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FBZbJkxHiEAWbMnVaKPR9z56Yjz0Q8FRWEmxuJnXG9JiPrc/KwlxUROuq5F17NEXVB1Kn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\nppDsYrZkF+8DKggptemiQ7pDdEi7opz3E2CS6JA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                "text/plain": "<Figure size 432x288 with 2 Axes>"
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lIoHsIuNvBzAe+I3ohQsJvW4WCj+/M16PfeYZon7/e0pffY3yjz6idssW4l56\nCb/+/VwUsecRt9UOIlutFP7lLxTMf4jKVV8C4NunT6vJI4C6Rw9in32WhLfewlJRSc6NN1H36692\njWlHThkWq8ylPT3jJAq2i3iOoY6CinpXh+JRan78iaOXT8R48CAA2uuvbzV5BFBoNITddBOpX67C\nf8gQChc9SdmKFXaNx2yxsjW7jFFp7j1563QjxTjILjGXl1P47HPIjY2owsJIfPst/IcNa/Nz9x88\nmKQVHxD1yB+o/v57qr9fZ/eYfs4qJUCjZGB8qN337Qgalbh57gpZlmk4ZuuSDpk+nbSNGwm79ZZz\nksdmqvBwYp74E8krPkAVFYUyNMSZ4Xo8kUA6gGy1Uvjkk1Ss/Jjwu+8iZOaMDr83cNRIkld+hCo8\nnIbDh+0a15ajBjQqBUOSPKc1RVzEO6/mx5/InzcPVXQ0yoiO3yyoIiNJeON1wu+9l6DLJ9o1pt9O\nVFHTYPaIsbfNwgI09IsN5scj4rvXUebycnLvuJOKjz+mISurw++TFArC776b1NVfEjpzpt3j+iXL\nwLCUMI8aijCqVyTZpbXi5rkTSl54kWMzZmI8ZLt2drQl2y8jg+SPPkQTH29LQo8edWSYXsNzfps8\nSMkLL1Dxv08Iv/8+Iv/wByRF537Mmvh4Ur/4HO2cOYAtIbWHn7MMDEnU4qtW2mV/ztA7OpCIQB+R\nQHZQ/W+/kf/gg2h69iTp3XdQR3VutSFJpSLq9w+jjo5CNpup2bzZLnE1T4S6ODXMLvtzllFpEezK\nLRfLGnaAtaGB/N/Nw5SdTfwrr+Cbnt7pffik2obX1O/bR+6999qlUkBxlZGsklouSfWcmxcQlQA6\nq2zZcgxvvEHIzJn49Or6OG7D629w7NpZ1O3aZcfovJNIIO2s8eRJypZ/QOjs2UTOn9/l7rrmru6a\nH38iZ9Z1WCq6tyKLoaYB/ckqRqZ51klUkiRGpoXzc5ZBFHU+D7PBQP68B1BqtSS++QbK0O5115V/\n+CF599xL9caN3Y7tlywDPSMDiApyzOQwRxnVK4JGi8y2Y2WuDsWtybLMySf+TP3u3cT+4x8EjhrZ\nrf2ZS0up/fEnTv75z93+vd/a9Nl5Uus3iJvnzqj54QeKFi8m8PIJxDy5qNONNqcLvf461DEx5D/w\nII0nT9oxSu8jEkg7U/foQcon/yPmiT/ZZayXIiAA45EjFDz2WLdaIrdmN59EPWf8Y7ORPSMoqW7g\nSHGNq0Nxa4qgIILGjyPhPy+j6kTXdVtCr78e3759OfHoY5iOH+/yfhotVnbmlHncBRxgaFIYaqXE\nVlFKql2NBQXU/PADkQ/NJyvLim0AACAASURBVHjypG7vL2j8eCJ//zBVX31N2Xvvd2tfv2QZCPJR\n0S/Ws8a3iZvnjmksLubEY4/j06cPcf/4B5Kyez1sqrAw4l99BbmhgfyHHsZqEnWI2yISSDuxNjRQ\n/f33APikpSGp7VMmx3/wIKIXPE7t5h8p//CjLu/n56xSAn1UDIz3rJMowKVNrabiTrxtstWKQqMh\nZtEifPv2tcs+Fb6+xP/7X6BUcuKxx5HNXevG3V9QSa3JwsUe1oUI4KdRMiA+lG3ZogWyPZr4eFLX\nrCb83nvtts/wu+8maOLlFC9dijEzs8v72ZptYHhKGEqFZ0zeOt2IlHBKqhvEikjtUIWFob31FuKW\nPo/C3z4VHnxSU+nxt+cw7ttH6cv/scs+vZFIIO2kZOlS8h94sGXWqz1pb7yRgNGjKV6ypGWGWWf9\nnGU7iXrSIPJm8VpbUeftohuxVfUHfiP7qqs6NWmho9RxccQs+jP1e/diePudLu3jl6zm8Y+el0AC\njEgJsyXBDWIc5Nlkk4mKzz5HtlpRR0V1q+vwbJIkEfP00yhDQqj45NMu7aOwaTlKT2z9BhjRNGZY\nDKFonWyxIKlURM6bh09Kil33HXzFFYTfcw/+w4bZdb/exPOyCTekOnaMsmXL0d54o91af04nSRI9\nnnkGyceHqq++6vT7T1TUc6y0lks99CQKtjvx7cfKHNqV07xvS00thc88S82PPznsWPYim0ycWPA4\n1uoau3RbtyZ4yhTC776LgEsu7tL7t2YbWsZzeaIRqeFYrDK/Hi932DGsRiM1mzcjN9pWHjEeOkTZ\n+++7/dJrhnff4+TChdQ7aMKBSqsleeVHRC/8Y5fe/0u2rdfCU29eUiMCiAj0YZsThlBUff01Ze+f\nGi5g1Ovd+vtnOn6crClTqNu922HHiPq/3xM4epTD9u/pRALZTbLZTPCHH6KKiiLy//7PYcdRR0eR\nuuoLIufN6/R7m8dveepdONhagQy1JrJK7D8O0lJZyfFbb6N8+XIA5EYTlV9+iaXCljA0FhWRc8Mc\n6g/8Zvdjd5fh/fcxHc0i5i9PoQxxzPAESZKIeuQR/AYM6PR7TWYrO3PKPfYCDjAkSYtSITm0Bbxy\n9Wry7rm3Zaxp3datFP1tccua0ab8Are7mJvy8ih95RWCJk7Ef+hQhx1HEx+PpFDQWFSMKb+gU+/d\nmlVGiJ+avj08c4URSZIYkRLGNgfcPFtNJmq2bGl5XPXtd1R99XXL48JnniV37ly7HtNeZFmm8C9P\nYzGUoY6Nc/ixil98kdLXXnfocTyRSCC7qfzDD1Hn5RP9xz/affWEs6ljbCsSmHJyOjUre0dOGUG+\nKtJjPPMkCjA8xf5dObLFAoAiOBhVdDSKwCDA1urRZ/s2QqZPB8BiMGCpqEAZFGi3Y9tDY2Ehpa++\nRuD48QSNHevw41nr6yl8+mkq16zt8Hv2F1RQ32jxuBIqpwv0UdE/LoRtx+zXCiSbzZS+9jq1P/8M\n2LrLEt58E3VCAgDaW2+l189bWmbSF/7lL+Rcd32Xx6HamyzLFD79VySlkug/LXT88RobyZk9m8In\nn+xUIvVL0/hHhQeOf2w2IjWMk5VG8svtWw+y9JVXyLt7Lqa8PABin32GpJWnxtlHP/4YUb//PWD7\n+Ze+/oZdyirZQ9WatdT+/DOR//d71NGdK1XWWZIk0Zhru1lq/lkJNi5bylCfrksAlgHRgAy8ocvU\nv6RP14UBHwPJQA5wvS5TX65P10nAS8AUoA64XZepd3mhJlVMDPWXXELQpCuccjxzeTnZM68m9Jqr\niVm0qEPv2X6sjKFNrSieKincn6ggH7YfK+OmEUnd3l/tzz9T+MyzSL+7H0mSiPvnP9rc1rdvX1K/\n/qplVn3x88/j26+/XWabdkf5ig/BYuly915nST4+1O/bT/W67wkcO7ZDN0zN4x9HeHACCXBxShjv\nbsnB2GixSx1V2WymctUqzGUGAi69FGVIyBldZZIkoQo7VTMz8uGHMBcVI6lsp2zZarXreMPOqt28\nmdoffyRqweMtN7aOJKnVhN95B0XP/Y2aDRsImjDhvO8pqKgnt6yO2y9Ndnh8jjQixfa7szXbQEJY\n9yaJyLKMtbYWZWAg4Xffjf/QYajj4wFbxY/Tnd7jULt9OyUvvogmKRF8XVuKy2o02s7B/fujnT3b\nKceMevwxqjdtomrtV0TcZ7+JYl2VvGDtO8A0oDhn8dT+rorDlS2QZuAPukx9X+BiYJ4+XdcXWACs\n12XqewHrmx4DXAn0avpzD/Cq80M+V/AVV1B1261OW55NpdUSOmsW5Ss/7lC1/NKaBrJKahmW4lkF\nnM8mSRLDU8LYlm2frhylVotSq0XqYImG5s/XajRSt2Mn9Xv2dDuG7op8+CGSVqxA03QBcDRJoSDm\nz09gLinB8NabHXrPL9kG0mOCCAtofQlPTzEiNQyTxcqu3O6Ng2wsKrbNmPf1JfmjD4lZ2LHWO79+\n/QgaPw6Aqm++4fjNt7i0S1vy9SNw3DjCbrzRacfUzpmDJq0nRYv/jtyB39utWZ4/dAegV1Qgof5q\nu/S+nPzjQvLm3oNsMqEMDCRw1MgOXbsCR46k53ffEjx5MoBLS9tUrf0Kc2Eh0Y8/1u2SPR2ljo4m\n9csv3SJ5bPIeMNnVQbgsgdRl6k82tyDqMvXVgB6IA2YAzSN53wea17WaASzTZeplXaZ+KxCqT9f1\ncHLYbiFi3u9Q+PlR8tK/zrvtzhzbSWeEhyeQYPs/FFYZySvreleOKScHAF+djqQPlmMN69zPReHr\nS+Ky94l65A+AbcKNszW3IkhKJX79+zn12H4DBxI85UrKli3HXNp+WaVGi5Vfj5d7xXdvSFIYkkS3\nyvmYy8vJue46SpYuBehGoXcJha9PS2ukKwSMGE7Cq6+0LHjgDJJaTfRjj9GYl0fFp+eflb0jxzb+\nsU90kBOicxyFQmJ4cphdxuAGThhP4Nix0IUyc5qm4RXKggKyJk2mdtv2bsfTFSHXXE3ShyucPjta\nE+/YsZadkbN46mbA5VPz3WIMpD5dlwwMArYB0bpMfXP590JsXdxgSy5PH4CQ3/TcBUel1RJ2xx1U\nr1tH/f797W67/Vg5PioFF8V1b1USd9DcDdrVsWg1mzeTNWUqNT/8ANDlVmOFRoOkUmGprCTn+usp\nW7GiS/vpqtotP3N0/ASHlIzqiIgHH0RuaKD0jTfa3e7giSqMjVaPb/0GWiZidGccpDI0FO1NNxF8\n1VXdiiV48iQS3n4bhb8/ssXi1NYgq9FI6WuvY6lxTVH/gNGj8R86FNPx3PNuuyPHNnTHk8c/NhuR\nGk5uWR0nKzt/8yzLMqZc288reOJEIu69p1s9ZtagIHx690Id6/z2G6vJhCRJ+A8e7PRjC+dyeQKp\nT9cFAp8CD+sy9Wf0yegy9TK28ZEdJknSPZIk7ZQkaafZTQacO0LY7bejioo6bwK5I6eMjIRQNCqX\nf9TdlhYZiNZf3eU7cf8RI4iY9zv8L+5aOZqzKQID8bvoInx69bLL/jpClmVKli5FERiIJq3r6712\nh09KCj2efpqwW25pd7sdTa3fQ5M8P4EE21i03bkVNJgtnXqfta4Oc2kpkiQRce89+Pbu3e1YJElC\ntlrJf+BBTi78k9NWKqn45FNKXnwR42+uuXmRJInEd94mesHj7W5naBq6MzTZW757TZMIu9ACXr5s\nGdlXzaAhO9suscjBwSS+/npLi6S13r6Te9piLinh6NhxVH37nVOO50Kq5hym6c89rg6oLS7NKvTp\nOjW25HGFLlP/WdPTRc1d001/Fzc9XwAknPb2+KbnziDL8huyLA+VZXmoyoVdPI6mDAyg53fftjsG\nqabBzG8nKltmMHs6hUJiWHIY23M6dxI1l5ZiNZlQ+PgQOW8eCh/71COUlEpi/76YgOHDgVOzuh2p\n5ocfMB48SMS8eSic2H14ttBrr2m5gLRlZ045CWF+xIR41vrXbRmRGkaD2crevMpOva/wL0+Tc/1s\nu19oJYUCv8GD8B8y2CljsK0mE4a33sJvyBD8h7uuuHJzt7nx8GGsdXWtbtNcs3NostZpcTmSrkcw\nQb6qLrWAB0+dSsT996Oxc6FtgNJXXyVnzo1YjUa77/tshnffw1JRgW96H4cfy8XMzTlM05/2u3pc\nyGUJZNOs6rcBvS5Tv/S0l74Ebmv6923AqtOev1WfrpP06bqLgcrTurovSIqm2XAN2a2vTrPreDlW\nGYZ5yV042Mr5HDfUUVjZsROWbLGQd//vyH/gAYe20lR89jk5N8xx+InU8MabqGNjCZk+zaHH6YiG\nY8fIf3A+jcXF57wmyzI7j5cxzEtaHwGGN/0e7ejkDUzYbbcSfu+9KPz87B5TxNy5aOfMAXB4K2Tl\nF19gLiwk4v77nTZpsC0NWVkcu2pGmyvU7Dxejkap4KI4z1u6tTXKppvnzkyksdTUIssyqoiIbndb\nt8W3b19bd7KDvw/m8nLKV64keOpUNEndr8Ih2IcrWyBHArcA4/Xpuj1Nf6YAi4GJ+nTdEeDypscA\nXwHZwFHgTeB3LojZ7VR89jnZU6ZgPHT4nNd25JShkGBwknfchcOpkhYdvROXlErC77oT7ezZDr3o\nqSIjUIWFITc0OOwYxoMHqd+1i7A777TbWuvdISmVVK9fT/myZee8lmOoo7TG5DVdiADaAA1pUYEd\nXpGmeaawb9++aGdf78jQqNuxg5zZN3SqPmxnyI2NGN54E9+LLiJg5KUOOUZn+PTsid/QIRjefbdl\n9Z7T7cgpY0B8iF1KLrmLoclasktqKas9/5hX2Wwm7557OPnEEw6NKXDMGGIW/dluvTptKXvvfeT6\neneaBe1SyQvWfgT8AvRJXrA2P3nB2rtcEYfL+nh1mfqfgLau6OcU+WoaD9n5ZVi8XND4cRT5+2N4\n661zahluP1ZGv9gQAn28pytf1yOIAI2SnTnlzMhofw6VLMtIktRSesKRAkePJmDUKIcmqb59+5L0\n4Qp8dTqHHaMzNImJBE+eTPlHKwm/5x6UwacK1TfP/h/mJV2IzYYkavn2YCFWq9zu5AxrQwM5c+YQ\nMm064Xfe4fC4JD9/sFgwGwzdmN3dNktVFZqeqQ6/EeuMiLlzybv3PirXriV05syW5+tNFg4UVHLX\nqFQXRmd/QxJtv0u7jpdzed/o9jeWJIImjEcV7fganQCm3FwKn/oLsX9fjCoy0q77ttbWUr5iBUGT\nJuHTs6dd9+2pchZPnePqGMANJtEI3aMMDSV09myqvvoKU35+y/MNZgt78iq8qvsaQKVUkJEYet56\nfNaGBnKun0319987KTLbAH+zwUDh03+1e3mf5u5J/8GDHdIV2lXhc++2neA/WnnG8ztzygn1V9Mz\n0r1W7+muIUlaKuoayS49zyxksxnfPun49HROEuPXvx/Jn/zPYRdYVXg4ia+/TtD48Q7Zf1cEXHYZ\nPr17Y3jrLeSmJR8B9uZX0GiRve7mZWBCKCqFxK8dqEVq63m5i5BpU50QGchmCw1ZWS1l0uxJERBA\n0gfLifr9w3bft9A9IoH0AmG33wYKBWXvvtfy3IGCShrMVq+ZQHO6IYla9CerqG1oe5a9paISyUeD\nwr97Kzd0lik3l4pPP6V+t30XSTr55z9T9I9/2nWf9uCr0xEwejRly5ZhPa37fsdx7ymhcrohTUnJ\n+bqxFQEBxP7tOQLHjHFGWIDtBsZqMlH65pt2LbPTkJ19xs2pu5AkifC5c2ksOIEpK6vl+ebW7yFe\nNHQHwFetpF9cSLvfPdlq5cTjC6jd7twajT6pKaSt+85htRl909PF2Ec3JBJIL6COjiZkypVUr1/f\nslbuzhzvmoV4usFJWqwy7M1re7yXOjqKpOXLCbjUueO1/AcNIm3jBgJHj7bbPs0lJVSu+rLVsV7u\nIOK+e9HecANyo+27Z6hpILukliFeNIGmWWpEAKH+6jYv4rIsU/z88y1195yt4fARSl54kepvvrHb\nPouXPE/ODXPcZh3u0wVfOZm0DevPKKW1I6ec3tGBhPp79upHrRmSqGVvXgWNFmurr5uLi6nbtYvG\n/HMKlDicpNEgyzJV331ntzWzq9ato+Cxx1y66pLQNpFAeomoRx6h59o1LatT7MotJyncn4hAxw5u\ndoVBiW23AsmyTNmKFVhqalw2Vqt5DWPjocNndK11Vfl//wuNjWhvdIthL+fwHzKEyAcfaFkbe2fT\n5+JtXYhga/Uakqht+T+ezXT0KOUffkTt1q1OjszGr38/en61ltBZs+yyP1NuLjUbNxJ63SyXrnzT\nFkmlQqXV2lZnamjAYpXZdbzcqyZvnW5IkpYGs5WDJ1pPqNQxMaSuWU3I1TNbfd3RGg4fpmD+Q1R8\n8old9lf27nvU795zzjrdgnsQCaSXUEVGoggIsJ1IrVZ25VYwKMHzV59pTYifmt7Rga2OBWo4dIii\nZ56lavVqF0R2St2vv3Jsxgyq1n7Vrf3IJhMVKz8mYPRofBxQx81eZLOZqu++w3joML8eL0ejUnBR\nvHeUUDnbkHZmw/r06kXP79fZLYHrCk1yMmArfdLd0j5lH3wAKhXaG9zz5gVs3bbHb7yJ4r//g0OF\n1VQ3mL3y5gVOdcu3dgNTt3s3stmMwsfHZTfPvn36kPjO22hvvrnb+6rfv99WdeLmm5y25rXQOSKB\n9CKmnByyp03nyLqfKKlu8KryPWcbkqRld24FVuuZF0jf9HRSvvic0GuvdVFkNn6DBhH9xwUEjhvb\nrf1UrVuHuaSEsJtvsk9gDmI1NnDyjwsxvP0WO3LKGBgfgo/KO0/6zbNhd591A9NYVATYlhqVFK49\ntdbvP0DWhMup2bipy/uw1NRQ+elnBE+ejDo6yn7B2ZmkUKBJSqLyiy/YfthWGthbVj86W0yIL3Gh\nfuw6K4FsPHmS47feRsnLL7soslMCLr0USans9pCHsuXLUQQEEOLic7nQNpFAehFVbCyWigp+Xr0J\ngMGJ3ptADkrUUll/5mzYlrp7ffq0rFbhKpJCQdhtt6EM7N4sZL/+/Qm//z4C7Dim0hGUgQGEXHMN\npd9+z4H8Sq8c/9hsQLxtNuzprUBmg4HsKVMpfd09Fo3wTe9DyKxr0aQkd3kf9Xv2YjWZ3P7mBUB7\n001Y6+rY9stvRAb5EK91n0oF9jYkScvO42VntC6rYmKIf2Ep2jltr0zmTA1HjpB15RTq9+3r0vvN\n5eVUf/MtITOu6vY5VHAckUB6EYVGg3b29ewpqsNPpSA9JsjVITlMc1dO8zhIa10dWVOmUr5yZXtv\nczrjocPkzXugy4PANUlJRD30kMtbtDpCO2cOWf5RNFplBid65/AJAD/NubNhFQEBhN93L0ETL3dh\nZKdIajUxCxd2a9hD4KiR9Nr8A74DBtgxMsfwu6g/vgMHsLe4nsGJoW5Tq9IRhiRpKapq4MRpq3FJ\nkkTQ5Ze7TUuxqkcsmoSEri/vKstob7mZ0Nk32Dcwwa7c/6okdEroddehD0tCp6hFpfTej/fs2bCy\nyYT/iOH49O7t4sjOYrVQv28vDaeVGemoylWrqNuxwwFBOYZPagpZgy8DIMNLxz82O3s2rMLXl4i5\nc/FJda/i1Y1FRRQvfaHTM/ibux9VWq3HJGPyrDmc8Amln2S/Ekbu6Oyb54JHH6Pis89dGdI5lIEB\nJL7zNv6DBnXp/aqwMKIffRTfPm52PhfO4L0ZxgXKEhFFVmg8vY/tdcuyG/bSPBu2+SSqDA0l9tln\nbeuyuhFfnY5e69d3+kRqNRopfPY5yld+7KDIHONIykBiGirR1nZsuT9P1Twb9rcTVRjefY+aLVtc\nHVKrjHo9hnff7XRXYuEzz5B7510OX1/bnrJ6DwFg+BDvTjrSY4Lw1yjZdbwca20t5qIiLFWVrg6r\nVdaGBipXr+nU96jhyBFqfvzJLhUsBMcSCaSX2V9QiUVScMnEi10disMNTtKSVVJLwfc/YDp+3NXh\ntKm5PlpD9rEOv6d63fdYq6oIvc51s3m74oDZl2HD0lHHxro6FIdqrq+6M6uE8pUfUbN+g4sjal3g\nmDG2As9DhnT4PdbaWqpWr0EVGekxrY8AewvrUCokBvR0j25cR1EpFWQkhLLzeJltlZZl7xN2662u\nDqtVlV9+yYlHH8W4d2+H32N4510KHnoIub7egZEJ9iASSC/TPDtv5DWXu2XdNntqniS08T/LKHzm\nWRdH077Sf/+bY9dcg7msrEPbV3zyCer4ePyHD3dwZPZTVGXkRIWRQYlarA0NWI3G87/JQ0UH22bD\n7i6opufq1US66TJrkiShjrGth3z6SkHtqfrmG6y1tYRef50jQ7O73Xnl6GKCqP/wA6q++87V4TjU\nkCQt+hNVVJXZWh7ddYx06IwZJC1fhl9GRoe2t1RVUfX11wRPny5qP3oA9/zWCV3WXEA8tLGO0jfe\nxFxS4uqQHGZgQghKhcSJWx8g5k8LXR1Ou4KnTSfmiT91aEahKTeXum3bCJ11rdteGFrTXNZmQDAc\nuWwMFZ986uKIHGtIfDA7j5eBWo0yyL0nrJX85z8cmzGzQ8NaKv77PzQ9e+LnZsNB2mOxyuzNqyQj\nMZTKTz/D8OZbrg7JoQYnabHI8N29j7j1MANJo+nU8oaVq1cjG42EXudZNy8XKs+5OgnnJcsyu3Ir\nGJyoxVJeQcnSpVR88YWrw3IYf42Kvj2C2V3S0FI82V35pKYQOmtWh8oLmY4fRxUZScjVVzshMvvZ\nnVuBRqngor6JqONiqfjUuxPItMO/2mbDGtx/0oZvv34EThjfUuqqLQ1HjlC/d69t5RkP6r4+WlxD\nTYOZwYlaQmddi3H/foyHDrs6LIcZnGDrfTl+hWd8TmXLllHw6GPn3a7i00/x6avDr38/J0QldJdI\nIL1Ifnm9rYB4Yig+qSn4DRlC5Rer3PoOtTsqPv2M3sf2sievHHMba8O6E1mWqVy9hsovv2x3u8DR\no0nbtBF1dLSTIrOP3bkV9IsLxkelJPTaa2nQ66n/7TdXh+UwQ/onArD3pPsnkEFjxxL96KMo/P3b\n3U4VHU3Mk4sIueoqJ0VmH82t34MStQRfdRWo1VR+5r03MCH+alIjA9Brwl0dSodY641Y6+ravYEx\nl5djKTUQOtOzbpwvZCKB9CK7TjuJAoRcdRWmrCyMvx10ZVgOI5sa6FtfTH2jlcNF7n8RlySJis8+\npeLztktuWKqqkK1Wj1u6q9FiZV9BBYOaWkZCpk1D8vGh8tPPXByZ4wy95go0SgV78ipcHUqH1R/4\njfr9+9t8XRkcjHbOnJb13D3F7twKQv3VJIf7o9JqCZowgcpVX2I9T4urJzLlF1C8ZAkDovzYk1fh\nEQ0E4ffMJeE/L7fbA6PSaknbuIHQ2dc7MTKhO0QC6UV251bgr1G2FBAPnjwJSa2m8stVLo7MMbRz\n5nD5M7ZuEU+5iMctXUri22+3+XrhX5/h2NXXeMRF4XSHCqsxNloZ1FRAXBkSQtCE8VR99VWnaxC6\nO6vJROWqVaitFvrGBrMn1zO+e7LFQsH8+ZT8+9+tvl6/fz/lKz/2yMlPu/PKGZRwqoB46LXX4KNL\nx9LBSWuepG77dsqWf8DAKD9KaxooqHD/2crNn4u5rKzViYSy1YpssSAplSh8fJwdntBFIoH0Irty\nyxkQH9JSQFwZEkLg5ROwVnZtFRR3ZjxsG9+UGOaP1l/NXg9JIJvXSZbN5nOSRGttLdXff4/fwIEe\nMa7pdKe6EE+tQBN+zz3Ev/IKeFk1gJoNGznx+ALqduwgIyGU/QWVHjGEQlIqifv3v4hburTV18s/\nWEHx88+Dh333qoyNHCmuael5AdswkKR3322Zge5NQq+5mrRNGxnSzzaEwlNuni01tWRNvALDG2+e\n81rdtm0cHTsO46FDLohM6CqRQHoJY6OFgyeqzln/Ou7554n9+2IXReUYRr2eY1fNoOLzL5AkiYEJ\noR5zEgXb8oZHJ15B3fYzV5mp3rABub6ekKumuyiyrtudW0FkkA9xoafWIPZNT8d/8CCPS4bPJ2jS\nFSQtX0bApZcyKDGU+kaLRwyhAPDr16/VSgDWujqq1q0jePIkj2sB2ptXgSxzzrkPwFxSgrWuzgVR\nOUZzl7xKqyU9JhiNSuExN8/KwACi/7iA0FnXnvNa5RersBqNaJKSXBCZ0FUigfQS+wsqMVvlc06i\nzWVgLNXVrgjLITRJSUQ/8QRBE8YDkJEQyuHiamoaPGPlHU1SIr79+qLwOXM8UOWXq1HF9vCo8inN\ndudVnNGF2Kzh2DEKn/4rlhrPSLA6QpIk/IcNs928xNtaXD3qBkavJ/fuuWd0JVavX49cV+dxk2fA\ndvMiSTAg4czlMxuysjgyZixVX3/josjsSzaZyL5yCoZ33wNAo1LQPzbYo757obNm4ZOWdsZzZ9y8\n+Pq6KDKhK0QC6SWauxAzTutCbFb2/vscHTMWS02ts8NyCIW/P2E334QyOBiAgQmhyDLsz3fP5bzO\npvD1JeHll88ormsuLaV2yxZCpk33qNqPAOW1Jo6V1p7RhdjMUl5B+YcfUr3uexdEZn8n/7yI8o8+\nanmcFG4bQrEnz3OWbpR8fGjIzsKUc2r1pspVX6KOjcWvEyvWuIvdueX0igok2Fd9xvOa1FTU8fFU\nrlntosjsy2o0Ejh+/BnrQ2ckaNlfUNmyJrsnMB0/Tsm//t2yVKEn37xc6DzrSiW0aW9+JfFaPyIC\nz+1+8r1oANa6Oqq9YHWGytVrqFq37oznMjywFQjAUlND3Q5bN7YyNJSE1171uNU/4NTPfVArNy9+\ngzJQx8dTtdrzL+JyYyONJ09iLjW0POeJQyh8UlNJ+/57/Afb1meXGxux1tURPOMqj7t5kWW5qfX7\n3JsXSZIImTaNuq3baCwqdkF09qUMDibmTwsJuPTSlucyEkMxNlo5VOg5PUz1+/ZhePNNGo4cBTz7\n5uVC1+bZQp+u877RZwraIwAAIABJREFUx15sX34FA+JDWn3Nb1AG6sREKle3X3/QE5R/9BEVKz8+\n4zltgIakcH+PagUCKHr2OfLu/x3WujoklYrAyy5DEx/v6rA6bXduOQqJVr9/kiQRPH0atVu30ljs\n2RdxSa0m8a03iXhg3hnPZySEcqS4hmqj58w2lxQKZFnGbDAgqdUkf7iCyAcecHVYnZZjqKOirrHV\nmxeA4OnTQJapWrvWyZHZV2NBAQ1HjpzzvCfePAdPmkTaxg0tLalhd9xO1ILHPe7mRWi/BXKPPl33\nvT5dd5c+Xdf6b6fgFspqTeSV1TMgvvWPSZIkgqdcSd227ZgNhla38RRJy5e1OikoIyGUvXme0YXd\nLHzu3SS+8zbmkhKKl76AubTU1SF1ye68CtJjgvHXtD7bOmT6dLBaqfrqKydHZj9Wk6llHPHZ4zwz\nPGwIRbP8Bx4k/3fzsNbbysB4Wu1ROLOAeGt8UlLwvegiqtascWZYdmd4+x2OzbrunLHsCWF+hAVo\nPGYiDdiWN1RFRAC2FuTAkSMJvuIKF0cldEV7CWQc8E9gFHBIn65bpU/X3aBP1/m18x7BBfbl204e\nbbVAAgRfOQWsVo/uxpZlGUmpbDn5nC4jIZTCKiOFlZ5Tw84nNRW/AQOo+uorDG++iWyxuDqkTrNa\nZfbkVrQ69raZT2oqAaNGeXQ9yOpvvuHImLE0ZB8757WMhKZWoHzPuYiDLbEPuvJKDo24mKpvPHOi\nyZ68CgJ9VKRFtb3GfMyTTxL/yn+cGJX9RTz4AHEvvXjOmuuSJJHhYUMowFb3Mf+hh8mbe0+rLauC\nZ2gzgdRl6i26TP23ukz9HUAC8A4wAzimT9etcFaAwvnty69EkuCiuLYTSJ/evejxzF8JHD/BiZHZ\njyknh+yp06jfu7fV1wcmeF5XDoClooKyZcvxSe/jcUsXAmSX1lLdYG5JotqS+NabRMyd66So7M+n\nTzphN85Bk5J8zmuh/hpSIgI8pqB4s+DJk0CWwWTCV6dzdThdsjevgv5xwSgVbZeK8uvfz+PrQaq0\nWoLGjm31tYyEUI6WeN4QCoWvL7VbtnjNLPkLUYcGHegy9SbgIKAHqgDPPNt4qX35FaRGBBB01izE\n00mSROisWaijo5wYmf1YqmtQhmlRx8a2+nrfHsGolZLHJZCmvDws5eVoEj2z/llz6/fANoZPnE6W\nZczlnjVOtZlvn95EPfJImzUtB8aHeMyycqerXLsWdWwsqshIV4fSaSazFf3J6g5992q2bKHw6b86\nISr7kmWZwr8+Q92u3W1u0zyEYp+HDaHwvegikGWCr5zs6lCELmo3gdSn6xL06bpH9em6XcCapu2v\n0mXqPa9QnZeSZZm9+ZUdvoBXfP4F1Rs2OiEy+/K7qD/JH3zQ5oXOV61E1yPY4ybS1GzeDED0nxa6\nOJKu2Zdfib9G2W4XYrOC+Q+RN/ceJ0RlX1XffocpP7/dbTISQimubuCkBw2hMOXn03DgAI0nTnjk\nOSGzsAqTxdrm2O/TmY7lUP7hhzQcPeqEyOzHXFhI1TffYMrOanMbT+19qfr6azRpPUFMnvFY7c3C\n/hn4CYgC5uoy9X10mfqndJn6TKdFJ5xXYZWRkuqGdsc/NpMkibJ338Xw5rlLSbkzU25uh9bnzUgI\nZX9+JRar57QCyfX1BIwejToqyiPHQO7Nr6B/bEi7XYjN/DIyMB44gCk31wmR2YfVaOTkwoWUvvpq\nu9tlNE3i8KSLeHXTuMfYF18geOoUF0fTeXubWtw6cu4LumIiSBJV33zr6LDsSt2jB702biC4nRqJ\nIX5qUiMDPOq711hURP2vv6JJTCR76rRWxxYL7q+91H8BkKzL1D+qy9T/6qyAhM5pnnk84Dxj0JoF\nT7mS+t27aTx50pFh2dWJxxeQe8ed590uIyGUWpOFo8Wes+pJ1COPkPDG65T8+2VyZt/gUV2gjRYr\nB09UdegCDk1j7sCjLuIKX19S1645b4kbXY8gNEqFR13Egy6/nJinniRk8mSPXG5yX14FYQEa4rXn\nn9epjvp/9s47rqnr/eOfhL0REBDRgCsBBOveG7ete7aOLrWtba22xtFqh7V1tFVr1Vq/WkfddQYV\nRdx7FdQwZBOG7BAg+z6/PwIpKkjGBbE/36/XffFKOPecc3Puvec5z3mGJ+w7dEDxqZN10DN2qAi0\nzbG2Btfa+rllX/N1falMKJSxseDY2cHt3ffgvXQJLD1fTtOq/+88z4nmYkBszMtxN/4/JlpSBEsu\nB4GNnA0q7zxYZ2/yMk3iDT/9BO4za976/Hcr5+XYxq5I78fhcGDtx4Pda6+BynPdvgzEZcug1DAG\nL16sGjeGbZuQl2oSBwArb29YNWr03DI2lhYI9HF+qRxprP380GDiRAC6MDG5Gza84B4ZR7REihBf\nF4OFX6chg6FKSHxpvH4L9+xB0ujR0BYX11j2taauyJUpkfmSmFA49u6NVteuwqF9OzSYNAkWjg4v\nukuvMIFXxgcvOdESKfjeTrC1MiyGm7WfH2wCA1B88uWZxB26dKnWA7Ey/u4OcLa1xD8vQTxIIkLK\nhInI+moJAF1IFe8vF4Nr82wmofpKhdF+GwM1kADgPGQIlOIYqFJSaqlX7KF89AgZX8yHOiPDoPKv\nNXHV5aR/CdLKySIjIYuM1H9WPnoE5UtknVSm0uBRjswg+8cKnAcMgI1A8NI4cll6NIRNixb6lK3P\nQx9K6iVYwFRoVivyXpNWi+Lw0yi9fuNFdusVJvA8G8iuMYKAl29f4/8RRFSegca4OO/OQ4aAVCow\nZWW11DN2II0G+f/banAGEy735Ukrp3z0CKrERNgECJ78PiEBTOnLkbM8WlIEV3srNHWzN/gc56FD\n0fjXdbCsQaNXH1AmJaP06lVw7AwLfftaE1fI1Vok5NZ/E4rcteuQ/8cW/edGy76D76/rXmCPjONB\nRjEYMm7xYtmwIZodOQyHTp1qsWfs4TxoIBqvXGlQWYG3M6wtuYh6CWKRFmz7E0mjRv87/3A4yFm9\n+okc8694OXieBnIqgDsxgoC9MYKA6a9SG9Y/UvLLUKzQGPUSBQD3d95BsyOHwbU3fOJ/Ecj/+Qc5\nq1ZB8eCBwee81sQVcdnFKFNparFn5iM7dQrgcuE8YID+O0VcHJKGv/7SaIejJFIENzZ8CxHQ2aI5\nDxjwUmhanQcNRMsL52Hp5mZQ+eDy5zC6nmvAlUlJUMbFwXnIEP13HEtdFiFGqXxR3TKKf5MnGJ8k\njVEoDNoWfpEo4uKMMmextuQioJGz/nepzxSfPAmOhYV+/uFwuWi6bSsa//zTC+7ZK4zleTaQH5SH\n6/kaQAMAf8YIAq7FCAKWxwgCesUIAl6+vFf/MUx9iVakLKvv9nb2HTqg+elwOPbsafA5bXxdwRAg\nzqy/EwQRofhUOOw7dnwiLJFNq1bw/vYbOPbt+wJ7ZxhylRbxjw2Lwfc0moIC5G3cCFV6ei30jB2Y\n8mejQrAyBH93BzjZWNZ7LVBFNiqnQU+mj5OdO4dHXbu9FF7yURIpfFxs0dDJuIUIU1aGRz17IX/b\ntlrqmfkwSiVSp05D9nfGxa1s4+ui08zW4ygU6sxMKB480DvUVWDt6/tSptL8/06NNpABsTGxAbEx\nvwTExgwG0A+60D7jALwyWHjBRKVLYWvFRSuvmmPwPU3xqXDEd+0G9WPDtodfFNZNm4JjVX2A9Kep\n8AiOqsdBdZXxj6BKSnrmJcrhcNBg/HhYuru/oJ4ZjjhLFy7JUA/sypBSidy16+p1+ryMjz+BZM5n\nRp3D5XIQ7OuC+xn1994DANmZCNi2CXkm85FtQACchg55KeLyRUuK9E5zxsC1t4dtYCBkp8/UQq/Y\ngWNpCZ8VP6LBpElGnRfi64oSpQZJefXXhEJ2Vmd36xQa+sz/isNPI23GDL2N5CvqP0a9KQJiY+QB\nsTEnAmJjPg6IjelQW516hWHczyhCkI8LLC2Mf+HbtGgOprQUsrMRtdAz8yncswdZXy0xOn+yp7Mt\nvJ1t6/VWjpVPIzRavhxOAwdW+f+SS5cgDQur414ZR0X4KFMmcatGjWAbHAzZmfp57wGAfZcusO9o\n/Csu2NcFMVnFUGrqZ0xPprQU2qKiKidwK29v+CxbBmtf3xfQM8MpKlMhNb/MpO1rAHAaMACqxEQo\nk5JY7hk7cCws4NSnD2wDA406r2IxV58z0sgiImDdojms/fye+R+pVGBkJdAWFNR9x15hEvV/qfmK\nKtFoGTzIMDwG39NYN28Oa39/yM7Uz5W4Ji8f6owMo7SPFYT4uuB+PX6JWjg5wXX0qGo1jQU7dyL/\nf/+r414ZR7SkCF7ONvBytjXpfKfQUCiio6HOzma5Z+zg/vZ0uL35ptHntfF1hVpLiM2S1UKvzIfr\n4IDmEWfgPm1atWVU6elQpabWYa+MwxTv/8o4hfYHgHq5gFFnZiL/zz9NstFs3tAR9tYW9VqAdB09\nCh4zZ1X5P+fhw+C3ZzcsPTzquFevMJVXAuRLSkJuCeRqrUk2aIBuu9RpwACU3bxVL8NaNPx4Npr8\nb0vNBasgxNcFSXmlkMqN017WBeqsLBTs3AVtUfUa0kbfLYP/3r112CvjiZJITdYAATotEADIIs6y\n1SVWICKUXr9uclag4MblWqB6uo1NROBwOOBUE5ia1Gokjx2H3PW/1XHPDKdid6G1iQKklbc3bENC\nIIuofwJkycVLyFmxEozM+AWIBZeD1j4u9Xr3xWXECLi8PrzK/1U44zFKpd4G+RX1m1cC5EtKhadn\nsIkvUaB8EtdqUXLuPEu9YgdtiS6MjanZMSoEm4f1cBKXnT6Nx99//1wNg5WXZ7UTfH1AKlcjOa/U\nZA0QANg084dNYAA0ubks9sx85HfuIG362yg+aZp9pm8DO7g5WCO6HoaS0hQWIqFXbxSXO9FUBcfK\nCo1XrYTnXOPsP+uSKIkUzRo6wNnW+N2JCjw/nwfvJV+x2Ct2aDBxApqfOQOrxo1NOj/E1wUPM4uh\nroexSGXnztWYAU0lycCjnr1QfFxUR716hTnU6GIYIwiQAXjarUsK4DaAeQGxMfXTkOQ/TpSkCE42\nlvB3Nz2Cv23rIDSc8ynsXmvDYs/Mg1EqkdC/P9zffRceM943qY4KLVCURIpuLerXdojsTARsWrWC\nddOmzy1XdusWcn7+BU1+32RQIOG65L4+B7HpGkgA8D9woN55XtoGB6PxmjVw7NnDpPM5HI7OhKIe\nLl5Kzl+AJje3xqw6jr161VGPTCMqvQjdzXyu63MsSGtf04RHQJfSVnk5GfGPZQjyMX2BxzZMWRky\n5nwG13Hj4P3l4mrLWTX2geuokbBp1bIOe/cKUzFEA7kGwBcAGgPwBfA5gN0A9gLYWntde8XziJZI\nEezrAi7X9FjvHA4HHrNmwaZZMxZ7Zh6kVsNt6hTYd2hvch0NHKzR1M0e9zPqlxZIU1CAsrt39TZY\nz4NjZw9GoYA6q/7ZCEbpw0eZN0Hpw0kZ6ShVm3BtbOA8eBC4DqYvzEIauyD+sazexSKVnY2Apbc3\nbFu3rrGsPCoKub/Vv23sbKkCOTKl2fceAJTdvYv8rfUnnE/Oz78g5yfzYiGGlC+e65sNeMnlyyCl\nskrnrcpwOBx4LVwIu+DgOurZK8zBEAHyjYDYmN8DYmNkAbExxQGxMZsBDAqIjdkHXXzIV9QxSo0W\nsdnFZmuAAF0aqZJLlyGPjmahZ+Zj4eiIhh99BPt27cyqJ9jXRe8pXF8oOXcOYJgaX6IAYNc6CM0O\nH4Itv1Ud9Mw4oiVF4Lnbw9Xe/G12yZzPIPl0Dgu9Mp/SmzdRuHev2fZXIeWxSB/Wo1ikjFyO0stX\n4NS/v0GmIWW3bqHgz+31zj46yowA4k9TcvEiclavrjfXqJVKn2sbbQg8d3s421rWuzBmJWfPwsLF\nxWDFgPpxDspu367lXr3CXAyJklsWIwgYD+Bg+eexACoyttffiKV1RPK48XArKEDKxk3g2NnB0q0B\nLBs1gpV3I1j7+8M2QMB6XL+YLBnUWjLLBq0ymQsWwL5TR/j+8gsr9ZmKtqgIyoQE2LVrB46Zseja\n+LogLDoL+SVKuDvWj6wnquRkWPn6wiYgwOBz1NnZUIjFUEskUGdlQ5ObC01uLhi5HKRWg9QqcK1t\nwHV0BNfREZaeDWHt66trp2VLWPv5sb5NHC2RooOfYdlZasLKyxOFe/ZCW1IKC0fTtX5sIAs/jeLT\n4XAdN86seiqHU+nI0u9kLqVXroAUCoO03wDQYNIkuIwdC3W6BCWRkVBnZkGdnQXN4xwwZWUghQKM\nSgmOlTW4dnbg2tnB0sMDVo19YOXjA+vmzWHL57Oe7SpaUgRLLgdBPuabdTgNGID8Tb+jJPIcXMeM\nZqF35tHom69BZN6UqjOhcK1Xuy+kVkN27jyc+vUzODB/1uLFUD56BM9FC6FJT4c6+zE0j7OhLSwC\no1KBlEqAYcCxtwPX3h4Wzi6watQIVj4+sGraBLYBgbD0bGiyHf3/Z/wWhFmk/DjMIC9CQ0bzTQBr\nAWyATmC8DuCtGEGAHYDZJvfyP4JNixYoSkoC18EBjFwO+cOH0EScfSLLi6WnJ+zatIF91y5w6NIV\n1v5+Zt3Y+gw0JsTgexqOhQWcQkMhPX4cjFL5QlPMSU+cwONvv4P/0aNma96CG+t+m+gMKfryPdno\nntl4fv45PGbPrnbsiQiqxESU3ryJspu3UHr7Fpi8fP3/OTY2sPT0hKWHByycncGxsgLH0hKMSgmm\npBTqjAzI79yBVvqv9qEicLJd+/Zw6N4N9q+9ZpaDTo5MgSypgrXFi1NoKAq270DppYtPpNZ7EXh9\nuRges2aaLXDXx1ikVk2awm3aVNh3qD62pbaoSHfvXb+Bslu3dHESK7zRORxYenjA0ssLXEdHWDg5\ngWNtDdJowMjl0MpkUCYmQpOTA1QEguZyYe3vD/t2beHQtSvsu3QxOC1kdURLpGjl5QRbK/MXRbaB\ngbDy8YHszJkXLkBWLKDYEHhCfF2w+WISFGotK7+TuShiYsDIZHAaUP3OC6nVkN9/gLIb11F6/Qbk\nUVEghQKZ5bsTXCcnWHp5wrKBGyxcXMCxsQaHwwFTJgdTVgZldjxKzp/XCZblWHh4wC4oSDfvdusG\nm5YtXwmUz8FvQVhvAJMBjABgUOrqGgXIcieZ16v592WDe/cfxeeH5Yg/fx5t+/TRf0dE0ObnQ5mQ\nCEVMDBRiMcru3NbHXLTy9YXToIFwHjwYtq1bG31TR6VL4eFoDR8X02LwPY3TgAEo2rcPpVeuwqnf\ni0uj5zpiBCwbNmRl27Z1Y2dwODpboPogQFaET+Ha2j7zveL+fcjOnEHx6dNQp+rSyFk2agTHrt2g\nLSqEQ5cucBkxAhbu7gbdK9qSEqjT06GIjYPiwQPI799H/pYtyP/9d3Dt7eHQowechw+DY+/eRi8Y\nos0IIF4Vdu3awcLNDbIzZ164AMnhcJ5ILWkO9S0WqS2/FWwXLnzme01enu7eO3lKt2XIMODY28O+\nXTvYd+yA0hs34fHRh3AeMMCgmKykVkP9+DGU8fFQiGOgePAAxafCUXRAt4Fl2yYEzoMGg+tqQgYj\nIkRLpBgabNDcViO6UGahL1wDriksREK//vBasAANJow3u74QXxdoGEJMVjHaNn3xVmZ2ISFoefEC\nuE85A5JKhZKrVyE7FQ5ZZCSY8sgUNgEBcB09CjYBAbAVCGDt38ygsSEiaAsKoEpO1t17YjHk9+6h\n5MIFADpFjtPAgXAeMhh2bdsavcv17XExUvJLsXV6R6POq8/4LQjrAJ3QOAaAB4BPAFTv5fQUhnhh\nb0MVW9UBsTHvGN7N/19wKlbrHh5w6NIZgO7mVqelofTadcgiz6Jg+w4U/G8rrHhN0WDcOLiMqj6w\n9NNES4oQ4uvK2mrKoVNHcJ2dITtz5oUKkFwHBziXxwc0FydbKzTzcKg3WqCsRYtBajUar14FANDk\n5qLo8BEUHTwIdVoaYGkJh86d4f7223Do0QNWjRubPL4Wjo6wCAiAbUAAMGokAEArk6Hsxg2UXLoM\nWUQEZKdPg+voCKcBA9BgwnjYtmljUHvRkiJwOWBlCxEo14D374/isLAXqgGXfPIp7Dt0gNvUKazU\nF+LrgtPix5DK1XCxMz3cDBsok5PBSKWwDQkBh8vVbSlGntMtGq9fBxgG1s2awX3mDDj27Am71q3B\nsbYGU1qK1GnTddpGAwP6c6ysYO3rC2tfXzj16wcAII0GCrEYpVeuQHYmAjkrV6IhgJRDh9BgwgQ4\nDRxo0Lin5pdBKlezYv9YgVNoKKQnTkCVkgK71kGs1WsURHB76y3YvfYaK9VV/D73M6T1QoAE8MTC\nTBEXh6J9+yA9LgIjk4Hr5ASnfv3g2Lcv7Dt3gmUDXZ+ViYko3LMXnvO/MKgNDocDS3d3WLq7P6Fp\nV2dmovTaNZScP4+iAwdQuGsXLL284DpmDFzHja0xKkEFN5Lz0YAFu+/6gN+CsG8BTACQDWAPgA4A\nbqb8OMyoDBaGbGFXDshkC2AUgExjGnmF7ua25vFgzeOhwcQJ0BYVQXY2EtLDh5Gz+ifkrF0Hp/79\n4TZ1Kuzbta22nhKlBgm5JRgWYthNb1DfrK3h1LcP5Pej9ZqyCogIcrkc9vb2KCsrw6JFi5CWloa8\nvDzk5+ejoKAASqUSDMPoz/3ggw/www8/gIjw1ltv4c0338TQoUOh1WpRUFCAhlVoeaTHjoEYBq4j\nR7J2XW18XXE5IY+1+kyFVCrIIiJ0gdvv3kPBn39CFhkJaDSw79ABHrNmwalfX1i4Vj0xagoKoHyU\nAIfOpocesXByglNoKJxCQ+H91ZcovXEDxWEnIAsPh/TwYdgGBqLBm2/CedjQZ7SklYkq30K0tzbM\nlskQXMeNhW1gwL9bn5UoKiqCWCxGQkIC2rdvj6CgIDx8+BBTpkzBtGnT0KdPH4SHh2P8+PEgIjg4\nOMDV1RWurq7w8PAAj8dDs2bN4O/vjx49elR575FKBdJoTA4eXhUVk/iDDKnZIWfMpXDXXyj6+2/4\nHzsK6eHDKDp4ENrcPFj6NILHrJlwGjy4yu09roMD/A8eMLt9jqUl7EJCYBcSAo8PPoAqLQ3/bNgA\nq3v3kPnFfFh8vxwuI0fCbcpbz41/yJb3f2Xs2rdHywsXqtRGqdVqxMXF4dGjR0hISEBCQgIkEgkK\nCgqQn5+PwsJCaDQa3L17F/7+/ti0aRNWrlyJqKgoODk54ejRo4iKikKzZs3QokULBAcHw6EK735L\nNzd4zpvL2jU1crGFh6ONzomwK2vVmoT8/gPkrl0LL+F8KGJjUbh7D+T37unmnEGD4DxsKBy6dQO3\nCrMadWYWig4ehMuIN8zyyrby8dEJi2PGQFtSipLz5yE9dhR5Gzcib9MmOPbujQZvvgmH7t2qXUQr\n1FrEZcvwfq/6E7HETD4C8BDALwBOpPw4TOW3IMxoA1xDtrD/rvw5RhCwB6+2rs3GwtUVrmNGw3XM\naCgTE1F04CCKDh+G7NQp2LVvD/f334Nj797P3NAPMqQggskZaKrDa/FicB0dUVZWBolEAj6fDwBo\n1aoV+vXrh99//x22trbYtWsXvLy80LBhQwgEAri5ucHOzk63PcvlQqvVom1bnQBcVFSEGzduoHfv\n3gCA+Ph4BAYGwtfXF+3atUPnzp3Rp08fdOzYEdIjR0HErgAZ7OuCQ/cykC1VwJul7X5TKL15E4xM\nBnl0FKSHDsHCxQVuU6bAddxYg0IoPV7+A0ouXUKrSxdZCTDOsbSEY/fucOzeHcziRZAeP47Cv/5C\n1uLFyPnpJ7i9PR0NJk1+ZttIt4VYhAGBXmb3oTIVwgXDMIiKikJkZCRu3bqF27dv49GjR/pyK1as\nQFBQEBwdHeHj4wPLcoN8X19fvP322+BwOCgtLUVRURGKioqQnp6OS5cuQVpuExoeHo6BAwfixo0b\n2L17NxYuXAhvb29wrK3RZAO7IWsqO9K8SAGSiFB8OhyW7u5IHv46SK2GY+/ecJ04AY49expk70kM\nA6a4uNoFjrFYN22KssGD0XH5cpTduIHCfftRsGsXCnbuhMvwYXB7913YtnrWjCVaIoWNJRetvJxY\n6QcAveBIRCjMz0f4mTMYOHAg3N3dsXbtWnzxxb/aL3d3dzRt2hTu7u7g8Xho0KABrK2t4ejoCABo\n0qQJunXrphcSz507h7Vr1+rP53K5EAgEaNeuHbp27Yp+/fqhWYMG0ObkmGTKVO016WORvvjdl+KT\nJ1B65QrS3p8BTXY2rP384LlACJcRI/Saxupw6NYVLS9fZtW0wMLRAS7Dh8Fl+DCoJBkoOnAARX//\njZL33oNNYAA83n8fTgMHPvNcxGbLoGFIHybpP4A3gEEAJgFY77cg7AwAO78FYdyUH4cZHoWeiIw6\nxHwBX8wXJBh73os47O3tqS44d+4cK/VoS0spf/t2iu/bl8R8ASW+MYKKz0YSwzD6Mr9fSCCeUER5\nMgUrbTIMQw8ePKBly5ZR7969ycrKilq3bq3//08//USHDh16orypZGVl0erVq2ny5MkkEAgIOtMI\ncnBwoMGDB9PalStJIpGYdT2VuZ1SQDyhiMIfZFVbhq2xqwqGYUh6+jTFdu5CYr6A4nr3ofztO0hb\nWmpUPYqEBJLHxtVSL3UwDEMl165T6jvv6vraqTPl/PYbaaRSfZm0/FLiCUW081oKq22XlJTQhNGj\nycPZWX9PNGnShEaNGkXff/89iUQiiouLI6VS+cR5ho5dQUEB3b59m0pKSoiIaNu2beTk5ERFRUXE\nMAxt+e03EgqFdO3aNdJqtaxdV88VkTRr523W6jMWRVISpb73Pon5AhIHBlHmV0tImZpqdD1JY8ZS\n+sefsNq3p8dOlZlJ2ct/oJi27UjMF1Dahx+RPO7Je37sxis06rfLrPVBrVZTZGQk3Tt1iuJ796Hw\ndesIAB07dozd/NC+AAAgAElEQVSIiOLj4+mvv/6iO3fuUFFRkUltyOVyio2NpSNHjtDSpUtp+PDh\n1KhRI/193tLLix7yBaTKzmb13vvlTBz5LxBRiULNWp0VGPLcacvKKO+PP0gcGERivoCSJ0yk4shI\ns+aP2kKrVFLhwYOUMGgwifkCejRwIBUdO0ZMpfHYcTWZeEIRSQrLar0/AEqpDuUknlBkxxOKJvCE\noiM8oSiLJxTtMPRcQwRGmZgvKK50xIv5gjF1eYGmHi+bAFkBo1JR0ZEjlDBwkP7hK7l+g4iIPvzr\nDnX74ax59TMM3b17lxYtWkR8Pp8AEIfDoTb+/jSTL6DwU6fYuIwaycnJoQMHDtCHH35ILVu21L9U\nb9zQXau5L9QypYaaLQyjVadiqy1TWwJk6c2blDx+gn7yThozlhiVqlbaYpuyqChKmzmLxHwBxXbq\nTHlbt5FWqaTjURnEE4ooOt20ybQyO3bsoDVr1hCR7n5sz+fTG87OtHnJUkpPTzeoDnPGTq3WTaxl\n9+7R2+4eZGlhQQCoUaNGNGvWLDp79qzZ999HLDyrpqDOyaHMpUt1k3dQaxILAkgeH29yfYUHDpD0\nVDiLPax+7NQFBZTz63qKbd+BxIIAypg/n5Tp6aTWaEnw5UlaevSBWe2qVCoKCwujd999lzw8PAgA\nzZ0zh2Lbd6AUoZBu3bpFGo3GrDZqgmEYSkhIoM2bN9OP335LxWd190jHjh1p4cKFrLRxNiabeEIR\nXU/MY6W+yjzvuWPUairYv5/ie/bSvfv4AspatsxkwVEjk1Hq2+9Qwf79JvbWcBiNhqSnwilxxEid\nAuf1N/RC77z9/1C7b0/XiQBc1wJk5YMnFLnyhKJ3DC3/woW82jxeVgGyAkalooJ9+yi+V28S8wWU\n+u571H1ZOH2wy3StxokTJ6h169YEgCwsLKh///60YcMGysrKosLDh0nMF1BZVBSLV/F8GKWSksaO\nI+nJUyQWi2nFihX6yf2LL76g/v37m/VCH7zmIk35341q/8/22Mlj4yh1xgwS8wUU36s35f/1F+Vu\n3Eiyi5fMqleZnk7Zy394QiNY28gfPqTUd9/Trcr7h9KSX0XUctEJUqqNF6xUKhVFRkbqP0+aNIm6\ndeum/6wtKaGY4BDK+v57g+tkY+yUaWmUvfwHyktPp127dtGYMWPI3t5erwVdtGgRxcWZpv3dfCGR\neEIR5bK0W1ATGlkJ5axdp9PiBbWmrG++ocSRoyhl2vQ6ad8Yaho7dUEBZa9cSTEhbUjcOpgufb2a\neEIRHbpr2OLiaR48eEDz5s0jT09PAkBOTk40adIkOnjwIJWUlJBk7jyK69qNmFoWHqtDrVbTnDlz\n6M8//yQiosLCQpo8eTKdPHnSpPdfTrGCeEIR/XExke2uVjl2DMNQcUQEJQwdplN6jJ9AmV9+RWK+\ngFSZmSa3xTAMpc6YQYUH/zajx0a2qdVSkUhEjwYM1F3LpMkUuvwUTd9a/TzCJi9SgDT2eOEdqM3j\nZRcgK9DK5ZT3v610o1tv4glFtHLJZtIYuKWi1WopLCyMEhN1L5LTp09T586dadOmTZSbm/tEWU1h\nIYkDg+jx6p9Yv4bqUGVlUerb75Ds4sVn/rdhwwb65JN/t85Wr15Nd+7cMar++QeiqM034dWuHFkz\nPygpoewfV5A4MIhiO3aivD/+IK1czkrdRERl0fcppnUwyS6xt4VnKLJLlynxjRE0/N21NOjTbVR2\n757B5z569Ig+//xz8vLyIgB0//59ItJtWz89JmmzPqD4vn0NXuXXmva4tJT27NlDgwcPJi6XSwDo\n888/N7qea4l5xBOKKDLmcS308l8YhqEikYjiuvcgMV9A6Z/OIWVyMhHptudUGRlmt6GRSvWaMjYw\ndOxU2dmU+eVX9MvAqcQTiuju1r1GCXm7du2ijh07EgCytLSkUaNG0dGjR0mheFKol548SWK+gEpv\n3jTmMsyiYO8+kp4+XeX/Ll++TG5ubgSAfHx8aMmSJZRh5Dh2XR5Bs3ffZaOrT/D02CmSkihl2nQS\n8wWUMGgwScN179uiI0coY+Ei1tuvKxiVigr27KV/evUl//nH6Ov560mVVb05FFu8EiDryfFfESAr\niLils3/c02M4xXXpSgV7an6ZZmVlkZWVFS1apHuQa5qcU6ZPp4TBQ1jrM1vk5uaSg4MDAaBu3brR\n7t27n7GJq4pd11OIJxRRWn7VdodsjF3x2UiK76OzW81cspQ0hYVEVL4qj4wkbbntnTkwDFOn2sen\nUavUFLDwOH089VsS8wWUsXgxqQsKqizLMAydPn2ahg8fThwOhywtLWnkyJF07NgxUj1nG7/w4N86\nDfgDw7YpzR07eVzcM3Z2T5ORkUErVqzQt5WamkpLlix5ZvFVFTKFmvwWiGjNGdO3j2tCmZam1xIn\njRlLZf/8Uyvt5G7cqNMmsSCMEhk/dsKtFynwi0P0gB9AiW+MoJIb1WuDUlNT9eYHX3zxBbVu3Zp+\n/vlnevy4ekFeI9NpwLOX/2BUv0yFYRhKHDmKJJ99Vm0ZhUJBBw8epKFDh+qfowkTJtClS5cMWmTN\n3HGbeq+MrLGcsVSMnVappJzffqOY4BCK7dCR8nftIkbNvs0lke73Uufk1ErdNXH9oYR4QhFt6z9e\nt8CtpWusoC4ESJ5Q5MlGPS9cyKvN478mQK6NiCe/BSLK/ecBpbz5lm7SGD3miQk3MTGR5syZQyNG\njNB/d+XKledO3JXJ37WLxHwBKRISWO//02jLyowSrgoLC+mXX36hFi1aEADy8vKib7/9lvLz86s9\nJzq9iHhCER2PqnriM2fsVFlZlD57ts5eZvjrVHrnydW+4tEjEvMFlL9rl8lt1Bfis4uJJxTRvssJ\nlL1iJYmDWlNsp85PLGJKS0tp06ZNFBgYSADI09OTlixZQpkGbmGpCwpIHBhE+Tt2GlTe3Ocu/dM5\num1LIyaErVu3kqWlJaWWO6PIa9Ay9//pPL2zjX2tFqNSUe7vmykmpA3FtmtP+Tt2PrOYlHzxBRXs\n3cdKe+qcHCqLjmbNBszYsRu+7hJN2nyNpCdP6p0MJZ9/Qeq8J238zp8/T1wul06V23ErFAqD+5y/\nfTuVXLtmVL/MgdFoDN5JevToEc2dO5dcXV0JAL322mu0ffv2517bb+ceEU8ooqJSdm2vz507RyU3\nblDC4CG6cfjsM1I9JZwr09JYWThXIJk7jxKGDH0hTjhbLiURTyii9Ngks02RDKGOBMjTPKHoKk8o\nWsYTinrwhCKuKfXUWEDMF+w05Lv6ePzXBMh3/7xJ/Vbr2npi2yogkM598ilNHDeOuFwuWVpa0uTJ\nkw3S0D2NKjubMhYuIkVSEsu9f5aCffso5rW2pEw3zvNaq9XSyZMnaciQIXov7jlz5lBaWtozZZVq\nLbVcdIKWh4mrrMuUsWM0GsrfvoNi27ajmDavUe7mzVU6yORu3KTT2mRnG91Gle2qVJT2wYeUt+V/\nrNRnDAdupxNPKKL47GIiIlLEx1PKW1N0i5ix40gWFaV3hGrbti39+eefz2wTGoLaAM1eBeY+d+q8\nPJO2LLMrjeeQIUNowIABFBERUeXk9tm+e9T+uzOsTnyld+5S4vDXddvVsz+ucltNlZ1NYr6Acjdu\nZK1dNjFm7BRqDbVYFEY/nIghIt3CM2ftWhK3DqaYjp1ot1BI+/buJSIipVJJX3/9NavRHOoTJSUl\ntHnzZgoKCqIBAwbov1dXsQi6FJ9LPKGILsazp7lTFxTQnbff1tlF9+tPsgsXqiyX+vbblPjGiCr/\nZwqyCxeo8O9DL8RG9ZM9d6nz9xF11l5dbWHzhCJ7nlA0nCcU/cYTiu7yhKIDPKHoHZ5Q5GNoHYYI\nkHef+mwh5gvEdXGB1fRnsJgviBPzBQlivmDB88r+1wTIjsvO0Jy9T9qfXYuMpEHlntT2XAv6ZPx4\no21lXhRysZger1lj1uQaHR1NU6ZMIQsLC3J1daWysmfDLLzx6yWa8PvVKs83duzK7j+gpNFjdE5N\n771Pyud4DCeNHUdJ48YbVX9NpH86h/K3b2e1TkP46sh9CvzqJGm0/45VdnY2ff/uuxTbvTuJAwJp\n3fgJFBlevb0p29TVc1cdDMPQihUryNvbmwBQ+/btaf/+/U84Pfx5RRf+I7PI/PAfmqIiylyyVOeg\n1afvc20S8//6S7eT8OiR2e1WoC4ooMc//Uzy2OqjGhiKMWN3L62QeEIRnYj+V5OtVqvpz59/Jn65\nRq5Dw4as7JrIY2Ko5Np1s+t5HhqplBJHjiLZZdPtmRmG0e+8pKWlkbe3N504ceKJMkWlKuIJRbQ+\n0vx7gGEYKjx8mOK6dKWHAYH0ePVq0lbxriXS3afioNb0ePVqs9utD/RddY7e336rztozRIDkCUWD\neUJRHE8oSuAJRc+Vgww9eEJRK55Q9AlPKDrBE4puGHJOtckgYwQBC2MEATIAITGCgOLyQwYgB8BR\ngwNNskiMIMACwG8AhgAIBDApRhAQ+CL6UtdkSxXIkSkR4usCIsKFCxcwcOBAdO3XDzdzcrBoxgxc\nDA3FrKhoMD/8AHV2tsltEREUMTHQFBayeAXPYhsQAM9PPzUrgG5wcDB27NiBxMREbNu2DXZ2diAi\nfP7557hz5w4AXVaQBxnFYBijA+3r0ZaU4vEPPyBl/Hiocx6j8S8/o8nm32Ht61tleXV2NhT378Mp\nNNTkNqvCd80vcJs6ldU6DSFaIkXrxi6w4P47VuHh4fhy61YULVsG17FjERoVhSYrV6H06lWT2yGV\nCumzZ6Ng5y42ul0tuet+Ren1G2bVweFwMH/+fCQnJ2Pz5s2QSqUYP348BAIBtmzZAqVSieDygOJR\n6abnxSYiSMPCkDhsOIoOHIDb9OloLjquTxVYFSURZ3WZr5o3N7ndp+FwuSjYuRPye/+wVqchVKQj\nDWniCqVSic2bN4PP52P63Lmw8PHBxtmzsY3nh6SRo5C7bh0YpdLktrKXLcPjH35gq+tVosnLB9fe\nHhbOpqcD5XA4cHNzAwAoFAp06dIFgYG6qTA2NhYSiQQu9lbwc7c3O52rMjkZadPfRtaChbDm8VCw\neBE8580D186uyvIlFy4AGg3r7z6mrAxSURhIo2G13uchlauRlFfKavYjc/FbEPaMHOS3IMxsOSjl\nx2HxKT8OW5fy47ChAHobdFJNEqaYL/iBDemWjUPMF3QV8wXhlT4vFPMFC6sr/1/SQJ56kEU8oYhu\npxSQTCYjFxcX8vT0pBUrVlBxsW5b8Qm7qLbtqrSLMgRlcrLOds9AWzRTKL15k5QpKbVSd3JyMrm5\nudHmzZuJiGjvDZ0jzaPHsmfKGjJ2xWfOUHzvPiQWBFDWN9+Qpvz3fh6FBw/qNECJ7IfRqGuDcqVa\nSy0Xn6B5Oy/TO++8o4/fqFKpKLaSNqr05k19MN6M+cJqnWxqInHUKEqeMLHGcqY+dxpZCcV170G5\nGzaYdH619Wo0dODAAWrXrh0BoMaNG9Oqn9dQ84VhtPJUjEl1Pu0kI3/4sOZ+VGiAVq0yqc3n1i1j\nx67NmLGbt/8favttOK1e/RP5+PgQAOrYsSMdPnxY7yyjzssjyedf6DyBBw4yWYuYt20bifkCUlZh\nDvOyMGTIELKysqL33nuPpv9+gbouN237VatUUs769RTTOphiO3TU2TtrtTWOXfrsjym+Z68nAnGz\ngfT0aRLzBWZpbo3lyiOdGcCFuBxas2YNlRqZBMIUUIMGkicUdeUJReGVPi/kCUXVykG1eVSrgayE\nKEYQ4AAAMYKAt2IEAT/HCAJ4pkq5ZtIYQHqlz5Ly7/7zHL30DzjEIMDbEY6OjggPD0dKSgrmz58P\nJyddai+OlRU8ZryPZsePwa5tWzz+/nukTJoMRVycUW1Z+/nBunlzyCIiauNSAABZ33yDrMVf1krd\nfn5+SE1NxbRp0wAA4ssnAQBbj5wFU0W+5epQZ2Uh/aPZkMz+GBYuLvDbuwfeS5bAwqnmVGouo0ej\nmei4QakKjeXx8h+QNGp0na3Ewy7fhUrDYNPyhdi9e7c+NaCVlZU+5SUA2HfsCP+jR+D+wSxIw8KQ\nNHQYpMdF0L0TDccpNBTyf/6BOieH1euowMLRAS3PRcKt/P5grV4LC4wdOxa3b99GeHg4WrRogW+W\nfIkWHvaIlkiN+h1IrUbe5j+QNPx1yO/dg9fixfDbtxe2gTUrGrQyGZz69oXToEHmXE6VVKSVM3ZM\nzeF2Ui5yYm7h88/nQSAQICIiAjdu3MDIkSPBLU9FaOnujsarVqLJ/7aAiJA2fToyFy4yehelQmsm\nO1M77z5tSalZGlJD2LBhA2bMmIGdO3fi0P/WIFOqwIXrd42qo/TmTSSPGIm8X9fDacAAND8RhgYT\nJ1SZM7wyjEKBksuX4di/X41ljcWxd2/wdu6AQ9e6SfBNRNh75hoAILixC/r27ftEatUXSL2RgwwZ\n4Y0AymIEAW0AzAOQCGBHrfbKDDgczgwOh3Obw+Hc1tShqrs2UKvVUCgUAIC4XAUgzURB7mMAQOfO\nnWFXzRaCddOmaLLlD/isWgm1RILkMWOR89NPYORyg9t2Cg1F2e3btbaN3XTLFnh9ubhW6gYAR0dH\nWJfnjQ5o7AZolNi0/wSCg4Oxc+dOqNXqas8ljQb5f/6JxGHDUXr1Kjy/+AL+Bw/Ark0bg9vncDiw\nadHC7OuoCqcBoWg4+yOQEcKwKdy6dQsjR47E1Dk6Qf/tN/ohNTUVS5YsqfYcro0NPD/9FP5//w2r\npk2Q+cUXSJ8xE+qMDIPbrZjESyLPmXcBz4FjZQWuvX3t1M3hYODAgTh//jzEYjHa+rkhWiJF3379\nnsiNXB1ld+8iefRo5P78Mxx79UKzMBHcprxlUN5qALD29YXvr+tgFxxs7qVUSdbSr5H9nHuADfLz\n83HmzBmUKjVIK1IiqJETrl69irNnz6J///7Vmr04du+OZseOwn3GDEiPH9ctYo4dM1jgtfb1hY1A\nANnZs2xejp7CXbvwqGcvaIuLa6V+QLeAXr9+PVJSUjCmbwcAwJC3ZmH48OG4WoN5iaawEJkLFyFt\n6jSQWo0mf2xG459/gmXDhga1zbGxgd/evXCfPt3cy3gGrrU17Dt2ZF0wfRqtVosDBw6gbdu22HP6\nGhyoDA0crBESEoI2RswBZmBZIcOUHzNqqyG/BWGvVfHdEEPPN2QkNAGxMQRgBID1AbExvwFgL5u9\ncWQAaFLps2/5d3qIaDMRdSCiDpaWlnXaObZQKBTYuHEjWrVqhXXr1oGIkMfYY3xoF/hWY3P3NBwO\nBy6vv45mYSK4jHgD+X9sQdIbI1By5YpB5zuFhgJaLUrOnTfjSqrHytsbtgJBrdT9NBMnjEeH5l5o\n3WsouFwupk6dilatWmHjxo1QqVRPlJXfv4/k8eOR8+MKOHTsiOai43B/9x1wrKwMbq84/DQyFyyE\ntqSE7UsBADh06oQGEyeCWy4gs83ly5cxePBgdOrUCRcvXkTXYRPhYmuJdT8shaenp0F12PJbwW/3\nbngtWoSyO3eQ+PobKNi+HaTV1niuTcuWsOI1rRUNuPz+AySPHgNFXDzrdVdFkyZNEOLrCqlcDbcm\nLeHiorOlKi0tRXJy8hNltVIpspYsRerkN6EtKYXvhg3w/XUdrLy9DW6PUamgkhgurJuChYsLLFxq\n1ybss88+w7hx43AnKQcMAfPfm4CuBmqeuLa28Jz7Gfz//hvWTZsic74Q6e++B1VamkHnO4WGQiEW\n18rza9+5E9zfedss+0dD8fb2xq/fzAeXAwyaNAPXr19H9+7d0adPn2c0aUSEosNHkDRkKKTHj8P9\nfd1OlmPPnka1yeFwYMtvBWte7WxSMkolcn/7DbLISNbrVqvV2LFjB4KCgjB+/HgoFAo0bt0FfYLZ\n30WqAU2FDFN+bH7q/zXKQUawtbL9pN+CsHEAvjX0ZEMESFmMIGAhgCkAwmIEAVwAhs+m7HILQMsY\nQYB/jCDAGsBEAMdeUF9YRyaTYdWqVfD398eHH34ILy8vtG3bFqn5ZZDK1XitaQOj67Rs0AA+33+P\nptu3g2NhgfR330PG/PnQFBQ89zzb1kGwbNQIJefY1QKRRoOsr7+GQixmtd6aaNOkAR6rrHHn7j0c\nO3YM3t7e+PDDDzFx4kSsXLkShZmZyF72PVLGT4A2Lx+N166F76aNsGps/M5Aseg4Sq9dqzUNF6B7\nkRaHnwZTWspqvVevXkXPnj1x7949rFixAqmpqbD0bI6QJq5GOztxLCzgNnUKmouOw75jBzz+4Uek\njBsP+f0Hzz+Pw4Hbm2/Bvn07cy6lShh5GTg2NrDy9mK97uoIbqwTtqbP+xrTyzUzf/zxB1q2bImp\nU6fi4cOH/zrJHDxYyUmmr9FtlV6+gsTQUJSVO5DVBp5zP4Pn55+zWmd6ejo+/vhjPHz4EACwdOlS\nXLlyBfF5uh2YEF9Xo+u05bcCb/df8FryFeRRUUh6/Q3kbf4D9JzdBwBwmzoFra5choWjo/EXUgP2\nbdvCY9Ys1uutDgcbS7TwdIRrsxCkpqbil19+QU5ODjw8PAAAmZmZkCckIm3adGQtXAhrPz/4//03\nPOfNrdZJpjoq3u01Pd/mwLG2hvToMVbvb6VSid9//x18Ph/Tpk2DjY0N9u/fj4s376FIbYE2TY2/\n92qZWwBa+i0I8/dbEGauHDQewC6/BWGt/BaEvQ1gDoCBBp9dk5GkmC/wFvMFc8V8Qc/yz03FfMHU\nF2GwWd7+UDFfEC/mCxLFfMHi55V9WZxo8vLyaOnSpdSgQQMCQKGhoRRZnsSdiOjIPV0k/AcZhgWd\nrQ6tQqGPnxbXqTMVHjr83JAr8pgY0rJsNKyIj6fYDh2pOKLu4moR/fsbPszQZXNhGIbOnTtHHdq3\nJwC0MSBQ5yTz3TLSyJ51tjEUbVkZxbR5jbK++ZatrldJ6e3bJOYLqOjYcbPrOnjwIK1fv56IdL/L\njh079MbicpWGmi0Mo1WnzAvdwjCMLgh0j5663/nb7wxyRnoeLzqMj6GoNFpqtfgEfXf8XwcYiURC\nc+fOJXs7OwJA/R0d6XDffgY5yTyPjEWLKLZDR2JMiAFrLObkOK4Yu6ioKJo6dSpZWVmRpaUl/fHH\nH0+U+3j3XZOdQCqjys6m9Nkf64L+v/6GUek42aLk6lWzfjNTmbf/H2r/3Wn9u77ir6asjIJ8fKi/\nk/MTTjI1Ud1zV3L9Bon5ApKGh7PW96qoLnyQKURGRlLjxo0JAHXq1ImOHTum/30iYx8TTyiia4l5\nNdTCLjAsjM9QnlAUzxOKEnlC0XPlIAPqEvCEIjFPKDrDE4rsjTnXUKHNS8wXDC8/WEmBUxdHfRcg\nExMTaebMmWRXPomMGDGCrl9/1nvw2+MPqdXiE6TSsOPVpnj0iJInTSYxX0Ap06br8+bWFVqFosrA\n27VJUm4J8YQi2nszVf+dIimJ7o0YSQd5fpQwajSVRUfT999/T3PnzjU5lmHxmTMk5guo5MoVtrpe\nJQzDUMm1ayYH1q0cL3Py5MnUvn17vUdrZW6nFBBPKKJTD9jJAauRySjru2UkDgikuB49SBoWVu1v\nrVUoqOx+9WkNjX3u1Pn5dX7fVTDyt8s0buO/sUi1cjk9XrOGrgoC6MNGjci1PE3n04tHY2DUaorr\n0pUk84zP220s+bt2kTggkFTZxuf5ZhiGVq5cSQMHDtQnAvj4448ppYqoDL1XRtKMHezF4CuOiKD4\nXr3LIyp8W+1iseTKFUqeMNGsxWRlGI2G4nr0oPTZs1mpzxi2X9XFIpUU/vvMy86fp7h+/emnRj70\n5+jRpM7JIZlMRhs3bqwylm5lqnvuspZ9TzEhbVhXOlSHqe/opKQkeli+UEtOTqb+/fvTmTPPBvtf\nc0aX+U2mqN3UhU9jiABp7sETiu6VBw+vODLLhci7PKHorqH11LiFHSMIGA/gJoBx5erOGzGCgLFG\nKUn/w+SsXg3n//3P4PJEBHm5M0tSUhL+/PNPTJ48Gffv38eRI0fQuXPnZ86JlhQhyMcZVhbsGA/b\ntGgB3q6d8P76aygePkTSGyOQu2EDmHKHncoU7PoLuet/Y6Vd3bOhc7QwxqaQDXhu9nCytUSURApG\noUDO2rVIfmMErFJT0Xf592h2YD/sgoORnZ2NjIwM/XZtnJEe7LKIs+A6O8O+Y8fauAw9HA4HDl26\nGOxYUUFycjLmz58PHx8fREdHAwB+++033Lx5U+/RWpn75THk2piwhVgVFo6O8P5yMfz27YOVpxcy\n5s5D+vszoEpNfaZszoqVSJ06lTWv1cfLf0DSGyP092Fd0sbXFQ8ypdAyhJILF5A0/HXkb9yEpq8P\nx9qoKKRlZWHVqlV48OAB+vXrh65du+L8+fNGtVF29y60hYWsx9+rCsdeveA5/wtw7WwNPqesrAzb\ntm1DmzZtMH/+fNy/fx/Lly9HWloa1q1bB95TdnPSMjVS8stM2r6uDqf+/dEsLAwN3noLhXv2IGnY\ncBSfCn/mnuBYW0P+zz8ovXiRlXY5Fhbw++svNPz0U1bqM4aK3y86vQjqzExIPv4Y6TNnwcLGBjMP\nHcK0v/+GZcOGOHz4MD744AP4+/tjyZIlkEgkBrdBRJCdjYBD9+61arpTQd6m35H65ltGn6fVatGz\nZ0/Mnz8fgM7hKCIiAqGhoc+Y6ERLitC8oSMcbV5OX4oaGAudTFdx9ATweqXPhlGThCnmC6Iqax3F\nfEFDMV8QVdsSMhtHXWggczdsoNvvzzBoNaRWqykkJIQ+++wzItKtoHJrSN2m1mhJ8OVJWnq0ek2M\nOageP6b0OXN02S369n1GI5SxYCFrW2KFhw9T8sRJpH5O7uraZPLmazRkWRg96h9aHqtwPl0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msZ6XQ6ycrKan9XVq9ezaJFi3oc84cffmDKlCkcOnSIrVu3cvjwYbKysrjpppu48cYbyc3Nbf+b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\nBq7UBTwkLc5Pp7OboL153lxaS9bQOAqyg/cBx1CiA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                "text": "UR= (6+16j)\nUL= (-77+27j)\nUC= (121-43j)\n",
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                  "\u001b[0;32m<ipython-input-11-ccdb826be566>\u001b[0m in \u001b[0;36mrun_complex\u001b[0;34m(omega)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mKazalci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mUL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mUC\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mcomplex_plane3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKazalci\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# po potrebi razširi za ogled kode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_complex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0momega\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFloatSlider\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m650\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m950\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0sMEXVqtWNA",
        "colab_type": "text"
      },
      "source": [
        "#   Obravnava resonančnega pojava"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISL9pg10tWNC",
        "colab_type": "text"
      },
      "source": [
        "<br>\n",
        "<center>Dejan Križaj, 2019</center>\n",
        "\n",
        "**Namen:** Zvezek je namenjen seznanjanju študentov z uporabo Jupytra za analizo resonančnega pojava. Pri tem bomo uporabili analizo vezja s kompleksnim računom. Spoznali boste različne načine obravnave in vizualizacije resonančnega pojava, tako v časovnem prostoru kot tudi s kazalci v kompleksni ravnini in predvsem s prikazom v frekvenčnem prostoru. Pokazali bomo tudi, kako se računalniško določi pomembne parametre kot so resonančna frekvenca, bočni frekvenci in pasovna širina.\n",
        "\n",
        "**Povezani zvezki**:\n",
        "* O obravnavi vezij vzbujanih z izmeničnimi signali s kompleksnim računom"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_Eu8sJDtWND",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-block alert-info\">\n",
        "<b>Namig:</b> Obstajata dve verziji tega dokumenta. Ena je v obliki html datoteke (končnica html), ki je ni mogoče izvajati, druga pa ima končnico ipny (Jupyter Notebook), ki jo lahko izvajamo z Jupyter aplikacijo. To aplikacijo imate lahko naloženo na vašem računalniku in se izvaja v brskalniku, lahko jo ogledujete s spletno aplikacijo nbViewer, s spletnimi aplikacijami Binder ali Google Colab pa jo lahko tudi zaganjate in spreminjate. Več o tem si preberite v \n",
        "<a href=\"http://lbm.fe.uni-lj.si/index.php?option=com_content&view=article&id=59&Itemid=135&lang=si\">tem članku</a>.\n",
        "<br>    \n",
        "Za izvajanje tega zvezka ne potrebujete posebnega znanja programiranja v Pythonu, lahko pa poljubno spreminjate kodo in se sproti učite tudi uporabe programskega jezika. Več podobnih primerov je na Githubu na https://github.com/osnove/Dodatno/\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxtR2mGptWND",
        "colab_type": "text"
      },
      "source": [
        "## Teorija - supernakratko\n",
        "\n",
        "Analizo vezij s kompleksnim računom smo že spoznali v predhodnem zvezku. Analiza resonančnega pojava se nanaša na analizo vezja, vzbujanega z izmeničnim signalom, s pomočjo kompleksnega računa. Resonančni pojav nastopi tedaj, ko pride v vezju do največjega prehajanja iz magnetne energije v električno in nazaj. Tedaj pride v vezju do bolj ali manj izrazitega povečanja tokov ali napetosti, kar  imenujemo tokovna ali napetostna resonanca. Očitno je za to potrebno, da imamo v vezju vsaj en element, ki je sposoben shranjevati električno energijo (kondezator) in en element, ki je sposoben shranjevati magnetno energijo (tuljava). V praksi pa je to lahko tudi en sam element, na primer tuljava, ki pa ima zaradi realne strukture tudi medovojne kapacitivnosti, ki povzročijo, da ima sama tuljava resonančne lastnosti pri določeni frekvenci.\n",
        "\n",
        "Vzemimo preprost primer vzbujanja zaporedne vezave upora, kondenzatorja in tuljave na izmenični vir napetosti. Za analizo takega preprostega vezja bi morali rešiti enačbo \n",
        "\n",
        "$u_g(t)=R i(t)+\\frac{1}{C}\\int{i(t) dt} + L \\frac {d i(t)}{dt}$. \n",
        "\n",
        "To enačbo bi morali prevesti v diferencialno enačbo in jo rešiti, kar je tudi potrebno, če analiziramo prehodni pojav. Če pa nas zanima kvazistacionarno stanje (prehodni pojav že izzveni), pa tako tok kot napetosti nihajo z vzbujalno napetostjo. Analizo takega vezja lahko opravimo s prehodom v kompleksni račun, ki zgornjo enačbo prevede v algebrajsko enačbo za kompleksor toka $\\underline{I}$ \n",
        "\n",
        "$\\underline{U}=R \\underline{I}+j\\omega L \\underline{I} + \\frac{ 1}{j\\omega C}\\underline{I} $. \n",
        "\n",
        "Od tod sledi, da je kompleksor toka \n",
        "$\\underline{I}=\\frac{\\underline{U}}{R +j\\omega L +1/(j\\omega C)}$. \n",
        "\n",
        "Če analiziramo zgornjo enačbo, ugotovimo, da bo tok največji tedaj, ko bo imenovalec najmanjši, to pa bo, ko bo $ \\omega L = 1/(\\omega C)$. Tedaj bo vezje v resonanci, tok pa bo enak kar $I=U/R$.\n",
        "\n",
        "V nadaljevanju si oglejmo, kako izračunavamo in analiziramo resonančni pojav z Jupytrom."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ush0doG7tWNF",
        "colab_type": "text"
      },
      "source": [
        "## Obravnava zaporednega RLC vezja s kompleksnim računom\n",
        "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/impedanca_RLC2.png\" style=\"height:120px\" align=\"left\"> \n",
        "\n",
        "Izračunajmo tok v vezju za naslednje podatke:\n",
        "\n",
        "$\\quad u_G(t)=50 \\cos (20 t) $  V  <br>\n",
        "$\\quad L=100 $ mH <br>\n",
        "$\\quad R = 2 \\, \\Omega$ <br>\n",
        "$\\quad C = 2 10^{-6} \\, $ F <br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZb50iaWtWNG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "148cef63-750e-4711-eb06-932ac95e0b98"
      },
      "source": [
        "import numpy as np\n",
        "L=150e-3\n",
        "R=20\n",
        "C=1e-5\n",
        "U=50\n",
        "omega=1000\n",
        "ZL=1j*omega*L\n",
        "ZC=1/(1j*omega*C)\n",
        "# Izračun impedance\n",
        "ZRLC=R+ZL+ZC\n",
        "print('ZRLC = ',ZRLC)\n",
        "om0=1/(np.sqrt(L*C))\n",
        "print(om0)\n",
        "\n",
        "# Izračun kompleksorja toka\n",
        "I=U/ZRLC\n",
        "print('I = ',I)\n",
        "\n",
        "# Izračun amplitude in faze\n",
        "Iabs=abs(I); print('Iabs = ',Iabs)\n",
        "fi=np.angle(I,deg=True); print('Faza = ',fi)\n",
        "print('i(t) = ',np.round(Iabs,2),'sin (',np.round(omega,0),'t + ',np.round(fi),') A')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ZRLC =  (20+50j)\n",
            "816.496580927726\n",
            "I =  (0.3448275862068966-0.8620689655172413j)\n",
            "Iabs =  0.9284766908852593\n",
            "Faza =  -68.19859051364818\n",
            "i(t) =  0.93 sin ( 1000 t +  -68.0 ) A\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVZLJpsBtWNK",
        "colab_type": "text"
      },
      "source": [
        "### Prikaz toka in napetosti v časovnem prostoru\n",
        "Določili smo amplitudo in fazni kot toka. Sedaj lahko tvorimo časovni signal, kot smo to opisali v uvodu. Formalno to naredimo tako, da pomnožimo dobljeni kompleksor z $e^{j \\omega t}$ in vzamemo realni del. V praksi to pomeni, da je rezultat $i(t)=I \\cos (\\omega t + \\varphi)$. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOardC_NtWNK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "7315700b-6638-4529-ddbe-d7b6cea8d2b3"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "\n",
        "t = np.linspace(0.0, 20/omega, 1000)\n",
        "ug=U*np.cos(omega*t)\n",
        "i=Iabs*np.cos(omega*t+fi)\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Čas (s)')\n",
        "ax1.set_ylabel('Napetost ug / V', color=color)\n",
        "ax1.plot(t, ug, color=color)\n",
        "\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "ax1.grid(axis='x')\n",
        "\n",
        "ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "ax2.set_ylim(-U/R,U/R)\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('Tok / A', color=color)  \n",
        "ax2.plot(t, i, color=color)\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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62SwonoKj0eve1PtZ7JifaeB/7lxE48l+frJZioiMvh2tpmNypem6h4wEtUrg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\n3Y7TIYk4FwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MiHXKA-tWNN",
        "colab_type": "text"
      },
      "source": [
        "#### Opravi naslednje analize:\n",
        "1. Spreminjaj vrednosti krožne frekvence od 200 do 1200 s$^{-1}$ in opazuj amplitudo in fazni kot toka. X os je izdelana tako, da se normira na ogled par časovnih period, Y os je fiksirana na največjo možno amplitudo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABXuG5mOtWNO",
        "colab_type": "text"
      },
      "source": [
        "Nekoliko nerodno je stalno spreminjati krožno frekvenco in opazovati spremembe na grafu. Bolj elegantno se da to storiti z uporabo interaktivnih gradnikov, kot je na primer ipwidget. Spodnja celica kaže, kako narediti drsnik (slider), s katerim lahko spreminjamo krožno frekvenco vezja in opazujemo spremembe na grafu."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          2
        ],
        "id": "ZAUe7Z6utWNO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313,
          "referenced_widgets": [
            "e086060fcbd248da96cac694a3c8254a",
            "0ce19e31692d4948885f407db9638997",
            "1f4f5e502b8347b8ad651b2917aef0ed",
            "2fa210640b7b49888ed93fc211d09c51",
            "90eb45f0926b4a17b4c4784395ebacfc",
            "08c7568f11f84707a1d9e95c13327205"
          ]
        },
        "outputId": "79a8518e-d909-4bd4-839c-fdb7432d4e09"
      },
      "source": [
        "from ipywidgets import interactive\n",
        "\n",
        "def f(omega):\n",
        "    ZRLC=R+1j*omega*L+1/(1j*omega*C)\n",
        "    I=U/ZRLC\n",
        "    t = np.linspace(0.0, 20/omega, 1000)\n",
        "    ug=U*np.cos(omega*t)\n",
        "    i=abs(I)*np.cos(omega*t+np.angle(I,deg=True))\n",
        "\n",
        "    fig, ax1 = plt.subplots()\n",
        "    color = 'tab:red'\n",
        "    ax1.set_xlabel('Čas (s)')\n",
        "    ax1.set_ylabel('Napetost ug / V', color=color)\n",
        "    ax1.plot(t, ug, color=color)\n",
        "    ax1.tick_params(axis='y', labelcolor=color)\n",
        "    ax1.grid(axis='x')\n",
        "    ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "    ax2.set_ylim(-U/R,U/R)\n",
        "    color = 'tab:blue'\n",
        "    ax2.set_ylabel('Tok / A', color=color)  \n",
        "    ax2.plot(t, i, color=color)\n",
        "    ax2.tick_params(axis='y', labelcolor=color)\n",
        "    plt.show()      # tok se izračunava znotraj funkcije, ki tudi vsakič znova izdela graf za vsako spremembo omega  # Razširi, če želiš videti vsebino\n",
        "\n",
        "interactive_plot = interactive(f, omega=(500, 1500,25)) # spreminjamo omega od 500 do 1500 s korakom po 25\n",
        "output = interactive_plot.children[-1]\n",
        "interactive_plot"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e086060fcbd248da96cac694a3c8254a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=1000, description='omega', max=1500, min=500, step=25), Output()), _dom_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kiLc8qgtWNR",
        "colab_type": "text"
      },
      "source": [
        "Ob spreminjanju frekvence vzbujalnega signala ugotovimo, da pri določeni krožni frekvenci, ki je v konkretnem primeru okoli 825 1/s, tok doseže največjo amplitudo. To stanje vezja imenujemo **tokovna resonanca**. \n",
        "\n",
        "Zanimivo je pogledati tudi napetosti na posameznih elementih vezja pri spreminjanju frekvence. Pripravimo naslednjo celico tako, da bo prikazovala napetosti na posameznih elementih in tok v vezju. (Graf bo nekoliko natlačen, zato ga za lažje opazovanje povečamo.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [],
        "id": "C8Wov4hitWNR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422,
          "referenced_widgets": [
            "f3669e074ccd4910b0b0f79bc320d77b",
            "81d742305c51448a9901b0be53074de1",
            "d35ec25657954e57bbb7553087e7c5d8",
            "27d85c6ac42241a7b622014c2a00c4f1",
            "77f2bcdae8df42258bbd0ad5ef2da345",
            "6aa36c675ab347f09db7fd6926aa0a6d"
          ]
        },
        "outputId": "4ca20c37-b06e-4b5d-d230-fe7df198e5a2"
      },
      "source": [
        "from ipywidgets import interactive\n",
        "\n",
        "def f(omega):\n",
        "    ZRLC=R+1j*omega*L+1/(1j*omega*C)\n",
        "    I=U/ZRLC\n",
        "    UL=1j*omega*L*I\n",
        "    UC=I/(1j*omega*C)\n",
        "    UR=R*I\n",
        "    t = np.linspace(0.0, 20/omega, 1000)\n",
        "    ug=U*np.cos(omega*t)\n",
        "    i=abs(I)*np.cos(omega*t+np.angle(I,deg=True))\n",
        "    ur=i*R\n",
        "    uL=abs(UL)*np.cos(omega*t+np.angle(UL,deg=True))\n",
        "    uC=abs(UC)*np.cos(omega*t+np.angle(UC,deg=True))\n",
        "    \n",
        "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "    color = 'tab:red'\n",
        "    ax1.set_xlabel('Čas (s)')\n",
        "    ax1.set_ylabel('Napetost ug / V', color=color)\n",
        "    ax1.plot(t, ug, color=color,label='ug')\n",
        "    ax1.plot(t, uL,linestyle='--',color=color,label='uL')\n",
        "    ax1.plot(t, uC,linestyle=':',color=color,label='uC')\n",
        "    ax1.plot(t, ur,linestyle='-.',color='black',label='uR')\n",
        "    ax1.legend()\n",
        "    ax1.tick_params(axis='y', labelcolor=color)\n",
        "    ax1.grid(axis='x')\n",
        "    ax1.set_ylim(-10*U,10*U)\n",
        "    ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "    ax2.set_ylim(-U/R,U/R)\n",
        "    color = 'tab:blue'\n",
        "    ax2.set_ylabel('Tok / A', color=color)  \n",
        "    ax2.plot(t, i, color=color)\n",
        "    ax2.tick_params(axis='y', labelcolor=color)\n",
        "    plt.show()      # tok se izračunava znotraj funkcije, ki tudi vsakič znova izdela graf za vsako spremembo omega  # po potrebi razširi za ogled kode\n",
        "    \n",
        "\n",
        "interactive_plot = interactive(f, omega=(500, 1500,25)) # spreminjamo omega od 500 do 1500 s korakom po 25\n",
        "output = interactive_plot.children[-1]\n",
        "interactive_plot"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3669e074ccd4910b0b0f79bc320d77b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=1000, description='omega', max=1500, min=500, step=25), Output()), _dom_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qklljkfctWNU",
        "colab_type": "text"
      },
      "source": [
        "**Analiza opazovanja:** \n",
        "1. Najprej ugotovimo, da so napetosti na posameznih elementih vezja precej večje od amplitude napajalne napetosti. To je seveda možno, saj mora biti vsota vseh napetosti na elementih enaka vzbujalni napetosti, ker pa so napetosti na elementih vezja med sabo zamaknjene za četrt periode, so lahko v določenih razmerah zelo velike, a se med seboj \"navzven\" odštejejo. Predvsem to velja za napetost na kondenzatorju in tuljavi. Napetost na kondenzatorju zaostaja za tokom za četrt periode, na tuljavi pa ga prehiteva za četrt periode. Napetost na kondenzatorju in tuljavi sta tako fazno zamaknjeni za pol periode ali 180 stopinj, kar pomeni, da se med seboj (navzven) odštevata.\n",
        "2. Ker napetosti na posameznih elementih vezja precej presegajo napetost vzbujanja, to popravi v zgornji celici (išči ylim) in jo ponovno zaženi.\n",
        "3. Kako izrazita je resonanca (kako izrazito je povečanje toka), je odvisno od velikosti upora. Če je ta majhen v primerjavi z reaktancama pri resonanci ($\\omega_0 L$ in $\\frac{1}{\\omega_0 C}$), bo povečanje toka ob resonanci izrazito, sicer pa ne. Preveri to s spreminjanjem upornosti in ponovnim zagonom celic.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKcD93HLtWNU",
        "colab_type": "text"
      },
      "source": [
        "### Prikaz  kazalcev (kompleksorjev) toka in napetosti v kompleksni ravnini \n",
        "\n",
        "Način prikaza vzamemo iz datoteke <a href=\"https://github.com/osnove/Dodatno/blob/master/Obravnava_vezij_kompleksni_racun.ipynb\">Obravnava_vezij_kompleksni_racun.ipynb </a>\n",
        "  in uporabimo že izdelano funkcijo complex_plane3. Funkcija se nahaja v datoteki  <a href=\"https://github.com/osnove/other/blob/master/funkcije.py\">funkcije.py </a>. To funkcijo morate imeti naloženo v isti mapi od koder zaganjate Jupytra. Če delate preko Colaba pa je najlažje, da se iz datoteke funkcije.py skopira funkcijo complex_plane3 v novo celico in se jo zažene, s čemer postane aktivna znotraj celotnega zvezka. V tem primer je potrebno zakomentirati prvo vrstico *from funkcije import complex_plane3*, saj je funkcija že zagnana znotraj zvezka.\n",
        "\n",
        "Vsi kompleksorji napetosti so relativni na napetost vzbujanja, ki ima kazalec usmerjen v smeri realne osi (ker je fazni kot enak nič). Kazalec toka je odvisen od vrednosti impedanc, te pa od vrednosti elementov v vezju in frekvence. \n",
        "\n",
        "Kazalec napetosti na uporu je na sredini med kazalcema napetosti na kondenzatorju in tuljavi in je v fazi s kazalcev toka (ga ne rišemo, a vemo, da je tako). Če ima ta kazalec pozitivni fazni kot, pomeni, da tok v vezju prehiteva napetost na zunanjih sponkah. Takrat ima vezje kapacitivni značaj - napetost na kondenzatorju bo večja od napetosti na tuljavi. To bo pri nižjih frekvencah od resonančne, saj je tedaj reaktanca kondenzatorja velika.\n",
        "\n",
        "Z višanjem frekvence pa narašča napetost na tuljavi. Pri frekvenci večji od resonančne bo napetost na tuljavi večja od napetosti na kondenzatorju.\n",
        "\n",
        "Pri resonančni frekvenci bo kazalec toka poravnan s kazalcem vzbujalne napetosti - torej usmerjen v smeri realne osi. Kazalca napetosti na tuljavi bosta tedaj enako velika."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pm8u-LPtWNV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7628ee6f-bd1e-454b-b84c-a1458cfd2fb4"
      },
      "source": [
        "complex_plane3?"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Object `complex_plane3` not found.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIXCDwyztWNY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "149c5ba7-1a33-412f-ca76-b25c83e0e637"
      },
      "source": [
        "#from funkcije import complex_plane3\n",
        "\n",
        "omega=820\n",
        "ZRLC=R+1j*omega*L+1/(1j*omega*C)\n",
        "I=U/ZRLC\n",
        "UL=1j*omega*L*I\n",
        "UC=I/(1j*omega*C)\n",
        "UR=R*I\n",
        "print('UR=',np.round(UR))\n",
        "print('UL=',np.round(UL))\n",
        "print('UC=',np.round(UC))\n",
        "\n",
        "Kazalci=[UR,UL,UC]\n",
        "complex_plane3(Kazalci,2,300) "
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UR= (50-3j)\n",
            "UL= (16+307j)\n",
            "UC= (-16-304j)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-0e516b7d37cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mKazalci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mUL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mUC\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mcomplex_plane3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKazalci\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'complex_plane3' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ra0Fl2jDtWNc",
        "colab_type": "text"
      },
      "source": [
        "Za lažje opazovanje zopet izdelajmo interaktivni gradnik (widget), s katerim lahko spreminjamo krožno frekvenco. Ugotovimo, da se kazalci \"vrtijo\" v odvisnosti od izbrane frekvence. To je OK, saj je fiksiran kazalec vzbujalne napetosti (ker gre za vzbujanje s kosinusnim signalom brez dodane faze, je njegov kompleksor usmerjen v smeri realne osi), kazalec toka pa je odvisen od impedance, ta pa od frekvence. Kazalec toka ne izrisujemo, vendar vemo, da je usmerjen v isti smeri kot kompleksor napetosti na uporu, ki je umeščen med kazalca napetosti na kondenzatorju in tuljavi. \n",
        "Kazalec napetosti na tuljavi prehiteva kazalec toka za četrt periode, kazalec napetosti na kondenzatorju pa zaostaja za kazalcem toka za četrt periode.\n",
        "\n",
        "Ko je vezje v resonanci, je kazalec toka usmerjen v isti smeri kot kazalec vzbujalne napetosti - torej v smeri realne osi. Tedaj sta kazalca napetosti na kondenzatorju in tuljavi največja. V bistvu so največji vsi trije kazalci. \n",
        "\n",
        "(Za bolj natančno spreminjanje frekvence spremeni step v ukazu interact.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          4
        ],
        "id": "31iO4cbstWNd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427,
          "referenced_widgets": [
            "c28964bf8ec749c3b180d8cb9e1a43c6",
            "10adc0be74f04ac0b7cda0486a02a1ea",
            "ef281d48afc947b09838145b1eaec236",
            "32add9493f304a0fbf51fd8b7e038c61",
            "228f94b113674abbaa4865b989d70b80",
            "2a2e352af55947d6883726d799bce72c"
          ]
        },
        "outputId": "4f6c9422-a9ab-409d-89c4-0908090f6950"
      },
      "source": [
        "from ipywidgets import FloatSlider\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "import ipywidgets as widgets\n",
        "\n",
        "def run_complex(omega):\n",
        "    ZRLC=R+1j*omega*L+1/(1j*omega*C)\n",
        "    I=U/ZRLC\n",
        "    UL=1j*omega*L*I\n",
        "    UC=I/(1j*omega*C)\n",
        "    UR=R*I\n",
        "    print('UR=',np.round(UR))\n",
        "    print('UL=',np.round(UL))\n",
        "    print('UC=',np.round(UC))\n",
        "\n",
        "    Kazalci=[UR,UL,UC]\n",
        "    complex_plane3(Kazalci,2,300)  # po potrebi razširi za ogled kode\n",
        "\n",
        "interact(run_complex,omega=FloatSlider(min=650, max=950, step=25));"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c28964bf8ec749c3b180d8cb9e1a43c6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(FloatSlider(value=650.0, description='omega', max=950.0, min=650.0, step=25.0), Output()…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70i81kvZtWNf",
        "colab_type": "text"
      },
      "source": [
        "### Prikaz toka, napetosti in faze v frekvenčnem prostoru\n",
        "\n",
        "Še najbolj informativen pa je prikaz v frekvenčnem prostoru. To pomeni, da nas zanima, kako se spreminjajo veličine (tok, napetost, faza) s spreminjanjem frekvence. Na X osi bo torej frekvenca, na Y osi pa absolutne vrednosti toka ali napetosti,  ali pa fazni kot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          14
        ],
        "id": "1SAJDirRtWNf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "6bd95649-4978-49bb-e0aa-5e22d41a1ed1"
      },
      "source": [
        "## Prikaz v frekvenčnem prostoru\n",
        "L=150e-3\n",
        "R=50\n",
        "C=1e-5\n",
        "U=50\n",
        "freq=np.arange(1,500,1)\n",
        "omega=2*np.pi*freq\n",
        "\n",
        "\n",
        "ZRLC=R+1j*omega*L+1/(1j*omega*C)\n",
        "I=U/ZRLC\n",
        "Uc=I/(1j*omega*C)\n",
        "UL=I*1j*omega*L\n",
        "\n",
        "def f():\n",
        "    fig, ax1 = plt.subplots()\n",
        "\n",
        "    color = 'tab:red'\n",
        "    ax1.set_xlabel('Frekvenca (Hz)')\n",
        "    ax1.set_ylabel('Napetost  / V', color=color)\n",
        "    ax1.plot(freq, abs(Uc), color=color,label='Uc')\n",
        "    ax1.plot(freq, abs(UL), color=color,Linestyle='--',label='UL')\n",
        "    ax1.plot(freq, abs(I*R), color=color,Linestyle=':',label='UR')\n",
        "    ax1.legend()\n",
        "    ax1.tick_params(axis='y', labelcolor=color)\n",
        "    ax1.grid(axis='x')\n",
        "\n",
        "    ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "    ax2.set_ylim(0,U/R)\n",
        "    color = 'tab:blue'\n",
        "    ax2.set_ylabel('Tok / A', color=color)  \n",
        "    ax2.plot(freq, abs(I), color=color)\n",
        "    ax2.tick_params(axis='y', labelcolor=color)\n",
        "    plt.show() # po potrebi razširi\n",
        "f() # razširi po potrebi"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PakWKlsLGjUUTEUHVqlUnPPaMIfEM6xPJS98f6Bbp4YbUFJLuuJ30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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLFWkvxttWNh",
        "colab_type": "text"
      },
      "source": [
        "**Razlaga slike:** Zgornja slika nazorno prikaže frekvence, pri katerih je odziv vezja (tok) največji. Ko je tok največji, so največje tudi napetosti na posameznih elementih vezja. Napetost na tuljavi je enako velika kot napetost na kondenzatorju. Sta pa ti napetosti v protifazi (zamaknjeni za $\\pi$), kar pa na tej sliki ni razvidno. Za ta vpogled je boljši prikaz v časovnem prostoru ali prikaz s kompleksorji v kompleksni ravnini. Napetost na kondenzatorju in tuljavi lahko tudi večkratno preseže amplitudo napajalne napetosti.\n",
        "\n",
        "Opazimo tudi, da je pri nizkih frekvencah večina napetosti na kondenzatorju (ki ima veliko upornost pri nizkih frekvencah ($1/\\omega C$), pri frekvencah nad resonančno pa je večina napetosti na tuljavi (saj upornost (reaktanca) tuljave linearno narašča s frekvenco - $\\omega L$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VXZV3MvtWNi",
        "colab_type": "text"
      },
      "source": [
        "#### Opravi naslednje analize:\n",
        "1. Spreminjaj kapacitivnost kondenzatorja in opazuj spremembe odziva.\n",
        "2. Spreminjaj induktivnost tuljave in opazuj spremembe odziva.\n",
        "3. Spreminjaj upornost in opazuj spremembe odziva.\n",
        "4. Spremeni graf tako, da bo namesto napetosti in toka prikazoval fazni kot in tok. Poišči razlago za spreminjanje faze in poglej, koliko je fazni kot v resonanci. Za boljše opazovanje nariši še dodatno črto pri faznem kotu enakem 0 za celo frekvenčno področje. (Rešitev je na koncu zvezka)\n",
        "5. Spremeni graf tako, da boš izrisal več krivulj toka za več različnih upornosti (lahko tudi kapacitivnosti ali induktivnosti). Za primer poglej zadnji primer v https://github.com/osnove/Dodatno/blob/master/Primer_diff_enacbe_analiticno.ipynb. (Rešitev je na koncu zvezka)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uliCtT_ptWNk",
        "colab_type": "text"
      },
      "source": [
        "### Dodatni izračuni - resonančna frekvenca, bočni frekvenci, pasovna širina, kvaliteta vezja\n",
        "\n",
        "Iz teorije vemo, da je pri zaporednemu RLC vezju resonančna krožna frekvenca $\\omega_0 = \\frac{1}{\\sqrt (LC)}$. Za bolj splošno RLC vezje je težje priti do analitičnega izraza, zato si poglejmo, kako bi ga določili iz grafa. Poiskati moramo največjo vrednost toka in pogledati, pri kateri frekvenci nastopi. Podoben primer smo obravnavali že pri iskanju časovne konstante v tem zvezku: https://github.com/osnove/Dodatno/blob/master/Primer_diff_enacbe_analiticno.ipynb.\n",
        "\n",
        "Bočni frekvenci in pasovna širina sta podatka, ki dajeta informacije o obliki resonančne krivulje. Bočni frekvenci poiščemo tako, da poiščemo frekvenci, pri kateri je amplituda toka za koren iz 2 manjša od amplitude toka pri resonanci, torej $f_{bočna} = f(I=I(f_0)/\\sqrt2)$. Za zaporedno RLC vezje jo je mogoče določiti analitično, mi bomo to naredili numerično, podobno, kot smo poiskali resonančno frekvenco.\n",
        "\n",
        "Pasovna širina je preprosto razlika med zgornjo in spodnjo bočno frekvenco: $B=f_{zgornja}-f_{spodnja}$.\n",
        "\n",
        "Poznamo tudi pojem normirane pasovne širine - normiramo jo z resonančno frekvenco, torej $B_{norm}=\\frac{f_{zgornja}-f_{spodnja}}{f_0}$. Obratna vrednost od normirane pasovne širine je kvaliteta vezja $Q$. \n",
        "\n",
        "**Sam dopiši enačbe za izračun in izpis normirane pasovne širine in kvalitete v spodnjo celico.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [],
        "id": "W3p6Xi1vtWNl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "01c604fa-85a7-4f60-98c6-668d18d162e0"
      },
      "source": [
        "## Analitičen izračun resonančne frekvence\n",
        "freq_0=1/(2*np.pi*np.sqrt(L*C))\n",
        "print('Resonančna frekvenca = ',freq_0)\n",
        "\n",
        "index_tok=np.where(abs(I)==max( abs(I))) # indeksi, kjer je izpolnjen pogoj\n",
        "res_freq=freq[index_tok]\n",
        "print('Iz grafa določena resonančna frekvenca = ',res_freq)\n",
        "print()\n",
        "\n",
        "## Poiščemo indekse, kjer je I>I/sqrt(2)\n",
        "index_tok=np.where(abs(I)>( abs(max(I))/np.sqrt(2))) # indeksi, kjer je izpolnjen pogoj\n",
        "#print(index_tok) # prvi indeks je kjer je spodnja bočna, zadnji, kjer je zgornja bočna frekvenca\n",
        "f_spodnja=freq[index_tok[0][0]]\n",
        "f_zgornja=freq[max(index_tok[0])]\n",
        "f_pasovna=f_zgornja-f_spodnja\n",
        "print('Spodnja bočna frekvenca = ',f_spodnja)\n",
        "print('Zgornja bočna frekvenca = ',f_zgornja)\n",
        "print('Pasovna širina = ',f_pasovna)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resonančna frekvenca =  129.94946687227934\n",
            "Iz grafa določena resonančna frekvenca =  [130]\n",
            "\n",
            "Spodnja bočna frekvenca =  107\n",
            "Zgornja bočna frekvenca =  159\n",
            "Pasovna širina =  52\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajyf6EXZtWNn",
        "colab_type": "text"
      },
      "source": [
        "Spodaj je izris spodnje in zgornje bočne frekvence, ki sta izračunani v zgornji celici. \n",
        "\n",
        "**Opravi naslednje analize:** Spreminjaj vrednost upornosti in opazuj spremembe pasovne širine. Ali sprememba kapacitivnosti ali induktivnosti vpliva na spremembo pasovne širine? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          0
        ],
        "id": "XD-JBAGbtWNo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "bf347c38-6571-4611-9aae-75944ff95a04"
      },
      "source": [
        "def f():\n",
        "    fig, ax2 = plt.subplots()\n",
        "    ax2.set_ylim(0,U/R)\n",
        "    color = 'tab:blue'\n",
        "    ax2.set_ylabel('Tok / A', color=color)  \n",
        "    ax2.plot(freq, abs(I), color=color)\n",
        "    #ax2.plot([min(freq),max(freq)], [max(I)/np.sqrt(2),max(I)/np.sqrt(2)], color=color,Linestyle=':') # naredi linijo pri faza=0\n",
        "    #ax2.plot([f_spodnja,f_spodnja], [0,U/R], color=color,Linestyle=':') # naredi linijo pri f_spodnja\n",
        "    #ax2.plot([f_zgornja,f_zgornja], [0,U/R], color=color,Linestyle=':') # naredi linijo pri f_zgornja\n",
        "    ax2.axhline(max(I)/np.sqrt(2),Linestyle=':') # horizontalna linija\n",
        "    ax2.axvline(f_zgornja,Linestyle=':') # krajši zapis za vertikalno linijo\n",
        "    ax2.axvline(f_spodnja,Linestyle=':')\n",
        "    ax2.axvspan(f_spodnja, f_zgornja, facecolor='g', alpha=0.1) # zelena površina\n",
        "    \n",
        "    ax2.tick_params(axis='y', labelcolor=color)\n",
        "    plt.show()\n",
        "f()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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UQgjnMeISQU55Y8/Ik/bOLlas2tYzW7W5rZMVq7axNs2y9XFdSzsrVm3r2RWrqrGNFau2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\nBEII4eL+HyDNkGB2G0iIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xP86tLuVtWNr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Dodatni prikazi in izračuni"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkAyEqdatWNt",
        "colab_type": "text"
      },
      "source": [
        "### Zdaj pa ti\n",
        "\n",
        "V tem zvezku smo pokazali možno obravnavo zaporednega RLC vezja in resonančnega pojava. V bistvu ima vsako vezje, ki vsebuje več kot en reaktivni element (element, ki shranjuje energijo v magnetnem ali električnem polju) možno resonančno stanje. Druga najbolj pogosta analiza je analiza vezja vporedno vezanih RLC elementov. Tam je seveda skupna enaka napetost, različni pa so tokovi v vezju. Privošči si analizo takega ali kakšnega podobnega vezja. Najlažje tako, da ta zvezek skopiraš, ga preimenuješ in preoblikuješ po svojih potrebah.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WE1lfMsltWNu",
        "colab_type": "text"
      },
      "source": [
        "## Zaključek\n",
        "\n",
        "V zvezku smo spoznali način obravnave resonančnega pojava s kompleksnim računom. Pokazali smo, da je za resonanco značilno izrazito povečanje toka ali napetosti na zunanjih sponkah vezja, kar pa se odraža tudi na tokovih ali napetostih v vezju, ki lahko celo večkratno presežejo vrednost napajalne napetosti ali toka. To je lahko v določenih aplikacijah izkoriščeno v prid, v drugih pa je morda lahko velik problem in povzroči uničenje elementov oz. naprave.\n",
        "\n",
        "Za karakterizacijo resonančne krivulje uporabimo pojem spodnje in zgornje bočne frekvence, ki je določena kot frekvenca, pri kateri se tok ali napetost (odvisno kaj \"resonira\") pri resonanci zmanjša za $\\sqrt 2$. Razliko med bočnima frekvencama imenujemo pasovna širina, obratna vrednost od normirane pasovna širine pa je kvaliteta.\n",
        "\n",
        "**Naslednje branje:** O frekvečnih lastnostih realnih elementov."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiEOmbBrtWNu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cecgf-ZqtWNw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUnWuCIytWNy",
        "colab_type": "text"
      },
      "source": [
        "## Rešitve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          0,
          12
        ],
        "id": "jKy09tR_tWNz",
        "colab_type": "code",
        "colab": {},
        "outputId": "367db2ac-8c39-4a39-b5ba-123f87ae473e"
      },
      "source": [
        "## Izris faznega kota in toka v odvisnosti od frekvence - izziv 2.3.1\n",
        "L=150e-3\n",
        "R=20\n",
        "C=1e-5\n",
        "U=50\n",
        "freq=np.arange(1,500,1)\n",
        "omega=2*np.pi*freq\n",
        "\n",
        "ZRLC=R+1j*omega*L+1/(1j*omega*C)\n",
        "I=U/ZRLC\n",
        "faza= np.angle(I)\n",
        "\n",
        "def f():\n",
        "    fig, ax1 = plt.subplots()\n",
        "\n",
        "    color = 'tab:red'\n",
        "    ax1.set_xlabel('Frekvenca (Hz)')\n",
        "    ax1.set_ylabel('fazni kot / stopinje', color=color)\n",
        "    ax1.plot(freq, faza, color=color)\n",
        "    ax1.plot([min(freq),max(freq)], [0,0], color=color,Linestyle=':') # naredi linijo pri faza=0\n",
        "    ax1.tick_params(axis='y', labelcolor=color)\n",
        "    ax1.grid(axis='x')\n",
        "\n",
        "    ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "    ax2.set_ylim(0,U/R)\n",
        "    color = 'tab:blue'\n",
        "    ax2.set_ylabel('Tok / A', color=color)  \n",
        "    ax2.plot(freq, abs(I), color=color)\n",
        "    ax2.tick_params(axis='y', labelcolor=color)\n",
        "    plt.show()\n",
        "f()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C0NDYauh5F0CKzYzTZpaWlxAi4SR5CUNsM2diSkujsT1kqJt8t7x0u0yLIoRIOElewhDFZMIxfz5NUZOhAcrdcp1S31AIkXiSvIRhylFz6TRZyUoxPjwwN80utw2FEAknyUsYFpg5F4CMQKvhY/LSZFoUIUTiSfIShnVOmwFAWkOt4WPy0mw0tHcRiUqtZCFE4kjyEoa15hYBkFq9x/AxuU4bUT02gaUQQiSKJC9hWGMw1npy7t5h+Ji89O76hpK8hBCJI8lLGNYQT0Ap2zcbPqZ7oLJMjSKESKRBu41pblUB/guYqfq0GzS3WgoUqT7tvaRHJ8aU+vYgCjqpe3YR7ezElJIy6DHdJaL2S8tLCJFARlpedwInAt3Tl7QCdyQtIjFmNbR3kWlRMKMT3L3b0DF5UpxXCJEERpLX8apP+w4QAFB9WiNgfJSqmDAa2oPkxIvyBnftMnRMZooVs0mRgcpCiIQykrxCmls1AzqA5lbzgWhSoxJjUn1bkNzM2KSSQb/f0DEmk0KO0yYDlYUQCWUked0OPAUUaG71/4A3gRuTGpUYk+rbg+Smp2ApLqbLYMsLZKCyECLxBu2wofq0hzW3ugH4PKAAX1R9mpb0yMSY09AeJGeGDfsMF8FdfsPH5aVJfUMhRGL12/LS3GpG/D0HqAUeBR4BajS3mh2/lSgmiUhUp7EjSK7Ths01g+CuXei6saoZuU6bVJYXQiTUQLcNH4m/bwDWH/L+AbBPc6ty+3CSaOoIouuQ47RhmzGDaFsbkfp6Q8fmSXFeIUSC9XvbUPVp58bfZ/S1Pd7y+gT4WXJCE2NJ9wDlHKcN67SpAAQrKrDk5Q16bG6anY5ghI5gmFSb8Yr0QgjRH0PfJJpbLQGm995f9WlrATVJcYkxpru8U16aHVtaLHmFKvfC4sWDHpsbH6hc3xYkNUeSlxBi+IxU2FgFfAXYAkTiq3Vg7XAvrrnVs4DbADNwt+rTvIdsvxy4GdgbX/Un1afdPdzriqE7qOWVVQJAaG+loWPz4wOV97d1MS0nNTkBCiEmFSN/Bn8RmKv6tIQ+cY/fdrwDOAOoBN7X3Oqzqk/bcsiuf1d92rWJvLYYuu6WV67ThinFgTkvj2ClseTV3fKqk/qGQogEMTLOaydgTcK1jwO2qz5tp+rTgsBjwAVJuI5IgO7yTtnOWCKylZTEbhsaUJjhAKBGkpcQIkGMtLw6gI2aW30V6Pn2UX3adcO8dglQ0Wu5Eji+j/0u1NzqKcBW4H9Un1bRxz4iyRrag2Q4LFjNsb93rFOn0rlpk6Fj89LsWEwK1U2dyQxRCDGJGGl5PQv8FlhHrJt892u4lD7WHTpw6DnApfq0BcArwN/6PZmiXKUoynpFUdaHw+EEhCd6q28Pkht/dgVgLSkhVF2NbuC/tdmkUJjhoLo5kMwQhRCTiJEKG/0mjGGqBKb1Wp4KVB1y7d4Die4CVvV3Ml3XVwOrAZxOp8w5n2ANbUFynAfqMVunlkAkQmhfDbapJYMeX5zpoLpZWl5CiMToN3lpbvVx1addornVjzm8RUS8NTQc7wNzNLc6g1hvwq8CXzskhmLVp1XHF88HpCzVKGloD1Kae6CnoG1qd3f5SmPJKyuFjyqbkhafEGJyGajl9b34+7nJuLDq08KaW70WeIlYV/l7VZ+2WXOrNwDrVZ/2LHCd5lbPB8JAA3B5MmIRg6tvD7K4NKtn2VoS7y5fVdXfIQeZkungpc0BdF1HUfq6YyyEEMYNVGGjOv6+W3OrRcR6B+rA+6pP25eIi6s+7QXghUPW/W+vzz8FfpqIa4kjF+2ua5h24LahpbAQgHBtjaFzFGU6CIajNBzy7EwIIY6EkUHKVwL/C7xGrJPFHzW3eoPq0+5NdnBibGjuDBGJ6uQ4DyQdk8OBOTubULWxv2OKM1MAqG4OSPISYpxylZXfS+xuXK3f6zm6j+3LgWeA7jmTnvR7PTckIxYjXeV/BCzu7jyhudVcYj0PJXlNEnXxMV756QcnHUtREeF9xpLXlKzYWK+qpk6OLslMbIBCiJFyP/An4IEB9nnD7/Uk5XFTb0a6ylcCrb2WWzl4fJaY4GpbYsmr4JDkZS0sJFRj/LYhwL4W6S4vxHjl93rWEut/MOqMtLz2Au9qbvUZYs+8LgDe09zq9QCqT7s1ifGJMaCuLZZwDm95FdK5caOhc+Q57VjNClVNkryEGKMsiqKs77W8Oj4EaahOdJWVbyI29OmHfq9nc2LCO5iR5LUj/ur2TPw9PfHhiLGo35ZXURGRpiaigQAmh2PAc5hMCkUy1kuIsSys6/rSYZ7jA2C63+tpc5WVnwM8DcwZfmiHMzJI+TcAmltNB3TVp7UlIxAxdtW1dpFiNZNmP/jXxVJUBEC4pgbb9OmDnqc4I0WqbAgxgfm9npZen19wlZXf6Sorz/N7PfsTfa1Bn3lpbvVoza1+SGziyc2aW92gudX5iQ5EjF21rV3kp9sPG59ljSev0D5jz72Ks6TlJcRE5iorL3KVlSvxz8cRyzHGplwfIiO3DVcD16s+7XUAza0uJ1aq6aRkBCTGnrrWrsNuGUKvsV41xrvL72uuJhrVMZlkoLIQ442rrPxRYDmQ5yorrwR+RXzWEb/X8xfgIuBqV1l5GOgEvur3epJSrs9I8nJ2Jy4A1aet0dyqMxnBiLGptjXAUYWHP+LsaXkZHuvlIBTRqW8PHtb5Qwgx9vm9nksH2f4nYl3pk85I8tqpudVfAg/Gl7/OgQFoYhKoa+3i5Nl5h603paRgzsw03PIqyYoNVK5s7JDkJYQ4jKus3Oz3eiJG9jWSvL4B/AZ4Mr68FqkxOGkEQhFaAmEKMvruTWgpKjL8zGtaTqywb0VjJ4tLsxMWoxBifHOVlZ9CrDD7F4EiI8cYSV6nHzrxpOZWLwb+MeQIxbhTF5/9OL+fkk6WokLDVTam5cRaXhUNHYkJTggxbrnKypcQS1gXAvnAdcAvjB5vJHn9lMMTVV/rxARU2528MvpOXtbCIgKfGBuDmGqzkJdmZ0+9JC8hJitXWflvgK8ANcCjwGeA9/xezz1DOc9A83mdDZwDlGhu9fZemzKITVEiJoHaeDmnvnobQqzlFamvJxoMYrLZ+tynt9KcFCoaJXkJMYldC2wG/gC84Pd6gq6y8iH3SBxonFcVsB4IABt6vZ4FvjDkcMW41D2ouLsq/KGsRcVAbKCyEaU5qeyR24ZCTGZFwM3AJcBOV1n5fUCKq6zcSK3dHgPN57UJ2KS51UdUnxYC0NxqNjBN9WmNRx63GE/2tQSwWUxkp1r73G4pLADiVTamTRv0fKU5qTy7qYpQJIrVPKTfVSHEBOD3ekLAc8BzrrLyVOB8IAfY6yorf9nv9aw0ch4jz7xejs9mbAE2AnWaW/2P6tOuP8LYxTiyrzlAcaaj39mPrQXx5FVba+h803JSieqxqVGm58pwQSEmM7/X0wE8BjzmKivPBr5s9Fgjf/pmqj6tJX7S+1SftgQ4/YgiFePOvuYARf10k4cDVTZCNcaSV2m8u7zcOhRC9Ob3ehqH0mnDSPKyaG61mNj9yeePODIxLlW3dFKc2X/yMqWnozgcxp955caSl196HAohhsFI8roBeAnYrvq09zW3OhPYltywxFgQjerUNHdR1E9nDQBFUbAUFhi+bViY7iDFasa/vz1RYQohxhFXWXlBIs5jZEqUf9BrTJfq03YSG1QmJriGjiDBSHTAlheAtaCQUK2xlpfJpODKc7KzTmbWEWKSeshVVp4GvAa8CKzzez3RoZ5EunuJfu2Ld5MvGiR5WQoKCNfWGT7vzHwnu6TlJcSk5Pd6ziTWb+Id4FLgfVdZ+T9cZeXfcJWVTzF6HiO9DcUkdWCM1yDJq7CQ8CuvoOt6v70Se5uZ5+TFT/YRDEexWeTvJyEmm3gvw+fjL1xl5UcBZwF3u8rKc/xezwmDnWOgChsnAu+oPi0pc7HEr3EWcBtgBu5WfZr3kO124AFgCbEJzb6i+jR/suIRB9sXnzhysJaXtbAAvauLaHMz5qysQc87M99JJKqzp6GD2QVpCYlVCDF++b2ercBW4HZXWbmhKScG+rP3MmCD5lYf09zq5ZpbNVTp1yjNrZqBO4CzgXnApZpbnXfIbt8EGlWfNptYKZFViYxBDKyqOYDVrJDnHPh3yRIf6xUy2GljRl4sYclzLyHEofxeT5eR/fpNXqpP+7bq044Ffg1kA/drbvVtza3eqLnVU+LJZziOI9aDcafq04LEBqpdcMg+FwB/i3/+J/B5za0mdQre3StW0vTkUwDooRC7V6yk+dlnAYh2drJ7xUpaXngBgEhra2z53/8GINzYyO4VK2l9LTZ3Z7iujt0rVtL2xhsAhKqr2b1iJe3r1gEQrKiILb/3HgBdO3exe8VKOj74EIDA1q3sXrGSzo8/ji1rGrtXrCSgaQB0fvxxbHnrVgA6PviQ3StW0rUzNt1a+3vvsXvFSoIVFbHldevYvWIloepqANreeIPsW24lXBd7XtX62uvsXrGScGOsgMquLTsp6GpBb48lmZYXXmD3ipVEO2MtsuZnn2X3ipWYc3Njy08+xe4VBwbHNz7+OLuvuKJnueGRR9jzrauYkRcbnLz5pbVUXH1Nz/b6e+6l8rsHJjDYv/ou9l5/YCx83Z13svdHPz6wfPvtVP30Zz3LtbfcSvUv/7dnuWbVTey74Yae5X033si+G288sHzDDdSsuqlnufqX/0vtLbf2LFf99GfU3X6grOfeH/2YujvvPLB8/fXsX31Xz3Lld6+j/p57e5Yrrr6Ghgce6Fne862raHjkkZ7l3VdcQePjjx9YTvLvXvYtt46p373dK1b2+7vX8u9/s3vFSiKtrbHlfn739FAIgCaDv3s9yw88MKF/9yaDQR84qD7Np/q0P6g+7SzgNOBN4GLg3WFeuwSo6LVcGV/X5z6qTwsDzUBuXydTFOUqRVHWK4qyPhyWusGJsDcAReHBO1Z0t7wirS2GzpuZYiUvzcau4HD//hFCjFeusvJFfaw72/AJdF0fldeWue6Lt8x1391recWWue4/HrLP5i1z3VN7Le/YMtedO9i5U1NT9SPx+uuvH9Fx49lAP/OS376s/+SfmwY9R6SrS98y163X3Xmn4eteuvpt/fw/vmF4/0SajP/Ouj45f275mY0D2vURzAHTf/L8B9N/8vy8XssXT//J8+8bPX40extWAr0ruU4lVsm+r30qNbdqATKBhpEJb3ILhCLsb+tianb/A5S7mWw2zNnZhAxW2QBwF2Xw6Ht7iEZ1TKak3gkWQoxNlwCPu8rKvwqcTKyPw5lGDx7NfsrvA3M0tzpDc6s24KvEplvp7VliHUcALgJeS2bvR3FAZWPs2cLU7FRD+1sKCggbrG8I4C5KpzMUkRqHQkxSfq9nO7GZlJ8mNt7rDL/XY3jGkkGTl+ZWD+vh19e6oYo/w7qWWOkpDXhc9WmbNbd6Q7yKPcA9QK7mVrcD1wNlw72uMKZ7wkgjLS9gSCWiAOYWpQPg22fsOZkQYmJwlZV/6Cor/8BVVv4B8AiQRax/w5vxdYYYuW14BvCTQ9ad3ce6IVN92gvAC4es+99enwPEOoeIEXYkLa/unmhGHFWYjqKAb18rZx1dfEQxCiHGpYsScZKBBilfDVwDzNTc6ke9NqUDbyXi4mLsqmzswGY2UZBuaLwg1oJCIvvr0UMhFGvfE1f2lmIz48p18um+1uGGKoQYR/xez47uz66y8qOJPe8CeMPv9Ww2ep6Bbhs+ApxH7LnTeb1eS1Sf9vUhRyzGlcrGTkqyUwx3prAUFoKuE66vN3yNuYXpkryEmKRcZeXXAo8DpfHX466y8msGPuqAflteqk9rJjau6lLNrS4EPhvf9AbS42/C21PfYfh5F4ClIB+AcE0N1iJjxVjcxem8tGUfncEIKTYZ8yXEJHMVcJzf62kDcJWV3wisA+4c8Kg4Ix02rgMeBgrir4c0t/rdIw5XjHm6ruPf395TCcMIa8+MykPpLp+OrsO2Wml9CTEJKUCo13Iovs4QIx02rgSOV31aO/T0NHwb+OMQghTjSH17kNauMK5c48nLEk9eQ5kaxV2UAYCvupUFUwcv6CuEGP9cZeUWv9f6MWaqAAAgAElEQVQTBh4E3nGVlT8R3/QlDpQDHJSRcV4KEOm1HGEI2VGMP92zHA+l5WXOzgarlfAQWl6lOamk2y18vLd5yDEKIcat9wD8Xs9NxG4ddgCdwLf9Xs/vjZ7ESMvrPuBdza0+FV/+IjC5KkBOMv762Bgv1xCSl2IyYcnPI2xwRmWIzaq8YFomGyuahhyjEGLc6mn8+L2e94kVrBiyQZOX6tNu1dzqGmLdGRXgCtWnfXgkFxPjg39/O2aTMqQOGwDW/ALD06J0Wzg1i9VrdxIIRXBYpdOGEJNAvqus/Pr+Nvq9nlv729bboMlLc6vfVH3aPcAHvdZ5VZ8m1S4mqF317UzNTsFqHlr1MEthIV3btw/pmEXTsghHdTZXtbBkevaQjhVCjEtmII1hPn4yctvwIs2tBlSf9jCA5lbvBIyNXBXjkn9/+5A6a3SzFBbS/tbQxq8vmhbrqLGxokmSlxCTQ7Xf67lh8N0GZiR5fRl4VnOrUWJloRpUn2Z4IJkYX7q7yX/GlTPkYy0F+UTb24m0tWNOM5b8CjIcTMl0yHMvISaPhHT46/e+kOZWczS3mgOkEOsu/2OgBbghvl5MQNXNAdqDEWYXpA35WGtPd/khPvealsUmSV5CTBafT8RJBmp5bQB0Ylmy+90Tf+nAzEQEIMaWbbVtAMw5guRlKYxV1gjvq8Y+c4bh4xZNy+Jfn+yjvq2L3DS5Iy3EWOUqK78XOBeo9Xs9R/exXQFuA84h1gX+cr/Xc1CleL/Xk5AKTQOVhzL+7SMmjG01sWoXcwrTh3ysdUqsOnyounpIx3U/9/pwTxOnzysc8nWFECPmfuBPwAP9bD8bmBN/HQ/8Of6ecKM5GaUYg7bXtpHrtJHjtA35WGthISgKoaqhJa+F07KwWUy8vdN4UV8hxMjzez1rGbi27QXAA36vR/d7Pe8AWa6y8qTMeSTJSxxkW23bET3vAlBsNiz5+YSqqoZ0nMNqZklpNm/vkOQlxCiyKIqyvtfrqiM4RwlQ0Wu5Mr4u4SR5iR66rrOtppU5hUeWvACsU6YM+bYhwImzctH2tdDUETziawshhiWs6/rSXq/VR3COvnoS6sMNrC8DTUbpVn2aT3Orx/a1XfVphqdrFuNDXWsXLYEwcwqG/ryrm3VKMZ2fGJ5PrseJs3K59WV4Z2cDZx1tbEoVIcSYUwlM67U8FRjarRiDBupteD2xoom39LFNB05LRkBi9GjxiSGPOoLOGt2sU6bQ+vIr6NEoisl4w37h1CxSrGbe2VkvyUuI8etZ4FpXWfljxDpqNPu9nqHfijFgoN6GV8XfP5eMC4uxZ0tVCwDzijOO+ByW4mL0UIjw/v1YCwoMH2ezmFjqkudeQoxlrrLyR4HlQJ6rrLwS+BVgBfB7PX8BXiDWTX47sa7yVyQrFiMVNtDc6kmAq/f+qk/rr6ukGKe2VLdQkpVCZqr1iM9hnTIFgHB19ZCSF8RuHd704qfUtgYoSHcccQxCiOTwez2XDrJdB74zErEYmUn5QeD3xKrKfyb+WprkuMQo2FLVzLwpR97qArBOiXUsGmqPQ4BTj8oHYM2nxie0FEJMTkZaXkuBeapPS0qPETE2dATD7NzfzrkLpgzrPNaS2PGhvXuHfOy84gyKMx28qtVwydJpgx8ghJi0jCSvT4AiIGEP3eK1Ef9O7FakH7hE9WmNfewXAT6OL+5Rfdr5iYpBHOzTfa3oOsNueZnT0jBnZxPcUzH4zodQFIXT3AU89eFemd9LCDEgI8krD9iiudX3gK7ulcNMJGXAq6pP82putSy+/JM+9utUfdqiYVxHGLQ5AZ01utlKSwnu2XNEx56uFvLwu3t4Z2c9y+cO7ZmZEGLyMJK8fp2E615ArMcKwN+ANfSdvMQI2VTRRI7TNuTZk/tiLS2lc8OGIzr2xFm5pFjNvKrVSvISQvTLyECcU4Cdqk/7T/cLmDvM6xaqPq0aIP7e37eUQ3Or6zW3+o7mVr840AkVRbmqu6xJOBweZniTz6bKJhZNy0JRhj/Vjq20lFB1NdHg0KtlOKxmls3O41WtBl2Xx6xCiL4ZaXl9F7hUc6vfUX3a6/F13wYGLB2iudVXiD0rO9TPhxBfqerTqjS3OhN4TXOrH6s+bUdfO8ZLmawGcDqd8q03BG1dYbbVtuE5ZnidNbrZppeCrhOq3DukqVG6nX10Ea9oNXywp5El02XqOCHE4Ywkr73EbvP9Q3Or/1R92s0YmAlT9Wmn97dNc6s1mlstVn1ateZWi4E+Zy9UfVpV/H2n5lbXAIuBPpOXOHIfVTah67BwWmZCzmedFuspGNyz+4iS15nzC7E/ZeKZjVWSvIQQfTJUv0f1aXuAU4F5mlv9B7HZlYfjWeCy+OfLgGcO3UFzq9maW7XHP+cBy4Atw7yu6MOmimbgwLxaw2WbPh2A0BH0OARId1g5XS2k/KNqwpFoQmISQkwsRpLXegDVpwVUn3YFsc4VQ5/s6WBe4AzNrW4Dzogvo7nVpZpbvTu+jwqs19zqJuB1wKv6NEleSbBhdyMz8pxkpQ73nzXGnJ2Nyek84h6HAOcvmkJ9e5C3pFyUEKIPg942VH3atw5ZvgO4YzgXVX1aPfD5PtavB66Mf14HHDOc64jBRXWd9/0NnDU/ccVwFUXB5nIR3LXriM+xfG4+6Q4Lz2zc21N5Qwghug2avDS3Ogf4HTAP6C44p6s+bVYyAxMjo7I1SnNniONnJvbZkn32LNrffe/Ij7eYOXdBMU9/WMWvzguRmXLk9RaFEBOPkduG9wF/BsLA54AHgIeSGZQYOZ82xp4pHTcjscnLNms24X37iLS1HfE5vnbcdDpDEZ7+cOilpoQQE5uR5JWi+rRXAUX1abtVn/ZrZC6vCePThgglWSlMzU5N6Hnts2cDENy+/YjPcczUTBZMzeThd3fLmC8hxEGMJK+A5lZNwDbNrV6rudUv0f+gYjGO6LrOp42RhN8yhNhtQ4CuHcMb2fBfx5eytaaNDbsPK30phJjEjCSv7wOpwHXAEuDrHOjmLsax7bVttAbhhBm5CT+3taQExeGga9uRt7wAzls4hXS7hfvX+RMTmBBiQui3w4bmVh9UfdoK4CTVp70PtJHEWTHFyHtnZ6wbejJaXorZjG3mjGG3vFJtFr52fCl3vbGTPfUdlOYm9vamEGJ8GqjltURzq9OBb8QHDOf0fo1UgCJ5/rO1jvwUhdKc5CQE++zZdG3dOuzzfOPkGZhNCne/uTMBUQkhJoKBktdfgBcBN7DhkNf65IcmkikQivDW9noW5JsTUoy3Lw51HuGaGsL1wxtoXJjh4EuLS3h8fQX1bV2DHyCEmPD6TV6qT7td9WkqcK/q02aqPm1Gr9fMEYxRJMG7uxroDEVYmJ+8CR8d8+cBENi8edjnuuqUWXSFo9z1xpEPfBZCTByDdthQfdrVIxGIGFmv+2qxW0y4c5KYvOYlLnnNLkjj/IVTuH/dLmpbAsM+nxBifDNUmFdMPGs+reWkWbnYzMm5ZQhgTkvD5nLRmYDkBXD9GUcRjujc/tq2hJxPCDF+SfKahHbWteGv7+A0d/KH6znmzyfwSWKS1/RcJ189bhqPvVeBf397Qs4phBifJHlNQi9u3gfA50YieR19NOF9+wjX1SXkfNedNge7xcRvn5cJBoSYzCR5TULPb6pmcWlWwktC9SV18SIAOj74MCHnK8hw8L3T5/Cqr5ZXtZqEnFMIMf5I8ppkdta1saW6Bc8xxSNyPce8eSgOBx3rEze64oplM5hdkMZvnttCIBRJ2HmFEOOHJK9JpvyjagA8C0YmeSk2GymLFtGxIXHJy2o2ccMF89nT0MFNL36asPMKIcYPSV6TzPMfVfMZVzbFmSkjds3UpUvp0nxEWlsTds6TZuWx8sTp3PvWLt6W2ZaFmHQkeU0ivn0tfFrTOmK3DLulLl0Cup7QW4cAZWe7ceWm8qN/bqKtK5zQcwshxjZJXpPIY+9VYDObOH9RyYheN2XxYhSHg/Y330roeVNtFm65ZCFVTZ3879OfyJxfQkwikrwmiUAowlMf7uXM+YXkOG0jem2T3Y7zhBNoW7s24QlmyfQcvvf5o3jyw7089M7uhJ5bCDF2SfKaJF7avI/mzhBf/UzpqFw/7dRTCFVUEPT7E37u7542m8/NzeeG57fIpJVCTBKSvCaJv79fwdTsFE6alfiJJ41wfvYUANr+85+En9tkUvh/X1lMcWYK1zy8gaqmzoRfQwgxtkjymgQ+3dfKuh31XHpcKSZT8moZDsQ2tQT73Lm0vvTvpJw/M9XK6pVL6OiKcNm979HUEUzKdYQQY0O/Myknk+ZWLwZ+DajAcapP67MbmuZWzwJuA8zA3apP845YkBPIPW/uxGE18bXjRueWYbeMc86h7g9/ILR3L9aSxHcacRdl8NeVS7j83vf51gPrefCbx+OwJq9qvhCTjaus/KDvZL/X4z1k++XAzcDe+Ko/+b2eu5MRy2i1vD4Bvgys7W8Hza2agTuAs4F5wKWaW503MuFNHHWtXTz9YRUXLZlK9gh31DhUxjlnA9Dy4otJu8ZJs/K45ZKFvO9v5KoHN0gFDiESxFVWfth3squsvK/v5L/7vZ5F8VdSEheMUvJSfZqm+rTBSiMcB2xXfdpO1acFgceAC5If3cTy4Nt+QtEo3zx59OcPtU2bhmPBApqfeTap3drPWziFVRcewxvb6vjWA+vpDEoCEyIBjgO2+72enX6vZ9S/k8fyM68SoKLXcmV8XZ8URblKUZT1iqKsD4dlwCpASyDE397ezelqITPynKMdDgBZF15I19atdG7cmNTrfOUzpdx80ULe3L6fb9z/Pi2BUFKvJ8QEYOn+Do2/rjpku9Hv5AtdZeUfucrK/+kqK5+WtGCTdWLNrb4CFPWx6eeqT3vGwCn66lnQ75/ruq6vBlYDOJ1OGa0K3Pemn+bOEN/7/JzRDqVHhsdD7U030fTY30ldvDip17poyVSsZoUfPL6Ji//8Nvdd8RmmZI1cWSwhxpmwrutLB9hu5Dv5OeBRv9fT5Sor/zbwN+C0RAXYW9KSl+rTTh/mKSqB3ll7KlA1zHNOGs0dIe5+cydfmF/I0SWZox1OD3Oak4zzz6P5iScp+OEPsOTnJ/V6FywqIS/Nzrcf3MCX71zHXSsH+n9TCDGAQb+T/V5P70KjdwGrkhXMWL5t+D4wR3OrMzS3agO+Cjw7yjGNG3e9sZPWQJjvn37UaIdymNzLLkMPh2l44IERud6y2Xn84+oTMSlw4Z/XsaYiJKWkhBi694E5rrLyGa6y8j6/k11l5b0Lp54PaMkKZlSSl+ZWv6S51UrgRKBcc6svxddP0dzqCwCqTwsD1wIvEfsP8Ljq0xIzn/wEt7epk7ve2Ml5C6egFmeMdjiHsblcZJz1BRofeZRIc/OIXNNdlMHz132W42fmcP/mID/650fSkUOIIfB7PYd9J/u9ns2usvIbXGXl58d3u85VVr7ZVVa+CbgOuDxZ8SgT8S9Qp9Opt7e3D/m4NWvWsHz58sQHNMK+++iH/HvzPl774XJKBnnGM1o/c+DTrez64hfJuewyCst+MmLXjUR1/ueel3l2R4iZeU5uuWQhi0uzR+z6o2mi/H4PhfzMximK0qHr+tjo2WXAWL5tKI7Aen8Dz22q4r9PmTlo4hpNjrlHkXXRhTQ89BBdu3aN2HXNJoUvz7Hx8JXHEwhFuPDP67j5JR/BcHTEYhBCDJ8krwmkKxzhp09+zJRMB99ePmu0wxlU/ve+h8lup3bVTSN+7WWz83jxf07hwmOncsfrOzjrtrW8sa1uxOMQQhwZSV4TyB2v72BbbRv/9+VjSLWNSuWvIbHk5ZF3zdW0rVlD8/PlI379DIeVmy9eyP1XfIZoVGfFPe9JYV8hxglJXhOEVt3Cna9v58uLS/jc3ILRDsewnMsuI2XhQvb95jeEqkZnJMTyuQW8+P1T+OGZR/Gar5bTblmD918+Ke4rxBgmyWsCCIaj/PifH5GZYuWX546v8o+KxcKUm29Cj0So+vFP0EOjUwnDYTVz7WlzeOX6Uzn76GL+unYHn131Ore/uo22LqnYIsRYI8lrArjpRR8f723m/750zKgX3z0SttJSin/9KzrWr2ffDb8d1TFYU7NT+cNXFvHi907hxFm53PryVpZ5X+P3L31KXWvXqMUlhDjY2H8wIgb0ypYa7n5zF5edOJ2zju6rGtf4kHn++XRt30H96tXYppeSe+WVoxrP3KJ0Vq9cysaKJv68Zjt3rNnO6jd2cuGxJVz52ZnMyk8b1fiEmOwkeY1jlY0d/OAfmzi6JIOfedTRDmfY8r//PUKVFdT+/hYwW8i94vLRDolF07L464ql7Kxr4+43d/HPDZU8+l4FJ83K5WvHl3LmvCJsFrmBIcRIk+Q1TrV1hbnyb+uJRnX+dOmx2C3jf9JFxWRiyqpV6FGd2lWriHZ2kHf11SjK6Mz+3NvM/DRu/NIxXH/GUTz23h4efa+Cax/5kFynjYuWTOXLx05lblH6aIcpxKQhyWscikR1vvfoh2yrbeO+yz+Da4xMd5IIitVKye9vptpuZ//tfyTo91P8299isttHOzQA8tLsXHvaHK5ZPps3tu/n0Xf3cPebu/jr2p3MLUzn/EVTOH/hFKblpI52qEJMaJK8xhld1/n/yrfwqq+W314wn1OOSm5V9tGgWCwUe3+HbYaLuv93G12+T5ny+5txHDV2igybTAqnHpXPqUfls7+ti399XM0zG6u4+aVPufmlT1k0LYsvzC/ijHkFzMpPGxOtRyEmEkle48wfX9vOfW/5uWKZixUnukY7nKRRFIW8b38bx7x5VP3s5/gvupj8//kfclZ8HcUytn5t89LsrDgx9u9R2djBc5uqKf+4ilUv+lj1oo/puamcrhZyulrIUlc2VrM8IxNiuMbWt4AY0L1v7uLWl7fy5WNL+KVnfI3nOlJpp5zCzGeepvoXv6R21Sqan3ySwl/8Aufxx412aH2amp3K1ctncfXyWVQ3d/KqVssrWg0Pvr2be97cRZrdwvEzcjhpdh7LZucytzBdWmVCHAFJXuPEfW/t4obnt3DW/CJuunABJtPk+cKz5OYy9c47aHv1VWpu/B17LrsM58knk3fNNaQem9zZmIejODOFr58wna+fMJ32rjBvbKtj7bb9rNu+n1d9tQDkpdk4aVYeJ87KZcn0bGbnp02qf1shjpQkrzFO13X++Np2bn15K2fOK+S2SxdhmYS3nRRFIf3003GefDINDz5Iw333s/trXyP1hBPIuWwlaaecgmIeuz0unXYLZx1dzFlHx+bqq2zsYN2Oet7avp91O+p5dlOsNFa6w8KxpdksmR57LZyWRZpd/jcV4lDyf8UYpus6N76gcdcbu/jy4hJuumjBpExcvZkcDvK+9S1y/uu/aPz74zTcey+VV1+DZUox2RdfTMZ552ObWjLaYQ5qanYqlyxN5ZKl09B1HX99Bxt2N/LBnkY+2N3IH17Ziq6DSYHZBWkcPSWT+SWZzJ+SwbwpGWQ4rKP9IwgxqiR5jVGBUIQf/fMjnttUxcoTp/Pr8+bL7aReTKmp5F5xOTlf/y9aX3udpr8/Rt1tt1N32+04Fi4g46yzyTjzDKwlYz+RKYrCjDwnM/KcXLRkKgAtgRAb9zSxYXcjn+xt5q0d+3nyw709x7hyU5lfksm84gzmFKRxVGE603JSMcvviJgkJHmNQbUtAb71wHo+2tvMj8+ay9WnzpKH+v1QrFYyvnAmGV84k2BlJa0vvkjLv16kdtUqaletwjZrFs5lJ5F28smkLl2KKXV8jL/KcFg55aj8g4ZC1LV2sbmqmc1VLXyyt5mPKpso/6i6Z7vdYmJ2QRpzCtKYU5jOUYXpHFWYxtRsSWpi4pHkNcZsrGji6oc20NQR4i9fX8IX5o/feoUjzTZ1KrlXXknulVcS3LOH1ldfo/2tt2j6++M0PvAgitWKY9480vLyaAl0kbJ4EdbCwtEO27D8dDvL5xawvNeUN62BENtq29hW08q2mja21rbx7q4Gnt54YHoZq1lhWk4q6QT4T+tmZuQ5mZ7rZEaukylZjkl/K1qMT5K8xghd17nnzV2setFHQbqDf159IvOnZI52WOOWrbSU3CsuJ/eKy4kGAnRs2ED7unV0friR1P/8h72vvgqApbgYh9uNfe5ROObOxT7XjW166Zju/NFbusPKsaXZHFuafdD6lkCI7fGktmt/B7vr2/lkdyd/f7+CjmCkZz+LKZbYpuemUpKVQkl2CiVZKUzNTmFKVgoF6Q5ptYkxSZLXGNDUEeSH/9jEK1otZ84r5OaLFpKZKg/kE8XkcJC2bBlpy5YBsOaVVzi+oIDOjRvp3PQRXVs/pW3tWojEvtQVux3bzJnYpk8/8HLF3s05OePiFm5GH0ltzZo1nHrqqdS1duGv78Bf387u+nb8+2OfN1U00dhx8HxqFpNCcZaDKZmxxDY1K5bUCjMdFKY7KMywk51qk+exYsRJ8hpla7fW8ZMnPmJ/Wxe/Om8el5/kGhdfjuOaxULKggWkLFjQsyra1UVwxw4Cn26ly+eja9dOAtoWWl9+uSepAZjS07FOmYK1qAhLcRHW4ilYi4uwFBXF1hcUoNjG7pxqiqJQkOGgIMPBcTNyDtve3hWmqqmTyqZOqpo62dvYyd74+9s76qlpCRA9ZLo1i0mhIN1OQUYsmRVmOCjMcBy0Li8tluSkFScSRZLXKGkJhLixXOOx9yuYle/kiatPYsHUrNEOa9Iy2e045s3DMe/gyiV6KERo716Cu3fHXv7dhKqrCVVX07lxI5Hm5sPOZc7KwpyXiyU3D0tuDubcPCy5uVjycjHn5mLJy8OSk4MpMwuTM3VM/bHitFuYU5jOnMK+K+SHIlFqWgLUtHRR2xKIfW7toqYlQG1LFzvr2nl7Rz0tgcNnn1YUyEm1kZtmI8dpIzfNTl78PTfNRm735/h7hsMypv7biLFlVJKX5lYvBn4NqMBxqk9b389+fqAViABh1actHakYk+l1Xy0/e+pjaloCfPvUWXz/9Dk4rOPjGctko1it2FwubC5Xn9ujHR2E9tUQ3lcdT2r7CNfvJ7K/nnB9PZ2bNxPZX0+0vb3vC1gsmDMyMGdmYs7MxJQZ/5yR2bPOnJmBKT0DU5oTk9OJOS0Nk9OJKS0NxW4f0S94q9nE1OxUpmYP3GszEIpQ29JFTWsswdW3Balv62J/e+y9oT2IVtXC/rauPhNd7FoKWak2slKsZKVayUyxkZVqJSvFSrbTRmZ8fVZ8ffdyml2S3mQwWi2vT4AvA381sO/nVJ+2P8nxjIiKhg5ueH4LL2+pYXZBGk9cfRKLD3nQLsYXU2oq9pkzsM+cMeB+0UCA8P56Ig31Pe+R5mYizS3x92aiLc1E6vYT3L6DSEsL0dbWwQMwmzGlpWFypmJ2HkhqsXcnplQnJocDU2oKqXv30rhvH4rDgcmRginFgZKSgiklBZMj/tnhQOneNoxOKw6rmdLcVEpzBx+aEAxHaewIsr+tK5bk2mPv+9uCNHcGaeoI0dQRYm9TJ1uqmmnsCNEZivR7PotJ6UlmSqiT+3a+R7rDQrrDSobD0vM53WEhzX7gc0b8Pd1hkR6Y48CoJC/Vp2kAmnv8z/5rRCAUYfXandzx+nZMisJPznLzzZNnyAy8k4jJ4YhV/hhC9Q89HCbS2kqkqYloWxvR9vae90h7O9G29oPWRdvj21pbCFVXx9e1owcC6KEQ6cC+fz5h+PqKzXYgodntmOw2FKst1tqz2VDsNhSbDZMtvmzr3mbF1L2P7cC+JluvY23x/Ww2sFrJtFjJslqZk2JBybCiWBwoVmtsBoH4u2I68P9LIBShpTNEU2contyCNHWGaO4I0dQZpLEj9tlf1UlTR5CKhg5aAmFaAyG6wtFBf/YUq7knkaX3SmpOmwWn3UKqzYzTbsFpM5Nq715vPrAtvp/TbibFapaWYBKM9WdeOvBvza3qwF9Vn7a6vx0VRbkKuArANkYemEejOs9/XM3NL/moaOjEc0wxP/eoTMlKGe3QxDigWCxYsrOxZA+/da6HQqx95RWWLV1KNBAg2tmJ3tlJtDNANNCJHgjEPnd2oPe1riuI3tWFHgwSDXahB0NEOzoOW9e9rHd1JeC/wCHM5p6EduDdQo7VSq7F2ue2hpZWcgsLD9oWtlhps9hoV6y0m2PvbYqVdsVCG5bYu26mTTfTHjDR1mGiUTdRGVXoiCp0RKAjotB/2+9gCpBqNZFqNeG0muLJLZbgUuMJL8UWe6XazDjsVlK711nNpNhMpFgtpNhiiTDVZsZhNfcsT9ZOMElLXppbfQXoa4Ttz1Wf9ozB0yxTfVqV5lYLgJc1t+pTfdravnbUdX01sBrA6XTqfe0zktZt38/v/uXj473NuIvSeeibx3PynLzRDktMUorVip6SgiV/ZCYv1XUdQiGiwWBPMtODQaJd8SQXPJDk9HAYPRRGD4Vin8Mh9FAIwvF1hreF0UPB2LZgiGhHJ6aWFkLd20OhnuMtkSgZkQgZ4TB6NBo7JhKB6OCtMoj9VR0yWei02AhY7HRY7ATMdjotNjotdjotdgIWO53m3ssHPnda7DT12tZlthIw2wibh/6VbI2GcUTDOKIh8vQufnLB9CGfYzxKWvJSfdrpCThHVfy9VnOrTwHHAX0mr7FiS1ULq1708Z+tdUzJdHDLxQv54uKSSfvXkZicFEUBmw3zKN8FWbNmDQuWLze8vx6NQiSCHomghyMQjX3uTm56OAKRMHokGn/vvS6CHg5DNHr4ut7nC0fQIwfW6ZEgRDrRI1HCkQiBcJTOMAQiUQIRnc4IdEYgEIVABDqjEIgqBHSFTl0hoCgEFBMBs4J9EnX8GrO3DTW36gRMqk9rjX8+E7hhlMPq1+aqZm5/dRsvba4hM8XKz8CTObcAAAnZSURBVM5xs/JEl/QiFGIcUUwmMJlQrOO3SMCaNWtGO4QRMVpd5b8E/BHIB8o1t7pR9Wlf0NzqFOBu1aedAxQCT8U7dViAR1Sf9uJoxDuQT/Y2c9ur23h5Sw3pDgvXfX4O31w2QypkCCFEEo1Wb8OngKf6WF8FnBP/vBNYOMKhGfZRZRO3v7qNV7RaMhwWvn/6HK5YNoPMFElaQgiRbGP2tuFYpOs6/9lax+q1O1m3o57MFCs/OOMoLlvmkskBhRBiBEnyMiAYjvLcpiruemMnvn2tFGU4+Nk5bi49rpR0SVpCCDHiJHkNoDUQ4tH39nDvm372tQSYW5jOLRcv5LyFU2SAsRBCjCJJXn3Y1xzgvrd28ci7e2jtCnPSrFy8Fx7DqUfly0h5IcSk5SorPwu4DTADd/u9Hu8h2+3AA8ASoB74it/r8ScjFklevVS2RvnB45t4dtNeIlGdc44p5r9PmcUxU2VSSCHE5OYqKzcDdwBnAJXA+66y8mf9Xs+WXrt9E2j0ez2zXWXlXwVWAV9JRjySvOJaAyFueLuT/7+9e4+xojzjOP79hVWh1aBQbRGoBwI12oSuaLkEaykxBIWABOI1sVYT+kfb2NpLlvRitiRK7EVTmzTWaPCPtiiKkbKJQLmkTSUCcqeILmVrBdK1VWkClBb69I/3Oet0PStHOLtnZ87zSSYz8847c97nMMu7c9nnHTDgMHdMvIx7rh3FyCGnTyoaQggNYgLQ3rF45p8BSi1tS4E5QLbzmkMaMQTgWeDnpZY2dSyeWfOsR4XsvI4dO2aSjp/Brk3AyVagtcZt6seagMpjUhRXI8YMjRl3xFy9QZKyw1P90tPulQ0H/ppZfxOY2O0YXXU6Fs88WWppOwIMBWo+MkghOy8zO6O3KSRtMbNCjBlWrYi5cTRi3BFzbQ9doaz7FVU1dWoiXpkLIYRQjTeBkZn1EcChnuqUWtqagMHA273RmEJeeYUQQqi5zcDYUkvbKOAgcCtwe7c6K4AvAhuB+cC63njeBXHl1V2P44UVWMTcOBox7oi5RjoWzzwJfBVYBewFnulYPHNPqaXth6WWttle7QlgaKmlrR24D2jpjbYAyKzuQ1+FEEIIH0pceYUQQsid6LxCCCHkTnReTtIMSfsktUvqtfu0fU3Sk5I6Je3OlA2RtEbS6z6/yMsl6Wf+HeyUNL5+LT9zkkZKWi9pr6Q9ku718sLGLWmgpE2SdnjMrV4+StLLHvPTks718vN8vd23l+rZ/rMhaYCkbZJW+nqhY5bUIWmXpO3lv8sq8rndk+i8SCc/Ke3JDcCVwG2Srqxvq2pmCTCjW1kLsNbMxgJree+h6g3AWJ8WAL/oozbW2kngm2Z2BTAJ+Ir/exY57hPANDP7DNAMzJA0iZSe52GP+R1S+h58/o6ZjQEe9np5dS/pBYKyRoj5C2bWnPl7riKf25WZWcNPwGRgVWZ9IbCw3u2qYXwlYHdmfR8wzJeHAft8+THgtkr18jwBL5DysTVE3MBHgK2k7Ad/B5q8vOs8J70xNtmXm7ye6t32M4h1BOk/62nAStIfyRY95g7gY93KGuLczk5x5ZVUSnsyvE5t6QsfN7PDAD6/xMsL9z34raGrgJcpeNx++2w70AmsAfYD75pZOVVQNq6umH17OY1P3jwCfAf4r68PpfgxG7Ba0iuSFnhZoc/tSuKPlJM+S2nSzxXqe5B0PvAc8HUz++cHDGdTiLjN7BTQLOlC4HngikrVfJ77mCXNAjrN7BVJU8vFFaoWJmY3xcwOSboEWCPp1Q+oW5SY3yeuvJJq0p4Uyd8kDQPweaeXF+Z7kHQOqeP6lZkt9+LCxw1gZu8CG0jP+y6UVP4lNRtXV8y+vdfS+PSiKcBsSR3AUtKtw0codsyY2SGfd5J+SZlAg5zbWdF5JZuBsf6W0rmktCcr6tym3lRO4YLPX8iU3+lvKE0CjpRvReSJ0iXWE8BeM/tpZlNh45Z0sV9xIWkQcD3pJYb1pDQ98P6Yy9/FfGCd+UORvDCzhWY2wsxKpJ/ZdWZ2BwWOWdJHJV1QXgamA7sp8Lndo3o/dOsvE3Aj8BrpOcF3692eGsb1G+Aw8B/Sb2H3kO7zrwVe9/kQryvSW5f7gV3ANfVu/xnGfC3p1shOYLtPNxY5bmAcsM1j3g38wMtHA5uAdmAZcJ6XD/T1dt8+ut4xnGX8U4GVRY/ZY9vh057y/1VFPrd7miI9VAghhNyJ24YhhBByJzqvEEIIuROdVwghhNyJziuEEELuROcVQgghd6LzCoUl6ZRn3i5PpQ+xb0mZTPz9jaRhmSzqU8vLme1LJM2vvDdI+rGkab3dzhB6S6SHCkV23Myae9ooqcney4GXN/cBj5/F/o/6/utq05wQ+lZceYWGIukuScsk/RZY7WXflrTZxztqrbDPaB8v6rM+DtSnM9s2SLraMx886cfZJmlO5vOWS3rRx1p6KLPvDElblcbgWutlEyS95Md4SdLlPYQyD3ixinivyVx57pJkAGb2F2CopE9U/+2F0H/ElVcoskGeZR3ggJnN9eXJwDgze1vSdNJYRxNI2QhWSLoOeAPAO4+lwJfMbLukpcDNwP2eQ+5SS4lhHyClG7rb0zRtkvQ7/7xmUmb7E8A+SY8C/yJd+VxnZgckDfG6r3rZSUnXAw+QOqoukkaRxqU6kSn+XCZWgE+SMk5s8c9H0o/4/w5vKyk/4HNVf6Mh9BPReYUi6+m24RozKydkne7TNl8/n9SZvQFcTMoRN8/M9vj2Z0jDjdxP6sSWZY4zW9K3fH0gqQOBNEjgEQBJfwIuAy4Cfm9mBwAy7RkMPCVpLCnF1TkV2j8MeKtb2R/MbFZ5RdKS7EZJNwPjvZ1lncClFY4fQr8XnVdoREczywIeNLPHshX85Y4jpLGQppDyyGFmByX9Q9I44Bbgy5njzDOzfd2OM5F0xVV2ivRzJyoPTbEIWG9mc70NGyrUOU7qHKvitzlbSVd0pzKbBvqxQsideOYVGt0q4G4f+wtJw32cJIB/AzeRsnLfntlnKWkAxMFmtitznK95RnskXXWaz90IfN5vAZK5bTgYOOjLd/Ww72uk0bFPS9Jgb++dZtb9au1TpCS+IeROdF6hoZnZauDXwEZJu4BngQsy248Cs4BvlF/C8Dq3km4hli0i3eLb6a/YLzrN574FLACWS9oBPO2bHgIelPRHYEAP+x4F9ksaU0WIN5FuUz5efnEDusY7GwNsqeIYIfQ7kVU+hBySNBe42sy+dxb7jzez79e2ZSH0jXjmFUIOmdnzkoaexSGagJ/Uqj0h9LW48gohhJA78cwrhBBC7kTnFUIIIXei8wohhJA70XmFEELInei8Qggh5M7/ACr2RCv0YY/3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          0
        ],
        "id": "XZpj2ZZwtWN3",
        "colab_type": "code",
        "colab": {},
        "outputId": "2641a79b-3646-4fa7-cb60-69dc3e8a6895"
      },
      "source": [
        "## Izris resonančne krivulje za več upornosti\n",
        "R=[10,20,50]\n",
        "C=1e-5\n",
        "U=50\n",
        "freq=np.arange(1,300,1)\n",
        "omega=2*np.pi*freq\n",
        "\n",
        "Linestyle=['-','--',':']\n",
        "\n",
        "\n",
        "fig, ax2 = plt.subplots()\n",
        "\n",
        "ax2.set_ylim(0,U/min(R))\n",
        "color = 'tab:blue'\n",
        "for i in range(len(R)):\n",
        "    ZRLC=R[i]+1j*omega*L+1/(1j*omega*C)\n",
        "    I=U/ZRLC\n",
        "    labela=r'$R = $'+str(R[i] ) + r'$ \\;\\Omega $'\n",
        "    ax2.plot(freq, abs(I), Linestyle=Linestyle[i],color=color,label=labela)\n",
        "\n",
        "ax2.set_ylabel('Tok / A', color=color)\n",
        "ax2.legend()\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "plt.show() # po potrebi razširi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ja7nPIKItWN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}