sample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebookサンプル\n",
    "* [実行結果を残す](#実行結果を残す)\n",
    "* [実行時間を計測する](#実行時間を計測する)\n",
    "* [matplotlibを用いてグラフを描画](#matplotlibを用いてグラフを描画)\n",
    "* [markdown](#markdown)\n",
    "* [latex形式で数式の描画](#latex形式で数式の描画)\n",
    "* [shellを実行できる](#shellを実行できる)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 実行結果を残す\n",
    "出力結果がそのまま残ります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01回目\n",
      "02回目\n",
      "03回目\n",
      "04回目\n",
      "05回目\n",
      "06回目\n",
      "07回目\n",
      "08回目\n",
      "09回目\n",
      "10回目\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 11):\n",
    "    print('%02d回目' % i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 実行時間を計測する\n",
    "`%%time`や`%%timeit`をセルの先頭に書くことによって、セル内の実行時間を計測することができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01回目\n",
      "02回目\n",
      "03回目\n",
      "04回目\n",
      "05回目\n",
      "06回目\n",
      "07回目\n",
      "08回目\n",
      "09回目\n",
      "10回目\n",
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 1.05 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 実行時間を計測\n",
    "for i in range(1, 11):\n",
    "    print('%02d回目' % i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# フィボナッチ数列の生成にかかる時間を計測\n",
    "def fib_loop(n):\n",
    "    if n < 2:\n",
    "        return [0] if n > 0 else []\n",
    "    res = [0, 1]\n",
    "    for i in range(n-2):\n",
    "        res.append(res[-1]+res[-2])\n",
    "    return res\n",
    "\n",
    "def fib_recursive(n, l=[0, 1]):\n",
    "    return l[:1] + fib_recursive(n-1, [l[-1], l[-1] + l[-2]]) if n > 0 else []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.8 µs ± 2.58 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "res = fib_loop(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.2 µs ± 19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "res = fib_recursive(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlibを用いてグラフを描画\n",
    "`% matplotlib inline`を設定すると、出力にグラフを描画できます。\n",
    "\n",
    "指定がない場合は、jupyterを起動しているホストでqtやgtk等で描画されます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x = np.arange(0, 10, 0.1)\n",
    "y = np.cos(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## markdown\n",
    "`Cell > Cell Type > Markdown`を選択するとmarkdownの記述が可能です。\n",
    "\n",
    "markdownについては、[これ](https://guides.github.com/features/mastering-markdown/)を参考にしてみて下さい。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "# Headers\n",
    "\n",
    "# This is an h1 tag\n",
    "## This is an h2 tag\n",
    "###### This is an h6 tag\n",
    "\n",
    "\n",
    "# Emphasis\n",
    "*This text will be italic*\n",
    "_This will also be italic_\n",
    "\n",
    "**This text will be bold**\n",
    "__This will also be bold__\n",
    "\n",
    "_You **can** combine them_\n",
    "\n",
    "# Lists\n",
    "* Item 1\n",
    "* Item 2\n",
    "  * Item 2a\n",
    "  * Item 2b\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Headers\n",
    "\n",
    "# This is an h1 tag\n",
    "## This is an h2 tag\n",
    "###### This is an h6 tag\n",
    "\n",
    "\n",
    "# Emphasis\n",
    "*This text will be italic*\n",
    "_This will also be italic_\n",
    "\n",
    "**This text will be bold**\n",
    "__This will also be bold__\n",
    "\n",
    "_You **can** combine them_\n",
    "\n",
    "# Lists\n",
    "* Item 1\n",
    "* Item 2\n",
    "  * Item 2a\n",
    "  * Item 2b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## latex形式で数式の描画\n",
    "`$`もしくは`$$`でlatex形式の数式の描画も可能です。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "$\\mu < 1$かつ$t \\to \\infty$で\n",
    "\n",
    "$$\n",
    "\\lim_{t\\to \\infty} \\Delta w^{(t)}=\\lim_{t\\to \\infty} \\left \\{ \\mu ^{t-1} \\left( \\Delta w^{(1)} + \\frac{\\epsilon}{1-\\mu}\\nabla E_t \\right) -\\frac{\\epsilon}{1-\\mu}\\nabla E_t \\right\\} \\\\\n",
    "= -\\frac{\\epsilon}{1-\\mu} \\nabla E_t \n",
    "$$\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\mu < 1$かつ$t \\to \\infty$で\n",
    "\n",
    "$$\n",
    "\\lim_{t\\to \\infty} \\Delta w^{(t)}=\\lim_{t\\to \\infty} \\left \\{ \\mu ^{t-1} \\left( \\Delta w^{(1)} + \\frac{\\epsilon}{1-\\mu}\\nabla E_t \\right) -\\frac{\\epsilon}{1-\\mu}\\nabla E_t \\right\\} \\\\\n",
    "= -\\frac{\\epsilon}{1-\\mu} \\nabla E_t \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## shellを実行できる\n",
    "`!`から始まる行はshellとして実行される\n",
    "\n",
    "`{}`で囲まれた部分はpythonの実行結果が埋め込まれる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\r\n"
     ]
    }
   ],
   "source": [
    "!echo hello world"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "Fizz\n",
      "4\n",
      "Buzz\n",
      "Fizz\n",
      "7\n",
      "8\n",
      "Fizz\n",
      "Buzz\n",
      "11\n",
      "Fizz\n",
      "13\n",
      "14\n",
      "FizzBuzz\n"
     ]
    }
   ],
   "source": [
    "# pythonとshell scriptを併用する\n",
    "for i in range(1, 16):\n",
    "    !echo {'Fizz'*(1-i%3)+'Buzz'*(1-i%5) or i}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Videos以下の動画(.avi)をすべてpngに変換\n",
    "video_path_list = glob('./Videos/**/*.avi')\n",
    "size = '171x128'\n",
    "fps = 25\n",
    "\n",
    "for video_path in video_path_list:\n",
    "    output_path = os.path.splitext(video_path)[0]\n",
    "    !mkdir -p {output_path}\n",
    "    !chmod -R 777 {output_path}\n",
    "    !ffmpeg -i {video_path} -f image2 -vcodec png -s {size} -r {fps} {output_path}/frame_%05d.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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