sigmod.07.20.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import Counter\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import numpy as np\n",
    "import probscale\n",
    "import itertools\n",
    "import csv\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "class Result:\n",
    "    def __init__(self, path):\n",
    "        self.path = Path(f\"results/{path}/flamestream\")\n",
    "    \n",
    "    def lat_data(self):\n",
    "        with open(self.path.joinpath(\"latency/lat.data\"), 'r') as file:\n",
    "            return list(map(int, file.read().split(\", \")))\n",
    "    \n",
    "    def notification_await_times(self):\n",
    "        with open(self.path.joinpath(\"notification_await_times.csv\"), 'r') as file:\n",
    "            return list(map(lambda row: int(row[0]) / 1E6, csv.reader(file)))\n",
    "        \n",
    "    def duration(self):\n",
    "        with open(self.path.joinpath(\"duration.txt\"), 'r') as file:\n",
    "            return int(file.read())\n",
    "        \n",
    "    def durations(self):\n",
    "        with open(self.path.joinpath(\"durations.txt\"), 'r') as file:\n",
    "            return list(map(int, file.read().split(\", \")))\n",
    "    \n",
    "    def throughput_hz(self, skip=0):\n",
    "        durations = self.durations()\n",
    "        return (len(durations) - skip) / (durations[-1] - durations[skip]) * 1E9\n",
    "\n",
    "    def plot_latencies_evolution(self, **kwargs):\n",
    "        plot_quantiles_evolution(self.lat_data(), **kwargs)\n",
    "        \n",
    "def plot_quantiles_evolution(data, window_size=100, q=0.95, stabilize=True):\n",
    "    quantiles = []\n",
    "    prev_quantile = None\n",
    "    is_stable = not stabilize\n",
    "    for window in each_slice(data, window_size):\n",
    "        quantile = np.quantile(window, q)\n",
    "        if is_stable:\n",
    "            quantiles.append(quantile)\n",
    "        elif prev_quantile and prev_quantile < quantile:\n",
    "            is_stable = True\n",
    "            quantiles.append(prev_quantile)\n",
    "            quantiles.append(quantile)\n",
    "        else:\n",
    "            prev_quantile = quantile\n",
    "        prev_window = window\n",
    "    if not is_stable:\n",
    "        raise \"has not stabilized\"\n",
    "    plt.plot(quantiles)\n",
    "        \n",
    "def results_path(parallelism=3, rate=5, watermarks=False):\n",
    "    return Path(f\"results/sigmod/parallelism={parallelism}/distributed_acker={False}/rate={rate}/watermarks={watermarks}/iterations=10/flamestream\")\n",
    "\n",
    "def plot_probabilities(results_by_legends, legend_title=None, plot_title=None):\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    ax.legend(results_by_legends.keys(), title=legend_title)\n",
    "    for result in results_by_legends.values():\n",
    "        plt.plot(*probscale.plot_pos(result.lat_data()))\n",
    "    plt.xlabel('Percentile')\n",
    "    plt.ylabel('Latency, ns')\n",
    "    plt.title(plot_title)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_percentile(percentile, results_by_rates):\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    plt.plot(\n",
    "        results_by_rates.keys(),\n",
    "        [np.percentile(result.lat_data(), percentile) for result in results_by_rates.values()],\n",
    "        marker='o'\n",
    "    )\n",
    "    plt.xlabel('Rate')\n",
    "    plt.ylabel(f\"Latencies {percentile} percentile, ns\")\n",
    "    \n",
    "def plot_percentile_by_throughput(percentile, results):\n",
    "    plt.plot(*plottable_percentile_by_throughput(percentile, results))\n",
    "    \n",
    "def plottable_percentile_by_throughput(percentile, results):\n",
    "    return (\n",
    "        [result.throughput() for result in results],\n",
    "        [np.percentile(result.lat_data(), percentile) for result in results]\n",
    "    )\n",
    "\n",
    "def plot_throughput_by_number_of_iterations(results):\n",
    "    plt.plot(\n",
    "        [result.iterations for result in results],\n",
    "        [result.throughput() for result in results],\n",
    "    )\n",
    "    \n",
    "def show_max_throughput(result_factory, bad_rate, max_rate):\n",
    "    pass\n",
    "\n",
    "def plot_throughput_by_parallelism(results):\n",
    "    plt.plot(\n",
    "        [result.parallelism for result in results],\n",
    "        [result.throughput_hz() for result in results],\n",
    "    )\n",
    "    \n",
    "def plot_latencies_barh(results_by_labels, ax):\n",
    "    w = .70\n",
    "    ind_fs = [1, 2.0, 3.0]\n",
    "\n",
    "    def do_plot(q, c):\n",
    "        ax.barh(range(len(results_by_labels)), [np.percentile(result.lat_data(), q) for result in results_by_labels.values()], color=c, height=w, left=0)\n",
    "\n",
    "    do_plot(99, '.8')\n",
    "    do_plot(90, '.6')\n",
    "    do_plot(75, '.4')\n",
    "    do_plot(50, '.2')\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(60)\n",
    "\n",
    "    ax.set_yticklabels([])\n",
    "#     plt.xlim((0, np.percentile(latencies[8], 99) + 100))\n",
    "\n",
    "    ax.set_xlabel('Latency(ms)', fontsize=60)\n",
    "\n",
    "    for i, label in enumerate(results_by_labels):\n",
    "        ax.text(np.percentile(results_by_labels[label].lat_data(), 99) + 10, i, label, fontsize = 60)\n",
    "\n",
    "    patch_99 = mpatches.Patch(color='.8', label='99 %-ile')\n",
    "    patch_90 = mpatches.Patch(color='.6', label='95 %-ile')\n",
    "    patch_75 = mpatches.Patch(color='.4', label='75 %-ile')\n",
    "    patch_50 = mpatches.Patch(color='.2', label='50 %-ile')\n",
    "    ax.legend(handles=[patch_99, patch_90, patch_75, patch_50][::-1], fontsize = 60, frameon=False, bbox_to_anchor=(1, 1), ncol=1)\n",
    "\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_visible(False)\n",
    "        \n",
    "def each_slice(generator, n):\n",
    "    group = []\n",
    "    for element in generator:\n",
    "        group.append(element)\n",
    "        if len(group) == n:\n",
    "            yield group\n",
    "            group = []\n",
    "\n",
    "    if group:\n",
    "        yield group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latency overhead of tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120aa2f28>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.legend([\n",
    "    plt.axhline(y=np.median(Result(\n",
    "        path=f\"sigmod.07.20/tracking_frequency=0\"\n",
    "    ).lat_data()), color='r', linestyle='-') and 'No tracking',\n",
    "    plt.plot(*zip(*(\n",
    "        (tracking_frequency, np.median(Result(\n",
    "            path=f\"sigmod.07.21/rate=5/tracking_frequency={tracking_frequency}/tracking=watermarking\"\n",
    "        ).lat_data()))\n",
    "        for tracking_frequency in [1, 10, 50, 100]\n",
    "    )), marker='o') and \"Watermarking\",\n",
    "    *[\n",
    "        plt.plot(*zip(*(\n",
    "            (tracking_frequency, np.median(Result(\n",
    "                path=f\"sigmod.07.20/tracking_frequency={tracking_frequency}/{path}\"\n",
    "            ).lat_data()))\n",
    "            for tracking_frequency in [1, 10, 50, 100]\n",
    "        )), marker='o') and legend\n",
    "        for legend, path in {\n",
    "            \"Centralized acker\": \"tracking=acking/distributed=False\",\n",
    "            \"Distributed acker\": \"tracking=acking/distributed=True\",\n",
    "        }.items()\n",
    "    ]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.20/tracking_frequency=0\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.21/rate=5/tracking_frequency=10/tracking=watermarking\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.20/tracking_frequency=10/tracking=acking/distributed=False\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.20/tracking_frequency=10/tracking=acking/distributed=True\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.legend([\n",
    "    'No tracking',\n",
    "    'Watermarking',\n",
    "    'Centralized acking',\n",
    "    'Distributed acking',\n",
    "])\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "plot_quantiles_evolution(Result(\n",
    "    path=\"sigmod.07.20/tracking_frequency=0\"\n",
    ").lat_data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.20/tracking_frequency=0\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.21/rate=2/super_warmup_tracking_frequency=0\"\n",
    ").lat_data()[6000:], 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.21/rate=2/super_warmup_tracking_frequency=0\"\n",
    ").lat_data()[6000:], 50, density=True, histtype='step', cumulative=True)\n",
    "plt.legend([\n",
    "    \"Rate 5\",\n",
    "    \"Rate 2\",\n",
    "    \"Rate 1\",\n",
    "])\n",
    "len(Result(\n",
    "    path=f\"sigmod.07.21/rate=2/super_warmup_tracking_frequency=0\"\n",
    ").lat_data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x121cdfac8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(*zip(*(\n",
    "    (rate, np.median(Result(\n",
    "        path=f\"sigmod.07.22/rate={rate}/super_warmup_tracking_frequency=0\"\n",
    "    ).lat_data()[15000:]))\n",
    "    for rate in [1, 2, 3, 4, 5]\n",
    ")), marker='o')\n",
    "plt.plot(*zip(*(\n",
    "    (rate, np.median(Result(\n",
    "        path=f\"sigmod.07.22/rate={rate}/cpu_idle_level=5_tracking_frequency=0\"\n",
    "    ).lat_data()[15000:]))\n",
    "    for rate in [1, 2, 3, 4, 5]\n",
    ")), marker='o')\n",
    "plt.legend([\"1\", \"5\"], title=\"CPU idle level\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": [
    "plot_quantiles_evolution(Result(\n",
    "    path=\"sigmod.07.22/rate=2/cpu_idle_level=5_tracking_frequency=0\"\n",
    ").lat_data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12301b438>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.legend([\n",
    "    plt.axhline(y=np.median(Result(\n",
    "        path=\"sigmod.07.25/rate=1.75/tracking_frequency=0\"\n",
    "    ).lat_data()), color='r', linestyle='-') and 'No tracking',\n",
    "    *[\n",
    "        plt.plot(*zip(*(\n",
    "            (tracking_frequency, np.median(Result(\n",
    "                path=f\"sigmod.07.25/rate=1.75/tracking_frequency={tracking_frequency}/{path}\"\n",
    "            ).lat_data()))\n",
    "            for tracking_frequency in [2]\n",
    "        )), marker='o') and legend\n",
    "        for legend, path in {\n",
    "            \"Watermarking\": \"tracking=watermarking\",\n",
    "            \"Centralized acker\": \"tracking=acking/distributed=False\",\n",
    "            \"Distributed acker\": \"tracking=acking/distributed=True\",\n",
    "        }.items()\n",
    "    ]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120b1ac18>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.legend([\n",
    "    plt.axhline(y=np.median(Result(\n",
    "        path=\"sigmod.07.22/rate=2/super_warmup_tracking_frequency=0\"\n",
    "    ).lat_data()), color='r', linestyle='-') and 'No tracking',\n",
    "    *[\n",
    "        plt.plot(*zip(*(\n",
    "            (tracking_frequency, np.median(Result(\n",
    "                path=f\"sigmod.07.22/rate=2/tracking_frequency={tracking_frequency}/{path}\"\n",
    "            ).lat_data()))\n",
    "            for tracking_frequency in range(2, 11)\n",
    "        )), marker='o') and legend\n",
    "        for legend, path in {\n",
    "            \"Watermarking\": \"tracking=watermarking\",\n",
    "#             \"Centralized acker\": \"tracking=acking/distributed=False\",\n",
    "#             \"Distributed acker\": \"tracking=acking/distributed=True\",\n",
    "        }.items()\n",
    "    ]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xc+WVV1Z6/4VFRkb6Lqg6U4X7VVig+lhVngx3ES/af3Q/W8eUXX+9smJeiin3deccv/jFLxgzZgyLFy8GYMuWLRw4cOCMw71z586lhvvJkyf9uqBp5syZld7v2VZb+ujJaRkRqbq1a9cSHh7OxIkTfctiY2Pp06cPUHYJ3w4dOjB+/Hg6derEVVddxbFjx1i6dCkpKSlcf/31dO3alWPHjhEdHc3dd99NXFwcS5Ys4fnnn6dHjx7ExsZy3XXX8e2335bo09ixY33b6tq1K127diUmJsZXi+azzz4jMTGR7t2706dPH3bu3AnA559/zmWXXUZMTAz33XdfmWP+r//6L7p3706nTp2YN2+eb/mbb75JXFwcsbGx9O/fv8R6zz//PFdffTXHjh3z9REgOjqaBx54wFcS+XR/sg5/zYABA+jUqRM33ngjF110EYcOHarU51MRhbuIlCo9Pb3MEreFS/impaWRmprK+vXrAdi1axe33HIL27Zto3HjxixbtoyhQ4cSHx/PokWLSEtLo169/O/EmjZtyubNmxk+fDhDhgxh06ZNbNmyhQ4dOvDCCy+U2bf4+HjS0tJIS0sjMTHRNzUyYcIE5syZQ2pqKrNmzfLdLOT2229n0qRJbN26lQsvvLDM7c6fP5/U1FRSUlKYPXs2hw8fJisri/Hjx/vK/y5ZsqTIOnPnzmXVqlWsWLHCN67CmjVrxubNm5k0aZKvHvyD//Mc/fr1Y9u2bQwdOpQvvwzgHbYKBPW0zMClA4mqHwUE/o0RkbKVV8K3bdu2dO3aFYDu3bv7yvSW5le/+pXvcXp6Ovfddx/Z2dnk5uYycODACvvx6quvsnnzZtasWUNubi7JyckMGzbM9/p3330HwIYNG1i2bBkAo0aN4u677y51e7Nnz2b58uUA7N27l127dpGVlcXll1/uKyPcpEkTX/uFCxfSunVrVqxYQXh4eKnbHDJkCJD/Xrz++usA/PPDNJbf/wgAiYmJnH/++RWOtbKCN9yfiGF/E9j6+ZfsIOqHgkqN2kDJiqMiUkmdOnXyTS8U58op4Vu8fO6xY8fK3Efhgl9jx45lxYoVxMbGsmDBghI31CguPT2dGTNmsH79ekJDQzl16hSNGzf23SCkuNNTN2VZt24d77zzDhs3buS8886jb9++pZYvLiwmJoa0tDQyMzOL1JAv7PT7cbZLCQfvtEzBjYJ3r48nPCoq/96SM3LKveGviPivX79+fPfdd0Xmnj/++GPef//9MyrhW7wMcHFHjhzhwgsvJC8vj0WLFpW7rezsbEaMGMHChQtp3rw5AA0bNqRt27a+aRPnHFu2bAHyy/ee/lK4rG3n5ORw/vnnc95557Fz504++OADAHr16sX69ev5/PPPAfjqq69863Tr1o3nnnuOpKQk9u/3/0ymhB6xvPbaa0D+X0Fff/213+v6K3iP3Avk7d9Ph507arobItUuqn5UhWe4VHZ75TEzli9fzpQpU3j00UeJiIggOjqaJ598knbt2lW6hO/YsWOZOHEi9erV892btbCHHnqISy+9lObNm3PppZeW+4vgjTfe4IsvvmD8+PG+ZWlpaSxatIhJkybx8MMPk5eXx/Dhw4mNjeWpp55i5MiRPProowwePLjUbSYmJvLss8/SoUMH2rdv77s7VPPmzZk3bx5Dhgzh1KlTvlv8nfbTn/6UWbNmcc011xRZXp4Hpt3EiKn/n5dffpnLLruMH/3oRzRo0MCvdf3mT+lIIBH4BNgNTC/l9TbAWuAj4GPg5xVts8olfx9oWLLcr3Ou93+/63r/97tV27YfVPJXqptK/nrX8T0fuLy8POecc8nJyS42NrbUdtVa8tfMQoGngQFAJrDJzFY657YXanYf8Jpz7k9m1hFYDUQH6PdPpegCJhGp7b7c9x9+OaQHp06dok6dOjz//PMB34c/0zI9gd3OuT0AZrYYGAwUDncHnL61SSMgcJfRiYh4TLuL2/DRRx9V6z78CfeWwN5CzzOBS4u1mQGsMbNbgfpAzV53KyJyjgvU2TIjgAXOuVbAz4GXzazEts1sgpmlmFlKVlZWgHYtIiLF+RPu+4DWhZ63KlhW2G+A1wCccxuBCKBZ8Q055+Y55+Kdc/GnT18SEZHA8yfcNwHtzKytmdUBhgMri7X5EugPYGYdyA93HZqLiNSQCufcnXMnzGwy8BYQCsx3zm0zs5nkn5KzErgDeN7MppL/5erYglN2RCRAdvfrT14lLpSpSHhUFD9+791y24SGhhITE0NeXh5hYWGMHj2aqVOnEhISQkpKCgsXLmT27NmlrpuRkUFycjIjR44s9fX9+/dz2223sXTpUhYsWEBKSgpz5871u/9nUkI4IyODQYMGkZ6e7vc6pRk7diyDBg1i6NChRZbfeOONTJs2jY4dO1Zp+4Hg10VMzrnV5J/eWHjZ/YUebwcSAts1ESks0Bfs7bikQ4Vt6tWr57uc/+DBg4wcOZJvvvmGBx98kPj4eOLj48tcNyMjg1deeaXUcD9x4gRRUVFlljfwR3klhGvKn//855rugk/wlh8QkbOqRYsWzJs3j7lz5+KcY926dQwaNAiAf/zjH74SvN26dePIkSNMnz6d999/n65du/LEE0+wYMECkpKS6NevH/379ycjI4POnTv7tr9371769u1Lu3btePDBBwFKtJk1axYzZswotYRwamoqV1xxBd27d2fgwIH8+9//BiA1NZXY2FhiY2N5+umnSx1bbm4u/fv395XmfeONN3yvLVy4kC5duhAbG8uoUaNKrPv73/+esWPHcvLkSfr27UtKSgqQf9XuvffeS2xsLL169eLAgQNAflniXoNG+8oPR0ZGVuVjKZPCXUT8dvHFF3Py5MkSdWRmzZrF008/TVpaGu+//z716tXjkUceoU+fPqSlpTF16lQANm/ezNKlS/nHP/5RYtsffvghy5Yt4+OPP2bJkiW+kCxN8RLCYWFh3HrrrSxdupTU1FTGjRvHvffeC8ANN9zAnDlzfHVmShMREcHy5cvZvHkza9eu5Y477sA5x7Zt23j44Yd577332LJlC0899VSR9e68806ysrJ48cUXS5ReOHr0KL169WLLli1cfvnlvguVbr/9dm6/cSRbt26lVatW5bzbVaNwF5EqS0hIYNq0acyePZvs7GzCwkqf8R0wYECRkrnFX2vatCn16tVjyJAh/POf//R7/5988gnp6ekMGDCArl278vDDD5OZmUl2djbZ2dlcfvnlAKUeeUN+GZZ77rmHLl26cOWVV7Jv3z4OHDjAe++9x7Bhw2jWLP/kv8J9f+ihh8jJyeHZZ58tteJknTp1fH/ZFC59vHHjRoYNyr8UqKzvIwIh6AuHFaYbY4tUrz179hAaGkqLFi3YseOH+f/p06dzzTXXsHr1ahISEnjrrdLvzVq4xG9xxQPSzAgLC+PUqVO+ZWWV4HXO0alTpxIFybKzsyscE+RXiszKyiI1NZXw8HCio6MrLPfbo0cPUlNT+eqrr0r9hRUeHu4b09ku9wseO3Lfl32MDdP71XQ3RDwpKyuLiRMnMnny5BJB/NlnnxETE8Pdd99Njx492LlzZ4Ulfot7++23+eqrrzh27BgrVqwgISGBCy64gIMHD3L48GG+++47Vq1a5WtfePvt27cnKyvLF+55eXm+O0E1btzY91dAeeV+W7RoQXh4OGvXruWLL74A8sseL1myhMOHDwNFy/0mJib6fqlVZpy9evVi2d/zz1I6XYa4OnjqyF3Ey8Kjovw6w6Uy26vIsWPH6Nq1q+9UyFGjRjFt2rQS7Z588knWrl1LSEgInTp14uqrryYkJITQ0FBiY2MZO3ZshXcb6tmzJ9dddx2ZmZn8+te/9p2Jc//999OzZ09atmzJJZdc4mtfvITw0qVLue2228jJyeHEiRNMmTKFTp068eKLLzJu3DjMjKuuuqrUfV9//fVce+21xMTEEB8f79tPp06duPfee7niiisIDQ2lW7duLFiwwLfesGHDOHLkCElJSaxevbrUbZf2Xv36V0P4w59eITExkUaNGvm1XqX5UzqyOn6qo+RvdZTLLYtK/kp1U8lfbzp69Kg7lZnqnHPur3/9q0tKSiqzbbWW/BURkcBJTU1l8k2/wYVF0LhxY+bPn18t+1G4i4icRX369GHLO69CVLdq3Y+nvlAV8RqnKh7nrKp+9gp3kVoqIiKCw4cPK+DPQc45Dh8+TERExBlvQ9MyIrVUq1atyMzMRPc+8KDsg5BTfp2giIiIKl3BGtThPveZE36dziUSjMLDw2nbtm1Nd0Oqw4xeMCOnWncR1OHeIgd+/K/yS5aKiJyLNOcuIuJBngl31ZUREflBUE/LFLYv+xgZj1xT090QEakVPHPkLiIiP1C4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8yBPhrguYRESK8sRFTLqASUSkKE8cuYuISFEKdxERD1K4i4h4kMJdRMSDFO4iIh4U9OGu0yBFREoK+lMhdRqkiEhJfh25m1mimX1iZrvNbHoZbX5pZtvNbJuZvRLYboqISGVUeORuZqHA08AAIBPYZGYrnXPbC7VpB/wOSHDOfW1mLaqrwyIiUjF/jtx7Arudc3ucc98Di4HBxdqMB552zn0N4Jw7GNhuiohIZfgT7i2BvYWeZxYsK+wnwE/MbIOZfWBmiYHqoIiIVF6gvlANA9oBfYFWwHozi3HOZRduZGYTgAkAbdq0CdCuRUSkOH+O3PcBrQs9b1WwrLBMYKVzLs859znwKflhX4Rzbp5zLt45F9+8efMz7bOIiFTAn3DfBLQzs7ZmVgcYDqws1mYF+UftmFkz8qdp9gSwnyIiUgkVhrtz7gQwGXgL2AG85pzbZmYzzSypoNlbwGEz2w6sBe50zh2urk4XpguYRERK8mvO3Tm3GlhdbNn9hR47YFrBz1m1YXq/s71LEZFaL+jLD4iISEkKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeJDCXUTEgxTuIiIepHAXEfEghbuIiAcp3EVEPEjhLiLiQQp3EREPUriLiHiQwl1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8yK9wN7NEM/vEzHab2fRy2l1nZs7M4gPXxZIGLh1ITNs21bkLEZGgVmG4m1ko8DRwNdARGGFmHUtp1wC4HfhXoDtZ3P6j+9n6+ZfVvRsRkaDlz5F7T2C3c26Pc+57YDEwuJR2DwGPAscD2D8RETkD/oR7S2BvoeeZBct8zCwOaO2c+3sA+yYiImeoyl+omlkI8D/AHX60nWBmKWaWkpWVVdVdi4hIGfwJ931A60LPWxUsO60B0BlYZ2YZQC9gZWlfqjrn5jnn4p1z8c2bNz/zXouISLn8CfdNQDsza2tmdYDhwMrTLzrncpxzzZxz0c65aOADIMk5l1ItPRYRkQpVGO7OuRPAZOAtYAfwmnNum5nNNLOk6u6giIhUXpg/jZxzq4HVxZbdX0bbvlXvloiIVIWuUBUR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeJDCXUTEgxTuIiIepHAXEfEghbuIiAcp3EVEPEjhLiLiQQp3EREPUriLiHiQwl1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeJDCXUTEgxTuIiIepHAXEfEgv8LdzBLN7BMz221m00t5fZqZbTezj83sXTO7KPBdFRERf1UY7mYWCjwNXA10BEaYWcdizT4C4p1zXYClwB8D3VEREfGfP0fuPYHdzrk9zrnvgcXA4MINnHNrnXPfFjz9AGgV2G6KiEhl+BPuLYG9hZ5nFiwry2+A/y3tBTObYGYpZpaSlZXlfy9FRKRSAvqFqpn9GogHHivtdefcPOdcvHMuvnnz5oHctYiIFBLmR5t9QOtCz1sVLCvCzK4E7gWucM59F5juiYjImfDnyH0T0M7M2ppZHWA4sLLwvOxJAAAG5ElEQVRwAzPrBjwHJDnnDga+myIiUhkVhrtz7gQwGXgL2AG85pzbZmYzzSypoNljQCSwxMzSzGxlGZsTEZGzwJ9pGZxzq4HVxZbdX+jxlQHul4iIVIGuUBUR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeJDCXUTEgxTuIiIeFLThvntlC8Kjomq6GyIitZJfV6jWRnnfhtFh87s13Q0RkVopaI/cRUSkbAp3EREPUriLiHiQwl1ExIMU7iIiHqRwFxHxIIW7iIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8SCFu4iIByncRUQ8SOEuIuJBCncREQ9SuIuIeJDCXUTEgxTuIiIepHAXEfEgv8LdzBLN7BMz221m00t5va6ZvVrw+r/MLDrQHRUREf9VGO5mFgo8DVwNdARGmFnHYs1+A3ztnPsx8ATwaKA7KiIi/vPnyL0nsNs5t8c59z2wGBhcrM1g4KWCx0uB/mZmgetmUXOfOUH4eSeqa/MiIkHPn3BvCewt9DyzYFmpbZxzJ4AcoGkgOliaFjnw46SD1bV5EZGgF3Y2d2ZmE4AJBU9zzewTP1dtBhwqsuRB4MFq++PAP2f+x0nJ8RTebDVMatnYan+vyh1TEPLaeMB7Ywru8ZSeX/6M6SJ/Nu9PuO8DWhd63qpgWWltMs0sDGgEHC6+IefcPGCePx0rzMxSnHPxlV2vtvLaeMB7Y/LaeMB7Y/LaeCCwY/JnWmYT0M7M2ppZHWA4sLJYm5XAmILHQ4H3nHMuEB0UEZHKq/DI3Tl3wswmA28BocB859w2M5sJpDjnVgIvAC+b2W7gK/J/AYiISA3xa87dObcaWF1s2f2FHh8HhgW2a0VUeiqnlvPaeMB7Y/LaeMB7Y/LaeCCAYzLNnoiIeI/KD4iIeFCtCXcvljjwY0xjzSzLzNIKfm6siX76y8zmm9lBM0sv43Uzs9kF4/3YzOLOdh8rw4/x9DWznEKfz/2ltatNzKy1ma01s+1mts3Mbi+lTdB8Tn6OJ6g+JzOLMLMPzWxLwZgeLKVN1fPOOVfjP+R/UfsZcDFQB9gCdCzW5mbg2YLHw4FXa7rfARjTWGBuTfe1EmO6HIgD0st4/efA/wIG9AL+VdN9ruJ4+gKrarqflRzThUBcweMGwKel/LsLms/Jz/EE1edU8L5HFjwOB/4F9CrWpsp5V1uO3GtdiYMA8GdMQcU5t578s6HKMhhY6PJ9ADQ2swvPTu8qz4/xBB3n3L+dc5sLHh8BdlDyivKg+Zz8HE9QKXjfcwuehhf8FP/ys8p5V1vCvdaVOAgAf8YEcF3Bn8ZLzax1Ka8HE3/HHEwuK/jz+X/NrFNNd6YyCv6U70b+kWFhQfk5lTMeCLLPycxCzSwNOAi87Zwr8zM607yrLeF+rvobEO2c6wK8zQ+/qaV22Axc5JyLBeYAK2q4P34zs0hgGTDFOfdNTfenqioYT9B9Ts65k865ruRf8d/TzDoHeh+1JdwrU+KA8koc1CIVjsk5d9g5913B0z8D3c9S36qLP59j0HDOfXP6z2eXf61HuJk1q+FuVcjMwskPwkXOuddLaRJUn1NF4wnWzwnAOZcNrAUSi71U5byrLeHuxRIHFY6p2DxnEvnzicFsJTC64GyMXkCOc+7fNd2pM2VmPzo9z2lmPcn//6U2H1BQ0N8XgB3Ouf8po1nQfE7+jCfYPicza25mjQse1wMGADuLNaty3p3VqpBlcR4sceDnmG4zsyTgBPljGltjHfaDmf2V/DMTmplZJvAA+V8G4Zx7lvyrmH8O7Aa+BW6omZ76x4/xDAUmmdkJ4BgwvJYfUAAkAKOArQVzugD3AG0gKD8nf8YTbJ/ThcBLln8jpBDgNefcqkDnna5QFRHxoNoyLSMiIgGkcBcR8SCFu4iIByncRUQ8SOEuInIWVFSorljbNgUF0z4quIL955Xdn8JdROTsWEDJi5XKch/5p0h2I/80yGcquzOFu4jIWVBaoToz+39m9qaZpZrZ+2Z2yenmQMOCx42A/ZXdX624iElE5Bw1D5jonNtlZpeSf4TeD5gBrDGzW4H6wJWV3bDCXUSkBhQUQ+sNLClUzbduwX9HAAucc4+b2WXkX63a2Tl3yt/tK9xFRGpGCJBdUB2yuN9QMD/vnNtoZhFAM/JLBPu9cREROcsKShd/bmbDwHf7w9iCl78E+hcs7wBEAFmV2b5qy4iInAWFC9UBB8gvVPce8Cfyi4mFA4udczPNrCPwPBBJ/perdznn1lRqfwp3ERHv0bSMiIgHKdxFRDxI4S4i4kEKdxERD1K4i4h4kMJdRMSDFO4iIh6kcBcR8aD/A/jwRu2yiikRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.25/rate=2/tracking_frequency=0\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.25/rate=2/tracking_frequency=2/tracking=watermarking\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.25/rate=2/tracking_frequency=2/tracking=acking/distributed=False\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.hist(Result(\n",
    "    path=f\"sigmod.07.25/rate=2/tracking_frequency=2/tracking=acking/distributed=True\"\n",
    ").lat_data(), 50, density=True, histtype='step', cumulative=True)\n",
    "plt.legend([\n",
    "    'No tracking',\n",
    "    'Watermarking',\n",
    "    'Centralized acking',\n",
    "    'Distributed acking',\n",
    "])   \n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}