Docker_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 動作環境\n",
    "1. コンテナはVirtualBox内のUbuntuで動かす\n",
    "2. 開発環境はMac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remote（Dockerコンテナ）\n",
    "`sudo docker run -it -p 8888:8888 -v /home/user/notebooks:/home/jovyan/work -e PASSWORD=\"password\" -e GRANT_SUDO=yes --user root  jupyter/tensorflow-notebook start-notebook.sh`\n",
    "\n",
    "[docker-stacks](https://github.com/jupyter/docker-stacks)<br/>\n",
    "[ローカル環境でJupyter Notebookを動かすときの俺的ベストプラクティス](http://qiita.com/yacchin1205/items/efaa498ca68e7c169015)<br/>\n",
    "[How do I start tensorflow docker jupyter notebook](http://stackoverflow.com/questions/33636925/how-do-i-start-tensorflow-docker-jupyter-notebook)<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## リンク\n",
    "[matplotlib入門](http://bicycle1885.hatenablog.com/entry/2014/02/14/023734)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Jupyterって何ができるの？](http://www.hirotsuru.com/entry/2016/05/11/201337)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 計算ができる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7071067811865475"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**3\n",
    "\n",
    "import math\n",
    "from math import sin, pi\n",
    "sin(pi/4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数式が描ける"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "$\\displaystyle \\lim_{x \\to \\infty} f(x)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\displaystyle \\lim_{x \\to \\infty} f(x)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### グラフが描ける"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f226bb999e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k169Du3amQ+eIEaY6p539LZJLKUX/qv1Z3W41O87soMq0Khy5eMTusIRNtmzZ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\neFmmUSe/fvEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f226bd12978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "\n",
    "# x,yの範囲\n",
    "xmin, xmax = -4, 4\n",
    "ymin, ymax = -3, 3\n",
    "\n",
    "# グラフ初期化\n",
    "def init_graph():\n",
    "    plt.title(u'$y=sin(x), y=cos(x)$')\n",
    "    plt.xlabel('x-axis')\n",
    "    plt.ylabel('y-axis')\n",
    "    plt.xlim(xmin, xmax)\n",
    "    plt.ylim(ymin, ymax)\n",
    "    plt.hlines([0], xmin, xmax, color='skyblue')\n",
    "    plt.vlines([0], ymin, ymax, color='skyblue')\n",
    "\n",
    "# 主処理\n",
    "init_graph()\n",
    "x = np.arange(xmin, xmax, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "plt.plot(x, y_sin, label='$y=sin(x)$')\n",
    "plt.plot(x, y_cos, label='$y=cos(x)$')\n",
    "plt.legend(loc='right')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Jupyter事始め](http://qiita.com/taka4sato/items/2c3397ff34c440044978)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python codeを入力してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017/03/05 14:04:37\n",
      "2017/03/05 14:04:38\n",
      "2017/03/05 14:04:39\n",
      "2017/03/05 14:04:40\n",
      "2017/03/05 14:04:41\n"
     ]
    }
   ],
   "source": [
    "# libraryのimport\n",
    "import datetime, time\n",
    "\n",
    "# サブ関数をコールして１秒待つことを5回繰り返す\n",
    "def main():\n",
    "  for count in range(0, 5):\n",
    "    print_current_time()\n",
    "    time.sleep(1)\n",
    "\n",
    "# 現在の時刻をprintする\n",
    "def print_current_time():\n",
    "  print(datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))\n",
    "\n",
    "# 主処理\n",
    "if __name__ == '__main__':\n",
    "  main()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chartを作成してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P9KEtkiRpEvU0IpHkU8D/orkM9N8AHwDeBryjlLIpyZeAy5NsBJ4CrgTuLKX8oK3iVprA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f228d01a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.random.randint(0, 100, 10000)\n",
    "plt.hist(x, bins=20)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数式を入力してみる"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "$$r=\\frac{1}{f}$$\n",
    "$$\\left(x + y\\right)^{5}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$r=\\frac{1}{f}$$\n",
    "$$\\left(x + y\\right)^{5}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## コマンドラインを実行してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Mar  5 13:54:55 UTC 2017\n",
      "-----\n",
      "drwxrwxrwx 1 root root   136 Mar  5 12:08 コハル\n",
      "-rwxrwxrwx 1 root root 47593 Mar  5 13:53 Docker_Jupyter.ipynb\n",
      "-rwxrwxrwx 1 root root   338 Feb 21 07:24 hello_tensorflow.py\n",
      "drwxrwxrwx 1 root root   136 Feb 25 19:03 Moeka\n",
      "-rwxrwxrwx 1 root root 44959 Feb 25 19:12 Python_Libraries.ipynb\n",
      "drwxrwxrwx 1 root root    68 Mar  3 22:58 tmp\n",
      "-----\n",
      "hello, tensorflow!\n",
      "-----\n",
      "Sun Mar  5 13:54:58 UTC 2017\n"
     ]
    }
   ],
   "source": [
    "!date\n",
    "!echo '-----'\n",
    "!ls -ald *\n",
    "!echo '-----'\n",
    "!python ./hello_tensorflow.py\n",
    "!echo '-----'\n",
    "!date"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}