{ "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 13 Цветовая шкала\n", "\n", "### Содержание главы\n", "\n", "1. Цветовая шкала colorbar;\n", "\n", "2. Подпись цветовой шкалы;\n", "\n", "3. Положение цветовой шкалы на области рисования;\n", "\n", "4. Деления цветовой шкалы;\n", "\n", "5. Особенности в отображении цветовых шкал разными методами;\n", "\n", "6. Цветовая шкала для метода сontourf();\n", "\n", "7. Цветовая шкала для метода сontourf();\n", "\n", "8. Цветовая шкала для метода scatter().\n", "\n", "**Цветовая шкала colorbar** - это аналог легенды для отображения трёхмерных полей, заданных обычно в виде матрицы или двумерного массива. Цветовая шкала является неотъемлемой частью рисунков, создаваемых с помощью методов ax.contour(), ax.contourf(), ax.imshow(), ax.pcolor(), ax.scatter().\n", "\n", "Каждая цветовая шкала является по сути ещё одной областью рисования Axes. Это значит, что она имеет такие же методы и атрибуты, как и обычные экземпляры типа Axes или AxesSubplot. Имеются у неё и свои особенности.\n", "\n", "### Электронные ресурсы:\n", "\n", "+ [Документация по цветовой палитре colormap](http://matplotlib.org/users/colormaps.html);\n", "\n", "+ [Примеры создания цветовых шкал разных видов](http://matplotlib.org/examples/api/colorbar_only.html);\n", "\n", "+ [Примеры создания цветовых шкал для метода contourf()](http://matplotlib.org/examples/pylab_examples/contourf_demo.html);\n", "\n", "+ [Предустановленные наборы цветов для цветовых шкал](http://matplotlib.org/examples/color/colormaps_reference.html);\n", "\n", "+ [Сокращения и условные обозначения цветов](http://matplotlib.org/examples/color/named_colors.html);\n", "\n", "+ [Подписи осей для colorbar() с форума stackoverflow](http://stackoverflow.com/questions/15908371/matplotlib-colorbars-and-its-text-labels);\n", "\n", "+ [Регулирование расстояния между заголовком и цветовой шкалой](http://stackoverflow.com/questions/17475619/how-do-i-adjust-offset-colorbar-title-in-matplotlib); \n", "\n", "+ [Пример комплексной настройки внешнего вида цветовой шкалы](http://stackoverflow.com/questions/18403226/matplotlib-colorbar-background-and-label-placement);\n", "\n", "+ [Пример 1 по созданию общей цветовой шкалы для нескольких областей рисования](http://stackoverflow.com/questions/9912008/how-to-add-a-single-colobar-that-will-show-the-data-from-2-different-subplot/9914204#9914204);\n", "\n", "+ [Пример 2 по созданию общей цветовой шкалы для нескольких областей рисования](http://stackoverflow.com/questions/13784201/matplotlib-2-subplots-1-colorbar)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.1 Цветовая шкала `colorbar`\n", "\n", "Цветовая шкала, которая связывает цвет и числовые значения, отображаемые на рисунке тем или иным графическим методом, является самостоятельным элементом на рисунке. Цветовая шкала может быть создана при условии, что на рисунке существует \"mappable-object\". К таким объектам относятся результаты выполнения графических команд `cs=ax.pcolor()`, `cs=ax.contourf()`, `cs=ax.imshow()` и др. \n", "\n", "Если условие на наличие \"mappable\" объекта на рисунке выполнено, то при использовании метода `plt.colorbar()` или `fig.colorbar()` на рисунке будет отображена цветовая шкала. \n", "\n", "Цветовая шкала может располагаться непросредственно на графике (привязывается к конкретной области рисования ax через атрибут \"cax\"), так и рядом с ним (привязывается через атрибут \"ax\"). И то и другое отрисовывается на текущей области рисования, однако в первом случае она именуется для цветовой шкалы как просто область рисования axes, а вторая - как родительская область рисования (parent axes). Обычно удобнее располагать цветовую шкалу вне графика или диаграммы, отдавая часть текущей области рисования под цветовую шкалу (см. атрибут fraction). Хотя это и не обязательно, лучше напрямую \"привязать\" цветовую шкалу к конкретной области рисования ax через атрибут colorbar \"ax\". По умолчанию, без указания данного атрибута, реализуется именно такой вариант для текущей области рисования, а если её нет, то создаётся новая. Если же необходимо отобразить цветовую шкалу на самом графике, то нужно воспользоваться атрибутом \"cax\". Оба атрибута принимают экземпляры Axes или AxesSubplot.\n", "\n", "Метод `fig.colorbar()` имеет следующие параметры, определяющие внешний вид шкалы:\n", "\n", "+ **ticks** [None | list of ticks | Locator object]- задаёт положение делений на шкале в виде последовательности или массива;\n", "\n", "+ **extend** ['neither' | 'both' | 'min' | 'max'] - позволяет задать на концах шкалы значения \"больше чем\" и/или \"меньше чем\" в виде цветных треугольников. Цвета треугольников задаются методами cmap.set_over('цвет') и cmap.set_under('цвет') для текущей цветовой палитры cmap;\n", "\n", "+ **extendfrac** [ None | 'auto' | length | lengths] - определяет размер треугольного интервала цветовой шкалы (будет показано на рисунке, если атрибут \"extend\" определён \"положительно\"). По умолчанию он определяется как 0.05 длины шкалы. Если 'auto', то длина треугольника равна длине остальных интервалов (при условии, если атрибут spacing='uniform') или пропорциональна доле данных, попадающих в данный интервал (если spacing='proportional'). Если задано число, то оно определяет длину обоих треугольников и для минимума и для максимума в виде доли длины цветовой шкалы. Можно задать два значения в виде списка - отдельно для минимума, отдельно для максимума;\n", "\n", "+ **spacing** ['uniform' | 'proportional'] - uniform задаёт одинаковую длину для каждого отрезка шкалы; proportional определяет длину делений пропорционально долям данных, попадающих в градации шкалы;\n", "\n", "+ **format** [ None | format string | Formatter object ] - задаёт формат для подписей значений цветовой шкалы;\n", "\n", "+ **drawedges** [ False | True ] - если True, между интервалами будут нарисованы линии;\n", "\n", "Также есть параметры, которые определяют размеры, положение colorbar на области рисования шкалы:\n", "\n", "+ **orientation** ['vertical' | 'horizontal'] - определяет ориентацию цветовой шкалы (вертикальное или горизонтальное);\n", "\n", "+ **fraction**=0.15 - доля площади родительской области рисования axes, на которой будет нарисована colorbar;\n", "\n", "+ **pad** [0.05 для вертикального, 0.15 для горизонтального положения] - расстояние в долях от размеров исходной области рисования axes между цветовой шкалой и новым изображением области рисования;\n", "\n", "+ **shrink**=1.0 - доля, на которую цветовая шкала будет сжата;\n", "\n", "+ **aspect**=20 - соотношение во сколько раз длинная сторона шкалы больше короткой;\n", "\n", "Обычно ссылку на результат метода `fig.colorbar()` передают в какую-либо переменную, например cbar. Это удобно для последующей настройки цветовой шкалы." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.12667932 1.5273198 2.92796028 4.32860076 5.72924124 7.12988172\n", " 8.5305222 9.93116268]\n", "Тип CS: \n", "Тип CBAR: \n" ] }, { "data": { "image/png": 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eCnxhyRJHnrX82lOHQzEH6oNVam49FD6ICw9sWB/6pHIejIGjDhuMWwfH5Uih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\nKfpelCI/Q5W/yl90yfjkM1SFr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.1\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "a = np.random.random(100)*10.\n", "a = a.reshape(10,10)\n", "\n", "fig = plt.figure(figsize=(12,4))\n", "\n", "N = 7\n", "bounds = np.linspace(np.min(a), np.max(a), N+1)\n", "print(bounds)\n", "cmap = mpl.cm.get_cmap('RdBu', N) \n", "\n", "ax = fig.add_subplot(131)\n", "cs = ax.pcolor(a, cmap=cmap) # вызываем метод pcolor. Вводим пользовательскую раскраску через cmap\n", "print(u'Тип CS: %s' % type(cs))\n", "cbar = fig.colorbar(cs, ax=ax) # рисуем шкалу colorbar для изображения cs. В качестве черточек шкалы указываем bounds. \n", "print(u'Тип CBAR: %s' % type(cbar))\n", "# ticks=bounds\n", "ax.set_title(u'Подбор делений под количество цветов')\n", "\n", "# ---------------------------------------------------\n", "\n", "ax2 = fig.add_subplot(132)\n", "bounds = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0]\n", "cmap = mpl.cm.get_cmap('ocean', len(bounds)-1)\n", "cmap.set_over('red')\n", "cmap.set_under('grey')\n", "cs = ax2.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # Ставим границы для шкалы vmin и vmax.\n", "cbar = fig.colorbar(cs, ax=ax2, ticks=bounds, extend='max', extendfrac=0.1)\n", "ax2.set_title(u'Подбор количества цветов под заданные интервалы значений')\n", "ax2.grid(True)\n", "cmap.set_over('pink')\n", "\n", "# ---------------------------------------------------\n", "\n", "ax3 = fig.add_subplot(133)\n", "cs = ax3.imshow(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # В методе pcolor/pcolormesh/imshow присваиваем пользовательские границы цветов norm\n", "cbar = fig.colorbar(cs, ax=ax3, ticks=bounds, extend='both', extendfrac='auto',\n", " orientation='horizontal')\n", "ax3.set_title(u'Масштабирование цветовой шкалы')\n", "ax3.grid(True)\n", "\n", "plt.tight_layout()\n", "\n", "save('pic_13_1', fmt='png')\n", "save('pic_13_1', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.2 Подпись цветовой шкалы\n", "\n", "Подпись к шкале осуществляется методом `cbar.set_label()`. Однако, мы помним, что цветовая шкала - это особая область рисования, а значит ей доступны и другие методы для создания подписей." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Положение делений на цветовой шкале subplot 1\n", "[ 0.07979106 1.47394994 2.86810882 4.2622677 5.65642658 7.05058546\n", " 8.44474434 9.83890322]\n" ] }, { "data": { "image/png": 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CbBT0yVG2/a/zmG4nO7C6nTypHMfsfLuezmMpfju1luF16hOu8YiIu5VdNugbyury15Ed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\n1U5e3yNpR7KLetZlVNn9pZlNR6+VtAx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S8PaPtU5mZ5gzOTlzZvY5e36f5zlPZt/fvc85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\nY2ZmDXJTdjMzM7MKkLQLcCcwA1gIjAUOB46OiIXlRWZmZuv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.2\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from matplotlib.ticker import MultipleLocator\n", "\n", "a = np.random.random(100)*10.\n", "a = a.reshape(10,10)\n", "\n", "fig = plt.figure(figsize=(14,4))\n", "\n", "N = 7\n", "bounds = np.linspace(np.min(a), np.max(a), N+1)\n", "print(u'Положение делений на цветовой шкале subplot 1')\n", "print(bounds)\n", "cmap = mpl.cm.get_cmap('gnuplot', N) \n", "\n", "ax = fig.add_subplot(131)\n", "\n", "# вызываем метод pcolor. Вводим пользовательскую раскраску через cmap\n", "cs = ax.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1])\n", "cbar = fig.colorbar(cs, ax=ax, pad=0.01, aspect=30) # pad=0.01 приближает шкалу близко к графику\n", "ax.set_title(u'Подбор делений под количество цветов')\n", "# ---------------------------------------------------\n", "\n", "bounds = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0]\n", "\n", "ax2 = fig.add_subplot(132)\n", "formatter2 = MultipleLocator(2)\n", "cmap = mpl.cm.get_cmap('cool', len(bounds)-1)\n", "cmap.set_over('red')\n", "cmap.set_under('grey')\n", "cs = ax2.pcolor(a, cmap=cmap) # Ставим границы для шкалы vmin и vmax.\n", "cbar = fig.colorbar(cs, ax=ax2, ticks=formatter2, fraction=0.1, drawedges=True,\n", " extend='min', extendfrac='auto')\n", "# методы ax.set_xlabel() и ax.set_ylabel()\n", "cbar.ax.set_ylabel(u'Правая подпись цветовой шкалы', rotation=270, labelpad=10)\n", "cbar.ax.set_xlabel(u'OX', labelpad=10, rotation=90, position=(1.,0.))\n", "ax2.set_title(u'Заголовок')\n", "ax2.grid(True)\n", "\n", "\n", "ax3 = fig.add_subplot(133)\n", "cmap = mpl.cm.get_cmap('brg', len(bounds)-1)\n", "cmap.set_over('white')\n", "cmap.set_under('grey')\n", "cs = ax3.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # В методе pcolor/pcolormesh/imshow присваиваем пользовательские границы цветов norm\n", "cbar = fig.colorbar(cs, ax=ax3, ticks=bounds, extend='both', extendfrac='auto', pad=0.075,\n", " orientation='horizontal', drawedges=True, aspect=15)\n", "\n", "ax3.set_title(u'Масштабирование цветовой шкалы')\n", "cbar.set_label(u'Подпись цветовой шкалы 14', fontsize=14, labelpad=0)\n", "cbar.ax.tick_params(labelsize=12)\n", "\n", "ax3.grid(True)\n", "\n", "plt.tight_layout()\n", "\n", "save('pic_13_2', fmt='png')\n", "save('pic_13_2', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.3 Положение цветовой шкалы на области рисования\n", "\n", "Метод `fig.colorbar()` принимает три необязательных параметра:\n", "\n", "+ **`mapable`** - собственно объект, с которым связана цветовая шкала;\n", "\n", "+ **`ax`** - родительская область рисования (экземпляр Axes или AxesSubplot), из пространства которой будет выделяться место для расположения цветовой шкалы. Шкала будет создана вне графика-диаграммы.\n", "\n", "+ **`cax`** - область рисования в которой будет нарисована цветовая шкала. Шкала будет создана \"на графике\".\n", "\n", "Собственно, третий параметр \"cax\" позволяет рисовать цветовую шкалу в любом месте соответствующей области рисования. Стоит отметить, что cax в качестве входящих данных берёт экземпляр axes. Чтобы расположить цветовую шкалу в заданной области рисунка, необходимо знать границы соответствующей области рисования ax. Иначе цветовая шкала скорее всего перекроет область, где отображается график или диаграмма. Границы текущей области рисования ax можно узнать при выводе на экран экземпляра axes (print ax). Для автоматизации можно использовать следующий приём:\n", "\n", "> `bbox = ax.get_position()`;\n", "\n", "> `rect = bbox.bounds`\n", "\n", "Здесь rect - это кортеж границ соответсвтующей области рисования ax ([левая граница, нижняя граница, ширина, высота]). Отступив от неё какое-то фиксированное расстояние (в долях от всей ширины-высоты), можно автоматизированно рисовать в нужном месте цветовую шкалу colorbar. Такой метод прямого размещения плохо работает с методом `plt.tight_layout()`, но при тонкой настройке он вряд ли потребуется." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Положение делений на цветовой шкале subplot 1\n", "[ 0.01992804 1.42603898 2.83214991 4.23826085 5.64437179 7.05048272\n", " 8.45659366 9.8627046 ]\n", "Область рисования ax имеет следующие границы\n", "Axes(0.125,0.125;0.352273x0.775)\n", "('Rect:', [0.54772727272727262, 0.94000000000000006, 0.35227272727272729, 0.025])\n" ] }, { "data": { "image/png": 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bwCZQ45qZwLJ+aQD7Ub80gP1o57/eunYJGl2PjoizgDuA+YA/KEaZmcAyc29l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\nr9j5RNi5dhUPOvOyo2uXMOyHnAZPgx/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.3\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "a = np.random.random(100)*10.\n", "a = a.reshape(10,10)\n", "\n", "fig = plt.figure(1,(14,4))\n", "\n", "N = 7\n", "bounds = np.linspace(np.min(a), np.max(a), N+1)\n", "print(u'Положение делений на цветовой шкале subplot 1')\n", "print(bounds)\n", "cmap = mpl.cm.get_cmap('gnuplot', N) \n", "\n", "# ---------------------------------------------------\n", "\n", "ax2 = fig.add_subplot(121)\n", "print(u'Область рисования ax имеет следующие границы')\n", "print(ax2)\n", "bbox = ax2.get_position()\n", "rect = list(bbox.bounds)\n", "#rect[0] = rect[0]\n", "rect[0] = rect[0] - 0.025\n", "rect[2] = 0.01\n", "cax = fig.add_axes(rect) \n", "\n", "bounds = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0]\n", "cmap = mpl.cm.get_cmap('PiYG', len(bounds)-1)\n", "cmap.set_over('red')\n", "cmap.set_under('grey')\n", "cs = ax2.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # Ставим границы для шкалы vmin и vmax.\n", "cbar = fig.colorbar(cs, cax=cax, ax=ax2, ticks=bounds, drawedges=True,\n", " extend='min', extendfrac='auto')\n", "cbar.ax.set_ylabel(u'Левая подпись цветовой шкалы OY', rotation=90, labelpad=10)\n", "cbar.ax.set_xlabel(u'Нижняя подпись цветовой шкалы', labelpad=10)\n", "\n", "ax2.yaxis.set_ticks_position('right')\n", "cbar.ax.yaxis.set_ticks_position('left')\n", "cbar.ax.yaxis.set_label_position('left')\n", "cbar.ax.xaxis.set_label_position('bottom')\n", "ax2.set_title(u'Подпись сверху')\n", "ax2.grid(True)\n", "\n", "cmap = mpl.cm.get_cmap('Spectral', len(bounds)-1)\n", "cmap.set_over('white')\n", "cmap.set_under('grey')\n", "\n", "# ---------------------------------------------------\n", "\n", "ax3 = fig.add_subplot(122)\n", "bbox = ax3.get_position()\n", "rect = list(bbox.bounds)\n", "rect[1] = rect[1] + rect[3] + 0.04\n", "rect[3] = 0.025\n", "print('Rect:', rect)\n", "cax = fig.add_axes(rect)\n", "cs = ax3.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # В методе pcolor/pcolormesh/imshow присваиваем пользовательские границы цветов norm\n", "cbar = fig.colorbar(cs, ax=ax3, cax=cax, ticks=bounds, extend='both', extendfrac='auto',\n", " orientation='horizontal', drawedges=True)\n", "\n", "ax3.xaxis.set_label_position('bottom')\n", "ax3.set_title(u'Подпись снизу', position=(0.5, -0.125))\n", "cbar.ax.xaxis.set_label_position('top')\n", "cbar.ax.xaxis.set_ticks_position('top')\n", "cbar.ax.set_ylabel(u'Левая подпись цветовой шкалы OY', rotation=90, labelpad=15)\n", "cbar.ax.set_xlabel(u'Подпись цветовой шкалы')\n", "\n", "ax3.grid(True)\n", "\n", "save('pic_13_3', fmt='png')\n", "save('pic_13_3', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.4 Деления цветовой шкалы\n", "\n", "Как и для других axes объектов с цветовой шкалой можно работать через методы \"set-get\". Так как для работы с делениями colorbar доступны методы, аналогичные для axes и axis, то можно управлять положением делений и их подписями на шкале разными методами. \n", "\n", "> Здесь `cbar=fig.colorbar(cs, cax=cax, ax=ax)`\n", "\n", "Например, так:\n", "\n", "+ `cbar.ax.set_ylabel()`\n", "\n", "+ `cbar.ax.set_yticklabels()`\n", "\n", "+ `cbar.ax.set_yticks()`\n", "\n", "Или воспользоваться методами xaxis/yaxis:\n", "\n", "+ `cbar.ax.yaxis.set_ticks_position()`\n", "\n", "+ `cbar.ax.yaxis.set_label_position()`\n", "\n", "+ `cbar.ax.xaxis.set_ticks_position()`\n", "\n", "+ `cbar.ax.xaxis.set_label_position()`\n", "\n", "С помощью локатеров и форматтеров:\n", "\n", "+ `cbar.ax.xaxis.set_major_locator()`\n", "\n", "+ `cbar.ax.xaxis.set_major_formatter()`\n", "\n", "+ `cbar.ax.xaxis.set_minor_locator()`\n", "\n", "+ `cbar.ax.xaxis.set_minor_formatter()`\n", "\n", "+ `cbar.ax.xaxis.set_major_locator()`\n", "\n", "+ `cbar.ax.xaxis.set_major_formatter()`\n", "\n", "+ `cbar.ax.xaxis.set_minor_locator()`\n", "\n", "+ `cbar.ax.xaxis.set_minor_formatter()`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Axes(0.125,0.125;0.352273x0.775)\n", "Subplot 1 bounds: [ 0. 1.42857143 2.85714286 4.28571429 5.71428571\n", " 7.14285714 8.57142857 10. ]\n", "new_yticks [ 0.07142857 0.21428571 0.35714286 0.5 0.64285714 0.78571429\n", " 0.92857143 1.07142857] 8\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.4\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "a = np.random.random(100)*10.\n", "a = a.reshape(10,10)\n", "\n", "fig = plt.figure()\n", "\n", "# ------------------------------------------------------\n", "\n", "ax2 = fig.add_subplot(121)\n", "print ax2\n", "bbox = ax2.get_position()\n", "rect = list(bbox.bounds)\n", "rect[0] = rect[0] - 0.035\n", "rect[2] = 0.02\n", "cax = fig.add_axes(rect) \n", "\n", "N = 7\n", "bounds = np.linspace(0., 10., N+1)\n", "print 'Subplot 1 bounds:', bounds\n", "\n", "cmap = mpl.cm.get_cmap('rainbow', len(bounds)-1)\n", "cmap.set_over('white')\n", "cmap.set_under('grey')\n", "\n", "cs = ax2.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # Ставим границы для шкалы vmin и vmax.\n", "cbar = fig.colorbar(cs, cax=cax, ax=ax2, ticks=bounds, drawedges=True,\n", " extend='both', extendfrac='auto')\n", "cbar.ax.set_ylabel(u'Цвета радуги 1', rotation=90, labelpad=10)\n", "ax2.yaxis.set_ticks_position('right')\n", "cbar.ax.yaxis.set_label_position('left')\n", "cbar.ax.yaxis.set_ticks_position('left')\n", "\n", "ax2.set_title(u'Шкала слева')\n", "ax2.grid(True)\n", "\n", "# ---------------------------------------------\n", "\n", "ax1 = fig.add_subplot(122)\n", "\n", "bounds = np.linspace(0., 10., N+1)\n", "cmap = mpl.cm.get_cmap('rainbow', len(bounds)-1)\n", "cmap.set_over('white')\n", "cmap.set_under('grey')\n", "\n", "cs = ax1.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # Ставим границы для шкалы vmin и vmax.\n", "cbar = fig.colorbar(cs, ax=ax1, ticks=bounds, drawedges=True,\n", " extend='both', extendfrac='auto')\n", "cbar.ax.set_ylabel(u'Цвета радуги 2', rotation=270, labelpad=10)\n", "cbar_yticks = cbar.ax.get_yticks()\n", "new_yticks = cbar_yticks + (1./((len(bounds)-1)*2))\n", "print 'new_yticks', new_yticks, len(new_yticks)\n", "\n", "cbar.set_ticks(10.*new_yticks)\n", "cbar.set_ticklabels([u'Ф', u'С', u'Г', u'З', u'Ж', u'О', u'К', u'Б']) \n", "\n", "ax1.set_title(u'Шкала справа')\n", "ax1.grid(True)\n", "\n", "save('pic_13_4', fmt='png')\n", "save('pic_13_4', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.5 Особенности в отображении цветовых шкал разными методами\n", "\n", "Методы `pcolor`/`pcolormesh`, `imshow` и `contour`/`contourf` немного по-разному отображают цветовую шкалу. В методах `pcolor` и `imshow` по умолчанию отображается \"плавная\" шкала, переход от цвета к цвету в них постепенный, а в методах изолиний `contour`/`contourf`, наоборот, шкала дискретная с резким изменением цвета при переходе от одного интервала к другому." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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aRxioVqo2qjaCbvQjOunUzXhDTtdcKzhVco19/ytSkhU41CxIUkIqjElwSYmp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\nD35BQQhrqeAlcviBbj7FVA2m2pIbGQWetkjkdIWbUpWN3teWI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.5\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "def m_draw(i):\n", " '''\n", " В зависимости от целого i выбирается метод рисования 2D\n", " '''\n", " if(i == 0):\n", " md = ax.pcolor\n", " elif(i == 1):\n", " md = ax.imshow\n", " elif(i == 2):\n", " md = ax.contour\n", " elif(i == 3):\n", " md = ax.contourf\n", " else:\n", " md = ax.pcolor\n", "\n", " return md\n", "\n", "dat = np.random.random(200).reshape(20,10) # создаём матрицу значений\n", "\n", "fig, subs = plt.subplots(nrows=2, ncols=2, sharex=True)\n", "titles = ['pcolor', 'imshow', 'contour', 'contourf']\n", "\n", "for i, ax in enumerate(fig.axes):\n", " new_method = m_draw(i)\n", " cs = new_method(dat)\n", " fig.colorbar(cs, ax=ax)\n", " ax.set_title(titles[i])\n", "\n", "plt.tight_layout()\n", "\n", "save('pic_13_5', fmt='png')\n", "save('pic_13_5', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.6 Цветовая шкала в методе `pcolor()`\n", "\n", "Покажем как можно задать количество цветов для шкалы, определить сами цвета и отобразить значения, которые выходят за установленные рамки для метода `pcolor` или `pcolormesh`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xticks: [ 0. 2. 4. 6. 8. 10.]\n", "yticks: [ 0. 2. 4. 6. 8. 10.]\n", "cmap.N= 5\n" ] }, { "data": { "image/png": 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8b9Jaa9bAyEhxe9kyWL584k2f+jV0a3nq49q/WCzPgLRqRaYut+6b9fHW2YFW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\nOi46xw1lHaWac8U1JH1uxzU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.6\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "a = np.random.random(100)\n", "a = a*10.0\n", "a = a.reshape(10,10)\n", "\n", "fig = plt.figure(1,(14,4))\n", "\n", "\n", "ax = fig.add_subplot(131)\n", "\n", "bounds = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0] # список числовых границ. Длина N. Обратите внимание шаг регулярный.\n", "cpool = ['blue','cyan','green','yellow','pink'] # список цветов, которые будут отображены между заданными границами bounds. Длина N-1\n", "cmap = mpl.colors.ListedColormap(cpool, 'indexed') # Задаём дискретную шкалу цветов из списка cpool\n", "cs = ax.pcolor(a, cmap=cmap) # вызываем метод pcolor. Вводим пользовательскую раскраску через cmap\n", "cbar = fig.colorbar(cs, ticks=bounds) # рисуем шкалу colorbar для изображения cs. В качестве черточек шкалы указываем bounds. \n", "\n", "# ---------------------------------------------------\n", "\n", "ax2 = fig.add_subplot(132)\n", "\n", "bounds = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0] \n", "cpool = ['blue','cyan','green','yellow','pink'] \n", "cmap = mpl.colors.ListedColormap(cpool, 'indexed')\n", "\n", "cmap.set_over('red') # задаём цвет для значений, выходящих за рамки границы bounds[-1] (сверху шкалы)\n", "cmap.set_under('grey') # задаём цвет для значений, выходящих за рамки границы bounds[0] (снизу шкалы)\n", "\n", "cs = ax2.pcolor(a, cmap=cmap, vmin=bounds[0], vmax=bounds[-1]) # Ставим границы для шкалы vmin и vmax.\n", "cbar = fig.colorbar(cs, ticks=bounds, \n", " extend='both', extendfrac='auto') # Добавим треугольные индикаторы, которые отражают превышение \n", "# заданных vmin и vmax \n", "kx = ax2.get_xticks() # Передаём в kx значения черточек по оси OX xticks\n", "print 'xticks: ',kx\n", "xlabel = ['one','two','three','four','five','six'] # список подписей\n", "ax2.set_xticks(kx+0.5) # Смещаем xticks, чтобы они были по центру \"пикселей\" метода pcolor\n", "ax2.set_xticklabels(xlabel, rotation=45) # Подписываем пользовательскими значениями черточки оси OX и поворачиваем их на 45 градусов\n", "ax2.set_xlim(bounds[0],bounds[-1]) # Ставим ограничения на область изменения оси OX (иначе будут белые полосы по краям)\n", "\n", "# Аналогично для оси OY\n", "ky = ax2.get_yticks()\n", "print 'yticks: ',ky\n", "ax2.set_yticks(ky+0.5)\n", "ax2.set_ylim(bounds[0],bounds[-1])\n", "ax2.set_yticklabels(xlabel)\n", "\n", "# Рисуем вспомогательную сетку основных черточек xticks-yticks\n", "ax2.grid(True)\n", "\n", "'''\n", "Область 3\n", "'''\n", "\n", "ax3 = fig.add_subplot(133)\n", "\n", "bounds = [0.0, 1.0, 4.0, 6.5, 8.0, 10.0] # Обратите внимание шаг НЕрегулярный.\n", "cpool = ['blue','cyan','green','yellow','pink'] # список цветов, которые будут отображены между заданными границами bounds. Длина N-1\n", "cmap = mpl.colors.ListedColormap(cpool, 'indexed')\n", "cmap.set_over('red')\n", "cmap.set_under('grey') \n", "norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # Ставим границы для цветовых отрезков шкалы\n", "# cmap.N показывает число цветов-отрезков(прямоугольных)\n", "print 'cmap.N= ',cmap.N\n", "\n", "cs = ax3.pcolor(a, cmap=cmap, norm=norm) # В методе pcolor/pcolormesh/imshow присваиваем пользовательские границы цветов norm\n", "cbar = fig.colorbar(cs, ticks=bounds, \n", " extend='both', extendfrac='auto',\n", " spacing ='proportional', shrink = 0.9) # Т.к. список bounds имеет неравномерный шаг, то ставим \n", "# spacing='proportional', а не 'uniform', то есть цветовые отрезки будут пропорциональны заданым bounds, а не одинаковыми.\n", "# shrink позволяет пропорционально изменить шкалу от исходной величины: 0.9 уменьшит шкалу, а 1.2 - увеличит.\n", "\n", "# В качестве подписей можно использовать любые значения. Пусть по оси OX это будут значения от 1 до 10\n", "kx = np.arange(0,10) # 0 <= kx <= 9\n", "xlabel = kx + 1 # 1 <= xlabel <= 10\n", "ax3.set_xticks(kx+0.5)\n", "ax3.set_xticklabels(xlabel)\n", "\n", "# А по оси OY будут значения от 0 до 9, как более принято в Python =)\n", "ax3.set_yticks(kx+0.5)\n", "ax3.set_yticklabels(kx)\n", "ax3.grid(True)\n", "\n", "save('pic_13_6', fmt='png')\n", "save('pic_13_6', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.7 Цветовая шкала для метода `сontourf()`\n", "\n", "Для методов изолиний настройка цветовой шкалы происходит немного по-другому. Так как рисуются изолинии, то необходимо определить интервалы для их проведения. Шаг может быть равномерным (через 4 гПа, например), так и неравномерным. Деления, формирующие интервалы, задаются через параметр `level` метода `contourf()`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('New norm:', )\n", "('Number of colors in discrete colormap:', 5)\n" ] }, { "data": { "image/png": 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MngKexpkla88WezJusDfLsG5dzchHNBpl9Wo9/cpO7yRaDN1nqKPYIOhxkjaY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KAAAg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\n8DLzUfBqqevWrUtYXrsWzjgD7rwTBg40eQTPAMZg5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.7\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "# Создаём массив, который будем отображать\n", "a = np.random.random(100)\n", "a = a*10.0\n", "a = a.reshape(10,10)\n", "\n", "cpool = ['blue','cyan','green','yellow','pink']\n", "cmap = mpl.colors.ListedColormap(cpool) # Задаём дискретную шкалу цветов из списка cpool\n", "cmap.set_over('red') # задаём цвет для значений, выходящих за рамки границы levels[-1] (сверху шкалы)\n", "cmap.set_under('grey') # задаём цвет для значений, выходящих за рамки границы levels[0] (снизу шкалы)\n", "\n", "# Задаём список границ изолиний. Причём значения попадают в интервал z1 < z <= z2\n", "\n", "levels = np.linspace(0., 10., len(cpool)+1) \n", "\n", "fig = plt.figure(figsize=(14,4))\n", "\n", "ax1 = fig.add_subplot(131) # Первая ось\n", "cs = ax1.contour(a, cmap=cmap, linestyles='dashed') # вызываем метод изолиний contour. Вводим пользовательскую раскраску через cmap.\n", "cbar = fig.colorbar(cs, ax=ax1) # рисуем шкалу colorbar для изображения cs.\n", "ax1.grid(True)\n", "ax1.set_title('contour')\n", "# -----------------------------------------------------------------\n", "\n", "ax2 = fig.add_subplot(132)\n", "\n", "cs = ax2.contourf(a, levels, cmap=cmap, extend='both') # Задаём границы для изолиний levels.\n", "# Также для метода contour и contourf треугольные индикаторы, которые отражают превышение заданных \n", "# границ levels указываются в самом методе contour. \n", "cbar = fig.colorbar(cs, ax=ax2, spacing='uniform') # Устанавливаем черточки шкалы согласно спискам уровней изолиний levels.\n", "# Задаём равномерное распределение цветовых сегментов шкалы.\n", "ax2.grid(True)\n", "ax2.set_title('uniform colorbar')\n", "# -----------------------------------------------------------------\n", "\n", "ax3 = fig.add_subplot(133)\n", "\n", "# Обратите внимание шаг НЕрегулярный.\n", "levels = [0.0, 1.0, 4.0, 6.5, 8.0, 10.0]\n", "# len(cpool) = cmap.N\n", "norm = mpl.colors.BoundaryNorm(levels, cmap.N) # Ставим границы для цветовых отрезков шкалы \n", "print('New norm:', norm)\n", "print('Number of colors in discrete colormap:', cmap.N)\n", "\n", "cs = ax3.contourf(a, levels, cmap=cmap, norm=norm, extend='both') \n", "cbar = fig.colorbar(cs, ax=ax3, spacing='proportional', # сделаем цветовые сегменты шкалы пропоциональными границам levels\n", " extendfrac='auto', # изменим длину треугольных индикаторов\n", " shrink=0.9, # уменьшим размер шкалы\n", " orientation='vertical') # можно изменить положение шкалы на горизонтальное (horizontal)\n", "\n", "# В качестве подписей можно использовать любые значения. \n", "xlabel = np.arange(50.1, 51.1, 0.1) # Сделаем подписи произвольных долгот \n", "ax3.set_xticklabels(xlabel, rotation=45)\n", "ax3.set_xlabel('lon')\n", "\n", "ylabel = np.arange(60.5,65.5,0.5) # Сделаем подписи произвольных широт\n", "ax3.set_yticklabels(ylabel)\n", "ax3.set_ylabel('lat')\n", "\n", "ax3.grid(True)\n", "ax3.set_title('proportional colorbar')\n", "\n", "save('pic_13_7', fmt='png')\n", "save('pic_13_7', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.8 Цветовая шкала для метода `scatter()`\n", "\n", "Нерегулярно расположённые в пространстве данные можно изобразить маркерами с помощью метода `scatter()` с добавлением цвета и/или размера в качестве третьей координаты. Значения поля нужно указать в качестве массива или списка параметру цвета маркеров \"c\" и/или параметру размера \"s\". Тип цветовой палитры задаётся параметром cmap (см. главу \"Цвет\")." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min y 0.0\n", "Max y 21.3\n" ] }, { "data": { "image/png": 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iFnH8eBSVK1fir7/+okSJwuTIGczjjz9ChYoleOmVh4iKOk2/PoOYPf8ratQs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\nvUFERFe0kPj5iIzszKVL99m6dauu2hJy/vxFNOdJW/JhsSgePHiQpnN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.8.1 \n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "fig = plt.figure()\n", "\n", "x = np.arange(-np.pi, np.pi, 0.1)\n", "y = np.exp(x)\n", "z = np.sin(x)**2\n", "\n", "ax1 = fig.add_subplot(211)\n", "cmap = plt.cm.get_cmap('YlGnBu')\n", "sc = ax1.scatter(x, y, c=y, s=35, cmap=cmap)\n", "plt.colorbar(sc, ax=ax1)\n", "ax1.grid(True)\n", "\n", "ax2 = fig.add_subplot(212)\n", "cmap = plt.cm.get_cmap('jet')\n", "sc = ax2.scatter(x, z, c=y, vmin=0., vmax=25., s=35, cmap=cmap)\n", "fig.colorbar(sc, ax=ax2)\n", "ax2.grid(True)\n", "ax2.set_title(u'Заданная область изменения')\n", "print 'Min y %.1f' % np.min(y)\n", "print 'Max y %.1f' % np.max(y)\n", "\n", "plt.tight_layout()\n", "\n", "save('pic_13_8_1', fmt='png')\n", "save('pic_13_8_1', fmt='pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min y 0.0\n", "Max y 1.0\n" ] }, { "data": { "image/png": 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LMJMTazDgnen8snqdS6aRcud8uSvNmXM8JV8tW7akSrFQAiY9DPs3wu/LCXyj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LAAAg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\nffSR0aEIIVLBsO/+5G5OY6X1U6zp09fj63USsw6nVzYPd4gIS3mniDBcH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Пример 13.8.2\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "fig = plt.figure(1, (10, 8))\n", "\n", "N = 100\n", "x, y = np.random.rand(2, N)\n", "z = np.random.rand(N)\n", "\n", "ax1 = fig.add_subplot(211)\n", "cmap = plt.cm.get_cmap('jet')\n", "cmap.set_over('w')\n", "cmap.set_under('grey')\n", "\n", "sc = ax1.scatter(x, y, c=z, s=z*100, cmap=cmap)\n", "cbar = fig.colorbar(sc, ax=ax1, extend='both')\n", "ax1.grid(True)\n", "ax1.set_title(u'Плавная цветовая шкала для метода scatter')\n", "\n", "ax2 = fig.add_subplot(212)\n", "\n", "#cpool = ['cyan', 'green', 'yellow', 'orange', 'red']\n", "cpool = ['#ffffff', '#ffff7f', '#c0ff00', '#00c300', '#00c3ff']\n", "cmap = mpl.colors.ListedColormap(cpool, 'indexed')\n", "\n", "cmap.set_over('r')\n", "cmap.set_under('b')\n", "\n", "ticks = np.linspace(0., 1., cmap.N + 1)\n", "norm = mpl.colors.BoundaryNorm(ticks, cmap.N) \n", "\n", "sc = ax2.scatter(x, y, c=z, s=100*z, cmap=cmap, norm=norm)\n", "cbar = fig.colorbar(sc, ax=ax2, ticks=ticks, norm=norm, extend='both',\n", " extendfrac='auto', shrink=0.8,\n", " orientation='horizontal')\n", "cbar.set_ticks(ticks)\n", "cbar.set_ticklabels(ticks)\n", "\n", "ax2.set_title(u'Дискретная цветовая шкала для метода scatter')\n", "ax2.grid(True, color='grey')\n", "print 'Min y %.1f' % np.min(y)\n", "print 'Max y %.1f' % np.max(y)\n", "\n", "plt.tight_layout()\n", "\n", "save('pic_13_8_2', fmt='png')\n", "save('pic_13_8_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 }