notebooks/display_results_test_rw.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a9179a10",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/ZosoV/aco_random_walk/blob/main/notebooks/display_results_test_rw.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "# Display Results\n",
    "\n",
    "In this notebook, you can find some results of our work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f00952db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/israel.pineda/oscar/aco_random_walk\n"
     ]
    }
   ],
   "source": [
    "COLAB_ENV = False\n",
    "\n",
    "if COLAB_ENV:\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/gdrive')\n",
    "    %cd /content/gdrive/MyDrive/Proyectos/aco_random_walk/\n",
    "else:\n",
    "    #%cd /home/zosov/ACO_Random_Walk\n",
    "    %cd /home/israel.pineda/oscar/aco_random_walk/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce84e08c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "histories_greedy_params_q_0\t      histories_params_proximity_dif_size\r\n",
      "histories_levy_greedy_params_omega    histories_params_q_0\r\n",
      "histories_levy_proximity_paramas_q_0  histories_proximity_paramas_q_0\r\n",
      "histories_params_p\t\t      histories_rw_greedy_params_q_0\r\n",
      "histories_params_penalty\t      histories_rw_levy_greedy_params_omega\r\n",
      "histories_params_penalty_dif_size     histories_rw_levy_proximity_params_q_0\r\n",
      "histories_params_penalty_p_0-3\t      histories_rw_proximity_params_q_0\r\n",
      "histories_params_penalty_reduced      random_walks\r\n"
     ]
    }
   ],
   "source": [
    "!ls stuff/results/aco_pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0020b599",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31199aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "RESULTS_DIR = \"stuff/results/aco_pp\"\n",
    "SAVE_DIR = \"stuff/results/plots\"\n",
    "EXPERIMENT_FILE = \"stuff/experiments/rw_experiments_1.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1f315fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Handy Functions    \n",
    "def plot_many_distances(data, x_values = [], legend_labels = [], \n",
    "                        legend_title = \"var params\",  title = \"\", \n",
    "                        save = False , name2save = \"default\",\n",
    "                        ylim = None, list_x = None):\n",
    "\n",
    "  markers = ['o','s','v','D','x','+','p','H','*','^','<','>','1','2','3','4','8','s','p','P','*','h','H','+','x','X','D','d','|','_']\n",
    "\n",
    "  # title = 'Average Distance vs Iterations \\n' + title\n",
    "  figure(figsize=(6, 5))#, dpi=80)\n",
    "  plt.title(title)\n",
    "  \n",
    "  i = 0\n",
    "  for label, distances in zip(legend_labels, data):\n",
    "    print(f\"min dist: {np.amin(distances)}\")\n",
    "    plt.plot(x_values, distances, \"-\", label = str(label), marker = markers[i])\n",
    "    i += 1\n",
    "\n",
    "  plt.legend(title=legend_title)\n",
    "  if ylim is not None:\n",
    "    plt.ylim(ylim[0], ylim[1])\n",
    "  if save: plt.savefig(os.path.join(SAVE_DIR,name2save + \".eps\"), format='eps')\n",
    "\n",
    "  plt.show()\n",
    "  plt.close()\n",
    "    \n",
    "#@title Handy Functions    \n",
    "def plot_many_distances_ax(ax, list_distances, legend_labels, \n",
    "                        legend_title = \"var params\",  title = \"\",\n",
    "                        set_ylabel = None,\n",
    "                        set_xlabel = None,\n",
    "                        ylim = None,\n",
    "                        disable_y_tick = False,\n",
    "                        optimal_distance = None):\n",
    "  #ax.grid(axis = 'y')\n",
    "  list_iterations = np.arange(0, len(list_distances[0]))\n",
    "\n",
    "  #title = r'Average Distance $\\langle x \\rangle$ vs Iterations'+' \\n' + title\n",
    "  \n",
    "  #figure(figsize=(6, 5))#, dpi=80)\n",
    "  ax.set_title(title)\n",
    "  \n",
    "  for label, distances in zip(legend_labels, list_distances):\n",
    "    print(f\"min dist: {np.amin(distances)}\")\n",
    "    ax.plot(list_iterations, distances, \"-\", label = str(label))\n",
    "  if optimal_distance is not None:\n",
    "      ax.plot(list_iterations, [optimal_distance] * len(list_iterations), \"b--\",linewidth=1.0, label = \"optimal distance\")\n",
    "  \n",
    "  if set_ylabel is not None:\n",
    "      ax.set_ylabel(set_ylabel, rotation=0, labelpad=10)\n",
    "  if set_xlabel is not None:\n",
    "      ax.set_xlabel(set_xlabel)\n",
    "  if disable_y_tick:\n",
    "      ax.set_yticks([])\n",
    "        \n",
    "  legend = ax.legend(title=legend_title, ncol=2, loc = 'upper right')\n",
    "  legend.get_frame().set_alpha(None)\n",
    "  legend.get_frame().set_facecolor((1, 1, 1, 0.1))\n",
    "  if ylim is not None:\n",
    "    ax.set_ylim(ylim[0], ylim[1])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9c1488",
   "metadata": {},
   "source": [
    "## Plot Greedy Random Walk -> Vary $q_0$ and advantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0c4cf72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>n_rw</th>\n",
       "      <th>advantage</th>\n",
       "      <th>q_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type  size   n_rw  advantage  q_0\n",
       "0  greedy    50  10000        2.0  0.0\n",
       "1  greedy    50  10000        2.5  0.0\n",
       "2  greedy    50  10000        3.0  0.0\n",
       "3  greedy    50  10000        2.0  0.1\n",
       "4  greedy    50  10000        2.5  0.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet_name = \"rw_greedy_params_q_0\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name).sort_values(['advantage','q_0'])\n",
    "params.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18ccceb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "history_exp_00.npy  history_exp_05.npy\thistory_exp_10.npy  history_exp_15.npy\r\n",
      "history_exp_01.npy  history_exp_06.npy\thistory_exp_11.npy  history_exp_16.npy\r\n",
      "history_exp_02.npy  history_exp_07.npy\thistory_exp_12.npy  history_exp_17.npy\r\n",
      "history_exp_03.npy  history_exp_08.npy\thistory_exp_13.npy\r\n",
      "history_exp_04.npy  history_exp_09.npy\thistory_exp_14.npy\r\n"
     ]
    }
   ],
   "source": [
    "!ls $RESULTS_DIR/histories_rw_greedy_params_q_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90e5334b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "EXECUTIONS = 10000\n",
    "\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e39d7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "val1_name = 'advantage'\n",
    "val2_name = 'q_0'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d04efbb6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min dist: 106.0662\n",
      "min dist: 104.9948\n",
      "min dist: 104.1688\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_many_distances(data = test_data, \n",
    "                    x_values = params[val2_name].unique(), \n",
    "                    legend_labels = params[val1_name].unique(),\n",
    "                    legend_title = r'$\\omega$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e7ae21e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 10000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "EXECUTIONS = 10000\n",
    "\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append(arr['distances'])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a851be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>advantage</th>\n",
       "      <th>q_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>476.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>322.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>422.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>262.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance  advantage  q_0\n",
       "0     476.0        2.0  0.0\n",
       "1     322.0        2.0  0.0\n",
       "2     422.0        2.0  0.0\n",
       "3     314.0        2.0  0.0\n",
       "4     262.0        2.0  0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    tmp_df = pd.DataFrame(data[i], columns =['distance'])\n",
    "    tmp_df['advantage'] = params.loc[i]['advantage']\n",
    "    tmp_df['q_0'] = params.loc[i]['q_0']\n",
    "    tmp_df.head()\n",
    "    frames.append(tmp_df)\n",
    "    \n",
    "test_df = pd.concat(frames)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ad584aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle x \\\\rangle$')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(x=\"q_0\", y=\"distance\", hue=\"advantage\",\n",
    "                    data=test_df, palette=\"muted\")#, split=True)\n",
    "ax.set_ylim(90, 450)\n",
    "ax.set(xlabel=r'$q_0$')\n",
    "ax.legend(title=\"\")\n",
    "ax.set_ylabel(r\"$\\langle x \\rangle$\", rotation=0, labelpad=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123a82f8",
   "metadata": {},
   "source": [
    "## Plot Proximity Random Walk -> Vary $q_0$ and proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17f8761b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>n_rw</th>\n",
       "      <th>proximity</th>\n",
       "      <th>q_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type  size   n_rw    proximity  q_0\n",
       "0  proximity    50  10000  proximity_1  0.0\n",
       "1  proximity    50  10000  proximity_2  0.0\n",
       "2  proximity    50  10000  proximity_1  0.1\n",
       "3  proximity    50  10000  proximity_2  0.1\n",
       "4  proximity    50  10000  proximity_1  0.3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet_name = \"rw_proximity_params_q_0\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name).sort_values(['q_0'])\n",
    "params.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c04724ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "EXECUTIONS = 10000\n",
    "\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee8d6a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "val1_name = 'proximity'\n",
    "val2_name = 'q_0'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92bc7b51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min dist: 108.1894\n",
      "min dist: 108.329\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_many_distances(data = test_data, \n",
    "                    x_values = params[val2_name].unique(), \n",
    "                    legend_labels = params[val1_name].unique(),\n",
    "                    legend_title = '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e7423d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "val1_name = 'q_0'\n",
    "val2_name = 'proximity'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9ae3c1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 10000)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "EXECUTIONS = 10000\n",
    "\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append(arr['distances'])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cbbbadc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>proximity</th>\n",
       "      <th>q_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1966.0</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1822.0</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3716.0</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3722.0</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3572.0</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance    proximity  q_0\n",
       "0    1966.0  proximity_1  0.0\n",
       "1    1822.0  proximity_1  0.0\n",
       "2    3716.0  proximity_1  0.0\n",
       "3    3722.0  proximity_1  0.0\n",
       "4    3572.0  proximity_1  0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    tmp_df = pd.DataFrame(data[i], columns =['distance'])\n",
    "    tmp_df['proximity'] = params.loc[i]['proximity']\n",
    "    tmp_df['q_0'] = params.loc[i]['q_0']\n",
    "    tmp_df.head()\n",
    "    frames.append(tmp_df)\n",
    "    \n",
    "test_df = pd.concat(frames)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "478c2280",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle x \\\\rangle$')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(x=\"q_0\", y=\"distance\", hue=\"proximity\",\n",
    "                    data=test_df, palette=\"muted\")#, split=True)\n",
    "#ax.set_ylim(90, 450)\n",
    "ax.set(xlabel=r'$q_0$')\n",
    "ax.legend(title=\"\")\n",
    "ax.set_ylabel(r\"$\\langle x \\rangle$\", rotation=0, labelpad=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0dade53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: probarle con escala logaritmica \n",
    "# Check table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b571f819",
   "metadata": {},
   "source": [
    "## Combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd903dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "sheet_name = \"rw_greedy_params_q_0\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name)\n",
    "params.head()\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "767b7803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[316.806 , 272.5928, 209.4202, 160.9008, 127.9744, 106.0662],\n",
       "       [242.2852, 218.238 , 181.2306, 148.465 , 123.361 , 104.9948],\n",
       "       [205.2216, 189.6516, 164.8052, 140.1148, 119.8692, 104.1688]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val1_name = 'advantage'\n",
    "val2_name = 'q_0'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8550747e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['greedy $\\\\omega = $ 2.0',\n",
       " 'greedy $\\\\omega = $ 2.5',\n",
       " 'greedy $\\\\omega = $ 3.0']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_labels = [ f\"greedy $\\omega = $ {i}\" for i in params[val1_name].unique()]\n",
    "legend_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5992d352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "sheet_name = \"rw_proximity_params_q_0\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name)\n",
    "params.head()\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3fac1143",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3468.218 ,  739.3348,  295.666 ,  187.3082,  137.0352,  108.1894],\n",
       "       [3510.6074,  733.2034,  296.6062,  187.008 ,  136.935 ,  108.329 ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val1_name = 'proximity'\n",
    "val2_name = 'q_0'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data2 = np.array(temp1)\n",
    "test_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c414d710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 316.806 ,  272.5928,  209.4202,  160.9008,  127.9744,  106.0662],\n",
       "       [ 242.2852,  218.238 ,  181.2306,  148.465 ,  123.361 ,  104.9948],\n",
       "       [ 205.2216,  189.6516,  164.8052,  140.1148,  119.8692,  104.1688],\n",
       "       [3468.218 ,  739.3348,  295.666 ,  187.3082,  137.0352,  108.1894],\n",
       "       [3510.6074,  733.2034,  296.6062,  187.008 ,  136.935 ,  108.329 ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data = np.concatenate([test_data,test_data2])\n",
    "plot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2be154be",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['greedy $\\\\omega = $ 2.0', 'greedy $\\\\omega = $ 2.5',\n",
       "       'greedy $\\\\omega = $ 3.0', 'proximity_1', 'proximity_2'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_labels = np.concatenate([legend_labels, params[val1_name].unique()])\n",
    "legend_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8ba777e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min dist: 106.0662\n",
      "min dist: 104.9948\n",
      "min dist: 104.1688\n",
      "min dist: 108.1894\n",
      "min dist: 108.329\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_many_distances(data = plot_data, \n",
    "                    x_values = params[val2_name].unique(), \n",
    "                    legend_labels = legend_labels,\n",
    "                    legend_title = '',\n",
    "                    ylim = (90,800))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c68f7c",
   "metadata": {},
   "source": [
    "## Plot Levy Greedy Random Walk -> Vary $q_0$ and advantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "141c8b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>n_rw</th>\n",
       "      <th>advantage</th>\n",
       "      <th>q_0</th>\n",
       "      <th>omega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>levy_greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>levy_greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>levy_greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>levy_greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>levy_greedy</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           type  size   n_rw  advantage  q_0  omega\n",
       "28  levy_greedy    50  10000          3  0.0      1\n",
       "0   levy_greedy    50  10000          3  0.0      2\n",
       "1   levy_greedy    50  10000          3  0.0      3\n",
       "2   levy_greedy    50  10000          3  0.0      4\n",
       "29  levy_greedy    50  10000          3  0.0      5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet_name = \"rw_levy_greedy_params_omega\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name).sort_values(['q_0','omega'])\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b03b394b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'stuff/results/aco_pp/histories_rw_levy_greedy_params_omega/history_exp_28.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-b1ecc50cbf0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"history_exp_{}.npy\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'TRUE'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mtemp_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'std'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sem'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/aco_rw/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    414\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'stuff/results/aco_pp/histories_rw_levy_greedy_params_omega/history_exp_28.npy'"
     ]
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bbf3f60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "val1_name = 'omega'\n",
    "val2_name = 'q_0'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "acd58859",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min dist: 108.99009644367275\n",
      "min dist: 104.61721061147175\n",
      "min dist: 104.27673103504289\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_many_distances(data = test_data, \n",
    "                    x_values = params[val2_name].unique(), \n",
    "                    legend_labels = params[val1_name].unique(),\n",
    "                    legend_title = r'$\\phi$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "acc42cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 10000)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append(arr['distances'])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b09f722f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>omega</th>\n",
       "      <th>q_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337.193754</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>194.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205.062258</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193.071068</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>209.482279</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance  omega  q_0\n",
       "0  337.193754      2  0.0\n",
       "1  194.000000      2  0.0\n",
       "2  205.062258      2  0.0\n",
       "3  193.071068      2  0.0\n",
       "4  209.482279      2  0.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    tmp_df = pd.DataFrame(data[i], columns =['distance'])\n",
    "    tmp_df[val1_name] = params.loc[i][val1_name]\n",
    "    tmp_df[val2_name] = params.loc[i][val2_name]\n",
    "    tmp_df.head()\n",
    "    frames.append(tmp_df)\n",
    "    \n",
    "test_df = pd.concat(frames)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dc1741bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle x \\\\rangle$')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(x=val2_name, y=\"distance\", hue=val1_name,\n",
    "                    data=test_df, palette=\"muted\")#, split=True)\n",
    "ax.set_ylim(85, 300)\n",
    "ax.set(xlabel=r'$q_0$')\n",
    "ax.legend(title=r\"$\\phi$\")\n",
    "ax.set_ylabel(r\"$\\langle x \\rangle$\", rotation=0, labelpad=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fde1ce",
   "metadata": {},
   "source": [
    "## Plot Levy Proximity Random Walk -> Vary $q_0$ and proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87323054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>n_rw</th>\n",
       "      <th>proximity</th>\n",
       "      <th>q_0</th>\n",
       "      <th>omega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>levy_proximity</td>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>proximity_2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              type  size   n_rw    proximity  q_0  omega\n",
       "28  levy_proximity    50  10000  proximity_2  0.0      1\n",
       "0   levy_proximity    50  10000  proximity_2  0.0      2\n",
       "1   levy_proximity    50  10000  proximity_2  0.0      3\n",
       "2   levy_proximity    50  10000  proximity_2  0.0      4\n",
       "29  levy_proximity    50  10000  proximity_2  0.0      5\n",
       "18  levy_proximity    50  10000  proximity_2  0.1      1\n",
       "3   levy_proximity    50  10000  proximity_2  0.1      2\n",
       "4   levy_proximity    50  10000  proximity_2  0.1      3\n",
       "5   levy_proximity    50  10000  proximity_2  0.1      4\n",
       "23  levy_proximity    50  10000  proximity_2  0.1      5\n",
       "19  levy_proximity    50  10000  proximity_2  0.3      1\n",
       "6   levy_proximity    50  10000  proximity_2  0.3      2\n",
       "7   levy_proximity    50  10000  proximity_2  0.3      3\n",
       "8   levy_proximity    50  10000  proximity_2  0.3      4\n",
       "24  levy_proximity    50  10000  proximity_2  0.3      5\n",
       "20  levy_proximity    50  10000  proximity_2  0.5      1\n",
       "9   levy_proximity    50  10000  proximity_2  0.5      2\n",
       "10  levy_proximity    50  10000  proximity_2  0.5      3\n",
       "11  levy_proximity    50  10000  proximity_2  0.5      4\n",
       "25  levy_proximity    50  10000  proximity_2  0.5      5\n",
       "21  levy_proximity    50  10000  proximity_2  0.7      1\n",
       "12  levy_proximity    50  10000  proximity_2  0.7      2\n",
       "13  levy_proximity    50  10000  proximity_2  0.7      3\n",
       "14  levy_proximity    50  10000  proximity_2  0.7      4\n",
       "26  levy_proximity    50  10000  proximity_2  0.7      5\n",
       "22  levy_proximity    50  10000  proximity_2  0.9      1\n",
       "15  levy_proximity    50  10000  proximity_2  0.9      2\n",
       "16  levy_proximity    50  10000  proximity_2  0.9      3\n",
       "17  levy_proximity    50  10000  proximity_2  0.9      4\n",
       "27  levy_proximity    50  10000  proximity_2  0.9      5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet_name = \"rw_levy_proximity_params_q_0\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name).sort_values(['q_0','omega'])\n",
    "params\n",
    "#comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "360d3111",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'stuff/results/aco_pp/histories_rw_levy_proximity_params_q_0/history_exp_28.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-ce009701920d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"history_exp_{}.npy\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'TRUE'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mtemp_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'std'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sem'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/aco_rw/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    414\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'stuff/results/aco_pp/histories_rw_levy_proximity_params_q_0/history_exp_28.npy'"
     ]
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ec786fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "val1_name = 'proximity'\n",
    "val2_name = 'omega'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6cf25f1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min dist: 187.7221096280845\n",
      "min dist: 187.5318219043357\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_many_distances(data = test_data, \n",
    "                    x_values = params[val2_name].unique(), \n",
    "                    legend_labels = params[val1_name].unique(),\n",
    "                    legend_title = r'$\\omega$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d86aec69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 10000)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append(arr['distances'])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48483248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>proximity</th>\n",
       "      <th>omega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>229.708204</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238.000000</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>207.341664</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>304.983069</td>\n",
       "      <td>proximity_1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance    proximity  omega\n",
       "0  229.708204  proximity_1      2\n",
       "1  156.000000  proximity_1      2\n",
       "2  238.000000  proximity_1      2\n",
       "3  207.341664  proximity_1      2\n",
       "4  304.983069  proximity_1      2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "\n",
    "for i in range(len(data)):\n",
    "    tmp_df = pd.DataFrame(data[i], columns =['distance'])\n",
    "    tmp_df[val1_name] = params.loc[i][val1_name]\n",
    "    tmp_df[val2_name] = params.loc[i][val2_name]\n",
    "    tmp_df.head()\n",
    "    frames.append(tmp_df)\n",
    "    \n",
    "test_df = pd.concat(frames)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b98dbf62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\langle x \\\\rangle$')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jkdgwTaqEm1yBuEAq9vPoz2a3H8RlQlNzM5jjwAp/UaMhpiaHpo22tLSQ6b14buAKxAWB8Pv9ZGZmJtGagdHZ6Y/8nfprEHYy0BCh2w+if1yBGCahUIiW5iak4Eo0EKlW7gkPLnbsZIwHzc3NZHYLZ7oC0dnjz04mameqeDzdsXOtJ3Y2niprSEPxeobbD2Kw93RDTMOkoaEhvFHJG5MWZ6Qjhofa2uEVH4sXzc3NZJgXf1B8kamCU8Ni8Sa23WqqhGw6/WGBSBVBi8Xn81FbW2tbaOhyDDHF4361tbWDSkJxPYhhUlcXrgAinmyij1tEMLzZzhWIpkaKPRfPsEwRMjyGY8Ni8SY2EyhVenMHAlGBSA1Bi6WkpISysjJbykFA2IuKeuwdHR22bBKLN62trbS1tXWJZSLw+XyUlJQM+HxXIIZJ9MGKN/ui45aZ5ViBaGlpYXLWpS545gjeTR3bLClVBKKzMxxaCqSgB+H1epk6dapt1ztw4EBXeuq//uu/kp+fb9u148Urr7zCG2+8wY033sh3v/vdZJvTI86XWYfTJQKeiwUCbzbVNc4TiFAoRGtb+0V7IKJkmCM3xJTaHkTqCYTdxKYpp0rp9uhzc3JI0xWIYVJbW4sYHjAv3hAnniwa6usd92Fta2tDVS/aAxEl02TEFuzr6OjoSi5w8hc2lqgH4QrExaKQKqXbo5MSJ3/eXIEYJjU1NRhpOZdmTXhzsKwQjY2NyTGsF3raJBcl0wPNzSNYICIbHFNhZ7JlWXRGspf8flcgum90TAWinzMne6yuQAyT6uoaLPPSwl7RNYmamppEm9QnUYHI6GH1KdMrNI/QEFN7RwfiCQuEk2d0UWJTW/0puEhtN6koENF1r/ZWuzrp2Y8rEMOkqroaPDmXHJfIMbuyNOyiLw8iwyN0dPhTNq9+OPg7/CnlQURtzPRAIBAc8T0hYkUhVT6/bRFhaG1zBeKyJBAI0NTYcEkGEwDesEA41YPoOcQUPjYSF6r9fj8YXsf38ogStTE7kmyQCjbHk1hRSBWBiIZzW1vb+jkzeSSqJ/VEEVkrIgdF5ICIfDdy/M9E5FwPbUgRkf8uIsdE5IiI3JEIOwdL1x4Ibw8ehJmG4Ul3nAcRXRPJ7iGLKTsiEE5bN0kEfr8fMbyI6U2JwTZqY7TpUyrYHE9ScSd8S3N4ItYZCDj2+SXKgwgCf6Sqs4HrgG+JyOzIa3/bvQ1p5LVHgTnAncA/ikjvtW6TRNfg34NAAODJcZwHUVdXR7rHIN28VCBy06TrnJGEqoY3m4kHMbwpEWKKxq+jz8zJC52JIPaZpYJAWJZFc2srWZG1QKfuP0pUT+pyVd0Z+bkZOARM6OMt9wO/UlW/qp4EjgFL4m/p4IgKRE8eBIB6sqmsqkqkSf1SV1fXNah0Z6QKRDT112o7TygYSIkQW1QgCtLDX+FWBy90JoJYgYzd9OhUWlrC3RvHZIafn1O99oSvQYjIFGABEC0/+KKI7BWRfxeRgsixCcDZmLeV0YugiMjzIrJdRLYnOpxzwYPoYQ2CsHDUVDvLg6itrSXX03MNmByvIIw8gegqLxJqB7Uc+2WNJSoI+ely0e8jldh/fyr8X0Q/Y2MyXIHoQkSygdeB76lqE/BPwBXAfKAc+N+Dvaaq/lRVF6vq4kQ3uqmursZIy6bX6Jc3h46Odkd9YOtqa8hN6/mxm4aQlWaOXIEQA8SgscmZ7n4sUZtH+YyLfh+pNDc3Y6TnA6nxfxGtGzUuyxUIAETES1gcXlbV3wGoaqWqhlTVAv6VC2Gkc8DEmLeXRI45iqqqatTs2XuAC6GnKoeEmUKhEA2NTb2GmAByvSPPg+j694oJYtBQX+/46qDRAWZPbfCi30cqhw8fxgoFMLyZKfF/ERWEsZEQk1NtTlQWkwA/Aw6p6t/EHB8Xc9pKINq94/fAoyKSLiJTgenA1kTYOhiqqqp6X6AG8OYCztkLUVNTg2VZFKT3LhAFaVBRfj6BViWfLgEXE8RDMBhw7Bc2Sn19PaZAdbuFxxDq6+uTbVJSaW5uBrXA49wimbFEn9f2qiA+j+HY55eoaq5LgSeBfSKyO3LsT4DHRGQ+oMAp4OsAqnpARH4NHCScAfUtVXVUUaNgMEhdXS1SMKnXcyQiEJWVlYkyq0/Onw8P/MUZvc8LijMMDlVUEwgEUqIvsx1UVlZieHyoGKhhdh0rKCjo553Jo7a2Fo+AALlpRkoMivEiGAyGN8p5fKgnl/IKZ3zf+iIqCFXtFjle5wp8QgRCVTcS/ix3590+3vOXwF/GzahhUl1djWVZmGl5vZ9kpmOY6Y4RiLKyMgCKfX0LhGUFqKioYOLEib2edzkR9gTDYo6EvxKVlZXMnDkziVb1TWVFOZ7IYyxIU6oc8hlLBl0eoOFB0vKoqT5FKBTCNB2XGd9FXV0dHiM8KOZ4lTqHCry7k3qIdA36fQiEiEBaLhUVFQmyqm/OnTtHltcgq4dNclGKM6Tr3JFCeXkFGi2XEkk4cMq6UU+EQiFqa+vwGOFnVegzqKoauQLR9VkVD5KWTygUcvTzA6itqSFazCAvzaC21lnZjlFcgRgiUYGIhpF6Qz25lJc7QyDKysoo9vXdqzfqXYwUgQiFQtTU1CARoRcRjLQcysvLk2xZ79TW1hKyrAseRLrQ1NwyYjfLdX1WDQ+kh8OCUW/ZqdRUV2N2CYRQ39DouNYA4ArEkKmoqAj3gfBcWsk1FknLo6amJukPX1U5V1bW5SH0RpopFPhMx3/B7CK8cB9CvBc8QfXkUuHgOHZ0chJ1BAsjm+WcEspMNGfPngUxEDGQtMILxxxKMBiktr4eb8QDLEgXLMtyXNUFcAViyJw/fx5Jy720D0Q3JC0Py0q+y1tfX09La2vXxpy+GO2D06dOxd8oB9CVYZYWk43mzXXcDvhYoiFLb1eIKfz3yBWIMpBwQoWYaRhpOY6e4FRXV6OqMR5g+IdkjxE94QrEEDlz5izqLez/xPTwOcn+wJ48eRK4sDGnL8ZlGpw7f96xBcTs5EJP8QsCId5smpoaHVsVtKqqClMEM/IoR7IHoaqUnSsLh5eix7z5YdFwKBcEPvx7UUTgnRjWdAViCHR0dFBTU42k9y8QTnF5T548iRAe/PtjfJaBZVmcOXMm/oYlmS63PqanuHizQdWxGwZra2vJTTe60gIzPEK6Z2SmutbV1RHo7OzKPgOQtHzKy8sdu9kxmm4e9QBz0wSvKV3HnYQrEEMguig2IIGIuLzJFogTJ05QlGH2WMW1O+MjXkbU67icqa6uxvBmhteTonic2csjSm1tDXneiwe/vDQZkQLRlSEY+/zS8vD7OxxbcqOsrIxMr0FEHzBEKPYZSY8y9IQrEEMgOtgPRCAA1FvAmTNJ9iBOHGd85sDOzU8TMrzGiBCIiooK1HNxJpqkOWuDY3dqqqsvKZeS61FqHLJjP5F0rSHF1EOLZhY6MaYPcObMGUb75KKNYaN9cPb06aTZ1BuuQAyBs2fPhgv09bVJLgZJL+T8+XNJy2Rqbm6mprZuQOElCKd6jssQTpw4HmfLks/58+WXPkdvDojhmP0rsUT3QEQXNqPkpxtUVztzQIwnPQuEcz1Ay7I4c/o04zIvFvhxWSZ1DQ2O83pcgRgCx44dQ3xFiAxwwPUVEwwGkxbTP3bsGAATsga+s3RClsHp02dSovnKUOns7KS+of6iFFcAEQPDQRscY6mrqyPUQz2twhG6FyIcIsy6OJvQ68x+8BD2Sjv8/q4ifVGikzenee2uQAySUCjE0WPHwDdmwO+RjLEAfP755/Eyq0+OHj2KACXZA3/ck7INQqGQ4z6wdnLmzBlQRdIvrbmk3gJOnDyVeKP6ISpa3T2IAp9zUyXjSUVFJdqtYKaY6RgenyMFPjpZK8m+eLIWXfc7ceJEwm3qC1cgBsmZM2cIdHZ2DfoDwpuD4c1MmkB8/vnnjMka2AJ1lKiYHD16NF5mJZ0jR44A9PgsJWMslRXljnP5owNI93BhdEbqtAEm3pw9exa8PQh8Wr4jF32PHj2K15RLNqxmeIRRGWbSxojecAVikEQf4GAEQkTQ9DEcPnwkXmb1imVZHP38CBOzBi4OEK4Qmu8zuwbRy5EjR45gpOWE01q7IZnJ9fp64+jRoxT6zEvqaY3yCT6PcVkLendqampoaWlGfEWXvCZpozhx8mTSKxh058jhw5RkGZg9bLCdlCUcOXzYUem5rkAMkiNHjmB4s/ruA9EDkjmWqqrKhHeOOn/+PG3tHUwcRHgpysRMOPr55SkQqsrBQ4dRX89CL77RiBgcPHgwwZb1jqpy9PMjlPRQ3cUQYUKm8PllLOjdiU5ejIxxl7wmmWPxd3Q4ai9PW1sbJ0+dYkpOz9/FyTkGzS0tjvJ8XIEYBKrKwYOHUN+YfktsdCfqcRw+fDgepvVKdAY8KXvwpY8n5ZjU1tVflvn1p06dorGhHiOr55LmYnggcxw7d+5KsGW9c+bMGerqG5iW2/OzvCLX4PSZM5fl8+qJ3bt3Y3jSwTfqktckM9zCfu/evYk2q1cOHjyIqjItp+fnF32u+/fv7/H1ZOAKxCA4f/48tbU1SC+DSl9IxhjETGP37t32G9YH+/btIzvN6NrOPxiiMx0nfWDtYteu8MAv2X00fMqazLlzZY7Jhtm6dSsCzMzveYCZVRjeLLZ9+/YEWpUcQqEQ23fsQDMn9ZhNKN5sDN8otm7dlgTrembPnj14TWFSLx5Eoc+gyGeyZ8+eBFvWO4lqOTpRRNaKyEEROSAi340c/18iclhE9orIGyKSHzk+RUTaRWR35M8/J8LO/ti5cycARvbkQb9XxITMEnbs2JmwGGMoFGL3rl1cmWtgDNLjgfDCZ3aa0TWYXk5s37EDI2M00kc1XiN7CnDhuSebrVu3MDHHJCet569tsU8oyjDZunVLgi1LPIcPH6aluRkjZ2rvJ2VP4/OjnzumW9vuXTuZmmN09fHoiStyhf379jmmDliiPIgg8EeqOhu4DviWiMwGVgNzVXUe8Dnw32Pec1xV50f+vJAgO/tk586dGL7Ciwq7DQYjezL19XUJi4uePHmSltZWpucNrbOWIcKVuQZ7du923GLfcGhububY0aOQ1bv3AEBaPkZ6niME4syZM5w6dZq5Bb0/SxGhtMBg3779l32Y6bPPPkMMDxIR8Z4wcq8AVTZv3pw4w3qhvLycisoqZvTzXZyRb+Lv7OTQoUMJsqxvEiIQqlquqjsjPzcDh4AJqvqhqgYjp20GShJhz1Bob28PP7TMwXsPUSQr/N5Ezcj37NmDwJAFAuCqPJOW1tbLaj/Enj17UFWkH09QRCBzEvv27U/6hsE1a9ZgijB/VN9dghcWe1BV1q1blxjDkoBlWXz22WbImowYvfdNl/RCDF+hIwRix44dAFzVS3gwyrRcE48hjgkTJnwNQkSmAAuA7n7wKuC9mN+nisguEflERG7q43rPi8h2Edkez1jxvn37CIVC/Q4qfSHeLAzfKHbsSMyMdNeunYzPujQlcjBcmWci4Ki46HDZsWMHhicDGcBmR8meTCDQyYEDBxJgWc8EAgE+WbeW2QV9t4sFKPIZTMs1WfPxR1iWlSALE8vRo0dpamrEyJnW/8nZ0zh06FDCswe7s23rVsZmmhT20Q8ewg27rsg12L5tqyPSXRMqECKSDbwOfE9Vm2KO/4hwGOrlyKFyYJKqLgD+C/CKiPTY21NVf6qqi1V1cXFxcdxs37x5M+JJ78qPHzLZUzhy5HDc46LNzc0cPXqU6XnDe8RZXmF8tsnOyAwo1QkEAuzctQvNmjSgTDTJnIAYHrZtS95i54YNG2hpbeOa0b3PlmO5ZrSHquqarlnr5caOHTtAZECTNcmegqomPDkklubmZg4dPszM/IF9F2cVmFTX1HLaAcX7EiYQIuIlLA4vq+rvYo4/A9wLPK4RyVRVv6rWRn7eARwHrkqUrd3p6Ohgy5atkH1FeLF5GBi501FVPvvsM5us65lNmzZhWcqcwuHZCzCnwODzo0cd2dBksOzZs4e21laMnCsHdL4YHsieyqZNnyVl4dCyLH7/1luMyzKZljuwr+ucQpP8dJPf//6tOFuXHHbs2IlkjEPM9H7PFd9oDG9mUteRtm3bhqoye4DfxVkFHkRgy5bkJxskKotJgJ8Bh1T1b2KO3wn8MXCfqrbFHC+WyEgsItOA6UDSagjs2LGDzk4/Ru7wNSocFx3F+vXrbbCsd9Z8/DHjskzGD6JAX28sGBX+wF4Oce3169eHw0vZA09VNnKvorW1JSlhtl27dnHu/HluHOsZ8N4bU4QbxpgcPnzksttZXVtby5kzp7vW8/pDRNDMSezevSdpiRabN2+mwGcyfoDVlLO9wpQck82fbYqzZf2TKA9iKfAksCImdfVu4P8DcoDV3dJZlwF7RWQ38FvgBVVNWnuvDRs2YHizkczx9lww9yqOHz8etxn5qVOnOHHyJItGDV8cIFx246o8k3Vr16R0NlNrayvbtm2HnCsH5QlK9kQMTwaffPJJHK3rmbfefJO8dIPSQXqCi0d7yPAYvPnmm/ExLEkMJdXcyJ5MW1trwjepArS0tLB3717mFBiD2lw7p8Ck7Nz5pDcaS1QW00ZVFVWdF5O6+q6qXqmqE7uns6rq66o6J3Jsoaq+nQg7e6K5uTmcdZRz5aB3T/eGkTsdgI0bN9pyve6sXbsW0xCuLuo742UwLCr2UFff4KidqYPlww8/JBgMYOTNGtT7REzIncGWrVsT2kToyJEjHDp8mKVjPJh95M73RLopXDvaZNu2rV0dEC8HNm/ejJGe19XrfSBI1iTEMJMSstm2bRuhUIi5gxT4OYXh5JBkZ2C5O6n7IRzLtzDy7FsCEW82RuYEPvlkve2ZCoFAgPWffMKsfIPMYWQvdWdGvkmW12DNmjW2XTORtLe389Zbv0eyJyMZg09mMArnA8Lvfve7/k61jTfffJMMr8Hi0UMT+uvHejFF+P3vf2+zZcmhvr6effv3R9YCB/7ZFjMNsiaz8dNNBIPB/t9gI5s2fUqBz6Qka3BDbW6aweQck40bNiQ1m8kViD5QVVav/gjDNwrSL633MhwkbyaVlRW2F4Pbvn07La2tLCq2z3sA8BjC1UUG27ZtS3rK4FBYvXo1ra0tGKMWD+n94s1C8mazbt26hJTeKC8vZ/v27VxbPLgy7bFke4WFo0zWf/JJSj6z7qxbtw61rEF7gABG3kyamxoTWhWgqamJvXv3UjrI8FKUeUUm58vLk1pw0BWIPjh+/DinT5+C/Dm2hZeiSO4ViJnO6tWrbb3ue+++S4HP5MphbI7rjWtGewmFQnz88ce2Xzue+P1+3nzr90hWCcZg+nh0wyhagCoJieuvXr0aQ+DaMcMT+uvHeAmGQqxdu9Ymy5JDKBTi/Q8+QDLHI+n5g36/ZE/G8Gbz7nvv9X+yTXz66adYljKvaGjfxbmFHgyRpKx9RXEFog9Wr16NmF5bspe6I4YXyb2KzZu32NaU5tSpUxw6fJjrRpsDqr30h9N+ytssytss/u1gO3847e/z/NEZBlfmmXzw/nsJd9WHw9q1a2luasQoGpr3EEW8OZA3kzVr1sR1H4vf72ftmo+ZlW+S20vdpYEyOtNgaq7J6g8/SOmNc1u3bqWuthaj8OohvV/EgPy57N+3L2H7C9as+ZjxWSbjhphJmOUVZuYbfLJuXdK+b65A9EJraysbNm6EnOnhGGY/hCo2oB01aEcNwdNvEKrY0O97jII5hEJB29JH33vvPbymDDi8VN5q0RGCjhCcbLYob+1/ALluTHixOpkbxwZDMBjkjTffxMgcZ0sWmlm0gGAwxB/+8AcbrOuZbdu20dLaxpIxA9sY1x9LIhvn9u3bZ8v1Eo2qhp9hel6ftZf6wyiYg5he3nor/vtDjh07xqlTp1k4zEzCRcUempqbk/Z9cwWiFzZu3EigsxMjf86Azld/DVidYHWibefDv/eDpBdhZI7jww9XD3shqqmpiQ0b1jO/yCTDY284LJYZ+SaFPpN34zhA2snGjRupq61FihbaEiaUtHwk90ref/99WlpabLDwUjZu3EhuujHgjXH9MavAJN1j8Omnn9pyvUSzb98+Tp44gRQs6LG090AR04fkzWbjp5/GPRvt7bffxucxWNBP7az+uCryfXvn7eQkcroC0QOqyvsffICRUYyRMTqu95L8OVRWVgx7dvfxxx8TCAS53qZZZ28YIlw32uTwkSOOL+Cnqrz9zjsYvqIBb6waCGbRwnAYKA5x/ebmZnbv3k1pwdBKtPeE1xBm5xts2Zyc3eDD5Te/+W14H1LezGFfyyicDypxXUeqqqpi8+bPWDzKwDfMyZoR2fT4+dGjSWn/6wpEDxw6dIiys2eR/Llxv5fkXIHhyeD9998f8jVCoRAffvA+03JNxgxwt+ZwWFjswWsK7yVwwW8oHDlyhDOnTyP5pbYmGYhvFEbmeN57733bNw5u3bqVUCjEPBv3sEA4I6atvSPlenscOHCAw4cPQeF8xBh+4oV4s5G8WaxZuzZu2WhvvPEGAtww1p7J2qJiD1leg9/+5je2XG8wuALRA++//z6Gx4dENrTFEzE8kDeL7du3D/kDu3fvXmpq67h2iPnygyXDI1xdaPLppxtpa2vr/w1J4r333kPMdMTGPSxRpGAu1dVVtheB27B+PUUZJhMGmTffH1fkhfexbNjQ/9qYU1BVXn31VQxv1oBDvQPBGLUQteD111+37ZpRampqWLt2LYtGmeSl2/MM00xh6ViT3Xv2JLx0iisQ3aivrw/vuMyd0WeteTsxCuagypBTXj/6aDVZXoOZfTSTsZvFoz10dgYcG9eura1l85YtSJyeo+RMw/Bm8Yc/vGvbNWtqajh46CBXFw4tb74vTBFKCw12bN9Oa2urrdeOF7t27QqHVQoXhSdSNiHeHCR/NmvWrrV9l/nrr78OlsWy8fZ+5q4b4yXTa/Daa7+y9br94QpENz76KFxH3yiIf3gpinhzkezJrP7oo0HHiOvr69mxfQcLRpl9tjK0m5Isg7GZJh9/9FHC7jkY/vCHP4Sf4xDTIvtDxIT8Uvbt22vbWsyGDRtQpd+mQEPl6iIPgWAw6eUbBkIoFOKll36JkZ6HUTDb9usboxaDmLz88iu2XbOyspK1a9awuNikwCbvIUq6Kdw01mTPnr0J7TbnCkQMwWCQDz74MFy7JS0/ofc2CkppaW4edBnw9evXE7IsFtu8c7o/RITFxSbHT5zg1KlTCb13f7S2tvLhhx8iOVciaT22EbEFo2AuYqbZkjYZCAR4/713mZZrUtRPU5mhMjE7LOrvvP22I5rR9MVHH33EuXNlyKjrhl1ivyfEk4kULmTbtq3s37/flmv++te/RlBunhCfyMN1Y7zkpBm8+sorCXt+rkDEsGPHDhobGxLqPUSRrIkY6Xl8OIgwk6ry0erVTMkxKc5I/KO8epQHjyGO21n94Ycf4vf7MYsWxvU+4fWNOWzatGnYaZOfffYZdfUN3DgufkIvItw41qTs3LmkNtDpj6amJl559VWMrAlIzhVxu49ROB8jLZd/+7efDXsj2tmzZ9mwYT3Xj/GQN8zNjb2RZgq3jPNw6PDhhJWedwUiho8++gjDmzWstqJDRUQgdzZHDh8ecFz00KFDVFRWsrg4cWsPsWR6hDkFBus/+STpPZujtLa28uZbb4WL8vnsrZ/VE0bh1SAmv/3tb4d8DcuyeOutNxmdaQ6rf/hAKC3ykJtu8JaDy4D/8pe/pK2tHWP0TbavxcQihgcZvZRz58qGvfHxV6++SpppsGxcfNctF4/2UOAzE+ZFuAIRobq6mt179kDerGFtxhkORv5MEIOPBhjXX7t2LekegzmFiQ0vxbKw2Etbe7tjmqy/8847tLW2Yo66dlDvG8pOeIgU8cufyyeffEJZWdlQTObTTz/lzJmz3DzOY9veh97wGMKNYzwcOHjQkX3G9+/fz7p16zAK5yO+orjfz8iZhuRM5bXXfj1kL/D48eNs3baNpWNMWyso94THEFaMNzlx8iRbt26N673AFYgu1qxZA6oY+YOvFGkX4slEsqeydu26fherOzo62PzZZ8wtMEgbYrVPO5iWa5CXbrLOAcXgmpqaePvtt5GcKwZd0nsoO+GjGEULwfDw2muvDdZkAoEAr7z8MuOzzEEVdRtsHa1Yrh3jodBn8suXXnJUAyi/388//fM/Y6TlDarq7lDFPYo5ZhlBC/7lX346pFn5a6+9RqbXYOkg9j0M5/ldPcpDcYbJa7/6VdzrayWyJ/VEEVkrIgdF5ICIfDdyvFBEVovI0cjfBZHjIiJ/JyLHRGSviMQtoByuULomvDjtjd+i5kAw8mfT2trS7+xgy5YtdPj9w97KP1wMEeYXGezesyeuBewGwltvvYXf34lZvCSh9xVPBlJwNZs3bx50RtP7779PTW0td070Dsp7GEodrSgeQ7i9xMPpM2cctS/ijTfeoKqyEhl786BSk4cj7hDZPDfqOvbt2zvoJl7Hjx9n165d3DjGHNSu6eE8P1OE5eM9nC0ri3uNpgELhIisEZEeRyMRKRCRN/q5RBD4I1WdDVwHfEtEZgM/BD5W1enAx5HfAe4i3It6OvA88E8DtXWw7N27l/r6Oox8+9PpBotkTcRIy+Hjj/tuzPPJunUU+kwm5yTfCVwwyoOqJnWwaWxs5L3330dypyOD6DZmF0bhfMRMH9RaRFNTE7/9za+ZnmdyRZzXHrpTWmhSkm3yysv/SUdHR0Lv3RMVFRW8+eZbSO5VGFkD7xduF0bBXIyM0fziFy/R3t4+4Pe9/vrrZHgNro1ziZvulBaZjMow+e1vfxPXtYgBjS4iMgeoVdUel/pVtR4oFJFeg4aqWq6qOyM/NwOHgAnA/cAvIqf9Angg8vP9wEsaZjOQLyLjBmLvYNm4cSOGJ31YlSLtQkQg5yr279/Xa5OXmpoa9h/Yz/wi++r1DIfiDIOJOSZr165JWvrk22+/TaCzE3OIDYGGi5jpSEEpW7duHXA56ddee42Ojg7untR/tWC7ERHunuSlvqExIdVN++OXv/wlFgbm6BuScn8RQcbcRGNjA2+80d9cN0y4qdM2ri0enPdgB0YkI+3UqdO2pen2eJ8Bnnc/8CZAJEx0e+Tn/yEifx855w/AFwdyMRGZAiwAtgBjVLU88lIFMCby8wQgtmN3WeRY92s9LyLbRWT7UEpV+P1+tmzdimZNs6XWix0YudNR1V73REQ3VCU7vBTLgiKTsrJzSdkT0dzczHvvRb2HgoTfP4pReDVipg3Iizh79iyrV69myWgPoxNQP6snJueE1z3eeutNamoGF5axk5ORBVcpuBrxZiXNDiNjLJI7nXfffXdAPVr+8Ic/hItXDrOp01CZP8pDdprBO+/Er9LrQD+ZdxMWAIA/BX4kIo8THuS/Fzn+Fhdm/70iItnA68D3VPWip6Dh6eegpqCq+lNVXayqi4uLB99rePfu3fg7OjDy4l93aaCIrwjDV9RrPPSzTZuYmG1SGKcNVUMh3P2KQW/0s4ONGzfS2Rn/fQ/9ES4nPYctW7f22+Lz1VdfJd0Ubp2QeO8hli9MTMMKhfhNEgrBRfnd736HmOlx2/U+GMxRi/H7/bz7bt8lVNra2vhk3TrmFZrkxGnfQ394DeGaYpNdu3bFrXx5v/8yEckE0lS1AUBV1wMC/BfgUVUNRY4fAWb0cy0vYXF4WVWj3d8ro6GjyN9VkePngNhgZEnkmK1s3LgRw5uJZF7inCSXnCs5cuTIJTO7yspKTp46xdxCZ3g7UbK8wtRck882fZrwMNO6dZ9g+EYlZN9Dfxj5M1DL6rNG1fHjx9mWoLTI/ihIN1hSbLJu3VoqKioSfv+amhq2bN2K5M1GzPSE3787kl6IZE/hgw8/7DOTcMOGDXT4/cNuCTtcrin2IAy9jlt/9CsQqtoGaGTmj4iUAuOAzshaApHjk4FeUzgkvOPlZ8AhVf2bmJd+Dzwd+flpwp5I9PhTkWym64DGmFCULbS3t7Njx07IviJpex96w8i9Erh0Rh6tozPHYQIBMLfQpKKyKmEtHQHOnTvHiRPHIbfPuUnCkPQijIxiPvlkfa/n/PrXvybDa9hWDnq4LBvvxYBhbfYbKh999BFqaVKqF/SGUTivz7I3qsrqDz9kfJZJic1VdwdLXrrBzHyTtWs+jkuvj4H+6z4A7ozM8F8mvCbRIiJ3xpxzPxcG955YCjwJrBCR3ZE/dwM/AW4XkaPAbZHfAd4FTgDHgH8FvjlAWwfM3r17CQQ647qdf6hIWj6Gr5jNm7dcdHzTpk8pyba/GJgdzC7wIEJCi8Ft2rQJCK/bOIacqzhx4niPM/Lz58+zc+dObhhkWmQ8yU0zWDLaZMOGDQlNVQ6FQnz00cdI9qS41swaLJJZgpGex+rVPW9YPXHiBKfPnGFxsRnXnd4DZfFoD03NLezYscP2aw90lHkL+ArwO8KpqoeA/4fwekSUL9KHQKjqRlUVVZ2nqvMjf95V1VpVvVVVp6vqbapaFzlfVfVbqnqFqpaqqu1bdffs2YOYaUjmWLsvbQ9Zkzh67GhXz4WqqipOnDjJnALniQNAtleYmpPYMNOu3bsxMsYkdXGzO0bOFIAedyqvXbsWQ0h4ccX+uGa0F8uyEpqqvGfPnnDtMwekl8cSLnszi8OHD/UY2//444/xmsLVNjd1GirT80zy0k0+/tj+ysoDGmlUdQdwJXCTqq6OHFuvqtdDeB8EkK6qiQ9iDhFVZeeu3ZAxIS7VIu1AsiailsWBAwcAuspZJLO0Rn/MLTQ5X17B+fPn436v1tZWjh07BpmJz5vvE28eRlruJQIRCoVYt3YtV+WZ5CZpYbM3RmcYTMoJhyoSJe4bNmwIN+ZKQu2z/jAiTaa6J4r4/X42btjAnALneICGCAuKDPbs2Uttba291x7oiZGZf6/7IFR1mX1mxZ+Kigpqa6qRJGzKGSiSORYxvV0DzaFDh8j3xa8ctB1Myw2L7eHDh+N+r/3796OWhWQ76xmKCJpZwr59+y+qEnrgwAEaGhtZ6DDvIcqCUSZl584nJFU5FAqxfccONGuqIydo4s3ByBzLli0XVzTYunUr7R0dLHJQijmE25KqKuvX9772NRScO9LEmeigazhscIlFxISMCezatRtV5cjhQ0x2TiSlR0b5hEyvkZAG61u3bg1vcMwY0//JCcbInkxHR3uX9wewc+dOPIbEvWLrUJmVH7YrEX2rjx07Rkd7O0b2pLjfa8hkTuLkqZMXpSxvWL+e/HSTKbnOGjoLfQaTc0w+WbfOVg/QWf/KBLJnzx6M9Fzw5iXblD6RrIlUVVVy6NAh6hsamZRt3+DSEVJ8Ph933303Pp+PjtDwP1giwqQs4fChgzZY2Dt+v58tWyIbHJ04A82ahJhpF4Uodu3cwdSc5BZX7IucNIMJWSY7d9q/2NmdqIfpuPTyGCSrBFT5/PPPgXBplD179zCv0BkVDLozv8jk3PnztmYRjliBOHHyFJo+xrYsBA11XjTYasie/gjR2fGWLeFspkk21l7qCMKKFSt49tlnWbFiBR3D65nSxeQcg/KKyn43iw2HXbt24fc7a4NjLGJ4IHsamzdvIRAIUF1dzfnyCq7Kd56YxTI9z+Dzz4/GvW/10aNHMdLzEE9GXO8zHMQ3CkQ4fvw4EP7MWZY6MsUcYHZkbdLObKYRKRAdHR3U1dYgaTaWZbD8Fw22WAMv39sXURuPHj1KumkwxsayDD5PuMz5f/zHf7BmzRp8NoVVo15OdOYVDz799FNnbnCMwcidTkdHO7t37+bgwbBHFV2jcSpX5JrhcGacQ4THT5xE05K/sbEvxPBipBd2VejduXMn2WkG45O896E3sr1CSbbJThsFwlkrLQki2rHN1qqfRnq4pwSR3hJGti2XFTMNIy2HivJyJmQJpo2urc8UOto6usoK+GzyTiZkGxgS3jF8zTXX2HLNWC5scJzhuA2OsUjWBAxPBp9++ik+n48Mr8HoDOeFJmIpyTYwRTh06BALF8andElrays11VUYxVPjcn070bQijh0/AcDnR44wNVscGV6KMjXHYNOJEwQCAbze4W/EdO63K45cEAj7PAgx0+joCA+2HR0diGlfjR315tPR0U6uMzbe9ovXEDK85oAKng2FnTt3hjc4RnabOxURE82eyrZt2zl08ACTspw9uEC47/H4LIMjccxCO3EiPOCKb3Tc7mEXkjGapsYGzpw5Q01tLRMc6j1EmZBlEAqFhtzdsDvO/tfGibKyMhAD0py9QB1F0gsJBkNkOCTveiBkmMQtjr1p06Zw7/AM+zY4xmsNyci9ks5OP5VVVRRn2Lh+FIcEgyjFPqGyMn5bmo4dOwbYJxDxenYA4guvAUb3INkV4o3X84vad/bs2X7OHBgjUiAqKysx03Idmf3SE+rJRSHlBKKlpSUu1/786DE0s8Te8FK81pAyJwBCKGSRl2bf84tXggFAbprQ0NgUt3ak4QXqfMTjs+eCcXp2EF2ovpC2bVcF5Xg9v4L08GfMruquI3INIhAIoCkiDgAipJ5AeKClubn/EwdJMBiksaEeKZpm74XjtYYkBuLNRAOt5NhYuTWaYADhvwts/CbnpgmWZdHY2Ehhof3d+Y4eO46m2xheitOzg3A2muEroqysDAHybRL5eD0/ryHkppu2CcSI9CAsywqPuqmCFZ5eZKaQQGSa0NJiv0DU1taiqog3x9brxnUNybR/d6PPlIvs9cVhb0U8CtH5/X4a6utSZv0PwmuADQ2NFPhMPIZNAhHH51eUDhXl9hS+HrkCkVL/9HBD8xTSBzyGxKXXcdfeCtO5+fOX4MkEsHWdIJ50BMN2ZmXZL2x1dXUAiMe+WX68EW8OgU4/hclvVzEgitKF8+ftaZ2TSqOkbYQFIoVGWzM8wNT7rSQbMnDq/Upxsf1ZKqNHh6+pgfhkSMUDCYbXYtqDqSEQ7SHwej2kpcWx210KefDR0hXFvtSwuTjDoLmllWYbQrwjViA0hQRCzDQEqO5IjQEGoMYP4yfYv4ktPz+fnJxctCN5PZQHg6qF+uvwej3U+VPj+dV1WBTFYe0BIDMzPNnRYHtcrh8Xgi0oMMrGLLR4MioiZHZUVE6Nf7HNZGRkIDZmOsQbDXWgQG2KCERnSGn0h5gQB4EAmDp1CuJPDYGgswHUorh4NBVtqfH8Kjpg8pT4bGLLy8sLC7zf3rLUcSVia3aS28MOlKidrgcxREpKSrA6G1ArPml8tuOvwzAMalJE02o6wqGw8ePHx+X6s2fPxuqoQTvjV+vJLqzm8Kawq666iop2CyvB/boHiz+k1LWHmDJlStzuMWPGVUj7+YT3Lh8KGupE/eEue/FIBIgHUTujjcaGQ8IEQkT+XUSqRGR/zLHXYtqPnhKR3ZHjU0SkPea1f7bTlokTJ4IqdCauveJwUH8d+fn5tHSGUiKOXRPxdOIlELfccgsigtUQ34qxw0XVgsaDzJ07l3nz5hEIKedanb2OdKo5hAJXXhm/XeqLFi3C6mwCf13c7mEX2nqGcJI5tAac/90DaLUxySCRHsTPgdge1qjqI9H2o8DrhFuaRjke05r0BTsNmTgx3ANCU+ADCiCB+q5wjdMHGIDzrRamYTB2bHxauRYVFbFo0SJoOoyqc71AbT2D1dnMF77wBebPn4+IcLjeufYCHK4P4UtPZ86cOXG7x+LFixHDwGo6Grd72IXVdJTsnFxMw6CizfnfPYDKiJ2TJg2/10bCBEJV1wM9jsgSTrh+GHg1EbaMHz8ewzBSQiA02I4VaKO0tJS0NC8H6mzcMhsHVJUD9Ral80pJT49fXuDtt9+OFWhDIyEcJ2LVHyAnJ5fFixeTk5PDjBkzONLo3EFGVTncqMy7+mpbCr31Rn5+PvNKS6H5c0eHmTTUAS2nWXbTjUybNo0jjZaj7Y1yqCFEfl4eo0YNv1quU9YgbgIqVTV2SjFVRHaJyCciclNvbxSR50Vku4hsr66uHtDNvF4vY8aOQzsGdn4yido4depUFi1azIF6i5CDP6TnWi3qOkLccMPSuN5n/vz5jB49Bq3fG9f7DBXtbEBbTvGFL9zeNdguWbKE8tYQVQ6diZ5utmjyh1iyZEnc77V8+XKszma01Z6aQfHAajyCaojly5dz6223UdkW4nSLM59dlHq/xdGGELfedpstGx2dIhCPcbH3UA5MUtUFwH8BXhGR3J7eqKo/VdXFqrq4uLh4wDe8ZvEitO0sGoxvY5ThYjV9js+XwcyZM7n++utpDVicanLuh3R/XQjTMFi8eHFc72MYBvfcczdWWwVWe1Vc7zUUrLp9GKbJHXfc0XVs2bJlmIbB9upAEi3rne3VQTJ8Pq699tq432vJkiVk5+Rg1e+L+72GgqpCw36uvHI6U6ZMYenSpWRmZrDhvDOfXZRPywOIIdx66622XC/pAiEiHuBLwGvRY6rqV9XayM87gOPAVXbe99ZbbwVVrIb4lTUeLhrqgObjLFt2Ez6fj4ULF5KW5mW/Q8NMseGlnBx7S2H0xC233EK6z4dVtyfu9xoMGupEmw5zw/XXU1BwoaREXl4ei6+5ht21FkHLWV5ge1DZXx/ixpvCn7V44/V6ufOOO9CWU11ZQk5CW05i+Ru49957APD5fNx//wMcbghxptmZ60j1foutVSFWrLiVwUyW+yLpAgHcBhxW1a4C5iJSLJFSqyIyDZgO2BpsHj9+PDNnzoKmQ46NK1qNR1Er2DUbSE9PZ+HCRRxscGa6ZDS8dP31NyTkfpmZmdx2661o8zE0EJ/KsUPBajiIhjq55557LnnttttuozVgsa/WWSK/szpIIKS2zTwHwp133onH4yVUuzNh9xwIqorW7qKoaBTXXXdd1/G77rqL3JwcPiwLOHLM+LgsgGGaPPjgg7ZdM5Fprq8CnwEzRKRMRJ6LvPQoly5OLwP2RtJefwu8oKq2ryjffvttWP5GtG34Ow7tRlWh8SBTpkxl2rQLlUtvvPFGWjotR2bD7KgO4jHNuHSR64277roLQR0TqlC1oGEvM2bO7DFVdN68eZRMmMDGipBjBpmQpXxaGWLWzJlcccUVCbtvXl4ed9zxBbTpiKP2tGjrWaz2Cr785S9hmheqPmdkZPDQww9zsinEkQZnff/KW0Psrgly1913U1RUZNt1E5nF9JiqjlNVr6qWqOrPIsefUdV/7nbu66o6J5LiulBV346HTddeey0ZGZlYDQeGfS1JHwVGGhhpSOb48O/DQDuqsTpquO22i2d0ixcvZnTxKNZXBIc9wIzLMvCZ4DPDrQrHDaNbVktA2VkT4uZbbklIeCnKmDFjWLLkWrTxAGoNvVGMXc9Pm49jdTZz3xe/2OPrhmFw/wMPUNEW4mjj0AcZO5/dvroQjf4Q9z/wwJCvMVTuv/9+PKaHUPXWIV/Dzu+eqqI1WygsKuKWW2655PXbbruNsWPG8P7ZIKFhhAntfH6qyntnA2RlZfKlL31pyNfpCSeEmJJGeno6y5ffgjYfRzsbhnUtc+xNiG8U4huFZ/JKzLG9Jl4NCKt2B+npPm688caL72Oa3Hf/A5xtDnGqeXiL1fdMTmdcpsG4TIOvzs7gnslDT0vdVBEgpMr9998/LJuGwhe/eC8a9GPV7+//5F6w4/mpWmjtTkaPGRPep9ELS5cupbCggDXnhi7ydj27kKWsOx+kZMIEFixYMKRrDIeCggLuvfcetOnzIWcV2vnd0+ZjWO1VPPbooz2m+no8Hp5+5hmq20N8Vjn0MKGd370D9SGON4Z4+JFHba/AO6IFAmDlypWkp6URqtqUbFO6sFrPoc0n+NKXVvb4wG+55RZyc3JYX+6MjIqOoLKlKsS1117LuHHjEn7/GTNmsGDBArR2Z1KLwGnjEayOGh579NGLQhPd8Xq9PPrYY5xtCbG3Nrmhii1VQarbQzz+xBMYRnKGgwceeICsrGxClRuTGnZTK4BWf8bEiZO46abeRWbRokUsWLCANeeDNHYmN6PQH1LeOxNk0qSJfOELX7D9+iNeIPLz8/nyl7+ENp/EarWn0fdwULXQqo0UFhX1uMgJYc/nnnvv5fOGkCN2d26rCtARtHjggZVJs+HJJ58EqxOrZltS7q9WAK3ZwrRpV3DDDf0v0t98881MmTKZD8oCdCapT0RbQFlzPkhp6dw+PZ54k5WVxeOPfwVtO48mcXe1VbsLq7OZ555b1afAiwjPPvssKiZvnexMqqh9cKaTxs4QX/va833aPFRGvEAA3HPPPRSNKkarPg0vMiYRbTyM1VHDU08+2edO5DvuuAOfL51PztvXoH0oBCMLnKWlcxO6wNmdiRMncuutt2I17E/KDnmrdhdWoJVnnnl6QDNxwzBYteo5Gv0W65KUW//B2U46gsozzzwbl+5xg2HFihVMnToVrd6EhhJflVL99WjtTpYuXTqgMiPjxo3jK48/zpGGELtqkpORdrwxxJaqIHfffQ8zZ86Myz1cgQDS0tJ46sknwhVCk7gvQkOdaM1Wpk+f3u8sNCsrizvuuJN9tSHOtSYvTLGpIkBzp8XKlfYujg2FRx99lMyMTKzytQkVeu2oRWt3cP31NzBr1qwBv2/WrFnccsstrC8PcLYlsc/w84Yg26uDfPGLX7SlZs9wMU2TF154AQ22YVV9ltB7qypW5Sf4fOk888wzA37fXXfdxcyZM3jnTJC6jsROLNuCyusnA4wdM4bHHnssbvdxBSLC9ddfz1VXzUBrtyRlBgNg1e7ECrTy7LMDm9GtXLmSnJwc3jkdSMq+iEa/xdrzQRYvXkxpaWnC79+dvLw8vva1r2K1V2DV7krIPVVDWOUfkZ2dzXPPrRr0+5955hkKCwp4/USAQII2z7UHlTdOBSmZMJ5HHnkkIfccCNOmTeOee+7BajiAlcDUc204iNV6jqeffor8/PwBv88wDL7zne9ietP4zYnOhJXAUVXeOumnJaB87/vfj2vNM1cgIogIq1Y9Gy6OV70l4fdXfx1at4tly5Yxffr0Ab0nKyuLJ596ijPN4RzoRPP+2U5UzEHNuuLN0qVLufba69CabWhH/JvSWDXbsTpqeOGFr5OXlzfo92dlZfHNb71IdXuID88mJlz4zik/LQGLF7/9nfi2FR0CjzzyCKOKi9GKtagV/8+0Blqwqjcxe/YcVqxYMej3FxcX8/zXX+BMc4hPziUmVLirJsj+uhCPPPpo3MO6rkDEcMUVV3DnnXdi1e/Daq9M2H1VFaviEzIyM3jqqacG9d5ly5Zx1fTpfFAWTGiviBNN4QycB1auZMyYMQm7b3+ICF/72lfJzs7CKv8oroNM2FPZwbJly4ZV4G7evHnceeedbKoIcrQhvoPinpogu2tDPPjgQ0ldM+oNn8/HN7/xDSx/A1bN0PdGDARVJVTxCR4DXnjh60Neh1m6dCk33XQTa8/HP1RY12Hxzukgs2fN4r777ovrvcAViEt49NFHyS8oQCvWJSyOrY2HsNrO8/RTTw16FmoYBs999au0Biw+LkvMDDRkKe+cDjC6eFRS9j30R15eHi+++C2sjpq4xbM15EfPr6aoaBSrVg0+tNSdJ554gpIJE3j9ZCBujWnqOix+fzrAVVdNt31DlZ2UlpaGEw5qd8d1oqZNn6Mtp/jKVx4bdnr2c889R2FhEb85Eb+sNEuV1092YnjTePHb345L1lJ3XIHoRmZmJl997rnw4JKAInAabEOrP2PmzFksX758SNeYNm0at9/+BTZXBROS9rq5MkhlW4hnnl0V1/jncFi4cCF33303Vv1erOZTtl47OvPUYAvf/953bdmclJ6ezve+/306LIPfnfTbnjppqfLbE52IJy0cN0/A4DIcnnzySQoKCtDyj+PiBWqgFa3ayPTp07nrrruGfb1wqPBb1LaH+ChOE7XNlUFONYV4dtUq24rx9YcrED2wZMkSFi1aFI5jB5rieq9Q1SbQAF//+vPDSjV89NFHyc7K5q1TnXFdsG70W3x8LsCC+fPjXtJ7uDzxxBNMmjQZrViDBuwr666NR9Cmozzy8MPMmDHDtutOnjyZrzz+OIfrQ+yotndQ3Fge4HRziOe++lVHhQR7Iysri29965tY/nqs6s22Xjss8GsxCPHiiy/aJpalpaXcfvvtbKoI2h5qqvdbfFgW/t71VAIkXrgC0QMiwnPPPYfXYxCq2BC3+1itZWjjEVY+8AAlJSXDulZOTg5PP/MMZ5pDbKuKXxz77dOdqOHhua9+Nem58/3h9Xr5/ve/h0GAUOV6W2blGmjFqtrIzJkzeSAOtYvuvvtuZs+ezbs2pk5WtFl8VBbk2muvZdmyZbZcMxFcffXVfOELX8Cq22PrJlar4QDacpqnnnrS9r7pTzzxBPn5ebx1KmBbVpOq8vapTgzTy9eeH95EcrC4AtELxcXFPPzwQ2jLKdtDFBBOj9SqDYwqLmblSnt2IC9btoy5c+bwYVmQpjiUADhYF+RQfYiHH3kkJWahACUlJTz66KNo8wm0+fiwrhWeea7DYyjf/OY347Nz1TB48cUXEa+X108M3xsMWuHQUnZODl/72tccL+rdeeqpp8LdHyvW2JJ+rv4GtGoTpaXzLmrmZBeZmZk8u+o5yltDbK6wZ6J2sD5cPfaRRx9NWGgpiisQfXD33Xczbtx4tGqj7XFQq24vVkcdz62yL44vIjz/9a8TwuAPp+2Ng3YElbcjNV96KwHiVO69916mTpuGVq4fVq0mbTqKtpzisccejWvNqeLiYp555llONQ8/1PRpRYDy1hBff+GFIaXhJpv09HS+993vQLCVUMUnw7pWeM/KajIy0njxxW/FrfbUddddx/yrr2bN+SAtw0w46Awp754NMrGkxJa1ksHiCkQfeL1evvrV57A6G23deKWBVrR2GwsWLLQ9jj9u3Di+/OCD7K8LcbjePlH7qKyT5s4QL7zwDTwej23XTQSmafKtb34TtHPIRRk15EerNnLFFVdy991322zhpaxYsYJZM2fyQVlwyFlNdR3hjYxLrrkmoT067ObKK6/koYceQpuOYg2jVpNVsxOrvYoXvv51CgsLbbTwYkSEZ1etIqDCB2eGN1HbUB6goSO8dpSM750rEP0wb9688Marup1opz0L1qGqTzFFWbXqWVuu15377ruPkgnjeft0EL8NKXdlLSE2Vwa54447B7yJz2lMnjyZe++5B208jA6hh7VVsw0r1MHXvx6fomjdERG+9vzzdFrCe0McZN453YnpSeNZG9Jwk83KlSuZNu0KtHLDkPrIa0c1Vu12brzxRq6//vo4WHgx48eP595772VnzdAXrOv9FuvLg9xww/UDqg8VDxLZUe7fRaRKRPbHHPszETknIrsjf+6Oee2/i8gxETkiIvYHCwfBM888jcdjEKraOOxrWZGKlQ888ABjx461wbpL8Xq9PP/1F2jwh9hUMbzdnarKu2cC5OXlxrXmSyL40pe+RHZODlbV4MpKq78Bq34fK5YvZ+rUqXG08GImTpzIF7/4RXbVBAddb+tYYzhu/dDDDzNq1PCaVzkB0zT59rdfRDRAqPLTQb1X1cKqWEduTo4te1YGype//GXy83L5wxBL4bx/phPD9PDkk4PbPGsnifQgfg7c2cPxv410jpuvqu8CiMhswq1I50Te84/RHtXJYNSoUTz45S+HS4K3VQz5OqqKVn1GQWFRXDJgYpk1axaLFy9mY0WItmHssP68McTp5hAPPvQwmZmZNlqYeLKysnj8K1/BaitHm48N+H2hqk9JT09LikA+8MADZGdl8eHZgQu9pcoHZwOMKipKStw6XpSUlLBy5QPhUFPr2QG/z6o/gNVexapVzya022FGRgaPP/HkkPp+nGoOsb8uXKkgmQKfyJaj64GB1mG+H/iVqvpV9SRwDBh6LQMbuOuuu8Kzz2Fs/9fWM1jtFTz04JcTssHssccewx+y2DDEctKWKqvLgowuHjWkOjVOZPny5ZSUTAzvcRnATnmrvQJtOcWXVq4cVCE3u8jKyuLLDz7IscYQxwbYonR/XYjzrSEe+8pXeuyKlsqsXLkyXKupatOAnp+G/FC7lTlz5g6oT4fdLFu2jClTJrO6LDjgYoyqyvtnAhTk5yWknEZfOGEN4kUR2RsJQRVEjk0AYqcIZZFjlyAiz4vIdhHZXl09tJaFAyEjI4OVDzwQbmjeVj7o94d73W5l1KjihG10mTRpEjfeeBOfVQ0t7fVAXYjy1hCPPPrYZTPQmKbJI488jOWvR5v69yKsmm1kZWUndSZ+xx13UFRYwJoBFIOzVFl7PsikiSWXtKu9HEhLS+PJJyKl+RuP9Hu+VbsTK+jn6aefSkqKr2EYPPXU0zT4Q2wZYIvSA/UhzraEeOwrjye9UkGyBeKfgCuA+UA58L8HewFV/amqLlbVxfHOEb7jjjvIzc1Dh+BFaMtprPYqHnzwywkdbB9++GEsFdYNstJkSJWPzoXT65YuXRon65LDkiVLKJk4Ea3d3ucsNOw9nOH+++8jIyMjgRZejNfr5b77H+B0czjc1xdHGkJUtYVY+aUvJ62FaLy5/vrrw82Faneg2vv/hwbb0Pp9LF16Q0LXjrpTWlrKvNJS1pcH+63TZKmy5lyQ8ePGOWJTY1I/QapaqaohDX9L/5ULYaRzwMSYU0six5JKeno6K1c+gNVaNqh69aqK1m6juHg0N998cxwtvJSxY8ey4tZb2VYdGtTO3F3VQWraw2EKp9ftGSyGYfDwQw+FvYjmk72eZ9XuIjMrizvv7GnpLLGsWLGC7KxMNvTTh3xDeZBRRUUJydRJFiLCQw89hNXZiDb2nvZq1e1BNchDDz2UQOt65uFHHqE1YLG5su/nd6AuRGVbeDOqE753SRUIEYndbbQSiGY4/R54VETSRWQqMB2Ib+3fAXL77beTm5eHDqL3sbaGvYeHHnowKbnMDz74ICLClgGW4FBVPqsMMXXKFMfXWxoqS5YsIb+gAKvxUI+va7ANbTnFrStWJNV7iOLz+bjjzrs4VN+70J9rDXsYX7zvPkcMLvFk8eLFTJhQgtbv6TEjTa1OtPEA1y5ZwoQJPUanE8qMGTOYV1rKpspQr2sRqson5UHGjxvrGIFPZJrrq8BnwAwRKROR54D/V0T2icheYDnwfQBVPQD8GjgIvA98S/vyJRNIeno6d991V7iOUmfjgN5j1R8kNzePm266Kc7W9UxhYSELFy1id22I0AAWys61WlS0hbj9C19IudIMA8U0TVYsX462nEEDLZe8bjUeAbUctTh/2223IUKvPZB3VAfxej0J91KTgYhw331fDK9F9ODNW42fo0E/9957bxKs65kHVq6kudPqtbnXscbwmt/9D6x0THgwkVlMj6nqOFX1qmqJqv5MVZ9U1VJVnaeq96lqecz5f6mqV6jqDFV9L1F2DoSbb74ZRLAa++9frcE2tPU0N9+8LKk7kFesWEFLp8XnA8iEiQ40ycj6SCThZAENi0EMqgpNh5k+ffqwiyjayahRoyidW8rOWuuSvPqApeyttViy5Fpbyo+nAkuXLsXny+jZC2w8RMnEibZW2x0uc+fOZcqUyXxWGerR6/msMkhebm7SJpI94QyZSjFGjRrFvNJSaDrS74Yrq+koqDXkXg92sWDBAvLzcvut7ROwlL11Ftdee91lP9CMGzcuvDO85cTFL3Q2YHXUOXImvnzFCho6QpxuvjjMdKQhRHsw+Z+zRJKens7SpTdA84mLvofa2YDVXsWK5csd5QGLCHfddTeVbSFOdnt+dR0WnzeEvXYnZQy6AjFEli9fjtXZjLb1vnauqtB4mGnTrmDixIm9npcITNPk5luWc6Qh1GcBsYN1ITqCzgqtxJPFixdjtVddVL7BajkNhJsOOY1Fixbh9Xg41K3O1qH6ENlZmcydOzdJliWH66+/HrUCEFPp1WoOC/51112XLLN6ZenSpWRm+C6ZqO2oDoIIt912W5Is6xlXIIbINddcgy8jA6uh50VOADpqsDpqWLHCGbO65cuXY2nvMWyAHTVBikcVJa32S6JZsGABANpypuuYtp5mwoSShJdWHggZGRnMLS3lcIMSlfmQKp83WixafM1lvzjdndmzZ4f3CoQ6uo5pyxkmTpzkyOeXnp7O9Tcs5WB9iOhyoKqyp85iXmkpRUVFyTWwG65ADJH09HSW3nADtJ7sNRfbaj6GYZqO2UcwYcIEpk6ZwqH6nu3tCConm0LceNMyxyySxZspU6aQl5eP1RoWCFWFtnIWLlyQZMt655prrqG2I0QgEqU422zRFrBYtGhRcg1LAl6vl1mzZoMVLmioVhA6Krj66nlJtqx3brrpJjpDSnukBM65Vov6jhA3OmjtIcrIGAXixIIFC9BQAO2tsXpbGVdNn052dnZiDeuDq+fP52xLqMcqryebw7Oaq6++OgmWJQcRYc6c2UhHBaqA1YlqyNEe1Lx54cGvIzLAHG8KIXLh+EhjzpzZoEFULbSjCrVCzJ49O9lm9crMmTPJzPDRFvkOHm0MP7+oN+skXIEYBrNnzwYRtPXSdQgNdWC1V1NaWpoEy3qntLQUS8PFwLpzvDFEmtfLVVddlQTLksfMmTOxOltAQ10zUSf/H4wePZpRRYW0RwaYk80WUyZPueyTCnqjqwS91dk1WXNyWXrTNCmddzXtkUjvsUaLqVOmOrKhkysQwyAnJ4cpk6egbZf2yw3nZqvjFg1nzJiB1+PheA/priealVmzZjkqiyIRzJw5M/yD1QmhTsaPn5DQqp+DRUSYM7eUjhAocLbFYo7DPmeJZNq0aeEfrADaUUN+QWFSCisOhhkzZhBSCCqca7OYEf0MOgxXIIZJaelcaK8MZ1LEoK3n8HrTHDeTSU9PZ8aMGZxovjjE1NRpUdkWonQEhikmTZoUXty1AqABrrzyimSb1C/Tp0/HUmgLKkFLufLKK5NtUtLIyMjA4/WCFUA6a5k2dUqyTeqX6PNqCSiBkHLFFc78zLkCMUxKS0tRDaHt3fpEtJ9j5syZjpyNl86bR3lr6KJWlieawiueTvN4EoFpmowePSYiEBbjx49Ptkn9MmnSJICulOUpU6Yk0Zrk40tPByuIdjY4orRGf0Q3YLZFnl/0eToNVyCGSTRWre0XSo2rFcTqqGXmTOfs4owlGlIpi+lSdq41RFqad8QONOPHjwMrnEs/bty4fs5OPpMnTwagLQhpXm/cuhOmCmlpaeGFaiuUEgKfk5ODaRj4I5loTn1+rkAMk6ysLPLyC9DO+gsHOxsAHFWmIZbopr2q9gseRGWbRUlJyYjLo48S+wV16pc1lszMzK5nNXbsmBH73KKkpaV1/ezE/Q894Y3YnJeb44iCkD3hCoQNTCwpQWIEQv3hn53q6ubk5JCXm0NV+4Xt/lV+YeJEZ7q5iSA2g8SJ2SQ9EQ1fFhalfs/p4RJb58xpm816I/r8ihzcM9wVCBuYOLEE7awnWg5GO+sQEUe7uhMnTe7yIEIKzf5Q0suBJJPYrCUn7Vvpi64BJkUGxHgSKxCpIvBRmwsKCpNsSe+4AmEDEyZMQEOd4Tx6wh7E6NFjHLlAHWXixIlUt4fLNUTr049kgYgVhWS3eRwo0bBSbm5uki1JPrEhtlTZDxIVCCfb6wqEDXStNWh454sEGigpcWZ4KcrEiRPxhyyCFnSGLhwbqTj5S9ob0UqlTo1fJ5JYgUiVMjFRm51UcbY7qfE/6XC6XPxoTaZgK6McHFcEGDNmDABBVYIKpmGM6FCFz+dLtglDJnaBdqSSKqIQi5OFIUoiO8r9u4hUicj+mGP/S0QOi8heEXlDRPIjx6eISLuI7I78+edE2TkUunZtargRiBXsoKCgIKk29UfUvpAFIUvJy8tNyS+ZXaSiQETXTUbyc4vStZs6hXD6JBIS60H8HOje/X01MFdV5wGfA/895rXjqjo/8ueFBNk4JHw+H+npPlAr/Accv9U/al90u7+TF8oSQaqsO8Qya9asZJvgGJ599tlkmzBoop3jnOxJJLLl6HqgrtuxD1U12pxgM+DMjQMDIC8/PxxiioSZnC4Q2dnZeD0eQhoWiYLCkS0QbpjGJVn015UymTjJN10FxPaenioiu0TkExHptVC6iDwvIttFZHt1dXVvp8WdosKCizwIp4eYRIS8vFyCloYFwuH2xhsnZ5y5XN64HkQ/iMiPgCDwcuRQOTBJVRcA/wV4RUR6zOVT1Z+q6mJVXZzMHZR5eXkRgQh7EKmQelhQWEhIwVLnezzxxhUIF5dLSbpAiMgzwL3A4xrxtVTVr6q1kZ93AMcB5xboJ1z6ACyINIJMhbTJnJzcrq5kTi5vnQhiN1q5uCQSN8TUCyJyJ/DHwH2q2hZzvFhEzMjP04DpwInkWDkwMjIyQBXUQkRSIismIyODaGO5VLA3nriZQC7JwskhpoRNm0TkVeAWYJSIlAF/SjhrKR1YHflP2hzJWFoG/IWIBAhPy19Q1boeL+wQwh6DAkq6z+fohx4lVhTczVYuLsnByR5EwgRCVR/r4fDPejn3deD1+FpkL10DrBUiw5eZXGMGSKxAjHQPwsUlWTh5Mun61TbRJRAaIiMzNQQi1mtwPQgXl+TgZA/CFQib6JqBa4jMFBlsY0XB9SBcXJKD60GMAC7sxLXw+VJjV27s5jB3o5iLS3JwPYgRQGyphlQp2xCb2ummebq4JAfXgxgBpOJs3BUIF5fk43oQI4BYryFVBCJ297C7k9jFJTm4HsQIIBUFwvUgXFxc+sIdFWwidoBNRYGI7cjl4pKK3HfffV2NsFIJJ4eYXIGwidgQTarMxmNFIVVsdnHpjSeffDLZJgwJN8Q0AogViFTxIGIFwskfUpeeiYq6W0cqNZk7dy6FhYXcfvvtyTalV9xpo02k4oKvG1ZKbe655x7Onz/PDTfckGxTXIZAcXEx//Iv/5JsM/rEFQibSMUFX1cgUpvi4mL+5E/+JNlmuFzGuL6pTcS6+aniQaSKkLm4uCQHVyDiQKoIhOtBuLi49IUrEHEgVWbmrkC4uLj0hSsQccAVCBcXl8uBhAmEiPy7iFSJyP6YY4UislpEjkb+LogcFxH5OxE5JiJ7RWRhouy0A1cgXFxcLgcS6UH8HLiz27EfAh+r6nTg48jvAHcR7kM9HXge+KcE2WgLqTLwuvnzF3P33Xfzwx/+sP8TXVxGCIlsObpeRKZ0O3w/4T7VAL8A1gE/iBx/ScN70DeLSL6IjFPV8gSZOyxSxYNIFTsTxbPPPptsE1xcHEWyp5BjYgb9CiBaSGUCcDbmvLLIsZTA9SBcXFwuBxwzQkS8hUFXrRKR50Vku4hsr66ujoNlgydVZuauQLi4uPRFskeIShEZBxD5uypy/BwwMea8ksixS1DVn6rqYlVdXFxcHFdjB0qqeBCpYqeLi0tySLZA/B54OvLz08BbMcefimQzXQc0psr6A7gehIuLy+VBwkYyEXmV8IL0KBEpA/4U+AnwaxF5DjgNPBw5/V3gbuAY0Aak1OphqszMXYFwcXHpi0RmMT3Wy0u39nCuAt+Kr0XxI1UG3lSx08XFJTm4I0QccD0IFxeXywF3hIgDqTLwpoqdLi4uycEdIeJAqngQLi4uLn3hCkQccGfmLi4ulwPuSBYHXIFwcXG5HHBHsjjgCoSLi8vlgDuSxQERSbYJLi4uLsPGFYg44AqEi4vL5YArEHHADTG5uLhcDrgjWRxwPQgXF5fLAQlXtbg8EJFqwjWdLldGATXJNsJlSLjPLrW53J/fZFW9pBz2ZSUQlzsisl1VFyfbDpfB4z671GakPj83xOTi4uLi0iOuQLi4uLi49IgrEKnFT5NtgMuQcZ9dajMin5+7BuHi4uLi0iOuB+Hi4uLi0iOuQLi4uLi49IgrEA5HRCaKyFoROSgiB0Tku8m2yWXgiIhPRLaKyJ7I8/vzZNvkMjhExBSRXSLyTrJtSTQJ60ntMmSCwB+p6k4RyQF2iMhqVT2YbMNcBoQfWKGqLSLiBTaKyHuqujnZhrkMmO8Ch4DcZBuSaFwPwuGoarmq7oz83Ez4gzohuVa5DBQN0xL51Rv542aGpAgiUgLcA/xbsm1JBq5ApBAiMgVYAGxJsikugyASotgNVAGrVdV9fqnD/wH+GLCSbEdScAUiRRCRbOB14Huq2pRse1wGjqqGVHU+UAIsEZG5STbJZQCIyL1AlaruSLYtycIViBQgErt+HXhZVX+XbHtchoaqNgBrgTuTbIrLwFgK3Ccip4BfAStE5D+Ta1JicTfKORwJ1w7/BVCnqt9Lsjkug0REioGAqjaISAbwIfDXqjriMmJSGRG5Bfivqnpvkk1JKK4H4XyWAk8Snr3sjvy5O9lGuQyYccBaEdkLbCO8BuGKg0tK4HoQLi4uLi494noQLi4uLi494gqEi4uLi0uPuALh4uLi4tIjrkC4uLi4uPSIKxAuLi4uLj3iCoSLi4uLS4+4AuHi4uLi0iOuQLi4xAkRuU9EXu927Bsi8vfJssnFZTC4AuHiEj/+EvjTbseOA7OSYIuLy6BxBcLFJQ6IyNWAoar7RWSyiHwj8pLbD8IlZXAFwsUlPswHomWibwemR36eDexJhkEuLoPFFQgXl/hgANkiYgJfAnIi1VyfAV5JpmEuLgPFFQgXl/jwLjAN2A38MzAH2A78NNpC1sXF6bjVXF1cXFxcesT1IFxcXFxcesQVCBcXFxeXHnEFwsXFxcWlR1yBcHFxcXHpEVcgXFxcXFx6xBUIFxcXF5cecQXCxcXFxaVH/n8Oexjlew+g2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(x=val2_name, y=\"distance\", hue=val1_name,\n",
    "                    data=test_df, palette=\"muted\")#, split=True)\n",
    "ax.set_ylim(85, 300)\n",
    "ax.set(xlabel=r'$\\omega$')\n",
    "ax.legend(title=\"\")\n",
    "ax.set_ylabel(r\"$\\langle x \\rangle$\", rotation=0, labelpad=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65bd8d1",
   "metadata": {},
   "source": [
    "## Combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee67e579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "sheet_name = \"rw_levy_greedy_params_omega\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name)\n",
    "params.head()\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n",
    "\n",
    "#mean, std, sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ee3fe999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[168.48593646, 161.88682256, 160.97399934],\n",
       "       [156.81792373, 149.27965427, 148.62307319],\n",
       "       [147.30318799, 141.19269577, 140.41660537]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val1_name = 'advantage'\n",
    "val2_name = 'omega'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data = np.array(temp1)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d3727b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['greedy $\\\\omega = $ 2.0',\n",
       " 'greedy $\\\\omega = $ 2.5',\n",
       " 'greedy $\\\\omega = $ 3.0']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_labels = [ f\"greedy $\\omega = $ {i}\" for i in params[val1_name].unique()]\n",
    "legend_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d6e8ec5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot histories_params_p\n",
    "sheet_name = \"rw_levy_proximity_params_q_0\"\n",
    "params = pd.read_excel(EXPERIMENT_FILE, sheet_name=sheet_name)\n",
    "params.head()\n",
    "\n",
    "res_dir = os.path.join(RESULTS_DIR, f\"histories_{sheet_name}\")\n",
    "\n",
    "temp_list = []\n",
    "for exp_idx, row in params.iterrows():\n",
    "    file_name = \"history_exp_{}.npy\".format(str(exp_idx).zfill(2))\n",
    "    file_name = os.path.join(res_dir, file_name)\n",
    "    arr = np.load(file_name, allow_pickle='TRUE').item()\n",
    "    temp_list.append([arr['mean'], arr['std'], arr['sem']])\n",
    "\n",
    "#temp_list\n",
    "data = np.array(temp_list)\n",
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3eb14297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[197.11191062, 188.62233574, 187.72210963],\n",
       "       [198.31129037, 188.12185718, 187.5318219 ]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val1_name = 'proximity'\n",
    "val2_name = 'omega'\n",
    "temp1 = []\n",
    "for i in params[val1_name].unique():\n",
    "    temp2 = []\n",
    "    for j in params[val2_name].unique():\n",
    "        temp_df = params[(params[val2_name] == j) & (params[val1_name] == i)]\n",
    "        row_id = temp_df.index[0]\n",
    "        temp2.append(data[row_id,0])\n",
    "    temp1.append(temp2)\n",
    "    \n",
    "test_data2 = np.array(temp1)\n",
    "test_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f146074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[168.48593646, 161.88682256, 160.97399934],\n",
       "       [156.81792373, 149.27965427, 148.62307319],\n",
       "       [147.30318799, 141.19269577, 140.41660537],\n",
       "       [197.11191062, 188.62233574, 187.72210963],\n",
       "       [198.31129037, 188.12185718, 187.5318219 ]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data = np.concatenate([test_data,test_data2])\n",
    "plot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "717a9e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['greedy $\\\\omega = $ 2.0', 'greedy $\\\\omega = $ 2.5',\n",
       "       'greedy $\\\\omega = $ 3.0', 'proximity_1', 'proximity_2'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend_labels = np.concatenate([legend_labels, params[val1_name].unique()])\n",
    "legend_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "20b24677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min dist: 160.97399934258698\n",
      "min dist: 148.62307318580014\n",
      "min dist: 140.41660537040673\n",
      "min dist: 187.7221096280845\n",
      "min dist: 187.5318219043357\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_many_distances(data = plot_data, \n",
    "                    x_values = params[val2_name].unique(), \n",
    "                    legend_labels = legend_labels,\n",
    "                    legend_title = '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c10059c",
   "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": "3.6.9"
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