eda_md.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data science tools\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Image processing\n",
    "import nibabel as nib\n",
    "from ThreeDLabeler.preprocessing import tag_parser\n",
    "from ThreeDLabeler import images\n",
    "\n",
    "# Base operations\n",
    "from tqdm import tqdm\n",
    "from io import StringIO\n",
    "import os\n",
    "\n",
    "# Cloud interface\n",
    "from google.cloud import storage\n",
    "credential_path = \"/Users/michaeldac/Downloads/mouse-labeler-cff0443f5b5e.json\"\n",
    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_475 = nib.load(\"/Users/michaeldac/Code/CUNY/698/Skulls/475.mnc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data shape is: (698, 397, 456)\n",
      "The type of data is: <class 'numpy.ndarray'>\n",
      "[[[-242.   -186.99 -304.03 -101.02]\n",
      "  [ -59.98 -216.98 -267.03  -55.02]\n",
      "  [  31.01   29.98 -118.01   68.97]\n",
      "  [ -35.98  230.02  337.03  221.01]]\n",
      "\n",
      " [[-179.02  -62.    148.97  143.02]\n",
      "  [ -72.02    7.98   93.98   99.02]\n",
      "  [  59.02  125.    152.    146.  ]\n",
      "  [  64.     -3.98  -45.98   40.99]]\n",
      "\n",
      " [[   8.03  128.02  128.99  -11.  ]\n",
      "  [  92.01  181.01   90.02    1.02]\n",
      "  [  88.99   41.98 -118.01  -69.98]\n",
      "  [ 137.02   43.98 -114.99  -23.03]]\n",
      "\n",
      " [[-117.    -31.99  -94.99  -12.  ]\n",
      "  [ 103.03   32.02 -155.98  -89.  ]\n",
      "  [  -3.99   32.02 -208.   -107.98]\n",
      "  [ 208.03  132.99 -178.99   26.98]]]\n"
     ]
    }
   ],
   "source": [
    "img_475 = img_475.get_data()\n",
    "print(\"The data shape is:\", img_475.shape)\n",
    "print(\"The type of data is:\", type(img_475))\n",
    "np.set_printoptions(precision=2, suppress=True)\n",
    "print(img_475[0:4, 0:4, 0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.65,  7.05, 14.94],\n",
       "       [10.07,  9.49,  8.1 ],\n",
       "       [ 9.73,  5.17,  0.57],\n",
       "       [ 6.82,  9.39,  7.67],\n",
       "       [ 1.93,  6.43, 13.68]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_475 = tag_parser('/Users/michaeldac/Code/CUNY/698/Skulls/475_landmarks.tag')\n",
    "tag_475"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# from google.cloud import firestore\n",
    "# credential_path = \"/Users/michaeldac/Downloads/3DLabeler-ff833c2b4aaa.json\"\n",
    "# os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path\n",
    "\n",
    "# db = firestore.Client()\n",
    "# doc_ref = db.collection(u'test_collection').document(u'tag_475')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looks like Firestore only allows JSON style objects and does not like arrays. [See here for more details](https://firebase.googleblog.com/2018/08/better-arrays-in-cloud-firestore.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doc = db.collection(u'test_collection') \\\n",
    "#             .document(u'another_test')  \\\n",
    "#             .set(tag_475)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "update_time {\n",
       "  seconds: 1573093271\n",
       "  nanos: 722280000\n",
       "}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_ref.set({\n",
    "    u'tag_name': u'tag_475',\n",
    "    u'num_rows': u'50'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dict = {\n",
    "    \"looks_like\": \"JSON\",\n",
    "    \"walks_like\": \"JSON\",\n",
    "    \"talks_like\": \"JSON\",\n",
    "    \"ItMustBe\": \"JSONNNN\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "update_time {\n",
       "  seconds: 1573093561\n",
       "  nanos: 217360000\n",
       "}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.collection(u'test_collection').document(u'another_test').set(test_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As you can see creating json/dict style files successfully pushes up"
   ]
  },
  {
   "attachments": {
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n6NzQHdD7YoAz5DSSNdY3sTf1MAs2j7aIs0y0OmGdbJNim2Z3yj7NIckxxsnX2c5Fb5+mq8F+lwNRB3PcbpDa3Xs8ujzvehV6H/Zx9JX2o/Sb8x8IaAqsDCoIzghJCCUfciVrhXGHrYU/j7gemRzlHq0fIx3Lf5jzCOtRxjiaY5hji/Hvj/ck3EzMSYo6sT/Z5KReilkqKe14+pVTD0+/OfM1Y+HsUubCuZ9Z89lfcuZyv55fvkCTr1IQXFhe1Hdxsnj20ofLb0telg6WPbrSUt58tafiyzXeqv3Vhddf1jLcsLiZAp9eK3cl6zzrCxqGmtD35JsP3j/RUt7a3Nby4Fr72Y6jnVFd8Q8zuoselT6+2HPmSUSv7VOJPlTfWP+tgfRB/yHrZ/rD+s+tX7iPRIwmvTw5dvSV92vdcbbxuTeNb09OOL2TeI97/3GyY6row6GPWtOU08MzpbPHPvl99pzz/RL0NfRb6Hzod/KPyJ8xv6IW/BYNl6iXbi/rLz9ZcVn5sjqwTrkxtj3/YqATMoVGEV5IDDIDJYbqR8dipDCz2Cs4X7wUfoXQQ1FMGUW0oZKlpqJepHlJ20ZXSZ/FcJTRh8mGWZ1FhJWRdZ1thn2Io4WzhquUu4Anlzd7TwZfMn+kAElQX4hHaFm4V6RYNEzMSJxXAiExKzki9Ui6SeaabJ5cvLybgooiRrFfKUfZSYVV5aVqkZrnXll1jPobjTrNDC1fbT0dQV0aPaD3U3/a4IXhPaNcYy8TAZMJ0zwzS3OseYdFoqWxFYvVJ+sWmyxbXzs1e6L9G4frjkecTJwZnd+6VOwLgeP/yv77B+IP6rrh3AZJhe4BHns9KT3HvK56H/JR9ln3bfWL99cKAAFtgceDdINRwV0hJ0K1Q5cPVZGd4ZhdGW4Z/isiL3Jv5Juo+GjO6PsxbrFMsWOHq44kHnWKE45bPNYRn3XcJ0EvUTSJ5QRFMkj+dXIy5WlqbdqpdNIp+dPY02NnbmSknQ3INDxHd+5h1r6suezYHO1cnfMpF3D5aQVTRawXZYtVLqlcViiRKhUu473CWk57lVCBr6SGV5J6tdv1kzXXa5/dWL8lfNvlzrm7g/UMDc6NhU0jzej7Ii2Gre5txx5cbG/peNu5+ZC3W/eRz+NTPTefvOjd6BPp3zdwfvDNM9nh08+/jtiONo7xvsoZl3pL9S5qKn0m5ovFj8UV66353/net1UwigBkwXmmw2m4zgKQWQ/nmfcAYCYAYEUEwE4FIE7WAIRhDYACTvyNHxCceOLgnJMJcAMRIA9nmmbABc6ao0EqnFFWgxYwBD6CdYgOEoG04PwwDDoN54Nd0CQCQvAidBCeiJNwljeEWEXyIc2RscgK5AgKh1JFBaFKUS/RdGhTOCPrwEAYLUw8ph2Lxppgz2JHcby4QFwjHot3xFfgVwnmhMuEJQoLigpKFKU7ZQdRgJhK/EplR9UMZzqZNIDmEM0UrTNtP50B3X16Zfo6BlWGDkYbxkmmCGYMcy6LIEsDqwXrDFsKuwz7JEcxpzuXGNcy90OeHF7PPfJ8GL5X/LcFMgQDhUyFxUSIIvOiz8XuiV+UiJN0lVKRZpCel3kqe00uVd5XwURRUolRaVP5i8ob1SG1nr1d6p0a3Zp9WmPaMzqLekAfA59zOCOcMd6E0pTBjNdc3sLCMtgq27rZ5oMd0V7ewdnxqNMl506XaVeK/dIHHA4ecSsj9bkve/J72Xqf8Gn2XfXXDbgQuBLsETJ0yIDcHC4fURslEX0zdu/hgaMhxzjiXyRkJ5mdWDyZnSqe1nXK6wxjxtvMp1njOZt5PPkqhWYXD16KKblUNnZVovJStXTNxM3Ldw7UUzTWNO9vFWvn7jJ4VNJL2S88uDicOSL8cvD1xbfn3w99dJtd+UL3rfoH+CW9qLK0uZK22rA2vH5vo/R36KbS9vkBbX9zoAPsQBDIAk1gDlxBEIgDmaAMNIJ+8AFsQEyQFGQCeUOJUAn0AHqPQCGEEGYIMiIf0YH4huRAmiKPIGuRUyg2lA0qHdWFhtDq6MPoe+h1jCYmEfMYS4N1xl7B/sBp47JwH/Fq+Cz8HMEAnvN1CieKO3AmTKYcJqoQL1FRUEVTTVM7U/fRGNC00WrQttLp0vXQ29KPw5npKmMGkyjTE+ZDLEwsdazWrB/ZYtiJ7GUcWhxTnJlcJtxU3OM8t3nP7PHj0+Fn4f8scF/wrJC3sI6IgCidGE4cLYGTpJKik6aVwcmsyM7Ijcj3KDxQfKDUo/xK5Yca1V5pdWsNP81wLbK2r46TrqGeir68gbKhodFB4ziTy6bdZvMW7Jb6VgFwTMu2PW+XY5/tcMmx1em7i8K+eNenB7gOhrv1u/N5eHvmeN317vOZ8l3zZwqQC7QLigzOD2kL/URmDjMIj4y4GjkWTRNjHptxePSoYNzRY5PHfRJpknqSw1MwqSfTUadSzrBndGQmZDnl6J5Xu6BWoFakUixyGVXysCyynP3q/Ur3Ksbq8ZquG/23Fu7K1B9pfNJM3aLXRm4v75zt1nl8q1emr3BgfOjX8PcX06OTYzOvf72F3hEmGT7wTxvN5s4pfUv7Wb4UuNK3lrTesfHr98r2/CPg3U8LuIAE0ADWwBscBbngBugFnyA8JAaZQ2QoD2qDPiGYEHqIcEQ5YgxJizRGJiHbkBsoNVQsqgm1jtZGp6FHMCKY45hxrAa2BIfDheCG8Sr4IgKC4E94TqFHcY9ShfIB0Yr4kSqBmpe6jcaVZpH2LJ0E3VP6YAYiQwWjDuNrphhmLuY+ljOs7mw67KIcDBxrnONcDdzneIJ4zfZI87HwY/hXBL4LfhP6KbwhSiXGL64l4SYZL1Uk3SDzTPanPJuCsWKCUocKpaqr2g11LHxXbdHeo5Olx6RfY+hiTGsyaJZvEWJlbyNrO2bv4tDrZOT8bJ+36/KBRDeIFOr+3FPJq9AH73vcnxBQGmQeAkLrySHhXBEdURExnoe/xpXFxxx/kbCehDiBS6Y5KZcSljqcbn9q9kzKWcnMl1kpOWq53/Mq8w8UEoquFitdul+iWdp2Ra+8p8KqcrjKrnqgxqC28abwrfN3cHeP1q03pDYJ3hu4n9Cq2DbbXthp+RDVfe9x2BOx3qm+iwNOQwzPhp5njJiMbo5Vv7Ycn3kbMbHxPmEK+SFhGjGT+An1+djc168G32Lmi76f/hHxU+/n0q9rCxYLrxZ9FxeXIpdml12X+1d0V6pWiauhq0NrCmt5a9/XjddL1tc27Dau/0b+dvpdvQlt2m9e25r/MG852e3wAVHqAIB+s7n5UxAA7DkANjI3N9dKNjc3SuFk4zUAbYE7/yFtxxoaAArfbqEe0efx//4v5/8ABP3ffwVT3t0AAAGdaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjY5MjwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zNjY8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4K2ra87AAAQABJREFUeAHsnQd8FNX2x082mx4SCL2DBRFFbKigYC/Prn+xPxv2LvjsvXfFXrD38kTF3gVEnwVpCgICoXfSe7L/87vhLpPN1mST7Ca/k89mZ2fu3HvnO/fOnDn33DMJ6enpnr2vWy4zc6uEQgIkQAIkQAIkQAIkQALxRsBdVVUlHk9NvNWb9SUBEiABEiABEiABEohRAu6aEtmm5g/Jzv9dstxrJKtmjUjROpFEjyR36Cntdxgp1Z13ltmrsuW3pR2ktNLdqCNxJyYmSkKCSzOhUtsoktyZBEiABEiABEiABNo4geTChZKZ+6bsk/Y/2bprphRVbJR1G8pkwaoiXU4QT0KVdG23WrKTS2SXvgkycvfuUrxdoUxa3Es+n99T1pTlNIigu6amhhbaBqHjTiRAAiRAAiRAAiRAApZAzpynJP2fV9UKWyFJ2/SQ8hqPVFSUS3l+gVRXeqRSPQK6pbolze2RJXPnSeee3aTdllWSlJoshXNfk/WffCvVfU6RxIGX2yzD/jYW2rBTMyEJkAAJkAAJkAAJkAAJOAikFi6RbrMelIT86VKjI//VCQmyvrBMOndvL+UJHklKSpZ2KeWSUuWSnLQE6dcrQ7K7dpSy/HVStmSJrFyXL59M/EYqq2qkZtFrIht+l8TBN4u028pRSvBFt8fj0RQJwVNxKwmQAAmQAAmQAAmQAAn4EMjYOFf6TLtDpGKdKrJuqUyolOqaBCkoK5cy9WZNSE6X8sRKSVGlNj3FJVmZSVJRo/6yialSVFgki2d/L+vzKyUp0S1l1RUmd0/+HEn89UKp2ek+8XTY0adE/z+NQkt11j8criUBEiABEiABEiABEvBPILVoqfSbfpckVuZJtc7F8qhl1lWTKEmSpkqtSxbmrpKcpCrp07OLpKenSk5WtoirUnKXrpYF81fKxpIyUS1YPGqZrVT3BP0SF6Z1qVSWbxSZNlZcuz0TlqXWBR9anRVWuzf/kwAJkAAJkAAJkAAJkEAYBHrOfkYSykukSpJVE82UGk+yJLj0k5Ak+UXlsnZ1vkhpqWS4PNI5p71ktsuUjJQ0SUuokeXL1smSNcVSUlwuK2HNFY+YQAVaLrRSD/4q8qVm5q1h1ESLd7vV7EuXg7BgMREJkAAJkAAJkAAJkIBI93mvSVrhAlVma/SjKqi6G6itVTw1+l6D6mopr6qQknKdHJaUIhnpGYjWJW5VbAs2Fkh6Zob0zElRxdYlmcmq/JarQqoWWgTdSvQk1Pm48ueKZ+64kMjd1Voo49CG5FQnwcE7Jst2vd3Sq6NLlq2vkaXrquXneVWydH11nXQt+aN3x0QZNTzF1O+dqWrSj7JkpSfIoF54GBIpLPXIn0v5Yo4oI2Z2JEACJEACJBCTBFIKcyX7n/fVoposiWqRdZmR/mpJMPOyEqXCVaXrkiQrLVkq1JF2+oJV0rtbiWRnp8vCVRWSovbYzu0zzCe3oETK16qeogqv6ruqk6piu8lzwH7LwtdFeh6urgdbBuTBKAcB0dTfcIgqsjefkCFQFv3JQxNLBJ9YkF6dXDLmiHRVtCslmgrtsG2SZPT+aQIWToEyP/7rMnn+m1Lv6mXPdjLlH/eADjm0kLx3ZbbsMSBJep2rwZwDCOrpKzien/6ulIcnltZ5UGnKY8L5OlvZ4mHhr2VVctBteb7VapW/0abeHat+VZsE3Ec92HJtxtaD3yRAAvUJoL+OOTzdGHVwrcL95d2fys31sn7q2F/z5U3tVYESOfj2+Lve4j58hd43cB5ufbtYPp9eO6GqOah3yP3EKJ2V1WVGoa2USn2dgbqwaoSDxGq3uFUhLa+plpzMZEl110iP3l0lMz1JMtJSZNcBGme2pkyWLl4iiSmpkrtghU4S81/r2sAF2KYW4MVviQy+3n9CXWveFObVgAMmi94Gf8pDQ3IPpqA0JL9Q+xw/PFUeOiPTJKtVEsu9ig6stWO1UUEhKVBr5fivNyt1ofKNp+04PnwgULg+/6O28/TulGgU3FtV2T9ercJQRgpK9AoRZ4LzagVKcO/hiXo8qeZC19QWaMsW1m48FCxVy39bEbQbp+CGiQ8UWwoJkEDsELDXKWeNcI0098cYMug46xdoGdcYGGfsSOP4C7PMtTeerju3nJBpRopxjFBsm0uhTagqkayV30pldbGJaOD2JKkuWyOqyoqrCvd+dTuApTUhTZYXlUpylluGtEvT36KTxXTSmH671begTI2yk/78R9arq0GNbJoJFuiEwXy77CORbTU+rbvuPcPuUhu2y7wpzK7ity8BNHyrzI55qaiexRMdAE+pw1UJaq4G5VvHpv6NCxYuZlC4znqyoJ6ycTOeELVzbdfbv/W6qesXjfx9LclnH5AmtxyfIeMvbCfDrtXZlo0U+zDn72EM7iEQWGVjyXWlkYcccne0K3tDcSZGf2ssc7c2xaoW8AI6eUSqLFhZLb8siFwh36m/W3bo65aXv4++m5CTL5dJIFICMNzYe8DNxhpYbgwX6MN4KMW2L9RC2NQP/5HW2196f4o5rJ34xNJIq7+6B1rXnHP7M9b8okqpes0mJkm1R10PVYdMUYW00lMbeqvG45LUJJcUlRbI6kKX5FUWysC1azXKQYaUp3aQ6pJCcdeU67YSKYXfrIb4wqUaKq2GrNV/qvE6xOuCoC9lkNWT1PXgX46tmxfdLsRHMD4Pm1c25ZK/m3lTlheNvK0yi4YeaPgeFsnWqszCxQKKHZRZKH3+Llg4/iteLDRDH/FonfXXTmBph8UZCheGdJryuKwbS1tSZv0xt+sSMLzUQMGNFw8juDmtLaiRib+VyyOflGjAbpHOWS758c4OJudqvXD+pb7fU/WBFFbxvGJcSWvl8xvay4T/lcszX0U+2nKKKrSfTitvoEKbJKeOTKVCa08Ev2OGAJRACK7zznudvSfiPok0o9XgEcsCA5U9Fn/1xLafdLQuHiy1t7xdJGOOVJeDNJc89FHzuTu22zhbJ3OlSFVCtSq0VfpRTwOFWQ4LrCq4FXr9rq52qSuCrq1JkoKqZFm0rlh2GthJcjq0U4U1TZYsXS45224vGYVzpaikSK0Pm6+/sOQa0VV2cdMa88KFoAotJ4V5UdVbwM3RKhvjHf6h9RKGWIFONEwtuIM0P9xE35262WXBuSvKgrUO6ZbpZDN0LDz1+grSjd4/1fgxQQmCDxPWYaJaMD9emz/qAsX0ixkVITvu2QekGoUO+fpTZp11C0fpGzUsxSgcYAtBnlAecQxW7AXH37H424bjuuKINDNUjTqAWWPOl61HOM96OB5YKeC3vGxdjTlnKNuysPW1edrfODa77LsNv53HHi4zTFL8fHq518dt2YYacwNCOWhLqB84gVf+JvcGXLgtP7v+PR1xcN60bP2i/Y2bIdqXr5X2ipcKTVHWB9qWC3eWYDeatOQEee2yLFUoK2T4dRuke4dEef6iduoiUy2f/F7bvtJTEuTspwq03VVLv84uM+z42qXZcuLD+VJUVntRRT5Jtc3TFs1vEmjTBLL1oR7i77qAa85Dsnn4O5ZBYZ5CKEGaYNeZUPs313acC3/no6nLTy38x0QkcKm/rEstrdVqoXUl6LyaGnjSVulHHQg0Bq1bLbjqgyDZ6WnSadAu8vucGTJkC7Whul3Sc5c9JCFPoyBUzZLszHaSnu3Wt4VtkBLEolX3AkQ6gLE1YZMHgfWlTSiYF/Dw3PB7qN2hBcbmAlYrdjYgogEE/pVWQYmkdrDsPXRGuzqTqGA5goJxyzvFdfxtoRRZa7AtA5YmdKzRegO25fumG6ZeKViH7aY8VZT8iRlO0bogDQRKNvKH4uRUnnz3tYonlPDGCo4PdYW11yrqw7UeD5+pw/p4claXDohV9PzVy3cb9nv+gixzXFDoEnTMAmnsuWtoneFfhWNHnpa9b172eLAebQT+xKgPHkowyQD72frafe1vHJtd9t2G3/bYbRnhMEMd7IMO8rAPCSgHFz64xWA0Bw9BqCfaxNlqUUEbxXo8XGAdPqGUR+QfDcHwpXNSGI4BbR71gy+zUzBSEGzyRudsl9kX7DaqxXVjcZV8qcd9zO4pXoUW+a3Oq41Oggglk+dUylS12l57bIZc/0Zt+3OW6W+5fUaC7D84WTq1c8m3sytkvroY+Mreg5JkSL8kWbCqWj77o7zOQBgePA/YIdmsw3lBfQLJ4D5u2WvbpE3W5gop1/ehQ3COFq+tkQE9EiVdFfC3fqSbQiCGXN94Ar590V+OffT6F+uC+00ogcEnHgTXgJaYFOYuXCGpiWlSUKGuA/pXoWG67Otuy2tKpVxdDxDKqyKxRtxJNZKemiZdevaSjNRkWbhimYz41/6S1bmLfP7Sm+qt4Jbk5BTZql9f6dO9m75wYZksz89X/1wEAKvVVeqci5IVdX46f2yyQWw29To3NsWy9SNsbN6x4roQ6HisMoDJYmh0UIowPACrGDoLFAzcnKFA2Ju3VWZxM4Y1LVvfdwynb9zY4Z+KoR5Y0LAfxFohkQ7DDr5WLidjq1hbhRcWxGwdpoD1CnUJNsRiL2SNHQ6HIosPWIxS1wWbH47pXY1GgG0If2aHsJz1D7SM44EyC2XMMkdaKKKw7rVTNuGKU6kCcyuBhtBQNi7gmCAHVwyr9FrfW5x7KGu2rdq2Yn8jf7vsbxu2R8oM5wqKL5RU3yd3tEO0GXs8tp6YDOE8BpSJtthcVgq0f8sBx2zlYVWyfQXnFfUL1EaWrK2WC5+rte5iX7SLXbdKkqlzg/uzfjWzQkYMqhu5w7ds+7tP50R5T6MyzFdFNVfLQzt78dsy49Zg05yxb5pp3xsKPXLlUenyxuQkuea1WmV5r4FJ8tIlWfLtrArpogr4lUdlyDH35hnF1+5vvy8/LF3OOyjNuDD8e+9Uc06OuCfPuE9ccmi6dG3vMgrulzrSEo6gr+MTjuA6ZB+qwknPNK2bAB6OMVKE60j9a0vtHADf9bFIJJx7Aq7t8SAtNSnMU10inqQstcxqaC59nW2l+spquAJVQF1SrMosQnZVqeOBOxGW20TpmN1RZk2fKwkaJjYtI0vWbiySBUtX6SQxtyQm6YQytcS61D0hKytLeqpSW6NW2iUb9DquL1+o9ax1nI2qzdd3x1qzCB9clfg4ebV1jd3/sCxBWXMKrHwQKHDo7Lh5O28UUHogdhjEbkM6pMd+UFBwIYHiZ4f/jSVu0+QspBv9RKFJZzLz8w9KADopLkooA8osrIi9cmqfqG35fnaN2ipYDiFQ7K0yi99YtjdO+KxGIofsmGKO60E8BCgzK3hQwOS1SARKrP3gPEL5w8Qk5OVPoMBCkcVELixDmYTCBfcJ7I/lxkpDmOG4A91YblEF24ozGgf8r6xCbpXFxlq4bTkt8Q2r5gOnZ8r/7s6RNWr9HKc+tMHkG1Uut+6WKO1SQ18LB/VKlI/VfeGUR/LluteL5IY3i+X0fVP14r25hD8WVcqx9+Ub14bTHi2QY9VCDBcGpHn2/Cy5VUdnzn26UI6+N1+ma9pLVTn1FUwOG6sPqkeqAjv25SLZ56Y88wB13B61/QjpZ+VWyZ7XbzT5+e7v77e9vvjb5lwXbjrnPlxu3QTsdQGjObg2WKUP9xavkUVHIigtQwAP780lCeo3W6nuBdWqvLp0AlhlYqlO6iqT4qoyVXITdbKYSLIqqkmJbumY01GvfRqLtrJaemzRSzJzOsinE7+TmTPnSVJKinTu0UOy23eQjRs3SnFxqUY+qJCyigqpcdlpYnWPyroe1F1b+6vxd1x/uQZZ588SEyR5i2+CUgOLBpQVdGB707cV8z0eWGswnAixVj4on04FDtuc+eL3oE3RAbDeKdgPChXKh4+mVZIwkcUpznTO9XbZ7oenU1jk8JQNgWVu/Dvwlwx8IUL9Ax2/zT+cb1uHqZqfr1gfLJQTidioCl9sCiHm3Nep4DrX+1vGMfpGOfCXzncdHjJgzbTnGtttG4n0WHzzxu+GMAt23L7t0JYZSAG225vyG+yc1nHUH9Z2PPigrToF7dXeWJ3rfZfhC/uPWlC7ta+SrbsnmmH5aQv9P5hg35LAzd83axOuDm4MUDh32zpJ+qrFFpPN2qe7ZF1h7QPtjMWby5oyV6dI6M1m6Ja11nP0QXUhM5ZmZJ6vD0PbqwLuK2g/q1QZ36l/kvlge666SNg2gd+//VO/L2F9MLEPj4EstVRmg9Fru9vQL9E20G6e9+mXlgqGv3F9t9dAu57fTUOgpSaFFauCmq0qLCyrmIOlVzT9laC/8WIFl3E3SHcnSXJqinTs1EnfFJYkWw3aQkbsO1RciQlqic2UmXPmS5WGoUlLS5W1hQWSlp4phcU6eRcv+6qukhT1zzVuB8aPVt0P9NuIu/7InaW76SrafC4HtuB4+YYyCWsblFQ8hVofT3/1h3KDmxAsquj8TiXHX3qsQ1qIndVtn3rNSj//EJ90Dz/rQ60qKKm90VpFFpZaKAbBlB+bp1WoYQ0NR5mw+zXHd5YqEZD80rqW8eYoG2UgpBfcDjBLHpOM0E7gRwuLOiU8AoHi0ELJtg9TNie4cIQji9ZUy1NflJoPLLX/0WH9k3TSVyDpq5PDFq6ulsJNk8ICpcP6If3cOvEsW37WPj5zSZXevGvbXiALCUKHQdHdoqtOxFPlVUfTZHefBzd/imm/LonGorvf4M0PeXBxWKyfxkogpZbKbGPJtu790T5gdEGftQ/s+P2u3kvg9oaHLTyc4oGUSm3TtwVcI1vCGFGS1kk85WqVVWW2GkqsuhogVFeluiDoWJRRSpPcaWppVSuuKqhut1uK84skTSeJJaobQhHCdqn1tkK3l5XqW8LKK9XXtlp/I22KpOhwVrJadDG5rJ4HQXqPgGD1ZQ6wU1OhDUhIN1hLEYZW0EkxmctXoIhCuYE8t+nFCnaoGh0fHd3+Rho7nGstrfBhtf5JTiUTSrL1jcUMdeSBdHAXsDcl5GeVaSz7EyhbEFjo7GQlm86fT5Tdhm+8AQyuE5gdD0tqsAsV6hHICgjrGo7Fn2KMdRAoMMEE+TsFk3og/mIA+6Z17heNZeSP8wpl1qloYX20FNpoMIvGsTZVHuhTtn07yzBWb3X3iNRqjrzOUteWG94olrJNk6f+VKVz7xD+sfBPDTdu7HkHpskv8yvlnKdrXVoG9nTLZerrag0IOA6ncgs/WbQJ9PVMdWnQ67k8+XmpzFR3gWACf+AktWZc8nyh8ZkNlrYh2+z1w1pqqcw2hGLb2wf3IH/9EpZZjFDGslKLycfhCq5BwQxY4ebTlOlw726JSWFl2VtKzeoZ4lG/V8Q1UFusuhpAk6zRP/WfVUW3tKJcqlQfLS4oEFf79lJYhOtYlWxQ/9l5/yyRVJ0opi8Sk5ISVXr1Kb+yslyXKyQnJ0uSVdlNUT/cMs2natOF1fsCsOxtAiJ1ec24AZNEdwMmv0TjE91aBc8NT0C2YZuoA3d3MMMuUEqhuMBy+/NdOaYjQwGxNwooflB2IOapVdNCuTUdZZPvrA0tZf0ZoThiO9Ih7y9ubG9cHZAPFEVYSGExxg0Sedo6vHNl3aFZ3yPCfqgb9oMPFPLHvsgDw7r2pua7H36jXByTLRPf/gR5/KRsoKT4EyjGECjGuOhZwTLWQd7ZFEnBKrbWomzTQpF3io28YDu1cxteU9yUAhcQX8GDjT0W320N+R0Js4bk39L7BBqRsCMWkdZvfVGNHL1bilx4SJog9BYmcI3S9mgfHG1+WTrsjwgFQ3XC2AsXZQlCed3xXt0HVeyPaAbOD5TRhWr97af5dtMJWbDIX3tsrf9rmuZh5bS904ybA8r4j04Kg2UVFmAoA7NVwb766HTZSn124Vd75n5pcq4qyb7ykcbPTdbt1xyToa+PdJnrwD2nZsru6uYQLbFKrP2OVr7Mp+0RwP0Oii7uM1apDdS/W4pOJIaGQPexlqq7v3IxKQyscU/GPbC5pDJnZ6O6JqizbFV1jaS60kws2lSdIJZgJoKp20FVpT7YqxuCbi/Ky5O8vHypUIX211//MFENqlTbhYU2MzPDTAYryC9Ql4MiM1qdmppe+zYxf8bWnF0CHmathRYevOY9DQHTtfkNUAgxtIgGhMbjTwHEMP7N6vfnFDPLXdNDeXQ+HcLVANusNRY3OijNGMpBR3J2JgzpYNITBBcNzFJ/+Mxav03r1oD8rGuEs3znMiaOYT8oiU5FEcqjVayd6Z3LRqHdNJQOpRVKPuLpQqAcHLxTsuGCi1kgf1wwRH1xUYEijWOGoEPiwmfdILAOCn6t8t9OeuqxL4NlWesNLjhWO1MVyjaUfTwIQPm3Ci7SwhUgFBOU1VBB/VEXlG3rg+ODL6W/cnEe7UME6m0fkoKVHwmzYPnE6jacZ0xItH7ntp42Dq39He43wl+d9USBOSfTHsiRQu0vE3UC12Of1p0U9vrl6gGm1gMolxgdueoVjUCiaZ0Cqys+TsHoxksa0WAH9Xn9SR9iq9V6gIeO/QeLUVDtiMGkORXyyiXZGgfXpfnWyCXja2fmosxzdDLYw/rQ+u2tHXRIzmMUAGdkBlveCo0hfKYey33/zpSzNGpChUYvx+um/1gU3LJr9w/3G32bQgLRIGCVWmup9TdyFo1yGpqH75yXhuYTi/s5R4Waun41XUfImlkPatzZ2tfdVqqFtbRGJ3MlVmj82Gp9EFedUs2v6jor5Tp5LLESFtt2sm7NBlm1Cm/ddEmpvkzBpZZYlyrAiDxbXqajv5UVOnmsXFI0jFeSvh9XLa6adrOhAG8kk64jAx5eQnJysme/G1fJrCWN98sKWEor2wBlEC8+QDw7+LTiJgZlxSpo/g4Xio6NbVegShAmMUGp8RUodlDaoCRCAoXTQjoogsgTyhOUSFiJ0ai3vWy9US5hzcQ2KEVOQV2MEqllhKq3cz8sY18o3b7DxLYc580Rlngoy77DUzg+ZzB9KMFQCnzriXQoyyqLUB6tOwceKJwXJ6SDYmkF9YHij/VQjJ1pbRr7Haiedrvz2zctONoykA7HcsWLtQ8mvuUiLR4oLDtnaC3kCwlUz3CYBcsj0LZI15tKRvkf2hQecKz4azN2WyTfGWoxLVGF0ekKEMn+odIiIkKp5o9htUDSUS20G9Rq7K8OqB8u1yXldRVpf3l1UEsx2n+wsvztx3Uk0BIEcH+KNWXWHwcbuhDbfOPC+0sfa+ugi9g3hcE1sjn9advPfkhSl31qXvCV4U6VNVXr1fXALRnqS+vBrbj2HbaSoMNQ6aqYdO3VU7bbbitZtmS1lCKSQVmZTgjThKqjFuTnmfizeZWVqlv1VouvS1atXikbSiukCJMOrPQ+SmTwDfZXve+ElJQUo9CG8ueqtydXtAgBWIcRyglvgLJuCqgIlCVYKKOlDIQ6ONTDDrnDt9efch4qj3C3G+VbL5DWmh1oP6vkY3uotIHy4HoSIAESIIG2QQD3DFiTITC8wMJMCY9AcmGu9P/pSlE3V7XClsuqhDUa+SBdjahqodVRyqQUffFCsbpxabSCdmqq7dqtsxnRys7KlryN+UaZTdTJYjDA5m9YL/OWLFHlVcNd9tLQXuprtWbNalmvCm0hZtVaGfGmSLut7K96326XasLGjFtvE1fEIgHM5j9hz1RjteyV4zJPZHgl4S1qiYRYH9SmrjsU2KZUYp31D2b5dqbDxYiKrJMIl0mABEiABAIRwD0DccQpkROoaNdX1vY9XDrmfiL51RvMhLBEj1tKEjT0aKWOPumkrkrVdpOqVGNNdUmB+tB26dpdCnVSWJoqu251S0jQSa8acFattcVSraqoxkDQyWFVkpCmkQ6S08RVWm4MvR4MPfc/JagyiyNwY1KYB1PNKHFBwPooIQ4ghkvwseL0QbXr+E0CJEACJEACJEAC0SawYZvTxb1hptRsXCaJqnTibV+IUNCjazcp0nBcxRraKzEpUcorqnQ9/GZLNLqBTvjSQNwudUkoLy1WxTZVSlVxrVRLLN40VqUTx/DCBfjWbo5sMBC+lCGrbxTakKmYIKYIwGJ50O0bjY8SfHkhkfrCxtQBsTIkQAIkQAIkQAJxR2Dj9pdK0tQZ+lrbQhP5IEXdC1JT9HW2CRrtSCMbeuBLqxO8yso1jJfGpE1UyyzWIUyXWy20Sfq7TK2yieqaoJv12y2paemS1aG9LF+/Xl85pi4hO9wcFhej0CaoVs0oB2HxiplEsNTCAbw5ncBj5uBZERIgARIgARIggRYnUKmuBzVDH5Tkaf+RRPUbKK8o0ZizBVKiSmqZWltTUlP1zV+V4lGFNVXfCoagWvC7TU5BBBmPxp4tUUVW3zamei8sshvyNkqP7t10ndprU3LEs8M96mqwZVjHqS606rhAIQESIAESIAESIAESIIEICVTnDJGK3Z+SyqytpELttGkZmZKelqkWWVVYNZpBouqZaarY4o1hELgmQPes0QgGxQWIPevRVzPURh9PgE6qrrDFKf2lcNcnRHJ2DLs2rhqdVWbU5bB3YUISIAESIAESIAESIAESqCVQrVbU1cMek4StT1Pra6p5WxgiGFRqKK52WVn6AoV26itboi4F6o6AOIaqt1Zo+K6C4kJVQWv0zWAu0RcjaLztKlmWcbDM6Xu3VGeGZ5m158BtFFqUSiEBEiABEiABEiABEiCBBhLI63+eTK86XDISPhBZ+bp541fnnGx9cUKFhupKN9ENairy1Q0hQ0qK80WtqpKmllt9r5hUdzpEKvqdIAszt2hQ6bX2XzX3UkiABEiABEiABEiABEigMQSK3T2luPdFIt3PkIR1U2Rj1jppV62REKrXi1vfHuZulyN5GoK0JEndCrr1kNKsIVLUYZjUJNZ9M2OkdTCvvvXAQ5dCAiRAAiRAAiRAAiRAAtEg4M6Qim4Hyyx/eWXqys7+NjR8nQuhEhLwflwKCZAACZAACZAACZAACcQhAZd5sQIttHF46lhlEiABEiABEiABEiABEDAWWqIgARIgARIgARIgARIggXglYCy0Gs42XuvPepMACZAACZAACZAACbRxAiYOLdXZNt4KePgkQAIkQAIkQAIkEMcEXNV4ea4Gs6WQAAmQAAmQAAmQAAmQQDwSoMtBPJ411pkESIAESIAESIAESMBLICE5Odkzc+ZMWbNmjb6WrFQqKiqkqqpKX6XLly14KXGBBEiABEiABEiABEggZgm4EYc2KSlJX0mWpp4HCWbZuCFolZ1KrXM5Zo+GFSMBEiABEiABEiABEmgTBKC3WnHX6Ht0U1JSJCMjw6vMYp1TgXUu2x35TQIkQAIkQAIkQAIkQAItScAqtUahhXUWSmxlZaVRZLEMoSLbkqeIZZMACZAACZAACZAACYRDwO1yuYyFFsorXA3wTUU2HHRMQwIkQAIkQAIkQAIkEAsEjEILP9rU1FSvMkuFNhZODetAAiRAAiRAAiRAAiQQDgHjcoBJYVBqrauBc0cqt04aXCYBEiABEiABEiABEoglAvCjdaNCWIDrAT4UEiABEiABEiABEiABEognAkahhSJLS2zw0/bTTz+ZSXPOVO3atZOddtpJfv/9d+nVq5d07drVuZnLJEACJEACJEACJEACzUAgQUN2ecrKykxRVGoDE+/UqZN0795dcnJyvIm22WYbefbZZ+WYY46RI488Us4880zvtsYuTJkyRebNmydnnXVWY7Pi/iRAAiRAAiRAAiTQqgnUUWhj7Uh/++03Y/3My8uTvfbaS/bcc88Wq2LHjh3l1VdflUMPPbRZ6vDwww/Lp59+Kl999VWzlMdCSIAESIAESIAESCBeCRin2ViyzOL1u7BO3n333fLOO+/IP//8I3///bc8//zzctVVVxkFr6SkJKZ4n3jiifLaa6+ZOu27775y5ZVXSu/eveXRRx8161544QUZMmSIdOnSRS644AKxFnG8Znjs2LGy5ZZbytChQ2XcuHEm/XnnnSd33HGH/Pjjj7LFFluYcGoIqTZmzBjp37+/yfuaa67xukB88MEHcsghh8hxxx0n3bp1M3msXr1ajj/+eIFleccdd5QffvjBrOc/EiABEiABEiABEmhtBNz2DQstfWAbN240iiysslBq/cm6devkzTfflA8//NBYaw888ECjsPlL2xTrCgoKBHWw0qFDBxMdYsWKFYJtkKVLl0pxcbG88cYbMmjQIFPXW265Rd566y3p3LmznHzyyfL++++b7xdffFF++eUX+fnnn2X58uWyzz77yMiRI+Wxxx4zSu7nn38uH3/8sSnjkksuEbDBvoWFhXLhhReaN7vdfvvtUlRUJF9++aU88MADcttttxkF+IgjjjCM5syZYx4MzjnnHOPCYOvObxIgARIgARIgARJoLQSMQtuSSi0ssJhUBWUtXIGFFkPx+MANAZ+BAweGu3uD08Fympyc7N0flmT40frKtddeKyNGjDCrX3rpJTn11FONcosVp512mlFSodhC+cWxrF+/3lhR//zzT0lPTzdl2FBq+I1waq+88oq8/PLLZhIa8rniiisEVlootJDBgwcbCy6WZ8yYIVBkJ0yYYJRelAWLN3xyBwwYgCQUEiABEiABEiABEmg1BNz27WDNrdRa/1gotI0RDMvjA4XWKreNyS/YvrAOh+ND62Q5ffp0M9wPa6yVHj16mMUbbrhB4HYASzOsvWeffbbAEusrubm5xgLs9CEeNmyYsRbDGgzxLbO8vFx23XXXOlnhwYEKbR0k/EECJEACJEACJNAKCDS7hXby5MnGtQAuBuEI3mCWlpYW0A3B5jF37lzBB+4IUBDxiQWBmwEU1euvv75edcAALgL33nuvTJo0SU444QRBKDDfaAkIBwaFdfbs2QIfXci0adMEinFGRka9fFEmmC1cuNB810vAFSRAAiRAAiRAAiTQighoCNrmfZnCxIkTJRxlFhZL+IFi+P7++++Xk046SRBpIJRYP9tQ6ZprOyZqYVLYzJkzTZHfffedCfWFH+eee65cdNFFxud1+PDh0rNnT69LA44VrggQuB0cffTR8sgjjwgme8Fia8OFmQQ+/xARAgot3Azgj4wP3BPWrFnjk5I/SYAESIAESIAESCD+CbgQ4cDfK29b6tAwqx+z860fKhQzKHSwuEKxHT16tF+/1Zaqb6hy4esKN4XddtvNxLG99NJLBcorBFEQ/vrrLxP9oF+/fsbPFhETIAcffLBxM0DcW7gVPP3008Y9ARER8DKHvn37CkJ7+ZOsrCxB5ANEiUDs3D59+phzDMsthQRIgARIgARIgARaGwEThxYWPKcPZlMeJEJv+ZNddtnF+HxCYQtH4F5g/Wf9pYdVNJakqqrKKKbZ2dn1qoUoBXCtcLvNi9u82/GwAWu282UOUG4xYcw5Oc27g58F7I8ym9sS76cqXEUCJEACJEACJEACTULAaFDNpcz6HgGUOExcQkQAuBhEIpgEhs9RRx1loh0g4kCgcF+R5NtUaaGs+lNmUV5mZqbfYnFenMosEvnzmfW786aVkXINlhe3kQAJkAAJkAAJkEAsEmiROLRQsuDnCWUWLgWBxIb0QtzVQKG58OIA+NdCsYXFFvFYre9poHy5ngRIgARIgARIgARIoPUQSNChaw9CPDWXIFyXbzgp37J9Q3rZlxmEG5oLiq0zxJVv/vxNAiRAAiRAAiRAAiTQeggYH1r7KtaWPCy4CyBOKsJ6+UZBsAqtrR+sspgkBqUVE8YoLUvg1ltvFbwNzZ9g/c033+xvE9eRAAmQAAmQAAmQQFQIJKhC6LHB+aOSY4SZQHmF/yussoF8YH0VWlsElFkotVBuoeRSWo6AP6WWymzLnQ+WTAIkQAIkQAJtiYBxOYAi2dyz4K1/LBTZUBJIoXXuB8UWH7glUFqGgFOppTLbMueApZIACZAACZBAWySQoJOyPCUlJc127L7+seEUHI5Ca/MJ18/Wpud3dAlAqYXQzSC6XJkbCZAACZAACZBAYALGhxYKbXNZaAPFofWtojOkV3V1dcShuWItDq3v8fE3CZAACZAACZAACZBAdAi4ExMTo5NTlHIJFNKrpUNz4U1d48eP9x4lXlO79957yz777GPe3NVSsXy9FeICCZAACZAACZAACbRRAubVt7GgjPl75a3vOWnJV+AuX77cRGHAm8yGDh1q3uz11FNPCd5whtfZNlTwatyRI0c2dHfuRwIkQAIkQAIkQAJtnoAbr1dtSYn0lbe2rnYSWKhX4Nr00fq+8cYbZfvtt/dm9+STT8pll10mWVlZcuedd3rXh7uwaNEimTVrVrjJmY4ESIAESIAESIAESMCHgLumpkaa20Lr9I9t7KtZMQkMn5Z6Be6FF15oFNK77rpLRo8eLbA0W4GyOmHCBJkxY4Z57e1hhx0mBx98sNn8yy+/yMcffyxQyBEH+KabbpIBAwbIqaeeaneXn3/+Wb744guZP3++9OrVS04//XTZdtttvdu5QAIkQAIkQAIkQAIkIOJyu93NyuGII46Qa6+9Vo488kiJVJmF4vfuu+/KaaedJmPGjJFvv/3WW3f7Ctz777/fvArXu6EZFi6++GJTytSpU72lffLJJ7LjjjvKHXfcYV7F+9Zbb8khhxxiFFckWrFihXmJBF7TW1VVZZZnz57t3f+2226T4cOHy0svvWT2HzdunOywww7m1b7eRFwgARIgARIgARIgARIQE+UAcWib20obCftly5YZa+bEiRONNdO5LyZnQUnGB5bfphK4GkA5hXuA0+UA5eHVwRr+TM4//3yBCwIEvrZg+ueff0pKSoogUsNee+0lv/76q+Tl5UlmZqZJd/TRR8sPP/xQ5+1oubm50r9/fznllFPk1VdfNekQuqxPnz4yePBg+d///mfW8R8JkAAJkAAJkAAJkIBIzEU58HdSMNQeSDBZCxEIYMmEUnv44Yeb4flA6ZtiPRTW9u3by7x580z2cOP45ptvjF8ttkEQTWLUqFHGjQBuBrvuuqtZ7+9f586dZeHChdKlSxfvZligDzjggDpWae9GLpAACZAACZAACZBAGybghuUwlq2z4Z4bDM8PGzas2ZVZ1K+yslIKCgq8ZSOmb79+/WTSpEkCVwNYXLF95cqV5nDgYhBMEM0BPrNvv/22fP311wILNazof//9t3FPCLYvt5EACZAACZAACZBAWyNgJoXF60HDxcC6G8D1oKUECiseDPbYYw9vFfACCfjzIk4trLGY8DV58mTBK39DRZaACwOssdOmTTPfcFXo3bu3PPPMMzJ9+nRvGVwgARIgARIgARIgARJQl4PmekNYNGE3l99suHV++eWXTVJYiCFwg4AyC2X7o48+MuvwD64I8IkNZRF/7733ZMqUKfLggw+ayW82g6+++spETLC/+U0CJEACJEACJEACJLBJoYXFMJSSFQuwMMsfSuJ+++0XC9UxdUCkhYceesjUCxO2IAjXBUGMXacgVBfEaaFNSkqq50bgb//i4mIzwcy5rzNvLpMACZAACZAACZBAWyXQInFoI4W97777GoVxyJAhke4a9fQffPCBiVSwatUq+e2330yc2f3339+EE7PWbijeCEn2+OOPS0ZGhnEX+O9//2vSoEJLly41/r5YhjsBLLKPPvqo4GURUILxSl0IXtiAN4nBBQGv3bUvYFizZk2dCWMmMf+RAAmQAAmQAAmQQBsl4IqH477hhhskFpRZsEL4rrPOOstEVoCy+txzz8mHH35oQnNZlnhrGF6aAGUVdT/ppJMECjB8YCFz5syxSeXYY4818WahvCK2LmTEiBECNwbkf+aZZwri3ML6e/nll5vtzv3NCv4jARIgARIgARIggTZMwMShxQsLKE1DID8/37gYIKxXMIHfLay6iHDglNWrV5uQYDb8l3Mbl0mABEiABEiABEiABKT2xQpUaNkUSIAESIAESIAESIAE4pVAXLgcxCtc1psESIAESIAESIAESKDpCbgQ3YAz55seNEsgARIgARIgARIgARJoGgK00DYNV+ZKAiRAAiRAAiRAAiTQTASMhTYeYtA2Ew8WQwIkQAIkQAIkQAIkEGcEXHhlK10O4uyssbokQAIkQAIkQAIkQAJeArTQelFwgQRIgARIgARIgARIIB4JuPF2K8Q6ZeiueDx9rDMJkAAJkAAJkAAJkIB5sUJJSYnY17YSCQmQAAmQAAmQAAmQAAnEEwET5YCTwuLplLGuJEACJEACJEACJEACTgIuTAijQutEwmUSIAESIAESIAESIIF4IqCeBgxFG08njHUlARIgARIgARIgARKoS8BVU1NTdw1/kQAJkAAJkAAJkAAJkEAcETBhu+KovqwqCZAACZAACZAACZAACdQh4EpMTKyzgj9IgARIgARIgARIgARIIJ4IuKqqqoRuB/F0ylhXEiABEiABEiABEiABJwFjoeXEMCcSLpMACZAACZAACZAACcQTATMpjBbaeDplrCsJkAAJkAAJkAAJkICTAH1onTS4TAIkQAIkQAIkQAIkEHcE+GKFuDtlrDAJND2BH374QUaPHi34ppAACZAACZBArBNw401hFBIgARKwBP766y956qmnzE/7vffee9vN/CYBEiABEiCBmCNgfGib89W31dXVMm/evJgDEUsV+v777+W9994zVVq8eLE8+OCDUa1eeXm5QGmh+Cdw//33y+OPP+5/Yytfu3btWhk0aJA4Fdh33323lR81D48ESIAESCDeCbjcbnezHsMff/whH330Udhl3nbbbXLNNddIcXFx2PsESjh37lz57rvvAm0Oe3208glU4Jo1a2TZsmVmM47777//DpQ0rPUbN26Ut956y5s2NzdXxo8f7/3d1AvRPIdNUdcpU6bI888/780avBcsWOD93RQLt9xyi+A8NEZWrFgh119/fWOyqLMv3AsuueQS42ZwwQUXeJXaUaNG1UnHHyRAAiRAAiQQawRcsJg2p9sBFMp99903bA5Q6GClvP322xut1C5cuFCmTp0adtmBEkYrn0D5R3t9fn6+TJw40ZvtgAED5KGHHvL+buqFaJ7Dpqjrr7/+Wkfhb4oyfPN8+OGHvQ8tvtvC/b169Wq57777wk0eNB2UWetegG/8hlJ70003eRXboBlwIwmQAAmQAAm0IIFmjXKQl5cnixYtkh133DHsQ8YNtW/fvo1Wap977jl5//33jbXz0ksv9b5MAgr2VVddJeeee66x0lVWVpq64YUTr776qiDtddddJ5999plZHygffweEvJD+oosuMoqB0zoMFlBqzjnnHBkzZoxMnjzZXxb11sFdA8o9Juzcfffdsn79em+aGTNmCCx/559/vrHAwtKLMqH04MEFx4L98cExWQlWF1hXP/30U7n22mtNvq+88orJy+4bzne0zmFFRYX85z//kX79+sk222xj6mRDzqF+OI9HHHGEdOzYUU466SRZsmSJt3qwwu6///7SoUMH2XPPPeX3338328477zy544475Mcff5QtttjCe2xQFgPl9cILL8iQIUOkS5cuRukrKyszeaEOaEe77LKL/N///Z+3bOcCXD1QTlFRkcDyaR8sUN7xxx8vnTp1Mv3DORnr22+/lUMPPVS6desm//73v00bRh1QP7RT5NeYBzWnMmvrCqXWuh/YdfwmARIgARIggVglYCy0zeVDO2nSJKNMRPK63YyMDLnyyislPT29UUrtmWeeKUceeaRsvfXWAh9JvEzit99+M76qZ599tkBx++eff+SXX34x5wp+rPgNZQdKCvwIYSn2l0+gk4thfSiVGBY+44wzjBVw6dKlRmm65557JC0tTfB9zDHHyIsvviizZ88OlJVZDwUDdYeFG8owlDMo6RC4KIwbN0722WcfkyY5OVlef/11GTlypFx++eUC5tgXxw9Fe926dWY/KLrB6gKFGcqSVeyheEfqAhGtcwjl75tvvjH1+fLLL825+/jjj81xgA3cBsaOHSszZ84UKIhgCsF5xLmHAjhnzhzZb7/95IADDjAMHnvsMbn66qtljz32MPxt20T+/vL68MMPzUMDFD4owWhD9hygDvB9vvHGG+WRRx4xZfv+23bbbU05mZmZ5oEJQ/w4B6hbz549Tf3wkIMPBEo82hw+eBDJzs42ivtpp51m2iRchtBuUP+GiD9lFvnAOtu5c+eGZMl9SIAESIAESKDZCZhJYc1VKpTESNwNbL1wY4WVzyq1DZkkhRs/PlBkU1JSTNa4mY8YMcIoEu3atTPK37Rp08w2KHKYPAVLGiyCDzzwgLnB+8vH1tP5Dcvhzz//bBSpHj16GCsaLKeFhYXGdxL+k6eccoqxJqIOsOp99dVXzizqLUN5gtKz0047GQX1kEMOEVtfbOvdu7dRaHEssPah3hAotxAct+/DC+oRqi5HHXWUdO/eXfr06SPbbbed/Pnnnya/SP5F4xzCko0Hjq5duwqsorvvvnsdV4pTTz3VHD8YHXfccQLLJmTChAmm7lDsYeWEhRuM3n77bcMmKSnJ8ET7shIor5deekmwDROncExQLK1SjX1hQT366KPNubB5Ob/B35aTmpoqKBsKKRRtPLjh98knnywlJSVGgYVCC+UcH2xDO8QH7RD7Q5BfQ972F0yZdU4Kc9afyyRAAiRAAiQQiwTcDbkRNuRAMJEKVikodw0VX2WsofnY/WBxxRAwFG0rsHpCjj32WDOcCwst6g2r3sEHH2yThfyGBRQKMSyiVqCIQlAelC4oVVaQDgpGMEF94bIBy6FTYBlcvnx5nbKg7MDyG0owLB9JXaBUQclqqDTmHMIKfcUVVxhXAijXUAL32msvb1WceeP4S0tLzbZZs2aZkQFvQl2ARTPYpKxAeU2fPt2cJ2v9RZ7ONu3cz1lesGXkibay66671kkGtwj4O8NaDCUcll+0QSw3VqjMNpYg9ycBEiABEoglAkahxaSwhtyIIzkQKHENsc6iDChsuIljchH8aX0Vukjq4UyblZVlFFV/ih/KgpUT1jIoThjOh5K0jw7phyMYGobAxcAqtQUFBcZ3t3379sbiBiXGWouhqMJ6GExQXyg4/ma2I0+nzyjOKZRcp7LlL++G1sVfXsHWReMcwkcWPqYY9ofAYgv/31ACrrCWOwWW7QMPPNC5KqxlWGXhouLvHISVgZ9EyBPuJ5hsiG+noB3CXxjWezCEawzcJYIp4879/S1TmfVHhetIgARIgATimUCzxKHF8DDCdQ0bNixiVrihw8XAKrNwPYBPZkMEllYM+VvBkDUmTVlFEEPp8NGEPPvss4KJN3AdgEKRk5NjhnmxzTcfrPMVKKo77LCDsa6h7vjceuutAmsc/ChhnUX4MvizwjcSfHbbbTffbOr8xnZYlGG5g8IKK/Abb7xhlocOHSqwguMDwXA7fGPxoIL64jhQB19paF188wn2G+VG4xyCGR4CwGz+/PlGscXvUHLiiScahRauAfBXxcQ2DOFDMYRgEplzcl2w/ODKgHYBP10I2g/aSqTiLBNWZiiymOQHqzI+CFUH/2ucT1ij8Q3FFw9UeLDC+UQeOJ5wlHpbPyqzlgS/SYAESIAEWhMBd3MczE8//WT8PnEjjlRgkYI1CpbZxiizKBcz0//73/+aCAFPPvmkHHbYYUaRgbUNSjIUJkx+gmASFyYMYUIYfFC33357GT58uNnmm4+1spqNjn+YQY88Lr74YuPjCL9ETNKCmwf8JRG8HxEEkP/hhx9ufEIdu9dbhKUX1kHUHZOX8IEPJ5RWWG5hTb733nvNeliIUQa22ZnzqAeG7O3EJxSAujekLvUqF2RFtM4hJm9hchSsylDudt55Z/MwEKRoswmuHog2cfrppxsFEBZbtIP+/fub7RjGRx3x0AKLejABP6TBwwXcU8D2zTffDLaL322Y9IXzhagNN998s3zwwQemzT366KPGVxbHiWNEJAW4veBhEL6y+I3RArQhKLqIfoBvTEY76KCD/JblXGlDcznXOWPOOtdzmQRIgARIgATihUCCKjQeG3aoqSoNixYmPtmh90jKgaIBC19jlVlbJiybyA9WSyuwcsHSZyfr2PX4Bhv4jTqVQKy3+cCVwt+blC688EKvggqLG5RW3zyQD+qCciNx+UDZmKzm9MFFXhBsw4Qif1Zs7IP1gcpqSF1qSw3+P9rnEHF1rUtH8JLrbgUbWDOtn7RzK7bhBRRQasMRhMsCr2D1QDm+/sYDBw70hgyDCwoss2hfVlAH5Onr24764bihzPvKhg0bzDEFOq/O9LBWO4XKrJMGl0mABEiABOKVQLMotPEKh/UmgdZGwKnQUpltbWeXx0MCJEACbZcAFdq2e+555CRAAiRAAiRAAiTQKgi4MEyJ4UwKCZAACZAACZAACZAACcQjAVc8Vpp1JgESIAESIAESIAESIAFLwFhow5lMYnfgNwmQAAmQAAmQAAmQAAnEEgEXZvjT5SCWTgnrQgIkQAIkQAIkQAIkEAkBWmgjocW0JEACJEACJEACJEACMUdAw13SjTbmzgorRAIkQAIkQAIkQAIkEDYBF9wN8BpNCgmQAAmQAAmQAAmQAAnEIwGj0HJSWDyeOtaZBEiABEiABEiABEgABIy/ARVaNgYSIAESIAESIAESIIF4JeCGMrt69WopKyuL12NgvUmABEiABEiABEiABNowATf8Z/Hp27dvG8bAQyeBxhHIzc1lH2ocQu7dxgmwD7XxBsDDJ4FGEmDYrkYC5O4kQAIkQAIkQAIkQAItS8CVmJjYsjVg6SRAAiRAAiRAAiRAAiTQCAKuqqoqhu1qBEDuSgIkQAIkQAIkQAIk0LIEjIWWL1do2ZPA0kmABEiABEiABEiABBpOwIUJYdXV1Q3PgXuSAAmQAAmQAAmQAAmQQAsSMBZaxqFtwTPAokmABEiABEiABEiABBpFwLwprFE5cGcSIAESIAESIAESIAESaEECfPVtC8Jn0SRAAiRAAiRAAiRAAo0nYHxoG58NcyABEiABEiABEiABEiCBliHgcrvdwigHLQOfpZIACZAACZAACZAACTSegAsRDhjloPEgmQMJkAAJkAAJkAAJkEDLEOCbwlqGO0slARIgARIgARIgARKIEgFjoWXYrijRZDYkQAIkQAIkQAIkEOcEdtxxR4FuGOwTa4doJoU1hw8t3BomTZrk/cyfPz/WWLA+JEACUSJw//33y5FHHmly++qrr2SfffaRhQsXRin32mw+//xz+e2336KaJzMjgWgROOCAA+SJJ56IVnbMhwSalcCMGTOatbxoFKa6rEs8Hk808gqaR1FRkey9997ez4ABA2SrrbaSm266Keh+4W585JFH5Pnnnw83OdORQFwSiFY7j1Y+gSD+/fffMmXKFLN59erV8sMPPwiuAY0R3zpffvnlcs899zQmy7jfN5j1JNi2uD/wGDuA0tJSufrqq+W7777z1gxtfsGCBd7f0V7wV2ZDyohWPg0pm/vEPgHoh4E+sVZ7o9Di9bfNJZdeeqkUFBTIvHnzZI899pDbb79dJk6c2OjiX3zxRXnnnXcanQ8zIIFYJhCtdh6tfJqTlW+dYZ194403mrMKLIsE/BIoKyuT++67T37++We/25tiZbTKjFY+TXGMzDM2CIRyP9hpp51ioqLG5SAxMbHZKpOcnCzt2rWTrbfe2jzRouA///zTlL948WI55phjpFOnTrLNNtvIc889V6de48aNE4DNyckx6ZYtW2a277DDDjJ37lyZPHmyxArYOhXnDxKIAoFA7fypp54y/QL95pRTTpG8vDxTWn5+vlx22WXSr18/2XXXXb3WzED5+Kvi2rVr5aSTTjJ9cs8996zTJ0P1V3/5YV1lZaWpV58+fQSfW2+9VexD9fTp02XkyJHSoUMHOfbYY+Xbb7812fir8xFHHGHyseUEuj78/vvvssUWW8hdd90l22+/vSnz5ptvtrvF9QbHV0sAAEAASURBVHcgy0mo9XF90I2s/KJFi+Twww+Xjh07yvDhw+u0aYwcjhkzRvbff3/T5s8880xZv369t0R/bWz27Nne+w5cbc4//3xvevSR/fbbTzp37iznnHOObNiwwbstUL8dNGiQSTt48GA5++yzvemdC4HKDNa3YDg6+OCDpWvXruY6gb4WKB9nWVwmgVDuB2hLMSEpKSmeFStW6PWvaUVvsvBr8Fx55ZWmIO14nhtvvNGs05uWp7Cw0KMXE/NR1wHPCSecYLYpSJP+mWee8egQmueCCy7wqF+Sp0ePHh5Vfs22b775xtO/f3/PLrvs4kFeFBJobgJ642ryIv2181dffdX0k2uuucbz7LPPevQm7dFRD1OXW265xfxWH1bPQw89ZNJ9+eWXHn/5BKr8gQce6OnSpYvn8ccf91x77bUmj19++SVkfx09erRHlVKTra2j7ct6kzbbnnzySY+6HJk89WHUg2uE3vg9+lDqwT5HHXWUqb8Oifqt85Zbbum9BgS7PqjrgyljyJAhnjfffNNz8sknm9/6EBzosLm+BQg0Rx/CYaEdqNLoQV+46KKLPGrQ8eTm5poj1rjsnvbt23vUmOK58847TTt5+umnzbZAbUxHHD3vvfeeSasKsOePP/7w5oU+MH78eM8dd9xhtiNfiO0T/vptWlqaqRP60LvvvmvS+/4LVGagvrVx40ZzXKpse77//ntzr1Rl3hMoH9/y+LvtEYC+hg/EuexLItg237RN/Vug0K5cubKpyzE3Kxw4OjgUV1w08Hvs2LGeqqoqj07wML9xYYCoxcbc3C6++GLzW90TPGpdMcv4hxssLj627mrB8Rx00EHe7VwggeYk0Fw3Y992vu+++3p01MKzbt0688FNcLfddjOHPmrUKNOHvv76a9PHcKPVUQ2zzTcff6yQFn0UN10r6idobrKh+msghba8vNyDaw76ta3zwIEDPVdddZXntddeM+Uhb4j63nrOO+88z8yZM81v3zo7Fdpg1wer0E6YMMHko36Nphw8GLcWsTeVUN+xfLzN1YeWLl1q7kdr1qzxfPrpp6YtqH+2QYN7io5qeDH16tXLg34ECdbG1PJq8tFRAO++vnn17NnTGGqQIFi/hUILxTiU+JYZrG/pKKipHwxCy5cv96xatcozbdo0j07W9vjmE6pcbm8bBOy1BEfrXPY9+mDbfNM29W+XVqZZBS4D2qlk6NChkpqaKldccYXA5UFvWqYeu+++u/nGhAYMk37xxRfmN7bbbViB/VUR9g5JmkT8RwJtjIAqqYLhHrgb4IOJkb/++quZgKVWWdl5553NMGP37t3lpZdeMsOs4SLSG79JqiMf3l0wCeu4444L2V+9O/gszJkzR/TGK/pA6q0z3IUwmca3PLUMi1rHBEOvoSSc6wOuKZDMzEzzDd9BStsj8Nlnn8lee+0l+oDknZRcUVHhBWHbCVagrdh2Ek4b82ayacGZF1ztSkpKzJZg/RYJ0tPTN+UQ/lewvgU3BkRc+OCDD0SVdNOH4ZrUHBGOwj8CpiSBxhEwCm2tAt64jMLdGzdHzE7GjQqhvHRY1OyKGy4EndIKLiDwpYVgu+82rLfbsUwhgbZGAEofHvzgL2s/uFFlZGSIuvGIDm2KjmLI9ddfL48++qjcfffdYSOyffKvv/7y7jN16lTjd2e3+fbJUP0R9YXgQdbWF99QaG2etjwoGQj5pS5R3vIDLbT16wOu4eF8AvFrK+vRN+DjOmLECNMv1EIb9qFHs40F67dhV8gnYbC+paMholZhwbwThM+Ez/qhhx4qiHBAIYHWQsDlfIJszoPCJA0dlhTMXIaF5l//+peZCIIJIgh3csMNN4gOjZiJIagXJruo7548+OCDor5PAud8HXI0T9nYrsM5Amd/PPlSSKC1EvBt5yeeeKKJxfrYY4+J+gEaKwz6Dvq1+oqKuuGYSWKY0KVD/cYqCja++fjjhQlbUJahFCPm6+uvv24UAfTPUP3VX35YB6UAk75eeeUV+eijj0xsWnU/MJFOMGEFFjFM3sJs8euuu87UX/3/THbB6hzq+hCoPlzftgioG4CZlIyHPEx0sQ94GDUIJcHaGKyvWVlZ5v6D+1AoCdZvQ+1rt/uWGaxv/fjjjwIrLQxJCJepbj5mcjWuE7752Pz5TQJxR0CH/VtkUhh8KeCjh/Lt5C44q6uia/w19GnTo8otkhnBxBCdJerRKAnGYV7dDzzwhbMC31v458I/j0ICzU2gufz/fNs5/Obgj4p2rxcfD/xK7cRI+M1hgpXetIzPut5EPTp8atD45hOIl4bXMz65yBu+fSgLEzohwfprIB9a7Id+r8qrR4c7TV/W4V/PkiVLsMn4NKriao5FXSg8mCBqxbfOTh/aYNcH60Orw60mK/gP4nj04dhm3Wq+cVz+PvFwgM3VhzAxC76x2dnZHh0tNO0aEwUh8HvFOivw79ZoGuZnsDaGBPA1x/0JbRsSLK9g/Rb9DJPVwhHfMgP1LcxJ0Qddjyrd5nqA64L1KUc5vvmEUzbTtB0C9pri74iDbfOXvinXJaDz6IVE7HCFVq7FBRYZnTRmrEy+lUFYEvg04anSVzBEiSdtf9t80/I3CUSTAKyjffv2jWaWAfPy187hvoP4zvpQV28/uB7AJ883PJ/NB64IsOr6yttvvy3HH3+8WY289VohSUlJvskkWH+tl9ixAsOdGPqEe4SvIFQSwir5iq1zoD4e7Prgm1dr/B1oxE1vIjF/uM3Zh8AD9wrM44hUgrUx9DWMhCA8ZTgSrN/a/dHnMF/EKdtuu61Y1xx/ZQbqWzhu9GVV5p3ZmWV/+dRLxBVtkoC9rvi7jgTb1tywEvBEiSESDYPV3GWzPBJoNQSa82YcbWjwUYVvna9gaBIxnykk0BwE4rkPNSUfTPL0VSTwcBnOZMmmrBfzbjsErNIa7Ih922iwtE21zY2KhlPZpqoA8yUBEmhZAniY5QNty54Dlk4CgQggog+FBFqSgMZuNj7ngeqA7bEgboTtYOiOWDgVrAMJkAAJkAAJkAAJxBaBmHkTWAgsLpiJ4cdDIQESIAESIAESIAESIIF4JGAU2nisOOtMAiRAAiRAAiRAAiRAAiBgXqzgO/uZaEiABEiABEiABEiABEggXgi02IsV4gUQ60kCJEACJEACJEACJBDbBFyIA4kPhQRIgARIgARIgARIgATikYCx0DJsVzyeOtaZBEiABEiABEiABEgABFz0n2VDIAESIAESIAESIAESiGcCLrxSjy4H8XwKWXcSIAESIAESIAESaNsEjIWWL1Zo242AR08CJEACJEACJEAC8UzATArjixXi+RSy7iRAAiRAAiRAAiTQtgkYCy0nhbXtRsCjJwESIAESIAESIIF4JpCQlpbmWbBggVRWVsbzcbDuJEACJEACJEACJEACbZSA2+PxCCy0ffv2baMIeNgk0HgCubm57EONx8gc2jAB9qE2fPJ56CQQBQLGhzYK+TALEiABEiABEiABEiABEmgRAi632y2MctAi7FkoCZAACZAACZAACZBAFAi4EOGAUQ6iQJJZkAAJkAAJkAAJkAAJtAgBvimsRbCzUBIgARIgARIgARIggWgRMBZahu2KFk7mQwIkQAIkQAIkQAIk0NwEzKQw+tA2N3aWRwIkQAIkQAIkQAIkEC0Cqsu6BKG7KCRAAiRAAiRAAiRAAiQQjwSMQltTUxOPdWedSYAESIAESIAESIAESECMy0FiYiJRkAAJkAAJkAAJkAAJkEBcEnCh1rTQxuW5Y6VJgARIgARIgARIgASUgFFoGeWAbYEESIAESIAESIAESCBeCRiFNl4rz3qTAAmQAAmQAAmQAAmQgFFoGeWADYEESIAESIAESIAESCBeCbhjxd1g+MnzDMOpbwyICktEIvt7UZkUFNfItlukSLuM2olvM+eVqs+w/yL69kiWDlm16dZuqJLlaypl+61TxZ2Y4N0hv6haFi2rML+xto9jn5KyGpm3uNyb1ndhuy1TJSlpc152e2WVR2bPL5OyctQ1VdpvqoPdju91G6tk7qJycywd27u9m3AsOKYuHd3So3OSdz0WwtlWZwf9kaiPOIMHpHlXB6pbKI7Yb4XyG6THnOw4ZjCaMbdUumtd+/VM9paDhb/1+KprPGYfu6G62iOzlE1XPT7sQwlMINp9CCWF0w9SUxJk6z4pkujoJ9gX57NU2zRafK9uSeJst9juFNtWsQ5tcCvNLy3V/wAS2sTsBWXi0ozRvnzLRR6B2m2ptr+/tY/29qnPn5pfupbXv1eyabdrtP937uCWnl3rtjm7P64JuDY4rwco10p2u0Tpr+07VJtG3dFP/EmK9ptt9fgozUegKfrQirWVsnBphekjXTvVXrsXLi039yZ/R5aTnSh9utdeG9F/0Ib66X3GeV9wtrto3Ydwz/xH64X2uGXvlHptH3UNdf1O0MoM6JdS77BwDMG24Th9ZZv+eg1Iqb0GBOrPoTh20j6MezJ4gquVYNcQHD/6v+/9Gsdgrk196x+fzZffLUtgs2bUsvWIaukffJMvL7y/XjbkV5l8XdqTrj6nqxw2Mksuu2uZlFf4j7t70wXd5JARWWafh19ZI9//UiR3Xd5D9tkt01u/mX+XytUPrvD+RieFEnrioR2kR5ckufC2pd5tvgsTHtvCKGbO9V//VCh3P7ta61QjqsuZDnz9+V1lv93bmWSVlR75z4PL5ZeZJeaCgIvO8J0y5J4xPYyijQsMyjz58A5y8cmdnVmbi0+obXV20B+Z6S75cvxWZnWwuoXiuC6vSp58c52890h/wwUZPvP2Onn9441GacVx9FJl4bEbenuZ3PXsKpmfWy6PXtdLdt0+3dShuLT2+E4/KkfOO6GTWcd/zUcg3H4A5XNPbZc3nN/N+wBjz6etLW4ql57a2bRfu85+23Zsf0PRg7J684XdvO0H26ZMK5Zbn1gpaBcQtNfbLukuewzJML/xL1i7zV1RYfrLNXo9OHLfbO8+1zy0QgbqDfT+//SUd7/Ik7c/2yi4ob54Z19vGix8/H2BgEl2ZqJ89uyW4ns9sIlH7JIp947tIZZBoDadofVHP/EnuJ6g/1Dik8AsNTTc9/waoyQmuRPMQ9axB7SXMWd0kSf0nP80vdjvgR2+T7Zcd25Xs+2THwrkoZfWyHEH1e5nd/Btd429Dy1eXiGX3b3MPMBqaHqNTS9y0mF17ynhXL/Rb1+4o4+tpvcb/SDYNlz3feXlu/vK1qo8BuvPoTgetV+26e/gCa6QUNcQ2//PPKajnDOqo7dadzy9StLTXPLMLb2967gQWwSMhbY1he3aWFAtD7ywWnbXG9wtF/VV5UnkvvGr5a5nVskwXffp01uK9lVj8Txx7GI545gcOfWIHHNWYBGB4On3R71xwpr68Q/5dRRak0D/3XFZ7U0U1tr7tbzbtbFDYf36hc3K4D3PrZZHrukl2w+otbLYp02bx5x/ysx+w3fMkAtO7CQpyS69AK6WJ99YJ/sMbSe4sNyjdZ81r0wevqan7DwoXX6bXSLXj1uhx7hGcFNurIw6pL2cd/xmRbGWgEiouoXi+OanG+tUDRfmlz/cIBep0o2LM6wW16oSceV9y+V5vQA6rbhg8Np9/eqsq5MZfzQLgXD7ARRPKHrvfZkn1z2yQu4b29O0XVQS1ponb+ot0+eUGiUR2x+6urYt+zsItMezju1oHibf0jZ06Z3L5JlbexvrLqwxNz+2UvbQ/nLV6C7mpos+dsO4laYNYYQlVLv1V2agdbDIwGIFa5WVTybl28U63/Z6YFcmwnzskEBt+oR/dZBjD2xvUkJx+XxKgfeBEkoKJX4J4NpdUFQj/x3X37RftOen9aF+p0FpxlCC0SjIadfkSrdOSXLflT3Mb+eI4Cd6/8F96MuphXLJKZ3rjfDZdteY+1BRSY2M1etwlo5iwqgzUA00r0/cIK/o9RoKYe9uyRLJ9dscRIT/7HXCuVuqWmdD9WcYnIJx9FWUw7mG2Dq8pgwOHN6u3kii3c7v2CPgqq6ulqqqWktm7FUv8hoVFlcbSyeGaOBm0F6H/i44qZNcdFJnwXAhLEkYWrTKZbK79jfW4QkS8uWUQnOzxI315xnFsl6tjb6Sqson9tluq1Q5bO9sgSV1pQ5VYB0+Kcm1eaXocKxd53uD+vrnQrMfrFawXmFYHWXCajNDLcGwJH/xY4FRxHffIUPwlD9Mb+ZDt88wN74qHXptrCBPWz9822HeUHULh6Ozbh99l2/cOU7Wp36wwXDs0ftnG4UBFy0rOF8r11XJSxPW21X8biEC4fYD3IzPPq6jjDq4vUz9o1iHSDefT5cqdmhXGFWA9RN9AFb6QIL2COsnbqRXje5qHnw+1/4I+Upv6hiaRFlIg7aC/gLr7rf/q00Tqt0GKtd3PVyPcFyf6oOYlQVLys3wJRR4X7HXA9uXbP9HumBt2tn/rCJj87DXKN+y+Ds+CECZhdsKXKXwwI5RPIyioW2hfdjzjGcfGC/sb/twj/aGh6rzRnVSxbhaJk8rqnfgtt015j6Ee81KNTCcqNfmXbZLlwy1Qp52VEej2E75vdaKHO71u14Fw1xhrxOWAb7BJVR/DoejswrhXENs+iy9xtyrxhVYqynxQUBH4xP0JlOrfMVHlYPXEorhbjuky9ufb5TrHl4h0/4qMU+YGJL39YcLlBOsMLtrHlC44K7w2eTNNzXffTD0iZs4LkLw+4tEcLHCxc7pG4oLE4Zadto2TXBBg2+hHX63ee+o2ypUgYZfVmMFZXz0bb73Y/35QtUtknJxDPNzy8zF0tnUdtq21q1gzsLNClA35XHiv9rLaxM3yiIdBqO0HIFI+gFqiYctyKz5pebb9x/6CEYZ4C8ezk1iBx3ZwM0VQ7cQ+JDDDxcPQ1a20iFJuB3MXVg7ZBmtdov+ddjeWebBEf52kE8nFRg3GViwfOV/M4u9fQj9yenWxDbtS6tt/D5OH/Dm6sPd2TcuMYYJXPtwH0IfCEfQ3qBUHa/XQ1gwYSUNJI25D83ddP3dRS3HVuAXP/72PsbtIJLrt90/0u88HVl13ocwRwMSrf5s6xPONcSmvfy0zma+x0Q1xlDig4Bb332rT4e1jtfNVWXreO+vPH/bIp0ohiHPd1Shff+rfLn4jmVmogesPUO22dxh/ZWNdQvUlwdO5KccnmOsQFCOcWGxbgl2Pzy5wYKCJ1u8aO26c7t5rbI2TahvTPTCE3wgWbWudrIIfOmcAt9TyFrd33ebM104y3+qcrF05eZJKR21PsgzVN3CydumwdB1WblHevoch33AwHE4ZfT/dVKLW5Hcq8PJ96gfIqU+AX/9xKbyty3SPhRuP7Bl4tsqmivW1D2fzjSYCAjfQdyAoYgGE4yYYLgT7imQ1esr67V3WHHwQIjJa5BotdsyHR05VH3u4Ys/VesLt6Av1B3geHURWLaq/oMW/Pzgm2dl5NBMvR5snoTCNm3JxM63v35ia+dvW6R9CH7/uObBH/vWJ1bJo6+uVZ/MTsZQYssJ9I3RN7if7KvzN2C5P2h4ljz51lrTvjHRyUq07kPIL9C9KNLrt61bJN8YBYVLmpWD92wnO+jk5Gj1Z5tvONcQmxajSvvv0c74O++lPvGU2CfgRsguuB20JoElCAroyYflyOTfi4zf0ph7lss7D/cLOtMaDD7e9BS8bHWFwA8UFxM4zGMWNKynVmCNwg383c/zJF1vzPC1iVRwI4Z1EtYqp+USrlWwmcMnEIIn6N0Gb36qt5EU7HaTqIH/jlIrtO9kMmQVqm7O+oYqGkNssDTMcQxFY595i2sts77HAevAf87qImPuXS58Og5Ft2m2h9sPnKVj6BKCiRyBBJNZMNM7lDKL/aH0YgTB9i20E0yOxM3eDs/DkrpYJ3rtt1tt/wvVbgPVC+udbRpW2c45bjOqgAdaCG7sh+qk0cdeX2t+O/9df143v5PdbBq2aUuibX0fMKyd4AO3qvH/XW/mSEAh3WvnzZMY/RHBqB+slmjfuA+t1wnOsJRitPDfR+Z4d4nGfcgaRTAi5hxltKMokV6/vZWLYGFLHd30N5ksVH929tlwigvnGuLMB1bak65cLONeXeNczeUYJWAU2uaum78nXftE7G9bJPVDZIJXPtog15zd1QzT7K2WElwUbn58pVFKR+4a+EkLN8ov1WcV/nlfbPLbQ9m4eX78fX4dhXakPrHhCQ7+cbc+uUqd9gvk4D1rIySEW18oyBiqhMKMcEEQ1B8TZ26+qJscOCxLcCOE28Rp+rRv5Q+dYAOFAE//uOk3hYSqW6THOlBDp836u6yOMjJdw3dBBvbf/KBgjwWz1nEjgIWMUp+Av34SrT4UST9w1mzSb7U+foM3TYJ0bsMyrDB/6Y0dVo9wBJZc1MWGkUM0kR9+LTLKgV2H/OC/jogEkFDtdudNbi7OySKoFyKidGxfV8nAc/7h6h+P2c35hdXqhpRhlNwqDUnXEGGbbgi1ptunKfsQRu6u18mKcFtDNA2EX7sWkTUuWmgm9oZSaOFeAP9QGFLwgeCaj/VOhTZa9yHkj3CKVqFFnzjmkkVmlAKTjyO9fiO/aEio/hzpfSica4iz3nBxulDn32BSJ87HgH7171XO9FxuWQJm7KI1RTkYoDc2zEx++OU1xv8HFwE4tMOrwtcS6IsekQ3y9MaFcDsIu2PlJp1Z/Y1O4Lr8tC52lff7QFViMRv0uXfWm1BbmOQRrsBHb8LXeXKt+vqeeWytwvrKBxtMvDxEZECdz9UhKliEnn13nZkM9uvsYvljTolccXqXOhYl+L5iApsVKOW91Z8YEsk2+AzDzSJU3Ww54X6P1sk7l+iMdbBE6JqV6k6BWb8ITxbIogfeOCanP2K45TFdwwlE0g/gIwjLJR748KA1+v86emNoogbFOoMaiulvf5aYCVZdO2poO52kGUjQVqEYo7/h4Q5KJMLtQaAYTNRy7tVQSOdvCuGGWePwm4d7ACRUu8VIAXxgMTkEE0cRR/r9r/LMKIkzPJ/JTP9h3QMvJggevjCjOpCAg9NzCw+6/nxt2aYDEWxd62FZLFFjw/NqlYVbTP9eKcaFAEe5xSbjRaAjRqQeuLlgkiUmDFuZ8HW+iagDH3Rfacx9CA+HQwamyXPvrZcirfMAHWFBn6jRocJ/bQpjGe71G/3deR+CBRV9GBLJNqTffuu0kP0Z6SKRcK4hvvlhgios49aX33c7f8cOgZh5sUK0kODlAogTd7+GtUI4nwRVCjHBCrEqQym0CJGCyAjOmJaoF4Y84SMH61Baal2FFRerczTsFSagfaAXHIQdCldw0Xvwqp4y7rW1cufTq034ETxB3nRhdzNEj3wwMxYWZjyZvzRhg/EpRIgvhL5yCm7++FjBbNW7r6i9AUeyDX6Lk1/d2rgchKqbLSucb1w077ish4lecLnGO8zSGz6s51ee2bWOYu7MC4GwEebrXg1/Q2k+ApH0g/F6E8SMZJxf+KnDIuWUpepvipBA6HtQDmFdQvSAQIK2ipnVsLgiDiZiQdqXkSCwPNrkI6+slRsfXWHazY4D001cTyiqkHD61F2Xd9c2tcY89MK9B+0MIzr2xuusG9yX4L+IKAp7BhkmBgenDNU4yuM0nrKvsE37Emm9v5+6ube5dj2s7RUvzdlGR6LO1XvFEY74x/6OHr6zcHk5UEeonLLfHpmmzSKUJGI+O6Ux9yEYYe7VuOYIgfeajm5i1A9uQbdc3N0ouign3Os3+jtcxaygXlNeH2B+RrINOzyt93H40UbzPhTONcTW3X5DKcf14fRrc+0qfscogYTk5GTP4sWLpXv37i1axWgNlzoPAkORuGFhqCDWpfaNSgnGxSBQXXGhwazv5pZw6hZJnRBmCRPqIvV/iqSM5k6bm5srffv2be5i65TXFH2oTgEx9AN9G2KVXX9VC9Vu8fYh9ClYUyktT6C19iHcg2CtDcdvvKXPAhRpXJ/tmzX91aelrt+h+rO/ugZbF841JNj+3BZ7BIyFtjWF7XIiDnazc6aLheVwYk62hDILNuHULRKGsOhRSKAxBMLp26HaLSxTVGYbcxa4bzgEYKWMB2UWx4IRumDKLNK01PU7VH9G3SKRcK4hkeTHtC1PwB0r/rP+HPRbHg9rQALxQ4B9KH7OFWsamwTYh2LzvLBWJBAOARfeElaDeCAUEiABEiABEiABEiABEohDAi5YaJv7xQpxyIlVJgESIAESIAESIAESiFECLlhnW9uLFWKUNatFAiRAAiRAAiRAAiTQBASMhba1TgprAl7MkgRIgARIgARIgARIIMYIuPDqWwoJkAAJkAAJkAAJkAAJxCsBo9DSQhuvp4/1JgESIAESIAESIAESMD60xEACJEACJEACJEACJEAC8UrA5Xa7GeUgXs8e600CJEACJEACJEACJCAuRDhglAO2BBIgARIgARIgARIggXglYKIcxGvlWW8SIAESIAESIAESIAESMBZaTgpjQyABEiABEiABEiABEohXAmZSGN8UFq+nj/UmARIgARIgARIgARJwQ5llLFo2BBIgARIggZYmkJub29JVYPkkQAJxSsAotHj9LYUESIAESIAEWpJA3759W7J4lk0CJBDHBIzLQWJiYhwfAqtOAiRAAiRAAiRAAiTQlgm4cPC00LblJsBjJwESIAESIAESIIH4JmAUWkY5iO+TyNqTAAmQAAmQAAmQQFsmYBTatgyAx04CJEACJEACJEACJBDfBIxCyygH8X0SWXsSIAESIAESIAESaMsEXHQ3aMunn8dOAiRAAiRAAiRAAvFPgC4H8X8OeQQkQAIkQAIkQAIk0KYJGAstw3a16TbAgycBEiABEiABEiCBuCbgqq6ulqqqqrg+CFaeBEiABEiABEiABEig7RIwFlr60bbdBsAjJwESIAESIAESIIF4J+BSEXwoJEACJEACJEACJEACJBCPBNwI2QW3g7ySeKw+60wCsUOAfSh2zgVrQgIkQAIk0LYIuBiDtm2dcB4tCZAACZAACZAACbQ2Am4cEKMctLbTyuMhARIgARKIlMDwk+eZXaa+MSDSXQOmLy2vkb8XlUu/HsnSPivRmy6/qFoWLaswv1NTEmTrPil6L07wbscC9sP+WNurW5J0bG9u2XXS2B81NSIz55Xan97vtBSXbNM/RRYuLZeyCo8M2jJVnGV7E+pCdrtE6d8z2ZSboIUO6Jfi3GyWI6lTvZ25ggSakICbE8KakC6zJgESIAESaNMEPvmhQB56aY0cd1B7GXNGFy+LmX+XytUPrvD+Tkt1yZ47ZcgN53eT5KRaxfauZ1fJ/Nxyb5o+3ZPl0lM7y3BN5yslZTVy4W1LfVfL1n1T5OW7+8rjb6xTBbpcJjy2hfiWbXcasUum3Du2h6BcKNcv3NHHbvJ+R1In705cIIFmIOCu0cc6fCgkQAIkQAIkQALRJfDJD/mSpArql1ML5ZJTOptlZwl3XNbdWE0//r5A3vsyT657ZIXcN7anTtauTQUr6ZM39Zbpc0rl7c82mu0PXd1Tdh6U7szGuzzqkPZy3vGdvL81lJF32XcBZe8xZLNynOgKnNa5b6R1cu7LZRJoKgIM29VUZJkvCZAACZBAmyawYEm5Gb4/b1QnKVAXg8nTiurxSE12SbdOSXL2cR1l1MHtZeofxbpPmTedS5XMdLXewip7/396CvTT1z/e6N3uu5Dkrk2PffCBO0MgQdk2Hb5TkgOndeYRaZ2c+3KZBJqKgIv+s02FlvmSAAmQAAm0ZQKfTiqQrMxEOf5f7Y0/KtwPgsmwHWutpbPm1/eFxX5wRYBldvb8MtEARX5lQ361UaLh64oPXBECyf9mFstH3+Z7P+XqYxuphFOnSPNkehJoCAE33hJGl4OGoOM+JEACJEAC8UrATgDzV39/2yKdKFZV7ZHPpxTIvrtlilv9UQ8aniVPvrVW1m2skk4d/E/uwoQsyIo1gd/e2U/T/DS9WIpLayQzvX4M+c8nFwg+Vh69rpfsur1/94SvfyqUKdOKbVIZOTRTrbSbJ655N4RYCFWnELtzMwlEhYAbFlq+WCEqLJkJCZAACZAACRgCcB3IK6iWikqPvPnpRlmfD+ORyGeqbP77yBy/lGboRDEIJnIFEkzo6trJ7VeZxT7HHthezv6/jt7dMzPqK7124/XndfM7wcxuD/c7VJ3CzYfpSKAxBMykMLxYgUICJEACJEACbYWAP4urtcz62xYpF7gXwCf1zwVl5oP9YVHF+kAK7aTfan1sBw9I9Vvc+rwq+eufMtl/j3Z+t2MlfGad4cECJozShnDqFKWimA0JBCVgLLQM3RWUETeSAAmQAAmQQNgENqpldqq6BWCSF8JsWZnwdb7c/8Jq4wNr183VCWCIC/vx9/nyh0YyGK3WVYTnslJcUmNcDH77s0Q+VWW4a8ckufCkzVEMbLqGfKNsG00B+7fXOLQDt6hVplHuzzM2uyNgMtruO9T6+DZlnRpyHNyHBEDAvPqWKEiABEiABEiABKJDAL6z1epDe+CwupbU/fbIlIdfXiMfaygvxJyFjH9vvYk0MHhAmlw1uqscvX92nUosXVUhY+9bLn31xQz7qD8urLuIihANQdlOGaq+tuPU5xaCcsfcu9y7GRG9prxe+8KJpqyTt0AukECEBBJSU1M9CxculLTs7hHuyuQkQAKWQP7aXMnu3Nf+5DcJkECEBNCH+vZt2T4UTZeDCA+fyUmABBpJwMUIB40kyN1JgARIgARIgARIgARalIBbhVEOWvQUsHASIAESIIFYIBCNyWCxcBysAwm0RQIuRDhglIO2eOp5zCRAAiRAAiRAAiTQOgjwTWGt4zzyKEiABEiABEiABEigzRIwFtqWDtv1ycQPZOSwnWTIoP6CZQoJkEBkBNiHIuPF1CRAAiRAAq2LgJkU1tJvCrv2qstl1szpsiR3sdx7162ti3AjjuaaKy+TTz/+0OTgXG5ElnV2Xb5sqfz156w66+Llx+GH7GMegvLz8+Klyk1az9bYh9atXSs4z8XFtcHmmxRgG8ycfagNnnQeMgm0YgKqy7rE4/HEzCE2R13eeuMVmfzDd40+5nvuvEWWLsltdD6BMpg54w9ZuaI2DqBzOVD6UOt//mmKvPry895kE/77jjzx2EPe3/G0kJ+XZx6CjjhkX6FSW/fMNUcfqlti9H45+1RFZbn8OPkHqaoK/F77xpbs2ycaml+08mlo+Q3Zj32oIdS4DwmQQKwSMAptS4fuuvu+R2T7wUOkd5++cs31tzQ5q48m/Fd++d/URpfz5OMPy/LlyxqdT3NlMO23X+X9d9/yFnfxZWPliadf9P6Op4WPv/jetBlY9qnUijR3H2qqttLcfcq3TzT0uKKVT0PLb8h+7EMNocZ9SIAEYpWAcTlITExs0foddsTRMvnn6TJzzmLBclPKiD2GyPfffS2Pj3tQzvz3CaYoRHm48borZftt+hg/3mefftxbhdzFi+T8s0+TAf27yXFH/0u++epz+XvuX7LjdltIcVGRnHHqKHni0eBWzsWLFsrJo46Srft1kVNPPEamTpnkzR+WnSMP3V/69eggh/x/e+cBJkW1tOFiXXLOOYPkJBIkSlaQIOESRETilSQIkpPkICIiOUeRIAgS5KLAlSQgOcefKDlLVv75ittNz9AzDOzO7szuV88zO90nVr89vV1dp87pcsVl184/zDxPG7NnThUcC9r87NNP5P79+1r8wYMH0qFtSz2WSmWLCcpBkPbl0AHy+5aNqjuOeezokdKlY1vNx58Z0yZJ6WJvSOa0SeSjD+rIlcuXNO/SxQtaZ/rUiVIgd2Yp8kYOwXZ4Svz4CWTO90skXrz49NQ6TkRYXkM47zu2b9XfK34rDepUky2bN5o/h6rvltHf1tvFC0r2zKmke5cOjpVUnnpZ8bvr0fUzvc5yv55OvujVVR4/euTxmpo9Y6r+/lx/d+6uW+P32q9PN8mUJrFgdMNO7K4JlHN3bT18+FB6duuo10DZkoVk/JhR2qy7duz69Kc0XkP+dDaoCwmQQEgJBKGB8PbQhuWEltVrt0jJ0mWkZat2Mn7yTOXXu8fnsnvXTln+83oZO2G6DOzXU87/b6i/f9/ukjhJEtmx56jUqdtAPmnRWNKlzyibt++T2HHiyIQps6TlJ8+MQtcTAgPzXzUrS4ZMmWTT1r1S/f060qxxfcHN8f9OHpf6tavJu5WrytadBx16lZUaVcrL1atXXJtx2kdcLYZmv/x6nKxas1F27dguy378Qct0aNdSYIT/sHS1DPnyG+nXp7scPLBPho4YLZ927CJvFi6quuMh5saN63Lx4kWtt3TJQhk8oI906d5H/rN+ixogH9avpXmPHcYI2kQ/P/+yUbr36ifdHXHP9+7dc9IrrHfSpc8g8DIZRm3Duu+HtQp+019YXkN4eHq/WkVp8OHHsu/IGSlSrITTg9HZM2dk0cJ5Mnn6d47f4c8y/7vZsmXTU4MXv5utv2+WWd/9IBOnzpZVK3+SoYP7yevZcri9po4eOSSrf9303O/O3XVr/F4vXPhTFv/0H8n6enbb82R3TXi6tubOmiZ/bNsq/3H8Dxk1ZpIMHthHjWW7dmw79MNEXkN+eFKoEgmQwCsRUIM2vFc5CMsJLTFjxpTXgl6TqFGjSvTo0TV+eO6s6dLWMfyeIGFCyZ03n5QtV0lWr1quQDFxCobf/fv3pG79D2Xlf34T8IoZM5bmR48eQ4IdbbmT3bt2qDHYq+9ASZosuRrFn33eXS78eV5+WrpY0qRNJ5+0aS/JkqeQHr37S5y4cWXxwu/dNafpc2dPd+jSULLnyClJkiaVeh80cuj7k64nvGTRAmn3WWeHgZBdCrzxpnw7bopcvXJFokWLJlGDo+qxG7pbO5njYPB+zX9J5feqS6ZMWWTA4C8dXrcNcujgfrNY3wFDVU8Y5TDmt2/dYuaF54bx+30iT8JTjXDtOyyvIVw3h47/KY0aN9NRireKlVTDzngIBIgOHbtKlqyvS85ceaREqTLy3/W/6oPzd4749fafdZG8+QpIsRKlpFXbDjJt8niP15Td7w5xwp6uW+gwaOhIyZf/Dce1GhO7z4ndNeHu2kLls47/Bffu3ZVr165Knrz5HQb4fsfDbYYXXlvPdeyHCbyG/PCkUCUSIIGXIhD8UqXDoHBYT2g5e+a0GqxtPmnqdHTJkiXTfXiZ+vbqIoXyZ5c8jpvwpx0+l8xZsjqV9bSD8ATc1K1GZLOWrbXK/n17pchbxZ2qv1moqJx+wUQzxI1isszsmc/iX1OmTKUT1HDDLeTwwhpS8Z0qxqbHb6x20KDhR2aZDBkzq7GMSW+58uTVdOOmhx0Y8jDyw1OwKoYRP4sYbIQgUMTnkzzxO5gycawsWbzA8ZAU7BjBSKrY/3GEExhi/a3EiBFDvfn4Ld2+dUuKFH32my9c5C0dkbh79y+JFSu2Ud3p29qW8bvzdN1WfKey1rfWc2rQw467awtVOnXpqSMrNatWlPgJEkijj5tJi3+7H53x0I3fZPEa8ptTQUVIgARCSEAN2rA2Il11xoQWDKFjtnpYTAqz9o9wAtz4ZsxZKEVdjEvE6EEmTp0jCB1YtOA7R/hAFdm2+7B6Ma3tuNtOkSKlHD1yWOMEDU8ubiIJEyaS5A6v7LZtzl5OeHTLlKvgrjlNT5w4qXrHOnbu4VQORgHk4P59GlqAbcTBIqQEHmBPgvwDjnrwvkLOnz/rqHtZMmX23nj31H5o5+G3gnhkfMOYRegBYgIjq4TlNYQQFoTi/L7jgODBB0ZqupTxX4g+WfLkeq2hPsJ+IPi942HMnTHrrlFP1y1+u68q7q4ttHfTMVKDcJsvHCMViIP/uFFdiRMnrnzgCL0IROE1FIhnjTqTAAm4IxD0Kl4Md429anpYT2hJlDixDhtCX9xIy1V4R74eMUQwmQTGPSaFIc7P4euSEkXyCYYh4WWCoRkrdmwJfu2pYztRosRy/fpVj4dd9K0SEjNWTJns8GhhYsy+vXukcIHs6pWqWaeeDtv/7IgjhPGM5cQuX7oob5cp77HN6jVr68QVtAXBEmSYpIVjKVu+okyaMEYNPYRKVKn0tvxn9UotZz1uTbD8+Ve9D2TR/O9UP6z7OfLLIZIjZ26HQZvFUsp/Nt9zHBe8aTRmn56TsLyGMNrwj+M6uXv3rnosJ4z7RpUwJia6+5WgHvQc9+3X+juHx3b6lIlSpdqz2Gdvrim07/m6dafB8+mu14S7aws1P23TQj7v0NrxgPi3w8tcTFKlSq3hBshzbQdp/i68hvz9DFE/EiCBlyGgMbQvU8EXZcNyQgv0r/Wv+hp/h6FDCOJMHz16qDOvsZrBGocBmD17TnnNYbhOnfW9eo+RXrlCKenctZfGzaFeo8bNpdlHDTy+DCJuvHgyfdYCGTfma8mSLpm8X7W8jHb0lyFjJo0jxOSST5p/JJnSJpavhg+SmXMXSfoMGdG8W2ndpoNUrFRZypcuLNkypZQundrpDRYVvhkzWUMPsGJD/pyZpHzFd6Reg0baVrnyleSWw5uWMXUihzHy1JtrdNKsRSup+G4VqfB2EcHs830OY3HB4hXqUTPK+NM3hnxpzD47I2F5DeG3+1mnbgKDCKsIwDBFnOqJ40efKeRma+Q34+Wh41rLnyuzvhgDS/UNdsS6GuLNNWWUdXfdGvnefLteE56uraGOSZaHDh3Q6zhvjgySzfE/ombtetqNazve9B3eZXgNhfcZYP8kQAKhSSCK40b05P/+7/8kWpynMaOh2bi3beHmgJsiBJMt/rvZfpkdb9vzphy8SfC0WIc6YeRFiRJkO4kEw3N2Q9oYbsXNPEv6ZOqtsvaNiVnrNv5hJl2/fk1DDcyE/23AK4z2EyRI6JrlcR8e37/u/CXx4j8/3GvoZYQ5GA2hL3huEfJgJ1hCCWwwOY3iPYGbl09J/KTpva8QyiXD4xrCb+XR40dO8eHeHhautWDHJEVMzHIVd79d13LGvqfrFmW+HfWlDOrfxyhufmOVk2o1aumojOs14enawggGRmzwwGuVF11b1rLcfp4ArqH06cPvGnpeI6aQAAkEEoEojhvKk5MnT0qsBKnCTW/rzRheN6xJSyGBQCLgTwYtr6FA+uVQV4MADVqDBL9JgARehYDG0IZ3HG1EecvRq5wA1iGB0CDAayg0KLINEiABEiCBQCWgIQfw0EaPmzxQj4F6k0C4EwhvD224A6ACJBBCAvTQhhAgq5NAJCcQhLgvY3mqSM6Ch08CJEACJEACJEACJBCABNSgDUC9qTIJkAAJkAAJkAAJkAAJKAFdtuu1114jDhIgARIgARIgARIgARIISAJ+8WKFgCRHpUmABEiABEiABEiABPyCQBBei4oPhQRIgARIgARIgARIgAQCkYBfLNsViOCoMwmQAAmQAAmQAAmQgH8QCGL8rH+cCGpBAiRAAiQQeAS+/PJL+fjjj6VBgwbSqFEj3f7ll1+cDmTTpk2O16s/ckobO3astGvXzkybPn261jUTQrCBV5zv2vX8C4p++OEH+fDDD6V58+bSsmVLGTlypL4OPQRdvbBq5cqVZfHixTJp0qTnyv7xxx8CnQy5ceOG7vfu3VvmzJkj9+7dM7L0GxyHDBki48aNk8OHD5t5WK1p2LBhcvz4cTMNGzgP69at07QNGzbIzJkzddv4g7diop5xbubOnWuWN8ocPXrUrHf69GkZMWKEvk3TyMf3tGnTzL49Ha+1DrdDn0DQ48ePGXIQ+lzZIgmQAAmQQCQg0KlTJzVocuXKJU2aNNHtcuXKOR35N99885xxhgJXr14VGEkwyHbs2OFUJyQ7Z8+eldmzZ9s2Ubp0aTUuYVDHd7w23dXIs61kSYTODx48sKR43owTJ44eX1yb16nv2bNHVq1apQ2cOnVKoBsMyIIFCwqMVzwkPHz4UPMnTJgg/fv319cjwxGHh4fff/9d88Bv/Pjx8vnnnzvZMzBiN2/erGW2bdsmffv2lbVr1+o+/sCgRT3DoP3xxx/l008/lYsXL5plTpw4IYsWLdL9c+fOyZgxY/RBwCzg2Jg/f76eR6R5Ol5rHW6HPgH10AYF6WIHod86WyQBEiABEiCBSEpg6tSp0rZtWz367t276/bKlStNGlmzZhUYWgcPHpRUqZ69fh4GKbyUhsDA6tq1q7ErN2/eVM9i48aN1cu7ceNGMw/9wID+888/tT/0f+zYMTPf2IBRmCNHDrl06ZKRJDt37pTPPvtMPbhTpkwROLysgjXr27dvL7NmzbIme9zOmDGjxIwZU1KmTOmxHLypOXPmlC5dukj16tX1GG7fvq064eVP8ITPmDFD6tevLy1atFA+0MUqFy5c0AcKa5p1O0WKFNKjRw+5c+eONdlpO3bs2NKrVy+nNOsODPN58+YJjHE78fZ47eoyLWQEdFIYX6wQMoisTQIkQAIkQAKuBDC8jyFtCAzU4cOHS4UKFcxiefPmVYMWXsQ33njDTIcheeXKFXMf9+jLly+b+zCokiRJIpMnT5bWrVvLxIkTBcP1kD59+mg4QfLkybU/9JkpUyazLjysu3fvlt9++02+//57wRA5BAYwvJVt2rSRr7/+Wr2Uy5cvN+thA0Zw3bp1pWTJkk7pnnaGDh0q7777rnpfPZV7++23Ze/evQKvKgR9rVmzRooUKaJGbfbs2SVZsmRmE8WKFVNjHHobAg/sqFGjTG+pkW58lyhRQvLlyyeDBw82kp77/uSTT/QBY9myZc/lISFBggRq9Hfu3Pk5gx/53h4vylJCl4B6aKNEiRK6rbI1EiABEiABEojkBKJGjareSWCIESOGfoKDg00qGPKHbNmyRfLkyWOmv2gDBi+G2fHJli2bGmjwgkKiR4+uH4y8Gn1aR2ER4gAvMTyiWOEoadKkWg+xpjD40qdPr/WqVq0qW7du1Tzrn/fee0/7tKaFxjaMbhj/AwYMkEqVKgliig1PKsIQrAY/+kuYMKHAG4o8Q3Lnzq0xwt26dVM2Rrr1u1+/fhrmYIQiWPOwDQ/toEGD5IsvvpDr16+7Zus+wh3gqUX4AcV/CPBNYf5zLqgJCZAACZBAJCIAzysMNRhzMES9lYYNG+pkLkzsgkcQIQje1i9QoICGL8Cb+cEHH8jo0aO1W3g6f/75Z40DRiwwhvj/+usvb1UKlXLwFsPYRtgBvLN16tRRoxaGP8IJrAJjHgYnPKZWQQwswijgxbYTGPAIO4DRixhaO4EHumzZsgJGdgInILgjLOPIkSN2RZgWDgSC8aOghzYcyLNLEiABEiCBSE+gVKlSgtADq8CL6ykUEJ5XxI/CINu+fbuGFmAYHWEGhuDe/iJBDC3ic9EXPI61a9eWGjVqvKjaK+cjRjdWrFimhxf6G57lFStWaBxx/vz51ZhECEKZMmU0BAF8MIkNeiIUAbJ//3416uGhtkq0aNHU2IRR/tZbb0m6dOms2bpds2ZNQUgBVixwJzB64SlGe3aCh5BWrVoJQg+4lr8dobBP0xjasO+WPZIACZAACZBA5CAAD+P58+dtDxYTlRAfahXEx969e1dXQUC66woImCCGcAAYg0WLFlWjyzo8Dq/ltWvXPK5GAIMXKwxkzpxZjUR4ihGGAG8vBPoa8aya8L8/MAQPHTpkTfJ6G+EOiCXGKgk4Pnhjq1SpovWRh8lYiPGFwCOLbbCDbmnSpDFXZMCKCIiVhWFq55lG+Vq1aqnHWRuz+TNw4EBzhQWbbO0X4QnGCgd2ZbD8GYxZa9iDXTmmhQ0Bx4NgsFjja8KmW/ZCAiRAAiRAAoFPAF4+eAthpGFtVCyDhaF869Jd8HoiPjRx4sQ6kx/r1noSeGDhLYXhiphOGGhWQagBJoItWbJEjT5MnLJ6KrFiAiY/YZ1b1Ifhh/hYyPr163W5K9z34WU01sLFUlkwKjHcD28tlrJq2rSptVs13rBuLJbXcjXCnQq62XnnnXcEKzKUL19eR4bRDob2ITAOsX4uysCLGy9ePF1CC15WCFaMwLJcWM8WrOG9RbytO+nYsaOGLbjzVIMR+Hpa0aBixYpqcMOAthN4i3Feq1Wr5jZm164e03xDIIrj6eYJ1lmLleDZkiG+6YqtkkDEJXDz8imJnzR9xD1AHhkJ+JgAriFMSIqoAiMMHxhq3grKw/DE5DI7waQpGL/WiWbWcsiH0WUM61vz3G3DAEQ9rKdqF46IMAEMw4fEEYZJbWjbCB9w1cXo3zUd++gfPNzVtavDtMhBINj4USSIFTkOmEdJAr4ggEE6XkO+IMs2IwuBpwPdEfdoMTRuNzzu6YhfVB5Gpyd5Ub5dXRia8NC6ExjQIRV3BrjRrie9Q6N/ox9+RywCQQiytnsKi1iHyaMhARIgARIgARIgARKIqAR0UlhIhg4iKhgeFwmQAAmQAAmQAAmQQGAQcNiyQQxmDoxzRS1JgARIgARIgARIgARsCKhByzXUbMgwiQRIgARIgARIgARIICAIaMiBMTEsIDSmkiRAAiRAAiRAAiRAAiRgIRCEbXpoLUS4SQIkQAIkQAIkQAIkEFAE1KDlKgcBdc6oLAmQAAmQAAmQAAmQgIWAGrSWfW6SAAmQAAmQAAmQAAmQQEARUIPW3avhAupIqCwJkAAJkAAJkAAJkECkJBDEcINIed550CRAAiRAAiRAAiQQYQgw5CDCnEoeCAmQAAmQAAmQAAlETgLqoeWyXZHz5POoSYAESIAESIAESCAiEAj6+++/5fHjxxHhWHgMJEACJEACJEACJEACkZCAemgZRxsJzzwPmQRIgARIgARIgAQiCAF99a3jTwQ5HB4GCZAACZAACZAACZBAZCMQhCW7EHZAIQESIAESIAESIAESIIFAJKAGbSAqTp1JgARIgARIgARIgARIAASC8YerHIAChQRIgARIgARejsDhw4fl1KlTWil58uSSL1++l2vApvTNmzdlz549UrJkSZvcV0+6dOmS7Nq1SxuIGjWqlClTxraxR48eCfIN8ZU+Rvv8JoHQIMAXK4QGRbZBAiRAAiQQKQk8fPhQ7t69K3v37pVffvklVBhcvnxZFixYECptWRv5559/VNcLFy7InDlzrFnm9smTJ6Vt27bmPjZ8pY9TJy47zZs3l+3bt0vTpk1dcp7ujho1Si5evKg7OAfr1q2Tfv36yYQJE+TcuXNOdaD/3LlzpW/fvrJ8+XK5d++emb9hwwaZOXOmuY+N+/fvy7BhwwSG/b59+6Rz587SpUsXPcdOBbnjVwSC8APHh0ICJEACJEACJPByBPLkySM1atQIFc/sy/X88qVTpEihupYvX95t5YwZM8rIkSPd5r9qBpYHhYfYW8H8njhx4kjcuHFtq0ybNk2uXLmiy47WrFlT5s+fr+cAaXXq1JETJ05ovatXr5r7OFfLli2T1q1bm3OHtm3bpobu2rVrzX5g0I4fP14NWiRCB+jjThezIjfClUAwluzisl3heg7YOQmQAAmQQAQlAK/tjz/+KPAilipVSurVqyfGykKe8gwcMKTGjRsniRMnlrp162oyQgDgiTxw4IDEixdP04sXL25UeaVveJgnTpyodWPHji1DhgyxbcdOH3g8p0+frh5V6NmkSRPJnj27U/2lS5eq1xnHkiBBAqc8u50kSZJouQwZMthlm2lHjhyR48ePy8KFCyVGjBhSvXp1uXHjhqxcuVIN1wEDBkiJEiWkZ8+eWqdWrVpSqVIl1QXnAgJDv0ePHrJ69Wo1ojXxf3+Cg4MlTZo06vXFsVH8l0Aw42f99+RQMxIgAVEPzNGjRwU3tpgxY2q8IoZ4XSVp0qSCmyDk0KFD6lExysSKFUvSp09v7PKbBMKEwP79+2XRokXSrVs39e6NGDFCVqxYIe+99554yrMqB08kvIwtWrSmq3J0AAAaXUlEQVQwk+fNm6e/9cmTJ6sxN2jQIMmVK5dXhqLZiMtGlixZdFgdw/NjxoxxyX22a6cPjFkYfmPHjlUj+6uvvpLRo0dL9OjRzYqFChXS0WAY4N6IYVC3b9/eY/Fs2bJJokSJZNasWfLRRx9JtGjRZPjw4WadHTt2SK9evcx9bMD437lzpz5cYB8G7+3bt2Xw4MEycOBAJJkCw9zVODczueFXBIIwDMCQA786J1SGBPyewH//+1+9IYeFoosXL5YvvvhCzpw5o9399ttvsmTJEqfP0KFD9QaFAhgu7OuIlZs0aZL5wTAjhQTCmsD69evlnXfekbRp06qxiaFwpEE85Rl64nd+8OBB6dixoxqMRjru2/CU4gODDoYYHvZCIqgPTyQmtrkTO30ePHggGzduVOMQRm3evHklVapUatha2wGD2rVrm95pa15ItuGUg7d68+bNapgi9vXs2bPaJDzH+L9RsGBBpy4KFCggx44dc0pD/O2qVau0HacM7gQMAfXQGsMfAaM1FSUBEghXAr///rvAowOvkC8FNyNM9sCN0pCGDRsam/qNuDx4wOBlgVy/fl2HHuElopBAeBKAt/PNN980VUiZMqVOsEKCpzzkX7t2TYfRX3/9df09I80QXANTp06Vli1bqkFbrVo1NSKNfF98u9MHE7Owln2bNm2cur1165bTvi93cufOrTwQggFvMcIOMNELnlUY6ufPn5eECROaKuBY4sePb+5jAyM8CDvA/xJ4wCmBR0AnhfHFCoF34qgxCYQXAcz4xczfn376STDTGIKYNUwkwbBo7969TS8U8vD/BTcXzFrG8CGGYHHjeJHA+wTPC27enkKjoEfp0qVNDxUMWgxBQtAGhQTCiwCG162GHbaNIXdPedAXD3EIJbhz545eM9ZjQKworiWEBhQrVkyH2I0Z/9ZyobntTh9MlMISXzCwZ8yYYX5wTYaWwNOKURbjesb/FKxAgFAiLEOGMA5Izpw5dXUCLEeGVSLwfwMP3fAgWwUrG8AIdhVMLsOkOISGUAKPQBBOOCeFBd6Jo8YkEF4EMMkCN453331XWrVqpWpgogcmksDAbdCggUyZMsW8keOGg+HAf//732rsYvYxZiK/SDCpAzf9t956y21RxL0hBKFy5cpmGRi0SO/UqZM0atRIjQIY3BQSCGsCb7zxhmD2PIwvCCYdIQ3iKQ/5+O2nS5dODVcYbIi5NaRr166ydetWfYgrWrSoxo3id+9LcacPPJ+pU6fW5bCM/nHt4hq0yunTp9XIfJUQR3hZ8cBshGuAB2LqYXzCyMW1jrh5CIxfLD1mTDzDAzEmjGEkB4YwQpiwxm/VqlWt6pnbiKFF6AEl8AgEG088gac6NSYBEggPAph0gTAlPAwbi6/jhgIPDmZyw2MDDyluGggDwOSLIkWKmHFsiCn89ttvPaqOmw9mRbtO0HCt9PPPP+tSPcZkMOQjVg8GbuHChXXNTUxiwQ3KmNHs2gb3SSAkBDA8DUMVca0wmIwZ/hjJwIsRYFx16NBBrxXEluIhC+Ipz6oP6jRu3FgnWSFWHEPlCDXAigSIacWEMVxfiKV9keBaxGQt3PcRaw5d4dDCA6inPGu7dvpg3VpMCEN4ENqF99N1abDdu3ervhUrVnxuuN/avrttjOrggbl79+4a64tvCOJhwQVMcA7w/wn/d4xJdAjHwAM0VoiAhxxhH4izzZo1q21XOD48MLhOJLMtzES/IhDFMXTxBB4TnGQKCZDAqxHAm4Ii0yx6zCJGDO3777+vwOCFxZAgvCOIRUNIAmYcY5ki3NhxUzFiXLE8EAxahBO4ExiyuOFUqFBBi2B4tV27duoZNia/YDIK4vaw4Dl0cSeI9/3uu+/k66+/dleE6X5AICJfQzB0YWxZZ/0byD3lGWXcfSMcAeEH1hjz/v37C16cYBUYkYgr9bXg+seDLoxKVzGMaOP6dc33dh99uGsDPLB2rTvB6igIU6BETALBr+L+j5goeFQkQAKvSgCxfPA8Ga/qhIfDkMyZM+ukDGP/Rd/wzmLCDD6bNm3S4vD8wouEYUKEOkAw/IgHcVdjFp4mDINiSBKCGxiMCQoJhBcBGHn42ImnPLvy1jQ74y08PYvuDE3oDE+wp3zrcXna9tSGHQ9rWzRmrTQi3rbjwS441JfRiHiYeEQkQAJWAggrMGLk4HnBTQZDjdiGtxavnjTiBvPnz6+zhhELi+E8w0i1tmfdTpYs2XPeVHh74aXFjG8IHsQxGeyDDz6wVtVteG6xPieGJHETXbNmjcc43OcaYAIJkAAJkEDAEQjGcAc+FBIgARLwlgAWJscQPpbVQmwbJl7Mnj1bFzfPlCmTxrj9+eef2hwmv2CWMUIDYPhiKZ2QTkRFGAEEi7W7CuIJMUEEoQ4YgsTamh9++KFrMe6TAAmQAAlEIAJRHC74J3gLDzwnFBIggVcjEJHj/9wRgQcWnlJrXKBdjBq8thjyNyZsINYWvKpUqSKI93OVsmXLavyta/rL7kM3eI05zPiy5MKnfGS8hsKHNHslgYhJQD20IfWWREw0PCoSIAFPBIwVDqxl7IxHrHYwf/58XXkAM7KxpA+W+0LsK9at9JVgJQY7fXzVH9slARIgARIIPwI6KYxvCgu/E8CeSSCiE8iXL596cv/44w+dgYwXL2DdSgoJkAAJkAAJhBaBYBizGBKkkAAJkICvCGCtSHwoJEACJEACJOALAg57Nki9J75onG2SAAmQAAmQAAmQAAmQgK8JBGHihLv18XzdOdsnARIgARIgARIgARIggZASCEIDMGopJEACJEACJEACJEACJBCIBNSg5SoHgXjqqDMJkAAJkAAJkAAJkAAIqEFLFCRAAiRAAiRAAiRAAiQQqATUoOUqB4F6+qg3CZAACZAACZAACZBAEMMN+CMgARIgARIgARIgARIIZAIMOQjks0fdSYAESIAESIAESIAERD20XLaLvwQSIAESIAESIAESIIFAJRD0999/y+PHjwNVf+pNAiRAAiRAAiRAAiQQyQmoh5ZxtJH8V8DDJwESIAESIAESIIEAJhCMV9/iQyEBEiABEiABEggbApcuXZJdu3ZpZ1GjRpUyZcqYHd+8eVP27NkjJUuWNNNCY8NTn9b2Hz16JNDJEF/pY7TPbxIIDQJBWLILYQcUEiABEiABEiCBlydw69Yt0zj1tjbe0Hn37l25cOGCzJkzx6na5cuXZcGCBU5pobHjqU+j/ZMnT0rbtm2NXf32lT5OnbjsNG/eXLZv3y5NmzZ1yXm6O2rUKLl48aLuPHz4UNatWyf9+vWTCRMmyLlz55zqQP+5c+dK3759Zfny5XLv3j0zf8OGDTJz5kxzHxv379+XYcOGCQz7ffv2SefOnaVLly6yd+9ep3Lc8S8CatD6l0rUhgRIgARIgAQCh8DZs2dl9uzZL6VwihQppEaNGlK+fPmXqheSwt70mTFjRhk5cmRIurGti7k68BB7K3C2xYkTR+LGjWtbZdq0aXLlyhWdA1SzZk2ZP3++5MuXT9Pq1KkjJ06c0HpXr14VYz9PnjyybNkyad26tenI27Ztmxq6a9euNfuBQTt+/Hg1aJEIHaCPO13MitwIVwLB6J2rHITrOWDnJEACJEACAUqge/fucu3aNbl9+7bp2fz0008lS5YsekSLFi2SNWvWCLyIhQsXlmbNmr3UPReG1Lhx4yRx4sRSt25dbRMhAPBEHjhwQOLFi6fpxYsXDxFBeB8nTpyobcSOHVuGDBli256dPvB4Tp8+XT2q0LNJkyaSPXt2p/pLly5VrzOOJUGCBE55djtJkiTRchkyZLDLNtOOHDkix48fl4ULF0qMGDGkevXqcuPGDVm5cqUargMGDJASJUpIz549tU6tWrWkUqVKqku9evU0DYZ+jx49ZPXq1WpEm407NoKDgyVNmjTq9cWxUfyXQDAnhPnvyaFmJEACJEAC/k2gT58+aljOmjVLBg0apMpGixZNv3fu3CkbN26UoUOHqhE7ePBg+fXXX6VChQpeHxQ8kfAytmjRwqwzb948gcE3efJkNebQb65cubwyFM1GXDZggGNYHcPzY8aMccl9tmunD4xZGH5jx45VFl999ZWMHj1aokePblYsVKiQIOQBBrg3YhjU7du391g8W7ZskihRIgH/jz76SMB++PDhZp0dO3ZIr169zH1swPjHuTEMWhi8eCDB+Rk4cKBTWRjmrsa5UwHu+A2BIPzA8KGQAAmQAAmQAAm8HAEYbfhgcjU8hPgYE61z586tRlLMmDE1bjNr1qxy5swZrztYsmSJHDx4UDp27KgGo1ERw/fwlOIDgw6GGPoIiaA+PJHJkyd324ydPg8ePFCjHcYhjNq8efNKqlSp1LC1NpQ2bVqpXbu2ycaaF5JtjDDDW71582b1xCL2FSEgEHiOwbtgwYJOXRQoUECOHTvmlIb421WrVmk7ThncCRgC6qGllzZgzhcVJQESIAESCBACMDgNryUMOoQmwMj1RlAWw+ivv/66GsnWOg0bNpSpU6dKy5Yt1aCtVq2aGpHWMqG97U4fTMzCxPI2bdo4dYmJcmElYAoeCMGAtxhhB5joBc8qDPXz589LwoQJTXVwLPHjxzf3sZE0aVINO+jWrZvAA04JPAJBjJ8NvJNGjUmABEiABPyLAIxXV0HsLOI5J02aJL1795ZSpUqJtw4keDsRSnDnzh1BHK5V4AXGUDxCA4oVK6ZD7MaMf2u50Nx2pw8mSmGJLxiUM2bMMD+lS5cOte7hacVkLoMxDGisQBArVixdXWLFihXaV86cOXV1AiyBhlUiYN8gFANhH1bBygZ2DxaYXIZJcSNGjLAW53aAEAjC0AVDDgLkbFFNEiABEiABvyOASU7w+mH43SqIyzQmEiEPE6+8vd8i1jRdunRquMJg279/v9l0165dZevWrep9LFq0qMaNXr9+3cz3xYY7feD5TJ06tS6HZfSLCVk4dqucPn1ajUxvj99aF15WrLywfv16TQYPTBaD8Qkjt1OnTnLo0CHNg/GLpceMiWfwZsPTjRUWYAgvXrxY1/itWrWqtQtzGzG0CD2gBB6BYDzBGPE+gac+NSYBEiABEiCB8CWAmFEsGdWuXTvBCgHw9GGiUbly5TS+FcPxuM/C8MMELwgmJWHiFAwyLBOFlQHgvZ0yZYrTwaDtxo0ba1lMLsNQOUINsCIBYlrRXpEiRTT0wKmizY6nPj3lWZuy0wfr1iK0AmvB4ljg/XRdjmz37t2qb8WKFZ8b7re2724bqxBg7VmsKoFYX3xDEA8LLmACBx0mhYG9MYkO4RhY3gsrRCAMImXKlOrFRTyzneD48MDgOpHMrizT/ItAFEcw+xMseYELjUICJPBqBE6dOiXp06d/tcqsRQIkIBHhGkJ4AJxErhO04K3E8Hhoh/ihP4QfIBzAkP79++vLGox9fMOIRFyprwXeURyjscqDtT/DcHdlYy3jzTb6cNcGeGDtWneCF1ngPFAiJoEojpP75OjRoz4PKI+Y+HhUJPCUQES4GfNckkB4EuA1FJ702TcJBD4Bviks8M8hj4AESIAESIAESIAEIjUBNWi9nXUZqUnx4EmABEiABEiABEiABPySgL5YwS81o1IkQAIkQAIkQAIkQAIk4AWBIASTc5UDL0ixCAmQAAmQAAmQAAmQgF8SCMK6bPhQSIAESIAESIAESIAESCAQCfBNYYF41qgzCZAACZAACZAACZCASUA9tJwUZvLgBgmQAAmQAAmQAAmQQIAR0ElhjKENsLNGdUmABEiABEiABEiABEwCDls2SF+9Z6ZwgwRIgARIgARIgARIgAQCiIAatP/8808AqUxVSYAESIAESIAESIAESOAZAQ05CO33Sz9rnlskQAIkQAIkQAIkQAIk4FsCQWieHlrfQmbrJEACJEACJEACJEACviOgBi1XOfAdYLZMAiRAAiRAAiRAAiTgWwJq0Pq2C7ZOAiRAAiRAAiRAAiRAAr4joAbtkydPfNcDWyYBEiABEiABEiABEiABHxIIYriBD+myaRIgARIgARIgARIgAZ8TYMiBzxGzAxIgARIgARIgARIgAV8SCIaHlst2+RIx2yYBEiABEiABZwKXLl2SXbt2aWLUqFGlTJkyZoGbN2/Knj17pGTJkmZaaGx46tPa/qNHjwQ6GeIrfYz2+U0CoUEg6O+//5bHjx+HRltsgwRIgARIgARIwAsCWC7z7t27cuHCBZkzZ45TjcuXL8uCBQuc0kJjx1OfRvsnT56Utm3bGrv67St9nDpx2WnevLls375dmjZt6pLzdHfUqFFy8eJF3Xn48KGsW7dO+vXrJxMmTJBz58451YH+c+fOlb59+8ry5cvl3r17Zv6GDRtk5syZ5j427t+/L8OGDRMY9vv27ZPOnTtLly5dZO/evU7luONfBDSGlnG0/nVSqA0JkAAJkEDEJpAiRQqpUaOGlC9fPswO1Js+M2bMKCNHjgx1neA4g4fYW8Fk9Thx4kjcuHFtq0ybNk2uXLmiDrmaNWvK/PnzJV++fJpWp04dOXHihNa7evWqGPt58uSRZcuWSevWrQXOPMi2bdvU0F27dq3u4w8M2vHjx6tBi33oAH3c6YIylPAnEOx4963gQyEBEiABEiABEvCewJYtW+T8+fMCg2rRokWSJUsWNaqGDx8uH3/8sSRJkkTT16xZI/AiFi5cWJo1a/ZSYX4wpMaNGyeJEyeWunXrqnIIAYAn8sCBAxIvXjxNL168uPeK25SE93HixImaEzt2bBkyZIhNKVHDzlUfeDynT5+uHlXo2aRJE8mePbtT/aVLl6rXGXUTJEjglGe3A3YolyFDBrtsM+3IkSNy/PhxWbhwocSIEUOqV68uN27ckJUrV6rhOmDAAClRooT07NlT69SqVUsqVaqkutSrV0/TYOj36NFDVq9erUa02bhjIzg4WNKkSaNeXxwbxX8JBOFiMZ5U/FdNakYCJEACJEAC/kUgWbJkcvjwYVVq9+7dOjyNHXgHYfzs3LlTNm7cKEOHDpVvvvlGzpw5I7/++utLHQQ8kfAywhAzZN68eWosT548WY02GKIw4kIiMMYxrA5jFJ5Pd2KnD4xZGH5jx46V+vXry1dffSUPHjxwaqJQoUJ6DDDAvREY1DA027dv77F4tmzZJFGiRDJr1ix9aEBhPFDACwvZsWOHvP3227pt/IHxj3NjCAxeeHcHDx5sJJnfMMzxcAKDmB5aE4tfbqhB65eaUSkSIAESIAES8GMC6dKl0xhYxFpipBOewmvXrkny5MkFoXy5c+dWIylmzJgat5k1a1Y1ar09pCVLlsjBgwelY8eOajAa9TB8D2cUPjDoYIihj5AI6sMTCd3diZ0+MFxhtMPbCaM2b968kipVKvUeW9tJmzat1K5dO9RHhDGpHd7qzZs3qycWsa9nz57VruE5xkNEwYIFrapIgQIF5NixY05piL9dtWqVtuOUwZ2AIaCxBlzlIGDOFxUlARIgARLwEwIw4GLFiqWe2UyZMmk856lTp8xhchic8Fq2atVKv+EVxMQsbwSGMYbR4RXEULpVGjZsKLdu3ZKWLVuq9xchCNGjR7cWCfVtd/pgYhZGedu0aaPeXXh4MbEM+oWV4MFh6tSpGvYA7zLCDvbv3y/RokVTQx9hIVbBscSPH9+aJEmTJtWwg27dumkMrVMmdwKCAF+sEBCniUqSAAmQAAn4IwHEeGKGPYamYdRu2rRJMLEKgthZhAJMmjRJevfuLaVKlVLPrTfHAWN50KBBcufOHY3DtdaBgYuh+DFjxkixYsV0iN2Y8W8tF5rb7vSBwY0lvmBQzpgxw/yULl061LqHpxWTufCAAIEBDa84Hiaw9NmKFSs0PWfOnLo6AZZAwyoRcNblypVLPcha4H9/sLIBjGBXQSw0zt2IESNcs7gfAASC8LTo7RNjABwPVSQBEiABEiCBMCMAAwjxszBoc+TIoTGbMGwht2/f1lhabGNoHhOvvL3fItYUIQ0wXGGwweNoSNeuXWXr1q3qfSxatKh6Iq9fv25k++TbnT4JEyaU1KlT63JYRseYkIVjt8rp06fVyPT2+K11EQ6BlRfWr1+vyeCBBwmwh5HbqVMnOXTokObB+IWH2Jh4Bm82PN1YYQGG8OLFi3WN36pVq1q7MLcHDhyooQdmAjcChoC+WIHLdgXM+aKiJEACJEACfkQARhWMJxh8iGeF5xAGHqRcuXIa34rheMTYIh0TvCAIPxg9erQaZFgmCkP1uBdPmTJF840/iEdt3LixlsXkMgyVI9QAE8EQ04r2ihQpon0bddx9e+rTU561PTt9sG4tQivgqcaxwPvpuhwZjH7oW7FixeeG+63tu9vGKgRYe7Z79+4a64tvCOJhwQVMEFuMMANM8mrRooXmV6tWTSe5YYUIhEGkTJlSvbiIZ7YTHB8eGHr16mWXzTQ/JhDF4bJ/gkB2zCakkAAJvBoBxM2lT5/+1SqzFgmQgETkawjeSgyPh/Z8FYQjIPwA4QCG9O/fXyeqGfv4hhGJuFJfC7yjOEYYla4CTyqM3ZBOXkMf7toAD6xd607wIgucB0rEJBDF8cN7Avc8nkooJEACr0YgIt+MX40Ia5HAyxHgNfRyvFiaBEjAmUAQnqb4YgVnKNwjARIgARIgARIgARIIHAI6KYwvVgicE0ZNSYAESIAESIAESIAEnAmoh5aTwpyhcI8ESIAESIAESIAESCBwCPBNYYFzrqgpCZAACZAACZAACZCADQE1aOmhtSHDJBIgARIgARIgARIggYAgoDG0AaEplSQBEiABEiABEiABEiABGwJBWL+OqxzYkGESCZAACZAACZAACZBAQBAIwgoHXOUgIM4VlSQBEiABEiABEiABErAhoKsc2KQziQRIgARIgARIgARIgAQCgoB6aDkpLCDOFZUkARIgARIgARIgARKwIaCTwhhDa0OGSSRAAiRAAiRAAiRAAgFBwGHLBsmTJ08CQlkqSQIkQAIkQAIkQAIkQAKuBP4fkIAW0LIdDVsAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --------------------\n",
    "## Lets try Google Drive instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pydrive.auth import GoogleAuth\n",
    "\n",
    "\n",
    "# gauth = GoogleAuth()\n",
    "# gauth.LocalWebserverAuth() \n",
    "# # Creates local webserver and auto handles authentication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/michaeldac/Code/CUNY/698/3DLabeler'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading 177Fq_r_b5E84RKHR0s2eJhaXevP3Um-0 into ./Users/Code/CUNY/698... Done.\n"
     ]
    }
   ],
   "source": [
    "from google_drive_downloader import GoogleDriveDownloader as gdd\n",
    "\n",
    "gdd.download_file_from_google_drive(file_id='177Fq_r_b5E84RKHR0s2eJhaXevP3Um-0',\n",
    "                                    dest_path='./Users/Code/CUNY/698',\n",
    "                                    unzip=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --------\n",
    "## Nope, back to GCP, this time buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import storage\n",
    "import os\n",
    "credential_path = \"/Users/michaeldac/Downloads/mouse-labeler-cff0443f5b5e.json\"\n",
    "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#client = storage.Client(project='quickstart-1573095918077')\n",
    "storage_client = storage.Client(project='mouse-labeler')\n",
    "bucket = storage_client.get_bucket('skull-images')\n",
    "blob = bucket.blob('475_reduced.npy')\n",
    "blob.upload_from_filename('475_reduced.npy')\n",
    "\n",
    "\n",
    "#blob.download_to_filename(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'os' has no attribute 'listcwd'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-c0a3c8fb9042>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'os' has no attribute 'listcwd'"
     ]
    }
   ],
   "source": [
    "os.listcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnc_files.sort(),tag_files.sort(), mnc_names.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['475.mnc', '711_2_6.mnc', '717_2_6.mnc', '717_2_8.mnc', '930.mnc']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnc_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['475_landmarks.tag',\n",
       " '711_2_6_landmarks.tag',\n",
       " '717_2_6_landmarks.tag',\n",
       " '717_2_8_landmarks.tag',\n",
       " '930_landmarks.tag']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['475', '711_2_6', '717_2_6', '717_2_8', '930']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnc_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data shape is: (698, 397, 456)\n",
      "The type of data is: <class 'numpy.ndarray'>\n",
      "[[[   7.98   93.98   99.02  301.98]\n",
      "  [ 125.    152.    146.    356.  ]\n",
      "  [  -3.98  -45.98   40.99  198.01]\n",
      "  [ -51.02   83.    129.    -26.01]]\n",
      "\n",
      " [[ 181.01   90.02    1.02   -0.98]\n",
      "  [  41.98 -118.01  -69.98  -13.  ]\n",
      "  [  43.98 -114.99  -23.03  -62.  ]\n",
      "  [ 108.99   19.02 -105.99 -132.02]]\n",
      "\n",
      " [[  32.02 -155.98  -89.      0.99]\n",
      "  [  32.02 -208.   -107.98  -92.02]\n",
      "  [ 132.99 -178.99   26.98  -58.98]\n",
      "  [ 328.99  -59.99   38.01  -43.98]]\n",
      "\n",
      " [[  81.98   30.    134.01  -18.  ]\n",
      "  [  77.03  108.02  156.02  -16.01]\n",
      "  [ -69.01  -71.97   19.02  -19.99]\n",
      "  [-108.99 -174.    -16.98   45.98]]]\n"
     ]
    }
   ],
   "source": [
    "data = img.get_data()\n",
    "print(\"The data shape is:\", data.shape)\n",
    "print(\"The type of data is:\", type(data))\n",
    "np.set_printoptions(precision=2, suppress=True)\n",
    "print(data[1:5, 1:5, 1:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.65,  7.05, 14.94],\n",
       "       [10.07,  9.49,  8.1 ],\n",
       "       [ 9.73,  5.17,  0.57],\n",
       "       [ 6.82,  9.39,  7.67],\n",
       "       [ 1.93,  6.43, 13.68]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 128, 128)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = nib.load('/Users/michaeldac/Code/CUNY/698/Skulls/475.mnc')\n",
    "tag = tag_parser('/Users/michaeldac/Code/CUNY/698/Skulls/475_landmarks.tag')\n",
    "im = images.Image(img.get_data(), (0.035, 0.035, 0.035), tag)\n",
    "im.cube().scale(128)\n",
    "\n",
    "im.voxels.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['475.mnc',\n",
       " '930.mnc',\n",
       " '717_2_8.mnc',\n",
       " '717_2_6_landmarks.tag',\n",
       " '711_2_6.mnc',\n",
       " '717_2_6.nii',\n",
       " '717_2_8_landmarks.tag',\n",
       " '711_2_6_landmarks.tag',\n",
       " '475_landmarks.tag',\n",
       " '475.nii',\n",
       " '930.nii',\n",
       " '717_2_8.nii',\n",
       " '930_landmarks.tag',\n",
       " '711_2_6.nii',\n",
       " '717_2_6.mnc']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "skulls_folder = os.listdir('/Users/michaeldac/Code/CUNY/698/Skulls')\n",
    "\n",
    "mnc_files = [f for f in skulls_folder if 'mnc' in f]\n",
    "tag_files = [f for f in skulls_folder if 'tag' in f]\n",
    "mnc_names = [i.split('.mnc')[0] for i in mnc_files]\n",
    "    \n",
    "mnc_files.sort(),tag_files.sort(), mnc_names.sort()\n",
    "skulls_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def package_to_npy(mnc_files, tag_files, mnc_names):\n",
    "    \"\"\"\n",
    "    INPUT:  List of .mnc files, \n",
    "            List of corresponding .tag files, \n",
    "            List of .mnc prefix names\n",
    "    \n",
    "    The .mnc file is loaded \n",
    "    The .tag file is parsed and converted to an ndarray via tag_parser()\n",
    "    Image class is instantiated with the .mnc and .tag file and cubes\n",
    "    any images shaped as rectangular prisms and scales down image \n",
    "    resolution to 128x128x128.\n",
    "    \n",
    "    OUTPUT: Tuple of the processed .mnc and .tag files stored as .npy file \n",
    "    and saved to disk locally.\n",
    "    \"\"\"\n",
    "    \n",
    "    count = 0\n",
    "    for i in tqdm(range(len(mnc_files))):\n",
    "        img = nib.load(f'{skulls_folder}/{mnc_files[i]}')\n",
    "        tag = tag_parser(f'{skulls_folder}/{tag_files[i]}')\n",
    "        im = images.Image(img.get_data(), (0.035, 0.035, 0.035), tag)\n",
    "        im.cube().scale(128)\n",
    "        npy_file = (im.voxels, im.point_position)\n",
    "        np.save(f'/Users/michaeldac/Code/CUNY/698/ReducedSkulls/{mnc_names[i]}.npy', npy_file)\n",
    "        count += 1\n",
    "    \n",
    "    print('f{count} .mnc/.tag file pairs have been saved as .npy files')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/5 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "No such file or no access: '['475.mnc', '930.mnc', '717_2_8.mnc', '717_2_6_landmarks.tag', '711_2_6.mnc', '717_2_6.nii', '717_2_8_landmarks.tag', '711_2_6_landmarks.tag', '475_landmarks.tag', '475.nii', '930.nii', '717_2_8.nii', '930_landmarks.tag', '711_2_6.nii', '717_2_6.mnc']/475.mnc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/nibabel/loadsave.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(filename, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0mstat_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: \"['475.mnc', '930.mnc', '717_2_8.mnc', '717_2_6_landmarks.tag', '711_2_6.mnc', '717_2_6.nii', '717_2_8_landmarks.tag', '711_2_6_landmarks.tag', '475_landmarks.tag', '475.nii', '930.nii', '717_2_8.nii', '930_landmarks.tag', '711_2_6.nii', '717_2_6.mnc']/475.mnc\"",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-33f1045eb549>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpackage_to_npy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmnc_files\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag_files\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmnc_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-50-c03b2f9786ae>\u001b[0m in \u001b[0;36mpackage_to_npy\u001b[0;34m(mnc_files, tag_files, mnc_names)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmnc_files\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{skulls_folder}/{mnc_files[i]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mtag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtag_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{skulls_folder}/{tag_files[i]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.035\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.035\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.035\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/nibabel/loadsave.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(filename, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mstat_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No such file or no access: '%s'\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstat_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mst_size\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mImageFileError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Empty file: '%s'\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or no access: '['475.mnc', '930.mnc', '717_2_8.mnc', '717_2_6_landmarks.tag', '711_2_6.mnc', '717_2_6.nii', '717_2_8_landmarks.tag', '711_2_6_landmarks.tag', '475_landmarks.tag', '475.nii', '930.nii', '717_2_8.nii', '930_landmarks.tag', '711_2_6.nii', '717_2_6.mnc']/475.mnc'"
     ]
    }
   ],
   "source": [
    "package_to_npy(mnc_files, tag_files, mnc_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " hello\n"
     ]
    }
   ],
   "source": [
    "print('\\n', 'hello' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "717_2_6.npy\n",
      "711_2_6.npy\n",
      "717_2_8.npy\n",
      "930.npy\n",
      "475.npy\n"
     ]
    }
   ],
   "source": [
    "file_path = \"/Users/michaeldac/Code/CUNY/698/ReducedSkulls\"\n",
    "os.listdir(file_path)\n",
    "for filename in os.listdir(file_path):\n",
    "    print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_files = '/Users/michaeldac/Code/CUNY/698/ReducedSkulls/'\n",
    "project_name = 'mouse-labeler'\n",
    "bucket_name = 'skull-images'\n",
    "\n",
    "def upload_to_gcp(path_to_files, project_name, bucket_name ):\n",
    "    storage_client = storage.Client(project=project_name)\n",
    "    bucket = storage_client.get_bucket(bucket_name)\n",
    "    \n",
    "    count = 0\n",
    "    for filename in os.listdir(file_path):\n",
    "        blob = bucket.blob(filename)\n",
    "        blob.upload_from_filename(file_path + filename)\n",
    "        print(f'{filename} successfully uploaded to {bucket_name} bucket.')\n",
    "        count += 1\n",
    "    print(f'{count} blobs were uploaded to Project:{project_name}, Bucket:{bucket_name}')\n",
    "\n",
    "# ** pip install --upgrade imagekit **      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "717_2_6.npy successfully uploaded to skull-images bucket.\n",
      "711_2_6.npy successfully uploaded to skull-images bucket.\n",
      "717_2_8.npy successfully uploaded to skull-images bucket.\n",
      "930.npy successfully uploaded to skull-images bucket.\n",
      "475.npy successfully uploaded to skull-images bucket.\n",
      "5 blobs were uploaded to Project:mouse-labeler, Bucket:skull-images\n"
     ]
    }
   ],
   "source": [
    "upload_to_gcp(path_to_files, project_name, bucket_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = \"/Users/michaeldac/Code/CUNY/698/Downloaded_Skulls/\"\n",
    "\n",
    "if not os.path.exists(folder):\n",
    "    os.makedirs(folder)\n",
    "\n",
    "def download_blobs(folder, project_name, bucket_name):\n",
    "    storage_client = storage.Client(project=project_name)\n",
    "    bucket = storage_client.get_bucket(bucket_name)\n",
    "    blobs = bucket.list_blobs()\n",
    "\n",
    "    count = 0\n",
    "    for blob in tqdm(blobs):\n",
    "        print(blob.name)\n",
    "        destination_filename = f'{folder}{blob.name}'\n",
    "        blob.download_to_filename(destination_filename)\n",
    "        count += 1\n",
    "    \n",
    "    print(f'{count} blobs were downloaded to {folder}')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "475.npy\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mWantReadError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/contrib/pyopenssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOpenSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSysCallError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1821\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1822\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36m_raise_ssl_error\u001b[0;34m(self, ssl, result)\u001b[0m\n\u001b[1;32m   1621\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_ERROR_WANT_READ\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1622\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mWantReadError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1623\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_ERROR_WANT_WRITE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mWantReadError\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-37c50b9b8f99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdownload_blobs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproject_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbucket_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-59-5661338bdd93>\u001b[0m in \u001b[0;36mdownload_blobs\u001b[0;34m(folder, project_name, bucket_name)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mdestination_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'{folder}{blob.name}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mblob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_to_filename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestination_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/google/cloud/storage/blob.py\u001b[0m in \u001b[0;36mdownload_to_filename\u001b[0;34m(self, filename, client, start, end)\u001b[0m\n\u001b[1;32m    713\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile_obj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_to_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mresumable_media\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataCorruption\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m             \u001b[0;31m# Delete the corrupt downloaded file.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/google/cloud/storage/blob.py\u001b[0m in \u001b[0;36mdownload_to_file\u001b[0;34m(self, file_obj, client, start, end)\u001b[0m\n\u001b[1;32m    685\u001b[0m         \u001b[0mtransport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_transport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_download\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    688\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mresumable_media\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidResponse\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m             \u001b[0m_raise_from_invalid_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/google/cloud/storage/blob.py\u001b[0m in \u001b[0;36m_do_download\u001b[0;34m(self, transport, file_obj, download_url, headers, start, end)\u001b[0m\n\u001b[1;32m    623\u001b[0m                 \u001b[0mdownload_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             )\n\u001b[0;32m--> 625\u001b[0;31m             \u001b[0mdownload\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconsume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m             download = ChunkedDownload(\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/google/resumable_media/requests/download.py\u001b[0m in \u001b[0;36mconsume\u001b[0;34m(self, transport)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stream\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_to_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/google/resumable_media/requests/download.py\u001b[0m in \u001b[0;36m_write_to_stream\u001b[0;34m(self, response)\u001b[0m\n\u001b[1;32m    121\u001b[0m                 \u001b[0mchunk_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_SINGLE_GET_CHUNK_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_unicode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m             )\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbody_iter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0mlocal_hash\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    748\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'stream'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    751\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_fp_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 564\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    566\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m    505\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m                 \u001b[0mcache_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfp_closed\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m                 if (\n\u001b[1;32m    509\u001b[0m                     \u001b[0mamt\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    455\u001b[0m             \u001b[0;31m# Amount is given, implement using readinto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m             \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/contrib/pyopenssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    323\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOpenSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWantReadError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_for_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The read operation timed out\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/util/wait.py\u001b[0m in \u001b[0;36mwait_for_read\u001b[0;34m(sock, timeout)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0mReturns\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msocket\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mreadable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0mexpired\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \"\"\"\n\u001b[0;32m--> 146\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mwait_for_socket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/util/wait.py\u001b[0m in \u001b[0;36mpoll_wait_for_socket\u001b[0;34m(sock, read, write, timeout)\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpoll_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_retry_on_intr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo_poll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/util/wait.py\u001b[0m in \u001b[0;36m_retry_on_intr\u001b[0;34m(fn, timeout)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m# Modern Python, that retries syscalls by default\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_retry_on_intr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/3DLabeler/lib/python3.7/site-packages/urllib3/util/wait.py\u001b[0m in \u001b[0;36mdo_poll\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0mt\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpoll_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_retry_on_intr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo_poll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "download_blobs(folder, project_name, bucket_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from preprocessing import mri_point_plot as mpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 },
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