{
 "metadata": {
  "name": "blog_note_20140211"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "\u5404\u7a2e\u30e9\u30a4\u30d6\u30e9\u30ea\u8aad\u307f\u8fbc\u307f"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%pylab inline\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Populating the interactive namespace from numpy and matplotlib\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "\u65b9\u5411\u3068\u9577\u3055\u304c\u5909\u308f\u308b\u5834\u5408\u306e\u56f3"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x_min = -1\nx_max = 20\ny_min = -1\ny_max = 20\nplt.axis([x_min, x_max, y_min, y_max])\n\n#\u63cf\u753b\u7bc4\u56f2\u306e\u9762\u7a4d\u3092\u57fa\u6e96\u306b\u3001\u7a0b\u3088\u3044arrow\u306e\u5e45\u3092\u751f\u6210\narea = ((x_max - x_min) ** 2 + (y_max - y_min) ** 2) ** 0.5\ncharacteristic_length = area ** 0.5\nar_width =  characteristic_length * 0.03\nar_head_width = ar_width * 3.0\n\n#arrow\u3092\u6307\u5b9a\n#before\nplt.arrow(0., 0., 5., 1.,\n          width = ar_width, head_width = ar_head_width, length_includes_head = True, \n          color = \"b\")\n#after\nplt.arrow(0., 0., 16., 14,\n          width = ar_width, head_width = ar_head_width, length_includes_head = True, \n          color = \"r\")\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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O7yBJys6Wxo2TJk2SJkxwPr7xRudC5qBBP3zvP/7hTIaKi53fUb/4hfPjAkgsyRHkUaoT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\nwHAEOQAY7v8BzI4v5a1BUvkAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x8c48940>"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "\u65b9\u5411\u304c\u5909\u308f\u3089\u306a\u3044\u5834\u5408\u306e\u56f3"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "x_min = -1\nx_max = 8\ny_min = -1\ny_max = 14\nplt.axis([x_min, x_max, y_min, y_max])\n\n#\u63cf\u753b\u7bc4\u56f2\u306e\u9762\u7a4d\u3092\u57fa\u6e96\u306b\u3001\u7a0b\u3088\u3044arrow\u306e\u5e45\u3092\u751f\u6210\narea = ((x_max - x_min) ** 2 + (y_max - y_min) ** 2) ** 0.5\ncharacteristic_length = area ** 0.5\nar_width =  characteristic_length * 0.03\nar_head_width = ar_width * 3.0\n\n#arrow\u3092\u6307\u5b9a\n#before\nplt.arrow(0., 0., 1., 2.,\n          width = ar_width, head_width = ar_head_width, length_includes_head = True, \n          color = \"b\")\n#after\nplt.arrow(0., 0.+0.1, 5., 10+0.1,\n          width = ar_width, head_width = ar_head_width, length_includes_head = True, \n          color = \"r\")\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x8c692b0>"
      }
     ],
     "prompt_number": 3
    }
   ],
   "metadata": {}
  }
 ]
}