03-PyconUS-Graficar-Discontinuidad-Walter-Garzon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "killing-mainland",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as Funcion\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "genetic-lodge",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.linspace(-0.1, 0.1, 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "partial-nightlife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.1       , -0.09411765, -0.08823529, -0.08235294, -0.07647059,\n",
       "       -0.07058824, -0.06470588, -0.05882353, -0.05294118, -0.04705882,\n",
       "       -0.04117647, -0.03529412, -0.02941176, -0.02352941, -0.01764706,\n",
       "       -0.01176471, -0.00588235,  0.        ,  0.00588235,  0.01176471,\n",
       "        0.01764706,  0.02352941,  0.02941176,  0.03529412,  0.04117647,\n",
       "        0.04705882,  0.05294118,  0.05882353,  0.06470588,  0.07058824,\n",
       "        0.07647059,  0.08235294,  0.08823529,  0.09411765,  0.1       ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adverse-kingdom",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.1        -0.09411765 -0.08823529 -0.08235294 -0.07647059 -0.07058824\n",
      " -0.06470588 -0.05882353 -0.05294118 -0.04705882 -0.04117647 -0.03529412\n",
      " -0.02941176 -0.02352941 -0.01764706 -0.01176471 -0.00588235  0.\n",
      "  0.00588235  0.01176471  0.01764706  0.02352941  0.02941176  0.03529412\n",
      "  0.04117647  0.04705882  0.05294118  0.05882353  0.06470588  0.07058824\n",
      "  0.07647059  0.08235294  0.08823529  0.09411765  0.1       ]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "piano-christopher",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10272/1460129213.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  Y = np.sin(X)/X\n"
     ]
    }
   ],
   "source": [
    "Y = np.sin(X)/X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7e467e",
   "metadata": {},
   "source": [
    "## Función discontinua con numpy\n",
    "\n",
    "La función *sin(X)/X* es discontinua en x=0, no está definida, y **Python numpy** informa el error, pero lo grafica igualmente "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "novel-dutch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99833417, 0.9985243 , 0.99870293, 0.99887005, 0.99902566,\n",
       "       0.99916976, 0.99930234, 0.9994234 , 0.99953294, 0.99963095,\n",
       "       0.99971744, 0.9997924 , 0.99985583, 0.99990773, 0.9999481 ,\n",
       "       0.99997693, 0.99999423,        nan, 0.99999423, 0.99997693,\n",
       "       0.9999481 , 0.99990773, 0.99985583, 0.9997924 , 0.99971744,\n",
       "       0.99963095, 0.99953294, 0.9994234 , 0.99930234, 0.99916976,\n",
       "       0.99902566, 0.99887005, 0.99870293, 0.9985243 , 0.99833417])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "offensive-crown",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff86ee8b898>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 8x6 inches, 80 dots per inch\n",
    "Funcion.figure(figsize=(14, 10), dpi=80)\n",
    "Funcion.grid(True)\n",
    "Funcion.plot(X,Y,\"bo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "premium-accessory",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.linspace(-20, 20, 505)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "young-recording",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10272/1460129213.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  Y = np.sin(X)/X\n"
     ]
    }
   ],
   "source": [
    "Y = np.sin(X)/X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "innocent-empty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8be4afa910>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 8x6 inches, 80 dots per inch\n",
    "Funcion.figure(figsize=(14, 10), dpi=80)\n",
    "Funcion.grid(True)\n",
    "Funcion.plot(X,Y,\"ro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "polished-breath",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}