visualize_binary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import numpy.random as rand\n",
    "import networkx as nx\n",
    "from random_graph import random_graph\n",
    "from itertools import product\n",
    "from ast import literal_eval\n",
    "import qutip as qt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_probs(filename):\n",
    "   with open(filename) as file:\n",
    "       amplitudes = file.read()\n",
    "   result = [[literal_eval(amplitude)] for amplitude in amplitudes[1:].split('\\n ')]\n",
    "   n_qubits = int(np.log2(len(result)))\n",
    "   q_state = qt.Qobj(result, dims=[[2]*n_qubits, [1]*n_qubits])\n",
    "   prob_vector = np.abs(q_state.full())**2\n",
    "   n_qubits = len(q_state.dims[0])\n",
    "   strings = product('01', repeat=n_qubits)\n",
    "   return {''.join(string): prob[0] for string, prob in zip(strings, prob_vector)}\n",
    "\n",
    "def draw_best_coloring_2colors(probs, edges):\n",
    "    labels = list(probs.keys())\n",
    "    probabilities = list(probs.values())\n",
    "    best_prob = np.max(probabilities)\n",
    "    best_coloring = labels[np.argmax(probabilities)].replace('0', 'r').replace('1', 'b')\n",
    "    print('Probability of coloring: ' + str(np.round(best_prob, 4)))\n",
    "    nx.draw(nx.Graph(edges), node_color=best_coloring, with_labels=False)\n",
    "    \n",
    "def draw_best_coloring_4colors(probs, edges):\n",
    "    labels = list(probs.keys())\n",
    "    probabilities = list(probs.values())\n",
    "    best_prob = np.max(probabilities)\n",
    "    best_coloring = labels[np.argmax(probabilities)]\n",
    "    best_colors = [best_coloring[2*i:2*(i+1)] for i in range(int(len(best_coloring) / 2))]\n",
    "    color_dict = {'00': 'r', '01': 'g', '10': 'b', '11': 'k'}\n",
    "    best_colors =[color_dict[color] for color in best_colors]\n",
    "    print('Probability of coloring: ' + str(np.round(best_prob, 4)))\n",
    "    nx.draw(nx.Graph(edges), node_color=best_colors, with_labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_graph_2colors = [file for file in os.listdir('results') if (file[-10:] == 'binary.txt') and (file.split('_')[2] == '2colors')]\n",
    "to_graph_4colors = [file for file in os.listdir('results') if (file[-10:] == 'binary.txt') and (file.split('_')[2] == '4colors')]\n",
    "structures_2colors = [file.split('_')[1] for file in to_graph_2colors]\n",
    "structures_4colors = [file.split('_')[1] for file in to_graph_4colors]\n",
    "graphs = {'triang': [(0, 1), (1, 2), (2, 0)],\n",
    "          'line': [(0, 1), (1, 2), (2, 3)],\n",
    "          'square1diag': [(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs with 2 colors\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------3_triang_2colors_p1---------------\n",
      "Probability of coloring: 0.1667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d42fa1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_square1diag_2colors_p4---------------\n",
      "Probability of coloring: 0.4628\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3f0f5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_line_2colors_p1---------------\n",
      "Probability of coloring: 0.2288\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d42fb6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_line_2colors_p2---------------\n",
      "Probability of coloring: 0.3213\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3f0905f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_line_2colors_p3---------------\n",
      "Probability of coloring: 0.4268\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3f02d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------3_triang_2colors_p2---------------\n",
      "Probability of coloring: 0.1667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3efc9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_square1diag_2colors_p3---------------\n",
      "Probability of coloring: 0.4644\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3ef9c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------3_triang_2colors_p3---------------\n",
      "Probability of coloring: 0.1667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3eed2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_square1diag_2colors_p2---------------\n",
      "Probability of coloring: 0.4327\n"
     ]
    },
    {
     "data": {
      "image/png": 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tiOgxunpedGV5enpi/fr1UCgUvOiBtI5rwhUgSRLefvttSJKEbdu2ce2ICEDbtm2xadMm+Pn5iY6iFtOmTcNff/2FH3/8Ua92e5N+439pFSCTyfD111/jzJkz2LRpk+g4RMLdu3cPV69e1csNWc+zZMkS5OTkYPHixaKjkBHhmnAFWVtbIyYmBv7+/vD29oa3t7foSETCHDt2DJ06dYJ5Va4q01Hm5uaIjIyEj48POnbsyIseSCs4Eq4ET09PfP311xgyZAiysrJExyESRl/Oi66sRo0aYefOnQgJCcHt27dFxyEjwBKuJIVCgQEDBiA0NJQXPZDR0tdDOiqiR48eeP/99xEUFITi4mLRccjAcWNWFRQVFaFbt27o378/Zs2aJToOkVY9ePAAzs7OyMjIgIWFheg4GlF+0YO7uzvCwsJExyEDxpFwFdSoUQN79+7FmjVrcOTIEdFxiLTq5MmT8PX1NdgCBsoO69m+fTu+//57HtZDGsUSriJnZ2ds374dI0aMQGpqqug4RFpjyFPRj6tbty6ioqIwYcIEJCcni45DBoolXA29evXC+PHjMXToUK4dkdEwlEM6KsLb25uH9ZBGcU24mpRKJfr27YvWrVtj2bJlouMQaVRubi7q16+Pu3fvwsrKSnQcrZAkCW+99RYA8LAeUjuOhKvJxMQEO3fuRGRkJL777jvRcYg06tSpU3jllVeMpoCBssN61q9fjzNnzmDjxo2i45CBYQmrgZ2dHaKiovDuu+8iJSVFdBwijTGmqejHlR/WM3fuXCQkJIiOQwaEJawmfn5+WLBgAQIDA5Gfny86DpFGGGsJA08e1nPv3j3RcchAcE1YjSRJwvDhw2FtbY3NmzeLjkOkVgUFBbC3t8edO3dga2srOo4wH374IZKTk/HDDz/wogeqNv4XpEYymQybNm3CqVOnEB4eLjoOkVr99ttvaNmypVEXMAAsXboU9+/fx5IlS0RHIQPACxzUrGbNmoiOjsarr76K9u3b45VXXhEdiUgtjHkq+nHm5ubYu3cvfHx80KFDB/Ts2VN0JNJjHAlrQIsWLbBmzRooFAo8ePBAdBwitYiLizOKQzoqwsnJCTt37sTIkSN50QNVC9eENWjixIlITU3Fvn37+Gwh6bWioiLY2dnh5s2bqFOnjug4OmPRokU4ePAgjhw5YlDXOpL2cCSsQatWrcLt27exatUq0VGIqiUhIQEeHh4s4KfMnj0bderUwcyZM0VHIT3FEtYgCwsLREVFYdmyZTh27JjoOERVxqlo1covevj2228RHR0tOg7pIZawhjVp0gRbt27FsGHD8Pfff4uOQ1Ql3JT1fPXq1UNUVBTGjx+Pixcvio5DeoZrwloyb948nDhxAocOHYKZGTelk/4oKSmBnZ0dUlJSYG9vLzqOztqwYQPWrVuHX3/9FdbW1qLjkJ7gSFhLFi5cCBMTEyxYsEB0FKJKOXv2LBo3bswCfol3330Xbdu2xfjx48GxDVUUS1hLTE1NsWvXLmzfvh0//vij6DhEFcap6Iopv+ghMTERmzZtEh2H9ARLWIscHR2xZ88ejBkzBlevXhUdh6hCWMIVZ2Njg5iYGMyZMweJiYmi45Ae4JqwAGFhYYiIiMDx48dhaWkpOg7RcymVStjb2+PPP/9EgwYNRMfRG9HR0ZgxYwYSEhJQr1490XFIh3EkLMAHH3wAV1dXTJ06VXQUohc6f/48HBwcWMCVpFAoMGDAALz11ltQKpWi45AOYwkLIJPJEB4ejsOHD2Pnzp2i4xA9F6eiq27ZsmW4d+8eli5dKjoK6TCWsCC1atVCTEwMpk6digsXLoiOQ6QSD+moOnNzc0RGRmLt2rU4cuSI6Diko1jCArVu3RorVqyAQqFATk6O6DhET5AkCXFxcRwJV4OzszN27NiBESNG8KIHUoklLNhbb72FgIAAjB07ls8Wkk7566+/ULNmTTRu3Fh0FL322muvYcKECQgODkZxcbHoOKRjWMI6YO3atbh06RK++OIL0VGIHuFUtPp8/PHHqF27NmbNmiU6CukYlrAOsLS0RHR0ND777DOcOnVKdBwiANyUpU4mJibYsWMH9u3bh5iYGNFxSIfwOWEdsn//fkyaNAmJiYlwcHAQHYeMmCRJcHJywvHjx9GsWTPRcQxGQkIC3njjDZw8eRIeHh6i45AO4EhYh/Tv3x/Dhw/HyJEjUVpaKjoOGbGUlBSYmpqiadOmoqMYFB8fH3z66acIDAxEXl6e6DikA1jCOuazzz5DYWEh/vnPf4qOQkYsNjYWAQEBkMlkoqMYnHHjxqFNmzaYMGECN2MSS1jXmJmZYc+ePdi0aRN+/vln0XHISHE9WHNkMhk2bNiAhIQEfPPNN6LjkGBcE9ZRcXFxCAoKQnx8PFxcXETHISPTpEkTHDp0CF5eXqKjGKzk5GR06dIFP//8M9q3by86DgnCkbCOCggIwIcffoigoCAUFRWJjkNG5Pr16ygsLISnp6foKAbNy8sLX331FRQKBbKyskTHIUFYwjps+vTpaNCgAT766CPRUciIcD1Ye4YMGYL+/fsjNDSUFz0YKZawDpPJZNi6dSsOHDiAyMhI0XHISHA9WLuWLVuGzMxMLFu2THQUEoBrwnrgzJkz6N27N44dOwa5XC46Dhk4Dw8P7Nu3D61btxYdxWjcunULvr6+2LVrF7p37y46DmkRR8J6oF27dvj8888RGBiI3Nxc0XHIgKWmpiIrKwstW7YUHcWoODs7Y/v27RgxYgRSU1NFxyEtYgnriTFjxsDPzw/vvfceny0kjYmNjUXXrl1hYsL/NWhbr169MH78eF70YGT4naYnZDIZ1q1bh/Pnz2PDhg2i45CB4qUNYs2ZMwe2traYPXu26CikJSxhPWJtbY3o6GjMnz8fCQkJouOQAeKmLLHKL3qIjo7Gvn37RMchLeDGLD20b98+TJs2DYmJiahXr57oOGQg0tPT4enpiczMTJiamoqOY9Ti4+PRr18/nDhxghc9GDiOhPXQ4MGDMXjwYISEhPDZQlKbY8eOwd/fnwWsA/z8/LBw4UIoFApe9GDgWMJ6asmSJXjw4AGWLFkiOgoZCE5F65bx48ejVatWmDhxIjdjGjCWsJ4yNzdHZGQkvvjiCxw+fFh0HDIALGHdIpPJsHHjRsTHx2Pz5s2i45CGcE1Yzx0+fBghISE4ffo0nJycRMchPXXv3j24uroiMzMT5ubmouPQY5KSktC1a1de9GCgOBLWcz179sTEiRP5bCFVy/Hjx9GxY0cWsA6Sy+VYt24dhgwZwoseDBBL2ADMnj0btWvX5rOFVGWcitZtQUFB6NevH95++21uxjQwLGEDwGcLqbp4SIfuW758OdLT07F8+XLRUUiNuCZsQE6fPo2+ffvy2UKqlOzsbDg5OSEjIwMWFhai49AL3Lx5E76+vtizZw+6desmOg6pAUfCBsTX1xeffPIJny2kSjlx4gR8fHxYwHqgcePG2LFjB4YPH447d+6IjkNqwBI2MOPGjeOzhVQpnIrWL7169cK4ceO4GdNAsIQNjEwmw4YNGxAfH4/w8HDRcUgPcFOW/pk7dy5sbGwwZ84c0VGomrgmbKD++usvBAQE4NChQ2jXrp3oOKSjcnNzUb9+faSnp8Pa2lp0HKqEzMxMtG/fHmvWrMHAgQNFx6Eq4kjYQDVv3hxffPEFFAoF7t+/LzoO6ahff/0Vbdu2ZQHrITs7O0RFReHdd9/F5cuXRcehKmIJG7ChQ4fizTffxNtvv831YVKJU9H6zc/PDwsWLIBCoUB+fr7oOFQFLGEDt3LlSqSlpWHFihWio5AOYgnrvwkTJqBFixaYOHGi6ChUBVwTNgI3btyAn58f9u7dy12w9EhBQQHs7e1x584d2Nraio5D1fDw4UP4+flh2rRpGDNmjOg4VAkcCRsBFxcXbN26FcOGDUNaWproOKQj4uPj0aJFCxawAahZsyZiYmIwa9YsnDlzRnQcqgSWsJHo06cPxo4di6FDh6KkpER0HNIBnIo2LNyMqZ9YwkZk/vz5qFGjBubNmyc6CumA2NhYLk8YGG7G1D9cEzYyd+/ehbe3N7788kv0799fdBwSpKioCHZ2drh58ybq1KkjOg6pUVFREQICAjB48GDMmDFDdBx6CY6EjYyDgwMiIyMxduxYXLlyRXQcEiQxMRHu7u4sYANUo0YN7N27F6tWrUJsbKzoOPQSLGEj1KlTJ8yZMwdDhgxBQUGB6DgkAKeiDZuLiwu2b9/Oix70AEvYSE2ePBlubm6YMmWK6CgkQFxcHDdlGbjevXvj3Xff5WZMHcc1YSOWnZ0NX19fzJkzB6GhoaLjkJaUlJTAzs4Oly9fhoODg+g4pEFKpRJvvvkm2rZti6VLl4qOQypwJGzEatWqhZiYGEybNg3nz58XHYe05OzZs3B2dmYBGwETExPs3LkTe/bswffffy86DqnAEjZyrVq1wqpVq6BQKJCdnS06DmkBp6KNi729Pfbu3Yt33nkHKSkpouPQU1jChJCQEHTv3h1jxozhs4VGgId0GJ8OHTpg/vz5CAwM5EUPOoZrwgSg7Bxhf39/hISE4IMPPhAdhzREqVTC3t4e//nPf9CwYUPRcUiLJEnC8OHDYW1tjc2bN4uOQ//FkTABACwtLREdHY3PP/8cJ0+eFB2HNOTChQuwt7dnARshmUyGTZs24dSpUwgPDxcdh/6LJUyPNG3aFJs3b0ZwcDDS09NFxyEN4FS0cSu/6GHmzJk4e/as6DgEljA9pV+/fggJCcGIESNQWloqOg6pGUuYmjdvjrVr1/KiBx3BNWF6RklJCXr37o0uXbrg008/FR2H1ESSJNSvXx8JCQlwcXERHYcEmzRpEm7fvo19+/ZBJpOJjmO0OBKmZ5iZmWH37t0IDw/HwYMHRcchNUlKSoKNjQ0LmAAAK1euRGpqKlasWCE6ilFjCZNK9evXx+7duzFq1Chcv35ddBxSA05F0+MsLCwQFRWFlStXIi4uTnQco8USpufq2rUrPvroIwQFBaGwsFB0HKqmuLg4XtpAT3BxccG2bdswbNgwXvQgCNeE6YUkScLgwYPh5OSEL7/8UnQcqiJJkuDs7Iy4uDi4ubmJjkM6ZuHChTh69Ch++eUXmJmZiY5jVDgSpheSyWTYsmULfvrpJ+zevVt0HKqilJQUyGQyNGvWTHQU0kHz5s2DhYUF5s6dKzqK0WEJ00vVqVMH0dHRmDx5Mv7880/RcagKyqeiuQuWVDE1NUVERAR27dqF/fv3i45jVFjCVCGvvPIKli5dCoVCgYcPH4qOQ5XETVn0MuUXPYwdO5YXPWgR14SpUsaMGYP8/HxERERwVKVHXF1d8dNPP0Eul4uOQjruiy++QHh4OE6ePAkrKyvRcQweS5gqJT8/H506dcK7776LCRMmiI5DFXD9+nX4+fkhLS2NPzjRS5Vf9GBjY4NvvvlGdByDx+loqhQrKytER0dj4cKFiI+PFx2HKoDrwVQZ5Rc9nDhxAlu2bBEdx+CxhKnS3N3dsWHDBgQFBSEzM1N0HHoJrgdTZZVf9DBjxgycO3dOdByDxhKmKhk0aBCGDBmCkJAQKJVK0XHoBXhIB1VFixYtsGbNGigUCjx48EB0HIPFNWGqsuLiYvTo0QOvv/46ny/UUXfu3EGrVq1w9+5dmJjwZ26qvIkTJyI1NZUXPWgIvyupyszNzREZGYmvvvoKv/zyi+g4pEJsbCy6du3KAqYqW7VqFVJTU7Fy5UrRUQwSvzOpWho1aoSIiAiEhITg1q1bouPQU2JjYzkVTdViYWGBvXv3YsWKFbzoQQNYwlRt3bt3x+TJkxEcHIzi4mLRcegxcXFx3JRF1dakSRNs3boVw4YNQ1pamug4BoVrwqQWSqUSAwYMgIeHB1atWiU6DgG4e/cuPDw8kJmZCVNTU9FxyAAsWLAAcXFx+Ne//sWLHtSEI2FSCxMTE2zbtg3ffvstoqOjRcchlI2CO3fuzAImtZk/fz7Mzc0xb9480VEMBkuY1KZevXqIiorC+PHjcfHiRdFxjB6nokndyi96iIiI4EUPasISJrXy8fHBZ599BoVCgby8PNFxjBoP6SBNcHBwQGRkJMaOHYsrV66hvrH6AAAVq0lEQVSIjqP3uCZMaidJEkJDQ2FiYoKtW7fy2UIBsrKy4OLignv37sHc3Fx0HDJAa9euxdatW3Hy5ElYWlqKjqO3OBImtZPJZFi/fj0SExN5ALwgx48fR8eOHVnApDHvv/8+PDw88P7774uOotdYwqQRNjY2iImJwccff4zff/9ddByjw6lo0jSZTIZvvvkGx44dw9atW0XH0VssYdIYLy8vrFu3DgqFAllZWaLjGBUe0kHaYGtri5iYGEyfPh1//PGH6Dh6iWvCpHFTpkzB1atX8d133/H4RC3IyclBw4YNkZGRwbU60oqIiAgsXLgQCQkJqF27tug4eoX/RySNW758Oe7evYvly5eLjmIUTpw4AR8fHxYwac2IESPQq1cvjB49GhzXVQ5LmDSuRo0a2Lt3L8LCwnD06FHRcQwep6JJhLCwMNy8eZMn5lUSS5i0onHjxti+fTuGDx+OO3fuiI5j0HhIB4lgYWGBqKgoLFu2DMeOHRMdR29wTZi06pNPPsG///1vHD58mGfPakBeXh4cHR2Rnp4Oa2tr0XHICB08eBDvvPMOEhMTUb9+fdFxdB5HwqRV8+bNg5WVFebMmSM6ikE6deoU2rRpwwImYd544w2MGjUKw4YNQ0lJieg4Oo8lTFplYmKCnTt3Ys+ePfj+++9FxzE4nIomXbBw4UKYmppi/vz5oqPoPJYwaZ29vT0iIyPxzjvvICUlRXQcg8JDOkgXmJqaYteuXdi5cyd++OEH0XF0GteESZgvvvgC4eHhOHnyJKysrETH0XsFBQWwt7dHamoqatWqJToOEU6dOoWBAwfi1KlTaNasmeg4OokjYRJm0qRJ8PLywuTJk0VHMQinT59G8+bNWcCkMzp16oSPP/4YQ4YMQUFBgeg4OoklTMLIZDJs2rQJx48f59mzasCpaNJFkydPhru7O6ZMmSI6ik5iCZNQtra2iI6OxvTp03Hu3DnRcfQaD+kgXVR+0UNsbCy2b98uOo7O4Zow6QSePVs9xcXFqFevHm7cuIG6deuKjkP0jAsXLqB79+7497//jdatW4uOozM4EiadwLNnqycxMRFubm4sYNJZrVq1QlhYGAIDA/HgwQPRcXQGS5h0RlhYGG7cuIGwsDDRUfQOp6JJH4wcORI9e/bkD9uPYQmTzig/e3bp0qU4fvy46Dh6hYd0kL5YvXo1f9h+DNeESeccOHAA48aNQ2JiIhwdHUXH0XmlpaWws7PDpUuX4ODgIDoO0Utdu3YNHTp0QExMDLp06SI6jlAcCZPO6du3L9566y0MGzYMpaWlouPovLNnz8LJyYkFTHrD1dUVW7ZswbBhw5Ceni46jlAsYdJJn3zyCQBgwYIFgpPoPq4Hkz568803+cM2WMKko0xNTbF7925s27YNBw4cEB1Hp3E9mPTVJ598AplMZtQXPXBNmHTaiRMnMHjwYPz2229wdXUVHUfnKJVKODg44Pz582jUqJHoOESVlp6eDm9vb3z99dfo16+f6Dhax5Ew6TR/f3/MnDkTQ4YMQWFhoeg4OufChQuoV68eC5j0lqOjIyIjIzFmzBhcvXpVdBytYwmTzps6dSpcXFwwdepU0VF0DqeiyRB07twZs2fPhkKhMLqLHljCpPNkMhnCw8Pxyy+/ICIiQnQcncJLG8hQTJkyBc2aNcMHH3wgOopWcU2Y9MYff/yBnj174ujRo2jZsqXoOMJJkoQGDRogPj4eTZo0ER2HqNqys7Ph6+uLuXPnIiQkRHQcreBImPRGmzZtsHz5cgQGBiInJ0d0HOGSk5NhZWXFAiaDUatWLURHR+PDDz/E+fPnRcfRCpYw6ZW3334bXbt2xTvvvGP0Z89yKpoMUevWrbFq1SooFApkZ2eLjqNxLGHSO2vXrkVycjLWrVsnOopQPKSDDFVISAi6d+9uFBc9cE2Y9FJKSgo6deqE/fv3o2PHjqLjaJ0kSWjcuDGOHj0Kd3d30XGI1K6goABdunTBiBEjDPrJCI6ESS+5ublh06ZNCA4ORkZGhug4WnflyhVIkgQ3NzfRUYg0wtLSEtHR0ViyZAlOnDghOo7GsIRJbw0YMABDhw7FyJEjje7s2fKpaJlMJjoKkca4uroiPDwcQ4cONdiLHljCpNcWLVqE/Px8LFq0SHQUreIhHWQs+vbti9DQUAwfPtwgf9hmCZNeMzMzw549e7BhwwYcOnRIdByt4c5oMiaffvopJEkyyFvVWMKk9xo2bIiIiAiEhobi5s2bouNo3I0bN5Cbmwu5XC46CpFWGPKtaixhMgjdunXD1KlTERQUhKKiItFxNCouLo7rwWR0HB0dsWfPHowePRrXrl0THUdtWMJkMKZPnw4HBwfMmDFDdBSN4lQ0GSt/f3/MmjULCoXCYG5VYwmTwTAxMcG2bduwf/9+7N27V3QcjeEhHWTMPvjgA7i6uhrMRQ8sYTIodevWRXR0NCZOnIjk5GTRcdTuzp07yMjIQOvWrUVHIRKi/Fa1w4cPY+fOnaLjVBtLmAxO+/btsXjxYgQGBiI3N1d0HLWKi4tD165dYWLCb10yXrVq1UJMTAymTp2KCxcuiI5TLfxOJoM0duxYeHt7Y9y4cQZ19iynoonKtG7dGitXrkRgYKBeX/TAEiaDJJPJ8PXXX+PcuXPYuHGj6Dhqw0M6iP4nNDQU3bp1w5gxY/T2h21e4EAG7eLFi/D398fBgwfh4+MjOk61ZGRkwM3NDZmZmTAzMxMdh0gnFBQUwN/fH6GhoZgyZYroOJXGkTAZNE9PT3z99dcYMmQI7t27JzpOtcTFxaFz584sYKLHlF/0sHjxYpw8eVJ0nEpjCZPBUygUGDhwIEJDQ6FUKkXHqTJORROp1rRpU2zevBnBwcF6d9EDS5iMwrJly5CVlYWlS5eKjlJlPKSD6Pn69euHkJAQvbvogWvCZDRu3boFX19f7Nq1C927dxcdp1KysrLg4uKCzMxM1KhRQ3QcIp1UUlKC3r17w9/fH//85z9Fx6kQjoTJaDg7O2PHjh0YMWIEUlNTRceplOPHj6NDhw4sYKIXMDMzw+7du7Flyxb83//9n+g4FcISJqPy2muvYfz48QgODkZxcbHoOBXG9WCiiqlfvz727NmDUaNG4fr166LjvBRLmIzOnDlzYGtri48//lh0lArjIR1EFdelSxfMnDnziYse0tKAhQuBVq0AZ2egcWPglVeAFSsAkQ9OcE2YjFJmZia8vb0RFhaGQYMGiY7zQjk5OWjYsCEyMjJgaWkpOg6RXpAkCQqFAhYWrZGTsxD/+hcgkwEFBU9+nbU1oFQCAweWFbKTk3ZzsoTJaMXHx6Nfv344efIk3N3dRcd5rp9//hmLFy9GbGys6ChEeuWnnx7izTcBwBqS9OKJX1NToE4d4MgRQJv3o3A6moyWn58fFixYAIVCgfz8fNFxnotT0USVd+4coFDUhCTVfGkBA0BpKZCZCbz6KnDtmubzlWMJk1GbMGECWrRogUmTJomO8lx8PpiocpRKoF8/oCqXqD14AAQGqj/T87CEyajJZDJs3LgRp06dQnh4uOg4z8jLy8PZs2fRqVMn0VGI9MYvvwD371fttUol8NdfwB9/qDfT87CEyejVrFkTMTExmDlzJs6ePSs6zhN+/fVXtGnTBjY2NqKjEOmNZcuAhw+r/vqiIiAsTH15XoQlTASgefPmWLt2LRQKBe5X9UdoDeBUNFHlZGUBx49X7z1KS4Hdu8tGxZrGEib6r2HDhqFPnz4YNWqUztxNykM6iConLQ1Qx8FykgTk5FT/fV6GJUz0mJUrVyI1NRUrV64UHQWFhYU4ffo0/P39RUch0hsFBWXPA1eXqSmQl1f993kZXkxK9BgLCwvs3bsXfn5+6NChA7p27SosS3x8PORyOWrVqiUsA5EuKy0txfXr15GUlITk5GQkJSXh7NlsZGd/A6B6+yiKisqeG9Y0ljDRU5o0aYKtW7di2LBhSEhIQIMGDYTk4FQ0UZmcnJxHJVv+e1JSElJSUmBvbw+5XA65XI42bdpg8ODmUCisqrUxCwAaNQKsrNST/0VYwkQqvPHGGxg9ejSGDx+OQ4cOwcxM+98qsbGxOv38MpE6KZVK3Lx584mSLf/z/fv34enpCS8vL8jlcgQGBsLLywuenp4qnxwYPx5Ys6ZsNFsVNjbA9OnV/AtVEI+tJHqO0tJS9OnTB76+vli8eLFWP7u4uBh2dna4du0a6tWrp9XPJtKk3NxcXLx48ZlR7aVLl1C3bt1HRVv+u1wuh7OzM0xMKr6F6cYNwMvr2XOiK8rSsmyDV+3aVXt9ZXAkTPQcpqam2LVrF7y9vdG5c2f069dPa5/9+++/o2nTpixg0kuSJOH27dtPFG3573fv3oWHh8ejkv3HP/6Bjz76CF5eXrC1tVXL57u4AIMHA99+C1T2RFpra2DSJO0UMMCRMNFLnTx5EoMGDcKvv/6Kpk2bauUzly1bhlu3bmHt2rVa+TyiqsjPz8elS5eeGdVevHgRNjY2j0ayj49qXVxcYGpqqvFsRUVAQEDZyVcVLWJra6BHD+D774FKDLyrhSVMVAGrV6/Gjh07cOLECa1cJ9i3b1+MGjUKCoVC459F9CKSJCEtLU3lWu2dO3fg5ub2RMl6eXnBy8sLdbSxtfgl8vOBIUOAo0fLHjd6XtuZmJRNQQcHAxs3AtrcAsISJqoASZIQFBQEOzs7rF+/XqOfVVpaCjs7O1y8eBGOjo4a/SyicoWFhbh8+fIzo9rk5GRYWFioXKt1dXUVsmmxMiQJiIsDli8vO1Pa3BwoLi57ltjcvGzEPGAAMG0a4Oen/XwsYaIKys7Ohq+vL+bOnYuQkBCNfc7vv/+OkSNH4s8//9TYZ5BxkiQJd+/eVblWe+vWLbi6uqoc1drZ2YmOrhZ37gD//nfZlYUmJoC9PdCrFyDyr8cSJqqE8+fPo0ePHjhy5AhatWqlkc8ICwtDcnKyxkfcZLiKi4uRkpKiclQLQOVabbNmzWBubi44ufHR7XkEIh3TunVrrFy5EoGBgUhISFDbbs7HxcXFISgoSO3vS4YnMzNT5aj2+vXraNy48aOS9ff3x+jRoyGXy2Fvbw+ZOs51JLXgSJioCt577z1kZWUhMjJSrf9DUyqVcHR0xLlz5+Dk5KS29yX9VVJSgqtXr6oc1RYVFakc1bq5ucHCwkJ0dKoAljBRFRQUFMDf3x9vvfUWJk+erLb3PX/+PAYNGoTLly+r7T1JP9y/f1/lDuQrV66gUaNGz6zVyuVy1K9fn6NaPcfpaKIqsLS0RHR0NDp27AhfX1906tRJLe/L86INm6oLB8r/nJub+8QO5GHDhkEul8Pd3R1W2jjEmIRgCRNVUdOmTfHNN98gODgYiYmJcHBwqPZ7xsbGavVkLtKM7OxsJCcnP7NWe/nyZTg6Oj4q27Zt2yIoKAhyuRyNGjXiqNYIcTqaqJpmz56NxMREHDx4sFonAUmShAYNGuC3336Dq6ur+gKSRpRfOKBqVFt+4cDTa7UeHh4qLxwg48USJqqmkpIS9OrVC6+++ioWLlxY5fdJSkrC66+/juvXr6svHFXb4xcOPF64T1848HjhVvbCATJeLGEiNUhLS4O3tzc2b96MPn36VOk9Nm7ciOPHj2P79u1qTkcvo+rCgfI/Z2RkwN3d/ZlRraenp0YeUSPjwjVhIjVo0KABdu/ejaCgIMTHx8PFxaXS7xEbG4vu3btrIB2Vy8/Px8WLF59Zq01OToatre0TJfvmm29q9cIBMk4cCROp0fLlyxETE4O4uDjUqFGjwq+TJAmNGzfGkSNH4OHhocGEhq/8wgFVo9ryCwdUjWp14cIBMj4sYSI1kiQJgwYNgouLS6WuIbxy5Qq6dOmC27dvc4dsBRUWFuLSpUsqR7WPXzjweOHqw4UDZFz4XyORGslkMmzduhU+Pj7o3Lkzhg4dWqHXxcbGIiAggAX8FFUXDpT/ufzCgfKS7d69O8aPHw8vLy/Uq1dPdHSiCmEJE6lZnTp1EBUVhd69e6Nt27Zo3rz5S18TGxtr1Id0FBUV4cqVK8/sQE5KSoJMJns0opXL5QgICOCFA2QwOB1NpCGbN2/GypUrER8fj5o1a77wa5s1a4Yff/wRLVq00FI6MZ6+cKD8z+UXDjy9Vuvl5cULB8igsYSJNESSJIwePRpFRUXYuXPn/4qkqAg4dgxITwdKSnC3pAT+H32E5IwMgyibkpISXLly5Zm12qSkJJSUlKhcq+WFA2SsWMJEGpSXl4dOnTph3LhxGN+3L7BuHVB+T7AkAZKEotJSoLAQNQIDgY8+Anx9AT0o46ysrCeOZiwv3PILB1SNannhANGTWMJEGnbp0iVEtGuH+cXFMAHKRsKqmJgAVlZAt25AVFTZnwV7/MKBp0e1eXl5Kke1vHCAqOJYwkSaNmsWSlavhllhYcW+3soKaNmybMra0lKz2f7r6QsHygu3/MIBVaNaXjhAVH0sYSJN2rIFmDQJyMur3OusrIA+fYB9+9QW5fELB54e1T548ODRhQOPFy4vHCDSLJYwkaaUlgL16wOZmVV7vZUVkJAAVHLH9MOHD584mrG8cC9evIh69eqpHNXywgEiMficMJGmHDjw/PXfiiguBlavBjZufOZfSZKEW7duqdyBnJmZ+ejCAblcjv79+/PCASIdxZEwkab4+wMnT1brLSQrK1w4fBh/PTWNXH7hgKpRLS8cINIfLGEiTbG0BCq6Ges5HgB4r2lTFLdr90Thenl5oXbt2urJSUTCsISJNKG0FDA3L3sWuBqk2rUh27sX6N1bTcGISJdwJwaRJpiYlP2qJhkA8CQpIoPFEibSBJkMUMd0cXFx2Q5rIjJILGEiTQkNBWrUqN57NGoEeHmpJw8R6RyWMJGmvP9+9aaka9YEZs3Si3OkiahquDGLSJO6dQOOHy/bqFVZNjbA33+X/U5EBokjYSJN2rGjamvDVlbA3r0sYCIDxxIm0qTGjYG4OMDODqjoARpWVmWnZL35pmazEZFwLGEiTWvZEjh7tqxULSxU34xkagpYWwNt2wI//QSMHKn9nESkdVwTJtKmtLSyUe7WrUBWFlBSAtjaAr16AR9+WFbCRGQ0WMJERESCcDqaiIhIEJYwERGRICxhIiIiQVjCREREgrCEiYiIBGEJExERCcISJiIiEoQlTEREJAhLmIiISBCWMBERkSAsYSIiIkFYwkRERIKwhImIiARhCRMREQnCEiYiIhKEJUxERCQIS5iIiEgQljAREZEgLGEiIiJBWMJERESCsISJiIgEYQkTEREJwhImIiIShCVMREQkCEuYiIhIEJYwERGRICxhIiIiQVjCREREgrCEiYiIBGEJExERCcISJiIiEoQlTEREJAhLmIiISBCWMBERkSAsYSIiIkH+Hx6CYMatnwsoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3eed7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------4_square1diag_2colors_p1---------------\n",
      "Probability of coloring: 0.2202\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3eef1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for graph, structure in zip(to_graph_2colors, structures_2colors):\n",
    "    probs = get_probs('results/' + graph)\n",
    "    print('---------------' + graph[:-11] + '---------------')\n",
    "    draw_best_coloring_2colors(probs, graphs[structure])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs with 4 colors\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------3_triang_4colors_p1---------------\n",
      "Probability of coloring: 0.0278\n"
     ]
    },
    {
     "data": {
      "image/png": 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Iy8tDbGwsfH19RUciPVVdXQ0nJyfs3LkTSqVSdBx6TJyciWRmw4YN8PX1ha+vL06ePMlipgZp1qwZpk6diqVLl4qOQnXAyZlIJq5du4bp06cjOTkZMTExCAwMFB2JDERBQQE8PT1x/vx5tGnTRnQcegycnIlk4Ndff4VSqYSjoyMSEhJYzKRTnTp1wtNPP43o6GjRUegxcXImEujGjRt45513cPToUURHR2PAgAGiI5GBOnr0KIKDg5GRkQETE85lcse/ISJBdu3aBaVSidatWyMpKYnFTI0qKCgILVu2xO7du0VHocfAyZmoiZWUlODvf/87du/ejVWrVuHJJ58UHYmMxOrVq7Fp0yZs375ddBR6BE7ORE3owIEDUCqV0Gg0UKlULGZqUq+88gri4+ORk5MjOgo9AidnoiZQVlaG999/H5s2bcKKFSvwzDPPiI5ERmrOnDnQaDT4+uuvRUehh+DkTNTIjh49Cj8/P1y/fh0qlYrFTEJNmzYN0dHRKCsrEx2FHoLlTNRIKioqMGfOHLzwwgtYsGAB1q5di3bt2omORUauW7du6NevH9atWyc6Cj0Ey5moEZw+fRoBAQE4d+4cVCoVxo4dKzoS0V13ztvmqqZ8sZyJdKiqqgqffPIJnnnmGXz44YfYuHEjOnToIDoW0T2GDRuG8vJyHDlyRHQUegCWM5GOnD17Fn369MHp06dx5swZvPLKK1AoFKJjEWkxMTFBWFgYn1YlY7xbm6iB1Go1Fi5ciH//+9/46quvEBISwlIm2bt16xacnJyQkpICOzs70XHof3ByJmqA9PR09O/fH/v378fp06fx1ltvsZhJL7Ru3RqvvPIKVqxYIToK3QfLmageNBoNvv32WwwYMAAhISHYvXs3HBwcRMciqpOwsDCsWLECVVVVoqPQ/zATHYBI3+Tk5OCtt94CAMTHx8PFxUVwIqL68fb2hoeHBzZv3ozx48eLjkN/wcmZ6DFJkoRly5ahb9++GDNmDA4cOMBiJr13Z1sVyQtvCCN6DPn5+ZgwYQKKi4sRExOD7t27i45EpBNqtRrdunXDtm3b4OfnJzoO/RcnZ6KHkCQJUVFRCAgIwJAhQ3DkyBEWMxkUMzMzTJ06FUuXLhUdhf6CkzPRA1y+fBmTJ0/GxYsXERMTA6VSKToSUaMoLCyEh4cHcnJyeMSsTHByJvofkiThxx9/hJ+fH/z9/REfH89iJoNma2uLUaNGISoqSnQU+i9OzkR/cfXqVUyfPh0pKSmIjY1Fr169REciahLx8fF49dVXkZmZCVNTU9FxjB4nZ6L/2rx5M5RKJbp164aEhAQWMxmV3r17o127dti1a5foKAROzkS4ceMG3n77bRw/fhzR0dHo37+/6EhEQsTExGD9+vXYuXOn6ChGj5MzGbWdO3eiR48eaNu2LRITE1nMZNTGjx+P06dPIysrS3QUo8fJmYxScXEx/v73v2PPnj1YvXo1hg4dKjoSkSzMnTsXFRUV+Pbbb0VHMWqcnMno7N+/H0qlEgqFAiqVisVM9BdTp05FbGwsSktLRUcxaixnMhq3b9/GjBkzEBwcjGXLlmHFihVo1aqV6FhEsuLo6IiBAwfihx9+EB3FqLGcySgcOXIEfn5+uHXrFlQqFZ5++mnRkYhkKywsDEuWLAFXPcVhOZNBq6iowOzZszFu3DgsXLgQsbGxaNu2rehYRLL25JNPorq6GocOHRIdxWixnMlgnTx5Ej179kRubi5UKhXGjBkjOhKRXlAoFHxalWC8W5sMTlVVFebPn48VK1Zg0aJFGD9+PBQKhehYRHqluLgYTk5OOHv2LLp06SI6jtHh5EwGRaVSoXfv3khMTERiYiJefvllFjNRPbRq1Qqvvvoqli9fLjqKUeLkTAZBrVZjwYIFWLRoEb766isEBwezlIkaKC0tDUOGDEFeXh6aN28uOo5R4eRMei8tLQ39+vVDXFwcTp8+jZCQEBYzkQ54enrCx8cHv/zyi+goRoflTHqrpqYG33zzDQYOHIjQ0FD8/vvv6Nq1q+hYRAaFN4aJwcvapJdycnIQEhICExMTREVFwdnZWXQkIoOkVqvh4uKCTZs2ISAgQHQco8HJmfSKRqPB999/jz59+uCFF17AgQMHWMxEjcjMzAzTpk3D0qVLRUcxKpycSW/k5+cjNDQUpaWliImJgYeHh+hIREbh6tWrcHd3R3Z2NmxsbETHMQqcnEn2JEnC6tWrERAQgGHDhuHw4cMsZqIm1KFDBzz//PNYtWqV6ChGg5MzydqlS5cwadIkXL58GTExMejRo4foSERG6dSpUxg3bhxycnJgamoqOo7B4+RMsiRJEtatWwd/f38EBgYiPj6exUwkUK9evdCpUyfs2LFDdBSjwMmZZKewsBDTpk1DRkYGYmJieIcokUysWbMGa9aswe7du0VHMXicnElWNm3aBF9fX7i6uuLUqVMsZiIZefHFF5GUlISMjAzRUQweJ2eShevXr2PGjBk4efIkYmJiEBQUJDoSEd3HBx98gNLSUixatEh0FIPGyZmE++2336BUKtG+fXskJiaymIlkbOrUqVizZg1KSkpERzFonJxJmOLiYrz77rvYv38/oqKiMHjwYNGRiOgxvPDCCxg2bBimTZsmOorB4uRMQuzbtw9KpRJmZmZQqVQsZiI9cue8bc52jYflTE3q9u3bCA8PR0hICCIiIrB8+XK0bNlSdCwiqoM7/5g+ePCg0ByGjOVMTebw4cPw9fVFSUkJzp49i5EjR4qORET1oFAo+LSqRsY1Z2p05eXl+Oijj7Bu3TosW7YMzz//vOhIRNRApaWlcHBwQGJiIhwcHETHMTicnKlRnThxAj179sSFCxegUqlYzEQGwtraGm+88QaWL18uOopB4uRMjaKqqgqfffYZVq5cicWLF2P8+PGiIxGRjmVkZGDgwIHIy8uDhYWF6DgGhZMz6VxSUhICAwNx9uxZJCUlsZiJDJSHhwf8/PywceNG0VEMDsuZdEatVuPzzz/H8OHD8be//Q2//vorOnXqJDoWETUi3hjWOFjOpBOpqakICgrCoUOHkJCQgODgYCgUCtGxiKiRPfPMMygsLMSJEydERzEoLGdqkJqaGnz99dcYNGgQJk6ciF27dsHe3l50LCJqIqamppg2bRqWLl0qOopB4Q1hVG/Z2dkICQmBmZkZVq9eDWdnZ9GRiEiAoqIiuLq6IjMzEx06dBAdxyBwcqY602g0WLJkCYKCgvDiiy9i//79LGYiI2ZjY4OxY8di1apVoqMYDE7OVCd5eXkIDQ1FWVkZoqOj4eHhIToSEclAQkICxo4di5ycHJiZmYmOo/c4OdNjkSQJkZGR6NWrF0aMGIHDhw+zmInorp49e6JLly7Yvn276CgGgZMzPdKlS5cwceJEFBQUIDY2Fj4+PqIjEZEMrVu3DqtXr8bevXtFR9F7nJzpgSRJwtq1a+Hn54c+ffogPj6exUxEDzRu3DgkJycjLS1NdBS9x8mZ7quwsBBTp05FZmYmYmNj0bNnT9GRiEgPfPzxx7h+/ToPJmkgTs6k5eeff4ZSqYSHhwdOnz7NYiaixzZlyhSsW7cOxcXFoqPoNU7OdNf169cRHh6O06dPIyYmBn379hUdiYj00EsvvYSBAwciPDxcdBS9xcmZAADbt29Hjx490LFjR5w5c4bFTET1due8bc5+9cfNaEbu1q1bePfdd3Hw4EGsW7cOgwYNEh2JiPTcE088AXNzc+zbtw/Dhg0THUcvcXI2Ynv37oVSqYS5uTmSkpJYzESkEwqFgk+raiCuORuh0tJSzJkzB9u3b0dkZCRGjBghOhIRGZjbt2/DwcEBCQkJcHR0FB1H73ByNjKHDh2Cr68vysrKoFKpWMxE1CisrKwQHByMiIgI0VH0EidnI1FeXo4PPvgAGzZswLJlyzB69GjRkYjIwGVlZaF///7Iz8+HhYWF6Dh6hZOzEYiPj4e/vz8uXboElUrFYiaiJuHm5oaAgABs2LBBdBS9w3I2YJWVlZg3bx6ef/55fPbZZ1i/fj1sbGxExyIiIxIeHo7vvvuO26rqiOVsoBITExEYGIjU1FQkJSXhpZdeEh2JiIzQyJEjcePGDZw4cUJ0FL3CcjYw1dXVmD9/PkaMGIHZs2dj8+bN6Nixo+hYRGSkTE1NMX36dG6rqiPeEGZAUlJSEBwcjPbt2yMyMhL29vaiIxER4fr163BxcUF6ejqHhcfEydkA1NTUYOHChRg8eDCmTJmCnTt3spiJSDbatWuHcePGITIyUnQUvcHJWc9lZWUhJCQE5ubmiIqKgpOTk+hIRERaEhMT8dxzz+H8+fMwM+PJ0Y/CyVlPaTQafPfddwgKCsL48eOxb98+FjMRyZafnx+cnJywZcsW0VH0AidnPZSbm4vQ0FBUVFQgOjoa7u7uoiMRET3Shg0bEBERgQMHDoiOInucnPWIJElYuXIlAgMDMXLkSBw6dIjFTER6Y+zYscjIyEBycrLoKLLHyVlPXLx4EZMmTcLVq1cRExMDb29v0ZGIiOrs008/RUFBAZYtWyY6iqxxcpY5SZIQGxuLnj17IigoCMeOHWMxE5Hemjx5MtavX49bt26JjiJrnJxl7MqVK5gyZQpycnIQGxsLf39/0ZGIiBrslVdeQVBQEN5++23RUWSLk7NMbdy4Eb6+vvDy8sKpU6dYzERkMMLCwrB06VJoNBrRUWSLm81kpqioCGFhYUhMTMSWLVvQp08f0ZGIiHSqf//+sLS0xN69e/lM+Qfg5CwjW7duRY8ePWBnZ4czZ86wmInIICkUCoSHh/O87YfgmrMM3Lx5EzNnzsShQ4cQFRWFgQMHio5ERNSoysrK4ODggJMnT6Jbt26i48gOJ2fBdu/eDaVSCSsrKyQlJbGYicgotGjRAiEhIdxS9QCcnAUpKSnB7NmzsXPnTqxatQrDhg0THYmIqEnl5OSgb9++yMvLQ4sWLUTHkRVOzgLExcXB19cXVVVVUKlULGYiMkouLi7o06cP1q9fLzqK7HBybkJlZWWYN28eNm7ciOXLl2PUqFGiIxERCbVr1y7MnTsXCQkJUCgUouPIBifnJnL8+HH4+/ujsLAQKpWKxUxEBGDEiBEoLS3FsWPHREeRFZZzI6usrMT777+PMWPG4IsvvsC6detgY2MjOhYRkSyYmJggLCyM26r+By9rN6KEhAQEBwfDzc0NERERsLW1FR2JiEh2bt68iW7duiE1NRWdO3cWHUcWODk3gurqanz66acYOXIk3n//ffzyyy8sZiKiB2jTpg3Gjx+PlStXio4iG5ycdSw5ORnBwcHo2LEjVq5ciS5duoiOREQke2fPnsXIkSORm5uLZs2aiY4jHCdnHampqcGCBQswZMgQTJ8+HTt27GAxExE9ph49esDNzQ2//vqr6CiywAdf/FVZGXDjBmBqCrRtCzRv/lgvy8zMRHBwMFq0aIFTp07B0dGxkYMSERmeOzeGvfjii6KjCMfJuaICWLsW6NEDaNUKcHcHnJ0BKytg6FBg1y7gAY8102g0WLRoEfr164fXX38de/bsYTETEdXTmDFjkJ2dDZVKJTqKcMa95hwZCfztb4AkAaWl9/8Za+vaXz/+CAwefPe3z58/j7feegtqtRrR0dFwdXVtmsxERAZs/vz5uHjxIpYvXy46ilDGOzl/8AHwzjtAScmDixmo/V5BAfDMM8DPP0OSJCxfvhy9e/fGqFGjEBcXx2ImItKRSZMm4aeffsKNGzdERxHKOCfnpUuBOXNq15jrQGNhgdk9euAPSUJMTAy8vLwaKSARkfF67bXX0KtXL7z77ruiowhjfOV88ybQuXPtWnN9Xt6mDawLC2HGW/2JiBrFsWPH8MYbbyAzMxMmJsZ5gdf4/tTR0UAD/rLbVFfD7Phx3eUhIqJ79O3bF61bt8bvv/8uOoowxlXOkgQsXFjny9n3KCurfQ8iImoUCoUC4eHhRn3etnFd1s7MBHr2BG7fbtj7mJsDlZW6yURERFrKy8vh4OCAY8eOGeVNt8Y1OV+7Bpjp4NwVtbrea9ZERPRolpaWCA0NxbJly0RHEcK4yllHJIWi9hI5ERE1mmnTpiEmJga3G3q1Uw8Z1/Gd7drVTr2OTN3aAAAO6ElEQVQNpK6pgbevL3x8fODt7X33q7u7O8zNzXUQlIiInJyc0L9/f6xbtw6TJk0SHadJGdeas0ZTu42qsLBhbzN8ONK+/RbJyclISUm5+zUvLw8uLi7w8fG5p7hdXFxgamqqoz8EEZHx2LNnD2bNmoXExEQoFArRcZqMcZUzACxYAHz6KVBeXr/XW1sDv/4KPPmk1rfKy8uRkZGhVdoFBQXw8PC4Z8r28fGBo6Oj0e7hIyJ6HBqNBl5eXoiMjMSAAQNEx2kyxlfO164BXbvW/4auLl2ACxeAOvwLrrS0FGlpafeUdnJyMm7evAkvLy+t0u7SpYtR/QuRiOhhFi9ejKNHj2L9+vWiozQZ4ytnAPjyS2D+/Lrvd7a0rJ2aR4zQSYybN28iJSXlnik7OTkZlZWVWoXt7e0NW1tbljYRGZ1bt27ByckJKSkpsLOzEx2nSRhnOUsSMGMGEBX1+AVtaQl8/z0QEtKo0QDg6tWr9y1tU1PT+5Z2u3btGj0TEZFI06dPh62tLf7xj3+IjtIkjLOcgdqC/uYb4OOPa4/zfNCt+tbWtXuj164Fnn22aTP+hSRJKCgo0FrPTklJgbW1tVZpe3l5oVWrVsLyEhHpUkpKCoYNG4a8vDyj2BVjvOV8R3ExsGZN7ZGcV64AzZrVFndVFdCjB/Dee8CYMbW/L0OSJCE/P19ryk5PT0f79u3vlvWd4vb09ESLFi1ExyYiqrOhQ4di8uTJePnll0VHaXQs5zskqfa5zTdu1E7KNja1v/RUTU0Nzp8/r1XaWVlZ6NKli9alcQ8PDzRv3lx0bCKiB9q0aRP+/e9/4/Dhw6KjNDqWs5Gprq5Gdna2Vmnn5ubCyclJq7Td3NxgposjT4mIGkitVqNbt27YunUr/P39RcdpVCxnAgBUVlYiIyPjnq1eKSkp+PPPP+Hm5qZV2t26dePBKkTU5L744gucO3cOkZGRoqM0KpYzPVRZWZnWHu2UlBRcu3YN3bt31zoNrWvXrtzuRUSNprCwEB4eHsjJyTHonSosZ6qX4uJipKamapV2aWkpvL29te4e79SpE0ubiHTizTffhFKpxKxZs0RHaTQsZ9Kp69eva61nJycnQ6PRaF0a9/HxQfv27UVHJiI9c+LECbz88svIysoy2OU1ljM1OkmSUFhYqDVlJycnw8LCQuvSuLe3N1q3bi06NhHJWO/evfHxxx9j1KhRoqM0CpYzCSNJEv7880+t0k5NTUXbtm3ve7CKlZWV6NhEJAOxsbFYt24ddu3aJTpKo2A5k+xoNBrk5eVplXZ6ejo6d+6sVdrdu3eHhYWF6NhE1IQqKirg6OiIw4cPw83NTXQcnWM5k95Qq9XIycnRujSek5MDR0dHrdPQ3N3d0UymJ7sRUcPNnTsXFRUV+Pbbb0VH0TmWM+m9qqoqZGZmapX2hQsX4OLiorWe7eLiYrA3kRAZk7y8PPTs2RN5eXmwtrYWHUenWM5ksMrLy5Genq5V2leuXIGHh4dWaTs6OsLExER0bCKqg7Fjx2LkyJGYMmWK6Cg6xXImo1NaWorU1FSt09Bu3boFT0/Pe0rbx8cHdnZ23KNNJFP79u3DzJkzoVKpDOq/U5Yz0X/dvHlTa8pOSUlBZWXlffdo29raio5MZPQkSYKXlxciIiIwaNAg0XF0huVM9AhXr16978EqZmZmWqXt7e1t0EcKEsnR0qVLcfDgQWzcuFF0FJ1hORPVgyRJuHz58n0n7ZYtW2pdGvfy8kLLli1FxyYySCUlJXB0dIRKpYK9vb3oODrBcibSIUmSkJ+fr1XYaWlpsLW11bo07unpCUtLS9GxifTejBkz0KZNG8yfP190FJ1gORM1gZqaGpw/f16rtLOysmBvb69V2u7u7mjevLno2ER6Iz09HYMHD0ZeXp5B/LfDciYSqLq6GtnZ2Vqlff78eTg7O2uVtqurK8zMzETHJpKl4cOHIyQkBK+99proKA3GciaSoYqKCmRkZGitaV+6dAnu7u5aN6J169aNe7TJ6G3ZsgVffvkljh07JjpKg7GcifTI7du3kZaWplXaRUVFWnu0vb290bVrV4Pa+0n0MDU1NXB2dsamTZsQEBAgOk6DsJyJDMCtW7fuOVjlzteysjJ4eXlplXanTp1Y2mSQFixYgIyMDKxevVp0lAZhORMZsKKiovvu0Qag9aAQb29vtG/fXnBiooa5du0a3NzckJ2dDRsbG9Fx6o3lTGRkJEnClStX7rtH29LS8r4Hq7Ru3Vp0bKLHFhISAi8vL8yZM0d0lHpjORMRgNrSvnjxolZpp6WloW3btlql7eXlBSsrK9GxibScOnUK48aNQ05Ojt4+gY7lTEQPpdFokJubq1XamZmZ6NSpk9ZpaB4eHrCwsBAdm4xc3759MW/ePIwePVp0lHphORNRvajVauTk5GhdGs/JyYGTk5PWHm03Nzc0a9ZMdGwyEmvXrkVsbCx2794tOkq9sJyJSKeqqqqQmZmpVdoXLlyAq6urVmk7Ozvr7aVHkq/Kyko4ODggLi4O3bt3Fx2nzljORNQkysvLkZ6erlXaV65cQffu3bVK28HBgQerUIN8+OGHKC4uxuLFi0VHqTOWMxEJVVJSgrS0tLvbvO4Ud3Fx8X33aNvZ2XGPNj2WCxcuwNfXF3l5eXr3VDiWMxHJ0o0bN5CSkqJ1I1p1dbXWlO3t7Q1bW1vRkUmGxo0bh6FDh2L69Omio9QJy5mI9EphYeF9S7tZs2b3Le22bduKjkwCHTx4ENOnT0dKSopeXXFhOROR3pMkCZcvX9a6NJ6amopWrVppnYbm5eWld5c5qX4kSUKPHj2wePFiDB06VHScx8ZyJiKDpdFokJ+frzVlp6enw9bWVmvK9vT0hKWlpejYpGMRERHYvXs3Nm3aJDrKY2M5E5HRqampwblz57RKOzs7G/b29lql7eHhAXNzc9GxqZ5KS0vh6OiIM2fOwMHBQXScx8JyJiL6r+rqamRlZd3zkJCUlBTk5ubC2dlZ6zQ0FxcXmJmZiY5Nj2HmzJmwsrLCP//5T9FRHgvLmYjoESoqKpCRkaG1R/vSpUvw8PDQuhHNycmJe7RlJjMzE0888QTy8vL04nhZljMRUT3dvn377h7tv5b29evX4enpqVXa9vb2enXHsKF56qmn8Prrr+ONN94QHeWRWM5ERDp269YtpKamapV2WVnZ3cdw/rW0O3bsyNJuAtu2bcPnn3+O+Ph40VEeieVMRNREioqKtNazk5OTAeCerV53vtrY2AhObFhqamrg6uqKDRs2oHfv3qLjPBTLmYhIIEmScOXKFa0pOzk5GVZWVvc9WKVVq1aiY+uthQsXIjk5GTExMaKjPBTLmYhIhiRJwsWLF7VKOzU1FTY2Nlql7enpCSsrK9GxZa+oqAiurq7IzMxEhw4dRMd5IJYzEZEe0Wg0yM3N1bo0npmZCTs7O63T0Dw8PPTi7uSmNGHCBLi6umLu3LmiozwQy5mIyACo1WpkZ2drXRo/f/48HB0dtS6Nu7m5oVmzZqJjC5GQkIAxY8bg3Llzst2nznImIjJglZWVyMzM1CrtixcvwtXVVau0nZ2dYWpqKjp2o+vfvz9mzZqFsWPHio5yXyxnIiIjVFZWhvT0dK27xwsLC9G9e3et0nZwcDCog1V+/PFHREZGYt++faKj3BfLmYiI7iopKbnvHu3i4uL77tHu3LmzXu7RrqqqgqOjI/bt2wcvLy/RcbSwnImI6JFu3LihdWk8OTkZarVaa8r28fGR9Z3Qd3zyySfIvJGJlsNbIi43DsVVxTA3NYedtR2mB07Hi94vwsJMzM10LGciIqq3wsLC+5a2ubm5Vml7e3ujbdu2oiMDAA6cP4CZv82E6rIKZuZmUEvqe77f0rwlJEiY1HMSPh/6OVo0a9Gk+VjORESkU5Ik4dKlS1qXxlNSUtCmTRutKdvLywvW1tZNlm/F6RWYuWsmytXlj/xZCzMLuLR1wYHgA+hg1XRXA1jORETUJDQaDfLz87VKOz09HR07dtQq7e7du8PS0lKnGX48+yMmbpuIsuqyx35NM5NmcLdxR/zEeFiZN81BLyxnIiISqqamBufOndMq7ezsbHTt2lWrtN3d3WFubl7nzykoLYDzIufHmpj/l4WpBSb0nIAlzyyp82vrg+VMRESyVF1djaysLK3T0PLy8uDi4qK1pu3i4vLQQ0U+i/sM/zr8L1SoK+qVx6qZFQpnFzbJ+jPLmYiI9EpFRcU9e7TvfC0oKIC7u7tWaTs5OUEDDTp93QlF5UX1/lyrZlZY/PRihPqH6vBPc38sZyIiMgilpaVIS0vTKu0bN26gy4AuON/nPKpNqhv0Gf6d/JEwJUFHiR+M5UxERAbt5s2b+GbvN1iYuhCVUmWD3qujVUcUzCrQUbIHM5yz2IiIiO6jTZs26NqtK0zNGn5meFVNlQ4SPRrLmYiIDF7r5q1hqmh4OVubN81+bJYzEREZvAC7AFRrGrbebKIwQV/7vjpK9IjPapJPISIiEsi1nSv8O/k36D0szCwwq98sHSV6OJYzEREZhff6v9egy9L2rewRaBeow0QPxnImIiKj8Kz7s7BvZQ8zkwcfVPIgLZq1wMLhC5vs8ZgsZyIiMgpmJmY4EHwANpY2dSroFs1a4KOBH2G0x+hGTHcv7nMmIiKjcrnkMobEDMGfJX+itKr0gT9nbmoOU4UpFo5YiLDAsCZMyHImIiIjVF1TjS0ZW7Dg8AKkXE2BicIEao0aJgqTu1P1pJ6TEN47HN3admvyfCxnIiIyamlX03Dy0kncrLiJ5qbN0bllZ4xwGQELMwthmVjOREREMsMbwoiIiGSG5UxERCQzLGciIiKZYTkTERHJDMuZiIhIZljOREREMsNyJiIikhmWMxERkcywnImIiGSG5UxERCQzLGciIiKZYTkTERHJDMuZiIhIZljOREREMsNyJiIikhmWMxERkcywnImIiGSG5UxERCQzLGciIiKZYTkTERHJDMuZiIhIZljOREREMsNyJiIikhmWMxERkcywnImIiGSG5UxERCQzLGciIiKZYTkTERHJDMuZiIhIZljOREREMsNyJiIikpn/BzhC+LsK7F0YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d3ee6fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for graph, structure in zip(to_graph_4colors, structures_4colors):\n",
    "    probs = get_probs('results/' + graph)\n",
    "    print('---------------' + graph[:-11] + '---------------')\n",
    "    draw_best_coloring_4colors(probs, graphs[structure])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}