evaluation/visualization_batch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization: Batch evaluation results of backoff configurations\n",
    "#### Load the data and enrich with config information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# use LaTeX fonts in the plot\n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "# load data\n",
    "df = pd.read_csv(\"results/BatchTestResults-takeMoreButCommon-2.csv\",encoding = \"ISO-8859-1\", skipinitialspace=True)\n",
    "df['Config File'] = df.index\n",
    "\n",
    "# load details from corresponding config files\n",
    "for i in df[\"Config File\"]:\n",
    "    with open('./configs/config_'+str(i)+'.json') as json_file:  \n",
    "        data = json.load(json_file)\n",
    "        df.at[i, 'backoff'] = data['Layers'][0]['Backoff']\n",
    "        df.at[i, 'Condition'] = data['Layers'][0]['Condition']\n",
    "        df.at[i, 'threshold'] = data['Layers'][0]['Threshold']\n",
    "        df.at[i, 'threshold_float'] = data['Layers'][0]['ThresholdFloat']\n",
    "        df.at[i, 'merger'] = data['Layers'][0]['Merger']\n",
    "        df.at[i, 'splitter'] = data['Layers'][0]['Splitter']\n",
    "        df.at[i, 'stepsize'] = data['Layers'][0]['Stepsize']\n",
    "        df.at[i, 'Parallel Execution'] = data['Layers'][0]['ParallelExecutions']\n",
    "               \n",
    "        if df.at[i, 'Condition'] == \"tooUnlikelyRecommendationsCondition\":\n",
    "            df.at[i, 'Condition'] = \"tooUnlikelyRecommendations\"\n",
    "        \n",
    "        if df.at[i, 'backoff'] == 'splitProperty':\n",
    "            df.at[i, 'Parallel Execution'] = 2\n",
    "            df.at[i, 'Description'] = \"Split Property (\" +  df.at[i, 'splitter'] + \" \"+ df.at[i, 'merger']+\")\"\n",
    "        if df.at[i, 'backoff'] == 'deleteLowFrequency':\n",
    "             df.at[i, 'Description'] =\"Delete Low Frequency (\"+df.at[i, 'stepsize']+\")\"\n",
    "                \n",
    "df['Parallel Execution'] = df['Parallel Execution'].astype(int)\n",
    "\n",
    "df[\"f1\"]=2*df[\"precisionAtL\"]*df[\"recall\"]/(df[\"precisionAtL\"]+df[\"recall\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final plot of the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmzatOBwOM378+L7Lly/fmJWV5bPZbPLXv/61yGq1sm7dOnthYaG18XFjxoxxA9xyyy29Zs6cubOpsi+//HLXk08+GZo3b17CTTfdVDVo0CDP+PHjK08//XSPx+NR8+bNSwAYNGiQZ8OGDfYuXbq4CwoKrMuWLYsaMWKE58knn9x7HTZt2mQfOXJkswFwa4wYMcLjcDjC9b+z3333XZTNZts7HK2p89m/f39PXl6e/eyzz3avX79+7zV8/vnnU/bs2WP74IMPtnm9XvXWW2+l1v+s/rovX77cecYZZ3jqt2dlZfmqq6stXq9XORwOyc/PdwwdOnTvz5uqPzc31zl//vztPp9PjR49euAPP/xQdfbZZ7fpedE0TTsc5hkzZnR0G7TjTHFx8Z3p6ekt+ifntJmD328uiw+FZe9/T7vFFL7n0kGFw3okeA517KH8/PPP9i+//DKuurra3LdvX98nn3wS/8UXX8RlZGT47rjjjvLBgwf71q5da2+8/b///W/U3LlzU3bu3GmzWq3hESNGeOvLHDx4sG/NmjWOjz/+OH7p0qXRP//8c9TEiRMrTSYTffr0CTz88MPdHn300d29evUKrlixwtmrVy//RRdd5AKYMWNGlyVLlsR5PB7OPfdc14ABA/zvvfdewuLFi2MmTpxYuXPnTmvjck866aS9bVy8eHGM1WqV8847r+7MM8+se/HFF1NXrlzpmD9/ftKjjz66MzMzM/DII490Wb58eeyAAQM8CxcujF2wYEFC9+7dvatXr3Z8/PHHSXa7Pej1ennrrbdS9uzZYxk4cKD3pZdeStuxY4d9+PDh7hdffDFt8+bNjlGjRrl+9atfVUfqcH744YcJmZmZvpNPPtlbX0dmZqZnxYoVUR9//HGS0+kMXHPNNTW7d+82z549OzUnJ8e5detWe0FBgeOMM85wud1u9cYbb6Tk5+fbvvvuu9jCwkL7BRdcUJuSkhJ8/fXXU5cuXRrdu3dv/5tvvpmcl5cXNWDAAM8NN9xQ+Y9//CN5xYoVUfPmzUuaOnVqyejRoz3PPfdcyqJFixK6du3qLSwstL777rvJbrdbDR061PPQQw91X7dunT07Ozt6woQJFUOGDPEd/B1yFFijghR8G084tO9O0GIPc8HDhXQ/5bDfy3D472cwbpwXLVoU+/LLL6cuWLAg9t1330285557inv37h0oLCy0PPvss+mbN292jB492vX666+nLFu2LDYzM9Pz8ccfx//nP/+JT0pK8m/evNn24YcfJlVVVZmioqJCb731VkpRUZHlV7/6VfW8efNSFixYEHv66afXDR8+3P3cc8+lZWdnOz///PP4CRMmVP74449Rb7/9dvKePXssl1xySa3dbher1RrOzc2Nmj59ehlAw9+7tLS0wODBg329e/f2TZs2rbfT6QzeeuutpTNnzuyyatUq53vvvZd49tln12VmZvpHjRrlevbZZ7vk5uY6Pv3004Qbb7yxcvDgwX6fzyevv/568rfffhsTCATU448/XrRkyZKolrz3X3311ZTmfo/GjRtX4/F4mDt3btKyZcuilixZEnPjjTdWLlu2zFlfx6hRo9zPPfdcl/pze8kll9Q++eST6dnZ2c7Nmzfbly5dGpeQkOA///zzXW+99VbyypUrnStWrIj67rvv4rp27eo9+eSTvfXXfdWqVVFut1vVX4PrrruuumvXrr6ZM2emLV++PKqgoMD+7LPP7tq0aZOt/nWdc845rpkzZ6bl5eVFDR061L1w4cL4r776Ku67776LslqtMmXKlPL6Hs56xcXFUenp6bOO5L2qaZrWWno2La3NdYbZtDStQxyl2bSOF4FAAJPJxNy5cxO7du0abOlsXFr70LNpaZrWEXQworW51gYjmqadGD755JPYN954IyUpKSlYP0Od1nnoYETTtI6gc0Y0TdO0dnHVVVfVXnXVVbo3RNM0TdtLT6WhHQ3hcDh8XK6crGmadjyK/M0ON7ujpmlaG9PBiHY0rC0tLY3XAYmmaVrnFw6HVWlpaTywtqPbomnaiUcP09LaXDAY/H1RUdHrRUVFQ9ABr6ZpWmcXBtYGg8Hfd3RDNE078egEdk3TNE3TNE3TOoT+1FrTNE3TNE3TtA6hgxFN0zRN0zRN0zqEDkY0TdM0TdM0TesQOhjRNE3TNE3TNK1DdOrZtFJSUiQjI6Ojm6FpmqZpmnZ88HqNrw5HhzUhJyenTERSO6wBWqfSqYORjIwMsrOzO6bygBe81RgzHiqITgWTuWPa0tY8lRD0Gc+VGaJTQLXRkiB7z1tkljZbNNhjW358XSmEgmCxQ1RSK44rh3AgUmcM2GOaP8ZTDQH3vu8VYIsDe3TL69WOCVVuP/5gmDBgN5tIjLZ1dJMOqcYTwBsIAaCUIiXGhmrh72jDYwESoqzYLEf+t0tEKHX5EIEom5lYh/WAfSrr/PhC+9bNMwFpcc3f8NR6A3j8RpvtFjPxUQeWrWlaG8nPN74OGNBhTVBKbe+wyrVOp12DEaWUCfgMWA7YgH7AZBHxtGc7WsRbBWveh125xk3qr14FjpNgpGApuIrBXQGbF8E1cyGxd9uUHXDDjmUQCsCGzyG+B4y5G6ISmz+2rgw+nwbrP4O7NrasPhGo2ArLX4HeZxrbBv6iZccGPfD2ryDraiMoi+0Cg64AdDByvNlaVkf2tgpm/XsTi6ad0+mDkTW7qtlU7MITCLFwXREzrx1GZlrLgvpou5lFecWEwmE+yN7Jny/IZExmKibTkX3gUOry8ad5OZS5/Hx065nENhFjeAIhrn/tR64Z2QOAwelxXJh16GCktNbLS99uYXDXOBKcVi4+Kf2I2qlpmqYdWzqiZ2SZiDwKoJT6BBgHvNMB7Tg0RwKcPgV2rzRudI8nmeeDNdoIGExmyHkTLny4bcq2RcPgK8FkgswL4W+D4czbmz8u4IHC5TDuNXiqFYFRXSl8chv89mOwRG56QoGWH5+cCWPvM44x609jj1eD0mPJTI3h7R93dHRTWmRk70TO7JdMWITkGBsvfbuFp8YNw2ppPs0vFIZfR4KBvqkx/P27AkZlJOG0Hf6f+xpPgO82lfHqjadyzcs/HHLfs/unMPW8TEJhwWI+dHt9gRCz/7OZP43tR7d4B0opAqEw1maO0zRN044f7foXX0TCDQIRC9ADaOFH4O3M6gBzpx7FdvhsMcawrHDA6B3JGNN2ZVvsRq9SxVajh+O+wpYN06otgoTeIKHm920o6DPqC3iNoKpgKfhdLT8+5IdlLxg9Mtu+A18rjtWOGVE2C75guPkdOwmH1Ry5MRcq6/ycnZmCxdyyng2bxUStN8CWEhcrCsp55YaRRxSIhMPC9nI3ozKS2Dv88iCUgp0VHl5dspW//GsNm0tchEIHP+/V3gCL8oqId1j5eNUuPs3dRa23FR8maJqmace8Dvn4SSl1CfA58LmIZDf62R+UUtlKqezS0tKOaN6JwVsDW7+FxAzofkrblx8OQdAL3882ej0OxVUKhSsgpb+RLwL7ejmarScIpRuMPJjBV0L2XKhq4afftmi46mUY8Vs473/gw8lGYKNpnUCdL8iqHZV4AyHOG5jW4pwRAIUiGA5TVO1l+dZy/EcQiJXV+Siv85Ee7yAciUXsB+mhiXdaefa64Vx/ei/+dG4/rn3lB8rr/ActW8QY2vXTtgrGDkzDEwjzYc5ORA4d9GiapmnHjw4JRkRkoYhcCvRRSk1p9LM5InKqiJyamqonWjgq3JXw83tGwDD8N+BMaLuyRYzE85T+MPQaWDUPAnWHPibohdx/wuJH4T//a/R2/HsGuEqar8/qAGcSWJ1GvT1HQeW2lrXVbIfoZHDEGT068d3B727+OE07ymo8Af69vpjtFW5uPqtPq3JcRIQYh4WB6XHcOjaTv/y/NVR7Dr+3odYb5IPsQp5esJHHv1xPcY2XN3/YRmmt74B9LSZFcoydOKeVeKcVbyBM+BCBhdWsyEyNoW9qNInRNob3jGdDUS3BkA5GNO1EopQapZT6Vin1g1JqhlLqqcjjkDcoSqmJSqnKFpR/c3NlNdo/TSn1plJqt1LqNy09rjWUUvcopUQp9XjkNc9QSv3YmnYeL9o7gT0L6CMiX0Q2FQB927MNLeauMD4lL1wO1TuhajvEpLdslqbOLBw2AoS1H8IVzxuvyxEPsV3bJmdCKShaY/Q67PgRYtKa7+WwRcO4OcZzqxNWvQUXzjACm+Y4EuDsabA7F7qcBAVL4PLnWt7WLd9AQi/jOIujbQMzrdOocvupdPuN4UulLkxKkRhtw2ntnJNSfLOxhGcX5TN7wgh2VbqpcltIjbUT1YLhVoGQsLOyDqVgzpKt/HpkD6Lth/86k6NtPHzlSQD4AmGWb61g0lkZTe4rAqt2VBLvtPLO8h1cM7I7jkOc48QoGw9cnsUnubsZf1pP3v+pkN+d1adFuTGaph0/RGSFUupbIEZEZgAopS4DFiulRolI8CDHvaOUeqwFVdwMfAu0aPiDiJQopd4EhojI/JYc01oi8rRS6ingcRFxASilsgHv0aivM1Pt2R2ulOoHPAOsBKzAYOB2ESlqav9TTz1VOmxqX3eFkccQbvD+T+5n3Dgfy8JhqNllDGuq50xou2Ckrgx8NUZvhzXKOF/RrejhcldAzW6jl8LZghm4ADxVRp5IwA32OIhOMxLoW1KXv8441uIwAs3WtFU7ZlTW+dldvW+4YIzdQlqsA6etcwYjJTVeSl37eh6i7RbSWhiMlNZ6qfEGCYeFaLuFaLuFeGfbTM5Q7QlQUuMlLdZOfNSBvTXlLh9ufwhPIES0zUy03UJCE/s15PEHqfEGqfUGiLFbSYyyYu+kQaKmHRc6x9S+OSJyaqNtMzCCkbsbbPsRmCEiC5RSj2B8iB4CakXk6cg+20QkI/L8VmAgUAbEA/cAFwFzgI+BDSLySlP7SaMbYqXUWGBmE+00AzOBciAR2Cgic5RSbwHDgclAJfCFiGQppX4BPAT8VkTyG5UlQKyIuJRSj0bKVcC7ka8bgPOBByP1TQbyIm2/T0TKIqkPU4CfMHKxRwO3A78ARovIWKXUdcAcEUmI1Psr4FJgK9AbmA6MAF7GuEf3R17Ln0UkWykVCzwb2b8LUBFp21vAVBGZq5SaC0SJSKt7ktq1Z0REtmDMntX5RSW1bp2LY4XJBAk9jcfREJ1iPA7X4Zx3Z8Lh9Wgcr9dYO0BitK3TT+fbUFqco0XrczQlNdZBaiuW9mmN+Mjwq4NJjrGT3MoynTYLTpuFLof5ejVNO65tB3pFbrhHi8jFAJEhXYtEJLd+R6XUYIyb8CwRkUjPxpUi8olSahswS0S2HWw/4JMWtun3gLXBhExrlVJLgTuAFcAq4G4grJQaBhQCrzcORBp4SikVAM7ACH4qlVJPAk+LyB1KqXSMtSVWAKNEZJdS6mbgf5RSd2EEBCeLSJFS6veAQ0T+q5TagRGYICLvK6Xqg7dE4CWgr4h4IkHgH0XkeaXUx4BdRP6ilLoWuAnIBu4HNjcIACeLyAdKqV8C9TP/VETOa6sdp9NFaZqmaZqmace43sAOYBgQpZS6L7K9EGg8lGEIxkrV90Ym/AgAcU2U2dL9DmYYsKXB9wUYw7nWK6U2YAQAXYBXgPEYvQyzD1HevZGekX7su7EHWA8QCTJSgSTgxkibkzB6iFIweiPqRxhtBc5upv2ZGFMj3tGgrIb11gdNpUD9R1vDgL/X7yAicyNPnwdmKaUWA9764WatpYMRTTtc4ZAxRCwcWa/lWBriFfAaw+kkbOTpOOKPrDxfZLgbYkwG0NIhdq0R8O6b7Sy2BQvj+VwHTvNsjz30UEt3JYR8YLIYEyO0ZLhfC4kINZ4gvqAxfXVStK3ZdTiOFhGh2hPAHwyjlCIhyoLV3PZDo1zeAO7IyurOg6zafrRUuf34g2ECoTACmJWia4Kz3eoH4zxX1gUIhMPYLCYSnNZWzYqmaScypdTFgAP4N0bwcIaIPBn52fnA5kaHrAE8DfY5BSPQAOPGXSmlhmAMczrYfs216U5gNcYQpnp9I3UDvAc8DLwPfAEsARaISLNJ9pHRQ/ttavC8DCM4eDXSc5KM0ZNSBniUUl1FZA/752HXEgmylKTjwQIAACAASURBVFIO9gVvmzHyUmaKSDASBHU7SL31VmMsVI4y/oj9QUReFZGcyPcvAH9p7jUeTHsnsPcDHsUYj9YDKBeRR9qzDZrWZtzlsOpt2LIYhl8PI67v6Ba1XP4CY/KCujIo3wKXPwtxXQ+/vLAfNnxulLfrJ7j8b20/FNBVDPOuNhbT/MUzze/vr4MPboauw43JCmLS4eTrmw5GQn4oy4c1H0LKAEjqa3xtw2F8pbU+Plm9m593VuGwmHn06iFYOig1orTWxz9+3E6s3cLKHZVclNWFS05Kb/NgoaLOz9JNZeys9LClzMVjVw8lNdbepnUcjNsf4pZ/ZHN6H+MajuqT3K7BiNsXZO3uapZuKqNPSjTDeiTgtJoPmdCvaScqpdSpwDmATSn1ABCFkVt8fiR5fVFkxq0nMG6yE4H7lFITgXil1J8iuSCvKqWew7hx78a+G+QFwH0Yw6smH2K/+vakAjcCXSPtqZcJ/A54NjK8KRF4XkQ2RH7+KcYQqAmRoGEnxjCnpl7z9MjTe5RSL4pIcWS7PVL3MKXUNSLyUWQ42W8whnRtBXoB/1dEwpEhW69H8mv2DtIVkXKlVK5S6nGMHpPqBudpGvC8UqoQo/fpr0qpAZFrMFQptbBBG04Fnoi85v8BEoCvGryU54HrRaSgqdfZEu2dwH4a0E1EPol8nwfcKCI5Te3foQnsmtYcb7XRC5D9hjFN8GmTO7pFLRcKGJ99hAOw5Cmj/efff/jl+euMCQtCAfjub0ZPy+g/tVlzqSuDTYuMHpf1n8LVLzd/TG0xfHk3jJ9n9AAJRlDS1CfTtUVG4HLDR0awEvRHeo3aLo/B5Q0gQEFZHS99u4Xnx5/cYYnawVCYWl+QxCgbxTVexjz1DUvuGUvX+La9Wa/zBYm2W/D4Q0x/P5eJp/fm7P5HkFPWCoUVbub9uJ37LhvEtxtLGNk7kXhn++UN7a7y8L+f5/HcdSfjtJnxBkJYTKrDesM0ba9OmsCuHblI0v3NInJzO9RlweixuhYoFpFvD7es9l6B/af6QKRB/c0sQqFpnZQjvuWLM3Y2ZitYrMbwMosdumQdWXm2aCNgqN1jnJdTbmibdoIxPGtnNgy8DGytnFq7rhR+fAX+/QiUrj/4ApyV26DnaKOHZN2/jF4YU9t2HMc4rMQ6rHSGQToWs4nEyCxXSkFanB3zURg+5LCaKa7xsqfaw7kDUhnW4wiHA7ZCWITcwipe++9WVhRUUOsNtutiij9sKecXQ7tSXOPlyzV7qPYEMJs6w9XXNO14FJnxqr43o7m8kbZwLkby/BlHEohABy16CHunFVvYoGurfrtegV3T2kPADXtWGzfgfc458vLCQdiTawx3OthN/+FwFRlDvhytnDHNFgUT3oUh4+DUyfD2uP2ntG7IUwmr3zECn16j4Z1fG4HMca7c5WPW15t4Y9JppMQcjeFTgtsfYuWOSraXuwmG2ycYKHf5SIq2Mfs3J/PL4d04pVciE19fTpnr4KvBt7WyWh8vfrOZGLuFod3j271+TdNOLCJSKyK/E5FTROS7dqjvPyJyo4jceaRldUgwopQ6DzgPmNb4Z3oFdk1rB94a2LwYdq2EM+9om9yIuK6QdRUM/iUs+Isx1KktbPkGCpYa+TnZc2FXDmz73lgz51CsUcawrpg0cMRBUr+DByOJvaHLEKOHxxZjrHMTbMOAqhMqqfXy2Jfr+eO5fembEoPpKHxqbzaZ6JMSzbgRPUiNtfN+dmGb19EUtz/EJ6t2sXRTGZ/9vJvkGDu7qzwEQs28Z9rQgC4xjOydSHKMDYfVjIgQbO49q2nafiIJ421d5iNKqSsP47iLIzkYXyqlBjX62WSl1J/brpUnlnafTUspdTkwBmM+5q5Kqd4isqy926FpR8xdDkEfVBeCyWos1mixQ1RrV1poZ+GwkWz+02tw3dvGDFVBj7FYpPkw/iQEvUY+hrvMWIL7p7/DSVe3zSKaYAQ3ATcok5HH4XdB2qCmcz8aUiYoXmcEIqWbjJ6Og814Fp0KKPBVG19DgTZf4LTK7cftD1FQVkeV20+1J4A9GG6zBQlbo7TWx4Q5y/nz+ZnYzCZ2VrpJcFqbXMjwcFV7AoTCgi8Ywh8M89nPu5kRWcn9aItzWrjhjAy2lLqwmU28u2IHF2V1OSpD0Q5maI943vhhG6W1Pmp9QfqkRGPvqBkLNO3YdScwq43LfLjxAoctISKLlFLXA9mNR/VEFv3T4zAPU3snsI/EmOasPis9GnhRRN5san+dwK51au4K2NEoju51RudfSDEchuI1UL1z37bYbpA+5PACiFDAyBUp32zkWST0NvJGDmchyua4y6F6F3Qd1vy+dWXG/lU7ICoF4rsZAdfB/l+4SqByu/HzhF5Gj0obqqjzs2ZXFb7Avk/HR/dNJq4DgpFKt5+fCir223Z63+Q2DYxqvQHKXX62l9fhtJnpmRhFnNNKtL19PgMrc/korvFSXOOlS5yDlBh7uy6sKCKUuXwUlLmJspnpGu8g+agMhdO0VuqkCexKqYEYM16twVgNfA3GYnuzMFZOn9/UKuxKqd9irOPxOMZsUicDt4tIgVLqBWAXxirruzAWNpwN5IrIDKXU+xhT/ZowRutMwJh5axZQEjkuV0TmRdr4JkYw8kKDdsdFykREblZKPQVcD7wJZAHVIjI5su8BK5+LiFcp9VqkfTHAHhF5Vil1BsaK6DmAD/g1MEBEqg7/zHdO7RqMtJYORjRN0zRN09pQ5w1G7gQGA38GumOshbFMRDIiP78EuKvhKuzAnSKSG1lh/UIR2ayUGo9x4/4HjPUxzoisWn6miPwQmQo3IxKMDBWRNUqpx4BYEbldKfVHYKSI/CHS27EeOEdESpoKRiJtGUuDWayUUl6gi4hUK6XWYUyZG8YIfBqufF4ZWfn8qgYzzeYCY0SkNrJPlIjco4zV3NeLSIvWRDmW6EUPNU3TNE3TtI72GkbPyFJgIzC90c+bW4V9a+TrZuCkyDofd2CsweHEWOduP5FAZBzGquUXNqina4N61gLpGD0lLVUsItWR5/Urmady8JXPu0bWA6nBWKgwGWM9Fdi3EvvPraj/mKKDEU3TtLYQCho5JyJGnoq5jfIvQgHw1Ro5MPZYYzrmJviDIWq9QYC2Hw4U9EdWs1dGG1qYWxQOC7W+IGERom0WbJZGc6a4y43zZYtpdk0XEaHOF8QfEuwW04HDvYI+8LnAYgOL08iFasjqPCAPSESoqDMmWoh3Wlu0BkgwFKbas/8Hk06rmahDDD+r9QYIhoQYuwVr43OgaVq904EnReRBpdQzwG+BUKR3Yhj7ejkOtgp738j3A4A8pVQCUCoilymlTgLejZSzl1IqC2N417kiEoj0PqwGfA3q+RWwrakGK6UuoOkgpalhR02ufK6UGg7cIyJ9I2U2Tq7vvEOY2kh7r8CejhGZDheR09qzbk3TtKOmrgw2fGFMlRz0wMWPtU3ukKsUVs0zApGyfMgYAwMvNWYJa6DM5eOD7EI2FNXSLzWG2y/of+R11wv6jdyoknVQUQCx6XDKTRB96MULPf4QX+cVsbvKy54aL7F2M5PO6rMvUKorhU9uA2s0XPZ0s8FIlTvAx7m78AZCbCxycevYvmSmxRprd9QWQe67RrDhiIPMC4zFPEWMPCiTBc6YekAwUury8ed/rmJw1zj+zyUDWxSM+IJhZi/eTCgUxmYxYTIp/nBO3yaDkVpvgPziWr7bXE5qjI2xA9Po1o6rwGvaMSYJeC6ywngqxkrmGcBMABG5q6lV2Bscf6FSahJGvsmfATNwZ2TNjRRgllKqJ/BLIDESiHwMrANuifRWKIzg5Gml1F+BYKTuf0UCj2FAciTQIVLXm8DVGOt7nImRJxKvlLoGqMDIDZksIg81tfI5UA2sV0q9DmzAGKI2WSn1NvtWRF8rIsdt3kJ7J7D/GiMJ5+GWrLypc0Y0TTsm+GqNGb/8dfCvP8KE99omGPG7jeDG4gR/Lbx6Dkz6CpL67rdbtdtPWGDtrmq+WLOHJ69pQYJ/S4WCRq+INQp8NfDGZTBhPiT3O+Rh3kAIq9mExx8kGBamvZfL1SO6c9XJ3Y3ztPnfEN8TPp0KN34CMYeeyr3OZyxaaDabWLermmcWbmTOjSOJlxr4cDL86hVjgoKgz1g757PbYfw8Y+2YkN8ISqz7AoEaT4Cv84pJjbXzxvfbeOH6ES1Krq/zBbnrg9W8csNIvIEQgVAYq9mEw3pgj9Xmklr+sWw7/+eSgUTbLLj9QWIc7T9hgabtp5PmjBxhedvqc0u0Y097r8D+IfvGwGmaph0f7LEQ08X4JL4t2aKMqaJtUaDMxnPTgTez8VE2EqNtqKPxF91sMYaceSqMoGT4BGO2tGY4rGbMJkWMw4rTZqZ7onPfbF01eyBlIMR2bXEzou0WgmHB5Q2ws9LDX688yeiNCLiNXpW4blC43Bgqp5Qx5fTKebDyH8bCmeHQ3rLCYWFbeR2j+yYf1gxbgVCY+St28N5PhRTX+Ag1sZhjMBxm/opC7rp4ILurvKzfU4MvqNcZ0bS2ppSaiNETMaWj26Idnk43eFWvwK5pmtaIp8oYBjbm7o6ZOjrkh9KNsOwlSBt80LyVpngDITbsqcVuMTO8R4IxPKtojTF9ciiyMGYLoyiLycR/NpTwdV4xfVNjjI0BD2z6GnbnQlIfeG+i0fMy/m3of5ExrO2da42AJKLM5aPSHSAtbl9uTUtXCDCbFC9PHMn5g9K4YHAak9/8ieIa7wH7BUNCYaWHW9/OISnaRqnLx6NfrD8g30TTtCMjIu+ISKKIvNTRbdEOT6cLRvQK7JqmaQ24K+Cn1yGhJ/S/uM0XY2wRZwL0PRcu+ius+QD2tGxSF5cvwI9by/lxazlTz8skMdpm9IrUlcKuHMhfAK5iY40aT/NT58c4LPzmtF48cPlgznn6GyrdfmPoVVx3o5cltiv0OhsqthgLkMamQ1wP6He+kc8TsXpnFTWeANnbKvji591sL69jW5kbjz90iNoNDqsZm8VEWpyD7glOxg5MZc3O6gP2s1tM9EmJYtyI7qTG2snqFsfSTaX4As3XoWmadiLpdMGIpmnaMcfngtpiY8hQwGskTLsrmj+uOXXlsPB/jPJ6jjJyNpoot9odoNzlo9odwO0PUeMJUOX2H3n9YPQyuEqMJHF3BZTkGYFRM7yBEAvXFjPvx+3cdGYGvmCY0lqvke/S77xIoNDdGAIWk4qRN3poJTVeSmq9rCqsol9aNFaTyZiJK20QhHzG7FwlayGpH1RsNa5J1XbY+i103zc8/fS+yZzULY4ucQ7S4x04bWYSo60t6h3xBkJsL6+jpNZLYaWHJfmljOideMB+Sil+c1ovfiyooKLOT7nLzzkDUnHY9CrsmqZpDbV3Avu5GFO1XYqxquSzIuI52P46gV3TtGOCuwI2LTJu2utlXQmJGUdWrqsE1n5kTO9bb8g4iO+x325lLh+f5O7aL3fh2pE9jZ6II+UuN3pCqguNXojup0J0KthjDt10X4D/5pexs9K9d9vwHgmc3jd53051pcYQqwGXGPkwh1Dm8vHdplJ8wTA9EqMY2CWWlNjIMCtXCRStNQKSLkOMZPvKrVCcZzzvdbrRZuuBM1lV1PlZUVDOOQNSibI1n8BeUedjR4WbDXtqcVjNnJqRSHKMHWcTCewef5BSl591u6tJcNoYkB5DcrRehV3rYMdhArt2bNMrsGuapmmapp0odDCidTKdOhhRSpUC2zu6HVqbSQHKOroRWofR1//Epq//iU1f/xNb4+vfW0R0YrAGdPJgRDu+KKWy9SchJy59/U9s+vqf2PT1P7Hp668dik5g1zRN0zRN0zStQ+hgRNM0TdM0TdO0DqGDEa09zenoBmgdSl//E5u+/ic2ff1PbPr6awelc0Y0TdM0TdM0TesQumdE0zRN0zRN07QOoYMRTdM0TdM0TdM6hA5GNE3TNE3TNE3rEDoY0TRN0zRN0zStQ+hgRNM0TdM0TdO0DqGDEU3TNE3TNE3TOoSloxtwKCkpKZKRkdHRzdA0TdM0TTs+eL3GV4ejw5qQk5NTJiKpHdYArVNp12BEKWUCPgOWAzagHzBZRDxN7Z+RkUF2dnY7tlA7aurKoHQDFOdB91Ogx6kd3SJNO764y2HXSqjeBUl9oEsWROv/9Z1NcY2XdbuqODOxBtu2xZiGXQchPxQshZCPcNpJuJzd+Pe2APFOK8N6JPDFmt17j79sSFfCYWFhXhFXDOtGSoy95ZXXlRFe8yHBsLG+mLnX6XhThxJttx7ysPI6H9vK6thU4iLeYWVErwTSYh2YTOqwzoHWwfLzja8DBnRYE5RS2zuscq3T6YhhWstE5BEReQCIAsZ1QBu09mayQFJfUAo2Lero1mja8ScqGdKyYNAvYPPXsOgh8NV2dKu0RuwWE+d2V5i3fI3pxxfBUwGx6dBnDOHMCzF9OR17WR7nDkhlaPd4nDYzabF2Lj0pnYuz0rGYFIvyinj/p53srmryc7yD89Viqi7EPGQcpsFXUKJSKHP5mz0swWkjMcrG+QPTiHVY+OUL31Pm8h3mGdA0TdtfuwYjIhIWkUcBlFIWoAewsT3boHUQZwLEdYOopI5uiaYdn8JhiO8OMWlwys1Quh6C+oaxs0kw+TAVLME0+o/G38R60WkElINgl2HY41MREcwmxbpd1dgtZoJhQSnYVl7H9af3pldy1GHVLyKYLBZMSrG2xIdJNd+7oYC+qTGkxTkYmB5HIBQmFOld0TRNO1IdksCulLoE+Bz4XET0OCxN07QjZYr8OfdUQs4bcOlT4NTBf6ciAtU7CPc4laXrGo1S2ZWD/ZsZyKAr8CVkkrOjkns++pk+KdGkxNjZXeXlD//IweUNEj7cQCChFzLocgJl25Ev7uYC12ekO4PNHlY/HMsbCLG5xMXE03sRbe/UKaeaph1DOiQYEZGFInIp0EcpNaXhz5RSf1BKZSulsktLSzuieZqmacemunL4fjZkXQXpw/YFKFrnUFcKm/+Nik7llB5x4HeDpwrcFZDSH9859+PPX4w17/9x0eB0RvRMYOWOSob3TGBQegwP/TKLF77ZTLXHj9sXpMzlp8rd/DCrvZQJ6XUmnpQh8Mu/YfrvM/jdLRvK5/IF+HFrOT/vrOKWMX2Jcx46z0TTWkMpNUop9a1S6gel1Ayl1FORR0Izx01USlW2oPybmyur0f5pSqk3lVK7lVK/aelxraGUukcpJUqpxyOveYZS6sfWtPN40a7/qZRSWUqpyxtsKgD6NtxHROaIyKkicmpqqk6+PG6EAsaQERHjEfQbD03T2kZdKXwxHU6eCOlDIRzQw7Q6IVFmWPYCtuxXoLoQWfYiEg6BLQYx2yAqBap3EgqHqfEGSY934vYF8YeEgrI6usY7WbOrhvxiF2//uA1vINzyypUiWFdJjCWIcpdDXFfcLTi+yu3n41W72VHhZtJZfTCbFB5/6AjOgqbtT0RWAN8CP4jIDBG5N/L94siw/oMd9w5Q3YIqbgZafJMvIiXAm8BuEZnf0uNaQ0Sejjx9PPKaZwCPAt6jUV9n1t79rD7gd0qpEYAVGAzc3s5t0DqCuwJ+eg1K8iDggf8+DaP+YIxv1zTtyJVtgrJ8WP6yMWGEPQZOu8VIjtY6h5g0tmbexKrCKnrFCKNchYTG/g/mknVQvA6Hv45w2EvN0N/x3boi+qVGE2U1848ft+P2B6n1BnnwiiwSoqxcPqwr40/tSXp8y6dnDZdswJ6/AMIBpHI77us+xNqCv8GeQIi3ftjG4K5xFJTVATBlbD+cNvNhnwpNa46IfKWUehi4EFiglHoE4741BNQ2uJnfSyl1KzAQKAPigXuAi4AM4E6l1AYReaWp/USkReMflVJmYCZQDiQCG0VkjlLqLWA4MBmoBL4QkSyl1C+Ah4Dfikj+Icp9NFKuUyn1MUa61gbgfODBSH2TgbxI2+8TkbJI6sMU4CeMXOzRGPfWvwBGi8hYpdR1wBwRSYjU9SvgUmAr0BuYDowAXgZWAv7Ia/mziGQrpWKBZyP7dwEqIm17C5gqInOVUnOBKBFpdU9SuwYjIrIFPXvWickWZQQfDVlaMSWlpmmHljoYfvvJ/tts0R3TFu2gEqPtjB2QhpUAvi6PEDbbUD1Ox9xlCCgTJlsMlqDirEwh1mEkmqfF2QkLRNvM2K1mQmHh1nP7YbW0bnCDKXUggeh0QkEfYonC6ogmytx8GTF2C+/eMnq/bbZW1q1ph2k70Ctywz1aRC4GiAzpWiQiufU7KqXqP+DOEhFRSr0JXCkinyiltgGzRGTbwfYDGv0BPajfA9YGEzKtVUotBe4AVgCrgLuBsFJqGFAIvH6IQOQppVQAOAOYKSKVSqkngadF5A6lVDpgjpQ9SkR2KaVuBv5HKXUXRkBwsogUKaV+DzhE5L9KqR0YgQki8r5S6ulIexOBl4C+IuJRSs0A/igiz0eCILuI/EUpdS1wE5AN3A9srg8AlVKTReQDpdQvAVfkdVRwmB0MOgNNax/2WOOhadrREZXY0S3QWiAp2hZ5Zgdi9v3A5tz7NKbRf+aEKNt+35tNipTYw/gwRyms0Qm0Ntsj1mEltuPWx9NObL2BHcAwIEopdV9keyHQeCz/ECAM3KuMWeICQFwTZbZ0v4MZBmxp8H0BMERE1iulNmAEAF2AV4DxGL0Msw9R3r0i4lJK9WPfjT3AeoBIkJEKJAE3RtqchNFDlILRG1EUOWYrcHYz7c8EBLijQVkN660PmkqB+hu3YcDf63cQkbmRp88Ds5RSiwGviDQsp8V0MKJpmqZpmqZ1KkqpiwEH8G+M4OEMEXky8rPzgc2NDlkDeBrscwpGoAHGjbtSSg3BGOZ0sP2aa9OdwGqMIUz1+kbqBngPeBh4H/gCWAIsEJFmk+wjo4f229TgeRlGcPBqpOckGaMnpQzwKKW6isge9s/DriUSZCmlHOwL3jZj5KXMFJFgJAhqMM84TQ1XW42xUDnKiGD+ICKvikhO5PsXgL809xoPRgcjmqZp9erKoHqnkQzea7TuzdOODX4XuCuhZhdIGBIzwJkIVmezhzZW7fZT4w2yvbyOLnEO+saFCW5bBoDVbMLUJQviexx4oKsUqdkF7jJIO4mQp5qwPZ5qcxLxlgC2ys0QqDOGE0bWm/IEghSUualy+xmUHteg10g70SilTgXOAWxKqfpFsa3A+SISBBZFZtx6AuMmOxG4Tyk1EYhXSv0pkgvyqlLqOYwb927su0FeANyHMbxq8iH2q29PKnAj0DXSnnqZwO+AZyPDmxKB50VkQ+Tnn2IMgZoQCRp2Ygxzauo1T488vUcp9aKIFEe22yN1D1NKXSMiH0WGk/0GY0jXVqAX8H9FJBwZsvW6UupH9vVkICLlSqlcpdTjGD0m1Q3O0zTgeaVUIUbv01+VUgMi12CoUmphgzacCjwRec3/gzERwFcNXsrzwPUiUtDU62wJ1cJ8nTYRib4exUiO6QGUi8gjB9v/1FNPlexsvQyJpmntxFNprFr+wU1w9SuQOrCjW6RpzfNUQs1uI3iuK4V3J8DvvobE3q0qptYbYO73BfRJjmZUn2SUgmTvDkw5bxAe9QdMEsYUlQBRyfsf6CqBT6fC+Q8hjnhUQk88+d/iXPwAWy59h4woH+bs12Hz1zB5AcR0AWB7eR3/WLadz1bv5vM/n01anB4L1i7yI6NwBgzosCYopXJE5NQOa8BxSik1FrhZRG5uh7osGD1W1wLFIvLt4ZbV3hloScB8EXlGRO4AfqOUGtnObdA0TWuaM3HfQ9OOFbYYo8choRd0GQp9xkDp+lYXU+cPsbqwmitP7o5JQZ0vSEhAJfXBFJUE9uimJ0XY8SOMvR+S+xFSFjbsKkelDACBnvEWKPoZxu4/gqPc5SNneyV3XdxxN8SadjyJzHhV35vRXN5IWzgXI3n+jCMJRKD9Z9P6qdEmE1DXnm3QNE3TtOOKuUFKuqcCaoug24hWF1NW62NLqYuFa4sodfkoKK3jgcsy8e+MwbtuIfG5c5Cz7kRlXgTWSAJ9OAz5XxlfB/8S844fGNj7bNSWPJi8ELVxIZIxxhhKVt/EQJDs7ZWM6Z9KjadFQ/U1TWuGiNRiDCFrr/r+A/ynLcrqsLn5InMcL2wwzq5+u16BXdM0TdNaQ8TIGVn0AIx7DaJbv4aTw2rCHwxzWp9Efj2yB3tqPBTX+LCPvJ6K3r+g4tKXUF/eDd4GubhKgTMJ4rvDgEuRM25HfX4H4SG/hsoCLAndMVvtxn7hEFgc+N0uYuwW7BYTJpMiFBacNjO1Xh2YaNqJqEOCEaXUecB5wLTGP9MrsGua1qFMel4P7RgTCkLFVlj0EFz1AsR1M27+WynWYSXOYUUwVlvrmRhFWpydYMBHWEKYgh4jL6Vh2UrBgEtAmQkF/WAyGcPGlBnMVlTu26hvHoOv7oHa3fDDbOxhD5/k7ubZr/OZ8ek6Kt1+Zv9ns17VXTtAZPaqti7zEaXUlYdx3MWRhPAvlVJ/VUrNbZTc3m6UUgmRxPX67wcqpdp0pXilVE+l1EeRRP2jql0T2AGUUpcDYzBmLugK9BaRZU3tqxPYNU1rV9W7YPU/Ifef0H2kMaPW4CuhBatUa1qHqSuFD26GoA96RHKCs6423r+t4A+GyNleycaiWhKjbZhNisuS9qC2/0A4FMCyZxVy/gOo5P5G0LG3/nJYNQ9J6AkVBdDnHNSOHwj3GM1OWwYhs4M+Ti/88zq46TOC1mgk8llojSfAda8u419TziLabsFsan0QpbXSMZTArpTaJiIZbVy3aulq600c+yaQLSIvRKa0LQB+ISJ5bdnGFrQjA3hTRMY22HbYr+sQ9dwMZIjIjLYs94B62nk2rZEYcy7XRxjRwIsi8mZT++tgRNO0duWugJB//222GLDHNL2/pnUGT5yG3wAAIABJREFU/jpjFriGlBliWj+6IBwWKtx+wiJEWc2Eg37swVrMSrBY7Xun5T1AwEuwrhwkTNhkJxQO4bUm4A8rY5FGU53xu9Xo96nK7ccXDBNlMxPraO1yjNph6aTBiFJqIMb0u2uAEZGv9wOzgA0iMl8p9QhGvnMIqBWRp5VSv8VYVPBxjKltTwZuF5ECpdQLwC4gPvL1k8i+uSIyQyn1Psa6IyaM0ToTMKYBngWURI7LFZF5kTa+yb5gJBZjgcBTgecw1vhYApyJsc7IQuCuyD6DgGcw8qRnAzbgB2AgsO7/s3fn8XFW1R/HP9/J2jTd042lDS0tWMpqWURABBQEEREEFBEoi4CKiCj8ZJXdIoggoAhYEIosIrIpe5FVSJGyllJs2bolXZM0e87vj3tD05CtbTrPJDnv16uvzDzzLGfmCS/m5N57jpldLikL+A2whFAy+F0zu1HSzwm9S86L584iNET8LjA1xrsTcHpT4ibpPEJpZAG1ZnahpGMJJXr/SCjnOwb4upmtlHQysBWwECgGToo9SI4hDclIuhewz2CNlrPOOZdB2vqi5Vwmy22jytU6SKVEUWHz7u45hL8bdiAnn+yBG6+xac0uJ613jG/ZXd71al8jdCv/PbAxoTHfKU1fhCXtC+xiZl+Nz6dLeszMbotJyn1mNkfS4cAUSScC3yBUe/pE0q5m9qGk+wlfuAEuMrM3JF0C/NnMHpL0AyA3foEX8I6kR81scTxm/9iHpIhQRvcTSWcCzxNm/eQTZv7cCvzYzEok7QzcbGa7xuvvY2YXx/fxtqSHgC8S+qA0bX9T0rNmdoWkHxKaJ14V+36UAbs2SxJeaupbEj+nnczs6/H5PyV91cz+LOlo4AUzO1/SdcBXgL8BHxMaKjZKugbYl9C0MS18crRzzjnnnEvanwgjI88C7wKnt3h9G6BA0lnx+Ues7ioOobEfhA7jW8Wmgz8hNATsQ+hzt4aYiHwL2A3Yp9l1Rja7zpvACMJICcAjZvb7VuKfY2Z1hG7u5ZK2aRFT867t/2v2+H1gQrxu8y7sc4GJhFEQmn7G5Ka4les3aX7d5td+LD6PQ2OUsrpJ4ipCAlcWY3m1nfN3OU9GnHPOOde+uiqoLIOGWiyVHZ6nsqjPH0xO4ZDQ+LCmAhqqof/GkD+A5atqWVJRS2F+NsNjQ8OK6no+WV5FSlBcUE1WZSmpQaPWmLq1oqqW8up6BlNObkMlqcY6UrkFYYpXwWd7AJWW11DX0MAQW05OYzUpqw/75g+E3IJ239aSihpW1TZQU99In5wUhfnZDOjjozUJ2Rm43MzOlXQF8H2gIY5ObAPMJIxyXA4gaS/CF+0mY+Lz8cDbkgYCpWb2NUlbAXfG83xK0gTC9K4vmVldTCBmAjXNrnMwMK8T8bdc9zATGAssBcYBr7WItcnmhKliA1kzYRlDmKoWTr7muooGwhQsJG1rZjNbXHfPZs/HETrDtxUnwL3AtnHkqH8rr29Qaa2mJWmEpJsktew34pxzzrlM1dgAz0zBGmrhqYuhsR5QSEQAqlfCqjL4y7egahkNDY28t7iCs+9/gysfe/fT05RV1HDd03MYnrOK7JI/kbrtAChfsMalhKiua6Rg0HBqqlexoqoWe/h0eO2OEEcLK6vrqKs3sivm01hdHtZ+TT0ASt/9zL4tZWelmL+8ikYz/u++N7jjPx96Va/kDAauilOehgJ/J0wV+g3wfTN7DHhV0mWSfkmY1vVJs+P3idOtjgLOJKytOC2OcBwLXC1pU+BAYI+YiNxPGHE4IVbGOgi4GbBYMetcYGJcV7E3IZnZNz5u7nhgtKTJzbYdB5wSYz057tMkV9L/SboTuN3M3ozXrZN0gaTfAb8zs1mSvg0MkHSOpKaFVQuA6pi07SjplLjPd+Ln9J/4OV0OvGhmj0v6CmGtyGRJY4A9gAPjlLM/ANfFz/4LcXvLz2qDSfcC9kOBGuD8zlRR8AXszjnnXAaoqYBZD8Ob98KR99BYUUqd8sktKERNpX5XLYFbD4QjprEwNYK5ZRXUNxoPzpzPlEPDH3znlVXyQely9sh5F0btjKYdBl+/GorGtXrZlVW1XPjQO0yZVE7qzXthv8shJ/8z+62oquPx1+ay85absOnAPvDKn2DZR7DvRe2+req6BvJzsgCYW1rBCX+ZwZ0n7MLQfq2vcekRMnQB+3qer8urbm0o6VoU3p2kdWTEzO4Fyjvc0TnnnHMZxUZuAx++iM16GL33L3L/fQl1KxZR19AYdmgMPxsajWffK2XixgNIteh3UlSYy+eH1FKbU8i7i0JX9tb+JLqsspbH315Ibl05vxn7Opr9Tzjw6lYTEYC6hkZufGkBZkD1cpj3AuxwZIfvqSkRqaqr59UPl3PMrsUM6ONVvboTSUcSRgVOSTqWjrQYbZiYdDyZIrEO7G2Rd2B3zjnnMkt2HuQPAGVR328TaDQYvSt66TpW1azZOb2+0fjqViMwC0mCWVgrApBfv4K+H04nd6OJjBmUA2ZYY0OY5tWMBNtuMpCGxgYWDd8d67cRTP91GKFpQ35OiuFZlfD8tfCFU2BgcafeWnl1Hc+9t4QVVXUcuM1IcrMz7quRa4eZ3WFmg8zs+qRj6YiZfWRmh5jZXnFqliMDkxHvwO6cc85lmIZalJUbmoHmFMCSOdBnEFg9NE33zgo1cYwUd7z0ATdMf5+rHp/N9HdLeeKdRQCooQ59/Ap64gJy/30ZzP8vqX+dCfXVn16qqraeAX1yGNbHqM8bxC8fL8XG7wdv3x8WzrciJyWu/cam5E7/Fezwfdh4hzZHUZpbvqqWv834mPqGRo7ceRQDvNSwc2nn1bScc8451766VfDm37F9LyH7v7fC6C/Am3+j8Ys/oyAvF5Z9AO8/BSs+Ife1qZw0+ovUbTSJXcYO4aGZ8/nqhOEALKjNp3Lbs0ilIC8nh1GL3sK+NgXlrK56lUqJJ99ZxF4ja8l+/zmunQhZjz8Q1ovkD/hMaB8sqWTh8lXs/M41MO9Z6gdvTiqVIlU0HjbfC7LaTjBmL6rgkkfe4Ud7bc5Hy1aRn53F/luPpKgnrxlxLsOkewH7lwil2vYDbgCuNLPW/8yBL2B3zjnnMkJDPVQtBaCxvhYk6iybVL8icrKyoHIJNNQ0O0CQP4Bl9dnU1Tcy7NPSvnWsqm3AgALVUWgVKDt/jZK95dV1VNc20M9Wkk0DKUFKKSgogqysz4S2tLIGs0YGNCwHC5WwUhKpVFY8pu2/uy5bVUt13erqWQL65GQzoKAHrxvpgQvYXfeW7g7szwDPpPOazjnnnFtPWdlQOAxYPb97jbGDvkNaPWxQi0GJwvwcCvObvujns7rn2mr98nPol58TX+/Y4L5NkfRpd79W4/NpWc4lLq0jI2tLUinwQdJxuC5TBJQlHYRLjN//3s3vf+/m9793a3n/R5uZLwx2QIYnI65nkVTiw7K9l9//3s3vf+/m97938/vv2pNx1bScc84555xzvYMnI84555xzzrlEeDLi0unGpANwifL737v5/e/d/P73bn7/XZt8zYhzzjnnnHMuET4y4pxzzjnnnEuEJyPOOeecc865RHgy4pxzzjnnnEuEJyPOOeecc865RHgy4pxzzjnnnEtEdtIBtKeoqMiKi4uTDsM555xzrmeorg4/8/MTC2HGjBllZjY0sQBcRsnoZKS4uJiSkpKkw3DOOdebVK+AuqrwOK8f5PZNNh7n1lFDQyPLq+qobzRSgsEFuWS9Pye8OH58YnFJ+iCxi7uMk9ZkRFIKeBD4D5ALjAUmm1lVOuNwzjnnWlWxCP57O2T3gQUzoc8g2OMM6FuUdGTOrbWlq+q48+UPyctO8fycMo7bbQy71DeQl5OVdGjOfSqJkZEXzexiAEn/AL4F3JFAHM4559yacgvh85Ohz0CoWgp//hrscJQnI65bGtovj8m7FdM3N5uvTBjOwde/wFMHjvRkxGWUtCYjZtYINCUi2cAmwLvpjME555xrU27f1dOyzCA7H/IHJBuTc+uhMC8HgJTE2KGFZEkJR+TcmhKppiVpX+Ah4CEzK2nx2omSSiSVlJaWJhGec8653m7VUphxK3zlYijwURHXfZkZi1ZWc+Vj73LdkdszsG9u0iE5t4ZEkhEze9TM9gM2k3RKi9duNLNJZjZp6FAvtOCccy7NKsvg2Sth871h0x0hJ7mqQ86tj4ZG4+NlVfz6X7M49+sTGDmgT9IhtUnSSZJukHSBpNskXd7B/rtJelXSnvH5hZK+ER8XS/pmG8cdJmmepGnxmGmSjuvyN9QJ7cXZm6Q1GZE0QdIBzTbNBcakMwbnnHOuTZVlcP8poBQUDIaV80N1Lee6oaWVNRx03fMcPmlTauobmVdWyYqq2qTD+gxJ/YGLgFPM7AJgMjCwvWPM7Dng9WabzjezB+LjYqDVL/lmdjcwD5hmZufFa10rqd96vIV1VUwbcfYm6V7AXgMcJ2l7IAf4HHBqmmNwzjnnWifBF34IDXVQOjts8zUjrpvqk5vNVYdtS1VdA++XVgAwyBIOqnU1gIDTJd1qZmXASQCSTgXOBq4BCoEtgOPNbGnTwZJGAddIeg24FDgG2E7SBcBfzWxWO9cuAqqAAkn3xDhmAXsB5wKlwNHAHGBL4Jx4zDXAAuBNYHvgATO7LSZWVwPvEdZGP2Bmj0r6NfAd4M/ALsD7QEGzOJ8AbgGeN7NjJR0N/AD4rpnNW9sPtDuRWWb+VgJMmjTJvM+Ic84551wXmR2T7GT7jMwws0kttm0FnAXsRyhudLGZ/Su+VgUMNbMKSWcCRWb2c0lTgalmNl3SMUCxmV0Qp24dY2bHtHH96cB8QoIxBLjVzF6Ox00xs50kjQCyCO0otjezUkmHAweZ2XdjApFtZudIyiOMtkwEzgDKzexSSX0Iic1YM6uXVA2MBFYCWxNGfz6NU9JkYJKZnSLpe8C8OALUo2V000PnnHPOOdfzmdlbwFGSsghtH+6TtKmZLQFKzawi7joH+FIXXHKamT3UyvZ3YjwLJQ0F+ptZU0WlOcC2zfb9X9y3RlIZoX/eNsASSWfFfd4ABgOLgUVmtixuf61pvUvzmIBzJQ0AdjOz29frHXYTiSxgd84551wbaldBZWlYq5LBsxdcZqpvaGRZZS1LK2uprW9MOpxOiQu5bwYwswbg70B1s12GSiqMj8cDb3dwyoZwWuVL2mItw2n+H10ZsELSsPh8HPBas9fHxPjzgWGEqVczgRIzu9zMLgf+Cixp5dyfidPMqgm9924CXljLuLutdHdgH0voM/IqYR7dEjO7MJ0xOOeccxmrYjGU3AypbFj0dli/MnyiV/RynbKyqo6HXp/Pyqp6Plq2iqLCPI7edTSD++YlHVpHVgBDJP02Pt4MODOOigAsBU6QNJKwZuQ4SbsRRiGOkjQXOBAYJGkC8Bbhe+aVhFYSn/a0k3QIMBo4XNLCphYTcarVUcA2kg4xs7+ZmUk6ArhM0vvx2mc0i3uYpF8COwFnmdkSSZcBUySdA+QCC8ysQdLxwABJp5vZVfH41uK8njA17Mgu+Fy7hbSuGZG0I7CRmf0jPn8bOMrMZrS2v68Zcc451+tUloVO8Ms/gJv2gR+9Av1GJB2V6wYqa+pJxaaG1fUNHH9rCaftM47dxzVrlZCha0Y62H+emRVvwJDWWlwzMs/MpnbhOXOBocCxZnZxV50306W7A/srLTalgMp0xuCcc85ltL6xyWJeP+gzKFT4cq4T+uat/lrXaEZRYS4D++QkGNH6i/3oBkg60szuSDoeAEkTgT2ArSU9aWYfdcE5C4AHCW0vzupg9x4lsQXskg4GHm1Zbk3SicCJAKNGjUoiNOeccy5ZlaXw9GVw5L3Qd1jH+zvXTGVNPa9+sIxtNxnIqCF9kw5nvZjZ9YSpSxnDzN4klP7tynOuAvbuynN2F4ksYJf0ZeDLwE9bvuYd2J1zzvVq5YvgsXNh99NhyFgfGXFrZUVVHY++tZBFK6s5cudRDOjmIyMbiqSDJd0SO77/UdKtCcQwUdJTsSwxkn4hySRdHJ9v10rFrR5nnZIRSeucZscO7PsCPwFGSPrCup7LOeec61EqFsOtX4dJk0OzxfIFsGppx8c5B1TU1DHtPx/w3Jwy9tt6JJW1DSypqEk6rEz1J+A0M7vAzH4A1KU7gDjC8u9mz6fEh5fHn9sBe6Y5rLRrd5qWpD3aeOko4IS1vZikzwN3ASXA00Bf4DrgxbU9l3POOdfjpLJhxxNg/mvhH8DEg5ONyXUb9Q3GpoMLKMjN5m8zPgZgx+LBDCnM+GpaSLqQ8L20ASgHriL03diC8L2zHrgVuAR4BbiM0P18c+CPZjZD0l2EcrvPALsC9wG/AK4zs19JOi9uPwJYCJwt6Roz+8TMjm8rlqYkIR6fS+gYv62ZHSppI+BCYDah9O9UM3s+xjI2xjIBeNnMzo/nOStueyP+nNfK5zEM+CYwMC6W/wOhQeOZ8bgt42exDLgzfj6zCN3d/0RowLgDcJeZ3djK+f8eP8dNCF3f75B0FXBI/LznEL6z3wHcE8/5LlABnApcYmbXtDzvumi3mpakGcDrMdB8QnOXMQBmtntXBNAer6blnHPOOddFVq6Exx6D6upQTWvLLaF//7SH0bKalqR9gZ+Z2Vfj8+nAaYTF3DMJX7xTwEVm9jNJdwIPmtk0ScXA381s+/j4eaCY8L11JPA9oMLMpsQk4E4z+0DSpsAvgYOARcBv4hfytmIZDvzEzPaP248zs5tjLH8zs3slDQdmAJsSygc/G2MB+NDMNo6L3+82swnxPLcDTzRV5ZJkQL/Ybf4YYlf5+NqLMbYX4vStn5jZwfHxRWa2u6TtgPsJ39cHAP82s61buQcHmdk/YpPJd8xsfHw8i7A4fyFwvZmdLOkKoMzMfh37vZSaWZ+O7nNndbSA/dSY3Z1hZr9p9gbO66oAnHPOOefcBlRRAY88AnPnQr9+0LcvLFgATz8Nm20G++8PhYUdn2fD2QYoaNa1/CNgqJm9JumfhJGMPOD2ZvsvljQKUHzctPRgjpnVEaZdlUu6AXhG0jXAxmb2AUCsgHVyrNb1ZULH99fbiiVun9MUsJnd3CyWK+K2RbF7eiyJx/9iE0ckNU0Dm9D8PMQu7mvxOX01zlzqQxilaPJ+/LmcUHK4EVgmqV/Lk0jKBiZI2gGoiu+P2A/lJuAHhFlLj8VDtiI0YiQmSaUtz7k+2k1GzOz5+HCzFi9t0pVBOOecc845oKEWGuohp0/XFC+oqIBbboGttoKDDqJ+7lzq6hvJm/A5UvvtB889F16fPDnJhGQm8IXYsRxJe7H6C/vvgamEruZ/bLb/k2b2gCQBn5hZY3i4ZpdzM1sg6WXgL8C1TdtjSd69LUwRekrSR0BOO7GIZus3JE0mJEczCdOxXpU0gpAMlBGWIrQ2/egdwnSuJmNoOyFp6tA+COgXr3Wfmb0emzSu6xzOA4CvmNle8b38uNlrNxGmb20EnBy3vQ2Mj/sWEpOXrtLZ0r71kh4G3ovBvN/B/q2KN+liwjy7HdflHM4555xzPdKHL8GHL4aiBX2LYLvvQt/1/N73yCMhEdl7b+Yvr+LpVz6itr6R+R/A8buPYfjee6/e77DD1v89rAMze0zSTrF7eTkwiNhrw8zeklROWGvc5AzgQklbAYNZvfb4eGC0pMlmdkuz/X8H3Ghm/262bamk64HFhC/ed5vZqwCtxWJmH0raJW6vBpaYWa2kM4BLJI0jrF85InZub4plb6A/oVfKZDO7RdLtcXrXrBj/gZKeBL7d7P1dALxM6MR+BWGN9XHAzyTNIUxBu0drdo7fAfhGvO6XCVPFBkj6tpnd0+y9vwj8VNK1wMdA36bYYhf56YQRpoa4/+XAzZKmAAuALq2q0ekO7JL2JwzTvG1mD6/TxaRDCYt+zu9M501fM+Kcc865XqWiNIyIPHo2DN8Kdv3xuo+QrFwJN9wAp50GeWER+9JX3ySVgsfrB/Dw6wu45jvb0z/VCFdfDSefnJY1JC3XjGzA66QIa00+D3yuK7ul9yaSRgJZZvaxpHzCYvxtuur8nS7ta2aPmNkVZvawpMPX5WJmdi8hy3TOOeeccy0VDoU+g2Hjz4fn6zNVa9assEg9b3U1rcGFuQwsyGWrjQas3i8vL+w3a1YrJ+nWNgfuJYwoTEs4lu5sCPBbST8HriQs6O8yHZX2nctn57uJMNx0V1cG0uya3oHdOeecc71TYyOs+AjmPQcHXLl+56qqCgvWW1haUcv1JXO45OCJ9G9qilhYGKps9SBmNptQHteth9gP5dsd7riOOhoZmWJmY1r82ww4Z0MF5B3YnXPOOdcr1ddC2Wx47rdwwG/CKMn66NMHyldPSDEzSstruP6ZOfxy/8+x0cBm1VkrKiA/f/2u59w6aDcZMbMbmh5L2kLSIZLGNd/unHPOOee6wIKZ8PBP4etXhelZ5QtDZa111TT1qiZ0YV+wopqf3zuT0/YZR2FeNqXlNZRX14XXm6Z0OZdmnVozIukEwpy7o4G/xwoBa03Slwgr/kdKOkdSlzVMcc4555zr1pa+D0MnwKPnwLO/hfefgsb1SEb69w99RJ57DoDlq2rZdpMB3PrCPK558j3ufOVDquoawuubbZZIA0TnOlvad4vm3RslXb0uFzOzZ4Bn1uVY55xzzrkebdsjwr+utP/+oY8IMGG33ZjwlS3C9vHjw4jIc8/BW2+FPiPOJaCzyUjLesJdWl+4LTNmzCiT9EE6ruXSoojQCMj1Tn7/eze//72b3/8EZUFqEAzIg7wqqG6AhizI6gP5NVCzDFY0/OQnjRswhJb3f/QGvJbrZjqbjBRJuobQIXIsUNfB/l3CzHwFew8iqSQddcVdZvL737v5/e/d/P73bn7/XXs622fkDOBNQr3m1+Nz55xzzjnnnFtnnRoZMbNG4Mam57HF/NMbKijnnHPOOedcz9epZETS14CTgUJC08NRhOlazq2NGzvexfVgfv97N7//vZvf/97N779rk8xaNlhvZSfpWULr9zJCMnK0mf1qA8fmnHPOOeec68E6u4D9v2Y2o+mJpL9soHicc84555xzvUS7IyOSzosPJwI1wJz4fHcz22cDx+acc84555zrwToaGdkGeBBo2etj61b2dc4555xzzrlO62hkZBMz+7iV7cPMbPEGjcw555xzzjnXo7XbZ6QpEZH0Q0m7SNpL0mvA/mmJzjnnnHPOOddjdbbp4cZm9hJwDvBdYMKGC8k555xzzjnXG3S2mtYKSSOBajN7W1LZhgyqSVFRkRUXF6fjUs4555xzPV91dfiZn59YCDNmzCgzs6GJBeAySmeTkU2AF4GfSPoisOO6XExSirAg/j9ALqFx4mQzq2pt/+LiYkpKStblUs4555JUWQYVi2HFx9C3CAZsCoX+3cO5DWlJRQ3zl1dTVlHDRgP7MLRfLoP75q250+zZ4ef48ekPMJLUsjCS68U6NU3LzH4MbGdm/wDeBH6wHtd80cwuNLNzgALgW+txLuecc5mobxFgMHwCzHsO7vpuSFCccxvMkMI8CvOz2GJEP/7wzByuf/p9qmobkg7LuXZ1KhmRNBg4V9IU4IvAOv15y8wazezieM5swojLu+tyLueccxmsoR6GbwUDNoFtD4f5/4VG/1Lk3IZU39DIZkWFbDSwDz/aaxwvz1vKqtr6pMNyrl2dXcB+OfA60Ai8BPx4fS4qaV/gIeAhM/N5WM4519NkxVnAtVXwyX9hz/+D3L7JxuRcD5edFb7WlVfV8eDM+fx83y0YWJCbcFTOta+zyci7ZnYrsMzMlgIfrc9FzexRM9sP2EzSKc1fk3SipBJJJaWlpetzGeecc0mqXglzn4Glc2DSZMgrTDoi53q8ZZW1THv5Q7bbdCA7jBpEVkpJh9Qpkk6SdIOkCyTdJunyDvbfTdKrkvaMzy+U9I34uFjSN9s47jBJ8yRNi8dMk3Rcl7+hTmgvzt6kswvYt4rVtEzSAGDMulxM0gRgMzN7OG6a2/JcZnYjcCPApEmT2u7I6JxzLnOtWgZv3gt5/WCnE0GpME0rlZV0ZM71WEsqarjq8dkcufNoxg0vRILGRiOV4QmJpP7ARcAwM7M4lf/37R1jZs9Jer3ZpvNtdSfvYuCbwP2tHHd3/EP4NDN7SFI+sFTS3WZW3hXvZy20GWdv0tlk5BbgFWAwcApwxDperwY4TtL2QA7wOeDUdTyXc865TFWzEqZfCuP2DetFAHb7KfQbkWxczvVgb81fyWNvLyIlkZ0l+uVn872dRzOsf3JlfDupBhBwuqRbzawMOAlA0qnA2cA1QCGwBXB8nKlD3GcUcE1szH0pcAywnaQLgL+a2ax2rl0EVAEFku6JccwC9gLOBUqBo4E5wJaEnntFMZ4FhMJO2wMPmNltMbG6GniPsDb6ATN7VNKvge8AfwZ2Ad4nFHJqivMJwvft583sWElHEwpGfdfM5q3tB9qdaHUS2c5OYdirBKiNvyBpMWnSJPPSvs451w2tWgYNNWtuy86DPoOSice5XmBpZQ31DWt+ryvIzaIwP2f1hswo7TvDzCa12LYVcBawH6G40cVm9q/4WhUw1MwqJJ0JFJnZzyVNBaaa2XRJxwDFZnZBnLp1jJkd08b1pwPzCQnGEOBWM3s5HjfFzHaSNALIIrSj2N7MSiUdDhxkZt+NCUS2mZ0jKQ+YB0wEzgDKzexSSX0Iic1YM6uXVA2MBFYCWwMDm8cpaTIwycxOkfQ9YJ6ZPbfOH3Q30dmRkYuAr6QzEXHOOdeNFXjS4Vy6faanSDdiZm8BR0nKIrR9uE/Spma2BCg1s4q46xzgS11wyWlm9lAr29+J8SyUNBTob2ZNi5jnANs22/d/cd+a2BB8LLANsETSWXGfNwgzixYDi8xsWdz+WtN6l+YxEarXDgB2M7Pb1+vIMk5lAAAgAElEQVQddhOdXcD+tJktbnoi6eANFI9zzjnnnOtF4kLumwHMrAH4O1DdbJehkpoqYIwH3u7glA3htMqXtMVahtN8aKkMWCFpWHw+Dnit2etjYvz5wDDC1KuZQImZXW5mlwN/BZa0cu7PxGlm1cAdwE3AC2sZd7fV2ZGRLSW9RMwWCUNLf98wITnnnHM9QNUyqK2EmnJI5UB+PygcnnRUrgeqqWtg2ao6KmvrMTP65ecwqCCH3OxuUzBiBTBE0m/j482AM+OoCMBS4IRYTGkLwvrj3QijEEdJmgscCAyKxZLeIqzXuJLQSuLTnnaSDgFGA4dLWtjUYiJOtToK2EbSIWb2t7iY/gjgMknvx2uf0SzuYZJ+CewEnGVmSyRdBkyRdA6QCywwswZJxwMDJJ1uZlfF41uL83rC1LAju+Bz7RY6u2bkn4ReI02OMrPj1/pi0ljgYuBVwoe/xMwubGt/XzPinHOu26oph9mPwUbbwpynoORm+P4D0M8TEte1VlbV8eb8FQzvl8+CFVWccc/r3HPSF9h0cMFnd87QNSMd7D/PzIo3YEhrLa4ZmWdmU7vwnLmExuLHNjUJ7w06OzLyHTNb3vREUkfDY20ZTKhq8I+m80h62MxmrOP5nHPOucyUyoaJ3wIJ8gbAi7+H+qqko3I9UH5Oih2LB5OTlWLUkAJ2G1fEzI+Xt56MdDOxDO8ASUea2R1JxwMgaSKwB7C1pCfNbL3678VzFgAPEtpenNXB7j1KZ5OR+rhOpF98fiDw7bW9mJm90mJTCqhc2/M455xzGS+nT/hpBnWVMHA05HrjR9f1mk/HWllVx4dLVzFp9OAEI+o6ZnY9YepSxjCzNwmlf7vynKuAvbvynN1FZ5ORBwl1lJuqaa33b3hMbh5tWftZ0onAiQCjRo1a38s455xzyWmohxUfwjNT4NCboW9R0hG5Hqy0vIYrHp3Frw/ZhqH9um9lLde7dDYZmWdmP256Immz9bmopC8DXwZOa/mad2B3zjnXI9RVQ9lsePVW+PrVkJPxjedcN2VmLFxZzaUPv8PFB29N//xspMzuup60+EfxA4EPCb0/8s3s6DTHMJHQPPE2M5sq6RfAr4FLYv+S7YCBZjY9nXGlW7vJSOxoCTBX0t6E+spG6ER5wbpcUNIBwO7AT4CRkkab2Yvrci7nnHMuY1Uvh5v3gfFfgxeuDdsmHeujI67LLa+q4/S7Z1JeXcdtL84D4Itji9hhtPf7acefgDFmthJA0k3pDsDM3pT072bPp8RO7U1Fo7YDioHp6Y4tnToaGZlO6CgpwkhGk1GsQzIi6fPAXYRu7k8DfYHrAE9GnHPO9SxZuXDqzDW3pbpNqVXXjeRlpbj68O3W2JaV6l4jI5IuJHwvbQDKgasITQC3IJTcrQduBS4BXgEuIywh2Bz4o5nNkHQXoffHM8CuwH3AL4DrzOxXks6L248AFgJnS7rGzD5pXiW2ZSxmNiVuP49QrrcG2NbMDpW0EXAhMJvQh2SqmT0fYxkbY5kAvGxm58fznBW3vRF/zmvl8xgGfBMYGCt3/YHQLf7MeNyW8bNYBtwZP59ZwC6ERGsisANwV5x11PzcBwK/JfRAGUHo3fI7YB9CU8fvmNkHkr4JHEQoObw1cDJhqcb1hKq4exKWcjxmZr9q+R46q6Nk5Edm9oikw83srvgGtgVuXpeLxapZvnrPOedcz1fQMxYQu8xXkJdNQV5nZ95nHkn7AruY2Vfj8+nAY8AJhCaCswlFj/5tZvdLuhN40MymSSom9L7bnvBF/Xng/4B8wvSr/kBTGbta4AdmtlzS14BfAq9IWgT8xszuaC0WSY8Bw+P2/eP24+I5rwT+Zmb3ShoOzJC0aYzlWUIyBGE62Plxatb3zWxCPE+rXdbNbLGk+4FiM7sg7vt34Gdm9kLs3n6lmR0s6XLgIjM7I07tup+QlA0A/k1c/tDs3A/Gfitz43Swq4HtzOxkST8FDo3vaxlwmpmtkHQ6obXHdfHY54HvxPd+Feuh3d9cM3skPtyi2baZ8ZfEOeecc8659bUNUBBHDAA+Aoaa2Wux190RQB5we7P9F8flBIqPU/G1OWZWB9QB5ZJuAJ6RdA2wsZl9ABDL8Z4cSwd/GbhP0uttxRK3z2kK2Mya/jC/DXBF3LZI0gCgaS7m/2JHeSTVxW0Tmp8H+N9afk5flbQH0AeoaPba+/HncsJa70ZgmaR+tG2NY+LjZYSpYcTznyepjDDK8haAmVVJ+hHwJGGEZL10tGbkJ4RF5gMlHUO44fXAw+t7Yeecc865jFK1HOqqoKEGUjmQ2xf6DEwklPqGRpZW1lLb0IgZ5GWnGFKY1+2mX3XSTOALZnY5gKS9WP2F/ffAVKDEzP7YbP8nzewBhZX6n5hZY1y0v0bxIzNbIOll4C/AtU3bY3+QvS10/35K0kdATjuxiDAtqen4yYTkaCZhOtarkkYQvtiXEZYitFaI6R3CdK4mY2g7IWkIl9IgQnuNmcB9ZvZ67Bh/cBvHdZWbgJ+Y2b9jtduNiAERRk9OB/4s6SDrTBf1NnQ0MvI74HeSvm1m96zrRZrEm3QxYZ7djut7Puecc865LvXuP2H4BHjpBug7FL58NhSkfyF4dX0DJR8sY2BBDguWV3PtU+9x+/E7s8mg7t/IsCUze0zSTpIuI6wXGURs/Gdmb0kqJ6w1bnIGcKGkrQhrGJrWHh8PjJY02cxuabb/74AbzezfzbYtlXQ9sJjwJftuM3sVoLVYzOxDSbvE7dXAEjOrlXQGcImkcYT1K0eYmUlqimVvwlSxAU1xSbo9TjWbFeM/UNKTrO7hdwZhbfbLwJGEkZfrgOOAn0maQ5iCdk9MSo4CtpG0A/CNeN0vA6Pjddf4Hi9pJ8Ioy1GS5rO6geMLhApjgySNJyzLOFfS08Dn4/adCWtHVgCPEKa6/VnST81sWZs3uR1aj0Rm7S8mHUpY9HO+mU3qaP9JkyZZSUnJhg/MOeecc65uFWTlQyoFFYvgyi3g9FnQb0TaQ6mtb6S+sZGC3GzqGhq56KG3mTCyP0fstJ492GbPDj/Hj1//INeRpBmd+R7YBddJEdaafB74nJlN3dDXdGsv1fEuXcfM7iVkmc4555xzmSWnICQiZrBqaSjLnJWbSCi52SkKcsMElsqaeuaWVfKFsUMSiaUb2xy4lzCiMC3hWFwbMq70gndgd84551xiGuph+bzQG+Yb1yReFW1pZQ03PTuX078ynhH9vXHm2jCz2YTyuC6DpXVkpDPM7EYzm2Rmk4YOHZp0OM4555zrLeqqYOHrUPJn+NqUsGYkQWUVNVzx6LscvuOmbL3xAPJyvE+N63kyLhlxzjnnnEvEqqVwy75hNOS9x+DN+2DVkkRCqaiu41cPvMWsBeW8/vEK/vXWQmYtXJlILM5tSGmdpiXpS4QV/yMlnUNo1lLVwWHOOeeccxtebgH86JUWG5MppZtKiV8e8DnqGlYXGurjIyOuB0prMmJmzwDPpPOazjnnnHOd0mdQ+JcBCnKzP13A7lxPltbSvmtLUinwQdJxuC5TRGgE5Honv/+9m9//3s3vf+/W8v6PNjNfGOyADE9GXM8iqSQddcVdZvL737v5/e/d/P73bn7/XXt8AbtzzjnnnHMuEZ6MOOecc8455xLhyYhLpxuTDsAlyu9/7+b3v3fz+9+7+f13bfI1I84555xzzrlE+MiIc84555xzLhGejDjnnHPOOecS4cmIc84555xzLhGejDjnnHPOOecS4cmIc84555xzLhGejDjnnHPOOecSkZ10AO0pKiqy4uLipMNwzrnuyxrDPwSprKSjcS5jNDQaBqQEKSnpcNKnujr8zM9PLIQZM2aUmdnQxAJwGSWtyYikFPAg8B8gFxgLTDazqtb2Ly4upqSkJI0ROudcD1KxGEpuAaVg4Ruw22kwfCJk5yUdmXOJenv+Sp5+dzHVdQ3MWVzB2ft/jo0H9UG9ISmZPTv8HD8+sRAkfZDYxV3GSWJk5EUzuxhA0j+AbwF3JBCHc871bIXDYMcTIL8fLJkDU78OJ78A/UYkHZlziZqwUX82GdyHnFSKx99eyLn/eItrv7Mdhfk5SYfmXK+T1mTEzBqBpkQkG9gEeDedMTjnXK/Sd0j4mVsIhcPDKIlzjv75OZgZQwrzGDEgn6yU/7fhXBIS+S9P0r7AQ8BDZlbS4rUTJZVIKiktLU0iPOec61kqFsNTF8N37oS+Pk3bOYDGRmP+8irufPlDTv/KePrk+poq55KQSDJiZo+a2X7AZpJOafHajWY2ycwmDR3q/9N0zrl1ZgYr58NjZ8Ne58LA0dAb5sQ714G6hgb+V1bJ75+ewwXf2Iqh/XwdVdIknSTpBkkXSLpN0uUd7L+bpFcl7RmfXyjpG/FxsaRvtnHcYZLmSZoWj5km6bguf0Od0F6cvUm6F7BPADYzs4fjprnAmHTG4JxzvUZlGdyyHxx2G2Tnw4qPIbcACoYkHZlziZpbtopj//wK9//wizQ0GgtWVFGYm02/Pr5mJAmS+gMXAcPMzOJU/t+3d4yZPSfp9Wabzjczi4+LgW8C97dy3N3xD+HTzOwhSfnAUkl3m1l5V7yftdBmnL1Juhew1wDHSdoeyAE+B5ya5hicc653yMqB3X8GC2aGfwBbHpBsTM5lgOyU+PFem/PkO4sA6N8nh13GDE44ql6tBhBwuqRbzawMOAlA0qnA2cA1QCGwBXC8mS1tOljSKOAaSa8BlwLHANtJugD4q5nNaufaRUAVUCDpnhjHLGAv4FygFDgamANsCZwTj7kGWAC8CWwPPGBmt8XE6mrgPcLa6AfM7FFJvwa+A/wZ2AV4HyhoFucTwC3A82Z2rKSjgR8A3zWzeWv7gXYnWp1EZp5JkyaZl/Z1zjnnnOsimVHad4aZTWqxbSvgLGA/QnGji83sX/G1KmComVVIOhMoMrOfS5oKTDWz6ZKOAYrN7II4desYMzumjetPB+YTEowhwK1m9nI8boqZ7SRpBJBFaEexvZmVSjocOMjMvhsTiGwzO0dSHjAPmAicAZSb2aWS+hASm7FmVi+pGhgJrAS2BgY2j1PSZGCSmZ0i6XvAPDN7bp0/6G4io5seOuecc865ns/M3gKOkpRFaPtwn6RNzWwJUGpmFXHXOcCXuuCS08zsoVa2vxPjWShpKNDfzJoqKs0Btm227//ivjWSygj987YBlkg6K+7zBjAYWAwsMrNlcftrTetdmscEnCtpALCbmd2+Xu+wm/A6ds4555JTXwM15dDYmHQkzq03M2NVbT2raurJ5JknmSYu5L4ZwMwagL8D1c12GSqpMD4eD7zdwSkbwmmVL2mLtQyn+Y0rA1ZIGhafjwNea/b6mBh/PjCMMPVqJlBiZpeb2eXAX4ElrZz7M3GaWTWh995NwAtrGXe3le4F7GMJfUZeJcyjW2JmF6YzBueccxliwesw54mQjADscnJo1OhcN7SsspbH317I0so6Pl5exc6bDWb3cUUMLMhNOrTuYAUwRNJv4+PNgDPjqAjAUuAESSMJa0aOk7QbYRTiKElzgQOBQbFY0luE75lXElpJfNrTTtIhwGjgcEkLm1pMxKlWRwHbSDrEzP4WF9MfAVwm6f147TOaxT1M0i+BnYCzzGyJpMuAKZLOAXKBBWbWIOl4YICk083sqnh8a3FeT5gadmQXfK7dQlrXjEjaEdjIzP4Rn78NHGVmM1rb39eMOOdcD1dZCsqCF6+D6hWw32Vh4b1z3UxFdR0g6hsbqWswvnXD89z0/UlsMaJ/0qGtKUPXjHSw/zwzK96AIa21uGZknplN7cJz5gJDgWPN7OKuOm+mS3cH9ldabEoBlemMwTnnXAbpOzT0Qxm5LXz0UtLROLfOCvNXJ9ErqurYZGAB8r4+6y2W4R0g6UgzuyPpeAAkTQT2ALaW9KSZfdQF5ywAHiS0vTirg917lMQWsEs6GHi0Zbk1SScCJwKMGjUqidCcc86lixms/ATeuAcO+I2Pirhub1VNPW9+soIvbj6E4d5Mcb2Z2fWEqUsZw8zeJJT+7cpzrgL27spzdheJLGCX9GXgy8BPW77mHdidc66XaKiHpf+Dpy+FA66EwuFJR+TcellZVccTsxbzwZJVfG+X0Qzw9SKdJum0LjrPSEm3x+7ql0j6l6Stu+Lc6xHTds0rZ0n6hSSTdKmkiyQ90NQ9vjdKezIi6QBgX+AnwAhJX0h3DM455zJA2btwzzHwjWsglQ3li6CuKumonFsnK6pqueM/H/L2Jys4bMdNqG1oZGllbdJhdSddkowAPwNeNrPzzOxsQjWrAV107nW1HbBn0xMzmxIfXmpm5wLnA1ckEFdGSHc1rc8DdwElwNNAX+A64MV0xuGccy4DVCyGzfaAJ34Vng8qhonfgpw+iYbl3LqorW+kvrGRBjOm/CsUb/rW9hszuK+PjnRE0mHAwLgofBbwOULX88mEylKzCRW2JgFXAacC5YTEYzahM/oVZvYOsBD4nqRXgJeaFphL2hL4AzCV0M/jRgAzO6atLu+EJobXAC8BHwM7AtfHjuobARfG648jNF98XtJdhJK/zwC7AvfHn03v7w9mtrDFR1BE6ENCLGF8F/DvGMc0M3tC0rHAZcAfCdXAxgBfN7OVknYAfkX4fp1P6PR+KvAIoRv8YkJC9pqZ/UXSFwmVw94jVAI7qVn/k1ZjAKbHn1vEY+uBW4FLCOWMryGUPS4Hfgmcama30Qnegd0555xzrrfI0GpazStmSUoRvihvSyjZeyZwHvAc8CMzu1DSi8CPzaxE0s7Ab81s19g08RTgGELZ3L8C55hZefMKWC27tHfQ5f05M7spdmV/jdBFfRrwNzO7V9JwYAawKSFReB4oJiQGIwnJSLGZXdDs/RphRGTjeMz3zWxxXMi+a0xABhPWV+8Yj5kOXBaToeuAp8zsbzHx+pGZ/UfSPvH97inpB8DnzexEhWoK7xAW3v+S0PvkEkLC9b6ZVTaLrdUYYjPGmYTkLwVcZGY/k3QPcK+Z3SVpc+CJtal+5k0PnXPOOedcxjCzRuBhQiKyA3AOcDhwCHBf3G0bYgd0mnVGN7MGM7vWzD4fj92S8KW/Iy27vG/V7LWmTusLCbN6hja/vpktIow8FDUdb2Z1ZlZuZrPbueZVwElADmHUB0DAnpLOJRR0armAuul8pUC/+HgrQvL2aazRNsDI2A3+TOBNYAQhCRkCvAycALTsOttqDGa2AvgncARhdKSpQ3xb1+8UT0acc845l7kyeAYH4J3Wu06Dgm3j87sIX4SXA48CuwGTYiUrCH+hHxsff9oZXdJUSZsCmNknwGOE5oMQphA1NX5pWbK1vS7vTZ3WRwKrCInAp9ePIybLCR3boe1O64MkrXFdC79AZwOXxtGL4wk9+S4iJCsttfYL93aM+dNYo5nAe826wd8BzAN2MbPTCNPOhgFfa3G+9mL4PfBDYDsz+28H1++UdK8ZGUHowL5t05CTc84559xnVC6B2f+CikVQXwPbHg4DNs2Y8s//K61g+rulLKusZcywvuwxbihDCr2U73p4GPhNfPwz4AXCl/0fmVmdpMeB5v08jgN+Iek9Vq/xAHgWuFrSm0AeYQpUU/XW+4DrJWUDgwnd1nc1sxdopct7s2uNj6MEuxCmdpmkM4BLJI0DNgeOiNuPB0ZLmmxmt8TjXyZ0VL8CuE7SoXH7LyRdYmYvSVpIKGF8O3CopCtiTANi1/iV8b1MjlPHmvqcPEwYXblI0stADasTlpsJ3eB/RVjjgZn9XVKxQrf7xUAVYT1Ic4+2FkPsSv+WpHLC2u8mZwLXxqplH9F6wtSmdHdgP5TwIZ3fmc6bvmbEOeec68XKF4IUih1MPQB++DL0G5F0VJ9avLKarJS4+OF3GDeskJP3HJv5jQ4zdM1I0trq8h6/+E81s+npjqmzJG0PvGFm9ZL2IKw/Ob6j47rw+uMI09yWx5GfW8xsn84en+4O7Pc2r7PsnHPOOdemTxMPhX8ZNiVqWP98APbcYihvfLyCRjOyMj0ZcZ/RVpd3SbsR1l0cFROo8sSCbN9E4AeS3idMl/pVmq8/GjhX0huEUaLT1+bgxDqwO+ecc851qHpFmK61/28gv3/H+6fZopXVPDhzPpccvDVZKV+K2x211eXdzJ4jLILPaGb2F+AvCV7/CeCJdT0+4/6rkXSipBJJJaWlpUmH45xzzrmkrFoK/70dCopgi/0gt2/SEX2qsdH4ZNkqLn3kHS4+eGuGx1ES59zaybhkxMxuNLNJZjZp6NCW1cycc8451ytUlsGTF8HA0TB2L6hdBVXLk47qU3PLKjn97plccei25GenWLyymrqGlhVSnXMdSXc1rS8R6hKPlHQOcKWZVaUzBuecc851Aw21ULEQZv8T5j0btu22VlPRN6gZHy5jYEEOl/3zHQA2H1rIQdtvRE5Wxv2d17mM5h3YnXPOOZd56muhsX7NbaksyM6M8rlVtQ2f2ZaXnSKVyvAF7F5Ny2WYjE5GJJUCHyQdh+syRaxuCOR6H7//vZvf/97N73/v1vL+jzYzn4vvgAxPRlzPIqnE/xLSe/n97938/vdufv97N7//rj0+sdE555xzzjmXCE9GnHPOOeecc4nwZMSl041JB+AS5fe/d/P737v5/e/d/P67NvmaEeecc84551wifGTEOeecc845lwhPRpxzzjnnnHOJ8GTEOeecc845lwhPRpxzzjnnnHOJ8GTEOeecc845lwhPRpxzzjnnnHOJyE46gPYUFRVZcXFx0mE455xzzvUM1dXhZ35+YiHMmDGjzMyGJhaAyygZnYwUFxdTUlKSdBjOOde2yjKorYTq5ZBTAPkDodD/H+tcT7W0soaK6gZWVtfRLz+bfvk5DO6bm3RYnTd7dvg5fnxiIUj6ILGLu4yT1mlaklKSHpZ0nqSLJd0pqU86Y3DOuS6VWwhL50JeP3j9brj32JCgOOd6pIEFuSwqr6YwL5s/PPM+lzzyDitW1SYdlnPdVhJrRl40swvN7BygAPhWAjE451zXkGDsnjB4DOx8Inz4AjTUJR2Vc24DqW9oZMfiwRQX9eXUvcfx+NsLqalvTDos57qttE7TMrNG4GIASdnAJsC76YzBOee6VHZe+NlQD8s/gu2+BznJzcV2zm1YudlZANTUN/D2/JWcuPsY+uZl9Kx35zJaItW0JO0LPAQ8ZGYlLV47UVKJpJLS0tIkwnPOubVTVwWL3oA37oV9zoc+g5KOyDm3AVXU1PPKvGXMWljO93YZ7cmIc+shkWTEzB41s/2AzSSd0uK1G81skplNGjrUF4E65zJc9UqY+yzMeSokIgVDko7IObcBraiq45E3FrBweRUn7L4ZAwu60eL1DCbpJEk3SLpA0m2SLu9g/90kvSppz/j8QknfiI+LJX2zjeMOkzRP0rR4zDRJx3X5G+qE9uLsTdKaykuaAGxmZg/HTXOBMemMwTnnulT1crjzMNjmO/DMlLBt5x9A4bBk43LObRBLKmr4xb2vc/QXipm3ZBUAR+9azNB+eQlH1n1J6g9c9P/t3XmYXFWdxvHvm6VJQgIBkrApYdhc2CEoICLoOKKgw4wrCBoWEXiQYdhl3wkwimYEUUQiCAMMoqBoEBV0gkgABUWILBpGliys2Tvp5PWPe9pU2gRIqrqqk34/z8PTVbfvved361DV9cs95/yAEbZdhvJ/7bWOsT1B0u9rNp1p2+XxxsC+wA+WctxN5R/Cr7f9I0kDgJck3WR7ZiOuZzksM87epNn3FduBQyRtD/QH3gYc3eQYIiIap20wHDtpyW19MmQjYlW19uptTDzlfUts699XLYpmldEOCDhW0ndsvwAcDiDpaOBUYCwwGHgLcKjtlzoPlrQRMFbSQ8AFwGhgO0lnATfY7vIhvYRhwFxgkKT/LXFMAt4LnA5MBz4LPAm8FTitHDMWeB54BNgeuM32NSWx+grwBNXc6Nts3yHpImA/4GpgZ+ApqoWcOuP8GfBt4B7bB0n6LPB5YH/bk5f3BV2ZaHES2fOMGjXKqTMSERER0SA9o87Ig7ZHddm2JXAysBfV4kbn2R5ffjcXGG57lqSTgGG2T5A0Dhhn+25Jo4GNbZ9Vhm6Ntj16Ge3fDTxHlWCsA3zH9sRy3MW23yFpPaAvcB+wve3pkj4J/Kvt/UsC0c/2aZJWAyYDWwHHAzNtX1DKV0wCNrXdIWkesD4wA9gaGFobp6SDgVG2j5R0ADDZ9oQVfqFXEvnnu4iIiIhoKdt/BA6U1Jeq7MMtkt5s+0Vguu1ZZdcngfc0oMnrbf9oKdsfK/FMkTQcWMN254pKTwLb1uz757Jvu6QXgE2BbYAXJZ1c9vkDsDYwDZhq++Wy/aHO+S61MQGnS1oT2M32d+u6wpVEkpGIiFhx82dD+wzoKKMs+g+A1UdU9VcieonZ7R3Mau+gvWMRAgb278uwzCF5wyRtDJxu+xDbCyV9H/hGzS7DJQ0uCckWwKOvc8qF1Wk1ABhpe3nKSNQOGXoBeFXSCNvTgM2Bh2p+v0mJfwAwgmro1cPAFNtjy+8OAF5cyrmXGqek64BvAT9cjphXas2uwL5pqbp+gqSvSjqjme1HRESDLZgDfxoPs1+Ex34I1+wLs6a1OqqIpmrvWMT4R6bw0uz53PrQsxw07n6mz2xvdVgrk1eBdSRdWoY/XQWcVO6KALwEfE7SxVTzLcZI2o3qLsSBkkYCHwZ2L4sl/ZFqvsaX6LJQkqSPAiOBT0oaVbN9NeBAYJuyD2VC/KeACyWdAnyQahhWpxFl+w3AySXeC4G3STpN0jnAkJJgHQqsKenYmuOXFufl5RpvWP6XceXU1DkjknYCNrB9a3n+KHCg7QeXtn/mjERE9HDzZ0Of/tCvDea+DNd9HPa5FNbbutWRRTTNnPkd9OvTh7Z+fXhpdjv7ffM+vn7ADmwyfHCrQ5QU4wwAAAzUSURBVPtHPXTOyOvsP9n2xt0Y0nIrSdNk2+MaeM42YDhwkO3zGnXenq7ZFdjv77KpDzC7mTFEREQDta2++PHC+bCoA1ZPjajoXQa1Lf46tWChaevXhyEDMhK+EcoyvGtK+rTt61odD4CkrYDdga0l/dz2XxtwzkFUQ7P+QjWRv9do2TtF0r8Bd3Rdbk3SYcBhABtttFErQouIiOU1ayrceSZ8/DsweN1WRxPRdLaZNrOdC378GN84cEeGDc6ckUawfTnV0KUew/YjVEv/NvKcc4D3ve6Oq6CWVGCXtCewJ/CfXX+XCuwRESsRG159Bu44FfYaA0M3yuT16HUWLjLPvDyXi8ZP4rx9t2KDoQNR3gfLRdIxDTrP+pK+W6qrny9pvKSWjhuVtF3tylmSTpRkSRdIOlfSbZ3V43ujpicjkvYGPgD8B7CepF2aHUNERDTIrGlwzUdgYQc8fgf8/iaY+Xyro4poqhdnt/PvX/81GO58dCq3/PYZXpyVCezLqSHJCHAcMNH2GbZPpZoIvmaDzr2itgP26Hxi++Ly8ALbpwNnApe0IK4eoanDtCTtCNwIPADcBawOXAbc28w4IiKiQfq2wYG3ssSKlX0zPCV6l/59+/D9I3ddYlu/VGV/wyR9AhhaJoVPAt5GVfX8YKqig48D/wSMAr4MHA3MpEo8HqeqjH6J7ceAKcABku4HftM5wVzSW4ErgHFU9Ty+CWB79LKqvFMVMRwL/AZ4BtgJuLxUVN8AOKe0vzlV8cV7JN1ItTLWL4FdgR+Un53Xd4XtKV1egmFUdUiQNJjqu/KvShzX2/6ZpIOoVur6BtVqYJsA+9ieIWkH4Gyq79cDqCq9Hw38mKoa/DSqhOwh29dKehfVymFPAO8ADq+pf0JNW1+iWrFsGFXl+A+Ua+1s9whgy/KabwwcXl6zy4FZwGiqeTBX2r6CZUgF9oiIiIjeooeuplW7YpakPlRflLelWrL3JOAMYAJwlO1zJN0LfMH2A5LeCVxqe9dSNPFIqi/Cb6K6M3Ka7Zm1K2B1rdL+OlXeJ9j+VqnK/hBVFfXrge/ZvlnSusCDwJupEoV7qL6cDyj77kqpDl9zvaa6I7JhOeYztqeViey7lgRkbar51TuVY+4GLizJ0GXAL2x/ryReR9m+T9I/l+vdQ9LngR1tH6Zq3OBjVBPvT6GqfXI+VfLwlO0lFpQqbZ1r++eSfgD80PZVkr4K/Kq0+2HgdtuLJI0tsd4uaQRVMnUZ8KLt61/r/4eWzBmJiIiIiFga24uA26kSkR2A04BPAh8Fbim7bUOpgE5NZXTbC23/t+0dy7FvpfrS/3q6VnnfsuZ3nZXWp1CN6hle277tqVR3HoZ1Hm97ge2Zth9/jTa/THU3oT/VXR8AAXtIOp1qQaeuE6g7zzcdGFIeb0mVvP091mIbYP1SDf4k4BFgPaokZB1gIvA5YNEy4nuq/Hyl5vHLNe3OAS4u5397Z6ylQOQ5pc2bl3n1RZKRiIiIWPXNnwMzp8CMZ6uf82Y0vIm5CxYybcY8nn91LtNmzGPmvAUNb2MVtlCVbcvzG6m+jL8C3AHsBowqK1lBVel80/L475XRJY2T9GYA288CPwXayn4zgTXK465Ltg4vQ6TgH6u8d1ZaX5/qC/j02vbLHZNXqCq2w7Irra8laYl2S2HFU4ELyt2LQ6lq8p1Llax0tbQhTY+WmP8ea/Ew8ITtMbbHANcBk4GdbR9DNexsBFUxxxVxMzC2nHti58ZSQHJn4EqqoV6vqdlzRtYDzgO27bzlFBEREdHt5s+C314Dw7aA398Ia24Ee5wEg9ZuWBOz2zv4n4n/z+YjhnDbw8+xzuA2jvuXt7D26m2vf3DcDvxXeXwc8GuqL/tH2V4g6U6gtp7HIcCJkp5g8RwPgP8DviLpEWA1qiFQnau33gJcLqkfsDZVtfVdbf+axVXe1y/nO6SmrS3KnYqdqYZ2WdLxwPmSNgc2Az5Vth8KjJR0sO1vl+MnAp+mmqR+maSPle0nSjrf9m8kTaGaa/Fd4GOSLikxrVkqws8o13JwGTrWWefkdqq7K+dKmgi0szhhuYrqzsXZQAeA7e9L2ljSpVRzSeYCd9d2hKT3l7ZGS7qNxZXun6tp9ydUc3AukzQB2AV4i6THgC9SzZm5C/iVpA7gWC9jbkizK7B/jOpFOvONVN7MnJGIiIhoiAVzAUH/ATBrOnx1azjqAVjzTQ1rYu78hUgwoH9fXpjVzp6X3M34Y97NhmsNalgbdeuhc0ZabVlV3ssX/3G27252TG+UpO2BP9jukLQ71fyTQ1/vuJ6iqcO0bN9MdYssIiIionn6D6wSEYCF82GdzarV4BpoYFtfBvTvC0DHwkVsuNZA2vr1bWgb0Xi1Vd67bN+NxXcFhiz14J5hK+Brkk6gWiXr7BbHs1xaVoF9WVKBPSIiIrqFXc0X+dkZsP9NMHhEtzQzdcY8xoyfxLdH78TwIVnquqdbVpV32xOoJsH3aLavBa5tdRwrqsdNYE8F9oiIiGi4RQvhlafh52fDBy+GNTZofBOLzDMvz+Gi8ZM4Y5+3s8HQgQ1vI2JV0+OSkYiIiIiGmz0drtwTRrwdpj8OT9/b8BW1Xpjdzr6X3cNmwwfz1LTZ3P+Xl5g1r6OhbUSsapq9mtZ7qMayrS/pNOBLtuc2M4aIiIjohfoNgIN/2q1NtPXtw42f36Vb24hY1TQ1GbH9S6qlviIiIiKaZ+DQ6r9uNHRQG0MHZRnfiOXR1KV9l5ek6cDTrY4jGmYYiwsCRe+T/u/d0v+9W/q/d+va/yNtZ2JwAD08GYlVi6QHetq64tE86f/eLf3fu6X/e7f0f7yWTGCPiIiIiIiWSDISEREREREtkWQkmumbrQ4gWir937ul/3u39H/vlv6PZcqckYiIiIiIaIncGYmIiIiIiJZoap2RWHVJOgHYmGrpvs2BQ4CBwBjgz2XbKbandjluBHA1MAEYAbQBX7C9qGnBR91WtP9rjh8B/A640PbXmhFzNE49/S9pMjC5PH3W9qe7P+JopDr7f2fg/cAiYE/gINt/bU7kUa86/vbvAVwGTC+bRgA32T6rGXFHz5JhWlE3SesBjwLDbC+SdCtwE/Bu4Be2b5L0YeATtg/scuwGwN62ryzPHwaOtH1Pc68iVlQ9/V+O7wNcAawL3JlkZOXSgP4/K19AVl51fv6vAVxt+6Pl+SbAVNuzm3sVsSLq7PstgNVt/648/xZwru3UluuFMkwrGmEOMB9YozwfDPwR2Bu4t2y7pzxfgu3nahKRIeXYfBitXFa4/4uTgKuAl7sxxug+9fb/uyWdKOlcSbt2a6TRHerp/w8BsyQdK+kMYIckIiuVev72P16TiKwLDEgi0ntlmFbUzfaMcqv2RknPA88AT1Lddp1ZdpsBrCWpn+2OrueQ9CngCOBi2880KfRogHr6X9J7gTm275N0RLNjj/o14P3/RdsTJQ0CfitpH9tPNu0Coi519v9I4J3AocBC4C5JL9i+u2kXECusEX/7iyOo7o5HL5U7I1E3SdsBJ1ANtxpNNXb0DGAaMKTstgbw8rI+jGzfQDVeeD9JH+r2oKNh6uz/jwADJZ0MbA28X9JBTQk8GqLe97/tieXnHOAh4F1NCDsapM7+nwH8zvaCMk/wXmCPZsQd9WvE335JqwGjbE/o/oijp0oyEo2wIfBSzYfN88AA4HZgl7LtXeU5kvpI2qg8fo+kdwCUP0ZPA5s0Mfao3wr3v+1jbI+xPQb4A9WckaubGn3Uq573//sk7VVzrs2Ap5oSdTTKCvc/cBfV5OdOI4HHuzvgaJh6+r7TfsANTYg1erBMYI+6SeoLjAXmAa8AWwHHAO3ARVQJxqbAybanln9Nudb21pLeCRxPtZLSEKoPtyMybnjlUU//15zjYOAo4Fngcts/ae5VxIqq8/2/NXAW8CCwAfCc7QuafxWxoup9/0s6kiohWUC1CtNxzheTlUKDPvtvB/a1vaDZ8UfPkWQkIiIiIiJaIsO0IiIiIiKiJZKMRERERERESyQZiYiIiIiIlkgyEhERERERLZFkJCIiIiIiWiIV2CMiWkTS/lTr8a8G/ImqCOT9to9vaWARERFNkjsjERGtcxBwKXA41Tr9v2xtOBEREc2VZCQiogUkfQTYHDga2Mv2VcCi1kYVERHRXElGIiJawPZtwGTgK7Z/3OJwIiIiWiLJSEREREREtESSkYiIiIiIaIkkIxERLSBpb2AkcJSkTSR9Btgd2FnSYa2NLiIiojlku9UxREREREREL5Q7IxERERER0RJJRiIiIiIioiWSjEREREREREskGYmIiIiIiJZIMhIRERERES2RZCQiIiIiIloiyUhERERERLREkpGIiIiIiGiJvwHcZCpmmZ3bMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(6, 1, figsize=(11, 4), sharex=True, sharey=True)\n",
    "markers = {1: r\"$\\mathbf{1}$\", 2:\"$\\mathbf{2}$\", 3:\"$\\mathbf{3}$\", 4:\"$\\mathbf{4}$\", 5:\"$\\mathbf{5}$\", 6:\"$\\mathbf{6}$\"}\n",
    "\n",
    "for ax, column in list(zip(axes.flat, sorted(set(df[\"Description\"])))):\n",
    "    sns.scatterplot(\n",
    "        x=\"f1\", \n",
    "        y=\"threshold\", \n",
    "        hue=\"Condition\",\n",
    "        style=\"Parallel Execution\",\n",
    "        markers=markers,\n",
    "        edgecolors='none',\n",
    "        data=df[df[\"Description\"]==column], \n",
    "        ax=ax,\n",
    "        s = 50\n",
    "    )\n",
    "    ax.get_legend().remove()\n",
    "#     ax.set_title(column, fontsize=10, rotation=90, loc=\"left\", horizontalalignment='right', verticalalignment='center',)\n",
    "    ax.set_aspect('auto')\n",
    "    ax.yaxis.set_label_position(\"right\")\n",
    "    ax.set_ylabel(column.replace(\" (\",\"\\n\")[:-1], rotation=0,\n",
    "                  horizontalalignment='left', verticalalignment=\"center\")\n",
    "    ax.axvline(x=80.6785,c=\"r\",alpha=.2)\n",
    "\n",
    "f.tight_layout()\n",
    "\n",
    "f.text(-0.01, 0.54, 'threshold', va='center', rotation='vertical')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#f.legend(handles, labels, loc='center left', bbox_to_anchor=(1.01, 0.46), borderaxespad=0.)\n",
    "f.legend(handles[1:3], labels[1:3], loc='upper center', bbox_to_anchor=(0.42, 1.03), borderaxespad=0.,ncol=2)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "\n",
    "axes.flat[2].plot((80.6785), (1.055), 'o', mec=\"r\", mew=1,mfc=\"none\",ms=11, alpha=.5)\n",
    "\n",
    "axes.flat[2]\n",
    "\n",
    "# plt.savefig('batch.pdf',bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Different additional visualizations of the same data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hzTXXU5oWFm02G3Vr1lG3Zt2k7Va2mXeCjK1eWmQsphBCLH7SRS5mJT1cAtjsingsyWBPiFgke/fpuYN+PMVOtt29grGzzUxl8T8rXG66rYFla8twOGZ/s8lC8rc209/VQTxi3Bm+YvM2To4ZNwnga2uh0Oul9fABAn4f/R3nOfL8U6AhHo0w6Oumu/ksgz09DPR08crTv84YLgF0KkXQ76Pl8MFRXd1Ot4eisvJRH+7ConH7isrKZzVVUKbqpUXGYgohxOIm5QFBKpnCZp/+3xpjwyVA6+FeGtdXAdB5aoDSJYVZK5nnDvhZsamKbXevYM//nkNro9vWOUn1cmy4tC/ycJmIxSj0lnLdm95GMhHHZneQjMdoP3Y44/RBvpZmbn7HvRQUl9By+OBI9dJy+OknWLp6Hb2tzThdHiqXN2a9tlIKf3sLQ31+vNU1WY/rPd9GcXkFrsJCnK7Mq+tMh9aawtIybnn3+7Me43C6Zn0dIYQQC0MC5iUuFIjSfryf5VdUUlDsnPLzMoVLFNRfUUGwL0z9unJgeiHzpV8243BeWuESwOFyUd24gmrz8/BQkF0//g+KSssyHh8LhwCjW/3Is0+OVC8tRhXzDEtWrOLyV9/McP/Ec4suaVqJp8Sb9fFQIMChpx9n9TXX462qHjVB+kwppSitXjLr8wghhMhPEjAvYaFAlEPPtONvH2JoIMrlNyydUsjMFi4br6qk4/QgVcuKpx0y115Xy3WvW8W+x5uz9ehelOEyk77z7QTSJkvPpPmVA3irqsZVLy0Hn/gVt777/dSuXItjsorjJL3cfe2tBHp6OPD4L7ni5lspqarOSRVTCCHExUsC5iUqPVyCEQKBSUPmROHy/MkBzp/o5/yJPjbe2jDlkFlc6aF2RSlOt1G9fPnX40PmpRIuw0NBju96btLjirylHN/5PErZRqqXBd5SGq7ayIldzzPo66an5Rw1dgf1ly2f8c0yoUCA47ufJx6L0nfutDGtkd+XkyqmmMSwH1BQVGl8nkrCUDeU1DFu4LIQQuQZuclnERnqjxKLzH5+wLHh0tJ5aoBjuzoID41f5xogGo7TdrRvwnAJoFNw8OlWXB7nSMisW1NGeChGMqkpKnOjbMYvyOJKD5vvaKSkwoOnyMnabbVs+Z2mUb8/L5VwCVOrXhaWljE00EdvW8uo+SLXXnsDS1evo7jCCCSHn34CUsmReTFn1B6zehka6AetObH7BfztrcRj0RmfU0zBsB/2fhv2/zuEeo1w2b4XnvoMdB3KeuOWEELkCwmYi0TAH2bvr5s5e8A3q5CZLVxaJgqZ7gInl11fR3WDOV4vQ7i0jA2Za7fVsnrzEhqvqmDZunLWb19G08aqkXBpGRsyL6VwOdXqZc3K1Zzc9TyxSASlFA6na2Tey32/+hnrrn81NpuNYK+PnpZzhAcHZzRxeXr1MjJsfL/0tbcSGhgg6M/NKj4ig3gEjv4Mzr8Mg23gO0mi7RSRcy1EyreTCIShv2WhWymEEBOSLvJFIOAPs++JVob7I5x52egOXbmpGpdnem/fZOHSMlF3eaHXxVU3L+Pwc1BY6swYLi1WyNz62hUsaSqhenkxx3Z3gopx+Ll21mytyTg5uhUyUYrmQ75LIlyCcfPO0jWXs3TNZVmPcXoKCAUGcJhTCSmlKK6oZOWWbZz67S4G/T04XC7K6pYx3N/HiV3P03DlRmwzmK9ybPXScmL3CxSWlclYzLni9MCaO4wqZkkN/PJ+Etv+Dt9X/hncXqr/7L04enbBlndCYeVCt1YIITKSgJnn0sOl5czLxvyA0wmZUw2XlslC5mU31PLyr1qyhkuLy+Ok+aCPolKjW7yk3MPeXzcTHU5wdGcnNruN1VuW4BoTND1FTi67rpZla8ooqym46MMlQGl1DaUTTBUExt3iAZ+PirplBMwqos1uvDbNB16mYmk9XadPcvPb3zNSZYwMB4mGQ3iKiqfclkzVS0t6FVPGYs6RsuVw5evh0XeQUi70QDu6oBLCfRALQ+eLUFIL6/8A7FOf/UEIIeaLdJHnsUzh0nLm5Z5pdZcP9Ufp6xqe1vV9LQHCgdi4/eFgjJN7ugn2hccFw3TuQiclFW40ileeaUMpOPzCeaLhBMoGQ/0RDj9/ntMv9xALj/86XB4HlcuKL4lwOVVOtwdvdTU2u51CbxmF3jK81TWc2fsiDpcbh8tNOBgkOjxEWe1SSiqr0VqTiI1/HyeSrXppkbGYcyyVJBFWRLZ9juiaPyHR1Uuq9RUIDZAMhIg0vYOIez2J/sDk5xJCiAUgFcw8NVG4tEynkllVX8ym2xo48FQrqcTkNwg4XDauvqOR8rrCUfvDQzGO7uyg++wgdocNb6WHQG9kXEC0wiVKkUykWLmpmud/dAKH047LbR+5UcgKmUDGSqYYz+n2sHTt5SOftx87TCwcptB7YS7Lllf286q3vJOissxzaU4kFAhw/sRRCrylxCIhCjLMkalTSZLxuFQx54J5Q0/iXAu+r/4r2l6A7m+naPsdhF/eRd83vw5LrsR9+ZWUvukPcFRJN7kQIv/Ib/M8FArEOPhM+4Th0nLm5R5cHgdNG6qw2bJPXWKz26hZ4Z1SyLTCZXVDybilAB0uO+U1hXSdGQTIGDIzhcuDT7YSHopTusRBPDr62hIyZy4UCHB81/glJYcH+vG3NVNUtmna53R63Ky/7Q5629oyzrE5QkNf53m8VUtwuGTVnZxJJUjE3aTcteiCKlR8gIr3vQ9H+DSuxrcy+N8/puCyVZRc7sGR6gbdJNMWCSHyjvwmz0MFxU7WbFnCgSdbScZTEx5bVlPIkibvhOHSMpWQOVG4BHC67Cy/0qiYHNvVCYwOmUqpCcJlAdHhBMnE+K9JQubM9J1vJZhlze4Tu56nannTtKuYTpcbp8tN4ZXTr37ORiIex+GU8YQ43CSSBSh7iKp73oyrthK95xvE9z+Da91t1H3pK9gCJ+GV/0QN7oHCQqhdLyFTCJFXZAxmHlI2RU2Tl023N2B3Zn+LymoK2Xh7A8VlU7+TNz1k2hxjq5MTh0uLFTIvv6FuZJ8VMmcSLi1jx2QmEylSyYkD9qUsW/XSYlUxF4vB7s6JK6YLKJVKTn5QLik7qb5O1PH/Av9JlMMBziL0mZ3Y9v0zPP5JbLYYNodd5sQUQuQlCZh5arKQOZNwackUMqcaLi3ZQuZMw6XFCpmnXu7B3zZE97mAhMwsJqpeWk7sep7hgYnXIs8H8WiEky/upPvs6YVuSkaDPd0MD0w8Y0IuOcrLsa/ahPN1D6JSYfT196HLGsDmhGE/tmWXoYvr4Ma/gLoNUr0UQuQdCZhT0Nc5xFD//N8tmy1kziZcWtJDprvQMa1wackUMlOJFI1XVXJmX8+0w6UlHktydMd5+ruHObW3W0JmmlTSqKRNVr20LJYq5mB3F91nT3HqtzvzroqZTCQ4t+8lfC3n5u2ajqpKPKtX4ai/jOTaNxDzhXHd8Cbcq1ahCopQRZXYqlYZ0xlJuBRC5CEJmJPo7Rji5V+3cODJ1rwImbkIlxYrZF77ulVUN5Qw1Bed8jyZlrEh0+m203l6kLXbaqhbUzrtcOkucuJy26m/rIKh/gjB3ggHnmqVkAmEAoMMdBvjXqdSvbTkexUzHo1was9utNaEg4G8q2IGfN20HT3Eid0vzGsVc0Qyhb13H4kn/wUcDmwuNygb+E/Avu8bS0kKIUSekTspJtDbMcS+x1qIhRPEwgkOPNnKptsbKC6f39VLrJC5+c5GCkvdOQmXFpvdRkmFh2BvhH1PtBAdjnP1nY1ULy+Z8jkSseSoG3+cLhtV9SUUlrh46VfNDA9MLZiPhMvLK3C6bXSfNeb4SyU0B55qZdNtDdSs8GKzX5p/F/W2t3L++FGuvutu3EXFXPemt07j2bkbp6e1nlalezJW9dJy6rc7qVm5muLyipxdY6aSiQTn9u8llUwSGhzA13KOorLyeW2DwxlCdz8NK1egikrhhg/C4Z9CoB1ad0HValj3WqlkCiHyigTMLNLDpWWwJ7SgIXNJ4/j5CHPBCpdDfca0SPsfb5lyyPS3BTn5Ujfrt9cbIVMpSio9VC8voa9ziKvvaGD/E62Thsxs4dJyqYfMUGCQE7ueZ6ivl2DvdVQ3NGU9Ntjrp6DEO2dTBw10d+IuLB417+ZMpVcvLVYVs3jLtlmff7as6qXlxO4XqG5cMa8h01a9Gm76U3jlUbj+w8aYy7IG2PEVqFwFDTdIuBRC5J1L67f0FGUKlxYrZC5Ed/lcGBsuAeLRJPsfb8HXFpzwuf62IPseb6G/c5h9T7QQGYrTeGXFSDCtqCumqr6Yq+9ooGiCqutk4dJihcxLsbu8t72VYK8frTWnX3qReDTz918ymeDsvj0EfN1z0o5kPM7Zl/fQ196ak/ONrV5a8mEsZnr10mJVMeeVww0rboLbHrhwQ09ZA7z6ftj4Niic34qqEEJMhQTMMVIpTTSUIBHLPi1JNBQnEZ/naUvmQKZwaZksZFrhcmRFHvNcoWB81HGThcyphkvLpRgyreqlpev0CQZ7ujIeG/D10PLKfk6/vCfr8pDh4MyXFxz0ddN+7DDHdz1HKDC7ZQozVS8rlzdS4C3Ni7GYocAAroJCVm25dtRHNBwiHJz4j6+cc7jH39BTukzCpRAib0kX+Rg2m6J2hRduW87Bp9pIJUePXfMUO9l8ZxNlSwqznGFxmChcWqyQOba7fGy4tFghc/MdjZRUeEb2V9QVA4zrLp9uuLRcat3lVvXSYlUxS5fU4nRfCO3JZILmAy+TTCToOHGU1Vu2UbFs+ahzRUPDnNi9g3XXv5qCkqmPswWjemlV9IK9fvraWym84qoZf13xaJSmTVto2rgZAKUUqWQSh8tFMpHA7nQSC4dxFRTM+BqzUVJRxVW3vGZBrj1isB0KysFVBECip5NEby84PDjKy2WZSCFE3rq4fzPPkM1uo3ZlKRtvW47NfqFiYIXL8tqFC5eR4fjkB01iKuHSMraSmS1cWqyQGRxz7rGVzJmGS8ulUskcW720ZKpiBnw9tB15BQCdSmWsYg50dXJu/0v0dbRNuy1W9dIy2ypmobeUutVrqVuzjro166ioX87Z/XsJ9vVSt2YdS5pWLli4zAu9p+GZz8OpJyA2DNEgibYT+D7xIXxf+CyJ/gW4o10IIaZIAmYWY0NmPoTLQG+YA79ppb9reMbniMeSnDnQM6VwOfKcaJKjOzro6xyeMFxaJguZ171uJeU1hTMOlxYrZE53aqXFZGz10jJ2LGZ69dLSceLoqLGY0dAwJ17cAcDxnc9Pq5s3vXppsaqYuTLY1YWv5WxejL9ccEM+2PNNCPnh4I/g5GNw9H8g2AWJCAy2QTLzEAghhMgHEjAnMBIyb2/Ii3C57/FWes8bNyDNNGQ6XXbWbK2hfGnRyD6tIZlhXXKLu8jBZdfV8srTbZOGS8tEIXNJk5cb3rSa0mrPjMPlyPlqi+b9jv75kq16aUmvYqZXLy1jq5gDXZ30trWYx3dPq4o5tnppycVYTIBYJDwSfvNh/OWCK66Gq98B7hISK15PxB8nMugkORgGuxO8y0j2DRI5dZrIqdMk/DIXphAiv0jAnITNbqNuVWlehMvhfiOsRYbjswqZRaVuNt66nPKlRWgN0XCcQG+YRIYJ0d1FDtbfXM+xXZ1Tns/SMpSlK76g2EXZkkIarqykdApjWW12lTFEVtUXs/6Wegq9F2fAzFa9tFhVzGg4PK56abGqmOnVS8tUq5iZqpeWXFUxreqlRaqYQO2V8Kq/IBG14Xvoy/ge+jJ93/pXqFgFriL6vvc9fA89hO+hh6S7XAiRdyRgTkEuJ5WerrHh0pKLkLnhluUUl7sJ+CLEwgkC/siokOkucrDlriYKvK4ZT9NdVOrKuJY6QHGZh02vWT5hyLTZFU0bqqhaXjIq5F/s4XKy6qWl6/QJ+s63cf74kYyPW1XMYK9/pHppmWoVM1v10jLbKmZ69dIiVUwgEoTOA4yaJF+nIBqE1OKfxUIIcXGTgJnHsoVLy2xCZiqlCfaGqV1VStVy4y7veORCyLTCZXltEd7KAmMVoWmuIFSz0suVNy6joCT7hN8ThUwrXJ5+uf8mpr0AACAASURBVIc9vzhHYamb8trCiz5cwuTVS0sqleLIc09SWd+Q9Zj2o4fp7+pA2cb/d5+sipmMx2k+uB8Am92e8WN4oJ++89O/acgytnppuaSrmJEAHPs5HPs5Drem+qN/RfVHP0rFvR+EwHkY9lHxjj+m+iMfofojH8FRLtMVCSHyi0xTlKeCfZEJw6XFCplbfqeJspqpdeOnUpruc4McfLINndKs3rIEAF9rkHgkQTyaYtNtDZTXXhinWVplhMx9j7cQmkJX+VTCpcUKmQd+08ZgTwgYHS59rUYA2v+bVrb93gqaNlRd1OFyqtVLgEQsxpm9v+XGt76TnuazGbuxY+EQp/fsprqhiZ7m0UHOqmIuW3dF5vMnEqy+5lpWX3PtpG3RqVTGEDuRTNVLSz6t6DPvtB65icfR9RyOG++HZJzIyWPm4ynsVZV41qxewEYKIUR2EjDzlMNpo9DrmjRggjGfpMM9tV/s6eEyaXaHNx/0j4TM4YEo629aSvPhXgq8LopKLwS5qYbM6YRLS3rIDPaGx4VLMG5QOnugh5JKD+4iBw6HfcrnX0zCgUHKapdRVrtskiNTDPf3411SQzQ0THXjinHdyqlkkqH+Pga7O7n1PR9A2Wzjjjm+83kqli7POC+mu6AA9xxOFZSIxViz7XpWb7su4+N2u4NYJILL48n4+EWroBSueqMxwfqyrVC91tjvj4J3KRRVg825sG0UQogJqPRVNBarrVu36r17987pNeKRBE7P/ObxcDDGoefO42vJPr6tdEkhm16znOKyyX8BZwqXFptN0bSxCpvdRtvRXqKhBBXLitlwS/2okAkw6A9nDZnTDZdDA1EKS5wjk6UPDUToPhvg6M6OUeHS5XHgrfJgd9hQNrjq5nqWri27aEPmVAz1+Tn5250jN/dEh4eJhUOjjolFIgR8PQBsfu3vo1Mpju98btz8mNte/+asVUyxgOJhcF4I+Al/L4k+Pyj7XE60LgubCyFmTQLmFAT7Ipza082abUsoqZjfiZ8nCpm5CpcWZVPYHYpE7MLj0wmZ0w2X/V3DHHiylcuur6OmyViRZ3ggyr4nWmh+xT8yJVJ6uLzQVgmZ8Wg0Y5e4JRGL0nXmJKHBAVyFRQz399N56hiNGzbjb2sedWxJZTVXvPrWaa/uIy5KEjCFELMmXeSTCPZFOPCbVgL+MMGBCJvvaJjXkFlQ4mL9zcs49ByjQuZ0wiUYSxjbnXbUBD3pOqVJxEb/weHy2LHZxv++GdtdPpNw+fJjzUSHExx8so2Nr1lOSbmHV55pJ+AL460qIOAPo5QaFy6NtsLh59oBLtmQmb5MZLbHG9dfDcDwQD87fvg9oqEQlfUNXHbDTeOOt9kvvddQCCHE3JC7yCeQHi7BmtexlWBfeF7bYYXM6kYvMP1wCcZUS9XLi7n6ziYcrqm97bWrSrniVUuzhkYrZDZcWTkuXIaHYlknZU8Pl2DM5zjYE2bvr87R12HcEe9w2vBWFWQMlxYrZHacHCCRkGlbxrLZ7ThcLuxOJ11nTxGLhFA2xanf7iSZiONwuUZ9SMAUQgiRK3PWRa6UWge8DQgDNwMPAv8CpM+J0qC1XjnmeS7gG0AzUAN0aK0/M9G15qKLfGy4TFdc6Zn3SiYY3eVnD/hoXF85rXCZTmuNr22I/Y83j+oKH2uycJkuEUvicF0IJ+FgjKM7O0YmU3e6Lzw2Nlym3y3ubxvCW+3BXeBkOlOPSnf5xIK9Pp7/wXeIhS6Mz7z+D/6I2lVrpn0urfWCzgu7UOLRCMl4AqUU7qKiyZ+QIwl/78gk6nM45nKsS+8NFkLk3Jx0kSul7MCXgbu11iml1PeABPAlrfWj5jHbgRszPP0NQLnW+h6llA3oVUp9W2t9fi7amkk4GOPQs+0ZwyUYlcyDT7ez+Y5GCr1Tv1N6tgpKXFx2XS1258xDVHolM1vInE64BMaFyyM7Oug+O0jXmUGAkZA5NlyiGHe3eMAXwVvNtEKmdJdnp7Wm49SJUeES4OSLOyivW4q7cOphKRQYJBYOUVZTl+tmApBMJrHnaRV1sLuLWCSCzW6fUTCfqUR/P76HHgIw5rucn4AphBCzNldd5Ndg/BX8YaXUXwN3A34rXJreD3w9w3O7gSpz2wt0APM627KnyMnKTdXYnTaSiRSRUIL0Qq/dYWP15iV4iud3mpB4NDmrcGmZqLt8uuEyXXq4tBzf3cn5E/30dgyNDpcA2hj3md4FrrUm4IsQDceZTnFdKYXNbkNJ8WWUoT4/p1/aPW5/b3sr/Z0d0zpXb3sr5/bvJZkcvyTlbGmt6WtvnfCmpYUSj0boaT7Hoaef4PjOZ4mGZrZ6lhBCXErmKmA2AtcD39Fafx64CXiX9aBSaiUQ0FqPW6pEa/0ssM+sev4I+K7WelwpUSn1PqXUXqXUXp/Pl9PGK5uipsnLhlvqCQVjDPpCRIaNwGN32Nj0mgaWNHkz3vwyV6KhOKf3dtPbMZST82UKmbkOlwDuQgeRUJxd/3WacDA+7nnNh/w0ra+kdmXpyL7phkybXbH+luXUrSrNOl7zUpStemk5+eKOKYcla/L3tiOvjEx7lEtBfw/7fvUzgr25/b+cC4PdXWhSnDuwl+6zZ+jvnNvOlIS/l8ip00ROnSaZ9rMt6fON7E/4e+e0DUIIMVtz9ds4ABzXWltpYwewPe3xDwNfzfREpdSfAy6t9TuB1wJvVkq9duxxWutvaK23aq23VldX57TxYNyk4msLsv7mehwuO4HeMPFYko23L1+QcHnqpW7OHvCx77GWOQmZy9aVz0m4rL+8ggO/acXXFiQcjJNKjU6MOjW7kCnhMrts1UvLdKqY1tKVyUSC5gMv57SKqbWm/dgRQoFBWg+/kldVTKt6eW7fXnQyyfBAH8d3PjenVUyrW9z30EP0fe97I/v7vve9kf3WuEwhhMhXc/Ub+bdApTkWE4yK5kkApZQXaNRaH7YOVkq5lFLWsiXLgU4ArXUKo8t8XpfxCAWiHHq2nfZj/fjbgmy5sxFPkZO119SMdOvOFytcthw2Khal1R6CvRH6u3MbMi+/oS6n4RJgxaZqDj3TTihgTOo91B+ZUciMxzIHDgmX2U1WvbRMpYo5dunKXFcxg/4ezu5/CYDmg/vyqoppVS/bjxk/rqKh0LxUMYUQYrGbk5t8tNZ9SqmPAQ8rpXxANfBp8+H3AN8e85Q7gQ9iVCwfAr6qlHoAKABagZ/NRTszscKlv80IcEP9xkTi179hFa1Hemk53Mv67fUsXVM256FmbLisbihG2Wzs+M+TbH1tEwDlNcWzvo5SCnfh+PGkY+8OH2uicAnQeWqAddfXsf/xZpIJI1QOmUtfFpQ4R1WBL4RMY/htl3nOwlIXDuf411nC5cQmq15arCrmRDeuWNVLi1XF9L5mCXb77H6EWNXLeMT4vkjEorQefoUrt1djsy3sDT+jqpcp82Y4rUeqmOV1y6Z1k9RUOcrLqf7IRwCjW9yqYla8853Yzd4aR3l5zq8rhBC5NGcTrWutfwr8NMP+hzPs+1/gf83tLuDNc9WuKRlToBzqj3L0BaMrUdkUWoMee1COZQuXh55rBw0v/aqZa3IYMsca6o9wZr+P1ZuXUFQ2fkLvyJAxFVG2cAkw6DOGzlp3rI8LmV4nNpU9ZAb7IhR6XeOGI0i4nJzN7uCGN799asfabFmnHxpbvbS0HXmFpk1bKK9dOqt2plcvLc0H99Fw1UZKl9TM6tyzlYjFqV29lsr65Vzz+28a9Zjd6ZyzrnxHVeXI3eKR9GtWV+NZs3pOrimEELkmK/mMUeh1s/6Weg49046/fXQ3tLIpc77F0jmdCmeycAkQjyTnLGQO9UfY/5tWAr4wwwNRNt66fFzIdLjtVNaX0H12cMIxkplCps2mcLhsGQONFTI33d5Ay5E++s6Pfg8kXE5NUVk5uaitja1eWnJRxRxbvbQYVcyDXLn9tgWtYhaUlMjSmUIIMUPyGzoDK2RW1V8IbfMVLsFYNzwWNm6iyBQuLfFIkr2PtTA8ECOVyj5p+nSkh0uA/s5hDj7dxnDamuMADqed+nVlXHFT/aTzVQ76wgR6Qlx9ZxNOlw1vdQGuAkfWCYVWb6mhankJG2+tp2r5hfdAwuX8yla9tMx2LGam6qWl+eA+gv7xwfZSY3WXV3/kI9ItLoRYVKSCmUV6JbO3Y3jewiVAQbGLy29cSnGFh0FfOGO4BCP0Fpa4OPz8eVxuB9WNJbNaZWVsuLRYIXNsJdMKmQBHn2+ftJLpcNp49VvXcXRnBzqZ+eA119TQtKEKl8eBy+Ng/fZ6Dj3bTl/HsITLedbb3spQX2/W76lUMknLwf14q2dWxUwmk6y/9a6sj6fmYL7NxSa9u1wIIRaTOVsqcj7NxVKRllAgyqAvTHVjybyvENN5ZoBnvn+cyPD4+SOVTVGaVgl0FTi47vWrKKmY2Q332cJluvK6oozd5Yl4kvYTAxOGTKfbztV3NlJeV0j32QCvPN1GakzITA+X6UKBKMG+CFX1JRIu54lOpQj2+qc00thTXIK7YH6XTRVzSlYrEELMmlQwJ1HodVPoHX+Ty3yoqCti/fZlHHrmPJHQhZA5Nlza7IrLX7V0xstWTiVcwswrmVa4rKovRilF7SpjKqL0kJktXMLCvgeXKmWz4a1estDNEEIIsUhJOSiPuQudrN1Wy/pbluExpxHKFC5n03U81XBpme6YzLHhEsBut1G7qpQNty7HZlcThkshhBBCLD7yGz3PWSET4NBz5/EUOXMWLgGioQRhcyL0qRoeiBINJ8bfWT6mkulwjQ+XFitkFpa4KCp3S7gUQgghLiLyW30RGAmZStFyuDdn4RKgclkxm+9qZN9jLcSjk8/r5ypwsPmuRirqMk+CY4VMpaDQ68oYLi12u43yLOcRojfcS39k4iURyz3lVBbITTBCCJFvJGAuEu5CY6lKm03ReqQ3p3dUV9WXTClkWuGycunEc24aIbMcm13N6q52cWnrj/Tz8L5x6zKMct/m+yRgCiFEHpIxmIuIu9DJ6i1L2Hb3ypxP12OFTKc7853yUw2XFrsj80TqQgghhLj4ScBcZNyFTiqXFc/JdD3ZQuZ0w6UQQuSKUmqbUupZpdQupdSDSqkvmh9lc3jN9yilPjyF4+5Jb4dS6nGllEy/IAQSMKcsFIgyNObO6YvR2JAp4VIIsZC01nuAZ4FdWusHtdYfMz9/Wik1J8O8tNbfBv5pCofeA6QH3bu01jNf3kqIi4iMwZyCUCDKoWfaScRTbLy9geKyi3tORitkHnn+PFdtr5dwKYTIK1rrXyulHgBuV0rdgPG7LAkEtdZfUkqtAz4OHAKuBj6jtT6plPobwAVEgY1a6z9QSj0KrASeA24AHjM/B7hHKfVl4E7gUYwwuQR4L3Az0ATcp5Q6DhwFHlFK3ae1flYp9SrgXcBp4DLgk0AR8P+ALvNjM/A5rfUv5+q1EmKhSMCchBUu/e1DABx8svWSCZlbf3cFRaUX99cphFi0WoBG4Dqt9R0AZlf6E8B2IIZRhVwGRJRSd5rHvtY89r3meT4G7AT+GvAAdcBSjOokwCPAHwCf1VqnlFL/Atyrtf5npVQz8LDWutk85wHzX4URSK/WWvuUUm8B/kFr/UdKqW8Cd2qt/0wpdQ3wN4AETHHRkS7yCYwNlwAD3SEOPtl6SXSXS7gUQuSxRoxqYqFS6uNKqY8DbUA18G9AD/AC8LdAHNiAUU0EQGv9rbRzndZax7XWQa31yQzXatZap6xjgSsnaVsV4NVa+9KeszHtcesaPqBkknMJsShJwMwiU7i0XEohU4iFpLUmFEvQFYjQ7B+mYyDMcDRBKtui9+KSoJS6A6Pa+FugR2v9Ba31FzC6n08A1wJf0FpfC3QD7wQOAqvSzvEepZS1vu5k31CNSinr9+VajO5wMLrllVLqKqVU+t2RfmAw7YafNcCBtMflG1hc9KSLPItgX5S+zuGsjw/6wgT84Yu+q1yIhVLmLuPdl/8prX3hUYFSKVhaVkBlkYtyT/kCtlDMB6XUVuAmwKWU+iRQCDiBW7XWfeZd5p8HgkA5xtjLbcCXlVJnMSqaX9Nan1NKXWceGwF6tdYxpdS9GAHyPVrrbyulSoB3ABvM8Z0dQB/wl0qpZRgVx2+azXvMvJ4T+DZGlfQdwEvAW4HPK6XOAOvM59cAdwPlSqnVwNvNa9+mtX5qzl5EIRaA0hdBJWDr1q167969OT1nKpmi6+wgB59qI5Uc/Ropm2L99nqWrimbk+mChBDQ1hfiC786RiSRGveYTcFH77qMtTXSuzgHZALbNEqpJuA7WuvtC9sSIRYXSUdZ2Ow2aleWsvG25djsF37eSrgUYn4c7wpkDJcAKQ0vnumd5xaJS9SHMKqMv7vQDRFiMZGENIGxIVPCpRDzp2tw4jHOXYEIyWTmACpErmit/0prvUKmEhJiemQM5iSskAmQTGgJl0LMk9pJZjGoK/Vgt8v/RSGEyEcSMKfACplaI+FSiHlyWa0Xj9NGJJ55DOa1KysXoFVCCCGmQtLSFNnsNgmXQsyjpWUFvPP6Jlxj/t/ZbYq3bGugqbJwgVomhBBiMlLBFELkJbtNsbWpgrpSD6+cH6StL8wSr5url5exrLwAl8M++UmEuAQppexa6+RCt0PM3mJ+LyVgCiHylt2maKgsoqGyaKGbIqao6eO//CPg74AGoBX4RPMXfvcHszmnUuoDGCvhdGOsE96htf74BMffiLHE4/3muuCfBvZqrX9uTju0SWv9Pxme94fAl4BdGKvvrAaeGrPqz7yYqJ2TPO9eoJMFWH5SKXUF8H+BUxjzla4H3q217prHNpQADwN2rfU9Y97TjwMB4PVa6+/MV5tm6YNKqSNa62cWuiHTJX2+QgghcsIMl/+GsYyjMv/9N3P/jCilvMBngA9prR8E3gOUTfQcrfUO4JW0XQ9orX9uNRN4fZbn/SfQDPxAa/035rW+aoaW+dZElnZmo5S6CWO99YW64/3TwKNa6we11n8F7AAK5rMBWusg8P20z9Pf01aM75175rNNs/TPwANKqeqFbsh0SQVTCCFErvwdRuUqXaG5f6ZVzChGWL1fKfVdrbUf+ACAUurPMSpmjwDFGCvm3Ku17rOerJRqAB5RSh0w23EPsEkp9SDwI6318QmuXQWEMdY7/7HZjuPArcCnMNYSfxdGtfMy4JPmcx7BqCIeBq4Gfq61/p4Zlh/GqPDVm/sfV0p9EXgbxlKX1wFnMF43q51PYqwUtFNr/W6l1LuA9wN/pLVuTmvv+4D/Svva3wDcBZzFCPv3m8d8BmPFoSeBR4EjZtsfxljDvRQ4oLX+vlLqo8ADwN8ANwB2YJn59b0DuBFjvfc/AbqADyilWrTWB7XWn5uoLVrriFLqdcCdGCHwerONrcA/AL0YqzOd0Fp/I60tDwBbzHb+vtY6qZS6E2PO0hfN52TzPqDJfF0fw1j2c6L35GvAqzGW+hwArgGGtNbvST+pUmoNxvt3DujHXE3K/JquAT6vtf6lUmqluX8XRoX3Ia31AaXU14A/xnj/PwRUAvdprXuUUjvNtjwywdeVf7TWi/5jy5YtWgghRE7M+Gdx48d+kWr82C90ho/UbM4LXIlRlfJhVMXuSnssDBSb2x8D/t7c/g6w3dy+B3jQ3N6OsTJPtms9ixGGP41RPdqW9rw95nYtRshqB6rNfW/BqJIBPAh81tx2Y4SxSuDzwCfM/QVAC+AwP49gBCM7sGlsOzGqqV8zt98O3Jih7fuBa8ztcvO6BWlt+j/m9gvAFnP7axi9me8HvmHus4L0EvPzZuAKc3srRsD+lfn5jcDb076mT2GEtlaM4OnM1hZzfzvgTnuNV5lt+ae0r+swcHlaW9aZ27/ECJo2jHBba+6/d8xr9yzwe+Z2E/Bs2mMTvSdhjD9cbBhhd5W5/yBQmeH1vwf4vrn9euAFc/tq4H/N7Xpgs7m9Gfhx2vO/BXwW+CpgS9v/IeBfZ/N/aCE+pIIphMhLveFe+iP9Ex5T7imnskCmK8ojrRjVqUz7Z0xrfQR4h1LKDrwR+G+l1HKtdS/g01oPmYeeBm6ezbVMP9Ba/yLD/mNme7rMLkuv1tqXdu2NaceeNY+NKqX8GMFpA9CrlLLGjx4CKjCqht1aa+sb/oBSavvYNgGfUkqVYoTLf8/QPjeQMLdXAxr4P0opzOtYr9M/AR9WSn0BozqYUkptAOrS2nYYI0j3jPna9wIopf7RrNr9IfCX5mNhjOroZ5RSa4EfYfxR8NssbVkN9Gmto+bznzXPfT9GFddyDrjKagNw0vzXh7E2fBVQqC+M9TyLEXynYrL3ZMhsU1BrbbWp37xupuXErGMG0rat4wHiwFuVUr8DeIH0ru8PA+cxAnv6/Gxx5nmoQS5IwBRC5KX+SD8P73t4wmPu23zfvAbMof4+3IWFON2eebvmIvMJjDGY6d3kIXP/jJg3u3xKa/1ebXSF/hT417RDqpVSxWYQWItRPZtI0jit8gCNWusT02iOTtv2A4NKqSVa6x5gDUY3qmWl2X4PsAQjbBwEurTWj5iPvZ0LISX93BnbqZT6D+CbwP9maV8bRjgCI/BGgH/QWieUUquApeZj/40xXCABfMTcdxCIaq2/YLbtDRjVQqNxZiktzVeBL2LcPBUzn/ML4M1a67DW+qTZteuaoC2ngQqllEtrHTNDdZfZlvSwvhIj+GVrix8IK6XqtNad5vHZJDEqtCilNjLxezIXPg70a60/Z4bwa9Meew9GtfKLSqnd+sJQjwpm+UfaQpCAKYQQU6C1puPkMSrqllHV0LTQzclLzV/43R80ffyXkNu7yAeBSqXUP5rbK4CPmdVLgD7gT5RSdRhjMN9r3kW+AaPqeQ64Gyg373I+gtFN+RDwC2AkYCql3oRRgX2LUqorrVrnxhhvuEEp9Sat9U+01lop9Vbg80qpM+a1/zKt3UuUUp/AGIv3ca11r1Lq88CXlFKfxAhenWZovhcoVUrdr7X+svn8TO38GkY18I+zvFY/xRjD95TWul8p9RfAV5RSbebX9bcAWuu4Uuq7GF3Kg+Zzv2W27W8xq6Ba658qpd5stu2TwBe11nHz+P8wz/eBtOvvAr6tlDqB0bVcjNG1O5ypLWYb/xRjjGwLRpD6BMZ4yIfMcZLlwFe01sfT2vIejDC/wXxfdmJ0T39TKfUSRuV1g/l9UJP2nh7E6KqPKKX+3nxNJ3tP3mB+baVKqXswutAbMbrhP2l94UqpGi58n63lwvfLZuD3Mdazfw3wE4zvGbd5vUal1G0Y4zR/D7gN+CDwU6XU/9FaHzDf009nec/zlhr/h8Dis3XrVr13796FboYQIodO95+eUgVzdfnqeWlPwO/jhR9+h4q6erbe/YaLuYqpFroB06GUatZaNy10O9KZwahZ53AqHKWUC6M79d1a689mOcYBfBfjrvnTubp2huu4McYlfk5rff9cXUeAGUpv0VrPuBdgocg0RUIIMQmtNZ2nThALheg+e4rB7nmb1k9MQCn1IYzKUraK3rxTSl0F3ATcrZRanqNzFgK/xqgYfj3bcVrrBPBOjLGYc+l/MO68/tfJDhSz1r0YwyVIF7kQQkwq2Ovn9N7dgBE2T+3ZTWlN7cVcxVwUtNZfw+g2zhta68MYd1nn8pwhjK7TqRybxOhenzNa69+Zy/OLC7TWr0x+VH6SCqYQQkwgvXppkSqmEEJMTCqYQggxgaG+XjpOHKWgxDtqf9uRQ1LFFEKILOQmHyFEXsqXeTCjoWESsXjGx1wFnosxYC6qm3yEEPlJKphCiLxUWVCZF5OouwuLcI9d/FCIPKaUsptjMcVFLp/fawmYQgghcuPBUhfwM/OzNwM/Nrdfx4ODsZmeVin1AYyJt7sxJtHu0Fp/fILjb8RYt/l+rfWzSqlPY0wI/nNz4vZNWuv/yfC8PwS+hDGf42mMlWae0lp/a6Ztn6mJ2jnJ8+7FmOvxl0qp12OsKd48y7Y4MNbPDpsfVwM/1Fr/aDbnnWWbyoDXW1NBjXnvTmGsnHRKa/23C9XGefIppdRPtNaHJj90fknAFEIIkSs/48JSje0YE0lb+2d057FSyoux/OASc3JzB8ZSh1lprXcopdLvvn0gbfWXJox1oscFN631f5pTH/1Aa/0LcxWdPqXUf2qtgzNp/yxkbWc2SqmbgOu01veau16PsWRh8yzbchdQr7V+o3mdK8x9C6kMY3L170DG984G+JVS3zBX97lYfQl4Sil1U9ok+HlBAqYQQohcK+DC2snhWZ4rijEu9H6l1He11n7M1WOUUn8O/F+MamUxxmo696YtsYdSqgFjpZgDGCsM3QNsMidD/5HW+vgE164y21+olPqx2Y7jGNMQfQpjLex3YVQ7L8NY2aXKbE8nxnreVwM/11p/zwzLD2NU2OrN/Y8rpb4IvA34fxirtpzBWG7TaueTwLeBnVrrdyul3gW8H/ijMdXJ9wH/ZX7d24BNwD1KqevMtvwA+IjW+t+UUs8CH8NYXvJR4F8wlqD8B4y1vBuBx7XWP8OoHN+olHoj8Cut9VHgqBnAvw6gtb5HKfU+4BNa6yal1O+br8MPMZaJ3Ao8gLGs4yMYf3zsMt+zI1rrLyhjrfl/wFiqsRxjnfRvKKU+aj73b4AbADvGuuRN5uvzmNb6xTHvXTHGikQh8/X4N4x1vosxVut5SCl1vfl17wNiGFXyD2ut9yqlKjGWPT2BsW76n2NMLP+IUuqDZrv9QCnwVxiT4P89xpKW64Dvaq13WI1Ju9YOs/2bzONvMV8b67rXAfeZbdqEsbRkAGM6rhsxVob6BsYfcPdprUNKqbMYf8D9nHyitV70H1u2bNFCCCFyYuY/jx/wFusHvAP6Aa9O+xjQD3iLZnNe4Erg+xiBbgdwV9pjYaDY3P4Y8Pfm9neA7eb2PcCD/uvYvQAAIABJREFU5vZ24DsTXOtZjCD2aeCfgW1pz9tjbtcCyzB+yVeb+96CUT0DeBD4rLntxgiblRjLEn7C3F+Aseygw/w8ghGqrPAxqp0Y61R/zdx+O3BjhrbvB65J+3zkNTA/fxQjHC/FCDBfNvd/xfz388BfprW7HSg3P/9d4HGgH2O5wzWZXk+MFYzSX8vbze1rgZfS3o9/TzvuKHAVRmj+p7T9h4HLrfMCV5jbWzEqvM9meO++A3wB2ANsTXvsdWnbB4CStPfq8+b2m4Gvmtt/j7EkKRihNGxuX44RblXaa/w6jGrxL4Ai871em+H9eRD4jLl9H/B9c/sNadddD6wwt9/Ihe9nG/AEcD/wd2PO+yXgr2f1f3cOPqSCKYQQIld+zIVucYsLo6o248m5tdZHMNYVt2P80v1vpdRybaxH7tNaD5mHnuZCF/1s/EBr/YsM+4+Z7elSSlUDXq21L+3aG9OOPWseG1VK+THGBG4AepVS1vjRQxjrb/dgrNhiTZtwQCm1fWybMMbblWKEy3/P0D435jriWTyKEYSPYoS8/1FKfR04aT6+AWNNcqvd/RjjUF/SWv8SY1xnCUbV+IcYQW8yZ81/T2P8oTB2PxgV2yvM659J238OI3geMz+3Xv+95hjVTP5LG13kD2AERmuKmTql1N9hVAO9GCHQGvZgff0+oMTcvhL4pnm9IaWU9T5fBaSAjymlAOLm+X6I8Vo9bp4n2xKa1tc3kLbdn3bdMPBn5vfMCsz/T1rr/8/emYfJVVXr+/3SnZHME0kkAyHMSZhaBC4q4oCgXEWuIP6uEFERBwRBGbzMIpOAzAKiFxAHUAS5gKCCqDgAARIIJEAIISQkkInMQw/r98faJznpVFVXj6kk632ePFXn1Dl7rzqn0vXV2nuvr0HSl9I1yX/Oshi6U2FEofUgCIKgrVkFLKH1w+NIGiUpEz31wL14ti9jkKSe6flOuHgqRb03q26Sdm5mOPm6fguAJZIGp+0d8cxYxugUfzdgMC4mJuOLjS41s0uBX+PDwY3b3ihOM1sN/AIXPf8sEt+buGBt3MYO6Ro9BHwUGGXuEPMS8CPSsHqKb4dc3P2AVyVNkPQFAPO5qPkfEstwgZVNR2jM6PTY+N6Mzj0fk15b13/umHWLVyyl6/LvLfXbWHABXAl8XtKw9PrpZva9dN0buyQUqtf4UoqZdO0Gpf0v4NnM7B7+GL/v4/CFTwfiUxq+XaDNcvghMNnMLmH9fcn4Ev7D4KfJmz6jPzCrhf21G5HBDCqK1StqWbu6jt4DKu7HWBAETfMpiq0ibzlLgAGSfpSeb48PXWbCbBHwFUlD8blvX0qryMfjWc/XgcOBfmlxyov4/Mcr8SHNl7OOJB2Jzz08WtI8M5uY9ncFvgCMl3Skmd1jZibpc8Alkl5LfX8nF/dgSd/D58ydaWYLJV0CXC7pbFygzTWz+rTyu4+kU83sqnR+oThvBJ4Einmv34vP4Xw0bf8J+DIuoE4ws9WSshXy4AJ3gpm9nbYvAa5K8Y0AvmFm70qaBpwtaRdc2O0CfC2dMwnoJOkcfBi7j6RjzOxX6fX3Snp/ug4n5mLtIumsdJ/uNLMpkqYCV6Z5lf3woftpkj6b2j0buMx8MctcYLWkHwIvSxqTu3evmNkrkm7A565+H5gq6VZ8Du17gOMl3Yn7xo+T9EjuHtfgw+w/lXR56msRQIrnZklX4ZnKYcBZ+FzbUyS9hP/Y2MCnXdJOub7+yfrP5E65fvcF7gROkrQ9MDztfx8+R3cU/oPgDOBuSaeYz8HdBx9+ryii0HpQMaxeUcvL/57LwjnL2fvjo+g7OIoPBsEmYLMqtC5pppmN2tRx5EkCaaalEjpt1GYXPIv2RTO7qMgx1cDt+Kr56YWO6UjSQqIJ1qhMkqQJeBb1/I6PqjzSD5YqM5udsrlPmdn4TR1XYyQdD3Qzsxs3dSyNadYQuaRjJJ3bXsEEWy+ZuJw9bTGrltXy7MMzefedlU2fGATBVou8LE0fScUyeh2OpLF4pupwScPbqM0ewB+AC0irtgthZnXAsfhczE2KpE/gGcWvN9o/HM/efSBdq0plAPCjtIL9SnxRTiUysRLFJTQzg5kmNZ9uZt9tv5CaT2QwK5/Vy9fS0AA9ejee/7+huMzTvVfnyGQGQcezWWUwgyCoTJqVwUyr5d5tp1iCLZTVy9cy7d/zmPLX2axcuqGZRzFxCUQmMwiCIAg2U4oKTEkfK/LS5j9pM+gwMnE55+XFzJ+1bAORWUpcZoTIDIIgCILNj6JD5JJexFexNWaXSpvoGkPklUleXOYZNKIXu/7HMGY8905JcZknhsuDoMOIIfIgCFpNqTJFrwAPFtgftTODJikmLgG69ezMpD/PYvHcFVRVl/dxyjKZITKDIAhKI6kq1QwNNkO2lPtXSmCebmav5nfIzeOfKHL8BqQCtsfghXY/iNdo+jHrK+cDjDCz0Y3OmwCclDtuBHBhW5Z7CNqOlcvW0qVbFdWdq9btKyUuh+/WnwWzlzPz+QV06V5N7wHdQmQGwRbCuNvHLWW9I0meZS8c90LvlrYr6UTcveRtvPj2W2Z2ZonjD8T9rk81s8clXYivtr0/OcDsaWb3FTjvKNx2L6sVOQZ41Mx+2tLYW0qpOJs478t43cZCCaJ2JdUZ/R/ca70HXnz8i2bWuLB5e8bQC/d7rzL3R8/f08zX+9MVrinOkXSPmb3Q9KGVS9Fv9kxcys3rM3bFC4qWJNl5XYULw8vw6vOvA5eb2UFmdhAuOH9W4PSXgSNyx01m42r2QQWwYskann/sTWZPW0xdrf/YKldcAqxdVcfShaupr2sou8+YkxkEFU0hcVlqf5NI6o0Xyv56qpt4PNC31Dlm9gTwfG7XeWZ2f3o+CveNLnTe3Xix8F+a2bmpr+uSaOloRlEkzmJI+gCwX7J13BRcCNxlZueb2el4QqpDXTOS09DPc9v5ezoL/+xM6MiYWsDlwC2SOm/qQFpD0Qxm+k/dF9glZ/+0gtI+pxnvxefxnJTqdy0EfmJmd+WO+SqeqdwAM/tXLoY9gek5n9l8fCcAJwCMGFHInSpoT1YsWcPzf5nNojnLWTTHb8/gkb145am3C4rL6s6d6NG7C3Onb1iEIBOZzc1kLnprBb0GdKOqKmZsBMEWzhr8++RUSbeb2QKSI4ykb+EZs2uBnribzpfNbFF2cvr+ulbSJOBiXFzsmYqh/9rMppXoeyA+CtdD0m9SHNOAg4FzcCeX4/Bs5y7A2emca/Es4hTc4eV+M7sjfa9ejWf4tkv7H5F0GT7i97+4E89reAYwi/PPeELmH2b2RUnH4d+hn29UxPwEcgkZSUcAH8d9v0fi/tgn4IL9C6ndu/D1Fmen2N4B+gCTzOznqQ7kecC5wAFAFe6EMze1cSBen/MruAXjiZLeMLPJZvaDUrEkZ6FPAYfgInD/FOMs4ApcO/QDXjazW3KxnIe71/QB/jO5IR2C19z8dzqnGCcAo9J1fRi3hCx1T24E3o87Fr2L65vlZnZ844YlfR93aFqLFz//bnIhuh34ppn9TG57uo2ZfU7StUBnYA5waIrhW2a2VNKMtO/+xv1sLpRa5HMc/h9xFJ59FC4uHzazK0s2Kh2NF4MdZWZLkh3Tn7OUtKTRuNXXV5to51bg+2b2RqnjYpFPx5IXlxkjdu/PiiVrmf/mMjqp8BqB7r06M2zHfjzz8EzWrNzwd0pzhst3et8QRo0bSOeuVU0eGwRBs2nxIp9xt48rWmXkheNeaHG7knbHhzc/jo9yXWRmD6fXVgGDzGy5pDOAgemL/TbgtjREPoHkHCPpINxdZkKRvh4H3sJF4wDgdjN7Kp13uZntK2kILrSeBPYys/npe+9TZvb5JF6qzexsuc3kTGAsbiW5zMwultQdF6s7mFmdpNXAUHwIdxwp05bFmRxbaszs65L+G3cK2mDKmqTncEvIpyX1w8XTaDNblWJabGbXSPo7cIqZPSPpRuCbuEDcx8xOkCRgKvABM3tH0kzgMDN7Kdko9ga+Y2aHpekIo8zszvSevoMLs564YL4oPd8oFuAO3Nt7BzNbk67xm8BHgHFm9s30vqYAnzWzqSmWQ8zsZUkP4sL3uXTP9jSzeWmawIG5a/c4cIWZPZCmHtyWRkiR23cWuyercPeklfiPiX3N7DVJk4GDbb1daXb9P2Vmv0/P7wfOMrMXkwa6z8x+K7e2vACfOvhNMzs0Hf8EcLaZPZ62L0/36xI2U4pmMM3sduB2SR81sz9l+yVtW0a7S4FpZrYkbT8BHATclrZPAq4r1UDqp1tT4jLoWAqJy4HDe7JqWS0vPfEWvQZ0o1vPzgVF5qpltbz16mL2+fiojUTm2lV1rFiylp79utKpU/HvoRCXQbD1YWYv4r7iVcBngN9JGp6+4OfnRrmm41/creWXZvZAgf1TUzzzkvFI71QfOut7j9yxM9KxayQtAHbAfbcXSsrmj74A9Mezhm+bWTb8MymJrQ1iwufm9cHF050F4uvK+lHGMXhZwZNdL9IfyK7T9fgI46V4drBB0nhgaC62KcCQFFv+vWf+7D+StCNwFMmD3cxW4dnR78s9tn+NC7Mni8QyBlhkZmvS+Y+ntk/Fs7gZr+MCfWrafiU9zsenXwwEeuTmes7AM6vl0NQ9WZ5iWmZmWUyLU78LG7XVJQnDRXiWd1Dafx1wmaRHgdr0Y2h3PGOZMaNRW7V08PSCtqbUIh8AGonLwcANwH81cdqTwIDcSqiRpA9EGiIYaWZTcu12wX+Bzsm18TVKWGIFHU8hcQmw6K0VjBo/kEEjejF/lq/Naq7IrO5cRY9eXUJcBkGwASnjdI6ZfSkNhd4L3Jw7ZJCknkkI7IRnykpR782qG/5d9HIzwslnaBcASyQNNrN3gB3xYdSM0Sn+bsBgXDBNBuaZ2bXptf9mvUhpnP3dKE5JvwBuBf6vSHxv4uIIXPCuxjN3dZJ2AIal136HTxeoA05L+yYDa8zs0hTbEXjm1YPbeLjzOuAyfPHU2nTOA3imcZWZvSLpH/iQcbFYpgP9JXUxs7VJVM9LseTF+mhc+BWLZQGwStJQM5ubji9GPSlLL2kPSt+TspHUF5/72Tu9l3XlHM3sSfl0wavw6w7+OT240XvM0x94prlxVBJNCkwASR/B53t8GL+RJTGzRWmo4mpJ83EVf2F6+Xg2XtxzCC4oD0v9dcWHAs4vJ76g/SkmLgEa6o2Zzy9gzD6DAZotMutrjT6DulPdpfjweIjLrZcFy1Yze/Eq5i9fQ78eXdiuX3e27d0NFZmKEWxSllFkFXkr2lyCJyx+lJ5vj0+xykTAIuArkobiczC/lIZtx+NZz9dx7+t+8lXOL+Jz7a4EHsCH3AGQdCSeEDla0rxctq4rPt9wvKQjzeweMzNJnwMukfRa6vs7ubgHS/oesC9wppktTMOxl0s6Gxdec5No/jLuqX6qmV2Vzi8U5414AqeY9/q9+BzOR81ssaRvA9dIejO9rwsAzKxW0u3AkNxI409TbBeQsqBmdq98DmGfFPNlZlabjv9Fau/EXP//BH4m6WV8WLwncLOZrSgUS4rxG/gc2TdwUfU9PLN3ZRpK7wdcY2bTcrEcj4v58em+/AOf0nerpKfxzOv49DnYNndPJ+NzR1enoeqXgabuyRHpvfWRT7V4I7X3ZXzeasYS4G7gDkkTgd3wz98/0jW7Hvicra/O8yBwqKSf4EJ7DRv+yNgHXwy92VJqDuYgfPV3VvJgHnAsMN7MnuywCMsg5mC2L6XEZZ5OVWLU+IFMf+Yd5s9aVnK4HHxO5vDdBjD96bdpaChuEBXicuvl5XlL+d9/zGT+sjXr9vXuXs2x+49iz+F9Q2S2D5vVRZU008xGbeo48iRhNNPasBRONtKHl/25qMgx1fiCkvPMbHpb9V2gn654FZofmNmp7dXPlo6k95rZ0+n5o/i9nZUEdDczu3HTRtg6Sq2oeBNX0J8xs/cDU1Lau6LEZdC+rFxanriEDTOZg0b0YtnC1axZXkeR3zBUda5i6A592ffw0XTuVlg8hrjcepm3ZBW3/G3GBuISYOmqOm792wzeWBilqrZ2JH0dzywVy+h1OJLGAh8ADpc0vI3a7AH8Ac8YFp06ZmZ1eCKoa1v0W4L78JXXNzd1YFCSCySdKekH+Or1WWn/xM1dXELpIfKxwH8Dn5OnCcKDfAtj9YpaVi5dS78hPYpmgjp3rWbwiF5lCUzYUGR2qhJrV9dTqOme/bux98dG0rNfV3r268o+h4zimUdmUrt6vXlBiMutm9fmr+DdlbUFX1td18CzsxYzauA2HRxVUEmkL+GK+iJO6wsObvLA5rW5Ep+iVs6x9RS2eW7LeA5tz/a3FszssCL7ny+0f3OjVKH16WkO5Hn43JLRkr4i6dyOCi5oP1avqOWVp97mqf+bwTszl1JsqkTnrlWM2H0Au+w/tOy2G+qNudPfZf9Pj2HbURubd2TisteAbuv2DdiuJ/scMmpdJjPEZfD6ghUlX5/+znLq68sv0h8EQRB0HE0WHTSzWjO7O9WT+gvwvnaPKlhHQzt8gWbi8s2XFlJf28Bzf5rVpiKza49q9vzoSPoP24Y9Dh5Ov6Hrs0yFxGVGJjJ32X9oiMuA3t1Kr0Hs3b06Cu0HQRBUKM3665wmDd/WPqEEjVk4ZzlzX1vSplmavLjMaEuR2bVHNXt/fBT9k6jcpm/XdSKzlLjMGLBdT0aOGxDiMmDse/pQVaJs1YFjBhV9LQiCINi0NFmmSNKheAmhnvjqwhHAb9o5rq2ehXOW8+zDM6mrdXE5ZIc+rc7WFBKXGZnI3OujIxg8qnfBOZmZyASY9q+5G73eWFxmZCKzod5KisuM6s4hLgMY1rc7/7nnMO57ds5GE8A/uNMgRg7osUniCoJKJ1eDOtgCkdTJzCp+flA5dTD/BzgFr5gv3Hc1aEcycbk2LXh5/rE3gdaJzFLiMqM1IrOYuMzYpm97L2oMtjS6da7i4F0GM6J/D/4y7R3mL1tD7+6d+dAug9l521706tZ5U4cYdBCSTsQLb7+NF6R+y8zOLHH8gbgf+KnmVpEX4itz70+F2/c0s/sKnHcUcDlez3E67jTzqJn9tI3fUpOUirOJ87LSgg9K+jTuKT6zlbFU40XCV6V/ewG/MrNft6bdVsbUF/i0rbegzt+7V3HnpFfN7IJNFWN7IKkTXiP0SjObvanjKUU5AvO5rNgsgKSft2M8Wz2NxSX4opnWiMxyxGVGS0RmU+IyCFpKjy7VjN+uL7sM6cWq2nq6VlfRLTLcFc/UXXZdihdcX7brtKkbr/RrBsn97fvA4FTcvBovWl0UM3tCUn4l7nk595dRwKfxUjuNz7s7lT76pblvdTdgkaS7zaw1xeJbQtE4iyHpA8B+ZvbltOvTwLvkHHlayMeB7czsM6mf3dK+TUlfvLj6bVDw3nUCFki6Jbn7bBEkW88f4q5BZVUW2FSUIzCHJHuqzPvz/bgRfdDGFBKXGS0VmdZgvPXK4rLEZUZ9bQOTH32T/Y/YgV4DCluhZiJTnUTfbXuEuAzalS7VVXSpDmG5GdGr0WNrWIOPnp0q6XYzW0Byj5H0LXyU7Vp8GtfOwJfNbFF2sqQRuFPMJNymbwKwZyqG/mszm1ai74F4xq6HpN+kOKbhZYjOwUf2jsOznbvgzi4DUzxzcT/vvfAah3cksXw1nmHbLu1/RNJlwDHA/+JOPK8BPXJx/hl3wPuHmX1R0nG4u97nG2UnTwB+m973vsCewARJ+6VYfgmcZmY/kfQ4cAZe8/ou4Me4BeUVuC/2SOARM/s9njk+UNJngIfM7CXgpSTAbwIwswmSTgC+Z2ajJP1nug6/wm0ia/CqNAvS/i54tnFn4EUzu1TuNX8FbtXYD/dJv0XSd9O55wIHAFW4L/modH0eNrN/N7p3PXFHopXpevwEmJP2zzWzKyXtn973s8BaPEt+kplNlDQA+Anu9rMc+BZeWP5aSV9LcS8A+gCn40Xwf4hbWu4M3G5mT+QDKhLDt/AfUF9I9/kuvMzUebi70jv4j4Sj0/041czektRN0vhKLmlU1Mln3QHS33Hv04zDzawpL/IOZUtw8iklLvN0qhLjDx7eLJG5culapvx1DvNnLS3reHUS4w7ajmE79qWqunQf9XUNTR4TBMFmRYudfHKZyzo8gZE9tiqTKWl34Ew8a/YycJGZPZxeWwUMMrPlcovigWb2XUm3AbelIfIJwCgzO1/udz0hVUYp1NfjwFu4aByAC4Wn0nmXm9m+kobgIudJYC8zmy/paOBTZvb5JHqqzezs5HozE68t/R1gmZldLKk7LlZ3SP7cq4GhwFJgHClDl8WZ3F1qzOzrcr/smQUEzHPACTl3mHXXIG3fhRdHn4bbTz5uZqdKusbMTpZbWS40sytS3K8B45Kl4ydwkbUv8Bhuf/lq4+uZd1ZK1/IiM/uzpPcB15vZe9P9+IiZ/Xc67iXgKOA/Un/fTPun4N7mUyXNBA4zs5ck1eDi7jYzO6jRvZuJOw8eDHzd1tt9fiqJZdKPjfeb2bJ0r7qa2VlyK8oPmNlJKUu4wMwuk9QTmG9m3SXtinu575Yy6rfhFp3CnQ+PBroBA8wsS8zRRAx/B04xs2ck3Qh8E1/7MtbMvpaysTNTbDPT+XcD95rZr6hQyslgftFyllOS/tCO8WyV1NfVs+SdVU2KS/BM5oI3lzPgPT2p2qY8YdejdxfGfvA9TPkrTYrM5ohLIMRlEAR5soxldaPHVmUyzexF3Ne5CvgM8DtJw839yOebWeYEMR34YGv6SvzSzB4osH9qimee3E65t5nNz/W9R+7YGenYNZIW4HMCxwMLJWXzR1/A/bffAd42s8Vp/6Qk3DaICThHUh/gQDO7s0B8XUk+4kW4CxdAL+GZ3Psk3cT6EcrxeNYsi3sxPg/1aTN7EJ/X2QvPGv8Kz0o2xYz0OB3YvcB+cCG7W+r/tdz+13FhPjVtZ9d/YpqjWojfpiHy84DPAln2aaiki3EB3xv/8ZBNe8je/3zWf1Z3JyXX0o+X7D6PBRqAM9IUstrU3q/wa/VIaqeQhWaxGK4HTpJ0KZ61bUg/ql5N/TckgZ2nFig8xFghlKMOlkv6haQX0vzLzcqndnOgqrqK4bv1Z+f9mq4zOWynfuz8vm3ptk3zFjhkInPQiOJJhOaKyyAIgkZkX9h1jR5bPH9R0ihJmeipx7NFq3OHDEoZJoCdcPFUinpvVt0k7dzMcPJDfguAJZIGp+0dgUm510en+LsBg3HhNBlfbHSpmV0K/BofDm7c9kZxmtlq4Be46PlnkfjexAVr4zZ2SNfoIeCjeDb3efxa/Yg0rJ7i2yEXdz/gVUkTJH0BIM1F/Q0+xA1+b3unc0YUiGl0emx8b0bnno9Jr63rP3fMC9lGbh7tuveW+s0L+4wrgc9LGpZeP93Mvpeu+7xGxxYayn0pxUy6dlldtBeAVbl7+GP8vo/DFz4diA91fzvfWBMx/A6ffvgd1peCzPffCZ+Tm6c/MIsKppwM5g+A3+M3a0fgUuCL7RnU1kjnrlWMHOsLZ17+d+H5yMN26seu+w+hW88uBV9vilKZzBCXQRC0lmwYfOouu2Zf2NW7Tpva2qTEEmCApB+l59sDZ6TsJcAi4CuShuJz374kX0U+Hs96vg4cDvSTL055EZ//eCU+TPxy1pGkI/G5h0dLmpcbXu2Kz5EbL+lIM7snDY9+DrhE0mup7+/k4h4s6Xv4kPKZZrYwDUFfLulsXKDNNbN6+crvPpJONbOr0vmF4rwRH5Yv5r1+Lz6H89G0/Sd82NbwofPVkrIV8uACd4KZvZ22LwGuSvGNAL5hZu9KmgacLWkXXNjtgg/hgourTpLOwYdx+0g6Jjd0+15J70/X4cRcrF0knZXu051mNkXSVHyF9Pm4uL3GzKaloes+Ka7LzKwWn+O6Og1lvyxpTO7evWJmr0i6AZ+7+n1gqqRb8ekB7wGOl3Qn7hs/TtIjuXtcg2udn0q6PPW1CCDFc7Okq/BM5TDgLHyu7SlpuH9HNvZpf6VQDMC5ZlYr6XZgiJktScffCvxM0jX4vM2F6T5mq/pHAn+jgilnDuZ3zeyHue2zzOySdo+sGWwJczAzatfU88aUhRuJzNaKyzyN52SGuAyCIEerR6nachV5U+Tn/FUKSSDNtFRCp43a7IJn0b5oZhcVOaYauB1fNT+90DEdSZoTOaHRQiSUmxPb8VGVR/rBUmVms1M29ykzG9+B/fcEhpvZ1LQ9BdjbzNbKLbsnmdn9HRVPSygngzlGUn8zWyRpIBumtYM2plAmsy3FJWyYyVwwe1mIyyAI2pT2FpUZ8rI0fST9PzP7RUf02RSSxrI+K/aomb3ZBm32wFd4v44vdipIWix0LJ5h3KTIFwWNBL6Or7LO9g9nfUZ5rJlN2UQhNsUA4DxJT+HD06d0cP/dgcskPQEMwRdLrU3zkO9J85IrmnIymAfgE4N74cMTnzOzf3VAbGWzJWUwM2rX1PPGiwtZtnB1m4rLPCuXrmXpwlUMGt4rxGUQBBkxzz4IglbTZAbTzP4JDJc00MwWyEsrBO1M565VjNx9APV1Dc1e0FMuPXp3oUfvtheuQRAEQRBs3RQVmDlB+YHcPvBJsF/pgNi2ejp3raJz1yguHWz5mFlB16ggCIJg86RUBvPnwKHANWxYemFcu0YUBMFWx7vz5tJn8LZ0qoofVEEQBFsCRSfemdmh6em3zOyL2T/g5I4JLQiCrYEV777Lcw/fz9IF85s+ONhsuOHEx2JBaFAWaeFKUAab07UqZ2VHvaQxkkZKupoNC9xukZgZC99azsqlazZ1KEGwxbNg1usseedtZk2ZREN9025WQeVzw4mPnQm8lh5bhaR9JT0u6Z+Szpd0WfrXt4nz/p/ciaap9ic01Vaj4wdLuk3SW6ny1KUlAAAgAElEQVQOZpsj6XRJJuni9J7Pl/Tv5sS5OSCpKtUGLfpjRFKbr96WdKHcK725531M0iRJD6WaoPnXjpd0UttFWZSekq6W+9pXNOUIzOPwAqNX4YVCv9quEW1izNyK8ZmHZvLCX2aHyAyCdmTFu+/y8r/+DsAbzz8XWcwtgCQqz0mb57RWZJrZU8DjwD/N7HwzOyNtP5bqPhY77xd45ZOmmID7fpcbzzu428pbZvbrcs9rDmZ2eXp6cXrP5wMXseUleM4CpprZqyWOaY/yQOe1pIakmf0RnzL4kJlNa/Taz3DLx3YlFWK/Ay+6X9GUUwfzZWAVMNjMbpR0elMnbK5k4vK5P75B7Zp6Fsxezgt/mc24D21Hj95dN3V4QbDFsWDW66x415NMdWvXMmvKJHoP/GjMxdxMyYnLHmlXD1xk8o2bDr60rfoxsz/IvaY/Ajws6UL8+6weWJYTaOuQ9DXcbWcB0AevzfhRUo1DSdPM7KZCxzWyKCxKGr68Andd6Yf7St+SXFr2wJ1bFgMPmtlukg4DzgWONbNXSrR7UWq3u6T78FJS04CD8eu9MLX9Uor9zLRI9xC8DuXTuCvQfsC3gMOA/czsIElHAbeYWd/U1xHAx3Gv8JG4p/ZeuCXis8Da9F5OSp7gvXC3oRnAtnhCahpe8P2bZvYzST8DephZ44zviaR1HXLbzjNxK8a9cPedPYG+qXD9NDP7daF7nWp/XgtcjJdU3BOf3ve6pOtxJ5w+6fH3wLWSJpnZ+ZLuTtetE27veAzwMHA17hHfBy9q/vMS96d36h9ggqTLgM/jP0R2A5aY2fHFrm9yWPpJiq8n7vB0paT903V/BlgD/Bewk5k9K+lASQNyjlYVRzkCczxeB/NeScPwi7XF0VhcZoTIDIL2IZ+9zHjj+ecYMXZP+m47ZBNFVXnULVhI3WIX4dX9+lE9cMAmjqgwBcRlRruITOANYEQSUfuZ2cfA3WMk/dHM1i1OlbQrLqx2SxaPtwH/aWa/lzQTuNrMZhY7Dhcl5fBloHPmtCNpiqS/42sXngKew+0kGySNx73Dby0hLi+TVAvsD1xhZoslXQpcbmYnSxoCVKW29zWzOckl538knYaLvD3NbJ7cjrKbmf1N0ixcbGJmd8vtEJHUD8+MjTazVUnYfdXMrknCtquZnSW3bjwOmAh8D5ieiXpJx5vZbyQdDixP72NRuq7rkNQH6Gdm2TSGQ3Hxej1uo7g6iy1z/Clxr+9IwvN3ZjZd0tG4JecJ+P3bP12bA8xsVnovo1K/3zezFyT9APhfM3tA0leBLmZ2oby8xVRJj6Ts9UaY2dL0WZmQts+QdHK6Z0skvShpANBQ6Prii6kfMLPfp/c1SdItZvavFGsPMztd0k3AitTtO7hXeUXVJc9TjsD8LnAA7oU6DvfH3KIoJi4zQmQGQduTz15mRBZzY+oWL2b+lVcCMOi00ypSYKYFPaUshHsAl9xw4mN3f+Omg2e0UbcjgVl4EqSHpGwo/k3cUjHPWPzL/YxUDqsWKDSHrdzjijEeeC23/Tow1symyv2898OzfDcBR+OC6tqNWlnPGWa2XNIOrBdrAFMBknAcBPTHfddJz+uBgbgwmZfOmQEc2ET8Y3C/65NzbeX7zYTwfDxTmL3nn2YHpKFicNF0taTHcLGYbwegK1CX2/4JnsH8Oz5yemqB+Jq619lnazqwexLkJwO3ymt4b2SxmcTlZ/Br85FcP0Nz/UzB3XQKCswivJ3zFc+u1yCKX9+hki4GluKfuQHAsvRadr+fz7Vfi7v9VCzlFFqfz/pfb5PSL4In2jWqDqah3li9opa62oaix6xcVkvd2uKvB0FQPoWylxmRxdz8+MZNB8+44cTHzqJwBhNgJfD9thKXkj4GdAP+jAvC/c3s0vTawbjAyPMCsCp3zN74FzS4GJPc4vGlEsc1FdMpwGR8+DhjdOobfCTwPOBu4EHgr8DDuQxeUczstca7cs8X4ALm5iSoBuAZzwXAKklDzWwuGy6kWUYSznKf7UykTcfneV6RbCd3AIYV6TdjMrBDakvACWZ2s5k9k7avx+daNmYBUC2ps5nVAu8DLjWzcyT9EDgWX/tRn9oZn/oqda9Hp+2dgJfki6Lmm9mhknYHfpXaWYek3fCh9Q+aWW3KLE8G1uT6OQKYWeA9IOnDFBaeha5VwesraQ98Ksbo1GbjBUiF2uqP/8CqWEoVWv+LmX1Ivgov+w8g/EN5S0cE11FUVXdi2I59MYMpf52NNWx4L3v06creh4yk98CK/rEQBJsNtatXMWLsHkVfX7Ni+VZdfD0/LF4/f/3Cp/r589et8qi04fJv3HTwpTec+BhsLDIzcdmi4XFJNbi3dxdJZ6e2OwMHm1kd8Ef5SvNLcOHUDzhT0v/DfcpPTHMrb5Z0FS7GhrFe9DyMZ846m9nxJY7L4hmEG44MTfFkjAG+BFyZhj77AdfkFoPcjw+PHpOE4Gx8iLnQe86yd6dLusHM3k77u6a+x0s60szuSUP5n8OH02cAI4DrzKwhDZffKunfrM84YmYL0zDsxXjWb0nuOn0buEbSm3iW+AJJO7HeX/2RXAw1eOb6Skn/gy+W+kPurVwDfN7MXm/8HlN8/wfsC/wDF0xXpfcwiPWLWB7E559iZqcVute5Zj8i6Yv4HM6T8OkDp0g6EM/oXq0NvdB3A+4DXgS+kv7eCBecl0u6gJRlNbN7k5gcDwzQ+hX9e+FzLT+drskB+FTCPpKOxKcHjASON7NzC11ffDHaVEm34vNX3wMcL+nO3HWfYmYT0+egP/5DqPEPqYqiqBe5pF5mtkzSF/KTWyV93sx+2WERlkFbeZHX1zUw55V3NxCZmbjsMyjEZRAEHcPqV6evGxYvxqDTTqPbjmPao/tWqfpGczFbJS6DtkPSQcAEM5vQAX1V45nlz+JDxY8XOW474IfAl8xsZSv7nGlmo1rTxuaApE7AdcBtZvb0po6nFKUKrWdj/3+TtD1A+hVzV0cEtimoqu7Ee3bqy9gPboc6KcRlEARBM0li8vtpM8RlBSBf6Z1lHZuah9kWfBBfYLR/MXEJYGaz8ZXkjefMNotctvrrrWlnM6E3PpWgosUllMhgrjtAugdP8/9N0geBw8zrkFUMbZXBzKiva2Dua0vo1b9biMsgCDqcxkPki+64A4D+xx5L1SD/Lm7HIfI2mZdww4mPjW7DBT1BEGxmlLOK/N9m9jcAM/urvC7TFk2Wydxa538FQbBpqR44YJ14zFfWrho0qL2GxducEJdBsHVTjpPPyDSfIptXMaJ9Q6oMQlwGQRAEQRC0jHIymH8EXpe0EF/l9Y32DSkIgiAAYNnbVPftzaDTTgOguk8vWP4O9By8iQMLgiAoTZMZTHO/znG4Q8F4M/u/do8qCIJga+fdN+GJH1G9dArdth9Ot1HvoXrRRPjndbB07qaOLgjaDLnFZlDhNPc+NSkwJXXBV599ANhdUvx0DoIgaE+Wz4cnb4bFr8NTt8Ib/4Dpj8FzP4cFr8Az/wsrF23qKDuEVPfwcUn/lHS+pMvSv75NnPf/Uh3nptqf0FRbjY4fLOk2SW+l+pNtjqTTJZmki9N7Pl/Sv5sT5+aApKpU03J0iWNOaYd+LyxQzLyc8z6W6oc+JOkCST9rVAu1w5DUN9U5zbZ3lvTrNu5juKR7Ul1XgP+S23CWd34Zq8hvwKvU98MtrU4zs4oaJm/rVeRBEASblIYGmPMM/Os6qFuz4Wudt4EDT4Yh46F95oq3utErj/7kUNzx7T9Ou+uBeU0d32RA/gXX08y+k7YPBX6A+2/XlTivydqIkh7H60PObEY8B+FuLDXlntNcJBnQK7NYlPRJ4M9mtrr0mZsPSZzNMrM7ShzT5vUtJcmaEj/Fz70NmGhm18sXa7yOV9d5qS1jLCOOUXgtzINy+1r8vkr0MwEYlfOD/y1wnpm92NS55czBnGlmP5R0hpnNkFTR1kRBEASbPZ06wXv2gQNOgr9fBZZsaqs6t7e4bCvOAUalxzZPSJjZHySdh3tHPyzpQvz7rB5YZmaXNz5H0teAnXGLwj7A6cBHU5ynSJqWnGw2Oq7cL+00hHgFsBBPyrxsZrdIuh23kDwed8Z70Mx2k3QYcC5wrJm9UqLdi1K73SXdh/8ImAYcjF/jhantl1LsZ5rZAkmHAF8Hnga2w73QvwUcBuxnZgdJOgq4xcz6pr6OAD6OO/yMxD3B9wJ+DDyL+6fvAZxkZhNTjc0r0/Hb4s410/A6mN80s59J+hnui94443siPgUPSTvjrjwvpP6+D+wJ9E0/MKaZ2a8L3WtJx+IJsItxx6I9gW+Z2euSrgfm4PdyDm59fa2kSWZ2vqS703XrBHwbOAZ3d7oaT671ASblDWdy9MQ91ZdIugvPxP4VOAC3BH0EOA33cN8FLyq/IsXaBfhnul8vmtmlJT4/38VtRs9NbVfh/uSj0rV5GHdEOhX/PCPpXNzxSsBaM7tQ7nJ0CXBzurejgU+a2dL0ud8dmJfaOLHIj7dHcMeqQl7xG2JmJf/hH6qu+H/GTsD1TZ3T0f/22WcfC4Ig2KJYu9Js6gNmvzhq/b9ffs5s+qNmtavbs+dW/T2+4qhPDL3iqE+suuKoT9gVR31i5RVHfWJIa9sEzsczhvl9dwEnAIcAf8ztfxzYMz2fmR53TV/I2ajdbcCncsePauq4Rn0fhGexGu//av47EpiS2uyLiwwB3037x5PWNxR5zwbcgAudJ4G+ub6fSs+H4LaCc4D3pH0TgB+l7+t5wJC0/8t4xgtcQDye6yu7Tv2AuUD33HU/Off8kvT8s7gdJbhgOT3X1vHp8Q7gqPT8CjwDnX9/fYAVue1TcOHTBdgeGJqPLT0vea+BMen50cBv0vuZlbs2B+Su0fnp+bj0+APg2tx9vCU9z8T84Nxn4iHc4vEG4JDcNZ2Di7peuB/6v4Ca9Pr7gH/m+r8z9z5eAsZS5POTe3+7pec1je9ho/t4CPBAbv8fgI/lrlkW8w3Aken54UCn9Pxa4BONr1XaPgx4pJz/t+VkMB/BU8CG/2f+dhnnBEEQBC1l7SqYkeZcAlR39Sxmfa3PyQQY+R++v/I4h/XD7J1opywmnoGZhQu1HpIyT+o32dgZZixuXXhGKkFXizuiNKbc44oxHngtt/06MNbMpkqahmcQtwVuwkXQWvzLvBhnmNlySTsAy3P7pwKY2Ty5N3p/4Asp5v54dm8gnjXMpijMAJpy8RmDf9efnGsr32+WZZ3Pem/z8cBPswPM7Gfp6TW49/djwGpLQ/05upJ8vhM/wTOYfwdepnCGrKl7ndVenQ7sbu75fjLux94duKhxg2b2gqTP4NfmI7l+hub6mYKL+XfS9kNmdn2B+KabWS3+uVkmaXyjmPYoECv4Z2Y3inx+SPeb9fd9YhoiL0a+33zff0zbhe7jStx/fUGK5dkibdcCZTnQlCMwZ6TOxuAX791yGg6CIAhaSO1KmP8ymK2fc1m3dv2czAWvwrC9K05gprmXX8TFA+nxi1ce/cnvt8VczAxJHwO6AX/GBeH+ZnZpeu1g/As1zwvAqtwxe+NflOBiTJLG4pmkYsc1FdMpwGQ2FBGjU9/gGdfz8KHTB/Gh1IfNrMmFSGb2WuNduecLcKFwcxJUA4D90/5Vkoaa2Vw2XEizjCScJXVjvUibjtf2v8LM6pKwHVak34zJwA6pLQEnmNnNZvZM2r4eOKvAeQuAakmdkyh7H26BeI6kHwLHAlcB9amd8amvUvd6dNreCXgpLYqab2aHStod+FVqZx2SdsOH1j9oZrVJFE4G1uT6OQLPIDZF4+uTXZtFwI7ApEaxZozBP3t9Kf75wVIKMVFP+iEnaQ8zm9yo34Ny2zsC95eIE+C3wB5mNktSqR9V/fEfdk1SjsC8D/iwmcUqmiAIgo5gmwGw93FQ1RW2P9DnXJrB/ifB3Mkw7kjoXpELivPZy4xWZTEl1eBVTLqkRSE98GHIg83niP1RvtL8Elw49QPO1Hp/6hPN51beLOkqXIwNY73oeRjPnHU2s+NLHJfFMwivrDK00QriMfjctCvTvLh+uM3ytPT6/cCNwDFJCM4GCn6vSsqyd6dLusHM3k77u7LeU/xIM7vHzEy+mv0ySTNwM5TrzKwhLdC4VdK/WZ+pwswWyldDX4wnkZbkrtO3gWskvYlniS+QtFO6B+MkPZKLoQYfIr9S0v/gAukPubdyDfB5M3u98XtM8f0fPnfwH7hwuSq9h0HpWoGL8SvSOacVute5Zj+S5hnuBZyEz1U8Re6/PhDPqA7Hh4P7JXF5H/Ai8JWUtRUuOC+XdAEpy2pm90r6MC5QB0iaamaP5vr+Mm5Mc3wui/uldA9fxedafjl3fBdJZ6X27jSzKZKmUuDzI+mz+Gf5bOCyJMjnAquTGH9Z0n+kY44xs19J2i9dJwH/MrM/SfpouqfHyxcrZff0QTyrfoOkJ/AfKDtLejR/rcwXMu0P3NP4fhainFXk15rZt3LbHzKzv5TTeEcRq8iDINgiWb0UuvZav6CnoQHWLoduzRm1bTYtXj105dGfnI3PCWzMnNPuemC7locUtBb5yvcJZjahA/qqxjPLnwXeNrPHixy3Hb7w5UtmtrKVfc60Nl5t3l6o0crszQVJe+DC/SvWlHikvAzmEHltpWwOwPuBihKYQRAEWySNhWSnTu0tLltFiMjKRL7SO8s6HmhmT7Rzlx/EF4csNLOidSzNbLakE/GM5Rst7SyXrf66md3Y5AmbkEYZ1LFmNmVTx9QMGihTXEJ5Gcy/45NvwX/ZHm5m/9WqENuYyGAGQRC0GRVd/ygIgs2DcjKYX8TnI+yOz1O4rF0jCoIgCIIgCDZrmrSKxAuo/hZPr/8OXwUXBEEQBEEQBAUpJ4M52czuTs+fTaUcgiAIgiAIgqAg5WQwl0raHkDSaFL9ozQxNwiCIAiCIAg2oJxFPotx71TYcPJ3bzMb0F6BNYdY5BMEQdBmVNQiH0n7ApfjFoJ/xFccN+AWhg2tbLsXbsVYZWYTJF0O7GtmB5U45+u4NeKoRvt3SnHuhBdTB0/i9C61krqtSKVv7svMULJ6lWb2TskTg6CdKGeI/FtWwORd0ufbIZ4gCIKgEXULFlK32H/nV/frR/XAivht3yGY2VOSHse9rM8HkPRX4OO4J3Rr2l4m6ed4SR3w4t77NnHOjZJOL7D/FUn3AZ/MxdkJ927uCCbgPtOZ297Hyy0nEwTtQZMCs5C4TPt/2fbhBEEQBI2pW7yY+VdeCcCg007bqgRmY1IR74HAAkn7Aafgvsl7Amcmq7vLgGOA/8X9v6fi4utTuM/1OOBrZra0iX6uxv2n+wCTin0fluBGMzsxFTm/EXgitXknbot4t6QL8e/iemCZmV2e+j8Xz9quwe0D/xt3WyFlW08Avmdmo+TWmaNw15ppuO3gtZJOMbPHk8vLcbiN4i7A2cA26frMS//2Bn5gZg828z0GQUHKyWAGQRAEwabmgGShNwAXQk9JGgecZWavS/oM7jLyXTM7Q9LJuJhbigvKPsApZrYkWTF+AbihRH9fArqY2YXJC3uqpEfKGHLeQ9LV6Xk1QBJ5nwHuxUXet83sr5IOAfYzs48BSHpc0h+BbdP+w9L+L5nZ6mTvNyG1eYuk76Xnf5Q0E7jazGamcyalR+E+6HuZ2XxJR+Ne45+XdCtwiJl9U9J7gXNxa8YgaDXtJjAl7Yz/glyFV/U/H/gx7h+aMcLMRhc49xPAWKA78CHgI8l7MwiCYKsgPyxeP3/+uv318+ezmq1vqBz4ZwFrvVXANyUtALbHM34Zb5tZtn5gkqR9gHPTsXvjdZ1LMR73G8+8rqcAQ/CMZikmZ3MuJe2S7Uye0j8H/ivLUqY+euT6eBOfYzoeF6LZuT9tos9SDMTngWYfoul4RjTjlfQ4n5xfeRC0lnYRmJKqgKtw158GSXfghvGXm9ld6ZiDgAMLnLs98CkzOyFt/xYfOgiCINhqyA+L51l0xx1ADJUnfgjca2Z3pGHi/NqAxvMPb8UXBv0tDS8Pa6LtycAaM7sUQNIRwMzmBJdE5SfN7AFJ/YF+wGRJp5nZlamP/XN9HIwLQAEHZe1IOh4fVl8G9E77RjTqrt53ayzrrZ0BFgBLJA1O2dcdgUn5MJvznoKgXNorg/le/D/ISZJ6AAuBn2TiMvFVfDijMUcDKyR9G+gP/KWQV2f6A3ECwIgRjf+fBUEQBFsCkmqADwBdJB1pZvfkXr4T/57ZHhiOe23X4PMx+0g61cyuSsf+FDhH0l+AfXAv6HGs9+g+APg0MFLSoen4yyVdgCdIMLN70yryPpKOMbNf5eIcg3tM7yzp7FyMwyW9hZuWXA8sB+6VVGVml0vaV9IluHjsx/p5pPul/atxT++1adi7k6RzcLGbj+Nh4EygM/AzPAv6BeBp4HPAJZJeA3YGviNpW9Z7Yo/B53iOlPRhM3u0BbcqCDagyTJFLWrU53jcBIxK813uBP5sZrel10cDZ5jZVwuc+2O8zMNH8OGO54BPm9krjY/NiDJFQRBsaTQeIs8yl/2PPZaqQYPac4i8osoUBUGwedJeGcylwDQzW5K2n8DT/bel7ZOA60qc+1Qqr7BG0vPAAayfJxIEQbDFUz1wwDoBuTq3v2rQILrtOGbTBBUEQVAm5Tj5tIQngQFpLibASJJAlNQbGJkf9pbURdJ70uajQH7hz7pzgyAIgiAIgsqnXYbIYd2E6IPxlWkjgJPMbJWkU4DpZvZA7tjD8ZpkWUmG83Hxuw2wwMwuKdVXDJEHQbAl08GF1mOIPAiCVtNuArMjCYEZBEHQZoTADIKg1bTXEHkQBEEQBEGwlRICMwiCIAiCIGhTwioyCIIgaDNqamp64fWMx+BFw++aOHHistJnlUbSR4DP4C46ZmYXtDrQIAjalRCYQRAEQZtQU1NzIPAQ6xdprgCuqqmpOWzixIlPtKTNZNZxE7C7ma2RdE8UAw+CyieGyIMgCIJWkzKXD+F+1tuk3duk7Ydqamp6trDp/YE3zGxN2v4H8InWxBoEQfsTAjMIgiBoC46m+HdKp/R6SxiM2yhmLE37giCoYEJgBkEQBG3BGNZnLhuzDbBDC9t9B8+CZvRO+4IgqGBCYAYdxqpla1mzsnZThxEEQfswHZ9zWYgVwGstbPdfwEhJXdP2fwAPtrCtIAg6iBCYQYewculaXnh8DtOffSdEZrBZ0FBfz/LFizZ1GJsTdwENRV5rSK83GzNbCXwNuFbSRcDzscAnCCqfEJhBu7Ny6Vqm/HUO82ctZebkBSEyg82CpQveYfpT/6Kudu2mDmWzIJUiOgyfL5llMlek7cMmTpy4vKVtm9mfzOyrZnZ2lCgKgs2DEJhBu5IXlxkhMoNKp6G+njeen8QbLzzH0vkx3a9cUimiYcDJwCXpcVhLSxQFQbD5EnUwg3ajkLjMmDl5AQBj9h5M1x6dOzq0ICjJ0gXvMGvKJBrq65nx7NP0HjSY6s5dNnVYmwUpU/nTTR1HEASblshgBu1CKXGZEZnMoBLJspd1a31ofM60FyOLGQRB0ExCYAZtTjniMiNEZlBpZNnLjCyLGXMxgyAIyieGyIM2pTniMiOGy4NKoaG+nnmvvUqX7j3o0r3Huv3vznuLZQsX0G/IsI4NqL4eqqoa7auDqvjTHQRBZRN/pYI2oyXiMiNEZlAJmBkjxu7BiLF7bPRap8ZCr71ZtQReexRG7A+9h/q+eVNg9RIY/r6KFZnJMvIIYAgwD7g3rTAPgmArojL/QgWbHbVr6njl6bdbJC4zZk5eQLdtOrP9HoPo1EltGF0QlEdVdTU9evfZ1GHAqnfhhd/C9D/BW8/B/l+HFQvhH1dD7WrAYPh+FSUya2pqBJwBnAvUA92A1cBNNTU1FwKXTZw40VrStqQdgIuAZ4HtgIVmdmGbBB4EQbtQOX+dgs2azl2r2X78ABbNWc6qZS2bq9Zv6DYM2b5PiMsgqF8Ly9/25wtegb9eAavfhbXLAcHKhX5MBQlMXFyeA3TP7euZHs9Jj5e2sO3+wK/N7PcAkl6S9KCZPdPC9oIgaGdikU/QZvQZ1IN9Dh1J917NL+fSb+g27HHwcLbp27Xpg4NgS6fnYNj3KzBkvG8vnb1eXO55DIz5KHTpUbKJjiQNi58LFAuqB3BOTU1NzyKvl8TMns7EZaITxW0pgyCoAEJgBm1KS0RmiMsgKEDPwbDLYRvu694X3lNTUeIycQQ+LF6KhnRcq5B0BPCImU1rbVtBELQfITCDNqc5IjPEZRAUYd4U+NcNG+5btRievAmWzt00MRVnCD7nshRdgaGt6UTSh4APAd9uTTtBELQ/ITCDdqEckVlIXNbXNbBwznLWrq7riDCDoDJZ+hb8+8ewZhkg2P0zMGScv7bgFXjhblhTUSPE8/AFPaVYA7RYGUv6BHAIbj85RNL+LW0rCIL2JwRm0GrMCi8MLSUyi4nLOa+8y5P3z2DGpPkhMoOtl55DYJ/joLqbz7nc9XDY9wSfk9lrGOz2aei6zaaOMs+9QFN1nDql45qNpH2Au4D9gL8Avwd2bklbQRB0DComDjYnampqbOLEiZs6jK2ShvoG3p65lB69u9BnUOF5YUvmr+SZP7yxbnV5KXE55a+zsQb/TO6wz2BG7zmILt0qaqVsEHQMDQ3w7kwXm9mcy+XvQN1q6DuiPXtuURmHmpqaM/HV4oX+EKwALpo4cWJLV5EHQbCZERnMoMU01Dcwb8ZSJv1pFs8+Mosl81cWPC6fySxXXAK89sw7kckMtl46dYL+ozdc0NNzcHuLy9ZwGfB9YBWwHKhNj6vwGpaXbbrQgiDoaCKDGbB04SqqqjuxTZ/yF9pk4nLyo7NoqPfPUI8+Xdn7kBElM5nVnavKEpd5IpMZBB1KqwrRppJFn8YX9MzFnXyWt0VgQRBsPoTA3MpZukYhuEkAABKtSURBVGAVzz7yBt16dmbcQduVJTILicuMpkRmnnLEZUaIzCDoMMLpIAiCVhND5Fsxmbhc8e4aFs5ezguPz2bFkjUlzyklLgFWLllTcrg8ozniEmK4PAiCIAg2JyIdtJWSF5cZmcgslslsSlxmZCKzWCazueIy47Vn3gGITGYQVCg1NTWjgZOAo3CbyOXA3cB1EydOnLEpYwuCoGOJIfIKp76+gaqqtk00FxKXeQZs13MjkVmuuMxTaLi8peIyTwyXB0G70uwh8pqaGgEXA6fgI2P52mRrcRefq4HvTZw4cfP/0gmCoEliiLyCqa+tZ860xSx6q+3mxzclLoGNhstbIi5h4+HythCXEMPlQVCBXAx8C3fzaVz4tkvaf1I6rkVI6i7peUlXtDjKIAg6jBCYFUp9bT2zX3Yx9uwjb7SJyCxHXGbkRebqFXXMmDy/WeIyY+WSNbz9+lLq6xqoq21g2YJVrRKXGUveWUnd2oZWtxMEQetIw+KnULj+ZZ5tgFPS8S3hIuC5Fp4bBEEHEwKzAsnE5Yt/m40ZrFlZ12qRuXLZWp5/fHZZ4jJj4ezlvPLkPKq7dGKPDw+n14Duze53+z0GMnLcQKqqO9G1ezVjagYzcuyAZreTZ2Aawu/Ru2mv8yAI2p2TKP+7pBPwzeZ2IOkLwD+A15t7bhAEm4YQmBVGY3GZ0VqR2b1nZ3Z677ZUdyn/lnfr2Znt9/C5jr36dWOvQ0Y0S2Ruv8dAdthnW7p2Xz9XsmuPzuz43m1bLDIHbteTcR/ajh69y6/ZGQRBu3IUGw+LF6MLcHRzGpe0G7Crmf2uuYEFQbDpCIFZQRQTlxmtEZmSGDSiF3t9bGRZIrNbz87s8/FR9N12/ahXc0RmIXGZ0VKRGeIyCCqSns08vrkm6kcAqyWdCRwI7CvplGa2EQRBBxMCs0JoSlxmdITILCQuM8oRmaXEZUZzRWaIyyCoWJr7x2hFcw42sx+Y2YVmdinwBPCUmV3dzD6DIOhgQmBWCGvX1rPoreUlxeW6Y1fVsXzxGurrm7/IpSmRWUpcZpQSmeWIy4xyRWaIyyCoaO7GSxGVw1rgrpZ0IulI4APAfpKOaUkbQRB0HCEwK4Tu23RhlwOGMnSHPiWPk2D3D2zHe3bq2+L6mMVEZjniMqOQyGyOuMxoSmSGuAyCiuc6vM5lOTQA17ekEzO7x8wONrMDzexXLWkjCIKOIwRmBdG9Zxd2PXBYUZGZicvtdu5LVeeqVvXVWGQ2R1xmrBOZA7u3SFxmFBOZIS6DoPJJDj1XA6X9YX1o/Opw9AmCrYOwQqkwMpEJMPe1Jev2t6W4XN+mi8y9DxlJ567VzRKXGb36dUvnV7VIXGZkIhPgjSkLQ1wGwebF99JjKSef63LHBUGwhRNWkRXKquVrmfrEW8x9bUm7iMtKZc3KWua8vJghO/QJcRkEm4ZmW0VmpCLq38RLEW2DZy3vAq6PzGUQbF1EBrNCyTKZ6iT6D+u5VYhL8Ezm9nsMQp1a/B0XBMEmoKampitwAPBRYChQC/RO28/W1NTMmThxYvlOD0EQbNZEBrPCWbV8LV26Vm0V4jIIgoqg2b/uampq9gX+AHQGehU4ZBkuOD8+ceLEp1sXXhAEmwOxyKfC6d6zS4jLIAgqlpqamvcCjwH9KSwuSfv7A39JxwdBsIUTAjMIgiBoEWlY/GHKd+fZBng4nVc2knaWdL6kMyQ9JGnf5sYaBEHHEnMwgyAIgpbyWXxYvDl0Af4L+EU5B0uqAq4CDjezBkl3AHXN7DMIgg4mMphBEARBSzmD4sPixegJnNmM49+Lzws9SdJZwOHAgmb2GQRBBxMCMwiCIGg2NTU1VcDuLTx993R+OYwE9gduM7NLcLvI41rYbxAEHUQIzCAIgqAl9MRXhreEunR+OSwFpplZ5jzxBHBQC/sNgqCDCIEZBEEQtITlNH/+ZUZ1Or8cngQGpLmY4BnNV1rYbxAEHUQIzCAIgqDZTJw4sR54sYWnv5jObxIzW4TP9bxa0rnAIOBHLew3CIIOIlaRF2HNqjpWLllD38E9wlUmCIKgMJcBN9K8hT7LgEub04mZ3Qvc25xzgiDYtLSbwJS0M3AMsAr4IHA+8GP8j0vGCDMb3ei8UXhdtXlp1zNmdlp7xVmINavqmP7M27z50iL2/MgIth3VO0RmEATBxvwGuKaZ59QCv22HWIIgqCDaxSoyzZW5n/V1y4bik7oPNrO70jEHAQea2UWNzh0FHGRmt5XbX1taRWbicuZkr4JR1blTiMwgCLYmmvWHLjnz/IXyiq2vAD4UdpFBsOXTXnMwC9Yty8Rl4qvATUXOP1zSdyVdJGm3QgdIOkHSREkT58+f3yZBNxaXAPW1DUz68yzenrkUa9j8fduDIAjakiQWPwT/v707j7GrLOM4/n2oXQy0IGUqgrSmLIqKpWRwQSOoUdSkaA3uioqKoNQtRjABgohicI24IAZBkMQFNO5xiWu1AUZBaQQRFKSQblamZbHr4x/nDLmMM3Tu3PfcO8v3k5z0nu32edJJ++t7znsOm3j4FapWW+r9hktpmmgqYD7ic8siYjGwOTNHeljuBuCczPwEcBHwo4jYZ/hBmXlJZvZnZn9fX1/HBY8ULocYMiVpdHVoPAA4DVgNJNWl8ARuqrcfYLiUpo+m7sEc7blll9frK6jC4//JzPupZyZm5rqIWAcsAX7TUK0AbN+6k41rRn9qxs7tu7h3/QPMP3BPZs52bpQktRoYGNhK9frHq+qHqO8F3DfW2eKSppamktJDzy3LzJ20PLcsIuYBizJz9dDBETEL6MvMuyPiJOCGzLwpImYCjwfuaKjOh+y1z2yOetEi/vSzO7nv3//9v/2Ll/axeEmf4VKSdqMOlYO7PVDSlNVIWsrMTREx9NyyDVTPLTuv3n0y8NVhpxxPdQnlpcAa4KyIuBE4BDg7M+9sos7h5u47Z8SQuXhpHwcvXcCsRxsuJUmSdqeRWeTdVnIWOcCWTf99KGQaLiVNMz4uQ1LHTE0jGBrJXHfHZhYevq/hUpIkqQ0mp1HM3XcOe+49iz1m+DZNSZKkdpieHoHhUpIkqX0mKEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlEGTEmSJBVlwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCRJUlGPauqLI+KJwGuBB4FjgXOBLwFbWg5bmJmLRzn/ScD1wGsz84dN1SlJkqSyGgmYETED+DSwLDN3RcQVwA7gwsz8Zn3MccBzRjn/0cAHgZuaqE+SJEnNaeoS+dFAACsi4kPAMmDjULisvQO4eJTzPwp8BNjWUH2SJElqSFOXyBcBz6K6vD0YEV+nCouXA0TEYmBzZm4cfmJEnASszMx/RsSov0FEnAKcArBw4cLiDUiSJGl8mhrB3AzckpmD9fpK4LiW/SuAi0Y593nAYRFxJrAQODEiXjH8oMy8JDP7M7O/r6+vXOWSJEnqSFMjmNcC8yNiRmbupBrRvBUgIuYBizJz9dDBETEL6MvMuzPzLS3bXwxc7SQfSZKkyaOREczM3AScAXw2Is4B+oDP1LtPBr467JTjga+0boiI91MF01dHxDFN1ClJkqTyIjN7XUPH+vv7c2BgoNdlSNJUMPrN75I0Rj5oXZIkSUUZMCVpBIMb1rF969ZelyFJk5IBU5KG2bFtG7euWsng+rW9LkWSJqXGXhUpSZPV4Pq13P23v7Jz+3b2XrA/M2fP7nVJkjSpOIIpSS12bNvG7QPXkrt2sfb2Wx3FlKRxMGBKUovB9Wu55++3AJCZ3HbdKu/FlKQ2GTAlqdY6ejnEUUxJap/3YEpS7b5NG9n64APMP/Cgh21f/8/bvRdTktpgwJSk2ty+x3LMK18/4r49ZszocjWSNHkZMCWpNsMQKUlFeA+mJEmSijJgSpIkqSgDpiRJkooyYEqSJKkoA6YkSZKKMmBKkiSpKAOmJEmSijJgSpIkqSgDpiRJkooyYEqSJKkoA6YkSZKKMmBKkiSpKAOmJEmSijJgSpIkqSgDpiRJkooyYEqSJKkoA6YkSZKKMmBKkiSpqMjMXtfQsYjYANzZwFfvB2xs4HsnGvucWuxzaul2nxsz88Vd/P0kTUFTImA2JSIGMrO/13U0zT6nFvucWqZLn5KmFi+RS5IkqSgDpiRJkooyYD6yS3pdQJfY59Rin1PLdOlT0hTiPZiSJEkqyhFMSZIkFWXAlCRJUlGP6nUBvRIR7wMOBO4HZgMfAo4CzgQGgGcAn8jMVSOc+3FgGzAHWJuZn+5W3e0apc8jgfcAfwWeApydmf8a4dw3AEuBncDtmfnlbtXdjg57PAT4JLAjM0/sWtHjMN4+I+Jo4L3ADcATgesy8ytdLL0tHfS5ALgMWAksAGYBKzJzV/eqH7tOfm7r8xdQ/ZlekJmf70rRkjRWmTntFqrQdGPL+jXAcuAnwPJ623Lg5yOc+3Lgey3rfwSO6nVPbfZ5I7C03rastZ+WYx9fHzd0n+71wKG97qlkj/W+1wOnAFf3upcG/yxPAJ5ef54J/AfYr9c9NdDnAcDbW9b/DDy71z2V7rPetwfV5J/vAaf3uh8XFxeX4ct0vUR+CHBXy/o/gBcA64C+elsfVXgc7lCgdUThH8DzG6ixhNH6bO1htPqPB/6YmUOzwFYBL2mozk500iOZeRXVaPREN+4+M/P7mXldy6YdwPaG6uxUJ33ek/XIbETMBfaimTd8ldDRzy1wBnAp1X8WJGnCma4B83rg8IiYExEB9APzgLOAkyLik8CbgG+PcO7vgaMjYo+ImEl1SWtel+pu12h9rgSeWR/zdGDPiBh+u8QCYEvL+uZ620TTSY+TSak+Twc+lpmDjVY7fh33GRGvAX4IXJiZa7pQ83iMu8+IeD7wQGZe282CJakdk/kf3HHLzDsi4hTgbGADsBoYBL4PvCszV0XEEcAvImL/llE8MvMPEfEp4BzgXqp/KEa8R6rXHqHPDwDvru8/3Azck5k7hp2+nmqUZcg84Lbmq25Phz1OGiX6jIjXAXtm5vldKrttJfrMzG9ExLeAX0bEXZn54y6VP2Yd9nkCsDYizgSOAB4TEfdn5mXd60CSdqPX1+h7tQBHtny+Ejic6i/6J9Tb9qMKkEE10ruw3j4HeFLLuT8F5ve6nzb7XNKy7UVUkwQY1uekuAezkx5b9r+ZCX4PZqd9Am8D3lt/PgI4rNf9lO4TOJb6XtN6/WtM4PsTO/25rbdfPpF7dHFxmb7LtH3QekT8FvgdsBW4OTO/HRHLgVcBfwGeDHwnM78bEUcCV2bmERGxH9UN+b+imjDx88z8dU+aGINR+jwLWEw1U3VvqkumD7b2WZ/7BqpLdzuBW3PiziLvpMeXASdRza6+IjMv7EkTYzDePuser6CacQwwn2p29a+73sQYdNDnM6hGAG8A5lLN0D4tM+/vSSO70cnPbX3+yVS3PNwNfDEzf9L1JiRpFNM2YEqSJKkZ03WSjyRJkhpiwJQkSVJRBkxJkiQVZcCUJElSUQZMSZIkFWXAlCaAiDgoIq6JiHMjYm5EXBoRl/e6LkmSxsOAKU0AmXkX8IP68xaqB29LkjQpTctXRUqdiIi3ABcAFwMHA08B/kD1es29gRsz88qImAF8CthI9QaofTPznRFxWn3OWuAJwKk5iV9jKUnScAZMqU2ZeVlEvAm4LjPPjYgzgIMz87yICODmiPgpsByYlfW7vyPirfVXrAG+nJm7IuJzwPHAj3rQiiRJjTBgSuN3c/3rQuBxEXFmvb4a2B94GnDb0MGZeWn98QHgwojYSPVK0j91p1xJkrrDgCmN39B7Vv8MbM3MjwPU77S/o96+ZOjgiDg1My8GrgaWZOa/ImJed0uWJKl5TvKR2hQRLwQWAadHRB9wKZAR8eGIOBt4amZurrdvr2eGnw/cW3/FxcAX6kvrzwKWRcRBwDLguRGxBHgj8LSIOKa73UmS1LnIzN0fJUmSJI2RI5iSJEkqyoApSZKkogyYkiRJKsqAKUmSpKIMmJIkSSrKgClJkqSiDJiSJEkq6n/FSq578WekqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 698.625x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 619.75x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,25))\n",
    "g = sns.relplot(x=\"recall\", y=\"precisionAtL\", hue=\"Description\",style =\"Description\", size=\"Parallel Execution\",\n",
    "            sizes=(50, 200), alpha=.7,\n",
    "            height=6, data=df)\n",
    "\n",
    "# plt.savefig(\"backoff_comp.pdf\", bbox_inches='tight')\n",
    "\n",
    "plt.figure(figsize=(15,25))\n",
    "g = sns.relplot(x=\"recall\", y=\"precisionAtL\", hue=\"Condition\", style=\"Condition\", size=\"threshold\",\n",
    "            sizes=(50, 200), alpha=.2,\n",
    "            height=6, data=df)\n",
    "\n",
    "# plt.savefig(\"condition_comp.pdf\", bbox_inches='tight')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2ec104eb8>"
      ]
     },
     "execution_count": 4,
     "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": [
    "sns.stripplot(x=\"f1\", y=\"Description\", data=df, jitter=True, dodge=True, hue=\"Condition\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drilling down into additional data details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>backoff</th>\n",
       "      <th>Condition</th>\n",
       "      <th>threshold</th>\n",
       "      <th>threshold_float</th>\n",
       "      <th>Parallel Execution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               backoff                   Condition  threshold  \\\n",
       "16       splitProperty  tooUnlikelyRecommendations        1.0   \n",
       "17       splitProperty  tooUnlikelyRecommendations        1.0   \n",
       "18       splitProperty  tooUnlikelyRecommendations        1.0   \n",
       "19       splitProperty  tooUnlikelyRecommendations        1.0   \n",
       "20  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "21  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "22  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "23  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "24  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "25  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "26  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "27  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "28  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "29  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "30  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "31  deleteLowFrequency  tooUnlikelyRecommendations        1.0   \n",
       "48       splitProperty  tooUnlikelyRecommendations        2.0   \n",
       "49       splitProperty  tooUnlikelyRecommendations        2.0   \n",
       "50       splitProperty  tooUnlikelyRecommendations        2.0   \n",
       "51       splitProperty  tooUnlikelyRecommendations        2.0   \n",
       "52  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "53  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "54  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "55  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "56  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "57  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "58  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "59  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "60  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "61  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "62  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "63  deleteLowFrequency  tooUnlikelyRecommendations        2.0   \n",
       "80       splitProperty  tooUnlikelyRecommendations        3.0   \n",
       "81       splitProperty  tooUnlikelyRecommendations        3.0   \n",
       "82       splitProperty  tooUnlikelyRecommendations        3.0   \n",
       "83       splitProperty  tooUnlikelyRecommendations        3.0   \n",
       "84  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "85  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "86  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "87  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "88  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "89  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "90  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "91  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "92  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "93  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "94  deleteLowFrequency  tooUnlikelyRecommendations        3.0   \n",
       "\n",
       "    threshold_float  Parallel Execution  \n",
       "16         0.033333                   2  \n",
       "17         0.033333                   2  \n",
       "18         0.033333                   2  \n",
       "19         0.033333                   2  \n",
       "20         0.033333                   1  \n",
       "21         0.033333                   1  \n",
       "22         0.033333                   2  \n",
       "23         0.033333                   2  \n",
       "24         0.033333                   3  \n",
       "25         0.033333                   3  \n",
       "26         0.033333                   4  \n",
       "27         0.033333                   4  \n",
       "28         0.033333                   5  \n",
       "29         0.033333                   5  \n",
       "30         0.033333                   6  \n",
       "31         0.033333                   6  \n",
       "48         0.066667                   2  \n",
       "49         0.066667                   2  \n",
       "50         0.066667                   2  \n",
       "51         0.066667                   2  \n",
       "52         0.066667                   1  \n",
       "53         0.066667                   1  \n",
       "54         0.066667                   2  \n",
       "55         0.066667                   2  \n",
       "56         0.066667                   3  \n",
       "57         0.066667                   3  \n",
       "58         0.066667                   4  \n",
       "59         0.066667                   4  \n",
       "60         0.066667                   5  \n",
       "61         0.066667                   5  \n",
       "62         0.066667                   6  \n",
       "63         0.066667                   6  \n",
       "80         0.100000                   2  \n",
       "81         0.100000                   2  \n",
       "82         0.100000                   2  \n",
       "83         0.100000                   2  \n",
       "84         0.100000                   1  \n",
       "85         0.100000                   1  \n",
       "86         0.100000                   2  \n",
       "87         0.100000                   2  \n",
       "88         0.100000                   3  \n",
       "89         0.100000                   3  \n",
       "90         0.100000                   4  \n",
       "91         0.100000                   4  \n",
       "92         0.100000                   5  \n",
       "93         0.100000                   5  \n",
       "94         0.100000                   6  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "df[['backoff','Condition', 'threshold', 'threshold_float', 'Parallel Execution']].query(\"Condition=='tooUnlikelyRecommendations'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Config File</th>\n",
       "      <th>subjects</th>\n",
       "      <th>duration</th>\n",
       "      <th>mean</th>\n",
       "      <th>meanOfHits</th>\n",
       "      <th>median</th>\n",
       "      <th>stddev</th>\n",
       "      <th>top1</th>\n",
       "      <th>top5</th>\n",
       "      <th>top10</th>\n",
       "      <th>...</th>\n",
       "      <th>backoff</th>\n",
       "      <th>Condition</th>\n",
       "      <th>threshold</th>\n",
       "      <th>threshold_float</th>\n",
       "      <th>merger</th>\n",
       "      <th>splitter</th>\n",
       "      <th>stepsize</th>\n",
       "      <th>Parallel Execution</th>\n",
       "      <th>Description</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40663</td>\n",
       "      <td>24.8525</td>\n",
       "      <td>3.9176</td>\n",
       "      <td>2.7069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.1357</td>\n",
       "      <td>79.1776</td>\n",
       "      <td>93.3601</td>\n",
       "      <td>96.4316</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>avg</td>\n",
       "      <td>everySecondItem</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (everySecondItem avg)</td>\n",
       "      <td>80.678406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Config File  subjects  duration    mean  meanOfHits  median   stddev  \\\n",
       "2            2     40663   24.8525  3.9176      2.7069     1.0  27.1357   \n",
       "\n",
       "      top1     top5    top10  ...        backoff              Condition  \\\n",
       "2  79.1776  93.3601  96.4316  ...  splitProperty  tooFewRecommendations   \n",
       "\n",
       "   threshold  threshold_float merger         splitter  stepsize  \\\n",
       "2        1.0              0.0    avg  everySecondItem             \n",
       "\n",
       "   Parallel Execution                           Description         f1  \n",
       "2                   2  Split Property (everySecondItem avg)  80.678406  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['precisionAtL']==max(df['precisionAtL'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Config File</th>\n",
       "      <th>subjects</th>\n",
       "      <th>duration</th>\n",
       "      <th>mean</th>\n",
       "      <th>meanOfHits</th>\n",
       "      <th>median</th>\n",
       "      <th>stddev</th>\n",
       "      <th>top1</th>\n",
       "      <th>top5</th>\n",
       "      <th>top10</th>\n",
       "      <th>...</th>\n",
       "      <th>backoff</th>\n",
       "      <th>Condition</th>\n",
       "      <th>threshold</th>\n",
       "      <th>threshold_float</th>\n",
       "      <th>merger</th>\n",
       "      <th>splitter</th>\n",
       "      <th>stepsize</th>\n",
       "      <th>Parallel Execution</th>\n",
       "      <th>Description</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40663</td>\n",
       "      <td>24.9412</td>\n",
       "      <td>3.5964</td>\n",
       "      <td>2.9118</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6400</td>\n",
       "      <td>79.0129</td>\n",
       "      <td>93.1830</td>\n",
       "      <td>96.3529</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.647379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>40663</td>\n",
       "      <td>25.1936</td>\n",
       "      <td>3.5968</td>\n",
       "      <td>2.9122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6036</td>\n",
       "      <td>78.9612</td>\n",
       "      <td>93.1732</td>\n",
       "      <td>96.2915</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.627810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>40663</td>\n",
       "      <td>25.0576</td>\n",
       "      <td>3.5886</td>\n",
       "      <td>2.9040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6015</td>\n",
       "      <td>79.0079</td>\n",
       "      <td>93.1855</td>\n",
       "      <td>96.3210</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.645903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>40663</td>\n",
       "      <td>28.2981</td>\n",
       "      <td>3.6504</td>\n",
       "      <td>2.9659</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6319</td>\n",
       "      <td>78.8038</td>\n",
       "      <td>92.7551</td>\n",
       "      <td>95.9054</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.494229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>40663</td>\n",
       "      <td>25.1729</td>\n",
       "      <td>3.5933</td>\n",
       "      <td>2.9087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6081</td>\n",
       "      <td>78.9932</td>\n",
       "      <td>93.1928</td>\n",
       "      <td>96.3308</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.641761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>40663</td>\n",
       "      <td>29.6406</td>\n",
       "      <td>3.6649</td>\n",
       "      <td>2.9804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6657</td>\n",
       "      <td>78.7448</td>\n",
       "      <td>92.6838</td>\n",
       "      <td>95.8586</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooUnlikelyRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.443993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Config File  subjects  duration    mean  meanOfHits  median   stddev  \\\n",
       "1             1     40663   24.9412  3.5964      2.9118     1.0  22.6400   \n",
       "17           17     40663   25.1936  3.5968      2.9122     1.0  22.6036   \n",
       "33           33     40663   25.0576  3.5886      2.9040     1.0  22.6015   \n",
       "49           49     40663   28.2981  3.6504      2.9659     1.0  22.6319   \n",
       "65           65     40663   25.1729  3.5933      2.9087     1.0  22.6081   \n",
       "81           81     40663   29.6406  3.6649      2.9804     1.0  22.6657   \n",
       "\n",
       "       top1     top5    top10  ...        backoff                   Condition  \\\n",
       "1   79.0129  93.1830  96.3529  ...  splitProperty       tooFewRecommendations   \n",
       "17  78.9612  93.1732  96.2915  ...  splitProperty  tooUnlikelyRecommendations   \n",
       "33  79.0079  93.1855  96.3210  ...  splitProperty       tooFewRecommendations   \n",
       "49  78.8038  92.7551  95.9054  ...  splitProperty  tooUnlikelyRecommendations   \n",
       "65  78.9932  93.1928  96.3308  ...  splitProperty       tooFewRecommendations   \n",
       "81  78.7448  92.6838  95.8586  ...  splitProperty  tooUnlikelyRecommendations   \n",
       "\n",
       "    threshold  threshold_float merger          splitter  stepsize  \\\n",
       "1         1.0         0.000000    max  twoSupportRanges             \n",
       "17        1.0         0.033333    max  twoSupportRanges             \n",
       "33        2.0         0.000000    max  twoSupportRanges             \n",
       "49        2.0         0.066667    max  twoSupportRanges             \n",
       "65        3.0         0.000000    max  twoSupportRanges             \n",
       "81        3.0         0.100000    max  twoSupportRanges             \n",
       "\n",
       "    Parallel Execution                            Description         f1  \n",
       "1                    2  Split Property (twoSupportRanges max)  80.647379  \n",
       "17                   2  Split Property (twoSupportRanges max)  80.627810  \n",
       "33                   2  Split Property (twoSupportRanges max)  80.645903  \n",
       "49                   2  Split Property (twoSupportRanges max)  80.494229  \n",
       "65                   2  Split Property (twoSupportRanges max)  80.641761  \n",
       "81                   2  Split Property (twoSupportRanges max)  80.443993  \n",
       "\n",
       "[6 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Description']=='Split Property (twoSupportRanges max)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Config File</th>\n",
       "      <th>subjects</th>\n",
       "      <th>duration</th>\n",
       "      <th>mean</th>\n",
       "      <th>meanOfHits</th>\n",
       "      <th>median</th>\n",
       "      <th>stddev</th>\n",
       "      <th>top1</th>\n",
       "      <th>top5</th>\n",
       "      <th>top10</th>\n",
       "      <th>...</th>\n",
       "      <th>backoff</th>\n",
       "      <th>Condition</th>\n",
       "      <th>threshold</th>\n",
       "      <th>threshold_float</th>\n",
       "      <th>merger</th>\n",
       "      <th>splitter</th>\n",
       "      <th>stepsize</th>\n",
       "      <th>Parallel Execution</th>\n",
       "      <th>Description</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40663</td>\n",
       "      <td>24.8525</td>\n",
       "      <td>3.9176</td>\n",
       "      <td>2.7069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.1357</td>\n",
       "      <td>79.1776</td>\n",
       "      <td>93.3601</td>\n",
       "      <td>96.4316</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>avg</td>\n",
       "      <td>everySecondItem</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (everySecondItem avg)</td>\n",
       "      <td>80.678406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>40663</td>\n",
       "      <td>24.9193</td>\n",
       "      <td>3.9297</td>\n",
       "      <td>2.7190</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.1494</td>\n",
       "      <td>79.1383</td>\n",
       "      <td>93.3010</td>\n",
       "      <td>96.4046</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>max</td>\n",
       "      <td>everySecondItem</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (everySecondItem max)</td>\n",
       "      <td>80.672158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>40663</td>\n",
       "      <td>24.8553</td>\n",
       "      <td>3.8081</td>\n",
       "      <td>2.8055</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.4850</td>\n",
       "      <td>79.0547</td>\n",
       "      <td>93.2814</td>\n",
       "      <td>96.3702</td>\n",
       "      <td>...</td>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>stepsizeProportional</td>\n",
       "      <td>2</td>\n",
       "      <td>Delete Low Frequency (stepsizeProportional)</td>\n",
       "      <td>80.631517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>40663</td>\n",
       "      <td>25.8503</td>\n",
       "      <td>4.9539</td>\n",
       "      <td>2.7036</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.3375</td>\n",
       "      <td>79.0620</td>\n",
       "      <td>93.2150</td>\n",
       "      <td>96.2693</td>\n",
       "      <td>...</td>\n",
       "      <td>deleteLowFrequency</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>stepsizeLinear</td>\n",
       "      <td>5</td>\n",
       "      <td>Delete Low Frequency (stepsizeLinear)</td>\n",
       "      <td>80.541176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40663</td>\n",
       "      <td>24.9412</td>\n",
       "      <td>3.5964</td>\n",
       "      <td>2.9118</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6400</td>\n",
       "      <td>79.0129</td>\n",
       "      <td>93.1830</td>\n",
       "      <td>96.3529</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>max</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges max)</td>\n",
       "      <td>80.647379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>40663</td>\n",
       "      <td>25.1123</td>\n",
       "      <td>3.5999</td>\n",
       "      <td>2.9153</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.6287</td>\n",
       "      <td>79.0055</td>\n",
       "      <td>93.2051</td>\n",
       "      <td>96.2988</td>\n",
       "      <td>...</td>\n",
       "      <td>splitProperty</td>\n",
       "      <td>tooFewRecommendations</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>avg</td>\n",
       "      <td>twoSupportRanges</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>Split Property (twoSupportRanges avg)</td>\n",
       "      <td>80.646756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Config File  subjects  duration    mean  meanOfHits  median   stddev  \\\n",
       "2             2     40663   24.8525  3.9176      2.7069     1.0  27.1357   \n",
       "64           64     40663   24.9193  3.9297      2.7190     1.0  27.1494   \n",
       "7             7     40663   24.8553  3.8081      2.8055     1.0  25.4850   \n",
       "12           12     40663   25.8503  4.9539      2.7036     1.0  35.3375   \n",
       "1             1     40663   24.9412  3.5964      2.9118     1.0  22.6400   \n",
       "35           35     40663   25.1123  3.5999      2.9153     1.0  22.6287   \n",
       "\n",
       "       top1     top5    top10  ...             backoff              Condition  \\\n",
       "2   79.1776  93.3601  96.4316  ...       splitProperty  tooFewRecommendations   \n",
       "64  79.1383  93.3010  96.4046  ...       splitProperty  tooFewRecommendations   \n",
       "7   79.0547  93.2814  96.3702  ...  deleteLowFrequency  tooFewRecommendations   \n",
       "12  79.0620  93.2150  96.2693  ...  deleteLowFrequency  tooFewRecommendations   \n",
       "1   79.0129  93.1830  96.3529  ...       splitProperty  tooFewRecommendations   \n",
       "35  79.0055  93.2051  96.2988  ...       splitProperty  tooFewRecommendations   \n",
       "\n",
       "    threshold  threshold_float merger          splitter              stepsize  \\\n",
       "2         1.0              0.0    avg   everySecondItem                         \n",
       "64        3.0              0.0    max   everySecondItem                         \n",
       "7         1.0              0.0                           stepsizeProportional   \n",
       "12        1.0              0.0                                 stepsizeLinear   \n",
       "1         1.0              0.0    max  twoSupportRanges                         \n",
       "35        2.0              0.0    avg  twoSupportRanges                         \n",
       "\n",
       "    Parallel Execution                                  Description         f1  \n",
       "2                    2         Split Property (everySecondItem avg)  80.678406  \n",
       "64                   2         Split Property (everySecondItem max)  80.672158  \n",
       "7                    2  Delete Low Frequency (stepsizeProportional)  80.631517  \n",
       "12                   5        Delete Low Frequency (stepsizeLinear)  80.541176  \n",
       "1                    2        Split Property (twoSupportRanges max)  80.647379  \n",
       "35                   2        Split Property (twoSupportRanges avg)  80.646756  \n",
       "\n",
       "[6 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['duration']<26].sort_values('precisionAtL', ascending=False).drop_duplicates(['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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