{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Построение графиков\n", "\n", "Для построения графиков чаще всего используется `matplotlib.pyplot`. Больше информации можно найти по этим ссылкам:\n", "* [Научная графика в python](https://nbviewer.jupyter.org/github/whitehorn/Scientific_graphics_in_python/tree/master/) --- уроки по `matplotlib` на русском языке.\n", "* [Галерея `matplotlib`](https://matplotlib.org/3.1.1/gallery/index.html) --- галерея примеров на официальном сайте `matplotlib`, выбираете нужный вам пример и смотрите как он сделан.\n", "\n", "Построить график достаточно просто:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "x = np.linspace(-1,1,100) # Создаем массив из ста точек на промежутке (-1;1)\n", "y = x**3\n", "plt.plot(x,y);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как вы можете заметить построеный график хорош всем, кромо того обстоятельства что он нарушает все правила оформления графиков для лабораторных работ. Постораемся оформить его. \n", "Следующая иллюстрация поможет нам узнать как называются элементы изображения:\n", "\n", "\n", "![](https://matplotlib.org/_images/anatomy.png)\n", "\n", "\n", "Используя поиск по сайту [matplotlib.org](https://matplotlib.org/3.1.1/index.html) можно подробно узнать как настроить тот или иной элемент изображения. А мы приведем краткое описание полезных функции:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "mpl.rcParams['font.size'] = 16 # Управление стилем, в данном случаем - размером шрифта \n", " # Создаем фигуру\n", "plt.figure(figsize=(7,7))\n", "\n", "# Подписываем оси и график\n", "plt.title(r\"Это название графика $y = x^3$ - да, можно использовать LaTeX:\")\n", "plt.ylabel(\"Это ось Y\")\n", "plt.xlabel(r\"Это ось X, $F(x) = \\int f(x) dx + C$\")\n", "\n", "\n", "\n", "# Добавляем данные\n", "x = np.linspace(-1,1,100)\n", "y = x**3\n", "plt.plot(x,y, label=\"Синия линия\")\n", "\n", "# Еще данные\n", "x2 = x[::10]\n", "y2 = np.sin(x2)\n", "plt.plot(x2,y2, 'r^', label='Красные треугольники')\n", "# 'r^' - задает стиль линии - красные (red) треугольники (^), подробнее в документации\n", "\n", "# Данные с ошибками\n", "mu = np.sin(x2)\n", "sigma = np.abs(mu)**0.5\n", "y2 = np.random.normal(mu, sigma)\n", "# Можно рисовать ошибки\n", "plt.errorbar(x2,y2, yerr=sigma, xerr=0.1, fmt='.', label='Кресты') \n", "\n", "# Активируем сетку\n", "plt.grid(b=True, which='major', axis='both', alpha=1)\n", "plt.grid(b=True, which='minor', axis='both', alpha=0.5)\n", "\n", "# Активируем легенду графика\n", "plt.legend()\n", "# Внимание, запускаете вашу программу как сценарий, то что бы показать график\n", "# Используйте эту команду\n", "# plt.show()\n", "# Сохраняем изображение в текущую директорию\n", "plt.savefig('example.png')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+W+S5JGAfsD/AtYgExVdffcWYMWNo0KBBYTO6tm3bUrFiRY8rEymZQL/L6CoK9hnOtB+o7Pu+MvCGmV0E5FMQBn9wzrkA1yJSppxzzJgxg4ceeoiffvqJp59+Ws3oJKyVxdtOz/aL3QqfdG434Pd/NWY2GBgMUKVKFdLT00tcUF5eXqmOEymOc44xY8bwySefULt2bUaMGMFtt93GJ5984nVpEoGC9Tss0IFwgIKrhDNV5uxXDufknJsGTANo0KCBa9GiRYlfIz09ndIcJ3KmU6dOccEFF2Bm9O3bl+TkZAYPHsyKFSs0x6TMBOt3WKD3ELIp2Ec4UwywNcDnEgmq7OxsmjRpwqxZswAYNGgQQ4cOVTM6iRiBnskLgUZmVv30A74b2Jr4nhMJO8ePH2fs2LHUq1ePnJwcLrnkEq9LEikTfi8ZmVk337dxvq8dzOxb4Fvn3HLfY9OB+4AFZvY4BfsJTwD/Bl4KTMkiwZOVlUVqaiqbN2/mnnvu4fnnn+eaa67xuiyRMlGSPYQ3z/j7FN/X5UALAOfcYTNLAJ4DZlGwmbwEGOWcyzu/UkWC7z//+Q/79+9n4cKFdO7c2etyRMqU34HgnLNzjwLn3F4K7i0QCUvp6ens2LGDwYMHk5iYSOvWrbVMJFFBu2EiPgcPHmTo0KG0bNmSCRMmFDajUxhItFAgiACLFi0iNjaW6dOn89BDD6kZnUQlfWKaRL3du3eTmJhITEwM8+fP54477vC6JBFP6ApBopJzjrVr1wJQvXp1PvjgA7KyshQGEtUUCBJ1vvzySxITE4mPjy9sRtemTRt92L1EPQWCRI38/HymTZtGbGwsS5Ys4dlnn9UVgUgR2kOQqOCco2vXrrzzzjskJCQwffp0qlevfu4DRaKIAkEiWtFmdF26dCExMZEBAwZg5tdtNSJRRUtGErG2bNlC48aNC5vRDRgwgIEDByoMRIqhQJCIc/z4cf785z9Tv359cnNzueyyy7wuSSQsaMlIIsq6detISUkhOzub5ORkJkyYwNVXX+11WSJhQYEgEeXrr7/m4MGDvPvuu3Tq1MnrckTCipaMJOwtXbqUqVOnAtC5c2d27NihMBApBQWChK0ffviBQYMG0apVK1544YXCZnQXX3yxx5WJhCcFgoSlBQsWEBMTw4wZM3jkkUf49NNP1YxO5DxpD0HCinOOPXv2kJSURGxsLAsXLqRBgwZelyUSEXSFIGHBOVfYd6h69ep89NFHfPrppwoDkQBSIEjI27t3L506deL3v/89mZmZACQkJKgZnUiAKRAkZOXn5zNlyhRiY2NZvnw5EyZMUDM6kTKkPQQJSfn5+SQmJrJo0SJat27NtGnTuOmmm7wuSySiKRAkpJw8ebKwGV1SUhJ33303qamp6j8kEgRaMpKQsWnTJuLj45k1axZmRmpqKv3791cYiASJAkE8d+zYMf7nf/6HBg0a8OWXX3LFFVd4XZJIVNKSkXhqzZo1pKamsm3bNvr27cuzzz7Lr371K6/LEolKCgTx1L59+8jLy+O9996jQ4cOXpcjEtW0ZCRBt3jxYqZMmYJzjj/84Q/s2LFDYSASAhQIEjQHDhxgwIABtGnThhdffJETJ05gZlx00UVelyYiKBAkSN566y1iYmKYOXMmo0ePZu3atbrTWCTEaA9BytyuXbvo3r07tWrVYtGiRdSvX9/rkkTkLHSFIGXCOceqVasAuPnmm/n4449Zu3atwkAkhCkQJOD27t1Lx44dadKkSWGH0pYtW+rzCkRCnAJBAiY/P5/JkycTGxtLRkYGEydOJD4+3uuyRMRP2kOQgDj9FtL333+ftm3b8tJLL1GtWjWvyxKRElAgyHk5efIk5cqVw8zo2bMnPXv2pG/fvuo/JBKGtGQkpbZhwwbuuOMOZs6cCUC/fv3o16+fwkAkTCkQpMSOHj3KH//4Rxo2bMhXX32l3kMiEUJLRlIimZmZpKSksH37dlJTU3nmmWeoXLmy12WJSAAoEKREvv/+e44ePcqHH35I27ZtvS5HRAJIS0ZyTh9++CGTJ08GoFOnTmzfvl1hIBKBFAhSrP3795OSkkL79u2ZNm0aJ06cAKBixYoeVyYiZUGBIGc1b948atSowezZs/nTn/7EmjVrdKexSITTHoL8l927d9OrVy/q1KnDhx9+SN26db0uSUSCQFcIAhTcaZyRkQFA9erVWbp0KWvWrFEYiEQRBYKQm5tLu3btaN68eWEzuubNm1O+vC4gRaKJAiGKnTp1iokTJ1KzZk1Wr17N5MmT1YxOJIrpfwGj1OlmdB988AHt27fnpZde4sYbb/S6LBHxkAIhypw4cYLy5ctjZtxzzz0kJyeTnJys/kMioiWjaJKVlUXDhg155ZVXAOjbty/33nuvwkBEAAVCVPjpp58YPXo08fHxfPPNN/z617/2uiQRCUFaMopwq1atIiUlhZ07dzJgwADGjx+vZnQiclYKhAh34MABTp48yeLFi2nVqpXX5YhICNOSUQR67733mDRpElDQjG7btm0KAxE5J08Cwcz+aGbbzSzfzLp6UUMk+u677+jTpw+dOnVixowZakYnIiXi1RXCEqAjsMKj80cU5xxz584lJiaG119/nTFjxpCZmalmdCJSIn4Fgpldb2aTzGy1mR0xM2dm1YoZe4OZzTOzg2Z2yMzmm9nP7nhyzq1xzu06//IFCprR9e7dm6pVq5KVlcVf/vIXXRWISIn5e4VwC9ADOABkFDfIzC4BlgK3A/2APsCtwDIzq3R+pUpRzjmWL18OwM0338yyZctYvXo1tWvX9rgyEQlX/gbCCudcFedcR+DNXxg3CKgOdHXOve2cWwAkAlWBIedXqpy2e/duWrduTYsWLQqb0TVr1kzN6ETkvPgVCM65fD9fLxHIdM7lFDl2D7AS6FLy8qSoU6dOMWHCBGrVqsWnn37K1KlT1YxORAIm0P9LGQssOMvj2UD3AJ8rqjjn6NixIx999BGdOnVi6tSpXH/99V6XJSIRJNCBcBUF+wxn2g8U3h5rZo8DQ4FrgJpm9gLQwDn3/8480MwGA4MBqlSpQnp6eomLysvLK9VxoaBoM7q4uDgaNmxIq1atyMnJIScn59wvIEERznNMQl/Q5pdzrkR/gIGAA6qd5bnjwN/O8vhTwMmSnuvMP3Fxca40li1bVqrjvLZ27VpXq1Yt9/LLL3tdipxDuM4xCQ/nO7+Adc6P37GBvg/hAAVXCWeqzNmvHOQsjhw5wiOPPEKjRo3Yv38/1157rdcliUgUCPSSUTYF+whnigG2BvhcEWnlypWkpKSQk5PDkCFD+Pvf/84VV1zhdVkiEgUCHQgLgX+YWXXn3G4A3w1sTYDRAT5XRDp06BDOOZYuXUrLli29LkdEoojfgWBm3Xzfxvm+djCzb4FvnXPLfY9NB+4DFvg2jh3wBPBv4KXAlBx53n33XXbt2sXIkSPp0KEDW7dupUKFCl6XJSJRpiR7CG/6/gz1/X2K7+9/OT3AOXcYSAB2ALOA2cAeIME5lxeIgiPJt99+S+/evencuTMzZ84sbEanMBARL/h9heCc8+tzFp1ze4GkUlcUBZxzvPHGG9x///0cPHiQP//5zzz22GNqRicinlKvAw/s3r2bPn36UL9+fdLS0qhZs6bXJYmI6ANygiU/P59ly5YBBc3o0tPTWbVqlcJAREKGAiEIcnJyaNWqFQkJCYXN6Jo0aUK5cuU8rkxE5P8oEMrQyZMn+cc//kGtWrVYv34906dPp1GjRl6XJSJyVtpDKCPOOTp06MDixYtJTExkypQpXHfddV6XJSJSLAVCgB0/fpwLL7wQMyMlJYWBAwfSo0cPzPx6k5aIiGe0ZBRAa9asKXznEEBycjI9e/ZUGIhIWFAgBMDhw4d58MEHady4MQcPHtTnFIhIWNKS0XnKyMggJSWF3bt3M2zYMMaNG8fll1/udVkiIiWmQDhPeXl5lCtXjvT0dO68806vyxERKTUtGZXCggULeO655wDo0KED2dnZCgMRCXsKhBLYt28fvXr1omvXrrz22mucPHkSQD2IRCQiKBD84Jzj1VdfpUaNGrz11ls8+eSTrFq1ivLlteImIpFDv9H8sGvXLlJTU2nQoAFpaWnExMR4XZKISMDpCqEY+fn5LFmyBIBbbrmFFStW8MknnygMRCRiKRDOYufOnbRs2ZLWrVuTmZkJQOPGjdWMTkQimgKhiJMnT/L0009Tu3ZtPvvsM2bMmEF8fLzXZYmIBIX2EHycc7Rv354lS5Zw1113MXnyZH772996XZaISNBEfSAcO3aMChUqYGb079+fIUOG0K1bN/UfEpGoE9VLRqtWraJu3bq8/PLLAPTu3Zvu3bsrDEQkKkVlIOTl5TFy5EiaNm3KkSNHqFatmtcliYh4LuqWjJYvX05KSgq5ubncd999/PWvf+Wyyy7zuiwREc9FXSAcPXqUihUrkpGRQdOmTb0uR0QkZER0IBw7doyNGzcyd+5c1q9fz4MPPki7du3YsmWL2k6IiJwhIn8rTpw4kddee40NGzZw/PhxoODGsgceeIDy5csrDEREziJsfzMePHiQtWvXsmbNGtasWcOmTZvYuXMnFStWZN++fVSsWJFRo0YRHx/PqVOn6N69u9cli4iEtLAKhPz8fACmTp3K8OHDcc4BUKNGDVq1asWPP/5IxYoVefLJJ392XHp6erBLFREJO2EVCD/++CNQsPwzduxY4uPjadiwIVdeeaXHlYmIhL+wCoRKlSoBUKdOHerUqeNxNSIikSWsbkzTZrCISNkJq0AQEZGyo0AQERFAgSAiIj4KBBERAcBOv5c/HJjZQWBnMU9fARws5rmrge/KpKiy9Us/Uyif63xeq6TH+jven3HnGqM5FjrnisQ5Vpbzq6pz7ppzjnLOhc0fYFopn1vnde2B/nlD+Vzn81olPdbf8f6MO9cYzbHQOVckzrFQmF/htmT0TimfC1fB/JkCea7zea2SHuvveH/GnWuM5lj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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Логарифмический масштаб по оси x (аналогично для y)\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "# Сетка\n", "plt.grid(True)\n", "# Добавляем данные\n", "x = np.linspace(0,100,100)\n", "y = x**3\n", "plt.plot(x,y,\"k--\", label=\"Синия линия\") # \"k--\" --- черная прерывистая линия" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": false, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }