L03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:11:12.081454Z",
     "start_time": "2020-03-15T06:11:12.076470Z"
    }
   },
   "source": [
    "#### Lecture 3 - Numerical errors and differentiation\n",
    "\n",
    "Floating point representation: print range of values for 64-bit decimals on this system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:17:59.967092Z",
     "start_time": "2020-03-15T06:17:59.959602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine parameters for float64\n",
      "---------------------------------------------------------------\n",
      "precision =  15   resolution = 1.0000000000000001e-15\n",
      "machep =    -52   eps =        2.2204460492503131e-16\n",
      "negep =     -53   epsneg =     1.1102230246251565e-16\n",
      "minexp =  -1022   tiny =       2.2250738585072014e-308\n",
      "maxexp =   1024   max =        1.7976931348623157e+308\n",
      "nexp =       11   min =        -max\n",
      "smallest_normal = 2.2250738585072014e-308   smallest_subnormal = 4.9406564584124654e-324\n",
      "---------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import numpy module that will be used for matrix operations\n",
    "import numpy as np\n",
    "\n",
    "print(str(np.finfo(np.float64)))   # 64-bit reals by default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the attributes using `.max`, `.eps`, etc.\n",
    "A few examples that create Inf/NaN values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:31:55.011367Z",
     "start_time": "2020-03-15T06:31:55.002390Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3q/q4pw14qn3r95vp8fslqnfl340000gn/T/ipykernel_19840/1288780920.py:3: RuntimeWarning: overflow encountered in scalar multiply\n",
      "  print(np.finfo(np.float64).max * 2)   # Inf\n"
     ]
    }
   ],
   "source": [
    "# multiplying the largest number by 2\n",
    "# this will give an overflow warning (value too large to store)\n",
    "print(np.finfo(np.float64).max * 2)   # Inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:31:55.011367Z",
     "start_time": "2020-03-15T06:31:55.002390Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3q/q4pw14qn3r95vp8fslqnfl340000gn/T/ipykernel_19840/1673797736.py:3: RuntimeWarning: divide by zero encountered in divide\n",
      "  print(np.divide(5,0))\n"
     ]
    }
   ],
   "source": [
    "# dividing 5 by 0 \n",
    "# using numpy here because default python will raise an error instead of a warning\n",
    "print(np.divide(5,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:31:55.011367Z",
     "start_time": "2020-03-15T06:31:55.002390Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3q/q4pw14qn3r95vp8fslqnfl340000gn/T/ipykernel_19840/2659034772.py:2: RuntimeWarning: invalid value encountered in divide\n",
      "  print(np.divide(0,0))\n"
     ]
    }
   ],
   "source": [
    "# dividing 0/0 - note the error is different\n",
    "print(np.divide(0,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:31:55.011367Z",
     "start_time": "2020-03-15T06:31:55.002390Z"
    }
   },
   "source": [
    "Functions to check if a value is Inf/NaN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "x = 5\n",
    "print(np.isnan(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(np.isinf(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T06:25:11.197074Z",
     "start_time": "2020-03-15T06:25:11.193082Z"
    }
   },
   "source": [
    "##### Roundoff Error\n",
    "Example of subtractive cancellation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T20:36:51.746261Z",
     "start_time": "2020-04-06T20:36:51.737258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not equal\n"
     ]
    }
   ],
   "source": [
    "if (0.3 - 0.2 - 0.1 == 0.0):\n",
    "    print('equal')\n",
    "else:\n",
    "    print('not equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T20:36:51.746261Z",
     "start_time": "2020-04-06T20:36:51.737258Z"
    }
   },
   "source": [
    "In general, floating point numbers are almost never strictly equal\n",
    "instead test within a tolerance, `a-eps <= x <= a+eps`\n",
    "\n",
    "Another example of roundoff error - small numbers added to big ones may be chopped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T20:36:51.746261Z",
     "start_time": "2020-04-06T20:36:51.737258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "equal\n"
     ]
    }
   ],
   "source": [
    "x = 10 ** 16\n",
    "if ((x + 0.25) == x):\n",
    "    print('equal')\n",
    "else:\n",
    "    print('not equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Truncation error and total numerical error\n",
    "##### Textbook Example 4.8\n",
    "\n",
    "$$ f(x) = -0.1x^4 - 0.15x^3 - 0.5x^2 - 0.25x + 1.2 $$\n",
    "\n",
    "Use a centered difference approximation of $O(h^2)$ to\n",
    "estimate the first derivative $f^\\prime(x=0.5)$.\n",
    "\n",
    "Start with $h=1$. Then progressively divide the step size by a factor of 10 \n",
    "to demonstrate how roundoff error becomes dominant as the step size is reduced.\n",
    "The exact value of the derivative is $-0.9125$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T08:04:12.146166Z",
     "start_time": "2020-03-15T08:04:11.947480Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import the numpy and matplotlib module \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "\n",
    "def f(x):\n",
    "    return -0.1*x**4 - 0.15*x**3 - 0.5*x**2 - 0.25*x + 1.2\n",
    "\n",
    "x = 0.5 \n",
    "exact = -0.9125"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, if we wanted to calculate the error using a step size of 0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0034999999999990594\n"
     ]
    }
   ],
   "source": [
    "h = 0.1\n",
    "D = (f(x + h) - f(x - h)) / (2 * h) # centered difference\n",
    "Err = abs(exact - D) # absolute error\n",
    "print(Err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to do the same thing for a range of step sizes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T08:04:12.146166Z",
     "start_time": "2020-03-15T08:04:11.947480Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n  = 11 # number of step sizes\n",
    "H  = np.zeros(n) # h values to store\n",
    "Ea = np.zeros(n) # absolute error values to store\n",
    "\n",
    "for i in range(n):\n",
    "    h = 10 ** (-i)     # h = 1, 0.1, 0.01, 0.001, ...\n",
    "    H[i] = h  # store the value of h in the array\n",
    "    D = (f(x + h) - f(x - h)) / (2 * h) # centered difference\n",
    "    Ea[i] = abs(exact - D) # absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(H,Ea)\n",
    "plt.title('Abs. Error estimating df/dx at x=0.5 with centered diff.') \n",
    "plt.xlabel('Step size')\n",
    "plt.ylabel('Total Error')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the left side of the graph, roundoff error dominates. \n",
    "On the right side of the graph, truncation error dominates.\n",
    "Note the right side slope is ~2, so the error decreases by 2 orders of magnitude for every 1 order of magnitude decrease in the step size.\n",
    "This is what we would expect for an approximation with $O(h^2)$ error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.12.8"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": "40",
    "lenType": 16,
    "lenVar": "100"
   },
   "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": 4
}