example/_run_jnb/Power_function-output (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Power Function\n",
    "$ y=x^b $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import matplotlib and numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the argument as a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_arange_args={'start':-10,'stop':10,'step':0.01}\n",
    "\n",
    "\n",
    "# !!! start assign jupyter notebook parameter(s) !!!\n",
    "\n",
    "np_arange_args = {'start': -20, 'stop': 20, 'step': 0.1}\n",
    "\n",
    "# !!! end assign jupyter notebook parameter(s) !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(**np_arange_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the exponent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "exponent=2\n",
    "\n",
    "\n",
    "# !!! start assign jupyter notebook parameter(s) !!!\n",
    "\n",
    "exponent = 3\n",
    "\n",
    "# !!! end assign jupyter notebook parameter(s) !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x**exponent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('$y=x^{'+str(exponent)+'}$',fontsize=20)\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$y$')\n",
    "ax.plot(x,y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}