Greatest Common Divisor Algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Comparison between different Euclid GCD and Binary GCD algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Intuition Behind the Binary GCD Algorithm:\n",
    "- $Since,$  $d = c.gcd(a, b)$ $Given$ $that:$ $d = gcd(ac, bc)$\n",
    "- $This\\ intuitively\\ shows\\ you\\ that\\ if\\ two\\ numbers\\ share\\ a\\ common\\ divisor,\\ then\\ the\\ difference\\ between\\ the\\ two\\ numbers\\ also\\ shares\\ the\\ same\\ divisor$\n",
    "```\n",
    "# if u and v are even, then\n",
    "    return 2*binaryGCD_rec(u >> 1, v >> 1)  # 2*gcd(u/2, v/2) (From 1)\n",
    "```\n",
    "\n",
    "- $Since,$  $gcd(a, bc) = gcd(a, b)$ $if$ $gcd(a, c) = 1$ \n",
    "- $Since$ $gcd(odd\\ number, 2) = 1$\n",
    "- $This\\ intuitively\\ shows\\ you\\ that\\ if\\ one\\ of\\ the\\ two\\ numbers\\ is\\ even\\ then,\\ we\\ can\\ divide\\ it\\ by\\ 2\\ and\\ its\\ gcd\\ won't\\ change$\n",
    "```\n",
    "# if either u or v is even, then\n",
    "    return binaryGCD_rec(u >> 1, v)  # 2*gcd(u/2, v) (From 2)\n",
    "    return binaryGCD_rec(u, v >> 1)  # 2*gcd(u, v/2) (From 2)\n",
    "```\n",
    "\n",
    "\n",
    "- $Since,$  $gcd(a, b) = gcd(a−b, b)$ $assuming$ $a > b$\n",
    "- $This\\ intuitively\\ shows\\ you\\ that\\ we\\ can\\ reduce\\ numbers\\ using\\ their\\ difference$\n",
    "```\n",
    "# if either u and v are odd, then\n",
    "# their difference is even, then\n",
    "    return binaryGCD_rec((u-v) >> 1, v)  # 2*gcd((u-v)/2, v) (From 2, 3)\n",
    "# or\n",
    "    return binaryGCD_rec((v-u) >> 1, u)  # 2*gcd((v-u)/2, u) (From 2, 3)\n",
    "```\n",
    "**Here, are the proofs of some of the used theroms and corollaries,**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I. Prove that: if d = gcd(ac, bc), then d = c.gcd(a, b)\n",
    "\n",
    "- $If$ $d = gcd(ac, bc)$\n",
    "- $Then,$ $d|ac$ and $d|bc$\n",
    "- $Then,$ $ac = dk_1$ and $bc = dk_2$\n",
    "- $Then,$ $a = (d/c)k_1$ and $b = (d/c)k_2$\n",
    "- $Then,$ $(d/c)|a$ and $(d/c)|b$\n",
    "- $Then,$ $(d/c) = gcd(a, b)$\n",
    "- $Then,$ $d = c.gcd(a, b)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II. Prove that: if gcd(a, c) = 1 then, gcd(a, bc) = gcd(a, b)\n",
    "\n",
    "- $Let,$ $d = gcd(a, bc)$\n",
    "- $Then,$ $d|a$ and $d|bc$\n",
    "- $Assume,$ $gcd(a, c) = 1$\n",
    "- $Then,$ $d$ ~~|~~ $c$ \n",
    "- $But,$ $d|bc$\n",
    "- $Then,$ $d|b$\n",
    "- $Then,$ $d|gcd(a, b)$\n",
    "- $Assume,$ $e = gcd(a, b)$\n",
    "- $By\\ definition,$ $e|a$ and $e|b$\n",
    "- $Then,$ $e|bc$ $Theorem$\n",
    "- $Then,$ $e|d$\n",
    "- $But,$ $d|e$\n",
    "- $Then,$ $d = e$\n",
    "- $Then,$ $gcd(a, b) = gcd(a, bc)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# III. Prove that: gcd(a, b) = gcd(a−b, b) assuming a > b\n",
    "\n",
    "We should prove it in the two directions:\n",
    "- $Every\\ common\\ divisor\\ of\\ a\\ and\\ b\\ is\\ also\\ a\\ common\\ divisor\\ of\\ a − b\\ and\\ b$\n",
    "  - $If$ $a = dp$ and $b = dq$\n",
    "  - $Then$ $a − b = d(p − q)$\n",
    "- $Every\\ common\\ divisor\\ of\\ a − b\\ and\\ b \\ is\\ also\\ a\\ common\\ divisor\\ of\\ a\\ and\\ b $\n",
    "  - $If$ $a − b = dp$ and $b = dq$\n",
    "  - $Then$ $a = d(p + q)$\n",
    "- $Therefore,$  $gcd(a, b) = gcd(a − b, b)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implemnatation\n",
    "\n",
    "Comparison between different implemnatations of Euclid GCD and Binary GCD for\n",
    "  randomly generated integers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions Implementations \n",
    "from numba import jit\n",
    "\n",
    "@jit(nopython=True)\n",
    "def euclidGCD(a, b):\n",
    "    assert a >= b and b >= 0 \n",
    "    return euclidGCD(b, a % b) if b > 0 else a\n",
    "\n",
    "\n",
    "@jit(nopython=True)\n",
    "def extendedEuclidGCD_utility(a, b):  # returns gcd (a, b), x, y: gcd (a, b) = ax+by\n",
    "    assert a >= b and b >= 0\n",
    "    \n",
    "    if b == 0:\n",
    "        d, x, y = a, 1, 0\n",
    "    else:\n",
    "        (d, p, q) = extendedEuclidGCD_utility(b, a % b)\n",
    "        x = q\n",
    "        y = p - q * (a // b)\n",
    "    assert a % d == 0 and b % d == 0\n",
    "    assert d == a * x + b * y\n",
    "    return (d, x, y)\n",
    "def extendedEuclidGCD(a, b):\n",
    "    return extendedEuclidGCD_utility(a, b)[0]\n",
    "\n",
    "@jit(nopython=True)\n",
    "def binaryGCD_rec(u, v):\n",
    "    if u == v:\n",
    "        return u\n",
    "    elif u == 0:\n",
    "        return v\n",
    "    elif v == 0:\n",
    "        return u\n",
    "    # u is even\n",
    "    elif u & 1 == 0:\n",
    "        # v is even\n",
    "        if v & 1 == 0:\n",
    "            return 2*binaryGCD_rec(u >> 1, v >> 1)  # 2*gcd(u/2, v/2) (From 1)\n",
    "        # v is odd\n",
    "        else:\n",
    "            return binaryGCD_rec(u >> 1, v)  # 2*gcd(u/2, v) (From 2)\n",
    "    # u is odd\n",
    "    elif u & 1 != 0:\n",
    "        # v is even\n",
    "        if v & 1 == 0:\n",
    "            return binaryGCD_rec(u, v >> 1)  # 2*gcd(u, v/2) (From 2)\n",
    "        # v is odd and u is greater than v\n",
    "        elif u > v and v & 1 != 0:\n",
    "            return binaryGCD_rec((u-v) >> 1, v)  # 2*gcd((u-v)/2, v) (From 2, 3)\n",
    "        # v is odd and u is smaller than v\n",
    "        else:\n",
    "            return binaryGCD_rec((v-u) >> 1, u)  # 2*gcd((v-u)/2, u) (From 2, 3)\n",
    "\n",
    "@jit(nopython=True)\n",
    "def binaryGCD_nonRec(u, v):\n",
    "    # if u is 0, return v\n",
    "    if u == 0:\n",
    "        return v\n",
    "    # if v is 0, return u\n",
    "    if v == 0:\n",
    "        return u\n",
    "    # make sure u is positive\n",
    "    u = abs(u)\n",
    "    # make sure v is positive\n",
    "    v = abs(v)\n",
    "\n",
    "    # temporary varible to store\n",
    "    # the common factors of 2\n",
    "    k = 1\n",
    "\n",
    "    # run this loop until u or v becomes odd\n",
    "    while u & 1 == 0 and v & 1 == 0:\n",
    "        k <<= 1\n",
    "        u >>= 1\n",
    "        v >>= 1\n",
    "\n",
    "    # remove all the remaining powers of 2\n",
    "    while u & 1 == 0:\n",
    "        u >>= 1\n",
    "\n",
    "    # remove all the remaining powers of 2\n",
    "    while v & 1 == 0:\n",
    "        v >>= 1\n",
    "\n",
    "    #  run the while loop\n",
    "    while v != 0:\n",
    "        # check if u > v\n",
    "        if u > v:\n",
    "            # swap the values\n",
    "            u, v = v, u\n",
    "        v = v - u\n",
    "    # return value\n",
    "    return u*k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the function to let numba compile it\n",
    "euclidGCD(7, 5)\n",
    "extendedEuclidGCD(7, 5)\n",
    "binaryGCD_rec(7, 5)\n",
    "binaryGCD_nonRec(7, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import random\n",
    "\n",
    "MIN_NUM_BITS = 8\n",
    "MAX_NUM_BITS = 64\n",
    "# Utility Functions\n",
    "def getProperRandomNum(maxNoBits):\n",
    "    a = random.getrandbits(maxNoBits)\n",
    "    b = random.getrandbits(maxNoBits+1)\n",
    "    if a < b:\n",
    "        a, b = b, a  # swap the values\n",
    "    if a == 0 or b ==0:\n",
    "        a = 2 ** maxNoBits\n",
    "        b = a >> 1\n",
    "    # print(a, b)\n",
    "    return(a, b)\n",
    "\n",
    "def calcTimeOfAlgo(algo,minSize, maxSize):\n",
    "    runTime = []\n",
    "    for i in range(minSize-1, maxSize):\n",
    "        a, b = getProperRandomNum(i)\n",
    "        start=time.time()\n",
    "        algo(a, b)\n",
    "        end=time.time()\n",
    "        runTime.append(end - start)\n",
    "    return(runTime)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot') # use ggplot style for more sophisticated visuals\n",
    "\n",
    "# Plot Functions\n",
    "\n",
    "# plot all the functions on the same graph\n",
    "def plotOnSameGraph(x):\n",
    "    # this is the call to matplotlib that allows dynamic plotting\n",
    "    plt.ion()\n",
    "    fig = plt.figure(figsize=(13,6))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(x,calcTimeOfAlgo(euclidGCD,x[0], x[-1]),'-o',alpha=0.8)      \n",
    "    ax.plot(x,calcTimeOfAlgo(extendedEuclidGCD,x[0], x[-1]),'-o',alpha=0.8)      \n",
    "    ax.plot(x,calcTimeOfAlgo(binaryGCD_rec,x[0], x[-1]),'-o',alpha=0.8)      \n",
    "    ax.plot(x,calcTimeOfAlgo(binaryGCD_nonRec,x[0], x[-1]),'-o',alpha=0.8)      \n",
    "    ax.legend(['euclidGCD', 'extendedEuclidGCD', 'binaryGCD_rec','binaryGCD_nonRec' ])\n",
    "\n",
    "    plt.ylabel('Runtime (sec.)')\n",
    "    plt.xlabel('Number of Binary Digits')\n",
    "    plt.title('Title: {}'.format(\"GCD Algorithms Comparison\"))\n",
    "    plt.show()\n",
    "\n",
    "# plot all the functions sided\n",
    "def plotSidedGraph(x):\n",
    "    # this is the call to matplotlib that allows dynamic plotting\n",
    "    plt.ion()\n",
    "    fig = plt.figure(figsize=(13,6))\n",
    "\n",
    "    ax = fig.add_subplot(221)\n",
    "    color = random.random(), random.random(), random.random()\n",
    "    ax.plot(x,calcTimeOfAlgo(euclidGCD,x[0], x[-1]),'-o',alpha=0.8, color=color)       \n",
    "    ax.legend(['euclidGCD'])\n",
    "    \n",
    "\n",
    "    bx = fig.add_subplot(222)\n",
    "    color = random.random(), random.random(), random.random()\n",
    "    bx.plot(x,calcTimeOfAlgo(extendedEuclidGCD,x[0], x[-1]),'-o',alpha=0.8, color=color)     \n",
    "    bx.legend(['extendedEuclidGCD'])\n",
    "\n",
    "    cx = fig.add_subplot(223)\n",
    "    color = random.random(), random.random(), random.random()\n",
    "    cx.plot(x,calcTimeOfAlgo(binaryGCD_rec,x[0], x[-1]),'-o',alpha=0.8, color=color)     \n",
    "    cx.legend(['binaryGCD_rec'])\n",
    "\n",
    "    dx = fig.add_subplot(224)\n",
    "    color = random.random(), random.random(), random.random()\n",
    "    dx.plot(x,calcTimeOfAlgo(binaryGCD_nonRec,x[0], x[-1]),'-o',alpha=0.8, color=color)      \n",
    "    dx.legend(['binaryGCD_nonRec'])\n",
    "\n",
    "\n",
    "    plt.ylabel('Runtime (sec.)')\n",
    "    plt.xlabel('Number of Binary Digits')\n",
    "    plt.show()\n",
    "\n",
    "# plot solo graphs\n",
    "def plotter(x_vec,y1_data,identifier=''):\n",
    "    # this is the call to matplotlib that allows dynamic plotting\n",
    "    plt.ion()\n",
    "    fig = plt.figure(figsize=(13,6))\n",
    "    ax = fig.add_subplot(111)\n",
    "    color = random.random(), random.random(), random.random()\n",
    "\n",
    "    ax.plot(x_vec,y1_data,'-o',alpha=0.8,  color=color)        \n",
    "    #update plot label/title\n",
    "    plt.ylabel('Runtime (sec.)')\n",
    "    plt.xlabel('Number of Binary Digits')\n",
    "    plt.title('Title: {}'.format(identifier))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#Display Tables Function\n",
    "def showTables():\n",
    "        # solo Graphs\n",
    "        size = list(range(MIN_NUM_BITS, MAX_NUM_BITS))\n",
    "        \n",
    "        # dictionary of lists\n",
    "        dict = {'Number of Binary digits': list(range(MIN_NUM_BITS, MAX_NUM_BITS)), 'euclidGCD': calcTimeOfAlgo(euclidGCD, size[0], size[-1]),\n",
    "                'extendedEuclidGCD': calcTimeOfAlgo(extendedEuclidGCD, size[0], size[-1]), 'binaryGCD_rec': calcTimeOfAlgo(binaryGCD_rec, size[0], size[-1]), 'binaryGCD_nonRec': calcTimeOfAlgo(binaryGCD_nonRec, size[0], size[-1])}\n",
    "\n",
    "        df = pd.DataFrame(dict)\n",
    "        df.set_index(keys='Number of Binary digits', inplace=True)\n",
    "        display(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotOnSameGraph(list(range(MIN_NUM_BITS, MAX_NUM_BITS)))\n",
    "plotSidedGraph(list(range(MIN_NUM_BITS, MAX_NUM_BITS)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we can see that **binaryGCD_nonRec** is always **faster** than **binaryGCD_nonRec**\n",
    "\n",
    "### we can see that **binaryGCD** is always **faster** than **extendedEuclidGCD**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>euclidGCD</th>\n",
       "      <th>extendedEuclidGCD</th>\n",
       "      <th>binaryGCD_rec</th>\n",
       "      <th>binaryGCD_nonRec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Binary digits</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.291534e-06</td>\n",
       "      <td>1.907349e-06</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>2.384186e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.430511e-06</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>7.152557e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.430511e-06</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>1.668930e-06</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>4.768372e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            euclidGCD  extendedEuclidGCD  binaryGCD_rec  \\\n",
       "Number of Binary digits                                                   \n",
       "8                        4.291534e-06       1.907349e-06   1.192093e-06   \n",
       "9                        4.768372e-07       7.152557e-07   2.384186e-07   \n",
       "10                       7.152557e-07       4.768372e-07   2.384186e-07   \n",
       "11                       2.384186e-07       2.384186e-07   4.768372e-07   \n",
       "12                       7.152557e-07       2.384186e-07   4.768372e-07   \n",
       "13                       2.384186e-07       4.768372e-07   2.384186e-07   \n",
       "14                       4.768372e-07       4.768372e-07   2.384186e-07   \n",
       "15                       2.384186e-07       4.768372e-07   2.384186e-07   \n",
       "16                       2.384186e-07       2.384186e-07   2.384186e-07   \n",
       "17                       2.384186e-07       7.152557e-07   4.768372e-07   \n",
       "18                       2.384186e-07       4.768372e-07   7.152557e-07   \n",
       "19                       4.768372e-07       4.768372e-07   4.768372e-07   \n",
       "20                       4.768372e-07       4.768372e-07   4.768372e-07   \n",
       "21                       2.384186e-07       4.768372e-07   4.768372e-07   \n",
       "22                       2.384186e-07       2.384186e-07   4.768372e-07   \n",
       "23                       4.768372e-07       2.384186e-07   7.152557e-07   \n",
       "24                       7.152557e-07       2.384186e-07   4.768372e-07   \n",
       "25                       2.384186e-07       4.768372e-07   4.768372e-07   \n",
       "26                       2.384186e-07       7.152557e-07   7.152557e-07   \n",
       "27                       4.768372e-07       4.768372e-07   7.152557e-07   \n",
       "28                       4.768372e-07       7.152557e-07   4.768372e-07   \n",
       "29                       7.152557e-07       4.768372e-07   7.152557e-07   \n",
       "30                       4.768372e-07       9.536743e-07   4.768372e-07   \n",
       "31                       4.768372e-07       7.152557e-07   7.152557e-07   \n",
       "32                       4.768372e-07       4.768372e-07   4.768372e-07   \n",
       "33                       4.768372e-07       4.768372e-07   4.768372e-07   \n",
       "34                       4.768372e-07       9.536743e-07   9.536743e-07   \n",
       "35                       4.768372e-07       7.152557e-07   7.152557e-07   \n",
       "36                       7.152557e-07       7.152557e-07   7.152557e-07   \n",
       "37                       2.384186e-07       7.152557e-07   9.536743e-07   \n",
       "38                       4.768372e-07       7.152557e-07   4.768372e-07   \n",
       "39                       9.536743e-07       7.152557e-07   7.152557e-07   \n",
       "40                       9.536743e-07       7.152557e-07   7.152557e-07   \n",
       "41                       7.152557e-07       7.152557e-07   7.152557e-07   \n",
       "42                       7.152557e-07       7.152557e-07   7.152557e-07   \n",
       "43                       7.152557e-07       9.536743e-07   1.192093e-06   \n",
       "44                       7.152557e-07       1.192093e-06   9.536743e-07   \n",
       "45                       9.536743e-07       9.536743e-07   7.152557e-07   \n",
       "46                       7.152557e-07       9.536743e-07   1.192093e-06   \n",
       "47                       7.152557e-07       1.192093e-06   9.536743e-07   \n",
       "48                       4.768372e-07       9.536743e-07   9.536743e-07   \n",
       "49                       4.768372e-07       1.192093e-06   1.192093e-06   \n",
       "50                       4.768372e-07       9.536743e-07   7.152557e-07   \n",
       "51                       4.768372e-07       7.152557e-07   1.192093e-06   \n",
       "52                       9.536743e-07       1.192093e-06   9.536743e-07   \n",
       "53                       7.152557e-07       7.152557e-07   9.536743e-07   \n",
       "54                       7.152557e-07       1.192093e-06   1.192093e-06   \n",
       "55                       4.768372e-07       9.536743e-07   9.536743e-07   \n",
       "56                       7.152557e-07       9.536743e-07   1.192093e-06   \n",
       "57                       7.152557e-07       1.192093e-06   9.536743e-07   \n",
       "58                       9.536743e-07       7.152557e-07   9.536743e-07   \n",
       "59                       7.152557e-07       1.430511e-06   9.536743e-07   \n",
       "60                       7.152557e-07       9.536743e-07   9.536743e-07   \n",
       "61                       9.536743e-07       1.430511e-06   1.192093e-06   \n",
       "62                       7.152557e-07       1.192093e-06   9.536743e-07   \n",
       "63                       7.152557e-07       1.668930e-06   1.192093e-06   \n",
       "\n",
       "                         binaryGCD_nonRec  \n",
       "Number of Binary digits                    \n",
       "8                            7.152557e-07  \n",
       "9                            2.384186e-07  \n",
       "10                           4.768372e-07  \n",
       "11                           4.768372e-07  \n",
       "12                           2.384186e-07  \n",
       "13                           2.384186e-07  \n",
       "14                           2.384186e-07  \n",
       "15                           2.384186e-07  \n",
       "16                           4.768372e-07  \n",
       "17                           2.384186e-07  \n",
       "18                           2.384186e-07  \n",
       "19                           4.768372e-07  \n",
       "20                           2.384186e-07  \n",
       "21                           2.384186e-07  \n",
       "22                           7.152557e-07  \n",
       "23                           2.384186e-07  \n",
       "24                           2.384186e-07  \n",
       "25                           2.384186e-07  \n",
       "26                           4.768372e-07  \n",
       "27                           2.384186e-07  \n",
       "28                           2.384186e-07  \n",
       "29                           2.384186e-07  \n",
       "30                           2.384186e-07  \n",
       "31                           2.384186e-07  \n",
       "32                           7.152557e-07  \n",
       "33                           2.384186e-07  \n",
       "34                           4.768372e-07  \n",
       "35                           1.192093e-06  \n",
       "36                           2.384186e-07  \n",
       "37                           2.384186e-07  \n",
       "38                           2.384186e-07  \n",
       "39                           4.768372e-07  \n",
       "40                           7.152557e-07  \n",
       "41                           4.768372e-07  \n",
       "42                           4.768372e-07  \n",
       "43                           2.384186e-07  \n",
       "44                           4.768372e-07  \n",
       "45                           4.768372e-07  \n",
       "46                           4.768372e-07  \n",
       "47                           4.768372e-07  \n",
       "48                           4.768372e-07  \n",
       "49                           7.152557e-07  \n",
       "50                           7.152557e-07  \n",
       "51                           7.152557e-07  \n",
       "52                           4.768372e-07  \n",
       "53                           7.152557e-07  \n",
       "54                           4.768372e-07  \n",
       "55                           7.152557e-07  \n",
       "56                           4.768372e-07  \n",
       "57                           7.152557e-07  \n",
       "58                           4.768372e-07  \n",
       "59                           7.152557e-07  \n",
       "60                           4.768372e-07  \n",
       "61                           4.768372e-07  \n",
       "62                           4.768372e-07  \n",
       "63                           4.768372e-07  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showTables()"
   ]
  }
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