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 - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib](https://link.springer.com/book/10.1007/979-8-8688-0413-7) (ISBN 979-8-8688-0412-0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "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,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "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,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.10.12 64bit [Clang 14.0.6 ]"
        },
        {
         "module": "IPython",
         "version": "8.12.0"
        },
        {
         "module": "OS",
         "version": "macOS 10.15.7 x86_64 i386 64bit"
        },
        {
         "module": "numpy",
         "version": "1.22.3"
        },
        {
         "module": "matplotlib",
         "version": "3.7.1"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.10.12 64bit [Clang 14.0.6 ]</td></tr><tr><td>IPython</td><td>8.12.0</td></tr><tr><td>OS</td><td>macOS 10.15.7 x86\\_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.22.3</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td colspan='2'>Sat Nov 02 18:11:39 2024 JST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.10.12 64bit [Clang 14.0.6 ] \\\\ \\hline\n",
       "IPython & 8.12.0 \\\\ \\hline\n",
       "OS & macOS 10.15.7 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.22.3 \\\\ \\hline\n",
       "matplotlib & 3.7.1 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sat Nov 02 18:11:39 2024 JST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.10.12 64bit [Clang 14.0.6 ]\n",
       "IPython 8.12.0\n",
       "OS macOS 10.15.7 x86_64 i386 64bit\n",
       "numpy 1.22.3\n",
       "matplotlib 3.7.1\n",
       "Sat Nov 02 18:11:39 2024 JST"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information numpy, matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "npbook_py310",
   "language": "python",
   "name": "npbook_py310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}