LOG_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45a66028",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-th1_6av5 because the default path (/home/gabri-bot/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(420)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88662d06",
   "metadata": {},
   "source": [
    "### Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e83edfac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "USER_PATH = os.getcwd()\n",
    "SAVE_PATH = USER_PATH + \"/plots/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941871f1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed6f7420",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('text', usetex=False)\n",
    "plt.rc('font', family='serif')\n",
    "plt.rc('font', size=16)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=18)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=16)     # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=12)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=12)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=16)    # legend fontsize\n",
    "plt.rc('figure', titlesize=18)   # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44c79a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "in_width      = 64\n",
    "# Out width\n",
    "frac_width    = 10\n",
    "integer_width = int(np.floor(np.log2(in_width-1))+1)\n",
    "out_width     = integer_width + frac_width\n",
    "LUT_input = np.linspace(0,2**frac_width,2**frac_width,endpoint=False, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b29316",
   "metadata": {},
   "source": [
    "## Look up table method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bec2874d",
   "metadata": {},
   "outputs": [],
   "source": [
    "LUT_output = np.log2(1+LUT_input/(1<<frac_width))*((1<<frac_width))\n",
    "LUT_output = LUT_output.round()\n",
    "LUT_output = LUT_output.astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b793035",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(LUT_input,LUT_output)\n",
    "plt.title('COMPUTED: LUT respose')\n",
    "plt.xlabel('LUT input')\n",
    "plt.ylabel('LUT output')\n",
    "plt.grid(True)\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"LUT_response\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf0a99e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 2**20\n",
    "\n",
    "# Generate random input from 1 to (2**64)-1\n",
    "Test_input    = np.random.randint(1,2**64-1,N, dtype=np.uint64)\n",
    "#Test_input    = np.linspace(1,2**64-1,N,endpoint=False)\n",
    "#Test_input    = np.floor(Test_input)\n",
    "#Test_input    = Test_input.astype(np.uint64)\n",
    "\n",
    "# Compute the exact logarithm\n",
    "Exact_output  = np.log2(Test_input)\n",
    "\n",
    "Test_input_ord   = np.sort(Test_input  , kind='heapsort')\n",
    "Exact_output_ord = np.sort(Exact_output, kind='heapsort')\n",
    "\n",
    "\n",
    "# Priority encoder output\n",
    "prienc_out    = np.array(np.log2(Test_input), dtype=np.uint64)\n",
    "# Extract the fractional part and convert in fixed point number\n",
    "frac_in      = ((Test_input - 2**prienc_out)*2**(in_width-prienc_out))/(2**(in_width-frac_width))\n",
    "frac_in      = frac_in.astype(np.uint64)\n",
    "# Computed result\n",
    "Comp_output   = ((prienc_out<<frac_width) + LUT_output[frac_in])/2**frac_width\n",
    "\n",
    "# Evaluate the error\n",
    "Error         = Exact_output - Comp_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4645ee92",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "facada07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of input, computed result\n",
    "\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "plt.plot(Test_input    , Comp_output     , '.',label=\"LUT result  \")\n",
    "plt.plot(Test_input_ord, Exact_output_ord,     label=\"Exact result\")\n",
    "plt.title('COMPUTED: LUT result')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Log$_2$ result')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"LUT_LOG_out\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b951336f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean error  0.000492\n",
      "Standard deviation error  0.000412\n"
     ]
    }
   ],
   "source": [
    "# Error distribution\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "plt.hist(Error,bins=120)\n",
    "plt.title('COMPUTED: LUT error distribution')\n",
    "plt.xlabel('Error')\n",
    "plt.semilogy()\n",
    "plt.grid(True)\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"LUT_LOG_error\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"Mean error  %f\"               % np.mean(Error))\n",
    "print(\"Standard deviation error  %f\" % np.std(Error ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fab2cf0",
   "metadata": {},
   "source": [
    "## Polyfit\n",
    "\n",
    "\n",
    "$PE_{out}     = MSB(input)  $  \n",
    "$I_{order}    = \\frac{input - 2^{PE_{out}}}{2^{PE_{out}}}$  \n",
    "$II_{order}  = I_{order}^2  $  \n",
    "$III_{order} = I_{order}^3  $    \n",
    "        .  \n",
    "        .  \n",
    "$Log_2(input) = PE_{out} + a \\cdot I_{order} +b \\cdot II_{order} + c \\cdot III_{order} + ...$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51b25bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def I_func(x, a):\n",
    "\n",
    "    return a*x \n",
    "\n",
    "def II_func(x, a, b):\n",
    "\n",
    "    return a*x + b*x**2\n",
    "\n",
    "def III_func(x, a, b, c):\n",
    "\n",
    "    return a*x + b*x**2 + c*x**3\n",
    "\n",
    "def IV_func(x, a, b, c, d):\n",
    "\n",
    "    return a*x + b*x**2 + c*x**3 + d*x**4\n",
    "\n",
    "I_or   = LUT_input/(2**frac_width)\n",
    "Exact_value = np.log2(1+I_or)\n",
    "\n",
    "popt1, pcov1 = curve_fit(I_func, I_or, Exact_value)\n",
    "popt1 = popt1*2**frac_width\n",
    "popt1 = popt1.astype(np.int32)\n",
    "I_int= I_or*popt1[0]/(2**frac_width)\n",
    "\n",
    "popt2, pcov2 = curve_fit(II_func, I_or, Exact_value)\n",
    "popt2 = popt2*2**frac_width\n",
    "popt2 = popt2.astype(np.int32)\n",
    "II_int= (I_or*popt2[0] + I_or**2*popt2[1])/(2**frac_width)\n",
    "\n",
    "popt3, pcov3 = curve_fit(III_func, I_or, Exact_value)\n",
    "popt3 = popt3*2**frac_width\n",
    "popt3 = popt3.astype(np.int32)\n",
    "III_int= (I_or*popt3[0] + I_or**2*popt3[1] + I_or**3*popt3[2])/(2**frac_width)\n",
    "\n",
    "popt4, pcov4 = curve_fit(IV_func, I_or, Exact_value)\n",
    "popt4 = popt4*2**frac_width\n",
    "popt4 = popt4.astype(np.int32)\n",
    "IV_int= (I_or*popt4[0] + I_or**2*popt4[1] + I_or**3*popt4[2] + I_or**4*popt4[3])/(2**frac_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0047018b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "plt.plot(I_or,Exact_value,label=\"$log_2(1+x)$\")\n",
    "plt.plot(I_or,I_int,label=\"$poly_1$\")\n",
    "#plt.plot(I_or,II_int,label=\"$poly_2$\")\n",
    "plt.plot(I_or,III_int,label=\"$poly_3$\")\n",
    "#plt.plot(I_or,IV_int,label=\"$poly_4$\")\n",
    "\n",
    "plt.title('COMPUTED: Polyfit response')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Correction')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"POLY_response\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2981c3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "I_order     = frac_in\n",
    "II_order    = I_order**2/2**frac_width\n",
    "II_order    = II_order.astype(np.uint32)\n",
    "III_order   = I_order**3/(2**(2*frac_width))\n",
    "III_order   = III_order.astype(np.uint32)\n",
    "IV_order   = I_order**4/(2**(3*frac_width))\n",
    "IV_order   = IV_order.astype(np.uint32)\n",
    "\n",
    "\n",
    "Corr1 = (I_order*popt1[0])/2**frac_width\n",
    "Corr1 = Corr1.astype(np.uint32)\n",
    "\n",
    "Corr2 = (I_order*popt2[0]+II_order*popt2[1])/2**frac_width\n",
    "Corr2 = Corr2.astype(np.uint32)\n",
    "\n",
    "Corr3 = (I_order*popt3[0]+II_order*popt3[1]+III_order*popt3[2])/2**frac_width\n",
    "Corr3 = Corr3.astype(np.uint32)\n",
    "\n",
    "Corr4 = (I_order*popt4[0]+II_order*popt4[1]+III_order*popt4[2]+IV_order*popt4[3])/2**frac_width\n",
    "Corr4 = Corr4.astype(np.uint32)\n",
    "\n",
    "Comp_output_fit1 = prienc_out + (Corr1)/2**frac_width\n",
    "Comp_output_fit2 = prienc_out + (Corr2)/2**frac_width\n",
    "Comp_output_fit3 = prienc_out + (Corr3)/2**frac_width\n",
    "Comp_output_fit4 = prienc_out + (Corr4)/2**frac_width\n",
    "\n",
    "\n",
    "Error_fit1       = Exact_output - Comp_output_fit1\n",
    "Error_fit2       = Exact_output - Comp_output_fit2\n",
    "Error_fit3       = Exact_output - Comp_output_fit3\n",
    "Error_fit4       = Exact_output - Comp_output_fit4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b126687b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficent in 11_fix_10\n",
      "Polyfit1 coefficient\n",
      "[1108]\n",
      "Polyfit2 coefficients\n",
      "[1389 -375]\n",
      "Polyfit3 coefficients\n",
      "[1457 -601  169]\n",
      "Polyfit4 coefficients\n",
      "[1473 -694  331  -86]\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficent in 11_fix_10\")\n",
    "print(\"Polyfit1 coefficient\")\n",
    "print(popt1)\n",
    "print(\"Polyfit2 coefficients\")\n",
    "print(popt2)\n",
    "print(\"Polyfit3 coefficients\")\n",
    "print(popt3)\n",
    "print(\"Polyfit4 coefficients\")\n",
    "print(popt4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e9df4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of input, computed result\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "plt.plot(Test_input    , Comp_output_fit1  , '.',label=\"fit1 result  \")\n",
    "#plt.plot(Test_input    , Comp_output_fit2  , '.',label=\"fit2 result  \")\n",
    "#plt.plot(Test_input    , Comp_output_fit3  , '.',label=\"fit3 result  \")\n",
    "#plt.plot(Test_input    , Comp_output_fit4  , '.',label=\"fit4 result  \")\n",
    "plt.plot(Test_input_ord, Exact_output_ord,     label=\"Exact result\")\n",
    "plt.title('COMPUTED: Polyfit result')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Log$_2$ result')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "#fig.savefig(SAVE_PATH + \"TE_LOG_out\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76e61c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean error  0.017305\n",
      "Standard deviation error  0.037879\n"
     ]
    }
   ],
   "source": [
    "# Error distribution\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "plt.hist(Error_fit1,bins=120)\n",
    "plt.title('COMPUTED: Polyfit error distribution')\n",
    "plt.xlabel('Error')\n",
    "plt.semilogy()\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "#fig.savefig(SAVE_PATH + \"TE_LOG_error\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()\n",
    "print(\"Mean error  %f\"               % np.mean(Error_fit1))\n",
    "print(\"Standard deviation error  %f\" % np.std(Error_fit1 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15ecc2be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of input, computed result\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "#plt.plot(Test_input    , Comp_output_fit1  , '.',label=\"fit1 result  \")\n",
    "#plt.plot(Test_input    , Comp_output_fit2  , '.',label=\"fit2 result  \")\n",
    "plt.plot(Test_input    , Comp_output_fit3  , '.',label=\"fit3 result  \")\n",
    "#plt.plot(Test_input    , Comp_output_fit4  , '.',label=\"fit4 result  \")\n",
    "plt.plot(Test_input_ord, Exact_output_ord,     label=\"Exact result\")\n",
    "plt.title('COMPUTED: Polyfit result')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Log$_2$ result')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"POLY_LOG_out\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "495a169b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean error  0.001048\n",
      "Standard deviation error  0.000655\n"
     ]
    }
   ],
   "source": [
    "# Error distribution\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "plt.hist(Error_fit3,bins=120)\n",
    "plt.title('COMPUTED: Polyfit error distribution')\n",
    "plt.xlabel('Error')\n",
    "plt.semilogy()\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"POLY_LOG_error\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()\n",
    "print(\"Mean error  %f\"               % np.mean(Error_fit3))\n",
    "print(\"Standard deviation error  %f\" % np.std(Error_fit3 ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd534186",
   "metadata": {},
   "source": [
    "## Taylor expansion method\n",
    "\n",
    "$PE_{out}     = MSB(input)  $  \n",
    "$I_{order}    = \\frac{input - 2^{PE_{out}}}{2^{PE_{out}}}$  \n",
    "$II_{order}  = I_{order}^2  $  \n",
    "$III_{order} = I_{order}^3  $  \n",
    "$Log_2(input) = PE_{out} + \\frac{I_{order}}{ln(2)} - \\frac{II_{order}}{2ln(2)} + \\frac{III_{order}}{3ln(2)}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "044e6c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "I_or   = LUT_input/(2**frac_width)\n",
    "II_or  = I_or**2\n",
    "III_or = I_or**3\n",
    "IV_or = I_or**4\n",
    "\n",
    "Exact_value = np.log2(1+I_or)\n",
    "I_corr  = I_or/(np.log(2))\n",
    "II_corr = I_corr-II_or/(2*np.log(2))\n",
    "III_corr = II_corr+III_or/(3*np.log(2))\n",
    "IV_corr  = III_corr-IV_or/(4*np.log(2))\n",
    "\n",
    "TE_out = III_corr\n",
    "TE_out_mean = (II_corr + III_corr)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a4cda8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(I_or,TE_out,label=\"III order\")\n",
    "plt.plot(I_or,TE_out_mean, label=\"III \\& II order\")\n",
    "#plt.plot(I_or,I_or, label=\"Mitchell approx\")\n",
    "plt.plot(I_or,Exact_value,label=\"$log_2(1+x)$\")\n",
    "\n",
    "plt.title('COMPUTED: Taylor expasion response')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Correction')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"TE_respnse\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "090367de",
   "metadata": {},
   "outputs": [],
   "source": [
    "I_order     = frac_in\n",
    "II_order    = I_order**2/2**frac_width\n",
    "II_order    = II_order.astype(np.uint32)\n",
    "III_order   = I_order**3/(2**(2*frac_width))\n",
    "III_order   = III_order.astype(np.uint32)\n",
    "\n",
    "ln2_1          = int(1/np.log(2)*2**frac_width)\n",
    "ln2_2          = int(1/np.log(2)*2**frac_width/2)\n",
    "ln2_3          = int(1/np.log(2)*2**frac_width/3)\n",
    "\n",
    "Corr = (I_order*ln2_1 - II_order*ln2_2 + III_order*ln2_3)/2**frac_width\n",
    "Corr = Corr.astype(np.uint32)\n",
    "\n",
    "\n",
    "Comp_output_te = prienc_out + (Corr)/2**frac_width\n",
    "\n",
    "Error_te       = Exact_output - Comp_output_te"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9bcf1185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of input, computed result\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "plt.plot(Test_input    , Comp_output_te  , '.',label=\"TE result  \")\n",
    "plt.plot(Test_input_ord, Exact_output_ord,     label=\"Exact result\")\n",
    "plt.title('COMPUTED: Taylor expansion result')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Log$_2$ result')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"TE_LOG_out\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "245324c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean error  -0.042785\n",
      "Standard deviation error  0.055396\n"
     ]
    }
   ],
   "source": [
    "# Error distribution\n",
    "#######################################################################\n",
    "# FIGURE\n",
    "#######################################################################\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "# plt.axes(axisbelow=True)\n",
    "\n",
    "\n",
    "\n",
    "#######################################################################\n",
    "##### TOP PANEL\n",
    "#######################################################################\n",
    "fig.add_axes((0.,0.35,1.0,1.0))\n",
    "\n",
    "\n",
    "plt.hist(Error_te,bins=120)\n",
    "plt.title('COMPUTED: Taylor expansion error distribution')\n",
    "plt.xlabel('Error')\n",
    "plt.semilogy()\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "#######################################################################\n",
    "##### SAVE FIGURE\n",
    "#######################################################################\n",
    "fig.savefig(SAVE_PATH + \"TE_LOG_error\", bbox_inches='tight')\n",
    "#######################################################################\n",
    "\n",
    "plt.show()\n",
    "print(\"Mean error  %f\"               % np.mean(Error_te))\n",
    "print(\"Standard deviation error  %f\" % np.std(Error_te ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f922e9d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "849bcfd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c875e27",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TFenv",
   "language": "python",
   "name": "tfenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}