Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.linspace(0, 6, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.12244898, 0.24489796, 0.36734694, 0.48979592,\n",
       "       0.6122449 , 0.73469388, 0.85714286, 0.97959184, 1.10204082,\n",
       "       1.2244898 , 1.34693878, 1.46938776, 1.59183673, 1.71428571,\n",
       "       1.83673469, 1.95918367, 2.08163265, 2.20408163, 2.32653061,\n",
       "       2.44897959, 2.57142857, 2.69387755, 2.81632653, 2.93877551,\n",
       "       3.06122449, 3.18367347, 3.30612245, 3.42857143, 3.55102041,\n",
       "       3.67346939, 3.79591837, 3.91836735, 4.04081633, 4.16326531,\n",
       "       4.28571429, 4.40816327, 4.53061224, 4.65306122, 4.7755102 ,\n",
       "       4.89795918, 5.02040816, 5.14285714, 5.26530612, 5.3877551 ,\n",
       "       5.51020408, 5.63265306, 5.75510204, 5.87755102, 6.        ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.sin(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this exersise, we set all values > 0.9 to nan\n",
    "Y[Y > 0.9] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.12214321  0.24245733  0.35914064  0.47044581  0.57470604\n",
      "  0.67036003  0.75597537  0.83026995  0.89213121         nan         nan\n",
      "         nan         nan         nan         nan         nan  0.87233593\n",
      "  0.80608763  0.72776815  0.63855032  0.53977018  0.43290697  0.31956097\n",
      "  0.20142953  0.08028167 -0.0420684  -0.16378849 -0.28305585 -0.39808444\n",
      " -0.50715171 -0.60862436 -0.70098285 -0.7828441  -0.85298224 -0.91034694\n",
      " -0.95407918 -0.98352405 -0.99824062 -0.99800852 -0.9828312  -0.95293597\n",
      " -0.90877049 -0.85099614 -0.78047811 -0.6982724  -0.60561003 -0.50387864\n",
      " -0.39460166 -0.2794155 ]\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1094ada30>]"
      ]
     },
     "execution_count": 33,
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38",
   "language": "python",
   "name": "py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0rc1+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}