{ "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": false }, "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 10 Особенности координатных осей\n", "\n", "### Содержание главы\n", "\n", "1. Дополнительная координатная ось;\n", "\n", "2. Работа с легендой дополнительной оси;\n", "\n", "3. Единая легенда;\n", "\n", "4. Графики с логарифмическими координатными осями.\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)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10.1 Дополнительная координатная ось\n", "\n", "Иногда требуется нанести на один рисунок две величины, имеющие общие единицы измерения по одной оси, но разные по другой. Например, это могут быть временные ряды сильно отличающихся по масштабу величин. Или ряды величин, имеющих разные единицы измерения. В таких случаях удобно нарисовать дополнительную координатную ось (обычно - ординат). \n", "\n", "Такую возможность обеспечивает метод **ax.twinx()** для оси OX и метод **ax.twiny()** для оси OY. В результате создаётся ещё одна область рисования, совпадающая по размерам с исходной. На каждой области рисуются соответствюущие значения, при этом с помощью пользовательской настройки подписей осей ординат можно добиться, чтобы вспомогательные сетки grid обеих областей совпадали." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Результат работы метода twinx \n" ] }, { "data": { "image/png": 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N58nNTd62NQFNngjxj1BJ+UTL3Am3Sgpsu/xKikpcz9VIyxwPVVi3zigcp3Ts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\nZ2XB6dPhNRzLa1JwxkiFxe7dJsxSnTpnDg1NGBqxKb9wWZi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 10.1\n", "\n", "from matplotlib import rcParams\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.arange(-2*np.pi, 2*np.pi, 0.2)\n", "y = np.sin(x) * np.cos(x)\n", "f = np.sin(x) + np.cos(x)\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "ax2 = ax1.twinx() # Создаём вторую область рисования ax2\n", "print 'Результат работы метода twinx %s' % type(ax2)\n", "\n", "ax1.plot(x, f, label = u'Сумма cos и sin', color='green')\n", "ax1.set_xlabel(u'Аргумент')\n", "ax1.set_ylabel(u'Функция 1', color='green')\n", "ax1.grid(True, color='green')\n", "ax1.tick_params(axis='y', which='major', labelcolor='green')\n", "\n", "ax2.plot(x, y*333, label = u'Произведение cos и sin', color='red')\n", "ax2.set_ylabel(u'Функция 2', color='red')\n", "ax2.grid(True, color='red')\n", "ax2.tick_params(axis='y', which='major', labelcolor='red')\n", "\n", "ax1.set_title(u'Несколько разномасштабных переменных')\n", "\n", "save('pic_10_1', fmt='png')\n", "save('pic_10_1', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10.2 Работа с легендой дополнительной оси\n", "\n", "Если график, где присутствует дополнительная координатная ось, выполнен в чёрно-белом варианте, понять какой график относится к левой шкале, а какой к правой невозможно. В таком случае необходимо использовать легенду для идентификации данных. При работе с легендой такого графика есть свои нюансы." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0S2vGqm2ronaXMmNZUBDRoqwjZ+/e5vewebP3cjkkZcbzKCUZllT9CWQBBgPr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\nvn+dmc1tvW4L7/qUlPN+qUDnz5lj/HK19WcU/75wtnNycsh56SWznZF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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 10.2.1\n", "\n", "from matplotlib import rcParams\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.arange(-2*np.pi, 2*np.pi, 0.2)\n", "y = np.sin(x) * np.cos(x)\n", "f = np.sin(x) + np.cos(x)\n", "\n", "fig = plt.figure()\n", "# Явно задаём область рисования в виде четырёхуголника\n", "ax1 = fig.add_subplot(111) \n", "ax2 = ax1.twinx() # Создаём вторую шкалу ax2\n", "\n", "ax1.plot(x, f, label = u'Сумма cos и sin', color='green')\n", "ax1.set_xlabel(u'Аргумент')\n", "ax1.set_ylabel(u'Функция 1', color='green')\n", "ax1.grid(True, color='green')\n", "\n", "ax2.plot(x, y*333, label = u'Произведение cos и sin', color='red')\n", "ax2.set_ylabel(u'Функция 2', color='red')\n", "ax2.grid(True, color='red')\n", "#ax2.legend()\n", "\n", "ax1.set_title(u'Несколько разномасштабных переменных')\n", "\n", "# Легенда для всего рисунка fig\n", "plt.legend()\n", "\n", "save('pic_10_2_1', fmt='png')\n", "save('pic_10_2_1', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Несмотря на то, что было указано нарисовать легенду ко всему рисунку, отобразилась легенда только для одной координатной оси из двух!\n", "\n", "На самом деле обе подписи легенды отобразились верно, но более новая легенда перезатёрла предыдущую. Потери можно избежать, если указать другое место расположения. Чтобы каждая подпись была в своём месте, нужно воспользоваться методом `ax.legend()`, а не `plt.legend()`.\n", "\n", "Расположение легенды задаётся параметром loc метода legend(), который принимает значения в виде цифр (1-9), начиная от верхнего левого угла и заканчивая нижним правым, и в виде условных обозначений. Значение loc='best' автоматически выберет место на рисунке, где легенда меньше всего будет \"портить\" график. Также можно убрать рамку вокруг легенды для визуального уменьшения места (frameon=False), занимаемого легендой, снижая тем самым геометрическое давление рисунка в целом." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MI1JTVdevD1ymvFy1RQvVnTujI1MdxXx2Vn2epgLfmN+/ATLM77cCjwEdgNUY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\nu2A243uMD/6iUDJPBFjEGy/3PV6xTqoh4QRPBEUQa6R8Gd9jPDnbc9h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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 10.2.2\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.arange(-2*np.pi, 2*np.pi, 0.2)\n", "y = np.sin(x) * np.cos(x)\n", "f = np.sin(x) + np.cos(x)\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "ax2 = ax1.twinx() # Создаём вторую шкалу ax2\n", "\n", "ax1.plot(x, f, label = u'Сумма cos и sin', color='green')\n", "ax1.set_xlabel(u'Аргумент')\n", "ax1.set_ylabel(u'Функция 1', color='green')\n", "ax1.grid(True, color='green')\n", "ax1.legend(loc=2) \n", "\n", "ax2.plot(x, y*333, label = u'Произведение cos и sin', color='red')\n", "ax2.set_ylabel(u'Функция 2', color='red')\n", "ax2.grid(True, color='red')\n", "ax2.legend(loc=1)\n", "\n", "plt.title(u'Несколько разномасштабных переменных')\n", "\n", "save('pic_10_2_2', fmt='png')\n", "save('pic_10_2_2', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10.3 Единая легенда\n", "\n", "Если есть необходимость объединить обе легенды, сделав её единой, то можно пойти на следующую хитрость:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eUVRQZH5IySqpsWNtWVKhuJRTl19GxKTatqXVoMGmHx9Rc6SGkpoSxueY+ErM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\nJWnrwAqbPzt/XuT220UsrO/zJrDBBR2znhTasF8ScIe+Px0YhzYn9at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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 10.3\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.arange(-2*np.pi, 2*np.pi, 0.2)\n", "y = np.sin(x) * np.cos(x)\n", "f = np.sin(x) + np.cos(x)\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "line1 = ax1.plot(x, f, label = u'Сумма cos и sin', color='green')\n", "ax1.set_xlabel(u'Аргумент')\n", "ax1.set_ylabel(u'Функция 1', color='green')\n", "ax1.grid(True, color='green')\n", "\n", "ax2 = ax1.twinx() # Создаём вторую шкалу ax2\n", "line2 = ax2.plot(x, y*333, label = u'Произведение cos и sin', color='red')\n", "ax2.set_ylabel(u'Функция 2', color='red')\n", "ax2.grid(True, color='red')\n", "\n", "lns = line1 + line2\n", "labs = [l.get_label() for l in lns]\n", "ax1.legend(lns, labs, loc=3, frameon=False)\n", "\n", "plt.title(u'Общая легенда для двух осей')\n", "\n", "save('pic_10_3', fmt='png')\n", "save('pic_10_3', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10.4 Графики с логарифмическими координатными осями\n", "\n", "Иногда очень удобно использовать не стандартную равномерную шкалу на координатных осях, а логарифмическую. \n", "\n", "Сделать шкалу координатной оси логарифмической позволяют методы `plt.xscale('log')/plt.yscale('log')` или для областей рисования - `ax.set_xscale('log')/ax.set_yscale('log')`.\n", "\n", "> Из определения натурального логарифма и экспоненты вытекает следующий факт: если ось ординат имеет логарифмическую шкалу, то график функции, которая равна экспоненте от аргумента, будет выглядеть как прямая линия." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DdF9tuiEifwTuUNXjg/V7cNUtRiSNYbpjFJVcdcfnN6mxQFtgmIgcCHyYvIMP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\nrsC1GN8F+Leq9k81iK95Ur7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 10.4.1\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.arange(20)\n", "y = np.exp(x)\n", "\n", "fig = plt.figure()\n", "\n", "# 1 x и y=x\n", "ax = fig.add_subplot(221)\n", "ax.plot(x, x)\n", "ax.grid(True)\n", "ax.set_xlabel('x', fontsize=14)\n", "ax.set_ylabel('y = x', fontsize=14)\n", "\n", "# 2 x и y=exp(x) \n", "ax = fig.add_subplot(222)\n", "ax.plot(x, y)\n", "ax.grid(True)\n", "ax.set_xlabel('x', fontsize=14)\n", "ax.set_ylabel('y = exp(x)', fontsize=14)\n", "\n", "# 3 x и y=exp(x). OY с log шкалой\n", "ax = fig.add_subplot(223)\n", "ax.set_yscale('log') # log здесь - натуральный логарифм!\n", "# для работы с типом axis -> ax.set_yscale('log') \n", "ax.plot(x, y)\n", "ax.grid(True)\n", "ax.set_xlabel('x', fontsize=14)\n", "ax.set_ylabel('y = exp(x)', fontsize=14)\n", "ax.set_title(u'OY log шкала', loc='center')\n", "\n", "# 4 x и y=x. OX с log шкалой\n", "ax = fig.add_subplot(224)\n", "ax.set_xscale('log') # log здесь - натуральный логарифм!\n", "ax.plot(x, x)\n", "ax.grid(True)\n", "ax.set_xlabel('x', fontsize=14)\n", "ax.set_ylabel('y = x', fontsize=14)\n", "ax.set_title(u'OX log шкала', loc='center')\n", "\n", "# Автоматическое форматирование риснука\n", "plt.tight_layout() \n", "\n", "save('pic_10_4_1', fmt='png')\n", "save('pic_10_4_1', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для того, чтобы выразить обе координатные оси в логарифмических шкалах, существует метод `plt.loglog()` или `ax.loglog()`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\pashabanov\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:475: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labelled objects found. \"\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 10.4.2\n", "\n", "x = np.arange(50)\n", "y = np.exp(x)\n", "\n", "fig = plt.figure()\n", "\n", "# 1 Обычные шкалы\n", "ax = fig.add_subplot(211)\n", "ax.plot(x, y)\n", "ax.set_xlabel('x', fontsize=14)\n", "ax.set_ylabel('y = exp(x)', fontsize=14)\n", "ax.grid(True)\n", "ax.legend(loc='best', frameon=False)\n", "\n", "# Log шкалы\n", "ax = fig.add_subplot(212)\n", "ax.loglog() \n", "ax.plot(x, y)\n", "ax.set_xlabel('x', fontsize=14)\n", "ax.set_ylabel('y = exp(x)', fontsize=14)\n", "ax.set_title(u'OY log и OX log шкалы')\n", "ax.grid(True)\n", "\n", "plt.tight_layout()\n", "\n", "save('pic_10_4_2', fmt='png')\n", "save('pic_10_4_2', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "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)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }