{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Научная графика в Python\n",
    "\n",
    "Автор: Шабанов Павел Александрович\n",
    "\n",
    "E-mail: pa.shabanov@gmail.com\n",
    "\n",
    "URL: [Заметки по программированию в науках о Земле](http://progeoru.blogspot.ru/)\n",
    "\n",
    "Дата последнего обновления: 12.03.2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Преамбула\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def save(name='', fmt='png'):\n",
    "    pwd = os.getcwd()\n",
    "    iPath = './pictures/{}'.format(fmt)\n",
    "    if not os.path.exists(iPath):\n",
    "        os.mkdir(iPath)\n",
    "    os.chdir(iPath)\n",
    "    plt.savefig('{}.{}'.format(name, fmt), fmt='png')\n",
    "    os.chdir(pwd)\n",
    "    #plt.close()\n",
    "\n",
    "rcParams['font.family'] = 'fantasy'\n",
    "rcParams['font.fantasy'] = 'Arial'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Глава 9 Деления координатной оси Ticks\n",
    "\n",
    "### Содержание главы\n",
    "\n",
    "1. Контейнер Ticks;\n",
    "\n",
    "2. Locator и Formatter.\n",
    "\n",
    "Деления (ticks) неотделимы от координатной оси на которой они находятся. Однако свойства самих делений (цвет, длина, толщина и др.), и их подписей (кегль, поворот, цвет шрифта, шрифт и др.), а также связанные с ними линии вспомогательной сетки grid, удобно хранить в отдельном хранилище-контейнере Ticks, а не в контейнере Axis. Повторюсь, что эти вещи очень связаны и одно в отрыве от другого теряет смысл. Тем более, что для удобства пользователей разработчики сделали множество методов для работы с делениями из контейнеров более высокого уровня (Axes, Axis). \n",
    "\n",
    "### Электронные ресурсы:\n",
    "\n",
    "+ [Описание элементов рисунка в matplotlib](http://matplotlib.org/users/artists.html);\n",
    "\n",
    "+ [Пример работы с главными и вспомогательными делениями координатных осей методами Locator и Formatter](http://matplotlib.org/examples/pylab_examples/major_minor_demo1.html);\n",
    "\n",
    "+ [О Locator и Formatter](http://matplotlib.org/api/ticker_api.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1 Контейнер Ticks\n",
    "\n",
    "Контейнер `matplotlib.axis.Tick` - это последний и самый низкоуровневый из Artists-контейнеров, самая маленькая \"матрёшка\". Он содержит экземпляры делений (ticks), линий вспомогательной сетки (grid lines) и подписей (labels) для верхних (upper ticks) и нижних делений (lower ticks). К каждому из них есть прямой доступ как к одному из атрибуту экземпляра Tick. Также контейнер существуют логические переменные с помощью которых можно определить с какой стороны будут нанесены подписи и деления: для оси ординат справа/слева, а для оси абсцисс - сверху/снизу в прямоугольной системе координат.\n",
    "\n",
    "Для работы непросредственно с экземпляром Tick, необходимо \"спуститься\" к нему с более высоких контейнеров-уровней. \n",
    "\n",
    "> Axes - Axis(YAxis) - методы \"set-get\" -> `ax.yaxis.get_major_ticks()`\n",
    "\n",
    "Для такого объекта определены следующие атрибуты:\n",
    "\n",
    "------------------------------------------------------------------------\n",
    "\n",
    "Атрибут Tick -> Описание\n",
    "\n",
    "------------------------------------------------------------------------\n",
    "\n",
    "+ **tick1line** - экземпляр Line2D;\n",
    "\n",
    "+ **tick2line** - экземпляр Line2D;\n",
    "\n",
    "+ **gridline** - экземпляр Line2D;\n",
    "\n",
    "+ **label1** - экземпляр Text;\n",
    "\n",
    "+ **label2** - экземпляр Text;\n",
    "\n",
    "+ **gridOn** - логическая переменная, разрешающая рисовать tickline;\n",
    "\n",
    "+ **tick1On** - логическая переменная, разрешающая рисовать первую tickline;\n",
    "\n",
    "+ **tick2On** - логическая переменная, разрешающая рисовать вторую tickline;\n",
    "\n",
    "+ **label1On** - логическая переменная, разрешающая рисовать tick label;\n",
    "\n",
    "+ **label2On** - логическая переменная, разрешающая рисовать tick label.\n",
    "\n",
    "------------------------------------------------------------------------\n",
    "\n",
    "Атрибуты с номером 1 - это стандартно отображаемые деления, которые располагаются слева и/или снизу.\n",
    "\n",
    "Атрибуты с номером 2 - соответственно справа и/или снизу.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Major ticks on Y-axis <class 'matplotlib.axis.YTick'>\n",
      "Major ticks on Y-axis <class 'matplotlib.axis.YTick'>\n",
      "Major ticks on Y-axis <class 'matplotlib.axis.YTick'>\n",
      "Major ticks on Y-axis <class 'matplotlib.axis.YTick'>\n",
      "Major ticks on Y-axis <class 'matplotlib.axis.YTick'>\n",
      "Major ticks on Y-axis <class 'matplotlib.axis.YTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n",
      "Major ticks on X-axis <class 'matplotlib.axis.XTick'>\n"
     ]
    },
    {
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9nEFFMQPpgw3DfbAltg9OIo8yeBZSN+oPgHchI8aDl7yekNI54+PjjI+Ps3F8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\ntxljetRkiqIo5bMDgzLWixiM67HWVh4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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2e9828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 9.1\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "rect = ax.patch\n",
    "rect.set_facecolor('k')\n",
    "\n",
    "N = 31\n",
    "x = np.arange(N)\n",
    "y = 100*np.random.rand(N)\n",
    "\n",
    "ax.plot(x, y, color='red', linewidth=2.0)\n",
    "\n",
    "#formatter = mpl.ticker.FormatStrFormatter(u'%.2f руб.')\n",
    "#ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    print 'Major ticks on Y-axis %s' % type(tick)\n",
    "    tick.label1On = True\n",
    "    tick.label1.set_color('green')\n",
    "    tick.label2On = True\n",
    "    tick.label2.set_color('blue')\n",
    "    # серые деления на оси ОY слева\n",
    "    tick.tick1line.set_color('grey')\n",
    "    tick.tick1line.set_markeredgewidth(2)\n",
    "    tick.tick1line.set_markersize(25)\n",
    "    # линии вспомогательной сетки для оси OX\n",
    "    tick.gridOn = True\n",
    "    tick.gridline.set_color('white')\n",
    "    tick.gridline.set_linewidth(1.5)\n",
    "    \n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    print 'Major ticks on X-axis %s' % type(tick)\n",
    "    tick.label1On = True\n",
    "    tick.label1.set_color('red')\n",
    "    tick.label2On = True\n",
    "    tick.label2.set_color('orange')\n",
    "    # розовые деления на оси ОX сверху\n",
    "    tick.tick2line.set_color('pink')\n",
    "    tick.tick2line.set_markeredgewidth(2)\n",
    "    tick.tick2line.set_markersize(25)\n",
    "    # линии вспомогательной сетки для оси OY\n",
    "    tick.gridOn = True\n",
    "    tick.gridline.set_color('yellow')\n",
    "    tick.gridline.set_linewidth(2.)\n",
    "\n",
    "save('pic_9_1', fmt='png')\n",
    "save('pic_9_1', fmt='pdf')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.2 Locator и Formatter\n",
    "\n",
    "Деления могут быть как главными (major ticks), так и вспомогательными (minor ticks). Работа с делениями в обоих случаях одинаковая.\n",
    "\n",
    "Однако, если главные деления будут автоматически созданы при вызове графической команды (например `ax.plot()` или `ax.hist()`), и с ними, соответственно, можно работать c помощью `ax.xaxis.get_major_ticks()` или `ax.yaxis.get_major_ticks()`, то вспомогательные деления необходимо задавать вручную. Удобнее всего это делать с помощью методов Formatter и Locator.\n",
    "\n",
    "Методы Locator и Formatter относятся к модулю `matplotlib.ticker`. Этот модуль содержит классы, позволяющие наиболее полно определять форматирование и местоположение делений. Это самый низкоуровневый способ форматирования делений на координатных осях. \n",
    "\n",
    "Контейнеры коодинатных осей XAxis и YAxis имеют специальные методы для работы с объектами типа Formatter: `.set_major/minor_locator()` и `.set_major/minor_formatter()`. Например:\n",
    "\n",
    "+ `xax.set_major_locator()`;\n",
    "\n",
    "+ `xax.set_major_formatter()`;\n",
    "\n",
    "+ `yax.set_minor_locator()`;\n",
    "\n",
    "+ `yax.set_minor_formatter()`.\n",
    "\n",
    "В качестве входящих данных данным методам нужно передать какой-либо экземпляр класса из модуля `matplotlib.ticker`, например, `matplotlib.ticker.MultipleLocator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'matplotlib.ticker.MultipleLocator'>\n",
      "<class 'matplotlib.ticker.FormatStrFormatter'>\n"
     ]
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
    "\n",
    "majorLocator   = MultipleLocator(5)\n",
    "majorFormatter = FormatStrFormatter('%d')\n",
    "\n",
    "print(type(majorLocator))\n",
    "print(type(majorFormatter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Класс Locator является базовым классом для всех производных классов-локаторов, отвечающих за расположение делений. Локаторы работают с автомасштабированием в пределах области изменения данных, и исходя из этого определяют положение делений на оси. Одним из наиболее удобных является полуавтоматический локатор MultipleLocator. В качестве входящих данных он принимает целое число, например 10, и самостоятельно подбирает пределы изменений на оси и располагает деления в местах, кратных заданному числу. \n",
    "\n",
    "Другой пример - субкласс AutoMinorLocator(). Он позволяет автоматически определить положение вспомогательных делений. В качестве входящего параметра даётся целое число n - число промежутков, разделённых вспомогательными делениями, между двумя главными делениями. Это один их самых простых способов задать положение и значения вспомогательных делений. Форматирование подписей к таким делениям (их строковое представление), легче всего осуществить с помощью методов-форматеров. Подробнее о субклассах Locator смотри в электронных материалах.\n",
    "\n",
    "Класс Formatter является базовым для всех производных классов-форматеров, отвечающих за форматирование делений. Форматеры в качестве входящих данных принимают строку формата, например \"%d\", которая применяется к подписи каждого деления. Разные субклассы предоставляют разный функционал. Так субкласс NullFormatter() позволяет скрыть все подписи на выбранной оси. Подробнее о субклассах Formatter смотри в электронных материалах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1hagLPbD4QdzqUyfejLWWkXE+5czYGhXPnRusDqUkq9oJXsLUAI2M162j8KtQ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\nIAiCYBgxY0EQBEEwjJixIAiCIBjm/wNJrIRaoXWmIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa347278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 9.2.1\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter, AutoMinorLocator, NullFormatter\n",
    "\n",
    "majorLocator   = MultipleLocator(5)\n",
    "# Автоматический подбор промежуточных делений. Количество созданных делений равно n-1\n",
    "minorLocator   = AutoMinorLocator(n=3)   \n",
    "\n",
    "majorFormatter = FormatStrFormatter('%d')\n",
    "minorFormatter = FormatStrFormatter('%.2f')\n",
    "\n",
    "N = 10\n",
    "x = np.arange(-N, N+1, 1) \n",
    "y = (np.random.random(len(x))*2.-1)*N\n",
    "\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "xax = ax.xaxis\n",
    "yax = ax.yaxis\n",
    "\n",
    "xax.set_major_locator(majorLocator)\n",
    "xax.set_major_formatter(majorFormatter)\n",
    "yax.set_major_locator(majorLocator)\n",
    "yax.set_major_formatter(majorFormatter)\n",
    "\n",
    "xax.set_minor_locator(minorLocator)\n",
    "xax.set_minor_formatter(minorFormatter)\n",
    "yax.set_minor_locator(minorLocator)\n",
    "yax.set_minor_formatter(NullFormatter()) # скрываем подписи вспомогательных делений по оси OY\n",
    "\n",
    "ax.grid(True, which='major', color='k', linestyle='solid')\n",
    "\n",
    "ax.grid(True, which='minor', color='grey', linestyle='dashed', alpha=0.5)\n",
    "#yax.grid(True, which='minor', color='grey', linestyle='dashed', alpha=0.5)\n",
    "\n",
    "#for label in xax.get_ticklabels():\n",
    "#    label.set_color('red')\n",
    "#    label.set_rotation(30)\n",
    "#    label.set_fontsize(12)\n",
    "\n",
    "# для вспомогательных for the minor ticks, use no labels; default NullFormatter\n",
    "\n",
    "save('pic_9_2_1', fmt='png')\n",
    "save('pic_9_2_1', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'matplotlib.ticker.FixedLocator'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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lbpOMBIC/IiCOosCArD9ZukSjqQAw3/N+/UwPjSzi8+wAiQR4iu/pANtNgioR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\nSKSFEpWASCQSiURaKP8Hfjp0R4tZeMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa355b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 9.2.2\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter, FixedLocator\n",
    "\n",
    "majorLocator   = MultipleLocator(5)\n",
    "majorFormatter = FormatStrFormatter('%d $')\n",
    "\n",
    "# FixedLocator позволяет задать последовательность подписей. \n",
    "# Если множество подписей имеет пересечения с множеством других подписей, то \n",
    "# они будут перекрываться (случай пересечения главных и вспомогательных делений)\n",
    "minorLocator   = FixedLocator(np.arange(-7.5, 12.5, 5.))\n",
    "print(type(minorLocator))\n",
    "minorFormatter = FormatStrFormatter('%.2f')\n",
    "\n",
    "N = 10\n",
    "x = np.arange(-N, N+1, 1) \n",
    "y = (np.random.random(len(x))*2.-1)*N\n",
    "\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "xax = ax.xaxis\n",
    "yax = ax.yaxis\n",
    "\n",
    "xax.set_major_locator(majorLocator)\n",
    "xax.set_major_formatter(majorFormatter)\n",
    "yax.set_major_locator(majorLocator)\n",
    "yax.set_major_formatter(majorFormatter)\n",
    "\n",
    "xax.set_minor_locator(minorLocator)\n",
    "xax.set_minor_formatter(minorFormatter)\n",
    "yax.set_minor_locator(minorLocator)\n",
    "yax.set_minor_formatter(minorFormatter)\n",
    "\n",
    "ax.grid(True, which='major', color='k', linestyle='solid')\n",
    "\n",
    "ax.grid(True, which='minor', color='grey', linestyle='dashed', alpha=0.5)\n",
    "#yax.grid(True, which='minor', color='grey', linestyle='dashed', alpha=0.5)\n",
    "\n",
    "for label in xax.get_ticklabels(which='minor'):\n",
    "    label.set_color('red')\n",
    "    label.set_rotation(30)\n",
    "    label.set_fontsize(12)\n",
    "\n",
    "for label in xax.get_ticklabels(which='major'):\n",
    "    label.set_color('blue')\n",
    "    label.set_rotation(-30)\n",
    "    label.set_fontsize(14)\n",
    "    \n",
    "for tick in yax.get_major_ticks():\n",
    "    tick.label1On = True\n",
    "    tick.label1.set_color('green')\n",
    "    tick.label2On = True\n",
    "    tick.label2.set_color('blue')\n",
    "    # серые деления на оси ОY слева\n",
    "    tick.tick1line.set_color('green')\n",
    "    tick.tick1line.set_markeredgewidth(2)\n",
    "    tick.tick1line.set_markersize(25)    \n",
    "\n",
    "    tick.tick2line.set_color('blue')\n",
    "    tick.tick2line.set_markeredgewidth(2)\n",
    "    tick.tick2line.set_markersize(25)    \n",
    "\n",
    "for tick in yax.get_minor_ticks():\n",
    "    tick.label1On = True\n",
    "    tick.label1.set_color('green')\n",
    "    tick.label2On = True\n",
    "    tick.label2.set_color('blue')\n",
    "    # серые деления на оси ОY слева\n",
    "    tick.tick1line.set_color('green')\n",
    "    tick.tick1line.set_markeredgewidth(1)\n",
    "    tick.tick1line.set_markersize(12)    \n",
    "\n",
    "    tick.tick2line.set_color('blue')\n",
    "    tick.tick2line.set_markeredgewidth(1)\n",
    "    tick.tick2line.set_markersize(12)\n",
    "\n",
    "# для вспомогательных for the minor ticks, use no labels; default NullFormatter\n",
    "\n",
    "save('pic_9_2_2', fmt='png')\n",
    "save('pic_9_2_2', fmt='pdf')\n",
    "\n",
    "plt.show()\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также некоторые элементы форматирования делений и их подписей вынесены в настройки rcParams :\n",
    "\n",
    "+ **xtick.color**='k' - цвет делений по оси абсцисс;\n",
    "\n",
    "+ **xtick.direction**='in' - направление деления [] по оси абсцисс;\n",
    "\n",
    "+ **xtick.labelsize**='medium' - размер подписи деления на оси абсцисс;\n",
    "\n",
    "+ **xtick.major.pad**=4.0 - расстояние от координатной оси для главных делений оси абсцисс;\n",
    "\n",
    "+ **xtick.major.size**=4.0 - размер главного деления оси абсцисс;\n",
    "\n",
    "+ **xtick.major.width**=0.5 - толщина главного деления оси абсцисс;\n",
    "\n",
    "+ **xtick.minor.pad**=4.0 - расстояние от координатной оси для вспомогательных делений оси абсцисс;\n",
    "\n",
    "+ **xtick.minor.size**=2.0 - размер вспомогательного деления;\n",
    "\n",
    "+ **xtick.minor.width**=0.5 - толщина вспомогательного деления;\n",
    "\n",
    "+ **ytick.color**='k' - цвет делений по оси ординат;\n",
    "\n",
    "+ **ytick.direction**='in' - направление деления [] по оси ординат;\n",
    "\n",
    "+ **ytick.labelsize**='medium' - размер подписи деления на оси абсцисс;\n",
    "\n",
    "+ **ytick.major.pad**=4.0 - расстояние от координатной оси для главных делений оси ординат;\n",
    "\n",
    "+ **ytick.major.size**=4.0 - размер главного деления оси ординат;\n",
    "\n",
    "+ **ytick.major.width**=0.5 - толщина главного деления оси ординат;\n",
    "\n",
    "+ **ytick.minor.pad**=4.0 - расстояние от координатной оси для вспомогательных делений оси ординат;\n",
    "\n",
    "+ **ytick.minor.size**=2.0 - размер главного деления оси ординат;\n",
    "\n",
    "+ **ytick.minor.width**=0.5 - толщина главного деления оси ординат."
   ]
  },
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   "metadata": {
    "collapsed": true
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   "source": [
    "Автор: Шабанов Павел Александрович\n",
    "\n",
    "E-mail: pa.shabanov@gmail.com\n",
    "\n",
    "## Научная графика в Python\n",
    "\n",
    "### Оглавление\n",
    "\n",
    "+ [Глава 1 Библиотека matplotlib. Pyplot](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P1 Chapter 1 Pyplot.ipynb)\n",
    "\n",
    "+ [Глава 2 Основные графические команды](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P1 Chapter 2 Main graphical commands.ipynb)\n",
    "\n",
    "+ [Глава 3 Работа с текстом и шрифтами](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P1 Chapter 3 Text and Fonts.ipynb)\n",
    "\n",
    "+ [Глава 4 Цвет и цветовая палитра](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P1 Chapter 4 Color.ipynb)\n",
    "\n",
    "**Часть II Структура рисунка в matplotlib**\n",
    "\n",
    "+ [Глава 5 Рисунок](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P2 Chapter 5 Figure container.ipynb)\n",
    "\n",
    "+ [Глава 6 Область рисования](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P2 Chapter 6 Axes container.ipynb)\n",
    "\n",
    "+ [Глава 7 Мультиоконные рисунки](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P2 Chapter 7 Subplots.ipynb)\n",
    "\n",
    "+ [Глава 8 Координатные оси](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P2 Chapter 8 Axis container.ipynb)\n",
    "\n",
    "> + [Глава 9 Деления координатных осей](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P2 Chapter 9 Ticks container.ipynb)\n",
    "\n",
    "**Часть III Специальные элементы рисунка в matplotlib**\n",
    "\n",
    "+ [Глава 10 Особенности координатных осей](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P3 Chapter 10 Twinx and log scale.ipynb)\n",
    "\n",
    "+ [Глава 11 Графики в полярной системе координат](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P3 Chapter 11 Polar plots.ipynb) \n",
    "\n",
    "+ [Глава 12 Легенда](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P3 Chapter 12 Legends.ipynb)\n",
    "\n",
    "+ [Глава 13 Цветовая шкала](http://nbviewer.ipython.org/github/whitehorn/Scientific_graphics_in_python/blob/master/P3 Chapter 13 Colorbar.ipynb)"
   ]
  },
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