From c2dfb29816b319545bf24da005f1e513ad190198 Mon Sep 17 00:00:00 2001 From: 6f892419cc99326ee525ed439d8ff5df <6f892419cc99326ee525ed439d8ff5df@app-learninglab.inria.fr> Date: Wed, 3 Mar 2021 18:46:46 +0000 Subject: [PATCH] matplolib --- module3/exo2/exercice.ipynb | 1128 ++++++++++++++++++++++++++++++++++- 1 file changed, 1127 insertions(+), 1 deletion(-) diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 6364e59..e272d97 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -24,7 +24,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -2128,6 +2130,1130 @@ "sorted_data = data.set_index('period').sort_index()" ] }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'W'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'week'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" + ] + } + ], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameyear
0202108713464963317295201426FRFrance2021
12021077136331032516941211626FRFrance2021
2202106713383979316973201525FRFrance2021
3202105712210898815432181323FRFrance2021
4202104712026882615226181323FRFrance2021
52021037891363751145113917FRFrance2021
62021027779554301016012816FRFrance2021
7202101710525775013300161220FRFrance2021
8202053711978840615550181323FRFrance2020
9202052712012828515739181224FRFrance2020
10202051710564757413554161121FRFrance2020
11202050770634744938211715FRFrance2020
1220204975026314569078511FRFrance2020
13202048766834312905410614FRFrance2020
1420204774999296370358511FRFrance2020
152020467375219635541639FRFrance2020
162020457369620165376639FRFrance2020
1720204474391237564077410FRFrance2020
1820204374376250562477410FRFrance2020
192020427400019796021639FRFrance2020
202020417396120995823639FRFrance2020
21202040720786753481315FRFrance2020
22202039710492371861213FRFrance2020
23202038722537823724315FRFrance2020
24202037715844052763204FRFrance2020
2520203679191001738102FRFrance2020
26202035782801694102FRFrance2020
27202034722723714173306FRFrance2020
28202033712841772391204FRFrance2020
29202032726506894611417FRFrance2020
....................................
15481991267176081130423912312042FRFrance1991
15491991257161691070021638281838FRFrance1991
15501991247161711007122271281739FRFrance1991
1551199123711947767116223211329FRFrance1991
1552199122715452995320951271737FRFrance1991
1553199121714903897520831261636FRFrance1991
15541991207190531274225364342345FRFrance1991
15551991197167391124622232291939FRFrance1991
15561991187213851388228888382551FRFrance1991
1557199117713462887718047241632FRFrance1991
15581991167148571006819646261834FRFrance1991
1559199115713975978118169251832FRFrance1991
1560199114712265768416846221430FRFrance1991
156119911379567604113093171123FRFrance1991
1562199112710864733114397191325FRFrance1991
15631991117155741118419964271935FRFrance1991
15641991107166431137221914292038FRFrance1991
1565199109713741878018702241533FRFrance1991
1566199108713289881317765231531FRFrance1991
1567199107712337807716597221529FRFrance1991
1568199106710877701314741191226FRFrance1991
1569199105710442654414340181125FRFrance1991
15701991047791345631126314820FRFrance1991
15711991037153871048420290271836FRFrance1991
15721991027162771104621508292038FRFrance1991
15731991017155651027120859271836FRFrance1991
15741990527193751329525455342345FRFrance1990
15751990517190801380724353342543FRFrance1990
1576199050711079666015498201228FRFrance1990
15771990497114302610205FRFrance1990
\n", + "

1578 rows × 11 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202108 7 13464 9633 17295 20 14 \n", + "1 202107 7 13633 10325 16941 21 16 \n", + "2 202106 7 13383 9793 16973 20 15 \n", + "3 202105 7 12210 8988 15432 18 13 \n", + "4 202104 7 12026 8826 15226 18 13 \n", + "5 202103 7 8913 6375 11451 13 9 \n", + "6 202102 7 7795 5430 10160 12 8 \n", + "7 202101 7 10525 7750 13300 16 12 \n", + "8 202053 7 11978 8406 15550 18 13 \n", + "9 202052 7 12012 8285 15739 18 12 \n", + "10 202051 7 10564 7574 13554 16 11 \n", + "11 202050 7 7063 4744 9382 11 7 \n", + "12 202049 7 5026 3145 6907 8 5 \n", + "13 202048 7 6683 4312 9054 10 6 \n", + "14 202047 7 4999 2963 7035 8 5 \n", + "15 202046 7 3752 1963 5541 6 3 \n", + "16 202045 7 3696 2016 5376 6 3 \n", + "17 202044 7 4391 2375 6407 7 4 \n", + "18 202043 7 4376 2505 6247 7 4 \n", + "19 202042 7 4000 1979 6021 6 3 \n", + "20 202041 7 3961 2099 5823 6 3 \n", + "21 202040 7 2078 675 3481 3 1 \n", + "22 202039 7 1049 237 1861 2 1 \n", + "23 202038 7 2253 782 3724 3 1 \n", + "24 202037 7 1584 405 2763 2 0 \n", + "25 202036 7 919 100 1738 1 0 \n", + "26 202035 7 828 0 1694 1 0 \n", + "27 202034 7 2272 371 4173 3 0 \n", + "28 202033 7 1284 177 2391 2 0 \n", + "29 202032 7 2650 689 4611 4 1 \n", + "... ... ... ... ... ... ... ... \n", + "1548 199126 7 17608 11304 23912 31 20 \n", + "1549 199125 7 16169 10700 21638 28 18 \n", + "1550 199124 7 16171 10071 22271 28 17 \n", + "1551 199123 7 11947 7671 16223 21 13 \n", + "1552 199122 7 15452 9953 20951 27 17 \n", + "1553 199121 7 14903 8975 20831 26 16 \n", + "1554 199120 7 19053 12742 25364 34 23 \n", + "1555 199119 7 16739 11246 22232 29 19 \n", + "1556 199118 7 21385 13882 28888 38 25 \n", + "1557 199117 7 13462 8877 18047 24 16 \n", + "1558 199116 7 14857 10068 19646 26 18 \n", + "1559 199115 7 13975 9781 18169 25 18 \n", + "1560 199114 7 12265 7684 16846 22 14 \n", + "1561 199113 7 9567 6041 13093 17 11 \n", + "1562 199112 7 10864 7331 14397 19 13 \n", + "1563 199111 7 15574 11184 19964 27 19 \n", + "1564 199110 7 16643 11372 21914 29 20 \n", + "1565 199109 7 13741 8780 18702 24 15 \n", + "1566 199108 7 13289 8813 17765 23 15 \n", + "1567 199107 7 12337 8077 16597 22 15 \n", + "1568 199106 7 10877 7013 14741 19 12 \n", + "1569 199105 7 10442 6544 14340 18 11 \n", + "1570 199104 7 7913 4563 11263 14 8 \n", + "1571 199103 7 15387 10484 20290 27 18 \n", + "1572 199102 7 16277 11046 21508 29 20 \n", + "1573 199101 7 15565 10271 20859 27 18 \n", + "1574 199052 7 19375 13295 25455 34 23 \n", + "1575 199051 7 19080 13807 24353 34 25 \n", + "1576 199050 7 11079 6660 15498 20 12 \n", + "1577 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name year \n", + "0 26 FR France 2021 \n", + "1 26 FR France 2021 \n", + "2 25 FR France 2021 \n", + "3 23 FR France 2021 \n", + "4 23 FR France 2021 \n", + "5 17 FR France 2021 \n", + "6 16 FR France 2021 \n", + "7 20 FR France 2021 \n", + "8 23 FR France 2020 \n", + "9 24 FR France 2020 \n", + "10 21 FR France 2020 \n", + "11 15 FR France 2020 \n", + "12 11 FR France 2020 \n", + "13 14 FR France 2020 \n", + "14 11 FR France 2020 \n", + "15 9 FR France 2020 \n", + "16 9 FR France 2020 \n", + "17 10 FR France 2020 \n", + "18 10 FR France 2020 \n", + "19 9 FR France 2020 \n", + "20 9 FR France 2020 \n", + "21 5 FR France 2020 \n", + "22 3 FR France 2020 \n", + "23 5 FR France 2020 \n", + "24 4 FR France 2020 \n", + "25 2 FR France 2020 \n", + "26 2 FR France 2020 \n", + "27 6 FR France 2020 \n", + "28 4 FR France 2020 \n", + "29 7 FR France 2020 \n", + "... ... ... ... ... \n", + "1548 42 FR France 1991 \n", + "1549 38 FR France 1991 \n", + "1550 39 FR France 1991 \n", + "1551 29 FR France 1991 \n", + "1552 37 FR France 1991 \n", + "1553 36 FR France 1991 \n", + "1554 45 FR France 1991 \n", + "1555 39 FR France 1991 \n", + "1556 51 FR France 1991 \n", + "1557 32 FR France 1991 \n", + "1558 34 FR France 1991 \n", + "1559 32 FR France 1991 \n", + "1560 30 FR France 1991 \n", + "1561 23 FR France 1991 \n", + "1562 25 FR France 1991 \n", + "1563 35 FR France 1991 \n", + "1564 38 FR France 1991 \n", + "1565 33 FR France 1991 \n", + "1566 31 FR France 1991 \n", + "1567 29 FR France 1991 \n", + "1568 26 FR France 1991 \n", + "1569 25 FR France 1991 \n", + "1570 20 FR France 1991 \n", + "1571 36 FR France 1991 \n", + "1572 38 FR France 1991 \n", + "1573 36 FR France 1991 \n", + "1574 45 FR France 1990 \n", + "1575 43 FR France 1990 \n", + "1576 28 FR France 1990 \n", + "1577 5 FR France 1990 \n", + "\n", + "[1578 rows x 11 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MDL/fB2A66d6AboTcHC47dNRUtntyDiaxUldQBx/Fss4yKYuNPas3jvuJmNBeZjjtVy/i6QVrsunQF+Gzzd9dP6TSOYTinsMBzAqLLiGit4noViLiQfNHAVgmXLY8LBsVflfLpWsYYyUAmwGMSDO2jkYytlLXjCOHP3yslV5qXIf3VgXy/tZSWTJ/7a56JR0BycK7ubMU0pt3tGHu8s347r1zO6U/jpxzSAdv4kBEgwDcD+DbjLEtCERE+wGYBGAVgGt5Vc3lzFJuu0Ydw4VENIeI5jQ1NWkuydEZKLWXcdOzi7AjI4VeZ76rKlE/5+ZZuOXFxQAC4iBuHF1xAPDZn/Ue0voL/6BkfbMhaz8H0wm9LZTd1RQ71x6GD8fXxLmvw+vpEFEtAsJwJ2PszwDAGFvDGGtnjJUB/B7A1LD6cgB7CZePBrAyLB+tKZeuIaIaAEMBbFDHwRj7HWNsCmNsSkNDg98dZoSOUEj3VPb2z2+swNWPLcANT3+QSXvdZR6efG+NJIrqEuLgUUc3LhNR+eFf5nv3nTXnYNqDOXFoam7F/a8v11fqAPB1liuk/eBjrUQAbgHwHmPsOqF8D6HaZwHwVfgQgLNDC6SxCBTPsxljqwA0E9G0sM3zADwoXHN++P0MAE+z7rJjGNBVIofOnpW/zl2Jr858LXqhAUTRP3eWssmV2lG3VIna6vF3Yjl4lzxhjzHrxpXFtp61ms90Qi8Jca6++6e5FYdUT4OL/vA6fvVMI4BcJOyLGo86RwM4F8A8InorLPt/AP6JiCYhWKtLAHwNABhj7xDRvQDeRWDpdDFjjMsfLgJwG4D+AB4N/4CA+NxBRI0IOIazq7ut9Ni4bSfmr9yMY8f7cSTZcA4VXFN9t6nwzbveBABs2t6GhsH1ABA5itV2slggC7jmr6tMWTn8OAd/sVIaFDNWOZhO6G0KMeiMWX50/uroey5W8oOTODDGXoR+zT5iueZKAFdqyucAOERT3gLgTNdYOhIX3fk6Xv1wA97+8ckY0q828Xt3CZ/RVQyV+KJzjoFb+VSL7nqS626mrBy6cWWhkC5mHLPb9Fx3KsTBJeZZsWkH+tcWMXxgXSbjysVKfuh5R78OwrINOwAAmz1j8WexSVfSQleta4k4hC93vUActrWW8PDbqypqu7taBXXXPUQ3rmxMWTMWKxkmUBVHuub56KuexpSfP5HVsLrtc+1uyIlDiMH9AiaKy9NVJEN2V49KCExXbaQiJx5xDoJY6fIH5uHiP76B+SvS51/oqJdVN79p5jzt88niwFB9EIzKkXWyH9MJXRXr+ExblpKgnHPwQ04cQsTB1+JTzTk3v4oxlz6srd9V66vLOAcpcQ7XOcS7yYpNAefVneLVVDtVaa/P4tlUenrPgnPI2lqJGfTM6jR19madNsd2X4WPQrpPIN774oXzUuN6yxV9V6zEvxZSHDVb2tpx28tLMh5Vx6I3mLKmQdbEwbQJdwQXnga5PtoPOXEIEaUQ9Fw41W4ci5q2yrkDGPM6NXbmuv7PxxdE36t9oX7z3CL895N6v4gO24Q17bq6IhIyhqWc7Sxuo1KFdBb7uk8bi9dtw/KN272s+kwcgSp+62zOoZtbyXcb5MQhBPd90i2bpuZWtJRkcUm1y2v6tXJgsrZ2hroaD+LQiQv718/ESeslE0/h7ltL7di4za3E3xbmP9aho/QolbRaIIpOvGmnurOeTVqFtO/Bw4dzOP6/ngUALLnq08Y6nMAaiYP6f8e7OUjIdQ5+yHUOIcqWDeHIK59MBBPLen2p5n0mdCcTWgLwzT++iWm/fMpaLyjvvHHZfnONQyaCnQ+fwHva8BmW+r52/VkppHkzpriHafKxL9+43dlfc0sbTrzuOW9jCB76PIcdOXFQ4Hv6q+a0e9mfk7H723y9jbuIOpjkx39/N/AoTit2kpLqVHFPpmvnLNmAbVUqx1NbK1XVWwgfsVJKuZIUTNByT1npHDiXop7QV29uQbnMEu+Obe0cc/Uzzv7mLNmIxrVb8Z+PL/Qa34frtrkr5ciJA0ecCCRd/Upw1+xliTJ/zqGLTFkdw3OdTtV95/kP1kXfs76jra0lnPGbV1Jdc8Bug6vutyOlFQtXN1fcjy/nkJUTXMQ5CANdtmE7pv3yqSCEhTIcsd6WljaMufRh3DlrqTdx5paGLW05R5AlcuIQIhIreW5VmYuVPDmHrrK00MlpmeN3G8QXuRpZve7KW8Moq/5X6C2vUuscMiBzpu35Px6KA+ilFSuVhEVj0z2YwqG0tZexfmurpQcZnAMRDxSrNgeJip5/vympcxAKVof1bntpifdar68Nxj1r8QZc8aB/oMEcduTEIUS0uflyDhVuBGXDivfdXLuDKasIvtd0p3g1lZx/dYfmruDSvKyVUg5LPHjYCHGNIbjS9+97G5N//qT3M+ZcsLhmxPtKRjj2s2oyQSRqt7/iH6I8hx05cQiRkjZUvEmbxEfe4qxu4CHNQYg3YrdYyWJNU/mwtBtIsYIIcjqRSnprpdTdJmBSSLvEejai4ituMYmV/vZ2EFk/7QHAbK1k1jmIl/h2150OJr0JOXEIwZeX7wt+47ONFfXja/ttQvfjHPTKRxeYYUNIC92lRatZp748C+KQBUxDLwnUobmllEq+3tIWX2sj0jUOnUPag4l5rbvbJfJfU656Jm7dhA+btlb8fvcm5MQhRFqdwyPzVrsraWBax7b1K1n2VNRr9TC9YL6cQxIddyeVKFZ1BKVLTFkNQ1+2cUf0/Z9+/yo+fcML3m366ndce/Gdr37k3SegX9MMuvAZqZpNQMwPoUPacBnn3DwL1zy20DsIpw+Wbdje4xTmfZ44PLNwLSb+x+PYFto+dzSHamre9tJKY+qykN36cr6ZuebNul1nbMrqOgHroDPjrIYbyhKLmraiqblVKZPNMW133Fry25Rcw//p395NlO3Y2Y6/vLlCu34lnYPYj+ohXaUezrX5p32OPD5YViLc9jLDsdc8g0v++GYm7XUW+jxx+M/HFqK5tYR1oTVGR3u5+nqNmq7pKs7BxBlwGTn/vbnF7Altgu4lrMbfxMY5mFrV5k/uEmul5Ni5BY+9bzPEYJJW3U8Fw//Zw+/i2/e8hVmLE1l9tWuGseQsmfr1HY9LH+P63YRbX1yMOUuS95UW/P19ZuHaqtvqTPR54tDZSXzMYiUb5yAQh06iDi81rpP+NzqtKdZKX719jr5By9FWd0/V3GclkU1rNNQhdWyllGPWbfq6oV/58HvpGlYg63csYiXD/dq8tvk9bNUcCsSupPvy0DkA/if+kmP3rzQK6w1PN6b2l9Ghp0bryImDuu47WqxkWCm29e37cgPAw2+vwphLH8a0Xzxlrbdq8w5s3LbT+Ps5N8+S/l+9RXN6pXjb4CG7fZHVC5NVO1n4OaTFpRpPeRUfNm3Fu6u2OOvZyKH3fWR8v5sMMnt10zeJJH2H41RIp3yQGQenFULz9CwqkRMH5bXqaFNRIwtt6Vdc3C7Z/sV/fAOAYTMXcNQvn8bHHARExLMLm6Lvi4XwA1m8SLpb6qinYLRW0vo5pGw7Rd23lm2KRJkiVK7HyImlgOxvkP6B2bz3+Yana/aLt8xKFiL5DIyiVk9xkDo8NUd1WmuljGlDj4WTOBDRXkT0DBG9R0TvENG3wvLhRPQEEX0Qfg4TrrmMiBqJaCERnSKUTyaieeFvN1C4UomonojuCctnEdGY7G/VdH/y/x1N3M0KafM1kh14hmPxDdkBAA/NXYl5y4PAZi8vivNc+ASKc6HajG1ZoKgTK1mGoAsIl2bMp//6Jcxf4eYIsoAslrSJlSoHf4907e/Y2R7ponTWSpWIWkW0K2z3TCVvSFpLuqzTpfbUKLA+nEMJwHcZYwcBmAbgYiI6GMClAJ5ijI0H8FT4P8LfzgYwAcAMADcSUTFs6yYAFwIYH/7NCMsvALCRMTYOwPUArs7g3ipCR1sr+dp+m67pStb0ufeTCrVKw/Eww/fU7Wgu3m1Iv9Tt8BAMUtuWkZ2VgSxaB3U6B9QVtfUS12XgZJjF2tK9Pydc+yy+9H+vGfuRTbXj72ua3Yp4IMk5PDJPzmXe1T5yvP+eRiKcxIExtoox9kb4vRnAewBGATgNwMyw2kwAp4ffTwNwN2OslTG2GEAjgKlEtAeAIYyxV1iwGm5XruFt3QdgOmVNvj3R0ZtvJaekzo53b4LukVT6mFzT7C8m11krpasPxMHbpLqWQTRpREJZrBx1OrPgLpinWCmL8etO6asUxbtaQ+chTSB85565Xn2qYiT1Hrv65N7V/VeKVDqHUNxzOIBZAHZjjK0CAgICYNew2igAYtjR5WHZqPC7Wi5dwxgrAdgMYISm/wuJaA4RzWlqalJ/rgidrI+uyDJDDrmc+ZAqhhg+w103nQllVV7TFVyrJQ6W+oP71WbSb2dAlLpU4wSnvUbty9EIY34e0gvXNHsp4oHAD0SESqBSi5VS1XYjCs3TTdeHCd7EgYgGAbgfwLcZY7anpptbZim3XSMXMPY7xtgUxtiUhgZ3mkIvKKeMDtc5VGCZUTaw3d0CmSikNTqHTo6OW1+jEd9YGtfedgZj6Qh2uSONlcSTPuC7ESvWSlVyxqrncVenIVXR06yUOLyIAxHVIiAMdzLG/hwWrwlFRQg/uUB6OYC9hMtHA1gZlo/WlEvXEFENgKEAqvc+8UBHBU04+qqn8dkbX0q2biIO3czPwReVbmbS5l8Nl6Ars7SXKraSpd+OEnqKIpGsNhXX5rhfw0DsO3JgRjoHdxu+1kr+fcr/q34NaYlP9qas2bbXWfCxViIAtwB4jzF2nfDTQwDOD7+fD+BBofzs0AJpLALF8+xQ9NRMRNPCNs9TruFtnQHgadaB5PaMm17Gr58JAmt1lLXSik07EqlFAZtYydwWy2Yf9ULaaW+25IYWkfaFq0qsVME1WtbV2pCOmFT/dMRW01iT2eB6pizsOIu15bMRf7B2q7tSCqi3p06bSCxGDqrDKRN2c7SYLXUQ5//9Nc2Wmt0LPpzD0QDOBXACEb0V/n0KwFUATiKiDwCcFP4Pxtg7AO4F8C6AxwBczBjjfN9FAG5GoKReBODRsPwWACOIqBHAdxBaPnUEGGOYs3RjlFJQXQYdb61kGpf5GimvcZXjW7OlBU+GqT1VvL50A8Ze9ogxZIBug8+CmFbTxAvvJ3VPlcjV9fdmESs55mLHznaMufRh/Oa5RcY2XO3uSJHmtFonOAKqtGUNPlzeyAzAdU+8L5VVzzmonIJZrFRTKDjNr8VnMGxAUreUfnzx95Ovfz7Vc+1K1LgqMMZehHntTTdccyWAKzXlcwAcoilvAXCmayxZYMsO+aRbUhaS6/R3yfHjglSHFryz0pzo3OghbVNIS0nvq3uRTrz2OeNp/8n3AsmgLk4OkI1Pgw7V7A2Pzl+NUyfuYfz980eMxv1vLDf+zqGz4qlmpl9eFIQfufmFxfj6J/dLcWU8ju0ZbSLOAw8DqEAVrS31ikpyK2R9ILMRi5piuvvMInWq+s63lcvoDz8T5a5En/OQVk82anpO20Y1bEAtTp24OwDgyDGRzx8YY5E53XurtuDTN7xobKMSU1bRVM/qD+HxltnEQNvC3wZ62tb/9K/JKJ0mJKzCHEP1JRgPzV2JMZc+jFWb4/Ad4qX7NgzEd07a368xBbbpdFlQXDAz8GxWzSzTICvi4CPKIFRorcQ9pJX/LRcY26gU6rujvuPi/7XFQipiZBvab59bhAtue81cgbeh/P/tu9/yH0AXos8RBy/5qwHFAmHCnkOxy4BaHLzHkKj8N899iPGXP4rNO9qwxhG2wti9t1jJwmFU+ZJt5cSh3slQAqhOJs6k75VbK3Fc+/dYVCFOA5ESLtoYXE4zRmU+H39nNT72iyexs1T2FkOVUs5RxWIlywH3f576wHptsUCZKWE37UifA6FaziGh4LaYstYUSAkimezcEiNQwi8fXYCnFrgjrarE62mPa7oD+h5xUP5XH5xdzhwsmwKRtKD/8GqQt3bLjjbnllaJQrrkqXOoNl1ia5gxrL42e5bXtvmI9/TiB+vwyLxVqU+x8r3H3wmeG59HnR8/9A7WbGnVxkSSe43RVsUz2b5zGsExAAAgAElEQVQzffjzSlATeg1moT9KG4Ax6Lc6nZr6DidExcK/NcWC9L/undnhmRyJY7ODIPZaa6XehqRlQwr5Y0Qc5AXJk6nU1RSci8kcgdJX52CGTdeRBqZ9sprTpTot766MXWXEn754yyx84843nIEDE2Mz9FVmzMuLW6dPsT1Ksf42i6iu1F7Gg2+twFdn+gXQE9dPVtZKLtQUCITKdA4cfI7bSunHXO3emTjwWTiH2iJJ9XXctpiTxGdsp1z/vH183c3+3BN9jzioDjgqS2p5kFw3RQrnIObodfZvVEjL/8/6cD2m/eIpbG0teXMONz6TzjLGBNNemqU62mXFk1ZWbyIAvjolrZhIWSuquIpjyfptxrbLDPjW3W/hyff0FmKJPpn+e0eiphhwV9X0d9tLi/Evt8/Br5+1P1ddF2mjpibaVAY+tmGg9L9IAD5Ys1Wq704U5B6b6yDTQ2lD3yMO6upUOQfbg+Qx/wskL0juoakLDeDoPoJKlK55fCFWb2nBglVbpKiTptPdE++u8ZJ/djQG99PrK260bBpZnKzEzVpszZUIxgZxWA+8uTzaBFRCMqCuJuy3+vuQw7PL7Z14kMs+vzLUhhFpTaM/9ZDdnW08s7AJT7y7BnOXJX17RLy9PMndylGH089hUueg/h5X2NHWLvXnDPeRejRJdLWHdqXoc8RBfUypiIOkc4gr8pN9mTEPKxx9hRc/WKctZ5ATqJvaX7Jum/6HDOEjVtLFKHJBd0+6nM42iBaHYntt7Uwa921KOGcOQjICKl8aW1ra8G9KEDhxdJXkrDbBFp59hscmDQDXnXWY8be1zUl9SaCQpi474YoEIQudQ8JaSSEWYn2nEUcGc9JDaUMfJA4uzsFybbEgEofk72XGvAKP6XDLi4ul/8XtpuSpc+gOyOpFSLvfijoAcbNRxUr3va73eSACrv/CJKmMc2z/78/zEvVX6vI6Z7GRWNqrsxBe8f6nH2jmMHTmsTVFfrVJ5Gm+sUycIKtsw2WtZFPsu8RGOefQh6CyrerJwccrlkj/wH3WgO8yiZOnKNYXKRfaX+euTOSDNo8tW7FINdiwLZ1JpJirRxxCqVz2ct4jTS0+72quZ7VmlAbSf7hGbNy2M1qD6vOo06Wr02DogFo8ePHRiXLd2q4rFnDV5w816hyee7+pw5MSVRs7zBVbqVnJb+2yVpLrVv9Ud7T1DI9oFX2PODhOGbalwBdSgUh74mDMvUH4bp7RWY4xSedgWssmKcw373oT59w8C2ubW/Dtu9/06ts1Jht8X6WzpsQxGHVTctWj73m2FMCkkG4rsYqtrLg4zxkSgsmf1WDeis0RF6m217/Oz/8EMAQS1Izvl5+biFG79A+Ig6ad82+dbTVPzeJAIYpUbe2ZNuqEkYnykmxpkQ8aacRK1d7dix+sszrFdmf0PeKg/J+Gc1i6PkgNWVMkrf365X+Zh4Wr7d6oaTeQjdvbIv8DoPKX8ddPN+Ivb6201kkTc8YEX8sTUUSid4JLB5PjUlt72cvKSrc5Rrok5Z6WKSlCsxYbcCcpdSoH1Vv8T5Sb1OlsbKMkUCan5Erw2+c/jL7bhmD6TS1X/RxUa8I0nEo1U7J5Rxt++3w2FoRdgb5HHBLKKvn/O2d95GyjvqYobdgcL3ywLhFULNm/xyAFfP0Pr+NHD86v+HqOof2rDyAGAG8vt1uj+A4va5NNcTOsxFdADR8xoK4YeTerB4gzlRShLPqs7Ea+feJ46X9+6lfX6oAUnEONRgSVNpCgiKljhitK/44hJLpWLzl+nPE33ViSRibq/+a6KnwJv9rH+q2tOOwnf8cLBkOTnoA+SBzi7xP/4/GEPFJnaqeiX20hcnxLC9NiSygbhRdx3dad0fdKX8lBBhPT9QZvXx0Wrm7GP/4qmaNChO+eIQb3081JWtN30+bW1q4PdaFvIO60pkDRCdRFXyIdgWbM/YTc1KYN9VvTZeLARWRq7UGeYU0Af2svZviuoqZIeOcnMxLlpud9zLiRGL/rIK8xiFDXQk2BIgs407uTxldp1C790+kcrL/GuGDmHOn5bmvtmXoGEX2OOIjwzUWgop+Bc6gGYiA/Gyo9sJlERpN//qR3G7q8ySKKBfI+aTWu3RpZkWjfz5Q3Km6G3xICm5XamadCWkZtsRD5SDgtWiw/62wJVCKh6kv4oT+pc/APa+KbvEgMnGe7j5piQeJGXHqWGsUT2Rdqe8UCRf5Fpr7ENTdyUL3RPP2uf5mG+y46CqOG9ddeK/bJsbNUxtL1bjPxpxesxaPzV0f/19Z0UDaoTkSfIw5p9pwDdx+sLa+vLaClQs7BV25qWlpp8wNE7Xu8qtUqF4uFdLby6yOOKHnRXA8OToTpoJwmBIU49mKBIoW0y5GO70W6WxdNacsWDkNETOiCinVh7CObKauKok7nYFU62Dfz2gIZ9To6qAHu/CFfI27UpoOHWDqgrqhEL2jHlY8Exg2H7TUUewztj5+ffghOPjgw99VxDurcPePpXLpFiLHUU+MpifDnU3sJsrCu6FdTTOSt9YVxgVcxrJa2dqdsM037lY6lVhDF+IDPRRYvkok7KLX7WSupCunaYgFt3FrJKXrgm76fiMJ1u+pJ+a4LP4ah/esiIuGDgqaqTfEfcA7mkdUUSaPXMU9sTaGQDedA5BSRSYH7wKT19MO/xPo6vkYG1tfgHw7bE39/d4127dUUCaI7SHsFN9JT4ymJyDkHC0wbebFA3gH7EsowU70qiNYVD87Hc5qMaJXCdFJ2zV2xkE6UwOcwC2ufW19arC33jdEkEpfDRg9FTZEiE2IXwePT5boN3/vkh2Xe7aD6WozbdRD61Ra1hgVL12/Da0r2vhoNdbB17yKgNcVCKpPgYtFgG+uAeglRPDafgxVj8v8LVsc+GuL4OXega1P1ePf1+pf0Nz2fNvRB4uD4fe/hA+K6hsomm3AdVCLiyzmYsrHp0OiRkzfNWlVDRfiituiOSiviv58M8gx05IsUKKQ9dA4Ux4U6fO9hKJKokPbjHFww6RxURNZKYbvi8I8cMzxR/5P/+WxiDnWcw0cbticLQ7h0DrVhiA2OmAvSX1RwiKlMUMcg8iemxyC+U3sNG+BlqsqZMN2zrVE4NJ04z6WH6qle0SKcxIGIbiWitUQ0Xyj7MRGtUHJK898uI6JGIlpIRKcI5ZOJaF742w0UrjQiqieie8LyWUQ0JttblGF7MYcNqJUsQkw108ShUdeQUefg11zF11716IIqeghgCqrHkVbn8NDcwO+iI1+kqWOTm6kJx4wbievOOgyXnnqgdABwcQ6m4R87fqS2nlOsRLJYSSZtfnOl0zmcrAstzfsge8hudYN0PbJK1bE6s1ROLNWN/MOmrWgttYOxQD8478cnY9Sw/saxieIp/l1HHHwOOH9+c4X1996gc/DhHG4DkLRhA65njE0K/x4BACI6GMDZACaE19xIRNzE4iYAFwIYH/7xNi8AsJExNg7A9QCurvBevGB7ZqJY5Ht/mps4ke8bhgJWo7LaoMZ1aVxrcJLzXEy+nq9Z4uP7jQAA9HMkAQpSMKYfTCXjH7VLf2edOy6Yih/MONDPCQ7BBvm5I0ajX20xOPlynYgncVDvQzU9jXUT9rFExIGPzRBU0Ia0uY9dnIPqY+Fl4FDJWlD+L5UZasOTvJhVb0tLG0649jn84L63UWbBsxvcrxYEs5ObJFYqmMVKaskP7n87kdHv3/+U5K5fWbRe6LfnUwcncWCMPQ/AV8ZxGoC7GWOtjLHFABoBTCWiPQAMYYy9woJZux3A6cI1M8Pv9wGYTpRGupkOtmcmbgh/CgO0ifLHfjXB5hgswKDshQ/ssn4uOuF48yO9E5nPy3bMuJE4aI+kBVVWy9DMgvs9jrQ6B1e/rr5cGDNioLesXK0ThEgJvjt1DuENbFMOAgVljLFVU7K9W86fEn1POsElxTkupH2FXPkc1Ii10XgqEL3uO3Kg4Zdke+Uyi0xoxeewPfQjeHnRegAs0tMULFy9OCOFSOfgHgNjwPyV7vhSnBM2tdvTUI3O4RIiejsUO3Ej/VEAlgl1lodlo8Lvarl0DWOsBGAzgBFVjMsB81PThV7+xnH74Y4LpgIAakPWuiCw4OsFBzUdVM6hrZ1h1C79ce2Z5rDKJtQWSe+QZXgbtlbox6HC16GqplhZ2OfuIJ9VN1MxuKIrDzQf/Rd+K3tOq6Idm7PcdCFXQ5JQJdtwQV3KJu4n0ms4MsGpxMFHrGSqM3H0UMyYoA8/ro6hVGZRvgnRuEDUxwScQ1BeKJjXk/iMCwZRFaCfY0nc7PEM+gTnYMBNAPYDMAnAKgDXhuW6XYRZym3XJEBEFxLRHCKa09RUmXWOlXPQyMyHD6xDfcgx1Eap4GILFdEDVjtmVWLMGAqF4AXxHReHSeFruvTe15YZfkkHftuuMUZJYzQVB1u8eyshDmm4gUrYUDEs+yGjhlrr8vFvUbztVe7G9zSpWtJUwkirBN0UYE7DnGhhEila9XIWYqNTmJsa5A5lurwmPCYUv181S6PUp4e10ubtbYnnCEAyIxbDnk/bV6/T6vmkoULiwBhbwxhrZ4yVAfwewNTwp+UA9hKqjgawMiwfrSmXriGiGgBDYRBjMcZ+xxibwhib0tDQUMnQPXQOco0BdTXRyZHLPqXcAa4TlHqCCxeyerLzWUwmD2TTGForyOerQ8HwIqng7L/u5ay3ENGOYsFJ2DDSolCIidy+DWYxCGAXrcgV7fU5OIeqU0j7TlWtYnHjY3pttVZK4WMBuHUYJr8U3SU1Ws4hwOotLVjUtC1qTfXXENuTOYfgU52XbxoiF4v7woZtsbRgouHg0B244WpREXEIdQgcnwXALZkeAnB2aIE0FoHieTZjbBWAZiKaFuoTzgPwoHDN+eH3MwA8zTqQJ7O1XAzllWLcpH51RfQLWep9RgRmrgVB5KBLniJC3ZjKjG+2epGDDQXy968AULGjXqLfQlLmq0NNQlYew35pBZxDmrqV6hwsYiARpmdnzvtgb7A2msfk2HzfjLqaAv7+b59I9K1CZBzEGqX2Mv73qVhfpnJBznE4dBimZ6K7pjY8dHxXUAKLc/7Rhu3Re6aa0JqeTcQ5KAvTFCpDbEZ8501Es4rstN0GTg9pIroLwHEARhLRcgD/AeA4IpqEYD0tAfA1AGCMvUNE9wJ4F0AJwMWMMT6TFyGwfOoP4NHwDwBuAXAHETUi4BjOzuLGTLCzusHCOuf3s6KyAbVFHLH3MPz6n4/A9IN2BSAr71xOVgnZLws8divhHNImgc+MOHAnJBdxKPIAacnfbMRv7rJ0oTJ8kYqAKLVF8YSTOBjK1X2D1+PtXfiJffGpiXtABZ/HeOOOx5YmJ7YY+M51qCACmND0Q3NX4lohwnCCOERskKE9y+wzZtZj6d5PzjmIQTHVZxLpHEgmhKYDTSGyVlLKw4aOGTcSV3zm4Mj8V6wmts9Fzip6A+fgJA6MsX/SFN9iqX8lgCs15XMAHKIpbwFwpmscWcHFOZTKZcxZujEq47LWTx8av8SiQtp1kB8xqD7Rf0ETEsBnLRnFSoY3NDviYFbeiVCdt0TYrv3p395NPSZfxzbAj0jolMC+J30T0UzqHHh7ARoG1WPSXrskruOn0SgontCMmpvABnGOlm80J+wBuEI6bls1ZkjLOZDD3NtkbaZeMnpYf2348UR/Ub9yIi6TMQGfY/VwR9HvhN2H9hPGldR3AOniXfU09N47M8CpkFbKdA+fW0gA7hNCw6A66f9A55DcjHzOGQUirWLRNISsZPmcOLjESpz9V8fT3KJX8nU0otNrBToHUWZeKeegM4kMPpObvohoHvlYJOJgJ/imsN6n/s8L1jGqXKmae1vnVAeYdUmqmEr6jfSWgVCuOedje+OBbxwtiW4i3xNlcvkJXr2PNkNgJO7QqWaJE29TPMBJ0XWFUdYaCFdv4Bz6HnGwbMPFAhIrWvfwRQ9pl65A3VBjhbR5w3r47VXactH2XoRpCCYLlUNGDTH2re03MvszncKC34sFWZHK8V+PL0zVnwrVHLilrb0ibsBaV/lf5A5dr7lpI3h/jezwyJhfezUFlXOIR+ciDil93yKofgnqppoUKwUYMVA+/EjtGW6UsWSICg5xjU0ZMwwNg+uld4WPSz2ncKtBVedgEsPxGFWbtqvEIe5LJoh6zsEUd6mSg9n9ry93mp+3tLXjjleXOs2rs0DfIw42sZImkqRO4SRaRLgWQTK2UrAAk5Yscb3731gOHUye2eaTq/6XT+6fztKL7wvifqE7+dVGcly535mvLE3VnwrR8/ShuStx4I8ew4fr3DH2OSoTK5F3QD3TA0iIDvlndFrXj6xG4cDEWgftYSfspk3XBTVNqCpuSYqVgromhazLb8LEOagci9o3X1vqe8VzXSR0DgbOgXNY6mYcmW3DbAggfjcqpDWL5sJP7KutCwBvfLQR3/3TXPxIiCKrw20vL8GP/jI/ctLtSPQ54mBDUbP56k4G4unExT6qJzAWipXEA80n92/wsrAwi5X09U26yy8fPVapZ78HzoKLp7rjDtg1US/WOWQLMcnNE++u8b4ulkP71FUV0m6dw4mhgYLZrl6vc4Bm0weAB77xcel/nVjpmjMOxcfGDjf61/g6LHKM321Q1IfMOTiIg/Ip4rFvH2vlHNZsaTFyODrza3H/5URB5SZ59ALRygww5/MwTZMY10oKUS7UEdvfqHAeUX3NvY9rMGfG4x7fa5tbjHUAYFtIzHyjDVeDPkcc7JxDUuewr+aBihvHglWGWEkhVPaPm7Jub4sXd0F5kUxDLBTMDj46GEOOK29Gm8MCZvSwwIS3rSSy1kk2uzayVqqMPBy1r94xvr8jppMRkULavWG+s1K2mJLCMBhvx6yA1yGmDXqdw4Q9h4b1mFRfHP+Auhocvvcw4zpIwzi8+IPjpSivks7BQRw4dM+6X03RGj5j1uINRq5JJA68aXGT5mJaNbYRNzcHycTaxDnwOTVl5eO6QXUsgHxfuw2RDU7i+sl+z5wyGoeOHqoNuy56qvvg3Gn7eNWrBn2PONhMWZVTx/EHNGhfCnHjuGeO3Qt5m+IHwRfdboNjSwjVm9S0oNME/Av60perp0v+Qpqa5pvzbCFngMTphJ+qOMSF/RTnMtOzSeuAxeH7ogHAX95aKf0vhmEw3Q6fRiPBVrpX/SaSeg5Iv/P5UJegbR2YFMc6cKIP8DUYQzwIAEnu0kY4gyHYQ6kkrfWCyk6xUjiO99fIQTFFzkEck0nnQMpcx+MKPtvLTOEckgejn542QWuKDOjXBBHhiL2H6UXDnu9Ma6mMfrWFihw706LvEQfHghV/twV38z0d3/e6TDy4zmHYwDrsObRf8KCVcZlYRpMTnDlHhGGMym05lZyaeZAjXwbfVUWqC4eNls04fS5LQxwjU1aP9+gTih5GdoIznT7tYzKplWJxkSrK4qdWSJ9qQ2JoDxW6Z1UJ1DWoWvUgGmNyIIVIp2Z+VgkfkLCqTqyk4xxUDDDoHEzWStGzU8sF4mDSOfAh7D18gMUk1ywa1v2kEyHqsLNUTpURsBr0wTShjt+FCibqrJ5ObFi3dSc272iLWEmucwCA579/PADg6394XeqXvwBTxw7HVZ+biIH1NWhpa8f/vbREuynoTluAPwFrddjO12qJQ7Je7CEdl5ky1O05tJ/3+Ko1C/SJ4HrJ8eOk/yUnOMM1ptOn+jsHr8dPs+rv8YbFpAts+hAVk/cZpi13gcQBAnheiTZsTrRjbi8N51BmDAWQtJb59T55pGOFtN4XKDE+Sq5VcVyBs6qpXy4WJCOn9qtnGg39GsKE+76rpXbUVypmTYk+yDmkOHmayi0vp77T+KvIrtYUC6F1iWJ+F57a+tcWsW/DIOw2pB/2GTHQ2K8phpJv7lsxXIgOOgsY3TzGsZXi395frdfJ1NcWExuLabiV+mvw52eyjBFhE90wps8fwTmlMmO48dnkZpAIuhje4e+e+xBAUrmuEpv73lghlcf1zCKbqz9/qP4HB9TzztL1ctY49QBiM/MtFMiZLVEXVmb24g1aLlYkDiZnSu6sSpDXi5pwKeo//FRFmbxc7UdnrVQgM6em5nR/9bLp0TVp5kVFa1vZO21pteh7xMHym8urM6qnaefzR4xO1PvuSfsDkDfLMmOJBaWuB84Kq5taQfH+5FCJw89ODxzRXVZIHNzr9rF3VkdlophFx8Xacu+K3ZpEVlpCZzydMlcVK3w4B/WlLCicg84Zkre7bMN2XPNY0pdDjTzK21u5OfBWXr1ZtkyJTrMI5m3usiD3h1k3kZwNV0ImE1wnffUAwevq1gEhaRrrwtML1uCs376C37/wYdxH+ClyGSbiwPViMUcQ1Bs5SK8wNnF9/HrbwYoPIY1Oa9fBwThMnI1BgphAa3u507yy+x5xsCxYX2mRLrT3NWccKoXYAIAhXJQklAXWSvZxcbGDSkSKBmulVmUDjm21DfJWpf/WUnviZCgSFl2yelHPx6vyRSv222LgSmxJWVSk2WR0XJAfcZD/l8JnMKZ9aTkx9PUtUHUzuo2OH1DE33SEC8g2mq0rxPbR45SUp/zTqnPQ499O3D9RxvNbi0HtuC+C+Pj4vKgWbP3rYie4YFxyfRUiIZbLEV5v4xyYVNcHUV0yPDem1NPgntc+wsNvr4oITUejDxKHZNmeYQyVIf1qvUxKA9ZV/rVAQLty3IitWYLy1lI7Zi/egC07ZBttdT0Yze8MYiXVIkp9QQDgX44dG33vpwQLa2krJzaGdok4JFeszqGnTgk1DZj1GariEDATM9cmePaRcZR4kWjFpzEP4qD+r+ocNE1womPyLVCJKm+Pi990JsT8BC/OjU7kFbSXHXUQOQfdhs/NbFXohhBtsIa+xjYMjOoMCX1oxGc8oK6Iqz8/EadM2C1sL6mQ5r9xcGKhzo0pSoD1BqARK4nWSuFnOuIgrBVNl1HuDksbP7h/HgBgT48UuVmg7xEHTdnlnz4Yb11xUkAcDG7yIlSzP16mWlJEcs2w+K9zg7AY766SUw6qDkMmvwNvZRtvRyAym3fE1iYqW8qTtIs4ar/Y50B38j5MEzCOx7cR29K9nPdf9PHoXsSNaK/hAxJ1gZiL2byjDa8v2Zj4ffjAOm2YE5cT2x2vLo2+qxt8sFnG1IGQjFvEN3nTBpTMAyGLC3WHAH6CF5eSLmIs4JejwYTvnXKA0qYoKpLrXvEPByeuN8U44m3ZxFSiEpdzx2Ldwf1q8IUj99bK303mxZHOISIO4adljnT7tKpz+Nb08YnxxRt5CuoQQncoEtv3MVFNY65cDfoecdCslbqaAnYZUOcdEtukm1BtqmPWNTzFmGyuDaEGVPlx0WLCyHHHBVOFQHlxf0XlFMu9noGQcxDaHVRfg0uOH4c9Qo5KFxVTtzw50eGL/52Vm6XEKByT9xkWnczFDe5gQ2gIXuWC217D6i1JD1JZP5CcINMmKoYqSIqVSCIuRIRvnzheqpPM9SxD9c+IOQceEVQnkgnWoNSmMjZOXColDmdOHo2LFess0SjC5wASi5WSvxEoVJozrc5JPGvoEkmphxfx9vk9q7ee0DlE75yFOBjGL46HR80Vx8cPebY9eqAh5zbBfsDz2fZ9c7pXiz5IHJIPhr9syZOE/iGqHs0c6kJ0mTqK9cQ6x4Ty3Z/844REv3xh7djZjoWrmzFniZw0b+zImGUXT6Z1ygb/4vdPwF3/Mg1AoPwUFyyFVhjjwnwAOrGJrqw+0jkAjWu34tM3vIgHFecy8V4YY/j5w+9FZas260MH8Gc2b4U+70OBgrl/dN4qea4jMYn2MgnqKTDIBBdfrxMl2nQHwwbU4l+ny8SEXz4yDFa3/24a73sEhE7iHFTiEBIXV5RcE3Sbmnjgudfh2ClCNwQxPtHLi9Ylfxc2t5i7FteqeVuKiYPcsWjKGrQHbT0ROj1LgitTRGTbd5Zw1+yPpL5E8Pv44rR9tH2arJXSiKqy8mVx9tMpvXQj6B5MTIn9lKQm6s8348tOPRB/veQYwUXf0Z6yYMrlwC1/uBLxkpswMsbwr3e/iVP++3lc/dgCqU7gvBPKtAVHJnVBDR1QizEjAzFOa6ks3Q+fD15WLCTTmuqOOBHnUGb41A36ENHReMLT/m0vL4nKVOsdDtceyO/3ojvf0Fo2+dm9J9sUPZqJANU3MZ6nZHvXfWFSQgzF2xsbipv+4zMTEteBEIqVmFgkIRZLBQM6epw+7IgJOnGIWHL5A3LwN92GZdNPEFFkcKDz59xtSL9E3CuxGTWBjth/e8wiSuBrT9U52NaOjnPgXSm0Iap34e2va8fF4fKNqSkWwFjSyTCex1ys1GXQ7RMi5yA+VtOeYqb+QemEPYdi4uihCashk4xSLS+VmdZCSNyMXgs5hmYlT4JoDeU6WRYF8YRYU03uUyTSWMwk2xNPfCbHPPF69cQ9pL/eJ9O1uYtiCIlxCP/xEb/oxEqxyoFpDwSR6a6m/VrN84tOs+HU6GJG8SUoc3Ly4LiYr1RmWLe1Fe854nsl+jBwDmlg9XMgs2wdgJTgSPUKB+wJdEwEn78vqs7BKlYyvMfidfHcB/+/2BhzQropizgbQ79cnLtVeW/TcA65WKmDoJNJ88lWTxLGZRVuHGsU+fex4wPfAL64E8qxsPHjD0iGzFZNWXVyfvFUxDemHYpMt7YY54oQTyc6whSz4ExKEclDEfBNrFBIXl0gwl7D+yfKxPu0QTyZc1z+6YPxu3Mn4wczDpTKXe2JupnfPx/byXOvdD8lfpL4qZyDekqOiKtBKasiPs0yYx0e8VdUT6nVOOEplRmO/89nI73Os/9+nO7WvMYG2OTvusr8N829g4xZC5N1k+2oxGHv4QMiEacprzc3SBDXdFt72ergSRpJAX+WEXEIy/VWWcmJdHEsg/sFa+xt+rEAACAASURBVFINRxIppI2j7Xw4iQMR3UpEa4lovlA2nIieIKIPws9hwm+XEVEjES0kolOE8slENC/87QYKZ5aI6ononrB8FhGNyfYWFeg4h6JJ56AH36RfapTlqd84bj/86etHYerYINKlGvmRK+euPWuS3KDSb6mdac1HRXkofwlET9abzjkCo4cNiF5+UeGpPS2Gn2UmR+H8wwUfAwD84nMTceohu+OIvYclrXkIePRbn8DrPzwR//flI3HWlNGR05ePaK6o8RUZVF+DkyfsjtHDZKLjak8MX/2/T8eeylwO7SOaVw/6qhMckBSRcIV9WsUwvx+d7JgoOHWKm+UARblZFMRKzUI+gjEjVesof9jyL+gMKaK50W2ahZD4W5jHseFYx4wYGLYTN6R6ABMRfhRaTJmspLgeRuREpvz8Sbz6oayTkxtOHhb5iV8l4LyWeGr3OQCo4JyDyvG7IK6xNHnEq4EP53AbgBlK2aUAnmKMjQfwVPg/iOhgAGcDmBBecyMR8ZV9E4ALAYwP/3ibFwDYyBgbB+B6AFdXejM+0LPBnHOQvTrNAdWC+mqiECKSQiCr8tSWUNSixuJXpFloay9rI5HG1jF634NTwwiR/AVxZYsST1m/eCRWDPNNZtyug3DTFycHJzmN2GVQfQ1GDKrH8QfsimvOOCyaF58To03soFpp2dorFijht6HCxy7cms+B6TmdY0JOUSt31/Shbhw66QAhWAq8zjVnHGoVK1UGPVEyTbPOqqrdsgkSAq/6MmPGZ3fG5NG492tH4TOT9gQgv5f6HCrBZ6xoln/n8b9E73HRfFsH9b0D4jmNn1H8zgHye6dTSPNN3LR3cK4omWfCzE0CshSgE1I5APAgDoyx5wGo5Pc0ADPD7zMBnC6U380Ya2WMLQbQCGAqEe0BYAhj7BUWzNrtyjW8rfsATCf1bcgQumcmyvt8Xjc+Ohf1V4N7rd3SCiDphKb6TZTKzClWsqUT5B7TIlehm1DR0/bFxqRViTRG669hHeWUZa9rFjsM6acqcs3tHLd/gzNkxCf3b0g4TalIOpqJnE3gIX3mlNFSjKU4vHOyPU5gP3fEKJx6yO5BK8rGpttcYqMDcx0uX7/uifet9wTI8v24j2Q929rX+WOUo01Q1xZFianEE+9j3z4WD//rMVGdqWOHR8pVm1gJgHDw4CUGzoGPz2MR6u6ZX9dfiNUkjk8kDvoDgL1/rpdrU3RynAA//o4+mZVqTdgZqFTnsBtjbBUAhJ88LdgoAKId3PKwbFT4XS2XrmGMlQBsBpDO/CIFdKxzWZAv+ugc+PpQw1aoiFVZDI1rt+LWlxYH16uxlaDqHFjCLyHoN5Zxb7EQJk44xNOJdkMIuygrL7EO6iZl2tgA86lJvt78Ah2hRBaNPczlF2rsyIH49TlHaDeTr39yP+n/KfsMT9QRkVRIJ3UOo4cNwEuXnhBfY+CUFv58RsStXHfWJJw1ZS+pnk3nsLW1hKatrVbugp/aTbnGRRyvydinA+eaffx3gjKGt5Ztig4Vd371Y9FvRclaKW7vwN2HJDytVb0coM/focaTUtcOP0zFTnU++g5ZUrBh285I7DPzK1N5pbA9Pg6HWEljanvAboOj77UGHxdXZjfxfn3Etlkga4W0jqYxS7ntmmTjRBcS0RwimtPUpA8F7YJuYkXLBF2GM804guscT0mUwTeuNVuUqCeYUntZGyY7YnEdbOWJB9lPyYn2mFsElTT11LWHqD0djh43Ald+9pCob3GuPzVx9+i7ujmYXvR9RgxAv9qiNjDgqF36Sf+7s7UliV+ZMSzbsB1PLVhrtfBRiYPJFJPXYtHGrz8C/nXuSit3sXzj9kSZCd6nzHAN6g4J4kb2xWl7A2G9OwUPczH2UqEQj9slH9etmb9piJ6oS2hrLyfGWVtI6hxcEEVp763agiN+9gRmL96AY8ePxD6hLiTSG2qss3R96BTSXz8uDjVTqwmd8tj8Vbj5hcXWsVbjDV8pKiUOa0JREcLPtWH5cgB7CfVGA1gZlo/WlEvXEFENgKFIirEAAIyx3zHGpjDGpjQ0JC1+fKCbYokqS+X6B2ILfyCCL6z731iOW19aYqknvxyldrdYyQa9olNThrg91+JTr9ZyDgnWX8a1Z07COR/bJ7perPe9Uw7UX2Rpj4/BxsX4Qp0y7sH92RtfCv43yOmBIGeHDSpHZdv4gSAciI27OHNy8Ipxhbi1b2cNoR7TH3g+f8So6Pu0MJVrmbFIBzZC8ccpEkUEW80ol+hXw22aAhLyeuMvfzSRJyTiHBQOAwCOO6ABS676dLJNxO/7e0JIG9EkW7Fkdeok+fy5vOZFsdLX//BGIqSOCrm9ziEUlRKHhwCcH34/H8CDQvnZoQXSWASK59mh6KmZiKaF+oTzlGt4W2cAeJqlCcOZErqm9wxPmSSuFpipNX8pTMnLOfjC+t+nGzF7sdlqQt3I2sp6hTTf9HUv8JFjYlGMzkmGAPzw0wfhvq8fFbfHX0wAbSnFSnodBsL29G2pclPxf11sJPG699ckOS87cZD/160o0SpK58fBGIs2fp3fBu+Xe8yakFSmmkVGn9i/AXsPH2DlLhoG1+OUCbtpcxGr8KWRXO+lO+iPF8QifG21l1l0P6rdfbEQ+8U435Hw03ngcXAEka+S5oBichoT82KIB716wWBEoQ3Su8f7EPVQ6jNWwUWgpgx1JnRLzoGI7gLwCoADiGg5EV0A4CoAJxHRBwBOCv8HY+wdAPcCeBfAYwAuZoxxwfxFAG5GoKReBODRsPwWACOIqBHAdxBaPnUUxCnee/gAzL58usRCSuIdB3FwyQnTnF4ZAnnvNY8twJJ124y5q4Fg4X3p42Ok38S49SYfma8euy+maKypRM5BjN4qwUOsFLVnmBY50qjMOdhTsgLn3TI7Uf7dk/c3jkWNBptOqMSJV7zpbG9L6ni8N15lw4q8bzUNcF2Hi7tIm/FMhFaJjIAYqgePm8+bIvcrOE7yuupBhoii5+lMQasYbZjAb0MnpjpyzLDoPnUHFFO4CQrrvdS4TsplIho4qEYlsuw/+Of+iz6Og8K4YH8PEziZAifGOod0JkddQBvcaUIZY/9k+Gm6of6VAK7UlM8BcIimvAXAma5xZAZlQ9p1cMyaE7nZWyCNWMkPXKz0wdqtuPHZRWEfZoU0Y/LpBgB+elo8tVqipNURxO3FJnj6MSb9HMyiHCPnILwPBWWubSEBGGPaNg/cPTjR6kQ+2yzWXDroFO6MMfSvLaK5tYTtrboAcn5PWBV1MAvnUKQgT7iNu+B9l1mQ6UzNOuYeo15swwA8pMTCOvFgWX8lWhdxPZVmqUaJisTYWdrxRUYR1mrRPOhO3KcesodQz59zQKhzOOfmWVLxn99YgetCXySV2EhipfBz96H9sPuQery3Crj3tWU4d9o+RusiziG7OCoVUliYTiIUfTCHtP6hAbIMErBxDiZbZaW9FNRBfeA6MUtsOsmk+pP3GYYGIQGI7hSu95AOPkVXf9O687mXiAU3NDJqmGgGKp98bcHETCdkTox0Y1Ov8D2ZyuMLxQCtsk/Lz04/BAfsNtjfmzXi0PinWWRUCBM6cUJq4j65WM4lbvDNRc97eWbhWms9MeTKbkOCg5XOAGKlIU5Wst+Y2PiMUHfiFq8kzZo2caU+z0+1phKnW+wjyt5YtHNCdRVyDuJzzolDB0GcWPVEUQhd/rmZpSnEdk0kVkonpzeBvyDiGtZlF+MbqJoHQV38vqFXdJYdRgstj/ZMIoI5PzwRQ/vXKt6lsgetjXNwOYRqz8bKGNzWSkqb4eZLob2vaEZ77rR9AABNza1ebcW6nWAMNpFRgWQPadOz5PJylyPcyk1+mzQQmuw66ohrkCvEv3nC+EQ9nbWdDr4HqJhz0BAH6ZSeXINGsZJiMcchR9Pl7QX1xE1anHo+rtpiAfNXbMYDb67Q9qlTSIsYNkCvR5IPUtoqmaPvxVYSF41GycpYLNIxiY0KkVjJT9nmAhdniadEnQe0uPna2GZTWIZke8GnHMnUcM8eb7HJtHPkoPqEXFr1kLa1X0nGs7TEQJ0zbqdvu21/nUM4JoVz0F1fLATOYzq7enl8wZpxrcEdO+0y/2iMFITPcN1TrJCOCZPOss43OJxvPC5ezyego9qeydaBi9JUfOek/aU6gL6euMb4XNQVC/iH/31RqifqA2sdCmmdIQogH5B0IueOQN8jDsL3xCYaLpYow5dD5+DiHNJsHgwyMdETh+CzVGa45cXFUXmSc9CJlXTj4yfaGGbfDn25rl+fbVnNIW1b78GJ1rZL66+x/Q/I96Q2wYmX7ba9dQ5C2BPx05QnQ+IcDPPCxV42Z0gTdHNRCDk5V3YzUazE3w/dWlUJxr6GuE+mA4Wpni+3ftdrHyXKEm3CQ9zIv2jqideed1TATaoOnLd9+UjJB8SlczBxgrqQ+h2NvkccpNOq/BuF1MEWbRPI3lqJQp2DWF+X2Ib3q+Y9SMrLvbqN6vpZDlfOOWj7LQALBfNUu7WSY+PQjM12BX9uNi4ySmNq7TeJBy8+2lgvcpAqWxTSoWiTW8+4dA66THtqPR/U1xbQWmp3cw4CceAbme7ZiR7+uw/ph0e+day2PZeJqlpPfOeKBcI+IwZEvjNAfL8vvB8r6a1iJcdRxmZkIa4fTgBGDpJ9Po5TPNS5s55p7zBxguJeZDP7zhJ9jzgI33WRRhmYMw1jjS9xSDk2cRNcsDpp12/KHezDOZigKoZNhMKH4KQJn6FuerYTq2vj0BvkyBedL5j+6kQTCeME4ophc+fqNb/54hHa3Noqh2Y3ZQ1ykd8UWq3ZTFkZi/MCnBYGsHON0YT6mgJa28rYX/Bp+O8vTErU41KPdsYisatOzCFyE0P61xjjX/FqvitWfOeO2ncEnvve8VH0XUA/p0Y/B/hzDrp6sujKvmdwFAqEmgJp945igYyckfhO6fSRHYG+RxyEuT9m/EjpN75Yit46h+wU0tzPgUOUe8btBZ+qrXdC56A1MzWP0eQh7nO9bnw+jIiOMJvg5hx018j/i1nZ+CnMJVbS2f1L/SYGbT7lB2PiSs1y1IeKApG0rsymrKG1EmO45Phx+J+zDzeN0jR8CfU1RbSWylJu8cH9kvYq0Um/zNBeLoNIzznImexsY/DcVMN+3wpNZAG7Hs0qPhaudy1VlbMRIyofOjqOE1XQHCg/c5ieYNcWC1oiMHxgHXa0tWsPJO2SzqFzOIc+Z63El8Pvzp2M6YoJHl8svpxDVqasXKwkdjd+12R+YU4EVKKkbmC6tWN8QcnPhnrNFrdljit8hgh1jOrJ8uVLT8CKTTtw5m9e8RR7+YPHppKaTYjmKLRY8+ccbAQ46DD4aCsz1BaT2fWA4GQuyp1N4rYCUST3t5kBa818NbfUr7aAne1l55pWxUqmjUqcN9t7IJpn28CZEzHuki23uY3ji+FOCxw9ukgkGAR1vPRUOdyLmloXQCJNLEdNkSLuVTQYGNKvBk3NrWgptWNAnT7FLBCHT+lo9FnOYa/hAxIvHo/SGCVTqdpD2m9MnCiJL4iOdYzyNCjjUh20dBvKQMNCLRCk45OPlc/3ZxxgbgsyC/y1T+xrqBuP8YQDk5FD99ylf6TELDOX1ZBO52C+D1fAxGB8oaeyZZPxCSkilvMXvNReNlqcBGKleF2Z6nGdQ1DHQhw0Zbq54cECxbVkUlwDiMJxm4jX54R4TDYOWk1JawI/eBwrcPs2zkEmcvq2g+vl3x7/9ie0dXmtdsa0viOiFRdHU7PejLi2WIie8UFXPBaVDwnDoWzXWJjx+fntuZOx94gB2nazRt8jDuGnbmFFnENkrWTyc0javuvgHz6DQs5BJA66U1HwqRIlXdIhjrOmjMZlpx6IC44Zq+1Z1TkcvtcwbT2Ow0YPxTeOG2e6DQDAik07oqJhSlC2eIyJy7RjA7hfh3lMumm27TV3C5YsEZT6m3e0gTFgm8UUNCFUsugHgHizbTMEVgQC8YRoA2+qRxTLp23KfN8lyGP+bNsZr6WhGpv76IRcZsF9CMTr4uP3w9HjgsB8nz18dBT7yWaJxsfn8teIRKqSyE13cyFXL8xhS5v+PVV1Dvs2DMQBuw+W6nDTUt5ee5lpdRixpzeLxHEbt+uTDXE/KhVDwhSiOm98/o7a/IGyRt8jDlwZqNmSeOrKouGELtYDYM1PG/ThhyhrlrCGdQnq437lxa6LX8Pr9qst4muf3M+YtJ2LTw7baxfs1zBQOvHpx+o+BX7n3rlRWZ1BeSYuctemWmb2uR5uIEAmXPPYQmzfWbISnJmvLHW24805hD/8z1MfAAiIu21exCCIJq6gQPEhIY0BAhDnOpf7DT7FTVXMahjVEyz52pVc59875UDc+dVpibHbdA7enIPmYKTX2STrmRzSuDiXQ5dRkHv1L9u4PbYy01lnCffBxUlbDJno1HD1HDzBkC5+FJ+ezjJjBfoicbCk4+OnhMiqxEUcDCcSDl/OobmlhA3bdkphg3ULRGfOB8ix4eO68jXmMcZxcsaMGGgcM4/jb1ucuhe81kKUxDFoxxblw2DGUxgA7NeQ1M+4JEcHX/G4xOG4BE06HZCvzoGDmyebQrIDwXNr99Q5xNZCNrFS8jedopRveG4nMz+dAxCP3bafxZyD37skRw826xx8YhepebPV9L1AoDcoUHCafyFMbKQ7vYu6GJ7o6UtHj9H2y31KVPB4abr3KM45Y7mhjNH3iEPEOSTBFzqn6qYw1mrI7tu+fKS2ni+RX7AqMFv94YPzozKdyIovDPUF1sXM5y+Ty7KBEBCbTdvbjN6ZQEw4bcSmuSW5gdcZNkHREsZskaM/Ve46uB6vXX6iVHb43rIJaVoltqv6pybukSjzJQ7qs2wrW3QOBZJ1TxbdBD8UpFVIm9rTjVUFX/tbWkq4c9ZHVnFQxDlYBmEyz07UCz/Fta/lHMLpcuWPDvqWn/tew/WyfO61fv6ts8M+kh0TUXTQ6l9bxOR9hkn+F9IYSW+Bx7lJnU6MJwuzvaNZo+8Rh/BTt15rFM7BZa3U2lbGuF0HJRxdOFzephzcTlt0gNG9pEXD6U5rMx3eoIsNLRQIf3j1I6zYtMN4mgVimbStva0aWalpMe8uJKoxzRPvSpUZNwyulwINBnU77khVV1NQ4u0EUMdt2gRVkVipnRkdmdRTqVnnEG9stgMAD47ngq+olPf1m9APY5OFoysqCXh00FkrXfW5iZp6IVESNn2t3jCKhRSXnXiQ6f2UN+LdDcmTuNc6h+kd4BF128vMwS2RlgBwLltnIv+D++cB6DwzVqAvEofooSQnuVYJm3Hywfp0m+KLZGXpUz5HkRZN2HNI4ncTy6xjofmwnMRBGKRJDi7+ZmtPd1rXnbgBoF4wXbWFiACAne3yhqXfFJSxGEeph+hIBcjxcCbsOcSQX8M+Bg5V/Fgql42OTGo/Zp1DXG7jHC40WIsl+uVz7RIrpdicapTUndr2NPq9Y/dP6kR4PVEUqI0+rOnqvKPGaPsmIul+TXNdLJBsXmzSkYUcRhC80y5uYwxYu0W2ZoozBVq4sU7yjgb6IHHg0HIO4WJevnEHjh0/Ev99dtJDFIgfUJllYymiWwo6OTpvT7VO0p00+EvsJg7xd9vCi/Qxll339MMDZTbPsHbtmYc5PWMBM+dgEqMVLbkuONKKldSsajd98Yi4P8ODTHh5G+qpcxBY+fi16RNu2mbB4iuGKEQHHodYKexrQJ3+uUp1fXQO4afIOWj9dDRlPusgsKAamajHIYfjMBNsaXwWzqEchtO3m+8GBGDqL56SyvkhwiZi66yge0AfJA5WnYOwOU7Yc2giUTyH+DKmVQY+8q+aGDOeGxnvd/1W2SFthyXblos4SJFgbZxDjVlZxlFbLOCw0UOj2zFZSAF+CmlTJM5+mnbVNtLQBl2Y5F0EYmGLjDpxVOwla5rpafsGVj88lWtrqSxxTiLUR2DTTcR1qj9N8n5dxIEPZ/G6bc420+gcxJO5LV+3CN1ZRnxWA+qK+N4pB5oJLMmB/Gycg0+U14CIBBu/bQ9XoxJwtJbcxCG3VupAxNZKZrESYH/hxAdk21B1TRysERf5gm8I985ZLpVz23IdXHbRZPiuotaiLJPaI4rk1rZTq9Svw5RVFZu5IqwC6cRKurriczW96ESEXwrycaPVFRGm7DMsmo/WtnbUGwhnMhSKqU2hTgYbBp/r15dutNZzmXlLdT04B18PaW0cKoeozzTHcZvywcOlS+AwjTRynHSIlYj0lpAnTwjE2K4DWGehz4XPsHEOIltpo9Diae7t5ZvM9TwfpO9GphvS7MunY3C9JkEIt4t2yCh9TvBAbHXkCktQoFiBbHs5JZm5kXMIPtXTrE5pmhQrWYfprCseDnx8OwC3PT/vp7VUjrxhVaiboMmJUOw3G87Brw213hBN/CUOHz8HHeegk7m3a0SnuoOP+M6ZRJpR3yCJ6zbqdxTOwRQZgYuf2pmdW1I5EQB45t+Pw5pQB2E7gHUi41Ad50BES4hoHhG9RURzwrLhRPQEEX0Qfg4T6l9GRI1EtJCIThHKJ4ftNBLRDeTvWpwaEXHQ9FDruSGIG64tvrzIiew7ciD+eskx1jG5wDcEUXG86+B+CWUqEBMcF+fgu9gisZJjsAWiyCnPJlYS9wvTEPgyUMVKZ0xJxpZRb3PPXfysdAC9fkJ8/i7lomkM8gDjTa+1VDZzDkJ715xxqPGkKD63LDgHX89bta+/fVMfihsQOAeriCX4dKWqtXlrixDXnA/nIOYa9+UcTMQuTtTEjKInIBmVAADGjhwo+UqYkNbQohpkwaMczxibxBibEv5/KYCnGGPjATwV/g8iOhjA2QAmAJgB4EYi4rvaTQAuBDA+/JuRwbi0iG2V7GyqlTh4vkhiG185ZiwmClEc5TH5PfI0Dj58w0ujc6grmk9afJNycg6CZYevzsFGYAskn9Re+P7x+OLH9k7UW781yGuw1/D+uOX8KfjK0WOt4xShFSt5EgdZsW5GgYJ+PljTjPdWbYlOiSp8idIbS2OO1Tclpw2+BEZd+zbOlHPYdhFL8JuYv0S3xob2r8U/HCpbvunGLB7ITDrDqG/IcYxsOgfxlbOJD5mXWEnWOXC9lRdx6ETq0BECrNMAzAy/zwRwulB+N2OslTG2GEAjgKlEtAeAIYyxV1iwo90uXJM5+Kape3biA7USB88XSTz1ZaFISmPL7+tuL57q6jUeohz8Xnxj4AD6kM8c4qgenrfKWK+gmBvuNXyAlmU/PgzeV1MoYPpBu6U7TWtuSZy3dVvNEWnFfqyK1zCo4/1vBKEc3l6eTOYEqGvQ2BxOEsyss4jvX8mBB7CLtLiBh10h7e7DVK4bc50kVnJxDiQRh6KJSyv4pbTlHEa57DLflQkgt5Tj92MzZU2b/rYaVLuqGIC/E9HrRHRhWLYbY2wVAISf3ANlFIBlwrXLw7JR4Xe1PAEiupCI5hDRnKampooHbIJk1mnVOfi9SJLcOsXGvoshybgaJkP1ChbBF5FrrMs2xHbj/S0yWh9rJUB+ccaM0KeGBOyiBrU9H06Jz28lNFgXsVaUV7+zckvid3F8HLZHXCggNHG0j0USF1kaFP1gsrB9r1Tn4CNys3NU8q8/O21CFH4i0Z6Hma94IGsYbBctEoAdQqBBEwdWJJIy7pnmW7JWsty0qnPgoUOiaNAWTnqPofq56QhUSxyOZowdAeBUABcTkT7ebQDddDFLebKQsd8xxqYwxqY0NCQdZbxg0TnISlI/nYMN4kK1bRz/cmzsqLRfw0C8dcXJ2nobhQX6yf0bcN/XP25sk6+9NCfofSyhgPmJzDcRPGCX+fp6j4sWJRcdt5+l3+DTZgf+aY1D3u5D+uHuC6clyl3KTA4pgKClHpczuzbhoicnIuqZdEEa08J3naiHDZ9DlI+1Ese5Boc1wM8HRBRlNgx2BGQkOUSO0X+hQHju/fgwasqnwDmMtGIlfuDS5YTgGDtyIP7xsD0T/jgdiapWFWNsZfi5FsADAKYCWBOKihB+rg2rLwcgzupoACvD8tGa8g6BzZRVLLKdxrw5B6EN26Zw2qRR0cZ8+iRzVFTRmWdgfdHaJrfwSWPJ8o+GzFWAv1iJz6EpmY1az4UCUXQvx443OzPxvmybnJgqlOOyTx2IMSOTHI7vSVq8D5cdRdlhxQL4WXEBshNampzCujSmgEzkDt5jCOb88ERtvcQGbem76KNzSJFMV5X6uDgHl8NYURFZmvuV+9EZgPD2ePgMu4UbFM5BJg46RnlnqdypZqxAFcSBiAYS0WD+HcDJAOYDeAjA+WG18wE8GH5/CMDZRFRPRGMRKJ5nh6KnZiKaFlopnSdckzlspqy+nIOv7L/G0zQWAJau3x60banXr7YYmQ6OGFhvrCfC5PWpg23j4icyl0KMz43rxRT70oUK4SgW4hfY9nL4iTCSZTyGfqXwtVYqEIEBuCEM222uJ19jgqhs9RUrXf35iXjw4qP1/QpTu+uQeil8iA02camfE5xXN0FfHiKtuhR6PtVgwpav2wc8fEapbI66y9uTiEMoRopDicjU4YE3l2PFph1e4tUsUY2fw24AHggffA2APzLGHiOi1wDcS0QXAPgIwJkAwBh7h4juBfAugBKAixljXBt0EYDbAPQH8Gj41yGwBd7ztR33PY2LJzpf69ymZnc6TsA/h0FWh43aSLlorxeLd1wn5Pj7jeccYawnipVcpzHAYTapud5l7uiC5BBpGR+RbDJrDLwnWSuZ+xUJpe+eYTuli2K0NCEafEy+XUTTF+o79NaypMNebY3fAQ9IEgdTbW99DAXhM0pKEiRdvXXNsYiYcw58/ahipR/cFwTdW+e5N2SFiokDY+xDAIdpytcDmG645koAV2rK5wA4pNKxpIEt2Y9kluhQKPlAtCLxFvc0/wAAEqlJREFUVUi7NtUtLYECbeQgP+KQVbRSn5Dd4u+u06w4/zZCJyqkbbL1SKxk0xVpfrNZaPlAEkVaxic6wQHAMYZ4P77WT+IadKWrjdsz/zZ8QPwM0oipbNwcjxVkW9KpOAel8qsfbkjUkTkHe3tqoEnT4/M9DHInuLb2MupqzNcQAYuatkb/tysKafVx8vXv+5yzQt/zkLYk+xFfRttz8OUCZC9bv/H5Ep7hnmKlrMDH5QzHEREHtwMSECTRGWwR7RQEzsHFqgN2sZJubl228C6I82ENwgj5RPjLzx2qrSeZsnqIbAD/TcPmdyLmMkhjGmu75yffWwMAWL1Z79MByPerjTvm2Zeuzkcbtlvr+oqV5hrMjlUsWN2MBauDvAtvfmSOnBDkCY/XAldIFyLioH+enS1W6nuxlaw6h/i7TxJ6F8RTle8J3pc4DLL4EIjIinMoW/xDRPDffcVjuuizIoqFWCFtO9HyafMJDy3CZQvvgiSKtIyPFM7BptTksG3moiJ48j72vN8cNhFasUD4Uqiw9z0p+y4t3/hjttO2rr9vnpDMZS4+f1ecKHE+ph+4K2Ycsru1fhpwIqGDyqG0K2IlEw04Ym+/55wV+h5x4F90OgdPea+Ir3/SbF7p6+1qusYGX9Y/qyiOkWms4z5WCvH2s4AYc98ltgnqm9vSzYXOxyENfKOjEsmcg+n5kbRZmu9X7MvGeYmwhjMR+vYlDr7nJxsHJDbh4uLUdnS5sEWYCDCHOB+//NxELy4yi7OWGguNcxFR5kNhYrme6tjxI/Gjfzi4+s5ToM8RB76itWGBhe8D6/w2DZvtvQjfTdr3hbMl5hGRFecwfrdBOGbcSFz1+WSWLhFZu/cXKGan7SdzXt8mp0+W2ZyKPjXRfZKUHSf9I/SaHA5dyWx8+jLBFh4FiOeuWoLJcbVjrQCI4nABcR4QE5LWSva2bU6dwe/xfVYS9aBS7DZEFglza6VI5yCwDpxwTB0zvFPDdQN9kDjYrJVEltQUDVOFr7VLFsHRRPjKhbNKK1hfU8QfvvoxHDra7JUNZHOyElGgOOiZ7cX00TmohMN1ErvxnMnO8UnmyjbiBdl80ST+svmayP2mn2gX58A5NDUFa6WIzGEtQ90Rhq84cswwtw+Ics+u+v0dB7xRQnBGb+JgqWeKbKBC5fQSfg7CAYsTjixCpKRF3yMOnjqHT1gcrkR4Z9ryfJd9N9fOFiv5wiPMf1iPK+Hs9dLGu0ojVjpHE8BPxcRRQ3HpqQcafxdFF7YNu1Dwy4Gwr6CDsRK6DiAOPEKpKZw4x79qZP06+LwbPG/zSYaUvCIGKmIi1xS4AhIOFSy0bGvrjgumetX7xWdjTmnv4ZZoA8pzUK2VxLhLPGROGguyrND3rJUixWpysuU4Odluvr6mrAM8xVm+YiVfxfX0A/VJ2NPCZGmhgq9/92kx/m43ZQ0+t7Was+KpnIPPI/nrN/Vh1nWwp4yNdSe+J8ysuTDXmuEE27WpuogMRxR4z1JnnxEDMfvy6WjwcLpTYy6ZRIj3fu0onPXbV5wh6uo8IxiMHhZv9LawKuJ4fn/eFGM9VdoQcQ6RE1zw/4dNW7EqtPTq7EMe0BeJQ/ip5xz8H8BbV5zkHX8H8D/pfeWYMV71fDmWKZ6WLLd86Uivei64AvNxRCHFHXMuBga06Rxe/XA9AODdVeYgeWmCxv3/9s41SIrqCsDfYXddhGV5LCwgjwUVUF4KCwi+QBOD+AgmaAUfgGBprGjFmFQqmhitinn4iFZUrCiVQPAVEytJBY3GMiZqmUgUIqAEERSjKCXRqDxUBDn50bd3x52Z7p7Znume3fNVTW3vnTs99/Tp6XPvufeeUwzBKWNbl5x+LeI81ZiDcod4L5awvM++OyMuF8aOj/YCUBcyh9EYEiDPp23HaVCeOQpfDWF5xKNGTc7stUfZpQ/Be3d84+DvizhvapPXbndq30ifeNOTLZ9Jwq3U+YxDQOC9Qp4VvbpFm5Pwifogirruviak97bhB15KjBLmTcpJ1AnplrXdBTQvyDj42eeCaPsAiPvKBC/ZjLbqKpNCOh9RCDMO/kMpbD7D13HYCGjaIX05+pAGrj49nlU2/lLXpoZuPP7N6Xmvt3/Ph/VTosZhyhxx3Tkv/zxUZnOCNqke4H7jVSK8/ONZWUEjc3Ww4sjZUSidzzi4v1GTmMdFXIb/2jPGcu/K/9AQMmEetoyvVIRFbW2t5/0tpPce5FaKcp62m8XKOnKQ1gnfMMMelcMH1keewAboFtKD933dUWM1nXdUU+D7PQ+s4b4LsyPeFov/8FQNNsS+WsPuRF8PzU29Q0YOrd81dlD+0VxUt3RtRlbFXOFScs1N2cihDGjAjHQpsiwN79udLe/spi5XnucimDe1iXlTg3+USeLf12ErXvYHzP3kI8g1F6Vj1bYnHndnIMwf3RoGJJ4vfuSy4B3FbQlb2rk/oqsvKaIaLf8hHeZW8o152HxHVGMedUSYLzeKiGQlAmo9t40cykau+78UxsH3u/YOGYI/cPE0Xn83eLt/JeA/YO654KjAeq1pTOP53igTdn3ramlu6t2yc7acLjdvziG5ZYkQfo2iLhJICr8HH5YNrXXOIZ7v9Xv6Fx43PPh7nVrDjHDQ8vfqqi5ZSb28c5txKDlBS1mjukQK4cdfHseNj24MjaI6eVgfJg/rE/v3lxv/EoataPE9PHG5dqL+eA7tVxcaVqFQvIirYXUyJzXT+fD1DXYCz6FItBiHkGs9oD7a8tioMalqqrqw8YcnhyZV8kdcYfqtDTAetdVdcuaYSEIlnc84BCT7KYVxmDlmADPHxBezJe341zDMOBTjVoqDUuTgXXnl59i1Z19gnUwxw1aandU8OLZdyuC1L8qD0HeL7A1IUwnhvvxS4btWwn6mjfVdWXP1SaG5Osa5+YNzIux3ibJQxB+ZhY0Mg5YU11ZXtcQSyyTu+bEodD7jEDhyKGtTOiT7I/Y+4+6l5vLT5uKo4Q38dtXW8IoF0L++K2FbuDLlfGdXcFDCG8/KioTfLvyNZmF0q/UegB9+EmzofMr9vPIfvlGiEkRZTXhwvzq2/OSU2Doo/ug1zH0XFCa+troLe/buzwpcmYSnr/PtkHZ/c885eO9GjXLZEVh8zgRumJM7fHQxDHIblcJ6x/6zPK7NPV+Z7GWgDQqECDCneXDg+6Vi7RutYZ93fhzt4Vtu/KWumfGOcjF9pBfwbsaoIvO4F4k/Oou6iTAKcY5cfbdSeyaPa6u7sGffp2za/tmorkm4+jrdyGHW2AGM7F+Xc5jYuryyzI1KkNPGR18KGYUl8yex8tV3Q9NMftomEmV7mTGqkdeuOzVS3TEH1bP+rfyb5UrBlnd3txyntfNx+edHsuOjfXxpYrABPWJIr8jXOk5GD6zn4H7duarM0Umj4nd0wu7pILfYzj37eGjdNh5at+0z5cePLK8hhk5oHJoautPUkJ1QHmBIH6/XG/cDszPRt6420vXzd83G2QuMyv0XTeXtHfkT0JSCA2uqWiYapx7cUNbvjkpDXS23nj0h6WbkpXttNX/91oykm5GXHi5UTdjIa+LQXlxz+mhOHTcw671ceVBuPHN85LA6cZIa4yAiJwO3AFXAL1T1unK3YWDPA3np2pPbnVfYCGdO82A+2vspc6cMiVT/1PHZP6Ri6dG1JnIOhLjwJ+gPG9CjrN9rlI/+bpVUY33wHI+IsPCY4GWxPnHOiRRKKp6CIlIF3A7MAkYDZ4tIImPHrjVVqV3n3ZGo6iIsOHpY6CoQf7/EiMbgjHFpx/dH3zI3vT1zo310rali2fmT+dXCeOKUHTagR6LPolQYB2AKsFlVX1XVT4D7gdkJt8lIAceO6MvyRVO49IRoYaLTih8krhRLaY30cMJhjS0jiGKYlZGqNOm5qbS4lQYBb2T8vxUI3mJrdBqmJzAZFze3nzORu1e+xshGcysZ+fn5eV5gv83bd4Vmxis1aTEOucZOWV0sEbkIuAhg6NDwjSuGkRYG9OzKt2fmTxpkGJkcmgI3alrcSluBzJnJwcBbbSup6hJVnaSqk/r1q/zepGEYRlpJi3F4DhghIsNF5ABgLrAi4TYZhmF0WlLhVlLVfSJyKfAo3lLWpaq6PuFmGYZhdFpSYRwAVPVh4OGk22EYhmGkx61kGIZhpAgzDoZhGEYWZhwMwzCMLMw4GIZhGFlIWBLutCIiO4GNOd4aCrwe4RQ9gQ9CayVXLyk5SnFO00m66tm9lb565ZRjlKqGb9VX1Yp8AavylP834ueXpLxeInJ0JFlMjnTJ0ZFkqWQ58j072746olvp/Yj1Hkx5vaTkKMU5TSfpqmf3VvrqJamTnFSyW2mVqk6KWl5pdBQ5oOPIYnKkj44iSznliPpdlTxyWFJgeaXRUeSAjiOLyZE+Ooos5ZQj0ndV7MjBMAzDKB2VPHIwDMMwSkTqjYOILBWR7SLyYkbZESLyjIi8ICIPiki9Kz9ARJa58rUiMiPjM82ufLOI3CoJ5N+LUZYnRGSjiKxxr8YyyzFERP4mIhtEZL2IXObK+4jIYyKyyf3tnfGZK9213ygiMzPKE9NLzHIkppNC5RCRBld/l4gsbnOuRH8nMctSSTo5SURWu2u/WkROzDhXMjqJugQtqRdwPDAReDGj7DlgujteBFzrji8BlrnjRmA10MX9/ywwDS+x0CPArAqW5QlgUoI6GQhMdMc9gJfxcn/fAFzhyq8ArnfHo4G1QC0wHHgFqEpaLzHLkZhOipCjO3AscDGwuM25Ev2dxCxLJelkAnCQOx4LvJm0TlI/clDVp4D/tSkeBTzljh8D5rjj0cDj7nPb8ZaHTRKRgUC9qj6j3tW+Czij1G1vSxyylKGZoajqNlX9lzveCWzAS/U6G1juqi2n9RrPBu5X1T2qugXYDExJWi9xyVGu9uajUDlUdbeqPg18nHmepPXh2haLLElThBzPq6qf4Gw90FVEapPUSeqNQx5eBL7ojs+iNYvcWmC2iFSLyHCg2b03CC/bnM9WV5YGCpXFZ5kbKn+/3EP/TERkGF6v559Af1XdBt6PA2/EA7lzhA8iRXpppxw+ieskohz5SI0+oN2y+FSiTuYAz6vqHhLUSaUah0XAJSKyGm/I9okrX4p38VYBPwP+AewjYo7qhChUFoBzVXUccJx7zStrix0iUgf8DviGqu4IqpqjTAPKy0oMckAKdFKAHHlPkaMskd9JDLJABepERMYA1wNf9YtyVCuLTirSOKjqS6r6BVVtBn6N5/tFVfep6uWqeqSqzgZ6AZvwHrKDM06RM0d1EhQhC6r6pvu7E7iPBFwbIlKDd9Pfq6q/d8Vvu2Gw76LY7srz5QhPXC8xyZG4TgqUIx+J6wNik6XidCIig4E/APNV9RVXnJhOKtI4+KsORKQLcBVwh/u/m4h0d8cnAftU9d9u+LZTRKa6oeV84I/JtP6zFCqLczP1deU1wGl4rqlytlmAXwIbVPXmjLdWAAvc8QJar/EKYK7zoQ4HRgDPJq2XuORIWidFyJGTpPUB8clSaToRkV7An4ArVfXvfuVEdVKOWe/2vPB609uAvXhW9ALgMrzZ/5eB62jdzDcML1LrBuAvQFPGeSbh3RyvAIv9z1SaLHirM1YD6/Amrm7BrZgpoxzH4g1t1wFr3OsUoAFvEn2T+9sn4zPfc9d+IxmrLZLUS1xyJK2TIuV4DW9xxC53L45OWh9xylJpOsHrGO7OqLsGaExSJ7ZD2jAMw8iiIt1KhmEYRmkx42AYhmFkYcbBMAzDyMKMg2EYhpGFGQfDMAwjCzMOhlECRORiEZlfQP1hkhGt1zCSpjrpBhhGR0NEqlX1jqTbYRjtwYyDYeTABUv7M16wtAl4mxTnA4cDNwN1wDvA+aq6TUSewIt/dQywQkR6ALtU9aciciTezvdueBuZFqnqeyLSjBdD60Pg6fJJZxjhmFvJMPIzCliiquOBHXg5Nm4DzlQvFtZS4EcZ9Xup6nRVvanNee4CvuPO8wJwjStfBnxdVaeVUgjDKAYbORhGft7Q1jg39wDfxUvE8piL/lyFFw7F5zdtTyAiPfGMxpOuaDnwQI7yu4FZ8YtgGMVhxsEw8tM2tsxOYH1AT393AeeWHOc3jNRgbiXDyM9QEfENwdnASqCfXyYiNS7+fl5U9QPgPRE5zhXNA55U1feBD0TkWFd+bvzNN4zisZGDYeRnA7BARO7Ei6J5G/AocKtzC1XjJWJaH3KeBcAdItINeBVY6MoXAktF5EN3XsNIDRaV1TBy4FYrPaSqYxNuimEkgrmVDMMwjCxs5GAYhmFkYSMHwzAMIwszDoZhGEYWZhwMwzCMLMw4GIZhGFmYcTAMwzCyMONgGIZhZPF/fFH9cqE/N1YAAAAASUVORK5CYII=\n", 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