mkatsura/invert_axis.ipynb

## invert_axis.ipynb
{
 "metadata": {
  "name": "invert_axis"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Matplotlib\u3067\u30b0\u30e9\u30d5\u306e\u5ea7\u6a19\u8ef8\u306e\u5411\u304d\u3092\u53cd\u8ee2\u3055\u305b\u308b\u65b9\u6cd5 #\n",
      "\u6842\u8aa0\n",
      "\n",
      "[INDEX](https://sites.google.com/site/makotokatsura4ipythonnotebook/ipythonnb4jpnexp/Tips)\n",
      "\n",
      "3\u3064\u306e\u65b9\u6cd5\u3092\u6319\u3052\u307e\u3059\u3002\n",
      "\n",
      "1. xlim,ylim\u306e\u5024\u3092\u6570\u5024\u3067\u76f4\u63a5\u66f8\u304d\u8fbc\u3080\u65b9\u6cd5\n",
      "2. Pyplot\u304c\u8a2d\u5b9a\u3057\u305fxlim,ylim\u306e\u5024\u3092\u8aad\u307f\u51fa\u3057\u3001\u53cd\u8ee2\u3057\u3066\u66f8\u304d\u8fbc\u3080\u65b9\u6cd5\n",
      "3. \u898b\u305f\u76ee\u306f\u4e00\u898b\u30b9\u30de\u30fc\u30c8\u3060\u304c\u3001II\u3068\u7b49\u4fa1\u306a\u65b9\u6cd5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_test():\n",
      "    x=np.arange(-10,10)\n",
      "    y=(0.1*x)**3\n",
      "    plt.plot(x,y,'ko')\n",
      "    plt.grid()\n",
      "\n",
      "plot_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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XrlyR+vv7R7yQ+7e//Y0XcieAWnVTUTVoEbNbpbVs3/uzs7Nl18LDrZ4eDljTF0tO7lQ0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\n1GgAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## \u65b9\u6cd5\uff11\uff0e ##\n",
      "\n",
      "\u4e0a\u306e\u30b0\u30e9\u30d5\u306ex\u8ef8\u3092\u53cd\u8ee2\u3055\u305b\u308b\u65b9\u6cd5\u3068\u3057\u3066\u3001\u30cd\u30c3\u30c8\u4e0a\u3067\u4e00\u756a\u898b\u304b\u3051\u308b\u306e\u306f\u3001"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_test()\n",
      "xlim(10,-10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "(10, -10)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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H3IH28bYC4Wwr8pKTQe7cuSPNmzdPunbtmjQ0NHTfgeG//OUvHBieBux3lT824fn9zMzM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\nGJMAEVEY+3+i+kze6GceywAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u3053\u306e\u3088\u3046\u306b\u3001\u76f4\u63a5x\u8ef8\u306e\u7bc4\u56f2\u3092\u6307\u5b9a\u3057\u308b\u65b9\u6cd5\u3067\u3059\u3002\n",
      "\u3057\u304b\u3057\u3001x\u8ef8\u306e\u7bc4\u56f2\u306f\u81ea\u52d5\u3067\u6c7a\u3081\u305f\u3044\u3051\u308c\u3069\u3001\u53cd\u8ee2\u3057\u305f\u3044\u3001\u3068\u3044\u3046\u5834\u5408\u306b\u306f\u56f0\u308a\u307e\u3059\u3002\n",
      "\n",
      "## \u65b9\u6cd5\uff12\uff0e ##\n",
      "\u81ea\u52d5\u3067\u7bc4\u56f2\u3092\u6c7a\u3081\u3066\u304b\u3089\u3001\u305d\u308c\u3092\u53cd\u8ee2\u3059\u308b\u3068\u3044\u3046\u65b9\u6cd5\u304c\u3042\u308a\u307e\u3059\u3002\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_test()\n",
      "plt.xlim(plt.xlim()[::-1])\n",
      "# 2\u884c\u306b\u5206\u3051\u308b\u3068\u3001\n",
      "#_xlim=plt.xlim()\n",
      "#plt.xlim(_xlim[::-1])\u3068\u3044\u3046\u4e8b\u3002\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "(10.0, -10.0)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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H3IH28bYC4Wwr8pKTQe7cuSPNmzdPunbtmjQ0NHTfgeG//OUvHBieBux3lT824fn9zMzM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\nGJMAEVEY+3+i+kze6GceywAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## \u65b9\u6cd5\uff13\uff0e ##\n",
      "\n",
      "\u5b9f\u306f\u53cd\u8ee2\u7528\u306e\u547d\u4ee4\u3082\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\n",
      "[\uff08\u53c2\u7167\uff09](http://stackoverflow.com/questions/2051744/pyplot-reverse-y-axis)\n",
      "\u3002\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_test()\n",
      "plt.gca().invert_xaxis()\n",
      "# 2\u884c\u306b\u5206\u3051\u308b\u3068\u3001\n",
      "#ax=plt.gca()\n",
      "#ax.invert_xaxis()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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H3IH28bYC4Wwr8pKTQe7cuSPNmzdPunbtmjQ0NHTfgeG//OUvHBieBux3lT824fn9zMzM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\nGJMAEVEY+3+i+kze6GceywAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u73fe\u5728\u53cd\u8ee2\u3057\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u5b9a\u3059\u308b\u547d\u4ee4\u3082\u3042\u308a\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_test()\n",
      "plt.xlim(plt.xlim()[::-1])\n",
      "print plt.gca().xaxis_inverted()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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H3IH28bYC4Wwr8pKTQe7cuSPNmzdPunbtmjQ0NHTfgeG//OUvHBieBux3lT824fn9zMzM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\nGJMAEVEY+3+i+kze6GceywAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "y\u8ef8\u306b\u5bfe\u3057\u3066\u3082\u540c\u69d8\u3067\u3059\u3002\n",
      "\n",
      "## \u65b9\u6cd5\uff12\uff0c\uff13\u306f\u7b49\u4fa1 ##\n",
      "\n",
      "xlim,ylim\u3092\u53d6\u5f97\u3057\u3066\u304b\u3089\u53cd\u8ee2\u3055\u305b\u308b\u306e\u3068\u3001\u7279\u6bb5\u306e\u5dee\u306f\u7121\u3044\u3088\u3046\u3067\u3059\u3002\n",
      "\n",
      "\u305f\u3060\u3001\u3082\u3057\u304b\u3057\u305f\u3089\u5c06\u6765\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u306f\u3042\u3089\u304b\u3058\u3081axes\u306b\u53cd\u8ee2\u3092\u8a2d\u5b9a\u3057\u3066\u304a\u304f\u4ee5\u4e0b\u306e\u69d8\u306a\u4f8b\u304c\u4e0a\u624b\u304f\u50cd\u304f\u3088\u3046\u306b\u306a\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3001\u3068\u60f3\u50cf\u3057\u3066\u3044\u307e\u3059\u3002"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax=plt.subplot(211)\n",
      "ax.invert_xaxis()\n",
      "x=np.arange(-10,10)\n",
      "y=(0.1*x)**3\n",
      "plt.plot(x,y,'ko')\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAACDCAYAAABlYxuxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niz8RkQ36P0ZyRIL+QMwIAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u73fe\u72b6\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u304b\u3089invert\u3057\u306a\u3044\u3068\u4e0a\u624b\u304f\u50cd\u304d\u307e\u305b\u3093\u3002\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax=plt.subplot(211)\n",
      "x=np.arange(-10,10)\n",
      "y=(0.1*x)**3\n",
      "plt.plot(x,y,'ko')\n",
      "ax.invert_xaxis()\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u304a\u305d\u3089\u304f\u3001invert_xaxis\u306e\u5834\u5408\u3067\u3082\u3001\u5185\u90e8\u3067\u306f\n",
      "\n",
      "plt.xlim(plt.xlim()[::-1])\n",
      "\n",
      "\u3068\u540c\u3058\u52d5\u4f5c\u3092\u3057\u3066\u3044\u308b\u306e\u3060\u308d\u3046\u3068\u601d\u3044\u307e\u3059\u3002\n",
      "\n",
      "*\u306a\u306e\u3067\u3001\u6700\u521d\u306e\u4f8b\u3067\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u306exlim\u306e\u5024(0,1)\u3092\u53cd\u8ee2\u3057\u3066\u8a2d\u5b9a\u3057\u305f\u306e\u3067\u3057\u3087\u3046\u3002*"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}