tm_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spiegel Online news topics and COVID-19\n",
    "\n",
    "## A topic modeling approach\n",
    "\n",
    "November 25, 2020 | Markus Konrad <markus.konrad@wzb.eu> | [WZB / Berlin Social Science Center](https://wzb.eu/)\n",
    "\n",
    "This is the main part of a small project to showcase topic modeling with the [tmtoolkit Python package](https://tmtoolkit.readthedocs.io/) via [LDA](https://lda.readthedocs.io/), where I use a corpus of *[Spiegel Online (SPON)](https://www.spiegel.de/)* news articles to create a topic model for before and during the COVID-19 pandemic. In the notebook at hand, I load and investigate the generated topic model and identify COVID-19 related topics. I then show how the share of COVID-19 related topics – as a measure of media coverage on COVID-19 – developed during the pandemic and, as a quick example, how this relates to national and global COVID-19 infection rates.\n",
    "\n",
    "For an introduction to topic modeling via LDA see [Introduction to Probabilistic Topic Models (Blei 2012)](https://oar.princeton.edu/jspui/bitstream/88435/pr1bv3w/1/OA_IntroductionProbabilisticTopicModels.pdf) or [Topic modeling made just simple enough (Underwoord 2012)](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/).\n",
    "\n",
    "Currently, a time span from Oct. 2019 to end of Aug. 2020 is covered but I plan to give an update for a time span until end of Nov. 2020. The time span begins well before Jan. 2020 in order to make sure that also enough topics are generated that have nothing to do with the pandemic.\n",
    "\n",
    "The results of other Python scripts in this repository enter this notebook, namely:\n",
    "\n",
    "1. text data preparation in [`prepare.py`](prepare.py)\n",
    "2. topic model evaluation in [`tm_evaluation.py`](tm_evaluation.py)\n",
    "3. generation of final candidate topic models in [`tm_final.py`](tm_final.py)\n",
    "\n",
    "Please have a look at the [GitHub repository](https://github.com/WZBSocialScienceCenter/tm_corona) which contains all necessary files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:02.528266Z",
     "iopub.status.busy": "2020-11-23T16:59:02.526422Z",
     "iopub.status.idle": "2020-11-23T16:59:02.787704Z",
     "shell.execute_reply": "2020-11-23T16:59:02.788111Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "pd.options.display.max_rows = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load a candidate model that was generated in `tm_final.py` (for this example, we only investigate the first of the candidate models that are stored in `'data/tm_final_results.pickle'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:02.791924Z",
     "iopub.status.busy": "2020-11-23T16:59:02.791431Z",
     "iopub.status.idle": "2020-11-23T16:59:03.343285Z",
     "shell.execute_reply": "2020-11-23T16:59:03.343665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32921, 180), (180, 3278))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tmtoolkit.utils import unpickle_file\n",
    "\n",
    "ldamodel = unpickle_file('data/tm_final_k180_eta0.7.pickle')\n",
    "\n",
    "doc_topic = ldamodel.doc_topic_\n",
    "topic_word = ldamodel.topic_word_\n",
    "\n",
    "doc_topic.shape, topic_word.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we have a model with 180 topics. The shape of the document-topic and topic-word distributions tell us also the number of documents (32,921) and the vocabulary size (3,278) for which the model was generated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the document labels, vocabulary and document-term matrix (DTM) which were genereted in `prepare.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.347354Z",
     "iopub.status.busy": "2020-11-23T16:59:03.346874Z",
     "iopub.status.idle": "2020-11-23T16:59:03.388141Z",
     "shell.execute_reply": "2020-11-23T16:59:03.387711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32921, 3278, (32921, 3278))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_labels, vocab, dtm = unpickle_file('data/dtm.pickle')\n",
    "doc_labels = np.array(doc_labels)\n",
    "vocab = np.array(vocab)\n",
    "len(doc_labels), len(vocab), dtm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dimensions of the DTM fit to the number of documents and vocabulary size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First ten document labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.391865Z",
     "iopub.status.busy": "2020-11-23T16:59:03.391303Z",
     "iopub.status.idle": "2020-11-23T16:59:03.394288Z",
     "shell.execute_reply": "2020-11-23T16:59:03.393868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['007-drehorte-in-europa-klimavertraeglicher-james-bond-a-1295233',\n",
       "       '070-shake-in-berlin-vertrauensuebung-mit-einer-emo-rapperin-a-7c4a0eb3-7c1b-474c-aaee-f2de6e165362',\n",
       "       '1-fc-heidenheim-in-der-relegation-demoliert-in-die-groessten-spiele-der-vereinsgeschichte-a-383ce249-6865-4bfa-a8ea-2a89894ad7b2',\n",
       "       '1-fc-heidenheim-unterliegt-werder-bremen-in-der-relegation-da-helfen-auch-keine-klatschpfannen-a-ea0861b0-dd7c-403a-99ab-38b6c25c7aed',\n",
       "       '1-fc-kaiserslautern-beantragt-in-der-3-liga-insolvenz-pfaelzer-sauplan-a-69b7ee5a-5cc8-4f8c-9f67-c5f455a3ec9f',\n",
       "       '1-fc-kaiserslautern-fussball-drittligist-stellt-antrag-auf-plan-insolvenz-a-fb530286-9b7b-4cd7-ae1f-9a876cc3f700',\n",
       "       '1-fc-koeln-beendet-kooperation-mit-china-totaler-ueberwachungsstaat-a-1301960',\n",
       "       '1-fc-koeln-holt-horst-heldt-und-markus-gisdol-neue-gesichter-alte-zweifel-a-1297292',\n",
       "       '1-fc-koeln-kontert-kuendigung-eines-mitglieds-wegen-moschee-auf-dem-trikot-a-0fc58882-dffd-4f84-a157-6ba1d86061ca',\n",
       "       '1-fc-koeln-markus-gisdol-soll-neuer-trainer-werden-a-1297085'],\n",
       "      dtype='<U190')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_labels[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First ten terms in vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.397825Z",
     "iopub.status.busy": "2020-11-23T16:59:03.397315Z",
     "iopub.status.idle": "2020-11-23T16:59:03.400159Z",
     "shell.execute_reply": "2020-11-23T16:59:03.399736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10', '11', '20', 'ab', 'abbauen', 'abbrechen', 'abend', 'abends',\n",
       "       'abgeben', 'abgeordnete'], dtype='<U27')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DTM is a large sparse matrix with the expected dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.403581Z",
     "iopub.status.busy": "2020-11-23T16:59:03.403006Z",
     "iopub.status.idle": "2020-11-23T16:59:03.405485Z",
     "shell.execute_reply": "2020-11-23T16:59:03.405868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<32921x3278 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 4247828 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigating the topic model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate how quick the probabilities in the topic-word matrix fall with the term ranks. For each topic (i.e. each row in the topic-word matrix), we sort the term probabilities in descending order and plot these sorted topic-specific term probabilities.\n",
    "\n",
    "Note: I will add a function for such plots to the next major version of [tmtoolkit](https://tmtoolkit.readthedocs.io/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.410246Z",
     "iopub.status.busy": "2020-11-23T16:59:03.409647Z",
     "iopub.status.idle": "2020-11-23T16:59:03.912510Z",
     "shell.execute_reply": "2020-11-23T16:59:03.912119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2zruhbMEZY4wx42ZgVfSq2hCRNwIfBsLAO1X1QRF5G3BSVe8FfhGYBP6kE0TPq+o3AM8CflNEWrQzIT/X1fv+hrISvDHGmHEz0DZ4Vf0Q8KGufT8ReP7VO5z3j8BzBpm2a2FrwhtjjBk31mOsD66K3krwxhhjxoUF+D5YCd4YY8y4sQDfJ5uu1hhjzDixAN8nm67WGGPMOLEA3ycRsQBvjDFmbFiA71MoFLIqemOMMWPDAnyfrARvjDFmnFw1wIvIN4nIdGB7ZqfFX/YyK8EbY4wZJ/2U4N+qqlm3oapbwFsHlqIRZZ3sjDHGjJN+AnyvY0ZiFbobyYbJGWOMGSf9BPiTIvIrInJb5/ErwP2DTtiocVX0FuSNMcaMg34C/H8AasAfdx5V4AcGmahRZNPVGmOMGSdXrWpX1SLw5huQlpEWnK7WPTfGGGNG1Y4BXkT+u6r+sIj8OfCEYmtnWdebhq0Jb4wxZpzsVoJ/d+fvL92IhIw6WxPeGGPMONkxwKvq/Z2/nxCRGPBM2iX5U6pau0HpGxlWgjfGGDNOrtoGLyL/CvgN4FFAgOMi8r2q+peDTtwosRK8McaYcdLPePZfBl6iqqcBROQ24P8F9nSALxaLxGIxotEoYGvCG2OMGS/9dAfPu+DecQbIDyg9IyObzVKpVPy2DZMzxhgzTvopwZ8UkQ8B76XdBv9vgPtE5JsBVPX9A0zf0PSauc6mqzXGGDMu+gnwCeAK8FWd7TUgCXw97YC/JwO8qtJoNLbts+lqjTHGjIt+Jrr5rhuRkFGiqqyvrzMzM8Pc3JzfbyV4Y4wx46Kf5WIPi8gHRGS18/hTETl8IxI3LCLSM5hbCd4YY8y46KeT3e8C9wIHO48/7+zb08LhMM1mc9s+K8EbY4wZF/0E+EVV/V1VbXQe7wIWB5yuoQuHwz3b4C3AG2OMGQf9BPgNEXmNiIQ7j9cAG4NO2LCFw+EnBHO3ZKwxxhgz6voJ8N8NfCtwGVgBXgW8boBpGgm9quitBG+MMWZc9DNM7nD3ynEi8s+BC4NJ0mgIhUI92+Ch3cveTXxjjDHGjKJ+SvD//z737RmtVovNzU1KpdK2ErtNV2uMMWZc7LYe/IuArwAWReRNgZemgPCgEzZMoVCIcDhMuVze1uZu09UaY4wZF7uV4GPAJO1MQDrwyNFuh9/TJiYmqNfr1GqPr4xrJXhjjDHjYrf14D8BfEJE3qWq525gmoZOVSkWi5TLZUqlEslkErASvDHGmPFx1Tb4my24QzuQx+NxRIR8/vGF86wEb4wxZlz008nuphSLxQiFQts62rkSvAV4Y4wxo84C/A7i8TiRSARV9evCB4fJGWOMMaPsquPgRWQR+B7gWPB4Vf3uwSVr+OLxuJ+5rlKpkEqlrARvjDFmbPQz0c0Hgb8DPgo0r3LsnhGLxYB2tXy1WqXVahEKhWy6WmOMMWOhnwCfUtUfG3hKRkw0GvXLxnaX4q0Eb4wxZtT10wb/FyLyyoGnZMREIhG/4Ew4HN7WDm8leGOMMaOunwD/Q7SDfEVEciKSF5FcPxcXkZeLyCkROS0ib+7x+ptE5CER+YKIfExEjgZee62IPNJ5vLb/W7o+3Gx2jUaDZDJJtVpFVW1NeGOMMWOhn3HwaVUNqWpCVac621NXO09EwsDbgVcAtwPfLiK3dx32WeBuVb0TeB/wC51z54C3Ai8EXgC8VURmr+XGngpV5cqVK7RaLarVKolEwlfTi4iV4I0xxoy8qwZ4aXuNiPyXzvYtIvKCPq79AuC0qp5R1RrwHuCe4AGq+jeqWupsfhI43Hn+dcBHVDWjqpvAR4CX93dLT51rZw+Hw9RqNWKxmJ+b3krwxhhjxkE/VfT/E3gR8B2d7QLtkvnVHGL7krIXO/t28nrgL6/lXBF5g4icFJGTa2trfSSpf6FQiEgkQr1eR1VJJBJUq1XAhskZY4wZff0E+Beq6g8AFYBOiTp2PRMhIq8B7gZ+8VrOU9V3qOrdqnr34uLi9UwSoVCIaDRKq9Wi2WySTCZRVWq1mlXRG2OMGXn9BPh6pz1dwU98008R9hJwS2D7cGffNiLy1cB/Br5BVavXcu4guU52zWaTRqPhp651vemtFG+MMWaU9RPgfw34ALBPRH4a+HvgZ/o47z7ghIgcF5EY8Grg3uABIvI84DdpB/fVwEsfBr5WRGY7neu+trPvhhERP1WtWzI2mUz6KnsrxRtjjBllV53oRlX/UETuB14GCPCNqvrFPs5riMgbaQfmMPBOVX1QRN4GnFTVe2lXyU8Cf9KZBva8qn6DqmZE5L/SziQAvE1VM0/mBp8sV4IXEer1OgCJRAIRoVKp+E54xhhjzCjqZy76XwPeo6r9dKzbRlU/BHyoa99PBJ5/9S7nvhN457W+5/USCoUQkW2T3MTjccLhMMVi0UrwxhhjRlo/VfT3Az8uIo+KyC+JyN2DTtQocPPOh0IhX0UPMDExQbVapdm8aablN8YYM4b6mejm91T1lcDzgVPAz4vIIwNP2ZC5KvpQKORnsQN8b3pXqjfGGGNG0bWsB/804JnAUeDhwSRndATb4N1QOWgH+FAoRLFYHHIKjTHGmJ31M5PdL3RK7G8D/on21LJfP/CUDZkL8NAeEtdoNIB27/p4PE65XLZ2eGOMMSOrn+ViHwVepKrrg07MKAlW0TebTV+CFxG/+Iybp94YY4wZNTsGeBF5pqo+THuo2hERORJ8XVU/M+jEDVMwwAdL8NDuTV+tVimXyxbgjTHGjKTdSvBvAt4A/HKP1xR46UBSNCJCodC258EAHw6HfZBXVTpj+I0xxpiRsWOAV9U3dP6+5MYlZ7S4YXLAtgAvIn6eequmN8YYM4r6mejmm3vszgIPdE0vu+cES/HNZtOX1kOhELFYjGazSaVSsQBvjDFm5PTTye71tJeL/ZvO9otpT35zXETepqrvHlDahs7NZuc0m00ikYjveJdIJKhUKlZNb4wxZuT0Mw4+AjxLVb9FVb8FuJ12G/wLgR8bZOKGTUT8A9g2VM6tEd9qtbbNdGeMMcaMgn4C/C2qeiWwvdrZlwHqg0nWaOhVgnf7W60W8XgcEaFcLg8ricYYY0xP/VTRf1xE/gL4k872t3T2TQBbg0rYKAgGeBF5Qgke2ivMVavVHa9hjDHGDEM/Af4HgG8GvrKz/fvAn2o7wu3pHvYuwDcaDcLhsA/wrvOdqhIOh2m1WsNMpjHGGPME/awHr8Cfdh43lVAoRCQSoV6v+451gC/Vt1otQqEQqmod7YwxxoyUa1ls5qbjxsG74N1oNFDVbSV499xK8cYYY0aJBfhduOlqg8G72Ww+oQTvnhtjjDGjYtcALyJhEfnDG5WYUeNK8K1Wywf1ZrNpJXhjjDEjb9cAr6pN4KiIxG5QekaKK8EH29cbjca2oG4B3hhjzCjqpxf9GeAfROReoOh2quqvDCxVQ6aqFAoFotGor6IPtsO7YG8leGOMMaOq3/XgH6Vd2k8PNjmjQUTY2NhgZmaGSCTil4uNRCJPKMEH2+ONMcaYUdHPMLmfAhCRyc52YdCJGjZVJZvNEg6HiUTaH5Gbua5eb0/e5ya7cVPZWoA3xhgzSq7ai15Eni0inwUeBB4UkftF5I7BJ214RIRYLEalUvFt8L0mu3FBPfjcGGOMGQX9DJN7B/AmVT2qqkeB/wj81mCTNXzJZJJqteqr491KcoBvh7cAb4wxZlT1E+AnVNUtFYuqfhyYGFiKRkQikaDZbPphca1Wywd4t8/NR28B3hhjzKjpqxe9iPwXwK37/hraPev3NLcUbL1eR0SuWoJ3VffGGGPMKOinBP/dwCLwftrz0S909u1Zqkomk6FarfoA73rPB58HS/DuuTHGGDMKdizBi8i7VfXfAv9OVX/wBqZp6NzQt3A4TK1W27bQTCQSodlsbpvC1qrojTHGjJrdSvBfLiIHge8WkVkRmQs+blQChyUajfrArap+eJwbCx9cE94muzHGGDNqdmuD/w3gY8CtwP1AcC1U7ezfs2KxGOFwGIB6vU6z2aTVahEOhymXy9uWie01da0xxhgzTDtGI1X9NVV9FvBOVb1VVY8HHns6uEO7BA/4IN9sNlHVbRPfgE1Xa4wxZjRdtbipqv/+RiRk1ESjUVSVaDRKo9HwJfjuAG8LzhhjjBlFVp+8g3A47FeTExFfTR8s0YMFeGOMMaPJAvwOwuHwtiBfqVR8G3xwDLxV0RtjjBlF/cxF/x9EZPZGJGaUuECuqsRiMWq1mp/Mxq0wB4+vKGcLzhhjjBkl/ZTgl4D7ROS9IvJycYPE9zhXeq/X6ySTSV9F714LluABC/DGGGNGSj+d7H4cOAH8DvA64BER+RkRuW3AaRsqESESiVCv10mlUtTrdarVKtAuwbvAbpPdGGOMGUV9tcFrO5pd7jwawCzwPhH5hQGmbejcrHWJRAIRoVKp+P3w+NA5sABvjDFmtPTTBv9DInI/8AvAPwDP6Qyd+3LgW65y7stF5JSInBaRN/d4/V+KyGdEpCEir+p6rSkin+s87r2mu7oO3OIybmiciFAqlQC2DZWzErwxxphR1M9qcnPAN6vqueBOVW2JyL/e6SQRCQNvB74GuEi7Hf9eVX0ocNh52tX+P9LjEmVVvauP9F13qsrly5ep1Wp+adhoNOpL8N2T34AFeGOMMaOlnyr6W7uDu4i8G0BVv7jLeS8ATqvqGVWtAe8B7gkeoKqPqeoXgJGLjGtra35onJvwxgV818MetrfB24pyxhhjRkU/Af6O4EanZP7lfZx3CLgQ2L7Y2devhIicFJFPisg3XsN510VwWJwbKtdoNKjVasDj7fPdS8ZakDfGGDMKdgzwIvIWEckDd4pIrvPIA6vAB29A2o6q6t3AdwD/vVevfRF5QycTcHJtbe26vbGIUC6XKZfLiAjNZpN4PE6z2fQB3g2VC5bgwSa7McYYMxp2W2zmZ1U1Dfyiqk51HmlVnVfVt/Rx7UvALYHtw519fVHVS52/Z4CPA8/rccw7VPVuVb17cXGx30v3JRaL+er4YLV8cKicBXhjjDGjarcS/DM7T/9ERL6s+9HHte8DTojIcRGJAa8G+uoN31l/Pt55vgD8c+Ch3c+6vpLJJI1Gg1Ao5DvauTnpgz3rXTW+BXhjjDGjZLde9P8R+B7gl3u8psBLd7uwqjZE5I3Ah4Ew7WVnHxSRtwEnVfVeEXk+8AHa4+q/XkR+SlXvAJ4F/KaItGhnQn6uq/f9wMViMer1OqFQyAf6cDiMqlKr1XyJ3lXZW4A3xhgzSnYM8Kr6PZ2/L3myF1fVDwEf6tr3E4Hn99Guuu8+7x+B5zzZ932qVJVWq+WHxbkqetejvlqtMjk56Uv3tuCMMcaYUbNjgBeRb97tRFV9//VPzuio1WrUajUf1F0wj0QivgTvSvdulTmwAG+MMWY07FZF//W7vKbAng3wIsLk5CS1Wg1V9W3wzWbTj4dXVSKRCJVKxSa7McYYM3J2q6L/rhuZkFEzMzNDq9WiWq0SCoV8r3pXgq/Van6fC+q2opwxxphRsVsV/WtU9Q9E5E29XlfVXxlcsoZvenoagGq1SjKZ9JPYuKr4arVKNBq16WqNMcaMpN2q6Cc6f9M3IiGjRFWp1+t+wptkMulL561Wi1gs5kvw7th4PG4B3hhjzMjYrYr+Nzt/f+rGJWc0iAi5XI5YLObb2F0pvdlsEovFKBaLJJNJgG1D5dy4eGOMMWaY+lku9lYR+XMRWRORVRH5oIjceiMSNyyqSrlcJhKJ0Gg0fE95VaXRaPiSu1Ov1wGrojfGGDM6+lls5o+A9wIHgIPAnwD/a5CJGjZXNe/a2IOd6FwJHtpD4tzsdmALzhhjjBkd/QT4lKq+W1UbnccfAIlBJ2zYEomED+BumVj33K0PX6vViEajT5iu1gK8McaYYdutF/1c5+lfisibaa/nrsC30TU73V7kOtaJyLZOdy6Yx2IxSqUS4XC453S17rkxxhgzDLv1or+fdkCXzvb3Bl5ToJ8V5cZWMpn0Y96bzSaVSoVQKOSr4+PxOMVi0RacMcYYM5J260V//EYmZJS0Wi2Wl5ep1Wq+5N5dHe/a4d289cFSuwV4Y4wxw7ZbCd4TkWcDtxNoe1fV3x9UooYtFApRLpf9ynGuJB8KhbZVx7s14YPT2YIFeGOMMcN31QAvIm8FXkw7wH8IeAXw98CeDfAAqVTKLw/rArerrldVRMQPl2u1Wn4aW7AAb4wxZvj66Qn2KuBlwOXO/PTPBaYHmqoRkEql/Lh2V/1erVb9NrTb4V0nO9fL3uajN8YYMwr6CfBlVW0BDRGZAlaBWwabrOFLpVK+RO6mpa1Wq9vmno/FYkQiEer1uk12Y4wxZqT00wZ/UkRmgN+i3bO+APyfQSZqFCSTScLhMOFwmEqlwuzsLKpKpVLxATwcDvvJcFyAtxK8McaYUXDVAK+q3995+hsi8r+BKVX9wmCTNXzhcJhYLOar4KPRKCLiq+kdNyGOleCNMcaMkn570X8z8JW0x7//PbDnA3woFCKRSPie866znVt8xonH40B7+Vh3ni04Y4wxZtj6WWzmfwLfBzwA/BPwvSLy9kEnbNjC4TDxeJx4PE69XqfZbBKNRqlWq9sCeCwW2xb4rQRvjDFmFPRTgn8p8CztFFtF5PeABweaqhEQDodJJBJEIhGazabvaFcsFimVSqTTaYBt1fiut70FeGOMMcPWTy/608CRwPYtnX17WigU8p3oAN8OD1AqlfxxkUhk27KytuCMMcaYUbDbYjN/TrvNPQ18UUQ+3XnpBcCndzpvL4lEIsTjcT9VrQvm+XzeHyMiJBIJisXiE2azC4fDw0q6McaYm9xuVfS/dMNSMWJUlZWVFer1OvF4nFgs5oO3q46v1+u+RB+Px9na2vLt9GAB3hhjzHDtttjMJ9xzEVkCnt/Z/LSqrg46YcO2srJCLBYjFottWzLW9ZKvVCo+mCcSCZrNJs1m0/eqt3Z4Y4wxw9RPL/pvpV0l/2+AbwU+JSKvGnTChi2Xy5HP54lEIn7OeWgHbjce3olGo6jqtjZ4C/DGGGOGqZ9e9P8ZeL4rtYvIIvBR4H2DTNiwtVotSqUSU1NTRKNR6vU6rVaLarXq2+Rdr3lXyi+Xy8zNzfnzjTHGmGHppxd9qKtKfqPP88ZavV6nUCggIr6jnSu5u1K6K8W7oXLlctlK8MYYY0ZCPyX4/y0iHwb+V2f722gvG7tniQgTExPkcjkf4AuFgl9YBvCT2ySTST/ZjY2FN8YYMyp2DfAiIsCv0e5g95Wd3e9Q1Q8MOmHDlkqlfMc5EfFrwzebTRqNhl9dDh6ft97NeGcB3hhjzLDtGuBVVUXkQ6r6HOD9NyhNI2FiYsIHczc8TlVRVUqlErFYjEql4sfHx2Ixvy68BXhjjDHD1k9b+mdE5PlXP2zvaLValMtlarWa71QXCoWIRCKEQiFKpZIfIlepVAiFQr4jnivxW4A3xhgzTP20wb8QeI2IPAYUAaFduL9zkAkbplAoRDqdptFoUK1WicfjRCIRX0VfLpcBtlXTu7HwwbHyxhhjzLD0E+C/buCpGDGqSjgc9uu8u0VnXAe6UqlEs9kkkUiQy+VoNpt+qFytViMej1sJ3hhjzFBdtYpeVc8B88A9wDcA8519e5YrqddqNUqlkm9/D4fDfqracrlMIpEA2sPl3GQ4bhida683xhhjhqGfmex+Avg92kF+AfhdEfnxQSdsmEKhELOzs35hGdfJzq0uV61WKZVKRCIRwuEwlUplWwneVpQzxhgzbP1U0X8n8FxVrQCIyM8BnwP+2wDTNXSzs7PE43E2Njb8PreaXL1ep1KpAO2FZiqVCqlUikgk4jvlAb5K3xhjjLnR+ok+y0AisB0HLg0mOaNBVbl8+TLpdJpcLker1aLZbPrx7s1mk1KphKr6tnnArwvvWDu8McaYYeknwGeBB0XkXSLyu8A/AVsi8msi8muDTd5wuGFxi4uLqCqbm5v+NdeBrl6v+zHwjhsLH1yYxhhjjBmGfgL8B4D/BPwN8HHai898ELi/89iRiLxcRE6JyGkReXOP1/+liHxGRBrdK9SJyGtF5JHO47V93s91Ew6HmZmZIR6Ps7m5iarSarVIJBJ+3vlqteoDfKvV8qX7ZrPp9xljjDHDcNU2eFX9vSdzYREJA28Hvga4CNwnIveq6kOBw84DrwN+pOvcOeCtwN2AAvd3zt3kBgmHw6RSKSYmJigWi6RSKZLJJIlEAlX1s9il02mgXa3vAnytVgMswBtjjBmeQfYAewFwWlXPqGoNeA/toXaeqj6mql8AuiPh1wEfUdVMJ6h/BHj5ANP6BJFIhHg8TiKRoNFoUK/XaTQafmW5Wq3mA7kbRheNRhER6vW6zWZnjDFmqAYZ4A8BFwLbFzv7rtu5IvIGETkpIifX1taedEK7qSrZbJZarUYymfRt6m5oXCgU8qvK1Wo1PylOOBwG8GPhLcAbY4wZlrEew6Wq71DVu1X17sXFxet2XRGhVCrRaDR8lbybwS7Yc95NZet6z7tx8W4svAV4Y4wxw9LPRDd3i8gHOp3hviAiD4jIF/q49iXglsD2YfofXvdUzn3Kms0mlUqFtbU1ksmkD/QA5XKZeDxOtVql1Wr5nvStVgsRIRqNWoA3xhgzdP1MdPOHwI8CD/DEtvLd3AecEJHjtIPzq4Hv6PPcDwM/IyKzne2vBd5yDe/9lITDYQ4cOMCVK1eo1+v+EYlE/OpxjUYDVaVWq5FKpQB8Nb17zQK8McaYYeknwK+p6r3XemFVbYjIG2kH6zDwTlV9UETeBpxU1Xs7y9B+AJgFvl5EfkpV71DVjIj8V9qZBIC3qWrmWtPwZLlq+KmpKRqNBuVymXw+z9zcHCJCo9EgGo36AO7+uvnqG40GzWbTpqo1xhgzNP0E+LeKyG8DHwOqbqeqvv9qJ6rqh4APde37icDz+2hXv/c6953AO/tI33XXarU4deoUhUKB+fl5kskkm5ubJJNJZmZm/IpxLtgHA7ybq94tG2uMMcYMQz8B/ruAZwJRHq+iV+CqAX5ctVotLly4QD6f584772R+fp58Ps/W1hZTU1OEw2Gq1arvWOfa3BuNBrFYzHfAcyvMubnpjTHGmBulnwD/fFV9xsBTMkJEhPPnz5PNZrntttuYmJigVqsRi8WoVquoqu9lr6q+fb5erxONRlHVbcHeDZ8zxhhjbpR+6pD/UURuH3hKRkg4HGZqaop8Ps/a2poP1LFYjMnJSZLJJFtbW+TzeQC/9rsrrbt2eLDZ7IwxxgxHPwH+nwGf68wpfy3D5MaWiHD48GHC4TDr6+vUajU/e10kEmFxcZFWq8Xy8rLvUOfmn3ft8DZdrTHGmGHatYpe2o3H3wucuzHJGQ1uUZloNEo+nyeRSBCJRGg2m9TrdVKpFNPT07RaLa5cucL8/DyTk5OEQiGazaYfTueuZYwxxtxouwZ4VVURebuqPudGJWgUuOVio9EopVKJarVKOBxGRCiXyySTSWKxGPPz89TrdbLZLMlk0ne4c5mBZrNpAd4YY8xQ9FNF/5nOePWbhogwPz/vp6l1Y+FFhEqlQjweR1VJJBKk02kqlYpfjAbw89XXajUL8MYYY4ain170LwReIyKPAUVAaBfu7xxkwoZtenqaiYkJH7hrtZqfe96V8FWVmZmZbe30rg0e2DZG3hhjjLmR+gnwXzfwVIygmZkZpqenWV9f96X4YrFIMpmk2Wz6BWmmp6eJRqPkcjkWFha2DYuzAG+MMWZYrlpFr6rnaC/88tLO81I/542zVqvF6uqqb2uvVqtEo1HK5TKVSsVPVVutVkkmk8Tjccrlsh8jHwqF/FA5C/DGGGOGoZ/V5N4K/BiPL/YSBf5gkIkatkajwcMPP+w710E76DebTUqlkp/QplKp+Dnra7UalUrFd6yLRqMW4I0xxgxNPyXxbwK+gXb7O6q6DKQHmahhi0QiJBIJcrkcIuKXggWoVCrk83lCoZAP6NPT04TDYcrlMs1mc1sJ3wK8McaYYegnwNe0vSyaAojIxGCTNBr279+PiFAsFqnX64iI70RXKBS2zUfvZrcrl8u+U14kEvFt98YYY8yN1k+Af6+I/CYwIyLfA3wU+O3BJmu4QqEQIsLs7CyqSi6X81PRAlSrVWq1Gs1m089RPzU1hapSLBZ9Rzs3T70tG2uMMeZG66eT3S8B7wP+FHgG8BOq+muDTtgw1Wo1NjY2aDabzM3N+fXgXSm+Xq/7se/VapVIJEI6nSYSiZDL5XwbvTvWArwxxpgbrZ9Odj+vqh9R1R9V1R9R1Y+IyM/fiMQNSywW48iRI0QiESKRCPF4nM3NTUqlkh/n3mw2qVQq1Go1otEo0WiUiYkJSqUS5XKZSCSCiFg7vDHGmKHop4r+a3rse8X1TsgoaTabhEIh3zs+kUjQbDbJ5/PUajVf/e6msQ2FQoRCIWZnZwF857xYLEa9XrcAb4wx5obbMcCLyL8XkQeAZ3RWkXOPs8CeXk0OYHV1lVgsRjwe90G9Xq+Ty+Wo1WrE43Hq9TobGxu+VD85OUksFmN9fR1oLztr09UaY4wZht1msvsj4C+BnwXeHNifV9XMQFM1Aur1OgBTU1O+bX1iYoJisUgulyOdTtNoNMhms77XfK1WY35+nocffphms0k8HqdQKFiAN8YYc8PtGOBVNQtkgW+/cckZDW4mOoClpSUuX77M8vIykUiEaDTK1tYW6XSaVqtFpVKhUCj4BWhmZ2dpNptkMhlfte8yC8YYY8yNsqennH2yGo0GFy5c4OLFi6RSKRYWFpicnGRjY4NIJEK1WqVQKNBoNKhUKmSz2W0ZAhHh8uXLfjU6C/DGGGNuNAvwPbRaLbLZLMvLy6ytrdFqtVhaWkJVyWQyiIgfA+8mt3FBPBaLMTk5yZUrV4hGo6gqtVptyHdkjDHmZmMBvodYLMaxY8f8srBuvvlIJMLW1hb5fJ5EIkGtVmN1ddXPeBcKhWg0GiwtLfkSvgV4Y4wxw2ABvgcR4fjx4378+/T0NFNTUxw6dAgRYWNjg7W1NUKhEJlMhkKh4Oegr9fr7Nu3j0ajwebmpl+UxhhjjLmRLMD30Gq1fHv7mTNn2NjYIJfLMTMzs61XfTabZX19ndOnTxMKhfySsVNTU8RiMTKZjJ+z3hhjjLmRdhsmd1ObnZ3lyJEjZDIZ8vk89XqdeDzu13lPJBLEYjG2trZ44IEHCIfDTE9Pk0gkmJqaolKpcObMGSYnJ8lmsywtLREKhZieniYejw/79owxxuxxFuB7aDQarKyskEqlSKfTVKtV4vE4c3NzzMzM+DHxLuBHo1Hy+TwAxWKR2dlZ9u3b5zvehUIhP71tJpNhdnaWRCIxzFs0xhizx1kVfQ+RSISVlRWq1SqHDh2iVquxtbVFtVr1K8w1m01mZmZIpVI+8M/Pz5PP59na2iIcDvvgH4vFmJiYYGFhgUgkwubmprXLG2OMGSgL8D245WIvX77M0aNHSaVSbG5uks1mmZiYQESoVCokk0k/FK7VarG4uEgymWRzc5NIJOKnss1kMtRqNUKhEPPz80SjUTKZDOVyedi3aowxZo+yAN+DWyBmfX2dUqnE3NwclUqFc+fOMTk56YO3iDA5OUmz2SSXy9FsNjlw4ADlcplms0kikSAej7O6usrFixf9krPz8/PEYjE2NzctyBtjjBkIC/A9hEIhstksuVyOTCZDOp0mFotx7tw5lpeXabVafoKbdDrN5OQkhUKBlZUVv+161Mfjcb8wTT6fZ3NzE4D5+flty9AaY4wx15MF+B3Mzc1Rr9epVCosLS3xzGc+E1XlkUceoVKp+FL5+vo6iUSCaDTK8vKyH16XTCapVqs0m00KhQKqyvT0NJVKha2tLUSEubk54vE4W1tbFuSNMcZcVxbge1BV4vE4oVCIYrHIvn37uPPOOzl48CCqytzcHJFIhFAoRKlUIpvNkkqlaDQabG1tsbKywsbGBvl8nmKxyObmJg8++CC1Wo10Ok25XCabzfogn0gk2NraolgsDvvWjTHG7BEW4HtQVQqFAqVSiZWVFYrFItPT0xw4cIBms0k4HEZEWFxcZG5ujlAoxMLCAktLSxw5csTvA5icnCQcDnPlyhW++MUv0mq1mJycpFgs+jZ5N2wum81akDfGGHNdWIDvIRwOs7CwQCgU4tKlS2xubpJMJllcXERVaTQafra7yclJWq2WH/OeSCQ4fvw4+/fv98cePHjQT3hz+fJlpqamSKVS5PN5CoXCE4J8oVAY8idgjDFm3FmA76FarbKxsUEikSCTyXDq1ClEhKWlJb8ErFt4BvAT3RQKBQqFAvPz8xw4cID5+XkymQzFYpFIJEKz2WRlZcVPe5tMJsnlcpRKJV9d7/ZZkDfGGPNUWIDvIRKJkMlkmJiYoF6v84UvfIFcLsfBgwdJJpPUajU/Fh4gmUwSi8XY2Njg/PnzNJtNZmdnOXToEOl02revu972Dz74IMVikZmZGd/Jzg2Xm52d9UHe5rA3xhjzZFmA78GV0Dc3NwmHw5w+fZpPfepTAExMTPh2+FKphKoSDoc5dOgQqVSKlZUVLl68SKPRYGpqiiNHjpBOp9nY2GBra4ulpSVqtRoXL15kY2PDD8FzM+UBzMzM+PdvNpvD/CiMMcaMKQvwPUQiEe68804OHz7M1NQUpVKJz33uc1y8eBERIZfL+Ylw6vU6xWKRTCbD4uKir7rPZDLkcjnm5+c5dOgQqsra2hr5fJ54PI6qUqlUWF9fJxQK+aVnXe3A3NwcqurHzRtjjDHXYqABXkReLiKnROS0iLy5x+txEfnjzuufEpFjnf3HRKQsIp/rPH5jkOnskS6i0ShHjhzh2LFjhMNhLly4wNmzZwmHw9TrdSKRCLVajdXVVVSVK1eukMvlqFQqXL582Y93v3jxIul0mqmpKcLhMIVCgUql4gP35OQklUqFZrNJpVIhk8n4609PT1Or1fxCNsYYY0y/BraanIiEgbcDXwNcBO4TkXtV9aHAYa8HNlX1aSLyauDngW/rvPaoqt41qPTtptFokMlkaDQaHD16lIWFBbLZrK9Gz+VyTE1NEY/HyWazJJNJH6hTqRTFYpFms8nExATlcplarUahUCCRSFCtVikWi8RiMVZXV/0Qu3w+T7PZZGNjg1qtxsGDB0mlUj7Ax2IxW2bWGGNM3wZZgn8BcFpVz6hqDXgPcE/XMfcAv9d5/j7gZSIiA0xTX1SVs2fPUqvVmJ2d5c4776TZbFIqlVhaWkJEWF1dBaBcLlMoFKhWq1SrVVKplF8zfmlpiaNHj3LixAn27dtHq9XyPeRrtRrxeJz19XU2NjZIpVIsLCwwMzNDJpPh/PnzAExPTxONRq093hhjzDUZZIA/BFwIbF/s7Ot5jKo2gCww33ntuIh8VkQ+ISL/otcbiMgbROSkiJxcW1u7bglvtVqsrq7y0EMPcfnyZb8c7Pr6Oul0mkOHDvn56HO5HMVikXK5TD6fZ2VlhWw2y5UrVygUCjQaDebn5zl27BhHjhyh0WhQKpU4e/YsKysrvpTvFp5ZWlpi//79fkY8N0betcer6nW7T2OMMXvXqHayWwGOqOrzgDcBfyQiU90Hqeo7VPVuVb17cXHxur15NBrlzjvvJJlMcuXKFWq1GolEglwux8rKCuFwmGQySSqVolqtUigUiMfjLC0t+c5yjz76KGfPnvVt6uFwmGPHjnHixAm/AM2VK1c4deoUpVKJaDRKtVplfX2dqakpZmdnWV1dZWtri0gkwszMjLXHG2OM6dsgA/wl4JbA9uHOvp7HiEgEmAY2VLWqqhsAqno/8Cjw9AGm9QmSyaQfk+7Wca/VapRKJebn51FVkskk8XicTCbDuXPnaDabzMzMkEgkaDabXLx4kc3NTTKZjF9Jzq02l06nmZ6eRkQ4e/Ysy8vLiIifzz6VSpFIJFhdXfXt/BMTE76TnjHGGLObQQb4+4ATInJcRGLAq4F7u465F3ht5/mrgL9WVRWRxU4nPUTkVuAEcGaAaX2CUChENBr149xTqRStVotLly5Rq9WYnJxEVUmlUuzbt8+30U9NTZFOpwmFQhQKBU6fPk25XPYd5hKJBIBfPhYgnU6zvr5OsVgkFApRqVT8cDqAXC5HNptlamqKaDTK1taWtccbY4zZ1cB60atqQ0TeCHwYCAPvVNUHReRtwElVvRf4HeDdInIayNDOBAD8S+BtIlIHWsD3qWpmUGntVi6XefTRR31VfKlUYt++faRSKTY3N/249Vqt5teEr9frFAoFotEoBw8eJBQKISJkMhlOnz7tJ6+Zmppi//79bG5ukkgk2NzcRET81LS33347k5OTbG1tkcvliMViqKpfhGZ2dpa1tTU2NzeZn59nBPokGmOMGUEDC/AAqvoh4ENd+34i8LwC/Jse5/0p8KeDTNtuEokEtVqNlZUVDh8+TCgUotFosH//fs6cOUOpVCISiRCNRtnY2ODMmTMsLi6ytbXF+vo60WjUt7u7avVWq0UkEuH8+fP+9VtuuYV0Ou1L/m5M/dGjR4lEIuRyOTY3N/1Kdpubm0xOTvre9+Vymfn5eVKp1LA+KmOMMSNqoAF+XNVqNRqNBrlcjrNnzzI5OUkikeDYsWN+nHqhUGBqaorp6Wk/SU29Xmd6eppsNouq0mq1aDabiAilUolUKsXy8jKJRIJLly6RyWS47bbbEBFarRbxeJyNjQ1EhPn5eQ4ePIiIsLKyAsDBgwd9e7yIsLa2Rr1eZ2FhgampJ/RBNMYYcxOzAN9DLBaj0Wj4leNKpRIbGxvEYjGmp6f9jHP5fJ5kMsnhw4eJx+NcvnyZUCjE0aNHWV1dJRKJEIvFuP3226lWqzSbTaanp0mn00QiEc6ePcv6+rrvfR+NRqnVav55JBLh2LFjflrc+fl59u/fT6PRIJVK0Wg02NjYIJvNEolErCRvjDHGG9VhckNVLpf9OPRCoeBnqstkMpTLZb8yXDQaJZfLEYlEOHLkiJ+dLpvNUqvV/JS0W1tbTE9PE4lESCaTFAoFDhw4wOzsLBsbG1y6dMlnANyMeVtbW7RaLSYnJ7ntttuYmJjgoYce8kPvMpl2l4TJyUmq1ap/T2OMMQasBN9TIpHgyJEjXLp0iZWVFYrFIocOHdoWTPP5PJOTk7RaLc6cOcORI0dIpVJ++tlqtUqpVGJ2dpaLFy+yf/9+X6Ufj8eJRCKcOHGC1dVVYrEYtVqNhYUFqtUqZ86cYWVlhTNnznDo0CEWFhaYnZ31HfbcinYTExOk02lmZ2eJRqN+wZtwODzsj9AYY8yQWYDvQVWZmprilltuIZ/Ps7y8TKVSIRaLUS6XiUQifia7eDzuq/D37dvHpUuXWFtbIx6Ps7y8zNraGqrK+fPnOX78OKpKLBbzVfOpVIr5+XlisRgTExPs37+fgwcP8sgjj5DP51lfXycej7Nv3z6OHTvG+vo6ExMTNBoNNjc3WVtbIxqNMjU1xb59+8jn835VOxHxvfnd314PY4wxe48F+B5cG/jhw4f9tLJuAZnV1VUqlQrVapVoNMr8/DyRSIS1tTUOHDjAwYMHOX36NKFQiHg8zsWLF/0MdI899hgveMELuPXWW32P+ng87qvs4/E4rVaLgwcPsri4yGOPPcbq6iqZTIZ4PM7i4qKfRGd2dpZwOMyjjz5KNpulWCxy/vx5v7b8oUPdswLvrJ/Af7Xtq+1PJBIkk8m+02SMMeapsQDfg6qysbHhh8aVSiVWVlb8wi8PP/wwpVKJVqtFKBTixIkTVKtVVJWZmRmSySTZbJaZmRmq1SqRSISJiQk2Nzf527/9Wy5fvszRo0fJ5XI0m02SySTRaNQvOOPmnD9w4ACJRIJKpUI2myUajfqlZd3wuWc84xmsrKzQarUAfMk+FovxtKc9za8932q1UNVdH+4Y9xl0fyZB7v12OyZ4bLlc9hMDGWOMGTwL8D3U63WWl5eJRqMsLi76znCFQoH9+/dTqVQ4c+YMuVyOjY0N1tfXSaVSPPLII9xxxx3Mzc1x5coV8vk8ly9fJhKJsH//fo4dO0az2WR5eZlkMkkulyOXywGwtbVFoVDgy77sy1hcXKRUKhEKhSiXy9TrdaLRqC+5ZzIZms2mX39eVSkUCoRCIebm5piYmGB1dZVSqcTBgwfZv3//UJeaVVUymQxbW1uEQiE/m58xxpjBsQDfQ6vVolqtsrKyQjwe9zPPPfLII5RKJSYmJti3b59vk7906RLhcJhoNEosFuPpT29Pm3/+/Hk2NzdZXV3l1KlThMNhFhcXqdfrXLlyhQMHDnD58mUymQybm5s89thjnDp1imc84xnccccdLC0tkUgkOHfuHNVqlXK5TCwWI5lM+qVpW62Wf1+XMdm3bx/z8/Osra352of9+/dz+PBhIpEb/5WLCHNzc2xsbLC5ucnc3JytbW+MMQNmw+R6SKVSfMVXfAUTExM8/PDDrK+vc+DAAZaWligUCly6dMnPHb+4uMiRI0eIRCIUCgW+9KUvcenSJRKJBAcOHODIkSN+/vhqtepL3FeuXCEej/OiF72Iffv2+RXmMpkMn/rUp3jXu97FX/3VX5HL5ZicnEREaDabXL58mXK5TKlUYm1tjVgs5sfEu2rzlZUVQqGQ77nfbDY5deoU//iP/8jly5d9f4IbyQX5cDjsV9gzxhgzOBbge3Djz5///Oezf/9+8vk8xWKR2dlZ9u/f7xeOyWQyLC8vE4vFOHDgAPv27aNSqfDAAw9Qr9eJxWLceuutpNNpv0Z8vV6nXC6zvr7OJz7xCXK5HE9/+tNZXFzkrrvu4nnPex6veMUrOHToEKdOneKTn/wkW1tbJBIJZmZmSKfTFItFP5XuxYsXmZ6e5s4772Rubo7Z2VmazSZnz55lY2ODarVKOp3myJEjNJtNTp8+zeXLl7ly5Qpra2vk8/kbNn7ercoXCoV8HwdjjDGDYVX0Pagq5XKZaDTKLbfcQi6Xo1wu+xXi5ubmqFarfv/y8jKtVssPdVtfX6derzMxMUEsFvPt5ufPn2dycpKpqSnC4TArKyv8xV/8BQcOHKBSqVAoFDh48CCJRILnPve5XLhwgc3NTU6fPs3BgweZmJjg6NGjNJtNGo0G6+vrnDt3jocffpg777yTo0ePUq1WufXWW1leXvYl/eXlZebm5ti/fz+5XI5SqeTbwfP5PPl83reNJxIJ4vH4wIbPhcNh5ufnWV9fZ2Njg4WFBRu3b4wxA2ABvodwOOx7sk9PT1Mul/2wuEgkQrVa9RPWVKtVRMSPO0+lUlQqFRqNhm/vdtPTtlotNjc3qVarfpKaUqnEhQsXKBaLXLx4kQMHDpBKpXjuc5/L9PQ0ExMT1Go1Hn74YUKhEGfOnOH48eNMTk6yb98+31fgs5/9LPv37/dD95aWlvwc95cuXSKbzfqx/KVSiXg8TiwWY2Fhwc+lX6lUKJVKiIhffz4ajV73zzcSiTwhyIdCVplkjDHXkwX4HiqVCp///Of9AjPz8/PMz8+Tz+eJRqO0Wi0ajQbpdJpqtUo8HmdhYcEPdZuZmeHcuXOUSiWmp6eZnJz0Q+aKxSKFQoFIJMLMzAypVMpPRLO6ukqr1WJiYoJsNsudd97J5OSkH1OfzWZZWVlhZWWFmZkZFhYWuPXWWymXy2SzWeLxuJ+f/uDBg9TrdZaWlnjmM5/JpUuXWF1dZXNzk3K5TLlcRkRoNBpMT08zOzsLtBfaKZVKvvTv2vjdAjfXi8uIbGxs+CBvk+4YY8z1YwG+h2azSbPZ5Etf+hJf+tKX2LdvH7Ozs0xPTwPtxWiazSZLS0t+mNypU6eIxWJ+OFwikWB5edkvKzsxMUEkEiESibC5uUk2m2Vzc9O3qycSCSKRCJlMxk91+/nPf55nPetZTE5OMjc3Rzqd9oHeDZHLZrNMT09TKBSoVqu+ZFyr1Xzb/5EjR3wfgeXlZc6fP+/b4p/97GejqlQqFWZmZojFYsRiMaampvy8+25t+lQqRSqVum498YPNF5lMhrm5OQvyxhhznchOk5OMm7vvvltPnjx5Xa7VbDb5u7/7Oz772c/y4IMPks1mqVarzM3Nsbi46ANSoVBgfX2dxcVFPxa91WoxPz/P8ePHyWQy1Go1FhcX2dzcBGBhYYFyuczq6qrvaJZMJqnVaoiIX3LWVf+n02kOHTrE9PT0tg5qrpnAzbpXr9f93PexWIxKpcL8/DyJRILp6WkWFhZIpVK+R/7a2hqf//znqdVq3Hbbbdx6661MTU3584PcvPrlchmAeDzOxMTEdRvPXi6X2dzcJJFIMDc3d12uaYwxNwMRuV9V7+75mgX4J2q1WvzZn/0Z4XCYSqXCww8/zIULF/zCMIlEgkwmQy6Xo1AocNdddyEirK+vs7a2RrFYZGpqivn5eXK5HKFQiIWFBVqtFslkElWlWq3SaDT87HfNZpPHHnuMTCbj28BTqZRvApiamiKdTjMzM4Oqsrm5Sb1eJ5VK+RqGra0tyuUys7OzlEol328gkUiwsLBAOp321fGxWAxV5fTp0+RyOfbt28fhw4f9NLmxWOwJ89i3Wi0qlYqflS4SiRCPx/04fNcP4ckoFotks1lSqRTT09NWkjfGmD7sFuCtir6Her3O5cuXCYfDTE9Pc+zYMfbv38/58+c5c+aMn11ORPwENHfccQczMzMkEgkuXrzoO9PFYjFyuRyZTIZnPetZHDhwgFKpxObmJsVikUqlAsBznvMcnvGMZ3Dq1CnW19dpNBpMTEwwNTXlx7i7sfKpVIq5uTlKpRK5XI4rV67QbDbZt2/ftlqDUChErVZjY2ODCxcuMDEx4TvXJZNJ4vE4zWaTYrHIZz7zGR544AEmJyc5evQod9xxB7Ozs75HfXfAdavqhcNhksmkD+4u2Lu//faQn5iYoNVqkc/nqVarTE1N2dz1xhjzFFiA70FVue+++4jH46TTadLptF8Fbv/+/b563gVn11FscnKS48ePE4/HuXDhgh9+5trNH330Uer1OlNTUz4AZjIZstks0O5t32g0SKVSPvAmk0kWFxd9r3bXYc9NrFOpVFhbW/Or1i0sLPihcG7GvX379rG+vk40GvVBv16vE4lEfCe/aDRKNpslk8mwsrLCY489xm233cbCwgIzMzPbAn04HEZEfE1ELpcjGo0SjUZRVZrNpp+XPhQK+XZ9136/0+I2IkI8Hiefz2/riOhmvQtmNLr/dj/f6TWrGTDG3CwswPdQLpep1WpsbW2RzWZJJBJ+KJwLbpOTk2xsbPi278cee4xkMsns7Kxf171YLLK5uUk8HiccDlMsFllfX/eZhUgkQiwWI5vN8sgjj3D06FHq9TpbW1t+OtpsNsva2hpTU1O+Pf348eMsLS0xMzNDOBzm1ltv5fLly1y6dIl6vc78/LyvHdjY2GD//v3ccsstbG5u+kl6SqUShUKBZrPp2/er1SrVapWLFy+SyWRotVp+3P/MzIxv049EIn5Ym2v/dzPjpdNpJiYmgHaPfJeZcMPwXBOHa+fvXvAG8FMAb21tsbGx4TNa16tzXzDY75Rh6Pfvk3nPndKw23Wv5f26rxcKhbY1txhjbg4W4HuIx+PccsstXLlyxf8AN5tNstms72Ffr9cJhUI+WCeTSer1Omtra37muImJCb70pS+xublJrVZDVbl8+bLv2e7azmOxmG/jP3ToEM1mk3w+72e9cyXaVCrFlStXOH/+PLfccgsLCwt+Njg3v/vGxgZbW1vEYjHfZr65uenXnI9Go5w4cYJSqcTq6iqrq6t+at1EIkE6nebEiROcP3+ebDbLxYsXKRaL5PN5rly5wvT0NIuLiz3HyOdyOV9T4Nr60+k0gP/MarUajUaDUCjExMQEqVTKXycY5N3zQqFALpfzc+67YYPBY53gdq/XulfK6/V+vV7vtf9a9XqfYXEBv7uPxU56vdadOdppO3j9q72PMeb6sk52PTSbTd71rnexsrJCrVbzQ93q9ToXL14kn89TqVRYWVnxwXFubo5kMkk4HPaT3ExMTPilZ9fW1mg2m4TDYWq1GqFQiMXFRQ4fPszk5CTnz58nn8+TTqdZWlqi0Wiwtrbm2+pFhKmpKaampvw0uIcOHfKz4rnOdPV63ffmTyQSfgicW2q2VqsxPz/Ps5/9bI4fP+6bCZaXl9na2qJUKvlSvhsvXyqVSKfTPjPSbDaJxWLMzMwwOzvrl7sF/PtVKhXC4bAf4ucyBG6kQLVapdlsEo1GSSaTpNNp33QB24OKq0lwn0M6nWZyctLXpoyrfjMO1/J/tFfGptVq+cdO273e42rpebK/HS7QB4P+bpmE4LZ77vTzvN/XrtakM87/1szeZb3or1G9Xuf9738/y8vLrK+vU61WfTCLxWKsrq76VeDOnDlDo9HwwcuNdXc/oK492vVqdzUAtVqNarVKIpHwndkqlYqvAned5FwVveu5Pj09zdTUlA/ahw4dIplM+rHrU1NT1Go1v368m75WRJiZmeHMmTNcuHABVWViYoLZ2VkWFxeZn5/3tRQusLs+BI1Gg2w26zMt8/PzRKNRarUasVjMV+G76nfXZu7G0auqHwng7jUSifh+BoVCwWd6XDNEIpHwGRcXyBuNhu+EFw6Ht/Xgd+/tvofu4BG027ZVYV+b3Wo7gpkH93enfTudPw6/T70Cf6+MS69/l6lUyqZqNk+JBfhr1Gq1OHnyJLVajXPnznH27Fnfdu6CWT6f59KlS5w7d465uTkikYhfuMUFwlgsRq1WI5/Ps7Gx4Uu/rqTsgn4oFCKVSgH49u1Wq+UzDO44F6hdhzUR2dae7TrNJRIJWq2WH6J38OBBX+I+cOCAX0u+VCr59Lpe+u78yclJCoUC+XweaAfrQqHg16YPLmzjRhSkUilfUnfT+rqe+sGRB6lUymdG3DS+gP9MXGc7VzvhmkFcMIf2bIP1ep1Go0GtVvMr6bkOg+79r6UnvxMOhwmFQk/42/3cMgM3xm7NG72aZXY7Zqdzdjr/ydSu9Mq07JShiUQiNlWzeUoswF+jZrPJRz/6UaDdiSyXy/nSfLlcptlskkwm/dzwLvCUSiWy2awPyG7xmUQiwebmJpFIxLfVu9no1tfXKZVKNBoN30PdlWBdid/1VndV390lUxd0VNVX1cfjcUKhkO/p79LhptN1wc+Ndw9OweuqzicnJ4nH4/56mUzGl+5doJ+ZmfHD2VTVt627jonuRy0UCvmq+Vqt5psrQqEQkUiERCLB1NSUzyS4Uv709DSpVMr3zned+VyAdcE8WEJytSeAzySl02lSqZR/v3A4vC1Yu/QHa16Cz3cSDPb9PoyB9lDTTCbjp222JgDzZFiAv0atVstPz5rL5cjn874zWrFY9P8x6/W6n8LVldjdSnRbW1t+LHyr1fKd7FwJ1A07i0QibG1tsbq66ieqCYVCvvTqSvNuLHywg58LovV6nXq97oNgq9Xy1duuKtz1IwgGVFet7Y6dnJz029AOeK5KcX5+npmZGWq1mm8Pz2az1Go139buPgOR9kQ9MzMz2/Y1m01ExGdUGo2Gz1C0Wi2fiXGB3d27m+p3amrKj0hwmRx3z/B4VWkw6Lt7cJkZl2EJtt+7Un8kEmFycpJ0Ou1HOQT/Tbh0BoN/92O3Nm2nO+AHawq639fsbW4WRzcCx5hrZQH+SXBrsLvpWN3kNw888IBfDnZ5eZmLFy/69nFX5RYsTboe8IVCwZewXZAOlpbd0DCXoXBV0y54uSViVdVXSbtJZtz4c5cJcMHeBbDuEq37zl0GI1i6d7UMri+B6zToSutuTnxXInaT9gRX3HPV4q59cXp6mpmZGaLRqE+jq1p3M/q5avl6vc7m5iaNRoOpqSnfaS8YqF3wdmPs3QRA8Hgp3L2Pa+pwn7Obrtc1tbjPDvCZMPcZBD+X4AQ+3b3Bez3frVp2p0xC8BruPYPNDVbC25vcSBG3lLQx18IC/DVqtVp+NjlXsnXV0OVymdOnT5PP5/1a8PV6nUwm4wOdK7GXSiX/4725uUk+n/c93V0QclXe1WrVB3TX2c5lEtx+FxRdiT1Ycg1W/QarqV1gDB4bDCZuX3C8dPDhaiVchiJYte3a2LuDqwuqLjAFl7Ddt28fU1NTPgPh7s+NmXdBrFAoEA6HWVhY8Ev0ukDf3aQA+NKvm6HPNVG4Zgr3ubp2ftdB0E0cNDc3RyqV8t9JrVYD2NZxMBwO+/sJVvMHaxS6dQfr3XqMu+/YZfzcvbnvJtis4j6/YODfqce5DU8bfdlslmKx6JeINqZfuwV4qwvswQU2127sgnM6nSaZTPL0pz+d06dP+6rjer0OtNvU1tfXKRQK/ofatcvPzs6Sz+eJx+Pbgro7zpXei8XitlK7K/W5H2o3jMyVrF3buzsnWGJ09+KCYnCGuaBgyfJauDS56m4X9IKl1WA1uQt2buTA5OSkLxm7NneXVlWlXq8Tj8f9j55Lo5uFz/VxcKXwYrHIxsaGb98Pln7dd7W0tOQX8slmsxQKBS5cuMDy8rIfweCqyN24fZf+4Gvu34gr0bvgH2zXdyMAXNMEsC1NrpnG3XN3JisY9IPNL8C2GpZgZqN7X6/va6chajt9x92CmRzrU3B9TE9P+1Es4XD4ui3kZG5uFuB7cCVwV4JyE84Ui0XfA/wZz3gGjz76KNlslsXFRR/kbrvtNgqFgp/cxpX+1tbWuHjxIpOTk34CmkqlQrFY9NXy+XyetbU1Ll++7DvedVflunbpYCB0P+7w+A9ysB042KYevFawTTlYbd9vrU53x7dqtbrtdZdWFwSCGYjl5WVf6g5W67vAFY/H/fVF2jMHuklxgpkGd47LILiA60q87nkwQAbb5F0Adk0mwVK5C8bBKv9g4A7Ov+/a9oM9910tQvCzct+p+4yDATlY0t5piFWwNsZ9n92B2l3Lpd99NsHP130/O014s9NQwuC/jWCmJzgzY7AJxWoO+jc7O8vGxsa2iamMeSoswPcQCoVIJpN+vLoLMuVymUwm4ztjLS4uIiIUCgUAX7oM9ny/fPkyAMlkkoMHD7K0tORLnMHOVSJCrVajUqmwurrK+vo6q6urXLlyha2tLT9JjTvGVTe7Kvtqtdqzs10wMAWDnYj4kqFLq3vuagmCJcYn05Tjzt2pZsANn3PL0Pb6HoLPXdAOBij32bmHq3kJTiscLCG7e3GZg+BYe/dasPbDvYc7JhiY3fHBAOlec+/vAp/r2JdMJn2/iWDtghsq2V397tLo0tvd5OIyWO7fXKPR8CMnghk4d41g04rLoAQzK901CcHMh3veK4PY3VwUrM0I3muvZp7uUSHuOr3slvHZqYZiXIgIc3NzrK2tkclkfPOUMU+WtcH3oKqsrKw8YZ8b4uVK3I1GY1uv7OCPomvrrlQqZDIZLl26xMbGxrYSU/Czd4E3WNJynd3cKmtbW1vbevG7YOSCeLCN2WUIXAANBvNg23VwvytdBoNi8P6BbTUA4yZYwnXcPQaDjdsO1mrA4xkD9whmLtw5vc4PdngM6s6gBDNhwU59rlnD1ToEO/oF2/+D7x/8DoP7gkHPvXew1mOn6vvgoztYB5sToHczUfA76M4AhELtBZnchE3Bjo3BtAVrQ4IZqp36NAQzJb2aM4LpH6XJZhqNBuvr64RCIRsjb67K2uCvkYhw8ODBXSeqcG3zGxsbFItFH0S7e0O7UuXRo0d99bwrbXWXkIM95MvlMtls1pfIg+3AbphcLpfzVdmq6nt+B2sD3FSzbvraYDVxsPrfBXpXVe2u2Z3G4I9/MBPgfuDdNUZR8DMepO5MgNvn+hgEBZsxgk0PwTR3B65gxqO7g18wg9GrZBssyXcHSVe1HkxzMFAGr7/TMcHn3ZMNBWtXgn1Jgv1E3PnB5gXXR8Nlbrr7O3RnUoKfbff/XZfm7uYIl+buzEd3RqY7Y9Irs9BdG+Heo9d2sEkl+PnNzMyQyWTY2NhgYWFhrGoizOiwAL+LXv9RHbcwy5EjR7aVgF2HOTfznCtJNxoN0un0trHs7gcnOMa9118XdF0VfL1eZ2FhgbW1NT8OPdgBTVVJpVK0Wi2fIXBD0twPYDCT4ar3XcYiOKWuS6vLBAQXjAn+aHaX0txrwV7hTrBT4F4ULL0G77O7j8KN0Cvg93otWNoPBspgz39gWxAKfve9qthDodC2DobBWoLuawG+f0R3Jsjt6zWzYLDWpbsvg8sMuMyE2w4GZ3fd7tqZ4OcTzBi5DEt3himY2equVQm+HmxuAbY1jbi0u2Ya938zlUr52TKDGaZgM1SwBqI7w9GrNqY7g9P93QX/dj/f7bVreW41E4NnAf46CJbU3TSy3YKlZNje6So4LCoYULvn7w6Wtt287G6FO1fy776O++tK1b1+PN1/ONffIPjXDc1z7ynSbrt34/pFxC8H6/666v/uQOeu4TIswUxAMOi77eDEMe4awWuaq7taP4hxF8wguO3uZo/ga8ATgluwNiJYW7FTMNwpeLsMgDuuu19DMF3B2q/gOhbB4AyP97EI9pfobgoMzr3RHczh8YwUsO3Y4MiLYEage597T/fZdQ/3DKYp+Fl1D+Hs7twZnOwr+Jl296vZKdMV/I6CGYjujGPwefB9grUx3fcY/DfT/f7d+7qfB//2ciNHn1iAv0Fczvx6684gdAfRYLV5rwlWgsE0+B8h2CzgrumCuAvkbp76YCnVvbfrr+DGoAc7YwXf09UsuOu6YWnBSXvce7ix7MEmBHg8Y+IyFsF7c+8dzCj1aiveqwFwr+tVG+T+HZirc4Goe3REd4ahu/mjO4AFMwPB7e5A1h2I3SyXwWaK7uaK7gxSdzNJryaqYIYiWKPS3efCFczccTs1zQQ/j+B9dWdCujMM3fcjIrzkJS/hxIkTT/Gb648F+DEX7FXeSzCw7vQAtlWnu20X4IO1DvD42u7FYvEJQTU4uY5rXw727g+WzIPNBMGe4O563W2nrjahUqn4hXSCaRSRbU0gvfobBDMRLl3BDJJ7P5del1EIXrt7prxg+oOfR7DGIfgIZjCC31Pwcx/XjoxmvPSqDdupiclcHy972cv8WieDZgF+j3O5TdcO2S0YxIKBsLtTYfd2q9VidnZ2W0e87qaEYNDrzgB06w76wer77j4JquprE4LHd5fW3XV7/Uh1j/8PntOdGQlWcwePE5Ft1w7eV3CkgluPIPgZBz8Xl9FwGZVgzQk8Xnvh3q874xBMX/f9BjtJBu83eI1gWrtf7/430OvR/Z12p8kY87iPfexjN+y9BhrgReTlwP8AwsBvq+rPdb0eB34f+HJgA/g2VX2s89pbgNcDTeAHVfXDg0zrzSrYFnetdvrB3+m14P5gsO+VoQjqLul2j0TYLZPRKw07Xbu7lB3MdASbP4IZjuCx3Rmk7iAJjwfc7uaSYP8Dl5ZecxrsdB873WN37Uuv+91pO3gfwc8+eD3A98MIZha67w/Y1g+kO+PU67rdNR3BPizd5wf/BjMYO33/wWO6793V1gS/t17p67Ud3Lfbsd3HuExdrwxnrzQa04+BBXgRCQNvB74GuAjcJyL3qupDgcNeD2yq6tNE5NXAzwPfJiK3A68G7gAOAh8VkaerqtUXjZDuzi2jqDtz0c++a7l28NzukutOgaXX3532BZtQgn0LuoPjTrUNwWPc8+736x4y2Z1JCpb2gzUI3Rkyt6+7hqHXZ9adAesO5r0Cc6/jd8pU9hLMnAXT2/1e3c1C3ekO3kvwcw3WkPT6DoJp7/Vvpvs9en2+vY7v3u++0269Mjjd/252+jcYvJed/s3u9H33+jfZrfv6O2X2g5/PTuf3uuerpb/7WsHatN3ucadM4m6/JV//9V+/42vX2yBL8C8ATqvqGQAReQ9wDxAM8PcAP9l5/j7g16UdMe4B3qOqVeCsiJzuXO//DDC9Zg8ah0yI2V2/P5zdr/c6r/uHvp9ScncQ3Ok9dwt87r2vloZewWOnwLVb5nC3TMxO196pxqvXe/VzTK/36JWJ6rZbcN/p/rs/g+7Xupvwgp9BdxqDGdhgJqj7c+tu4ut+72AmNHjsK1/5yh3v63obZIA/BFwIbF8EXrjTMaraEJEsMN/Z/8mucw8NLqnGmFFlmTRjnpyxnmlARN4gIidF5OTa2tqwk2OMMcaMjEEG+EvALYHtw519PY8RkQgwTbuzXT/noqrvUNW7VfXuxcXF65h0Y4wxZrwNMsDfB5wQkeMiEqPdae7ermPuBV7bef4q4K+13VBxL/BqEYmLyHHgBPDpAabVGGOM2VMG1gbfaVN/I/Bh2sPk3qmqD4rI24CTqnov8DvAuzud6DK0MwF0jnsv7Q55DeAH1HrQG2OMMX2z5WKNMcaYMSW7LBc71p3sjDHGGNObBXhjjDFmD7IAb4wxxuxBFuCNMcaYPcgCvDHGGLMHWYA3xhhj9iAL8MYYY8weZAHeGGOM2YP2zEQ3IrIGnOvj0AVgfcDJuZHsfkab3c9o22v3A3vvnux+dndUVXsuxrJnAny/ROTkTrP+jCO7n9Fm9zPa9tr9wN67J7ufJ8+q6I0xxpg9yAK8McYYswfdjAH+HcNOwHVm9zPa7H5G2167H9h792T38yTddG3wxhhjzM3gZizBG2OMMXveTRXgReTlInJKRE6LyJuHnZ6nSkQeE5EHRORzInJy2Om5ViLyThFZFZF/CuybE5GPiMgjnb+zw0zjtdjhfn5SRC51vqPPicgrh5nGayEit4jI34jIQyLyoIj8UGf/WH5Hu9zPWH5HIpIQkU+LyOc79/NTnf3HReRTnd+5PxaR2LDT2o9d7uddInI28P3cNeSkXhMRCYvIZ0XkLzrbN+z7uWkCvIiEgbcDrwBuB75dRG4fbqqui5eo6l1jOozkXcDLu/a9GfiYqp4APtbZHhfv4on3A/Crne/oLlX90A1O01PRAP6jqt4O/DPgBzr/Z8b1O9rpfmA8v6Mq8FJVfS5wF/ByEflnwM/Tvp+nAZvA64eXxGuy0/0A/Gjg+/ncsBL4JP0Q8MXA9g37fm6aAA+8ADitqmdUtQa8B7hnyGm6qanq3wKZrt33AL/Xef57wDfeyDQ9FTvcz9hS1RVV/UzneZ72j9QhxvQ72uV+xpK2FTqb0c5DgZcC7+vsH6fvZ6f7GVsichj4V8Bvd7aFG/j93EwB/hBwIbB9kTH+z92hwF+JyP0i8oZhJ+Y6WVLVlc7zy8DSMBNznbxRRL7QqcIfi+rsbiJyDHge8Cn2wHfUdT8wpt9Rp/r3c8Aq8BHgUWBLVRudQ8bqd677flTVfT8/3fl+flVE4sNL4TX778D/D2h1tue5gd/PzRTg96KvVNUvo93s8AMi8i+HnaDrSdtDPMY6Bw/8P8BttKscV4BfHmpqngQRmQT+FPhhVc0FXxvH76jH/Yztd6SqTVW9CzhMu5bymcNN0VPTfT8i8mzgLbTv6/nAHPBjw0th/0TkXwOrqnr/sNJwMwX4S8Atge3DnX1jS1Uvdf6uAh+g/R983F0RkQMAnb+rQ07PU6KqVzo/Wi3gtxiz70hEorSD4R+q6vs7u8f2O+p1P+P+HQGo6hbwN8CLgBkRiXReGsvfucD9vLzTtKKqWgV+l/H5fv458A0i8hjtJuGXAv+DG/j93EwB/j7gRKcHYwx4NXDvkNP0pInIhIik3XPga4F/2v2ssXAv8NrO89cCHxxiWp4yFwg7vokx+o467YW/A3xRVX8l8NJYfkc73c+4fkcisigiM53nSeBraPcr+BvgVZ3Dxun76XU/Dwcyk0K7vXosvh9VfYuqHlbVY7TjzV+r6ndyA7+fm2qim87wl/8OhIF3qupPDzdFT56I3Eq71A4QAf5o3O5HRP4X8GLaqytdAd4K/BnwXuAI7dUBv1VVx6Lj2g7382LaVb8KPAZ8b6D9eqSJyFcCfwc8wONtiP+Jdrv12H1Hu9zPtzOG35GI3Em7k1aYdmHtvar6ts5vw3toV2d/FnhNp/Q70na5n78GFgEBPgd8X6Az3lgQkRcDP6Kq//pGfj83VYA3xhhjbhY3UxW9McYYc9OwAG+MMcbsQRbgjTHGmD3IArwxxhizB1mAN8YYY/YgC/DG7HEiMiMi3z/sdPRLRD4uIuO4eJIxI8UCvDF73wxwTQFe2p7S70Ngti5jzBBYgDdm7/s54LbOWtq/CCAiPyoi93UW8HDrbh8TkVMi8vu0Zwv7FyLycGc97i+JyB+KyFeLyD9Iey34J0wZKiKvE5F7O5OTfExEJkXkYyLyGRF5QETuCbzXF0Xktzprf/9VZ/ay4LVCnff+b4P+gIzZiyzAG7P3vRl4tLOW9o+KyNcCJ2jP6X0X8OWBhYpOAP9TVe+gPUvd02gvvvLMzuM7gK8EfoT2LHC9fBnwKlX9KqACfFNnUaSXAL/cmXLUvdfbO++1BXxL4BoR4A+BR1T1x5/i/RtzU7IqNGNuPl/beXy2sz1JO9ieB86p6icDx55V1QcARORB4GOqqiLyAHBsh+t/JDB1rQA/08lAtGgvjemWlz2rqp/rPL+/63q/SXuq0rGaftmYUWIB3pibjwA/q6q/uW1ne430YtexwTmyW4HtFjv/fgSv8Z205xH/clWtd1bWSvS4dhMIVtH/I/ASEfllVa3sejfGmJ6sit6YvS8PpAPbHwa+u7MuOiJySET2Dei9p2mviV0XkZcAR/s873eADwHvtc56xjw59h/HmD1OVTc6HeP+CfjLTjv8s4D/02kOLwCvoV2Kvt7+EPjzTpX+SeDha0j3r4jINPBuEfnOznrtxpg+2WpyxhhjzB5kVfTGGGPMHmQB3hhjjNmDLMAbY4wxe5AFeGOMMWYPsgBvjDHG7EEW4I0xxpg9yAK8McYYswdZgDfGGGP2oP8PUqgn32dL934AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# rowwise sorting \n",
    "# (NumPy still doesn't support descending order, hence the \"-\" hack)\n",
    "topic_word_desc = -np.sort(-topic_word, axis=1)   \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "n_terms = 40  # show ranks 1 to 40\n",
    "term_ranks = np.arange(1, n_terms+1)\n",
    "for t in topic_word_desc:\n",
    "    ax.plot(term_ranks, t[:n_terms], color = 'black', alpha=0.1)\n",
    "\n",
    "ax.set_title('Term probability decline per topic')\n",
    "ax.set_xlabel('term rank')\n",
    "ax.set_ylabel('term probability for given topic')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us that if we rank the terms in each topic, only the first five to ten terms really represent the topic. All other terms are really close to each other regarding their probabilities, so their ranking becomes more or less meaningless.\n",
    "\n",
    "This finding helps us to later focus on the top five or so terms in each topic, when we try to interpret the top terms per topic.\n",
    "\n",
    "When we look closely, we see that the ordered probability distribution for three of the 180 topics seems special compared to the other topics. Let's highlight these topics. We want to identify the three topics that stand out in the plot above, i.e that have the highest term probability at the term ranks 1, 7 and 25:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.915847Z",
     "iopub.status.busy": "2020-11-23T16:59:03.915244Z",
     "iopub.status.idle": "2020-11-23T16:59:03.918359Z",
     "shell.execute_reply": "2020-11-23T16:59:03.917930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([131, 158,  93])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use indices for the ranks here, hence we start at 0\n",
    "highlight_topics = np.argsort(-topic_word_desc[:, [0, 7, 24]], axis=0)[0, :]\n",
    "highlight_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot these results. This time, we use a log-scale for the y-axis to be able to better descern the individual topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:03.934091Z",
     "iopub.status.busy": "2020-11-23T16:59:03.933062Z",
     "iopub.status.idle": "2020-11-23T16:59:04.542430Z",
     "shell.execute_reply": "2020-11-23T16:59:04.542776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZMj75yU+STCbPq7ygAngujV4klSCZSV/iEimKoihv1sVeTrSgoIDvf//7/NVf/dVprxuNRnbs2MHRo0c5cuQITz75JHv37gWyAby9vZ3jx48TjUa5++67z6u8cIUHcI3QYJseyAbZtKqKoijK5eViLydaXFzM2rVr0etPX8lSCIHNZgMgmUySTCZzlcQbb7wRIQRCCNatW8fQ0NB5f+4rdh74DLvBnAvcgUSUApP1EpdIURTl8vXMQOu8nfva6qVnfP1iLyd6Lul0mtWrV9PV1cWf/umfsn79+tP2J5NJfv7zn/O9733vTZ33TK7oGjicntBFrUymKIpy+Zvv5UTPRavVcuTIEYaGhti/fz8nTpw4bf+f/MmfsHXrVrZs2XIenzDrig/gaiCboijKleNCLCc6Fw6Hg6uvvponn3wy99pXv/pVJicn+e53v3tBrnHFN6Hb9EY0QkNGZoilksTTKYzaK/62KIqivCVna+aeTxd7OdGzmZycRK/X43A4iEajPPPMM/zN3/wNAHfffTdPPfUUzz33HBrNhak7X/GRSgiB3WDCH48A2Vq4y5x3iUulKIqizNXFXk50bGyMNWvWEAwG0Wg0/Nu//Rutra2Mjo7ysY99jHQ6TSaT4c477+Tmm28G4LOf/Sw1NTVs3LgRgNtuu42///u/P6/PfcUuJ5rJZEgmkxiNRtp9YwxOZQcq1Oe7WJDvuiDXUBRFuRKo5UQvDLWc6ByNj4+zc+dO4PSMbAHVD64oiqJcBq7YAJ5MJmltzU53OD0nuhqJriiK8k60ffv2t23t+624YgO43W7H4/EAYNEZ0Gq0ACTTKaKpxKUsmqIoiqK8oSs2gMdiMVpbWxkZGUEIcVotPKBq4YqiKMrb3BUbwPPy8jCbzezcuZPR0dHTVyZT/eCKoijK29wVG8CtVislJSU4HA5OnDhBcHwyt08tLaooiqK83V2xATwQCKDX6xkZGWHTpk2M9w8x3NcPQDAZ43KaXqcoinIle7ssJwrZRVGWL1/OihUrWLPm1dlfR48eZePGjSxfvpz3vve9BIPB8yovXMEBXKPRYDKZGB8fB+Cardvwj00y0NlNOpMmogayKYqiXBbeLsuJznj++ec5cuQIs/OWfPrTn+ab3/wmx48f59Zbb+Xb3/72eZUXruAAbrFYMBqNWCwW+vv7MZlMbNq8mYDPT3drO/646gdXFEW5HLxdlhM9l46OjtyKaNdeey0PPvjg+X7sKzeVqlarJT8/n1QqxdDQEI2NjRTZ7Cxd1cKpoyfYe2Aft2679oLlrFUURbkShF55dN7ObVv53jO+/nZaTlQIwXve8x6EEPzRH/1RLtd6U1MTv/vd73j/+9/Pr3/9awYHB9/CHTjdFR2dXC4X4XCYvLw8xsbGsBvMaHU6Glc2MxWPsX//flKp1KUupqIoivImXMrlRHft2sXhw4d54okn+OEPf5jL+Pk///M//OhHP2L16tVMTU1hMBjO+3Ne0QG8qKiIQCBAVVUV/f39uaVFNRoNlUsXYTAa2Lt3L8lk8hKXVFEURbkQ5ns50YqKCiDbzH7rrbeyf/9+ABobG3n66ac5dOgQd911FwsWLDjva12xTeiQXcEmkUjgcDhobW0lGYtj0umJpZJIJAuWLmGoq4eXX36ZLVu2oNVqL3WRFUVR3tbO1sw9n94uy4mGw2EymQx5eXmEw2Gefvrp3IpjExMTFBcXk8lk+PrXv85nP/vZ8/7cV3QN3OFwkEwmicViVFZWTtfCX03oMpWM0dTUhBDitD8ORVEU5e1j9nKiX/ziF7n11ltzg9Cuueaa3HKiQG450SVLllBXV3fO5URbWlr4i7/4i9ddb2xsjMrKSr773e/y9a9/ncrKSoLBIOPj41x11VW0tLSwbt06brrpJq6//noA7rvvPhYtWkRjYyPl5eV84hOfOO/PfcUuJwrZqQdf+cpX+PM//3NKS0vZtWsXC9evojuYTepSYXOwtKCcAwcOUFlZSVlZ2QW7tqIoyjuFWk70wlDLic7R0NAQO3bsQKvVEggEsFqt2O12or5A7piZnOgmk4lYTGVnUxRFUd4+rtgAbrVaOXXqFAaDITf3r6amBs/IWO6YcDJOOpNRAVxRFOUdQC0n+g6h02XH70UiESYmJgAoLS0lFo6gSWe7FaSUBJMxzGYz0ahK7KIoiqK8fVyxAVwIgcFgQKPR0NfXB2Snj1VVVRF2vzpxP5iIqhq4oijKG7icxlO9Hb2V+3fFBnCr1YrBYKCuro6Ojg7S6TQA1dXVBCc8ZKZvZjAeUwFcURTlHEwmEx6PRwXxt0hKicfjwWQyvan3XbHzwIUQuFwupqam0Gg0tLa2snz5cqxWK6WOAtxeH4WFBdkauKNENaEriqKcRWVlJUNDQ0xOTr7xwcoZmUwmKisr39R7rtgADtk+74mJCRwOB+3t7SxYsACLxcLi2nraTu6nsLCASCqB1Ai0Wi2JROKCpL9TFEV5J9Hr9dTV1V3qYlxxrtgm9ImJCUZGRojFsmt/l5aW0traCkBFWTnEk0Snm82nEqoZXVEURXl7uaID+KFDh4hEIqRSKQoLCwkEAkxOTqLRaKgrq2RyenR6QA1kUxRFUd5mrtgAXlxcjMfjwWg0otFo8Hg8LFu2jBMnTpDJZFhcXYfbkx3MFkyoqWSKoijK28sVG8AtFgs+nw+r1YoQgsnJSUpKSrBYLPT29lLqKMBqseDz+tRUMkVRFOVt5w0DuBCiWAhxqxDiT4UQnxRCrBNCnFfgF0J8WwjRLoQ4JoR4SAjhOJ/zvRUzNe9IJJIL4ADLli2jq6sLbTpDSXEJE5MTxFJJhF6nAriiKIrytnHWQCyEuFoI8RTwOHADUAYsBf4/4LgQ4qtCCPtbvO4zwDIpZTPQAfzdWzzPW5ZIJCguLmZoaIhMJoPX60VKidVqpaamhva2dqqKS4lFY0RjMVJaoZrQFUVRlLeNc00juxH4P1LKgdfuEELogJuBa4EH3+xFpZRPz9rcC9z+Zs9xvrxeL4lEAiEE4XAYp9NJIpHAaDSycOFCduzYQZ7TgsvlYnJiguoah6qBK4qiKG8bZ62BSym/eKbgPb0vJaV8WEr5poP3GXwSeOJsO4UQnxFCHBRCHLyQSQIKCwtJJpOYTCa8Xi/hcJhIJAKAVqulqamJkZ4+ilxFuD0eYqRVAFcURVHeNubSB14ihPiJEOLJ6e2lQohPzeF9zwohTpzhccusY74MpIBfnO08UsofSynXSCnXuFyuuX2qOQiFQuh0OjweD6WlpQwMDBAOh3P7y8vLcZisBPz+7Ipl4SDpdJpUKnXByqAoiqIob9VcBqPdAzxFtg8csn3WX3ijN0kp3y2lXHaGx+8AhBAfJ9sM/yF5CRLoZjIZYrEYHo+H6upq/H4/AwOnNzisWd7C2Mgoep2OYCSMxqBXtXBFURTlbWEuAbxISvkrIAPZ5nMgfT4XFUJcD/w18D4pZeR8zvVWFRQUUFxcTCaTIZFIYLFYOHz48GnH2O12KopL8QcCxGNxtHoVwBVFUZS3h7kE8LAQohCQAEKIDUDgPK/770Ae8IwQ4ogQ4j/P83xvmsFgoKKiAiEEwWAQjUZDIBBgaGjotOPqa2qIRWMEggF0RhXAFUVRlLeHuSxm8hfAI8ACIcTLgIvzHDUupWw4n/dfKNXV1RiNRjKZDOFwGJvNRltbGyUlJej1egDMBiMFhQX4fD50rmo1lUxRFEV5W3jDGriU8jCwDdgE/BHQJKU8Nt8FuxgcDgeFhYW5ZnSPx0NxcTEdHR25Y3QaLU6HM1tLV03oiqIoytvEWWvgQojbzrJrkRACKeVv56lMF8XIyAhtbW1YLBYgm5mtv7+fBQsW8PLLL1NVVYXdbseg0WLLs5FOpUnKFLG4GoWuKIqiXHrnakJ/7zn2SeCyDuCTk5McPnyYoqIi8vPzycvLY3h4mGQyyeLFizl+/DibN29Gp9FitViztfR0img0eamLriiKoihnD+BSyk9czIJcbEajkaGhIaqqqjCbzRgMBhKJBJ2dnaxdu5aBgQGGh4cx5FvR63UYTUaC4TAilrnURVcURVGUua1GJoS4SQjx10KIv595zHfB5ltlZSWZTAatVksikcDhcKDVajlw4ABCCJYvX05raytMT1F3Ohx4An6SySSZjAriiqIoyqU1l0xs/wl8APgzQAB3ADXzXK55l8lkkFISDoeRUlJcXAzA2NgYk5OTOJ1OXC4X/T29QHbAm8fvw2AwEI/HL2XRFUVRFGVONfBNUsqPAj4p5VeBjcCi+S3W/NPpsr0Hfn+2Vl1WVoZer2dqaoquri4AlixZwvjIKNFoFGdBAR6/D7PZrKaSKYqiKJfcXAL4TLSKCCHKgSSvplW9bKXT6Vwu9FgsRkFBAVarlfHxccLhMH6/H6PRyJJFi+nr78fhcBCKhNHp1LrgiqIoyqU3lwD+mBDCAXwbOAz0AffNY5kuCr1eT319PUNDQ0QiEcxmM06nE6/Xi9PpzNXCG+rqiMfjpFMp9CYjkUhEBXBFURTlkptLIpevSSn900uH1gCNUsr/N/9Fm1/RaJTy8nISiQRutxvINqvr9Xp8Ph8ej4dQKIRWoyXPYiUWi5Nnt+OfCqomdEVRFOWSm8sgtj+droEjpYwDGiHEn8x3weabyWTKNZuHw2F6e3sxGAxYrVZ6enooKSmhu7sbAKvZQiwew+Fw4PX7VQ1cURRFueTm0oT+f6SU/pkNKaUP+D/zVqKLZDqbHBaLBYvFQmdnJ2azGZ1Ox8zqpqOjo8RiMWxmM/FYDKfDgS8YUAFcURRFueTmEsC1QggxsyGE0AKG+SvSxREMBunr68NqtRKNRiksLCQUCpFOp0mlUoyNjVFWVkZPTw82i5V4PI7TWaCa0BVFUZS3hbkE8CeBB4QQ7xJCvIvsALYn57dY889kMhEKhZiamiIWi5FOp1m4cCEejwe3243L5UKv1zMwMIDFYCIWi5OfbyeZThEMBnO1dEVRFEW5FOYSwP8G2AH88fTjOeCv57NQF4OUktLSUvLz8wkEArS1tVFRUYFer6e3t5eSkhJGR0cpKSkh4PORSCZACGyOfKampkgkEpf6IyiKoihXsLmMQs9IKf9TSnk78Blgj5QyPf9Fm1/Hjh3jmWeeIT8/H6fTSSwW49SpU1gsFtxuN6OjoxiNRux2O5OjY+i0OhKJBM7CAkKhkOoHVxRFUS6puYxCf0EIYRdCFACHgP8WQvzr/BdtfkWjUQYGBhgZGcFgMGAwGKiursblchEIBHjqqadwOp2Mj49jt9pIZ9LEYjHyC5yEw2HVD64oiqJcUnNpQs+XUgaB24CfSSnXA++a32LNr0wiSnOVkzWlGiLjvUgpyWQyeL1eli5disPhwGAw8Mgjj9DZ2YlJbyCZTBKPxXAUFuT6zRVFURTlUplLANcJIcqAO4HH5rk8F0XKO4Sc7KQ034xdE8Pr9SKEYGhoCLvdjsFgoLKykm3btjEwMMDBvfuIxWLE43Fs+fmkUtmBbIqiKIpyqcwlgP8j8BTQJaU8IISoBzrnt1jzS+csx2g0UlBQgNOQIZ2I4/P5SKVSQHaAWyQSYfny5Wzbtg2f24Pf5yMciaA3GTEajUxOTl7iT6EoiqJcyeYyiO3XUspmKeWfTG/3SCn/YP6LNn80RitGeyHFxcVoNBqchjSBQIB0Os3k5CQGg4FIJEIwGGT9+vVYTCbCwSl8Xh9Cp8VqtTIxMXGpP4aiKIpyBZtLDfwdKWl0Yrfb0Wg02LUJ0uk0wWCQwcFBXC4XXq83tyJZy/JmQlMh3B43iXSKsrIyRkZGLvVHUBRFUa5gV2wA7xwLcOhYKxazGYuMokGSTqeZmJggPz8/F8ABlixejE6jwev1EonHqKioUDVwRVEU5ZK6YgN44/KVBJICbyiKRoDDkCEajWI0GgkGg/j9/lzGtcKCQsqKS/B4PETCEVwlJUSjUcLh8KX+GIqiKMoVSvdGBwgh/uIMLweAQ1LKIxe8RBdJIpGgvLaB9kMTJONxivKtBCMRSktLiUQiufnh4XAYq9VKmauY490djI6OsmpJOSaTiYmJCerq6i71R1EURVGuQHOpga8BPgtUTD/+CLiebEKXyzalaiAQoHd8Cleeial4Bl0qjN/nwWw2U1hYiNvtJplM4vf7sVqt6DUail0u2trbMFrMGI1G1YyuKIqiXDJzCeCVwCop5V9KKf8SWA0UA1uBj89j2eZVXV0dK9dvJhCJU+Kw4I8kiAc8JBIJjEYjBoOB48eP4/f70Wq1GA0mFixYQE9PD3pTdr8K4IqiKMqlMpcAXgzEZ20ngRIpZfQ1r19WBgYGiMfjRDQ20okYRXYL6XiI/v5+SktL0el0DA8P093dDUCe1cqC2npCoRDDoyMUFhYyOjp6iT+FoiiKcqWaSwD/BbBPCPEVIcQ/AC8DvxRCWIHW+SzcfEqlUoRCIZImJ1qtlkQ4SFmegYDPS1dXF2azmUQiwfHjx4lGo+TZ8hACCgsL2bn7ZUpKSvD5fCSTyUv9URRFUZQr0FwSuXyN7CpkfsAHfFZK+Y9SyrCU8kPzXL55U1BQgN/vp6SyFmHKI5VKoSdJoVVPa2srxcXFtLe3U1lZyZ49e7DbbCRjcaqrqzneehK73U46nU0AoyiKoigX21ynkSWBDJCefn7Za29v5/jx44RCIYLSREFBAfFYDBmaoKioCCklY2Nj2Gw2PB4PiViMWCRCZWUlsUScQCBAJpNRAVxRFEW5JOaynOjnyTajF5HtD79XCPFn812w+VZYWJjLfd7njmA2m9FqIBH04Pe6KS0tJRaLsXfvXhwOB+7RcWKRKA6HA0dRIT09PSQSiVyyF0VRFEW5mOZSA/8UsF5K+RUp5d8DG4D/M7/Fmn/19fVUVVURiURICT0WRzFOR3alMc9gJz6fD51OR19fH0eOHMFqteIeG8doMFJeWYnf7ycUCuH1ei/1R1EURVGuQHMJ4IJs0/mM9PRrlzW3201RUREDAwOEw2G6PFFMVjsptFQ4TAD4/X4mJiZ45plncBW68Lk9CMBit5GXl0cgEMDv9+dq8oqiKIpysbxhJjbgp2RHoT80vf1+4CfzVqKLxGKx0NDQwLFjx5icnGTQE2FTrZP2vlEq9Rp0WkFBQQH9/f3k5eXx9a98hbTFiFanR6PTYAnE6evr4+jRo1itVpxOJxqNhpKSEmpray/1x1MURVHe4eYyCv27wCcA7/TjE1LKf5vncs27wcFBvF4vFRUVZDIZQrEkixYtwmbSUVVkp74knxUrVlBeXk5NTQ1OhxNp0jM8MoKpwMHSpUspKytDr9dTUlJCQ0MDNTU19Pb2curUqUv98RRFUZR3uLPWwIUQBbM2+6YfuX1Sysu68zcWi3HixAmuvvpqjhw5gtvtJmMpwmjQkwG0ETd1dXUMDw9TUFCAVqtl0YI6utraGR4doaJAgxCCsbExMpkMLpcLAKfTyd69e0mlUjQ1NV3aD6koiqK8Y52rBn4IODj9c+b5wVnPL2sul4vh4WE0Gg11dXUEg0GG/HGsFhNToTA2kcBk1JOfn08gECAWi1HoLKCstAx37yCukhKKiooYHBzkySefxO12A2A0Gtm0aRNer5djx44hpbzEn1RRFEV5JzprAJdS1kkp66d/zjyf2a6/mIWcD1JKtFot/f39lJSUAPDoU89hyy/C5w9QWOAk7h3D6XRit9sZGRkhmUxiy8sjP8/OqY4OhBAUFRURDoc5cuQIL774IpOTk+j1ejZu3EgoFOLIkSMqiCuKoigX3FkDuBCi9lxvFFmVF7xEF8nw8DBer5e+vj5KSkowGAwMDAzQ4w7h8fkZGxujNE/L+Pg4y5cvx2w2M9zXh8/toXrRApKpZK4JXa/XU1tby+LFizl8+DB+vx+dTsf69euJx+McOnSITCZzqT+yoiiK8g5yrib0bwshHhRCfFQI0SSEKBZCVAshrhFCfI1sTvQlF6mcF1xTUxNWqxWj0UhLSwuFhYUYjUbSOhvheJq+/j4S3hG6uzo5efIkVqsVvy+A1+NmqKePWCqJw+FACMHExAQvvvgipaWlrFixggMHDhCJRNBqtaxbtw4pJQcOHCCdTr9xwRRFURRlDs7VhH4H8P+AxcAPgZeA3wGfBk4B10gpn7kYhZwP4+PjQHZVMqfTmVuBLC20RDCQQcdHP/IhakqcSCnJy8ujsNhFYVkpyUSCzlMdTE5OMjU1xejoKI8//jg/+9nP0Gg0LFy4kL1795JIJNBoNKxevRq9Xs/+/fvVnHFFURTlgjjnNDIpZauU8stSyu1SysVSypVSyg9KKe+VUsYuViHng1arJR6PMzExQTKZpKqqCiklGo0GjcFKz8gkE5MeViyuJRqNIqVEJzS0bFjHosWLqKqvJRaLUVNTw9KlSykuLmZiYoKnn36a2tpaSktLOXDgAJlMBo1Gw8qVK7FYLOzdu1etYKYoiqKct7kuZvKO43Q6qa2txefzMTk5SVlZGTqdjry8PIIJyDPA83sOUV9WSCKWDeDeSTdoNNQuWUx+QQFLly6lra2N8vJyLBYLiUSCjo4OIpEIS5YswWQy8corryClRAhBS0sLTqeT3bt3k0gkLvUtUBRFUS5jV2wA7+zsJJPJkEqlePbZZykoKMBkMhGLxdBb8piKxDFqMgTCMUrzjUSjUSbHxgmFQ6QSSRpblrF06VIKCwt58sknsdvtFBYWMjExwc6dOxFCsHLlSmKxGO3t7bnrNjU1UVJSwu7du1WfuKIoivKWXbEB3G63Ew6H0el0HDlyhKmpKZxOJxMTEyxYsIB+TxSRilFRVkx8ykdzczORcJjW1pNMBYOYbTaamppYv349gUCAU6dOYbVaaWpq4umnn+bo0aNIKVm7di2jo6P09/fnrt3Y2IjNZqOrq+sS3gFFURTlcjaX5URvFULkz9p2CCHeP6+lugiSySSxWLYb3+v1cvjwYXQ6HZFIhOLiYvxpA93d3Sytr4JUjEwqQV1tLaGpECePnyAQniIej3Pbbbeh1WrJz8/nqaeeQqvV0tLSQldXFzt37iQcDrN+/XpOnTqVGzgHsGzZMvr6+giHw5fqFiiKoiiXsbnUwL8ipQzMbEgp/cBX5q1EF0ldXR3XXXcdeXl5JJNJJicn8Xq9pNNpTp48SVpjoL13iNGRYYqcDnwjfbgKCknE4xQUFjI8MsLBgwdxOBwsXLiQ/Px8pqamaG1txePxYDKZqK+v58CBAwwODrJ69WqOHDlCIJC9lSaTiYULF3L8+PFLfCcURVGUy9FcAviZjpnLKmZnJYT4mhDimBDiiBDiaSFE+fmc762Ix+PEYjHKy8uRUhKJRAiFQtmpZOk0LpeL8VCGrs4OTAY93pF+uju6CPj8jIyN4iwowO/3s2/fPvLz86msrCQ/P594PE4kEqG3t5dAIMC2bduYmpri+PHj1NXVsX//fqLRKJD9EhGPxxkeHr7YH19RFEW5zM0lgB8UQnxXCLFg+vFdsvnQz8e3pZTNUsoVwGPA35/n+d60eDxOfn4+CxYswGAwEAqFsFgshMNh+vv7MRqNjEylGBrox2I2s6CyGJdFh0lvIBAMEpwKEovFSCaTtLe388orr6DT6chkMgSDQerq6njsscfweDysXbuWhQsX0tvbixCCPXv2kExmM7ktX76c1tZWNbVMURRFeVPmEsD/DEgAD0w/4sCfns9FpZTBWZtW4KInC/f5fAwPD2M2m7FYLIRCIYQQOBwOYrEYoVCIjM6EtaAEv99HcZGTBZYo1lQIs9UKQlBfX88HP/hBbrjhBux2O/F4nEAgQDAYZHBwkI0bN/L000/T0dFBeXk527Ztw263MzAwwIsvvghAQUEBxcXFaglSRVEU5U2Zy3rgYSnl30op10w//k5Ked4jr4QQ/ySEGAQ+xCWogU9NhenvHyEYDFFaWorJZCKRSGC1WnE4HIRCITweDyc9koDfz8TEBLFEmsXGCPa4h76eXjR6HYlEgrKyMioqKrjjjjsYGxsjnU6za9cuCgoKKCoqYnx8nP3796PRaFi3bh3XXHMNhw4dYmBgAIAlS5YwMjKS6x9XFEVRlDdyrsVM/m3656NCiEde+3ijEwshnhVCnDjD4xaA6QxvVcAvgM+d4zyfEUIcFEIcnJycfNMf8Ezck2HckymCgRj9fUOkUikSiQSxWAwhBDabjcWLF2MymWjrHmAMJ75QnKQUREJT2AKDiOE2IrEokUgEpzObbrWxsZH169fj8/kwGAz853/+JzqdjoqKCvLy8ti5cyd+v5/q6mo2bdrEM888QzqdxmAw0NjYqJYfVRRFUebsXIPRfj798ztv5cRSynfP8dBfAL/nLCPbpZQ/Bn4MsGbNmgsS3cxmPXW19bS0tNDZ0UFGxmeuRSAQQK/X09XVRVFREclkEn8oyrCpgE2NC2g76UOn1ZL2DnLy+d+Qb7YgNFpsNhtut5vm5mZisRg2mw2DwcAjjzxCc3MzH//4x3E6nezbt4/GxkbWrFnDsWPHOHr0KKtWraKqqorBwUEGBgaoqam5EB9TURRFeQc712Imh6Z/vgjsAXyAF9gz/dpbJoRYOGvzFqD9bMfOB4tVj883AWSw59uw2ZxkMplcfvQNGzaQSCTQ6/Xo9XpisRiDw6PsPjVGSGMjk0zjcDiwZMLEe/bT193F8PAw+/fvp7y8HJfLhUajweVysWzZMnp6enjggQeYmppiw4YN9Pb2curUKZqbm+nu7mZoaCg3oK29vZ14PH4xb4eiKIpyGZpLIpebgG7g+8C/A11CiBvO87rfnG5OPwa8B/j8eZ7vTRFCUFhkR683MBUKkIgnycvLIxwOEw6HiUQiFBUVEY/HsVgsLF26lFQqhdFkJumso9eXIJ5IEJgK4B7s4qpaC02LG0gmk4yMjHDq1Cmqqqo4fvw4a9asoaCgAJ/Px9TUFPv27aOqqgq/34/RaKSwsJCTJ08SCoWw2+1UVVXR2tp6MW+HoiiKchmayyj0fwGunl6RbBtwNfCv53NRKeUfSCmXTU8le6+U8qJPhLbl6UkmktjtDgxGI/Y8ey43emtrKyaTCbPZjNvtpri4GJvNhs/nQ4OGoLWcCZ2T4pIS2k+d4vjhfWT6D+Cym9mwYQM1NTUEg0GEENx3330sXbqU0dFRotEoGzZswO12EwwGGRkZIRwOU1tby6FDh8hkMixatAiPx4PH47nYt0RRFEW5jMwlgE9JKWcn7e4BpuapPBfN+PgoQyODlJZWEI2Gsdrs6PV60uk0wWCQUChEOp0mk8nQ29uLzWYjPz+f0eFhdEJLX1gQtNdSXV2DVqPBatShGzvGS089QmNjI+l0mk984hPk5eXR1taGEIL//d//ZWBggHXr1tHc3Mzk5CR+vx+dTofVauXkyZPodDqampo4duwYmUzmUt8mRVEU5W1qrolcfi+E+LgQ4mPAo8ABIcRtQojb5rl888bhcODxjBGPpSksKMJqsaPT6dBqtaTTaYQQJJNJdDodBw8eJJFIMDY2xlQggMc9CekM+zsGsC7ayMjYOAaDgcryMsrkBFp3B77xIaxWK1u3bsVoNHLDDTfgcDi47777eOihhwgGg7lUq08//TQlJSV0dXXR0dFBfn4+BkM2F7uiKIqinMlcUqKagHFg2/T2JGAG3ks2Actv56do8ysvLw+DQUN31ylcxYVoNHqMRhOZTIZwOIzRaKSgoAC9Xo/b7SYSiRCPx3EUF9LW00lGpyU44ebxSAp9Js7R409QU1GKy+XC6XSS6d1H67NOprQOKivreOqpp1iyZAmdnZ34fD58Ph9lZWVs2LCBBx98kIqKCsxmM7/73e9YsmQJyWSS5557jubmZtatW0d5+UXPNqsoiqK8jb1hAJdSfuJiFORiO3XqFFqtlmDQizFoJhT2YbXYSSTi6PV6MpkMQ0NDVFZWsm3bNtrb28lkMrQ0tzDudaMXguKqSpYtWcYdd9zBD777bUrKTehTYaqqq+jt7WWs+yT19fXUmgtxO414PR7S6TThcJjjx4/T2NjIli1b8Pv9HDt2jM9//vMsXLiQ0dFRNm3aRFNTE/39/bS1tVFaWopGc8Wu/qooiqK8xlxGoVcKIR4SQkxMPx4UQlRejMLNp5UrV5LJZNDpoKysHK/XTTIlEUKg1Wqprq4GYGxsjEgkkqsVHz18GKeriFggTDA0xeTkJKFQiMXLWuhLOYkVLcET09DS0kIqlUICZk2a27c0URrrY2tzPalEjCVLllBWVsbg4CClpaUMDw/z3HPP4XK5MBgMtLe309DQgFarJZFIMDg4eGlvmKIoivK2Mpcq3U+BR4Dy6cej069d1l5++WXGx8dJp1NMTXmpKK8glYyTTGaIxWIkEglKSkoIh8P4fD7q6+sxm83EIlGSGkEsEkEjtPh8Po4ePcqiRYuwWq3ENWZ+/8oAPWkXY1ENU1NTHDlyBL1Ox7pVzXhP7aMiMYC/6xDBiSE2btjA1q1bKSsrY+/evTz++OPU1dUxPDyM2+3OXbejo0MNalMURVFy5hLAXVLKn0opU9OPewDXPJdr3jU1NWE0GpmYmGBktBe9wUhBQREGvZFQKITb7cblcmE2mzl16hRbtmzBZrOh12gw5VmZCk0hUym0Wi1Hjx7FYrFQUFDAokWLaGpqorqhEelaRLR0Fa8M+Hnq2R14vV6MRiMe9wT+3mP4jj3L0Iu/RDt6lDuvWY3TJAj5Pfz0pz/Fbrdz5MiR3OIqeXl59Pf3X+rbpiiKorxNzGUQm0cI8WHgvuntu4DLfpKyRqNh2bJl7Nq1i5GRYaTUkWezI5GYjGai0SiDg4MUFBQQDAb52c9+RklJCfsPHMBVbMfmdNDX2c2SkirC4TBtbW00NDRw9OhRamtrcznPrXYHV73vw6RTCcz5BlaWVvLs7x/JLV4SjkQoLysjnU5j9nfj8w1QVuTg4f/8BmmDnT0vPkv9oqXU19fT1dVFdXU1Wq32Ut8+RVEU5RKbSwD/JPADsslbJLAb+Pg8lumiiMViABiNRuLxOLFoiEgoRDwex2gy59YGt1gsFBUV4ff72bBhA0eOHGG4sxedwUBwwkN7xylWLG+mo6OD6upqysrKiMVidHZ2UllZid/vp7KykuLiYgYGBliy5GpuqWnmNz/9ERHvKAG/n3ddcw0GgwGn00nfqJvSkhK2b17PsWPH6BwaY3BvL9rxEyyqr6XvJR8V1XUIvRGhNyF0xuxDP/1TqwetDiHEJb7DiqIoynyaSwCvlFK+b/YLQojNwGU9qio/P5/6+noGBgZob2/HoDciNFpSqSQgc1PJ7HY7kUgEm83G4OAg9fX1jKbDJBMpxuO9dHd24bTns3r1ap5++mmuv/56mpqaePnll7HZbJjNZlKpFOFwmPXr17N3715WrVrF+z/7JX784x/jiUzx40f38MHbbsJVvRB/cIqRCQ/lxYW0tLQQThwmGI7R1t5Bid1MyDdJkc3whrVwodUjdHrQ6rPPpx8z22g0gMgGeqEBIQCR/SkEQmhO254+66tfDGaOn34uNDqEyaa+OCiKolwkcwngPwBWzeG1y0oikaC6ujqX9jSRSJDJCKTMkEgkyWSyS4w6HA7MZjOdnZ1s27Ytmy9dm4BUGqszn2QgwtGjRwkGg0SjUaSU1NfXc9111/H73/+esrIyIpEIqVSKVatWsXr1ag4ePMiGDRvYtm0bHR0dTE1N8ZsXXqG5uRmvuYEly2vpGOlnw/Imlsclzzz/Io2LFzI4MoZeI3G5XG+4YplMJ5Hp5EW6m1m6/FJM9Wsv6jUVRVGuVGcN4EKIjcAmwCWE+ItZu+zAZd8JG4vFOHz4MMlkkng8TjKZpLKyhkxGMjk5hk5nIZPJMDw8zLJly3C73cTjcRYvXkx0chC/309evoNEWrBu9RpisRg6nY4dO3ZgtVrZtm0bJpMJj8fDwMAAFRUVhMNhCgsLaW5uZv/+/TQ2NjIxMYGUkiVLlmQXPAmFOHS8jcLCQsakkyXXf5Tne0KYi4sosVnp7zrFg7vb+ezSTThsFmQqTiYVRybjyFQc0qmLHrhnpAJjpEMetLbCS3J9RVGUK8m5auAGwDZ9TN6s14PA7fNZqItBq9ViNBpZunQp/f39uN1uxsdHKC+vxePxkEjE0Wqz/eNCCCorK3G5XNhsNl48+Qoy30KwP4A2lSQQCHDdddfR2dnJ0aNH6evrw263Mzo6isPhIBQK0d/fj8fjwWq1UlZWBsCxY8ewWCy4XC5aW1u57rrrOHXqFB6PB41Gw29+8xvuuOMOVq1axalTp4gn09z+kU/xH//xH/z3r5/g05/+NCXlJa/7bFLK6UCeQKZTyFQCpmvkMp3M7pMZQEJm+qeU2ffJV7eREjnr+fTZp5/LmYuRScaQiSgAibFOzA0qgCuKosy3swbw6TW/XxRC3COlfMfNXwqFQkgpGRoaoqmpiR07diCEoKy0mK4uEyDQaCShUIhjx46xaNEiHnroIb7whS9Qc+II4/EQbr2W8KSXY8eOUVNTQ2NjIxqNhrGxMRKJBBUVFezbtw+73Y5Go2HPnj1UVVVlr1NWhtFo5Le//S12u528vDw6OztZvnw5Dz/8MJs2bcJut/PMM89gNpvRarX09fURCoX4zGc+ww9/+EPuv/9+3ve+91FXV3faZxNCgG66D/wiyMTDRNqeBylJT02SDvvRWh0X5dqKoihXqjecB/5ODN4Ak5OTvPTSS5SUlKDX6zEYDCSTSTo629Dr9RgNJqoq6jCbzYTDYerq6vD5fAwNDVFWWopJCvLt+aRT2dSoTz/9NM8++ywej4fJyUnWrFlDSUm2djxTA3/yySfZvXt3LiFLQUEBN910E4cPH6aqqopoNMrQ0BCbNm3iqaeeYvHixdTX17NgwQLi8ThSSn784x8TCoW47rrrKC4u5uGHH+bYsWOX8laiMVrROV7N1Z4c77yEpVEURbkyXLHJtSsqKkgmk7S2tpKXl5er5fp8bpLJZDbNqsFEZWU1Op2O3bt3o9fr2bdvHyVFLsqrqygudmFzZlcxM5vNpNNpJiYm0Gg0PPjgg7mad0dHB/X19UxNTXH33XfzX//1X8TjcQDKyspYsWIFkUiEiYkJjEYjsVgMl8vF/v37qa2tJRaLEYvF+NjHPoYQgkOHDjEyMoLH46GlpYVnnnmGl1566ZJmatOXNOSepwJjZKKX/YqziqIob2tXbAB3OBzU1tbmVhrbtGkTer2eRCJBMpkkkYwTCoXJzy+koaGBWCxGNBrl1KlTdHd2ktBrKKmrxmKxkkgk8Pv9WK1W9Ho9NpuNaDTKokWLuPbaa3O18plVyH71q19xxx134PV6AaitraWmpgan00l3d3c2JWs8TllZGd3d3TidTrRaLS+//DLLly8nnU6zZcsW6uvrGR4epry8nCeeeIJHHnnkkt1PrdmOLr80t52Y6DrH0YqiKMr5mstiJi4hxJeEED8WQvzPzONiFG4+GQwGVqxYgdvt5uDBg8TjcQoKCqisrETKNNFomHAoQjAwRVlZOTfddBPpdJqpqSmcDgcjfYPo8qzYXNklR2OxGCMjI0SjUYxGI8PDw/T19fHpT3+ampoatm/fzubNm/ngBz/IZz/7WSYmJvjYxz7GE088wejoKPv27aOpqYmBgQHGxsYoLCxk//79bN68GavVisPhoKuri+PHjzM+Ps6xY8fYtm0bLpeLa6+9lve+97089thjPPnkk/j9/ktyT0+rhfuGycQjl6QciqIoV4K5zAP/HfAS8CyQnt/iXDwGg4H3vOc9jIyMMDQ0RFdXF2NjY9TX15Ofb2diYoJUKoHPn0QzkGHx4kWsXr2a3bt3E4tEWNTQQJ7NTmltNd6uAaampgiHw5SWluL3+wmHw/zud7+jqKiIoqKi3KC29vZ2vvzlL+NwOPjhD3/IwYMH+chHPsLIyAhTU1O4XC66urqYmppicHCQRCLBRz/6UW644QZ2796N0+nk5z//OT09PUxMTFBSUsLIyAgbN25kYmKCvr4+tFotRUVFNDY2YjabL9o91VqdaPOKSE+5QUqSE10Yq5ov2vUVRVGuJHMJ4BYp5d/Me0kuMp/PR1tbG0ajkZqaGnQ6HV6vF7/fT0VFBW63m3A4gMViJRyOceDAAWpqaigoKGDH089RvmIpJouJ8rJyJoqKCAaD+P1+zOZsHnWn08nExAS/+tWvSKfTvPzyy3zta1/jX/7lX9i5cycrVqxg8+bN9PX18eyzz7Jp0yZSqRTvfe97aW1tZcGCBTz66KOMjY2xa9cubr75ZoQQrFy5kkgkQjKZZMeOHTQ3N7Nr1y5CoRBNTU10d3fT0NBAPB7nxRdfpLa2loaGBnS6ufyqz5+hZCHRKTcASc8g+pJFaAymi3JtRVGUK8lc+sAfE0LcOO8lucgsFgtTU1M4HA7S6TSjo6OsXLmSYDBISUkJGo0WoRHoDWbMJhuBQIDR0VHsdjtGg4FoMo5nfJLBzm40Gg1msxmPx0N7ezupVAqz2UwymWRycpL8/Hz27dvHjh07KCgoIBKJ0NramguuAwMDvPzyywwMDFBUVEQmk8FsNnPDDTcQj8c5fPgwU1NTFBQU4PV6qaiooKqqio985CMMDAywbNkywuEw3d3d1NbWcuDAAWpra9m2bRvRaJTnn3+egYGB7DzveabNK0JrdWY3ZIbkZM+8X1NRFOVKNJcA/nmyQTwmhAgKIaaEEMH5Lth8k1LidDoJh8NUVFTkBqmZzWZ8Ph92ex6pVBzQEI1FKCosRwhBNBolLy8PrdmIsdhJOJN89TWtFrfbPZ0IJkFtbS0TExMcPnyYTCbD4cOHGRkZ4dixY9hsNiYmJigvz06/CofDHDp0iNHRUerq6ujp6aG2tpZVq1bhdrs5fPgwBQUFeDweysrKGBkZYdmyZVx77bWMjo7S2tpKYWEhOp0OvV7P0aNHMZvNrFy5krVr1zI0NMTOnTuZnJyc93urL1mYe55092UTySiKoigX1FzmgedJKTVSSpOU0j69bb8YhZtPiUSCUCiEyWSivb2dkpKSXBN6NBrNjUY3GEzIjCSVBp93irKyStyTk2jSGYwWM2UL6zBZLJjNZkwmExaLhWAwiNvtxmKxZGvsRiORSIQdO3YQjUbZv38/J0+e5N3vfjd6vZ68vDy8Xi9CCO6//37Kysrwer1EIhGuvfZa0uk0+/fvx+fz4fF4ctPT/H4/q1atYvHixeTl5XHw4EGEECQSCcLhMAMDA0B2xP2mTZtYvHgxx48fZ9++fbnV2OaD1l6Mxjz9J5JJk5zsnbdrKYqiXKnmMgpdCCE+LIT4f9PbVUKIdfNftPlltVrRaDRs3LiRRYsWUVxczOLFiykvz9a0i4qKEEKSSsZwOAqx2ezkOwpoWrKBJY0bmfJGsJisJFMplrc0E4lkR1wnk8ncCmYmkyk3iE2r1aLX65mYmKC7u5sHHniABx54ALfbzfXXX4/X68Xj8XDgwAHuv/9+9Ho9hw4dIpVKsWnTJgoKCnj66afp7u4mkUhQXl7O6OgoGo0mlzQmk8ngdrvx+/24XC7a2tqIRqO5z1xaWsr27dtxOBzs2rWLqan5masthDhtRHpysheZTs3LtRRFUa5Uc2lC/xGwEfjg9HYI+OG8legiaW9v5ze/+Q2Dg4PodDqqqqqYmpoiPz+fVCqFy+VCp9MRCI6TSiWwWR2UlFTgdo9QWVlOvq2QeASSST0pqaWurgGTyUQymSSZTBKLxejp6cFmswHZhC0+n4+rr76ayspKAoEAPp+PZ599lpdeeinX363RaHj00UfZt28fBw8e5OTJk7lELu95z3toa2vjhRdeoKioiNHRUQDsdjtLlizJzWsPhUK5gXBHjhw5re9bo9GwePFiGhsb2b17Nx6PZ17ur85RjsZoBbIroyXdffNyHUVRlCvVXAL4einlnwIxACmlj+xCJ5e1oqIi6urqeO655+ju7qa8vByLxZKrKZvNZiwWC/F4lOGRboLBEVKpGA6nk7q6BkQ6RdzrxeksxOuL4POGWbZsDVqtgeHhkVza1Y6ODvr7+xFCMDk5yT333JNLjarX6/nUpz6Fz+djfHycTZs2sXDhQrZv387ISPYcZWVlbN68mf3791NSUkJTUxNDQ0McOXIEv99PMJgdjlBfX09DQwPV1dV4PB72799Pfn4+6XSa/v7XZ8OtrKzMLW06PDx8we/v62vhPcjMO2YWoqIoyiU3lwCeFEJomV5+SgjhAi5dzs4LpLe3lyNHjhCJREin0zz66KNUVVVhMpmIRqO5QF5RUUEoNEVnVxtjY/2MT/SwctUiKuprScbjRCfdOIsKsNvzmZz0UFu7kLLSCrK3DPLz87HZbBQVFVFfX4/H4+HUqVMEAgFeeukl3G43a9eupb6+HgC/38/Jkyf50Ic+RCgU4nvf+x5arZbly5fz+OOPU1NTg8/no6mpCa/Xy4svvkg6nQ2MixYtYvPmzdTW1jIyMsJjjz3GihUrOHXqFOFw+HX3oKioiI0bN9LW1kZX14XPnKZzViL02SlkMhkn5Rm84NdQFEW5Us0lgH8feAgoFkL8E7AL+Ma8luoiWLZsGStWrKCjo4OhoSEGBgbo6OggnU4Ti8UYGxtDCIHRaMRut2Oz2Th69CgvvfQShw7twWHXoc/Tke+yo9dLNFqoKK+msqIGg8GExWLH5wszMjxKIpFkfHwci8WSy4m+YMEChoeHefjhh7n77rvRarUsWrSIZcuWEQqFeOqpp9i+fTtSSn7zm9/gdrsZHh5mamoq9/y9730vIyMjvPTSSwQCASBbs77ttttYsmQJ9957L6FQiIULF76uKX2G3W7nqquuYnh4mOPHj1/QqWZCo8EwqxaemOhCXsJ87YqiKO8kcxmF/gvgr4F/BkaB90spfz3fBZtvWq2Wa665hg0bNjAxMcHk5CSlpaWEw2F0Oh0+ny83tzsvLw+73U5lZSWJRIKHHnqIns4ufKEgXR1t3HDbjdTWljIy2kVdXT0tK1ZTVVVDgaMIoymfyUkvg4MjBAIB0uns6mXhcBiLxcLY2Bgul4t7772Xo0ePotfrEUJw7NgxxsbGqKiooKuri9LSUvr7+zlw4AD9/f384Ac/4OTJk1gsFkwmEzt37qSzsxMpJUVFRXzhC1+goKCAL37xi9TW1gLZVoczMZlMbNq0iVAoxMGDB3M1+gtBV1iN0GV7XGQiSsp34ZvrFUVRrkRzGYX+faBASvlDKeW/SynbLkK55l1hYSGbN29m8+bNXH311QSDQU6dOkVpaSkmk4n8/HwSiQQajYb169czOTmJ0WhESkksFmPJosVoDTr6e3u59/77EEJQWOhgcKgNs1mPTgeu4lIaFy9jw/otaDU6RkYmGB2dpKqqGsg2YY+Pj+N2u4lGo5w8eZJAIMANN9yAVqvl6NGj1NbWUl5eznPPPYfP52PhwoXceOONWCwWfv7znxMOh+nt7SWTyfDkk0/yu9/9jkgkgt1u5+tf/zojIyP80z/9E83NzXR2dhIKhc54P/R6PevXr0en07Fnzx4SiQszd1totOhd9bnt5ETXRUkooyiK8k43lyb0Q8D/J4ToFkJ8RwixZr4LdTHMjAJPJpMYjUZaWlo4cuQIPp+PdDpNYWFhLrnLkiVLaGpqQqfTYTKZGB8fp7+7l6qaGgwmI5MTE7S1tTE+Ps6hQ4doaztGOp1gYnKAsdER8u2FrFy5jrKySoKBEIcPHae3t4/x8UmkhJ6ebAB2uVwcP36cF154ITcIbv/+/XziE5/gz/7sz7Db7Tz66KN0d3dTUlKC0+nk2WefZd++fdTV1fH+97+fWCzGD3/4Q44dO8bChQv56Ec/yr59+7j//vupr68/a1M6ZEeor1y5kqKiInbt2nXGfvO3Ql9Ui9BmU7lmYiHSgbELcl5FUZQrmZhrbUgIUQD8AfCHQLWUcuEbvOWCW7NmjTx48OAFOVdvby/PP/88FRUV/OpXv8LhcPDQQw+h0+kIhULYbDampqaIRCKsW7eOj3zkI/zoRz8inU4zMDCAxqBn/V3vY6C3j8ikjxtXbcTr9ZLJZOjq6mJiYgKDwcCWq7agN9goKizl6acfJxxJgkzg9rhxOoqYmvJhMlvQa41U19aRZ8sjGgmj1Wnp6m4jkUjidBSwds1axsYnkAiKCovo6+tmaVMzoakp+vq6KSwspqCgkBtueA96g+To0cMUFxezYMEC2tvb2blzJzfeeCOlpaVUVVXR0NBwzvvT39/PqVOnWLVqFUVFRed9vxMj7STGOwHQWPKxLN563udUFEV5pxNCHJJSnrHi/GZWuGgAGoEa4LJvRrdYLAwPD2OxWCgqKqKgoIC1a9fS1dWFRqMhkUig1+sxGAwEg0Ha2tpYtWoVjz/+OFqtlkAgyIEdL2HMsxGYmMTv9zM0NMT27dsRQqDT6WhtbWXXy7u45ZZbWLlqEcUlDn70w/+kuroWn99LUVExixub6O05RXl5LVqthsbGJbhcLiYmxikqKsTj8TA2OklN7RLKyxdw/OQxSkrKMJnNaNBR4CzDbLZRVlaO2+3m0MFjrFixlvLSJYyM9nL06EMUFRWyatUq9u/fz7p163JJYywWy+seer0egJqaGsxmM0eOHCEvL48lS5Zgt7/1BHz64noSkz2QSZOJBEj5RtA5yy/Ur1NRFOWKM5c+8G8JITqBfwROAGuklO+d95LNM4vFQnl5ORMTEyxbtiwXuGcysWk0GrRaLZlMhmQyicvlorGxkW3btmG1WqmuriKVTCKEIB6JsH//fux2Ow6Hg8rKSvLy8iguLmZ4eJhIJMKvf/1rbDYjK1YuJRIN4nQ6GR8forysjOKSUmw2O3k2B319fXR0tGE2myguLmXb9neTlnEOHT6AyWymrLQKo9FG8/IVlJS6MJm1DA0NcfjwAerrFjE0PMyJE6+waPFSNm58Nyuar+bw4XaOvNJKPJ6kvb0dyE5vs1qtxGKx3LzyZ599lieffJKdO3dy8OBB3G43K1eupLi4mL1793Lo0KG33KwudAb0RTW57fjQcZUjXVEU5TzMpQbeDWyUUrrnuzAXk9/vZ3BwkLKyMvx+P+vWrePll18mEomg1+tzK5WFw2HGxsbIZDKEQiE2bNjAK6+8AkJgSKfQ28zkl7roPd6F3+9Hp9Nx++23A9l+9omJCXbt2kVtbS19fX0UFDjp7+8jlYqSziQ5fmIP69evZ2RkBJfLhdmcrQWbzBn6+weIxX3ceOO72LdvH4FgBaHwKH39R/jQBz9EXf1S1q/fwPHjx/nWt/5/HDz8HHl5Lg4ePkh/fz+NS5ZTXFzMVZvfxckTxxgecnP0SDtHj7Rxx523c/3111NSYkKIbOIVIQSpZIJoNEo0FiEcDnPgwEG2bt1CdXU1PT097Nq1i9LSUhYtWvSm1xo3lCwi5RtBJmPIVIL44DFMde+IIRWKoigX3VkDuBCiUUrZDhwAqoUQ1bP3SykPz3fh5lMymWRoaCi33GcqlaKlpYVjx44Ri8UYHh7OBlKTCY1Gw4EDB2hqaqKrq4vFixez4/nnKWlcQGoqQklNFfGeEXQ6HceOHWNkZITy8nJSqRROpxOHw4Hb7eb3v/89a9asIT8/n/z8fI4cOYLb7WZycjLXGrB582bGxsYIBoO8/9b3c++99xII+IhGI3i9k2QySfLyLPzb9/6F+vp6NBpYsGAB73//+wgGg5SXl+Px1HP48DHi8QDRiI3ysgrS6Thbiq7m8OHDHDm6l18/8DCDgxNsvepd5Oc7EUK85g4Zsg8Z4MHfPMvKlWvRau1UlC1jcLifgwceoby8gpqaeswmY7bbQK9Bp9Oi12vQ6TTo9Vp0ei06nQaNRiB0eozVK4h17wUg5R9VTemKoihv0blq4H8BfAb4lzPsk8A181Kii2R0dJSxsTG0Wi0mkymXrczv99Pc3Ex3dzfBYDCXka2lpQWDwTDdfF5NV1cXMSFJ+qew1ldjsVoxGow0NjZitVqx2+309fXh8XgQQvD5z3+eX/ziF+zbt494PE5ZWRlSSvLz82ltbWXRokWUlpbS2dnJqlWrSCQSdHR0sH37dgYGBnjllVcYGxtj48aNBAIBKisr6e/v5/HHH6e0tBStVptLRLN48WIKCwvo7OwkkRrHmmcnmYwhZZp169YTT0QIBIKMjY7x/AtP43KVUFhQRGGhi4KCInS6V/8sqipr8Pu89PZ2saB+ERqNjpqqBZSVVNLf38NLL71IRXkVVVW1uf7zM9HpNNMBXodIFyCCwwitINR6ANPibeiMZjRagU6rQaPNBnyN5rVfKhRFUZQZZw3gUsrPTP+8+uIV5+KxWq2Ew2FKS0s5efIkJpOJoaEhJiYmiMfj6HQ6pJRoNBqSySTPPPMMtbW1FBQUMDQ0hJQSz9gkriIXnqFxSspK6e/qob+/n6uvvpqBgQGuvvpqRkZGctPLPvWpT3HffffR399PIpEgFosxODhIS0sLhw4dYs2aNeh0OtLpNEIIVq1axeDgINdddx0lJSXcc889WK1WHA4H8Xgco9GI2WxGq9XicrkYHh4mk8nw/PPPs3btWpYuXUo8HicU8lC/oJShoW4qyusxW/R4vTEWLGggHJ5i0aIGhNAy6R6jq7sNuz2fgoJCCguLMRnNNC5ZxsEDe3DkOyksdAFgMBhZuHAJVVW19PZ1s//Ay7Q0r8Zmyzvj/U6lMqRS01nYDLWI6AikYkAMGdkHZSte9x6hAc10074QIDTZ55qZJn+NyDX/a7XZcQsarUCrEWi1mtNfm/5SoNWKM7Q2KIqiXH7esA9cCHHbGV4OAMellBMXvkgXRzgcxufz4XK5chnW7HY7sViMVCqF2WxGp9PlluYcHx8nk8nQ0NBAU1MTeXl53Pfow4zHE2RGBSWOAkwmE2NjYzzzzDMsWLCAvXv3kpeXx+TkJI899hixWCwXPEKhEAUFBfj9fkKhEGVlZbzyyisMDg5SVVWF0+lEp9NRWlpKX18ft9xyC4cPH8ZmsxEIBFixYgWTk5NotVo2bdrEkSNHCAQCLFiwAKfTycMPP8zSpUtZuXIlOp2OvLw8RkdHMRgldXXFDA62c+jwC2zcuJETJ/fysY99jLy8laRSKdxuN+Pj44yPd2C321mxYgWVVe/iwIEDrFxdP53QBqSUSClZ0lTB8FA2FevKleswmcwkkxlSyTTJmcA9e7aiRod0NSFGDwEgIhPIqRHIO70pXWYgjeT0N58/IUAzE9w12YCf3X71NY1GwBzjvBC8+gVBI3ItCLOfq9YERVEutLkMYvsU2eVEn5/e3k42uUudEOIfpZQ/n6eyzavVq1ezdu1aOjo6sFgsuN1uYrEY69atY8+ePUgpWbBgASdPnqS0tJSGhobc3PFt27bxvve9j94pD8dbT0Iggtfrw6Y3YLFYWLhwIXl5eZSXl7Nw4UIymQxer5eenh7q6+uZmJigr6+P8vJyAoEAp06d4qqrriIcDpPJZDh8+DA1NTWkUimKioqoqalhcHCQDRs2MDo6mpvi5vF4SCQSJBIJPv7xj+N2uykpKSE/P59kMsnu3bs5fPgwV111FZFIBIPBwNDQEOFwmLy8PHQ6HVarldbWVv7t3/6NP/qjP6K4uJjS0lJKS0uRUtLR0cHOnTtZsWIFDQ0LOHr0FTZt2vS6WmzDwlpMZh1tbcfYtGkTVuurU86klKRSGZLJNKlkhnQ6QzptI2EIknL3k8lIZKibTEExGWEkk86QTs9ftjYpIZ2SpLl4q6PNfGkQQmS/F8y0KmQ30Ijp18i2NLz2va8/X/ZFrVZgNOowmfQYTTp0urnkZlIU5Z1gLgFcByyRUo4DCCFKgJ8B64GdwGUZwHt6ekin05SXlxMMBqmpqeGll15Cr9dTWVmJEIKOjo7ciPS1a9dml8jU60kkEhw7dgyHw0lBZRlTcow8jQH/xCTxeJxYLMaGDRuIRCLs3r0bgIaGBmw2G9XV1Wi1WtxuN6Ojo7k1xAcGBigsLKS1tRW9Xs/SpUtJJBIMDQ3R09PDokWLKCkpySVkaW9vp7m5maNHj/Lzn/+cvLw81q1bRyAQ4A//8A/p6emhsbGRHTt2cPjwYT73uc8xODjI2NgY119/fS5z3IEDB/jCF77Afffdx49//GO2bt3KggULKCsry60dXlRUxOHDh6moqEAIwalTp2hsbHzdPa2srCSdTrNnzx42b96cG6WevW9a9HrtacfLgjVET4XJxCMAaHVDmBesz+6TMhvYczX9V2v8r3s9k30tnc6QzkgyaUkmk/0SkJ7+MpDJyOwXg4y80BX6OZn50jDfF9fpNZhM2YCe/alDb9CqbgNFeQeaSwCvmgne0yamX/MKIZLzVK55l0qlGBkZQQiBzWZjbGyMsrIyDh06lKt9zwTvYDDInj17cDgcHDhwgEQiwYYNGygI2DFIQUYIPF4PVpMJr9fLyy+/TEVFBY2NjWzevJn29nZGRkbQarVs376dhQsXMjExwalTpwgGg5jNZsLhcO66w8PDtLa2UlJSwsjICENDQ2QyGU6dOoXf7+fJJ58kGo0yOTmJz+ejoKCAr33ta9xwww259LD19fXk5+cjhGDnzp18//vf56Mf/ShDQ0OkUiluuOEGDh06xPDwMA899BArVqxg9+7deL1e9Ho9J0+epLa2luLiYvR6PatXr+bYsWNEo1FGR0dz89xn5szPTEOrqanJBfFNmzZhMpnO+jsQWh3G6hVEO7NfctLBCZKeAfSF1bl+7fmQyWQDfCYtswE/82qAz0iyPzNzC7SS6S8baTn9ReE1573IXxpSyQyhZILQ1Ktz7DXTtfTZzfivjeezA7xmZgyBLjuoMPs8+1One7WbQVGUS2suAfwFIcRjwMwKZH8w/ZoV8M9XweZbSUkJ9fX1hMNh1q1bl5vfXFFRwdGjR+nt7SUWiwEQi8WQUlJcXMw111zDk08+yeLFi7nm3e9mwD1OWV0Nhx55BovFgtlspqenh9///vfEYjGWLVtGVVUVPp8Pr9fL/fffT3NzM5lMBp1Oh0ajIRwOs3z5crxeb+4f6dDQEEuWLEGrzdZaw+FwboGR9vZ2Fi5ciBACKSVerxebzcY999xDUVERtbW1rFixgtLSUm666SY0Gg3PPfccjz76KDU1NTz//PNYLBZWrVpFf38/k5OT3H777Wi1Wu6//37e//7309LSQjAY5NixY2QyGaSUpNNp3G437e3tHDx4kA0bNqDX68lkMgghKC8vp7a2lvr6+tNq4gaD4ay/B62tEH1xPcmJHgASwyfR5rnQGN7cHPM3I9snrX1zeQjP08yXhmzLASBlLvjPZDOWmexrSJAzEf/0HzAr9bGUkEpniEVTxGNJYrHU7N2vXjstiUYu7HdtrVag1WkwGLQYjToMRh1GY/a5Vqua8RXlYpjLv7A/BW4Drpre/hnwoMwmUb9sR6i7XC4qKioYHR1lZGSEv/7rv+ab3/wmFouFRYsW4fF4aG1tRaPRkMlkGB4eZnJykrq6OhYsWMC9997LhokRnEWFjPq8VDXU4enN9lPPTEfr7u6murqa8vJyotEozc3N9Pf3o9PpKCgoIBQKEYlECAaD7N27lxUrVhCPx5FS0tvby/Hjx7n11lsJBoMANDU1kU6nefbZZ6mtrUUIQTQazdXC6+rqOH78OHv37qW6upru7m70ej2LFi0iEonwwgsvIKWks7OT/Px80uk0yWSSJ554Ar/fz5YtW1i9ejW//OUvOXHiRG7K3OLFi3E4HLnsbYFAgIcffphgMMhdd92FTqcjkUgwODjI4cOH0ev1udr7TE38XFPMDGWNpAPjZOJhZDpFfOAo5oYNF+tP4aLIfWmYR1JK4vF0LphnH8nppvsLK9s9kSYRT59W24fslEHDdDDPBvds94luuhavKMqF8YYBfDpQPzj9eMcYGxtjcnKSkpISOjo6ALjrrru45557WL16NXv27KG0tJSxsTH8fj9CCKampjh+/Dj5+fkYDAYO7d1LXnkpGYseR1kJAyfa6ezsZPny5ezevZvh4WFOnjzJ+vXr6e7uJi8vL1fzXr58OVNTU6RSKVwuFx0dHYRCIfLy8sjPz8doNPLKK6+g1Wqpq6sjGAwyMjKSq+V3dHRw5513UllZyeTkJJs2beLXv/41ZWVl7N27l9LSUm6//Xby8/Nz64qXlpayb98+rr76atra2pBScsstt/D888/T19fHbbfdRn5+fi6v+y233ILP5+PYsWNUVVURj8dzo/Wbm5vZsWMHP/3pT/nABz6A3W5nwYIF1NfXMzk5SX9/Px6Ph1gsxo4dO3jXu9512vzy2YRGO92U/jIA6alJku7+01KvKm9MCJHr986f9XoymSaRSL+6Ct1ZavUzu2fGE6TSmexgv3SGdGp6e/q1c5mZMhgJv77WLwRoddlEP7pcs/z08+ltjRDZKYQaMf08O4o/212jmu4VZcZFbER8e5nJWb5r1y6sViuPPPIIX/rSl3juuedwu92UlZVhNBrRarWEw2EmJyepqanB5XKh0Wgwm82c7DpFeHgUMimMNhtGo5HOzk5isVhuGlpnZycaTbbWUVdXh8fj4cSJE7ksbyMjI7S0tDA4OIjf78dms1FVVcXx48e55ppr6O7uzvUtz+Rlr62t5bnnnmPXrl2YzWb27duHRqPh6quv5sEHHyQWi3Ho0CGEEKxcuZKNGzfS2NjI6tWr+dGPfsTu3bux2WwMDw9TV1dHTU0N/f39/OIXv+Bzn/scH/jAB9ixYwf79u3jqquuoq+vj3379rFs2TKWLl2KVqslHo+zefNmHnnkEb7xjW+wZs0aFi9ejNPpxOl0snbtWqLRKP39/bzwwgvcfffd3HTTTVRUVOTux2xaWwH64gUkJ7oBiM80pRstF/Xv4p3oTAMIz0e2O0WSSqWJx9Mk4ini8VTu+bkWOJQy20+fSmbe2sXFrMA+OzfArJwArwb7V1MEa7TZ9+R+al59iOkpf7PzCsDMe08fH6AobydzXk707eBCLifq8Xh49tlnefTRR6murubQoUN84hOfoLi4mHvuuQcpZS7ZyvDwMFNTU0A28NfX1/Oe97yHlw8eYCDmR48G78QkRRg4dOgQBoMBj8dDJpOhuLgYp9NJLBajoqICp9NJb28v73nPeygoKODHP/4xmUyGRCJBPB6npKQEh8PB8PAwW7ZsyfWRG41G7rzzTvbv309jYyM7d+6koKCAv/mbv+E73/kOmUyGpqYmHn/8cXw+H8PDw6xZs4a6ujpKSkq47rrrqKqqIpFI8D//8z+cOHGCsbEx1qxZg9FoJJ1Oc99993HLLbdQVlZGNBrl6NGjNDQ08Md//Mf4/X727t1LKBSiqKgIm81GQUEBmUyGtrY2DAYDxcXF2O12fD7faWlk7XY7r7zyCkNDQ9TU1OB0OqcHQr3mgUQzfBhNKoZGo8HkLKVi/c3qH+hlREpJMpkhHk9NB/ZsUE+ls7XyzDxOD5xPQpD98jD9hUCrExgMOvR6LQajFoNBi0GvVSP+lQvuLS8nKoTQAj+TUn5oXkp2Cc0sGWqz2QCora3l5MmTpFIpysrKctO5PB4PW7ZsYWxsjEQiwcjICP39/XzrW9+icVkTk4kgDquN6sZFlMRFtmn90CFWrFiRXfQEGBkZIZFIEA6H2bJlCxMTE3R3d2Oz2VizZg3PPfccFRUVTE5OYrfbcblchMNhnn76ae644w7i8Tg9PT10dXVhs9l44YUXsNvttLe3k06nuf322/nBD37AV7/6VZqbm/nxj39McXEx7e3taDQaRkdHOXr0KCtXrmTz5s3ceeedLFy4kGeeeQa/35/r354Zif53f/d35Ofns2TJEn7+85/zr//6r9x88838wR/8AYODg3R2dlJaWordbsfv9yOlZHBwkLy8PGpqali9ejWxWAy/34/P56Ovrw+ATCbDwMAA5eXlFBUVTU/1SmdHbs88SpeSGTxEOpnE03WSseEBGpa2YLYXoDHZ0BitCIMVjeHso9uVS0cIkQ1mBi3kGV+3P5PJ5gRITwf07CNNOvXq61LK7NRAKclkmPX80kwBhJmBh68mFkqlIB47Qx4BkW3xmLkHJrOe/HyTavpX5sU5A7iUMi2EqBFCGKSU76i1H6WU6PV6NBoNp06dory8nEQiQU1NDclkksnJSY4cOZJbVtRqtZKXl0dtbS1ut5vVq1fz5DNPk0wkOHnqKP5IhMlggmuuuYahoSEgW1sPhUIsW7aMkZERvF4vJ06cIB6P8+KLL2IwGCgsLESj0eDxeHKD30pKSli3bh2PPPIIvb29NDY2MjU1xSuvvMKnP/1pRkdHOX78OGNjY7z00ku8973vxWKx8PLLL7Nu3TqWLl1KcXExR44cYc+ePVx33XUYDAY6OzsZGxtjwYIFuS8wLpeLwcFBwuEwX/rSl/i7v/s7nnrqKW6++WZSqRRNTU0YjUb27dtHe3s7GzZsYMOGDZw4cSKXEW758uX89re/ZWBgIFf7qK+vzyWEmbnfgUCA559/niNHjlBfX8+SJUsoKCh43e8m4cojMd6JRDI2OsaxPTuorKzE5SpiOg0KQqtDGK1ojFY0BivoDAihAY0WNBqEmPmpmc7JqkUIDUJvROjOPipemV8aTTbAw1tr0s/mADg9F8DMazLz+lwBmZkvA2d5yEx2yt/s8zD9PiTn7A44IwnJRJpkIk124d0oyUSa4hLbW/q8inIub9iELoT4GbAEeATILQYtpfzueV9ciL8EvgO45rJc6YVsQgdIJBLs2bOHf/zHf8RisZBOp7nhhhsoLy+nvLycr371q/T29rJ48WKam5s5fvw4jY2NuRSsy5YtY693iP07XsQ9NEJy3EtpaSl5eXmMjY1hMBjo7e3F5XJRWFhIb28v69ato7i4mAceeICCggJsNhuRSASfz5ebVlZeXk5TUxOtra0EAgGqq6upr6+ns7OTdevWceedd/L000/zi1/8Ar1ez1e+8hXGx8dxu9187nOfY/fu3SSn1yrXaDT85Cc/4aqrrqK4uJjx8fHc6PJ9+/ZRWFjI8ePHOXbsGNXV1VitVtra2njXu97FmjVrcl0NH/jAB0gkErlBdw0NDUgp6evrY+HChVRXV3PvvffmFnxpbm6mrq7udfc8FAqxa9cuGhsbc60QjY2N5Oe/OuxKZjLEeg+QDmYz9UajUXp7e9FoNNTV1WE0vr5m92ZoLA50+aXo8kvRmM+cu11RZsxOJASvtiIk4ikSyWywTsTTJKYzDb6WyayjfkHhxS628g5xrib0uQTwr5zpdSnlV8+zUFXA3UAjsPpSBPAZ3/rWtwgEArz88ss0NDRgtVpZs2YN+/fv58CBA2i1WrZu3crAwAATExNcd9117Nu3j+uuu44+fQK3203r3oNctaSFZ554ErfbTSaTweVyMTIyQiqVwuFwkMlkSKVSuUxnzz77LNXV1YTDYSYmJnLJXCwWC/X19ZSUlNDd3Y3VamVycjLXV5yXl4fT6SQSidDe3k5LSwvRaJRIJMIf/uEf0tDQQGlpKYODg2zbto3W1lYefvhhWlpasNvtDA8Pk0qlchnmrFYrL7zwAocOHWLVqlX09fURCoVyiVhOnjxJZ2cnN910E6lUinQ6TVFRUS6TXTqdJi8vD5fLRV9fHwaDgVgsxvr166mtrX3d/T548CAFBQXU1tYyMDBAZ2cnTqeTxsbGXJcGgEwlycTDZBJhMrEQg92nGOrtpLq0CFeRM1cbPx8aowXtTDC3Fqj+S+W8ZDKSRCJNPJ5ieDAAZPvPG5cWq78t5S05rwA+6yQ2ACll6AIV6jfA14DfAWsuRQBPJpPo9XpeeOEF2traaG1tpa2tjdtuuy03gO3Xv/41mUyGT37ykzz33HN4vV7y8/Npbm7ONm/fuB30Op55+BG2LG5mZdNyotEo//AP/0AgECCTyWAymXC5XESjURYuXJg7t9vtJpVKEY1GyWQyTE1NkUwmMRgMaLVaHA4HWq2WxYsX4/V6SafT2Gw27HZ7rk+9u7ubpqYmrrnmGn75y1+STqcxGo1UVVURjUYpLi5my5Yt9PT0MDo6yooVK8jLy6OhoYHW1lZMJhPr16+npKSE73//+/h8PqxWKx6Ph+rqam6++WYymQw/+clP8Hq91NTUEIlEiMVi1NXV5cqv0WgIhUJMTExQVVWFwWAgnU6zadMmqqqq0Ol0aLXaXNN9d3c3733ve3Orr/X29tLd3U1JSQmLFi3CYjnz6PNQKJTt2pBpli+ux6QFGQ8jZRoyaWS20zT7fPonMoOc/pmJh8/aLiq0erT5JejyS9HmuRDaK3aShnIBdJyazNXIGxYWYjCqvyflzTvfGvgysvnOZzor3cBHpZQnz6NAtwDXSCk/L4To4xwBXAjxGbLrklNdXb26v7//rV72NBMTEzz//PPccMMNxGIxfvrTn7JixQr+67/+C6vVSmVlJddddx3/+7//y8GDB7nzzjtZvXo13/3ud+nv72fhwoXZmmaRlcaVLfhGxxk7dor3bLuazZs3E4lE+M53vsPhw4fx+/3odDpsNhtFRUV86Utf4vHHHwdg//791NbWMjQ0xNjYGBqNJhcQZzKg5eXlUVhYSDgcpqamhry8PJYtW0YoFOLZZ58lHA7natJutxur1UosFmPBggUcOnSIsrIyMpkM4+PjxGIxSktLaWpqyg2W02g06PV6LBYL4XCY4eFh4vE4ADfddBM33ngjXq+X//3f/6WlpSW3EMr+/ftP+zKh0WgIBoO88sor5OfnMzk5STKZZPXq1dTW1uY+U11dHV1dXVgsFurq6sjPzyc/Px+LxcLk5CRDQ0NUVlaycOHCMzaXzyS66ezsZOHChdTV1c25diNTSVJTE6QDY6SDE8h06swHCg1aqxNtXhFaWxEaiwNxhulvinI2/X0+wqHs0KHKqnzs+WrgpfLmnW8A3w18WUr5/PT2duAbUspNb/C+Z4HSM+z6MvAl4D1SysAbBfDZLmQNvL+/n//4j//AarXy8Y9/nN/85jdce+21PP744+zduxebzca6desYHR2ltbUVgOXLl9PQ0MCvfvUrenp6KC0txaNNo7dbWVhXR2LEzeTQKC6Xi6amJnp6ehgYGODkyZPE4/Fcv3RzczPXXHMNPp+P0dFRBgYG2Lx5MwcOHMDtdhOJRHC5XKTTaQKBAKlUKpfJzGAwoNPpWLhwIRqNhomJCTo6Oli8eDFarRadTkdlZSXJZJKGhobcsQsWLODUqVPs3buX7u5uxsbGWL9+PWvWrGHVqlUMDAxw+PBhBgcHcyuUzbRQvPvd7+baa6/lwIED+P1+lixZQiAQyM0fLysro6ioiHA4zNTUFOPj45hMJsrLy9mzZw9tbW25+eipVAqTyURxcTEnTpxg5cqVufdNTU0RDodzOeij0ShLliyhpqaG4uLi180fD4fDHDlyBOCsA+LORWYypEMe0oExUoExZDJ29oM1WrTWglkBPV81iSrnND42hcedXainqNhKcbEayKa8eecbwI9KKVve6LU3UZjlwHNAZPqlSmAEWCelHDvXey90E7rH4+H+++/nxIkTFBcXs3z5cux2O3v27GHfvn3U19eTSCRYuHAhkUiEsbEx9Ho9BoOBkydPYjAYsC2s5uRQLzKZ5t1Nq1hW18Djjz/ODTfcQDKZJB6Pc++99zI1NZVbSGSmxrlo0SKCwSB9fX25xC5+v590Oo1er8fhcKDT6UgmkwQCAaLRKHl5eWi1WqLRKLfeeitarZZnnnmGsrIyysrKcnOyCwoKyM/PJxAIkEwm+eQnP0lFRQWBQIBIJMLBgwd58cUXcTqdrFy5kq1bt7J161Yee+wxDAYDp06dYmhoiNHRUfr6+mhubub2229n586dbNq0ieLiYjo6OqitrWV0dBSdTkd1dTWxWIzR0VH2799PWVkZer2eQCDA0aNHWbRoERUVFWQyGVatWsWRI0coKyvLzSefGSMQiUSYmppibGwMj8eDxWLBYDBQVFSUm2s+Mx5ACIHb7cZoNGK326mvr8+tpPZmpSOBXDDPRIPnPFZodWhshWhtRdmgbraf83jlyuP3RxkZyv4d5dmNVFU7Lm2BlMvS+Qbwh4DDvLps6IfJDjq79QIVro9L1AcO2bnJO3fu5IEHHsDn83Hrrbfi8Xjo6+tjYGCAQCDA1VdfzaJFi4jH4+j1erq7uzl69CiHDh2iatUyalcvZ9/efUS6Brnj+pvp7+8nEAiwePHi3MjVnTt3EggEcqPAN2/ezODgIJWVlXR0dGA2m7HZbHR0dOBwOIhEIrkc4zqdjiVLltDT00MoFGLx4sX09/cTi8VYu3ZtbrT6Rz7yESYmJhBCcPz4cW6//XZisRivvPIKixYtYsWKFXi9XoaGhnA4HBw5cgSPx8PatWvp6ekhEAhQWFjI0qVLWbRoEb/+9a9Zv349Go2GF154ASEETU1N+P1+tm/fjlarpb+/P9fHLaXk6quvxm63093dTXt7O4sXL2ZoaIhXXnmFJ598EofDgUaj4brrrqO5uZmpqSm2bdt21t/P+Pg4x48fByAvLw+v14uUkvLy8ly2vJkR+A0NDbl7VFtbS01NzTkXUjnn30UiRjrsITPlJh1y55Y8PRuhN6HLL0GbV4w2r0j1nytEo0l6u70AGIxaGhYWXeISKZej8w3gTuCrZBczkcBLwFellL4LVLg+LmEAn3HixAn+6q/+ittuu43BwUFsNhvPP/88NpuNaDTKn//5n3PixAmuv/56BgYG8Hq9tLW18eDTv6eyZQlLWpp54n/voya/iM2bN/PCCy9QW1tLNBolGo3S09OD0WhkYGCAcDjM9u3bc8t9er1eDh8+nBvZrdFoSKfT3HTTTbS2ttLV1YVGo6GkpIS2tjaKioqoqalhdHQUKSUlJSUcO3YMm83GVVddRUVFBYODg6TTaZqbmxkaGmJycpKtW7dyzTXXYLVaefTRR3E4HDzyyCN84AMfYOPGjfh8Pp544gl27NhBSUkJRUVFHDp0iJtvvplVq1axd+9eBgYG8Pl8XHvttXzwgx/E5/Oxb98+otEoHo+HoaEhVq1alVu4ZenSpdTUZHOaj4+P88Mf/pDe3l6cTicjIyMA3HzzzWzfvp3q6uoz1pwzmQz9/f10dHRQXl6Oy+ViYmKCkZER8vPzqaio4OTJkyxZsoTa2lqCwSA9PT2MjY1RXl5OfX39aaPb34pMPJJtbg+5SU+5z93cLjRobYVo7cXo8kvQGK3ndW3l8pTJSNrbJnLJZxqXFquELsqb9pYCuBDi51LKjwghPi+l/N68lnCO5jOAA/zoRz+ivb2dkpISXC4XnZ2dHDhwgHg8zp133onb7SadTnPXXXdx+PBhnE4nT+56gUMDXdRWVlGW5+TQsy+yaNEiGhoackElkUiwd+9exsbGGBsbIxwOU1JSQnNzMxqNhoaGBo4ePZrr/47FYkSjUTZu3MiWLVvYt28f+/bto7i4mEgkkktC43Q6yc/PJxgMkkgkGBgYIJ1OY7VaWbduHT6fjxUrVnDq1Cl6e3vZsGEDWq2WgoICbrnlFnbt2sW+fftYt24djY2NrFq1CpfLxTPPPEMqleLEiRPs3buXYDDIxo0bufHGGxkaGuLkyZO8+OKL3Hbbbdxxxx24XC6GhoYIhUIMDw/z8ssvk8lk0Gg0DA4Osn79eiorKykrKyMej/PSSy9xyy234PV6efTRRzlw4ADFxcU4HA4aGxtpaWnJjZafLZFI0NHRwfDwcG7u+eTkJMPDw7lsczPvs1gs6HQ6vF4vk5OTuFwuGhsbqaiouCB/K5l4mPR07TwdnESmz75cp8ZoRWsvRmsvydbOVd/5FaOr000ins3YVregALP57KvyKcqZvNUA3gq8G3gC2A6nT7qVUnovbDHf2HwH8FdeeYXdu3fnBpdt3bqVY8eO8dxzz9HU1MSmTZt47rnnWL16NXl5edlR0CuW89DeFzCYjLTULGTkSGsuYHg8Hmpra7nmmmvo7+/PnXv//v0kk8lcf7bNZssN7srPz2d0dDS7VGMiwWc/+1mMRmNudbOpqSn8fj8GgyHXD9zQ0EA8HsftdhMIBNBqtSSTSQoKCli2bBmf+MQnuO++++jp6eG6664jGAzmVhv7/e9/TzAY5K//+q8JBAI0NzcTj8fxer0sXbqU//mf/+Hpp58GYPXq1WzYsCE3TmDHjh1s2rSJjRs30tzcjNPpBLKB9vDhw7kxAH6/H6vVmlsB7tChQ3z4wx+mqamJ0tJSduzYkUsKMzU1hdvtxuv1UlhYSEVFBVVVVbkA73Q6yWQy9PT0kEgkaGlpoaysDIBjx46RSqWor6/PzYuPRqOEw2EGBgbo7e2loKCAu+66C6v1wtWKpZRkwj5SwXHSwYlz9p9r84ow1qxCoz+/ZDTK5WFo0E8wkJ3RUVZhx+mcv3XulXemtxrA/xz4Y6AeGOb0AC6llPUXuqBvZL4DeDQa5V/+5V/4zGc+wze+8Q0CgQDvete7eOihh7BYLDQ3NxMMBunt7eW6666jra2NwYkx0kV59E+M8u5NW1hsKcwthqLX6/F6vZSVlbF161b6+/sZGhpifHwcvV5PIpHA5/Pl8oZLKdFqsykmzWYzgUCATZs20dTURCQSYXx8nFAoxPj4OBqNhsnJSaLRKCaTKbec50yymaKiInw+H+l0mpKSEiorKxkbGyM/P5+6urrcoLny8nJ27txJIpHgL//yL/H5fLS0tNDX18e73/1uhoeHefTRR3niiSdwOBxs27YNs9nMihUreOqpp/D5fJjNZmpqarBYLLnVyPLz83G73QwPDxONRnnXu95Ffn4+iUSCb3zjG5SWlrJq1Sqi0SjBYBAhBFu2bGFoaAivN5vRLhAI0N/fz8DAAKlUCq1WO71kpgmDwUA0GmViYgK73c4HPvABamtref7551m3bh0Oh+N1v990Os0vf/lLLBYLGzZsuGC18dfKJKKkgxOkgxOkpiazc9FnEXoTpro1aK3Oebm+8vYxORFiciKbwLKg0EJpmcr8p7w559sH/h9Syj+el5K9SfMdwAHuvvtu1q5dSzAY5LHHHmN0dBSr1YoQglQqxdatW3nqqacoKSlh6dKlPPfiC4QMgmAiSiwR53v/98tkMhn++7//m1QqRW9vL0ajESklJpOJZDJJLBZjfHyclpYWJiYmcklLOjo6mJyczAWrTCZDSUkJmzdvzg2AKy0tzdUqS0tL6enpYXh4GL1eT1lZGRUVFfT39xOPx3E6ndTW1ubmS3d3d+P3+ykrK+P/z96fB8l1X3ee6Cf3fc+sytr3BYUdIBaSAC0ustqy3JbtZ7fdHj/L87rHM9Mxr3t6wp7oeRNvPEt0T7g7ZtoR/Rzdbrs17W3csiTLkkWLEiWSIAmA2JcCUCjUvua+79t9fyTOj1kESIIEZFFWnYiMqsrKyvrlzZv3/M4536VUKmGz2XC5XBw5coSbN2/idDr5+3//73P58mUATp8+zdGjR7l69Sq3bt3iT//0T5menuZzn/scCwsLHDx4kFu3bjE2NsadO3dotVqUy2U1AqhWqzSbTSKRCC6Xi5MnT2Kz2ZidncVsNjM8PMz09DSDg4N8+9vfpru7m0AggMPhIJPJYDKZePbZZ9Hr9aRSKWKxGNFolFwuh8PhwG63YzAYuHLlCrOzs/zar/0a3d3dLC0tcfr06Ye2qtfW1rh79y5GoxGfz8f+/fvVpun7Ee9S1bapJzo0DHR6LH17MYWGv2//ezd+8JHPVVhfayuyOZxmhoZ3N2278dHiiSixfRLibyKBnzlzhu3tbU6cOMHly5e5fv06yWQSq9WKTqejUChgt9tZWlriyJEjRBIxbkTX8bvcrG9tMmb3c/z4cXK5HHNzc1QqFTY2Njh9+jSXL1/G5XLR1dVFIpHA7XZTKBRYWFjAbDZjt9uJxWI0Gg0ymQyNRltkZN++fbjdbra2ttDpdNRqNdxuN9VqFaPRSDabpVgsYjabqdVqil5ltVrx+XxKRGZ4eJhsNkskEiEUCrG9vU0mk8FutzMzM8PW1hZDQ0N84Qtf4MKFC1SrVU6fPs2RI0c4e/YsS0tL/NEf/RGf+9zneP7553njjTcIBAJ0dXXx2c9+VsmsNhoNGo0G9XqdTCbD5uYmf/7nf47b7WZ8fFzJp46Pj6vNhtfrxeVyMTY2RrVapVgskk6nlX2r2WzGbDZjMplotVoUi0UKhQKVSgWv10sikeDOnTvKpnVkZISxsbEH3l9N03j99deZnJwkHo+TTqc5evQobvf3nwbWyMWorlzZMS83+vuxDBxAp//+bSJ24wcXtVqThfk2Ptdg1DE13fUDXtFu/LDFbgJ/SKTTaRYWFjh27NiO+xOJBP/+3/97/sk/+Sf89V//NZubmzz77LP8+Z//OZubmxw7doyenh7OnTuH0Whk34ED/Okb32Jjawub2YIlnlctZWkfb29vU61W6e/vZ3V1FbvdztDQEM8884yiXL399tv09/eztLSkpFUjkYjSVD9y5AjNZpOFhQVKpRIOhwOn00mlUqFSqZBKpbDb7QQCASKRCPl8HpPJRDAYZHJyku3tbfbs2YPL5ULTNOLxOIODg4oTXqlUqNfrBAIBRkZGOHHiBG+++SY2m43+/n7MZjP1ep35+XmuXbvGT//0TxMMBrl16xZGo5GXXnqJ06dP09fXh9X6oOJUIpHgS1/6EmNjY4TDYb7yla/wX//X/7V6TefOnVOCNB6PB5vNRi6XI5fLcfjwYYLBINVqlVqtRq1Wo16vU6vVKJfLJBIJlpaWWF1dJZ/Pq3n5c889h9/vV+I3JpMJk8mkwHbPPfcc6XSapaUl9u3bx8TExBM5tz4oWtUSlZVLtEpZdZ/e5sY68tQuWv1vYWhaG4mu3fc4mZwOYjTubtZ249FjN4E/JFqtFq+++irPPvvsA4Cmf/2v/zU//dM/TSQS4erVq+zbt4++vj6+973vEY1G2dzcZGZmhmw2y/79+7mQWCcVi7O6ssKAzobNaqVSqdDT08ONGzfI5XKsra3h9XqJx+P4fD6lkz44OMjRo0eZn58nlUrxmc98Rs3c6/U6V69exWq10mw28fv9aJpGvV5X8/LR0VFSqRTpdJparaYeK9KmIsXa39+vKvbR0VFVyQcCAer1Ok8//TSvvPIKFy9eVDPi559/nkqlwurqKi+88ALRaJT+/n5+93d/lzt37vCbv/mbZDIZzp8/T6vV4uTJk2iaRjAYZHx8nMHBQfx+vzq+169fVzz1L33pS/zDf/gPOXDgABaLhXq9zssvv8zW1hbPPfcc0PZRv3TpErFYjF/5lV9henr6fTXSNU1jaWmJP/mTP2FzcxNN0/D5fBw5cgSPx4PD4cDhcOB2uzEYDJw9e1bN69PpNLOzs1gsFvbs2YPT6cRqtaqk3/n1YfeZTKaPhCzXWk1qG7PUk2vqPp3BhGXwEEbvw8QLd+OHOZaXUpRL7a7L4LAXp3MXwLgbjx6POwP/b4A/flK878eJJ91Cl7nt3r17d9z/zW9+E51Ox759+3jzzTcpl8t84Qtf4Jvf/CaapjE9Pc3v/d7vsb29zd69e5n+3PO8/dobZAp5DBtJ1pZXcLvdiuq1trZGNBoF2pV/uVzm137t14jFYly6dAmPx4Pf7+fChQuEw2E8Hg+pVIqJiQnm5uYoFovYbDYKhYJCYSeTSUqlEq1WC6fTSavVolQqYbFY8Pl8qj0spiJGoxGr1crIyAh2u529e/dis9m4evUquVyOgwcP8qlPfYqrV6/y1ltvEYlECAQCHDx4EIPBQCaTYWRkBL/fzwsvvMD/+D/+jzQaDf7lv/yX3Lp1i+9973u88MIL7N+/n3w+z+bmJsViEYPBoGbxLpeL27dv89JLL/HFL36RUCiEz+fD7/czMDCAz+fjL//yL3G73YTDYfbt20ej0eCb3/wmb775JidPnqSrq4tQKEQoFCIYDCqJWYlKpcKXvvQlbt26RaFQ4Pjx4+zdu5dAIEA+n2d7e5ve3l66urq4ceMGL7zwgsIb3Lhxg/X1dfbs2YPNZqNer6tRwMO+yvetVgu73a66IrJZcDgcD+1GSNSTa1TXb6LKM8DcPYGpZ2qXava3KLY2c2TSZQC6w04Cwd1Oy248ejxuAv/fgF+krcb2H4BXtB9Q2f6kE3i5XOaNN97g05/+9A4g09LSEl//+tf5R//oH/HNb36Ta9eu8Qu/8AtomsbFixf5zGc+g9ls5v/6v/4vZmdnqQ8E2LN3Bl2tyb6ufvwOF6+99houl4tGo0EymeTtt9+mUqkQDAZ5++23sdlsDA4O4vF4WF9fx+12q5ZxuVzGbDarRBaJRNDpdDSbTQqFAhaLRYmetFotzGYz1WqVSCRCqVRCr9fT1dXF5uYmuVwOq9VKoVBo051aLYxGo7IsPXbsGJcvXyYcDmO1Wunp6aFcLrOxscH6+jomk4lwOIzb7cbhcLC5uYndbqe/v5+XX36Z/v5+fumXfokLFy5Qq9X4uZ/7OSVEI9VysVgkl8thsViIxWLKelSv1/PZz36W7e1t1tfXyefz6hh5PB41bnA6ndy+fZv19XWefvppenp6yOfzpFIpnE4nXV1djI+Pq/ewUqnwve99j6tXr7KxscFzzz1HIBDg8OHDeDwe7t69y9bWltJan5qaUu99JBLhxo0bDA0NPSDZ+n5fBRFfKBQoFovqVigUFC9fErvL5cLlcuF0OtvHqZihsnIJrVZWazC4QliHj6AzfjwVud34ZEUqWSKynQfA67PS2+f5Aa9oN36Y4rFb6Lp2OfDjwK8BTwFfAv5A07TFJ7nQD4vvB4hNRERELQza7fX//X//3/n1X/91Zfyxvr6uZEifeuopfvEXf5FYLMZXvvIVkg492XqF+OoGnzt+iqDTw9DQEN/73veUfnc0GuVf/+t/zec//3neeustbDabcgaTJC4Wordu3WJ5eRmDwUBPTw9ut5uFhQX6+vqUCUi1WlVz4pGREZxOp2qv53I5hTJPp9N0dXVhMBiIRqMkk0k1YwewWq2YTCbq9ToWiwW73Y7FYsHv95NOp9HpdFgsFlqtFvV6nZGREdWeLhQKXL9+nVOnTnH69Glef/11Dhw4QDgcpru7W9HkMpkMlUqFVqtFpVLh7t27WCwWhXp3u93YbDY0TSMajfLGG29w8uRJJiYmlIJaLpfje9/7HplMBovFwtjYGB6Ph2azSSwW49ChQ+zZs0e9h4VCgbfeeovZ2Vk0TaO/v59Go6Hm+eVymfn5ee7cucNnP/tZent70ev1GAwGarUaGxsb+P1+jEaj0mmXDZB8bbVaNJtNPB4Px44de2jVXK/XdyR0MW0plUrY7fZ2QrdbsOdWsWllLBYLOp0OncmKZeAARk/3Ez3fd+NvPoqFGqsr7QamzWZkZCzwA17RbvwwxZPyAz9IO4H/HeA14CTwHU3TfvNJLfTD4vuRwOPxOLdv335Aj/s//af/RG9vLyMjI2xtbXH8+HEymQzf+MY3+M53vsP4+DhdXV1tepNWoXtmApNOz/xr59BXGwo8dvPmTUKhEOFwmCtXrnD27FnGxsaoVCpK51z4zSsrK1itViUnurS0hNls5sSJE0QiEe7du6eq10gkQnd3N6lUilQqpRzIMpkM3d3dNJtN6vU6iUSCQCDAsWPHFFJ9dnaW9fV1lciFTiaJutlsqvZ7q9VSSdxgMNBoNFSF7XA4SKVSSlkuHA5z6NAhXnzxRZrNJuPj4+zfvx+r1UqpVCKRSBCPxzlz5gxzc3PcunWLmZkZnnnmGTweD5qmqVl2tVpVFbzX62VoaIhwOMzy8jK9vb1EIhEmJiaw2+2srKzw9ttv84UvfIGurndRvul0mjNnzjA/P8/09DQ+n091EOQ2OztLLBZj7969jI2NYbfb1Thie3ub4eFhxsbGHmjVS7RaLS5evIjFYuHQoUOPfN61Wq0dCT2fy1HevEMrsYTFasFitrRBd/4BTH17sDncWCwWdXu/9ezGJy8ajRbzc3EAdHqY3tO1OyLZjUeOx22h/2Pg/0nbB/z3ga9pmlbX6XR64J6maQ9ydb5P8f1I4Jqm8dprr3Ho0KEddpTXrl3j8uXL/Oqv/irf+c53eP7557HeB6f9/u//PqdOncJoNHLz5k3+7Z/+IfaQj/F9M/zU0z9Gem2Lc+fOMTIywlNPPcWrr76KxWKh0WjwjW98g1arhcfjwWq1kkqlOHLkCG63m42NDXp6erh27Rq5XA6Px0M0GsVisRAMBtna2lKzdIvFoow68vk8er1ezcK3t7dxuVzUajVKpZKqYsPhMIODg0xNTRGPx7l48SLlclkJvlgsFkKhEKOjowwPD5NOp7lx4wYnTpwgk8mQzWbJZrPqtdhsNsxms5qjm81mXC4XMzMzAKq9v2fPHk6dOsXBgwfp7e2lXC7z2muvUalUiMViZDIZhoeHmZiYUNKwFy5cYHJyUtmKyv9PpVIYjUaOHz9OuVxmZmaGo0eP8vLLL1OpVNi/fz9TU1NqExSNRhV2YXJykqNHj1Kr1VTiTCaTnDlzBp/PR6lUYmhoiH379hEIBLBarSwtLRGNRhkbG2NkZOShnPFms8m5c+cIBAI7ugAfJ6qpLbLz56mVi9Tr9fZN01FxDVIxuqhWq1SrVVqtluqY+P1+/H4/Pp9vN7F/QmN+Lk6j0e56jU8EMFt2zW5249HicRP4/wz8B03TVh/yuz2apt15Msv88Ph+8cCXlpbIZDIcOXJE3VepVPjt3/5tfuM3fkOZl5w8eRKdTscbb7zB2toav/IrvwLAX557na98/Wu0DAZ6LE6MpRqZTIbZ2Vm8Xi8vvPAC29vbyu5zdXUVp9PJiRMn+Ou//mu2t7fx+/0YDAZyuRw//uM/ztmzZ6nX62r+XCgUaDQaxGIxUqkUpVJJobJXVlaUcEo4HCYcDnP37l00TWNzc1MlUkC16QcHB1lfX6dYLOJwOGg0GhSLRUqlEjqdDpfLxcDAAA6Hg2QyyeTkJF1dXVgsFjKZjDJMkSr9nXfeIZVKUS6XmZyc5Gd+5meUecvc3ByRSNsp1maz0d3djdlsRqfT4ff7GRwcZGFhQXUD/H4/5XKZcDjMsWPH0DRNAcby+TwXL16kUCjgcrnIZrOKltdqtfD7/VitVo4fP04gEMButxONRvnyl7/MwYMHqVarGAwGbDYbVqsVm81GJBKhUqkwNjbGwsICW1tbCp3v9XpxOBwUCgXlsT40NITRaFQtd+lMvP322wwODj6Uf/5RQmvUqG7M0khv7rjf6OvF3LcPvclCs9mkWq1SKBRUFyabzarjJ7f3Q+3vxt9srK6kKRZqAPQPeHB73h/cuBu70RmPm8D/SNO0X/mw+/4m4kkmcJEvDYfD1Ot1vvvd7/L8889jsbxL8fj93/99nnrqKQ4ePMj58+fx+XxMT09TLBb5nd/5HX7913+dQCDAvVSE//j1r9AsVTg8vZcBm0f5Yn/lK18hGo0SCoVU4k0k2sIOJ0+eZGhoiPPnz6PT6SiVSpRKJQVUM5lMxGIxXnjhBXp6epienmZ2dpa7d+8yPz+Ppmm89NJLyoa0u7ubCxcuKLCUVPizs7M4nU6KxaKa37rdbvbv308mk2F9fZ1AIMDY2BjLy8skk0my2SzNZhODwaCkS0X5TObnbrebQCCgPMFl3lyr1RgYGGBmZobDhw+zZ88e9Ho9S0tLbG9vs729TTQapVQqMTo6yokTJ/D7/cTjcUqlEuFwmGQyyezsLKdPn+bw4cPqPatUKuTzec6ePatoXYuLi2rjMT4+rubNAwMDeDwejEYj8XiclZUVfvVXf1V1EKTCrVQqXL58WdmPSmVuMpl2zK4rlQrVahW9Xs/AwAB+vx+dToemaaolura2Rn9/P+FwGKPRqARozGYzNpuNnp4efD6f2kB8kG95IxOhunFzh/OZzmjG3L8Pk+9BGdhWq0Uul1MJPZVKodPpFF3OaDSqdep0uofeAMWd340nF9FInmSibUkb7HLQ1fV47ni78aMTj5vAr2iadqTjZwNwU9O0mSe7zA+PJ5nAS6USb775Js8++yxOp5Pr169jt9t3iHm8+eabbG5u8ou/+ItUq1XOnDnDwYMH6erq4itf+Qp2u52f+ImfYD2f4ru3rrC2sES3xcGAw6cSZaPR4Ny5c0QiEXUhlxa5w+Ggp6eHZrPJ3Nycon+5XC42NzcxGAyq+guFQjgcDgYGBkin02SzWRYXF3cgpc1mM8FgkNXVVYxGI263m0OHDvHGG28ornKhUMBms6mLvV6vx263k8vlOHbsGP39/WxtbREMBtnY2ODevXuqqqvX6wo9De1qenNzUymkxeNxotEoBoOBUCiEy+VidHSU3t5eJiYmGBgYIJvNsra2RiaT4cKFC4yOjuLxeJicnKS7u5tqtcri4iLDw8NkMhnS6TR79+7lhRdeIBQK7Xj/zpw5w759+zAYDLzyyivMzc2xf/9+BgYGWF5eZn19ne7ubiYnJ2k2m7z88sskEglOnTqFx+NRmyTRrE+n02qzkM1mcblc7N27VwHZ8vk80WiUpaUlZmdnMZlMHDhwgKGhIXVMisUily9fVhamskkQdbloNKo6DTabjUAgoHjncp/NZqOrq6vtsd6oU9u6vYMzDmD0hDH370dv/uBKrlQqkUqlyGQyNJttTXbBGshnv/NnYTocP378b0Sd7kclMukyW5ttkxuX28LAoPcHu6Dd+KGJj2tm8s+A/wGwASW5G6gBv6dp2j/7Pqz1A+NJt9CXl5eV0lo+n+edd97hpZdeUpVIMpnk3/7bf8s/+2f/TOlxX7p0idOnT5PNZvniF7/Ib/zGb5ColbgaXeX69eu8cOxpjve920JttVqk02neeustKpUK58+fJx6PU6vVSCQSPPfcc4TDYba2tlhaWsLpdGI2m0kmk1y6dElR0WRzIYAzh8PBq6++is/nU615kWdttVqsrKwoypjb7cZsNqvqv9FoqGSvaRqZTIZSqYTZbKa7u416LpVKCqGeyWQIBoMMDAzgdDoVHaxarfLss88q3+2RkRH+43/8j9y+fVvN05PJJF1dXQwMDOByuTh69CjHjh0jEonwh3/4h0rG1WKxsL6+riRgpQOQTqcJBoP09/fzC7/wC0xOTqr3JxqNcvPmTZ577jk0TeOb3/wm3/rWt/in//SfMj4+TjQa5ezZs2xvbzMwMEAwGOTq1ausra1x8OBBBgcHsVqtitN98eJFhaAX6dxqtcrRo0cfMEdpNpvcvXuXS5cuEY/HFTpdkuD29jZTU1N0dXUpwJzT6VTjAdmcJJNJLBaLek8tFosCOE5PT6vNQSMXp7p+fQfdTGcwYe6bwejtRWd4cjPVzc1NZmdnOXz48A5Q4G58/CiX6ywvtg0czRYD4xPBH/CKduOHJR63Av8XP4hk/bB40glc0zTOnTtHOBxmdHSUt99+W0l9SvzO7/wOn/vc59Rcc3Fxke3tbZ555hn+4A/+gIMHDzJ2YIYrsTU2NjYx1Br8Z8995oHWaCQS4ebNm4yNjfF7v/d7nDt3jlKphMlk4siRI1gsFu7evUuj0eDUqVMsLi6SSCSo1Wpks1mSyaQChH3605/m1q1b5PN5NjY2OH78OFNTU9y4cYMrV67Q399PoVDg5s2bZDIZarUaTqeTY8eOEY1GlW1ns9lUFWiz2aRSqSj5VRErKRaL1Go1NUeXRCQguVqtxujoKCaTCa/Xy9/9u3+Xf/Ev/gWFQoH9+/fz0ksv8Vd/9VfkcjkFSiuVShw5cgSz2cydO3ewWCz81m/9FsPDw2xtbbG4uKhcyS5fvky1WiWdTpPP5zly5Aif//znmZ6epquri4WFBXK5HHv27FEKb3fu3OEnf/InOXXqFAaDgfX1dWZnZ+np6cHpdHLx4kXVKbBarWq2n0ql2N7eVpaqyWSSgYEBGo0Gk5OTjI4+3ICvc0Yv8q5ra2tcuXKF8fFxdDqd4sIXi0WCwaDqUAhzQFre5XIZl8tFqVRS3PmDBw+yZ88evG4XuuQSZDd3gul0OvQ2NwZnAIMjgMHpf2wOuWxWp6amdlAsd+PjRavVllTl/uV2eqYLvX4Xib4bHx4ftwKf1jRtTqfTHXnY7zVNu/IE1/hI8f0AsRWLRd566y1OnTpFJpNhbW2Np59+Wv3+5ZdfRtM0fvInf1Ldd/HiRdWGfvPNN/nl/9evcTG2QkvT2Fxc4ZC3h8OHDz9AFRGA1MmTJ/nTP/1Tzp07x8WLF+np6cFut5NIJLh37x5HjhwhFApx+/ZthoaG6O/v59y5c0SjUarVKh6PhwMHDih/cL/fT09PD0NDQ/T09FCv13nxxRc5d+4cX//617l37x6xWIwjR44wPDxMNBolFouRTqcplUpKmtVisVAul5U3ubR6jUYjqVSKZrOJTqfDZDLhcDgUFzyfzytBE7vdjtFoZG1tDavVyujoKD/1Uz9FJBJhfn6eo0ePEgwGuXnzpqpE5X8/99xzfOpTn6K3txeHw0GxWGR9fZ3/9J/+E36/n2KxyLVr16jVavT399Pb28vw8LBCZDudTmq1GrOzsyQSCXw+HydOnMBoNFKtVlleXlb0vrm5ObUR2dzcxOFwMDg4yNLSkkqwzWaTS5cu0Wg0FJXt9OnTH6iu1hmbm5vcvn2bZ599VoHJSqUSm5ubbGxsoGkafX199Pf3K7lZ2TgUi0VSqRTLy8vMzs6SzWYJBALtxG9sEmjEcFlN2Gw2pWbXObfWW10YnH7095P6h7Xa3++z8c477xAOh9UGaTc+fizcS1CrtscYI2N+bLZdxsBufHh83AT+7zVN+4c6ne61h/xa0zTthSe5yEeJ7ycKPRKJcOLECb773e/yzDPPKOrVysoKX/va1/gn/+SfqMfX63XOnDnD2NgYf/EXf8FnfvKzbFrbH0yT3oBlPYXNZuPgwYMPXPSuXr1Ks9nk8OHDnDt3jtdee435+Xl+8zd/k2azyb/6V/+Ks2fP8tnPfpbr16+TTqfVnD6bzXLjxg2i0ShdXV309PTgcDhotVoqqYogzK/+6q/ysz/7s7zzzjv8yZ/8CW+++SZ6vZ5Dhw4xPj7O+vo6V65cYX19XYmSAEoBTua2rVZLIbeFW67T6Wi1Wuj1enp6elTbt9ls4nQ66e3t5fr166RSKZXsBB2eTCYJBoMcO3ZMibyIHGytVqO7u5vx8XGGh4cV2EvMSgS5vrKyQjweV21unU5HOBxWxzoWizE4OMjGxgaNRkMdv0ajwcLCAsPDwxw8eFCJ48jcf2lpSYHknnvuOaLRKIODg+TzeW7evKkAhidPnlTt8Q+jbS0vL7O8vMyzzz67AyAJ7Tn7xsYGm5ubyjSmt7f3gccBSpe/Vqu1OyTVCuWtu9i1Ek5TO9larVaFPn8vCE1ntqE320BnaLfb9UZ0BiM6vRH077nPaEZnsqEzt+VkL126hNls5vDhw99X69W/7bG+liGfqwLQ2+fG67P9gFe0Gz8MsWtm8iGhaRpvv/02fX19VKtVGo0G+/btA9oz7N/+7d/m8OHDHD16lEAggE6nI5vNcv78eer1OtlcjtCpQ+gAvU7Pp3onOH/+PC6XiwMHDuz4X61Wi3PnzuH1egmHw7z66qucP38eTdP4X/6X/4VUKsXv/M7vcPHiRV588UVWVlbwer20Wi2y2SzhcJi1tTVu376NTqcjGo0qidCpqSmeeuopvvzlL3PlyhU+//nP8xM/8RNsbW3xH/7Df6BWq3HkyBEFkmo0GszOznLhwgW2t7cxm82KEmUwGHC73UqaNZvNUqlUFG1KlOTy+TwWiwWv10ulUlF8dLENzWaz9PX1kc/nsVqteL1eVldXsVgsOBwO0um0sju1Wq3E43ElPdrf38+ePXsYHh4mlUopBoB0HlKplGpxr6+vc+DAAXw+H5FIhFQqhd/vZ3Z2lq2tLZ599lkmJiaoVCpcuHABp9NJs9kklUqxZ88exsfHsVqtypzF4XDw9NNPYzKZKJfLSjSn0WgQjUZxOp3Y7Xa8Xi9jY2MKO/CwuHv3LtFolGeeeQaj8cFZtaZpJBIJNjY2iEaj+Hw+BgYGCIfDO0Yxmqaxvr7O3bt38fl8jI+PE4vFWF68x1hfCDs1khsLpCPrWC3m903mjxz3FeEwWlhY2aBYa3Lw6AksTg96sx2d2bZrg/oRIh4rEI8VAfAH7IR7XD/gFe3GD0N83Ar8Zz/oSTVN++oTWNtHiu+nH3ihUODtt9/mqaee4uLFi7z00kvqYhuLxfj2t79NrVajr6+P4eFhBgYGiMfjXL58me3tbYLPHlRCMC/0T6O1Wop69l6zlGq1yo0bNygUCqTTaV5++WWSySTDw8P81E/9lJrZXrlyBZ/Px8zMDCaTCafTydLSEs1mUymWpdNp4vE4Q0NDym5zz549bG9vs7y8zNjYGD09Pdy7d49oNMqJEycYHh7G4XCg0+lwOp1Eo1G+973vsby8rMRgAoEA0WhUzck9Hg+NRgNN00ilUmiappy9otEojUZDVfIGgwGTyYRer6der+NwODAYDFgsFkX9kpZ1LpejXq8TjUaZnJykUqmwubmpZvKhUIiJiQklvnLkyBGCwSDlcplsNksmk2FoaEi10vfs2UOhUODMmTM8++yzCpX/ne98h7GxMaampsjn88zNzTE5OUkul+PatWt4vV4CgYDiTl+/fp1isUiz2SQYDKpE6HK5FCjQ4XDQ19fH4uIi3d3dzMzMvG+FKu/3sWPHPrBqbzabSrpXNj9Ch+t8zPLyMouLiwwMDDAwMMCNGzfQNI2DBw9it1lJbCwSXZkntbmETVfH7/UooNzHbYVvbW0Rj8eZnJzEZrOBTofR14c5PIXesss3/7DI5yqsr7VtZB1OM0PDvh/winbjhyE+bgL/4gc8p6Zp2n/+JBb3UeL7mcChPaOOx+MYjcYH9NEbjQY3btxga2uLUChEJpPB5XIptTLT1AAHnjqCDjjdO4HV2NYXP3fuHKFQ6AMVum7fvs2/+3f/jmq1yt69ezGZTPT39/PHf/zHRCIR3G63Msvo7e2lu7ubu3fvotPpaDQaAEobvVqtks/n6enpIZvNUiwWMRqNSjO8Vquxd+9e+vv76enpUV2HTCbDN7/5Tfr7+1lbW8PtdnPy5Enm5ua4fv069Xodm82mpDyF7jY8PKz468JBFolWg8GgBE+CwSCNRkNZh4rnt8/n49ixY7z99tuMj4/z9NNPUy6XuXjxIq1Wi7t375LNZhkbG8PlcmG32xVgDNqGNLVajWKxiMlkYmRkhGeeeYZMJoNeryccDlMoFIjH49y4cQO3283BgwfR6/VsbW0xMzNDqVRiYWFB4QeEciXJLhaLEY1GlXlJuVxWHY/u7m6eeeYZstms2mA8jH6laRq3bt0ikUhw/PjxRxJYKZVKrK+vK1OZwcFB+vr6VEVdr9e5ePGiam9vbGwwNzenVO3E7CYRjxNdXyKTTlLK53DYrXhcTtwuJx6nA5vFBK0mWqsBzQZao0qrVt7BP5dIJJOsra21tejlder0mIJDmLon0Jt2rTLfL2rVBgv3kgAYjXomp0Mf8he7sRu7LfRHDk3TeOutt3A4HOTz+Qf00aEt1HHnzh2mpqawWCwsLy/z5S9/mWizwgs/+1MMjgxxMjyK6z5oqFarcfbsWXp7e5mcnHzf//31r3+dN954Q3Gd//E//secPXuWP/iDP+DEiROqXX358mX6+/vp6+tjfn6eGzduqBn14cOH0TSNWCxGLBZrV0lAf38/yWSSVqulZsl2u12JkPh8PoLBIMvLy+RyOQKBAMlkkkAgwP79+7HZbIqOZTabmZqaIhaLsbGxgV6vZ2RkBJ1Oh9vtRqfTEY/HSSQSFAoFhbS3Wq04nU7y+TzValW5lWmaht/vJxAIYDQacblc7N+/n4MHD7K6ukogEKDZbPLWW28pWpyYj0glPT8/Ty6XY319nUgkgsfjYWRkhEwmQyAQwGQyqRl+LBajXC4TDAbVhmBycpJqtUo8Hmdqagqn00m1WmV7e5t4PE61WsXpdCqjE9nAbG1todPpcDgcamNVq9WYmZlh//79eL1erFYrVqtVSd8uLy+zsLDAU089hc/3aBWYtNjX19eJxWKEQiEGBwcVDfDq1atUq1WOHTtGs9nk5s2bFAoFDh48uEMeGNrVu3Qu5FatVvF6vXi9Xnw+n1q31mqh1ctotTKtWun+1zKZ+Dbzt2/QE/ThcDgUk8FotmDuGsPUNfZEaW1/W0LT2kh0cY+dnA5iNO6OIHbjg+PjVuD/maZpf6zT6f7pw36vadr/8QTX+Ejx/U7g0NYVf/vtt9E0TSmEvTcKhQKXL1/G6XRy4MAB1tfX+c3/9X/CN9zP6NgoJ0amGe7pIxAI4Ha7VRL/IJnNTCbDH/7hH7K+vo7FYuHUqVOcOnWK//P//D85ePCgoqE1Gg02Nzf59Kc/TavVYnNzk/n5eZXsPve5z2EwGNjc3OTKlSsUi0U1k15fX2dtbY1KpYLD4UDTNBwOB6VSSdGfyuUyzWaTcrnNN/Z6vUq5LJvNKgOWiYkJJWwiiXdoaEihutfW1tA0DZPJRC6XQ9M0nE4n3d3duFwu5ufnKZVKO2bCZrOZcDhMudx25RKt9Z6eHrxeL4lEgmw2i9vtpq+vD4/Hw/T0NKFQCLPZTK1WY3Fxkb/4i79QgLipqSkmJyffte9sNlldXWVzc5NCoUAmk1Gbq1wux9bWFr29vdTrdXw+H/39/fj9fjKZDNvb26ysrLC+vq4od61Wi1QqpcB/XV1dCtnv9XoVit9gMGC329X4IhqNMjMzo9zRJAmqZPiQWTm0q245vtVqld7eXjweD1tbW5RKJU6ePInValXKeOFwmOnp6Q9s29dqNZXM0+m0wiWIvrrf78flcu2YxxcKBeZvXKQRuUuzkKJer6txi8FiwxAcwRQcxmJtd206bVmBHd+LNaucA16v9wMV6n6YY3kxSbnc7poNDftwOHcV73bjg+PjJvBf1zTt3+l0uv/pYb/XNO1/foJrfKR4kgk8lUpx8+ZNgsGgmn1Ka/LevXvcuHGD4eFhjh49+tC/bzab3L59m1gsxtGjR/m9r/zf3F5e4Pmf/AkOBPswlOuqnezztSuVe/fuceDAAcbHxx/6nBcvXuT1119nfn6el156icHBQer1Om+88QbT09OMjIxw7do1Xn75ZSKRCMePH2dgYIDt7W08Hg9//dd/jclkoqenB71ej9VqZWFhgY2NDVwul9JVbzQaqtIXQBa0uwvQliwtlUpEo1FVQQJKe1tU3wQIls1mlSBKV1cXBw4cwO/38+1vf5tYLIbdblcAOKvViqZpCjCm1+up1WoYDAZKpZKSPpULuCjIeTweTCaT6kSEQiG8Xi/FYhGDwUBfXx9PPfUUfX19OJ1Ovva1r7G6ukpXVxcjIyOq4m802k5xJpNJJcF79+4RCAQYGBhQdLgTJ05gt9tJp9OsrKwoR7ZwOIzL5SKXy7GwsKAEdKLRqKLEuVwu+vr68Pv9TE1N4fP5aDab5HI5kskk1WpVUdv27NnD9PS0GoeIcpt0GySZd34vX0UOuFQqUSwW2d7eplwuc/LkSSWcs7m5SSKRYN++fXR3dz9yYhR8hijUlUolVaFLYpfPSyMbpbZ1h2Y5R73RoCEmLBiou/qpW/00O4RuPuhWqVQoFAp4PB71ufzbZNKytZkjk25vjrvDTgJBxw94RbvxSY/dFvpDQlDdyWSSRCJBOp3GbrcTCATw+XzMzs4SjUb5e3/v7z2U1iOxvb3NzZs32agVmJ27TbaQ59j4DKcPP6UqMBEi2dra4p133mF6epqZmRmlbCaRz+f57ne/y3e+8x1CoRCf/exn0TSNqakpzpw5Q7Vapauri62tLb72ta9RrVbbKPhslh/7sR9jcnKS733ve/T29rJnzx6SySRLS0tcuXKFWCwGQE9PD+VyWbV8R0dHiUQi6PV60uk0hUJBIaEjkQi5XFv+UbTUt7a2KJfLbZlPTcNsNu+onkQ5TpJqJBJRSHVASYVqmkY2m0Wv1ysRmPX1der1OoODgxgMBjKZDIlEQgHGJLmn02lMJpNyTpOKM5VKKQS71+tVSnujo6P09/ej0+nUBqBSqeDxeDAYDAoDIMlJNMR7e3sZHx/nyJEjBAIBUqkUa2trmEwm1R0Q+pbX6yUWi3Hu3DklRlMqlTAYDIqv3t/frzYJW1tbxGIx5ubmcLlc7Nu3j8nJSeX/LvatchOvd1GNk++l8m00GpTLZe7du8f8/DyBQED9XqfTkc/nMRqNhEIhRZ3r6+vboTr3QdEpOCNVulDfBgYGMJvNNNKb1LbndqjFQZuTbgpPYHR3P1JrvdFoqM2DyMB2uq75/X41Hvphi2SiSDRSAMDrs9HbtytXuxsfHI+rxDYK/A5t/28NOAf8t5qmLT3phX5YfD9b6J0JPZlMsrGxwVtvvcXp06d55pln6O7uft+LXLlc5stvfIdzVy5y4vnnKKxto08V6enpwWg0Uq/XcbvdeDwe9Ho9V69epa+vT7l+DQ8PK8rQ1atXuXr1KmfPnuVnf/Zn6e7uJpfLMTMzQyKRYHt7W82Hv/SlL1Eul7l58yY6nU61Y7PZLA6Hg6mpKZXgvvSlL3Ht2jVV6dpsNgUue/rppxWCXEBmy8vLqp3ebDbVrDuTyXDmzBnsdjsOh4PV1VVKpZJCqIvsq1SYcp+0SaUzIDPv7e1tHA4HQ0NDeDweIpGIaslLsrtw4QKZTIauri4GBwex2Wxcv34dAIfDgdfrJRgMMjQ0pIxi7HY7FouFO3fuqEq3t7eXYDCIXq/HaDQSjUaVq9nW1haJRIJwOEwul1N2oxIOh4Pp6Wmmp6eViI+06VutlnJb6+vrU9aqzWaT8+fPs7y8jM1mU8j7vr4+Dh48SCgUIpfL8e1vf5uVlRWF1Bd9dOlESPXvcrkUkNBsNquvncdWJH9nZ2fZv38/Pp+PXC5HJpMhl8up2X0ikVAqew6Hg66uLnp7e+np6aGrq0u19eV/CI5Abnq9Xo0cotEogUCAwcFBQsEgjeQq9eg9tEZt5wdFp8fgDGD09mBwdz+yuEynSUsymVSWsqKv/8MkMFMs1FhdSQNgsxkZGQv8gFe0G5/0eNwEfh74/wH/9/27fhH4bzRNO/FEV/kI8TcxA5fQNI3XX3+d119/neeff14But4vlrJx/sOf/9+g1xMId1EplshvROmyOBjpG1CypIVCgWQyyfLyMuPj44yOjipE9dTUFMPDw7z22mvcvXuXer3OL//yL+P1erl9+zZOp5NgMMjCwgIjIyOsra1RKBR4/fXXyeVy+P1+JQkqF9iRkRHlgPXtb39bIdLj8Tj1ep1cLke5XGZgYIBcLkez2eTIkSOUSiV6e3tZWFhgYWFBJf19+/axsrLC3Nwcx44dw+PxsLq6yvb2NkajUZlnuN1uNf8X4JjonEvVKA5dUonb7XaazaaicombV19fH8lkkoWFBRwOB+FwWKHahb6Wy+VotVr4fD61YZBEu7y8jMvlwmg0EgwGCQaDWK1WRW+T1nej0aBWq/Hiiy+q2b3Io+ZyOfWeyExYkprT6cTtdlMsFlXXQkRvgsEg+XyelZUVZQkruuxdXV2cOHGC48ePq01jKBRia2uLZDKpFPJarZZah8ViUcYnVqsVt9utxiHyWNmM3rp1i4GBAbq6upT9aOdz6PV6hW1IJBIqOdbrdVwul9pEyDEGFPAR2nNsMYQR7/lms0l3dzfdoSAeLYupEMWg0wiHww/oyevtXoyeMEZvD3rrR3PnSqfT3Llzh3q9zszMzA6jm09yNBot5ufiAOj0ML2n64dqA7Ibf/PxuAn8hqZpB95z33VN0w4+wTU+UvxNJnBo7/x/93d/l2PHjlGpVAiHw0xNTT30sblahe/dvc787C1MZhO9Q4N4ggGikSi5WIKDw+Mc37Mfq6k9N4xEInzta19D0zTGx8cVQnl9fZ1SqUQsFmNmZoZsNsvf//t/n2AwSCKRUHNd0dU2Go1YrVa++MUvMjg4qGbXHo+HH//xH+fChQuUy2VsNhurq6tqvigXZ6PRSC6XI51Oc/ToUW7evInBYFDe0l1dXTidTlZXV5VByr59+3jnnXew2Wz8+I//OPF4nCtXriiQmHQdnE4ne/bsoVKp8N3vfpdYLIbL5cLr9ZJMJsnn8+j1etWKl9mvzWZTbeNAIKCq0EqlotrnPp+PSqWiwFWhUAij0ahQ52ITarfb2d7eJpvN0tPTQ6vVolgsKt63eHpXKhXVrtXpdMrEo1QqYbfblYVoq9Via2uLVqulwHWiSjc2Nsbk5KSqrJeXl8lkMhw4cIDR0VFmZ2d5/fXXeeutt6jVauh0OtXd6O3tVQ5kTz/9NENDQztoe61Wi0qloroD8jWRSFCpVBRfXGRohaZ2/fp1hoaGlMuePE+5XFZYB/lZbtJ9yOVymEwment76evrU5aknTx/0XjP5XJks1mFk8jlcgSDQcaGB+i2Nlm7fYXegGuHz0Bn6C0ODJ4wRk83eqsbnfHRZt6RSITbt2/jcDjYu3fvjpHUJzXm5+I0Gu0N0fhEALNlF7G/G+8fHxfEJvDr/x5IA39Gu4X+9wDf3wY3skeJO3fu8K1vfYuf+ZmfYXFxkdHRUUZGRh762GK9yno+zezyPVaXlmk1m/QOD+Ly+9jY2KBcKnFkYg/7BkcJWB2Uim1L01qthtVqZWxsjP7+fm7fvs1v//ZvK3WvarVKX18f4XCYQCCgfKm9Xi8LCwv82I/9mOKFb29vs7q6yuXLl6nX64o/HQi0W3VXr14lHA6reabMpGWG2tXVxalTp5S/djAYpFgsYrfbmZub4969e1QqFer1OktLS5w+fZp/8A/+AUajkX/+z/85VquVu3fvKvGTYrHI5OQkpVKJubk55ubmFP2pXm8D/YTD7Xa7KRQKqq0rCU4qTqkY0+m00k+XSl6+B+ju7sbv96uKWpI7gNVqVX7fMhf2er04nU4cDgflcpmlpSUMBgPBYFBJvFqtVprNJjabje7ubiqVimrBiy0ooPjvfr+f3t5e9Ho9q6uruN1uXnjhBWZmZmi1Wrz22mvKNS4ajRKPx9E0TXUxxMJULGmNRiM2mw273a7a8YILkPGLGNTIujVNU9V/KBRieHgYt9utNm7vB5KTMYpQ64SLLsj6TuyAz+eju7ubrq4udfzy+TzZbFaJzSQSCcbHx6kWsgz4bRya6MeqVeADigedyYre6kJvc7W/3r89bIYux/HevXv09vYyNTX1ifYzX11JUyy0xwsDgx5c7o+uU78bPzrxcRP4Mu2E/bD+jqZp2sOtmb6P8YNI4K1Wi6985StkMhksFguapvH8888zODj4/n+jtUhWitxaW+bm3G2KhQI9g/1YXU42Njaw2mxMjIwy09VPyGTnwoULCtmdyWSYmJjgjTfewOl0KlOO+fl5BRwSi8pEIkEikSASiTA8PMznP/95pY0ei8X4q7/6K6U3XigUMBqNFIvFHW1PSZ4Wi4VqtcrCwgJjY2OqhTw1NUWr1aJUKhEKhVSb2Gg0sr6+rmwvXS4X2WyWlZUVenp6uHnzJna7XWl7d3V1YTAYuHTpklIry+fzquqr1+tK+KVer6sKz2w2q+TZbDYVJalaraoEL8l5YGCAWq2mNNjdbreyQ43H48pJbXp6mmAwiNFoVB7m8vy1Wk1Jx9rtdtxuNw6Hg0qlgqZpigvu8/lUu17U8KSlLJuhTo36RqOhNgBiSqLT6Th58iR+v19x58WwRjYVwlVvtVqqapZjJhu5RqOx41iZTCbFrx8aGqKrq4toNEoul1P0MLfbjcvlUuj3Tjc1i8XCsWPHHjBtyWQybG1tsbW1pcSOrFarAj7q9Xq6urro7u4mEAio9zmdTvP666/j9/tZW1sjmUwyOjxI2GXCa6zj1Ndw2KyPpLOuM9tUMjfYvegdfjVLr9VqzM/Ps7m5yfj4OCMjI59IOlpkO08q2XZoDnU5CHV98rsGu/GDi10U+mNGvV4nEokwNzfH5cuXWV9f5zOf+QwvvfTShypq1ZoN7m1vcOnWDbZiEUK9PWAykkglCfj9HBufYV9XP1evXqXRaDA+Ps7i4iKXL1+m1Wrx1FNPYbVa2bdvH1tbW8zOzjI7O0u5XMZut1Mqlbh79y75fJ6nnnqKoaEhNTve2tqi2WxiMBiIx+Ok02nu3r3L2NgYmqaxurpKLBZDr9fj8/mUOlomk1FSsYlEQs2Ks9ms8siOxWIUi0U1b/Z4PAq4VSqV6OvrY3V1lUqloqo7ae3KZsDj8ZDP59na2iISiai1inWpw+GgUCjgcDgUsKtQKKh2uniWy1eptg0GA8ViUUm9SnWdSCQUat/r9WIwGFSC1Ol0ynZV/MiFGiYVuIDrwuEw6XSaRqOBzWbD7/ersYC0/UUvXyhYxWKRarWK2+3G6XSqDUoymVRsBQGHtVotms0mbrcbn89Hq9VSGxKXy6XWrGkapVJJecBLa79are54L5LJtvqX8NY7QW/CXZfE63K5iMViJJNJnn32WXp6eh44pzVNU6yKzc1NpVvvdDpJp9Nqs+D3+1V1rmka58+fZ3BwUEnBjo2NtTdAqSSF2BpOXRWHoYmxVcOg16nxhl6vR28wYLj/Va/XY7jPXjCajBgsTvQOPwanH4MzQKmucefOHWUz29vb+325LnzcyKTLbG222R0ut4WBQe8PdkG78YmOx07gOp1uHzADqC25pml/+MRW+Ijxg0rgnVGv17ly5Qpf/epXMRgM7N+/n6eeeorh4eEP5arGMiku3LrBneVFbB4nuUKRdCbNcFcPL+w7SiwSIZvNcvLkSW7fvs2FCxdwu914vV7lCS1rWF1d5c6dO+oi/eUvf5larUY4HMbj8eD1enG5XCwtLTExMUGpVGJtbY21tTVqtRoul4vjx48zMTHB1772Nebm5jCZTEpX3eFwMDk5qQBc6XRaIdil4otGo0ppbXBwUFW0wkf2+/2KJ16r1dA0TQmZxGIxnE4n4XCYSqWiVM9EVEZmw1arlWq1isPhUHQnQbdrmqbEQ+r1ukqyQpXL5/OK8mW1WikWi/h8PiWvOjIygtVqVUA+vV5PqVRSfyfgP0BV5eJYduTIEaanpxWtSpzTtre3SafTdHd3EwqF0DSNTCZDPp9XQjCCHJc5uKZpTE5OKgEZcXYTGp0kbmhTrAQsJih1sX8dGRnB4/EoS1iz2awEdGw2Gx5PWw9d1pTJZFSrXKp6sWWVzsXExASTk5Nq4yE3GXG0Wi3i8TgbGxvEYjGCwaASv0kmk0oV0G6309XVxfr6Or29vbjdbmZnZzl48CDhcFgB74rFYhvkWMnTLOVoltu3VjlPq1qk1Wx3MpqtJvV6g+b98YII4JjNZoxWO2Z3iJJmZiWSxuYNceTo0YeKMv0golyus7yYAsBsMTA+EfwBr2g3PsnxuCC2/wn4FO0E/jLwE8Bbmqb9P57wOj80PgkJXCIej3P27FkMBgNLS0vo9Xr27NnDxMQE4XD4A2dwpXKZ129fJZJLK5R2Pp5kwtuNWW8gkUjg9XopFApsbW1htVrZ3Nxk//79zMzMEA6H6e7uxu12t92olpe5cuUKpVJJgcYEVZzL5ahUKjz1VJuXfu3aNfR6PX19fVy5coW1tTXF4y2Xy6RSKQWU83q9nD59GrPZTCAQUBdZUTDL5XKcOXNGAcqkXVsoFLhw4QKNRgOfz0exWFS83kajQU9PDx6PRyXmp59+WnUNtre3WVpaYnV1lWg0CqDEXmw2G6FQCJPJRLFYVC1kQfFWKhVVXXaC3+r1umrTa5qG3W5XHtz79u3jwIEDqkshj8nn8ywuLipqVyqVwul0KtbA2bNnqVarjI6OEgqFqFarjI2NYbfbFVVLRGNEdMbj8ZDJZLh586baeEkVvry8jMFgwOPxqBn+zMwMBw8eZG1tjeXlZSWq43A4VKvebDYTCoW4c+cON27cUHNqGUUIsM/n85HP55V9q9/vJxwO4/f71fGUBKppGrVaTUmuCnhTErZU8aFQiH379ilKZL1eZ3t7m42NDXK5HL29vfT39+P1eonH46ytrRGJRIjH44yPj7N//34uXbrEyMjI+4obdYbWaqFVi7QqeVrlHM1iimYxRa1We1c8puNWq9ep12rEEgk2Ikmc/hDD49N4Qj3YPQEc3iAOb1CBFP+motVqS6rKgHJ6Txd6/S4SfTceHo+bwG8CB4GrmqYd1Ol03cAfa5r26Se/1A+OT1ICB9jc3OT27dscP35c+TU3m008Hg/BYFDxah9WmWuaxmI2znKuzVtuNltkkkmsiSIWrW2g4fP5eOaZZ1hYWOD27dtcu3ZN6Y1L+1YqdE3TuHbtmppxdnd34/P5MBgMXLt2TW0oCoWC0knv7u7G6XSytrZGPB5XLmMyr9U0Tf0PES4R3rVOpyOdTjM/P08mk+H06dNsb28rBTExD9E0Tbmx1Wo1xd+WpCsUJRE8eW/VeOHCBbWGToOSvr4+AFZXV9WoANqocUnSgUCAarWK1WpVbfJOPIAg3Z1OJz09PYyMjOxQ5Eun02xubqqEFYvFyGazqjpvNBokEgkajYZCw8/MzLBv3z6GhoZIJpOsrKwo0ZF4PK6Oj3QKtre3yefz6vkNBoMC2BUKBVqtlhLckdZ5X19bprderzM3N8epU6cIhULY7Xai0SgrKysEAgGsVitra2vMz88Ti8WUwI4ongkCXa/XK4EUmffLa87lckSjUUwm0w6hGUn66XQaq9XKxMQEQ0NDqlI3Go0kEgnV1hfHOGgr/r366qsYDAZOnTqleORiMvNRQmu1aJUyKpm3Cim0Zv2BxzWbTVZWVtjc3MTlcuHxeNrdjmqNagvMdg9WTwB/zxDdQ5M4vN9ffvbCvQS1avucHRnzY7P97VCa240nH4+bwC9omnZcp9NdBp4H8sAdTdOmn/xSPzg+aQkcYGVlhaWlJZ599lmMRiM3b94kmUwyNDSkwFOCSA6Hww8k861ChjvpCC1xONDAV9ZYvD7La6+9xn/1X/1XHDzYZuzduHGDYrFIf38/sVhMgcgkobdaLXWfz+dTrXCpxl988UWKxSJXrlxRwKWpqSk2Nze5e/euSoZ2ux2Px6OQ0cPDw8r/ulPNzGAwkE6nOX/+PN3d3XzqU5+iXq8zMDDA008/TTwe59/8m39DrVZTCa3VarGxscHg4KCibgklzmAwKNEQuajrdDo2NzcVr7tWq2G325UgSyAQaKuANRpKGrVQKCh3tp6eHrXxqNfragMjSG/RhDcajWqWL/rl0oUQBbOpqSn0ej3nz59nZWVFIbBTqRTb29tqzu1yuZTSW1dXl6p4RZM8l8tx7949TCYTBw8epFgssra2pvjfwmlOpVLqWFerVbXR0el02O12enp6KJVKqjMgPHDBKDQaDYaHh+nt7VWPkfa4pmkUi8Udrf1EIkG1WlW0NaHHaZpGoVAgn88rIKDFYlEAP5PJpCiBcrw6pXNlTbVaDb/fT1dXFxaLhVu3bqnORrFYxOVy8eyzzyrtgveC6B4lNE1rV+iFlErqncpwtXqdzY0NMpkMff39hEJB0NpMj1Kp1MYtZDKYrHa84SGCA2P4eoYx2L3onmCVvr6WIZ+rAtDb58br++FUltuN7388bgL/XeB/oC3g8t8BBeCapmm/9qQX+mHxSUzgAPPz82xvb/PMM89gMplYXV1lbm6OQ4cOKV9tUb8KBAJKxlSSebpS5Fpig0arqZ5zzB3iu3/+NZLJJHa7nbGxMbxeL7OzswwMDDA8PKwoR53uUrVajUgkwuLiIuVyWRmEZLNZlcii0ahqzcbjcY4dO6bm3WfOnOG1116jv79/hzBHuVymWCzi8XiUH7bZbMZoNHLr1i1WVlZUpVgul6lWq/j9fur1OteuXePgwYPMzMxw584dVbnv27dPVdZifiIiMD6fj1QqRSgUIhaLsbq6qirdYrGoEM8yQzabzfzYj/0YXq+XN954g4WFBWUJKuudnp7G7XZTr9dJJBLcuHFDaXxLqzseb4tsmEwmGo2GoqMJet/n8zEw0BbmWVhYIJvNqs2ZcPCbzaayhRUpWukMSBUt6m8iRNLX14fVamV5eZmbN2+qToRQ3sQgRToHMsoQMJqcEyJYI92Gra0tdXwajYbCRrhcLuU8JgBGEdnJ5XIqoYkCmmAUksmkauULCLC/v59wOKxc8fr6+jhx4gRdXV2KJ76xsaE07+XcGB4eZmNjg1QqRV9fHxcvXuT27duq4yOua0NDQwSD7Vb3oyDV3xtas9F2U6sWaVUKaLUSuWSE1YW71CsFBgcGd/ita2gUC0UymYzitnu8Pnw9gwT6x7C4Q23hGYMJncH0sRJ7PFYgHisC4A/YCfe4PvJz7MaPRjwxFLpOpxsG3Jqm3XhCa/tI8UlN4ACzs7MKgCaV6aVLlxgcHGRyclJVyZFIhK2tLeLxODqdToFvGnpYaRRo6MBg0GMwGkkvb/Bj0wfR63SKS9zd3c3Vq1fZt2+fqkjVzK9WU7dyuUwkEmF9fV0hxfP5PAcOHODGjRuEw2HsdrtK9kJDstls3L59W+l8i7DJwMAAXq9X6YYDSmazUqmo9rBUZ8ID7+3t5datW1y7do3x8XFlVZnJZFhYWFBz2EQiQbPZxOv1cuPGDQXS8vv9hEIhZZNZqVQIBAJks1mazSaBQACLxaJm4haLhaGhIZxOJ7OzsxSLRVWJ2+12QqEQpVKJRCKhwFzyXHa7XbWBG40GJpOJRCKB3W5Xs3x5zaOjo2ozJJsP0UWv1WqYTCaGh4cV2E480wcHB1WrWroRMpPv7e1l7969CgXfKa4SjUa5d+8eW1tbSsCm1WqxsLDA1taWEnwRQRiHw6EU7aR6Hh4eJh6PKyEW2YAJNU5MVoQJIBx0sYM1m80UCgWWl5cxm820Wi22t7fVLH9wcFBZrebzeZxOJxMTE/T09ChuerlcVptJOV80TUOv13Py5EkA9TrFIGdrawtoy+MKRU02QZ1Uu4+jaLa9ucHczavYTTrGB8LY9Q1apTRas6EeU7uvA5+9j/uw2+07MS56A+iNaHojGIygN4LehKY3Ynd5GJ7Yg9HuRm9xoNO3NyC5bIWN9SwADqeZoeFHs5bdjR+9eBIo9J8FTtGGXbyladpfPNklPlp8khO4zKCj0ai6sJhMJpaWlnA6nTzzzDMPANvea0pRqla4kdwkVSnSbLRndl1ON4NGJ73dYYxGI5ubm9hsNiqVCi+88MIHIt8bjQa5XI7bt29z7949Njc3ARgZGSGXy9HX10ej0VBmK2NjY5TLZRYWFnC5XOzdu5fFxUVefvllNRuVxFAqldja2iKTySjaVjQaVTKdYrfp9/sZHR1laWlJeaybTCbK5TKLi4tEIhHVjRBd8pGREWKxGLdu3VJKZr29vRSLRc6fP0+5XCYQCKj/LVV1PB5X+u0CZpPWs1SqMh7o6elRqPpsNqsqWuFPB4NBBSoTzrXD0XaOkgQr1WsqlVJMgdXVVdXZMBqNzMzMKCqYWJkODw9jtVopl8tYrVbi8bjSGJCq/9SpUxw6dIhQKITT6VTJKRaLcfPmTRYWFnC73Rw9epS33nqLa9euYbfbuXr1KhaLRanEyQYpEokodTzhmksnyOl0Kvc06TbIhrBer1MsFpUMsKisLS4uKpEeq9VKJBJR/uvSLZLRgNvtViBIEcgR/IAcF6G6HT58WLX4BV+wZ88e/H4/+XxedUgECyKStc1mUyVz+fqoib3VarG6usrCwkIbnBcMEnTb8Fr1GOoFWsUUrWpJPTZfKNCoPzhnf1gI5mJkZKS9ZpMFvcVBQ2dlfbsGJjtGq4OJfUOPrD63Gz9a8SRa6OO8q4X+94BFTdP+0RNd5SPEJzmBS8gMtvNid/v2bSKRCIcOHVIXTJfLRTAYfMDprKW1uJ3aZruYZeN+wh3s7SMbiZOLJxkO92LR9Ny4cpVQKMRP//RPP1JbMZfLcfPmTV599VWmp6fZ3NxkampKtXlXV1eVrebq6qqiOT3//PM4HA7i8ThTU1Pcu3ePQqFAf3+/Mr1IJBLcvXuXpaUl7t27x/DwMBaLhXv37pFKpVRCWVhYANqGH88//zxTU1PMzc2xvb2trDF1Op2aAz///PO88sorxGIxJRMbiURIJBKYTCYlZuNwOOjr61ObAKmUs9msapcLl1ou9nLeW63WHX7VQiOTi75I1b6X9mWz2dDpdEohTRL8vn37FO5gZWWFer1OX18fk5OTOBwOpVEuCG7RYs9ms8qdrpNuNjo6Sl9fn1LpE3paNpvlwoULSqf+0qVLWK1WgsGgmp1LG1yQ66FQiP/yv/wv8Xg8LC4uqlm+zLONRiNDQ0PqPHW73e+rc9Bqtbhw4QJ/+Zd/qRzg0uk0t27dolwuMzQ0hNfrVSI2YtMq9rAGg0Gh4w0GA/V6nWQyydraGmazWYnoaJqmHPC6u7tVq1uqd7GIFb64IOXFoU247qLW914jmM775PwR/YNkMqmOW9DnxmPRoZUy7Qq9XkVr1tuAuQ+5horAkdvtVk57Glq7Ar//p719HoxmEzqTrS1WY7a1vzdZ29/Lz59AYZrd+P7G4ybwOWCPdv+BOp1OD9zSNG3PE1/ph8QPQwJ/v1hbW1MtdbfbrZzPxAlKAFOSOJaycd5ZmiObyTI21ha9q9ZqbG5uks1kCfn9LN25i8di58VnTzM5OILdZP7QauPatWucO3cOi8VCrVbj1KlTSlWsUCgopTSpDF9++WXS6TStVotDhw5x9OhRnE4nt27dolgsMjY2RjAYVG3pt99+m7W1NSYmJhQP+vz588omtFgs0mq1uHPnjgJ3GQwGhoaGqNfrfPazn2VlZYWvfe1rNBoN3G63ahEPDAwQCATUuAJQOABRPRPeOIDP56NUKinam1RpIs0qYiuimqbX65XYTKHQtnyU57JYLArtLy1xQZMDSpFMp9OpxOJ0OkmlUopXLomyt7eXlZUVhS+wWCzq+Al7QbABsVgMi8WikqQ4qYmPvTAdotEowWBQVXzC1xcfermFw2H279/PoUOHGBwcpFarsbCwoNTrUqkUsViMQCBAd3e3Wo/X61UGNTJnh7YW+ZkzZ5Ssa09PD1euXOHb3/620hsQGp9QASVhSkdKpHMdDoeSiZXNh4ACjUYjhUIBk8mkVN7kfZU1ibqgjANqtZpS0Ot8fknwkuRlVKBpmrKUDQaDyqBG3NwqlQo+n4+uri6Gh4fp7u5ubwSbjXeT+f2vWqPW/lqv0KoWaZRyrC3dI51qA1x9Ph/RSJ5arY17CXU5sVo/XBNdZzSjtzoxOAIYXEH0Dp9qy+/G38543AT+V8A/0jRt9f7PQ8C/0TTtp574Sj8kfpgTOLSr4EuXLtHV1cXMzAzQpiqJ2EWlUiEUCtHV1UUoFGI5GeU7Ny4yPbNzr1Qul9nY2CCTzZJMJAkEA9isVgZ7+5nx9xC0OXeqWN2/CYL50qVLqg0qLct0um1xKJam4+Pj/NRPtd/iS5cu8Qd/8AeqRRoOh9m7dy/1el2B5Y4dO0ZXVxfZbJY/+7M/w2q1Mj09zcrKigK6LS0t0Wg0GBwcVKAnmQMLWGhwcJAjR44wNDTE9evXlVHG+vo6FotFOZqJHro4uZnNZnXh7qywZbYs7VypTKGdlEOhECMjIxQKBRYXF5WBh9vtViYwgHLzEr66y+VSkrTZbFYBv8SBTIxTxKWrM2lomqbEZEqlkjIJqVar5PN5lawdDodKQtJuF/9umU3La6jVaopfLa34UCiEw+HgqaeeQqfT8dWvfpUrV66wf/9+9XdHjx5VDmhzc3PodDrFNBBMgegAiEe5dCAE75BMJlldXaWnp4d0Oq1c9q5cucKlS5cUGK1Wq7G4uKg0BESERsBttVpNARUnJyex2+0KhyEe5JlMRlnWjo6OcvToURwOhwILDg0NKUEbQeXn83n1Gcvn83g8Hvx+Pz6fT7EQxORF5IllHCM0TaEJJpNJEokEGxsbymJ3fHyc8fFxJbbzfqFpGqnoJrNXL+IwGwi4/JSzRXT1El5nC5frY+i36/QYHF4MziAGZ2A3of8tjI+rhf4N2g0eD3AMuHD/V8eBC5qmferJL/WD44c9gUNbRe3q1auUy2U8Hs8OEwlRLRNEudVqZW19jZ/5e78AFhO5eoV8rUK+XqXZapIvFJi7c4eNjQ323bc61WkwZfVhQa+ASM1m84HvRUktEonw4osvKuBXJpPh2rVrvPLKKxw9epTh4WFCoRBms5lvfOMb5PN5NjY2MJvNfPrTn1bz7YsXL5LL5VS19OqrrzIxMaEkYOX/nz17lmQyqWROi8UiXV1dChwngKbe3l5GRkbo6+tDr9ezsLBANBpVSGRBogsQTS62YpAiPGrRNhd+sVDGWq3WDq9ys9msElI+n6der+Pz+ZRSmcFgwGaz7eCbi5BKT08PoVBIzWhTqbbKliDgbTabomdJUioUCni9XqV/HwqF0Ov1FAoFpXrndrsZGBhQKnDpdFp5lAcCASUMI+C7RCLBwMAADoeDUqnExsaGkqcdHh5mYGCACxcuKGc2j8dDMpnE4/EwMzOjXO5ExEXkckWONhgM4vF4VBXs8/mUUczS0pKSR11eXiafz9Pf3w+0DYG2t7cB6OnpIRAIoGmaUuyT5xcvc6HTBYNBpqamlEyvVP25XI7NzU2WlpZIJpOYzWa6u7vxer1qJi5+8sKZF7lbp9Opxlxi2SoMhFAopDZsgEro0lIXc59QKITL5SKdTrOysqLOTY/HoxJ6T0/PDuxCZ7RaLe7du8ed28t4vX2EQiF8Pjs93VZatTJarYxWL7e/r1d2fP9h7fp2QvdhcAYwOAPoLA50Rstu6/2HOD5uAv+xD3pSTdPeeAJr+0jxtyGBQzuJxONxVbG9F8wmwLJUKsXrr7+Oz+dT4CBJtAabhZbJQFWn8dalC8wu3CXc14vJZMJpNLPX2YX5vl/1e286nY5isUgikeD1119XLVuv18vExAR+v59z585hMpmUcUo8Hufy5ctKmObSpUusrKwwOjrK5z73Ofbs2YPFYmFjY4ONjQ3W1tb4+te/zq/8yq/wEz/xE2xubnLr1i30ej1vvPEG6+vrSoJU+Mdra2vKf1r43m63m5GREcxms1JmMxgMCp0s3GaZxff09DAxMUGlUlFAuIWFBTV7lSpPWqbSQpXELNWYtMqli9H5syR8QMm+mkwm5dIlNLp6vb7jAt6pQy665PJ3rVZLJZFWq6UMQqQrMzY2pkRm8vk86+vrZLNZfD6fkmq1Wq14PB6VYILBIOVyWfmal8tlJd4i1fXBgwdptVpqE7F3716F8A4EAjQaDRYXFxVoTapfoYPJxmloaEgBGF966SVlLOLz+ZicnARga2uLW7duce/ePSVQ4/f7yWaz7dFQNqv82g0GA7OzsywvL9Pb28vY2JhSlwOUfK/dblcqdI1GY4cLm3RbhOkh1zpB4UtlLQYwolC4Z88eJYwjcrWCj0ilUqRSKdU90ev1Ssc+nU4TiUSUII/P52NiYoLBwUG1QZMOR3sDneL82VsYDAZmZibZu3/gQ68bWr3SFq7JJ2gWkrQq+Ue65uhMFnQma3umfn+2rn4229pJfhdE94mMJ4FC76ZdhUO7+o49wfU9cvxtSeAfJd58800mJyfJZDLcvXtXIZ5dLpdKQna7ndmFu0StGoFgEK/Hw7A3xLgrqNq2nTfZKFSrVTY2Nrh9+7ZCKktFWiwW+cxnPkM6neb48eNMTU0Ri8V4/fXXmZ6eVj7Mly5dol6vc/ToUUZHRxkdHaW3t5dqtcorr7zCX//1X/PpT3+av/N3/g7hcJiVlRWVECwWC2+99ZZKPqIl7vP52NraIp1OUywWKZVK2Gw2TPc3JJqmqTavWJ0KWEkoWS+++CJ+v59qtcry8jLf/e53FUipUCjgdDoJBAIKhd6ZiM1ms9J/F410g8Gg2t9iOCLJXNDagGqri5a7JHHhlcvfdL4XUiULuEta1VK9i7ypz+dTGxGhe0ll32w2VYu4u7t7hymMdHeEDiciOdlsllKpxNjYGA6Hg83NTdUZ2LNnj6KjzczMMD4+TqlU4t69e6TTaer1Oj09PUp3QAxplpeXabVaDA8Pq0TcbDaV/K9U4MVikc3NTdLpNB6PRxnXFAoFJWcrYi6SyEdGRpiamsJms5HP51leXqZarTIxMaEocpubm4RCIaVxr9PpFDBRNhhy/FOpFIlEgnQ6rY6F0WhUrm0iWSsdss6vMqvvBMDJxkAkhyORiBIvEj2Grq4uNa83Gs1k0m1Fw2QywaHDQ5w+fUoJ+TxKtOpVWoUkzUKSZiFBq1L4+BcbnR692Xo/mVveTfoP+3m3ov8bi8edgf8C8C+B12lbi54GfkPTtC8/4XV+aPwoJnCpLA4fPqy43Kurq2xsbCgQk81mI5VK8ebNK9xKbpHP5dHQmLD7GetqU4XE5ERAXBKapnHmzBmmpqZU4lxeXuYrX/kKgUCAcrnM+vo6e/bs4dSpUwq9Ltz2eDzON77xDd544w2FsJdWo9fr5cKFCySTSeV21dvbS19fH5FIhD179tDX16fQ1CIFOzMzw9DQEIuLi6rCfOedd1QrOxgMEolEqFQqapYqbmCi/d5qtRSNqlM3XixHr1+/TrlcVm1s6XwACszWaDRIJpNEo9EdnGUxZpE5tDibCUDP5XLh9XrJZDJqMyCSpZ2bLuHENxoN4vG48v222+2KHSBjAOkAWK1W/H6/SkSiUCcgrXQ6TaVSUZKnonRXq9VUIpGugIxsyuWyEl/RNI27d+8SjUYxGAyq2hZL2ImJCQYGBtDpdAq011lVNptNbt26pfjmxWJR4QfkJq9TkqGch4L4lgQvKHKRJhZa2eDgoDp3RFBHAIBms5nFxUUajQZjY2NqrCKAN+muGI1GBgYGGB0dpaenB5fLpaSRjx49SjweZ3FxEY/HQ29vr9oIlEolZZQj/HoZicjvZEQlx1pojdCWEJbPo9PpJJUEs8mMBqytXSeVTjA6OsqePXsYGBh4YAMhG8z3A6u+m9ATNEvZ+y34yhO/Lu2o3k3W+yj5+9W9VPS7Sf6JxOMm8OvAp6Xq1ul0IeBVTdMOPvGVfkj8KCbwZrPJhQsXMJvNHD58WM1yG40Gm5ubCs08PDxMf38/d3IxIsUs5VKJXCZLVwmyiaQSLhGEud/vJxAIEAy2q/RoNMrzzz+vLgxvvfWWcm9aX1/nlVdeoVAo4Ha7yefzTExMqAuVy+Wi2Wxy9+5dBQoT1zC73c65c+eUapr4hkciEZaWllQLvFgskkqlWFtbQ6/X43Q68Xq9VCoVRa9aWlrizp07aqabz+cVn1iqXPmq1+tJJBJqZiuc+Ewmo1DcAjzz+XxomqZcvKRlKuCrQqGgTDoAJR9qMLSNZ4rFogK2SeXdWTF3d3ermWulUmlz/u9f7AFltSrzW5FKFd15sQuV49bpGtbZ4peqXFTfzGYz+/btY+/evUqSVjYKlUqFmZkZTCYTKysrXL9+HU3TOHz4MEePHsVmsynQYS6XY3t7W402pPUvKmydXHU5/tJGHhsbo16vKzc0cY4T8KZgBkQBLhqNsmfPHn75l38Zo9HI9vY2CwsLCuQJqNcP7YQoSa5SqVCr1RS9MRKJKHtXj8ejOhWd2gcChnQ4HAwODjI8PEwqleLw4cPKRGhra4vu7m7VqZDzo3PzIjdAOcoJAyKbzSrHtmg0SiqVUqDEvt499PYMYrc7sDkapNNR5ufnARR40Ofz7bCZNZlMhMNhJWgjN5vNht1uf0CCVmu10BpVlczfna235+tavUKrXoUOJcjHDp0Og8OH0T+I0duDzvDhCPvdeHg8tpmJpmn7O37WA9c77/ubih/FBA7t2emlS5fQ6XQcPXr0AcOHVCrFyspK+4Kl17Gqq6A330cou9yc7B3HbrNRv68olUgklA2mtA/n5ubo6+tjcHBQVbMDAwNMTk6qOeHly5cplUrcuXOHvXv3snfvXuUfnkqlWFxc5OzZs2puKKIvkUiESCTC6dOnsVqt7N+/n/Hxce7evUu5XMblchGJRFhZWeHcuXNMTExQKBTo6upia2uLWCzGgQMH8Pl8ZLNZXnnlFXQ6Hf39/eRyORKJhEqeMu8Vm9NSqaTa6jIzvXfvHuvr68rxbXt7W0mLyt+KsIvQwoReVq1WlRJaV1cXe/fuJZvNcv36ddbX11Ur3el0qrZ1OBxm3759aqO0tbWlrFIlqXaKz8hzCPAPoPNzKnN6oZRJ4pbfNZtN5QcuQLtgMIjL5VI0K9FQF7ewSqXC4uKiajU7HA76+/tVC763t5dyuUwymSSbze44B6UjEAgEVKfF4/GwurqqNlvJZFIJ78imRuxa7XY7rVaLcrnMnTt3+OIXv0gymeTYsWO89NJL7N27V3Uy7ty5w71795RQTn9/v2rlC24im83SaDRUhb69vU0ymdyBc5DjbbVaaTabCoBYKpXo7e1VynB9fX0K27G5uYnf76e/v18J73TeOrETcpNNmWzMBG0vQLx79zZIJYv3LW/BbGkpqdxWq6U6CML+kM2cGPKI4qGM1MR1TxK6yN7K+EC+Wq3WB64jWrNxP9HfT/byfaNKq2MDoNWrH+0Cpjdg8vVh9A9gcH4yLF1/mOJxE/i/BA6wU8jlhqZp//0TXeUjxI9qAof2xfzq1avU63WOHTv2vuIt1WqV7XSS89uLlCsVqpUqPs2At65XCUVoSqJW5XQ6icVinD17lqmpKVKplFJBEyCTzHk7JWAF+btnzx4lo1kul3n77bcJBAKkUilu376NXq9na2uLUqnE9PS0SrRSzX/mM59henoaq9XKn/3Zn1EsFrl37x42mw2bzUYikWBpaYnBwUH6+/tptVrcunWLZrOpZuMbGxuKZy1e2pFIhGazqebbImgilKB8Pq8qw2g0qmaaggGQ6lOq6kKhoCroer2uAExiyen3+8lkMiSTSZU4RdFMAGaiDCZzbUE+J5NJVV0LL1mQ8SJjKnPtZrNJuVxW6HuZxwtKWzYCIlwjoDoBMUorX2bygrAXHrX8TpKcxWLBaDTicrmU4ItsLKSiDoVCSvteLF6l7ex2u9m3bx/T09M4HA7K5bJC1JvNZpVYXC6X6vq8/vrrXLt2TSnjice41+tVqP5MJqPm6KFQiN7eXoU/kO6Kw+Hg2LFjii6Zz+fVTTae0tKv1+ssLS0p0ZVUKsXU1BRut5tQKKSojwK2E77/e6vg93NUE+yJbDbq9TqJeI7VlTjrG+vUGyWMxqrCfUSjUba3t9VmrFAoqPdKXN/kPBbOfTgcJhgMqla7COqIwYxsQOV86Dz2Ho9HIfU/SE9CVfS1Mq37Fbx2v6rvrO4fFnqLA2NgEKOvH735o5vV/CjGx07guva72E8bwHbq/t1vPq6Uqk6n+y3gHwLx+3f9D5qmvfxhf/ejnMDhXbnWUqnEiRMn1AX7YbGSS3IvE1U/Hw4N4jVZKZVKamYnF7JCoaCMNIaHh5mamlK/O3TokEpYlUqFZDLJ22+/zY0bNzh8+DCZTIb19XUlkuJwODAajUSjUaamphgcHCQej3P37l0uXLhAIBBgfHxcVSGJRIJKpaIAaOJpLnQwAULVajW2traU8Uiz2cTlcpFIJMjlclgsFpaXl4lEIkqrutVqkUwmlS1nrVZT9DVpN0rbtVarKWtMSVgCUJOKU4B28lipdoxGo6q2JMnK3Fq42iKRKk5nMksXu1OZccqFWnjJslGQv5GKu7Na7/Q6l2g2m+oiL6IvcrxFlEbAaNLSlWQtLWKbzUaj0VDubp1Vq6indf5sMBgUa6HZbKrNkADTZCwxPT2tKltpNcuGxGAwKDeytbU1VlZWFA5AJF8FtS8St1LNyphINgIi6iPyugMDAxw/fpznnntOeZiLKE/nPHthYYHz588rYKB0buQ1yesSASaPx0Oj0VAIf6ELirhNf3//+35Wy+U6y4tt2qHZYmB8Irjj96VSievXr1Or1dTmemVlheXlZVZXVxUIUBgQpVJJUVCFfigKfeFwWI07gB1yuSJ8I9oMImgjXgQ+n08B9h7F8rVVr9JIb9BIrj0cWKfTYXCFMAUGMbi7d+flHxBPtIX+hBb0W0BB07R/9VH+7kc9gUM7id+8eVMZp7yfFrqmaVxNrJMstz88JoORk90jWB9CFZGL2NraGu+88w779+8nEolw69YtDhw4oJKGtGr1ej1vvvkmtVqNv/t3/66SAxUVMEkUgm4Ph8OKN3vjxg0175U5+dbWluLnChdcqDlms1lVEKLRHQgEFGL58OHD6uI4NTVFMBhkdnZWcYH1ej0bGxusrKxQLpfZ3NxUyOh6va4uyKFQiKGhIVZXVwkEAkxOThKPx5V0q1iVSoWq1+tV8n/vBU280yUxSDUqcq4CUhM6nHQSpLoWpL2AwmRGK6ptuVxOtdClWuqs/DVNI51Oq2MsiaoTJ9BpXiPUNek6dG4uZBMi55lUkVK1Cd1R1g1tjEBvby/BYFBV4WKwk8/nKZVK6piLkpzMqGu1GplMRlmtBoNBpVGfSCRU61jojrI2QX9Ho1Fu3brF+vo6mqZRrVZJJpOEw2Hy+TwrKytks1mcTicjIyPs37+fvXv3qs6CdDJKpRKzs7NYLBaFbBe9/3q9rgRihMEgnHgZCwjIsFqt4nQ6mZycZGRk5AFp2lZLY+72fVKPDsbHA5gtDyZ7cTgcGxtjZGRE3S/dqoWFBVZWVmi1WvT29iqhoUajwcbGBltbW2qDKFa5XV1dCqchXR95n3K5nBIoEhCmVPWibS+KdS6XC4fDsUOittMoh0oOctu0sts7TGJ2hE7fFqDR3/+q04PecP97ud94n+MeRG/70XFve9wE/h9pK69dfIIL+i12E/hjxa1bt0gkEpw8efIBPXWJWrPB+cgy1WZ7puq3OjgSGvzA9tg777yj5pOvvfYaL7zwgkoyna3VUqnEH/3RHzE2NsaRI0fURb+zRZjNZrly5YpqHRcKBe7cuUOj0VCa3na7XalbDQ0NKV104XVLq7anp4fR0VElquH3+1leXqZYLKo24draGsViUQmOiB+3nOPLy8s0m03S6TSBQEApya2vryvHK5mDezweTp06xdTUlKJl3bp1S83NZ2dnWVpaUhc5sV6V19+phFcqlRT1S9rd0nJ1Op1KbU2qX6l2paUtCGfhn0uHQS6QshkQ323ZNAigqROZ/zDOuzynCNVI4peLemeI+p2MFVwuF319fQqY1qk6JwItktCGh4cZHBxUpjwi+NJoNLBarcp4RdrCkUiEXC6nKshDhw6hu+/Z3rnZ6Dyf5XXLhq9arWI2m1ldXaW3t5fnn3+ecDjM7OwsFy9eZG5ujnw+T09PD9PT04yNjdHX14fX66XVanHlyhX1XHv27MFsNt+nfSV3bKCy2Szb29tsbW0pkSGHw4HP58NsNiur166uLkZHRwkEAmrNy4tJyuX2cTZbDIyM+jEYHqxIy+Uy169fV8Yv7w0RNdrc3FQucXa7nb6+PsVWEXqeAARbrdYOoZv3q64FZJjJZIjH4wp8mM/nabVainYnYy/pUnTqLGitJvZmAVsji7lRRG8woL+/mZTPilKPNBgw6PXoDXoMeoPqQslnRGeyonP40TsD6BwBDGabAhaKzoJgAD6oU/nDEI+bwOeACWAFKNKmkmmaph14jAX9FvAFIAdcAv47TdPS7/PY/wL4LwAGBwePrq6uftx/+7cu7t69y9bWFk8//fQDyFOJVKXI5di7x2zME2LU8/48UzHJeOGFF3jllVf49Kc//b5V/u3bt7l16xZ9fX0cP378oR+UYrHIW2+9RV9fH319fVQqFVZWVrh9+7ZyR+vp6SGbzTIxMUG9Xmdzc5O+vj4l0RmJRFQbNxAIqHns+Pg49+7dw+FwkMlkmJ6exuv1srW1xd27dxUSev/+/bjdbkqltqOUy+ViZWVFSZOKW9j58+cVUE9mqELhEQlV0ReXKlrEQ4LBIPl8XhmJ5PN5tdGRqkaqaRF0kcQj7XNJ0FKBw7tIZDFIkWq309JSNlXwLvBNZp2S5IUKJvNvWYtU5fJ30joXJL88V6fLm2xEZKMmrWVB51cqFfX80sqVxC9dFZm/ykhBqloBw4nuuqDJl5eXSSaTitZms9kYHh5WZi9yjOSc69TAT6VSJJNJ1YUxm82qMySJdHt7m2g0Sq1WUwY1Ho+H7u5upcrncrl4+umn6enpURtDee5UKoVer1ddAaEGrq6uUq1WmZ+fR6/XMzMzQzgcxuv1dmgmNFlZTqG196o4nGYGBr3o9R/dHvW9n+Xl5WXm5uaYn59ne3tbJVWXy8XRo0fZs2ePoqfm83m8Xq9iqIjE7weFjFhkUyNgRRlrdWIDpDNlNptpVYu0slto2QharbRDp6LVatFstWg1m+9+bTZpNJvU778/wr03m82YzCaMNg8mTwjN6qVpdlNrtpRgkYx53nsT+uWHSeD+oONxE/jQw+7X7mujf8DfvQqEH/Kr/w9wHkjQlmr9X4EeTdP+8w9cCLsV+MNiYWGBtbU1Tp48+b7OUYvZOEvZNtxAp9NxNDSIz+p43+e8dOkSPp+PjY0NDh8+jNvtfujjKpUKr732Gl1dXWou/17LVGhXDufOnWNoaIixsTF1v8h9zs3NcevWLdbW1jh+/Dh6vZ6f//mfV4IdGxsbXLt2TdGJUqkUmqYpYNPW1paq5IUa53K5MBgMrK+vc+3aNbq6ujhw4ABbW1sKYd1sNhkfH1eJVz7Ily5dIhKJYLVaVcIVxL5U2zITlHa1cN/9fr9q9xaLRbWpiEQi3L17F7vdTiqVIpvNUqlU1P+QahJQmxRJBFJRS6tY7D07K2dJuJqmqeonlUqpkYRcGEWop7PCFo67qOAJTUzaoZJ0BaEvyVaet7PdLq35VquluPWyKZCWrlDcgB0dgU6evDxPp1CKpmlKnEVCNgIC9LPZbIpREAgECAQC9PT0MD4+jsFgUO53mqapDaTD4dixkRF7WaHbyVxZgIWid9DV1cXQ0BADAwMKMNgJkhOdAIBjx47RarU4c+YM6XSagYEBent7cTqd7N+/H78/TDz6LvDL57fR0/vwz93HjVqtpqRfb968yfz8PKlUSiHcQ6EQbrdbbYTEflbm4V6v9wPtiztD9AnkXO20pBW2gGgXtIsPDT0aekCv09DrQI+GTgc6rYVBBzRrUEpTy8aoV9rnYbVWpVqpqnGWyWTCarNhdAUxBgYx+XrR6fQ7PgNy3gtItdVqqZGCAAA/SfEklNiO8K4f+Nuapl15gosbBv5K07R9H/bY3QT+8FheXmZxcZGnn35affg6Q9M0LsfWSFeLAFgMJk6GRzC/Dzczn89z7tw5XC4Xw8PD9PT0vO//vn79uprfbm1tceLEiYeuoVKpcPbsWQYGBpiYmHjg941GQ1mHXrp0ieHhYQVEkjm1ANlkLjc7O4vNZlO+3DabTVG0hoaGdjiVra6uEo1GldqbzCYNBgPT09OKriPtb5nNOxwODh48SH9/v2pnr66ucufOHdWm83g8CgwoVC1JeNvb21gsFsLhsFJOE9T/xsYGiUQCs9msZohah8yuJEWpoAHloibVhV6vVzxouUk7vXPuLkC3zvm/JGGZMYt0rSRfqXSFEuV0OnE6nWrDIAlaWqudgjXC+ZfqTpDp0iYVnIN0HqQt33mTDoHFYlGYCNnUSEIXVzKRLpWLsAAfpQsifH8RJ8rlcgQCAYW7ECCedEw6PzvS7ZDqUt4Hh8OhEoLb7VYIcJmHixPb6uqqOlePHTuG2WxmdnaWUqlEV1cXBoOh3dWxBTAa28BDm9VKd9hFMOR8gAb2QSOwjxLlclnhXgQEKK9RRjiy8ZL3rDPxOp1O1aGS91Vu5XKZmZkZenp6HpByFmyCdHYA1e2RRCsb5/feGo0GjXodrZJDK6Ux1nKYGiUM+o5NabOJxWLG7XJjsNio20LUbEFqLd2OMZ+AQmu1Gvl8XgkniQyxONJJF+tRj7vf76e7u/uJvEfw+BX4/xf4eeCr9+/6PPDnmqb9b4+xoB5N07bvf//fAic0TfvFD/u73QT+/rG2tsb8/DynT59+6Ey80qhzPrpM/T6IxG91sD/Q975J/MqVK2xvb6u54PtFoVDg7NmzvPjii2xsbDA/P8+xY8fwer0PrqFS4dy5cwQCAWZmZh5oz0m7vV6vc/jwYYWOlgpPdtnxeJyVlRWlSidtXmmtS4IKhUJ4vV5F3YrFYsqGVAQ1BEgmgjKS+KQSXV1dpVKpMDQ0tGM2WigUVIKXJC0f8P7+ftxutwIxpVIpTCYTmUyG1dVVXC6XSl75fF7N7mUMIhaz0t5dX18nFouphC7VuQDPOg1qZIYpLUI5LrJZkWpbVNLEuEW026WFLApiMt+UJCYgNpnlA6pKbjabSt5WkqckYPm9VOfSdRBXM6vVqjYHAhAUbIQg7KUrACiuvHCeW62WSr5STUvbVqpzeU1SpRsMBtWCF/Blp0RtpxCLbGjk3JDXKMfGaDQqY5ZOloGg4h0Oh9JRl+QnuIEd67V6MZud99dtw2JtYrW+2zIG1PrFRtXr9e5QWRR8ROeY5sNCErAkY6GxiZCNsAikKyGfR2FnyP+VMY2005955hkCgYDi23e68sn3lUpF4QXkJl2RD4tms0mjXmtX5dkY9WyUeqHdKUsmk5gtFgJ+P4FgEFugH2NwGIMrqJL9ezeO9XqdRCJBLBZTIxWfz6eO8fuNKjtDmDJPKh43gd8FDmqaVrn/sw24pmna1GMs6I+AQ7Qr+hXg1yWhf1DsJvAPjrm5OVKpFCdPnnwoGCVeznMtvq5+NhmMTHq76XV4HnhssVjkL/7iL+jp6eGZZ57Z4dL03rh48SKhUIjh4WGi0SjXrl3j0KFDD92F1ut1bt++rcRZ3vuYW7ducenSJX7yJ3/yQz8E29vbXL9+nRs3biiXL+GpGwwG4vE4PT092O12hUqXyq1Wq7G2tsbt27cVMnpwcJBAIKA+zHa7ncHBQWKxGLlcTnGUxXFKWpI+n48bN26wsbGBwWBQaPBOQJjFYuHYsWMYjUbi8bjyWxe0uAiJSLUpc3Oz2aySeblcVsAhuV82BqLtLdWjJCBAqcRJZVyv19Uc32AwUKlUiMfjO+bjcqHq1M2XpC1zTKn+OytuoYYJN1ksUkXBrRN0JlVXZ2UuCUG6AXa7XVG0BJQmWAGRuy0Wi6oTIhuEQqGwQ7teZvcykxf+vOifi7+A8KHlfwsISlDnyWSSra0tBZaTkUStVqOnp0d5uxsMBnp7e7Hb7UqsR3ATsVhMcbm7urqUmUxb8rVINlujXm+g1+kxm8yYrQ0MBr1qS8v7BqhjKhWqJHqr1aoMacSe+IOSj1S3nfr88lUojcViUW2ohY0hDIdObX+r1apApSIXLN08wStIt0jwJTJHl5vgDuT2Udr3rVqFRnKVWnKVbDJOMpEgk8ngcDjaY5VwP5buMUz+gQ81cCmVSkoxUCyXJZl7vV68Xu/7goifVDxuAn8N+BlN0zL3f/YCX9U07YUnvM4Pjd0E/sGhaRoXLlzA4XCwb9/DJxKd83AJv9XBHl8PdtPO+fX169eZm5ujUCjw9NNPMzIy8tBEnkqluHLlCsePH8ftdpPJZLh48aIymXhYJBIJbty4gcfjYd++fTsqq9///d/nueeeY+/evY/0mjc2NlhcXFS84XQ6rdqqJpOJqakpBgYG1IVOxFlcLhetVkv5gC8vLzM4OMjU1JS6SG1tbak26+joKM888wzr6+tEIhGlRS+88fX1dWZnZ5XByOTkJA6HQyGeY7GYMigZHR3F7/fTaDRIp9PE43G2t7dZX19XlB0R6pDqp7PFnUqlKBaLqn1uNpsVfzqZTKLT6RR16tq1a0oPXBKa/A+xfxUkvLSepdXvcDjU/xcBGal+Oy/y0mYXiVfhJQv/Xa/Xk8lklPa6vNeCuBd6U2dSl+pWWrgytxRdAnkPLRaLqog7FdDk/e4E8MnvOymRUt1LlSibFblPcACyEQBUApMNl8PhUH8ntrTSZZAKXLjr0v2Q9cr5+O5Gxoheb8egN4KmQ6drYTQ3sVrfBSGaTCZ1bN6LE5BOgyRXOV6iCtjT08PAwICivAlY8P2is2ruPK4yniiXy8rprlQqqfNZ7hsfH1fndy6Xw2AwKOS7x+PZgRoXTAe0O3byHOVyWUkL+3w+lUClY/Pe9wfagjPNXJR6YoV6NtZWoUwmKOQLuD0egqEuAoNTGBzedzXcTba2acv7VP+VSkVRZuUmG8DOpP4k5+iPm8C/RlvI5Tu0K+ZP0/YG3wDQNO3//cRW+iGxm8A/POr1unIwEz/m90a8nGcuHaHSqKv79Do9I+4gw24/el27em82m8TjcZaWlrh06RIej0eZonTeHA4HKysrzM/P09XVpdTW3nnnHUXPedgHotlsMj8/z/r6OjMzM2q9r732GpFIhF/6pV/6WMcgGo1y7949Ll++zKVLl9je3sbpdPL0008zPj6uKDXSQo7FYty4cUNVWNLiF7nRWq3GjRs3WF1dZf/+/Rw7doxisahAbW63W0nLut1url+/ztWrV1lbW1Ne2zJ7v379OouLi9RqNVWRiDlMf38/BoOBV199lW984xuqbV6v1xUASBKLOLAJd1cudkLtMpvNqp0sbWOn06nan9KF6Kyia7WaqmrloimuaLLB6qSiSdtbElylUlESvdVqW26zUzNcfpaEIjNdmUsK1a2T5iadJGmfS7ta1ihteHkuYMdGANgxm5fugSQ3oSeJyYp0FiT5yiig01ZU/laSYydyXapy2TRIUu2sUuV76WZ0d3erSlTW02xqFAt1Go06GqC1GjRb7wr7dIKyZCMlnZ7OTYckZhm5yPnUKcwjdL+uri56e3uV/r7X61UgM1mbvA+dN/lfnTx6nU7H0tISr7zyCp/5zGfYt28fmqZRqVSIRCKsrq6ytrZGPp9XLAQ5Nzsrewk5X2W90qESgKTcZDwh83aFDdHqWKtJzJUkWrOhZt7yHDq9Hv3981RvMILRjGa0gtEKRgs6sw1HsJ9Q74Bq78u5UC6XlS5FoVBgYGCAkydPfqxr18PicRP4r37Q7zVN+4+PsbaPFLsJ/NEin89z9uxZTp48icfzYHscoNFqsphNsF5I7figOEwW9vh78Fl2ItpLpRIXL15U8owyG5T2rVSzGxsbRCIR5QI2Pz+P1WplYmJCXYwHBwfp6+tTz53NZrl27RoWi4UDBw6QTqf5+te/zs/93M/R1dX1WMeiVqtx8+ZNvvzlLysgkdvtVr7ZctGy2+3cvXuXRCJBNptVwKtkMonZbGZmZoZUKsXZs2c5duwYX/jCF6jX62xsbHD16lWi0agCSY2MjODxeEgmk8TjcUwmE0NDQ8rVanNzk29961v09/fTaDSUuIimacr/vLe3V6m9SeXZ1dWl5Gm3trYeaGd2amF3XrwkEQlSvTNBSWKV+b/IgwpITbyvbTYb3d3dSvRH8AiSPGROLcA3SaaySZDkJqA1qdTkd9L+7nQNEzqYINMlIctFXIRJ5GIq3wOqPSvVPLBDk1yqcNmQvBfZ3kkdlNl353O/t4UtYwIB58l65JzvxCjIekUYqJOLL7iQVquF1tIDRpqt+x2OVoNmq6oSbqfFrljGymZIzgepWO12u9psi/+5ABoFxNUJ0hOwmRwbkbztFAKS90LRue5/lWpYBJgWFxd56aWXmJqaUuegPIdsrPx+vzq+QlHs7LZIghccgYyF5DyrVCqKPVCv13G73WqWLs/TaDSoVytY6zms1QTGZptiJskb2WS+Bzsg50uhWCRebFGzd+HpaYNsHQ7HjnNT0zTC4TCjo6OPdd3qjMdGoX9SYjeBP3psb29z69YtnnvuuQ9sj+VqFW6ntsjXdmoX9zv9jHtDmPTvaq43m02uX79OPp/n2LFjirYm4iFyElcqFRYWFojH4wwNDZFOp2m1Whw6dAi9Xs/NmzcfaK+3Wi2WlpZYXFwkHA6zsLCgLDffW+1/XBTuzZs3lXWp6KAXi0VFtRITiVwup+ahuvsqZ7lcDrvdrnyo9Xo9Y2Nj7N27l/HxcTweD/fu3VMoVolSqaREPwSJLjSz+fl5xsbG8Pl8QBsVnM1mSaVSqrKSY2q325V1qlQZnXxyQWk3Gg3W1tZIJBIquQqoSo5zp0KbOHNJRS2SuJJYOs03arXaQ/2vZSzQeS5UKpUdIh7yP6TClGTWWW11SrjKaxJ2gLTeO49Lp7DQe4F6Uq3JGEL+t2AABIgnrXo5HvLaBNAkjwPUpqfzuEt7X85JWVsnc6CT5y9/L2uR+6WlLvcJD95uc6NpBoxGEzodaNQplXLqXJCqWKrtzg5BpVJR3G8RBZL1SctaznMBxXV3d6vNm8PhUBiSSqVCoVCgXC6rNn2ncpuwIuT1yXnTaLSrXaFuHjt2jKGhIfUZ0TRNGc0MDg4yODj4gdcr+RsBqclN9BJEDyCTybC2tka9XmdgYICBgQHsdrvqXDQaDWq5OLVsnEalQKNcpFEt0qwUadYqin/eeQ7L/y6Xy6RLdSIVIzWLr92KDwSURG1PT88OtbzHjd0E/iMaHwZqk9A0jbVCioVMnJaoSdCmm035uum27+SjLi0tsbCwwOHDhwmF3l8UJp/Pc+fOHXWx0el0nDx5Ek3TFC98fHx8x98Ui0WuXLnCuXPn+LVf+zUAlUCFOy0VtNwEoPVRYmNjg9u3b9Pb28vk5CT5fJ5oNEokEmFhYYHNzU0F+pGLRTQaVTPj1dVV1X0AlHqbrEUMMBwOB7lcjnQ6rdqPckEXtSyZm0mLVZKn+I8L93zPnj04nU6lVifzc2CH7GogEMBgMJBOp1VCE19wae9KO1WSoFS7AkYTVLNUe6FQiHQ6zb1791TbWi7kkuAEJNZp6CEdAklowgfuVKsT4JMkeqnO5MIpCH+pouR1Cgdcu69AJpW5JFe5WMuGoXM2LhVrJwCscyMhCVGAbNIO70Tnd860O1vI8t4Ih1rav7JJkspfxh7S1pYuhmweJIxGMwZDu7K1mM04nHbc7vZGthO9D6jPhhyXcrnM/Pw8rVZLmb0IYFLGHZ2bb9nEyPPJaECqSlFxA1RVLuhzObdlHJNKpdje3mZubo579+4B7U1qp+ys3IxGo3K6EwGd9+pPyNo6/5/8XKvV1EhMdBOsVqvScMhkMvj9fkZGRhgeHla0zYeF1mq2jVpq5bbdarVEs5ShmY8/8NhipU6qaWG7bEDTm+jp6VEslCcVuwn8RzQE1OZ0Oh8JEFZu1JlLR0iU8zvuH3QFmPR27ah8k8kkly9fZmxs7ANpZtAGrN25c4etrS2MRiNHjhzBYDAwOzvL8PAwMzMzD6z7T/7kT3C5XBw4cGDHblYkHaVdls1mFYJcWnSPGrVajTt37hCLxdi3b5/iu5dKJRYWFlhdXSUejytvck3TuHr1Kvl8Xqmiia+zoGhlkyFJwuPx0Nvbi8PhUAlSNjOi+y2oYql+9Hq98g+XSqherxOLxdDr9XR1dSmAWX9//462tiibCa0pk8koUJlsOLLZLMlkUrXYJYnk83kFNJLWe+eFXdDFUuFKC1MqceFSG41G5VUtbUzxxBYRG9lACE+/1Wrh9XoVhQvebaU2m00cDgehUEglJVHLExqdcP5Fg1wSoVT+8hqUVGcHbUo2A1K17pD/vH99lHNf/r5zZi2bGGEAdHKdAaX2JdoAMmuXjZsg40W0RxDfnRsFo9GJ2WwFrUWxmMNqs6PT6alUijTqDeqNGiaj6T7QzYjZbMHhcGKxWjEZjeTz7RFNuKeXoaER3C4nZosZq8XaVjIzQi6XZmtri7W1NWKxmOpySJUtGwzZhHQC+OT1yWvX6/UEg0F6e3txu90sLS3h9/uVxnytVsPr9arPgSDy3W63ev0yrhseHlYCM4K5kI1hpwmLfBWDIJnny3m2ubnJysoKyWRSGd6IHrzcpOv2sGhVCtTjy9RT6w96p+v0VE0eEg0Ldn+YgYGBR74OfVjsJvAf4XgUUNt7I1LKcTcdodZhPNDj8DDj71EAN2jvpi9duoTdbufQoUOqZfiw0DSNra0tzp07RyQSUR/SlZUVnE4nQ0NDanZqtVpZWVlRGts9PT0899xzDxWIgXYiXlhYIBKJKAe0j9JmT6VSXL9+XaH3OxXtms0m29vbbGxskMlk6O7uVmCVarWK2+0mnU4zMzOjqFxra2vcuHGD69evs7q6qpSnuru7FQdceK/pdFqBqrLZLCMjI6rik+6G+Iy7XC7W1tYUlSwej9NqtVT7UzYShUJBJQmdTkcul9uBfG+1Wko+ViRAZd5bLpcVFkAqKgHpSCdA2s4ChhMUtyTDTmqYIMldLpdCTUuXQSpQcQGTyl6AX82OFqagziVpyFfZaAjFTEYYkmCFG92Johc1MLnoS2KXDoJsRDpn+5KM5XGdTnFynxxveW2dLXNpt7tcLqXaJuY8cjylpS3VswDkBOtgMDiwmOzoDHqKhSxGgxG3x4fFbKHZapLP52i2GjjsNqxWO+h06IEWGvV6jXw+Q6VSxWQy4vX4cTpdGE332+5AV7eXvr6wmkdns1klkSprlZDjJO+3eAFIx0a4+dlsVrX4C4UCoVBIbUZlQyrnhHDm+/v76e7uRqfTqYQstEfZ0AjVr9M1TUR0rFYrmUyGpaUlCoUCw8PDDA0NqYq7UqmwsbGhtPir1XctXJvNpkKTS1KX80GAfWaDnnpqjXp8Ga1WfuB6YnD6MYVGMXrfXwDro8Tjgtieoi1/OgQYeQJa6B83dhP4x4tHAbW9N+qtJreSW8Q7qvGgzcWBQB+GjnZ1q9Xixo0bpNNpJiYmlPLS+0Un4APaM9O33noLaPOry+UyxWKRmzdvqsQgOs5jY2P09/cr+kjnc4rXdzwex+FwcODAgR2GER8WQidbWlpifHyckZGRB9rylUqFzc1NNjY22NzcVMC1Z599lqWlJQ4fPrwDdKdpba3s119/nW9+85vE43HVTpWEJepfgkAvFAr09vZitVqVnrpUBdJeNplMSku7UCiwsrJCNBpVlYe0/iWhydqlTSstVqGqOZ3OHahymftPTEyQzWZVMhPxm42NDXVhFgqRJFipLKWSFvCRXGdkvivYBjlX6vU6kUhE0Y86W9mdibDTXKUTIyCPlfazbD7eywXvNIGRmbagnzu7DfKcnUpj8j8FVyD0O6ny5TV2zqM7pWk7zWe8Xq9Sa5MNiCCjgR38eukoNBoNyqU6zZaGXg+VUpWm1kCn07dFX8wO0GmUS2VqtRI6vR6j0YTRYEKv193fGLWo12v3NxRW7A4HZrMFHVCvV2i22sI/wWBQVcTy2sW6tVarqc6JjDY6gYqy4ZEqXHjeqVSK8fFxBgYGVDdieXlZmfWIi6F0ijoZAbIhtNvtSiVNxjyCBC8WizvEZQS8J/iH0dFR9u3bR29vrwJNim6/jGZEa0E07mWDLVoShUIBs9nc5qZ7PPgtLdytLPpaoX0O3j9nrN4ubJOneBLxuAn8LvAbwE1AXXm1D9FC/37EbgL/+PGooLbO0DSNO+ltNgsZdZ/XYudQaGAHuA3a1K2VlRUymQz9/f0MDQ0pUMeHRbPZ5NKlS+j1eo4ePYper2dxcZFKpaJa/9vb25w5c0bRRfR6Pd3d3XR3d+P3+ykUCmpXv7y8TCaTYWhoSClBPWrI5qFcLittaAH5dCrH5XI57ty5w1e/+lVarRaf/vSn0TRN2Ua+NxqNBnfv3mV5eZmtrS01h87lcszNzZHJZFQFnEgkFA1JDEI66UJyMZeqWnyvBZCXy+V2cLJl7CDuVKJ+pWnaDh1wmSdqWlvfvVKpMDw8rAwuZmZm8Hq9FItFVldXuX37tmqHC/guFotRLLYle/1+P/39/WqWLhzaQqFAsVhU3YNAIKASuswmxXM9kUiQTqcV2E0AVoCqdkXkRRD7nW1qaXnL8YZ3qW1SxUtbVqo7qcw7FeXgXTCaqMo1Gg3VXZBkLoCuTk66JHkJaeEDOyRwhQ4n7690o2Tj0jkn97g91Bt1JXoiSVP+p4SmtS1LjUY9Op0g4jW1EWpvauwY9Hp06Kg3SjSaDfU8ch52HkuLxUIgEFDjDEmsggiXTUcn2t/j8Sh1N8GKiIWs0Wjk0KFDHD9+XAkpzc/Pc+PGDaWWKKyESCSiumGycTIajaoaF+qb8MnlXJKRVLFY3PF40YEXrr5IxcrG1mQyqfNLzj/RxhcWTj6fx2Fo4tMVcenK9Pf1Mf2pn8Po633k684HxeMm8Lc0TXsyW4nHjN0E/nhx584dMpkMJ0+e/Egt5oVMjOVcQv3sMFk4Ehp8qLe4+Iqvra2p1nhPT8+HgsxarRZXr16lXq9z7Ngx4vE4y8vLO9ZarVa5evUqzWbbhCSXyymVNAHTdLajJWEODg5y6NAh+vv7P7RDICFCKaK7XigUsFgsKqELSthoNPLtb3+bb33rW/h8PkZHRzl69CgHDx5832PcaDSIxWJsbW0pmtn8/DwnTpygVqspKp7YQiYSCeUGJrQlacEK77vTW1vEXgShKxfTXC6nVMQksUsylRm2VOWa1jb7kCpWkNNer5fBwUHcbrdKyvl8u0sj88RsNqvMMkSeVCqinp4ehRSuVqtsbGwQj8cplUoq0UrS6wSXdaLHO5HOgiAHFMBMEqlUgZKMOufZkkQ7KzxJOPKYTuqYdI7keaQCb8+n3zWeea/YSSfFrbOdDjz0M9E5W5bWs2rd3t9cCSgyGAwqn/JqtapmzEL7E3CdoPhFNldCNhloRvQGA1qrXYXXGxX1OuU9EFOazq6G1WrF4/Eo1L5sUgTIKJ9Fi8WijpPY5sqYQcCLMvYRoNnAwIDChIiiYF9fn0qqQt1LJBKsra0RiUQoFouq2yJzb3HkkzVJu142HvK+yfhCqI5yTersnMhXmZ/7/X71N/IeN6slum0ao0c/he4jAmvfLx43gb8I/BLwXUBtIzVN++r7/tH3KXYT+OOFpmm88847uFyuRwK1dcZqPsl8Oqp+thpNHA0NPaDeJtFqtZRgQy6XY2BggKGhofedY8v6RFDl8OHDXLt2jVKppGggwrmUVveBAwcIh8OqTfteP3LxJL969SobGxuqvfwwHfaHrb+rq4vx8XEFYOtsc+ZyOfL5PJVKRc3mzp8/z+LiIiaTiaeeeoqf//mf/9CRRbPZJBaLceHCBVZWVjh27Bg9PT3KHawT2ZxMJtnc3FSKc+vr6ywvLyu98E5etySVzs1KIBBAZDslaXcKtwiAqxP0Va1WsdlsO+hUer2eUChEX1+fss+sVCoKxGc0GtXFTVr8IvMqGwFJLk6nU22KZNYtFpSCkBdHMJmZy1dZkySWTgc1o9GoWqrCfZb3sJMOJa9VnqOTiyzP3flevRe1LklfEPHvumuh1t5qtdQcXl6H/F+pcAUMKJuPzgpZkodUjVLtCmhvcHAQaANLvV4v9XpddWLkdXZytKVqNZvN941e6mitdhK32azY7e+C8OTYS4KWY945OpBzVJ5bjlEn9UpGB9DuOgQCgXeT3v3HibKbKAJK0pUZtGw+5LlkvCLnuGzexAZYjlMnDVAolLIRAZRynjzOYrEoIxMBYspoQKp52TR1jlZko3P8+PGPLUL1sHjcBP7HwDRwi3db6Jr2CPafTzp2E/jjh4DavF4vMzMzjyTOL7FdzHIrtfXuPNNg5EhoELf5g59DWq7r6+u43W6mpqaUrvh7Q9M0bt++TSKR4OTJk0qzfGNjA5fLxdDQEOFwmFwux5UrV+jq6mJmZuZDq+pkMsns7CwLCwvodDqefvppJSMpvOPOED710tISdrud8fHxh4rKNJtNlRQKhQK3bt3izJkzLC0tYTKZOH36NHv37qWvrw+fz0c4/DCH3XbC+M53vsPg4KC6cHYKcsiFU5J6ZztX09pysqurq2xubiqueCfHW9roBw4cYHBwkEqlws2bN0kmkwSDQfr6+tA0jfX1dZU8stksKysrlEolbDabqraz2azSGhfwldPpVG3ITg6ww+FQ4L1isUgqlSKRSCg5UKnSxIhExgU6nQ6Px6P4tZIgOispqTJl8yBuWmJ5CqiNg9DWhHpotVpVS1845p3VvVTO8h53bmrkq1T8nUlfftepBCaUOLlPZvqCLRAlN+kGyEaiEwXe2QWRzWdnt6BTDEckUjurSXH+klGKnBPyOgx6GyaT8KQrmMya2lRIopWOTblc3oEGj8ViSu1PHASlypWumMis6vV6NS7qpCp2IshlQy68f2AHKFFGaJ2aAu89tvJeyudEr9crnIZ8pqSTJZ2DTgGeTrxJp+ugXC9kDCkbFjlfNE3jqaee4h/8g3/wgdejjxKPPQPXHsO45EnGbgJ/MtFoNBRN6v0AW+8XiXKB64mN/397bx4cd5qehz1f3zf6Qje6cREkQYLXDMmZnZVm9hqVjlXssqSyKiVbKjnl1CqWpcipiiTLcSpOUlEslUuyoooTSdaZSLG9iZRYiRxZ0mizq53ZmeUOhzM8QQLE2ej77v718Tu+/NH9fvwaBEgQAIcE53uqutBoNLp/R/fv+d73fd7nFf3idpsdF+NTiD5ivjjBsixkMhncvn0bs7OzOHXq1K5p5sXFRayvr+PYsWOYmZmBg5sQ5QAAVKNJREFU0+lEPp8X/deTk5NIpVJYW1tDs9kUgz3oC7vTfbvdLkalNhoNzMzMiLnNclo8FAqNpOq2trawvLwMADhx4gTS6fQjj1er1cI3vvENfP3rXxfiPkq3RyIRvPrqq4hGow9lATY2NrC+vo433nhjx9el2qhM7HRhpsi0VqtheXkZy8vLqNVqcDqdopVrc3MT5XJZeKqn02mYpon19XWYpil6fWlE68zMDCKRCL7yla/g2LFj6PV6KBaLKJfLIv1NiwDaBrroyW1TrVZrpHULwEjWhCJUIgxZMEQXfbpoysRIE9LkbAMZsdBCgIRXmqYJkx4Aoq+b9A1+v1+QAdnr0gxwipJpu+WhHhTVUcpZdoYjwqXUOz0um83I/e4EOYsie7ZTdkm2U6XvlXwdJ1KWF3my/wB9H4AHUauNOWAYg8WDzx+Ax8MQiUQQj8fFApdIr9PpIJ/PC3+HYDAIzgfmRPL5Ig0M1eZJX0BCMyrDkG88HRtqhbTZbKIVkj77RK4+n09EznTuaYFH5j3U4UDHkRbQchcAtaKWy2XRwkfPp+fQYlTOstD7breUdTgceOONN/ClL31p1+vDk+KgBP47AP4Z5/zWoW3RPqEI/HDRbrfFXOLz588/0pRFRq2n4YPiBoxhL6SN2XA+ln7I8GU3dLtdXLt2DYZh4PLlyyNtWzLq9TrW1tawtbWFeDyOY8eOIR6Po9PpCLIjMwkAImoBHqTLhIXicPVNtd779+8jFArh8uXLIzaU9KUnYpdNOhqNhhgxODMzI9TiU1NTD2UUOOdYXV3Fn/7pn2J5eRmf/vSncfz4caysrOD+/ftIJBLC5UoW3Xz44Ye4cOECZmdnn9icRgalypeWlnD9+nWsrq7CNE1sbm5C13U4HA40m01hY2kYBorFIlwuF0KhEM6fPy9GwtJc69dee00cYzLMsNlsWFlZwdLSEjweD/r9PtbW1pDL5UT61uFwIJFIiH52at2RIyAStdF5jMViomxCoiEieqql0sAaKgFQlCrXrdnQmCaRSAixH4kdSYQGQESAVIMmwiN7WmB0ehoJx+TaOnnGU/paJm+v1ytEi2T+QsRHWRS5jAFAZAOI3OS+cxE5SzoAudOAXkNO9cu1f1oUyGltxpywTAu60YdlGeDcGvnOuFwuBAIBYVQi+xXQVLftbnByTz8dQ7vdjnw+L7ZF7s8HMLIgkmvwwGBRQ6U0AMLjwO12i8UsRfKUsaJrAZUmqAsgkUggkUgI73cqARSLRZElSKfTSCaTME0TS0tLWFpaQr/fFwsFUsOTiNFms+FTn/oUfvAHf3Df393tOCiB3wZwAsAKBjVw1Ub2giGXy+HmzZsYGxvDuXPnHjk6lNDSe7haWEfPfCCKOTE2julg9CGF+k7gnAtHt3Pnzj2yR13XdWHCwDnH7Owspqen4XA4UCqVsLm5OZICJcKmaFCO0MLhMFKpFJrNJt5++22R4pUjSIoeTp8+Da/XK1b+9LNcLmN5eRnFYhGJRAKdTgef+cxndlS7a5qGv/qrv8JXvvIVJJNJnDhxAqZpYm1tDQ6HA7Ozs4IoDMNAqVTCysoKTp06JVb7FG3IrmB0k9vGHgVqVWo0GnjnnXdEnZrsa8lalqaylUolxONxLCwsCEHbiRMncObMmRGf8Xq9Dq/Xi0gkglqtJiauud1u0Y5TrVaRzWYRi8Vw/vx5nDt3DpFIBL1eD/l8HltbW6ItL5vNiu1stVpi/4hoKAokExQilePHjyMejwtDmmazKdznqDWNxGAUYVE5gBZPVCenLIHX6xUpfGohpOhRbnsiH3saBiPPTXe73SKTQMp4UtJTTz0tTOSOAxJuOZ1OIciShWT0HZIzABSVU1lCVutTjVduw5MJckA+DtjtDtiYDZbFwaGPtH0SKculHEqRy0RJ20WlnO1CSLkkQZPF5LY+2WxHrmOTip2EmeT41ul04HA4EAwGhcER9W/LnRuUFep0OsJUSB7qQosdebIbpfTJz4A0PHLbIX1WaCHwxhtv4Md//Mcf+53cK/ZN4GywlP0sgIdaxrhqI3uhYJqmEIeRu9rjIsCOoeNqYQ2a0ReP2W12zAQimA5G4bY/WigGDKLsq1evYmxsDBcuXHisk1qlUsHq6ioKhQImJiZEm9Nu+7Sd1G/evCnsU3u9Ht5++23Mzc2J1i+6IJRKJSwtLeH8+fMjg1dktNtt3L9/H4uLi7h37x5ee+01nDhxArFYbMQPHQDu3buHP/uzP0Or1cL58+dx8uRJVCoVrK2t4fjx40gkEiJ6+vDDD1GpVBCPxxEIBEYUtfIFkshmYmIC4XBYXGDkdpid6vu6ruPtt9/G1NQUTp48CcuyUCwWcefOHaysrMDn86FSqeCtt96Cpmm4cOEC4vE4FhcXMT8/j1OnTmF+fl5EkCSuy+VyIu1oGAYmJyfF3PNsNovFxUXU63VhMUpWujMzM8JgxjAMobwfGxtDJpMR253JZMSUNOoSoJYvqsdPTEyIcgUdB4/HA03TsLGxgV6vJ4bXUOo0n88LR73tRETaA5fLhWg0KkorlAYnBXWr1RKkEAgEkE6nRc2ZCIvakYhE6XEiGCIumRiI9OSJcUTkckqeUu30WaeomURotHig2i2pyEm7EIvFoGkdeN1hmJaFdrsJw+ig022LWjSVO3RdF9GsrAmQFwT0HgQ5c0DXFfp/eoxq1TKBE2TRYywWg2EYwhqY2vrkaJ/6yGm2PD1Onxd6TSovyAsR2c6YFiK0n/Q3Os5kE0z7YVkWPvOZz+A3f/M3d7xm7AcHjcCvc84vHNrWHACKwJ8+NE3DzZs30Wg0cP78eSSTyUc+v28a+KC4gcY2RyIbs2EyEMZsMAbvDu1mMkzTxM2bN1EsFnHp0qVdBW4yer0eNjY2sLa2Juqhe4HdbsfGxgbOnDmD+fl5aJqGt99+G+fOnUM6Pdq32Wg08P777yMcDuPChQuPVK5vbGzgq1/9KiYnJwUR0XAFInTGGFZXV/Huu++iWq2KmciFQgGJRAKvv/662I9KpYLl5WVUq1Xh30yRujyhKZfLYX19HRMTE0gkEsLhjpylSEVOgiaKnAzDwLVr1wSBEgFYliWsNKempnDz5k289dZbQlBGqvtGo4F4PI7jx49jfHwckUhEGMI0m02sra0hm83C7Xbj05/+NBKJBBqNBr75zW/i6tWr0PVBD7PX6xUiuunpabHwqdVquHv3LmjsqNfrxYULF+D3+4WYjrzry+WySOkXCgXEYjFcvHhReH+TwI0mhhUKBfj9frz88suYm5uD3+9HuVwWHRPFYlFMdKOokEidUv+k0JeJnoRa5XIZnHPR1ifX/qm0QSlnUoyXSiX0eoNJY/S5oLp3tVpFtVoFY0wcC1LUW5YlxHyySIuEfET+1LYlR+pEthR5DhYCTnjcfrg9XrjdLrjddrjdLqE/8Hg8onWKdBDdbhfBYFDUlznnwsWNsg+0rfKiiBahJAak7yeVBGhRILcC0j6SVoIWJHKKnjiNftLxp5q13C5GNXfZU4AWWFQOoEUVPS4bBlH5p9VqiUXR5z//efzGb/zGnq5He8FBCfz3APyPnPMrh7ZF+4Qi8I8PxWIR169fF2YhiUTioUiOYHELuXYDK80yNL038jfGGFK+MRwLxeB3uh/5nrlcDh999NFjBW4yKAKU+1sfhWazifv37+PGjRt46aWX8Prrr8OyLHzjG9/AK6+8gng8PvJ80zRx48YNlMtlvPLKK49sCyuXy/jWt76FV155RZAC3cj7mWqJ1WoVKysrcLlciMfjWF1dRSaTweXLl/HSSy8J0RS1YRUKBUxOTmJ2dnYkZU4XoXv37qFSqeD06dOYnp4eUUYTscgXeNMceKK///77YpqanKkg/UG328XY2Bhu3bolFOvf+73fC5vNhvv376NerwvnNdlshvaz2Wwik8mIui/14ebzeZFd2Nragq7rYmRpLBZDJBJBIpEQtXk6F5OTk0gmkyM911QjJ396Km/Y7XZEIhFMTk6KwTKUWs5ms6jX6wgGg5iensb4+Lio4afTaUQiEVEuoYUC7QMtKsgWl1znqC95a2sLS0tLqNfrcLvdCIfDwtKViJz6tanPmzGGUqkkjGsYY4hGo8KfOxgMotfrodFoiPeijAFlAACIMsHs7Cx8Ph80TUOlUhHiMuq9pvq/3T4YMFMoFMQEO3A7wIYkb/bEOFNgVCAnj1qlkhWJuyjypVQ0LSpIY0EOhNlsVpxbimgpMqcIWbbEpVq/PEp3e32dUuCyIp0WKtu7Ceg7AkAsYun/Zcc+uaOAsgG0/5T1MozBcKFLly7hV37lV/Z0PdoLDkrgdwDMA1gF0IaqgX9iYFkWtra2sLKyAl3XhSJ8t0iUc45Cp4mVRumh8aQAkPSFcCwUf2TbGQncTNPEpUuX9hxZPyny+Tz+5E/+RNShfT4fstksPv/5z+9I0plMBjdu3MD8/PwjZ/3KJC4vBmheMQlsaLW/urqK1dVVhMNhuN1uLC4uot/vC4tIigAMwxCKWZpl7vV6RVRIUQSJb+bn5/fkQNdqtXD79m2cPn1aTFCiKIWiXbK7LJVK+NrXvgZd1/GjP/qjYsZ0s9lEKBTCiRMnwDkXbV1yDZzERdQX3e12sbGxAQAIh8Oo1WrIZrOo1WqilkgGIbSPlK6kFshYLDZyIaYo2TRNNBoNbGxs4P79++j1ekKZTM8BHvQdm6aJRCKBsbEx4VTXbrdFlEc1fdIRbE+DAw9qoNTN4PP5sLq6itu3b6NSqYjeZrmfm6Lsfr+P2dlZTE1NiRrt1772NXzzm9+EpmkibSzbgjocDlFW4ZwLQ5VisYh8Po9msykiRdmQRk770vb4/X4kEglMTEwMP2dttFtduFxeuN1OuNyDCJ1U2+RdT+l9ioxlgx0iO+BB+ptMUyi7QUJBGlNL2gAie9m8iM6vTLCyCQ/9fXt0L6fjaXvkMgURsGx9S0p/Inz5/Wmf5ZT69g6AS5cu4a233nrsd2+vOCiBz+70uKqBf7JQqVSwsrKCYrGI6elpMZJvN5S7LazUy6j22g/9LeTywu90wedwweNwwedwwutwiZo5CdyWl5cfO7L0IOh0Onj77bcRjUZht9tx8+ZNFAoFfM/3fA9OnDjxkOWspmm4evUqnE4nLl68KKLk7diNxHdDr9fD4uIistksZmdnR4hFjjSp/kZObSQeky+gNMYxl8shFArh5MmTSKVSoudWNt2gC3i9XsedO3dw+fJlRCIR9Pt9kZKmn+RlbZqmSIF/z/d8D3q9HsbGxpBIJFAul7GwsICTJ0+Ki+x2UOqRVN+3b9/GxsYGTp48Cb/fj+XlZbz33nuw2WxialQ8Hhez2JeWllAoFETESdGPPJdbdnQDICag0aJEFjlSupcGa1CbFYmv6HhRS1UwGBSKZjLzofQ01VaBwSKIrGHpeMrDYEggRWTR7XZF5EqDbCYnJ2EYBpaXl4WVp0yOlEomoSVlS+LxuFDDExnS1D6KRMmVTY5iH5CaE06nCzabHR63B6bVR7/fE5H3drMbAKJXmgR+pNyXFw8ESkdTh4JsLkNZBXoefa6JKGlb6TWJjOVWOiJ1Ol7bF3n02vS7bC4kC/PkSJ5KFQ6HQ6jdqX+fFiK0vRcvXsQf/uEfPvZ7v1cciMCHL/AZAPOc899hjI0DCHDOVw5tC/cIReDPHp1OB6urq1hfXxfWoY8iqWpPw0qjhHKn9djXttvs8Dqc8DkG5K7VGrhx7UPMHp/D7NyxXf+PMQaP3YmA072jveujoGka3nnnHczPz2N6ehpXr17FlStXMDk5iVgshvHxcRGd0Rd5cXERm5ubuHTp0q77/qQkDgzS+7du3UK73RZp6Z0Il9KwNPqTaohE4EQo5Npms9mEAQYREdXmKerIZrO4f/8+zp079xDxEtm0221sbm7izp07uHnzpohyKD1PM83JMEWeFU2pfrkOT/epLh2LxUTNmFKRuq6L/nVSm1MJwuv1CjU4CdjS6bRoeyOCJNUxGQRRdE/1WSJUEsaRw56sTiaicDqdiEajomd+YWFBCPA6nY7IkhSLRRQKBdGqR69D/e3UgkeLQCIFWqjRcA7ZQx2A6Oum6JPKHhT9ErkzxhAOh8XnmEoc8tAPykwEAgEhBKQRre22gV5v0AYYi8YQjjxws6N2KSK0fr8vfMbpdegzSovLnVz46DMLQKS7SXxGkbOcOZAja3oPOhZEtLR99DxarFHWQhacyYp4OZKWH5MXAPQaJIykRSKl/Qmf/vSn8Ud/dHhGpQeNwP8JgFcBnOacn2KMpQH875zznd0mniIUgT8/oJ7ilZXBOm5ubk6IhnZCs9/FSqOEvNZ4ovfpdjq4++EN+ENBzC2ceqwy3mV3IOD0IOTyIOB0I+TywOdwPbKe3m638c4774hRpPfu3cPGxgbm5uagaRoKhQL6/T7Gx8fFrdls4tq1a5iensapUztv135IHICoWcoXEjkSl+/L0azNZnuIMAFga2tL1JnJXpZqqdQjPTExIS6qb775pkjJUkuSDNM08dZbb+HmzZtIpVLweDzI5XJYWloSPbmkok+n0yLSkiNa2TmLUtaLi4tixnqlUkEmkxFkS+9LdWi6gNJChFraiDBTqRSmp6eRTqeRSCQwOTkpUvW3bt1COBzG3NwcDMMQOgWKUjVNQ7VaRbFYRKlUQqvVQr1eF9EgtRYRcVKdm8ZZUqRP9VtKrZNtLNlyZrNZbGxsoN/vIxKJAICooXPOMTk5icnJSWiahnv37mFpaQn3798faZmUIfsdkCZEjtKpxYq2i8oyjDHhiPbApYzBzrxothvodDS4XDb4fIMFGdX+SYRHnztqeTPNwdhXyiyQXgCAsCGmeee0rfS5pmhaVoaTSJUyDfR8SuWTjzktcKk/ndT6pKCnRQtB7jvf3jdPCwJaeMkiNrnVVDbHkZ3YnicR2zUAlwBc5ZxfGj72kaqBKxCKxSJWV1dRKpVE6i+RSAgCkdEzDbT1HjSjj46hQzP64r5pmTu8OmAaBpZu3obR1zH/0nm43HubpkawMRuCLg+CTg/8zkGqXr7ZmE2Q+MLCAqanp7G6uorFxUVMTU3h9OnT0HVdRFWlUknUPPP5vKih0k1OrROJX758+amVAghELhR5yj/JS73b7YoLOUWh1H+dy+WwsrIiertpQhilgsfGxkS/tNvtFsfD6XSKDMXi4iIsazBvnNThiURC1Hgp4qfFBNldkv85kSFNpbpy5QqOHTsGr9crrFLX1taEWQyNnoxEIpiamsLExAQsyxKWrbJNKWUifD6fqB2fOXMGx48fF4pmSsnTRCoSedXrdeTzeWHEUygUkMvlRB+4rPoGHvh1k4iLHqfIj/QD1MtOZAdAlKZarZYQtNEii8xoiPyazaY4r/QejUZDqMAp6tY0bcQQhlqsqC5NKW3yFhhEs24YhoV2s45qvQTLetBXLkexsp0p/T8t0CgCp2NDx1kmT+BBT7s8YIcWAvSZAR60oxHZy9E9pfjlSJvKEqTgdzgcIz76219T1jZsJ28qYW3vvaf3oqzBuXPn8N577x3a9/qgBP5NzvlrjLGrnPPLjDE/gG8oAlfYDlL3ZjIZ1Ot1TExMYHJyEvF4fE+K8p5poGP0oRk6ukYfXXPUdGJ1aRm5zBbOX76IYEh2feNo63009d6ui4BHwWGzw2V3wOr2cPv9D3Hm7BnMTE/DaTFklldQK1dw5swZYTbDORcRGvU+T05Oot1uo1QqCVFQMpnE2NgYqtUqrly5gvPnz+95xOphgzEmBoRQWxINH6GBDeQNr+s61tfXsbKygvX1dXFx2p5OpAtku90W9qokams2m0gmk9A0DZubm2KONL2P3Bokb6N8QaTXr9frmJ+fx9mzZ5FIJBCNRkfqvCsrK/jggw9w8+ZN0YdNBiEOh0OIyILBoKjR0jaXy2XY7Xak02nEYrGH5oiTajoUCgnCpRQ6GYwUi0Vsbm6KWjVNriN1NtmBUuRJaWx5IAiVHUhISMREUTEtwsgd7MKFCzhx4oToSCC9AgkKZ2dnEY1Gsby8jNXVVSFIvXfvnsjWEEHa7XZhxUuthwN1fBh+/zgcdid0o4+trXuw2diIUYtMppTCJuEXie8CgQAsyxKlA875iJ0rGcPQZ4yItdVqiRIRPU7ZHIqQ6X9k/wG5R1tO3cttaXK7mfzZo+fItXJK0dNccMpU0f5QpE+LktOnT+Pq1auH+d09EIH/NAYq9O8C8E8B/F0A/4pz/quHtoV7hCLwo4NutysctjqdDtLptBjocRBsbW3h+vXruHDhwkN925xzdAwdDb2LVr87/NkbcYt7HLRWG7euXsOxUycRnxj0wOudHvJrGwg63Xjl3EuYjI3DJZnUULROJE9zsfP5PHRdFz7txWLxsSWApwVqJ6MUqDx8BBjU34nUyQKViJTGVzabTRGJU0pUVvbabDYh1CJ/6zfeeAPRaBT5fB7379/H/fv3oes6FhYWcP78eWGLKnu5Z7NZMWbVZht4Wt+6dUukSCkV6na7xYKAhGz9fl8Yqsj+6m63G+12W6jlKeVKwjjKHMi+19vruBSBUa06Go2ODPyg3nBaIAAQteh0Oo2pqSmEw+ERgiEyr9frIpqnXm/Z9IRIzTRNlEolYQUbi8WQTCYxNTUlZpNzzoWvN6XsyWkwHA6j2Wzi3r17wnnQsiyRcXA6ncLIqFgswjQdcNqdgA0w9D7aWk2I1MiNTM4u0LEgNzoSlJEYkb6zJHKjiJzIluxsSRzGOUexWBRpfhLlyRkDWWhGtq5+v1+Y+pB9rqZpCIfDYjEhG9TQAkmuhdNr0nEfFfrZBalTGYD+Nj8///wQ+PAFvgvAd2PQQvbvOed/fmhb9wRQBH400W63sbW1hc3NTViWhWg0KsxFvF6vuO2Uct8J9XodV65cEfXnx0X3PdNAS++i0e+iY+jomQb6pjH4aRnY/h1oN1u4++EN6P0+PD4v3F4PPD4ftG4X5UoFiVQSZ+ZPI+oLIOTywutwottu4+aHH8HvD+DSyxfh9XjgYDb0ul1B5rVa7ZHObk8bnPMRr3e6tVqtEWKnkZiyYIqiwGw2K+aYUxsbtQFRGxZFe7dv30apVMKFCxdw9uxZRCIRuFwuFItFXLt2DeVyGWfPnsWrr74qFnaUxqbINpfLCd/sra0tLCws4OLFi+h2u8hms1hfXxclDRLqUYZkY2MDm5ubIpqnaFjTNEF6U1NTSCQS8Hq9Ih0vj4wkQxxq2aKULEXaCwsLwgmQ6vlk6kJ96bKgzW63IxqNIplMCn920itQL7xpmvD5fJidnYXX60Wr1UKtVkOxWBTpeuo22NzcFN0BdPwpWiUrU6/XC7/fj1AoJCLQ48ePwzQH/t6kt6CyAn0vB9mVPhp1HTYbg9/vQyDI4HDYheUr2efSkBvSVsgtfQBGsjfUq06LRyJseWwoecJTPVwWx8kqeCJWeh/adjpvlAnz+/3iONLUNHIwTKfTCAQCwhhpc3MT2WxWuM1RacNuH0y+o3PabDZF2xyp0O12Oz7/+c/j93//9w/te3vQCPwXOef/8HGPfRxQBH70QfU5ighoNd/pdEQrCpE7TczaKWrt9Xr41re+BZfLhUuXLj3SJe1R4JxDt0xB5j3DQM8y0DF0VLUWSvUaOlobXa2Drqah1Wpjc30DjVodqak0JtKpQRTmcMDusKOwlUWjXMWJ82cQSw4ib7fLDYfNDqPbQ3F5DSfjKbz80kv73ubDxnZil88JRePyQotIh1K/5ICWz+eF2Mtut2N8fByGYWBxcXGEPC3LEkRPLV7JZFLMXgcwMh0uEAig0+kgl8vhypUr8Hq9ePnll3H27FnRL0/q72q1ikAggImJCTHG9M6dO9ja2hI+9Ovr6/jggw+wvr4u3LnIizyRSIi2NdnpC3jQN07Kd1Lkk5COeryJoEhDQEIsy7KwubmJu3fvYmlpCcViEdFoVLwnLQyq1SoKhYIoSRw7dkx4e8tKfkKxWMTNmzfx0UcfodVqwel0YnZ2FslkUgzgKRaLaLVaIookLcT8/PxIiYsGhZCOwev1oloxkM8XkM1m0evVwdETETA9lwib/A3q9brIZGiaJpT9wAPbVSb5jtPnEHhA6PLfqI5OBjFEmLKwk7aJ1PgABMlS2p0ibvrs0vuR730qlUIymYTX6xWlD9o2WgxNTEwglUoJDczS0pJw1ysUCrh48SJ++Zd/+dC+nwcl8Kuc88vbHlMiNoVDBbUqyaReLBahaRpOnz6NycnJHftJr1+/LsaLygTj8XgOJV1tcQttvY9Gvzu8ddDSe6jVa1i+O0hDBgNB2BgDAwAOtOsNrN1bRiAURGQ8JtLRbo8H0cT4QHRl2vEdn/r2A5cUPg4YhjEyspSi0UKhIGadE0iEVK1WkclkkM1mhxf+Hi5fvoxPfepTcLlcqFQqKJfLqFarwoEvl8uJSIxUyFSr9Pl8ghDX19eFPzlNk5NTsPV6XUTPnHNRLy+Xy4hGozh+/Ljof19aWkKv10M4HBbqdzLG8Xg8CIfDIlILh8PCMpYItNVq4d1334Xb7cb09DQajQZqtRpqtRqazaYYokJlB1r4GIaBZrMpoj0qr1AdnEouW1tbaDabiMViOHXqlHCnk+vVbrdbuJ5pmob19XXkcjkhXCMilAVhNJu9UqkIrYHcny37s7vdPoyNJTAWCsHj9WJyMiKO6XZTFSLVfr8vzj29luyLT9PlZDW9nIoHHoxJpZo5vQcJ8KgbgQifWrroc0rqdIroybTF7XaLRQap1EnwSS5wlAkiARwRP20vbSvVxeXxrG+++SZ+6Zd+6dC+f/sicMbYjwP4+wCOA1iW/hQE8Dbn/EcObQv3CEXgnzyUy2XcuXMH/X4fp0+fRiqVeojIt7a2UKvVxBeX6nMU0VNNcaf7exHXbYdM6sub6yhWK+j0uuj0uuj2e+gZOiwO5Dc2YZkmjp87A7/fB1M30W220KzVYNkYYJj4rle/Ha+de3lf2/GsQbPVt5P4TiAPeJrx/sorr2B6elpEuIZhIJ/Pi55vSslWq9WHVMKNRgPZbBaVSgWtVgsTExNi0Ijc4kQKbDJRabfbQg09NzcnlPMkPgyFQmKwiuxRTr3i8gCRYDCIVColbFA3NjbAOcfCwgJ8Pp84n/1+X7Si0evE43HRsUBjORljKBaLIu1fr9dFG5nP58PS0hI+/PBD2O12JBIJ0WJFSm9KN5PCn2rgcieCPMdc7q9utVqCvChNTXVlYSpk2MEAOFxu2GwcDgcXx9rj8YhMA9WuyRY3lUrBbrcLS1oC1baz2ayI2uWUuOyGRoI76u0nwpe7FiKRiDDg8Xq96Pf7yGQyaLfboquAskBsaFNLiyFySJTft16vo9/vi/IGBQQUucvqdFo8AoM+/S984Qv41V89PInYfgl8DEAEA+Haz0l/anLOK4e2dU8AReCfXNC0LMuysLCw8NghK3JET6S+/X6/34fL5RpJD29P4e8HtKLvdDq4t7SEGzdvYiKdgul3Y6maR0/XYfb7aFXryGezODY5hb/9xe/D/Oyxfb3fs0Sj0cC7776L8+fPPyQq3Anlchnf/OY3sbq6Cp/Ph4WFBczMzCCZTO6YMaH+7nK5LHzCKRpPpVIoFAr4+te/jng8jm63K2qTbrdbGPGQDzql7MkLP5lM4uLFi0gmk3A6nVhZWcHKygry+bxQchMxkJ1rtVoVCmSanx6PxxEOh5HNZlEoFIS5EX2O5NY5Ur9XKhURhZIxiKzKJn/3Wq2Ger0OAGLB4fV6xQAZIlnZE4DEVLJzmiy0o88nCbdowUsaFIpY6Tb4Hxs6HQPtdhOa1oamtWGa3RGhnd/vFy2AY2Nj6PV6aLVaME1TdAaQcp/Gf1IvOYARASBtF5Ur5G2SJ7LRLHVS7MvHnPaVWuxoDrjX64XL5RLKeYqg6XiQUp4yT/SeFJWTcJHEjbLfer/fx8WLF/HlL3/54F+uIQ4sYnteoAhcIZfLYXFxETabDQsLCwfqrZaJdvuNvsSTk5OYmpo6UPtXq9XC1atX4XA4EInHULH62NRqaHU0VEpl3L5+A4WtLM7MzOG7v+0zuHjuwlPvGT9MPCmJA0C1WsXt27exsrIiUsDT09OYmpp6ZFmBCL1YLGJ9fR0ul0uMHb106RJSqZQQeG1tbYn6L9UwacSozWbDtWvXsLm5KRZr1O5EKuxyuSz+h9K9VBePRCLC/IaidHKRW15exsTEBILBIDqdjhCQUU89tbMBEH3c5AJHnQLkYkcRYLvdFsYvmUxGCNfi8biwyiV/dZp8R9E6ia+IoLbfSHRGJA9AROyUmu71eiiXOtC0Hvr9HjodDW2tiUajhFarPuLWRz3xJJ6j7aFFA/DAt14Wgcl95PI4XLkcRvtSLBaRyWRQKpXAGBMCPcouyBkbWtzIdXuqsdPiKRQKifG9drt9ZHFDtXy5lERiQNkJjko/b7zxBn72Z3/2IF+pESgCV3ihQC5Wi4uLcLvdws7ysFGv15HJZJDJZOB2uzE1NYV0Oj0yDWyvsCwLuVxODPco16poMAM9jwO+YAA9Q8eND6+jXavDZwAJbxDzc8eRTqdHXKl2u+1Vwf+0QCR+4cIFpFKpPf9fvV7H3bt3kcvlRoZ1UNvho6a/URp2bW0NGxsbKBaL+PZv/3a89NLD8hxyV6tWq6Inm8afbm1tIZFIYGpqSkRWlDpeXV3F1tYWwuEwkskkdF1Ho9FALpcTtVxyN6P08+TkJPr9vrBa7ff7gtgoXU1p31gshng8LqJrzrkYCUvRKP0kP3Qi90ajgbW1NaHSlxEKheByudBqtTA9PY3z58+LvngymJHtbGlqGd1IQCfrEAYjXFvoaBx2mw0W5+AWB1gPrXYe+XxOjJsFgLW1NayurgobXPLMp6EltD+UzieipUUFZcm2ewZQrZ0WVeRHT4sAEqnJPd2y4I0eI+U68GCSGWkwiIzJpEb+f2qZpG2XfQsA4I033sCv//qv7/k78DgoAld4IcE5F6pet9uNubk5EQUd9vuUy2UxRIQ8plOp1L6V5KY5GOeZKeTw0cZ9bBRyME0LrU4b5lCo02m34YUdJ6ZmMD81i+BwihuJhKi+SVEHkflOhE9p16dZa98viQMPiLxQKIi+6FqtBrvdjmQyiYmJCUFAO90Mw8Da2hq++tWvIp1O47Of/SxmZ2eFocp2UCRPhjwffPABCoWCqJ8TGZCamaarjY2NYWpqShgUBQIBoRzf3NwUiwkSYlLUTfVhcmaTzyG5jlFtnAx1IpGIaLdyu93C4Y/S+bLjWrvdRqPRgM/nQzKZRCgUEgNolpeXkcvlEI1GMTMzIxYcsjCM6tbUhiUP7aBFBGURDMOGarkHiw+Fm4zB4bBhfNyDTrcpFr3UX25ZFtbW1sQwJLfbLcoOci88mdrQcadylrzYkF0EAQhB3vr6Ou7cuYN2uy368yn1TRPw5LR4p9OBzWYTiyVyWJOHohA5AxjZJspUEKFvF929/PLL+Iu/+Iv9fIV2hCJwhRcanHPk83msrq6i0WhgZmYGx44d21ek/DiY5mCWNaXvSIi0FzgcDkSjUSF8klHrabiR3cBmMYtsZgv1Wg2dtoZatYpyqYy+3sdYeAzj4ShigTHEAyGMh8IYCwRFlCRbXMrRBwm/YrEYXnnllV396g8D9Xod7733Hl566SVMTEzs6zUo2iGf8s3NTUEI4+PjiMViYqa17I1Ngqx3330X7XZbOMyRwC0QCAgi3T5QhQxi5MlW8k+qbxJJy73Bdvtg7Gg0GhUpbF3XsbKygtXVVVGOofY7EojRdlONvVKpoNEYzAqg8yerwWXbUr/fL6L3SCQi2vIKhQIymQzK5TJisRiOHz+OcDgMh8MhWv38fj/i8bio4fp8PmF/LE8o226uInuGm6aFRt1At2OCg1rAGMbGXIiP+0WpQa7N075sbm5idXVVzF6nUam0QCHSlX0KaMhMtVoVUTmNvaWBQ9FoFI1GA4uLiwgGg6L1jkiXvnOMMTSbzZFuhna7jVwuh1arBZ/PJ1wlG42G+IxRaUD28qeFFfXeW5aF11577fnpA3+eoAhc4XFotVpYXV3F5uYm4vE45ubm9jQXez+gVhlN0/b0fF3XheUo9Q7TiEq6uJQ6Law1K6j3O8IW1jRNNOoNbK6to1AswOce1AiNfh9MN+HgDE7OYDc5bKYFbjyo+ZHXONller1evP7668Jq82mQ+WGQ+E6o1WrIZDLY2toSIqidBIh2ux1XrlyBZVmIxWLCYKTVagmV9PZMBWVS5EWBbMtJKm85TUtDTur1+oi1KKmTyVyGyjsUMZPiOhQKPWSeY7PZxJCOZrMJxpjwone5XELoRapu6r7odDrCTIei22aziUqlIlT6VHOm3vuJiQkcO3YMnHOsrKyIaW90HIiw5WOxve+62eygWmlD1w2Y5uC5nJtwuQGHwyaOrVznppQ0ifSox5tq8ZSiljNKlNImhzqKvInUqS5NqvBmswmPxyNEjKQToGwNteJVq9URHUylUhEOilTDJ/IGIAbv0JAWACLlT4uzN998E7/2a792aJ97ReAKnzgYhiGmpTHGMDc3h8nJyefCPKXX6wmRlEzoNOaTbEqbeg+1noZ6r4NaX0PX0NHXdeTzeRQKBYTHwhhPjCMQCMAmRfQOmx1BpwcBlxt+uwtuMFjdPu7cuo3V1VXU63WRXiZXqlgsJhzyDgNPi8SBQbQn+4pvv9GEsI2NDTDGcPHiRcTjcUGk2+vK9DuNp6T08vaf8oQqWX2t6zqq1SpKpZI4rwBEmpXStZQSl/uVydGt3W6LqV02m02QJpEUOZyR4I+iTxrOQg50ZJpCbVAejweVSgWGYYi597RYrFaraDQaImVPC4YLFy4I9zLKXJCgbKfvj66byG010NYeWBYzAA6niVarjmarhlarjl7vwcAVWgiQqRP1v1M/OwnsqNedUt2yDwF9FoAHLniUveGci7ICnWNg0LcdCASEBkHu7ZcV7fl8XmRa6PiSWx05upVKJXS73RGhXr/fx6VLl/Bbv/Vbh/Z5VwSu8IlGqVTCysqKMPLYSx3YbrcLm82nDZoURhd/XdfFhZnUuD6fD8zlRAcmGnoXZa2J+5kNlGjCmD+AYCg4uKj7/TvqAPxON8xGG2t37qGayeHyyxexsLCAer0ujFXI6pMWE2R3uR/UajW89957olXr4wKRpqZp+Oijj7C+vi5GjZJiWR5OEg6HRRqUzjeljyk6lH+Xf1IKloieIk6K0Gu1Gjjnone7VqthbGwMqVRKdBpQ9ElT4Xw+H9LpNNLptFhQUQq/UCiIunetVhPRNO0fLWBoAUAtVDSrPRqNIhKJjNSDi8WiqD/TwiaVSiGVSo1Y6RqGIdrmqBQk+8V3O4DWhkipO+wOeH1eqZZth93OoetddLotdLQWbHbA7XZB09piIAv1ysfjcYyPj4t2M5fLhWg0+tCIUOoaoV57uk/HhWrhtFCjYwVAkC4tnlKplEjr+/1+IRSksoU8qpdElLdv30YmkxHDet5880381E/91KF9nhWBKyhgMECBemofh16vh9XVVXDOceLECUxOTn5sg0holU+2nfJNTu25PG7oNqDZ7SJfLaNYr6BUr0HrdeD1eOH3B+AP+OEf+mM73S54fF643B4USyXcfu9biPqC+Jt/4/twLJmGy+4QwifKDlAamm6UHdgriMTJhWy3yJbuH5aD3k6gqIwMYqgXu9FoiH5p0zRx7tw5XLhwYcRGdTfI5h87/STHOhJwUc2axlpOT0+PfL4452ImeC6Xg9/vF2S+F00HCdx6vZ5oUVteXsbNmzfR6XTEIuPUqVOYmJgQiw06Dr1eD+VyGXfv3hWDeIjcZLU96QUSiYQY1DLoS3egUtZhmQx9vf+QXXKn04Fj2K/t9frgcNjhcNoRCHjhdDI4HIBhdlGrVUayGcFgUAxImZiYEC2HezlHtECizAS1IZJTIJUa5Ho7iUJp1C21F5ITnM/nE1G7zWYTx61SqeD111/HL/zCLzz+A7lHKAJXUNgnisUilpeX0Ww2MTc3h9nZ2acqAnscZD9uGmqxHe1eF/lqCblKBcV6BZV2EzaXCwyDth/LMGG32eDxeZHLbGFrYxMXXr2MV195BZPRcUxG40j4Q7Axm6i30o0mYMViMYTD4T2RrZzu3E5y2wnPMAwxaGSvY2gPCppOVa/Xsbm5iY8++gilUgmnT5/GSy+9JIZdHBSdTgdLS0u4d++e8GyndG2v1xO96JQapzY3UuWHw2HMzMyI4SuPKweRRSotDFZWVnD16lVBYj6fDxMTE8J4xe/3i4i61Wrh2rVrWFlZES1uZKrSaDREVE315kajMeJ86HB4wJgdNmYD4ABjNtiYHTa7A5xb0HVjeP77sCwOt8uDWCyO8cQEwsOhLB6PA063DZbZQ7tdR6vdEK55pVIJjUYDoVBIGPbI6vrtWRF5BvlOx4hGhFKmhbIhuVwOuVxOEDf1s5PBi8fjEVkNmvp2+vRpfP/3f/+BPy/SNioCV1A4CBqNBpaWllAoFDA9PY3jx48fWr34acM0TZQqFeQaFeQbNRRadZSadTQbDWjtNhqVKpZu3IbT7cL03DEE/QGEAgEcS6RwcmIK0dDYiAEKRXZ7zWZQ5EsCK4pCdxtSk8lksLm5iV6vJ4x09uuKt1+Uy2VcvXoVi4uL8Hg8IqVMWoWDlBYAiNRsJpNBNBoV/e7UHkaiO3mGtjwURNf1kWEgO5EWZTSIXMgvgJT9pmnC4XAIW1lSfXc6HWFu0+/3sbGxIdzlAMDtdmNrawtbW1swTRPJZBKRSATNZlNMJuv3+6IERN+TQbq7j163D8AGxhwAc8A0TLTbGhqNOhqNGsAZ3G4P4uNJJJMpjI8nEAwE4XY7EY64EAwNom6aHkYOeE6nE8FgUIyKlZX0ZKG7vb2SFjjyPG/ZupWm8LVaLaGRIDOZarUq3pv+Ho1G8dnPfhY/8AM/cKDPhwxF4AoKh4ROp4P79+9jY2MDyWQSJ06c+NjJ5TBgWCbq/Q7KnRay9SqylSKuvXsF2VwO/vAYQsPxiv1uFz5mx5jNDT8bRHzkREaiI7lWT1HKdgQCgZFaf6vVGjEyCYfDD/1fq9XC5uYmNjc34XA4xPjPp9EeuBu63S6Wl5dx7949eL1e0V8dDofx8ssv7ymF+yiQkczGxgZarRYMwxDub3Tb7u1OrVyUot/NYY3S181mUwz2GBsbE9mETCaD9fV1YZdKZivUu01Znlqtho2NDRGxp1IpXL58GceOHRNmR4VCAdFoFOl0GvF4/CFjGLfbLdrs6FxT1oUxO8BtaDY1NBptFAqDeee5XBbFYgH9Xg92uwOhcASxaBxT0xGEQv6RrgPq66f3czqdooWQNB3hcFi0H1KZYacb2bJSixjpI0h42Gq1EAqFxMQ7v98PTdOQyWSwsrKCWCyG11577cCfPYIicAWFQ4au68Kcwuv1Ip1OI5VKHZmofDtMy0K118b7N6/j/VvXkcnlEBoLIRaNwTIt9PU+uGHAy+1w9y30Wm3hKibfTNN8KMohO06/349UKiUiakpZUhtQJBIZGZtJ4JyjXq8jl8uhWCyKcaGRSEQYpYyNjT1VN7p+v4/l5WWsra0hkUgIq9YzZ8480vZWjpLlqG+3bSXluayOp/sU1e510SC/N0WRcgo8EAiIKW3y3xqNhrCoDYVCgtBp4VooFFAqlWCz2UQPNqnniQDpnMjRt7xfch2Z/BFOnTqFhYUFpNNpGIaFbkeHpukoFivY3MhgM7OJfD4Lux2YSI2J6JnKDXLpwTAM1Go1UfohF75+vy96xInc6X44HBavuZ3kqS+9Xq+j0WiICJ2yFcePH8f09DRisdihazgUgSsoPCVYloVyuSxERz6fT6RbD5pmfVawuIVso4qv37yG9VIBMzPTD1nVht0+pP1h+JwuOG12OJgNLrsdlmk91KJFrVKlUkmQMPVU08Xb4/EIIwwAIvUqp2GpvlqpVFAoFEbSzN1uF263W7T0BAIB0WdNCwNS9h/kAqvrOlZXV9HtdlGv13Hnzh2EQiGcPHlyR23EbiI3qr3KCx3KYsimMzKoHk7DPx4HiqJJmCW3y5EegdLxoVBIjBsl21Tq06Zxq/KMc7fbjXw+jzt37ogFARkHkTqdFnOJREK0KdJ7NxqNESU9H86kz+fz4Jxjenoac3NzSKfT8Pv9MAyOXLY7XBBw+PwchtEWXug0oIT8Drrd7shiknr+qRQhlydo0dLtdsVzaUFAx8XhcIjpajabTSjmm80mstksNjc3hYL+jTfewBe+8IV9f8a2QxG4gsLHALJcpTnIbrdbROaHIYJ6Ftgq5vHVa99C02YiNTMNz9AtazfYmG1A6DYbXDYHHLbB7067HQ5mh9Nmg53ZoDVbqJUrqJbKKBeL8LrcIv1OTlvba5SGYUDTNNELLROxZVlioSC3E1EKeWAwwsEYg9/vFy1kcqTo8XhGUteBQOCxI2cNw8Dt27eRz+fx8ssv73kIjSyYIpW0TLatVgsAHurF3onYd4MsYiMQUcqLK4oqO52OGCRC70fCtmaziWKxKNTasqc41YIBYHJyEn6/X4jb6vW6SPkDEI5pNOUNGCyKisUiisWiMOepVCrIZDKiz3pychI+bwS67oSN2RAMhXDmbAqRyJhIb9OCIxgMjrRBytE0OcvRTd42+nwRqZMqnWrf9BiVfsjznUBK95MnTyoR205QBK5wVMA5R6VSEWTudDqRTCbFNCr/Lr3azyMoffrB3dtwJ6PwxCM4THG4YZpoNhpoN5rotDXo3R64roOZHDaTg5smYJjweb1IJpIYCwThdbvhdg3qk65d/M4JDpsN3HjQ40wDZaiNTNM0cM6FCEoeAQpA1I5l0qdhIYRisYiPPvoIyWQSZ86cESly6l/eD6iXXDZ7abVaTxSBk/XndoOanW5klyrXeonke73eyOKGhHNUf+52u9jc3MStW7dQKpXEc+TecnKiI4taUpCT37nL5RKZDY/Hg8nJSXi9XlE+GQw6sYPBPvRM5/D5mbA1Jce1SCQCu90uJrzRiNNYLPbQQtrhcIx4rm8H51wQOu0neb17vV5MT08jkUiMfJeDwSDC4fC+zvlOUASuoPAMwTlHrVZDoVAQaTdN00S6l1J1RB4fR+vUftDpdHDjxg2UGzUkTh4D83mgW6a4GZaJw7qe6LqBbu+BQKvX7aHRqKNeqaLb1mCZJlwOJ9wuFxx2O5xEUG43XG4XXG734DG3C8HwGLweL3wO1+DmdMHrcMHncMLncMHGIdrltra2kM1mhYUpnQ9qlaJbp9MBABG5k885KbBPnjyJUCgEwzCEQEyu0waDwcdG94eF7WY0O922/51GZMrT7uTxu3IrI2U1qORhmiaq1eqOhGiapojMC4UCqtXqyMQwsqOlBUS/3xd1eNM0weCEYbig6304HC44nTqcrgedDnSz2+1Ip9MIBoMjfuwOhwPhcFicD3lmAA1Loe+iPAJWNq0BBosjmoTXaDQwNTWF6elpsXA5TKGlInAFhecM1HtMxEDETpHOXnvNHQ7HyAXn44jsc7kcbty4sXOdllswuAWTcxjivoVYIoGxaGSE7PvDn7plCd/3vcAYOq3RxLJutwu/zwe/1wevxwPLMKH3++j3euh1e2g3GpiZP4FEeucJaQ6bXZC7026Hk9mgNduolyuol8votDUk4uNITUxgciIlUqckDJNT961WS/i1z87O4vTp0yNRKJmatNttGIYxouCnGiup+SlifhYgT/HtNznlTJBdzmj/qARAbmo7dRkAg8UFfQ/keejygJlCoYBarQaPxzPQPTR19HuAy+WEy+3EWHjQHUGtjh6PB5lMBktLSwiHw5ibmxMCtUajgVKpJPaFonNSqDudTrHvpLGgBdtuiy0SYlLm4HOf+xx+6Id+6NDOhSJwBYUjApqoJc8qfhRkIw260MgCHCL3w1bH01S2vVw/DMPA/fv34fV6ce7cuR3d3CxuQbesh4hdtwwYloW+OUr6hjWYgkXHoFaroVqrolFviN5n6n+u1GpYunV70PN85hS822qXj4Pe76NeqaJWqqBercLjHIxndQzFe067DXY2qPs7mR02MHTaLSzfvYdqpQqnbWfFOUW0FAFTbZ/U0bFY7KGUN7U07QU7jZiVB7g87YUeCbxyuRw6nQ4mJiYwMTGB8fHxXd+bPv90ozo0lToYY+h2e9jKNIaGNDUAOrw+m/i8+/1+4TC3traGzc1N2O12eDweoWughRENg6HvG81bp0l2iUQCiUQCExMTmJycRCgUgtfr3XH7LcvC1tYWnE4n0un0oR1HReAKCp8QmKY5oqwlcjdN89Av2DabbSTVSOnh3S5ua2truHv3LtLpNE6fPn3gHuqdQNO2aOCLrutIJpMIx2MoVMq4u7yE5MwU4tNpdEwdmt6Hxa09vz7nHFqztWMkuhNM04SD2eB1uuC1O+F1uOAf3vc5XUOnssEi5NatW1hZWRFGIsViEWNjY4L4KONB07kel34nBbw8FlT+SfV5Up/vBdunhNFP+f5OCwxN04SrWaPRwPj4uOij3suAIeqBp22vVNrIZhrDiWgNWLyGreyG6EkHMFLaoEEolD4nwo5EImK0KtXoK5UKOp2OOG7Ua6/rukiPR6NR0UI3NTUlpp1R/ZuMeQ4DzyWBM8b+UwA/AcAE8Cec85993P8oAldQ2B9IhX2YGFw8H64N+/3+EVIfGxsTZN3v93H37l1kMhnMz8/j2LFjTzUSbLfbyOfzyOVyqNfrCAQCqFQqCAQCePXVVzE2NoauoaNj9NExDeiWAd18EP3rUtTfN80nIvu9wOtwCVJ32x0wOj2sLi2jWatj/sRJOGw2bG1sYmNjQ/TS+/1+MMaEWlxWqAcCgT2l3WmYCREite89DnLWYKefvV5PRP67nVdKOdP887GxMTH2k8oIexGBra1W0W4NrITdHgeOn4iKca5kjSunwQuFApaWlsRQo0AggEajgUqlAr/fj2PHjomOEZfLNbIAarVaiMfjwgMgm80KL/VSqYROpyNGpn7uc5/Dl770pT0dz73guSNwxtibAP4xgL/GOe8xxhKc88Lj/k8RuILC8w0SKcmk3mg0YLfbR0RBNpsNKysr0DQN586d+1imldE0r1wuh9u3b6NUKuHMmTN4/fXX9+ymZ/FBOl+u4+tDcje4Cd2UCN8y0TX0fZF+q97A+tJ99LpdzJw8jvFkEt1GE/VSBbV8EZMTKczNHoMbNpjdHrraA9U4mZsctOd9v6BpaHt9Lok6KW1eq9UAQLj0JZNJcZNbt/p9EyvLFVhDDkskA4jFHl8eKZfLuHHjBvr9PpLJpEi1Ly8vo9/vi0WQYRgim8AYw8bGBnq9HiYmJgRZUxZDtlw9e/bsi10DZ4x9GcBvcM7/4kn+TxG4gsLRBA3CoKioXq9D13WYpilsOF999dU9E7nNZjuQ85plWchkMnjnnXewurqKhYUFzM/Pi4Ei5BB2GOgaOjSjj7beg2boaBs9aHofXfPxWZFauYL1pfsAgJmTxxGORaH3+9haXUdhK4vxdArp2RmE/H74HG74nS7YDQvo6YBuwmWzP7ddDQSKcOkGQBiyyMpyGgFKanev1wsb88OyPHDY7QADIlEGu/3x+0ueDTTtjEAiN9M0EYlERB85DQ2iGfSnTp1COBweMcUhtfvCwgK+8zu/89COz/NI4NcA/FsAXwTQBfDTnPMrj/s/ReAKCi8OdF0XSvI7d+7g9u3bcLlc8Hg8I3XV7daqwICAp6ensbCwcGCldi6Xwze+8Q1wzmGz2cQQi0AgICZ1yS1gh1W7t7gFzRjU4TWjj/4wff8gZW8MInzTQKVQxPrSfXi8XiQm0/AFfGA2O7LrGyhlc0hOppGanYZzh21z2Oxw2exw2R1w2uxw2e1w2RwDxb3NAZfNDvseSd7GbMJ972mBItntt06nI+xLSe2uaRpKxQ64xeByuxAIeJFKjwkHtcfB6XQKf/d4PC4yFqVSCUtLS2g2mzh+/DhmZ2dhmiYajQaWl5fx/vvvI5FIwO/3w+PxCAFdKBQSivbDwjMhcMbYXwCY2OFP/xjAzwP4CoCfAvApAP8GwHG+w8Ywxn4MwI8BwMzMzCtra2tPZXsVFBSeLXRdR6FQeGiGtKZpsNlsI5GX0+lEJpNBvV7HxYsXMTc3d6D3NgwDmUxmhBjq9Tqq1SoMw4BpmrAsC6ZpisEc5PYVi8X2TBj7AeccumWiZ+hYWVvDZi6Lcq2CWqsJuBywbAy1ShVaq4XpE8cxtzAP11PaFoLb7oTf6UbAOYj66f7TJPbduErT+ri7mBfnzeM10GqVEQgEhMBst0VXp9NBLpfD1tYWWq0Wkskk0um0UMrLUwjlhaKmaVhcXMTY2BgSiQS63a5oozt9+vQLH4H/KYBf5Jx/Zfj7MoBv45wXH/V/KgJXUPhkgtzBZILtdDooFAq4efMm7HY7zp49KywuZS918sneDzjnI/OfO52O8O+mIRnNZhOGYcDj8SAcDosWMKrh7vW9Keuw15S3EGs16ijVatjIbeHmrVvIZDOIpVNIzEwiGIsgFA7DFwx8LKl0l90xJHU33HYH3MNI32VziMjf/hTq8rlsA5XywFzH4bBh7kQElcpgbGqxWEQsFsPk5CSSyeSupZdutyucExuNBhKJhCBz0zQf6jzQdR3Xrl2DZVm4dOmSOM8Hcd/bCc8jgf89AGnO+X/FGDsF4C0AMztF4DIUgSsoKGwH5xxLS0u4fv06YrEYUqkUdF0fMRbhnI+MPpUHpfh8vgPVu6nFqVaroVQqoVQqibGW9XodoVBIROu7kQctFHRdF33IZPFJN1H3fQwBNptNfPjhh1hZWYFpmujpfcBuQzI1gcTkJMKxCNx+H3RuiZT9Xnmgbxno7FOYR7ANB98MSN0B95D0g0Pid9mf/FyYpoXlpTIMfbBdwZAb4bAHNrsN3DJRKOaRy2ZRb9SQTqcxOTmJWCy266Km1+uJyLxWq2F8fHzHDAvnHCsrKygUCjh//jwCgQDi8ThSqZ1Ng/aD55HAXQB+G8BFAH0MauB/+bj/UwSuoKCwG3q9nvDiPn/+/MhFdDuhy1agcop+L2CMIRqNYnZ2dkdTGhmGYSCfzyOTyaBcLmN8fFxEgjsRsWmaIh1LJQS5lNDtduF0OkcIXb4vL0ZIY9BoNFAsFrG5uYlsNoterweXy4V0Oo2ZmRnMzMw8URcA5xwdQ0dL76Gl99DWe2gNhXmH0WY3iOI9CAzT8pSmdzwmPd9sdLGxXn/kc/p6H5VKGZVyGaZpIBoLIxRywevb/bV1XRczzUkLsV13kcvlsLi4KMSQ26f3HQTPHYHvF4rAFRQUHodyuYzr16/D4/HgwoULexrrSoYde7kecs6Ry+WwsbEBn8+H2dlZpFKpx6ridV0X/cP1eh3JZBKTk5Mj4qm9vLfsR/4ovYDf7xe1+mAwKHy/W60W8vk8NjY2sLm5ia2tLdhsNiwsLODMmTOYmpralwhLJva20Ru22xnoDX9S+91+Sd7jcMKGR5cBCoUWNG1vLWz9oc++hzkR9nmQjvkR8Xrh3iEDQNPuaEHkdDqFRzr9NAwDV65cwdzcHI4fP76vfdwJisAVFBQ+UbAsCysrK1haWsL4+LgYYRkMBg+tN9qyLBQKBayurqJer2Nqagqzs7N7Gh3b7XaxtbWFra0ttNttpFIpRKNR0cZ2kG2Up4mVy2Xh/U01+VgsJgidsLm5iY8++gjLy8vgnGNychKnTp3C+Pi4sHQ9LJApTm+ouqcWu6beRfuAUbxlcTQaPRjGYOKZZXJYnINzDOfN7853DIPUezTsR8jtQcDpQdA1SOsHnW6RAaCZ4tvbIk3ThNfrRTqdxvz8/L734aHtUgSuoKDwSQQNw6hWq6hWq9A0DWNjY8IrPRKJHErLj6ZpWF9fx/r6OgKBgIjK90LEmqYhm80Ovb3r0DRNTMWSbwdRuXe7XUHm5XIZuq6PCO3oGJAa/86dO8jlcqKlj4h8fHwcXq/3iQV3e8Voer4r0vSa0T80J0GLA9yyYFpAq9lDq9Ub+bvdbkM47IHfPypEc9jsA2Ge3SHq9y67He7hfZgWuq02fG4PopHIoWwroAhcQUFBAcCAoGq1miD0arUKm82GSCSCUCi058iX0tPbSdWyLOTzeaytraFerwsPc5qU5ff7H5tqJz/7er0uRm82Gg3YbDZB5AclTpq7XavVUK/XYbPZhKCPyFzTNBSLRRSLRViWJd7X4/GI2jv1PG/v26dWO98TDo7ZDRa30LesA5n37IS+ZaDcbGM1W0GlpUGz+jD4wCfd7XYgEvXB5XzybMhkIIyz0cMZaKIIXEFBQWEXaJom2sH2WgNvNpti5jVND6MUPZGrpmkoFApiTCY5irlcLkHmdNuL7Wm320Wj0QDn/FCj3+0pYdM0R2q7wWAQmqaJlixaVFC2QCZ0h8MhZm83Gg0xf5te6yClAUpRj42NidccGxs7lPQ+5xz1eheFXAtav4+22Ydm9qFZPTj8NjgDNjzJ4Z4JxnA6cjj2wI8i8MPxClRQUFA4oqDI80lBgjAazLG8vIxer4dIJCIIfWJi4qH/6Xa7I6RO5jV7HShCs6z9fj+8Xq/wPafFAEXQdrt9X+TW7/dH1OvLy8vQNE3MLJf3iZ5brVZRq9Wg6zrC4TDC4TDi8bgQENLrySM69yIulME5R7vdRq1WQ61WEwNqqP+ebqFQ6InbAhljCIe9CAbdKBbaqFQ0RPjwM8EBu8bgD7tgdwHMaYNl50OnPAN9cyDUo/t904DrkDMFu263isAVFBQUDgfypK1KpYJ2u/1U3ofazXa6maYpxmi6XK6RITL7nQtvmiY0TYOu66JdjTIHMll2Oh1UKhXRD18ul9Fut+F2uxGJROB0OkUaPBqNYmpqas+DZKiEIAsRaRFFpE4LBZ/PJwh9P+LFbtdALtuA1t5Z0W53MHg8Tng8jsHN64TLZRdKfw4uRsUeFCqFrqCgoPAJgdzz3mw2R2r+nHMh3guHww+p0R8HwzBEKYCmnzkcjpGRppQJIMKkRU0ul0OxWBTkXqvVRE/69pnidH/7tnHOwRhDIpEQugUaW0sLA7K8pRp/rVZDu90WPuVE6j6f77H7Xq93kc81hUHMo8BsEKTu97sQGjscP3RF4AoKCgoK0DRN2MBWKhV0u11EIpE9K9ydTidisZgQ8FFfOpE5/Wy3248tCei6LuZ10yzx3rA3u9frwTCMEUJ3OBzodrtivjeROf0MhULC0CYcDuP8+fOYmppCIpEAY2yE0CndHwg83mLWNC1omo5+z0S/b6DfNx/ZjjYeT+Lc+VOYmg7v6Zg+DqoGrqCgoKAg6v1TU1MABtFxpVLZ8/zuXq+HjY0NfPjhh/B4PKIVjdrRDhOWZY2I/zqdzsjfaEoZie8ow0DWul/72tfgdDrh8/lw7NgxnDhxAtPT0yJDQFqCdDr9RN7lnHPofRPdnoFe10CvZ6DbMWCYgwWL2+WGx3N4ffOPgorAFRQUFBSeCJxzMTubonm32z1C6Ic1T51gs9keWce2LAuNRkNE191uV/TmLy0toVarod/vY2JiAidOnMDs7CzGxsbQaDSQSqUwNze353r8TtB1E72ugU5XRzB4eCSuUugKCgoKCk8NROjlclkQummah/4eNBgmHo8jGo3uuS+cc45qtYrNzU1cv34dq6uraLfbYoqcx+MBYwypVAonT57E1NTUvtv0QqEQIsrI5WEoAldQUFD4ZMKyLFSrVSGCq9frol0tHo8jHA7vWWlOo2hzuZwYNpPJZETngNvtFin3UCgkFPd76b8fHx9HOn04Ji6AInAFBQUFhRcMhmGMtKy1220xvGW/9rjNZhP5fB53797F+vo6+v0+kskkUqkUDMOAaZrw+Xwj7nqBQEBE8MAgAlfTyHaAInAFBQUFhZ3Q7/dHhrccFORRf/fuXeRyOTDGYFkW+v0++v2+cJzTdR2WZcHlcsHhcOCzn/0sfvInf/IQ9mgApUJXUFBQUHih4XK5kEqlRubAHxbIIGc36LouVPF7mUZ3WFAErqCgoKCg8AjsJSUfDoef/oZsw+F4vSkoKCgoKCh8rFAErqCgoKCgcAShCFxBQUFBQeEIQhG4goKCgoLCEYQicAUFBQUFhSMIReAKCgoKCgpHEIrAFRQUFBQUjiAUgSsoKCgoKBxBKAJXUFBQUFA4glAErqCgoKCgcAShCFxBQUFBQeEIQhG4goKCgoLCEYQicAUFBQUFhSOIIzUPnDFWBLC2h6fGAZSe8uZ8nFD783xD7c/zDbU/zzfU/jwas5zz8Z3+cKQIfK9gjH1rtwHoRxFqf55vqP15vqH25/mG2p/9Q6XQFRQUFBQUjiAUgSsoKCgoKBxBvKgE/hvPegMOGWp/nm+o/Xm+ofbn+Yban33ihayBKygoKCgovOh4USNwBQUFBQWFFxovFIEzxr7IGFtkjC0xxn7uWW/PQcEYW2WMXWeMXWOMfetZb89+wBj7bcZYgTF2Q3osyhj7c8bYveHPyLPcxifBLvvzXzPGMsPzdI0x9h88y23cKxhj04yxrzDGbjHGbjLG/sHw8SN5fh6xP0f1/HgYY99kjH043J//Zvj4HGPsveF17t8wxlzPelv3gkfsz+8yxlak83PxGW/qE4ExZmeMfcAY+3+Gv39s5+eFIXDGmB3AvwDwvQDOAvhbjLGzz3arDgVvcs4vHuE2i98F8MVtj/0cgLc45/MA3hr+flTwu3h4fwDgnw/P00XO+b/7mLdpvzAA/Oec87MAvg3ATwy/M0f1/Oy2P8DRPD89AN/BOX8ZwEUAX2SMfRuAX8Rgf04CqAL4j5/dJj4RdtsfAPgZ6fxce1YbuE/8AwC3pd8/tvPzwhA4gNcALHHO73PO+wD+NYDve8bb9IkH5/xrACrbHv4+AL83vP97AL7/49ymg2CX/TmS4JxnOedXh/ebGFyEJnFEz88j9udIgg/QGv7qHN44gO8A8H8MHz9K52e3/TmyYIxNAfhrAH5z+DvDx3h+XiQCnwSwIf2+iSP85R2CA/gzxtj7jLEfe9Ybc4hIcs6zw/s5AMlnuTGHhJ9kjH00TLEfiZSzDMbYMQCXALyHF+D8bNsf4Iien2F69hqAAoA/B7AMoMY5N4ZPOVLXue37wzmn8/Pzw/Pzzxlj7me3hU+MXwHwswCs4e8xfIzn50Ui8BcRn+GcX8agLPATjLHPPesNOmzwQRvEkV6FA/ifAZzAIC2YBfBLz3RrnhCMsQCAPwTwn3HOG/LfjuL52WF/juz54ZybnPOLAKYwyDIuPNstOhi27w9j7DyAf4TBfn0KQBTAP3x2W7h3MMb+OoAC5/z9Z7UNLxKBZwBMS79PDR87suCcZ4Y/CwD+Twy+wC8C8oyxFAAMfxae8fYcCJzz/PDCZAH4lzhC54kx5sSA7P6Ac/5Hw4eP7PnZaX+O8vkhcM5rAL4C4NsBhBljjuGfjuR1TtqfLw5LH5xz3gPwOzg65+cNAH+DMbaKQcn2OwD8D/gYz8+LROBXAMwPFYAuAD8E4I+f8TbtG4wxP2MsSPcBfDeAG4/+ryODPwbwd4b3/w6Af/sMt+XAILIb4gdwRM7TsF73WwBuc85/WfrTkTw/u+3PET4/44yx8PC+F8B3YVDX/wqAHxw+7Sidn5325460WGQY1IuPxPnhnP8jzvkU5/wYBnzzl5zzH8bHeH5eKCOXYXvIrwCwA/htzvnPP9st2j8YY8cxiLoBwAHgfzuK+8MY+1cAvoDBhJ48gH8C4P8C8GUAMxhMl/sPOedHQhi2y/58AYP0LAewCuA/kWrIzy0YY58B8FcAruNBDe+/wKBufOTOzyP252/haJ6flzAQQdkxCLa+zDn/b4fXhn+NQbr5AwA/Moxen2s8Yn/+EsA4AAbgGoC/J4ndjgQYY18A8NOc87/+cZ6fF4rAFRQUFBQUPil4kVLoCgoKCgoKnxgoAldQUFBQUDiCUASuoKCgoKBwBKEIXEFBQUFB4QhCEbiCgoKCgsIRhCJwBYUXAIyxMGPs7z/r7dgrGGP/H2PsqA7oUVB4LqAIXEHhxUAYwBMROBvgQNcAyXFKQUHhY4YicAWFFwO/AODEcJ7yPwMAxtjPMMauDIdE0OzlY4yxRcbY/4KB49VnGWN3hjOZ7zLG/oAx9p2MsbfZYB74Q7aWjLH/iDH2x0MDjrcYYwHG2FuMsatsML/++6T3us0Y+5fD+c9/NnTgkl/LNnzv/+5pHyAFhRcNisAVFF4M/ByA5eE85Z9hjH03gHkMfKUvAnhFGoYzD+B/4pyfw8Bp7SQGAz4Whre/DeAzAH4aAyeznXAZwA9yzj8PoAvgB4aDd94E8EtDW0x6r38xfK8agL8pvYYDwB8AuMc5/y8PuP8KCp84qPSXgsKLie8e3j4Y/h7AgEzXAaxxzt+VnrvCOb8OAIyxmwDe4pxzxth1AMd2ef0/l+xVGYD/frhAsDAYn0gjSFc459eG99/f9nq/joGd5pGzCFZQeB6gCFxB4cUEA/BPOee/PvLgYE52e9tzZZ9mS/rdwu7XCPk1fhgDL+tXOOf6cDqTZ4fXNgHIKfR3ALzJGPslznn3kXujoKDwEFQKXUHhxUATQFD6/d8D+LvD2dhgjE0yxhJP6b3HMJiLrDPG3gQwu8f/+y0A/w7Al5UYTkHhyaG+NAoKLwA45+Wh8OwGgP93WAc/A+Abw3J0C8CPYBAFHzb+AMD/PUy5fwvAnSfY7l9mjI0B+F8ZYz88nNmtoKCwB6hpZAoKCgoKCkcQKoWuoKCgoKBwBKEIXEFBQUFB4QhCEbiCgoKCgsIRhCJwBQUFBQWFIwhF4AoKCgoKCkcQisAVFBQUFBSOIBSBKygoKCgoHEEoAldQUFBQUDiC+P8BHQFZC7unkuQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = plt.get_cmap('Dark2')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "highlight_topics_handles = []\n",
    "for i, t in enumerate(topic_word_desc):\n",
    "    highlight_index = np.where(i == highlight_topics)[0]\n",
    "    if len(highlight_index) > 0:\n",
    "        color = palette(highlight_index[0])\n",
    "        label = 'topic ' + str(i+1)\n",
    "        lw = 3\n",
    "    else:\n",
    "        color = 'black'\n",
    "        label = 'other topics'\n",
    "        lw = 1\n",
    "    \n",
    "    hndl, = ax.plot(term_ranks, np.log(t[:n_terms]), color=color, label=label, lw=lw, alpha=0.3)\n",
    "    \n",
    "    if len(highlight_index) > 0:\n",
    "        highlight_topics_handles.append(hndl)\n",
    "\n",
    "ax.set_title('Term probability decline per topic')\n",
    "ax.set_xlabel('term rank')\n",
    "ax.set_ylabel('term probability for given topic (log scale)')\n",
    "ax.legend(handles=highlight_topics_handles, loc='best')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highlighted topics are #94, #132 and #159. We will later have a closer look at them.\n",
    "\n",
    "We will now further investigate the topics and try to identify topics of interest to us, i.e. topics related to the COVID-19 pandemic. Before we continue, we apply a transformation to the topic-word matrix: We use [topic-word relevance](https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Topic-word-relevance) ([Sievert & Shirley 2014](https://www.aclweb.org/anthology/W14-3110/)) which *\"helps to identify the most relevant words within a topic by also accounting for the marginal probability of each word across the corpus\"*, i.e. this transformation puts a penalty on terms that are more common (have a higher marginal probability) and therefore pushes more \"specific\" terms to higher ranks in each topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.546552Z",
     "iopub.status.busy": "2020-11-23T16:59:04.545887Z",
     "iopub.status.idle": "2020-11-23T16:59:04.599889Z",
     "shell.execute_reply": "2020-11-23T16:59:04.600266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.56622682, -6.47633154, -6.48707646, ..., -6.72499435,\n",
       "        -6.64484627, -5.20960509],\n",
       "       [-7.43442808, -7.3445328 , -7.35527771, ..., -6.70589241,\n",
       "        -5.11515226, -7.42773306],\n",
       "       [-6.78071434, -6.69081905, -6.70156397, ..., -5.58955515,\n",
       "        -5.97203059, -6.77401932],\n",
       "       ...,\n",
       "       [-7.14246662, -7.05257133, -7.06331625, ..., -7.30123414,\n",
       "        -5.31684861, -7.1357716 ],\n",
       "       [-3.15177778, -4.50491935, -3.50897469, ..., -7.03836263,\n",
       "        -5.02925396, -6.09519551],\n",
       "       [-7.59616309, -7.5062678 , -7.51701272, ..., -3.97399302,\n",
       "        -6.00977477, -7.58946807]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tmtoolkit.topicmod.model_stats import topic_word_relevance\n",
    "from tmtoolkit.bow.bow_stats import doc_lengths\n",
    "\n",
    "doclengths = doc_lengths(dtm)\n",
    "\n",
    "topic_word_rel = topic_word_relevance(topic_word, doc_topic, doclengths, lambda_=0.6)\n",
    "topic_word_rel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now try to identify our topics of interest. We can do this computationally, e.g. by using [filter_topics()](https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Filtering-topics) and by specifying a list of search terms. By this, we identify all topics that contain at least one matching term in their `top_n=10` terms (remember we found out that 5 to 10 of its top terms sufficiently determine the topic). Note that we use the topic-word relevance matrix `topic_word_rel` here instead of the topic-word matrix, because we want the top terms list to be determined by the mentioned relevance metric instead of the topic-specific term probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.603861Z",
     "iopub.status.busy": "2020-11-23T16:59:04.603361Z",
     "iopub.status.idle": "2020-11-23T16:59:04.707647Z",
     "shell.execute_reply": "2020-11-23T16:59:04.707970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13,  28,  76,  90,  93, 108, 120, 126, 131, 155, 168, 180])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tmtoolkit.topicmod.model_stats import filter_topics\n",
    "\n",
    "covid19_topics_automatic = filter_topics(['corona*', 'covid*', 'pandemie', 'sars*', 'infektion', 'rki'],\n",
    "                                         vocab, topic_word_rel, match_type='glob', top_n=10)\n",
    "covid19_topics_automatic + 1       # covid19_topics are topic *indices*; we add 1 to get topic numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively (and you should do this anyway) is to look at the top terms of each topic and try to interpret each topic. This is feasible for 180 topics. By this, you can also identify COVID-19 related topics that don't involve the keywords above but maybe synonyms that you didn't think of. Furthermore, you make sure that the topics in your topic model make sense. As long as there's only a small fraction of \"nonsensical\" topics (which happens most of the time with LDA), you know your model is okay and you can identify and later exclude such topics.\n",
    "\n",
    "There are several ways to display and export topic modeling results, specifically the document-topic and topic-word distributions. See [this section in the tmtoolkit documentation](https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Displaying-and-exporting-topic-modeling-results) for the available options. We will export the results to a Excel file, which allows to easily investigate topics and mark them accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.711486Z",
     "iopub.status.busy": "2020-11-23T16:59:04.710775Z",
     "iopub.status.idle": "2020-11-23T16:59:04.712642Z",
     "shell.execute_reply": "2020-11-23T16:59:04.712999Z"
    }
   },
   "outputs": [],
   "source": [
    "from tmtoolkit.topicmod.model_io import save_ldamodel_summary_to_excel\n",
    "\n",
    "# this takes some time since the topic model is quite large\n",
    "\n",
    "# save_ldamodel_summary_to_excel('output/tm_final_k180_eta0.7.xlsx',\n",
    "#                                topic_word, doc_topic, doc_labels, vocab,\n",
    "#                                dtm = dtm);\n",
    "# save_ldamodel_summary_to_excel('output/tm_final_k180_eta0.7_tw_relevance.xlsx',\n",
    "#                                topic_word_rel, doc_topic, doc_labels, vocab,\n",
    "#                                dtm=dtm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I manually identified the following COVID-19 related topics (yellow background) as well as nonsensical (gray background) in `output/tm_final_k180_eta0.7_tw_relevance.xlsx` (see the `top_topic_word_labels` sheet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.716860Z",
     "iopub.status.busy": "2020-11-23T16:59:04.716420Z",
     "iopub.status.idle": "2020-11-23T16:59:04.718644Z",
     "shell.execute_reply": "2020-11-23T16:59:04.718242Z"
    }
   },
   "outputs": [],
   "source": [
    "# substract 1 to get topic *indices* (these start at topic 0)\n",
    "\n",
    "nonsensical_topics = np.array([7, 8, 46, 47, 61, 63, 64, 74, 78, 80,\n",
    "                               93, 94, 97, 111, 113, 122, 124, 129,\n",
    "                               139, 159, 170]) - 1\n",
    "covid19_topics = np.array([13, 24, 28, 35, 50, 76, 90, 108, 126,\n",
    "                           131, 135, 152, 155, 168, 180]) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.722015Z",
     "iopub.status.busy": "2020-11-23T16:59:04.721470Z",
     "iopub.status.idle": "2020-11-23T16:59:04.723886Z",
     "shell.execute_reply": "2020-11-23T16:59:04.724257Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nonsensical_topics), len(covid19_topics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most topics can be interpreted very well (see the Excel file). With a little more than 10% nonsensical topics (mostly consisting of very common words), however, we could probably remove more common words in the preprocessing step and generate a model that consists of a bit fewer topics. But I think for our purposes here the model is sufficient. It's also interesting to see how the model captures different perspectives of the pandemic in separate topics, e.g *topic_24* relating to wearing masks, *topic_50* relating to relaxing the Corona countermeasures or *topic_131* relating to vaccines against the virus. \n",
    "\n",
    "We also see that we found a few different topics as compared to the \"keyword search\" approach. We found the following topics additionally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.728051Z",
     "iopub.status.busy": "2020-11-23T16:59:04.727505Z",
     "iopub.status.idle": "2020-11-23T16:59:04.730001Z",
     "shell.execute_reply": "2020-11-23T16:59:04.730378Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{24, 35, 50, 135, 152}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(covid19_topics+1) - set(covid19_topics_automatic+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the top 10 terms for these additionally found topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:04.734460Z",
     "iopub.status.busy": "2020-11-23T16:59:04.733743Z",
     "iopub.status.idle": "2020-11-23T16:59:05.537359Z",
     "shell.execute_reply": "2020-11-23T16:59:05.537755Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank_1</th>\n",
       "      <th>rank_2</th>\n",
       "      <th>rank_3</th>\n",
       "      <th>rank_4</th>\n",
       "      <th>rank_5</th>\n",
       "      <th>rank_6</th>\n",
       "      <th>rank_7</th>\n",
       "      <th>rank_8</th>\n",
       "      <th>rank_9</th>\n",
       "      <th>rank_10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>topic_24</th>\n",
       "      <td>maske (0.7224)</td>\n",
       "      <td>maskenpflicht (0.02156)</td>\n",
       "      <td>tragen (-0.2841)</td>\n",
       "      <td>trage (-0.5087)</td>\n",
       "      <td>schutzmasken (-0.6864)</td>\n",
       "      <td>pflicht (-1.121)</td>\n",
       "      <td>nase (-1.261)</td>\n",
       "      <td>mund (-1.328)</td>\n",
       "      <td>einkaufen (-1.596)</td>\n",
       "      <td>verstoß (-1.604)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_35</th>\n",
       "      <td>spahn (0.3342)</td>\n",
       "      <td>test (0.007197)</td>\n",
       "      <td>jens (-0.08562)</td>\n",
       "      <td>gesundheitsminister (-0.4132)</td>\n",
       "      <td>testen (-0.8518)</td>\n",
       "      <td>labor (-0.9737)</td>\n",
       "      <td>robert (-1.62)</td>\n",
       "      <td>deutschland (-1.705)</td>\n",
       "      <td>kostenlos (-1.951)</td>\n",
       "      <td>sagen (-1.975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_50</th>\n",
       "      <td>lockerungen (-0.8067)</td>\n",
       "      <td>öffnung (-1.114)</td>\n",
       "      <td>öffnen (-1.175)</td>\n",
       "      <td>schrittweise (-1.263)</td>\n",
       "      <td>mai (-1.375)</td>\n",
       "      <td>lockerung (-1.425)</td>\n",
       "      <td>normalität (-1.545)</td>\n",
       "      <td>lockern (-1.547)</td>\n",
       "      <td>land (-1.697)</td>\n",
       "      <td>sollen (-1.72)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_135</th>\n",
       "      <td>hand (-0.5646)</td>\n",
       "      <td>abstand (-0.7781)</td>\n",
       "      <td>waschen (-0.8674)</td>\n",
       "      <td>husten (-0.9921)</td>\n",
       "      <td>sollen (-0.9941)</td>\n",
       "      <td>empfehlen (-1.103)</td>\n",
       "      <td>meter (-1.122)</td>\n",
       "      <td>halten (-1.294)</td>\n",
       "      <td>luft (-1.602)</td>\n",
       "      <td>einhalten (-1.695)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_152</th>\n",
       "      <td>arzt (0.08259)</td>\n",
       "      <td>patient (0.04521)</td>\n",
       "      <td>klinik (-0.1198)</td>\n",
       "      <td>krankenhaus (-0.4784)</td>\n",
       "      <td>krankenhäuser (-0.7729)</td>\n",
       "      <td>behandlung (-0.778)</td>\n",
       "      <td>intensivstation (-0.8414)</td>\n",
       "      <td>medizinisch (-0.9082)</td>\n",
       "      <td>behandeln (-0.9243)</td>\n",
       "      <td>mediziner (-1.016)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          rank_1                   rank_2             rank_3  \\\n",
       "topic                                                                          \n",
       "topic_24          maske (0.7224)  maskenpflicht (0.02156)   tragen (-0.2841)   \n",
       "topic_35          spahn (0.3342)          test (0.007197)    jens (-0.08562)   \n",
       "topic_50   lockerungen (-0.8067)         öffnung (-1.114)    öffnen (-1.175)   \n",
       "topic_135         hand (-0.5646)        abstand (-0.7781)  waschen (-0.8674)   \n",
       "topic_152         arzt (0.08259)        patient (0.04521)   klinik (-0.1198)   \n",
       "\n",
       "                                  rank_4                   rank_5  \\\n",
       "topic                                                               \n",
       "topic_24                 trage (-0.5087)   schutzmasken (-0.6864)   \n",
       "topic_35   gesundheitsminister (-0.4132)         testen (-0.8518)   \n",
       "topic_50           schrittweise (-1.263)             mai (-1.375)   \n",
       "topic_135               husten (-0.9921)         sollen (-0.9941)   \n",
       "topic_152          krankenhaus (-0.4784)  krankenhäuser (-0.7729)   \n",
       "\n",
       "                        rank_6                     rank_7  \\\n",
       "topic                                                       \n",
       "topic_24      pflicht (-1.121)              nase (-1.261)   \n",
       "topic_35       labor (-0.9737)             robert (-1.62)   \n",
       "topic_50    lockerung (-1.425)        normalität (-1.545)   \n",
       "topic_135   empfehlen (-1.103)             meter (-1.122)   \n",
       "topic_152  behandlung (-0.778)  intensivstation (-0.8414)   \n",
       "\n",
       "                          rank_8               rank_9             rank_10  \n",
       "topic                                                                      \n",
       "topic_24           mund (-1.328)   einkaufen (-1.596)    verstoß (-1.604)  \n",
       "topic_35    deutschland (-1.705)   kostenlos (-1.951)      sagen (-1.975)  \n",
       "topic_50        lockern (-1.547)        land (-1.697)      sollen (-1.72)  \n",
       "topic_135        halten (-1.294)        luft (-1.602)  einhalten (-1.695)  \n",
       "topic_152  medizinisch (-0.9082)  behandeln (-0.9243)  mediziner (-1.016)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tmtoolkit.topicmod.model_io import ldamodel_top_topic_words\n",
    "\n",
    "ldamodel_top_topic_words(\n",
    "    topic_word_rel,\n",
    "    vocab,\n",
    "    top_n=10).iloc[sorted(set(covid19_topics) - set(covid19_topics_automatic)),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that by manually investigating the top words per topic, we identified Corona-related topics which didn't find with our set of keywords, because it's hard to create a comprehensive set of keywords.\n",
    "\n",
    "The following topics we didn't manually identify as COVID-19 related topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:05.541904Z",
     "iopub.status.busy": "2020-11-23T16:59:05.541401Z",
     "iopub.status.idle": "2020-11-23T16:59:05.543672Z",
     "shell.execute_reply": "2020-11-23T16:59:05.544053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{93, 120}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(covid19_topics_automatic+1) - set(covid19_topics+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate the top 10 terms for these two topics in order to make sure that we correctly identified these topics as *not* Corona-related:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:05.548019Z",
     "iopub.status.busy": "2020-11-23T16:59:05.547281Z",
     "iopub.status.idle": "2020-11-23T16:59:06.495085Z",
     "shell.execute_reply": "2020-11-23T16:59:06.495487Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank_1</th>\n",
       "      <th>rank_2</th>\n",
       "      <th>rank_3</th>\n",
       "      <th>rank_4</th>\n",
       "      <th>rank_5</th>\n",
       "      <th>rank_6</th>\n",
       "      <th>rank_7</th>\n",
       "      <th>rank_8</th>\n",
       "      <th>rank_9</th>\n",
       "      <th>rank_10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>topic_93</th>\n",
       "      <td>uhr (-0.5045)</td>\n",
       "      <td>coronavirus (-2.113)</td>\n",
       "      <td>sagen (-2.135)</td>\n",
       "      <td>mensch (-2.176)</td>\n",
       "      <td>mehr (-2.316)</td>\n",
       "      <td>angabe (-2.326)</td>\n",
       "      <td>infektion (-2.35)</td>\n",
       "      <td>teilen (-2.354)</td>\n",
       "      <td>zahl (-2.368)</td>\n",
       "      <td>coronakrise (-2.396)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_120</th>\n",
       "      <td>prozent (-0.4142)</td>\n",
       "      <td>quartal (-0.9742)</td>\n",
       "      <td>wirtschaft (-1.244)</td>\n",
       "      <td>minus (-1.258)</td>\n",
       "      <td>rückgang (-1.299)</td>\n",
       "      <td>erwarten (-1.527)</td>\n",
       "      <td>rechnen (-1.565)</td>\n",
       "      <td>einbruch (-1.58)</td>\n",
       "      <td>coronakrise (-1.594)</td>\n",
       "      <td>stark (-1.595)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      rank_1                rank_2               rank_3  \\\n",
       "topic                                                                     \n",
       "topic_93       uhr (-0.5045)  coronavirus (-2.113)       sagen (-2.135)   \n",
       "topic_120  prozent (-0.4142)     quartal (-0.9742)  wirtschaft (-1.244)   \n",
       "\n",
       "                    rank_4             rank_5             rank_6  \\\n",
       "topic                                                              \n",
       "topic_93   mensch (-2.176)      mehr (-2.316)    angabe (-2.326)   \n",
       "topic_120   minus (-1.258)  rückgang (-1.299)  erwarten (-1.527)   \n",
       "\n",
       "                      rank_7            rank_8                rank_9  \\\n",
       "topic                                                                  \n",
       "topic_93   infektion (-2.35)   teilen (-2.354)         zahl (-2.368)   \n",
       "topic_120   rechnen (-1.565)  einbruch (-1.58)  coronakrise (-1.594)   \n",
       "\n",
       "                        rank_10  \n",
       "topic                            \n",
       "topic_93   coronakrise (-2.396)  \n",
       "topic_120        stark (-1.595)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldamodel_top_topic_words(\n",
    "    topic_word_rel,\n",
    "    vocab,\n",
    "    top_n=10).iloc[sorted(set(covid19_topics_automatic) - set(covid19_topics)),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*topic_93* was not selected as Corona-related since it seems nonsensical (it's actually in the list of nonsensical topics). *topic_120* was not selected since the topic is mainly about economy and \"coronakrise\" as only Corona-related term is only on the 9th rank. However, we should later include these topics in a sensitivity analysis.\n",
    "\n",
    "We can now [exclude nonsensical topics](https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Excluding-topics) from our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.500815Z",
     "iopub.status.busy": "2020-11-23T16:59:06.499642Z",
     "iopub.status.idle": "2020-11-23T16:59:06.568812Z",
     "shell.execute_reply": "2020-11-23T16:59:06.569437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((32921, 159), (159, 3278))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tmtoolkit.topicmod.model_stats import exclude_topics\n",
    "\n",
    "doc_topic, topic_word, new_topic_mapping =  exclude_topics(\n",
    "    nonsensical_topics, doc_topic, topic_word, return_new_topic_mapping=True\n",
    ")\n",
    "doc_topic.shape, topic_word.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This leaves us with 159 topics. Note that the topic indices of the previously identified COVID-19 related topics now don't match to the new document-topic and topic-word distributions anymore. We can update the indices by using `new_topic_mapping` as returned from `exclude_topics()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.575537Z",
     "iopub.status.busy": "2020-11-23T16:59:06.574771Z",
     "iopub.status.idle": "2020-11-23T16:59:06.577489Z",
     "shell.execute_reply": "2020-11-23T16:59:06.578106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10,  21,  25,  32,  45,  67,  79,  94, 108, 112, 116, 132, 135,\n",
       "       147, 158])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_topic_ind, new_topic_ind = map(lambda x: np.array(list(x)),\n",
    "                                   (new_topic_mapping.keys(), new_topic_mapping.values()))\n",
    "covid19_topics = new_topic_ind[np.searchsorted(old_topic_ind, covid19_topics)]\n",
    "covid19_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also [generate labels for the topics](https://tmtoolkit.readthedocs.io/en/latest/topic_modeling.html#Generating-labels-for-topics) from their top-ranking words. This helps when refering to specific topics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.582771Z",
     "iopub.status.busy": "2020-11-23T16:59:06.582233Z",
     "iopub.status.idle": "2020-11-23T16:59:06.911271Z",
     "shell.execute_reply": "2020-11-23T16:59:06.910832Z"
    }
   },
   "outputs": [],
   "source": [
    "from tmtoolkit.topicmod.model_stats import generate_topic_labels_from_top_words\n",
    "\n",
    "topic_labels = generate_topic_labels_from_top_words(\n",
    "    topic_word,\n",
    "    doc_topic,\n",
    "    doclengths,\n",
    "    vocab,\n",
    "    lambda_=0.6\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the topic labels for our COVID-19 related topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.914960Z",
     "iopub.status.busy": "2020-11-23T16:59:06.914339Z",
     "iopub.status.idle": "2020-11-23T16:59:06.917149Z",
     "shell.execute_reply": "2020-11-23T16:59:06.916731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['11_pandemie_coronakrise', '22_maske_maskenpflicht',\n",
       "       '26_neuinfektionen_zahl', '33_spahn_test', '46_lockerungen_öffnen',\n",
       "       '68_wuhan_coronavirus', '80_virus_sarscov2',\n",
       "       '95_schließen_maßnahme', '109_coronakrise_kurzarbeit',\n",
       "       '113_impfstoff_medikament', '117_hand_abstand', '133_arzt_patient',\n",
       "       '136_zahl_todesfälle', '148_quarantäne_positiv',\n",
       "       '159_coronakrise_corona'], dtype='<U33')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_labels[covid19_topics]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick look at the marginal topic distribution shows us the most prominent topics in our corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.921296Z",
     "iopub.status.busy": "2020-11-23T16:59:06.920483Z",
     "iopub.status.idle": "2020-11-23T16:59:06.940946Z",
     "shell.execute_reply": "2020-11-23T16:59:06.941367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('26_neuinfektionen_zahl', 0.015712434406330807),\n",
       " ('75_minute_tor', 0.012762950363237005),\n",
       " ('108_trump_donald', 0.01272651858438072),\n",
       " ('105_prozent_quartal', 0.012365290954533646),\n",
       " ('80_virus_sarscov2', 0.012150155180471557),\n",
       " ('46_lockerungen_öffnen', 0.011952432146179335),\n",
       " ('95_schließen_maßnahme', 0.011331682833765531),\n",
       " ('158_bayer_dortmund', 0.01112611539262225),\n",
       " ('93_gates_foundation', 0.010949357258572412),\n",
       " ('119_leben_mensch', 0.010779585583945827),\n",
       " ('136_zahl_todesfälle', 0.010306918241285478),\n",
       " ('107_prozent_zahl', 0.010126169620470383),\n",
       " ('68_wuhan_coronavirus', 0.010071376604688579),\n",
       " ('159_coronakrise_corona', 0.010035174941218018),\n",
       " ('32_gericht_verurteilen', 0.009998688185347377),\n",
       " ('34_begriff_medium', 0.009733718116205612),\n",
       " ('11_pandemie_coronakrise', 0.00972157511571275),\n",
       " ('101_song_musik', 0.009688675866130446),\n",
       " ('87_modell_motor', 0.009622671837856773),\n",
       " ('66_politisch_demokratie', 0.009520435686119617)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tmtoolkit.topicmod.model_stats import marginal_topic_distrib\n",
    "\n",
    "marg_topic = marginal_topic_distrib(doc_topic, doclengths)\n",
    "list(sorted(zip(topic_labels, marg_topic), key=lambda x: x[1], reverse=True))[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the whole corpus (which includes about as much news articles from before the pandemic as from during the pandemic), we can calculate the overall share of Corona-related topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.945004Z",
     "iopub.status.busy": "2020-11-23T16:59:06.944403Z",
     "iopub.status.idle": "2020-11-23T16:59:06.946992Z",
     "shell.execute_reply": "2020-11-23T16:59:06.947371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13442846454584428"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(marg_topic[covid19_topics])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily COVID-19 cases and COVID-19 related topics in the news\n",
    "\n",
    "We now focus on the chronological development of the share of COVID-19 related topics as well as how this relates to the number of daily COVID-19 cases. We will calculate a share of COVID-19 related topics which represents our measure of COVID-19 media coverage on SPON.\n",
    "\n",
    "First, we load the corpus metadata that was generated in `prepare.py`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:06.950355Z",
     "iopub.status.busy": "2020-11-23T16:59:06.949876Z",
     "iopub.status.idle": "2020-11-23T16:59:07.030362Z",
     "shell.execute_reply": "2020-11-23T16:59:07.030792Z"
    }
   },
   "outputs": [],
   "source": [
    "corpusmeta = unpickle_file('data/meta.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We retrieve the publication date for each news article (in the same order as `doc_labels`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.053702Z",
     "iopub.status.busy": "2020-11-23T16:59:07.048389Z",
     "iopub.status.idle": "2020-11-23T16:59:07.093803Z",
     "shell.execute_reply": "2020-11-23T16:59:07.094206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2019-11-13', '2020-01-28', '2020-06-29', ..., '2019-12-28',\n",
       "       '2019-12-30', '2020-08-24'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_dates = np.array([corpusmeta[d]['pub_date'] for d in doc_labels], dtype='datetime64')\n",
    "doc_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we calculate the marginal distribution of COVID-19 related topics per news article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.099825Z",
     "iopub.status.busy": "2020-11-23T16:59:07.098845Z",
     "iopub.status.idle": "2020-11-23T16:59:07.104384Z",
     "shell.execute_reply": "2020-11-23T16:59:07.104782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00674433, 0.01073171, 0.00950735, ..., 0.01285347, 0.04862579,\n",
       "       0.48698885])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marg_covid19 = np.sum(doc_topic[:, covid19_topics], axis=1)\n",
    "marg_covid19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put this all together with the document labels and document lengths in a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.121778Z",
     "iopub.status.busy": "2020-11-23T16:59:07.120912Z",
     "iopub.status.idle": "2020-11-23T16:59:07.132895Z",
     "shell.execute_reply": "2020-11-23T16:59:07.133480Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>date</th>\n",
       "      <th>length</th>\n",
       "      <th>marginal_covid19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007-drehorte-in-europa-klimavertraeglicher-jam...</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>310</td>\n",
       "      <td>0.006744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>070-shake-in-berlin-vertrauensuebung-mit-einer...</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>265</td>\n",
       "      <td>0.010732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-fc-heidenheim-in-der-relegation-demoliert-in...</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>226</td>\n",
       "      <td>0.009507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-fc-heidenheim-unterliegt-werder-bremen-in-de...</td>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>244</td>\n",
       "      <td>0.004907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-fc-kaiserslautern-beantragt-in-der-3-liga-in...</td>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>269</td>\n",
       "      <td>0.094440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32916</th>\n",
       "      <td>zwoelf-kinder-als-chefsache-a-d4533bfa-668c-49...</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>421</td>\n",
       "      <td>0.129314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32917</th>\n",
       "      <td>zyklon-amphan-trifft-auf-indien-und-bangladesc...</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>203</td>\n",
       "      <td>0.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32918</th>\n",
       "      <td>zypern-diebe-stehlen-koffer-mit-urne-darin-a-1...</td>\n",
       "      <td>2019-12-28</td>\n",
       "      <td>57</td>\n",
       "      <td>0.012853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32919</th>\n",
       "      <td>zypern-mutmassliche-urnendiebe-gefasst-familie...</td>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>70</td>\n",
       "      <td>0.048626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32920</th>\n",
       "      <td>zytomegalie-was-schwangere-wissen-muessen-a-00...</td>\n",
       "      <td>2020-08-24</td>\n",
       "      <td>56</td>\n",
       "      <td>0.486989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32921 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   label       date  length  \\\n",
       "0      007-drehorte-in-europa-klimavertraeglicher-jam... 2019-11-13     310   \n",
       "1      070-shake-in-berlin-vertrauensuebung-mit-einer... 2020-01-28     265   \n",
       "2      1-fc-heidenheim-in-der-relegation-demoliert-in... 2020-06-29     226   \n",
       "3      1-fc-heidenheim-unterliegt-werder-bremen-in-de... 2020-07-07     244   \n",
       "4      1-fc-kaiserslautern-beantragt-in-der-3-liga-in... 2020-06-17     269   \n",
       "...                                                  ...        ...     ...   \n",
       "32916  zwoelf-kinder-als-chefsache-a-d4533bfa-668c-49... 2020-04-11     421   \n",
       "32917  zyklon-amphan-trifft-auf-indien-und-bangladesc... 2020-05-20     203   \n",
       "32918  zypern-diebe-stehlen-koffer-mit-urne-darin-a-1... 2019-12-28      57   \n",
       "32919  zypern-mutmassliche-urnendiebe-gefasst-familie... 2019-12-30      70   \n",
       "32920  zytomegalie-was-schwangere-wissen-muessen-a-00... 2020-08-24      56   \n",
       "\n",
       "       marginal_covid19  \n",
       "0              0.006744  \n",
       "1              0.010732  \n",
       "2              0.009507  \n",
       "3              0.004907  \n",
       "4              0.094440  \n",
       "...                 ...  \n",
       "32916          0.129314  \n",
       "32917          0.004484  \n",
       "32918          0.012853  \n",
       "32919          0.048626  \n",
       "32920          0.486989  \n",
       "\n",
       "[32921 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_covid19 = pd.DataFrame({\n",
    "    'label': doc_labels,\n",
    "    'date': doc_dates,\n",
    "    'length': doclengths,\n",
    "    'marginal_covid19': marg_covid19\n",
    "})\n",
    "docs_covid19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by marginal distribution gives us the documents with highest share of Corona-related topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.137660Z",
     "iopub.status.busy": "2020-11-23T16:59:07.136921Z",
     "iopub.status.idle": "2020-11-23T16:59:07.154665Z",
     "shell.execute_reply": "2020-11-23T16:59:07.153811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>date</th>\n",
       "      <th>length</th>\n",
       "      <th>marginal_covid19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8017</th>\n",
       "      <td>coronavirus-der-mund-nasen-schutz-ersetzt-nich...</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>338</td>\n",
       "      <td>0.959401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8505</th>\n",
       "      <td>coronavirus-infizierte-genesene-tote-alle-live...</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>394</td>\n",
       "      <td>0.946813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6949</th>\n",
       "      <td>corona-mundschutz-tragen-oder-nicht-tragen-a-e...</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>391</td>\n",
       "      <td>0.934810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7130</th>\n",
       "      <td>corona-robert-koch-institut-meldet-hoechste-za...</td>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>165</td>\n",
       "      <td>0.930165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5184</th>\n",
       "      <td>china-wie-gefaehrlich-ist-das-neuartige-corona...</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>432</td>\n",
       "      <td>0.916635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22974</th>\n",
       "      <td>niederlande-dsgvo-oma-muss-facebook-foto-ihres...</td>\n",
       "      <td>2020-05-22</td>\n",
       "      <td>881</td>\n",
       "      <td>0.001030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11708</th>\n",
       "      <td>eine-kuh-erklaert-die-welt-a-b4a34c61-2b89-447...</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>855</td>\n",
       "      <td>0.000996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>berlin-alexanderplatz-auf-der-berlinale-kaltes...</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>856</td>\n",
       "      <td>0.000975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>berlinale-2020-perspektive-deutsches-kino-a-78...</td>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>867</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14862</th>\n",
       "      <td>globale-konjunktur-unruhe-ist-die-neue-normali...</td>\n",
       "      <td>2019-10-13</td>\n",
       "      <td>896</td>\n",
       "      <td>0.000943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32921 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   label       date  length  \\\n",
       "8017   coronavirus-der-mund-nasen-schutz-ersetzt-nich... 2020-04-24     338   \n",
       "8505   coronavirus-infizierte-genesene-tote-alle-live... 2020-03-18     394   \n",
       "6949   corona-mundschutz-tragen-oder-nicht-tragen-a-e... 2020-04-02     391   \n",
       "7130   corona-robert-koch-institut-meldet-hoechste-za... 2020-08-19     165   \n",
       "5184   china-wie-gefaehrlich-ist-das-neuartige-corona... 2020-01-20     432   \n",
       "...                                                  ...        ...     ...   \n",
       "22974  niederlande-dsgvo-oma-muss-facebook-foto-ihres... 2020-05-22     881   \n",
       "11708  eine-kuh-erklaert-die-welt-a-b4a34c61-2b89-447... 2020-02-25     855   \n",
       "2697   berlin-alexanderplatz-auf-der-berlinale-kaltes... 2020-02-26     856   \n",
       "2929   berlinale-2020-perspektive-deutsches-kino-a-78... 2020-02-22     867   \n",
       "14862  globale-konjunktur-unruhe-ist-die-neue-normali... 2019-10-13     896   \n",
       "\n",
       "       marginal_covid19  \n",
       "8017           0.959401  \n",
       "8505           0.946813  \n",
       "6949           0.934810  \n",
       "7130           0.930165  \n",
       "5184           0.916635  \n",
       "...                 ...  \n",
       "22974          0.001030  \n",
       "11708          0.000996  \n",
       "2697           0.000975  \n",
       "2929           0.000969  \n",
       "14862          0.000943  \n",
       "\n",
       "[32921 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_covid19.sort_values('marginal_covid19', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to calculate an estimate of the *daily* share of Corona-related topics. Since for each day we have several news articles of different lengths, we can compute this estimate as the weighted average of the marginal COVID-19 topic probability of each news article per day. The weights are the share of an article's length compared to the total length of all articles on that day.\n",
    "\n",
    "Let's first compute the weights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.159386Z",
     "iopub.status.busy": "2020-11-23T16:59:07.158642Z",
     "iopub.status.idle": "2020-11-23T16:59:07.268775Z",
     "shell.execute_reply": "2020-11-23T16:59:07.269330Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>date</th>\n",
       "      <th>length</th>\n",
       "      <th>marginal_covid19</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007-drehorte-in-europa-klimavertraeglicher-jam...</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>310</td>\n",
       "      <td>0.006744</td>\n",
       "      <td>0.013959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>070-shake-in-berlin-vertrauensuebung-mit-einer...</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>265</td>\n",
       "      <td>0.010732</td>\n",
       "      <td>0.013212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-fc-heidenheim-in-der-relegation-demoliert-in...</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>226</td>\n",
       "      <td>0.009507</td>\n",
       "      <td>0.011430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-fc-heidenheim-unterliegt-werder-bremen-in-de...</td>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>244</td>\n",
       "      <td>0.004907</td>\n",
       "      <td>0.011631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-fc-kaiserslautern-beantragt-in-der-3-liga-in...</td>\n",
       "      <td>2020-06-17</td>\n",
       "      <td>269</td>\n",
       "      <td>0.094440</td>\n",
       "      <td>0.011453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32916</th>\n",
       "      <td>zwoelf-kinder-als-chefsache-a-d4533bfa-668c-49...</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>421</td>\n",
       "      <td>0.129314</td>\n",
       "      <td>0.021682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32917</th>\n",
       "      <td>zyklon-amphan-trifft-auf-indien-und-bangladesc...</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>203</td>\n",
       "      <td>0.004484</td>\n",
       "      <td>0.008875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32918</th>\n",
       "      <td>zypern-diebe-stehlen-koffer-mit-urne-darin-a-1...</td>\n",
       "      <td>2019-12-28</td>\n",
       "      <td>57</td>\n",
       "      <td>0.012853</td>\n",
       "      <td>0.004819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32919</th>\n",
       "      <td>zypern-mutmassliche-urnendiebe-gefasst-familie...</td>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>70</td>\n",
       "      <td>0.048626</td>\n",
       "      <td>0.004296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32920</th>\n",
       "      <td>zytomegalie-was-schwangere-wissen-muessen-a-00...</td>\n",
       "      <td>2020-08-24</td>\n",
       "      <td>56</td>\n",
       "      <td>0.486989</td>\n",
       "      <td>0.002623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32921 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   label       date  length  \\\n",
       "0      007-drehorte-in-europa-klimavertraeglicher-jam... 2019-11-13     310   \n",
       "1      070-shake-in-berlin-vertrauensuebung-mit-einer... 2020-01-28     265   \n",
       "2      1-fc-heidenheim-in-der-relegation-demoliert-in... 2020-06-29     226   \n",
       "3      1-fc-heidenheim-unterliegt-werder-bremen-in-de... 2020-07-07     244   \n",
       "4      1-fc-kaiserslautern-beantragt-in-der-3-liga-in... 2020-06-17     269   \n",
       "...                                                  ...        ...     ...   \n",
       "32916  zwoelf-kinder-als-chefsache-a-d4533bfa-668c-49... 2020-04-11     421   \n",
       "32917  zyklon-amphan-trifft-auf-indien-und-bangladesc... 2020-05-20     203   \n",
       "32918  zypern-diebe-stehlen-koffer-mit-urne-darin-a-1... 2019-12-28      57   \n",
       "32919  zypern-mutmassliche-urnendiebe-gefasst-familie... 2019-12-30      70   \n",
       "32920  zytomegalie-was-schwangere-wissen-muessen-a-00... 2020-08-24      56   \n",
       "\n",
       "       marginal_covid19    weight  \n",
       "0              0.006744  0.013959  \n",
       "1              0.010732  0.013212  \n",
       "2              0.009507  0.011430  \n",
       "3              0.004907  0.011631  \n",
       "4              0.094440  0.011453  \n",
       "...                 ...       ...  \n",
       "32916          0.129314  0.021682  \n",
       "32917          0.004484  0.008875  \n",
       "32918          0.012853  0.004819  \n",
       "32919          0.048626  0.004296  \n",
       "32920          0.486989  0.002623  \n",
       "\n",
       "[32921 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_covid19['weight'] = docs_covid19.groupby('date')\\\n",
    "    .length.transform(lambda g: g / g.sum())    # here \"g\" contains all articles' lengths on a given day\n",
    "docs_covid19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check that all weights sum up to 1 per day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.273796Z",
     "iopub.status.busy": "2020-11-23T16:59:07.273070Z",
     "iopub.status.idle": "2020-11-23T16:59:07.278072Z",
     "shell.execute_reply": "2020-11-23T16:59:07.277636Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(docs_covid19.groupby('date').weight.sum(), 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now compute the daily share of Corona-related topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.281801Z",
     "iopub.status.busy": "2020-11-23T16:59:07.281271Z",
     "iopub.status.idle": "2020-11-23T16:59:07.376181Z",
     "shell.execute_reply": "2020-11-23T16:59:07.375788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2019-10-01    0.031422\n",
       "2019-10-02    0.039168\n",
       "2019-10-03    0.034717\n",
       "2019-10-04    0.031006\n",
       "2019-10-05    0.026473\n",
       "                ...   \n",
       "2020-08-26    0.137607\n",
       "2020-08-27    0.151143\n",
       "2020-08-28    0.118545\n",
       "2020-08-29    0.111597\n",
       "2020-08-30    0.071660\n",
       "Length: 335, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_covid19topics = docs_covid19.groupby('date').apply(lambda g: (g.weight * g.marginal_covid19).sum())\n",
    "ts_covid19topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that we're within the limits of a valid probability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.380604Z",
     "iopub.status.busy": "2020-11-23T16:59:07.379931Z",
     "iopub.status.idle": "2020-11-23T16:59:07.382285Z",
     "shell.execute_reply": "2020-11-23T16:59:07.382814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all((0 <= ts_covid19topics) & (ts_covid19topics <= 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's plot the share of COVID-19 topics over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.400899Z",
     "iopub.status.busy": "2020-11-23T16:59:07.393588Z",
     "iopub.status.idle": "2020-11-23T16:59:07.762420Z",
     "shell.execute_reply": "2020-11-23T16:59:07.762865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.plot(ts_covid19topics.index, ts_covid19topics)\n",
    "ax.set_ylabel('share of COVID-19 related topics')\n",
    "ax.set_title('COVID-19 related topics in SPON corpus over time')\n",
    "fig.savefig('plots/covidtopics_time.png')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see clearly how media coverage on SPON got a first uptick in February when the first reports from the new lung disease in Wuhan arrived (this is how it's called in the first articles on SPON). By March and April media coverage skyrockets with a daily topic share of almost 40%. Note however, that we don't have information on how the respective articles were positioned on the website (i.e. frontpage news or rather positioned at the bottom of the page) and hence this doesn't need to reflect actually percepted media coverage by the public – it only reflects what was published anywhere on the page on that date. Furthermore, the LDA approach that we used for topic modeling views documents as *mixtures* of topics. This means all documents, including those that cover COVID-19 in some way, also cover other topics to some degree. For example, an article may cover the pandemic and its impact on the economy and hence contain a mixture of, say, 60% COVID-19 related topics, 30% economy related topics and 10% other topics. So you shouldn't think about the topic share displayed in the figure above as something like \"x% of all articles covered COVID-19 on a given day\". You should rather think of it like that: In the topic mixture of all articles on a given day taken together, x% were related to COVID-19.\n",
    "\n",
    "It is hence no surprise that the peak of COVID-19 related topic share is \"only\" at around 40% although you may have had a different impression from the news at that time: First, our sample includes *all* articles, also those dozens of articles on soccer results or \"lifestyle\" at the bottom of the page. Second, only the fewest articles solely cover COVID-19. Almost all of them will be a mixture of several topics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to see how this relates to the number of daily COVID-19 cases, we first load data that I fetched from the [COVID-19 DataHub](https://covid19datahub.io/) *(Guidotti, E., Ardia, D., (2020), \"COVID-19 Data Hub\", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376.)*. The dataset is not part of the repository. You can download it at https://covid19datahub.io/articles/data.html#vintage-data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.766609Z",
     "iopub.status.busy": "2020-11-23T16:59:07.766024Z",
     "iopub.status.idle": "2020-11-23T16:59:07.944741Z",
     "shell.execute_reply": "2020-11-23T16:59:07.945129Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>...</th>\n",
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       "      <th>key_apple_mobility</th>\n",
       "      <th>key_google_mobility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37172386</td>\n",
       "      <td>...</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2020-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AF</td>\n",
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       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id       date  tests  confirmed  recovered  deaths  hosp  vent  icu  \\\n",
       "0  AFG 2020-01-01      0          0          0       0     0     0    0   \n",
       "1  AFG 2020-01-02      0          0          0       0     0     0    0   \n",
       "2  AFG 2020-01-03      0          0          0       0     0     0    0   \n",
       "3  AFG 2020-01-04      0          0          0       0     0     0    0   \n",
       "4  AFG 2020-01-05      0          0          0       0     0     0    0   \n",
       "\n",
       "   population  ...  currency  administrative_area_level  \\\n",
       "0    37172386  ...       AFN                          1   \n",
       "1    37172386  ...       AFN                          1   \n",
       "2    37172386  ...       AFN                          1   \n",
       "3    37172386  ...       AFN                          1   \n",
       "4    37172386  ...       AFN                          1   \n",
       "\n",
       "   administrative_area_level_1  administrative_area_level_2  \\\n",
       "0                  Afghanistan                          NaN   \n",
       "1                  Afghanistan                          NaN   \n",
       "2                  Afghanistan                          NaN   \n",
       "3                  Afghanistan                          NaN   \n",
       "4                  Afghanistan                          NaN   \n",
       "\n",
       "   administrative_area_level_3  latitude  longitude  key  key_apple_mobility  \\\n",
       "0                          NaN      33.0       65.0  NaN                 NaN   \n",
       "1                          NaN      33.0       65.0  NaN                 NaN   \n",
       "2                          NaN      33.0       65.0  NaN                 NaN   \n",
       "3                          NaN      33.0       65.0  NaN                 NaN   \n",
       "4                          NaN      33.0       65.0  NaN                 NaN   \n",
       "\n",
       "   key_google_mobility  \n",
       "0                   AF  \n",
       "1                   AF  \n",
       "2                   AF  \n",
       "3                   AF  \n",
       "4                   AF  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coviddatahub_full = pd.read_csv('data/covid19datahub-2020-11-19.csv')\n",
    "coviddatahub_full['date'] = pd.to_datetime(coviddatahub_full.date)\n",
    "coviddatahub_full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a subset for our observation period and the variables of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.949563Z",
     "iopub.status.busy": "2020-11-23T16:59:07.949056Z",
     "iopub.status.idle": "2020-11-23T16:59:07.968308Z",
     "shell.execute_reply": "2020-11-23T16:59:07.967829Z"
    }
   },
   "outputs": [],
   "source": [
    "coviddatahub = coviddatahub_full.loc[\n",
    "    (coviddatahub_full.date >= '2020-01-01') & (coviddatahub_full.date <= docs_covid19.date.max()),\n",
    "    ['id', 'date', 'confirmed', 'administrative_area_level_1']\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable *confirmed* contains the *accumlated* number of confirmed COVID-19 cases per day. We need the daily cases so we apply `diff()` on that column per country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:07.974186Z",
     "iopub.status.busy": "2020-11-23T16:59:07.973470Z",
     "iopub.status.idle": "2020-11-23T16:59:08.021186Z",
     "shell.execute_reply": "2020-11-23T16:59:08.021751Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "    id       date  confirmed administrative_area_level_1  daily\n",
       "0  AFG 2020-01-01          0                 Afghanistan      0\n",
       "1  AFG 2020-01-02          0                 Afghanistan      0\n",
       "2  AFG 2020-01-03          0                 Afghanistan      0\n",
       "3  AFG 2020-01-04          0                 Afghanistan      0\n",
       "4  AFG 2020-01-05          0                 Afghanistan      0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coviddatahub['daily'] = coviddatahub.groupby('id').confirmed.diff().fillna(0).astype(np.int)\n",
    "coviddatahub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's investigate Germany and create a subset for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:08.029408Z",
     "iopub.status.busy": "2020-11-23T16:59:08.028689Z",
     "iopub.status.idle": "2020-11-23T16:59:08.040638Z",
     "shell.execute_reply": "2020-11-23T16:59:08.041287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>15229</th>\n",
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       "      <td>2020-01-02</td>\n",
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       "      <td>Germany</td>\n",
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       "    <tr>\n",
       "      <th>15230</th>\n",
       "      <td>DEU</td>\n",
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       "      <td>Germany</td>\n",
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       "    <tr>\n",
       "      <th>15231</th>\n",
       "      <td>DEU</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15232</th>\n",
       "      <td>DEU</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id       date  confirmed administrative_area_level_1  daily\n",
       "15228  DEU 2020-01-01          0                     Germany      0\n",
       "15229  DEU 2020-01-02          0                     Germany      0\n",
       "15230  DEU 2020-01-03          1                     Germany      1\n",
       "15231  DEU 2020-01-04          1                     Germany      0\n",
       "15232  DEU 2020-01-05          1                     Germany      0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coviddata_de = coviddatahub.loc[coviddatahub.id == 'DEU', :]\n",
    "coviddata_de.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:08.057138Z",
     "iopub.status.busy": "2020-11-23T16:59:08.056305Z",
     "iopub.status.idle": "2020-11-23T16:59:08.428922Z",
     "shell.execute_reply": "2020-11-23T16:59:08.429285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.plot(coviddata_de.date, coviddata_de.daily)\n",
    "ax.set_ylabel('daily cases')\n",
    "ax.set_title('Daily confirmed COVID-19 cases for Germany')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's repeat this for a dataset that represents worldwide daily COVID-19 cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:08.434214Z",
     "iopub.status.busy": "2020-11-23T16:59:08.432857Z",
     "iopub.status.idle": "2020-11-23T16:59:08.437207Z",
     "shell.execute_reply": "2020-11-23T16:59:08.437602Z"
    }
   },
   "outputs": [],
   "source": [
    "coviddata_world = coviddatahub.groupby('date').daily.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:08.456297Z",
     "iopub.status.busy": "2020-11-23T16:59:08.455779Z",
     "iopub.status.idle": "2020-11-23T16:59:08.755229Z",
     "shell.execute_reply": "2020-11-23T16:59:08.755571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.plot(coviddata_world.index, coviddata_world)\n",
    "ax.set_ylabel('daily cases')\n",
    "ax.set_title('Daily confirmed COVID-19 cases worldwide')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between infection rate in Germany and share of COVID-19 related topics on SPON\n",
    "\n",
    "We will now investigate how the share of COVID-19 topics relates to the number of daily COVID-19 cases in Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:08.759768Z",
     "iopub.status.busy": "2020-11-23T16:59:08.759332Z",
     "iopub.status.idle": "2020-11-23T16:59:08.761408Z",
     "shell.execute_reply": "2020-11-23T16:59:08.761029Z"
    }
   },
   "outputs": [],
   "source": [
    "ts_covid19topics2020 = ts_covid19topics[ts_covid19topics.index >= '2020-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:08.766798Z",
     "iopub.status.busy": "2020-11-23T16:59:08.766337Z",
     "iopub.status.idle": "2020-11-23T16:59:09.496360Z",
     "shell.execute_reply": "2020-11-23T16:59:09.495913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b6wjqfd1LUPw2LUoazKytme1N8OOxOlw/1bKF4ZNuK6C1mbWL5TiEadvRArsQFEtdkfCU02Dh9u4ErjOz7cN9l5jZ8HCRu4HTzex7ZtYqnBdr+v0FkLQvIA+KKh8H/hqe477ABQQ5OJnQgeDGsSJM8+nUvAF+QRBct4l4nJl0GfDdsDijJNzXdgR1mGLuBM4Oc3XMzDqY2Y8sbHKfAZ0IivbWhGkYXcf1JxAUzZxLUB8r3X7KgTILWgheWod9PAn82MwODD+nK0hxTzSzNhb0Pdc5zMH+mvRFoIlpXOXuG8xsX4IK9OncBow1s13DfXc2s2Q/mhBc4z8Kvx+FBMHqRoIGHmmFRWoXAJeY2elmtk34HTvQzO4IF7uOoGHN3Wa2Q3iP+ClBcdLoWLFc+P8B4O8E9SL/Vdv+w2NrbWY7E+TS70Bwn0m3bDuCOpIFYVpi3Qm1M7Nvh9dyH+AO4PoUOViw9b3jBeBbFnSD1NrMTiSok/hc3DKnWNDVRHuCa+VJT+g+w92XAy8S/PB3Ce+xB4Vp/LGZDQwD6DUE9bgyej81s2PM7KRw3xZebwcT1B2sTaTfpgQPEtTfOjLNdu8iKErdO0zTwPB+PIUgF/DC8DwdQhCgP1pLOn8Y9539C0F95iUeVJ8oJficCizIGasOCs3seNvSldBqgvt3Rs9/Jik4yzB3v4bghncxwQ/3EoLclQnh/LkE3RnsQZD1vpzgyXW4u7+VsLn7CZ7m0v04xVxoZt8QFFU8AEwnaG20Ls06dxL8sMVutuVsKUrdji2tZF4kqPd1R7KNZMAfCbL737WgaOq/bKmv8x5BC8HrCG5or7HlCfd64DgzW21myfpw+m2Y/gUET/aPkKQZfn24+0cEddjeIbjR70ZQFBczmSC38XMziz1dpzzOTHL3Twkq+vcCZoXXxVsET5eXhMtMI2gMcRPBjWoeW+qeZMLlBMHVGoK6L0/XZWUP6jE9RVDxPN26/yCoY/UVwQ/Qv+uwjw+B3xBcF8sJzkO6YrCfAwvDz+5soNbittD/A64IP4c/U0uOm7s/QxDkPBru6wOCot1ky84hyKm6keAcHEXQ0nVTlIS5+5ME9dZ+QXB9fEFQj+jZcP5KgvtVO4JWdisJ7m8/96CeZbwHCHI+HgvrA6VzopmtJbg+Jobb3Tss0k7lYoJ71JjwmMvDaYTpe4TggeA9gu/lJWm2VePeER7njwmC25UERWA/dvf4nLEHCSqkfx7uL1Wnvj8nyFX7hKCRzHnh9EEE3/m1YfpucfdXkm2gAVYTfK/nEjxAPASMj5L7WMffptg6lQTXdMq6gu7+BEH9tEcIiponENSt3kRwvf6A4Nq9BRjltdcpfoTgIWwVQd3GU+LmnUnwILiSoOuX+IeUfYAp4XU3ETjX86BPzFTMI9dHFBFpPGb2Z4LuH06pdWGRLDKzVwlaZ96V67S0ZBZ0PrzU3S+ubdmmTp1LikjeCYsozyChc1MRkZZAxZoiklfM7EyC6gAvuvvruU6PiEhjU7GmiIiISB5RzpmIiIhIHlFwJiIiIpJHmk2DgO2228779euX62SIiIiI1Gr69OlfuXvSUWKaTXDWr18/pk2L1E+riIiISE6ZWeKQYdVUrCkiIiKSRxSciYiIiOQRBWciIiIieUTBmYiIiEgeUXAmIiIikkeyGpyZ2ZFmNsfM5pnZmDTLjTQzN7OhcdPGhuvNMbPh2UyniIiISL7IWlcaZlYA3AwcASwFpprZRHf/KGG5TsC5wJS4absAJwG7Aj2B/5rZt9y9MlvpFREREckH2cw52xeY5+4L3H0T8ChwTJLl/gL8HdgQN+0Y4FF33+junwHzwu2JiIiINGvZDM5KgCVx75eG06qZ2V5Ab3d/vq7rioiIiDRHORshwMxaAdcCpzVgG2cBZwH06dMnMwkTEZFmYcKMUsZPmsOysnJ6FhcxevhgRgzRc77kv2zmnJUCvePe9wqnxXQCvg28amYLge8AE8NGAbWtC4C73+HuQ919aLduSYenEhGRFmjCjFLGPj2b0rJyHCgtK2fs07OZMGOrnxKRvJPN4GwqMMjM+ptZG4IK/hNjM919jbtv5+793L0f8C5wtLtPC5c7yczamll/YBDwXhbTKiIizcj4SXMor6jZhqy8opLxk+bkKEUi0WWtWNPdN5vZOcAkoAC4x90/NLMrgGnuPjHNuh+a2ePAR8Bm4DdqqSkiIlEtKyuv03SRfJLVOmfu/gLwQsK0P6dY9pCE938F/pq1xImISLPVs7iI0iSBWM/iohykRqRuNEKAiIg0O6OHD6aosKDGtKLCAkYPH5yjFIlEl7PWmiIiItkSa5Wp1prSFCk4ExGRZmnEkBIFY9IkqVhTREREJI8oOBMRERHJIwrORERERPKIgjMRERGRPKLgTERERCSPKDgTERERySMKzkRERETyiIIzERERkTyi4ExEREQkjyg4ExEREckjCs5ERERE8oiCMxEREZE8ouBMREREJI8oOBMRERHJIwrORERERPJI61wnQESya8KMUsZPmsOysnJ6FhcxevhgRgwpyXWyRETqrbnf1xSciTRjE2aUMvbp2ZRXVAJQWlbO2KdnAzSrG5mItBwt4b6mYk2RZmz8pDnVN7CY8opKxk+ak6MUiYg0TEu4ryk4E2nGlpWV12m6iEi+awn3NQVnIs1Yz+KiOk0XEcl3LeG+puBMpBkbPXwwRYUFNaYVFRYwevjgHKVIRKRhWsJ9TQ0CRJqxWOXY5tyqSURalpZwXzN3z3UaMmLo0KE+bdq0XCdDGlFzb0otIiLNl5lNd/ehyeYp50yapJbQlFpERFom1TmTJqklNKUWEZGWScGZNEktoSm1iIi0TArOpElqCU2pRUSkZVJwJk1SS2hKLSIiLVNWgzMzO9LM5pjZPDMbk2T+2WY228xmmtmbZrZLOL2fmZWH02ea2W3ZTKc0PSOGlHDVsbtRUlyEASXFRVx17G5qDCAiIk1e1rrSMLMC4FPgCGApMBX4qbt/FLfMNu7+dfj6aOD/ufuRZtYPeM7dvx11f+pKQ0RERJqKdF1pZDPnbF9gnrsvcPdNwKPAMfELxAKzUAegeXS6JiIiIlJP2eznrARYEvd+KbBf4kJm9hvgAqANcFjcrP5mNgP4GrjY3d9Isu5ZwFkAffr0yVzKRSSvqMNhEWlJct4gwN1vdvcdgT8CF4eTlwN93H0IQeD2iJltk2TdO9x9qLsP7datW+MlWkQaTazD4dKycpwtHQ5PmFGa66SJiGRFNnPOSoHece97hdNSeRS4FcDdNwIbw9fTzWw+8C1AlcpEWph0HQ43Zu5ZS829a6nHLZJL2cw5mwoMMrP+ZtYGOAmYGL+AmQ2Ke/sjYG44vVvYoAAzGwAMAhZkMa0ikqfyocPhlpp711KPWyTXshacuftm4BxgEvAx8Li7f2hmV4QtMwHOMbMPzWwmQfHlqeH0g4D/hdOfBM5291XZSquI5K986HC4pQ4X1lKPWyTXsjrwubu/ALyQMO3Pca/PTbHeU8BT2UybiDQNo4cPrjHIPTR+h8P5kHuXCy31uEVyLavBmYg0fbmucxTbVy7T0LO4iNIkAUlzHy6spR63SK4pOBORlGJ1jmK5VrE6R0CjB2i5rISeD7l3udBSjxty/1AiLVvOu9IQkfylOkeBljpcWEs9bjWEkFxTzpmIpKQ6R1vkOvcuV1ricedL9y3ScinnTERSyoeWkiKNTQ8lkmsKzkQkpdHDB1NUWFBjWkupcyQtlx5KJNcUnIlISi21zpG0bHookVxTnTMRSasl1jmSli0fum+Rlk3BmYiISAI9lEguqVhTREREJI8o50xEJM+oA1SRlk3BmYhIHsmXURlEJHdUrCkikkc0KoOIKOdMRCSP5KoDVBWliuQP5ZyJiOSRXHSAqrEkRQJr165l7ty5zJ49O6fpUM6ZiEgeGT18cI06Z5D9DlA1lqQ0Z1VVVXz11VcsX76czz//nOXLl9d4HT9t3bp1AOy888589NFHOUuzgjMRkTySiw5QNZakNEUbNmzYKsBKFnx98cUXVFZWbrV+p06d6NGjBzvssAN777139esePXrQp0+fHBzRFgrORETyTGN3gNqzuIjSJIGYxpKUXNi8eTNffPEFy5Yto7S0lGXLltX4i01btWrVVuu2atWK7bffvjrI2n333WsEXbHXO+ywAx06dMjB0UWj4ExEpIXLRVGqtExr165lyZIlLFmyhKVLl7J06dKtAq8vvvgCd6+xXkFBATvssAM9e/Zk4MCBHHTQQfTo0YOePXtWB1477LAD3bp1o3Xrph/aNP0jEBGRBtFYkpIJj709j78/9RbLSpfSqXIN+3RzOm5ew9KlS6sDsjVr1my1Xrdu3ejZsyc9e/Zkzz33pKSkpPp9z549KSkpoVu3bhQUFCTZa/NkidFpUzV06FCfNm1arpMhIlIn6sJCmoLy8nJKS0urg6xYzlfs9YJFi1m7pmyr9bbpsi0D+/eld+/e9OrVi969e9d43bNnT9q2bdv4B5QHzGy6uw9NNk85ZyIiOaLRACQfbN68maVLl7Jo0aKtgq7Y66+++mqr9bbddlt69+5N3759+aKoH63bFlOwTTdad9qWgk7B/17bdeatMYfl4KiaNgVnIiI50lK6sFDuYG5VVlayfPlyPvvsMxYuXMjChQurX3/22WcsWbJkq9aMXbp0qc7l2nfffbfK8erVqxdFRVsajPQf8zztkuy7qbX4zZdrVcGZiEiOtIQuLJQ7mH3uzueff75V0BULxBYtWkRFRUWNdXr27Em/fv0YNmwY/fr1q/6LBV51bcmYrRa/jRks5dO1quBMRCRHWkIXFi0ldzCb3J2vvvpqq8Ar9n/RokVs2LChxjrdu3enX79+7L333hx33HH069eP/v37069fP/r06UO7dsnyueovGy1+GztYyqdrVcGZiEiOtIQuLFpC7mAmlJWVsWDBgpQB2Pr162ssv+2229K/f3922203jj766Oqcr/79+9O3b1/at2/fqOnPRovfxg6W8ulaVXAmIpIjLaELi5aQOxhFrOhx7ty5zJs3j/nz5zN//nwWLFjA/Pnzt+pQtbi4mH79+jF48GCGDx9enesV++vUqVOOjiS1THee3NjBUj5dq7UGZ2Z2LnAv8A1wFzAEGOPuL2U5bSKSRL5UWJXMaOzRABpbS8gdjIkVP86dO5e5c+fy6aefVr+eN28ea9eurV62devW9O3blx133JETTzyRAQMGsOOOO1YHYcXFxbk7kDzR2MFSPl2rUXLOfuHu15vZcKAL8HPgQUDBmUgjy6cKqyJRNMfcwdWrV1cHXYlBWHwnqwUFBfTv359BgwZx0EEH8a1vfYtBgwYxcOBA+vTp0yx6ss+mxg6W8ularbUTWjP7n7vvbmbXA6+6+zNmNsPdhzROEqNRJ7TSEgwbNznpk2RJcZH6EpIGU67sFt98802NACw+EFu5cmX1cmZG3759GTRoUPVfLAjr168fhYWFOTyKpq85X5MN7YR2upm9BPQHxppZJ6Aq4o6PBK4HCoC73H1cwvyzgd8AlcBa4Cx3/yicNxY4I5z3O3efFGWfIs1ZPlVYlealJebKrl+/nvnz59fI+Yr9ff755zWW7dWrF4MGDWLkyJE1grABAwa02B7uG0NzL/ZPJUpwdgawJ7DA3deb2bbA6bWtZGYFwM3AEcBSYKqZTYwFX6FH3P22cPmjgWuBI81sF+AkYFegJ/BfM/uWu9dstiHSwuRThVVpXvKpG4FM2rhxIwsWLEhaBLl06dIayxZv242qTjtQ1W03+uzxY046fB9+9v3vMHDgwEZv/SgtW5Tg7BhgsrvHCtIrgQHA/2pZb19gnrsvADCzR8NtVQdn7v513PIdgFgZ6zHAo+6+EfjMzOaF23snQnpFmq18qrAqzUtTzpWtqKhg4cKFSYsgFy9eTFXVlsKebbfdlkGDBnHooYdWFz8OGjSIT9a15y8vLazx3Zq4poD9K7dldwVm0siiBGeXuvszsTfuXmZmlwITalmvBFgS934psF/iQmb2G+ACoA0QqzRTArybsG7TfXQTyZB8qrAqzUu+58pWVlayZMmSpEWQn332GZs3b65etnPnzgwaNIj999+fUaNG1agH1qVLl6Tb/+24yc0y51CapijBWat6rheJu98M3GxmJwMXA6dGXdfMzgLOAujTp0+mkiSS11pqHQzJrnzIla2qqmLZsmVJiyDnz5/Ppk2bqpft0KEDgwYNYs899+T444+vUQ9su+22w8zqtO+mnHMozU+UIGuamV1LUH8Mggr80yOsVwr0jnvfK5yWyqPArXVZ193vAO6AoLVmhDRJI2nOLWxEmqPGypV1d7744oukXVHMmzeP8vItwVDbtm0ZOHAgO+20E0cddVSNFpE9evSocwCWTr7nHErLEiU4+y1wCfBY+P4/BAFabaYCg8ysP0FgdRJwcvwCZjbI3eeGb38ExF5PBB4Jg8KewCDgvQj7lDzQElt9iTQHmcqVraysZOnSpSxYsKC6B/z4SvnffPNN9bKFhYUMGDCAQYMGcfjhh9eoB9arVy9atUpWeJN5+ZBzKBJTa3Dm7uuAMXXdsLtvNrNzgEkEXWnc4+4fmtkVwDR3nwicY2aHAxXAasIizXC5xwkaD2wGfqOWmk1Hc231JSJbfP3119XBV/zf/PnzWbRoERUVFdXLxnrDHzRoEMOGDatRBJkvnbGqPqfkk5Sd0JrZP9z9PDP7F1taUVZz96Oznbi6UCe0+aP/mOe3vmAAAz4b96PGTo60QM2tWD0Xx7Nu3TqWLFnC4sWLWbRoEQsXLqwRhH311Vc1lu/atSsDBgyoHoYo9nrAgAH06tUrLwIwkXxS305oHwz//1/mkyTNmepuSC5lu1i9sQOlTB1PRUUFK1euZMWKFXz11Vdp/5eWltboBR+CoYhiY0GOHDmyRvA1YMAAjQUpkkEpgzN3nx7+f83M2gA7EeSgzXH3TanWE1HdDcmlbBar56I+ZbLjWb9pM+MmzmD3zhsjBVtfffUVZWVlKffRpUsXtttuO7p160a/fv044IAD6NOnD3369KFv37706dOHnj17KvdLpJHU+k0zsx8BtwHzCUqm+pvZr9z9xWwnTpom1d2QXMpmlwgNCfw2btzI2rVrq/+++eabWt+vW7eOmVMXUFWxEd+8kapN5VSt/5rK8jUsrtzMjpdtvZ/CwkK6detGt27d2G677Rg6dGh14JXs/7bbbqvxH6XRNbeqB5kW5THoGuBQd58HYGY7As8DCs4kJfXFlZpuStmVzWL1xACvcsNaNq9cyqezS/nT1/9l7ty5rFq1KmmwFV9BvjYdOnSgU6dOwZBB650qa4MVtqX1Nt1o1X1HCtp3pkvXbbnshP23Crg6deqU0S4mRDJNLfprFyU4+yYWmIUWAN+kWlhEUtNNKfuSFasbwbkeNm5yvYPh9evX02HVp5R+OouNpR+zcdmnVK0vq54//qXWDBgwgO23356uXbvSp08fOnbsSMeOHenUqVP162Tv46e1b9++RvcRidcMBNUErjp2N10z0iSpRX/tonZC+wLwOEGds+MJBjE/FsDdn85i+kSaFd2Usi++WL20rBxjS3PzugTDS5Ys4e23367+mzlzZvUQQa279qJowN4UbteHDtv3YexJh3HWj/fPSvGgqglIc6PRGGoXJThrB3wBHBy+XwEUAUcR3PMUnIlEpJtS44gVqw8bN3mrIs5kwXBFRQUzZ86sEYwtXboUgKKiIvbdd19Gjx7NAQccwKr2fbn9vRWNGiipmoA0J2rRX7sondCe3hgJEWkJdFNqXKmC3iXLPudf//pXdSA2derU6mGDevfuzbBhwzjggAM44IAD2GOPPbbKERt1WNaTLtJsqUV/7aK01uwF3AgMCye9AZzr7kuzmTCR+srnCve6KTWuWDBcsaqUDYtns7H0EzYu+5jNq0o5+sag5/ohQ4Zw1llnccABB7D//vvTu3fv2jcsIvWmovraRSnWvBd4hKCuGcAp4bQjspUokfrK9wr3zemmlM9BMMC8efPov/Qlpj/xOBu//AyAVkXb0L7Xzoz42c8587gfMHTo0KBFZAbk+/kQyScqqk8v5fBN1QuYzXT3PWublmsavkmApHWMAEqKi3hrjMqiMiVfWxB+9tlnPP744zz++OO8//77AOy0x1A29N6Xih12p8+AgVx45E4ZT2O+ng8RyV/1Hb4pZqWZnQL8M3z/U2BlmuUlD2Xiqb4p5Ayown3jyKdWp4sXL+aJJ57gscceY+rUqQDsu+++XHPNNRx33HH06dMn62nIp/MhIk1flODsFwR1zq4jaJ35NnBaFtMkGZaJor58Ly6MUYX7xpHrILi0tJQnnniCxx9/nHfeeQeAvffem7///e+ccMIJ9OvXr1HSEZPr8yFNX1N4+JXG06r2Rejl7ke7ezd3397dRwDZfxSVjEn3VN+Y22gMo4cPpqiwoMY0VbjPvFTBbjaD4C+++IKbbrqJ7373u/Tq1Yvzzz+f9evX87e//Y25c+cybdo0LrzwwkYPzCA350Oaj9jDb2lZOc6Wh98JM0pznTTJkSjB2Y0Rp0meysRTfVPJGRgxpISrjt2NkuIijKCuWT7U+5kwo5Rh4ybTf8zzDBs3ucnfdBszCH7vvff42c9+Rq9evfjtb39LWVkZV1xxBZ988gkzZ85k7NixDBw4MOP7rQs9FEhDNJWHX2k8KYs1zWx/4ACgm5ldEDdrG6Ag+VqSjzJR1NeUigvzrRVQUykSrotstzqtqKjgqaee4vrrr+fdd9+lU6dO/OY3v+HMM89k1113zcg+Mqk5tcKVxtdUHn6l8aSrc9YG6Bgu0ylu+tfAcdlMlGRWJvrWUv9c9ddcKosnqxOT6RawX331FXfccQe33HILpaWlDBw4kOuvv57TTjuNbbbZJqP7yrR8eyiQpqMpPfxK40gZnLn7a8BrZnafuy9qxDRJhmXiqV45A/XXHJ6Ks537N3v2bK6//noefvhhNmzYwOGHH85tt93GD3/4wxqDgIs0R3r4lURRhm9SYNYMZOKpXjkD9dMcnoqzkftXWVnJc889x/XXX88rr7xCUVERo0aN4ne/+11eFl2KZIsefiVRlK40RKQBmsNTcSZz/9asWcM999zDjTfeyGeffUbv3r0ZN24cZ555Jl27dm1oUkWaJD38SjwFZ5JTLaFvn+bwVJyJ3L9PP/2UG2+8kfvuu4+1a9dy4IEHcvXVVzNixAhat246t6KWcM1Ky6JrOv+ka615I0Gns0m5+++ykiJpFLn+Mk6YUcrl//qQ1esrqqc1h1aMqTT1p+L65v65Oy+99BLXX389L774Im3atOGkk07id7/7HXvvvXe2k51xzbHlrbRsuqbzU7rH1dhAlcOAXYDHwvfHAx9lM1GSXbn+MiYbhzCmKbZizKZcB9Exdc39W7duHQ888AA33HADn3zyCd27d+eyyy7j7LPPpnv37llPb7bOW3NpeSsSo2s6P6VrrXk/gJn9GjjQ3TeH728D3mic5Ek25PrLmGz/8ZpSK8ZsynUQnShK7t+iRYu46aabuOuuuygrK2PvvffmgQce4IQTTqBt27aNks5snrfm0PJWJF5jXNP58pDZlERpo96FoOPZmI7hNGmicv0DU9t+0tVjam497afTVHoNd3def/11Ro4cyYABA7juuus44ogjePPNN5k6dSo///nPGy0wg+yeNw3TJM1Ntq9pDU1VP1GCs3HADDO7z8zuB94H/pbdZEk2pfrStTJrlC9Mui99unpMjfUlz5cAMNdBdG02btzI/fffz957783BBx/Mq6++yujRo/nss894/PHHGTZsGGbW6OnK5nnTME3S3GT7mm4qD5n5ptbgzN3vBfYDngGeBvaPFXlK05TsywhQ6d4oTzSp9l9cVJh2HMzG+JLn01NevubSrFixgr/85S/069eP0047jY0bN3L77bezZMkSxo0bR+/evXOavmyet3wdu1WkvrJ9Tef7Q2a+qrX9ugWPvocDA9z9CjPrY2b7uvt72U+eZEPsS/f7x2dR6TUb5DZG3bP6di3RGF/yXNfHi5dv/aN9+OGH/OMf/+Chhx5iw4YNHHnkkZx//vkcccQROckhSyXb562pt7wVSZTNa7o5dMKdC1E6F7oFqAIOA64AvgGeAvbJYroky0YMKeH8x2YmnVeXYKe+FT3rczNojC95Pj3l5UP/aO7OpEmTuO6663jppZdo164do0aN4txzz2WXXXZptHTURT6cNxEJ5NtDZlMRJTjbz933MrMZAO6+2szaZDld0ggaGuw0dmvCxviS59tTXq5yacrLy3nwwQf5xz/+wccff0yPHj248sor+dWvfsV2223X6OmpK+VuieQHPSzVT5TgrMLMCgg7pDWzbgQ5adLENTTYaewiwIZ8yaPm8LX0p7zly5dzyy23cOutt7Jy5UqGDBnCAw88wIknnkibNnomE5G608NS3UUJzm4gaAywvZn9FTgOuCTKxs3sSOB6oAC4y93HJcy/APglsBlYAfwiNtC6mVUCs8NFF7v70VH2KdE19IkmF0WA9fmS1yWHr6U+5c2cOZPrrruOf/7zn2zevJmjjjqK888/n4MPPjiv6pOJiLQE5p5yhKYtC5ntBHwPMOBld/84wjoFwKfAEcBSYCrwU3f/KG6ZQ4Ep7r4+7Oz2EHc/MZy31t07Rj2QoUOH+rRp02pfUDJm2LjJSYsAS4qLeGvMYTlIUXJNJZ2Nraqqiueee47rrruOV199lQ4dOnD66adz7rnnMnDgwFwnT0SkWTOz6e4+NNm8WrvSMLMH3f0Td7/Z3W9y94/N7MEI+90XmOfuC9x9E/AocEz8Au7+iruvD9++C/SKsF3JE02lz6d8quSfD9auXctNN93E4MGDOeaYY5g/fz5XX301S5cu5cYbb1RgJiKSY1GKNXeNfxPmiEUZsbgEWBL3filBf2mpnAG8GPe+nZlNIyjyHOfuExJXMLOzgLMA+vTpEyFJkknZLALM5HAfqSr5dy4qbGgym5QlS5Zw4403cuedd1JWVsZ+++3HlVdeyciRI2ndOsqtQEREGkPKO7KZjQX+BBSZ2dcERZoAm4A7MpkIMzsFGAocHDe5r7uXmtkAYLKZzXb3+fHrufsdsbQMHTq09vJZybhsVPTMdCvQ0cMHM/qJWVRU1bxE1m3azIQZpQ1Kf1MYM27KlClcd911PPnkk7g7I0eO5Pzzz2f//ffPddJERCSJlMWa7n6Vu3cCxrv7Nu7eKfzb1t3HRth2KRDfVXivcFoNZnY4cBFwtLtvjNt/afh/AfAqMCTKAUnTl+mRAEYMKaFju62fQyoqvUGjC+TTaAKJNm/ezJNPPskBBxzAd77zHV588UXOO+88FixYwOOPP67ATEQkj9ValuHuY82sCzAIaBc3/fVaVp0KDDKz/gRB2UnAyfELmNkQ4HbgSHf/Mm56F2C9u280s+2AYcDV0Q5Jmrps1BErW1+R8W3m02gCMWvWrOHuu+/mhhtuYNGiRQwYMIDrr7+e008/nU6dOuUkTSIiUjdRhm/6JXAuQc7XTOA7wDsEIwak5O6bzewcYBJBVxr3uPuHZnYFMM3dJwLjgY7AE2Fz/ViXGTsDt5tZFUHu3rj4Vp7SvGWjI9hsbDOfGhosWLCAG264gbvvvpu1a9dy0EEH8Y9//IOjjjqKgoKtxzEVEZH8FaUW8LkEQzW96+6Hht1q/C3Kxt39BeCFhGl/jnt9eIr13gZ2i7KPlqQp1G/KhGx0BJus3llhK2vQNnM9moC78+abb3Ldddfx7LPP0qpVK0466STOO+889t47SpsdERHJR7V2pQFscPcNAGbW1t0/AfKrr4QWIJ/rN2XaiCElXHXsbpQUF2EE/ZFddexuDQ9EE/tSbWDfqrnqSmTz5s08+uij7LPPPhx00EG89tprjBkzhkWLFvHggw8qMBMRaeKiBGdLzawYmAD8x8yeBRZlM1GytUxXks93I4aU8NaYw7juxD0BOP+xmQwbN7newej4SXOoqKzZWrOhDQKyFkSmsGHDBm6//XZ22mknfvrTn7J27VpuvfVWlixZwl//+ld69uyZlf2KiEjjitIg4Cfhy8vM7BWgM/DvrKZKtpJP9ZsaSya71MjW+WuMMePWrVvHrbfeyrXXXsvy5cvZZ599GD9+PMcccwytWkV5vhIRkaYk5Z3dzLom/hGMdfkmQSV+aUSp6jE1Vv2mXMhkbmFTPH/uzsMPP8zgwYMZPXo0O++8M//973+ZMmUKP/nJTxSYiYg0U+nu7tOBaeH/6QnvNYhlI2sqQyVlUiZzu5ra+Xvvvfc44IADOOWUU9hhhx14/fXXefnll/ne976ngchFRJq5lMWa7t6/MRMi6WVzqKR8lcnWkE3l/JWWljJ27FgefPBBdthhB+655x5OPfVU5ZKJiLQg5p5+1CMLHtN/BvR397+YWR9gB3d/rzESGNXQoUN92jRl6DUniXXOIMjtymal+1wpLy/nmmuu4aqrrmLz5s1ccMEF/OlPf6pTx7EtpasVEZHmwMymu/vQZPOi9HN2C1BF0OnsX4BvgKcI+j4TyZqmktvVEO7O448/zoUXXsjixYs59thjGT9+PAMGDKjTdjI9HqmIiOROlOBsP3ffy8xmALj7ajNrk+V0ST00x5yTxmgNmSvTp0/nvPPO480332SPPfbg/vvv55BDDqnXtvJxKCkREamfKBVZKsysAHAAM+tGkJMmeaQldVLb1G3atInf//737LPPPsyZM4fbb7+d6dOn1zswg5bZ1YqISHMVJTi7AXgG2N7M/krQlUak4Zuk8bS0Tmqbqvnz5zNs2DCuvfZazj77bObOnctZZ53V4PEvm2JXISIiklzaYk0zawV8BlwIfI9gwJsR7v5xI6RN6kA5J/nv0UcfrQ7Enn76aX7yk5/UvlJE2RiPVEQyqzlWPZHsSBucuXuVmd3s7kOATxopTVIPuR6EW1Jbt24d5557LnfffTf7778///znP+nbt29G99ESGk+INGVqtCN1EaVBwMtmNhJ42mvrd0NyRjkn+Wn27NmceOKJfPLJJ4wdO5bLL7+cwsLCrOyrOTeeEGnq1GhH6iJKcPYr4AJgs5ltICjadHffJqspkzpRzkl+cXfuuOMOzjvvPDp37sykSZM44ogjcp0sEckRVT2Ruqi1QYC7d3L3Vu7ext23Cd8rMMtDI4aU8NaYw7juxD0BOP+xmQwbN1ktNhtZWVkZJ554ImeffTYHHXQQs2bNUmAm0sKp0Y7UhcaEaWbypUuNCTNKGTZuMv3HPN+iAsQpU6YwZMgQnn76acaNG8eLL75I9+7dc50sEcmxpja+r+SWgrNmJh+61MiXALExVVVVMX78eA488EDcnTfeeIM//vGPGhNTRICgZOOqY3ejpLgIA0qKi5rlUHSSGVHqnEkeStUku771GjLZxLulVXz98ssvOfXUU/n3v//NyJEjueuuuyguLs51skQkz6jRjkRVa3AWjgjQC6gEFrj72qynStJK1yS7Pl1qZLqJd0uq+Pryyy9zyimnsHr1am655RbOPvtszCzXyRIRkSYsZZmLme1iZv8F3gGmAHcCs83sPjPr3FgJlK2ly5mqT72GTBeFtoSKr5s3b+biiy/miCOOoLi4mPfee49f//rXCsxERKTB0uWc3QOc6u5zzGxf4Dfuvp+ZnQncDRzXKCmUraTLmapPlxoNzelKLBI9dKduPDW9tNn2ubZkyRJOPvlk3nzzTU4//XRuvPFGOnTokOtkiYhIM5EuOCty9zkA7v6emd0Wvr7TzC5olNRJUrUVXUat1xALqlL1LBwlpytZkehT00sZuXcJr3yyotn1ufbss89y+umnU1FRwUMPPcTPfvazXCdJRESamXTB2XwzuwSYDBwLzAQws0LUyjOnMjEaQGJQlSjq9lIVib7yyQreGnNY5PTku40bNzJ69GhuvPFG9tprLx599FEGDRqU62SJiEgzlC44+wXwJ2AsMAs4N5zeHjg1y+lqljLVIjITowEkC6piSuqwvZZQ+f/TTz/lpJNOYsaMGZx33nmMGzeOtm3b5jpZIiLSTKUMzty9DLgwyfQ1wLtZTFOzlOkWkcmKLusS/NUWPJ3/2MzqBgbp0tfcB1x/8MEH+fWvf03btm2ZOHEiRx11VK6TJCIizVy9iifN7I5MJ6S5a2iLyNp63E/V8evFE2YnXS9V8GThulE7j22uvV6vXbuW0047jVGjRrHXXnsxa9YsBWYiItIoUuacmVnXVLOAH2YnOc1X1OK/ZLlfQK25bqmCv4ffXVxd4T9+vWT11gy2ahxQW+exzXHA9VmzZnHiiSfy6aef8uc//5lLLrmE1q3VX7OIiDSOdL84K4BFBL/ZMR6+3z6biWqOohT/pSr6bFfYqtYe91MFf6mCrVhl/figKln6YttOV2TaXHq9dnduueUWfv/739O1a1defvllDj300FwnS0REWph0wdkC4HvuvjhxhpktyV6SmqcoLSxT5X6lqrgfH5ClC65SrZcYVA0bNznpNjoXFWa0vlw+Wr16NWeccQbPPPMMP/jBD7j//vvp1q1brpMlIiItULo6Z/8AuqSYd3WUjZvZkWY2x8zmmdmYJPMvMLOPzOx/ZvaymfWNm3eqmc0N/5p869Aog97WtYVjfK7b6OGDKWwVrXf6VPXNktUfK2xlfL2hIueDqWfT22+/zZ577slzzz3HNddcw3PPPafATEREciZda82b08y7sbYNm1kBcDNwBLAUmGpmE939o7jFZgBD3X29mf2aIOg7MazvdikwlKBkbnq47uooB5Wvaiv+S5X7VVxUyMbNVWlz3UYMKeHyf33I6vUVadOQrrJ+Yv2xzkWFrNu0maqq5Ntq6t1lVFVV8fe//51LLrmEvn378tZbb7HPPvvkOlkiItLC1am1Zh1bae4LzHP3Be6+CXgUOCZ+AXd/xd3Xh2/fJRhgHWA48B93XxUGZP8BjqxLWpuiVC0fLzt611pz3QDK0gRm6daLN2JICW+NOYzPxv2IDm1bU1GZavyApt1dxvLlyxk+fDh/+tOfOO6443j//fcVmImISF6oaxO0oXVYtgSIr5u2FNgvzfJnAC+mWbd5VG5KI13Lx3TdWcSkynkrKS6qV2/96XLGmnJ3Gc888wxnnnkm69ev58477+SMM87QgOUiIpI36hqcfZmNRJjZKQSB38F1XO8s4CyAPn36ZCFljS9V57JRKuRnYlineKmCvQKzWnPgMjUaQiZ99dVX/OEPf+D+++9nr7324uGHH2annXbKaZpEREQS1alY093rUrRYCvSOe98rnFaDmR0OXAQc7e4b67Kuu9/h7kPdfWhzrsAdtQPbKI0O6iJVMes1J+xRa2CWrEPcKLl/2eDu3Hvvvey00048/PDDXHTRRbzzzjsKzEREJC+lzTkLW0meC8SyXj4GbnD3ByJseyowyMz6EwRWJwEnJ2x/CHA7cKS7x+fKTQL+Zmax1qLfJxjjs0Wqy/iVmexzrL4dzKYLJhs79+zDDz/k17/+NW+88QbDhg3jtttu49vf/najpkFERKQu0o0QcCpwHnAB8D5BnfK9gPFm5u7+YLoNu/tmMzuHINAqAO5x9w/N7ApgmrtPBMYDHYEnwjo/i939aHdfZWZ/IQjwAK5w91UNOdCmLJfjV9Yn2MuHwdDXr1/PlVdeyfjx49lmm2246667OP3002nVql4jluWFfCwqFhGRzDP35K3xzOxd4CR3X5gwvR/wqLt/J+upq4OhQ4f6tGnTcp2MrEiscwZB8WJDiiyzKVVntvVtmFAX7s4zzzzD73//exYuXMhpp53GIT8/nzve+6pJBzVN7RoQEZH0zGy6uydtaJkuG2GbxMAMIJy2TWaSJlFkui5ZtuVqMPQPPviAww8/nJEjR9KxY0deffVVjvndlYx7ZVne1H+rr6j1DkVEpOlLV+csXRlU0+59tAlqrPErM1F01tiDoa9atYpLL72UW2+9lW222YabbrqJX/3qV7Ru3Zph4ybnTf23hsiHomIREWkc6YKznc3sf0mmGzAgS+lpsfKhPlHULjuiaIxgcsOGDdx5551cfvnlrF69mrPPPpsrrriCbbfdtnqZ5hLU5LLeoYiINK60wVmjpaIZqU+QlcmgqCHq08oyF0HlmjVruPvuuxk/fjyff/45hxxyCNdffz277777Vss2l6Am033YiYhI/kpZ58zdF6X7a8xENhX17d8rX+oTpcpNKi0rZ9i4yVsdR2P2Z+buvPnmm5x66qn06NGD3//+9+yyyy5MnjyZyZMnJw3MIHf13zKtqdU7FBGR+kvXlcY3BIOObzULcHdXo4AE9e3fK1+K3lLlMkHy3Lxs92fm7syePZvnn3+e+++/nzlz5tCpUydGjRrFL3/5S4YOrX00scau/5ZNjVXvUEREcitlcObunRozIc1BfYOsfCl6S1Z0Fi8x8KrteGPdtEQZt7Kqqoply5Yxd+5c5s6dy7vvvsu///1vli9fDsABBxzAvffey/HHH0+HDh3qdFwKakREpClJl3P2PPAIMMHd1zVekpqu+gZZDa1P1NB6X/Hrdy4qpF1hK1avr0i6bHxAlni8leVfs/6TNylYMZd9Xr6MTz75hLVr11JYWEibNm0oLCys/ot/X15ezueff055+ZZtdenShSOOOIIjjzyS4cOH07Nnz8jHIyIi0pSlaxBwB8GQS/8ws1eAfwLPu/umRklZE1TfIKshRW8NbUyQuH5ZeQVFhQV0aV+YNECLDzRjx7tq7nS+ef85yudPg6rNdO3WneI9duP000+nuLiYioqK6r9NmzZt9bpdu3Z0796dgQMHMmjQIAYNGkTv3r2bdG/+IiIi9ZWuWPNZ4Fkzaw8cBYwCbjWzF4FH3P0/jZTGJqMhQVZ9i94aWu8r1fptW7eiqLAgbaC5f8/WbDf1Nj6Z9CytOhTT44ARjD7nLM4/8Yg6H0djyYcuS0RERNJJO/A5gLuvBx4DHjOz3YH7CQK1grQrtlB1DbISg4VDd+rGK5+sSBs8xK+TfPCt6I0JUi23pryC607cM2kg4+7cc889/OEPf2D9+vVcdtlljBkzhrZt20Y+7lzIly5LRERE0qk1ODOz7sAJBEWcPYDHgdOym6yWIVmw8NC7i6vnJwseko2xmEzUxgTp6sklCzTnzJnDr371K1577TUOOuggbr/9dnbaaaetjisfc6ey3bpUREQkE1JW6jGzM81sMjAD+BYw2t0HuPsYd5/VaCnMMxNmlDJs3GT6j3k+ad9fdZEsWEiU2N9ZlHXq0pggaj9gVVVVXH311ey+++7MmjWLu+66i1deeSVpYNZYfZ/VVb50WSIiIpJOupyz/YGrgJfdvaqR0pPXMl0sFjUoiM/ZSreOQZ1zqqLUk/v8888ZNWoU//nPfxg5ciQ333wz3bt3T7q9fM6dypcuS0RERNJJ1yDgF2a2PXCpme0aTv4QuMXdv2iU1OWZTAce6Tp9jWcEgeGIISUp1ykpLuKtMYfVOQ2Qvp7cSy+9xM9//nO+/vprbr/9ds4888y0/Zblc+6UhkASEZGmIF2x5jBgavj2gfAPYEo4r8XJdOCRrEgxGYfqos36DkdU1+LYTZs2ceGFFzJ8+HC6devGtGnTOOuss2rtUDZVLlQ+5E5pCCQREWkK0hVrXgOMcPcZcdMmmtkzwO3AfllNWR7KdLFYsiLFVDlpsQAwancd8ZXyi9sXsnbDZiqqgradtRXHzps3j5NPPpmpU6dy9tlnc+2111JUFO0Y8z13SqMFiIhIvksXnG2TEJgB4O4zzaxFDu2UjcAjMVgYNm5yrQFgbQFGYt24ZJ3JJiuOdXfuvPNOzj//fNq0acOTTz7JyJEj63w80DzGshQREcmFdMGZmVkXd1+dMLEraYpDm7N0gUfiEEhmULa+os7BSSYCwCgtOqFmceznn3/OL3/5S55//nm+973vcd9999GrV6/I+4yn3CkREZH6SxdkXQe8ZGYHm1mn8O8Q4MVwnoQSu48oK69g9fqKenUl0ZB6UbF6ZVEaGcCW3LhnnnmG3XbbjZdffpnrr7+el156qd6BmYiIiDSMuafqYx7M7MfAhcCuBPXSPwLGu/u/Gid50Q0dOtSnTZuW1X0k6wDWgPZtCli3KX1OVUNaU0Zx8YTZPPzu4pQjBiQqKizgku/347/3Xs29997LkCFDeOihh9hll12ylkYREREJmNl0dx+abF7aEQLc/TnguaykqglKVlzoUGtgBlu36MxkL/oTZpTWGpgVFhgd2rRmTXlQ1PqjbmVcNOoHLF68mIsuuog9jj6DMyd+xrIHPlM9MRERkRxKGZyZ2XhgnrvfnjD9V0B/dx+T7cTlm4b01RVfoT9VZ7bTFq2qdVzNZMZPmpM2MOvSvpBLj9qVEUNK2LhxI5deeikX/+lq+vfvzxtvvMGXRX3r3bluvg7VJCIi0lSlyzk7jKBIM9GdwP+AFhecRe00NlFihf5UndnG537VJUCqLWhs36Y1I4aU8MEHH3DKKacwa9YszjzzTK699lo6duzIsHGTU3auG0tvsuArEyMm5ENwlw9pEBERiUnXIKCtJ6mQFg7llL4n0mZq9PDBKQ+8uKiwuhJ/cVEhXdoXpqzQnyqYSjzZieNqplJbP2ulq9dxzTXXsPfee7N8+XImTpzIHXfcQceOHdOmJxZspRonM92ICVHkwzic+ZAGERGReOlyzsrNbJC7z42faGaDgNyPxZMDI4aUMG3Rqq3qdxUVFnDZ0btGzm2pSw5cLHBKl7uTrPuNmM1rvuSbSdfzh89mccwxx3DHHXew/fbbR0pPgVna4aoaOmJCPozDmQ9pEBERiZcuOPsz8KKZXQlMD6cNBcYC52U5XXnryhG7MbRv1zoXgyX22F/Yyqp77IcgKzJZvbGexUW1Fh/G9n35vz6s7nDW3Vn3wcus/u/ttGndirvvvpvTTz896fBLqfpWS9VXWiz4auiICely7PqPeb5RihgzMSSXikVFRCST0g18/qKZjQBGA78NJ38AjHT32Y2QtrxV105Wk/XYX1hgFBW2oryiCgi64yjfVElV3HqFrYzRwwdHyt2JpWnCjFIueuQtPnv2OsrnvkuHvrtxzU2384sf75/2eGDrumXjJ81JG3w1tMPcdDmI8UWM8WnMtFRpKG5fGGn9TNS7ExERiVdbVxofAKc2UlqarWTBVUWls7lyS15Z0u44wkyuuuTuTHl1EnNuuYDKjevocugv6LTPCP4xZQ3dS0rTBgupAs50wVdDh2pKVxwbk+0ixtHDBzP6yVlUVNbMt1y7YTMTZqQ/Z6BiURERyby0wZlkRtQGAIkqKp3xk+ZEKj7cuHEjF1xwAbfccguF2w9g+5P+Sptu/YD6BwtRgq+GDNWUuP1U56MhXZhEScNlEz+krLzm+KMVVR7pnGWiWFRERCSegrNGUN8uOCD4kb/uxD2Tjkxw6E7dAFi4cCHHH38806ZNY5t9fkLxwaOwgsKttlMf2R4nM377UQZ9z4bEwCwmyjlraL07ERGRRCm70jCzv4f/j6/vxs3sSDObY2bzzGyrftHM7CAze9/MNpvZcQnzKs1sZvg3sb5pyAejhw+mqLCgxrSofZEUty9kxJASRu5dUmMdB56aXsrFNzzAXnvtxaeffsrTTz/Nt489Z6vADJpGsJDsPAGs37Q5a11bTJhRmvKziHLOkqW5rgPVi4iIxEuXc/bDMKAaCzxR1w2bWQFwM3AEsBSYamYT3f2juMUWA6cBf0iyiXJ337Ou+81HyYoHD92pG09NL01b3wq21H165ZMVNYr9vKqSZS8/wF/feZw999yTJ598kh133BFLMf5nLJctn1sWxtKRWMy4en1FvSvZ13a8qUZXMIgUYDW03p2IiEiidMHZv4HVQEcz+5otvT0Y4O6+TS3b3pdg+KcFAGb2KHAMweDpEGxkYTivKtkGmpNkxYOJXXKsWrexuvVmTEWVc95jM2tMq1y3mhUTx7Nx8f/ouPv3efvtCRQVFVXvJ7EvtlguG+H/fG5ZOGJICeMnzdmqqLE+9eaitKRMVx8w6r6yXfQrIiItS7quNEYDo83sWXc/ph7bLgGWxL1fCuxXh/Xbmdk0YDMwzt0nJC5gZmcBZwH06dOnHknMvnQ5N4k/6v3HPF/r9jYs+YCvJl5N1Ya1bPvD8xj83aOqA7OYxFw2CIKbf05ZQmXCoA/52LIwU5Xso7SkLG5fWN03XLwuEbvSEBERybRaGwS4+zFm1h3YJ5w0xd1XZDdZAPR191IzGwBMNrPZ7j4/IW13AHcADB06tLbGj42urn1gpe33q7KCsjcf4espT9G6uDs7HH85nUsGJi16SxXEJAZmtS2fK5mqZB8lyEtxSlJOFxERybZ0Y2sC1Q0C3gOOB04A3kusvJ9CKdA77n2vcFok7l4a/l8AvAoMibpuvog69uSEGaXVLRWTVU7ftGIhnz/4B75+9wk67nY4PU69nqLuAxi5d/LitFRBTEGS0QEAWpnRf8zzDBs3udaK97G0Rl2+PjJVyT7VeYifviZFS81U00VERLKt1uAMuBjYx91PdfdRBHXJLomw3lRgkJn1N7M2wElApFaXZtbFzNqGr7cDhhFXV62piJJzEz/wNmyp1AdQsaqUlZNuYvm9v2Pz1yvo9pOL2PYHv6NV2/ZUuvPU9NKkwVGq4Oan+/VO2hqy0j3SoN+NNUj4iCElXHXsbtUDyScbPD6KKEFelABORESkMUXp56yVu38Z934lEYI6d99sZucAk4AC4B53/9DMrgCmuftEM9sHeAboAhxlZpe7+67AzsDtYUOBVgR1zppccBaleC4+d829ioqvlrCx9GMq5r/DN/OmQ0FrOg35AZ2HnUxB+841tpOqvli6FoTxjRBamdWpDlptdbgy2RI0E5Xso7SkbOgQVCIiIplmXkvlGjMbD+wO/DOcdCLwP3f/Y5bTVidDhw71adOm5ToZNSTWOYPghz8+F6j3b+5n7UevsXHxbDYu+4SqDWsBKNimG8eddAqzO+3LutadUu7DgM/G/ahe6es/5vmU3Ugk22a65ZN1lJt4rPkqSlCZz12QiIhI02Nm0919aNJ5tQVn4QaOBQ4M377h7s9kMH0ZkY/BGaT+Ud+0aRM/+9nPePKpp8GrKNy2N21LdqZtr11oW7IzffvvyIVH7lTr2JMQFPvVJ1hI1SN/SXERb405rE7LA8kHEC8qpEPb1kmDmqYS8EQJskVEROoiXXAWafgmd38aeDqjqWqmEgOOWOevif7973/z5JNPcsRPTmZO90Oxzj2q5xUWGBceuVPSYsRkYnW/pi1axSufrIgc7CQr0itsZazftJn+Y57fahvpigDPT+iLLaasvKK6z7L41qpAnVqy5kLss0wWdOZjFyQiItI8aGzNDErWdcZD7y6unh8fgDz1xBN06dKFs8b8lbETPqKiKi4HM3xZly4uyisqa3Q8W1pWzvmPzWTaolVcOWK3pOsk65G/osqr+/1KDJhS1eECktZfS5XOWGvV2vogq49M5cZNmFHK6CdnUVGZ+pjyrQsSERFpHhSc1UOqACBKTld5RSV/f242H02cyHHHHcd1kxfUDMwIAqTxk+bUecD0xDDCgYffXczQvl3TBigbN6ceoCExYEqsqB8LSKMEZjHpgpqGBDx17Vcuncv/9WHawAzUolNERLIjSlcamFmRman5Gum7k4gaWCyY+TZff/01J5xwQtruNlINBF4XDlv1qxYvSkCZ7rhSrV9glrKX/Z7FRVnpwiJqv3JRJBs1IJ5adIqISLZE6YT2KGAmwVibmNmeZhapv7LmKF0AEDWw8AXv0LVrVw477LC0QUpif19d2hdSXBQEPIndySbvXjbQ0JyqdMeVav0qdy49ateU/YxlqqPZKGnJdPFjfftdExERiSJKztllBB3PlgG4+0ygf9ZSlKfie/FPJpbTlS5IAijwCtbOfZef/OQnFBYW1hqkjBhSwltjDuOzcT/i0qN2pUPb1hjBmJDtC7d8fO0KU3+U6YKr2gLK2gKmugSX8UFNpjqajZqWuooFwcmmvzXmMAVmIiKSNVHqnFW4+xqrOfRPixp5MFlXColiwci0RatqVMxPtGnhDMrXreX4448HonWUmiwNicVu5RVVtAISa4/VFlwla4FpBB9wlC46auvENV1nspnoaLYuaamLy47eldFPzKpRH7CwlXHZ0btmJK0iIiKpRAnOPjSzk4ECMxsE/A54O7vJyi+11cuKDwCuHLEbQ/t25bwUXUus/uhtOnTqzGGHbelHLEqQEqVuWBVbcnxirS/T5ajF9h3bfn1aODZ0/UzKZFry6bhERKRliTJCQHvgIuD74aRJwJXuviHLaauTbHZCm6pnfEidu5SsCNS9iqU3jaLrwCGsmDU5Y2lIVFRYELnD1KbSEayIiEhzkq4T2rTZKmZWADzv7he5+z7h38X5FphlW6o6S7Ge9JMFM8mK0jZ9Po+q9WXQZ6+MpSFRgVnkFouNNZC5iIiIRJc2OHP3SqDKzDqnW665q0/LwhFDSraqVF4+bypgdN95v4ykIVFRYUHK/saStVjMZNcTIiIikhlRWmuuBWab2d1mdkPsL9sJyyf1bVl42dG7UtgqaEjh7qz7+FXa9t6VjYUdkuZOxVqE9h/zPMPGTa6xTLI0nPKdPlulqaQOLRYbq+sJERERiS5KgwCNq0n9WhaOGFLC5f/6kNXrK9hY+jGbVy+n8/4nUlHpWw1TFKV3+1gaYvXEHnp3MQVmNeqi1aXFYqoRCNTzvYiISO7UGpy5+/2NkZDmqizs8mLdBy9jhe1oP3gYsHXuVLoixnRBXKwYMxbMXXXsblx17G5JK/knG5T9qemlGel6QkRERDKj1uAs7D7jKmAXoF1sursPyGK6mo2exUUsWbGadR+/QfvBB9CqTVH19HhRixjTdakRC+aSNVJIljP31PRSRu5dwiufrFBrTRERkTwRpVjzXuBS4DrgUOB0Io7JKUEx4/+74gZ803o6fvtwIHnuVNQixtoGQi8tK6ffmOeBYLinS4/aNeWg7OUVlbzyyQreGnNYsk01WeoeREREmrIowVmRu79sZubui4DLzGw68Ocsp61ZGDGkhMs+f5dVxd1p1+fb1f2iQdAXWl2LGAvMUrbITLR6fQWjn5wFtJzK/1Hq7omIiOSzKMHZRjNrBcw1s3OAUqBjdpOVf+qbG7N06VL+N+UNLrnkEi6//KjqbdWniHHCjNLIgVlMrPFBS6n8H6XunnLWREQkn0UJzs4F2hMM2/QX4DDg1GwmKt/UNzdmwoxSzh17Ge7Oc+sHMWRGadoixn9OWcI1J+yRsif/WC5YXS0rK+e6E/fM2LiT+ay2HELlrImISL6L0lpzavhyLUF9sxYnakvKeBNmlDLmqf+xbOq/adtrV1YWdKkOAlIFEJXuKQOF8ZPmUFFZv/HmY4Oyx7bTnHOMasshrM9nKSIi0piitNb8FjAa6Bu/vLs3r1rkadSnvtb4SXNYs+gjNq8qpfN+I4EtQUCqACJ+mcRAob51wwoLrDp3rD59tcU0laLA2vp5ayl170REpOmKUqz5BHAbcCeQvA+HZq59mwLWbdr60Nu3ST2c0rKyctZ+8F+sdVvaDz6wxvRkRYyJ6yZKF9ClEt9asyFSFQVOW7Qq77rhqC2HsKXUvRMRkaYrSnC22d1vzXpK8tj6JIFZuukA3Tu0YlGsb7O27aunxxcx/v7xWUkr+CcLFEYPH8x5j82MlF4DPhv3o0jLRpGqKPDhdxdXj06QT3W30uUQ1mUEBRERkVxIGZyZWdfw5b/M7P8BzwAbY/PdfVWW05Y3UtX0SlcD7MDChUzZuI4O3/5e9bT4ICAWPCQLFA7dqVuNbjZiOT/TFq3ioXcX15rezgkDrjdUqiK/xONvCnW3WkrdOxERabrS5ZxNJ/j9tfD96Lh5DmiEgDQ+fG0i2+3Qkx1334/lX29MGgQkCxQS+zuLz5G6csRuDO3btXp5DJL1rGG29bSGqEuRalOou9WQunciIiLZljI4c/f+jZmQ5qS0tJSXXnqJsWPHcuWfDk+7bGKgMGzc5LStCeOX7x+OBJAoNp5npiQrCjSS5xyq7paIiEjDRGmteTzwb3f/xswuBvYC/uLuM7KeujxRVNiK8oqqpNOTtWKc89JDVFVVceqp0bqDi99GqqLSujQSyHSAFCWHD1R3S0REJBOiNAi4xN2fMLMDgcOB8QStN/fLasrySLLALDY9sRXjmKf+x7pH7uaAAw5g0KBBabc7YUYpl//rQ1ZHyOlK1UigsSq3JysKjC9iVd0tERGRzIgSnMV++X8E3OHuz5vZlVlMU5OSWAS5ZskcPv9sLn8eOzrFGoHE7inSSQy44nPaOhcV0q6wFWXrKxo9QFLdLRERkcxrFWGZUjO7HTgReMHM2kZcDzM70szmmNk8MxuTZP5BZva+mW02s+MS5p1qZnPDvyYzXNS6D16GgkKOP/74tMsl654ikQElxUVcdexuNcaFHPv0bErDItCy8go2VFRx3Yl78taYwxQsiYiINHFRcs5OAI4E/s/dy8ysBzVbbiZlZgXAzcARwFJgqplNdPeP4hZbDJwG/CFh3a7ApcBQgnrn08N1V0dIb8Z1SNEJbWKleK+sYN3Hr7PtLsMoLi5Ou80orRqT9VWm4YdERESat1pzwNx9vbs/7e5zw/fL3f2lCNveF5jn7gvcfRPwKHBMwrYXuvv/gMRKXcOB/7j7qjAg+w9BgJgTf/3JbhS0qtk/RUEr42ff6UNR4ZZRAsoXTKeq/Gt+fWbtQ5DWVmm/S/vkfZWlCupKy8rZcewL9BvzPDuOfYGLJ8yuNQ0iIiKSfyIVT9ZTCbAk7v3ScFrG1jWzs8xsmplNW7FiRb0TWpsRQ0q45vg9KCkuqi5qvOb4PbhyxG5cdexu1dMr57xG567bcemvT65ed8KMUoaNm0z/Mc8zbNxkJswoBYLK/PGBXbzCAuPSo3ZNOi9dUBcbbaDSnYfeXawATUREpAmKUqyZt9z9DuAOgKFDh6brsL/BUlV+j01fuXIlPa59l3POOYfWrYPTmmpMyth6EBRTlpaVU2BGpTsltVTqT9ZCM5V/TlnClSN2q9fxioiISG5kMzgrBXrHve8VTou67iEJ676akVRlQLK+zZa98ywVFRWMGjWqerna6ofVp7Vjsj7HUvXen2zcThEREclv2QzOpgKDzKw/QbB1EnBy+lWqTQL+ZmZdwvffB8ZmPonRXTxhNv+csmSrgCeWG1Y14S5233139txzz+p5qeqHNXSIo8SgbsexLyQNxAoyPY6TiIiIZF3W6py5+2bgHIJA62PgcXf/0MyuMLOjAcxsHzNbChwP3G5mH4brrgL+QhDgTQWuyOVA6xdPmM1D7y5OmRP19eeL+PSDGTVyzSB1/bBM9+D/0/1612m6iIiI5K+s1jlz9xeAFxKm/Tnu9VSCIstk694D3JPN9EX18LuL085f++FksFacfHLNjMHG6sE/Vq8slrNXYMZP9+ut+mYiIiJNUJNuENBY0tXccq9i3QevUDxob3r06FFjXrL6Ydnqwf/KEbspGBMREWkGFJw10MbFH1D5zQp+MfbSpPM1xJGIiIjURTb7OWsRqua+RvuOnbjyvF/kOikiIiLSDCg4i6CoMPlpauubKJ/zFj898QSKijJbyV9ERERaJgVnEaTqkGLtnLdZu3btVq00RUREROpLwVkE6ysSh/4MrJr1Mv369ePAAw9s5BSJiIhIc6XgrJ42f/MVGxbOZNSoUbRqpdMoIiIimaGoIoIObbYeoHzdR68DzimnnNL4CRIREZFmS8FZBIUFW5+m9R+/RlHPbzFo0KAcpEhERESaKwVnEawpr6jxvmLlEjZ9MZ92Ox2coxSJiIhIc6XgLILORYU13gdFmkaPIYfmJkEiIiLSbCk4i8Di+tJwd9Z9/Crt+u5Om222zV2iREREpFlScBbB6vVbijU3fT6XzauX02GXg2tMFxEREckEBWcRtIrLOVv30WtQ0Jr23zqgxnQRERGRTNDA5xFUefDfqypZ/8kbFA0YSqt2Haun54sJM0oZP2kOy8rK6VlcxOjhgzXouoiISBOj4KwONiz5gMq1q+iwyyG5TspWJswoZfSTs6ioDCLG0rJyRj85C0ABmoiISBOiYs06WP/Ra1ibIop23CfXSdnK5f/6sDowi6modC7/14c5SpGIiIjUh4KziHxzBevnvEX7b+1Pq8K2uU7OVlI1TlCjBRERkaZFxZpRFRTQ7diLaNWuU65TIiIiIs2YgrOIzFrRrs/ukZdv7Mr5xUWFlJVvnUtWnNCBroiIiOQ3FWtmwYQZpYx9ejalZeU4QeX8sU/PZsKM0qzt87Kjd6UwoW+PwlbGZUfvmrV9ioiISOYpOMuC8ZPmUF5RWWNaeUUl4yfNydo+RwwpYfzxe1BSXIQBJcVFjD9+D7XUFBERaWJUrJkFy8rK6zQ9U0YMKVEwJiIi0sQp5yyCtq2Tn6ZU03sWF9VpuoiIiEiMgrMIigoL6jR99PDBW80rKixg9PDBGU+biIiINC8q1owgWSvIdNNjRYsaSklERETqSsFZlqj+l4iIiNSHijVFRERE8oiCMxEREZE8ouBMREREJI9kNTgzsyPNbI6ZzTOzMUnmtzWzx8L5U8ysXzi9n5mVm9nM8O+2bKazNiUpusBINV1ERESkvrIWnJlZAXAz8ANgF+CnZrZLwmJnAKvdfSBwHfD3uHnz3X3P8O/sbKUzCnWNISIiIo0lmzln+wLz3H2Bu28CHgWOSVjmGOD+8PWTwPfMzMgzI4aUsFefzjWm7dWns1pjioiISMZlMzgrAZbEvV8aTku6jLtvBtYA24bz+pvZDDN7zcy+m8V01uriCbN5a/6qGtPemr+KiyfMzlGKREREpLnK1wYBy4E+7j4EuAB4xMy2SVzIzM4ys2lmNm3FihVZS8zDUxbXabqIiIhIfWUzOCsFese97xVOS7qMmbUGOgMr3X2ju68EcPfpwHzgW4k7cPc73H2ouw/t1q1bFg4htp+6TRcRERGpr2wGZ1OBQWbW38zaACcBExOWmQicGr4+Dpjs7m5m3cIGBZjZAGAQsCCLaRURERHJC1kbvsndN5vZOcAkoAC4x90/NLMrgGnuPhG4G3jQzOYBqwgCOICDgCvMrAKoAs5291Vb70VERESkecnq2Jru/gLwQsK0P8e93gAcn2S9p4Cnspk2ERERkXyUrw0CRERERFokBWciIiIieUTBmYiIiEgeUXAmIiIikkcUnImIiIjkEQVnIiIiInlEwZmIiIhIHlFwJiIiIpJHFJyJiIiI5BEFZyIiIiJ5RMGZiIiISB5RcCYiIiKSRxSciYiIiOQRBWciIiIieUTBmYiIiEgeUXAmIiIikkcUnEXQyuo2XURERKS+FJxFsP+ArnWaLiIiIlJfCs4iWLiyvE7TRUREROpLwVkEy8qSB2GppouIiIjUl4KzCHoWF9VpuoiIiEh9KTiLYPTwwRQVFtSYVlRYwOjhg3OUIhEREWmuWuc6AU3BiCElAIyfNIdlZeX0LC5i9PDB1dNFREREMkXBWUQjhpQoGBMREZGsU7GmiIiISB5RcCYiIiKSRxSciYiIiOQRBWciIiIieUTBmYiIiEgeUXAmIiIikkcUnImIiIjkEQVnIiIiInlEwZmIiIhIHlFwJiIiIpJHzN1znYaMMLMVwKJG2NV2wFeNsJ/mROesfnTe6k7nrH503upO56zudM5q6uvu3ZLNaDbBWWMxs2nuPjTX6WhKdM7qR+et7nTO6kfnre50zupO5yw6FWuKiIiI5BEFZyIiIiJ5RMFZ3d2R6wQ0QTpn9aPzVnc6Z/Wj81Z3Omd1p3MWkeqciYiIiOQR5ZyJiIiI5BEFZxGZ2ZFmNsfM5pnZmFynJ9fM7B4z+9LMPoib1tXM/mNmc8P/XcLpZmY3hOfuf2a2V9w6p4bLzzWzU3NxLI3FzHqb2Stm9pGZfWhm54bTdd5SMLN2Zvaemc0Kz9nl4fT+ZjYlPDePmVmbcHrb8P28cH6/uG2NDafPMbPhOTqkRmNmBWY2w8yeC9/rnNXCzBaa2Wwzm2lm08Jp+n7WwsyKzexJM/vEzD42s/113hrI3fVXyx9QAMwHBgBtgFnALrlOV47PyUHAXsAHcdOuBsaEr8cAfw9f/xB4ETDgO8CUcHpXYEH4v0v4ukuujy2L56wHsFf4uhPwKbCLzlvac2ZAx/B1ITAlPBePAyeF028Dfh2+/n/AbeHrk4DHwte7hN/btkD/8PtckOvjy/K5uwB4BHgufK9zVvs5WwhslzBN38/az9v9wC/D122AYp23hv0p5yyafYF57r7A3TcBjwLH5DhNOeXurwOrEiYfQ/AlJfw/Im76Ax54Fyg2sx7AcOA/7r7K3VcD/wGOzHric8Tdl7v7++Hrb4CPgRJ03lIKj31t+LYw/HPgMODJcHriOYudyyeB75mZhdMfdfeN7v4ZMI/ge90smVkv4EfAXeF7Q+esvvT9TMPMOhM8rN8N4O6b3L0MnbcGUXAWTQmwJO790nCa1NTd3ZeHrz8HuoevU52/Fntew6KjIQQ5QTpvaYTFczOBLwlu2POBMnffHC4Sf/zV5yacvwbYlhZ2zoB/ABcCVeH7bdE5i8KBl8xsupmdFU7T9zO9/sAK4N6wGP0uM+uAzluDKDiTrPAgn1pNgZMws47AU8B57v51/Dydt625e6W77wn0Isi52Sm3KcpvZvZj4Et3n57rtDRBB7r7XsAPgN+Y2UHxM/X9TKo1QRWXW919CLCOoBizms5b3Sk4i6YU6B33vlc4TWr6IsyeJvz/ZTg91flrcefVzAoJArOH3f3pcLLOWwRhUckrwP4ERSGtw1nxx199bsL5nYGVtKxzNgw42swWElTBOAy4Hp2zWrl7afj/S+AZgocBfT/TWwosdfcp4fsnCYI1nbcGUHAWzVRgUNjaqQ1BpdmJOU5TPpoIxFrYnAo8Gzd9VNhK5zvAmjC7exLwfTPrErbk+X44rVkK6/HcDXzs7tfGzdJ5S8HMuplZcfi6CDiCoK7eK8Bx4WKJ5yx2Lo8DJodP7ROBk8KWif2BQcB7jXIQjczdx7p7L3fvR3CvmuzuP0PnLC0z62BmnWKvCb5XH6DvZ1ru/jmwxMwGh5O+B3yEzlvD5LpFQlP5I2hh8ilBfZeLcp2eXP8B/wSWAxUET05nENRTeRmYC/wX6Boua8DN4bmbDQyN284vCCoazwNOz/VxZfmcHUiQtf8/YGb490Odt7TnbHdgRnjOPgD+HE4fQBAozAOeANqG09uF7+eF8wfEbeui8FzOAX6Q62NrpPN3CFtaa+qcpT9XAwhap84CPozd5/X9jHTu9gSmhd/TCQStLXXeGvCnEQJERERE8oiKNUVERETyiIIzERERkTyi4ExEREQkjyg4ExEREckjCs5ERERE8oiCMxHJCDO7zMz+UMsyZ5vZqPD1fWZ2XLrl84WZHW1mY2pfssY63c3sETNbEA4H9I6Z/SRbaRSR5qN17YuIiGSGu9+W6zTUh7tPpA4dT4cdDk8A7nf3k8NpfYGj67CN1r5lLEwRaUGUcyYi9WZmF5nZp2b2JjA4bvqZZjbVzGaZ2VNm1j6cvlXumpkdZmYT4t4fYWbPJNnXPmb2drjN98ysk5n1M7M3zOz98O+AcNkeZva6mc00sw/M7Lvh9O+HOVjvm9kT4TinmNk4M/vIzP5nZv+XZN+nmdlN4ev7zOyGMC0LUuT+HQZsig9G3X2Ru98YbqPAzMaH5+h/ZvarcPoh4fFMBD4K379mZs+G+xpnZj8Lj3+2me0YrneUmU2xYODp/5pZ97jzfY+ZvRqu/7tw+hVmdl7c8f3VzM5N/UmLSGNScCYi9WJmexMMD7QnwUgH+8TNftrd93H3PQiGWzojzaZeAXYys27h+9OBexL21QZ4DDg33ObhQDnBeH1HeDBY9YnADeEqJwOTPBgwfQ9gppltB1wMHB4uPw24wMy2BX4C7OruuwNXRjj8HgQjPvwYGJdk/q7A+2nWP4Ng2Jp9CM7bmeEQSRCMS3iuu38rfL8HcDawM/Bz4Fvuvi9wF/DbcJk3ge94MPD0o8CFcfvaCRhOME7kpRaM73oPECtebkXwOT4U4bhFpBGoWFNE6uu7wDPuvh4gzO2J+baZXQkUAx1JM0aeu7uZPQicYmb3EgxsPiphscHAcnefGq7zdbjPDsBNZrYnUAnEApqpwD1hIDLB3Wea2cHALsBbQakjbYB3gDXABuBuM3sOeC7CsU9w9yqC3K3utS1sZjcTBHObwoDs+8DucblunQnGrtwEvOfun8WtPtWDsQcxs/nAS+H02cCh4etewGMWDDDdBohf/3l33whsNLMvge7uvtDMVprZEKA7MMPdV0Y4bhFpBArORCQb7gNGuPssMzuNYIzHdO4F/kUQJD1Rh7pW5wNfEOQutQrXx91fN7ODgB8B95nZtcBq4D/u/tPEjZjZvgQDNh8HnENQLJnOxvjVk8z/EBgZe+Puvwlz7qbFrfNbd68RtJrZIcC6NPuqintfxZZ7+I3Ate4+MdzGZSnWr4xb5y7gNGAHEnIqRSS3VKwpIvX1OjDCzIrMrBNwVNy8TsDyMOfqZ7VtyN2XAcsIih3vTbLIHKCHme0DENY3a02Q47Q8zMX6OVAQzu8LfOHudxIEIXsB7wLDzGxguEwHM/tWWO+ss7u/QBDs7VHXE5HEZKCdmf06blr7uNeTgF+H54cwHR0asL/OQGn4+tSI6zwDHElQrJoyZ1NEGp9yzkSkXtz9fTN7DJhFUPdratzsS4ApwIrwf6cIm3wY6ObuHyfZ1yYzOxG40cyKCOqbHQ7cAjxlQfcc/2ZLrtMhwGgzqwDWAqPcfUWYi/dPM2sbLncx8A3wrJm1I8jRuiDiKUgpLKodAVxnZhcSnId1wB/DRe4C+gHvW1DGugIY0YBdXgY8YWarCQLD/ukXrz6nrwBl7l7ZgH2LSIaZu+c6DSIihK0hZ7j73blOS0sQNgR4Hzje3efmOj0isoWKNUUk58xsOrA7ajHYKMxsF2Ae8LICM5H8o5wzERERkTyinDMRERGRPKLgTERERCSPKDgTERERySMKzkRERETyiIIzERERkTyi4ExEREQkj/x/SBGUvshHX4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "smoothed = sm.nonparametric.lowess(ts_covid19topics2020, coviddata_de.daily,\n",
    "                                   frac=0.5)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.scatter(coviddata_de.daily, ts_covid19topics2020)\n",
    "ax.plot(smoothed[:, 0], smoothed[:, 1], c='black')\n",
    "ax.set_xlabel('daily cases in Germany')\n",
    "ax.set_ylabel('share of COVID-19 related topics')\n",
    "ax.set_title('COVID-19 infection rate in Germany and share of COVID-19 topics in SPON corpus')\n",
    "fig.savefig('plots/covidrate_topics_germany.png')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that for daily numbers in the range of up to ~1,000 cases, higher case numbers are associated with a sharp increase in media coverage on SPON in our time frame. After that, the curve flattens until it consolidates at a high level.\n",
    "\n",
    "Note: No, the *LOWESS* (aka *LOESS*) smoother in Python's *statsmodels* package doesn't support confidence intervals, yet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could try to fit a function to this data to get more insights about the relationship between the number of cases in Germany and the share of COVID-19 related topics. We could fit a simple linear model with a single term, i.e. *covid19topics ~ dailycases*, but this wouldn't account for the damping effect that sets in at higher numbers of cases. Adding a quadratic term, i.e. specifying the model as *covid19topics ~ dailycases + dailycases²*, may be used to account for the damping effect while still providing a simple, interpretable model.\n",
    "\n",
    "Let's specify such a model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:09.509242Z",
     "iopub.status.busy": "2020-11-23T16:59:09.508331Z",
     "iopub.status.idle": "2020-11-23T16:59:09.521442Z",
     "shell.execute_reply": "2020-11-23T16:59:09.521862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>covid19topics</td>  <th>  R-squared:         </th> <td>   0.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   260.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 25 Nov 2020</td> <th>  Prob (F-statistic):</th> <td>7.71e-61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:58:55</td>     <th>  Log-Likelihood:    </th> <td>  397.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   243</td>      <th>  AIC:               </th> <td>  -789.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   240</td>      <th>  BIC:               </th> <td>  -778.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>             <td>    0.0919</td> <td>    0.005</td> <td>   20.336</td> <td> 0.000</td> <td>    0.083</td> <td>    0.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dailycases</th>            <td>    0.0001</td> <td>  6.5e-06</td> <td>   15.980</td> <td> 0.000</td> <td> 9.11e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.square(dailycases)</th> <td>-1.169e-08</td> <td> 1.21e-09</td> <td>   -9.638</td> <td> 0.000</td> <td>-1.41e-08</td> <td> -9.3e-09</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.503</td> <th>  Durbin-Watson:     </th> <td>   0.778</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.105</td> <th>  Jarque-Bera (JB):  </th> <td>   4.600</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.326</td> <th>  Prob(JB):          </th> <td>   0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.828</td> <th>  Cond. No.          </th> <td>1.20e+07</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.2e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          covid19topics   R-squared:                       0.684\n",
       "Model:                            OLS   Adj. R-squared:                  0.682\n",
       "Method:                 Least Squares   F-statistic:                     260.3\n",
       "Date:                Wed, 25 Nov 2020   Prob (F-statistic):           7.71e-61\n",
       "Time:                        10:58:55   Log-Likelihood:                 397.52\n",
       "No. Observations:                 243   AIC:                            -789.0\n",
       "Df Residuals:                     240   BIC:                            -778.6\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "Intercept                 0.0919      0.005     20.336      0.000       0.083       0.101\n",
       "dailycases                0.0001    6.5e-06     15.980      0.000    9.11e-05       0.000\n",
       "np.square(dailycases) -1.169e-08   1.21e-09     -9.638      0.000   -1.41e-08    -9.3e-09\n",
       "==============================================================================\n",
       "Omnibus:                        4.503   Durbin-Watson:                   0.778\n",
       "Prob(Omnibus):                  0.105   Jarque-Bera (JB):                4.600\n",
       "Skew:                           0.326   Prob(JB):                        0.100\n",
       "Kurtosis:                       2.828   Cond. No.                     1.20e+07\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.2e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_cases = pd.DataFrame({\n",
    "    'covid19topics': ts_covid19topics2020.values,\n",
    "    'dailycases': coviddata_de.daily.values\n",
    "})\n",
    "\n",
    "lm_topics_cases = smf.ols('covid19topics ~ dailycases + np.square(dailycases)', data=topics_cases).fit()\n",
    "lm_topics_cases.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the model fit and interpret the results below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:09.539741Z",
     "iopub.status.busy": "2020-11-23T16:59:09.538002Z",
     "iopub.status.idle": "2020-11-23T16:59:09.939302Z",
     "shell.execute_reply": "2020-11-23T16:59:09.939712Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "predinput = np.sort(np.unique(topics_cases.dailycases))\n",
    "predresults = lm_topics_cases.get_prediction(pd.DataFrame({'dailycases': predinput}))\\\n",
    "    .summary_frame(alpha=0.05)\n",
    "\n",
    "ax.scatter(topics_cases.dailycases, topics_cases.covid19topics)\n",
    "ax.plot(predinput,\n",
    "        lm_topics_cases.predict(pd.DataFrame({'dailycases': predinput})),\n",
    "        c='black')\n",
    "ax.plot(predinput, predresults.mean_ci_upper, 'r--')\n",
    "ax.plot(predinput, predresults.mean_ci_lower, 'r--')\n",
    "ax.set_xlabel('daily cases in Germany')\n",
    "ax.set_ylabel('share of COVID-19 related topics')\n",
    "ax.set_title('COVID-19 infection rate in Germany and share of COVID-19 topics in SPON corpus\\n'\n",
    "             'Fitted function: y={:.4f}{:+.4f}x{:+.4e}x².'\n",
    "             .format(*(lm_topics_cases.params.values).tolist()))\n",
    "fig.savefig('plots/covidrate_topics_germany_lm.png')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with a share of Corona-related topics of around $0.09$ for no reported cases in Germany, the linear term can be interpretated as an increase of $0.1$ in Corona-related topic share **per 1000 cases**. The damping effect of the squared term with the negative coefficient sets in at higher numbers of daily cases. For example, at 1000 cases, the dampening effect is only around $-1.17 \\cdot 10^{-8} \\cdot 1000^2 = -0.0117$, but at 4000 cases it is already $-1.17 \\cdot 10^{-8} \\cdot 4000^2 = -0.19$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between worldwide infection rate and share of COVID-19 related topics on SPON\n",
    "\n",
    "Let's have a look at the relationship between *worldwide* daily Corona cases and share of COVID-19 related topics. We can make a similar figure as for the cases in Germany:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-23T16:59:09.956805Z",
     "iopub.status.busy": "2020-11-23T16:59:09.955942Z",
     "iopub.status.idle": "2020-11-23T16:59:10.365585Z",
     "shell.execute_reply": "2020-11-23T16:59:10.365154Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "smoothed = sm.nonparametric.lowess(ts_covid19topics2020, coviddata_world,\n",
    "                                   frac=0.3)\n",
    "\n",
    "ax.scatter(coviddata_world, ts_covid19topics2020)\n",
    "ax.plot(smoothed[:, 0], smoothed[:, 1], c='black')\n",
    "ax.set_xlabel('daily cases worldwide')\n",
    "ax.set_ylabel('share of COVID-19 related topics')\n",
    "ax.set_title('Worldwide COVID-19 infection rate and share of COVID-19 topics in SPON corpus')\n",
    "fig.savefig('plots/covidrate_topics_world.png')\n",
    "fig;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there's not such a clear relationship between the worldwide infection rate and share of COVID-19 related topics on SPON as compared to the data from Germany. There's a sharp increase in the share of COVID-19 related topics on SPON until around 40,000 cases which coincides with the sharp increase of Corona cases in Germany in spring 2020. Despite of increasing daily infections in the world, the share of COVID-19 related topics decreases first and then stays constant at around $15\\%$ for 130,000 daily cases and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "This notebook is supposed to showcase how topic models can be used to identify topics of interest in large-scale text data and how certain properties of these topics like their marginal probability can be used for further analyses.\n",
    "\n",
    "In the case of SPON, we found that national daily infection numbers clearly drive the volume of media coverage on COVID-19 during our time frame (January 2020 to end of August 2020), which is probably not very suprising. Even though infection rates increased dramatically in the world in summer 2020 (e.g. in Brazil, India and USA), media coverage first decreased and then stayed at a moderate level. But also regarding the national situation, media coverage on SPON stagnated on a high level with increasing infection rates in Germany. It will be interesting to see if this pattern holds with the second \"Corona wave\", that hit Germany in late autumn 2020, or if there's a \"weariness\" about media coverage that may lead to decreasing coverage despite of rising infection rates."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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