Evaluation_vA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import numpy as np\n",
    "import csv\n",
    "import os, sys, getopt\n",
    "import csv\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readfile(tfile_path, c, t):\n",
    "    # readfile, see if there exists and quality above cutoff and less than time\n",
    "    # t: time\n",
    "    # c: cutoff\n",
    "    csv_f = None\n",
    "    with open(tfile_path, 'r') as f:\n",
    "        csv_f = csv.reader(f)\n",
    "        for time, quality in csv_f:\n",
    "            if int(quality) <= c and float(time) <= t:\n",
    "                return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(algo, dataset):\n",
    "    record_time, record_nV = {}, {}\n",
    "    record_time_detail = {}\n",
    "    combined = []\n",
    "    t = None\n",
    "    for fn in glob.glob('./{}_output/{}*.trace'.format(algo, dataset)):\n",
    "        sub_detail = {}\n",
    "        with open(fn, 'r') as f:\n",
    "            fin = f.readlines()\n",
    "        line = fin[-1]\n",
    "        name = fn.split('/')[-1].split('_')[0]\n",
    "        try:\n",
    "            if algo == 'approx':\n",
    "                time, nV = line.split()\n",
    "            else:\n",
    "                time, nV = line.split()[0].split(',')\n",
    "        except:\n",
    "            print(fn)\n",
    "            continue\n",
    "\n",
    "            time, nV = float(time), float(nV)\n",
    "\n",
    "        # add number vertices and time respectively\n",
    "        record_time[name] = [*record_time.get(name, []), float(time)]\n",
    "        record_nV[name] = [*record_nV.get(name, []), float(nV)]\n",
    "\n",
    "    return record_time, record_nV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_cutoff = {'karate':14, 'football':94, 'jazz':158, 'email':594, 'delaunay_n10':703,'netscience':899, \\\n",
    "              'power':2203,'as-22july06':3303,'hep-th':3926,'star2':4542,'star':6902}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qrtd(record_time, record_nV, algo=\"GA\", dataset=\"power\", percents=[0.05,.1,.2,.5,1], maxtime=None, mintime=0, log=True):\n",
    "    # max time from runs\n",
    "    if maxtime is None:\n",
    "        maxtime = np.max(record_time[dataset])\n",
    "    interval = maxtime/200.0\n",
    "    optimal = opt_cutoff[dataset]\n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    ax = fig.add_subplot(111)\n",
    "    x = np.arange(mintime, maxtime+interval,interval)\n",
    "    for p in percents:\n",
    "        c = optimal + (optimal * p/100.0)\n",
    "        y = []\n",
    "        for xval in x:\n",
    "            num_runs = 0\n",
    "            num_solutions = 0\n",
    "            # find percent of runs with solution > cutoff at this time\n",
    "            for tfile in glob.glob('./{}_output/{}*.trace'.format(algo, dataset)):\n",
    "                result = readfile(tfile, c, xval)\n",
    "                num_runs += 1\n",
    "                if result:\n",
    "                    num_solutions += 1\n",
    "                    \n",
    "            yvalue = float(num_solutions)/float(num_runs)\n",
    "            y.append(yvalue)\n",
    "        # plot\n",
    "        if log:\n",
    "            ax.semilogx(x,y,label=str(p)+\"%\")\n",
    "        else:\n",
    "            ax.plot(x,y,label=str(p)+\"%\")\n",
    "\n",
    "    # making plot look pretty\n",
    "    ax.set_xlabel('Time (sec)')\n",
    "    ax.set_ylabel('P(solve)')\n",
    "    ax.set_title(f\"{algo}: Qualified RTD ({dataset})\")\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    box = ax.get_position()\n",
    "    ax.set_position([box.x0, box.y0, box.width*0.8, box.height])\n",
    "    lgd = ax.legend(handles, labels, loc='center left', bbox_to_anchor=(1,0.5), title=\"Solution Quality\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sqd(algo, dataset, maxquality=0.005, times=[10, 25, 50, 75, 100], min_bound=0):\n",
    "    # go from zero to this maximum quality, 200 intervals\n",
    "    interval = maxquality/200.0\n",
    "    fig = plt.figure(figsize= (10,5))\n",
    "    ax = fig.add_subplot(111)\n",
    "    x = np.arange(min_bound,maxquality+interval,interval)\n",
    "    optimal = opt_cutoff[dataset]\n",
    "    # max quality is a decimal value, compute percents for plotting\n",
    "    x_percents = [v*100.0 for v in x]\n",
    "    for t in times:\n",
    "        y = []\n",
    "        for xval in x:\n",
    "            c = (xval + 1) * optimal\n",
    "            num_runs = 0\n",
    "            num_solutions = 0\n",
    "            for tfile in glob.glob('./{}_output/{}*.trace'.format(algo, dataset)):\n",
    "                result = readfile(tfile, c, t)\n",
    "                num_runs += 1\n",
    "                if result:\n",
    "                    num_solutions += 1\n",
    "\n",
    "            y.append(num_solutions/float(num_runs))\n",
    "        # plot with percents\n",
    "        ax.plot(x_percents,y,label=str(t)+\"s\")\n",
    "\n",
    "    # making plot look pretty\n",
    "    ax.set_xlabel('Relative Solution Quality (%)')\n",
    "    ax.set_ylabel('P(solve)')\n",
    "    ax.set_title(f\"{algo}: Solution Quality Distributions ({dataset})\")\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    box = ax.get_position()\n",
    "    ax.set_position([box.x0, box.y0, box.width*0.8, box.height])\n",
    "    lgd = ax.legend(handles, labels, loc='center left', bbox_to_anchor=(1,0.5), title=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplots(sa_times, ga_times, dataset=\"power\"):\n",
    "    fig1, ax1 = plt.subplots()\n",
    "    ax1.set_title('Plot')\n",
    "    ax1.boxplot([sa_times, ga_times])\n",
    "    plt.xticks([1, 2], ['SA', 'GA'])\n",
    "    plt.title(f\"Local Search Running Time Box Plots ({dataset})\")\n",
    "    plt.ylabel('Time (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots - Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ga_record_time, ga_record_nV = get_data(\"GA\", \"power\")\n",
    "qrtd(ga_record_time, ga_record_nV, percents=[100 + 5*i for i in range(5)], algo=\"GA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sa_record_time, sa_record_nV = get_data(\"SA\", \"power\")\n",
    "qrtd(sa_record_time, sa_record_nV, algo=\"SA\", percents=[0.05,.1,.2,.5,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqd(\"GA\", \"power\", times=[1, *np.arange(100, 600, 100)], maxquality=1.6, min_bound=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqd(\"SA\", \"power\", maxquality=0.005, times=[10, 25, 50, 75, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "dataset=\"power\"\n",
    "ga_record_time, _ = get_data(\"GA\", dataset)\n",
    "sa_record_time, _ = get_data(\"SA\", dataset)\n",
    "boxplots(sa_record_time[dataset], ga_record_time[dataset], dataset=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots - Star2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset=\"star2\"\n",
    "ga_record_time, ga_record_nV = get_data(\"GA\", dataset)\n",
    "qrtd(ga_record_time, ga_record_nV, percents=[250, 300, 350, 400], algo=\"GA\", dataset=dataset, maxtime=4, mintime=3, log=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset=\"star2\"\n",
    "ga_record_time, ga_record_nV = get_data(\"SA\", dataset)\n",
    "qrtd(ga_record_time, ga_record_nV, algo=\"SA\", dataset=dataset, percents=[.1,.2,.5,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqd(\"GA\", \"star2\", times=[1, *np.arange(100, 600, 100)], maxquality=2.4, min_bound=1.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Noq2hLYejSlN4D1DTCHj9+b82UYEEw3G01LszzUyUL6zuqiC6s8zS71IgY/b0AEB2U0uxEFpqhh8IDVtkD1DHaSWqLIGwPbVEVMoYyFQQwwlkfFXu/NiNYMg+/zAzMnEzjrAeRnN1gWpkWB9DFcbOyDCQodLGQKaCGHELAOCvdikjEwoCALzDrJEJRe1AqDA1MkFmZKiiRHUTkbjJQIZKHgOZCqLHEhkZlwKZoB3I+IY5tRSK2YFMwaaW6lnoS5UjEI4DAKeWqOQxkKkg+ZpaGm6xbzBqB0J5z8goZa9aYkaGKkiwzw5kmJGhUsdApoK4XuwbDAIi8DY1DevzCza1FO2x91hijQxVkEA4BgBo5fJrKnEMZCpIokbGraklIxSEt7ER4h3e+ROBTEt1nqeWwokeMgxkqHIEw4mMDJdfU2ljIFNBEjUyrhX7BkNZ95Dxihcjq0fmcFRpiCS6+rJGhirH/kCGGRkqbQxkKojbNTJmKJR1D5nG6kZ4JM9Py8T2BMzIUAUJhuPweQQjawq55R5R9hjIVBC3p5bMUBC+5iz3WSpED5lERobbE1AFCYbjaOY+S1QGGMhUELeLfY1gaNg9ZABuGEmUT+zqS+WCgUwFMWImxCPw+HL/DkxZFsyenqymloLRYOE2jPTXA/7a/F+bqEDY1ZfKBQOZCmLELfiqPK6kks29ewHTHPb2BADQE+spUDO8AAt9qeIwkKFywUCmguhx070eMiGnGd4wp5ZMy8Te2N7CTS2x0JcqTKAvxqklKgsMZCqIETfdW7HkbE8w3KmlnlgPFFThin1ZH0MVRDct7Isa7CFDZYGBTAWxp5bcKvRN7LOU3YaRBZlaCnN7AqosoUQPGXb1pTLAQKaC6HHT1WZ4wPD3WUpsGFmYfZa4YSRVFm4YSeWEgUwFcXVqKeRMLQ2zj0zBNoyMhwEjyowMVRR29aVywpaOFUSPmRjRlHpOfO8jj2DfH55J+1wBbQ+2923vv928ow+1NT5868XvDGtsO8I7AORhaikeBp64Eojus28bUft/1shQGdiwfS9uXfsBTKUOedyuXmfDSAYyVAYYyFSQQ9XIBH99P+JdXfBPmJDWufr6PoEn3ocqrx0YaT7gnWNGHhDcZOpzEz/nfiCz/a/Amw8ALYfbvWMAoG0uMHGeu9clyoMn396BNRt3YvrYofcrO3nqKExsqcvDqIjcxUCmghhxE75BamSMUBANCxdi/I0/Tetcv3j2UuzR9uB3Z/zugPuXZz1KlyW6+J5zPzB2dmHHQpRjQadb71OXn1zooRDlDWtkKogeN+H3p/6RZ7pztWZoqPWVYCfcCLcjoPIV6GOTO6o8DGQqiBGzUmZkrEgEKhrNqAdM1IiWZiATDtj/c4NIKkPs1kuViIFMhbAsBdNIXSNjOEunM+kBEzVLNJCJ7AFqGgGvv9AjIco5e2qJTe6osjCQqRDGIXa+3r90OrOppRpfTW4Gl0/cjoDKWIAZGapADGQqhBG3ACBlHxmzvytv+lNLJV0jw/oYKkO6aWGvpjOQoYrDQKZC6DE7I3OoqaVMi31LMyPD7QioPIUiTrdebjtAFYaBTIXon1pKUey7f8PH9AIZpVTpFvtyOwIqU+zWS5WKgUyF0OOJjEyKqaVQEOL3w1Nfn9a5YmYMCqr0AhmlgAgzMlSeGMhQpWIgUyESNTKpin0Np4eMiKR1rqjT1r/kAploD2AZrJGhshTs3wiSq5aosjCQqRDGIWpkzGAwo80eNUMDANR4S6xGhj1kqIwxI0OVioFMheifWqo++EduhIKZrVgy7UCm5DIyia6+nFqiMhToswOZ5jr2SKLKwkCmQhyyj0wwlHEPGQClt2opsc8Si32pDAXDcTTV+eHz8s86VRY+4yvE/j4yg0wtZdLVt1RrZJiRoTLG7QmoUjGQqRCDrVqy4nFY4XBGU0ulG8g4NTIs9qUyFAjH0MpAhioQA5kKMVixrxlymuENY2qp5AKZcADw1wP+Ehs3URqYkaFKxUCmQhhxC16/Bx7PgUus9zfDG8aqpVKrkWEzPCpjDGSoUjGQqRB63EzZDM/o32dpGMW+Jbf8mhtGUnmyLIVQhPssUWViIFMhjLg56IolILN9lvprZEptioYbRlKZ2hfVYVoKLWyGRxWIgUyF0GNW6hVLIWdqaRgN8Wq9JRbIcMNIKlOB/q6+zMhQ5XE1kBGRRSKySUQ+EJGrUjzeLiLPichfReQtEfmim+OpZIZuptww0ggGAY8H3sbGtM8VNaPwiQ9+bwk13lLKzsjUpZ95IioV7OpLlcy1QEZEvABuA3AagJkAzhORmQMO+yGA3ymljgZwLoDb3RpPpTNiqWtkzGAI3qYmiCf9p4JmaKVX6BsPA0aUU0tUlhJdfRnIUCVyMyNzHIAPlFKblVJxAP8N4MwBxygAI52PGwFsd3E8FU2PW6lrZELBjFYsAXaNTMktvWYzPCpj/RtGjmAgQ5XH5+K52wB8nHR7G4DjBxxzLYA/iMgqAPUATnFxPBXlidvfwtYNgf7blqkw+ejR+OS738W+p57ef6Cuo+644zI6d0lkZPa8D9y9EIhH7NvK7myM+tGFGxPRIP7jj+/j5rXvD/vzTUsBAJrrGMhQ5XEzkJEU96kBt88DcJ9S6iYROQHA/SIyW6nEq45zIpGLAVwMAO3t7a4Mttzs+KAHoyaMwIQZ+2tCJh89Gr0r3kZVRzsaPr+w//4RCxZkdG7N0Io/I9O9AYjuBT594f7drqvqgEmfKey4iFJ4tSuElvoqnPXpCcM+x6RR9ajxH5x1JSp3bgYy2wBMTLo9AQdPHf0DgEUAoJRaJyI1AEYB2JV8kFLqLgB3AcDcuXMHBkM0gGlaiEUMdM4ZhWMXTzrgsb3RKOrnz8eYK7497PNHjWjxZ2QSU0kLvgeMHFfYsRANIRiOY/rYkfjuoumFHgpRyXGzRuYVAFNFZJKIVMEu5n10wDFbASwEABGZAaAGwG4Xx1QRon06AKB2xMGriixNg6c2u2xKSWRkws60Wh07+VLxC/TFuXSaaJhcC2SUUgaAlQDWAHgH9uqkDSLyIxFZ4hx2JYCvi8ibAB4AsEwpxYxLlrReO5CpSVH4pzQNntrssilRM1r8PWQie4DqRsDHFwcqftxegGj43JxaglLqSQBPDrjv6qSPNwI40c0xVCLNWYpZ23BgRkaZJlQ8DqmIjAz3VaLSoMVNaLqJFq44IhoWdvYtQ9HexNTSgX8YLc3eWsBTk30gUxI1MlxqTSUgEI4BYFdeouFiIFOGBs3IaPZSZE9dJWRkAmx+RyVhf1de7pNENBwMZMqQ1qsDAlTXHxjIWFE7IyNZZmRKZtUSC32pBAS4vQBRVhjIlCGtT0dNvR8ez4GtfCzN3uwxm1VLhmVAt/TizsgoBUSYkaHSEOzjho9E2WAgU4aivfGUS69VfyAz/GxK1LCzOkUdyER7AMtgjQyVhP6pJRb7Eg0LA5kypPXpqG04+I9iotg3m1VLUbMEAplI0P6fGRkqAYFwHH6voKHa1UWkRGWLgUwZ0gbJyFiJYt8sAhlNt7M6RV0jE+YGkVQ6guEYWuqrIJJqVxciGgoDmTKk9emoSZGRUdHE8uvhByGa6QQy3iIOZBLbE7CPDJUAuxkeVywRDRcDmTJjWQrRsJ46IxOxgxCprRv2+TXDPkdRTy0xI0MlJBDm9gRE2WAgU2ZiYR1QB/eQAQArmrti36KeWurPyDCQoeLH7QmIssNApsxog3T1BZJXLWVRI+NkZOp8w8/quC4cAPz1gL+Is0ZEjmAfAxmibDCQKTOJrr41qTIyiVVLWdTIlExGhvUxVAJihonemMGpJaIsMJApM9G+wTMylqZBamognuH/2EumRob1MVQCeiL27yt7yBANHwOZMqP1BzIpGuJFtaxWLAH7A5miz8hwewIqAQEng9pSx0CGaLgYyJQZrdeZWhpk1ZLkYMNIoNgzMtyegEpDkPssEWWNgUyZ0fp0VNX64PUd/KO1olF4st0w0unsW+0t4r4XkQAzMlQSAuEYAKCVU0tEw8ZApswMts8SYHf2zWbFEmAX+9b6auGRIn3qxMOAoTEjQyVhf0amiN8YEBW5In01ouGy91lKHcgoLQrJoocMYE8tFXVXXzbDoxISDMfhEaCpNvXvLBENjbuUlYjQzjD6grEhj9sXiKJlXH3KxyxNg7exMaPr9kR7sDG4sf/2x70fu1sfY8SBj1+2d68ejsCH9v/MyFARCobj2LB9b//td3bsQ3NdFTwe7rNENFwMZEqAshQevOFV6DEzreM7Z6euD1FRDZ6xYzO69vX/ez3WbFlzwH2zW2dndI6M/PV+4Ikrsj9PU3v25yDKse//f2/j6Q07D7jvqAmZvbkgogMxkCkBsYgBPWbiqM9PxORPjx7y+FHtDSnvt4YxtRSMBnFE8xH4wbwf9N/X3uBikLB3G+DxARc+Dgx3N+DqBuCwWbkdF1EObN+r4dPtTfj+F2f039fRmjqDSkTpYSBTAhLdesd0NmDclKZhn8fSNHgy3DAyakQxqm4Ujh5z9LCvm5FED5iOE/JzPaI8CvTFcfzhLZjb2VLooRCVDRb7loBD7Z+UCaVl3hBPMzTUevPYMyYcYKEula1gOM7md0Q5xkCmBPTvnzTIsup0KKXsLQoybIinGVp+u/hG9gB1fLdK5UeLm9B0k9sREOUYA5kS0J+RGWRZdTpUPA4olXFDvETfmLwJ7+GKIypL/c3v2MWXKKcYyJSAqJORyWZqyYpEACDjhniFycgwkKHyw+Z3RO5gIFMCtF4d/hovvP7h/7hU1N5aIJNVS0opRM08ZmRMHYjuZUaGylKA+yoRuYKBTAnQ+vRBtx1Il6XZmz1mMrUUt+KwlJW/QCYSsP/nPklUhoJOZpVTS0S5xUCmBGi9cdQ2ZPfHrz+QyaDYN2rYWZy8BTKJ7QWYkaEy1D+1xGJfopxiIFMCcpGRUU4gIxksv9YM+3PytrdShPskUfkKhOPwewUN1WzfRZRL/I0qAdE+HaMH6dabLkuzsyuZNMRLBDJ5n1piRobKUCgcR0t9FWS4HauJkrz22mtjfD7f3QBmo/yTEhaA9YZhfO2YY47ZNfBBBjJFTikFrS+egxqZxKqlYWRk8rVqKZyokWEgQ+UnEI5zxRLljM/nu3vs2LEzRo8eHfJ4PKrQ43GTZVmye/fumTt37rwbwJKBj5d7FFfy9KgJy1DZd/WNJjIyRVwjE9kDQNgQj8pSMBxDS312b0iIkswePXr0vnIPYgDA4/Go0aNH74WdfTr48TyPhzKU6OqbTTM8ALAiTo1MBoFM3qeWwnuA2mbA483P9YjyKMiMDOWWpxKCmATna00ZszCQKXKJrr7ZbE8AAFbUWbU0jIxM3qaWIuzqS+UrEI5z6TWVnS9/+cudLS0tR02dOnVW4r7u7m7v/Pnzp3Z0dMyeP3/+1N27d3sBwLIsLFu2bGJ7e/vsadOmzXzxxRcz28V4EAxkipzWl9ieIPsNIwFktGmkZuY7I8MNI6k8xQ0LvVGDzfCo7CxfvnzPo48++n7yfddcc824BQsW9HZ1da1fsGBB79VXXz0WAB588MHGzZs312zZsmX9HXfc0XXppZe252IMDGSKnNab2J4g22LfKODzQarS/0NakOXX9WyGR+UnFGFXXypPp512Wt/o0aON5PuefvrpphUrVgQAYMWKFYGnnnqqGQAeeeSRpvPPPz/g8XiwcOHC8L59+3xdXV3+rq4u/9y5c4+YPn36zKlTp856+umnR2QyBq5aKnLRHGVkLE3LKBsDJBX7+vNYI9N+Qn6uRZRHAXb1JRf94/+8OfG9nb05maZJmDa2IfLzs4/6eDifGwgEfB0dHToAdHR06MFg0AcAO3bs8Hd2dsYTx40bNy7e1dXlX7t2bcPChQv33njjjTsNw0Bvb29GSRYGMkVO643D5/fAX51dAayKahntswQkFft68xDIWBagBVkjQ2UpyH2WiKDUwbXJIoJ58+aFV6xY0anruufss88OzZ8/X8vkvAxkipzWp2dd6AvYU0uZNMMD7IyMT3zwe/OwZFQLAcriPktUlgLhGACgldsTkAuGmzlxS2trq9HV1eXv6OjQu7q6/C0tLQaSuvNdAAAgAElEQVQAjB8/Xt+yZUv/L8GOHTuq2tvb9Y6ODv2FF17Y9NBDDzUuW7Zs0mWXXda9cuXKQLrXc7VGRkQWicgmEflARK4a5JhzRGSjiGwQkf9yczylKNqnZz2tBAxvakkztDyuWGIzPCpfof6MDJdfU/k79dRTe+68885WALjzzjtbFy1a1AMAS5Ys6Vm9enWrZVlYu3ZtfUNDg9nR0aG/9957VW1tbfqVV16554ILLtjz+uuvZ/Su27WMjIh4AdwG4G8AbAPwiog8qpTamHTMVADfA3CiUiokImPcGk+p0nqz7+oL2KuWMll6DdiBTH6b4YHFvlSWguE4PAI01rIhHpWXM844Y9LLL7/cEAqFfIcddticq666avt11123Y+nSpZM7OjpGjR8/Pv7www9/CADnnHPO3ieeeKKxo6Njdm1trXX33XdvAYA1a9Y03HLLLWN9Pp+qq6szV69e/VEmY3Bzauk4AB8opTYDgIj8N4AzAWxMOubrAG5TSoUAQCl10B4KlU7r09E0NvsaLkvTMmqGB+Q5IxPmhpFUvgLhOJrqquD1cJ8lKi+PPfZYyqBj3bp17w28z+Px4P7779868P5Vq1YFVq1alfZU0kBuBjJtAJLn7bYBOH7AMdMAQEReAuAFcK1S6mkXx1T03vzjx+jdE+2/HdkbH9b2BPueegraG2/03453daH2qKMyOkfUiLoXyAQ3A6/80q6LAYBd79j/s9iXStDad7rx0geD/x1e92GAhb5ELnEzkEn11mNgybIPwFQACwBMAPBnEZmtlOo54EQiFwO4GADa23PSP6coxTUDL/7ufXh9Hnh99rfPV+XBuMmNGZ+r+yc3wOjpgad6/5x87dFHZ3QOV6eW/vobYN1/ANUj99932GygnrOLVHpufPpdbN4dRq1/8NWFX/p0Wx5HRFQ53AxktgGYmHR7AoDtKY55WSmlA/hIRDbBDmxeST5IKXUXgLsAYO7cuWW7t4QeMwEAJ50zFbM/M/w/ekopGKEQWpcvx5grvj3s80TNqHtLr8O7gRGHAd85KPtIVHICfXGcc+xE/GTpkYUeClHFcXPV0isAporIJBGpAnAugEcHHPMwgM8BgIiMgj3VtNnFMRU1PW4HMv6q7H4s1r59gGHA29Kc1XmiRtS9jAy3I6AyYVkKoQj3USIqFNcCGaWUAWAlgDUA3gHwO6XUBhH5kYgscQ5bAyAgIhsBPAfgH5VSwy74KXWGE8j4qrJrfmcEg/Z5WlqyOo+rxb7cjoDKRI+mw1JsdkdUKK42xFNKPQngyQH3XZ30sQJwhfOv4hlxu/DVl2UXXzMUAgB4m7MPZNzLyOwBxs1x59xEeRR0mt0xkCEqDG4aWUQSNTLZTi3tD2Sym1pyNyPDqSUqD8GwvR8aAxmqNB988IH/+OOPn3b44YfPmjJlyqwf//jHYwCgu7vbO3/+/KkdHR2z58+fP3X37t1eALAsC8uWLZvY3t4+e9q0aTNffPHFnOwPxUCmiOR+aqlIa2RMHYj2cKk1lQVmZKhS+f1+3HTTTds2b9684ZVXXnnnl7/85ZjXXnut5pprrhm3YMGC3q6urvULFizovfrqq8cCwIMPPti4efPmmi1btqy/4447ui699NKcLEPOeGpJROoBRJVSZi4GQPv1Ty1lGciYQScjk0WNjGmZiFtxdzIyETvQ4r5KVA4C4cTO1tx+gCpLR0eHntjlurm52Zo8ebK2devWqqeffrrpT3/60yYAWLFiReCzn/3sEQA+eeSRR5rOP//8gMfjwcKFC8P79u3zdXV1+QHgrLPOOryvr89rmqbceuutXYsWLepLdxxDBjIi4oG94uh8AMcCiAGoFpHdsOtf7lJKvZ/pN4AOpvdnZLKcWgoGIXV1Ge+tlCxq2k356nw53Rne1r8dATMyVPqCfXYg01zP7QeoQB7+5kTs2pjbP9ZjZkbwt7elvRnlpk2bqjZu3Fj32c9+ti8QCPgSAU5HR4ceDAZ9ALBjxw5/Z2dnPPE548aNi3d1dfnXrl3bsHDhwr033njjTsMw0Nvbm9GLYDoZmecAPAt7T6T1StmtWEWkBfbS6Z+KyO+VUr/J5MJ0sMTUkj/LYl8jFIQvB/UxAFDjdSEjw+0IqIwEwnE0VPtQ7cvu95aoVO3du9fzpS99afJPf/rTj1taWqzBjrPX9xxIRDBv3rzwihUrOnVd95x99tmh+fPna5lcP51A5hSnYd3AAQUBPATgIRHhW5EcSBT75mJqKZtpJSApkHFlaokZGSofwXAcLcPYRoQoZzLInORaLBaTxYsXT/7yl78cvPDCC3sAoLW11ejq6vJ3dHToXV1d/paWFgMAxo8fr2/ZsqX/l2XHjh1V7e3tekdHh/7CCy9seuihhxqXLVs26bLLLuteuXJl2q1YhkzfJAcxInKSiFzkfDxaRCYNPIaGr79Gxp/91FK2zfASgYwrxb5h5/nJjAyVgWA4zkJfqkiWZeHcc8/tmDZtWvTaa6/tTtx/6qmn9tx5552tAHDnnXe2Llq0qAcAlixZ0rN69epWy7Kwdu3a+oaGBrOjo0N/7733qtra2vQrr7xyzwUXXLDn9ddfz2iaLO1iXxG5BsBcAEcAuBeAH8BvAJyYyQVpcHrchK/KA5Hsdsg1QiFUT5uW1Tmihl0j415GRoC67LJGRMUgEI6jrSlPu8QTFZFnnnlmxMMPP9w6depUbfr06TMB4Lrrrvvkuuuu27F06dLJHR0do8aPHx9/+OGHPwSAc845Z+8TTzzR2NHRMbu2tta6++67twDAmjVrGm655ZaxPp9P1dXVmatXr065o/ZgMlm1tBTA0QBeBwCl1HYRacjkYnRoRtzKuj5GKeVkZLILEhKBjDsZmT1AbTPgYU0Blb5gOIYj20YOfSBRmTn11FP7lFKvpXps3bp1B22k5/F4cP/9928deP+qVasCq1atGnZX/0zmMOJOJ14F9C/DphwyYiZ8h9g9Nx0qEoGKxbLuIePq1FJkD5deU1lQSiEYjqOZU0tEBZNJIPM7EbkTQJOIfB32Sqb/586wKpMRN7PensDI1fYEpss1Miz0pTLQGzOgm4obRhIVUNpTS0qpX4jI3wDYB7tO5mql1DOujawC6XEz++0JnK6+WRf76i6vWmqdkvvzEuVZoodMC5vhERVMJsW+3wbwIIMX9xhxq2h2vk40xHOtRqZ9Xu7PS5Rn+7v6MiNDVCiZvP0fCWCNiPxZRL4pIoe5NahKZcTNotieAHCxIZ5lAVqQS6+pLATDiYwMAxmiQkk7kFFKXaeUmgXgmwDGA/iTiDzr2sgqkB4z4a/OdudrZ2opyxoZ15ZfayFAWayRobLADSOJCm84r5q7AOwEEAAwJrfDqWy5mlqSqip46rPbdkMzNNR4a+CRHG+QHuH2BFQ++qeW2NmXKlAkEpEjjzxyxhFHHDFzypQps7797W+PB4B33323as6cOdM7OjpmL168+PBoNCoAoGmaLF68+PD29vbZc+bMmb5p06ac/OKk/SolIt8QkecBrAUwCsDXlVJzcjEIstnFvrnZniDbpnqaoblT6JvYZ6mey6+p9AX74qjxe1BXlUlLLqLyUFNTo1588cVNmzZt2rhhw4aNa9euHbl27dr6K664YsLKlSu7u7q61jc2Nho333zzKAC4+eabRzU2Nhpbt25dv3Llyu4rrrhiQi7Gkcnb7Q4A31JKzVJKXaOU2piLAdB+htPZNxtmKARvlhtGAvbUkjs9ZLg9AZWPYCSOVq5Yogrl8XjQ2NhoAUA8HhfDMEREsG7duoaLLrooBADLly8PPPbYY00A8PjjjzctX748AAAXXXRR6C9/+UuDZVl49dVXa4488sgZ06dPnzlt2rSZb7/9dka/VEO+jXB2uQaAnw24DaB/80jKkrKUPbVUBDtfAy5mZLhhJJUR7rNExeCfX/rniR+EPsiunmCAKc1TIj8+8cdDbkZpGAZmz549c+vWrdUXXnjhrhkzZsQaGhpMv9/eS7qzszPe3d1dBQDd3d1VkyZNigOA3+/HiBEjzO7ubt+tt946+tJLL+3+xje+EYxGo2IYRkZjTScf+hqcbr4ABs5XKACHZ3RFSskw7A0jB5ta2rZqFWLvvT/keeLbt2PkqadmfP3/fPM/8fjmx/tv74rswqTGSRmfZ1Cbngb+8EN7xRLAzr5Ukq59dAP+9N7u/tvbezQcfzify1S5fD4f3n333Y179uzxLl68ePKbb7550DtgEVGA3Qk71WMnnHBC+Be/+MW4bdu2VZ177rmhI488MpbRGIY6QCmVw1czGowRMwEg5dSSMgz0PvMsqo84AtVTDt1Irmb2bDR/5ZyMr/9s17OIGlF8+rBPAwBmts7EggkLMj7PoLb8GQh9BMz8W2D0EYCP6XgqPX/YsBM+rwefmtgEADiyrRFLj24r8Kio0qWTOXHbqFGjzJNOOqn3pZdequ/t7fXqug6/348tW7ZUjRkzRgeAsWPHxj/66KOqyZMn67quo6+vzztmzBjzkksuCZ588snh3//+942nnXbatNtvv33LkiVLetO9dkYVaiKyBMBnnJvPK6UeP9TxlD49nghkDs7IWFF7KXTjmWeidflFrlw/FA3hxLYT8aMTf+TK+WHGgeoG4OxfunN+ojyI6CaWzDwMPzpzdqGHQlRw27dv91VVValRo0aZfX198vzzz4/8zne+s3PevHm99957b/PFF18cuueee1pPP/30HgBYvHhxzz333NN6yimnhO+9997mE044odfj8WDjxo1VM2bMiM2aNWvX5s2bq994441aVwIZEfkpgGMBrHbuulxETlRKfS+jr5xSMuKDTy0pzW5O56lzofgWzsZ3sSCaa7KvrRmUEQO8zMJQadPiJmqzXFlIVC4+/vhj/7JlyyaZpgmllJx55pnB8847b+9RRx2lfeUrX5l8/fXXt82aNSty+eWX7wGAyy+/fM9ZZ501qb29fXZjY6P529/+9kMAuP/++1sefPDBVp/Pp0aPHq3fcMMN2zMZRyYZmS8C+JRSygIAEfkVgL8CYCCTA0YiI5Oi2NdyAhmpcaH4FkCf3gfDMtBSk10TvUMy44CPRZFUukxLIWZYqM1yh3qicnH88cdr77zzzkErmGfOnBl/++233xl4f11dnXrqqac2D7z/hhtu2HnDDTfsHO44Ml3r25T0ceNwL0oH6w9kUtTIWJo9teSpzWlRer9Q1N7WgBkZosFFdft3tI4ZGaKikklG5gYAfxWR52CvXvoMmI3JGT12qKmlCADAU+tORiYYtVcSNVU3DXFkFsw4C3yppEWcNxvMyBAVl7QDGaXUA05n32NhBzL/pJQadiqIDmSkUezr1tRST6wHANydWjJigJdTS1S6EhmZWnbxJSoqmWxRcCKAfUqpRwE0APiuiHS4NrIKk1i1lGrTyESNTElPLZkxZmSopDEjQ1ScMqmRuQNARESOAvCPALoA/NqVUVWgxKqlVBmZ/lVLLk8tNVe7WSMTZ0aGSprWn5HJ8UaqRJSVTH4jDWW35TsTwC1KqZthZ2YoB3SnIV6qGpn9xb7uLL8ORUOo8dagzu9OxgcAMzJU8iJxu216rZ9TS0TFJJNApldEvgfgAgBPiIgXgN+dYVWeQ69acpZfuxXIxELuTisBzMhQydtfI8OpJaJkhmFgxowZMz/3uc9NAYB33323as6cOdM7OjpmL168+PBoNCoAoGmaLF68+PD29vbZc+bMmb5p06acvChkEsh8BUAMwD84Rb5tAH6ei0GQHch4fAKPN8UWBdHE1JI7gUww6nIzPIAZGSp5iRoZLr8mOtD1119/2JQpU7TE7SuuuGLCypUru7u6utY3NjYaN9988ygAuPnmm0c1NjYaW7duXb9y5cruK664YkIurj9kICMiAgBKqZ1KqX9VSv3Zub1VKfXr5GNo+PS4NeiGkVbEychUuxMIhKL5ysgwkKHSpbHYl+ggH374oX/NmjWNX//61/cAgGVZWLduXcNFF10UAoDly5cHHnvssSYAePzxx5uWL18eAICLLroo9Je//KXBsiy8+uqrNUceeeSM6dOnz5w2bdrMt99+O6MXi3Qme58TkYcAPKKU2pq4U0SqAJwE4EIAzwG4L5ML04GMuJmy0Bewl19LbS3E406RYSgawuGNLm9ibsYAL2ciqXRxaomK1fbv/2Bi7P33c1rkWD11amT8T/5lyM0ov/nNb0782c9+tm3v3r1eAOju7vY1NDSYfr/9976zszPe3d1d5TxWNWnSpDgA+P1+jBgxwuzu7vbdeuutoy+99NLub3zjG8FoNCqGYWQ01nReGRcBMAE8ICI7RGSjiGwG8D6A8wD8m1LqvoyuSgcxYmbK+hgAsLQIPC71kAHsGpmmGheb4QF2HxlOLVEJ4/JrogM98MADjaNGjTJOPvnkSOI+e03QgUREHeqxE044IXzTTTeN+8EPfjD2/fffrxoxYsTBBx7CkBkZpVQUwO0AbhcRP4BRADSlVE8mF6JD0+MW/Cn2WQIApUVdq4/RDA2aobnbDA+wO/uy2JdKWP/yawYyVGTSyZy44cUXXxzxzDPPNLW1tTXGYjFPOBz2XHLJJRN7e3u9uq7D7/djy5YtVWPGjNEBYOzYsfGPPvqoavLkybqu6+jr6/OOGTPGvOSSS4Inn3xy+Pe//33jaaedNu3222/fksnu1+nUyNSIyLdE5D8AXARgN4OY3DPiJnyD/IG0NM29FUuJZnhu9pABmJGhkqfFTVT7PPB4WBJIBAC33XbbJ93d3W998sknb993332b582b1/voo49+NG/evN577723GQDuueee1tNPP70HABYvXtxzzz33tALAvffe23zCCSf0ejwebNy4sWrGjBmxH/7wh7u+8IUv9LzxxhsZveClM7X0KwBzAbwNewfsmzL6Siktdo3MIFNLUc21qaW8dPW1TECZLPalkqbpJlcsEaXhpptu2nbrrbeObW9vnx0KhXyXX375HgC4/PLL94RCIV97e/vsW2+9dewvfvGLbQBw//33t0ybNm3W9OnTZ77//vs1K1asCGRyvXSKfWcqpY4EABH5JYD/y/SLoqHpMQu1DamnXtycWkp09XV9nyUA8HFqiUpXJG5yWoloEKeffnrv6aef3gsAM2fOjL/99tvvDDymrq5OPfXUU5sH3n/DDTfsvOGGG4a9d2M6GRk98YFSKrNSYkrbIVctuTm1FMvTPksAMzJU0jTd5IoloiKUTkbmKBHZ53wsAGqd2wJAKaVGuja6CqLHTfgHmVpSUQ2e2nGuXDcvU0tG3P6fGRkqYVqcgQxRMRoyI6OU8iqlRjr/GpRSvqSPDxnEiMgiEdkkIh+IyFWHOO5sEVEiMnc4X0Q5MOLW4BmZiObqhpE+jw8Nfhe3zWJGhsqAFjdRx32WiIqOa9u4Onsx3QbgNAAzAZwnIjNTHNcA4DIA/+vWWEqBETPhG2T5daIhnhtC0RCaq5vhanPm/owMAxkqXRHdRA0zMkRFx823F8cB+EAptRkAROS/Ye+cvXHAcT8G8DMA33FxLEUn2qcjGrHLj5SlYFlq0KklS9PgqclNIBM1otgV2dV/e0d4R372WQLYR4ZKWjRuYtxI9xpTEtHwuBnItAFIbtKzDcDxyQeIyNEAJiqlHheRiglk4pqB+656CaZhHXB/dd3BLfyVUlCaBk9dbgKZS9deild2vnLAfSe2nZiTcw+qf9USMzJUuiK6wRoZoiLkZiCTaq6iv+2wiHgA/BuAZUOeSORiABcDQHt7e46GVzjRsA7TsDDz5PEYP8XeGsDjFXTMbj3oWBWLAUpBcpSR6drbhWPHHoulU5b233f0mKNzcu5Bmc7UEjMyVMK0uMVAhmiAtra2I+vr602PxwOfz6fWr1//Tnd3t3fp0qWHf/LJJ9VtbW2xRx55ZPPo0aNNy7KwfPnyiX/84x8ba2pqrHvuuWfLSSedFBn6KofmWo0M7AzMxKTbEwBsT7rdAGA2gOdFZAuAeQAeTVXwq5S6Syk1Vyk1d/To0S4OOT8SmZi2aU044vixOOL4sZg69zBU1RwcV1qavfN1LhriKaUQjAUxe9RsnDH5jP5/ExpyspP64JiRoTKgxQ32kSFK4U9/+tN777777sb169e/AwDXXHPNuAULFvR2dXWtX7BgQe/VV189FgAefPDBxs2bN9ds2bJl/R133NF16aWX5iQz4WYg8wqAqSIyydkp+1wAjyYeVErtVUqNUkp1KqU6AbwMYIlS6lUXx1QUTMNOTHl9Q3/7VSKQycHUUp/eB8My0FLt8r5KA/VnZBjIUGlSStl9ZBjIEA3p6aefbkp0512xYkXgqaeeagaARx55pOn8888PeDweLFy4MLxv3z5fV1eXv6uryz937twjpk+fPnPq1Kmznn766RGZXM+1qSWllCEiKwGsAeAFcI9SaoOI/AjAq0qpRw99hvKVyMikE8hY0SgA5GRqKS89Y1JhZ18qcTHDgqXAqSUqSmt//c7E4Cd9dbk8Z0vbiMjCr85IazPKhQsXThURXHTRRbu/853v7AkEAr6Ojg4dADo6OvRgMOgDgB07dvg7Ozvjic8bN25cvKury7927dqGhQsX7r3xxht3GoaB3t7ejJIsrjZFUEo9CeDJAfddPcixC9wcSzHJKJDJYUYmL118U2EfGSpxUe58TZTSSy+99G5nZ6f+ySef+D7/+c9PmzVrVnSwY5VSB90nIpg3b154xYoVnbque84+++zQ/PnztUzGwO5OBZBJIKNyWCOTyMi4uq9SKuzsSyUuErcDGW4aScUo3cyJGzo7O3UAaGtrMxYvXtyzbt26+tbWVqOrq8vf0dGhd3V1+VtaWgwAGD9+vL5ly5b+F4IdO3ZUtbe36x0dHfoLL7yw6aGHHmpctmzZpMsuu6x75cqVaW8c6WaNDA3C1DPJyDhTSzloiFewqSVmZKjEaYmMDAMZon779u3zhEIhT+Lj5557buScOXO0U089tefOO+9sBYA777yzddGiRT0AsGTJkp7Vq1e3WpaFtWvX1jc0NJgdHR36e++9V9XW1qZfeeWVey644II9r7/+ekbTZMzIFICVKPb1D91N19LslWm52P06sdN1c3W+a2TY2ZdKmxbn1BLRQNu2bfMtXbp0CgCYpilnnXVW4Oyzz9530kknhZcuXTq5o6Nj1Pjx4+MPP/zwhwBwzjnn7H3iiScaOzo6ZtfW1lp33333FgBYs2ZNwy233DLW5/Opuro6c/Xq1R9lMg4GMgWQ0dSSU+ybi0AmFA2hxluDOn9Oa8KGxs6+VOKYkSE62MyZM+ObNm0a2K0fY8eONdetW/fewPs9Hg/uv//+rQPvX7VqVWDVqlVpTyUddN7hfiINX0bFvhG7RiYnq5ZiITTVNGV9noyZzMhQadNYI0NUtBjIFEBmy69zt2opGA3mf1oJ2D+1xIwMlahEsW8Np5aIig4DmQIo5KqlvK9YAuypJW8V4OYO20QuSiy/rqvibDxRsWEgUwCmbhf7enzpFPtGAZ8P4j94Q8lMhaKh/K9YAuyMDFcsUQmLsNiXio9lWVbFvDt0vlYr1WMMZAqgPyPjT68hXi4KfQG7RqYggYwZYw8ZKmks9qUitH737t2NlRDMWJYlu3fvbgSwPtXjzJMWQCKQ8XiGfv6pqJaTaSXN0KAZWmGmlowYMzJU0rS4AYAZGSoehmF8befOnXfv3LlzNso/KWEBWG8YxtdSPchApgBMw4LX54GkUTNiaVFIDgp9e6I9AArQQwawVy15s58aIyoUTTfh8wiq0qhrI8qHY445ZheAJYUeRzHgb2UB2IFMetlAS9PgycHS62DMaYZXkBqZGJdeU0mLxLnzNVGxYiBTAKah0qqPAexVS7lqhgcUYJ8lwMnIMJCh0hXVTdbHEBUpBjIFkJhaSoelaZDa3G0YWbiMDIt9qXRF4gxkiIoVA5kCMHULnnQDmWgUntrstxTo32epIKuWmJGh0qZxaomoaDGQKQArg4yMikRy1gzP5/Ghwd+Q9bkyxowMlTiNU0tERYuBTAFkVOwbzc2qpVAshObq5rRWSuWcyeXXVNqYkSEqXgxkCiDTGpmcrFqKBgszrQTYnX2ZkaESFomb3DCSqEixj0wBmIYaNJDpffZZ6Nu399+2IhF4hlHs+3r369gY2L+7+gehD9DW0Jb5YHOBGRkqUs9v2oXNu8NDHrerN4rDR9fnYURElCkGMgVgGhaqag/+1pt9fdi2ctVB91d1dGR8je+/+H180vfJAfctmLgg4/PkhBFnHxkqOpalcPGvX0PcTLl9y0EmjWIgQ1SMGMgUwGBTS+aePQCAsddei5GnLbLv9Hjgbci8QDesh/GlqV/CFcdc0X/fyKqRwxtwthK7XxMVkb2ajrhp4buLjsD5xw39ZmFkijcfRFR4/M0sAFNPXexrBO1eL/628fA2NmZ1Dc3Q0FjViMbq7M6TE8zIUBEKhOMAgLamWjTWcQsNolLFYt8CGDQjE7J7vXibs+u+aykLMTOGWl9uds3OGjMyVISCTiDTUs/nJlEpYyBTAIMV+xpBO5DxtWS3uihqRAEANb7s+89kTSnutURFKRiOAWAgQ1TqGMgUwKAZGWdqyduSXUZGMzQAKI6MjGUAUFy1REUnMbXUWs/nJlEpYyBTAIMHMkFIXV3WnXwTgUxRZGQM+10v+8hQsQn22YFMcz3rY4hKGQOZAjANC15/imLfUBC+pqasz5+YWiqKjIxpv1gwI0PFJhCOY0S1D9U+NrojKmUMZPJMKQXLUCk3jTSDoaynlYAim1piRoaKVDAcZ30MURlgIJNnlqEAYNCpJW+Whb4AEDWdYl9vEUwtmU4gw4wMFRkGMkTlgYFMnpmG3UU05aqlnhB8WS69BootI+NMLXHVEhWZYDiOVgYyRCWPgUyemebggUyup5aKoti3PyPDFwwqLszIECNEwSoAABf/SURBVJUHBjJ5ZuqJqaUDi32tSAQqGs3N1FIxFfsyI0NFSCllBzIjGMgQlToGMnnWP7XkP/Bbn9iewMeMDJHr+mIG4qbFqSWiMsBAJs8Gq5HJ1fYEwP6MTJ2vLutzZa1/1RIzMlQ89m9PwOclUaljIJNngwYyOdqeANifkakuhpVC7CNDRWh/V19mZIhKHQOZPEsEMh7vgTUyRo62JwAAzdRQ7a2G11MEjb7YR4aKUKKrL4t9iUofA5k8swapkUlkZLzNOcjI6Fpx1McASRkZvmBQ8eDO10Tlg4FMnu1ftZSiRsbvh2fEiKyvETWjxdEMD9ifkWEgQ0UkwECGqGwwkMmzwWpkjGAIvuZmiBy8B1OmNEMrjqXXwP5VSyz2pSISDMdQ7fOgrqoIpl+JKCsMZPLsUMW+uaiPAexVS0UTyBgs9qXiE3C6+ubijQMRFZargYyILBKRTSLygYhcleLxK0Rko4i8JSJrRaTDzfEUg/2BzIBi31AwJyuWgCILZEwW+1LxYTM8ovLhWiAjIl4AtwE4DcBMAOeJyMwBh/0VwFyl1BwA/wPgZ26Np1gMnpEJ5aSHDGBPLRVNsS8zMlSE7O0J+JwkKgduZmSOA/CBUmqzUioO4L8BnJl8gFLqOaVUxLn5MoAJLo6nKJiJ3a9TrFrK1dSSZhZhjYzXX9hxECUJ9HHDSKJy4XPx3G0APk66vQ3A8Yc4/h8APOXieIqCqdsZmd3/cj0CgR32nUrBCodzNrWUl+XX7z4BvHL30McFPrCzMaxFIJeZlsJVD72FnfuiQx7bvS/KFUtEZcLNQCbVK5dKeaDIBQDmAvjsII9fDOBiAGhvb8/V+AoiMbXU9/ijqJkwFj6nb0zt3GNQf9LJOblG1MxDjcxbvwW61gFjZx/6uBGHAVO/4O5YiABs79Hw4Gvb0Nlah+YhgpRPTWzCKTMOy9PIiMhNbgYy2wBMTLo9AcD2gQeJyCkAfgDgs0qpWKoTKaXuAnAXAMydOzdlMFQqEoGMKANjvnU5Rn7xizm/hmZo7veR0TVg9BHA15519zpEaUr0hvnn02diIYMUoorhZo3MKwCmisgkEakCcC6AR5MPEJGjAdwJYIlSapeLYykapmFBBPAoC1KT+6yJUio/q5biEcBfBJtSEjmCYft9EKeMiCqLa4GMUsoAsBLAGgDvAPidUmqDiPxIRJY4h/0cwAgAD4rIGyLy6CCnKxumoeBxvuueutwHG7qlw1Sm+4GMHgGqGMhQ8Qj0JTaC5Gokokri5tQSlFJPAnhywH1XJ318ipvXL0amYcGbCGRqcj/9k9j52vViXz0C+NvcvQZRBvr3T2J/GKKKws6+eWYaFjweu8xHanOf0Yga9oqNvGRk/PXuXoMoA8FwHFU+D+q57QBRRWEgk2eWbsEjdiDjqS3hjEw8AviLpFcNEbjtAFGlYiCTZ6ZhwSv2yiVPbe4DgaiZr4yMBlQxI0PFw+7Wy2klokrDQCbPTEPBA2cJtguBTCIjU+t1MZBRyplaYkaGikeAgQxRRWIgk2emYcEDE4C7xb61bgYZRhSA4vJrKirBcIyBDFEFYiCTZ6ZhwaNMiN8P8eV+0Vh/jYybDfHizvZYDGSoiAT7mJEhqkQMZPKsP5BxYVoJyNOqJd0JZNhHhopEVDcRjpvcCJKoAjGQyTNTt+CxdFcKfYE8rVrS7WswI0PFIhRxesiwGR5RxWEgk2emoSCW4Up9DJCvjEzY/p+BDBWJRFdfTi0RVR4GMnlmGhY8pg6pcycI6C/2dTWQSWRkuGqJikOiq28ru/oSVRwGMnlmGhbEjLuWkdEMDR7xwO/xu3J+ACz2paLTvz0BMzJEFYeBTJ6ZhgWPEXetRiZq2jtfu9rdlMW+VGQCiYwMAxmiisNAJs9Mw4IYumurljRDc3fpNbA/kGFGhopEMByD1yMYWeNiJpKIihIDmTwzDQXRY+5lZIxofjaMBBjIUNEIhuNorvPD4+E+S0SVhoFMnlm6BY8edWXDSMDJyORjw0iAxb5UNAJshkdUsRjI5JGyFCxLAXoMUuNeRqbO53KmJLFqiZtGUpHghpFElYuBTB6ZhrNZZFxztSGe6xkZPQJ4fICX9QhUHILhOFrZDI+oIjGQyaNEIGOvWnJvaikvNTJ+ZmOoeHDna6LKlftdCyuUshTUEMcYuhPIWEbOVi0ppWApq/923jIyrI+hYbDS+D3JlGFZ2KvpDGSIKhQDmRzYF9DwwI/+D0bMTOt4jxWHJ0c1Muc/eT7e3vP2Afd9+rBP5+Tcg4pH2EOGMvbWth6c/Z/rEDesoQ8ehlENnFoiqkQMZHIgtDMCI2Zi1snjUd80xB/T3r2ofvFNeOqWZn1dS1nYENiA48Yeh2PHHgsAEAi+0PmFrM99SLrGpdeUsXd39CJuWPj6yZPQkON+L36vB2fMGZfTcxJRaWAgkwPRXrur6KdOaUfTYYd+gY+++y4+MjRIDrYo2BvbC0tZ+Hz753H+jPOzPl/a9DADGcpYovvut06Zhvpq/ukhotxgsW8OaH06AKC2Yeh3mVbEXrrsqc0+EAhFQwCA5urmrM+VEV1jjQxlLBiOodrnQV2Vt9BDIaIywkAmB7ReHR6voKp26HeZKpoIZLLPyASjQQBAc02eA5l4hD1kKGOBcByt9VXu7gNGRBWHgUwOaH1x1Izwp/UH2tLsQCYXDfFCMTsj01LTkvW5MsJVSzQMwXAcLSO4soiIcouBTA5ovTpq0/wDbWlRAICnLgeBTGJqKd8ZGT3CGhnKmN19lyuLiCi3GMjkQLQvnlZ9DABYmr1PkScHxb79U0t5r5FhIEOZC/TZU0tERLnEQCYHtD4dtSPSC2SUk5HJRUO8nlgPGvwN8Od7qwAW+9IwhCLsvktEucdAJgeifTpq0p5aShT7Zh8IBKNBNNU0ZX2ejJgGYMZZ7EsZieomInGTgQwR5RwDmSyZpoVYxEh/aimqASKQ6uxrBULRUGHqYwBmZCgjiR4ynFoiolxjIJOlaKKHTLpTSxENUlubkyWooWgILdUFWLEEsEaGMhLsswMZZmSIKNcYyGRJ67UDmbSnlqLRnEwrAYXOyDCQofQFwjEAQCuXXxNRjjGQyZLmvNNMd2pJRbWcrFhSSiEYCxamGR7ATSMpI8FwIiPD5ddElFsMZLIU7U1MLaWZkYloOekh06f3wbCMAjTDs4uVmZGhTOwPZJiRIaLcYiCTpUwzMlY0mpuuvgVrhhe2/2cgQxkIhOPweQQja7hZJBHlFgOZLGm9OiBAdX36DfFKuxleIiPDVUuUvmBfHM3cZ4mIXMBAJktan46aej88nvT+QCstCsnh9gR5n1qKOxkZ9pGhDCQ2jCQiyjUGMlmK9sXTXnoN2A3xPDmYWuqJ9QAoxNQSMzKUOXb1JSK3MJDJktaroyaTQCaq5ayrLwA0Vee5s29/IMOMDKXP3jCSgQwR/f/t3Xu0VOV5x/HvzwMCKoIVTZWL0IixxguFg6Y28VKtVduIrqLiUqOJMa02prHRxKx2WaurNkZdtjRxGWusMUuDBtMUqC5NRapRIXITgUSKQgzRglUkXjjK5ekf+z24HYdz9pwzF/bh91nrLPbMvHvv5zlzmHnmfd/Zb/25kOmljW9tYtDg4i/Q2QXxej9HZn3Hega2DWS3Zk+63TbZ1z0yVtxrb73roSUzawgXMr1U89BSRwe7DOp98bH+3RZcDA88tGQ127RlK7/p2OxryJhZQzS0kJF0sqTnJa2UdFWVxwdIui89Pk/S6EbGU2+xNeiooUcmtm4lOjrq9q2llhQy772dffXa3z6xgtZ3XkPGV/U1swZoWCEjqQ34NnAKcAhwjqRDKppdBKyPiAOBW4AbGhVPI3S8s4kICs+RiY4OgLpcEK8lyxNA1iPja8hYDbxgpJk1UiOvTnUksDIiXgSQNA2YBCzPtZkEXJO2pwPfkqSIiAbGtc3PHpnFCz+d3+P9t2weBExk6ezv8eLPXuq2fVvHJsYC81Y+xv/O/HWPzwuw5o3VDB0whsU/ubdXx6nVyJeWsRsD+OnytU09r5XXirVvAr6qr5k1RiMLmeHAr3K31wBHba9NRGyWtAHYG/i/fCNJXwC+ADBq1Ki6Bfjzh57irY0n9vo4H33kSYb8ZnXh9j/SYua9vqTX5z3s5ScZt/zBXh+nVou2HsjFd/e8ALSd04i9PK/KzOqvkYVMtUkUlT0tRdoQEbcDtwO0t7fXrbfmqPMm8cKCBb06xi5tW3n7mBN5p+iUkX5tnDl8H87s5RwTIUYN2p+VauvVcXpiwOCRzGr2176t1PYc2J8Re3lI0szqr5GFzBpgZO72CODl7bRZI6kfMAR4vYExfcDBE47i4AmVnURmZmZWFo381tIzwFhJYyTtCkwBZlS0mQFckLYnA7ObNT/GzMzMyq9hPTJpzssXgYeBNuDOiFgm6VpgfkTMAL4LfF/SSrKemCmNisfMzMz6nkYOLRERDwIPVtx3dW67AzizkTGYmZlZ3+Ur+5qZmVlpuZAxMzOz0nIhY2ZmZqXlQsbMzMxKy4WMmZmZlZYLGTMzMystFzJmZmZWWirbhXQlvQr8so6HHEbFIpV9hPMqF+dVLn0xr76YE/SNvA6IiH1aHcSOqnSFTL1Jmh8R7a2Oo96cV7k4r3Lpi3n1xZyg7+Zl7/PQkpmZmZWWCxkzMzMrLRcycHurA2gQ51Uuzqtc+mJefTEn6Lt5WbLTz5ExMzOz8nKPjJmZmZXWTlPISDpZ0vOSVkq6qsrjAyTdlx6fJ2l086OsXYG8/lrScklLJD0q6YBWxFmr7vLKtZssKSSV4lsJRfKSdFZ6zpZJurfZMdaqwN/gKEmPSVqU/g5PbUWctZJ0p6R1kpZu53FJmpryXiJpfLNj7IkCeZ2b8lki6SlJRzQ7xlp1l1Ou3URJWyRNblZs1gQR0ed/gDbgBeB3gF2BZ4FDKtpcCtyWtqcA97U67jrldTywW9q+pK/kldoNBh4H5gLtrY67Ts/XWGARsFe6vW+r465DTrcDl6TtQ4DVrY67YG7HAOOBpdt5/FTgIUDAJ4B5rY65Tnkdnfv7O6UMeXWXU2rTBswGHgQmtzpm/9TvZ2fpkTkSWBkRL0bEe8A0YFJFm0nA99L2dOAESWpijD3RbV4R8VhEvJNuzgVGNDnGnijyfAFcB3wT6GhmcL1QJK+LgW9HxHqAiFjX5BhrVSSnAPZM20OAl5sYX49FxOPA6100mQTcHZm5wFBJ+zUnup7rLq+IeKrz74+SvGYUeK4ALgMeAHb0/1NWo52lkBkO/Cp3e026r2qbiNgMbAD2bkp0PVckr7yLyD5B7ui6zUvS7wEjI2JWMwPrpSLP10HAQZKelDRX0slNi65niuR0DXCepDVkn4Yva05oDVfr/78yKstrRpckDQfOAG5rdSxWf/1aHUCTVOtZqfy6VpE2O5rCMUs6D2gHjm1oRPXRZV6SdgFuAS5sVkB1UuT56kc2vHQc2SfhJyQdGhFvNDi2niqS0znAXRFxs6TfB76fctra+PAaqoyvGYVJOp6skPlkq2Opg38CvhYRW3b8jnar1c5SyKwBRuZuj+DD3dudbdZI6kfWBd5dV2WrFckLSScCfwMcGxHvNim23ugur8HAocCc9KL028AMSadFxPymRVm7on+HcyNiE7BK0vNkhc0zzQmxZkVyugg4GSAinpY0kGz9m7J38Rf6/1dGkg4H7gBOiYjXWh1PHbQD09LrxTDgVEmbI+LHrQ3L6mFnGVp6BhgraYykXckm886oaDMDuCBtTwZmR8SO/umq27zSEMx3gNNKMN+iU5d5RcSGiBgWEaMjYjTZOP6OXsRAsb/DH5NN0EbSMLKhphebGmVtiuT0EnACgKTfBQYCrzY1ysaYAXwmfXvpE8CGiHil1UH1lqRRwI+A8yNiRavjqYeIGJN7vZgOXOoipu/YKXpkImKzpC8CD5PNXL8zIpZJuhaYHxEzgO+SdXmvJOuJmdK6iIspmNeNwB7AD9OnkZci4rSWBV1AwbxKp2BeDwMnSVoObAGu3JE/ERfM6SvAv0q6nGzo5cISfEhA0g/IhviGpfk9fwf0B4iI28jm+5wKrATeAT7bmkhrUyCvq8nmB96aXjM2xw6+6GKBnKwP85V9zczMrLR2lqElMzMz64NcyJiZmVlpuZAxMzOz0nIhY2ZmZqXlQsbMzMxKy4WMWU5aGXexpKWSZkoaWmCft7p5fKikS3O395c0vQ6x7pJWX14q6TlJz0ga080+q9P1abpqc6Gk/XO375B0SG/jTcc6Pa2q/IsUd49XIZY0unO1Y0ntkqam7eMkHd2D431Z0mfS9g0pzrtzj58v6a9ytw+TdFdP4zez+nAhY/ZBGyNiXEQcSnY9ob+swzGHkq2uDkBEvBwRPX4Dzzkb2B84PCIOI1tLph5LGVyYjgtARHw+Ipb39qCSjgBuAiZFxMHAp4EbJE3o7bEjYn5EfCndPI5sBedaYusHfA64V9IQ4OiIOBxoSwXLILLfy625cz4HjEgXkDOzFnEhY7Z9T5NbBFDSlanXY4mkv69sLGkPSY9KWph6SDpXgf4G8NHU03NjRU/CPEkfzx1jjqQJknaXdGc636LcsfL2A17pXLMoItZ0rlos6ZwUw1JJN1SJdVsM6fYVkq5JPSTtwD0p3kEppvaujivpLUn/IOlZZYtdfqRKvFcA10fEqhTvKuB6sgvmUXGeYZJW52J9Iv1eF1brbUm9MLMkjQb+Arg8xf8pSask9U/t9ky9Uv0rDvGHwMK0YOxWYFdlV4MbBGwCrgSmpqUj8mZSgotnmvVlLmTMqpDURnZZ/Rnp9klkax4dCYwDJkg6pmK3DuCMiBhPtszAzenN8CrghdTTc2XFPtOAs9I59gP2j4gFZGtjzY6IielYN0ravWLf+4FPpzfsm5UtR0EaFrqB7M15HDBR0ulF8o6I6cB84NwU78bc76Sr4+5OtkbUEcDjwMVVDv9xYEHFffOB7oat1gF/lH6vZwNTu4h/NdkKx7ek+J8A5gB/kppMAR6oUpD8QWdsEfEm8ACwCFgFbAAmRsR/VDnlfOBT3cRvZg3kQsbsgwZJWgy8BvwW8JN0/0npZxGwEDiYrLDJE3C9pCXAf5H15lTrmci7HzgzbZ8F/DB3vqtSLHPI1if6wBBGRKwBPgZ8nawX4VFJJwATgTkR8WrqYbgHqCy6eqKr474HzErbC4DRVfYXxVadr9SfbImD58h+P7XO17mD95cP+Czwb1Xa7Edu/aeI+GYqhL4CXAdcLenzku6X9Le5/daRG4Yzs+bbKdZaMqvBxogYl+ZJzCKbIzOV7A33HyPiO13sey6wDzAhIjaloZGBXZ0sIn4t6TVlqw2fDfx5ekjAn0XE893s/y7wEPCQpLXA6cCj3SUJbOaDH2S6jDMX0/Zsyq2ftIXqry3LyIatluTuG0/Wq1EZUz6ey4G1wBHp8Y4CsW4TEU+m4aljgbaIWFql2Uaq/A46e7mAFcA/R8QxkqZJGhsR/5P22Vi5n5k1j3tkzKqIiA3Al4Ar0nyKh4HPSdoDQNJwSftW7DYEWJeKmOOBA9L9bwKDuzjdNOCrwJA0gZR0vsvS0FT+DXUbSePTcA+SdgEOB34JzAOOTfNM2oBzgP+u2H0tsK+kvSUNAP4099j24i1y3K7cBHw9zWMh/ftlsoVNAVYDnRN/85Ohh/D+XKDzyRan7Eq1+O8GfkD13hiAnwMHVrn/OrJFFPvnzrsV2C1tHwRUK4zMrElcyJhtR0QsAp4FpkTEI8C9wNNpiGM6H36zvAdolzSfrHfmF+k4rwFPpgmyN/Jh08nmbtyfu+86sjfPJWlS7nVV9tsXmJkeX0LWo/GtiHiFbLjpsRT/wsr5HWmOyLVkxcmszliTu4DbOif75vbp9rhdiYjFwNdSzCvIejkuyfU63QRcIukpIP8V8VuBCyTNJSsc3u7mVDOBMzon+6b77gH2IitmqnmIiuG3NP/nmfQtszd4/7mPiHg2NTse+M9u4jGzBvLq12bWEpK+ARwF/HFEvNfgc00m+9r3+V20+Xfgq2nIqMgxB5D1SH0yzRkysxZwIWNmfZqkfwFOAU6NiBVdtPsY8JGIeLzgcccCwyNiTl0CNbMecSFjZmZmpeU5MmZmZlZaLmTMzMystFzImJmZWWm5kDEzM7PSciFjZmZmpeVCxszMzErr/wEfEwfQcGnNNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqd(\"SA\", \"star2\", times=[100, 200, 300, 400, 500], maxquality=0.015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "dataset=\"star2\"\n",
    "ga_record_time, _ = get_data(\"GA\", dataset)\n",
    "sa_record_time, _ = get_data(\"SA\", dataset)\n",
    "boxplots(sa_record_time[dataset], ga_record_time[dataset], dataset=dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_cutoff = {'karate':14, 'football':94, 'jazz':158, 'email':594, 'delaunay':703,'netscience':899, \\\n",
    "              'power':2203,'as-22july06':3303,'hep-th':3926,'star2':4542,'star':6902, 'dummy1':2, 'dummy2':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>dataset</th>\n",
       "      <th>mean time (s)</th>\n",
       "      <th>std time</th>\n",
       "      <th>mean nV</th>\n",
       "      <th>std nV</th>\n",
       "      <th>err</th>\n",
       "      <th>Opt nV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karate</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>football</td>\n",
       "      <td>0.023672</td>\n",
       "      <td>0.020169</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jazz</td>\n",
       "      <td>0.060323</td>\n",
       "      <td>0.027542</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>email</td>\n",
       "      <td>4.105720</td>\n",
       "      <td>2.545893</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>delaunay</td>\n",
       "      <td>75.335668</td>\n",
       "      <td>55.165849</td>\n",
       "      <td>703.4</td>\n",
       "      <td>0.489898</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>netscience</td>\n",
       "      <td>0.049218</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>899.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>power</td>\n",
       "      <td>85.026874</td>\n",
       "      <td>40.980768</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>as-22july06</td>\n",
       "      <td>38.619063</td>\n",
       "      <td>13.192088</td>\n",
       "      <td>3303.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>hep-th</td>\n",
       "      <td>89.589120</td>\n",
       "      <td>143.510165</td>\n",
       "      <td>3926.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>star2</td>\n",
       "      <td>476.713831</td>\n",
       "      <td>81.301102</td>\n",
       "      <td>4547.8</td>\n",
       "      <td>1.661325</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>star</td>\n",
       "      <td>583.389166</td>\n",
       "      <td>12.420653</td>\n",
       "      <td>6963.4</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.008896</td>\n",
       "      <td>6902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>dummy1</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dummy2</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dataset  mean time (s)    std time  mean nV    std nV       err Opt nV\n",
       "0        karate       0.000588    0.000008     14.0  0.000000  0.000000     14\n",
       "1      football       0.023672    0.020169     94.0  0.000000  0.000000     94\n",
       "2          jazz       0.060323    0.027542    158.0  0.000000  0.000000    158\n",
       "3         email       4.105720    2.545893    594.0  0.000000  0.000000    594\n",
       "4      delaunay      75.335668   55.165849    703.4  0.489898  0.000569    703\n",
       "5    netscience       0.049218    0.000268    899.0  0.000000  0.000000    899\n",
       "6         power      85.026874   40.980768   2203.0  0.000000  0.000000   2203\n",
       "7   as-22july06      38.619063   13.192088   3303.0  0.000000  0.000000   3303\n",
       "8        hep-th      89.589120  143.510165   3926.0  0.000000  0.000000   3926\n",
       "9         star2     476.713831   81.301102   4547.8  1.661325  0.001277   4542\n",
       "10         star     583.389166   12.420653   6963.4  5.200000  0.008896   6902\n",
       "11       dummy1       0.000251    0.000004      2.0  0.000000  0.000000      2\n",
       "12       dummy2       0.000254    0.000004      3.0  0.000000  0.000000      3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = \"SA\"\n",
    "table = pd.DataFrame(columns=[\"dataset\", \"mean time (s)\", \"std time\", \"mean nV\", \"std nV\", \"err\", \"Opt nV\"])\n",
    "for i, dataset in enumerate(opt_cutoff.keys()):\n",
    "    record_time, record_nV = get_data(algo, dataset)\n",
    "    opt_nv = opt_cutoff[dataset]\n",
    "    err = (np.mean(record_nV[dataset]) - opt_nv)/opt_nv    \n",
    "    table.loc[i] = [dataset, np.mean(record_time[dataset]), np.std(record_time[dataset]), np.mean(record_nV[dataset]), np.std(record_nV[dataset]), err, opt_nv]\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>dataset</th>\n",
       "      <th>mean time (s)</th>\n",
       "      <th>std time</th>\n",
       "      <th>mean nV</th>\n",
       "      <th>std nV</th>\n",
       "      <th>err</th>\n",
       "      <th>Opt nV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karate</td>\n",
       "      <td>5.602412</td>\n",
       "      <td>0.043596</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>football</td>\n",
       "      <td>13.627329</td>\n",
       "      <td>0.115419</td>\n",
       "      <td>97.1</td>\n",
       "      <td>0.943398</td>\n",
       "      <td>0.032979</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jazz</td>\n",
       "      <td>25.024943</td>\n",
       "      <td>0.213199</td>\n",
       "      <td>164.1</td>\n",
       "      <td>1.445683</td>\n",
       "      <td>0.038608</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>email</td>\n",
       "      <td>111.720097</td>\n",
       "      <td>0.560437</td>\n",
       "      <td>750.9</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>0.264141</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>delaunay</td>\n",
       "      <td>97.444624</td>\n",
       "      <td>0.623481</td>\n",
       "      <td>774.4</td>\n",
       "      <td>4.317407</td>\n",
       "      <td>0.101565</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>netscience</td>\n",
       "      <td>134.430244</td>\n",
       "      <td>0.668064</td>\n",
       "      <td>1013.1</td>\n",
       "      <td>3.753665</td>\n",
       "      <td>0.126919</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>power</td>\n",
       "      <td>438.900043</td>\n",
       "      <td>2.612832</td>\n",
       "      <td>4129.2</td>\n",
       "      <td>11.061645</td>\n",
       "      <td>0.874353</td>\n",
       "      <td>2203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>as-22july06</td>\n",
       "      <td>598.975279</td>\n",
       "      <td>0.482429</td>\n",
       "      <td>22651.7</td>\n",
       "      <td>10.060318</td>\n",
       "      <td>5.857917</td>\n",
       "      <td>3303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>hep-th</td>\n",
       "      <td>598.227039</td>\n",
       "      <td>0.055295</td>\n",
       "      <td>6853.3</td>\n",
       "      <td>10.109896</td>\n",
       "      <td>0.745619</td>\n",
       "      <td>3926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>star2</td>\n",
       "      <td>598.835696</td>\n",
       "      <td>0.375247</td>\n",
       "      <td>13691.4</td>\n",
       "      <td>8.151074</td>\n",
       "      <td>2.014399</td>\n",
       "      <td>4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>star</td>\n",
       "      <td>598.676681</td>\n",
       "      <td>0.296839</td>\n",
       "      <td>10517.3</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>0.523805</td>\n",
       "      <td>6902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>dummy1</td>\n",
       "      <td>3.012015</td>\n",
       "      <td>0.012770</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dummy2</td>\n",
       "      <td>3.034706</td>\n",
       "      <td>0.027307</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dataset  mean time (s)  std time  mean nV     std nV       err Opt nV\n",
       "0        karate       5.602412  0.043596     14.0   0.000000  0.000000     14\n",
       "1      football      13.627329  0.115419     97.1   0.943398  0.032979     94\n",
       "2          jazz      25.024943  0.213199    164.1   1.445683  0.038608    158\n",
       "3         email     111.720097  0.560437    750.9   8.300000  0.264141    594\n",
       "4      delaunay      97.444624  0.623481    774.4   4.317407  0.101565    703\n",
       "5    netscience     134.430244  0.668064   1013.1   3.753665  0.126919    899\n",
       "6         power     438.900043  2.612832   4129.2  11.061645  0.874353   2203\n",
       "7   as-22july06     598.975279  0.482429  22651.7  10.060318  5.857917   3303\n",
       "8        hep-th     598.227039  0.055295   6853.3  10.109896  0.745619   3926\n",
       "9         star2     598.835696  0.375247  13691.4   8.151074  2.014399   4542\n",
       "10         star     598.676681  0.296839  10517.3   4.100000  0.523805   6902\n",
       "11       dummy1       3.012015  0.012770      2.0   0.000000  0.000000      2\n",
       "12       dummy2       3.034706  0.027307      3.0   0.000000  0.000000      3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = \"GA\"\n",
    "table = pd.DataFrame(columns=[\"dataset\", \"mean time (s)\", \"std time\", \"mean nV\", \"std nV\", \"err\", \"Opt nV\"])\n",
    "for i, dataset in enumerate(opt_cutoff.keys()):\n",
    "    record_time, record_nV = get_data(algo, dataset)\n",
    "    opt_nv = opt_cutoff[dataset]\n",
    "    err = (np.mean(record_nV[dataset]) - opt_nv)/opt_nv    \n",
    "    table.loc[i] = [dataset, np.mean(record_time[dataset]), np.std(record_time[dataset]), np.mean(record_nV[dataset]), np.std(record_nV[dataset]), err, opt_nv]\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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