{
 "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 8 Координатные оси Axis\n",
    "\n",
    "### Содержание главы\n",
    "\n",
    "1. Контейнер Axis;\n",
    "\n",
    "2. Главные и вспомогательные деления;\n",
    "\n",
    "3. Настройка координатной оси.\n",
    "\n",
    "Данные, насённые тем или иным графическим способомна рисунок Figure и область рисования Axes, не представляют особого интереса для научного анализа, пока они не привязаны к какой-либо системе координат. При создании экземпляра axes на вновь созданной области рисования автоматически задаётся прямоугольная система координат, если не указаны атрибуты polar или projection. Подробнее про области рисования с другими системами координат смотри главу \"Графики в полярных координатах\".\n",
    "\n",
    "Система координат определяет вид координатных осей. По умолчанию задаётся прямоугольная система координат: ось абсцисс (ось \"OX\") и ось ординат (ось \"OY\"). В полярной системе координат координатными осями являются радиус и угол наклона, что выражается в виде своеобразного тригонометрического круга.\n",
    "\n",
    "Для хранения и форматирования каждой координатной оси и существует контейнер Axis.\n",
    "\n",
    "### Электронные ресурсы:\n",
    "\n",
    "+ [Описание элементов рисунка в matplotlib](http://matplotlib.org/users/artists.html);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 Контейнер Axis\n",
    "\n",
    "Axis (не путать с областями рисования Axes) - это третья \"матрёшка\" (после Figure и Axes) или контейнер в matplotlib, которая привязана к области рисования Axes и на которой располагаются деления осей (ticks), подписи делений (tick labels) и подписи осей (axis labels).\n",
    "\n",
    "Координатные оси являются экземплярами класса matplotlib.axis.Axis.\n",
    "\n",
    "Любая область рисования Axes содержит два особых Artist-контейнера: XAxis и YAxis. Они отвечают за отрисовку делений (ticks) и подписей (labels) координатных осей, которые хранятся как экземпляры в переменных xaxis и yaxis. Чтобы обратиться к экземпляру axis, отвечающему за ось ординат, нужно обратиться к контейнеру yaxis соответствующей области рисования ax. Причём запись \"`yax=ax.get_yaxis()`\" идентична записи \"`yax=ax.yaxis`\".\n",
    "\n",
    "Каждый экземпляр axis содержит атрибут подписей (label) координатной оси и список главных (major ticks) и вспомогательных (minor ticks) делений, а также является хранилищем для экземпляров делений (ticks). Деления - это экземпляры класса `matplotlib.axis.Tick`, которые визуализируют деления (их размер, цвет, толщину и т.д.) и подписи к ним. Деления создаются динамически исходя из области изменения переданных данных. В результате на координатной оси появляются и хранятся экземпляры классов `matplotlib.axis.XTick` и `matplotlib.axis.YTick`. Они родственны классу `matplotlib.axis.Tick`. Хотя все графические примитивы, с помощью которых создаётся облик координатной оси содержатся в делениях ticks, о которых будет показано в следующей главе, у экземпляров axis есть средства для управления линиями делений (tick lines), подписями делений (tick labels), а также местоположением делений (tick locations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Major X-ticks locations:', array([   0.,   20.,   40.,   60.,   80.,  100.]))\n",
      "('Major X-ticks labels:', <a list of 6 Text major ticklabel objects>)\n",
      "('Major X-ticks tick lines:', <a list of 12 Line2D ticklines objects>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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sAmURnxa1dNx9FTAs+/m0nMenAFPaevDVq1czfPjwtn65iIi0QqL30jn11FMZP348o0eP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\nGvssKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6efb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 8.1.1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "x = np.arange(100)\n",
    "y = -np.exp(-0.3*x) + 2.33\n",
    "\n",
    "ax.plot(x, y, 'k')\n",
    "\n",
    "xax = ax.xaxis   # или xax = ax.get_xaxis()\n",
    "\n",
    "xlocs = xax.get_ticklocs()\n",
    "print ('Major X-ticks locations:', xlocs)\n",
    "xlabels = xax.get_ticklabels()\n",
    "print ('Major X-ticks labels:', xlabels)\n",
    "xlines = xax.get_ticklines()\n",
    "print ('Major X-ticks tick lines:', xlines)\n",
    "\n",
    "# Линии вспомогательной сетки (главные деления) только по оси абсцисс\n",
    "xax.grid(True)\n",
    "\n",
    "for label in xlabels:\n",
    "    # цвет подписи деленений оси OX\n",
    "    label.set_color('blue')\n",
    "    # поворот подписей деленений оси OX \n",
    "    label.set_rotation(45)\n",
    "    # размер шрифта подписей делений оси OX \n",
    "    label.set_fontsize(14)\n",
    "\n",
    "save('pic_8_1_1', fmt='png')\n",
    "save('pic_8_1_1', fmt='pdf')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2 Главные и вспомогательные деления\n",
    "\n",
    "В matplotlib деления на координатной оси могут быть главными (major ticks) или вспомогательными (minor ticks). По умолчанию наносятся главные деления, к ним привязывается нанесение вспомогательной сетки grid, за которую отвечает параметр axes.grid.which': u'major' из rcParams. Работа со вспомогательными делениями ничем не отличается от работы с главными делениями: используются те же методы, но явно указывается тип делений через определённый атрибут. Обычно этот атрибут называется which (принимает значения 'major'/'minor') или булевый атрибут minor.\n",
    "\n",
    "Ниже представлен список некоторых методов Axis. Они применяются к экземплярам xaxis или yaxis. Так как при работе с атрибутами поддерживается идеология \"set-get\", то списке указаны методы только с приставкой \"set_\". Для получения значений атрибутов какой-либо характеристики, нужно в методе заменить приставку \"set\" на \"get\": set_scale -> get_scale; get_data_interval -> set_data_interval и так далее.\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "Вспомогательный метод -> Описание\n",
    "\n",
    "----------------------------------------------------------\n",
    "\n",
    "+ **set_scale** -> определяет характер изменений значений на оси: линейный 'linear' (по умолчанию) или 'log' логарифмический;\n",
    "\n",
    "+ **set_view_interval** -> экземпляр интервала области изменения отображений по оси axis;\n",
    "\n",
    "+ **set_data_interval** -> экземпляр интервала области изменения данных по оси axis;\n",
    "\n",
    "+ **set_gridlines** -> список линий сетки для Axis;\n",
    "\n",
    "+ **set_label** -> подпись оси, экземпляр Text;\n",
    "\n",
    "+ **set_ticklabels** -> подписи делений, список экземпляров Text. Тип делений определяет параметр minor=True/False;\n",
    "\n",
    "+ **set_ticklines** -> конфигурация делений, список экземпляров Line2D. Тип делений определяет параметр minor=True/False;\n",
    "\n",
    "+ **set_ticklocs** -> положение делений. Тип делений определяет параметр minor=True/False;\n",
    "\n",
    "+ **set_major_locator** -> экземпляр matplotlib.ticker.Locator для главных (major) делений;\n",
    "\n",
    "+ **set_major_formatter** -> экземпляр matplotlib.ticker.Formatter для главных делений;\n",
    "\n",
    "+ **set_major_ticks** -> список экземпляров Tick для главных делений;\n",
    "\n",
    "+ **set_minor_locator** -> экземпляр matplotlib.ticker.Locator для вспомгательных (minor) делений;\n",
    "\n",
    "+ **set_minor_formatter** -> экземпляр matplotlib.ticker.Formatter для вспомогательных делений;\n",
    "\n",
    "+ **set_minor_ticks** -> список экземпляров Tick для вспомогательных делений;\n",
    "\n",
    "+ **grid** -> определяет будет ли отображаться линии сетки для главной или вспомогательных делений (параметр which='major'/'minor');\n",
    "\n",
    "----------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Major X-ticks tick lines:', <a list of 12 Line2D ticklines objects>)\n",
      "('Major X-ticks location:', array([   0.,   20.,   40.,   60.,   80.,  100.]))\n",
      "('Major X-ticks labels:', <a list of 6 Text major ticklabel objects>)\n",
      "('Major X-ticks tick lines:', <a list of 0 Line2D ticklines objects>)\n",
      "('Minor X-ticks location:', [])\n",
      "('Minor X-ticks labels:', <a list of 0 Text minor ticklabel objects>)\n",
      "----------------------------\n",
      "Тип изменения оси: linear\n",
      "Область изменения отображения данных: [   0.  100.]\n",
      "Область изменения отображения данных: [  0.  99.]\n",
      "Линии сетки: <a list of 6 Line2D gridline objects>\n",
      "Подпись оси: Text(0.5,0,u'')\n",
      "Подписи делений: <a list of 6 Text major ticklabel objects>\n",
      "Линии делений: <a list of 12 Line2D ticklines objects>\n",
      "Расположение делений: [   0.   20.   40.   60.   80.  100.]\n",
      "locator главных делений: <matplotlib.ticker.AutoLocator object at 0x00000000083C4B00>\n",
      "formatter главных делений: <matplotlib.ticker.ScalarFormatter object at 0x000000000847D978>\n",
      "Список главных делений оси: [<matplotlib.axis.XTick object at 0x00000000083BFF98>, <matplotlib.axis.XTick object at 0x00000000083A8940>, <matplotlib.axis.XTick object at 0x0000000008504748>, <matplotlib.axis.XTick object at 0x0000000008504CF8>, <matplotlib.axis.XTick object at 0x0000000008510390>, <matplotlib.axis.XTick object at 0x00000000085109E8>]\n",
      "locator вспомогательных делений: <matplotlib.ticker.NullLocator object at 0x00000000083C8748>\n",
      "formatter вспомогательных делений: <matplotlib.ticker.NullFormatter object at 0x000000000710DC88>\n",
      "Список вспомогательных делений оси: []\n",
      "----------------------------\n"
     ]
    },
    {
     "data": {
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/L5W2WE5wkPZrwB2YOcHzX01w5s7NFHNbBJlQQdCDrwauw/032Xm3AB8laJt7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\nEIW+iEh+seqSAAAAB0lEQVQJ+f9OhUWo7QF3VQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6974ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 8.2\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "rect = ax.patch\n",
    "rect.set_facecolor('lightslategray')\n",
    "rect.set_alpha(0.25)\n",
    "\n",
    "x = np.arange(100)\n",
    "y = np.exp(-0.3*x) + 2.33\n",
    "\n",
    "ax.plot(x, y, 'green')\n",
    "\n",
    "xax = ax.xaxis   # экземпляр xaxis\n",
    "\n",
    "print ('Major X-ticks tick lines:', xax.get_ticklines())\n",
    "print ('Major X-ticks location:', xax.get_ticklocs())\n",
    "print ('Major X-ticks labels:', xax.get_ticklabels())\n",
    "\n",
    "print ('Major X-ticks tick lines:', xax.get_ticklines(minor=True))\n",
    "print ('Minor X-ticks location:', xax.get_ticklocs(minor=True))\n",
    "print ('Minor X-ticks labels:', xax.get_ticklabels(minor=True))\n",
    "\n",
    "# вспомогательная сетка для главных делений \n",
    "ax.grid(True, which=u'major', color='w', linewidth=2., linestyle='solid')\n",
    "\n",
    "# вспомогательная сетка для вспомогательных делений \n",
    "ax.grid(True, which=u'minor', color='b')\n",
    "\n",
    "# Ось абсцисс\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    # label - это экземпляр текста Text\n",
    "    label.set_color('red')\n",
    "    label.set_rotation(-45)\n",
    "    label.set_fontsize(15)\n",
    "\n",
    "# Ось ориднат\n",
    "for line in ax.yaxis.get_ticklines():\n",
    "    # line - это экземпляр плоской линии Line2D\n",
    "    line.set_color('blue')   # задаём цвет линии деления\n",
    "    line.set_markersize(14)   # задаём длину линии деления\n",
    "    line.set_markeredgewidth(3)   # задаём толщину линии деления\n",
    "\n",
    "print ('----------------------------')\n",
    "print u'Тип изменения оси:', xax.get_scale()\n",
    "print u'Область изменения отображения данных:', xax.get_view_interval()\n",
    "print u'Область изменения отображения данных:', xax.get_data_interval()\n",
    "print u'Линии сетки:', xax.get_gridlines()\n",
    "print u'Подпись оси:', xax.get_label()\n",
    "print u'Подписи делений:', xax.get_ticklabels()\n",
    "print u'Линии делений:', xax.get_ticklines()\n",
    "print u'Расположение делений:', xax.get_ticklocs()\n",
    "print u'locator главных делений:', xax.get_major_locator()\n",
    "print u'formatter главных делений:', xax.get_major_formatter()\n",
    "print u'Список главных делений оси:', xax.get_major_ticks()\n",
    "print u'locator вспомогательных делений:', xax.get_minor_locator()\n",
    "print u'formatter вспомогательных делений:', xax.get_minor_formatter()\n",
    "print u'Список вспомогательных делений оси:', xax.get_minor_ticks()\n",
    "print ('----------------------------')\n",
    "\n",
    "save('pic_8_2', fmt='png')\n",
    "save('pic_8_2', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3 Настройка координатной оси\n",
    "\n",
    "Одну и ту же настройку координатных осей (тип, частота, подписи делений, их цвет, шрифт и размер, а также положение и многое другое) можно провести разными способами. Так как в экземпляре Axes всегда есть выход на экземпляры Axis (XAxis и YAxis), то и большинстов удобных для пользователя методов настройки координатной оси привязаны к экземплярам Axes. \n",
    "\n",
    "Одним из способов привести координатную ось в нужный пользователю вид заключается в применении методов \"set-get\" для объектов типа Axes:\n",
    "\n",
    "+ **`ax.get_xticks()`** - получить список местоположения делений (по умолчанию главных) на оси абсцисс;\n",
    "\n",
    "+ **`ax.set_xticklabels()`** - задать значения подписей для текущего местоположения делений (по умолчанию главных) на оси абсцисс;\n",
    "\n",
    "+ **`ax.get_yticks()`** - получить список местоположения делений (по умолчанию главных) на оси ординат;\n",
    "\n",
    "+ **`ax.set_yticklabels()`** - задать значения подписей для текущего местоположения делений (по умолчанию главных) на оси ординат.\n",
    "\n",
    "Методы `\\*ticklabels()` принимают множество параметров, с помощью которых, можно настроить внешний вид (цвет, поворот вокруг оси, шрифт, стиль шрифта и т.д.) текстовой подписи. Любая подпись - это экземпляр Text и работать с ним нужно соответствующими методами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Xticks:', array([-6., -4., -2.,  0.,  2.,  4.,  6.]))\n",
      "('Yticks:', array([ 2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,  6. ,  6.5,  7. ]))\n",
      "Yticklabels: <class 'matplotlib.cbook.silent_list'>\n",
      "Каждый label - это <class 'matplotlib.text.Text'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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WYuktsexPXSaFwskUvQiZEtBIXwe2bmp9yk0ul8tawYQD2PCw4AA2PCw4gA0P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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6f98160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 8.3.1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(-5, 6)\n",
    "y = np.exp(-0.3*x) + 2.33\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.plot(x, y)\n",
    "ax1.grid(True)\n",
    "\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.plot(x, y)\n",
    "ax2.grid(True)\n",
    "\n",
    "xticks = ax2.get_xticks()\n",
    "yticks = ax2.get_yticks()\n",
    "\n",
    "xx = np.arange(-10, 11, 2)\n",
    "yy = np.arange(0, 11)\n",
    "\n",
    "print('Xticks:', xticks)\n",
    "print('Yticks:', yticks)\n",
    "\n",
    "xlabels = []\n",
    "ylabels = []\n",
    "for i in xx:\n",
    "    xlabels.append('X point %d' % i)\n",
    "\n",
    "for j in yy:\n",
    "    ylabels.append('Y point %d' % j)\n",
    "    \n",
    "ax2.set_xticks(xx)\n",
    "ax2.set_yticks(yy)\n",
    "ax2.set_yticklabels(ylabels, color='green')\n",
    "ax2.set_xticklabels(xlabels, color='green', rotation=315)\n",
    "\n",
    "yticklabels = ax2.get_yticklabels()\n",
    "print('Yticklabels: %s' % type(yticklabels))\n",
    "print('Каждый label - это %s' % (type(yticklabels[0])))\n",
    "\n",
    "save('pic_8_3_1', fmt='png')\n",
    "save('pic_8_3_1', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для удобства пользователей многие методы настройки, требующие работы непосредственно с экземплярами Ticks, были продублированы в виде заскриптованных методов для объектов Axes. Далее показаны основные приёмы с помощью которых можно работать с координатными осями.\n",
    "\n",
    "Для облегчения доступа к настройкам делений, требующим работы непосредственно с экземплярами Ticks, в matplotlib был сделан метод `ax.tick_params`, который позволяет быстро и просто настраивать расположение и отображение делений координатных осей и их подписей. Ниже приводится список атрибутов данного метода:\n",
    "\n",
    "1. **axis** : ['x' | 'y' | 'both'] - экземпляр Axis на котором находятся деления; по умолчанию 'both';\n",
    "\n",
    "2. **reset** : [True | False] - если True, то значения всех параметров сбрасываются на значения по умолчанию. По умолчанию равен False;\n",
    "\n",
    "3. **which** : ['major' | 'minor' | 'both'] - определяет принадлежность к типу делений. По умолчанию 'major';\n",
    "\n",
    "4. **direction** : ['in' | 'out' | 'inout'] - определяет направление делений (внутрь, вовне или и снаружи и внутри);\n",
    "\n",
    "5. **length** - длиная деления в точках (points);\n",
    "\n",
    "6. **width** - ширина деления в точках (points);\n",
    "\n",
    "7. **color** - цвет деления. Возможен любой цвет, приемлимый в matplotlib;\n",
    "\n",
    "8. **pad** - расстояние в точках между делением и подписью к нему;\n",
    "\n",
    "9. **labelsize** - размер шрифта подписи деления в виде строки (например, 'large') или числа;\n",
    "\n",
    "10. **labelcolor** - цвет подписи деления. Возможен любой цвет, приемлимый в matplotlib;\n",
    "\n",
    "11. **colors** - изменяет цвет деления и цвет подписи деления на одно значение. Возможен любой цвет, приемлимый в matplotlib;\n",
    "\n",
    "12. **zorder** - zorder деления и подписи деления;\n",
    "\n",
    "13. **bottom, top, left, right** : [bool | 'on' | 'off'] - каждый из параметров контролирует отображение делений на соответствующей оси;\n",
    "\n",
    "14. **labelbottom, labeltop, labelleft, labelright** : [bool | 'on' | 'off'] - каждый из параметров контролирует отображение подписи делений на соответствующей оси.\n",
    "\n",
    "Правда метод tick_params не имеет параметра rotation. Чтобы повернуть подписи делений, нужно воспользоваться иным способом, например указать этот параметр явно через метод `ax.set_xticklabels`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QkbFBy6mdb3UVnFKWr7gIOEGEz8So2SNZ0UxStxq17xWbz+0ZPHqu4tz1wPXh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\nRl3y/wEwnNvu1AGiigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6f80a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 8.3.2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(-5, 6)\n",
    "y = np.exp(-0.3*x) + 2.33\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.plot(x, y)\n",
    "ax1.grid(True)\n",
    "\n",
    "# Настройка оси главных делений для оси абсцисс \n",
    "ax1.tick_params(axis='x', which='major', direction='inout',\n",
    "                bottom=True, top=False, left=True, right=False, \n",
    "                color='b', labelcolor='g',\n",
    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "# Настройка оси главных делений для оси ординат \n",
    "ax1.tick_params(axis='y', which='major', direction='inout',\n",
    "                bottom=True, top=False, left=True, right=False, \n",
    "                color='r', labelcolor='pink',\n",
    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "\n",
    "# Метод tick_params не имеет параметра rotation. Поэтому чтобы повернуть подписи\n",
    "# нужно сделать это явно через метод set_xticklabels\n",
    "xticks = ax1.get_xticks()\n",
    "ax1.set_xticklabels(xticks, rotation=45)\n",
    "\n",
    "ax2 = fig.add_subplot(122, frameon=False)\n",
    "ax2.plot(x, y)\n",
    "ax2.grid(True)\n",
    "\n",
    "ax2.tick_params(axis='x', which='major', direction='out',\n",
    "                bottom=True, top=True, left=True, right=True, \n",
    "                color='blue', labelcolor='grey',\n",
    "                labelbottom=True, labeltop=True, labelleft=True, labelright=True)\n",
    "ax2.tick_params(axis='x', which='minor', direction='both',\n",
    "                bottom=True, top=False, left=True, right=False, \n",
    "                color='r', labelcolor='pink',\n",
    "                labelbottom=True, labeltop=False, labelleft=True, labelright=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "save('pic_8_3_2', fmt='png')\n",
    "save('pic_8_3_2', fmt='pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R1ZB1zTMAayUutTSmpUEFFEE/A3dqTB+TuOyB3fjKt0iswH6nXRU0pqMlLjuV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\nTJxzzmXs/wNXRP6HuLcSbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8e7c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример 8.3.3\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(-90, 92.5 , 2.5)\n",
    "y = 113.3*np.cos(x*np.pi/180.)\n",
    "z = 113.3*np.sin(x*np.pi/180.)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(211)\n",
    "ax.plot(x, y)\n",
    "\n",
    "ax.tick_params(axis='x', which='major', \n",
    "               labelleft='off', labelright='off', labeltop='on', labelbottom='on', \n",
    "               bottom=True, top=True,\n",
    "               direction='in', length=10, width=4, colors='g')\n",
    "\n",
    "old_major = ax.get_xticks()\n",
    "N = 0.25\n",
    "lag = (old_major[1] - old_major[0])*N\n",
    "new_minor = np.arange(old_major[0] + lag, old_major[-1], 2*lag)\n",
    "ax.set_xticks(new_minor, minor = True)\n",
    "\n",
    "ax.tick_params(axis='x', which='minor', \n",
    "               labeltop='on', labelbottom='on', \n",
    "               bottom=True, top=True,\n",
    "               direction='inout', length=5, width=2, colors='cyan')\n",
    "\n",
    "ax.tick_params(axis='y', \n",
    "               labelleft='off', labelright='off',\n",
    "               left=False, right=False)\n",
    "\n",
    "ax.grid(True, which='major')\n",
    "ax.grid(True, which='minor')\n",
    "\n",
    "ax.yaxis.set_label_position('left')\n",
    "ax.set_ylabel(u'Слева')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.set_xlabel(u'Сверху')\n",
    "\n",
    "# ************************************************\n",
    "ax = fig.add_subplot(212)\n",
    "\n",
    "ax.plot(x, y)\n",
    "ax.tick_params(axis='both', reset=True)\n",
    "\n",
    "ax.tick_params(axis='y', which='major', \n",
    "               labelleft='on', labelright='on', \n",
    "               left=True, right=True,\n",
    "               direction='out', length=10, width=1., colors='r')\n",
    "\n",
    "old_major = ax.get_yticks()\n",
    "N = 0.25\n",
    "lag = (old_major[1] - old_major[0])*N\n",
    "new_minor = np.arange(old_major[0] + lag, old_major[-1], 2*lag)\n",
    "ax.set_yticks(new_minor, minor = True)\n",
    "\n",
    "ax.tick_params(axis='y', which='minor', \n",
    "               labelleft='on', labelright='on', \n",
    "               left=True, right=True,\n",
    "               direction='inout', length=5, width=1., colors='k')\n",
    "\n",
    "ax.tick_params(axis='x',\n",
    "               labeltop='off', labelbottom='off', \n",
    "               bottom=False, top=False)\n",
    "\n",
    "ax.yaxis.set_label_position('right')\n",
    "ax.set_ylabel(u'Справа', rotation=270)\n",
    "\n",
    "ax.xaxis.set_label_position('bottom')\n",
    "ax.set_xlabel(u'Снизу')\n",
    "\n",
    "ax.grid(True, axis='y', which='major', color='red')\n",
    "ax.grid(True, axis='y', which='minor', color='k')\n",
    "\n",
    "save('pic_8_3_3', fmt='png')\n",
    "save('pic_8_3_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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