ipython notebook - matplotlib

gistfile1.txt

{
 "metadata": {
  "name": "",
  "signature": "sha256:5693ce12d3ee8ef5f8290516954fe3d0548eefe589f70792f5d66fc06a709a8a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import math"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u6563\u5e03\u56f3"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "4\u3064\u306e\u70b9\u3092\u5e73\u9762\u4e0a\u306b\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u4f8b\u3067\u3059\u3002``plot(x\u306e\u30ea\u30b9\u30c8, y\u306e\u30ea\u30b9\u30c8)``\u3068\u3044\u3046\u66f8\u5f0f\u3067\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f4\u3064\u306e\u70b9\u3092\u7d50\u3076\u76f4\u7dda\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([1,2,3,4], [1,4,9,16])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xb984da0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u7dda\u3067\u306f\u306a\u304f\u70b9\u3067\u8868\u793a\u3059\u308b\u306b\u306f\u3001plot()\u306e\u7b2c\u4e09\u5f15\u6570\u306b'o'\u3092\u52a0\u3048\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u8d64\u3044(r)\u70b9(o)\u3092\u8868\u793a\n",
      "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1C8CZVfUcYC9w3Sq3p5MkTwE+ClwymMX+UJF5n6dmDJfo21SPX1U9XlXP5cg/8POT9BYo\nNrVjt4z+Te34JXkF8K2quoPFf7tY9viNO8y/AZw55/MZg31NqKr/PvqrYFX9E3BCkqetcrOGkuQE\n4GPAB6tqoX8MUzuGS/WthfEDqKpHgH3AC+Ydmtqxm+t4/Zvy8XsRcGGSrwLXAi9J8oF5ZYYav3GH\n+SeAi+DYU6PfraqDY77mxCQ5LUkG2y/kyFc9F1rXW5MGbX8/cF9Vvfc4xaZyDJfTt2kevySnJjll\nsP1kYAtwx7xiUzl2sLz+TfP4VdVlVXVmVZ0FvAb4VFVdNK/YUOPX6aGho5JcC1wAnJrkAeAK4IRB\nY/+6qm5I8vIkXwH+B7h4lOtN2lL9A14NvDHJo8D3OTIo0+SXgN8A7k5y9B/KZcAzYerHcMm+Md3j\n93TgmiRP4sik7O+q6pNJfgemfuxgGf1jusdvvgIYZfx8aEiSGuCfjZOkBhjmktQAw1ySGmCYS1ID\nDHNJaoBhLkkNMMwlqQGGuSQ14P8AGTGlG2xI8vsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaae4978>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u70b9\u3068\u7dda\u306e\u4e21\u65b9\u3092\u8868\u793a\u3059\u308b\u306b\u306f\u3001plot()\u306e\u7b2c\u4e09\u5f15\u6570\u306b'o-'\u3068\u66f8\u304d\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u8d64\u3044(r)\u70b9(o)\u3092\u8868\u793a\n",
      "plt.plot([1,2,3,4], [1,4,9,16], 'r-o')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nunlqFLZ2igAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb0be4e0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u5404\u7a2e\u30e9\u30d9\u30eb\u306e\u8ffd\u52a0"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u30bf\u30a4\u30c8\u30eb\u3001X\u8ef8\u3001Y\u8ef8\u306b\u540d\u524d\u3092\u4ed8\u3051\u308b\u65b9\u6cd5\u3067\u3059\u3002\n",
      "\n",
      "\u3064\u3044\u3067\u306b\u3001\u30b0\u30e9\u30d5\u4e2d\u306b\u30c6\u30ad\u30b9\u30c8\u3092\u66f8\u304f\u65b9\u6cd5\u3068\u3001\u30b0\u30ea\u30c3\u30c9\u7dda\u3092\u8868\u793a\u3059\u308b\u65b9\u6cd5\u3082\u793a\u3057\u3066\u3044\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
      "# x\u8ef8, y\u8ef8\u306e\u7bc4\u56f2\u3092\u8a2d\u5b9a  x\u8ef8\u306f[0,6], y\u8ef8\u306f[0, 20]\n",
      "plt.axis([0, 6, 0, 20])\n",
      "# \u30b0\u30e9\u30d5\u306b\u30bf\u30a4\u30c8\u30eb\u3092\u4ed8\u4e0e\n",
      "plt.title('graph title here')\n",
      "# x\u8ef8, y\u8ef8\u306b\u540d\u524d\u3092\u4ed8\u4e0e\n",
      "plt.xlabel('x-axis name here')\n",
      "plt.ylabel('y-axis name here')\n",
      "# \u30b0\u30e9\u30d5\u4e2d\u306b\u30c6\u30ad\u30b9\u30c8\u3092\u8868\u793a\n",
      "plt.text(1, 15, r'text here. $\\alpha, \\beta, \\gamma$')\n",
      "# \u30b0\u30ea\u30c3\u30c9\u7dda\u3092\u8868\u793a\n",
      "plt.grid(True)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ETAWOAD6ZtVtTMQJ4N3BpRLwbWAuc2d+O9Vb0nwB2Knu+E6WrfWsQkt4A/BC4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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb087ba8>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u65e5\u672c\u8a9e\u306e\u30e9\u30d9\u30eb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u53c2\u8003\uff1ahttp://sucrose.hatenablog.com/entry/2013/06/10/001250\n",
      "import matplotlib.font_manager\n",
      "prop = matplotlib.font_manager.FontProperties(fname=r'C:\\Windows\\Fonts\\meiryo.ttc')\n",
      "\n",
      "plt.title(u'\u65e5\u672c\u8a9e\u306e\u30bf\u30a4\u30c8\u30eb\u3092\u8868\u793a', fontproperties=prop)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kSZMzTrivBXYPLO/p183l7cDNh1KUJOnQrB6jzdhzKUleD/w88OrZtk9PTx94PTU1xdTU\n1LiHlqRnhJmZGWZmZg75OOPMuZ9CN4e+sV++GHiiqi4favdS4EZgY1XtmuU4zrlL0gIt5Zz7NmB9\nkhOSHAmcA9w0dPLn0QX7ubMFuyRpeY2clqmqfUkuAG4FVgFbqmp7kvP77ZuB9wLPAa5OArC3qjYs\nXdmSpPmMnJaZ2ImclpGkBVvKaRlJ0tOM4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMM\nd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCX\npAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq\nkOEuSQ0y3CWpQSPDPcnGJDuS7Exy0Rxtruq335fkZZMvU5K0EPOGe5JVwIeBjcBLgE1JThpqcybw\nwqpaD/wicPUS1dqMmZmZlS7hsGFfHGRfHGRfHLpRI/cNwK6qeqiq9gJbgbOG2vwL4H8AVNVdwLOT\nPHfilTbEX9yD7IuD7IuD7ItDNyrc1wK7B5b39OtGtTn+0EuTJC3WqHCvMY+TRe4nSVoCqZo7h5Oc\nAkxX1cZ++WLgiaq6fKDNbwEzVbW1X94BvK6qvjZ0LANfkhahqoYH0COtHrF9G7A+yQnAI8A5wKah\nNjcBFwBb+w+Dbw8H+2KLkyQtzrzhXlX7klwA3AqsArZU1fYk5/fbN1fVzUnOTLIL+C7wtiWvWpI0\nr3mnZSRJT08Tf0LVh54OGtUXSd7S98H9Se5M8tKVqHM5jPN70bf7yST7kpy9nPUtlzH/PqaS3JPk\ngSQzy1zishnj72NNkluS3Nv3xXkrUOaySHJtkq8l+fN52iwsN6tqYj90Uze7gBOAI4B7gZOG2pwJ\n3Ny/fiXwhUnWcLj8jNkXPwU8q3+98ZncFwPtPgv8L+BNK133Cv1OPBv4v8Dx/fKala57BftiGvjA\n/n4AvgmsXunal6g/Xgu8DPjzObYvODcnPXL3oaeDRvZFVX2+qr7TL95Fu88HjPN7AXAhcAPwjeUs\nbhmN0w9vBj5RVXsAqurRZa5xuYzTF18Fju1fHwt8s6r2LWONy6aqPgf89TxNFpybkw53H3o6aJy+\nGPR24OYlrWjljOyLJGvp/rj3//MVLV4MGud3Yj1wXJLbk2xL8tZlq255jdMX1wA/luQR4D7gXctU\n2+Fowbk56lbIhfKhp4PGfk9JXg/8PPDqpStnRY3TF1cA766qShKe+jvSgnH64Qjg5cCpwNHA55N8\noap2Lmlly2+cvngPcG9VTSU5EfhMkpOr6rElru1wtaDcnHS4PwysG1heR/cJM1+b4/t1rRmnL+gv\nol4DbKyq+b6WPZ2N0xevoHtWArr51TOS7K2qm5anxGUxTj/sBh6tqu8B30tyB3Ay0Fq4j9MXrwLe\nD1BVX0ryFeDFdM/fPNMsODcnPS1z4KGnJEfSPfQ0/Md5E/BzcOAJ2FkfemrAyL5I8jzgRuDcqtq1\nAjUul5F9UVUvqKrnV9Xz6ebd39FYsMN4fx9/ALwmyaokR9NdPPviMte5HMbpix3AaQD9/PKLgS8v\na5WHjwXn5kRH7uVDTweM0xfAe4HnAFf3I9a9VbVhpWpeKmP2RfPG/PvYkeQW4H7gCeCaqmou3Mf8\nnbgMuC7JfXQD0V+pqm+tWNFLKMn1wOuANUl2A5fSTdEtOjd9iEmSGuR/sydJDTLcJalBhrskNchw\nl6QGGe6S1CDDXZIaZLhLUoMMd0lq0P8HYx0hN/7yzJcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xab3e550>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "y = f(x)\u306e\u8868\u793a"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u5909\u6570\u306e\u6570\u304c\u3072\u3068\u3064\u306e\u95a2\u6570\u3092\u66f8\u304f\u306b\u306f\u3001\u4e0a\u3067\u66f8\u3044\u305f\u6563\u5e03\u56f3\u306e\u8003\u3048\u65b9\u3092\u5fdc\u7528\u3057\u307e\u3059\u3002\n",
      "\n",
      "\u4e0b\u306e\u4f8b\u3067\u306f\u3001\u307e\u305ax\u30920.0\u304b\u30895.0\u306e\u7bc4\u56f2\u30670.2\u305a\u3064\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u307e\u3059\u3002\n",
      "\u305d\u3057\u3066\u5404x\u306b\u5bfe\u3059\u308b$f(x)$ (\u3053\u3053\u3067\u306f$f(x) = \\sin(x)$)\u306e\u5024\u3092\u8a08\u7b97\u3057\u3001fx\u306b\u683c\u7d0d\u3057\u307e\u3059\u3002\n",
      "\u3042\u3068\u306f\u6563\u5e03\u56f3\u306e\u66f8\u304d\u65b9\u3068\u540c\u69d8\u3067\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(0., 5., 0.2)\n",
      "fx = np.sin(x)\n",
      "plt.plot(x, fx)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nQ8FEApozx6/ftGQJ1KwZOo1EUaJaN/n50KyZvznqsssyGEwksN69/d//J54InUSiKFGt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\nLyKScP8PLKy9Bglvlr4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf1b198>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "y = f(x), g(x), h(x)\u3092\u540c\u6642\u306b\u6bd4\u8f03"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u540c\u6642\u306b\u8907\u6570\u306e\u30b0\u30e9\u30d5\u3092\u66f8\u304f\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(0., 5., 0.1)\n",
      "\n",
      "fx = np.sin(x)\n",
      "gx = np.cos(x)\n",
      "hx = np.sqrt(x)\n",
      "\n",
      "plt.plot(x, fx, 'r--', x, gx, 'bs', x, hx, 'g^')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Qqz/pJCo63UqkTuj7bR5DhoD7NJ79+42sHbeWvh+exfDPPs+QIUbLoy/DkhGwpOMHqfNO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\nS/jc5+JuTd1RoJf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       "text": [
        "<matplotlib.figure.Figure at 0xbaf68d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}