Владимирская область, города и дороги

GraphVlad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Граф владимирской области\n",
    "Ребра - дороги, толщина ребра пропорциональна скорости по этой дороге.\n",
    "Синим выделены федеральные трассы.\n",
    "Вершины - города. Диаметр вершины пропорционален количеству населения в городе. Интенсивность - плотность населения (более темный - более плотный). Подписи: по первым буквам названия: \n",
    "<code>\n",
    "labels={'Владимир': 'Вл', 'Ковров':'Ков', 'Шуя':'Ш', 'Александров':'А', 'Гусь-Хрустальный':'Г-Х', 'Кольчугино':'К',\n",
    "        'Сергиев Посад':'С-П', 'Ногинск':'Ног', 'Муром':'М', 'Дзержинск':'Дз', 'Переславль-Залесский':'П-З',\n",
    "        'Нижний Новгород': 'Н-Н', 'Иваново':'Иван.', 'Тейково':'Т', 'Ростов':'Р', 'Ярославль':'Яр.',\n",
    "        'Вязники':'В', 'Балашиха':'Бал', 'Орехово-Зуево':'О-З', 'Егорьевск':'Е'}\n",
    "</code>\n",
    "\n",
    "<i>Источники:\n",
    "<li>Wiki - данные о населенных пунктах (база данных cities.sqlite). Там в базе приведена гиперссылка на страницу Вики каждого н-п - таблица Localities</li>\n",
    "<li>Росстат, справочник \"Регионы России\", Tabl-36-17.xls - количество жителей в н-п, по муниципальным образованиям (с кодами октмо), оценка 2017 г. (база данных cities.sqlite) - таблица MunObrsPeople2017</li>\n",
    "<li>Вики - данные об областях (база данных cities.sqlite) - таблица Regions</li>\n",
    "<li>Вики - данные о федеральных округах - код и название, (база данных cities.sqlite) - таблица FDistrics</li>\n",
    "<li>Минфин - коды ОКТМО муниципальных образования и населенных пунктов, (база данных cities.sqlite) - таблица All_places</li>\n",
    "<li>vlad_obl_roads.txt - расстояния и время пути между населенными пунктами. Данные взяты из построителей маршрутов Гугла, Яндекса и https://www.avtodispetcher.ru</li>\n",
    "\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Борисоглебский', 'Гаврилов-Ям', 'Данилов', 'Константиновский', 'Любим', 'Мышкин', 'Некрасовское', 'Переславль-Залесский', 'Пошехонье', 'Ростов', 'Рыбинск', 'Семибратово', 'Тутаев', 'Углич', 'Ярославль']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/egor/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "conn=sqlite3.connect(r'/home/egor/git/jupyter/OlegAIGK/Data/Base/cities.sqlite')\n",
    "strSelect=r'''\n",
    "select FedRegCities2017.* from FedRegCities2017 where FedRegCities2017.norm_reg in (\"Ивановская\", \"Московская\", \n",
    "\"Ярославская\", \"Нижегородская\", \"Рязанская\", \"Владимирская\", \"Москва\")\n",
    "'''\n",
    "dtf=pd.read_sql(strSelect, conn)\n",
    "#print(dtf.columns.tolist())\n",
    "print(dtf.ix[dtf['norm_reg']=='Ярославская', 'norm_name'].sort_values().tolist())\n",
    "#print(dtf[dtf['norm_name']=='Москва'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Александров', 'Кольчугино'), ('Александров', 'Сергиев Посад'), ('Александров', 'Переславль-Залесский'), ('Александров', 'Струнино'), ('Александров', 'Карабаново'), ('Кольчугино', 'Киржач'), ('Кольчугино', 'Юрьев-Польский'), ('Кольчугино', 'Лакинск'), ('Кольчугино', 'Владимир'), ('Сергиев Посад', 'Струнино'), ('Сергиев Посад', 'Киржач'), ('Сергиев Посад', 'Орехово-Зуево'), ('Сергиев Посад', 'Ивантеевка'), ('Сергиев Посад', 'Переславль-Залесский'), ('Переславль-Залесский', 'Юрьев-Польский'), ('Переславль-Залесский', 'Ростов'), ('Карабаново', 'Киржач'), ('Киржач', 'Черноголовка'), ('Киржач', 'Покров'), ('Киржач', 'Ногинск'), ('Киржач', 'Орехово-Зуево'), ('Юрьев-Польский', 'Владимир'), ('Юрьев-Польский', 'Гаврилов Посад'), ('Юрьев-Польский', 'Суздаль'), ('Юрьев-Польский', 'Лакинск'), ('Лакинск', 'Костерево'), ('Лакинск', 'Владимир'), ('Лакинск', 'Собинка'), ('Владимир', 'Суздаль'), ('Владимир', 'Радужный'), ('Владимир', 'Гусь-Хрустальный'), ('Владимир', 'Судогда'), ('Владимир', 'Камешково'), ('Владимир', 'Ковров'), ('Владимир', 'Вязники'), ('Черноголовка', 'Щелково'), ('Черноголовка', 'Балашиха'), ('Черноголовка', 'Ногинск'), ('Покров', 'Петушки'), ('Покров', 'Орехово-Зуево'), ('Ногинск', 'Орехово-Зуево'), ('Ногинск', 'Обухово'), ('Ногинск', 'Электроугли'), ('Петушки', 'Костерево'), ('Орехово-Зуево', 'Егорьевск'), ('Гаврилов Посад', 'Суздаль'), ('Гаврилов Посад', 'Тейково'), ('Суздаль', 'Лежнево'), ('Суздаль', 'Камешково'), ('Собинка', 'Радужный'), ('Радужный', 'Ковров'), ('Радужный', 'Судогда'), ('Радужный', 'Гусь-Хрустальный'), ('Радужный', 'Вязники'), ('Гусь-Хрустальный', 'Судогда'), ('Гусь-Хрустальный', 'Курлово'), ('Гусь-Хрустальный', 'Меленки'), ('Судогда', 'Муром'), ('Судогда', 'Вязники'), ('Судогда', 'Ковров'), ('Камешково', 'Ковров'), ('Камешково', 'Шуя'), ('Ковров', 'Шуя'), ('Ковров', 'Вязники'), ('Ковров', 'Южа'), ('Вязники', 'Муром'), ('Вязники', 'Гороховец'), ('Тейково', 'Иваново'), ('Тейково', 'Лежнево'), ('Тейково', 'Ростов'), ('Иваново', 'Лежнево'), ('Иваново', 'Кохма'), ('Иваново', 'Гаврилов-Ям'), ('Лежнево', 'Шуя'), ('Лежнево', 'Кохма'), ('Шуя', 'Кохма'), ('Шуя', 'Южа'), ('Южа', 'Балахна'), ('Муром', 'Меленки'), ('Муром', 'Навашино'), ('Муром', 'Гороховец'), ('Курлово', 'Тума'), ('Меленки', 'Касимов'), ('Гороховец', 'Павлово'), ('Гороховец', 'Володарск'), ('Гороховец', 'Дзержинск'), ('Гороховец', 'Нижний Новгород'), ('Гороховец', 'Балахна'), ('Тума', 'Егорьевск'), ('Тума', 'Касимов'), ('Нижний Новгород', 'Балахна'), ('Егорьевск', 'Балашиха'), ('Обухово', 'Щелково'), ('Обухово', 'Балашиха'), ('Обухово', 'Электроугли'), ('Щелково', 'Балашиха'), ('Балашиха', 'Королев'), ('Балашиха', 'Электроугли'), ('Королев', 'Мытищи'), ('Королев', 'Пушкино'), ('Пушкино', 'Ивантеевка'), ('Ростов', 'Гаврилов-Ям'), ('Гаврилов-Ям', 'Ярославль')]\n"
     ]
    }
   ],
   "source": [
    "GV=nx.read_edgelist(r'/home/egor/git/jupyter/OlegAIGK/Data/Base/vlad_obl_roads.txt', delimiter=',',\n",
    "                    nodetype=str,create_using=nx.Graph(),data=[('dist', int), ('time', float)])\n",
    "\n",
    "print(GV.edges())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "for node in GV.nodes():\n",
    "    sr=dtf.ix[dtf['norm_name']==node, ('lat', 'long', 'oktmo', 'people2017', 'square')].to_records().tolist()[0]\n",
    "    #print(node, sr)\n",
    "    GV.node[node]['location']=(sr[2], sr[1])\n",
    "    GV.node[node]['oktmo']=sr[3]\n",
    "    GV.node[node]['population']=sr[4]\n",
    "    GV.node[node]['square']=sr[5]\n",
    "    try:\n",
    "        GV.node[node]['dens']=sr[4]/sr[5]\n",
    "    except:\n",
    "        GV.node[node]['dens']=sr[4]/1\n",
    "\n",
    "#добавим время стояния в пробках на ремонтах (ооочень оптимистичная оценка)\n",
    "GV['Владимир']['Лакинск']['time']+=0.30 \n",
    "GV['Лакинск']['Костерево']['time']+=0.20\n",
    "GV['Покров']['Орехово-Зуево']['time']+=1.0        \n",
    "        \n",
    "\n",
    "for edge in GV.edges(data=True):\n",
    "    edge[2]['weight']=1/edge[2]['time']\n",
    "\n",
    "#print(GV.edges(data=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"1100\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "norm=mpl.colors.Normalize(vmin=500, vmax=4500)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "\n",
    "s_nodesM7={'Балашиха', 'Обухово', 'Ногинск', 'Орехово-Зуево', 'Покров', 'Петушки', 'Костерево', 'Ростов', 'Сергиев Посад', \n",
    "           'Переславль-Залесский', 'Ярославль', 'Ивантеевка', 'Королев', 'Мытищи', 'Гаврилов-Ям',\n",
    "           'Лакинск', 'Владимир', 'Вязники', 'Гороховец', 'Нижний Новгород', 'Суздаль', 'Лежнево', 'Иваново'}\n",
    "\n",
    "node_color = [nx.get_node_attributes(GV, 'dens')[v] for v in GV]\n",
    "#print(nx.get_node_attributes(GV, 'dens'))\n",
    "\n",
    "norm_dens=list(map(lambda x: int(norm(x)*10), node_color))\n",
    "\n",
    "node_size = [0.005*nx.get_node_attributes(GV, 'population')[v] for v in GV]\n",
    "\n",
    "edge_width = [GV[u][v]['weight'] for u,v in GV.edges()]\n",
    "\n",
    "edge_list = [x for x in GV.edges() if set(x).issubset(s_nodesM7)]\n",
    "#print(edge_list)\n",
    "edge_list.remove(('Иваново', 'Гаврилов-Ям'))\n",
    "\n",
    "pos=nx.get_node_attributes(GV, 'location')\n",
    "\n",
    "nx.draw_networkx(GV, pos, node_size=node_size, \n",
    "                 node_color=norm_dens, alpha=0.6, with_labels=False, \n",
    "                 width=edge_width, edge_color='gray', cmap=plt.cm.Reds)\n",
    "\n",
    "nx.draw_networkx_labels(GV, pos, labels={'Владимир': 'Вл', 'Ковров':'Ков', 'Шуя':'Ш', 'Александров':'А', 'Гусь-Хрустальный':'Г-Х', 'Кольчугино':'К',\n",
    "                                        'Сергиев Посад':'С-П', 'Ногинск':'Ног', 'Муром':'М', 'Дзержинск':'Дз', 'Переславль-Залесский':'П-З',\n",
    "                                       'Нижний Новгород': 'Н-Н', 'Иваново':'Иван.', 'Тейково':'Т', 'Ростов':'Р', 'Ярославль':'Яр.',\n",
    "                                        'Вязники':'В','Орехово-Зуево':'О-З', 'Егорьевск':'Е', \n",
    "                                        'Балашиха':'Бал'}, font_size=8, \n",
    "                 font_color='black')\n",
    "\n",
    "nx.draw_networkx_edges(GV, pos, edgelist=edge_list, edge_color='b', alpha=0.2, width=6)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15677729774257457\n"
     ]
    }
   ],
   "source": [
    "print(1/nx.average_shortest_path_length(GV, weight='weight'))\n",
    "#print(nx.closeness_centrality(GV))\n",
    "#print(nx.betweenness_centrality(GV, normalized=True, endpoints=False))\n",
    "#print(nx.edge_betweenness_centrality(GV, normalized=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}