plot_axes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Simple axes example\n",
    "====================\n",
    "\n",
    "This example shows a couple of simple usage of axes.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.axes([.1, .1, .8, .8])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(.6, .6, 'axes([0.1, 0.1, 0.8, 0.8])', ha='center', va='center',\n",
    "        size=20, alpha=.5)\n",
    "\n",
    "plt.axes([.2, .2, .3, .3])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(.5, .5, 'axes([0.2, 0.2, 0.3, 0.3])', ha='center', va='center',\n",
    "        size=16, alpha=.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}