{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute powers with numpy and matplotlib\n",
    "\n",
    "This is a simple notebook that you can run with parameters to see SageMaker notebook execution in action.\n",
    "\n",
    "It takes two parameters:\n",
    "\n",
    "* _n_ the number of points\n",
    "* _p_ the power to raise to\n",
    "\n",
    "This will compute $ i^p $ for $ i $ in $ [0,n) $ and draw a graph of the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, show the date so you can see that it really ran."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook run on 2020-03-13 17:49:12\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "print(\"Notebook run on {}\".format(datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up numpy and matplotlib to display our data the right way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters\n",
    "\n",
    "The following cell is the _parameter cell_. If you select it and then click the wrench icon (assuming you're using JupyterLab), you can see that it has the `parameters` tag set in it's metadata.\n",
    "\n",
    "Papermill will add a cell after this cell that sets any parameters that you added in the call. Note that it is not necessary to reset all the parameters (_e.g.,_ just setting `p` is fine here to get the same range but a different exponent. See the documentation [Parameterize][parameters] in the Papermill documentation for more information.\n",
    "\n",
    "[parameters]: https://papermill.readthedocs.io/en/latest/usage-parameterize.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "n = 100\n",
    "p = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computation\n",
    "\n",
    "Now use some simple numpy to compute the values and then use matplotlib to draw the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(range(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.power(x, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,      1,      8,     27,     64,    125,    216,    343,\n",
       "          512,    729,   1000,   1331,   1728,   2197,   2744,   3375,\n",
       "         4096,   4913,   5832,   6859,   8000,   9261,  10648,  12167,\n",
       "        13824,  15625,  17576,  19683,  21952,  24389,  27000,  29791,\n",
       "        32768,  35937,  39304,  42875,  46656,  50653,  54872,  59319,\n",
       "        64000,  68921,  74088,  79507,  85184,  91125,  97336, 103823,\n",
       "       110592, 117649, 125000, 132651, 140608, 148877, 157464, 166375,\n",
       "       175616, 185193, 195112, 205379, 216000, 226981, 238328, 250047,\n",
       "       262144, 274625, 287496, 300763, 314432, 328509, 343000, 357911,\n",
       "       373248, 389017, 405224, 421875, 438976, 456533, 474552, 493039,\n",
       "       512000, 531441, 551368, 571787, 592704, 614125, 636056, 658503,\n",
       "       681472, 704969, 729000, 753571, 778688, 804357, 830584, 857375,\n",
       "       884736, 912673, 941192, 970299])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b93bbe0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Environment\n",
    "\n",
    "The SageMaker Run Notebook tools will pass through and set up environment variables in the running notebook. You can see what they all are here. Note that this list or the structure of the values may change over time as the tool evolves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pprint\n",
    "\n",
    "pprint.pprint({k:v for k,v in os.environ.items()})"
   ]
  }
 ],
 "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": "3.8.1"
  },
  "sagemaker_run_notebook": {
   "saved_parameters": [
    {
     "name": "p",
     "value": "0.5"
    },
    {
     "name": "n",
     "value": "20"
    }
   ]
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}