From 56f085d79d337b6e276ed8e1c8bd19e16e527b94 Mon Sep 17 00:00:00 2001 From: Edwin Brown Date: Fri, 12 Apr 2024 12:10:21 +0100 Subject: [PATCH] Update to noteoboks --- examples/001_Data_Ingress.ipynb | 882 ++++++++++++++++--------------- examples/002_model_example.ipynb | 772 ++++++++++++++++++++++++++- 2 files changed, 1205 insertions(+), 449 deletions(-) diff --git a/examples/001_Data_Ingress.ipynb b/examples/001_Data_Ingress.ipynb index badd3c2..5c802da 100644 --- a/examples/001_Data_Ingress.ipynb +++ b/examples/001_Data_Ingress.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -64,16 +64,24 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): api.tomorrow.io:443\n", + "DEBUG:urllib3.connectionpool:https://api.tomorrow.io:443 \"GET /v4/weather/forecast?location=new%20york&apikey=1sm88wahfTQ7oWUU8q2OXwNY5wPTjaHf HTTP/1.1\" 200 None\n" + ] + }, { "data": { "text/plain": [ "" ] }, - "execution_count": 2, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -104,13 +112,13 @@ "" ] }, - "execution_count": 3, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -148,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -167,7 +175,7 @@ "['default_user@localhost']" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -281,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -336,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -363,7 +371,7 @@ "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:5000\n", "DEBUG:urllib3.connectionpool:http://localhost:5000 \"POST /sensor/insert-sensor-readings HTTP/1.1\" 201 37\n", "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:5000\n", - "DEBUG:urllib3.connectionpool:http://localhost:5000 \"POST /sensor/sensor-readings HTTP/1.1\" 200 6078\n" + "DEBUG:urllib3.connectionpool:http://localhost:5000 \"POST /sensor/sensor-readings HTTP/1.1\" 200 6079\n" ] }, { @@ -371,7 +379,7 @@ "output_type": "stream", "text": [ "Existing Sensor Data: []\n", - "Existing Sensor Data: [{'value': 12.0, 'timestamp': '2024-04-08T15:00:00Z'}, {'value': 15.05, 'timestamp': '2024-04-08T16:00:00Z'}, {'value': 16.46, 'timestamp': '2024-04-08T17:00:00Z'}, {'value': 17.24, 'timestamp': '2024-04-08T18:00:00Z'}, {'value': 16.48, 'timestamp': '2024-04-08T19:00:00Z'}, {'value': 15.95, 'timestamp': '2024-04-08T20:00:00Z'}, {'value': 15.81, 'timestamp': '2024-04-08T21:00:00Z'}, {'value': 15.59, 'timestamp': '2024-04-08T22:00:00Z'}, {'value': 14.8, 'timestamp': '2024-04-08T23:00:00Z'}, {'value': 13.87, 'timestamp': '2024-04-09T00:00:00Z'}, {'value': 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'timestamp': '2024-04-16T20:00:00Z'}, {'value': 17.7, 'timestamp': '2024-04-16T21:00:00Z'}, {'value': 15.99, 'timestamp': '2024-04-16T22:00:00Z'}, {'value': 15.28, 'timestamp': '2024-04-16T23:00:00Z'}, {'value': 14.84, 'timestamp': '2024-04-17T00:00:00Z'}, {'value': 14.88, 'timestamp': '2024-04-17T01:00:00Z'}, {'value': 14.92, 'timestamp': '2024-04-17T02:00:00Z'}, {'value': 14.97, 'timestamp': '2024-04-17T03:00:00Z'}, {'value': 14.72, 'timestamp': '2024-04-17T04:00:00Z'}, {'value': 14.48, 'timestamp': '2024-04-17T05:00:00Z'}, {'value': 14.24, 'timestamp': '2024-04-17T06:00:00Z'}, {'value': 13.64, 'timestamp': '2024-04-17T07:00:00Z'}, {'value': 13.05, 'timestamp': '2024-04-17T08:00:00Z'}]\n" ] } ], @@ -415,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -424,13 +432,13 @@ "" ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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KZgghhBDi0qhmhhBCCCEujTIzhBBCCHFpFMwQQgghxKW5fdM8g8GAa9euwc/Pz+I5JIQQQggRFsuyqK6uRlRUFESi1nMvbh/MXLt2rck0YkIIIYS4hry8PERHR7d6G7cPZriBZ3l5efD39xd4NYQQQgixRFVVFWJiYiwaXOr2wQy3teTv70/BDCGEEOJiLCkRoQJgQgghhLg0CmYIIYQQ4tIomCGEEEKIS3P7mhlCCCHCMhgM0Gg0Qi+DOBmpVAqxWGyT+6JghhBCiN1oNBpkZWXBYDAIvRTihAICAhAREdHhPnAUzBBCCLELlmWRn58PsViMmJiYNhufEc/Bsixqa2tRVFQEAIiMjOzQ/VEwQwghxC50Oh1qa2sRFRUFb29voZdDnIxCoQAAFBUVISwsrENbThQmE0IIsQu9Xg8AkMlkAq+EOCsuyNVqtR26HwpmCCGE2BXNxSMtsdXvBgUzhBBCCHFpFMwQQgghxKVRMEMIIYQ0Mnr0aMydO1foZdjda6+9hn79+gm9DJugYIa4nIuF1VCpdUIvgxBCnJKjGxSyLAudTti/yRTMEJdyLKccE1bswRNfHBV6KYQQNzRr1izs3r0bq1atAsMwYBgG2dnZOHPmDCZPngxfX1+Eh4fjwQcfRElJCf91o0ePxtNPP425c+ciMDAQ4eHh+PTTT1FTU4OHH34Yfn5+6NKlC/744w/+a3bt2gWGYfDbb7+hb9++8PLywtChQ3HmzBmzNe3btw8jR46EQqFATEwMnnnmGdTU1PDXx8fH44033sBDDz0Ef39/PPHEEwCAF198Ed26dYO3tzcSExOxaNEi/tTQhg0bsGTJEpw8eZL/Pjds2IDs7GwwDIO0tDT+/isqKsAwDHbt2mW27j/++AMpKSmQy+XYt28fDAYDli1bhoSEBCgUCiQnJ+P777+39VPULApmiEvZfr4QALA/sxRHs8sEXg0hxBosy6JWoxPkg2VZi9a4atUqDBs2DI8//jjy8/ORn58PPz8/jB07Fv3798fRo0exdetWFBYWYsaMGWZf+/nnnyMkJASHDx/G008/jX/961+46667MHz4cBw/fhwTJkzAgw8+iNraWrOve/755/Huu+/iyJEjCA0NxZQpU/igIzMzE5MmTcL06dNx6tQpfPvtt9i3bx9mz55tdh/vvPMOkpOTceLECSxatAgA4Ofnhw0bNuDcuXNYtWoVPv30U6xYsQIAcPfdd+O5555Dr169+O/z7rvvtur5fOmll/DWW2/h/Pnz6Nu3L5YtW4YvvvgCa9euxdmzZ/Hss8/igQcewO7du6263/agpnnEpRzKaghg1u7OxH/jgwRcDSHEGnVaPXq++qcgj33u9YnwlrX9kqdUKiGTyeDt7Y2IiAgAwNKlS9G/f3/85z//4W/32WefISYmBhcvXkS3bt0AAMnJyXjllVcAAAsWLMBbb72FkJAQPP744wCAV199FWvWrMGpU6cwdOhQ/r4WL16Mm266CYAxIIqOjsbmzZsxY8YMLFu2DPfffz9fw9O1a1e8//77GDVqFNasWQMvLy8AwNixY/Hcc8+ZfS/cWgBj9mb+/Pn45ptv8MILL0ChUMDX1xcSiYT/Pq31+uuv8+tWq9X4z3/+g23btmHYsGEAgMTEROzbtw8ff/wxRo0a1a7HsBQFM8Rl1Gn0OHWlgv982/kiZBRWo2u4n3CLIoS4vZMnT2Lnzp3w9fVtcl1mZiYfzPTt25e/XCwWIzg4GH369OEvCw8PBwC+hT+He/EHgKCgICQlJeH8+fP8Y586dQpfffUVfxuWZWEwGJCVlYUePXoAAAYOHNhkbd9++y3ef/99ZGZmQqVSQafTwd/f3+rvvyWNH/PSpUuora3lgxuORqNB//79bfaYLaFghriME7nl0OpZRCq90DdaiT/PFuLjPZfxzl3JQi+NEGIBhVSMc69PFOyx20ulUmHKlClYvnx5k+sazxSSSqVm1zEMY3YZ1yDOmqGbKpUKTz75JJ555pkm18XGxvL/7+PjY3bdgQMHcP/992PJkiWYOHEilEolvvnmG7z77rutPh43P6vxtlxL3XkbP6ZKpQIA/Pbbb+jUqZPZ7eRyeauPaQsUzBCXwW0xDU4Iwqzh8fjzbCF+TruK5yZ0Q6RSIfDqCCFtYRjGoq0eoclkMn4UAwAMGDAAP/zwA+Lj4yGR2H79Bw8e5AOT8vJyXLx4kc+4DBgwAOfOnUOXLl2sus/9+/cjLi4OCxcu5C/Lyckxu8313ycAhIaGAgDy8/P5jErjYuCW9OzZE3K5HLm5uXbfUmqOoAXAy5Ytw6BBg+Dn54ewsDBMnToV6enp/PVlZWV4+umnkZSUBIVCgdjYWDzzzDOorKwUcNVEKIeySgEAQxKC0T82EEMSgqDVs/hsX5bAKyOEuJP4+HgcOnQI2dnZKCkpQWpqKsrKynDvvffiyJEjyMzMxJ9//omHH364STDQHq+//jq2b9+OM2fOYNasWQgJCcHUqVMBGE8k7d+/H7Nnz0ZaWhoyMjLw888/NykAvl7Xrl2Rm5uLb775BpmZmXj//fexefPmJt9nVlYW0tLSUFJSArVaDYVCgaFDh/KFvbt37zarvWmJn58f5s+fj2effRaff/45MjMzcfz4cXzwwQf4/PPP2/2zsZSgwczu3buRmpqKgwcP4u+//4ZWq8WECRP4I2fXrl3DtWvX8M477+DMmTPYsGEDtm7dikcffVTIZRMBqHV6nMitAGDMzADAU6M6AwA2HcpFZV3HhpQRQghn/vz5EIvF6NmzJ0JDQ6HRaPDPP/9Ar9djwoQJ6NOnD+bOnYuAgAB+W6Yj3nrrLcyZMwcpKSkoKCjAr7/+yg/n7Nu3L3bv3o2LFy9i5MiR6N+/P1599VVERUW1ep+33XYbnn32WcyePRv9+vXD/v37+VNOnOnTp2PSpEkYM2YMQkND8fXXXwMwFjfrdDqkpKRg7ty5WLp0qUXfxxtvvIFFixZh2bJl6NGjByZNmoTffvsNCQkJ7fipWIdhLT2v5gDFxcUICwvD7t27ceONNzZ7m++++w4PPPAAampqLEr3VVVVQalUorKy0qaFT8SxjmSX4a61BxDiK8ORhePBMAxYlsWklXuRXliN5ycmIXWMdWlYQoh91dfXIysrCwkJCfypG9Jg165dGDNmDMrLyxEQECD0cgTR2u+INa/fTtVnhts+Cgpq+bgt9021FMio1WpUVVWZfRDXd7hRvQxXRMcwDJ4clQgA2Hgwp8WvJYQQ4t6cJpgxGAyYO3cuRowYgd69ezd7m5KSErzxxht8d8PmLFu2DEqlkv+IiYmx15KJAx283FAv09ik3sb+CPmV9aiodWwLb0IIIc7BaYKZ1NRUnDlzBt98802z11dVVeGWW25Bz5498dprr7V4PwsWLEBlZSX/kZeXZ6cVE0fR6Q04llMOoKFehuMtkyBSaUxNZhbXNPlaQghxVqNHjwbLsh67xWRLTnFGbvbs2diyZQv27NmD6OjoJtdXV1dj0qRJ8PPzw+bNm5uc5W9MLpc75Ew7cZwz16pQq9FDqZAiqZkGeYmhPsivrMflYhVS4gIFWCEhhBAhCZqZYVkWs2fPxubNm7Fjx45mK56rqqowYcIEyGQy/PLLL1RE5oEOm45kD4oPgkjENLk+McTYlfNyCWVmCHFGTnTOhDgZW/1uCJqZSU1NxaZNm/Dzzz/Dz88PBQUFAIyzMRQKBR/I1NbWYuPGjWYFvaGhoRCL29/RkbiOQ5eNxb9DEpovDO8cauxCmVmkctiaCCFt4/5GazQaKBTU2JI0xQ3dbG3HxRKCBjNr1qwBYNw3bGz9+vWYNWsWjh8/jkOHDgFAk+6HWVlZiI+Pd8QyiYD0BhaHTdOxhyQ2H8wkhlJmhhBnJJFI4O3tjeLiYkilUpv0ZCHugWVZ1NbWoqioCAEBAR1OTggazLSVXuKKo4jnulBQhep6HXzlEvSMbL7PQKIpM5NTWgOd3gCJmP5gEuIMGIZBZGQksrKymrTSJwQAAgIC2j21uzGnKAAmpCVHs42nmFLiAlsMUqKUCnhJRajXGnClvA7xIT7N3o4Q4ngymQxdu3aFRkOtE4g5qVRqs3IRCmaIU7tYWA0A6N2p5e6PIhGD+GAfXCioxuUSFQUzhDgZkUhEhzeIXVE+nji17FJjHUx8cOsBSmdT3UxmEdXNEEKIp6Fghji17BJjpTtXF9MS7kTT5RI60UQIIZ6GghnitOq1elytqAPQdmaGO9FEXYAJIcTzUDBDnFZOqTEr4+clQZCPrNXbcpmbyxTMEEKIx6FghjitLFPfmIQQH35SdksSTEW/JSo1Kuu0dl8bIYQQ50HBDHFajYOZtvh5SRHmZ5zJdbmY6mYIIcSTUDBDnFZ2iWUnmTjciSbaaiKEEM9CwQxxWlmllmdmgEZ1M3SiiRBCPAoFM8RpZVuxzQQ0mtFEmRlCCPEoFMwQp6RS61BUrQYAizv60okmQgjxTBTMEKfEZWWCfGRQKiwbDd85xJiZySqtgd5AA0oJIcRTUDBDnFLDGANvi7+mU6ACMokIGp0BV8vr7LU0QgghToaCGeKUsoq5ehlfi79GLGKQYDr5lElFwIQQ4jEomCFOqeEkk+WZGYDqZgghxBNRMEOcEt9jxsLiX05DMEOZGUII8RQUzBCnlG2ay2RpwzxOYgg3cJKCGUII8RQUzBCnU1mrRVmNBoDlPWY4tM1ECCGeh4IZ4nS4epkwPzl85BKrvpZrnFdUrUZ1PQ2cJIQQT0DBDHE67a2XAQClQgp/L2MAVFhVb9N1EUIIcU4UzBCnc9kUzCS2I5gBgBDT9Oziao3N1kQIIcR5UTBDnE5HMjMAEOJjDGZKVGqbrYkQQojzomCGOJ2G7r/tzczIAAClFMwQQohHoGCGOBWWZRt1/21fMBPMZ2Zom4kQQjwBBTPEqZTWaFCt1oFhgDgr5jI1FuIrN90XZWYIIcQTUDBDnApXLxOlVMBLKm7XfXDbTFQATAghnoGCGeJUsvji3/ZlZYCGbSbKzBBCiGegYIY4FS6YaW+9DACEmjIzdJqJEEI8AwUzxKnklrVvJlNjfGaGCoAJIcQjUDBDnEpRlTGbEu7v1e774Jrm1Wr0qNXobLIuQgghzouCGeJUiqqNIwjCTAFJe/jIxJBLjL/aJVQETAghbo+CGeI0WJZFoSkzE9aBzAzDMPzx7BIqAiaEELdHwQxxGiq1DnVaPYCOZWYAIMTXVARcTcEMIYS4OwpmiNMoMgUevnIJfOSSDt1XQ+M82mYihBB3J2gws2zZMgwaNAh+fn4ICwvD1KlTkZ6ebnab+vp6pKamIjg4GL6+vpg+fToKCwsFWjGxJ674t6NZGaAhmKHMDCGEuD9Bg5ndu3cjNTUVBw8exN9//w2tVosJEyagpqaGv82zzz6LX3/9Fd999x12796Na9eu4Y477hBw1cRe+OJf/44HM8GmbSbKzBBCiPvrWC6/g7Zu3Wr2+YYNGxAWFoZjx47hxhtvRGVlJdatW4dNmzZh7NixAID169ejR48eOHjwIIYOHSrEsomdNGRm2l/8y+EyM8XUOI8QQtyeU9XMVFZWAgCCgoIAAMeOHYNWq8X48eP523Tv3h2xsbE4cOBAs/ehVqtRVVVl9kFcgy2OZXP4zAwFM4QQ4vacJpgxGAyYO3cuRowYgd69ewMACgoKIJPJEBAQYHbb8PBwFBQUNHs/y5Ytg1Kp5D9iYmLsvXRiI1wBsC22mUK5mhnqAkwIIW7PaYKZ1NRUnDlzBt98802H7mfBggWorKzkP/Ly8my0QmJvhVXGzExHuv9ygvlghjIzhBDi7gStmeHMnj0bW7ZswZ49exAdHc1fHhERAY1Gg4qKCrPsTGFhISIiIpq9L7lcDrm84+/sieNxmZlQm5xmMm4zVdRqodUbIBU7TdxOCCHExgT9C8+yLGbPno3Nmzdjx44dSEhIMLs+JSUFUqkU27dv5y9LT09Hbm4uhg0b5ujlEjsrtmEBcIC3DCLG+P9ldKKJEELcmqCZmdTUVGzatAk///wz/Pz8+DoYpVIJhUIBpVKJRx99FPPmzUNQUBD8/f3x9NNPY9iwYXSSyc3UanSoVhuHQtqiZkYsYhDkI0eJSo0SldomW1eEEEKck6DBzJo1awAAo0ePNrt8/fr1mDVrFgBgxYoVEIlEmD59OtRqNSZOnIiPPvrIwSsl9sYdy1ZIxfDrYPdfToivzBTMUGaGEELcmaDBDMuybd7Gy8sLq1evxurVqx2wIiKUxieZGIaxyX0ae81U0/FsQghxc1QVSZyCLXvMcPhhkxTMEEKIW6NghjiFQq7414a1Ldzx7FLaZiKEELdGwQxxCvbJzNBIA0II8QQUzBCnYMtj2ZxgfpuJMjOEEOLOKJghToEvALZhZiaU32aizAwhhLgzCmaIU7DlKANOMBUAE0KIR6BghjgFWw6Z5IQ0KgC2pA0AIYQQ10TBDBFcvVaPyjotANtuMwX5GDMzOgPL3z8hhBD3Q8EMEVyxKSsjk4igVEhtdr9eUjH8vIx9IakImBBC3BcFM0RwjY9l26r7L4crAqa6GUIIcV8UzBDBFVXZ/iQThysCpsZ5hBDiviiYIYJrOJZt+8nWIZSZIYQQt0fBDBFcw7Fs+2VmKJghhBD3RcEMEVzDsWx7ZmZom4kQQtwVBTNEcFwwE2qXmhnaZiKEEHdHwQwRXJEduv9yQvkCYApmCCHEXVEwQwRnj7lMnGDaZiKEELdHwQwRlEZnQFmNMdCwRzATQsMmCSHE7VEwQwTF1bJIRAwCvWU2v/8Q0zZTjUaPOo3e5vdPCCFEeBTMEEFxx7LD/OQQiWzb/RcAfOUSyCTGX3MqAiaEEPdEwQwRFH+SyQ7FvwDAMAyNNCCEEDdHwQwRlD2Lfzk00oAQQtwbBTNEUMWNtpnsJcBUi1NeS8EMIYS4IwpmiKAKTUMm7dFjhhPoLQUAVNRq7fYYhBBChEPBDBFUUbX9MzOBlJkhhBC3RsEMEVTDXCZ7bjMZMzPllJkhhBC3RMEMEVQxXwBsv22mIB9TZqaGMjOEEOKOKJghgtEbWP64tD2GTHKoAJgQQtwbBTNEMGU1GhhYgGGAYB/bd//lUAEwIYS4NwpmiGC4LaZgHxkkYvv9KlIBMCGEuDcKZohguJNM3DBIewlolJlhWdauj0UIIcTxKJghguGLf+3YYwZoKADW6A2ooWGThBDidjoUzKjVNOuGtF8xV/xr58yMQirmh03SiSZCCHE/VgUzf/zxB2bOnInExERIpVJ4e3vD398fo0aNwptvvolr167Za53EDRVV2f8kE2AcNklFwIQQ4r4sCmY2b96Mbt264ZFHHoFEIsGLL76IH3/8EX/++Sf++9//YtSoUdi2bRsSExPx1FNPobi42N7rJm6Ay8zYs/svh4qACSHEfVkUzLz99ttYsWIFrl69inXr1uHJJ5/ElClTMH78eMyYMQOvv/46du7ciczMTAQEBGDjxo0WPfiePXswZcoUREVFgWEY/PTTT2bXq1QqzJ49G9HR0VAoFOjZsyfWrl1r9TdJnBNXM2PvzAzQuAswBTOEEOJuJJbc6MCBAxbdWadOnfDWW29Z/OA1NTVITk7GI488gjvuuKPJ9fPmzcOOHTuwceNGxMfH46+//sK///1vREVF4bbbbrP4cYhzcmQww2VmaJuJEELcj0XBTGv0ej1Onz6NuLg4BAYGWvW1kydPxuTJk1u8fv/+/Zg5cyZGjx4NAHjiiSfw8ccf4/DhwxTMuIGGUQYOCGZMJ5rKqACYEELcjtWnmebOnYt169YBMAYyo0aNwoABAxATE4Ndu3bZdHHDhw/HL7/8gqtXr4JlWezcuRMXL17EhAkTbPo4xPFqNTqo1DoAjsrMcAXAFMwQQoi7sTqY+f7775GcnAwA+PXXX5GVlYULFy7g2WefxcKFC226uA8++AA9e/ZEdHQ0ZDIZJk2ahNWrV+PGG29s8WvUajWqqqrMPojz4bIyCqkYvvIOJwjb1FAATNtMhBDibqwOZkpKShAREQEA+P3333HXXXfxJ51Onz5t08V98MEHOHjwIH755RccO3YM7777LlJTU7Ft27YWv2bZsmVQKpX8R0xMjE3XRGyjcb0MwzB2fzwaNkkIIe7L6mAmPDwc586dg16vx9atW3HTTTcBAGprayEWi222sLq6Orz88st47733MGXKFPTt2xezZ8/G3XffjXfeeafFr1uwYAEqKyv5j7y8PJutidiOI4t/ARo2SQgh7szq/P7DDz+MGTNmIDIyEgzDYPz48QCAQ4cOoXv37jZbmFarhVarhUhkHm+JxWIYDIYWv04ul0Mud8wLJGm/IgcW/wJUAEwIIe7M6mDmtddeQ+/evZGXl4e77rqLDxzEYjFeeuklq+5LpVLh0qVL/OdZWVlIS0tDUFAQYmNjMWrUKDz//PNQKBSIi4vD7t278cUXX+C9996zdtnEyTg+M8MdzaZghhBC3E27Ki/vvPPOJpfNnDnT6vs5evQoxowZw38+b948/r42bNiAb775BgsWLMD999+PsrIyxMXF4c0338RTTz3VnmUTJ8IHM3aey8ThtplqNHpodAZ+VhMhhBDXZ3Uw8/rrr7d6/auvvmrxfY0ePRosy7Z4fUREBNavX2/x/RHXUVRdDwAI83dMMOPvJYWIAQysMTtj70ndhBBCHMfqYGbz5s1mn2u1WmRlZUEikaBz585WBTPEc/ETsx20zSQSMVAqpCiv1aK8VkvBDCGEuBGrg5kTJ040uayqqgqzZs3CtGnTbLIo4v4atpkcF1QE+shMwQzVzRBCiDuxSeGAv78/lixZgkWLFtni7oib0xtYlKiMAYWjtpmARo3z6EQTIYS4FZtVQXJ9XQhpS3mtBnoDC4YBgkxHph0hkJ+cTb1mCCHEnVi9zfT++++bfc6yLPLz8/Hll1+2OjSSEA63xRTkLYNU7LhTRdQFmBBC3JPVwcyKFSvMPheJRAgNDcXMmTOxYMECmy2MuK8iB/eY4dCwSUIIcU9WBzNZWVn2WAfxII5umMcJoGGThBDilqhzGHE4oYIZrj6HCoAJIcS9WJSZueOOO7Bhwwb4+/vjjjvuaPW2P/74o00WRtwX3zDPz7G9XhoKgCmYIYQQd2JRMKNUKsEwDP//hHSE0NtMNDmbEELci0XBTOORAu4+XuDQ5VI8+20alk7rjbHdw4VejlsSKpgJpNNMhBDilqyumamrq0NtbS3/eU5ODlauXIm//vrLpgsTyqbDubhWWY8vDuQIvRS3xQUzYQKdZqqs08JgaHkmGCGEENdidTBz++2344svvgAAVFRUYPDgwXj33Xdx++23Y82aNTZfoKMdzS4HABzLLoeeXvDsQuhtJgMLVNXTVhMhhLgLq4OZ48ePY+TIkQCA77//HhEREcjJycEXX3zRpKGeq8mvrMPVijoAQLVah/SCaoFX5H7qNHpUq3UAHB/MyCQi+MqNO6tldKKJEELchtXBTG1tLfz8/AAAf/31F+644w6IRCIMHToUOTmuvTXDZWU4R7LLBFqJ++KyMl5SEfzkVrc56rAAGmlACCFux+pgpkuXLvjpp5+Ql5eHP//8ExMmTAAAFBUVwd/f3+YLdKRjOcZgRiYx/lgOUzBjc8Uq47HsUD85f0LOkQL5E02UmSGEEHdhdTDz6quvYv78+YiPj8eQIUMwbNgwAMYsTf/+/W2+QEfiMjEzBkYbP88qA8tS3Ywt8fUyvo7dYuJQZoYQQtyP1cHMnXfeidzcXBw9ehRbt27lLx83blyTuU2uRKXW4Xx+FQDgsRsSIRUzKKpWI7esto2vJNYo4k8yObZhHofrAkyZGUIIcR/tKlqIiIhARESE2WWDBw+2yYKEkpZbAQMLdApQID7EB32jA3AspxyHs8oQF+wj9PLchlAnmTjcNhMVABNCiPuweJyBpVx1nAG3xTQwPpD/77GcchzNLsddA2OEXJpbETqYoW0mQghxPxaPM3B3XPHvwDhjMDM4Pggf775MJ5psrEighnkcKgAmhBD3Y/U4A3ek0xtwItcYzKTEBQEABsYFgWGAyyU1KK5WC5ZJcDfOk5mhYIYQQtyF1QXAAKDT6bBt2zZ8/PHHqK42Npa7du0aVCqVTRfnKBcKqlGj0cNPLkFShLGHjtJbiqRw4/8fpeyMzQgdzATSsElCCHE7VgczOTk56NOnD26//XakpqaiuLgYALB8+XLMnz/f5gt0BC5Y6R8XCLGooffJoHhjlob6zdiGwcCiRCVsMMOdZqLMDCGEuA+rg5k5c+Zg4MCBKC8vh0Kh4C+fNm0atm/fbtPFOcrR6+plOIMSjMEM1c3YRkWdFjrTvKsQofvM1GiphxAhhLgJq49m7927F/v374dMJjO7PD4+HlevXrXZwhyFZVl+jAF3kokzyPT5uWtVqK7Xws9L6vD1uRNuiynQWwqpuF07nB3GbTNp9AbUavTwEWCkAiHEM1TXa3HnmgPo1ckf79yZDJHI8V3PPYXVrygGgwF6vb7J5VeuXOFnNrmSqxV1KKiqh1jEoF9MgNl1kUoFogMVMLDAidwKQdbnTrgtJqGyMgDgLRNDZgqkaKuJEGJPR3PKkV5YjR+PX8Vn/2QJvRy3ZnUwM2HCBKxcuZL/nGEYqFQqLF68GDfffLMt1+YQ3JHsXlH+8JY1fZc+OJ62mmxF6OJfwPj7ym01UREwIcSecksbOsgv33oBZ65WCrga92Z1MPPuu+/in3/+Qc+ePVFfX4/77ruP32Javny5PdZoV/wWk+lI9vW4upl9l0octiZ35QzBDEBFwIQQx8gxBTMSEQOtnsUzX59AjVon8Krck9XBTHR0NE6ePImFCxfi2WefRf/+/fHWW2/hxIkTCAsLs8ca7SqjyHi0vE908xO/x3YPA8MYt5muVtQ5cmluxxm2mYCGImAaaUAIsafcshoAwJxxXRHh74XLJTVY8utZgVflntpV/SiRSHD//ffj/vvvt/V6HC6vzBigxAZ5N3t9uL8XBsUH4XBWGX47dQ1P3NjZkctzK86SmaFeM4QQR+AyM31jAjAwPgj3/fcg/nf0Cm7sFopb+0YJvDr3YnVmZtmyZfjss8+aXP7ZZ5+53DaTVm9AfqUxmIkObD6YAYApycZfui2n8h2yLndV7DSZGdpmIoTYl8HAIrfMGMzEBXljWOdgpI7uAgBYuPkM6rVND9KQ9rM6mPn444/RvXv3Jpf36tULa9eutcmiHKWgsh4GFpBJRAht5QV2cu8IiBjg1JVK5JTWOHCF7sV5MjNUAEwIsa+iajXUOgPEIgadAo092eaMN243VdZpcTiLDpXYktXBTEFBASIjI5tcHhoaivx818pc5Jmi5uhARavn/0N85RjeOQQAZWc6gu/+K3BmhoZNEkLsjXvjGxXgxffVkopFGNUtFACw+2KxYGtzR1YHMzExMfjnn3+aXP7PP/8gKsq19gDzyo3BTEwrW0ycW/saA7hfT16z65rcld7A8gW3IX6yNm5tXw3DJikzQwixjxx+i8nH7PLRScZgZld6kcPX5M6sDmYef/xxzJ07F+vXr0dOTg5ycnLw2Wef4dlnn8Xjjz9ujzXaDVf8GxOkaOOWwKTeEZCIGFwoqMalItccqCmk0ho1DCwgYoBgH+eomaHMDCHEXrgeM7HB5m+Wh3cJgVjEILO4ht8dIB1ndTDz/PPP49FHH8W///1vJCYmIjExEU8//TSeeeYZLFiwwKr72rNnD6ZMmYKoqCgwDIOffvqpyW3Onz+P2267DUqlEj4+Phg0aBByc3OtXXazuMxMa8W/nABvGUZ25baaKDtjrZJqY+AQ5CMzG+YphEDKzBBC7CynUfFvY0qFFANiAwAAezJoq8lWrA5mGIbB8uXLUVxcjIMHD+LkyZMoKyvDq6++avWD19TUIDk5GatXr272+szMTNxwww3o3r07du3ahVOnTmHRokXw8vKy+rGaw0XFlmwzAeCP0m05lU9DCq3kLCeZADrNRAixv1xTzUxccNPXF75uJp2CGVtp95Q9X19fDBo0qEMPPnnyZEyePLnF6xcuXIibb74Zb7/9Nn9Z58626/NypdzybSYAuKlXOGQ/inCpSIX0wmp0j2i+0R5pqsRJTjIBDZmZ6noddHoDJAINvSSEuC8uMxN7Xc0MAIxOCsM7f13EP5dKoNEZIJPQ36COsugn+NRTT+HKlSsW3eG3336Lr776qkOLAowDLX/77Td069YNEydORFhYGIYMGdLsVlRjarUaVVVVZh/NqdfqUWR6gbU0M+PvJeWLt6gQ2DrFTnKSCTCmeTkVdbTVRAixrco6Ld/64fqaGQDoGemPEF8ZajR6fj4g6RiLgpnQ0FD06tULN998M9asWYMjR47g6tWrKC0txaVLl/DLL7/ghRdeQGxsLFasWIE+ffp0eGFFRUVQqVR46623MGnSJPz111+YNm0a7rjjDuzevbvFr1u2bBmUSiX/ERMT0+ztuKyMj0zMn26xxK2mBnpfHcrFgcxSK74jz+YsPWYAQCIWwd/LmJSkImBCiK1xxb8hvjL4yptugIhEDG7sSke0bcmiYOaNN97AxYsXMWLECHz00UcYOnQoYmNjERYWhqSkJDz00EO4fPkyPvnkExw8eBB9+/bt8MIMBgMA4Pbbb8ezzz6Lfv364aWXXsKtt97aanO+BQsWoLKykv/Iy8tr9nb8sewgbzCM5QWpE3qGo3cnf1TUavHAukP4eHcm1c9YwFnmMnECfWikASHEPnJMM5laGpMDAKOSKJixJYtrZsLDw7Fw4UIsXLgQ5eXlyM3NRV1dHUJCQtC5c2erAgJLhISEQCKRoGfPnmaX9+jRA/v27Wvx6+RyOeTytl8wr5RZfpKpMS+pGN89ORwLN5/GjyeuYtkfF3A8txz/d1cy/L0sz/B4GmfKzADGIuCc0lo60UQIsTluJlNccNN6Gc7IrqFgGOB8fhUKq+oR7m+bgy2eql1VR4GBgUhOTsbQoUPRpUsXmwcyACCTyTBo0CCkp6ebXX7x4kXExcV1+P6tLf5tTCET490ZyVg6tTdkYhH+PFuIaav/QTlNYW4R3/3XWYIZBXc8m54zQoht8T1mWsnMBPnI0Dc6AABlZ2xB0BJqlUqFtLQ0pKWlAQCysrKQlpbG95F5/vnn8e233+LTTz/FpUuX8OGHH+LXX3/Fv//97w4/tjXdf5vDMAweGBqH/z01DBH+XsgsrkHqpuPQ6g0dXps74jIzTrPNxM9nomCGEGJb3DZTc8eyG6PRBrYjaDBz9OhR9O/fH/379wcAzJs3D/379+d71kybNg1r167F22+/jT59+uC///0vfvjhB9xwww0dfmyu+290oPWZmcb6xQRgwyOD4C0TY39mKd787XyH1+ZutHoDv53jNJkZvtcMbTMRQmwrl99msiyY2ZdRAh29Ee6QdveZsYXRo0e3WTz7yCOP4JFHHrH5YzcuAO6o7hH+WHF3Pzz55TFs2J+NpAg/3Ds4tsP36y5KVcbsh1jE8Ns7QqNhk4QQe1Dr9MivqgfQfI+ZxvrFBECpkKKyTouz16qQHBPggBW6J4/s1FNd39ADwBbBDABM7BWB527qBgB49eczNN69kYYtJlmr08kdKdDHVDNTQ5kZQojt5JXVgWUBb5kYIb6tD9UVixj07mRsvppeUO2I5bmtdgUzOp0O27Ztw8cff4zqauMTcO3aNahUrjGAkdtiCvSWNtsDoL1mj+2CW/pEQqtn8a+Nx6gg2MTZin+BRsMm6+g5IoTYTm6jY9mWHI7pFu4HAEgvpGCmI6wOZnJyctCnTx/cfvvtSE1NRXGxsXBp+fLlmD9/vs0XaA9XbLjF1BjDMHjnrmQkhPigtEZDRV0mzlb8CzQuAKbMDCHEdnIsrJfhcMHMRQpmOsTqYGbOnDkYOHAgysvLoVA0FM9OmzYN27dvt+ni7CWv3DbFv81RyMQY2z0MAHA0h7aaAOcaZcAJpGGThBA7sKTHTGMUzNiG1cHM3r178corr0AmM98LjI+Px9WrV222MHuydlq2tVLiAgEAx3Iq7HL/robPzDjVNhPXZ0ZLHZwJITaTW9Z2j5nGuoX7AgAKq9SopExxu1kdzBgMBuj1+iaXX7lyBX5+fjZZlL1x20zRNt5m4nDBTHpBFarr6ZfTGTMzXM2MRmdAnbbp7zMhhLRHTqllPWY4fl5SRCmN3X8vFlF2pr2sDmYmTJiAlStX8p8zDAOVSoXFixfj5ptvtuXa7IYrAI6xwzYTAIT7eyE6UAEDC6TlVdjlMVxJiZONMgCMA0alYmNxHvWaIYTYgsHA8mUMcW0cy26sW4SpCJhONLWb1cHMO++8g3/++Qc9e/ZEfX097rvvPn6Lafny5fZYo02xLNuQmbHTNhMADDRlZ45m03j3YicbMgkYg3C+cR6dOiOE2EBBVT00OgMkIgZRAZbPWuLqZjKobqbdrD6XHBMTg5MnT+Lbb7/FyZMnoVKp8Oijj+L+++83Kwh2VuW1WtRojNsK9igA5qTEBeKntGs4nkvBjDNmZgDjiabiajWdaCKE2ER2iXGLKTpQAYnY8lwBHc/uOKuCGa1Wi+7du2PLli24//77cf/999trXXbDFf+G+cnhJRXb7XFS4oIAACdyK6A3sBA7SbM4R6vX6lFVrwPgXDUzAPWaIYTYhsHA4rtjeXjrjwsAgC5h1tWPJvEnmlyjV5szsiqYkUqlqK+vt9daHMKWYwxakxThB1+5BCq1DukF1egZ5W/Xx3NWXMM8mVgEf4Wg0zOaCGx0ookQQtrj7LVKLPrpDI7nVgAAukf4YcHN3a26jy5hvmAYoKxGgxKV2qm25F2F1TUzqampWL58OXQ6nT3WY3f2Lv7liEUM+scGAACOeXC/mRLTXKZQP7lF3TAdiZ/PRDUzhJB2+OdSCaZ8sA/HcyvgIxPjlVt6YMvTN6BzqK9V96OQifmj3BepCLhdrH6rfOTIEWzfvh1//fUX+vTpAx8f84rtH3/80WaLs4c8BxT/clLiArE3owTHcsrx4LB4uz+eM2o8l8nZ0ORsQkhHbDl1DQYWGJoYhJV390eE0vKi3+t1C/dDTmktLhZWY3iXEBuu0jNYHcwEBARg+vTp9liLQ1wxHZuLCbJ/sTLXb+ZojucWATvjXCZOAD/SgDIzhBDr5Vcayy6m9uvUoUAGMDbP+/tcIdKpbqZdrA5m1q9fb491OMxVB2Zm+sUEQMQYA6jCqnqE+3fsl90VOeNcJk5DzQwFM4QQ6xWYgpnwDgYyAI016Kh2Tc12ZZV1xlof7l25Pfl5SZEUYSz8Peah2ZliJz2WDdA2EyGkYwqqjMFMpA2CmaSIhmCGRqxYz+rMTEJCQquFnJcvX+7QguyNGy/g72X/YAYwNs87n1+FYznluLlPpEMe05mUOGHDPA5XAFxZR8EMcR+FVfUI8JZCLrFf6wlibDvB9aiK9O942UJCiA/EIgbV9ToUVNUjUun8fducidXBzNy5c80+12q1OHHiBLZu3Yrnn3/eVuuyC7VOD7XOAMBxwUxKXCC+PJjjsXUzzpyZoW0m4m5+OHYFz39/Ejd2C8WGhwcLvRy3xm0xeUlt03ZCLhEjIcQHl4pUuFioomDGSlY/A3PmzGn28tWrV+Po0aMdXpA9Vdc3HCf39XJMzxOuCPjs1UrUafRQyDzr3ZJzFwA3ZGY8ubEhcQ/fmwIZlgWOZZeDZVmna4fgThq2mBQ2+zknhfsZg5mCaozqFmqT+/QUNquZmTx5Mn744Qdb3Z1dcMGMj0zssBeu6EAFwvzk0BlYnLpS4ZDHdCbOXADM1U2xLFBFW03EhX13NI8PZACgWq2jMR12xhf/+tvub1vXcGN/GhprYD2bBTPff/89goKCbHV3dsHVy/g5aIsJMA405LIzJzxsgnatRsfPwXLGzIxULIKf3Jiho60m29AbWLz+6zl8sD1D6KV4jO+O5uGFH06BZYEHhsYizPRvLdc0uoXYR+PMjK0k0cDJdrN6r6V///5mKTWWZVFQUIDi4mJ89NFHNl2crXGZGT8HbTFxendS4o8zBTh7rcqhjyu0kmpjgOAlFcHHSbfXlN5SVKt1dKLJRjYdysFn/2QBAHpE+mN8z3CBV+Te9l8q4QOZB4fG4fXbeyG9oBpF1WrkltUiOSZA6CW6rYbMjO1abnSLaJjRZDCwENHWt8WsflW//fbbzYIZkUiE0NBQjB49Gt27WzePwtEaMjOOD2YAY92MJymtMW4xBfs43ygDTqC3DFfK66hxng2UqNT4vz/T+c8X/3IWw7sEw1vmXDO53MnHey6DZYFp/Tvh9dt7gWEYxAb54Eh2OWVm7IwLZmxxLJsTF+QNmViEOq0eV8rrEBts/35o7sLqvzKvvfaaHZbhGNz0Zn+F47aZAKCXachkVmkNVGodfOWe8ce9zDTzKMjH+UYZcBq6AFNmpqOW/3EBVfU69Iz0R2WdFlcr6rByWwZevrmH0EtzS7mltdiTUQwAmDOuK/+GgZvxk1tKwYw95VfZPjMjEYvQOcwX5/OrkF5YTcGMFayumRGLxSgqKmpyeWlpKcRi59xK4HBFno6smQGMxa+RSi+wLHA+33O2mlwhmAnkG+dRZqYjjuWU4btjVwAAS6f1xhtTewEA1u3LwjkP2151lK8O54BlgZFdQxAf0jAjLzbYWMNBmRn7KrRDZgYAEkONz2VOaY1N79fdWR3MtNSZUK1WQyZz3hctQLiaGaAhO3PGg7aauGAm2KmDGcrMdJROb8Cin84CAO4eGIMBsYEY2z0cN/eJgN7A4uXNp6E3UEdTW1Lr9PjuqDF4fGBonNl1fGaGghm70ekNKKo2BjMdncl0vegAYzB6raLepvfr7ix+VX///fcBGE/n/Pe//4Wvb8OIc71ejz179rhAzYyQwYwS284X4cxVz3mXWmbKdgQ6cTATQJmZDtt4MAfn8qugVEjxwqQk/vLFU3phz8USpOVVYNPhXDx43Ysuab8/ThegrEaDSKUXxnUPM7suNsj4zj6/sg4anQEyicdNrbG7EpUGBhYQixibt52I4oOZOpver7uz+FV9xYoVAIyZmbVr15ptKclkMsTHx2Pt2rW2X6ENOXqUQWN8EfA1D8rMqFxhm4kyMx1RVqPBu39dBAA8PzEJwY3+sIf7e+H5iUlY/MtZvP3HBUzpG8kHj6RjNh7MAQDcMygWErF5sBLiK4NCKkadVo+rFXVIaLQFRWwjv9IYaIT5yW3es4wPZiopmLGGxcFMVpbxuOWYMWPw448/IjAw0G6Lshdn2GbKKFKhXquHl9S564tsgct2OHUw40OZmY7Yd6kE1WoduoT54t7BsU2uf2BoHDYdykV6YTV+OH4Vj96QIMAq3cuFgioczSmHWMTgnsExTa43nmjyRnphNXLLaimYsYPCKvtsMQFAVIDxPq+WUzBjDavzjzt37nTJQAYAqtXCZWYilV4I8pFBb2CRXuAZDZFKXaAAmCZndwzX3GtgXGCz71DFIgYPDDNuL319OJemAdsAl5WZ0DO8xZM0MVQ3Y1f5puLfCBueZOJEBxifu9IaDeq1epvfv7tqV4riypUr+OWXX5CbmwuNxvwd7XvvvWeThdmDkJkZhmHQK8ofezNKcPZalUc0syp3hWBGwW0zUWamPTIKVQCArqbOpc2Z2i8Ky34/j0tFKhzJLsfgBOfuFO7MVGodNh+/CqBp4W9jccHc8Ww6EWMPXI8Ze2Rm/BUS+MjEqNHoca2iDomhvm1/EbE+mNm+fTtuu+02JCYm4sKFC+jduzeys7PBsiwGDBhgjzXajFBHszm9opTYm1GCMx5SN+MKmRnuaDbVzLTPxSJjZqZrWMt/cP28pLgtOQrfHMnDpkM5FMx0wC9p11Cj0SMxxAfDOwe3eDs60WRf3CgDe2RmGIZBVIACGUUqXKuop2DGQlZvMy1YsADz58/H6dOn4eXlhR9++AF5eXkYNWoU7rrrLnus0WaEzMwAQO9OxroZT+gErNEZ+J93kBMXfQb4GAPbOq2eUrpWUuv0yDE1ZuvWSmYGAF9P8/uZAj5jR6x3IrccADAlOarVrtoNwQzVXdhDvh0zMwCdaGoPq4OZ8+fP46GHHgIASCQS1NXVwdfXF6+//jqWL19u8wXakuDBTJTxRNP5gmpo9QZB1uAo3LaNiAGUDu64bA0/uQQSU60HZWesc7m4BnoDCz8vSZuTg/tGK9Eryh8anQE/HL/ioBW6n2KVcURIp8DWhxtyNTN5ZbVUp2QHhXbMzAANwcwVCmYsZnUw4+Pjw9fJREZGIjMzk7+upKTEdiuzsXqtHhpTACHUNlNskDf85BJodAZkFqsEWYOjcFtMgd4ypx6WxjAMP9KATjRZ56Kp+LdbuF+bs7cYhsF9Q4zZmU1UCNxuRVXGYKatKfTRgQowjLHGpsxNMmH/3XsZn+/PFnoZYFmWz8zYcmJ2Y9GBlJmxltXBzNChQ7Fv3z4AwM0334znnnsOb775Jh555BEMHTrUqvvas2cPpkyZgqgoY8r0p59+avG2Tz31FBiGwcqVK61dMgBAZcrKMIzx3bgQRCIGPfhOwO7dPM8Vin851DivfS4VGQPybuGW7enf3q8TvGViXC6uwaGsMnsuzW1xmZnQNhq1eUnFfNbAHepmSlVqLP3tPBb/clbwYv2KWi00OuMb47A2MpLtxR3PpmDGclYHM++99x6GDBkCAFiyZAnGjRuHb7/9FvHx8Vi3bp1V91VTU4Pk5GSsXr261dtt3rwZBw8eRFRUlLXL5XHHsn1lEkEzBdxWk7uPNeAzMy4QzFDjvPbhMjNdw1qvl+H4yiW4vZ/x3/DXh3Ptti53pTewKDUFM5a8iLrT8eycRt/DxUJhs9pcVibIR2a3fmFRSsrMWMuqFIVer8eVK1fQt29fAMYtp450/Z08eTImT57c6m2uXr2Kp59+Gn/++SduueWWdj+W0PUyHL4I2M1PNHFZDmeey8ShzEz7NBzLtvy0xX2D4/D14Tz8cboAi6doXCJz5yxKa9QwsMY6tGCftoOZ2CBvHM4qQ54bBDONJ4CnF1QJeiLO3vUyQOMC4HoYDKxTb9U7C6syM2KxGBMmTEB5ebm91mPGYDDgwQcfxPPPP49evXpZ9DVqtRpVVVVmH4Bx7xgQrl6G08uUmTl3rQoGNx6+V6pyncxMQ68ZysxYql6rR7aph0lbJ5ka6xOtRJ9OSmj0BvycdtVey3NLxdXGrEyQj2Ut9ONMmZmcUjcIZhoFZOmFwjYdtfdJJu6+RQyg0RtQUqO22+O4E6u3mXr37o3Lly/bYy1NLF++HBKJBM8884zFX7Ns2TIolUr+IybG2O5b5SSZmc6hPpBLRKjRNLwYuCNXysxwAZfQe/Gu5HJxDQws4O8lQVgbxajXu2tgNADQqSYrFZmCGUt/3rHB7rPNZBbMCNxBvcCOoww4UrGI7+5M07MtY3Uws3TpUsyfPx9btmxBfn5+s1kQWzh27BhWrVqFDRs2tHlSorEFCxagsrKS/8jLywPQMGRS6GBGIhahR6SpCPia+xYBu0LDPA63xhIVBTOWyiiy/CTT9W7tGwWpmMGZq1WCvzC5Ei4z09ZJJk7j49mu7vpgRsjTcAWmAZD23GYCqNeMtawOZm6++WacPHkSt912G6KjoxEYGIjAwEAEBATYdGbT3r17UVRUhNjYWEgkEkgkEuTk5OC5555DfHx8i18nl8vh7+9v9gE4zzYT0FA3485FwK50mok7BukO72AdxZIxBi0J8pFhTFIYAOBHys5YzNpghttmyq+qh1rn2g0hG9fMVNXr+OyIEApMx+PtmZkBKJixltVpip07d9pjHU08+OCDGD9+vNllEydOxIMPPoiHH37Y6vurrjf+Y/ZXCJuZAYDk6ABsRC6O5zim9kgIZS4UzMQFGacKUzBjuYYeM+1rtX7HgGj8da4Qm09cxQuTultUA+Lpiq3cZgrykfEzfq6U16Gzi7bFr9fq+eAlxFeGEpUG6QXVduvx0hbHZWaM93+FpmdbxOpX9lGjRtnswVUqFS5dusR/npWVhbS0NAQFBSE2NhbBweazR6RSKSIiIpCUlGT1Y3FHs50hMzMw3liJf+pKJeq1ersd7xNS46Z5zo6rLSiuVqNWo4O3TPiA19llmHrMWHos+3pju4chwFuKomo1/rlUghu7hdpyeW7J2swMwzCICfLGhYJq5JbVumwww72Y+8jEGJIYjN9O5SO9oBqjTdk9S+n0Bvx2Oh9DE4NbnDZuiQK+YZ59g5loysxYxeptJsC4BfTAAw9g+PDhuHrVeCLhyy+/5JvpWero0aPo378/+vfvDwCYN28e+vfvj1dffbU9y2pVdZ1zFAADQHywN4J9ZNDoDW651cSyLL/NFOzr/MGMUiHluwBTdqZt9Vo9cviTTO17gZRJRLgt2dhzhgqBLWNtMAM0zGhy5boZbu2xwT7obtrWbM+Jpq1nCzDnmzS8+dv5dq+lVqNDlekwSbijtpkqKZixhNXBzA8//ICJEydCoVDg+PHjUKuN/8AqKyvxn//8x6r7Gj16NFiWbfKxYcOGZm+fnZ2NuXPnWrtkAIDKiTIzDMNgYLyxvuioG241VdXroDMdO3eFzAzQ8EffHY6x2ltmsQoG1hgEWvPCer07BhhPNf15toAv0CctK6o2ZgTC/Cx/EY3jTjS58O81FzjHBinQLcIUzLSjcJxrtteRNyxcVsZHJrZ7J/nGvWZI29p1mmnt2rX49NNPIZU2BAYjRozA8ePHbbo4W1JxNTNOkJkBgIFxxq2mo9nuF8xwWRkfmdhlttDc4R2sozQeY2DtSabGkqOV6Bzqg3qtAX+cLrDV8txWRzIzrpxx5CZ/xwZ5o7spmMkoUkFvZZ+uK+XGn0GpFX1bajU6PoABGoKZcKVXh373LcEFM2U1GtRqdHZ9LHdgdTCTnp6OG2+8scnlSqUSFRUVtliTXTTUzDhHMJNiyswcyylzu6F7rjTKgMO9g6XMTNv4MQbtOMnUGMMwfHaGtppaV6PWoUZjfENmTTDjDiMNuLXHBnkjJtAbCqkYGp3B6j5dV021N6VWtGB44L+HMPyt7Vjx90Xo9Aa+ENne9TKA8Y23ryn7Q9mZtlkdzERERJgV7XL27duHxMREmyzKHrij2f5OsM0EGGc0ySUilNdqkVnsXs3z+HoZVwpmTCeaclz4j76jcOn6rmEdLyid2r8TGAY45CZt9+2Fy8p4y8T8C5wlGmdmXLXjeOOaGZGI4eu0Llq51cQVEtdq9BZlOvLKanE8twIGFli1PQN3f3IQx0xlAR0pILYUwzDoREXAFrM6mHn88ccxZ84cHDp0CAzD4Nq1a/jqq68wf/58/Otf/7LHGm2ioQOwcwQzMokIyTEBAIzZGXdS5oKZGf4drBt3ZbaVjMKGhnkd1SlAgWGJxlOLf5zJ7/D9uSt+WraVNUoxQcZMRq1Gj0vFwg5obA+WZc0yM0DD790FK4KZxlkVwLLszI4LRQCMfaj85BIcyynHV4eMA1IdkZkBaHq2NawOZl566SXcd999GDduHFQqFW688UY89thjePLJJ/H000/bY4020dA0zzm2mQBgYJypCNjN6mbKal2nxwyH22a6Ul5n9V68J6nX6vnslTUDJlvDBTPWvDh5Gmt7zHCkYhEGxAUAMGa/XE2xSo06rR4MAz5LkWSqm7loxYmmgqp6s3/X3FZ4a7abgpkHh8bh9zkjMSA2gL/O3j1mONQ4z3JWBzMMw2DhwoUoKyvDmTNncPDgQRQXF+ONN96wx/psRqs3/iI7UzAzyNRvxt1ONPEN81zkJBNg/OMkk4igM7D0h6MVmcUqsCwQ4C1FqG/7TzI1xgVFXGExaarIlFVoz+mxIQnGYPHQ5VKbrskRuC2mKKUCMonx5YoLZqw5nn31usZzparWi4Br1DoczDT+vMb1CENMkDf+9+QwPDu+GwbHB2Fcj3CLH7sjuGDmCv1NalO7+swAgEwmg5+fHyIjI+Hr6xrNmBgG8HGihmgDYo2ZmaySGpS08Y/LlfDBjAv0mOGIRAxiaKxBm7gxBt3CrJ/J1JIuYQ3BjKvWddgbv83UjgBycILxTdPhLNc7bHD9FhMAJJm2mbJLalCvtWxMw/VddNvaZtp3qQQavQGxQd58s0GJWIQ547vif08N44MMe6OaGctZHczodDosWrQISqUS8fHxiI+Ph1KpxCuvvAKt1rl7RfjKJRA5Udt0pbeUL2Y75kbZmTIXLAAGqNeMJQ6a3t33Ms0Xs4W4YB9IRAxqNXrkCzhzx5nx20zt2N7oFxMAmViEomo1sl3sd5v7t9g4mAn1kyPQWwoDa3k27+p1wUBJG8ezd5q2mMZ2D7P7EezWdAqkXjOWsjqYefrpp/HJJ5/g7bffxokTJ3DixAm8/fbbWLduHZ555hl7rNFmnOUkU2PcaIOj2a63n90SVxpl0FhcMHeiiYqAm2MwsNh23vhHflx326XZpWIREkKMP/uMdnR29QRF1e3PzHhJxehnOmxwOMu1tpr4zExwQzDDMAxfBGxp8zyuxwz3Xra1zIzBwPLFv+N6WDcywda4DFB+ZR1lLdtgdTCzadMmbNiwAU8++ST69u2Lvn374sknn8S6deuwadMme6zRZpypXobDFwG7UWbGlUYZNEaN81qXdqUCJSo1/OQSfuvCVhpvNZGm2tMwrzHu+Tp02bXeNOU1s80EgG+eZ2kRMJeZ4YKg1mpmzl6rQlG1Gj4ysc1/z60V7ieHiDHWfBa7USmCPVgdzMjlcsTHxze5PCEhATKZc794OWVmxtQJ+MzVSov3f51dmctmZmibqTXbzhUCAEZ3D+OLMW2lKwUzrepoMDMk0RTMuNiJpuZqZgDwYw0sPQHH1cwkRwcAaP000/YLxt/zG7qGQC4RtoO5RCziT05dv1VGzFn9F2n27Nl44403+JlMAKBWq/Hmm29i9uzZNl2crTljZiYmSIEwPzm0ehYn8yqEXk6HqXV6/hh8sI9tTrs4SuM5Nq5WKOkIf5uCmfF2SL13CW9oU0/M6Q0sf0DA2qPZnAGxgRCLGFytqOO3XJxdvVaPwirj9319MMMVAVuSmTE0OqHI9fYqaWWbid9isuFWakfQ8WzLWP3qfuLECWzfvh3R0dFITk4GAJw8eRIajQbjxo3DHXfcwd/2xx9/tN1KbcAZgxlu6OTvpwtwNKccQ0w9N1xVeY2xCFwsYpzy592a6EDjH8xqtQ7ltVqX6pNjb9klNcgoUkEiYjA6yQ7BTGhDZoZlWUGLLp1NWY0GBtZ4GrO9v5M+cgn6dFIiLa8Ch7PK+N91Z8ZtMfnJJfxUew6XmcmvrEdlrRZK75az7kXVamj1LMQiBr2ijIXrLW0zFVXX49SVSgDA6O6hHf4ebKFToAJHc8opmGmD1a82AQEBmD59utllMTExNluQPTlL99/rpcQF4ffTBdibUYzUMV2EXk6HNN5icqaTY5bwkooR4e+Fgqp65JbVUjDTyLbzxqzMkMQgKBW2/3eUGOoDEQNU1mlRrFJbNRna3XFbTME+ckjE7d/eG5IQhLS8Chy6XMbPxHJmjYt/rw9u/b2k6BSgwNWKOpy9VonhXUJavJ+rFcb7ifD34scQlNVoYDCwTf5G7bpQDMA4BNVZfgejTSeaXO0kmqNZHcysX7/eHutwCGfNFEzoGY7//H4eBy+X4Uh2Gd9MzxXxPWZ8nDNwbEtssDcKquqRU1rDnwAhjbeY7JN695KKERvkjezSWlwqVDnNC4kzKKpuf8O8xoYkBuHjPZdx2EVOTrZUL8NJiQvE1Yo6HM0pbzWY4eplOgUq+DcoOgOLqnotAq6r6+PqZcY6yRYT0Gh8Q36VwCtxbrat4nNy/nZ4R2kLMUHemDHQ+E7pnT/TXbpewxVHGTQWF9RQN0OMyms0/Gk7ewUzQKMTTS44Q8ieOlr8y0mJCwLDGJt0FrlAP5+2gplB8caToEfaCM64YCY60NhF2N/0pvb6uhmt3oC9GSUAhD+S3VjPSOPW2IWCajqe3Qqrg5nS0lKkpqaiZ8+eCAkJQVBQkNmHM3PWzAwAzB7bFTKxCIeyyrA/07V6QTRWZtqLdtVghm+cR8ezebsuFkFvYNE9wo8fyGkPXcJMRcCFFMw0VtTOuUzXUyqk/AujK5xq4t5QtPQ7x/XoOp5TDp3e0OL9cKeAok2FtCGmXj3X181cLa9DrUYPL6mI/zk5g4QQH8gkItRq9NSdvBVWv7o/+OCDuHTpEh599FGEh4e7VKGes9bMAMa21fcNicWG/dl45690DO8c7FI/W05ZrbEA2GWDGe5EE/3R4HFbTDf1tG/qnTuenVFEjfMas1VmBjD2mzl7rQqHskoxJTmqw/dnT9y/wbjg5oOZbuF+8POSoLpehwsF1ejdSdns7RoyM8b7CfaV4XJJTZPj2Y0zQc5U7ycRi9At3BdnrlbhQkEV4k0NJok5q4OZvXv3Yt++ffxJJlfizJkZAPj3mM745kguTuRWYGd6kVPt21qqzNQm3JWGTDbGdQGmbSYjtU6P3enGokh7bjEBjRvnUQfmxjoyl+l6QxKCsf6fbKdvnseybJvbTGIRg5S4QOxKL8aR7LIWg5mrpqPo3GgArmXE9ZmZth5PSD0i/HHmahXO5VdjUu9IoZfjlKzeZurevTvq6lzziJi/kwczYX5emDk8HgDw7l8XXXJ/tKEA2EWDGdMfsoKqerdpYtgRBy+XoUajR7i/HH1aeLGwlc6mYKZEpea7SBOguIqby2SbzAxg7OfDFRY7o+JqNdQ6A0QMWh3qOIgfB9N8B3WWZRu2mbhgxtSZ/PqaGS6YsedWanv1MG17naci4BZZHcx89NFHWLhwIXbv3o3S0lJUVVWZfTgzZ95m4jx1Y2f4yiU4e60Kf54tEHo5VuOPZrtoMBPgLYWf3Bj00lgDYIfpSPa4HuF2T737yiX8lGAqAm5gy8xMkI8MydHGoHTjwdwO35+9ZJUYs3NRAQpIWzmOzo2DOZLd/ETw0hoN6rUGMAwQqeSCGVNm5rphk7nNDLV0Ft0juY7Hzv0aKySrg5mAgABUVVVh7NixCAsLQ2BgIAIDAxEQEIDAwEB7rNFmnHGcwfUCfWR45IYEAMD7Oy4JvBrrNUzMdq3uvxyGYahuphGuUHRkK0dfbakzjTVooiMTs5vzxI2dAQCf78/mu3U7G25MATeDqSXJMQGQihkUVauRV9Z0x4Crlwn38+JHcISYMjPXD5tsq0ZHSFxBcl5ZHarrtQKvxjlZHczcf//9kEql2LRpE7Zv344dO3Zgx44d2LlzJ3bs2GGPNdqMs9fMcB4dkQCxiMH5/CqXyw6U1bh2ATBAM5o4VfVapJvaxafEO+aNCl8ETCeaAAC1Gh0fcNiiABgAJvWOQGKIDyrrtNh0KKfV214uVmHRT2cw4I2/Hdo2gttO6R7R+qkiL6mY3/5s7oj21UY9ZjgNNTMNwQzLsi0OtXQGAd4yRCqNwayl86g8jdWv7mfOnMGJEyeQlJRkj/XYjVjEwFsm7NAwSym9pRgQG4Aj2eXYk1GM+4fECb0kixgMLMpdvM8MAMQGmYqAXSyQtLUTuRVgWeMfd0c1saMTTea4rIxCKoaPjf5+iUUMnhrVGS/8cAqf7s3CQ8Pi4SVtuG+WZXEgsxTr9mVhu2lOEQB8uPMSxCIGz97UzSbraA0XzPSw4Ij0oPggHM+twNGcMkxPMe9szM2him4czHA1M422mcprtag2BY3OOuqhe4Qf8ivrcSG/yqUbq9qL1ZmZgQMHIi8vzx5rsSsfmdiljjrf2NU4F2TPxWKBV2K56nod9Kai5UAX7QAMNOo1U+rZp2qOmt7pDnRQVgZoONGUSdtMABpvMclt+vdrav9OiFR6obhajR+OX+Ev1xtYvPTDadz330PYfqEIDGMcLPrUKOPW1KrtGfhkT6bN1tEcvYHlM4I9IlvfZgIa+s0caaYImCv+7dSoiLi5bSbujUu4v9wssHMmXGB3Lp8C/eZYnZl5+umnMWfOHDz//PPo06cPpFLzF62+ffvabHG25OsiW0ycG7uF4t2/L2L/pVJo9YZWi+CcBVdQ5yuXQC5xzj8IloijmhkADSdEBsY57l0gF8xcq6yHSq2Dr9y1/t3aGtcwzxbFv43JJCI8cWMilvx6Dmt3Z+LugTEwsMDcb0/g99MFEDHAfUNi8ciIBCSahoD6eUnwf3+m4z+/X4BCJsGDQ+2TMc4urUG91gAvqYhvldCaFFMR8KUiFcpqNGZZ4SutbDNV1mmh0Rkgk4ga6mWCnLeHC51oap3VfynuvvtuAMAjjzzCX8YwDD/pVq93zuOsrnCSqbHenZQI9JaivFaLtLwKl0grusMWE9CQmblSXtfsMDpPoNUbkJZXAcCxmZkAbxlC/eQorlYjs0iFZA+fj2XLhnnXu2dQLD7YcQl5ZXX47tgVbD1TgN0XiyETi/D+vf0xqXeE2e1Tx3RBjVqHj3ZlYtFPZ1BcVY/JfSLRPcLPplkj7sU6KcIfYgv+7QX5yNAlzBeXilQ4llNu1tzx6nUN8wBjJ2SxiIHetC0e7u/F18s447FsDhfMpJvGGnji36XWWP12Pysrq8nH5cuX+f86K1d7hycWMbjBtNXENS1zdlza1lWPZXMilV4QixiodQb+WKynOXetCnVaPZQKKbqY3pk7Cvd4GbTV1LDNZIdgRiET45ER8QCABT+exu6LxVBIxVg3a2CTQIbz/MQkzDL1wnp/xyVMXrUXQ/6zHc/97yQf/HYUF8z0tGCLicPNaTraqAiYZVm+ZqbxNpNIxPBvuEpM/765LWVnLP7lxAd7Qy4RoU6rp3ErzbA6mImLi2v1w1m5ykmmxm7sajwOuyfDNYKZCtMog0Bv18qCXU8iFiEqwFjw6mqnyWyFGyyZEhfo8HeAXcOpCJhjq4nZLXlwWDz/Rs/PS4IvHx2MkaY3Uc1hGAav3toTy6f3weikUHhJRSgy1d3c/fEBPvjqiAv5XL2M5fORuK3QxieaKuu0qNEYdwoaFwADQLCPed0M3/03uOUGfUKTiEVIMh1Vp62mptpViPHll19ixIgRiIqKQk6O8WjfypUr8fPPP9t0cbbk52KZGcBYNwMAp69W8v1bnFlFnSkz46KjDBqLCfSMupnc0lqcvVbZ5PJjOcYXBa4ewZG6hRv/YJ+52nRdnqawyn7bTIBxy2XRrT0wKD4QXz8+lC+mbY1IxODuQbHY8PBgnFw8AV89NgTdwn2h1hnw/bErbX59Wyw9lt0Ytw1/+mol37mbq5cJ8ZU1KeoNua5xHtejJtaJa2YA41gDALhAwUwTVgcza9aswbx583DzzTejoqKCr5EJCAjAypUrbb0+m3HFzEy4vxe6R/iBZYG9LpCdKTdlZpQK187MAA3p5uYacbkLlmVx76cHcduH/+DctSqzy4/wxb+OD2YG8dOQK6BtZRqyu9PqDTiea3weuIni9nD3oFh899TwFmcbtUYuEWNElxA8dkMiAODrw7kdGsNSUavBtUpjNqq7FdtMMUEKhPnJodWzWLj5DA5eLuXfiHRq5qh1cKMTTRqdAdcquWDGebeZgIafCZ1oasrqYOaDDz7Ap59+ioULF0Isboh2Bw4ciNOnT9t0cbbkK3fNF9hR3bgj2iVml399OBfz/pfmVPODGraZ3CAzE+T+mZncslpcraiD3sDivb8v8pfnldWhuFoNqZgRpAC3a5gvAr2lqNPqcdqDszNHsstQXa9DkI8M/Zy8EPrW5Ej4ySXILavFP5klbX9BC7iGcNGBCqs6tjMMwxf+/nD8Cu755CDmfHPCeF/NzHbiTjSVqDS4Ul4LljX28uGObTsrOtHUsnYVAPfv37/J5XK5HDU1ztuXw9fLNY8Kc1tNezKK+e6b3xzOxYIfT+PH41exzTQ7xxlUmE4zBbh4zQzQEMzklbtvMHPqSkOgsO18IU6YsgBHTVtMvTspBem5IRIxfHbG2ac729P288aGdWOSwiw61SMkb5kE0wZ0AmB8o9Ve7dli4rx+e2+sf3gQ7kqJhr+XBFq98e9l59CmW0cNmRm12bRsZ+9Fxm0zXa2oQxWNNTBjdTCTkJCAtLS0Jpdv3boVPXr0sMWa7MLXxY5mcwbGB0IhFaO4Wo3z+dX462wBXt7ckAE7kuU8f+y5zIxbBDOmgkF3LgDmalK4F0ouOyPkFhNnSGIwAOBwVqlgaxASy7LYbnqjMr5HmMCrscx9Q2IBAH+dLWz3RO72nGTiiEUMxiSF4f/uSsbRV27C+ocHYcHk7vysu8b4xnk1moYxBk44k+l6Sm8pfzLrAm01mbE4mHn99ddRW1uLefPmITU1Fd9++y1YlsXhw4fx5ptvYsGCBXjhhRfsudYO8XfBmhnAuCc9NNH4LvXDnRl4+usTMLANbd+b63oplHI+M+PcqVpLcHvnBVX1UOucZyvPlrgtnH+P7gypmMHejBIcvFzKF/9aUgxqL0MSjI99NLuc7yrtSTKLa5BdWguZWISR3Vo+XeRMukf4o39sAHQGFt8dbV8hMLfNZM1JpubIJCKMSQrDk6M6N/v3qGE+k3lmxhV0pxNNzbI4mFmyZAlUKhUee+wxLF++HK+88gpqa2tx3333Yc2aNVi1ahXuueceqx58z549mDJlCqKiosAwDH766Sf+Oq1WixdffBF9+vSBj48PoqKi8NBDD+HatWtWPQbHVTMzQMNW0++nC6DWGTC+Rxg+f2QwAOB8QZXTpBsr69zjaDZgbMTlLRODZRsab7kTlmX5YGZS7wjcPSgGALD0t3O4aBryKMRJJk6PSH/4ySWoVus88o82l5UZkhjkUj2y7htszM58c8T6QmCd3oB0blp2B4OZtvDzmVQafqCsqwQzXKB3ocDz/l20xuJgpvG01Pvvvx8ZGRlQqVQoKCjAlStX8Oijj1r94DU1NUhOTsbq1aubXFdbW4vjx49j0aJFOH78OH788Uekp6fjtttus/pxAMBX7po1M0BDMAMAA2ID8MG9AxAVoEBcsDdYFjiW4xzZGT4zo3D9zAzDMA0nmtwwmMkprUV1vQ4yiQjdwv0we0xXyCQinLlq/AOZEOLDH18VgljE8J2HD172vK0mrl5mfI/wNm7pXG7tGwU/Lwnyyuqw95J1hcDZpTVQ6wzwlokRZ+fAovHRbJfLzJi24Gh6tjmramauL47y9vZGWFj793MnT56MpUuXYtq0aU2uUyqV+PvvvzFjxgwkJSVh6NCh+PDDD3Hs2DHk5lpfYOaKR7M5iSE+uC05CsMSg/HZrEFQmKbnckWSzlA3U6/Vo15rPEYb4MJDJhuLduNeM6dMWZkekf6QikWIUHqZzdoRMivDaaibEf7325HKazR8EfY4F6mX4ShkYkwfYJxc/fUh6/5Oc8eNkyL87N6okcvM1GsNuFxs6v7rAjUzQEMfpkuFKrMkg6ez6hW+W7dubVZ7l5XZ7w9PZWUlGIZBQEBAi7dRq9VQqxu6UFZVGd9p+rno0WzAGES+f2/TE2SD4gPx/bErZl0vhcIV/4pFjEs2KGwOP6PJDYMZrvi3T6eGdP6/RnfGpkO5qNPqBS3+5Qw21c0czi7zqFk0uy4WwcAaayOim+mR4uzuHRyLDfuz8ff5Qlwpr7X4e7jQgZNM1vKWSaCQilGn1UOjN4BhzEceOLP4YB9IRAyq1ToUVNUjUuka67Y3q151lixZAqXS+sZKtlBfX48XX3wR9957L/z9W/5lX7ZsGZYsWdLkclebmm0JLjNzMs/Y9VLI0fVc998AhdTpjzdaKibI+EfCHTMzp03Hsvt2CuAvC/GVY/mdffHX2QJMSY4SaGUN+nRSQiEVo6JWi4wiFd/K3d1tc9EtJk5ShB9GdAnGP5dKsXTLeax9MMWir+vISab2CPaV8V2CI/y9BP37aQ2ZRIT4EB9cKlIho1BFwYyJVa/w99xzT4e2ldpLq9VixowZYFkWa9asafW2CxYswLx58/jPq6qqEBMTA4WL/KJaw1jXIEOJSoNTVyr5d7JCKK8xdf91g+JfTqyb9poxGFg+M3N919fbkqNwmxMEMgAgFYuQEheIfZdKcCir1COCGY3OgD2mwbKutsXU2Ku39sLN7+/F1rMF2JVehNFJbX8v59sxk6kjgn3lfDDjzNOym9PVNCU8o0hlVlPpySyumRHq3TYXyOTk5ODvv/9uNSsDGJv3+fv7m30Awq3fnhimobmY0FtNlW40l4nDdwEuda9gJqesFtVqHeQSET/U0VlxAfohD6mbOZJdhmq1DiG+ciRHBwi9nHZLivDDw6bp2q/9crbN9gblNRoUVNXzX+sIIT4Nf6vsXXBsa1xrjoxCKgLmtOs0k6NwgUxGRga2bduG4OBgh6/B2TlLMMPNZQpwg7lMHG7YZFW9DpW1znH83RZOXakA0FD868y4fjOHLpd5RLEj19F7bPdQl68RmjO+K8L85MgurcWney43uV6t0+NCQRV+PXkN//dXOgDj1q6fg9poBDcaXeAqJ5k4XU1FwBlFKoFX4jws3mYyGGw/8E2lUuHSpUv851lZWUhLS0NQUBAiIyNx55134vjx49iyZQv0ej0KCgoAAEFBQZDJ3CcD0BHcO9djpuZiQrU9b+j+6z7Pi0ImRoivHCUqNfLKa6H0FqZezNYain+d//tJjgmATCJCiUqNrJIaJIY6dyapo3ZeMNbLjHPRepnG/LykWHhLD8z5Jg0f7ryE2/t1QkyQN9LyKvDfvZfx59kCfuQAp3+M4wrPgxu1HnCVk0wcLqN6sbAaLMu65c6DtQStij169CjGjBnDf87VusycOROvvfYafvnlFwBAv379zL5u586dGD16tKOW6dS6R/jB19Rc7EJBFXpFCfMC5U5zmRqLDVIYg5my2nZNFXZGXLO8PtHO//14ScXoFxOAw1llOJRV5tbBTFW9FtmmLc0hAta/2dJtyVH4+nAuDl4uw7PfpoGFeV8sPy8Juob5olu4H7qF+2Fq/04OW1two20mV6uZSQjxgVjEoLpeh6JqNcL9vYRekuAEDWZGjx7daurYE9LKHSURi9A/NgB7M0pwJKtMwGDGfbr/NhYT5I3juRVuc6LJWPxrPDXiCpkZwPjCfjirDIezynCvqcOsO+K630Yqvdwmw8kwDF6/vTduXrUXR01BjFTM4LbkTnh4RDx6RfkLllVo3BTS1baZ5BIx4oK9cbm4BhcLqymYQTsGTRLnM5ivmxGuEzDX/VfpJn+EOe52oim7tAYqrvg3zDWyHAP5FgQVwi7Ezhr6rLjXqa1u4X54aXJ3JIT44OmxXfDPi2Px7oxk9O6kFHR7hKuZ8ZGJzbI0rqJbmKluppDqZgCBMzPENgY1ai4m1P5phRvNZWoshu8C7B4jDbgtpp5R/pA4efEvp4sp6Morr4VOb3CZdVvrgoPmEgnhsZGJeGxkotDLMNMrSokAbylu6BLikjUnXcN9sfUsFQFzKJhxA/1iAiAVMyiuNs4ZiQv2cfgaKtxoLlNjMW7WBZhrlucqW0wAEOnvBZlEBI3OgPzKeperb7AUH8y4WWbGWQX5yHD45fGQil0vkAEagnw6nm3knm9xPIyXVIy+pp4Uv53OF2QNDaeZ3CwzY+oCfKW8zuopwM7otAudZOKIRA1DP7NLawRejX0YDGzDxGgHtPMnRjKJyCWzMkDDjKaMIprRBFAw4zbuSjEOd1v5dwbOXqt06GOzLOu2wUykUgGJiIFGb0Bhdb3Qy+kQg4HF2Wum4l8XOMnUWHwwF8y4R4bselcr6qBS6yAVM0gMdXxmlbiehBAfiBigsk6L4mp121/g5iiYcRN3D4rB+B7h0OgNeObrE6jV6Bz22NywNsC9OgADxsGZnQJNM5pc/IU0p6yWL/7t4mJHnLmt05wS98zMcHOJuoT5OX0jQ+IcvKRixJv+XVDdDAUzboNhGLx9Z1+E+8uRWVyDN7acc9hjc91/pWIG3jL3m4HVcKLJtYuAudMySRF+LldE25CZcc9ghtti6kH1MsQKVDfTwLX+opFWBfnIsGJGPzAM8PXhPPzuoPqZhoZ5Mpfdf25NNH+iybUzM+dduMCUy8y46zZTw0km13tuiHC4upmLlJmhYMbdDO8SgqdGdQYAvPTDKVytsH82ocIN5zI1FusmJ5oa+pi4XoFpQogxmMktrYXeDQqxr3e+wHWfGyIcbqzBJeo1Q8GMO5p3UzckxwSgql6Hj3ZeavsLOqih+6971ctwuBNNrp6ZSS903cxMpNILUrGxEJubruwu6rV6ZJtqgVzxuSHC4baZLhZVe/yJJgpm3JBULMIzY7sAAP65VGL3x2vo/uvemRlXDmZq1DrkmLZoklzwBVMiFvENDN2tCDijUAUDa9wmDvWTt/0FhJh0DvWFiDG+oSxRaYRejqAomHFTgxKCIGKMNQb5lfbdaqp00+6/HC6YKapWo06jF3g17cNlZcL85GbTgl1JnJsez27YYvJzy5ozYj9eUjH/98nTi4ApmHFT/l5SvjHagcxSuz5WeU1DAbA7CvCWwd/L2CzbVbMzF/Jdv1U+fzzbzU408c8N1cuQdujaqHmeJ6Ngxo0N7RwMwP7BDDeXyd0a5jXm6i+k6QWuP8TQXY9nX+CeGzrJRNqBGxibUUSZGeKmhiWagpnLdg5m3HQuU2PcFoerZmZc+Vg2Jz6ECyhd8zloDsuyNJOJdAh/PNvDTzRRMOPGBsUHQSJicKW8Dnl2fBFuOM3kzpkZ180KsCzr0seyOfF8r5katzm5UaxSo6xGAxEDdA2jYIZYjwtmzl2rgtbUid0TUTDjxnzkEiTHBACw71YTt83krqeZACAuyHWzAvmV9aiq10EiYtA5zHXn/nQKVEAsYlCvNaCwyj1m0XD1MvEhPlC4YfdsYn/dI/wQ5CODSq3D8ZxyoZcjGApm3Jwjtpq4bSZ37TMDALEuvM3E1WQkhvpALnHdF0ypWIRo05wsV8yQNYd7bnq4cMaMCEskYnBj1xAAwK6LxQKvRjgUzLi5YY2KgO2Rmm88Mdudgxlui+NKeZ3LpXIbajJc/wXT1Quxr9dwkom2mEj7jU4KAwDsTvfcYEYi9AKIfaXEBUImFqGgqh7ZpbV8W3hbUal10Jnay7vzaaYwPznkEhHUOgOuVdTxL6quoOFYtuu/YMYHe2MPXLPXTGFVPeZ/dxJiEYMBsYEYEBuIs9cahn8S0l4ju4aAYYBz+VUoqqpHmL+X0EtyOApm3JyXVIx+sQE4nFWGA5mlNg9muKyMl1QEL6nrbmG0RSRiEBvkjYwiFXJKa10rmHGjrQxXzcxU1mkx87PDfJZs13XvoHu4cP8fIrxgXzn6dFLi1JVK7L5YjLsGxgi9JIejbSYPYM+6mYYhk+67xcThTjTluFDdjFqnR2ax8YXfHd79J4SYTpWV2PY5yCmtwbz/pWHziSs2vV/AOHvp8S+O4kJBNUL95Fh4cw9MSY7i63+6hPmiU4DC5o9LPMvobqEAgN0eWjdDmRkPMKxzMFZtz+DrZmzZMr2ijuv+675bTBw+K+BCs4Eyi2qgN7Dw95IgUun6qefGmRlb/S5vPnEFi346C5Vah1/SriEp3B89o2yTKdEbWMz9Jg2Hs8rgJ5fg84cHm913qUoNXy8JRCIaY0A6ZlRSKN7fcQl7M0qgN7AQe9jvFGVmPED/2ADIJSKUqNTILLZtY6XyWvfv/stxxcxMQ3dZf7eY+xMdqICIAWo0+g4P1quu12LuNyfw7LcnoVLr4CMTQ2dg8eIPp6CzQZE3y7J49ecz2Hq2ADKxCJ88NLBJkBTsK3fpE2bEeSRHB8DfS4LKOi1OXqkQejkOR8GMB5BLxEiJCwQA7Ldxv5lKDziWzeGnZ7tQ8SlXo9HDDbaYAOPvcpRpS6a9dTManQE/HLuCm9/fi5/SrkEsYjDvpm7Y/txo+HtJcPpqJT77J6tD6yyqrseTXx7DV4dywTDAynv68ScLCbEHiViEkaatputrsjwBBTMegqubOWjjuhlPysxwx7NzylynA+35/IbMjLvgnocsK7f7ymo0+HBHBm5YvgPPfXcSeWV1iA5U4H9PDsUz47oiQumFV27pCQB47++L7QqWWJbFTyeu4qb39uCvc4WQihn8Z1of3Nwn0ur7IsRaozy4boZqZjwE1wk4w8bzO/gCYA/IzDTuQFtUrUa4Cxx/TDdlZtyh+JcTF+yNfZcs78bMsiw++ycb//fnBdRrjdtH4f5yPDQsHg8Ni4OfV0MgftfAaPx88ir+uVSKl344jU2PD7F4e65EpcZLP5zGtvOFAIDenfzxzl3JbtHfh7gGLpg5daUCZTUaBPm4/99lDmVmPESMaYvkSnmdTbMKDUMm3T8zIxWLEBVgDGBcYaxBqUqNompj2/+kcPcJZhrPaGqLRmfAiz+cwhtbzqFea0DvTv5YcXcy9r4wFqljupgFMgDAMAyWTesLL6kIBy6X4r97s1BVr23zcYqq63H3xwew7bwxGzN/Qjds/vcICmSIQ4X7e6F7hB9YFtib4VnZGcrMeIioAC8wDFCn1aO0RoMQX7lN7peby+QJNTOAcUZTXlkdsktrMDghSOjltCq90JiViQ3yho/cff6pWzo9u1Slxr82Hsfh7DKIGGDhLT3xyIj4NjMtscHemD8hCUt/O483fzd+RPh7oWu4L1LiAvHw8ASzOWRFVfW499ODyCyuQZTSC589PIiCGCKY0UlhuFBQjd3pxbi9Xyehl+MwlJnxEHKJGBGmbRFbTtAuN2Vm3HnIZGPciSZXKALmerF0DnWdBn+WiDc9BxcKqrBqW0aTzInewOKfSyW4ffU/OJxtPBK9btYgPHpDgsVbRg+PSMDMYXH8v5mCqnrszSjBym0ZGPXOTqzblwWNzoCiqnrc0yiQ+eaJYRTIEEFxW017MophMLhGbZ8tuM/bNdKm6EAF8ivrcaW8Dv1jA21yn5UeMJepMVc6ns0VsLpSt2JLJIT4IDkmACfzKrBi20Ws23cZj9yQgJS4QPx5tgBbzxSiRGXcXosL9sa6mQPRJcy6bTaxiMGS23tjye29UVmnxaUiFdILqrFhfxYuFqrwxpZz+Hx/NsQiBlklNegUoMDXjw/lB5ISIpSUuEB4SUUoUWmQVVqDzqG+Qi/JISiY8SAxgd44kl2OvHLbZ2Y84TQTAMQGuU47fW4bJs7NXmAlYhF+/Ndw/H46H+9vz0BGkQort2WY3UapkOLmPpF4YWISAjtYBKlUSJESF4iUuEDMGBiN749dwbt/X+QnqHcKUOCbJ4bydWmECEkmEaFXlBLHcspx6koFBTPE/XDt0/PK6mxyfwYDi8o6zzmaDTTKzLjANhOXPXK3YAYwZk6mJEfhlj6R+P1MPj7amYmiajXGdg/FLX2jMLxzMKRi2++iS8Qi3DM4FlOSo/DfvVk4l1+JV27pSYEMcSp9o43BzMm8SkzrHy30chyCghkPEs2faLLNC3F1vQ7clqwnzGYCGgKDyjotKmo1TnsknWVZ5JqyR1w2yR2JRAxu7RuFW/tGOfRxfeQSzBnf1aGPSYilkqMDABiPaHsKKgD2IFxm5kq5bTIz3FwmH5kYMoln/Cp5yyQI9TOeBHPm7EyJSoMajR4MA8QE0RBDQjxJ32glAODstSpobTCawxUI+gq0Z88eTJkyBVFRUWAYBj/99JPZ9SzL4tVXX0VkZCQUCgXGjx+PjIyM5u+MtCkm0JhVuFpeZ5Mq93IPapjXWFyQ8xcB55YZszJRSgXN/iHEw8QH+8DPSwK1zsA3znR3ggYzNTU1SE5OxurVq5u9/u2338b777+PtWvX4tChQ/Dx8cHEiRNRX1/v4JW6h0ilF8QiBhq9gW+m1hEVHlb8y+FOB+U6cREwlzWKpVoOQjyOSMQ02mqqFHYxDiJoMDN58mQsXboU06ZNa3Idy7JYuXIlXnnlFdx+++3o27cvvvjiC1y7dq1JBodYRiIWIVJp7Jthi7qZCg+ay9QYVzeT7cTbTNza4kMomCHEE3FbTZ5SN+O0hQ5ZWVkoKCjA+PHj+cuUSiWGDBmCAwcOtPh1arUaVVVVZh+kAbfVZIvj2Q2ZGQ/bZnKBxnmeUPxLCGlZX1Nm5iRlZoRVUFAAAAgPDze7PDw8nL+uOcuWLYNSqeQ/YmJi7LpOV2PL49llfMM8z8rMxPI1M068zeTGx7IJIW1LjjFmZi4WVqNOoxd4NfbntMFMey1YsACVlZX8R15entBLcioxNjyeXVxtrF0K9XX+6dG2xA06LKxSO+0fCXdtmEcIsUyEvxdC/eTQG1icy3f/7IzTBjMREREAgMLCQrPLCwsL+euaI5fL4e/vb/ZBGtgyM1NUZSwiDvO3zdBKVxHgLeWzURcKnG8bs7pei7Ia4xagu40yIIRYhmEYJJvqZtLyKJgRTEJCAiIiIrB9+3b+sqqqKhw6dAjDhg0TcGWujc/MVHQ8M8OdiArz86xghmEYDDDNtjqWUy7waprisjLBPjL4utG0bEKIdfp6UPM8QYMZlUqFtLQ0pKWlATAW/aalpSE3NxcMw2Du3LlYunQpfvnlF5w+fRoPPfQQoqKiMHXqVCGX7dK4AuBrFfXQdbCZUrEpmAn1sGAGAFLinTeYyaV6GUIIGp9ocv/MjKBv244ePYoxY8bwn8+bNw8AMHPmTGzYsAEvvPACampq8MQTT6CiogI33HADtm7dCi8vz6rRsKUwPzlkYhE0egMKquoRHdi+FzyDgeUnE4f5ed7zkWLKzBzNKQfLsmAYRuAVNch202nZhBDrcL1mskpqUFmnhVLhvoc1BM3MjB49GizLNvnYsGEDAGM6//XXX0dBQQHq6+uxbds2dOvWTcgluzyRiEEnG9TNlNVqoDOwYBgg2NezjmYDQHJMACQiBsXVapuNh7CVXGqYRwgBEOgj4/8OnHbz7IzT1swQ++GLgDtwookr/g3yltllOrGz85KK0auTMYXrbFtNOdQwjxBiwm01nXTzuhnPexUi/NZSRzIKxSrPrZfhDIzjtprKBF6JuRxqmEcIMeG2mk7mVQi6DnujYMYD8dOzOzAosajK2GMmzN/z6mU4KXFcEXCFsAtpRK3TI9/03FABMCHEU4qAKZjxQA2N89qfmfHUY9mNccFMekEVquu1Aq/GKK+sDiwL+MjECPbxvFomQoi53p2UEDFAQVU9Cqvcd0gzBTMeyBY1M558LJsT7u+F6EAFDCyQ5iQp3NyyhpNMznTCihAiDB+5BN0jjM1jd6cXC7wa+6FgxgNxvWYKquqh0bWv10wxZWYANKqbyXaOIuDsEuoxQwgxN7m3sWv+r6euCbwS+6FgxgOF+MrgJRWBZYFrFe3baioyzWXyxB4zjaXEBwEAjuc6RzDDNcyLpWCGEGJya3IUAGB/ZilKTYc33A0FMx6IYRj+RFN7t5qKaJsJQEPzvBO5FdAbWIFX03CSKZ4a5hFCTBJCfNAryh96A4utZwuEXo5dUDDjoWK4E03tKAJmWbZhyKSHBzNJEX7wlUugUuuQXlAt9HIapmVTwzxCSCO39jVmZ7aczBd4JZbLLFZZfFsKZjwUn5lpx/HsGo0edVo9AM+bmH09sYhB/9gAAMAxgfvN6A0sn2mjbSZCSGO39o0EABzMKuVbazgzg4HFQ+sOWXx7CmY8VExQ+zMz3D8EX7kE3jKaytzQb0bYupn8yjpo9SxkYhEilQpB10IIcS4xQd7oFxMAlgV+P+382ZnzBVWorNNZfHsKZjxUR2pmqF7GXEpcw9BJIXFbTNFBCohFdCybEGKOy85sOeX8wcyBzFKrbk/BjIfiho9dLKhGfqV12RnqMWOuX0wARIwxyyVkU6qsElOPGaqXIYQ04xZTMHM0p7zdJ1kd5eBlCmaIBXpE+qNnpD9qNHr8+6vjUOv0Fn8tdf815+clRY9IU1Oqi8I1pdpxoQgA0Mc0i4UQQhqLVCow2NROwpm3mvQGFoeyrKtBpGDGQ4lFDNY+kAKlQooTuRV4Y8s5i7+Wesw0xTWl+jntqiCPX1ajwR5TIHVbcqQgayCEOL9bTX8ffnXiraaz1ypRXa+Dr1xs8ddQMOPBYoO9sfKefmAYYOPBXHx/7IpFX1dcRdtM17u9XycAxqZU1m7b2cJvp/OhM7DoFeWPLmF+Dn98QohrmNw7EiLGOEX7shVHnx2Jq5cZGB9o8ddQMOPhxiSFYe64bgCAhZtP48zVtierFqtom+l6MUHeGBwfBJYFfklzfMvwn08YM0JTTUEVIYQ0J9RPjhFdQgAA93xyEEezhW0p0ZwDpnqZQaYtMUtQMEPw9NguGNc9DGqdAf/66lib9TN8wzwP7zFzvan9jYHE5hOO3WrKK6vF0ZxyMAwwxdS2nBBCWrJ0am90DfNFUbUa93xyEOv2ZYFlhe9gDgBavQFHTPUygxMomCFWEIkYvHd3P4T4ypBXVoeDl1uP1LmaGdpmMndLn0jIxCJcKKjG+fwqhz3uLyeNmaChCcGIUFIdEyGkdXHBPvgpdQSmJEdBZ2DxxpZzmL3pBGrUlvd1sZfTVytRo9EjwFuKpHB/i7+OghkCAFAqpLipZzgAYPv5whZvp9EZUF6rBUAFwNdTeksxtnsYAMdlZ1iWxU/cFlN/ysoQQizjI5fg/Xv64bUpPSERMfjtdD5mbzoOg8Az5rh6mSEJQRBZ0S+LghnCG9edC2aKWkw5lpjqZaRiBoHeUoetzVVMG2Dcavo57apDBk+ez69GRpEKMrEIk3rTKSZCiOUYhsGsEQnY9PhQyCUi7Ewvxvs7MgRdE9dfZlhisFVfR8EM4Y3oEgK5RISrFXW40MLQRL77r68cDENdZq83OikUSoUUhVVqqztYtgd3FHxMd+PjEkKItQYnBGHp1N4AgFXbM7DT1LPK0dQ6PY6YCpKHm4qULUXBDOEpZGLcYPoFammriZvLRPUyzZNLxHzLcHtvNRkMLF8vQ6eYCCEdcdfAGNw/JBYsC8z55gRyS60fddNRJ/MqUa81IMRXhq5hvlZ9LQUzxMy4Hsatpm3nm4/MG+YyUb1MS6aZTjVtPZOPOo3lnZWtdTi7DPmV9fCTSzDGVKtDCCHt9eqUnugXE4Cqeh2e3HjMrn+/msPXyyQGW535p2CGmBnXw/iiePJKBT+DqTHuMjqW3bKUuEDEBClQo9HjJzt1BDYYWHy0KxMAMKl3BLyklnfKJISQ5sglYqx5YABCfGU4n1+FJzceQ0WtxmGPf+ByCQDr62UACmbIdcL9vdCnkxIsi2b3TRvXzJDmMQyDB4bEAQD+8/t5uwx0W7U9A3suFkMuEeGxkYk2v39CiGeKVCrw4X0DIJeIsOdiMW79YJ9FzVQ7ql6rx/HcCgDAsM4UzBAb4LIz25qpmynm5jJRZqZVj96QgOSYAFTX6zD/u5M2Pe6440IhVm03njj4z7Q+SIqg8QWEENsZmhiMH/89HLFB3rhSXoc71uzHt0dy7fqYx3PLodEZEOYnR2KIj9VfT8EMaWK8qW5mb0YJ6rXme6YNE7OpZqY1ErEIK2YkQyEVY39mKdbvz7bJ/eaU1mDuN2kAgAeHxmF6SrRN7pcQQhrrFaXEr0/fgPE9wqDRGfDiD6cx/7uTdmusd9BULzO8s/X1MgAFM6QZvaL8EeHvhTqtnp+RweFrZug0U5sSQ33x8i09AADLt15ARmHzx90tVafR48kvj6GqXocBsQFYdGtPWyyTEEKapVRI8cmDA/H8xCSIGOD7Y1cw5YN9OH3F9ttO3GtNe7aYAApmSDMYhsFY01ZT4yPaBgPLBzN0NNsyDwyJxahuodDoDJj7bRo0OkO77ientAaPfn4EFwqqEeIrw0f3p0AmoX++hBD7EokYpI7pgk2PD0Wk0guXS2pwx5p/8MmeTJttn9dqdEjLqwAADEu0rr8Mv06brIS4nfF8MNPQDbi8VgOd6Zc3hAqALcIwDP7vzr4I8Jbi7LUqvLz5NHR6ywOaeq0eK7ddxE0r9mB/ZilkEhE+vG8AzWAihDjU0MRg/DFnJCb1ioBWz+I/v1/AzPWHUapqeurVWkezy6HVs+gUoEBMkKJd90HBDGnW8M4h8JKKkF9Zj7/PGbMzxaZf2iAfGWUFrBDm74Xl0/vyadonvjyGWk3b+877M0swceUerNyWAY3OgBu6hOCPOSMxtB3HFgkhpKMCvGVY88AALLujD7ykIuzNKMGtH+zDidzyDt0vt8U0tB39ZTj0ikSa5SUV4+6BMQCAOd+k4URuOYqq6Fh2e03sFYG1D6RALhFhx4Ui3PPJQX7OVXPSC6rx8PojyCmtRZifHB/c2x9fPjoYnUOt64pJCCG2xDAM7h0ci19m34DEEB/kV9ZjxscH8OWB7BZn+rWFa5bX3noZgIIZ0opXbu2JG7uFok6rxyMbjuBQlvEXjo5lt8+EXhHY9PhQBHpLcepKJe74aD+ySmqa3K5eq8fTXx+HWmfAyK4h2P7cKExJjqJZWIQQp9Et3A8/zx7Bbzst+vks5n6bhqp6rVX3o1LrcNrUx4aCGWIXUrEIa+4fgORoJcprtVi909hxlop/2y8lLhA//Gs4YoIUyC2rxfQ1+5ukaN/87TwuFqoQ4ivHirv7wc+LBkgSQpyPn5cUax4YgIU394BYxODntGuYuGIPdqVbPqjySFYZ9AYWsUHe6BTQvnoZwMmDGb1ej0WLFiEhIQEKhQKdO3fGG2+80e5UFrGej1yCz2YNQkKjJkbUY6ZjEkN98eO/RqBPJyXKajS499ODfF3SX2cL8OXBHADAezOSqdCaEOLUGIbB4zcm4psnhiIu2Bv5lfWYtf4Inv/uJCrr2s7ScPUywzuQlQGcPJhZvnw51qxZgw8//BDnz5/H8uXL8fbbb+ODDz4QemkeJdhXji8eGcxnZKID2x89E6NQPzm+eWIoRieFol5rwJNfHsXqnZfwwg+nAACPj0zAjd1CBV4lIYRYZlB8EP6YMxIPj4gHwwDfHbuCiSv2NDsWp7H9maZ5TB0MZhjWidMct956K8LDw7Fu3Tr+sunTp0OhUGDjxo0W3UdVVRWUSiUqKyvh7+9vr6V6hJzSGvx5tgD3D4mDj1wi9HLcglZvwMLNp/G/o1f4y3p38seP/xpBJ8YIIS7pcFYZXvj+JLJLawEAd6ZEY9GtPaFUmG+ZV9Zq0e+Nv8CywOGXxyHM3zzrb83rt1P/tRw+fDi2b9+OixcvAgBOnjyJffv2YfLkyS1+jVqtRlVVldkHsY24YB88cWNnCmRsSCoWYfn0vpg7visAwFsmxvv39KdAhhDisgYnBOGPOTfi0RsSwJhaUkxYsRs7LpjP+zuUVQqWBRJDfZoEMtZy6lell156CVVVVejevTvEYjH0ej3efPNN3H///S1+zbJly7BkyRIHrpKQjmEYBnPHd8OobqFQKqRIpOPXhBAXp5CJsejWnpjcOwLPf38KWSU1eGTDUdwxoBMW39oLSm9pwwgDG/TOcuq3f//73//w1VdfYdOmTTh+/Dg+//xzvPPOO/j8889b/JoFCxagsrKS/8jLy3Pgiglpv/6xgRTIEELcysD4IPz+zEg8ZsrS/Hj8Km5asRvbzhXapL8Mx6lrZmJiYvDSSy8hNTWVv2zp0qXYuHEjLly4YNF9UM0MIYQQIrxjOWV4/rtTuHxdf62jr4xv9uSm29TM1NbWQiQyX6JYLIbB0L5hfYQQQggRRkpcEH6fMxJP3JgIkakHaLdwX5u0oHDqmpkpU6bgzTffRGxsLHr16oUTJ07gvffewyOPPCL00gghhBBiJS+pGC/f3AOTekfgo52XcGdKjE3u16m3maqrq7Fo0SJs3rwZRUVFiIqKwr333otXX30VMpnMovugbSZCCCHE9Vjz+u3UwYwtUDBDCCGEuB63qZkhhBBCCGkLBTOEEEIIcWkUzBBCCCHEpVEwQwghhBCXRsEMIYQQQlwaBTOEEEIIcWkUzBBCCCHEpVEwQwghhBCXRsEMIYQQQlwaBTOEEEIIcWkUzBBCCCHEpVEwQwghhBCXRsEMIYQQQlyaROgF2Bs3FLyqqkrglRBCCCHEUtzrNvc63hq3D2ZKS0sBADExMQKvhBBCCCHWKi0thVKpbPU2bh/MBAUFAQByc3Pb/GE42qBBg3DkyBGhl0FM6PlwLvR8OBd6PpyLJzwflZWViI2N5V/HW+P2wYxIZCwLUiqV8Pf3F3g15sRisdOtyZPR8+Fc6PlwLvR8OBdPej641/FWb+OAdZAWpKamCr0E0gg9H86Fng/nQs+Hc6HnwxzDWlJZ48KqqqqgVCpRWVnpMVEsIYQQ4uqsef12+8yMXC7H4sWLIZfLhV4KIYQQQixkzeu322dmCCGEEOLe3D4zQwghhBD3RsGMA6xevRrx8fHw8vLCkCFDcPjwYf66zMxMTJs2DaGhofD398eMGTNQWFgo4Grd1549ezBlyhRERUWBYRj89NNPZtezLItXX30VkZGRUCgUGD9+PDIyMoRZrAdo6/n48ccfMWHCBAQHB4NhGKSlpQmyTk/R2vOh1Wrx4osvok+fPvDx8UFUVBQeeughXLt2TbgFu7m2/n289tpr6N69O3x8fBAYGIjx48fj0KFDwizWCVAwY2fffvst5s2bh8WLF+P48eNITk7GxIkTUVRUhJqaGkyYMAEMw2DHjh34559/oNFoMGXKFBgMBqGX7nZqamqQnJyM1atXN3v922+/jffffx9r167FoUOH4OPjg4kTJ6K+vt7BK/UMbT0fNTU1uOGGG7B8+XIHr8wztfZ81NbW4vjx41i0aBGOHz+OH3/8Eenp6bjtttsEWKlnaOvfR7du3fDhhx/i9OnT2LdvH+Lj4zFhwgQUFxc7eKVOgiV2NXjwYDY1NZX/XK/Xs1FRUeyyZcvYP//8kxWJRGxlZSV/fUVFBcswDPv3338LsVyPAYDdvHkz/7nBYGAjIiLY//u//+Mvq6ioYOVyOfv1118LsELPcv3z0VhWVhYLgD1x4oRD1+TJWns+OIcPH2YBsDk5OY5ZlAez5PmorKxkAbDbtm1zzKKcDGVm7Eij0eDYsWMYP348f5lIJML48eNx4MABqNVqMAxjVqnt5eUFkUiEffv2CbFkj5WVlYWCggKz50qpVGLIkCE4cOCAgCsjxDlVVlaCYRgEBAQIvRSPp9Fo8Mknn0CpVCI5OVno5QiCghk7KikpgV6vR3h4uNnl4eHhKCgowNChQ+Hj44MXX3wRtbW1qKmpwfz586HX65Gfny/Qqj1TQUEBALT4XBFCGtTX1+PFF1/EvffeS/27BLRlyxb4+vrCy8sLK1aswN9//42QkBChlyUICmYEFBoaiu+++w6//vorfH19oVQqUVFRgQEDBljUvpkQQhxNq9VixowZYFkWa9asEXo5Hm3MmDFIS0vD/v37MWnSJMyYMQNFRUVCL0sQ9IppRyEhIRCLxU1OJxUWFiIiIgIAMGHCBGRmZqKoqAglJSX48ssvcfXqVSQmJgqxZI/FPR+tPVeEeDoukMnJycHff/9NWRmB+fj4oEuXLhg6dCjWrVsHiUSCdevWCb0sQVAwY0cymQwpKSnYvn07f5nBYMD27dsxbNgws9uGhIQgICAAO3bsQFFREZ0ScLCEhARERESYPVdVVVU4dOhQk+eKEE/EBTIZGRnYtm0bgoODhV4SuY7BYIBarRZ6GYJw+6nZQps3bx5mzpyJgQMHYvDgwVi5ciVqamrw8MMPAwDWr1+PHj16IDQ0FAcOHMCcOXPw7LPPIikpSeCVux+VSoVLly7xn2dlZSEtLQ1BQUGIjY3F3LlzsXTpUnTt2hUJCQlYtGgRoqKiMHXqVOEW7cbaej7KysqQm5vL9zJJT08HYMyiUbbM9lp7PiIjI3HnnXfi+PHj2LJlC/R6PV9LFhQUBJlMJtSy3VZrz0dwcDDefPNN3HbbbYiMjERJSQlWr16Nq1ev4q677hJw1QIS+jiVJ/jggw/Y2NhYViaTsYMHD2YPHjzIX/fiiy+y4eHhrFQqZbt27cq+++67rMFgEHC17mvnzp0sgCYfM2fOZFnWeDx70aJFbHh4OCuXy9lx48ax6enpwi7ajbX1fKxfv77Z6xcvXizout1Va88Hdzy+uY+dO3cKvXS31NrzUVdXx06bNo2NiopiZTIZGxkZyd52223s4cOHhV62YGg2EyGEEEJcGtXMEEIIIcSlUTBDCCGEEJdGwQwhhBBCXBoFM4QQQghxaRTMEEIIIcSlUTBDCCGEEJdGwYydHDhwAGKxGLfccovQSyGEEELcGvWZsZPHHnsMvr6+WLduHdLT0xEVFdXu+9Lr9WAYhoZPEkIIIc2gV0c7UKlU+Pbbb/Gvf/0Lt9xyCzZs2MBft2vXLjAMg99++w19+/aFl5cXhg4dijNnzvC32bBhAwICAvDLL7+gZ8+ekMvlyM3NFeA7IYQQQpwfBTN28L///Q/du3dHUlISHnjgAXz22We4PgH2/PPP491338WRI0cQGhqKKVOmQKvV8tfX1tZi+fLl+O9//4uzZ88iLCzM0d8GIYQQ4hIomLGDdevW4YEHHgAATJo0CZWVldi9e7fZbRYvXoybbroJffr0weeff47CwkJs3ryZv16r1eKjjz7C8OHDkZSUBG9vb4d+D4QQQoiroGDGxtLT03H48GHce++9AACJRIK7774b69atM7vdsGHD+P8PCgpCUlISzp8/z18mk8nQt29fxyyaEEIIcWESoRfgbtatWwedTmdW8MuyLORyOT788EOL70ehUIBhGHsskRBCCHErlJmxIZ1Ohy+++ALvvvsu0tLS+I+TJ08iKioKX3/9NX/bgwcP8v9fXl6OixcvokePHkIsmxBCCHFplJmxoS1btqC8vByPPvoolEql2XXTp0/HunXr8H//938AgNdffx3BwcEIDw/HwoULERISgqlTpwqwakIIIcS1UWbGhtatW4fx48c3CWQAYzBz9OhRnDp1CgDw1ltvYc6cOUhJSUFBQQF+/fVXyGQyRy+ZEEIIcXnUNM/Bdu3ahTFjxqC8vBwBAQFCL4cQQghxeZSZIYQQQohLo2CGEEIIIS6NtpkIIYQQ4tIoM0MIIYQQl0bBDCGEEEJcGgUz7bRs2TIMGjQIfn5+CAsLw9SpU5Genm52m/r6eqSmpiI4OBi+vr6YPn06CgsL+etPnjyJe++9FzExMVAoFOjRowdWrVrV4mP+888/kEgk6Nevn72+LUIIIcTlUDDTTrt370ZqaioOHjyIv//+G1qtFhMmTEBNTQ1/m2effRa//vorvvvuO+zevRvXrl3DHXfcwV9/7NgxhIWFYePGjTh79iwWLlyIBQsWNDv2oKKiAg899BDGjRvnkO+PEEIIcRVUAGwjxcXFCAsLw+7du3HjjTeisrISoaGh2LRpE+68804AwIULF9CjRw8cOHAAQ4cObfZ+UlNTcf78eezYscPs8nvuuQddu3aFWCzGTz/9hLS0NHt/S4QQQohLoMyMjVRWVgIwTsAGjFkXrVaL8ePH87fp3r07YmNjceDAgVbvh7sPzvr163H58mUsXrzYDisnhBBCXBvNZrIBg8GAuXPnYsSIEejduzcAoKCgADKZrEmX3/DwcBQUFDR7P/v378e3336L3377jb8sIyMDL730Evbu3QuJhJ4uQggh5Hr06mgDqampOHPmDPbt29fu+zhz5gxuv/12LF68GBMmTAAA6PV63HfffViyZAm6detmq+USQgghboWCmQ6aPXs2tmzZgj179iA6Opq/PCIiAhqNBhUVFWbZmcLCQkRERJjdx7lz5zBu3Dg88cQTeOWVV/jLq6urcfToUZw4cQKzZ88GYMwCsSwLiUSCv/76C2PHjrXvN0gIIYQ4OQpm2ollWTz99NPYvHkzdu3ahYSEBLPrU1JSIJVKsX37dkyfPh0AkJ6ejtzcXAwbNoy/3dmzZzF27FjMnDkTb775ptl9+Pv74/Tp02aXffTRR9ixYwe+//77Jo9JCCGEeCIKZtopNTUVmzZtws8//ww/Pz++DkapVEKhUECpVOLRRx/FvHnzEBQUBH9/fzz99NMYNmwYf5LpzJkzGDt2LCZOnIh58+bx9yEWixEaGgqRSMTX4HDCwsLg5eXV5HJCCCHEU1Ew005r1qwBAIwePdrs8vXr12PWrFkAgBUrVkAkEmH69OlQq9WYOHEiPvroI/6233//PYqLi7Fx40Zs3LiRvzwuLg7Z2dn2/hYIIYQQt0B9ZgghhBDi0qjPDCGEEEJcGgUzhBBCCHFpFMwQQgghxKVRMEMIIYQQl0bBDCGEEEJcGgUzhBBCCHFpFMwQQgghxKVRMEMI6ZBdu3aBYRhUVFQIvRRCiIeiYIYQYpXRo0dj7ty5/OfDhw9Hfn4+lEqlYGuigIoQz0bjDAghHSKTyZpMgieEEEeizAwhxGKzZs3C7t27sWrVKjAMA4ZhsGHDBrOsyIYNGxAQEIAtW7YgKSkJ3t7euPPOO1FbW4vPP/8c8fHxCAwMxDPPPAO9Xs/ft1qtxvz589GpUyf4+PhgyJAh2LVrF399Tk4OpkyZgsDAQPj4+KBXr174/fffkZ2djTFjxgAAAgMDwTAMPx9t69atuOGGGxAQEIDg4GDceuutyMzM5O8zOzsbDMPgf//7H0aOHAmFQoFBgwbh4sWLOHLkCAYOHAhfX19MnjwZxcXFZj+HqVOnYsmSJQgNDYW/vz+eeuopaDQa+/3wCSEtoswMIcRiq1atwsWLF9G7d2+8/vrrAICzZ882uV1tbS3ef/99fPPNN6iursYdd9yBadOmISAgAL///jsuX76M6dOnY8SIEbj77rsBALNnz8a5c+fwzTffICoqCps3b8akSZNw+vRpdO3aFampqdBoNNizZw98fHxw7tw5+Pr6IiYmBj/88AOmT5+O9PR0+Pv7Q6FQAABqamowb9489O3bFyqVCq+++iqmTZuGtLQ0iEQN7+UWL16MlStXIjY2Fo888gjuu+8++Pn5YdWqVfD29saM/2/vbkJS28IwAL+yKUIkKAmhKKUfw9CiYQQ1cFCTotGJCKwoKOg/GxREGhQOomlBBQ5rJA0yKDBr1g8GJQSRoBVNtDDICAJdZ3Bpc7ync8/tXu85d8P7wB58Hwt9QYSPtZb45QtmZ2flP5gFAJ/Ph5ycHBwcHCASiaCnpwdarRYLCwv/5UdARB8RRESf0NjYKEZHR+Xa7/cLACIejwshhHC73QKACIVC8pr+/n6hVqvF8/Oz3GtqahL9/f1CCCFubm6EJEni/v4+7b2sVquYnp4WQghhsViE0+n8MNOfM/xILBYTAEQwGBRCCBEOhwUAsb6+Lq/Z2NgQAITP55N7LpdLVFZWynVXV5fIz88XLy8vcm9lZUVoNBqRTCb/MgMRZR6PmYgo49RqNcrKyuRap9PBYDBAo9Gk9aLRKAAgGAwimUzCaDRCo9HIz+HhoXwsNDIygvn5edTX18PhcODi4uKnOa6vr9HR0YHS0lLk5ubCYDAAAG5vb9PWVVdXp+UCAIvF8mHWdzU1NVCr1XJdV1eHRCKBu7u7n+YiosziMRMRZVxWVlZarVKpPuylUikAQCKRgCRJCAQCkCQpbd37ANTX14empiZ4vV7s7e3B5XJhaWkJw8PDP8zR0tICvV6PtbU1FBYWIpVKwWw2f3e35dtsKpXqw957ViL6/+HODBF9SnZ2dtrF3Uyora1FMplENBpFeXl52vPtL6WKi4sxMDAAj8cDu92OtbU1OROAtFyPj4+4urrCzMwMrFYrTCYT4vF4xjKfn5/j9fVVro+OjuQ7PET0a3GYIaJPMRgMOD4+RiQSwcPDQ0Z2LIxGIzo7O2Gz2eDxeBAOh3FycgKXywWv1wsAGBsbw+7uLsLhMM7OzuD3+2EymQAAer0eKpUK29vbiMViSCQSyMvLg1arxerqKkKhEPb39zExMfGvs757e3tDb28vLi8vsbOzA4fDgaGhobSLxUT0a/BbR0SfMjk5CUmSUFVVhYKCgu/un/xTbrcbNpsNdrsdlZWVaGtrw+npKUpKSgD8sesyODgIk8mE5uZmGI1GLC8vAwCKioowNzeHqakp6HQ6eajY3NxEIBCA2WzG+Pg4FhcXM5IVAKxWKyoqKtDQ0ID29na0trbC6XRm7PWJ6O9TCSHE7w5BRKQk3d3deHp6wtbW1u+OQkTgzgwREREpHIcZIiIiUjQeMxEREZGicWeGiIiIFI3DDBERESkahxkiIiJSNA4zREREpGgcZoiIiEjROMwQERGRonGYISIiIkXjMENERESKxmGGiIiIFO0ri9VKtZUmX04AAAAASUVORK5CYII=", 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ghhBCCCEOjWpmCCGEEOLQKDNDCCGEEIdGwQwhhBBCHJrTN83T6/UoKCiAl5eXyXNICCGEECIslmVRXV2NsLAwiEQd516cPpgpKChoNY2YEEIIIY4hNzcX4eHhHd7G6YMZbuBZbm4uvL29BV4NIYQQQkxRVVWFiIgIkwaXChrMrFy5Ej/99BMuXboENzc3jBo1CqtXr0ZCQgJ/m4aGBjz99NP49ttvoVarMXXqVKxbtw7BwcEmPQe3teTt7U3BDCGEEOJgTCkREbQAeM+ePViwYAEOHz6Mv//+GxqNBlOmTEFtbS1/m8WLF+PXX3/F999/jz179qCgoACzZs0ScNWEEEIIsSd21WemtLQUQUFB2LNnD8aMGQOVSoXAwEB8/fXX/ITfS5cuoXfv3jh06BBGjBjR6WNWVVVBqVRCpVJRZoYQQghxEOa8f9vV0WyVSgUA8PPzAwCcOHECGo0GkyZN4m+TmJiIyMhIHDp0qM3HUKvVqKqqavFFCCGEEOdlNwXAer0eixYtwnXXXYe+ffsCAIqKiiCTyeDj49PitsHBwSgqKmrzcVauXInly5eb/fw6nQ4ajcbs+xHnJpPJOj0SSAghRFh2E8wsWLAA586dw/79+7v1OMuWLcNTTz3F/5mrhm4Py7IoKipCZWVlt56XOCeRSISYmBjIZDKhl0IIIaQddhHMLFy4EL/99hv27t3b4ix5SEgIGhsbUVlZ2SI7U1xcjJCQkDYfSy6XQy6Xm/zcXCATFBQEd3d3aqxHeFzDxcLCQkRGRtLPBiGE2ClBgxmWZfH4449jy5Yt2L17N2JiYlpcP3jwYEilUuzcuROzZ88GAKSmpiInJwcjR47s9vPrdDo+kPH39+/24xHnExgYiIKCAmi1WkilUqGXQwghpA2CBjMLFizA119/jV9++QVeXl58HYxSqYSbmxuUSiUeeOABPPXUU/Dz84O3tzcef/xxjBw50qSTTJ3hamTc3d27/VjEOXHbSzqdjoIZQgixU4IGM+vXrwcAjBs3rsXlmzZtwn333QcAWLNmDUQiEWbPnt2iaZ4l0fYBaQ/9bBBCiP0TfJupMwqFAmvXrsXatWttsCJCCCGEOBo6c0oIIYQQh0bBjIMaN24cFi1aJPQyrO6VV17BwIEDhV4GIYQQO0bBDBFEY2OjTZ+PZVlotVqbPichhNhCZlktKmpt+5pqbyiYcUD33Xcf9uzZg3fffRcMw4BhGGRlZeHcuXOYPn06PD09ERwcjHnz5qGsrIy/37hx4/D4449j0aJF8PX1RXBwMD7++GPU1tbi/vvvh5eXF+Lj4/HHH3/w99m9ezcYhsG2bdvQv39/KBQKjBgxAufOnWuxpv3792P06NFwc3NDREQEnnjiiRYDQ6Ojo/Haa6/h3nvvhbe3Nx5++GEAwHPPPYdevXrB3d0dsbGxePHFF/lTZps3b8by5ctx+vRp/u+5efNmZGVlgWEYpKSk8I9fWVkJhmGwe/fuFuv+448/MHjwYMjlcuzfvx96vR4rV65ETEwM3NzcMGDAAPzwww+W/hYRQohNZJTWYOqavZjz4SHo9HYzatHmKJi5BsuyqGvU2vzLnHmf7777LkaOHImHHnoIhYWFKCwshJeXFyZMmIDk5GQcP34c27dvR3FxMebMmdPivp999hkCAgJw9OhRPP7443jsscdw++23Y9SoUTh58iSmTJmCefPmoa6ursX9lixZgrfeegvHjh1DYGAgZsyYwQcd6enpmDZtGmbPno0zZ87gu+++w/79+7Fw4cIWj/Hmm29iwIABOHXqFF588UUAgJeXFzZv3owLFy7g3Xffxccff4w1a9YAAO644w48/fTT6NOnD//3vOOOO8z6fi5duhSrVq3CxYsX0b9/f6xcuRKff/45NmzYgPPnz2Px4sW45557sGfPHrMel9hOdYMGD31+HF8fyRF6KYTYne3ni9Co0yOtpAZ/nm97zI8rsIsOwPakXqND0kt/2vx5L7w6Fe4y074dSqUSMpkM7u7ufCfkFStWIDk5Gf/5z3/423366aeIiIjA5cuX0atXLwDAgAED8MILLwAwjH5YtWoVAgIC8NBDDwEAXnrpJaxfvx5nzpxp0cvn5ZdfxuTJkwEYAqLw8HBs2bIFc+bMwcqVKzF37ly+hqdnz5547733MHbsWKxfvx4KhQIAMGHCBDz99NMt/i7cWgBD9uaZZ57Bt99+i2effRZubm7w9PSERCJpt+NzZ1599VV+3Wq1Gv/5z3+wY8cOvulibGws9u/fjw8//BBjx47t0nMQ6/rtTCH+vlCMk9kVuGtYBB2XJ6SZXZdK+P//cG8GpvcNccnfEQpmnMTp06exa9cueHp6trouPT2dD2b69+/PXy4Wi+Hv749+/frxlwUHBwMASkpKWjxG847Lfn5+SEhIwMWLF/nnPnPmDL766iv+NizLQq/XIzMzE7179wYADBkypNXavvvuO7z33ntIT09HTU0NtFptp6PezdH8Oa9cuYK6ujo+uOE0NjYiOTnZYs9JLGtfWikAoLy2EVnldYgJ8BB4RYTYh8q6RpzIrgAAyMQinM6txNHMqxge63od7SmYuYabVIwLr04V5Hm7o6amBjNmzMDq1atbXRcaGsr//7VdbBmGaXEZF9Hr9XqznvuRRx7BE0880eq6yMhI/v89PFq+CR06dAhz587F8uXLMXXqVCiVSnz77bd46623Onw+bop186259iaeN3/OmpoaAMC2bdvQo0ePFrczZ54XsR2tTo/9aU11X8eyrlIwQ4jRnsul0LNAr2BPDIn2w9dHcvDxvgwKZojhzdzU7R4hyWQy6HQ6/s+DBg3Cjz/+iOjoaEgkll//4cOH+cCkoqICly9f5jMugwYNwoULFxAfH2/WYx48eBBRUVF4/vnn+cuys7Nb3ObavydgmJcEAIWFhXxGpXkxcHuSkpIgl8uRk5NDW0oO4ky+ClUNTafQTmRVYM6QCAFXRIj94LaYJiQGY86QcHxzNAc7LpbgSkk14oO8BF6dbVEBsIOKjo7GkSNHkJWVhbKyMixYsABXr17FXXfdhWPHjiE9PR1//vkn7r///lbBQFe8+uqr2LlzJ86dO4f77rsPAQEBuOWWWwAYTiQdPHgQCxcuREpKCtLS0vDLL7+0KgC+Vs+ePZGTk4Nvv/0W6enpeO+997Bly5ZWf8/MzEykpKSgrKwMarUabm5uGDFiBF/Yu2fPnha1N+3x8vLCM888g8WLF+Ozzz5Deno6Tp48iffffx+fffZZl/9tiPXsu2zIyvi6G7KHx7OvCrkcQuyGTs9i92XDFuyExCDEBnpicm9DmcDHezOFXJogKJhxUM888wzEYjGSkpIQGBiIxsZGHDhwADqdDlOmTEG/fv2waNEi+Pj48Nsy3bFq1So8+eSTGDx4MIqKivDrr7/yQxj79++PPXv24PLlyxg9ejSSk5Px0ksvISwsrMPHvPnmm7F48WIsXLgQAwcOxMGDB/lTTpzZs2dj2rRpGD9+PAIDA/HNN98AMBQ3a7VaDB48GIsWLcKKFStM+nu89tprePHFF7Fy5Ur07t0b06ZNw7Zt21pNbCf2Ya+xXuahMbEAgPRS6qdBCACcyqlAZZ0GSjcpBkX6AAAeGWv4PdlyKh8l1Q0Crs72GNacM8EOqKqqCkqlEiqVqlVhaUNDAzIzMxETE8OfuCEt7d69G+PHj0dFRQV8fHyEXo7N0c+IcFT1Ggx67W/o9Cz2Pzce8z89ivTSWnxy7xBMSgoWenmECOqN7Zewbnc6ZgwIw/t3NR1gmL3+IE5kV2DB+DgsmZoo4Aq7r6P372tRZoYQYpcOpZdBp2cRG+iBcF93DInyAwAcN57eIMSV/cPXywS2uPyh0YbszJeHc6DRmX6Qw9FRMEMIsUt7jaeYxvQ0vFgPjvYFAJyguhni4goq63GpqBoMA4ztFdTiuslJwZBLRFDVa1Ckcp2tJgpmSIfGjRsHlmVdcouJCIdlWew1FjeO6RUAABgSZQhmTuepoNZ2v6idEEe1K9WQlUmO8IGfh6zFdWIRgzAfNwBAfmW9zdcmFApmCCF2J6u8DnkV9ZCKGQyPMfTMiAnwgL+HDI1aPc7lqwReISHCaTqSHdTm9WE+hvq+AgpmXIuT10CTbqCfDWFwXX8HR/nCQ27om8QwDAYZszPHs6huhrimBo0OB66UAwDGtxfMKA2ZGQpmXATX+fbaoYqEcBobDceAxeLudWgm5mnaYmpZ3MhtNVERMHFVRzKvol6jQ4i3AkmhbZ/wadpmcp2aGftvdWtFYrEYPj4+/Bwid3d3lxzQRdqm1+tRWloKd3d3q3RVJm1r1OpxKN3wyZMr/uUMiTacaDqZXQGWZen3lbicE1mGAvhR8f7t/vz38HG9zIzLv0Jz05ivHaxICGCYAxUZGUlvmjZ0KqcCtY06+HvIWn3y7NvDGzKJCOW1jcgsq0VsYOvBqoQ4s9N5hnqx5Aifdm8TRsGM62EYBqGhoQgKCmp3WCFxXTKZzCIdlInpuC2kkXH+EIlaBpFyiRgDwpU4llWB49kVFMwQl8KyLE7nVQIA+of7tHu75gXArpLBdPlghiMWi6kughA7kFZcDQDo3U49wOAoPxzLqqChk8Tl5F6tR2WdBjKxCImh7Q+S5DIztY06VNVroTTONnNm9JGTEGJX0kpqAAC9gtt+seaKgI9R8zziYlKMWZneoV6QS9r/8K2QiuFv7D/jKr1mKJghhNgNnZ7FFWMw0zOo7S2kIdG+YBggo7TWpTqcEnImtxJAx1tMHFerm6FghhBiN/Iq6qDW6iGTiBDh597mbXzcZfyLOXeEmxBXcMZY/Dugg+JfDl83o6JghhBCbCqt2JCViQv0hFjUftHiWGP/md2X6RSi0FiWxaH0cpw2Zg2IdWh1epw1dr4eEK7s9PauNtKAghlCiN1I62SLiTMuwRDM7Esrg9aFJgPbm0JVPR76/ATu+vgwZq8/iDPGmg5ieVdKa1Cv0cFTLjHpFF9TrxnX2IqlYIYQYjfSSgwnmXoFd/xiPSDcBz7uUlQ3aHGKMgI2p9Oz+OxgFia/vRc7LhYDALR6Fk98cwo1aq3Aq3NOXOarbw/vDrOWHKqZIYQQgXDbTPFB7R87BQyTgUcbuwPvTqWtJluqa9Tiro8O4+Wt51Gj1mJQpA++f3QkwpQKZJXX4eVfzgu9RKd02ox6GYCCGUIIEYS++UmmTjIzADCOq5tJpSJgW/rmaC6OZl2Fp1yC12b2wQ+PjsLQaD+8c2cyRAzw48k8/JKSL/QynQ63hTfAhJNMQFMBcHFVAzQusBVLwQwhxC7kV9ajXqODVMwgqp2TTM1xQyjPF1ShpNo16gKExm0vAcDS6YmYNzKa79I8LMYPCyf0BAC8sOUccq/SAF9LadDocKnQsAXb34TiXwAI8JBDKmagZw0BjbOjYIYQYhe4rExsgCck4s5fmgK95Ojbw9AleO/lMquujRjsulSCnKt18FZIMGtQj1bXPzEhHkOifFGt1uLJb09Bp2cFWKXzuVBYBa2eRYCnjC/s7YxIxCBUabhtoQv0Y6JghhBiFy4bxxiYssXEGdcrCADVzdjKpoOZAIC7hkXCXdZ6Go5ELMI7dw6Ep1yCkzmVOJ5FXZot4XSzZnnmzFlqPqPJ2VEwQwixC03Hsjsu/m2u+RFtygJYV2pRNQ5cKYeIAeaNjGr3duG+7nwfoGMUzFgE3yzPxHoZjiv1mqFghhBiF9LMKP7lDIzwgbdCAlW9Bil0RNuqNhuzMlP7hCDct+OapqHRhvlZR7MqrL4uV8BPyo4wrV6G08OFTjRRMEMIERzLsrjCbTN10jCvOYlYxB/R3kNbTVZTUduILacMJ5TuGxXd6e2HRPsBAE5mV1DGrJtU9RpklNYC6HpmxhUa51EwQwgRXKGqAbWNOkhEDKL8Pcy671jjVtMemtNkNd8ey0WDRo+kUG8Mi/Hr9Pa9Q73hJZegRq3FxcIqG6zQeZ0zjjCI8HODn3EStqlcqdeMoMHM3r17MWPGDISFhYFhGPz8888trq+pqcHChQsRHh4ONzc3JCUlYcOGDcIslhBiNVzxb0yAB2QS816WuPqMM/kqXK1ttPjaXJ1Wp8cXh7IAAPdfF21SAapYxGBQlGGriepmuoffYjIzKwMAPYwFwFQzY2W1tbUYMGAA1q5d2+b1Tz31FLZv344vv/wSFy9exKJFi7Bw4UJs3brVxislhFiTOc3yrhXsrUBsgAdYFjhfoLL00lzezkslKFA1wN9DhhkDwky+H5fBoWCme7j+Mn3CvM2+L3c0u7pBi6oGjUXXZW8EDWamT5+OFStW4NZbb23z+oMHD2L+/PkYN24coqOj8fDDD2PAgAE4evSojVdKCLEmU8cYtCchxHC/1KJqi62JGOy4YJi9dEtyDyikYpPvN4TPzFSAZalupqu4rGVCsPm/Gx5yCXzcpQCAQievm7HrmplRo0Zh69atyM/PB8uy2LVrFy5fvowpU6a0ex+1Wo2qqqoWX4QQ+8YNmDSn+Lc5Lpi5WEjBjCWxLIu9aYZaJO4YvKkGRPhAJhahtFqN7HLqBtwVWp2eL/7t1YVgBgDClK5RN2PXwcz777+PpKQkhIeHQyaTYdq0aVi7di3GjBnT7n1WrlwJpVLJf0VERNhwxYQQc7Es26Vj2c0lhhhS8KnF9OHFklKLq1FcpYZCKsLQ6M4Lf5tTSMV86/2jtNXUJdlX69Co08NNKja58++1XKXXjN0HM4cPH8bWrVtx4sQJvPXWW1iwYAF27NjR7n2WLVsGlUrFf+Xm5tpwxYQQcxVXqVHdoIVYxCAmwLyTTJxEY2YmrbiGjgJb0F7jCbERsf5mbTFxuCPa1Am4a9KadcXmZmCZq4eLdAFu3Y/aTtTX1+Pf//43tmzZghtvvBEA0L9/f6SkpODNN9/EpEmT2ryfXC6HXC635VIJId3AbTFF+btDLjH/DRMAIv3c4SYVo16jQ1Z5LeICu5bhIS1xx93H9DRvi4kzLMYXG/YY6maI+S4Xm98V+1qucjzbbjMzGo0GGo0GIlHLJYrFYuj1zj/OnBBXkca/YHc9ABGJGPQyblFdoroZi6hr1OJYpiEIGWtmvQxncJQfGAbILKulyeZdkGrMzPTq4vYr4DqN8wQNZmpqapCSkoKUlBQAQGZmJlJSUpCTkwNvb2+MHTsWS5Yswe7du5GZmYnNmzfj888/b/f0EyHE8Vwp5U4ydS+bwtfNFFHdjCUczihHo06PHj5uiO3i9p/STcqfwjlB2RmzpfHBTPczM1QzY0XHjx9HcnIykpOTARj6yiQnJ+Oll14CAHz77bcYOnQo5s6di6SkJKxatQqvv/46Hn30USGXTQixoELji2xEJ/N+OsOdaLpEx7MtYu/lMgCGrIw5k5qvxRUOUxGweTQ6PTLLDCeZuloYDzTNZyqqanDqejJBa2bGjRvXYf+BkJAQbNq0yYYrIoTYWqHKkP4OUSq69ThcETCXmifd0916Gc7QGD98cTibmueZKausFhodCw9Z108yAUCglxwSEQOtnkVJdQPfSM/Z2G3NDCHENRRVGYKZ7r7IcpmZ7PI61Kq13V6XK8spr0NmWS0kIgaj4v279VjDjJmZCwVVqHbyLrSWxBX/xgd7dSszJhYxCPZ2/hNNFMxYwPZzRfglJV/oZRDicOobdaisM7zBdTcz4+8pR6CX4STjZcrOdMseY6O8QZG+8FZIu/VYIUoFIvzcoGeBUzmVFlida+B+hnt1s5YMaPrdKqlSd/ux7BUFM91UUtWA//vqBBZ9l0LV+oSYicvKuMvE8FZ0f9c7kcYaWATXX6arp5iu1b+HDwAKMs3BtSzoTvEvJ9jbEOQXVznvexQFM9207Wwh9CzAsk1HTAkhpilUGdLeIUpFt1LpHO7kDBUBd12jVo+DVwzFv92tl+FwzRAzjAWtpHN8j5luFP9ygrwMmZniasrMkHZsO1PI/38afeogxCxFKq5epntbTJzEUMPx7Et0PLvLTuZUoLZRB38PWZcmNbeFC2ayKJgxSaNWz/9bWSYzYwxmKDND2lJQWY/j2U29E7j5MoQQ0/Anmbwtc8Ki+TYTTWrumn3GepnRPQO63EL/WjGBhmAmk4IZk2SW1UKrZ+Ell1gk0Oe2mahmhrTp97OGrIzY+AtPwQwh5uG2mSyVmYkP8oSIASrqNCh14pS6NZ3NN2S1hsaYN1iyI1zTvUJVA+oa6aRZZ7jaovhgT4tsv1JmhnToV+MW022DwgEA6RTMEGIWfpvJxzLBjEIqRrTxjfMi1c2YjWVZXChQAQD6hCkt9rg+7jL4uhtORWWV1VnscZ0V3/m3GzOZmqMCYNKu3Kt1OJ1bCREDLJwQD4YBymsbUV5DnwYJMVWhhWtmAKA3jTXostJqNcpqGiFimoqpLYWrm6Gtps5ZsvgXAIKMmZmqBi0aNDqLPKa9oWCmi34zZmVGxPojws8d4b6GPf8rlJ0hxGRFFq6ZAWisQXecLzQEgLGBnnCTdW2CeXtiAgxvzJll9BrZmcsWmMnUnJdcAjep4fvprHUzFMx00W9nCgAAN/UPAwDEBxp+UaluhhDTNGh0KK9tBGDZzEwC9ZrpsgsFhmAmKdQyp5iaiw2k49mmaNDokFVu+Dfifpa7i2GYpq0mJ+2HRsFMF2SW1eJ8QRXEIgbT+oYAAHoaI2jKzBBiGu4Tolwigo9797rMNsdtM6WV1ECr01vscV3BBWNmJslCR7Kbo20m02SU1kLPAt4KCYKMHa0tge8146R1MxTMdMFvpw1ZmeviA+DnIQNgOEUBNHVtJIR0rPlJJkuc2OCE+7rBXSY29OoopzdOc1gzMxPtT8GMKZp3/rXk70UQXwRM20zEiKuXual/KH9ZTy6YoS7AhJiEG2XQ3ZlM1xKJGD49fy6fioBNVaPW8sGfNTIz0QHuAIDKOg0qjNuLpDWuXqanhQuwuePZJZSZIYBhHz61uBpSMYOpSSH85VxmpqRaDVU9TYYlpDNNJ5ksV/zLGRzpCwA4mnXV4o/trFKLqsCyhmO8AZ6W297guMuaGsBlUsasXdwH4l4WOsnEcfbj2RTMmOnHk3kAgPEJQVA22+f3Ukj5X1SqmyGkc/xJJgtnZgBgmLHh29FMCmZMZc0tJg5fN1NKwUx70ksN7x9xgZYOZriaGdpmcnkanR4/ncwHANw+JKLV9Vx25grVzRDSKUt3/22OC2aulNSgjHo/mcSaxb8cKgLumEanR3a5oalgXJBlg5mmYZOUmXF5ey+XoqxGjQBPGcYltJ4mG091M4SYrKnHjOWDGR93GT+n6RhlZ0xyns/MWK7z77UomOlY7tU6aPUsFFIRQi38e+Hs85komDHDDycMW0y3DOwBqbj1P11PY+tp6jVDSOesWTMDAMON2ZkjFMx0SqvT800GLTUpuy3Ua6ZjGcbtt5gAT4sN+eRwXYBr1FrUqJ1vPhYFMya6WtuIHReLAQCzB4e3eRuu9TTVzBDSMY1Oj1Lj9o81amYAYFiMPwAKZkyRUVaLRq0eHjIxIv3crfY8XBfgrLJa6PU01fxaGcbuyFzQZ0mecgk85RIAznmiiYIZE21NyYdGx6JvD2/0bqdAjusCnF9Zj1onjHwJsZSSajVYFpCKGfgbezVZ2tAYw4mmS0VVUNXRCcOOcMW/vUO9LZ4RaC7c1w1iEYN6jc5paze6g8vMWLr4l+PMvWYomDHRD8ZTTLcPbl34y/H1kPFHGrmKdEJIa4WVhuLfYG+F1d48g7wUiA30AMsCx+iIdodsUfwLAFKxiM/80Imm1ppOMlk+MwMAwcYi4BInDCQpmDHBxcIqnMuvgkwsws0Dwjq8bXyQ4YeQioAJaZ81pmW3hauboX4zHbPFsWwOVwRMdTOtcZmZ2ADrZGacuQiYghkTcIW/k5KC4NtJSpyKgAnpXFOPGesU/3KGc3UzGeVWfR5HxrIszheoAFg/MwM0BTNZFMy0oKrT8INXY6yVmfF23vlMFMx0QqPT4+dTht4yt7VT+NtcUxEw9ZohpD22ysxw/WbOFVQ55QkOSyiqakBFnQZiEYNeFm6h3xY6nt22dGPxb4i3gi/UtTTuRFNxNWVmXM7+K2Uor21EoJccY3q27i1zraaBk5SZIaQ9RVWGmhlr9JhpLszHDeG+btDpWZzIrrDqczkqbospPtATCqnY6s8XS8FMm9JLrHeSicNN4abMjAs6ZXwBHNcrEJI2estci9tmyrlahwaNzqprI8RRcZmZMB/rBjNA01bT0UzaamoLXy9jgy0moGkLJedqHTQ6vU2e0xFwNUTWDGacedgkBTOdOJtv2EvuF25aV8wATxl83KVgWeo3Q0h7bFUzAzRrnpdBRcBt4U8y2aD4FzCcqHGTiqHVs8irqLfJczqCDONJJmsV/wLNh02qwbLO1eeHgpkOsCyLs/mGX/S+PUwLZhiGQf9wHwCGLSpCSEtanR4lxj17a9fMAMDwWEMwczqvkrKlbUgtNtT3JYRYv14GAEQiBtH8VhN94OPwJ5msus1k+H2r1+hQ7WQ1ZBTMdKC4So2yGjXEIsasTy2TegcBAHYaOwabS6PT443tl/D98dwu3Z8Qe1ZW0widnoVYxPB9mawp0s8dwd5yaHQsTuZQ3UxzjdqmwYbxFh5s2JGYAEOvmQzqNQPAEOBnlVu3YR4AuMnE8FY4ZxdgCmY6wG0x9QwyrzBuYu9gAMCJ7AqUd2Fi739+v4h1u9Px7I9ncIpefImT4aZlB3vJIbZit1kOwzDNjmjTVlNz2eW10OlZeMjENsmScfjj2eUUzABAXkU9NDoWcokIPXysu/XadDzbuU40UTDTAS6YMXWLidPDxw1Jod7Qs8Cu1FKz7vtLSj42HcgCALAssPTHs2jUUpEccR5N9TK2e/Mcxg+dpCLg5ri6vrggTzCM9QNLTpS/IZjhskKujpvJFBPgYdVxEoDz9pqhYKYD57lgpgtV/pOSDNmZHRdM32q6WFiF5348AwCYPzIK/h4ypBZXY8OedLOfnxB7Ze1p2W0ZEWvIzJzKqYRaS3UzHC6Yibfi1kZbov0pM9OcLeplONx8phIn6zVDwUwHzD3J1Nxk41bT3rRSk4oOVXUaPPLFCTRo9BjTKxAvzeiDl2YkAQA++OcKNeEjTqOoyvaZmbhADwR4yqHW6nE6V2Wz57V3V4wnaOKDbR3MGGpm8ivqKfMMIN3KAyabo8yMiympakBJtRoiBkgKNT+Y6dvDG8HectQ16nC4k1bqej2LRd+dQs7VOoT7uuG9OwdCLGJw84AwjE8IRKNOj6U/noVe71xH6YhrslX33+YMdTPcEW3aauIIlZkJ9JLDTSqGngXyKmiriRswaYvMTLCXc85nomCmHVxWJj7IE24y87tiMgzDFwLv6ORU057LpdiVWgq5RIQN9wyGj7uMf4wVt/aDu0yM49kV+OpojtnrIMTeNJ+YbUsjjEe0D1PdDADDhyjuTdSWJ5kAw2tblDE7Q3Uz1h8w2RxlZlxMV4t/m+O2mnZeLOmwQdGlIsMW0vS+Ia2er4ePG56dmgAAeOOPS9Qngzg8W3b/bW64sW7mRHYFbW0AyK+sR4NGD5lYhEg/d5s/P9XNGFQ1aFBmPPVqm5oZbj4TBTMWs3fvXsyYMQNhYWFgGAY///xzq9tcvHgRN998M5RKJTw8PDB06FDk5Fg/Q3GOq5fpRjAzMs4fblIxClUNOG9sGd6WbOMvM9dI6lrzRkbDXSZGtVrLvxEQ4oh0epb/RGjLAmDA0GLBz0OGBo0eZ/Mrbfrc9oirl4kOcDdpVIulRQVQZgZoysoEecnhpZBa/fmctQuwoMFMbW0tBgwYgLVr17Z5fXp6Oq6//nokJiZi9+7dOHPmDF588UUoFNb/RHfWAsGMQirGmF4BADreauI+mXCfVK7VvLnY1Vrn2uckrqW8Rg2tnoWIaRp6ZyvN62YOU78ZfrChrbeYOJSZMbDFgMnmAo2/d41aPVT1Gps8py1YZ864iaZPn47p06e3e/3zzz+PG264AW+88QZ/WVxcnNXXVVLdgOIqY/FvN4evTeodjD/PF2PHxWIsmtSrzdtwn0y4PeS2+HnIkHO1DmU1jd1aDyFCKjBmFoO8FIJkA4bH+OGPc0U4nFGOBePjbf789kSo4l8O1cwYcD1mYm30fZBLxPDzkOFqbSOKq9R8jaajs9uaGb1ej23btqFXr16YOnUqgoKCMHz48Da3oppTq9Woqqpq8WUubospLtAT7rLuxXvjE4PAMMC5/Cq+82lzDRodv3XUXmYGMAywBIByCmaIA+OKf0NtXC/DaV434+oTm5s3zBMC93qXe7UOWhf+XjQV/9omMwM0ZUWdqQjYboOZkpIS1NTUYNWqVZg2bRr++usv3HrrrZg1axb27NnT7v1WrlwJpVLJf0VERJj93GfzDAFQd7aYOAGecgyK9AUA7GmjG3DOVcOnEm+FBD7u7e+X+nvQNhNxfEIcy24uIdgLPu5S1DXq+K1kV8SybFOPGYGCmRBvBWQSEbR6FgWVzvOmai4umLFlUMmdaMqvdJ6p5XYbzOj1hkh95syZWLx4MQYOHIilS5fipptuwoYNG9q937Jly6BSqfiv3FzzhzVa4iRTc0OiDMHMhcLWWaKssqbi347aifsbMzO0zUQcGZedtHXxL0ckYjAsmus347p1M+W1jais04BhbNOorS0iEYMo4ykqV62bYVkWucY+O7Y8UdbHWD5xItt5Zv/ZbTATEBAAiUSCpKSkFpf37t27w9NMcrkc3t7eLb7Mda4bnX/bkhDiBaDpCHZzTfUyHacY/TyM20y1FMwQxyV0ZgZo2mpy5TlN3BZTuK+bWUN0LY07wZntosGMql6DukZDuw1rD5hsjptVdjTTeQJ6uw1mZDIZhg4ditTU1BaXX758GVFRUVZ73tJqNYqqGsAwQFJo94p/OVwwk1pU3eooXNNJpo6jcu40U1emcBNiL4SYy3Qtrnne8awKl63VSBO4+JfDve5luWgRMLfN4+8hs2lQOSTaDyLGUObQVi2nIxL0NFNNTQ2uXLnC/zkzMxMpKSnw8/NDZGQklixZgjvuuANjxozB+PHjsX37dvz666/YvXu31dbEZWViAzzgIbfMP098kCfEIgaqeg2Kq9QtZtKYmpnhtpmuUmaGODChC4ABIDHEG94KCaoatDhfUIUBET6CrUUoQh/L5jRNz3bNzAxXKxRmw6wMAHjKJejbQ4kzeSoczbyKmQN72PT5rUHQzMzx48eRnJyM5ORkAMBTTz2F5ORkvPTSSwCAW2+9FRs2bMAbb7yBfv364ZNPPsGPP/6I66+/3mprOpPX/f4y15JLxHyl+sWilnUzpmZmuAJgqpkhjkqnZ1FsnNQr5DaTWMTwaXZX3Wq6YifBTFOvGdfMzBQYg3tbbjFxuNoxZ+m5JGgwM27cOLAs2+pr8+bN/G3+9a9/IS0tDfX19UhJScHMmTOtuqYTOYaCqEHGol1Lab7VxFFrdfwPs+mZGTUNnCQOqbRaDZ2ehVjEIMhLuGAGAEYY62Z+O1PoVF1QTWUvwQzXayanvA46F3xd417/bZ2ZAZpqx446SUBvtzUzQtDpWZw0VncPifKz6GMnthHM5FXUQ88CHjIx30emPb7GxkZ6Fqh0oq6NxHVwe/PBXnKIRe2f3LOFW5N7QCEV4UyeCofSnePF3FTVDRoUGfuLxAd6CbqWMB83SMUMGnV6fk2uJJ8PZmwf3A+N9gXDAOmltSitdvxaTApmmrlUVIUatRZecgmfSbGUhBBv43M0BTPcPnGUf8fHsgFAJhFB6WboQ0O9Zogj4op/QwTcYuL4e8pxxxBDD6r1e9IFXo1tpRv7mgR4yqHsoLeVLYhFDCKMR5Kzy1yvbiZfwG0mH3cZEoIN73PHshx/q6lbwYxa7VxvqsezDFmZ5Chfi39y5DIzV0qq+c6jWWWGfeLoANP6C1CvGeLICvjiX+FOMjX34OhYiEUM9qWV8YX/rqBpi8l2HWc74sp1M0JuMwHgZ5UdyXD87KRZwcwff/yB+fPnIzY2FlKpFO7u7vD29sbYsWPx+uuvo6CgwFrrtAkuOh1q4XoZwBB5e8ol0OhYZBo/gTTPzJjC34NGGhDHVcQdy/YWPjMDABF+7pjRPxSAa2Vn7KVehhPl75qN8xq1epQYt3d6+AoUzPA9l1wkM7Nlyxb06tUL//rXvyCRSPDcc8/hp59+wp9//olPPvkEY8eOxY4dOxAbG4tHH30UpaWt2/bbO5Zl+WBmSLRl62UAQ7fLXsGGFw9uq4n7JNLZSSYOd6KpnLaZiAPie8zYSWYGAB4dZxhc+8fZQv5DhrMTesDktfjMjIv8+3OKqxrAsoYSAu6Dqq0NNb7XpRZXo7LOsT8km9RI5Y033sCaNWswffp0iESt4585c+YAAPLz8/H+++/jyy+/xOLFiy27UivLq6hHcZUaEhGDgVbqO5EQ4o2TOZVILaoCBoSZn5mhYZPEgXEFwGF2UDPDSQzxxoTEIPxzqQQf7c3Ayln9hF6S1aXzM5mELf7luOr07Ob1Mp3VTFpLoJcccYEeSC+txbGsCkxOChZkHZZgUjBz6NAhkx6sR48eWLVqVbcWJJTj2YasTN8eSrjJrNOJkaubuVRoqJvJqzD8MHc0Lbs5fpuJMjPEAdlTAXBzj46Nwz+XSvDjiTwsntQTQXayDWYNWp0eucbhtrGB9lUzk321Fno9C5HAJ91spUDAk0zNDYvxR3ppLY5mljt0MNPt00w6nQ4pKSmoqHDsgVXHjMW/Q6MtXy/DSWw2o6mgsh5aPQuFVIRgb7lJ9/fnRxpQZoY4Fq1Oj+IqYbqddmZotC8GR/miUafHxv2ZQi/HqgoqG6DVs5BJRAixk6Cth68bxCIGDZqmGhJXwAczAo72AJoVATt43YzZwcyiRYuwceNGAIZAZuzYsRg0aBAiIiKsOmbA2o5bsV6Gk2g8np1fWY9z+YZOwNEmHMvm0DYTcVSlNWroWUAiYvg5Y/aCYRg8NtZQO/P5oWyUVDtvv5McY1YmwtfNbjIgUrEI4cYCWFcqAs4X+CQTh+uGfS5fhRq1VtC1dIfZwcwPP/yAAQMGAAB+/fVXZGZm4tKlS1i8eDGef/55iy/QFirrGnG52LCPPMQKJ5k4Sncp/2norwtFAJr2i01BBcDEUXEzaIK9FYI3zGvLxN5BGBjhg3qNDu/uSBN6OVaTfdW8Oj1bccUZTfnG3wmhTjJxwnzcEOHnBj3b9KHeEZkdzJSVlSEkJAQA8Pvvv+P222/nTzqdPXvW4gu0hRPGrr+xgR78Vo61cM34/rlYAsD0ehmgWWaGhk0SB8Mfy7azehkOwzBYNj0RAPDtsVxkGItknU2Oscg20s/0D1G24IrTs4Wcy3St4TGGI9r708oEXknXmR3MBAcH48KFC9DpdNi+fTsmT54MAKirq4NYbLsR5pbE18tYeIRBWxJDDcFMtTGdZ84nJK4AuLJOwzfeI8QRcCeZ7K34t7nhsf6YmBgEnZ7Ff/9MFXo5VsGdGDInI2wLUS52PJtlWcEb5jXHFf7+nJLvsO8tZgcz999/P+bMmYO+ffuCYRhMmjQJAHDkyBEkJiZafIG2wKXWBlux+JeTeM2YBFN7zACG9tNchr6CsjPEgXDbTPbwwt2RZ6clQsQAf5wrwskcxz7U0Jbsq/YZzMQEuFZmRlWvQV2jDoB9ZCsnJAYhwFOOsppG7LxYLPRyusTsYOaVV17BJ598gocffhgHDhyAXG7YlhGLxVi6dKnFF2htDRodzuQZWpkPtWLxLych2LvFn6MCTM/MiEUM/Dxoq4k4nqIqY2bGTk7QtCchxAuzB4UDAFb9ccmpJmqzLIscY01KpJ991cw0b5znTP/m7eGKfwM8ZVBIhd/RkIpFuH2I4ef+m6O5Aq+ma0zqM3Ot2267rdVl8+fP7/ZihHA2X4VGnR4BnjKzsiRdFRfkAbGIgc54PNLc1u5+HjKU1TTSiSbiUJoyM/YdzADA4sm9sPV0AY5mXsWu1BJMSHTc3hvNldc2orZRB4YBf3rIXkT4uUMsYlCv0aG4Sm3X25GWYI+ZyjuGRGD97nTsTStFXkUdwn3tK3vXGbODmVdffbXD61966aUuL0YIR41n64dE+dmkC6NcIkZsgAfSSmoQ6edu9vFIw4mmGjrRRBxKUwGw/bx4tyfMxw33XReND/dk4I3tqRifECRYh1ZL4uplQrwVdpENaE4qFiHC1w1Z5XXILKt1+mAmv8LwvbCH4l9OdIAHRsb641BGOb4/nofFk3sJvSSzmB3MbNmypcWfNRoNMjMzIZFIEBcX5zDBTINGh/f/ScOHezIANJ21t4XEUG+kldR0KRNEk7OJo9Hq9HzvFnuoDzDF/42Nx+YDWbhUVI2LhdVICvPu/E52Lucqt8Vkn5+4owM8kFVeh6zyWoyM8xd6OVZVoLK/zAwA3DkswhjM5OKJiT3tso1Ce8wOZk6dOtXqsqqqKtx333249dZbLbIoazuYXobnt5zjB8tN7ROMu4dH2uz5h0X74tfTBegf7mP2fbmGY1cpM0McREm1/TbMa4/SXYrRPQOw42IJ/r5Q7BzBTLmhTsPein85hrqZUpcY+GkvDfOuNbVPCHzcpShQNWDv5VKMTwzq9D4lVQ3YfbkUu1NLoNGxePO2AVC6S22w2pa6VDNzLW9vbyxfvhwzZszAvHnzLPGQVvP2X6l4758rAIAgLzlendkX0/qG2HQNdw+PQv9wny69QPIFwJSZIQ6CO5Yd7K2wm66zppicFGwIZi4W4clJPYVeTrfZa8M8TozxMIQrBDNNPWbsK1OpkIoxKzkcnx7IxLfHctoMZirrGnEiuwLHsiqw/0op382e8+Iv5/DeXcm2WjLPIsEMAKhUKqhUKks9nFWcyavkA5l7RkTi2WmJ8FbYPoIUixgM6OJkbtpmIo7GkYp/m5vYOxgMcxbn8qtQUFlvd5+izWWvDfM4XDDjCr1m7KnHzLXuHBaBTw9kYufFEpRUN6BWrcPxrKs4kV2B49kVuFLSuqHkgHAlhkT7YfPBLGw9XYBJScG4eUCYTddtdjDz3nvvtfgzy7IoLCzEF198genTp1tsYZbGsiyW/3oBAHBrcg+suKWfwCvqGhppQByNIxX/NhfgKcfgSF8cz67AjovFuHdktNBL6hZ77THD4YKZ7Kt10OlZh6rXMEejtmmgpj0GM72CvTAo0gcncyox5o1daNC0bqIXG+iBIVG+GB7jjzG9AhHoZXhf8pBL8N7ONLyw5SyGRfvZtJDb7GBmzZo1Lf4sEokQGBiI+fPnY9myZRZbmKX9frYQJ7Ir4CYV47lpjtncDzD0JQCAq9RnhjiIAuM2k6MU/zY3OSkYx7Mr8PcFxw5m6hq1KDW+gUbZWY8ZTpiPG2RiERq1ehRU1iPCTjNI3VVc1QCWBeQSEd/V3d7MGxmFkzmVaNDoIROL0C9ciSFRhunyQ6L9+HKHaz0+IR67U0twJk+FJT+cxuf/Gmazk4BmBzOZmZnWWIfVvf33ZQBi/N+4OIc+9kc1M8TR2Ptcpo5MTgrGyj8u4VB6OVT1GijdbL8tbQnctGylm1SQ4kxTiEUMIv3dcaWkBlnltU4bzORVNM1kstcj/7cM7AEfNxm8FBL07aE0+Si/VCzC23MG4sb39mFfWhm+OJxtsw8BZncAdlTFVWr08HHDQ2NihV5Kt3CDMGvUWjRodAKvhpDOccdQQxxsmwkAYgM9ERfoAa2exe7UEqGX02X2OpPpWtEuMKPJnutlOAzDYHxiEIZE+5ndkyg+yJMf2vqf3y+2WWNjDSZlZmbNmoXNmzfD29sbs2bN6vC2P/30k0UWZg3P39jb7ppFmctbIYFUzECjY3G1ttGufyEIAYBC/sXb8TIzADA5KQTpe9Lx94VizBzYQ+jldIm9F/9yuBlNmWXOO6OpwMF/H0xx78ho7LxUgn1pZXjy21P46f9GQS6x7nuvSZkZpVLJp8OUSmWHX/ZqSJQvptv4CLY1MAzTVARMW03Ezun1LMpqDLUaQV6O+eI9pY9hnMGe1FI0apuKIXenluDdHWkOkSFtOpZt38FMNH882zaf5oXA1ZA58wdRkYjBm7cPgK+7FOcLqvDf7dafQm9SZmbTpk1t/r8jeW56gt3uT5rLz0OGoqoGlNGJJmLnKus10BvnBrZXNGjvBob7INBLjtJqNQ5nlGNErD9W/XEJnx4w1A9KxAwWjI8XeJUdy7lqbJhnp8W/HP54thNPz863w7lM1hDsrcB/bxuABz8/jk/2Z+L6ngEYl9B5E76uMrtmpr6+HnV1TT9o2dnZeOedd/DXX39ZdGGW1jvUfrNG5uJ6zVBmhti7cmNWRukmhUzimCV6IhGDSb0NL8JfH8nB7R8e4gMZAPj8UFaLjI094qZl23tRLRfM5F6tg0Zn3/+mXcVtM4U7eTADAJOSgnHvyCgAwDPfn+ZP1FmD2a8uM2fOxOeffw4AqKysxLBhw/DWW29h5syZWL9+vcUXSFqjkQbEUZQbWwhwAbijmpxk2Grafr4Ip3MroXSTYt3cQQj0kqO4So1tZwsEXmH7tDo9f4LG3reZgr0UUEhF0OpZfs3OhGVZhygAtqR/39AbCcFeKKtpxJIfTkPPpWotzOxg5uTJkxg9ejQA4IcffkBISAiys7Px+eeft2qoR6zDn45nEwfB/YwGeDjGTKb2jIoLgIfMUMA4MMIH2564Hjf0C8V846fOjfszwbLWeZHurkJVA7R6FjKJCCHe9l23JBIxTn2iqbJOg7pGQ42VI7cIMYdCKsZ7dyVDLhFhd2opNh3MssrzmB3M1NXVwcvLCwDw119/YdasWRCJRBgxYgSys7MtvkDSmh+NNCAOgutU7eiZGYVUjPX3DMZLNyXhf4+MRLivIcNx9/AoKKQinMuvwpHMqwKvsm3csewIXzeHmI3lzDOauAGTgV5yhz9Za46EEC+8cGNvAMDqPy7hXL7lRx+ZHczEx8fj559/Rm5uLv78809MmTIFAFBSUgJvb8efLOsIAmikAXEQXMDt6MEMAIzpFYh/XR/TovbHz0OGWYPCAQCf7LPPhqL2PmDyWtF8EbDzBTN5FYbAsoeLbDE1d8+IKExOCkajTo8nvz2FukatRR/f7GDmpZdewjPPPIPo6GgMHz4cI0eOBGDI0iQn235Spivyp5EGxEFwBcD+Dr7N1JF/XRcDANh5qdguswmO0mOGE+PvvJkZvvuvr+sFMwzDYPXs/gj2liO9tBav/XbBoo9vdjBz2223IScnB8ePH8f27dv5yydOnNhqbhOxDq4LMNXMEHvH18w4QWamPfFBnpiQGASWBTYdsL/sjKN0/+XEBDpvMMNtM4W7YDADGDKZa+YMBMMA3xzNxe9nCy322F06KxkSEoLk5GSIRE13HzZsGBITHXeAoyPhCoDLatR2W3RICNC8ZsZ5MzMA8MD1huzM98fzoKrTCLyalux9Wva1uALggsp6qLX235DQHPkVrnMsuz2j4gPw6Ng4AMDSH8/wp7u6y+RxBqay53EGzoLbZlJr9aht1MFTbva8UEJsgsvM2Ot0YEsZFeePxBAvXCqqxpdHsu2miR7Lssi9ym0zOUbNTICnDJ5yCWrUWuRerUN8kJfQS7IYV95mau6pyb1wML0cp3Mr8fq2i1g7d1C3H9PkcQamfhHrc5dJ4GashL9KW03EjnGjDJw9M8MwDB4Zaxhi+8m+DNSqLVvc2FVXaxtRo9aCYRxna4NhGEQbZzRllDrXVhO3zdTDxzGyZNYiFYvw2sw+AIBdqSUWycCZPc7Akvbu3Yv//ve/OHHiBAoLC7Flyxbccsstbd720UcfxYcffog1a9Zg0aJFVlmPI/H3lCGvoh6lNQ2IdJD0MXEtjVo9qhoMb+rOXDPDmdE/DO/tvILMslp8figbj42LE3pJyDVmAkK8FQ51FDgmwBPn8quc6kRTjVoLVb1hC9LVMzMA0K+HEkFecpRUq3EsswLX9wzo1uN1qWZGq9Vix44d+PDDD1FdXQ0AKCgoQE2NecPBamtrMWDAAKxdu7bD223ZsgWHDx9GWFhYV5brlHoGeQIADmfYZ28LQrjTdhIRA2+FVODVWJ9ELOK3lz7el2Hxo6ddkXOV6zHjWB94Yvydb3o2Vy/j4y6l0gAYMnDjEgIBGLIz3WV2MJOdnY1+/fph5syZWLBgAUpLSwEAq1evxjPPPGPWY02fPh0rVqzArbfe2u5t8vPz8fjjj+Orr76CVOr8L4immpxkmAD+5/kigVdCSNu4LSZfD5lDNGuzhFsGhiHK3x1Xaxvx5WHhm4hy9TL2PpPpWnyvGSc60eTKPWbaM944eFKQYObJJ5/EkCFDUFFRATe3pm/Krbfeip07d3Z7Qc3p9XrMmzcPS5YsQZ8+fSz62I5uclIwGAY4k6eyWDU4IZbEz2Vy8uLf5ppnZz7am4H6RmFP4zQFM471BuqMXYCb6mUc63thTdf1DIBExCCjtBbZ3dxSNDuY2bdvH1544QXIZC1foKKjo5Gfn9+txVxr9erVkEgkeOKJJ0y+j1qtRlVVVYsvZxToJcfgSF8AwN8XigVeDSGtcQ3zApy8+Pdatyb3QISfG8pqGvHVEWGzMzlXHathHic2wLCNXlTVYDfF1N2VTyeZWvFWSDEk2vA+tju1tFuPZXYwo9frodO1/rSRl5fHz2yyhBMnTuDdd9/F5s2bwTCmp6hXrlzZ4nRVRESExdZkb6b0MUzy/esCbTUR+1PuRKMMzCEVi7BgnCE78+HeDDRohMvO5FY4ZjCjdJfyGT1nyc7kUWamTZbaajI7mJkyZQreeecd/s8Mw6CmpgYvv/wybrjhhm4tprl9+/ahpKQEkZGRkEgkkEgkyM7OxtNPP43o6Oh277ds2TKoVCr+Kzc312JrsjdTjHUzhzOuorKOjmgT+9K0zeRamRkAmDUoHD183FBarcY3R3MEWYNGp0dBZQMAx6uZAYBYYyfg9FLzDpbYK75hnoMVY1vb+ERDMHMovbxb27JmBzNvvfUWDhw4gKSkJDQ0NODuu+/mt5hWr17d5YVca968eThz5gxSUlL4r7CwMCxZsgR//vlnu/eTy+Xw9vZu8eWsogM8kBDsBZ2exT+Xul9ARYgl8XOZXCwzAwAyiQiPGo9mf30kR5BO3YWVDdDpWcglIgQ64FYft9XkLL1m8ipce5RBe3oGeaKHjxvUWj0OZ5R3+XHMPh8WHh6O06dP47vvvsPp06dRU1ODBx54AHPnzm1REGyKmpoaXLlyhf9zZmYmUlJS4Ofnh8jISPj7+7e4vVQqRUhICBISEsxdttOa2icYqcXV+PN8ET+9lxB7wGVmXKHHTFtmDgzDa79dQFpJDS4WViMpzLYfrLgtpnBfN4c8TcZlZjKcYJupQaPjT/fRNlNLDMNgbEIgvj6Sg92pJXymxlxdOuwukUgwd+5czJ07t0tPyjl+/DjGjx/P//mpp54CAMyfPx+bN2/u1mO7iil9QvDeP1ew53Ip6ht1cJM5TmMs4txcYWJ2R7wVUkxICML280X45XS+zYMZRy3+5cQGcpkZx99m4k6cusvE8HGnFiPXGp8QhK+P5GBXaileYVmz6mQ5Zm8zrVy5Ep9++mmryz/99FOzt5nGjRsHlmVbfbUXyGRlZVH332v0CfNGDx83NGj02JfWvWpwQiypzEULgJu7JdnQ6PPXlALo9bbdasp1+GCm6Xi2ow/UbT4tuytv1M5uVJw/ZGIRcq7WdTkTZ3Yw8+GHH7Y5HbtPnz7YsGFDlxZBuo5hGP5U05/n6Yg2sQ8sy/ITs13taHZz4xKC4CWXoEDVgGNZtu3WneOgDfM4kX7uEIsY1DXqUFylFno53cIPmKQtpjZ5yCUYHusHANjVxfpPs4OZoqIihIaGtro8MDAQhYWFXVoE6Z6pfQynmnZeKoZWpxd4NYQAdY06NGgMP4uunJlRSMWY1tfw+/lzSoFNn9tRu/9ypGIRn1Vy9K0m6jHTuXHdPKJtdjATERGBAwcOtLr8wIEDNDtJIEOifOHrLkVlnQZHbfzpj5C2cD1m3KRiuMtcew7NLck9AAC/ny1Eo9Z2Hza4IZOONpepuVhjJ+B0By8CpmnZnRtvnNN0NPMqarrQKNHsYOahhx7CokWLsGnTJmRnZyM7OxuffvopFi9ejIceesjsBZDuk4hFGN3T8IOQklsp7GIIAVBW67rHsq81ItYfQV5yqOo12HPZNnVtNWotP+jT0UYZNMefaKLMjNOLCfBAlL87NDoW+9PKzL6/2R+ZlixZgvLycvzf//0fGhsNvywKhQLPPfccli1bZvYCiGVEG6fMcnuzhAipqfuv69bLcMQiBjMGhGHj/kz8kpKPyUnBVn9ObovJ110KLweeWN50osmxMzN5zY7Jk7YxDIMJiUHYdCALuy6V8NuzpjI7M8MwDFavXo3S0lIcPnwYp0+fxtWrV/HSSy+Z+1DEgsKNe8vcixghQuLnMrnQkMmOzBxo2ILfcbG4Syl0czn6sWwOt82UUea4mRmNTo+iKkMn5nAqAO7QhMSmuhlzT7CZHcxwPD09MXToUPTt2xdyOX36EhoX8edTZobYAX6UAW0zAQD69VAiNsADDRo9/jpv/Vlqjl78y+EyM3kV9YLOuOqOIlUD9CwgE4tc+mSfKYbF+MFdJkZJtRrnC8wbEm1SMPPoo48iLy/PpAf87rvv8NVXX5m1CNJ9XJFfXmW9zftZEHKtMn6UAb14A4aM9s3G7Mx3x3Kt3jfFWYKZAE8ZvBQSsCyQXe6YWWeu+DfMR+GQnZhtSS4R4/r4AADAzovmnWoyKZgJDAxEnz59cMMNN2D9+vU4duwY8vPzUV5ejitXrmDr1q149tlnERkZiTVr1qBfv37m/y1It4QqFRCLGDRq9SitceyeDMTx8TUztM3Emz0oHDKxCEcyr+LXM9ZtY+Es20wMw/DZmUwH3WrKo+Jfs3BbTf+YeUTbpGDmtddew+XLl3Hddddh3bp1GDFiBCIjIxEUFISEhATce++9yMjIwEcffYTDhw+jf//+5v8NSLdIxCKEeCsANBWbESIUapjXWoSfOxaMjwcALN96HhW11pt07wzHsjn88WwHLQLmp2XTsWyTcLOZzuRV8rV3pjC5ZiY4OBjPP/88zp49i7KyMpw8eRIHDhxAamoqKioq8MMPP2DatGnmr5xYDFc3QyeaiNDKaZRBmx4bF4dewZ4or23Eim0XrfIcej3r8KMMmuOLgB01mKk0fC8oM2OaYG8F+oR5g2WBfWYc0e5SAbCvry8GDBiAESNGID4+nmZN2IkIOtFE7ARfAOyiQybbI5OIsHJWfzAM8OPJPKvMUyutUUOt1UPEAKE+Cos/vq3xx7MddJupqWEeBTOm4raa9pnRl6nLp5mI/aHMDLEHej3LN2wLoMxMK4OjfDF/ZDQA4N9bzqKu0bJHtbkPM2E+bpCKHf8lvqlxnmMOnKSGeebjtpoOpFs5M0PsE3+iiYIZIiBVvQY644k6XyoAbtMzUxPQw8cNuVfr8fZfly362M5S/MuJCfAAwxh+rq5asc7IGvR6FgWVhh4zlJkx3YBwH/h7yFCjNv04PgUzToTLzORSATAREFf86+MudYrMgDV4yiVYcWtfAMCmg1koMTZVs4Tcq85T/AsYhnWGKQ2vbRkONqOptEaNRp0eYhGDUKXjb/nZiljEYKxxVpOp6JXGiXBdgAsq6/lPxoTYWpmx+NePsjIdGp8QhEGRPtDpWfxw0rQ+XqbgMzP+zhHMAI47o4k7WRrirYCEAnuzcHUzpurSv65Wq8WOHTvw4Ycforq6GgBQUFCAmhrH+kFzNiHeCkhEDDQ6FiXVlvukR4g5uJNMAVT826k7h0UCMDTSs1SzS65mxpnmAMU56IymzDLD9yLKiQJLWxndMxBiM5oMmh3MZGdno1+/fpg5cyYWLFiA0lJDtfHq1avxzDPPmPtwxILEIgZhxn1ZLtVMiK2V08Rsk93UPxSecgmyy+twOKPcIo/JbTM7S80M0Cwz42DbTFnG9UYbj5cT0yndpHjo+hiTb292MPPkk09iyJAhqKiogJtbU+R/6623YufOneY+HLGwphNNVDdDhFFGPWZM5i6T8GMOvjmW2+3HU2t1/FBDRx9l0FxMgGNuM2WWG4KZGH8KZrpi4cSeJt/W7GBm3759eOGFFyCTtXyhio6ORn5+vrkPRyyMTjQRoXFdO6nHjGnuGmrYavrzXFG3uwLnV9SDZQF3mdipRklwvWayy+scauAkZWZsx+xgRq/XQ6dr/cOUl5cHLy8viyyKdB1/ooka5xGB8DUzlJkxSb9wJfqEeaNRp8dPp7r3gZAbxhjp5+5UzUzDlAqEeCug1bM4ZKHtOGtjWZYPZmICnCdLZq/MDmamTJmCd955h/8zwzCoqanByy+/jBtuuMGSayNdEO5HjfOIsJpqZigzYyquEPjbozndagyXbtyG4QpmnQXDMJjY23C6ZceFYoFXY5rSGjVqG3UQMc615WevzA5m3nzzTRw4cABJSUloaGjA3XffzW8xrV692hprJGbgtpmo1wwRCk3MNt/MgWFQSEVIK6nByZyKLj8ON4yRK5h1JpOSggEAOy+WOEQn4Kyypk7McolY4NU4P4m5d4iIiMDp06fx3Xff4fTp06ipqcEDDzyAuXPntigIJsIINwYzhaoGaHV66m1AbK6shjIz5vJWSHFjvzD8eDIP3x7NxeAovy49jrNmZgBgZKw/3GViFFU14HxBFfr2UAq9pA41bTE5X2Bpj8x6p9NoNIiLi0NaWhrmzp2LN954A+vWrcODDz5IgYydCPKSQyYWQadn+VMNhNhKo1aPqgbDrCGqmTHPXcMiAAC/nSlEjbpr85oynDgzo5CKMbpnAADgbwfYasoynmSiHjO2YVYwI5VK0dBAb5D2TCRi+IFm1GuG2FpFnWGLSSxi4K2QCrwaxzI4yhc9fNxQr9HhXL7K7Pur6jR8VizWCTMzADCpt2GracdFxwlmoulYtk2YvQexYMECrF69GlqtZSe9EsuhXjNEKKp6DQBDwyuRGd07iaHItVewIQi5UmJ+P5X0MsN9gr3l8JSbXUHgECYkBoFhgPMFVSiotO8Pa1z3X9pmsg2zf+KPHTuGnTt34q+//kK/fv3g4dHyG/XTTz9ZbHGka8Kp1wwRSPNghpgvPsgTu1JLuxTMcFtMzlgvw/H3lGNwpC+OZ1dg56USzBsRJfSS2sSyLLLLqceMLZkdzPj4+GD27NnWWAuxEJqeTYRSZQxmvBXOmRmwtvggQyCS3oVOt85c/NvcxN7BOJ5dgR0Xiu02mCmpVqOOO5btJNPL7Z3ZrzibNm2yxjqIBTVtM1FmhtgWl5nxpsxMl3DBTFpxVzIzhvs4Y/Fvc5OTgrB6+yUcSi9HjVprl1tqmcaTTOG+7pBJ6ESpLdC/shPiGjTlUzBDbKyKgpluiQ80dFEvqmpAdYPGrPumu8A2E2D4+0X7u6NRp8e+y6VCL6dNNMbA9swOaWNiYjpsk52RkdGtBZHu4zIzhap6aHR6SKnXDLERVb3hYADVzHSN0l2KQC85SqvVSC+txcAIH5Pup9Xp+RoNZ8/MMAyDSb2D8cn+TOy4WILp/UKFXlIrTQMmaYvJVswOZhYtWtTizxqNBqdOncL27duxZMkSS62LdEOgpxxyiQhqrR6FlQ2IpF8oYiNVDVzNDAUzXRUf6InSajWulNSYHMzkVtRDo2OhkIoQpnT+nl8TjcHMP5eKodOzENvZyTnKzNie2cHMk08+2ebla9euxfHjx7u9INJ9DMMg3NcN6aW1yKuoo2CG2AydZuq++CBPHMooN+tEU7rxtjEBni5xJH5otC+UblJU1Gmw82IxpvQJEXpJLXCjDCiYsR2L7T9Mnz4dP/74o6UejnRTOM1oIgJoqpmxv6JMR8EVAZsTzGSUcSeZXOPNUyIW4S7jcM73/kmzq1lNej3LN8yLoYZ5NmOxYOaHH36An1/X5okQywunLsBEAJSZ6b6uHM9OL3GN4t/mHhodAzepGOfyq7ArtUTo5fCKqhqg1uohETH86zCxPrM/PiUnJ7coAGZZFkVFRSgtLcW6dessujjSdT2NL4gnsrs+gZcQc3Fzmahmpuu4YCa7vBZqrc6kicvpLnIsuzl/TznuHRmFD/dm4N0daRifENTh4RRb4eplIvzcadCvDZkdzMycObPFD4xIJEJgYCDGjRuHxMREiy6OdN2ExGC88usFHM26isq6Rvi409A/Yn10NLv7grzk8JJLUK3WIqusDgkhXp3eJ6PM9TIzAPDQmFh8digLp/NU2H25FOMTgoReErLKjfUyVKtoU2YHM6+88orFnnzv3r3473//ixMnTqCwsBBbtmzBLbfcAsBwSuqFF17A77//joyMDCiVSkyaNAmrVq1CWFiYxdbgrCL93ZEQ7IXU4mrsSi3BrcnhQi+JuIAq2mbqNoZhEBfkiZTcSlwpqek0mKmobcTVWsOAT1fKzABAgKcc9wyPwif7M/HujjSM6xUoeHamaVq2a30vhGZ2DkwsFqOkpPX+ZHl5OcTiztOhzdXW1mLAgAFYu3Ztq+vq6upw8uRJvPjiizh58iR++uknpKam4uabbzZ3yS5rcpJhwuzfF+x/wixxfDo9i2o1t81EBcDdYU4RMFf8G6ZUwF3mev/uD4+NhVwiQkpuJfallQm9HL77Lw2YtC2zf/LbqxpXq9WQyczbypg+fTqmT5/e5nVKpRJ///13i8s++OADDBs2DDk5OYiMjDTruVzR5KRgfLDrCvaklpq8905IVzXvWEvbTN3DBzMmFAFzxb+xLrbFxAnyUmDu8Ch8eiAT7+5Mw+ieAYJmZ6jHjDBMDmbee+89AIYU6CeffAJPz6ZfHJ1Oh71791q9ZkalUoFhGPj4+LR7G7VaDbVazf+5qqrKqmuyZ/16KBHirUBRVQMOppfbxX4ycV7cSSZ3mZi6TndTfKDpmZmmAZOu++b5yNhYfHkkGyeyK7D5YBbuvy5GkHXo9SyyrxpqZuhYtm2ZHMysWbMGgCEzs2HDhhZbSjKZDNHR0diwYYPlV2jU0NCA5557DnfddRe8vb3bvd3KlSuxfPlyq63DkYhEDCYlBeHLwzn4+0IxBTPEqqpolIHFcJmZjNKaTjvccjOZXDUzAwDB3go8OjYO7+1Mw/JfL6Cgsh7Lpve2eQPBAlU9GrV6SMUMwnwUNn1uV2fyx6fMzExkZmZi7NixOH36NP/nzMxMpKam4s8//8Tw4cOtskiNRoM5c+aAZVmsX7++w9suW7YMKpWK/8rNzbXKmhzF5CRDZ8y/LxRDr7efxlLE+fATs+lYdrdF+BmmLau1euR10vgyg8/MuG4wAwCLJ/XEkqkJAICP92Xi/746ifpGnU3XwHX+pWPZtmf2v/auXbvg6+trjbW0iQtksrOz8ffff3eYlQEAuVwOb2/vFl+ubESsHzzlEpRWq3E6r1Lo5RAnxs1losxM94lFDGKNNRcdbTVpdHrkGLc14oJce1uDYRgsGB+Pd+8cCJlYhO3ni3DXx4dRqLJd49ArJdUAwH/viO10qfQ9Ly8PW7duRU5ODhobG1tc9/bbb1tkYUBTIJOWloZdu3bB39/fYo/tKuQSMcYmBGLbmUL8faEYyZG2C0SJa1HRKAOLig/yxKWialwpqcHE3sFt3ia7vA5aPQt3mRgh3rStAQAzB/ZAqNIND31+HCm5lZjw5h48Ni4OD4+JhUJq3UMQ5woMNZpJYUqrPg9pzexXnZ07d+Lmm29GbGwsLl26hL59+yIrKwssy2LQoEFmPVZNTQ2uXLnC/zkzMxMpKSnw8/NDaGgobrvtNpw8eRK//fYbdDodioqKAAB+fn5mn5xyZVOSgvlg5tlp1NiQWAc1zLMsU45nN+/8K3R/FXsyLMYPW/5vFJ778QyOZVXg7b8v47tjuVh2QyJu7BdqtX+rc/kqAIbDF8S2zN5mWrZsGZ555hmcPXsWCoUCP/74I3JzczF27FjcfvvtZj3W8ePHkZycjOTkZADAU089heTkZLz00kvIz8/H1q1bkZeXh4EDByI0NJT/OnjwoLnLdmnjEoIgETFIK6nhjw0SYmlUM2NZphzPPptnePPsFdR5l2BXExvoif89MhLv35WMMKUC+ZX1WPj1KSz54YxVBlM2aHRIMwaefXu4dnmDEMwOZi5evIh7770XACCRSFBfXw9PT0+8+uqrWL16tVmPNW7cOLAs2+pr8+bNiI6ObvM6lmUxbtw4c5ft0pRuUgyPNQwBpQZ6xFqoZsaymmdm2nvzPZhuaBI3Io624NvCMAxmDAjDzqfH4cmJPSEWMfjhRB7e2ZFm8ee6WFgFnZ5FgKeMtvwEYHYw4+HhwdfJhIaGIj09nb+urEz47oukbZONe+7/XLKf6bLEuaiMR7Npm8kyYgI8IGKA6gYtSqvVra6vUWtx2piZGUXBTIfcZGIsntwLK27pCwB4d2cafjiRZ9Hn4Opl+oQpactPAGYHMyNGjMD+/fsBADfccAOefvppvP766/jXv/6FESNGWHyBxDKGxhgyM+cKVFZJsRJCc5ksSy4RI9LPMKwwrY26mWOZV6HTs4j0c0e4Lw01NMVdwyLx2Lg4AMDSH8/g4BXLfQA/ZwwsaYtJGGYHM2+//TbfT2b58uWYOHEivvvuO0RHR2Pjxo0WXyCxjJ5BXpCKGVQ3aJFXYbujisR1NNXM0GkmS+lrLCTdm1ba6jpui4myMuZZMiUBN/UPhVbP4pEvTyCtuNoij3uugIp/hWRWMKPT6ZCXl8fPRfLw8MCGDRtw5swZ/Pjjj4iKirLKIkn3ySQi9DQWCV4odN0RD8R6qGbG8qb3DQUAbDtT2CqjejC9HAAwkoIZs4hEDN68fQCGRPmiukGLez89ityrHTcm7Ixaq8NlY1DUh45lC8KsYEYsFmPKlCmoqKiw1nqIFSWFGdKfFwoomCGWR0ezLW9CYhDcZWLkVdQjJbeSv7yitpH/UELBjPkUUjE+vncI4gI9UKhqwNxPjqBI1dDlx7tcVAONjoWPuxThvm4WXCkxldnbTH379kVGRoY11kKsLCnUEMycp2CGWBjLsjSbyQrcZGK+Yd5vZwr5yw9nlINlgV7BngjyopMzXeHrIcNXD45ApJ87cq7W4e5PDrdZaG0KboupLxX/CsbsYGbFihV45pln8Ntvv6GwsBBVVVUtvoj94jIzF2mbiVhYg0aPRp0eAGVmLO2m/k1bTdx8NW6LaVRcgGDrcgYhSgW+fmg4wpQKZJTWYt7GI6iobez8jtc4a2yW14eKfwVjdjBzww034PTp07j55psRHh4OX19f+Pr6wsfHx6Yzm4j5ehszM/mV9aisM/8XlpD2cPUyYhEDD5l1W8a7mrG9AuEll6CoqgEncgxb/FzxL20xdV+4rzu+fmgEgrzkuFRUjfs2HYVaa96AyvPU+VdwZh872LVrlzXWQWxA6SZFhJ8bcq/W40JhFX2qIxbT/CQTpdktSyEVY3JSMH46lY/fThcg0s8d6aW1YBhgRAwFM5YQHeCBrx4cjts/PITTeSqs+TsNS6ebNvpFo9PjYpGh+LcvFf8KxuxgZuzYsdZYB7GRpFBvQzBTQMEMsRzqMWNdNw0IxU+n8vH7uSL0D/cBYHjjVLrTv7el9Az2wqpZ/fHolyfw4d50TEgMwjBjf66OpBXXoFGrh5dCgih/6vcjFLO3mQBg3759uOeeezBq1Cjk5+cDAL744gu+mR6xX0mhhk8OdDybWJKKTjJZ1fXxgVC6SVFarcba3YbhvNRfxvKm9Q3BbYPDwbLA09+noEat7fQ+XPFvnzBvykoKyOxg5scff8TUqVPh5uaGkydPQq02VH+rVCr85z//sfgCiWXR8WxiDdRjxrpkEhGm9jGcasooNQyLHRVPmVVreHlGEnr4GLbjX/v1Qqe35yZl0xaTsLp0mmnDhg34+OOPIZU2vXBdd911OHnypEUXRyyPC2aulNSgQWNekRsh7VHV0cRsa7upfxj//xIRg6HRdODCGrwUUrw1ZwAYBvjueC7+Ol/U4e25YKZfOAUzQjI7mElNTcWYMWNaXa5UKlFZWWmJNRErClMqoHSTQqtncaWNeS+EdEVVAw2ZtLZRcf7w85ABAJIjfeAuo7ER1jIi1h8PjY4FACz76SyulLQ98kCnZ/kte+r8Kyyzg5mQkBBcuXKl1eX79+9HbGysRRZFrIdhGPShrSZiYU01M/QGay0SsQg3DzBkZ8YnBgm8Guf39JRe6B3qjfLaRsxad5A/Dt9cemkNGjR6eMjEiA3wEGCVhGN2MPPQQw/hySefxJEjR8AwDAoKCvDVV1/hmWeewWOPPWaNNRIL4zoBUxEwsRR+lAFtM1nV0umJ+GjeYD5rQKxHLhHjqweHY3CUL6oatJj/6VH8eCKPv16t1fFbUElh3hCJqPhXSGZ/jFq6dCn0ej0mTpyIuro6jBkzBnK5HM888wwef/xxa6yRWBgVARNLU9HRbJtQSMWY0idE6GW4DD8PGb56cDie/v40tp0pxNPfn8bx7AqU1ahx4EoZ6hoNdYfccXkiHLODGYZh8Pzzz2PJkiW4cuUKampqkJSUBE9PT2usj1gBH8wUVkGvZ+kTBek27jQT1cwQZ6OQivH+ncmI8HXHhj3p+OZoDn9doJccExKC8PAYypQJrcsb3DKZDF5eXvDy8qJAxsHEBXpCJhGhRq1FbkUdovxpr5d0j4qGTBInJhIxWDo9EXGBHth6ugDDY/wwLiEISaG0vWQvzK6Z0Wq1ePHFF6FUKhEdHY3o6GgolUq88MIL0Gg01lgjsTCpWISEYC8AtNVELKOq2TgDQpzV7UMi8MUDw7FwQk/07aGkQMaOmP3K8/jjj+Onn37CG2+8gZEjRwIADh06hFdeeQXl5eVYv369xRdJLC8p1Btn81W4UFiF6f1ChV4OcXDUNI8QIiSzg5mvv/4a3377LaZPn85f1r9/f0REROCuu+6iYMZBUBEwsRSdnkU19ZkhhAjI7G0muVyO6OjoVpfHxMRAJpNZYk3EBrhg5nh2Bcpr1AKvhjiymoam+TV0NJsQIgSzg5mFCxfitdde42cyAYBarcbrr7+OhQsXWnRxxHoGhPsgLtADqnoNFv/vNPR6VuglEQfFHct2k4ohk3Rpdi0hhHSL2dtMp06dws6dOxEeHo4BAwYAAE6fPo3GxkZMnDgRs2bN4m/7008/WW6lxKJkEhHWzR2MmWv3Y+/lUqzfk44F4+OFXhZxQFQvQwgRmtnBjI+PD2bPnt3isoiICIstiNhOQogXXr25L5798Qze+isVQ6J8MTzWX+hlEQdDowwIIUIz+9Vn06ZN1lgHEcjtQ8JxOLMcP53Mx+PfnMLvT45GgKdc6GURB1JF3X8JIQKjDW4XxzAMVtzSFz2DPFFSrcbjX5/itw0IMYWK5jIRQgRmdjBTXl6OBQsWICkpCQEBAfDz82vxRRyPu0yCdXMHwU0qxqGMckxbs7fNCbGEtIVqZgghQjN7m2nevHm4cuUKHnjgAQQHB4NhqAOiM+gZ7IWvHhqOxd+lILu8Dnd/fAQPXB+DJVMToJCKhV4esWNNNTMUzBBChGF2MLNv3z7s37+fP8lEnMegSF/8/sRorNh2Ed8czcHG/ZnYn1aGLx4chiAvhdDLI3aqqp4a5hFChGX2NlNiYiLq6+utsRZiBzzkEqyc1Q8b5w9BgKccqcXVeOSLE2jQ6IReGrFTKprLRAgRmNnBzLp16/D8889jz549KC8vR1VVVYsv4hwm9g7G94+OhNJNilM5lfj3lrNgWedsrMeyLIqrGpz272dtVDNDCBGa2cGMj48PqqqqMGHCBAQFBcHX1xe+vr7w8fGBr6+vNdZIBBIT4IG1dw+CWMTgp5P5+GhvhtBLsopfUgow/D87MX/TMajqhDvJ9fbfl/HdsRzBnr+rqGaGECI0s/PCc+fOhVQqxddff00FwC7g+p4BeOmmJLy89TxWbb+EnsGemJAYLPSyLGrzwSwAwN7Lpbh57X58fO8Q9Ar2suka8ivr8d7ONEhEDGYMCIO7zHG2bKjPDCFEaGa/Yp47dw6nTp1CQkKCNdZD7NC9I6Nwqaga3xzNwRPfpOCXhdchLtBT6GVZxJWSaqTkVkIsYhDirUB2eR1uXXsAb98xEFP7hNhsHZV1jQAArZ5FSk4lRsUH2Oy5u0vFFQBTnxlCiEDM3mYaMmQIcnNzrbEWYqcYhsHym/tgeIwfatRaPPvDGacZTPn9iTwAwPiEIGxdeB1GxPqhtlGHR744gU/22W5brVbdVGB9LKvCZs9rCXzNjDsFM4QQYZgdzDz++ON48sknsXnzZpw4cQJnzpxp8WWOvXv3YsaMGQgLCwPDMPj5559bXM+yLF566SWEhobCzc0NkyZNQlpamrlLJhYgk4iw5o6B8JCJcSK7Al8dyRZ6Sd2m1emx5WQ+AOC2weHw95TjiweG475R0QCA13+/iF2XSmyyllq1lv//49lXbfKcltCg0aFRqwdAp5kIIcIxO5i54447cPHiRfzrX//C0KFDMXDgQCQnJ/P/NUdtbS0GDBiAtWvXtnn9G2+8gffeew8bNmzAkSNH4OHhgalTp6KhocHcZRMLCPNxw3PTEwEAq7enoqDSsY/o70srQ0m1Gn4eMkxIDAIASMUivHJzH8wdHgmWBZ789hSyy2utvpaaZsHMyewKaHV6qz+nJXD1MiIG8JRTMEMIEYbZrz6ZmZkWe/Lp06dj+vTpbV7HsizeeecdvPDCC5g5cyYA4PPPP0dwcDB+/vln3HnnnRZbBzHdPcOj8POpfJzMqcSLP5/DJ/OHOGwR+A/GLaaZA8Mgk7SM61+akYQLhVU4lVOJR744gS3/dx3cZNbrhNw8M1PbqMOlomr07aG02vNZSvOTTI76c0AIcXxmZ2aioqI6/LKUzMxMFBUVYdKkSfxlSqUSw4cPx6FDh9q9n1qtpt43ViQSMVg9uz+kYgY7L5Vg29lCoZfUJZV1jfj7QjEAwxbTteQSMdbPHYwATxkuFVVj6U9nrNqHpnlmBgCOZTnGVhP1mCGE2IMuTc3+4osvcN111yEsLAzZ2YbaiXfeeQe//PKLxRZWVFQEAAgObnkMODg4mL+uLStXroRSqeS/IiIiLLYmYtAz2AsLxscDAF7Zeh4VtY0Cr8h8W08XoFGnR1KoN/qEtZ0BCVEq8IGxz84vKQX8EW5r4IIZLrlx3EGKgCvraGI2IUR4Zgcz69evx1NPPYUbbrgBlZWV0OkMpzB8fHzwzjvvWHp9Zlu2bBlUKhX/RSevrOOxcXHoGeSJsppGvPFnqtDLMRu3xdRWVqa5EbH++PcNvQEAK/+4hNyrdVZZD7fN1D/cB4ChCNgROhJzwYwPnWQihAjI7GDm/fffx8cff4znn38eYnFTDcGQIUNw9uxZiy0sJMTQ46O4uLjF5cXFxfx1bZHL5fD29m7xRSxPLhHjP7P6AQC+O5aD1KJqgVdkutSiapzJU0EiYjBzYFint//XddG4Pj4AjVo9/vP7RausqcZ4NPu6OH9IxQyKq9TIq7D/AusKY38cX3eZwCshhLgys4OZzMzMNk8tyeVy1NZa7tRHTEwMQkJCsHPnTv6yqqoqHDlyBCNHjrTY85CuGxrth+l9Q6BnDceYHcUPJwzZuom9g+DvKe/09gzD4MWbkiBigD/OFeFgepnF18RlZvw8ZOhnLPx1hLoZyswQQuyB2cFMTEwMUlJSWl2+fft29O7d26zHqqmpQUpKCv94mZmZSElJQU5ODhiGwaJFi7BixQps3boVZ8+exb333ouwsDDccsst5i6bWMnS6YmQihnsvVyKPZdLhV5OpzQ6PX7ie8uYXk+VEOKFe0YYCtxf/fUCdBZuGsgFM55yCYZE+wFwjOZ5XGbGhzIzhBABmRzMvPrqq6irq8NTTz2FBQsW4LvvvgPLsjh69Chef/11LFu2DM8++6xZT378+HEkJyfzmZ6nnnoKycnJeOmllwAAzz77LB5//HE8/PDDGDp0KGpqarB9+3YoFAqznodYT5S/B+4dGQ0A+M+2ixZ/k7e0nRdLUF7biEAvOcYnBJp138WTekHpJsWlomp8a+GBkNVcMKOQYEiUYWDrcUfIzBiPZvtSZoYQIiCTg5nly5ejpqYGDz74IFavXo0XXngBdXV1uPvuu7F+/Xq8++67Zvd+GTduHFiWbfW1efNmAIb0/quvvoqioiI0NDRgx44d6NWrl1nPQazv8QnxULpJkVpcjf8dt++C6++N65s1qAckYvMSk74eMjw12fDz9+afqRadsM1lZjzkEgw2BjNpJTV2f1KskmpmCCF2wORX8+YnK+bOnYu0tDTU1NSgqKgIeXl5eOCBB6yyQGL/fNxleGJiTwDAW39dbtUzxV4UVzVgV6phPMGcIV07sj93eCR6BXuiok6Dd3dabrRG820mf0854gI9AAAnsu17q6milmpmCCHCM+uj6bUdPt3d3REUFGTRBRHHNG9EFKL93VFWo8ZHe9KFXk6bfjiRBz0LDI327fLUb4lYhBdvSgIAfH4oC2fzVBZZG3eayUNmaMo9lKubsfM5TZVUM0MIsQNmBTO9evWCn59fh1/ENckkIjw7zTC36csjOfzwQXvBsiy/xXR7F7MynNE9A3Fjv1Bo9SwW/y8FDRpd53fqRPPMDAC+CNjem+dV1FHNDCFEeGbNZlq+fDmUSvufF0OEMSUpGEFecpRUq7ErtQRT+7TfD8jWjmZeRVZ5HTxkYtzYL7Tbj7filr44mnUVV0pq8Mb2VLw0I6nLj6XTs6g3BkQeckPvpqHRhrqZM3mVaNDooJBaby5UVzVodPy6KTNDCBGSWcHMnXfeSdtKpF0SsQi3DuqBD/dk4PvjeXYVzPzvuKHj7039w+BhgenOvh4yvHFbf9y/6Rg+PZCJSb2DMCo+oEuPVdvYVGPkqTCsLdLPHWFKBQpUDdhxsRg39e+8uZ+tcUMmxSIG3gqamE0IEY7J20w0EZeY4nbjeIBdqSUorVYLvBqD6gYNfjcOxJwz1HKzusYnBOHu4ZEAgGe+P82/uZurpsEQzEjFDOQSQwaGYRjMGmT4t/zumH2eEON6zChpYjYhRGBdOs1ESHvig7wwMMIHOj2LX1LyhV4OAODX04Wo1+gQF+iBQZE+Fn3s52/ojSh/dxSoGvDK1vNdeozmx7Kbu32IIZjZf6UM+ZX2N9qATjIRQuyFycGMXq+nLSZiEm544/fH8wQPghs0Onx6IBMAcMfQCItnEDzkErw9ZyBEDLDlVD5OdOH0EXeUnTvJxIny98CIWD+wLPCjcTCmPVHVU48ZQoh9MHucASGdmdE/DDKJCKnF1TiXXyXoWt7++zKulNTA30OG280YX2COwVG+uMFYVLznsvlzm2qNx7I926jlucO4Lfa/47nQ21l3ZTrJRAixFxTMEItTukv54l9uqKMQDmeU4+N9GQCAVbP7w9fDehmE64zFv4czys2+L5+Zkbc+sTStTyi85BLkVdR36bGtieYyEULsBQUzxCq4raZfThdAre1+HxZzVTdo8PT/ToNlgTuGRGByUrBVn29ErD8AICWn0uy+M3yPGUXrDIebTIybBxpOMn1nZ6Mi+InZbpSZIYQIi4IZYhXXxwcgxFuByjoNdl4ssfnzL//1AvIr6xHh54YXu9EDxlTR/u4I9pajUafHSTNHENTwDfPa7iXDjV7441yRRedBdRc/l8mKGS9CCDEFBTPEKsQiBrMG9QDQNNzRVv48X4QfTuSBYYC35wxssxbF0hiGwUhjdsbc7aD2CoA5/cOVSAzxQqNWj62n7eOEGNBUM0OnmQghQqNghljNbONW0760MlQ32Caj0KjV44WfzwEAHhkTx884soURfDBj3omm9o5mcxiG4UcwcM3/7AFNzCaE2AsKZojVxAV6IibAA1o9iwNXbFO8uudyKUqr1Qj0kmPx5J42eU4OXzeTW4n6RtPrZq6dy9SWW5N7QCpmcDZfhYuFwp4Q41RQzQwhxE5QMEOsamyvQADAnsu2qZvh+rHcmtyD76ZrK1H+7gjxVhjqZnJMr5vhJ2Z3EMz4ecgwpif3b1navYVaCE3MJoTYCwpmiFWNTTC+AaeWWr2BXmVdI3ZeKgYAvl7HlhiGwcg48+tmmk4zdVzbwx3/Ppgu/BFtlmX500y+HpSZIYQIi4IZYlUjY/0hl4hQoGpAWkmNVZ/r1zOF0OhYJIV6IzHE26rP1Z4RsYYaHbOCmcaOTzNxuGDmWOZVNGr1XVyhZdSotdAam/hRzQwhRGgUzBCrUkjFGG6sJdmdat2tJm6LSYisDKcrdTPVDR2fZuL0CvaEv4cM9RodUnIru7XO7uKyMgqpCAqpbbfzCCHkWhTMEKsb18v6tR7ppTVIya2EWMRg5kDhgplIP3eEKRXQ6FiT62ZMKQAGWm5jHUw3f2yCJfHdf90oK0MIER4FM8TqxhnrZo5lVvBv3Ja25aSh/8rYXoEI9JJb5TlMwTAMn505ZGJtS2dHs5sbFWcfdTOV1GOGEGJHKJghVhcT4IEIPzc06vRWeRPW61lsOWUIZoTcYuKMMLN5Xo1ZwYzhsU/lVJh1/NvSKqjHDCHEjlAwQ6yOYRiM6xUEwDpHtA9nliO/sh5eCgkm9bbuDCZTcMHM6bxK1DV2nIliWRa1xqDEq5PTTIDh+De3jXU827zmfJZEJ5kIIfaEghliE9xW024rHNH+ybjFdFP/MLsoRo3wc0MPHzdD3Ux2ZYe3VWv10BlPBZmSmWEYBqPs4Ig2l5lRUs0MIcQOUDBDbGJknD9kYhHyKuqRXlprsceta9Tij7OFAIDZdrDFBBgCjuExhiPax7I6zp7UNKshcjcxEOO2mg5eEa4ImM/MUM0MIcQOUDBDbMJdJsEw4xu8JU817U4tRW2jDhF+bhgc5Wuxx+2ufuFKAMD5go5HD9Twx7LFEIkYkx6bO9F0Nl8FVb0wU7RpLhMhxJ5QMENshhttYMl+M78bszI39A0Fw5gWDNhC3x5cMKPq8HbmFP9yQpVuiA3wgJ4FjmYKUzdDE7MJIfaEghliM1zdzJHMqxY5idOg0eGfS4bAaHq/0G4/niX1DvUGwwCFqgaU1ajbvZ2pPWauJXS/GcrMEELsCQUzxGbigzwR6CVHo1bfacbCFHsvl6KuUYcwpQIDjNs69sJTLkFMgAeAjrea+FEGJpxkao7rN2NqLxtLo8wMIcSeUDBDbIZhGD7oOJPX/WDmj3NFAIBpdrbFxOkT1vlWEz8xu5NRBtfiZkBdKqruMPNjLTQxmxBiTyiYITbVr4cPAEPxaneotTrsuGCYkH1Dv5DuLssq+oYZhl2ez+8gM9OFmhkA8PeUo3eo4fFtnZ3R6vSoMhYu02kmQog9oGCG2FR/PjNT2a3HOXilHNVqLYK85BgUaT+nmJrjioDPdZSZaTBtYnZbrovjBnhab+ZVW5qfoFK6UTBDCBEeBTPEprg3+IyyWlQ3dP1YMXeKaVrfEJOPNNtaH2NmJru8rt0j1F05zcSZ0NvQVXl3agnfeM8WuHoZL4UEEjG9hBBChEevRMSmAr3kCFMqwLKd92Bpj0anx1/GLabpfe3rFFNzPu4yhPu6AQAutPN37eppJgAYGu0HL4UE5bWNSMmt7PI6zaWqp5NMhBD7QsEMsbl+3dxqOpReDlW9Bv4eMr4Rn73isjPtFQHzp5m6EMxIxSK+d88/l4q7uELzVdRS919CiH2hYIbYXP9wHwBdP9HEnWKa0icEYjvdYuL0NZ5oOtdOwTN/mqkLwQwATDRuNe28aPkBnu2poJNMhBA7Q8EMsTmuCLgrJ5q0Oj3+Om8IZuz1FFNzTZ2ALb/NBADjegVBxBiOaOdV1HVtkWaiuUyEEHtj18GMTqfDiy++iJiYGLi5uSEuLg6vvfaaxacuE9vqZ3yDzy6vg6rOvCLgI5lXUV7bCB93KUbE+ltjeRbVp4dhmym9tAZ1jdpW13enABgAfD1k/EwqrhuytVFmhhBib+w6mFm9ejXWr1+PDz74ABcvXsTq1avxxhtv4P333xd6aaQbfNxliPRzB2B+duaHE3kADIW/Ugc4SRPkpUCglxx6FrhYWN3qen7QZBeOZnMm9g4GYLutpsp66v5LCLEvdv1ucPDgQcycORM33ngjoqOjcdttt2HKlCk4evSo0Esj3cQXAedXmnwfVb2GP5I9Z0i4NZZlFX07KALuTgEwZ2KioW7mUHo5v21lTTSXiRBib+w6mBk1ahR27tyJy5cvAwBOnz6N/fv3Y/r06e3eR61Wo6qqqsUXsT/9jVtNZ80oAv71dAHUWj16BXtiYISPlVZmeXzzvDayUHzNjJmzmZqLD/JEpJ87GnV67L9i/cGT3GkmyswQQuyFXQczS5cuxZ133onExERIpVIkJydj0aJFmDt3brv3WblyJZRKJf8VERFhwxUTU3XlRNP/jucCAOYMibDLWUzt6cOfaGodWPM1M2bOZmqOYRhMSORONVn/iDbVzBBC7I1dBzP/+9//8NVXX+Hrr7/GyZMn8dlnn+HNN9/EZ5991u59li1bBpVKxX/l5ubacMXEVH2NhbH5lfUmDUq8WFiFM3kqSEQMbk3uYe3lWRT3d00rqYZaq+Mv1+r0aNDoAXRvmwkAJhnrZv65VAq9lbsBc92M6TQTIcRe2HUws2TJEj47069fP8ybNw+LFy/GypUr272PXC6Ht7d3iy9if7wUUsQGegAwrQiYy8pM6h0Mf0+5VddmaT183KB0k0KjY5FWXMNfXtvYFNh09TQTZ1iMHzzlEpTVqLs9xLMzFVQzQwixM3YdzNTV1UEkarlEsVgMvV4v0IqIJZlaN6PW6vDzqXwAwB1DHW/bkGEYPjvTvG6G22KSiUWQSbr3qyiTiDCmVwAA6241NWh0fDaJamYIIfbCroOZGTNm4PXXX8e2bduQlZWFLVu24O2338att94q9NKIBfQzsW5mx4USVNRpEOwtx+ieATZYmeVxnYCbZ01q1d0/lt3c+ARD3cyey9abos1lZSQipttbY4QQYil2/Wr0/vvv48UXX8T//d//oaSkBGFhYXjkkUfw0ksvCb00YgFNnYArO7wdt8V02+Bwh53SnMQfz24qAq6xwEmm5sYY5zSdyVehorYRvh6W3wZqfpLJkYqwCSHOza7fGby8vPDOO+8gOzsb9fX1SE9Px4oVKyCT0V69M+gT5g0RAxRXqZFfWd/mbQoq67E3zZBpuH2w420xcbgTTZeKqqAzFujWWuAkU3PB3gokBHuBZWG1I9qV9XSSiRBif+w6mCHOzV0m4TMWd398uFUflgsFVbh/0zGwLDA8xg/RAR5CLNMiYgI84C4To0GjR0apoQi4u3OZ2sJtw+1Ls85WE81lIoTYIwpmiKBWzeqPHj5uyC6vw6x1B/HF4Wzo9CzW707HzLX7kVpcDX8PGZbd0FvopXaLWMSgd2jLrabuTsxuC7fVtC+tzCozzKjHDCHEHlEwQwTVt4cS2564HpN6B6FRp8eLP5/Ddav+wertl6DRsZicFIw/F49xqI6/7ekT1vJEkzUyM8Ni/CCTiFCoakB6aU3ndzBTeY0hmPGjYIYQYkcomCGC83GX4eN7h+CFG3tDImJQVNUAD5kYb9zWHx/NG4wAB+sr054+YddmZix7mgkAFFIxhsf4AQD2XLZ83UxJdQMAINjbOb4nhBDnYNenmYjrYBgGD46OxZBoP/x5vgh3D4tEhHGytrPgioDPF6jAsmzTaSa5ZetPxvQMxL60MuxLK8UD18dY9LGLqwzdmoO8FRZ9XEII6Q4KZohdGRjh4xRbSm3pFewFqZhBVYMWeRX1zbaZLJeZAYDRvQKA34HDGeVQa3WQSyz3+CXVxmDGizIzhBD7QdtMhNiITCJCzyAvAIbsTNM2k2U/UyQEeyHIS44GjR7Hsyos+tglVdw2E2VmCCH2g4IZQmyoed1MrZWCGYZhMLqn4VTTXgse0dbrWZQaMzMUzBBC7AkFM4TYUN8eXN1MFWqNR7OtMRaAm9O0z4JFwFfrGqHVs2AYIMCTTjMRQuwHBTOE2FDz49nVVsrMAMD18YZg5kJhFZ9N6a5i4xaTv4fcYcdKEEKcE70iEWJDvUO9wTCGQtqc8loA1snM+HvK+Und+69YZquppIrbYqLiX0KIfaFghhAb8pBLEGMcy1BhHA1grenTfN2MhbaauB4zdJKJEGJvKJghxMa4fjMcSzbNa260cavpaOZVizxecRUV/xJC7BMFM4TYGFc3w7FWZqZ/hA8YBsivrOezKt3B1cxQZoYQYm8omCHExq4NZqxRAAwYgqRexr42p3NVndy6c3zDPMrMEELsDAUzhNhY820mhgHcZdbZZgKAARGG50rJ7X7zPGqYRwixVxTMEGJjfh4yhCkNAYGHTAKGYaz2XAMjfAEAKbmV3X4sGmVACLFXFMwQIoAkY3bGWvUyHG7O1ZlcFfR6tsuPo9ezfDBDmRlCiL2hYIYQAXB1M9Y6ycTpFewJN6kY1WotMspquvw45bWN0FH3X0KInaJghhABJEf6AACCvKyb5ZCIRehnHKFwKqeyy4/DnYai7r+EEHtEr0qECGBsr0C8cVt/vHZLX6s/10Bj4NSduhnq/ksIsWfW3bAnhLSJYRjMGRJhk+fi6ma6E8xQjxlCiD2jzAwhTo4LZi4VVaNBo+vSY1DxLyHEnlEwQ4iTC1UqEOglh07P4lx+15rn8ZkZCmYIIXaIghlCnBzDMN3eaqIeM4QQe0bBDCEugAtmTnU1mKHuv4QQO0bBDCEuINkYzJzuYjDDTcymzAwhxB5RMEOIC+gXrgTDAHkV9SirUZt1X72eRWkNFQATQuwXBTOEuAAvhRTxgZ4AgBQzm+dR919CiL2jYIYQF9HVImDuJBN1/yWE2Ct6ZSLERXCdgE/nVZp1v9Jq6v5LCLFvFMwQ4iL4zExOJTQ6vcn3K6aTTIQQO0fBDCEuIjHEGwGeMlSrtTiUXm7y/egkEyHE3lEwQ4iLEIsYTOkTAgD441yhyffjJmZT919CiL2iYIYQF3JD31AAwF/ni6E1cauJMjOEEHtHwQwhLmR4rB983aUor23E0ayrJt2ntJpqZggh9o2CGUJciFQswuSkYADA9nNFJt2Hy8zQaSZCiL2y+2AmPz8f99xzD/z9/eHm5oZ+/frh+PHjQi+LEIc1vZ9hq+mPc0XQ69kOb6tr1v03yIsyM4QQ+yQRegEdqaiowHXXXYfx48fjjz/+QGBgINLS0uDr6yv00ghxWNfFBcBLIUFptRonciowNNqv3dtepe6/hBAHYNfBzOrVqxEREYFNmzbxl8XExAi4IkIcn0wiwuTewfjpVD7+OFvUYTBD3X8JIY7Arl+dtm7diiFDhuD2229HUFAQkpOT8fHHH3d4H7VajaqqqhZfhJCWuK2m7ecKwbLtbzWV8MW/VC9DCLFfdh3MZGRkYP369ejZsyf+/PNPPPbYY3jiiSfw2WeftXuflStXQqlU8l8RERE2XDEhjmF0zwB4yMQoUDXgdJ6q3duVVNG0bEKI/bPrYEav12PQoEH4z3/+g+TkZDz88MN46KGHsGHDhnbvs2zZMqhUKv4rNzfXhismxDEopGJM6G041fTH2fYb6FGPGUKII7DrYCY0NBRJSUktLuvduzdycnLavY9cLoe3t3eLL0JIazf0NXQD/r2DraZLRYZtWur+SwixZ3YdzFx33XVITU1tcdnly5cRFRUl0IoIcR7jEoLgJhUj92o9fkkpaHX98ayr+ONcERgGmGzM4hBCiD2y62Bm8eLFOHz4MP7zn//gypUr+Prrr/HRRx9hwYIFQi+NEIfnJhPj/8bFAQBe+fU8X+wLGPrLvPjLeQDAHUMi0C9cKcgaCSHEFHYdzAwdOhRbtmzBN998g759++K1117DO++8g7lz5wq9NEKcwqPj4tC3hzcq6zR4fss5frvpqyPZuFhYBaWbFM9OSxR4lYQQ0jGG7ehcphOoqqqCUqmESqWi+hlC2nCxsAo3f7AfGh2Ld+8ciOvjAzD+zd2oatDitVv6Yt4I2tYlhNieOe/fdp2ZIYRYX+9Qbzw+oScA4KVfzuPfW86iqkGLPmHeuHtYpMCrI4SQzlEwQwjBY+Pi0CfMG6p6Df48XwwAeHVmX4hFjMArI4SQzlEwQwiBVCzCm7cPgFRsCF5uGxyOwVE0A40Q4hjsejYTIcR2eod6Y9Ws/th5qRj/vqG30MshhBCTUTBDCOHNHhyO2YPDhV4GIYSYhbaZCCGEEOLQKJghhBBCiEOjYIYQQgghDo2CGUIIIYQ4NApmCCGEEOLQKJghhBBCiEOjYIYQQgghDo2CGUIIIYQ4NApmCCGEEOLQKJghhBBCiEOjYIYQQgghDo2CGUIIIYQ4NApmCCGEEOLQKJghhBBCiEOTCL0Aa2NZFgBQVVUl8EoIIYQQYirufZt7H++I0wcz5eXlAICIiAiBV0IIIYQQc5WXl0OpVHZ4G6cPZvz8/AAAOTk5nf5jOKOhQ4fi2LFjQi+DGNH3w77Q98O+0PfDvgj9/VCpVIiMjOTfxzvi9MGMSGQoC1IqlfD29hZ4NbYnFotd8u9tr+j7YV/o+2Ff6PthX+zl+8G9j3d4GxusgwhowYIFQi+BNEPfD/tC3w/7Qt8P++JI3w+GNaWyxoFVVVVBqVRCpVLZRYRJCCGEkM6Z8/7t9JkZuVyOl19+GXK5XOilEEIIIcRE5rx/O31mhhBCCCHOzekzM4QQQghxbhTMOLi9e/dixowZCAsLA8Mw+Pnnn1tc/8orryAxMREeHh7w9fXFpEmTcOTIEWEW6wI6+3409+ijj4JhGLzzzjs2W5+r6ez7cd9994FhmBZf06ZNE2axLsCU34+LFy/i5ptvhlKphIeHB4YOHYqcnBzbL9YFdPb9uPZ3g/v673//K8yCO0DBjIOrra3FgAEDsHbt2jav79WrFz744AOcPXsW+/fvR3R0NKZMmYLS0lIbr9Q1dPb94GzZsgWHDx9GWFiYjVbmmkz5fkybNg2FhYX81zfffGPDFbqWzr4f6enpuP7665GYmIjdu3fjzJkzePHFF6FQKGy8UtfQ2fej+e9FYWEhPv30UzAMg9mzZ9t4pSZgidMAwG7ZsqXD26hUKhYAu2PHDtssyoW19/3Iy8tje/TowZ47d46Niopi16xZY/O1uaK2vh/z589nZ86cKch6XF1b34877riDveeee4RZkIsz5f1j5syZ7IQJE2yzIDNRZsaFNDY24qOPPoJSqcSAAQOEXo5L0uv1mDdvHpYsWYI+ffoIvRwCYPfu3QgKCkJCQgIee+wxfgQKsS29Xo9t27ahV69emDp1KoKCgjB8+PAOt2qJ7RQXF2Pbtm144IEHhF5KmyiYcQG//fYbPD09oVAosGbNGvz9998ICAgQelkuafXq1ZBIJHjiiSeEXgqBYYvp888/x86dO7F69Wrs2bMH06dPh06nE3ppLqekpAQ1NTVYtWoVpk2bhr/++gu33norZs2ahT179gi9PJf32WefwcvLC7NmzRJ6KW1y+nEGBBg/fjxSUlJQVlaGjz/+GHPmzMGRI0cQFBQk9NJcyokTJ/Duu+/i5MmTYBhG6OUQAHfeeSf///369UP//v0RFxeH3bt3Y+LEiQKuzPXo9XoAwMyZM7F48WIAwMCBA3Hw4EFs2LABY8eOFXJ5Lu/TTz/F3Llz7bZ+iTIzLsDDwwPx8fEYMWIENm7cCIlEgo0bNwq9LJezb98+lJSUIDIyEhKJBBKJBNnZ2Xj66acRHR0t9PIIgNjYWAQEBODKlStCL8XlBAQEQCKRICkpqcXlvXv3ptNMAtu3bx9SU1Px4IMPCr2UdlFmxgXp9Xqo1Wqhl+Fy5s2bh0mTJrW4bOrUqZg3bx7uv/9+gVZFmsvLy0N5eTlCQ0OFXorLkclkGDp0KFJTU1tcfvnyZURFRQm0KgIAGzduxODBg+261pKCGQdXU1PT4lNkZmYmUlJS4OfnB39/f7z++uu4+eabERoairKyMqxduxb5+fm4/fbbBVy18+ro+xEZGQl/f/8Wt5dKpQgJCUFCQoKtl+oSOvp++Pn5Yfny5Zg9ezZCQkKQnp6OZ599FvHx8Zg6daqAq3Zenf1+LFmyBHfccQfGjBmD8ePHY/v27fj111+xe/du4RbtxDr7fgCG+Ujff/893nrrLaGWaRqhj1OR7tm1axcLoNXX/Pnz2fr6evbWW29lw8LCWJlMxoaGhrI333wze/ToUaGX7bQ6+n60hY5mW1dH34+6ujp2ypQpbGBgICuVStmoqCj2oYceYouKioRettMy5fdj48aNbHx8PKtQKNgBAwawP//8s3ALdnKmfD8+/PBD1s3Nja2srBRuoSag2UyEEEIIcWhUAEwIIYQQh0bBDCGEEEIcGgUzhBBCCHFoFMwQQgghxKFRMEMIIYQQh0bBDCGEEEIcGgUzDuzQoUMQi8W48cYbhV4KIYQQIhjqM+PAHnzwQXh6emLjxo1ITU1FWFhYlx9Lp9OBYRiIRBTfEkIIcSz0zuWgampq8N133+Gxxx7DjTfeiM2bN/PX7d69GwzDYNu2bejfvz8UCgVGjBiBc+fO8bfZvHkzfHx8sHXrViQlJUEul9MwN0IIIQ6JghkH9b///Q+JiYlISEjAPffcg08//RTXJtmWLFmCt956C8eOHUNgYCBmzJgBjUbDX19XV4fVq1fjk08+wfnz5xEUFGTrvwYhhBDSbRTMOKiNGzfinnvuAQBMmzYNKpUKe/bsaXGbl19+GZMnT0a/fv3w2Wefobi4GFu2bOGv12g0WLduHUaNGoWEhAS4u7vb9O9ACCGEWAIFMw4oNTUVR48exV133QUAkEgkuOOOO7Bx48YWtxs5ciT//35+fkhISMDFixf5y2QyGfr372+bRRNCCCFWIhF6AcR8GzduhFarbVHwy7Is5HI5PvjgA5Mfx83NDQzDWGOJhBBCiM1QZsbBaLVafP7553jrrbeQkpLCf50+fRphYWH45ptv+NsePnyY//+KigpcvnwZvXv3FmLZhBBCiNVQZsbB/Pbbb6ioqMADDzwApVLZ4rrZs2dj48aN+O9//wsAePXVV+Hv74/g4GA8//zzCAgIwC233CLAqgkhhBDrocyMg9m4cSMmTZrUKpABDMHM8ePHcebMGQDAqlWr8OSTT2Lw4MEoKirCr7/+CplMZuslE0IIIVZFTfOc0O7duzF+/HhUVFTAx8dH6OUQQgghVkWZGUIIIYQ4NApmCCGEEOLQaJuJEEIIIQ6NMjOEEEIIcWgUzBBCCCHEoVEwY8dWrlyJoUOHwsvLC0FBQbjllluQmpra4jYNDQ1YsGAB/P394enpidmzZ6O4uJi//vTp07jrrrsQEREBNzc39O7dG++++267z3ngwAFIJBIMHDjQWn8tQgghxKIomLFje/bswYIFC3D48GH8/fff0Gg0mDJlCmpra/nbLF68GL/++iu+//577NmzBwUFBZg1axZ//YkTJxAUFIQvv/wS58+fx/PPP49ly5a1OfagsrIS9957LyZOnGiTvx8hhBBiCVQA7EBKS0sRFBSEPXv2YMyYMVCpVAgMDMTXX3+N2267DQBw6dIl9O7dG4cOHcKIESPafJwFCxbg4sWL+Oeff1pcfuedd6Jnz54Qi8X4+eefkZKSYu2/EiGEENJtlJlxICqVCoBhAjZgyLpoNBpMmjSJv01iYiIiIyNx6NChDh+HewzOpk2bkJGRgZdfftkKKyeEEEKsh2YzOQi9Xo9FixbhuuuuQ9++fQEARUVFkMlkrbr8BgcHo6ioqM3HOXjwIL777jts27aNvywtLQ1Lly7Fvn37IJHQjwQhhBDHQu9cDmLBggU4d+4c9u/f3+XHOHfuHGbOnImXX34ZU6ZMAQDodDrcfffdWL58OXr16mWp5RJCCCE2Q8GMA1i4cCF+++037N27F+Hh4fzlISEhaGxsRGVlZYvsTHFxMUJCQlo8xoULFzBx4kQ8/PDDeOGFF/jLq6urcfz4cZw6dQoLFy4EYMgCsSwLiUSCv/76CxMmTLDuX5AQQgjpBgpm7BjLsnj88cexZcsW7N69GzExMS2uHzx4MKRSKXbu3InZs2cDAFJTU5GTk4ORI0fytzt//jwmTJiA+fPn4/XXX2/xGN7e3jh79myLy9atW4d//vkHP/zwQ6vnJIQQQuwNBTN2bMGCBfj666/xyy+/wMvLi6+DUSqVcHNzg1KpxAMPPICnnnoKfn5+8Pb2xuOPP46RI0fyJ5nOnTuHCRMmYOrUqXjqqaf4xxCLxQgMDIRIJOJrcDhBQUFQKBStLieEEELsEQUzdmz9+vUAgHHjxrW4fNOmTbjvvvsAAGvWrIFIJMLs2bOhVqsxdepUrFu3jr/tDz/8gNLSUnz55Zf48ssv+cujoqKQlZVl7b8CIYQQYnXUZ4YQQgghDo36zBBCCCHEoVEwQwghhBCHRsEMIYQQQhwaBTOEEEIIcWgUzBBCCCHEoVEwQwghhBCHRsEMIYQQQhwaBTOEkG7ZvXs3GIZBZWWl0EshhLgoCmYIIWYZN24cFi1axP951KhRKCwshFKpFGxNFFAR4tponAEhpFtkMlmrKe2EEGJLlJkhhJjsvv9v735Cotz+OI6/nzsUOU7iJCIo6lCpWGoERYT9IVxokKEZSQVmf0BBs1IXBeGfKFxEkC0KMtBWSZC10KECU1tVYlSSoFmORRunMHA0EsbzW/zouXdudu/tXu+fgc8LnsU5czjz4RkGvnPOGZ7SUvr6+mhubsayLCzLoq2tLWRVpK2tjejoaDo7O0lLS8PpdLJ7925mZma4fv06Ho8Ht9tNVVUVwWDQnvvLly/U1taSkJBAZGQkGzZsoLe31359fHyc/Px83G43kZGRrF69Gq/Xi8/nY9u2bQC43W4sy7KfXXb37l02bdpEdHQ0MTEx7Nixg9evX9tz+nw+LMvi5s2bbN68mYiICNavX8/IyAj9/f2sW7cOl8vF9u3b8fv9IfehoKCAxsZGYmNjiYqKory8nNnZ2b/v5ovId2llRkT+sObmZkZGRsjIyODMmTMAvHz58ptxMzMzXLp0ifb2dqampti1axeFhYVER0fj9Xp58+YNRUVFZGdnU1xcDEBlZSVDQ0O0t7cTHx/P7du3ycvLY3BwkJSUFCoqKpidneXhw4dERkYyNDSEy+UiMTGRW7duUVRUxPDwMFFRUURERAAwPT1NdXU1WVlZBAIB6urqKCws5NmzZ/z008+/5err67l48SJJSUkcOnSIffv2sXTpUpqbm3E6nezZs4e6ujr74a8A3d3dLFmyhN7eXnw+HwcPHiQmJoZz5879nR+BiMzHiIj8gK1bt5pjx47Z7Z6eHgOYyclJY4wxra2tBjCjo6P2mLKyMuN0Os3U1JTdl5uba8rKyowxxoyPjxuHw2Hev38f8l45OTnm1KlTxhhjMjMzTUNDw7yZfp3he/x+vwHM4OCgMcaYsbExA5hr167ZY27cuGEA093dbfc1NTWZtLQ0u33gwAGzbNkyMz09bfdduXLFuFwuEwwGfzODiCw8bTOJyIJzOp2sWLHCbsfFxeHxeHC5XCF9ExMTAAwODhIMBklNTcXlctlXX1+fvS1UVVXF2bNnyc7Opr6+nhcvXvxujlevXrF3716WL19OVFQUHo8HgLdv34aMy8rKCskFkJmZOW/Wr9asWYPT6bTbGzduJBAI8O7du9/NJSILS9tMIrLgFi1aFNK2LGvevrm5OQACgQAOh4OBgQEcDkfIuK8F0JEjR8jNzaWrq4v79+/T1NTEhQsXOHr06Hdz5Ofnk5ycTEtLC/Hx8czNzZGRkfHN2ZZfZrMsa96+r1lF5L9HKzMi8kMWL14ccnB3Iaxdu5ZgMMjExAQrV64MuX75T6nExETKy8vp6OigpqaGlpYWOxMQkuvjx48MDw9z+vRpcnJySE9PZ3JycsEyP3/+nM+fP9vtR48e2Wd4ROSfpWJGRH6Ix+Ph8ePH+Hw+Pnz4sCArFqmpqezfv5+SkhI6OjoYGxvjyZMnNDU10dXVBcDx48e5d+8eY2NjPH36lJ6eHtLT0wFITk7Gsiw6Ozvx+/0EAgHcbjcxMTFcvXqV0dFRHjx4QHV19V/O+tXs7CyHDx9maGgIr9dLfX09lZWVIQeLReSfoW+diPyQ2tpaHA4Hq1atIjY29pvzJ39Wa2srJSUl1NTUkJaWRkFBAf39/SQlJQH/X3WpqKggPT2dvLw8UlNTuXz5MgAJCQk0NjZy8uRJ4uLi7KKivb2dgYEBMjIyOHHiBOfPn1+QrAA5OTmkpKSwZcsWiouL2blzJw0NDQs2v4j8cZYxxvzbIUREwklpaSmfPn3izp07/3YUEUErMyIiIhLmVMyIiIhIWNM2k4iIiIQ1rcyIiIhIWFMxIyIiImFNxYyIiIiENRUzIiIiEtZUzIiIiEhYUzEjIiIiYU3FjIiIiIQ1FTMiIiIS1lTMiIiISFj7HwbnNkRQPJymAAAAAElFTkSuQmCC", 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" ] @@ -468,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -565,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -594,217 +602,127 @@ " ('/sensor/insert-sensor-readings',\n", " {'measure': {'name': 'temperature', 'units': 'celsius'},\n", " 'unique_identifier': 'Tomorrow.io Weather Forecast - New York',\n", - " 'readings': [12.0,\n", - " 15.05,\n", + " 'readings': [14.63,\n", + " 14.93,\n", + " 15.04,\n", + " 15.78,\n", + " 16.65,\n", + " 17.11,\n", + " 17.62,\n", + " 16.56,\n", + " 15.99,\n", + " 15.01,\n", + " 16.24,\n", " 16.46,\n", - " 17.24,\n", - " 16.48,\n", - " 15.95,\n", - " 15.81,\n", - " 15.59,\n", - " 14.8,\n", - " 13.87,\n", - " 13.09,\n", - " 12.74,\n", - " 12.09,\n", - " 12.29,\n", - " 11.53,\n", - " 11.28,\n", - " 10.57,\n", - " 9.15,\n", - " 8.92,\n", - " 8.32,\n", - " 8.12,\n", - " 9.99,\n", - " 12.49,\n", - " 14.74,\n", - 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"execution_count": 11, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1100,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1131,7 +1139,7 @@ "[, , , ]" ] }, - "execution_count": 12, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/002_model_example.ipynb b/examples/002_model_example.ipynb index bfd02a1..1dbd2b7 100644 --- a/examples/002_model_example.ipynb +++ b/examples/002_model_example.ipynb @@ -6,33 +6,781 @@ "source": [ "# Models \n", "\n", - "This tutorial covers how to use models with DTBase. \n", + "This tutorial covers how to use models with DTBase. We define models as some form of operation on data stored in the database. \n", + "\n", + "For this tutorial, we will run a simple forecasting model to forecast the temperature. \n", + "This tutorial assumes you have already run 000_Local_Deployment.ipynb and 001_Data_Ingress.ipynb. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What data is already in the database? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Source Env variables and login to backend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# imports \n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pandas as pd\n", + "import matplotlib\n", + "plt.set_loglevel (level = 'warning')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Load environment variables from .secrets/dtenv_localdb.sh\n", + "\n", + "\n", + "env_vars = !cat ../.secrets/dtenv_localdb.sh\n", + "env_vars = [x for x in env_vars if x.startswith('export')]\n", + "\n", + "for env_var in env_vars:\n", + " key, value = env_var.split(' ')[1].split('=')\n", + " # remove string quotes from value\n", + " value = value.strip('\\\"')\n", + " os.environ[key] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:5000\n", + "DEBUG:urllib3.connectionpool:http://localhost:5000 \"POST /auth/login HTTP/1.1\" 200 391\n" + ] + } + ], + "source": [ + "from dtbase.core.utils import auth_backend_call, login\n", + "import os\n", + "\n", + "# Login to the backend\n", + "username = 'default_user@localhost'\n", + "password = os.getenv(\"DT_DEFAULT_USER_PASS\")\n", + "access_token = login(email=username, password=password)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List available sensors" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:5000\n", + "DEBUG:urllib3.connectionpool:http://localhost:5000 \"GET /sensor/list-sensor-types HTTP/1.1\" 200 421\n" + ] + } + ], + "source": [ + "# Get list of Sensor Types\n", + "sensor_types_list = auth_backend_call('get', '/sensor/list-sensor-types', token=access_token).json()\n", + "sensor_type = sensor_types_list[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:5000\n", + "DEBUG:urllib3.connectionpool:http://localhost:5000 \"POST /sensor/list-sensors HTTP/1.1\" 200 353\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weather: Tomorrow.io\n" + ] + } + ], + "source": [ + "# List all Sensors with type \"Weather: Tomorrow.io\"\n", + "print(sensor_type['name'])\n", + "\n", + "sensor_list = auth_backend_call('post', '/sensor/list-sensors', payload={'type_name': sensor_type['name']}, token=access_token).json()\n", + "sensor = sensor_list[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract data for measure \"temperature\" from sensor called \"Weather: Tomorrow.io\" " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost:5000\n", + "DEBUG:urllib3.connectionpool:http://localhost:5000 \"POST /sensor/sensor-readings HTTP/1.1\" 200 6071\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'value': 14.69, 'timestamp': '2024-04-12T10:00:00Z'},\n", + " {'value': 14.85, 'timestamp': '2024-04-12T11:00:00Z'},\n", + " {'value': 16.31, 'timestamp': '2024-04-12T12:00:00Z'},\n", + " {'value': 15.92, 'timestamp': '2024-04-12T13:00:00Z'},\n", + " {'value': 18.12, 'timestamp': '2024-04-12T14:00:00Z'},\n", + " {'value': 17.59, 'timestamp': '2024-04-12T15:00:00Z'},\n", + " {'value': 17.18, 'timestamp': 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{'value': 15.24, 'timestamp': '2024-04-16T00:00:00Z'},\n", + " {'value': 13.7, 'timestamp': '2024-04-16T01:00:00Z'},\n", + " {'value': 13.0, 'timestamp': '2024-04-16T02:00:00Z'},\n", + " {'value': 12.34, 'timestamp': '2024-04-16T03:00:00Z'},\n", + " {'value': 11.99, 'timestamp': '2024-04-16T04:00:00Z'},\n", + " {'value': 11.84, 'timestamp': '2024-04-16T05:00:00Z'},\n", + " {'value': 11.74, 'timestamp': '2024-04-16T06:00:00Z'},\n", + " {'value': 11.66, 'timestamp': '2024-04-16T07:00:00Z'},\n", + " {'value': 11.41, 'timestamp': '2024-04-16T08:00:00Z'},\n", + " {'value': 11.21, 'timestamp': '2024-04-16T09:00:00Z'},\n", + " {'value': 10.96, 'timestamp': '2024-04-16T10:00:00Z'},\n", + " {'value': 11.01, 'timestamp': '2024-04-16T11:00:00Z'},\n", + " {'value': 11.2, 'timestamp': '2024-04-16T12:00:00Z'},\n", + " {'value': 13.09, 'timestamp': '2024-04-16T13:00:00Z'},\n", + " {'value': 13.67, 'timestamp': '2024-04-16T14:00:00Z'},\n", + " {'value': 15.1, 'timestamp': '2024-04-16T15:00:00Z'},\n", 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'2024-04-17T07:00:00Z'},\n", + " {'value': 13.05, 'timestamp': '2024-04-17T08:00:00Z'},\n", + " {'value': 12.46, 'timestamp': '2024-04-17T09:00:00Z'}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get data from the sensor\n", + "temperature_measure = sensor_type['measures'][0]\n", + "unique_sensor_identifier = sensor['unique_identifier']\n", + "# Set startt and end time to a large range to get all data\n", + "start_datetime = '2021-09-01T00:00:00Z'\n", + "end_datetime = '2025-09-02T00:00:00Z'\n", + "\n", + "sensor_data = auth_backend_call('post', '/sensor/sensor-readings', \n", + " payload={'unique_identifier': unique_sensor_identifier, \n", + " 'measure': temperature_measure, \n", + " 'dt_from': start_datetime, \n", + " 'dt_to': end_datetime}, \n", + " token=access_token).json()\n", + "sensor_data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:matplotlib:matplotlib data path: /opt/homebrew/anaconda3/envs/dtbase_env/lib/python3.10/site-packages/matplotlib/mpl-data\n", + "DEBUG:matplotlib:CONFIGDIR=/Users/wbrown/.matplotlib\n", + "DEBUG:matplotlib:interactive is False\n", + "DEBUG:matplotlib:platform is darwin\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "module 'matplotlib' has no attribute 'pyplot'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmatplotlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpyplot\u001b[49m\u001b[38;5;241m.\u001b[39mset_loglevel (level \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwarning\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 6\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(sensor_data)\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemperature\u001b[39m\u001b[38;5;124m'\u001b[39m})\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Change timestamp to datetime\u001b[39;00m\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/dtbase_env/lib/python3.10/site-packages/matplotlib/_api/__init__.py:217\u001b[0m, in \u001b[0;36mcaching_module_getattr..__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m props:\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m props[name]\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__get__\u001b[39m(instance)\n\u001b[0;32m--> 217\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__module__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib' has no attribute 'pyplot'" + ] + } + ], + "source": [ + "\n", + "\n", + "\n", + "df = pd.DataFrame(sensor_data).rename(columns={'value': 'temperature'})\n", + "\n", + "# Change timestamp to datetime\n", + "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", + "\n", + "df.plot(x='timestamp', y='temperature', title='Temperature from Tomorrow.io Sensor')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weather Modelling\n", + "\n", + "**Please note the aim of this tutorial is how to use dtbase and not how to do modelling!**\n", + "\n", + "The following cells will use a decision tree to forecast the model over the next few days. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import tree\n", + "\n", + "# Extract Hour of the Day and day of week from the timestamp\n", + "df['hour_of_day'] = df['timestamp'].dt.hour\n", + "df['day_of_week'] = df['timestamp'].dt.dayofweek\n", + "\n", + "# Train a Decision Tree model to predict temperature\n", + "X = df[['hour_of_day', 'day_of_week']]\n", + "y = df['temperature']\n", + "\n", + "# Create the model\n", + "model = tree.DecisionTreeRegressor()\n", + "model.fit(X, y)\n", + "\n", + "# Predict temperature for the next 2 days\n", + "min_timestamp_for_predictions = df.timestamp.max()\n", + "max_timestamp_for_predictions = min_timestamp_for_predictions + pd.Timedelta(days=5)\n", + "\n", + "timestamps_for_predictions = pd.date_range(start=min_timestamp_for_predictions, end=max_timestamp_for_predictions, freq='H', tz='UTC')\n", + "\n", + "df_predictions = pd.DataFrame({'timestamp': timestamps_for_predictions,\n", + " 'hour_of_day': timestamps_for_predictions.hour, \n", + " 'day_of_week': timestamps_for_predictions.dayofweek})\n", + "df_predictions['forecast_temperature'] = model.predict(df_predictions[['hour_of_day', 'day_of_week']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "\n", + "df.plot(x='timestamp', y='temperature', title='Temperature from Tomorrow.io Sensor', ax=ax)\n", + "df_predictions.plot(x='timestamp', y='forecast_temperature', style='r--', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding the model into the database\n", + "\n", + "This next section will take our model, its parameters and its results and add them to the database.\n", "\n", "There are a few key concepts to understand with how DTBase handles models: \n", "\n", - "1. **Models**: \n", - "2. **Scenarios**: This is a model with a specific set of parameters\n", - "3. **Runs**: This is the specific execution of a model. A new run is entered into the database after each run of the model. \n", - "4. **Measures**: " + "1. **Models**: This is specific type of algorithm: i.e. Random Forest, etc. \n", + "2. **Scenarios**: This is a model with a specific set of parameters. A model can have many scenarios. \n", + "3. **Measures**: Measures are the type of values that the model is returning. E.g. Forecast Temperature\n", + "4. **Runs**: This is the specific execution of a model. A new run is entered into the database after each run of the model. A Scenario can have multiple runs. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insert Model\n", + "\n", + "The model only requires a name. " ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "Models and " + "model_payload = {'name': 'Tree Model',}\n", + "\n", + "auth_backend_call('post', '/model/insert-model', payload=model_payload, token=access_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insert Model Scenario \n", + "\n", + "A model scenario refers to a specific setup for a model. \n", + "\n", + "The payload requires a description associated with the model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scenario_payload = {'model_name': 'Tree Model',\n", + " 'description': 'Forecasting temperature using day of week and hour of day'}\n", + "\n", + "auth_backend_call('post', '/model/insert-model-scenario', payload=scenario_payload, token=access_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insert Model Measures\n", + "\n", + "The model measures are what type of data the model returns. It defines name, units and dtype." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_measure_payload = {\n", + " \"name\": \"Forecast Temperature\",\n", + " \"units\": \"celsius\",\n", + " \"datatype\": \"float\",\n", + "}\n", + "\n", + "auth_backend_call('post', '/model/insert-model-measure', payload=model_measure_payload, token=access_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insert Model Run \n", + "\n", + "A model run is a record of the time the model was run. \n", + "The payload includes model_name, scenario, measures, values and sensors. \n", + "\n", + "The model outputs must be associated with a sensor (can be thought of as a data source). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_run_payload = {\n", + " \"model_name\": scenario_payload['model_name'],\n", + " \"scenario_description\": scenario_payload['description'],\n", + " \"measures_and_values\": [\n", + " {\n", + " \"measure\": {\n", + " \"name\": model_measure_payload['name'],\n", + " \"units\": model_measure_payload['units'],\n", + " }, \n", + " \"values\": df_predictions['forecast_temperature'].to_list(), \n", + " \"timestamps\": df_predictions['timestamp'].astype('str').to_list()\n", + " }\n", + " ], \n", + " \"sensor_unique_id\": unique_sensor_identifier, \n", + " \"sensor_measure\": {\n", + " \"name\": temperature_measure['name'],\n", + " \"units\": temperature_measure['units'],\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert model run\n", + "auth_backend_call('post', '/model/insert-model-run', payload=model_run_payload, token=access_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BaseModel\n", + "\n", + "To simplify this process, the user can write a class that inherits from the `BaseModel` class. \n", + "\n", + "The BaseModel class handles the authentication and responses. The user only needs to overwrite the `get_service_data` method. \n", + "\n", + "The only important rule is that the `get_service_data` needs to return a list of tuples in the format `(API endpoint, payload)`. \n", + "These endpoint, payload pairs will then be looped through to ingress data into the datbase. \n", + "\n", + "We will redo the example above but using a class inheriting from `BaseModel` class. I user other methods to make the code in `get_service_data` more readable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dtbase.services.base import BaseModel\n", + "import pandas as pd\n", + "from sklearn import tree\n", + "from dtbase.core.utils import auth_backend_call, login\n", + "import os\n", + "\n", + "\n", + "class WeatherForecastModel(BaseModel):\n", + "\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " # Login \n", + " self._get_env_vars()\n", + " username = 'default_user@localhost'\n", + " password = os.getenv(\"DT_DEFAULT_USER_PASS\")\n", + " self.access_token = login(email=username, password=password)[0]\n", + "\n", + " def _get_env_vars(self):\n", + " env_vars = !cat ../.secrets/dtenv_localdb.sh\n", + " env_vars = [x for x in env_vars if x.startswith('export')]\n", + "\n", + " for env_var in env_vars:\n", + " key, value = env_var.split(' ')[1].split('=')\n", + " # remove string quotes from value\n", + " value = value.strip('\\\"')\n", + " os.environ[key] = value\n", + "\n", + " def get_sensor_type(self):\n", + " # Get list of Sensor Types\n", + " sensor_types_list = auth_backend_call('get', '/sensor/list-sensor-types', token=self.access_token).json()\n", + " sensor_type = sensor_types_list[0]\n", + " return sensor_type\n", + "\n", + " def get_sensor(self, sensor_type):\n", + " sensor_list = auth_backend_call('post', '/sensor/list-sensors',\n", + " payload={'type_name': sensor_type['name']}, token=self.access_token).json()\n", + " sensor = sensor_list[0]\n", + " return sensor\n", + "\n", + " def get_training_data(self, sensor_type, sensor) -> pd.DataFrame:\n", + " \n", + " # Get data from the sensor\n", + " temperature_measure = sensor_type['measures'][0]\n", + " unique_sensor_identifier = sensor['unique_identifier']\n", + "\n", + " # Set start and end time to a large range to get all data\n", + " start_datetime = '2021-09-01T00:00:00Z'\n", + " end_datetime = '2025-09-02T00:00:00Z'\n", + "\n", + " sensor_data = auth_backend_call('post', '/sensor/sensor-readings', \n", + " payload={'unique_identifier': unique_sensor_identifier, \n", + " 'measure': temperature_measure, \n", + " 'dt_from': start_datetime, \n", + " 'dt_to': end_datetime}, \n", + " token=self.access_token).json()\n", + " \n", + " df = pd.DataFrame(sensor_data).rename(columns={'value': 'temperature'})\n", + " \n", + " # Change timestamp to datetime\n", + " df['timestamp'] = pd.to_datetime(df['timestamp']) \n", + " return df\n", + "\n", + " def build_model_and_predict(self, input_dataframe: pd.DataFrame) -> pd.DataFrame:\n", + " \n", + "\n", + " # Extract Hour of the Day and day of week from the timestamp\n", + " input_dataframe['hour_of_day'] = input_dataframe['timestamp'].dt.hour\n", + " input_dataframe['day_of_week'] = input_dataframe['timestamp'].dt.dayofweek\n", + "\n", + " # Train a Decision Tree model to predict temperature\n", + " X = input_dataframe[['hour_of_day', 'day_of_week']]\n", + " y = input_dataframe['temperature']\n", + "\n", + " # Create the model\n", + " model = tree.DecisionTreeRegressor()\n", + " model.fit(X, y)\n", + "\n", + " # Predict temperature for the next 2 days\n", + " min_timestamp_for_predictions = input_dataframe.timestamp.max()\n", + " max_timestamp_for_predictions = min_timestamp_for_predictions + pd.Timedelta(days=5)\n", + "\n", + " timestamps_for_predictions = pd.date_range(start=min_timestamp_for_predictions, end=max_timestamp_for_predictions, freq='H', tz='UTC')\n", + "\n", + " df_predictions = pd.DataFrame({'timestamp': timestamps_for_predictions,\n", + " 'hour_of_day': timestamps_for_predictions.hour, \n", + " 'day_of_week': timestamps_for_predictions.dayofweek})\n", + " df_predictions['forecast_temperature'] = model.predict(df_predictions[['hour_of_day', 'day_of_week']])\n", + " return df_predictions\n", + "\n", + " def get_service_data(self) -> None:\n", + "\n", + " # Get sensor type\n", + " sensor_type = self.get_sensor_type()\n", + "\n", + " # Get Sensor\n", + " sensor = self.get_sensor(sensor_type)\n", + " \n", + " # Get training data from sensor \n", + " df = self.get_training_data(sensor_type, sensor)\n", + "\n", + " # Create model and generate predictions\n", + " df_predictions = self.build_model_and_predict(df)\n", + "\n", + " # Define Model Payload\n", + " model_payload = {'name': 'Tree Model'}\n", + "\n", + " # Define Scenario Payload\n", + " scenario_payload = {'model_name': 'Tree Model',\n", + " 'description': 'Forecasting temperature using day of week and hour of day'}\n", + " \n", + " # Define Model Measure Payload\n", + " model_measure_payload = {\n", + " \"name\": \"Forecast Temperature\",\n", + " \"units\": \"celsius\",\n", + " \"datatype\": \"float\",\n", + " }\n", + "\n", + " # Define Model Run Payload\n", + " temperature_measure = sensor_type['measures'][0]\n", + " unique_sensor_identifier = sensor['unique_identifier']\n", + "\n", + " model_run_payload = {\n", + " \"model_name\": scenario_payload['model_name'],\n", + " \"scenario_description\": scenario_payload['description'],\n", + " \"measures_and_values\": [\n", + " {\n", + " \"measure\": {\n", + " \"name\": model_measure_payload['name'],\n", + " \"units\": model_measure_payload['units'],\n", + " }, \n", + " \"values\": df_predictions['forecast_temperature'].to_list(), \n", + " \"timestamps\": df_predictions['timestamp'].astype('str').to_list()\n", + " }\n", + " ], \n", + " \"sensor_unique_id\": unique_sensor_identifier, \n", + " \"sensor_measure\": {\n", + " \"name\": temperature_measure['name'],\n", + " \"units\": temperature_measure['units'],\n", + " },\n", + " }\n", + "\n", + " # Now we need to return these payloads as a tuple along with their \n", + " # corresponding API endpoints\n", + "\n", + " return [\n", + " (\n", + " '/model/insert-model',\n", + " model_payload\n", + " ),\n", + " (\n", + " '/model/insert-model-scenario',\n", + " scenario_payload\n", + " ),\n", + " (\n", + " '/model/insert-model-measure',\n", + " model_measure_payload\n", + " ),\n", + " (\n", + " '/model/insert-model-run',\n", + " model_run_payload\n", + " )\n", + " ]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test the get_service_data() function works as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = WeatherForecastModel()\n", + "model.get_service_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To send this data to the database, simply call the object. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model(dt_user_email='default_user@localhost', dt_user_password=os.getenv(\"DT_DEFAULT_USER_PASS\"))" ] } ], "metadata": { + "kernelspec": { + "display_name": "dtbase_env", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } }, "nbformat": 4,