Vander_Plus_4_Matplotlib_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48133f47",
   "metadata": {},
   "source": [
    "# Визуализация с помощью библиотеки Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "771c4ebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d6d014",
   "metadata": {},
   "source": [
    "# Общие советы"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b75bec",
   "metadata": {},
   "source": [
    "## Настройка стилей\n",
    "Для выбора стилей директива ``plt.style``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b9f1dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c6c9561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.4.3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpl.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "479dc049",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.style.use?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae737126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8462b326",
   "metadata": {},
   "source": [
    "## Использовать метод ``plt.show()`` или не использовать? Как отображать графики.\n",
    "Зависит от контекста. Существует три возможных контекста:\n",
    "- Использование Matplotlib в сценарии (``модуля.py``)\n",
    "- В терминале оболочки IPython\n",
    "- в блокноте IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad158780",
   "metadata": {},
   "source": [
    "### Построение графиков из сценария (``модуля.py``)\n",
    "\n",
    "Пользуемся командой ``plt.show()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f5d275e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting myplot.py\n"
     ]
    }
   ],
   "source": [
    "%%file myplot.py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0f98398",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (Temp/ipykernel_11984/4103115203.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_11984/4103115203.py\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    $ python myplot.py\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# $ python myplot.py\n",
    "# синтаксис устарел"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26011812",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(640x480)\n"
     ]
    }
   ],
   "source": [
    "! python myplot.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73f22461",
   "metadata": {},
   "outputs": [],
   "source": [
    "# короче, у PyCharm все хорошо, только надо накатить библиотеки в env...\n",
    "# plt.show() можно выводить только 1 раз за сеанс, т.е. в конце модуля..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af42025",
   "metadata": {},
   "source": [
    "### Построение графиков из блокнота IPython\n",
    "Существует 2 вида backend'a:\n",
    "- ``%matplotlib notebook`` для *интерактивных* графиков\n",
    "- ``%matplotlib inline`` для *статических изображений* PGN\n",
    "\n",
    "Backend должен быть установлен 1 раз за сеанс для одного ядра, *до импотра библиотеки* matplotlib, и его действие распростаняется на все дальнейшие вызовы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d8417ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "# хотя похоже он установлен по default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "29a8a658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ab77297730>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)\n",
    "fig = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151cbdd7",
   "metadata": {},
   "source": [
    "### Сохранение рисунков в файл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05ba0dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('my_figure.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "22281d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !my_figure.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0494d046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('my_figure.png')\n",
    "# открывает быстрее чем прога просмотра изображений 😊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa8d912c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eps': 'Encapsulated Postscript',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'ps': 'Postscript',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fig.savefig поддерживает разные форматы\n",
    "fig.canvas.get_supported_filetypes()\n",
    "# для сохранения рисунка не обязательно использовать plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8042c656",
   "metadata": {},
   "source": [
    "## Два интерфейса по цене одного"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83acc440",
   "metadata": {},
   "source": [
    "### Интерфейс в стиле MATLAB\n",
    "MATLAB-подобные инструменты содержаться в интерфейсе pyplot (plt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67a94333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ab791de670>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# создаем рисунок (подложку, окно)\n",
    "plt.figure()\n",
    "# создаем первую из 2х областей графика и задаем ось\n",
    "plt.subplot(2, 1, 1) # row, columns, panel number\n",
    "plt.plot(x, np.sin(x))\n",
    "# создаем вторую область графика и задаем ось\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c247ec8",
   "metadata": {},
   "source": [
    "Данный интерфейс *сохраняет состояние*: он отслеживает текущий рисунок и оси координат.\n",
    "- ``plt.gcf()`` (get current figure) Получить текущий рисунок\n",
    "- ``plt.gca()`` (get current axes) Получить текущие оси\n",
    "\n",
    "###### ``Недостаток`` - сложность при внесении изменений (в 1ю облась после перехода ко второй)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3252e6",
   "metadata": {},
   "source": [
    "### Объектно-ориентированный интерфейс\n",
    "``Объектно-ориентированный интерфейс`` подходит для более сложных ситуаций, когда нам требуется больше возможностей для управления рисунком. В данном интерфейсе *ф-ии рисования* не полагается на понятие текущего рисунка и осей, а являются *методами* явным образом определяемых объектов ``Figure`` и ``Axes``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "34dd1f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сначала создаем сетку графиков\n",
    "# ax - будет массивом из двух объектов Axes\n",
    "fig, ax = plt.subplots(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "920fbe08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ab79b25e50>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сначала создаем сетку графиков\n",
    "# ax - будет массивом из двух объектов Axes\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "# вызываем метод plot() соответствующего объекта\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6e197fc6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Single argument to subplot must be a three-digit integer, not 2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11984/4224436411.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# сначала создаем сетку графиков\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# ax - будет массивом из двух объектов Axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# вызываем метод plot() соответствующего объекта\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1271\u001b[0m     \u001b[1;31m# First, search for an existing subplot with a matching spec.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1272\u001b[1;33m     \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSubplotSpec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_from_subplot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1273\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1274\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\matplotlib\\gridspec.py\u001b[0m in \u001b[0;36m_from_subplot_args\u001b[1;34m(figure, args)\u001b[0m\n\u001b[0;32m    630\u001b[0m                     \u001b[0mrows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 632\u001b[1;33m                     raise ValueError(\n\u001b[0m\u001b[0;32m    633\u001b[0m                         \u001b[1;34mf\"Single argument to subplot must be a three-digit \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m                         f\"integer, not {arg}\") from None\n",
      "\u001b[1;31mValueError\u001b[0m: Single argument to subplot must be a three-digit integer, not 2"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сначала создаем сетку графиков\n",
    "# ax - будет массивом из двух объектов Axes\n",
    "fig, ax = plt.subplot(2) # важно, есть разница\n",
    "\n",
    "# вызываем метод plot() соответствующего объекта\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3e9cd5",
   "metadata": {},
   "source": [
    "###### основное отличие в коде  в замене ``plt.plot()`` to ``ax.plot()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba516944",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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