Tree_billig_mod.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "sys.path.insert(0, r'D:\\uni\\Masterarbeit\\Code\\auswertung')\n",
    "import auswertung_ma\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "from auswertung_ma import f_exp, f_poly, f_poly_long"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hier erstmal meine neue Version des LP-Alg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = r'D:\\Uni\\Masterarbeit\\Daten\\Tree_billig_mod\\bfc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n",
      "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "# c = 2, GVBilligFirstCreator(0, 10, 0, 1)\n",
    "dic2 = {}\n",
    "\n",
    "dirs2 = [d for d in os.listdir(p) if 'ModTest_C2' in d]\n",
    "\n",
    "for ho in dirs2:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p, ho, os.listdir(os.path.join(p,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    \n",
    "    # noch einlesen, wie viel % der lp-Variablen ganzzahlig sind\n",
    "    \n",
    "    dic2[number_nodes] = df.mean()\n",
    "    \n",
    "ns_2 = sorted(list(dic2.keys()))\n",
    "mean_lp_constrs_2 = [dic2[n].lp_constraint_counter for n in ns_2]\n",
    "mean_total_lp_2 = np.array([dic2[n]['total_lp[s]'] for n in ns_2])\n",
    "mean_sum_opt_2 = [dic2[n]['sum_opt[s]'] for n in ns_2]\n",
    "mean_anteil_opt_2 = [dic2[n]['anteil_opt'] for n in ns_2]\n",
    "mean_anteil_lp_ganz_2 = [dic2[n]['anteil_lp_int'] for n in ns_2]\n",
    "print(mean_anteil_lp_ganz_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.set_title('Laufzeit in Abhängigkeit von N, c=2')\n",
    "ax.scatter(ns_2, mean_total_lp_2, label='BFC(0, 10, 0, 1)')\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_2, mean_total_lp_2, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "# popt_exp\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_2, mean_total_lp_2, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "\n",
    "# ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3} $e^{{N*{popt_exp[1]:.3}}}$\")\n",
    "# ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('Laufzeit[s]')\n",
    "ax.legend(fontsize=20)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das klappt ja wieder noch nicht so richtig. Weder exp, noch poly Fit passe gut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n",
      "[0.9964426009720126, 0.9866380625091742, 0.9872686291296001, 0.9876428347687904, 0.9889542056509341, 0.9895401637888771]\n"
     ]
    }
   ],
   "source": [
    "# c = 3, GVBilligFirstCreator(0, 10, 0, 1)\n",
    "dic3 = {}\n",
    "\n",
    "dirs3 = [d for d in os.listdir(p) if 'ModTest_C3' in d]\n",
    "\n",
    "for ho in dirs3:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p, ho, os.listdir(os.path.join(p,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    \n",
    "    dic3[number_nodes] = df.mean()\n",
    "    \n",
    "ns_3 = sorted(list(dic3.keys()))\n",
    "mean_lp_constrs_3 = [dic3[n].lp_constraint_counter for n in ns_3]\n",
    "mean_total_lp_3 = np.array([dic3[n]['total_lp[s]'] for n in ns_3])\n",
    "mean_sum_opt_3 = [dic3[n]['sum_opt[s]'] for n in ns_3]\n",
    "mean_anteil_opt_3 = [dic3[n]['anteil_opt'] for n in ns_3]\n",
    "mean_anteil_lp_ganz_3 = [dic3[n]['anteil_lp_int'] for n in ns_3]\n",
    "print(mean_anteil_lp_ganz_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27472e1f6a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.set_title('Laufzeit in Abhängigkeit von N, c=3')\n",
    "ax.scatter(ns_3, mean_total_lp_3, label='BFC(0, 10, 0, 1)')\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_3, mean_total_lp_3, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "# popt_exp\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_3, mean_total_lp_3, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3} $e^{{N*{popt_exp[1]:.3}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('Laufzeit[s]')\n",
    "ax.legend(fontsize=20)\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "# ax.set_xlim([-10,200])\n",
    "# ax.set_ylim([-1, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wieder das bekannte Problem für die Laufzeiten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27472abf1c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# constraints\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3, mean_lp_constrs_3)\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_3, mean_lp_constrs_3, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_3, mean_lp_constrs_3, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3}* $e^{{N*{popt_exp[1]:.2}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.2} * $N^{{{popt_poly[1]:.2}}}$\")\n",
    "\n",
    "ax.set_title('Constraints für C=3')\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('im Mittel Anzahl hinzugefügter Constraints')\n",
    "\n",
    "\n",
    "ax.legend(fontsize=20)\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "# ax.set_ylim([-2,8000])\n",
    "# ax.set_xlim([-5, 110])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Anzahl constraints wächst jetzt linear?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## c=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n",
      "[0.9902411354129458, 0.9884489110278705, 0.9887054350430715, 0.9909147415261752, 0.9908212298431698, 0.9913623866262996]\n",
      "[0.0026122865499999997, 0.009790208330000001, 0.039874287189999996, 0.16691450195000002, 1.0158340287999998, 23.02186044299999]\n",
      "[0.1520297115645778, 0.2597032466777659, 0.25377830423317677, 0.191137605836975, 0.15636940278094238, 0.3285857121496921]\n"
     ]
    }
   ],
   "source": [
    "# c = 5, GVBilligFirstCreator(0, 10, 0, 1)\n",
    "dic5 = {}\n",
    "\n",
    "dirs5 = [d for d in os.listdir(p) if 'ModTest_C5' in d]\n",
    "\n",
    "for ho in dirs5:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p, ho, os.listdir(os.path.join(p,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    \n",
    "    dic5[number_nodes] = df.mean()\n",
    "    \n",
    "ns_5 = sorted(list(dic5.keys()))\n",
    "mean_lp_constrs_5 = [dic5[n].lp_constraint_counter for n in ns_5]\n",
    "mean_total_lp_5 = np.array([dic5[n]['total_lp[s]'] for n in ns_5])\n",
    "mean_sum_opt_5 = [dic5[n]['sum_opt[s]'] for n in ns_5]\n",
    "mean_anteil_opt_5 = [dic5[n]['anteil_opt'] for n in ns_5]\n",
    "mean_anteil_lp_ganz_5 = [dic5[n]['anteil_lp_int'] for n in ns_5]\n",
    "print(mean_anteil_lp_ganz_5)\n",
    "print(mean_sum_opt_5)\n",
    "print([mean_sum_opt_5[i] / mean_total_lp_5[i] for i in range(len(ns_5))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: approx_teil[s]            0.000000\n",
       " lp_constraint_counter    10.160000\n",
       " lp_teil_loop[s]           0.005008\n",
       " lp_teil_setup[ms]        11.899351\n",
       " sum_opt[s]                0.002612\n",
       " anteil_lp_int             0.990241\n",
       " total_lp[s]               0.017183\n",
       " anteil_opt                0.138699\n",
       " dtype: float64,\n",
       " 160: approx_teil[s]             0.670000\n",
       " lp_constraint_counter    328.010000\n",
       " lp_teil_loop[s]            6.458024\n",
       " lp_teil_setup[ms]         30.315034\n",
       " sum_opt[s]                 1.015834\n",
       " anteil_lp_int              0.990821\n",
       " total_lp[s]                6.496373\n",
       " anteil_opt                 0.134563\n",
       " dtype: float64,\n",
       " 20: approx_teil[s]            0.000000\n",
       " lp_constraint_counter    26.560000\n",
       " lp_teil_loop[s]           0.024047\n",
       " lp_teil_setup[ms]        12.948696\n",
       " sum_opt[s]                0.009790\n",
       " anteil_lp_int             0.988449\n",
       " total_lp[s]               0.037698\n",
       " anteil_opt                0.247030\n",
       " dtype: float64,\n",
       " 320: approx_teil[s]             5.180000\n",
       " lp_constraint_counter    975.420000\n",
       " lp_teil_loop[s]           70.001487\n",
       " lp_teil_setup[ms]         52.129411\n",
       " sum_opt[s]                23.021860\n",
       " anteil_lp_int              0.991362\n",
       " total_lp[s]               70.063486\n",
       " anteil_opt                 0.140943\n",
       " dtype: float64,\n",
       " 40: approx_teil[s]            0.000000\n",
       " lp_constraint_counter    61.300000\n",
       " lp_teil_loop[s]           0.137294\n",
       " lp_teil_setup[ms]        15.510296\n",
       " sum_opt[s]                0.039874\n",
       " anteil_lp_int             0.988705\n",
       " total_lp[s]               0.157123\n",
       " anteil_opt                0.253326\n",
       " dtype: float64,\n",
       " 80: approx_teil[s]             0.000000\n",
       " lp_constraint_counter    131.850000\n",
       " lp_teil_loop[s]            0.846759\n",
       " lp_teil_setup[ms]         20.286875\n",
       " sum_opt[s]                 0.166915\n",
       " anteil_lp_int              0.990915\n",
       " total_lp[s]                0.873269\n",
       " anteil_opt                 0.185973\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schaue mir mal nur die Laufzeit an, die insgesamt auf die opt-Schritte fällt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x274737f15b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_5, mean_sum_opt_5)\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_5, mean_sum_opt_5, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "# popt_exp\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_5, mean_sum_opt_5, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3} $e^{{N*{popt_exp[1]:.3}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.legend(fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ist das Problem die Gurobi Optimierungszeit? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27473e38970>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# anteil von opt an Gesamtlaufzeit\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_5, mean_anteil_opt_5, label='mean_anteil_opt')\n",
    "ax.scatter(ns_5, [mean_sum_opt_5[i] / mean_total_lp_5[i] for i in range(len(ns_5))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huh?? Problem, dass ich hier mit mittelwerten rechne?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27472b78040>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "mean_setup_zeiten_5 = np.array([dic5[n]['lp_teil_setup[ms]']/1000 for n in ns_5])\n",
    "mean_approx_zeiten_5 = np.array([dic5[n]['approx_teil[s]'] for n in ns_5])\n",
    "ax.scatter(ns_5, mean_setup_zeiten_5, label='mean_setup_zeiten')\n",
    "ax.scatter(ns_5, mean_setup_zeiten_5 + np.array(mean_sum_opt_5), label='mean_setup_zeiten + mean_sum_opt')\n",
    "# ax.scatter(ns_5, mean_setup_zeiten_5 + np.array(mean_sum_opt_5) + mean_approx_zeiten_5, label='mean_setup_zeiten + mean_sum_opt + mean_approx')\n",
    "\n",
    "ax.scatter(ns_5, mean_total_lp_5, label='mean_total')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auswertung\n",
    "Der hauptteil des LP-Approx-Algs, liegt beim Bauen von neuen Constraints?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.011899351000000006,\n",
       " 0.012948695999999992,\n",
       " 0.015510295999999998,\n",
       " 0.020286875,\n",
       " 0.030315034000000005,\n",
       " 0.052129410999999994]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# schaue mir setupzeiten an\n",
    "[dic5[n]['lp_teil_setup[ms]']/1000 for n in ns_5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die werden fuer mehr Kanten schon größer, aber haben trotzdem kaum Einfluss auf die Gesamtlaufzeit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.005007937329999998, 0.02404741929999998, 0.13729421600000002, 0.84675875, 6.458024000000006, 70.00148700000001]\n"
     ]
    }
   ],
   "source": [
    "# loop zeiten\n",
    "print([dic5[n]['lp_teil_loop[s]'] for n in ns_5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.017182737000000003, 0.037697674, 0.15712252199999993, 0.8732687700000001, 6.496373399999998, 70.06348600000003]\n"
     ]
    }
   ],
   "source": [
    "print(list(mean_total_lp_5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wie man sieht, fällt der Hauptteil der LP-Laufzeit noch auf das while-loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# misch fkt zum fitten\n",
    "def f_misch(n, a, b, c, d):\n",
    "    return c * (n ** b) * np.exp(a * (n ** d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.73634975e+00 1.47795122e-04 1.62483203e-05 2.44122759e-01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x200ab933190>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# laufzeit\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.set_title('Laufzeit in Abhängigkeit von N, c=5')\n",
    "ax.scatter(ns_5, mean_total_lp_5, label='BFC(0, 10, 0, 1)')\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_5, mean_total_lp_5, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "# popt_exp\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_5, mean_total_lp_5, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "popt_misch, pcov_misch = curve_fit(f_misch, ns_5, mean_total_lp_5, maxfev=50000, p0=[0,0,0,1])\n",
    "print(popt_misch)\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3} $e^{{N*{popt_exp[1]:.3}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.plot(np.arange(330), f_misch(np.arange(330), *popt_misch), label=f\"${popt_misch[2]:.3} \\cdot (N^{{{popt_misch[1]:.3}}}) * e^{{{popt_misch[0]:.3} \\cdot N^{{{popt_misch[3]:.3}}})}}$\")\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('Laufzeit[s]')\n",
    "ax.legend(fontsize=20)\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "# ax.set_xlim([-10,200])\n",
    "# ax.set_ylim([-1, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "vergleiche $N^b$ und $e^{a N^d}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x200abf3c9d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSZKUK1VL4LU/QZdvwVYHxU5TtBzBlCRJklT8Rt4O8yfB0Tc4eplDdd2mRJIkSZIK07JKeO1q6LYH9Ng7dpqi5gimJEmSpOI27CZYNB1OuN3RyxxzBFOSJElS8Vq6AF6/FnruD912i52m6FkwJUmSJBWvof+AxbNhv1/HTlISLJiSJEmSitPiOfDm32Drw2GzHWOnKQkWTEmSJEnF6c2/w9J5sO+vYicpGRZMSZIkScVn0UwYeiNsewxs2jd2mpJhwZQkSZJUfN64FqoXwz6/jJ2kpFgwJUmSJBWXBVPhnZtguxOhw9ax05QUC6YkSZKk4jLkz1BTBXv/PHaSkmPBlCRJklQ85nwBw/8FA06DdlvETlNyLJiSJEmSiscr/wdl5bD3L2InKUkWTEmSJEnFYdqH8P49sNN/QevNYqcpSRZMSZIkScXhpd9Bk1awx09iJylZFkxJkiRJhW/CMPj4SdjtR9C8Xew0JcuCKUmSJKmwJQm8eAW06AC7nB87TUmzYEqSJEkqbJ+9BOOHwF4/gyYtY6cpaRZMSZIkSYUrk0lHL9tsDjueFTtNyWsUO4AkSZIkrbexj8KU9+Hof0CjJrHTlDxHMCVJkiQVpprqdOXYDr1huxNipxGOYEqSJEkqVO8Nhlmfwkl3QVl57DTCEUxJkiRJhahqMbxyJXT5Fmx9WOw0quUIpiRJkqTCM+xmWDAZjr0JQoidRrUcwZQkSZJUWJbMgyF/hp77Q/c9YqfRSiyYkiRJkgrLG9fB4jmw///GTqJVWDAlSZIkFY75k+GtG6Df8dC5f+w0WoUFU5IkSVLhePkPkNTAfpfETqLVsGBKkiRJKgzTx6Zbk3zrv6Bt99hptBoWTEmSJEmF4YXLoXEr2Ot/YifRGlgwJUmSJOW/8a/DJ8/AnhdB83ax02gNLJiSJEmS8luSwHOXwkabwc7fj51Ga9EodgBJkiRJWqsxD8PkkXDUDVDRLHYarYUjmJIkSZLyV/UyePE30HFb2P6k2Gm0Do5gSpIkScpfI/4Fcz6HUx+AsvLYabQOjmBKkiRJyk9L5sOrV0GPvWDLA2KnUR1YMCVJkiTlpzeug8pZcOBvIITYaVQHFkxJkiRJ+Wf+ZHjreuh7HHQeEDuN6siCKUmSJCn/vPwHyFTD/pfGTqJ6sGBKkiRJyi9TR8O7d8JO50Hb7rHTqB4smJIkSZLyR5LAc5dAszaw989ip1E9WTAlSZIk5Y//PA/jXoG9fwHN2sZOo3qyYEqSJEnKDzXV6ehlu54w8NzYabQeGsUOIEmSJEkAjLwNZn4MJw6GRo1jp9F6cARTkiRJUnxL5qUrx3bbA7Y5PHYarScLpiRJkqT4hlwDlbPg4N9BCLHTaD1ZMCVJkiTFNecLGHoDbH8ydB4QO402gAVTkiRJUlwvXgGhHPa7NHYSbSALpiRJkqR4JrwDHzwIu/0QWm8WO402kAVTkiRJUhxJAs/+ClpuArtfGDuNssBtSiRJkiTFMeZhmDgMjvwbNGkZO42ywBFMSZIkSQ2vagm8cBls0hf6nxo7jbLEEUxJkiRJDW/o9TD3SzjjUSgrj51GWeIIpiRJkqSGNX8KvPZn2OYI2GKf2GmURRZMSZIkSQ3rxSsgUwUH/TZ2EmWZBVOSJElSw5k4HN6/G3b9AbTbInYaZZkFU5IkSVLDyGTg6V+k25Ls+dPYaZQDLvIjSZIkqWGMvh8mDYejboAmrWKnUQ44gilJkiQp95YuTLcl6TwAtj85dhrliCOYkiRJknLv9b/Agilwwh1Q5jhXsfLMSpIkScqtOePhzb9BvxOg606x0yiHLJiSJEmScuu5S6GsHA64PHYS5ZgFU5IkSVLufP4ajH0M9vgJtN4sdhrlmAVTkiRJUm5kauCZX0LrzWG3C2KnUQNwkR9JkiRJuTHiNpj2ARx/G1Q0i51GDcARTEmSJEnZVzkbXvotdN8T+hwdO40aiAVTkiRJUva9+BtYMh8O/SOEEDuNGogFU5IkSVJ2TRqZTo/d+XuwSZ/YadSALJiSJEmSsieTgad+Bi06wD4Xx06jBuYiP5IkSZKy5/27YNJwOPof0LR17DRqYI5gSpIkScqOxXPh+cug686w3Ymx0ygCRzAlSZIkZccr/weVs+D0h6DMsaxS5FmXJEmStOGmfgDvDIKB50Cn7WOnUSQWTEmSJEkbJknShX2atoH9LomdRhE5RVaSJEnShhn9AHz5Jnz7OmjeLnYaReQIpiRJkqT1t3QBPHcJdB4AA06PnUaROYIpSZIkaf29ehUsnAonDYay8thpFJkjmJIkSZLWz7QPYeiN6chll4Gx0ygPWDAlSZIk1V+SwJM/hSat4IArYqdRnnCKrCRJkqT6e//udGGfI/8GLdrHTqM84QimJEmSpPqpnJ0u7NNlJ+h/Wuw0yiMWTEmSJEn18+IVsHguHPEXKLNSaAX/b5AkSZJUdxOGwYjbYJfzYdO+sdMoz1gwJUmSJNVNTTU8eRG06gz7XBw7jfKQi/xIkiRJqpthN8HU0XD87enqsdIqHMGUJEmStG7zp8BLv4ctD4A+R8VOozxlwZQkSZK0bs/+EjJVcNifIITYaZSnLJiSJEmS1u7TF2HMw7DnT6HdFrHTKI9ZMCVJkiStWdUSeOp/oF1P2P3C2GmU51zkR5IkSdKaDfkzzB4Hpz8CjZrETqM85wimJEmSpNWb/hG8/hfY7kTouW/sNCoAFkxJkiRJ35TJwOMXQpOWcPAfYqdRgXCKrCRJkqRvGnk7TBgKR90ALTaOnUYFwhFMSZIkSV+3YCo8fxl03xP6nxI7jQrIOgtmCKFpCOGdEML7IYQxIYQrGiKYJEmSpEieuRiql8AR17rnpeqlLlNklwL7JUmyMIRQAbweQng6SZKhOc4mSZIkqaF9/Ey65+W+l8DGW8ZOowKzzoKZJEkCLKx9WFH7keQylCRJkqQIli5M97zssI17Xmq91OkazBBCeQjhPWA68HySJG+v5pjzQgjDQwjDZ8yYkeWYkiRJknLu5T/AvAnw7eugUePYaVSA6lQwkySpSZKkP9AF2CmE0Hc1xwxKkmRgkiQDO3TokOWYkiRJknJq8rvw9o0w8BzYfJfYaVSg6rWKbJIkc4FXgENyEUaSJElSBDXV8NiPoEUH2P+y2GlUwOqyimyHEEKb2vvNgAOAj3KcS5IkSVJDeftGmDoKDv0jNGsTO40KWF1Wke0E3B5CKCctpPclSfJEbmNJkiRJahCzPoOXfg+9DoU+R8VOowJXl1VkRwEDGiCLJEmSpIaUJPD4hVBeAYf/2T0vtcHqMoIpSZIkqRiNvB3GD4EjroXWm8VOoyJQr0V+JEmSJBWJeZPguUuh+56w41mx06hIWDAlSZKkUpMk8ORPoKYKjvyrU2OVNRZMSZIkqdR88CB88gzsdwm02yJ2GhURC6YkSZJUShbNhKd/DpvtCLucHzuNiowFU5IkSSolT/8ClsyHI/8OZeWx06jIWDAlSZKkUvHx0/DBA7DX/8AmfWKnURGyYEqSJEmlYMk8eOIn0LEP7PGT2GlUpNwHU5IkSSoFz/8vLJwKJ94JjRrHTqMi5QimJEmSVOzGvQIjboNd/hu67Bg7jYqYBVOSJEkqZkvmw6MXQPstYd9fx06jIucUWUmSJKmYPX8pzJsI5zwLjZvHTqMi5wimJEmSVKw+fTGdGrvbBbD5zrHTqARYMCVJkqRitGQePPYjaL+VU2PVYJwiK0mSJBWj5y6BBZPhnOegolnsNCoRjmBKkiRJxeY/L8DIO2C3H0LXb8VOoxJiwZQkSZKKyeK58NgPYeOtYZ9fxU6jEuMUWUmSJKmYPPtrWDgVTrwTKprGTqMS4wimJEmSVCw+eQ7euxN2/zF02TF2GpUgC6YkSZJUDBbPgcd/BB16wz4Xx06jEuUUWUmSJKkYPP0LWDgdTroLGjWJnUYlyhFMSZIkqdCNeQRG3Qt7/Qw22yF2GpUwC6YkSZJUyBZMhScugs4DYK//iZ1GJc6CKUmSJBWqJEm3JKmqhGMGQXlF7EQqcV6DKUmSJBWqEbfBf56DQ66CDr1ip5EcwZQkSZIK0uxx6Z6XPfaGnc6LnUYCLJiSJElS4cnUwMPnQ1kjOPoGKPPXeuUHp8hKkiRJheaN62DCUPjOTdC6S+w00lf8pw5JkiSpkEwZBS//AfocDf2Oj51G+hoLpiRJklQoqpfCw9+D5u3giL9ACLETSV/jFFlJkiSpULz0O5j+IZz6QFoypTzjCKYkSZJUCD5/Dd78G+x4Nmx1YOw00mpZMCVJkqR8VzkbHvoetN8SDv597DTSGjlFVpIkScpnSQKPXwiLZsDJd0PjFrETSWvkCKYkSZKUz94bDGMfg/0ugc79Y6eR1sqCKUmSJOWrWZ/BUz+HHnvBbj+KnUZaJwumJEmSlI9qquDB70J5BRz9DyjzV3flP6/BlCRJkvLRK1fC5JFwwh3QerPYaaQ68Z9BJEmSpHwz/g0Y8mcYcBr0OSp2GqnOLJiSJElSPlk8Fx46D9r1gEOuip1GqhenyEqSJEn5IkngiYtg4VQ49zlo0jJ2IqleHMGUJEmS8sX7d8OYh2CfX8JmO8ZOI9WbBVOSJEnKBzP/A0/+FLrvCXtcFDuNtF4smJIkSVJsVUvg/rOhohl85yYoK4+dSFovXoMpSZIkxfb8pTBtNJxyH2zUKXYaab05gilJkiTFNPYJeGcQ7HoB9Do4dhppg1gwJUmSpFjmToBHfwCd+sP+l8VOI20wC6YkSZIUQ001PPhdyNTAcbdCo8axE0kbzGswJUmSpBhevRImDIVjb4H2PWOnkbLCEUxJkiSpoY17FV67GgacBv2Oi51GyhoLpiRJktSQFs6Ah/4LNt4KDv1j7DRSVjlFVpIkSWoomQw8/D1YPBdOewgat4idSMoqC6YkSZLUUF7/M3z2Ihx+DWzaN3YaKeucIitJkiQ1hM9fg5f/AP2Oh4HnxE4j5YQFU5IkScq1BdPggXOh/ZZwxLUQQuxEUk44RVaSJEnKpUwNPHguLF0AZzwKTVrGTiTljAVTkiRJyqVXroTxQ+DoG2GTPrHTSDnlFFlJkiQpVz59AV77E/Q/DfqfEjuNlHMWTEmSJCkX5k2Ch86Djr3hsD/FTiM1CAumJEmSlG01VfDAOVC9FE64Axo3j51IahBegylJkiRl24u/gQlD4dhbYOOtYqeRGowjmJIkSVI2jX0c3vxrutdlv+Nip5EalAVTkiRJypaZn8LD50PnHeCQK2OnkRqcBVOSJEnKhqUL4d5ToVHj9LrLRk1iJ5IanNdgSpIkSRsqSeCxH8LMT+D0h6FN19iJpCgcwZQkSZI21NAbYcxDsN+lsMU+sdNI0VgwJUmSpA3xxZvw3CWwzRGwx0Wx00hRWTAlSZKk9bVgKtx/FrTtDkffACHETiRF5TWYkiRJ0vqoqYL7zoSlC+D0R6Bp69iJpOgsmJIkSdL6eO5SmDAUjr0FNukTO42UF5wiK0mSJNXXqPvh7Rth5/Oh33Gx00h5w4IpSZIk1cfk9+CxC2Dz3eCg38ZOI+UVC6YkSZJUVwtnwL2nQfP2cMIdUF4RO5GUV7wGU5IkSaqLmqp0xdhFM+CcZ6Blh9iJpLxjwZQkSZLq4tlfwxevwzGDoPOA2GmkvOQUWUmSJGld3r0T3vkn7HoBbH9i7DRS3rJgSpIkSWszcTg8cRH02BsOuCJ2GimvWTAlSZKkNVkwNV3Up1UnOP42KPcKM2ltfIdIkiRJq1O9FO49HZbMg3Ofh+btYieS8p4FU5IkSVpVksBT/wMT30lHLjftGzuRVBCcIitJkiSt6u1/wMg7YI+fwLbHxE4jFQwLpiRJkrSy/7wAz/4KtjkC9rs0dhqpoFgwJUmSpOVmfAwPnA0dt4Vj/gll/ros1YfvGEmSJAmgcjbcdSI0agIn3w1NWsZOJBUcF/mRJEmSaqrgvjNg/iQ460lo0zV2IqkgWTAlSZJU2pavGDt+CBwzCLruFDuRVLCcIitJkqTS9vY/YcRtsMdFsP2JsdNIBc2CKUmSpNL16Qvw7C9h68Nhv/+NnUYqeBZMSZIklaYZH8P950DHPvCdQa4YK2WB7yJJkiSVnoXTYfBxrhgrZZmL/EiSJKm0VC2Gu0+GhTPg7CehzeaxE0lFw4IpSZKk0pHJwMPfg0kj4MR/w2Y7xk4kFRULpiRJkkrHi1fAh4/CQb+H3t+OnUYqOl6DKUmSpNIw4jZ441oYeC7s+oPYaaSitM6CGULoGkJ4OYQwNoQwJoRwYUMEkyRJkrLm0xfhiZ/AlgfAoX+EEGInkopSXabIVgM/TZJkZAihFTAihPB8kiQf5jibJEmStOGmfQj3nQkde8Nx/4JyrxKTcmWdI5hJkkxJkmRk7f0FwFhgs1wHkyRJkjbYgqlw1wnQuAWcci803Sh2Iqmo1esazBBCd2AA8PZqXjsvhDA8hDB8xowZWYonSZIkraelC9JyWTk7LZetu8ROJBW9OhfMEEJL4EHgx0mSzF/19SRJBiVJMjBJkoEdOnTIZkZJkiSpfmqq0mmxUz+A4/8FnfvHTiSVhDpNQA8hVJCWy8FJkjyU20iSJEnSBkgSePxC+OxF+PZfodfBsRNJJaMuq8gG4BZgbJIk1+Q+kiRJkrQBXv4DvDcY9r4YdjwzdhqppNRliuzuwOnAfiGE92o/DstxLkmSJKn+hv8LXvsjDDgd9rk4dhqp5KxzimySJK8DbhQkSZKk/PbxM/DkT2DLA+GIv7jXpRRBvVaRlSRJkvLSxOFw/1mw6XZw/G1QXhE7kVSSLJiSJEkqbLM+S7cjabUJnHo/NGkZO5FUsiyYkiRJKlwLp8Odx6Yrx576ILTsGDuRVNLqtE2JJEmSlHeWzIN/fwcWToMzHoONt4ydSCp5FkxJkiQVnqolcPfJMGMsnHwvdP1W7ESSsGBKkiSp0NRUwwPnwBdvwrE3w1YHxE4kqZYFU5IkSYUjSeCJC+HjJ+HQP0K/42InkrQSF/mRJElS4XjhMnj3Ttjr57Dz92KnkbQKC6YkSZIKwxt/hTeug4HnwL6/ip1G0mpYMCVJkpT/3rsLnr8Utj0GDrsaQoidSNJqWDAlSZKU3z56Ch69ALbYB475J5SVx04kaQ0smJIkScpfn70M958JnfvDiXdCoyaxE0laCwumJEmS8tOXb8M9p0D7reDUB6BJq9iJJK2DBVOSJEn5Z/J7MPh4aNUJzngEmreLnUhSHVgwJUmSlF9mfAx3fgeabgRnPAotO8ZOJKmOLJiSJEnKH7M/hzuOgrJGabls0zV2Ikn10Ch2AEmSJAmA+ZPTclm9BM56Ctr3jJ1IUj1ZMCVJkhTfoplpuaycDWc+Bpv0iZ1I0nqwYEqSJCmuytnw76Nh7gQ4/SHYbIfYiSStJwumJEmS4lk8F/59TLqwz8n3QLfdYieStAFc5EeSJElxLJkPdx4L08bAiYNhy/1jJ5K0gRzBlCRJUsNbujDd53LKe3DCHdDroNiJJGWBI5iSJElqWMsWwV0nwMRhcNytsM3hsRNJyhJHMCVJktRwllXC3SfBl2/BsTdDn6NiJ5KURY5gSpIkqWFULYF7ToHPh8DR/4C+x8ZOJCnLHMGUJElS7lUvhftOh3Evw1HXw/Ynxk4kKQccwZQkSVJuVS2Be0+D/zwHR1wLA06LnUhSjjiCKUmSpNypWpyWy09fSMvlwLNjJ5KUQxZMSZIk5cayyvSay3GvwJF/gx3OiJ1IUo5ZMCVJkpR9yyrh7hPTBX2Ouh4GnBo7kaQGYMGUJElSdi1bBHedCF+8Acf8A7Y/KXYiSQ3EgilJkqTsWboQBh8PE4bCMYNgu+NjJ5LUgCyYkiRJyo6lC+DO42DiMDj2Zve5lEqQBVOSJEkbbvFcGHwcTBoJx90C2x4TO5GkCCyYkiRJ2jCLZsK/j4bpH8EJt0Pvb8dOJCkSC6YkSZLW3/wpabmcMx5Ovge2OiB2IkkRWTAlSZK0fuZ+CbcfCYtmwGkPQvc9YieSFJkFU5IkSfU367O0XC5bAGc8Cl0Gxk4kKQ9YMCVJklQ/0z6EO46CJANnPQmb9oudSFKeKIsdQJIkSQVk8rtw22FQVg5nP2W5lPQ1FkxJkiTVzfg30mmxTVrB2U9Dh61jJ5KUZyyYkiRJWrePn4Y7vwOtNoWzn4F2PWInkpSHLJiSJElau/fuhntOhY590nLZerPYiSTlKQumJEmS1uyt6+GR76dbkJz5GLRoHzuRpDzmKrKSJEn6piSBl34HQ66G3kfCsTdDoyaxU0nKcxZMSZIkfV2mBp78KYz4F+xwJhzxl3TVWElaBwumJEmSVqheBg+fB2Mehj0ugv0vgxBip5JUICyYkiRJSi2ZD/eeBp+/Cgf+Fnb/UexEkgqMBVOSJEkwfwoMPh5mjIWjb4T+p8ROJKkAWTAlSZJK3YxP4M5joXIWnHIvbHlA7ESSCpQFU5IkqZR9+TbcfSKUNYKzn4TOA2InklTA3AdTkiSpVI19Au44Epq1g3Oft1xK2mAWTEmSpFI07Ba473TYZFs49zlo1yN2IklFwCmykiRJpSRJ4KXfwZCrodchcNyt0LhF7FSSioQFU5IkqVRUL4VHfwCj74cdzoDD/wLl/jooKXv8G0WSJKkUVM6Ge06BL9+C/f8X9vgJhBA7laQiY8GUJEkqdrM+S/e4nDcxnRLb99jYiSQVKQumJElSMfvirXTkEuDMx2DzXeLmkVTUXEVWkiSpWI1+IN2GpHk7+O4LlktJOWfBlCRJKjZJAq/9CR48FzYbmO5x2b5n7FSSSoBTZCVJkopJ1RJ4/EIYdQ/0OwGO+js0ahI7laQSYcGUJEkqFgumpddbThoO+/wK9v65K8VKalAWTEmSpGIw+b20XC6eAyfcAX2Oip1IUgmyYEqSJBW6MQ/Dw+dD8/ZwzrPQabvYiSSVKAumJElSocpk4LU/wiv/B112gpMGQ8uOsVNJKmEWTEmSpEK0rBIeOR8+fAS2PwW+fa2L+UiKzoIpSZJUaOZNhLtPhqmj4cDfwm4/dDEfSXnBgilJklRIJgxLF/OpWgyn3Au9Do6dSJK+YsGUJEkqFCP/DU/+FDbqBGc+Dh23iZ1Ikr7GgilJkpTvqpfC07+AEf+CLfaB4/4FzdvFTiVJ32DBlCRJymfzJsF9Z8Ck4bDHRbDfpVBWHjuVJK2WBVOSJClfjX8d7j8rvd7yhDugz1GxE0nSWlkwJUmS8k2SwNAb4LlLod0WcNaT0GHr2KkkaZ0smJIkSflk2SJ47EfwwQOwzRFw9I3QdKPYqSSpTiyYkiRJ+WL2OLjnNJj+Iez/v7D7RVBWFjuVJNWZBVOSJCkffPIcPPRdCGVw2gOw5QGxE0lSvVkwJUmSYqqphpd/D69fA5v2gxPvhLbdY6eSpPViwZQkSYpl/mR44Fz48k3Y4Qw45Cpo3Dx2KklabxZMSZKkGD59ER46D6oq4ZhBsP2JsRNJ0gazYEqSJDWkTA288n/w2tXQYRs44Xa3IJFUNCyYkiRJDWXBVHjwuzB+CAw4DQ79k1NiJRUVC6YkSVJDGPdKWi6XLYKj/wH9T46dSJKyzoIpSZKUS5kaePWP8OpV6VTYM5+AjtvETiVJOWHBlCRJypX5k+Hh78Hnr8H2J8Phf4bGLWKnkqScsWBKkiTlwkdPwqMXQPUSOOr69JpLSSpyFkxJkqRsWlYJz/0aht8KnfrDsbfAxlvGTiVJDcKCKUmSlC1TRqUL+cz8GHa/EPa9BBo1jp1KkhqMBVOSJGlDZTLw9o3wwuXQrB2c/gj03Dd2KklqcBZMSZKkDbFgGjxyPnz2Imx9OBz5N2jRPnYqSYrCgilJkrS+PnkuLZfLFsHh18DAcyCE2KkkKRoLpiRJUn0tq4QXLoN3BsEm/eDYm93bUpKwYEqSJNXPhGHp3pazP4Nd/hv2vwwqmsZOJUl5wYIpSZJUF9VL4ZUr4Y1rYaMucOYT0GPP2KkkKa9YMCVJktZl6mh4+Psw7QMYcDoc/AdoulHsVJKUdyyYkiRJa1JTDW9eBy//HzRrCyffC1sfEjuVJOWtdRbMEMKtwBHA9CRJ+uY+kiRJUh6Y+Sk88n2YOAz6HJ2uEuv2I5K0VmV1OOY2wH+qkyRJpSGTgbf/Cf/YA2b+B469BY6/zXIpSXWwzhHMJEleCyF0b4AskiRJcc36DB6/EMYPgS0PgCP/Dht1ip1KkgpG1q7BDCGcB5wHsPnmm2fry0qSJOVepgaG3gAv/R7KK+Db18EOZ0IIsZNJUkHJWsFMkmQQMAhg4MCBSba+riRJUk5N+xAeuwAmjYBeh8IR18BGnWOnkqSC5CqykiSpNFUvg9evgdeuTrccOfYW6Huso5aStAEsmJIkqfRMGgGPXgDTP4R+x8MhV7mIjyRlwTpXkQ0h3A28BWwdQpgYQjg397EkSZJyYFklPPtruPkAWDw33dfy2Jstl5KUJXVZRfbkhggiSZKUU+NeTVeInfM57Hg2HHgFNG0dO5UkFRWnyEqSpOK2cDo8dwmMuhfa9oAzH4cee8VOJUlFyYIpSZKKUyYDI2+DFy5Pp8bu9XPY8ydQ0Sx2MkkqWhZMSZJUfKaOhicugonDoPuecPg10KFX7FSSVPQsmJIkqXgsXQiv/B8MvRGatYVj/gnbnejWI5LUQCyYkiSpOHz0JDz1c5g/EXY4Ew64HJq3i51KkkqKBVOSJBW2OV/AMxfDx09Bx23huFth851jp5KkkmTBlCRJhWlZJbxxHbxxLYQyOPA3sMt/Q3lF7GSSVLIsmJIkqbAkCYx9DJ79NcybANt+Bw76LbTuEjuZJJU8C6YkSSoc08fC07+Az19Np8Oe9SR03yN2KklSLQumJEnKf4vnwitXwjuDoEkrOOxq2PFsKPdXGUnKJ/6tLEmS8lcmA+/dCS9cAZWzYMezYL9LoUX72MkkSathwZQkSfnpy6HwzC9h8kjouguc/hB02j52KknSWlgwJUlSfpk9Dp6/LF3Ip1Un+M5N0O94CCF2MknSOlgwJUlSflg8B179U3qdZXlj2OdXsNsF0LhF7GSSpDqyYEqSpLiql8Gwm+HVq2DJPBhwGux3CbTaNHYySVI9WTAlSVIcSQJjH4cXLkunxW6xLxz0O9i0b+xkkqT1ZMGUJEkNb9IIePYS+PJN6LANnPoAbHmA11lKUoGzYEqSpIYz8z/w0u/gw0egRQc44i8w4Az3s5SkIuHf5pIkKffmTYJXr4R3B0OjprD3L2DXC6DpRrGTSZKyyIIpSZJyp3I2DPkzvHMTkMBO58GeP4WWHWInkyTlgAVTkiRl39KFMPRGePOvsGwhbHcS7HMxtO0WO5kkKYcsmJIkKXuql8GI2+C1P8KiGbDNEemWIx17x04mSWoAFkxJkrThaqrgvbtgyNUw90voviecdDd0/VbsZJKkBmTBlCRJ66+mCt6/G177U1osO++Qrgzbc3+3HJGkEmTBlCRJ9VdTBaPuTYvlnPHQeQAc9mfY6kCLpSSVMAumJEmqu5rqlYrl59CpP5x8L/Q62GIpSbJgSpKkOqiphtH3wat/rC2W28PJ90CvQyyWkqSvWDAlSdKaVS+F9++BN66F2eNg0+3SxXu2PtRiKUn6BgumJEn6pmWLYMTt8ObfYMHkdCrsiYNhm8MtlpKkNbJgSpKkFRbPgXduhqE3wOLZ6XYjR18PW+xrsZQkrZMFU5IkwcLp8Nb1MOwWWLYAtjoY9vwpbL5z7GSSpAJiwZQkqZTN/TKdBjvyjvR6y22PgT0ugk7bxU4mSSpAFkxJkkrRpJFpsfzwUQhlsP1JsPuPYeMtYyeTJBUwC6YkSaUik4H/PJsWyy/egCYbwa7/DTt/H1p3iZ1OklQELJiSJBW7qsXpViNvXQ+z/gOtu8LBf4ABp0PTjWKnkyQVEQumJEnFatFMGHYzvHMTVM6ETtvDsbdAn6OgvCJ2OklSEbJgSpJUbKaNgbf/CaPuheol0OsQ2PUC6L6HW41IknLKgilJUjGoqYaPn0qL5RevQ6NmsN2JsOsPoMPWsdNJkkqEBVOSpEK2aBaMvD3dv3L+RGi9ORz4m/T6yubtYqeTJJUYC6YkSYVoyih4558w+oF0GmyPveDQq2DrQ6GsPHY6SVKJsmBKklQoqpfC2MfT0cov34SK5rD9ybDTebBJn9jpJEmyYEqSlPdmj4MRt8G7d0LlLGjTDQ76HQw4DZq1jZ1OkqSvWDAlScpHNVXpoj3Db4Vxr0AoT6e/DjwbttgPyspiJ5Qk6RssmJIk5ZO5X8KI2+Hdf8PCabBRF9j31+miPRt1ip1OkqS1smBKkhRb9TL45BkYeQd8+kK6V+VWB8GOZ8NWB7pojySpYFgwJUmKZdqY9LrKUfem11a26gR7/Qx2OAPadI2dTpKkerNgSpLUkBbPSbcWeW8wTH4Xyipgm8Og/2nQcz8o90ezJKlw+VNMkqRcy2Tg81fg3cHpNiM1S2GTvnDIldDvBGjRPnZCSZKywoIpSVKuTP8onf46+n6YNwGatkmnvw44DTptn15rKUlSEbFgSpKUTQumwgcPpsVyyvsQymCLfeHAK2Drw6GiaeyEkiTljAVTkqQNtXQhfPREWirHvQJJBjoPSKfA9j0WWnaMnVCSpAZhwZQkaX3UVKVlctR9abmsqoQ2m8OeP02vq+zQK3ZCSZIanAVTkqS6qqmG8UNgzEPpYj2L56TXVW53YvrRdWcoK4udUpKkaCyYkiStTaYGvnwLPngIxj4Gi2ZA45aw9aGw7Xdgy/2hUZPYKSVJygsWTEmSVpXJwMRh6UjlmEdg4VRo1Ax6HQx9vwNbHQQVzWKnlCQp71gwJUmCdPrrl2+l11OOfQLmT4TyJrDVgbDtMdDrEGjSMnZKSZLymgVTklS6qpfCuFfTqa8fPwWVs6BRU+i5P+x/KWx9GDTdKHZKSZIKhgVTklRali6ET19IF+n55FlYtgAat0qnv/b+Nmx5gCOVkiStJwumJKn4zZ8CnzyTFspxL0P1EmjeHvoeA72PhB57uVCPJElZYMGUJBWfTAamvJcWyk+ehinvp8+33hx2OBP6HAldd4FyfwxKkpRN/mSVJBWHZYvS6yk/eRo+eS5d+ZUAXXeC/S9LF+np2BtCiJ1UkqSiZcGUJBWueRNXmvr6KtQshSYbQc/90n0qtzwQWrSPnVKSpJJhwZQkFY5llfDFm/DZi/DpizDz4/T5tj3gW+emC/Vsvhs0ahw3pyRJJcqCKUnKX0kC08bAZy+lpfKLt9JRykZNodtusMPpsNXBsPFWTn2VJCkPWDAlSfll0UwY90o6QvnZS7XXUgIdesNO/wU994Vuu0NFs6gxJUnSN1kwJUlxLV0IE4bC56+l11FOeR9IoFlb2GJf2HL/9Lb1ZrGTSpKkdbBgSpIaVtVimPBOWijHD4FJIyBTDWUV0OVbsO+vYcv9oFN/KCuPnVaSJNWDBVOSlFvVy2DScPh8SFooJ7yTXkcZyqHzANjth9BjL+i6MzRuETutJEnaABZMSVJ2VS2GSSPhy7dg/Osw4W2oqgQCdNouvY6yx16w+a7QdKPYaSVJUhZZMCVJG6Zydloiv3wrXeV18ruQqUpf69gHdjgDuu+ZrvravF3crJIkKacsmJKkuksSmPslfDkUvnwzvZ3xUfpaWQVstgPs+t/pXpRdd7JQSpJUYiyYkqQ1q1oCU0fBxGEwcXhaKBdMTl9r0jotkdudkE537TzArUMkSSpxFkxJUipJYPa4tEhOGp6WyqkfrJju2rprOs11813SQtmxt6u8SpKkr7FgSlKpWjwn3SJk4oi0TE4aAYtnp69VtEinu+52AWw2ELoMhFabxs0rSZLyngVTkkrB4jkw5X2Y/B5MeS+9P3tc7YsBOmwD2xyeFsku30ofOzopSZLqyYIpScWmcvaKErm8UM4Zv+L11ptD5+2h/6lpmew8wO1CJElSVlgwJalQJQnMn5ReJzntg7RQTnkvXeV1uTbdoHN/2OHM9HbT7aFF+0iBJUlSsbNgSlIhqFoM08emRXLamBWlcsncFce07Q6dd4CB50Cn/tBpe7cJkSRJDcqCKUn5JJOBeV/C9I9WlMlpH8CsTyHJpMdUtIBN+sC2x8Am28Km/aBjH6e5SpKk6CyYkhRDpia9LnLGxzBjbO3tRzDjE6hevOK4Nt3SArntMbBJ37RQtu0BZWXRokuSJK2JBVOScqmmCmZ/XlseP15xO/MTqFm64riNukCHrWHgHulth23SfSYdlZQkSQXEgilJGypJYOH0dBrr7M/S21kr3WaqVhzbenPouA303CctkR22gY17WSQlSVJRsGBKUl0tnpsWxq9K5PIi+RksW7DiuLIKaLcFtO8JvQ6GDr3TUcmNe0GTltHiS5Ik5ZoFU5KWy2Rg4bT02si5X6S3c8anU1xnfQqVM1ccG8qgdVdovyV03Sm9bd8T2vWENptDWXmkP4QkSVI8FkxJpWXJ/FUK5BcrPf7i69dFEmCjzumiOtscnhbI9lumH227Q6MmUf4IkiRJ+cqCKal4ZDLpKOO8CTBvIsybVHs7If2YMx4Wz/n65zRpDW27pddC9jo4LY5tu0Ob7tCmqyVSkiSpHiyYkgrH0oUwf9IaCuREmD95lRFIoKI5tO6SfnQeUFseu9UWyW7QrG2MP4kkSVJRsmBKiq9qCSycCgumwoIpa7idCkvnf/3zQhm06pSWx812gD5Hptt9tF7po1lbCCHOn0uSJKnEWDAl5UaSwLKF6fYdi2ami+csmLr6IrnqtFWA8sbQatO0QHbsDT33g5abpAvrLC+PrTpBuX+NSZIk5Qt/M5NUdzVVUDkLFs1IPxbOWHF/0UxYNH2l+zOgesk3v0ZZI2i5aVoe220B3XZbUSS/uu3kyKMkSVIBsmBKpaqmKh05rJwNi2evuP3Gc3NWFMbFs1f/tcoqoGVHaLExtOiQLpjTosOKj5a1t606Q/P2UFbWsH9WSZIkNQgLplTIMjXpdYlL5tV+rHR/+fOrFsjlpXHV6xlXVlYBzdtBs3bpbYetofseXy+RLTpAi9rHTVs72ihJkiQLphRN9VJYtgiWLkivVVx+f+n81RfF1ZXIZQvW/X2atIbmbWvLYntov9XXy2Oztitulz/XuKWFUZIkSfVmwZTWpaYaqhdD1WKoqlzldnFaDJctTLfQWLagtigurH1uwSqvL0qPWboQMlXr/t6hLB0dbLJRetu0NbTrseL+qq813eibr5WV5/6/kSRJkkQdC2YI4RDgOqAcuDlJkitzmkpanSRJR/2ql0DNstr7S9N9D1d7f1l67PL7VauWxDUUxpXvVy9OP7c+yiqgSct0FLBxyxX3W236zedWvv/V7UYryqIjiZIkSSog6yyYIYRy4HrgQGAiMCyE8FiSJB/mOpzWUyYDSU16fV6yyv1MzUqPVz5mDa/VVKUjbTVVkKle6XH1Ss+v+ri6jp+3ynFrLIjL0ufrW/TWpKI5VDRb6bb2ftON0hK48nOrO275baOm6W3j5l8vi42aZCenJEmSVGDqMoK5E/BpkiTjAEII9wBHAQVZMN954BoajX8FgEACJIQkIZDUPl7peRJCwor7X3t+NcdSe2ySWXF/+eclXz8WoIwMJMk3j639uiFJKKOGkGQoI0NZkkkfk/nqtbLa1wIZypIaysnk/j9iHVXTiJrQiJpQnt7SiMwqj7+6HxpRFRpTHZpRHVpTHSqoDhVUVTSmunHj2tcqqC6rWHE/VFAdGlMdGn/tuarQmOqyitU834Sq0HjtI4IZYGntR50t/4RZG/KfS5IkSQLggN4dOWZAl9gx1ktdCuZmwISVHk8Edl71oBDCecB5AJtvvnlWwuVCzbzJbFL5GZBWugxlX91fpd59VQdJK93XK2YIrK5eZigjoXy1X2Plr59Zy9dYkSVQk1bL9KOsjAzl1FD2Vfa0cpaRCWUrPS7/6n5CoGal1746nvKvfU5mNV+vhnJqaEQ15VTTiOqw0uPw9ddqQu0xtY8zlG341M6k9iNr6t0cJUmSpAa33WatY0dYb3UpmKtrCd/4tT9JkkHAIICBAwdmtRZk067nXg1cHTuGJEmSJBWduux2PhHoutLjLsDk3MSRJEmSJBWquhTMYcBWIYQeIYTGwEnAY7mNJUmSJEkqNOucIpskSXUI4QLgWdJtSm5NkmRMzpNJkiRJkgpKnfbBTJLkKeCpHGeRJEmSJBWwukyRlSRJkiRpnSyYkiRJkqSssGBKkiRJkrLCgilJkiRJygoLpiRJkiQpKyyYkiRJkqSssGBKkiRJkrLCgilJkiRJygoLpiRJkiQpKyyYkiRJkqSssGBKkiRJkrLCgilJkiRJygoLpiRJkiQpKyyYkiRJkqSssGBKkiRJkrLCgilJkiRJygoLpiRJkiQpKyyYkiRJkqSssGBKkiRJkrIiJEmS/S8awgzgi6x/4ezZGJgZO4QajOe7dHiuS4fnurR4vkuH57p0eK4LW7ckSTqs7oWcFMx8F0IYniTJwNg51DA836XDc106PNelxfNdOjzXpcNzXbycIitJkiRJygoLpiRJkiQpK0q1YA6KHUANyvNdOjzXpcNzXVo836XDc106PNdFqiSvwZQkSZIkZV+pjmBKkiRJkrLMgilJkiRJyoqSK5ghhENCCB+HED4NIVwcO4+yK4QwPoQwOoTwXghheO1z7UIIz4cQ/lN72zZ2TtVfCOHWEML0EMIHKz23xnMbQvhl7fv84xDCwXFSa32t4XxfHkKYVPv+fi+EcNhKr3m+C1QIoWsI4eUQwtgQwpgQwoW1z/v+LjJrOde+t4tMCKFpCOGdEML7tef6itrnfV+XgJK6BjOEUA58AhwITASGAScnSfJh1GDKmhDCeGBgkiQzV3ruj8DsJEmurP1HhbZJkvwiVkatnxDCXsBC4I4kSfrWPrfacxtC6APcDewEdAZeAHolSVITKb7qaQ3n+3JgYZIkV69yrOe7gIUQOgGdkiQZGUJoBYwAjgbOwvd3UVnLuT4B39tFJYQQgBZJkiwMIVQArwMXAt/B93XRK7URzJ2AT5MkGZckyTLgHuCoyJmUe0cBt9fev530h5kKTJIkrwGzV3l6Tef2KOCeJEmWJknyOfAp6ftfBWIN53tNPN8FLEmSKUmSjKy9vwAYC2yG7++is5ZzvSae6wKVpBbWPqyo/UjwfV0SSq1gbgZMWOnxRNb+F5sKTwI8F0IYEUI4r/a5TZIkmQLpDzegY7R0yrY1nVvf68XrghDCqNoptMunVnm+i0QIoTswAHgb399FbZVzDb63i04IoTyE8B4wHXg+SRLf1yWi1ApmWM1zpTNHuDTsniTJDsChwA9qp9mp9PheL043Aj2B/sAU4M+1z3u+i0AIoSXwIPDjJEnmr+3Q1Tzn+S4gqznXvreLUJIkNUmS9Ae6ADuFEPqu5XDPdREptYI5Eei60uMuwORIWZQDSZJMrr2dDjxMOr1iWu11H8uv/5geL6GybE3n1vd6EUqSZFrtLywZ4CZWTJ/yfBe42mu0HgQGJ0nyUO3Tvr+L0OrOte/t4pYkyVzgFeAQfF+XhFIrmMOArUIIPUIIjYGTgMciZ1KWhBBa1C4aQAihBXAQ8AHpOT6z9rAzgUfjJFQOrOncPgacFEJoEkLoAWwFvBMhn7Jo+S8ltY4hfX+D57ug1S4GcgswNkmSa1Z6yfd3kVnTufa9XXxCCB1CCG1q7zcDDgA+wvd1SWgUO0BDSpKkOoRwAfAsUA7cmiTJmMixlD2bAA+nP79oBNyVJMkzIYRhwH0hhHOBL4HjI2bUegoh3A3sA2wcQpgIXAZcyWrObZIkY0II9wEfAtXAD1yJrrCs4XzvE0LoTzptajzwPfB8F4HdgdOB0bXXawH8Ct/fxWhN5/pk39tFpxNwe+0ODmXAfUmSPBFCeAvf10WvpLYpkSRJkiTlTqlNkZUkSZIk5YgFU5IkSZKUFRZMSZIkSVJWWDAlSZIkSVlhwZQkSZIkZYUFU5IkSZKUFRZMSZIkSVJW/D/EhNayJxBMeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ns = np.arange(330)\n",
    "pol = ns ** popt_misch[1]\n",
    "exp = np.exp(popt_misch[0] * ns ** popt_misch[3]) \n",
    "ax.plot(ns, pol, label='$N^b$')\n",
    "ax.plot(ns, exp, label='$e^{a N ^d}$')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der exponentielle Anteil ist größer, oder?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x200a8c5c5b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# constraints\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_5, mean_lp_constrs_5)\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_5, mean_lp_constrs_5, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_5, mean_lp_constrs_5, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3}* $e^{{N*{popt_exp[1]:.2}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.2} * $N^{{{popt_poly[1]:.2}}}$\")\n",
    "\n",
    "ax.set_title('Constraints für C=5')\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('im Mittel Anzahl hinzugefügter Constraints')\n",
    "\n",
    "\n",
    "ax.legend(fontsize=20)\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "# ax.set_ylim([-2,8000])\n",
    "# ax.set_xlim([-5, 110])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jetzt nicht mehr den bfc, sondern den rtc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "read_tracking_files() got an unexpected keyword argument 'b_read_lp_results'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_20436/589749647.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mp_konfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mho\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mho\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mauswertung_ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_tracking_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_konfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mread_opt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb_read_lp_results\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'anteil_opt'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sum_opt[s]'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'total_lp[s]'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: read_tracking_files() got an unexpected keyword argument 'b_read_lp_results'"
     ]
    }
   ],
   "source": [
    "p = r'D:\\Uni\\Masterarbeit\\Daten\\Tree_billig_mod\\rtc'\n",
    "# c = 2, RTC(0, 10)\n",
    "dic2_rtc = {}\n",
    "\n",
    "dirs2 = [d for d in os.listdir(p) if 'ModTest_C2' in d]\n",
    "\n",
    "for ho in dirs2:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p, ho, os.listdir(os.path.join(p,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, b_read_lp_results=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    \n",
    "    # noch einlesen, wie viel % der lp-Variablen ganzzahlig sind\n",
    "    \n",
    "    dic2_rtc[number_nodes] = df.mean()\n",
    "    \n",
    "ns_2_rtc = sorted(list(dic2_rtc.keys()))\n",
    "mean_lp_constrs_2_rtc = [dic2_rtc[n].lp_constraint_counter for n in ns_2_rtc]\n",
    "mean_total_lp_2_rtc = np.array([dic2_rtc[n]['total_lp[s]'] for n in ns_2_rtc])\n",
    "mean_sum_opt_2_rtc = [dic2_rtc[n]['sum_opt[s]'] for n in ns_2_rtc]\n",
    "mean_anteil_opt_2_rtc = [dic2_rtc[n]['anteil_opt'] for n in ns_2_rtc]\n",
    "mean_anteil_lp_ganz_2_rtc = [dic2_rtc[n]['anteil_lp_int'] for n in ns_2_rtc]\n",
    "print(mean_anteil_lp_ganz_2_rtc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schon mal gut zu wissen, für einen Tree, ist auch im rtc Fall die Lösung immer ganzzahlig. Und: Mit meinen constraints, brauche ich hier auch immer nur einen Opt-Schritt!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n",
      "[0.9635510216907274, 0.9419352965206206, 0.9315892800729969, 0.9368207435197229, 0.9442891624517568, 0.9463068461836056]\n"
     ]
    }
   ],
   "source": [
    "p = r'D:\\Uni\\Masterarbeit\\Daten\\Tree_billig_mod\\rtc'\n",
    "# c = 3, rtc(0, 10)\n",
    "dic3_rtc = {}\n",
    "\n",
    "dirs3 = [d for d in os.listdir(p) if 'ModTest_C3' in d]\n",
    "\n",
    "for ho in dirs3:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p, ho, os.listdir(os.path.join(p,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, b_read_lp_results=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    \n",
    "    # noch einlesen, wie viel % der lp-Variablen ganzzahlig sind\n",
    "    \n",
    "    dic3_rtc[number_nodes] = df.mean()\n",
    "    \n",
    "ns_3_rtc = sorted(list(dic3_rtc.keys()))\n",
    "mean_lp_constrs_3_rtc = [dic3_rtc[n].lp_constraint_counter for n in ns_3_rtc]\n",
    "mean_total_lp_3_rtc = np.array([dic3_rtc[n]['total_lp[s]'] for n in ns_3_rtc])\n",
    "mean_sum_opt_3_rtc = [dic3_rtc[n]['sum_opt[s]'] for n in ns_3_rtc]\n",
    "mean_anteil_opt_3_rtc = [dic3_rtc[n]['anteil_opt'] for n in ns_3_rtc]\n",
    "mean_anteil_lp_ganz_3_rtc = [dic3_rtc[n]['anteil_lp_int'] for n in ns_3_rtc]\n",
    "print(mean_anteil_lp_ganz_3_rtc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x200af2a5040>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# laufzeit\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.set_title('Laufzeit in Abhängigkeit von N, c=2')\n",
    "ax.scatter(ns_2_rtc, mean_total_lp_2_rtc, label='RTC(0, 10)')\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_2_rtc, mean_total_lp_2_rtc, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "# popt_exp\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_2_rtc, mean_total_lp_2_rtc, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3} $e^{{N*{popt_exp[1]:.3}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('Laufzeit[s]')\n",
    "ax.legend(fontsize=20)\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "# ax.set_xlim([-10,200])\n",
    "# ax.set_ylim([-1, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x200ad1554c0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# laufzeit\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.set_title('Laufzeit in Abhängigkeit von N, c=3')\n",
    "ax.scatter(ns_3_rtc, mean_total_lp_3_rtc, label='RTC(0, 10)')\n",
    "\n",
    "# fitten\n",
    "popt_exp, pcov_exp = curve_fit(f_exp, ns_3_rtc, mean_total_lp_3_rtc, maxfev=50000, p0=[0,0])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "# popt_exp\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_3_rtc, mean_total_lp_3_rtc, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "ax.plot(np.arange(330), f_exp(np.arange(330), *popt_exp), label=f\"{popt_exp[0]:.3} $e^{{N*{popt_exp[1]:.3}}}$\")\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.set_xlabel('N')\n",
    "ax.set_ylabel('Laufzeit[s]')\n",
    "ax.legend(fontsize=20)\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "# ax.set_xlim([-10,200])\n",
    "# ax.set_ylim([-1, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ich will checken, ob die Summe der Einzelteile des Alg. mit der Gesamtzeit übereinstimmen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "p77 = r'D:\\Uni\\Masterarbeit\\Daten\\Tree_billig_mod\\ModTest_C3_77_nodes_5_scenarios\\TreePlusC_3.000000_c_77_nodes_5_RandomScenarioCreator_RandomTestCreator_0.000000_10.000000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df77 = auswertung_ma.read_tracking_files(p77, read_opt=True, read_lp=True, read_constr=True)\n",
    "df77['anteil_opt'] = df77['sum_opt[s]'] / df77['total_lp[s]']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.052807622"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df77['total_lp[s]'] - (df77['sum_opt[s]'] + df77['sum_add_constr[s]'] + df77['lp_teil_setup[ms]']/1000)).max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auswertung: Das passt so :) es gibt immer nur einen kleinen overhead von maximal 0.05s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jetzt will ich wissen, wie die Einzelteile skalieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt = r'D:\\Uni\\Masterarbeit\\Daten\\Tree_billig_mod\\rtc\\ModTest_C5_320_nodes_5_scenarios\\TreePlusC_5.000000_c_320_nodes_5_RandomScenarioCreator_RandomTestCreator_0.000000_10.000000'\n",
    "dft = auswertung_ma.read_tracking_files(pt, read_opt=True, read_lp=True)\n",
    "# dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22e625b11c0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5e287M0y95mUg3bdrRM+8dNHzf2eDwpzPf0MhSRMTE25ubs7vZQAAAABdtfv40zW3xyYTcT174I6W36+ZoFl+XrMV0EOpBT36/KsqOCeTNLQ5quXLhXVdeuuNjWnGK8fv1M2//g39TaF7WSRiUtSkfHeKoavYptscM3vBOTdR6zEqpQAAAECPeNnIqFHVVVpb7Vy+fGVd99ty1bLyuZsia0Ock/TW5avvv//UvKYfn+846KXSma4G0q1DMR2+a4emxpN1/yLAK2zT7RyhFAAAAOgRLxoZNapS5vIFHTlzXm9fWVkTVuspB9nyc5sJm15UHsthuBvisehqIJWK52T3n5rvyr1237iNbboeYCQMAAAA0CPTk6OKx6JrrrXSyOhQakEPnJpvGDSzuXzTM0FNant+aCe6OeImly9o/6l5XX/gKe0+/rSkYuXUS6biOdKT99/m6fsOKiqlAAAAQI900sgolc543hzIr+4yjUbPeKm85dhLJ0pjbeCdDUOpmX1V0i9LesM593dK145Iul/SxdLTPuuc+0bpsYOSPi2pIOlfOudmu7BuAAAAIJQqu+y24uEnz3dhNb0XNdP05KgePDWvRjuBo1YMzSv+92VdtW/XCIG0C5rZvvv7kj5c4/qXnHM7S/+UA+nPS/qkpB2l1/x7M4vWeC0AAACAJqXSGV1arj1mJWzuvfU6TY0n9cW9OxWPXY0jESuGvleO36kTe3dq86ZoYAJpebvu0akxv5fSlzaslDrn/tTMrm/y/X5F0h86596W9Fdm9j1JH5T0XPtLBAAAAAZXuctuv6gMdtu2XLNuG3MqndFDj51bnXnqtyRzR7uukzOlv2Zm/1TSnKSHnHOXJCUlna14zmulawAAAADaMDO76Eszom4oV0ZrjbPZf2q+a11y22VSW/Nj0Zp2u+/+rqQbJe2U9LqkL5SuW43n1vwrDjP7jJnNmdncxYsXaz0FAAAAGHjd7FTba8f2vF9SeIJ2MGq1/a+tSqlz7oflr83sK5K+Xvr2NUnXVTz1vZKW6rzHlyV9WZImJib49w0AAICBUmveaNRM99563Zotrr3qVNttlU2CwhK0ky3Mj0X72qqUmtl7Kr79qKRvl74+I+mTZnaNmd0g6SZJf9bZEgEAAID+Ut6+Wh02C87pkbMXdCh19Qzp0OZ2NzcGx9ahmCbet231++EQhL1W5seiMxt+ws3sURUbFY2a2Wtm9mlJ/9bMFszsRUm3S3pAkpxz5yU9JukvJf2xpF91zgW/Lg8AAAD00EbbVx99/lVJ0n1feU7ffeOtXi3LU5Xn+i4t53Xw9IJS6YwkaXpyVLFIrZN//rpmU0SmYoX02J4xmhv1SDPdd++tcfn3Gjz/85I+38miAAAAgH620XbcgnNKpTN69uU3e7Qi71Wfz8vlC5qZXbwa9AKWSRn54p9Ouu8CAAAAaFG5WthI1ExHzpzvwWp6qxzGZ2YXlS8Eo63Mls1Rff6jVEX9FP4N6gAAAECIzMwubvice2+9Ttlcvger6b1UOhOYRkf7do3o/Oc+TCD1GZVSAAAAoMvKnXaXsrkNx4zsvnGbvvbCaz1ZVzUzyXW5gPnwk+cD0VF4KBZhu25AUCkFAAAAuiiVzmj6iXPKNBFI47GInnv5TeXyKz1ZW7VuB1Kp2PTo9pu3d/9GG/g3pZmp8B+VUgAAAMBjtWaQNsOvMNprz7x00df7V85Mhf8IpQAAAAi1yq2xw4m4pidHfQ0c5RmkjUa+DLJEPNbTM6WJeExmUnY5H4jPB9YjlAIAACC0qgNgJpvTwdMLkuRb8NhoBmkQbdkc1VuXu79mk3Tk7h1tVZHbkUzE9eyBO7p+H3SGM6UAAAAIrVoBsDwP0y9B6SzbjC2bozqxd6c+/9ExxSLdHxz6pb07NTWe1PTkaNfvFY9Fe3IfdI5QCgAAgNCqFwD9DIbDibhv925FMhFfOw6ly5l0943bVu81NZ7U1qFY2+9Vzs/JRFwn9u7UK8fv1Im9O5VMxGWl68f2MHs0LNi+CwAAgNCqN1qk28Gw0TnW6cnRwJ8pra4iPvzkeeUL3W29+19fflOHUgurY1gO37VD+0/NN/365AbnQafGk4TQkKJSCgAAgNCanhxVPBZdc63b2zbL51jLI14y2ZweODWvQ6mrZ1k/9oHuhqNWippbNke1b9dI3SpiKp3RpeV8V9ZZyUk6efaCUumMpOLv0+4bt234unisuMX42QN3EDr7FJVSAAAAhFY5pPSy+26tc6xO0iNnL0iSjk6NdX3kiVMxrH3sA0md+rNXlV9ZX+XcqLJY1svzt650v/KaTt5/m+77ynN69uU3az6/2Z8B4UYoBQAAQKj1ettmo/OqJ0vBtBedZXP5gr5+7vV1ZdNY1DTz8Vua+j1JpTM9WWul6t+/k/ffFrixPugtQikAAADQhHJwanTysrxFtVeyufXbbvMFt1r9PHLm/LrnRM10763XaeJ92zT9+LmerLNSrfO+nAcdbOZcdw80N2NiYsLNzc35vQwAAACgpup5qGEQi1jNbb1+iseidMUdUGb2gnNuotZjVEoBAACADdQ6RxpkUQtGIE3EY9pyzSa25aIhQikAAAACJYjnC/2cexoxqdV8WQjAbsh4LKojd+/w/d8dgo9QCgAAgMCo3iabyeZ08PTVUSuNXtfNIFtvHmq33fSuLbr4k8s1z44G0ZbNUS1fLgTmLxMQDoRSAAAABEatbbK5fGHNGJFq7QbZVlz/M70PpTe9a4u+9eAv6IYDT/X0vu04sXcnARRtI5QCAAAgMOptk628Xl0VXb58pWaQ3X9qXjOzix1X7FLpjP5rnTma3RKLSN968BckSYmhmC4t+18pjUWk/Mr66/t2jRBI0ZGI3wsAAAAAymqNC5Gka+MxSVeroplsTk7FqmijwFaumqbSmbbX9PCT5xuOgfFaPBbVzD07V78PwPFQmaSZe3Zq364RRa04GDVqpn27RnR0aszfxSH0qJQCAAAgMKYnRzX9+Ll1nWOzubwOpRb0zEsXW+6Cu9H230ZS6UzPq5Qf+8DVmZ2HUgs9O08aj0X1sQ8k9bUXMmt+j03SfaVq6NR4khAKz1EpBQAAQGBMjSf1U++oXTc5efZC2+c62+2eOzO72NbrOvH1c69Lku77ynN65OyFnt332J4xHZ0a07E9Y0om4jJJyURcX9q7kyCKrqJSCgAAgEAonxWtV5lstIt1KBbR1i3X1A2tTtLu40+vOV/aTMdePzruZnN53feV5/Rsj8+xln/2ckUU6BVCKQAAAHxX3UG3VZs3RfXsgTsavk8mm9MDp+Y194M3NfG+bWu2CWeyOU0/fk5SMZQV3+fF9n+gDvU6kJbPiQJ+IJQCAADAd7VGwbTix6Vzl+UK38zsYs0qp5P0yNkLNbfF5lec9p+a12dPv6jlWm1mWxSNmAorAehS1IR7b73O7yVggHGmFAAAAL5r98xnWcRMNxx4SruPPy2p2DCpXV4EUkm+B9KIpI0KoHTQRRBQKQUAAICvUumMImYqdDD7pPzaTDan/afmPVpZyJn0pU/sXLedOR6L6tieMc6NIjAIpQAAAOiJWo2FJOng6YWOAilqW3FrtzM3augE+IlQCgAAAM9VB9Dbb96+Zv5luaIZsWJ4QvPK80SfeemilrK5ul2Jy82L6KaLoCOUAgAAwFPVHXAz2ZxOnr1QMzwRSFsTNVu39fZQaqFm4yaaFyEsCKUAAADwVK1OumTPztU7C1puUvTo86+q4JyiZrr31utoXoTQIJQCAADAU5120sV6tSqklY5OjRFCEVqMhAEAAICnhhPxmtc3mE6CBr7wiVs4F4q+RSgFAACAp6YnRxWPRdddZwtve3bfuI1Air5GKAUAAIDnrtnEHzM3cmLvzg2fs2/XiE7ef1v3FwP4iP9aAAAAwDPlzrvZXN7vpbSlPEal25KJuKbGk9p947a6z9l94zbOiWIgEEoBAADgmVqdd8Ok4Lq/ydhU3OIsSSfvv61mMN194zYqpBgYdN8FAACAZzJ03t3QfbtG1pwRJXxi0FEpBQAAQF8zSVs2r2+85Be25AJrUSkFAABATal0RjOzi1rK5jSciGt6crRhF9hDqYUerq55TtJbl4OxpThZZ1wOMMgIpQAAAFin3LCofD40k83p4Oli6KwVTFPpjB45e6GnawybeCy6epYUwFVs3wUAAMA6tRoW5fIFzcwu1n0+1ksm4rLSr8f2jDFvFKiBSikAAADWWarTsKjedRocrUcHXaA5VEoBAACwznCds4/1rg+qRDymfbtG1l0nkALNo1IKAACAdaYnR9ecKZU4E1ktFjUduXuHpsaTdNQFOkAoBQAAwDrls4/Ndt+NmLTierlC/818/BbOiAIeIJQCAACgpqnxZMPQVR4ZM4jnSU/s3UkgBTxCKAUAAEDLqkfGDJJ9u0YIpICHCKUAAADYULkqupTN6dp4TD/O5TVgu3UlSfFYhPOjgMcIpQAAAGiouiqazeV9XpF/ju15v99LAPoOI2EAAADQ0Mzs4kBu062WiMfYtgt0AaEUAAAADS0NYCOjaibpyN07/F4G0JcIpQAAAGhoOBH3ewm+Mkn30dwI6BpCKQAAABq6/ebtfi/BU0OxiIZikTXfbx2KySQlE3Ht2zWiZCK++v2X9u6kuRHQRTQ6AgAAQENfP/e630to29ahmLLLeQ0n4pqeHKXaCQQQoRQAAAAN+dltNxoxFVZaHz6zZXNUn//oGCEUCAFCKQAAAAInWapsStKRM+drBuOtQzEdvmsHwRMIOUIpAAAA1kilM5qZXdRSNtfzJkfxWFTH9qytcE6NJ9etia24QP/YMJSa2Vcl/bKkN5xzf6d0bUbSXZIuS3pZ0j9zzmXN7HpJ35G0WHr5Wefcv+jGwgEAAOC9Q6kFnTx7QeUNs5kejoNpVPmcGk8SQoE+1Uyl9Pcl/TtJ/6ni2rckHXTOXTGz35J0UNK/Lj32snNup5eLBAAAQPel0pk1gbRXIiZ98RM7CZ3AgNowlDrn/rRUAa289s2Kb89K+rjH6wIAAIDHyltgM9mcomYqOLf6azIR1/LlKz0PpLGoaebjtxBIgQHmxZnSfy7pVMX3N5hZWtJfSzrknPu/PbgHAAAAOpBKZ3Tw9IJy+YIkqeDcml97uU23LMnZUADqMJSa2a9LuiLpZOnS65JGnHM/MrMPSEqZ2Q7n3F/XeO1nJH1GkkZGRjpZBgAAADYwM7u4Gkj9tm/XiI5Ojfm9DAABEWn3hWb2KRUbIN3nXPGv2JxzbzvnflT6+gUVmyD97Vqvd8592Tk34Zyb2L59e7vLAAAAQBOWfKiE1kMgBVCprVBqZh9WsbHR3c655Yrr280sWvr65yTdJOn7XiwUAAAA7ev1aJd6EvGY30sAEDAbhlIze1TSc5JGzew1M/u0it143ynpW2Y2b2b/ofT0fyDpRTM7J+kJSf/COfdml9YOAACAJk1Pjvq9BEVMOnL3Dr+XASBgrLTz1lcTExNubm7O72UAAAD0tesPPOXbvRvNIAXQ/8zsBefcRK3HvOi+CwAAgBDYOhTTpeV8T+9pkv7q+J09vSeAcGm70REAAADC5fBdvd86G5SzrACCi1AKAAAwIPzYOnv9zxBKATTG9l0AAIA+lUpnNDO7qKVsTsOJuC/Njs5+/1LP7wkgXAilAAAAfSaVzujImfPK5q6eH81kc9p/ar7naykEoKkmgGAjlAIAAPSRVDqjg6cXlMsX/F6KJClq5vcSAAQcZ0oBAAD6yMzsYmACqSTde+t1fi8BQMBRKQUAAAix6nOjmWzO7yWtcXRqzO8lAAg4QikAAEBIVW/VDVogTTIOBkAT2L4LAAAQUkHbqlvt9pu3+70EACFAKAUAAAippYBVRqs989JFv5cAIAQIpQAAACE1HPDtsUEPzQCCgTOlAAAAIVJubBS086O1BD00AwgGQikAAEBAVXfWvf3m7Tr1568qX3B+L60p05Ojfi8BQAgQSgEAAAKoVmfdR85e8HlVV0Ujpp9+xyZdWs7XfHzfrhFNjSd7vCoAYcSZUgAAgAAKemfdd16zSYfv2qF4LLrmuqkYSJlPCqBZVEoBAAACKOhNgn6cy69WQiu3GE9PjlIhBdASQikAAEAADSfigW5mVG5iNDWeJIQC6AjbdwEAAFqQSme0+/jTuuHAU9p9/Gml0pmu3Gd6clSxiHXlvTsVj0VpYgTAM1RKAQAAmlSr+dDB0wuS1JVq4RUXvC67SbboAvAYoRQAAKCO6pEs2eXL65oP5fIFPfTYOT1wat6zM5WpdEbTj59TADOpli9f8XsJAPoMoRQAAKCGWlXRegql9OhV5fTImfPKrwQwkUq6tJzX9BPnJHWnOgxg8HCmFAAAoIZ2R7Lk8gXNzC52dO9srvbsz6DIF1zHPyMAlBFKAQAAauhkJEvQx7l4YRB+RgC9QSgFAACooTzypNevlaStQ7GOXt8Lnf6MAFBGKAUAAKhhenJU8Vi0rdfefvP2ju59+K4dHb2+26IRYyQMAM8QSgEAAGqYGk/qYx9Iqp1Joc+8dHH163bmmgalgZBJ2n3jtnXX+QMkAC/RfRcAAEDrx79MT47qqRdfVzs9cMvnLXs919RrTtIrP1p/djS/Umx0FIafAUDw8RddAABg4JXDYyabk1MxPE4/fk6Xltvrgls+b/nwk+drzjXdqHNtKp1pq0LrtWQiXrehEY2OAHiFSikAABhoqXRGDz12bnXWaFknc0KnJ0eVSmfqhtpaga6yUhsxa6tC24qb3rVF333jrbqPx2NRTU+OamZ2seaMVhodAfAKlVIAADCwyhXS6kDaiZvetUVT40kdOXO+7nOqA111pdbL9dRb47ce/AXt2zWiqBVrsiZpy+aoTMUK6bE9Y5oaT9Zs+FQOrADgBSqlAABgYM3MLq7bXtuJTRHTr95+kyQpm6u/9ff1H+d0/YGnlIjHZKa2twm367tvvKXxz31Th+/aoYn3bVut0CaGNuvzHx1dc1a0/HX1eVvOkwLwirku/01cMyYmJtzc3JzfywAAAAPmhgNPeb5NNhY17f3vr9MjZy94/M7ei1hxvEu+cPV3IR6LrlZJAcArZvaCc26i1mNs3wUAAANraHN7c0gbyRecToYgkErSitOaQCo114gJALxEKAUAAAMplc7orcvebd2t5P8+tM7QWRdALxFKAQDAQGrUiGjQ0VkXQC/R6AgAAAyMQ6kFnTx7IfSVzG6isy6AXiOUAgCAvlM587PcLXbuB2+GovlQL8Uipr0fvE7PvHSRzroAfEMoBQAAfaU887M86iWTzeng6QW9faU750fDIhGP6cjdOxjtAiBwCKUAAKCv1Jo96uUs0jAySUfu3qGp8SQhFEDg0OgIAAD0FTrHrmWS7ts1QhgFEFhUSgEAQKhVnx8d2hzt2qiXsEmyRRdACBBKAQBAaFV3081QJV2VTMT17IE7/F4GAGyIUAoAAAKrVhfdctUvlc4w3qUBAjqAsCCUAgCAQKrXRVeSpsaTmpldJJA2EDXzewkA0BQaHQEAgECq10V3ZnZREg2NNlJwRHYA4UAoBQAAgVQvdJavDyfivVxO6CT5/QEQEoRSAAAQSPVCZ/n67Tdv7+VyAiOZiOvE3p1qtDk3HotqenK0Z2sCgE4QSgEAQCBNT44qHouuuRaPRXX7zdu1+/jTeuTsBU/u00lFcSjW2z9KlTvqTo0n64b2qJmO7RljDAyA0CCUAgCAQJoaT+rYnjElE3GZioHsYx9I6msvZDztLNvOe5mkfbtG9Je/+RHt2zXi2Voaqa5+1gvtX/jELQRSAKFC910AABBYU+PJNQFr9/Gn1zU/6rVk1WiaXqmufpa/rjcyBwDCglAKAAACLZXO6MiZ88rm8n4vRZL07IE71nx/8nlvthE3EjWrGTarQzsAhBHbdwEAQGCl0hk9eGre00C6dSjm2XtJUi8mrzDeBUA/I5QCAIDA+uzpF7Xi4fvt2zWiw3ftWHcWs1mJuLeBtlmMdwHQz9i+CwAAAum+rzyn5byXkVR65qWLktTWudRYxHTk7h2erqcZ0Ygx3gVAXyOUAgCAwDmUWtCzL7/p+ftmsrm2RslsHYrp8F07en5+c1PE9Nv30E0XQH8jlAIAgMB59PlXO36Pd79zs374k8sdvceWzVF9/qONZ35u2RzVW5e97QgcMekf3zqio1Njnr4vAAQRoRQAAASCl112YxHp7SvtNQdKxGOaP/yh5u8VjUjyLpSapO8fu9Oz9wOAoNuw0ZGZfdXM3jCzb1dc22Zm3zKz75Z+3Vrx2EEz+56ZLZrZZLcWDgAA+oeXXXYjJs3cs7Ot94q2cW70x03cZ9+uEVmT7/eOGH0oAQyWZv6r9/uSPlx17YCkP3HO3STpT0rfy8x+XtInJe0ovebfm1l77e0AAEAgpdIZ7T7+tG448JR2H39aqXSm4/c8cua8J112tw7F9MVP7GzrDOamiOkLbZzfHN6gM27UTEenxtRs3fZvPG7uBABBt2Eodc79qaTqTgO/IukPSl//gaSpiut/6Jx72zn3V5K+J+mD3iwVAAD4LZXO6ODpBWWyOTkVGwcdPL3QUTA9lFrouEJ6Yu9Ondi7U0ObN+mBU/Paffzppl8bsWIl83v/5pfaCrPX/0zjUFqeMdrsWJeNQi4A9Jt2z5S+2zn3uiQ55143s3eVriclna143mulawAAoA/MzC6uG6eSyxc0M7tYM9Cl0hnNzC5qKZvTcCKu6clRTY0nV69nsjlP1rX/1Pya75t93xN726uqVjr7/UsNHy+H0enJUR08vdBwHE08FmX8C4CB43Wjo1rHJWruVjGzz0j6jCSNjIx4vAwAANANS3XCXiab0+7jT+v2m7frmZcuaimb07XxmN66fEX5glt9zv5T85p+fF5B2aHqxaiVciW0nnLILN+rMqRX/n5VhnYAGCTthtIfmtl7SlXS90h6o3T9NUnXVTzvvZKWar2Bc+7Lkr4sSRMTE+21xwMAAJ6qV9ksG07E61Yhq2eA1tuSG5RA6hVTnb+BV7GTb+Xv39R4ktAJAFXabe92RtKnSl9/StJ/qbj+STO7xsxukHSTpD/rbIkAAGAjXjQfqnVedP+peY1/7pur7zc9Oap4rD96GCbiMU/eZ/Om+n+carWTLwAMog0rpWb2qKRfkPSzZvaapMOSjkt6zMw+LemCpHskyTl33swek/SXkq5I+lXnnLfTpAEAwBrlMFk+q1huPiS1tj314SfP1zzveGk5r/2n5rX/1LySibh++h3RhuciwyDWxuiXet6+Ur/0S1UUADa2YSh1zt1b56FfrPP8z0v6fCeLAgAAzWum+dBG23JT6YwuLW/cAderxkR+SnJ2EwACxetGRwAAoMfqNR8qX9+okppKZ/TAY/M9WaufEvGY5g9/qCvvW+v8rFfbgwGg37V7phQAAAREvbmW5euNKqmpdEbTj5/TBg1kQy8ei3btfOeRu3coFlk7gMDL7cEA0O8IpQAAhFyt5kOV8y4bVVJnZheVX+nPRBq1YlBMJuI6tmesa9t1p8aTmrnnFiUTcVnpfjP33ML2YABoEtt3AQAIuVrzLyvPTNYb4xIx64szorUkE3E9e+COnt2PUS8A0D5CKQAAfaA6FJVHxCxlc7o2HlPEpOqCaKFP9+xWVokBAMFHKAUAoM9UNzaq1YSnX9FZFwDCh1AKAEAfSaUzeuixc31bBW0kFjUCKQCEEI2OAADoE+UKaVAC6U3v2qJY1DZ+okfyBafPnn6xZ/cDAHiDUAoAQJ94+Mnz60a/+GEoFtFQLKLvvvGW8gVvA/LWocazP5fzK0qlM57eEwDQXWzfBQAghFLpzJpuu7ffvF2Xlv07O7p1KKbDdxXnck4/fq5rY2b+Jr+iE3t3av+p+brPOXLmPFt4ASBEzAVgi8/ExISbm5vzexkAAIRCdSMjSTJJfv8/ejIR1/LlK10Px4l4TD/O5Rv+vCf27iSYAkCAmNkLzrmJWo+xfRcAgJCZmV1ct03X70AqSZlsrifV2uwGgVQq/h4BAMKB7bsAAARI9bbcWt1kl7I5n1YXHvweAUB4UCkFACAgyttyM9mcnIqVx4OnF9Y17hlOxP1ZYIjwewQA4UGlFACANjRT0WxVrW25uXxBM7OLa957aDN/p9xIPBbV9OSo38sAADSJUAoAQIuqGw2VK5qSOgqm9bacVl6/7yvP6btvvNX2PfrV1qGYsst5z/6CAADQO4RSAABa1GxFsxWpdEYRMxVqdMWPmOn6A0+19b79ziTdt2tER6fG/F4KAKBNhFIAAFrUTEWzFal0RtNPnKsZSCXVvT6Idt+4Ta/8KOfptmkAgL8IpQAAtGg4EVemRgBtt7nOw0+eV75A8GzGyftv83sJAACPEUoBAFDtxkWSajYzmp4cXXOmVGq+uU6t+/RiticAAEFFKAUADLxajYumHz8nmVYrmLWaGbXafbdRg6R+tq905nP38adrVpibtXUo5uGqAABBQSgFAAy8Wo2L8ivrt9O228wolc7oyJnzyubWV0Sr79uPHjl7QY+cvdDx+xy+a4cHqwEABA2hFAAw8FppULSUzdWseO4/Na/9p+aVrKqaptIZTT9+rmbIRfP27RqhoREA9ClCKQBg4NVrXFSLk7T/1Hzdx6u3+c7MLhJIO7B1KKbDd+0gkAJAHyOUAgAGXq3GRZ2o3Obb7piYQffK8Tv9XgIAoEcIpQCAgVfduChi1vFs0Ew2p+sPPOXF8gZOIk5DIwAYJBG/FwAAQFC89fYVOanjQIr2xSKmI3fT0AgABgmVUgDAwLvvK8/p2Zff9HsZfclUPIdbSzwW1cc+kNQzL11sabQOAKC/EEoBAAPtUGqBQNoFlQ2KDqUW9Ojzr66pQFd3KQYADC5zAdiiNDEx4ebm5vxeBgBgAN1w4Km6lTy058TenYRNAMAaZvaCc26i1mNUSgEAoZJKZ1YbEnW63TOVzhBIPcY8UQBAqwilAIDQSKUza0a3VM8EbfW9ph8/5/kaB8nmqOlyoRjrmScKAGgXoRQAEBozs4vrZolWzgRt9b3yK9RJm0XoBAB0C6EUABAaS9lcS9fLam35zWzwGtCMCADQG4RSAEBoDCfiNcPkcCJe9zW1tvw+eGq+W0sMPSqiAIBei/i9AAAAmjU9Oap4LLrmWjwW1fTkaN3X1Nryu9KV1YVbxIpdc9O/8SECKQCgp6iUAgBCoxyWqrfiStLu40/X7Mi70dZeSLGoaebjtxBGAQC+IJQCAEJlajy5Jjxt1JE3MRTTpeW8L2sNqs1R05ZrNim7nO94rA4AAJ0ilAIAQiuVzuihx86p4NZ20S135JWk/+9vrvixtECicREAIIgIpQCAUCpXSKsDadlSNsfYlwon9u4kjAIAAolQCgAIlfJ4l41Gugwn4pwnlWSS7ts1QiAFAAQW3XcBAKGRSmc0/fi5DQNpuSNvo1ExYRCx9l53zaaITMXtul/au1NHp8Y8XRcAAF6iUgoACI0jZ843tR33Yx+42gxpf8hmkr5y/M7Vr1PpzIbrN0lOnBcFAIQXoRQAEGjl7bpL2ZyaPR36zEsXJUkHnjjXvYV1wZbNa2ewTo0n9dnTL2o5X3uyKkEUANAPCKUAgMCqHvfSrEw2p+sPPNWlVXkjYlJl0TcaMX3+o+u32f6bPe/X9OPn1lSIYxHTzD3MFQUA9AdCKQCgZyqrns3Mx5yZXWw5kIbBvl0jmnjftqZ+L8rXWvl9AwAgTAilAICeqK56ZrI5HTy9IEl1A9ZGDY3CaPeN21YbDzUbLKfGk4RQAEDfovsuAKAnalU9c/mCZmYXaz4/lc70Ylk9s3UophN7d+rk/bf5vRQAAAKFSikAoCfqzQytdf1QakEnz17o9pI8EY9F9bEPJHXy+QtyNToxmaQv7d1JpRMAgDoIpQCAnhhOxGtuxy3PEi2fNw3Tlt2tQzEdvmuHpsaTmnjfNj342LyqJ9ZsirY5bBQAgAHB9l0AQE9MT44qHls78iQei2p6clSpdEbTT5wLTSBNJuI6sXen0r/xoTUV0Foja/IFpyNnzvducQAAhAyVUgCAp+p12G3URXb8c99UvtDsFFJ/JRNxPXvgjjXXyk2cam3flaRsLq9UOsMWXgAAaiCUAgA8s1GH3XpdZC8t53u6znbFoqbpydF115sZXTMzu0goBQCgBkIpAMAzjTrslgNZuYlRuai4ZXNUYVB5frRavSZOrT4HAIBBRCgFAHhmow67933lOT378ptrHnvrcuMKo59qbdWtpV4Tp+rnAACA9Wh0BADwTL3gNZyIK5XOrAukQVZuwtSM6clRNeqx28p7AQAwaAilAADPNOqwOzO76NOqmrd1KCZTsUJ6bM9Y02dAp8aTum/XSM1gunUo1tJ7AQAwaNi+CwDwTKMOuw+cmvd3cQ2YpPt2jejo1Fjb73F0akwT79tW82cHAAD1tR1KzWxU0qmKSz8n6TckJSTdL+li6fpnnXPfaPc+AIDwqDcORpI2b4ro7SsrPq9wvaSH4bFed2EAAFBf26HUObcoaackmVlUUkbSH0n6Z5K+5Jz7bS8WCAAIh0bjYOZ+8GYgA+m+DqujAACgc15t3/1FSS87535g1qjVAwCgX9UbB3Pw9IvK5YMVSKNmuvfW6wikAAAEgFeh9JOSHq34/tfM7J9KmpP0kHPukkf3AQAEVL1xMEELpCbp5WO/5PcyAABASceh1Mw2S7pb0sHSpd+V9JuSXOnXL0j65zVe9xlJn5GkkZGRTpcBAOiBWmdGpWKV1Pm8tmYxLxQAgGDxolL6EUl/4Zz7oSSVf5UkM/uKpK/XepFz7suSvixJExMTYfmzDAAMrFpnRqcfP6eCc1oJ0X/FmRcKAECweBFK71XF1l0ze49z7vXStx+V9G0P7gEA6JF6HXRrnRnNhymNqtjYiO64AAAES0eh1MyGJP0jSf9jxeV/a2Y7Vdy++0rVYwCAAGvUQbfemdEw8HLsCwAA8FZHodQ5tyzpZ6qu/ZOOVgQA8E29Drozs4saTsSVCWEwZewLAADBFvF7AQCA4KhXDV3K5jQ9Oap4LNrjFbUvYgRSAADCwKuRMACAPlCvGlruWHulUFj3WJCc2LuTLboAAIQMlVIAwKrpyVHFIrbmWixiuv3m7dp/al4BGzm6RiIeI5ACABBChFIAwFq2/vvH517zZSnNiseiOnL3Dr+XAQAA2sD2XQDAqpnZReULa8e8FL8P7ugXOusCABBuhFIAwKowddfdfeM2nbz/Nr+XAQAAOkQoBQCsipqp4IJXFd0cNV0uVXC3DsV0+K4dVEYBAOgThFIAwKogBtJ4LKLv/OZH/F4GAADoEhodAQAkSfd95Tm/l1DTsT3v93sJAACgiwilAACl0hk9+/Kbfi9jnXgswjZdAAD6HKEUAKAjZ877vYR1YhGjSgoAwADgTCkADLBUOqOZ2UVlc3m/l7IGzYwAABgchFIA6GPl0LmUzWm4ap5nKp3RwdMLyuULvq0vYtKKu9r1l5mjAAAMHnMB6LQ4MTHh5ubm/F4GAPSVRqEzYlI8FtVbl/0LpCf27iR8AgAwIMzsBefcRK3HqJQC8F2jah7ak0pn9NBj5+qOeFlx8jWQJhNx/h0DAABJhFIAPquu5mWyOR08vSBJhJY2HUot6OTZC/J/H0x905Ojfi8BAAAEBKEUgK9mZhfXbS/N5QuamV0klLagXG3OZHN+L2VD+3aN8O8WAACsIpQC8NVSnRBV7zrWC0LDombFItLRqTG/lwEAAAKEOaUAfDWciLd0HevVqjYH1cw9O/1eAgAACBhCKQBfTU+OKh6LrrkWj0U5c9iCMGzZldi2CwAAamP7LgBflUMK3XfbV571GQQRk774iZ2S+HcKAACaQygF4Lup8SSBpU2pdCYwgTRZFT75dwoAAJpBKAWAEJuZXfR7CZKKgfTZA3f4vQwAABBCnCkFgBALwnlSzgADAIBOEEoBAC3bOhSTqVghPbZnjK26AACgbWzfBYCQOpRa8O3e6d/4kG/3BgAA/YVKKQCEUCqd0SNnL/hy7903bvPlvgAAoD8RSgEghA6eftGX++6+cZtO3n+bL/cGAAD9ie27ABBQqXRGM7OLa5oZRc10763XKZdf6ckaTuzdyXlRAADQVYRSAAigVDqjg6cXlMsX1lwvONezbbv7do0QSAEAQNexfRcAAmhmdnFdIO2ld79zs45Ojfl2fwAAMDiolAJAgKTSGR05c17ZXN63Nbz7nZv1/K//I9/uDwAABguhFAACIpXOaPrxc8qvON/WQCMjAADQa2zfBYCAmJld9DWQxiIikAIAgJ4jlAJAQCxVdNn1w5XeNPQFAABYg+27AOCTQ6kFPfr8qyo4p6iZzCTnX6FUw4m4fzcHAAADi0opAPjgUGpBj5y9oEIphRacUy927m7ZHNW+XSOKx6JrrsdjUU1PjnZ/AQAAAFWolAJAj6XSmZ7NGo2YtOKkZCKu6cnR1bmjE+/bppnZRS1lcxquegwAAKCXCKUA0EPlCmmvfP/YnTWvT40nCaEAACAQ2L4LAD3SywqpVKyOAgAABB2hFAB6IJXO6KHHzvX0npwRBQAAYUAoBYAuS6UzOnh6YbWpUS/EYxG25wIAgFDgTCkAdFG5QtrLQBox6die9/fsfgAAAJ0glAJAl/hRId06FNPhu3ZQJQUAAKFBKAUAD6XSmdVRKxGzngbSfbtGdHRqrGf3AwAA8AKhFAA8Uq6M5vIFSeppIN194zYCKQAACCVCKQB4ZGZ2cTWQ9krEpC9+YifbdQEAQGgRSgHAI0vZXE/v99PXRPXiwx/u6T0BAAC8xkgYAPDIcCLek/tEzbRv1wiBFAAA9AUqpQDgkdtv3q5Hzl7o6j2SibiePXBHV+8BAADQS1RKAcAjT734elffPx6LanpytKv3AAAA6DUqpQDggVQ6o0vL+a69fzIR1/TkKA2NAABA3yGUAkCHUumM9p+a78p7n9hLZ10AANDfCKUA0KZUOqMjZ84rm+tOhTQRjxFIAQBA3yOUAuhbqXRGM7OLWsrmNOzx9tdUOqODpxe6Opf0yN07uvbeAAAAQUEoBdAV3QyEzdznUGpBJ89ekCs9L5PN6eDpBUnqeB2pdEYPnJpffe9u2LdrhCopAAAYCOZcN/9Y1ZyJiQk3Nzfn9zIAeKRWFTEei+rYnjFPg1a9auVQLKLl/ErN1zQzUqUcdDPZnKJmKji32mhIUtfOj5bt2zWio1NjXb0HAABAL5nZC865iVqPUSkF4LmZ2cV1QTGXL2hmdtHTUFrrPpLqBlJJWsrmJK2vsN5+83Y989JFZUqPlxVKf3GXyea6EkY3R02XC8V7bB2K6fBdO6iQAgCAgdJRKDWzVyT9RFJB0hXn3ISZbZN0StL1kl6R9Ann3KXOlgkg6A6lFvTo86+uhrhalqoCX6eqA2QzEkOxdRXWTDanR85e8HRtzdh94zadvP+2nt8XAAAgSLyolN7unPtvFd8fkPQnzrnjZnag9P2/9uA+AALqUGqhqVA3nIg3fHyjc6iVj18bj7W1Vuekh58839UGRc1giy4AAEBRR2dKS5XSicpQamaLkn7BOfe6mb1H0v/pnBtt9D6cKQXC7caD32hYIZWkiEnXxmPKLufrBs7q86Emyal4DvT2m7fray9kfA+TXqBCCgAABk2jM6WRDt/bSfqmmb1gZp8pXXu3c+51SSr9+q4O7wEg4DYKpJK04qRLy3k5XT2fueM3/lipdEZS7fOhlZ1zHzl7oS8CqSQCKQAAQIVOt+/uds4tmdm7JH3LzF5q9oWlEPsZSRoZGelwGQD8FLFi6GzVW5cLmn7inCTvz5sCAAAgHDqqlDrnlkq/viHpjyR9UNIPS9t2Vfr1jTqv/bJzbsI5N7F9+/ZOlgHAR6l0Rp0M7MwXnI6cOa+ImXeLCrDogPycAAAAzWo7lJrZFjN7Z/lrSR+S9G1JZyR9qvS0T0n6L50uEkBwzcwuqv4AluZkc/mmtgD3g3tvvc7vJQAAAARKJ9t33y3pj6z4t/6bJP1n59wfm9mfS3rMzD4t6YKkezpfJoCgamcsyyAySffRcRcAAGCdtkOpc+77km6pcf1Hkn6xk0UBCIdykyLUtmVzVJ//6NiaLsMAAABYy4s5pQAGUCqd0YOPzfu9jMBi7AsAAEBzOh0JA2BAPfzk+bY67g6CfbtGCKQAAABNolIKoC2XlvN+LyFwrtkU0W997P1s1wUAAGgBoRQAPLJ49CN+LwEAACB02L4LoGU0OFqP+aMAAADtoVIKDJhUOqOZ2UUtZXMaTsQ1PTna0nbTVDqj6cfPdXGF4cT8UQAAgPZQKQUGSCqd0cHTC8pkc3Iqzhjdf2pe45/7ZtPVz5nZReXpcLQO80cBAADaQygFBsjM7KJy+cK665eW8zp4emHDYJpKZ5TJ5rq1vNBKJuJ+LwEAACC0CKXAAFlqEChz+YJmZhfrPn4otaAHTs13YVXhFouapidH/V4GAABAaHGmFOgjqXRGR86cVzZXHNeydSimw3ft0NR4Uql0RhEzFVz9rbf1QmsqndHJsxfEpt21Kn9/AQAA0B5CKdAnUumMHnxsXpXHPS8t5zX9xDnN/eBNfe2FTMNAKknDVdtQy02R2LK71r5dI5whBQAA8AihFOgTDz95XrX6D+ULrqkqp0lrtqEeSi1QHa1CZRQAAMB7hFKgT1xaztd9rJlg6aTVbb6VW4AH2ZbNUX3+o2OEUAAAgC4ilAJ9oNlxLo2YqI6aiuE82cb8VgAAALSHUAqEXHn2aKecpEfOXuh8QSETMWnFEUQBAAD8QigFQq7e7FFcFZGkUvgsi0VMM/fcQggFAADwGaEUCLFUOkNn3JJYxPRT79ik7HJe18ZjMpOyy3kNlyqgUjHAL2Vzq9cIpAAAAP4jlAIBVh7JUitIlc9/ovmtt4RQAACA4CGUAgFVPita3pqbyeb0wKl5zf3gTU28b9tANySSpKiZ7r31OuaFAgAAhByhFAiYcnW01rbccjOip158fWAD6e4bt+nk/bf5vQwAAAB4hFAKBEh1dbSeRjNJ+xmBFAAAoP8QSoEmNTrf6ZVB6qQbNdOKcxpOxHX7zdv1zEsXlcnmFDVTwTlGtAAAAAwIQinQhFrnO8uzQb0MTYPSSTcei+rYnjECJwAAAAilgLRxFbRWBTOXL+ihx85J8iaYptIZmRTKs6Jbh2LKLucVKVU5qyXiMW25ZhPjWAAAALAOoRQDr5kq6FKdCmbBuaYqpql0RkfOnFc2VzwLunUopsN37VgXfMMYSG961xZ968FfkFT7TGw8FtWRu3cQQgEAAFBTxO8FAH6rVwWdmV1c/X44Ea/7+urnVkulM3rwsfnVQCoVGxVNP3FOqXRm9VoYt+7uvnHbaiCVisH82J4xJRNxmYrzQ9mmCwAAgEaolGLg1auCVl6fnhxt2BW33ntI0sNPntdKjRJovuA0M7u4GtgipprPC6JYxDRzzy01w+bUeJIQCgAAgKZRKcXAq1cFrbxergBGzVp6D6nx+JbK6mhYAmkyEa8bSAEAAIBWUSnFwKtVBY3HopqeHF3zvHIIq35uLGJavnxFNxx4qq0mPql0Rr/zzHc7/Cm6K2LSFz+xkyAKAAAAzxFKMfDKQauZGaTVz702HtNbl6+sVkOrmyRVnhmtp7IBUhBtiph+m8ooAAAAusRcjfENvTYxMeHm5ub8XgZQU6NxMbuPP12zQVEyEdezB+7Q+Oe+2XD7bpAlGd0CAAAAj5jZC865iVqPUSnFwGsUOmuNi3ng1Lz2n5pXMhGv2zF3KZtTKp0JZSDdfeM2nbz/Nr+XAQAAgAFBKMVAqQ6gt9+8XV97IVN3RmmtcTHlvQWNRrgMbY7q4OkXu/IzeGEoFlG+4JSv6q60b9eIjk6N+bQqAAAADCK276KvVYbQ8vnPfOHqZ950NWRWSsRj2nLNplDODt1ILGqa+fgtkpo7RwsAAAB0iu27GEjVW29rNROq91cy2Vw+0M2H2hUxaebjV5sWEUIBAADgN+aUom/V2no7yGJRY6wLAAAAAodQir611Idbb1uVTMRlpV8rK6QAAABAULB9F31ruEF33EFQHksDAAAABBmVUvSt6clRxWNRv5fhi3gsqunJUb+XAQAAAGyIUIpASqUz2n38ad1w4CntPv60UulMy+8xNZ7U3x25tgurC5Z4LKp9u0bWbNU9tmeMrboAAAAIBbbvInCqu+ZWzw5t5X3+68tvdmWNQWEmAigAAABCjUopAqdW19xcvqCZ2cWW38f/KbzdEzHpS3TTBQAAQMgRShE49brmttpNt5+aHJmkodjV/7luHYox3gUAAAB9ge27CJx6XXOHE/GW3sekvqiUxmNRtugCAACgb1EpReDU6prbajfZQ6mFvgikNC0CAABAv6NSisApB7CZ2UUtZXMaTsQ1PTnaUjA7efZCt5bXEyf2sjUXAAAAg4FQikCaGk+2HcpS6Uzoq6QEUgAAAAwKtu+i7zz85Hm/l9CRZItnZwEAAIAwI5Sir6TSGV1azvu9jLa1enYWAAAACDu276KvtDrLNEiSbZydBQAAAMKOUIqeSKUzTTcuauW51a8L22xSk3TfrhEdnRrzeykAAACAL8w5/1vCTExMuLm5Ob+XgS5JpTM6eHpBuXxh9Vq92Zu1nhuLmH7qHZuUXc7XDamHUgt6JOAdd03S37txm175Ua7trsIAAABAGJnZC865iVqPUSlF183MLq4JmZKUyxc0M7u4LpDVem5+xa2eE81kczp4ekHS1Q61qXQm8IGUrbkAAABAbYRSdN1SnS21S9ncuq26zWy/rQ60QT9HmkzE9eyBO/xeBgAAABBIdN9F1w3XGXFybTymg6cXlMnm5KSWzoNWPrde6O21RDymeCy65hrddAEAAIDGCKXouttv3l7zer6wsm6rbrNMxW27kpQYirW7NM/EY1EduXuHju0ZUzIRl6lYIa11bhYAAADAVWzfRdc99eLrNa+/dbm9QCpJTlrdwvvW21fafh8vVJ8XJYQCAAAAzSOUoqsOpRZWmxR5LZPN6foDT3XlvZvBWVEAAACgc21v3zWz68zsGTP7jpmdN7N/Vbp+xMwyZjZf+ueXvFsuwiSVzuhkwLvitouzogAAAIA3OqmUXpH0kHPuL8zsnZJeMLNvlR77knPutztfHsJsZnZR/k/B9R7jXQAAAADvtB1KnXOvS3q99PVPzOw7kvhTOlYFpSuul7YOxdiyCwAAAHjIk+67Zna9pHFJz5cu/ZqZvWhmXzWzrV7cA+ETj4WruXMzy73z/e/p/kIAAACAAdJxajCzn5L0NUn7nXN/Lel3Jd0oaaeKldQv1HndZ8xszszmLl682OkyEDCpdEbL+RW/l7GONXjsyoq0b9dIw9c/8xKfVQAAAMBLHYVSM4upGEhPOudOS5Jz7ofOuYJzbkXSVyR9sNZrnXNfds5NOOcmtm+vPccS4ZJKZ7T7+NO64cBTeuDUvN/Lqemvjt+pZCJe87HhRFxHp8Z0Yu/Ouq/vxy3JAAAAgJ866b5rkn5P0necc1+suF65v/Gjkr7d/vIQFql0RgdPLyiTzclJgW5wND05qngsuuZaZTfdqfGktg7Far52uE6gBQAAANCeTiqluyX9E0l3VI1/+bdmtmBmL0q6XdIDXiwUwTYzu6hcvuD3Mhqy0t7dqfGkju0ZUzIRl6nYTffYnrE13XQP37WjYXAFAAAA4I1Ouu/+P6p9RO8b7S8HYRWGba2uonw7NZ5sONKl/NjM7KKWsjkNMwYGAAAA6IpO5pQCq4YTcWUCHkzrnSWtZ6PgCgAAAKBz4ZrZgUBKpTN68623/V5GQ2y9BQAAAIKJSik6kkpnNP34OeVXgtzaSOvOjAIAAAAIBkLpgEilM56dj6x8r4iZCi7YgTSZiBNIAQAAgIAilA6A8riWcnfcTDang6cXJKnlsFb9XkEPpGzbBQAAAIKNM6UDoNa4lly+oJnZRU/eK2gipZ7QtUa9AAAAAAgWKqUDoN64lkw2p1Q601JoC+LoF5PkVAyhjG0BAAAAwoVQOgAajWt58LF5PfzkeWWX802dNR3aHNVbl4NVKf3S3p0EUQAAACCk2L47AKYnR2V1Hltx0qXlvJyunjVNpTM1n5tKZwIXSGliBAAAAIQboXQATI0n1Ww7oly+oAcem9cNB57S7uNPrwmoR86c784CN2CSEvGYYtG10ZomRgAAAED4sX13QCQbbOGtVm6om8nm9OCpeUnFYJvN5bu0uvqSibiePXCHJG/H2gAAAAAIBkLpgJieHNX+UsBsxYqkg6df9CX8mbSmEjo1niSEAgAAAH2GUNrnDqUWdPLshaa379aSy6/UPWfaTfftGiGEAgAAAH2OM6V97FBqQY90GEjLunWeNBGP1by+b9eIjk6NdeWeAAAAAIKDUNrHHjl7wbP38uI8aa1GRb98y3vWfQgjkibet63j+wEAAAAIPkJpnzqUWvB7CevMfPwWJRNxmYoNjI7tGdPXz72ularnrci/Tr8AAAAAeoszpX0olc7opIdVUi+U54lWnxGt13zJj06/AAAAAHqPSmkfmpld9OQcqZeYJwoAAACgFkJpH1pqch5pr+y+cVvdLrpbh2o3Oqp3HQAAAEB/IZT2oeFE3O8lrPHKj+qH5MN37VjXACkWNR2+a0e3lwUAAAAgAAilfShoW2UbVW6nxpPrGiDNfPwW5pMCAAAAA4JGRyGWSmc0M7uopWxOw4n4ahgNWufajSq3tRogAQAAABgMhNKQSqUzOnh6Qbl8QZKUyeb04Kn5deNV/BaLWuAqtwAAAACCg+27ITUzu7gaSMuCFkglsRUXAAAAQEOE0pAKWofdWqJmBFIAAAAADbF9N8BqnRkth7zhRFyZgAfTggvatFQAAAAAQUOlNIBS6Yx2PvxN7T81r0w2J6fimdH9p+Y1/rlvKpXOhOKcZjJgo2kAAAAABA+V0oCpbmBU7dJyXgdPL+jvjlzb45W1Jh6LhiI4AwAAAPAXldKAqdXAqFouX9CzL7/ZoxVtbOtQTFLxDKlUrJAe2zPGeVIAAAAAG6JSGjBBPydabd+uER2dGvN7GQAAAABCikppgKTSGb+X0BICKQAAAIBOEUoDonyWtJfisWhHryeQAgAAAOgUoTQgmjlL6oWISaar5z7pkAsAAADAT5wpDYhenCWNRU0zH79lXQOiRt1+6yk3NwIAAACAThBKuySVzmhmdlFL2ZyGE3FNT4427EYbNVPBua6t55pNEf3Wx96/bg3l74+cOa9sLt/Ue8WipsN37fB8jQAAAAAGD6G0C1LpjKYfP6f8SjFkZrI5TT9+TpLqBtNuBlJJWjz6kbqPTY0nNTWeXA3Sjaq2ySYCNgAAAAA0i1DaBUfOnF8NpGX5FacjZ86vC3PFBkcvdnU9zZ4bLYdTqfVKLwAAAAC0g1DqsUOphbrbYLO5vFLpzGpVcvrxeeVXurueeCyq6cnRll9XGVABAAAAoFvMdXnbaDMmJibc3Nyc38voSLnimet2ymyCSXJiqy0AAACAYDCzF5xzE7Ueo1LqgfKM0SAE0n27RpgfCgAAACA0CKUe6NWM0Y2c2LuTqigAAACAUIn4vYB+sNSDGaPNIJACAAAACBtCqQeujcf8XoJ237jN7yUAAAAAQMsIpR6o123XC9bEc2561xadvP+2rq0BAAAAALqFM6Utqpzf+Y5YpOvNje7bNaKvvZCpeWaV7roAAAAAwo5Q2oKrXXaLAbGbgXRTxPTb99yiqfGkJt63bTUIDxNEAQAAAPQRQmkLetVlt7qL7tR4khAKAAAAoC9xprQFmS532d06FGOsCwAAAICBQqW0Cal0RvtPzXflvbcOxXT4rh0EUQAAAAADiVC6gb918Cldce2/fveN2/Tsy2/WfIyqKAAAAIBBx/bdBm7+9W90FEhP7N2pk/fftm6G6DWbIgRSAAAAABCV0ob+ptB+It23a2Q1dDJDFAAAAABqI5R67JpNEf3Wx95PFRQAAAAAmkAo9cAmk7537E6/lwEAAAAAoUMobeAdUdtwCy9nQwEAAACgfTQ6auClz/+S3hG1mo/t2zWiV47fSSAFAAAAgA5QKd3AS5//Jb+XAAAAAAB9i0opAAAAAMA3XQulZvZhM1s0s++Z2YFu3QcAAAAAEF5dCaVmFpX0O5I+IunnJd1rZj/fjXsBAAAAAMKrW5XSD0r6nnPu+865y5L+UNKvdOleAAAAAICQ6lYoTUp6teL710rXAAAAAABY1a1QWmuOypqBn2b2GTObM7O5ixcvdmkZAAAAAIAg61YofU3SdRXfv1fSUuUTnHNfds5NOOcmtm/f3qVlAAAAAACCrFuh9M8l3WRmN5jZZkmflHSmS/cCAAAAAITUpm68qXPuipn9mqRZSVFJX3XOne/GvQAAAAAA4dWVUCpJzrlvSPpGt94fAAAAABB+3dq+CwAAAADAhgilAAAAAADfEEoBAAAAAL4hlAIAAAAAfEMoBQAAAAD4hlAKAAAAAPANoRQAAAAA4BtCKQAAAADAN4RSAAAAAIBvCKUAAAAAAN+Yc87vNcjMLkr6gd/r2MDPSvpvfi8C6AE+6xgUfNYxKPisY1DwWQ+29znnttd6IBChNAzMbM45N+H3OoBu47OOQcFnHYOCzzoGBZ/18GL7LgAAAADAN4RSAAAAAIBvCKXN+7LfCwB6hM86BgWfdQwKPusYFHzWQ4ozpQAAAAAA31ApBQAAAAD4hlC6ATP7sJktmtn3zOyA3+sBWmVm15nZM2b2HTM7b2b/qnR9m5l9y8y+W/p1a8VrDpY+84tmNllx/QNmtlB67H8zM/PjZwIaMbOomaXN7Oul7/mso++YWcLMnjCzl0r/fb+Nzzr6kZk9UPrzy7fN7FEzewef9f5DKG3AzKKSfkfSRyT9vKR7zezn/V0V0LIrkh5yzv13knZJ+tXS5/iApD9xzt0k6U9K36v02Ccl7ZD0YUn/vvS/BUn6XUmfkXRT6Z8P9/IHAZr0ryR9p+J7PuvoR/+rpD92zt0s6RYVP/N81tFXzCwp6V9KmnDO/R1JURU/y3zW+wyhtLEPSvqec+77zrnLkv5Q0q/4vCagJc65151zf1H6+icq/sElqeJn+Q9KT/sDSVOlr39F0h865952zv2VpO9J+qCZvUfSTzvnnnPFw+j/qeI1QCCY2Xsl3SnpP1Zc5rOOvmJmPy3pH0j6PUlyzl12zmXFZx39aZOkuJltkjQkaUl81vsOobSxpKRXK75/rXQNCCUzu17SuKTnJb3bOfe6VAyukt5Velq9z32y9HX1dSBITkj6XyStVFzjs45+83OSLkr630tb1f+jmW0Rn3X0GedcRtJvS7og6XVJP3bOfVN81vsOobSxWnvNaVeMUDKzn5L0NUn7nXN/3eipNa65BteBQDCzX5b0hnPuhWZfUuMan3WEwSZJf1fS7zrnxiW9pdL2xTr4rCOUSmdFf0XSDZKGJW0xs32NXlLjGp/1ECCUNvaapOsqvn+vilsGgFAxs5iKgfSkc+506fIPS9tZVPr1jdL1ep/710pfV18HgmK3pLvN7BUVj1vcYWaPiM86+s9rkl5zzj1f+v4JFUMqn3X0m38o6a+ccxedc3lJpyX9PfFZ7zuE0sb+XNJNZnaDmW1W8eD0GZ/XBLSk1F3u9yR9xzn3xYqHzkj6VOnrT0n6LxXXP2lm15jZDSo2A/iz0vaYn5jZrtJ7/tOK1wC+c84ddM691zl3vYr/vX7aObdPfNbRZ5xz/6+kV81stHTpFyX9pfiso/9ckLTLzIZKn9FfVLE3Bp/1PrPJ7wUEmXPuipn9mqRZFbt9fdU5d97nZQGt2i3pn0haMLP50rXPSjou6TEz+7SK/9G/R5Kcc+fN7DEV/4BzRdKvOucKpdf9T5J+X1Jc0v9R+gcIOj7r6Ef/s6STpb80/76kf6ZisYHPOvqGc+55M3tC0l+o+NlNS/qypJ8Sn/W+YsUGVAAAAAAA9B7bdwEAAAAAviGUAgAAAAB8QygFAAAAAPiGUAoAAAAA8A2hFAAAAADgG0IpAAAAAMA3hFIAAAAAgG8IpQAAAAAA3/z/UDb8Ok5RDZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt0 = np.loadtxt(os.path.join(pt, 'simulation_0', 'Tracking', 'opt', '46_ms.txt'))\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(np.arange(len(opt0[1:])), opt0[1:])\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "p = r'D:\\Uni\\Masterarbeit\\Daten\\Tree_billig_mod\\rtc'\n",
    "# c = 5, rtc(0, 10)\n",
    "dic5_rtc = {}\n",
    "\n",
    "dirs5 = [d for d in os.listdir(p) if 'ModTest_C5' in d]\n",
    "\n",
    "for ho in dirs5:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p, ho, os.listdir(os.path.join(p,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    df['anteil_loop'] = df['lp_teil_loop[s]'] / df['total_lp[s]']\n",
    "    df['anteil_opt_an_loop'] = df['sum_opt[s]'] / df['lp_teil_loop[s]']\n",
    "    # noch einlesen, wie viel % der lp-Variablen ganzzahlig sind\n",
    "    \n",
    "    dic5_rtc[number_nodes] = df.mean()\n",
    "    \n",
    "ns_5_rtc = sorted(list(dic5_rtc.keys()))\n",
    "mean_lp_constrs_5_rtc = [dic5_rtc[n].lp_constraint_counter for n in ns_5_rtc]\n",
    "mean_total_lp_5_rtc = np.array([dic5_rtc[n]['total_lp[s]'] for n in ns_5_rtc])\n",
    "mean_sum_opt_5_rtc = [dic5_rtc[n]['sum_opt[s]'] for n in ns_5_rtc]\n",
    "mean_anteil_opt_5_rtc = [dic5_rtc[n]['anteil_opt'] for n in ns_5_rtc]\n",
    "mean_anteil_lp_ganz_5_rtc = [dic5_rtc[n]['anteil_lp_int'] for n in ns_5_rtc]\n",
    "mean_anteil_loop_5_rtc = [dic5_rtc[n]['anteil_loop'] for n in ns_5_rtc]\n",
    "mean_anteil_opt_an_loop_5_rtc = [[dic5_rtc[n]['anteil_opt_an_loop'] for n in ns_5_rtc]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26b3f664310>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# welchen Anteil an der Gesamtlaufzeit, macht im Mittel die Summer der opt-Werte aus\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_5_rtc, mean_anteil_loop_5_rtc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "das while-loop ist fuer grosse N komplett fuer die Laufzeit verantwortlich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.69905943e-10 4.36855109e+00 5.13346446e-02]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b40446940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wie skalieren die opt-summen-Werte\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_5_rtc, mean_sum_opt_5_rtc)\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_5_rtc, mean_sum_opt_5_rtc, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "popt_polyl, pcov_polyl = curve_fit(f_poly_long, ns_5_rtc, mean_sum_opt_5_rtc, maxfev=50000, p0=[0,1,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "ax.plot(np.arange(330), f_poly(np.arange(330), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "ax.plot(np.arange(330), f_poly_long(np.arange(330), *popt_polyl), label=f\"{popt_polyl[0]:.3} * $N^{{{popt_polyl[1]:.3f}}}+${popt_polyl[2]:.3f}\")\n",
    "print(popt_polyl)\n",
    "ax.legend(fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die opt-Summen skaliert selbst auch schon so komisch, obwohl sie nicht den Hauptteil an der Simulation ausmachen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22e645d6ee0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# welchen Anteil an der Gesamtlaufzeit, macht im Mittel die Summer der opt-Werte aus\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_5_rtc, mean_anteil_opt_an_loop_5_rtc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der Gurobi-Anteil (opt) ist nicht fuer die Hauptlaufzeit im while-loop verantwortlich. Koennte aber sein, dass sich das fuer grosse N noch aendert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22e6423a430>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0AwAAwPA2tnaztrmTvf1xrs+NsrK0kOXF+aHb4goTVgEA4Irb2NrN6vp2xgeHSZLd/XFW17eTRGBlMJYBAwDAFbe2uXM7qB4ZHxxmbXNnoI5AWAUAgCtvb388VR3Og7AKAABX3PW50VR1OA/CKgAAXHErSwsZzc7cURvNzmRlaWGgjsCAJQAAuPKOhiiZBkxPhFUAACDLi/PCKV2xDBgAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHeEVQAAALojrAIAANAdYRUAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHceGboBAADgdDa2drO2uZO9/XGuz42ysrSQ5cX5oduCUxFWAQDgAtvY2s3q+nbGB4dJkt39cVbXt5NEYOVCswwYAAAusLXNndtB9cj44DBrmzsDdQRnQ1gFAIALbG9/PFUdLgphFQAALrDrc6Op6nBRCKsAAHCBrSwtZDQ7c0dtNDuTlaWFgTqCs2HAEgAAXGBHQ5RMA+ayEVYBAOCCW16cF065dCwDBgAAoDvCKgAAAN0RVgEAAOiOsAoAAEB3hFUAAAC6I6wCAADQHWEVAACA7girAAAAdEdYBQAAoDvCKgAAAN0RVgEAAOiOsAoAAEB3Hhm6AQAAuOo2tnaztrmTvf1xrs+NsrK0kOXF+aHbgkEJqwAAMKCNrd2srm9nfHCYJNndH2d1fTtJBFauNMuAAQBgQGubO7eD6pHxwWHWNncG6gj6IKwCAMCA9vbHU9XhqhBWAQBgQNfnRlPV4aoQVgEAYEArSwsZzc7cURvNzmRlaWGgjqAPBiwBAMCAjoYomQYMdxJWAQBgYMuL88Ip3MUyYAAAALojrAIAANAdYRUAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHdeM6xW1Z+pqi9X1b+pqq9W1d+e1N9YVV+oqm9MHt9w7D2rVfVSVe1U1dKx+nuqanvyu1+uqno4XwsAAICL7CRXVn+Q5AOttZ9K8u4kT1TV+5I8m+TF1trjSV6cvE5VvTPJU0neleSJJJ+oqpnJZ30yyTNJHp/8PHF2XwUAAIDL4jXDarvlP0xezk5+WpInkzw3qT+XZHny/Mkkn22t/aC19s0kLyV5b1W9NcnrW2tfaq21JJ859h4AAAC47UT3rFbVTFX9XpJXknyhtfY7Sd7SWvtOkkwe3zw5fD7Jt4+9/eVJbX7y/O46AAAA3OGRkxzUWjtM8u6qmkvyG1X1E/c5/F73obb71F/9AVXP5NZy4Tz22GMnaREAAAaxsbWbtc2d7O2Pc31ulJWlhSwvuiYDpzXVNODW2n6Sf55b95p+d7K0N5PHVyaHvZzkbcfe9miSvUn90XvU7/V3PtVau9Fau3Ht2rVpWgQAgHOzsbWb1fXt7O6P05Ls7o+zur6dja3doVuDC+8k04CvTa6opqpGSf5Skj9I8kKSpyeHPZ3kc5PnLyR5qqpeV1XvyK1BSl+eLBX+flW9bzIF+EPH3gMAABfO2uZOxgeHd9TGB4dZ29wZqCO4PE6yDPitSZ6bTPT9U0meb639ZlV9KcnzVfXhJN9K8sEkaa19taqeT/K1JD9M8tHJMuIk+UiSTycZJfn85AcAAC6kvf3xVHXg5F4zrLbW/vcki/eo/99JfvpHvOdjST52j/rNJPe73xUAAC6M63Oj7N4jmF6fGw3QDVwuU92zCgAA/ImVpYWMZmfuqI1mZ7KytDBQR3B5nGgaMAAA8GpHU39NA4azJ6wCAMApLC/OC6fwEFgGDAAAQHeEVQAAALojrAIAANAdYRUAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHeEVQAAALojrAIAANCdR4ZuAAAAhrSxtZu1zZ3s7Y9zfW6UlaWFLC/OD90WXHnCKgAAV9bG1m5W17czPjhMkuzuj7O6vp0kAisMzDJgAACurLXNndtB9cj44DBrmzsDdQQcEVYBALiy9vbHU9WB8yOsAgBwZV2fG01VB86PsAoAwJW1srSQ0ezMHbXR7ExWlhYG6gg4YsASAABX1tEQJdOAoT/CKgAAV9ry4rxwCh2yDBgAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHeEVQAAALojrAIAANAdYRUAAIDuPDJ0AwAA8KA2tnaztrmTvf1xrs+NsrK0kOXF+aHbAs6AsAoAwIW0sbWb1fXtjA8OkyS7++Osrm8nicAKl4BlwAAAXEhrmzu3g+qR8cFh1jZ3BuoIOEvCKgAAF9Le/niqOnCxCKsAAFxI1+dGU9WBi0VYBQDgQlpZWshoduaO2mh2JitLCwN1BJwlA5YAALiQjoYomQYMl5OwCgDAhbW8OC+cwiVlGTAAAADdEVYBAADojrAKAABAd4RVAAAAuiOsAgAA0B1hFQAAgO4IqwAAAHRHWAUAAKA7wioAAADdeWToBgAAuLo2tnaztrmTvf1xrs+NsrK0kOXF+aHbAjogrAIAMIiNrd2srm9nfHCYJNndH2d1fTtJBFbAMmAAAIaxtrlzO6geGR8cZm1zZ6COgJ4IqwAADGJvfzxVHbhahFUAAAZxfW40VR24WoRVAAAGsbK0kNHszB210exMVpYWBuoI6IkBSwAADOJoiJJpwMC9CKsAAAxmeXFeOAXuyTJgAAAAuiOsAgAA0B1hFQAAgO4IqwAAAHRHWAUAAKA7wioAAADdEVYBAADojrAKAABAdx4ZugEAAC6mja3drG3uZG9/nOtzo6wsLWR5cX7otoBLQlgFAGBqG1u7WV3fzvjgMEmyuz/O6vp2kgiswJmwDBgAgKmtbe7cDqpHxgeHWdvcGagj4LIRVgEAmNre/niqOsC0hFUAAKZ2fW40VR1gWsIqAABTW1layGh25o7aaHYmK0sLA3UEXDYGLAEAMLWjIUqmAQMPi7AKAMADWV6cF06Bh8YyYAAAALojrAIAANAdYRUAAIDuvGZYraq3VdU/q6qvV9VXq+oXJvU3VtUXquobk8c3HHvPalW9VFU7VbV0rP6eqtqe/O6Xq6oeztcCAADgIjvJldUfJvkbrbU/n+R9ST5aVe9M8mySF1trjyd5cfI6k989leRdSZ5I8omqOppr/skkzyR5fPLzxBl+FwAAAC6J1wyrrbXvtNb+9eT595N8Pcl8kieTPDc57Lkky5PnTyb5bGvtB621byZ5Kcl7q+qtSV7fWvtSa60l+cyx9wAAAMBtU92zWlVvT7KY5HeSvKW19p3kVqBN8ubJYfNJvn3sbS9PavOT53fXAQAA4A4n3me1qv5skn+c5K+31v79fW43vdcv2n3q9/pbz+TWcuE89thjJ20RAIAT2tjazdrmTvb2x7k+N8rK0oI9U4GunOjKalXN5lZQ/fXW2vqk/N3J0t5MHl+Z1F9O8rZjb380yd6k/ug96q/SWvtUa+1Ga+3GtWvXTvpdAAA4gY2t3ayub2d3f5yWZHd/nNX17Wxs7Q7dGsBtJ5kGXEl+NcnXW2t/59ivXkjy9OT500k+d6z+VFW9rqrekVuDlL48WSr8/ap63+QzP3TsPQAAnJO1zZ2MDw7vqI0PDrO2uTNQRwCvdpJlwO9P8t8k2a6q35vU/laSjyd5vqo+nORbST6YJK21r1bV80m+lluThD/aWjv6r+FHknw6ySjJ5yc/AACco7398VR1gCG8Zlhtrf1vuff9pkny0z/iPR9L8rF71G8m+YlpGgQA4Gxdnxtl9x7B9PrcaIBuAO5tqmnAAABcfCtLCxnNztxRG83OZGVpYaCOAF7txNOAAQC4HI6m/poGDPRMWAUAuIKWF+eFU6BrlgEDAADQHWEVAACA7girAAAAdEdYBQAAoDvCKgAAAN0RVgEAAOiOrWsAAC6gja1d+6QCl5qwCgBwwWxs7WZ1fTvjg8Mkye7+OKvr20kisAKXhmXAAAAXzNrmzu2gemR8cJi1zZ2BOgI4e8IqAMAFs7c/nqoOcBEJqwAAF8z1udFUdYCLSFgFALhgVpYWMpqduaM2mp3JytLCQB0BnD0DlgAALpijIUqmAQOXmbAKAHABLS/OC6fApWYZMAAAAN0RVgEAAOiOsAoAAEB3hFUAAAC6I6wCAADQHdOAAQDO2cbWrm1nAF6DsAoAcI42tnazur6d8cFhkmR3f5zV9e0kEVgBjrEMGADgHK1t7twOqkfGB4dZ29wZqCOAPgmrAADnaG9/PFUd4KoSVgEAztH1udFUdYCrSlgFADhHK0sLGc3O3FEbzc5kZWlhoI4A+mTAEgDAOToaomQaMMD9CasAAOdseXFeOAV4DZYBAwAA0B1hFQAAgO4IqwAAAHRHWAUAAKA7wioAAADdMQ0YAGBKG1u7tp4BeMiEVQCAKWxs7WZ1fTvjg8Mkye7+OKvr20kisAKcIcuAAQCmsLa5czuoHhkfHGZtc2egjgAuJ2EVAGAKe/vjqeoAPBhhFQBgCtfnRlPVAXgwwioAwBRWlhYymp25ozaancnK0sJAHQFcTgYsAQBM4WiIkmnAAA+XsAoAMKXlxXnhFOAhswwYAACA7girAAAAdEdYBQAAoDvCKgAAAN0xYAkAuFI2tnZN8gW4AIRVAODK2Njazer6dsYHh0mS3f1xVte3k0RgBeiMZcAAwJWxtrlzO6geGR8cZm1zZ6COAPhRhFUA4MrY2x9PVQdgOMIqAHBlXJ8bTVUHYDjCKgBwZawsLWQ0O3NHbTQ7k5WlhYE6AuBHMWAJALgyjoYomQYM0D9hFQC4UpYX54VTgAvAMmAAAAC6I6wCAADQHWEVAACA7rhnFQC4UDa2dg1IArgChFUA4MLY2NrN6vp2xgeHSZLd/XFW17eTRGAFuGQsAwYALoy1zZ3bQfXI+OAwa5s7A3UEwMMirAIAF8be/niqOgAXl7AKAFwY1+dGU9UBuLiEVQDgwlhZWshoduaO2mh2JitLCwN1BMDDYsASAHBhHA1RMg0Y4PITVgGAC2V5cV44BbgCLAMGAACgO8IqAAAA3bEMGAA4Nxtbu+43BeBEhFUA4FxsbO1mdX0744PDJMnu/jir69tJIrAC8CqWAQMA52Jtc+d2UD0yPjjM2ubOQB0B0DNhFQA4F3v746nqAFxtwioAcC6uz42mqgNwtQmrAMC5WFlayGh25o7aaHYmK0sLA3UEQM8MWAIAzsXRECXTgAE4idcMq1X1a0l+LskrrbWfmNTemOR/SfL2JH+U5L9qrf0/k9+tJvlwksMk/31rbXNSf0+STycZJfknSX6htdbO9usAAD1bXpwXTgE4kZMsA/50kifuqj2b5MXW2uNJXpy8TlW9M8lTSd41ec8nqupovc8nkzyT5PHJz92fCQAAAElOEFZba/8iyb+7q/xkkucmz59Lsnys/tnW2g9aa99M8lKS91bVW5O8vrX2pcnV1M8cew8AcEFsbO3m/R//Yt7x7G/l/R//Yja2doduCYBL6kHvWX1La+07SdJa+05VvXlSn0/yr44d9/KkdjB5fncdALggNrZ2s7q+fXuv1N39cVbXt5PE0l4AztxZTwOue9Tafer3/pCqZ6rqZlXd/N73vndmzQEAD25tc+d2UD0yPjjM2ubOQB0BcJk9aFj97mRpbyaPr0zqLyd527HjHk2yN6k/eo/6PbXWPtVau9Fau3Ht2rUHbBEAOEt7++Op6gBwGg8aVl9I8vTk+dNJPnes/lRVva6q3pFbg5S+PFky/P2qel9VVZIPHXsPAHABXJ8bTVUHgNN4zbBaVf8wyZeSLFTVy1X14SQfT/IzVfWNJD8zeZ3W2leTPJ/ka0l+O8lHW2tH64U+kuRXcmvo0v+R5PNn/F0AgIdoZWkho9mZO2qj2ZmsLC0M1BEAl1n1vtXpjRs32s2bN4duAwDIrSFLa5s72dsf5/rcKCtLC4YrAXAqVfWV1tqNu+sPOg0YALiClhfnhVMAzsVZTwMGAACAU3NlFQCuCEt4AbhIhFUAuAI2tnazur59e5/U3f1xVte3k0RgBaBLlgEDwBWwtrlzO6geGR8cZm1zZ6COAOD+hFUAuAL29sdT1QFgaMIqAFwB1+dGU9UBYGjCKgBcAStLCxnNztxRG83OZGVpYaCOAOD+DFgCgCvgaIiSacAAXBTCKgBcEcuL88IpABeGsAoAF4A9UgG4aoRVAOicPVIBuIoMWAKAztkjFYCrSFgFgM7ZIxWAq0hYBYDO2SMVgKtIWAWAztkjFYCryIAlAOicPVIBuIqEVQC4AOyRCsBVI6wCwDmwTyoATEdYBYCHzD6pADA9A5YA4CGzTyoATE9YBYCHzD6pADA9YRUAHjL7pALA9IRVAHjI7JMKANMzYAkAHjL7pALA9IRVAHgNZ7HtjH1SAWA6wioA3IdtZwBgGO5ZBYD7sO0MAAxDWAWA+7DtDAAMQ1gFgPuw7QwADENYBYD7sO0MAAzDgCUALr3TTPO17QwADENYBeBSO4tpvradAYDzZxkwAJeaab4AcDEJqwBcaqb5AsDFJKwCcKmZ5gsAF5OwCsClZpovAFxMBiwB0LXTTPJNTPMFgItKWAWgW2cxyffoWOEUAC4Wy4AB6JZJvgBwdQmrAHTLJF8AuLqEVQC6ZZIvAFxdwioAD9XG1m7e//Ev5h3P/lbe//EvZmNr98TvNckXAK4uA5YAeGhOOyDJJF8AuLqEVQAemvsNSDpp4DTJFwCuJsuAAXhoDEgCAB6UsArAQ2NAEgDwoIRVAH6k0wxHSgxIAgAenHtWAbin0w5HOn6cAUkAwLSEVQDu6SyGIyUGJAEAD8YyYADuyXAkAGBIwioA92Q4EgAwJGEV4JIyHAkAuMjcswpwCRmOBABcdMIqwCVkOBIAcNEJqwCd2tjafeCrmoYjAQAXnXtWATp0tIx3d3+clj9ZxnvS+04NRwIALjphFaBD91vGexKGIwEAF51lwAAdOu0yXsORAICLTlgFeAhOc79pcmu57u49guk0y3gNRwIALjLLgAHO2GnvN00s4wUAEFYBzthp7zdNbl0V/cWf/8nMz41SSebnRvnFn/9JV0oBgCvDMmCAe+hh2xjLeAGAq8yVVYC72DYGAGB4wirAXWwbAwAwPMuAgUvntJN4bRsDADA8YRW4VI6W8B5dGT1awpvkxGHRtjEAAMOzDBjozsbWbt7/8S/mHc/+Vt7/8S9OteXLWUzitYwXAGB4rqwCXTntldGzmMRrGS8AwPCEVeBMnfZ+0ftdGT3J55zFEt7EMl4AgKFZBgycmdNu+ZKc/sqoJbwAAJeDsArcdpp7RZOzuV/0tHuULi/O5xd//iczPzdKJZmfG+UXf/4nXSUFALhgLAOGS+Q0S3DPYoruWdwvurK0cEcfyfRXRi3hBQC4+IRV6MRp7/U8bdg87b2iydlt+XLUj+FGAABXl7AKZ2Toq5qnDZu9XBVNXBkFAGCAsFpVTyT5u0lmkvxKa+3j590Dl89ZXJW86Fc1Txs2XRUFAKAn5xpWq2omyd9L8jNJXk7yu1X1Qmvta+fZx1k4bbg5i8/ooYcevsdpg+Jluap52rDpqigAAD0572nA703yUmvtD1trf5zks0mePOceTu0stuc47Wf00EMv3+O0E2jPYoLtWVzVnKZ+L6fdssUUXQAAenLeYXU+ybePvX55UrtQziLc9BCweviMHoLiWV3VnKZ+t7PYG/Qswuby4nz+5bMfyDc//rP5l89+QFAFAGAw533Pat2j1l51UNUzSZ5Jkscee+xh9zS1swg3PQSsHj6jh+WvZ3Gv5mmX0J7VvZ6W4AIAcFmc95XVl5O87djrR5Ps3X1Qa+1TrbUbrbUb165dO7fmTuoslmye9jN66OEsPqOH5a+uagIAQH/OO6z+bpLHq+odVfWnkzyV5IVz7uHUziLc9BCweviMHoLiWd2rKWwCAMDZqdZetQr34f7Bqr+S5Jdya+uaX2utfex+x9+4caPdvHnzPFqbymWYotvLZ5xFDwAAwMVUVV9prd14Vf28w+q0eg2rAAAAnN6PCqvnvQwYAAAAXpOwCgAAQHeEVQAAALojrAIAANAdYRUAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHeEVQAAALojrAIAANAdYRUAAIDuCKsAAAB0R1gFAACgO8IqAAAA3RFWAQAA6I6wCgAAQHeEVQAAALojrAIAANCdaq0N3cN9VdX3kvyfQ/dxH29K8m+HbgLu4rykR85LeuOcpEfOS3rzsM/Jf5skrbUn7v5F92G1d1V1s7V2Y+g+4DjnJT1yXtIb5yQ9cl7SmyHPScuAAQAA6I6wCgAAQHeE1dP71NANwD04L+mR85LeOCfpkfOS3gx2TrpnFQAAgO64sgoAAEB3hNVTqKonqmqnql6qqmeH7oerqap+rapeqarfP1Z7Y1V9oaq+MXl8w5A9crVU1duq6p9V1der6qtV9QuTuvOSwVTVn6mqL1fVv5mcl397UndeMqiqmqmqrar6zclr5ySDqqo/qqrtqvq9qro5qQ1yXgqrD6iqZpL8vST/ZZJ3Jvmvq+qdw3bFFfXpJHfvS/Vskhdba48neXHyGs7LD5P8jdban0/yviQfnfz30XnJkH6Q5AOttZ9K8u4kT1TV++K8ZHi/kOTrx147J+nBX2ytvfvYljWDnJfC6oN7b5KXWmt/2Fr74ySfTfLkwD1xBbXW/kWSf3dX+ckkz02eP5dk+Tx74mprrX2ntfavJ8+/n1v/CJuP85IBtVv+w+Tl7OSnxXnJgKrq0SQ/m+RXjpWdk/RokPNSWH1w80m+fez1y5Ma9OAtrbXvJLeCQ5I3D9wPV1RVvT3JYpLfifOSgU2WW/5ekleSfKG15rxkaL+U5G8m+f+O1ZyTDK0l+adV9ZWqemZSG+S8fOQ8/sglVfeoGa0MMFFVfzbJP07y11tr/77qXv/ZhPPTWjtM8u6qmkvyG1X1EwO3xBVWVT+X5JXW2leq6i8M3A4c9/7W2l5VvTnJF6rqD4ZqxJXVB/dykrcde/1okr2BeoG7fbeq3pokk8dXBu6HK6aqZnMrqP56a219UnZe0oXW2n6Sf55b9/s7LxnK+5P81ar6o9y6newDVfX345xkYK21vcnjK0l+I7dufxzkvBRWH9zvJnm8qt5RVX86yVNJXhi4JzjyQpKnJ8+fTvK5AXvhiqlbl1B/NcnXW2t/59ivnJcMpqquTa6opqpGSf5Skj+I85KBtNZWW2uPttbenlv/jvxia+2vxTnJgKrqx6rqx4+eJ/nLSX4/A52X1ZqVqw+qqv5Kbt1rMJPk11prHxu2I66iqvqHSf5Ckjcl+W6S/zHJRpLnkzyW5FtJPthau3sIEzwUVfWfJ/lfk2znT+7D+lu5dd+q85JBVNV/kltDQWZy63/WP99a+5+q6j+K85KBTZYB/w+ttZ9zTjKkqvpzuXU1Nbl1y+g/aK19bKjzUlgFAACgO5YBAwAA0B1hFQAAgO4IqwAAAHRHWAUAAKA7wioAAADdEVYBAADojrAKAABAd4RVAAAAuvP/Aw4C6zPpFFLWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(0, 50)\n",
    "ypar = 0.05 * (xs ** 3 + 6*np.random.random() - 6*np.random.random())\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(xs, ypar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Schauen, ob das break statement den Alg. verlangsamt hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_track = r'D:\\Uni\\Masterarbeit\\Daten\\Track'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "# c = 3, rtc(0, 10)\n",
    "dic3_track = {}\n",
    "\n",
    "dirs3t = [d for d in os.listdir(p_track) if 'Track_C3' in d]\n",
    "\n",
    "for ho in dirs3t:\n",
    "    number_nodes = int(ho.split('_')[2])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p_track, ho, os.listdir(os.path.join(p_track,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True, read_constr=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    df['anteil_loop'] = df['lp_teil_loop[s]'] / df['total_lp[s]']\n",
    "    df['anteil_opt_an_loop'] = df['sum_opt[s]'] / df['lp_teil_loop[s]']\n",
    "    \n",
    "    dic3_track[number_nodes] = df.mean()\n",
    "    \n",
    "ns_3t = sorted(list(dic3_track.keys()))\n",
    "mean_lp_constrs_3t = [dic3_track[n].lp_constraint_counter for n in ns_3t]\n",
    "mean_total_lp_3t = np.array([dic3_track[n]['total_lp[s]'] for n in ns_3t])\n",
    "mean_sum_opt_3t = [dic3_track[n]['sum_opt[s]'] for n in ns_3t]\n",
    "mean_anteil_opt_3t = [dic3_track[n]['anteil_opt'] for n in ns_3t]\n",
    "mean_anteil_lp_ganz_3t = [dic3_track[n]['anteil_lp_int'] for n in ns_3t]\n",
    "mean_anteil_loop_3t = [dic3_track[n]['anteil_loop'] for n in ns_3t]\n",
    "mean_anteil_opt_an_loop_3t = [[dic3_track[n]['anteil_opt_an_loop'] for n in ns_3t]]\n",
    "mean_sum_constr_3t = [[dic3_track[n]['sum_add_constr[s]'] for n in ns_3t]]\n",
    "mean_teil_loop_3t = [[dic3_track[n]['lp_teil_loop[s]'] for n in ns_3t]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b44449d90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t, mean_teil_loop_3t, label='loop', s=100)\n",
    "ax.scatter(ns_3t, np.array(mean_sum_opt_3t) + np.array(mean_sum_constr_3t), label='opt+add_constr')\n",
    "ax.legend()\n",
    "# ax.set_xlim([-2,100])\n",
    "# ax.set_ylim([-2,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rest + opt = loop Zeit. Das ist gut, weil das muss auch so sein"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# war der alg. ohne das break statement schneller?\n",
    "# erst anzahl an while-loop Iterationen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "# c = 3, rtc(0, 10), ohne break\n",
    "dic3_track_wob = {}\n",
    "\n",
    "dirs3t_wob = [d for d in os.listdir(p_track) if 'Track_woB_C3' in d]\n",
    "\n",
    "for ho in dirs3t_wob:\n",
    "    number_nodes = int(ho.split('_')[3])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p_track, ho, os.listdir(os.path.join(p_track,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True, read_constr=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    df['anteil_loop'] = df['lp_teil_loop[s]'] / df['total_lp[s]']\n",
    "    df['anteil_opt_an_loop'] = df['sum_opt[s]'] / df['lp_teil_loop[s]']\n",
    "    \n",
    "    dic3_track_wob[number_nodes] = df.mean()\n",
    "    \n",
    "ns_3t_wob = sorted(list(dic3_track_wob.keys()))\n",
    "\n",
    "# brauche die constr, die jetzt aber nicht mehr der constr anzahl entsprechen, sondern der Anzahl Iterationen\n",
    "mean_lp_constrs_3t_wob = [dic3_track_wob[n].lp_constraint_counter for n in ns_3t_wob]\n",
    "mean_total_lp_3t_wob = np.array([dic3_track_wob[n]['total_lp[s]'] for n in ns_3t_wob])\n",
    "mean_sum_constr_3t_wob = [[dic3_track_wob[n]['sum_add_constr[s]'] for n in ns_3t_wob]]\n",
    "\n",
    "# mean_sum_opt_3t = [dic3_track[n]['sum_opt[s]'] for n in ns_3t]\n",
    "# mean_anteil_opt_3t = [dic3_track[n]['anteil_opt'] for n in ns_3t]\n",
    "# mean_anteil_lp_ganz_3t = [dic3_track[n]['anteil_lp_int'] for n in ns_3t]\n",
    "# mean_anteil_loop_3t = [dic3_track[n]['anteil_loop'] for n in ns_3t]\n",
    "# mean_anteil_opt_an_loop_3t = [[dic3_track[n]['anteil_opt_an_loop'] for n in ns_3t]]\n",
    "# mean_sum_constr_3t = [[dic3_track[n]['sum_add_constr[s]'] for n in ns_3t]]\n",
    "# mean_teil_loop_3t = [[dic3_track[n]['lp_teil_loop[s]'] for n in ns_3t]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Anzahl an Loop-Iterationen')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t, mean_lp_constrs_3t, label='mit break')\n",
    "ax.scatter(ns_3t, mean_lp_constrs_3t_wob, label='ohne break')\n",
    "ax.legend()\n",
    "ax.set_title('Anzahl an Loop-Iterationen')\n",
    "# ax.set_xlim([-2,100])\n",
    "# ax.set_ylim([-2,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.46886121 0.31323529 0.29844961 0.29532808 0.29070417 0.37293806]\n"
     ]
    }
   ],
   "source": [
    "print(np.array(mean_lp_constrs_3t_wob)/np.array(mean_lp_constrs_3t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also bei 320 Knoten, wird der Anteil auf einmal wieder deutlich groesser, das muss ich noch genauer anschauen.\n",
    "Das ist ordentlich, aber wie wirkt sich das auf die Laufzeit aus?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gesamtlaufzeit in s')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t, mean_total_lp_3t, label='mit break')\n",
    "ax.scatter(ns_3t, mean_total_lp_3t_wob, label='ohne break')\n",
    "ax.legend()\n",
    "ax.set_title('Gesamtlaufzeit in s')\n",
    "# ax.set_xlim([-2,100])\n",
    "# ax.set_ylim([-2,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.90585901 0.63938377 0.53759165 0.50931124 0.47587917 0.63496209]\n"
     ]
    }
   ],
   "source": [
    "print(np.array(mean_total_lp_3t_wob)/np.array(mean_total_lp_3t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Laufzeit sinkt nicht im selben Stil, wie die Anzahl an loop-Iterationen\n",
    "Das sieht sehr vielversprechend aus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das selbe nochmal, aber 100 andere runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "160\n",
      "20\n",
      "320\n",
      "40\n",
      "640\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "# c = 3, rtc(0, 10), ohne break\n",
    "dic3_track_wob2 = {}\n",
    "\n",
    "dirs3t_wob2 = [d for d in os.listdir(p_track) if 'Track_woB2_C3' in d]\n",
    "\n",
    "for ho in dirs3t_wob2:\n",
    "    number_nodes = int(ho.split('_')[3])\n",
    "    print(number_nodes)\n",
    "    p_konfig = os.path.join(p_track, ho, os.listdir(os.path.join(p_track,ho))[0])\n",
    "    \n",
    "    df = auswertung_ma.read_tracking_files(p_konfig, read_opt=True, read_lp=True, read_constr=True)\n",
    "    df['anteil_opt'] = df['sum_opt[s]'] / df['total_lp[s]']\n",
    "    df['anteil_loop'] = df['lp_teil_loop[s]'] / df['total_lp[s]']\n",
    "    df['anteil_opt_an_loop'] = df['sum_opt[s]'] / df['lp_teil_loop[s]']\n",
    "    \n",
    "    dic3_track_wob2[number_nodes] = df.mean()\n",
    "    \n",
    "ns_3t_wob2 = sorted(list(dic3_track_wob2.keys()))\n",
    "\n",
    "# brauche die constr, die jetzt aber nicht mehr der constr anzahl entsprechen, sondern der Anzahl Iterationen\n",
    "mean_lp_constrs_3t_wob2 = [dic3_track_wob2[n].lp_constraint_counter for n in ns_3t_wob2]\n",
    "mean_total_lp_3t_wob2 = np.array([dic3_track_wob2[n]['total_lp[s]'] for n in ns_3t_wob2])\n",
    "mean_sum_constr_3t_wob2 = [[dic3_track_wob2[n]['sum_add_constr[s]'] for n in ns_3t_wob2]]\n",
    "mean_teil_loop_3t_wob2 = [[dic3_track_wob2[n]['lp_teil_loop[s]'] for n in ns_3t_wob2]]\n",
    "mean_sum_opt_3t_wob2 = [dic3_track_wob2[n]['sum_opt[s]'] for n in ns_3t_wob2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92185603 0.65637741 0.51359635 0.4587907  0.48634783 0.55945702]\n"
     ]
    }
   ],
   "source": [
    "print(np.array(mean_total_lp_3t_wob2)/np.array(mean_total_lp_3t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da gibts also anscheinend schon Variationen, aber es ist hinten raus doch wieder etwas mehr geworden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wie skaliert denn jetzt aber die Varainte ohne break?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.69905943e-10 4.36855109e+00 5.13346446e-02]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b497d27f0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t_wob2, mean_total_lp_3t_wob2)\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_3t_wob2, mean_total_lp_3t_wob2, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "ax.plot(np.arange(660), f_poly(np.arange(660), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "print(popt_polyl)\n",
    "ax.legend(fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antwort: immer noch komisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Schauen, wie der Rest im Loop skaliert und ob Rest + Opt gleich loop Zeit ist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b4978c040>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# schauen, wie der Rest skaliert\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t, mean_total_lp_3t,label='total')\n",
    "ax.scatter(ns_3t, mean_teil_loop_3t, label='loop')\n",
    "ax.scatter(ns_3t, mean_sum_constr_3t, label='sum_constr')\n",
    "# ax.scatter(ns_3t, np.array(mean_sum_opt_3t) + np.array(mean_sum_constr_3t), label='opt+add_constr')\n",
    "ax.legend()\n",
    "# ax.set_xlim([-2,100])\n",
    "# ax.set_ylim([-0.2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.array(mean_total_lp_3t_wob2)/np.array(mean_total_lp_3t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zusammenfassend kann man sagen: je groesser das Problem wird, desto groesser wird der Anteil des While loops an der Gesamtlaufzeit (hinten raus gegen 1). Fuer den Algorithmus mit break: Den groesseren Anteil an der Loopzeit hat der Teil, wo das neue constraint gebaut wird. Wie ist das jetzt ohne das break statement? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.12452323 0.36201967 0.68429468 0.86005321 0.91528513 0.89245   ]]\n"
     ]
    }
   ],
   "source": [
    "print(np.array(mean_sum_constr_3t_wob)/np.array(mean_total_lp_3t_wob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.11770478 0.35800758 0.67750108 0.85624338 0.90648694 0.85908707]]\n"
     ]
    }
   ],
   "source": [
    "print(np.array(mean_sum_constr_3t_wob2)/np.array(mean_total_lp_3t_wob2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse mit 640"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.69905943e-10 4.36855109e+00 5.13346446e-02]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b43d225b0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gesamtlaufzeit\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t_wob2, mean_total_lp_3t_wob2)\n",
    "popt_poly, pcov_poly = curve_fit(f_poly, ns_3t_wob2, mean_total_lp_3t_wob2, maxfev=50000, p0=[0,1])#, bounds=([-500, 0, -50], [500, 10, 50]))\n",
    "\n",
    "ax.plot(np.arange(660), f_poly(np.arange(660), *popt_poly), label=f\"{popt_poly[0]:.3} * $N^{{{popt_poly[1]:.3f}}}$\")\n",
    "print(popt_polyl)\n",
    "ax.legend(fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Gesamtlaufzeit skaliert immer noch komisch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b4a7f96a0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wie entwickelt sich der Anteil an der Gesamtlaufzeit\n",
    "# schauen, wie der Teil skaliert in dem die neuen constraints hinzugefügt werden\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.scatter(ns_3t_wob2, mean_total_lp_3t_wob2,label='total')\n",
    "ax.scatter(ns_3t_wob2, mean_teil_loop_3t_wob2, label='loop')\n",
    "ax.scatter(ns_3t_wob2, mean_sum_constr_3t_wob2, label='sum_constr')\n",
    "ax.legend()\n",
    "# ax.set_xlim([-2,100])\n",
    "# ax.set_ylim([-0.2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11770478, 0.35800758, 0.67750108, 0.85624338, 0.90648694,\n",
       "        0.85908707, 0.72212592]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(mean_sum_constr_3t_wob2)/np.array(mean_total_lp_3t_wob2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.11770478 0.35800758 0.67750108 0.85624338 0.90648694 0.85908707\n",
      "  0.72212592]]\n",
      "[0.09717035 0.13849104 0.14027554 0.09852389 0.0866668  0.13938102\n",
      " 0.27714088]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26b43e2dbb0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anteil des Constraintbaus an Gesamtlaufzeit\n",
    "print(np.array(mean_sum_constr_3t_wob2)/np.array(mean_total_lp_3t_wob2))\n",
    "# Anteil der Gurobioptimierungen an Gesamtlaufzeit\n",
    "print(np.array(mean_sum_opt_3t_wob2)/np.array(mean_total_lp_3t_wob2))\n",
    "\n",
    "anteil_constr = (np.array(mean_sum_constr_3t_wob2)/np.array(mean_total_lp_3t_wob2)).flatten()\n",
    "anteil_opt = (np.array(mean_sum_opt_3t_wob2)/np.array(mean_total_lp_3t_wob2)).flatten()\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "ax.plot(np.array(ns_3t_wob2), anteil_constr, 'o-', label='sum_constr')\n",
    "ax.plot(np.array(ns_3t_wob2), anteil_opt, 'o-', label='sum_opt')\n",
    "ax.plot(ns_3t_wob2, anteil_constr+anteil_opt, label='sum')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}