{ "cells": [ { "cell_type": "code", "execution_count": 147, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T11:14:45.221979Z", "start_time": "2018-10-25T11:14:45.100490Z" } }, "outputs": [ { "data": { "image/png": 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7HW3Xd/shMzMH6vXvTCssUzj5x4ieYUzO64KT2T9O8fxfU134wZpzaKUEWjmJObCffErF5Rfry5ihEYnYrIxt90ex7f7omnrLVzL07ent+DdvlZUBcIVqWcdkrplm7QShUYnogoh15AGKZ/+y/YpKmXjMx8DeDoTZFvT2mZj//pcpnftLUCskc2MMHF4v6L4T8QbeFmhNEnolixw5tfHBukrp4tfq/VlkWia0TcqItqZGX/jCF9b8/9JLLwGg6zpf+tKX2r6YbffHqOSiKNUFrHaBSqlz1XGn3Nghp2MKvtCOBY7uCKtDoFqxYPaOIsfPrduvJC8RXZpkbFTi8uvbZyjbOyBx+vIHcR4LoJWSpDPnODq82Lpih4RGLaQqh5Djr6/doWsIVi+u458m99J/JDIzz767HVzZxt/ciPCoxMIbZ9CVKpVrTyIf+Rf4zVeAzUXw7AaBQIqFk6n1OzSZVFzkyFEnkG7rXE6fSEnrRysm0AoRYvNujj3ir0kCbejrXT6RQub6cdFpmbs+aoPn2v016+nb3cNcch+wfilnNYlljd4hB0uRzV2np89MckkhOitzx/s7tzUKjkgk0/2o2WsA5KNJHO72+7OBOyaJRvLA+ncpeoZxHPoE1fkXkGNnqc6/uOF5BIsH1/FfIP29x8lEFXxBM+moQmjUQmRWwzb+QXIv/oc1dRZPX+DwvbtrmjFt55Zkw6MWYssOHAc/TPnad9GKMeJzMsEhy44YTIdHLcxfypKZ/zX0SqajupHpKocPdUfzYnOaqFbNKPlkvWz5ap6BCR/XXujKJW4qoTELkekqaDL5V/9ry+OV5GWU5GUwmd/WDnu25b521FK48Pr/QFdlojNVgjukXXF5RQqrhB10KGTUmz5Db4TNYaIiOyhd+SZo69fT9WqexWf+Dz392zcVsjoElIpOafZF5Pg5dLWCOPh+oKYa7yb9BwaIx/2omZl1+1Y0Wa7jP08yImzrb96I8ISPgq8W+VsrJymkqrh6bq6qeeDIbmKRxkZ8cvQsFcWDw9PeZ+3yicj2d1G6/LcAVPM5BF3GGmxvhrl6ImGb+BDs/te172qTzUQQwOG1Uiq27huWzlylx75+QtAuK9pLuaJjMtU8qzqqP2ph7qXnUdNXAahEzoLcnpAJ0LcvRHyD91idf5HMk78KJjPm3n1Nz6PlF5ETF7CNPfT2Upb09v1JpPW7UdLXUHNrPeyqmTR6eRnRvLPG9uExCznXj4HJjK4pWMceJJEb37aZ/I0ERySy4v01T7YOib369zht7S/xNiM0KpGqHMQcPFAvW35rikDfrekl3CnhUQvRWRXP+34TQWrP8NoyfB/Od/1f6Bq1pUZn90WTHRV2lPQUSmqKRHqQ8DbZgNyI0ydSSGuAgGDzA9urKtsKNqeJUjxK+dLXNzzGuuvDVBUXdvf2vLrwqIVEbgS9nKY6+yzlS18n/+ofEpuVCQ5395kFwhUWXmlkIFijuvADlPQM5uARKkUNu2vnmqtoBlGSqJavz9bTl0/icN1E42RBxBcUSC00DgVRXXqN6IKl7aUkp89EZnYaJXGxXhZflujbN9xWfZevNpEQ/RNYxz6A6NtFNmPHF9yc5tTfbyZbHkaOnW15bGRaZuCOSUyO4KauFRp9e/YJxOZkAh227dAuBxn9nvr/eiWDImvtLcmYzPj9OSIXpzc8RK9mKZ7+X7VljhaUL30d6/jDtX5t1IJJBIvdRDlXoXj2LxrWiV3NEJroaeNmu0fvqI9U0kXxzJ+hl1NopSQZ6QM7YlZgtgggiEiTP7Fm6bBddKVEvuCthWnYIuExC6nKYdTU1XpZJRVFy812fUJ5M+jpN5NT96ErFXS5udPSCnLkFFLfUQTJRXSV0N5NdjzruSBKFEKfIrxrZ9yJnT6RYsmB+93/L44DP43z2L8ingjfknY7FrsJ+7t/B8RmdhcaiczQthkM9+0Lk+Z+dOX6LMPsnyQt3N9VuxmzwwZqmdJCczuvwhv/HXnptbc78p0TUANDEslMADV/ffmudPUZTMLNiwDuGR6mWLTX4qU0QM3MsHDyXNvPyek1k7rwyhot4uKZafy25u/ken0TpbIHNTVF5pnPkP/B77N0ObPpjiq8y0kiM9hQ03cj+ZSK01muxe7YBN6ASL4cxPvgfyC6KHU0+RLNINmt6L7ja8qr1oO4gr6W9V0Do1QrInq1Ox6panaO7DO/QTqi4O+30TvmJ5X2U517AS2/1LBOZFFi+P4H63FPthu7y0Q5kyf/2h/Vy+ToGWRxDLvHirDNI1FwWCIRr7XVWlSkTtFJ6/cS7oKtU3DUTio/ilaKrymPLYn0Hdqz5fPfTBweE6W8hjl4B/Ly660rvI1ezSMvn8Q6cj+R6e1xYtpxYUeXi+QvPYXZ6d8RwzSXV0Qb+QRKaorCyT9CK8TIiSfoHbz1NDuCaENXK6BurM5UUldJlg9sm7AWHvew+MapmtHr26i5RTLSQ4THuueNMHTXu0kV2lsuMffsIev40R01Ug6PWYhHvWsGXsvAXSD1YLpJK6B9u33EFptrTQqejxMa97d1PnfQDRP/ck1Z5HKy9pxNrb8Pi13Edu/nagOmUkbNzZNI9dE32XrAb0R4WGD6239Bu4NRJqbQM965Uacn7CCXd6Hll6kuvUYx/AuEx9s3Lu4drHlhqdm1y0OFnIg73How7PUusni2tUDXCXo1h8k1SFXazb6H7yaeaK7xilxO0eu4hBTcmaj3g3fdSXTRgla+bqeCrlK+9n3SSTs9fdtrRxkes5DIjSIn2vHAakxkVqdvcmv2JGaLgIBO9uR6jV084mLwyM3xkusWodHa8rCuVNrS0K6mPPVt1FKC2JxMqMurCHAThB2A8tV/IF0aJzTRPev2jXB6TZRKdkqXvgG6RnXpNczhE8gVDavjFlMZihJKunknqGRmSOdHCQ53f+CXXF4EUaRwda1BpK4UKS7NYnU7uzbQ9+32s3S+8azzRpTUFdKlyQ0HcZMzhNR/HMG6+QBxNxIeszD3/JNrlnh0VaYk+zuOp9Itel1Xmf/BK02PUXJxNMGNxda6bYtWM3J+raFmKa9h9/uRgi1mmAIgWtHykTWCca46in9oc+/BN+gjXw63ffzy5SSh/g6NxQUTow8+QjI7CkDp/F+RvfYWjp6etrUL4VELiezoOluYQlqvCZAt6N/fT+Ra9+OMqbl5Fk9PMdp/ivmXm7eTUk7DYkpi6d+cZqwTTI4Q/fv6iVxd/67Kl77G0oXlbZ/IhEYlFl5+lcr09zd9jsR0DH9oa2NGcFgiOitTXVy/fL94ZpreQBLBuj0GujtBeEwiOi1TOv9/2tLQrkbNzCAvvoqiWdHpvo3oTRF2UKvMvvAcY3vL9I1b6Ntl2TY1pskskHnud+vrtGp2FjUzQ3SOmxrv50Ykq4BcASXVIlaRWiH5vc+gqTqStTuNITAk0TduYeKQzPLZaw09NCqzz5FKurpiKGxy9dHjnWtb2EHXyJ36SwR7kME9jlqbGbcwfNcRho7upX/SydCx/Yz/6C9i7t26GrjmlmyGsZ9dU66V4pTk3psWayc4ESS+2NxmSI6eIVXa27JtCyZAsDRc5khGzQQnBprWt7tMVGQXcnLtTLky8wxlJcCuOwfoG7fgC7c3Y3f3iBS1EfQOcgcsT+XxlL/Z1rFmi0DfuIWRu4/RH7zC3A+uC/Tli18ldjVKT3979xoak5h74Tmqy2sDfGbn57BbWxkpC4Qme4i3F7exY5YvxakUFfKp1kHZEvMFnMprLY/bLHaXqfbM77uHHvtbxK41DpqXER/uyvLQRggmsNpFFMsYejW36fNU5l+mmMq17QDQiPCYhYz0QaTwkXX7ynkFiSTW/mObPv/NJjAkkSrvxr7vn2yqvm3ih3Ac+KltsRG9OcIOsHAhh2qbYGi/h8MPOJk40n0vF9EMusmF1H9iTXn+1T8kcrVwS9nt2JwmSsk01Y0CMK1CCh8hnfHWc1dtBU+vyH0f9zC014F/73EuvtJ4piwvv8HimamuGI4JqJjV5Y4iMiuJC5x+yUL/uMjIkUFG7z7IyLEx+oez9PfH6VW/zoH+v6D/0NZjVTi9IvmcBbNvfE25VoxTLNpw3QTNjuj0g+RFU5o/M60YIzKvEx5v7gXh9IoU8yJK+tq6fYlFpeXA7/TWPLHkxRsGS03mrZfNhPcOMrTHyoOP+RDbaKZ9e0Mk0gNoxWjrg98mHVHwD7nr8ZiacfgBJ3tP2BncYyV6ZZnk0lqhKjoPIw98sK3rOtwiFYbgBq+ezMwUDkuDsACrMDkCmJCRC42NzLdKfF7mpa+1F4k3cjVHr2++hY3g5rn3Rz2MHrQxMFrm4ksJFLnx8mQmWsQb2j4vR0+vSDZtw77/J7d0Hr2SIbks4B/Y/L329JvJ6UdQNtB6xKcTuNSNnTZuZQQTCIKAyX8YfZNh1KuLr2IZuJvInNp1bd9NCzgjV3TeOn8ErTyM69S32HPCzpWT3Y2j4vCKlNQgwg2RVk22HoqhRwmNfLmr19sKVocJxboLk2OhZYdvsvVQFnpw+pZgdmsGs+ExC5dfL3Fl9iiWwRPkYxsHSUlpd3Ns/zxnn28dKbsZfl+a+HTnbpYzz73EDGAdPY7Ud4zi6T9bY+Q3vqzi80SY29LdvR1pVXGuMU6GmkdG4vW/x9+mBqCbuEO9lIrtDUpz3/8auz/R3G7H6TWRmZtDzayf6WYXlwntar7M4vKJ5BaWUFLrvS3mX3qB+RcUQEeUarO91RG5G9E36eFai2NuRNehXLbhnbyDRKy5UXVoVOLpv8hQKTYOZBK5kmXXvTlEzzBqduMWZBJBR8J5+GfJPPlv1+wrln14RoeBkxvWd4WClErbZy+oqbSdDDYyXeXEj+/l6lKoaRDHTSGAyy/y5J+ngebnri68TFG5E0/gGtl49x0AXD6RkhxASa5PPNkpsutevIOnWbhwZVP1HV4L5ZgTvdxYAxiZKtB/cIhEZHlLWqibgcNtophVkXr3Ujz3vzd1Dq2UQM3OkSzuZf/odFfv76ZpdgAqc89hHX6gK2HmG+HyipS1QZTU2oaplVPg3otgljqOr7Fd2JwmVOuuNfYPG6Gkr1JmrCuxgkKjEpGZKtbh91Cda55rqJDStxRHZYWRBz9GLLL5xHeVmafJv/z767wZItfK9O0LIXpHt3R/VodAVfWi5tYHUpQ99+Lq3fnEpK6gm3yuvfet2saQPIGmGhVXwIXsvrfhvlw0g92abLhvBXfYjxr+WOOdmoy5ZzeW4fcQnZbbmqH5PHEWnmucHqIZkWvllp5UNVdsM7Z7fxfMjY3sswkVh7SI1H+84f4VHB6RYtGBmlu/DlXJprHYaOrhZBOXyC4uN73GTpGNq7jsCaTQ4a6f2xsQycQF3O/5DK06DL2aI7ZoIzyxPbYqTp9ISR+qBa7bIrlkCVcHkbJvRDCZKE1tHDAzMl2lb9xVc4Z4h1GLu6WB2draHKMJpUtfp5zOY7GbuuoMclOFHTU1ha6UEJwDVIrdDyTkCjgplt1oxdgNe3SqS6+TzIQ7jq+xXVidIlW9pyaItUDNzlNhBKd/6/fe0y+RjAgIFhfV5Y1npABKepp8ObylWBOC2U4wkGT5Yrz1wR1SyGg43AK2kfds6Tw2h4nC7DnKU99et68iHcDV0z2vtLbvSZsic/VC6wMBvZIlU9ndNC2LO+ihpDZ2ECikVbxjk5jsG2dBd4V85NMb29foShnH/p8gMqu2XC62e23I+NGVzmf1SxfjBIOppksxvQMS6Xw/cuLiuqWn1WSjFbyB5lo7l0+kVPWu88QCQFPQNa1p7B+nB/LRzqL3bifZaBHfQKAt77tOCI9ZSBb3vq0la+1dN/vcdwkNbk9YB6dPJHP1PHK0vZAKzcjHC5u22TNbBJRylfKVje3MckkVhzWGZeDEhsfcqtTibilkn/q1hkFx20WJn0NJT5NYUruqBLmpwg5A9pnfQMstbksgIadLJv7yXzfcJy+/Tjzec8skBbV7nJSLQlth+tFVlr/7n3BucSJkd5uoFDV0RSb7zK+3bKBqboF4up/wrs0P9qJ/HIcUIZfYno4ttVSkZ3KEraifrE4Tiq2xC2g5k8Xm3HltoCvgopBclVpxAAAgAElEQVRu75lppQSJzDDhJi7grl4bhUTjgV+u6EhCAdHdv3H9Hiu5hY01FGp2FrUQQXEexe42ITR5ZP0Hx0jmN+dym5gv41ZfahquITxmISUfozL9ZNNzLV9O4qk0N3h2ek1kl+NUFn/QcH8pp+Lwbfx9+A/cQ1lrP//YdhO5VsJV+ApCl+12wrtsZM0PUr7y920dnykOE9i1PUbKLp9EbjmOLm/dTiozfRm7eXMTNafXRNk0jtgg2euaaywX8PY5u+pduhM4vSIVcTfmwIHWB7c61x2fJJmf7GpstZsu7ICO445/RmQbvKPc/SFK1cYfkBw9w/wLz94yRspWS4nUK+3nGasoPTi8WzPqC41KRKarOI/9fHvujrrKzHe/Qnhk8zYrHr9KJr4547V2iFzJ0OueRnRvfkCxOc0IAx9qKHhqpSQIO2+z4x0dp6S2/5si14pN4yI5PZCLbaxh0FS5qbBjtxbJzjVXVZev/gPm4EGSywr+JnFU+nZ7iFzd3ECkKqCoIo7+jYWl0JiV5UsJ1AbG2KuJTFfp3z+AZeSBDY9x+kTykTRagyVOgOzcLHbTxq5WTjdko+2nldhuIjNVep0X0ZXu2kv6+yzEzp1roFVvjK7IyAS3JTK8O+RBH3m0K+eqppYQhc3lxnP5RMqMoG1gr7NCZLqCM/Lf0CvtGZrfKjh9IlXnCYQurD1V5l8kbf5AV42UbwFhB0w2L1nteNddzdwhP8VyE9uQwR/F5rZte/TOdrD53Chi+4OZFDyMybo125HwqIV4zIe5dw96pT1juAIH8Q9tXqUU8M6wdK7BEkCXiEzLeKvfWRcDpRPsXiflXLWh/VT52vcpJlPbkrulGU5nlXys/UEy/sKfYbcVN9SoWIQ4mbc2th0oZapYTRvnAtIrCdRc89AB8tJrFE/9acts6IGwzPLZqxvub0Ui5mHwzqONdwrg8pmIv/q3Lc+TXFbw9VawNhF2XH4LwoF/s6FdTqnqx7v7XRvci4jDWSIX6U6OpW6QXFLoHXLged/nunZOh9dEMV2leP5v2q6j5hZI5Me3xW5HspqppNoTuloiiJjcowhi5wO6q9dBSfajtzBViMxUCQ5UsAy9e8ciXHcDl99M1XJgXTiKzaDEz1Eqe3EHHFu2EV3hFhjmoXzte4gjPwR6FwMJmcxYbBrF5Y0DGwmSg3S2p+34GtuJ3e9DcR5s+3g1v4SsObYUayc4LJFWjyIvvU7bIdQtXipqDw7vZpqOwOCJ40Rmty/bfTqm4A2asE1+eNPnsLktlDIbz96KJTdO385+OharTDHR/iApWL1kiv0balQEi2dDY12AXDSFpXy64T7bwEE0sb28SiZniLznZzZUR5stAkpugUqysaakHZYvpQgONLYf8gXNFIWDmOytl0h0DUqZIs6AB8HSODigK+AgHy9s6EhQyEt4Bvoa7hNEC1Z9jlL25qUcuRFdg3Kugt3Jlg37Vxg4OEqa+zq9E6LzZvomu7t0IwigmyxbmvysQVcpVRy4Qp0vubl67eQirW0yU8sKPWERy8CduO/9FQRzTYMvmB1Yxx6s/3+rYfdYKSYzTe3iOqFw5s/JxLSu5CODW0TYUWJn0eUisSVz17Q7omcYXVOaruXLiQskC7tviaUsqwPKqfYNF9X8EiU5uGmPLMkqoGmAta+lYfKa66avkcxPbuqZmVz9eD0ZUovdVZmvQa/lTeq9431NB/NmWMxFMmca2xoIZhuy+56ueMK1jSDWDNebtOUb0dUKGf0+QmPrbTGsLgnFPADqxoNuISsQuKtxYDDvyDiFfHvvXyvEKKhj+Pobz9j7D+4ivri1bihyJU6Pd76hQNN/YJB4qg+t1J6gGJmu4NVeQHQ1FlhsLgvF2MYarXwkhcvT2O5OV0rolXRbZnk7SWSmirvwVRwHfror5+vb38/y5daD+o3M/cMT9Aa6625t95goZuQteQfdSDEn4Q61l5JlNQ57kcSrrT0OdQ1KeRXtwhdQc4u43/MZBMmF4/BjWMcewnn0X2zmtrcfrUr2+d/u2umU2Nmad9pE66jk7dBWL/PII4/w+OOP86lPfQpJui6M7Nmzh9/+7d/ml3/5l/mlX/qlLd1I7vnfJDKV6doanaTMUYxGmh6jJC6QLB/a0ZxLG2GWBOR8c3ff1WjFKNnZq5v2DAiNSMRmZQpvfhElfq7tempugWRp36aMlN3DY5QLAvr2KXYAiM5U8VtOYna3l737RswWgUq6cawjvZKlLAdw+neuzdgcOqUOBGEAlDKxqLvhTNnTF6RYtDUNc5BPVnD1WBCk9cvAnoEg+Xi7NjY6pfN/RUkbwd27fiIzcGSSWLTzgWM11bIOxXkEbf1A2bfbz/Kl9jVi0WkZn/bkxm7KmkJ1aeMEh7loGrst33D27Zx8ANW0td+6HUSmZXocF6lGTrLVNQPB5qc3WGbp5ButD74B1eQGi7drkeGhFn4ktxRp23aoHXKxDA53+5G+V3D39VARxto6NjJdJTwqUTzzZ5QufAVdLlA4+UWyz/5/mOy9iN72zrNTWGwCiu7tuqF7Rnwv/QeGunKulsLOwMAAgUCAz3/+85w/f5777lurnnz99df5/d//ff7gD/5gyzeT8zxG34QTi03Y8K+daKwA3rEJ8qnm6mK9mmfxb38Td2/3ZunWwDC+ez+FfWBPR+6cWjnV2exD18hFUrh6Nte4QmMWEplhLIP3dFZR15j+6n8iMND5MwsP6URmt39aG5mu0uO8gujdnLBjsvdg6dvABgSdfM6EK7D5OEGd4p08TEXa23G91EISb3B9G3SF/BQyzQeUQlrFZlpEdN2QNkIQcTgqZJfbFyDk6BkiczqDe11rvmVbYIBgKMPiW80Nh9shNlNg8PDIuv7C48kQe6vxclzD88xXCYzYse/7sXX7rA6BUibfdHJQzKlIpbMNDX7dfUGK+Zu/ZH4j8TmZ4IgZffG72Pp2YbGZmvbBjWwcRXNtwHP5ROR8CrW8Ce2tppJRjnY175+7P4jie1/XzgeQnjqLXew8VpLFYUfW2psk1jQaltrzTr+BxUbtz6JQfvVziJUZTJbtiXy9GZw9VirSXvQtuJw3IvrmK/jDpq7YLrX88iYnJzl7tpa99OzZs3z84x/n6aefru8/evQoY2NjnDx5kiefbO7a2YpytkihOsj7Hq1ueIzTK/LVP4i3MDERCB57P7m5v2p5TXPvPvL5Mp5AmuwWvYR69+7hwZ80kU8+hfWeLJejP8fZL/9JS3dywQS6qqHLnVn5q4EP4rW+DnRutR8esTBz8b0gnOq4LtYQqq5hsQm1WXWb+G1vMX2q+wkQbySxKOO1z1Cd71wlLgiAIKEVN3YvTZ76HqP3dD6z2yzuXgfFbOez3eJbXyZ70Ia7RySXvN62nZZ5khebaz3zGRWbGEG40QheV5ES3yCa2PgbbcTVp1/m3h/tYfTwILpci7ps9oZIzaeo5LbeJq69pXHixwLsP1pAV2vnE0wSMydjqB2kZlBl0FQV1573U7r8t/WcegCuXiey4yiwcT+na2CSJKTgPuTY2rhIrh4b+VRnz20nUGSd6LTM+x71Yfb60KrjaKUUsL7fEkWBbELhha9c73MEAR75vwMUcibQNS4832bOuxvQ5QLxeID+vb0sXmlsw7XnhB2zReDcC+31le6gh0K+u0vOZSXM8K5eeKb95f+a7ZAVNdeeYB+flzl4n5P3Pdo4fIQg2jC5B/nW729fbrNO8AwNUcgKHS21t4OSXWDhvIBZ0kDf2nJ3S2HH4XCQydRU6KVSCafz+ox2ZmaGz372swB8+tOf5sqVK8zOzm76ZirXvsuZ0c+Re+F3UbPrz2Oy9XD/x8EXMpOObDzYiJ4hel1XOT/VWggQrB6SpXHCYxGy8a3ZkgzsD3Hu1Rxnv32K4LDEgY9cRAoeRo42FyisHg+qbRJoPgDdSC5eZOTQJmxnRDBbTeieo8iRP+u4vugdJVk0ERqNtx2aHpNEeE8/r31r6yHbW6GpUC2pOPqGKMx35hlgsQvImgu1tHHKgOz0RRwP79xyhMsvkUttbqacSA8SHr1KLnm9ft+kjx98pblBcCmrYWUJ+QYNjmX4PYR2XePUk50ZFOeSKt//ihv3PY9TePOLbwd4O7ul4GOriV7L872/dmIZ+CS5F38XkyOI54HPknnh1zo+V2ymis/+GinfOEriusDiGeijUGhtHJpJOwgfOcH899YKOw5ritzi9uTE2iqvfqs2MRC9HpzHfh7RGSL7/G+hV3M4Dvw0WiVNZfpJ1PwCP/KLa22j/H1mYvMyb87/PNWl17aUWTwyU+aeOzfWfgxMWDsSdlw9VuJX16c02Qr5jIbL35lWxdHrplx1b5gm4kZUBZ58otmxAo/8ah+Suxc5d/O9+zz9IXKx7XGVf+VrNeNyaYvmvC1FpWKxiN1e+8DtdjuFwvWGU6lUUFUVVVU5c+YMQ0NbW1vTSgmKp/7n9ZgvJgnbnh/BdfcvA2Db+whZ+yMMHGjuom3u3YvXPkN8oXVHqiQukFLv7IqRco/1DAtv1LRgiUUZr20ay3BrrwR7j59KufPZRy6W2lRgwcCgRDJuQ0lN1WfZnaCmr5EqH2gZpn819tAwmuCkg6TWWyI6pzPyvo90XM/mMFFK59HLG9vIWMY/htmxcwG/7JYU+Uj79lx1NIWc68cIjV8foE2uftxBD/lk8xeh62ASzVh3Pbym3DlxP4KpFniwU9T0VfIv/2dcxz6Nfe8jXRN0VqjMPIXJ5sPcswfn0U9Rvvx36JXOoxVHpqv4becw96yN3eMO+8gnWwudy1dyBIfWa0XM6Wc7Ch9wM1AzM2Sf+nekvvkp1Mw0ulyguvgyWiWL+75fQwoeoZBW13gjhkYtxJNhTHY/lZmnt3T99JtfQyS7YZoAb1DE4WkepHI1DkeZ7MLmNE0bUYjFsNkqdGLf5HSUSV8508W70ElErYQPbi4YZ7exVs93+fd1n5bCzpUrVzhwoBYR8cCBA0xNXbcrsdmuS+ATExNEo+1nLN6I6uIrKLG3sE18CN/Dn8fs20XxTC3YXvHUnzL/8iv0Hx5v6n5nyp6imsvQTuJVvZIlk7TRM7j5fCcA1vGH8Q24SMdqg4imQiWfx1xpHT/E4fdQKXU+eFSiU5joXFgJjVpYvhgn99LvdVwXQM0tkirvJ9QkaN2N9O0JElveOZfJyNU8va6rTVMeNMLqNFFYmqbZOqlWSqDqFsQdCr5tZY7M7PQmaurEryUJjFwXzEKHjpBJCLQTakCuCrh2v/d6gWgl0A+x6c3PlJXUFTJP/TtKF7++6XNsiK6Rfe5zmJxhEISG6T7aITpTpcc1Rfny360pdzhlcout+7jlywkC/qU1gTpNjiCekTEK6VvMFWsjdBX02tJ6dfEVype+Tvp7jyPHzxGL+Rk6cZyVwT48KpG1fIjimSfayu3X9LKVLKmkh2CDaMruHpFsUiUVUfCF27N9spjiZKe3niZiDUoFdLW9QKxv4x7oo1jc2hhzI0sX4wQCnXu9dR3Riivo6cBp4ebQUthZXFwkmUzy+OOPc+DAAV588UUefbQWjfLEiRP86q/+Kr/yK79COp3mypXNZYJtRGXuBXIv/wH5V/7LGkv62LkzuM1XaZYhLDQiEbnW/oPPPPvblLLlzUfvFER6D7+HfEpdM4ZErpXxak81zdsDIJkyFGOdC4pafgm1nOs4KGJ4TCJj/VDTZ9gcneSz/wWTqLdtMB4aNROd2Tl7hdicjN95FdE70lE9R98omrd5MkitGKNU8eP07oz7uWdwgGJ+c22zeOHrKGIYm6v2ovr2BFi61J6WKJ8s4/Sa6ob2Zt8u/NZTRK5tbblXKyW6rtVZQZcLVOdfJP/Kf6Xt2FE3UCnpmCWwTzzI6tm7jRnSV8+3rJ+JqTi0c2ts8ETPMC6vQD69fdHDtx2lDJpM5FqJ8LgT93v+PY47Pok3ZGbp+b9CjnVnZp/MjTFwZPe68vCYhWR+gpRygvB4G4KDyYzoDG62GTSlGJ1DEtpftvGOjFHWu5smZOnMVYK93fMy2yxSzx7cIS+FW7xttzVUfe1rX1vz/5e//GUAnnvuOZ577rnu3xW1TLhqoxT3es31z+mXKGuHUGJrpXaTe4DBu+5k5sn2Z3W6rpBIjRAeLTH9VufGklLfMfzWsyxfWdv4I9NVho8MkPI/Ru6F39qwvoUYhU0mQq5K+3AFz5KLtKmuF8DZY0dx3wva11ofvwFqfpFEtIfAkERkuvXA5bee5+TrOyf5yxUdrZJCKHc2qDp8Lirl5p+Fmlskn3Lh8olbNmpvhWB2YJIsaPLmhAM1O8fi6SKhEZHZcwpe9SlOn2/PNiyfVrBYT2IZupfq7LMoiQv4TDHOzNw6QfEaoqvo1a3ZDyQWZPruOMRs7ApqdhZBcuHu76OYbW8ZKpdQ8A4GyczVllBEZx+i6RJKdZvjLuwAsSvLnHhIpjqr47BnyKdUtBbRtDth/uQ57vupyZoHzipNUd/eEPPVD1CJz7Bvfz8XXmz+jm29fSj61qLMb0Sp7MYz4iV+abqt450+kcXz3dXCFLMazlAIKdSPHG0/fEi3MQf3Y9Jf3zEThc1ySwQV7JTItEzfhAvX0X+5zr1b6t1Pj+MK0dkOOmRNJWt/pL3ZQgNEzxB+8+tEbxgEYnMygWAO0TNci1i7Ae7Ju5HNm5P6iyUH7r72l2p8ITO5Qi/V5Tc3db0VRGeYrOWH24pRJFqtSE431eLOeqLErmbpDXbm4WZzWynnmhtd69Usmalzm45x1AmSpxdV2VrckciVHIPHDyP1HcPbUyUdba9XKqRVxNi3qc4+C4KIbfQBrA4Tpfw7ZClmC0Smq/gtpzD37gFqWi2T2dTW0jhALNHL0N131f8XXQE05RYfDdpEU0Gp6BB7lh7HBSLT3f2ucwvL2KQEUviONeX+QIn5p/43S8/+JW5HHKnvXZicjYM/Qs1otpDbnm+0JAfwjk60fbzTrZJb2rqZx42kEhKBPeNdP28nWEL7UKvb72W7Vd6Rwk50pkowmEFJT2Mdfd+afdbQJKiFzgwoNZnYbJHQ2OZmAeWLX8XnS5NYXCvs1GZxOuQvY/ZtHIrd5rLWcjFtgkJGwx1sf+04PCaRLOxGXu486Ndq1MwsycIk4fHW8WbCewbJlNvvGLpFLOpm5IEPdFTHYqlQSrWegSmBhzcVRbVTXAHnlmOzxOZlAsECofv/GcW8qe2gjoW0itOrg0nC++Dv0H/P+0ku3R4DdisiM1V6vXOIntoyqG3kLtRq+799+VKCUP+q5b6Zv6AQf2cldmxGZLZKaMxCeMyybpLXDVIzUZz6GRBERP8EPXf+OJWCipKcrkdJ9w4Prev/V+MKOCmktqe95pMVXG0HFhWQ5KsUEp0by7di+UqGvjb64O1EmPtL8onNJUfdSd6Rwk58QaZ3UKJ08avYdj20Zp9H+wGxmc4NKMtLZ9FFZ8fRO+37/gmusSPIZa3hrC82J+OuPokubyz52hxaR6kiVpOLpHE62+9swqMWZp76Jkpiqy7gOrmFORxee0vPiPCkh8jCzgdTW7oQJdATA7F9Tztz5Rr5uQstjysW3bh6u2tw2AirerUtO5FmKFUdtZjAX/1rIlPtLyXm02rNLkmTKV36W3zCK0Smb/0ZXDcopDXs1iyV6e8BYLdmyCfaf3bxa3G8rgiCzY/9wE/j7g9SSN/iy38dEJ2uEh610DsgrZvkdYPla0UGjx/B/5E/xnnkn9PjjxKdvq5xjUxX8YmvYx26l428oiyF17f87WxELp6rTQTaQJAcNRu1bVjBXD6/RI/zSkcBbLuJyRHEZopQSN/6k6B3pLCzokY1V2fIPFOL84NoxTr+QXrcVzrq0Feozr/I8qXYhgkLGyJasI59gF7fIpENZjeR6Sq9zssoqQ3ivYhWJFKU0pvLCZO+chqr0H5sI/+Ai3x1lG58eeUr3yS5XNkw2eQKoSGNyFR3c960QzmvIJkymL0DrQ9+G0ffMJVKa+EoH8/tSDJQ78gYpcrW3dyjMyV2j58j0oGReCGj1XOAVeeew2d6qetLFrcyqWUZj60WT0hc/gb5WPttWNeglErjHtmHpf9O7NbsO9s4+QaiszLD+6xUS40neVslMi0T8M6R/s7/Q/bpz9AjnVzT9qIzMqG+Aloljbm3cXRxz9gEhW1SpuUWI1hK7QVkdU3chyp2nji0HTIxBac4vW0G/62w7/9xvKPjt7xxMrxDhR24rkZFV3Ee/RSe+38Ds2eE8Ji1ow59BV0uElt2dJR11zJ4N0pqilB/ecNBIDojE9rlwP2ezzQ+iVpBrF6lUtqcHUSxYMc71F5aBJdPpKz1YbJ1Z/lFzcwQnVaa2u0IAliFKJmbFIMhuaTQE2xz1iGI2D22ljY7AKXEIhbL9n/gnqFBStX2Mow3IzpTxeUVic+33ykqVR2z5fqs2d0rkkvc+p1at4jOVOtt2+kXOxZWIlcyDN19J4U3v4jLo79z3M7bQK7oKLK+4SRvq6SjCt6AWo8DFhyWiK1qu4nFmnY/99J/XBP4cQXRN44n3EM+2d2IvisUUhWcHr2mtWmBZ2iAfHL7Jgn5vJ3Qu39iy+ex7f4oUnijNDmNEJCCh7EJc+8IQf7WS9TSJvMXK7znx7wceb+Oyf40aDJaJYtc1inlNtepZPR7ODhxEmjtzrfrDhtHHp5Dla9hwsLJ7zfWJpULGlabiuQbQjA70JW1a5uWofsQrTPo2iaCxgGlTBG7R0Iw2xvm41lNaJeDZOkAcvSvN3WtRuR7f47d/j/n/EuN12z9A3bSMRO6fHNiMCy+NUMgIBC92PpYk70HQZfR1dYfrrz4Kmqpt6ZB71BJ5u8zMzBp4ezzrde5XT6YWt5c21hNZLrK1dOljmfhpbzGR/91L4LAtthm3MosTlX5wM/6mTxux2ITePFrnakJFuec3H/87xj/qSwWm52nmkbEfecxc7bM0tQ2DeJ6LWXCx36hF12HYkZd48lWi5KuYTEXYPBhKrPPrklVYB1+DzbTixQy2zMIqwpILi9SaJTqwstNjhTwhL1kI9v37iPTZfom/URf7LwzEn27OPShOxgNvgDSBTTRx3f+16G3I5y3qOsfR6ukcfmrzBnCzvYRm5X56n9eyV/UnXDZhfnzSHYrJpGWg8LYIRvf/59zbc10E4syLt4k6x1ZNwvxTuynXFzY/E3rKpWSgHNgnPzs2aaH9u8NsLBobikUdUJqZhHv3o3jCPXv7ycjHAO6J2B1Qizq4a6Pj3DuhVdbHmuy96Jr7WqBTFTpw+5KdSxcj917mNE9Vc69MoveKLzCyiUsbryuJMnpzlIzNKJS1NfkM2qX7/2vWyBo2U0il1Brefg2SfTiAn/zH7p4Q7cYp5/ubhqGG3nxq83ba2RGrsXe8Rypx1eqIWAfehfI30HdRvk8nxHwDvURa9Z9m8x4lCeZn96+ZfzIlQyHDk4heoYbplna+N4kXMf+FQM9/53vfDFBpRTnvf90F+HjH2H+W62FHb2UovjWE/T8pNQ0fdOtwjt2GWs7kOPnSRdHCU20duX2DgYoW4+1dd7otIxfOtlQ5RkaEli+ujVL9tiimXAby2/+nixzT3ZX6JCjZylWQ7h7Grt49u32sXRx8wPGVsksxHG5q00jbq+gpS+gZNvMT6ZrVExjuDrwhAMwBw4QHndgFgrYesJNj7XbyhSiEXTtnR+bxcCg20RnakbS1bnnsY6+n+uGyjrSlc8Sm9leD6HI20barejxLBDrJBRKh6ykJjLZO1vuduz/CbTcNCL5egT/5QvLuLJfbqu+jo65eJ7KNtltdRtD2FmNprDw+mmC/c3Xeb1hJwVluG2PpshMlR7HxXXu3oLZQcC/yPLlrc2e518/TY+1uU2MzetEFbyope7OMOToaSLTlYYfvSC58PgVYqdvYmZeXSWXtdMzPtbyUEf/GOUWAQVXk8+acIc7s3/SS3GsyjWunYzT457FNvHDCNbGgmrf/r5/VAbBBgadEJ2VCY5IVJdeA0HAHNgPgHXsQcKjlraCnW6F5QtR/Jbmmc+9J/4Fot1Ftbx9ExZdg1IyiZg/3VG9avQ09sSXSazKIRmZlgkNaTgO/9OmHl6CxYX3wd8lNGZ9xyxvG8LODSydW6Rvd/MBbODoPqKLUttJBnNJFXePBcfhn11TritF3NoP1jS2zRCbrRLeE6JZ3oiBdx0gmWvPkLkjdJXFk2fp2+Nbt8vlkckup26avc4KkRmZ0EjrkAKeibuQtfYN1AtpBXeg3dhMAvb9P47HmyW5WKjd05gFkzOMbeyhhjUGj+wmnuwst5eBwT8WlKqOAJjNKrkXfgclfg7RPYT9wE8RGmZTjiqdEJ/L4/OmmgoFwVEn8dnumQ1sRHRWYeShH6lFnW6F2YZ1/IMosbcID6lrJlTpmIIvJGKy92DdoF8CkMLvQomfJzxqfsdMyAxh5wYKGQ1X0IvZu7FgEArnWTrVWR6wTKxK7979a2K+OAb3oWqWLYfZVqo6gsWNtXfje+7b7Wfp/CZzUrQgsaQR2BVC9O1aU95/aIjlS92xp9oKC6+fotfX2i7K5rF1FNwxdfYFbLS3Rm4ZOIEUOEh4xERkukpspkp4xEJl9hksQ/esryCI9PRmWD7fwRq8gcE/MmJzMsFhqZZWwiThvvdXUKJvYnOwaUeVdtE1UMz9eCbvbLjf5OqnxzPN8g6E3YhcqxDqK2G+oQ9uhPPQJxDdQwCExqS1wopeCyZqWvwb7JM/vKHwZBt7iMrsM4SGJWJzhmbnHYpOOgaBQ+9qvNtkxuuOE7vUOpv5amrh508jeq4LJIPHDhKPdmbzsRGJqI3w3sbxZEz2AL09SZbOdC9R62p0VaWcLeA7/EP1MnPgAAMHR7ddldwOqWWVwHh/81mPScLudVLKtm90mU/rbdrsCNj2PELp0ncvBMkAABIuSURBVNcIj0pEZ+RaskmLgJ67BrqGyRlaU8MWGkGVNdTyP44gfgYGmyEys8puRpPJvvBb2OL/h9TyzhjMRudFwpON+wDBJOGX3tyRBMixeRm/8xrm4IGmx0nho5gDByie/XItT6JHpJBZKxRGpmUCgRhy4hKiZ6jBWQQqs8+iJ2txht4p+d4MYacBSxdj9O/20ygyZ++9P0tFdqN3OGmIzlTpcV5GdF8XSIIDMstT3QkhHrlWJrzL1nCfSUmg5WaQy9tnRbZ8MUp4RESwejHZe7FNfhiP9SrJpZsv7OiaTkV24xpqkgFdU9AXv025g+COFdmDK9jaZsfk6keXi8iRU3iDZtKxWkccX5AJDEpknvr3aIW1eXMCgTSRqa27nBsY3M5EpuU1gWC1QpRgf2nbl7Dq15/KERpsPBho+VmsYopidvvjKylVHU0uYfWHmh5nDuyjcPKPQSnjD5lJNfCiikzX4ksVXv9vqJmZdftNrn4qM08RHDYTfYdodcAQdhqyfDGOV/3+unKTPUDfuJPlK53HTEgtK3isM1Rna1niBclJr2+ZyMXuJIdbOHURv7WBu6AgMnDve4nNbU9wrRUi18q4K99Er2QQvaNYpTyVbP6WsdKPLloZ2LuB95NJwjJ0LxZilAvtd0xKPoogimwUrn4FLb9I7oXfwrUSmO7tiVA9aJ0gYD/w02vOEx4Vt23Z0cDgdqFc0LA6TGvMFcNjlh2zI1m+uIDftV4gABj+8C+QjGwtiW8nRKbSOBN/tuF+weajdPYv6uFPannN1j+nxKJM70BNgHTc8UnMwYPXz2Fx4bn/NxAk144+527QlrDzyCOP8Pj/3969x0ZV7Qsc/87utNMZKH0MdEpbKdAX1B6hQEUFc7ncgxzl0Wh8whC56tVwxSO3TdBgMOgfCBI0RolRbi6koGKUKFwUsB4EK7ZIaRHqBQ60UIG2U/ruTB/zvH8MDIWWR6EzU2Z+n4SE6W7Zv27WrP2btdZev7w8XnzxRUJDL2fRKpUKo9FIXl4eRqMR1Y2KJN0hmi/YGRLVSXjqI5enF9ThRDywlEjHT5hu4VFxlwva2xxEj/8bmlF/RaETl/lPrJ39k/V3tnQQPigURdNtwawqhMGTXsYwQo3pjHeTnQtnbQzVu/fFsNWWMqhhw4Cqo2SqNDMssfdrrRk5nbD4bDSDVHS29+H/w2nDbgshLOrai4jVwzLR3v0MAIakK+fHTWes7k+lTjuhhntQx6R6jg2/eyT1F26/TIQQga77zRlgyNAQWut98ynL3tGFs73+ip3GQYXuLwvQR9VgOu3dvYi6qztjJS5tKIMn5/aoBxiWcB8R9/7XFV+LTQrtdZmB0wG2LicanQp782nCkx/2HNOM+BdstYdw2cyeKfk7xQ2Tnfj4eIYOHcratWs5duwYU6ZM8RzLzMzEbDazdu1azGYzmZmZXg3WZy4u0tJpmhky5Q33Oht7J5ay/yY6soYL524tm62rshKtLkUz8t8YMdNI/fn+fUO2dCZhyLh4w1SFMDj776BSiA4t9XqjtNtcuFx43vQDLeuvKTtMtLai54EQDdrU2XQc/4ZwnUJXH0Z2ANpqLjBI1zOpU6l1aNMfY/Ck//RsUXB1hWhLi9NdaFMF1vPFhCW4FyqrB8UQEuKgs6F/Rv2ECGR1VTYMF6ey/FHSpLFtBPETJ3pea8c8SkhkEpHOfdT5sA80VVkZFteJy2ZxJzYXnxJThUejyzRiObLxiu+Pir08pX419zUNw3quCHVkEqHD/gJAePLf6KwsQAmBMK3Sp5Fwf7vhpiIpKSn88Yd7Z94//viDRx99lL1793qOlZeXe47dfffdHD3qnxpI/a2mwsrkB47RZf8A9X+kY7tgB3sdDqtyy7ty1lRamfKolub6NUQlxHHkl2hupjTFTf/7px3cOxMs90/E3lSBotuOs6MRjU7x+pMJ4B6pmG6MwtrhYmhCKGU/+veR8+4cdnCERDPj72nuYTbA3lSBKnQQim4j9phWYpPC+vzmba1t4IEnErEpcbgcVnA5sTeeQqWJQNFU4jC/hetfu4Ao9PFqiv/3yl1hG2vs/HVBNA7XAdTRo7FlRhE+dDgNFwb1168uRECrPW3loX+PZtiIMHQRCpVHfDuibDqrZuI0K+mT7sPe8ic4C3E59xIdq9DioxEmcO+SrtZAdvqXqKNHwfgM7M1VhA5Nx9n5EY6YJsC9RYgS4t4S5VrVJWoqrUyeHcHo8Q4UzbswC5xdUSiaNTgfaSVUE92nOnsDwQ2THZ1OR0uLexFtR0cHgwYNuuJYR0dHr8fudMeK2qn6v06gGZWmybOnTlf7ra88v/CnjR82NLrnl13NPVbB365//lhCTfkwwIWzoxVw31itHb5ZLV/2DzPaCPdgod3qGnCr9P+R34w2+vI1t7e2olIsuFwmcHa56+30cfOvI3vN/LPkJKqwWpTwSFSqEOytreDqubjY3uXqsYapaFsL4YPd10wV1oXL2oqiC6e96Wzff0EhgpC5ycHOTxtRLt7N+rtfvZHT+w9TX3UXisqMw9yMy+FOtmxe3EjwWn74nybCtCrgCEp4NM7OVuC3Xr/3eh/saiutFGxsurgWqvsHtMt/98UH6P50w2Snvb0drda91b5Wq8VisdzUsTudy0W3KsX9Vx/IqyvzXU7aam6y3IEXOB0M6MrO1tZ6rK39W7rCYXdPeULzxT+38vOXrtnFn2+WhclC9EWH2Y/9jtNO25+n/Xf+bmxdLmxdl5Ks2+vrfPEUmS/dcM3OqVOnyMhwP7ufkZFBRcXldQ8VFRXXPCaEEEIIMRDccGSnurqaxsZG8vLyaGtrY+PGjcybN4/PP/+co0ePMm7cOPLy8qirq/Os37mW7k9yCSGEEELcjNvNH1TJyclen1jU6XTMnz/f26cRQgghRAD77LPPaG/v+/YvPkl2wJ3w2Gx31uptIYQQQgwMoaGht5TowE1MY/WXWw1QCCGEEOJ2BkykXIQQQgghApokO0IIIYQIaJLsCCGEECKgSbIjhBBCiIDmkwXKOTk5pKSk0NbWxoYNG4LqqaxRo0bxxBNPYLfbaWlpYcOGDaxYsYLmZvduuTt37uTYsWN+jtL79Ho9r7/+OjU1NQB8+umnpKSkMGPGDBwOB5s3b6auLvALXyYnJ5OTkwNAZGQk5eXl3HXXXahUKlwuF/v37+fAgQN+jtJ7wsPDWbJkCXFxcbz77rtUV1czfvz4Hu0gNjaWBQsWoCgKBQUFHD582N+h96urr0NTUxMvvfQSISEhOBwO8vPzaWxs5Nlnn2X48OFYrVaOHj1KQUGBv0Pvd721ibfffrtHHxlsbaKhoYGXX34ZgLCwMBRFYeXKlcyePZusrCwsFgtVVVVs3brVz5H3r97umffcc89t9xEhMTExK7wZeHx8POPGjWPdunXodDqSkpI4c+aMN085oLhcLgoLC9m/fz8jRozwXIM1a9ZQVFREfX3/li8YqHQ6HQaDgY8++oiioiLsdjsLFizg/fff59SpU+Tk5FBaWurvML2uqamJoqIiioqKSEpK4uDBg6Snp7Nu3Tp++eUXzp8/7+8QvcrpdFJWVkZ0dDSVlZVYLJZe28H8+fP56quv2LNnD0ajkaKiIlyugVVr7XZcfR3MZjPl5eXs3buXlpYWpkyZQnl5OePHj+ebb77hhx9+oLKy0t9he8XV16KtrY377ruvRx8ZbG2iubnZ01c4HA5aW1s5efIkaWlpHDhwgG3btgXkB+Xe7pkzZsy47T7C69NYV1dNT05O9vYpB5SWlhbPSJbD4cDpdKLRaMjNzeW5555Dp9P5OULfSU5OJi8vj5ycHGJjY6mtrcVut2MymYiOjvZ3eD6lKAojR47k1KlTOJ1OFi9ezKJFi4iJifF3aF7ldDoxm82e19dqB1FRUZhMJux2O7W1tQwbNsxfIXvF1dfBZrPR2uousnipnwB3x280Gnn11VdJSEjwS6zedvW1AHrtI4OtTXQ3YcIEDh065Hk9Z84ccnNzSU9P91V4PnP1PdNgMPRLH+H1aazrVU0PJnq9nszMTHbu3MnJkyexWCxMnjyZOXPm8OWXX/o7PK9raWlh+fLlWK1WjEYjWVlZdHR0eI4rSnAtH8vIyOD48eO4XC7Wr1+PxWIhNTWVp556io8//tjf4fmMTqfrtR10bw/B1G+EhIQwd+5cNm3aBMDWrVuxWCwYDAYWLlzI6tWr/Ryhb6xZs6ZHHxmsbUKr1TJkyBBqa90Fgvfs2cOOHTuIiIhgyZIlrFy5EofD4eco+9+le+bXX3/N4MGDPV+/1T7C63eYQK6MfrPCw8NZuHAhGzZswOFweK5BaWkpiYmJfo7ON+x2O1arFYDDhw+TmJjoaReA55NssOj+Se1Sezh58iRRUVH+DMvnuvcPcLkddG8PwdRvGI1GfvrpJ0wmE3C5bZhMJlwuFyqVyp/h+UxvfWSwtolx48bx+++/e15f2qC3ra2N2tragBwV737PNJvN/dJHeD3ZuV7V9GCgKArPP/88O3bswGQyoVarUavdA2qpqalBsSgX3I33kpSUFI4cOUJcXBxqtRqDweBZjBgMFEUhKSnJ817QaDQADB8+POh2Gq+rq+u1HTQ3N2MwGFCr1cTFxXHhwgU/R+p9s2bNoq6u7orpikvvm4iICNRqdUCtUbmWa/WRwdgmoOcU1qU2ERoaisFg8MycBIqr75n91Ud4fRqrt6rpwSQ7O5tRo0Yxa9YsZs2axa+//sr06dPp6urCbreTn5/v7xB9IjU1ldmzZ2O1WmloaGD79u1YrVZyc3M9K+yDxdixYzlx4oTnk3pubq5n1GvLli1+js77Fi9eTGJiIgaDgcLCQgoKCnq0g23btmE0Gj1PWgTiyF/361BeXs4jjzxCRUUFY8eOpbKykm+//ZYXXniB8PBwFEUJuKduuut+LY4cOcKECRN69JHB1iYKCwspKysjMjLSM9IH8PjjjzN8+HAURWHXrl0B93Tz1ffMffv29Usf4bNCoEIIIYQQ/hBcq0KFEEIIEXQk2RFCCCFEQJNkRwghhBABTZIdIYQQQgQ0SXaEEEIIEdAk2RFCXJM/duzV6/UsXbq0Tz+zdOlS9Hq9lyISQtzpJNkRQvhVsJUKEUL4ntc3FRRCBJYHH3yQKVOmoFarOXfuHPn5+cTGxjJv3jzee+89ALKyskhPT2fLli1MnTqVqVOnolarOXjwILt37yYtLY2HHnoIq9WKRqPhww8/7PVc999/PxkZGURERKDX6/nuu+8oLi5GpVIxf/58kpOTOX/+vGfHXaDX802aNImsrCzWr1/PyJEjefrpp1m9enVQ7EgshJBkRwjRR4cOHaKwsBCAJ598kqysLA4dOoRarUav19PQ0MDkyZPZvXs38fHxjBkzxjMd9sorr3D06FEARo0axYoVK2hra7vu+eLj41m1ahVarZbXXnuN4uJiJkyYgFar5a233iIhIYE33njD8729na+kpITs7GwmTpzIzJkzyc/Pl0RHiCAiyY4Qok8SExOZO3cuWq0WrVbrqedVXFzMvffey759+4iNjeX06dNMmzaN0aNHs2zZMsBdByw2Npb29nYqKipumOgAHD9+HJvNhs1mQ6VSoSgKo0eP9tQLOn/+vKcidFpaWq/nq66u5osvvuDNN99k3759nDt3zhuXRggxQEmyI4ToE6PRyLp16zCZTEybNs1Tqb2kpITc3Fza29spKSkBQKVS8fPPP7Nr164r/o20tDS6urpu6nzda/+4XC4URelR/fvS62udD9wLn202G0OGDLn5X1YIERBkZaAQok/CwsJobW1FrVaTnZ3t+Xp7ezsmk4mHH36Y3377DYATJ04wadIktFotADExMZ6qzbejoqKCiRMnApCQkIDBYLju+dRqNc888wwffPABer2eMWPG3HYMQog7h4zsCCGuKSIignfeecfzetOmTXz//fcsW7aMxsbGHtNBJSUlREZGUl9fD0B1dTU//vgjeXl5qFQqOjo6+OSTT247rtLSUsaOHcvy5cupqqri7Nmz1z3fzJkzKSsro7q6ms2bN7No0SJWrVoVcBWjhRC9k6rnQoh+89hjj2Eymdi/f7+/QxFCCA8Z2RFC9IvFixej1WrZvn27v0MRQogryMiOEEIIIQKaLFAWQgghRECTZEcIIYQQAU2SHSGEEEIENEl2hBBCCBHQJNkRQgghRED7f8dP56nSM+ZIAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import makeqx as mkq\n", "\n", "\n", "\n", "q_x = mkq.make_qx(200)\n", "mkq.plot_qx(q_x)\n", "\n" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T11:13:29.964067Z", "start_time": "2018-10-25T11:13:29.877244Z" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from jupyterthemes import jtplot\n", "jtplot.style(grid=False, fscale=0.8)\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "n_arr1 = np.array( 100*[1.45] )\n", "n_arr2 = np.array( 100*[1.33] )\n", "fracs = np.linspace(0,1, 101, endpoint=True)\n", "neff = np.array([LL_mixing(f, 2.5, 1.45 ) for f in fracs])\n", "plt.plot(fracs, neff.real)\n", "plt.plot(fracs, neff.imag)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-10-25T09:52:53.734151Z", "start_time": "2018-10-25T09:52:53.594151Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import numpy as np\n", "import pylab as plt\n", "from tmm import coh_tmm\n", "from numpy import pi, linspace, inf, array\n", "from scipy.interpolate import interp1d\n", "import matplotlib.pyplot as plt\n", "\n", "from jupyterthemes import jtplot\n", "# # # #currently installed theme will be used to\n", "# # # #set plot style if no arguments provided\n", "jtplot.style(grid=False, fscale=0.7)\n", "# \"5 * degree\" is 5 degrees expressed in radians\n", "# \"1.2 / degree\" is 1.2 radians expressed in degrees\n", "degree = pi/180\n", "#index of refraction of my material: wavelength in nm versus index.\n", "d_list = [inf,300,inf] #in nm\n", "lambda_list = linspace(400,1200,400) #in nm\n", "T_list = []\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "for lambda_vac in lambda_list:\n", " n_list = [1, 1.33, 1.33]\n", " T_list.append(coh_tmm('s',n_list,d_list,0*degree,lambda_vac)['R'])\n", "plt.figure(figsize=(4,3))\n", "plt.plot(lambda_list,T_list)\n", "plt.xlim([400,1200])\n", "plt.ylim([0,1])\n", "plt.xlabel('Wavelength (nm)')\n", "plt.ylabel('Reflection')\n", "#plt.spines['top'].set_visible(False)\n", "#plt.spines['right'].set_visible(False)\n", "plt.show()\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 2 }