max_unique_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "source": [
    "import math\n",
    "from decimal import Decimal\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from Helper import norm_list, to_dist_list\n",
    "from NOREC4DNA.optimizer.optimization_helper import diff_list_to_list, scale_to"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "source": [
    "%matplotlib inline\n",
    "sns.set(font_scale=1.10)\n",
    "# plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n",
    "matplotlib.rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "bmp_low_entropy_evo_dist = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004971830584445606, 0.021156042715781434, 0.0,\n",
    "                            0.0, 0.046598859494963854, 0.04050492331421386, 0.0, 0.04809096147307328, 0.0, 0.0,\n",
    "                            0.06925547416308825, 0.04532100335793773, 0.0, 0.0, .042668615395303304, 0.0,\n",
    "                            0.05816837349875859, 0.03192880067497832, 0.03802025041832258, .044968466096850894,\n",
    "                            0.03558473680486723, 0.029744521403035355, 0.05121639254344267, 0.0374629392718176,\n",
    "                            0.03647931445297305, 0.047747672087969276, 0.05476667945725357, 0.04528494162225304,\n",
    "                            0.07783895885703183, 0.04164461679912541, 0.03467221400441464, 0.01590341150809876]\n",
    "#bmp_low_entropy_evo_dist = scale_to(diff_list_to_list(bmp_low_entropy_evo_dist), 1048576)\n",
    "\n",
    "bmp_low_entropy_diff_dist = [0.0010843093439073137, 0.007585735667248767, 0.0028807487219476126, 0.0008633477404769521,\n",
    "                             0.04285990733936592, 0.013198093021637457, 0.0006570240115704037, 0.0506976852299126,\n",
    "                             0.03987067627381218, 0.03855953667866308, 0.04357898806706369, 0.007757985783490819,\n",
    "                             0.046984313365351686, 0.010214050507630095, 0.04493830021405755, 0.023652275921144446,\n",
    "                             0.00464181052056198, 0.04553260862747855, 0.02201611233315065, 0.007921113084693305,\n",
    "                             0.004631002645290646, 0.03662221952686662, 0.0027091735702709442, 0.04194512702766884,\n",
    "                             0.022288518262633495, 0.01999377137953349, 0.0160611981296819, 0.0500439906111607,\n",
    "                             0.0390939301508127, 0.046337594000175834, 0.021496185996437402, 0.009400421039175594,\n",
    "                             0.042341290846698146, 0.0031643277474615373, 0.02233346996464411, 0.02763039006158188,\n",
    "                             0.017722380330575195, 0.052653732160742844, 0.04302024630806095, 0.02501640778736209]\n",
    "#bmp_low_entropy_diff_dist = scale_to(diff_list_to_list(bmp_low_entropy_diff_dist), 1048576)\n",
    "\n",
    "evo_low_opt_dist = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004971830584445606, 0.021156042715781434, 0.0, 0.0, 0.046598859494963854, 0.04050492331421386, 0.0, 0.04809096147307328, 0.0, 0.0, 0.06925547416308825, 0.04532100335793773, 0.0, 0.0, 0.042668615395303304, 0.0, 0.05816837349875859, 0.03192880067497832, 0.03802025041832258, 0.044968466096850894, 0.03558473680486723, 0.029744521403035355, 0.05121639254344267, 0.0374629392718176, 0.03647931445297305, 0.047747672087969276, 0.05476667945725357, 0.04528494162225304, 0.07783895885703183, 0.04164461679912541, 0.03467221400441464, 0.01590341150809876]\n",
    "\n",
    "#evo_compress_encrypt_high_entropy_dist = scale_to(diff_list_to_list(evo_compress_encrypt_high_entropy_dist), 1048576)\n",
    "\n",
    "diff_low_opt_dist = [0.0010843093439073137, 0.007585735667248767, 0.0028807487219476126, 0.0008633477404769521, 0.04285990733936592, 0.013198093021637457, 0.0006570240115704037, 0.0506976852299126, 0.03987067627381218, 0.03855953667866308, 0.04357898806706369, 0.007757985783490819, 0.046984313365351686, 0.010214050507630095, 0.04493830021405755, 0.023652275921144446, 0.00464181052056198, 0.04553260862747855, 0.02201611233315065, 0.007921113084693305, 0.004631002645290646, 0.03662221952686662, 0.0027091735702709442, 0.04194512702766884, 0.022288518262633495, 0.01999377137953349, 0.0160611981296819, 0.0500439906111607, 0.0390939301508127, 0.046337594000175834, 0.021496185996437402, 0.009400421039175594, 0.042341290846698146, 0.0031643277474615373, 0.02233346996464411, 0.02763039006158188, 0.017722380330575195, 0.052653732160742844, 0.04302024630806095, 0.02501640778736209]\n",
    "\n",
    "evo_final_same_param = [0.0, 0.02088799265672318, 0.023699988666651232, 0.01643490319999933, 0.017412252369953574, 0.027759268973297727, 0.028157601781817997, 0.02258613853687647, 0.034596766258260137, 0.02887242478245877, 0.026818528623717332, 0.02316381047240955, 0.02529651292374893, 0.020982484715106033, 0.030756801808736363, 0.0, 0.02757494612860611, 0.021387981216317905, 0.024865855541047515, 0.0, 0.02891732486636941, 0.021864007284190537, 0.02681611820527676, 0.02254534410164049, 0.029479746203610668, 0.03455324392837791, 0.02804994960306137, 0.028725225100967516, 0.03515417907168796, 0.03361078343361947, 0.029009654487853788, 0.030314200195660612, 0.026546443549719655, 0.03402998655066935, 0.030580261587484246, 0.035575445677210965, 0.026881705960461674, 0.02603731249299422, 0.02325741010076499, 0.026797398942650603]\n",
    "\n",
    "diff_final_same_param = [0.006918697490223755, 0.019780025380762272, 0.02267846770384564, 0.018949709831797563, 0.03162725040000313, 0.028372166193997992, 0.01970551392921105, 0.010272036186997055, 0.010706960895218436, 0.013248559839070478, 0.040716915888522304, 0.04042797756960299, 0.03749710987201956, 0.0195278584236879, 0.030284211292832052, 0.018780709427957218, 0.03842539130082528, 0.041340697069709394, 0.03685813521559906, 0.01598522910624237, 0.021188427488971064, 0.029030349153101494, 0.02827441474282986, 0.024982859393149766, 0.041314007163255485, 0.025225402873826918, 0.04104964161719164, 0.035190273206113926, 0.014256329493491163, 0.005164065468535932, 0.0015369733299758481, 0.03772561343752855, 0.010087572829176671, 0.042695524183949826, 0.030344773883911994, 0.010256567637385703, 0.04164496368083376, 0.03586767853015908, 0.0073614066286906, 0.0146995322397952]\n",
    "\n",
    "evo_compress_encrypt_high_entropy_dist = [0.0013594048887460395, 0.024392575968035388, 0.015914749333043713,\n",
    "                                          0.018695909592454388, 0.023358685184162963,\n",
    "                                          0.03236783551192264, 0.018216166247821023, 0.03815209530861828,\n",
    "                                          0.02626279541362586, 0.026771738033088587,\n",
    "                                          0.02240981497836683, 0.02900653795560873, 0.029469219106741655,\n",
    "                                          0.03148959262239247, 0.026607500636491237,\n",
    "                                          0.029993377912122702, 0.02729437495737362, 0.026240710500059775,\n",
    "                                          0.029520514187713967, 0.03144957623804716,\n",
    "                                          0.026660139657590207, 0.0, 0.036582102310668094, 0.023023044857186127,\n",
    "                                          0.029136110541167947, 0.0405114142787493,\n",
    "                                          0.02593307536238757, 0.025394969853798632, 0.019287711635823702,\n",
    "                                          0.024887158571412053, 0.024965334073119294,\n",
    "                                          0.029143289300996692, 0.03509571086315818, 0.018402552246391504,\n",
    "                                          0.0270945738944279, 0.023436914977565768,\n",
    "                                          0.015901655364440288, 0.032297209895409, 0.028740382914874504,\n",
    "                                          0.004533474824396502]\n",
    "\n",
    "diff_compress_encrypt_high_entropy_dist = [0.0, 0.027742190569628983, 0.041381430387865605, 0.024661226394784533,\n",
    "                                           0.03168760035607533, 0.042586268544093045, 0.050172438239758727,\n",
    "                                           0.02240595277423825, 0.022744587962995572, 0.02219616498815092, 0.0,\n",
    "                                           0.0250794427132504, 0.02340166233385067, 0.0354141468356474,\n",
    "                                           0.005684106293662012, 0.04266178285648165, 0.004800692990094652,\n",
    "                                           0.02111713250802425, 0.04036783935327176, 0.045008748835130855,\n",
    "                                           0.010439857196132265, 0.020439399221880886, 0.04246549723441552, 0.0,\n",
    "                                           0.019110068800104227, 0.02766843648291822, 0.027110395639511172,\n",
    "                                           0.03395117226609973, 0.016734711153209517, 0.016655637276022293,\n",
    "                                           0.005009021579709849, 0.04196046472591439, 0.009162780801450403,\n",
    "                                           0.01917442849327642, 0.02673033190145107, 0.014242757680173164,\n",
    "                                           0.02024259118571735, 0.02817669668334466, 0.02678213620884411,\n",
    "                                           0.06483020053282014]\n",
    "#diff_compress_encrypt_high_entropy_dist = scale_to(diff_list_to_list(diff_compress_encrypt_high_entropy_dist), 1048576)\n",
    "#print(norm_list(to_dist_list(diff_compress_encrypt_high_entropy_dist)))\n",
    "\n",
    "raptor_dist = [0, 10241, 491582, 712794, 831695, 831695, 831695, 831695, 831695, 831695, 948446, 1032189, 1032189,\n",
    "               1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189,\n",
    "               1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189, 1032189,\n",
    "               1032189, 1032189, 1032189, 1032189, 1032189, 1048576]\n",
    "\n",
    "ez_robust_dist = [0.023562333363729804, 0.4170269125229677, 0.5512718027759727, 0.6199392631691046, 0.6620667485649615, 0.6907697449355179, 0.7117133181335238, 0.7277520727320201, 0.7404841621864, 0.7508758357854546, 0.759546661304677, 0.7669128051706923, 0.7732645434624459, 0.7788107597081224, 0.7837057670410399, 0.7880661492206742, 0.7919817076997616, 0.7955227929596026, 0.7987453434646052, 0.8016944288748958, 0.8044067923013483, 0.8069127075011525, 0.8092373577096685, 0.8114018743558798, 0.8134241299757269, 0.8153193508297388, 0.8171005954712023, 0.8187791324044125, 0.8203647409077455, 0.8218659527329247, 0.8232902478631458, 0.824644214248534, 0.825933679056777, 0.8271638172213974, 0.828339241762493, 0.8294640793713051, 0.8305420340036646, 0.8315764406562267, 0.8325703110588596, 0.8335263726741811, 0.9862809556490806, 0.9867271745661067, 0.987152639114899, 0.987558764366019, 0.9879468396059781, 0.9883180420094172, 0.9886734485659014, 0.9890140465158656, 0.9893407425086882, 0.989654370661798, 0.9899556996716485, 0.9902454391041972, 0.9905242449732534, 0.9907927246990113, 0.9910514415256506, 0.9913009184656243, 0.9915416418287568, 0.991774064386264, 0.9919986082130083, 0.9922156672455278, 0.9924256095884565, 0.9926287795977423, 0.9928254997654635, 0.9930160724279434, 0.9932007813161932, 0.9933798929654051, 0.9935536579982226, 0.9937223122947807, 0.9938860780610038, 0.9940451648053349, 0.9941997702329242, 0.9943500810653026, 0.9944962737926845, 0.9946385153652721, 0.9947769638292575, 0.9949117689126117, 0.9950430725652294, 0.9951710094575236, 0.995295707441152, 0.9954172879751898, 0.9955358665207329, 0.9956515529066285, 0.9957644516687677, 0.9958746623651417, 0.9959822798686597, 0.9960873946395379, 0.9961900929789016, 0.9962904572650979, 0.9963885661740763, 0.9964844948850774, 0.9965783152727598, 0.9966700960867969, 0.996759903119887, 0.9968477993650391, 0.9969338451629247, 0.9970180983400211, 0.9971006143382083, 0.9971814463364325, 0.9972606453649956, 0.9973382604129875, 0.9974143385293358, 0.9974889249179126, 0.9975620630270997, 0.997633794634187, 0.9977041599249488, 0.9977731975687151, 0.9978409447892334, 0.9979074374315939, 0.9979727100254708, 0.9980367958449136, 0.9980997269649069, 0.9981615343149004, 0.9982222477294957, 0.9982818959964667, 0.9983405069022728, 0.9983981072752203, 0.9984547230264079, 0.9985103791885924, 0.9985650999530932, 0.9986189087048521, 0.9986718280557556, 0.9987238798763164, 0.9987750853258112, 0.9988254648809592, 0.998875038363225, 0.9989238249648198, 0.9989718432734761, 0.9990191112960597, 0.9990656464810839, 0.9991114657401846, 0.9991565854686121, 0.9992010215647906, 0.9992447894489965, 0.9992879040811994, 0.9993303799781104, 0.9993722312294785, 0.9994134715136733, 0.99945411411259, 0.9994941719259107, 0.9995336574847555, 0.9995725829647514, 0.9996109601985501, 0.9996488006878202, 0.9996861156147394, 0.9997229158530114, 0.9997592119784303, 0.9997950142790136, 0.9998303327647242, 0.9998651771768011, 0.999899556996717, 0.9999334814547796, 0.999966959538394, 1.0000000000000004]"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-10T09:38:05.976604Z",
     "start_time": "2024-05-10T09:38:05.970403Z"
    }
   },
   "source": [
    "def plot_max_possible_unique_packets_per_deg(dist, name, n, seed_len=2):\n",
    "    # calculate\n",
    "    lst = []\n",
    "    max_deg = len(dist)-1\n",
    "    print(max_deg)\n",
    "    for i in range(1, max_deg + 1):\n",
    "        if n > 49999 and (i > 1 and lst[-1] > 2.6720589553888095e+138):\n",
    "            #pass\n",
    "            lst.append(lst[-1])\n",
    "        else:\n",
    "            lst.append(math.comb(n, i))\n",
    "\n",
    "    # Create a figure and the primary y-axis\n",
    "    fig, ax1 = plt.subplots()\n",
    "\n",
    "    # Plot the primary y-axis data\n",
    "    ax1.plot(range(1, max_deg + 1), lst, label=\"Combinations\", color=\"tab:blue\")\n",
    "    ax1.set_yscale(\"log\")\n",
    "    ax1.set_xlabel(\"Degree\")\n",
    "    ax1.set_ylabel(\"Packets\")\n",
    "    ax1.set_title(\"Number of unique packets per degree\")\n",
    "    ax1.tick_params(axis=\"y\", labelcolor=\"tab:blue\")\n",
    "    if dist == raptor_dist:\n",
    "        raptor_dist_func = norm_list(to_dist_list(dist))\n",
    "    else:\n",
    "        raptor_dist_func = dist\n",
    "    print(raptor_dist_func)\n",
    "\n",
    "    possible_packets = math.pow(2, (seed_len * 8))\n",
    "    res = [x * possible_packets for x in raptor_dist_func]\n",
    "    ax1.plot(range(1, max_deg + 1), res[:max_deg], label=\"Possible packets (dist)\", color=\"tab:cyan\")\n",
    "    if n > 5000:\n",
    "        ax1.set_ylim([0.0001720829380504742, 2.6720589553888095e+138])\n",
    "    print(ax1.get_ylim())\n",
    "    # Create a secondary y-axis\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    # Plot the secondary y-axXis data\n",
    "    ax2.plot(range(1, max_deg + 1), raptor_dist_func[:max_deg], label=\"Distribution\", color=\"tab:red\")\n",
    "    ax2.set_ylabel(\"Probability\")\n",
    "    ax2.tick_params(axis=\"y\", labelcolor=\"tab:red\")\n",
    "    # Combine legends from both axes\n",
    "    # ax1.legend(loc=\"upper left\")\n",
    "    # ax2.legend(loc=\"upper right\")\n",
    "    lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "    lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "    lines = lines1 + lines2\n",
    "    labels = labels1 + labels2\n",
    "    ax2.legend(lines, labels, loc=\"best\")\n",
    "    plt.autoscale()\n",
    "\n",
    "    plt.savefig(f\"{name}_max_unique_packets_per_deg_{n}_{seed_len}.svg\", format=\"svg\", dpi=1200)\n",
    "    plt.savefig(f\"{name}_max_unique_packets_per_deg_{n}_{seed_len}.pdf\", bbox_inches=\"tight\")\n",
    "    plt.show(block=False)"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-10T09:38:17.746001Z",
     "start_time": "2024-05-10T09:38:07.569606Z"
    }
   },
   "source": [
    "for (name, dist) in [(\"evo_low_opt_dist\",evo_low_opt_dist), (\"diff_low_opt_dist\", diff_low_opt_dist)]: # [(\"bmp_low_entropy_evo_dist\",bmp_low_entropy_evo_dist), (\"bmp_low_entropy_diff_dist\", bmp_low_entropy_diff_dist), (\"evo_compress_encrypt_high_entropy_dist\", evo_compress_encrypt_high_entropy_dist), (\"diff_compress_encrypt_high_entropy_dist\", diff_compress_encrypt_high_entropy_dist), (\"evo_low_opt_dist\",evo_low_opt_dist), (\"diff_low_opt_dist\", diff_low_opt_dist), (\"evo_final_same_param\", evo_final_same_param), (\"diff_final_same_param\", diff_final_same_param)]:\n",
    "    for i in [50,100,500,5000,50000,500000,5000000]:\n",
    "        plot_max_possible_unique_packets_per_deg(dist, name, i, 4)\n",
    "    for i in [50,100,500,5000,50000]:\n",
    "        plot_max_possible_unique_packets_per_deg(dist,name,i,2)"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-01T22:23:58.889729300Z",
     "start_time": "2024-02-01T22:23:30.196267400Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "    #plot_max_possible_unique_packets_per_deg(50)\n",
    "    #plot_max_possible_unique_packets_per_deg(100)\n",
    "    #plot_max_possible_unique_packets_per_deg(500)\n",
    "    #plot_max_possible_unique_packets_per_deg(5000)\n",
    "    #plot_max_possible_unique_packets_per_deg(50000)\n",
    "\n",
    "    #plot_max_possible_unique_packets_per_deg(50, 4)\n",
    "    #plot_max_possible_unique_packets_per_deg(100, 4)\n",
    "    #plot_max_possible_unique_packets_per_deg(500, 4)\n",
    "    #plot_max_possible_unique_packets_per_deg(5000, 4)\n",
    "    #plot_max_possible_unique_packets_per_deg(50000, 4)"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "source": [],
   "outputs": []
  }
 ],
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