ch04-figures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 4: Figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robert Johansson\n",
    "\n",
    "Source code listings for [Numerical Python - A Practical Techniques Approach for Industry](http://www.apress.com/9781484205549) (ISBN 978-1-484205-54-9).\n",
    "\n",
    "The source code listings can be downloaded from http://www.apress.com/9781484205549"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcIEAAACcIEADACQIE\nAHCCAAEAnCBAAAAnCBAAwAkCBABwggABAJwgQAAAJwgQAMAJAgQAcIIAAQCcaO36AOqSMcb1IQAA\nAtRsAmStdX0IAIBa4CU4AIATBAgA4AQBAgA4QYAAAE402wCVlJRo5MiRvjcnZGZmyuv1yuv1auXK\nlVd9zM6dOxUTE6M2bdpo/fr1fve1atVKQ4YM0ZAhQ3TXXXcFtP97771XvXv31m233abDhw9fdb1f\n/epXuuWWW3TDDTf4Lc/IyFDXrl19+1yxYoUkKTc3VxMmTKhx/wDQ2DWbd8FVtHr1av3gBz+QMUb5\n+flKT0/Xnj17JElDhw5VYmKigoOD/R4TFRWlzMxMPf/885W2FxQUpH/84x8B73/58uXq3Lmz/vnP\nf+qNN97QnDlz9Prrr1dab+LEifrZz36m3r17+y03xuj+++/XSy+95Lc8LCxMISEhys7OVkxMTMDH\nAwCNTbMdAa1du1YTJ06UJL333ntKSEhQcHCwgoODFR8fr3fffbfSY6KiojRw4EB5PNf/Z3n77beV\nkpIiSZo8ebK2bdt21fViY2PVrVu3SsuttVW+tTwxMVFr16697mMEAJeaZYBKS0u1f/9+eb1eSdKJ\nEyfUvXt33/3du3fX8ePHa7XN4uJiDR06VMOHD9eGDRtqXP/48eOKjIyUJLVu3VqdOnVSfn5+wPsz\nxmj9+vUaNGiQkpKSdOzYMd99sbGx2rlzZ62OHwAam2YZoFOnTlW6pnK9jhw5oj179mjNmjV69NFH\n9dVXX9Xp9iv64Q9/qMOHD2vfvn2Kj4/3jaYkKTw8XF9//XW97h8A6luzDJDkPzNCRESEjh496rt9\n9OhRvxHR1VSc1ic8PFySFB0drVGjRtV4PSgiIkJHjhyRJH333XcqKipSaGhowMcfGhqqNm3aSJJS\nU1N916+ky8+NaYcANHXNMkBdunTR2bNnfbfHjh2rrKwsFRYWqqCgQFu3btXYsWOrfHzF6y+FhYUq\nKSmRdHl09be//U39+/eXJM2bN09/+ctfKm0jMTFRmZmZkqS33npLcXFxtXoO33zzje/3t99+W7fe\neqvv9smTJxUVFVWr7QFAY9MsA9SqVSsNGDBAX375pSQpJCREv/71rzVs2DDFxsYqLS3N9w64tLQ0\nbdy4UZL0ySefKDIyUm+99ZZmzJihgQMHSpI+//xzDRs2TIMHD9bo0aM1b9489e3bV5K0f/9+3+io\nvNTUVJ0+fVq9e/fWiy++qGeffdZ335AhQ3y///KXv1RkZKTOnz+vyMhIpaenS5JeeuklDRgwQIMH\nD9aSJUuUkZHhe8zu3bt1++231+FfDAAanqluEk9jjG2qk3xmZGQoNzdXc+bMqdf9jBs37qrvqKtP\nU6dO1RNPPOEXMgC4HsYYWWsb9LX9ZjkCkqQpU6Zo06ZN9T5LdkPHJy8vT4WFhcQHQJPXbEdAAIDA\nMQICALQYBAgA4ERAAboyseelS5cCmtQzPz9f8fHx8nq9SkhIUGFhoSRp3759Sk1NrbujBwDUKWNM\nO2PMB8YYjzHmXWNMgTFmYzXrhxpjthpjDhhjsowxwWXLBxljlle3r4ACdGViz8LCQqWnp2v37t3a\nvXu3nn76aV9cynv22WcVHx+vAwcOKC4uzvcW5EGDBulf//qX8vLyAtktAKDhTZX0jrX2kqTfSfpx\nDevPlbTVWuuVtK3stqy1+yT1NMbcVNUDAwrQlYk9A53Us/xEnCkpKX4f1Bw/frzefPPNQHYLAGh4\n90vaIEnW2u2Szla/uhIlZZb9nimp/PfVbJGUVNUDawxQ+Yk9jx8/HtCknrm5uQoLC5N0+esDcnNz\nffcxkSYANE7GmFaSBlhrD9TiYWHW2isn+VxJYeXu2y2pyk/N1xig8hN7Xsv8Y8YYv8cxkSYANFpd\nJJ251geXfW6n/Gd3TkrqUdX6Ab0Ed+WzQIFO6hkWFuaby+zkyZO66ab/vgTIRJoA0KhVPEHX9GHQ\nXGNMN0kyxoRLKn+R31T3+BoDVH5iz4SEhCon9Sw/KWf5iTgzMzP9vsKaiTQBoNE6Jel/KiyrNGIw\nxvzWGHPlxP62pCvfF5MiqfzszOGSDle1sxoDVH5iz9DQ0Con9Sw/KefcuXO1detWeb1ebd++XXPn\nzvVtj4k0AaBxstaWStpvjOkjScaYDyWtkxRnjDlqjIkvW3WALr+8JknPSoo3xhyQNLrs9hWxkqq8\n6B/QVDyBTOwZ6KSco0aN0rp16/xelgMAuHVlKh5jzAO6/MaCRdWs+661dlwA29wh6UfW2qt+9iag\na0CBTOwZSHz27dunXr16ER8AaLzWSLrTVHOxPsD4DJJ0sKr4SAGMgGraCQCgeWjoyUirDRAAAPWF\nyUgBAE4QIACAEwQIAOAEAQIAOEGAAABO/D/WwqGcGC7X8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067cc650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0.05, 0.05, .88, .88])\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "ax.text(0.001, 0.01, \"(0,0)\")\n",
    "ax.text(1.001, 0.01, \"(1,0)\")\n",
    "ax.text(0.001, 1.01, \"(0,1)\")\n",
    "ax.text(1.001, 1.01, \"(1,1)\")\n",
    "ax.text(0.02, 0.92, \"Figure\", fontsize=18)\n",
    "\n",
    "ax.text(0.12, 0.06, \"(0.15, 0.15)\")\n",
    "\n",
    "ax = fig.add_axes([0.15, 0.15, 0.68, 0.66])\n",
    "ax.text(0.03, 0.88, \"Axes\", fontsize=18)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "\n",
    "\n",
    "fig.savefig(\"figure-axes-schematic.pdf\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CgQCBQIC8vDzy8vIiek9UPVYzOw+4B+hKaCmWo68qMMA55yKaBSu80us84Fvn3D3ltFGP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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10844bf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "ax.text(0.12, 0.06, \"(0.15, 0.15)\")\n",
    "\n",
    "ax = fig.add_axes([0.15, 0.15, 0.68, 0.66])\n",
    "ax.set_yticks([1, 2, 3])\n",
    "ax.set_xticks([1, 2, 3])\n",
    "\n",
    "#ax.set_yticklabels(['', '', ''])\n",
    "\n",
    "ax.set_xlabel(\"x axis\", fontsize=18)\n",
    "ax.set_ylabel(\"y axis\", fontsize=18)\n",
    "\n",
    "ax.set_xlim(1,3)\n",
    "ax.set_ylim(1,3)\n",
    "\n",
    "ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))\n",
    "ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))\n",
    "\n",
    "ax.annotate(\"Major tick\",\n",
    "         xy=(1, 2), xycoords='data',\n",
    "         xytext=(+35, +30), textcoords='offset points', fontsize=16,\n",
    "         arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3, rad=.2\"))\n",
    "\n",
    "ax.annotate(\"Minor tick\",\n",
    "         xy=(1, 1.5), xycoords='data',\n",
    "         xytext=(+35, +30), textcoords='offset points', fontsize=16,\n",
    "         arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3, rad=.2\"))\n",
    "\n",
    "fig.savefig(\"figure-axis-schematic.pdf\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]"
        },
        {
         "module": "IPython",
         "version": "3.2.1"
        },
        {
         "module": "OS",
         "version": "Darwin 14.1.0 x86_64 i386 64bit"
        },
        {
         "module": "numpy",
         "version": "1.9.2"
        },
        {
         "module": "matplotlib",
         "version": "1.4.3"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]</td></tr><tr><td>IPython</td><td>3.2.1</td></tr><tr><td>OS</td><td>Darwin 14.1.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.9.2</td></tr><tr><td>matplotlib</td><td>1.4.3</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n",
       "IPython & 3.2.1 \\\\ \\hline\n",
       "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.9.2 \\\\ \\hline\n",
       "matplotlib & 1.4.3 \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n",
       "IPython 3.2.1\n",
       "OS Darwin 14.1.0 x86_64 i386 64bit\n",
       "numpy 1.9.2\n",
       "matplotlib 1.4.3\n",
       "Mon Aug 03 22:57:26 2015 JST"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information numpy, matplotlib"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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