{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-31T20:17:44.768535Z",
     "start_time": "2024-05-31T20:17:41.704428Z"
    }
   },
   "source": [
    "from pprint import pprint\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import hoho\n",
    "from hoho import compute_WED\n",
    "from hoho import vis\n",
    "from hoho.vis import line\n",
    "\n",
    "from scipy.spatial.distance import cdist\n",
    "import cv2\n",
    "hoho.LOCAL_DATADIR = hoho.setup(\"../data\")\n",
    "from handcrafted_solution import predict\n",
    "from tqdm import tqdm"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using ..\\data as the data directory (we are running locally)\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-31T20:17:44.774691Z",
     "start_time": "2024-05-31T20:17:44.769543Z"
    }
   },
   "cell_type": "code",
   "source": [
    "split = \"val\"\n",
    "\n",
    "dataset = hoho.get_dataset(decode=None, split=split)\n",
    "import webdataset as wds\n",
    "\n",
    "targets = wds.WebDataset(\"../data/val/targets/hoho_v3_025-of-032.tar.gz\")"
   ],
   "id": "55c712587c694e96",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-31T20:17:44.779814Z",
     "start_time": "2024-05-31T20:17:44.775700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "\n",
    "# for i, sample in tqdm(enumerate(dataset)):\n",
    "#    # if i > 170:\n",
    "#    predict(sample, visualize=False, point_radius=40, max_angle=5, extend=30)"
   ],
   "id": "f36ee7b8f0427f72",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-31T20:30:50.830835Z",
     "start_time": "2024-05-31T20:29:08.806688Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pathlib import Path\n",
    "\n",
    "solution = []\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "with ProcessPoolExecutor(max_workers=11) as pool:\n",
    "    results = []\n",
    "    for i, sample in enumerate(tqdm(dataset)):\n",
    "        results.append(pool.submit(predict, sample,\n",
    "                                   point_radius=25, \n",
    "                                   max_angle=15, \n",
    "                                   extend=30, \n",
    "                                   merge_th=3.0, \n",
    "                                   min_missing_distance=300.0, \n",
    "                                   scale_estimation_coefficient=4.5,\n",
    "                                   ))\n",
    "\n",
    "    for i, result in enumerate(tqdm(results)):\n",
    "        key, pred_vertices, pred_edges = result.result()\n",
    "        solution.append({\n",
    "            '__key__': key,\n",
    "            'wf_vertices': pred_vertices.tolist(),\n",
    "            'wf_edges': pred_edges\n",
    "        })\n",
    "        # if i % 100 == 0:\n",
    "            # incrementally save the results in case we run out of time\n",
    "            # print(f\"Processed {i} samples\")\n",
    "            # save_submission(solution, Path(params['output_path']) / \"submission.parquet\")    "
   ],
   "id": "c4ea239a996b87e6",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:12, 27.31it/s] \n",
      "100%|██████████| 346/346 [01:28<00:00,  3.93it/s]\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-31T20:46:13.489460Z",
     "start_time": "2024-05-31T20:46:13.290404Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import io\n",
    "solution_dict = {}\n",
    "for d in solution[:173]:\n",
    "    solution_dict[d[\"__key__\"]] = d\n",
    "# for d in solution[173:]:\n",
    "#     solution_dict[d[\"__key__\"]] = {\"wf_vertices\":[[0,100,0], [0,-100,0], [100,0,0], [-100,0,0]], \"wf_edges\": [(0, 2), (2, 1), (1, 3), (3, 0)]}\n",
    "WEDs = []\n",
    "\n",
    "for i in targets:\n",
    "    try:\n",
    "        j = solution_dict[i[\"__key__\"]]\n",
    "        i = dict(np.load(io.BytesIO(i[\"wireframe.npz\"])))\n",
    "        WEDs.append(hoho.compute_WED(j[\"wf_vertices\"], j[\"wf_edges\"], i[\"wf_vertices\"], i[\"wf_edges\"]))\n",
    "    except KeyError:\n",
    "        pass\n",
    "\n",
    "import scipy.stats as ss\n",
    "\n",
    "ss.describe(WEDs)"
   ],
   "id": "825b2b42001620ed",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=173, minmax=(1.1048503424519986, 3.2776734068655204), mean=2.123907112995204, variance=0.1767523302203136, skewness=0.3492868616641026, kurtosis=-0.24007805364057333)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "best mean=2.123433669870156",
   "id": "1d3cde94dcfc4c56"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T16:39:13.457163Z",
     "start_time": "2024-05-30T16:39:13.444922Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "9fa1f52b8c1b08fe",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=173, minmax=(1.2170699811410024, 3.615513996539459), mean=2.2450021630442674, variance=0.22820626491502016, skewness=0.3915201081482274, kurtosis=-0.39830836419419846)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T12:17:05.230962Z",
     "start_time": "2024-05-30T12:17:05.227210Z"
    }
   },
   "cell_type": "code",
   "source": "from  handcrafted_solution import convert_entry_to_human_readable",
   "id": "657b56ff33bad8e7",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T13:39:37.452606Z",
     "start_time": "2024-05-30T13:39:37.449975Z"
    }
   },
   "cell_type": "code",
   "source": "from  hoho import color_mappings",
   "id": "79f75593d6753024",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T14:17:38.957436Z",
     "start_time": "2024-05-30T14:17:20.322850Z"
    }
   },
   "cell_type": "code",
   "source": [
    "color_range = 4.\n",
    "indices = set(np.where(np.array(WEDs) > 3)[0][:5])\n",
    "for i, sample in tqdm(enumerate(dataset)):\n",
    "    if i in (indices):\n",
    "        sample = convert_entry_to_human_readable(sample)\n",
    "        for gestalt, ade20k in zip(sample[\"gestalt\"], sample[\"ade20k\"]):\n",
    "            gestalt = np.array(gestalt)\n",
    "            ridge_mask = cv2.inRange(gestalt,\n",
    "                               np.array(color_mappings.gestalt_color_mapping[\"ridge\"])-color_range,\n",
    "                               np.array(color_mappings.gestalt_color_mapping[\"ridge\"])+color_range)\n",
    "            ridge_mask = cv2.morphologyEx(ridge_mask,\n",
    "                                    cv2.MORPH_DILATE, np.ones((3, 3)), iterations=4)\n",
    "            rake_mask = cv2.inRange(gestalt,\n",
    "                                    np.array(color_mappings.gestalt_color_mapping[\"rake\"])-color_range,\n",
    "                                    np.array(color_mappings.gestalt_color_mapping[\"rake\"])+color_range)\n",
    "            rake_mask = cv2.morphologyEx(rake_mask,\n",
    "                                      cv2.MORPH_DILATE, np.ones((3, 3)), iterations=4)\n",
    "            \n",
    "            intersection_mask = cv2.bitwise_and(ridge_mask, rake_mask)\n",
    "            intersection_mask = cv2.morphologyEx(intersection_mask, cv2.MORPH_DILATE, np.ones((11, 11)), iterations=3)\n",
    "\n",
    "            plt.imshow(gestalt)\n",
    "            plt.imshow(intersection_mask, alpha=0.5)\n",
    "            plt.show()\n",
    "# if i > 170:\n",
    "# predict(sample, visualize=True, point_radius=40, max_angle=5)"
   ],
   "id": "6d69e7c3fd4b6ce7",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3it [00:00, 16.18it/s]"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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7do2PxvNuSQDHu44TLg7Hu4xIhl++gjEDPIoJKjcp2WARAB0a6wfvP0TauN4SU161+GeWQF2Q5MOOonRNyhvhFq+FG/QkHYlXQfGCkInFp5g+cRr8CKMZ5fQstfQ1AtuEnYTm2JCtK8kVIN8bASDXu3R2tExfiN+E0ikhqsQcm7nMZmuMNCvtHgtoDlfOdTj2vipTR59m7dwLZKq8Ea4zqSVOJmOgLkna8e7hhIvD8a6imGATYwbsuIhyhDeCFtt4zOQNFoIG0rdMfGSDnx4/d50XaStP+Mql72Pqdwdot4em6fAbteNhQYGByXg1XOf1oEnXi4euw+GbLYaw2mDu1IeoTR7HIAT5JtX0FcrZFYxmhZUlF5INSC8p9M3w+B035PBEeywo1/WjD4PXlegxmBzf4vH5i7xx9SSZ9XaPEOh3M5bP9Tj8+JNk/S7bS2foG8v/iK4QqsfRpH7vBsvhuAYnXByOdxGRDM9v7TwDhFcl4+smJ/FWidji6bmTHFn1WPQ81INR9ulwLpowIb9w7AXO/1XD+WSGly4foXZWKK31seubTsQ84FhRLgYdXolWWDG9wkCihe1ERTEmYPLwk0wdfwY/KCGihPkSE/1vYugNNYhBB5b8CiQrIFb2xE3tsFPbX/c8vx4dQHLRUKkqx+cusr49yWp7en/klQrtrZQkrjP7+AfoNpdJei0GJuOlcIWJvEQ9D27YvsNx0Djh4nC8i6gGaNYAr1e4g0Q4gyFBQSwJCd9ZfY1X1HC0McOzhwcsUKPRNQSph+QW+gmi8FhgeSxY5YeeXCU7ZbmSG/7thQ8z+a0cVjbQLLvfl+vYgwq0TMKrwRpnwi0GJh++IoBBxFKqzzBz4jS1mSOIeHgaU4+/Rzk9j6eFaBFrsG0YnFVsV5C9xpXb7lTxy3aVdAOCOeHY/FU2++NkWbBvnzwXrp5rc+TUGAvv+z4uv/jfsOmAZa/LS9Eq39dfJFC5s344HLeBEy4Ox7vNsF6GAudEWREhDEokWZ+dEMrMKEu6zR9vX8DzPGYaU3z0ieeYrU1BK4b1DvbKJrRjJLH4eBzz4f918gUuHbN8u3OMy2cOM/5CE+310Tx/2y457i0W5arf4Y+iq2x78W6K845VxDNMzJ1i9rEP4JXrgBLYJmODF4jsCqL5MJrFYpvQfytD4r2VVG4WYXIrkSeKWIgvWryqx8zYKgvjk1zZOISq2bsbna2M869s8dizc8wcez+rbz2PFcuZYJPH0nHms+rdCSmH4xZwwsXheFcRUg1pGjiH8nIYMnPqOSrjc3Q2l+k1l+ltLpOlydB1AHmes9Rc4T99+2uMV8c4NH+Io0eOMfbkKcKBRTe66OUtZKuDpsqhQcrh8QvYj5znpWdrXBhMc35pnrHXFe/CCmROxLybDEzOG+EGL4QrJKbw++1kDBnjURqfZfLI+6hNLiLGA3Iq2UXq8asEukFhZhEYCNmSkK6A2kJQjBZ6vksUIDUMLuSUTglPHz2DRVhaX0SvKffV71rWryTMHX6C1uZl+pvLpKL8YekKn+wvcCiru0Bdxz3FCReH413EivKGZ/kWQiww+/gHGZt/DKOG8NAYU4tPkaZd2utXSSdXgUsoOaAkeczq9iorrVVeeutl6pU6h6YXOTRzmMXvP4anPtJPodVHltvIpU2e7fT5QOkipnGR7ScT/n9b72PrrQnq5xPkyhpYl5l0r8iMZdnr8kK0zKrfw+5EjezE3/oBi099jNrMCcQziEKgTerxK5SyKxiyYn8VbBvicxbbLmJZZKhYrl0k8c7YrZibtyBbh2jR8syRt+gPSmx1pvftrQprV/pUxxoceuZPcfm7v8dge4Mtf8C3oiUmbImKDXE2F8e9wgkXh+NdQ2l5Ka8EawwEqhMLzIwvEL76Al6ngzbGSCemkKkFJg6dYuPEx5irKu0rZ4k7W+RJH6RIlk5txlZ7i832Bt+7+BpzY7McmT/CwvQ8jdkxSofGMadmoRtjl7bIljvUOgl/xbyFTlq2nsv4v5c/hF4o03hhDTsY3O/Bec+gonS8jJfDNc74WwzMbqxRoTcEP6wydewpanOPIWLw6FNJz1HOzhPmzdGiiGKFdFOIzynEe0TKPdEEgqiStRR/LicylsMzy7T6dfI82ncNNoeVyz1OPDPG1LHTLL36x+TpgHW/z+tRkw8MZjBOtzjuEU64OBzvEirwZtBky4vx/IC5x58hvPgWpctvYVRha4XgskFrY2RjMwTZM/zfn/wMlVNNTgXnONF7lXTjKp3NFfJ8MKz3YsjznKsbSyxtLhGYkHqlzsLMPIdmFzg8ewRvvoHJLBLnsNYhv7rJxEqLv+G9TLqY8/85/GHCN6Ypv76BDvr3e5geaqwob4VNvhus0jQxudhhAX3BihKUGkwsnGRs8TH8UgMjQmDXGYu/Q5StoNiRFcXkkF5Q4lVFst2g11uzslybSXRtttHbXMMmJFeVaM5waHKZLAt5/fJj5OqzG50ldJsZl9/Y5uiTx0kOb7N6/rsoysvBCpNZyLF07NYGzeG4TZxwcTjeJXKUZdMuCv37PmUNCVavYKyOvmGbPIftTbztTfz1K3hnj5IeeZJzkyd47dCHaD0u/KnTv8/U1vdI166Q9luI3XE/KKmN2ejEbHQ2ePXCa8yNz/LYoeMcXzhOuVbF1Cbxjk+irQFcbeJv9fh/jr+BfSLmSzxH+cXL3OjrvPvy/PYIRSzL2WCbP4qukJq8UKoiw4RkISw1OPT0J6hOLhQxLppTS89QTc7gawuwGEyxf08YXIFsTSlWWLyTPglGPQyCYrFDZ5W+U3MK6SXBqBIcsRyfuUwcR5xfP4y1ZhRAjkJrPaU9kzJ+9BTd5ir9rWUGJufFaJWpvEzNhnfUd4fj7XDCxeF4l1j1e6x7PUAZmz5CtHQOE+/U5bg+jdTvdTHnXiG4eoF8ZhFZeZzgzDivj32IZOrPkH64yWP6ApXN7zC+fRVrizRpQVAdBvVuXGV54yrffeslDs3McWT2KLOTc1QbVbQxB4AkGTJIof84dlBkLEknhtTu1pBx0uUmFNVTNv0B/710iVVvQC45IEUcCuCHFaYfO01j9hhBWEUFfNummp6llr6KGcYwIQIZ0DHEZy3a2w2Kvd3RN+oxkY4xkY3jW59McmJJSL2MZtAkMfGOjGG/BWbozFJIli1exeBNZzx55A2CKObM5RNY3Z02rIVLb3Y48f4Gc6c+ytL3/pBBu8my3+MbpSv84OAooVvPyHHAOOHicLwLKHDJ7xJ7OYEXMUtAdOlM4SLixhPTTjExf9DFv/QmwcolkqNPkE3OEjSnCC5ELPnvR2ofZ3Mx52r+Ch8ce4kPxm+QZQlCoToswna3Q6vb5vULbzLdaPD0kWMszB6lVq4jnmBqERyZQD5+ApIcM0ixb62SX2lCJ0GS3K1JcwNik/NW0OSlcIVtLx6Wvi9EqPghjclFZk58gLA2AUaAnMiuU4+/Q5RtFPaVYQVc2zHEl3Nsy6K5ImJvwTxyPQpUbJmZZAZRQcQQWp+QELVKI6vR9rfZDLZJTHKDe294zhTiq0qpBl7JcmT6KsubU2x3J3fljkCWKCtne5w4Pc3C05/gwgtfI08TLvttvhOt89xgDs/dO44DxAkXh+NdYNtLuORvIwiRQm31crE4Hjui5cYTlOhujIJJBpTefJnc97Fj0+STc2TTs+S9BhObARMcIVn4EN/4xBWeSr/ObOsSaWsLmw+KlFYVVJX17TZ/2HqJ8ltnOD47zVOHj9Ko1rHpEpquIqaMrUfIBw/hP7MIrQTd6pEvNZEkRzc7kFkkyzGPaMGxxFiu+F1eD9a47LdH3hwZBk9Xp48w+9j7KdWLRREVCG2XSvYGlfQCnu0NWxK04xGfV7K2RXKzI3vuuKKcQTHWFLE1srcdHWYu+UwkU1TzOl2vzVqwRWZuVKxwmM10Rik9bSj5KacOX+Y7b5VJs/KeUwrtrYyVi11mDs8wtfgkqxdfJjeWM/4GR/w6C2nlTi7G4bghTrg4HPcc4bLfpun1EWA8y/DT7h22pXhZir+xhG4uYc/7ZOUaybGTZHMn4OoG/r8pc7b2k3xvscba5AYf9b7MeOcipbzoy07MRW8Q8+rFy7y1tMLRmRk2W0+wmLYQ6aC5h2IQqcFUDZmawH98skjB7aXQTYsieJc2kfUumlt2bA3vTSGzYxkTtr2YF8MVzgXNYV2W4qp3lhkcWzjJ3FPfh/ilYViu4tl1xgffJrTrhTtPi/L+eQsGZzKITWEd4SBSnIdrE+neNYp235WdR4ENmcgnMeqxHK2Sy7Wp8cWeWdswWMopHVZm6+s8sVjm1YuP7+mnohaWz7fxI2Hi+JP0Opt0Ny/T81JeDFaYzI4SqXMZOQ4GJ1wcjntMLDlvBpvYobVlTgNEd7NN4M7LtZs0I0qbBK98h+zSReKjJ8mmF9A0JWy1OCSGpfLP8eY4bJol0rEz2N4KNkvA5sNarIouC+aP/oSF9Q7z42McmZqmUanieSlimyAeKiUQD8oRVCJ0rox5Yhq2+tCO0Sub6EYX+hmSvbfqwygQm4zvRpu8GQxXcZYiq0uHdVW8oMz04aeYOPIkxi+jVkkHXWrZGSa9N/E0Hr3RYiFdVdKLIJlBRUCGou8ulZ8CiYnJJMPH42bWGxkuwNjIGlhgpbSMyvVLXYkq2WVDWoFgKufI1BWanRpXNxfZtxaSwuqFLpXaGItPfYKLr/wBg+YKV4IW/0Ov8InBIpG6Kcdx97i7yOG4pwhb3oBNLy6sLX5EOR7lZdxx5VO95rHYjKC5it/awEYVtFwhnZgnm13E5mNU+oaqjqGXPkKW9OhurbFx6Xsk22vFZAVYPc9FhUtr67x47hylMGR+fIK5yXEem50j8rOhPUVBfFQ8rKkg0xEyM455bAodpGgvgZVWIWi2+uStfpGOfddjeb9QLoRtvhUu0fQSVIp3TtBhdpBQGZtn4emPEVbHQQSbWfKNM0x7b1CPWoha2KlAOxB6Zyy2C+TDUTnghTEzk2ElB+vt8ULe+G4TNUQ2gOvsZbsWFbFCetHi1zyCUszjixfYaE0TZ8GedoWkb1k+2+XI0+PMnfgQV1/5b2RJjzfDLebzKk8mUw/xfeB4UDDvvMvt8Su/8iuIyL6f+fn50euqyq/8yq+wuLhIuVzmB3/wB3nllVf2tRHHMb/4i7/I9PQ01WqVv/gX/yKXL18+6K46HPccRTkbNsnFYryQqfHZfR/cBzddFdYbsTlev024sUb5rZeofuu/UnnpjyhdPoffbSNY/FKZ8bnjHHv/DzJ78jnCcgPZU4VVgTjL2e71ef3qVf7wlVf5r995iW+fu8B6u0VqFdUcsQnkTTRdRZNlNGtCKYapCJ6ZR/7UScynT+N9/+PIs4voXA0NDZjCqSQCe50ZDyKZUS5Gbf44usqWH6NSCLCdjCExPhNHnmbxmU8S1iYLy0nSpTZ4mUPRi9Sj5nBeL9Sh7UD/rGK3GYqWgyrav59AA4wWaxntD6q+8WinXvo2rRV91D5kmwpqqJZaHJu7hMgwI2pPu62tmKW3tqlNLDJx5OmhtU54I9ykZ5K36YXDcWvcE4vLM888w9e+9rXRc8/b9W3+g3/wD/hH/+gf8eUvf5lTp07xq7/6q/zoj/4or7/+OvV6HYDPfvaz/Pt//+/57d/+baampvj85z/Pj//4j/P888/va8vheNDpeDGXvRYKNManqQ8SRHVU3+PgKSYZK4VlxGQpZuk8/vJFbFgmmZ0jO/wEtjZOEJaZOv4+xhYfo716me72Eu31y2iWDufSoZBRuLSxyeXNDb57TqhVKhyenuXozAwTlTKR7yOSonkTtYD4xWRlIsSU4WgNc7SB2kNIO8aubKObPbK1NqbdR6xwXXjFfcYKdLyUV4N1Xg82iKVY32n0rolHuTbB7OMfpDJ5CIyHpwlRvkIpfoUSWxgvHYoGhZ5Hsq6kyzmkMlQT10rYg5vOPWsw7HxWvlPbykB2LElvt5sQX8kJaiFePeP43CW2OxVWt2exmN1zKDTXY2pjA6YPPUVn/Sq97RVW/R7fiVb5ULJAJXef4447554IF9/391lZdlBV/vE//sf88i//Mj/1Uz8FwG/8xm8wNzfHv/gX/4K/8Tf+Btvb2/z6r/86v/Vbv8WP/MiPAPCVr3yFI0eO8LWvfY0f+7Efu+E54zgmjuPR81ardQ+uzOG4PS74bVpmgMGjbgxeZ+seipYd5JrfIGrx4g6lSx3ylctk9SnyY6dIJ2fxogrjR55g/PBjxN0W7dXLbF56FZvGWGsZfW0XSDJls9Vls3WOl8+fp1Gu8MTCIkdnJhir1QkTHy8eFjwzXWy5jYjBegHq12G8jIzPYqwQZBbTHJCvttDLm9DsQaZIfn/De1WUM2GTF8MVWl6CDv8VA2nwSzXmjj9LY/4xxA+G7qKUevIS1fQtDCmKHaVF202I38rQZK996d7aHGKTkEuGpzuunHfinYKqh2HDsaH3ZkrpfQa/FPO+Y2eJ34xo9ib2HC/kOVx6q4X4Yxx68jnOfuf3sUmP14MtyhrwoXx2FMzscNwuB+4qAjhz5gyLi4ucOHGCv/yX/zJnz54F4Ny5cywvL/OpT31qtG8URfzAD/wA3/jGNwB4/vnnSdN03z6Li4ucPn16tM+N+OIXv8jY2Njo58iRI/fi0hyOWyY2ygWvhRqoIcxtbGHy/E5KcxwYAvhJTGljicqLX6f2za9Rfutl/M42YoWoNsHsiWd57Pv+PEee/TNUJxYwYriR1Mqtst3p8idvvsG/+9afcOmFJrXXxqm8MU71jXHqb0xSf3WS8ls1wqYig3UkXkLTdazdBj8hn43g9Dzyqacx/4/TeD/0JHJ6EZ2qYkv+u+5T6HkZL5RW+OPoCttePJrOBUGMz/Tx9/PYR36MxuHHwQ+L9PZ8g8nB/6CWvoHRbLQ/uSG/ZOi/rtjE7LRyg7MepMNsaCWTax+//TGiXpHV9LbqZbh0Qc8Qn1MkFyphlw+f/B7jtSaCjnLWUMVmhpULXSSaZvb4sxjPwxrL68EGS8GdZtU5HPfA4vKxj32M3/zN3+TUqVOsrKzwq7/6q3zyk5/klVdeYXl5GYC5ubl9x8zNzXHhwgUAlpeXCcOQiYmJ6/bZOf5G/NIv/RKf+9znRs9brZYTL477yorfZS3oIhjmxucINzZ2X7w3oQ3vwP4ZTPIcv7OF9+YW4flXySfmSR8/TVar45eLn8r0IXqbS6xdeIVBZx3NLAzrz+yGbghxZml2B5iSt3suFbyBwQx8gnZEUI+Jj7bJovawPo2ixkeJMF4JaiW0XkUXG2APYToxrLTJ31iB7R5ii/V77gVWYNXv8s1oiY3RSs67Y+aHJcYPPcHMifcjxkdRDAmV9BKN5Hl8m+5/O2PD4GxG1hw2vs9dc6/V2O22r1RtxCYUMTrvECgsgN0S0gs54TGhFPU4PnuJF7t1VL19t/agY1lf6rNw7H2kSYeN8y/T9lNeKK0ylZeIrMsPcdw+B37XfPrTnx49fvbZZ/nEJz7ByZMn+Y3f+A0+/vGPAyCy/w9LVa/bdi3vtE8URURRdNPXHY53EyvKFa9FKpbAC6j2++xU6didGB4MU7mo4qUpZvUSZnuNYLKIg0nHxsn9gOrMMWqTh0j6TTpba3TWLjHobpPG7eJ4irok/TzBCpi9FgQdPsqFcLuELiv2cAtrdJQNJWRge1jxivgYU0K8ElKPkMYM3rEptBNDkmOXtyHOsStNNM4wgwxj71wDWlHWvQFvBU3OB5t0JN1nF1EMlalF5k6epjw2S2E1ySnlq1TStyhlVxHNhiFBxfo92aohvazkgz3F5EbtvVvsTVN+pxgXYcf4fqv9K9K5BX/MIJPK7NgGTyyc58LaInFa3jOCwsbVHmMTJSYOPUl77TJpt8mK6fJ62OT0YBJzbwz/jvcw91zuVqtVnn32Wc6cOcNP/MRPAIVVZWFhYbTP6urqyAozPz9PkiRsbW3ts7qsrq7yyU9+8l531+E4EFom47zfLNalyXNKaZcHRahcj4z+95MBLF/AW7uCVx9Hpw+RTc2TjU3j1SeZrE4xsfgEeTJge/ktNpfeJO22GTcVnquc2i9a9rVezJ/heonMJMSH+ogZzvUCxZSZg03B9iE3KCHWBBgToWMhYqowX0Ws4OdHoJdCJ8ZebWI3u8ha+5bjYxTomYzXShu8ZtbpeelerVVM5X7E7JMfpT59BD8sD68lo5a+QjV5ncDGw0ysoUsmMSQXLfm6otneUb1f3HrAr299PGuwt2jSUoDc0D+rlD1DMJ7yxOI51ChvXnkMVb/YS5Q0gbPf2+TkB4pg5isv/SG5tbwQLhNajyeTcRfv4rgt7rnUjeOYV199lYWFBU6cOMH8/Dxf/epXR68nScIf/MEfjETJc889RxAE+/ZZWlri5ZdfdsLF8VCgwJvhJj2T4SlMWoP/gGXN3JThF3Uvy4i21onefIHyC79P9MYLhK0mkmeI8fHLNSaPv5+jH/xBJo4+zanKE8x5EwhmZLG4XsIoa7V1Xqif4eveK7xqLvGWWaYlPboMUIpaJ7LzsaQJknex6QaarmLjq9hkBWubqJdgGx56qIF89CjeDz6O96NPwekFdLKEVvz9hWP3YEVZD3p8rXKWbwfL9L1sTw8L15dfqjH9xIcZW3wcE5YA8LTL+OAb1OOX8TQZFqAbuk66yuAtS7qqaLab5y37RuPd5tbP6auPf7vF4RSIhcFZiwyKTKnj00vUy61h1lRhXRSELDY0V2LqE4cZW3gMxJCYnO9Fa0Wa+e2d2fGIc+AWly984Qv8hb/wFzh69Cirq6v86q/+Kq1Wi7/21/4aIsJnP/tZ/v7f//s88cQTPPHEE/z9v//3qVQq/E//0/8EwNjYGD/3cz/H5z//eaamppicnOQLX/gCzz777CjLyOF4kBmYjHPBFtZAudRgLs4Qmz2EH86KqODFA8rnvkd08Q3y+hTZ4ceI5+bRoExYmWD+1EeZWlO4fH1Nj730wphvnPgOG5VtBGWDLQTBUw/B0NAKgfrM6ziRlJimSqB+0ZrmqOSYHNT2ipwd9VETgBcgQRk7V0Zm6/CBw+ggRVoDJFfslSamuYW3uUoqwgtelzPBNm2zZ8IcRrCaIKI+c5S5Ex/EL9copIelnF2mmnyPKF8rxmVoJdLckF5R0jXBDnbK/usonfz+vee3J5bu3DqkaE8YXLSUHhcif8CpI+d58c0KqR267oeu0fUrA4KSYe6Jj5H0u/Q2r7Lp9fnD0mV+pH+Cau7tcdE5HDfnwIXL5cuX+St/5a+wvr7OzMwMH//4x/nmN7/JsWPHAPhf/pf/hX6/z8///M+ztbXFxz72Mf7Lf/kvoxouAL/2a7+G7/t85jOfod/v88M//MN8+ctfdjVcHA8FW96AtklQhNmxaUrrG8CNFrF70Nk7jSgmT/G2V/BbawRXpogfP006MQviMda3ezJSbjztRFlIlJWA5mibhaLCKzmbso2iLLOBp4Z5HWdOp5miRolwWFBNMDqMd5UMNIO0j9JGxQNTAq+MqdaQWogC3qEG4bfewvYv8rKBF1R3F0XcuUYRgtoEh9/3caLGLB7FWkJGB5Tzt6j3v4dhwMiGYg3ah8F5yLctqnqfAq5vhu75gXcSJXoL+7wddgPycTDTwmxtnSOTS5xdP1a0LGaYZQSr57vUG5PMnXw/lzrbZEmXDa/P98INPjSYwVfnNHK8M6J6wLWmHxBarRZjY2P8vSN/k5JxQbuOdwcF/rh8hRdLG3gqPB3WGO/H7LdEPFjBubfGsP97upz7Een8UfLHTnN6OeTQVr5n2Zrd7/AKZB7EvvL1ky+z1LgEJCh5kcUCyE7dkx0dsVM2RQ0lDWhQoaE1GlSZpIyHj2AIhmm8qI5cNwgYKkhSJWhFmO0MVt7iG61v8wIDUoR9cf7iMTZ/grlTH8IP6yiCISbM16jFr1LKVzGaY4euH1IhXYHBUoZJPfYtLbmncN/9xFeP4/0jhPnOZ9/b9ymXnIvly/S9/h2eUVE/p3zSx8xY4rjG8+eeZqs9CddkKlXHPU6cnqS98jrLZ/4Ym+WUrfDJ+DAn4vFCNN5hLxwPLwMb88uX/k+2t7dpNBpvu6/LRXM4DpBEci75bUSVqgqVwY5oedjZE7k6xEtjvMtvkW2vYxsfQ6mN9hwJFgNL44ark4ZOCbLwNOPZKXLZxtIn9q6SSxtLDxU7OsGOgLFYuhLTI2aZJgKUrIeIoaxlalJmghoztkFEgIfBywzBmlBaFUxaZAkNgiO86b1MZlOMDgvKiWC8kLnHP0jj0CmM54MqnsbUk5coZ2fxbDJM+xZQyFuQXlayFhj1dhKJHjhysbS9PpO29A61WQp2agzeDZJ5xJdyylWfqNzjiYULvNCvkeYRO3eEInRbOasXOywee4LO5lWaqxeIjeUb4RKVPGQxq95dRxzveZxwcTgOkGW/y7aJEYQJzPAPbG96bfH8vYNFjeGtEzVKV4T6QAlyoSuwXhOuTBi2agYNCo8BGuHbEE+qCEJkj6GSk0mLTFZIzTqZNFFJhqnUe2bT4erJfVOk7PRI2GSby2rwjUdDyyy25jl96RRRN9xXeS2UgAVvhi3bKWZoz6Mxc5KJQyepjs+iRhBVgrzFePI8Yb4CFFWDRcH2hWTdkl4VJB1abGR4igdQuSiWgRkUy0tguKVO3tVtOcys6nmkVy3BSZhurPPM0TO8dO4pcvyRFU5VWLsSU66FzD/+HFahs3aevpfyvXCNmbyEry4swHFznHBxOA6InpfzYrRKLhCpMKke5gGc1A4UBTC0S4Y/eczHbuek53tcGqT0uhZdKywVlUZAWDaMTUYEJUNY8lABIUDUJ9RZImaweUpuBmSskJiLJLIJYoeumGER/dEEW7hociy5KKvSpl+yLLLIgp3eZ0Iw6vF0+QRbtkUgBnv0CPUTzyDGK7KoNKGSnqWWvI5HuxAs1qCi2J6h94ZC14zO/4DqlX0MvB65WLxbKNUsaijbCn1v8I7F/29OcVy6LpgGeDPK7Pga4/VZNlozu/JdwVrl6rkOpz44xuFTz/FmZ52s1+aCv82rUZXTg5n3lLx3HCxOuDgcB8SmH7PpDTBAGaGy5+v4gz7J3Q2i4Pfh6mqX9bU+SZ/hBe8GhzbXYgRYu9QnqvhUasLYVJmg5FOqehhPCtcNEWIjfOqEeohU1slkm1y2h5aYFEj2GFNklHorCJ1Sl9cWzjLbHcezu1V8BTjmzzE79v2Upcyr9YDz4mFV8BlQj/+Eanp+FCMjCpIZ8i6kVzJsb2+ZtMLl8WC/qUJqctpBi0bawLyDBUNgKLIP4KIyJTkHpVAIxhKeOHSBfhLRixu7VjBV0r5l6a0ui4/XmTv5HEvf+wa5ZrwWbDKf15hJy3ffF8d7EidcHI4DYknapFKkyk7anepq97tX95LC+lDqK1NnLHkftpLC5bL7rf2aRFuFuJsTd2FrpY3xISgp9UaJylhIpRESVQxg8LSKsVUiyUEUywAlJ5M1rMQkskEu61gz2HsGumGPTCzenhWLVZRADQFVFDi+rlTjHMVgJAXf0i755F6ODIRk1ZJtWmzfFqtX3+ja92190OwDisWyEq6y7W9TS+uUtURoIwLrX3NbCplk9Ex3ZNm6M/ZUCE4hvqKUSsJMZZuPPP4G3zl/ilZnbN++W2sxaZ5z4uljxIc32Lz4Kk2J+UZ4mR+xx6nmwR32xfFexgkXh+MASEVZNV1ULFEQMWl8JHkYU6BvD0EomxlqScAzfsCUCflO3Gbd5txKCq7NlLgDg04fWSrqfNTHQ8anSlTHAkw4LEevgqGoq2K0ASglyUllg1jPk8o6KkWA72RvjMB6jCJkBMCS+hkq4FmPRh8afYuVlNSDzDzLhU5Ap50SaIeK2ULs7hWYkQaVveV+H2CK/uWidL0+PTNAMEQaUcojqnkFo2Z0TR2/Q8/fLwDvDiVrWnrnhNrjMFba4sTMZb7bbaB7Up5VhW4zY3szY+axDzBob9HdvMKGH/NasMX77TSBuiUBHPtxwsXhOAC2vJhNrw+iTE0cIlpefijiIO4OAXxKwWxR6l+VBeNRKjf4b/0WHZtet7ti0NGkv9eNJqhC0rds9AdsLg8oVQ218YhKPaBc9QjLHsYziOxU1hUCnSWwM1hicmmSyzZGxkl8QzSs4p+J5dz0Bc7Mnif2E2pxnfqgCA4e+DEbtU2sQNfG2KR418qJR9TKi3TsDcVPACtEHRDLNZntd2OleDeQ4WKYOX3p0zd9toLmvh7vd2gezLUYNbAJ6arFX4D5sTWuNtZYa02B7lrDrIWVCx3KlXFmn/gQV17pk3Q2+U60TKQez8RTD/ToOt59nHBxOO4SK7ASdIglx8djOs0RtSA7bpP3LoKHZ6pY0VEG0Dgen/RS1uPnd11GAv4iLKcztDfr9KmQSEBOiFGLHdVz2ck8gX7H0u/0EekjokQVj1I5ZGIxolIN8HzBmELEiJTxtAS6wPqY8MePK48v58y0FT8XDjUXafTqXJq6yma5Q7PcZqO2RewnsNNLASqFCOmq0p0Zxt2clCLmxUJtGWqXLEErJ0iHFUeus748qHV69vfn+vvyIPu7G8sSXxTUMwSzMR987Hu8ePYpVrdn95xPGPSUc6+1eOID08yd/CCXX/p9rLW8GC5zOK0ybiMevPF03C+ccHE47pKOSXjD28AaaGRQXVu+rtTce5lrr9EAY2JJdHsUM6EC5SllKuwQC7RXfTLKtBin65XpSJU+VSwGu1Pmf9i2auFS6Hdy+p2Y5vqAIDL4AdTHI0rVgHLNIyz7GE+wAtsV4TvHAya6lkbPAmVq/RIn1yY5nViMwtWxFd6Yu8Dl8SVkFI+0W0l3+ABrhlsNbB9W2guGqGsob0C4kVLaErwERAVjh8c+Cm/8LSGQK/G5HBEhmh3wzPE3yc4JW61pFG80VnE3Z/VCl7ljC1Smj9BZu0jPy/hWeYnvGxxiLA/v76U4HhiccHE47pIlv03TH+CJMGcCJL83pvcHFYF9E/Wo2PweN0rQAIkAP6N8HIJGjm7HzJS2MLWA3Bi6pkyuPmvb4yR5wGpziiyLyHcyYnaytBSSgSUZGHrtwa5FpuxTqvpUxwLECBPTIWsVj7WaKVbpthBmPmM9ZaKbY3SRybjOuo1IzFXAouQ3rMS2d0vuKf0G9BrgHQsI++DFQtDOCTrgJUptuXCd7VhqdlK5dW+d3UdG3AiSQ3IFTAMqpQ4fOvE6f3KmzHZvT4VUhfWrA8r1kMNPf4LLWUx3c5mLfpt6uM5HBwu3lNrteO/jhIvDcccomcA5f5tcLBUvYtyGSF64H97rgmUXRdgJ/LhBZI8oeRv0TTCVogxZHitkYHygn+NNZIyHMSLCVG0DMPTmS+S5x1p7ko3WJJutcTL1i2lfZXjO4nRWhUE3Z9DN2FobIAhrFw3iCfXxiKhiqI0H5CWPwRisjBkQD5iimo1Rlh5gyWWdXAbk0iWTbZQUK92h2LD7rkwA6ymDGlCzMAmCQSxsPVHs4ydQ2rCUNiBqK15cCJlHzygjaB+SK0p4zKPs9zkyfZnWpaewurOCdhHvcvVcm1pjnOmjp4k7bdKkwxv+JjNBjZPJ25eCdzwaOOHicNwxhjWvw5LfQ9SnmuX4ecq+tWve4+iwBNyIG0Uka7EWYr4BdlNQDGBBhXzTkImiYY4/YwgPW8QTwFINuyDQKLc5NnOZdq/OSmuS/qDMZmecNAvIch8weyrs7lSJFeK+omIZdIrCMsaHcsWjUg+pjQdUGgEmMBjj41EvCtExPlx6oAhmVUnIpUlilkhliVy66I5baafoy95LFUU9Ia4XLSSq9CYFToI/8ClvCuW1jOqa4g9Xg9g/ZO9hsauQrxgScqITsDC5wuWNeba6E/t2S2Pl4pkOx545wvxTlkuv/D4xOd8JlxmzAdOZq+/yqOOEi8Nxh6hYloI2mSiiyrh6w2nz0bG2qKbE6QZBNMnbX/PQeaaFKNndd7iycmJIrxTuleiIQbyheFAFUTyxjNc2Ga9uAoY0C2n2Gmx2xugOynT6FdIsIEkiFClWit4N9QUEm0G3Zem2B6xdHWA8oTHhU6r51MZD/NAQlnx2SuMKPqI+RisEdgFruiRygViuYk0f1WT36mSvi0lHv+xOHRsDaUVJK0r7kKG0BeVVJdjOKfUFv2sRax6BSsuK3VB02iNspDxz/E2+/eb76MU76xMV90B7O2F7LWZi+jCNiSNsr19gw+/xSrjKJ/MjLkX6EccJF4fjDtnwBrwWbAJQKpVpJIpm9j736t2nsEzsrgetO26QG+aD38AcM9QwBsiWQVs5Zhy8yMOrCIQ7De2W/Q9ImPE3mPTXycs+tuwzGAR0VkNyDWiaMfpSoatjxPhY2VNEbtgFmynNtQTWE0R6GA/KFR81ltp4iSA01OohYcnH+ILYGmXeR4lTWElQ6bATF5PIKpkMsLKFJUElHQpYvU7OqUB/EvoTYKyPyYWwpYRdIdpSoqbFSyh+7L4uvyfQ1NA/k1N+0jBW3+bU4kVeufgYWV4aXafmcOWNFsY0mH3iObIsptdc4mzQYiFr8UQ6VqwK7ngkccLF4bhD2iahL8WkvTA+T7S8dL+7dN+QPVYm4Z3sTde8OjxUVSEXbBuytoBYxCjc8Mt14a7RXBBSICWgz9RwMpu2K+TiMaDMtoyxZA7TMzXyoXun8PYMe6pFIHFuob1dFA3sbPUA8DyIyj5jMxETsyXCskEkxNcA1eqoLz6HKRacjMlpEZtzJLKMlRTIdy9yzyUjgvUg95V0GrrTYI4KXurhx0K0DZXljPIGmFQx9r0wUQ9FaOyRrCpRzbIwtcR6a4wrG4sjrSuAzWHpfJsnPjDJkfd9krPf/hpZ3OaPSksEKpxIGzwqlk3HfpxwcTjuCOWy18GilMp1GjkYtbvugkfo22CRVSTXFGXb8/ttXWeyZ1/dv7cK5MLeEJpr29v//85SA0X9HF8zarSoSYu5fJmONNjyJhhIRI8asZSxIqTslJWX6+KT8hx6nYx+N2PtcpdKI2BsqkxUUkr1CM83FKVkigq/omWMVvDsFKVhgG9iLpPJ+lDEZHuuYf+VKIoV0BCyUBnUobUIYQ+CbUN9yVDayjGp4l1XlHmveetBv/eGgbhrgp0UzGTKkdkl1lsTDNIKo+sQSHuwerHL/PEaU4efYPmtF+hLyuvBFofyGqHdLWTneHRwwsXhuAPaXsZVvw1Yyv0OpV7KwzNxHCxFTMleCbHDO9te9nOr+76DCLrBdp+Mcd1kPNtEgRwPi09qfDpSx+Kz5M3TlTo53jCAGPbGq+QptDcy2pvF6tGe36Fc9YjKHtOHqviBIQiLVa89DfB0HF8alOwhkJRMOuTaBWDgXcZKC0hBY6zYvdneI9QYBjUY1KCzCEFiCLrg95RoU4k2LX4ihPFQBN00z/oBvCetMDhvqfgeU7UNPvz4K3z7zdMM0tJI0CmwdnVAnucsPvYk/e0tWqtnueq3+V7U5P2DKTz7SH1PcOCEi8NxW+x8mF71u7RNggFmrMHTPQGn76WAhFtEVPcUbdvhQRiI6/OOBfDJgZzQxlSkiwBTdoWe1GlJgy0zScfUSAmKImnXNqKQJ9BOUjpbGVsrMcYXylWfct2jUo2oNAL8kiDiIdYnoIJPYZUL8gUgx5KQS5dU1kjMZaz0UCzXxsYooEZJI0gjQSfBLApe7uH3IegppfWc0oYS9AQvk+vq6zxoqCr0hcFbGeVTHpPVTd537E2+e+6pYbbYMFdMha3VlMZMhZmTH6DXWiYb9Pmuv8JUUOJoUn2HMzneazjh4nDcBgrExnLe38IK1NQwab0bx6E+Kuj9uvKDsHDpMMhTCUkJdZNxNlm0lxlIiZZpsMUkfanRMbXiiJ2v96LIcKFCmys2h3ac0dpKEQb4gVBp+IQln7HJCOMJlbGimJ7RogqsoYyv40QcQu0TpGaLlFVSWSUzzd0+sneYiwfWgDVKGsCgAe158JOQsAOVNYg2cspbtoiN2YmBlt079b69bXtRsF3D4IpSPgkLY8tszYxzfuXQsFZPYU2xKiydbXPy9AQLT32cSy/9NwaS8o3oEoEeZj6t3e8rcbyLOOHicNwmS36HZb/4ll4S8HYCSx9Ec/y7gbydq+jdmB3vZuzlmt8FvmbUtEPNdlmQZQaEtE2NhDLbZpKMgI6pkWlILnvOL7tJ2FkK2+spQsb6lT7Gg1ojwARCY6pMVDZE5YDA1yJESCtEeYWIRXJ6pGYLlT4Z66hAJj0sQ/fSqNbM3tAinzRUskmlP1HsUtrwiDqW0pYSdA1+32IAk+xc84Pg3hTyTSWbAm/ScmT2IqvbY/QG9eGrheUl7ipXzrY5emqBySNPs3HpFVoS80qwwXRWwXcp0o8MTrg4HLdBKpYXw1UScgSY1HD3m+yD8A32PnFvXUV7JlcDO6X0laLg271Niy1q9JQZUMoHCMqh/BIWj1gitmWSVW+WloxjxUNVdlPB2RUxINhcaW0WUbXN1QRjBD8QauMBtYmIqGwoVf1i8UgqRLY6bOgJQLFkqCRksomVNomskJlNIEPR/cMvhUWmNyf05or1gEwKXi6IFaJNS7lpibaKir6SDWvX3K97OBMGZyA8qTSmejw+f4nvXXycLI92yuqAQGszYWM5ZPbEB4k7m3Q2rnAuaFHWJT46WBzWd3mE/xAfEZxwcThug7ZJaXoxAjSsx6Sa0QJ9j+zH5bvkc/DGIDgmpKsWUsgThe61gvHeWb52bEoCeORUtEdVu8zZZbpSYclboCdluqaOVUMmwZ4j91h2hoYOmytJDhvLMZsrMUYgrHrMHKriBUK1FmA8MH6hKAw+aIBHDdQS6Sly0ybhPIlZLuJjTMYoNuSacbAB2KDIXoorhs5hg2RKuamMvaWETSVIZeQ6G607+W4VVMyU9Cz4VeXo5BXyXHj9yuNkdndxRc2FpXNdjF9j7vEP091ex6YxbwSbzNoKjyWT7/0ifg4nXByO2+FMuEUsFmN8FqM6XrfLI+si2uFdcBUJhXAxdaVUN4VWspBvCumaxbZyilzid/e9UMCQUdcW1awDDQ+d9UklYFsaiEKe+6y2J4fWGKU3qJJlIVZ3BYYqWIVB23LptRYI+IEgRqiPhxhfaEyGBJEhqgQY8fDEYOwEPmOU9WlyaZLbdpG9JM2iXTpYYizZTo75SNYoggZCd9rQn1RKHQi3lNIqlJqWICn6ZezOld7rsRVIIX4LSk/A8dkrdJMKF5aPsVveENTC6qUOjdNjzJ96jtU3nifNBnwnWGUyrzKRh5hH+YvEI4ATLg7HLTIwGZf9FoJSCSJq+U5Z+vvcsQeAe+sq2q3QstOuFGYP/BnwJwXte2StnGxVsN13M25D9jyy+DajPJ6gkTKl7WJdI5QTCxcRDIrQi0ust8ZpxVVWNqbJs4hczZ7xG8bIJMWxW8sxKrB+ZYDxlLDkUR8LaEyVqIyFeJ4HWsGTCrtrR9lhBeMeVvpkskkiS2SygUq25zRF1K71lN6YFiteHxGCvo+fGvxmhtcXqqsQ9MBL7+3NrkDeUuKrQnQ85+T8Jda2punEtZEMViDuK8sX+xw5dQo76LN67ttsm4SXwzX+1GAes7OiuOM9iRMuDsctci5o0jRFnMNkkhJYe/9iAh4oDmIQbtaG7Hsoo6lrZ4MFH6RmCGqCNyXYLUu+JWTbWiyL9K5YYQpLRt6F/puCGReCMZAK4BVWGRm6jOqlhEbYIsPwxOx5BoMKK60pNjpj9JMKWRZQrLdt9gxL4RKzGQw6OYNuzuZqjBcItXpIue5RHQsJyx7GM4iY4cg18LRBIPNEPEbKKolZJjMbWNrFGkv7xhNyD7KaImrRiaKYX/Ok4MdC/bJSWbdELUHye3Tzq5CtKH7DUJ7qc2TmCm9cPYm1u9OVqLC9njE+k9I4dIpue43u2lXOBk3q1ueD8Zz723wP44SLw3ELpEY5GzSxogQqjFkKm/Wj7iYCeNsCdLc6ewjkJfLuPKRlkByvvI56KRJ0QfLCqrMv6kJ3K/YO82ZNpJh5wZ81hH3I2ha7oUU8zMCg9mbWmINwhRTiIt8urAaJyfFKHsECBNMG3VPkVUXwsFSiHuWox3hjE1WPJAlI8pA4LbHZbmAxbLYbpFlEPy1j7agB8lTIU9joDZBVCwJh5OP5HtWxgKhsqE+EiDGEJYOREpEeJbKHsSYmo0km66Rmi1xaIIqV/qiOjA6zpVSELFJspGw8BRsKlQ2orAiVdSXoKia/0ZjeBTkMzkPZU47PXUSxvHXlcVIN2FleIs8sF15rceLZMQ49+XHODf4raXubV8J1FrMac5mr7/JexQkXh+MdEVb8LusmAYQKUNE9Pvf72bUHAr1LV5EieUR+5cPY7uFRcGgmGWpyvLnvYsbPDtu3KILvQSmw9GKzU+WkaGrYB/Us1BS/BjJnIBOSrRxJPSQ1JK0McrAxYA1GhbuToXuO3Lns3GA7RcxG3lRMWQnG/EIQiCKRgUARKVxfKjnlKKFEDxBmxlaKZqyPVcPy1iwbrXE2u2PEaYhqAKMUYAOqxP0cwdJvZ4BijMH4MD4bMT5Twg8NUcng5SU8mSeUOdTmhXtJUhJZIpeETNbIpYvSL1xLwxhoEbDG0JmF3ozgxZbyCkQdobSpeCl4/SIuxlhTWMTu9A9koAzOQuW0cnzuCv2kysXVQ+xdvCpPlZVzXY49Pcbik9/H5e/+NwZJn/9WusL3x0eYT0t7Mrsc7xWccHE43gErljP+JrHJ8FAOqUegioo40XIgCJrUsf0ZZJTOKmADRAPs2jN4YQsTbgKCGMtMTamHGUnZ0EmFTuxhraIqpPtSYgU1ICGEcwLkqCplK5AL2hey7RwdAG2BXNFkb6zJnVhidBg6MjzOCtla0U5s8pEA80rgjQtBXZCpYdyOmD2WpeJ43xQxKUemL3Noaol+ViJJSqy16vTjKs1Og15axlofkd1ic4pgrWITWL/SZ+PqAOMLYUmo1kPGpkLCikcQ+RhjUA2J7KkiS05yrPRJZYNYLpKZTawMhl63oVQUJYuE7aNgFHzr4WXg9ZVw2xI1IdyCYCD46Z4i/rf0RzMUoAMlXVX8QzmPL1ykMyix2Zre9560mykrl/osnJhj8vCTrJ9/mS2/z3/nEp+yJ2jkwU3O4XhYccLF4XgH+iZnw3QAJVShqsZ9i9vH3buKNKkU+bp7999pLK2Rbz6NV/5jwFL2oBZlGJRyYCkFwmQ5R7GkmUcn9UitkCSm2GZ3q93qTrESD/AUCSBsFK9LJkhiSJo59IWsm6OxQbO3u4wbuZ5udm/ITt04EIN2layrpL7gx0o4ZZBwfxO7+T/FWBrJqAZdqkGX8do6Fg+blVjdHqMfl9nqjhHHZRLrESdRUdWX4f1qFZso/VTotXtsLHcxnjA+XSaqGGrjJaIyRYwMPmidUGqEHMLaJgOzRMoa1vSA3rCOTtE6AplRMh8oQW/CQ1QxqVDeNkRbOeX1YsVrk9nhOOyM09uIQxUGVy2VklCZavPssbd44c2IVr9YGVqHh69d6dKYDJk+/gGsTdm48D2a3oArfod6PuH+Wt9jOOHicLwDF4MWLS8parfgE+AVpv6i7Nf97t4DwN26iigsBaNjrne72MEkmtYQEsqhxexGuVBM6ACCF1hKQZGBpMNKcEnOKBW5GxsGGSS5R2aHaxDJ0IIQKARKUC1cEb416AA0UbKmoplH3rJgQZNhv3fibG4wJjt77P+9+/Ko1koG2XlDflWREMQo3oSgvhA0QD0QzyDeMAd8pAe1WCrS63N4skjLt2oKq1Me0EvK9JOItdYkW+1x4qxEnvmFK04EzQWbC5tX42GcUI+oYghCn8ZkSFQxVBoRnh/hyzRVO4VisSbGSo+cDjmbqFgyWSWX3p5g36IacB4KnRmlM+1hTiphF7y+obSmVNcFLwE/UchvJl0ULxX656CCR32qxQdPvszLF55ms70jSAS1yoXXtlk8VmP66Pvpbq0Rb6/ynXAZH+FkMoZxKzG+Z3DCxeF4GyywZWJywA8j5v0G0m7tSc59l4pzPdDc3MJw1xFAOym7KiORGKcGUblBsWLZ90iGx5Z8Rv2oBDlWIdWcrW5Ac+DtOCV2j9554IGpglYhGhdUc8ilqB/TUeymkHaBRNF0p4LvHVZuVdBY0Li4n2xTsEZIvRyhiI/RSJBIKE15SMUino+VYtHGnSAUI4Ww8UxGFPSZrMLi+CqZ9egkFTZbE1zdmCW1PoNk6F5iKPJU6HeVQSehvZkgRghLHUqVgIm5kFI1ICp5eFTwqBIwhXAM8LAMyGSD2FwmNSvk0hv2a9dFZA0M6qB16MwaNjNFrKHSgtolJdxW/F4RHzO8jNHgSAr9tyCyhsZMhw889gp//PqzdAdjw7dMyBJl6WKbUnWMuRMf4sLLv0eHmG9EFynb4xxOG7f/vjgeSJxwcTjeho6XcM5rogLV6iSlwd70UccO+1xFoiTZJgc6TsOAVoAsL853pxFGRpRIYKKSEWeFBWZ4EsDiDUuvqjX7ziAio09Mf8LAuBLioTEQQ9q02Nhim4JmtyJmb75PsXaRQlYItrwNtAFROkspUhG8QPAmwCsLpgZ4jGJQ9p/G4nmW8fI2Y6U2R2aukuc+zd4Ya9tj9AZlOnGZPA9I86BwY1EUnxv0lUE/obUZIx5Uaj5jk2XCkqHS8DGBKar7EhHoPL6dxtoemdkkk1USWR/WluntWiilyFiyAYClM2PoTYLkUGpD0LWEm0q0pXiZ4MdgrKK5Ep9X8AyVyT4LE5u8tdQYtllcahJbLrze4sRT0xx66hNcef2PSNIBL4XrNGyJRh7iePhxwsXhuAkKvBW06JsEQZmNU/xee88ej7qlZYdrXUWWfnKF3XJhBzNOMgo6ZTgZ3mnbxTGRp0xVc640d/OUZ2qWRjlHgTQRMgy9xBJnAUkKtkj/GZp0tDANVASpWMJxikDcTWVwZiee5Xb7uHffGwgzHZbk70AO5FuAsUgJvLKHNwbetIVA9sRh7VTLFUQsATGhH1Nu9JivL5NuBeQY4lKFblpmrTnO2vYMSRaOjrSqSGbobFk6W20wgudBqeIRlj3qkyUqdY+oHODJGF4+TijHKBGjZFjZBrFYYhLWyU2HXLbYcX3lnoIndKcUJkEOG7xUMLkQtSxBTwk3FT+G/HxOKTUcn15hvTnBdn+8iGGSwvIS9y2Xz3Y58fRxFo4PuPrmH3PV7/BH0RV+cHCUQL1RjRf3FeThxAkXh+MmWFGu+EXtlrrCeKc9zPhwSdC7vN3EfHDutMINs3fcD2b897YoYolCiz+c1YKSBbXUS5DnKWkO3cSjFRuyTFDMKMpJdsr3G/DrgimDdu+2j7c4ZlbQnpL1LOmm4rUVf2wnr2ivXarImMq297thtJOgBmpHYiZmN1kYX6bTu0w3LbO+PcZGZ5LeoIIdvQdDd5mF7nZGd9uytRITlk0hYiZCopJPbSzAC0vD09RH8UgRj2PzAVaaxN5bJGYDS8woHUqK1HQbFkck5aEIOw5eCmFHMCJ4qx61rTZez9CpVkkjDzu0vnS2My6f7bJw/Bi1rau01y9z2W/zcrjBB+IZvB0rjfszfig5cOFy/PhxLly4cN32n//5n+f/+D/+D372Z3+W3/iN39j32sc+9jG++c1vjp7HccwXvvAF/uW//Jf0+31++Id/mH/6T/8phw8fPujuOhw3Zc3rsm6KmhpjGAK9dgK+WTGzRw8d1ta4pyOho/8OjMzm7NQFua75Ua0exfdyfK+IkRmrCP3Eoxt75ApxKnssE4KEhnDSMujuSYm+5wyFigp2TUjWRhex5/Xdfa8bxVyIzxUWnOCQUq83qVebzI8vk2UB290xtjoTrLTG6PbrKIbceiMxggpJ35L0LJ2NFASCyFCth5RrhnItxC8ZwrLBGMFIGaNlvHyGkh2QcpXMrJBJGyt9YH8qlw4tXFkEWTi0rky04ESPSjOitF0iKfv02pMM1iaxcUhztQ+mxOyJjxD326TdLV6I1rDA+5MZQutWkn5YOXDh8q1vfYs8z0fPX375ZX70R3+Uv/SX/tJo25/7c3+OL33pS6PnYbjf7/jZz36Wf//v/z2//du/zdTUFJ///Of58R//cZ5//vliXQ6H4x4Tm5zXg01SUYzCuPo3mYIeZdGykxdj3p1R2AlCvePolusZpIKIQXWU47OHa/0JRZRGIIof5dSjIt4pzwWL0E+EduzRT3y8hhRRpnbHDZVf1/rB83ai+p0sY4X4yLYgbytaVaI5D79h8MUyVdpgqrTJY7M+A+threHi2iG6cYXtboUkq4IWFiiG9Y3SgaU5iGmuAfQwPviBYWq+QqXhUa5GSODjSx1fTkH+OFZicumTyEVU0uHz5mihSBkafIqEMYUoQ2czmO0SAGOsUn+sxODKHL2rszRXLVFU4/D7PsnK2W/T31jjxWiZcSIej8f3LQvgJMzDw4ELl5mZmX3P//f//X/n5MmT/MAP/MBoWxRFzM/P3/D47e1tfv3Xf53f+q3f4kd+5EcA+MpXvsKRI0f42te+xo/92I8ddJcdjmsQrgY9zoZNEIgQKrrrFtjZxwFGQkrB7D0/TzG17tgvDi4J3TcKuuNS2WduuUkvCmTPc98rREFYttRKlotbhsw3iPHQlSexeQWvulosXRC1UJPf/BQHxnCEjKK7cc1vv+8QzRRtCXHLEvt2KBIsYgx+Q4mmUvwGvO/Im+RqiLOIzfY4K81J+kmJzqBGngf7NROQZ2Azy9K5bpHy7RvKdY8w8hibLGECpVwt4ZsyvjeJUNTmUUnJpUUuW6RskJom1gxQ8n3vA4CI4pUGVB67SOXQCnGzTtioIuEki43TmItXmd8scTSeway3yePu0A25vx3Hg809jXFJkoSvfOUrfO5zn9tTpwF+//d/n9nZWcbHx/mBH/gB/t7f+3vMzhYffs8//zxpmvKpT31qtP/i4iKnT5/mG9/4xk2FSxzHxHE8et5qte7RVTne6+TGctXrkA0/7RvqEboloG9Akf4rYoZZRfewqs2w4d2YEg5EO5aDHQvu3b6/w35JEfnilQUZLJJuPgl5hG49BmaA1K/iTb8J0b36fBpeh4DXMASLYDcgWdspHHOj65RrHuvuZJ7uuLuK//MBZOuQ1IRwzuJNK5WgQ3Wyw6GJJVIN6MY1trZrbHQn2W6PkeZ+4UJDRlE3apUssbQ3LJCycTVBjBJEEJZ8qmM+k3NlvNDH832Mlglkjgglt11ys0kil8lkDSsJWkS37BqWRJHSgGiuTwZ0OI8YH/M4yKCKaaV4Jyxmtcehc1Wi1HO65SHingqXf/Nv/g3NZpOf/dmfHW379Kc/zV/6S3+JY8eOce7cOf7O3/k7/NAP/RDPP/88URSxvLxMGIZMTEzsa2tubo7l5eWbnuuLX/wif/fv/t17dSmOR4iOSbhkmgBEQZm5tFir1xWbu5bi2/wozmH4O8t7ZLY3fH5AY6Y6mvbuj7XrnSM5FTAIldASD3zy5gmwEYIpgjTyKtp8AptMYA7/d/D73KtrMXWhdMqioWIaBiuKXS8Kzu0Uh9uJhb2efSHL+64PtAgGbilxB8wa+FPgl0wRC0TCGJuMlTY5OnGFTEOavQZZ7tEeVGn168RpSHdQRtXf8zelaA5JH+J+SmcrYePKAD8o0q4rYwHlWki5OnQt6bCir8RksoFKj4RVVCxW2kWRPIrlFUZlCiUjQ9msNtmqNItxOqRcPlbm0PkxZlbKlLsGk/sYJ2IeaO6pcPn1X/91Pv3pT7O4uDja9jM/8zOjx6dPn+YjH/kIx44d4z/8h//AT/3UT920LVXdZ7W5ll/6pV/ic5/73Oh5q9XiyJEjd3kFjkeRddOnbzJAmB2bp76+CsMVc52L6Hp2Q1MLrMZYHWaJXLPnnZ8DdsZ/WCvtQN6JnbbeXpPINb+v3b5/U1HF15BnpRu0q9j+JAzGkdrgHtxNhbQzoYVg2CFfCR4HXVTyLRBbZBelGzosqDdMlR5Jkxv16gaDZAXbhGTbkO5xtY2sYlGKSMbUdI9o3qAzxV9RlgVs9RtcWZ1ndXuazAZF+8N8950MsixVsjRn0MvZXIkRA2HkEVV9yhWPaj2gMl4l9CuIKCU9BaLk9MlMm1w2hxV9W6iAJS6KFu4R27kHa3MDNmdiwoGhNPCYWK1Qb0XMX6oQxR5+bnA8WNwz4XLhwgW+9rWv8a/+1b962/0WFhY4duwYZ86cAWB+fp4kSdja2tpndVldXeWTn/zkTduJoogoig6m845Hllzgqt8nF4sRn7FBB+y7EVj5EKMMS6/ujQCSm0yAB3fKA+GGXdR32uHmjA7N8Up98hu/OAxovlf5uMMlD/YsoyCAVBRTGW6yin/Yw3YpUqlbu6s4mxzyjtyga9eOxfA9V9B91zaUmYNij/yqJdtQwlkPM6OEYcJsY42J6hbtQYOt9hhrrQa59en2aqgKuXpY9UZnVRU0h7hnGfQStlE8D7zAUK17hKWAsakQLxCiUg3P1MDMozyGlRxFyWQdS4fYXCGXDkhRr8coqChxKWdQytkeT/Bzw/mnAupbIdOrZWYuVyj3fSdiHhDumXD50pe+xOzsLH/+z//5t91vY2ODS5cusbCwAMBzzz1HEAR89atf5TOf+QwAS0tLvPzyy/yDf/AP7lV3HQ4Aul7KsmmjAmXPp9rpgjo30Tuz84F+L23sBx8avacg7zWht3dTg0ZRLyWYbZMvcUMBoHl4d7lRNihcUXkwcp4pYMqbEG0j2mfXSni980cNSCnHjwSdhEDN7qXmMHhTyTf29/l6bm1cJDdoT0guKLqSEy54BLNC6GVMVTaZLG/y2KyHxdCNK1g8Wr0y/biCAJ1eic6gTG59+nGFYoVMsLlgc8vWwCJkrF7uY3wljAzVRkB1LKRSjwjLPmIsgdYBpaQnyKRFKhuksoI1HawU6zXtvCe5Z2nVY1r1mKXDXWqnAhrNiENna9TaAdV2gGcNxguKIGy12Cy59ffPcVfcE+FireVLX/oSf+2v/TV8f/cUnU6HX/mVX+Gnf/qnWVhY4Pz58/ztv/23mZ6e5id/8icBGBsb4+d+7uf4/Oc/z9TUFJOTk3zhC1/g2WefHWUZORz3AhU46zfZ9gcgwrjv4yc79eCdm+hmqBTfWEfBkbfiUrntc9wbC46KomKLmfyg2oRiErxhKo+ACrY3gxk7f8dn0LiOXXkWsd6+NR6tyaC6jJkrVtKuhjmhJ/QSSK2gojBc3bxYMdsWj0WHaz8p4kNwxJC1LJIO+3xHXHOcCjowxOdzsm1DOGswZYVSEdCMyWiUm4gaJkpbo5Rnqx5WDUkesrY1wer2LO1+lTgNUesxkhsKeSr0U8ugk7C5lGB8aEyV8ALL+GQJLzJEpSqBqRLIPMiT5HmLTLbITbv4LW2sxBRl8MAapV2PaddjVhY7hInHqTPzPLV8gjCq4/khmiW0Vy+SJ4O7GC/HrXJPhMvXvvY1Ll68yF//639933bP83jppZf4zd/8TZrNJgsLC/zZP/tn+Z3f+R3q9fpov1/7tV/D930+85nPjArQffnLX3Y1XBz3lL5knAk2UCBSw1w/HbpAwH0YvTMK1ywQ/RC4iu4B1/dtx94ho19yt0JJhwG/w/ZHyx/YEEnGCOslIGW8pDSilAzIrIdV6CdF1dtOakjTa21LRZteGfzII09vfEV31OWd4Go12E1hsAn4OV7DEM6DlgQs2L7Fq3mYqFjWwUiGJxCYhMpsl2PTywyykF5SZr3VoN2v0+nVSfOAJA12HGOoKnmmNJcHqMD6lQTjCWEkVOs+0XCpgqgyTSAziCjWZOTSIZF1cmkPY2Q6WCkK4qW+knkplx/r8FRax0+iwtIVRpQmFuitXkTVHsh4OW7OPREun/rUp0YFnfZSLpf5z//5P7/j8aVSiX/yT/4J/+Sf/JN70T2H44Zse306pqj6OZ4byg/y7PjAcY9rYehOEPD+s90tortr+uzv+d0GEu8VBLpvmwyzse6c/X3eTTIeni0rQV7H0MIYRUXwUDyvmFDLfvF7QoU4g17i0Ut2XGZCZoXMmtG5DpR9FjmF1CPfUPpbO8tBF+JFAotfE/wFD1MSJGTk1xPJqQR9ykGfydoGVg1qPeI0opeUaHYbrDYnAaGXlMjyAGs9VA02U/qp0u/EgMW70KNcC4gqPtPzZYKShxeOUWKsqIFDjso2iVknlmVU2qgM6JR6bFXaVNLSyNIYlqr0jA+5cxnda9xaRQ4HxZfXC357GJRrmDAekjv30K0w/H5bPLlHuqWYHArXxv5Q0LtDZXexxmH5j51Xrj37LbM3buYGZ2Sfb+cOUZMhJkft7gKRw0RnsB5qfTyxhCMj9fWBtb4ofgCVQNHq7iuDzOfyJlABOu+GlbsQK/t6lyjpppJsWUwAhMPsqIYpXFqiYAR/zMcEigQZfpRSjtpM1Tc4MXcREPpJRJoHbHfrdOIqW606g7RMmgYoRdx9dzunu52ytdLDDw1+YPB8oT4ZUWkElCqTlIJJyvI4VnqoxIRJmyCPRgn6ajMG7U1U8+svz3HgOOHicACJybjid1GERqlKPcZ9c7oldG9+qQPwirCLe4oJO9iwi8YTRWVcYG9FHQUyFQY5BDfUHvsDnfcGCgeS4xmDX/HI5EZi7t0Q80MxpoImQFKIy7hpd88vlsQoEinBjODNgQQCYvGlCKivRUWi9kRlC4shtz69uEK7V2e9XafZbdAf1LAqqBXSgZIOiizCTrNXBPWGHvUJn/pEmcZUA+OBr1MkeZ+kvUWeZWS9JlncPxBR6nhnnHBxOICLfpeWiUGE6cnD+Jcv3e8uPQQISkqcbVAJx4fb7pWC2TFjFNPsvXAVwd55505TlZWyb+8uY+iWzqK7AdEjij4bf4AXtlDkjlxSgaeEgWVAhLYW0LQ8FC8K1Q2ktMW9Vao3CWrmmgKEKpBTZCxdBNm0RPMGb4bCpYSM3k9RxSPHeDlj5YSx8jaHpoR+UqIXV9nYHifJA7baE+QY4jTA5h7kHklP2einbC4llGs+9ZLyiW4bs9Gl0x0g+24Vp+DfDZxwcTzypMZy3m+SS04FobG2hLHZOx/oAASr9pqsop0HB+dq28lc2m9bOIh2b8VVdDsIO4sM3jsUJIewB/0provOEQve7d6/10ggBbIq+dIHkKyy+0LUxD/8zXu0ZIEgnmWnbK3mAlb2vf42R6Idj8GbircC/iRIWCy9kCUGtcMxEiWsGTTKEYFq2Kca9pmpF0H5WV6Yp1qDKnFcYX1jAdOdZyfdP93u89RSk7mkT6DDlcWvqWPjuPc44eJ45Gl6A676bazApDVU+gPuMnrykUHEJwqmgb1ZRXrnBov7ie6Io7ttRrhGBY2QndfuisJ/Y6ImOUe4fmofisa7PI9fN2AMqBlaLhSJx9HNxzDz3zkwr4gA6luCScGbEaRaiMisaUmXLNrejeO5GXuTQfKWkLekSEf3QPO9wkLJQiU47OGPWzCChLs3a+BlCMpUuE2ebbMQpYxnJxANijHprFONu4gWKfrOMXR/cMLF8UijKFdNlwRLoIZJDXj4Ztz7xc7CgsXEUsxt9yhIV3fcOgX3xlUkw2qtB2Vtu4f30Widgj0xH0NsVsImDYx27qp59exQfO24pIr/7WAaNECKIi930vqex4KKJTps8BYZGjaK8wUzQlCXohheS/ZpsVFZPbl5fJWqDN9KZa/EsLESn7XEnkV8i1eVkX0QiiZtHzSBgBRp7C73EQwf7njOnKXl/uCEi+ORRVTo+SlvBZuoWMpqqLzzYY4Rw3gD3R/cOdp+gN9Hi/ofe2JRDqjd/a4iYbtnqYZ313PBItcsgbDnxaEI81H1EbkTkaR7fm70sgdZODrdnRi/REGCPiZqofHOX8XQhZNWERuCuU3hoh62vYhk4c5tg5S28Rob+NOgJh/2uXivBbBlS3TKkF4F21HsoMjK0ozhmkbD2Ce91fttT3xMJmgm5IN9V8fOvSACGX2SvE3oT7OTji8qB2KZc9w5Trg4HlmswIo/YNukeGqYUIPnXES3zf7YloeXnUycg/BzqQjiDSgsItcXm9PuDDIYh/L6nZ3gJoOu1wrGO7yM0Fe6kiMm29eEopCH2P4Ept69rTY1LZMvfxiy3TXlxKQw/ybwKmiGEaEUKGlmyVUQNWioRMdArcVqEfei7SLgNt9W8mTn/dqx9gl5h2LoR9nJNxI1N6/fo0OziogFtftGdeQ2G/3veLdxwsXxyJJKxveCdTKx+J7HhBVwtVtuk2tTZO90wr+F4/Z8y70XrqKR++HuW0UBr9QEk0F+g49Z63H3ywxc29eDu2/rJaVpQ2y6a4MchTAriL2DvmsRL7PP4ZdH2NYhxL4JkjBRsUxWU3IrJBmkmdAc+NhcyUWGElBhujjeTBuKiix7+qOgicJAiC9a8vZQ2NxJUI7u3hUKpPUIzxokLhaltLJzD70HlPtDhBMujkeWLS9mzeuixnB46gjl9XXA1W65PeQGaxXtPLgNERj2wMSQl2/4ssrOt9t7V4Buz9a7bRVQ7LviTjDs9nfP7514EOEG1/fOCFrM82Z/EZi7uSJNaoVg29uagOYBOijj1bqMl7Oiyq+BMARCpVHOsAhxAlYVi0eSWZJM6KV+YYUR3dW1AhIJEgnREwY7jI9JmzlkkA1AkuGSCbdgYR1ZVnxL+UMhgTdJshnzxsUrrG5tM5fVqKvPdF7Gt5772vMu4ISL45FEgat+m1QsYVRiwgsw+Z0GGzqAPVlFcLsTpc1KMMzcuBXuxeQgyDBu4iDaYhTYeu/YG6q8V7QIyXKdymOGOIV6dGfX5JVjgvEOSXuM/csK3BnG72MlL2JwRig2LpFu1fGn14Zn2N/fnWJyQVQUhtOh/8eq4dKWYZDLNcM8tKCJQgm8UiHdotni/dW0WPk6vphDfhuxWMYipZg8AK8acnzhCGa9zOsXr7DZ7DCXVpjLqyzkNabyEoG9NwuDOpxwcTyi5KIse10ECI3B3950HzF3xO7X3H1ZRcNtt9qGWPO2rhNRHU2eBytbZP+ChwdiJRnm4NynAE7BoL06iiHOPa6rp3+L3Eh23c3Iq8lvavsRlSIg+Cb92Gu929nHE2Wyallue6gWhfPGyhbfU1BDL1GSXEgyb6TtLDniCf6cIV0RtPfOYkyxxX2tguYehBmCEPkBj88vcHR2jjNXLvPaxcssd1d4MV9h0TY4kY5RtxGTNiK4E9ea46Y44eJ4JNk2A9a8PhZhPDMEcY/3RITpu85OVtHetNy9WS8HKTJ22zuQd0osmQ5dIsMMlf1OqDvte3HtpuGhBuSeLV9zE4vOvoz0QvDdbvyOAir2gITcHvZ14Way6Nq4qRsejACNKKOXCN0EFsczSt7Q/UROo1TojcymiIBVoT0wtAYBqbEQ7Cx/efP7VMkZpMtE/hykHmkvwFRi/GFMjVEoG+HZI0c5OT/Pdq/H1Y0trqyv8SeDZZJBRt2GLGQNJvKAY+kYJetKLtwtTrg4HikUITU5f1JeJjY5HjARxwfmIngUKSwtcv3Ge/jZfPfvVtG5QbynKMe+Vu8uIRpA7vn6hLcqDu/0zdhNS76O7A4KB9yS6+zWBe/OXqGBvgiBt5PqrIXLUoav+7tutKiq1CPLpWaIGRPs9qhzN+1bsXBi0eDlQZMVeYujMkOgARWJiAjw1VAOA8rhOPNj47z/xDH6cUyvP+DVy1c539zmlX6fl4JNPpDMcTiv4KshsHLXLrhHESdcHI8URmHLxKxIB1QpI5R3SkHc7849lNwoq+jamINbbermbqD9uR0HgYCa69OHR8/v/jzCvVuzSAATtooA4HcS3fuDj26dUZG76y0jeX8a/zbHSfw+JtpGe7OjdvYefydZXQp0U8hyS5qB7+uuDt3Z45ouBr4S+oqd8uhdCbC9amExjNqFW+j6ng9bUlarq6x4W6zSRFSICIg0YEoajFOjrmVqlDEC9XJEvRQxMzFOL854c+kq55aW+ePuZV7MDLU84mg2xkxepmpDSuph9J5r/vcETrg4HimswGW/Q2wKE/ph9QmcaLkLdrKKCleR7Nl++1aLtxENshPoWKjMg3u/buRKuXayvt3rGKbnBn1M2N2XUnygRB12nB1vi1wrLt8OBSyeCfD2vK83aPT2PYEmHxWZu9F5VSwWuJ28nFwgV4OqYrE3uC+ub8tDqIaWgReQXf0weXMWMRZv4TtI7QrX3n+57aDk5EYZhANGSdliGRAzkJgmHTz1EDE0KFHSiGkdZ5IKdcpUI58PHDvKM0cO0+z16MQJF1dW+fbyEjZXqmnIlJaYs2WOpWNU8vCWx+BRxAkXxyNFapQ1rwuiBAh1a1xQ7oHw9vEIB8pNgjjvDNn3/8G4DIcJtF4KJuHdlMU7ayEpMtQrewTfLbYAHr4HvqfvmgtVgDz3iFM7zB66RazZXfWgSOXaI0RvjIpS8hWsh8aTSF6CXND1J5HqMuyrZqykeRPVDKMhE4MPkVc3yCVGyUjNMlaKandWLCqWTdpAh6u6QUl9KlpmhgY1rTBuKkw0ykxT4/jUJK1jR2j2e1xc3WBlu8nlQZtXk00+OJhjIi9RUp+S9biJw+6RxQkXxyODAit+h2W/jQB19QicYfYuuVlW0cGO6b3LKmJ/ewfatHCjqrkHyg19nEq+WYd+BOVkaBi5vQsrlizQIgX4FrxRt9wuN74zdrbZ26xcbYxijCL2NnLYFHYzrXbMRgp5WCzFcM0yDKMauWqoXamxLFXKYyVELGqfJqeFSkIm6+TSJjHLhctJcgYk9CVlgxYGIVCoUKOsIbOmQaVe4VB9ksXZSUwutHp9VrdbvHj+AtudAXUbcjitcTKbYSrznYAZ4oSL45EhNRlv+BukWBDDdFDBDFzBubtjJ431brOKFMS+wwx5bTzEQaDs5pZcG5Fyd1lFNi1jkxoHn121N0X4RiMhaBIieVAUZ7tTTEa42KR34ei+tu+cwg11a/vcqkDVkaVFcwHf3t7C7rLrJrRpBa81j4xf2GezUc3ItUegZQatnPMvdSiP9aiPl2hMBnjBJGHgUZJDWEnITYtUtsnpYtkmMy2sFG69RJRE2jSBJd0gkICKhpQ0YMKvU6oHzDXGqc1UWL66xdLmFi9vrXM2bXMyrTOTV5mwJaoa4OePrrXYCRfHI8PFoM2FoIUgNLwSY/nuqq+OO0WKwM99E6Rw6xPP8IioDX4X0voNjtOimJgUMkPkoMp6KaG3k9NxkK6u4bXnPmqDYUzEwVv1VBRMDvYGhfuGhphBJmRWCMzNYlXe9gyYYK+r625Fy43viZ3xv70RGko3FVSLYnSdVChHt5ZjBYL6OVQHSHus2GpDSKvFvbwnDdxqQpZ3CMwUVfEoZ9BZT2lvpCydtwShR1T2KVc9yrWQytgkgT9FKQCVHNWYnG1yaZNKk0w2sBKjEpOSsi0Z2yjLNDEq+Grwyz61k2XqxyPSVol+M+P19havbK4Tpj4L/TLvj+eJtJjCy9bgq7nh+L4XccLF8UhgjXApbJEJeOJxJIcgu5OVeR17Cf1xPCmz39pyuwiEHWT2NezKs0hWYt8HsFikcYE87ww/sA7OYB75eyfTvRaMOxe0e51n96Z26tA+5PcxYQvNSm/Tl7sTYLovsPdur0TxyhtknXn2utBEhaxZwx+J3zsc99uIfRIBCRKC8TbJ8izFu5WT9SbxRsLl+mse9zw+HGa8MOjSkgaaG5K+kgxSWpsJyADfN4QlmJgp44VCbSxCzBylaIFIFJUYK30SuUoqq+TSxUoPxGIFUs1JyekTg1FkSqhMe4gVNA2xMVy92mWjeZF826ID5UhW56l0hqm8hNHd+/m96gR3wsXxnkZE8CtjhOOTHLYey/1XqTQmqPZ7kPXud/ceeoSgmBz3ZRXdrqtIQXK8sXNI2EI3T0J3FtRDRTFTr8HkGeygOEexXg14o3O9fQ9v9UpGfdnbr9tqozgmy4ugkJ2InAMxWFyHgLGoubdTk0Kx0vVBLYVQW4X1p9hbJVnUYDfG9wzP7Y33/p9bwxPFGLvbxI5QscE1mVrF9swWwsIo1OKzvC+7yoY3y7ZM0pYJUhFyfFQhz5R+W+m3e8XbZLp4vlCqBtQnPGqNkLAyTslMUPGfwpKSSRM1MRmrWFEy2UalB8RAseSBGiBSTAS1RoTkgk0N8VbO2eVtrqRdTq9MczKeIIqL1Or3Kk64ON7T+OUq1dkjiCgf4RTvK52g5TU51/w6Lij3ILiRheJ2XUW7+5nyBixuIXk4MtmrF4MotiOIFTKF9U7ARHk3wFJlOBnJXivHTrzEzQJki/wTUTmw4FOAQb4bgzIqoXJPbrW36fTQg3cwJ75eeRndWbvodrKVbr63XLPfrXON4LzFQ0MPfLNzrt17V9Qg6rM/FseSZJvsxNNYTSiTcCi/xCJXyAhIJKIjhZtTgYFEbJhpUo1INCSzQjuG9uYA4/XwPCEoeZSqHuVawNjUDH5giLxjQ5dogpKRmxa59ElluRAzxKgkQI4aRUpQWjCUFgJQ4c14i6W0w5GzdRYu1Kh0fbzcG/0F7P5lPNyrKDnh4nhPI8Yf2oXBw1CnTLN7kduL4HO8HTsf/wc2T4pF/cGeDYUwyVtg28CY0ux7tPpmKDqKANvAg9Ar4klCUaIoxxhLyRf86xbCUVTl/8/euwfLcpWHvb9v9WMe+zH77PPa56AjkEAIgTAGE0A4lYjYFsgBKrFT2MGlOFUu4VtJmaKAikNSSfAf15Sdsn1TUMmlKLvsgFK4UhWTVOxSgh+B+IJ4GIuYh4UEQkLSeZ/9mj2P7l7ru390z0zP7Jn9nH3O2fusH2ydme7Vq1c/ptfX35N2Ms4csh9JQ5iNYRk3PE0fxCyhukPRYe8XZaz4qeC684itIGF7hz3poEMo+ZFISVey/5O0Cx1f3lKGl2k6i6RzSHB1Qs89Xd/AKT2mS6xdZlnr790hvEh+QCoxbZlhXWZZl3k2zAyZjUhdSJoqrfUMkS4Xvi9UaxFhLMyfqGACQSRgdn4JMRCHt4FYnNkgk9WBiUmbuR8NaS7kV5VmNeGJe5d55mVNFq5FvPjqbbzkylkCDQDF2ZSktU7a3iiixw4fXnDxHGni2nzxKf+BZqJ0nCu+Htir8C3H8GSxl6iibfpXIIXWdxzRYkBwHJwoMqNImO8jySDJislIFe0IIo7IQD2GSiRUgnxccWjpZgGrHTPmLhgdd3ntTo7HoKVMtQfr/n0dTEWFt04fAbWVwil4p4JLcZYlhSAhrwRe8sXoR5Tt3oF40P9+GGhcBnWwxzucV6LjdNIfbDkeA8SaEmvCjDQ5zmUcASkRHanRok4mASvBAh2doeXqbKQJCKxe7Ra7VMLIIEaYbcSYUJhfnKdabzBfvRMnCc50sdLG6hqZuYyVJlaWcYGjNdOhNdNl5fQzuOfrvOzyOaIs175UZ4+xfvUFkubK1MtRXQ+84OI50jibgubhzynKshVMdBZjvoNzXW4VL/yDZvgxv/uooq0p9ZMI6QUlvZRrGoJZkCKoRmr5d2PAzIEYAROTWmG17aA9CHiuRAangu1HYuSqc6eGzBoCo5hNKqSdH4/23+YPela4HvfvZuFgUiXnrREkakHUgmym36PKIBy9lQrz1d4+d7KHwX1m3S59Y/qXpjAZIqAh0lqE6pUJ+zIYCQd97HBXIkqgGQEpVW2xwFUU4UXuB3SpsSGzdEyNVTNPqhVaMouKknXzfS23c2Hm6vNtKvWAaj2kMhNQn60Qx3Uq9VNUoxfjxGLlKplcxpoNHAmdcJ1vnXmStdoyr3v2XqpZDCagNneMtLUO9sCqgB4YXnDxHGnayxfoNleJqlU2agtsVBYIzDyBzOK4vllNPVPE5ToAt9ZbkIdM26IooKs6TCUgmHGEDQMRmLrSk1PaWZFYvhzJIkpihWdWQmKjVKKeaQAMjplYEREqYS8ke9LbvuY+Oa0MuBkmBcNex7F1/Nbufzu5BkcLK9FIXSGBzO1RC6rQSQSnhkC2yxWTCxJRnBEdXyMxhTO15P5Okta2GL0AQW5+1lGhdty4ZbBp+XuvJ3XU2aCuG+DgRSIohowYK4ZVs5BXtTZztGSGLjWSjZhsI2H1soAKJoQggpn5CkEUMLd4gpm5MwSxoCbFuIy7zjtuW3ZENkZVsVlCa/UquO3P1c2IF1w8RxpVxSYtbNIiC+pIRemn2fSVFafG5ultuqaiHY1BS/4SHYPrgFuF9LzN3ZwqoKGhekogytuZmkDF5vJLAGDInGKd0s7oLQQCrrXyw4kDiCPHfOyoRI5QGM4tI4qoonYDE22g6ewBHvXWN7AUOU62T/w2HjPTQioJdIZDrlVy8874HDhbDcghUZtBuHH+j3YiJAv24Hs28JBxu6hhJQgVo5jGOs44jBs4cOtYca14bmhAJVrESITT8ovPuPPQW7aTc1SYmBTAEtAChbpr5mutQYHERKRUQAPOB6fpEEMC7WyGjXYKKKvPG0w1ZvZYzPHTNebqNdZNC20mpN2M7tpVsqSD2ozdZs+5WfCCi+eWQdIN4AT5k1K80DJFNj+ap2kq2gmj+yp7XeZJyrRwx2g3B8X4TCj5C3SkuXkJCI4JQWBQk5uf+t0AGCWxjq4Vmu2AwBgMSjWCIBBqEWQWlHyiIV6D9gnQYNCRAGJRG0/hDG3dgwXW2o6Te5SdZOkyZn4d16lR/sGICrKXhGdiCarL2LVzg21VyJbn0XYNZnaboqAn/Oznx6z0KmH3NUITmgGIREQnQqxLyMZZlMqjq+aRbZoY1JkJgsI4zd3orynX8FVdlyoJAsxmq32FYWIrWAny8UfnUPcK9BqkV1ssdK5w+3qTNLNkJRPfYTaSe8HFc8ugOng7O9S/Ws8uKKvwBxNl3y00BVJBO2DX8zfx9HyhnTAgtVJbgei4YGYMpgpEYB1kxpEk+bYr7SIQuhOQrWaEp7+JizdwG6fRbgNciFRXCBaehvpF9qWVEiBubtmHiDBb2evNXtJOTlN5NkHIUAWr4FQw2woiA7+k0AyGt6chlh4L+dgm7btw4JUK1VPH6CTNQnCZcO5rSv2eABeArhuSSw7JwLYKxZLdzhQ3fv+9IUvP5AZU6dCzu9XosBjFCELYTqheW8fooJjTUXhf84KL55ZB4xkGT+Feuv8eXpLZDzeDqWgyOxtDv1UvOZpVtDl8lyTrCiJICBooEkEwJ4iBcMEgoUMz6PxA0Q2BoIuceILw+HfyDLdqcFEXwZbCsMdFMUlJ5JpkhlCCuWfJll9cROmMOyYlDPZW2sKYfD/jZIg9m4oAqTQRyVCNR1Y4ujb3c4mD8duObIAAtcix1jX7nJF7qojxx1sWiwKgagJsw5DEmgu/OtpaCI8ZtObyLY87aot5O00EZwW7bLGXwbWm+xtJ02XUZYhUMGkrF1qOGF5w8dwy9KbSzK6T2VwlvUdXQM8IN95UtF/GjXXC+BU0VUhBO0K2nh99+kIvE+uoGbKotRS1yY1IZYPBGJFPA1SDosKRglhkgpCjxoHJUBv15t3+BIz29rT7O1xEqVaUVnu8TmDPpiKAaAM1GWTxYHMbkV4+RmVhZU93jfT/s5uNHEG9Q1BJ0HZcGIqK+3akXlFpL0jUYWnpIhsu43tiCr3PsOCpohCAUcEVWhzNg5KQmiNUIZgFThuSp5X08qRBTnIAn4ySAhlCBZN28n3qbpIF3vx4wcVziyEoFtWs+Ozx7IUx/ggTw3En+y4Mo6gGLF+8jaQzS69adlxr0jhxkSBMGIqCAqS6Qnj2a9jlOyCr5L2II5i9TLLRJZ4XVjcCFmYcoemZfXYwCSqllPFjJvH9+JT0ti0PI4uwz5+Gu57ee7+7ZK4iNGdbSL2Daw+cgGz7OKGL8nwzDIba8ycWsQRRSg2LEVO4PY8IlORal4G/jBKGDlEhdT1/GkEiJTwtpNcc2OkI+s51SOwy1XAmFzD7Izo6eMHFc8sw9F7kI4qmys1tKjosCNaFdFqzZGmNPBRKSdM6Nq2zuPQMQdSl59uRn14Lc88RzlygHKBtxJJetIQnhavtgGZimKsqUeioRrn5wogQDAkgg+ulCO2u9o2q4ybmvZmKJvzotH9Ue0LZrbO9EhpbZF0e6gVsyOgxZbaJ1TaB1FAMzoXFM2Sr41fUKCfqjnrFUTV5zaGkUMyttgLWuiA1gxhQO95JVwVMCOExRRXSFYekg/xD5bYUwpJiQaR/TwyEl6Pxe/SCi+eWoR8xoOThqkfoh3yjOfymopsBJe3UsFllMCUV2WQ77RrXWrPUGx0QR1UNQbHaKLnphdIEJYJ0wDUNZkHoZtDZyOs2GXGFb4hhpuKoRY5AHGGQ95DrJIXEFY4mZTuMSqGAMZDVINrY26GO3hoCKvkxZA6iHfm4DDbek6kI+snvpN/PeINKYpfJbJPA1HBpnW7zGNHs2ja7FIw45ioplf5M66iZfF113qHrEauJQKBIOqGWuIHay8Acc6CGcDmg86xCp/fyNUZo01yoUpN/3mzOOtxMqj42kc9//vO84x3v4OzZs4gIn/nMZ4bWqyof/vCHOXv2LLVajfvvv59vfvObQ2263S6/9Eu/xIkTJ5iZmeGd73wnzz333FCb5eVlHnroIRqNBo1Gg4ceeoiVlZVdH6DHA6AiuHDEGfCG/ZCHtRG5o+PefBE8RwnpOwb3Sgbklh3LysnneHLhPF83Hf6PpPwVKd+QjG9KyrMm47zJuCSWriiJFK7nFjpPWjrfgPRZyC4K2UWDawXYbshGR7i0HvLscsQzKxHPLoc8vxpwrRXywrIhzQBxmNnnkcpyrt2RIhymsoKaZNKBbHmMPcfeIRR0zeTVjrPd/i7z345VIduNuUUFCSzR0lWUkn/SFmPv/5tWGH5+jNlOIZJeMcdxvSknZlOqNUcwO0mwUOKTwLH81hAD4aIyc29A5VVK9V6lcieYuhu7tavOkU/zR0dogT0ILhsbG7zmNa/hYx/72Nj1v/7rv85v/uZv8rGPfYyvfOUrLC0t8RM/8ROsr6/327zvfe/jD/7gD/j0pz/Nn//5n9NsNnn729+OLaUefve7383jjz/Oo48+yqOPPsrjjz/OQw89tIdD9HgAMWhULd5kp1sNeA+DgQiCuiLzjspLhWDmRo5n/0w2FXl2Qxx3CIKM8vlLKi02Fi7hjMUJWFGaxrJiMlYk4xnJ+J5kPGkyHifl65LwpKSsiKGdCdma0nlBSb4rpE8ZOv/H0P46tJ8I6DxhSJ4y2LWQzkpMcyXm0npEuxuhnRB3OYCZJwhf8r+Qk/8HGt8jOPcFonP/H1JZLY1cJ/xtRsIOQWVt8wqbazzq8S7vG8mFf2cNqe2NZXuMCHFskWp3yO9GZJuHgwrJ8mmGcvOMb0hmhcwZZIKQE0kevZXn/Rl/zkTyatxzVcfZYxlnF1JOLXaYOaZUGo5wKSN+ORAOhMHclAcQHDGRJWfXpqIHH3yQBx98cOw6VeX/+X/+H/7lv/yX/NRP/RQAv/d7v8fp06f5T//pP/GLv/iLrK6u8tu//dt88pOf5Md//McB+NSnPsW5c+f44z/+Y9761rfy7W9/m0cffZTHHnuMN77xjQB84hOf4L777uOJJ57g7rvv3uvxem5lSr/gXpbV640GDjNvqN4uhLVchauBIlboNg/vRO9NRdNAkTBDjC0y9BfnTxSH5hqC4Zu42Ez622fFq+hlHNekQ4BQIy8k2RBDKAJOia1SX8kbR6p0LxdXzCjU82gY29G8jo0VCBLC40+hasCkgGCKyTaQiEAipO8N40htOqJV6d0hWmhuHEIpo6/kPjPIZhfXHZy23HQlWipssP22Io4wsEjghiLBdNvsvQIuABduaW7O/YMEVUVwY8w1uWkoYLy2pEf3mqV2O5yqW8Kgb/CmUXNk1rGRhKwaIakHaFGgOnMbxQiKgp+HtAr0JKbq4/L0009z4cIFHnjggf6ySqXC3/7bf5svfOEL/OIv/iJ/8Rd/QZqmQ23Onj3Lvffeyxe+8AXe+ta38sUvfpFGo9EXWgDe9KY30Wg0+MIXvjBWcOl2u3S73f73tbUxEr3nFuYmmEhDpXKnIVpUFuZSahGIEzKF85XD7W7mRZTpIJJRm1vBLldQDRGUQKo0Fu4kq7RxaZfW2lUgj1DRIefa4atggQylayxGhRVsv43gCMl9SqoiRAqzaqg7YX4tINJBf1LIGyoKxtJT1FelzqnaS5ivnKISzBIUQ8k0ZT1doWmXaaaXWUuWR45SMbWrZM2z9Oswh03M3HOgkGYBUZBuOp6tyX06stRBZScCz8BnJzpzDaJubv7ZkSpWyNrztK+exWVXgM2JXESFvBSa0kygOjbNTl7zqlYJtnyJEmeIAkcQ6KCeUrFtHEBcc9RCw0YsdAutTZJeg4ojq9aw8QxBt7mD4zo8TPVpeeHCBQBOnz49tPz06dM888wz/TZxHHPs2LFNbXrbX7hwgVOnTm3q/9SpU/02o3zkIx/hV37lV/Z9DJ6jiZowNxDn36733gHBzEHlhOX4rGOhbjEigCO1BpGAozX9+6ii3SOIOBonLhBXEjaWT5ClMfFLFzj2N+5Fjc2L461fQ7sd1i7/gI3V89huFyYkGetFH+ngG6CoCD0PlZQ8x8g1tYgqsziqQIOAGJjJ7ScIEGGITYWT8W2cqt5OLVrsx7L0uo+0SrUyy0k5R+JafPPaF2i5ZYaChBvPENRW6PlfSNhCKiu41YBLseNUINQDBlqlsRE0wwcpQBwH7Ob3HQaKCXp5cnIT8mRTkfb/dUkN150DvTamXa4Zy5oZFddzuO2Nf3PfRgbi5LixK6CFk3Z+OoqrqWVfKEeWDITKwVAMasyEng8vB/KaN3rhVXVbu+Fom3Htt+rnQx/6EO9///v739fW1jh37txuhu05sggurqImt0kn2bWBM951G4JSWXScamTMVW3p8VU81LqTNz0MeFPR9BAs9bkrVGeWc9OMVsmevExy7i6YmWH+2BlUhNkzL8ElHVpXLrB84Wk21i4iaQdHP86mJKuUTDUMr4Ky1UlYx7EuublJgLBYF6AsSMzrZl/F2fhOYkJ6NX7GHYWoUJEZlmZezNPrK6VpH4jaSNRGZGCyVYX0khIuBLywLNRjoRZnRKGjYiQ365BHRg0YRNYYUQKxZflsG5RqpKRXZlEb9c/JZlNR3qHV9mCsWZX2+ZcBz47dkaKINdhEAINgx3mvgEJoLOGcTigfIIhVWmtCelKIg4EIOpBhipeEvpTa05RJEVF0tIQWmLLgsrS0BOQakzNnzvSXX7p0qa+FWVpaIkkSlpeXh7Quly5d4s1vfnO/zcWLFzf1f/ny5U3anB6VSoVKpTK1Y/EcLcq5GjLX4rr/lI2jvqjMVmye2Kv/fMofK9na4Z7oJ1v5D+8x3RgG58sYC1hIEswz3ya4/AKuNouLQtyJJezMHDqzQHjmThpLL6W1doHO+jIbqxfIul06zWWcTVBn6XnHuJ6byQ5Ho0BaNE6B8yT8z+Zf8lxlhb9Tfx0VMYx1KC19OFV5MS27ygvp02RFVrsgE0xvMP37xJA2LeGKIZgNWe9CMzKIQBhmhIGhFkIlstTC/PfSyZSNTi6oGSNYDcicEphemUudLFvlW6GdSu6zMlEvkffTTS9Ti3vFIaUQdiZpFhXtgl2G7JgUwuTmfgWhFkJQ7XnLjLkyzuC6ww7bm8e3FUdPdJmq4HLHHXewtLTEZz/7WV772tcCkCQJn/vc5/i1X/s1AH7kR36EKIr47Gc/y7ve9S4Azp8/zze+8Q1+/dd/HYD77ruP1dVVvvzlL/OGN7wBgC996Uusrq72hRuPZ+9cX22LFG9WtdgQ9BwGRnQuR4+j9aC80Ygq4cYqbKwCCuefxYURWbWGWziBO36caHaR2TMvZeG2u3Dq0DShs75MmrRoLV8iaTVpb1yFLJtoWtpmFACkZDzRfZY31l9FRaJtL7UEIeFtS1w++QKdeooiVNYcQTcXVqpXM4IOhInFtIXmd7XI8iuEswYMBHOGoGZo1wUbZYSST/oWUM2Fm0yV86uGIHDUI2EmhpnYEWyKchr84tophGdWkSBBs3EvvmX1TUlnJJaw2oSJrpSDjDBJOsmFt1SQQca36LcsKpz32mzKNrPpGuSuwTBa9uFoPG12Lbg0m02eeuqp/venn36axx9/nMXFRW6//Xbe97738au/+qvcdddd3HXXXfzqr/4q9Xqdd7/73QA0Gg1+4Rd+gQ984AMcP36cxcVFPvjBD/LqV7+6H2V0zz338La3vY2HH36Yj3/84wC85z3v4e1vf7uPKPIcPXQ/OUNvDrypaNoU799iUB2NzgFwmKxL3OxCcxV94Xu4KMJWZnBn7iBtHMPOzhMsLoEJWTjzcsRZ0s4661fO01q5QNLeIG2v4zRBrS3e/ydPceXldRNRJdg2AbWinG9c5st3fINmJe0vTWoDgUDOhYgKJoUgcVRXhdlnLZV1xV5zGCd5LR/jCGeF6KxgZwM0AAnKIwPVkDRTVjPHekeJQuFYVahVUsLCwmREMApWlU4CBFlRmqjnKjyZXoq6sLpG9dQzmCsVnG1P3sApisnlxK2SjxgpnKDHCzAKtFJDJbSb1uUDU4zQj6rqZpdJsmXi4AQaxGx/ZIeLXQsuX/3qV3nLW97S/97zK/n5n/95fvd3f5d/9s/+Ge12m3/yT/4Jy8vLvPGNb+R//s//ydzcXH+b3/qt3yIMQ971rnfRbrf5sR/7MX73d3+XIBjExT/yyCO8973v7UcfvfOd75yYO8bj2Q4X1xg8EK7vhKr0nOgURDc/7OXw6yc2q7i9c+5+MRKzMPMqVlt/jXW9yXH8+TQOJEkIkgTWV6iYMBdk5hfRShVXrWGPnUTCmMXb7uL4uZeTOYe1KWlrlaS5wvrVC9i0Q3djBWdTcI6QQQi2AhUTs2jmuafyEuqmuoWPC6goTx9/jr88902alY1CMNJNglEvAaOrQFpVklmhuWSQTKmsClFbqKwYwpYlyJT4KUGMw1QgXgows4pUpdBaDEKLVSFJAy6mSrDROxKhVsnT/SdZQCsRkC5EG0gW5L4pYrd1Zg1qawTVLlHYILMrY9sIgl1Rui+C9a5hoTZZyxXMCcEc2LXRvRaRRChuqBbW8H0Qh476CWV1pXet0rwemwi2MoM2r25xNIePXQsu999//5Zx7iLChz/8YT784Q9PbFOtVvnoRz/KRz/60YltFhcX+dSnPrXb4Xk8Y9GwrAa+MZOplBwJyw/Gw61v6WXlPMzHcHMiBFTCUxj5HpYt3uopTAc6+CY2I3AZ4aXnyFcYnAENQmx1Bp0/TjZ/jGzpNqoLp3HHztK47R5AyTqrpEmH9OJF3rDW4JjLsyOqQCwhc1IjwIxUh9aR8eQc35gnTitIdaMUIbSp+eCYixBvGwKhkFWLxqoIBhzErYDqmiVuQtAV6i/k2WRDAXG9yBtT7CL/dSXkPixSUbLFABWDcYZsFTovrBOd+3Mkq+TaEcnQYHJWYJVc2Irnr2DC+S0d63vKMt0izNqIEsUWNeMy3AqiSu6WN6mPIuLI9D5rf1muwdnsmH3YOdzJIzyeXZDLDA6nneu+byPKTAV6du1hDusDRajFp6DwIhi8l4MXZqaDqmw56W2xJUOyDIo4RVyCSXOtTHTe4J59Atc4RVabITu1BDMNTG2esH6MxuwS1ecs1XVHNzYkIWBhI1HqaZ6o1fQnxv5eSwiRnedE+9Ws158mkw2srIO4/r2iYgdbbvUz6EUfBdCdc3TnBEQQB5VWhFEIVy3xKlSWLUEqhO28z8AObDRiIP2BLUouOTQzqBUkbKNBC/oapvJg8rs7tSs4zXL7lIKELQgGuXEmXodtMAaiQMmLao7bRLCtgKxvzhvfp0SA5IUYB90czd+gF1w8twSumEedZnTTZa6vGUORIT+W0QfP4X24iAlBxolj3lQ0HXRYU7EnxplIFVFL2FqHjSaRgH3mW7j6HNniKVx9DnfsFE+emkOXIpIoN3eKKnEGM4lwcs1x2xVLLe2PNJ84i31dnDc8c0JYnl9i1p5ExeJkI8+Qq2B1A2eWcSiZrOKkXfSjOJpQFmo2jb9oaYTuTD5TuzlBzgrGGYJMiDqKcUJ01RJk5a5yQUacEF+zhIlQbfX2MjlKKHMbKBZTOLxKkBLPODbWdpc7Zjw6OWVNsX49cRxzQmzG7yuYEyRU8vTJjk56gWo0Pgr3sOMFF8+RZ6t3wuvBQHl79MISc4RBSnPPdClHxEzz/EpJyVFoJrKMYG2FcG0ZFYMLAvT4WbrnXooeP5GbSIySyTrrgbIcVGiux5zouiIZGrxgLas2z1nyQlUIOjH1ICSqhyAhgRmYbEOOo9lt+VjEgtjCHywj40JussGSmEugGU7aWGnT8/vom1ylf0RgFGfAhdqvg6iLeQNDPsieYCUIJguJukL1kqV+Uak0lbgd5JlvC4+cPOdT6fyrIWstsvLkD7F25SmgJxVNOM/otkoZo0JYF+zqZLOOYpiUul8gT6InA+2SatncdbSeO15w8dwS3MgpVUf+HcWpIv2wg0M2+RdzauG6U8Kbig4nuSOoqMVkDi5+n+Dqc4SvnCdcyCd949YwZFxrHudPLr+SNK3Si0iylDwqLipc7mAMVGsBCMw2KoRRfl9IIMwtVMAoQRAQxlEu3EuFgJf2R1S1eSSpSgcrbVK5TGouY2UZJ11GX02GjqY00Q/cdgf3pQ0VGyrtO4SVlwRU2obXPP0ybr92WyE+WFa6F7nYeZpMB0KB7SzQbd+FZs+wteACkSgz8YRooIJa1SL1sJ/Sf9NxJOASQzuzxJtqO+aCUa3icrOiCojJnXNV80izIMLYdFO/hxUvuHiOPP0p9Aa9dAw/Ljc/lFbaIVa3ipW8+VGRwfFJ7w3Vm4oOJyWHW1FMnFCNr2Fs4eoqgDpOzFzl5S9+kmfOn6WT1EmzsCjnmG9rCcAJ6qC1nt8TG2stysnPTZAXAwwjQ7UeYkRYvK1KGOa5WSq1CJF8mjJmlkDniOQk6iyOday0yOQKiVzMzUUkOGmVjkRLKpmsODZXrBm5NwW6M7B8MuUVzQZBEcUzFx7nVP0lbGRtNojygpcIqMHZcOswZ/Js74EpFZQcc7qNgWAWJFAk3fyocomgeaAXg7DEwZNFpfA06wlqCkl2FacJZkKSwMOMF1w8twT7dhM4SLQnWt3Mg9weJVdXd9MrlOv0evbLQWqvRp1Qe7ss9hdA5YxB4nJsSvFJHC9auMDZ+cu0kjqJi+iFRytwZW2B1IbFS4NhpTlLNwtAhDSNyVyIWkEJSDIl6eSmjZXlpBArhGo9QHIZgfmFCkEozB6LCUJDFC0QBAvELFGXV6KAky6ODQYRNQOn/Ewu4iTXjjjWyKSN0kFJiwdE7mfijKDiEHqqDaEqs1SiOVJrWE8cmbNcuthkee0Ux5baBMEkbYbgFDppnhBvEoIgEUyoaNO/PoNIqc1eZRIIJja4rNe2VHn7iL1AeMHF4zlgtjMVDdYe5odLz1gATnsP8WkfzwRfgf7b59FxegZQHKp2rOlgrz3mDJ8rrUB8whDMjrlDYwjmQEUxtoLYChqv97szODDKbHWN4fMvHJ+5SlnPmNiQPGscNDt1OllMu1XnheXTODW0kgqqAepyfw5VaK0XxicRWiu5f4sJm4hAbSbGRHDyTJ0wNogRKrU6odQHxymCK5yBY3e6r3xRSUEdVppYWaYjz6AmpdEWfvi5uwjt5qmxm2V8+7tXWFlPyDKLtQrMsbFynPkTl4ZEiTyPnKIdSNrCeQk5PuNYqJdEiN5YgHZCLp3tKaMxiCqEilSBvsKpEHMkQONqnib4iOAFF8+RJ4urZNUZBg/s66sNGBfTcVQYTIWjgtekSJC97yVaEMJzUrL7OVAhXQEy0LZg23ldHnUgqSlPJVMYx/XFaUKSXWVTevdpIIqpQnTcEJxWpOL6Dq+lEQy/2WsEtgKs9zoZdDfyfcCgyF8UJP02x2Y6+dJ54SWnn8FhaHbqqAuwGnBltYHta3ByoWe1OYeTwa8pWc9IXcD6lU6ukzJQrUeF6Uao1ALq8zEqMDMbEcUBQSSYAFQjQAi0AixS4Q5ULHXbxpl6rvroWYSKE3Bppc3Fq80iPD23D2VhynmxNNViimifmgpVGUQuVTYstgqXNyqsdfP7sx5BEDhSJ3RT6NgAmyjqtvDXgaKi/NgLCuSRRXa5cEJWRclQE+OCLdQ9hxAvuHiOPGoC1OS3emrzFOeew4SgxhG+SDDzJf1VMbfEDc3ziTiDc/mE4VJBW65vInQJ2DUgAbtR5Lrom+huRnoml/0ILSUNlcknNXJLDdFigDQcxINE//0yWkNj6I0kr/YcpDVcbfdHMn4yzoXQQCwGy0J9tT/cE3OXB/ZdBdWA1IWDq6XQTqu0u3V6FZB7WBdycXWR1nqNFy7OogSIMblgUzNElZD6fEC1FlKdiYlrgkiA0ZAkivnL27uceuYSSysWRVnpXiJzXVY7EXG0SJLmDvVJtcnK0pN0qy2uyKCMYpCfbhQhMDD/gqWyLJw8WeFYIyIw0M4cWvIIEhXssuQ5ZcZoX/M7VWh1AyITU48slCKrQMkcBBWTR2gpOO2S2KvUzQwaRLu7aDc5XnDx3FI4l+Te9teRnZmKDhvlhyYMx6QOfZgKIiBFeGvZAbGvDoc8770pQl9DhdpALR8gRGdBU8W18gRidlnJmg7t5I6PfUXO0AQ+ehzjTHrlZdM2+TkmFwUdc0cZ0FhzzcKMILESNnLNk5lV6OcAcaVeeqqFrUcuGhC2jpPMPZ9PjuXrvy17OCdDLjeOOEiGeonDLo3a5iqHCrzo+At00irr3RnanRrLG3OsNOdpN2u01mJWruQHW6kaanXh2DHDcb1KnLSRyy9wbe0qK66I8CmczU1thrtf8UqaTeH5K+e5svB9kuoGQ9IUYEUKna6SOui0DNKCF5a7zM0YTixE1KuOxnxApI7IWdyGIb2i+f3bvw4DEUadI1tV1muGZiJUAoNBCYyjWjV0E2WjE5BccUVUUflMCa4yh3JxjGfM4cQLLh7PAXM0TUX5W6qYmH4Sj6EZf5qmopEuy2PYtGTyNwCJwDTySTdoQOwMmoFb1yKFfb4j14Fkw+bLNNc2ZC2LuHwqUUu/vRoFk5sFxssYexdmUruK1Xa+eVg4kDJw00TBxAJVJWoEmBlF6vnEJaHm4cDitpmwJt2hw6Y/NZYgmSdIa9h4Y0/Hs90Ihr9vd87Gr++dn2rUohq1YFa47biQuZirqyf47vnbaLWrVF2XufVVGmtr1C5sMOPWMJprnnph1CrD93FUSajwAxbrq1xuzBKaOdK0S7e1kptnbIKWkk0OGd6csLqurK4niChhmFIxlpkIxBmQgEyEeqg0IsOMM8RBlutlrGCvwFztGHXTYKZ1EqOGdmWF5swVknCdtJ2QrVuk8CNytsJG01BpgFQquOosQWdtB+f15scLLp5bCL3u2pZ8r0fhUbEZIxXicAEAEUEVVB3WbYz3ld0PTshWlKg+KiDtlpKwI6CBgwCCCuS+Tz3DCNR6GVa1iDZJi1TvFrqXMiTLx+BCpXLKYNfBNumPzbXANXsemHu7CxwOrWSEDSVaMpiwZxjQYmzkAk04KMY32FPv1X1STMkux6OAqxB0jk9dcJk2ZcFHUYwolaDLmcUXWKxeZe1bhjDLiNQi2hPsivYlzZOO9Lrefop2+gwcO87t97wRW53FOotNO6hzXH3+O7g0wWUJ7dXLuYO1zbBZBqKlfoU0hZSQZresPLRgQaxjJoRzc5ZQIBJYDCNOX3sZx+xLQA0qykLzduxyQide5anuX0KyDgJZt8Lypdu40OlytfECp07Os1idY7a7dnNHWO4QL7h4bh3E0k5f4EYYbSa73B1y1PR9TZDCb0Rl7wfrwqJPB2YgZOaFf4t8FJt8gPcfTTTeibf31l28eVcG/VbuKCfvyMcVzgweqApIy9D6lqLJ3gUtazaovUQIjvXq+ow6xPb215t4h/e1d4FZGZ5mQYM2rTP/H67SntBvoY0qjeJmENj7XiCioIpc6DDT3WTsHGLz1RLiYJF6vEQ3+QHR8jX48p/iFk5i6zO4hVNkjWOceenr0CAE53BZFwSS9gZpa5XOxhor558i62wweCKURKziP701zQy+fbXIYQPMS8idx05C4RuTp3MxhLbKTKfCS7LX8/3Zr7PhVrn6/G0krQVAWV7eYHl5g1N1y72NozHpH4Vj8Hh2iACOQQKn67ffIBBE9hbqePMyRhwTIQhqu1aKiIvRtdvIVm9HkzpEbUxtBYmaBI1nIeiOFMfNE3spri81bZ6ItjdX7cZLY3fkPjZmFtzVvXoWKDa6RtQwuJIfRU88GRZixn2edGxlc97gQpUFFe0lT0GRXsI2k6EVSz/iZqjH8gJTbOEOwmC4d1QwqSFbsWN0UFubyUAJTI1qdIYoPEE3O0/QyQgvtBEEJ3+Nq1ZwlTp24QSuMos7fQ4XGOLKDFTqcOw0p07cxuql73Px/HexSTeP/Cn7bY0ZCeQWyK4OkubpyO/LCVw73eb7t2V0BJLbljFfMwQXZ/JCnYHjhbDNyvzz1KxhLo25TecGDuoqRGqI1QxpZG6K6zYGL7h4bh36SRyu/64bS0J19igKLmOWTqinMhnBXnkp7trLUVfJu01n0dZJnMlw62cIjz2NBC+gZASiHJtJqAQG53ohu0KG0OmSP6jJ54Ik630f1KiZZAiYJpKrn6icNrSuDYSr3faCbPaYnYYmQ0d70YBuVmWtM0tiIy6vzoEaRJTbT5xncWal1HbceROsBlxcOcWl1QXi0LLYWGW+ukY9ahf6mxs7DQq5+U67g+8737K4eySmVjlDN7uQ1wbSXEQTFYJOm6DTIly5ghqDfv9bfY+kHAUMs0Y41VhiffEEnbCCE6W9fIluZ6MvNHTba9ikVMVelNBFeb4WKWuLlHbc5WvnvsWzx16gE3fzM33PCnp7G312Ng8yqlk42WUl6nJpLYFmwDcvXwEF21FsW5nTmHkNQYWKi1lKZ4jVcDqtE95kmb294OLxXAeMcRNTg2+aRA4xqhnd7DK7ml5VoNtAtdqf8HsYG6EbS2R2htmoieMaoVEWa4oZqRGj5E6qg7EImTMoSjc1dJzQ7YIrBNiuDcZampQxC0eOZadXS8P9XNvCuXabaJ+h9tDf3zjdE8UaQeikNTpplWa7xpXmAtdWF2inNfJifvnWRhxnF68WWpbJgp4A1kV894WzrLWPIQjfv2i56+yz3HX2aURvfCZlRbEJ+wiD7wkvxbRZyhIM2j8/Aohz0G2V9jPi5NveIF5fJ7vtLpLTLyI789JcGVy0ytI2LkvpC02inF0WZi/VSwkJB+bBlfoK3SjPkyPFDeNmu7h7upSvvgJxzaCnFe40GAWXKZqAxbGseR+aJjx7dRmTGs5crHMuPUZ9PUQzOGYrRDdYkPGCi8dz4OSRHRM1FBm4zlERXvbiAC2lWkflnvK5QRBwMYggUuQbGeNFsUnZL0oc5FquSmCZB7Q+cMC83FJWWoaBRDl4jxXpGZ8Go7EEuY/EFpqassJfhheMbbX1NRdsG1zikJobMgENhCsZbq+C04h2p0aWRSiQaZgndNPB415UWG3XabbrOA1QLXvnlIYmSmTGFM/ZdERKFCTMzXRYaxfBVRpwcfkYLz71PHEwyS/m+iGAXbOFCWzvfeS3X0+Qk5G1W21ZRgk3Vgm+85cEz3+P5I5XkJ1YwsU1FCGIZwhHcsatR7DezFjcGO6tksa89MqdXJv5JlZcbj7FFeMbFVlLJkHJTUzEgsTlPvMW8WIIClfu7rLiLpFdy8iWlWNXK1TXQ1xXqduIpWwWo7DgaoSa+98ctIbGCy4ezw1EEVwWoMlN5Q0wBXZ5HGMcekdcWob9KMqhH7vYb99gJMrJGUdMxOWWKV6WBSPKfC1jtiJEpmzaUxKb0clkyAcgc0I7Hd6nUyG1vTdlgCD323ERoJi4iZqdpV+XTHCXIby9N2UanBM2utXcopZVubp+rD8ZXWvOkiQxnaxKZkP6BoXCkXOgjXFUdYMq3TFnTHACXa0ShFmuLdx+pAhKHCZIIQiqQCupsbYxz4n59o6O90BRQbNpmAen8RvN7w+jlqi5TPjNx8jqs2S3vZT09IvJavXc5KMDC3cnhL86F/LSi46T60polWZVuDIXcGX+DubTU6goKikpV9C+T51iWcaa1pDuxbGBSsawAOz6Rj3p33JKpg45HRAuOdY1Y7WT4lIlW9vgO61roIZZjQhVMApn7HzuM9MNWLxUJcqEmWaerVgUjErJb2v3eMHFc+QRUxRLKwqpDZmdbyiKddBNc9v3rUr+fNxBjljNzT9bmSx2s1eDMl+3rHQNSZbfI3FkOTFrMaIjNYKUOIDZTZnTN1cZTjPhByshmROwFdylu3Gr5yCrgzjc7AsEs+cxMxdwYaffz9gDRuleNFgC4lNgIsXaiNX1ebK24dryMdpJBQQCtTR0OQ/vjZTK2QAi23eTGRgyFBHlWHaNMO3gVgdv4q7tIBO0LqzMn6B63DJfa7KdQ3vPaLK0eI3nr5zGasixmVVuO3WehbnlHV6TrRjcH3v2lVGBqWRDmNbDY6C7E6dEzXWiJ75O9NwzdO94BfbkWVwYgYJRRzeBCwm8MKsEy20WwoDmqQpZ6KhUI8Q28rvRCKGcLI2zuPquLIA6EjmPowPiSOUKVlIUi6O4IbD0CzZIL4tzbso1NcHUIJwPgDxNQIbrn97vsVy0Vow11DqGU8/PEicBC5frhBbq6xFROjDbJW7nPoBecPEcaRTIqrOoQJqt0c2u3oBRSKGeLgrG9ZYVfw4pTCJHiF3lLckfjr0H49Zb9R5u05A+ZaD6h/xttUg2J6UU7oP9je9jVOSshJb5qnCtbdBOA3f15XmYtwAuRFdfQrp2hmDxKYJT3yy9GY/DQCJkzzmyS5BXruiyqOdxqWMxe4Gy0GuKnCThHFROS54cL593cAmIGIjITZfGYtSgtxVJ9sh9HbAgsbIQXt5mbJs5Vl/lR+78a4LIMVNZJzTJmDM36VwOfhsjU25+TVLJa/nE0jdj7QqjSGXvms3eHapqDvDFRwmby5hvPoatz+MaJ8BlmOYG667Bc/YUXargTK6kXG5hBOKqoZctd65RIYyL1AEUGhsDc42YIMiXR3FIJby9/5urSk84SbG0+sKMkuIkI5Oi4rsKKk2c5M8y6b9yTIrTElwAzRlH8+VrCEJgVxGU2kZElA3u3TRJ4Xs7O0tecPEcaQRQ6UWZZLheSMH1xsDmp10RCsno2/0hRQEMRiLGhcxujTAadzJe9OlNZVOaOfo7yCf4NBMyC3H/eo06V45uuFlTJAi1WKA1MsH1PYEFcTF27XbMse9Dr9ryuMH1tlGgq7hu0B+LYMjdi+3wNghSWIU0UdovdEnPp2RtizEGEwthPaB6e0wmGXmdADBVMNWgv1vT608Gx7n5egwfvZGU441r+TSmtmeo6p+poaKNQ3qgXJOm5d+BgmuDqCG7CsnVDOfyt/34WECwJEgwwW+s12/hiKuS+whJKFAkSxxM7duRt0qyqzhNiMNjiMToQTxLigEZVertKlVXZ7X9LZSMM1xlUZ6jbepIRWnGC1wzx/vbOBew0ZlleSM3Q+bKSZMLWiiXTFE2WvJSB0EsiBGOn60RRQEgmCCiUl1EBEI5jpg85YCS9ofnzBpOUvI1TTryPCquEOw2UJL+jZIPbeDfp6LYXEFD2ugOm127O69e7QUXzy1D36HxOpuJVJSgMV4wyayg+zH23nQog9wqO92i9262Ex3HdO18Y0TJvALxUOTJpP1NmjTBDO42Nh9JsTSt4rrzmHinadhHj31cv7l40Kv03P5um9b32kXyPrBYLLkQ0H6u05PsAcHEgqlKX5NhZgJmXjZDND/sjzAqwkj530xIL2eoNYQNQYPSVs5gV2AQQqx9/w3jDMlqsaoX0aSKcxacQpqbCQVBM0hQqidBQiZeHhVFs6KicpB3G50zmLpi2w5dEZwF7WrhpTpZqwaaF2dVJTRz1OIztLrPTN75PqkEJzk++zcRY8hci2b3ewSq1LXLXJwQn1XkxApOfpBndkZwCs3uzJC83cliltcb/ePouZanWcRKcw6H4cpT17AupJtUECNUqgaVPPfU/LEqGsDMXEwUS3GrHCOuBIVQ6ojlrlyDJ+CkDZrl+xJQyci4jCMFLJms4aSJkw6qPWFo9+fQCy6eW4aydvl6yi4CSFh2NBg86luJ6YfnDkZ2mAUZxblRh7/tt9F+1NV220zTVDR6P2hp2U7MRJPJnSS3dgpR0fyBv2OhZbc4ooUIcZ3x+qIhh2jFdRVXViKsOJA2C6+dGdKTAJjuMUxWwc5cGPRpY+yFKsmzeVr5TAaCSX/8Fsb7c23289IQ6q8KwAjZhpKtOgyGcNEQLADBZDOWkJv8ejOc9IoOBkp4CkIEzjnUGVxLsVeE9ILCRM1nWVALma+9km5yCasbHMTvVYgQUzhXSy0395EPz3aV7vfBrAmu2yvC2RMilpEAwgUhqBkas8qp+cuDGqhFP07Lzx0hsxHNzgyokLqAyyvHcc6wfH6WdlpBxOAk18oEIlRnIozpmc+EMA6YOxYCMSIRiDK/UCMMhYjjpUvrULFY1nF0UUlJ5BKII3PJjs+PF1w8R5r87TP/wZWZ7nv7Ttg8EWYqrHV7b3p6A8Y0ZfZzAGrYmdPlwZiKer2GxhEYLS3bmyC57RYKEnYJ4uae9zF534UGawrdppe72PUKwVw4ML8gVNbPEK/cSWfpL0nrFzHdeaqXX4NdbdF2X6Gc0XivqBGIHcRKOCOEJ03RnRsRurbtaUhA62MEjBLMGUxdsF3FXttmxD35Ryr5BK07Nzjthn5Awcj93vOR1hTSS24kJ430/7Kr+cuSVCGaNVC1BI0w11BFLj/0kv9SGFqqs4OEd2caF1GgmcyQ2BjrAq6szaMuYLU5R7ddoZXG2J7vFobVC6XxClTqLYxRTCDMHatiIsN8owImIKwcIzS5PbMiua9Nmu088swLLp6jjQnQeAYoPUevv7WILIMkE4wRnBM6CaykAWnHkL4giD3UIsu+UFtFk/mbQnALDBizU+Foiymu5+S75ebD9Zj2TyFqiSVrKWqD4k17H8JDmicoM8WM2ZeFXIhJZqlc+GHc2S8Rrt1OtLGE6gVyk8ROM+X2euyd62LyNY5oCTTqaYcK36D+b7esedvTkRVb5z48EirV00JrpWc2Gr+Nqu37/IBul5dvz0ThLMOmSmXgN1aIupu0QyWtkAKpoCkkzeIcG4sJBakrlMoxTMLUoD7bpi4bBHPCyRNXcx+XEzEWYaNdJ7UxKKxszLLRrfa3VUADaKURnXaNqxtroIargSG1MZV6SBgZTADzx6sgQpZ4jYvHAxQvZmbwE70hE6MK62tCthxjjJA5h1qDtUL6AqQX84ew7Nqh9WggNkRttMMpqDzBTeFklUxF+ZQ56Hd/ve/UDHQAb+tF2K86wdQMEho022u5CaHvsVMS+rOZq3AtJZn/AWl1hWjlpbl5Zg/9iwouq+U7UIOalOhUm+hFhl5F5c2+SMP+Rz2n353vf3NL27Z9X6BxWNeh1X2Wueo9QEAcLtJOJjlW748kXYVqXjMqCo8hEqH9YONdnuXCNwgLakG7O9PvuhUhK2q75cmCCydwsahATItK4V8zV/Kf6RHOGLI5g61HCLlGK5UKF5aP40yda+tzOIXuJSEhJrXeOdfjyelVK+bGeo4kz0uumo4V0hC7ptgmsCFDk+cty44zbR6cqQgEq7kvqNmvnUXICxWWdzKyXlyI2iqEnc3r97NjAPJItaBmkEj3nr9ElaxtCYauj5LVLrFx+nGyxjOIZEVV8H7AMDs+d0kDe+Vu3PpS7tSCQmWZ+M6vIEGLaugITd5fZpW0cKLtTbxaOP3u/27Qwkl+q3Er3ewSs9yFSEg1WqKVfH/fe55ET7FTDU9iJMbuOiN1j0nur9tco9JGmgAE2zXrK4VEwXYtLFtCzUsXBOTCzktZzl/cNMxLWgi0ZJaWhe/v8Ii84OI50kjpQZ5rhfdaqXc/KNoRuk/1POPykZXX31ix6gZz0xy6klrFOiUc/4zeMZUwD6nu9M0KY/ZmYzSZQSor+9vZFuz7flchXU6pno0GixDUpKTHvttv05fRyiaYHfStl1+JWzvHIIpL0e4itnOS0HyPM/MZUZD3q1rUmeoV0SwSSiZJyOVWSHfTvD40pY4sGzM+01s++Yx1sysk2TUq4Ykt2+0XKQLdodCgKewuN9IUmeCwPHapDrsxb/ZPL14RnBIWIdYoNHSFmtu5YOYFF8+RxonrR3d00yuo3qgKzVpyKCw/gHS4DXATzeTXh6CDqaxBq76DxgdnKsoxKAFKOvQA3i15rSMo57DYtF/p+YIczISU59aA/Z+r4W1HRW5k8E5vJMZIlIcO76jrcp2o0rUVEFUC8rBoLWpUjWrCRJWomrLahSQLijNZ0nSJG5l487IOUWQJDfSsyB2r6PGA7JKQPf9iREOkuoZUVtFgEGqlatF+3pxdapd2QaVyGsjr/rie0viWVskO4wUXzxFn4LngbOsGOYDKhM/+SQSAyZCgW7gLbjcJHJypqJ9PRqcwFfX6mDTMwmlWDrAYXTlL/15F4jyh6uStpH8t8j2EZp5A6ljtbq91EYXqCrL+Isp5V02lS3RyZWDg2KKbXsbpRt3R7JqhxqE4TjUcqVWWWwGZE6qBZWkewsAWQlBOKw14ziphZYHkyqvQtI4EKcTLRGf/Eq2sjhmIGbNsOvTui/J5ucX1skN4wcVzpBFGH+A3o7CwhS/ELcikB/ThSi5cfhufNHBBs+qeXFp3P4799TDpqvR6zupXqazemR9Lv9L3dlOtjs8aLA7CtNyqvHK0MQChDIu0Io7jc8psnCICc7FjPTHUI0clHBQf7PVRCy2VIKKzfhuazeW92gDtnEQ7i1BZz9uLAg5ECKSKSJBHGk2dkif04brxrwu3bmU3z62BBIVKfosoxxvGds6Atxa9VFrj6E+/MphspsIYx+hpzBW5K8Z2E7fg2osHNjH1ykn03tv3spdtE+kVbVzU2kPveYbfgUPv4Npu3uPkIxAgNBCErm+im40c89UUkTyiJg6UxVpGNXRj++yb9eIU8uphxdoA7RwrmubCRCc9jwBxuICRTVU3p4wCwXXYz+HCCy6eI42Nq7hgn56WnuvGpOmpv7w/yR9EVFFpX9PovpSZdOyudUr7mcAgA/B+BCPZVrDal9jVN5UNj7NfunJHnSuBKWlSBIIwD+LL6x8NXIYn/+X9VJeWkSK3jhYOwdqXq7TozxXKkOuhvRWMCQpnYE8PL7h4jjheq3EYGBhVxkWCFN9uRivfBHY+1pv/3pQxOpHNbXrsxTQ1fA40jdDVWTbX3R5Pr3TjsdrAr2gmdKUonFFPny2GIZNalpb0w76vl5fazX+PXG+84OI54oyqgz03Iz0ny+1ngoM2FekgimOfyDYTpoqUcr1Mn8HUuvfpVXaSFnbTLna+PxO2EdMZ2kbTiKxZpVcHZ6dEgeZalolMybG7UJUd5LUb2hdKYKpM8Pza4d/RYteCy+c//3ne8Y53cPbsWUSEz3zmM/11aZryy7/8y7z61a9mZmaGs2fP8o/+0T/ihRdeGOrj/vvvz22Ppb+f/dmfHWqzvLzMQw89RKPRoNFo8NBDD7GysrKng/TcuvTeFXvqfy+83JwMR65s5RsyXVNRv/hcv1chzcpL9tE3kjuabu+5cyAIg/pPe91TPjlvkxN3SKm5Gw2nItWrSLyyqTswWBXa6e6vg6KDooqlHlspJHYKZuMil4whIjTz++9vB/urRKdGjqlHLvGbikCoYApXHMPI7H60BJhdCy4bGxu85jWv4WMf+9imda1Wi6997Wv8q3/1r/ja177Gf/kv/4XvfOc7vPOd79zU9uGHH+b8+fP9v49//OND69/97nfz+OOP8+ijj/Loo4/y+OOP89BDD+12uJ5bHI0q5O++jkw3isnkRo/KM0RWRbvzN+S5uvlWENL+ZDkFM2PcRKLJxeMONh3itLwwdMc99V4TdqqxKiW2HkJsHmrspnIAeSeZC+hsmVVeSq0nk2TLqCaIGMJgJ7mH9sF2J1KU+Kyh9mqovlqo3muo3ivU7hWqrxSiF0F0BqJFINC8onbgkJ6DlQwn6j8sj8Zdh0M/+OCDPPjgg2PXNRoNPvvZzw4t++hHP8ob3vAGnn32WW6//fb+8nq9ztLS0th+vv3tb/Poo4/y2GOP8cY3vhGAT3ziE9x333088cQT3H333bsdtucWRBE0rpNPD5bUrcH1UO96doVoCG6nURNlU9EUZrUxUUXTcqYRFA0y1IwPlz1on51eZtl8LHtjS1ORCoNyxaVzuIudje/d0D2/SP0VAbDz+jXl3Y/2qygbiSEQZZ4x10MgDh2dmQ6m1kGbg0zBUsrqC+C0i2Kv0yS/jXlLlOAYaMUW+rXhUO+gUSQ5tELYDUDzUhC25dDuIPWhomRrDkkF7cogb+Km3d4cz88D93FZXV1FRFhYWBha/sgjj3DixAle9apX8cEPfpD19UE8/xe/+EUajUZfaAF405veRKPR4Atf+MLY/XS7XdbW1ob+PJ78uatoPxpApzLfeQ6K8iQx7kIdrKmov3AqMpFgQpAJ1okDTc/hBLvhMPtMkjbOVKSASWeJV++gZ6ooKzJ3Ey01cWTdqNTH7i/GRjJ83Glm2OhAJxuvOxIgDsDUNjC1YQ2Z7RxDbTRmq4NmnOZve/foMn0vq0CRukVmHMw6wlMQnVPic1r8C/V7DLVXG2qvMtReaajeYQiWlOCUElQVE1O+3CN72SwqbvaxmZ7PzYEmoOt0Ovzzf/7Pefe73838/MAW+HM/93PccccdLC0t8Y1vfIMPfehDfP3rX+9ray5cuMCpU6c29Xfq1CkuXLgwdl8f+chH+JVf+ZWDORCPx3Pw3ICwoYkT55QUOlJVpKJoc8xK5eCkFwt2RQjn9p/eTm0u7PeUlQpIdwHTXoTG94ZOokxlYpKRf3ePK1QJvVQ6HWtI1WAcWCeEY17ZTfGSs2k0WTwhw/FBRywOtIuB1DFSxem4fDn5QU7KfrPjMZrCP2i20NI1oILJz2EGWOhe0Tz/HqAdIV1RyHR8dmWjmHou6fRy9Lks1+r0/diGtFk7v28OTHBJ05Sf/dmfxTnHv//3/35o3cMPP9z/fO+993LXXXfx+te/nq997Wu87nWvAxjriKQ66nQ14EMf+hDvf//7+9/X1tY4d+7cNA7Fc4g5Wi5pR5tBDMlWD9peq4OKKtr8ba8UaQ+3eEc+uImv54y+fy8XxbYs6hQJBmOV8kS5SUO1831O/n3uR+slOBVUQURxKqy1QwxC5gyZCtGEM7O7a3I9zCb5BB+YGoFUcbQ2rc7WHVFjUDF7e63lZKRvOOr9EouIhij/q9xW3rcStcCuGZLv6yYf9PC4EL+U3K7jHCC4BHplrLQNut47RkWtg2/vbJwHIrikacq73vUunn76af70T/90SNsyjte97nVEUcSTTz7J6173OpaWlrh48eKmdpcvX+b06dNj+6hUKlQqlamM33OEuDlMsp4dsLNJw+BcXsHZBFtFH+2Msqlo8Ig3xZe9999zUhUdmuJH9q25k+QBoP3/7s/9VxTiuRiCzaUghTwiVIfC9XYnjImAVJpsViTonk5977y3U6GdQj0C6wydrDgjKqRWqJnR/hVT+CKNuvWMH8beBINds53mTwXSXjOhG8SsBg2sREQuoe7aDCQKIXQpARmC9n138t9A3iYY2ddA56ObliEgMxBUFTkP2h65OyogvQ6D3NfG1ARqRYsGsDQwI2ki8LmdnZapCy49oeXJJ5/kz/7szzh+/Pi223zzm98kTVPOnDkDwH333cfq6ipf/vKXecMb3gDAl770JVZXV3nzm9887SF7jigaBmhUOH3qhCq9nkOGkikkzhAF+6/0PW5SGtaR7FF4KVQespXmYEq+NFux36l1b0PcjcbCYSprOFF0KCvy7kee2t5gcy1L1wbUI0c3K/qW3DSRpgbijGEhS6jGoEFGMNvBXRn061yEZDUkzH1fnHZJ7TIVU93V+PaE7kxAchhWokUuVF5ER2r0kvIFavslG0SFWDuEmpbkoeGA+YprUXWd/vpAHXXXRNQNaWFCzRAcomACCOeEtOQapIDtKFjtC72DfY1jdwLvrgWXZrPJU0891f/+9NNP8/jjj7O4uMjZs2f5B//gH/C1r32N//7f/zvW2r5PyuLiInEc893vfpdHHnmEn/zJn+TEiRN861vf4gMf+ACvfe1r+dEf/VEA7rnnHt72trfx8MMP98Ok3/Oe9/D2t7/dRxR5doxKgAtCBCWxy1jXudFD8mzBzk1FU1SkjfEpbKawiBDsV6rYweYDLdMBSjD71Or0kwOWuxz9vKddlDbqSUj7uLBJqn1tiSokKVAVrANXEor6IdgjYzYqaGAJT66Qfv+2wWBcjCazSHU53x6L69k7gIO9fjsTXBJT4QfVl5BJVHKFFZyExedcbkuZ7W8z0KaUWRiIJ5L7/eSCTu/XqQiOuWyDU8nz1O0GYiNMMguslvYs2BUHqSnUODK0zwGjLwk7Y9eCy1e/+lXe8pa39L/3/Ep+/ud/ng9/+MP8t//23wD44R/+4aHt/uzP/oz777+fOI75kz/5E/7dv/t3NJtNzp07x9/9u3+Xf/Nv/g1BqabMI488wnvf+14eeOABAN75zneOzR3j8WyP5BFFExOBeW4GpOest+XkZZjmJDE2h4jbf7Blbj3ZehZWyXU7ojvPe7K3sfSS9k+pPxRRh8lqiAY4cXsc/3QPuhIPpkcRoRr3JvzePbMTIUA3C3pbhITvtCzB3ijGKyAExOFJUrs8tqXL9R9b+FPtbBmYQR+F20oypsBjGlZZSK9QZwMlpMaL6fINes9YQelJiFYCWsEMa8E8iqFmW1Rdm0i7Qx452aTTMIZdCy73338/usWF3GodwLlz5/jc57Y3ZC0uLvKpT31qt8PzeIY44HdZz7TZdi6brl9B2TjBmM/7oV/PZgtlgmsvolkNiZsHdp/uTyhSstUEtZWSc67gRJDuHCarYOMUF23gwg4mDbgxvzghCoTAKNYJ1dAxE+Xjj0OHKZx080OSLU7KsG0sb2Uw/eiX3tK8XTU6w0b3e+xbXTSWsh+OITDjfTgViFyXqmuxEVyHTL7kp68VzNLIlom684TaYJD8ZeAi1m6HvHD8DtbDBpaA3EVXCdUiuKFDzMIu8Nlxu9uEr1XkObIMvzh58cUzmalPOYO5bbBs0z4FccG2mpkpjGZfW7u23aSsHIy4MCCEHdRk/WU3gtA44igfaBQ4IpOPLQwYCuVeTwzOTRJZx41dsJ2FIftS5jbINTsHmlFkxxh1ZV3JgaMIXVPBiWDSGuLGJCtS6KRVVsMFrISl0QmZhKQSk5qYpPjrmp0mofSCi+eIooCtzuOkd4t7M9GhYNtn7+6c+Pa6u6nsRQceO9Md9c7JfYT3s+fxI+8FEvUKDQ5eEvZxpBM326GDqoO0yBcy5N47YgXoZkIrHTU5KmHgqI/JM6eApDMlAVPpple2Hc/U2FJDBKS5f86NejcTwEiMjGZaVCGP0JPiEvQSGY7/240A7wUXz5FEABvXQPIHVCe9dKOH5NkB28+xB28qGrds1/3qIIR0+352Xgtotyj7NRWNE0RyPxAXJqjJRvax9+PY/spu7bSdqSGz+ZTmbO6UO7alCtfaIbafVK6IeRGhMhoPvGnPY87FgYql29+NWdOibr/XeS/kjrqKEppZAuk5/pbPx3R/rz284OI5koz+hp1L8eaim58b8Owds8/piRHb9uNibOs0N0YfszfysnyCq6yiJp2KoLdpH0OnYyf+I8JGkieeE6CT5tlyh/sYTKntVGh1yz4rg36KFLqlrSYTmhnMLkwcu0MG/0w4/J5Du5bbXyek/GHiGN2BDMsLLp4jjxdXDhFbXqyDMLpsjoeeitgiEAWGXj7TiXvXENc+NmYc0yE3FU2rt/KEPsl8s/drM3RlXQg6ocjTpNG5gblERfoFIpOhcOhiX5r7vozqUgQlOr2MRslQe1UzdjyBmcGMibq53ohIWd66PqgUEWvjRMtiMAckS3nBxXMkccaQxXmCqMPzLntrs5VZQ22IXZ5DUIwoItN4dOWZa6vRqKFo/3eMoFRDByLoFv5VAgdasHz/piJyM0RKSQRTbLyODbt9sWVw1vZuGhhsqdjlWXSjvqvtjXGbHKIBEmtwI9c0ChxhONwy378ilQSR4WvmWifQ7lypZe9oM/ZzzFsz5KmzZTujjjm7cgBj2AJRVBQbN4sK6MO/Iw0dwVzINDWYPbzg4jmSqBgIwpJjm9e73OxsKTK4kGx9FlCioBAKprRTI6NZlacjSeT+iJagfmW7pjc1rutI19L+dwEINkDS3PF1UwqM3Z2/3K90ZBtr8r+d98JMlAu1owyckwcCRxgI4SaJUREZnYDJt3Ex9CNnDNZ1cGSkdh3rNnYxzr2y1TnN1wW9SobXiVykEghbiHHE4QnK41RRCPRAaqd6wcVzJCnHczgylGTrDTw3BxMfclI8AAVVxamdqqbiwJQeopj5dPt2B7V7pmAq0l4f2p+s1FjS2Ut9abMc6rrrMZqMvDxCsbWyc2XDUEelvU/YLl/sqFcyRtO1CUI9lpG243uxrplnz1W7pUZtKkyMGBoRtuXgA+uHGbxqiBiq0SnK11+Gct9MFy+4eI4wuaYls9dIssP91nsrsLVZQ6FIX5VmAWkaTlWJNmrymNojV5SgeuO0ffk5ndL+SydExWKrV/M9aFk42r3ZxNSuYUx3aNmoSLETSaZn6hlcvdFtpGghhJuucKG1EEEqXUytU1o/bp+9+/Ggr23PQXf8fhRFU8Fu6FYJfg+IgXdLX/tS0Htx3C6Se694wcVzxNH+/zw3N9sLCwOR4iCv57SjY278nTe9mWMwCY3k3RjaxS5NRbgR4UqmIDmWOxjuTMYKNvlfHDoqs23M3EbfubevtbKjmWu10LrsZ5xboEWVcsnNcU4nJMV3AtlAKLteOAkYL1gV4qOaYlzTxwsuniPMjZ8yPLtky5fqsXELBzIEd0RuHUH2mYCu39GIZmV43X5MRZs2keI/Urb9bK8Hyx2d86rISIZIT/uyOXJsa7QvoJVNzrZ1cmhv6pIi3f/0J+fMNslDicvaonxso/TWxNrBcP38XFpBnUxi+t4uYhi6Rlax6ykHYcDygovnSFJ+kKp2+589Ny9KHqWwhYdu/o8o086EXJ4WMgdJOlWD0Q1DxeKmECe75dnep6loZ823MxUJkVGqRcr/mUioRgCOSqhFlZwiZkmkyNUyTgjIbRt5GLuW9iq5BqE3Gk25tvE1OukFDiIrd2qvbpISN4ddl2K5UqVmW5jrmSFcc42LEwUV4uDYpnpKgdLXXE0TL7h4jiQurqMmBFHa6WWujz3asx9k1PwwpgWUfWGmcz03daXm+qnc9WCdGCUQZs7W9tFD2XthizHuw1S0gw53vMVMRTCizFbyeskAkRH69SERQqAebaWZ2KxeGlVaKY7UrjCIVJrm9VOsdnCa9r6CQC0+Q2DGhIirkF50iDUHIiRsNc78ZcOiURshYFCJO79nqq5NxbWnvmcvuHiOJLnQAgMnOjjsb8+3BLswFU3jeqoK7awwS4wZyoEjoJ0G6g6mWJ8IBLX9PeYFIb02wb8ibzBdUxGCiVIIe9FYZeFgq/6VRiVhNnbMxvlvXgWMsVTjgSZCxfUj1Ib9YIrbz1iiO8/jjOuv2d6/eS9hUJPJbBPnOoOxqRCYKjD+PtFMwFoivd412RRMho3XEImIg8XBGpne+RjFCy6eo436W/ywsGNTESBTeXTlE1fmRnYoOrUpaEfTeFYdMkNMB0EJppZDw7WG3aGHjmuT78vuTUWj58ksrCG11u76QQofF0H6wpQAhnKBRKdCx26OKMpFNCUMLebYKio70bv1dFG7y/K7O/ITnNl1VMenddAuBO2M6nXJKTOgd15V8mSLxow6MB8M/qnuOdIoKWm2cqOH4dkBOzUV5Yq0A9SHDHtm7q+rHbY7iCRdWSvDTdHLWEb+HbuSkDg8tuu+R0eZO9ruuhsEmK9niNG+pkRGxFBVYaNTJKccE3hdj3o5ZUYzvYwnNPNU+jlMpqXVLe25GGYnvZRHMU1AOnJQCo4t0CGxT6SkGfN5XDye3VI44mGxrs0N+EV79sKuTEUHh6CFv8D1uW+mvxdFugZc75Qe4HGU5ktBMKa2i71pETI+vfH1BI/xgkT+fb1rSEc1bcV48lZjRGg1I84uuRk6juYJpDZmX/tBSewqPY2R4uhkLzD+TlFQSNdznxK5jg66fQNZtAEaUI1ObdKGHoTDsBdcPB7PTcHWpqLRSKIDFGKmaJvfiSbFHciL6eDNN5oLMWHPaXKv3ZWickZ7KpmKVHampRjqOuhi4rV+X/3lOA4qK61D8/M+5sT3hqAyvNa1ToCLRloLQsD0jIu9MWR00uehiG4S0W21Z5I4GukK5rr6ueRJ5tLaVRCLoTrk2K5rlrl0fep79YKL5wiiea2ino7Va1sOBWVTUTkQdfA2Xo4qms411TE5TkQgMPuTJKT8STQPGR0/guK/ByGIFcJLXWCkEvKu2UrtP7Jqd3sRxFioXwOxxS/W4WwKWUCrE03x3MjoUCe0gp4WY1hAKyVcK7U8MJOI5k7JCuiWptF837ap4EbLSR402h9Cvt9y9KagHUUOQJDygovnyCEItjpH/s7WQa9jUibP3lAprlHp+dz/KA6iFibq9NftLrh9vPCqCM0EEjvs6yCiiASlisK7F5IyFZrdfNKR6jUk6Aw3KGsWgtaQRmN65P2ZCKLZ/T7qZYtvpZHr+PWTURRHcPwpghPfhso6EjeR6vdIVhyJU6wz6IjzsgJOZeKfDvU/3lyW9R4LJbcMQTBBQC+Py/Axj7s+By0m7O6eMDoIA78uSF5DTKMmLuwgEiEyiHwauItN994+mBg8j+cG4kTycGiUNFsf5EPw3JyEG4Qnv4VbvR3XGXbsVLEEC09jGs+TxauEmosTl9cimhWHMQMHzLJephftOhcrsXGFlmZg0nACa62Q5baQ520ZYK3h+WUhipWTtYwoMCDQzYRWkptcegG6FPvq+VTUY4tzwnLH0LUmf9uUy8Qv+hLptZej62fy934BTIJpPEd47Ds4s0W48Z7ITQuuawhmwFQM7EeA7wtWA61Xf8oeMhUVzdnllBu0MSe/hSw8i7oAE62TPpfRWRCe1YDIOCqVXHgRBacB7S3qplYix2ysVGPHykbARrcX9VNouFQ4vxoxX0s5MeMwkis4UitstIBwHTP/HHb5JYOjqayCbL5OIgF6EOYZGYx3h40JXYeqa5EGo8nqpk9mIjaCOpHr4oIuNkgIwhkCUyez+cWRRKh2u5iqxcn0xA0vuHiOIAMTQ65h9aaimxk1GWbhaczc82PymSgSdlFx2DWHWxbMMSWxhm4rf1gbDXJfiM3elKwYzdXKY16MrS3Hm5SjTiBxkHRCuskglNYpOJe/i/fmslE3iZV2gCpYlTzlvILtWHTmIkFlHZ1doheiL3ETqV/BGcuIKDAVJBO0A8xMoesRc8hQd1ISWHoCzM5H2W8vKERr+anVfOzdZ4E7DKkYWklpB71tRWFTKLLSyQLWO4oRh+071Q6PyjphpRXRTSEOHamFbhpgrdC91CU4+Ve4zhx0GpiZSwSnv4EaO7QfkYiZyp20kud2fMS7p3fuh4XHUTQB1gSZv14O5QZHPMb9ueTjkgpxZwPTcFPVA3nBxXPE2fWT1HMDUIAggWBzeYa+YJEY2t9xBItKUCsCVisgs7mWrb9VqEiUr3duq4TsOthmgpOGdSOTYtlhs/zEtuCS/GE+6F3AOfKXcUXCDeTYdyeOZnOn+0SnWSVm5z0ZidiDzmXoauSbG9xFaK85MBDWAqSXODaXbpBQCecFFypSKYRFgMJJ2DrDIK66PKae74jQ6gobqwOtm2sqdjVAwzbm7F8gLs6dh4PhCta9fozEVOPjrHcM00//X/ivuA5Ou2x1HUQFdYrR62ka11zjqYWP2ojPWO+aal/4ms6D2AsuniPISMii6hR/Mp7pM8bhcVIba7CXS15LAok4nJRcFepKMJtfbScgagjmhKBamANWLKRb7GoPI9cEslWBcSaD653MFJi678UOTUUAlegUhgjHFracHaIA7XxP6caYX7BAIoqtZNRfEhI2BA0Kd16XC5FqimKFOuwELYCmhuQ5R3pZwQWgDlEtEgIKUlmll5BwMKDhEapo7rhbCFOD3qfHoGDkZLSQIGbtGqvh8QNy+C7tj4GmTUVxkmKojXo045oKp6e7by+4eI4go051+X+98HJUKD2Qc7m05G+i6Lrg1oe9ULILpdl1bP6O/Y1k4Mq5+76vbxTIHtmhqahwb93zuRjPFv1oHnEjnSDXxjUgmg2waW5aBJC6UL1doDq8qV0W0vMOu6LkRRSVQdK5cV7iE0bXn713e1zb0HPUAoxUCYMZErd1NuHA9fIPXQ+UXv1OZzKy+ecIO/eMvDcK0sqo2w3Ww9FQ8r3jBRfPESdXU8qQ+6bn6CIj/xaUXw+nyIjHxU3Kjbjve95DB31W8tcRUcAK7prQvZY7cDvJhRHdENrrjvhlgokFMkivOuwFyRVkmgtbgu76GWEkQiQ4QK1afg5FQqrhKZL08hZNheSqIkvX604UNkyDRa5gsLiolfuajfwqIk2ouw3WWZjanr3g4jmCjJiKhCJfhxdcPLcQQt9p2clwVNXu+xoWRCZHFeXtRPRAqzKMDG7Td6U3pmK8HUPybQWjuKykJSl5OO1luFEwjzFV1K1OX0wbiioSjNTIM5iMl5IEoK0E6q7Lk04Fkl4xW2dyx+Uhk1Y+CmMNpp9lcToj83lcPEeQcT8OL7R4bkWGTTx7pmcqGtdVOaqoHx90kEUH94ZaQdOy0FKKJNvz86GnrZmmIzRF5Nlwj/X4NgIzs+2m9axFqNMOrx9DEbGZy4cOV7mKBu2RCC4lWXfUk3WmmV/GCy6eI454mcVza6KQrSioENWj6fwOdtBHaOapRkvcnMazsqZlghS2C2FP1Y5ktZ2OoCgSUI9vH+5PdjJd5x5G18fPZXgfThREiMJaqUkeaRRpOtVCol5w8RxBRkxFsClMz3PQ+PN9U5AZEEfUCAahwnuhVL9pdGruhcMW3xAJCczBJ0C7GUjtKplu5F/6pp39z9B5DirL4Exv12fun2NTcB32YRPcDUrXVEh7ieXEYcOEcFx18CkXLPWCi+cIMiaqSA46ONADSmpXcNq+0QPxFJhqOeJpH4xGFbmQaPUckBfZU9Hcx6GYoDZF5hxJBKVIsa86xcMVUEsr+UEpvH6H/iFZ/nd9nnWGVGKs5PWk1CRoZW3sMKuuS8V1Nq/Y8549niOOjvzrOTicpsWboudmwMwe0H2vgqT1fudqMmztUrHyVnlFKIS0A7nfd3sOc8FSEALNpiokbIWK0DE1+vlsjGNcKUvBYqZYM84LLp4jyGhUkR54MiZPGX+ubxam5Jo7ZCrqd9yjcNJ0JmOQpfbWQDWj2f0eB/tatDszS6COyF2f+myWgLVogZ7jdjpzaVAwtXeTKIg1U30qeMHFcwQZ/ZH7UGiPZz+oKeorMeaXVI6LHl5wxBHA4A5cSNit+Dldf5Jt91ZWaYsr+dcUBsOukF6z1G1zavv0govn6CJKO7nI9f4h39r4c33TMC1nUYHKybKzbZGobbSitYaD/ZVyqBxtxr0kHcRx7/AaFmlUruevcDjuQfs+hT1EBXEQu87URrVrweXzn/8873jHOzh79iwiwmc+85mh9f/4H/9jRGTo701vetNQm263yy/90i9x4sQJZmZmeOc738lzzw1X11xeXuahhx6i0WjQaDR46KGHWFlZ2fUBem5FeipKRTUjr1XkjUXXgzCYxZjKjR6Gp8w0IkxC188rK+Q+LcnC9+nVAMIFBN1BNEklPMEtI7iIFhljD3AfwI7PpyqxdqeaN2UiAh1TI5Ne3p5+JsJxTafGrgWXjY0NXvOa1/Cxj31sYpu3ve1tnD9/vv/3R3/0R0Pr3/e+9/EHf/AHfPrTn+bP//zPaTabvP3tb8fagfPOu9/9bh5//HEeffRRHn30UR5//HEeeuih3Q7Xc0tSvG8Ub5y52f1WeIjeYDSvqTKoDuzP+Q2lp/WYUiqAcoo1ATB28MVYXPUKvVf+IJjh1rj+QmabtJMfwBSdT/eD3bDMZBtcl/OvkEplOOFgPwJN+l+Nwly2hhlXhHQP7Drl/4MPPsiDDz64ZZtKpcLS0tLYdaurq/z2b/82n/zkJ/nxH/9xAD71qU9x7tw5/viP/5i3vvWtfPvb3+bRRx/lscce441vfCMAn/jEJ7jvvvt44oknuPvuu3c7bM8tRDnV96Cyq/dzub74833D6ZmKpjB/CXnIsxknBGluPEpmLhGv3InJapvbHGGs28CYg84UnGfoDaSCZX1yMxVc60Y+8cbnnclaDqOWAIubQlblA/Fx+V//639x6tQpXv7yl/Pwww9z6dKl/rq/+Iu/IE1THnjggf6ys2fPcu+99/KFL3wBgC9+8Ys0Go2+0ALwpje9iUaj0W8zSrfbZW1tbejPc2siCEHSBoQoaJDf5rfC29/Nhpb+PDeOfQovRjGhQdQwnFygV740/5+tXSOrXp1G1phDhdOMzK5zcPd6LoKIBMRRYwf7mHL5gW0p55qBnvmsPE7tGCKXsJBencoepy64PPjggzzyyCP86Z/+Kb/xG7/BV77yFf7O3/k7dLtdAC5cuEAcxxw7Npxd7/Tp01y4cKHf5tSpU5v6PnXqVL/NKB/5yEf6/jCNRoNz585N+cg8hwVFMWleMyOOZhEpqsuJDGX03t6Y4Sfd/eGFxRtKoXkUNUTzIfGpGCKFQJFgkFG6l/k2/2yGvmOE2ktqxMfiEUPRmF+P6dJdfAobJtfrCG8KVDMym78oTf+eH01st30mXduEIMsIrke9oj6jQktpjYI6RTIlnFLOm6lXh/6Zn/mZ/ud7772X17/+9bz4xS/mD//wD/mpn/qpidupaj7BFMiYfACjbcp86EMf4v3vf3//+9ramhdebmkEDRKCV/w1s9lV7HpM93wV2xSCuiJG0a7BJWbTg0FFENcTWqT0r8dzuHAtCFSgrsy/fgbbquUTiRqSK100GzcBDpaZWkDtbAUN3Q5keCWrXyCrn4dufZqHcVNjJCYK5+hmV6f6nqNqNz95+sUXt9iRhZmsSc22WA8b0xvQRDaXWCmjotAB7QCz09nj1AWXUc6cOcOLX/xinnzySQCWlpZIkoTl5eUhrculS5d485vf3G9z8eLFTX1dvnyZ06dPj91PpVKhUvHRDJ4cUUXnX0BPfp9a1EG1S/3OJmkrIJoDMQ7bFlzXgAqD6rCCdgPa362QrgaQjRanu9VyVeyWYVOC5yDYuSCdXnSoNcRnA6hYglmDuFxYD2drudfkUL9jkPI9PxDoB0ulv7UGjmTh+8Qrd2EAN9TnuDGPCyc+bBhEIoTyc2Q/5Oc3sVdx2iWQss+QjPy7GWHEz+/AKd8XxfdyhWil/4wN1BVnaX9jO3DB5erVq/zgBz/gzJkzAPzIj/wIURTx2c9+lne9610AnD9/nm984xv8+q//OgD33Xcfq6urfPnLX+YNb3gDAF/60pdYXV3tCzcez1YEWRO39D00KnIHCEjNEdVc/2dm5hQzt3lbQagsdclWIjrnY7qXQ+iG2HYwSI2x7QP5FkQgzZpY52sVHTx5EvXUzYKGvblhMx3gOUUuG9rV40RVJW5dBWchVMK5CMTlpqEamPpm7wExjnhmnQAHONCQIK3Tu+9FwYUdXJAgajC2ijELpPoiEreB4rAa00pPF14xjmq4Qmxa9H5HkWkT0p0Qur29eWQ81+slQwHHdAsya1F1uhyFM0ZAGLvpDdYQjxOaVBFxVLSFUYuV/Ykeu9662Wzy1FNP9b8//fTTPP744ywuLrK4uMiHP/xhfvqnf5ozZ87w/e9/n3/xL/4FJ06c4O///b8PQKPR4Bd+4Rf4wAc+wPHjx1lcXOSDH/wgr371q/tRRvfccw9ve9vbePjhh/n4xz8OwHve8x7e/va3+4giz44QbSPBNUYFjPynP/zDH6t4DSzhccvs8Q6zmUG7IZ0fVGk9W8VthLlGx0fOjCFBuZ629VsHlVyTmLka17ovYS09Q5ItkLm4FD1XnjJ6d7uiGJxGIEpAylBq/t6GBtSUTUK90GbHzNwy87MdXrYYUHEBQTIPGvT34qImGnZQhLW1OZ68PMv55puxToueDE7jfr9GMkRc/3ts1omlxVzlEmbo/hFEIJIWs/EF2Dac1pC4WeJgHcFeF0dh1ZQkW2H6z4JR4WOz/8hNhZD7NwUJZc1cHzW0zQx2ClFFuxZcvvrVr/KWt7yl/73nV/LzP//z/If/8B/4q7/6K/7jf/yPrKyscObMGd7ylrfw+7//+8zNDV5tf+u3foswDHnXu95Fu93mx37sx/jd3/1dgmBwQI888gjvfe97+9FH73znO7fMHePxlJEsJFqNyTb7eO+wg94HhdBBmDDzipTKuTbppZjOD2LsRkTWMUNNN9t7j642xkiFsf796gW6g0A1YLV7O5dbd7OeHQcJc38VoXg7z+836Tt0FtehyGOkCKiSYXLH24Ke+K4WsGVjR96fy4TVbp31q8L6BcfpGWWx7qiGEAcQGdCsSis1PL+m/GAN2lmIEJbiW8oGJVAN0X6+D6VjK3QQVtPbGb13BDCSUjXNTes2nSOBTKtEdKkGa1SiZWKzQT28hsERmVauYcINzs8+f6OKQ920HZLHGVNGCzpuMfaBavj6oaBRC40GmrTRQWkvKmKf7Fpwuf/++4sfyXj+x//4H9v2Ua1W+ehHP8pHP/rRiW0WFxf51Kc+tdvheTzAIFhzp21hksvb4IfmcJjZlMpsSvziFnQN6XqIUehci7GtEOkIyUoFTaXQ8m718DjcQs1M5SUYhv3Khh8Nh/v4bjaWOy/luebrscTks8Q43WHxb//UlwWU8SYGLf13dIteC0WwDpY7huU2BMYgotRjpRpAaoX1jiHT4d0PRjjJl6VYLlIStjaPz7oKG24HPozFponOsmGPQ3IHgss1PFiq4SoBCY3KczSii0RBWRjaixBTCItS+Lds8oeTMZ9737c+CJEgF/ekECA1pZteZWvnXCWYFdYrs3TMwTtI947ISS4Mq2TYY1cxq7k1cnhk4KS81d45cB8Xj+dGIAjx5VmSOyJcmI2s28ywK+lY8WVoSwkU6hlx3YE4ZpeKNy4LLg3o/qCKW4voXBU0CXBJzz+meBBNKZvpjUQmqnzHTVaH/3hvNG07j9WAvJAd11+pVZKLrOYOmOttWOsJKvuZ/8v973bdNu0Vg9UYFJppDUVYTc8RB+ssVr/HQvwCNbOCSLqnoQemRhTO0k0vT3yGOK3QTE/SsQvEQZNquNqvlmBMQixdwIHY4hkhVILjGKkV33u9juZM2Xzg3TjmQvUcqYk3jeMgsBLSDOapuA4iFnPSoueB1rA21omZmjDlBRfP0USV8PvHiV98lfbZa4VJf9JEOnhcDT8OtpsdivVl00gAEmRU72piHMxYg9uIyNoBdi0kuRaSroCmIWQBg7y+UtrV4Z3kjdQQiVHt3uihHCmchqynpxi6J0W4MRLMAO1PqByO21ZBiheHxM5xYeOHuNp+KfPRBY7Vvkc1WCWS3Edjcq2f/LwrIBIyU30Z1rZIXRPF5Pqp4pJ03Swt22C182I20uM4IgSLkbQw6glR0KIqa9TjqzTi84TSRcVgs5PMY4pJujyWYfFq4MkkqBiuzp1hLZjprztoFCErZw4OIFyAtNUbp4JRWuEMG8H80Ja9p1/+bed4wcVzJFEgSKrUv3In8aljJLevkC60QRyumqDGDbTTfaPSJLXu7hE0T7lgLGbBEi8InBFqzuC6BtsW7GrQ/7HatYjuCxUMYNMAlwlGpTS+4sHU82G4CWaJcaGfYVAlMBGZ7dyAER1VFCXAaZjnxGDgMeJ9iXbBJl/XXLORujmudmZZTm6jGmxQC1eIzAaLlWcxRf0hQQlMh8zFOK1ytfNiMkKM1Kglp0nSNdY6ryQj7P86BSUjwrphXzDFYF3U/261SodFVpPbuRi8ghBbaIBrXGgJdx8X5qu5hlMkyvuSYvwImatiCbjSfTFdN0dSjRGu74tDK5jpe1gJECxCcoHCNhQQntqgW1sjdB1CDQm6EVXXpCvz9O7hLNn5vewFF88RRghW5ghX54mffhEYh4ojO95EQwsIrtYlO72MimJnE9xcBxenIA5FyJOc7y7zwDjdTY6CsUjdEtYhXCytch3qr2wiCLYZYFsByYUITQLS5RCsoNb0/WZUt9rP9UBpp88zU3kJUvJzSbM1rOvcwHEdRYSV5EV07Rz+vE6JTadRcK5GS2u0sxOAcqV9z1DzSrhK187gNMRpXPi0CLQBqoOuNil2x2jFxlxGJSBzM1gKryI1XGoq19rKsRrMViNarTfSsS16UWGKoZ01yDTGuQjFEH53nerSJVwwqjHamXZukl9vYJXA5i1CTai61b7f1GJ6iSBziIYggovarIcvp3n1LLWoy0t5gjN/WeUOriDOUF2dwajgWO3337Zd/nTb0eV4wcVz9FHFpAEUPhnmhXyi7fmc6F+/CFBcbNE4ITvZxEVZ7kw238KeaGIXNrCVhHI44qieZqeMjWg0eTZfgPCYJTgG8Yta+fi6QR4Nsh5imyGihu7lENcttDfNsPSwGY3yGShj95v0aZTMrqPYEaObG6uJ8UxiK+1Zfh5dLaRtFqHp6GdO7flKeabDkE9MviDrh2/npOnJzRuNu3Sblu3wOsngF9pzylUgdcKljYBLzQCRE5TdrMt7kGI7PV9h8WmDaeQmr17bJA7oVg1za+n43QON5ZSZ9TE+fgoLKxmVrgMMxmmR16e37UmEU/2BqMJSt4ZqmzDNiJ68nXIo++AUDfbldpEIxwsuniPPpp+Dlv8Z/ORNYiCJCJozg7aiuZbm5ArZuWtk822y4y1cmKGRRcUVD5jh16yykWdvfopa2NAVqvk7WFyzcCp3Aq7cmbeyrRC7EmEU0vWAZCUPkRXAbhiyjhQ1aAS1wXVyRrjVJtTRaJ1xwsjkKBCCPBlcuYkYJViwmNBSe1lKo/ZnnHzmOa48dwacoBqyfvU4WVrBZjHOxmO6nlCKzittDi3acyqaoNANsNyVXeM1f7lMIPlzo7eyVRdatZATV7OhO7T8uVenamt6AsjWUV4RNnc27u1kmxtPdhGw4AUXj2crVBANiC6eILx0DBWHq3exUQYnmtg4JT2zmud6YfDTdJWEdKG1xVNgOz+VcgaMcs/FMsnfpcKZlGAmf4MKgZpKf2tNBM3yN3TbMWTLgmtHdC9FuNVKLlTpXvQjk7aQkX9vLfJns2ICS9BIS8sFEzuqJ9JCWdJT2edOVuGMJWjY4Y5EkWqWa+KK9i+652ucfYVBMKgKSauOcwHttQWaKwv5vVp04VzE1efOYrPhR3zarbOxfAotjKA7im67NS/nzccWirlIMk6YJrcHq7wkXCZSi6ijrMOZa8JcM+ttsuVlvdlfPbzg4vHsCC0q5xpMM8yNTsvzGBT713bgNFk8EVwlwZ5ukp5Zwda62IUWrpKiYbZprtjrvDBWTJDBG5ZUQCr5hGhmej41LWrdkPRyTLYSkq1H2JbBtcygJyX3pyl5+5eTmm0yO/UdbvYS4ZKngO/tZ/TYSnEJI9uMdw7eEwJiYEeFa4U8FN4o4UKGBINqPGEjI5xLkAgqJ5PShSn0b6aYNGRwRNITNmSciW9z9IgUb9EiUJ1dBZTa/DUWb6PfVsgvye33hoyaDrvtGVYunGHl4hLttYX+8iyp0rx2HJtWce7WFkD3z0CLS5HoLsARlCo3jbpWV7AsmFaxsGeC0uL/hqtax2FQINPBb7MmCeeCa7zINDltVnItixp6DvyTruDu/PVuPkHGCy4ezx7oTRGKYOxmlbzJYsLvzVJ5+jSKorUEW0uwp9ZIj62TLi3j6gkauFKkSM+0dDBRQ70oKqlaKufaVM7lE5ymgqa9h6GgCtk1g3YH5gfXEpJrUT6dtgJIA4oyw6WhKlY3SnvUkSJ+YzBCMJsi4nClQx6nxtbS2sqJBKnqhFZlUWf7zyZUwnlLdzlCtrGzq8mFEgmUoNYzE/Z61MGEM8Q43VlZ5T8ozrf5quuYz8P/yqZ2+bWWIBnqUYHqbJell11j6aXfGlpnbUR3Y46Vi6dJ2zVAsGmF8999ORsrJ8eOzFOifxPkV9JgOSEb3BasIhiOSYtZafczJwzd0wKhKlXJzcDSvzeLoiIqtKjgACuGq1mdhJBAlLNmhRnplsTT/F6alqBxswksPbzg4vEcJJoHCJpWBdOqEl2dpyoOV+1iZzqkd1wma7TQSoZrtEmj3lv5Xr1jtmZTn6JIDBIPqxvCWSWv0NfTwij1IvzStQ2aBJi0i3yvCe284J6KJVraoNJt4hLFVJWZOzp5/ZvJAyKYzfqOyX3/o3FaqcKyoeQ+IFKYVPobSKmP/swgE9oMO7cqUD/e6WvMNvdDz7IzsqJ/Gktap83XbZy5b/P67ZZu9X1cDzL2W8+kVZ6WgrBLrdGh1riKSyuoC2kuL5IlNTZWjsMU6sscWbT0QYUqXW4Lr/G68CJVMvraM5XJkoD0zDo9LU1JTBdlhrxYrAKNcPBy0Nti2o73NztecPF4rgPDphWDadcw7SrBtQUEhwsz3GyL7PQ67sQq2VyCRhY710FF0cCWJs1h9iPg7FiZLFJobBxmxsFMBprh2n+NeXoR0Qjmn4NXPEYj7AIWFcm1SSXlwlYeP1CSJWTMuqHtpRBuhjUXxVAH28jg3E/6PDSOSbLAsD1u7HaHaepwNqa1egxne/lEhObKPM2rJ1g+v0TWrdHZmMNmFdCQHXhs3toIBDjuCK5xV3CJBekQ9iLseilyd3iDjP+dTNLI3Zp4wcXjuWEIuTIiIEgCgmsVomsLYM6iRnGhYuc30DhFl9bonlwjXWwWfjKDJ+HBGJZ2MnyHnvouvPBq6M5jX/R/sJUNhpOjDWs1Jptuiu8y3JqR3iabUsomkSHjzaaWo59vDQZnxXZn+evH3szFp186FI2kNkZdaUrYdC08Q5ROS6gZPxRf4OXmMiE9B9iepgW2/4UO7vLdiii34tXxgovHc1MhiMtDmk0mhJ04fzA9f5JKNcVWu9jTa6THV0lPrpHND0wxZct2TyTYzkSxh9H1PyuKra4gx57FXH4Fdu7ySNvdPlKHRzxqltne/+PGvI/e2LfgUZ8e+t83a5SE1tpxvvuVN3Phe3cPCyk9U0Zv89HuPRPIk/y/PLzEy4NLhFoOQQZ/8g4GL7h4PDcj2v9P4d8hSDtG2jHh8hwVcxYbZdiFJsntl0nPXcMZiwsV7SfKk8J59uBmHxWFIMlt7VMxl/iZcjeM6qWGRVch7cyQdOosX8jNP8996zW01xbQTYkKZfN9ciu+yu8GgVAtrwovck94cYzQ4jkovODi8dxk7OjR54SgGxFcPEZ8uYF+vcj022jTfdFVdKZNdrKNnW2jxqFm4CMjm6JY9iooFK6BAiq9jLnbOYyOLp/cZvtR7dA/50gjdNYWufDUXVTnNjhx+3cJ4w6d5jzNq0t89y//Bhsrx8nSaslkMaqh0dLyoa49MOEHqQTiuCe6wL3BRYSdxNJ7poUXXDyeQ444gyS5r0JwOSa83ECNxcYWt9CEKCU5s4ardXAnWthKkpc0EFsSYza7BG4fCZNPeFpdRWcuQdwqLd9626PC8JHqJg3IoN2wLmT006CvgYFn3NUY1amAsH6twdNf/2GiWgsTrXHqxc9x/qk7ePovf5QsnWX0So5med7abfoWZOKpyB3Na3Q5btqcNE1eHl5CdIuoOc+B4AUXj+eQM1bP4QLCToBcOIYiRM+dzDUvUYqd75CcWSF5ySWILK7ezbcyeRjm7ow1ilu4iHYWwGQHaZW6ackzELuhyKbx7Uq+R0UZhnx5vkakML0VIUrCsJtx+bMrlf88cfsz/PBb/4jabJvq3DIiltvvfYLKjOM7j/1NknavOOM4X5jyZy+8jPqNC2DEUpOUk7LB2WCVRWkzK928Vs8u6ut4pocXXDyeI8DkMOPibVsNYg3YkKBTI768gPvm7UiUkhzbwNUSstuukp5aw9a6DIqWDIxKm6fkfImbvYS58yqIG1q+/ZjHaSEmH48wiHM2SQRjEv9db8QJ4ZU5JAtwcYY93qSclG6obRYQXpnFWINs1IguzYNKUe/KoEGGO7uc+ymJkp1cx4bpJn2JKERXZyF22Pk2hJbFM8/nwk0hPIVRmzMv/wZRpcWz33gtKxduw2Uxveu4WcN2i0mbEwhw1CXhpGkRk3I2WCMio2G6hJpiJM+EO4w/d9cbL7h4PLciKpgsgCyg0q7mi54+jVZSspMrpKfWSW67hqt3cNGgpME4RBQ12S41NfkU6nC5UOUChl1NtcjXJQTdkLAbEVxoEFzKTR/h1TmCbv74UthRnrlpfO4H3vQUIyoE3RjRAA0cLu5lP9U8K3G/veTH2Q3ztdITKAttCwAOnj2RrxPyMHi0dO4LrYmCSQLUKBpZ3GwHO5dnZc1efAXbaPWvxG2Ni5x5459x7dpJNrqzfP8bP8TG2vFCCOxpbkrd905oedlOlAqTLvxW2+7mZtmtYmM0MmpMh6YQnSuSUSXlzvAK89Jh0bSpkSHkpRX6fUwQSD3XHy+4eDweIPeVMa0K5tnTRM+doPpXt2EbLexMgj27gpvr4OZa2DjDhW7whq89E0fPHCKlkkn5xJ7n8xdMOyJYqxYzugCOYLWGuTbLsNGr14HBNKsErRhsOLa8AmO23OvnvVFsbQ2mXekv2XLOLkWNlQU27WmVFEwnHlq7qQ8HZCFhO0YvN/LNnl3EGdffqCcSzVUy3HyHl9SeotlZARVWqLDiKojCmtZpaQw4KiajqxFdjXLBJg9rG4xjwskbVEQvnxlhtF7Sbqf/smi3o3IYYvNq6MW4q2SIKDVJmTcJokpFUk4EGwSqzEhCXVJi0pJ/VnEsm4Y6bt9enLneeMHF4/H06WsIXEDYDQguVfNJ+PtLaJji4i5Zo4urJxgUFzjcmTU0yqMqFJAkIDg/izgBDQiu1pEk1zRgDWE3N1n0ZmgnpiTsjAouOxMpJsUp7fbzXhmdjLefyrZvv9PpsG8OBCSNNwU6AwRJBdZnqAANWQYRzoqiQX7eU0JSgkLMcGQiXLU1UkIU4YqbYcPFDOox5YhAI2gzLx1M32enpKFCWNeYFVfrj7NJlaar7FhinC18SnK/n16/g3u1NBoEWDLrVMiLmRocx6VNKI4QR0UyUC0ElMGx9LMwexnkUOAFF4/HM5FeIWNRySfFNCbY6GlHcvTJMyP+KoK4UrXpbTB97cON91m5dch9ZXoahphc41Bazazp9r++1FwFkbG+qEY0d04eI2v2/KNcaWFTq6y7aOAv3GurA82KlsSxOUmYNZ1Sj1vrzYxaBiLU6GGPvydlaPkt6GF+yPCCi8fjGcuQNWPTJDB4sIsLpvCY39YpwXMDMbjJl2UL5VhPgAhKGzekxUIwEGr6oseE7RUtbDbjnHDGfZaRf3eLF1pudrzg4vF4bhK8wHKrUNLXbXnVJ8edeW5lvG7W4/F4PB7PocELLh6Px+PxeA4NXnDxeDwej8dzaPCCi8fj8Xg8nkODF1w8Ho/H4/EcGrzg4vF4PB6P59DgBRePx+PxeDyHBi+4eDwej8fjOTR4wcXj8Xg8Hs+hwQsuHo/H4/F4Dg1ecPF4PB6Px3No2LXg8vnPf553vOMdnD17FhHhM5/5zNB6ERn792//7b/tt7n//vs3rf/Zn/3ZoX6Wl5d56KGHaDQaNBoNHnroIVZWVvZ0kB6Px+PxeI4GuxZcNjY2eM1rXsPHPvaxsevPnz8/9Pc7v/M7iAg//dM/PdTu4YcfHmr38Y9/fGj9u9/9bh5//HEeffRRHn30UR5//HEeeuih3Q7X4/F4PB7PEWLX1aEffPBBHnzwwYnrl5aWhr7/1//6X3nLW97CnXfeObS8Xq9vatvj29/+No8++iiPPfYYb3zjGwH4xCc+wX333ccTTzzB3Xffvdthezwej8fjOQIcqI/LxYsX+cM//EN+4Rd+YdO6Rx55hBMnTvCqV72KD37wg6yvr/fXffGLX6TRaPSFFoA3velNNBoNvvCFL4zdV7fbZW1tbejP4/F4PB7P0WLXGpfd8Hu/93vMzc3xUz/1U0PLf+7nfo477riDpaUlvvGNb/ChD32Ir3/963z2s58F4MKFC5w6dWpTf6dOneLChQtj9/WRj3yEX/mVX5n+QXg8Ho/H47lpOFDB5Xd+53f4uZ/7OarV6tDyhx9+uP/53nvv5a677uL1r389X/va13jd614H5E6+o6jq2OUAH/rQh3j/+9/f/762tsa5c+emcRgej8fj8XhuEg5McPnf//t/88QTT/D7v//727Z93eteRxRFPPnkk7zuda9jaWmJixcvbmp3+fJlTp8+PbaPSqVCpVLZ97g9Ho/H4/HcvByYj8tv//Zv8yM/8iO85jWv2bbtN7/5TdI05cyZMwDcd999rK6u8uUvf7nf5ktf+hKrq6u8+c1vPqghezwej8fjucnZtcal2Wzy1FNP9b8//fTTPP744ywuLnL77bcDuZnmP//n/8xv/MZvbNr+u9/9Lo888gg/+ZM/yYkTJ/jWt77FBz7wAV772tfyoz/6owDcc889vO1tb+Phhx/uh0m/5z3v4e1vf7uPKPJ4PB6P5xZm1xqXr371q7z2ta/lta99LQDvf//7ee1rX8u//tf/ut/m05/+NKrKP/yH/3DT9nEc8yd/8ie89a1v5e677+a9730vDzzwAH/8x39MEAT9do888givfvWreeCBB3jggQf4oR/6IT75yU/u5Rg9Ho/H4/EcEURV9UYP4iBYW1uj0Wjwf5/7v6ga7/vi8Xg8Hs/NSsd1+Zc/+H9ZXV1lfn5+y7a+VpHH4/F4PJ5DgxdcPB6Px+PxHBq84OLxeDwej+fQ4AUXj8fj8Xg8hwYvuHg8Ho/H4zk0eMHF4/F4PB7PocELLh6Px+PxeA4NXnDxeDwej8dzaPCCi8fj8Xg8nkODF1w8Ho/H4/EcGrzg4vF4PB6P59DgBRePx+PxeDyHBi+4eDwej8fjOTR4wcXj8Xg8Hs+hwQsuHo/H4/F4Dg1ecPF4PB6Px3No8IKLx+PxeDyeQ4MXXDwej8fj8RwavODi8Xg8Ho/n0OAFF4/H4/F4PIcGL7h4PB6Px+M5NHjBxePxeDwez6HBCy4ej8fj8XgODV5w8Xg8Ho/Hc2jwgovH4/F4PJ5DgxdcPB6Px+PxHBq84OLxeDwej+fQ4AUXj8fj8Xg8hwYvuHg8Ho/H4zk0eMHF4/F4PB7PocELLh6Px+PxeA4NXnDxeDwej8dzaPCCi8fj8Xg8nkODF1w8Ho/H4/EcGrzg4vF4PB6P59CwK8HlIx/5CH/jb/wN5ubmOHXqFH/v7/09nnjiiaE2qsqHP/xhzp49S61W4/777+eb3/zmUJtut8sv/dIvceLECWZmZnjnO9/Jc889N9RmeXmZhx56iEajQaPR4KGHHmJlZWVvR+nxeDwej+dIsCvB5XOf+xz/9J/+Ux577DE++9nPkmUZDzzwABsbG/02v/7rv85v/uZv8rGPfYyvfOUrLC0t8RM/8ROsr6/327zvfe/jD/7gD/j0pz/Nn//5n9NsNnn729+Otbbf5t3vfjePP/44jz76KI8++iiPP/44Dz300BQO2ePxeDwez2FFVFX3uvHly5c5deoUn/vc5/hbf+tvoaqcPXuW973vffzyL/8ykGtXTp8+za/92q/xi7/4i6yurnLy5Ek++clP8jM/8zMAvPDCC5w7d44/+qM/4q1vfSvf/va3eeUrX8ljjz3GG9/4RgAee+wx7rvvPv76r/+au+++e9NYut0u3W63/31tbY1z587xf5/7v6iayl4P0ePxeDwezwHTcV3+5Q/+X1ZXV5mfn9+y7b58XFZXVwFYXFwE4Omnn+bChQs88MAD/TaVSoW//bf/Nl/4whcA+Iu/+AvSNB1qc/bsWe69995+my9+8Ys0Go2+0ALwpje9iUaj0W8zykc+8pG+WanRaHDu3Ln9HJrH4/F4PJ6bkD0LLqrK+9//fv7m3/yb3HvvvQBcuHDh/2/vbkObOPw4gH+jprGW7LBqesnKShjKxNaiVWuLTnFbaSE+4AsfKRWGw4dWihVRfFHhD84J6hsfNsb+bgNH98ZqQalGbOuKrYpWrDqloLNVGqMlxqi1Ufvbi+HBNX02TXvH9wOB5u6Xy903P+iP4y4BACQlJelqk5KStHU+nw9xcXEYP358rzUOhyPiPR0Oh1bT1c6dOxEMBrVHS0vLYA+NiIiIRqgxg31hYWEhbt68idra2oh1FotF91xEIpZ11bWmu/retmOz2WCz2XS1APCmM9zr+xIREdHw+vC/uj9XrwxqcCkqKkJFRQUuXryI5ORkbbmqqgD+O2PidDq15X6/XzsLo6oqwuEwAoGA7qyL3+9Hdna2VvPkyZOI93369GnE2ZyetLW1AQD+9/j/Azw6IiIiGg6hUAiKovRaM6DBRURQVFSE8vJyVFdXw+1269a73W6oqgqv14sZM2YAAMLhMGpqavDDDz8AADIyMmC1WuH1erFixQoAQGtrK27duoV9+/YBALKyshAMBnHlyhXMmTMHAHD58mUEg0FtuOnLh+tumpub+wyBPt6Hi6FbWlr6vLCKPh7zjh1mHVvMO7ZGSt4iglAoBJfL1WftgAaXzZs3448//sCpU6dgt9u1600URUF8fDwsFguKi4uxZ88eTJ48GZMnT8aePXswbtw4rFmzRqv99ttvUVJSggkTJiAxMRHbtm1DWloavv76awDA1KlTkZubi/Xr1+Onn34CAHz33XfweDzd3lHUnVGjRmnvx+aPnU8++YR5xxDzjh1mHVvMO7ZGQt79PckwoMHl6NGjAICFCxfqlh87dgzr1q0DAGzfvh3t7e3YtGkTAoEAMjMzce7cOdjtdq3+4MGDGDNmDFasWIH29nZ89dVX+PXXXzF69Git5vjx49iyZYt299GSJUtw6NChgewuERERmcxHfY/LSPbixQsoitKve8Lp4zHv2GLescOsY4t5x5YR8zbtbxXZbDaUlpbq7jSiocO8Y4t5xw6zji3mHVtGzNu0Z1yIiIjIfEx7xoWIiIjMh4MLERERGQYHFyIiIjIMDi5ERERkGBxciIiIyDBMO7gcOXIEbrcbY8eORUZGBv7666/h3iXD2b17NywWi+7x4feogP++onn37t1wuVyIj4/HwoULcfv2bd02Ojo6UFRUhIkTJyIhIQFLlizBo0ePYn0oI87FixexePFiuFwuWCwWnDx5Urc+WtkGAgHk5+dDURQoioL8/Hw8f/58iI9u5Okr73Xr1kX0+ty5c3U1zLt/vv/+e8yePRt2ux0OhwPLli3DvXv3dDXs7+jpT95m629TDi5//vkniouLsWvXLjQ0NGD+/PnIy8tDc3PzcO+a4UybNg2tra3ao7GxUVu3b98+HDhwAIcOHcLVq1ehqiq++eYbhEIhraa4uBjl5eUoKytDbW0tXr58CY/Hg/fv3w/H4YwYr169Qnp6eo/fBh2tbNesWYMbN26gsrISlZWVuHHjBvLz84f8+EaavvIGgNzcXF2vnzlzRreeefdPTU0NNm/ejPr6eni9Xrx79w45OTl49eqVVsP+jp7+5A2YrL/FhObMmSMbNmzQLfviiy9kx44dw7RHxlRaWirp6endruvs7BRVVWXv3r3asjdv3oiiKPLjjz+KiMjz58/FarVKWVmZVvP48WMZNWqUVFZWDum+GwkAKS8v155HK9s7d+4IAKmvr9dq6urqBIDcvXt3iI9q5Oqat4hIQUGBLF26tMfXMO/B8/v9AkBqampEhP091LrmLWK+/jbdGZdwOIxr165pv3H0QU5ODi5dujRMe2VcTU1NcLlccLvdWLVqFe7fvw8AePDgAXw+ny5nm82GBQsWaDlfu3YNb9++1dW4XC6kpqbys+hFtLKtq6uDoijIzMzUaubOnQtFUZh/N6qrq+FwODBlyhSsX78efr9fW8e8By8YDAIAEhMTAbC/h1rXvD8wU3+bbnB59uwZ3r9/j6SkJN3ypKQk7desqX8yMzPx+++/4+zZs/j555/h8/mQnZ2NtrY2Lcvecvb5fIiLi8P48eN7rKFI0crW5/PB4XBEbN/hcDD/LvLy8nD8+HFcuHAB+/fvx9WrV7Fo0SJ0dHQAYN6DJSLYunUr5s2bh9TUVADs76HUXd6A+fp7QL8ObSQWi0X3XEQillHv8vLytL/T0tKQlZWFzz//HL/99pt2YddgcuZn0T/RyLa7euYfaeXKldrfqampmDVrFlJSUnD69GksX768x9cx794VFhbi5s2bqK2tjVjH/o6+nvI2W3+b7ozLxIkTMXr06IgJ0O/3R0z4NDAJCQlIS0tDU1OTdndRbzmrqopwOIxAINBjDUWKVraqquLJkycR23/69Cnz74PT6URKSgqampoAMO/BKCoqQkVFBaqqqpCcnKwtZ38PjZ7y7o7R+9t0g0tcXBwyMjLg9Xp1y71eL7Kzs4dpr8yho6MDf//9N5xOJ9xuN1RV1eUcDodRU1Oj5ZyRkQGr1aqraW1txa1bt/hZ9CJa2WZlZSEYDOLKlStazeXLlxEMBpl/H9ra2tDS0gKn0wmAeQ+EiKCwsBAnTpzAhQsX4Ha7devZ39HVV97dMXx/x/RS4BgpKysTq9Uqv/zyi9y5c0eKi4slISFB/vnnn+HeNUMpKSmR6upquX//vtTX14vH4xG73a7luHfvXlEURU6cOCGNjY2yevVqcTqd8uLFC20bGzZskOTkZDl//rxcv35dFi1aJOnp6fLu3bvhOqwRIRQKSUNDgzQ0NAgAOXDggDQ0NMjDhw9FJHrZ5ubmyvTp06Wurk7q6uokLS1NPB5PzI93uPWWdygUkpKSErl06ZI8ePBAqqqqJCsrSz799FPmPQgbN24URVGkurpaWltbtcfr16+1GvZ39PSVtxn725SDi4jI4cOHJSUlReLi4mTmzJm6W8Oof1auXClOp1OsVqu4XC5Zvny53L59W1vf2dkppaWloqqq2Gw2+fLLL6WxsVG3jfb2diksLJTExESJj48Xj8cjzc3NsT6UEaeqqkoARDwKCgpEJHrZtrW1ydq1a8Vut4vdbpe1a9dKIBCI0VGOHL3l/fr1a8nJyZFJkyaJ1WqVzz77TAoKCiKyZN79013OAOTYsWNaDfs7evrK24z9bRERid35HSIiIqLBM901LkRERGReHFyIiIjIMDi4EBERkWFwcCEiIiLD4OBCREREhsHBhYiIiAyDgwsREREZBgcXIiIiMgwOLkRERGQYHFyIiIjIMDi4EBERkWH8C/iShfBQeaZoAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7it [00:05,  1.19it/s]"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "33it [00:09, 13.13it/s]"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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yMn/50svUmzFAV7SYMMPEobczvP8xMEo4f4HsmdP4y1fprrhmHFZ6nSB+tURhcYn9Dz/NmVf+ilZlkRjltWCJKVtgstW37R8JVMB6qT6TgVx6vif6MSN9jP4/R3jP2GWs6adz7KJwZfEy33v1e8zFLYxRGibiv5gW75KYt2Mwuioa01MoYHNbeZiOHYwTLnscT2E66mcqLVCR9v873ZTt2F2IYH1BpgYxU/2YmSHoy6CeoFjQMhqVUZuAxj1hoG3XjHT+ERpxwmtnL3Di/Hnq8apoURH6RmeYOPQ2skMTeFFE5o3XCC6/iYma3XbQHoAzSUjO5mmYOpHExCYBFFElc+pFpPkIB594mjMv/RVxrULVi3jZX2A0zhPY7RT0nrq7VATyGcgHmH3D+NOD6fH2hRAY8AyEISP9JYzpSg9accTZq2f43qvfo1avAkKC0FJDBuFxvDWiJe1dFGtDkriwdYft2NE44bJHsKIsmzqCMJRkMd2OZHWGW0SpGZ+6geE4wkW5OLYUI2hfgBwaw+wfxozk08JxmqBaR6MK2HSmZljrwbkepVxv8sKbZzl56cp14rwwtp99T/wYXpDFKy2Ree27BKUVjE16NpFaGALrM9WaJJdksVhiiVgMF1gJqihgkpjw7EkKYxNMHX0Xl1/7FjZpcimosBA1mLEFtuLx4DpjkSfYwCBj/ZhHppCRAhIYbGCwq3aR7nfTNluMbaI2IoorfPu113jj0lnU2q6wg/bc2FYxutYipaQWncTmUesCcx13hxMue4RYlCtenXGb3aAHWyUSwzU/YCiOnWxxPGAUjKL5ECYH8Y5OYoeykPEQAWvraKuMJC1g1bKy+iy/MYkqb12d44U3T1OsN1fDUlQxfsjwzKOMHnkC33iEl94iOHcSv7zY/rbAul9C3ubI2gyigoePb33GmtNUvfNEXgsAz0aEb7zM0Dt/jMbBx1k48wIxlpeCa+TtNEM2+2BjdEVBBMmH6HABHcxhpgcxhSC1XhnpllSR7iNLOwBZG5DUQRNoFFEWaEUxz7/5FqcvXkznbupROPNJxP959Z0cWix1d6/tv0WBlwXqXoN3eDWGkzw37ZAcjg1wwmWPEFrDk61R1vvk16IMJE0GktYDbJnDkcaumNEC8tAEMjuKZrz2QJpAtIxqHasRova6O/jGQ57SihP+5tWTnJmfJ9KkG7+lgBdkmTr2boamj6BiCU+/Su7s62khug3H0tQCYVTaGUKpqFFRfAyFJMeKaaVFdgX84gLZV7/H+MPvoLJwmUZpngthiZqJ+en6AfqSkBv/Fu8da4B8iJkaRPYPo4GHjPZBIIjp1rjtObZOzIpFbQ1NKmBbSDsTS0wA2qKVxHzrxElOXbm65hRZVf6yNsnZv95P3+vX0PEDnDw0xcjKPP3L1zjVqPIiSkkE/Bpx5ho/VT+Ip7srVdxx/3HCZZdhRSl7UWqyVgjUkLO9l/kmHaULynU8YFTADmSQoxNwdBxyAUoCWsfG1XTgtE0QTY0Gt71loRZFvHz2PG/Nz5FY2xP7Inhhlsmj72Bo5iheogQXT5E9exJJ4nbDui1k7V5lXWJR2+qjkLGZtL6LanfNcO4itn+YmWNv4/zLf0MU1VkM6lxr1eiPM5v+k1MBGxjMYB5zaAwzM4gOZbtp2939dduYINrCJq3UumLr7fPfvE64qcJrK/2cWHiJ1y4sYzUElLoN+eurD0HJZ+w7RXKtEsOH3sbYwUe56mW4tv9RCtVlri1epvLmDyCJEDVcCMqcj8scjAZcQoDjjnDCZZexbJr8VXiRSZvjQDxAgZCcu8yObYYNBKYG8fYNYQ6MQDZAsWi0lMavEIFaRG36hTuoKxTFltcvX+aNK5dZKpZ6xIHgZfKM7n+MgckDhPkBTLNC8PrLZOYuYDSNZ9Hbcl2sXUcQ8jaLUUh63CaiEFw+Q37fQQamDrN44TUsyhvBEjNx/7qHijtHjaK+h0wNYjMGf3oY0xdihgokgcF2mopAuwQ/qhDX0vo2NgKNVoWM2J6tr607s7C8wl99+hTLdUXZ1/4sXX+4fhVRCAcnmDz+LgqD42nAL2lGV6VviHyhn6FmhaXzJwBLovDdzFX6bcBY7OYuctw+bkTbZQzZDD/R3M+QDds1I9yTjGNr6dyB1giaD5CZQcyRMcxoP3hgiVBbRuIKJA1EbGq5QO8wQNxQj1u8cPotXr1wPhUgIt1x2wtzTD/6DP3jswiC16iSOfki4bWLG7qg7vgI12ud9nu/XiG8fI6R6YcoL1ygVS9zNdPirKnzSGsIr+t2Iq09ozcSTW1Xjhh0MAuTfcjUAPRlMcP51P0jqaBK1nxNUZI0qDZpoLbetqjY9vlVpCNStEd19ZyPxUqFv3ntBAsrq3ErvcXkQOgb3cf0Y88Q5Prb++ztfwQRj+nDb6e+fI16aR4RoeI1eDVc5BmbI7Sur3LcHk647DI8FUaT7H3fT5odoM7E67gl6gk60Q/HJjDTA9iMBxqhWkHjBmpriKapxIh2B8M7vbMaccS3TpzkrSvX0gounZFVID80zcRD7yQ3OI7YhGDhCtk3X8WrLGNY3WevDeXWXL/O9ZIjdXFl33od2zdI34fex1v+8xwcXGTFP402pzgSDSMqNGzApf8yhV6eu34rhrS43swQHBhFxvuxWa+7UyuKqIAqoq30/KKorSI2dQdp29rSmTJyTUTtupZ3xaYq86USf/nyyxRrjQ3PgIrQP36QmcefxfezbTe1JSyX8OcuYLN54tEpNMyBn2Xy4Xdx7oW/QOMWFuGsX+KIX2a2NeDCdB23hRMujrsiEUtNIgY0s9VNcWxT1JBaBp6YwRwYRXwDWkeSMsQ1LDFCsinid6lc5eVzF3jr6lVsx8SiYPwsg1OHmTz6NkxYwG/WCE69RHD1PF4cI7Iaoro2R+kujlfWWiC6yxGMjQnffI2Jf/hh/l69SPnqBRQlzBU5Xj1MX5KhoRH/H3/f6rZCg/F96M/CdD/y0CSa9+nG2XR2IBasBVtH40rbmpKssVgJvSFs2rZo3eRYgAR46exZXj57hkaUsN4KA4AXMjx1mMmj78ALcoDiNSpkLp4hPHcKEzWwgGZyxOMzNB55ivzgJMNTh1i6eAoFIhPzRrDEVNxPaMWJF8ctccLFcVf4apxocaxDUd9Afw4d68ObHUbG+9GMSeMo4gpJUmoHhPY6Gu5+mFLgarnMN158hXKtloZUkMaWhP1jTD30FPnRGQwQLM8RnHwev7jcri9yE6/MbbE2ETuwIXmbo+pVr1tHVfErK4yf+AYH3n2Ic5pQvpaKl8SA9QxWfHRmEDPkIUM5pD+LFEIIvNRq1et5UQWbINrA2hokLdBWarXq7nlt/ZTV07yBAFl3XAnKa+fP8cKbbxLZ69dWFPyQmYd+JK0yLAZBMfUKmVe+R7h4tSuNDECzgbl8Bts3SOPAw4wdepLq0hWatTIKXPIrXAzKHGoNYJxqcdwCJ1x2JEosikGcq8axDRA06yEPTyHTA5jhHBp6WFGwMSZaxtoKaMJqzdh7EysGSKzy2vkLvHzhApV6jxtDhfzQBDPHfxw/09cOkH2D7OnX8Jr1HqHU24Y7yVfaeN3A+gQ2A15t4++pEpRWyJ+/wuzx97AQ5JjyfHL5Q5jxIYxJMOZh5GKaZtyJ9OkWdlOL0RhNaqkryNZRG9/CdnKnCFFseencGV566wxxu64crNqRFPC8kKlHn2Zo8igq6fwAplEj8+rzZJauslHAjygEb75CNDQCg2MURvfTrJ1AgaYkfD+4zGScJ2+De1WUjl2OEy47kLpJeDFY4Gg0wLi6aHzH1qCBwHAe78gEdqIfhnJpSK1tYONlsC2wEYpdM7iuJiXfHYJQa0W8evECL711jsSuZsKI8RmePsLwweN42X5M3CBz7jSZs69j4laandQxyayzQNzOfhWlYZrEEuOrv+a7SoJykxpIIvh9g4zkldHA5+AT72E68DHGR1GstlCzalbpVqtNGmAbSFxHtQntOZgk/XhVWXC99epOzrGqUm22eO7ESS4uzJGortl2J4xXggyTD/0IQ5MPpZYWVfz5K2TeeplwZfHGokMVP26RPf8WtSfGGJw+wsrlt9AknVKh6Dd5PrzMM439+GynaREc2w0nXHYgWevxttYYOXWXz/Fg0M5An/GRwTzMDqc1QvpCktBgbIwmi9ikXXdlbV7LGu7FRmiBM9eu8dJbbzJfqYGV7tjqhznGDh9naDZ1XQT1MtlXvkewfC1Nq97I0HIHdERB3WtwLTPPUDSA1x5gFWUpKFLxG2nNGWR1P55HZmCEgX2zTPzEcfbPXE6nM+jUpWm/kLb1VDRCtbU6WaRuJIbaG193Mu/23CrKG1ev8IPTZ6jUm2u21htPkx+ZYd8j7yIoDKMiiMaEl8+SO/liOqfTrSwlCsHVc4TjM9ipacaPPMn8mZfQOEIRTgUrjNo+Hm+lAcvO7uLYiE2XtZ/73OcQkTV/U1NT3c9Vlc997nPMzMyQy+V4//vfz6uvvrpmG81mk1//9V9nbGyMQqHAxz72MS5evLjZTd2xCEJefZfo7LjvpAXNBJ3og6cPI+87hnz4MfSJSRgOUT+GuIxG8xCvILYG7ZluYPOS8QUltpZzc/M899oJFsq1brSpAl6mwMzj72XowOMgPkG1Ru6V7xEuXV0rWugUn++07k5a2K6Ui2UlLHI+f5Gz+Qvtv4usBEUSienUoPXCHAP7DzHz7vdy6Cc+yNgjT1C0Y7wW5XitmeNEK8drzSwnWiGvNeDlehZTn8e2rmGbcxAXYUPRsnmkLim4tLTCd0+eptyZEoG1+k4BE+bZ98i7CftGEBHAkpm7Qvb1FzGtRo9ouUXYr43JnH6JsFpl9ODjFEZnSSsQQ2zgZLhEzeudtdvhWMt9eWR/4okn+PrXv95973mrJZ1/+7d/m3/5L/8lX/7yl3n44Yf5rd/6LX76p3+akydP0t/fD8AnP/lJ/ut//a/80R/9EaOjo3z605/mox/9KM8///yabTkcjvuDipAE4D00iX90DO3LIIEBbZLYdEDVpJnWP9GkbWWAjfNq7rEtQLHR4genT3Pu2jwtm0Z/pMlDQpjrY/LYj1AYP4BYJShdIzz5Iv7KNei1fGziMCiq7ZIAnfekg7kYvEyW/ukZxg89QjA4AmZ1StPB/1ziRTMJtoXaBmg9raeiJs0Eqp9Ks6F6BJauHi03FwV3TqnR5OTFC5y8cJlmK25br3rdemlEUWFogpGDjxEUhtPW2Jhg7hLhiecxcfO6a39jUoebVysRnH6J6PiPMjb7CPWlKyRxHQSWvBpn/RKPxWObHL/j2C3cF+Hi+/4aK0sHVeVf/at/xW/8xm/w8z//8wD87u/+LpOTk/zhH/4hv/Zrv0axWOSLX/wiv//7v88HP/hBAL7yla8wOzvL17/+dT784Q9vuM9ms0mz2ey+L5VKG67ncDhuggENPTg8TnBkDDOcx5o0fsPGK0hSalezXY2m6A3e3Oxn5FiVi/PzfPv1Nyg3WnTKmnUKsQ3OHGX08JNksv2glszl82Re/wFe/GDm2xIEMR5BXz+ZgWHGDh/Gz/Xj5wuoMYhKe3oABY2xtSI2LuO1Y396xclqFMn6PcBmCxYFrq4U+cZLL1JstNLj6Ba+6dm7CMP7H2PyyNsxYS5dlsSEb7xAeOktvDi6u9apEsxfwVu5Qn5kkr6RSYrXzrZ1pvKWV+SIGSR7j5WFHbuT+3JXnDp1ipmZGTKZDE8//TSf//znOXLkCGfOnOHq1at86EMf6q6byWR43/vex3PPPcev/dqv8fzzzxNF0Zp1ZmZmePLJJ3nuueduKFy+8IUv8Ju/+Zs3b5isdiKdp6Xe+U9s+73DsacQ0hmCRwvI/iHMeB/alwFRrK215wxqQDuD5V6Da2+XOLG8fP48L7x1hsjaHseOIH6GqWM/wuD0Q4gX4LVqZE6/SnDlDCaJ0PbcRpvT0k7dk7QSrwlDglyBgfEpvP4B+ofHMPkCvh+ksyxruxicTSCporaJ2CZWExDbzgTcqHbM+s7nfpzhdB/zpQp//cqrlJpRKliUHlHYWdMwOHmU6YfeCX46L5FEdcI3XiJ76QxiO3FMvYLn9gv3mSQhd+p1GseH6Bvfz8rceaQdeDznV3kts8g7GpMuPdpxHZsuXJ5++ml+7/d+j4cffphr167xW7/1Wzz77LO8+uqrXL16FYDJyck135mcnOTcuXMAXL16lTAMGR4evm6dzvc34rOf/Syf+tSnuu9LpRKzs7NAu5jTQA7vwCh2qQL1KP2LErQ/i3oCiYVivVsHwf1WHLsd6xt0IJPOFfTIJJpJ46asxog20KSKJlVEkzXj0f0uEKakP8fvv3mG186dI6HXDSVIkGX/Y0/TN3EAg+CX5si+8gP88jKdGJbNHPLV8/D7+snk+xk98ih+vkAmn0e8TlZRT+vUYmyEjetYLSHaTlfWnoDdu8ho2iwiFa6Vijz3yqus1Ord3XeMLatWNENhZJrJh96B+CFWFD+Oyb7+Q4LLZ9uisLftd3ccfnGO4NKb9B84QnZwgtbKNUCxopwIFjkcDTIc3/9K4I6dxaYLl4985CPd18ePH+eZZ57h6NGj/O7v/i7vfe97gbYvuAdVvW7Zem61TiaTIZO5SUG0REkuLEJ7ThJjLahircUbyCNWscWN6y84HLsBFVJXUCGEiUH8o5MwnIPAYIkQW8MmJYyNQBPohpnCun/uK+VGi++eOMn5xfl2Sm57xyYgPzjO5ENPkR0YwUuU8MyrBJfeImjU7rJta2u5pOOxYIKAoDDA4PgUfRPTZIaHMH4GlbX5DGngb5KmKdsW1jbpzANkbrCXXoHwICk3G3z75Gkuzi0Q2XYlXNU19h6hnVI++yjjR45jvAyghMtzBOffwr96cROlVmoVM9cuEhw4wuwTP8rZ5/+cuFEBUeom5mRQ5Ok4c8PaOY69yX13IBYKBY4fP86pU6f42Z/9WSC1qkxPT3fXmZub61phpqamaLVaLC8vr7G6zM3N8eyzz95VG0RBKmmBqu7UX23zqFeqQ7Gepvbd3SE6HNsbI9jJfszsCGYwB8N5yBhULNbWkLiJ2iqQYLBdF8uDHlwVuLCwyPfeOM1StUp3KFUBA5NH38Hw7MOICfEbFbInfkiwcDkNEBZZl4p7q1/z2pwZQVDPUJicZXT/QcL+AcK+/h6rSopBUY2QpIHaJqpxmvnTjvu5oVjZwt5FgWaS8BcvvszcSnmNjagTAtxtnQhjh44zdujJblCxv7xA9qVv4TWq6b2xaYeSbsivlfAvnyU8/CT7HnkXF199jiRpYUV5M1jkieYwAzbsHovDcd+r/DSbTU6cOMH09DSHDx9mamqKr33ta93PW60W3/jGN7qi5KmnniIIgjXrXLlyhVdeeeWuhUsv0vmv/SC3GuOiG8a3qIDmBPqBfoP2e2i/QX0ncxzbkXQoUgHN++ixMfRDj2He9zA8OonO9KGZBLUlbDQHrXmIi4hGiNoHHuOVWjiUZmI5dfkaf/3yayxVKqym7AgmzDCy/1FGZh8HL8SrV8i+/F3CuUuI3ajNN/pt6ppVxPMJ+gbpnz7I6CNv49CzH2D2Xc/Qv/8gmcFh8IL2Y06SFtOLS9jWHNq8io0W0KQC2gCSTgTMthtYVZXlWp1vvnqChVIFpXd6xdUIGwW8IMfogScZPfgkeKnbMFhZIHvih6lo6ay8ySJMsITnTxNUVsiP7ad//GD3k7qJeTNcJhG77c6tY+vYdIvLZz7zGf723/7bHDhwgLm5OX7rt36LUqnEL//yLyMifPKTn+Tzn/88x44d49ixY3z+858nn8/zi7/4iwAMDg7yq7/6q3z6059mdHSUkZERPvOZz3D8+PFultFmcN2PYAPzrQCM+8h4BhnMwSAg6ZOVPdOEN2o3q7PlcDxw1BN0JI85NolMDKD9GdSktTM0XoGklloJeuNGesahrRgcFkolXnzzLOfmF7E9sROe55OfOMDEobcT5gcw1hJcOkP2zAn8ygod104ab2+5fkBddzQieJks+dFJ+sanCAeGyQ0OIcZHjAedLB+1oHEqVpIqSgQ2XrO97lSAG1intssAa4HTly7x3VOnqUfJmiTntNZw+xjEY3B8lvGjbycsDCHiIzYivHqRzKmX8BrlztdWv7OJiILfrBGefon4bT/K2KHHKS9eIo5qWFFeyMzTpxkeag5t6n4dO5dNFy4XL17k7/29v8fCwgLj4+O8973v5dvf/jYHD6Yq+p/+039KvV7nE5/4BMvLyzz99NP8+Z//ebeGC8Dv/M7v4Ps+H//4x6nX63zgAx/gy1/+8gOv4aIAcxF2PkZMFYK2gUoUWookwJo8C4dja1ADOpDFPDqFHBmDMC3Fjm1A1ECTEpCAWgTD1g+vSqJwebnEX730ErVWmr68mjUUMvPYe+mbOAhG8MsrZE6/TGb+CmLj7jZuLFbSDCBFCHN5vFyBvvFpxg4dQnJ9iKRl9tMMnzRGRW0dsRa1lbb1SdPspC0/V3eOVTi/uMC3Tp2mFaVPV+tDadOj8hiYOMK+x98LfgAIJrFkzp8kPP0qJomv2/bmk7r5gqV5vMoyQf8Y/WP7WL58CoCWSXg5mGM6LlBIggfQHsd2R1R352xWpVKJwcFB/sXsPyJrNmcW47WpgqvLduUJdOwIbGjQwSzmkSnM/mEIDRClcwTFVdA6qna1QJx2bAVbe9fWo4hXzl3glXPniZJkTaqzCbJMPfIuBicP48UJwfnXCS6fw6tVMALoDVovgheE+IU84eAwgxOzmCCkb3AYDQLE99vBqIqSgI0Q20KTCqrtOZV0XaxbT1r1xjEr2+/X34xiTly6yItvnaUVr9aK6UVR/LDAxKEnGJh5CC/IogjGJmTfepXMmdeQpPPdO4kduntUhNbEPhpPPkO1tsTZH/53bNRI3XAKzzZnebwxfF/b4Ng6GrbJb1z4DxSLRQYGBm66rqvucwds1EVtv27LsftRCDz0wAj+4/vQvhANBZIWGi2hWkMsKD2CQFe/u5X3rAJLlQrfePkVFitVVKXrdlGB3OAEU8eeIjM4gVdZJvPK8wTlRcQm7eMwpNaUztZAjEff+DQDs4foG5nAhAEShHRC+LrDnEaojbBJGUmaiCbd8NTV8m/rzs4NM4C25y+/kSR86/WTvHn1MlZvHMIofsi+x99LYfxAN6TYixpk3jxB5sJJJHnwPnBRJZy/jL30JsmhY4zsf4TFMy91PuWcV+SQ6aNgw2169h0PCidcHI4dggA28FILy9v2IzOD6WzC2kCjMprUMJq0XRuy7YwCjVbCD0+f4sz8HNVmRLcqrFiMCekfm2Xs6I8QFvoJVubJnvwhfmkZoZOanbp/JPARY8gPjpEdHCY/MUluZBTPz7B60Kn7R2wTTZqYTsqytsWctl28ndIq7W/t5Gf5ZpTw7RMnePPaHPYGeRdCKlomHn4X+bFZwCAoYa1O8Nq3CJbaE1J21+79936jiLUEF94kGp9m7NATNCvLlBfSGl+XgzInkkXe2Zje0dfJce844eJwbCdE22m9qwOPChB6cGQUc2wSLWSwoWCSGhpVwdbSwV17B5rVQorboZOvNJs8d+INzs/NkVYQ6bhkhCA/yORDTzEwfgDEwysvkn31eYJKsf1tIdM/QN/4PsKhYfKjY5ggg5/JIOK1C7spqpqKlaQKSRPRCIjXuHM7+1xNRbrxedou5+5WKFBrxnzr9Tc4O3ctDbxV1hw3pO8zhREmjz1FYWwGJLVe+aUlsq89j7cy1w7b3aqjbqdHV4uE504RP/ZORvYdodIWLopyKlzkYDTEWJJn2yhyxwPHCReHY5sgkuCFixhTQ5N+omQYOz6If3gCHcjCRB9IC2wNojoa1xF6gyc7jg7pvN0WA28tSvibV1/n/Px8Wm8FAEWCgKGpo4zMHCMzMAYIfrVI7sQPCFt18hMzhPkChYlJskOj+Pm+tDgc0K6okoo8baC2jtoW2GYqXtZUK1nP7VVY2Q7n7naYK5b4zsk3uLZcXNWurBctQm5wlH2P/zhB32C6TBO8epHciefxlufbRrrtcNSKLsxh6g1y/ePkBsaoF5dQlIpJeCW7wLP1fYT2vlfzcGxTnHBxOLYFihDjB0uIROBXsKN55L3H0BDUttBkLs180Xh99EbbOrC9bASKslRv8sM33+J8sQhBgEhqa/H9DDMPP0Xf+CE6szRmbcTI8jUmnnwHmUIBL1NIi6D1mA4UTdOU1UJSA221XUC258j3zoB2ebnIX7/yCqV6A9ZNktjrKSwMT7HvyR9LzymCqJI9f4rgzEm8RqV7DbbH/SNk60Xkwinqx44z8dBTXHjpL7FxEwXe8lYYyGR4R2MMg3GGlz2IEy4OxzYhFsVH8EjnEeJAAZUltBmBRu0iZzezH2yjoBaFa+Uyf3LsCkeePM8B0gq4ihAWhlgqf4y+M2lVWg8oGDiUzdD38GPddG3tZkJ16qpUU8tK0pkl+oaRs7uajlVtvljiGy+/QqXR6C5fK11Sl2O2b4jpR9+Dl+lLK4SrEsxfInv6NYgaPetuB9GSIijexTdIRvaTG5tmcPIQyxdPApAYOGtKPOYNko/TjNE9eBvsaZxwcTi2mJaJeSNY5oJfpN+PePvMFMMz+7FDfZCUV+MyrkNv+MlWosDl5SX++rWTjB1v8RP5JQCC/BDDM8cYmJjmP548iDlzmRnPYyyAjAie2DS4WG27lH466am1TbARYLtWpe12zA+SaiPi9OXLvH7pIpV6oxtgfN394HkM73uMsYOP4Wf7AcGoJbj0FrlTL2KiBrrNBEsHUQijFpmzrxOPPMPo4SeorszRqiwDsOI3mW81OEBmG7becb9xwsXh2DKUxaDBt8IrXPUr+AEcfuxx8hOTJN7OLWzYjCzfOnmScqPOkKZdjBHD6P5HGZ59FA/oN4b9gcd+nzTjx1bRpIZVy+rcP5CKlO05uG4FlWbE//XiCyyUiqmM63EP9Z4hL5tneOZhxg+9HfFMmpOlQubaOfInnm9XAt7+VagKSxdZuniRYHaW4dlHuPb6d0AtiVh+GC4ybvvIJe0UeceeYdcLF2vapR/snd7arrN03A/SgbhqEs6HJV73F1j060yODPEjRw8zMzyCuclgsn3vSsVYsCinL1+kWKmCeIAwWOjjsYceZmTyMEhMwYcnMgmjpgqtMqot0GS1nu+aAFPtebs9j/zBICxVqzx34lXmiqUNbU4d+WLCkNnj7yM7NEVHnHhJTHDlLNk3XkaSiE4Zvu16TrvHogkD539Ic3SAgZFpFjJ54kYFEWHJq/NqMMc77WS7ArJjr7DrhYv/Y8N4NsRea6DzMVK6eQnrSCxvhEsM2Qz74v7t/kCyK0lE8XZpR6RYql6Lv85e5pJfYrAvz08cPc7syAjZcLUU/42cQNvvrChmcQWvWEIXi5g4RqolArVgfIYLA/zUjzzEaF4QWUoLukUxfvMy0lqgOz9Qb/ROj3qRdfvai8SqXFxc5gdvvMFitdQtGNd7p0D7XJmAsQOPkR2cWE04jxpkTvyQ8NpFTBKvkYI7gbBapXH6LTLH38Hg2EGWLr6GKMQm5rVwkcPRAKNJYaub6XiA7HrhcmrkGgfmhyisWEz91h1fU2Le8Jd4RzSNqnSSLh0PCIuyYuqMJPkd1r3emlgsZ8IVXgjmKfstDk6M87ajR5jo7+f60uo7A7GKXJnDv3CFNAYF3ub7HBqeIj58gOH3jDJauIrYJA2wFcFoR6akFV22oxzbLjTimO+8fpKzcws04xjB0Kk0vP6s+bk+9j/6DNnhybS+jYK/Mkf2/Ov41y4gNq1OLDvwoSBcukSzfIjR/Q9TWb5Cq7YMCk0v4YXMPO+r5/BvUinYsbvY9cLlRHCeCzMLjJscR18fZHAxv2FmRoesBryvcZChJGTnRhnsXAzCaJLf6mZsOgtenVez87zpF/EC4fjsAd5x9Ai+15vOufMGFDUG+8hRokIeqdfTIxgaom9qDPUCqi9M8x/6plkvUAYvRuzE431QqEC92eJbr5/kzNW57i2iPYX7Vm0uSnZggulH30NuYLxdkM8SLF0j+8q38RuVbnHCtO7e9nUR3YhMq4xefAMef5rxo2/n8olvoXETgAt+iUt+hYPRwA6I2nFsBrteuHgM0cwknD9UoTjS5OGXR5k+X8CzG/9wfRVGks2ZlNHhSEQ5FxZ5Ppyj6jcY6ivw9oeOcGhsFCOyWsB1B6OBT3x4doMPEnIvXnzwDdrhJAiVeoNvn3idcwvzPUJltaDe6sOX4OcH2f/4M2T6h9ME6MQSXDpF5s1X8Zp1VkXKzhIrvQjteYyK8wxMHqJRWmTh7MsIEBvlB5lrjNs8+WTXD2kO9oBwydd+nD61+K0qmdJVglarp9z3zv0hO7Y3VqDsNXk+vMZFv4IGlnccO8qj+2fJGMNqcOUuUC6OTcNiePXsWV49d45Ks8WqRNngfhHD4PRRJg49TpBPRYsXJ2TPnCA4cwJjowfd/PuK16wTnnqR+B0/wfDMEZYvncJGDVRg0a/zWrjIjzQmdm18nGOVXS9cLjxf5gNezNRymTDy8awHKCJyk5lfHY67JxbLhbDKD8MrLHkNcmHAex59lKOTkxjp7VTd3efoRbi4tMQLZ96iEW08O3NvhtXQvmNMPfJujARpsT5rCU+9QHjhTYxNtmmVn3vDX57Hv3aeYN9hMn3D1JevgApW4DV/iX1+gZko3w1gduxOdr1w6W9cI25myDUjIpPQNAlGPbIa3DTWxbFKrB5JexZZX2LXJdwARSl6TV7OLPBmsIwEcHz2AI/MzNBfyK87b+7e26uIChL3TKLpW1SUehzx4lunaUbJDZOdFRAxDMwcY+Khd2Ik7cL9ygrBxbNkLpzC2J0Xw3Jr2gHJqoQXTpMMjjGy7yiXinNgLaJC0ySc9ouMx1kXqLvL2fXC5eH4BH1MUDXD/FX2PCumydtaEzzeGt3qpu0Iqq0CV5amSeIQY2Jmxi9SCGpb3axthxVlLqjyl+FFyn6D/lyeZx57hP2jI20ri3NP7nVM7JOZz+LVfLxKkFp8DdhCghrLucpZ5haLG94iHSkiJmB438NMHnsHxgSAIagtk33hW/iVlXXf2G2kv6OgvIQ98xoDjz3F0tA56ssX6WS0vRUscygZYLbVv8VtddxPdr1wCW2BRHxezM5zOawwZoY51BpZLXbluCHWKJWCUruWAwySeDRaOQpBHXf2VoujNYzlXFDkpXCeimlyYGyEZ544Tn8YgrgCanuVjqtGUIJ6huBCH2HZw2ha6bXzC/JbISoJk3aCDHlq1Nrf7N0OmDDP1EPvYHDqKGK8dLLKeonw1efbomVdPZxdSTrnVbB0lUxpmQNPPMPFE89RWbwAAi0v5mSwyExUwHNWl13Lrr+yPzRF/nt4mVeDebLDM4w8/ZPMPXGA2LhB5NYoSSZJk8IVFEO9nu+mVu51BCibFv9X7gLfylymHsY8PDPDjx0/Tn82RAQnWvYoXfuaQmYlS/ZMP2E5QNR0K7Cs5glZRA1TZoT39b+dAZMH1W5lHwG8MMvsk88wuO9hxPMREfzyEtkffJNgaa7nJ7m7ZUuKIq06/rkTBL7P2KEnEJNWaQbholdm3qu6bmoXs+stLhUvxniQLYww/ei7MYUBqkM1ktDHa+yuqPvNJsnExPm1lYbrUTaNefGaJCamEOeAvdFd9pIA58IyL4dXWAyaDObzvOvRY+wfHcO0C6q7vKG9TDoLc7iUJXuxDy9a39VeH4diVDgeHmbI6+PPSt+krHUQj2z/CCMHHqMwMktncJZWjfDE8/iVZXrrLO+Ne00RFfzlRfziCrmhcXJ9o9SLi4ASGcsr4RL9jRwF3fVD3J5k919Vgf7hWfY9+h4yns/0S+cYu7SC34xAzF75pd8Vkhi0vnZZZAPUGpqmSWIi8pLbkloknSn40hyxB8uy1+C1zCJv+svEvvK2wwd5YvYg2dBfNdSv/cex57CE5RyF84NIstGj//pl6Z1iMMx6E/zcwPv5m/prVA9MMrT/IQgy3e+YuEl46hWClQVoZ0eie+lOS8+Dl0Rk3niR5J0/yuC+Y9RLy6BpNtbZoEi/ZnmqMUWguFKiu4xdL1wyfWMcPPYUk6+8SaN+mUa9wZwaxvxhClH/hpOVOVK8lkHLPt1OViGxhlorg8laIrn5vE/3C0V5JbOAIDzRGn1gE6xZLOfDEt/KXqViWhQyPo/v28fbjxzBl+vvJNdV7nZunNruNQPCS31d0XIneT6CMOWP8tNjP853ZzzqQRrXgYBJErKnXyVz8fS6je722JbrEQW/tEhw7jT9+w8xH4TErVpqeRI4HSxxNB5iLHIFRXcbu164HHjb+wmrJWqlNzBJTAI0jNCQAoUdWPr6wdNTT0IANVgb0PQi6qZ+w2/dT1TgvF+iYlqM2Bz7ovs/wVrJb/FSMM+bwQotkzA7OsqPHDvC2EA/aahlu224O2o3oyiJJFRp0pK4PfcPNCRiSUvttYTQ+jwixxhn5I6f9oXUonhx3KMZdiJhNB2kz7xBcO0ioCB7yT10PUp67oNrFwhnjzCy/2Hmz7wEmtpj6xJxxZQZJeN+k7uMXS9c+s6/RbhwES+J25PO9haec7fznSImIfAjVP0tO32iwowt8MOgzFm/yEyUv4+WM6XqxXw/c5W3/CXEGB6amuK9jx4jFwTXt+0+tcKxVQhKQiwxRWkyT5EFWaEidVoSQ3fKQ8V2HJgCMiDE0x4/9tYgXnLnORCxB9cGBNueFsKrlsm99J3VlGfpBMzv7TtOUfxamcyF04wfeZx6cZ7K4iVAsUY5FSzxUDy0ZiqAvSr0dhO7Xrhkzr1GYPzu5GTpE7EQqu+ejm9It2oExGufVnwvIhM2sCr4WxT4JsBQ/zSFIGExbnAlqDEaZ8hsUns694kVy4WgyolgjktBhcD3eOrwwxw7MEXo7fqEvD2Jks5QrqJUqXNNilRMnSVKNCUm1giV3ulXtcfq0SmSlr5fyZWITIxJwrtui7EJwZWzhOffxOumPHf+cb0XAKoEl88R7TvCxEPvpFmvENWKGBVWvAavBwu83U6tWkadctnx7HrhgqadUBq81klDFEKbwexC5aKa5rMYsbdeeSM8C7kEKgGoT1KbBEx3NO8Pa/iSzqFSiPvSwNwHbK+uGY+XdZyZ7DiV4nf5av5NHmuN8N7G/rQ+zz22RYGqF/GDzDxvBcskxvLwzD4e3b+PsYF+RHqEnesFdyyxWOq0iCRmkXJqNZGYRSq0JKYuTWLiblrymij0nqKCqtenvQtQyVRZyZWZjG6/2GVnD1YERAguv0X29R/gRUl707usw9ok/EaNzPm3SB59O0OzjzB38vuAkgi8nJlnPO5jNu5zp2+XsPuFC1z3dOJrQGDTWgi7bdiJkgzNOKQ/W7r1yhthDdRSa4KI4ptW9/z5EjFUWMEAGRsSE2O7tqwHRywxtaSMLuZ4f/0ALwWXuOJVibBk9N7yjKwoS36T58MrXPTL5HMZ3nb4II/sm8EzpmdY4p7243iwaNuK0pSEGg2K1FiRCldkkUQsCbZraFy1zF63kdUXt3HpgyQkiDPtEnS3d6909rmch6Zpkb10Fj9O0oevNWs4ehGEYO48/sGHGJ44xMr514jrNRSlJZYXslcZrx1uW2Xd73anszeEyzqMGswurb1XbvTTigL6MhXkbqwu3cdLAMtwrkyrmSPMNsn5dbKZOtZAJgnJEJLWq3iwXUFAk8fjKww2ZxhM4EeY5JQuEeq9WUBiSTgVrvBasMSyX2ff6AjvOHqUqeF+ZE+lm+58ukJBoEVMWeqcZ44VqVKROooFadc+lrQuCLJ6+99KHtzs886dMlYdpK+ZQ7F3JO4VWBgweCtXCcor7UJq4sbbm6CiSLOK/9bLZB59D+MPvZMrr30LkhhFuOpX+X7mGu9tzOBtdWMd98zeFC6Quol2GQpU6gUacYYJnce7W3dRD335Mn3ZKiLJqql8A//6g3OaKEpEzlYZtkVEB8nYDNMycV2b7oSaSXgrWOJ72StksgFv33eQJw8fJed3ttlOH9HbGdYcW4G25YrFUpUGkcQsUGReyhSlRixxx7nTFivt1z3c25VtF4fDx7MDBHqQatbHNBTf0kkEukHbV7EG6iEEFy9j4rgtXJzF5VaIQvbyRXRwgsF9hyhdPUtl/gKQZiKe9Vd41B9l1KVH73j2pHDZzRSyFfy4hZh7Fy0ABttWeTeWJQ9WAwqGEE9DWiZBgfkgQCUH1NtrrLbq1m1Tlv0mf529xJxXYWJ4iGcff4TRQgERWRsws40DIq0onvXaLoVdqMo3RIlJqEmLRVOhTJkV6lgsZaljRdFOjFubtZXxpef1+i2vsqZai1jQ9Jnd0wKCYLQPo314miHUScDHo5/lPp9vH4NCQ5koW2YXLIXmjUrPQWKgnBPOjhkW8jF9y/OsFv533JSOgU0jMmdOEA2PM3X07ZwpzpO0GqBCzYv5YXiF9yUHCez2/B07bo89K1zu0auwLRGUsf5FUgP4ZloGtteJyiYZZuvTeBhEYCKKsFFMx0GQCo5bXWDFinA+qPKD8DLLfoPJ4UF+8vhx+rPBbcuerSStYyEE9QB/ISQeiYgLzV08R4tiBRIsZalygQWKpkaZWjs1eS0dD8vGp+PGJ2mtaBFQgxBgNI/gE9oj+BriM4Tgp0JG03uR3ulbBWJPKBagWPCYGzQ8dM0yVrSESTudOf0StVB4fZ/HYp/Q8kESVkXz9r4Ntx1erUzm/El49CkK47MUL53qXu1LfpkFv8ZMK6395E7tzmTvChe267PzvSBpxot2qkvs3h9maENAaXotGtIkZ0PQAESY92oMJ3n8Gxy8oiz6TU6EC7wVLBPmfN594BEe3j9B1ve3cW2M1aBgBWqtFt5SFp33iZpKsuixNF2nMByQC/32QLoz0XZIR1NaWLW0JGKJKouUqNOkZKokYnuCXmWDe/7GESsb3RrScfWoj0dIYEfwGMbXUYQAT/van6cWlzQuRlaNNz2uxE5tl/S3aFALixJTHDcMFYQD8wn5FhRzUMsJK6M+lZxgTboZ0d6yhrAbe6v7Q3rmg2sXSKYPMbbvISrXzmHjFgARyqvBPENxSN5eX4fJsTPYs8Jlt3YDe+khrWlaXMpeomliRlqDTDUmUIEyLYYkRzrIrC3LrgIXghJ/k7lC1WtyeGqCtx05zFhfP9v1rHWG43aYBBWpcW5hgZdeO4ttGsR2hmwhXowZKmT5mfe8k4y3c37enbo5RalQkQYLWiGRhDI1miYiwRJJAmLXuex6w171um32pidft5YIYn0CHUHIENgJjIZ45DEUEA1Si0qXTgzLqkVlzR5UUStUii2SWCktNUji9PM4ttQrrbQkrgieTSsPRGVBDDxa7eNwkMV24tJsQi3qFWaOO8FrNvDPnSL35LsYmXmE+QsvdePwzvglRoM+fqQ5ttXNdNwlO6dn2zSUWGISlEBljf/bsf3pfX62JmmXXbckom3vkHAoGW4PLml8QwubPp0LzHl1vh9eoeI1ODw5wY899jgZ32u717YHnTsyloSWRCxTJibimhRJxFLVJvNLVWqNFtojzAQY6+/jXceO4XnetrcqKhBJRFMiFqhw1SxTpEyTCGsspn09tTed+CYBrh3WJqt3hI1BNETw8HUUoz6+DuEziNFBBL8tcuW6Laxud32ArBLHFptAs9KiuNikUbHUKhHWpm1vHwKp/cWk1hSUpL0VEyeMieFY0acg7aWa6ptW4uPmr78b0uvjL13DrxQZmX2Y0uJ5mrWVduF05a1wkUdbQ+Tc7NE7kk2/aocOHeLcuXPXLf/EJz7Bv/23/5Zf+ZVf4Xd/93fXfPb000/z7W9/u/u+2Wzymc98hv/j//g/qNfrfOADH+Df/bt/x/79+zehhULTRBSDMqOtkU3YnuNBYCV1F0QmwqiQsRlKXqUtPIWWaZJIjKd+N2OsZWJeDBe54JepmhYq0JKEMPR5fGKWdx47QhikP4GO0HlwQ31vDI4QS4TFUtEWZVNlUcqUqFMzNVoka40MTagvtVYHxvbi0DM8/cijTA8Nsd0qFMWSoCgNYipSoyotlilTpkJDIlrEabxH+4A6VgwVWb026w5pY3ePQfDw7ACGDL5O4pHDaIixA6TzL4dIuxxCurv1Vpl0y1YFTXquUdPSrMdYC6WlOtZCvWKJWzE2sVjbsch0W4+HRVTxNKKgVRClYCv0aQWAUBtMBxMUZHh13wIGn0wwTtRcuqfzvndR/FadzBuvYt/xNBNH387FE38DUYwIrHhNzoQrPNYcvaNUdcf2YNOFy/e+9z2SZHVivldeeYWf/umf5u/+3b/bXfYzP/MzfOlLX+q+D8O1JbE/+clP8l//63/lj/7ojxgdHeXTn/40H/3oR3n++efxvM3Jwl8MlumLc2RsdlO257g/JMZS8+osBku0pEFiknRWWA2ITNyNI6h5deYz80w0p/BUsKKcDau8HC4RmTRwFyyD+QLvfvgYhybG28/WvWLlQXRgqe0gEaVFRFEqNIm5wjIVr0ZTI2LpBBqvSzlXg7ag8kYDW9Ku06IThDo9MsTEwOCWdsMK7UJvMZHGLJkKSsxllqhLi5iEBlHbsKBdgaWk1s9VA6h0M8+7Lp+Oa2aNlSmDqIfRLJ4O49FPxk5jNAsE9AqJVWHaGwUj3W0msWItVEtNbKJUixHl5SgNXRElSSxJtCpytP190bQwoaeKoUVeyxSo42vMtJ3DaIzBEmqz51urLfBkFOT6ulIiPuvb67h9FAiWr6GnX2Xg2NvpH9lP+do5UMWK8lJmnmk7wEgUdtd37Aw2XbiMj4+vef8//8//M0ePHuV973tfd1kmk2FqamrD7xeLRb74xS/y+7//+3zwgx8E4Ctf+Qqzs7N8/etf58Mf/vAmtFLI2wyeuuCs7UzDa7EYzlP1KiQ9masqSktaPWsKKlD0K2STIjnbz7mgzLcyl4jEIqQD4tToCM8+9hjD+dy6wf3BdFkWS8U0uCYrzFOkJBWaEqNq6ToiZNUlsTYuQ2hcjqmebqKVzgCcDmrZIODI9DTvfOgIngf3POfBbZJeC0ssCRVp0CSmLi1WbJkVqdMyLZq0UOlUWOkRHMJqWaCOlWXDZqfyRmyAEBDoAILBt+OpRYXhdiyKh2inPsc6wdfzqntmldTF04hp1iJKi02qxRibQBz1xtHo2n/b7RYsPjG+xuS0SlbrDFAmp2UKtkagqdtHxLavh17fhutYF49189PvuCldhzLetYt4Bx5m8tBxqotXSOImolCWmBf9BX40niF0J3tHcV8dfK1Wi6985St86lOfSlNU2/zlX/4lExMTDA0N8b73vY9/8S/+BRMTEwA8//zzRFHEhz70oe76MzMzPPnkkzz33HM3FC7NZpNms9l9XyrduOS9URiMBvHV1VDcrlixzIfzVPzyBp8K7SjH1SWqJJJwOTvPZV3gomkRYREsgefz0Mwkbzt6hP4wi7Ba4+b+OofSrcckLEuZt+Qqy6bcdv301BeR6wNI12CFxtmI6tkIW29PsSDg+8KxmVke3r+P0b5C2/lh17g/7s9RKTVpccnMU6XJEkXqEpOkObztMierNonV9Ka1lo5uC/X64xcNMOIRJlN4OoanYxg8jPat2se6MSlro1I0jZNFVNFEsTY9G6Vii1o5tdjFUUK52CJpJdiE1VgaSbfbuz1fWxgUERhO5ilojYCIflvEIya0zTReRdryTFedT2l7eu/TDU+o436hgtds4F06TXDsHfSN76d45TTp/GvKWX+J2aCPo62BrW6p4w64r8LlT//0T1lZWeFXfuVXuss+8pGP8Hf/7t/l4MGDnDlzhn/+z/85P/VTP8Xzzz9PJpPh6tWrhGHI8PDwmm1NTk5y9erVG+7rC1/4Ar/5m7+5wSfX9wqhhuSSDLs1KXo30DBN6n61O/51HAo3duuk76uiXPYaRKQVNWYnJnjbkUOM9fXjG2H9Nd+sq69AghJJREVqABSlRoUaZWlQpEokyU33l7bMw2g/nmYAIdARSufnKJ88n2q19oB/ZGqax2b3MzHQTzpRte1uZHPGQUvcHoibtKhLg4iYBa3Q9GKWqdCQBrbnynSQNUs6Q/gGR96ut2MIEM3gaR8eOXw7iq8jGHI9mT3rv9+xYlhUhcQKjVqEjZVmLabaFij1apOobZxLYoW2F9t2LT7SjkWJKGgN3yYM23l8TdrXQxnQIkE7TDbQCLPmqHvuR10roDovrj9y1+c8ONIrEV67TDJ5gJF9x6jMX8bGDUCJvIRTwQKzcR+h3Z3TwOxG7qtw+eIXv8hHPvIRZmZmust+4Rd+ofv6ySef5F3vehcHDx7kz/7sz/j5n//5G25LVddYbdbz2c9+lk996lPd96VSidnZ2c636X0yG4oG8HWjztCx1ShQ9kssh0VsezBOZ/e+sWm9I0dqBt6SBi3AE4+j+6Z498PHyPvepkcKdAa1NDPGclWWuCKLlKRKQyLSmYbTNdOn8PVDd8cVIAgGT/OEdpbQTuIzgrQng2ssXKV46lXErsqBQxMT/Ojjj5HxDWhvheS7OUpLp2iaojRMwgpVlqTIIlViadGSiCZxmu4roGJWY1M6RqOb7mNVMIr1Ec0QMoGhD89m8ewwIiEeuZ7rLD3fW+tqsXGadlMtt6iWIhrVhEY1IepYTzqBMZ06L+qBpPE0nasQaIusNhizcxS0SsFWyNLAqCJtdbN6fdaKXV3jQtro6G/lBrpdOgnwjnvFr5XJnXwR3vljzDz2bi6f+B5JXAeBK36V816Rh+zwrTfk2BbcN+Fy7tw5vv71r/Of/tN/uul609PTHDx4kFOnTgEwNTVFq9VieXl5jdVlbm6OZ5999obbyWQyZDLXz0GRtSHqrfqLfRtQSProFJtynuTtgaK0vBYlv8RysNx2O5ie63O9dUxFSRCawIIkzEtMSyxDhQKPHzjE0X1TZIzXniBxrQXg3tqaZixVpMaClligyJJXI5GNk1dT104nylQBg9E8nu0jYBRP8wRMYeyqGwQ1RPVlrrz5A2zcpBNcUQhCHpudJfTa/pANB9Hbu6cTsSQSU6bJNVao0WBJSkQS05JVwSDSE1MjnauiqzE5N7DyiHqk8/b042k/AcMYLeDrYBqPov5aE4Wsun5AU02mQr0a0apbKisNUKiUEpI4wSZKEvec454r5JEgahESclrHU4vRhGG7jIclT5mstsjYOtdnYK13Xd3onnkQDz7u4WozEBSvtEQwf4XB6UPUS8ssnH8FQYkl4VJQ5Gg0SCfbzLG9uW/C5Utf+hITExP8rb/1t2663uLiIhcuXGB6ehqAp556iiAI+NrXvsbHP/5xAK5cucIrr7zCb//2b99xO2YaMzS0TsmrEJuIkWiIMAk3MHA7tgorMQvhMkW/RCwx1vRMgLfhJRJaAsuSsKAxDVEiTxjq7+Ox/bMcnJgi53sYselcNfckWpRELBUikIQlKVKlxQIVKlIjMcltiV/P9mNsDl+G8bQfnwk8m8O046xSQ4MBEgQlaqxw5fT3aZQ66bBKIQz58SeeYHp0mI2cMzd+D4qlIg2sWErapGJSN1aFJlVpdjOZNtxKe+bkzoK1lghAUtlgbBYhh6hPwDi+HcTXfkTyiPrtarC9O0gfHATAGloNi9qE4mKa1VNebpLE0GokaO/cMrLaJkEJaJLRFjmtU9A0RX7AlshoE1Elq83UvSOKp0nPRrY7661OnWWOO0cwSUxy9jTJ2Cxjh5+gNH+OqFZEEK74DarG0ufcRTuC+yJcrLV86Utf4pd/+Zfx/dVdVCoVPve5z/F3/s7fYXp6mrNnz/LP/tk/Y2xsjJ/7uZ8DYHBwkF/91V/l05/+NKOjo4yMjPCZz3yG48ePd7OM7oTQhvS1+hiQAepenf6kf2N/u2OLSAMi616NyItB7Zqgyw3XR7hGzEUirIGc5/GOw0d47OA+sl5vXRa42zimlkSUqbNgylSkxhwlLEm3HknHcnK9spJ2eu4AaRbMOJ7NEOgERnMIPpKGenbbppp6fISEZm2RlSvnKC2cJ6oU04HZN+wbGeH44cOMD/a1z8C6vaqkEwAiaTE+LA1t0ZSIFalSlhrXZIVIYhJsT4zHasbPmuDYnrO35tjoWFICjBYQvPaxhXg6iK+DpMXegtXttC1e2rFyarosbiW0apbichMbQXGhTpKser9kzZGmbTQkeJqQ1yqFpEIfVXK2Qp+W8NTidQOvV/cpneu05mDuVQg8iD7EPVhtNl61SvPqEuG+EXKD40T1EgpUTIuLQYlHm662107gvgiXr3/965w/f56///f//prlnufx8ssv83u/93usrKwwPT3NT/7kT/LHf/zH9Pf3d9f7nd/5HXzf5+Mf/3i3AN2Xv/zlu6vhIunwkrUZMjbjJMu2QzHq4bVjOjrL1g+dAsQiNDVhSSyXvQhFmB0d5V3HHmGkr4DIal0QuNNuP609sixp8bc5ltsWld42dZqm0LYedIZ8Q4CXDBLoBL5OpXEbmn6yJlhV2nPQqGAjS2mhTnGpSau+iEnOEDfmsVELNHXRjA70846HDnNgdBRPzIaOIEWpS4OGaTIvZco0KEqdSOs0TRpIqp1S8ghp5lHbfdU509q7ve5S0vibAsYW8HUYT/P4jKZZP5rrHv1690rHYGatEjcTWnVLtRyBCqVinSSCJII40rY1RVcNKULXvedhyWqdQFsM2CKDLJG1DXJax6hd9cBt+MPuiDF7oxV2AL3WFse9oeSSGtU3XqbiP83U4XdSK84T18tYgdfCBQ5EfeRtJ3HDsV0R1QdU9OEBUyqVGBwc5F/M/iOy5vrYF8f2QVEuZ69QDEur0Z69FgmgLJaLJqEsCYkouTDDI9OzPHFwP9lMsFoThNsdojTNmhFLhSaLFDljrlGTBlZulEzc8ZUIqIfg4VEgtNP4dhqPATz1etYD2hPsQZqCG7diaCWULy7SX5sjV52jIsJFlrHU16TLTg0N8f63PUlfNi2QJSqIQCKp66cuLWrS4iLzLMgKTaJ2bBDd89bT6hu86Tlja46rn9COEegkvh2FdkXa1ViU9AAFAduxGimNRgSauncWrzSxsaVeb6FW0CTdn0rbDmINRiKEhLzWu5+NJIsM2jQN3tcWeaoYFKPJdS2/vau9/q64mZvtQaMUMscYLryL9W1cqb1IpXGC7dXenUx6H1gxnMk/Rv7tj1Gce4nFs6+k9lMVfjQ6xPFogiRqomp77jPH/aZhm/zGhf9AsVhkYODm6eluogbH1iO27YLoPHevogJzwHkiYk0zWg5PTvDUQw8xmMl2Z8OGG3UwqbsgMQl1WtRpskwNJWFBqsS0qNIkbk/kJ9KptJpaJlK3h4/YPD6DeFrAYyi1QqiPoa/tOmnX7FCIG0rUSqgUI5r1mFa5RkbrFJrLZBtlClQYTWoYIhYlYkFtOiswdOM3xgf7+YknH6cvG6KSBhBWaLJEiaJUKdOgRZO6RFjR9Bze1MW2ej7Sf3yEENGQQPvxdABDH4YBRD187WvPgmzWnI9OjZRWw6IJVFaqVEsRzVoaLNtqrFoIVE1a7RcPweKRYEgIbYN+LZPTBoN2CU8S8km9HQSsGLXdAODrRcca+9Vtst7xtVMGfzdkbi7pdTdqmaqdobQwxtjsE5QXrtIsL5AVn30jh+jzJmgUF2gUF0knnXJsN5xwcWwxQiQxVa9J6tRb7ShaIsyRcEkSYhFyGZ93P/woh8ZHyPgBG9pXuo9I6bBXlSZXZZF5KVGkTiwtEtI0WRVpGw/aHVpbfKQuHg+jQwR2msBO4OswRjzUeu2g0J4ZaRRazZiV+RblpSb1SoImCUHcYsguMa0LDNlFAk3dWwZDxVjO0aAkXccNHQvG/rExfuyxR8jnA2rS5ArLXGWZklRpmbg7P9Na/47pOQEdubY220gw+MkIvg4T6CSGwmrcjXrQdieJriYOq6bWnahhqRYjKsUmzbqlVo1RCzbpaUs75Tt1HCmQMKALhNogqw0GtIxPTMFW8DVGrisi2D6U7rlYjYvZvdzo2HT1hLjn/k1GKWiVoHyWujfBzMNv49wP/4oJM8r+YAKDR254giRqEVdKuHO//XDCxbHFKHWvQdKeBFFUsCJURblAixVJCAOfhyanObZvmonBQcwav39P594uLd8ipix1lkyZCyxTlkr7s45VQ7qD7GoMgSDqp6nJdoJAp9K5bzSP6YoC7VpEklY6mFfKDWqlmHo5IqnVydk607bCMAtktEne1jAogckwmtlHIDkymWH+v9XvsaztAFRRxFO8rDA808e+fUNcCeeZkwp1aVCVBrZdEXdtEG3PgLZ+/GtbXzwt4OsIvh3HZxijfRjNdOfX6cxcLKpgfaIoxlpL1FBq1YRWPaJWikgipVFPM54E8NQS0EIAn4h+XcFoGmA8aJcJSAvA5ajia5qxtFZIXR/P1Ds83L4baGcj+GT8jQJCxY2X9430N+QtzGGiBtmhaYamjvBYaRSf9gORCPmhCcq1Gmqvz7hzbC17QLi4G247I6QTJNq2FSEWuCgt5sUSG+HhmVmeOLiPoUJfGnzbfhDtlMtvSUJV6lSlQYkGJapUaVA3TRLS+WLWiBY6gaBZjAb4yRBCloB2ZowdRgjoZKIolihWbCumXomprrTQZoStVggaZdQmjGqFPlsno3UyWktnBEZJXSvCoD/J0YF3kpUCglChSZ/pY9Gv4g8ZMiMGb9jg9RnEt5zm4trhvS20Oq6TtY4Tg2iIaBajA3jqE+gU6dw9Hh7DqaurawfpTBlgwQpJpNSKMSuLDeJWQr0aY+O2pSVJLWC+RIS2wZQtk9Emg1okoEXBVukIkjTNWHsifaWd6bT6fiOXz83vjM1m+wkhkYBsMEk35Xztp7d477hbFDBJC6+0jEwe4PD+d3LsrfQ3AemZNkEOk8li6xtNO+LYSna9cFkJioxaj8BNqLgtUdK+wooQqXLNJFwiQvB4ZHqKpx99iMD0DnpKZBLqpsUSVc4xR8lUiYl7tthGel5oahtILSojhHoU3+ZWM2OkZ65iFeLIkrQs5eU6K5fL+M0a+VaREa0wpIsENsFvz3Is2inKtnZgFgwFf4ijA0+RM310XEF95Hlm39upHf0b4mzSzoby6NbSldUsm7Xnqt2paojRDIGO4esYgU5h1IdOCrKattvn+oEuiQSbKPVKk0YpYfFajbglbV++EtLCJyGkQcGm1pKp5CoBLUIiVD3Suvm9Mzmvu6Br3FUbHYnj9uix9LlzuOmIWqTZAIGMnyWUGGT1ntWkiTZTce7O//Zi1wuXucwCsVqmGxPrUm4d2wHFsmIS3iJmxcREoowODPHw9D4e2jdBYAwqEBNRNjXmtci8VChJhVgSrHTKordN693pAaRd9Cwk0EF8O5nWGWEQ0RDw6IS3qAo2hlY9oVGPKS/WYHmZXLNIf7zMpNbJ2wY+UTsuY21cB9CdvaaLCBmyHCg8Ts50KuKm36mFDU7ue4skn6QTAfYIlU58B/QKFR+jOQIdJ9CJdE4fLbRnQ5aeP7CxYjU9pnq5iU1SoVJarqGJ0KrFSKuJSSJySZVBsfgapxVlVclTwdcIjxivnQ7V68jplMPv+eAW19dxc9wZ2kq8RPBjqGaFUk4YrnarDRE16yS6k1Ppdy+7XrioKBWvTDHIMtwadrfgNuNaUOMFb5l6e8bk4UKeD77jbeQyPk2JqJoGK1Lhoi5QliqRabt/oB28mFoxVAUhxNgAwwC+5gjtDJ4OppP12XRuKsViFZLI0qjFtOoxcQuKVyqYVpNCVKRfK0zZy+2Kq+k+VCR1U7FWYlw/8KyKmrH8AYbDfV1x07FBXBq6yuXBufYxrIb5KgLqYUhrpBgdQlQIdJxQx7qBtJ05gzrNiFuWViOhVo5Zma/RqiWICkmUEGgEJORtlYy2GNcao3Y+ze6h2T2N0hvr09PWjd08bPDecffYG5zOtvvNcR9IrZzjcxkiqyxPKst5GKmmnyZqadXL7d+uuwbbjd0vXNpZEQvhIv1JP2Hiu9twKxFBxCCeR50W389coSYJEkB20iM3G3CycI4GLcrUSQQi4q4rp+NGoT3Ie9pHoCNti0ofnvaRVqf16eQJ2ViJIkujGlFZadKoxjQrdUycUIhL9NkSM7pETmv4Gqf1Qtrz5nSEiuhqTZlbHiKACh7BagBs24UiAoeXZki8iO8deAOVEJ9+jBo8O4nRPjz62nEpQbt2iwAW1GATxSaWWiUmjiylpSrNUoS2IvJRmRyWUVuhXyv4WAq2jBC3U5HTINxu0bZ2a1e75o1iUJxAuf9sdI478ULu/N8fBCEgr8OYBWGglDCBRW067URteZ5WecWd/W3KrhcukHbKiSTUTI0wuXlhG8f9QggKA4R9w/hhBjzhu9HzrEw0KRR8gqGAsF+ITMRFWWx/p3c4lTQTRn2MBoR2Ni2nr8OIZtrBqKSBuKrEkcVGloUrNaqLdUyrQaG1yICWGbMx/baE17Y6COtqNfRM9tc7wN/NMa8ex+r3gyRgqPYQQ639JMZDCAHBqJBW1l1FFeJWQtRIWLjcoLrSwtgmtFpMJnOMa9TO4mmR1WZPjtX6HJ2Olep6kbKRzcjxIJH1twg9F8pxX0krVPvNFtHCAsWojEqMxvHGMVyObcGeEC6Q9gmtdsqt6xAeLIqQ7R8iNzqFGA8rcHbsIqXJFfJ9AWJWnRNphTPoPBGhBo9hPA3wdSItlKYFPJvBqI+10GwlRI10vpvyYhWSiHo5tUBIHHHYLtKvy2Q0wtOYTo+krLWG3Bk3Wl+6nqGm1khsCyNhe1lapm4lJ5zcbxDTj6+KtUqjEhPHabtq5YhmNU4nBoxi6pWIXFLGxC1mbYk+LZHVOhlaGFWkG2GzfhaujW509wy57RC7sXZxMxXfV7LBJEZypD1PgiatdLqNjeokObYVe0a4BDZkMOqnZz5ax4NClGBgEDGduaaUS4NzlAYqrM7l087D0X4CHcOzAwSMtd0lOQxeWloeoVlrUakktEpVbHGBpB4x0FzER5nQJgO2BiSE2kTEtuNUTNvd027BqgGCtRPebFZnpczVz9OKKuT9YfqzI/iSw2rMmVyLZmOAoCHYqI/y1Sb1Uo1qs0xIg35bpk8FnyZ9WkawZLSJp0mapC2sOZaN9u0Eyg5iw1RocAPn/SXwBtou5U5WX9Ku3gzu3G9vdr1wEaA/zjMSjZLRENehP3hSF8+q+8WoMFYZ4fTEOQwGsQG+jhDqVFrRVfvTSq4qqFVsbKlWYxrlBlopEy8VGWguM2grDGgRweJrssYx0+14uv2Pbb/VnmXXJxzfHb3fk+4Dm2rMcjTHcrQADUNnlmS7IuQupBalsf4fJfTGqWmRpegFwLZrotxi+5vQasd2IRXO4rqmB0xvdFeIXhcM7R4Atiu7XrjMNGcY1gF8668mdzgeMJrWTGi/s6Kol6cQPZEWTWMYz+aIm4Y4gmo5olmvoVFCdaVFJqlimjWmk4sM2BKhJggxq9kuncnQemuz3tiVsxnHc90mRTF94A2lpd7iZSGpalus2PRPLN2y+AqBDJGVIQQl7w9Rxke13t3HqmXwVm2+UcaPu+G3L71xRzfKKnLX78GwUZbgRpl0ju3CrhcuA1EBz9v1h7nNUZqVEuJn8IIQRajkpsgl+4kbSqseUbpWorwUQdwgk9Qp2BITyTwikLc1PJJ2LMfGcRy93c1mtHeVdvxNu2ovXmo1MTlBfINiCYZABsDPee26MuCPe9Rei9CGl4qXjhlGerYrq8tFfETAaq9ccW7NvUFv2vn65dzgM4dj77LrR/ROTbLuG8eW0Cwv06yVyBQGEM8n4+cZGRyhtNgk04wYa75BK1ogICZjW6Bp2Xyjuk5GbCRRNqtTb4sgUfAULzSoZ/EHDeopYoRgwE/94KEgRlA1JPUYrUHlYo3GcguAkScHoWfm6vXuKQVsdw4fWVvrZW2+smNXkl7v0B/CkL3BOu4m2BqcSNzu7Hrh4thGJAmt0jIoTEQjDDaG2E8Ins+ijagn5TSTRnpTka+3ftw3BLwRxRsQvH6Dn/VQUaxJUo8PlqQeE89ZRIVmJSKqRCTlBGIDVrvBfeXTFQLyN0+nFgVJZ6pOUddn7jFEArSdAn/9pe+k6fe6MtwNcn/pqezo2LY44eJ4oChpEKIft4isRY0HCNoOXO3Ub90KZFTIHRWsB7Zuqc83iSoJSSXBltJS/xorSV3bmT2dbm6tU0dUoKpIvuPuWc/1AqW7jgvE2nu0iwJez9pwc8eDxImX7YwTLo4tQeImYpO2cOnQHuR705bTtW+xtd6n0bVPprK61ZtvR5RgRLBGqZxq0Dpbx7a03RRZF6on3XTktVtOXUMqQtwQfL8jW3r3u/oN3xTo/ATXtsyJl73Eja+2uw8cjo1wwsXxwOmIE7Ed+0qSZlYAGz/p3EZwYjtVRzIdmaIQCUS9K9ykRUbxCtC80KJxuoYk6RxC6/MNep1YN2yjKsQ2nWrmRk/SCr43AAQ933fsRdbUFFqDvW6J437jfoc7ASdcHFuCZy1+q4ENsyQ0SWyTe3nC9KeEYEyRvAFJa8XYBsQL0Lpsb68/UqVxqYkkN27HjTezxh6DBrdIxxbSTCJW3U3uCXuPcsMaLu5+ePC4c74TcMLFsUUoatJ/rW0Rx6v1S+648zDgjyt+P2SDmEwAjUhoBAapm9uKG/HyBuPrauqy3t6EihuhBvoO5knmuaHSMZKhL3ME11E67q+r6AEGt+8aXHzLdscJF8eWYI1HnMl13Toiyk2r2N8EyQiZgmWor8VwTjGiJNZwreKxTHhb2zB9CgFI3kOXk3vquwTFeEIs7UDdG6wl4nXTu6NkGasRbnDZe2zoKlJI/Zz3NoBasaixeMlGXb3LVLqejvvanZPtjJvFy7ElrAbddmqW3H0HLSLksjGjeYtppyMbzzKaT/Bvu/9RMJAZClb7rrtEr3txgzV0dVeJNtHrSv079gKd7LRelIhGNM+9VHCVjJI97FOYzUJBET9Nv++6JoW73vauQwDxnJ1lh+AsLo4tQ7qihZ5MorvpSPW6TlhUMQLaSrhdfa6qNJejblHbe+nEVDrp0Tc6nrVp0qpri/w79joegTdInKxwV+LFKJkp8KbSIo7+pI9tWJIW2Kpia0pSVaQFJCYturiHR21FSDL9eM0Ke/pE7BCccHFsEUI6x9C9mqt7zSOdiirtT6yhtWKR9vzQN92KpJ2Xrdvu+7vvv+7U3Nwp/e/Yi2zkKhI8PJPnzn8X7fvOU/wxD2ssVi14IEE6Y4U/AqhBY4utCHYZoiVFm73bud4GdP3yXeRSEcGaXXIsewAnXBxbQPoEudYjc7cD90Y1UtrLI8FWb7OOC3SnE7r3B647fUJ2GUV7Gbnh+H/3Yt7kQINOthpdi2a3pJ1J0BDMMHhDijepaCO1gCbLYJO1PwJbb5cXUEETRdTQEei7SXLvIim2q3HCxbGF9AzY0s7ouatuQ/DN+m+unSv6Vs0QP1239xv33iHfxr43TSw5dheK1RZ3N5QK2lCIQT029HumcTWrvz1TAC2kK3lj12fUaSSIFcQqUTlGYo9WUaAJNEgnP9e1DxHb/ZZWVydnx+KEi2ML6OYSbdK2LIXgegWw+upWxeuUYMDD1iy2sgmdmXBL148nOUQ6asu5ivYyG7mKlJhWvHAXW2uXSNT0f03Po+YNXFcSIGMbhLaFR7zqrtXe8opr70cJ6K4X5DxA8acVEkPSUuJFhbJHUo3BGjQR1qYJ9v42t4dNo966QF/mCEYKW90Uxx3ihItjawhzqEnNzRsX37p9PAOBp2seTu/E+aICVhS1isbpnESrW7lz/LxHkAtIsDd86gy8IQyZnv1sj87c8eC5savorisJpf8XWAqmuRruZ30VXk8jPE3I2DqZdvFHTyP64wpIx1piydoGvk2/K2g6CSrpjOYqIL7F+JDJp24k2/IwkZCUIa4pWkstMXEzoZM0Z2w6q/pqW7fi3hesNp3VZYfihItjS7DGTzs+II6r99SBGAOeuQd/S8/XNp4U8Q43Z4Tm4q2yNGTdI7brQB2bRee3IDRMDrtGGKf/WskQCTRMrh3SnmbC+XZVtCuWjG1i2nNXFJIKg/EyOVvF0wSjbWEuaUYekqRTbmTA9EEm3SiihqQFtCtSmxY05ixY0BhsTcGmFhpBsNwqI28zcBbOnYwTLo4tQNZYrmutSyjxXcZ6CEZXzdgd7qQUS6db37yuzELLcus07NVW1qNruNDAvcmN5yq66y2m21NhIF5mKRihqzCu25N0HxpE20UTO+4mMdQl336tVP0CC+EkoY0JtU4+KTMQr5CxdQLiVSHTe1wCSoLk0nZ19pwdbq9jQSPQspI0lWQZJFJoSqdGwG2cGfe72Ws44eLYErrBgdp+WuNWFoqbbg1IO9demu0siAfOXYgv1ejWqzl2JTd2Fd0jqhiN246dNR+sb0HPq95aSNe/tkDDhDQIKHkDzIXTBLZFaFv0J0WMJviaMBAXgQQBfJusi+FSuiWcPEF8kCwYBH8fafZSE7QKcQMkEaLlBOnWZ5TUQGllVWTd1elxVpedihMuji1AUdGur7y9aFO229sJV5pgb0O4KO2Ouevbv9dR5HYE0/YKVHRsHfdNt7QDv+Wus/VuuOXuK4uhabI0TZaS308aK5PGxnTW60sqeMRkbINsUsMjJrAtBDC6OgFqWrRRkFAhFOgXDBbRhKBheqwvQlyL0UY71gYlKQp2xf2W9gpOuDgeOIpgswU2v7uWdbkLt7n9ADS0UKenTffqPLrZvgXfa2cyqJBojcgu9Tw7ug54TyH356orkLRnIH8QtoXO782Koeblu8trXgFIK/gaFENMxjbx1NKXrGCwZJIGGdsk266Cpz1uVBUgZ7t7AQjybWtt+6SFk4baCYuWV+N7HLsXJ1wcW4I1mVuvdAdcX3tFQAxiOtEvN+7MTGAwoaV+sQkJN1zvzrhZ5ymE/hioAVEarWsktkFnVmrH3uK+uYqAsj+4TcJQBSvSjqbxaHnp77/oD5GKGouvEcPRMkYteVshtI2e7ysZ22iLozTzT2RVpBgf8sMFquUaTrTsfu54ksW/+qu/4m//7b/NzMwMIsKf/umfrvlcVfnc5z7HzMwMuVyO97///bz66qtr1mk2m/z6r/86Y2NjFAoFPvaxj3Hx4sU16ywvL/NLv/RLDA4OMjg4yC/90i+xsrJyxwfo2L6sWhjurWvdaKxXAF87qQ23aIhNO0TbG6Z7v7v7zn4szXiuHaXoOty9huBzz/UAbrjtzaqVdP9If2lCIh4Nk+VqZprL2X28lTvG64Xj3b+TheO8UXiSs/mjxCbs+Xb6m/XLB+mrP0tghrbsWBwPjjsWLtVqlbe//e38m3/zbzb8/Ld/+7f5l//yX/Jv/s2/4Xvf+x5TU1P89E//NOVyubvOJz/5Sf7kT/6EP/qjP+Kb3/wmlUqFj370oyTJ6uy4v/iLv8gLL7zAV7/6Vb761a/ywgsv8Eu/9Et3cYiO7Ymuzoor91KArT3r7fqCWSjEitY7YuQ2OvBNFQ7XVx/dYBV662u4ai57DSGXmURvYy6tvUDvvW/FrPlLxKPqFVgKRlnxRxFM9xsmzpJZeAw/GW67YN3Z3O3csavoIx/5CB/5yEc2/ExV+Vf/6l/xG7/xG/z8z/88AL/7u7/L5OQkf/iHf8iv/dqvUSwW+eIXv8jv//7v88EPfhCAr3zlK8zOzvL1r3+dD3/4w5w4cYKvfvWrfPvb3+bpp58G4H/73/43nnnmGU6ePMkjjzxyt8fr2Aao56Gel74G6OmE7hxhYEwRr3dJb9d1O3b4Xslw752exKlouuV+lZ6nbdfZ7i0694d3qxXvaQ+7izSuZS4cp5CUyCfVNGup2Y8fFVLrkoQ4+b/7uWOLy804c+YMV69e5UMf+lB3WSaT4X3vex/PPfccAM8//zxRFK1ZZ2ZmhieffLK7zre+9S0GBwe7ogXgve99L4ODg9111tNsNimVSmv+HNsTNQHqZ7p1HtKFd29vkHXWmnurN9rd6l1uBWzLoK07dzc9CAeVYzuhiMp9s7Tt1uG7aXJUvIHue685gtgAwSMbjLF7j9zRYVOFy9WrVwGYnJxcs3xycrL72dWrVwnDkOHh4ZuuMzExcd32JyYmuuus5wtf+EI3HmZwcJDZ2dl7Ph7H/aNTodZqRCte4u47G0XoLdPfXnpHm9tcyZAGBEs3HfXG++x+wwmWvYy7+HeEQLuab0o6nWq71q6b82tPsKnCpYOsCzZT1euWrWf9Ohutf7PtfPazn6VYLHb/Lly4cBctdzxoVC3WNtvv7k68bCRSRAXbkNscFNJn3s16TsuMeTc5FF39t5NO4gphOdZhtYVqfE/b2K13VWCbDEVL3fedWDnB0mgt4tKhdz+bKlympqYArrOKzM3Nda0wU1NTtFotlpeXb7rOtWvXrtv+/Pz8ddacDplMhoGBgTV/jm1MTzGp1QV309VKmla8QUcVzdvu/Ci3bo8StXoHirvv9tVrT0B3g00Y8TCSIa2Ym5BoLS285fravckG1z1KiiTa3PjDu9/srqBTuK5b6EB6HwcinNN197OpwuXw4cNMTU3xta99rbus1WrxjW98g2effRaAp556iiAI1qxz5coVXnnlle46zzzzDMVike9+97vddb7zne9QLBa76zh2NirrPft339mIuddOKjU0Z8KAtULq3th44BCMyRF4g6Quoog4Kafrqmyi3cexE+gMuhvfbW7wvX3SOtyhN5K+dS6jXc0dZxVVKhVOnz7dfX/mzBleeOEFRkZGOHDgAJ/85Cf5/Oc/z7Fjxzh27Bif//znyefz/OIv/iIAg4OD/Oqv/iqf/vSnGR0dZWRkhM985jMcP368m2X02GOP8TM/8zP8g3/wD/iP//E/AvAP/+E/5KMf/ajLKNrxCCrSnrH23p+MgmzC+H4PenzeAKpCbG9Xl6dtWi1Uly67F27ab6pJ99Odp8m2I36caHH00onduksX6qa2ZfvRnTBEknacW6citUnrIrnf1K7ljoXL97//fX7yJ3+y+/5Tn/oUAL/8y7/Ml7/8Zf7pP/2n1Ot1PvGJT7C8vMzTTz/Nn//5n9Pf39/9zu/8zu/g+z4f//jHqdfrfOADH+DLX/4ynreaGvgHf/AH/A//w//QzT762Mc+dsPaMY6dhMVm8yAd68I9CgRRvPD65RalkVy/fGM2v4O79RYVpD1Pi25k5XEd7l6gW8voOjbH4pdPaiz7u+teisSn7PczGBcBiPPzWBMhticV+kHNc+DYEu5YuLz//e+/6ayaIsLnPvc5Pve5z91wnWw2y7/+1/+af/2v//UN1xkZGeErX/nKnTbPsc1Jn5C8bv2Se+tSlQShFlkK68RLKxawacjt7fZfrp9zbAn3zTCgZJPOZIe7R7wk4tPwcl3h0pk2ujfWxbG7uS9ZRQ7HLenMCHuP7iJrhXLTtKNUOtuxNGKDteY2t735ReBuNkyEfj+C50r8Ox4IuzLcY/0xdX/+u/FgHetxkyw6tojOTEX3nvJZaRr6WkLgpdlFsRWWqj6qnWoxt96KtdBYbN1TW9a360YuAM/0k1ZM3WgNJ2b2Gir396rvSn3cPSa9fpFj1+OEi2NL6KQLt+IlrDZu/YUbIFaI68LlFW+NWyipC3HpDpxEKkh0183YBFy3uxfpiIrd5cy5/8QSbFgCyZ3DvYETLo6t5Z6KrwkaK403IHzIQ4I04Fet0jyrcJsGFC9j0gFkE63Mt9uBbpgV7nBsIrsvTlWomX4SBG99xewtapHjweKEi2Pr6AzYYu/enq1g69B4rf2m03XZ261Iq5i8oMbeetU7a9YN67isX8tIQGLdM/de5MZZRZvHbhzMddOrNaaVrFOb7e3Gxjm2Chec69gSugGDm9L/aFohN5G0nEvSXnbb274PndQN9i0EhN5Q+41iJEPojeJ+insYN0ZuC5IwTxp75i7Idsf1lo4tRNtGknu5DW9QC6ZbH+XBWzFEwNxgv0ZCMsF4972qYDVJJ4dzBhfHJiK6GSUHdih3+ttXUBPeB0uO436wB4RLOnuwmgSbbSCyuS4Bx72gxLayydsUbihmbtQKAS1biDbn3lhTU26jfSHdOjZW67TiBZfJuYe5H/VHNBaai2k12d13WymGGLPBFBmywas74qYzuju2C7s+xqX84ydphR7WsyR9dfKnZ8i+ug+5p6d8x+YgtOKlW6/2AIiXY5JWwj1reQ+8vE9SY8OoyNWaGqsxLalocTEue5H7k6osiCo0b73mTkQRQttsS7IbBec68bGb2fXCpXHgGmTSmhmqkIyVgH1b3SwHkM4RtMUzuYqA0VQ4bMIoIqJoy7S3d4N1oK1bVue0dexN7qdU3ZWF50jPWcPkScTg3VNWomOnsgfMDoK2/0ziE54d7fp+HVuPdDuerYlHwSiZgc3W73LDe0yl1/1+524txy5D7/cdcHslGHcaNzomwSCkhSjvCGmbR50Q2hHsCeEiiYdXyZF9bR/hhXHcQOHooALW2+y4p7Tz22hA2moDk2Mbch+T2vJJlVC3tLLifUfFtmeHhmwwSeANcccnVcFrVFg/y7xje7LrXUXZN2YYuDqBv9iH1wgRNbuzBPZORHfzKH6TiUg7H+9WW75jy+nM3WWwmF37G0tRv06UXcGvTiEEiNzd87gY0459dAkc251dL1z6vn+EjGTSMULEiZZtRGxrNJMVdpMFzC8E0EhN1RsZ6TuuIpX1+RCOPYmshlZt7v2gqKTCZfc+HKQoCmI3yC+6gyk/BGwmn7qMdvfp2hXseleRdAIKZDemBe50LKoJqxVvt+AKKWA7T6j39nNQA2E2vGk2R8dVtFFGhGPvcd/uAA/8YW/rg98fCLr2nztKadb1X3fsAHa9cOngbsrtwvrnoq29MmIhLib4Qz6SuftnXgG8MERt0BPft3HYpSsV4ejQ8RZutrUFo/h5A3cTqLoj0TXWq9v9ju2J/7kf9XQc94c9Ilzc7bhdUPGIsv1YAYhZ60/egg5WgQT8YUPmkI/JGDDSbt+dIIRhBrVp6v0Nd9fjKtobA4rjVtyvu0C7xTZ3+32m7f/o6epvfcxWI5rR8n1sl+N+setjXFJ2+w9352CNwQY5BKXWOAvaieLfumuULAvBlKH/WAHdpzSuxWjTptOWwNoOMVHiuZi4viqGje/jeR6e598yrK/rKmo/aou4u3OvIx1vxabPG9jjA9nVN9naqQ3kjspQW3b5ydmV7BHh4tg+SLuqDiTa3HJbmAJxBRpvJkgAqJCsBPh9ivFkzXoAGisSZvCD6xKdUfS2EoWucxXdSRyhY3dxX90T0hvCsauRu3Ie6HVvnYTZGTjh4njArAYLqtj242bvNPIPtutIRYRgF1ddNwZImoptF6XSNU9w7XmGupOxtRNP7/iRuV3uXyPU1Y7Yg3TuN+1O7Hd/qv/rnki7vz4E+c6fBpxo2TnskRgXx/aia9TtSJie5XqDv9vlTtdfnS9Ie/4DUNWNA22Vdkl/vctkqNXjasbLWNu60w04dgVKI5rnftUNse197H6TiyIk60Lhd/1B72mccHE8YHqfazr1znv/VTC256/XfnuvomYzuP657N5asBfSVR3Xk17zJElz5zf9aT8W4jlLJFm0E6y1S1FRmkNnwMRb3RTHA8K5ihwPFCVBoibqC4VjJcKhlTTN5mbmbGtozvusGiZWzewAoh7NRR9tGNRqu/rlrcTAzUzJ99No3BEq6f5jW71JOxy7m1SoSyfQZdPy5AVU0UhpSY7YBJuwze2MgN/Eiq56cF3g2K7GCRfHA8UkFr9eJBqp4u2/CPnGLWsvCBBMb7R0VbhEFYPUAurzBqKAxqJB64LG642KqegRL8Fk09gSbY8atu6BlW7cC+rRiWHpdWutjW7phOUq6wXVRp2n1Qa11iX6Mo8ASiteum4dxx6i43bsuiHTeyZKSmzafXGrB4NdQ29RnDs8XlVM1Lzz7zm2BCdcHA8UQRFTRA+/QZIvtZ8400/ujNUOxkqC359AX0RhIu2j+xKfpGzQZqf8/mogroogQYLfn5qWVRSxhrjkoYlBsMRVj/qKj6e98qQ3Bmc1Niau+FBbPcLV9gkkHhqZ7meBP0AmcBN9OqB7D6zRuumLKFnevDF0D4iWzoND+iu9C9eYgMTNNDPQ/Ta3PU64OB44wgI6dLHdod57J9Ht99tPWiqCmBgzot0n2I2+1DuDvQp42dV1PYRsuwu7rizEutorNjIQG0y3EasWl1bRpzHvIWowcZa+8nF8HWp3kBtxt+6r9d9zne/2J3UVdW6b9TWlNwVZK7R3J4IaC2KBgGwwSSO6xp1nFXXKGTg303bHCRfHFrD5AamywStPDQOEJEBdWsTri01t2JevDR7WjRbT23pFAosGsH6aNwA/36K/7eZSLcOVV+HNIYgLqMR4YzVCbRItB6jqmmJka17Tfq8GjTZ6JuyVQttpkNqaNPcdg5oNBMsm/jbuOE1/Z5CIjxUPT1OrqfUraFCEeBLB567OYddn7UTLdscJF8eDRw2S3OOEhu1/b9YlB3jkCRD18BBWpHFP+9yY23V0KYIlmX4Vgjr+yQ/B4Hnyj70Fpo5t+T3xDv//9u49OK76vv//83POXnSxtLYs6wbCdVIgJHZc2wRfSBNDEmOnxk1JB4gZFWYYU1ow47GZJm6mhcxvGid0INOpm4RhyA3cMdMZIExh1JgAJnxtczNKwDiOCQZMkHxDWt33cs7n98detKuLJdmSrF2/Hswa7TmfPXvOZ4/2vPX5vD+fMygtwZBtvvZiDvH2wLDv5yVc+t8Lk7pfwQhfvp7Bxgaa0ofLxhn888AsaWO9ABb7X/gTxRAOzkz/nK4zS3qOIF08TyfuhIg5QYJ++o6mxqZvKAk694qfAheZck53Ce6pCpIX9A907zDer5uBHJP8JQPixuOE7ccai2e8c/y3f6YvwMebfQRnxglsWSc20IeBbKIw5LedZF6beR4o9XBnxjFDwozUaKryP+vH2NzugcwrfcDixV286NDABfKDpczPBvC8APFjAU57AycLyfYgvmfTf7imQ56EGfK60XNFB//lO9L7Dvfpj9bVNnib4y0/kRwCbmVqArrBo/4nSrF2FVmDsanzeOKObOSkepleFLjIlHN8h9IDddiKPmKR7oHLss2k16UMfH3kX4gNZG8gZ6xJXQhhyDXGYkmYRHbh6b6OpiqoMRis8fEjH4JJ5ryfzSkz0t6NtD611BoLoURuBxeDv4SdsIdbMXTLI9VNpsWl9KJRasaC7Q/k5w0ByVMBbNzNWZIKXLxogP5jwfTQ9cynm1rvJ11Ictp8A5v5wNNlBkqNPKrr9Jejoe1Mky99Xk/iNdLipLoYiyxumZzPSF1FhUKBi0w5Yw3Btmpm/KqS8JwofiiGX9OD21aJ4wWGu0zlvjrVClDZS7I6imVgxJB1k/hVPfiuxQaT6UtRbmhgh2xtmL076+MblbH41e9AqHf0suMwMP5p8DGMfExmTKUYfWSKAVOWyPnqT+1NqDyWs0eZT8DBeIaypMlZMxC4JLtdvF4nZ49yLyg5PycC9B8PpHIy09Fr9hP2XZJRNzsprclcudMJ4dY32JzuykwO0cCem6HvN3otjZ8dCLcmQ9jvw8HDP4++6vN/y4suYhMUuMg5kbrCuD1h3J6a1KLD4/+z0A6+mDo+tjSBH0wSr2vHOANfXomaTvyy/qHvYSx+MI5f2T+m3AJrBm4JwKDQKPcSZEb74qw4OcrRjWZoyDG9vqIH9mkgFTunplxwXH9Q6ZRA2CMwewxvYSE8LzfIGKhz4xu8/nRQm+3+Mtl+Kj/m4n0cGrQ9Q+xUAPoG5V/5DoloJpA6zflh8z+H4UvmHmnqXl0mL0CaWEEbx7VJkub8+Ko/u1xkdRUVivPjbJbp7wzass3g13guptvFwRBoLyf3G6zkYCrHY5it4Idj+HUd+KlhO9ndyXZOZfImjSVZ24MtTSUE+qEkiZm9OKSnoAt4WMcfGIE9wiGdD/NEmJyfxtbeM9rS0xXM/1wtgGtxyke+B5BbniRUFRv0OkPJn5OXb2IBm3SJHw/ieM7QM8iAsam5P2LRIPFug2PTXVdJiHe5mLwcHwebdLPdotk3mbBTwg7ztPjON2uG+cOFQV3G46auokKhwEWK0NArgfFHGsVkcPtKcI/U5b0mN70id0v2XZ/s7esCPl5pHJNuhbGRXryK1H0JkmW9mDl9eI6PV9EHAT+VHusmBu2bmrQnTn6Lx3gDRJP5J+9lFhNKEr7Qyy2Vc2lLtZgYawhm1ttMTqzB6w/k9bJZDLGPXYg5GAxurA176gKw7sD7Z0++oV1+ZuDWidkjtMZPzV3kDxw5kJckXWyCfpzQCDcnLc4jllwKXOQ8NxDkDEnPHOYPr1QyaSoIMglwEjndDdFKjEmHNSZJekY6kpFecH2SJf3EFvyJRFU3uKkWoNS766t2+hr82QyXk5KTIJ6OdDN5Fs6MeM6rUmVKKzJbstBzEL/rEpzYzIHtGx93RhLXevieGYhjAhCoTKTa93JSdoLVSUwYYn9y8bpdbCIALpTMCZLM3Li0yPgmgDem7q/Bieq5y3NaWCwM3KVbLS/T3bgn03jxxRe59tpraWhowBjDk08+mV2XSCT45je/yYIFCygvL6ehoYG/+7u/46OPPsrbxsqVKzHG5D1uvPHGvDLt7e00NTURiUSIRCI0NTXR0dFxRgcpMhYT8TVl09cJ4wcwySAmGSR4KkLw+CxKP6ijctdCKl/4NKGPZoHnMpANoy/J6ciM4zHW7UBOonioD8Ld6WWphx+IU/oXR4lcdYrIX7Yz84vtVF19ktlfPMHMK09ReeXHRFZ8TGTFKSJXfkzZpzop+UQnkc+3M3NlOzOvOsnML5zCndNJZhh8sfGMg29GuHyNo6/RmCCpdjAft78rvTQ3X0qmo3EHLj09PSxcuJDt27cPWdfb28v+/fv5l3/5F/bv38/jjz/OH/7wB9atWzek7IYNG2htbc0+Hnzwwbz169evp6WlhebmZpqbm2lpaaGpqWm8uysyjRjceJCSo3OoeG4Blb+eT9kH1biZACaTUDMlfyHbMT5kMpj0f36wF7/+EJnp5q3bj533//Ar23BnJAnMjhGcFcOtSOLM8FKteNkHqf9nzhcDTqmHW5HErUgQLO/FwafM6zmnxzpp7NAYJRUQDrSKDg0ZB547JkQ4UAUYjJfESSaQwjDurqI1a9awZs2aYddFIhF27dqVt+w///M/ueKKK/jggw+46KKLssvLysqoq6sbdjsHDx6kubmZffv2sXTpUgAeeughli9fzqFDh7j00kvHu9si00I2NyHpEvqomuDJSkIXnSA27yTJ0iSUxUiG4jjWyZa2xsc6IwURg0dCjDB0OK/MePJBJ2/Ey/nMZs8EsJGPIBDHJEswla14dQdTI+RyOhEzn8JIY5CG+4SsY3HxqfC66AjMZqS7YxWiYVu50v11oeAcDDUk/QR4VVi/jEGj4QFwTQAoAWNwkkmMl5yy/ZezM+k5LtFoFGMMM2fOzFu+Y8cOHn30UWpra1mzZg333HMPFRWpzt+9e/cSiUSyQQvAsmXLiEQi7NmzZ9jAJRaLEYsNjBLo7OycnAMSmQCZS5KJhwi9cwGB92pTE9PN6McPx3DSiRLWgD+zl+TMviG5MH4wiV/dSSrv2GDMyIGLNR5eaRycgZv6jd6gUjwXumkp/fkaN56eUNFiAzF8J8mQRHHyw8+x8o1Dn1NaVEELDG4PtDiJctzEDIw1OKYCYp8nGTdYG8QaB+zQ2ait4xNPGlzH4ibjjOEXQqaJSQ1c+vv7+da3vsX69euprKzMLr/pppuYN28edXV1vPXWW2zdupXf/va32daatrY2ampqhmyvpqaGtra2Yd9r27ZtfOc735mcAxGZZE4y9avotodSyZdANt3zGISH+VK1jg/B+Gln4s/wXQ9/bjteKJF+7uPVdmIDyfRQbx+/PAZu6i99MjMTo6/zSZedZ8Ziy9qzXT/DtaqMNXhJBacWz7F0upWjlC5sqbzaAPipcV2+5xJLWHzrgkm3UBkzMKtzuvI8C/39PmUhD7e/sxhzmIvWpAUuiUSCG2+8Ed/3+eEPf5i3bsOGDdmf58+fz8UXX8zll1/O/v37Wbx4MZA60Qaz1g67HGDr1q1s3rw5+7yzs5PGxsaJOBSRKTXkb8MR5uIwnovxSsmfUP80DszI26h1vOwXO8bHm5WadRhjSdZ24JXGsYEE/pxubMDDK0kMyr/JdDvlzn87FsX11/+EMR5+xQms8fNa13Jrazw1Z4xNT8JYfPWd31WUmYF5oB0mNc9LzguyweHA6y0OfX0eTmkC0xdFIXrhmJTAJZFIcP3113PkyBGee+65vNaW4SxevJhgMMjhw4dZvHgxdXV1HDt2bEi5EydOUFtbO+w2wuEw4XB4QvZfpFCc8VetNRgv9euf+i53cY7PzK4Of1SVLmaxoQQ2mCT+iRPYYBJvVi/JGf0YfPySJH7Iwxpv2AnBRlJ8l9KzZSHUhSk/MWHbs76FpMsMp4eYUzpB250e8tPHMz9lwhkfQ2qenEx3XCauGXzepdoVHRwCgIcUhgkPXDJBy+HDh3n++eeZPXv0ubsPHDhAIpGgvr4egOXLlxONRnnllVe44oorAHj55ZeJRqOsWLFiondZREZgrMHEQhALUvq7uYDFD3ip1hp8/Io4fmkcr7oTvzyGX5LAq+rDYPGCSbxgKl8j1a7jn+HN/oYmFxdj6OOHu/CDsdELjoNjk8zwOvk4WFzJuZDfouIHuvGDXbix2emu1vz7UA2b0WxT/3iBEMnycgJdsSKroeI17sClu7ubd955J/v8yJEjtLS0UFVVRUNDA3/7t3/L/v37+d///V88z8vmpFRVVREKhfjjH//Ijh07+OpXv0p1dTVvv/02W7ZsYdGiRVx55ZUAXHbZZaxevZoNGzZkh0nfdtttrF27ViOKRM6JgeGkqXycdE7OqVLAYv9UlbowuhY/lLrZolfRg1fRCzhgDcnaKIm6KDaUwA8nyUwxm5mEb+h8NsN0j03a8U0D5afw3YkMXFK1NbaJ2gpPzijw1A1W3SRuXsouY7n9GOBjfV/TzhWQcZ/Rr732GldddVX2eSav5Oabb+bee+/lqaeeAuAv/uIv8l73/PPPs3LlSkKhEL/+9a/5j//4D7q7u2lsbOSv/uqvuOeee3BdN1t+x44d3HXXXaxatQqAdevWDTt3jIicawaTmbI+CU4y9Xvs9pbAsYEWV/teHdb18Mti2BkxkpFuknM6ic/pwgYT2FD2Vs5D2/dPezUphjwOg53QyeIMeC4+Lh8Hq4uutSV3dpZUMrnJyXvJ/XeU7ViDm0wQ6O+bpD2VyTDuwGXlypVYO/Iv1+nWATQ2NrJ79+5R36eqqopHH310vLsnItOU8RyM5+DEg9BRQfDD2akcmpI4fmmM5OxuvPJ+vNoucrMXvBkx/PKB4eCekwRjcayTvtgXvtRI9okNwPxkAGsdkiPNMFvg8oZDJ0twEmXDFxylSt14D2aE+x7J9FScbYgiMs2lh/xag+kL4/SVEPi4gnQ+Zd7oJVvRTTLSBxi80gSJuo7UBG2zYiTKe7Cul95ibrtCAbQw5OyiDcSws9+b4FmTDXaEUZiFLuT3EbSx9CduMX4Q4wfOqL3KifdjRvmDW6YXBS4iMg2kWxsyIz9yMi9NtJJQdGBkYskf6rHG4pXFsLM7SdR0k6yLkoh047upeWkG5sJJbyNnwPZ0upSn8jMS+KEu/PKOdPA1cXtYrHOTeCaQvldR6hP1g914oW4CfeMbPWWsxY31pnOsirSyipACFxEpKMY6GAtOdxl0lxL8oAYbjhGr6iJx8Um8il78il5812IDmdaYCe+JOSu5k/nbQC+mvH3Y4boT817FxzNBPIJAX3bEGiad8D3WW/BlhhtZD6XkFhYFLiJSwEyqu6m/lNLWEkpbq/GDHl44iS3vx4t0pxpyHI/EBR3YYConxp/Rh1eSSA/rziRzTv1Q60xiqcUB60zCe9vp18w0EWymY3BgtBtDno+2DXCSMQyxnNaWYquo4qTARUSKQ3okkhNPJwB3lULbrIHVv8/cxsDgl/WRqIiRqOvAVkdJzOnCDyZSM67m/PU99vEpZ84kyjDRCwgcuxjvz/ZjJ2IiNAteIpBq2TkPrsXW+PihLkxPDeCkx0GPzvg2fY8vKSQKXETkvGBSd6PEAG53GU53GeHWWfjhGMnKXpJzonh1UZKzevHK4/hOJoCY5Ds22QAmXoppvwjvwgPYYO/ZxxrWIZkIj/kCXuis8fHCXVgD8SSMP9f2/KinYqHARUTOK4OvaU4sTOhEmPCJmXiHknhlMfzZXSSre4lfeJJkZU/2rtoTmTg7ZIe66jGddTD73QnZrLEm1Z1iz5NWl/T/Ewnw/fPikM9bClxEREjlmzheEKcrCF0zCL9nKTnYQN+n3yf2qTY8dzLnjPHBD+Me/yR+1ftgzr67yGDpc2aQdIITsH/T0LCRSfpu0ONO7CnGRKDiVZwzE4mInDWD21NKWcsnKPn9BTiem5P/MnCLv4l7N4P1Qul82rMcnGt8AiX9eI7BL8LuIt849Ljl+QtNeuZhawbfhXEMiq+OipkCFxGR03ATQUp/O5fS92pwrMkmUNiccUgTxXhBjJ+59clZbN1YjJse5muL8WvekDSDWpJMKlJxNLS56KmrSETkNCwGJx6kZP88TEcJyZouErN7sCXx1E0iM3/pn9Vf7enxS9E63Ggt/uyjExAWFW/3R/5Q6NSSZPhjrJugJOziOAZ/XD17xVtXxUiBi4jIqAxuTwllb/4ZfsAjWdEDFb3YgE/vJccgnMCWx/ACHjiZPorRL4QGi/GCpBq/DcYrgd5qzOwPzu5Sal0SsXC6w6n4WiCGG6Zu3RgWD8fNyXEZcwUqaCkkClxERMbM4CSDhDoqse2p2xAE36/FOj52Zi9+MEmioQMvnMCb04UfTuCXxE57XTSddZhkKLt9kqH0SKCzCDgseMkAsUAJNnu37WJnwRlfArUxmfKTPORdJpQCFxGRcUkFAtk5Wr0AeMCJVPAR+qg61YUUTJIsjdGz/A+pG0MOuyWD8XO/hg3m2Gfgwjch2Htm+5bzvx53BsXYmmBz/s3wAzGSJR04fRWjvTD7JEBy8EIpAMWYtSUicm5Zg4kHMUmDN6Of018cB+Vr+O5ZtpAYrB08BX5xyd6fKHeJsZCddTini8zmdpelfs5U70yiSuYtQGpxERGZYAbwQjH6F72PLe8f12tt1bv4gf4zmIsk887gJwPgBzA2PQFdUV6czaBnmfoypIKagfWZFhqLoddCnw8Jz+eiYPK8mV24mChwERGZYD4GG/LxZ8Yw8SA2lBxjzorFlnRhnPFMQJe54SDgGUgYiDs5LQ3FF7RYDL5xs8+z99p2/FT3Gz7gpg/dkLQ+/dahM+nQZU26xgztfpgat2ug/qQgKHAREZlgBkugp5SKZ+fjhePE50RJzD1JoqEd3/WyqaBuPEDgw0py72rstF2GueBNCPaN+f38PpdEe5DYe6XEowHciEt4nnO209hNaz1uGZ5xCdh0kGd8EpXvU9LRgItDAkgC3Z5D1HeI2VQ7TH6NuGgodOFR4CIiMgmsBdMfJNAfIBgtw39/Dr2fe5feP2/DOqngJdBdRvBkJYlS0tdOg4mXEegpITkzk5w7/EU1ewHuD9L1WiXxkyEcm2qFMKUDbQiZ1oViYg34xsXikMlrsVji5a2UlvyBRipp8S/gI68EzzLQXWZMTuZP5l5OpGfdLa46KmZKzhURmVSpS6WTDFL62lxCrZHUUmtw22ZiYkFy2wHcmEvondpRe3hSW7X0fxQgfiKA8TNT1hmMTT2KlWMtPi5JJ5i9PYKxhlBfiHC0nYhtp9G04vjxVF2QuqVCJmixOASsR5gkVjkuBUctLiIiU8LgJEoIfTSL+AUfg4XgsXICfTESlT7WdcFagt3tOL0z6J8fwi9JZLt7Mq0mufeptkCo1qe0MU68LYiDwfcdKj6ehdvqwJ+7qeTTIugxMhiMbwgmPFxrueDDJDWdlfTXWQgkKWmbQcXRWQR7g4DPXOdjSsJJTnmlWJxUNZh0fxGGmU4fDW40tfEiDvKKkQIXEZFJZiHddwROfwnGOjjxIKazFJMzN70xYPwkbncpwdZqYvNaUxdszyF0pBaDJdZ4Ej+czIYzTnmCGYs68eMGcHFOlhJ56SKcP5QSrJmBqT4nh3zGLOCm55Ir6/EJx31K+gyzT8YJ9XvMOpXEsYZwzOL4EfyjFal7M8UDefeScoxPAx3UBzvSW06Heum0FpN5Myk4ClxERKaIseAeq8TpC+P2hXB6SlLdF+k5SIL93QT7ujBegLL9F2GCCfySOCW/m0voT7PBWkqqLiRxwcckZ3Xn5MFYsBA6ESHUOpugVwa+oaQ31WU0EUm6g1NYh7RR2JFWjMxYKO31CSZTLyrp95l1KkFJv2XWiRhl/RBMpMv6NpvbMHA0Dm5scMaDyW479/+KUoqHAhcRkSkU6CmhfN8nMQkXNxECkyTYdQrH6yXQ043je4Ah2F2Gs/cS8B3cvjCZuUlCJyOETlZiXZsa/mtzLuNezuR1xjLr4yQfXhhI53GM/8JtrCHgpXJpLBCOW8p6fEr6LDM/TuQEBSY9m3BqcrdUOvDAXbQzc6wM/te1lupjMcr60q0kvsX1AZzsUOZUHJI/NX9mQHO+wcen7p9ipcBFRGSKpLqMDOEP5qSXGLAe4Y7WzFpyGwicnpL0j7ntHekSnsF47siXZ2u48P1+YkE4Xh+me4aD54Ln5F/gAz7ZRN7M+5T0+1RGPSLRJLNOJgjFUuuDCUtpn4fjkw4w0n0uo+aIDN5/M8K6zJ2fB/fljCUIUaByvlDgIiIy5UZrLRiuzPiV9MOnD/TzicP9dEYCdEZcPrqohHgAfNdQ2ufR+F4/4f5M8GBxLMzo9gn3W0LJgenxh2+wGWvi7+mO5bQdUOPclpwPFLiIiBQxY6EsZig7kaT2RIJPvttPImDwXUM45uH4zpAWk+w8J5lep2Fva3gG+8KQdiUUiMh4KXARESlyNp28CwbHg7CXWsow6a4Dz2x28USltWZGQomcDU1AJyIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMMYduLz44otce+21NDQ0YIzhySefzFt/yy23YIzJeyxbtiyvTCwWY+PGjVRXV1NeXs66dev48MMP88q0t7fT1NREJBIhEonQ1NRER0fHuA9QREREise4A5eenh4WLlzI9u3bRyyzevVqWltbs49nnnkmb/2mTZt44okn2LlzJy+99BLd3d2sXbsWz/OyZdavX09LSwvNzc00NzfT0tJCU1PTeHdXREREisi4Z85ds2YNa9asOW2ZcDhMXV3dsOui0SgPP/wwjzzyCF/+8pcBePTRR2lsbOTZZ5/lmmuu4eDBgzQ3N7Nv3z6WLl0KwEMPPcTy5cs5dOgQl1566Xh3W0RERIrApOS4vPDCC9TU1HDJJZewYcMGjh8/nl33+uuvk0gkWLVqVXZZQ0MD8+fPZ8+ePQDs3buXSCSSDVoAli1bRiQSyZYZLBaL0dnZmfcQERGR4jLhgcuaNWvYsWMHzz33HPfffz+vvvoqV199NbFYDIC2tjZCoRCzZs3Ke11tbS1tbW3ZMjU1NUO2XVNTky0z2LZt27L5MJFIhMbGxgk+MhERETnXJvwmizfccEP25/nz53P55Zczd+5cnn76aa677roRX2etxZiBm2/l/jxSmVxbt25l8+bN2eednZ0KXkRERIrMpA+Hrq+vZ+7cuRw+fBiAuro64vE47e3teeWOHz9ObW1ttsyxY8eGbOvEiRPZMoOFw2EqKyvzHiIiIlJcJj1wOXXqFEePHqW+vh6AJUuWEAwG2bVrV7ZMa2srb731FitWrABg+fLlRKNRXnnllWyZl19+mWg0mi0jIiIi559xdxV1d3fzzjvvZJ8fOXKElpYWqqqqqKqq4t577+XrX/869fX1vPfee/zzP/8z1dXV/M3f/A0AkUiEW2+9lS1btjB79myqqqq4++67WbBgQXaU0WWXXcbq1avZsGEDDz74IAC33XYba9eu1YgiERGR89i4A5fXXnuNq666Kvs8k1dy880386Mf/Yg333yTX/ziF3R0dFBfX89VV13FY489RkVFRfY1P/jBDwgEAlx//fX09fXxpS99iZ/97Ge4rpsts2PHDu66667s6KN169addu4YERERKX7GWmvP9U5Mhs7OTiKRCP/WeDslTvhc746IiIiMoN+P8e2jPyYajY6ao6p7FYmIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMFQ4CIiIiIFQ4GLiIiIFAwFLiIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMFQ4CIiIiIFQ4GLiIiIFAwFLiIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMFQ4CIiIiIFQ4GLiIiIFAwFLiIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUjHEHLi+++CLXXnstDQ0NGGN48skn89YbY4Z9/Pu//3u2zMqVK4esv/HGG/O2097eTlNTE5FIhEgkQlNTEx0dHWd0kCIiIlIcxh249PT0sHDhQrZv3z7s+tbW1rzHT37yE4wxfP3rX88rt2HDhrxyDz74YN769evX09LSQnNzM83NzbS0tNDU1DTe3RUREZEiEhjvC9asWcOaNWtGXF9XV5f3/Je//CVXXXUVn/jEJ/KWl5WVDSmbcfDgQZqbm9m3bx9Lly4F4KGHHmL58uUcOnSISy+9dLy7LSIiIkVgUnNcjh07xtNPP82tt946ZN2OHTuorq7mM5/5DHfffTddXV3ZdXv37iUSiWSDFoBly5YRiUTYs2fPsO8Vi8Xo7OzMe4iIiEhxGXeLy3j8/Oc/p6Kiguuuuy5v+U033cS8efOoq6vjrbfeYuvWrfz2t79l165dALS1tVFTUzNkezU1NbS1tQ37Xtu2beM73/nOxB+EiIiITBuTGrj85Cc/4aabbqKkpCRv+YYNG7I/z58/n4svvpjLL7+c/fv3s3jxYiCV5DuYtXbY5QBbt25l8+bN2eednZ00NjZOxGGIiIjINDFpgctvfvMbDh06xGOPPTZq2cWLFxMMBjl8+DCLFy+mrq6OY8eODSl34sQJamtrh91GOBwmHA6f9X6LiIjI9DVpOS4PP/wwS5YsYeHChaOWPXDgAIlEgvr6egCWL19ONBrllVdeyZZ5+eWXiUajrFixYrJ2WURERKa5cbe4dHd3884772SfHzlyhJaWFqqqqrjooouAVDfN//zP/3D//fcPef0f//hHduzYwVe/+lWqq6t5++232bJlC4sWLeLKK68E4LLLLmP16tVs2LAhO0z6tttuY+3atRpRJCIich4bd4vLa6+9xqJFi1i0aBEAmzdvZtGiRfzrv/5rtszOnTux1vKNb3xjyOtDoRC//vWvueaaa7j00ku56667WLVqFc8++yyu62bL7dixgwULFrBq1SpWrVrFZz/7WR555JEzOUYREREpEsZaa8/1TkyGzs5OIpEI/9Z4OyWOcl9ERESmq34/xreP/phoNEplZeVpy+peRSIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMFQ4CIiIiIFQ4GLiIiIFAwFLiIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMFQ4CIiIiIFQ4GLiIiIFAwFLiIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMFQ4CIiIiIFY1yBy7Zt2/jc5z5HRUUFNTU1fO1rX+PQoUN5Zay13HvvvTQ0NFBaWsrKlSs5cOBAXplYLMbGjRuprq6mvLycdevW8eGHH+aVaW9vp6mpiUgkQiQSoampiY6OjjM7ShERESkK4wpcdu/ezR133MG+ffvYtWsXyWSSVatW0dPTky1z33338cADD7B9+3ZeffVV6urq+MpXvkJXV1e2zKZNm3jiiSfYuXMnL730Et3d3axduxbP87Jl1q9fT0tLC83NzTQ3N9PS0kJTU9MEHLKIiIgUKmOttWf64hMnTlBTU8Pu3bv5whe+gLWWhoYGNm3axDe/+U0g1bpSW1vL97//ff7+7/+eaDTKnDlzeOSRR7jhhhsA+Oijj2hsbOSZZ57hmmuu4eDBg3z6059m3759LF26FIB9+/axfPlyfv/733PppZcO2ZdYLEYsFss+7+zspLGxkX9rvJ0SJ3ymhygiIiKTrN+P8e2jPyYajVJZWXnasmeV4xKNRgGoqqoC4MiRI7S1tbFq1apsmXA4zBe/+EX27NkDwOuvv04ikcgr09DQwPz587Nl9u7dSyQSyQYtAMuWLSMSiWTLDLZt27Zst1IkEqGxsfFsDk1ERESmoTMOXKy1bN68mc9//vPMnz8fgLa2NgBqa2vzytbW1mbXtbW1EQqFmDVr1mnL1NTUDHnPmpqabJnBtm7dSjQazT6OHj16pocmIiIi01TgTF9455138rvf/Y6XXnppyDpjTN5za+2QZYMNLjNc+dNtJxwOEw6H88oC9Pvx076viIiInFuZa/VYslfOKHDZuHEjTz31FC+++CIXXnhhdnldXR2QajGpr6/PLj9+/Hi2Faauro54PE57e3teq8vx48dZsWJFtsyxY8eGvO+JEyeGtOaM5NSpUwD8f3/6yTiPTkRERM6Frq4uIpHIacuMK3Cx1rJx40aeeOIJXnjhBebNm5e3ft68edTV1bFr1y4WLVoEQDweZ/fu3Xz/+98HYMmSJQSDQXbt2sX1118PQGtrK2+99Rb33XcfAMuXLycajfLKK69wxRVXAPDyyy8TjUazwc1oMnk3H3zwwaiVIGcvkwx99OjRUROr5OypvqeO6npqqb6n1nSpb2stXV1dNDQ0jFp2XIHLHXfcwX//93/zy1/+koqKimy+SSQSobS0FGMMmzZt4rvf/S4XX3wxF198Md/97ncpKytj/fr12bK33norW7ZsYfbs2VRVVXH33XezYMECvvzlLwNw2WWXsXr1ajZs2MCDDz4IwG233cbatWuHHVE0HMdxsu+nk3/qVFZWqr6nkOp76qiup5bqe2pNh/oeayPDuAKXH/3oRwCsXLkyb/lPf/pTbrnlFgD+6Z/+ib6+Pv7xH/+R9vZ2li5dyq9+9SsqKiqy5X/wgx8QCAS4/vrr6evr40tf+hI/+9nPcF03W2bHjh3cdddd2dFH69atY/v27ePZXRERESkyZzWPy3TW2dlJJBIZ05hwOXuq76ml+p46quuppfqeWoVY30V7r6JwOMw999yTN9JIJo/qe2qpvqeO6npqqb6nViHWd9G2uIiIiEjxKdoWFxERESk+ClxERESkYChwERERkYKhwEVEREQKhgIXERERKRhFG7j88Ic/ZN68eZSUlLBkyRJ+85vfnOtdKjj33nsvxpi8R+Z+VJCaovnee++loaGB0tJSVq5cyYEDB/K2EYvF2LhxI9XV1ZSXl7Nu3To+/PDDqT6UaefFF1/k2muvpaGhAWMMTz75ZN76iarb9vZ2mpqaiEQiRCIRmpqa6OjomOSjm35Gq+9bbrllyLm+bNmyvDKq77HZtm0bn/vc56ioqKCmpoavfe1rHDp0KK+Mzu+JM5b6LrbzuygDl8cee4xNmzbx7W9/mzfeeIO//Mu/ZM2aNXzwwQfnetcKzmc+8xlaW1uzjzfffDO77r777uOBBx5g+/btvPrqq9TV1fGVr3yFrq6ubJlNmzbxxBNPsHPnTl566SW6u7tZu3Ytnuedi8OZNnp6eli4cOGIs0FPVN2uX7+elpYWmpubaW5upqWlhaampkk/vulmtPoGWL16dd65/swzz+StV32Pze7du7njjjvYt28fu3btIplMsmrVKnp6erJldH5PnLHUNxTZ+W2L0BVXXGFvv/32vGWf+tSn7Le+9a1ztEeF6Z577rELFy4cdp3v+7aurs5+73vfyy7r7++3kUjE/vjHP7bWWtvR0WGDwaDduXNntsyf/vQn6ziObW5untR9LySAfeKJJ7LPJ6pu3377bQvYffv2Zcvs3bvXAvb3v//9JB/V9DW4vq219uabb7Z//dd/PeJrVN9n7vjx4xawu3fvttbq/J5sg+vb2uI7v4uuxSUej/P6669n73GUsWrVKvbs2XOO9qpwHT58mIaGBubNm8eNN97Iu+++C8CRI0doa2vLq+dwOMwXv/jFbD2//vrrJBKJvDINDQ3Mnz9fn8VpTFTd7t27l0gkwtKlS7Nlli1bRiQSUf0P44UXXqCmpoZLLrmEDRs2cPz48ew61feZi0ajAFRVVQE6vyfb4PrOKKbzu+gCl5MnT+J5HrW1tXnLa2trs3ezlrFZunQpv/jFL/i///s/HnroIdra2lixYgWnTp3K1uXp6rmtrY1QKMSsWbNGLCNDTVTdtrW1UVNTM2T7NTU1qv9B1qxZw44dO3juuee4//77efXVV7n66quJxWKA6vtMWWvZvHkzn//855k/fz6g83syDVffUHzn97juDl1IjDF5z621Q5bJ6a1Zsyb784IFC1i+fDmf/OQn+fnPf55N7DqTetZnMTYTUbfDlVf9D3XDDTdkf54/fz6XX345c+fO5emnn+a6664b8XWq79O78847+d3vfsdLL700ZJ3O74k3Un0X2/lddC0u1dXVuK47JAI8fvz4kAhfxqe8vJwFCxZw+PDh7Oii09VzXV0d8Xic9vb2EcvIUBNVt3V1dRw7dmzI9k+cOKH6H0V9fT1z587l8OHDgOr7TGzcuJGnnnqK559/ngsvvDC7XOf35BipvodT6Od30QUuoVCIJUuWsGvXrrzlu3btYsWKFedor4pDLBbj4MGD1NfXM2/ePOrq6vLqOR6Ps3v37mw9L1myhGAwmFemtbWVt956S5/FaUxU3S5fvpxoNMorr7ySLfPyyy8TjUZV/6M4deoUR48epb6+HlB9j4e1ljvvvJPHH3+c5557jnnz5uWt1/k9sUar7+EU/Pk9panAU2Tnzp02GAzahx9+2L799tt206ZNtry83L733nvnetcKypYtW+wLL7xg3333Xbtv3z67du1aW1FRka3H733vezYSidjHH3/cvvnmm/Yb3/iGra+vt52dndlt3H777fbCCy+0zz77rN2/f7+9+uqr7cKFC20ymTxXhzUtdHV12TfeeMO+8cYbFrAPPPCAfeONN+z7779vrZ24ul29erX97Gc/a/fu3Wv37t1rFyxYYNeuXTvlx3uuna6+u7q67JYtW+yePXvskSNH7PPPP2+XL19uL7jgAtX3GfiHf/gHG4lE7AsvvGBbW1uzj97e3mwZnd8TZ7T6LsbzuygDF2ut/a//+i87d+5cGwqF7OLFi/OGhsnY3HDDDba+vt4Gg0Hb0NBgr7vuOnvgwIHset/37T333GPr6upsOBy2X/jCF+ybb76Zt42+vj5755132qqqKltaWmrXrl1rP/jgg6k+lGnn+eeft8CQx80332ytnbi6PXXqlL3ppptsRUWFraiosDfddJNtb2+foqOcPk5X3729vXbVqlV2zpw5NhgM2osuusjefPPNQ+pS9T02w9UzYH/6059my+j8njij1Xcxnt/GWmunrn1HRERE5MwVXY6LiIiIFC8FLiIiIlIwFLiIiIhIwVDgIiIiIgVDgYuIiIgUDAUuIiIiUjAUuIiIiEjBUOAiIiIiBUOBi4iIiBQMBS4iIiJSMBS4iIiISMH4/wGrqwLRgJljWAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:18, 18.58it/s] \n"
     ]
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T22:01:09.223414Z",
     "start_time": "2024-05-26T22:01:09.042800Z"
    }
   },
   "cell_type": "code",
   "source": "plt.hist(WEDs)",
   "id": "752385cb053a2467",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6., 12., 24., 37., 35., 17., 23., 10.,  6.,  3.]),\n",
       " array([1.2885549 , 1.54589969, 1.80324448, 2.06058926, 2.31793405,\n",
       "        2.57527884, 2.83262362, 3.08996841, 3.34731319, 3.60465798,\n",
       "        3.86200277]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T21:47:21.367117Z",
     "start_time": "2024-05-26T21:47:21.360926Z"
    }
   },
   "cell_type": "code",
   "source": "WEDs",
   "id": "14c980357f3ed519",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.2229805695077083,\n",
       " 1.9514464996164573,\n",
       " 1.7590658149651095,\n",
       " 3.1405426461515633,\n",
       " 2.8183054907145473,\n",
       " 1.140120966164992,\n",
       " 2.1558598709479813,\n",
       " 3.3662869370814827,\n",
       " 2.3891611651819735,\n",
       " 2.275638964174794,\n",
       " 2.1483604574233124,\n",
       " 2.628918854537702,\n",
       " 3.1136525432670066,\n",
       " 2.1715716989266127,\n",
       " 2.438280253712535,\n",
       " 2.653076550893886,\n",
       " 1.9852997999356443,\n",
       " 2.644463567775789,\n",
       " 2.2122967796132564,\n",
       " 2.3063106133427045,\n",
       " 2.518780917419762,\n",
       " 2.173287036879083,\n",
       " 2.5362215037708,\n",
       " 2.0293073338156344,\n",
       " 2.1645267490995206,\n",
       " 2.3771093706013082,\n",
       " 2.693419063975656,\n",
       " 2.131319438324366,\n",
       " 1.803577113450096,\n",
       " 2.170475125298135,\n",
       " 2.837187660790624,\n",
       " 2.902589943407295,\n",
       " 2.749964377946793,\n",
       " 2.8058926261061226,\n",
       " 2.958089603803645,\n",
       " 3.243547730539942,\n",
       " 3.4913308530124443,\n",
       " 1.987038402928909,\n",
       " 2.0877151308757904,\n",
       " 2.067962649465477,\n",
       " 2.747934312119973,\n",
       " 2.801020542277625,\n",
       " 2.1139353030967962,\n",
       " 2.301493743300777,\n",
       " 2.195098303241758,\n",
       " 2.1529734033032097,\n",
       " 1.5749234936721188,\n",
       " 1.5727846791999307,\n",
       " 1.519615355101089,\n",
       " 2.0767172339426225,\n",
       " 2.8079623652687906,\n",
       " 2.6162473141707556,\n",
       " 2.8657522459461346,\n",
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     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 80
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-26T21:38:25.804190Z",
     "start_time": "2024-05-26T21:38:25.653008Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "38c2267c4de5aaa3",
   "outputs": [
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       "   (8, 9),\n",
       "   (17, 15),\n",
       "   (6, 0),\n",
       "   (7, 8)]}]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# grid search",
   "id": "b276132bf3fa7390"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "wed_stats = {}\n",
    "\n",
    "\n",
    "for radius in range(10, 50, 5):\n",
    "    for angle in range(1, 10):\n",
    "        for extend in range(0, 40, 10):\n",
    "            solution = []\n",
    "\n",
    "            with ProcessPoolExecutor(max_workers=12) as pool:\n",
    "                results = []\n",
    "                for i, sample in enumerate(tqdm(dataset)):\n",
    "                    results.append(pool.submit(predict, sample, visualize=False, point_radius=radius, max_angle=angle, extend=extend))\n",
    "            \n",
    "                for i, result in enumerate(tqdm(results)):\n",
    "                    key, pred_vertices, pred_edges = result.result()\n",
    "                    solution.append({\n",
    "                        '__key__': key,\n",
    "                        'wf_vertices': pred_vertices.tolist(),\n",
    "                        'wf_edges': pred_edges\n",
    "                    })\n",
    "            \n",
    "            solution_dict = {}\n",
    "            for d in solution[:173]:\n",
    "                solution_dict[d[\"__key__\"]] = d\n",
    "            WEDs = []\n",
    "            \n",
    "            for i in targets:\n",
    "                try:\n",
    "                    j = solution_dict[i[\"__key__\"]]\n",
    "                    i = dict(np.load(io.BytesIO(i[\"wireframe.npz\"])))\n",
    "                    WEDs.append(hoho.compute_WED(j[\"wf_vertices\"], j[\"wf_edges\"], i[\"wf_vertices\"], i[\"wf_edges\"]))\n",
    "                except KeyError:\n",
    "                    pass\n",
    "\n",
    "            wed_stats[(radius, angle, extend)] = ss.describe(WEDs)"
   ],
   "id": "b9fc75215dbcfb87",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 346/346 [01:16<00:00,  4.50it/s]\n",
      "346it [00:11, 29.01it/s] \n",
      "100%|██████████| 346/346 [01:14<00:00,  4.62it/s]\n",
      "346it [00:11, 29.20it/s] \n",
      "100%|██████████| 346/346 [01:14<00:00,  4.66it/s]\n",
      "346it [00:11, 29.43it/s] \n",
      "100%|██████████| 346/346 [01:16<00:00,  4.54it/s]\n",
      "346it [00:12, 28.72it/s] \n",
      "100%|██████████| 346/346 [01:14<00:00,  4.64it/s]\n",
      "346it [00:11, 29.44it/s] \n",
      "100%|██████████| 346/346 [01:18<00:00,  4.43it/s]\n",
      "346it [00:11, 29.12it/s] \n",
      "100%|██████████| 346/346 [01:18<00:00,  4.40it/s]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T05:40:19.373552Z",
     "start_time": "2024-05-27T05:40:19.364100Z"
    }
   },
   "cell_type": "code",
   "source": "print(wed_stats)",
   "id": "539b45cc86fa2d0b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(10, 1, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.2688324308036), mean=2.918631217038793, variance=0.2655104551963414, skewness=-0.5301066567004962, kurtosis=0.6648724709751717), (10, 1, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.756003469399469, variance=0.28989047602798246, skewness=-0.2945369702420912, kurtosis=-0.02369081827086239), (10, 1, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.832138275353508), mean=2.7507068339662806, variance=0.25787775042947936, skewness=-0.37508484769566325, kurtosis=-0.03210901516830633), (10, 1, 30): DescribeResult(nobs=173, minmax=(1.7501416446074862, 4.191259803191946), mean=2.961261234054409, variance=0.2398959796022379, skewness=-0.1092311580556235, kurtosis=-0.26051346232955686), (10, 2, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.9003120200331316, variance=0.261910349388631, skewness=-0.5119892469577535, kurtosis=0.6424781625590157), (10, 2, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.703919186064029, variance=0.2910392479358703, skewness=-0.17569831800238767, kurtosis=-0.026657277400341073), (10, 2, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.681594986261088, variance=0.27842546188147577, skewness=-0.38497515584895964, kurtosis=-0.1392300490851719), (10, 2, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.931984625553082, variance=0.2494577589000683, skewness=-0.11959221264031848, kurtosis=-0.13815791364404895), (10, 3, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.894245688555616, variance=0.26682389539585616, skewness=-0.5105429044672873, kurtosis=0.6087195136790573), (10, 3, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.683735530723601, variance=0.2893644793141911, skewness=-0.24196472267608216, kurtosis=-0.01272835493523905), (10, 3, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.660704229257497, variance=0.26893152564304285, skewness=-0.33487550920479664, kurtosis=-0.16293684332682234), (10, 3, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.92518079836633, variance=0.24887539377691065, skewness=-0.14225847098489716, kurtosis=-0.08017538671679647), (10, 4, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8886994646476283, variance=0.27163703068449624, skewness=-0.5564449875702622, kurtosis=0.7075542805925097), (10, 4, 10): DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.676455843721493, variance=0.2937927870249197, skewness=-0.2234006371411064, kurtosis=-0.09048904303855432), (10, 4, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6498871800519215, variance=0.26492318590273994, skewness=-0.32446593956630665, kurtosis=-0.17512206191987056), (10, 4, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9231854043619783, variance=0.24314027663665091, skewness=-0.1893019934136203, kurtosis=-0.14083355822754395), (10, 5, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873477731939214, variance=0.2728824437420165, skewness=-0.5642008052784352, kurtosis=0.7097326337896264), (10, 5, 10): DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669744008221317, variance=0.29841910879887634, skewness=-0.22225993120261048, kurtosis=-0.0472763227484263), (10, 5, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6471995488373823, variance=0.26381906477320244, skewness=-0.320655946681577, kurtosis=-0.169339018678476), (10, 5, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9227759884362543, variance=0.24341675218182648, skewness=-0.1876547463116731, kurtosis=-0.1468739255370366), (10, 6, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.886892246290024, variance=0.27395091325862625, skewness=-0.5656360125824968, kurtosis=0.7011024739716984), (10, 6, 10): DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669214639548226, variance=0.2935420680896115, skewness=-0.25504270121568223, kurtosis=-0.05367597717982964), (10, 6, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.646515331167755, variance=0.2625843266390292, skewness=-0.33129530187229717, kurtosis=-0.17352394193686305), (10, 6, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (10, 7, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252), (10, 7, 10): DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.667071442043656, variance=0.29269861161342803, skewness=-0.23300886540344137, kurtosis=-0.09841994971348544), (10, 7, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6441927741135607, variance=0.2594721361604495, skewness=-0.34778411539796744, kurtosis=-0.15659274696374137), (10, 7, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (10, 8, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252), (10, 8, 10): DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853), (10, 8, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.644217801251108, variance=0.2594927958428506, skewness=-0.3477939113557061, kurtosis=-0.15687301104545748), (10, 8, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (10, 9, 0): DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252), (10, 9, 10): DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853), (10, 9, 20): DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6445636740626393, variance=0.25947719317193274, skewness=-0.34986189727592815, kurtosis=-0.15558152824255256), (10, 9, 30): DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824), (15, 1, 0): DescribeResult(nobs=173, minmax=(1.4871436032569523, 4.281798203709453), mean=2.836863894959103, variance=0.2729460542498244, skewness=-0.20357139230435106, kurtosis=0.09873747800409838), (15, 1, 10): DescribeResult(nobs=173, minmax=(1.3160117650280163, 3.6053806394700256), mean=2.601205819621667, variance=0.27289267701422726, skewness=-0.4952990792320176, kurtosis=-0.03882736218116145), (15, 1, 20): DescribeResult(nobs=173, minmax=(1.469281283482776, 3.6261780809782476), mean=2.5122558974038944, variance=0.2440282829471233, skewness=-0.12522757705704293, kurtosis=-0.48285495809641654), (15, 1, 30): DescribeResult(nobs=173, minmax=(1.2588005585814288, 4.191259803191946), mean=2.7124903538315728, variance=0.28517216483889607, skewness=-0.21273665971149405, kurtosis=-0.06564314346805888), (15, 2, 0): DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7671814146840363, variance=0.27029380686229254, skewness=-0.2091631733056473, kurtosis=0.0691644289164639), (15, 2, 10): DescribeResult(nobs=173, minmax=(1.280605540735518, 3.5448537261245736), mean=2.5393145110575412, variance=0.26180386621894675, skewness=-0.33957640223485785, kurtosis=-0.20905919146227614), (15, 2, 20): DescribeResult(nobs=173, minmax=(1.2259766995447088, 3.6261780809782476), mean=2.4641723063091154, variance=0.2159265085681024, skewness=-0.010343951137930279, kurtosis=-0.30470002743739455), (15, 2, 30): DescribeResult(nobs=173, minmax=(1.3898060381764048, 3.688916269874316), mean=2.609196850447406, variance=0.2716643462073717, skewness=-0.2642797975682462, kurtosis=-0.2848939064366114), (15, 3, 0): DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7458533421389415, variance=0.2675799369586586, skewness=-0.1809451788868796, kurtosis=0.05540112858880075), (15, 3, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5172126530759686, variance=0.2807755090016337, skewness=-0.36304760594914764, kurtosis=-0.09794600674215204), (15, 3, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.441581110876761, variance=0.22090427842471203, skewness=-0.03683980809263881, kurtosis=-0.37562510182765596), (15, 3, 30): DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.688916269874316), mean=2.580109759802578, variance=0.2821922595914078, skewness=-0.15430239150351965, kurtosis=-0.5342614515549466), (15, 4, 0): DescribeResult(nobs=173, minmax=(1.1184268068034193, 4.095658947639928), mean=2.728873529740259, variance=0.2771105516332162, skewness=-0.31565304601887006, kurtosis=0.26495658847041126), (15, 4, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.514454351924287, variance=0.2641923391146466, skewness=-0.2689525157400004, kurtosis=-0.16619392726022308), (15, 4, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.425698034696807, variance=0.21914834068542002, skewness=-0.06652177289464448, kurtosis=-0.3290842422376308), (15, 4, 30): DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.5666447985570335, variance=0.26843038578214184, skewness=-0.14633910677551268, kurtosis=-0.560670544130895), (15, 5, 0): DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.7177283637337104, variance=0.2723959536913965, skewness=-0.33348323143825864, kurtosis=0.24648247402512613), (15, 5, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5104525144988883, variance=0.258237832865022, skewness=-0.2592235877859567, kurtosis=-0.11572394159871147), (15, 5, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4254430153975073, variance=0.2139334478379118, skewness=-0.09371038896209857, kurtosis=-0.2511108814365266), (15, 5, 30): DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.554533540655466, variance=0.269587546488235, skewness=-0.10541615752597985, kurtosis=-0.5658111536655612), (15, 6, 0): DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.711894351727476, variance=0.278369156594265, skewness=-0.30086035835336405, kurtosis=0.2023964929072064), (15, 6, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.507056878471702, variance=0.2500685023613651, skewness=-0.2455081158344568, kurtosis=-0.06388626860450675), (15, 6, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4237830561768567, variance=0.2129392320893983, skewness=-0.08376772978701559, kurtosis=-0.2716619126555728), (15, 6, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.549041398461515, variance=0.2728931242961085, skewness=-0.15514934615427897, kurtosis=-0.505909867544049), (15, 7, 0): DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.7078749214557574, variance=0.2795285538018334, skewness=-0.2666613713523985, kurtosis=0.0995119864201448), (15, 7, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.502045838854079, variance=0.24585856220767538, skewness=-0.24920723946558182, kurtosis=-0.08569310815061959), (15, 7, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4252546445919934, variance=0.2075275053147006, skewness=-0.07069685472679284, kurtosis=-0.24599343619613867), (15, 7, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5504816395542744, variance=0.2695209952065683, skewness=-0.12792988710767947, kurtosis=-0.5327642254880787), (15, 8, 0): DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.704509705407346, variance=0.27797811695164676, skewness=-0.3262617881322571, kurtosis=0.09119239683428315), (15, 8, 10): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.499706581469198, variance=0.2470089007313356, skewness=-0.24007391357504945, kurtosis=-0.10916436838477361), (15, 8, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250234741780834, variance=0.20909704825092762, skewness=-0.06777194176027414, kurtosis=-0.27625274525067756), (15, 8, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5485245903964615, variance=0.26862635121426187, skewness=-0.12194093267641852, kurtosis=-0.5241112363445328), (15, 9, 0): DescribeResult(nobs=173, minmax=(1.3957580288394014, 3.9147376529767435), mean=2.7075434147500044, variance=0.27093066675680394, skewness=-0.25501352814444467, kurtosis=-0.08732045907999852), (15, 9, 10): DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.66441849400411), mean=2.4991184440663403, variance=0.24559035270808768, skewness=-0.21622575184806114, kurtosis=-0.16251586619206204), (15, 9, 20): DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250708875937916, variance=0.20883053530286166, skewness=-0.07138164793039069, kurtosis=-0.27475050421781333), (15, 9, 30): DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5464305599413226, variance=0.26795495027539973, skewness=-0.11469074924572617, kurtosis=-0.517523709902524), (20, 1, 0): DescribeResult(nobs=173, minmax=(1.3423468584053975, 3.897319842199613), mean=2.684073393901844, variance=0.2807864899381598, skewness=-0.2364426189045562, kurtosis=-0.20265061882354019), (20, 1, 10): DescribeResult(nobs=173, minmax=(1.3362673027274208, 3.6261780809782476), mean=2.514185778093504, variance=0.24260229343779774, skewness=-0.13507501269991837, kurtosis=-0.2711732535949607), (20, 1, 20): DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.461412940346188, variance=0.22261073233179457, skewness=0.013054843197617676, kurtosis=-0.4048394269957174), (20, 1, 30): DescribeResult(nobs=173, minmax=(1.2827429687623266, 3.6506824010059473), mean=2.506670301022558, variance=0.2521143544916601, skewness=-0.09572168770654672, kurtosis=-0.3066998890567092), (20, 2, 0): DescribeResult(nobs=173, minmax=(1.2722541239378204, 3.8656333191299255), mean=2.5990973697762323, variance=0.26878379224054205, skewness=-0.08572147612813517, kurtosis=-0.12335277334607442), (20, 2, 10): DescribeResult(nobs=173, minmax=(1.3251742111209324, 3.6261780809782476), mean=2.4703947628727625, variance=0.23315199031846423, skewness=-0.008292557084480892, kurtosis=-0.3128243051811035), (20, 2, 20): DescribeResult(nobs=173, minmax=(1.270472247990643, 3.6261780809782476), mean=2.395570462065189, variance=0.22346972511382623, skewness=0.11329897943777814, kurtosis=-0.3234502459445663), (20, 2, 30): DescribeResult(nobs=173, minmax=(1.0487238909712968, 3.5605306008619384), mean=2.42725248440732, variance=0.240912705804242, skewness=-0.027445577863958494, kurtosis=-0.2989541627256087), (20, 3, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.7118106452900848), mean=2.5648878081388222, variance=0.288071300712369, skewness=-0.1560031922606656, kurtosis=-0.110412069870931), (20, 3, 10): DescribeResult(nobs=173, minmax=(1.2702796676835728, 3.6261780809782476), mean=2.4429333572379126, variance=0.2317515347098559, skewness=0.030755596810590057, kurtosis=-0.38533107980366), (20, 3, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3788947741669393, variance=0.2264973495722334, skewness=0.09025408327789222, kurtosis=-0.42256332202067437), (20, 3, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.392065057934599, variance=0.23365257607206494, skewness=-0.04666906316083106, kurtosis=-0.474086583223138), (20, 4, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5435791505822083, variance=0.28893024616184776, skewness=-0.16093508869298748, kurtosis=-0.08748636816873168), (20, 4, 10): DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4355294363069016, variance=0.22844370985525814, skewness=0.04564180713737524, kurtosis=-0.4708318500785569), (20, 4, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.383163900730137, variance=0.2199302363476837, skewness=0.13714925299665656, kurtosis=-0.47939364685394814), (20, 4, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.386627232078584, variance=0.22454729567294118, skewness=-0.07781410951642269, kurtosis=-0.3910997560468208), (20, 5, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5374620593106982, variance=0.2851672776793523, skewness=-0.13276170000964915, kurtosis=-0.08378526032506972), (20, 5, 10): DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4385881982087474, variance=0.22429320966832825, skewness=0.04935373950656626, kurtosis=-0.4198054833251561), (20, 5, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3922155425953786, variance=0.22032420003471148, skewness=0.14340784428616138, kurtosis=-0.5120992116516523), (20, 5, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3814874348247117, variance=0.21826226065443943, skewness=-0.031130696417601945, kurtosis=-0.3603365136513217), (20, 6, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5335298487193905, variance=0.277852958678635, skewness=-0.06653145571842661, kurtosis=-0.09408401959396251), (20, 6, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.42404563849526, variance=0.2245852469553631, skewness=0.07550445732280753, kurtosis=-0.33011654543771085), (20, 6, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.392327612986733, variance=0.22581619892859958, skewness=0.19485470098090532, kurtosis=-0.4507202547441471), (20, 6, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3800291337752557, variance=0.21008321180837491, skewness=-0.014567494878515918, kurtosis=-0.38431393219096144), (20, 7, 0): DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.53252174939801, variance=0.2771156373425166, skewness=-0.07208813987939296, kurtosis=-0.1032661224771787), (20, 7, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.420607944985054, variance=0.22213448574790956, skewness=0.07369247045913387, kurtosis=-0.3204623682911296), (20, 7, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3895829009651486, variance=0.22363397726813797, skewness=0.20584534226522705, kurtosis=-0.4096143639898431), (20, 7, 30): DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.376325762160446, variance=0.2060657302187169, skewness=0.01250070338034022, kurtosis=-0.34430512545245584), (20, 8, 0): DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.533041000736945, variance=0.2762960956844567, skewness=-0.0518493542005356, kurtosis=-0.14641200049739478), (20, 8, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.4224237705113114, variance=0.22372683062153817, skewness=0.07733561784015744, kurtosis=-0.3741548157951047), (20, 8, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.391465728756091, variance=0.22394370233911176, skewness=0.1902320233296623, kurtosis=-0.41359222542326357), (20, 8, 30): DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.378366762261831, variance=0.202805898739558, skewness=0.044863602165060454, kurtosis=-0.40796058479310604), (20, 9, 0): DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.530455916115353, variance=0.277895703511729, skewness=-0.05581159504734332, kurtosis=-0.14130198306143615), (20, 9, 10): DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.421994549752394, variance=0.22368579559809232, skewness=0.07766446553557484, kurtosis=-0.37632005523926537), (20, 9, 20): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3897760002726582, variance=0.2225776378698793, skewness=0.18575382982618197, kurtosis=-0.40539773457991934), (20, 9, 30): DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.377521039722907, variance=0.20392050998976574, skewness=0.04554219008201783, kurtosis=-0.4196129929250443), (25, 1, 0): DescribeResult(nobs=173, minmax=(1.280605540735518, 3.66441849400411), mean=2.56181421211636, variance=0.2601095214113185, skewness=-0.32632240498411913, kurtosis=-0.12005309910500106), (25, 1, 10): DescribeResult(nobs=173, minmax=(1.454272571633673, 3.6261780809782476), mean=2.473339889745894, variance=0.2233933922107472, skewness=0.017829377188284472, kurtosis=-0.4460671739578528), (25, 1, 20): DescribeResult(nobs=173, minmax=(1.3793123740968751, 3.7195088150342186), mean=2.4150328016456037, variance=0.22965534837872242, skewness=0.11268451204073322, kurtosis=-0.3615049140619653), (25, 1, 30): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.4086178137902587, variance=0.23777282252768617, skewness=-0.018916891642525895, kurtosis=-0.21225144452113565), (25, 2, 0): DescribeResult(nobs=173, minmax=(1.280605540735518, 3.6261780809782476), mean=2.4906008151320926, variance=0.248685931657691, skewness=-0.15565003519874476, kurtosis=-0.2276167953822883), (25, 2, 10): DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.419065699428399, variance=0.21591709226718184, skewness=0.2058330707307416, kurtosis=-0.3095627615564962), (25, 2, 20): DescribeResult(nobs=173, minmax=(1.3187205857224815, 3.7195088150342186), mean=2.3789786957469414, variance=0.23032806261527008, skewness=0.20786286650802807, kurtosis=-0.25287251641055786), (25, 2, 30): DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3744699364369355, variance=0.2149962896981116, skewness=0.22963219434874785, kurtosis=-0.39122511587047537), (25, 3, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.6268822743745335), mean=2.4567771801373315, variance=0.26685758977245705, skewness=-0.13106930458537935, kurtosis=-0.23530203208601286), (25, 3, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4108899944823015, variance=0.21198194312074198, skewness=0.24085917574500354, kurtosis=-0.46285314189413684), (25, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.3796113176549283, variance=0.2313326274790801, skewness=0.18020655451793677, kurtosis=-0.3976050568640255), (25, 3, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5793431626907006), mean=2.365799431760512, variance=0.2283485251258321, skewness=0.14581362181613933, kurtosis=-0.4966945138162737), (25, 4, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.450736724300251, variance=0.255481258716427, skewness=-0.0916393786706801, kurtosis=-0.1946484500943786), (25, 4, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.410518885127268, variance=0.20550593327046196, skewness=0.2499724220330842, kurtosis=-0.4559192902865674), (25, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3795244890339258, variance=0.2267076428562555, skewness=0.18073800339766158, kurtosis=-0.46599314212218124), (25, 4, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5559859764406214), mean=2.367685477902936, variance=0.22928262970110813, skewness=0.1234810295855286, kurtosis=-0.6546653426007194), (25, 5, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.441599693225722, variance=0.25279254900744635, skewness=-0.049430446846301976, kurtosis=-0.23987523776176367), (25, 5, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.409439772520956, variance=0.20459373614192744, skewness=0.2593292649700985, kurtosis=-0.43989262943361895), (25, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3813709841735355, variance=0.23045884130101402, skewness=0.23712341428131212, kurtosis=-0.40079076893774035), (25, 5, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.369952540791647, variance=0.23642155447313948, skewness=0.15339830751247546, kurtosis=-0.6673466083790669), (25, 6, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4356151895352216, variance=0.2545402506829705, skewness=-0.014315337290249688, kurtosis=-0.23985265026039926), (25, 6, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.3997149663071773, variance=0.21211940441425559, skewness=0.3057379221458508, kurtosis=-0.4542345695303873), (25, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.375564585578571, variance=0.23205540022105775, skewness=0.191174590767773, kurtosis=-0.39762545239110425), (25, 6, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.3758489971585637, variance=0.24544595203606948, skewness=0.20399683624555676, kurtosis=-0.6183066207894132), (25, 7, 0): DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4332410339185766, variance=0.2491228344995086, skewness=-0.01261753485038881, kurtosis=-0.2375642444532593), (25, 7, 10): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.396406456777108, variance=0.21087635212900227, skewness=0.3024016257197271, kurtosis=-0.40876460192220465), (25, 7, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3704425282816417, variance=0.23182957468994056, skewness=0.22228340584379075, kurtosis=-0.3626070143296296), (25, 7, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.373490069485539, variance=0.24398496452787613, skewness=0.2351830610301328, kurtosis=-0.5279495534648584), (25, 8, 0): DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.4366509101256306, variance=0.24380395324511875, skewness=0.055522021814290286, kurtosis=-0.3745228956384281), (25, 8, 10): DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.401775050693766, variance=0.21072810372063794, skewness=0.29902784407963034, kurtosis=-0.4643048528579099), (25, 8, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3666799964968566, variance=0.23256287638977155, skewness=0.2351978824288662, kurtosis=-0.34650790205426807), (25, 8, 30): DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.371012587748782, variance=0.2450203543725082, skewness=0.2469304978724812, kurtosis=-0.4951650893264814), (25, 9, 0): DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.43477140368765, variance=0.2432307330156745, skewness=0.05414275019076038, kurtosis=-0.36907364977950285), (25, 9, 10): DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.398615207826399, variance=0.20887753685860447, skewness=0.3187548013407416, kurtosis=-0.4229798846403865), (25, 9, 20): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.368605800644157, variance=0.2294246885522206, skewness=0.27285275332363895, kurtosis=-0.39286936432663166), (25, 9, 30): DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.6427458988029624), mean=2.365431389151285, variance=0.24408016546768208, skewness=0.24970843688533947, kurtosis=-0.47489943956804215), (30, 1, 0): DescribeResult(nobs=173, minmax=(1.1532672794627605, 3.6261780809782476), mean=2.4826310994850456, variance=0.23833070883681687, skewness=-0.05751429986772294, kurtosis=-0.27576009544920854), (30, 1, 10): DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.433282778268923, variance=0.22134877479162188, skewness=0.08024460232414445, kurtosis=-0.3947248921866984), (30, 1, 20): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3975186504066417, variance=0.22094968513618016, skewness=0.07480434328399128, kurtosis=-0.16335538209499267), (30, 1, 30): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3861556873839587, variance=0.22842621158531876, skewness=0.1290414047412115, kurtosis=-0.07336495164965884), (30, 2, 0): DescribeResult(nobs=173, minmax=(1.4082365099263328, 3.6261780809782476), mean=2.438737352325921, variance=0.22805965113028293, skewness=0.1429818073521336, kurtosis=-0.4555229006368986), (30, 2, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.3902302733627527, variance=0.2306056632981275, skewness=0.13261356954170908, kurtosis=-0.27069709723457525), (30, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.637570667436926), mean=2.3786084222442034, variance=0.22573612749333788, skewness=0.288059435082738, kurtosis=-0.40815147247155315), (30, 2, 30): DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3740534257011907, variance=0.2220012467665858, skewness=0.23492208208246168, kurtosis=-0.40217807803242067), (30, 3, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4271405903951044, variance=0.23033291768137476, skewness=0.1631811023842746, kurtosis=-0.5828797184614842), (30, 3, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.384897415418424, variance=0.22637196926022032, skewness=0.19173071548573675, kurtosis=-0.43595503689616644), (30, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.637570667436926), mean=2.3829987003334803, variance=0.2311443756171956, skewness=0.14685703983398077, kurtosis=-0.5259863508655851), (30, 3, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.562558606945364), mean=2.3860009450938464, variance=0.23103302426248906, skewness=0.11204332345204361, kurtosis=-0.5490414886033808), (30, 4, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4275089000561385, variance=0.22327316852087933, skewness=0.17835340203663447, kurtosis=-0.5397748344422348), (30, 4, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.397930773143809, variance=0.22641718268691954, skewness=0.17481864720402482, kurtosis=-0.4152277535960023), (30, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.617632303538528), mean=2.383797434796488, variance=0.23380033902387592, skewness=0.18900269218480067, kurtosis=-0.5808358084581036), (30, 4, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5455110669933947), mean=2.3917898129538484, variance=0.24078771833868412, skewness=0.19864827152416367, kurtosis=-0.5474088816866427), (30, 5, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4345756829691187, variance=0.22113762404024856, skewness=0.14077885323381606, kurtosis=-0.5306541114660988), (30, 5, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3941387003152848, variance=0.23003758231830418, skewness=0.21058066331754136, kurtosis=-0.379609944610138), (30, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3790364690020165, variance=0.23647421694312673, skewness=0.22090866080940696, kurtosis=-0.514964156012657), (30, 5, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.385768838953655, variance=0.25181681694295693, skewness=0.28409827803721455, kurtosis=-0.3134374893329581), (30, 6, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.432507639466392, variance=0.2202826193726728, skewness=0.18051045766742263, kurtosis=-0.510441313183494), (30, 6, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3879080309744856, variance=0.22786302055822993, skewness=0.23902741677955017, kurtosis=-0.4056748763379603), (30, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716579318006623, variance=0.2422788765943085, skewness=0.20670554315360368, kurtosis=-0.5289317019408943), (30, 6, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.576250985624627), mean=2.3850795520255197, variance=0.24493882723561317, skewness=0.2203755344910728, kurtosis=-0.4931265530057929), (30, 7, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.429406136909439, variance=0.21858010355750818, skewness=0.2000876621727689, kurtosis=-0.5077100524895188), (30, 7, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389402864252692, variance=0.2320812051198164, skewness=0.3130484267618166, kurtosis=-0.32815775713868645), (30, 7, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716701561009708, variance=0.24731604266709206, skewness=0.22941321516711177, kurtosis=-0.5283369198285905), (30, 7, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.381611757332351, variance=0.2506468837033553, skewness=0.26675328203354126, kurtosis=-0.402185686093512), (30, 8, 0): DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4298489368050427, variance=0.21888683945586074, skewness=0.20063596567110728, kurtosis=-0.5176138357954292), (30, 8, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389938416093227, variance=0.2285287200579085, skewness=0.33902253204824595, kurtosis=-0.32922349087198244), (30, 8, 20): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.372573018808825, variance=0.248495576144028, skewness=0.26669420512826, kurtosis=-0.5578124932452173), (30, 8, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3846008662163936, variance=0.2547504201897208, skewness=0.29648509192680983, kurtosis=-0.4623848643623356), (30, 9, 0): DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4262628008241856, variance=0.21831525479458277, skewness=0.22263078724693997, kurtosis=-0.48808525476895026), (30, 9, 10): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389975063842067, variance=0.22608397671643873, skewness=0.35686742328669135, kurtosis=-0.2997528177957811), (30, 9, 20): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.377108364289762, variance=0.24495071976894514, skewness=0.3024991123083083, kurtosis=-0.5537863211020939), (30, 9, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3808467678446803, variance=0.25066438236687766, skewness=0.2895430956226167, kurtosis=-0.47877754061451405), (35, 1, 0): DescribeResult(nobs=173, minmax=(1.3273505012242865, 3.6261780809782476), mean=2.4442174494359254, variance=0.22270434251888466, skewness=0.06292702158159418, kurtosis=-0.41536878070008587), (35, 1, 10): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3906148388612825, variance=0.23612292896679513, skewness=0.06810877547812552, kurtosis=-0.30725869635176695), (35, 1, 20): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3776617261354445, variance=0.23247429066020053, skewness=0.10806676086801285, kurtosis=-0.16554652753591004), (35, 1, 30): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3945732197549288, variance=0.23483766651871996, skewness=0.16196716664718905, kurtosis=-0.10484682014817315), (35, 2, 0): DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.411574201138517, variance=0.21578778205407273, skewness=0.23369600888365885, kurtosis=-0.2867155946425828), (35, 2, 10): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3681322155779267, variance=0.2347349275743601, skewness=0.2734479351216136, kurtosis=-0.38666271102839156), (35, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.3748694433759123, variance=0.22888427449653234, skewness=0.28036566784897027, kurtosis=-0.5267513800129584), (35, 2, 30): DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.6212506595263343), mean=2.3854325367926594, variance=0.23457032730509317, skewness=0.2604446162510226, kurtosis=-0.3349483534626043), (35, 3, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.405989752988432, variance=0.212938956410703, skewness=0.24447258131202554, kurtosis=-0.5271312715451346), (35, 3, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.375368586764985, variance=0.23069279313015867, skewness=0.20332670930357677, kurtosis=-0.4795555488120784), (35, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.376378529726554, variance=0.2327546566086452, skewness=0.27519000763349377, kurtosis=-0.31771745086975933), (35, 3, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976350502222, variance=0.23944715281813153, skewness=0.17912464319890165, kurtosis=-0.2781964142653237), (35, 4, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.408989352750845, variance=0.20993256278594338, skewness=0.20771311895378974, kurtosis=-0.5114565720950774), (35, 4, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3862119885427275, variance=0.22383472123068804, skewness=0.20644346651108006, kurtosis=-0.48916590957336537), (35, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3842603364378356, variance=0.22992856792091504, skewness=0.3132728720903736, kurtosis=-0.28223856418657345), (35, 4, 30): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.400215914906969, variance=0.2465940458980044, skewness=0.24000447703882694, kurtosis=-0.2886091250801641), (35, 5, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.406311167879119, variance=0.20781769633334632, skewness=0.2642635361018867, kurtosis=-0.3980318498632758), (35, 5, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3872945246573107, variance=0.2244550722608443, skewness=0.250064567983947, kurtosis=-0.38361445269250094), (35, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3922777542401183, variance=0.23879292523118395, skewness=0.32127679699013006, kurtosis=-0.2637818045632927), (35, 5, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.3889835348948454, variance=0.2569148258105363, skewness=0.2728444039629951, kurtosis=-0.22732955648449993), (35, 6, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.397308803810493, variance=0.21627613395654613, skewness=0.3096471084250271, kurtosis=-0.41018903098186943), (35, 6, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.382804047460243, variance=0.22894779192643214, skewness=0.2291410779208234, kurtosis=-0.41091063392043337), (35, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.576250985624627), mean=2.3937824872694407, variance=0.2376169204304254, skewness=0.26069901598295314, kurtosis=-0.40435481472120305), (35, 6, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.6212506595263343), mean=2.3904977600986737, variance=0.2507129621263109, skewness=0.21630205116480614, kurtosis=-0.3522875594025541), (35, 7, 0): DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.401718522915244, variance=0.21725510948778534, skewness=0.32112902558791273, kurtosis=-0.397168495428879), (35, 7, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3917090310591567, variance=0.23053829906762635, skewness=0.2568710565884056, kurtosis=-0.36570028673332855), (35, 7, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7689006556014593), mean=2.394708380809293, variance=0.24331914134325108, skewness=0.3104609785236659, kurtosis=-0.34468360619940963), (35, 7, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.38699418676085, variance=0.2577835077161205, skewness=0.2766783845585309, kurtosis=-0.29476357152580457), (35, 8, 0): DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.4064772123154166, variance=0.21721735273717763, skewness=0.3199872090051091, kurtosis=-0.41302195380383466), (35, 8, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3908782343737354, variance=0.23036013923586704, skewness=0.28462534178743354, kurtosis=-0.38709455161808304), (35, 8, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3986405901311167, variance=0.24608385998671142, skewness=0.3549449464418198, kurtosis=-0.4001901870073876), (35, 8, 30): DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.39275698546612, variance=0.257313874546351, skewness=0.2951195589126923, kurtosis=-0.32469376243371917), (35, 9, 0): DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.402065366133696, variance=0.21630799827125896, skewness=0.3496875784127574, kurtosis=-0.38042501490776415), (35, 9, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3883592992402862, variance=0.23027758760347003, skewness=0.296765878163557, kurtosis=-0.34861854619750154), (35, 9, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3975590366723187, variance=0.24904851847767226, skewness=0.3568965017957376, kurtosis=-0.4302411959545829), (35, 9, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7689006556014593), mean=2.3920055363342487, variance=0.25763993287424103, skewness=0.30817427852592283, kurtosis=-0.3347297549631598), (40, 1, 0): DescribeResult(nobs=173, minmax=(1.32972052526856, 3.7195088150342186), mean=2.4194208077125197, variance=0.22735926912395726, skewness=0.08411152670063812, kurtosis=-0.36976518164125993), (40, 1, 10): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3863502776797856, variance=0.22824894724073475, skewness=0.10516468851421981, kurtosis=-0.2594414493181243), (40, 1, 20): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3819968370307967, variance=0.23273599874155798, skewness=0.14303336257018318, kurtosis=-0.13731717359344797), (40, 1, 30): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3843476299533783, variance=0.23531452343703144, skewness=0.20823115450386134, kurtosis=-0.08506840332276866), (40, 2, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.388169259178502, variance=0.2272718361825683, skewness=0.14745033955498554, kurtosis=-0.3275151957127749), (40, 2, 10): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.374294140016307, variance=0.23088831111757532, skewness=0.28134191057856595, kurtosis=-0.45529187922921555), (40, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5721712499597116), mean=2.3793735172074504, variance=0.23659306029041108, skewness=0.2553454827884794, kurtosis=-0.4934855282228545), (40, 2, 30): DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.3880867134387906, variance=0.23677233140443266, skewness=0.2746348945969141, kurtosis=-0.21305949285139691), (40, 3, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.385897158360537, variance=0.23011231408637273, skewness=0.189040499666452, kurtosis=-0.42683198131482225), (40, 3, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5793431626907006), mean=2.38261739715201, variance=0.23783393755543428, skewness=0.1711163972917373, kurtosis=-0.6522259873531335), (40, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3978819990437974, variance=0.23997789669492398, skewness=0.24892265230252733, kurtosis=-0.4017673562738979), (40, 3, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.407634797501566, variance=0.24371594638017943, skewness=0.2557220507793175, kurtosis=-0.15715980564058407), (40, 4, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.3919928188754604, variance=0.23184626352990195, skewness=0.16634687967225528, kurtosis=-0.4035931059127029), (40, 4, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5559859764406214), mean=2.3886814716207545, variance=0.23275117890602048, skewness=0.22684340335139833, kurtosis=-0.5936785076680828), (40, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976595418096966, variance=0.23818181554736112, skewness=0.2784549893503289, kurtosis=-0.3637520804075818), (40, 4, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.412501835410017, variance=0.2504678890342812, skewness=0.23613524618695558, kurtosis=-0.2089049468054367), (40, 5, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3948665465964374, variance=0.23229474512206372, skewness=0.1883658604164852, kurtosis=-0.3117502418808762), (40, 5, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3888673754435845, variance=0.23588824039933945, skewness=0.2881021402644574, kurtosis=-0.43324356622430704), (40, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.402673983236185, variance=0.2481837073735338, skewness=0.30838627997579465, kurtosis=-0.244559942461954), (40, 5, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7717977793967843), mean=2.4103906271074926, variance=0.2622513620731259, skewness=0.28525878762522766, kurtosis=-0.11029258861434332), (40, 6, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3839623101258454, variance=0.23607791753616203, skewness=0.22002487618359687, kurtosis=-0.37320922175372795), (40, 6, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3895841432380847, variance=0.24536500899154293, skewness=0.2599897242497131, kurtosis=-0.5167545964253102), (40, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.4077533351492275, variance=0.25320749575495544, skewness=0.2812107479281849, kurtosis=-0.4215619870544707), (40, 6, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.724442042751297), mean=2.415071859981076, variance=0.26238842898246284, skewness=0.2633242071284276, kurtosis=-0.2445055517514012), (40, 7, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.386188689872926, variance=0.23893696990231386, skewness=0.2967800859202916, kurtosis=-0.28282915584685053), (40, 7, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3944423590294193, variance=0.2465065536857468, skewness=0.2721538250896667, kurtosis=-0.4792545836240327), (40, 7, 20): DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406839724428981, variance=0.2603261771691146, skewness=0.3032186102576703, kurtosis=-0.3173295824803919), (40, 7, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7725779764160206), mean=2.4188623730204735, variance=0.26939255179747723, skewness=0.2788384614811644, kurtosis=-0.18288967289790214), (40, 8, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3930129707845498, variance=0.23215066111872443, skewness=0.2989655069270995, kurtosis=-0.2817878628513344), (40, 8, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403858377352788, variance=0.24749869146926223, skewness=0.3140850115861441, kurtosis=-0.528785556311719), (40, 8, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4124700839687243, variance=0.25532818691020326, skewness=0.35812542624780425, kurtosis=-0.3749700141329608), (40, 8, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.4264197520081887, variance=0.27049178724551853, skewness=0.318896684817541, kurtosis=-0.2539636343376732), (40, 9, 0): DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3957542543079233, variance=0.23310121388075183, skewness=0.30095341959348654, kurtosis=-0.2942558792821828), (40, 9, 10): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403932248288499, variance=0.2487742084683387, skewness=0.3381611420772938, kurtosis=-0.5165431694838332), (40, 9, 20): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4118508808603045, variance=0.26022119050841064, skewness=0.3682029744859333, kurtosis=-0.41218842818502033), (40, 9, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.425222538586002, variance=0.2758842502293917, skewness=0.33399224512560255, kurtosis=-0.27223767809946775), (45, 1, 0): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3924854857851394, variance=0.23275307803748638, skewness=0.07500074075038048, kurtosis=-0.2935617253280385), (45, 1, 10): DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3744571686507063, variance=0.2355163304370561, skewness=0.0878789749320027, kurtosis=-0.1818687679043296), (45, 1, 20): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3792466167629636, variance=0.2422172698332015, skewness=0.18995496774253304, kurtosis=-0.12741963477250806), (45, 1, 30): DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.378327126176745, variance=0.23824530454742882, skewness=0.19566459356086427, kurtosis=-0.1111469729646477), (45, 2, 0): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3766409284369105, variance=0.2295085075667106, skewness=0.24134106021315538, kurtosis=-0.37408846477628144), (45, 2, 10): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.373057696488171, variance=0.227657062156513, skewness=0.3100663573759112, kurtosis=-0.4733080035750321), (45, 2, 20): DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.6212506595263343), mean=2.38632458240834, variance=0.23813819810123346, skewness=0.29652378295341514, kurtosis=-0.3672361799208925), (45, 2, 30): DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.388144182780888, variance=0.24112210638197287, skewness=0.23756936364283102, kurtosis=-0.2909234719207676), (45, 3, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.658925758785262), mean=2.385371374710671, variance=0.23433663582029468, skewness=0.14176451720155842, kurtosis=-0.5927851397233956), (45, 3, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.38523972688129, variance=0.23853526447287604, skewness=0.27479949313624624, kurtosis=-0.37936201651186785), (45, 3, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.4076371514178407, variance=0.24068682096802357, skewness=0.25787115006032574, kurtosis=-0.23278661069579876), (45, 3, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.412821308581424, variance=0.24551095940694598, skewness=0.2924398069064522, kurtosis=-0.07143088476505399), (45, 4, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6046958484870792), mean=2.390131984426462, variance=0.23610747813350952, skewness=0.22710720795114606, kurtosis=-0.5667969121056742), (45, 4, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3914062869519253, variance=0.2393996096447048, skewness=0.3202604593262514, kurtosis=-0.2876938695682867), (45, 4, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4112285800591664, variance=0.24211863480665663, skewness=0.3127233163899829, kurtosis=-0.14450499897441826), (45, 4, 30): DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.4269174633995525, variance=0.25948011673042015, skewness=0.2999659622583222, kurtosis=-0.06571939509751745), (45, 5, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.396039294508347, variance=0.23706545805278192, skewness=0.2683386111757052, kurtosis=-0.43250740115100994), (45, 5, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3985213041603988, variance=0.24132064938614203, skewness=0.32115251612013973, kurtosis=-0.17855020892334617), (45, 5, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4124482991603977, variance=0.248005365526009, skewness=0.35134624885668897, kurtosis=-0.007504347438727521), (45, 5, 30): DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.9162282140460087), mean=2.432424925734303, variance=0.27393959139490665, skewness=0.373169478138468, kurtosis=0.006635598228321271), (45, 6, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.3993612139561167, variance=0.24377787252097266, skewness=0.22483344276706962, kurtosis=-0.4970600005238177), (45, 6, 10): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.405068165571497, variance=0.24646178290038326, skewness=0.2875176572527761, kurtosis=-0.34540566014415486), (45, 6, 20): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.414563246470491, variance=0.25243118749415416, skewness=0.32718722307293585, kurtosis=-0.1359966216084869), (45, 6, 30): DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9162282140460087), mean=2.436520062983622, variance=0.2731040454544077, skewness=0.3782243329854789, kurtosis=-0.038026118597547676), (45, 7, 0): DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.4051959917035837, variance=0.2450394814984553, skewness=0.23972419985337953, kurtosis=-0.4682877238864682), (45, 7, 10): DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406087392078327, variance=0.2580389356909127, skewness=0.29136942528153364, kurtosis=-0.29872794085361143), (45, 7, 20): DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.86758500469272), mean=2.418061266506169, variance=0.2617243674783607, skewness=0.3253382576002433, kurtosis=-0.06503236957059055), (45, 7, 30): DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.9643641477107323), mean=2.439372515277074, variance=0.2801733914247726, skewness=0.4032973011280843, kurtosis=0.004250745916431153), (45, 8, 0): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.4131517076091358, variance=0.24066940267301618, skewness=0.26131189253213666, kurtosis=-0.49633037705736216), (45, 8, 10): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4179154555308684, variance=0.25567498182680215, skewness=0.32706170459005446, kurtosis=-0.4164166171364596), (45, 8, 20): DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.4346991775070603, variance=0.2619768174726941, skewness=0.3410719204865325, kurtosis=-0.1540722090125799), (45, 8, 30): DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4478526744775175, variance=0.2808214793691462, skewness=0.45054333863678114, kurtosis=-0.022699653611266246), (45, 9, 0): DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.410540212549997, variance=0.2396217756873817, skewness=0.26811073141192715, kurtosis=-0.4597579615051637), (45, 9, 10): DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.416366874484952, variance=0.25708950801601954, skewness=0.3347859277339084, kurtosis=-0.41942847638872305), (45, 9, 20): DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.434922529220423, variance=0.26497488722201096, skewness=0.3487337194660302, kurtosis=-0.13525730518814028), (45, 9, 30): DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4480861377214076, variance=0.28607402615953886, skewness=0.46832740215081625, kurtosis=-0.013192895201957988)}\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T05:47:19.852077Z",
     "start_time": "2024-05-27T05:47:19.846831Z"
    }
   },
   "cell_type": "code",
   "source": [
    "min_mean_idx = np.argmin([i.mean for i in wed_stats.values()])\n",
    "min_mean_idx"
   ],
   "id": "db1e1dba3dda9dd9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T05:47:47.480595Z",
     "start_time": "2024-05-27T05:47:47.475232Z"
    }
   },
   "cell_type": "code",
   "source": "list(wed_stats.keys())[min_mean_idx]",
   "id": "bd23be6c6c8e8a40",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 9, 30)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T07:34:11.948907Z",
     "start_time": "2024-05-27T07:34:11.943280Z"
    }
   },
   "cell_type": "code",
   "source": "len(wed_stats)",
   "id": "9b75523b47a950da",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T16:04:31.248052Z",
     "start_time": "2024-05-30T16:04:31.244997Z"
    }
   },
   "cell_type": "code",
   "source": "import pickle",
   "id": "2a63560b980ed175",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T07:35:34.525747Z",
     "start_time": "2024-05-27T07:35:34.516556Z"
    }
   },
   "cell_type": "code",
   "source": [
    "with open(\"27-03-24_08_41_gridsearch.pkl\", \"wb\") as f:\n",
    "    pickle.dump(wed_stats, f)"
   ],
   "id": "a0c0d1247345ce8f",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T21:00:37.543885Z",
     "start_time": "2024-05-27T21:00:36.891049Z"
    }
   },
   "cell_type": "code",
   "source": [
    "with open(\"27-03-24_08_41_gridsearch.pkl\", \"rb\") as f:\n",
    "    wed_stats= pickle.load(f)"
   ],
   "id": "2b1bf6b71366261",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-27T21:01:35.844300Z",
     "start_time": "2024-05-27T21:01:35.821404Z"
    }
   },
   "cell_type": "code",
   "source": "sorted(wed_stats.items(), key=lambda x: x[1].mean)",
   "id": "ef3a11e047073f63",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[((25, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.6427458988029624), mean=2.365431389151285, variance=0.24408016546768208, skewness=0.24970843688533947, kurtosis=-0.47489943956804215)),\n",
       " ((25, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5793431626907006), mean=2.365799431760512, variance=0.2283485251258321, skewness=0.14581362181613933, kurtosis=-0.4966945138162737)),\n",
       " ((25, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3666799964968566, variance=0.23256287638977155, skewness=0.2351978824288662, kurtosis=-0.34650790205426807)),\n",
       " ((25, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.5559859764406214), mean=2.367685477902936, variance=0.22928262970110813, skewness=0.1234810295855286, kurtosis=-0.6546653426007194)),\n",
       " ((35, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3681322155779267, variance=0.2347349275743601, skewness=0.2734479351216136, kurtosis=-0.38666271102839156)),\n",
       " ((25, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.368605800644157, variance=0.2294246885522206, skewness=0.27285275332363895, kurtosis=-0.39286936432663166)),\n",
       " ((25, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.369952540791647, variance=0.23642155447313948, skewness=0.15339830751247546, kurtosis=-0.6673466083790669)),\n",
       " ((25, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3704425282816417, variance=0.23182957468994056, skewness=0.22228340584379075, kurtosis=-0.3626070143296296)),\n",
       " ((25, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.371012587748782, variance=0.2450203543725082, skewness=0.2469304978724812, kurtosis=-0.4951650893264814)),\n",
       " ((30, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716579318006623, variance=0.2422788765943085, skewness=0.20670554315360368, kurtosis=-0.5289317019408943)),\n",
       " ((30, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3716701561009708, variance=0.24731604266709206, skewness=0.22941321516711177, kurtosis=-0.5283369198285905)),\n",
       " ((30, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.372573018808825, variance=0.248495576144028, skewness=0.26669420512826, kurtosis=-0.5578124932452173)),\n",
       " ((45, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.373057696488171, variance=0.227657062156513, skewness=0.3100663573759112, kurtosis=-0.4733080035750321)),\n",
       " ((25, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.373490069485539, variance=0.24398496452787613, skewness=0.2351830610301328, kurtosis=-0.5279495534648584)),\n",
       " ((30, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3740534257011907, variance=0.2220012467665858, skewness=0.23492208208246168, kurtosis=-0.40217807803242067)),\n",
       " ((40, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.374294140016307, variance=0.23088831111757532, skewness=0.28134191057856595, kurtosis=-0.45529187922921555)),\n",
       " ((45, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3744571686507063, variance=0.2355163304370561, skewness=0.0878789749320027, kurtosis=-0.1818687679043296)),\n",
       " ((25, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.5793431626907006), mean=2.3744699364369355, variance=0.2149962896981116, skewness=0.22963219434874785, kurtosis=-0.39122511587047537)),\n",
       " ((35, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5793431626907006), mean=2.3748694433759123, variance=0.22888427449653234, skewness=0.28036566784897027, kurtosis=-0.5267513800129584)),\n",
       " ((35, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.375368586764985, variance=0.23069279313015867, skewness=0.20332670930357677, kurtosis=-0.4795555488120784)),\n",
       " ((25, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.375564585578571, variance=0.23205540022105775, skewness=0.191174590767773, kurtosis=-0.39762545239110425)),\n",
       " ((25, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6427458988029624), mean=2.3758489971585637, variance=0.24544595203606948, skewness=0.20399683624555676, kurtosis=-0.6183066207894132)),\n",
       " ((20, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.376325762160446, variance=0.2060657302187169, skewness=0.01250070338034022, kurtosis=-0.34430512545245584)),\n",
       " ((35, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.376378529726554, variance=0.2327546566086452, skewness=0.27519000763349377, kurtosis=-0.31771745086975933)),\n",
       " ((45, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.7195088150342186), mean=2.3766409284369105, variance=0.2295085075667106, skewness=0.24134106021315538, kurtosis=-0.37408846477628144)),\n",
       " ((30, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7017431454671503), mean=2.377108364289762, variance=0.24495071976894514, skewness=0.3024991123083083, kurtosis=-0.5537863211020939)),\n",
       " ((20, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.377521039722907, variance=0.20392050998976574, skewness=0.04554219008201783, kurtosis=-0.4196129929250443)),\n",
       " ((35, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3776617261354445, variance=0.23247429066020053, skewness=0.10806676086801285, kurtosis=-0.16554652753591004)),\n",
       " ((45, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.378327126176745, variance=0.23824530454742882, skewness=0.19566459356086427, kurtosis=-0.1111469729646477)),\n",
       " ((20, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.276159459798673, 3.496787964451057), mean=2.378366762261831, variance=0.202805898739558, skewness=0.044863602165060454, kurtosis=-0.40796058479310604)),\n",
       " ((30, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.637570667436926), mean=2.3786084222442034, variance=0.22573612749333788, skewness=0.288059435082738, kurtosis=-0.40815147247155315)),\n",
       " ((20, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3788947741669393, variance=0.2264973495722334, skewness=0.09025408327789222, kurtosis=-0.42256332202067437)),\n",
       " ((25, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3187205857224815, 3.7195088150342186), mean=2.3789786957469414, variance=0.23032806261527008, skewness=0.20786286650802807, kurtosis=-0.25287251641055786)),\n",
       " ((30, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7017431454671503), mean=2.3790364690020165, variance=0.23647421694312673, skewness=0.22090866080940696, kurtosis=-0.514964156012657)),\n",
       " ((45, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3792466167629636, variance=0.2422172698332015, skewness=0.18995496774253304, kurtosis=-0.12741963477250806)),\n",
       " ((40, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.5721712499597116), mean=2.3793735172074504, variance=0.23659306029041108, skewness=0.2553454827884794, kurtosis=-0.4934855282228545)),\n",
       " ((25, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3795244890339258, variance=0.2267076428562555, skewness=0.18073800339766158, kurtosis=-0.46599314212218124)),\n",
       " ((25, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7195088150342186), mean=2.3796113176549283, variance=0.2313326274790801, skewness=0.18020655451793677, kurtosis=-0.3976050568640255)),\n",
       " ((20, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3800291337752557, variance=0.21008321180837491, skewness=-0.014567494878515918, kurtosis=-0.38431393219096144)),\n",
       " ((30, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3808467678446803, variance=0.25066438236687766, skewness=0.2895430956226167, kurtosis=-0.47877754061451405)),\n",
       " ((25, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3813709841735355, variance=0.23045884130101402, skewness=0.23712341428131212, kurtosis=-0.40079076893774035)),\n",
       " ((20, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.3814874348247117, variance=0.21826226065443943, skewness=-0.031130696417601945, kurtosis=-0.3603365136513217)),\n",
       " ((30, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.381611757332351, variance=0.2506468837033553, skewness=0.26675328203354126, kurtosis=-0.402185686093512)),\n",
       " ((40, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3819968370307967, variance=0.23273599874155798, skewness=0.14303336257018318, kurtosis=-0.13731717359344797)),\n",
       " ((40, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5793431626907006), mean=2.38261739715201, variance=0.23783393755543428, skewness=0.1711163972917373, kurtosis=-0.6522259873531335)),\n",
       " ((35, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.382804047460243, variance=0.22894779192643214, skewness=0.2291410779208234, kurtosis=-0.41091063392043337)),\n",
       " ((30, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.637570667436926), mean=2.3829987003334803, variance=0.2311443756171956, skewness=0.14685703983398077, kurtosis=-0.5259863508655851)),\n",
       " ((20, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.383163900730137, variance=0.2199302363476837, skewness=0.13714925299665656, kurtosis=-0.47939364685394814)),\n",
       " ((30, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.617632303538528), mean=2.383797434796488, variance=0.23380033902387592, skewness=0.18900269218480067, kurtosis=-0.5808358084581036)),\n",
       " ((40, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3839623101258454, variance=0.23607791753616203, skewness=0.22002487618359687, kurtosis=-0.37320922175372795)),\n",
       " ((35, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3842603364378356, variance=0.22992856792091504, skewness=0.3132728720903736, kurtosis=-0.28223856418657345)),\n",
       " ((40, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3843476299533783, variance=0.23531452343703144, skewness=0.20823115450386134, kurtosis=-0.08506840332276866)),\n",
       " ((30, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.3846008662163936, variance=0.2547504201897208, skewness=0.29648509192680983, kurtosis=-0.4623848643623356)),\n",
       " ((30, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.384897415418424, variance=0.22637196926022032, skewness=0.19173071548573675, kurtosis=-0.43595503689616644)),\n",
       " ((30, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.576250985624627), mean=2.3850795520255197, variance=0.24493882723561317, skewness=0.2203755344910728, kurtosis=-0.4931265530057929)),\n",
       " ((45, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.38523972688129, variance=0.23853526447287604, skewness=0.27479949313624624, kurtosis=-0.37936201651186785)),\n",
       " ((45, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.658925758785262), mean=2.385371374710671, variance=0.23433663582029468, skewness=0.14176451720155842, kurtosis=-0.5927851397233956)),\n",
       " ((35, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2687938019168812, 3.6212506595263343), mean=2.3854325367926594, variance=0.23457032730509317, skewness=0.2604446162510226, kurtosis=-0.3349483534626043)),\n",
       " ((30, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.385768838953655, variance=0.25181681694295693, skewness=0.28409827803721455, kurtosis=-0.3134374893329581)),\n",
       " ((40, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.385897158360537, variance=0.23011231408637273, skewness=0.189040499666452, kurtosis=-0.42683198131482225)),\n",
       " ((30, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.562558606945364), mean=2.3860009450938464, variance=0.23103302426248906, skewness=0.11204332345204361, kurtosis=-0.5490414886033808)),\n",
       " ((30, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.3861556873839587, variance=0.22842621158531876, skewness=0.1290414047412115, kurtosis=-0.07336495164965884)),\n",
       " ((40, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.386188689872926, variance=0.23893696990231386, skewness=0.2967800859202916, kurtosis=-0.28282915584685053)),\n",
       " ((35, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6617761017497807), mean=2.3862119885427275, variance=0.22383472123068804, skewness=0.20644346651108006, kurtosis=-0.48916590957336537)),\n",
       " ((45, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3880417275094599, 3.6212506595263343), mean=2.38632458240834, variance=0.23813819810123346, skewness=0.29652378295341514, kurtosis=-0.3672361799208925)),\n",
       " ((40, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3863502776797856, variance=0.22824894724073475, skewness=0.10516468851421981, kurtosis=-0.2594414493181243)),\n",
       " ((20, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.386627232078584, variance=0.22454729567294118, skewness=-0.07781410951642269, kurtosis=-0.3910997560468208)),\n",
       " ((35, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.38699418676085, variance=0.2577835077161205, skewness=0.2766783845585309, kurtosis=-0.29476357152580457)),\n",
       " ((35, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3872945246573107, variance=0.2244550722608443, skewness=0.250064567983947, kurtosis=-0.38361445269250094)),\n",
       " ((30, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3879080309744856, variance=0.22786302055822993, skewness=0.23902741677955017, kurtosis=-0.4056748763379603)),\n",
       " ((40, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.3880867134387906, variance=0.23677233140443266, skewness=0.2746348945969141, kurtosis=-0.21305949285139691)),\n",
       " ((45, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.1849286366873704, 3.6212506595263343), mean=2.388144182780888, variance=0.24112210638197287, skewness=0.23756936364283102, kurtosis=-0.2909234719207676)),\n",
       " ((40, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.388169259178502, variance=0.2272718361825683, skewness=0.14745033955498554, kurtosis=-0.3275151957127749)),\n",
       " ((35, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3883592992402862, variance=0.23027758760347003, skewness=0.296765878163557, kurtosis=-0.34861854619750154)),\n",
       " ((40, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5559859764406214), mean=2.3886814716207545, variance=0.23275117890602048, skewness=0.22684340335139833, kurtosis=-0.5936785076680828)),\n",
       " ((40, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3888673754435845, variance=0.23588824039933945, skewness=0.2881021402644574, kurtosis=-0.43324356622430704)),\n",
       " ((35, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.7717977793967843), mean=2.3889835348948454, variance=0.2569148258105363, skewness=0.2728444039629951, kurtosis=-0.22732955648449993)),\n",
       " ((30, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389402864252692, variance=0.2320812051198164, skewness=0.3130484267618166, kurtosis=-0.32815775713868645)),\n",
       " ((20, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3895829009651486, variance=0.22363397726813797, skewness=0.20584534226522705, kurtosis=-0.4096143639898431)),\n",
       " ((40, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3895841432380847, variance=0.24536500899154293, skewness=0.2599897242497131, kurtosis=-0.5167545964253102)),\n",
       " ((20, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3897760002726582, variance=0.2225776378698793, skewness=0.18575382982618197, kurtosis=-0.40539773457991934)),\n",
       " ((30, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389938416093227, variance=0.2285287200579085, skewness=0.33902253204824595, kurtosis=-0.32922349087198244)),\n",
       " ((30, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.389975063842067, variance=0.22608397671643873, skewness=0.35686742328669135, kurtosis=-0.2997528177957811)),\n",
       " ((45, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6046958484870792), mean=2.390131984426462, variance=0.23610747813350952, skewness=0.22710720795114606, kurtosis=-0.5667969121056742)),\n",
       " ((30, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.3902302733627527, variance=0.2306056632981275, skewness=0.13261356954170908, kurtosis=-0.27069709723457525)),\n",
       " ((35, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.6212506595263343), mean=2.3904977600986737, variance=0.2507129621263109, skewness=0.21630205116480614, kurtosis=-0.3522875594025541)),\n",
       " ((35, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3906148388612825, variance=0.23612292896679513, skewness=0.06810877547812552, kurtosis=-0.30725869635176695)),\n",
       " ((35, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.7489880220512433), mean=2.3908782343737354, variance=0.23036013923586704, skewness=0.28462534178743354, kurtosis=-0.38709455161808304)),\n",
       " ((45, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3914062869519253, variance=0.2393996096447048, skewness=0.3202604593262514, kurtosis=-0.2876938695682867)),\n",
       " ((20, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.391465728756091, variance=0.22394370233911176, skewness=0.1902320233296623, kurtosis=-0.41359222542326357)),\n",
       " ((35, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7489880220512433), mean=2.3917090310591567, variance=0.23053829906762635, skewness=0.2568710565884056, kurtosis=-0.36570028673332855)),\n",
       " ((30, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.5455110669933947), mean=2.3917898129538484, variance=0.24078771833868412, skewness=0.19864827152416367, kurtosis=-0.5474088816866427)),\n",
       " ((40, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.7195088150342186), mean=2.3919928188754604, variance=0.23184626352990195, skewness=0.16634687967225528, kurtosis=-0.4035931059127029)),\n",
       " ((35, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7689006556014593), mean=2.3920055363342487, variance=0.25763993287424103, skewness=0.30817427852592283, kurtosis=-0.3347297549631598)),\n",
       " ((20, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2552570270665462, 3.496787964451057), mean=2.392065057934599, variance=0.23365257607206494, skewness=-0.04666906316083106, kurtosis=-0.474086583223138)),\n",
       " ((20, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.3922155425953786, variance=0.22032420003471148, skewness=0.14340784428616138, kurtosis=-0.5120992116516523)),\n",
       " ((35, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3922777542401183, variance=0.23879292523118395, skewness=0.32127679699013006, kurtosis=-0.2637818045632927)),\n",
       " ((20, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3277926313096529, 3.6261780809782476), mean=2.392327612986733, variance=0.22581619892859958, skewness=0.19485470098090532, kurtosis=-0.4507202547441471)),\n",
       " ((45, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3924854857851394, variance=0.23275307803748638, skewness=0.07500074075038048, kurtosis=-0.2935617253280385)),\n",
       " ((35, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3262757602011017, 3.7689006556014593), mean=2.39275698546612, variance=0.257313874546351, skewness=0.2951195589126923, kurtosis=-0.32469376243371917)),\n",
       " ((40, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3930129707845498, variance=0.23215066111872443, skewness=0.2989655069270995, kurtosis=-0.2817878628513344)),\n",
       " ((35, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.576250985624627), mean=2.3937824872694407, variance=0.2376169204304254, skewness=0.26069901598295314, kurtosis=-0.40435481472120305)),\n",
       " ((30, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.714742906872058), mean=2.3941387003152848, variance=0.23003758231830418, skewness=0.21058066331754136, kurtosis=-0.379609944610138)),\n",
       " ((40, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6427458988029624), mean=2.3944423590294193, variance=0.2465065536857468, skewness=0.2721538250896667, kurtosis=-0.4792545836240327)),\n",
       " ((35, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7635929866868563), mean=2.3945732197549288, variance=0.23483766651871996, skewness=0.16196716664718905, kurtosis=-0.10484682014817315)),\n",
       " ((35, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7689006556014593), mean=2.394708380809293, variance=0.24331914134325108, skewness=0.3104609785236659, kurtosis=-0.34468360619940963)),\n",
       " ((40, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3948665465964374, variance=0.23229474512206372, skewness=0.1883658604164852, kurtosis=-0.3117502418808762)),\n",
       " ((20, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.270472247990643, 3.6261780809782476), mean=2.395570462065189, variance=0.22346972511382623, skewness=0.11329897943777814, kurtosis=-0.3234502459445663)),\n",
       " ((40, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.845491352295706), mean=2.3957542543079233, variance=0.23310121388075183, skewness=0.30095341959348654, kurtosis=-0.2942558792821828)),\n",
       " ((45, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.396039294508347, variance=0.23706545805278192, skewness=0.2683386111757052, kurtosis=-0.43250740115100994)),\n",
       " ((25, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.396406456777108, variance=0.21087635212900227, skewness=0.3024016257197271, kurtosis=-0.40876460192220465)),\n",
       " ((35, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.397308803810493, variance=0.21627613395654613, skewness=0.3096471084250271, kurtosis=-0.41018903098186943)),\n",
       " ((30, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.196035841536656, 3.7195088150342186), mean=2.3975186504066417, variance=0.22094968513618016, skewness=0.07480434328399128, kurtosis=-0.16335538209499267)),\n",
       " ((35, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3975590366723187, variance=0.24904851847767226, skewness=0.3568965017957376, kurtosis=-0.4302411959545829)),\n",
       " ((35, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976350502222, variance=0.23944715281813153, skewness=0.17912464319890165, kurtosis=-0.2781964142653237)),\n",
       " ((40, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3976595418096966, variance=0.23818181554736112, skewness=0.2784549893503289, kurtosis=-0.3637520804075818)),\n",
       " ((40, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.3978819990437974, variance=0.23997789669492398, skewness=0.24892265230252733, kurtosis=-0.4017673562738979)),\n",
       " ((30, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2367128896999586, 3.6261780809782476), mean=2.397930773143809, variance=0.22641718268691954, skewness=0.17481864720402482, kurtosis=-0.4152277535960023)),\n",
       " ((45, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.3985213041603988, variance=0.24132064938614203, skewness=0.32115251612013973, kurtosis=-0.17855020892334617)),\n",
       " ((25, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.398615207826399, variance=0.20887753685860447, skewness=0.3187548013407416, kurtosis=-0.4229798846403865)),\n",
       " ((35, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.3986405901311167, variance=0.24608385998671142, skewness=0.3549449464418198, kurtosis=-0.4001901870073876)),\n",
       " ((45, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.3993612139561167, variance=0.24377787252097266, skewness=0.22483344276706962, kurtosis=-0.4970600005238177)),\n",
       " ((25, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.3997149663071773, variance=0.21211940441425559, skewness=0.3057379221458508, kurtosis=-0.4542345695303873)),\n",
       " ((35, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.400215914906969, variance=0.2465940458980044, skewness=0.24000447703882694, kurtosis=-0.2886091250801641)),\n",
       " ((35, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.401718522915244, variance=0.21725510948778534, skewness=0.32112902558791273, kurtosis=-0.397168495428879)),\n",
       " ((25, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.4985825938516006, 3.6261780809782476), mean=2.401775050693766, variance=0.21072810372063794, skewness=0.29902784407963034, kurtosis=-0.4643048528579099)),\n",
       " ((35, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.402065366133696, variance=0.21630799827125896, skewness=0.3496875784127574, kurtosis=-0.38042501490776415)),\n",
       " ((40, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7717977793967843), mean=2.402673983236185, variance=0.2481837073735338, skewness=0.30838627997579465, kurtosis=-0.244559942461954)),\n",
       " ((40, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403858377352788, variance=0.24749869146926223, skewness=0.3140850115861441, kurtosis=-0.528785556311719)),\n",
       " ((40, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6427458988029624), mean=2.403932248288499, variance=0.2487742084683387, skewness=0.3381611420772938, kurtosis=-0.5165431694838332)),\n",
       " ((45, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.405068165571497, variance=0.24646178290038326, skewness=0.2875176572527761, kurtosis=-0.34540566014415486)),\n",
       " ((45, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6967857019419847), mean=2.4051959917035837, variance=0.2450394814984553, skewness=0.23972419985337953, kurtosis=-0.4682877238864682)),\n",
       " ((35, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.405989752988432, variance=0.212938956410703, skewness=0.24447258131202554, kurtosis=-0.5271312715451346)),\n",
       " ((45, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406087392078327, variance=0.2580389356909127, skewness=0.29136942528153364, kurtosis=-0.29872794085361143)),\n",
       " ((35, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.714742906872058), mean=2.406311167879119, variance=0.20781769633334632, skewness=0.2642635361018867, kurtosis=-0.3980318498632758)),\n",
       " ((35, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4587772202865898, 3.714742906872058), mean=2.4064772123154166, variance=0.21721735273717763, skewness=0.3199872090051091, kurtosis=-0.41302195380383466)),\n",
       " ((40, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.7689006556014593), mean=2.406839724428981, variance=0.2603261771691146, skewness=0.3032186102576703, kurtosis=-0.3173295824803919)),\n",
       " ((40, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.407634797501566, variance=0.24371594638017943, skewness=0.2557220507793175, kurtosis=-0.15715980564058407)),\n",
       " ((45, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.7655032774739143), mean=2.4076371514178407, variance=0.24068682096802357, skewness=0.25787115006032574, kurtosis=-0.23278661069579876)),\n",
       " ((40, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.6344813683062345), mean=2.4077533351492275, variance=0.25320749575495544, skewness=0.2812107479281849, kurtosis=-0.4215619870544707)),\n",
       " ((25, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.1616185507986427, 3.7195088150342186), mean=2.4086178137902587, variance=0.23777282252768617, skewness=-0.018916891642525895, kurtosis=-0.21225144452113565)),\n",
       " ((35, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.408989352750845, variance=0.20993256278594338, skewness=0.20771311895378974, kurtosis=-0.5114565720950774)),\n",
       " ((25, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.409439772520956, variance=0.20459373614192744, skewness=0.2593292649700985, kurtosis=-0.43989262943361895)),\n",
       " ((40, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7717977793967843), mean=2.4103906271074926, variance=0.2622513620731259, skewness=0.28525878762522766, kurtosis=-0.11029258861434332)),\n",
       " ((25, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.410518885127268, variance=0.20550593327046196, skewness=0.2499724220330842, kurtosis=-0.4559192902865674)),\n",
       " ((45, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.410540212549997, variance=0.2396217756873817, skewness=0.26811073141192715, kurtosis=-0.4597579615051637)),\n",
       " ((25, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4108899944823015, variance=0.21198194312074198, skewness=0.24085917574500354, kurtosis=-0.46285314189413684)),\n",
       " ((45, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4112285800591664, variance=0.24211863480665663, skewness=0.3127233163899829, kurtosis=-0.14450499897441826)),\n",
       " ((35, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.411574201138517, variance=0.21578778205407273, skewness=0.23369600888365885, kurtosis=-0.2867155946425828)),\n",
       " ((40, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4118508808603045, variance=0.26022119050841064, skewness=0.3682029744859333, kurtosis=-0.41218842818502033)),\n",
       " ((45, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.4124482991603977, variance=0.248005365526009, skewness=0.35134624885668897, kurtosis=-0.007504347438727521)),\n",
       " ((40, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4124700839687243, variance=0.25532818691020326, skewness=0.35812542624780425, kurtosis=-0.3749700141329608)),\n",
       " ((40, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7655032774739143), mean=2.412501835410017, variance=0.2504678890342812, skewness=0.23613524618695558, kurtosis=-0.2089049468054367)),\n",
       " ((45, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.412821308581424, variance=0.24551095940694598, skewness=0.2924398069064522, kurtosis=-0.07143088476505399)),\n",
       " ((45, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3911588477979093, 3.6967857019419847), mean=2.4131517076091358, variance=0.24066940267301618, skewness=0.26131189253213666, kurtosis=-0.49633037705736216)),\n",
       " ((45, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3347463232592418, 3.819449071027997), mean=2.414563246470491, variance=0.25243118749415416, skewness=0.32718722307293585, kurtosis=-0.1359966216084869)),\n",
       " ((25, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3793123740968751, 3.7195088150342186), mean=2.4150328016456037, variance=0.22965534837872242, skewness=0.11268451204073322, kurtosis=-0.3615049140619653)),\n",
       " ((40, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.724442042751297), mean=2.415071859981076, variance=0.26238842898246284, skewness=0.2633242071284276, kurtosis=-0.2445055517514012)),\n",
       " ((45, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.416366874484952, variance=0.25708950801601954, skewness=0.3347859277339084, kurtosis=-0.41942847638872305)),\n",
       " ((45, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3981383478421074, 3.7689006556014593), mean=2.4179154555308684, variance=0.25567498182680215, skewness=0.32706170459005446, kurtosis=-0.4164166171364596)),\n",
       " ((45, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3268220109662052, 3.86758500469272), mean=2.418061266506169, variance=0.2617243674783607, skewness=0.3253382576002433, kurtosis=-0.06503236957059055)),\n",
       " ((40, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.7725779764160206), mean=2.4188623730204735, variance=0.26939255179747723, skewness=0.2788384614811644, kurtosis=-0.18288967289790214)),\n",
       " ((25, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3488849778228653, 3.6261780809782476), mean=2.419065699428399, variance=0.21591709226718184, skewness=0.2058330707307416, kurtosis=-0.3095627615564962)),\n",
       " ((40, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.32972052526856, 3.7195088150342186), mean=2.4194208077125197, variance=0.22735926912395726, skewness=0.08411152670063812, kurtosis=-0.36976518164125993)),\n",
       " ((20, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.420607944985054, variance=0.22213448574790956, skewness=0.07369247045913387, kurtosis=-0.3204623682911296)),\n",
       " ((20, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.421994549752394, variance=0.22368579559809232, skewness=0.07766446553557484, kurtosis=-0.37632005523926537)),\n",
       " ((20, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.4224237705113114, variance=0.22372683062153817, skewness=0.07733561784015744, kurtosis=-0.3741548157951047)),\n",
       " ((15, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4237830561768567, variance=0.2129392320893983, skewness=-0.08376772978701559, kurtosis=-0.2716619126555728)),\n",
       " ((20, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2639142043132883, 3.6261780809782476), mean=2.42404563849526, variance=0.2245852469553631, skewness=0.07550445732280753, kurtosis=-0.33011654543771085)),\n",
       " ((15, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250234741780834, variance=0.20909704825092762, skewness=-0.06777194176027414, kurtosis=-0.27625274525067756)),\n",
       " ((15, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4250708875937916, variance=0.20883053530286166, skewness=-0.07138164793039069, kurtosis=-0.27475050421781333)),\n",
       " ((40, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.425222538586002, variance=0.2758842502293917, skewness=0.33399224512560255, kurtosis=-0.27223767809946775)),\n",
       " ((15, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4252546445919934, variance=0.2075275053147006, skewness=-0.07069685472679284, kurtosis=-0.24599343619613867)),\n",
       " ((15, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.4254430153975073, variance=0.2139334478379118, skewness=-0.09371038896209857, kurtosis=-0.2511108814365266)),\n",
       " ((15, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.425698034696807, variance=0.21914834068542002, skewness=-0.06652177289464448, kurtosis=-0.3290842422376308)),\n",
       " ((30, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4262628008241856, variance=0.21831525479458277, skewness=0.22263078724693997, kurtosis=-0.48808525476895026)),\n",
       " ((40, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.7725779764160206), mean=2.4264197520081887, variance=0.27049178724551853, skewness=0.318896684817541, kurtosis=-0.2539636343376732)),\n",
       " ((45, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.184431415694061, 3.82122118576931), mean=2.4269174633995525, variance=0.25948011673042015, skewness=0.2999659622583222, kurtosis=-0.06571939509751745)),\n",
       " ((30, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4271405903951044, variance=0.23033291768137476, skewness=0.1631811023842746, kurtosis=-0.5828797184614842)),\n",
       " ((20, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.0487238909712968, 3.5605306008619384), mean=2.42725248440732, variance=0.240912705804242, skewness=-0.027445577863958494, kurtosis=-0.2989541627256087)),\n",
       " ((30, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4275089000561385, variance=0.22327316852087933, skewness=0.17835340203663447, kurtosis=-0.5397748344422348)),\n",
       " ((30, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.429406136909439, variance=0.21858010355750818, skewness=0.2000876621727689, kurtosis=-0.5077100524895188)),\n",
       " ((30, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4409555005564576, 3.6261780809782476), mean=2.4298489368050427, variance=0.21888683945586074, skewness=0.20063596567110728, kurtosis=-0.5176138357954292)),\n",
       " ((45, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2885549028222312, 3.9162282140460087), mean=2.432424925734303, variance=0.27393959139490665, skewness=0.373169478138468, kurtosis=0.006635598228321271)),\n",
       " ((30, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.432507639466392, variance=0.2202826193726728, skewness=0.18051045766742263, kurtosis=-0.510441313183494)),\n",
       " ((25, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4332410339185766, variance=0.2491228344995086, skewness=-0.01261753485038881, kurtosis=-0.2375642444532593)),\n",
       " ((30, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.433282778268923, variance=0.22134877479162188, skewness=0.08024460232414445, kurtosis=-0.3947248921866984)),\n",
       " ((30, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3892887019500362, 3.6261780809782476), mean=2.4345756829691187, variance=0.22113762404024856, skewness=0.14077885323381606, kurtosis=-0.5306541114660988)),\n",
       " ((45, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.4346991775070603, variance=0.2619768174726941, skewness=0.3410719204865325, kurtosis=-0.1540722090125799)),\n",
       " ((25, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.43477140368765, variance=0.2432307330156745, skewness=0.05414275019076038, kurtosis=-0.36907364977950285)),\n",
       " ((45, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.3923393481255855, 3.86758500469272), mean=2.434922529220423, variance=0.26497488722201096, skewness=0.3487337194660302, kurtosis=-0.13525730518814028)),\n",
       " ((20, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4355294363069016, variance=0.22844370985525814, skewness=0.04564180713737524, kurtosis=-0.4708318500785569)),\n",
       " ((25, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.4356151895352216, variance=0.2545402506829705, skewness=-0.014315337290249688, kurtosis=-0.23985265026039926)),\n",
       " ((45, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9162282140460087), mean=2.436520062983622, variance=0.2731040454544077, skewness=0.3782243329854789, kurtosis=-0.038026118597547676)),\n",
       " ((25, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2996440506088136, 3.680832989677557), mean=2.4366509101256306, variance=0.24380395324511875, skewness=0.055522021814290286, kurtosis=-0.3745228956384281)),\n",
       " ((20, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3579617561666846, 3.6261780809782476), mean=2.4385881982087474, variance=0.22429320966832825, skewness=0.04935373950656626, kurtosis=-0.4198054833251561)),\n",
       " ((30, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4082365099263328, 3.6261780809782476), mean=2.438737352325921, variance=0.22805965113028293, skewness=0.1429818073521336, kurtosis=-0.4555229006368986)),\n",
       " ((45, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3449456589241753, 3.9643641477107323), mean=2.439372515277074, variance=0.2801733914247726, skewness=0.4032973011280843, kurtosis=0.004250745916431153)),\n",
       " ((15, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1873023426231983, 3.6261780809782476), mean=2.441581110876761, variance=0.22090427842471203, skewness=-0.03683980809263881, kurtosis=-0.37562510182765596)),\n",
       " ((25, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.441599693225722, variance=0.25279254900744635, skewness=-0.049430446846301976, kurtosis=-0.23987523776176367)),\n",
       " ((20, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2702796676835728, 3.6261780809782476), mean=2.4429333572379126, variance=0.2317515347098559, skewness=0.030755596810590057, kurtosis=-0.38533107980366)),\n",
       " ((35, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3273505012242865, 3.6261780809782476), mean=2.4442174494359254, variance=0.22270434251888466, skewness=0.06292702158159418, kurtosis=-0.41536878070008587)),\n",
       " ((45, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4478526744775175, variance=0.2808214793691462, skewness=0.45054333863678114, kurtosis=-0.022699653611266246)),\n",
       " ((45, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3597777334547303, 3.9643641477107323), mean=2.4480861377214076, variance=0.28607402615953886, skewness=0.46832740215081625, kurtosis=-0.013192895201957988)),\n",
       " ((25, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.680832989677557), mean=2.450736724300251, variance=0.255481258716427, skewness=-0.0916393786706801, kurtosis=-0.1946484500943786)),\n",
       " ((25, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0581332812065112, 3.6268822743745335), mean=2.4567771801373315, variance=0.26685758977245705, skewness=-0.13106930458537935, kurtosis=-0.23530203208601286)),\n",
       " ((20, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.32972052526856, 3.6261780809782476), mean=2.461412940346188, variance=0.22261073233179457, skewness=0.013054843197617676, kurtosis=-0.4048394269957174)),\n",
       " ((15, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.2259766995447088, 3.6261780809782476), mean=2.4641723063091154, variance=0.2159265085681024, skewness=-0.010343951137930279, kurtosis=-0.30470002743739455)),\n",
       " ((20, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3251742111209324, 3.6261780809782476), mean=2.4703947628727625, variance=0.23315199031846423, skewness=-0.008292557084480892, kurtosis=-0.3128243051811035)),\n",
       " ((25, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.454272571633673, 3.6261780809782476), mean=2.473339889745894, variance=0.2233933922107472, skewness=0.017829377188284472, kurtosis=-0.4460671739578528)),\n",
       " ((30, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.1532672794627605, 3.6261780809782476), mean=2.4826310994850456, variance=0.23833070883681687, skewness=-0.05751429986772294, kurtosis=-0.27576009544920854)),\n",
       " ((25, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.280605540735518, 3.6261780809782476), mean=2.4906008151320926, variance=0.248685931657691, skewness=-0.15565003519874476, kurtosis=-0.2276167953822883)),\n",
       " ((15, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.66441849400411), mean=2.4991184440663403, variance=0.24559035270808768, skewness=-0.21622575184806114, kurtosis=-0.16251586619206204)),\n",
       " ((15, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.499706581469198, variance=0.2470089007313356, skewness=-0.24007391357504945, kurtosis=-0.10916436838477361)),\n",
       " ((15, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.502045838854079, variance=0.24585856220767538, skewness=-0.24920723946558182, kurtosis=-0.08569310815061959)),\n",
       " ((20, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2827429687623266, 3.6506824010059473), mean=2.506670301022558, variance=0.2521143544916601, skewness=-0.09572168770654672, kurtosis=-0.3066998890567092)),\n",
       " ((15, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.507056878471702, variance=0.2500685023613651, skewness=-0.2455081158344568, kurtosis=-0.06388626860450675)),\n",
       " ((15, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5104525144988883, variance=0.258237832865022, skewness=-0.2592235877859567, kurtosis=-0.11572394159871147)),\n",
       " ((15, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.469281283482776, 3.6261780809782476), mean=2.5122558974038944, variance=0.2440282829471233, skewness=-0.12522757705704293, kurtosis=-0.48285495809641654)),\n",
       " ((20, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3362673027274208, 3.6261780809782476), mean=2.514185778093504, variance=0.24260229343779774, skewness=-0.13507501269991837, kurtosis=-0.2711732535949607)),\n",
       " ((15, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.514454351924287, variance=0.2641923391146466, skewness=-0.2689525157400004, kurtosis=-0.16619392726022308)),\n",
       " ((15, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.66441849400411), mean=2.5172126530759686, variance=0.2807755090016337, skewness=-0.36304760594914764, kurtosis=-0.09794600674215204)),\n",
       " ((20, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.530455916115353, variance=0.277895703511729, skewness=-0.05581159504734332, kurtosis=-0.14130198306143615)),\n",
       " ((20, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.53252174939801, variance=0.2771156373425166, skewness=-0.07208813987939296, kurtosis=-0.1032661224771787)),\n",
       " ((20, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.1482231202046413, 3.706031535959886), mean=2.533041000736945, variance=0.2762960956844567, skewness=-0.0518493542005356, kurtosis=-0.14641200049739478)),\n",
       " ((20, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5335298487193905, variance=0.277852958678635, skewness=-0.06653145571842661, kurtosis=-0.09408401959396251)),\n",
       " ((20, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5374620593106982, variance=0.2851672776793523, skewness=-0.13276170000964915, kurtosis=-0.08378526032506972)),\n",
       " ((15, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.280605540735518, 3.5448537261245736), mean=2.5393145110575412, variance=0.26180386621894675, skewness=-0.33957640223485785, kurtosis=-0.20905919146227614)),\n",
       " ((20, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.706031535959886), mean=2.5435791505822083, variance=0.28893024616184776, skewness=-0.16093508869298748, kurtosis=-0.08748636816873168)),\n",
       " ((15, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5464305599413226, variance=0.26795495027539973, skewness=-0.11469074924572617, kurtosis=-0.517523709902524)),\n",
       " ((15, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5485245903964615, variance=0.26862635121426187, skewness=-0.12194093267641852, kurtosis=-0.5241112363445328)),\n",
       " ((15, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.549041398461515, variance=0.2728931242961085, skewness=-0.15514934615427897, kurtosis=-0.505909867544049)),\n",
       " ((15, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.201268847274602, 3.585988729019799), mean=2.5504816395542744, variance=0.2695209952065683, skewness=-0.12792988710767947, kurtosis=-0.5327642254880787)),\n",
       " ((15, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.554533540655466, variance=0.269587546488235, skewness=-0.10541615752597985, kurtosis=-0.5658111536655612)),\n",
       " ((25, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.280605540735518, 3.66441849400411), mean=2.56181421211636, variance=0.2601095214113185, skewness=-0.32632240498411913, kurtosis=-0.12005309910500106)),\n",
       " ((20, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0693913293578532, 3.7118106452900848), mean=2.5648878081388222, variance=0.288071300712369, skewness=-0.1560031922606656, kurtosis=-0.110412069870931)),\n",
       " ((15, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.5939585866713815), mean=2.5666447985570335, variance=0.26843038578214184, skewness=-0.14633910677551268, kurtosis=-0.560670544130895)),\n",
       " ((15, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.4226294658585026, 3.688916269874316), mean=2.580109759802578, variance=0.2821922595914078, skewness=-0.15430239150351965, kurtosis=-0.5342614515549466)),\n",
       " ((20, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.2722541239378204, 3.8656333191299255), mean=2.5990973697762323, variance=0.26878379224054205, skewness=-0.08572147612813517, kurtosis=-0.12335277334607442)),\n",
       " ((15, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.3160117650280163, 3.6053806394700256), mean=2.601205819621667, variance=0.27289267701422726, skewness=-0.4952990792320176, kurtosis=-0.03882736218116145)),\n",
       " ((15, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.3898060381764048, 3.688916269874316), mean=2.609196850447406, variance=0.2716643462073717, skewness=-0.2642797975682462, kurtosis=-0.2848939064366114)),\n",
       " ((10, 7, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6441927741135607, variance=0.2594721361604495, skewness=-0.34778411539796744, kurtosis=-0.15659274696374137)),\n",
       " ((10, 8, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.644217801251108, variance=0.2594927958428506, skewness=-0.3477939113557061, kurtosis=-0.15687301104545748)),\n",
       " ((10, 9, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6445636740626393, variance=0.25947719317193274, skewness=-0.34986189727592815, kurtosis=-0.15558152824255256)),\n",
       " ((10, 6, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.646515331167755, variance=0.2625843266390292, skewness=-0.33129530187229717, kurtosis=-0.17352394193686305)),\n",
       " ((10, 5, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6471995488373823, variance=0.26381906477320244, skewness=-0.320655946681577, kurtosis=-0.169339018678476)),\n",
       " ((10, 4, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.6498871800519215, variance=0.26492318590273994, skewness=-0.32446593956630665, kurtosis=-0.17512206191987056)),\n",
       " ((10, 3, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.660704229257497, variance=0.26893152564304285, skewness=-0.33487550920479664, kurtosis=-0.16293684332682234)),\n",
       " ((10, 8, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853)),\n",
       " ((10, 9, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.666691960592264, variance=0.29199551446842, skewness=-0.2379122845655639, kurtosis=-0.09918437143325853)),\n",
       " ((10, 7, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.2093113705231822, 3.897319842199613), mean=2.667071442043656, variance=0.29269861161342803, skewness=-0.23300886540344137, kurtosis=-0.09841994971348544)),\n",
       " ((10, 6, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669214639548226, variance=0.2935420680896115, skewness=-0.25504270121568223, kurtosis=-0.05367597717982964)),\n",
       " ((10, 5, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.1398911880523022, 3.897319842199613), mean=2.669744008221317, variance=0.29841910879887634, skewness=-0.22225993120261048, kurtosis=-0.0472763227484263)),\n",
       " ((10, 4, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.676455843721493, variance=0.2937927870249197, skewness=-0.2234006371411064, kurtosis=-0.09048904303855432)),\n",
       " ((10, 2, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.7360770031252333), mean=2.681594986261088, variance=0.27842546188147577, skewness=-0.38497515584895964, kurtosis=-0.1392300490851719)),\n",
       " ((10, 3, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.217735384196255, 3.912896675694414), mean=2.683735530723601, variance=0.2893644793141911, skewness=-0.24196472267608216, kurtosis=-0.01272835493523905)),\n",
       " ((20, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3423468584053975, 3.897319842199613), mean=2.684073393901844, variance=0.2807864899381598, skewness=-0.2364426189045562, kurtosis=-0.20265061882354019)),\n",
       " ((10, 2, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.703919186064029, variance=0.2910392479358703, skewness=-0.17569831800238767, kurtosis=-0.026657277400341073)),\n",
       " ((15, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.704509705407346, variance=0.27797811695164676, skewness=-0.3262617881322571, kurtosis=0.09119239683428315)),\n",
       " ((15, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.3957580288394014, 3.9147376529767435), mean=2.7075434147500044, variance=0.27093066675680394, skewness=-0.25501352814444467, kurtosis=-0.08732045907999852)),\n",
       " ((15, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.1626290271659017, 3.9147376529767435), mean=2.7078749214557574, variance=0.2795285538018334, skewness=-0.2666613713523985, kurtosis=0.0995119864201448)),\n",
       " ((15, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.711894351727476, variance=0.278369156594265, skewness=-0.30086035835336405, kurtosis=0.2023964929072064)),\n",
       " ((15, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.2588005585814288, 4.191259803191946), mean=2.7124903538315728, variance=0.28517216483889607, skewness=-0.21273665971149405, kurtosis=-0.06564314346805888)),\n",
       " ((15, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.0984114607111604, 3.9147376529767435), mean=2.7177283637337104, variance=0.2723959536913965, skewness=-0.33348323143825864, kurtosis=0.24648247402512613)),\n",
       " ((15, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.1184268068034193, 4.095658947639928), mean=2.728873529740259, variance=0.2771105516332162, skewness=-0.31565304601887006, kurtosis=0.26495658847041126)),\n",
       " ((15, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7458533421389415, variance=0.2675799369586586, skewness=-0.1809451788868796, kurtosis=0.05540112858880075)),\n",
       " ((10, 1, 20),\n",
       "  DescribeResult(nobs=173, minmax=(1.1905609076446098, 3.832138275353508), mean=2.7507068339662806, variance=0.25787775042947936, skewness=-0.37508484769566325, kurtosis=-0.03210901516830633)),\n",
       " ((10, 1, 10),\n",
       "  DescribeResult(nobs=173, minmax=(1.217735384196255, 4.095658947639928), mean=2.756003469399469, variance=0.28989047602798246, skewness=-0.2945369702420912, kurtosis=-0.02369081827086239)),\n",
       " ((15, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4211163358463492, 4.095658947639928), mean=2.7671814146840363, variance=0.27029380686229254, skewness=-0.2091631733056473, kurtosis=0.0691644289164639)),\n",
       " ((15, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(1.4871436032569523, 4.281798203709453), mean=2.836863894959103, variance=0.2729460542498244, skewness=-0.20357139230435106, kurtosis=0.09873747800409838)),\n",
       " ((10, 6, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.886892246290024, variance=0.27395091325862625, skewness=-0.5656360125824968, kurtosis=0.7011024739716984)),\n",
       " ((10, 7, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252)),\n",
       " ((10, 8, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252)),\n",
       " ((10, 9, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873207941438324, variance=0.2734748745700127, skewness=-0.5678949467015401, kurtosis=0.7141385046787252)),\n",
       " ((10, 5, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8873477731939214, variance=0.2728824437420165, skewness=-0.5642008052784352, kurtosis=0.7097326337896264)),\n",
       " ((10, 4, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.8886994646476283, variance=0.27163703068449624, skewness=-0.5564449875702622, kurtosis=0.7075542805925097)),\n",
       " ((10, 3, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.894245688555616, variance=0.26682389539585616, skewness=-0.5105429044672873, kurtosis=0.6087195136790573)),\n",
       " ((10, 2, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.128352337180152), mean=2.9003120200331316, variance=0.261910349388631, skewness=-0.5119892469577535, kurtosis=0.6424781625590157)),\n",
       " ((10, 1, 0),\n",
       "  DescribeResult(nobs=173, minmax=(0.9504744679463277, 4.2688324308036), mean=2.918631217038793, variance=0.2655104551963414, skewness=-0.5301066567004962, kurtosis=0.6648724709751717)),\n",
       " ((10, 6, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n",
       " ((10, 7, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n",
       " ((10, 8, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n",
       " ((10, 9, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9209003612750677, variance=0.24247978797774788, skewness=-0.180609788966947, kurtosis=-0.13042431966873824)),\n",
       " ((10, 5, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9227759884362543, variance=0.24341675218182648, skewness=-0.1876547463116731, kurtosis=-0.1468739255370366)),\n",
       " ((10, 4, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.036416795989875), mean=2.9231854043619783, variance=0.24314027663665091, skewness=-0.1893019934136203, kurtosis=-0.14083355822754395)),\n",
       " ((10, 3, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.92518079836633, variance=0.24887539377691065, skewness=-0.14225847098489716, kurtosis=-0.08017538671679647)),\n",
       " ((10, 2, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.424401844727044, 4.191259803191946), mean=2.931984625553082, variance=0.2494577589000683, skewness=-0.11959221264031848, kurtosis=-0.13815791364404895)),\n",
       " ((10, 1, 30),\n",
       "  DescribeResult(nobs=173, minmax=(1.7501416446074862, 4.191259803191946), mean=2.961261234054409, variance=0.2398959796022379, skewness=-0.1092311580556235, kurtosis=-0.26051346232955686))]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T16:04:31.242990Z",
     "start_time": "2024-05-30T15:47:14.196648Z"
    }
   },
   "cell_type": "code",
   "source": [
    "wed_stats = {}\n",
    "\n",
    "\n",
    "for merge_th in range(1, 50, 5):\n",
    "    solution = []\n",
    "    merge_th = merge_th * 0.1\n",
    "    print(\"merge_th: \", merge_th)\n",
    "    with ProcessPoolExecutor(max_workers=12) as pool:\n",
    "        results = []\n",
    "        for i, sample in enumerate(tqdm(dataset)):\n",
    "            results.append(pool.submit(predict, sample, visualize=False, point_radius=25, max_angle=15, extend=30, merge_th=merge_th))\n",
    "\n",
    "        for i, result in enumerate(tqdm(results)):\n",
    "            key, pred_vertices, pred_edges = result.result()\n",
    "            solution.append({\n",
    "                '__key__': key,\n",
    "                'wf_vertices': pred_vertices.tolist(),\n",
    "                'wf_edges': pred_edges\n",
    "            })\n",
    "\n",
    "    solution_dict = {}\n",
    "    for d in solution[:173]:\n",
    "        solution_dict[d[\"__key__\"]] = d\n",
    "    WEDs = []\n",
    "\n",
    "    for i in targets:\n",
    "        try:\n",
    "            j = solution_dict[i[\"__key__\"]]\n",
    "            i = dict(np.load(io.BytesIO(i[\"wireframe.npz\"])))\n",
    "            WEDs.append(hoho.compute_WED(j[\"wf_vertices\"], j[\"wf_edges\"], i[\"wf_vertices\"], i[\"wf_edges\"]))\n",
    "        except KeyError:\n",
    "            pass\n",
    "\n",
    "    wed_stats[merge_th] = ss.describe(WEDs)"
   ],
   "id": "711e13b1d26945f7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 29.29it/s] \n",
      "100%|██████████| 346/346 [01:30<00:00,  3.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  0.6000000000000001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 30.06it/s] \n",
      "100%|██████████| 346/346 [01:29<00:00,  3.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  1.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:12, 28.21it/s] \n",
      "100%|██████████| 346/346 [01:28<00:00,  3.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  1.6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:12, 28.47it/s] \n",
      "100%|██████████| 346/346 [01:27<00:00,  3.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  2.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:12, 26.90it/s] \n",
      "100%|██████████| 346/346 [01:28<00:00,  3.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  2.6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 29.23it/s] \n",
      "100%|██████████| 346/346 [01:27<00:00,  3.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  3.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 28.91it/s] \n",
      "100%|██████████| 346/346 [01:30<00:00,  3.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  3.6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 28.93it/s] \n",
      "100%|██████████| 346/346 [01:34<00:00,  3.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  4.1000000000000005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 29.65it/s] \n",
      "100%|██████████| 346/346 [01:30<00:00,  3.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merge_th:  4.6000000000000005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "346it [00:11, 29.16it/s] \n",
      "100%|██████████| 346/346 [01:34<00:00,  3.65it/s]\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T16:04:31.254958Z",
     "start_time": "2024-05-30T16:04:31.249059Z"
    }
   },
   "cell_type": "code",
   "source": [
    "with open(\"30-03-24_19_00_gridsearch.pkl\", \"wb\") as f:\n",
    "    pickle.dump(wed_stats, f)"
   ],
   "id": "65d847156f2f2319",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T16:10:49.234942Z",
     "start_time": "2024-05-30T16:10:49.230037Z"
    }
   },
   "cell_type": "code",
   "source": "wed_stats",
   "id": "bdeed1b59b16aff9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.1: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 0.6000000000000001: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 1.1: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 1.6: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 2.1: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 2.6: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 3.1: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 3.6: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 4.1000000000000005: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956),\n",
       " 4.6000000000000005: DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T16:04:31.261753Z",
     "start_time": "2024-05-30T16:04:31.255968Z"
    }
   },
   "cell_type": "code",
   "source": "sorted(wed_stats.items(), key=lambda x: x[1].mean)",
   "id": "5a50b1d4c4b80702",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.1,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (0.6000000000000001,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (1.1,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (1.6,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (2.1,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (2.6,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (3.1,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (3.6,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (4.1000000000000005,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956)),\n",
       " (4.6000000000000005,\n",
       "  DescribeResult(nobs=173, minmax=(1.3076459810782375, 3.711955004608544), mean=2.3875919110882218, variance=0.24435959160468504, skewness=0.23662307954617326, kurtosis=-0.35790560125656956))]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  }
 ],
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