P2 Chapter 6 Axes container.ipynb

{
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  {
   "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 6 Область рисования Axes\n",
    "\n",
    "### Содержание главы\n",
    "\n",
    "1. Контейнер Axes;\n",
    "\n",
    "2. Создание областей рисования;\n",
    "\n",
    "3. Круговые и прямоугольные области рисования;\n",
    "\n",
    "4. Методы области рисования Axes.\n",
    "\n",
    "Область рисования Axes - это центральный объект при создании научных графиков. Это тот объект, та область на которую наносятся координатные оси, линии графиков и столбцы диаграмм, легенды к ним и так далее. Взамен активного использования pyplot предлагается использовать более объектно-ориентированный подход для вызова графических команд и изменениях их свойств.\n",
    "\n",
    "### Электронные ресурсы:\n",
    "\n",
    "+ [Описание элементов рисунка в matplotlib](http://matplotlib.org/users/artists.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Контейнер Axes\n",
    "\n",
    "Структурно область рисования Axes необходима как элемент более низкого иерархического уровня нежели Figure (весь рисунок), на котором непосредственно будут располагаться различные графические элементы. Axes похож на Frame в веб-дизайне, то есть это область, на которой можно размещать графические примитивы. При этом в matploltib непосредственно на Figure нельзя рисовать (изменение цвета фона не считается за полноценное рисование) и соответственно нужен хотя бы один экземпляр axes.\n",
    "\n",
    "Область рисования Axes является второй \"матрёшкой\" (после Figure) или контейнером, на который можно наносить другие элементы рисунка (или контейнеры), в первую очередь координатные оси Axis. \n",
    "\n",
    "При создании области рисования создаётся экземпляр класса `matplotlib.axes._axes.Axes`. Этот класс является, пожалуй, центром вселенной matplotlib. Он включает большинство всех элементов рисунка Artists, которые используются при создании рисунка, а также большую часть вспомогательных методов для настройки этих методов. Также у данного класса есть родственный класс `matplotlib.axes._subplots.AxesSubplot`, который создаётся при создании мультиокон subplots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Список областей рисования после создания объекта fig [] \n",
      "\n",
      "Список областей рисования после вызова комманды scatter [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000003FB0908>] \n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x40d86a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.1.1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "print (u'Список областей рисования после создания объекта fig %s \\n' % fig.axes)\n",
    "\n",
    "plt.scatter(0.,1.)\n",
    "\n",
    "print (u'Список областей рисования после вызова комманды scatter %s \\n' % fig.axes)\n",
    "    \n",
    "save('pic_6_1_1', fmt='png')\n",
    "save('pic_6_1_1', fmt='pdf')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После создания экземпляра fig типа Figure список областей рисования fig.axes пуст. Но стоит вызвать любую графическую команду, создающую графический примитв, и в нём появляется экземпляр типа AxesSubplot. Таким образом, при работе с интерфейсом pyplot область рисования создаётся неявно, но она существует и именно на ней располагаются многие графические элементы (линии, текст и т.д.).\n",
    "\n",
    "Чтобы лучше понять сущность Axes как контейнера/хранилища графических примитивов (или других контейнеров подчинённого уровня), рассмотрим что происходит с экземпляром области рисования при вызове графической команды."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lines on the axes: <type 'list'> [<matplotlib.lines.Line2D object at 0x000000000A55C2B0>]\n",
      "Patches on the axes: 50 <a list of 50 Patch objects>\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa55c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.1.2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "y = [-1,-2,-3,-4,-5,-6,-7,-8,-9,-10,-11]\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "# Первая область рисования (мультиокно 1)\n",
    "ax = fig.add_subplot(211)\n",
    "line = ax.plot(x, y, '-', color='blue', linewidth=2)\n",
    "print 'Lines on the axes:', type(line), line\n",
    "\n",
    "# Вторая область рисования (мультиокно 2)\n",
    "ax2 = fig.add_subplot(212)\n",
    "n, bins, rectangles = ax2.hist(np.random.randn(1000), 50, facecolor='yellow')\n",
    "print 'Patches on the axes:', len(ax2.patches), rectangles\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.grid(True)\n",
    "\n",
    "save('pic_6_1_2', fmt='png')\n",
    "save('pic_6_1_2', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При вызове метода `ax.plot()` создаётся экземпляр `matplotlib.lines.Line2D()`, эта линия обновляется по всем свойствам Line2D, которые были переданы в виде ключевых слов (keyword arguments), добавляется в контейнер Axes.lines и возвращается пользователю в виде списка линий. Методы, которые создают геометрические фигуры patches (например, `plt.bar()` создаёт ряд прямоугольников rectangles), добавляют patches в список Axes.patches.\n",
    "\n",
    "### 6.2 Создание областей рисования\n",
    "\n",
    "Чтобы создать область рисования типа Axes или AxesSubplot, существует ряд способов (подробнее смотри в следующей главе). Одним из стандартных способов создания области рисования является метод `fig.add_axes()` экземпляра fig объекта Figure. В качестве обязательного параметра rect метод просит указать последовательность из координат начальной точки (x, y), а также ширины и высоты от неё до другого угла прямоугольника (в случае круговой области рисования - смотри главу \"Графики в полярных координатах\"). Значения должны быть выражены в относительных единицах от 0 до 1 включительно. \n",
    "\n",
    "Весь рисунок Figure по сути имеет начальную точку (0., 0.) и ширину/высоту (1., 1.). Поэтому относительно определения границ области рисования стоит помнить следующее: подписям координатных осей (axis labels), заголовкам (titles) и подписям делений координатных осей (tick labels) нужно место. Определив размер области рисования вплотную к границам рисунка и передав в качестве аргумента список [0, 0, 1, 1], можно столкнуться с ситуацией, когда подписи вылезли за пределы рисунка, и при сохранении рисунок окажется обрезанным. Это не всегда очевидно при работе в среде ipython, ipython notebook, поэтому лучше проверять результат рисования перед запуском поточного производства графиков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тип ax: <class 'matplotlib.axes._axes.Axes'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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hHD1tRtL5SnZJNoJo3f6kKdMTg3NKgJVMcvMtb5Jbb6XUfGADMAWw1/+oQcif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\niMFJcouIiMFJcouIiMH5C4nTiiLe/InSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa2f4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.2.1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "x = np.arange(20)\n",
    "y = np.exp(-np.sin(x))\n",
    "\n",
    "x0 = 0.05\n",
    "y0 = 0.05\n",
    "dx = 0.9\n",
    "dy = 0.9\n",
    "\n",
    "rect = [x0, y0, dx, dy]\n",
    "ax = fig.add_axes(rect)\n",
    "\n",
    "ax.plot(x, y, 'g')\n",
    "ax.text(7.5, 0.25, u'Зелёный график', color='g')\n",
    "ax.grid(True)\n",
    "\n",
    "# В отличие от интерфейса pyplot, команды для назначения подписей \n",
    "# нужно употреблять с приставкой set_\n",
    "\n",
    "ax.set_title(u'Метод add_axes')\n",
    "ax.set_xlabel(u'Ось абсцисс')\n",
    "ax.set_ylabel(u'Ось ординат')\n",
    "\n",
    "print ('Тип ax: %s' % type(ax))\n",
    "\n",
    "save('pic_6_2_1', fmt='png')\n",
    "save('pic_6_2_1', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также можно создавать области рисования с помощью метода `fig.add_subplot()`, который позволяет создавать мультиокона (subplots) или мультиоконные рисунки. Создаваемая таким способом область рисования имеет другой, но родственный, класс - AxesSubplot. В качестве обязательного параметра subplot просит указать триплет (набор из 3 цифр), который определяет место области на рисунки. Подробнее о subplot будет рассказно в следующей главе. Для создания одной области рисования типа AxesSubplot или просто subplot, триплет равен 111."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тип ax: <class 'matplotlib.axes._subplots.AxesSubplot'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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BC1X1AnslRSduyBEIxsQFTmfb4W00j29Op5bu8Ij2aduHY6XHOFB4wGkprsIt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa88bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.2.2\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "x = np.arange(20)\n",
    "y = np.exp(-np.sin(x))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y, 'g')\n",
    "ax.text(7.5, 0.25, u'Зелёный график', color='g')\n",
    "ax.grid(True)\n",
    "\n",
    "# В отличие от интерфейса pyplot, команды для назначения подписей \n",
    "# нужно употреблять с приставкой set_\n",
    "\n",
    "ax.set_title(u'Метод add_subplot')\n",
    "ax.set_xlabel(u'Ось абсцисс')\n",
    "ax.set_ylabel(u'Ось ординат')\n",
    "\n",
    "print ('Тип ax: %s' % type(ax))\n",
    "\n",
    "save('pic_6_2_2', fmt='png')\n",
    "save('pic_6_2_2', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ничто не мешает комбинировать методы `add_subplot` и `add_axes` на одном рисунке Figure. Стоит, однако, помнить, что размещая явно область рисования методом `fig.add_axes()`, можно перекрыть другие созданные области рисования. Иногда это оказывается полезным (создание \"окна в окне\"), а иногда это может привести к неприятному эффекту.\n",
    "\n",
    "\n",
    "**Правила хорошего кода**\n",
    "\n",
    "> В дальнейшем мы всегда будем соблюдать следующие правила при создании научной графики:\n",
    "\n",
    "> **1. Явно создавать экземпляр Figure:**\n",
    "\n",
    "> `import matplotlib.pyplot as plt`\n",
    "\n",
    "> `fig = plt.figure()`\n",
    "\n",
    "> **2. Создавать хотя бы одну область рисования Axes:**\n",
    "\n",
    "> `ax = fig.add_subplot(111)`\n",
    "\n",
    "> или\n",
    "\n",
    "> `ax = fig.add_axes[0.05, 0.05, 0.9, 0.9]`\n",
    "\n",
    "> **3. Интерфейс pyplot далее мы будем использовать редко, за исключением следующих случаев:**\n",
    "\n",
    "> + `plt.figure()`\n",
    "\n",
    "> + `plt.tight_layout()`\n",
    "\n",
    "> + `plt.show()`\n",
    "\n",
    "Считается хорошим тоном не использовать неявный импорт типа \"`from matplotlib.pyplot import *`\" или \"`from pylab import *\"`. Лучше использовать явный импорт: \"`from matplotlib import rcParams as rc`\". Также при создании достаточно сложных рисунков не стоит использовать интерфейс pyplot. Он хорош для ознакомления и быстрой визуализации черновиков. Но для создания high quality plots, которые требуются для полиграфии и при публикациях в научных журналах, лучше использовать методы axes, а не pyplot. \n",
    "\n",
    "### 6.3 Круговые и прямоугольные области рисования\n",
    "\n",
    "Как и Figure, область рисования Axes имеет атрибут patch, который является прямоугольным (Rectangle) для декартовой системы координат и круговым (Circle) для полярной системы координат. Этот patch определяет форму, фон и границы создаваемой области рисования. Подробнее о графиках в полярных координатах и областях рисования в полярных координатах показано в соответствующей главе."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jEcz9gmSvwsSCs5Y9afAxtklaq+f5oh6T53x/Hc+JmTxx+7ymYZ9RMR5/i4iI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\n/yB5fnAZu62DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb99a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.3\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(20)\n",
    "y = np.exp(-x)*np.sin(x)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "# Обычный график в декартовых координатах\n",
    "ax = fig.add_subplot(121, polar=False)\n",
    "ax.plot(x, y, 'r')\n",
    "rect = ax.patch \n",
    "rect.set_facecolor('green')\n",
    "ax.grid(True)\n",
    "\n",
    "# График в полярных координатах\n",
    "ax2 = fig.add_subplot(122, polar=True)\n",
    "ax2.plot(x, y, 'g')\n",
    "rect = ax2.patch  # Круговой\n",
    "rect.set_facecolor('red')\n",
    "\n",
    "save('pic_6_3', fmt='png')\n",
    "save('pic_6_3', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Методы области рисования Axes\n",
    "\n",
    "Все графические команды, применимые для интерфейса pyplot, применимы и для Axes. Разница заключается лишь в том, что вызывать их нужно не от plt (matplotlib.pyplot), а от экземпляра типа Axes или AxesSubplot. \n",
    "\n",
    "C методами настройки посложнее: почти все функции имеют аналоги, но бывают синтаксические различия. Например, чтобы сделать подпись оси ординат в интерфейсе pyplot мы использовали метод `plt.ylabel()`. Для экземпляра области рисования ax подобный метод вызывается через метод `ax.set_xlabel()`. \n",
    "\n",
    "**SET & GET**\n",
    "\n",
    "Вообще говоря, в matplotlib при работе с различными свойствами элементов рисунка поддерживается система \"set-get\". Её суть состоит в том, что если нужно получить текущее значение атрибута или свойства объекта, то нужно вызвать его с помощью соответствующей команды-функции с приставкой \"get_\". Например, чтобы получить список, состоящий из положений главных делений на оси абсцисс из области рисования ax, то нужно указать\n",
    "\n",
    "> `current_xticks = ax.get_xticks()`\n",
    "\n",
    "А чтобы изменить этот список на пользовательский, нужно вызвать аналогичную команду, но с приставкой \"set_\". В случае с делениями оси абсцисс это выглядит так:\n",
    "\n",
    "> `new_xticks = ax.set_xticks(custom_xticks)`\n",
    "\n",
    "Существует множество вспомогательных методов Axes для создания и изменения свойств элементов рисунков и графических примитивов, то есть множество графических команд. Всё, что доступно для pyplot, доступно и для axes-экземпляров. Далее приводятся некоторые из них:\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "Вспомогательный метод -> Описание -> Artist -> Контейнер\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "1. ax.annotate -> текстовая подпись -> Annotate\t-> ax.texts\n",
    "\n",
    "2. ax.text -> текст -> Text -> ax.texts\n",
    "\n",
    "3. ax.bar -> столбчатая диаграмма -> Rectangle -> ax.patches\n",
    "\n",
    "4. ax.errorbar -> график погрешностей -> Line2D и Rectangle -> ax.lines и ax.patches\n",
    "\n",
    "5. ax.fill -> заливка области -> Polygon -> ax.patches\n",
    "\n",
    "6. ax.hist -> гистограмма -> Rectangle -> ax.patches\n",
    "\n",
    "7. ax.imshow -> изображение -> AxesImage -> ax.images\n",
    "\n",
    "8. ax.legend -> легенда -> Legend -> ax.legends\n",
    "\n",
    "9. ax.plot -> XY график -> Line2D -> ax.lines\n",
    "\n",
    "10. ax.scatter -> точечная диаграмма -> PolygonCollection -> ax.collections\n",
    "\n",
    "----------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lines: <type 'list'> [<matplotlib.lines.Line2D object at 0x000000000BDD76A0>]\n",
      "Patches: 50 <a list of 50 Patch objects>\n"
     ]
    },
    {
     "data": {
      "image/png": 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630p+slybxYFPA4vNrNqcYnlKx5YWUl25OZolK7EZ8XXm7j7T3R9290fcveTu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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa22be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.4.1\n",
    "\n",
    "x = np.arange(10)\n",
    "y = np.arange(-1, -11, -1)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(211)\n",
    "line = ax.plot(x, y, '-', color='blue', linewidth=2)\n",
    "print 'Lines:', type(line), line\n",
    "\n",
    "# Изменяем цвет подписей делений оси OX с помощью \"set-get\" технологии\n",
    "for label in ax.get_xticklabels():\n",
    "    label.set_color('orange')\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "n, bins, rectangles = ax2.hist(np.random.randn(1000), bins=50, facecolor='yellow')\n",
    "print 'Patches:', len(ax2.patches), rectangles\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.grid(True)\n",
    "\n",
    "save('pic_6_4_1', fmt='png')\n",
    "save('pic_6_4_1', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "При создании того или иного графического объекта на текущей области рисования он добавляется в соответствующий его типу контейнер. Ниже представлен краткий список таких Artists-контейнеров, которые есть у Axes. \n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "Axes атрибут - Описание\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "1. patch - экземпляр Rectangle для фона (background) Axes;\n",
    "\n",
    "2. artists - список экземпляров Artist;\n",
    "\n",
    "3. collections - список экземпляров Collection;\n",
    "\n",
    "4. images - список экземпляров AxesImage;\n",
    "\n",
    "5. legends - список экземпляров Legend;\n",
    "\n",
    "6. lines - список экземпляров Line2D;\n",
    "\n",
    "7. patches - список экземпляров Patch;\n",
    "\n",
    "8. texts - список экземпляров Text;\n",
    "\n",
    "9. xaxis - экземпляр matplotlib.axis.XAxis;\n",
    "\n",
    "10. yaxis - экземпляр matplotlib.axis.YAxis.\n",
    "\n",
    "------------------------------------------------------\n",
    "\n",
    "XAxis и YAxis являются очень важными Artist-контейнерами. Через обращение к ним можно изменять свойства отрисовки делений (ticks) и подписей (labels) координатных осей. Например, с их помощью можно изменить размер шрифта подписей делений оси абсцисс XAxis с помощью вспомогательных методов Axes. Подробнее о работе с контейнерами XAxis и YAxis будет рассказано в главе \"Координатные оси\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lines: <type 'list'> [<matplotlib.lines.Line2D object at 0x000000000A3A8D68>]\n",
      "Patches: 50 <a list of 50 Patch objects>\n"
     ]
    },
    {
     "data": {
      "image/png": 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JSglQnksNFdOZLFnQmQWNYDp9Y4HdMAyjybBUjGHEwFIxhg8sFWMYhtHDqTuw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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7b8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.4.2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(10)\n",
    "y = np.arange(-1, -11, -1)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(211)\n",
    "line = ax.plot(x, y, '-', color='blue', linewidth=2)\n",
    "print 'Lines:', type(line), line\n",
    "\n",
    "# Изменяем цвет подписей делений оси OX с помощью работы с контейнером XAxis\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_color('red')\n",
    "    label.set_rotation(-45)\n",
    "    label.set_fontsize(12)\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "n, bins, rectangles = ax2.hist(np.random.randn(1000), bins=50, facecolor='yellow')\n",
    "print 'Patches:', len(ax2.patches), rectangles\n",
    "\n",
    "for axes in fig.axes:\n",
    "    axes.grid(True)\n",
    "\n",
    "save('pic_6_4_2', fmt='png')\n",
    "save('pic_6_4_2', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В настройках matplotlib у области рисования Axes имеется несколько атрибутов, которые заданы по умолчанию. Именно эти параметры определяют свойства графических элементов (цвет линии, наличие вспомогательной сетки grid, цвет и размер шрифта подписей координатных осей) для каждого вновь созданного тем или иным способом экземпляра Axes.\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "1. u'axes.edgecolor': u'k'\n",
    "\n",
    "2. u'axes.facecolor': (1, 1, 1, 0)\n",
    "\n",
    "3. u'axes.formatter.limits': [-7, 7]\n",
    "\n",
    "4. u'axes.formatter.use_locale': False\n",
    "\n",
    "5. u'axes.formatter.use_mathtext': False\n",
    "\n",
    "6. u'axes.formatter.useoffset': True\n",
    "\n",
    "7. u'axes.grid': False\n",
    "\n",
    "8. u'axes.grid.which': u'major'\n",
    "\n",
    "9. u'axes.hold': True\n",
    "\n",
    "10. u'axes.labelcolor': u'k'\n",
    "\n",
    "11. u'axes.labelsize': u'medium'\n",
    "\n",
    "12. u'axes.labelweight': u'normal'\n",
    "\n",
    "13. u'axes.linewidth': 1.0\n",
    "\n",
    "14. u'axes.titlesize': u'large'\n",
    "\n",
    "15. u'axes.titleweight': u'normal'\n",
    "\n",
    "16. u'axes.unicode_minus': True\n",
    "\n",
    "17. u'axes.xmargin': 0.0\n",
    "\n",
    "18. u'axes.ymargin': 0.0\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "Изменяя значения их непосредственно в файле matplotlibrc или по ходу программы с помощью rcParams, можно добиться пользовательской настройки шрифтов, цвета и других параметров области рисования axes. Описанные выше свойства применяются ко всем вновь созданным экземплярам типа Axes. Очевидно, что разные области рисования могут иметь разные параметры в рамках одного рисунка."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f650KgAyMLiISqRc1AUWWcjtkyrmB8qu71PMrQgVARKShVj0WUEU0FtDghLF5RPLTvldf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa466518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 6.4.3\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['axes.grid'] = True\n",
    "rcParams['axes.facecolor'] = 'orange'\n",
    "rcParams['axes.labelcolor'] = 'red'\n",
    "rcParams['axes.labelsize'] = 'large'\n",
    "rcParams['axes.labelweight'] = 'bold'\n",
    "\n",
    "k = 0.1\n",
    "b = -0.7\n",
    "\n",
    "x = np.arange(10)\n",
    "y = k*x + b\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "ax.barh(x, y)\n",
    "ax.set_xlabel(u'Ось абсцисс')\n",
    "ax.set_ylabel(u'Ось ординат')\n",
    "ax.grid(True)\n",
    "\n",
    "save('pic_6_4_3', fmt='png')\n",
    "save('pic_6_4_3', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
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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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