{
  "$schema": "https://ui.shadcn.com/schema/registry-item.json",
  "name": "weather-forecast",
  "type": "registry:component",
  "description": "Weather forecast with optional atmospheric backgrounds.",
  "registryDependencies": [
    "card",
    "button"
  ],
  "dependencies": [
    "lucide-react",
    "astronomy-engine@2.1.19"
  ],
  "files": [
    {
      "path": "packages/react/src/components/wxcn/cloud-texture.ts",
      "type": "registry:component",
      "target": "@components/wxcn/cloud-texture.ts",
      "content": "// Generated from src/assets/weather-*.webp by tooling/registry/cloud-texture.mjs.\n// Embedded so installed registry components never request a third-party image.\nexport const weatherCloudTextures = {\n\tfair: 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CtaAIQBonDiqV8SvcIxEYJEzFZLK1X6TIkRGOmF4uhWs/A8i6HVfj7miX5dTikUSD8DWINBQtL79/eBo22VseQnTJK2n1dnW3dR6zRsCOObt60xzKt2S0w1GSSGoY1vi9Rtxu8Fc1HKI7RdwpQbqe24PpECKxo30Eh6iVpk5hu4u41aWu5SB8SC0f3nU2N6qMRqrNe6Qxz6b1j7s6E3sHvOzdgbKSnp7pbgeglcNHXtbf6PhPR/eEbJIoSPqCu51ttXc/SolPqlGNz10dfjS5asBhsgQFUe427gNTAvFBCZsegOyaFFApGifx1uSJQ6Krn3TsJuxKTeuE1x6s6yQ3zBPcyAprgSLceYtCIxkpoU0MlOOGvRfSvMgLoqAm+2zIQiXBMcyIP0oekZcqUMRlEHIJYnmAN2Tv9RWBpuuRaOwzPnkJ+k/kFq3fwMA+G7Qc+jENPT+QFktfauMilZtz8ITCh8zvtj3TXu4n2fRj7E54Hltyerh0XMKEqUhvxqjezLg1EIT+xz9RPkRIT5Sl+rGZLQfRvDsZ6yr+qWoESOcZ9OI4M/2UMyyWDUqblXhv9bstaNdj0nthaqM7kpDQheEB9szcvfD34iPo6KIusnDS3InEgjzsWhj1rlCML7mu2NYXrJVWMbkJAjtTK2hvTavX1MQDk+5u9ikH9KoTfK2NPyrAU6TT3VtCYtQp1c3yU02dDHvUVX8f4jeXAvJJW0B87X4Y4YCU49+NANSM58KtGgfPkt4kcBEdiuDWwpAGAlRQXwts4+6ZNU+s0oHYDTxV+mc9Mhx+VWxTjJxsvitWT0DGvj7MEVd5yNqGXz/hZQP9hZ8j8+34xRTm6WuN87fDi1XX8KbaGcJ+LZkiPH38JkdQlmmzzRe+mxRNE5ZSw3VYHnsoFeIwNwFJdS6dHwUr580NQKJwu3ERXjESu5VTix9df132qeTWi2SAc09NsTMNFBvAqpTqZiDk1y8PMx85xbrCJU3lluos2IQGnsfzaQEYCADu9KgZOS9Kt8J8oybMnLwmHQj5jGR8HsmZ3Vn6rFH4QTcbVhiXFaYvI+07XxD5T4+Sz5iviK12mfWikwGjh0fVdbnxba1E8a2w0Q6KIix6N/b/kwkZC0wPfcsFKCOrx9eUwC6MdoUrjVbjOuqE/0thMBzwjMhAvkdbpYfmuzAeGG+wfSass11KNOeZ+I/PV5xjxBEZfa6OtGwKSQBZWjojmTDbq6llmEVNN1PZ4ltZ+dFw843RSuouC7/5NH6DJv1HgFcqVoNkEyMylx835iQ3PgvVNGvofVVyNq85h7oLcG4mO2JpigxhvcQHFCzwLOvP03icsDGpdrcrqnoEQ3yXz2NsEEdupBysHbDePusYY8rqIHTtOwIirm/X8YYMezKxVDNHGz7jXf3UX8vgHtT+HNhe81OCIRV8399BnvogPP2RCfizualL/7IXZc7Ax0t6u/7N+rq+BXpZDJuU3ks98WO41qm8nlaLzcFB3ZCVAPYFQuX66sxdFUG5AikPep9YKeLM/Mzmn7L3lcrUzrMtSsSd0y5opWso6WCEnWfR6CFkBx2dScdr6JFH5xj4vbLy0BkLXxWUaFmsex+qn/RrennGbgADV8yElLj3GIrzNkEIBWlAWU8r2yM/qq2i4261Hl3ELSTSiKzO77Kjzv4J+MoZ+RmDqb2VPVQ3g4jt8QVcDwgVa8nxjgJr3BzUvY+ucYb9FXWY3KKkhGa3I1/E+cIWPyRQkGphHKnrLKPVHsJaZjMNDaSPHSAzPZxleuXHMvUGU73vP3cWE+ZVkv6CrD0sIsQP60aKAoBmNmYrYAf1Hbdd6VqMo3j0jXF6sxKB7qSWsfsU72uQzfIZV8ilQSSkfmR5t76giEAOemRIkSMZGOIftZicbnNMwlCeDAmCT+gC9K7SqhOEqRLxQfpudylTjMJPVcvZgLQR50HLXYm7e1wEdWolOkx1ZAJ++lhJIv1WGvww98+0q1MG8AwvG6nogURammMLtTCQUUhUtfA4hn5iM4PGux++I/eED4j3ErhIfoNsUCM2Y9/TK5WzY6vJO91RDC4qc33pgSJaQsVG6ppemWURp6Kz8ceNeFl2Smy7Jfr8j9Cmk82o+6qCubLQIyWleSZNKy1kmOwFXqLtZ0oXrOx5JgBWUmZXwSMaKvJhS7LLI97Vf1Kp2nV5RvfwtSeXtzMz4zCWdUpHsmjg+CYY56ry06dqjW0AXC/vYStd3WUUAipNkxlTjGgMRmHiW8cGqIK7JouwN/8RwvzOGLu8KaF3nEydN+Lcme6Ea0Oadn5jL0ugfiRKi3CQq4xpipd5DsBQUwjz5lQMzELjzZTsuSHuDQ3S/ndi6L1rLqijUh/V8dmZrBtpfUfQjpMm8KE6KJ9G7XaQSk/rL6aa2xjepEWdgv3wWX6bQ1FLy3uhQZlh6DVlBJOu0wE+SARBZecRIMOq9KjHnvPnlEH+pgSOaIBggIAO1Knm4z/Zo2cQALUwJq1hkmTpRpx42Kqua/Ll3+npQUbQvUoMl5htsuB3m8hYD1VOYbdrJktH0bPyoMSH3ExmrDA6nlXT1pKhmULFX/x4hn0lfIaLdOTJZ2NrorfeGuMpcU8Lp0TyE/E1GF9aGT4LxbeJFLLR4wHMXBn64rCFXvE0cw6HLsJDyl2bJDxjkCWXOFsXJ5ILJ5vbp1m5qSpnh1wYuTwUK2lYer/kQYPttT5gxf9kgYSSD1T92PtQkOhNDk9vg26WIIuprAQsIG2gimIHqqASN/I8PH6CEZIiG0bj/9jed90WHuO2i5ZDD5uZ+N+qrtnljurn08KBJC7qM+1+gTGe7yipNb71GW/DJiGgFUZGVua5u4vBLZJ3By/b/Kzqy5w0QhgVpujMyD+dGrGDbnn5KdVgGvlXNAJzDbhErZ+pdpL3GDy0WHVokYgQMAwZ7DN1HuDaz1oJo9CAoW2C2PruLCEpHUDzqQ7WF8DkuUDyACK53rAY80XCd6PMc2cgCxlaV4yk78NpwFuEEumJp51Qmy9NVY8OuY9LOO9weBkzKmE9T2K+ex1clFWkvmz+hzwrHY8wwaQGs7XupdudrT/3SwpPwihMSOl1oyAH0V3aM8zhggQT6AiWpyYVd4stmosRizqrLjONFBvqbEF/KSr5SCr2bJrZjsNN+Pc5hKh5AvtIBspdDDexAL45KG8/ycVY3cuzk86sGLaRGzm5wbaLHNy3bYLLWjHat7JNw5W3FYl3Mem11YRKvhpExyd2DDqJqSFYyIgAhNWNq5LUpXxNLhP9veHzFyBbW80Ank1ZZa7CNJtTzaxnJWuK+3iBQuL4bzgFg5qT55PZqmhehiuN3sDItYGhmJt2i4Q8kscPaDW6X39MQ+Z9xLRYcTjp/gA4AhzAqUJrklVLLXQQ9JVlO5YydsflUsVsY3P3Du1f+oAhXx/TToM8oyNnakBXA+nBxC0j2yQtJKanlU8pB7H7sBnU/BtP7OdwqdE4qnNkYemeRdMjWkRjbYVgPv0/Ryah34KmqpSYulIvB0X0izSWSV3aenaldwH3QX5k9tKX3NDlrg6nWAj+j5/PLQhm+IWBxsgu0+0OkZCVy7eBXp4dV4tPwt3OtcZdiEthzKlc3ND40ikOoZMFsuprOe4nkMT1r8vWQT+sVBIjLSenKyQkNoBZOBbovjTQ6TAcnFj1mj9vbnXbE+H4su8SpCLYeRc3BuBBWEFjxAazqSv1/IJS1LR79bBwitEQmcvVBQ6QO39lCmkxenA4OW7DHMykxTW3zinh7TY+CVL6VUTEXL52vojLkrrgPM4ttfew83Dg6dSZh3vjaZt/7fSXnQP1NmDVQtdTqTQ3UWmB6eXhs3Ug2o2l1Zye/tCIyRr0CWoJ+iGhgEwPSQjxW4c1jEDpsIzoDIF1tLb+oMoQTDlsOkyJuLFjqX3pjox3hTCOoZCA5zY4WzmVr26hys1WhQcZyY6LyvmD2IEK5julloI7x1pCTQtOuKPnYm4F2puBajRdcPmNjFo7+gCTM3apysU3Mpj0C+gyCsDF7iU7QoNUA4SqE0Sj6nnned3UDEZ+qUh+IWfIjjEksHYfSN4q7icFyDzcsCXfKFx847Eqzj3zGa8FTdGwGyEFoxqup8dEZYH8kmQ8UP4kiLrxXP3xJWuniS3IEInABoHXxUBdIW7izmq6ddFvOZW7DhTJmVjmMjr8GMGVTo1Rk37M03X1za7EmErmCuptYeki8KpOZydbnViXMPsG9UZ9O/HkIZ79F1xnVreUYdaFL4Ffo4cBVbwC031Rdp7gLEbpvT1bheA7yQD7OEXOk/9OMYixAFzUjL6tFQrAFc8AC92cvl1PxDhZTsjgir4gTcvsVlERn17nmS0eJWMuAqlgt2bBsgfpeVuUHAQDBxQL5RW/8JhOpax5/Ts8v4MGvJ8spd0OlwxFVueDzC09nXSKpYhXe2yqm4+97bR/5EX5xTy7BWQRjZqKv48SEYEoCzdvgxgo6Me5kpGr3PmUgWGjxUi7Sgn78Nku/HpmFfoUlypXz7bIU8yZeLbV5om18toBeHS/gK/T9+LIgW/2FsIUNMjDEMRB9Stb7Vb5uTJkZ0z7RmEig8uxChA6EGnjfgwIv2Vwgdm3RAB/fkTnVfUe/8LnOY1NAgoNfQvCRFWWJh4EG1kuMU5+LGUs2ZZhsKHrXVTvkR6uxYht5l22gN9O4GJshzTVV9/kWYsHvVZtYfNXOc8j0/T5WQHXnYOg8uzkCif4dqUCTQ6iTfdOeoouwRO/Hbi2AGUSLOjg0qUOFKDGlcrk0gmaEWj7ReS7yy+Vhu8sCzSIXHP68eOzqRHfsLZ3DMvWS2i+JEA75AzgSrlULDUOP+V1bCTfG84aFxlGLz08dhJlLOceGLE22NZ7WkHVVGT44isENryN6ehWzWyH02MLqqgOqr+gzPWagkZK6oBte9S4oRvHkhX3WMVj3HfQxH6qm1FhrVBAVoDJ+D0bakNkdDoL1nw7TRJ7v5A7qmPKDDaqqiuStIZatlrzXQGzCGh5Xv3iMDa+OjIk6uaeJhypQLuRSLIs3mFuQTU+TSk8jKQ4Joo2P963SvW4OsqyZ/7OdL4nF0nL4GED9FRKE9DjApwS3Rsr3g1RxaKq3Meezybgi/ADRQnVSf69ojqOxarcOws7xeLEkXpAGJ3XFI4I4VmMeoEwkT4cEyHgtVQJ+2g9/EsrCpjCNgYSI7Cc87QI/pC16UFhDjsb2bEyfi3cp7bArUFhaFVau/yRaJ5VM+vyOgIY8+/+coLVwlTPLx+U0HnYCwk07NW5+ujj19F39Pg7PTEsbFiFdDaNdidBfZ2BH9+beQcR+e3i/RFEc4rmoCa5eBNCweiW/1d7NAT0GEZhtA+dYpzAuI217G/aZY1gvV5aLtqYMXKD8cK8xeiMhYCThTosXFqNC/wIIEOE14r5o9LBOECBSzfbtWdJW8oAR1zgbFham5cKQvBDVVFEOSWsmi42H+I8iauV6+zEWgft3vwX7Hkr0SVmbscZsos7AXIbtMgJ0MCxoqjji+Ma9VFGZ7Q3n5vSEYoEPka2xC7JTgKTogV1F+QjmXlMGNAGJOuIDPnJN1ZLPhu2W/e6AhR4sMiZd5XopHcVrltte89rFxf5xBhYyVWTTKtiAJC9gOKupV2D/10OwkaYMVo/eNsZ6M2+AShJQ7xfE1mUuaMMHf/20y4fBgx+1RZBP7TGBGUbZdnrPxTYue+cyAEID2GMukmKolS8Is3GZQjj+SM70uQ4sxiFc9g4rUz1Er6SlXiTtcRxbl3+ntPxEXRCL1yu1SlY5cAKJJObQlBfcQG433mbl7KXwcf4Y3aPNQmtKd5OaYmmm8V6uCpi2T5WYtvDiWXpBQIQ9OlhBbQuAKeyLpmwqIdY9X7gYJwPni3QpVkKMv1MeAJw2ys0oXf0NXQ6O8nqH6/KWWBvy5I+wvVXboqXOG0bUv5zpqIw9FT+JKpDLK3DL1th/4+5iIjvmI5Mvr7ilAo8auTp6eCJIt8UluhWrbzqjAX2yvkG3DWjspl4313a23ayOPt92waNRdpaDNUDu+AAEHBNksHmBlPctRWNY/xoEME6s/rGlhBmI1fNqAIeZsrEK/o7g1CVXGwOhZ3u344oOtZ2U1eTpGDtL4Oztui5KTrSbOgh41pfMtmlp1QT6cqrQY7ruT7FNU35LU+uHfDqmC+OC/6M6PJPmWLamL9KzAxj7cJi0KbGbmp/rDcgZoxoLMcL6KNwt0wiAsW0ytWwITpkx4D7TosORkPpHx9NpfvVXbvQsl1eRp4BsXa4QKdxN5k3Ph/ouY9TO14npn80ZOimfXeLDsGtFjLKPCiOw13c1QrA821AnlR3f/BeUuDgRUnsmwxsRQP6RXcU31y46TY7ftRQ2rCcWPtYV8+AuGWBrnmwv4yuRTnmkbo4m1PNQWWzB8Wdazf+BL3C8tnMpZ8WJ+R7l/Qbs0toOfZG68xmGA3vzlUPxgGx+edk4uINdYyCbCgKcrM1fNXn8fepu+hPKQuPFX4bCziuxBfdqIylrjYOwBKjEKuhEQuk/dbEGAa3qUNJ4wbnWslI8UeXX+YOGWI7mD9leC9R6IF/90oDt6GCDPBFklbyJyoX1T31xlQXR7O39JkTWv8xQi+gDEXLiW6feqKrQbamvXQK7ap5+PpqLxFUAxre+OVsiFc248wKJTKyGnvDQn9tA2p1nBizNcHtg//LsBYBnJMzn5RdFlL6XjYnl/WmqBCGFDBE1dsP5Z67xIrlyj/i2Ks+MR5NkB2Sx8LF88AmP8WOU7pWuFTtZivbe4qwqHHFtXT8ZQkeNERX+XoudUFIVyrbWxsK3fJFvt7RaTH/Ki5e+z2b6T1v9YFZOFd2mxjYaF8alb27GnVcrKE0XE2QYJukXbbHZ4AlAArHfb9OvfJEyTzEwQwfkcjSZuwejK3YzU3VMazXIH5lUeKVtPFXwlJ55k2DxNKZAD8Wp/cJowqSIuGr9o0ZDbCczI2kMnaqw9VL0pMxXEjAP0tP9XXowHcxG+ArnhG/pShF+vbAtyNALK+xcmH4WQPGwTkrbSdEtpvrdmtICWjepQ9JK2Odk4+Dy7tlafpgGef/C49/+trYwyWi4GH3AqkrbHzizvPjA7pAuXxHfOkMe7I9SmBja4aZFem7nM6o4guP8TdUz6zIBlq3Qj95r2aHsjiY07kapYXv5YfxorDBESxPFm0HVal5rpmuQt8Ncg/EkzV1qOUVcXrez7GV3YCsnU4ZZ2xA6dDDwOBkhK7c78cjKLgDBQcJyEKUYj0t+bzs45YPCPGIKTbrIWlZsyxl59SFZXdvzvXBtL0D24pZ/C0I64O2le7LFJzcDq3qvf96TY7mJezzAVzDltP3caVsKBnDeHasDy2KG71ayePnZGthy4Dkg1shSQAfT5sX6WZBMXV6yr+gs3+xsDUp3ju3PCvUo4hcuK4i8Az4E9nQeDG/qElLD31e8LrcNBPqRPdGsq40+sAs0/h5FPJZDXWujRNMDoYtOxzjAFXWAxdvnBEZ30Gt52wjQyduQdjdUaTEKwF/DXFD5ETlWWnijNRDavdr4OjyCACPrN3fwDY5i4x0xIEXlO1/x+JVLXvA+qsnP8Voi54hyZsKXkJTZnyYFc5t5+rPv1bUAOoAXlMyIzNQhWc+/cjX9eS5fhW8uN0ssg4/geZ/9FP8o2rY1WJG4/Y1LnXaFHLpSittNDretOijveTyhTZhd0rYmBTj/0Nay1PkHmlG/Kn2axnOq624dZJYGOcH7bgCvceLm+1/M9n3T0HcArTTqywCi/4ytqu6hpJu1/nJQRp/ydalVNIc/vmExnbxmDKZSXS3so6ByJVjy8qR1yUwBZRN4b+MLNpdqVVUUk8dFWfqP4gbJBYqFLfTvAYai7b7hbVkEMwF3yMnrlRQQ531fJrIHCbtqYA16EZuCREXW0wmJ2TxPWmyUl4OsJRXGfbjfLiQRPvPgtb8EdVApxZLR7bcDRNILApokICF90xi10trzxrmZKqmuzhN21PFu7W2UZf75znli4h/J8mKcu5DJuVLaALrT80nR081udCG+u/5drbQIxqfXyzCnUU6ruZ0IIAi3dhCs592sSnsqCJrlwB6JEJvQEE9PL1BPO8VyF2FFMYKabgc9LLEH6e961479VZnbrA/ZRwFl7tmOysVf4xdsR2eRzG2i4tNLZM+IY12QcX4nrokaSUueE0z0lLhO90bejuenLwY9EVJNfDN8ivGOB4RaarlGCI57Qo0CDYLy1s9Ql2mVdp6JtnEiGLP8su6/DxEIuuAHQ+qi/tCPYQYUcl1cZY5jjHBdVBbTowSWpIP5hDLWXi00WQePLQRi0BCAk05Z0ZfbNi1o9M67YvwyFdhbjZJKkuUPV+2wJVI8EobOX3BN+WE/Jqe/chgQfNF/UNT+iZtTTm83dIhOTv7oEtusJ7FnIUfxY0fju7L1HKA2QtbhmsMVlgSY0Dx/n9f1eqEwMsHnGDJVB1PP+yCldNYv2IQRVP0V8WuzultUBP69Oiufc9/zAQPCa+Psthv9oeByzLrjh5u8Y79qEr3kLhQZSJbcGT1Y63+mK2EeAMFEXs9clg8nwxIYFCE+CPP+SYyoa3zLMdX0q408KBnOT74yHnBAVjeHMcZTHmjjEpLwDiUcco9Ywq8Ke02i/JXd0esswouwTgqlbOEIwSUQJvfcCe/Zm+d497UaazTzWIoZX4S33A0lh44bX7TEkNcZSZEkGzbJn+P2jh39VXQ+SiZNtstr8gXS44KRU/QGCWeVr1cQhrqk2uo91gO1WrxjKLCpLI/NBK/sZzGDtz3V0uRL+RiyiaEDH8Em4odGzeH+OiGixmF20VBReEd1tEh6L/px1vuvn3hC6Ghj5HL+H94RpSjTRogP6WSUVRWl1FFBtut/zUFP4H7xgr+6F3qiMSsn50k6QCaM6UMfU+QP1BHCEdQnLMIpYjJ0f3bH40ZyhkCPQtHgSpNBNMB3Nbuy67B5H2bGcmH7Cqzesu6BOcHoAT6l9FpisyJRV7iQHHaP1QWLD2lMeOaGlPVGEwSCuZScLpxDiQHYgrC49Bw0G0M0P1tpqmKy7R5C8SgWJExDWEAYSsVLqQCYkxLjwNBpWdMK1rvaK8qDBUonVbtwuuHnS23L1J+ScXDGrdlz3oQNWO7fZh2eCBBmBawGhtUPqiaow/anPuUh2ZP0fddvyS2mJ986EmH+sKfFCgRVGe7/n3Vr2PADBXcJwa5WZWtcXfzumv+2cB5F4Xh2N4yJySpqbh7Eu0RramVYihfMWN2ctJnAdcEJ0WSfRT1VCH7eVc5vuXLcmoOr3HUVtfARhzCK28Ol5gAR++c74iTUb9f1RjKWcJLhFvKSqM067A+czaRt9kDd6/SPJAESAtPiUGjxo3fCsJhmTtmxIFkH6KmPr6RxY+TKvTu1w2JlpQ5Q7dCtQqRv33zsRiz0koqK5HXKbt6BWGpp4ySeERhivaL+oixjMb7YJVKrLG3FuCjFIS0pgcYPqJWZIkGSoEo8AdWqnHWr65ao2zlTILjcI1CxTQSJOG0zem3TqA/VJW5Jfrm7a2a71IJCmJowCuuFbEHQEAvahek6ErmFLsihp8j+6qPiw8Iw1VcwXQnLdrGzuxg8BgNVl526I4x6dOJsRosa7HRJUdrrEDQ73kfok9MxPXmZ5MQ7iF2UIT8EyOFDy0qPOjnbNec682j75K4SMSrek5tJQ1QaddYiSiEfHAdud1EKm6CJAMB6EmHLISW3pMy4hwJo7JFxIwljrNsLCme+KMimO8q7qw5UNKEBBQQln/AgisIvsMBTGpI5tR+u2/sbMPlkvkZP7JFEQxQEIiNHQZh8PyyfGHI4Krnu8VTNdhxozvVfJDrJEkI9Dc2/N5VVQ3iIpTgfOFBC3NBUFwZynft4j65wgraM5+XXNPNhfeMXTDA4nhcUR8KZWzDU0ycXJZWpd/WIGWPFvVZ1MvOAb1Z9kYxPwHe3/+NKuPZsFe0c53nySNxOpUN7ie0w7o8qIiZHaw+O1pdnjRaKevduvxTrIyYdZ7frk7Cq8saY5ybF6QH2DI3oOT+odUYJtgpuV/Fmcr/ToArzX2BZlw64Rqtl2VIrR5J8qUVQ2yrq9WZC20SXrvoVTNYrJJAqlBhKp/qoEJEDhrAlxZMngCc5fXlXE/bHFy00rgJJzF6s8DotUr6NuhwCSKHR2ImlzuB7ZLTaAvP2yq28khY5gJLQn87tIFOOHvRD02rzPoC/q3tlgMonrI363agh+IPKipQ+nE99t21nuSuNYSVStCmZTbHR8ybFrjMjNfyI1ucvBNrotkZOG+ADcho8sTBBWqRKUw+c4LMWeSK3wxpyhf7MDywmqrD0sMvA9L9/Ny0DmrVz1fjsyfQVlfm2XhP4ZAiSTuk7WxhrBzU7rm5O6bpdJYaa5JjNkNyVraDxbxIdYBKRD3qNru213ca1PUEuXf178RoIr7uGkKNbVe06GglKmmlhnha4T1WgjeL+HnJQsLkeZxhNvwnWKZMkmINHcsAbEpXykls2L62T8WIN6nfFGbadlV/nmzmPCjWXBBs5GpwRjdjA0Z3jY1EDSz+hoaFtnw7xXREkhaVGIYtyYXDErEMBB5IsKzLryTAHhmtVjVpWcnA/+2aG/FloGuzy7e/pQJnWBVYqH7div5Szx9XiHmAmh0kek3Zgf1UoDGbCHeVzNC5jSZ5GynQzX4CaBcvHwwCIz84saFobJYXr2thcLvXpby0TmVjjEe5o3SWzfxvdIL64wS9uNIZZOkKRxEeq5tysiKKtKacCOhLhzWuNEJQwZ0R6Xxmi1RIE6fxSqI8XoyoMCpe1IHnGDcPpdGXoaynvJm4w3m2jmjkEoiAgX/Dg8pVE66M8UrOV4fjPCM+i9is3m81szyrasxTze6hHMHa+PjxWLmlsPOVIE/EbvhcXLhVbNctEgAcgbjruwfkQX09WWwItgW8S6wowHRvk/tnmUXh6XAdDPxu+e8t4c/dQNRqFam3bMLDoeo+UJz8FXeAjwgBBI/lCqryGj6GMtcSE9sVviQw3cgVr94Ahx/ybcC9mTXn0fk48snNEppd2uCDX4XoG3g5Z9AGKBZwH94XkDVwFSjfr+rzJYZ8NupGAhH1lnBMZG8PrVEulTI9c55fIOUD5YHUJdsPh2mL2PzNFfvdfoKyfth3H26hEGb17NDc832fTVso+uWaBUIJ7oCeeAA967TXrtjCqWFsiwDRd5xqVWZIFBNQ5LXxiMXjGDkX4BorExhBkVvWvcRuzNRsCxXqMIHQT1fDJ4S3BEXe7OpUG0Gi/sm0oM9a7L+M37kJlEQVnr+iQUINjiJWvWB451Mp824uP03Gfj31gKg3VmldYq+34ji8Rot34R/dndQjSqNuxXr40oWkXifFx3v1lSb8+9lyLMdzhCo8z+mhwCk5Zyj3dmQ6m8izoaHN6Uw0BNd3Of/GasDCnbSm3CECTmQ9GDL2ViloxQxzV0AsVyu7zomYCEc739kzIvRlEqrronTRVNpRyWsMcM1F4kuw4B2ToIMZIOpBUACClkp/Ng2kxhNtyXtGaw3PGJumpACHLU7IIo/F8d3XygIBgc53aWz3sjnFwv4CI/Z6K8KsIXG5ajcsRWGEaSckErRnFecKl3Qe8/p97VhlZfwCLe+1c4xJktt23Hpo5v4CBCZZNnyyGe5hMsZ2Tk9QyFlmLqSXpv2t2YjXKqpaFEHMaTWICQvr/3n9pn8mtjDL4KsWkLwax/xGR4cxGpJUJecm//KMkLfGh+G641iusldtQPTyjJvdhzxCAus2D9Ac7ococaTb0oQSBZ/b9DuyDSPv3Ig3CbRUhEbyscqCszxuJUp65cKxs/feZfY9Wy7r/3sdq99wnnZXEVC9LpldOGloSkb9a6adpxkZBi6NIWjsiX1v3yRrtVN1wWTX6jTrRB2yc70CCMxPErInmlrFj/B6d7APwRxpTvn/GDNnsVDpiDl1JdGvzdbNzdToITpTo449ck1cT9S/RzM8U0Ka0tIZL3nuzIsCkO4lBiPt/UHQ/b/iznrCZPUM5ekCq01fSped/sAQXhTsc/TJHevkqKO+wIUdVCsjkqWZseCUn91W1G8uXm7GLnxPWYOH5AuaQfKrk5gUPlwQ5OqIdd1c5acNE5gWefEJlthL9cqHdS5gC5himoOVYYibqpt4wLy9ovZDeyPJ5ekt+luTg0sbLTEaO7dcd9pZMtYaURxKfpyJYdRtar8LvtaO+3MlbZlG32+gY/FFsNXLwwrIVDknvUX7bk59CaI0lT0xNETHcxDRvhwx2g8bP3KCqVPm/taj7JmffVc2PxKlPddgY/ORYOqdYbQHmqGgEHOY5pC5x/llAvkR7oMdeVus5A8gZVHvw/hYbumTh/+fWB8pH0dtPiOjlbgbhK6wqVQTFd1NUb/V5WNjZXERbhAWhsC7/44B959eFlXrGhPc8RB0kRGpfSRxuWxx0Cad+9DH8suekeaxUm5K1dt1voQEEjZ7wcGeHSxFEgU4snsm6YzZ2sHDEhCvbMjoWHQImWnrU/Py9dj+dsYvZQ3QlTA9QFh8OpGeG38AbUyhLBw2CKftmklKCt9thsq6FCQ2hFD0dKibYfWJXVhwJz7/UkxwBBk5bljGlaQ6LxI6wBhFpz081jhtYZmrQuRMBz8mjNtvDNpBFFZgf6H5sz8NMcNRNJODWL64ze67hXOe2T2DiWroRlLSlmIWYV/MJR5lIuH1hi2rDhDisoEAPteCZB3V1uWOPzUQBi097SD5mklGZsXr/fyMNOogYg0cNjZGMNpai8NO7xD3bt4Za0/ntCo905ENsrZeqvC1g0+u2ve4S7+ytG46kwhk593KGZYaen6ZQtAewTprTd6zz3XbwxBkLydqHtKm8A1Rg8HH9yQe0ywG3v9WPWQYZ7KLSbZTsWRYb+O32RHaOmt2KCAVaOXzCwyR44PJvLCHSmLwrS7j6iDAyloji/f+0rUAGUY17ZDh01xJyxqqvmnwnXn6dAF9dMwpld7WNlaY21vV0HUj9HpSL0hqWhwculaZbhGHh5B/V0QBzNz0KnX6RLXQ1gAsigXSMX58QlXYLI1zIlZ7AXHr3cpUTLfJTqtDZq8yVeWL5HOh/KP7r9+mG2r6shLyuPvg0XrbeQAl6clWcnqO2OKXkE2WZb5aGgXASpQciTDOxzSjDKH2mluECStLXLKinsdyPO8fx2CnVt8JPzboEq2b6+T1eUFpXsMFE+sN9WS1/kEGgW0EvElCrtV5wviQOEBF8+zea/Y9YAYxZuj8k0jMzVfN0qgigZ8MhGzg3KJAX1y8wLELg806h3gWR8OyAEhpr7p9E12Dk7agrgm0Zx4vs0qVA8Q/kP9dX5JA7n13YdselOfaNdtFiyzfmSotB8RXKBLzN3KNxOJcbHZyqKsSTcW89w0GWoyp+mj/4YWiKITCVyppOFRb60wFAIEMstg/PN+RlGZzIxwvfsSnrMnv7SPjfLCF12RrL+tDMdCnU3PFAyXxRFRVR6iGZFgYu551t77zS3Ra4h5YC0KotIiaA0wNceVvf/7lHLpwjhTWah2DMoO1//iRVV/S8dMLQLILJnqndMOqFHmWzNmlJPieLJlGeoMc1kXvGLj4GL3438mrjKYlnlRDuhE7poz1mPhdIS9ffmiAn4YlZ+H7xv+yKoKfqtt4lXnuua7MnrUz2rjIKJccXB6VoCIJZkYPVMSzRwb7xQJt3tGYil1jcomXaynN9Gi3Hx0cTtRZ9ye255w9cAyR9VKmVZNyfgmIqa4gm7JnH/MwWGQV7zrc41S4FOFM/pT+mlsRvOGUK3DqcRyJqNahHz9dnb+CC/0s+tYNQIOW/fXAyUP21sj6mmlLWg3SwQWZv1vjoxCSPoE26FkwG5njCwXM2N90OS6qwuTrYk+sq0DJvFUTpHIciBgvyHIh22HmlrI0QLxLWwE5AQG8eLFlE80KxwoM6trtVJQLoVNiL2pqu68w58O9MyqmZnr4WotMkpjU63AoTXcLmP1rCjHrEYtqgn+2ukM6oEF8JVlA69oOB5H3nXxOTTNse/ifLv2DBqkgoHmA6pWQkHa3r9pf0a8NwYG8yLi4ZZ4ava9QTV5AkCp+mEOWLuHg1GO1DW63WjyLafX0SJ6w7el2uQcodl7AlSmjqcVyHjEESDaI26llChdQOpSSphKQxCC19AQJxvAhgjQmDCfAk5h6oMe2n2JTYf7tE9GilScBaj/fiYlIz3hrRUqm8Ymg4z1kdxwKI+fTREVhsU1wvCkoZv7RshxpHqPhQYgk/0RL7dQ8Yrj7KvTQpF4Q1NLd4mX9z7zL144VoEJ8xHZNwMphcaY83ivz+ZS8J1iSRjuULx9aW4jm/KCvWJh9lghgQoC2ELG9uNangV6xaDkhUPhEjkV6isbffiAM799gKs6IZOQhHJiKpbHNXIecJ6KBYo5rocxn+tknOiM8GkVCDYoQvtYyh+1EgnIq0olnyRrzHWu7oeXnkuyVlrybQ11i+HvmqQI3p+xBZIf1cHdGXv7uOdEczcacb2+Pce7AdOrg7o3A3Y6wNt7WXdQI2kk7gEdDGTpIQ+AKaQDnEsUVxlfKjYOtydEmFAOl2GWu+qQttzC7tyLcnSFl6f4yyY8DJZttSUCrJ3OWIzNxflA22MjMg6yTEaEBS4Tovbbql1T28bxtxOT9CCJ9K4SUSqDm+c1ovAyuBpjQmKppan86VKL2xmu4z6rRgAA/peyk+X4ajxEN/+H6/Ev+QLLZ/6Z+XH6iqBbaVv76MODvYRdglcFnxIdplQZNuqHH9PwqpRcYGvrLBPOCMFskcZs+zF8w5RJ/XN/gS+3RYeAKOUZB35ad2g9lVQMqJm1xYj3ar60a8ZY6NJ2k8KYQ6CxWNNUWzBtaTmH1RgEYDOfNpPumnO+lLKfaVL+ij4hU97vDey9bWEDbv3FnXo3iIETgqcGSJUOSeJB0gYfCny+pd1clDnblXtymHNJRsAntlCq+7BmV1DoIGMqQQ14EU18EG8rs3gnDPsHxMH59JljbKg6JzHGvzUlIM3Cv0znBaD6F7udzFj6mjG7GZYGopS1xxEs7u+/h8kLLtVADXEZDT+NBZ5k6owef3CEWqT1LwNWj3A8w1A775eAgtxK5lV8HVfix5RB2THDaqat2ir3U0iJBaE/h/uIm+cVzdwu/6eTqbEx0+AwE4tWZssidczkrfelNNuXh7RXTZVnKqVKkIbkQpRe6srH29ywyTE0XxjQmZOk+C8QoGnqjP0X5DZVoFHmC6b5mDa3YVvJJJEy4RQmXufMCv3asITKhf4mxhN6NaPCXwQxq2pQvQdi4Jujd9hBectWDweDMw+lYUTBfsMurc/z9EnTfOQXtvzSWO/+GH3y8myIHH9CQOEG7hzHPpmWabp6xrErlqzp9c4gzJlOmjLslP1391WL3TVCa2veGIvzDFUZvFI7YLi7WM0ylq4mMjRLRzjHsgiPZvERRDxjAurv+W+vXMhlGKodxKBvryik0m/tTPnN9Hq/C6rvKcNbZDs/9y2MwLLSxKeWOexLrFU2nS9MBXcVLqQ2HbgVpQMNo/WGQivkRlxKsNe0juN6b8SflOsiJ7vsxA3JQ6typkoP1tK6IknaDqHyeAWkTd0mqZc7lbEyz7pVnFz4dYB2G/uboq00b6+KR5I3ME6GQp9B82PqLHiBbueg9z0h2gZ+KVyfHMaVC5Zwtt1LwnJ18yE/E/Ip4GXkinzt6I2/xzGdbbqDJ/AUydWi+cdal9PAABbYojZf1YELfIF3AAKMA9m5hVswBo7EHqyQkicAALNNWiqypImH6MB4yBAAGKS8avLkJ+D18E332xaOtrirOujzFNCmyzYQvJSPP4G0iBIwmrZVPxSXr8vkJiq/qHfNNxfur8QdxWPjNJ6IyEH3iMru6EL4R98tWAYHLRI5Mct0gcqEmKfGIFfx/Z7eSWdVSHtVtZ9nzEWKI3dA8IdnceNYDVZ2N0ppIDD6S4isG0S7DOwx177U+NDyyxbjy0ayZ/7N0usG4PxayEDYcO+KyclouHAG/WKQIN0HKvXEjg/Np4zYJW8uQDj0eD3W4Ly8SDJ0M+AVQ4n3vnm2I/rNJJEiRh37TljskOAHR+ZQh1tNF8QJU0p84GymrBiu/rS7bTMt0I/UaUv3lbcrJ0rrkoa0ljENGmlvJpBdz3PKEIrjTFVmDvDu0cdYjIgXda96w4XlSx/H5YP9EiVO3fw7dysa2vPX70SluAf+MjhpcvaC7fIMDwVeU41M63oo9gaCaQ8s1QREDoljo524xt5IvBbhHgh0qs7dFmBi4CMQ/xq4kCsY7PdQKMODhOBG/q/k1WlUQAI6DASd1OL8Tboy40gQwIA8XSQIAFOBqDrBGsE9NWKxkLNpVRW5VTRRRHyLfCM2uPro+gLsFAQDZRx0OE8exllOTpK3sP2oCukmtauuVC5yxm5FehDQvWwuJH7rUiuLbhCSYYTEF0fIhCYVG3lX1nO2mY+jc1MuO72nKnoQtp4YnjY0KXg1B0cfLQUg0AfqsWRVJzpfB1EpdEXRS3imZfEATUqriZvL1v9tBYMFdVa5SHmH5ZhRgoBgqdBp7UcGIPFsrQf2yXAjMofZ4Ti6KwusP43tT0ZWwxQZZHwfPJYCexkcbjhvz1gyhLqoNSRj3fvqPaIt9YtkAFQatIJHyaNf2RDQ38B0p/fjFsGhqDgAy2lZdbEU2SqA/iEw65rJPiFB+PW6H11RvHlEI47+Go2Bn+3tUfG3p5tGgZeno5zGDfAHdBMSh9LfQaPvgZGdtbqbhEFZXopLFwuKSJDzUqQelI3MLTKcFPHVrCRc9iHz7WiHhSIfoaYwYmJHHPW6OOTMoJEhTH7xZjl6HjZqQIb40OpjjroEpF58NdRKZq1EDbZFhlrYQmTgE5nwhK+HpiooeM12IiQ8ryrG2+XZrKhe+ukY5tCug66F4mWKNya+5MAiKGA7wRCMIAAAlJ6Mf7vIiBdjorSKHvkx0Iu0gBJdO8AAS2uJ7hN1VmoWtbc6u6MNnGnWHQO/A6C8wTalcTCNyYUmoyjStE7VltDOupj4tCQE4PvIm4NJAi7/fazxtNxTdxRGjt2l0UMLUo0TWBPFXe/7W+qnHCij17pXn2yHurHe3Och4yv0f8jU4OJ29tQBtxWAQUbYb1KWDaBproBapZnWe1/g8cxzJu2RGTa2pw1qlSnTDuddtBpSp6AIXwSXH8wL3BuqbMoJhx1/Mmvu1zvrGc7Oy/3ecjbhj4nnLvE2EsFeOS8IIAuPIGOhM3bsHqfgJdPcNWJ1hXguGj1PG18BsoVPAqz7iMD7CZwEvyyBrIY0njLEWkMBiTX18Ud+1vVYXeFZas0ufyWNyBza+XmLNute6Mfy/lMyh4wV5/IiaH5ODregiW2MLtn4GnrU5sR8/mx1xjJ1Qa21MFgRcrwlthunGa5fHrsKyjVT0m0ld+ThhU7j5z6u/fqzZMgy0A1/xQKFfSGzQeLC2xTrzPYMJ2O+YIdh+uKp6b2vjQK9Lj2LX7n7StSkLwON+8xE5FTtNt3hf1NtRgfGJdApxelGgCPgT2ELIzg7xHMltjPRlIx/wFF2/q5Rez9URn7pnWAnfQ42D+ZfXT7PMeNcgC1FXVpIi26VLCAy2aO/P0bEA1lymIE76FIgNO8YoQBUQsOtTGUf28OPYJ3LG/+T1gEbp9ZTN//ttuhDy5sKwi5ohOElXUDQVoB4j2o81i+u+D7WHj/6OJNxcur7L8KGAUF1kRNuoPKg7DoArvFAnjR64LE+Y9z2F7ml7Gr3qBvZIfqonakHbw+AJ1mW2+751WThMkUFOqZCnRaJIeKtXSEdX38mpM1Ii+VdCe4tBMfim/I/CUO8Ed6+Gngf8RwolqlN9ly+MnGq5V9YFSTSz/2AygBPeNrjeRvspQZAWjdTprGlCIlUmfFmNx5KdTSV9YsyPGOUeFkwJus9z/LoG7d4R3Qh1TXNO0eFSufGNwJHviDvMX3OhPQeo5+FHZB+T6QAA7MXxBfBPMwlrwlRYpPz1mdrC4QDbJhD6BC8wPa5ZjGte0DreCqNxTWuwl/J7r6TRM+W1LZj0IMPMR1D2J/+PecMTEQuzHZFnCxoFvJd8GQW0MpgdyXdfZ+NfGiQk13vk/Zv7xs4HLryW/oGd359Fehy4fsW6BjrLYN5yDfYsH4PSa5Vba8xTRm/r772xn64gv0pLYBGieTiUCr3wYJULaXfjqOnpi3ogRp9BpOlmi3uRb86X2pX5wQHkEwwDLME+SQBMwESg8EFSWrbKfcM4AAEL0JbAM8pofFVWG+fObw7jgxP6BU1S2WhxG6sLHz9a/5dIdgMCoyqxv7fjm0aymux4uOd0u/CyPLUwMXnNFkYZ6+V1QcienTU9SgoHwagEGWcy8+HqGd0qfGjJBCssN9HcgGqF//960MFxCbs4lOmyfjxJcUMgI2dy0crW2luu2X5uNKXzzbSCYJpbK7RndREaLCgZm82X2EYdIgJhY/1RTqLHhkSkz3xVhxGX67GxSv3wcJ0/x+O4ZfcCr5ehv4GZ8KF5uSrzpmaMIvcTKSS80CME755baDn6B9QibLgFrjCbL8oEdNhumIyWy6JUdzawM+nVrbAvX1OpXXk4JCFn4xF0kgXHLcFqaf+9wnStlsDQcl0+SALRrAaFCdutmJa110FHoVt7ws5ymnRL12cK+8jr6nqfNh8hV/LCszNL7UgokjHj5JpRXM5BNx8xPq46ELiqgSHm/xDCoPKQbsg9Mdl50rG0veX+HvrkwoNalzTffWErdo4y/TESmZqmUo6OBC2Ky0TbWfSE/wbYIKNSPFGTo90RoQTxLOCRk5IR6ncgKPYE87x5AFLQfL6WiG/mKaVjIvcJcgDJDRC8yXmXEPVTartaM5PKqHAioLdNftIHkpBmUNqhYUWEYs6PQI/DPuUsrzxGoIrdJLAgln/7cYhfNVaOzh/6Ouv1Kz6anMkPDvWdtI2wfHFqqVfaUUWsLxmP2n+mc9kGVgVQh7hk6MUBLg/odzFVWGAVZ0GePvlSAClBcFxf2O/8niSyJv3FUkmPm28aAAAAnzXK8WUGD6hjMor87+yUmXqOJHg9NYRvtkEvI7PKfyASl5aAGDX0/O90ngHGo8C1mzYwUwkCEh7iI6sKoATcueFJqk2Pa1RG5y8p2Mgvh90SBRYJCmpsf9WVRjRaZw/T04xzxxPbvvKqQ73J9axhJAK7RlzNOj3wR2ZzKZ/hYIAiHj53vnj1voMlWigQZdvCtSB6rbpwgL6U8/BTGvLg6EJ1kvezgjAYLpPRy5V4Yml0JmrpVSv4NPBbTWRIfwV/YUJ0wy83x8RTPvs+sZxdfPdL4U+/1jkTuZYyBP9PC6d/Layith514cawGDMjGyaflp9D0RlGhBn+94Mls9C38lGijsNoVU+2G3ltD4R/C62ih883+os7kg84gPyMy1SAqjOQXT9ASum+GlYxYLF+URNfCwMJBOffHEoTsa1KY7b2SW6kyiDgKCmUc9OUJ9RJqyC1ZYuuNwv4fbV+UKZPEt2iUWKunUHu1tDk2UOEgN3jU5kJ9BGIwNH0HYmn8aWuFXSbjb2E4edMhBWaq4gKgvl51M/o6HRdM1+S8q0Y8p0ZqZiQaVqptJxu2jaAdUVNlqIfxwxJNdrDpmcdLCg8qBiwh6bxrqSErJvJWg89uAYviJa5aXh85JF7gHGjQxpe4cgEqovyPkKxgy4L1/i6y9qmNuocHAvg8rgJ4HRt9ESqhMAnIccknjjPZ8IP/2ChyDuvoC0B1628s9cLx6seV01s+1oRCtiB3XUk+28nbwpI+c59bm5roZwF42PU6vNQX+GtKsHgmPaiFowCFEHgGil2leJgaFBpGNwaUfSVWDMqIaKMsokFnX5qSpXDj4Af9gZnHIYMgl2TgNlahwtiiNM/X7p09ikmknPPWcc+FFHQuKmShIG+g7ycHcn994wSzbMvxjMFStg0z6biLXoV6caU65Pc/sHaL5/j/NYtVK644Am2eeqZxc7f1VYJCGVOsJ2wJBNjuEK9W37wbPbcnktgDir/16uUivbICkMg5MJlj90anrllPCqGUQ6Rv8Dm50H5K5AIy0HiAsaydNhci0ScKsA8Ub/xbKFnIyDzS92IQGxbUAn3mdILKG3Wr/VjCDpmLyRUtTu9nMGWWEO11iLP7iZFDWr5aVgE4AHgc7wceJ2pT1cxI+N+FykvUXDuhP436Qnm9ormbUNOxe5oktL/kvpIHUZrEdG3oFmVCYloRQAWA8VUM3UXImt8EvNJDxnCMT+25c4h3oVSyaovW7eyiPZjr3qMU4c91vOKNl50KSIS+33qq9PwInjIkEqk6sCXFIUXQkfy0iAuCQq8UoSH3CHerUNTLeyG8IaDFIzJvPktry5C0Xxxc4KEMMt3q21Yv1PlzfkaeqDKE9DwwbpMqZiwCTyqKdz6WBZ3Pju1SjepDoKALa3XYlMJ+rwqs53tn2hHxRITkB63FGroRMfI7IKoXCzTDrtj87idUj8zlzaAfLyxVbg9soqDxHakYdUZguisaYvP6YEBBSNT07VNxkjPQmxMBWmceua05iezRJKfZmbtxh/hNvPbuNlRc0+WWhOPrYoAN2PZRnA5N0g4o8VNURIYWdX77X2KgBMQjxB8UYpiRdBGX3ABIEhcThBR/sAQkfyAd8rKIxuHjLrmcZb/04SZArlM0LNk9zdqfGGcuaYmySU4D5j7y/tpFAhqMn1Y2sB50tDrXP2oFsWMcdNanLGP55q4wjb7yT+7uMC1U/uKnpSrBh6YLiMgDSwr+UyGAkzCI8UtXHIeUI2hcQCJ9VRDp7l9dwHukYgnYoz7no1OY6oWXtzIa7J7pP9CPIUIFrqrJnMEdi4dfW9D0o26dRyU+rqbs42amAoOfvb32a/EUWkqabhCSeJ+F+jjyMDKrK2XAI0EYmzeg1HBNqFQB0I6Q3OjjxdXSwy2Y9CQ+iyBIj6ETd7kVdd4oIPxOqUM7k3Dxc+AcDpaXGiHrjrVciOeLwmb6wp6WNWLJ8QT7qLvKJ15ycMNWd3ULaK3/HeZvT7e5uzfK2gqc1qVhWB/tBbDoLDDr35BA4M0iyd1XABSPuNuVvLRU7Aln9bqJTFglQGbNoWH2bW2BsacFdb+gyss/GqLVSNgyVwHjdZeyZEqBJR4Vy7DHWuU5HN4M+5J+4dfTWuCVXEUCpkZGOi0xPdOk6JHQ1YIVxmAbTBAYV5lJKIsRt2Afv6qPnMEwCC6zqbEabw0U5wm40ryHY4g4L356lKDcmZEq1gGMF1t3mE1vi4cLfxxdOakXMwJhukEkiDR9lDgwZJ2H+IXc6VMmzdKJEYW7i2bysTbr4tbPNhC9P8q9YCkYImlvLF8/BA8XPG5SvKsD97cdolEQdNIIJf0n2p5C8nMv/jD4HCfcfCgt25nYGvdkPSbzm8cAkr64DRgW0Bm9SpHgyPNOJErmYRWRzXVLXt2kxpiiaiuqexDSuIGtxTJpTAxM3/BQ6u+73o63SlR+CMAcpTuCKApzsyHrMAA5QUcjkMcIe4FeYLHSyuuLEJoICEQZfihrXRIBWUlPw6/ePrUTaY8gpDJEsC/hLMj0eU5OVXyrcPMvqYwyq1lG9sOtf8WeWwVDlEMBZj+Opy+ZpDGaJvVLGfgzSBNhWSaZ2V4tohVoDzm1jhQO/gvEbBJhZF+TUEn9HdkafKk/5Oe0Ja36BhLx4tmuVBSJXsuQZA68weV8kKLegs0xNiZeczFLJ8NQtiT3lk9gdj+x4Sn/fnMVOLnfPCRTExZVXJtKlU162jPvXS0Q70bGk/BhwuuJOlupSk/avcSNY7WLUx0xUsN5ocNljy7My0ZlTMRie+dt5WBtv04R5lCckQre43lyVObUshvD/zBf84ZBdK/5PeCpm8u9vUQnjozD5ozE8l49RKkDVuzNJZ6+8Oj0QJS+ZBFC21ral6ySeTUsRqfdKmJ0bEtIYQMlhddSLT8Z6svUNsB+ARsIUum8P+2Gjpgcthi1ZBCSmV0D8VubxNMYhojKEd/8Rnfa3prrdVBnLOabRvoRSXgST2oHeils3N4VBqUpj8YaltlkesRESKIgLLmFN9tYeWmhouadoBRir4RxgikX61zyNnKjJt/LoTK7H6MC8EepjgL7kLtXjXiP3yB+E8sf6lbLYiGNauLFPubNXMQ2LKuixMPZ87xlkRrnbJa3dkJJ7YwITWXiyH6P+0nxUV0Wsy5hYEQc2kk47wG/u19khNvRiWzmQMaBxDoFOGFeEI1IEba2+aGWoQ+0TlO77kskxDSSof1hMJIS46S4ORWQbKU6FitkfqskUhqAJcZipLypUOwTMZPp2RiWmRKeiYfG4rRYAY8bBjSjBap8mLH0wxsnI6DVqFgcE/ArKKrftbqGcSvvcPHR03laHeK9VJ3MytCuXwaiyHnMDK6K1HZBiMMsBjWspS8N78/J6hVNa4NaovoP8n7F0geVHx1unBW8OUA9QI7keK9r9n8fGQJEu+6C+ax/WLrhkeJCce2F4f0WqQJ7zkvioJnbPhgbkepqdyYTqvHAcrkU4sXKfiWE51DP2oTYeEhmfD/QuAvlxAYL18/tCKyJ02Jfysuve1SIGbu6INOD2iuLHl6juweBYefUh3N7dg+u2HQRz0RwP/ImsrKOvoSpqBwiIVFVKoLFtaYE2vluzRQTWlAotCzIsUvmOT2tUsLdG00+R2XnwxrZw//y5HBDnxzvEuoyKqMVKPWUix3srAAhj7P+GBCvznk+0czAryprc8xQMyll4GU1HihwsX7l1HYvZSnS+EZhgkGrNiYI5C5BxV7lHTtsiLpFU0KPxGkGytMuzMKZ1aQcGFWVBoADpSywTwSHZeno7Ca7UIWAaOD2HgzIOSyZoeR44bfB4UTuNHOuJEmawd++8oNc/qdESYRvf/q7MtI3KWHQVWitNxt7l98JYQt0VXk666QgNivQRbGdTFxFomKUq9jDtoTPwllebSfvDCQsKQMXuUhFkmz54Nsq106mYb128qIIvbuaUFwTmhQMew0TQGDqBMCpP/ETUJXkd31grtwl1/Zwy1nPkEsAbtw1AufRG0fnkWyuTRpbTIG5npxCJ1ei9mWk3eSadRU4RbrRHT7TutI/QxUtOm53eHBdhEFdBt3QLtN4C42FI4YmljnbUizzh/ZphqZxr9C4KqDxmfs50yOh9w1zpiFtl4iO7KqwyI97PsWvrD9OSxJCbXeQVQkAQVSvqr0cG9FKFQD5L19GW2f9pWo91L5I5qlEdRAUZvY/M9o4vC6x4rxMeZ0dcHGRgsn2ewq6eUViTNRX2yRX3XbRiAA3l1BtsiQA586H3DGIx1kiggo4Zq7cW6jSQgiFdhg4EFu02c+pF4ZSD+N4kmFJggG4YqCkuywjxad5qb5tY0YM54OuP1a5/Hu4zhQYbfWWGdr13pKqJ0I5K4+VQSIzdtdfos54iVB7BPBRd/v0gVgWF6In1uB3ZDtSXeCSC7YVwYSjMdruIENuZ/aO183d/E08Xfz6Y7n8LU9I2Wylgxl+MD9Hux9qSDlSjWyMi1htxKfOPFzNEW48YzhCqTmNjeHb9rzNCJb/99VZMAaO6o+4fIPvt/syid1Fwx7JiDsISw8oY5WyfdSAD8cqC/q/zXXJEjppf6FThMuqQJJd9Lmy8nFJ+CC4didyoENz2WYVT4W2RCNBIEAH0GsAsMuq43Mui6nkzvrTCbAAAAKQMFBk10pFV/aVKHopy6uTM1vFs8IG4LzkleFBHhR5uIlLQwdDua3O9eF1BSbx74140FSBWu6W93/4xn+ZDJJtcl/jj/myv2IkrXgrUzjOPBwPHsH6d/9U6C4S5B2doOG7bECF8ZZh5/PH04V61p26xVUZyJoFj0QItxktSf+ebmVcJo4XWwc9oKeANUUwyfrURnd+H2lU+06ABYU2XTQGr/sTY3IJ445BcNcuDQhGhXOR6hFfXoDaXavFBOQ+gALN9EfklcTPSoz+pVItRgKnTCRb7EoZpDP8IlPTy7gXRDP4Eq0Z+8I4veWatCpM197KXwL0G34JuXM4RXnXtSR3No8ryoKCSc7xitLgz9X4oYQ5fii3f8cyBuJBD9Sd6EiKM/G12HMvyQ9hOSHDYfv574QmCXQjLTP0GTETN58cQKoveCDjFYbzUOYUvSHXcaWC3BCvIRHiUPANPnbLkx3iCULd4a7E/OPMOiVihcBRKD31kiDxc8KH4YXoDTUsxMBLzecSK95CzAqV78gBDj+FRgVHsAbvxlcd2SnltBV9PS4wh3iD3mj7UBKE50bPd7zIOqNfOX/Vr8dN1PrjsTnA84z2YHxixr7NSzKoS/L6oxjeOAQ7OaLb8xX2ni0cZNartJTWs4AAAArbBAAADfpQn/QpenLOmsiiAjtYDztxZEaS9e2+8iVEs2/j6qNEbHrPCyolExhfXzWQzy+1go9HXBNAzHgfofO8NHqXqtSnyU/rERciA1qKsmMSIjWumNV+UVJROEa4nOUoJfG+NXwBQkAub7/bo+CII1/xOS2zsiTz1Z6DqEp+sM/5qlcyIM6D2anxBRp4CeKhXKSjEz/X3whRjCIFCXsQd/uFpyHOGB854Vq3J7DNLCPeNAD6+1xmwZP7SvxQvIS62sXDUG0uA+duNuubnafs1rNn2730mBnCpJvvkSV+lYtuI88Hw3TkdCRmxlNJqk7+bg/hpDRiKN2AxKNzd3tzwexIr+lx9IaHEWrpvnYSD6aiNJOtL7SUu0fk5vKzvaw6xEi5V10VXIRFenpxUMzO2FgVxw1XQbdmK6NfsSHI9XtYVbwq4f3XugyPhlOGipeTLWZdXe1Woj7uxiU4no7026zytapGR+JbpROETUvg2igZdQVmqe/WqI7coTwerFgTcUztr0nAXGx6k1y7/4UoZdm4fWmJtMErQX3qyT76bszqu2bqagGLoNVnafNTgu6kImjSjbj4gQIajSNiRP3zd5WI0dNtgvc6I5jj9kFZ7rEUZG6ZC+Khg/3VPVf0aoPcShJUywNcuGaY/KIX4Tjrby+3MngGjPFuzOSxSmkDuTpGnMxol+Nddt5jbXcHjL+yEVM8a9jCSPH4dUsvBeADPeAlGk7mUebwygJKhCNVfyDkq5LVFaOGmVELjqGG7MObGxY6R7ua/OdhH/95vEkdkIliF/A0BDgO4B7IJVJHYWK38ytBL845If2cyrkltFgDbSS/AtK2018tU49pe6Wdh4MLXzStoueVpipa0OCRrsPIAmNQhGD30VqJUuO75/nDOSwkYTSaS3CD/wm0OfAHk1uOqB1DGZdQNUwG2MUk7wLjM96dA5bACNrpqoW8IgyMjsOpLeQ+bbgKNlmTdGm/hpf/oNGFAaTNtdYJuYACLjyGrjZH0WqM7NNNyUrn9+k3wTaEGW2r6SFqpSmel2GbVyy04k6Y72BrJVPs+q83pKb1hDIH1lVYpaNtbIcCDzg/nbR0S4lCRznOykXePLSc8nw7TxQHxULK/dZjIyc7dDmtAl2x1W3QQr+2Z4sFnwxkYgRK1sceWY/VOl9lOvpSeophAEh4fDUPgcOgizRvl3CN5ShLMwQa+fJb370ZRCkkr8ReZ0pGR62Eq0YH0IlzYI4XrRfB/fGm6gonOfTXFvH4S8GLI+4BZk4MhNe6yKuEP6yfwzFia5Y/72sPQc0fouvXGFZhZE69eLAxYorqpSFTDbO/2zp5+zsGDH/ZIMJniQHTiCkKZGTk327nartnxIr3DyRs6urLjyqcgXVCf/p8gaIh5coZDA28NwdVLLwZ9MwCndAAMuT8wlmq370sE9v6g2RIelxIglCZLEM4awUHyEMpsqVTLNaqR/s3Jd1kfXaUCBRim5hzUC4BKdFgamAwAAJec9FqZxBBf/OO7m50WBW1THZAPXBIzukD8MuX9CYACDbFCz/v+LliFbhR5P/pAt5uHjeQopRy80b2SLJkycde3+BzJ2QM0JEySxdYTlNlUMa/OLgSb+xN1h4AJALqu15xL9sVfncu2dNEuIIWkkGZkJPuEgKaTdIV7x9vcAmwWtfh91a8b2KpZNdgwAytNq9160EYk2k/wjq2aBHB0huGMRCKCD0+QTzthm0Wop3xGMs8+qXyVpFHZxT4k4xxqfyyEZaSKz3XSnuPORRGFtHm5+UGD1QQmymspOqcV5GqVXmxZXjdg5CPv3qYTEXgUqy52dnNQFcjvAxP5nW/ld/79tO5SSoY8ucECJ+yRhFhb+ndItEqe+Vc8QgSMc0SkpPUIduoC2SrpWP33fv8SrtAKAP8144GzKacUmA9YSsZDU3HXJKyEisINsuwUJ5FKnhjYrZP/z9PCZc3GLuC5sQa9cO4AVgGUNxf9YNxMVFdRXCJWhGAtk4762UQnjzdgI44dsKobmxpAllU02Ci2xDM91LNRgA95WZzd2fo+0ZTPjGVYshDtGnXdhSf5pxmoUXP1qsixk9a3zBBaUzCZeKlTMovbl5jVl0f48JevBeS8hxvGr4Zwr9YXWXHwg2+2L7gnpQLCeqKJ5HDR89WjVZCyzCOStuKy4JeI+s03NzA5fXzek7GV+p0SKLMDAwhVMZqfiYnCLyOc5nQGoIZMXSXifzzyhVmTOc8F/szfHfAqqwJZwoi2/Z8UhhK1u76dDYefyZLhAq7xYmhlO79G+ncT9wVeMxKq43j4uM6Gw+L8ZpYBjt+s1b2c+tmzsXgsTBJMWqWxsXYYibFHHrMoSdc8kdmBx7Mv3qOFT1b6WnQTHVku/bKZJPjFeU9i2m4PeWr1bbLErGml9VzdbBdLrkgnI6SfqAYTCOen5+y59hGqONRfOGnvDhiHD62TCXDdwiTsXp0gDP+qCTAbZZK8U/mHZm/zjo4jz7AZiL04TMF7xv+5ymihXZJMqAG/uiGZ4fhl4+/40ZmDWfEma+NB0spjMMglWkFDFqRUKCYNIcYHhrcqkl60Yb+Wi5kysHd9ZzQsbZ7tYOMl3FkTKB23W0Sd1ZlqPwZWPmOHHmHD+vMFwq+8tNVb1xwp4mxskvKEWWcaDV9jrrnGCMIa3WJsCWjDt5E8KUlAUAyPwW4WeE2Zmc3/t24AgI3fH4GQzm5VMvvWqp9OLTiNWI4LrBvKOoTHDVtuOw3Ip1ITmOCbBGzF5uyXJMnkATOR5DIqyNnnhukOQ3HSluBQHfEP9z6MVqXxkCHW67O7npYApB+m4YIy9zYUUQ938mH23Vo2D4ToTeewjEBhMv3ac8KGiPvcVIK299VBUvrtUQkzxJRwyb2leVqal4Fc3NpK5m89v32I/vYc+CTJ5+rMwbGpUAbu9VLtQXyU62rPU3hhGpxuLfCqfM3YaD+M2eKANfTtk33lXxXqRFWbjU3st04fsuIBMJPu5EnazolSVRzYrx8Cq50LdWbISzSEyVNN7OXbbuEZbJF0SRH7m+00wxx+niKbhTIKKNQhW+ifkwo7ELdm+MSXFgn4xiIalPmhj0cFdp54CJ0ezHZSWpNiREMeSx6xfVXox1RqFdH87awKa+7Np0EvK9asGqcukL1Nvj1MAsZGHcn5/s/FRcWEm+G0p0LT/YeDiVLMglwOUtq6M18U/nTEkxneQkFiHh/UQbPIlKEMlHTmXSZc7jfX9uPmeOJ+oy7RjdwRuIcyL+AAkqxfhULPyUq1jXgVvL7ciSmnO/2F7woVXNT0hOkPMRz9nxfnvGEA27u9ik1+8IcFEnyTQ+d6DZVTGEw1VLNYjMk0CQ4EU72A1e7xA384avQjd6oJuy4LvlLCA5elMIEhr77ysqJnXRF5QjF0S1gMcow+JWt9mpvX+ZLSQdVVevFT1pkOHjIkPXzSd/aMcTZgNISfsiyuqHRgPdx8ViwxxqMmxS2n612s/8XYFUtVObUkHJI5GRNTzfqdwmezENEaU0mKcKCKIBYlcUpPZDk+vQdTTWbvIfVup66MQFxtOea9AEF+M4MtBWQPGl39BjzK7hqHyugDT5OiQ78DI7xbPNG1BrOMPjdHhZAdTjEXWv2yBy1sysKzo75ib9lYxVxaKH/jVBY5Q51fgIXqDxHxCgDe4W2YljbAzZ61HqtT0YNRZfUvoKMqOR+tGW9YDQCBD8E6I0GMqmk4CB47a7kRkwecNPR3/UjsLA/4Oa/sC+HN1Ocl+yKLWZbt3QBj7VKQjT0DKRTq6KV8QxJgIPOxt66sA1LbhlUzSeBL5tDau27cMit9BDWDMzA2PVeVMH35W7Dj+cMMzm4gDFlDBnNyP3zBZSXr3vkjgvYox3pVb1SxDuHMRD/g44ihGvqdYfIXj0R8mD0oF1E4GX9CH4oAlmGzDR4xpOr8IzNSIVZ0HrjtU8H8uk1uQzus2cuI0RYhXTBMVDE7kSWRZlGvAU5+28Sxm2Sf3sqGd4QiDyD/4xVfByhIJvWIsISc/TqSyNP9eJNPiDbH/y5GVKzTXvlGGu4Rh98rlDDsmmE0oRKoszmYs6rx4sfZjUGnVSMKDU+YPnhFIf42aSPabEmP8aBRDQrmrK68s5DAkBfEBvQWMqf3krFuX/KBGnrk3Qtr05MicBu9e2u5JlLdK3SPTuCUsC/mLDwRFkB8gkud1SMSxbLcS9UvHgDvZU72WLMzLYyzOf658DTdhitvbQigPLkP8nWnmw6qXrYf/l0yZGCZ2o1MsSuaM38ZdgSUAGQK3xFrKoDmQg0/63V/yJ5t8/CvcqxMR/dHHPWyBxGvwL79Oi6Mp3Sui7WPvWxXhFGGAHkj8f1iWTPoFmMVmty3b40F2XVOzFbR6LDQJpXI5VGi6wkL2gVcB1bC2+tbgFYMZSdSDx+RZsNjABoq7d4TWIQHenRxBe/3uMJ8SIbDYjplvBgvDchkHYQ/4kkV15YkERVZd9vry7YTVdB8ZhnxMSzJm+0mNoyl0xDH5d0UShDs7X35r/zgxi31LqvkyJc1bQftgE0Ba2eX/wlepBSftBVSl2wl+zTrKcrTNegJeDleF9DUoAc0MSbpbvyTKWEZ5rq0YGshEuWNavXYKwJzTN6bchonIppntYqhyncunmUYXvKqaqUcBL1TR1nUN9bWGDjtHZkU6q8Xro96SFO0J2BArG67i4q+yxFy+1+xHQ965RagiEGSAiXR6hccmeGeC1k01oponjCWkrxWGX5VWTQXA+HlNMHp4pfQNTwMSwjb+8p4Y1xc6WvL+uU4WSR2LN4tcwktBQq0r0/AiAC7+EDjan9v0QAtAAGquEBkDubXfzhT9kFekVjZe6F/Njg3kNpp01lUd7FpLT7JoVUFEudz9swtk8BCGPE4f1rO4eHOZiDzkh1YTaNa1lN4KV0KWMKoZ337QF+rohyK+XkcsU5BzQgJ1rng8+SJseyb/7CFktU+aNnWS7nxAvg/wd6adn0BXlVySElrwkBxjx9kPIp6VgbJ7QPc4RFPxib/VQzXoClS6qUYyBpviGwsy5QmJw41DLRTIwzxOEKGU+n/vV96Lz8zGHySrYwRMrRYgy3TnCkA6ziaXI9KlYHVEHdnHCTbRvkmHPkqCTZTB0FPvCqJ0UFCJG/GToVbKRXX9N3y2yqSyGaSoQcMzKh7YSfaQyJcRN9PbMwPWLPaezJWh93uUg0fM3d/gXqi+Ilj8ZXfUaLY6nEbg0tT2ehMCulQdvxTcEG3EwRcc0tEcLhEILVpcDzEieHovzQBJj0H4ZGXe6AOckYjk2GFZzYSIwGrQdIH33nBGEP7suEbUg7xx4f9WJQy5SS+KcKr7n6lcEBzdGHXv2EGVlkQaofU2lij9Mrkbb5JFcuustGr0JW4nAPy0MuOgYMEFK5y1Tma1eNL2E3lV1PORN3lMxUkb8Hcpt8DhoLm/6LCWqs0XYZ5WXc1TVbI4oThheYwWHwqiFNLEsWCVcXOJxUQr5ewq3peviiOjOkelZZvCEvXQIcNr3InvC4T+XIhjTkBfcvReHc0lIfwdetaXghPk0yCXoXyNidyYIwZESrqWS3wHvuJdcaQKbl++xs/TYpZ5ktSHF/VljniexUACGoAZDLK3asmXLfd72uU1J18y79OXohQ8kdWtr/0uZAZ81ryrkHpVTuQGgEpkpJ5LTFpL2MW4REHT5RjXgy2pu1weazVX4JgyjXTbjVnA7RBpSBI3u1MOZfnBIQpPISl0iVAEYn1qRA547dnXxmxNIsDN11kV0zpJ2F5nP3P8wxbXtPL3jKlCKdkc4+vjHGkLamKoRT2/b9KFNyBYFTVY8iGHuwq5jd1Fm5nKdWch5o0nJ99jFDb6oeokGyVYel2B8wB/YXKKkPJfaKM9XCW1LyzdbdlkLSSWkVzLfl/KS4sJmCd1X9PUBsCYGYx4YQHN53vr27XKMuy3RD+TZzw/5yyIq27/XUZgZsTrrC95SZ/8i/kzGFUCcUjmz1qiicOCvslYBjNgCnSrNGkwLMo6a0CTIzrVNsbgh9d/lTanOfGxrhq5goV3kE2EvRLF/cW0ViBWR3f2GdaOanbSJ2BQAGBBwAAACR9lGNw6g8AEcBd5Om80JmClg+EOVd5ZTXKFrl97YZCw2vXwtBKDYqtrk25zMq/zj7nW4m18bqSeOhhRRb7vlv2gny/Bttjio76nytl+YTHds6dsvoq1OuXVXZ+IMAWDTuuAHJFsPJ6wZSx30FxNAiMgwN7B3Ru0qas86CagsauWPZZQQtIf5JFk27zkOlnC1aDC4dRQ17f3VVTzvcqZTzpu2zgP3MmgcwPh2qjLNgLkOcH2P1RT/oCZt+0rfC75gRtdGhj6By+y8mUIRtvHDEukLxXK2x5fb5UUoeLPZ0rvxPXG6lva9v+fXCpSKrIk1XDKqCy/E+a/NuuiD//UGEVlI0I1RgHX3RtltojIrtbN2es+gwb951B51NHnZFmW+738kX6Z4cdEx+472mtr5E5V8812qY6RS76Dn84h6sfoSluJyO1GDctdKR01QGrgU1Z36mK5xH6u03/tgNqyZhfJrsCPDy04PELfhD5r/n+YWHWirHSKdohWxn+zvRkMmR3qE55ygSWHtMsNr2VBENbUw9OC1Wi61OBsGDJCT00sOi9kpKJPhU3vpTJJZRUrWE2EEzOvRqtrCx1k3EPtbGQojBOAOh3Bhfv08AoTPR5FrhRorKHb1NCGFPOByDdIsneBsThN1dK5zMSnvsB5pnzpw5s7JTzGBVuxWptMLKILbezbfPuvcBVmHcgynTMJd3+YfkSUoWCogoo58OvJ2cX30wMnYogvEFAzzKnOeKPOF1w6IIuSPuSXT5DggI2Ah3xCnohBklVJJlSHC5b9QPEcmu8xH21L2lfUf7MwTuBlRWznxFk+BI5Qc+DBC61zXxKYLTJ5Htl82typN1Qz6fw2lnT7S1M92Ff8YyyWGsUZCyDyVSOxw2yl6DoTxM4WGwvgfXO4NzTijJ0JkCKSvyGH+5k0vG5AUDdYO1AV14B4Q6yF8HXc8oVoid5h2M/dGN8LUS8u3ZduOF/ADFr1J1ZloO87W/4lnOjDPU1YS3GFzdAgHpNTsaPhYtCzzKDHQvNPbimcrG0gG3G/FYsQqhDBz6bSwTa8Y6MpI0mulVmEuGB2ZwoFU6EnVaShga9TVNnlf8qgvlww9XrLR24cBHsb5/4cBIxV6wYUg1GgGkqYxOOnomgCMo8PFsb5T1lZPTe39PTWjznyBZp/gWkBZPY6O3NcpBU34Yvvp+dH4K7C/RPunp5mdgp0X4d+EYaZHTEk6z9buDnTPqKfDnBdPC5l6mYsOOn/k2+7elqhZxlYoq7iahcCuGRjVacwxjKgoCiT2rckzFIZiD95p8WWIrhTcqDek8FSpT0bUguVNXoamIHVvXy4GFBI/ur8QhHUhxtmS6BWs31nLwT2yBEPucRtfXN7E9izy756hd9BnNd3s86v5D/LX2qXMDl40/NqjzCSEUumtu+zs8OJD8QWFK0A3Pzg3h5bqz9VdVQgcq4X31O1Nso2HCNJZykiPA9+ycffKOoFtE/aFTnvFGqfOdrOf9TnIEam/Ssw7X/ZehfVRBh0zd1JmTKy5OJQhtLmZzbtqK8dTLM6W8qijhzAiLGsaoACdbAJqJZhumuyFXQj/lUc9eNLPxPtmkn3KgBjgq1dZAJQlHhtDdIvef3KNdRmY7b6DOtkFC1+uJ0XUvgq+gLgC7klSNUQsh1NvBaBYOmI8J7UdAf+mO6vXb41T4kQBxUTZLzGsHlVO/CymXfbvCFsH6QNmRd7ABvevYPPy+rL7n3Gi7p++s0MaxaxjfeuyqpjnTbgSDflak1AYyBvAA4GMHQh56+FlGcAAHg7cbzCx1er6FlCNLLgqgdo8zLA0QTKtnPsBVL6dadWwkjXNVXV8svjdl6LkyYv9ZpVgCQsl2ubzoVmgPpuxFF/Gpxh38rWxPwMqWJQk6YL/ajz3e1WMqHWBGChaw8U61pENdqrTnq4kheQERPjOOXWeQx5ylYDufnLTh1gTfTb2HT8Qgzu9NjO5mQ/U/r7kPSpmXsiSVz4v68+gAvfSTDvDCMafWXZKMfGX+kiMGAypQ10FsDs4UtoMR1MbIeH1KKcwWxfpIp5pNOboUsWuTweXeZVj1MUKNwfEe85BN2C6gnoIbE8b3/NnaJ63rWLKNCA4C6trPEqENO01rem+9+3hUeakimQr4wP7Uh6FWg890Rty/HkHcOJsx9XvF2oxFz7ENWb5xx514a7zBtn11PfV3BDvwKSv6GeXR81tk//SrNSDNErElzRastDB4mDEKFBc6eI5RF+ouVpC+CAB92ISSsJkxjigcXewbF8J843iGMgOX1hxG5MTbmJWvilS7hmZvRhv10ey9Jb9uQUJ4rWSGaCLrKGEE/0TDTutWijuwjcoqrkKnr+2SG98/YA7nsvjQsgbaIJCsCL29MEMORtSw1ul8/X3Ow5razus5fbgrdPTh727kZGhqgc1bdqyjUuYYsXsk7HghDSAdT2eD4Vq9J8j4cngDxcVKz3vii7czMn8opndkMRpb38fqtsqWvTRUGy0im9VxLDd/QEtfpl4CjxEIysVUnWCgc8R5uVkxAu1lXvgmSkA9j8FAIw04o7moe7nBrCPaE2QijUjIFIAjtVFb7yRNYZKQyp2YVszvp52LYCHXQQNB0WBXbzhX9EHY2FJEmQDi5HWsr2qQyn4YQ/UGYaxvL2CSQ2ehdzy1mMSY572hnvtoSOU9W/CsatAqllyruyi6W5Dp6+fhAS9QCjvrib7a2x6gnZsO91dTfBjfXyAJSVwcIdzjpjGtPysduJTcc1qA/6x58a5fhKyxB0PkC0bQqHFcGTgCnIDR+m4GxWZBuDbGfE9TnU6TgEBO9ZNPhQVoJ84qoZToMErAeeQr0DI0K8L+jWBrDK/f29mNe7TN6aL+G+1YD/cQeLOv8hSf/8wV56cCrKmpIIgXEq+R3LlljxALURG9Lazc4BMRrd13ouk07IAsUEAi2MP541mpzIKzK9BQPceAUN6R0O0gDwsKlf6zhG//w16rI7AiPWpgPj0//+eNFJzy1Qzuwf6tQiX1zxlhzIKboZgHewGftiNSLntC9WurfyY8zoT4klMUSXaUXRsDWhD7gBjaHCO/UMjFzVdWhKDohnJdhJByMitmHuDXolUopmR+NzsKAzzvy0+SBt4f3As60kRXIdpUYbbIi+HM/9iLPKsNjzFf6OjB+QmHL+A6TipruFg/JmeVV+WJQC6gAOINsp+RshhoxHU8sltiwzRCl1bxSYAD02oAAJGQy3Bfm251QbhCdW3UPEHHVxaVMS7sCS+W6PBkNzjMWCtO++Z8gObV66Kk1V/+UNQvw2ISUsbO4e1/07Rhh7XWPLSRC0qntHOE5kaHJvzPL/LrvfBIA+ltT2T1dgnrWlweh6xIKUhQWCH52X7pueysSaVU2+Q6UvU+8GE8PYT5LE4vlAEwcXQrrZyEaJVqQcZCGOkAoSCqOGfTd91iX5Q4/+K6O8R9Rt9q3DI5I/9Z3T4kT7pLQoJSzmsPUxwFdHI31FRvhsnBNKFk0xPvtwL83AC0bAk9VuWpM85j1BvHn2Cmn6B4Ggp2HhcM+kbAoalZAuTkZ/T0NNmb10eaTCb3iESZf/YGBj671HJCleZBNxBNisjwjUSiAdKiCCjYE6Glcr4EIAHspZfDVJ6s9DuO7/TRLVQMlVT1KTmcehjL4WOFd5vIVnNxmk4/ZZvx6tyQb42j1jzxGm0SV2dyUcVG34OIkW9ujvsau6PbGr8ynwDuwj7sBz0C7uqGwTy5wmojo6ORzKGkK1me6Si5LBlHh/xjv4GqeLq9cXuQ/DctF1M52c/Avo9MzHKb0e/MzBhi4RwFd2gmnRU0HqDstx4YNVruuYOKzkwg0eoJ2oGb7dZfYYl8Cu38BHcshmCIS5YGUfcDNE7HqoWlgSN338hKOWZMAtDWiklaN8a5QGWHd4a6LYok+bywmEEznkrHGHExtLf4qSeCfq3P/GexZB5KAllQ0mmWC2AO+GczRssEDnFipJJ3nQHfL58CXOBOk+p5PWflLcDWRRl/c/PXfOAzVzTKnH+25cqTaBqJFnk6hTawOemZs6almBGDQVf9bMw+o7ufWFt8Xks/3WJoDIwGQl/YOh650m8t6u7IAZBH/2ijmzoH1TwL7DhrnFMAf00qqFQPcAx2PhaDqseC3PGLlNP96X99TJBpzXAM+r1MDIm/Zh0pQYchUXrX1DbZmZXP8m762ahbkhJ+BipRtK5kniZyrn0jeuciRjT6A9lGB9CQG3bS0/UQ7DveU3q90fbFYxMAO+gaSc73HribAWOkGGQcb4sUlpiiziXfxDD3TBWfZkuj56wj3JrjDR65hU7od86lwRXt2PpWUupQm59pfPsfHYB4vc8Ltg9NZ90vftXz7HjDOut6RYTfXALFJi7PXCFVWMz7XtyNUjdtY0Goeh8jte4Fr6+nuNNdOlCjGGkqYgxSfUqnmgfeNrZurdpIxJC4JLLJ/vJ3LrFF/FWqQ8q3Mqa4VekuHomJuXs5/3TMCHGqFNMTlEaubqouX52o0CthY8VNqMByjYngaoKGVAdPse4i6EAukPGA6FQXcdUFWMKtOfcXV5u+oHkTngiGb374eBZUDkro+GyYPb9cRfWTOP65Q+m4+fsSxsyoK3eV04cAv+iwGog5th2sFAusJWn3Z7kL5TfiuLucHdunx7QexZwSvvUUlG4B6hipGjyWVYo2r1zctnesV7n1B6k34PjTVuA2arYtU2sddczgZ+ad8+CajSurh4HVRL614w/8dL/TbqVGCj4My45S8j1bPFiffNFaNNPJKIr7KaY4LN39ZlGzw4k8mKygmDwVT/wvzaELU7fz5Ptj6IVRhR4IKjSnkILFZJB669+MVRNSTzLhk9RQ9MxIDpUdfyiFXy7NLUCl5R6Nrrna9ujQchiy5yOEZkPIO3cIFth5QChvVBkcKQe+/eJ0QZQdjtFKysdI9pONSugM2O7FONPo/Y4PxfksXPMOsjyPMfan8c8PQkCtEQaePANqlFLMrlQrzOI8QEsKYIW1ByhQVl0pPiB8Ea6f7YMBErIL3isdtHjAT9Rq/qjuG2vWasZLQ0y+LlOP+Pl46kJ3Z5swj3FitNeszxp/iaVDsn4ggGOjvrNyTKjGUrHQU1a9fEOfzIsK/heuKs70VUQKCohlZVCw/wO8QHYjG+NOE+1u/4P2BT4w1irbm33qETmcQgyRS2MXe+Qynn+J3EDi0/VHsKPNunCq2sxV5xTW8tN0cl9UmVPnGsFnk9zwt6JvSor3Q4/bF2/Qayj0w7ICzsXkujcZ79JKhXMHXUXjFC3V9WF6ton9H7ZUz2fM2flBCUuX5FyvGZZ7OEtX14XWN2cBdeswprz8NgCgAUnVHA2wptV1eF64FTBLN1U+SCY0AAh+AI6jB4+ISfmB+LbvViu3CpT+WA3/U4UHFaMD+YBPeqqHR+FsjSpA+Hby1sDIA1z/TPIpFSTJkPj7CbryLDoHCfN2QLkZogp0PdoUhYxtD2DGu5shKMdSDzm5lZEy2pT72BhwM1kvE+4q/T3v9fCZSLMufCHTlinzW6QKb5FXvxhqtZ4I9SjFUx5+xFxpxpwLdI33NG85awQKh69lNVUz3RNnZnUENRm+IHwakFdkcAGZ0YeNSM0xyyARO9z5uoisOfRx4sUmBgsjO2QsGAQYrF+TvdUeP0t01vuBzLn1Sh+lzY3YrEtVKmxbzoIVQJkofU4yTxxd3r0qsPOF5xROY9d5WDudxq3khIqZY78+DA0Lxro8VniKeqPLOi6a2jWPH21vsfpt/FZRX6no/hTrOfkR1RsOphX9rVMLpF0LIkfVCTq6V7XJ1fuIetkbHr7KptOCQ+OY+Id1MQwPGwEQOqlZwUeVlpjIfwAlTvfmyHEBT5G+3mgnERe0Lqjyy7G/+EeAb5rWOpENNUjHN8WxJXZBMADCK/rRpX+brCJralwqFImeHqQF9WtZpayhLYGuF7lvyPdzal8mIbP1Ost1GsTC7bAyh0PnIw8aWQcudbJZQdLZPatXd26WJq/sx0YCRTPj5upyBR76tZ6mCatZGD3/kxzaLgZNk3ncgIFAIbEl8Hx1C8AhCBzReApyJUUfRoMtG1m0fPsM7GhYJe/FxlZiFElxFBFxeHjzGeCccxl1ItPYoxTwqG0wlR4tjouDjZmiNBNqZg0pqyc5HApqlwl/kF5hKZvhB4N//A2LDESW63IN9ePzPHqMoj44Iucu0wqEFDZctpVsz2m/84hvHODgXfk0oi1F8EjGPUXPCs0oW0oGj430yt4c99MmUpKmp0AxtewkRdlaPKp8v7NnIBljTKned/x+aEM2oHcFYErGndMjwsTlfk2HiDN4m1elZAiq3ZWCK3cGVgAZNB8C5aELNf5B0Mv563WUxZJRVhskKog9t42kBHwacZK0u/t6hA4f3mLebjEaJE04c5LvKxqqEJ9TT8+fiKmQIHVannaXJisciP2SWE881Xl6fsgrloHMhTZ+eZyn5BFKXXksjB87WuFxywX3h7BpYA+VXzGF2BbbGZWZO8dw/gBJgbTqr4vsKiE6LLW8uoyOyZ+SoITi+knuwMYw7B7Qph72Ml33SsbBngtHWAz1QgpN56aIwzZUni9+Ws0b4q+x2cj1p+JbAOYEYXf+Hzn7TspUYaq1omT4f3WGLy9YYRszAJcNwF+3EHrkAAHkqqoeon1XV4QzwPewAwsyznZtmSEJei7EJdS/7PlVMhm/fI6A5O3z4xwA5/d+O8gXuCN+Tug1mTHjbtvXhDuaa9pSe2jtcQrEJi3RHCBlwPTxaYh6MBS2WYv2xQlLr2Pcw+ntDwVZ0NwDeTcRzSP3SivYXeRv2I+VolIIrIfZK1yoeQiMKKbV155Jk7GkqhWfBgdlSZb+mWRtbsgdjXYs+Ae9CgKiduswyNYr1da/ksF2vhvXEE4vbrC7MvDHRasH8/pjIAvYsqpBwczU42Z4tScXgpqDd6ek3nurGLBcsIgAhX7r2w5z+awALBBpTU6vNkePSxSPB9tXfVUnRwKE39Y69QpdcZFWI358dSZdqDhgRXiRNnwLeTGW/r8QZYnQPg213iUyrU/ZnLqiGOSvr97f003lDJaAXrPSXMtuVgqNXGGeQwumpv2+DiAtDZq/INLhbJNwXcwDylw4exLJmi/7Ar3NR3v4r63fn1YU2e1lNJyMHcTposmg09E5/yGpfb0BXvTiQ+IhMLh2YiRZ/BussCcgiLC6szfGbjnuoA2Md5zdXVDCQBfcPCZCUKRTSWqGIawWq5Bj8O8y3NoBSRVlmhfHIcnnPElMVzPdkM2IZhr9kAKfEXLzDNePE3qZr2tbTlZg9FfG6zbK4QWlUmSbEAXYg/bspDF4nLD+TIOXG8OCB+1QOJAVPQUavs8aaqJBadca59sGLgZgYsvjA9zz7fDqLPbMoEoQgM/FmoeQip0KofF3NN/E0YiLCHSMXJDmh/volnABZrJ50T4oZXxrDYC/2cBDZ0F5LoA+f7yRIA+SSTyqmnv92kLs8CpG3gjPCa5XPZkTamZghts1653KO5bgfAAuuXfqtIv/W6afigsUOM3eqWxIVwPFSR8YAMD40h6TbBN7wjowQ7zCLoZSNTMZRFT3gbpdq18Vf3My39348huSXgiXSLK1cwyhrCDxOXbc2+El526+eoJGdoTwV0mXoyhgYQ3YMNhcZjjXuYXvmTdHQe8Ly5U3ApPm2CT3M2Mpm6uTT5aBQ9HhXj8IwipIy/6vkmhbZA7zWOvpX2/myU36/YXUQeNdx6+VyrVDFTW0kgCPN8chn9mwJGNodwDvzM/P39QqXqYlKTtinx0R1tK4J/YLWpAAi3R4RtY5b4n0GGBaCwKLqgBhn8uCF1NODQTiQOhMErDzxAGFq7PM4MW7s1lBNOH0EvEvHHXCWWwjbT/OOL99VvjnhrxShKEAj/6tbQv3rpeBKFOqBsVV0ePuQKtHkxt+nVLY/WnoJ0stUYEONWTU/KT1TDiKjdlb1l/ZamMRcXbmUv0h2QOi3C3s+5OcUHouLCvsBP8VhB6GAZEHtJtg5Iv6po6ODtIcV64RWMlUI0UAqloZdtqC1+85qikfzgrm05qGTp++Y+JjAHsL43/dsVmJypJXe4BVDMcGowdhi/kYulFKHKLdgvuFrP9NHOhoSyJjdGdAZ4CtobCSZ1FCSCMmP/eoXZvpBiWC+zPIDvKGd3amJddGXaVplB2HXKDxtoMaew35iNZlB+SJ9p0GwUPi6p+926oT9bn13xF7B/8scIGP5cs6oAfhfnGHtaSPCUDwKeB6cdtQrGC2WG/rhy9r9g01tWN3qNnmLD8HCd8d5fpmvIjeUn9Uu4Bg/LzVRuoQXhe2mwuu3kuvPmIuoeeNio+gMIvUXiFfigF7NYbPmK5z4kH4n87eeTEVF9UfQu95vU7JOYIXfDSCgZB3ailffXS2Y77WMkBgW8gGH2zBKXACMIA4hj7nVi2QcqSEFLIZuQgrFLGZKZGxRfjC5ZBNn/6oLJrWMrLxeiZ02MROCIlgGOnWzb5zkVx6oxKdzYaLAYxpm/Lf0XJbxqPKcrMRSXM3fF98Cgh47H87U91liM2PEH1TUDF/U0BVMEboQsbHfO+H9oO8SHKuUAHAe0v9RZvskl/E09gsoMsbHRDLexmMI4pC1Nx907cOg5MPSnoGroZ3eKiL7k+O0en92Eu+VXH4nNvVE3B/CNrInCkV60uWfSmoxLK9Ihi4yIwQlDSm3bCT7vlowFtWhu3eIBst5EYKsTxRT/0kFnVn4AfhE/evLGZPr5ecw7VN14es2F3LWm/+PKOqUODWLGGvHqj8aqGVd7EI921eLTQ/1yWtiq6WhOkP2bqVQ+gucfKHcH+Y2xJGDWETTelcDfI4r92k3RBZNlSmwL+fV9PZXVLawmO+PgjAv0FhqkDa4I6aTOsTxXRNQ+BEYKc0/suG1aV3f98/OPhhMRJ0wHhxrZdNi6G3qFtyZ3gm7JzYTez+196x9dSKaAVMpBd9nIIAAJaNuuv4yXdD3fAfb+aXvsKytC59A88D65Rf4sgAYA6LsKVRDHyMbAwnl/dJDkUzlZb6+C/ADru62MBSRx8VvCFrHNLPQNr0CzjBUEArRygdCF8lkG7VBO6cFBd4s8k2h1sIdFIVThFK5+OhYN5fKvwWRmrQGqLLoBrsUAbYEpe0dW2A0uHljGjllfNjJyvzRs55G3vcGwb1nAikXtTCHmHZ15jgglmVQ4NDzjJ/4a5bC5ePkXW6kAmdB6+uLsOD9tuwkHpVlzgYO1UUM7U0And/d2rMqAyPZ2ruHDQJvEXMmQtIS2qvL+3IOCWZokezIMNSngumMEZXC/SJbP5KG/x1t2r/2Z08Iw6XIWQUJTk4SplPzfkXe96gesM1hT8KPGDddp19ND6pN4WwO344PaijzicYbU4KOJd73xEtVDD+ToeEvGIN7zFQrSMpuHXc1CQv7eEbleaez9KXagqUu42qVlGq8gBhRxxq0J6co+yHqVSVpq9HvLnSKWucbtXulwrpNLZ9xmUo8pJkH1RbebB6JXjBpIqNux3MyvEQvrw+cj3tW8WcDlyBTkdZTYOrxfpkCiOHJ+hB3FAvkGUzHtzRlWGMdNaEIr1gWuw4iWOM4uokoDns9K9P4RhxecAt4ya0HcmWiFLGsWYnz6ZABwi7mlxhTYXNsnGiNwz5pXKKR6OcKvJQX7RnSOMhX0nlwP4DTLx8RmRDPctViOTgGC+T6LYwksnrKO9mcJFxTSOKJiUa0ofErzQgxvSmiIWYEbH9U2NZ974jSpy7QXwH6l35eRaVevSpJJRgiEi8x/47OxN5dzTgAOAO+C/8dVv6x0mdEtscM6Lc4ibaVXRSNDAqUiKXvnRzdbaU0hkTB009Yq+246isnu0AssNyR8fOx7Ywvr4P+qH+GJ2tkFxpd1DdjpZHmBKG528rRW8hfwyvV1yr1Vc1RjfpC9uqxY93/zQWNK0czLHWn62w3AR/F5WLq/28kWg4OBubEAdqj2XqrvponaOxkap9R7RPPnE9sQRHfmhJz7U0ezzu9Z1qXJdKtwjwvK7tzRfz1WRO+cDCwAUkLkzE6tPyYZGzV9a8M5zcxhJO5Ve4ooH5Z4aKfc8Ub4FClAaiT3T9D1aUT1mhu5L6Kpp3jkp1OtJKNV5cTkwTrjLwnAZ0CJL2Y1FrsTh7S8MINGy+xYyvXM4S5wYjqFWh78PNYLJw4x0qMpNoCTJMFr9drPDSWCtZ2niv/fEzCSKKLTbtqZOHoaa6OCZ1AtXwe3gtRZcUplEpZi5y6PX2bunn3Muw1x3yTwTwoX/0ixPHGs5aBCHSxSDrMeMjIb00OvLUJiRiKBBPYaR04gSl6Z97teMggd3QA3D+s685RMnRISv3gl2RgzAJwJq+SHygpQ+oFgfRjuXnMu8/UXmT3tb942qoxCDGY9c43a10dhvGSWa70RFrFdR+RuPFzoSsuyBcsHLxNO9jTUOpQRX34wn4T4d44USaEJAkJJOf1oR24OQ0r+40N7cMxLfa+GILE3BlxShpx3UKE6GwhrIv4sqH/9mVeitMnPcHV14tQ2h5JfoZQEXyux2XItIkRk9B+MF+2cQTDaEEuLHdWHQVerDD1poanrP2SNBSQ7+SukyKeawLYJbfB91YaONbteujIkFofP09j680BZnqFC1DrnaL6yUK9pfcx6n4OqlWFg2KntZ1zSyNA8105FXRF2YzwqCmwFe/RwuXc9jVyLjHKY4IZheFXhGw32ps5VPpBN+ADtXvwWlbVyOzypkWmi1AGE1M0YvYzvE5LlrHvx+jmGEmLAyWmGTjHzUeTztEqV3QsINebMAfu8xwvHJoB3xKGLiGhti6D7EtCH//zM5/XmplfC6zzXFwi/P0KNVP/ubmNWmY5gnKf0oNdcduoMvzXDPa1XSJtn2gv/BXlf9CrVmsqhUCxe5hYPvC7Vzg92snNzOkmDCTM5cnvoF7DmbBQxr++I1gWbMGf/HIvIlbH7npDYH1KDUy9dn36TTmFJuhys40IBULc+G5fcgUpUWu/zJZ0p9CPkvxe1W1T1yDvh+jQC74LsTjwSO0CDPsdwBVU4osD67lm2NBg1p+jHDt2MVYqcsx5rcHV/tIbiV9vdbGCAZFsUZyqswjgnnyt99iHOPf5POavP+zSPeielz+0yY0dbHL9C/OnpyfJOLTYldA6gxgsVrp//7WvlLALhuWH7d3paJn8n/WsYKPZAiXA4kykAWAdeUaZKR1K2WlZZqji4Pll1ORY6k3RuPrTYVyFQpQzm8rZraeYeZycNdeGASQqmFIdy7fJzCW4uBmquSTX1ohetjeA9zGAWfHQbkui+TOtioJZSsPI0WMY/0rzwzTLGB8zrfVR7YYZM5qBF1+LCNnJ+7SppJnRM2CAUEwT1cLx5N1k8oD6xqRAOZMZAmkM4r/l6bYAjDNfbtfn2mXS8cf3Z/VcsukJvUSJ1NJYombkYm0BZYzrzjQrTefehYKsBXRAygQ4PIjOLeu6ntmMdfPbDM0diqVn9lxgBNDUUVfnZmPlm4ABnxukb3PwUWaBfZEk7CYjz9Wg1Kugi0dMKvzh2CdoF3C6fwzJH31TrXNckSXsRCwoPFrTgT02gdUovL01xgLNMJOL4nDZKC31C8vt/kaymxrjeLW0uNz3Ke4euMcNIAn9OwG4XA0tIvjWl5vFODWq8GT2j7GblCZYO0/ZJn3L1RMkxD8xRVtLNUBFrx1/8CiXbRfOCpimrymiriALXNqfsvkePwQUVfJ5FTQUFfWwFlbcb2fLmySi/Hdp0emrQtm/kxcbsaRMROVOddklB0yM9dX0NiWnofY8KgeEL7aja7DqOwEoDnkHZdNrF2A+ua8gliY0hqfqKapXFpGxTuY3LjupXi+IAieQfm6Kkg0w5ihUFmSFSXua0joaYosrGFHhICCMAgfofTzFArJTQpnfOo4/o9smcuTtB2hOgESZX9rvnXNc4nihdK0d8jWOE0lHKNSBPdulbIBfrMcSeJT15Nz4EntjWtFQ/7VNNGxziCer4B3qgm4dGYL6fQLBrEGnKjLU4/7daeO+teJeNSWYlf+HjlQfrc+MgKSGXuUyQbI109Li527e2/P42/+WMDaQiMiogpGkWA04jHpSf91dxaDDyS+oyOj9wVTQQyV82NwFwy+YLoOol37OgV0b43eKaa+i2eTrWuWuYQa++BLwtMIOokNm45xqaIfKmTuA5rNZ1/BWQ5Q8DShuc2SVpuGOV7kjGhzvnUaM9Bttlg9C89JV270xrnyI91idzWjq8dMz1CIJQ6uvVqCA1Vlr1W2ECPpYAnCg1VhUNdFL7tzNTE+L/jWVbw8sJCdyk34TdZYj/sf2zVLjEQ/lqoJOrCjAdS9tLdmrVCeg5lJDwNx52lhU5LPTLexT1WZAj6H6zgAZ9E2zwF64nJZJ3z5iSse63ZDnxUVcEg7oZRWSNaDCKZUmPHGM6XBClJReuP3L+FAbMyS/Bif3BxSWY04NzyTmegQqdVEq0lBkxmh7CYUgXtWHeq56FgZBSdNlIEUrVVh3JQZnRHBlqmGlRuC2mSTUY50PxZFO2tlOdAamDDogMLKUF31TWrXkfqhackC8GQKCWMTbGR6EJ0dD/k0M43OD/g6IfeadXJA1TyWV3WZlFnxpKEReoK3h4ZwM7SprJYlhWB9VSMV4RrJ01aHH9ofzWX5tctjTpal3C+NL0LUPYz9fMN7bBeS6nrc9xBS3xZ2M1+13D6e0P9T6Da75IOHAz27HtJ41MJX2AH8/1Ncdarv8HWz9jMGO+J9Gle2e/12omgsKi6WpG5IF8sonoWKMHD4jMnyJJ7seeyq8osJfHTgGk+VLXgquzMBbKFPpV31gR/PB9w4ndAFYnN5ef+xqmuh87eNHUSk4MJGNgU9cVnK6BszjSK2pY7icaBL3YQQ/EoqfZBSbTOnBnjvFjIBCu2WPaTBvdnKSzlNVyIVuCqYFrO13al3/qzK4Lw8b655iOpffxz/H29JmrmF/kENZmZjjPJPO8aWsNCmF+cpqoGRi1GmYntVZ1u9YMqzPuy6Muw3k/pK6GChqRZ28YO2vlW7TRuaJUtH1wIxA6Rf5V74ADVSqgUAj8Stiy9tTM+T2h0fL5uM3libYr5j31k5GbY/3jCdwxHWyWSa+7+GVlsX/52vhTSjNX+QAWFGVI5el/X2PgCU20EOO51oPpp13gkfTwJOWl9yEP8lzHFY354gSZIKNzgXS2yrpDPi8MCfPUCuLtMncIvdgIRUdCKEeG0N0fYIYuxH/KXGhEl6SLFFXHXtghSVfkKFpGiwIQWQvwC7k4yvFRT3YqnrCe3/EY1haGeeql/FBx/zgDe/85uSv3JtlUSG/1tQT/+g6EXdVpe0/LTSbXLlSPIn+NnL9IKUxouhWNWqHNQNrPBhaqut/CoOYuSim7ALsMjbqfPWwP7xgv4Tyj8cO2sSg+9MoN5Z4GTsnSUw6eIw6rQJ5WM3F6geQ3v/vlID/eAZHzObXfs7FmEagEg39zwO8I1/Hx/9SLbPwaBKUKt/cZmHc72RBjjDcCsvCuWou1Uh8z9tXLszjJ3rGBcW90gn/+kGF6QLOSiVWCPuLOKEELN22KaC7jxj8O1zAdzxiABou1IBmx0rwe/N3AQT1vjZVfg4el27fcClXeanmMzDoUTolcPHzSTGvrqZS5RJ47ZdDPjdB1WMJT7AJ0x6Iz/p8hhBTborYkm84T3WN/Rj3mTz+p0r37pqCvGcEccLE86BI9RuVlHXR/oAiBgITBZYq54Wm6YT8V8KZgNjpG5HSJJGprLLs0UCUWz9UReklWwzA7y8LPzb3VGSSBbujEmUxV7U83F+W798CHkjeQO4Z6kTKcLNg0n2AwCGJv2w0Broj5UUGI4I+2by23q/8y88yh6HC39g4Pk5VmfFy/3ln0xaWvicknqSpCsAunAhT5teD9IyLS1l7p3glOllmb5+VSdrYuzrodmleQW0Jf2yDD1eWhGMGvEywa0BJQOwD+TgGFmYpPb3FwKBNkKVO4ZPnja4BpmaUUnm38UluFQNfVHxTBpru60SEBZKiwy3XvCo3dphyqhvc+u/u/TRYErl3ommenL/f1+ourQZ/GeATayrtY9jUu8YmFviWNEggelJZ3XDq3tusaa+s8TBtAx8P0VDgjlgChiHk1UgELUM4nbHPEkMh/aTcNU58bIsfL0bMT0SX8PYQ0MQgqffOoPIofXDKYAdFMRJPg2mWH6GJ5owp0yl/kwhVuvEHGTMvs5xmMoox5yHq90NXxLvLOJrYL+InGOR8h9eOoaquCO4CjrRw7rXggtjZf7lzbrgdfDtLSU/aXN0CNc17KwhlCaNCioOjC3p4M8bx/50HDNuvESIST2kMvjs27Pn0FuZxdW5yoAwtqNtX5gwjrzXMh8+g+vBAONQTB76HiHyDnGZaHGllsJi6FZI7rtzTvj92ZzPGcSKrMU7KhmS12pKESzYe1MLs6dnyRsVSw4Q/V0IzG/x1NQvpNlNmw4rgURyLTDNGnvShNJIG59FM/L3WlbAmjrq1ae1rEDW8N2ud2rv0uEvWwptB2DEmgfA8pLf2rLieaVdCMRxON2a06h0kC6gMSg/76cvH0+nyeQMgM3ILpq8OpjYvcExcqAWHlq7psMdXdiYXKWH3z3f7d4vzX64Bd/mO6WzplTIkcHgpqWHcL0ovTfAuJ8Pi/1G7u+QTR5I+yU0Pl0eYeaNWykFHqtZJy9KLnsDvdVBZ8JUJiILSEbIvz9TzmpxWFECLRtpM3dnMe8KRA2HG/EZ0jLv1ml3LkSVd0PuBU9KyQXf28fI8xjgqegmKLogOgjZx+8N1Ase7Nl5tGFwssn1XH43wjS2nFlnDdkzfLrkAHXsjhzR1+sR0p4uiso5a9REc+4urvxa9F3ZM+kOdbpQfv2Bhr/nCcI/DPNbPTYBI5SXs9BBAb9UPD/d0rsaNYdTOnyejyDwV5HsIiZeQ+E+0JotrTTthxWOJGT5OEKhjYfKoHIZETJ67jI2r6dpzEfe3G+GIcSkAWAMGOlk4sGtcyTkaiovP4QS6mstje1Wd/nMi/qQuL/wZMdQq5h/asEKl0JXMxqsOFjZ63zODCvWWxg6DQKZdGOCIX1qdM7oo16wbCLO8w6rH0CvHJv9Dpvxp1UoHhsXv/50NiNmGBygcw4bHQot5oXoR9cVgZFmYXyke/dSypmFLTCbNaa5yfO1W7RSR0QHiFjUPL2MrA8rJN+BFjf00u5qKPOwF80DHc8RA059aissRsOY6WVF3nuj3qsf1PjdMQy81QMRiPhU6bQ+l5MuqViUtJbTrxoYubevCzzxPgXbswt1SCxeQ3Pj5fsBq0jggKU0BJEjva0DrtHozkPnXGcKhLvoNmKtmBKlIXY4g58SEYB+Go0B41mr511zPZQMM4KA+eEnqzTFuInoReRstCSNRLilQwPyGfNpesfYvVN/BBxYZgq6tbu0xfqFcelsvOnWJ6t2GVd8gWxyWF/gIuAeIA9yp90JJftE+pwKt8PkRyJbrwBwF7oC5QoqMQAX6Z/C5MJfiIZNjb0yMUsjMu+6lg0DtluXk0AuPNDfACgp37Kp0AiejyoPVoxsskhOGZQd7LHznuYNO+ln3t1TO/JNPulFluFSz6KmkocVCCQGJJEUNeWiMm1QrvcZyD+cfTmPqxC9s8v86Cg4PvUPSBBtBwSfZD23UXlK7UFVJVWjo3VP2Qf6ggWvTHfQkIa40SL76+o+e0Fu4dTr7GaazVpvNdR0jnZDC4YF55hqBJFM5jTHMryfil4QSHbpgqaSmyTw0qi7JSBBRr1haUT/kOezlGCtc7mTnI9DJRFi5HBP8kW9p/JFyvA7hX86wvD0Xfwp7R1HucUJJ0zZt8pXmtQdD70N/IjlCzmGYoUzq1ha2OQUkhwcXD49FqIk8xkk+4aDwleo1EaUuanvzm1QagFeXf4UL8w1o+v63hTZQIw6xtm7tAhspDvStv2+32pSD2KLMNxedr+DFOijVfZ6r4YyqpQ8voLcVONlKzuQXzYlpZZV9n0+3muguWtmU6hw4X5G3O1BBPqfW26f2uEHM5nEBcgoqbgqq7OSl3+ar0zFSfdr4iWl50WeoT2qvjI5i4c1iKisO0eBFR1aGB+GvmUJJCxoCvEmqcEiSqrcg0lqBtFvhDurhaEkWaIYN0+JYDejVI6Q4+loagEJo312rMJgjkbw23cwqgRUtGsZIco+Q9v8/2BRuExQjMybuQsmYz0EG6tXXllugKAjDIe4rtFqYLYx7xQ0TW/HGf+5GvdCoLnKKtqiU1M6dbYq18agqwkatoMXGXfrNwfglOtPqjuCBx6t9aluWfAZI+IYYMpdGFNUCC/AsXine4EnYun78b/mU9sz+Da9MY/rPcSJv+IBaQFJpq1JbsqfSPjOSmZ7WYa50TdeVq+2RuaIDQnpTMooqBpepFn8s99J0WuMIocZ+xXBNemJSfy1HlQasFLV8bSSonEiFEF7ZSb2b0hfZTXT5VxdentxZ7TmFJrjXu9NN3mBbllwOvu//OcWcBDN84uXJzHKCc2IupbefgrepxpUbMI3V/nJ1qlX5CVHdXjH0aosXu/uT3XA81GevELClZQvI8qIFfVkOeHkq8IFPQY/Vz7IgkLhULltiFz5NwuBYvYvfSVoRd3VTwGajYnCHO2LXzQjEobXq8QNknY/7QQA97u4Ddf/ohajv8oom3jQSJJG6jGSJN2fuy280iBBzdUKyedFrmuf0B8jS+P78ARg4ZrPw6Q8Zz7t1/6F4iYb06FckUFfGKBypztQIMIvTwS2M3z+ARNC1fuVm7FsBGumFT4460peCbPJh/YwJbi/NNHh81+TvSqmpzqdcl9r6lsiH/u0eQzstYbnQtOoqd9iFSZHAcfYjlhEiS60JRPGPn+kltLiId24g7ZUdb5yywEuBVZ226cPlYvy7AbRunaI4Wj0S1lt+H7JlyrHCrD0MPcy3CvprYimnxZmNopD9mibbRs51xe2CgAJW01HIo3pB07NrxlTZ1qqJRrl4OjGs7/tiLRNnD0px9Yg4kZbMi2Rqtizrprf42lJwt89U3qyPtxwMl3Zz0Ddwz5Add3pI+M/ryNHZ/TAVnIaA3tfbZ3ms+d5OGM1GUg31ys+bA1fclyYHzZEmFe5geKE7JxiJg0UwTU04Q6Q5CF4yC+D7E5aFF0UUmRGC0cFF+E8+TEKJUwy9Fp49635B6+e2PRBmyK2Kv2FnnWD5ZyZLzb3TWwsM0qlNS0EtnG9IGiLwz2Q/QjbY9SwrXHSO5gzEhHc3jshGAE6sD8iji/tZh9qD9/jgHQ5MqUkC0y665aPawVAJHPdfJALUpj8TZoLhI+C/icnTkDe99L1wt3cb9lUreSXe/6pAYML19AOmyOjfI1cCuhPzntVWgJ5BfKor8eSjFu45PglW9dBEyOJW5x3F83sF9D68mTV7gTLqfb9G977d+pi03tuA7+FM0uvLTvz9QXIBWWQuqHHBzd5t2UxRSDrkSq55h+zy4XXDxDCKDDrriHzcGlPUUvjVxvUPDDZAxuvIIoNS8W/v9QXNeYxC22oIZF0yT7sPmljtT+TP/S1WwV/RokdFkmhhBNl3zNIv5+J6VZ27eFXKvOmStU3LnmVO//oBfK2MA9sl+2IVBqN9KksyJCDYO1a5E5HMQGaW7pm1DYtC9Pae4cTvPhrNyz0pLgpXI8NcLQ69zMRqcxijXU2wwhog9oqz+Vx1wHyMSZIT0KBX4Sxu+GyYsyHEB4HtyK43w3HAydaeKVGxCIpp7qn4Y04K3OB8MPyuqbYoyWCao+MPzK5YVXkx7WiPOTJW6QjdsV4ogAwnBi3eOgDJl1myU30p11NaHJUuwZ7zZG0Ir8IbKhiSSWR3nktbi/9menAyEN1HuQ8hmAVWDCiL0OgKdomG1kGlFnjYiNvzklC4Sxa4KGUWP3hrO3htaYgkmDGFbobFr50ibPB2StBFjQzoDiZK9G6fjkFVdKCSqzuODdo73uDojT9kmH2Qe5glBWLzmiTKA7ZhplOMX8wg/QTI+Di0BHUCHmdq1tLkaatPeZWFJL5whQ/plcQDjp672qc3jg9+1Kwb1AB7cpE9SHFOQ4wGkWsn4N/EHCqDOTAKJc8mdpc10Ab4pjqsf6Z71yg9ytIKR7/sai/VQjSc8/nf1IVa6/QJYLnJhb7UgkRAyrZ8YdRNQhSY2EocULOuQNc9S8Vfmd0Iq5Jj+1sKvD3hPa6hiwZzsHvsC851LRAGuyHpQw3Edo7tDDYSifoJvy8SnVa2InjPjAakgZSXY0eBUc2tWzxvkEUn1l/qQ8nw+w9NFY6QW2/RHxFJvwrckFVLmnypnJcQgLIlkshe3I9y2okJtL/7rO/u9eEl2ek3nOpjZLi7bsXgBa68344rkD6IDxB1z8ygtHJET7c6HZxi6iWjHUF+KFgaO68rTso3sdQVQs9MSmtAuRYn6ErxFa1Hqrz/P6Xab+aQp6pb3BpZH09TJTYEUWfR0KeLJhFH6HOzt4nkc8/3hPVHL0J57jNKzlgptBmbs+dERVlHFHmjyq0avgEnHMp+62MPjeS11FMBr9w3nC9LGeEAwCltSdl/nG4Xwnz46AV9jIYXBm/9EDlsCEBs+ZNodducuLvLT2r3+gvWBEAEvydLQatGRUMThK7lnA1u+bEDjNTJICpGt0gWDML/8yjThUUJodGKEKzuwg7cEX4f0m9Mvn7s2tODELadLPzn1Z2Qhgen3dwARKr+i5+cIkONodVUR8ACLNw+M0UkvJVXUp86+IK32gX+0ZcGlnwuKMVG36wWIuOdC4zIk5LsIxGbOtM1GpDBF1VPydC3ZoQZX7eYvR336vhM3tx9D/wqlBAQKcILxye0BqzGHmW6lSaWHpO6GaDtcjsc62grNG72CjBhcKb/+caUKUQX/Wm9e3N6GOEX2YPdk7LbPF1PTqH4b3ZscIxWy/5ItfMSC7dOv3Gyq+wWJDt3jjn3EyTOW2ImMxWXdxHVOS77c18lAtT8WJ5vtthpTUmt6PZ2WFTQWq9alybPiHhZLR5+J6Yu6W0rD845xKOR+FXUBHnHI/KaNFi/PE2J8G4/QoBZq7dP9+cchDeWoIxgNKN0lXmgNd+VswOmnmLnBnoNU4nAx3X6U7ySFC7gUZb8VuFs6KeIUby+hmIac14H8MxXHzbLEU2kQ0OTIlXGpCrts4XKCDu7bvruAgYb37FINOTxCJqs1aCn/d4zgpTND3Ztlm8P0CiW2QEtOSRiD4xVkDK47lNyCQPYASMLx1qDG3PBtyPEyhWF3beMFsOCn7LCeUK229cqE4qnyCqL/xbWDc5ZlPNvIMzOIltY3Ct21b0L0LvKkg8GJ7dqfStWJFSGnTiVntvamjbdDrrU4KKTWxtKQrbBNgnsjfgvA2EAeL/t9/Xpkwx1MH5a/XCq7kfweEgTQvBDAbheInoS4Dfvf31qKVz5dYLEmX8DQpJ6+MHN61oi3Gk1qUPcv0gLUrNBydLakzjCpmuydZWKPxACcbbRjmLgBpB7WCm+WVHAzsL/Vq+k0VJxc7UxF9Ij6pVKGjCoYsqLUMTyCm/ztY3aTrZHqv/WDZiEwODSJY4qk7U3OvbQbljfvurGRiHxRuhn22kLvhlbYFTHgQfWBTUwoQsKONk9lIjMGVFgPjTDNDbzy0/gTHnlxIOyxdvt++LixLEz9PA/Fd1zuOx9u/Xl3V7ZQD9cXqHFerbpUUlUYrqsHs3X2nxXiceZOkJelMff0jp0HuxPTe/iGZUhfSn5fRes8HJO0FOQwjLggiP31BRWIl9qXsGDuzwUcBuvdLF0IXoDlP5elO37s3fNh9KdeRYDBnw1QR+Bz2LhBfohw2lNnASJq2jwrtBYZ45HZh41MVQ24qyPjB7OugdeCYVrXsO1PViwlyNwaCXmRTkrYQJxjE014rqtXQO18xRiHZh6oTUsapF2WkBXcf8lMpC1n0zgip1ruh6hNEWE5nRzJpyvLy0U0I3ATijkK89UDuRoxmhi5qx3cjFUd/ZT0lii4BXi/COr4DgPXMSIdKEAhvgISKZXgudeOKtjqa4xUqJFjk3/ticHAAM+L18l1dkAoFhNR87dNLlPhlylxH2IWyLNdYP6ckOs7gI+u3NGle1avWMgvebUMH8ugRayhXEpsoaQMO0G19j0vERy/baPI5P/mvUYN4EP9aBJ8hzdxBzlyaENDqjvk2NYQ/NI9Ii/EyDK/2+TlgO9xGYbkgg4ed3ZgA7QmXwiWuuFFj20xZpxn7CGFb5BM1RZLglBzYwqGUtqdipFgMOc2WVdZiSiYffai8OHpuVfW/xRCuUr+6vxaQQtsvz+WY/hfSKoMMNcY5IQNxJwuqnGpjWpfW77JO2YpfWVzW5D9DVJ1qh7c1RRkgwvE0YYX6F54bo8UwePsBru2kVerM8shNFEZPVVjyjowNVKvTGvJYEGGnuMI/DvaKGhqGvaSnLs/F1e/HPUwZjpG9SQEBKHOYHs40TAp+BELr4kgmoWp1jpD2pyoahYcdVYDOnAJu5yCyZKs8PQh/jVb0aWrK27Ez7OGdde6kPOkdW4Wee5RKzh3KM01oYv6ZDplSxKsRyE4u4Mw7STu/X+v8cs3HfoOP5t0ar9c6HUtzXRhsz6cKcsZcpTd+96ibyRueiyW8uEsBHBMImDZjDunml/CgdplZuGRC908GdixUH4vinsrtnBM8Rxl+2QzxN6opMHZdQOMyyvWuQ1DjR5TGgzUCdE4NPMIpmIEGVK6s0nzrbzpkKfk1ZJGJvgUQAjnXRDbedWCxSic6bKDzsqiAmwDUDLxNfrJylbJdamjn7E/pkAfIPo/FA4wtrhh9Oz3VXsVccyG9GaMQwFHzbhVxybRplX48xF66mf/vEt1RvXJz2tYiECwJnSfXYGtE1I5VzdpmwjiWupmua/KVfcewDUWJG+bQhiJN/EWdnbFiAizg21SUNnHlpvX27BUrwbGCjbW4+s1vwpjIblV56Om+XC2yhOMWbgysLOOWI8IvOwRY923KOFMlvy9sHjN93TtlqoMXbSBU3Zpay1LqE531DYfswXuFv4+WNOVDzHFctSgwffbOk3rTHQ+A+cBMkd8R25I0G6yYX60bEqLD1Y1Ycrz3UJtlTLDXGk7ij5WsUniBeULyjdr55ssTJmYxem9I92qGIc4d/2k2vDGLZGzqHpbcUtEoQq+qnGlU5bE1A0jkFSVotXYF0yfX1+9vYc8/k59MLhSC9izdxVA2k7FQQVjwftEqW1KFYCK/FeyPvbCqdZnuRGKbE1PTCFQthxVuL/qZ4lyRosRg51Dgmn5W+m61/5QGg5JHWnFxzvLZ4UB5TMl+Yj/CwC4cR39jvEMk+eNvWt6yHaNrM6P640ydQ1We5lDYKp9vcpHF21T7lwVZ6m8WM9LpMEin36G6wI/Vxhr1QzhOPHMVTaw3a2CcA2eAHyWC+hjf27JXj21jI94m8jWupV4ALI3UiaJo/tVuQFOygzQB2K6smPSdtt6VdIL6vEx1/S3ZgzU1v3kgi9LoXCAeu0abzZmY/77zY5D1IXLVdi5bxAkxUyku5KBk7Q5iRS6DCgTDTffsx2OxVXNulfT1/bY/90kdL7Ep/eTeol/MQ1ftYNjpbnM6UYg4wjTE2lgwAGNamPkyyRStFG+thMtXxFrQxmbk7d4a4hZIhMZMMyav7HPTBpn8tOgIWgjaj8gygS8i7cvz9zdBF5doR23CuUSVjVTH4kbcOb48688sdUh/AsjqPMKXmENbHbV0OWoS4z7UPAzzVwAY+ZS5edKDX40hOPakmZ1rC52eiUBrEEk5c03NTdp+FnjTisH8kWPtWGA9bK4zP0QeZpElqVhvI2jbZDz/38RYhL5uLU39RHC2BC0TJnL7qJwZd80/gv9SjKN/Gz6lg5P4Ip4iScv5mxwZMHoqvc4VJPxG6/AKP5lfqqheBg3DV/IpyKjPRYf9IWrJtDLSqjsTXiTUULWQfzmUAiurhynUqTjNroOL+qTLPUJ+f/VlyFLjT1/39bwusnstPIEpMJ+z308RLNNCiVMm0lWvUy/1wpqh2/gSYOnNKQjtCWJsM2tAASeVbavVLnAQYcabDYBQQgOhsKkOST0zKPGSPOyWFV2QD728lx7XYM+MQsvWULJTPYpTkH9wbPdh6VAiqhW1s7LyxjbGP9z1nhFcIHX5yImyzNjHACWt0tMEVpcS3zbjF4nqu+9uRCBtnux1UUQJRy/9MaaFUpdM7aPG1CM1dEelZsUueg/+Kaq69vjZ+y3+gFF8vPyD878/7kXjmBUJLDVUpcKatip4N+SAZXW7PfACAmxe1T4Uuz2CYGWsBByXRRGZGZKTJWEJBCVnucC7+PT7gtfqoQMjAIX7WeyZSFPkG1N3kqrQ1bI0aW2XPDT4IOv05KeVaLbMSAB6uyfXCO5aievROietv0hbB0jwIZnxEUCLM4KQ8+cnB4R3hsLRE4dUwPif5R78ozt4DpgYsVlaQ65SQubQNcB0KnL8tirY3kQwrTzf1YcGpbQVca4JFB0v6TGdkNxD+WWsWaTHAhSjRcdAakxSTqmyBvaK3XFLVlwUPf8cCLsYYxu1W2G/GtrhC/mVR2Ek2i7oUBI43Bq2JkGBVxtSWdn+gMRh1YSIY/XMXTGAo5ymzoSmrCCzrmI7wOZi/VArXctDEmWgce17KHLoEuHOfyd8g5dz7wioqUgLQa/aykrb4GlxRmrpVNAFuZmTaaBwx7Lk/AGhm3jQ7b3vbFMnvztt30XaWMp3ok+eW9YI9JOFCIi7W8O+ZN+bWSTmv6NAB9GUAaDAMB3Tm9KgZsfGYwukcOqGkxzfIlU0273636/aXSB4MjjG8SbTlKFlWk5VqxNDF1V4B8uKKxy0nRjBXF610iSESjkD9Q3sfbO6IBrUrb9Pnvg3fb6y/JMUg2Z0sZ41YG9Ovk+VTbU5wvtz63fwKhXEmeGOcYC9n1NEwlgIXzKuhMrtth3x23iJS/EW9q4M+FnzURmtBau+GGDWwc+GRFNVG72lJ/7QnX5l5AFkIQ0P7NXk2GArwgonmhY3rXWkvPa63w6i9E0TBdBJCfAe+LMtRA7DNjNBvmL+LCr7zbaXIqUmsbhcNB0JX7AoNGw1YOofMHt5KXPQLdC7hQP+1QmNdmGXvPBXZkNg0V3I/klxo0czyyRjXf2uiESbnGVzNnNJlSJnLhBpIqJwjqPzle252Ryq1FkFDx4dCxQdu0a+ivQCF4XZcOItZ8mJbBaUH8SqUwsqV6db99V4GY8MHcxLCzSMqY6oRQnMS0kwtvL/GMav3/2P5RvlxyMxEcaj9cRJeYaQWWHZTzNM9xFFahED4bEzwiO0vm4JCI77vwXXVQ96oF75tea0gmMfd/tIc4qHb4eirqgTjASK0OOYTpaPZNkiIZ32tVax0Un4354Jbwq/oRGXQJhqmQCyXSCiPn1Rj3bmHZmYp3oZRVbv5nUJu0bfAKc+4DscVe478ZpKBc9dCgw8VpApz7cI/dSJVovjn/e8OldsN8YyXnpNwLj7tdVpe7nbIy/nbzdN0G3CqY1rKSY7jIyuVMSJpdW7QyT2sKckW7/3HZeHztclzh48y8xlDu16DZOfiau9sK+SRXjm3tYP75t3HflVSmFA5Z/dYe/XizRju7iwufmM+OKE81bHutNPt5cGy805D/mD6Ox8Ozgbwd+vL9TE2EMSRNF6PMguVQ1vqyMvo8lMDtxL4r31EZRBTyIT0ua+EyodcR5uMPUdLfuHdZl6lsWNW+ovc3rzCnEfrxMYFlb9XwPYLsfiPZtB5wX2AZPTUXRh4VoTnDg6HAMZEhnuQ73JvPjSoXhRJVgNDE4KL4zNpu11UOLQKWNnjtQpvQE4Q138ina4lrtWIK0XWWs92E32YwXDZmRzaHpgfP6trljr7dj7xS250Z4FUmb2wrd8CrMomwlZxqodaF5XeTyAVf7P+nCiqyq9UQPnqoca2tyJ7yjX3NccxeP20JQ1Jo71DhRJADvZVm6t15tRa5MAAgqWYZYCDT57hmMwkIoeW7oUIaG6KhWt+0TUMuRyLD91O2XG1bYB3Uns0mAIwV/vcvlhaaJ9AU+Bse/AKdiAO9v07Zc/f3ArB9foIS3YDwXgEx7MTLt0TfLA+Ycnm03oOtVbC+3kKHrjOXHgzkilrPYKV9Lz4E2WgLw/oZv6AekVd3qT5n4G+Za6a6klf+7+kkxKvJsf4dUL1WK7zeTySIPYdxguuEGjouDA3DnPVtO5YCMDa52L6XjC2z9q9AtbeP/pc89QHap1o87Za8sN7qnub7boeVRVs7FUdatbbebJ8lv/iypozsgLJnPlBy0tmLs8tJezyez5qfNaInFlAihfME3o8VFOgD1EUxMNrxCF6fsF/bfyjj5rqP3ZIFTlVx7DeYOpEciZVnHpudAkA2pJozR4NihD0qtKSKp14WfAanX6MRvPhEWzMh8OpDx5iX/COD2Jlz90HCvtU6EgP2eDsfqKFRxc3LhHbfefyxq5IR57wOZlTldjXswMleecIf3n2y5B1rJX6yaCQkNq8Z8GFc1Ce9b/FtTzg4UwH6nWu42lGUtufxs3MNbxXlHh8pF8e9QQli8UuvtMyBIBLBluAGRcJQJETtMlbnjE69CamtbBj6cvHt+0T6QXDel2E5QJoBi42snoB3VDbvFSuQg95ptS/AW2XRf5vduGwESj3Nt3hXGAkyNcO3F11rE5/V9r7iwU4psALvo3pD9v07HLqgDMpaGwreDN4c8OzV+GxXESmGs38RxnNNdRdcTscXkS6MuX1lA09hMYkfkPha8v3B31VheyfzvQaxD30vsAt0MGVCf3hdn8AJc+43LM3NR/78AVEY+otKmRIHujIdhz/xACoEda3V3272YGElErhbFRIqu35+xubxt0L0JNokf7fpx0QU4pGGqIvjG7nAbRthCZa/AB7RNzLLKfBLxUF06OLjWrAWyHbp2lGB/rdEEU/mijUEcbshd4/pPd2R82ghlPDkgHjNYPU1q/oAnOiDG5Pe1qmVz1jqDV9h0fcIeQe/haZV9GQCgxMUsXEndXL7sUDZU7ssCspG3MMXrUbXYZj13ibLa0BmNX735d8diI8SM6eIMGubjL1IwDuuDiLK8sr4MxJIMl9KLPpf3m+24BvRpIhyl5v50b+ovEeYDAgxWPFHsUp09CtM2JVkTjQ8hPm86/iHV8AQGYJW6f0w3j30GQhQyA9Fco6CoWnCtNdf/ZpJxCL78quIsYQC/gGGonJb6R01CfhtKeIar84oC1xGh98XiNxQBcf3sxiUCw8PjNE4DtNk6998Ou1iiIeQAVRdZNVtwfze9qo3JxKVTN3bE3AGbehaM6hxb+PrMW2ylYQrD3nIipEC3Iec86uHJR1JtfoaqAJ9JSKI9Msge6JogF3gzt2w8mE96r7AMXNoLeN5HvbUgNE+EjWtlOfd1UEK8ktZlC5Ix2YohCZq/AGzrBQq65rfH/2E7G28pAuT0RNVxx4ABpzOjD6wz/O43PEn0YgPvb/0YmRjCoTZ9wyMlh7kIZG6G0bXodaJPUQTq3y2j+1DQFaCP8OyHTwKT/bXhI/2+wG0Nk9o408W5M2/MRoefW6kgpJEBXN009RM1+QusY1eYqNuyxolxUk7/eWH+yCy+rh/FS5Oaah5/cn5Sv14fB4Ip1LBFQGiicWRfBKr680LepK229O8hyVRZU9GI1NUgFGCX8NVqjuE4a4UWiKiaoJDs78G995YuKjwUSLLJwITq71E6tgdW9VuSWFTeBaB8LLyiw4WEBGtfo4LxSnxF5SSZgHEXqwr7b+oZNrT9dSwpSxgEo/2uE3EsXEwB/dba9/YQLXrlfpnaCHIlheO2JxPPf9SSxmh0VAEj/d8cSATxjuUGVLiZj1d6UPmp45AXXJ181a2BHE/x5oVRoc/VKHusckrehdr5hweee/4UKZScC0FbYcsCHoqUL0fLkBQdHoDEOtqH/MQ2+Qpo8lcIgxK26pMMmrtY96PcQh10SyJ6NFXUxbaHQBg87L2I5JOpxqODE9qsL3/Wzu452HsK05LCqEdIdyKNuyjJTiSVIHFdi9f/yIh4PESyk4fhbZbnm4QymZ/vE7edkM/LTo7B1kNbIAMfoaSVZr+MErPf+4narUaZJow4gga1MzLX/i4YIlsxgajxbtqw0jEvsllf2M6MwQeDzcQfMuv4Cppwu32XhW4X8+beuIvi5nX9+PdzN8gyicDtpi4KSil6PAtXmLDh53I2RX8/66DwOjsj4sU1XqoRCYhiP0Atf9HWzcsEPxH+xRcnHeunFAHusH4f9hXoftCKfdkMZb7tElGoX3xCu0k4TKvXjfYI6WWWVHggjGKbMwaNt8yEI7z6WwmA9r9iGjCNWPAZ2Fl/2/GCSMNuXbZlEZ3z2dN6JTep7/5vLwUcpgElLqtTcKLmD2VGE+9jqlmlAA2QNDxlHH1cvViAoqNbt06MCVfZackeXDNYaDN0R6LTD2vmbcUFRUIjnJPDgHA765sq+t+xEasMD8GrbRstL5AEhY9FBq8lqcoeBw5yidvMGfXfjzv7DAGxzeup3sPtuXDiG7Zaeb24VTzzeHkzVZQ4DLquPDcT/e4LWUmlGAullLcXi1Mq8H7Ygl9cwUT49efP5hXn1LjR8gwzWZ9fs0sPE+o0fhWaCXc+c1SFLlotWKVSbSEVsOSyPElvXP25Ll93JwrUXYpsH2dVDoXwAv0t+uzwyhSERHkcA8IXraSgEA+UEL/z3xyDry+sSFLuTJ1+AfheNyVPlgYLGFPAEAPGcy5318Nm3n6fCgScaE/YGnwuNLRLsitaWvzieQiOcSlbSG6dyUXh+nbZ5j23I5GR+CppDgf+MEqR7bje4I9rQpbRvoXGSqSw4OSUyMps4EqJaJu9PhQ3z2qDhYbqg4JT4/SleXOe2SbCCsRnYQegtn7Nm4m0kbwosWW3F7xwFJjamSFHO5ytbPVSnG+xUJG4KGBJYd3lN4FipTaeoCbxP7HBYc4Bw6y3uJddM7sU34ywuYq1dt/6hcE5u9Tjl0i0Lne2IzQKPpzI6fD0Txz8t//1biS3UD9lqlZep9bR+21eJG46V/qytQl9kjsURMaMqHmdHxv93osMk3MuCsGdm0qDg5zhe4+nTJzDVWjNYjDnMPzs3dEmNkUqHpma1Ih3NvU1LR1Pwx/oLJtpeQI5KknMyGr1pceygEK7Yfhs7+Kp5yvXIpKarKNjfl/zj7obBbb680Q4igOoyqazHkeVjbu6j6sKtL+j82ue+DLuiSK2ckk2AB4EfNQYcIlscH8srp8fjWEHzuVm+BbhAi0tnfF8cH1Z5T6c49cjmkeOa0+S2ijJMSRb5bedAywsMfwhyseskUM7O3aWsYDvYYs+NyL7py4+ORe8ZUjxLESv0I050tB7U8tnhsDsp6nABwTNCI/cllm6JJ4TxiCKoaQSDZfegAbmT4ZqCJVTtNIXWYaDH+ISEwqj4duAxADouLc3nHPzDev9BcYHvwVnNBuCU6GH7h+jWjKXHfKP8QFLycGHLvZ56h2S8qXbruVDNRVxhYIwh5M/m342899znNhCUxljgGm7q/YktPt96q1QutRuVQrS9CielqrW3saegV/u9tK/1XISj57kJ+ZrNnRkAv73OIMYrAwuglKZo/43C+2+mPsSlQk0KstMdna1qmnnY3smKUAuCZ8ZDa52+ao/zPSl4CM0eeyO/3ofQnf+OlFJIH6BDPalvmiKGXUJrZFGQHISr3qbh4In0MEq3IlUvbnB4HBglv6vkb5PBY++vucHtCHjSSQBzHxC4jTZ9yRxg1wmysudS1d8YOug0oIuNyPJYwCflgKueH5tCpl/AnbzOPJcfEf63x2M28l2xNATGKHvU9wKIZzG7uwfy8IaUrMoqtAb2QIHxhz7divKd1OdXSlR68tQ4VC1GyXMLkhnDEzVlDanLmQuZEud98j88bREB0njmjTMW2HDaIdUPUtQh5Z/dC2CwEEsgbMW8h0lFwNreNsMZloCsJJOGkZwgfqTgyT32+V3AY1mPTTUoCljP0BGFFU/JhLyMgO9G5d7ycPicEpItbc8Hho7vZrRAJRozfIgqzqiSiXZb13s+9OkHCyL4Uvgm/bOgPEpOGT68KpWo2F9C8iHP2NtRrSkbVbCzW+FKsVopPd861vvqCyoVYHG12LnvFFYfGfXSVBaMtA7K8tDqpsYiiByzmEXzz4VWKx4WYHhzFB1/E0IkfzCWvYHtNEtzB6084iRsaeuql9LlLAQW0vkviI0yUbaA6n7KgtghJg58dBUWWR+2/BtXRkRNrQo44CrXrhuH70qbXk5icYzAkg+rb/cJ57bu6iJwsH05Dycia8sQiWnLS66mpiw+nXo4p+2+W4Au1PlS6q3e2OvmMoKMKPb2tuHDRl1JC5bxSYIlKhFRxSPWzl7fJovxJnEuBemy5Has5a0U0XvVww481WYDCq3Fs0jeM/mqJH7ECoqxtLlifvznUygpExYfLUjf9zu0BSa93IQvcJlCviuYobr8GM6tgiyOZCP+AT/124Spgy30QgYv68Co4bd18cs3IxyO8/LGctlfzXqiPYUtXSODj7+1++w0EGlV5wJgRmX4QJlQsO1OZRQ+66wo2aqgDh7zIafWXGhjVyvgg9D0jGzNA62hzKihnCbRwyLvh1zi9MmZpxY4IbjWdbYaCO3FtZxn8cUb2aZqmHTkO9uQUBBVP2S2L4s3rlF9RIjSj3w4LLFDHLBXKO5AcKErIkFFqXr/NNyqg36UthsgI4MhnGZfjUyarrNrMty6/UVvBR83X+qa99ifIJ193mO3kYI9gcQc6MZVPvOgqI614b8ccP58U7D7ylb8Ra9I7oqIzGHKzSU/iHWTI83ynoxZvgqIL+icmn98Q39XvA+2mg7qTtroswZuFvfsPjjcoDS9j8cJ9mXXspHQdqLHGTyYMDsocteyNOgRtVbAWjeB7XGp/ffFy5EQ1jx48MlcnHVAqei9G8ZOE++5ClKvAhCFGOPWOjFqtRz1trV21ydNy415qlbyLSSSysAkyE0qKaqdWF0QRi8GLcApTn4z/JImbasX6SsBTbX9Svv1R06NVvKJSlvBxrdGx9Znpl0q+/W3zpItZ3Sx3rzeT7u+PNmjw16ZZLGUhiEWCzSxaCh0m4EJ9CqtIG4zjIo6V37q+NhABk2+vsbBJOFycEyLL5y02NKPaOgthqTMPPQ0bM9u7OyNhv/ckdSrKbVd4vx8CGzJUoZpfbWg3nGX6k7JoVKZtYinWVsWqnLPn4LfI9EVV+SGQCFYSVZJ/32cbQN1ia9IO/uFJOAWFrDYqZARxC0pjRj1UrGjlxF3oaQMePW3jFT2cthllAkF+s5dF0WMphdZPUzyfCwc3PzGeiqn5BdYops8muocq3em5sEgXE602K5IX9660IgsYO+z5gAPC4cRb5jPmYHcJPICc5O2DklcmcIEOyIfRNWO522B53Emi4KH6DIR5wUqAgLwjptv7ZnVhV7prTQru8ta+O6vtlcpeZ+8/V4rIxNXGas3epSPHXFqgThP8nV7OrWSDKxlP2v0cMKi6Ui1lBlAEo62OJLIk3rioCIqB2f0K6lIpME3vxgRSqRThSw2YQDXhw6uRCdBRSVEZ462zFHckfkWWe1cren8rGke8zG3F/BqADYJVaUuRsGbJyiif1wsox8DrD2Iim/7PArTKGoF0o4Jh/KzTsphzoEkSpkSKyA/s56+XEoIRp6ZR95I13X/fq7K8kvwqv93QPrNZW+QfwJXeuh5OCB62mNCvEtLSEDZKBmistU/TQmPkS7fPhMq78IlyQDCUc5U9Pq6xr/7M/88ux7HkHTo7Gv6tsoYOnaS6m52CkYRrT93NLzviU3tAP/ki+u/dGLWaF2UNU6ThPzLbIDrrOr4McSn04ae7yEvNUMUuWtUwoTmlNQ5LhR0VM+4yli/9t/FK217L+BJVBe5m9aG64TePoQ97DOG8EeFy65cbf+SnhrWVuqMqZr7DXIzbQhdVqECOaVjmKgHYBNF4xWNOKXjEilwXQqKo8nauAcTA56YmEAIKLMajNig/o8tH6VwL3qlvkK9aAJOeuBx/lzHjulScO87vzAV2wL4eta9x6FWpkvbwrc3EutNzoJsssl74K+iOXBJmScCaxn0ss+t4ylBea4ZwG5yNYd/QOgGX4Cqgv+rpOnVoBo8gLK9zuL+tyraCURPlvI8/0rldrQlPhyGJLKXduLwcAlj3p64FLqJTUeaxSaoFcMtD7+U+/LnF3VSxjM50De1jaENQ1cgA8dAzMPg5umujvAgCgWB9lX9dKvYajJsQOA6MFBWz3fgZsnn9qlj3bPYMCxb9GjjuRjsWjZ2kK2+tpO+m5kczKsUi2g6Kms5p62bGVK+OiaxrMes/Ac/9o72DlrL9tVFudRQhV1lM1SxuQHow45ntEujQCUCGoFrfjlZSC4WcF466c3R8czYs3k6/+vam57cIHAb6Bz8AZmi8mmhii9qk3SFHh1tK6swDWa27Z4ARj+7C02SpX7iyjVv+8e2wWHiYYevrfmB73x3Ld0cnjf6/5lMZXCPc0H2fx+wJFtAbpKAuS23qiObj7nZ4Q3ZpWULPh+BpgsTMbvydG9G9kgcw8irGZlHyurT8OAGBI9rxm/DuNDBgCzqPVDMBAO5pMSpUaUCWuaMh8seBJ57qMv499mxB2SWiXtT7J/3Lg6TKVUOVFIWxCp5mt/QBEbat2K9jqMEPjUR0ViymLiSxBN26ZQod6m/iIVFmpEMMjDhvYZ75YkEAHGFV/22eB285iuRbj3YJdAO0ouOtSbDRVjbANrmDu36sqWmetaw1gmSHiUyUvp8TwJGTPxTRmxNXKessz2s/qxqHy4ExRujN6cO34FL1yFi/hdDE+1Gk/FrBe+TUoMpa6f+6FFT67HGNlRbMZe5+hH2H0ZA3wYP3uIintN8OaPjqZJIuLC30Kakjm6bAfa1Se5Ke7Ix6RWIV6FRfyEfuUgnndB0Wliia7sDJHestWURNNEy4XE068GM0+jbv4qJr6fVGXmTSw+ti2Wl239MDHZrH95KFY+ymBtmMo5/myjfIBPIzIw2FYecSIKobSG1jBW8QFXQ/b9xtjfpyRheVVtdxdfOf3f3RhQsJF6xp9ZMuC29oTyLjV+F8FpWzS6qVtaAIQAG/RqjN7q7FPZV2NBFslKnmZ0GHXdF4hq7+5dqzC0usGmuBJc8M+JkRDAe8OhkXTTNzgB1PdYYeeKiKQ5UfCIVb1CadWFNHQFFy6hvZUy0DQsYMZKDjI72oKvcfroVKhmen0QQdbVXn8nJJpaiOk3AJHJjJ/9tG57jJ1RzuWFY55WjUvEw1nH+k1asRHqEVvFRBYs3LJqVYXhOu+/cpUZzQS+0dmCVh/2Gf4yaEPUubc4+lk6MfO0sZk5/MmqqfaXx8nUoUOgCjgTE8MTdLNgvORi6xvMWPVwTonXw7aVS6BMAFRsPV/w9R91Wo55DdcLRuUvnMluah2CiP+rZvdZGbGymXNRcYQhEqyS2oFLQmJHyDfXz9cIENbBbtIanHhdfFsPGUWh/38c4UuRVjaOLslQDVjm9z6dzwafiwmx8MAcrAHOWfT0Mh//32uIsZxN4SBcSmHh4YaxGTpMaRWwQRKWsH387Azc5OsHguKNyfj0nVlpceDH9lUUJf4vFkGawIL+/ryvOMiAE7MOLzqtZq+Iyf/WWfLuMnekUNnC8bO7og69oZBGoj7Ejj6XLaa8FLdwpNEBtV2pZZTpbfHZ1XGSYt72bmHB0s/EC1S0ZlIqF/WS4h/b6SIOj28Tmp955jCKKTJsWucqRHvwOiXmDzeX7f0qi6PJbqZHPBw8RdTVNeAyf6e/2hZDJzBqeEb1pSEAOj6C5mMtD5oSRDQdmGjtnveY1xCC52GTnaUXht3OOQ2b/NXvHF8Xq0/4MxoGZtiKQ4dsT7eX0Wqx56o+5axOka83eh1/03rLuaCjD8bEq6YjGt8cIWzVbhsBzpLkcXD54vz5dA9S9OPJRqXZM+/dZXCN0LP9xJMu0hLSLGb8lqbEXvwyt8oex3hRG0gJSgZ7fvZnzjVwGlc17j8KYZM9BDpDFp1G9qpF7CsJVs+gC3HS+khpPXx1ZHOJTnbL10zoWCKS0yEqAqosw3yeTkTGwnMAicIfVhjAdLEGD8rKqpDgFvNz/WfbtNFgi+s7iHskD2P32T6pyAc26PiYmWwrVqTy6m+K/yGK3HfNkH4UKhBNV/uKXiDfWPXrtuDPPT/Zj6PO18NpDjVycN4uZYTuWd4vkWaVj3zCeZQFktvyWaKE8zMX+MEjk1m1hZUrhbLp3Ei0WZnhfEvpOiVvF23aurlBCGASaNBPdERl8zDV8xJ8Y+mTbLrrS2Y3aSCSxLdDuM/zUfNorqnsr8nh+y+sQtYbhrZoUni4/Hn/w020ABeEGq2X4Uujv5siQ+9E7Uw4HX/xcU1Vu0g605iM+d6gSLxUx4VlKzr+IWg0uU4fKYyn3S1xW+HunTJ6BPbwxelm22MyIHUk39bbiNWq5PDjfMak40MTr4RVcccPiP0jqlk2uBEkyy64K+/3TOh/08HmHJicBlSuTBUiX6l7C6G6ki1GQnAAlaU8Abeh1FH40WEBoUN+GvH6bVwG4GAqblMlScpKbHBG2Cb68GpoSAL4eb8UyT6QzTyXW6nLz0hSJo3UFvmiJTayy9DT+iN1vfYH+Pla7AplJxl4QCWjMIL5Z96NRGxfMyOvy9BvlCOXig3SaBZm3YjYJt8xtgD0nHhDbt1ESHXBHwLGyO1h0daOCu7jnQ85/Q9Z37li2vOvbnDW1CaVFA3TP/Py/9hqEGeN3Ho6i22hJf11kTbq+L/fyNydbetqWqNs9rzxxwZGnspad2EaQSeNUXv8hQ7rmTK57r0bRF38jGcXKZ+/IfyfIYv2D7LQla//tCumkUPGGEjM0JlLqOTAD+oHhdJZCJLjAaR2XGXQyDNNu9iviELU5INo6hFdzI7S8mM2hiKx49ovE+/LfCqTlEGODqSx294UvACK57KJlxJjnI/0zBIAnre9as8Bi9R58u9qpdiXIakkAm9UoZP2TMD/MdV/8kWgahQx29pcMi84Wnz1oXSeWszurpcpodhnZzaEzd8444YeRs2EGZ8BEx0Wf7iwtDuLuh2Y8STKXflcFtjf1kUE+XNLdUmtyvml0WsViugpwe+4Rowj3InTSEvVkPGwe8DmluqiJG/jBcO7kegskx1DrtdO+hTmbVwnxhsrqeI28uzkyap/oYM5icFBlvYjPWRbQibUX8fYt4LTEL1CEXdWR4ZX86Go2gkyc1jDaVoY+g6YvFpivJaH5d6MG13ISrITfD8p09DwOo0fYDdWncVhcDyzBANBNwIKxSf8eLtGg7X7Kw+JpYxq6E+ypJf0n98AKvoLjWA6VnCEJerPFtsMUyjNDCvUtRr81bRhMP2l2GvlyVCfh3YLT7UsMHgTuqbXkf8Jcj46GkTeotgOXvXk+F7637ATvDqFZwhsu/66vLmOApe7TG1h8Oop2pCNHCoNxuGWwlN65GcErJZPtuGx4OOoEVIOMKhJZzZxjogDVOALydbULv9fkj9dkpRJiMjiu1ZYOFcjwsddbP3KtA6DifIp9pbO6F0e60uwDu/r0EVaVd5ClxtlPiDuAA9gw2WdFL+CelhVdEOSLHyRmL2d99W0QyLddybUemGeXHwqCfDJ5Ip2E8mvbj+WCFNmC8l6kfxj2tO/rt/vEee7zyw3nnaTdYZyPP09/5Qn6Zfi3od+Nposa5j/+0Obb4gCzkezKWU+qNBY2oTCLZBIF4uaAkRwGBjIJmopwhiN2JjpMP4TDvPDDdKOSoBhCa00gdA/5RHXG3VSRIDqRRHZQQISJh2/yApO/VjzGyX141r+Q1EYuZhJibYUZ8OagZlKHzBS73Oow/SOZetbVCxxyU+YtJS0u2i/SFSlAKN4pStTKHGwiSLc5bf3JSuaaQ/ILsG5CJSqtDMGaIwPBaWB2U7dEe0zN/0raxhkKzMNgeOMuww3wnC9FdO33M6qB4gYPe3Eol0DFwLYLNOi27A08uvd3VhRPFVbAYjvEaDQPjk54YLbLt4VV9vZmMLhWW0FXyG7WQohfTR4Ezv/nIZQgKGuBrSZ/0pJxp2SvEOgqVMxuoRIA5s/60ZVBu9piXBRsIbM9/3bV8rvz0Q3YvkHGNBC2+MC2SpsKKgQbStUHv7J2Rf3LkHxGIxxVR0oGKk9QoDSTur9j7GhBbnh8V0M9Zyyti/obpEfXRIvdcOCKf6IyjG4fncGkjMFCLaxB+xw183bSmTfQQ5mW8AwiNwXNIFcyYF0pBaFu8rkstd7WZAku0edsWb8kSX+3vYhkqLQFrpkpEmnJcZfG0h/dQ0DFJmADG93ltNQ+ifzS40KrnEEhYHcjx/9+PoYPHdSXXaC3XDEM6eA9sOHmehJ6ApFspVli1XJ4Xx5tcOzBFRhd3hWUi0GHSXD2YsybV4NrHFMIb4BZjZn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',\n\tstorm:\n\t\t'data:image/webp;base64,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',\n\tfog: 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'\n};\n"
    },
    {
      "path": "packages/react/src/components/wxcn/weather-scenes.ts",
      "type": "registry:component",
      "target": "@components/wxcn/weather-scenes.ts",
      "content": "// Shared by both photographic and monochrome rendering. Coverage, mist, and\n// precipitation are independent so, for example, drizzle never looks like a downpour.\nconst scene = (\n\tplate: 'fair' | 'overcast' | 'storm' | 'fog',\n\tcoverage: number,\n\tmist = 0,\n\train = 0,\n\tsnow = 0,\n\twind = 1\n) => ({ plate, coverage, mist, rain, snow, wind });\nexport const weatherScenes = {\n\tsunrise: scene('fair', 0.65),\n\tsunset: scene('fair', 0.65),\n\tclear: scene('fair', 0),\n\t'partly-cloudy': scene('fair', 0.85),\n\thaze: scene('fair', 0, 0.55),\n\tfog: scene('fog', 1, 0.65),\n\twind: scene('fair', 0.6, 0.08, 0, 0, 3),\n\tcloudy: scene('overcast', 1),\n\tthunderstorm: scene('storm', 1, 0.12, 36, 0, 2.4),\n\train: scene('overcast', 1, 0.15, 24),\n\t'heavy-rain': scene('storm', 1, 0.3, 48, 0, 1.7),\n\tdrizzle: scene('overcast', 1, 0.3, 10),\n\tsnow: scene('overcast', 1, 0.25, 0, 20, 0.65),\n\t'heavy-snow': scene('overcast', 1, 0.6, 0, 40, 1.3),\n\t'wintry-mix': scene('overcast', 1, 0.3, 16, 14),\n\t'clear-night': scene('fair', 0),\n\t'partly-cloudy-night': scene('fair', 0.85),\n\t'drizzle-night': scene('overcast', 1, 0.3, 10)\n};\n"
    },
    {
      "path": "packages/react/src/components/wxcn/weather-forecast.tsx",
      "type": "registry:component",
      "target": "@components/wxcn/weather-forecast.tsx",
      "content": "'use client';\n\nimport { useEffect, useState } from 'react';\nimport { ForecastScreens, type ForecastAction, type OpenForecastDay } from './forecast-screens';\nimport { forecastDays, forecastDayNoon, type ForecastDay } from '@/lib/wxcn/forecast-days.js';\nimport { ForecastIcon, type IconSet, type IconName } from './forecast-icons';\nimport { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card';\nimport { cn } from '@/lib/utils';\nimport { WeatherShaderBackground, type WeatherShaderMode } from './weather-shader-background';\nimport type {\n\tForecastType,\n\tLocationInput,\n\tWeatherPeriod,\n\tCurrentWeather,\n\tWeatherUnit,\n\tWeatherBackground\n} from '@/lib/wxcn/types.js';\nimport { sampleWeather, sampleCurrentWeather, convertWindSpeed } from '@/lib/wxcn/weather.js';\nimport { weatherOutlook, weatherDayHigh } from '@/lib/wxcn/weather-outlook.js';\nimport { getSkyState } from '@/lib/wxcn/sky.js';\n\nexport interface WeatherForecastProps {\n\tinteractive?: boolean;\n\ticonType?: IconSet;\n\ttimeZone?: string;\n\ttype?: ForecastType;\n\tsize?: 'sm' | 'default' | 'lg';\n\tdensity?: 'compact' | 'comfortable';\n\tclassName?: string;\n\tunit?: WeatherUnit;\n\tlocation?: LocationInput;\n\tforecast?: WeatherPeriod[];\n\thourlyForecast?: WeatherPeriod[];\n\tcurrentWeather?: CurrentWeather | null;\n\tshowTemperatureTrend?: boolean;\n\tshowHighLow?: boolean;\n\tat?: number;\n\tsourceLabel?: string;\n\twindUnit?: 'mph' | 'km/h' | 'm/s' | 'knots';\n\tbackground?: WeatherBackground;\n}\n\nfunction condition(p: WeatherPeriod): WeatherShaderMode {\n\tconst s = p.shortForecast.toLowerCase();\n\tif (/thunder|storm/.test(s)) return 'thunderstorm';\n\tif (/sleet|freezing|wintry/.test(s)) return 'wintry-mix';\n\tif (/snow/.test(s)) return s.includes('heavy') ? 'heavy-snow' : 'snow';\n\tif (/drizzle/.test(s)) return p.isDaytime ? 'drizzle' : 'drizzle-night';\n\tif (/rain|shower/.test(s)) return s.includes('heavy') ? 'heavy-rain' : 'rain';\n\tif (/fog/.test(s)) return 'fog';\n\tif (/haze|smoke/.test(s)) return 'haze';\n\tif (/partly|mostly sunny/.test(s)) return p.isDaytime ? 'partly-cloudy' : 'partly-cloudy-night';\n\tif (/cloud|overcast/.test(s)) return 'cloudy';\n\tif (/wind|breezy/.test(s)) return 'wind';\n\treturn p.isDaytime ? 'clear' : 'clear-night';\n}\nfunction periodIcon(p: WeatherPeriod): IconName {\n\tconst c = condition(p);\n\treturn c.includes('rain') || c.includes('drizzle') || c === 'thunderstorm'\n\t\t? 'rain'\n\t\t: c.includes('snow') || c === 'wintry-mix'\n\t\t\t? 'snow'\n\t\t\t: c.includes('night')\n\t\t\t\t? 'moon'\n\t\t\t\t: c === 'clear'\n\t\t\t\t\t? 'sun'\n\t\t\t\t\t: 'weather';\n}\n\nexport function WeatherForecast({\n\tinteractive = false,\n\ticonType,\n\ttimeZone,\n\ttype = 'summary',\n\tsize = 'default',\n\tdensity = 'comfortable',\n\tclassName,\n\tunit = 'fahrenheit',\n\tlocation = {\n\t\tlabel: 'Austin, TX',\n\t\tlatitude: 30.2672,\n\t\tlongitude: -97.7431,\n\t\ttimeZone: 'America/Chicago'\n\t},\n\tforecast = sampleWeather,\n\thourlyForecast = [],\n\tcurrentWeather = forecast === sampleWeather ? sampleCurrentWeather : null,\n\tshowTemperatureTrend = false,\n\tshowHighLow = false,\n\tat,\n\tsourceLabel = forecast === sampleWeather ? 'Sample forecast' : '',\n\twindUnit = 'mph',\n\tbackground = 'none'\n}: WeatherForecastProps) {\n\tconst [visitorTimeZone, setVisitorTimeZone] = useState('UTC');\n\tconst [clock, setClock] = useState<number | null>(null);\n\tuseEffect(() => {\n\t\tsetVisitorTimeZone(Intl.DateTimeFormat().resolvedOptions().timeZone);\n\t\tsetClock(Date.now());\n\t\tconst timer = setInterval(() => setClock(Date.now()), 60000);\n\t\treturn () => clearInterval(timer);\n\t}, []);\n\tconst effectiveTime =\n\t\tat ?? (forecast === sampleWeather ? Date.parse(sampleCurrentWeather.observedAt) : (clock ?? 0));\n\tconst periods = forecast.slice(0, type === 'detailed' ? 8 : density === 'compact' ? 3 : 5);\n\tconst currentSky = getSkyState(location.latitude, location.longitude, effectiveTime);\n\tconst current =\n\t\tcurrentWeather && currentSky\n\t\t\t? { ...currentWeather, isDaytime: currentSky.isDaytime }\n\t\t\t: currentWeather;\n\tconst outlook = weatherOutlook(\n\t\tcurrent,\n\t\tforecast,\n\t\tunit,\n\t\ttimeZone ?? location.timeZone ?? visitorTimeZone,\n\t\teffectiveTime\n\t);\n\tconst temperature = (p: WeatherPeriod) =>\n\t\tMath.round(\n\t\t\tunit === 'celsius' && p.temperatureUnit === 'F'\n\t\t\t\t? ((p.temperature - 32) * 5) / 9\n\t\t\t\t: unit === 'fahrenheit' && p.temperatureUnit === 'C'\n\t\t\t\t\t? (p.temperature * 9) / 5 + 32\n\t\t\t\t\t: p.temperature\n\t\t);\n\tif (\n\t\tforecast !== sampleWeather &&\n\t\t(forecast.length > 0 || currentWeather) &&\n\t\tat === undefined &&\n\t\tclock === null\n\t) {\n\t\treturn (\n\t\t\t<Card className={className}>\n\t\t\t\t<CardHeader>\n\t\t\t\t\t<CardTitle>Weather</CardTitle>\n\t\t\t\t\t<CardDescription>{location.label}</CardDescription>\n\t\t\t\t</CardHeader>\n\t\t\t\t<CardContent>\n\t\t\t\t\t<p role=\"status\">Loading current weather time…</p>\n\t\t\t\t</CardContent>\n\t\t\t</Card>\n\t\t);\n\t}\n\tconst days = forecastDays(\n\t\tforecast.map((p) => ({\n\t\t\ttime: Date.parse(p.startTime),\n\t\t\tlabel: p.name,\n\t\t\tsummary: `${temperature(p)}° · ${p.shortForecast}`,\n\t\t\tdetails: `${p.detailedForecast || p.shortForecast} Wind: ${p.windDirection} ${convertWindSpeed(p.windSpeed, windUnit)}.`\n\t\t})),\n\t\ttimeZone ?? location.timeZone ?? visitorTimeZone\n\t);\n\tfunction dayHours(day: ForecastDay) {\n\t\tconst date = new Intl.DateTimeFormat('en-CA', {\n\t\t\ttimeZone: timeZone ?? location.timeZone ?? visitorTimeZone,\n\t\t\tyear: 'numeric',\n\t\t\tmonth: '2-digit',\n\t\t\tday: '2-digit'\n\t\t});\n\t\treturn hourlyForecast\n\t\t\t.filter(\n\t\t\t\t(period) =>\n\t\t\t\t\tNumber.isFinite(Date.parse(period.startTime)) &&\n\t\t\t\t\tdate.format(new Date(period.startTime)) === day.key\n\t\t\t)\n\t\t\t.toSorted((a, b) => Date.parse(a.startTime) - Date.parse(b.startTime));\n\t}\n\tfunction dayPeriods(day: ForecastDay) {\n\t\treturn forecast\n\t\t\t.filter((period) => day.entries.some((entry) => entry.time === Date.parse(period.startTime)))\n\t\t\t.toSorted((a, b) => Date.parse(a.startTime) - Date.parse(b.startTime));\n\t}\n\tfunction cardView(\n\t\tday: ForecastDay | undefined,\n\t\taction: ForecastAction,\n\t\toverviewVisible: boolean,\n\t\topenDay: OpenForecastDay\n\t) {\n\t\tconst displayedPeriods = day ? dayPeriods(day) : periods;\n\t\tconst view = day\n\t\t\t? (displayedPeriods.find((period) => period.isDaytime) ?? displayedPeriods[0])\n\t\t\t: current;\n\t\tconst sky =\n\t\t\tday && view\n\t\t\t\t? getSkyState(\n\t\t\t\t\t\tlocation.latitude,\n\t\t\t\t\t\tlocation.longitude,\n\t\t\t\t\t\tforecastDayNoon(day.key, timeZone ?? location.timeZone ?? visitorTimeZone)\n\t\t\t\t\t)\n\t\t\t\t: currentSky;\n\t\tconst skyMode = view\n\t\t\t? condition(day && sky ? { ...view, isDaytime: sky.isDaytime } : view)\n\t\t\t: 'clear';\n\t\treturn (\n\t\t\t<>\n\t\t\t\t<div\n\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\tday ? 'contents' : 'relative isolate overflow-hidden',\n\t\t\t\t\t\tbackground === 'realistic' && view ? 'text-white' : 'text-card-foreground'\n\t\t\t\t\t)}\n\t\t\t\t>\n\t\t\t\t\t{background === 'realistic' && view && (\n\t\t\t\t\t\t<>\n\t\t\t\t\t\t\t<div className=\"pointer-events-none absolute inset-0 -z-10\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t<WeatherShaderBackground mode={skyMode} sky={sky} paused={!overviewVisible} />\n\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t<div\n\t\t\t\t\t\t\t\tclassName=\"pointer-events-none absolute inset-0 -z-10 bg-linear-to-b from-black/35 via-black/15 to-black/55\"\n\t\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t\t/>\n\t\t\t\t\t\t</>\n\t\t\t\t\t)}\n\t\t\t\t\t<CardHeader className=\"relative px-[var(--card-spacing,var(--wxcn-spacing))] pt-[var(--card-spacing,var(--wxcn-spacing))]\">\n\t\t\t\t\t\t<CardTitle className=\"min-w-0 truncate\">{day?.label ?? 'Weather'}</CardTitle>\n\t\t\t\t\t\t<CardDescription\n\t\t\t\t\t\t\tclassName={cn('truncate', background === 'realistic' && view && 'text-white/80')}\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t{location.label ?? 'Local forecast'}\n\t\t\t\t\t\t</CardDescription>\n\t\t\t\t\t\t{action(background === 'realistic' && !!view)}\n\t\t\t\t\t</CardHeader>\n\t\t\t\t\t<CardContent className=\"relative grid min-w-0 shrink-0 grid-cols-1 gap-5 px-[var(--card-spacing,var(--wxcn-spacing))] py-[var(--card-spacing,var(--wxcn-spacing))]\">\n\t\t\t\t\t\t{view ? (\n\t\t\t\t\t\t\t<>\n\t\t\t\t\t\t\t\t<div className=\"min-w-0\">\n\t\t\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\t\t\t\t\t\t'mb-2 text-xs',\n\t\t\t\t\t\t\t\t\t\t\tbackground === 'realistic' ? 'text-white/75' : 'text-muted-foreground'\n\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t{day ? (view.isDaytime ? 'Daytime' : 'Overnight') : 'Now'}\n\t\t\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\t\t\tstyle={{ fontSize: 'clamp(2.5rem,18cqw,5rem)' }}\n\t\t\t\t\t\t\t\t\t\tclassName=\"leading-none font-medium tracking-tighter tabular-nums\"\n\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t{temperature(view)}\n\t\t\t\t\t\t\t\t\t\t<span className=\"align-top text-2xl\">°</span>\n\t\t\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\t\t\tclassName=\"mt-3 line-clamp-2 min-h-10 text-sm wrap-break-word\"\n\t\t\t\t\t\t\t\t\t\ttitle={day ? view.shortForecast : undefined}\n\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t{view.shortForecast}\n\t\t\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t\t\t\t{!day && showTemperatureTrend && outlook.trend && (\n\t\t\t\t\t\t\t\t\t\t<p className=\"mt-2 text-sm\" data-slot=\"temperature-trend\">\n\t\t\t\t\t\t\t\t\t\t\t{outlook.trend}\n\t\t\t\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t{!day && showHighLow && (\n\t\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\t\t\t\t\t\tclassName=\"mt-3 flex gap-4 text-sm tabular-nums\"\n\t\t\t\t\t\t\t\t\t\t\tdata-slot=\"temperature-range\"\n\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t{outlook.high !== null && (\n\t\t\t\t\t\t\t\t\t\t\t\t<span aria-label={`High ${outlook.high} degrees`}>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ForecastIcon\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tname=\"arrowUp\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ticonSet={iconType}\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tclassName=\"inline size-3.5\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t/>{' '}\n\t\t\t\t\t\t\t\t\t\t\t\t\t{outlook.high}°\n\t\t\t\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t\t{outlook.low !== null && (\n\t\t\t\t\t\t\t\t\t\t\t\t<span aria-label={`Low ${outlook.low} degrees`}>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ForecastIcon\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tname=\"arrowDown\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ticonSet={iconType}\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tclassName=\"inline size-3.5\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t/>{' '}\n\t\t\t\t\t\t\t\t\t\t\t\t\t{outlook.low}°\n\t\t\t\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t\t<div className=\"flex flex-wrap items-center justify-between gap-3 text-xs\">\n\t\t\t\t\t\t\t\t\t<span className=\"flex items-center gap-2 opacity-80\">\n\t\t\t\t\t\t\t\t\t\t<ForecastIcon name=\"wind\" iconSet={iconType} className=\"size-4\" />\n\t\t\t\t\t\t\t\t\t\tWind\n\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t<span className=\"tabular-nums\">\n\t\t\t\t\t\t\t\t\t\t{view.windDirection} {convertWindSpeed(view.windSpeed, windUnit)}\n\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t</>\n\t\t\t\t\t\t) : (\n\t\t\t\t\t\t\t<p role=\"status\" className=\"py-8 text-center text-sm text-muted-foreground\">\n\t\t\t\t\t\t\t\tCurrent conditions unavailable.\n\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t)}\n\t\t\t\t\t\t{!day && type === 'simple' && sourceLabel && (\n\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\t\t\t\t'text-[10px]',\n\t\t\t\t\t\t\t\t\tbackground === 'realistic' && view ? 'text-white/70' : 'text-muted-foreground'\n\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t{sourceLabel}\n\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t)}\n\t\t\t\t\t</CardContent>\n\t\t\t\t</div>\n\t\t\t\t{day && size === 'lg' && type !== 'simple' && dayHours(day).length > 0 ? (\n\t\t\t\t\t<CardContent className=\"relative flex min-h-0 flex-1 flex-col px-[var(--card-spacing,var(--wxcn-spacing))] pb-[var(--card-spacing,var(--wxcn-spacing))]\">\n\t\t\t\t\t\t<p className=\"mb-3 shrink-0 text-base font-medium\">Hourly forecast</p>\n\t\t\t\t\t\t<div\n\t\t\t\t\t\t\tclassName=\"min-h-0 flex-1 overflow-y-auto overscroll-contain\"\n\t\t\t\t\t\t\ttabIndex={0}\n\t\t\t\t\t\t\trole=\"region\"\n\t\t\t\t\t\t\taria-label=\"Hourly forecast\"\n\t\t\t\t\t\t\tdata-slot=\"hourly-forecast\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<div className=\"grid min-h-full auto-rows-[minmax(3.5rem,1fr)] grid-cols-1\">\n\t\t\t\t\t\t\t\t{dayHours(day).map((hour) => (\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\t\t\t\t\tkey={hour.startTime}\n\t\t\t\t\t\t\t\t\t\tclassName=\"grid min-w-0 grid-cols-[1fr_auto_1fr] items-center gap-4 border-b border-current/15 text-lg last:border-0\"\n\t\t\t\t\t\t\t\t\t\ttitle={hour.shortForecast}\n\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t<span className=\"opacity-75\">\n\t\t\t\t\t\t\t\t\t\t\t{new Intl.DateTimeFormat('en-US', {\n\t\t\t\t\t\t\t\t\t\t\t\ttimeZone: timeZone ?? location.timeZone ?? visitorTimeZone,\n\t\t\t\t\t\t\t\t\t\t\t\thour: 'numeric'\n\t\t\t\t\t\t\t\t\t\t\t}).format(new Date(hour.startTime))}\n\t\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t\t<ForecastIcon\n\t\t\t\t\t\t\t\t\t\t\tname={periodIcon(hour)}\n\t\t\t\t\t\t\t\t\t\t\ticonSet={iconType}\n\t\t\t\t\t\t\t\t\t\t\tclassName=\"size-6 shrink-0\"\n\t\t\t\t\t\t\t\t\t\t/>\n\t\t\t\t\t\t\t\t\t\t<span className=\"text-right font-medium tabular-nums\">\n\t\t\t\t\t\t\t\t\t\t\t{temperature(hour)}°\n\t\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t\t))}\n\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t</div>\n\t\t\t\t\t</CardContent>\n\t\t\t\t) : type !== 'simple' ? (\n\t\t\t\t\t<CardContent className=\"relative min-h-0 min-w-0 px-[var(--card-spacing,var(--wxcn-spacing))] pb-[var(--card-spacing,var(--wxcn-spacing))]\">\n\t\t\t\t\t\t{displayedPeriods.length > 0 && (\n\t\t\t\t\t\t\t<div className=\"divide-y\">\n\t\t\t\t\t\t\t\t{displayedPeriods.map((period, index) => {\n\t\t\t\t\t\t\t\t\tconst icon = periodIcon(period);\n\t\t\t\t\t\t\t\t\treturn (\n\t\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\t\t\t\t\t\tkey={`${period.startTime}-${index}`}\n\t\t\t\t\t\t\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\t\t\t\t\t\t\t'grid grid-cols-[minmax(0,1fr)_auto_auto] items-center gap-3 text-sm',\n\t\t\t\t\t\t\t\t\t\t\t\tdensity === 'compact' ? 'py-2' : 'py-3'\n\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t\t\t{interactive && !day ? (\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tclassName=\"min-h-8 text-left underline-offset-4 hover:underline focus-visible:rounded-sm focus-visible:outline-2 focus-visible:outline-ring\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label={`View details for ${period.name}`}\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tonClick={(event) => openDay(period.startTime, event.currentTarget)}\n\t\t\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t{period.name}\n\t\t\t\t\t\t\t\t\t\t\t\t\t</button>\n\t\t\t\t\t\t\t\t\t\t\t\t) : (\n\t\t\t\t\t\t\t\t\t\t\t\t\t<p>{period.name}</p>\n\t\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t\t\t{(type === 'detailed' || (day && size !== 'sm')) && (\n\t\t\t\t\t\t\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'mt-1 text-xs leading-5',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tday && 'line-clamp-2',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tday && background === 'realistic'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t? 'text-white/75'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t: 'text-muted-foreground'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t{type === 'detailed' ? period.detailedForecast : period.shortForecast}\n\t\t\t\t\t\t\t\t\t\t\t\t\t</p>\n\t\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t\t\t\t\t<ForecastIcon\n\t\t\t\t\t\t\t\t\t\t\t\tname={icon}\n\t\t\t\t\t\t\t\t\t\t\t\ticonSet={iconType}\n\t\t\t\t\t\t\t\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\t\t\t\t\t\t\t\t'size-4',\n\t\t\t\t\t\t\t\t\t\t\t\t\tday && background === 'realistic'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t? 'text-white/75'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t: 'text-muted-foreground'\n\t\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t\t/>\n\t\t\t\t\t\t\t\t\t\t\t<span className=\"min-w-9 text-right tabular-nums\">\n\t\t\t\t\t\t\t\t\t\t\t\t{temperature(period)}°\n\t\t\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t})}\n\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t)}\n\t\t\t\t\t\t{!day && sourceLabel && (\n\t\t\t\t\t\t\t<p className=\"mt-2 text-[10px] text-muted-foreground\">{sourceLabel}</p>\n\t\t\t\t\t\t)}\n\t\t\t\t\t</CardContent>\n\t\t\t\t) : null}\n\t\t\t</>\n\t\t);\n\t}\n\n\treturn (\n\t\t<Card\n\t\t\tdata-density={density}\n\t\t\tstyle={\n\t\t\t\t{\n\t\t\t\t\tcontainerType: 'inline-size',\n\t\t\t\t\t'--wxcn-spacing': size === 'sm' ? '1rem' : '1.5rem'\n\t\t\t\t} as import('react').CSSProperties\n\t\t\t}\n\t\t\tdata-card-size={size}\n\t\t\tdata-size={size === 'sm' ? 'sm' : 'default'}\n\t\t\tclassName={cn(\n\t\t\t\t'relative isolate min-w-0 gap-0 overflow-hidden py-0',\n\n\t\t\t\tclassName\n\t\t\t)}\n\t\t>\n\t\t\t<ForecastScreens\n\t\t\t\tinteractive={interactive}\n\t\t\t\tdays={days}\n\t\t\t\ttitle=\"Weather\"\n\t\t\t\tdensity={density}\n\t\t\t\tsourceLabel={sourceLabel}\n\t\t\t\ticonType={iconType}\n\t\t\t\tshowWeek={size === 'sm' || type === 'simple'}\n\t\t\t\tflush\n\t\t\t\tdaySummary={(day) => {\n\t\t\t\t\tconst values = dayPeriods(day);\n\t\t\t\t\tconst daytime = values.find((period) => period.isDaytime);\n\t\t\t\t\tconst overnight = values.find((period) => !period.isDaytime);\n\t\t\t\t\tconst representative = daytime ?? overnight;\n\t\t\t\t\tconst high = weatherDayHigh(\n\t\t\t\t\t\tday.key,\n\t\t\t\t\t\tvalues,\n\t\t\t\t\t\tcurrent,\n\t\t\t\t\t\tunit,\n\t\t\t\t\t\ttimeZone ?? location.timeZone ?? visitorTimeZone\n\t\t\t\t\t);\n\t\t\t\t\treturn (\n\t\t\t\t\t\t<span className=\"grid w-full min-w-0 grid-cols-[minmax(0,1fr)_1.5em_3.75em_3.75em] items-center gap-2\">\n\t\t\t\t\t\t\t<span className=\"truncate font-medium\">{day.label}</span>\n\t\t\t\t\t\t\t{representative ? (\n\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\t\t\t\tclassName=\"flex justify-center\"\n\t\t\t\t\t\t\t\t\ttitle={representative.shortForecast}\n\t\t\t\t\t\t\t\t\taria-label={representative.shortForecast}\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<ForecastIcon\n\t\t\t\t\t\t\t\t\t\tname={periodIcon(representative)}\n\t\t\t\t\t\t\t\t\t\ticonSet={iconType}\n\t\t\t\t\t\t\t\t\t\tclassName=\"size-[1.4em] text-muted-foreground\"\n\t\t\t\t\t\t\t\t\t/>\n\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t) : (\n\t\t\t\t\t\t\t\t<span />\n\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t<span\n\t\t\t\t\t\t\t\tclassName=\"grid grid-cols-[0.85em_minmax(0,1fr)] items-center gap-1 text-right tabular-nums\"\n\t\t\t\t\t\t\t\taria-label={high !== null ? `High ${high} degrees` : 'High unavailable'}\n\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t<ForecastIcon\n\t\t\t\t\t\t\t\t\tname=\"arrowUp\"\n\t\t\t\t\t\t\t\t\ticonSet={iconType}\n\t\t\t\t\t\t\t\t\tclassName=\"size-[1em] text-muted-foreground\"\n\t\t\t\t\t\t\t\t/>\n\t\t\t\t\t\t\t\t<span>{high !== null ? `${high}°` : '—'}</span>\n\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t<span\n\t\t\t\t\t\t\t\tclassName=\"grid grid-cols-[0.85em_minmax(0,1fr)] items-center gap-1 text-right text-muted-foreground tabular-nums\"\n\t\t\t\t\t\t\t\taria-label={overnight ? `Low ${temperature(overnight)} degrees` : 'Low unavailable'}\n\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t<ForecastIcon name=\"arrowDown\" iconSet={iconType} className=\"size-[1em]\" />\n\t\t\t\t\t\t\t\t<span>{overnight ? `${temperature(overnight)}°` : '—'}</span>\n\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t</span>\n\t\t\t\t\t);\n\t\t\t\t}}\n\t\t\t\tdetail={(day, action) => (\n\t\t\t\t\t<div\n\t\t\t\t\t\tclassName={cn(\n\t\t\t\t\t\t\t'relative isolate flex h-full min-h-0 w-full min-w-0 flex-col',\n\t\t\t\t\t\t\tbackground === 'realistic' && 'text-white'\n\t\t\t\t\t\t)}\n\t\t\t\t\t>\n\t\t\t\t\t\t{cardView(day, action, true, () => {})}\n\t\t\t\t\t</div>\n\t\t\t\t)}\n\t\t\t>\n\t\t\t\t{(openDay, action, visible) => cardView(undefined, action, visible, openDay)}\n\t\t\t</ForecastScreens>\n\t\t</Card>\n\t);\n}\n"
    },
    {
      "path": "packages/react/src/components/wxcn/forecast-screens.tsx",
      "type": "registry:component",
      "target": "@components/wxcn/forecast-screens.tsx",
      "content": "'use client';\n\nimport { useEffect, useLayoutEffect, useRef, useState, type ReactNode } from 'react';\nimport { CardAction, CardContent, CardHeader, CardTitle } from '@/components/ui/card';\nimport { Button } from '@/components/ui/button';\nimport { ForecastIcon, type IconSet } from './forecast-icons';\nimport type { ForecastDay } from '@/lib/wxcn/forecast-days.js';\nexport type ForecastAction = (onSurface: boolean) => ReactNode;\nexport type OpenForecastDay = (time: string, trigger: HTMLElement) => void;\nexport interface ForecastScreensProps {\n\tinteractive: boolean;\n\tdays: ForecastDay[];\n\ttitle: string;\n\tdensity: string;\n\tsourceLabel: string;\n\tsummary?: (availableHeight: number) => ReactNode;\n\tdaySummary?: (day: ForecastDay) => ReactNode;\n\tactionLabel?: string;\n\tsummaryTitle?: string;\n\tshowWeek?: boolean;\n\ticonType?: IconSet;\n\tflush?: boolean;\n\tdetail: (day: ForecastDay, action: ForecastAction) => ReactNode;\n\tchildren: (\n\t\topenDay: OpenForecastDay,\n\t\taction: ForecastAction,\n\t\toverviewVisible: boolean\n\t) => ReactNode;\n}\nexport function ForecastScreens({\n\tinteractive,\n\tdays,\n\ttitle,\n\tsummary,\n\tdaySummary,\n\tactionLabel = 'View week',\n\tsummaryTitle = 'Upcoming tides',\n\tshowWeek = true,\n\ticonType,\n\tflush = false,\n\tdetail,\n\tchildren\n}: ForecastScreensProps) {\n\tconst [screen, setScreen] = useState<'overview' | 'week' | 'day'>('overview');\n\tconst [selectedKey, setSelectedKey] = useState('');\n\tconst [availableHeight, setAvailableHeight] = useState(0);\n\tconst surface = useRef<HTMLDivElement>(null);\n\tconst table = useRef<HTMLDivElement>(null);\n\tconst host = useRef<HTMLElement | null>(null);\n\tconst fromWeek = useRef(false);\n\tconst originIndex = useRef(0);\n\tconst originTrigger = useRef<HTMLElement | null>(null);\n\tconst focusTarget = useRef<'surface' | 'origin' | 'day' | null>(null);\n\tconst selected = days.find((day) => day.key === selectedKey);\n\tconst active = interactive && (screen !== 'day' || selected) ? screen : 'overview';\n\tuseEffect(() => {\n\t\tif (active !== screen) setScreen('overview');\n\t}, [active, screen]);\n\tuseLayoutEffect(() => {\n\t\tif (active !== 'week' || !table.current) return;\n\t\tconst node = table.current;\n\t\tconst update = () => setAvailableHeight(node.clientHeight);\n\t\tconst observer = new ResizeObserver(update);\n\t\tobserver.observe(node);\n\t\tupdate();\n\t\treturn () => observer.disconnect();\n\t}, [active]);\n\tuseLayoutEffect(() => {\n\t\tconst target = focusTarget.current;\n\t\tif (!target) return;\n\t\tif (target === 'surface') {\n\t\t\tsurface.current?.focus({ preventScroll: true });\n\t\t\tfocusTarget.current = null;\n\t\t\treturn;\n\t\t}\n\t\tif ((target === 'day' && active !== 'week') || (target === 'origin' && active !== 'overview'))\n\t\t\treturn;\n\t\t// Wait for the retained overview or resized detail surface to become visible.\n\t\tlet frame = requestAnimationFrame(() => {\n\t\t\tframe = requestAnimationFrame(() => {\n\t\t\t\tconst trigger =\n\t\t\t\t\ttarget === 'day'\n\t\t\t\t\t\t? host.current?.querySelector<HTMLButtonElement>(`[data-forecast-day=\"${selectedKey}\"]`)\n\t\t\t\t\t\t: originTrigger.current?.isConnected\n\t\t\t\t\t\t\t? originTrigger.current\n\t\t\t\t\t\t\t: host.current?.querySelectorAll<HTMLButtonElement>('button')[originIndex.current];\n\t\t\t\ttrigger?.focus({ preventScroll: true });\n\t\t\t\tif (document.activeElement === trigger) focusTarget.current = null;\n\t\t\t});\n\t\t});\n\t\treturn () => cancelAnimationFrame(frame);\n\t}, [active, selectedKey, availableHeight]);\n\n\tfunction open(next: 'week' | 'day', trigger: HTMLElement, key = '') {\n\t\tif (active === 'overview') {\n\t\t\thost.current = trigger.closest<HTMLElement>('[data-slot=card]');\n\t\t\toriginTrigger.current = trigger;\n\t\t\toriginIndex.current = host.current\n\t\t\t\t? [...host.current.querySelectorAll('button')].indexOf(trigger as HTMLButtonElement)\n\t\t\t\t: 0;\n\t\t}\n\t\tfromWeek.current = active === 'week';\n\t\tsetSelectedKey(key);\n\t\tfocusTarget.current = 'surface';\n\t\tsetScreen(next);\n\t}\n\tconst openDay: OpenForecastDay = (time, trigger) => {\n\t\tconst day = days.find((day) => day.entries.some((entry) => entry.time === Date.parse(time)));\n\t\tif (day) open('day', trigger, day.key);\n\t};\n\tfunction back() {\n\t\tif (active === 'day' && fromWeek.current) {\n\t\t\tfocusTarget.current = 'day';\n\t\t\tsetScreen('week');\n\t\t} else {\n\t\t\tfocusTarget.current = 'origin';\n\t\t\tsetScreen('overview');\n\t\t}\n\t}\n\tconst weekAction: ForecastAction = (onSurface) =>\n\t\tinteractive && showWeek ? (\n\t\t\t<CardAction>\n\t\t\t\t<Button\n\t\t\t\t\ttype=\"button\"\n\t\t\t\t\tvariant=\"ghost\"\n\t\t\t\t\tsize=\"sm\"\n\t\t\t\t\tclassName={`h-6 border border-transparent px-1.5 text-[10px] font-medium ${onSurface ? 'text-white/80 hover:bg-white/10 hover:text-white' : 'text-muted-foreground hover:text-foreground'}`}\n\t\t\t\t\taria-label={summary ? actionLabel : `View ${title.toLowerCase()} week`}\n\t\t\t\t\tonClick={(event) => open('week', event.currentTarget)}\n\t\t\t\t>\n\t\t\t\t\t{actionLabel}\n\t\t\t\t</Button>\n\t\t\t</CardAction>\n\t\t) : null;\n\tconst backAction: ForecastAction = (onSurface) => (\n\t\t<CardAction>\n\t\t\t<Button\n\t\t\t\ttype=\"button\"\n\t\t\t\tvariant=\"ghost\"\n\t\t\t\tsize=\"sm\"\n\t\t\t\tclassName={`h-6 gap-1 border border-transparent px-1.5 text-[10px] ${onSurface ? 'text-white/80 hover:bg-white/10 hover:text-white' : 'text-muted-foreground'}`}\n\t\t\t\tonClick={back}\n\t\t\t>\n\t\t\t\t<ForecastIcon name=\"arrowDown\" iconSet={iconType} className=\"size-3 rotate-90\" />\n\t\t\t\tBack\n\t\t\t</Button>\n\t\t</CardAction>\n\t);\n\treturn (\n\t\t<>\n\t\t\t<div\n\t\t\t\tclassName=\"contents\"\n\t\t\t\tstyle={{ visibility: active === 'overview' ? 'visible' : 'hidden' }}\n\t\t\t\tinert={active !== 'overview'}\n\t\t\t\taria-hidden={active !== 'overview'}\n\t\t\t>\n\t\t\t\t{children(openDay, weekAction, active === 'overview')}\n\t\t\t</div>\n\t\t\t{active !== 'overview' && (\n\t\t\t\t<div\n\t\t\t\t\tref={surface}\n\t\t\t\t\ttabIndex={-1}\n\t\t\t\t\trole=\"group\"\n\t\t\t\t\taria-label={\n\t\t\t\t\t\tactive === 'day'\n\t\t\t\t\t\t\t? `${selected?.label} ${title.toLowerCase()} forecast`\n\t\t\t\t\t\t\t: summary\n\t\t\t\t\t\t\t\t? summaryTitle\n\t\t\t\t\t\t\t\t: `${title} • Week`\n\t\t\t\t\t}\n\t\t\t\t\tdata-slot=\"forecast-screen\"\n\t\t\t\t\tonKeyDown={(event) => {\n\t\t\t\t\t\tif (event.key === 'Escape') {\n\t\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t\t\tback();\n\t\t\t\t\t\t}\n\t\t\t\t\t}}\n\t\t\t\t\tclassName={`absolute inset-0 z-10 flex min-h-0 flex-col overflow-hidden rounded-[inherit] bg-card text-card-foreground outline-none ${active === 'day' && flush ? 'gap-0' : active === 'week' ? 'gap-2 py-3' : 'gap-[var(--card-spacing,var(--wxcn-spacing,1.5rem))] py-[var(--card-spacing,var(--wxcn-spacing,1.5rem))]'}`}\n\t\t\t\t>\n\t\t\t\t\t{active === 'day' && selected ? (\n\t\t\t\t\t\tdetail(selected, backAction)\n\t\t\t\t\t) : (\n\t\t\t\t\t\t<>\n\t\t\t\t\t\t\t<CardHeader className=\"shrink-0\">\n\t\t\t\t\t\t\t\t<CardTitle className=\"truncate\">\n\t\t\t\t\t\t\t\t\t{summary ? summaryTitle : `${title} • Week`}\n\t\t\t\t\t\t\t\t</CardTitle>\n\t\t\t\t\t\t\t\t{backAction(false)}\n\t\t\t\t\t\t\t</CardHeader>\n\t\t\t\t\t\t\t<CardContent className=\"min-h-0 min-w-0 flex-1\">\n\t\t\t\t\t\t\t\t<div ref={table} className=\"h-full min-h-0\" data-slot=\"forecast-week-table\">\n\t\t\t\t\t\t\t\t\t{summary ? (\n\t\t\t\t\t\t\t\t\t\tsummary(availableHeight)\n\t\t\t\t\t\t\t\t\t) : (\n\t\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\t\t\t\t\t\tclassName={`grid h-full content-start gap-x-3 overflow-y-auto ${!daySummary && availableHeight < days.length * 28 ? 'grid-cols-2' : 'grid-cols-1'}`}\n\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t{days.map((day) => (\n\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\t\t\t\t\tkey={day.key}\n\t\t\t\t\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tdata-forecast-day={day.key}\n\t\t\t\t\t\t\t\t\t\t\t\t\tstyle={{\n\t\t\t\t\t\t\t\t\t\t\t\t\t\theight: daySummary\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t? Math.max(32, availableHeight / days.length)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t: Math.min(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t28,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tavailableHeight /\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t(!daySummary && availableHeight < days.length * 28\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t? Math.ceil(days.length / 2)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t: days.length)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfontSize: daySummary\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t? `clamp(12px, min(4.5cqw, ${availableHeight / days.length / 3}px), 20px)`\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t: undefined\n\t\t\t\t\t\t\t\t\t\t\t\t\t}}\n\t\t\t\t\t\t\t\t\t\t\t\t\tclassName=\"flex min-h-0 min-w-0 items-center justify-between gap-2 border-b text-left text-[11px] hover:bg-muted/50 focus-visible:outline-2 focus-visible:outline-ring\"\n\t\t\t\t\t\t\t\t\t\t\t\t\taria-label={`View details for ${day.label}`}\n\t\t\t\t\t\t\t\t\t\t\t\t\tonClick={(event) => open('day', event.currentTarget, day.key)}\n\t\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t{daySummary ? (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdaySummary(day)\n\t\t\t\t\t\t\t\t\t\t\t\t\t) : (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span className=\"shrink-0 font-medium\">{day.label}</span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tclassName=\"truncate text-muted-foreground\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttitle={day.entries.map((entry) => entry.summary).join(' · ')}\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t{day.entries.map((entry) => entry.summary).join(' · ')}\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t</>\n\t\t\t\t\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t\t\t\t\t</button>\n\t\t\t\t\t\t\t\t\t\t\t))}\n\t\t\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t\t\t)}\n\t\t\t\t\t\t\t\t</div>\n\t\t\t\t\t\t\t</CardContent>\n\t\t\t\t\t\t</>\n\t\t\t\t\t)}\n\t\t\t\t</div>\n\t\t\t)}\n\t\t</>\n\t);\n}\n"
    },
    {
      "path": "packages/react/src/components/wxcn/weather-shader-background.tsx",
      "type": "registry:component",
      "target": "@components/wxcn/weather-shader-background.tsx",
      "content": "'use client';\n\nimport { useEffect, useRef, useState, type CSSProperties } from 'react';\nimport { weatherCloudTextures } from './cloud-texture.js';\nimport { weatherScenes } from './weather-scenes.js';\nimport type { SkyState } from '@/lib/wxcn/sky.js';\n\nexport type WeatherShaderMode =\n\t| 'sunrise'\n\t| 'sunset'\n\t| 'clear'\n\t| 'partly-cloudy'\n\t| 'haze'\n\t| 'fog'\n\t| 'wind'\n\t| 'cloudy'\n\t| 'thunderstorm'\n\t| 'rain'\n\t| 'heavy-rain'\n\t| 'drizzle'\n\t| 'snow'\n\t| 'heavy-snow'\n\t| 'wintry-mix'\n\t| 'clear-night'\n\t| 'partly-cloudy-night'\n\t| 'drizzle-night';\n\nconst modes: WeatherShaderMode[] = [\n\t'sunrise',\n\t'sunset',\n\t'clear',\n\t'partly-cloudy',\n\t'haze',\n\t'fog',\n\t'wind',\n\t'cloudy',\n\t'thunderstorm',\n\t'rain',\n\t'heavy-rain',\n\t'drizzle',\n\t'snow',\n\t'heavy-snow',\n\t'wintry-mix',\n\t'clear-night',\n\t'partly-cloudy-night',\n\t'drizzle-night'\n];\nconst particles = Array.from({ length: 48 }, (_, i) => {\n\tconst depth = i % 3;\n\treturn {\n\t\tleft: ((i * 61.803) % 112) - 6,\n\t\tphase: (i * 0.61803398875) % 1,\n\t\tdepth,\n\t\tspeed: [1.6, 1, 0.6][depth] * (0.85 + ((i * 0.4142) % 1) * 0.3),\n\t\topacity: [0.14, 0.27, 0.38][depth],\n\t\tblur: [0.15, 0.3, 1.1][depth],\n\t\tsize: [0.8, 1.5, 3.2][depth],\n\t\tsway: 5 + ((i * 13.71) % 18)\n\t};\n});\n\nexport function WeatherShaderBackground({\n\tmode = 'clear',\n\tpaused = false,\n\tdithered = false,\n\tsky = null\n}: {\n\tmode?: WeatherShaderMode;\n\tpaused?: boolean;\n\tdithered?: boolean;\n\tsky?: SkyState | null;\n}) {\n\tconst atmosphere = weatherScenes[mode];\n\tconst rain = atmosphere.rain > 0,\n\t\tsnow = atmosphere.snow > 0;\n\tfunction particleStyle(p: (typeof particles)[number], frozen: boolean): CSSProperties {\n\t\tconst duration =\n\t\t\t((frozen ? 6 : mode.includes('drizzle') ? 1.8 : 0.9) * p.speed) / Math.sqrt(atmosphere.wind);\n\t\treturn {\n\t\t\tleft: `${p.left}%`,\n\t\t\tanimationDelay: `${-p.phase * duration}s`,\n\t\t\tanimationDuration: `${duration}s`,\n\t\t\topacity: p.opacity * (frozen ? 1.4 : 1),\n\t\t\tfilter: `blur(${p.blur}px)`,\n\t\t\t'--size': `${p.size}px`,\n\t\t\t'--sway': `${p.sway}px`,\n\t\t\t'--drift': `${-12 * atmosphere.wind}cqh`,\n\t\t\t'--angle': `${(Math.atan(0.12 * atmosphere.wind) * 180) / Math.PI}deg`,\n\t\t\t'--length': `${(mode.includes('drizzle') ? 5 : 15) / p.speed}px`\n\t\t} as CSSProperties;\n\t}\n\tconst canvasRef = useRef<HTMLCanvasElement>(null);\n\tconst settings = useRef({ mode, paused, dithered, sky });\n\tconst redraw = useRef(() => {});\n\tconst [active, setActive] = useState(true);\n\tuseEffect(() => {\n\t\tsettings.current = { mode, paused, dithered, sky };\n\t\tredraw.current();\n\t}, [mode, paused, dithered, sky]);\n\tuseEffect(() => {\n\t\tconst element = canvasRef.current;\n\t\tif (!element) return;\n\t\tconst canvas: HTMLCanvasElement = element;\n\t\tif (!canvas) return;\n\t\tconst observer = new IntersectionObserver(([entry]) => setActive(entry.isIntersecting));\n\t\tobserver.observe(canvas);\n\t\treturn () => observer.disconnect();\n\t}, []);\n\tuseEffect(() => {\n\t\tconst element = canvasRef.current;\n\t\tif (!element) return;\n\t\tconst canvas: HTMLCanvasElement = element;\n\t\tconst gl = canvas.getContext('webgl', {\n\t\t\talpha: false,\n\t\t\tantialias: false,\n\t\t\tdepth: false,\n\t\t\tpowerPreference: 'low-power'\n\t\t});\n\t\tif (!gl) return;\n\t\tconst shaders: WebGLShader[] = [];\n\t\tfunction compile(type: number, source: string) {\n\t\t\tconst shader = gl!.createShader(type);\n\t\t\tif (!shader) return null;\n\t\t\tgl!.shaderSource(shader, source);\n\t\t\tgl!.compileShader(shader);\n\t\t\tif (!gl!.getShaderParameter(shader, gl!.COMPILE_STATUS)) {\n\t\t\t\tgl!.deleteShader(shader);\n\t\t\t\treturn null;\n\t\t\t}\n\t\t\tshaders.push(shader);\n\t\t\treturn shader;\n\t\t}\n\t\tconst vertex = compile(\n\t\t\tgl.VERTEX_SHADER,\n\t\t\t`attribute vec2 position; void main(){ gl_Position=vec4(position,0.,1.); }`\n\t\t);\n\t\tconst fragment = compile(\n\t\t\tgl.FRAGMENT_SHADER,\n\t\t\t`\nprecision highp float;\nuniform vec2 resolution;\nuniform float time;\nuniform float mode;\nuniform float dithered;\nuniform float pixelRatio;\nuniform vec4 sunPosition;\nuniform vec4 moonPosition;\nuniform vec3 moonLight;\nuniform float astronomical;\nuniform sampler2D cloudPlate;\nuniform vec4 atmosphere; // coverage, mist, wind, opaque plate\nuniform float snowCover;\nfloat hash(vec3 p){p=fract(p*.3183099+vec3(.1,.2,.3));p*=17.;return fract(p.x*p.y*p.z*(p.x+p.y+p.z));}\nvoid main(){\n vec2 uv=gl_FragCoord.xy/resolution;\n\n float night=mix(step(14.5,mode),1.-smoothstep(-8.,1.,sunPosition.z),astronomical);\n float storm=step(7.5,mode)*(1.-step(8.5,mode));\n float dusk=mix(1.-step(1.5,mode),smoothstep(-8.,-1.,sunPosition.z)*(1.-smoothstep(2.,12.,sunPosition.z)),astronomical);\n float fog=atmosphere.y;\n float cloudy=atmosphere.w;\n vec2 previewSun=mix(vec2(.8,.76),vec2(mix(.25,.75,step(.5,mode)),.12),1.-step(1.5,mode));\n vec2 sunPoint=mix(previewSun,sunPosition.xy,astronomical);\n vec2 sunDelta=uv-sunPoint;\n sunDelta.x=mod(sunDelta.x+.5,1.)-.5;\n sunDelta.x*=resolution.x/resolution.y;\n float sunDistance=length(sunDelta);\n float sunVisible=mix(1.-night,sunPosition.w,astronomical);\n float height=pow(clamp(uv.y,0.,1.),.65);\n vec3 sky=mix(vec3(.64,.77,.85),vec3(.085,.29,.52),height);\n // Low-angle light stays near the horizon and the solar azimuth.\n float horizon=exp(-max(uv.y-.10,0.)*4.8);\n float solarLobe=exp(-abs(sunDelta.x)*2.8);\n float warmLight=dusk*horizon*(.24+.76*solarLobe);\n vec3 twilight=mix(vec3(.38,.46,.60),vec3(.88,.56,.32),horizon);\n twilight=mix(twilight,vec3(.97,.72,.44),horizon*solarLobe*.45);\n sky=mix(sky,twilight,dusk);\n sky+=vec3(.10,.025,.006)*warmLight;\n sky=mix(sky,mix(vec3(.43,.48,.51),vec3(.23,.30,.35),uv.y),cloudy*.75);\n sky=mix(sky,mix(vec3(.10,.15,.23),vec3(.025,.045,.09),uv.y),night);\n sky+=mix(vec3(1.,.87,.63),vec3(1.,.48,.18),dusk)*exp(-sunDistance*9.)*.19*sunVisible*(1.-cloudy);\n sky+=vec3(1.,.88,.64)*exp(-sunDistance*60.)*.18*sunVisible;\n float solarEdge=max(1./resolution.y,.0008);\n sky=mix(sky,mix(vec3(1.,.99,.93),vec3(1.,.80,.54),dusk),(1.-smoothstep(.008-solarEdge,.008+solarEdge,sunDistance))*sunVisible);\n float stars=pow(hash(vec3(floor(uv*resolution/2.),1.)),180.);\n sky+=stars*.35*night;\n vec2 moonDelta=uv-moonPosition.xy;\n moonDelta.x=mod(moonDelta.x+.5,1.)-.5;\n moonDelta.x*=resolution.x/resolution.y;\n vec2 moonDisc=moonDelta/.022;\n float moonRadius=length(moonDisc);\n if(moonRadius<1. && moonPosition.w>.5){\n  vec3 normal=vec3(moonDisc,sqrt(max(0.,1.-dot(moonDisc,moonDisc))));\n  float lit=smoothstep(-.015,.015,dot(normal,moonLight));\n  float mask=(1.-smoothstep(.94,1.,moonRadius))*lit;\n  sky=mix(sky,vec3(.82,.85,.89),mask*mix(.5,1.,night));\n }\n // Photographic cloud structure, slowly drifting without a visible loop seam.\n float aspect=resolution.x/resolution.y;\n vec2 photoUv=(uv-.5)*vec2(min(aspect/1.5,1.),min(1.5/aspect,1.))*.88+.5;\n photoUv+=vec2(sin(time*.012*atmosphere.z)*.035,sin(time*.008*atmosphere.z)*.012);\n vec3 photo=texture2D(cloudPlate,photoUv).rgb;\n float cloudMask=1.-smoothstep(.045,.24,photo.b-photo.r);\n float cloudOpacity=mix(cloudMask*atmosphere.x,1.,cloudy);\n vec3 fairTone=mix(vec3(.38,.45,.51),vec3(.98,.98,.96),smoothstep(.22,.95,photo.r));\n vec3 cloudTone=mix(fairTone,photo,cloudy);\n cloudTone=mix(cloudTone,cloudTone*vec3(.92,.96,1.)+.06,snowCover*.4);\n // Preserve cool cloud shadows; warm only the illuminated structure.\n float highlights=smoothstep(.28,.92,dot(photo,vec3(.2126,.7152,.0722)));\n vec3 coolCloud=cloudTone*vec3(.62,.68,.82);\n vec3 warmCloud=cloudTone*vec3(1.08,.76,.48);\n cloudTone=mix(cloudTone,mix(coolCloud,warmCloud,highlights*(.3+.7*solarLobe)),dusk*.85);\n // A restrained silver/gold edge where the sun backlights thin cloud.\n float rim=cloudMask*(1.-cloudMask)*4.;\n cloudTone+=vec3(1.,.80,.56)*rim*exp(-sunDistance*8.)*sunVisible*(1.-cloudy)*.16;\n cloudTone=mix(cloudTone,cloudTone*vec3(.12,.17,.24),night);\n cloudTone*=mix(1.,.85,storm);\n vec3 color=mix(sky,cloudTone,cloudOpacity);\n color=mix(color,mix(vec3(.69,.73,.74),vec3(.10,.14,.20),night),fog);\n color+=(hash(vec3(gl_FragCoord.xy,0.))-.5)/255.;\n if(dithered>.5){\n  // Fine, fixed stochastic dithering preserves cloud shading without a checkerboard.\n  vec2 cell=floor(gl_FragCoord.xy/pixelRatio);\n  float grain=hash(vec3(cell,7.));\n  float luminance=dot(clamp(color,0.,1.),vec3(.2126,.7152,.0722));\n  float ink=floor(luminance*7.+grain)/7.;\n  color=vec3(.28+ink*.64);\n }\n\n gl_FragColor=vec4(color,1.);\n}`\n\t\t);\n\t\tif (!vertex || !fragment) {\n\t\t\tshaders.forEach((s) => gl.deleteShader(s));\n\t\t\treturn;\n\t\t}\n\t\tconst program = gl.createProgram();\n\t\tif (!program) {\n\t\t\tshaders.forEach((s) => gl.deleteShader(s));\n\t\t\treturn;\n\t\t}\n\t\tgl.attachShader(program, vertex);\n\t\tgl.attachShader(program, fragment);\n\t\tgl.linkProgram(program);\n\t\tif (!gl.getProgramParameter(program, gl.LINK_STATUS)) {\n\t\t\tgl.deleteProgram(program);\n\t\t\tshaders.forEach((s) => gl.deleteShader(s));\n\t\t\treturn;\n\t\t}\n\t\tconst buffer = gl.createBuffer();\n\t\tgl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n\t\tgl.bufferData(\n\t\t\tgl.ARRAY_BUFFER,\n\t\t\tnew Float32Array([-1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, 1]),\n\t\t\tgl.STATIC_DRAW\n\t\t);\n\t\tconst position = gl.getAttribLocation(program, 'position');\n\t\tconst size = gl.getUniformLocation(program, 'resolution'),\n\t\t\tclock = gl.getUniformLocation(program, 'time'),\n\t\t\tscene = gl.getUniformLocation(program, 'mode'),\n\t\t\ttexture = gl.getUniformLocation(program, 'dithered'),\n\t\t\tpixelRatio = gl.getUniformLocation(program, 'pixelRatio'),\n\t\t\tsunPosition = gl.getUniformLocation(program, 'sunPosition'),\n\t\t\tmoonPosition = gl.getUniformLocation(program, 'moonPosition'),\n\t\t\tmoonLight = gl.getUniformLocation(program, 'moonLight'),\n\t\t\tastronomical = gl.getUniformLocation(program, 'astronomical');\n\t\tconst plate = gl.createTexture();\n\t\tgl.bindTexture(gl.TEXTURE_2D, plate);\n\t\tgl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n\t\tgl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n\t\tgl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n\t\tgl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\t\tgl.texImage2D(\n\t\t\tgl.TEXTURE_2D,\n\t\t\t0,\n\t\t\tgl.RGBA,\n\t\t\t1,\n\t\t\t1,\n\t\t\t0,\n\t\t\tgl.RGBA,\n\t\t\tgl.UNSIGNED_BYTE,\n\t\t\tnew Uint8Array([35, 95, 150, 255])\n\t\t);\n\t\tconst photograph = new Image();\n\t\tlet disposed = false;\n\t\tlet loadedPlate = '';\n\t\tfunction loadPlate() {\n\t\t\tconst source = weatherCloudTextures[weatherScenes[settings.current.mode].plate];\n\t\t\tif (source !== loadedPlate) {\n\t\t\t\tloadedPlate = source;\n\t\t\t\tphotograph.src = source;\n\t\t\t}\n\t\t}\n\t\tphotograph.onload = () => {\n\t\t\tif (disposed || lost) return;\n\t\t\tgl.bindTexture(gl.TEXTURE_2D, plate);\n\t\t\tgl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, true);\n\t\t\tgl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, photograph);\n\t\t\trefresh();\n\t\t};\n\t\tconst reduced = matchMedia('(prefers-reduced-motion: reduce)');\n\t\tlet visible = true,\n\t\t\tframe = 0,\n\t\t\tlast = 0,\n\t\t\telapsed = 0,\n\t\t\tlost = false;\n\t\tfunction draw(now = performance.now(), force = false) {\n\t\t\tconst { mode, paused, dithered, sky } = settings.current;\n\t\t\tconst atmosphere = weatherScenes[mode];\n\t\t\tconst snow = atmosphere.snow > 0;\n\t\t\tframe = 0;\n\t\t\tif (lost || (!force && (!visible || document.hidden))) {\n\t\t\t\tlast = 0;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tif (last && !reduced.matches && !paused) elapsed += Math.min(now - last, 100) / 1000;\n\t\t\tlast = now;\n\t\t\tconst ratio = Math.min(devicePixelRatio || 1, 2);\n\t\t\tconst width = Math.max(1, Math.floor(canvas.clientWidth * ratio));\n\t\t\tconst height = Math.max(1, Math.floor(canvas.clientHeight * ratio));\n\t\t\tif (canvas.width !== width) canvas.width = width;\n\t\t\tif (canvas.height !== height) canvas.height = height;\n\t\t\tgl!.viewport(0, 0, canvas.width, canvas.height);\n\t\t\tgl!.useProgram(program);\n\t\t\tgl!.bindBuffer(gl!.ARRAY_BUFFER, buffer);\n\t\t\tgl!.enableVertexAttribArray(position);\n\t\t\tgl!.vertexAttribPointer(position, 2, gl!.FLOAT, false, 0, 0);\n\t\t\tgl!.uniform2f(size, canvas.width, canvas.height);\n\t\t\tgl!.uniform1f(clock, elapsed);\n\t\t\tgl!.uniform1f(scene, modes.indexOf(mode));\n\t\t\tgl!.uniform1f(texture, dithered ? 1 : 0);\n\t\t\tgl!.uniform1f(pixelRatio, ratio);\n\t\t\tgl!.activeTexture(gl!.TEXTURE0);\n\t\t\tgl!.bindTexture(gl!.TEXTURE_2D, plate);\n\t\t\tgl!.uniform1i(gl!.getUniformLocation(program, 'cloudPlate'), 0);\n\t\t\tgl!.uniform4f(\n\t\t\t\tgl!.getUniformLocation(program, 'atmosphere'),\n\t\t\t\tatmosphere.coverage,\n\t\t\t\tatmosphere.mist,\n\t\t\t\tatmosphere.wind,\n\t\t\t\tatmosphere.plate === 'fair' ? 0 : 1\n\t\t\t);\n\t\t\tgl!.uniform1f(gl!.getUniformLocation(program, 'snowCover'), snow ? 1 : 0);\n\t\t\tgl!.uniform1f(astronomical, sky ? 1 : 0);\n\t\t\tgl!.uniform4f(\n\t\t\t\tsunPosition,\n\t\t\t\tsky?.sun.x ?? 0.8,\n\t\t\t\tsky?.sun.y ?? 0.76,\n\t\t\t\tsky?.sun.altitude ?? 45,\n\t\t\t\tsky?.sun.visible ? 1 : 0\n\t\t\t);\n\t\t\tgl!.uniform4f(\n\t\t\t\tmoonPosition,\n\t\t\t\tsky?.moon.x ?? 0,\n\t\t\t\tsky?.moon.y ?? 0,\n\t\t\t\tsky?.moon.altitude ?? -90,\n\t\t\t\tsky?.moon.visible ? 1 : 0\n\t\t\t);\n\t\t\tgl!.uniform3f(moonLight, ...(sky?.moon.light ?? ([0, 0, -1] as [number, number, number])));\n\t\t\tgl!.drawArrays(gl!.TRIANGLES, 0, 6);\n\t\t\tif (visible && !document.hidden && !reduced.matches && !paused)\n\t\t\t\tframe = requestAnimationFrame(tick);\n\t\t}\n\t\tfunction tick(now: number) {\n\t\t\tif (now - last < 1000 / 24) {\n\t\t\t\tframe = requestAnimationFrame(tick);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tdraw(now);\n\t\t}\n\t\tfunction refresh() {\n\t\t\tloadPlate();\n\t\t\tcancelAnimationFrame(frame);\n\t\t\tlast = 0;\n\t\t\t// Prop changes still need a static frame when an offscreen card is paused.\n\t\t\tdraw(performance.now(), true);\n\t\t}\n\t\tredraw.current = refresh;\n\t\tconst observer = new IntersectionObserver((entries) => {\n\t\t\tvisible = entries[0].isIntersecting;\n\t\t\trefresh();\n\t\t});\n\t\tobserver.observe(canvas);\n\t\tconst resize = new ResizeObserver(refresh);\n\t\tresize.observe(canvas);\n\t\tconst lose = (event: Event) => {\n\t\t\tevent.preventDefault();\n\t\t\tlost = true;\n\t\t\tcanvas.style.visibility = 'hidden';\n\t\t\tcancelAnimationFrame(frame);\n\t\t};\n\t\tcanvas.addEventListener('webglcontextlost', lose);\n\t\treduced.addEventListener('change', refresh);\n\t\tdocument.addEventListener('visibilitychange', refresh);\n\t\trefresh();\n\t\treturn () => {\n\t\t\tdisposed = true;\n\t\t\tphotograph.onload = null;\n\t\t\tgl.deleteTexture(plate);\n\t\t\tredraw.current = () => {};\n\t\t\tcancelAnimationFrame(frame);\n\t\t\tobserver.disconnect();\n\t\t\tresize.disconnect();\n\t\t\treduced.removeEventListener('change', refresh);\n\t\t\tdocument.removeEventListener('visibilitychange', refresh);\n\t\t\tcanvas.removeEventListener('webglcontextlost', lose);\n\t\t\tgl.deleteBuffer(buffer);\n\t\t\tgl.deleteProgram(program);\n\t\t\tshaders.forEach((s) => gl.deleteShader(s));\n\t\t};\n\t}, []);\n\treturn (\n\t\t<div\n\t\t\tclassName={`wxcn-sky ${(sky ? sky.period === 'night' : mode.includes('night')) ? 'night' : ''} ${paused || !active ? 'wxcn-still' : ''}`}\n\t\t\tdata-background-style={dithered ? 'dithered' : 'realistic'}\n\t\t\tdata-sky-period={sky?.period}\n\t\t\tdata-sun-altitude={sky?.sun.altitude}\n\t\t\tdata-moon-altitude={sky?.moon.altitude}\n\t\t\tdata-moon-phase={sky?.moon.phaseName}\n\t\t\taria-hidden=\"true\"\n\t\t>\n\t\t\t<style>{`\n\t.wxcn-sky {\n\t\tcontainer-type: size;\n\t}\n\t.wxcn-precipitation {\n\t\tposition: absolute;\n\t\tinset: 0;\n\t\toverflow: hidden;\n\t\tpointer-events: none;\n\t}\n\t.wxcn-precipitation i {\n\t\tposition: absolute;\n\t\ttop: -20px;\n\t\tdisplay: block;\n\t\twill-change: transform;\n\t\tanimation: wxcn-fall linear infinite;\n\t}\n\t.wxcn-rain i {\n\t\twidth: max(0.5px, calc(var(--size) * 0.4));\n\t\theight: var(--length);\n\t\tbackground: linear-gradient(\n\t\t\ttransparent,\n\t\t\trgba(226, 236, 244, 0.65) 35%,\n\t\t\trgba(245, 248, 250, 0.9) 75%,\n\t\t\ttransparent\n\t\t);\n\t\ttransform: rotate(var(--angle));\n\t}\n\t.wxcn-snow i {\n\t\twidth: calc(var(--size) * 1.5);\n\t\theight: calc(var(--size) * 1.8);\n\t\tborder-radius: 45% 55% 60% 40%;\n\t\tbackground: radial-gradient(ellipse at 40% 35%, #fff 10%, #d8e3ec 45%, transparent 75%);\n\t\tanimation-name: wxcn-snowfall;\n\t}\n\t.wxcn-still i {\n\t\tanimation-play-state: paused;\n\t}\n\t@keyframes wxcn-fall {\n\t\tfrom {\n\t\t\ttransform: translate(0, -20px) rotate(var(--angle));\n\t\t}\n\t\tto {\n\t\t\ttransform: translate(var(--drift), calc(100cqh + 40px)) rotate(var(--angle));\n\t\t}\n\t}\n\t@keyframes wxcn-snowfall {\n\t\t0% {\n\t\t\ttransform: translate(0, -20px);\n\t\t}\n\t\t50% {\n\t\t\ttransform: translate(calc(var(--drift) * 0.5 + var(--sway)), 50cqh) rotate(110deg);\n\t\t}\n\t\t100% {\n\t\t\ttransform: translate(var(--drift), calc(100cqh + 40px)) rotate(240deg);\n\t\t}\n\t}\n\t@media (prefers-reduced-motion: reduce) {\n\t\t.wxcn-precipitation i {\n\t\t\tanimation-play-state: paused;\n\t\t}\n\t}\n\n\t.wxcn-sky {\n\t\tposition: absolute;\n\t\tinset: 0;\n\t\toverflow: hidden;\n\t\tbackground: linear-gradient(160deg, #608da9, #c3d0d3);\n\t}\n\t.wxcn-sky.night {\n\t\tbackground: linear-gradient(160deg, #101c32, #405269);\n\t}\n\t.wxcn-sky[data-sky-period='sunrise'] {\n\t\tbackground: linear-gradient(180deg, #475575, #bd8290 70%, #dda472);\n\t}\n\t.wxcn-sky[data-sky-period='sunset'] {\n\t\tbackground: linear-gradient(180deg, #424760, #b4716b 70%, #d39866);\n\t}\n\t/* Multiply grayscale ink by the base swatch. CSS keeps palette changes live,\n       including while the shader is paused for reduced motion. */\n\t.wxcn-sky[data-background-style='dithered'] {\n\t\tisolation: isolate;\n\t\tbackground: var(--weather-base-color, var(--muted-foreground, #737373));\n\t}\n\t.wxcn-sky[data-background-style='dithered'] canvas {\n\t\tmix-blend-mode: multiply;\n\t}\n\t.wxcn-sky[data-background-style='dithered'] .wxcn-precipitation {\n\t\tcolor: color-mix(\n\t\t\tin srgb,\n\t\t\tvar(--weather-base-color, var(--muted-foreground, #737373)) 25%,\n\t\t\twhite\n\t\t);\n\t}\n\t.wxcn-sky[data-background-style='dithered'] .wxcn-rain i {\n\t\tbackground: linear-gradient(transparent, currentColor 65%, transparent);\n\t}\n\t.wxcn-sky[data-background-style='dithered'] .wxcn-snow i {\n\t\tbackground: radial-gradient(ellipse, currentColor 25%, transparent 75%);\n\t}\n\t.wxcn-sky canvas {\n\t\tdisplay: block;\n\t\twidth: 100%;\n\t\theight: 100%;\n\t}\n`}</style>\n\t\t\t<canvas ref={canvasRef} />\n\t\t\t{rain && (\n\t\t\t\t<div className=\"wxcn-precipitation wxcn-rain\" data-precipitation=\"rain\">\n\t\t\t\t\t{particles.slice(0, atmosphere.rain).map((p, i) => (\n\t\t\t\t\t\t<i key={i} data-depth={p.depth} style={particleStyle(p, false)} />\n\t\t\t\t\t))}\n\t\t\t\t</div>\n\t\t\t)}\n\t\t\t{snow && (\n\t\t\t\t<div className=\"wxcn-precipitation wxcn-snow\" data-precipitation=\"snow\">\n\t\t\t\t\t{particles.slice(0, atmosphere.snow).map((p, i) => (\n\t\t\t\t\t\t<i key={i} data-depth={p.depth} style={particleStyle(p, true)} />\n\t\t\t\t\t))}\n\t\t\t\t</div>\n\t\t\t)}\n\t\t</div>\n\t);\n}\nexport default WeatherShaderBackground;\n"
    },
    {
      "path": "packages/react/src/components/wxcn/weather-gradient-background.tsx",
      "type": "registry:component",
      "target": "@components/wxcn/weather-gradient-background.tsx",
      "content": "import type { SkyState } from '@/lib/wxcn/sky.js';\nimport type { CSSProperties } from 'react';\nimport type { WeatherShaderMode } from './weather-shader-background';\n\nexport function WeatherGradientBackground({\n\tmode = 'clear',\n\tsky = null\n}: {\n\tmode?: WeatherShaderMode;\n\tsky?: SkyState | null;\n}) {\n\tconst period =\n\t\tsky?.period ??\n\t\t(mode.includes('night') ? 'night' : mode === 'sunrise' || mode === 'sunset' ? mode : 'midday');\n\tconst weather = /rain|drizzle|thunderstorm|wintry/.test(mode)\n\t\t? 'rain'\n\t\t: /snow/.test(mode)\n\t\t\t? 'snow'\n\t\t\t: /cloudy|fog|haze/.test(mode)\n\t\t\t\t? 'cloud'\n\t\t\t\t: 'clear';\n\tconst z = sky ? 2 * sky.moon.illumination - 1 : -1;\n\tconst moonPath = `M0 -1 A1 1 0 0 1 0 1 L${Array.from({ length: 65 }, (_, i) => {\n\t\tconst y = 1 - i / 32;\n\t\treturn `${-z * Math.sqrt(Math.max(0, 1 - y * y))} ${y}`;\n\t}).join(' L')} Z`;\n\tconst moonAngle = sky ? (Math.atan2(-sky.moon.light[1], sky.moon.light[0]) * 180) / Math.PI : 0;\n\tconst style = {\n\t\t'--sun-x': `${(sky?.sun.x ?? 0.8) * 100}%`,\n\t\t'--sun-y': `${(1 - (sky?.sun.y ?? 0.76)) * 100}%`\n\t} as CSSProperties;\n\treturn (\n\t\t<div\n\t\t\tclassName=\"wxcn-weather-gradient\"\n\t\t\tdata-background-style=\"gradient\"\n\t\t\tdata-sky-period={period}\n\t\t\tdata-weather={weather}\n\t\t\tstyle={style}\n\t\t\taria-hidden=\"true\"\n\t\t>\n\t\t\t{sky?.sun.visible && (\n\t\t\t\t<span\n\t\t\t\t\tclassName=\"wxcn-gradient-sun\"\n\t\t\t\t\tstyle={{ left: `${sky.sun.x * 100}%`, top: `${(1 - sky.sun.y) * 100}%` }}\n\t\t\t\t/>\n\t\t\t)}\n\t\t\t{sky?.moon.visible && (\n\t\t\t\t<svg\n\t\t\t\t\tclassName=\"wxcn-gradient-moon\"\n\t\t\t\t\tviewBox=\"-1 -1 2 2\"\n\t\t\t\t\tstyle={{\n\t\t\t\t\t\tleft: `${sky.moon.x * 100}%`,\n\t\t\t\t\t\ttop: `${(1 - sky.moon.y) * 100}%`,\n\t\t\t\t\t\ttransform: `translate(-50%,-50%) rotate(${moonAngle}deg)`\n\t\t\t\t\t}}\n\t\t\t\t>\n\t\t\t\t\t<path d={moonPath} fill=\"#d9dee6\" />\n\t\t\t\t</svg>\n\t\t\t)}\n\t\t\t<style>{`\n.wxcn-weather-gradient{position:absolute;inset:0;overflow:hidden;container-type:size;background:radial-gradient(ellipse at var(--sun-x) var(--sun-y),#f5dba560,transparent 45%),linear-gradient(180deg,#32658b,#88b4c3)}\n.wxcn-weather-gradient[data-sky-period=night]{background:linear-gradient(180deg,#0c1428,#29394f)}\n.wxcn-weather-gradient[data-sky-period=sunrise]{background:radial-gradient(ellipse at var(--sun-x) var(--sun-y),#edab8370,transparent 65%),linear-gradient(180deg,#475575,#bd8290 70%,#dda472)}\n.wxcn-weather-gradient[data-sky-period=sunset]{background:radial-gradient(ellipse at var(--sun-x) var(--sun-y),#fba44780,transparent 65%),linear-gradient(180deg,#424760,#b4716b 70%,#d39866)}\n.wxcn-weather-gradient:after{content:'';position:absolute;inset:0;pointer-events:none}.wxcn-weather-gradient[data-weather=cloud]:after{background:#71818b55}.wxcn-weather-gradient[data-weather=rain]:after{background:#263c52aa}.wxcn-weather-gradient[data-weather=snow]:after{background:#a4b8c455}\n.wxcn-gradient-sun,.wxcn-gradient-moon{position:absolute;transform:translate(-50%,-50%)}.wxcn-gradient-sun{width:3.2cqh;height:3.2cqh;border-radius:50%;background:#fff0bd;box-shadow:0 0 3cqh #f4c98966}.wxcn-gradient-moon{width:4.4cqh;height:4.4cqh}\n`}</style>\n\t\t</div>\n\t);\n}\n\nexport default WeatherGradientBackground;\n"
    },
    {
      "path": "packages/react/src/icons/forecast-icons.tsx",
      "type": "registry:component",
      "target": "@components/wxcn/forecast-icons.tsx",
      "content": "import { IconPlaceholder } from '@/components/icon-placeholder';\nexport type IconName = 'arrowUp' | 'arrowDown' | 'weather' | 'sun' | 'moon' | 'tide' | 'wind' | 'rain' | 'snow';\nexport type IconSet = 'lucide' | 'tabler' | 'phosphor' | 'hugeicons' | 'remixicon';\nexport type ForecastIconProps = { name: IconName; iconSet?: IconSet; className?: string };\nexport function ForecastIcon({ name, className = 'size-5' }: ForecastIconProps) {\n switch (name) {\n case 'arrowUp': return <IconPlaceholder lucide=\"ArrowUpIcon\" tabler=\"IconArrowUp\" phosphor=\"ArrowUpIcon\" hugeicons=\"ArrowUp02Icon\" remixicon=\"RiArrowUpLine\" className={className} aria-hidden=\"true\" />;\ncase 'arrowDown': return <IconPlaceholder lucide=\"ArrowDownIcon\" tabler=\"IconArrowDown\" phosphor=\"ArrowDownIcon\" hugeicons=\"ArrowDown02Icon\" remixicon=\"RiArrowDownLine\" className={className} aria-hidden=\"true\" />;\ncase 'weather': return <IconPlaceholder lucide=\"CloudSunIcon\" tabler=\"IconCloud\" phosphor=\"CloudSunIcon\" hugeicons=\"SunCloud02Icon\" remixicon=\"RiCloudy2Line\" className={className} aria-hidden=\"true\" />;\ncase 'sun': return <IconPlaceholder lucide=\"SunIcon\" tabler=\"IconSun\" phosphor=\"SunIcon\" hugeicons=\"Sun03Icon\" remixicon=\"RiSunLine\" className={className} aria-hidden=\"true\" />;\ncase 'moon': return <IconPlaceholder lucide=\"MoonIcon\" tabler=\"IconMoon\" phosphor=\"MoonIcon\" hugeicons=\"Moon02Icon\" remixicon=\"RiMoonLine\" className={className} aria-hidden=\"true\" />;\ncase 'tide': return <IconPlaceholder lucide=\"WavesIcon\" tabler=\"IconWavesElectricity\" phosphor=\"WavesIcon\" hugeicons=\"WaterfallUp01Icon\" remixicon=\"RiWaterFlashLine\" className={className} aria-hidden=\"true\" />;\ncase 'wind': return <IconPlaceholder lucide=\"WindIcon\" tabler=\"IconWind\" phosphor=\"WindIcon\" hugeicons=\"WindPower02Icon\" remixicon=\"RiWindyLine\" className={className} aria-hidden=\"true\" />;\ncase 'rain': return <IconPlaceholder lucide=\"CloudRainIcon\" tabler=\"IconCloudRain\" phosphor=\"CloudRainIcon\" hugeicons=\"CloudRainIcon\" remixicon=\"RiRainyLine\" className={className} aria-hidden=\"true\" />;\ncase 'snow': return <IconPlaceholder lucide=\"SnowflakeIcon\" tabler=\"IconSnowflake\" phosphor=\"SnowflakeIcon\" hugeicons=\"SnowIcon\" remixicon=\"RiSnowyLine\" className={className} aria-hidden=\"true\" />;\n }\n}\n"
    },
    {
      "path": "packages/core/src/types.ts",
      "type": "registry:lib",
      "target": "@lib/wxcn/types.ts",
      "content": "export type CardSize = 'sm' | 'default' | 'lg';\nexport type CardDensity = 'compact' | 'comfortable';\nexport type ForecastType = 'summary' | 'detailed' | 'simple';\nexport type IconSet = 'hugeicons' | 'phosphor-svelte' | 'lucide' | 'tabler' | 'remix';\nexport type WeatherUnit = 'fahrenheit' | 'celsius';\nexport type TideUnit = 'ft' | 'meter';\n\nexport type LocationInput = {\n\tlabel?: string;\n\tlatitude: number;\n\tlongitude: number;\n\tstation?: string;\n\ttimeZone?: string;\n};\n\nexport type WeatherPeriod = {\n\tname: string;\n\tstartTime: string;\n\tendTime?: string;\n\ttemperature: number;\n\ttemperatureUnit: string;\n\twindSpeed: string;\n\twindDirection: string;\n\tshortForecast: string;\n\tdetailedForecast: string;\n\tisDaytime: boolean;\n};\n\nexport type TidePrediction = {\n\ttime: string;\n\theight: string;\n\ttype: 'H' | 'L';\n};\n\nexport type MoonForecast = {\n\tdate: string;\n\tphaseName: string;\n\t/** Astronomical phase cycle: 0 new, 0.25 first quarter, 0.5 full. */\n\tphase?: number;\n\tillumination: number;\n\tage: number;\n\tnextFullMoon: string;\n\tnextNewMoon: string;\n};\n\n/** Feet above MLLW. Prefer ISO 8601 timestamps with an explicit offset. */\nexport type TidePoint = { time: string; height: string };\nexport type TideReading = TidePoint;\n\n/** Current observed conditions; daily extrema use temperatureUnit and the location's calendar day. */\nexport type CurrentWeather = WeatherPeriod & {\n\tobservedAt: string;\n\thighToday?: number;\n\tlowToday?: number;\n};\n\nexport type WeatherBackground = 'none' | 'realistic' | 'dithered' | 'gradient';\n"
    },
    {
      "path": "packages/core/src/forecast-days.ts",
      "type": "registry:lib",
      "target": "@lib/wxcn/forecast-days.ts",
      "content": "export type ForecastEntry = { time: number; label: string; summary: string; details: string };\nexport type ForecastDay = { key: string; label: string; entries: ForecastEntry[] };\n\n/** Group supplied periods in the location's calendar, without inventing missing days. */\nexport function forecastDays(entries: ForecastEntry[], timeZone: string): ForecastDay[] {\n\tconst key = new Intl.DateTimeFormat('en-CA', {\n\t\ttimeZone,\n\t\tyear: 'numeric',\n\t\tmonth: '2-digit',\n\t\tday: '2-digit'\n\t});\n\tconst label = new Intl.DateTimeFormat('en-US', {\n\t\ttimeZone,\n\t\tweekday: 'short',\n\t\tmonth: 'short',\n\t\tday: 'numeric'\n\t});\n\tconst days = new Map<string, ForecastDay>();\n\tfor (const entry of entries\n\t\t.filter((e) => Number.isFinite(e.time))\n\t\t.toSorted((a, b) => a.time - b.time)) {\n\t\tconst id = key.format(entry.time);\n\t\tif (!days.has(id)) days.set(id, { key: id, label: label.format(entry.time), entries: [] });\n\t\tdays.get(id)!.entries.push(entry);\n\t}\n\treturn [...days.values()].slice(0, 7);\n}\n\n/** Resolve noon on a forecast calendar date in the location's time zone, including DST. */\nexport function forecastDayNoon(key: string, timeZone: string): number {\n\tconst noon = Date.parse(`${key}T12:00:00Z`);\n\tconst format = new Intl.DateTimeFormat('en-US', {\n\t\ttimeZone,\n\t\tyear: 'numeric',\n\t\tmonth: '2-digit',\n\t\tday: '2-digit',\n\t\thour: '2-digit',\n\t\tminute: '2-digit',\n\t\tsecond: '2-digit',\n\t\thourCycle: 'h23'\n\t});\n\tlet instant = noon;\n\tfor (let attempt = 0; attempt < 3; attempt++) {\n\t\tconst parts = Object.fromEntries(\n\t\t\tformat.formatToParts(instant).map(({ type, value }) => [type, value])\n\t\t);\n\t\tconst wallTime = Date.UTC(\n\t\t\tNumber(parts.year),\n\t\t\tNumber(parts.month) - 1,\n\t\t\tNumber(parts.day),\n\t\t\tNumber(parts.hour),\n\t\t\tNumber(parts.minute),\n\t\t\tNumber(parts.second)\n\t\t);\n\t\tconst correction = noon - wallTime;\n\t\tinstant += correction;\n\t\tif (!correction) break;\n\t}\n\treturn instant;\n}\n"
    },
    {
      "path": "packages/core/src/weather.ts",
      "type": "registry:lib",
      "target": "@lib/wxcn/weather.ts",
      "content": "import type { LocationInput, WeatherPeriod, CurrentWeather } from './types.js';\n\ntype NwsPoint = {\n\tproperties: {\n\t\tforecast: string;\n\t\trelativeLocation?: {\n\t\t\tproperties?: {\n\t\t\t\tcity?: string;\n\t\t\t\tstate?: string;\n\t\t\t};\n\t\t};\n\t};\n};\n\ntype NwsForecast = {\n\tproperties: {\n\t\tperiods: WeatherPeriod[];\n\t};\n};\n\nconst headers = {\n\tAccept: 'application/geo+json',\n\t'User-Agent': 'wxcn/0.1 (https://github.com/wxcn/wxcn)'\n};\n\nexport async function fetchWeatherForecast(location: LocationInput): Promise<WeatherPeriod[]> {\n\tconst point = await fetch(\n\t\t`https://api.weather.gov/points/${location.latitude.toFixed(4)},${location.longitude.toFixed(4)}`,\n\t\t{ headers }\n\t);\n\n\tif (!point.ok) {\n\t\tthrow new Error(`NWS point lookup failed with ${point.status}`);\n\t}\n\n\tconst pointData = (await point.json()) as NwsPoint;\n\tconst forecast = await fetch(pointData.properties.forecast, { headers });\n\n\tif (!forecast.ok) {\n\t\tthrow new Error(`NWS forecast lookup failed with ${forecast.status}`);\n\t}\n\n\tconst forecastData = (await forecast.json()) as NwsForecast;\n\treturn forecastData.properties.periods;\n}\n\n/** Deterministic Austin fixtures for previews; not live observations. */\nexport const sampleWeather: WeatherPeriod[] = [\n\t['Today', 92, 'Mostly Sunny', true],\n\t['Tonight', 74, 'Partly Cloudy', false],\n\t['Monday', 94, 'Sunny', true],\n\t['Monday night', 75, 'Mostly Clear', false],\n\t['Tuesday', 89, 'Chance of Rain', true],\n\t['Tuesday night', 72, 'Partly Cloudy', false],\n\t['Wednesday', 90, 'Sunny', true]\n].map(([name, temperature, shortForecast, isDaytime], index) => ({\n\tname: name as string,\n\ttemperature: temperature as number,\n\tshortForecast: shortForecast as string,\n\tisDaytime: isDaytime as boolean,\n\tstartTime: `2026-09-${String(6 + Math.floor(index / 2)).padStart(2, '0')}T${index % 2 ? '19' : '07'}:00:00-05:00`,\n\ttemperatureUnit: 'F',\n\twindSpeed: '5 to 10 mph',\n\twindDirection: 'S',\n\tdetailedForecast: `${shortForecast}. South wind 5 to 10 mph.`\n}));\n\nexport function convertWindSpeed(speed: string, unit: 'mph' | 'km/h' | 'm/s' | 'knots' = 'mph') {\n\tconst factors = { mph: 0.44704, 'km/h': 1 / 3.6, 'm/s': 1, knots: 0.514444 };\n\tconst source = speed.match(/mph|km\\/h|m\\/s|knots/);\n\tif (!source) return speed;\n\treturn speed\n\t\t.replace(/\\d+(?:\\.\\d+)?/g, (n) =>\n\t\t\tString(Math.round((Number(n) * factors[source[0] as keyof typeof factors]) / factors[unit]))\n\t\t)\n\t\t.replace(source[0], unit);\n}\n\nexport const sampleCurrentWeather: CurrentWeather = {\n\t...sampleWeather[0],\n\tname: 'Now',\n\ttemperature: 86,\n\tobservedAt: '2026-09-06T16:00:00-05:00',\n\tstartTime: '2026-09-06T16:00:00-05:00',\n\thighToday: 88\n};\n"
    },
    {
      "path": "packages/core/src/weather-outlook.ts",
      "type": "registry:lib",
      "target": "@lib/wxcn/weather-outlook.ts",
      "content": "import type { CurrentWeather, WeatherPeriod, WeatherUnit } from './types.js';\nexport function weatherTemperature(value: number, from: string, unit: WeatherUnit) {\n\treturn Math.round(\n\t\tunit === 'celsius' && from === 'F'\n\t\t\t? ((value - 32) * 5) / 9\n\t\t\t: unit === 'fahrenheit' && from === 'C'\n\t\t\t\t? (value * 9) / 5 + 32\n\t\t\t\t: value\n\t);\n}\nexport function weatherOutlook(\n\tcurrent: CurrentWeather | null,\n\tforecast: WeatherPeriod[],\n\tunit: WeatherUnit,\n\ttimeZone = 'UTC',\n\tat = Date.now()\n) {\n\tconst day = (time: string | number) =>\n\t\tnew Intl.DateTimeFormat('en-CA', {\n\t\t\ttimeZone,\n\t\t\tyear: 'numeric',\n\t\t\tmonth: '2-digit',\n\t\t\tday: '2-digit'\n\t\t}).format(new Date(time));\n\tconst today = day(at);\n\tconst valid = forecast.filter(\n\t\t(p) =>\n\t\t\tNumber.isFinite(p.temperature) &&\n\t\t\tNumber.isFinite(Date.parse(p.startTime)) &&\n\t\t\t(!p.endTime || Date.parse(p.endTime) > at)\n\t);\n\tconst highPeriod = valid.find((p) => p.isDaytime && day(p.startTime) === today);\n\tconst lowPeriod = valid.find(\n\t\t(p) =>\n\t\t\t!p.isDaytime &&\n\t\t\t(day(p.startTime) === today ||\n\t\t\t\t(Date.parse(p.startTime) <= at && !!p.endTime && Date.parse(p.endTime) > at))\n\t);\n\tconst observedHigh =\n\t\tcurrent?.highToday === undefined\n\t\t\t? null\n\t\t\t: weatherTemperature(current.highToday, current.temperatureUnit, unit);\n\tconst high = highPeriod\n\t\t? Math.max(\n\t\t\t\tweatherTemperature(highPeriod.temperature, highPeriod.temperatureUnit, unit),\n\t\t\t\tobservedHigh ?? -Infinity\n\t\t\t)\n\t\t: observedHigh;\n\tconst low = lowPeriod\n\t\t? weatherTemperature(lowPeriod.temperature, lowPeriod.temperatureUnit, unit)\n\t\t: null;\n\tconst temp = current\n\t\t? weatherTemperature(current.temperature, current.temperatureUnit, unit)\n\t\t: null;\n\tconst highReached =\n\t\thigh !== null &&\n\t\t((observedHigh !== null && observedHigh >= high) || (temp !== null && temp >= high));\n\tlet trend: string | null = null;\n\tif (temp !== null) {\n\t\tif (current?.isDaytime && high !== null && !highReached && temp < high)\n\t\t\ttrend = `Going up to ${high}° today`;\n\t\telse if (low !== null && temp > low)\n\t\t\ttrend = `Going down to ${low}° ${lowPeriod?.name.toLowerCase().includes('overnight') ? 'overnight' : 'tonight'}`;\n\t}\n\treturn { high, low, trend, highReached };\n}\n\n/** Combine a day's forecast high with observations from that same local day. */\nexport function weatherDayHigh(\n\tkey: string,\n\tperiods: WeatherPeriod[],\n\tcurrent: CurrentWeather | null,\n\tunit: WeatherUnit,\n\ttimeZone: string\n) {\n\tconst highs = periods\n\t\t.filter((p) => p.isDaytime && Number.isFinite(p.temperature))\n\t\t.map((p) => weatherTemperature(p.temperature, p.temperatureUnit, unit));\n\tif (\n\t\tcurrent &&\n\t\tNumber.isFinite(current.highToday) &&\n\t\tNumber.isFinite(Date.parse(current.observedAt))\n\t) {\n\t\tconst observedDay = new Intl.DateTimeFormat('en-CA', {\n\t\t\ttimeZone,\n\t\t\tyear: 'numeric',\n\t\t\tmonth: '2-digit',\n\t\t\tday: '2-digit'\n\t\t}).format(new Date(current.observedAt));\n\t\tif (observedDay === key)\n\t\t\thighs.push(weatherTemperature(current.highToday!, current.temperatureUnit, unit));\n\t}\n\treturn highs.length ? Math.max(...highs) : null;\n}\n"
    },
    {
      "path": "packages/core/src/sky.ts",
      "type": "registry:lib",
      "target": "@lib/wxcn/sky.ts",
      "content": "import {\n\tBody,\n\tEquator,\n\tHorizon,\n\tIllumination,\n\tMoonPhase,\n\tObserver,\n\tSearchHourAngle,\n\tSearchRiseSet\n} from 'astronomy-engine';\n\nexport type SkyPeriod = 'sunrise' | 'midday' | 'sunset' | 'night';\nexport type SkyBody = { azimuth: number; altitude: number; visible: boolean; x: number; y: number };\nexport type SkyState = {\n\tat: number;\n\tperiod: SkyPeriod;\n\tisDaytime: boolean;\n\tsun: SkyBody;\n\tmoon: SkyBody & {\n\t\tillumination: number;\n\t\tphase: number;\n\t\tphaseName: string;\n\t\tlight: [number, number, number];\n\t};\n};\nconst rad = Math.PI / 180;\nconst cache = new Map<string, SkyState>();\nconst phaseNames = [\n\t'New Moon',\n\t'Waxing Crescent',\n\t'First Quarter',\n\t'Waxing Gibbous',\n\t'Full Moon',\n\t'Waning Gibbous',\n\t'Last Quarter',\n\t'Waning Crescent'\n];\n\n// Fixed 360-degree panorama: N at both edges, E at 25%, S at 50%, W at 75%.\n// Altitude maps from the horizon at 12% to the zenith at 88% of card height.\n// Discs are enlarged for readability; coordinates retain their astronomical values.\nfunction project(azimuth: number, altitude: number): SkyBody {\n\treturn {\n\t\tazimuth,\n\t\taltitude,\n\t\tvisible: altitude >= -0.2666,\n\t\tx: azimuth / 360,\n\t\ty: 0.12 + (altitude / 90) * 0.76\n\t};\n}\nfunction valid(latitude: number, longitude: number, at: number) {\n\treturn (\n\t\tNumber.isFinite(latitude) &&\n\t\tMath.abs(latitude) <= 90 &&\n\t\tNumber.isFinite(longitude) &&\n\t\tMath.abs(longitude) <= 180 &&\n\t\tNumber.isFinite(at) &&\n\t\tMath.abs(at) <= 8640000000000000\n\t);\n}\nexport function getSkyState(latitude: number, longitude: number, at = Date.now()): SkyState | null {\n\tif (!valid(latitude, longitude, at)) return null;\n\t// Cards request positions once per minute; preserve an explicit event timestamp.\n\tconst key = `${latitude}:${longitude}:${at}`;\n\tconst cached = cache.get(key);\n\tif (cached) return cached;\n\tconst date = new Date(at);\n\tconst observer = new Observer(latitude, longitude, 0);\n\tfunction position(body: Body, instant = date) {\n\t\tconst eq = Equator(body, instant, observer, true, true);\n\t\treturn Horizon(instant, observer, eq.ra, eq.dec, 'normal');\n\t}\n\tconst sunPosition = position(Body.Sun);\n\tconst moonPosition = position(Body.Moon);\n\tconst sun = project(sunPosition.azimuth, sunPosition.altitude);\n\t// Apparent upper solar limb at the horizon (refraction is applied by Horizon).\n\tconst isDaytime = sun.altitude >= -0.2666;\n\tconst rising = position(Body.Sun, new Date(at + 60000)).altitude > sun.altitude;\n\tconst period: SkyPeriod =\n\t\tsun.altitude < -6 ? 'night' : sun.altitude <= 6 ? (rising ? 'sunrise' : 'sunset') : 'midday';\n\tconst illumination = Illumination(Body.Moon, date).phase_fraction;\n\tconst phase = MoonPhase(date) / 360;\n\t// Project the Sun onto the Moon's local horizon tangent plane. This gives the\n\t// illuminated limb its observer-relative tilt, including hemisphere differences.\n\tconst sa = sun.azimuth * rad,\n\t\tsh = sun.altitude * rad;\n\tconst ma = moonPosition.azimuth * rad,\n\t\tmh = moonPosition.altitude * rad;\n\tconst sx = Math.cos(sh) * Math.sin(sa),\n\t\tsy = Math.sin(sh),\n\t\tsz = Math.cos(sh) * Math.cos(sa);\n\tconst right = sx * Math.cos(ma) - sz * Math.sin(ma);\n\tconst up =\n\t\t-sx * Math.sin(mh) * Math.sin(ma) + sy * Math.cos(mh) - sz * Math.sin(mh) * Math.cos(ma);\n\tconst length = Math.hypot(right, up) || 1;\n\tconst z = 2 * illumination - 1;\n\tconst tangent = Math.sqrt(Math.max(0, 1 - z * z));\n\tconst state: SkyState = {\n\t\tat,\n\t\tperiod,\n\t\tisDaytime,\n\t\tsun,\n\t\tmoon: {\n\t\t\t...project(moonPosition.azimuth, moonPosition.altitude),\n\t\t\tillumination,\n\t\t\tphase,\n\t\t\tphaseName: phaseNames[Math.round(phase * 8) % 8],\n\t\t\tlight: [(right / length) * tangent, (up / length) * tangent, z]\n\t\t}\n\t};\n\tif (cache.size >= 256) cache.delete(cache.keys().next().value!);\n\tcache.set(key, state);\n\treturn state;\n}\n\nexport function getSkyPreviewTime(\n\tlatitude: number,\n\tlongitude: number,\n\tat: number,\n\tperiod: SkyPeriod\n): number | null {\n\tif (!valid(latitude, longitude, at)) return null;\n\tconst observer = new Observer(latitude, longitude, 0);\n\tconst start = new Date(at - 12 * 3600000);\n\tconst event =\n\t\tperiod === 'sunrise' || period === 'sunset'\n\t\t\t? SearchRiseSet(Body.Sun, observer, period === 'sunrise' ? 1 : -1, start, 1)\n\t\t\t: SearchHourAngle(Body.Sun, observer, period === 'midday' ? 0 : 12, start).time;\n\treturn event?.date.getTime() ?? null; // Polar day/night can have no rise or set.\n}\n"
    }
  ]
}