From 729998faeef734d9914eabd3dddcdf7598043ed2 Mon Sep 17 00:00:00 2001 From: garrethmartin Date: Thu, 11 Jun 2026 15:46:12 +0000 Subject: [PATCH 1/5] SP-2645: added 301 notebook on image display for faint features --- .../312_1_visualisation.ipynb | 551 ++++++++++++++++++ .../312_1_visualization.ipynb | 543 +++++++++++++++++ 2 files changed, 1094 insertions(+) create mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb create mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb new file mode 100644 index 0000000..b29554c --- /dev/null +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "attachments": { + "1e2050a2-4e1c-4956-84ad-cc5f196353c8.png": { + "image/png": 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XmScMCnBA2DUCERc4d2VFvvnhGXlm8mKgR5pqaJSepibp1wbVtqoZVUSBcrNzBBBAoG4ECIbqZig5EQQQQACBoAXyp4tNrWVkfnOj6MsSBhWl4Q4EEiVgU8yevnBevnn2bDZIDmPL7UVEQBSGOK+BAAIIxFeAYCi+Y8eRI4AAAgiEJOBluhhhUEiDwssgEDOBD5aW5S/PnJbnpqZCPXKaVYfKzYshgAACsRQgGIrlsHHQCCCAAAJhCLgNhCwMuquzQw5pf497O7vloP5/VhMLY4R4DQTiI+C2asj5ebJDT+2khkl+VRg5zaqPav+hT/f0yIH2DvoQxefy4UgRQACBQAV2Brp3do4AAggggEAMBU4vL8sPJibK9g9yqoM+3z8g+9rbpH1nk3TqrbmxIYZnzSEjgECQAvZzYd+uNhltaSkZ9ixubmqPoJR8YXhIljc25Z2FefmZ9jOrtnH1gk57tdv8pSl5bXY2+7OKPkRBjjj7RgABBOIjQMVQfMaKI0UAAQQQCFDAqQ56eXpGPlxZlvErqwX7B1EdFOAgsGsE6lzAbRPquzs65c/275cHe9OyqGHOzPq6vDk3K89oYF1tQJRLTB+iOr/gOD0EEEDApQDBkEsoHoYAAgggUJ8CXqaLHUunheqg+rwOOCsEwhBwO53MVhf7cw2G/mTfvhuHtaSVRBOrq9mA6H1d9t5ufoVEBERhjD6vgQACCERXgGAoumPDkSGAAAIIBCjgJhCiOijAAWDXCCRUwG0T6j8a3SN/quFQWpeez90sIFra2BD7r99VRDSqTuhFyWkjgEDiBQiGEn8JAIAAAggkS8BtIER1ULKuC84WgbAE3FYNOdPJjvf3Fz20oKqIaFRdlJw7EEAAgboUIBiqy2HlpBBAAAEE8gXKBUJUB+WL8W8EEAhK4NWZafm/Pzwjb2lj6WKbTSf7oz2jOp3stluqhvKfE1QVkRMQ0ag6X5x/I4AAAvUlwKpk9TWenA0CCCCAQJ6Am0CI6iAuGwQQCFNgsLVVRvVWKhjKbF3V1cPm5EhPr5SqGrLj7ti5M3uzbVj3a8/xoxeRs5KZyKqMa38jOx5WMgvzSuG1EEAAgXAEqBgKx5lXQQABBBAIWcBLIHSws0P6dHlolpkPeZB4OQQSKuB2OlmhJtRuyZwqonMrK/KGNqz2Y8l7e20aVbsdAR6HAAIIxEeAiqH4jBVHigACCCDgQqBcIHRXR4d8cXhYDnd2SV8qRSDkwpSHIICAvwIWQt+vVT33aWDz3NRa0Z1b1ZCtRGbL1ec3oS76pOt3OFVEFnof0J97jwwO+dKs2qqI3lnayFYQLWoD7E/39MiB9o7sa7AhgAACCMRTgIqheI4bR40AAgggkCfgNhA60t0rI22tN6ZdAIkAAgjUQsBt1ZCbJtRujz+3WfUzExNVL3dPk2q38jwOAQQQiLYAFUPRHh+ODgEEEEDAhcDrszPynfPn5cTiokxm1sU+ZbfNGkpb/6AH+9Kyr62dQMiFJQ9BAIFwBKxqaN+uNhltaZGpTPGqodM6Fcz6Bd3T1eW5aij/TKyK6JBW9uT2IaomIHJ6EM1fmtL+Q7Paf6hXvrZnjOqhfHj+jQACCERcgIqhiA8Qh4cAAgggUFzg9PKy/EA/9X5RV/g5e2X1lkDo8/0DQv+g4n7cgwACtRU4d2VFvqmrkz0zebHkgfhZNZT7QkFUEI1p82saVJccTu5EAAEEIidAMBS5IeGAEEAAAQTKCTiB0Gv6Kfq4BkLz2vPCNqdCiEConCD3I4BAFATcTierpgm1m/MkIHKjxGMQQACB+hVgKln9ji1nhgACCNSdAIFQ3Q0pJ4RAogXCaELtBrjQFLP3l5bEbif05nXLbVDNEvde9Xg8AgggEL4AFUPhm/OKCCCAAAIeBZzG0s9OTspbc/NUCHn04+EIIBBdAbdVQ0FNJysk4yx1/9bcnLyulZmVBkTOvlnivpAyX0MAAQSiI0DFUHTGgiNBAAEEEMgTKLbSGFPGuFQQQKBeBKxqqKOpSVoadpQ8JT+bUJd8Ib3TWerejut+XY6+2oCICqJy4tyPAAII1FaAiqHa+vPqCCCAAAJFBGza2NMXzssLly/fWGnsLl1N54vDw3K4s0v6Uinp01XH7E0VGwIIIBBngQ+WluUvz5yW56amSp5GmFVDuQdCBVHJYeFOBBBAIPYCVAzFfgg5AQQQQKC+BKxK6IcXL8r/1FV6nMbSTiB0pLuXJefra7gTcTZ2Tc9m1n07155Uc9XLlvt2MOzIF4G9bW3ymZ5eeXthsezS9Wd1+frj/f2+vK7bnRSqIHpy/FxF/YfsNXMriBY3N1ni3u1A8DgEEEAgIAGCoYBg2S0CCCCAgDeB3Gljb80vaJXQWnaVsa8MDcujg7vlcHdPdnoDGwJhCRQKdGbXMzKtN9tm1zdkJnPt/zvHNKMh0OXr9ztfy1zdkszVq74ddqqxUVJFKuWsiq6vuTn7WmmtquttbrrpddN6v91sI2DybUiq3lFUmlCXO5HcgOiAVnC+qf2HnpmYqCogev7SlLy3uMQS9+XwuR8BBBAIUICpZAHismsEEEAAAXcC+dPGOjUAOpZOC8vOu/PjUd4FnNDHCXqckCc32CkU6KxtfxLyZLa2ZS0v8Fnd0vu3/AuBvJ6ZLWve2nBtemWLBUh5fWssVGrZce3+/IDJQqVD+mbfCZOcEIkAyesoVPZ4a0L9Vx+dkf9Hb6W2Wk0nK3RMfi5zT4PqQsJ8DQEEEAhHgI9ew3HmVRBAAAEECgjkTxtr0je0j+/eTSBUwIovuRfIrfSx4OeU9qtyKnuc4McJfZygxwl5ah3suD/Lwo+0UOpGMLW5UfhBRb5qodIrs7M3wiQnRHICJKcayalEIjgqAlnhl61qaLi1RQZSLWWnk1mlzj1dXTWfUlhomftKK4iYXlbhhcPTEEAAAR8EqBjyAZFdIIAAAgh4E8ifNnZ1e5sKIW+EiX90bsWPE/zkhz6GZMHP4sbmjcqeuAc/tRx4pxrJqURyExzdoRVI6etT22p57HF57ag3oS7n6FcFEdVD5aS5HwEEEPBXgGDIX0/2hgACCCBQRuD12Rn5zvnzcmJxMbva2O272uSJkVF5qK+PVcbK2CX1bicE+mBlSU5pLxILgM6vrmb79uQGP4Q+tb1CCgVH9gbf+iHlTlOzSiMCo8JjZdPJbDXGb549W7JqyKz/fP9++ZN9+wrvqMZfJSCq8QDw8ggggIBHAaaSeQTj4QgggAAClQlYH6EfaJPSF2em5eyV1Wwg9H+MjQkrjVXmWY/PKlYF5IRANtVkQat/CICiOfo3prEVmMKWO03NKo0sMBppbb3R04iw6NqYxqUJdbkrMHeKWaeO9es69e39pSXPTaqZXlZOmvsRQAABfwSoGPLHkb0ggAACCBQRcKaNPTs5KW/NzYv1EbLG0qw0VgQsIV9OUhWQra7XryuEOdu0VjxN6ap7Sd8sLOpp0ooibZDthEW1qC5yrsWoNNmOYxPqUteyVQ8tbWzoz/85efbSpJxcWq7o+md6WSll7kMAAQSqEyAYqs6PZyOAAAIIlBDInTa2pY/7jC45z0pjJcDq/C6rGntlZkZOaeVAFKuAcgOcvlTzjWXfnWGxXjlpbQxcaEvrsvA2XarQ1qxhaLNWyTjbuk6BW9fVy0ptNl3OAqRS26yGS7mPyfZY0umZcQ+eCgVGTnXRgfb2qqehWRB0Wq9B602Vey06TbYP6Wsc1N5IB9r1pv+txfZdnU72nz48UzJAMac/2jOq08lui0UfJwuIZvSafVMDom/bdGIdg0o2AqJK1HgOAgggUFqAYKi0D/cigAACCFQo8OzkRV16+SO5om+CCYQqRKyjp1lI+OT4uPxKewTNafVAmEu65wc+9sbfCXhyA53cAKdFq1ia9Y137mb3W3hQaEvpEvA2DcivzapGMto4u9RmPZZyA6Z1XZFsbWtbcoOn3IApN0hyQiTbfxyCJCcs6mzaecs0NC8BjoWT1sPHVl+zpuSFrkULHrr1dWwK1NHeHnl8aDj0gCjuTahLXbd+9h96ZPeAfG3PWOjjU+r8uA8BBBCIowA9huI4ahwzAgggEAOBtsad8hvaUPrezm452NlBY+kYjFmQh/i+Th/52cys74GQE/pYhY/bwKc92xD5WsDjd6Djl6GFTM1SOmiyPi7lttyAKTdIckIke74TJFlYdGpp8UYVkv270mk/5Y7L6/0WJE5m7Hbtmb/U5vUW7tg0NAtwurRiy6n0KdavyAknbUrrfIE+SM4xZXtZZe9flXFtcr6olS5hhw9729rkMz298vbCYsmqodMrK3JWb8f7+72S1uzxuf2HbmvbJW9o/6GfaSWh1woiG6PnL03Jexo25wd4zhTml6dn5PL69Yvm+hlHoSKsZvi8MAIIIFBEgIqhIjB8GQEEEECgOgH7VNjeiNqbNj8rKao7Kp5dCwF7k/ZXZ87If/94vKKXLxT+OJU+TpWPVfjEIfCpCCCkJ1mItKhvtp0qJAuPrKrG/h3l0MjhcSp9nH5F93Z1ZqeEWVA0u7Eu39Pm929oKOS1Ws32e39Pl/zxnr3ygPZHC2tzWzX0yMCA/G+3749l1YxzzdkU0yfHz3kOh3LHfkybmVtA1KfTPd9ZmL+x8mX+eDvXye3tu+RoT1oe6O2NpV1Y1yGvgwACyRAgGErGOHOWCCCAAAII1EzA7RLcxQKg4ZbWbI+e3PAnqpU+NUMO+IXjGBrlBkUWUk9q/6X8kMAtm01le2SgX/6d9vMJq++Q2ybU9dBzx8/pZa36s6LQFMH8sXamJx7rS4deEZZ/LPwbAQQQqLUAwVCtR4DXRwABBBBAIAECVv3wv6YuyS8XF7INkm3Lnf5lFUAEQPG8EIqFRhfX1m5MTYvStLRKlS2A+droqPzh3r2hNXt204TaOZ96CYjenZ+X5y5dkhe1iiiM1fvMjV5FlX5X8DwEEKgXAYKhehlJzgMBBBBAAIEICzjhwbJOVbIGybZRARThAfPh0HIDI2daWtzDors7OuXP9u8PraeP2+lkucOVGxDVenW1Si4ju26mtS/QyzPTVa1e5uW1CYe8aPFYBBCoRwGCoXocVc4JAQQQQAABBBCIoEDcwyKbfvTnGgz9yb59oei6nYZZ6GCcqXRHtYfOp3t6xMvqbYX2F/bX/Jpe5va4a1ER5vbYeBwCCCAQtED55SyCPgL2jwACCCCAAAIIIJAIAWtE39eYuulc79QKkSMaXFiT6/zKovd1efmorIxmB209iiZ0pTKbGpdubg58zMzrfl2d7D5dueu5qTVPr+esrjavK3e9piu4xa3Zcu7qZbaIwbOXJgO9Fszr5PKSTOr4hjG2ngaTByOAAAIBCxAMBQzM7hFAAAEEEEAAAQSKC5QKi2zqoa2M5kxBi0JQlLEAS5tZh7XZ0vW372rXl5uq6CWdgMiab59cXJYfXrx4y/LuFe04pCdZQHRcV167R1eZe3NuLtDpZT+fX9AVzRbk7q6ukM6Ol0EAAQSiIdD4F7pF41A4CgQQQAABBBBAAAEERBobdkibBgLdWpWzu6VFxlrbskvP25Soz/f3y6GOdhlIpbK3Va04Wrm6GRpbg+zQ6pt22bdrVyivaRarGkStbG5Wda5Xt7dlWZ2mtH/PmZUr8raGLB9fWdHqmJSk1THKW6qhIXst7GnbpddAr+xX+zmt2rqsNz83C/326LL3n9JgqE1XN2NDAAEEkiJAxVBSRprzRAABBBBAAAEEYirgVBX1XQ8wbtNg5uGNjRtTz97V1e7eX1rKTvEKeupZswY1dgtzu08DsbG21mzFzC+1osXO9YTeKt2siuidpQ0Z12lTr83OxaaCKHd62aYGXZfXN3xfuezC2irTySq9sHgeAgjEVoBgKLZDx4EjgAACCCCAAALJFLCAwG7OVigoekmXOw8iJLIG1M1awRLmdu1827Wyp0UeTPeJNWZ+U/sOPTMxkdiAqE8rnWxlQ7+3jFagrWkvKTYEEEAe2ovyAAAgAElEQVQgSQIEQ0kabc4VAQQQQAABBBCoQ4FCQdEhnXr21Pi456bN5Xj6dEqThRK12HLPc1inPB3RaVW1CohmtTrrtFYt2dLy2eloeutJNdO4uRYXBq+JAAIIVClAMFQlIE9HAAEEEEAAAQQQiJaABSgjLW1iq1n5vQ23tshQS6vfu/W8v9xpVUEERItalZS/zL2FQa/OzsjL0zNyXqeh2ZS0jPY/Smk/npYdDdn/3qtNoh8fGpYDGsyxIYAAAgjEQ4BgKB7jxFEigAACCCCAAAIIeBDo1coeqxoa0OlXUxlvS70Xe5m7dH+Hu7rFeh5FZQsqIHr++jL3R3t7swHRrPbzeWdhXk4sLoqtcJYpMt3q1PJyrPoWRWUcOQ4EEECglgIEQ7XU57URQAABBBBAAAEEAhGw8OZ+nWp1n/bieW7Kn2DoqAYkRzQoieJWKCCyJtWVNqp2lrmfvx4QZba2ZU4bfhcLhByT/MbWf7x3LFtB5Pd2oL1DLKizBtp+brbi3cGOTj93yb4QQACByAuwXH3kh4gDRAABBBBAAAEEEKhEwMKSndqg+PyVK1UvbW4hxBeHR7JVSFHebGl3W71t3652rW7qkn79/11NO8VqnCpZ3t2WcF/QaWW21L0tee92s+dNaf+hOa0uGmppkdG2NrdPdfU4G9tFDare0wqlFT02P7a4jLEf58o+EEAAgVwBKoa4HhBAAAEEEEAAAQTqUsCqhj6rq3hdXFuramlzCwy+MTYW2WqhQoPnNKruaGrSyqkeeUuXun9dq6cqrSAq9BpuvvauVi19b+KC2NQ+P/sOBVERtr9tl9y2a5eb0+IxCCCAQF0JUDFUV8PJySCAAAIIIIAAAgjkClgFzUhrm+zUlc0/0sohr9UlTij08MBusbAlbpudv4VDQ7qKmR8VRF7P36qMLq1lpE2PY78GbG3aoNqvzc+qIRvnr4yMZntINWqVGRsCCCCQJAGCoSSNNueKAAIIIIAAAggkUKBtZ6Ps1WqQgVSTXM5kXE+pOqbVRn96223ygP43jqFQ7lDXMiCyaWWrV7d0DNp0ipt/FTkW4AzqCnGVhn6OjxP+HesfELtW2BBAAIGkCezY1i1pJ835IoAAAggggAACCCRPYEl75Uxos+I3dUrVSzMzcnJp+aYVy/qbU9mePH2pZnkonc7ebNn7KK1C5teomcWS9ugJa4pZqqFR/nz/fvmTffv8OoUb+5nWPkZPXzgv3zp/3vMKdHGvCPMdkx0igEAiBQiGEjnsnDQCCCCAAAIIIJBcAQtFZnQJ+8WNTVnXapaZ9XXZ1v8Na/VJs051atFKlLQucx/3KiE3IxxmQPRHo3vkTzUcSmu/Ib83C4deuHxJvq3h0Anta+RmIxRyo8RjEEAgCQIEQ0kYZc4RAQQQQAABBBBAoKjAuk5zsq0eK4OKnnTeHbkB0ZPj51yHK273b4+7W5eB/zMNho7393t5muvH5laEPTMxUfAcrCrsmFaCPdiXln1t7TLS1pqIANA1Ig9EAIFECsSvg14ih4mTRgABBBBAAAEEEAhKIMmBkGOau4rZ7MZ6Vau4FRunZq3EsltQm53DIW0iPayNto/09MrZlRWZ1mqw3G1EK8EOdnZInwZEjHtQI8F+EUAgbgIEQ3EbMY4XAQQQQAABBBBAAIGABCxcsdDEptP5vVmfoWZdnSzozQmIbtOG45nta9VgzmumdjQQCAU9AOwfAQRiJ0AwFLsh44ARQAABBBBAAAEEEECgnIBVBDVL8EFUuePgfgQQQCDqAgRDUR8hjg8BBBBAAIGICszqFI1ZbfhqW4+u4hREQ9mInjqHhUBdCxxo7xBrzDyuK7j5ufVp02mrRmJDAAEEEIiWAMFQtMaDo0EAAQQQQCDSAk4Y9MHKkvxkakrOX7n2xjGlKznd29Upjw8NywF9Q8mGAALxFdjb1ia372rXE5jy9SSGW1tkSFd+Y0MAAQQQiJYAwVC0xoOjQQABBBBAIHICFga9OjsjpxaXZGJtNRsGLWxuyKRWC2W2rt443lPLy7Koy4B/bc8Y4VDkRpEDQsC9gE3BshBnQBs1T2XW3D+xxCOtAulwVzf9fUoYcRcCCCBQKwGCoVrJ87oIIIBAjoBThTG7nhF7c21bb3OTTs1JyR36xzRTdLhcwhbID4NOLC7KwsamrG5t3RQG5R6XhUXPX5qSdFOz9KZSXLdhDxqvh4CPAnd3dst9WgX43JQ/wdDRnh450tvr4xGyKwQQQAABvwQIhvySZD8IIIBAhQKnNQh6+sJ5+fncvKzp6imL+ubbtpSuCGPTc/brqioPDwzIA+k0b7QrNOZp7gScMOjl6Rk5r71FrFKgXBiUv2cLh04uL8mkPp9AM1+HfyMQHwGbTvbo4GD2Z8GJpaWqDtyqhT6ty8fbamFsCCCAAALRE+Cnc/TGhCNCAIGECDhvwp+dnJS3NBSa1zfUhbZzOm3nV0uL8s7CgnxlZIQpOoWQ+FrFAoXCoPwpYl53nrm6JWs5U8y8Pp/HI4BA7QVsOtln031i389Pjp+rOByyUOgbY2NUC9V+SDkCBBBAoKgAwVBRGu5AAAEEghX4l8uX5ZsfnbmlT0v+q1oPl3P6ie38xYvZKTz0b8kX4t9eBYIIg7weA49HAIHoC1iFz3GtWLWtknDomAZLXx/bI4e7e6gWiv5wc4QIIJBgAYKhBA8+p44AArUTsOljL85MZwMftxv9W9xK8bhCAmGGQYc62uVgR2ehw+BrCCAQMwEnHLLVBt+cm5VnJibKVg/1a3+8Yzr9+feGh3W1QhpOx2zIOVwEEEigAMFQAgedU0YAgdoKWCj01Pi4vDIz6/lALBx6cWZG7unuluP9/Z6fzxOSJeA0NXeWlrcG0tVOEysnSC+RckLcj0D8BCwcOqTB0HBrq9ymfe/e0IBoWlcrnNHbZV2d0P7/9vZ2Ngx6sC8tw7okfZ82oO/TgMimpLEhgAACCERbgGAo2uPD0SGAQB0KLGm48/HqlaI9hcqd8umVFTmrN4KhclLJvd+pDvrJ1FTRpeX91nEqBB4d3J2dNsKGAAL1J2AB0RFtIm3VQ+u6QuG6Tm9e29qW9atXsydLGFR/Y84ZIYBAMgQIhpIxzpwlAghESOC0ru5ycrHyFV6sz9CETkGzT2pZ9SlCA1vjQwlzqpidqhMEHezs1OuwiQqBGo8/L49AWAJWAdTXmArr5XgdBBBAAIEQBAiGQkDmJRBAAIFcgcXNzYqrhZz9XFhbZTlwLqusQG51UJBTxSwIuquzQw61W/8gnVJyfapIR1OTpHY0MF2E6xEBBBBAAAEEEIipAMFQTAeOw0YAgXgK2Jv4Ge3HUO3GcuDVCsb7+WFUB1kQ1K89Qu7UIMjpGdLZtFPadzZJp97oGxLva4ijRwABBBBAAAEEHAGCIa4FBBBAIESB9kZ7Y82P3hDJ6+qlrHH5K9p8/J2FefG7OqhQENTc2Kgh0E4ayNbVVcTJIIAAAggggAACNwvw7oQrAgEEEAhRwKoshltbZCDVIlOZtYpfmeXAK6aL3RNzq4M+XFnW62Zd5jY2xHpNVbvl9gka0WtysK2VIKhaVJ6PAAJ1K5C70uMp7RWY1qrKXu2xdqC9I9uQmw0BBBCIqwDBUFxHjuNGAIHYCtzd2S33dXXKc1OVB0M2lcdWh2GrX4Ggegc5YVDuktL0Carf6yiJZ5Ybpm4rgC2zzpv3JF4J/p3z67Mz8v2Ji3JeF37I6ApsC7q66MLGprRoVWWqYUd2em2XBkTWg+3xoWFCIv/o2RMCCIQkwLuKkKB5GQQQQMAR2NvWJp/R5X7fXlisqGroLn2TY81/2epTIIhAqFAY1Kc9hOgTVJ/XUJLPyqZbPn3hvLxw+bJMXu/n9srs7I0370d7e7I/P6nwSPJV4v7c7efxDy9elO9OXJCzVzQUyq/U1IDo2raa/c87+nvdFpj42p4xwiH3zDwSAQQiIEAwFIFB4BAQQCBZAvZm/H4Nhu6bm/VcNWSh0DfGxuRIb2+y0BJwtn4HQoRBCbhoOMVbBN7Wn6vPaGXH/I037KIBkTPtclXGteKjW5uoW4UHIdEtfHwhR8AJGZ+/NKXXkLsKX6sksse/p9PMfndoUB7T6qF0czOuCCCAQOQFCIYiP0QcIAII1KOAVQ09OjiYLUs/sbTk6hTtjf5j+pyHB3YzjcyVWPQf5OfqYoWaR/dp/wsqg6J/HXCE/gjYG/nX5uZuCoXy95ydApQNjQiJ8m349ycCdi09NT6uIc+lktdTITO7vt5Z2pAtvXOsbZcc7+8v9DC+hgACCERKgGAoUsPBwSCAQFIErGros+k+sVXKntM/PF/UlaZKNaO2SqEnRkY1FBogFKqDi8Sv6qDcMOjz/QM0j66Da4NTqFxgKdv3xZnaU34/hETljZL6CKs8qyQUyvU6vbIiz01elOGWFqaVJfVC4rwRiJEAwVCMBotDRQCB+hKw5tFHdErZ3l275LA2o/7lwoLMaD+Dk0vLsr29Lf1W7ZFqzjaz/HRPjxzu7iEUivkl4GcgdCydFsKgmF8QHL6vAr06ZcfC0ko2QqJK1OrzOW4qz9ycufUj+un0jIy0tEqv/j5nSpkbNR6DAAK1EiAYqpU8r4sAAgiowLXl61vlkcEheVAriNb1D8lFXenEtmZd7aRFVztp114Y1g+DRsHxvWT8CIToGRTf8efIwxEYSrXKSGtr1S9GSFQ1YWx3YD+rfzQ5Ka/MzPpyDnYtWUXwPd3dTCnzRZSdIIBAUAIEQ0HJsl8EEEDAg4BVD7H8vAewmDzUz0DIqoMOdnbQMygmY89hhi9g4fk9XV1yt069ddu7rdxRlgqJWJa8nF787p/Uvn8nlxY99xUqdaY2peys3ug1VEqJ+xBAoNYCBEO1HgFeHwEEEECg7gSqDYSoDqq7S4ITCkngPp12+/WxvfK69oh5Xxv7+xUQ2eHnh0Svzc6xsllI4xrWy2S2t2Rty9pG+7fZlLIJDZxsqjjTyfxzZU8IIOCvAMGQv57sDQEEEEAgwQLVBEIWBt2lFUHWU+rezm6qgxJ8HXHqlQtY5eVxbdJ/vwZES5ub8qYGRC/pVB7r3Vaqwb/XV3RWnhrXN/zdTTuz032P9vYIVUReJaP1+NnMukxrgOP3ltGwaf3qVb93y/4QQAAB3wQIhnyjZEcIIIAAAkkVqDYQchpJ72tvo6dUUi8izts3gdypucPac+ghbdRuvdveXVzwPSSiisi3YYvEji6urcn4lVXfj2V6PSP2e2LIhx5Yvh8cO0QAAQRUgGCIywABBBBAAIEKBSoNhJgqViE4T0PAo8C1kKg9+6zbtBovNySyqWZ+Tjejisjj4ETw4e07G6WrqUmry/yt7slctSlq/u4zgnwcEgIIxFiAYCjGg8ehI4AAAtUKWLBxWt8cXfs0cyO7u97mpux/D7R3yAFt4sp2q0C1gRCNpG815SsIBC2QHxI9vLFxY7qZnyFRsSoiW3nyDv2ZSp+ZoEe68v03N1xbDbTyPRR+5qGOdjnY0Vn4Tr6KAAIIRECAYCgCg8AhIIAAAmELvD47Iy9Nz8jE2qrYiikZ7X2Q2drOHkaqYUf2v9YzY79+wn5I38g80NtLSHR9kE4vL8vTF87LC5cvy6T2o7DGouU2p0KIQKicFPcjEI5A/nSz3JDIz55EuVVEtmz5/rZdcq8uXc7P1HDG2eur2Acid+nvPOsd5edmv09ZedRPUfaFAAJ+CxAM+S3K/hBAAIEIC1io8YOJCXlxZlourWVkVRtiFg82VuWUhkavzM7KDy9eTHxjVcfuNW1maz0o5jevVViVGm4CoVI63IdANATyQ6Lc6WbP6M9LP1Y2y1YRLW/IOf3Z8fbCAj9TozH0txyFXQsW4vi52e+BdKrZz12yLwQQQMB3AYIh30nZIQIIIBBNAasSenJ8XN6am3cVathZWGg0qb0WJjPakFM/QV3UVX6+tmcsUdVDNm3MgrH/OXnRVSDkrC5mby4Pd3ZJXyolffrGoLmxIZoXBkeFAAI3BPKnmx3p6c2ubObXVLP8n6nOkvesZhaNi7C3uTlbJTuQavFtFbvR1haxJuhsCCCAQJQFdmzrFuUD5NgQQACBagUsELmsU34e0DfqSe3tYAZ/ffasvKGhUPEKofLSXfpJ6iO7BxIRDuX2EXprfiEbjpXacquDbHWxtL6xYOpAKTHuQyAeArbs/VJA/YhMwH6ujmlwYMvdH9RQgv5utb0uPlhalr88c1qem5qq+kBsWto3xsbk4YHd/D6oWpMdIIBAkAJUDAWpy74RQKDmAvbm/mfaS+fn8/OyS0vEj/f31/yYwj4Av0IhO26bDvH8pWt/LNdz5ZCXPkJMFwv7iub1EAhXIH+qWW4/Ij+mmuWvZnZUe7p9uqeHgCjcYb7xanvb2uTRwUE5r1Wy1U4jPKrjSChUo4HkZRFAwJMAwZAnLh6MAAJxE5jUP+zOrCzL1HpGV58p3xMmbudX7ngt4Pie9siotlIo93WccCjd1Cy9Ok2qnqqwvPQRIhAqd/VxPwL1J5AfEvk51cxZzWxew/fXtLcbAVFtrh+b9vtZXUHOlph/cvxcxeHQMd3Ho7uHqBSqzTDyqggg4FGAYMgjGA9HAIF4CVxcW5PzepvSqWSndFn2mfR6XQUZ5Ubjbe2N8cLl6aqmjxV6DXsDYyvs3KOr69RDFZYzbezZycmyPZgIhApdEXwNgeQJWEhk/Wisf4yfVUQERLW/lmxsjw8MZA/EazhkvyO+NDwkvzc8LCMtbbU/GY4AAQQQcCFAMOQCiYcggEB8BSwYshWkrK/O4samrOuy7EnZrPrltbk5142mvbrYMvfWlPWerq5Yh2021e4758/LicXFksvPEwh5vUJ4PALJEChWRVTtNDMCotpeP044dEDDP/td52Y8rUroiZFR/dDk2sIDtT0DXh0BBBBwL0Aw5N6KRyKAQMwELBj5xcInzZbf04qhU/q1oYSsDmLVQq/MzAY2aha22Yo6NpUijlVDzrSxF2em5ez18LAQFoFQIRW+li9gVWezWpmYu83qFNZpvdk2u74hM5lr/z//uYX+ndZpmr3N7pbNTtty2HrL3Xp0eeyoTvPMt4rysRYam1Jfy60i8muaGQFRKfFg78sfz7P6gcippUX9vl6XGb3ZwhZ3trfLwc5O/X5rylaQWZUQq1AGOy7sHQEE/BcgGPLflD0igEBEBKyn0IKuJONsi/rvpPQZCrpayDG1qiH7QzlOwZDbaWMEQhH5Rg75MHJDCwt2LEzOD3SybwivBz7O4Vk/kkxeReLa9idfy2xty5qHisWWxkZJNexwdfYpfWzLjoabHmtfS2mvFNv6NDTq02W488MmJ1AKI5hxvu9OLS7JxNqqnNcw1tmcYz1kb7DrZFWuIKaZ5QdEnbqama1kxlL3rr5NqnqQM563te3SD0N6ZH1rS29XZU2/rzt12llHU5Ok9HuQQKgqZp6MAAI1FCAYqiE+L40AAsEK2Kf39qmesyWpz1B+KBaUtFUNTWiDb3ujHNXqBOfcc5efLzVtjEAoqKslOvt1wp/84MeuY1uJyAl4LNixKaj5gc6qvim0az/Qzcdm+amGRmltaJD8sMkJlEqFSBYe3aFhTTXf37mr/C2oZzG/dxYWpbtpZ7bpcr2sehjENDMnIBJZlXG9Xq1yk4Ao0O/GGzu34Kev8ebqvHBemVdBAAEEghUgGArWl70jgEANBZz+Qs4hJKnPUH4oFuQwZOyTUw+VEEEeS7F9u1l+nkComF78vl6s6sep9HGqe/KDn2KBRfwEbj5i+9mXDbJchE35IZKFRl1amWLVR1Z5ZFNlnClubkIj+957anxcnr90qWy/s9yKmPe0sqjewo78aUlu+9aUuv7M7J2ljWxAdObKihztScsDGqxZXxw2BBBAAAEE3AoQDLmV4nEIIBArgfz+Qs7BJ6XPkIU1+dNaghpA66Fib8Sj2LvJzbQxAqGgroxw9mtjfPp6/zCb8lWq6qdegx8/pUuFSBYavaLLqDtT3JzQaET7tuVOVXMCozkdG7ehUO455IYdTTo9p1f7LVVTseSnjx/7KhQQva/XsN1O6K2SzcxempmTk4vLcmZluW4qriqx4DkIIIAAAt4FCIa8m/EMBBCIgUCxqVRx7IkTdW6rvlgLelpNBQhuqoTu0k/VbQWZh/r6stUQ9IeoADrEpziVQB+saCN5rShxQiB7U+xM+SL8CW5ALDSazNw6he6XuqJf7lQ1JzCyx9uqkPMuKpUKHbWN64szM7rCU3es+pgVOpdCX8vvQ/SWriL5ui4aUGlA5IzP85empB4rrgoZ8jUEEEAAAX8ECIb8cWQvCCAQMQGrIDipbxzzN/vDOS49cfKPnX+7E3BTJWSB0BeHh+VId6+MtLWKvUFji5ZAsRDIKuGyU45K9KqJ1pnU/9GUqjKq9uwtzH9u8qIMt7TU7fQopw+RNTC+XxsbVxsQ5VZcxa3/UKHve5u6SIPtar+TeD4CCCBQWoC/hEv7cC8CCMRQwP6w/GjlStFPqS/oijiT2o+hnqYm5A+TLZtrFTDWdyLozaaQ2GtFYStXJUQgFIVRKnwMuVPCTmmw6zSBJgQq7JWUr1ro9NPpGbm3q7tugyFnLPMDoqXNTammD1HU+w85IZDTBL7Y932LNk63/kl/vGevPJBOJ+XS5zwRQACBUAUIhkLl5sUQQCAMAQt9zq9eKfpSF/R+Wy757q6uoo+J+x2D2vNjVG9vLcwHfirDrS0y1NIa+OuUeoFyVUJOH6FHB3fL4e4eKoRKYYZwX6GqAAuCnClhcxsbwa/65eE87frp1z43hba+lAWjzYXuyobP6VRLkfuaZIf+L3flRHvgbGbtlq85O8g2z9bVFnM3e/6UPqeeN7suPtLKoTisfujHOPi9kpn5RaH/ULHw12kCX+z73sJBO/4Lq2vyJa30fGxouK4/2PHjGmIfCCCAgFcBgiGvYjweAQQiL5DRJabXtPlyse1j/eNyQm/1vA2lWsUawga9WQXOYf0kv5a9ecpVCR1L92X7CB3s7KCPUNAXRJH95wZBL2v1R5SqgXJDHwt5DrW33whznMq7Zq1YaNbVuQptLQ07pFmbMhfa7HnWb6fQltKmyrbZz6vczabKrRf5+bWub5DXtrZverytCJj/eAtQLDByQiYnUIpziBSH1Q8LjXO1X8vtQ9Spq8NV2oMo7P5Dfoe/dvyndIW7/3ZuPPv7+ysjI3VfQVbttcPzEUAAAS8CBENetHgsAgjEQqBYfyHn4JPQZ8iCGqvkGdBqhSCrCWwZ646m2v0qeX12Rp7UpbDfmpu/ZeqgM23sIZ16MNLSVtPwKhbfOD4fZG51wDtauXZemxBb5cKkVrzY92AYm9fQx0Ke9uzS7NfCHAtvgg49m+VaQOR4+NHval0bwlvg5IRMTqCUGyJZWHRqafFGdVLUw6OkrChZ7PvCrovjAwM3ehA9e2lSTi4te/75HkT/oWIhUBD9wCa1Ou77Fy9mf4Z8bc8Y4VCxC4avI4AAAh4FavfXvMcD5eEIIICAGwH7A7VUfyFnH79YWJB39Xa8v9/NbmP5mLs7u+W+rk55biqY6igLXv718JAc7OgM3cfG+Yf65uC7ExfkrAYOuUED08ZCH44bL2jj8qqGdbZimE3XtMbBtlpYsSkifhypE/7kVvsUqvSpRejjx/lVsg8LsyxwKhUyWXh0RBsdO9VG+eFRbnBk/7+SEKKSYy/2nEUNFW21ySRvuT2I7tGf7W/qKmbfPn++oiXuK+0/5IRA5foCBRn+2rHbymvT6xn6DiX5G4JzRwABXwUIhnzlZGcIIFBrgXL9hZzjS8KbjL1tbfLo4GB22s4Jbebr92bVQsM6Xa3Um0+/X9P2Z1VC39E3Q2/NL2j1yc2hF8vPByFefJ+FpohZhVpQK4Y5IdCdOt3rYGen9hnRa1D7W9k0r9zgJ4xKn+Iq8bjHwqO+xsJ9k+wMcoMjC40s4Lu4tnZTlVGly6rHQyi6R3ktILo25fEencpbbYPqUv2Hciv/ZjKZbOBr1X/l+gIFref0TbK+Q8e0KpRVy4IWZ/8IIFDvAgRD9T7CnB8CCROwNy7n9VZum9LpLDN5TVzLPSdu99sbv89qfx0zuby+4XnKQanzrUW1ULkqoS9p9dIju4dYfr7UwPlwX35VUFBTxEqFQJ06rcaW9iYA8mFAi+yiUHB0Z16Vka2ataSNwsOoLrIG3/W8kmSRYSj55dz+Q0d6eisOiPL7D+3X4NW8bVxzm8KvaT+rVe1/FWQ1UMkTzrvT6Tt0aS0ji3otMrXMix6PRQABBG4WIBjiikAAgboSsBBkXD/NLLcloc+QGdgbh8cGh7XXyJZ8S6ts/Og3ZKHQN8bG5OGB3aFVCzkNpm36QG6VkDNt7PP9A3JPd5f0FVk5qtz1wP2lBZwwyGkc7XdVECFQaf+o3FsoLLJjy68u+pF+n/r188Y595Q2+LZm3my3CvgVEDnTy07pFNBWtY5SCHTrWX/yFWdqmX2FcKiUFPchgAACxQUIhorbcA8CCMRMwMKDX2iTW7efZiahz5ANofVe+bKuytWr024q7UfhXAphh0JOIPHs5OQtDaaZNhbsN2ihMMiPxtGEQMGOWy32nh8YjbQuZaf2+bkd6tDpgzXoZ+bnOQS9r9yA6La2XfLG3Kz8bGbG81Ri+x3q9vdoEOfk/IzwsoqeEw69p/3NfndokCXtgxgY9okAAnUtQDBU18PLySGQLAFrTLqg0xrcbknoM+RYWDj0yOCQ2KfuT46f8/xGoRYNnS2Y+O6FC/KPF87ftJKVHQvTxtxe5d4eF2QYZH1ArC/QiK6UN9jWKkwH8zY2cXr0gfYOseB2XPub+bHZvj6tU6XC7mfmx7HXYh/mZFPLDqjbbbvaK/qZH+Zx28/0uzo75JD1DtNjdvqGndAPerx8mOFUPF3W33Ua+NkAACAASURBVB0saR/mCPJaCCBQDwIEQ/UwipwDAghkBWa1Z5B9wuh2sz5Dp7Qp80x6PRG9K+zNgi13bG8W3DYrzZ2qdVD/cO/TP+CDXr7bxq/Q1DGmjbm9sr0/zgmEfjI1JScWF6teUj63KujBvnT2jZ5N86MvkPexieMzrPH9ZzSYeHth0Zfpq0d19bQjvb1xpKjZMTtVXF5/5gd9wMUqBjubdkq7LmjQqTfnd8yILm5gzbV/pBWjT+sqlG6nQrOkfdCjyP4RQKAeBQiG6nFUOScEEirgtr+Qw2Ol8rbSzro21EzKlt+L4qz2kji1tHgjULNmo9uKYZ/cprWyw6o7wgyEik0dszcTXx0dld8bHg4tnKr3a8Lv6iAnuMuvCgorTKz38YrT+dkb+9/cPZhtCFxtryGqhaob+fyf+W4/FKjuVa89u1gIZKsIuqkYdI49rT//h1pbPFcPWU862+g75Mdosg8EEKh3AYKheh9hzg+BhAh47S/ksLynFUOntDfRkH4ymaTN+YPb+lAc0U/j13WlGdtsWWrb7JPblP7xHuaqT8Wmjh3TldWe0B5JNJeu/go149PXr/l3dJpGNdVBuUGQs2w8VUHVj1G97MHpbTaznpG/+fjjik7L6WlGtVBFfDc9KTcgsqqcZy9NysmlZddVOOWOoNoQqNT+K50KTd+hUqrchwACCNwsQDDEFYEAAnUh4LW/kHPSp7Vixqpmjvf314WD15PIbxrr9fl+Pb7Y1DHrJWRVQiMtbaFMYfPrfKK2n9ypYnbNW6XcnPbj8tJglulhURvV6B+PvaH/wtCw7NKQ2WsTZAuEvz62Rw5399BbyMehdqYU39PVqVOK5zxV4RQ6DPu5YD+nH9aVId1WAhXaT7mvOcdtj/PSJ4++Q+VkuR8BBBC4JkAwxJWAAAJ1IWBVECd1NRKvW1KWrffqEubjX5+d0T/0x29adYwqoepHwI+pYvamz2kKe29n942m0UwPq358krKHg9qIerClxXUTZCdoIBAO7gqxkKVj57XpwtbDp5rpZTZdcHJtTVo1/LP+dUFuTjhkr0PfoSCl2TcCCCRRYMe2bkk8cc4ZAQTiLZA7JcYaSL+1MCfnrlS2As6g9tHZo1PJ7tVPUG1FFFtRJ+g/cOOt78/R2xj+UBuKfnfigpzVsbOQzpmeZG8K79U3LGE0uvbnbKKzl9zqoEqnijnj8HmtAtjX3nZLU9jonC1HEheBJQ0QJnSVsty+ZtbT7LIuAmCVRbkrUg3rz2OblsgWjoCNzQvaeN5LJU7ukXXp1LQxHbOjvT3yuFaIhfH7c1qvmxcuX/Jc8WTH+sjuAfoOhXNp8SoIIBAjAYKhGA0Wh4pAkgXsza6tOvbBypK8PD0j5/UNhpWIVzIlppij/cHYrSujWP+F/dp8+VA2JGqXO/S/6ebmYk/j6xUIFJo6Zv1ErJfQQ319NJiu0PQHExPyi4WFbN+QSf1+8TpVzJaUz11FjMqgCgaCp5QUWL+6pU2pN7J9zayn2drWtrQ07CB8LKkW/J1OcFdN9VDYoUulgVbYxxn86PEKCCCAQPUCBEPVG7IHBBAIQKBQEJTR1cMsDPL6hreSw0s1NEpPky6dq0GR/RFpy+YSFFUieetz8qeOOdUpjw7upp/IrVyuvmJB21M6He/5S5dkXr9H3G6OPWGQWzEeh0B9C1QatjgqYVcP2fG+Oz+vDc7H5YXpadeDY8d5f0+X/PGevfKABuJsCCCAQNIFCIaSfgVw/ghESMCZAnNKewVNrK3KeZ1eFFYQVI4hPyhi2lk5sVvvLzR1jCqhW50q+cqPNRD6z2c+lA80ICq3OWGQs6z8wc4OKrTKoXE/AgkSiFv1kFWhTaxdkf8xcVGe1unJVjHpZrPf60e6u+WLOnXZwiEqg92o8RgEEKhXAYKheh1ZzguBGAjkBkHWa8Kmh9kfdAu6YtKqTjPwMg0m7NPNnXbW1dx0oz8G/YkKj0T+1DGqhAo7VfpV69X0nz88LeP6PVRsy+0bZGFQh1bEpXY00MepGBhfRyDhAk41znMaPL84M+M6cHHYwq7KqaTvkIVDg9rj6kvDI/Ll0VHCoYRf85w+AkkWIBhK8uhz7giELFBoelhcgqByVIX6E/VqYERQJJI/zYkqoXJXk/f7P1halr88c1qe0wayuRtTxbxb8gwEEPhEwKpxptcz8vLMtOdGz7YXC172trWK9S8LozF1pVPhbBGK3xoYkK+MjITSPJtrDAEEEIiaAMFQ1EaE40GgzgScqiCnYXSYfYJqRelMO0tpQ9WkN7LOD4VsGfqvj+2hl5DPF6e9eXtncUFemr6sb+LWs5962wp7wy3XVneiibTP4OwOgYQJONPLvC4T7zCF2fC5mr5DrFiWsAub00UAgRsCBENcDAggEJiAM33ohcuXQ2kYHdiJVLnj3P5EX98zli1XT8KW22S6qaEh+4kxy9AHN/K5qz01q7eFks2NDcG9IHtGAIHECVQyXctBchpT/+7QoDymy9oH2dPHCcv/v7MfeW5KTTiUuMuaE0YAARVo/AvdkEAAAQSCELAwYEVXDNnWndsfgKta1bBydTOIl4rsPm0qz6iuaHZfV5d8rr9f7urolN0tLZE9Xj8OzKrEfnJ5Sp76+GN5dXZOxnQawb/duzcbiN22q52wwg/kAvto1Aq1tp07s72D7L/2bzYEEEDAT4G2nY2yp21XdrXOSV0k4rL+vHe7ZbR34JROS/tweUXm9Xm7UylJ6y2IzX7+WaXk4a5O6WzcKR+vrrn6+8OO8YI+dlX/VrGKy6COL4hzZp8IIIBANQJUDFWjx3MRQKCsgJV0L21syPrWVVnUptLv6nSX95eWsrcTeqvHLX/Vp0ENRjqvv2Gv92a/Fgp998IF+ccL57NVYkd7epg6Vo8XOeeEAAKJFqh25bIwp5ZZldPT+jvpW+fPu26gHebxJfpC4uQRQCAyAgRDkRkKDgSBZAg4QZHz31PL10KiOAdFFgTdpas8HWpvv6mvS9JWfcpdeezq9jZTx5LxLc1ZIoBAggUqbfZsZGGuWlbJFLgwjy/BlxCnjgACEREgGIrIQHAYCCRVIDcoOruyIqeWFuX95WU5qass2YplUdwsCOrX8vc7NQh6sC+dLTfvbNop7Vpan9S+LrlNpodaW+SJkVF5qK8vW8pPn5soXsUcEwIIIOCPQDXVQ2GuWlZJiOUc35eGh2/0RbLK2NNa8WyrtaX1d9wd2ug/yH5J/owSe0EAAQRKCxAMlfbhXgQQCFHAaZ67vLmRnXZ2cW0tEkFRoSCoubExOz2M4OPm5egtFPrG2Jg8PLBbOtSHDQEEEEAgGQKVBC+OTFhTtyo9RlvO3noFtujv/vOrq7Kgf6fYKqsp/bcde0ob/VvV8OPaVPuABkVsCCCAQNwECIbiNmIcLwIJEsgPisLsT1SsTxBB0M0XoLPy2MerV+RwR5c8OribpegT9D3KqSKAAAK5Aha8vDs/L89duiQvzsx4qvyNejhk1UO2ZbRnYqHNjn9MF5s42ttDQFQIiK8hgECkBQiGIj08HBwCCOQK5E47s4bWfvYnyg2C0s1N2elhfTpdLGl9gtxecVZK/+rsjHxvYkLGr1yRL+qnpLYUPcGZW0EehwACCNSngH2oY9OsXp6Zlm9rw2cvC0044UrQS9o7AdbffDzuaTl7NyNGbyI3SjwGAQSiJkCdf9RGhONBAIGiAjY1KXd60qHOTnlYAyL7A+9Hk5Py9MWLnj6ddF7oWLov2xPnoDaQJggqyn/THf9y+bJ886Mz0tvcLP9+7z6dOjaQDdLYEEAAAQSSLWB95Ya1cuaRwSGxKpsnx8+5DodsitY7SxtyWT98mNBl478yMhLI1Cz7W+JIT6/2wLtWBfTC9LRvg2bn8NLMnE41287u84F02rd9syMEEEAgKIHGv9AtqJ2zXwQQQCBIgVRDQzbIsUBiWv+IfHt+Tuf9b3p+yd/ZPSBf1mCoW0MO22djww7P+0jSE6xa6CNtFG4NuP+1Vgk91N8vPWrHhgACCCCAgCNgv0+HNCC6vW2XLGpYck6rS91uy1c3s49f1f9aBW86gA8e7He9Vbke7uqUzsad8rEGUSv6en5stjLnVGZdNnXa2VhbWyDH78dxsg8EEEDAESAY4lpAAIE6Edihf0SuyIcaWHjZ7tImkV/QaVD7O9q9PC3Rj22UHTKoTaYPd3XLbbvapW3ntU9cE43CySOAAAII3CJg4dBubdxs4cs+DUjm9IMFqwZys2W2tuSChjUfr65Ib1OzjOrz/d4sHLIPhW5v75Cd+pnQRxpG+RkOXVrLSJsa7Ne/NdquVyf5fQ7sDwEEEPBDgGDID0X2gQACNRewsnCrYnlDq4a8bHdosPHrtuS8fqrJ5k7A/pBuU2+7UV3lzoxHIYAAAkkVcMKXffr71vrvTK6tegqHLmq4cmJxQda1+sbCoSACFvuAY69WNg2kmuRyJuP6+MqNqYVbq9pzaa8e975du8o9nPsRQACBmgk01OyVeWEEEEDAR4FrPQ1a9I+6Fk97PaSVQgc7Oj09hwf7L2DT02Zcfors/6uzRwQQQACBoAXsA5zj2o/u62N75W4PS7rbKmCnlpflv50bl786c0Y+WFoK5FD7UlY51C6dOkXdz82m0S3pjQ0BBBCIsgDBUJRHh2NDAAFPAkMtLTKqN7ebrUR2m36Cl9vQ2u1zeZy/ApOrq3JiYYFwyF9W9oYAAghESsAJh/73Ow7I7w+PePowZzKzJt/XRSae0pXEggqHgsCyXkOnNMziw48gdNknAgj4JUAw5Jck+0EAgZoLDOp0sFEPU8JGtU8OU8hqPmzZA7BpAn8zPi7vajjEhgACCCBQvwLOimB/evvt8h9uv81T9ZCt+PX8panAwqFZDXFsMQs/N6t4WtzYlPWrV/3cLftCAAEEfBUgGPKVk50hgEAtBYZSrTLiIRiyZXSbtSkkW20FTusUgdfm5uTN+Xl5c26WT1VrOxy8OgIIIBC4QO6S9l6nlgUZDl1cW5PxK6u+n//0ekZsyjQbAgggEFUB3hFFdWQ4LgQQ8Czgtc8Q/YU8EwfyhLc1DHplZlbsU9XXZueoGgpEmZ0igAAC0RPInVp2vK/P9QE64dB/+vADeXVmxvXzyj3Qfg/Zze8tow2o1wLYr9/Hyf4QQCC5AjuTe+qcOQII1KPA3Z3dcp8ui/vc1FrJ06O/UEme0O50qoXmrzfmPK0ry53V2/H+/tCOgRdCAAEEEKidgDO1bEindz+UTsszExNywkWDaQuHXpqZk8zV7ezBP6DPrXZL69L1tojFlPYz8nPr0/32aV9DNgQQQCCqAlQMRXVkOC4EEKhIwP7A7NTlcMtt9BcqJxTO/bZSy8LGJ6u12Ce1E9qImiad4fjzKggggEAUBKzi15az/z1tSO1lapn9zrBpyP/nqfflv587W/XvDufDJb9NhjX0Gmpp9Xu37A8BBBDwTYBgyDdKdoQAAlEQ6LVP5VLlP5Ub0T/QhvkjreZDdlo/FT65ePPSw7/QBtQ0oa750HAACCCAQOgClSxp7+dy9m4/XPICYxXKttCFhV9sCCCAQFQF+AkV1ZHhuBBAoCIBt32G+PSuIl5fn5Q/jczZuU0nowm1r9TsDAEEEIiNQCXhkJ2cH8vZ24dLhzo6stPJ/NqoUPZLkv0ggECQAgRDQeqybwQQqIlAuVLwu/SPvsNd3Xx6V5PR+eRFz2kA9KHe8jeaUOeL8G8EEEAgWQKVhkPVrlhmHy7d39Ob7VXox2Z/b/zB6Kjco39zsCGAAAJRFiAYivLocGwIIFCRQLlS8C7tQdTRRO/9inB9epIt2/uOThkrtiyw04Tap5djNwgggAACMROodsWypz4elw9cNLHOZ9nb1iaPDg7K3RrqVLsd7emRhwd2i50LGwIIIBBlAX5KRXl0ODYEEKhIILcUvNDKIqwOUhGrr0+a1AbTZ1aWiy4LnNuE2laJYUMAAQQQSJ6ABSpHtILHViy7s71Dnr540dWKYU7l0PR6Rv54z15PK5ZZ1dBn031ycW1NLq9vuHq9QiNj1UKf1mMnFCqkw9cQQCBqAgRDURsRjgcBBKoWsD/qOpqapKVhR8F90V+oIEuoXzyxuCBvzy+UfE2nCTVL15dk4k4EEECgrgWcFcv+cGxvNiD69vnzgS9nb2HOY4PDsqux0fXrOYNgzaaPpdNadbRbDnf31PXYcHIIIFA/AgRD9TOWnAkCCOQIHNBPFu3TunGtTMnd6C9U+8ukWNPp/CNzmlDf09UlVA3l6/BvBBBAIFkCfalmeWRwSFINjfLk+DlX4ZCznL2cPZvFekADG7eb83q2gulzly7JizMzZauH7G+MJ0ZG5aG+PunTgIiVyNxq8zgEEKi1AMFQrUeA10cAgUAErEfA7bvadd9TN+2f/kKBcHva6dLmhixsbJR9jv1Bv7ixKetXr5Z9LA9AAAEEEKh/AafvkJ1pGOGQM5Vt765d8nB/v/bGm5dp7ZE3o7eTS8uyvb0t/amUTnNrlwf70rKvrV1G2lqZPlb/lyJniEDdCRAM1d2QckIIIGACucvW5/YZOtTRLgc7/FltBOnKBE5rM9CTi0uunvyePvbU8rJOH2h19XgehAACCCBQ3wJhh0PX/p5ozVYAWQXr+taW3q59cGHSzTrdrFOnnlEhVN/XHWeHQL0LEAzV+whzfggkWKBZy83z+wx12opkrA5Ss6vC7TQy5wCZTlazoeKFEUAAgcgKOOGQTTP+G1197IXp6bLH6kwrmz71vnxpeFgeGxr2NE3ZAqK+xlTZ1+EBCCCAQBwFWK4+jqPGMSOAgCsBp8+Q82Cb+3/Qh+VnXb04Dyoo8PbcrLwyM1vwvkJftD/kX5udk3d1aXs2BBBAAAEEHAFnmte/23ebHNeePm42+51iVaj/7dy4PK1NrG1KGBsCCCCAgEjjX+gGBAIIIFCPAvZH40crK/LG/Fz29O7QnkO/rj0AhpmWFPpwz+of3z+5PCXfnbgoZ66seHr9xc1Nke2tG0vbp7WfAxsCCCCAAAKNuvqoTeE63NUpnY075ePVNVm5qr8zymzL+phzV65Ioz7O+ge16XQwNgQQQCDJAkwlS/Loc+4I1LlAfp+hqPUXsrDk1dkZOaX9duxTy8vrmeyI2B+5h7Syqbe5Sazq6UBMq5yc83t5ekbO6+pw1utpMuP901n7hPenuo9fatWQTQU82tsjD6b75A51YbWyOv8m5vQQQACBMgK5y9mnNOD5llYC5fYWLPb0Sf2d9Hf6WNu+PDrK75NiUHwdAQQSIUAwlIhh5iQRSK7AUEuLjOrNVg65TT8VjEJ/IScw+cnUlJxYXNQVujZlVZtZWgBimy3F+8rsrP53x40g5HHthRCHgMjOzZpLW6m+rd5i52dhkHNulV6JC7aSmd5EVmVcQyZbNnh/2y65t7tbHujtjYVNpefO8xBAAAEEygvY8vJf1qXibSMcKu/FIxBAAIFcAYIhrgcEEKhrgUGdNjaqt239XxSmkL2uFULf0U8oSwUmFqJMZpwl2q8FIdZnxyplohoQ5YZd1jDalpmf0yXpqw2ECl2c2ZBoeUOnAazK21pF9MOLF7M21j8qzhVWhc6VryGAAAIIuBcgHHJvxSMRQACBXIEd+in6NiQIIIBAvQqsX92Spy+c14qcq/L7o3tqWjFkodBfnz0rb8zNVxSYdOk0qkd2D8jX9oxFokKm2FSxIMKgcten2XQ37YxdhVW58+J+BBBAAAHvAtNaqWq/+91WDtkrDKZa5Ks6pYxpZd69eQYCCMRfgGAo/mPIGSCAQBkB+wNRuxdLXw2bFlcbCjmnaAHI1/QP1z/cu7dm/RDyp8L5MVWszBB6utuMxrRKjCoiT2w8GAEEEKgrAfvd/8LlS/Jtq9LVKc5uNsIhN0o8BgEE6lGAYKgeR5VzQgCBSAmc1n47/+XsR/LspamKKoXyT6YWf7hGqToo36PYv6kiKibD1xFAAIFkCCzpqpYvaD+/J8fPEQ4lY8g5SwQQqFCgocLn8TQEEEAAARcCFgo9NT6un1pO+xIK2UvaSir/pH/ovqv9dcLabEre2ubV7PK+r8/PyTltAF2LKWNeztd6EdlxTmYycklvi9nm1WwIIIAAAkkRsAUnjg8MyNfH9srdLlf4dFYr+6n+nmVDAAEEkiJA8+mkjDTniQACNRFY0jDi49UrMu9zKGENnt+cm5V7urpCmVLW29wsx/r75VP6eva6L+mqYCeXll0tCVwTeH3R/uaUHEun5fP9A7KvvU3S2j+CDQEEEEAgWQJOOGRn7bZyyMKh701MZBeteEB/j7AhgAAC9S5AMFTvI8z5IYBATQVs6faTi+56G3g5UKvWsZW/1q86q5d5ebb3xzY3NkhfYyrbp8n+UH5I/1C21393cUGe0T+e3fZv8P7K3p7hhEEP9qVluKU1e7x9GhDZ8bMhgAACCCRToJJw6F39/f3/6oIRthEOJfO64awRSJIAwVCSRptzRQCBUAVsGtlrc3O+Vws5J/Ge/tF6Sl9jSIOaMDf7A7tjZ3v2JW9rb5cjPb3ZKqL39XjsVouQKLc66GBnB2FQmBcEr4UAAgjEQMBrOGQfwLw5Py9COBSD0eUQEUCgWgGCoWoFeT4CCCBQRMCmkS1sBNfXxqaTndXbcZ3iVavN/tA+pH0brIroYT1Xa/RpIVEYVURUB9Vq1HldBBBAIJ4ChEPxHDeOGgEEghcgGAremFdAAIGECszqUrnT6+uBnb19mpkJaSpZuZO4VkV07VeKhURBVhFRHVRuNLgfAQQQQKCYgBMOzW6sy+X1jbK98nIrh17R/nq2zejv9svrmWx1qn040tvcpP3+UnKH/v+09uRjQwABBOImQDAUtxHjeBFAIDYCma2twIMb++PUblH6Q7RYFVE1DaupDorNZc+BIoAAApEXsN9Tjw0O6+/oLfnW+fOuw6FfLS5mz23Vfr/rhzOphkZ5ZXZW/7tDUo2NclSnVn9lZEQOuFwBLfJQHCACCCRGgGAoMUPNiSKAQD0KtO9slI7GaP4oz68isobVP7o05eqP8Nyxukv/wH5iZFQe6uujd1A9XsScEwIIIFADgb5Us3xZf7fY5jYcsjAod7N/T2Y++dqltYz8QvsSHe3tkceHhgmIajCuvCQCCFQmwDItlbnxLAQQQKCsQFpLy63MPMjNPq2Mw4pbFhLt29Uu93Z1ymiLt2Xju3Y2aZPrXdk+RnE41yDHm30jgAACCPgn4IRDfzg6KgMpb7+bCh3FgvYWfGdpUb5zYUKe+nhcPtAFGdgQQACBOAgQDMVhlDhGBBCIpcCgBhmjAa4YZtOr0vqJZ5y2SkwOdbTLwY7OOJ0mx4oAAgggEBMBv8MhO20LiJ7XCtnnJyez073ZEEAAgagLEAxFfYQ4PgQQiK3AUKpVRgIMhkZbW7JVNHHazOTeri7Xn8zaNLJPa88GqzhiQwABBBBAIAiBoMKhF7VZ9bsLC0EcMvtEAAEEfBUgGPKVk50hgAACnwjYtKdhDW/8KE8v5GoByz1d3YXuiuzXzOR+DXru0yllbrajPT1ypLfXzUN5DAIIIIAAAhULOOHQbw/0V7yP/CeeXlmR5yYvMqUsH4Z/I4BA5AQIhiI3JBwQAgjUk8Ddnd2uQxAv5x3nSpq9bW1yu/YbKrfF+RzLnRv3I4AAAghET2Bel7D3c+qXNac+qX2Gzl5Zid7JckQIIIBAjgDBEJcDAgggEKCAhSCPDg7K3T4vXRvnShq3lVTWdLqjiSlkAV6e7BoBBBBAIEdgSXsDLWxs+Gry8eqaTOiNDQEEEIiyAMFQlEeHY0MAgdgLWAjy2XSfPDY05NuUsmO6v0d3D8W6746bSiqaTsf+8ucEEEAAgVgJzGbWZdrnZtFWNTSxuuprJVKsUDlYBBCIhQDBUCyGiYNEAIE4C1jj5McGh8WP5XAtFPr3+/bJIZ8rkML2tUqqz2ivoWL9l5hGFvaI8HoIIIAAAhfX1mT8yqrvEJmtLVm/etX3/bJDBBBAwC8BgiG/JNkPAgggUELAaWr5H26/raJpZbY0/VeGhrOh0L3acNoqkeK8lWtCHeepcnEeF44dAQQQSLKAVffYze9tej0jsz5XIvl9jOwPAQSSLUDzhmSPP2ePAAIhClg49MjgkKQaGuX1uVl5XxtSntBbuc2qZ54YGZWH+vqkTwOiuIdCzvl+0oR66iYCqoXKXRHcjwACCCAQhEC6uTlbyTqV8bcnUObqlqwFEDgFYcA+EUAgmQIEQ8kcd84aAQRqJGDTyo4PDOiS7T2ytLkpb2pA9NLMjFy+3tdge3tb+lOp7NHd2d4uD/alZV9bu4y0tca6p1Ah7twm1Ll/hNN0upAWX0MAAQQQCFqgWT+4aWnY4fvL0DPPd1J2iAACPgsQDPkMyu4QQACBcgIWDtnNtuHWVnkondZPErdv9B9obmzM3tepj6mnCqFCLk4T6uemPvl0lj+gC0nxNQQQQACBoAUOtHeIVa2Oa7NoP7dOW2Xz+u99P/fLvhBAAAG/BAiG/JJkPwgggEAFAtdCovYKnlkfT3GaUL+9sJgt3WcaWX2MK2eBAAIIxFHAfidbiOPnZr/XDsZ8wQg/PdgXAghEUyDe3UujacpRIYAAAgi4FLDpZPt2tcloS0v2Gfvbdsltu3a5fDYPQwABBBBAwD+BXu0xZFO47/YxyGExBf/Ghz0hgEBwAlQMBWfLnhFAAAEEXAgM6nS6Ub19rKX7h7u7Zail1cWzeAgCCCCAAAL+CtiHFZ9N94ktW395faPqJtRUwfo7PuwNAQSCE6BiKDhb9owAAggg4EJgKNUq93Z1ya91d2m1UFvdrLrm4tR5CAIIIIBAxARsOtmDvX1yX1dnVUdmodA3xsbkSG9vVfvhyQgggEAYAjt0BZztMF6I10AAAQQQQKCYwLSuyra8uS5pXSaYBp3F3cemZgAAIABJREFUlPg6AggggEAYAuu6vPwL01Py1x99JCeWljy/ZH9zSv7N3jH5/dE9/E7zrMcTEECgFgIEQ7VQ5zURQAABBBBAAAEEEEAgsgJLm5syoVOc35yblWcmJlwHRFYp9MTIqDw8MCB9qVRkz48DQwABBHIFCIa4HhBAAAEEEEAAAQQQQACBAgIWEL0wNSXPXpqUk0vLBfsOWYVQv4ZAv57ulUd2D8lIWyuVQgUs+RICCERXgGAoumPDkSGAAAIIIIAAAggggECNBSwcmsmsyeLGZrYx9amlRZle1+nPuoqZLUU/rIsmNDc2Sp/+myqhGg8WL48AAhUJEAxVxMaTEEAAAQQQQAABBBBAIGkC1n9ocXND1re2pLmhQTp3NrFoQtIuAs4XgToUIBiqw0HllBBAAAEEEEAAAQQQQAABBBBAAAE3AixX70aJxyCAAAIIIIAAAggggAACCCCAAAJ1KEAwVIeDyikhgAACCCCAAAIIIIAAAggggAACbgQIhtwo8RgEEEAAAQQQQAABBBBAAAEEEECgDgUIhupwUDklBBBAAAEEEEAAAQQQQAABBBBAwI0AwZAbJR6DAAIIIIAAAggggAACCCCAAAII1KEAwVAdDiqnhAACCCCAAAIIIIAAAggggAACCLgRIBhyo8RjEEAAAQQQQAABBBBAAAEEEEAAgToUIBiqw0HllBBAAAEEEEAAAQQQQAABBBBAAAE3AgRDbpR4DAIIIIAAAggggAACCCCAAAIIIFCHAgRDdTionBICCCCAAAIIIIAAAggggAACCCDgRoBgyI0Sj0EAAQQQQAABBBBAAAEEEEAAAQTqUIBgqA4HlVNCAAEEEEAAAQQQQAABBBBAAAEE3AgQDLlR4jEIIIAAAggggAACCCCAAAIIIIBAHQoQDNXhoHJKCCCAAAIIIIAAAggggAACCCCAgBsBgiE3SjwGAQQQQAABBBBAAAEEEEAAAQQQqEMBgqE6HFROCQEEEEAAAQQQQAABBBBAAAEEEHAjQDDkRonHIIAAAggggAACCCCAAAIIIIAAAnUoQDBUh4PKKSGAAAIIIIAAAggggAACCCCAAAJuBAiG3CjxGAQQQAABBBBAAAEEEEAAAQQQQKAOBQiG6nBQOSUEEEAAAQQQQAABBBBAAAEEEEDAjQDBkBslHoMAAggggAACCCCAAAIIIIAAAgjUoQDBUB0OKqeEAAIIIIAAAggggAACCCCAAAIIuBEgGHKjxGMQQAABBBBAAAEEEEAAAQQQQACBOhTYWYfnxCkhgAACCCCAQMwE5ubmZHZ29sZR279n9N+9vb3S29Nz4+s9+v/ta2wIIIAAAggggAAC/ggQDPnjyF4QQKCIwJkzZ+SD06eL3Fv4y/amb//ttwf65s/edH744YfZN55uNy/HVcl5uz2Oah7n5Ryc1wnqXHLf8MftzX4l10/+uFUyFvn7iOu/HT/72WC3mZkZyWQy2ZuzOf9OpVJiN2dz/p3WnxN3HDgg9l/b9u/fL3foLe5b0N9vtfhe83pOQXxvVPI9W+k1VclrVXLdOj9DwxzTD+13+gcfuD7c7e3t7PflAf1e9bpV6ljpuJU7vihcx+WOkfsRQACBSgUIhiqV43kIIOBKYPzjj+Ufn35apqenXT3eHjQ8PCxf/YM/kM997nOun+P1gT976SX526eeuumNaLl93HPPPfLVJ54oG1jZH7M//ud/lmefe67cLkO///hv/IbsGR319Lo///nP5W/Uyu8t9w2//f/hoaEbb/SD+sPer3Oo5PrJf+0wrvP816zlv+374vU33pBXXn1VJiYmZHFxURYWFrL/XVtb83xoLS0t8uprr4n917bOzs7s7eAdd8ijv/M7sQyJgvzZ4Xy/FQrWgv5+8/ozxO3PWi8XzcmTJ+Vbf//3cuHCBVdPS6fT2Z/3lYSNXl/L1QEVeFDumB6+++7sz8+gx/Kdd96Rv/3bv5WrW1sFjujWL9nxDPT333qHi69MTk7KD374Q/mlvqaX7ciRI/KE/g1RydiVeh2v13Elv29LvT73IYAAAkEKEAwFqcu+EUBARkdG9BP9Hnnrrbdca2zpH5yLS0uuH+/1gfbm69SpU54+9bTXOHbsmOzZUz5UWV9fzwZh4+PjXg8t8Mfbm3Cv29LycijnciLnjb69wf/0/feH8kbHq0el10/+60xNTclnH3hAgos/81+xNv92AqEXfvpTee+998TOu5IgKP/obR+F9nPixAkZ2L3b9zeF+a8fxL/D/NmRG6wF/f3m9WfI4OCgp9DezVjYMZw/f14+1g8r3Gz2eyi3gs3Nc5zHeH0tL/su9tjTWnnX1dWVDUftZ+dR/dkSROXtijp+rOHa5sZGsUO56ev9fX2uHlfoQZV+P1jgnLYppz5PO/V6HVfy+7aQA19DAAEEwhAgGApDmddAIMECHR0d0tHR6UnAQhX7I9f6jQTRS8Q+hbSKBS9bn/5xu2/vXulo7/DyNB7rQSD/jb69gct9oxOVKpBKrp9CDHa+dh0GdZ0Xes0wvxZUIFTuHJwqpHKPS/r9xb7fgqq2SLp30Odv173dbLOfnT97+WU5ogHRF7/4xViGpNV4mcNLr7wid2sVVZCVx9UcI89FAAEEoiZAMBS1EeF4EKgzAet9YL0F+rWU/PLly67Ozt6w2Cdt9mlhENslrVi44DEYGtHKpyGd6sQWnkD+G50FrSL77d/6rUA+BfdyVjZN5Oe/+IWXpxR97Lta3fKrX/2q7t68WCj0/e9/X76nN78qhIoicocvAs73m/3XeshY1UlUwlhfTjBBO3HG0r73LuoHIb/9m78pn/nMZwL5oCWqrNYP6K2335ZPfepTiTrvqI4Hx4UAAtEXYLn66I8RR4hArAWam5vl1/7Vv9LbvZ7OwxrSWtVQENvFixezUwq8bNbz4lOfusvLU3isjwL2RueftW/T//Uf/6M888wzN61e5ePLlN2VBR5nz5278cl82SeUeYC9eTkXwSmHZQ675N12Tv/lv/5X+btvfzs7BbHQdK+SO+DOmgrY99q7774rT3/ve/L3OoantUk/WzwFbCxf1Z5e3/yrv5J/+Zd/iedJVHjU9nPnueefT9x5V8jF0xBAAAEhGOIiQACBwAUqmU4W1Btm2+87J9719GaVaWSBXyKuXsCpZPj2P/xDzcIhv6aROSecO53MFULEH2TfXxYmWMNYq1Zgi6+AE8YSDsV3DO3I7WeMBbT/Q78nX3/99XifjMejt59BL+uUMsJNj3A8HAEEEilAMJTIYeekEQhXIHc6mdtXDuoN85JOR1pa8NaAmWlkbkctnMfZH/sWDtXiE/BKpiGWU3Gmk5V7XNTvd0Khf/rxj32rqIr6Odf78REO1c8I2xRYC4eSFpLYSog//qd/qlmVaf1cQZwJAgjUuwDBUL2PMOeHQAQEbDqZVQ05y0q7PaQg3jB/qNMiTurKSF42ppF50QrnsbX6JLiSaYjlRIKqjiv3un7eTyjkp2a09uWEQ7y5jta4eD0a+7DlZz/7WeJCErt+rRG19XJjQwABBBAoLkAwVNyGexBAwEcBWzb3zkMHPe3RGlD7uWx9Jf1hmEbmachCfXDYnwRXMg3RDUhQ1XFuXtuvx/z85z8XKoX80ozefnhzHb0xqeSIkjqO9rP7R1rJmLRqqUquEZ6DAALJFWBVsuSOPWeOQKgCY2Njcv/9R3Q1p1+6Xp3M72XrK+kPwzSyUC8TTy/mvMkJa0nicV0C+qMzH3k6RrcPjvPqZPam642336p6+piFsLZ6YTqdlgN33CHp3t5b+Ga1+ffMzEx2Wsh777/v+mfJLTviC54FbJxZ5ckzW+Se4PcHLpE7wQIH5FRLDQ8OSq+ulNpb4GdLgafxJQQQQCBRAgRDiRpuThaB2glUMp3M/pg7e/asTF665Msfcuvr65LJZDwhDOkfkoO7d3t6TiUP/tyv/7o89ru/68t5lnp9e/Md9B/Fd955pzz+2GPZN/fFNqvespXn7E3+KV0a2/pfVLI507A+V8mTPT7HyzQyM+hXa7fhRZjn4fG0Sz7cmUL22quvlXxcqTvtmnzooYfkN459ToYGhySVSkl7e7s0p5pvedp6Zl3s+9hu1i/Mvp+tCsCWV6/mOrrlhWL0hXI/O+x7LRum2fecOrm9JvMJ7Ofxm2+9Jfffd5987nNhfMflH0H9/tu+B77whS/IZ48eLXmSfvzc9PsDl5IHHKE7w/4gIUKnzqEggAACrgQIhlwx8SAEEPBDwJlO9rFWXrjdJnRpeXtD/qm7ql8qvpL+QkNDQ2K3oDerkjh8+LAMahAV962zs1MOHTok9/3arxU9FXtjf5++wXTe4L+tU5FsJSuvAVHuNKwgAy+v08iOHDkix48dk29/59vyo3/6cVEH546wzqPsgXh8gIUzFy5cqKhayAkQ77n77myVkN0sQC65td9670Gdorq8dCwbFNnqSxY4lgolb91DvL9S7mdHNhBfz4iFasvLy2LT/v7h6ac9f6+ZUhKrTcK4Oqz/3m379on93Ci1+fFz0+8PXEodb9Tuc6aUDerv9Dv274/a4XE8CCCAQE0FCIZqys+LI5AsAZtOtm/fbZ5O2t50njhxIhsyVPPGv5L+QvbG1d60ln2z6umMeLAJmKm9obUtG74ND0mnNih/6u/+zvMb1jCmYXmZRmbXzf16vQ4PD2vT9U7XAx7Gebg+GJcPrCRstV2b0R9+9avyG79xTDraO1y+WuGH2fPtZtfRPn1zbYFjoWqjws+u/6/a91r2Z5iGavY915vulbs0aP/x//r/2XuvKDmuM8/zq8os7y0KBU8AhDcECNCIlFoiKddqqtVyrenW7nSfM7PbZ86c3XNmX3b7bc887sO8zdHMnD4z3T1qmeZo5CgakSJFI5LwhvBAobz3Jiursqr2/0VVgMlCZGREZGRmRNb/SslChrlx7+/eDPOPz/xOfvnLX7pyx9uo1iZBmSV+nTfjHqxng8Igk3aYLmV6XacwlAlJ7ksCJFCIBBh8uhBHlX0igYAS0JtafXjTOCJOi97I6VtqfVOaSfESX2jnjh2yAx+W7BPQB/tn4Er05S99ydX80JaZbljZbKUbNzK1mKrBpwGxLPbu3eu4P7noh5+MtL1eYgv5KQqt74/54Jyp2LS+3kL6rmweffRReeG55+T4saOuupZsbeJqR26cFQI6lhpj7ZFd7l64ZKUxIalUXcpeR/r6M2fOhKTFbCYJkAAJ5IYAhaHccOZRSIAE1ggcgKWA24cRdQ25g08mZXBoSHr7+lxVkSs3MleNKuCN9SHn9KlTrueHPqzqJ1vFrRuZikF79uw2rDRqYAWlbiJOSrI7mZPt872NuiS5jS2UTVEo3zzCdnwzIYAboV77aLr3hq2/hdpedT8+fPiwYwG6UDm46Ze6LP8BKew1iD0LCZAACZDAKgEKQ5wJJEACOSXgxZ3MD0sKNxYfCoRuZDmdFg8O5vVhVW/ws3WT78WNzLRYMeNqOaVpupM53T5f23m1FtIYKn64j+Wr34V0XBUujx875lqI1YDfboP4FxK3oPXFrQAdtPbnoz0qwmsg9WvXruXj8DwmCZAACQSSAIWhQA4LG0UChUvAqztZH6x9vD74u7X4UPqmO1DhjkQwe+b1IUcfVDN1N0xFxI2ouH7euBVC/RBBU/XDz+Ua6Hl6cspVlWbsJbp5ucKW1Y3dzs+sNoaVeyag18bh4WHH+xsp2/HZyEXPta+/8YaR1ZCFBEiABEhAhMIQZwEJkEDOCXhxJ8vEksLLQ6zpDpRzODyg8cDSvBaYOt843IqK6+eNWyE0LO5k4xMTMoIU6G6KWgs9duIxN7tw2ywT0PlZVlaW5aOw+mwS0MQKKl67caelm7QYvN577z15A/GGvL50yua4sm4SIAESyDUBCkO5Js7jkQAJiJe31JmkSXabOYmWDfmdpEF6WHXjRtbc3CwasHy9RYxbITQTETRXI+fGikrblIpNrtrL4/hHQDOTqRjBEgwCbhMr0E36k3HTQNTvI9YQXcqCMZfZChIggfwSoDCUX/48OglsSAJurSgUUnKaZDfQvKSpX+8O5OZ43LawCLgRQLZs2WJk3Vtf3AqhQXcn82KhkIrNelb8HnwC1dXVUlVVFfyGboAW6rni57/4hVy8dMlxb3l9+zQqupQ5njrckARIoMAJUBgq8AFm90ggqATcWlGo2beXtPVu36Yqr/XuQEFlWKjt8uKmlA0Wbt3INDPQwYMHHmqKWyE06O5kXn5T6q6kHFiCRUBFvlGXLoGaZY9jmd9x1HF77fXX5T/+4AfyW8TJUcsXJ0Ut95584gkja2KhFu2jm0x7dCkr1JnAfpEACbglEHW7A7cnARIgAT8IuLWi0GOaaes1Pa/T4jZNfb7cyLRvP/ynf/I93see3bvlFFLANzY2OkWW9+3cWOmYjdX++d1HN25k2o4aWFKsdyMz22cKoa//9g1HfE13smeeecbR9rncSIN8u81KRbE1lyPk/FheRL4mxP/SD4u/BNTl+dXXXktZaXLmxT7EFLpx44YMDQ25ii30+T/6I/nqV76S8jyV8uAhWnH06FGJFBeJ03Otds10KTt06JAE8ZwbIvxsKgmQQIgJUBgK8eCx6SQQZgLJVhROs6mYLjZuHpXdigz5MrPXvvX29vo+pF/64hflGFJSh6W4tdIx+5UNixQ3c0cFxb179qTEXFNTIzU1tSnXr1/hZa6vryNI3+1EsyC1c6O15fr1667ckJRPNn5rG437+v6qq/Qrr74qb7399vpVD76rZYspyOpfN8GmtRI9R6m1UKGLeju2b5f9+/ZJT2+f6Px2WvSce/7CBVh9HvT9JYPTNnA7EiABEsgnAQpD+aTPY5PABifg1ooi2cXGiXWIF5EhX5YN2je3N/pOpo9byw4ndWZzm4sXL8pHH37k6hDpRBlXla1t7HbuaNDpHfikKg3ItKZzS10cnAihbud6quNmY3lQXP2y0beNVKfO8bMXzjt2QzLZ5OscWchjk63zv8lMz5F//t3vboisgCpcnjp9SgYGB43YhE7Ot8pJx0AttvRc/uKLLxbydGPfSIAESMCSAGMMWWLhQhIggVwQ8OJO5iZjk9s09flyI8sF6zAc49y5c/Iq4mY4jZdh9ikbVl5u3cjSpX9WC7njsNw6fuyo46FwM9cdV+rDhio2hk1w9KHbBVWFikI/+elPXYuwzC4Xrmmg46Uix7/5m7+Rz3722YJ2IUseGXXpPQ0XajfnW91fXfN+9fLLcubMmXANNFtLAiRAAj4QoMWQDxBZBQmQgDcC2XYnc2vZkA2BwRuZjbWXPqT+5pVX5L3335fu7m7Xnc+GBYNbN7LDiE2RLiDvRnUn04fTQndfcT1p87SDBi0+c/Ys4q/8Vi7AbcatCMvscnkaOI+H1ZhC3//LvzR+f+nOTx4PEdjd9MXTiRMn4Sp52bHVkHZG3c/+gBT2uxGfz4llcmABsGEkQAIk4JIAhSGXwLg5CZCAvwSy6U7m5uFee5UNgcFfWuGoTbMcffTRR8bbV7uiwVQ16HZHR4chCLl9SNW6s2Hl5daNzKmgWCjuZDpuTt0zdIyYxcruV5D5Oh0PFXvSPfjrdmqF5iVosdnKVJn3Mu8Fa8gGgQ8//FCqKivlS1/+smgigo1U9Pegwtj9+/flxz/5ieOuq0vZufPn5cRjjzEQtWNq3JAESKAQCFAYKoRRZB9IIMQE3FpRaFedZGxy+3BPFwn/JpEG0f7FL39pCAJ2RW/Ap6amMoqtdPLkSd/jZrh1I3MqKOqDis73dFySmTmZ63aMs7FOs5JlIx5WNtq6Eeq8fOWKdHZ1pe1qpr+3bIiwaRvNDTIioOeyn/3853IWQsfjJ05sOIFILaU0C5tmcXvnnXccs9T7h9ffeEPaNm/ecIKaY0jckARIoOAIUBgquCFlh0ggXATcWlFo71RMmJqetu2o2/hCdJGwxelqZbYDqZqNydaDqhtLM7dt2P3II8iY86hjlzl9QNGHfjeZ+FwNloeN1b3CaRBtrV4DwKoLE0t2CKilnRdrO7etyYYI67YN3N49AXN+GFaZuG5+59vf3lBixx5ki3z6qacMSzmnlo56DXvvvfekva1NGpE4gC5l7ucd9yABEggfAQafDt+YscUkUFAEvATl1QfNO3BBUteIVOXu3btyHTeCTgtdJJySCsZ22cqy49bSzKkbmUnNbcB1fUDp6+uzneu5HhH9zbqxesqVUJhrDhvpeG4F0I3EJix9VYHozTffNIKO38H1caMU06XsC5//vKsuK6/3EWvo2rVrrvbjxiRAAiQQVgIUhsI6cmw3CRQQAbfuZKZLhLq0WBW1Trjf2en4LTrdyKwoBneZjtcXX3ghK1l2suVGZtLUhxTNYKYWN05LULOTOW0/tws3gWyJsOGmEs7Wb1RxyHQpe/bZZ10NnOlStpGENFeAuDEJkEBBEaArWUENJztDAuEk4MWdTIMWq9VQG0y915eBgQHDysJpCYIbmYodbsQCp30rtGxQyumb3/ymETdCUxL7XbLpRma21W3A9SC6k7nlrtZ9+qFLhlty+d3eFIU2Uqrz/BLP/tFNcWgvXKw2UkBqP1zKsj86PAIJkAAJ5I8AhaH8seeRSYAE1gh4Ccpr97A8iGxYvS6Eoc0Ql9o2bcrreBw9elS+8fWv+/7grNZYhfIwbopCyikbgpdbN7LFxUXRuaZui26K7ldTU+t4l2R3siCMpcbcaEZQV41Z4rTE43FJZeHntA5ul1sCFIVyx1vPbV/72tfkydOnHR1UrWI1++MY/mpqdU2x7qaoOHQeAakfQ+atjSIOec1SZrqUHTp0yA1ibksCJEACoSNAYSh0Q8YGk0BhEnAblNfuYdmN1YfSVNce/eSz1FRXy86dOy0toPLZrqAcO9uikPbTbcByzb72ox//WF7+zW9cYxqCoOSmBCk72SaIqO3t7XLh4kXHXbCz8HNcCTfMGQGKQmKIz9kQoK0GUWN27cL5XwN8OykqssYXILbGF+S5L3xB3vjd7+SXyATpNLiyHqMDadw74XK9UYQh7XMmWcrOX7ggiUTCyfBwGxIgARIIJQEKQ6EcNjaaBAqPgAblPXHipFy8dNnxza3Vw7Jbqw99ADqMN4H6NpEluAT0wakd4l02H9TcBixXcbLLQZpwP6jaWcj5Ub+bOsrKykQ/boqTTIJu6uO22SWwc8cO47zol7um20x22e2ds9p1jgf1uqDtMtpW/YmApe3953/+Z8fXTxW2P/74Y3ns+PGCsSp1MrJeXcpefe01J9VzGxIgARIILQEGnw7t0LHhJFBYBDJxJ0sm4dbqQx+AduDDEmwCTjLRZdIDFV7OXjjvOGB5Jsfysm+QspNpTDC3Al22x88LU+6TmoAGYXfjKpi6ptU1en53k8nOcJOyyTqZ7nhW67U+NxY1VnUEdZn+Ho9AyNu6ZYvjJqZL4uC4opBtaLqUuc1Splaebi09Q4aGzSUBEtjgBCgMbfAJwO6TQJAImO5kTttk9bDs1uojCG5kTvu7kbfTsT6HmBjZSh3sVlDMx1gEJTuZPli5zayW7fHLx3gU8jFVKFXXGb/FGafMNCaVfvws6n6l87BQi+ni6aZ/RlB4xCnaaEWFtCeffFIOHDiw0brO/pIACZBASgJ0JUuJhitIgARyTcAPd7LpmRnHVh90I8vOCCvXp3DT3dTYmPIAtxGw+f3333f1Bt98WD148KDvrg9uBcWUHcviiiC5k2mwdrVOcGOBkc3xyyL2wFft5Pf24Zkz8s477zjuiynknUBw4meeecbxfn5tqBZmGmDZr6Jz7+btW35VF8h6vLh4xjVWkc8CXCDhWDTq2LGj8uUvfUl0rrk5j1lUxUUkQAIkUBAEKAwVxDCyEyRQGAS8uJMlxy5xe/NPN7LszJva2lojiOrBg6nfxnZ3dcvc7Iy8/ts3HDciWw+rQXcjMwElW8jlOzuZlwDU2Ro/xxOoQDfU89gLzz8vbZvbUvYwEo3KjRs3XD0A+ynkuc1k5/dc92IRqG3WD0thEtD4WadPnZKrV6+4ug4VJg32igRIgARE6ErGWUACJBAoAm7dyZJjl7i9+acbWfaG3hD5cOOd6qMBQDXYeEtLi6tGZMPFxe28cdVgnzcOijtZW1ubHD582NP4vf7GG3IHFmMs/hBQS5GamtS/Nf0NHj92DJ+jrg7op/ufFzcnP+e6F4vAsF0f1C1MYzOxOCegVsrPP/c8XcqcI+OWJEACBUyAwlABDy67RgJhJKA3ajt37nLcdPPh5b/+t/8m//jDH8p1vBV3UuhG5oRS9rZR4SjfD6tm77w8NGaPjH3NpjuZ/VbZX5vJ+L333nvyk5/+lOJQ9ofpwRFMN918CbEqJLa3t7vqsc51P0TEc+fOyauvv+7YxVgb2dzcbLRX53lYyvXr15HV81JYmhuIdur4njp9ynApc/vbCEQH2AgSIAES8JEAhSEfYbIqEiCBzAl4CWyrDxAv/+Y38sEHHzi++Vd3pxp8WPJHIN8Pq9rzsLiRmaOU7GKTv5FbPbJbEdds7+TkpLz55ptZE4c0Nk2+gibne0xSHT8TIc+PoO9ezus61zMVEfX3/atf/9p10PotiJ+lFkNhKV7PY3SXE8OqVV3K3FrUhWVusJ0kQAIk4JQAhSGnpLgdCZBAzggcQPBiNzdp+gChD5v612nZu3ev7Nmz2+nm3C4LBDJ5WL1//74MDA5m3KowuZGZnfXTxSYTgF4e9s3jJYtDr772WsbWQyoGvQarkP/33/97+b//9m/l3XffzaRrBblvvoVYnS9uUtbrIHgVEc358B9/8AN5B3PBzbVBj6vueWGwFkru50cffuR63obNXc51Bx3uQJcyh6C4GQmQQEETYPDpgh5edo4EwknAqyWC096qG9mJ48eNN4VBKWrhcObs2aw/jOzevVv24BOsM3l+AAAgAElEQVSUYj6sXrx02VVg3MtXrsjHH38sBzNMN+zFjUznz75HH/UNoY79jZs3HfffdCfLfa6oh7tsirhugoibtZgP/erqo8HKT544YWSb0zhjdsG1TYsg/Xv7zh3j09fXZ/AbGhoyRIBTjz/+cGM3+JJkIdbNeJnuuplmKDPjx3V3d7saCXOe3L59Wx7HHDn9xBMp54jOCT2Pvv373xvBts354OqA2DgfLw70XKQiabpipJjXeEL4rJ/36fZNXk936k9o6G9DXcr0ZQOzlLmZRdyWBEigkAhQGCqk0WRfSKBACCRbImQjjWwQ3chU6Ojs6sr6CP75d78bKGHI68OqPiyeP39eHkM6ba9Clxf3C32YUob6kOxXeRcxd+53djquLtmdzE5AcVxhBhuab9p7evtEY5y4LTqO5kcFIrXU0N9nO9x4mpqaHqrOfBjWFNv60X01M6Fbi5CHKt4gC7wKsfpbOX/hAjINHrQV7ewwej221mnOERWV3vvDHyznSKZCidn2fLw4UDHilVdflbfeftsOobFO57o5/zOZ90G8DqbtfBY3YJayLMJl1SRAAqEgQGEoFMPERpLAxiOQiSVCOlr5eBucrk3mg0+67TJdrw/RQSteHxjVMuAELAi8CkNe3Mg0NfjhQ4d8jT/yGKzXLlw4L24sKUx3smeeya/dkF9v2tfP/4/Lyw2RaH3RB+JMHobX12d+7+ntlY6ODkkkEpab7Ny5U3bhE/biVYj1w2rI67GTmSfPk/VzxK+5odZrj53wT/h1MmeUbzbmdapja3DtJ2F5RXfqTxPKVOhOxZvLSYAESCAMBCgMhWGU2EYS2IAEsuVOlo+3wRtw+Fx12esDY6ZWQ17cyLIRk8PLXA+SO1k23rTn+kF5dnbWcD+6ZJHVqQxuJt/9zncKQhjSH6ZXIdaPOefng3c25shGuT5ocO1H4Q4bJHdqVxeNLG3sl9CdpeaxWhIgARLIKgEGn84qXlZOAiTglUCyO5nXOqz2o/m8FZX8LzMfVt2mDFaroQtwcXFbvLqRqbWQzk0/i5e5rg/FGl8kKNm3dPy+/a1vy7PPPusnmtzVtbJiuKRpUPP1H7XkmpmZyV1bsnykZCHWzaF0zl29ejWjQOHmg3cQ04ObbqK5thZyMwZ+bKv9/Maf/incAg/4UV3B1cEsZQU3pOwQCZCAQwIUhhyC4mYkQAK5J+A2O5mTFgbRjcxJuwt9G31g3AFxYSveZLspptXQHQRudVO8uJFlU1T0MteDkp1Muev4HYJo9v2/+IvwikNuJlDIt821EJuMK4gP3upa9cUXXpDPfvbZgraiMcWvQu9npj9P07LtQIbJDTJtB/cnARIggVwSoDCUS9o8FgmQgCsCXlxs7A6wUdwE7BgEed3+A/vlyNGjrpvoxWrIixtZNkVFL3PddO1xDSxLO1AcyhLYLFTr1WrIqxC7vgtBsjBTUeib3/ymfPUrX6EotH6gNuj3IFu2bdAhYbdJgARyQIDCUA4g8xAkQALeCHhxsbE7UjYtPuyOy3XOCKglgQZidvuWVh9W1f3HqVuVVzeyE2hbtmJyeJnrQXMn01E2xaH/49/+W/m//t2/cz2WzmYKt/KDgBcxUo/rRYhd397kefLXf/3X4taFdH19Xr/ry4J//a/+lXzj61+3zILntd6g7UdLIfcjEkTLNve94B4kQAIk4JwAhSHnrLglCZBAHgh4cbFJ1cxsWnykOiaXuyNw7NhROXXqlLudsLUbt6qguZGZnfUy19302zVUjzvoQ/8OZG/76le/It/77nflWWROy9eDv8cubIjddJw0/bwXIfb8+fMZxRpSwOY8+c63viX/G8QZt+3IdJBMseT5558rWFFIraFefPFF+Td/8zcF7yaX6Xyw2p8uZVZUuIwESKBQCTArWaGOLPtFAgVCoKamRmpqajPujd4ga6rxbFl8ZNxAVmAQ0PHRcVIhYXh42DEVtQI6jyDU+qDb2Nhou1/Q3MjMxnqx4PAjU5QtrAxW6lg+8+wzxphcvHhRXvrZz+T69esZ1Jh+V/2d69xpampKvzG3EFOIdTsuajV04sQJ2bN7d8YUday+8NwXDGHoAubJr19+OWvzROfH008/LU+ePm2Il5vbNxfkNUFFr6eefBJxvw7Ko3sfNX4PKsSxuCOgzE6dPiUDg4MyMjLi6prk7kjcmgRIgATyT4DCUP7HgC0gARKwIdDQ0CBq6eNWKFhfpabn1VTjLMEnYFrOvP7bNxw3Vt2qzsGK4cRjj8kzsFBJVYLoRma2NdmdzKkoluxOlk4QS8Ukm8tVHNJPY1Oj8eDf1dUlt+/ckdu3b8uNmzczftAyhSA9Rzy6Z4+0bW6TzW2bKQw5HFSvQqwZa+gx/N78EIeMefJojSHUqDupCkQ6R27h41a0Wt/15DnyWQiVhSSUmH3TPpu/AT0PqOilYpC+WKEgtH5GuPtuupRdvXpF3FyT3B2FW5MACZBA/gkUraDkvxlsAQmQAAmkJjA6Oir9/f0Sj8dTb5Rmjd4g5+rt8MLCgtFefcMYtLK5vV3aXQpk2hdNje60ZMraK7+ysjJD/LOzFpmemZb+vn5RdzKnJdP+OD2ObudlruvDofY7DA+AOrbKXtO/69+Pr10zBACzaJyoEfzezbfzyQ++uo2O7V4IQE14+NUHYLPfOkbVNdVSVlrmicMU2tLb02OZlr64uFja8bvJhbDsZe5nOv5e5pyORTZ/F/o7nZlenSM6TzTroDlPzDmibUg3T3SuJM+RXFnOeGX64Ifg8B96zjN/95n+Bhwe0rgW9PT2itPHh+qqKtEXM/X19U4P8WA7L+drL9e4dA3z8rvUOjP9baZrF9eTAAmQgJ8EKAz5SZN1kQAJkAAJkAAJOCZgCgDmDvoApgKw+Tf5wVe30e/V1dVSWlbqWQSyatyyviPDp6ioyGq1scxuXcqduMIXAsnzxJwbWnG6eUKLGV/wf6oSFYScikLmjvrb4e/H/7FgjSRAAiTgJwEKQ37SZF0kQAIkQAIkQAIkQAIkQAIkQAIkQAIkECICzEoWosFiU0mABEiABEiABEiABEiABEiABEiABEjATwIUhvykybpIgARIgARIgARIgARIgARIgARIgARIIEQEKAyFaLDYVBIgARIgARIgARIgARIgARIgARIgARLwkwCFIT9psi4SIAESIAESIAESIAESIAESIAESIAESCBEBCkMhGiw2lQRIgARIgARIgARIgARIgARIgARIgAT8JEBhyE+arIsESIAESIAESIAESIAESIAESIAESIAEQkSAwlCIBotNJQESIAESIAESIAESIAESIAESIAESIAE/CVAY8pMm6yIBEiABEiABEiABEiABEiABEiABEiCBEBGgMBSiwWJTSYAESIAESIAESIAESIAESIAESIAESMBPAhSG/KTJukiABEiABEiABEiABEiABEiABEiABEggRAQoDIVosNhUEiABEiABEiABEiABEiABEiABEiABEvCTAIUhP2myLhIgARIgARIgARIgARIgARIgARIgARIIEQEKQyEaLDaVBEiABEiABEiABEiABEiABEiABEiABPwkQGHIT5qsiwRIgARIgARIgARIgARIgARIgARIgARCRIDCUIgGi00lARIgARIgARIgARIgARIgARIgARIgAT8JUBjykybrIgESIAESIAESIAESIAESIAESIAESIIEQEYiGqK1sKgmQAAk8IDA6Oyujc3MPEWmqrJSmqqqHlnNBcAikGrvkFnIcgzNebAkJkEDmBFKd93iuy5wtayABEiABEsicAIWhzBmyBhIggRwQ0JvqP3R2ybv3O2QE/44tLcl8YvGhI5dHo1IRiRri0IGWFtnX2iqPNjcHSiz6YK0fDzXe4YJmiF/Na+KX9jNo/UvuhvkwdGt4JO3YJe+XPI4t6KP2+amdO2UfxjQXJdMxctrG/ejPUzt2ZHV+mr+dG8NDTpv1YLsgzbV0/Xh6x055eucO131MtUO64+Vi7KzadntkWN7v6JThuVmr1Q+WBb19to3PcKXfc8Ftc9yc98xz3f6WVtnf2iJ79bqVo/Ock36l+x3Y1RGU80e6Pvh1fUn329Tf5Bf27pXKkhI7bFxHAiRAAnkhQGEoL9h5UBIgAScEksWg7qkpGZyeloGZGYklEml315vt97o6pb6sXJ7cvk2Ob24PzA33jaEh+dHly2n7kGqD8pKolEP80qL9rKsol8fa2uVPDh0MzAOFOXa/vXtHuicnZDI273jskvttPDThs62uTtpqa3LWv0zHKNXYrV/+4oH9cmLr1vWLff2+ABH1Yn+v/M9r113Xm2qu5eMBtqa8THqnJlL+dpZWljE//BOB+3G++e2d2/JuZ6clt786eVJe2Peo5bpsLuwYG5Of37guXRMTtofZ29QkVWWl8vnde2y383ul0/b5fdzk+hoqKnwVCZ22NZPz3sWBAakvL5c6fJ7atj0w5/NCOH/ouUNfJL15964M4cXS+qLnucn5eeMFRCYWx2e7e+Q/nzsj84sP36MchOh3Yku7lEYi6w/P7yRAAiQQCAIUhgIxDGwECZDAegL65u2nEE/evHvPk6AwD/Gof3rG+KiY9Na9DkMg+v6JkzkTF9b3yfweX1qUcdyEei4Wu96ERc4fursC8UCR6dglc9Fx1E9tWZnEF5c8I3O7Y8Zj5PCAVg8QDnd1tVkcDD3NuRRzLR8PsKVrloCl0YgMzjz8cPdBV7ec3rbNNyHkSn+/vAtx2YrbYVgiHsCDXtmaQOtqMDLcuG9yWm6NjKQVyC8PDsq90THwyPCALndfSCxBCI5ZcnNZlefN9ZyR66JWhj+6fEmuQuBx+gIjuY06z8y51gnRLyjnc21jNs4fKlz+8f79WbeY1PbruePI5s2ix7w5Ovrw1MB57q2ODjnavtnz+UOvex92dxv3HKlKLUS/aDHDu6biw+UkQAL5JUBhKL/8eXQSIAELAnqD/ffnz8pHvb0yDkuTTIt5wz2BG+/rQ8PyNdyMvnjwYEZvBjNtk9/7G33EA4k+UEzEY3kTwPTm+B/On5eXb93yZez85sT6Miew/gE2l/PtSNtmOdW+RX6F+bW+3MIDn59CyFQcv6kU5589eMDc1dC4vglZ/66/r4v9fWlFIW2IiiO9k5OiViyZWEFkvVMhP4BpJfQ/rl4xrllOLFrTdTko5/N07fSy3jx/9OOFzW38ZnU+f/vosay/sNnV2GAIx2f6ei2FZT1/fARh5yjOMV5+L1O4BqsgalVUSP6zQ4dlX3NuXKGt2sBlJEACJJCOAGXrdIS4ngRIIKcEVBT6Tx9+IL+/jzf1KR7KvDZIb0jVXP+/nDtrWCPpDX2hFe3jK7duQ5w5JzeHh3PaPYpCOcUdiIPler7pw93uJmtBJlkIyRSOzmUVkVOVrbW10l5fl2p11parm9bt0RHH9V+A1dPlgX7H23NDdwT0GvISBKH/8N67volCyS0wf1//4Z135H1cEwup6O+1Ay8y1MU1F9crtRp6HMKQCstWRdujVodefi96vvj5tWtyJUUctzq4sm3F+aIGFkMsJEACJBBUArQYCurIsF0ksAEJ6M3Vz65edXSDvam6SlorV7OPaXDiIvDS2AFDCMhq5WaSjHMApt7/cOki3gpWGm8qC62YDxMt4JNpzAQ3bDS+ghNLoeSx0/qTx888npNxdNM2bps9Armcb/pw1454UzqHrH7nphCSaVwdOwFG3/4fg8tJvtzIOicmHQ+m31ZUjg+8QTZ8+949+fsLF2zdh0wUXq9Z+vv6PeJcmckW/AywHoRhyuX5Q4Xlr+zfJ52Ie3dl8OFg/F5/L2ot1D0+Yfkyi9ZCQZhlbAMJkIATAhSGnFDiNiRAAlknYFqbvNFx19YUX2+yPrdrlxxDvID22tU39mWIOaIljtgWcQSYvAKroHcQL+Aq3t5ZPTzqtioOvXT1Y9lSW5+XIKWpgH5h10757M5HUq2WEQhfw7OrMQyuwSLI6uZWd9ab7UxjJqRshMUKM76CnZWXPhh9DpnFnt+z98HYaVXJ42dWreMYR9Dk0dk5w7KrtCSCDHPBMMNPN0YWeFIu2r+pxRAo81XMMTnYssmyCeZ8s5trumMu51su3Mk0jk8qASaIbmQ6J4uLiuS3iKWWXPLhTqZZtf71E6dlwUVMsOs4V7+FjJNW52svvzeNFZPtoue83+M6YxdTxvx9PbNzF641tTjXrWajsrpmvfTx1ZTncx3Hj3r7ZP/9e74GWM+UkXk91pcQViVo5w8Vlj+DsbjSP2DJWjlfgmubWts6zQyXzlpIYxs+t2cPrYWsJgiXkQAJBIoAhaFADQcbQwIbl0A6axO9wf7WwcN427dfmqsrRYM4pnpjvxvp6T8DAeJsT7f88NIlyxtAJX0FwVnfCdiN9sHWTfKnRw6nnAj61liDu2o5BwudXyE7USoBLNOYCSkbYbHCzsJCN9eHuz8/dhziTqu0YCxTjd36qheWEobgp0WDDgehpBsjN23UPjll4aZep9tqdrvH4FqhvyurYs63qXhcziL+ht3Da67mm1N3Mi9xQpSBPujZxfEJmhuZPpx/4/ARGZ+bkyvIeLheXPHLispqflgt0/FphwjiplTcvGkE7rUqXn5v2T5XmC8yNDh5qqLj8r1jx+QZvMhId87Ta5a6Odn9xlS0+MWNG/JIY2NgLF3VReoJtPtoe7slhiCeP2qQyODUtq1G1lKrFyvvw53siW1djoWhdNZCyocuZJbTgwtJgAQCRoDCUMAGhM0hgY1IIJ21iXmD/dzePXA7qk6LSG/89NNSXY207iXydwhkbXUDaMYU8DOTUdrGpdmgFKnZte2pSvI6Fcfa62oQn+GCZTBe7Z+miVfLm2wXOwsL88H1mUd2uRZB9A2vfoJU0o1RkNrqpC0qTKWac+ZyjcqxFS5cdWXlef89ZdudLF0Q2aC5kenDeSvE1uXlZamw+K14dY9xMnestvHymy3HeS9VCeLvTYXwczbJEfSc91cnTzq2FNHf2QHsk+43ppauag17dHO7Y+EiFVe/lpch1XuYzh/a75Nbt8lntu+wvC9Q60cVKR/ftt0R41vICJoqtpBaC53CsVhIgARIIAwEGHw6DKPENpJAgRNQayG7N696c6VpbZ2IQsmo9Gb18xCT/vrE43JkU6slRdPKIYyBqLV/+qZWhS21qLIqo3A9y0XfNL17qmw8On7PwoIrn5YxVmy4zB0B8/f04oEDKeebKUK4q9n91qY7mdWek3iwm4rFrVY5Wmb3oBdENzIVFDTbkbpwHd70sEtgsjuZIwDcKC2BdEK4G1Eo+WBOfmO3R8ekY8wi5XraVud3Ayd9y9X5Q9uiVkOp7gvUauhsd1daYHYvtVQcpLVQWoTcgARIIEAEKAwFaDDYFBLYiATsbqyUR6Y3V+luRjPJRBKE8UqXaUXFmjjcsbJZVHgaRiwgq6KClbo+0JTeik74lunvSWN02GX2MVOkZ7N3du5kg5iPN0aGPAmi6c5H+XIjSyWeJ58fzXTcViIxs5P5NxvTuRpmGlMm3W+sa3IScXD6Pc1v/yh4qyld33IpYppWQ1Y9Ma2G0mX2TPW71DppLWRFlstIgASCTIDCUJBHh20jgQ1AwM5tQx9w/gSWQpmaYqe7Gc3VW8psDeciXMU0Fk++ygKOr8GirYrGsFF3PpbCIWAnQGgv9eEu2+6Lye5k68lmIvbanY9UhMmHG5kKr3fhumQV2F3dyNSlVIsyqcO/7dzJ1rPid/cE7MSATF9kmK2x+41lMr/d99b/PexEXT1aLs4fepxMrYbsRGS/5oH/9FkjCZAACaQmQGEoNRuuIQESyAGBUQRM1dTkVmUbso6pm4Qf1ia1sHSoxUOUVcnlW0qr42e6TEWZ+UVrYUjd75oq8pf1KhPrjUy5cP/sELATIPSIuXJfzIY7WRDdyPqnp6UX6bWtiulGZq6jO5kVJX+XTcXnLUU6PYpfViLpLEEzdZf0l4i72uxEXXc1Zb61Wg2lco1NZzVkJxD6NQ8y7yFrIAESIAHnBCgMOWfFLUmABLJAwC5Ww3GkHD6KtPR+FE0Jvr+lNWVslFy9pfSjL+vrsBPXcuH6omxbqqxjHCnXe6Oj0jc1tb7Z/B5iAo2VGHMEd7cquXBf1OPaWR54ESTtLHP0eLn4LVnxvAK3oTN9fQ+tsrJKsGNCd7KHELpeoFYi14eGLffz223Wbix1fmsq+LAWtSS1smzT/uRKWNZjpbMmThVriNZCYZ15bDcJkIAdAQpDdnS4jgRIIKsE9EGsZ2oyZdDiWmRA8sNaSDsRFCsHv4HqDeobt+9K58TkQ1XnyvUl3Rvgc3398uPLlyRdvIaHOsAFgSUwsxCXaVhO5LPYzTsVJN1m5LOzzMnVb2k9TzuxKtmNzNzPjknYXWbXs8nHdztXQ7Vw3YqPX8VuLMNu5WrHqBrX/erSUrtNfF1n57anVkN3Eex7fQIHWgv5OgSsjARIICAEKAwFZCDYDBLYiATsHsT07WszLFH8LKncLPQYXZNT0guRKkxFRaF/OH9e3ui4aymu5dKc3e4NsN5cv3Lrtvw/r7wi/99bb8sfOjsfutEOE3e2VWQhsRQI90U7d7Lrw8MQI4ccD1cffv8a2Neq5Csbmd05cr0bmdnuVEwKWUywGrNsLLOzziyLRkRTt/tZ7M6rer0KqyXmCJIVqNWTVSmLKMfcxaVL57a33tKO1kJWo8ZlJEAChUDA3ytYIRBhH0iABHJGwC42jt9vX7VTdnGG4howd3EpZ333ciB9a6kPJmP43IQ7w4c9XfJRb69lvAsrNxMvx3S6jym63U/xYK3i0PjAACybJuStjntSV1FuvF0/gBTbzWtuaFrHPnyCXnQcbkF0yKQ0wRWrKYX7XSb15mJffTB6p+N+yge7XLpc2bnbqIXMR93dcrRtsyPWdm6tuexT8hi6cSMz97NjYj7kfn73nlxMlYI7Rq4FUdNl0+q8miuXTb8HMV1WN3VLVvfkXBbTauhMX68MznxasFp/HqG1UC5HhsciARLIJQEKQ7mkzWORAAk4JpCNt6+OD57HDX/28cdyAWKPVYkh+9c8Ak3HEWh6Am48E7F5S0shFYX+6uTJjLO5WbUh1TK7G+vkfQyBCB8t5dEBea+rE1nLVi9FmlGpAR+NBbW/tUWCKhS9eveOXBocSIUi7fLWyir5zrGjEtaHc3WnGZqZtpx7aunXXlcrZWtjmhZGhhsku9usf6Bz405m97AaFjcyE6UdE9Od7PO7MwTP3R8ikA1LF70OVsCCppCKnbCSr9/aA6shCMm/unXrU7j1PPJBV7ec3rZNttbVyYfYxipDYK5fxhTSnGBfSIAEgkGAwlAwxoGtIAESyDOBoATz7IBFjX68li/s2in/y8nH5TiCdvsVn8lJW/TG+kv79skkBKt/vHjxobeuVnXoDXf/9MxDqy7CsqgeApEKRU9t2y5/cuhgoCyJBtBm/XgtO/BwMRWLe909r/upgPLza9fkSgoXrWxY+qXrsOk6tf6BTvcz3cnaa2ttq7GLHRMmNzKzk6mYJLuThdVizXYguTLQBOzcsLThVnGzctUhu5cbptXQFVyb3sXLDKuSS9dtq+NzGQmQAAlkSoDCUKYEuT8JkEBoCKTLnqUPTWEuaq3x2V2PyOnt23JmsZHMS13CvnP0mLHIqThkxTvZqkhdzybiMfn+iZOBEoes2l3oyz7o7JK/P382pfui9t/PTIJOedq5Tq13A0lVp12a+jC5kZn9s2NCd7JUsyD9crvYONlwgWqGdWFziux/w4jTM5IiTk/6nuR2C3W//QPOHy/fvG6cP1KVVHGzUm3v5/J0VkO/uHFDinBAWgv5SZ11kQAJBIkAhaEgjQbbQgIbjIBdIM9soNAbv7Jo4Z72Jufj8qPLl2UQ1iz5srIxxaFmxND54aVLcmXQefBfqzE3A1frOopDVoQyX3YTVkC/ghWQVRnFw+cw0mLrA+jVwUG5D6FOY5tYlXy5Uti5TjlxJ7OzYsiXa4vbbGTrx8OOiVOxbH2d/C4SX1pMOf9LcW3x24VyemFBZlJk/wtCXDwVp96/f18GpqYtp4eeP67DurB7agrXpWkZmJlJyS9f54/khttZDdlZidJayHL4uZAESCBkBAr3CSlkA8HmksBGJFBdWiY1SE2bq6IPW3ojW6hFH4JvjIzAjWtG7o6Nyh/v3y9P7djhKPCun0xUHPrK/gNyZHO7nEU8hutDg3INwZq9ikSmONSib89DHLTZT8Z+1aUulL+4cV1eu33bssr5pYSRfUznVipBSHfMR1yr5Aancp3SbdK5kxWaG5nJJRUTJ2KZ5WTgQuNcqpaZ6+NZKRojOQA+frro2SVoUAslvzN3uh1izeL3k6tXH8SJW7+/nj9SxcJL3jbf5w+zLXZWQ+v7Zn4PgqCVqm1cTgIkQAJuCFAYckOL25IACfhKINeBNRcQvFlvtK2K3uzn+yZb26Xt0ODE6coQrDisHk50PxVSfo+U8LeRlakHN+7fPnrU14eVdG3T9TVlZaJuARqs8wvze2QqHhd9ANc3yJpCfHh2BlYoc3IV/07Vj+TjaJ/e6uiQo+2b8x602ekYpeK0DUyCMNe0faniPKVqu9Vy5fEnECGf27Mnp3Gtktti5zqVLuByobmRmVzsmKQTy6zGmcsQMB9WpxUpAqvrbymOa0yuShASNBTK+WP9uUQDTVtlKLMaW1oLWVHhMhIggTASoDAUxlFjm0lgAxDIdfwEveEvj5TknewX8XD93aPH07ZDBS4NhJnKGkdv2DWI9auwBNmHDF/5yoClApF+tqz1aAFvkE9t3yqa9lldIVQs6oMbgopFb9/vsLUqCooLjNMxSjWI+kCnb/sLoejb8u8dOybP7c2fKKQc7Vyn9LfQC4HUypojiG5kdm1yE5zXjklQfkuF8BtgHz79aEoAACAASURBVLwTCMr5I7kHbqyGaC3kfey5JwmQQPAIUBgK3piwRSSwYQjYBdbMRvwEjWmUKlhnEMzydeBbqqrlwKZWR3Ngd3OzYY1zrrtH/g5Bga1ctYL2AKg33drH5HKwbVUs+jKsTl5FgM+ffHzV0oooKC4wbsbI0UCGdCPTUkhdFnOZAS8VrlSuU7p9qoDLQXQjs2uT2+C8qZgE5beUaizDuHwUVpwqPqbLgOemb7mOw+embZlua7qP5dPSMFUf7GINJe9Da6FUBLmcBEggjAQoDIVx1NhmEigQAhqssxzWE1YlG+nj7eI1BMEs34qD3TLTGqcWad1HYrMyhIeS9W5Z+gD4QVe3qGl8vqyG7Pqg60yxSAUXFeg04Op/vXDBcrdsPHxZHogL0xLQYOdFRUWBEIW0sXauU6ncyYLoRmbXpkv9A/KDDz9MOzbmBiqwa+Bfq0J3Misq9ssaEeOsBVnC7sMCbX3pmpyS3qlJxFbbvH6V5+99k9PSOfHwsbRCPV82VVR6rjvfO6prd3lJNDDnj2QeTqyGaC2U7xnE45MACfhNgMKQ30RZHwmQgGMC6dLHp3L/cHyAdRvaPXCF+SZbBaLP7Nwll/r65Ve3bj2EZxLuWlOx+EPLg7hAA1erxVSqAK/ZePgKIodctkkfcA7B3TBVSRU4XEXHS/19cAMcln0tqfdPVa/fy+1cp6zcyewyfymTY4hn5XeWqXR9tnMj032vIDPcHcQOc1O071YlaNaEVm0M2rIttbWiMcLOWKRcz4aVq10WtK1oS3t9XV4R6Xn6UEsrRKrUAlWq84cGrr7U3y+ntm7LeQw8J9DSWQ3RWsgJRW5DAiQQJgIUhsI0WmwrCRQYAbsHOe2qvn3tw9tuP7K82D0E6rGCcJOdyfDWQhyqLS/LpIrA7JvK/UUbmI2Hr8B0PA8NMd3BvgJ3sFTl1Vs3La3RdPv3YY32xLauQAhD2h67ubPenawf6bN7Jycsu72nqUl2NTRarsvmQjs3Mj2uijyphB637aI7mVtiIpvrVBiqtdxRrVxvjAzJM7M7fblmqUh4fWjY8lj6u21HO3ItXK5vzLbaOvnG4cNyzMZKKtX5I+jWrHp/Ugdr3FTBxmuRUTUILrTrx4TfSYAESMArgWKvO3I/EiABEvCDgF2WF9M6xI/j2D0Eav3q1pbvm+xM+mlnfZUNt7xM2ppu30W4GGiQaqsSlFhQVm0L4zL9/am13BZYQaT6qDXaqXYzfPine6nZ4j7s7jashoJQnLiTme3sg/CsVgtWJV9CsZ1Vo1U7M11mupNlWs9G2T/5Zcb6PptCx+WB/vWrPH0/i9hx73Z1Wu6rgsxWfPJd1AW7FSJVqnOHLrc7f5hWa/rihoUESIAESCC/BCgM5Zc/j04CG57AXrigHN60yZKDaWrux03jFZisn+nrszyOuo0csHGlsdwpYAunEe9lKr5g2So/rQwsD+DzwkKLBeUznpxXZ7pUqJWCVVGrobPdXVarcr4s3YO76Z6qDUsVvyWobmTZgMkHc/dUTas0qz394pnOpXArBBc/g1xb9cWvZXbnD7/FNL/azHpIgARIYCMSoDC0EUedfSaBABFId9P4C2SpevPunYxanO4muxBiBdhZROkDfbNNDIiM4GZh50LOxJMFXFmv8kEg1pBYDdk9uJvuZHpOuIj4SDGL+DtBdSPLxkDzwdw9VTurNL942lkL6fn8OFy38h1fyCm5dOcPv8Q0p+3hdiRAAiRAAtYEGGPImguXkgAJ5IhAuuwfA9Mz8tLVj2VLbb08vXOH61bpA+A/nD+f0iS/EDKLaB9/cvlSSouooLgdOBk87csbt+8WbCYeJwyCuE26QKxBijVk9+BuZifTmFyTsZgl6iC6kel56nO7dklLpbXVlmVHkhaOIJX62/c7ELx66KFNwxSc/qHG52FButh4OsdevnETFj11nmJvfdDZJb/EC5Hx2Lxl7/R8/khjY6hcn+3OH0GPNWQ5CFxIAiRAAgVIgMJQAQ4qu0QCYSNgd9OofdFMPP/5ow/k1vCQPLVzp6ObbXU/+wNusF++eV0+QgaZVDfZ+bIO8GuMTOHrZWQjS9XHsLgdmH15o+OupSWHMsvXQ7tf4xXWetIJuGasoce3bXf0+8wmB7sHd30I/adLl+Q3CKh9f+LhwNNBdSNTq8bvnzwh5dEST+jmE4uSWF62FIb8DprsqYEh28kuyLnOsd/duycaf+f7J046/j2Y16z/cfWKXB4YSEnkOLLlHbUJ9pxyxzyuSHf+YIa8PA4OD00CJEACawQoDHEqkAAJ5J1AuptGvdH+qLdPbo+NyZ2xUXkSD58am8gqRbZ5c/1buJ9dxc31wMxMSpFBHwKf27MnUCb5NyF+/eratbRjMjo7J8OwArgHHnbCV676qG+5/+fHV2UFLT+AsdG085pNrqmycvWDf1sVHS91HdOgu+lEvHw9tK9vt9MxWr+f1Xdl9Ghzsy9ZjKzq93NZOgE3SFZDdg/uHSoIPawJGajyJRTbZSMzrRo1SLjXUgMLqV1NjaJuSIMznw70S4sN91T1t/CV/fukE1ntrKywVCh95dZtI6vYU7hefWbXzpS/c7fXrCe2bQtlNiy78wfnoPs5yD1IgARIwG8CFIb8Jsr6SIAEPBFId6OtN479cCvTm+0PEOxW08g24KMP1pqpagQCg35iyGg1iDTUdoKQNtBM0/0sLJCClI1MH66vpUhRnAx2Hlm75hdXU1dbxUkxt61DCnvNGpPtPo7MzsiZnh4ZwBi8h0w6mu2qXDO9laz+NVP+Wo2XWjNMwm0i3ZgFJRaU0zFy8kN4Ye8e2dHQ4GTTvG+TTsANktWQnTuZHch8WaTZZSPzS6yyE8voTmY3Kx5ep78FzbbVNzUlQzjnrRfbdA/9PYzj5UQnhMi3Ou5JXcVq6nOrc6CTa5YKhH918qSc2rrt4QaFYEm68wethkIwiGwiCZBAQROgMFTQw8vOkUB4CJg32guLS/J3589avoXV3hg32/iYRcWHCnxUOLITSJJJqCj0L44ek68fPhS4N6/r+5fJCOqDxJ8dOiz7mlsyqcbVvqaAl2onL+OldZlWEzUQA/Nd/BwjFcTCVOze+ms/gmI1ZOdOlop3vizS0gXH90usshPL6E6WalakXq5WWC8ePGRce/7x4kVLcUj3Xn++8HIONEUhtXANwjkwNRX7NXbnD1oN2bPjWhIgARLINgFmJcs2YdZPAiTgmIDeaH8eFhR/feJxObKp1dF+ejOpN95uRaHvHj8GSyPvrhmOGpfHjYL6IOF2vBRh2N+U53Ea+H7oB2/9Q5ChzC47mRUYvyxzrOq2W5bOjewYYsr4YfGXLJatb4/5UH55oH/9Kn63IaAus9/BS4b/8+mns3bNCuq53AZLylXpzh9m4O6bw8Mp6+AKEiABEiCB7BCgxVB2uLJWEiABjwRMcUh3t7Mc8lK93mB/79gxeQ7iU6GKQmoN9Tm4x311/wEjpXGY3y4XUl+8zNeg7mP31l/bHBSrITsLGSu2flnmWNVttywXbmTm8elOZjcS3tapOPQVnG/LIyW+XrMK9fxnd/5QgVIDdx/f3GYZQ9DbCHEvEiABEiABJwQoDDmhxG1IgARySsAUh/a2tsjZ7m55CUGNrQJ8Om2UCkLfPHRITiEI6Na62lCLJan6bD5EPL9nr+xDf1tyEFcoVVsyXV5IfcmURRD3TxcrJCixhty4kxW6G5k5j+zEMrqTef+1+XnNKvTzX1jOH95nA/ckARIggXASoDAUznFjq0mg4AnojfYBCByaav1xZGFRd4tbMC+/PjSIINNzchXZu6wCfupNdWvlakBq3V8zlz3S2BQoQUjb9F3EN8q0NMMVrgV9bayqlC21tdJcje95EoSe3LFD/ra0RC719cswAlFr0XHSwKxDyJ5mNVZm/9eP2TFYOuVb3PJrjNKN8TO7diGAemW6zTJevz/FnNuC39c+CLBuiwoMmpWpCmNuVerKK2Q6nv/4SWoh85fHjksPskfZldMQjfMR1Le0OCJH2tqkEkHa1xf9fX8BMWX8cCMz69aHck11nur8s6WmVuII4J/r0ojshZ/Fb+EJBKJfXw54mJ/r68jF9/XXrI7RMdEMhteHhlJer7Rd5vlP+3mwZZO04+VFvs9/63mlOn880tQkTRXuz19hOX8UwrxcP5b8TgIkQAKpCBStoKRayeUkQAIkECQC0/G4IRDFYW5u/LV4gCmLRKQsWoJPBJZBZVKLYMV+Plj5wcPsR6Z1laKP5eir/g1CHxeQKU2zGy0kVh8sdZzi+HccD3vJYzUKwWgF/1MXDC1BHDO/xijdGNdCAM22u9/6cUluk84dr7+RdIxy0bd0fO36nrxvvtpq175MxsaOi924ZeuYdu3RdXZtytfYpGtzuvXm2E7Pr123LK5XWod5/tPrlWbbDMr53Oxftuao3ZjrsYMw7nZtDEL70s1BricBEiABNwQoDLmhxW1JgARIgAQyJqAPGvpGIghiVsadYQUkQAIkQAIkQAIkQAIkEHICFIZCPoBsPgmQAAmQAAmQAAmQAAmQAAmQAAmQAAl4JcB09V7JcT8SIAESIAESIAESIAESIAESIAESIAESCDkBCkMhH0A2nwRIgARIgARIgARIgARIgARIgARIgAS8EqAw5JUc9yMBEiABEiABEiABEiABEiABEiABEiCBkBOgMBTyAWTzSYAESIAESIAESIAESIAESIAESIAESMArAQpDXslxPxIgARIgARIgARIgARIgARIgARIgARIIOQEKQyEfQDafBEiABEiABEiABEiABEiABEiABEiABLwSoDDklRz3IwESIAESIAESIAESIAESIAESIAESIIGQE6AwFPIBZPNJgARIgARIgARIgARIgARIgARIgARIwCsBCkNeyXE/EiABEiABEiABEiABEiABEiABEiABEgg5AQpDIR9ANp8ESIAESIAESIAESIAESIAESIAESIAEvBKgMOSVHPcjARIgARIgARIgARIgARIgARIgARIggZAToDAU8gFk80mABEiABEiABEiABEiABEiABEiABEjAKwEKQ17JcT8SIAESIAESIAESIAESIAESIAESIAESCDkBCkMhH0A2nwRIgARIgARIgARIgARIgARIgARIgAS8EqAw5JUc9yMBEiABEiABEiABEiABEiABEiABEiCBkBOgMBTyAWTzSYAESIAESIAESIAESIAESIAESIAESMArAQpDXslxPxIgARIgARIgARIgARIgARIgARIgARIIOQEKQyEfQDafBEiABEiABEiABEiABEiABEiABEiABLwSoDDklRz3IwESIAESIAESIAESIAESIAESIAESIIGQE6AwFPIBZPNJgARIgARIgARIgARIgARIgARIgARIwCsBCkNeyXE/EiABEiABEiABEiABEiABEiABEiABEgg5AQpDIR9ANp8ESIAESIAESIAESIAESIAESIAESIAEvBKgMOSVHPcjARIgARIgARIgARIgARIgARIgARIggZAToDAU8gFk80mABEiABEiABEiABEiABEiABEiABEjAKwEKQ17JcT8SIAESIAESIAESIAESIAESIAESIAESCDkBCkMhH0A2nwRIgARIgARIgARIgARIgARIgARIgAS8EqAw5JUc9yMBEiABEiABEiABEiABEiABEiABEiCBkBOgMBTyAWTzSYAESIAESIAESIAESIAESIAESIAESMArAQpDXslxPxIgARIgARIgARIgARIgARIgARIgARIIOQEKQyEfQDafBEiABEiABEiABEiABEiABEiABEiABLwSoDDklRz3IwESIAESIAESIAESIAESIAESIAESIIGQE6AwFPIBZPNJgARIgARIgARIgARIgARIgARIgARIwCsBCkNeyXE/EiABEiABEiABEiABEiABEiABEiABEgg5AQpDIR9ANp8ESIAESIAESIAESIAESIAESIAESIAEvBKgMOSVHPcjARIgARIgARIgARIgARIgARIgARIggZAToDAU8gFk80mABEiABEiABEiABEiABEiABEiABEjAKwEKQ17JcT8SIAESIAESIAESIAESIAESIAESIAESCDkBCkMhH0A2nwRIgARIgARIgARIgARIgARIgARIgAS8EqAw5JUc9yMBEiABEiABEiABEiABEiABEiABEiCBkBOgMBTyAWTzSYAESIAESIAESIAESIAESIAESIAESMArAQpDXslxPxIgARIgARIgARIgARIgARIgARIgARIIOQEKQyEfQDafBEiABEiABEiABEiABEiABEiABEiABLwSoDDklRz3IwESIAESIAESIAESIAESIAESIAESIIGQE6AwFPIBZPNJgARIgARIgARIgARIgARIgARIgARIwCsBCkNeyXE/EiABEiABEiABEiABEiABEiABEiABEgg5AQpDIR9ANp8ESIAESIAESIAESIAESIAESIAESIAEvBKgMOSVHPcjARIgARIgARIgARIgARIgARIgARIggZAToDAU8gFk80mABEiABEiABEiABEiABEiABEiABEjAKwEKQ17JcT8SIAESIAESIAESIAESIAESIAESIAESCDkBCkMhH0A2nwRIgARIgARIgARIgARIgARIgARIgAS8EqAw5JUc9yMBEiABEiABEiABEiABEiABEiABEiCBkBOgMBTyAWTzSYAESIAESIAESIAESIAESIAESIAESMArAQpDXslxPxIgARIgARIgARIgARIgARIgARIgARIIOQEKQyEfQDafBEiABEiABEiABEiABEiABEiABEiABLwSoDDklRz3IwESIAESIAESIAESIAESIAESIAESIIGQE6AwFPIBZPNJgARIgARIgARIgARIgARIgARIgARIwCsBCkNeyXE/EiABEiABEiABEiABEiABEiABEiABEgg5AQpDIR9ANp8ESIAESIAESIAESIAESIAESIAESIAEvBKgMOSVHPcjARIgARIgARIgARIgARIgARIgARIggZAToDAU8gFk80mABEiABEiABEiABEiABEiABEiABEjAKwEKQ17JcT8SIAESIAESIAESIAESIAESIAESIAESCDmBaMjbH+rmz033yvjAFZkavS9zk/0SLa2ULY++IM1bToS6X2w8CZAACZAACZAACZAACZAACZAACZBAOAhQGMrjOMVnR2Vq+LpMj3bK9FinREsqpWXryTy2iIcmARIgARIgARIgARIgARIgARIgARLYSAQoDOVztFeWcPQVKa9ukKWluCQWF/LZGh6bBEiABEiABEiABEiABEiABEiABEhggxGgMJTHAU8sxmQJn0i0VEoraqVIZvPYGh6aBEiABEiABEiABEiABEiABEiABEhgoxFg8Ok8jviKLEtRkUhRccT4LMQnJR4bz2qLEvEZScSns3oMVk4CJEACJEACJEACJEACJEACJEACJBAOArQYWjdOicVZxPy5g2DQXVLTtFdqm/dnZSTjcyOyMDcmxZFSVYYkEinDX5HYzJAsxCZgQVTv+3EX41OIZXRXZsd6IEiVSf3mQ1LdsMX347BCEiABEiABEiABEiABEiABEiABEiCBcBCgMLQ2TomFGZmb6pGZifsyA/EkDoFmaWlRKmrapaSs1vfRXEbdKysJgTKEuldwjAopQVYyFYZis0NZEYb0mInEnMyin1ODndJz423ZtOsJ2bLvc1JaWed7H1khCZAACZAACZAACZAACZAACZAACZBAsAnQlWxtfFaWFiQGUWh65IYsJWJSFInKrIpE4x2+j+Di/CQEqA5ZnJ+A5U4xZCEYC+FvtKQCx7srk8O3fD+mBreenxuW2FSvJBbGIBCNycL8qBRDmIpAkGIhARIgARIgARIgARIgARIgARIgARLYeAQoDK2NebS0FlY7VSKwqJGVZYmW1xqxeKZHb0PAmfJ1ZsRjoxKb7jcsktRaCB5kRqmobjGshiYGP4ZA1OXrMTXItYpCs+P34Ko2CvFrQcoqGqWqcQuCX5f4eixWRgIkQAIkQAIkQAIkQAIkQAIkQAIkEA4CFIbWxkkthCrrd0klxBlZisGSptTIFrZqNXTPt9FciI3JPFzFlhPzRor6FTUXWivFOF5ZVQOsie5Jz82X8dcfcUhjC6mFkh43sTgPoWtOEvMLUtu6R+pb9/rWN1ZEAiRAAiRAAiRAAiRAAiRAAiRAAiQQLgIUhpLGK1JSBTGoUoqWF0SWlyRSUg23qxkZ6zsrswhGnUlZghCkdUyN3YZAMwiBBqnpYZmUXNRyqByWShXVjbBUuiF9t16FMNWdyWHFCDgNK6HpMQTUnupCf6YhRiGmEYJbV9VvlahaSbGQAAmQAAmQAAmQAAmQAAmQAAmQAAlsSAIMPp007NHSGqmo3SZzE3dhzTMrRSV1Ei2tkmlY8KzceU3adr8g1fU7XE2UxOIcXLi6keWsx0hHr4JQYmEO9S8YsYW0GDGGzL+wXCopqzEslmLTPdJx+b9LTcMj0rz9aamq2+rq2Kui0F1DFJpFrCTDUgnua0uJhJRVtkplbaur+rgxCZAACZAACZAACZAACZAACZAACZBAYRGgMJQ0nkXFUQRirkLgacTcgTWPERAaYtHKypJMwYJnFu5Y9a1HpWHzMYgqW4x164taGC0iNtH8zOCa69Ycvo/L8nICVS7js2TUZ8YWMkUhrccUh4px3KKSMuyzBBFnViZHrsHaqBNuZirmbJUSWBVVIluatmF9UcukpURcFhemHrjBxVSUQqDrlSVkQUNZXlJLpQgSopWu353fSYAESIAESIAESIAESIAESIAESIAENhABCkMPDXYRBKGIFK0F/ykqLoYQ0yDF0XJZmBuXiaHLRgwgFYWisOyJRCuMvysQkhLINqbC0DICOy+rO1pRkUQi5TgC5B/8WwMK6XZmSQovZCxKtiDS7ypMRXBcFZPU+mchPgHLo06sUcGqEintG5FmvglBpOulqLgE28EaaDFuHGtVGOqUhdkRCEXzWLcqChn1FqOP6NeDqNfG0VlIgARIgARIgARIgARIgARIgARIgAQ2GgEKQ+tGXMWYYlgOwbzmwRoVikpUCCqpNFyxVDNakVWxRrN9LcJFTOP2LMM9TNcsLsRh4TMps9NTcB1bRH0qNi3DRaxM6hpbEEOo1qjbtBAyD/TAncyoBTGwFxPYHy5nEJMWECxaLYiiJfP4ROCKBkskWAHNz/RJMdqlbVRhSNtgWCVBCFpKxFbbpA1OOlgxRKGECk2xyQd95D9IgARIgARIgARIgARIgARIgARIgAQ2HgEKQ+vGPIF09QlkC5PiVUsfTSZfBJnG+AvBKAr3qyK1/jGW6n/x1xBuYjIxPCQjA31QdcphRVQtdc17pWHTTsNYKD43Zggx0xO9MjM5KA0tWyHorLpyme5kyRZDsckpGR/oRzsqYBlUL5EyWAYhY1lsbkKmIDrF58dlaWFWqmurIDRVSXlVqUSjaIvhqgarpCTrpPWWSJFoFG1BdrTp4Y0349ljEiABEiABEiABEiABEiABEiABEiCBBwQoDCVNhoW5YYnPDEDtKYEIVIY1KvusFvPvg83VskhdtuIxmRztlbnpGalq2C2HP/M8hJoWuICVwMWr2vhoWYJ7mX56b78tvbdel9jMuFQ3bFoTlz45hoo4C7E5CELV8siJb0sdUsoXo64I4h4VR8uM+EFqhbSUWEQdIzIzrkJTv/HvaDQOqyS1eFpzS1uzEjL+JBkNRUqiUow6Zsbu4dMl1Y3bH3SL/yABEiABEiABEiABEiABEiABEiABEtg4BCgMJY31/HQvXLMGZKWoFIINAkAnrTOtelYXra6JzY4hwPMw4vy0S/ueL0lt004j1Xwkmjqoc8OmvTLWd2HN7ezTE8207IlNTeHY9VLbvFOat+5PORtVHFqYn4U4NScTI/dkoOMM4hANSHmFdb0PlkLQipaWII39bRnq/ADtr5dSBLRmIQESIAESIAESIAESIAESIAESIAES2FgEKAytjfdCbNQQhZaWEFuoKPopUSjZcsicHuoaNt53DRY9x2TnkT+FK5e9IGTuV1HTIlX1bbBM6n5opq0Z+KANCamHq1l9686HtkleoFZJFdX1xqeypsGIczTc/QdZXpmArIVYSGu9KCpSVzgUU3nCPyNRjUm0IJND16S2ZS8EqMdsj8WVJEACJEACJEACJEACJEACJEACJEAChUcATkcsi4jXMz1yHe5Y/bKMNO4qqKxqKGuCCr4lx+nRb8tw6Sqr3iRN7Uelqq7N1koomXACcYEW8dH6kuvUbR58X16BRU81sqGtuqE5GaFoaYVhrVSC/TQQtVHWKlxLsPag/tWVyJgGYWlxflgG7r6BeEbXnByG25AACZAACZAACZAACZAACZAACZAACRQQgVAKQ4lFBIhenPVlGDTF/MzobaSB70ad8Qfiyaqz2MMxhkz3sggygRUjRfxyUhp4Jw3SrGZIUgbhp/yh+EVm3YaQk2Td46Re3WZlOQ4XtRnUq9ZCKLAU0oqMevHPZNc4Y7WRtn5ZYlP3pf/2KzLU9SHc0qZ1leeyiGxn06N3ZH6Wga09Q+SOJEACJEACJEACJEACJEACJEACJJAjAqFzJUsgBfvE0FUjVXv9pqOwkmnzhGoZgaDjsWGklb8vcxM9iNWD9O0IJD07NWG4cpWWl0sZLHbKKqqM+lViMYUVlYvUKmd+ZghBpxGs2mXRdPHLK2qZ9Olifo+UlMjCwjTqn5ByuIk5KRp8OjbVBy1oHvUiK1lSST5OYjEBiyUVjlakBGnvS9SlbCWBWEn3wGAMLO5L685npLJ2i5PDfmobFYUmYHk11vMxBKda2fzIU1LT5L4e1wfmDiRAAiRAAiRAAiRAAiRAAiRAAiRAAp4IhEoYWkrMy9TITcTFuQoRYxzi0JQ0b3vK/nnKKAAAIABJREFUtYihAkZsqldiCDat7mOTI30yitTwK3Aja9l2GGJTnUwMXkdw5gFYBbUj01fVQ9Y2avFTgoxjU6Md2P8eUtM/4mgAFmMQnhJzqHPVYihZtDFjDBUh45nGOkpAvHJa5qaHJDar7S2SJehCZl26v/Fv/GduJi7D/WOwcqpATKJGWPWMY92sVFSVSUUl4hWtLMpY/xlsvQRx6HOuuH4iCp1BcO27Mju9LJPj47Ln+BeloXWH025wOxIgARIgARIgARIgARIgARIgARIggRwSCI0wtLQUh1BzD7GAbkEUmpTE/IxMxC7LEtzKWnf+EQI6p0+5rtZG87DwmZvqMQJNz04NyUh/DwSVOII975P23SelYdMupH0vgZixV/rvvgUxZVhqkGWsGJ9PXLHgnlUckaraNhntvyPDPZfSCkNz08M47rCMDSBF/BTS0UOQmY0NyMz0hKwsQQCKIpUYXNOqykuksrRYFmB5c//j30jbzlMQaFog5LSknBbzyI42N9UlifgIRJ/YJ9utqU76JxFPyPjQpFTW7ZN9J78s1Q1tiJO0AIunYZkY7pS+u+dkbKhHGltVjDpnMKpp2i9NWx/HsdtTHlvHZX52CGnvOyCmXZbp4WtwZ4O109yiDF0cwb5tFIZS0uMKEiABEiCBXBOYxrV4ZvLT7s7VdS1Sg2stCwmQAAmQQIES0PAfsSFZiY8ZHSyq2CRSwfN+gY42u+WBQGiEocX5mPTfuQqx5gOpqimVipoyiCALCJp8EVY7NyHMHJCWHZ99SCDSWERzE52IIQThBBZHKoYsxidlcmxI+jo6je0PPPGnEC92wVKowQjIrEWthJYhegx0vA3Xqxkpjzauw1skZUjzXlXbKqO9l6WmYYds2nHC2EbFloHOy9LTC8FlZEZGJmOyUIR08JE6GRmbkf7JnTKTKJd5iDXzcOtSnzLEm0aMoCUpixZLOYShyuJpabsxKrva3pb6spiUlkSxbFJqqspl2+4TEIyOPhCL4nNwI5vug6EPYiStIKsaiqEJrZkN6Z94fFHKa7bKI0efl237TkH8KjW2SyT2yKbthyE+bYYQ9XuZhQWVyKgsLcxBgJswXO1qmvYa1kPR0hopRXBrqGJwpUPeM3CJQWBTEWkGYzALzkvqAgdRaGZqHiLbMWnZstc4DgsJkAAJkAAJZJuAij6SGJWVxJj0wa1Zlkaluioi05ODEIMGjcMvLy8/uFbq9+nZJVjbFktt9ep1sRoJJWrqWnHNbkKS0iasT0hd40HZvPVAtpvP+kmABEiABLJFYHFalu/9WFY6fyZFzSelaO+/pDCULdasN5QEipDiPNmbKbCdWErA4mXwrtw+9zKshm5CyKmX0opSiDfzECNmjHaXVjTBImY7BBvczMGip7g4amQA01T0S9hOu6rxgWamEQtneFIaNh+TR458HqJO2wOhJBnA3PSgdF9/GdZFPRCQ2iGGaGyhVVFkVRwpMgJWjw3cx25VEitqkM6eXpmIV8ps+WGZi5fLyDjc1mKI6QNfru7pJemdWpb5xWW0B+IN2rMCRWhlZRk3qoYypHesxvciCDzb2+rk0M4Wqa8qkbm5eSkvKZJNzbVSL71Su4I2VUalESGQmptwM4ub36XFGQhMRsVG3ajswb8He0alcesz8tjn/gXiJj2c7WwhNgNRaFyGuq/JvcuvgWmfNDZXG7GWSsrAuqoJ/26QaEkVxLNyiZSUG0Lb3DhiNE12ymJs3GC8MJ+Q4cG4VDcfkaOf+Za0bt9vyTawE40NIwESIAESCBUBFYP67r8t1dH7sI4dktt3OnClX5aK8hWZmcGLilhCqiuLpTSyJJVlgn+vvhOrwB91s47BUFbFH7yrwTUuist7sczO6TW0GC9jSo2YfEVFJRCHtkpR2aNSXP4oRKKDtDAK1SxhY0mABDY8AVgKLV//gazc+ycpan1Kivf/73iIemzDYyEAEjAJhMZiKBKNwgJlt+w9+VW5e6FIJkdvwUqoDiJHhRRHyiCEIKgyhJHp0RsQKpAxLKI3d+ieijnaWwSLVmFnZmYG+85I647TsufYF41YO6lKOQSmmsaduNHE20eYHxZFVq2JdPt4bBZCzKKMjE7Kzf5l6Zguk8HlRrk/uVlWEOC5pXRJqiMQrHBvGdV2oNSUR6RiCZY2cdxuGmIQRKC1z7IqRYZIpGLR6rqqmhpY6tQix1ixJBAkehFZxu70zkt8oVaa645L2RSEbtzkbh24LjULHVIRmZVWiEkV1TVrxkKrzm9LaE9V/TbZhPhJVqKQtq0UYpF+qmoaIKiVyq3zv5axUYhDTWgjXN0WYyMyi36YwpgKbypgqWXR8lIMfVmCKLQko8NxKSnfInuOPkdRKNXE4nISIAESIAHPBEyroKmxG9IPQWhpYUgmp+CePReXloZi2VxfJFFJSEnRkqxAHFqA4jM4jhh6k8syjqNqRs6hmWUZnV1N1KDvUZqrivEpkpZq/VssTdVRWMkiQUPJguDdjkxAKOq6P4Vr4C3jfqL7WonsO3gaF889EjGEIloTeR5Q7kgCJEACJEACJJB3AqERhpSUKQ5t3fcZuXN+Em5OI7BcqcMbvhJIJ/gUQyUx3uwV639XLXCwRq171IpmDqLQ1DhEoe1PyG4ERa6sTi0K6fFUXNIYOZMjFZKAVVJpRYPE52dlYGBMunsHpG++Xm4tHZTO2RoZjZXI/EoUxy2WbTXFUl+RkChcu9Dq1UHG8TeVw7AHbmL3Z4sMq6FiQxRaQTBqWAhBGPpEJFqWCISWZQhZettaGi2SSgSIhnmXVJXA4mhiWe4PQ5DB/qUQzIZr9khFdJtUFw/Lpu4+aSgZk9qyCWmsL5XyqiqZn8fr0KKylKLQagNX/1uKTGw79p9GvxvkzsVXZGL8qtTXoz3oxqppmVo26b/0Har+Wf23/nN6ekEipVtk3+mvy7ZHT9JSKBks/00CJEACJJARARWEpoffk4mB30tfX7dMz8zDAmhJmuuLZUdTQiKNEIAmFuW9q3G5PgDrVYg/A2sftcpVw1yj4PZgCaZCuIQ+KDASkgiWQzMy/mppw7X8aHtUmiuKZf+miBzcUi7lleUyPruCGIci16+8hfuNd+GqVi69d47Ilj3foED0CVL+iwRIgARIgARIIEQEQiUMKVcVh1q3H0Kg4w7puvYG3MYWJFq3asmj5t+GfZAKQWs3dmolpCU2OyNDPV3Suu0JI1NWRRpRyBzDsqpGZOxqQRyj2zIw2I2bzFK5Mr9Hzk2dlNF4icRWEOtIj1e5eshi3HgmSnADWoYYRBEVpFCTiif4lOGzrbZM4lPF0jkWgyXOqnVQURQi0FIEH8hZSCmmGclW8BmfS8jCIm56a8tlBm885/S1JW5ma+uqJI71HT0jMjkdk71bm2X7pkaZXKyRoYUdsByKS1vJXWmduStNlWN4axqXmmbECYLY46SUlldJ245DEN6GEXeoHy5jU4YwtIbU+Id26cF3VLoIoWse7d2897DsOviUIxHKSVu4DQmQAAmQwMYmkCwI9fZ0SBmseLbVLyFsX0IGYAnU1bNoCEFv3l2QPrhsw0hWErhGLeEqhcuq8Um+L9haB6siiD79yJ6p9wh6CzE+syLjC3iBpGKRIRitwGoWVroQmophrVuKC140MiePtZfIgRYIRa0RObarGvcg5TI5Ny3jUx9J56UrsjT1uNQ2Py1F5Xvoaraxpy17TwIkQAIkQAKhIhA6YUjplpRVIiCyppGvRYwf3AEmFeP+T7+b/1j7u5RYNOIEbdp53LEopNUsJZaku6tbegeGZKL2lIzX7ZG+lYiM4EZyXg+ED+SfJAEIN5i4q5xEcOematx0LiO+kBE/SD/QjxC/oA2bD81FYTW0KgwJ3MtUECo2rIaKEDQTHyybiS/LuXsjMjwVkwheY2og67lYXBpryuFCBoEM8Yb0+Nc6h/AmdEW2tjbKgpRKfDEq0wuHpav4Edm6cFeai2/LjmqNtaRR+Ldpa9MWDcJd37IV8Zc2y+KcxnD65NWqIk0u+n0xvgSz+zppaEGMJ4sYRmkPyA1IgARIgARIIImABpBenvtIxu6+Kbdu3TYEoe2NS4jdF5OX/hCTa0MJ6ZtaMgSeRbw0QYg7OJAVy5a6ImkrK5YBWPbsaiyWP94blfryIghHS/IBXL+/tq9UdtQWyf3pItnfXia1mvChAtc8uHvrC6YbvXH5uw/G5fIYEkQYF7wVmdXrODy+f9eVkPe6V4WibfXzcqStRD67KypHdyLOIFzae+69KwMf/V627jglbduO0M2MM5oESIAESIAESCAUBEIpDClZzSLWsHkv0qRfh4XNMtyW1GVLg0Ov2bGY5ixrf5eQjayqdjviEjkTRjSz2JUzL0MQ6pKJygMy3Pa0xIthFoSU8u1NK4hjUASLnhW5Nbwo4/NwCVPVR28g8akqK5XaWlgIlSMu0TLc2jR2kGHDDkcz/GmuWpHW2qh044ZW10sElkLqVqZWQxCiiooRYwgi0TJczMbnlmSuV+MarEhsbkEakTVlU0MN3lwKYirMGebwNZUVMreQkLHpObSrFkZFMJFfKpLZpWq5M3dYeiKPyOQg0tK//ZYcPhqXg8cQF8FBWUZsITQKLnrK9hNhSJEa98pa8AVNg2XTMt6ctkJIQupHFhIgARIgARLwSEAFoanh9+XW1dfhMtYhTbVIxpAkCL1+e0E6ES8I71aMK1NbTUR2wgpoX1NE9kAIqsSFtrqiSN7uXpZf312RnbPF8hd7S+WbTbhunl+QDwZEusawP66xXSOITwhL5K+fKpciuIe9fXNaWpH5dG9bpVyenjUudt850Sqfe6Rabt4ekSm8uLmBa/cHd8dhSbwit8cX5Nc3F+TU1kX51uESQyCq2lwmU9Nn5aN3PpStO0/J0vwxCkQe5wJ3IwESIAESIAESyA2B0ApDlfWbpLZpi8yM34QxDu7cVKmASGG4OJnKRdJfTeM+M94p0+M92G+7Ld3OO+fl6vnfyNhiuQxWPSWzpc2GIGSKIOWI+YMYlYj3o45rUbk5gqCWRgYTFFj2xCAC6Q1rGd4+ygqEH0MYwjptnGHXji+oI4L9Ne5BET7LsBSCAoP/q7UQ1uPmEyZEEIeKkClFRSOIRVg/Dcuc0dl52dFaI3UInN3aWCMJdUHTzfGfYgRH0KrUjqm4CG5pyxGZWayWa2OV0oVAm0Nzd6S347wceexpuIsdTclB0/qOD12H9dIwRDe8Jk0qaz39RBzCOu1HtLQU8Z40zhMLCZAACZAACbgjYLqMTQ7+XtRlrKQ4Lo9uXpYxBPR56YOY/BaCUBcye8bw4kPj/+hLkP45kS1NUfnOsYi0wd/rpWvL8vFkEaxkiyDO4JqIS/DUQrH0IIv9zpZS+YvjpfLKnQVYG4v0QOA5O74i++oiUoYYhXO4/n6EOISDXTOCdx2yXBqV7x5rkf/1dItcvDEir9ydlaeObJK/PFQtlbiG3x2LGy99uuAa/nb3opzpS8ipLWsC0Y4qxPpTgei8XD93QYorjwDG9xiDyN2U4NYkQAIkQAIkQAI5IhBaYSiCwNAqQhTjTd9a3jEgM0JOo+C/q/948Le6rlmmxgZl4N77Ul3XJnUtjzyEWK2Erl76QO4MTchIxVMyWV0HL69SVAXzct3aFJ3wRY9UAt1nK24oVZC5MQpxKKbqD1LY66ZYVhY1nMyMooGjH2Qdww3pItLmFkEYikAx0hvLCFzHliMahBoxESACbaorkzq4i03CSmhsch7ZVmC9g3ULsCQam4nL5qYqaaivlPLyUsQA0jpwTENg0jrwF2Y8mrlezXn030vYdwK+b5cGWmViZkEG+38sO3edkxOf+RpiD7U8aKf+QzkM3f9QZkcvIwjnLHq0Fmh6bStloeKQyUQXq6WWZnrRcEssJEACJEACJOCGwDQyjHXffkl6759djSHUsOoy9rMPVwWhTghC8yoI1UbkW3uish+Zx97sWJF+vIW5OCJypF9k974iaUBWsc9UIWlDSbH8eKxYRiHgIDih/K5HpH1yRb51vEZqa5blQsec9CJuHzyvkX2zUhobq6W7f8YIWN2TwEJcs5/Z2yDfPLVZKvGS5eJgXG4limXrdEKeryqRx7dXI7FDQr737Hapri6RX7zbKR/dnYBABLFpYElOtS/JNw/CxWxHpcyWlyFY9WUZvz8ty/HPS23LU4w/5GZycFsSIAESIAESIIGsEwitMKRkihHcOaJuTqYIhGWfEiySlqs1S21Tm8Sm+uTWmX+QHYe/hiDWJx8A7oKV0M0bZ6V7ebP0lh2CwQ7QaIp73UJ9peCyZRQVh3QRlqkgpRnDtsCEvQUBokcQz+DGmApESxCFolKBuAUq8ugOuo9aNpUgivMSBKSp5XkIQ2pNhDXGNhCHcIwViCtqNTQDUace8YiOwXx9CkLQx90TMgqBaHkRglNZifEx3Oawn2FlhP+pLKXht1f/qok9vul6/AuakbZa4rAguj2xVXpmNsnYyrDE4y/JkRPPICj3YbxBnZPpsR4ZG7gsk0OXZXlhBPtDdDJNhAwAq4zX/vngewlEsMTClMRnJ5JX8d8kQAIkQAIkYEtgaug96bn1U+nuvoOMnnhpsrIAC6Fpw0LIFISW1PQHL1O2NETl9I6IbK8RuTCG694Irmu4Fk7jrUSkMip/dlSvzcVya2hF2vuWZXgugut5Cd6rrMirnYtSX7soLx6tl7l4sbzRN43rMK6e6uOtLzVwfV4xUpKtpuEshcVQOd4A3R6YkSvDSBiB67q2oxrX+1q8mGluWZQtm6rhqj0BC6USeeoLO6V7fF5euzspbyFO0ZnBJTkNgeg7RxfhmoYkEtN3ZWqiRyJdl2Tr3m/Resh2VnAlCZAACZAACZBALgmEVhianRoyLFuiEE8iesO4quCsiiUgqJpJctGvJRCHiusaJTYzLnfO/r0Md5+Rbfu+KPfu3ZA7N8/IUNVRGa3YjhtIWCGtiTkqCKku9OC7WsaovZD+xTbQZHDjiHTyuClF+B9D2Lk2mpBJCD6TsAyqxTqz6J6jyHrSD2FoR3M5gl+WyDREpI7RmIzMrAWpxv4R3JiWQ2iJQDgansWNbGWJPHtoE0zh52RkMialWF+EG1gNSK0mOireGGl3ISappZJhJaSykB4aH5WG9L+qba0Y6xB/aDEC66HNCHDdJ+PTb8jhY1PS0lwjoz1nZHb8tizFx7GPVrra92RxSKt9oBWtHg6BwItlfnJUZqfw6paFBEiABEiABNIQUNex3ts/Q8DmVyU+PyObEEuoF+5ZPzo7Ix/1LiH7ZhGWReSFPSVSAzexN+6v4JolMoHQP4eQFew43MzO4pJzJxaRM0NFchoWQg24br10FWnrY0Xy6KZy2Qx37lLsq3H8pmGZ+/OOhOzYBKtcXO+akN6+GBWWIE6fWhUlYJFbhBdOxcVqMYTrO0ShudiCDE/MC8L9QUSKyhO76gTxquW1gWl5bEedbMGub3RMyi9uTMqLJytly+YaqeiZhTt5QpDQTH6HQNVdOMaXHlmU5/dVSm05YgdOn5PxHtwzVH5Lahr3paHE1SRAAiRAAiRAAiSQfQKhFYaWF+dx47ZgiCeGKKRKhQofayKOIWSsiRYPlmNRBO5nlbVNSMU+CBEEFkJdd5FFbJP01XxGphFLCJFyjOoeKB+qChnfIQcZ9ekBVheV4AZyKr4iCZjjNJTDGkgtd+DTNQ8LoB4k8opCsDpQBzN1zXOLsoAb1E6kxO2NrUgbzN2bapANBTeJfRB/9K5TrXuMuiHu1EIMam8s1+A9RoBMDa69b2utNMCEXWWe1rpyWVxLb1+kQpHupk1F+wxtSFWhNRa6g9FubGU0X9+O4v9qPXRzZLOMx6pkdOo92dUak5a6OUlAFFrRYNhGq7XOT/359Pe1dVGMQ0npEjKfjUh8bhKBqNFxFhIgARIgARKwIKCuY9cv/li6738Et+m4bKlOIKBzTH50MS53x5cRADoCUahYntwclRf2RqS+pkja65flzW6RTsQFOti8LCe3ROTieJHcux+VUSSBmJovka1IDBHDtagKL15eQPaxRCyBly1R2YWXMc3Vc/LP9+LyXy5OyRHkkhhCqvsdcNveXR2V8cE5iY7Pyr4IEjksQBDC9btnalH6pxZkDi9gYsjU+cyOGjm9q1YG4Nld21YvR3bUwrZoRf7oSIsc29kgV/pn5bcfD8so3LWX8XKnMboiTXhxMwo3uP90Ba7co3PyL08gN2g9xK3hM7DQvQorpyOyZc83aD1kMUe4iARIgARIgARIIHcEQisMxeZGJQ4RotiI4wNxBtm8FvBmbwliSVllGVKmw3xHy5qIo2KJ+TWCVPKllQ0yhLhAI0tlMtz0/7P3HuCRHee14Gk0cs4ZA2ByzpzInNOISdkKtOggW+Ht87M/ebXez6tnr3a98mfLT7JlipItWbQsiWKQSImZnCFnOJyccwBmkHNOjQ57Tt2+mIuebqCBiaS6yB70vV23wn9vd1WdOv/5l2MgqYiAigSbrXyGKaS3QXDF6OcED3Q+kaDQEHcfz5AWJCmf+WQGcWPTvOcmp4kWVksQqIMTyBLiO3ncYRwmKkN2OWnvDKM77MO2hgGjC9TFsPRxArjYdjF+AoxK1jnqQ0qfB9KvbusZQSFp6rkpCkPvRx7fuwgUxVkiQtQU4rW8Rkn/2kwmG9ixXMuMQ1nQHLIZ36udvgQ09WcTHJqH4f7tGMisR3EhWVhu1qECHaCQbRrZ2Med1QD/iqwVT5c5ubOlpNImvbWc8J5FUeUK055YilkgZoGYBWIWiFnAaQH/8Gl0NjyP0f59mFnIyJvcHPnNoUG8fGoUNd0BLC9NoD5PAoqSXNhey7HytA+3zHVj7QyGos+hy3ajC6d76GpNt7LFuX5sa+Q4lJ6MrOxkzCqKw+/PG0AtmUc7Tnqwg+5mPdwEcccNoYtRQAcpAriD0UT3ckCTwDRDReDnx0fxnDZ2OKaN+JLhCU4Eahlx7IWjXVjpHsafzErEovnZGKDb2POHu1BakoUcbvq8sr+VTCS6eHPK8dbpbpxgkAevoo1yduXhRtEnbyrHnNI0fG9bC95tHIB/3xA+PXcEC8h46hsdQNP5d9HTx42umDB17EsSs0DMAjELxCwQs0DMAtfQAh9IYGh4oANDPVSaDIwwatcoenv74RtNRFp2GdKyOHHrbqa7WCdDp6eTacPZH/8XvmGDQ57hYTQ0dqElbhaa8m6C180wtYaHw2SAkCAo5LjGotpYWfRvDaOZnO71cmIn5EhnvCgkMMINSIutw8qICYHYEzhPxHnO+8TB8YiOToaPPmtjhDFNRJVcBLgC3FmUcDTDj5kJZT3dy0R/Hxnx8roAMpK561mcJuUg6xrDFOJ/vCSObCQBSuY96xEEZNhBfHk8owxfH6AeUlAzybKGxTBihoB2Q71pONiznvUkUPboPEryR2k76/GwWmiqxPCAB32MEJOYlI1kRkXzDHVRV6gXSWIyESUa6W9GV/NJ5BTNRmIyRSBiKWaBmAViFohZIGaBoAUECjWcfgbnT72HJH8/XamH8YsDw4zo5UcumbSfWZmIdaVxOMbjlznMdxF0mcXAoL6TPlQzHP3yaur8cBzd3OBGfE8CNlQK2gngneYAnt3di19yvG3wxaM/EE93LgI9dOsuJqO3IIUsJI6RZQwpuiiXrN1MjvrUEWxliPpWMna9RIkauZnTPOhCM93QmjhmezkO72z1YD9H1QyO2hk1dRjg+9sW5DKEPZ3WGM7+IAf5em7YaIw9MUI9I46D0vxz8doVMzKxfGYWqjPj8cQSMn5dXmxr8uBgVxxWFQXwmcXUTGJb6jt24ug+GSgWtSz2RYlZIGaBmAViFohZIGaBa2OBDyQwNNTXQuCHAo6c5HV39sDjzcDMJXdQQHk+XcUS0NF4BnVHtzA8fTf999MpHXAB6BkaHEJTax/q3YvQmrESvoQUA7OMMYqMv1gQ6zHnxzOF9JnAoDqGp28j08dE4eI156kb1Ez9IO1ASqMgiN2YuyoQiPNGk0zxRtySyegDSfiS7zmpVF3SKJIYtVhAclHzy4WNE80eFtBIWnsaI5VlUM9H7SVnKAhkCbpRZDKLCeRXuZyYBj3GkMrIZUNDw2jv7uMVnOCSUSUQJ46TZiNaTUBKadifiqP9q4lLJVO7qRWlBSqTUclYvGoYkriDuxhz196CvJJ5tDXLpUZE3dHX0NuyH0kZibT1KAZ7TqO/8xxySxebcmMpZoGYBWIWiFkgZgEbFKo9SVDI14+95wfxX/uHcbY3gCVF8XhgTiKKEuh+TZbt3OI4vN9Jzb4+NxqpI5SdFMBgdxzazxEcoktZVjI3YvrjcGrAiz31XhzqdRO0iTdjFQk6+DgBo7uqE1CWncjxLI6u3cGNFI6dYttq0yVAHaCAAljEaR6gUZNRQTkujpKBLBZyE13A9jWNYh9FpA+0x+EsI3uq/FeOdMAzPIr5bg/6W3twzJNIdzMGllCoUo7bLo7hG8sz8KUNBaigBtHTWxuxt2kI94p1RFe3n50cJZDF/IS0Hl+SiPI8Vwwcin09YhaIWSBmgZgFYhaIWeCaWuADBwwNkZHS33MWPm8fBvt7yLTJw8wFN2PG/DVkqHA2yJSYlGYmeQ2ntlLvppeaQsmGPTPY04Xmdu7uJSxBa/piagBw9mloNZwparYXxGv03hya4wABE8tFK5GCzx0Uiz7LXcVOgkCZ9PPKoBtVPzWABBaJDaQksEjXm/fBv6F/dN6AQPrfQpd4jXg+asuFqwTkiGkUYB0NfSPoYtj6iswk7nYmMvIZdX3EGgo21kRKE5DFyxMIhnUPUNS6exCZdKtLZRQzxA2jpokC0QMjyCbtvjw/Bxkp9HFTdebF6C6+VBzvXsbJ8TF4Rs+gLF/6DG4MU6fBlVCKigX3onzuOto63XQpI7dA1ko7AAAgAElEQVQC6bllaDyxhSHuN7Pt/dR0aCAodwaZ+VV0M7PyhfY/dhyzQMwCl8cCDQ0NeOGFF8x3/5FHqFVSUnJ5Co6VErPAZbRAOFDo6X3DqO0hM0djjJfjzzCZPwSD4vtd+PjCOGzi2Ndx1IWakXgMU2OogJp8r50N4FQiBZwJ7hyoGYX2WZIYPWwOwZWVJS7cPTsRM4gskftKX7FBtLQOoI0MHQVoaO5y0zWbLCG6obX1yN2c4x9TYdYoCrI51nE3pSDLy5cPxbkuzC6Iw7zCNHyMA6REqHfVe/BqjQUSvXh2AC+zfV66o3v4uZ9zCTP+skEa2zfOzMCSgiTUNvXCQ9Hq5NRE9HEclwZhObWTXP54bGtlo4748PjieIJDiIFDl/F5ixUVs0DMAjELxCxgWaC5pR3NLR0oKc5HUSEHm0mS8u/ff8JcY6eSknwsXzYvqusnKf536mPb9s1NwXtwndvxAwUMeYZ70N9dC89AC8Wju9DTRcHKOesJCt0wBgrpaUtISkVx9RLj0tV89j0MDfRwx5AMn24PGgLVaE4mkyWB7mMW8mOAFFcQjDHvL+AyBHxgtIIEBLnd0h8IYICvLAJCczLikE/2jo+Unx7qFtTQZ6wtSA1yFDHuC+A8b96T5q5IYZbmj94H2UbGxUz0I0U+46SWh9rF7KI20ZBnEHWdQyilaGZ1fqrRO5LotZzIJLnU3NGPUw20Ty8jqXAyW5GXhvzMVCPAOWdGEVo7GVZ+eATDnmGkpyQgOTEBw3RXM5VxcjvsT8KJ7gVI9rchztuFXFLuu0j3n7HgpnGgkJrvpiBnRm45qpbej1EP9RJOvYo4dx8jm53EYO88gkPzlS2WorSAFvlNTU0oKyuLLfCjtNnvajYbEPrpT3+KAwcO4Bvf+AYKCwt/V80R6/d1bAEnKNTT24v95wbxCvWEBAoVZVlRx6qyyLzthHGzGulzYRkZQzdWAHW9LvzXuTi0eOOxkW5XG0ZG8eu6eI7l8SgiG+eBmcB9c5NQzoAMbjGAPENooO/25v3JePtgFpq7kziSik1L8IbjrZ9cWzFxpQNoUX45jpE1qy0YizPE9wSI3ByXi3M8WFw5hDuWDWHJ7ADumJOKm2fT9Zpt2Fk/gp8c8WNvh8q+kCq5aXMDQ5WtrkjFIWoKNZDpe0tlMuL6hrC3dhBtjHqqQBT30JXs1EActpMN5Trqw+cXumPg0HX8DMeaFrNAzAIxC3zQLLBv/3H827+/gPd3HSGhgqMfF4kKjLTmhkV44gsPY+mSueO69PKrW/GDf3sB5+taqDebi6KiXNQ3MHooX26uNRO4CbNh7ZKw137QbHM12it7fv+p53DufAvnH366wfMe0I43bVyGP/yDRy+y/9Vo02R1fGCAodGRXvSRhTLYXUcKdw+629vhTq5AcdXSMfaKs7MGHKpaglGGwG05+w6aGxrR5CtDQ+ZyUooYtj6YORSosY7tfy3XsF6vgCFewMmi5VrmQiE3GssZ1SSR5+KIJGVThHKAWggkJBl8xSRn4aGMJJtSJBzI5AtOUsVgMskCesxfTiLJUjeuX6IiDREgGiQAFRc3Qk2GBEZvSTLAlvClIeoJNXQN4Tx3Sb2jXgMaUccadW296OodRFFOGlIS3cjNz0J2Wgq6OVltokZQGm2SnpJCho/KoTbDqAuH+tazb166oTWgtGIZgaGbw9parU1Ky0H5/Fsx3EchzvYDZAzVoqtxH/G3bKSkFwf7dG3+7Ny5E88/z5DI9fURG1BRUYHHHnsMq1atiphHZTz33HMRP4+mjNCL7cX9z3/+c5w7d47uC17zkr6TXrfccotp15o1ay4LUBSpD+vXr58S28Ru9/vvvx/apbHjqZYZsaDYBxdZQPb/9re/je9973sYHBwknmv9bojdF0sxC1xPFghlCp1vH8ZvKQp9vheYV+DGzTMSsYKaQpX5HEM5wLU300XM68YLZ10MtOAiGMPxheTes+0j+PFu6Qe5kUw3ss9Xu7BpQTJmcIOmrW0ERw714WBtEsGgXIJBiUYvb8SfyBGU0xxFSdDL3vmR+5gZcy0oiCp8ZlNEukD0nzbvRTHqbfOhhkLVr+0fZdRRH5ZWDeK2pQNYOjeAO2elYsMMYDtBrh8TINrXac0qZmQn4NGFmZiR4McPTvaQgcvIZ4ySdrCDuoTDKSZAxRq6zd00Lw7LubnVww2l99rI6L0OwCEtIl55bRs3Jy7sEod7llavXIB77tkwbud4sms3PXAT7rpzfbjiIp7TTuvLr76HffuOUxeyFY1cnJSXF3KcXoDiIgpPMe3Zc5SLnMX47GcejFjOdD5w1j3R9aWl+WNtmcqO+mT2mqhO+7NQm0Zb5nTbbNfrrEdl3X/vjVEvcK5EG6O9V5PZ1NmXydo51X5rgfjyK++FbUJoWZPVHbYQx8mplBeaN7TsidoyFXuFlhvpONxvS6S8H/TzTlZOU3Pb2O+u8/u5csX8qL9bofbQvfvX7z9DIoAH/983v2qAiad++Bx27DrK3/ntBCua8KU/+bj5Xba/Q7989g3k5mSY/CuWU56FDFiBQk/+4Fm88Ostpgpdu2zZ3Gm3K7SdH9Zjfee/+y+/wJJFs/DNv/kyGpvaxuz/2hs7UFScR+CNrygYXFfTRh8IYMjv9TBiVjNdweqoc9OM3s5WJKQUkCl0E7IKyiPaKz4xBaWzb8CRI1txri8TTVk3IZCYwQW3NHqM09ZFTCELy7EWWcqjncMygi/ETdBINzLNKylZwJ3KOCRJs0Dl8EUCEXLJHsokgNRHRHA8KhTaRE1AHec0DzV7ldyx5B0JEGAi3MQMF8Ahq7VxSGXbk5lJX/AM6hmkkKIutzGzLuRLbdEi0a92cZEovQSxnJIpPO3l37ON3YZVlETAKD+L4FleJplDo2jp6MVM0tuy01PJMhqChwwiH13yTvYtQ3ymjyBYMTWDZoZ2ZNxxRl41Cmasovj3OQz1daGzeS/7k8RzG64pOCQWxV133YXz589DAMzrr78+1u4FCxbgU5/6FARi5OdbE81InVy6dCkyMjIMwOQsZyplOMsWSPP3f//3/GEowuOPP47Vq1cb8EeLfoFZqkNskOPHj+PrX/86Hn744UhNi/q8+tDT03ORHY4ePYrS0tKo6yguLsbdd9+NtLQ0CBx69913ceLECdMO2x76/FowWGQ/aWh9mF2qZP8nnngC7QTIf/SjH0V9/2MZYxa4mhYIBYWkKfRThqMXKLSsLB73zE3AcbKE/nG/H48v524kx6s4bnL4OXbt6SUrtzUOdxV4ObH1Y39/Asc64KHZwIPzk1DJiGTtFIb+r3fdeG1vtgGDvAGBQWQIxXGQFhDEcS/ACJt+7qwYNq4RhhYzV8NlECiSQcx4rE+44RIEhQQS+fXys34/d3w4Jr57MgU7T2fShduH1bMH8ak7B3HH3DSsLffhvdohPH08gB0NjH7Wdh63M3paeW4qWlLcONXvxwA1+UbI+F1bHI9PzqGLWlYA71MzSdFKR0kJfq+N7QkBh+rPFCE9Mx8ZmQVX5bbl0708PS0VR47tQj13jMOlihnFyM5Ox5LGtnETWm1E9fcN8tqz465V/jLOLUZGtLsWfbJ3Wk+fqTcTaO1eb9ywDGWlhThw8AT+86evmsI8HrK6FvOhuMxJO7vSpAztT2g12oF3x1lTaeeO+v333zih20U0tg6tK/R4yZLx/Y50D3SdfR/qed/eeGtH2DaHYxCE1qkF5K9e3IxfPPO6tfvN/hcW5Ea9SIymjarz+InasYWyvcO+atVChFso616dOX0er3LBeimpqqoUSxbPMX2Z7P7ovkuHLJqFnRbozz33Jt7Zuv+i5um+ZGakcjM2qEXBHBPZ6KICwpxQ25z3ZKK+KO/5c8344h9/NOw9nOjajRuW4q47LLBXvxdvb97DDU5GDLiElENQQuubD3OyQZhf/3ozamqbzPfI5/OitKzQrN+c389EDno3kl0SzXfTaTMbFDpzppH39jHcdKMVKbqtvQuNFO2ra2jFkaNnUUtwSOnF37yD//Wdn/E3aw7++A8fw7q1S8cCEM0hU1YAkpLAIQ9/6wUqtbR2XHegxvXy3NjfeYFsH9l0C+bOq8KcOTPgtH/N2Xrei/Hj6PXQ/useGPLSPWmoj7pCXecIDDXAM9hJd7JeZJWsRcXclXxwpRMQOZ05cwR1fTk4Njqbws0ZSFBY+CAoY2Mz40AaFWU2Ei2IKIMWSqcbWiZBnwQJNnPiWMHoY3lk1lxIKslF1k0c6N2FfuokTJws8GasHSY0rlWGuc52awuCQwEep3KiPDs7lSAVQSECPH4JXDJrMkEheZ2puXo1kS1Uz5dLk2KeV1j5tt4hLJ5BRk9+Og6caUFjZz83RX3cqRxkmNwhzp1d5v0ZPqCzOIFLTUpCZ28fXdJoawIhafFzkdPcg9rThzB7fmRGjdzKcssWoavpKF87yB5qQWfjLtOlawkOickj1yzZQj+6p0+fNq+5c+fif/yP/4GPf/zjfC6sqGqmsRFSVVUVZsyYMa4cARBTKcMuWsDP008/jczMTHzpS1/Cxo0bTRvE+MjNzcX8+fOh+pTkWjY6KirapSeVKVtIi+ZnP/uZAaZki3379hkQSraaiDVlt0DtnDlzpsm/bt06w3ISuCQA7s///M9x4403IonP0dVksMim//RP/4TNmzfj1ltvxZ/92Z9F1ZdLt+rVL0F2nTNnDm677TZs3brV3MNYilngerJAY/0RNJ56Bt6BA0hlqPhdNYN4et8I3ccCmF8Qj3urErCWTKEqCkmX0l05l+yguZnEbkieOTVA9mvPCBJJXPlxeyJ2UBforgoXPr8sEdUFHPNHBvHGu3789O1M1HemYAQpBIM4+HIMEr1WARv8BIS8KoyAUECDI4dEJen7bFpbjgdvJwuztAT7TjRg//FG7DpWj/ePNXCED47fWptwvHAboWof20U2J0GiUR9/izkx3nwsGYPDbqycNQCuV3HPwjTksC/bzg5jb0sAP2x2YT3H54/Oob/b4DD+8+QwiosTsCrDj3NkIeWnxWOE0SpyqJf00cIAmhgN7b02Tjgc4FDDud8iM6cY85Y+dlVubWlJAf7gC4/is7/3IF767RZS4J83Cwg7ffTR27lAeQTlZUVIlm6hI63gzvaCBTOxa/cRPPnUL82u9Ia1i80iQ59pPIg22ZPqw0fOYM3qhWNluMj0cvPenq9vxr8++czYDna05U4lnxhJv//5h/HpT95/kS1mVBThi3/0GO6+awOam6XFcZx/O7Brz5Gx3fgt7+6b0F3AtvVdd6wbtxuvNspuD5BhZbOidK6Ju83OOnSuvr5l3OIs3D2w+7ya90D3roDgn48Lb7XZZg8oj1gAo3yuIwEEdjliD9TWNGCILAST+HWYyiJxojaW0G3lD554BMuXzuU8K4A9e4+O22Hf/I5l03Bt1OJaC1bbdiuWzUchd+ND+6kmf/VLn8DnPrtp7N5pUazn9awWarSz0kT3x+73uwR6VvPLf+/dG82pSEmgydmaRtM+Z7K/HytXLRr3fZrIRlE9G/qJctQ1YV+Y9823dxGUKAgLcoW7Vm34gyceZd8XICWFdE4mH0F0RUBWH/U7cd89G42eje7BD//9eXzvyWfHum5fv5T32f7+2Pegvq7ZPOclxVcHDI90z67Uef22PfWD5/Dutv0YpqTHww/dOmarEv7+GluGfD9tds+jD99+EVMzUjt37jqMzVv2kk25EJUVJWMgzyMP3WHAnJf4zGudu5RAqNq0fTs9PahLu3bN4nGgkMoXMFtdVY5HH7nd/A418f5Ia0igYSyFt4DsLyB4Pb8rZi2kNTlfTvuvX7+MQPTl39QI36Loz151YMjrZQxYzrviqfEzWfKO9JN9Us9XLcGgGs4F2zA8OICk9BIUVSyK6NZkl9ty/iDOHt+DVk8W3FnlcBO4iRtj+Vi5BK6YF/+x/upf671OJEnZkshRotuPdIpfChpK4Tmxbsw15jqLz8MNTMxMoYAmXb86KUY9BjwFGzQGFzkwIFMXj4NYUDCnZrCGRmQ+iGN9JemJmJmXjHRWrAFQwJDYQPZ7fcHjOQlVJLMhur5pAmVR412cyFK4unMQ88tyMLM0FwNDo+hhlLKMtGRUleUaAevjFPFs7OymrgIn5xWFyMvOQAejuh2v60ZzZzJFrKnd4HrDiHxWzFwWbOfFf9KySpBbvphuf6cxOtKNuP5WgkR7TcZowCGvd5DssDayprxkGRUhPunSxau1iNZLwEtyMnUo+F5JwIYW16mp9AmMIoUrp7q6ekplqBoBGN/61rcMw+ZrX/sabr75ZtM2O9n1aNGvJPBGLJjLkeyyZYc777wTu3btMqCCADO5yVVVVRnmUDRsG7ss2VB92LJlC5l1iYZVFa1NL0ef7DIEnnV2dhKBbzSMKAFg0fblcrbjapVlP9P283y16o3VE7NANBbwDhyhVtB+5KR6UdPsw2snR1BDUMjPMexkP/Cdwz60ULvvo4s56aQb2bkuho2nC/PNlXG4gxjCltMJeLYGGOCk9LMUZ36YbmOlSQwdf3AQP9ucjj1n0jHkY2AJBZHgBpFhBrnJGhJ7gxMwE9zBjOoCg4bwwLJe3LaskGDQHOon1GH/5u9geMlNuGHBGqxbuByr9ybC13oAO2sJBGUUBYEkbrwYRIll+hKNq5mAojiCQ15vInafS8C+2nTsPdOH37u1B8vpYrZ8XRLePzeCpw768U7DCM60DWNdjh+bZiUZIOi1Gi8qCIylcoxvHmQAB7rF3VDgo26iBz39iQSHklB4ZpSAkpsC2G5urrxBtnMxSionXnxGc08my6MFQAoDUuiVnp7GuUcQTQteWE1GxZzZM6xJbkhhcn1O58aVdqb3HzhhFtqVzD9z1gyOCVMbx+1JtZdzl/Xrl2LDhuVjCxtVO3tmxbgd7Mn6NZ3PJ7KFm/OrDLKr83Lpkp+Vgdnso1gOu3YdYlXPmL57Rvshd4E6gje2u4azHXb5c+ZUYgMXCHv2HBsD4RLIEpjDMgUQ2ElAieoY9VJji4wdgXZ+Mxe8wK6w78Fs3qMZM0pMO+ykMtPTU8j2yjCnbrl51bjday3oBRCsJhAXqjvibPdugl/bdxx2nsL7PF6/bumkAIkuCvec2IVp8ZRKSQP7eVEbE8j6u2BTq42hIIaABC1YBdg98sgd2PTALVxjWCBiZkYa51fjlzqpqcnj7t1quiLaQKNsquS8P9WV4QM6CEjS/Z0oiR2yc+8R/uY0X5RN7Urj9yydsg7ONJGNJno2IgGmzr7Yi3vns6F7/9OfvWK+7088/vA4Fkg4O6gNmZnpYb/XAn0eeeg2496pDWrrno4Hhe3rnd8fN++zmCz2muYiY30ITogF+b1//SVOnDw3BqD9yR9/jM9t8Zit7G7q2b+BOkD2d13snjNnG1Hf1HrRPQo1jZ65uromU4fs7/wdTyKgr9/oG1YvMgtYAfw/pAbRtvcOmmK0FtHzF5r0HKwni2gZwTyRE/QdMmBHLF1kAQFtO3YeuggIVkan/ROp7xvO1hcVeJVPXHz3r3AD+lrPEig4xkhh3HEiPTo5LQ/J6eNdeLyjw9wtbCJLqI4gQRPBoGaCDF2kdXO3bsQHN/WDklInnmi0cbfywJ6tONiahtZABdI40ZFrlc0EEuhiwTnssDV3jNhz5ZOhMuItlo6d3wKE5EqmMlxI4N/SZBep4RTPZAX0PHMkCxZS5BJdb4tdG+yH/0koW0CO2EFWc3Ssy8UucqGNUcE6+coiJclHAU2rDF7HelW/BKxVdGoyf7BTEzjB5G6OtIl0kv+39w3jfHsvGUYUHguiWvpoyMMdDA6g8bTNICetdW2dvLbPCG/2kUXUP8ww9329PO8ja6oX2ZlbkFdQitSM8Gh+HHdsc0oWoLv5BO/zdv7ID5LpdR5eT7/5m1XAHYaMUt5z3v80SyhXYKGfr1EPRavpLtjXUUNm2ADyK9aigNpGlzMJLBDrR6CMFtRi/Ewn2eVMp4yamhrDrlE7BE45QSFnW3RerCaBNWI7Xe6k8p11qw652W3YsCFqlzK1yQlQVFZWGjDmWiTdEzGYlNSXgoKCa+LKdi36HqszZoHryQJyIevtOMLIYGQItYzgR9u6sauRdHlOLl18jZLF4/G78XwN2bCJATy0gILOHGX/61w8fGdcKI4bxakhNxcfcfjyEjfWVSairXUY//BGMl7bl4lBL8dBMYQoPhQgKGQAIQJDAQNkWCOobY81Fd34/bVnsGpGB1JzFiIpp5K70auwZPmtiE+mq1ZGvpmU37hhDcU5R+D/+TvYQ2FpRrFwmJRlcgNF7mecahtak5t1+gkWxbEtu88l49DT6Vhd3YtP3TGIjbM5aY734Ml9o9jb6qJYNZlBCSNGX+gEQbFVVS4kkX2kKGvL8ulSVh+gCHUKisiamk09opfqGaUs04+7K3x0wWrA+dOvsf5clJQvuGa3WYvustKiSRcDmugmcE6hpIWDNCqmkkIn1W3t3ejo7LlosaodbBtQmUr508lbQlc2ucOdO3/x4l4LJr2UBGAVcdffBhm0MNOC7n9992fm83AaS7pWC+VQEC6O8xPnosFeq6Uwkl440M7ZL7E8ykvDz9HsfCp7/dpl2E2NprrnLVbYZC4i9r0ppj0C3IS02WQCSHZwh1yaJNHqZTifE2fbne+Vp5ogoBPkUhtDXTD8HO/FstAzKnaEFl/RJPveCWiM9CwpT1l5MSro5qN5urPfAi5DWVuh9YphVUf3KjHf1EYnICPXIbmiRUqRbBTp2ZioH6pDfUnm5l0oUKbPhhioZgsZJit5D8MxoJx2CNfuRmqANZBdKLaQQKFoF7z2PbCfxfY27hB8CJPNgjx2vIZz5ALDONz04K0GhA8HsMh+mQTUP/bYPeZzMa70fXv6P3+LUrKpHv/cRyJaSc9cff0FpmdoRpUtEN9O2lQNZbOFXqNju03hPoudu2ABuYI6GXuhtgm1f+jn1/r4qgJDw/3tnCyeZcSqGrqH1cHdmsIfqlTEUYfGhDUnUCEAQewFH8PM+ry9XOD1ExAizMJdOiEgihuSnJpDNsnEFLbjNU14/3wyTg9SGycvkZM0ggCcoIgxZAMzFqBjTSMNwMK7Yc7pb/DOOM8bIMhk0v/Wex0rjymWL8JPyKWbWRZZOwKHlMy/1ltOKe2SDQ5khbg1ggeCh6R/QCK7CiKoEyBLSduWkkYYpJr8SUZVE4OpkMCPwZeC9epy5QkYnzILHVbI3AvAEMum+Ro6Blk/o46RzSRApJ8+/6fqO8x+6MgIJ8Isb5DtHqTrmUA4znqN3sMIB90R1v/s3mRk5VJv6NRxLF8ZedIh1lA6w9h3Ne/iIEqeqmuEbK8W+HhvpRWVlJqHxJQ83vMM3hMO4GyLi/R/H8EhgUf9HbXo6x5AfErpZQeGbJaLuTGXkATqVFVVGXbKVJNEpo8dO2buQXPzxRNNZ3kCOuQSFe0gO9W2KL/AJ+06ijmk6Fbf/OY3DdizadOmKRcnm5SXR9b9mnKBU7hAtvrEJz5h2E5qgxhXMTbNFAwYyxqzwGWwQF/ncdSffgadTXuQEhjGrlP92FHnw6gBhQSqaGyT9k8cOjgi/VcNIG+EBxe6qJnnx28OebGbYeVnUIvnD1Yk4IZSNw4dH8XTb6Rj55lMhobn7npikmEK2e5iATFkDUPIkTQE8nBNVQfm5jajqYVjUet+FA0loWhOObLzCCK7szj2WIt6/cbOnjUTVaXHCfQ0wMU67HH7ooI5vvoIDPnITnJTz0gA0Qhf754iK4iRPD/Lucs66iBJi/D7u4fxm9oAfn5aLWdUU2oExvFVnBeHBdRO+skx4NWOROSzWw/OJBOJG0A/OZWIF2vdKKGL3Yp8P7rojtfVxHH1KuoNhYIhYslMFeSZzuMk9wSni0IkNkokQGU6dU52jSLDhgI34a4xz5ADZNBCTuCB9HLe23EQcp2JFjgJV759Tm5SH3nwFjKgwwMgsk1U7SWAl2AjTsHCQ1lIznaIySVmwSN0f/FyXmgDSurjezy/loBAOFBhor5M9lk4kCsSq0RuMCtWzJusyIs+n+xZsu25juU7+62CIj2fdiU2w0ouJXEOdpc+nw5welHjHSfUzjWrF9M96bZxbojhrhGAU0jdrhdffGccwPfCC28bN7pwrDHbDuHarTmkwK8bWP905qvl5UWYwdeHERgSKCQRaLkW6bsiAHP2zBkXMcXC3SeBnBvWr8AeCvDr+yYA7xe/fJ0gfUFYoFll2EBpuPJCz4ldJHc+JbXL6b4amjd2/LthgasKDPn9Ah88FCT2kB3SyZeAGoZxZRh1FydKAjt8PjFdBI4QyRCaIZcqIwJkTfriODlJSk7jD0/4AVG3ran2AI6d68R7dZmormCkLekKmY1E7fhZJVkh4nnaoCqaQRqkxZoI2n/seebY3wvtcD4eI3LrYrtSVD7LaSfw0iMNoOB15o99wLfKY1cn1y0jFq12qHVkA7nUXx06XsSJuMMaYKQVtcEujhnsvOYkBbAzEpHHKGW9Ax62iWVxEp7M/heQqipQyUOGUMCXRMkDD9p7huhSxntCAT2/Ad6sgg0vyrSHQJxplv4B2gZ92H6sD4sqKVRckIPCisXmfGgaIgA4MkDAybhseUxfdS+9pFb7CDoN9zfyM913CYQSsNO95+6p3zfM+z/Mz4fQ2+VFdwv92Pu7CQJmh1ZxzY8F6gh0mA5DRgOoAT85sZpMZE91yFXrSiYBKLbmkISMd+/ejZ/85CeG+RON3pDaZjOobLtcyfZGKlu2kkubhMTVjkhMrEjXx87HLBCzwKVZoK+3DccOvYbupp0oyvCSLTSK/U3cPeM49+lFiVhQ7Mb28wHs72CkEzFv+J3t4KbAr1s4TqX44Onz4tBgPCrLk/DHKxmtLI95D4/iJ29mYtQBdv0AACAASURBVHdNJllBqQgkUE+IoJBhCIUBhDRcKfiCXMC0yfLbo3l4e/csM/Yvnx+P0jNnsa5nF1bdWMnfCboRuy4wg8rJDCjPTyMzuR9xqQKNNMRaTGHDvg1NXIgZgIgR0NwcV+FNwM5zcTj8oxRsuqEPn7zThb++1YXFB4bwo8NkWNCTvoI6SoVJAdS3juKFo3Q3a0/AYpKmPzPHg2W5jALKPC0DXvyyPg4vniariHlnk1TQ07sPfZ2LrpoQdbRgSKhJLvVYm3fOHXSxUf7y69/Fyy9vu0iA9b57bsT8uVUESJzsrkttwaVdby/O16xZdNmBEzuKkVgmckFRSoySHXNpvYLRIZF7xOqVBKUoptpATcqdO6nhGdSgisatajptaGvvRCuZ7BMludz91f/xR0iK4AYz0bX2Z9E8S2LalJHlI/ZQNGwpJ8PqQQJ5dXQn2xp02YmmTVPJE/ps6DmcKCUnJVLUeAVaWjrGPadvb9mDcgIE0YhqO8t/4P6bCHzOo33ohjuNpPZu4nO1fMWCCVlU0yj6ml9i6/3YrJypApgCzdauWTL2fTt16vyEQLNcK+WWF02yhK+tse1qgf/RtOuDnGcq9r8e+3lVgaH2hiM4ufcVAkKjZJ5Qh4T+zn4w1DIp5SYF510WaBM8sDCJMdsl8sdsdKQTgwyLnpKee5FNB/vacJhuOruPtWPYR5clikbH0XVKuye225WBPDh7NJM+U/4Flyy7OtvlzORRfr4Ze88zOpZLVrvXhTMjZNlw0phNjR+BKM0EhrzMw7WplRzzSb3VtdYc09IhMtiOJIUkkkmWkBG/NK5logHxSO+J0Kg8SyNJhZiNV35s94Nl8Zw0hvTSh1lJ1CggUFTMqGqZSQTfWK7QajGLRuk2Nkz6YFv3IM7Wd6K5b4AgGQU71RDTP2k0sByVZRrLylj+gXPczT1Ui1z6qd8VBhiSLlR/Z63RgyLed+Ge6q36olDAZBGJsm/bWEbSeS+jMgwN0YWNekaD/T6ktDVQq6jlugSG1GalS2EgKdqYInpJqHkiTZ8rzXqRLpDErj/zmc+YyG1vvfWW0RsSuPIXf/EXUYFDl2IH25YT/Y020lioe9xEZcY+i1kgZoHLawH/0Al0tuxDAgH+Bv6OP71rALubKC6dFYeKbBejd8VhYzUZsCTvHCI49F4dRXC7XOgcjsOrp/zoZpj6ldXJeGJlMkqSPXh1WwBPv51L/aF0+OIFCiXDR2DIa0Smxy98BAZ5NW5Zg7r1l++P99NlOSB3dReOnhS714/qww34s+F9eHDTeDq+3MPkDq0Rz2sGWI3XEqC24oSaiGUXJQ6MYhC5uNFh6qernCcev3yPmhkdPfjcPX58clUax8MB/GDPCDq7fXh3zxDe47j+7kAillBf6HNzR1HNKGWbTwFbWxMIHrmwmi5m29rdqKgLIIsuZwmuJtSdeYMba/nX1KXsou5f5hPasRZbyU6as/T09o+JI68ha8OOTKXddGn0mEnadZRs9oOzSaPclJOLwaWkPXT7+vVLWwjM3Grcti412do8znJK6DJXGCZ8su0eIXBEYqpipoSCXzt3HDKsoXBsk+m2NZxbjHSQli29sGFmM7Uu5TmI9llaTW0WiW1Hw5ayGVa2AHAzQ5JfqSTx5l8zYpSEuaN9NioIhD/0kVuNO5zt4jaR3tBEbZ9RUQqVZwLfTDNdjjKmWfUVuyzUNVYaTFNlVYWCzeEYeob5Q5BPSTpEclu03lMc/8Bxjj/h74vc/6TPZadGiekz6mOk1Nx0oR79VkiEejIWpB2FbR9ZT85UWpqP+++9Marfi9AydK3GinBRCiO1PZrzofXommjqmor9Jco+mc2iaeuVynNVgaH88uUYpOr56X1v4RzFt9Kz0pFXnEuAaLz4L2GJYH/5V/Ow4ERPJxnxlcySLjTXbCM41IK8koXj9G4OHDqMNw52cZKZjJkz6Lgl1kzwekcx4+1pf2DqCr4cb/XxuHlH8EC+xgKD5rOOswSH6uU1xetG+a9VZDCjYTxZ+IrAmbEuCcnRRFOZBcIEP9CCPBCnULm6iGUZ5o8L/QScDrUPYdBDKmIGdyiD7TK1sAwJW+oHI54gWEZKAhYWpqIil/6r2vFkWfqhsCOa+anrkO6jnyknVj66iQ0QrOsbJHvIJBWstrmQTf/XGXk5yOTfnv4BtFDcd+/5JMw6dRJ5236DlRsfMFd46frX33kePa0nqRF0Gp6hBp61olZYvVcrrXdBCpHVP13M9nmowdDd6eUubyaKZq5E2axVyMovZ0SW6e0+mEZdp0luTrNnzzauW//yL/9iBJu/+tWvTggOXemu2GwbW8tI4NAvf/lLU2204NCVbOMzzzzDna2Wa26nK9nHWNkxC3yQLSBdoe6WLYgfbaDL1DC2nhoecyFrIfCzr4lRNKkROMrNmnmVbtw+iwK4M7m50gO8dmIEv2kiCFKSjEcWJiFheBTffiEBL+/OwlCAMs0Eg8QU8pEp5JP7sWMwFxDkC75MSHobHDLDmCbDCjrBjSG+k64spaXhTyELNUHzDjN6OpILFUU5qCzJQa3X2tkQu9PLsXMMIBJIFA4g4hjtM1HQyHjha5hC2u+eIEhELODx+wbx0SXsA8faf9/rwSvtLtzNoe0vl/owsyweWRwbm9u86B2MQ/0QN3WoD7wqfZgRRROwvSkRi7I9WJoziuGBPVfdpSzEQFf80GYO1FCXRaGR7aQFq8SR3912wET7cgJEV7xRU6zAdrmZ4mUTZtfC4326cp1hdKtAiEvShBdO8GGoy4kWratWLgyrebKXjKGdFLP+ypc+bkAZfbfk+uNMNdRgkhjz5QKG1OcXf7NlnNh1pIX1ZAyZCcww9lE0ZQj8cLI3dHE4jSUnIGAzRHbssgR+o2nLVPKoLkWVGhgYntKzIddQCQoLHLBDmKveyfSGwrXNsl148CFc/nDnLkcZ4cq9ludC9WYkpr9q5dS14kLB5lCGngDUp37wLHbuPmaiGCvimdK52kZ8+3/9LKyulD6X18KQaKpMdYz2+J3v/oJ5nzPH4ZLyi2Wk9NHHbud3PbJHgw2y/PrXmzE8MorCgmzjpljf0GJ+SwRWPfPMm4wMfafRTAoHlujZ/jeKY7+/6wjy87JNGfVkK77x1g7+TsVj1sxSTCVaW7g+2eckDv79p57D6TP1KArqyukzuQI3NXUgkaD4jRx7nvjCwxf9xk3F/l/98icm1IiaqI1X47OrCgylZhRi5uI7kF+2GC21h1FzSADReeTyRufR1zWeIMUYKOSY+DkNodNuqvd4R1rQS5DEQ2Fq6RMlp+VSG6cLNcd2UzeHE8vkCuTnpHN3TRo9QXCFF48xhSwCDAc3a2KoP3pnmEGq0D4Otmjc53YeTuboBMWXtI/EtrHKitPfIAZi2m6OLfcxeyKqyabQGglLK6/BgNgm83XTAX+w9RMrcMhuvyanXQRQzvVSq4Akq6xk+vwzjwhFbX0enGwmW4f+p3G8aElpJqOYJalV5kuseYQYQRYApQOrQkUZq+YkOIF2qmvpMQPCIPWGeofFoqKANsEmidWV5ecjPz0DpTk55tLmztM4d/YIKacF7D11ixh9TJHjBhlBzuvpIQClHyXWE7TjuHl00B6WtRh1ZsCHjjb2KbMK81c+gIq5qyhQnUNGFlHAD2GSq9Mtt9xigKFhint/+9vfNkydax1eXWwbOxKazG6DQ9JTutzRvaJlAKkdiuL29ttvG7e9aEW4dY3Armhd4abzmKkPTU1Nxo1uIsbXdMqOXROzwAfNAk11+9B0fjdy0hid6fQIN2foMs7f+jIKSFdlu+mKzAmWO4DFxQGcqPdh60k3ZhcoaibwTmcCygvd+MNlbizK9ODpVxLw4s4ceFxkCSUKFEolU4iC04aGaiXLZYxi0KxjDBAyG0F089V/Zpwms5euamZMZtK/el/fMox6zh/CJQVCwHAfy0mny5pYQAKFuFkjVmsUAJGPmi1+tlv1jo7G4f2zBKZedeEL9w/jVkYtO9zkwQtkR9UOuOgG5EJvrw8/PhpAQXY8WUI+bOSEPo1M5AFuNHUMM/AE379yDoxMGofyLGoFntsFd9o8zFt4W7jmfyjOiTmgSF5KoeBQKEAULnT59WCEkpLxLkdadDUFd/Qna5+ifh06/LfcNLrwvOvZG+gfvGQNEHtnWzv/z//qLbNAU7IjeoVbtDrdyJxsB1uU2Xar0iJ0N0PMK4R7uEXeZP2uZ5tsdsO+/SfIgNk8FsFJ7XuITKlND96E6qqyyYq6Yp8LTAnHljrPvjdywWr32wYEIgFZ021guGdjhGy0AQaLKSu1ArpMpWyxrRRCW2CjLXCs6wU8TKQ3NJU6fpfz2oCqbYNIUb8ms5FAM+f3LVT4XGDtf//vn0UDgdk9+46NaUdJ9+mBB26K+LshtpDYZmKMSRRb37Hly+aGbY4zrzJMxIDU78z3f/gcgZ83WB7b9tVPmyiLipjtp36tHW1Nvx0//slLhrgQGhHP1mbaTFH0j2y6mYDMIyYIgCI07uHvzFMsf8/e41FHawvbKZ5UWxWZTW0dGByiRtetY3XpGmd9r7y23QhLh447U7H/zOpro8Maqf+h568qMKTKE5PTkV+SbpggxVWLUXtkK2qOvsUHpQMFvOHxZiDk9G0MSdBET0iJDSOoFAIOAWroyKWMIIQEjEf6s3D0MF3V6Or0fsMSZOdSY4RsnuB80AJeBM4E2TvG70pF8pwBh8aqsN27gp8767YLU+bg+17uBtZy4tYqly61MthMFedM2tUUgKJkMCEem786tutQlaYQ6wOj72O/xgoj7Z7ilPs4EHBv0myBFqQSnqJOkKSU5hakUcSSTCCGSJPQtB5oO+md0TfS3+BLn0mDqCI/E3np1G3gLN3Hstp7BlBDhLSlrx8DFKn2Mnx8IgeQBH6pVUZTfxmKmxn5bfuvUJTvhme4i4LhFK32a8dC2lBWHVaXL7TB+sA6VktGhvzoJCiUVbQCi9czTOaM+UhKmTjinCnjA5yqqqrwqU99CopOJvBF4JDCqwv00Pk1a9ZcM6DBBocEegisEnj1ne98x4Asl8pqUpkvvPCC6euZM2fMHdSkRCDZf/tv/+0iEMfO/x//8R8M5bsHS5YsQXt7+zhBaYFs0kcSMOMsX1Hn/uqv/uqiMp2PzfPPP29c5iQGrpdSdXW1icr22GOPRbzWruenP/2piYImQE+7KOqbXAOd5UXqW+jjKyBL7dm2bdtYW6K9NrSs2HHMAlfbAo2MAlp/bj8yk0et0PQEhc6RCbSq3I1PL6e7FAWkdzPy1m5q9Y8SvGklK+ZXLYmoHOKmCoeDNP7kf2FpPJbT4+v17W78dncGRqj/40pMIzCUilFShQMEWuzkdBsLGIatXMUtQCiefwXKxPOcQCCNqRpvtaFhoCK+L+T3tqS4JKyZNL4Rv0IidyP9fHkJCHF7hVdqY8UaQ31jIyi3hMbmKheKk7j2KPUT1RZ44vDeqQAKt7Xjc/f68UcbqJXk7carNfzNOOXFPM5Vzo3EYaAb6Ox1Y1t3AlISOXlmG1bnE1zjRthbHUlY2U7wiJHNEgJN8PUfQ1/v4qumNxTWUFfwpBZBijr2N//Xn+JBapdoARAaXtsGiNSM0En6FWxa1EWHsoa0kLNDoU9WiKJ+3UBwRS4HSo1cvO3afZTjHx+SaaZnX3gLr7z+vrlaLHGbKSA7CnQRECdB63DCwbZLlNhCTuBoNdlFoe5klyJC7WQ32KwH2U1CyVoUKuKYNJXCRXGaplmmdVmo5osKOccoULpPthuXk2EVDmybVsW8KNKz0dXdO90iTRQ3p8CxCpLdp6s3NO2GfMguFOBgh41X1y5V3Lm8rIDuehf0rZxC8bY75azqMnR1942Jz8v1c86sGQaUCZfEdDlw6JT5fZXG0KyZ5bj5ppXhspr1ZCujxjl/i8Nm5EmBTT/72atYQZDpj//wMawjM83523LTxlUEgU+OCWoPke3m1FwNBYWkqVZZWTr23b/l5lVc47vxL08+Y9ozUUS9SG20z6ut//nTl6nv5zG/NaF1KZ/qa2vvMsw6MVeVnOPOVOx/Ke6Wk/Xlcnx+1YEhu9ECiPIIEKVmkCnE6B/nj71BXZlBag9xS8yZBIyESS5p1XBypl08/dfdchoN7QPYer4IfaSAV6cncueSXB4zYbRAGxVj4SxBEEfHwc/M5NF8br2x3tsTSWc+61pKaKPDF4cmvjo4gROVXcwdlaNkzxXHIBG90Wf8qzzmkP+YfHIX4xm/DVqZNoiOTghMeVW2Cmdf/HQx085Rr4f5+cMd4EsT07k5iZidnYhEXiSpI31GnMFUqmoFrpk+akdVbRg7x2OeS1J0CgpCS39ILnLpDBOTn5lGEWtu89LUGiTcdvQKHvd7SbXvLiPl8BwBql5ez11WqzNW/4J1qAWqzYKjrCM7g0Ch9lZGWkuvwtwVd6F0ln44PpwsoWDPzZ9wblsCheS6pXDx99577zVlDwkcEkClNv3d3/2dAYfEalKaLjgk0EPuYCtXrsTnP/95Azpt3brVAGMCWIqLi8exkgSU/MM//ANeeuklDA5Sh4zPliKmHTp0yGlK89nq1avNgKI2fu973xvL7/FYrozjLuCBgB2BXU899RQ++clPGne+oqIiw0z6p3/6J/zjP/4jfvjDH+Kee+65CLAKbVdvb69pZ11dnQH41I/W1lbTPyX1Te2I5I5n13n27FkDRN1xxx145513xuwy0bWh/YodxyxwrSzgHTgC7+AROg+P4FzzIOp7AigmU2hlQTy4iU43ZR+qKLocIOPnMMWlZ1JD50aOIyf7GQmTjf6juQlYXeLCweN+uo9lor6XvlRkCvn48kYAhfwcDxW8QK5jbgFB/KuNCwFCYgu5eWzGdw12TM55wMyyXDKUwgc1kKtv1YxytDdTWFph6TnH8JFtawAiBlGQCLXNHiL0xHqsMXhs+LZvAuv3MuKqNdr78OKeXC5qe/Dpu+Lw6Aovdf16sbV2FAV0+95ENv75bva/k6510hPkT9eGHC8eqPShmayhk6QP7WpxM7R9HOaXJjLC6AG46xd8qFlDAlbSKTOgCfkNNyzCLroShAOINEkv5YJpqmK5V/u7MpVFofI++sjtBBkslxONxV5u2L302y145dXt02p6AaO9CWzSWKrvgkCnEup0KMJZCd07UlOTyVAy243jUiS2kDKFurfoXDi3qtAyIx3b7AYJW1vRsixW38FDp1Fb22CExq+HpGdT98i5SHeypcQcsoW6p6onM1n/Ij0b3//hs3jllek9G6pT93IyvaHJ2hb7fLwFnMLO+uRSxZ1DAwI0NrYyolgHv8tWlGjbFc/6e6EtCgQUDvBVjniu/ZxAqxhxEfMSMZgxo2Sc+Hq4e267NgpoketcKCika0LB1dC+2ILdErt/hFH2nKCQaTfXoxs2LDfR2gQMTfd3x9lWsfvC1WXXt37tMuymztuzz79twKHQcWc69g9nv2t97poBQ3bHFXZ+xvx16GmvpUbNMaRlkILNhamVhIrwXXBi5zSW87QmaQat74rH7tZiZBAUykpPoj8gJ44GAbGKMe5bjgutWCMXiheJSGmsOntGGTzjBJH6ycdRVBWBQoKoNEcNn1ShVaZdn2mE/tek1XwaZCnZNWuGycmhOcsMwoR8ymhteVq4il0ozxcQAKvISuKElRNYgjoGMDLlMo11ZswUlkkEFAU/tmCp4BU8qXYlEonNZiSzdIp9e+lOZoq5kIV9Jh2/rxI5SW3ISOqlLkOwvgvZxk44LWBOBlGzEbrFpWVWY87qj2AG3cd+F0Ah2yjh3LY0+eukhpMYNQIKvv71r08rZPyFOzH9d8nJycatTAwY2+XtlVdewdq1a/Hwww9HXbDNrlG0M7Fw/vRP/9TofQjI2bhxoylH4JAYPgJXbJesFStW4Mknn+SOw8/w93//96YNTzzxBL7yla+MY1NJnyk1NdWUKUBt7969prxIyQZ2BFR97nOfM0CX9J4E1t19992orq7Gt771Lai94QArtUuAkRhMatORI0eMi9tf//VfG4BK5eg+bt++3QBGE7njqS2qa2hoCN/4xjewfsN6M3H42Mc+NtYGgYWy05V0iYtkq9j5mAWisYDYQk11B+h9NYDjrX1449QIGvrJFqpwcYMB2M+AQp5+ik4Tw+nxkx3Tn4BlJOssCJD5ShbMpsVJuG+2G0dOB/AfDEm/pzbL0hQy4ejHM4V8/J4b17EgKOQiIJTAqJY2Q0igULwBgyxASIwhmzWkvmgcK0gdxf3rqrFsYXXY7skdo7wwE3vqu5CQRNFoTgx8bLcAIq8BiBjZ0xqaze+YZKnFztXroshlHLx9BLZcgTQMM++7hz2YXzGC21al4I84rj65tQ9vHh9hNLYElFLyKDDsxgriVZ+iy9mSHD+1+gg6cRNofloc3uhIwA201+wc5h+oQ3frIfSVX9+soUhCxmENH+GkFgCZGekGINIOMWDtENvZxXh5l2Gg5b50uUOlR2hSVKe10JF2i520oJe+RjRJCzUJPKdQ29GZ0tP5PE4SbSpS+evXLsEf/uGjKCqgsDe/F6pD42bowjD0etslajddNv70y//vOPc25e2nbqgzhRPGDS0z0rHNbpBrUzndWmzXJi36/vlffmHafNed6yNdflXPS4tJQtTv0e1Pydnvc9TH2sboYwpRH2mRPd3GRno2Zs2s4OYa1eunmfRcTaY3NM2if2cvCxV3LyuT6LDFArwcRrFkQsZWe5ejyEnLCGVChrvAZhhO5EapcuSS+Sdf/CgjIR8lgLQMSxbPNsXZYLR+222x+3AsQX23nEDVdNiKdltV70R16fNQMOt6HHfC3Y+pnrvmwJAanJlbhsqFN6LmQDeGBvuQkWUJOE7UmTHMhpmG+rox6k9mKNgy0q65w8cHLp6op2HHmP+CoIgNbPCEATr0N/jeHCuvfayJpc7wH4sJdOGYgWzRxIlit+g8+jxYlikimKzyhKUoj3YYg3Wa3UbrnGEMKaOhBfGvTqiwoJubab+AHuUx7y3mkCbHFBJidmkruMj00Uv0YLstltOaxRKyGuhSNHrhZGqH+cvy7GOBROqv6lHTTCYdW3aUTpFYRDbNzzSVDfaTLdU+VIRCupEpFL0mGOqHilEek1SejsdO6Jz6S0FP7opmFFbQfWwBN4c/3O5jtjmcf21wSGCL2DM2kCBgYceOHXj88cfxxS9+EV/+8peviWtZVVUV/vzP/9wAHQJKxNj55je/acCPTZs2hevSRefEnHnttdfo/z4AlScQx05z587FrFmzDHgiIEzAkNzolGSbrKwsLFiwANXV1QaESUlJQW5uLgoKwk+uFaperlgqT2VL+8eZBFIJaBLYcvvtt+P3fu/3xkAhu8558+bha1/7mrlMfRaDS65lNhimdqnNdpvuvPNO+nX/d6hufWYngUw2c8gG1mRHO9mgkACqv/3bvzUgnH292qBjPRO6NhLzaayw2JuYBa6hBWy2UD51hLoH6VbMx3wFXcc+vSwJS0vjsKzdh8P1fhypjcMJsl8SGaHs/QYfGgkWzShOwobKBJys9ePHr6Vg99nMYPQxW2j6whTFAoUstzEBQ3EEhBL1EktIY76YQxwnbTBI47Z5b/JqFCIolOLB5+5bggduXWF+x2z2hNN8CllfyqANgcFuJKbnGSavxjsTsJNJMwGFSfDHyb2MQxkBH23c6D1bZ8ZYZ5ILnJhD8QSWGvoD+PHrfiQl9mHjEj8aGbntB9sHcLDLj7ICN762EMhP84L63DjMiG2/OuNGerILefF+lMdbrKHlBQGUZbnJGm6hO1nrVXMnm4pGjt1/2TjchH68hcYf2Ro4oZFb7B3i5XSJcOpT6OpQIdaJyr9an2m+JLBgzBZ6DqcJ6thlKKT6wgUzkZw8HjCKpk9a9KSSCS4WVrRJ9+I3r2w1OkTauV/JUOLh0q49R8a5l1zK/RC7QYCY07VJdpQA7LPPvWmYYZdL3DpcX6I9F7oo1XU2gDVKdpcWtaHR06Itezr55NZXQL3WqsqJ9ZfsUNpVdMsJTeqTQLkGbrR/91+fGfvY1huSIG8sRW+BQmrnFvJ5tVNTSyda27lT8iFOoaDORMCogJZHH74Tmx64xbAV7bxOwe7SskLznY+UnEDVVCM/hrr6TfZ9VV1OpuBU64vUh+vt/HUBDLkZHja3cCZasorpEsbw5Gmpxm1JEzCTgn+cxjNAS/B8PVUtDzUnYUttDmdmEpsmQCHwhEmsG0MUGnvZwEywaJ0fq8Y6MMe8wJQv8MU0QRXqGhe6CAq1ciaoId/k4T92E8fmhMEyzMUCYfRH/5m8dj3BSWTwYk0/rbeGK6QLrGQ1xLwfOxV8n0cB6myGpeccxKoqeD5YgnW96QD/seco41Ca4HWmH84agteM1Ris2fRH7beKbhqoQCWj0fT2+ZCbY2lA2MWbLHaDL7wx18uNDO5sFJTPR/qHMPKYZZ3J/7UBEAEJNjAgYEMLFrGHBBZJ+PlLX/rS5IVd5hya1M+ZM2dcGPvdu3fjb/7mb0xNk4FDAmLkPiY9IbmmCQBzJunziG2jJNDE6V9s5zMLC76iSbKfGERK4a4Ti+fNN980dQkYEhMntGy7zzYwIzDs3XffNW232UwS466qqjL12IwlJyik8zq2gSqBO9Idamxs5I6DJTonfSm5wT1O8E/Ak/N6tUHHoW0zFcZSzALXkQWcbKETrf14+eQwDrQGsKjIhdeOerH9PKNvLeZiY20cDnRQr+coRZd7RrB/JAGVdC17fKGLYtOj+I9tidhxygKFFIHMRB+jvo+dnKCQxna3i991jvPmxe+LodgTCRJbKF7gkMnD8TQ4Nuu9Z7AL99+2AA/etsJEF4mUFLJ+FYU8bzjRySigI3AnJJsx2zCPCHrZDqoKPM6pgKlDuno6jic0E1tg3gAAIABJREFUpI2Ui8AhtpFhHMg28qGm04+XtvtQnBPAumoPdtcO4dWzXopzk8XB8fJUPd1dKReSkhKHwlzpDVETyeVB7kAAuzricEObHyUpPvR0HUBPx3KUli+K1JVLOl9MdotcjOykxbnCWre0dkwoLGyAHYY8nm5SSPYnGRlm3folCBUjtdlDTn0K1RNt26bbpqlep8XR3pDQzJMtOqKpQ1owYoZorqY6FK1nDcPDf/YzD0Zz+ZTzSH+ktqbBgEJ/8sWPUV8o/LP2qxffHhfV6nLcj9DdeZV5vWnehNNYEoClNBFbYso3IooLZK8yulRKPkPPxiuvbeN9W3gRw8pnPAsuAJahResZe5Q6K/VkvNnC77bttTCW8G4sRWcBjUlOYNxPba/LFVEwuhZc/VyhUdgmaoGeJ730zDmTDV7q3AsUoH/9zZ3GRTxcGhoaMYLRSlPdvNDvWz1BUDtp3j0RkKV8Tne+qdYXrv3X47nIs6Or3NrUzAKGrp9HAcYzZJ+Mmpvj83oZaawfI7zpAorieNOSU1MYgYyilI729XvSUduRiNYB3rTkOAzzy8eouAYgGstn0AoLpRjDQHiGczor2W94bD7XcfAz6yNNMkmb5bSviy9NVK3H1M4ULCaImZizmjCyXgsg0b8sQ5/rwrHmWG1SXu1wWk5guvpCW3Ve7TG7ksGXBWZxJ5HC03mpjObG8wrIa9ViTWINoYkvI25tWEj8q6JVoMloHY+VGWyz+ZAnzYQ3WIboRS5tmZrydNIq28+Zcc9wOnqH0sjmYBgV61KDHfmpV+Slu5jfq8o4aeYEN55RWFS2T+wnaholpWbyXo//UTCZr0KyF+tXoapJq9APksAIARBO9ynp1ggoWrdu3TVxJxI4IYDDZruoLQKHfvKTnxjAaiIXJ+ntfON/fsOIQMs1TdR4ZwoH3kxqqEvIIFaSrVEke4eCOXbRTmBG31/1+aabbhpjDZlFZxRglbMO2c/JGHrooYeM65t2kJOTki+hV7FLYxa4dhYoyHVjmBsCGYEk9HPcHmIAglH+3Nd2072bUbcGuuONjtDdJX5smO3D/3lnMt6mEPO/U0uoklEzq/LcOFobwL6z1BKiWDPDiTISWDAkvcYpJglNS8MvoGONu9T9SSZTKEEAKsd4sYQE/AgQMvpCysNj6+8FnaG5ZQlYVEEdlTCgkH7TNN4J/NXf9WtX8vvqxw9e2I2DnDfGCRzSWoquTGLuKpKakgGHOOjZUct0Wts71khs5bH/9Uk8O56+dWTfbj/txXKCaB+/NR5rqlOwh753u+u8iKc+Yh7dxuYXu1CR4seiPA/SCBDtaoyjdiIXZby2roegEIN+5pM11NN2EE31i1FSHp7JMb4FUzsKXdTo6vq65nGRl8KVaE+2tTBevmxeuCwmCsx+Rp1SFJxQ9kd+fjblAFIQKkbqLEjs5ARb8zD4QRF35/Opo+NME9UTtmGX6aTTPUFFXk6QQAspJdXx2hs7sDjofnGZmj6umN1kAikKlkSnw+mE2Jlt7Y265y8sst7ndevXLZ22e5/tauIUt75WboOhboF2v0PBK523WWLTDUs+3ftoPRcXng1FclqyyHLNmWqZigoojStFobKFhmV7umTE0nVkgcnYNNe6qdNtnxO8XLF07oRR1UL7OJWIX37NyycASUPL1rFzw0Tf9WgDCoQr63o9d90AQ2INJaWmc0JHjQ4CQkoDPb3ULfAit2IlUkjpbjq7F231NUjNyiDDKJ/C1WkYYIj65p54vFebjRQXASVOnOI8fgz1xqO7wwsX3ay0iItjOHa99OOZlpNBbRuGsmcdAjCEcwSREus+2ccGFSEYRHBqqG8QQzxo5Z5gJ9UErAlg+NvqzsxAfAbFMzVhNFNE/ad3OsMKWa63tw9eRvzKGxlErmcIhYwkVpDM28EGuelP30zRrr1n29DqSoQ7v4CAF+m/miB3tKGgoxGLChLR7M6Ay5uPDIl0EoTpp8bDIMv0CYyS65f+sqF+7hD4jSuYipeAZvA8T5j3PM+PTYQz5ZNNdL2QbfOdscvSgf2eYI/y6Z+z3eUoyBwioNdIkIcaDPxshCHoEaCdc6t4X3N4TDfBvlpgpJcAkWVzCY+npIcXAA1v2ct7NhJLJZpaxIaR69PMmTOjyR5VHgEJcp+SOLOeWVv4+Ve/+hVuvfXWCUGYqCqYZiYbtLIvF1CiaF4CSCKJKiuv+pCRru/B5EnMIQFNVyrJdUtuZgJ6wrmZhdYrl7Ybb7zRaB+NjIxwl+zyzogElOkVSzELfFAt0NfbhoZTb6H51C6OPb144+QIDrb4sbosHo8uTETXQByeqQEO9btxui4e7w/H4T4CRIe7uKGR4cJ9VS7kuz146Ugi9tXQlZhBKAJ0uZJAtdm8YNImhJebEn75PWujx7iPKVCCxRYyoBB/hxIECvGcEZ12gEO2G9mSWbn4+O3rsWw2xxtqBO3ZxwgsnAssX7bY6JL98z9/n2y+Mqy+YTVamlu4817EjY48rJ6Tg3aOXc2jZAizCfToMjuGZlcouOlOxSFrs0ft1ZgZ/PCiaGVsI2OHEmRK4uIxCbtPMNLYXB/WzWS0trPD+O2ZUbriefH7K6ibkObGmyeAYwxCxakKTg3H46zHYk7uo0D1KgJvKwu9DN6wD1kFS68IMFRclM9gAQuolXIAdjjyrdRNEcAfKQqYmArf/8EvDZjwGFkHsmNoUp6nfvAc3nv/kLlX/9tXPzWO8WLvyO6k7sTGgyfHhFWd5YRqdwh4keaLc3c+tJ6Pf/xOPP65j0zIdgpt63SOna4Uul5ts9g2kcG7SMBDpPrtOhShSvcpNF0qa0vlOeuoKC+ZcCc9HFA3XTFYZ1/CCSLbbk3SawoFFUPtcLmObbfAUIHccOCV6gwHBF6OexJNf+z7Vki3snB6NvazFtoXZ9nqVzi9oWjqjyZPfRNZhQSHP+xJ380Sh/vdpTJMnEwa2a6cblbScbuekrONLWSOtrV1mnZON1VWleLGjSupzxZdP690xK/QDRNFIpyMRTvdvl+r664bYEgGSErNQjLFqL2eVowOj2CEIEdB5UbMXfso54SJKJm9Di21h3D20FtoqKlDWlYmJ20uHK9Px6o5+Xj01nko5ZdQQI/ZLQxOLo1xLYzHvDlZ347azi4k5eWYvNbLAm40HbVeYxcw9F8/yslEWj2rSLDOpOmNY/V4v60DqbOqrMLkSkZ2joAUAUJZ7a24pSgVt982EwU5adwZ6yezZpiAikQtA1wwphDQom7CXYtxrKYFB07U4QBd7PY29WF1VT4e/8jtSPEO4N9eP4LGjg6MFlLYkvZK8vZx4pvF8PHByG4GubmQxo7CdMI6FeYD+3J+FPppX18f9h6spT6EC31DyehmeOIkTvT9/gzkV6xAycw1Rj9KE2H/qIeLiPfQcuY1Iqw9BLK0s5uMBC4GrmaS7oxcmC41aaIgYEll2S5Gl1qmfX2o8LPqEdulqanpstcVbZvDgUPS6lESODTVZItS/+IXvzCXGvCW39krlQTs2Fo90TCVnGyf48ePm0hmVyM5xbpPnjx5NaqM1RGzwLQs4B9tQ1dHHVKSA+jzJOBc9wCok4xZZLI0tQVQT1rNDOoOcSjExuIA5iWO4P0zARweTcT9C5Oxgu5mh08nYP/ZNDJwCZISFPJxnPfxt0BJ443FFLJBIYJBRlOIbKEgU0gMIWnsxUtnKAgIWWO/xRpSOfNLeF3fKbz+WgNef6UfRblpjGDqI/M4gwSlLLz4+h4cactGHfKw7exBE/3J7W5hUAcvcojKxPn42+QdhktsJhXIf1SHxmodS3b6AjgU0P4Kk/i7cikLGTU5J/G7yfAlK2pXTTYqd/nx6Tu8WDtTrKFRbl4FkJHKOQ3XTK81uzFAhtJ6nltf4ENehw8Hu+NwfjARe1u8qEr1IJv7RYERaQ0xCARZ15czhVv0OkPEr1mzeFx1Tc1tFN0/hBMnzxnXI4WZD7doEdNFEV0MA4Hp9Nm6sBPrSACAFpQC9uocLgBaPISCUKH19HNzL5y7cjQ2s0OPh8trFvwtHWjmYvc3v30X22iDAUVzZbJBoYnYNrpei3SbaRKuDp2z69lHptXzzzNgA+285oaFYQWtQ10kdP1EQFtonarrVy9uNgLKEmNXpKKJUugCWHnVn4nEYAVghLrbhQIGNkAhcVqbuWK7NWm+EAmgnKit+mwqizknoBMakl5lCbyawZczSc8p1CVlOvcknI3C9S3csyHh61BNK5WnqEqyYbi+OMuOpDcUrv6JzjntZ+f7XXCpUl/1/JZRs66CwIjAddk9GnfcSPZ0MmmkdVNWWjQODI903dU872zj5RDHNhs/ZIiGfp8uR59C3aWnU2Y4pup0yrmerrmugKF4Ru+Ip0aIz0OqNoGSlKxSFFWvIABk/egqgll6diEKKxej5dwRAg1b6FvYhcFR7sLROT+fotWLZuVRXC+D4o6Rw56vWlyFt3afwo46Cxyybog9gbN3Kjm146l+7k42+d0YamhBaaKPkUyqGNUhi9oiSWN1eDzc6evpRj2/+C1t3ehrbMLIaAqSyGoS+0fAkwSnh+obcHOyF5+8eyFK8jLgGx0h66Qeb+84gWO1ZAf1DHFiGI/5BH9mF2Vi2bxyrJpfjrVLqhmiPmBo5An8oUlJjCeToQZuqjfHjXJSauhsnJBy8dvU1EIQZhiL51WYH6OJ7KB+C4zq6+8nsEQXAF6vya5xk2Hf3ZyUJpDJpS9kaloaKfjjXb7a29tx7nwjenobUUv/uCxO2GfPrMas5Q8hp7iabKBclnHhmhL/Wkaeq0Vv6w7WzEdPO68Ghrt6yekGdCkL/vr6egNkCBiKxq1oqj2sqqoyLlxbtmwxAsShrkhTLe9y5LfBIYEXEpVWu2xwSK5ukyUb9FDUNT1nEpGWzs71ntRWpxvYlWivbRtFQpP7oMS6VW8sxSxw3VpgtB1piZ3wDpGBc2YA+xp9WFVOl2/+Lr7V7MKCche+vNiN420ubG2NQ2pmMlI5sBTQDWrtDDfONMbjP9+Mx/7zZBVybPEJGHLoCikcvY9jREBuzAR+FH1MekISmraYQmIJuVBekInFVdlmYZ5MNktpCRey/iFuuqRi7/EWjl8u5JYtMeeykv3Yc7AGCQx2UFFahs0ce8tnVGHVmhzj0llMkcu+zmYwcjfqm3s4l0jDrKUF6OW5eG5w1DZ2o6urDympSdQBCqBp0GL5+g2LmN9X016Ft5d7GdlLOhfyNZZLmYtljfqSsf1YMpbP8eCGmUlYXROP35zy4mirHzOz4/CZBQFGb2NZxE88VPS+hbjPnrY4PNfkwt4uN9aUu7EyJx69PU1XTIQ6HGPDBofe3XZg3KPpY0OlP6KQ42LISPvGyeCxM8vdS5Npm4UkKn44wMYGAHbtPkZmbumY3lEd3dlOn6k3m2FaHD206VY8/NCtF4FQofVMxJCI9B17+dWtePmV93Dy1PkxIEt5tfP/V3/9r/i//59/49xM98jqw9DQMMTiuf3WVXiAwNjqVQuoFZUcdlGjBfPLr76HX1NDQyCPM4lxdejw346LAGbXo3ma+i77GBA06Fqm67Xwl7aMDdA5y7SFkfcfOIEVy+cbN7+iwotFXdXn71PjSW3SvVZfpfl0+OgZ3H/vjRexdOx+/PbVi0Olq86//Pp38fLL2/DEFx4219r5bXDL2UaFfP/XJ3/Jvp8a10bd3/O877bmjf0MCuBZt25J2HbZ966WZYba95ln36Bb+WkGu1gU9lq1Kdz9cbbPtkXo4l/P5IMP3oJl1ClzlhOuv8574rTtRDaK9tlw2jVceeH64rxG78PpDYXmiXQczn52Xvv78zpdIe+//8aIz2Kksj9I50N1qKJxxw3XP9lzz75jY5EOpxLlMFx5V+qcGFJlZPecO9+MUKB3OnVOBdCeavnTYf+EslXNRjN/hz9M6boChgSgaPHpIzqogS+3dD4KKsbvSiUmpyOvJB0ZFCxOSkmkfsFpbK+nm1dgEEe++x4+dmMtPv/IBlRWRKa+xhPwWFxdiFN1bTjPnZp8Q3OzAIoEukIJfFHYdw28qWxLFndMTg+O4OSuerjeOY2FBcn4xB3LsHrZXAOm9HR34+1dJ/HrvQwhy51ArzsJCVmWq5omyVrgDdcRFEoaxWfXz8GMkjyz8Nu1/ySefec4Djb303WXdHmJD41QQ4DbhW8eaUXB+7V44IZK3LthASc9xcgI+tSrzlGBQmxyMifGanlaZhq6PcPYcfI82t45SXDpLD5630qsXDyTE5PwUSgGyFTaufeEAaZO1rSjo3vowhyWk9m87BTMnpGN7IxkzKW9BIppMZ9GkEhJE0EvmUDqX9cIz5fOxPz1K2nPhZwIXQzMpeeWcnFQjrbznEBwsj4ySEBqkCqbVzGJMWQLAKvd0hmaDhNH12niPxX2kdyZFIVKYsOTCTfrx0bgUHW1FZHrKppowqr0/ZSQtIAS29VN4NCpU6cMoBEuhYIeivD16KOPGvtJXFvuWlc6XQpTLBrXs+m0PxQok+7Qk08+aVzq5Kpn23c6ZceuiVngSlugsameIsP13F3wo6nXxzGMv4dkCD20hOxaCik3kEG65WQAexhiPYfne/zxOE0X4/WVcVic48N+up31DdEN2iUXsmS6Y3ETweFCpphfRs+O56ThI5aQIo/Z0ccSOLaKKaTQ85+4az7O13J85rns7HSMcKPDTz/qqjzqDXq8HLcykcONJh9FotetqGSghFyOY+kYGhpADzd1blrDNubkIC01jWP6CLq7OjHMDR8Pr9VvXV7uPOPiPsKxNzuLbuuMkPibd4/g+y8eIVMqwG0OMoIJA1l6Q7Q82y1c13YmH3cv5FIW4GYWgbC6rlTsOTGEhZXJWDsrDbvre/Dr417cUA48OpfuYy0+bG6Iw8mBBNBDHsvyA1iQ5uN8hOMva/Up+pGnEX7PlXHLsBkby6jzsGvXEfz25a1oonuABDttYEd9s3avCwiELMKmB29CdVVZWDBEecspkLuObIZybqTJHei+ezeiMAxA8VG6oi1nvWKV7Nl3Age5kFcSg2XJ4lmmLi0qNddLpHhp6ORcCzKFQv4VgZem5g7cd99NY+GQx92PCAdaiNUy7HhPTx/y87KQyzaHS+qDIqiVlOYbty7papTThS6ZbZpol1u76UPUz0xLS+HCeG64oic9J40mZ5Lwq5hRmZwPriJrKzRpPnqSgM8wpQoKC3LDAkMjI6MXtUnXqdxRW1zLUbCYMGfP1E1oo7S05LFr7fyTtTE3N4tAkgWu2Jo30g7T82cnuz9LFs8ZB1g5791E9UTqk8qPdH/C1aln7eGHbiMb5wgqyB5yrj+i6W9oOya7JvS+hjtetWrhWDQnuy+htgjXl9CywukNheYJd2z3YaLnu5vfrXPnm8buc7hyPujnQhllkzG1IvVX9qzj75HWpUprydhcsSK8hlukMq7G+VKymMrEoCO4LfBx915GNNSzGOY3PlJ7nOCSwNM6Bj24GinSJoWzbqcu0UQ6elejvVeqjusKGBr1UHdntJfMFw88A0NkD6WR6p0etu8CiAb9qWjqoe4PJ6EMUoK4QR+eoWN+HCeLT3zsZsOYiZTKinPxkY0L8PrB89z1405iXjY6KSJwmlIiw9yhS6frV5HLixROCt1JiUiumoHRzEz4ufg9Qm785iMNKOcEJYPCz3sPncHmU21oTkhFUnEh0ozPN3cLGVJUoM0gmUIzu1tx98Z5BhQSS2ff4bP4xdtHsZ/uYRDDqawYbk44XdzxDLDO0a5uNDS34qnXj+HA8Sb8PsGuVUtnW93hjNOQCax/zJRU8FBGdg6KZ/iRyMltJxlXOw7VoYw7c1WV4YEhAUz7jzdgy546Tqjjye7hji7p9RKD9hCY6x7yYOfRbox6RvCbd85g7oxj+Nj9q7CBuywChwSsWC+gdzSH7gTxZA9xx6zyYlBIDRd7KCWjAEnJuZxA95NOT4aStkOvYhLgIj2b2bNnG8bL5s2bccstt4wJC0fTFFuvRuXYIFM01xUWFqKbIKKidEWTnC5PV8JlTcCEdJJUjzNi1mRtC+fqtm/fvrCXyVYCf1566SUT3ewrX/mKAbukU6H6BTRdjaQJkM3sElPs/fffNxpCkdwAbduobTOpIzUVADCa/jjtImFrhbxXe2QXtVPfL/29UqBUNG2M5YlZIJIFFI2sp/0IyvJcONtM5kw/J6tctKUR8KfUHxZku7COrKDWLoaIJ6ulY5iuZQ0D1BQKYHVxJiNvxePnmxNwuIFsIbJv/YYtdCH6oASnrSAHEo8mW4igi1ugkBgS/MzWEkqL9yErJYC05ATMrC7j2DpqIgW63TlmbPKMeMmm9fI3p8qEw9W5IYJGciulGh7LA3Kzs9De0YX2lmZkVFfS/YtlUsdwtL3LvJ9dXWHGbA3m7TzX2d7G8iqxYVE5DnDcb9jZYIIpKNRFQJs7cht3sIbCh7DnRhDHWx83eHaeSMEN831YXTmClaVuvHSakBhtuKfWj110HauhK5tfLF5uWOWRGZxBG3Z6XTjA6GQzU9nHwQYyhq7cxFngRiaZz7fcvIqLkSXso9yo5TIXFFniQyJQRu5GmjuEA2mcz5G0gBYtnG3KERNMAIoT1FnBcPT//J3/3czjJDD90EduM+59dn3R1mWHQhZzR4zqVNLAJgJqQp/1gvxcPP7Zj+Azn7ov9KNxx+qDYe4EbSBwMhSkCleAQKTf//zD+OzvPRDu46jO2fazM8t2CxjOXradKIVe58x73z034o7bbrjo8kjXiBkzb27lhHU6r40mvyq3nyW9twHKFcvmh60nNLDFVO6dnr9wabL746xTz9qf/PHH+Jw+PPZM22VG099Q20ZzTbg2O8857TeVvoSW6wSHbWAwiWuilJSJZSCi7YOznaF1fxiOZb/VqxdhLd0+5Q45XbDEFoKXTcJpWF0vtnKycARiTeROardZTEdnhEUnuKQydu44RPstvoitGNrnf/vRC2bMiDZKY6iOXjTsJKeGUjiX0dA2fRCPry9gaKgD3uF2I3yckVdJUcWqCW3a0NKLOr5cFGjWpNRPpk4fB+WfbzmDZNJ3v/DYTSj8/9l7DyjJrvJqdFdVV+ecu6fj5JxHo9EoBwQoS8TfgPmJwsbghVnLy/Zafr/X+9dv+5llvB42CATG9gOMESAhkgAJxdGMJo8m557OOXdXV357n1u3u7qmqqe7J2hk3SPdqaobTvhudZ3v7rO//dl6Owk1aeKuW1CKtb3DuHCkFS8HaQpqFtF/NVlQhuiESt+gOmI5j14KXXtJKzcLmksaMRwYRi8RpEzG/x9pHcTJHh/y1q5EbgOX+kTtVT0xyGZNrhePLFsChbDJeZDz2k49hrYx1l1ehuwli5i5vcjSKxDYwj8EF8EdT3kFgn39eKOpGcVcnSwtzEF9XRXpvUGTuU1F+JBAIf3vpmNWRJp2flGBWe0YHB1Cc/cIgaGLzShH1zhbPC/EFd/CknyUVlSSIp9riVFzv5DRKB2q8bER9PZ04ci5AYR+scc42DcRrdbEmM7NZYziQl+PgAY3lq1JHVZUXL0UfW3L0N+xh/RxZpzjiu21Ltu2bTNgkIAhpSNX+I4e/GfKsGX3UYCBsoZpFfn973//RcCQju/Zsydpfbr3Kkp/LjBgNu3Z7cYDG9qndsR0UhEbJhXAYV+f7NXWSdKxuYZKNTQ04Mtf/rK57t/+7d/MA1disW0lRtGdd96JD33oQ1i+fPlVCb1LbDvxs2yk+/7888+b+65NoFgqu8XbRt8ViVHbJR40Smxntp+Vrl4Z0sQcVP3KSBcPkqlv0hh6q+w123E4570zLaBsZOfDQfQMMi6MhVM2QSHglXMWw6WS4tLbF4Rx69J0PLg6E81kB313yIsJhkHrOWxiMIzRiWyyXtLJFEqnsDMvnoEtZPSDOB9b2cbsDGSc8zn3v3l2GKuX9CPq60d3dxcWLWowIaqdFJHu7RtCX+8QhqmHV0MB3d7ePuOYCwhat3YFfzsreV4nBeaZLp4imZpbG+prCbSMcEEkwFTdLeY3bs3qlUZHJjsrB92jY9R8azKsimiAtCjqD6XR91AiB2kJSn7aympqsYbM5Gxm6rhiWEPULmJImVhDLfQfblpBn6SEdjgZIlM5ih7mbzjJ6fGRBoJb3P1sdzpWFkZQnRbC2WGv6bPC2/LKCHhRl/Fq6AzFd1mgSm7u5buMl6rHBqLsthNTGU83ZOpPeiDTdjnXWw+/Mz8Ap+7BzEfUtytd/6VsO3OPrKOy11xsNtc253q+3ee5XHclbDuXOmY6dy79ns9YZ3NPZ+rfbK5P/Juc7TVX4vdiNm1d7+corFaMsvb2XsO0FFhSV1tpwhhnw6SxBcUVQil25iOP3EVGYGox+7nYIz406nLFsdVuItiSSivO7qPG9sS3niLLN8CFUOthVSzMLQTTdjFBgez1ymsHjX+cSlPMDls839RGbbtbZz18/V3csHm1CSlVRsXZsJNa2jpNWKvKZoZN2yGjs270bXDi5c/yV2iQIf8IWSqDZpXII8o4NQC8GcnZQmqyhyuWgwMdaCKr1AJH6GxSuDKSnsfVOj9Ot4+io3swJTCkOgQObaZ+T9dYAMfOD2OI1HKzGsXnd+YzQw+p2tmcJKV1wNQoBniRY6rMaV094+gZoLBYToGJMXdzhTGDrCM3Q8/kJNvFd6EVlaWZWLysgSsgMVFNHu/qHWF2My9yli6Gt0QMGjqPoggKGNLKo8Qr6YillZQimp6J3d2tWHG4iZR4ayVUZ6kvGXQw45k7otxbottaD1Va+Isf2Kd6p1r0H516ZlHLyaNotWEBsR8cswAngVvZHGMN78Xo2DCaOtrx/I5jho2VlxNjBgkUYPe7RsvhD2czM1oPM8YlF8JMY3Y1lzvHgE6utADPJWV/fASZ2bPLXjWHmcNjAAAgAElEQVTV9/m/E6ihcCg9nM82w5ZaEyDwT//0T8xg8y/4m7/5G2zfvn0ayGGzQH73u9+ZlUmd8/jjj0921A5je/LJJ/nwssiEDKUCJnSRWC2q65577sGWLdNX8J566in87d/+ran7E5/4BL7whS/MWNdkJ5K8UVicGENzKWKzLFmyBH/+539uLhM4lFj0QOXz+cyDlUCsPgql26wdnRsPsMymDwLUBDDNhaVl90nt6n7ZgKDEvAW+xAM+9rnq109+8pNJ2yv0Lx60iQeNEsc828/xdXRRWF56XfHfBR3X3/Xl6GDNti/OeY4F5mqB7rbDFL45hTwKT790YhSHOrnAQPrN6lIP7lrKbF9kvgxyseU/jkbR2BFFPplEZ0aiuGclFw4Ibvzn7kwcacli7DjDoSjGPCu2ECdmsxrJuaY824eKHLKKOU8NEbD5xjdfZgKHMaQze2n2q68TqyGw4ylAWtSPgG8U+4++iWjQh/T8UmRyjta+fW8eQpaXDFteIy/C7xvBxAsMH0+ndgv1hvT3pxXyiYkwcjlOkzGN/2lBZYw+g4AsnycX66oz0DESQcsY2X5mzpTOEKskSDST1pC0k6IExMIEt9p63RgY96K2OBPVORM40R/B+1am4YOMBsqg2OEOinmvZAjZ+f4omul/pLHuUxT1Pkudo3uXeQguHUZ76yosW3nHXG+lc75jAccCjgUcC1xFC+h5YNvWdQYYeuZnLxoA4h+/+gMTFvnJjz88IzhkAycSgjeg0MN3GP021ZmqxGc5vJTOT3xolKVXOzPb8FIZFBPBFlsrTovbSlighAQV1PJTsYX0xynV8vhnH5sEuxLrsDXF3th9lBnK1pkQ4vg6pNMm3bnPxdWRyjaJ+8X2ExN2N+sWCDUTOylevH0m1tZc7J/Yn+vhc+pv1jXunX+CbBSmMlfcfYRsFjeFJhMV9eO7FA4p1CkTJ7rogREgsVblCODwyxdKz0eQYWjxVOdUwxFY856t1ApyncYPm/swxNTwWu0T6JKbkYbC/AwU0HldIICFzWjVcpQMm0ECRgKI7CJAxkXH1VxL55HMcopXk3FUmo81tczSxZCz+KLVRYFBEpuWg0kP0jqsZkxTAmisYaVlZ2O4rAZPH2OmlKJzuHUzwSQKdWvYEuFMVuy9yY9OXSHwx/SZdpPTrfT2FoXfalxtiBHkpp1ycvLNhSebBnDoaBO2bmgw19qro2N+N/UcLKAqWZ+0LzO7kJnnis01acr9S2WKCFeer2URSCBhZ5slI3DIFlEWk0dCyvFsHgEFzzzzjGEW7d27Fx/72Mfwvve976IwKJsF0t/fb4Yj7Zx4/SK1q/CzxsZGfO1rXzPAyJe+9KWLmEN2e9///vcNECLwRWwXuwiAevHFF7k63m12Pffcc9i6deucwuF0nQ08CYQQSDLXYoNDChGTGLXsGF/i9ZyOHj0KiU43NDRQnLPSsKo0PvVdRcdff/11o7+k/uj7KKAkXhsokd0lO8m+NmNKnyUKrpIMUFHbNiAo+0nDR2EniXpPAt0E/gm8++hHPzrN9tMGyA8zAVqzBb7iwxnte29na9PDqc0wk91su9j9sJlF+qyxz0cvK3FMzmfHAjNZQMyUoYF2kyDiTOcEDrZT847TxeZqNx5cSjFohjd5mZ5sVZkL9Xle/P5UAE+dpc4e55DaPAIglJVr6WYaei6MML6Y89/UPGohKmxdE5eZW6zwMcMW0nzJV815dSU+fGDjSSwr7zGhYkoTr78VMx9pccMsafAKvedCh714AvdZM2+azwqFMtfQb+D8Lk0iA8hy0/ysc3SKwpD4lpvmNgI/3Bdbw2EbpqN45UQRfnh8FdpRxVZ5vbXbjMG0pXmW/04rmm/FlKIN9p/OwI0rAlhfm44NC9LwHJlXJ5k5bUm+B37W4aXWEddRcI7aRI/WhNGYG8HT7V60Utupu9fHBRnqKF0dYstMXwXnmGMBxwKOBRwLzMICdrjhhvXLKOb+YxNW9r3v/wod1A66//5bpwlwiwEjhmp8BkKJ2v/x5z6AB+67zYTrpirx4IXOEUP25z9/2WhfSfw9sSRmXNy79yi23bg26bmJdacKvUpMWhCfsECgjx09ITmTMka5PPjgrReBXcnq6B8cprj+Trz86oFpdUiTWAL17333LTMCZolj12cbhBKjS6BdMnZSoni7nW0yGWsr0UaXsn+yPr3V+64bYMg4aXLq6CSJji0tmsycopT26R1gKFbPMAYnCK946YUpSkfOmIqcMXl/LCOjYiL5kUdGjMKekpV0Aj9bGsvwZksvXmH2sTSGa2Xwy1uVnY6yLC8yWVeAzl2AzmMWH1jzuDLq4TVyUlXUdGmGG0VeN6gYNK2UBHwoCQlEmtqtEKwqiRl6uynAPE62kZg6PC7n03oxjqj5R74rBxbhCmbTSDb2tgxSWHrQVBZhfwIClCYvsi6xnFirvQRXdKoTie8m24+rL96R5W453dlZeRjjiumFLj+WDI1bTi8blAM+6s/HYE8rmk56sGrLexNbMJ+lM5RTUI506gyFwn6M9LViuLedDKPipOdfrZ12hi3VL22X1157zYBD0sKRvot0dBobG82DgjSBhoaGUFdXh7/4i78w4IKOJRYbyDh27Jg5JEaQdIXiix3G9p3vfMcAJQptuvfeew2wo6KH+1deecUIOT/yyCMGgJIeUjxjRXVWVEylRxVgIFHw2RRbAHvHjh3Yv3//JDj213/91wb8UkjdBz7AyeeBB2ZTnWEAxYNs8eCEzdIRuGUzs5RlTSsdAnMkQi02lOyg41//+tdNmN7tt9+OL37xiwYYUn90fwTkKPxLosyqQ/dH+z//+c8bG0vU+ytf+YoB7lT0MGaPSeORLVWf+qpXtSPw5eMf/zjuu+8+Y3/13baLwL8//dM/nWZ723bq6759+0w7ArQEMOnzY49xxWPTJq48WCLjied95jOfwc9+9jMzNjGV1H+BhwK8PvnJT5r9+o5JhPqP//iPDRCpOnSNvp/SG/qzP/szYzeN96tf/aq5hxqryhNPPGHGr++nPV5zwCmOBa6gBZSmXlt5ITXlmJWL6yRmzq2gr0pZO3y9B/Dx/eK8EB5d7CFg4UaOJ4KyAi6U5FGPqJeaZn08gYs/AkaiWmCJTZCS6JkUneacKQaOQCExg6xFCJ2qzyQbYRy9zMJ56JwPPeNJZroku4wZYvO25S+wEX2enJ/1RkDOVLHmYutz/P4ysoiWlLqYnIHZ0sID8ASoCJ0uAIidMxov8kMEOMWyk8XVab9V1jU3GVP+sJfaR7ycvkxmOsPYuTj2m6YwXmF2twgBtUyOvyTXhQ80hHFnVQivXOAiC7Oldvo86Ke2Yoabmcn6j2OkbPUVT1ufpNvOLscCjgUcCzgWmIMF7HC+W27eYBY3tmw6hj0UK/8tM7Mpw6OXYdYNdZVmUaK5pctkOrSBk89++jHDkllIUX9JeSQrysL37X99hpEQHWS0TmX7Tczu+JlPPQplbFT2wr37jlOyYHrGRQEj+w+cMpkgde49d29Lme3Qzjr4Hbb7wH238lnmJsN+0li3bV1rZFGApwwIJnBIW3wRA0pMoWRgl12HEhM88c2npmUkDARHJ6uJz0xZS/vNpySCdi+8uAdiJ6XznqgoKmjC5zcg1gP33WJAqBs5vnjW1mztH2+n+fT1Wlxz3QBDfuoLBckaMkwhAiw5hVVklkjEOXnp6BsxwJBbq3107OQsGuaK8fAmvTz09A3jDWbeqmX2hi3rlxpdnGSlhmLUH6c4dNqOk3iJ6TnT6xegw8eMY6SS649YQJMAGjmkpi1+waNRK5QqjcclulmaQQeXJ4zIUzboDHsSA03i29SXqZ6ZORYUd+AsVwKninWNhQpZeI+OaTRqM1pYjL09g1hzrocOtpG0NCu1+gOySsxt5QUWlT3eEvE9mP5eruyk2WTDWHXDg/2oKhD7KYL2ITdyswsscCinkH/gpN4ze0UmhUONwy7Hnv+PTGTSyWWIwAzFQ7aTh9oIwBj8o63oaz+Doop6ZFzDcDJ1zwaH9ICuMCO9qoiFoc0Oe/r0pz9tHuRtto8tEJw4RIE+0t1RqJMe8PVwHh86pfMbGhrw1X/6qgE0BCCItaNQohdeeMFUJzBJIIGAisZGS6Q5sQ6BJR/84AcNwKG21G+BR7MpGzZsYMaSfAO+JOoKqR1tYsrMpcSDbGLgxINhsqsYT+qjbKqVAo1NzCCNT59lE/u47Kax2eNRfwTsCPiSreLrsMetc9auXWuAoGRj0njsPqmvy5YtM32SRtTOnTsN80oAjUBAAUX//M//jMaFjQRBs6fdv1S2U/sad2mp9XuV6jzZ1D5P903fA31HFLamcclW6pNtFxsU0zHZRcdsu2i8f/VXf5V0vNJXS7TDXO6nc65jgZkskJutDJpRatr40TUUoOYOBaUXuLG4LA1v9jEbWVAsGer0jXgw1urGxvwow7dc2FjhoSg18MO9Xhy6wIQIEoMmKGJCtWNlUnRabB8CRoRNrHldcwznlzT+oznfzMGc7jqHIvj5GYo098VDNjP0XpWwvajmK82beiVjybB9zaZ5bKo/qWqqIij0BwuZoSxfFFuepXmeb3SlNukTKlTMYi9pDk2eut6Ek9EGXVz0Odw0gnWLvdhQl4UdF4IMg4uigB7aAF+z0qN4cGEENzMrmbK99U1QO4c+R8sw0zgwaUZJLuuJ0pcwKJZTHAs4FnAs4FjgerSAnv9uumk95SHWMLt00GR7PHjwJNo7etDVbUUb3EbwyM50uJFi/akyL8aPb+XyRfiTP/qQ8WNTFfnbCxfWMBtkKckSOSa8LdX59rmqa83qxTOeb/z4+moDONnFHud6CuLHj1Hhc9IQ2sisiRrbTGCX6li8sBb/9//6I2oH3WLspCJb6bnTrmM29kllE+2PB+02bVox2d9kbaXKODlb+yfaaaZ+vVXHrgtgyD/WC99Iu8nQodXvNG8ms5FR6Fk08xTFR/ROcYkRZNIRozOkvwXjFHGLc470pT9wrA1HT3cw/XqO+RImA4ekLVRPMep7lg6i+3AHzjCN4nBhPkbJyFG2DYFDxhk1CAo1eUJTq4ohMne6yFzKZj7ZXDpoQV4zbqjpqZ20LasbKSAdxvd2n8fp7l5qCRUb0W2z6TqtOJpXvY3tZ/vNAa62BvhHSKdaYWjn+/zIHgxgYREf6uNMYJxVwza6dJkciTFfzH58XbW4AresKcKBoxdwlmJbGQSBPG6vsUBT2whaOqgBZZnD9FmX+sd7MdQ/MzCkrB1ywt10ZD2hMQx2n8ZQzxKU16+6dGev8BkCCuIBIlWvlMZKW2gX+xz9+CWCNPHdsevZdtM22smT/HtGECGPWejWrFljhIU//D8+PK0thU96yapSXana0n4xXwREqU8znZtoLp0rbaCZwB+b5pl47Uyf7bHrnPh+x9tWdlVJHJ+ElwXy6Lhtt/g6xA66++67jT5QqjoaGhoMeJKsJN431S1W2MqVK40t9BuRqt74+i5lO9tuszlPfdD34F3vehduve1W8x3QdfGg40x2mWm86vN87mEy2zn7HAskWmBsuBujyoLFeamDwEwbwYlqilGvqHHjkU1p+JNRF472e3B6hEwahmMH/Eys7g6hNp8LK+QD+XzUI4qSGczfSLGFrHAstmJWM1j0ork2FkbG2cLsshd/zEJErFMizE7wn9FZJLfUdVG1adUWa8iq3ZrZTcOxZAqxBpK8VGcCH1gSwm3VZCuzXZrBFNUhKItS0ea9vTijsVwqnCzMeXXvKaasX05x0VI/aije3cVF3wdXubG9NhZqxwl2VyvwTJsXw7Sknw2HeE4vQ7gb2PXhoS6MUN8vj4xcpzgWcCzgWMCxwPVpAQEe2rKQYbI93rRtvVnMs7MJam60Mx2mURNvNlkOxXpZQMLBpYqdNbGRz8IN9VUznq5zVdTXS52fLBujrrMzWsaPUeNT9rDZjE2gTW5u9qSd1B+z8Ml5dbZ1zDjIuIOJ/Z1LW3O1/2z79Faclxp5uYa98Y11Yny4bTLm360wLT4cpyoSNx4d6sbQKIPuCQyZou8vvyh2mXpHFRt+CZ/f00wEtohi1PnmwVVfgMSiP74taxcZuZ+nWsfRFM2PMX5YW8z5EzXcqtuGXOT0yTmNoD8QRiad4GAM0FH9aWRnePMvFtFOIz182wayPFjZf+w4hVP+ADOUxRw6u2q+Wg6lduiDnFAX9h45j7YDwzhxvh+jaUU4So2BDFLeGvMpvh17+E4cW+Jn+4dJJot3tI0NtRTLEiFQJzeXT6105sf4IFtMUU5lgONKbe8YXtx1GqOjw+gbHEcJgS1dOujLwjjT3M8kQB2aGKU46Ch/FFiVN2DS7fZ3nkdheT3SKTr+VhQ9lGu73DLbevQd1JZpf3/n2PBs20lWrd12smOXsy+V/S7V10sdV58udc58xnQ1r5lt3TONa6Zjs63/cu6nc61jgWQWkPOam02NIE6/QTJeBfdqKaCdAslhHwGgIi/uWpyB+6iNl5aWjp8dGMHxLuYf45x3ojUdx5rJFlWiCIIhAmoMa4clMYzMTOnaBASZN3pvn27Nu+bCFKW+wCwZoYXglS40oJDaMnOcVV8DZfO0r2WWyTG3VuXhD5dHsa5gEBEmZPPJP7Xbj/VT2oIGGDL7BRBp7tZZ1p7E7trhZAGGk/mZ5KKeAFp1vpuZTkMMhSfw1s+U9X0RCl27qXfownur6WdkhHBk0IP9I2RcxaqNSEtplvN/Yh+cz44FHAs4FnAscO0tYD+LXW7LVuTIpdmudjtX+/z48VyJMV6JOmZr4/m0NVd7zrYvb8V5F6Mj17gXYguNj3QgHBgjCjhB14nCs3xgpogBGTXMOuK9mH0yOsgYSoZ5NfVPumRWr+UgGSdJjljcQPh+jPH4z73RhOKCLNyzfQ0KCwuTgkMCbG5ct4ggzyn8iHpDY0zhbuQCWMLMDiIfT396tq6HGjLNEgySBpGJqeQOOWu6LsS0tyGuoAIXI7kGHFq/GOtXNmD/sWZ8b8cJHE8rgJdhMzZxx1Su9q1GiINlYf8Enc2uMa7EkgLPFdkxAlJtIyEUp3uogWT1Zap/puspi41hmeqN3bRx9XFwAEvLC1CQl0m9G79hLVmAkXVmdlYBmqiwPzDEe5SWZWNJGA7kIcJsMKnaH6Vo6Uh/M31zKknQnhlkhgWDEXQ3v0Fh6mxUL7mB+kNvDTiU0kjOAccCjgUcCzgWMBZoZ2bKzs526u9Z04XAjxNkwJ8di2KYAIg7I4LarHFspBbOmjIPBarDyGf4WVkWGS5DDH8OC93hRMUtEhe2pZnFKhZqY7N4pjg9asn6z5oQNTFOMTt1bUNRFrbW56Iyl4saLh8ZTVHsbHbjAsX/JkEhEz4mJg7r4usu6vhsX8A0u9Q/2tvtTgkS1TD67YFGP7ZURBAgRWmATKlwmGHvHJeK5Y3wX9stsRlQsV7GeD/WEOP/NWgXQSzaQrqBCldPI0t5lArWR9qCOMYcA29Qy4nIGkOwPVjItbC7iwPIIOM4i2FkPJvZ1YBu+kUSBneKYwHHAo4FHAs4FnAs4FhgPhZ4S4Eh/zizgPWfxNhQK4WdhokFBYw4sfyqCV8PWUTNyMgSoBIDLPjON9qNvpY91C8YRvNwhqGXG9BE/1jpvKzTrZ2WTSw/E2cHg3j+YDsqSgtww9oM5FKQOlkRYPPuLUvQ4z9J5lA7cutqTFiY26QaoSuqJtWl2MValzRcE+40i4M6wH8EjnQRwOrjKmqqorbyuG1fvxAbV9Zh37EL+N5rJ3EU+UhjhjQLrFF9sRo43jD1eaIVtXCVVHO/Mq640ToSABPAYEkB9Xt4qu2bpmrX2m+dZZGttCrLzxobB1FVVoANq+pRVc4l1ZhdLT83do3offklyMpmTKlZIWWhj+4Pug2bq/fCDoxlU1Caq8JKhxiJcGWZr2MDrRgfPIG09JBZuPX7BrkFObxBdJ7fyXBCHyoXbqZA9ZS48sxjcI46FnAs4FjAscC1skBWJpMQcOthivbeMbFxCEowCxl/5BEmqqGpapBz0pmWKI5QIzIYSUNBLudPLgQEDV3emmsEhFw0UyVOXPqsaUlb4gBjc7A9NerwTQuL8fDaUlTn9jGlfQT7Wtgew9giLUCzMkPEQKGGQjdDtVy4keFv9y8D3uxxoduncPHERqzPmykf9sl1GdhcyQWgEYpeD0RxqJ1h5vSg1lZNuVFWd2Od1qWT/ok1hSer3lYn6hryondYyTfYDwJDUVKXX9fiF0m6fjahsDX5O8eoLzTQm2WYzT0Epnr9lnGs7GvTgbLko3H2OhZwLOBYwLGAYwHHAo4FLrbAWwIMBSeGMDp4Ab6xbgI9nWSQXMDESLfRF9EKXjAwSgDhPEOZ/EjLyDMAi0lHGw4wRS61f/rPYHzMT6VwKlky24nSwxpvlFuY50q0cRJI0W591LHcQuzpYmr0Xx9ElJm1bti03Oh8JCvpDDW7e2kF2rpOcsWxDdm11dMkENQnm7SnV/qZqGY2kXF6lgNkwFh9jqKXbJpjA36s7R1iGBuzj6UoNkB08/pF2LSy3gBE//HySRyO5CCtuIQaKLKBBTaZocqpJgIUJfVctvFxO9Htoxp9iHpDXuSTPSSfdOZCR512mODKo+lvbBsa6Ed2fgQFOQuNKr6fYW6mqrgVUNnTReffCHPH+iWjj/rzMNR3Ei0nXkdOfikdZ7KJuKKpVMBigIV4/0KhIfYtZOoOTPhMOIJWd8f6qc0QVVtcYS6tR3ZBFfvh6CXMfA+do44FHAs4Frh2FhB4EjIZRgj4cyLQ3GCHM1kojsXj8XG+eHNYE4UL2xgepWM9Q2noJvihfSZzaMIcpbnN7OOxqf/ix2bOsOb0yVUY67jYQtsai7FtIRcrwowNCw+hvsTF0Dbq2RHEMRM2O1/Pafixlcz2xR19PkAR6XXcV8M1EOFbF6YSnpiKt5S58dmN2dhUHsb40DB6mUb+UEcYPzsbwopyF9ZWk9MUG4c1ytiw9MHqrtVBDUyfY6HasZ3awf/lxXi4MBJFCRlIZWRYqQQ05xpiFC+Mzc88BR0S+NZu/qtDo2PK5eZiiN9b4tJNDcV551jAsYBjAccCjgUcC7xtLXDNvIgwU5OHg+MI+IYwPtrO1wFMjPURFGqi8HQXWSVBSx+HwsrykwK+foIIo0agUkVAhPGqYnH0AhumFwvYsE6c5o1ZfqauTctAMD+PdPF2lO87jwqyYhYvZDp6itsmK8pUdveScvQc68QFilHnFOShkCuiBcpcQsaMTQ2XC6feCGLK5Ac/HT+JVqvPbmoM7T17Gsu8Qdx72/pkzUzblwwg+vcXT+DNUBbchSWWY2kWBWULjdMaq96OEpA6G5hghpMJFDHErCjiwzalLUtRPAS/pOUk8WzLs3WRveNDXYkXD96xDOtW1DFjVseUox5flXlvecPGKVZ3Yr0JBycQ5H2ecBPkEYClE9hBgUMmvo7urLSQQgECfQSFBEhFogKgeuAedqG/jUDXcBNDygqQW1SHrLwpcChAVllWThnySmpSjMrZ7VjAsYBjAccCV8MCClUapfh0HgGIHv5WdzEjmX73rZkAeBd/lhu4XvMKw59OUYNH048gjIosD7N2RtHCjJ1hhj+ZbFzaJq+M720MEhI4ZFc8eTi2Q/NNbM6xD7k57x5qHcCiEh/WVIoeRMYuQZ79nMJa9DEGRNUXunCoi0LRnMDLcxhCxsjlspwofn3Ohc4xAkecyDUltvDaGyq8eHxTDjaUhrhYRT29/hAOUy/p2XMhHCBAtKyMSSAsDMeeQu0pOW5ksgD/U9d1rpk7pxeT9ZRbF3WDBn3Su4tVqpPNnOnBbeURLC0MYdegG8fYT7sECdJl8PzoKO8N9Red4ljAsYBjAccCjgUcCzgWmI8FEtGV+dSR9JqRgSYM950wIUQqEkWM8n2ErJ8Aw4d8I71kiwwRHBgzoJB9TjDg44IanSHqU0bdMVr05HKc5TAG/T6kG8BIHtZ0L8taRIx5jbGeySlT7L5LoFJ2LoKeavzmRCtckX348HuiWLKwjtI9F2sZSYz6hrULjQP669O9GCUjqKgoD+n0yZgIZNLxM73QSWwjn3T5nEwPU9ZH0T4RAv1m9BWWYt/AGBY3taK+pjKptlGsq5MviQDRXmYG+/ffH8MhpoN35TODGdszjrFpVowp64OfgJTPTyHscWY38QhsU++SlyzqFWVStyDo96PlfBNc/mHctW0Z7rxxA+qqS9hPinXG2tDrtCK/NLZiq0PWppPtPql/1j2futR6FyFLyU+mkF/aRZMV83x+PxRCGAoOM7wwl5pDRWSSNVOQWoLhGTzmR1/vCGqX3+YAQ8lvqbPXsYBjAccCV80CZh7nfBMJkzFEUN9ML2Z+dqE8w4UtNZlYSmDlzEDIAEP2JCnNHJ3WNUjGEFlDlub+dNTHMIiSAkVTM721iCAlQs0vBm6ZHGvzoB+vnenCAsZUl1NsryIG8JQT+FlAxlLzCOsnGPVGhwvvX0FeKpGafZ10NRiCvbkqivUVZOtkh7Gn043d3LZWeQwotK44iNG+AfQPhnFUoBCZQvt7mGSC066LDCRbHNtMZVPdmf09EAYkBW+OP8Q4dXGRK/O9qCJo1Uk2k6os8kSxtTod60mGaiUr+Jjiy2JFIXD9AdlOfkDMZ5p9686ZjgUcCzgWcCzgWMCxgGMBY4GrBgz5mM624/ROPuQPEWCgsxMLQzK+k9giDBOLKuVcvCelVTM6NgEyTuQkpWWQJRNjDMXfrzHSqHu4mmetqcWcSwE/xqUSADTdO7Pczdg+9SOnEMGSMPZ39mPZKWkOUYiaGamSZSoTOLJ13UIMkza/k41mGF6Q1db075AcVZHBZVSluAd1FcjG4TgupBXjldPD6Hp2Lz64bTE2r1uK9PQpx256PdM/2VvvuN8AACAASURBVADRLRsWoYZi0L8+1IpfX/CjQ8gUx2nGaoxqjc9ylCnyqZXZiyjriXWn4cG7N+PeW9eRPVVoUiKmEyhKp8Clne5avrqxH/9R1pipErO7jqkDsZOi0pGIpFOIO8NkqtF+HbJvifSGJkEhhr+pYlOTeSiga0uQMEiAKBSaIINoCGPD7YY1pv6MUNNiYiIHFY1bzLlOcSzgWMCxgGOBa2eBEbKFtOWTMZSXE+c+8Oe7l0DQU4d8yOUCSlOYx2xKLbtXQZCjJJ1zEuclhXAJoLF/8+3em3lEmwAfbgpT02Zmttgh+1wz7SXsE/P1LBk9uy5QIHvEbUSczw+68HILF3J0bmyOChB8eeY0P/P45ioXSrPVDsPeetzYS0Bogm7Jlmovw8cIChUGMNpLUGgojOMEg35GptDe7hgoxDlL85JbsXXW9GX1NdZJu0m7zzO+Gnt4lACUG/0HftRmj3uQgt0/PerHi6QkX4hI0XCqdoFzfmZDzSJrKCcWgjZjW85BxwKOBRwLOBZwLOBYwLFAEgtcNWBIYUI+pjIPExjyMqwpvtgggeXcxIADnRA7YMAhsoLkHHozsq0sZbbnxdOCdC79FAPQqqFWyNxaJYuxV6QxxMU1u2rTrHEimR5XQsjS4xHC4SoqQSdXEH/8ehPcBCvuu2MDiksYqpWkCJy5a/NiMCYMuzo6kVFZKZ3NqcIGTLXcDNbFor7LsRNQtSjHi8KlDTh1LIwfvHrKHJ8LOKTz1YeFtWX4dFUJKnacxL/ubkdHNNu0YxxpeoeTmz7H9pvGUpRQKETdIy6nsgo5t0qPnQiOmWp4vaH823XGv/KI+U9tqh3VFfXxAYAry0YEKbaTL2IKTRimEIG/SSaTriM4pDpj4JDeykMOBfg9YE5gk4GXzfvGWZ1rdoBaiiE7ux0LOBZwLOBYYJ4WyMki04Y/yOPMhGmSFcQVTYlnqdljdO+YVr2eWneFfG1lBi8vGUMeLhqYWUIgiLWEw9e4OvS7P73KafVb81BsvonNd9NO4Afp77x0ngtEzewN5zTxlY1OjwFvLC0g9buMGcbMbnZlHzORPXfBjVJq+5BoixsbyvE/12WjLEr9Q2oDDjDd/SvNITx9JoQLzERG9wMe1hEhkEPnxAKHEhZhYrNhrHtmEpyxWEtNFMBmKFn/qMLTZKOpojnxHMPsPVHq9QnxYtOLmbJeNg9zoYzWZUKHNIb59ZjMZHn5F2dBnbEDzkHHAo4FHAs4FnAs4FjgHW+BqwYM6bk/6GdoEEWi0xhSlUYWShoFkd1yauT88bgBGyzkwLoRhpZiOVECh4LUy1HxZhIccifpqu1v6Ro5igmAhX13jf9qt2kDKHJMKTzdMjKE3+9vQkVJLrZtTkdeikxlEqNeVVeKU20n0XShDflpduN25RqJ1QdzhH0JKm0I/5cTWUBgZ8XyRpw/n44f7DxnwJG1TFOfl5c/a/aQGD1ZXI194MbFCHJA393dhS46isZkVlPWkGN2mO6c2taYeh0dHcF3n3oFP/7tEWYfK8CqhSW4/YYl2LppJbKZOl5FtjOb+aB7Jy0ENhfvCMfYYCZUj6eFkMNTlV3ORs/4MMHQA8MU8gkU4n5zU0ylCa+sm4fH+YAxMU7mk2SJYqbWZYwEdIpjAccCjgUcC7wFFnB7S5DPhACeMSITg9M7UJufhhUV6aguykRpXgZqi7NxtseHZ04Q3I+tpCi5hJmsOH9can6arF1zGycBzQMK//LofWyunZwceLKmFKW2ZyS1tdvCn7TTKjzB8FP5f5s0eri/y2T0EldJGclceHRVOT65PhulkXaM9A2blPSvEhT66ZkwQSGLWSSMKaJrFEamhBnSHHRpIcSMLOm4NMtpS13MICf7Iv/Jnvfsawpp8ipmd9tcFcamoiAGyaD9ZVc6faoM+LiIovC+srxS5OYmX+BK3fZ/3yOdXb3o7Oq75gOsqixFRXny+zCfPs1UX6rBzacd1ZWsrfnWlapvs92frC/x116Nfl2qzdn2fT7n2ePp7OjFgUMn0NFhfXfb2rvR3taDmppyVFWVobq6FJUVpdi4YTnWrlk6ral//bdn8OS3n8bDD9+Oj3/swSv6PYxvaDZ2muv9mU2d87Grc41jAccCs7dAErRl9hfPdGZeUQ2qFt1MrRiqUNJVGu69gN728+aSdDKIsvOzCPiYJO9TJcETkkaNNIdUvJk5XJyzupubTjFLk+WExVwjL0ov8ghtr9A6bJ8iNo0BJAQMhZXhjI4dHSpXSQUOdbbA/fxReAn+3Lh5JbKyLFBkqgbr3YLKIjx4w2LqE3XjbBt/sJXGxLiBdBhZp5xfW9PHGorl+orFpM9ZBMcWNdagu92L/+dXx7H490fx6J2r5gwQZWdl4IaGAmYua8FvmscQzcy1zGB7kzGTGJtY/yQOhaFayioTxMh4AB2DAfT5x5l+OIpdh7uw8oWj+Mij27Fp7eJJ85oKzDhk65i9J2u11jtNcyzpFJ1Od0tgwrq/SlM/QaTHYgrF6D+T19pvZEMXfNRLmCAzyJNeiIz8IlRXLkLpArK1tDrLkkax7JJqfnaKYwHHAo4FHAtcYwsodMoDP2UBJ5TCKzYNP7wyAx/fVoiakhwyTzPhTdeWgRdODOGFlgBFna1Qs/KCMMrzg0xvbwLKJpcO7EGoOmseic2rnGvCYgXHGEaafzTHmhAzbdNGLxaPrhdQE7eZz4KMWMyCBJlE6jpBpjAvkE6Qrnp4USY+tiJAUGiAmU9HMGhAoXAMFOLcHlsACRPcMm+1TsL3SrBgFkHMtBjXJ3VOc6Vd9JanJUWIeJ7qqCiMoCQvhLMUttaJ1QzBu2uJBzdWSSA7woUhZl3lVOhiPQeDaQhwwUxDymVYX8AXxdgE60lgG0114J337ue/fAXfeOIn13TgtbXl+Nxn34d3v2t70nbn06ft29bg05969CIQIGkDsZ1tBBKe/PZPsHvv8ZlOu+jYFz7/QQMoxJf51nVR5XPc8b733YlPfvzhlODGfGx5qS54uHjdUFeJTZtWpARfLlXHXI8LQPn1b17Hs8++hAvNTMbDZ4kAoy7CfK5Q0ecQ3/f2DeLNw2eY7VcZgSX7kIabt6/De997MyoqSnDg4En86MfPo7O7n0L045OAfLL+XK7tLvWd1Ji+9Z2f4plnXk7W/EX7LvV3c9EFzg7HAo4FrooFrhowlFu0AI1r7uEPW9D4RkH/GMWmRzHQ2YSmIy8wG1kr05lnG3DIsIhU5OHEOVLSIwgpXboviDRu2blk12RmEBxwI0PXsF5zpZxFbm5dy/8FxExzyFR17BxXDCCKMDMKme30qPIRrarDgdZzKNt5EsWFOVi6uD4pOCTGTt2CUqznSmIbAZTxEekNxSgsWkGkt2h3P95BtLplgUPpjC+rqq5gWtksdJ08g7/9/k4srzqKR+YIEDUsKMOW2jzsb+0ka8h2kq1xm7aT2MDYOKFYwpkuimRnoLq4BvXM1Nba24FfvXgIxQVZIDvdcmSNV23dIrPCavnXsZ18kS2tf9i2NKTIFht3EXwK0U4BhpAxJT3PyKBAKSPWphVVFaGDPsLMMVHw4WL5JtSv3I784ipkEPTKyJadp4rH64STTbeg88mxgGMBxwLXzgL6uS/LocBzrhu1BREs5hpJZ4cPv9wzihYCKj4ushRSY68kNx0ZZLmGqBun0GLpDSoxu5mH7ckyvtuq2OxXqDg1iTRvaz6L7bbn0klgKFaHwXvMVSyx99aeuM/2STxuwuDi5rCHF2fiMxsyUOUdYUbMUYyTrTrMzBG9XCwZ5hpHSHO7DQjpQnMtASGKCbqNsLaCxu15eHImnOzCpd6Is6RFLQ+ZyHrfORxC52gEi4o9qKdYXxmnvI5+HiPTSmyn40NqmwspFE+qzmfGt8womocYB2cM4BTbAkpwMTA4fE0NMjLCv4HWrpRtzqdPv33+DVRUUkaAD/+pmEiJDUrPcYwLcnMdv99PxfOEMt+6EuuZ62ff2MSM4MZ8bDmbPgwyA+HR4+emgS+f/MTDcwLmZtPOgYMn8K/ffQa79hw1yVUm+H0VAJSqmGPaTL4e6z4999udePnVAyb8NMjnJdUxm3K5thsZGWN7Me2MJA0K1NL9m+33r7Agl897VrKiJNU5uxwLOBa4Rha4asCQJy0dntzii4ZRXLEQ5fWr0d38Ji4c/T3GetqRV8RVRrGHYk6e9GnGRv0YHfSROZLH9OT5mBgYgr+p2/z45eRnYGSC6TnkOmo10bCEFHNE8Ua+evhqgJG4Yj6L1cPJ0qzy6b2b7+lcuQUOVVTjd6ea6HgdwEfvT8fSJfUX6e2oOoFDm1c3Mq4/hEylM2FR1YYtxH7YYpl2+6YX8vli/dGLfMq8/Dxkb1iDoQVVaD51Dv+HANGKagJEd8yOQSQ7rFpUhtVnSZVuJaKSwaVFraSKEaXN8qBN/2Yq1qqr5XyPM/RPmVYKS6vQ2j+AI6c6sHFlFVcl6JWyPlvo2gadzLUGaIs5xBosN2kDDQ0EKOmUw3tXjpziApTkFGGg+xyGus8wAxzZU1lho+9gLqBFxgkiRaJFWLb5fizbdA+yC5QVjanpnOJYwLGAYwHHAteFBfIYRpaTVw5/v8FFjHRPLufQU51h/O7sBE4RtAhKXI9To5uhTl5vkAkY3FhcqDnCAmQMCMJNDBltShmhonlR7/RYpDM0l4bNHE/QRVM8mTI6JpFpzbUKN58OhAiwseqygJsYgGMqt5McxBoywI4LDUX5uJ9MoQdIQq3N4YMZ/Q490Cg72BAfvprJXu2mhk9E4WKqNPZi3sR0i6b2xfpu+qWeqmgkdjSbDGB9jh2cejHzNZnQTEcvxlCYlCZFop9nko2XOjhefohwbH5OmqeGXNg57MUE3wuwUteUMCItm6FLHvlFTrEt8In/+Qg+/KH3orOzFwf5EC6WxBt7jl1VA+nhXQzyVMXuk/rzJBkVs+mPFthefe0gNjPMPxUTKbG9DQwz+pev/QVaWrpmHHtdbQUeeuB2vOfem1BO4Ck7KzOxKsy2rosuvMo75nN/3/fonRyrxebq6Ojhd8MK19qz7+jkvdA9DPkEvEyBL6/tOGTYOVcCILIZQk8//XucPHXBLKDGl8R7Yh+zv8fqs91fXRsI8odijmU+38Obtq7Gfffdgg3rlpuwtqwk3xW7Gwp1+6u/+BS+/Gd/iF8/9yq+9eTTaGlTBMlUsce5ngl5qheUY2HDgjmOwjndsYBjgSttgasGDKXqqJcMkOLKXOQWlpMRUoPT+36Bga7DfJ87CQ6NjfgJDCzHym13MOSsjKtoXjpKdDBDFLQeGSTr6BiyBgfoZMUj45ZjJbBC1HPjhdol9tasOxrgRKFkfEsH1s0JQFoB7uJysmBcOEhx6WVvnkFxEXUGSsuSgkMmU9mGJab2vt4e4xeqWJFqVmNqa2JoBL3NHRgfHkFJbTWKqistN1GnyK/UqmpFOVOvl2Copw9t55rw9//5BpZWWgDRhjWLqReQm6IPaVyJTUeaQu181DfwSk3TssHka6xfM70YxlBsBOEYsOSmXSaYVWyQ9P8gmT9W1pVJj3iyOuPjxu0Wi6gkdwxVNVVYvWIjSms3m3uXRpBQm5+MsaajrxIQ/A3vZS+1lSy6fJCO9wRp8A1rbuY9v58PHslj82cah3PMsYBjAccCjgWurgW0qKJFiQmFkplVa04//OHPYEYs5ZgY1/xKbEZzQy7n1lvKvBhj6FkXVZ17J9woL4qgvCCITl88eDLVZ4FG2kySCK0csCIDEHFOF8NXIJHmKxNOprbihiuchks9qC9w4ZHVSlfvwbOnItjZFpuhDWPInq2tC+sKM7CwNAO5XKjwpFHMmXX66VZ0kin0iwthvNylRRNeM3lp7I3wpxjryFTLIojLsJzUf43DzMeWLfRmestxHTcYgs6NMExM7CPWofHzCoW87eqMYP+ABxsqXXh3dZQZ01wMcSFoRc2j/bZ8jumW7BUDxuKqfzu+tbVJFBrTxZCUVHoqlxqbQA5thQV5WLyoDrU1NByeSgnG6EH18c88hnfdc9OMVSd7QJ/xgriDdp9uu3UTtmxZhWd//lLSh+bE+s6da2VYzouopsZMoq5M4rn6rEQi+Xm5WL4s24x985bVeOKbT+GZZ1+edvq2rWvw6MN3opbhU1r4TFYS67qUHVWHAJhP/OFDBmxKVS7Hjqoz8f566Ju3t/deBEDY7ev+3rRtHW69ZaPZFRbgav7W5OsGsYfMnWRgncCXfjLPxM4pKy+aE3PLbtt+FUvoyW//FK/uOHgRQ8gGShQaVl/LRVkuPsffE/t7rD7v2XMYC6pfxp69x1KON7Ht+M/x30Ov4lNn+Luwr6tvqCY4thELCOKk+q7Y53r4HJGbm21iKm7ZvgkHD51Cy9NTwJBAps9++jH+DazhAgK12vjdu1SdM43HOeZYwLHAlbHANQeG7G6nEyCqaFjHELNxpiTvoxZRJ/I9uSadbVZONRpW3o7a5TcaQCG+hPjjXVK5AEe6dmJlxWkc646BQGYVkWuPBp2xfuinrjMepdEWEmso6hZbiA6mUucKGNGmH8bcAnRRkPmpHecIWnhx353ZKCgoSGppgUN2ERhlVvRim/bLMRzpG8K9y8uxvm4pfrbrNM43t6F4QbVcwElGj96Ljp5fVkpmTRFGevrRTIDo/3x/F5YxxOyj99+ATeuWJAWHSgqzUZxJj5DZ35BVSGCLfWJfxOKxqfamsRnKJGOIppmgp++jeEQmmTpeAk09A2MYHS9EHkW6wQwtNu5kDcC4shqJEaI2OJwBmcLIKVmE+hVbkFdcN61lBYS5OdaxwTb0XLCcE9lsnKvMucVLUbdsiwMKzXCvnEOOBRwLOBa4Hiyg3/tShpIpnKz1TBCNZW48tDIbH85MJ4jjQRZ/59MZhxzhPPmr1gCO9AXRQ/24Igk2CzXSHG0mjaligUL8zP0W2GKBLCaczGycYmLAk/msHdxswMWe9UVYyiZQlUvCKV+ml2nojAtvtAwg25ONhrxM5GeNYWwkiAsM2frp6Sh+10ah51rq/OS50MYptpVbM8m5pkE9QBvgih/0v6k3DqhKmCynNZvQJV2nxA2V1F4q4dbDELZusq1U74YKD+5cmIYh5mx4tduFfziZhg15ETza6OcDl5faTR4UurVoRhAgv5KR8W/vbGTxWiu6x2WlhWgm40UivLaeynwYG3pI1da4sBZ1dVUpgSEPQcw8PsyWkOU8U0l8QJ/NQ3VifTbYkuyhOfFcfRaL5cWX9xlg9nFqGM0GHNJ19tgXc+wCRfbtOz4JJOjh/MEHbpsRFIrvi13XTTetRztZWDOBMJkZ6SgsypvRlvF2FDDzxLd+jG98c+6aUHa/tm1dh737CJTEARDT+s/7KxDEzr5L7GyyZCEDAutmAknmw9yKb1/f758RCHzhxT0XsYR03mMP30EtqfddBAjZddjj1Gfdg40bVxIYOopvPvnjlN/p+PaTvZctVJdVUoOmOr5v/wkcO34WdbWVyapKuU/aSL09XMyPFX3vPvf4+3Hj1rVJn21SVuQccCzgWOCqW+AtA4Y0MrFJqhZtZMhYJ84d/AW1hBh+xMk7PZNhVvkKJbpYS0biwwKVvHQ608T0MWI3KnHO5qRTZtuP51g+pMUQ8hAcomMqYpEEj8WIiagu1o3iUrS3juG3u8+hmALZN3FFJz9fItOXKKzfCq2y3VMBRWFq9DCTGNFwX/8gxiJBsoaqpkAh2zGWw8v/PBTXzi8rQU5RAXrae7Dv8HEEn95pfjgFDiWWDE68CyqKuApLcU85pazPDu0yXnQMukm8Lv6zxRaybCjUXpR0OaSyycmmPiyqziKSr+PWOcYBtjf6xVFL7dOqku3np48hM70YuRQfT1YszaD8mCNNnC5EB764EUs2PYjqhWuTXeLscyzgWMCxgGOB68QCeQVkXXhKuKBzwURTab3l9QsTONAVRA7njzAfvPycs9wURt1S4sU452izJsNFgDKyhSoKGOrdxmkkcZ4286DJxE6mjAWyKGRM4WRhMoUUMu4mI0Yi0NbChwxizUvCfzTzatZTaaPOkTKGzVyiWF8SxYeWu7Ewx4fB3lGc74vix2ei+C3752ebXWTNLiwmC4lR8aY6TY9sUm5HC6M3LMatWlGfYj5AQhiZcDCzqYNWd6d1ywBiHK+HJ3iFahl78QK+XUxQKsRwthfZnyb6KmFqNAV4TP08SQ2iEPcp1HuAYNIg91e8jRlD8UyK9fR3Pv2JR0wI0y8kIE2wQGEoYmysY9jJbEGRxPsvtk1N9eWDZ4kP6JcCSRL7Ef+5hiymOsNkunQRMCFQoZoak3PRG1LN6rMYKbUMAbJDerz8G83IyJgzU0M+aSb1PuWvX06Jt6OAmfc/djdBwN6LWE2zbUPZj5VEZr7FBklmup9zZW7F92UfQasdO99MCgoJLNlKoCSLyWVmU9TX3Nw03HLzBnR1980I0l2qvtmMW3Vo7DPpZ6VqZy/D9Ha+cWTy8LZtax1QKJWxnP2OBd5iC1zer/oV6LxAnurFWwiILDVCZQEyVgyNfIaSTZ2D3JwMNNCpM7o+EcWFCeXhxhgxQw21QRfVIydTn7lfFG3Ff0epYxQhYyhK8EavZlNQfyZBi6o6vDnsxo+YnWvPgZPUv6FnNkORM2o0hlRvbFNdBqTh/yXFOVhWV4ycKAWZR8dizCL1mxR5blY2M11PErocSHqgQeoeBRevwp5gIX7+xlm0MF1lYgkEuFLIC1wMsYv6fDGwi/WY8U43QeK19mfjfsdOzkrnSi8dBetjlNTaCI4z/vn02dbJyux6bRDK2DW2jfrGMTwxwqhsEvpTOKiZOYXIzpMOghxgOt8Uz3R7ClFQUo2MLCcXfar75Ox3LOBYwLHA9WCBsfEImaQEMjhXeLhAozlEYMUQs5S18/e8k9sAt/6JCIqy0tCQ7UEm5zbNVWleJR8QmhSkzh/DlCdBlMSRcXLgnCodIQFDIb2audJmD+l9/Bw3hbjUMJSsgvRUzYMXlbh9W6vz8PgNTCZRGsY4F27Od4fx1KkYKMTq6gkIVRZwniJDo4Os1k66AV+40YNffDQNj6wQQiT3KX5pRZpIssb0MDLLydLEnqQ/6qBZoQoy+UMA5dQY6hoOM5QtjOpcF4YJ/LzSA1C+CSGhSkSlOqJetIfTMEzgKi/LjUJPyITxZWddCgi7yBrX1Y7de44Y0GPt6kUmxEQshjyGQt20bQNuuGGV6auAkQsMz9eD8HyKgIjLBTMS29VDtZgqdh8Tj1/q81z7ZLNWBKTNtQhYjR+/dF0EMF0vpY6SC2I11bJf8ykKOayiSPflFN3PRoZMCUBLVsTcOnO+bc4Aie7XMz97EU28NrHYDJpNG1ckHrrk58v9/tkNzKYejX33G4eZGe3UJftln6Bxv7H78CQYprFu2bzaYQrN2oLOiY4Frq0F3nJgSMPNL6lhWNlaZp8qMjTZ4MQQJsYGU1pCoEOhdwINpfYaoRwuy2G0QAo5lNOdMCN4SafSgDUCYOR08kdOAI7EJiMxoEhAE5WhESkpxwFmWfntzlM4efrCDOCQlg/VPOtWPTHnNWoDQzyYRbXlreuXYlFZNkPFBoyza7OLDJAU65f9OsYMXkO+EJ1hro4WlWN3dwiHz/VOs4eyfikDgVLBG6FD9dsANFZfbLAmpRFjB0x2lhgFKEQbBFmfKVy1dKcV4GBTmG2PGHZXTCN0qkpzqeWsjkz40NTdybC4MKnYqVc8VI/Xy5UmXhcMcGV5guEFTElfUrVoql7nnWMBxwJXxQJtbW3Yu3cvV2U7rkr9TqX//S2gaSaXYE8eU6SXMytZFVktdQVeZifzctohu4a6PZ/aWIKvP1iHx+9txIZV5QhmZONQFwVWvSGsaQwxZb2fwBDBIbOoM2UzzdMmaxkbMYwiM6+LMWRtFpPImsc1leuMxNJO7Z0uhny18VUhYJMlzieoL8zHw0vSsS5/EL7BQZwj2+lHDB/7Df8s/ArJJnu3vjgNN9aloXXUjZ3tTGzB/b87D3xjN3CEYI2mvqyMKBk7ZnnFgEJKfjE191r9nwyRS+xo7LPC32WLBWVhlJJR1dwXMP32kI5VmuNCQZYl021AKLZZlefGkoo0FOQyIxo/i+krvSdPWily8y6fDZOim1d1d/zDo1gsWVlZkw+OiYyaivJi6j9eXyLbiX28HGMZVs8lgBGbtTKXB/RkfZKmi8cIq18fRSCZ7r8NXrVzQdQWh55ND+cKsqWqs5qZgxfMwOK60NSOdgpXz7bYIWSvUEA8WdaxxO/8bOu1z9P3b+sNay75vblUvarnoQdvx9YtK1OeqjE8+4uXZw3OCvDd8fqbk/WJLTQfACxlh5wDjgUcC1xRC8yfc3kFuyGwICungI5NJhfOJuj+UASSLJiZisSrXV493Axxk4dogSMCetzUEDLO2WSxHEkbDOIiHH0sOmP0sozGEP8zLhzf65WEdbgLSw2T51BvH5afbEFFaT4dQAopJ+Zat640q5kG2JFapKnLAorUDSHxcuBG+/oxFshAYVWFAYZUzKqmaZzn6y23MFODhZkG0oBG7FvbhBdd/ukYnp8Kmdp6mUu3Z4ypgOksWuwdAU0yR6xeU3nqovbUisCqMV8Q42RspVMfwvSHAFxWdjHTpGab9PNuOsZWG+rv1DZMplBTVwdaB/pRUVKG6tKZ4/Pl3Ho8FiOpkFnqqhpXO2yh1LfIOeJY4LItIEDomWeewQ9+8APU1tbiS1/6Eqqqqi67XqeCd54F8ouXw5W5FIHxTtQUp6Mq10ddOjfev7kUjVX5ZN6M48CFIaTVZKMi0w1f7zBCIz74CpXSPQ015VEycThvUZDaxTT2gj3szGSaVzQ9i/sSimkRKTlEWILMCicTiMIT4kEhfbYmJOsBV8eqyBgqJ2B1diCKs3IR4guPw4mlKAAAIABJREFUb6vNxJYFacw81IdznUH88DTwXIcHAfkDsXCx3V1MRsH1mMpsl6mvmeFa7Qwte40EWpGL1ZG09EJwnYMllp6c86YdIqfuc5qLLbsk9MH+yM66aQPmaUZNaQiluX609PoNqOVKpz9CH+APFrqwrcqNs2RqZdFWNzewj1levNzjwQL2y83O+NhKQ3Ut8hXm9zYs8Q+PmzevxLq1U6Hzetj/1Kcew1133WgWlBqYueh6E6lVHxfUVJoH88TMS3O9HQKGNq5fQc2Ywyl1Y+arN1TJULoqbtdzUcarzZtWkBnWaS3a6g/6GhdJKcz0HbtU5rnE7ra19RimUGL2Mfu8xO984vWX+qzv3w1k4Yi1lkpf6VJ16Ljq2cZwtjaGbabSj5qLzpLDFpqN1Z1zHAtcXxa4LoAhmaSwopHZypaiv13iehY9eyZT5aUHsKCErBa5XQak4D/auG5n0qJcBIgIuCFiIqZQhGuSZpWO19PREDhkqolhL8a9pKPryqMY9dgYfvzqGUN5f9cta1FWxkxlF4FDFpmG64VsVaiM1Xz8dLaA9NaVjRU4d6AFo/39zLpGsWidaHfbvAqgiWKU4MzwhFhN6rNWISl82T2I7t4hlMdAl7HxMRw4fA7Hz/fTabbCv3SimUNjjCQD7sxkRB6zoDBr/GNsV1thNtOVyghmJZeaT+lavZNdTdVWv80JBLsICl3o6jTp7X10YiXcmJ/LfPQpin98iIywQUvXiZ31ZhQiM3sWGk4p6pvvbj0o79mzB2+88QZaW1tx4cIFs6ncdtttjPXeihtvvBGbNm2abxPOdVfAArt37zb3ab6lpqaGztIN72gQ5Gtf+xr+7u/+DkNDQ4b5WFRUZJiGTnEsMB8LuNxphhUqxmcBgZ8qhjydHGJ4RX8Ar7b24PfM8f7BdaXYtLAYO07140fHhtAe8eAAdXGO9ruwkswYgSBvtnKxgQtAbiY7CEvIOa5YujvaYc2JYuIo45gJ3qKunYuMHhfnvVKyacqzxTGaoh1tpWD0xgXMhDbGcDelMosVvRNYs70hB/cviaLU3Y8eLqy0jqUjStHsMqaJb+cii0GeWAJsL8BqLzCEbFtVFNVc79ilKBAzyfIc+Smm35x3vZmIilUbP6HzvTUOTZzJZ2KJTosttLFhBGsbJnC8M4wDFOvWX+dSAk5NJCUc4usdjVHcnksNIjYxykSk/3nKjb3dHnx2jQdraigAPlAIX6gw1h/T/bdNiX94TBVmogxKy5c1GrPP9MD+Vg76/vfeilUrFhomycLG5BqLs+mfxnfTTWtRVU29yxkybM1Hb+hSgMds+ne1zxFr5fHPvA/vfffN9LXTrqtQt/mOPVFjJ76eVN/5ubZ1pVhrdkjZTCLeYqy9QSbQhvXLUVGeOnTPYQtdfBfFHjvIbIsHDp0w5ASFP6pspJ6arZ2m30SBdPPVUru41el7bJF/ZXzU39nVaudS/XCOX58WuG6AofTMHGTlFhrQJegfwXBfMwQiZGQnZ59UM/Qs/2g/lpT042SfGC5KZ0svTmFVcYyhirJCVDKzBULn4GLolULG5F3IjZRLp1cLA5ETZ60Euo0TJ+YRvbDSSrQx89lLb7ahrCgX229gprJC1hdXBOZY4Whc1aQuj3EOWUyIV+y9spzdsHYhDp9px5FT57BgxXJ4Kd5n/Eida4AtpvsNhNE/GuDKQixEjE5omOBUeW4pSouV1wsY4+fRkVG8ea4Ph5qo9J9Twksp+qllTAJeRmNJoFKsTnNRQtGPf2ZGpkXX1nk8f3jch96hERQTGEqngKgFXMXqkYsroMmATdZ/cnb7hofQQiaUjyuedywKoSyXAqQFyWOz1YWJ0R74uUnQOhiicCYZSdl5VPa8hkVgwz/+4z9SSO8cHntMK5F3mQdlAUMCjHT8z//8z7FmzRr85V/+JR544IFr2DunKdsCuhdNTU04efIkXnvtNZw6NRXXvmzZMtTX119kLJ0ff95nP/vZdzy494lPfILgbjq+8pWv4MyZMxfZzNnhWGAuFsijxt+CujU40XuIWb8mqI2Tjt9QfPrZI/0Ikm6zua4A960vw0kyh757oB+Fpbm4hwDN3rYx7CM7Z80qN9YtDGLvuQn0TDC5gcLJmGHLLmLcaG5mAmPDGhJ4ormRgdPcy1maH1sHyKIdzmAWLyWPiF3Jc5TxrJui0Ic77UUSu1bNsi5sLnfhM5tzsbWxAIFhsmO9Y1iQ5scypqpv4kNoh0m+YC1M1ZFxtKVSYd9A74QbvZIaZNiY8TG0k11Jdw2TA2X1Ty25NPfyWDxbyO5Bslfjs5AxVEcWVXlxGPuagmhhFD2j9HDPMuozlaXhMFlLT50Sj5pgVYYH5yfSwehurCqIYGlOGC2d1CJyl6M6/+3PFlJIjXyTZEUPStdT0cNVZ5eldVRVWWoekJcsqcfixbWXDdApycojD92FttZu/PMTT6Uc9lzYGykreYsPJLNjY0MNGuqrTc8s8PUt7uRlNJ/ImkmsaqbvfOK5M32+kqw1OzRt9+6jSRlwYky9zvCwrVtW493v2p60W4njvlIAWNLG3gY7ZY9//e4z2LXnKBdVQgQ8i83zlDIuqthZFyurSrBz52E8/NDtVw2w+TkF/f/fr/2QU1kYu3Ydxh9/7gO45+5tbwMrOl28FhZIPgNfi5YT2sjIKkB2bgkFLZnuNuCnr+Tjw3rqcLLcomoUM137smo3gSE6aXLU6GQJYHHboAjbkL5PdiZDuSjuaNQqbeCIq47yRxXHbzKT8Vyu3U2CRaZ7WhFUZrSCEhzqboXnpeMmg8M2ZirLzs6eHIG1vsjmRaoxjmHsUMx/tE+sp5jde29ejeEXDmOguxfFNdbENxX2RcePwFXACGFb2kPq89bV9di4ssHoL6mMjo7iwBGyhZqHEOLqbZRi1RGlTJkEgyx7LCgvREVZcmBN9eTl5WHpwhrUVpzHKdowQHBEDCyvQslULF/aQs7sMWmwsf3SFeodGSFTiAOnM12U7zWgUM4MdPYIBUejET8rkbFIzPJkMoTQcPGvSRHo8w//8A/wUaz7f//v/42bb76Zq7DWQ4nRa6K9BUIEAgGjwzIxQdXPK1TU9tNPP40dO3bglltuwec///l3NJPlUmatrKzEgw8+iPe89z34rx/+F/7+7//eABtLly7Fn/zJn+CRRx65qArdw507d+InP/kJXn31VaPD9U4vOTk5uPvuuw3z6noBhvR38NWvfpVhIQ344he/+I4H795O31E9qI0z9fwIBaiJnTAszINypq3v8EWwbUkRPnl7HQEjN147N4A1lVm4oyEXXX0+dPb6yFgTt8dLlqhHSxmcs8gDUjgZf3ejcawhMW0srSGFn3Gi0NzGBZ+QS7whzpNcBOHayeRUK/vpTEFHFwYZ8nWYgAkBokrpH5kwMJ0RZfhaBM8d6kIef/NXMClEMNSPJp8bLw96cWqCUFQcw0g4RD818FpHmbI+l+FvzI1wboStcK5TiFd5hjSAxFwKoHvAZTYUaHK0NpstZGklJbnDHJMZu/SFSgOsa8IKIxthtjQynhqLPVhZ6saaGN7TxHGdYijbqsIgDnCOHpQlCMR56d/k0E+RsPDbseg32g6xud7EkGeypzJMffPJn5pFrs8xfbwekC3w6srch4wMLx599E60UmfnmWdfTtmVy8mSlbLSa3hAYVZPfvsn2L33OL7w+Q/i4x978IracT5Dka5RxwwaQgI51q9bNquqQxRMD3K7FmUz09c//NAdEEOpqspiosyn3dmEpl2KNRTPFrocce359P96u+bXv3kN33jixzjJZD4PPnArPvGHD02GdCoh0kGCRk9+56cm66Jsr+/Lfe+9+aoMQ0DshaY2jI6Respy9Ng5NFHQ3ymOBWwLXDfAkJvU8CwyR7KzGWIV9mN8uJVCzc1kkySPh5YAdVlRHqq4GokolRrprOmh3k3HMZPOVn1pFnWBCszq05Y1i7BldSsdVXpVyuAlqrjOl4fJ3+spX1A7YjiIgCY6qi7+kbqZLSuYX4p9Lc0ofv0EigpysJwrQ9l84FLRqRaQQ+fWMIYs19AShbYRFSt+d+uGJcZt/M/fHEQPVwdLqPdhA0PaL52fER/7SMfTsIgG+5kdIYLqPAusESh0oaUTLzMkTWyhCLOoRTwZpg6LIcRreJ2uT4sGqXEgVzl5EY0xjSFzCqdzETjLI+iVnSEH02IHmassH9eqU/VOfuYKriY8ag/ZOxfVlGFxbRlNm9oxCjCMzD/Rb0LSIrwBWcxQlpU3nYGVvLeXv9cGhfRQKlDojjvumASFVLsNEGm/2CoCIvR6pYqACjGV5AiPEFDbsmULHn744StV/X+7ejx8+NGWyf90T3bt2mWADe0T2FFQkBz0fNe73oVFixZNAqn/7QwzjwHpu21/v+dx+RW9RH+H3/ve9wwA+/rrr5u/Aydk84qa+KpXlle0HEuWrkeQ4YkrOO+uqwriwrAXf7C5HGurszE0OIGVnLN+d3oQXzvXjzxOsuNBN8PJ3DjGBdLV9X6srXej80guNXKCXLhhqFgcMKR5R7OxeKtWpi9rwUWp6ilZjdbxTPT4slBYS1AqO4x0zn8hPqBrYqmm3l4VmURjLWQN8TKrWsFLLrTQBejwe6nPN4wBavns64jiZx3peHPUY7KmJRaRcAVAqdgavQIClJGskqmiixhKp0kyTH3EEOdh029uRltIm9wBy61IrJrHuRBDf2RD3RDWNgZwvCuM/S0TtAMBKQJZ3z5ICI0e2ppKN1bzGW9VWRTrGt14vcmNZo7jrqXpBLciGGHY+YKF61C5ILVg7EWNXyc7tIq+/8BUdq0aavRczgPttRxWkIt4wyNjBpDTw//VKMrS9egjdxqQ4o09x5I2YesN1VCbaK4p7JNWeI13KhvvGNnqA4PD/LtMvRh8LbulPumBPVWZC8tHIJNCAlOVKwmGiunzuc++n5qgD9O39yKd4OJ8iy1E3dralfS7NxNrKJEtdLni2vMdw/VwnWzx05++gOMnzlMHaiWZgHdg6bKGaSGxt926iX7QKjz785fwrSefNiwthaQq++JMoXrzGV9ZaTHuvXc7zjDbtH5T1Ke1q5fMpyrnmv+mFrhugCHZN40qjulZOWRpeOEbakd/G0GYqiXcl1yDJhcdWJBLvRqtmnEdMo2eGAPRcMfaOjx4x0pUkTGjsmX9IjxOxzP6wx14neFXYYVeGeeOfpwBiQjokDUkt1BgkVw9FwWHtEIp0CNCz9JdUIwQnc/nj7fQ0dyPD/AcgUPxxYhb8xwV/WuAmlixmQt6OLuR4FAl+3bkLH9wz3Shze9BfnExM5EFmU0lYNLEm3CwoX7cUBTGfZvqCIDlo5/aRMdOnsNPnj+KF4/0IpiZhwhD7SK0m2lX1wQm4A76sX1pCT5931qsXT5zrPu7b1uH/LxsfOO/dmLPKdrGP4aAPwvedGomxA+O7y2CvVm/NSVAAC5ABpDsWFWQjcr8ADIowhkKjFKUM3nqef/4AAIMJbPD3NLIEEsTK+salPPnz+PYsWO48847GcN/U8oHZd2jW2+91TB7rqQWS0VFBerq6gy4ITaM9KqcMjsLzAXY0LlLliwx91AgRHt7O6Q15JTrwwL6Ozxx4oT5DdDfl9h5Tnl7WSC/sBpD3RUYJ9CTkcmkDFxg6GMmzed2t+GXe9pxaCgM5rKEj5OFmEA3N+RhMefn3Qwn20sdndXUxtm0LMSsl+PoZmiUm3OAWK8Xs4ZkF35PNOlofuVkQ4lA+KlZ1DmaS0ZPDgEapnrnGk0zF0CFwdQxXf3GWp7Eeb1NTCHu1PQo1lBtYQHeTfGeWu8QdjKU7cctXhwcSQ4KqeUFZAptKI/QiQf2dEwteGhRpbioGPk5GThyxoej5+hK8fssdpDALLGXjEchdChZYYc8ysoWmsDmJX6sbPBj/9kQ/AyvlgMS8rqxkmFvAyTXPtfmxi960lB9Moy7aqMYoJPSPkFfh2tFWcxT3z1eiIlQMQHza+PO2aE/nR1xoVRkJ4hBMdeHmEQ2hVkMiAcIk9nuOtmnjFkS6a2l6PTVKrMRA1bbYlz94IfPmWxen/z4w3O+D1er/2/HevX9/uVzr2F3CiBO7JdPffKRaQLpM42zpa0TzVzMTVWuJBiq70tWlhj4l8/Cn813LxVrKJEtNBd7pbLT23W/bGFno1NGthsYfpf4GycCQ35eLm7ZvgkHD50yAuIiFpjnwCtc7Psq8sCDD3Rjy+ZVaKSgv1McC9gWuDaexCzt7aZTYAEFaUwZO46BrhMY7F6J8voN02oIBUcxPtSMTC+BCOoDLKVEze3btuKhO1ajrDgXlQyfykx3cxWCy2qxsn5FDb7+f30IHT3DOHSsGc+80YwdrT7CSZZQskcAUFSqBiwCdLjJSZXQpZvLfwKSXNl5CJDRtO9sPxYfPW/CtMwfrs7VH7EAnRhjyFQjMIrHxPJRWJK0gRS+JYCgsbYcdRQXLMs/h0On2jAYGURLSx8GWvuwoCADm+sLsP6GWmxe3YDqsjz09HRj176T+PHzR5jxZYz2oSOdlmVWKk34GgGhW2vTcf+WRVi/sp5sqTxmF5Me0QiBNoVuXVzSqCOkVYUNKxbgif/1fnR2D6Onf4Qi18M4eGoY7QwvswWwLZOIMSRHl2NiGFkXs5ANUsxW419V1MIYtwn4+4bQdy6KwprtyMi1QuXslseH2jA+0ESbBWh3rbBNOdoX9+7K75GG0PHjx9HY2JgSFLJbbWhowJe//OWUegfz6Z1AoY985CNGy0gMGIkiO+XqWEAPGNu3bzcZuPT35pTrxwIPPfQQ8vPz8dRTTxmgVJ+d8vaygMLJXN4yjPgLGJblw9oFXuzrCeJXbRNmrgwS5HGRYlNfkoV7Fubi4dUlGOP8MEL9vNcpOr2lygKG9p0cw28OZ3KOJTjELRlrSCCLFmw080hvSFOQ3OUg3wgwWrEgA6v7PWg6o3nOhX2tEbKJeJwXGZaPsBYzsQM3sN3lzPwlZs6PLrhxkGRjBjdbB+P+rWf4mUChzjGGiI1bF2sNqZbrHQpRU1lVHMJCspUO+fzweRcgyiQKBhRivwQQpQwh47UeLqq4gz5sqO3HlmUBnO4O4Hu7hnCQeoYbK7348Jo0rCr34GxvFD8468Ibo26co5pRQY+EqUNYXJyG5fkRavsxvXdWNXKugb6QLVj67LMvMWNUl/F9pK2oooeNW7avw/333zojQGTrbOzdd9xc56fWxtiYxJus8i9ffwr/9u8/n/z8R4+/Hx/9yP2Tn6+HN7Z4rMR5k6Uev9J91EPjbPSGfGSav/zyfmY0W55S9+VK9+1y69P34dvffTolCHO59c/neumv/Oip3yXNIKaMcY88chduZOauVFpYiW3q72Sm78n1DIZeSog6GWsokS0kMGQu9kq039v5s34rWlo6zHdJ353amqoZvzfx2k5Xc9y6rzfdtN7o0qZxhSERqLqabTt1X/8WuK6AIY+ACq72y8lIo8hiwNeF3qa9dHrKkFNUQ2HncTKJWjA+eB6+kU74x7pQmjnIlTWu+BEYGR7oQX9vO06fsbzIKXdPq3eWhyhKY8A3Ac9YH7wjQWoWlJI1lElnS5oHlpNjCUHzGtLfFUpmrhZARD0fV0kFOgmY/OS189QuysSmVUJaY+1xFdCANCoWtmReh4eH8dreM/jtrrMoZDaXRqY3Wb+qHpuZknX9ylpsXN1oVjSDJnyM4XCsz0uPVno8IyND2H/wKF7h9S+/2YWWIYZvCRAiWyickW1CsgTWKI1vhEyh4NgQTp84iWMUgzbd4LF45pLVudi/CiHT29irxp3m8WJ8LICB/m7090UYssN0vLYGUGz1c4Sxqec7mZ6+b4DOt8brwurKcZRlDKCndYDhdKOYGGlFdvEy5FduREZOpWERDXYcwnDPMVo5pvsiT9vqwbRuXY0PCglrbm42thAwIxbJTEWTtVgnV7Js27YNmzdvNoJv6WRKXS+hPVdyjNdTXQL3BDzY2lzXU9/eyX3J5O+mNI9uvuVmOiQeo9vmlLefBfLyK8neKSU404u8wlymVx/hg34UKwrTsaA4m1m2CnA/AaHaogz4OKec7x1EtTuEA8MR7G4PYvXadGxZGbZYQ74MhlUlYQ0JBWKRo6KlBCkUuTTPcuo40F2EjRUlWF98HvkEWrTEI4bSmX6mqGcbmtcU8qW4LkWD3VSTgbtrxtHe78d/EmzZT00ghZ81kJAsDSGV1hGGmxH4aaOuUAdBocSieVqT+vqSDKa7L0AWQbE3mzJxuC2HWUzpI8wCFNJkb2sLbVkWxMo6sqjO0n+hnF2QA6tg9Mfe8xG8zLWWzTUe3NFABhRDbGq4YDQSycJzTZyXM6jzFKLOENe+8surCbReXfBbD3tPfvuneHXHQZSVFuEj/+M9eM+9N6Gc9rf1MX77/Bs8fsgARJ/+1KNJhVNLee0iijP39Q9Z9qa4skKIVPTgtKB6ikVbzbTqS5fWJ96Cq/q5pbXT6AZpLMlKG1lCzS1dRjzWRz/yWpW3o97Q7r3HMPC330k6/8qO7dQWsoHBVGncr5V9bbDvl796FS+9sh++iYtZrPp+SqT3wftvm/HhPr7Phl3XmTqM7FqN73LauZQQdSJrKJEttGXz6lnb63L6eT1eK+DcJAViaW/vwcHDJ5nteE1KRl+8ttPVHo/AoRQ6/1e7aaf+69wC1xUwJMaQR99UOnPuNGkAhTDUcxwXDvlQUF5vHqT9vm6MDpJ9M9JLrZoxFKYPMgtKBn765jG8tvtkbFkxZnWjdUPwwwYg5CcScBHQM0FsIpxOfR7Gibs8loplmA6kEb2MgSluAUMGdOFeOpCGQeRm2FNOEUUiudK564yJBT15vo+rZxncLPFi07p+C/ij0Nk1gNfG+vHqgWZ0+zwYZ/a11jPj+M2eHcjN2IWVi4kiV+QzC5jXaBcprGJgiJ4pr29nevpD1GfoHAqQYi76vIsrqgyayy5CKCPHrJwaZIiOpva/0ebHkfMn4QmM83IeNMdjnYk506ZvxruNAUYxx9t6oZgoM5Ll8MFN9yCT4JN1PV9UHTfdowEysZp7e+EzcfVRbK4OoiJvAl7XGIGsKCb8J5HT147iijYCeecMMKSY7aHOE3xA6KZTZYmeqSu+sUH4RgdNRrqrWRS6VVVVZZo4evSo0TZROJm9L1nbAoeuZJlLONSVbPedWpetUfROHf/1PG7nb+F6vjuz65vbW4r8gkpEx85gYX4Ya8qYNp3PyncsK8KntgusyEIvWai/erkNeymK008QMDs3Gwu5aPF6axCbmfFr4xKmal/ow68PZRtgyGINTQ8ttpk3k+AQ5yEXtQRbhrLQMZqDWwmerCmjgCc1ek4wRX2IixVRgiaGkGoEBF24sbYAH1/tNYsW3z8ZwV6GwN1Q7UIVAaFuTkeKXnpwEcMa6Qp841AawaHY/JnCFLlZWQSFgswyykQQTdlcYGISBYFCnA+toPQUF3K3zRbaXE/fhWFkx9sD+P92jlhsITKaVnBrH+CcHoyiNDuK1WVheAUEUdz7ewwnO0YB7M80uLG9zo0TTT54GIaeP0PCh9Q9md0RgUJPfOspvEQ2yuaNy/HZTz9GPYw1Rr9EK83Sx/ByUe/r33zKaFYIVKmoLEmqdyOw57Ofeh/DnSyW4LcIwthZt/Tg/RnWvX7tUtMxF78vmZehkTK70U0/SwyIEyeZ2fJ0c9LLL8X+SHrRFdr5dtMbEmDQ1PT/s/ce0G1cZ9rwCxLsvfcmkRRFiaJ6c5XcZEtusmOnOE5xNutkN2WTzcnZL1/+k93N+TY5m7MpTl0njhOXuMQ1jotcZMmyZMmS1XujWMTeK0AC+J/nDi44hAAQpEhZsnFtCIOZO7e8c4dz7zPP+7y+X8B9mHbc+cFhaQOzvbGpVUWE8gb7vEEqjstbb75aboIY8Ay43DDCcLDJDAwEe87Fls8MVtDFyTuZWUN0Jd2+Y7+HbUW20KKFs71P+Vj+9sWu8mUIAnGF+LTiRcp4SQOau/ceUWM5F14o2VnpsnBBhU9gXpen3YF378F6EevjGwDyj+cGPNm6WGeg+njslde2ym5ozQXbfl92MZfD4xMt63z656s9l/K+iwoYMkAcgC/qTR9duPCW0NkjvZ3HAQg1AiwKh3DvIDRw+iH2B4FmTABjY6GvQ+FHiCe3YzKnhCGRtMA0y9JikSrsJXz4lTgyt6FNRJBEsXwUeMQTsUvNKelaZoTNpeClha8oyc6hSxlAmZHUXAhptsiJtlZEYLFIbBJcuoAMmxlDienp8kFjF1hPNWDWhEt22UzkS1KsFTvo020dXbLxdLfYEWEManFoEqaUqN9hvJYE0MRIYUCcGXkMTB44D4MlFI+3tJjgupEaw9WL4BBCwbvAhnJgggqwS7t8cVqsgCDkN85hH9kRUvJ5lAKd/G3koY3joTkUCYDODIwY1kEkGrS7HYKjgxSdVjvxhjMOf8BcNtD1h1GWBYDdALpjl0G4mUU11mAxANAHNu/v6UBUmm5UDX4WzB8OkM6BfCNudhNLm67EvjC8eWlpqdL4eeutt1RksJD483RZPFTux9ECDzzwgALw77///o9j9z9WfSZjKDF9LkCMAwAw2mRZUbTs7bTL0cZ+2b73LCJ4OeRFhLFvG7ZIGtyHL8+Lk+tmJ8t2iP08vN8mOwAOVS3ApHSZHS9aeuRAE9nCbtaQFyhvBofoVsaXsIwm2twXI+0IeT8ryyHzW8PlDJg2/YRmjEebep4zLQKhxoLnzqMHh2VHmyFgvbMZDFls54Hpc2spwBc8k9oQba0gwQVgyD0f8HFFF2ZEyadmR0pp/IDsRkCLISsKh5u54har56iPk9y7GH0tDME1LGA4LyoblMqCQXn4nUF594xDhvFQrEy3yuoSvCArtUgrdIQaW13yINhNh6BDOASgqA2gWzZlAAAgAElEQVTP8rnJTqlKtsBzG0Bb2nyITs/F45Uo2NQnMyiUDbCHbjR0QTC70Wi3hF2Y2BMYChRC3ayBwol4Z5fBHGLLCwqyhWHKE6C18WEm5fbjdpH7MNvhXXcwmi8852LRG7oY7UhG2FN/fR3zTyuYHCPKFdIfSHUnIsLp6FFRCMqigVDv6/Jx+D2eELWOjMcX0u8ijD3Txz08va9xEUwEQd7nX/rSHWr9Z+hFnVuSBkHo1juC9V56erIaxwS1CRBFwjPmcjA37/vibR6ASJ/zElwlyXp0QPaE2rfLl86Rqqoyv8CQua7TNY0A/KG3ixf9b7y1Xd1HvurSLWY0tt8/9LzP+rj+JCDEPlAI225HsCRoFbLMmTNyFTvvumtXnNt5rz3aPfm99w9KelqyZGYkI5Jja1DtY1HB9O+uu65V0RLHA8/GbewlkuGiAoacEGJ0IEy9Z16lXJcAjmDfcL9B23UB0FBAhjtXOBYhmQl2qcjql33NaQbAQ8YRUSW6fmGCSRcwfvh2i8CHBSKN3K+O478w3FjKi4yhRPjBgHVCfJqMId6cBJaUpgIZRARvCDZFxootJUfA7kb9eGig3HCAN85hFjSaIuOTJK2kVIEokbGgm+MQwaoohLu3IEpL30gsdIsywSEHdRU3qYtR09g/tNFFMArNoSinEyCWYRdOernTAKG4TUCKLmjcR92hkTCAZe7fY78N2zkUMER2k7scimwzVDv2FyIyXHZ2jnrR6uIMHElHPOsBKHS68aw0tHe4zW+0KCe2B5Fh3BM87uLEHcDRkKMPIN4ANAS6lZ0dENtke7hNII5amT0tJ6Wz6RRcEaZfiJmuXFdddZUChvbu3Sv33XefCuH9YYSNp2sbXZwCMZbM4yjY7ekqN9j6L5Z8lxJAQYFsApeXemQuRvv74x//KJ/5zGeCHgYXYryyDmq85eXlTfn9FnRHP4IZ+UxMTpsrPW1zxNa1SSoQqn1BhkXea+RLAZv04pl12qaCzksfwkI0wX2sP6JHVuRFQ4w6Xt5r6pelAD6ugjvVB0cH5UAdABPrINyZ4RoOBo5ZiJrm0+CQgkD4jMGzZndTGsSh0+WKwkGZDwbS/lZ8evl44RLFeH4tTsejEVHIHtkfJtvxwptRzSDhJ0vAGGqAfhAZQyAoq5Qd55JlOU6AL9QSssgrdecCLokJOdAyjIV49EnZU5soB1tSJQyh68cDhfh8DsfzPQyM2SUz+mRxuUMONwIggwC3Hc/D/GiL1DQ75bd9DilH/cuKwHQqD5MZ8LRa0OWQzS3hAu8buMRFyMLcMDkK0C2uYI7kFc4zGj/F/5pBIYINK5ZVKeFUX9oq3FdYmCMFiCpGFjUXQHWIZhQoMUx5PVzJdJpKEd5A9QY6RnbI/V++Q66/bqXPbHQLouscdWj8RQnzeeIU7RxP80VXczHoDWlghe6G3knbkRG73kdo9QtlS4JVI9BiAnzm3aRzfne0d4H9bpfkZDDnL5LERewf/vi8vPTSOxNu0VJEvTIDBRMpQIOSO+Ee6Ota0a4bN+1SRWrGVYgthOcJ2Dtkmp1GeHjaTduJc//7//FOv6ye2Bh4bfhJZrfetTdeJp8H+zIP4v9M1G773YN/VXW9umGbihyt6yEImpQYJ5evmC/brfs915F51FrQR+J4+98/PAs9yDegHVcmP/3Jt2QBNMyYdn1wSF5+ZYu8jzHhXReP81y7bdhnfRTu/9vLm1W5dE2umjtTPQv47OC9efDQKfnFL59Q9QQChwg8/ea3fwXhwCbf+san4d4MeQISDjA5eOnlTZ4Ib2xfRmbKOSxWsy199e9B9J22/NMjL6kohR8XYf+LBhiy9bdCl6ZRuYfZ4QplJM7WOGAJaHAP0Qo1J3SDJEauTExIZ2QOSVnqkBztMMSkFUuGsWIRwsRC4MQNMjkApETgY4SRNcAVFm1M/9w3B6vhB8gtxS7DFGOI5xAYIkBEcAjFIzytA7TScLhfxYDqTcBDRyUzWmY0NAqR1nTb3V1QA3doxCU2zm7JYILmjAtuaq7IGAWesH4F9PCb4A82DMYPQZ2xv8lsYts8+WkhNJBTY1qL4X75P6tiYlZiQ9zHOC8sj2Uk4q1IbFyMWMlQYpmqapbiUkyh01hc1ba3j2ELXZXfLKVpnaDVu2P6uifYhvseymcfAHq5d3uIWWxHZKRTBnrrpKX2kGQWVEh0nO/w40arz//f4uJi+dSnPiWMikTGEKO8MXx8DRhd3/rWtya8MOeC8/nnn1dh1JmoXcQ/+HRR4yJ0+fLlY8pkfgJRFN1l+sY3vuF3oarzbt++XUVH0+XecccdPtup2/L444+raFxf//rXVdnebaQYs78yvC1sbkN9ff24/dPn64U4265tw3p92cS7zqn4zfqPHj0qJSUlfovzZ19/bdR9oh30Nfn0pz99DjPGOx/z33XXXXLzzTef0xYCQj//+c+xiDqlhM4JDJmvl3k8ne81y83NVS6UwSbvdvDeYRsomO4NZuq8Dz/8MBZNe5TdqSNkTr7uBd47HK+04z/90z/5bZp3WwLdY+ZC9DV+4gljgsF73PuemIyN/Tb0Y3iA0ckS06ug23NQ0mKaIJzskv09FtmN9zh8gqjHNhL1cT5ZkSi3VKZIbCSZPsOys0HkvVq7VIOBc/NKB1ynu+SNg3iRA0CJ/40wWqWbAaxN6w0O1XXHyd6WdKnMaJNyuJPNz3LJ6X6EcFcnwM0ZQNV8vHPYAwBqJ4ScwctVz6DCRJeUAHxpQTvroCtEkemFAJaaAAbtbcVLIZxt143XleN7SXa0fA4R1WbFdcvOD4ZkT0OuDEckGs9fUz5fm4xCFoa5zYL8dvnctb1gCw3JQ2/3y446p+THhsk98yJkZmKYbDgj8lSTVTZ1h8k1GcNyRWm4ZCLsWk+XRWal2aUqhSHq4WaVWi05+dWYCE/PNI4RwwYGhtRCj2//b7n5KvwNx0ssPyk/LwPiqgYwxAUQw1wHCrfsHRL8YhDhpeZZAtwd01J9z0WSkxBdb2ahLAZA9lu4zj3/4iY/1pi+3WRv3P/lOxXbJVD9wbATpq+VAjfASElOSfBpS21HvuR94W8bVTh3Y1E4nS0ydKwI/M2rKh8X4NsC5gv17wIt4Ke3teeWzmve1zuAQDF4OTvB1N3dC6DAPU+f4LnMTlBy2dK5sgyuYr7AIbMLXogtZBjYF8uPdnpz4/twuTt4DqtnvMtiButvuflK5XpbVJTrEY++4vIF6m+uvp9Yz2KGo8d4J0i19sYrFbEiBy5nwdxzBMCfeOI1WVBdrlyIzSLidCFm3Ra4GT/z3EbVJ10X+5GRniprANR417dt+wHZs+8E3N3K5Rc//Y4CmnyBOWQ/bd2+T+bBtdgXU4e2ePbZNyUJEiz/+i/3YL1VPcbN0xzhzR+LlXpYqt1uF2nv/tFFWsRwkb7UhP3HG0uBjk/PjCJQjX6O2fqbpKfjlAwN9ijRZSMZSIYCSUzJ66cK2VqYYpPq3B4AQ1yQAIzAf9QGAhwCUAisIGxrAIhvMI1tuoqBiaMAErccAXESJTrtRk/w9tJBYEiJTzMjwSH8RtEEP4bsYRDQc0gcKHDqHaVboNnTRnNjTR0ZgGhQ/xCQWqI13E8ARn3Qcp7jbpQZJBpl/7hBIoJFGuUhAEO0RwE+gHtw8xvMILYZ20TWWLb7m+WrunkCadP4PQh9ozaIQvJWsIHWNzA4CEsZfSVbqLkTekDa7cvdlxnJPZIaBzFvPZJYJGzDw7QRtowd3PRKVism6mCJtdYfkLazVZJfttg7y5T+5sSToAlFqPkhc2hoaEiefPJJeeONN+Bne4MCa8ZjbuiFKhe1PJ+LZQIKdFMjiPPTn/5UMUC++tWvws81Vy1GCQIQhHr99delG654a9aswcTO90Na5yVgwMX49773PUyy64WMjD/84Q/ntJP7f/KTnygW1ADc99h+aig9++yz8vbbb6v62E4muvmwjC996Ut+mVLm/pFqyv7NmjUrYP/0heK5P/vZz2TLli2q7Z/73OdU2wmIvfrqq/LOO+/Id77znXFtfD4XnteWYJ8/YEjbi33TbWS7afdf/OIXEFyPO8fGLJPX9c0331RRBnmur2ha3vl4jb3HkwaEOObaAbSWl5er8firX/1KASW8jrxeBjNSFBDFsRDIbt7XjP3iNSPwwTH51FNPecoLZFtdDs/JyIDoP2zR0tKixtPf//53+fKXv+wBHVmOHqsvvfSSGnv8G/bCCy/Ia6+9Nqaa//qv/1J20H3X45LnrF+/3meTvO8z9unqq69W4Jm+VuvWrfNpF+/7bfbs2Qqk5DicrI19NvJjvtPDGkqdIyNdbYhO6ZB5cHV6tSlcqlMjJBds2vKceFkHQCgN3tAHTrbLjjN9cAGH8DTAjhfODEpM5IjcOy9MbrncJi3dXbLvLJ7KeFniwofaed5JvaRRsBOe43g272/NkEWIkLki94xcNzNc+u0Wef2sU7oRZRSPZTmENdROAEPgq+KhxGe0Ea2sCVJ+LCcHLCGyg147HS71+GZgUUYkoxC1ORUkRMj6yiRZlAF28r42eXRTKnSBEBIVTJ/xUjiem9T+y4npRuTQfllQBlDpZK9sP2MTO14mVKZZpQfg1HMtLugkQWMIbW8B+HMc7nX1cAtIwPknzjpk/bwYxRbqgcp2btGiaWULmaNFMWgHF8mBoteEkalNH3F3Gi/cMtkiXJwwkanDhcvFnrjQ46d0RoGsXFEtu9zR1S5ku1k/Xe7W374aTMhWn4t0tkezE/Jh2yywdnwtri5ku811aTtyX0lxrgdQnO72aOCvYlbJuACfXsDnAvCcrP04pnPgghkoncU1DASgep+7cNFs5TLD88jWGA9Q4721ZFEl5nFzVT/OJ5ExeNutq8YFFUJsoVErE1BjVEGuyX7zu2fU9eLY6oDovmba8NqMpwvEEglkaK2323EdzKAQj3szClkPGUFM5nsuOhrBHkx/q0dbO7pF4GXbtr1KiN1XZDnWxTD3y5ZWYT52UPWL+RdjrBGI8nePs0233LxUvowABTzfzED9xB03qGeMtlMtNMoo2u39t0sDZJu37AGTda7Excedo/3lLZpOF7O0lFEtW5ah9bD89Y9u02fBEuVz6sMG2n1do+nad1EAQ4M9ddLXeQJCxB1gDJEHju4ST3BvEGBw4xBqF7EXdVgn7MtOsciMDDtC1w/KsU4IWWp0ggAIARMs0MJYEAWcQRei4JZRvvHtlhAydAJwxKUAHkxy1BfyYLByEsrKySBS01L87MUErRnUNbwERaQQQipGMjXfs8/TaBy0weVsCOg99XbUfrSTiyojIpr7N4EfdpzHFAA0CggpkEedZ0RpUX10/+Y+ZTA3aGQARNiny1Dfxrkqn/vDCGMNiDTW1tOrwLlh6gghKaAI24ya5rk46O+Vec1SltYl0VYCTm7gjOZkLvc385s2vQ66cDM7xNZbKw3H3pOElExJSi808kzTvwRGyBqiGDXBFDKHuHjn4pcLRop/j7cAJ/DB86hPxEVtYVEh/pgZ1/4Tn/gEEPYn5C9/+Yta3GvwJzMzU4XoJkspUCJo8aMf/UhFL/v+97+vwA22meXQFe6///u/5ZFHHlHt16BTWlqaWsD39yNEDdKDDz6oXGe4aP7ud7/rYUkQKGK7CIi9/PLLEBBdco7GkgZ2fvOb38g999wjX/va18b075vf/KbqH21HVk5dXZ2HQaIX41zs//u//7tcfvnlnrbfcecd8uQTTypwhX0IZONA9uGxI0eOKKYOgRQykcyJQAjbRcBl1apV5xRFF7Mf/OAHCuz6l3/5F9VGMlAIwhDs0X3jWDDbmNeD59HOZMb4S77yeQOACxYsUFG5aC+OO/bn0UcfVePyd7/7HSZvRoQhbU+Otb/+9a8qwpq+5ub6db7jx497xiTHje4XxyRtHqjdLE+Xw3uADJ6ly5aqara8s8VzrxDcW7ZsmWfccFzTJTMG+me6/C984QvQIrl9jIkIfjGZ+87IgNxPUNU76bYQcNLj0NwnAn/6Xti4ceM5QKf3/Ua21J/+9KdJ29i7faHfoxYYwxqKbYJrl0v2QnYOanjymTlJsrgiQ+oRrOHB7c3yyqk+mZkVLzclRkhH75BEI8+meovMARPmmopI+fxIv/zxVYA9TQY45LQgyAImoOckPLYIEPHRfKY9Wt48VSCZcf0yM7VNri+HexiCS7wDDaEP8HHh+aziYJq8wuoxzcBtLw0AYwgenQHLiSkjxiXvd4bJzuYwBRAZD0qj9vVzcmRdBVy1u07Je4fgltaQJ/YgWK4W/O0OHx4CW6hf1l7ZI9ctHkAksz75/bu98n4jBLghNp0FptB2YCR5EPC+s9gibzQBvIqwSCK6Ti2mHkwTilMgsg220PHaYelyVsiS8lnTxhbSCxDNAuCb4GpEUQ2UsiEsnYOPTnQXIPiTk+3bTbyuoQnaE00qOxevFKa+VBIXPQTLxltcTVd/fDERfNXF6/f4E6+qdtIN4mJM3oDihWijGeCjlgmTL/aVP6ZBsG1kPeONkfEAVHNdBJpuWXu1mq9wXvH+zoMe1yFfbSJzh0yPhYvmQCAeUagQmvx8kgEGBAbyQmyhcy2sogrefh3mlBljrpcGH3U0x0DsNDOQUQQwtbBwlClkrtGsB0WQZX71rHMbFMQeHV2Of5sL8nN8uhBzfBvHDabo6dom5UJMYMiczPc4xwdBrRkA171fNNBOMbExnnvG0ADjYnVs0mxW6t6tQ5RAX88mtm0pIuJ95f47hS6QxbBZjinqpe5foPHK8a5BNALtJ043+Oyfd/su9d8+ZlwXrksMPz+A8PO97UcBDNVDYNrMFmI7jAFB3MKT3JiG/u0AwNLXZ5OhAbvkxkXIZcVRcgzuZGQMKRADf0A5eVRzPPWPAW2ost2AiIJ58IbOxbC22KfcxziLdHAfgRfkwA2m3MjUW0yCQigU/5OJ04LQuAlQrozHRwE7Oo3ZNH7YwdLpGhwBZd0ufYN4h0kGD5Ii8qj24Afq19seYAjHXG5mjwEejbZf+YcSDFJAEplC+M0C8VuDQop9oCpRFQH40b+pLWSczzz9AKr6sTBUtqHBVHvUP0avTNuz82Ph21oojsEu6e/qgM6SEw8eslIg6hkBuyl0iOcZk24WMPYXJ+cEn/qkrX63nIb7yYzq6+GaUGjUNU3/6nDZWlybC28mPmi5ACe48H/+z/85x/1HgyZklaxevVrpFVVUkAY5+rAlS4NgDBfjkZGRHtCEAMbdd98tJ0+eVKCSr8TFMAGCVESZ4fnmsgkOadYO20nmEV3WCE4RMNIucgR9KKpN4OSyyy5TwAzbxzIJanAxrjWWuLj2TnRzY//Yt09+8pPn9I/5yQIiSMH+caGvE9lAZNMQcCEzi3Uz8Tsa/7F+bWO2zZtJ490Wf795T6RD2J328AaGeG0JihFw8U60L1k6BOfYPwJHuo06LwEbskpoI4JIFCwnSGK2P/P6AzR0PpZN1hTL8U7MQ/uQlcPj7A8BRjLM1Ft593i6/vrrVT80u80biGO57BMBEgKKBONoe3MZzKPHJLc1eOPdJnM5P/zhD8dcP20TjlubzaYANJ20iyBtrvtL21x33XVjqiBIxcS+c6xy7P3Hf/yH6qv5/tEncSzxXuR95mscsk8EjGgbtssb6GS76DrGtrLPwdrYDOZ62yj027cFfLGGqpMd0BByyt76ATnTXCN/Odwtp6A3NITnX3RzjzwJLZ35M5LlU+Xh8uLRHvn9fi5ahuXKOWHS1D4gLW9GKsZMBMbNsAXu1X7cpZRrGUCT7TV4E+golburnFKW0SHrMS8dALKzE9G91FMH4JA51UASLz/JAlFqAEHNFjkDdhBz0K2MbKFMRAN7D+CMTrfNjJGbiu3i7OuQVzfb5bWjM8UeDyBj9A2IT+NQbDoCrFgFCi3qkbXLbXLk7KD8fnO3bK11SRZcyNYUWeXK0gjZB1bTMQBUsXBLX5HmlE7MP+bghddOzDdeQhs/WWBVbKGufqsU5a2A6PT0aAuxI7xvNCgUaOJs7nQL3Ftamts9u/xN6nUGzjeUQDGS0n8c5w22ua7Q9rnsAH82MesNVc0NDO75K2M693sDitNZl3fZXDySfaXZX77YN2QK/OrXT6kxGkjvxLts/TsnJ9OjveUrz3gAqvkcMwuD++k6tGfvUb+sMYKXZFPEQyJiqtJ4QB7rNLNApqreS70cgh68Xrlwx90J5o/WKePfWQ0QBXIvIxii2T+B9Ng0aFwNFyw+wWIC6BX5syn1gerqGlW7xgPtc3OzJA/urQIXsTNk+IDJFiiNxz4lw46aSWcAMtU3timtIu/0Adg+O6D9k5ebqSJX+htvBMnW33at3Lz2KjXPpIA8kxlkG2+8LqiuAAtqtmpPMP3zbuul+PtDA4bsA20QrTysgKH+3iYAQ2chVAVu95hkAAuceyk8wp0YYr6/1wZf2yGxRiZIVtFSScmcgdkFxJ+BDC5rbZPtdVCFVqwfAiAGHMEoJmEQXVZ3C3ZrRx4tY0CASP0P4WkycLjfgm1GIlMNYDlKlRk50Sg1J8Q/NpTVC0G7oWhMZL0moObu9MB97GwvwtEjrx0RRgwvMrZN12t8m4Ehhc24gSINCCk9IbSHDnPEeUZBIWO/ZgaNMoVYiMpodENVbOwzNIZUJUZT+a223W3hXuMyGMe5jR3Xlo3IktlpsnjZcklILUTkuBbpaDwmZ0/tlJ5WhKiPdmGBCpYW7MXS1GnYMLAivcEDjD4HgMzRKu11H4jD1iXpuaDKFi+UuGQjvLxR8dT+ywUqF+9kP3BBSzcvzR6i685//ud/qgrN2jBchB4+fFiBCtSEISDjvajlbwIglZWV6nx9nN8zZ85UH3/AEBfDdMOhm1lbWxvCN+72dFpr22gXHbobaWBHAS8ARHRdZWVlyu3LrPPCYzNmzFAL/k2bNilAggAYF/RaM4bgANkX7B/BLYI33v1jgzSwxm0NrPDczZs3qzLJzkhC9D2d2HaCauz3sWPHMLxcyjXKXLcncxAbBB7+7d/+TchC0kwtfZoDwCaZST/+8Y/HlKSBD7pDEbQgqOYNCvGE4uLiMTZ66KGHFAjGcUBbmK+nL9uwDO5n2f6OMw+P6/rZH46LWAjSmxOPmwEk2owMLbKddOKYIShEAIWAoHcZuj3c7637Y67LXI63bdgOjh2OKbKbeC110jYx95cgkC/b6nPYDjKM/CU9lggisl++xiHr1bbhuOL9wD7wfuZ45nFzn3nNfdnH28YcvwRP8/Pz/TUvtN+HBcgayiuaLw22I5LmalCsoX3QxHnhcLtEAwCoQaCFATdj58QQdNjy42TV7BQpSwCojsfBnw/3yXNHnIhwGSk3XzECtk6XPLYRzB94wOI9A3R8AA75Yg652zKMZ/v22jTJSSiUzHi7zMvtk9vwgO87DNcsiDerhxCfa+pBBDcbPMO3ncULDKhOD2N7BMdq3KwhzR4yThK5Y1aC/OPiBMkM75H39/TIy4dy5YwdE2FSjQIkC1i3EYiiSl2hxcXdcssKbOMlyPM7u2VLLRiKWJRSW+E47JQN/aO5aRYpTXLKK6dFXmyKlDm5kRINqtCx7hGpTglTbKHGDrxYclTI7AS+kJie6Rsn4hQH1mm8ibPO560ZFMA0RvhiUx1cLJ2vm0ug+qbjGIVOr77ScH+Phc7kh5EmqjfkT2T2w2i7rpNMsf/ve1+W737nCxJIdHe62qiZBUsRnclfOHbqnTwDPROOUW82xHjtMmtv+cp7BqK7XExrUV9fefzt44I4IgAL6FK8r/z19aOwn9eLbqjFYPsUEExx69ewbwRhzO5l3uwhs+utmne5X7b5sgvrSTyP6I7mwAAEYO7/p/+HF8G+nzcjJBT0GbrAwWjL+WqveZ8ZeHTi5Y4n0rc7kxm0ystLh5uZfxdkDaQSlDMnM8hGzaP9B34YdP8UqeIjnnxf6Wnq9Aiiiw0CCBrsQWi6oU6x9bdLX3eTDPa1QmvGhgHgrbnC2dwoZsFt29Aw3lpHSkruApldslDikzJVOPTImATo3FilratXljFcbi3L4gWknhDeRrnL5p4whKx3Euwx9x5VhWFyqPQLCAbh46DWEAEibCvWECOduBlECuFQYe/5Dbcwe7jSGrKCNaQnn2oy6k79ULJsAJDV0j/sfktmgDRqsmr8P9pR7DP0hLhLu5cxE9rjBoVGWUMGqMN8Ksy9G/BRmkLu34b2kMFCMlzONCjkBoRUHe5tYwaNVrPxnoaNbrs1lBZV5sqipQtkRtVKADtWScstk9wZVTJj3mppObNP6o+8KUM9J2AmXFezIUZNYtqi6tMArm2NdDZ2iL2vGZpDhyU2pUhySpdJckahz7POdycXhQQwyMzQC1kuMmnLnTt3Kn0eugZpwVqyaehKQ+CGi85Ai19foAAXzL72sx/mxTAXp9Q90iwLHucimS4+dJEhg4NMloULF/o0gV6sex/kfgIfJSUlChhimWY3Jy6sCZwE0z9z3wkU0AWL5xLkoN3IhtGJdfDtMNkm11xzjdpNNzb2ZTKJ/SD4lhDvO1oIAT8teq3Lp+D4oUOHVH8JnNF+vhLLJhCho9cNQmdL6zP5yj8V+wi6eDOfdLn6evG3tqM+Zh4zgfoUTBvJMLv33nsVC8wfuETbsB662E1n0uOQdXCc+bvPuF8DVhzPvHcJ/pBF553Yr0Dl6GPe94R3OaHfvi1A1pA1ZrZYoisQ9adZVuP2ahx0yiPHEQUUz1/1vHWn6wqi5f5laZJscciGrQ1i6R2WamjsbIW+jnWvXe5fECW3AxxyOTvk8U0QiIa7NsIyALyJAYkXD21TWebWDCPQxN8O52HC7JLbK0/Iiny41uLZ/NgRkcOGGvWYxtvxTLcDUNK6gASHVHJ/FcLd7ZZZEMyeFS2ZYRCbBij06OY02dWaK2SPn9AAACAASURBVM5o/8Ami7BAE0iBQrZ+WVTUKV9cMySVhX3y8Ntd8sKhYRmGvRbmWKE5BLe1NovsanPKtZlOua7CIvcsDJd5EJ5u6xxRLKL4GIt8DkyqhblW6cb8Oz1v6bSyhbyjhY0x2gR+BFqUetcR6A34BKq8oFm52PBecLABDz38vDz4++cQwW3yUaCC7QgXPhPRG2qAXscQ9CMvpsQ+xMfHwvV0bOJb/YcQgYuRlr56/yfks/esm7ZmjxeO/Xz0msyMCl8d8LX49ZXP375AjKRL8b7y18+Pyn4NVlC/Zv6CCjDUD4qOfsU+avaQt7aV2fV2um1hBvnprkWNqpxs/wCMuT3U60lHpLHpSuZnx1QwTSfav8m65k2XPaaj3AsKDHXUH5azx9/GghfxYTEptDMCma0XCx536EiCLXpypnrLySTFIo3ddrhgWcIzZOaC1ZI/c4nEJKbijR8il5jSnOqVcra+Ru6sbpO/7sMbeEzyKAttgByGFg4BI77rU5HJkEigoda0TtzU01gKYCq3NDQijMKVmkHEHARJMEll+wYBWA0gtGVs+Nj2sEy6jzUTFOqxy7BiL+FD8IcH1U935XqfZgQpwMbIawBIBIzcgBKBH3WMgA/LGP19Diik8hh53TSl0XJVXbSRO7mbopqJxDm43jaMYpEbKl0ypzRXyqtWKFCIyQofMmtCmsTik5iajQkTgLBD3dCNalaAhkraqMYv9S8FsLlwsECI2uWCEOdIpzi6+2W49aQMHN4NVlfUtAFDuhlcFBJMIEtA6wNxofnrX/9aLTzpSkS2ENkuXDgSVKDWy1QmRd2HnchsIBuJzIdAibT76KiJv6X0BxqZQYaJ9s9sG4Yq/+d//udATVfHzAyncTNPMAPBFLJJCAboRLCNbC8mHg/ECDGDEd4MmQk2JajsvCZmENB8kr/rxTwEu7Sr2nh9Gq8hBNr4GS8Fas945wZz3DwO/bnrmcsha0+DeN5ubsHUF8ozdRZISMqVhDREKGs/iL/pbbIq1waGrFM2QIi6KDFScgG0LM2NlltmJ0uq2OTx99vkT8cQdaswRtYC9LDW2eWds3QrGpZ/mA9NhlUimSltYA4Ny0Fo8VghUh0WEauilXmHste9GBoJk+cP5ktjd5TcOfe4rMjrBAvJJY8fE9loyNkE1WGCQl9dli83lUWKo+cMQKF+eWRrnuzoKpbhSDL7fDzMWDIelow+ZmVkVTCgb1rYJV+4cUiykwfk1V0d8vwBu5A3RJDnM1XQL0Axncct8l6XVR7rCJeaM3DVTbBIJTDvl07bZBe0jm6rjpEZ8U453YwXT/FzJSl++rSFVBf4woDzC3cKBPB4MmHD/Eab+wMtSr3rUH9XArwBN9dzMW8TzNjy7m4VNSrYKFDeDK2J9o8LzRXL5oHJCeZJgOheXHCS+XKpJGqAbHhjO15EYE6Z6A0bTW0vgrEh7fcOxG4prrvm+suCbgAZUUsWz8HLqv0+haLr6ptkF8bNEgg7e4vsBlMJ2+7PDfOjcl8FY4dLLY9m9TC6lzn6Ffvha6yZXW8vZF/pSkah+wXzZwdVLRls0/m33Pzs8OdqFlRD3Zkm07+JlH8p5r2gwFBkTBJYIQ7paDoqsYnRAFScMgjAxG4zmEIaH+EfuZg4q0TCNYtJ76fPemRUnCSnF8F9yfdb6/QshBJdeqV02nbIoeYOfOhwxULwL/4nAGJMebAYg7yNk4gQ3cOAjjAntYbIEAI1CAfxG5MVF95EEiFxACRim1W0M6JBZAwRj8G2wwGxSrSP6D/VCty4ivTjYXIWfWwFU8iuNIKMYaK+NOJCgImHNAikDhltUp1HPuMntnUegj1uAElpCuG3/tYgkcEUwrlsFytQ+VmRqkz1b5QpRBOY6nRvK1vp/dhcNWNYbl4QJ4vmFCkwyFeKjI6XpNRM6YRv89CgB3JSWTHnlCEb3O8GYTOysFglTMlc1G+OwOQ/JgaC1Da4CcZkSVxicCi1r3ZMZB/BAOqWfPazn/Xo8JApwoU3XZ602B/LPN9FuK920d2KAAsBgt7e3jGuWL7yT/U+DUyx3Iku/s22IeBjdiOb6nYGUx7bzxDxhsYVBGbBaKJt1f2FFIi5xeMUQ9bAEc/xdrkLpg0XIo8Z7BqvT+fTHoI11PshMErdq+lM5nFIUNYfk0q34UKDeNPZ90u9bD4r82esFpe9Wc6efFpyEaJ+XemwNA8h2AOes1+oSkDo9WTZf7JDXj/eITMzouXfF1mkpg9sQjwBVuRFyRDKeLseWj5Ou9xXHSZXLooEwNstD7/mlP0NyXg2AyASgkPRAcGhrbUZKMMid807JqXpnfKNBRa5DLJjz9cgShn0hQKlFfkx8uVFEM3OHoTQdJ28+o5d/rytQGpGiuD+RaaQb1CIekLheMnF6GPZMV3y2Rs7Ze1Km3T2DMif3u6Wvx0chpYR/gZhfpMANzQLnoP5eLytnxUmg6fCZE93uGxutUj8sTApsDpkY4dVslMtyoXMjrnKgFRIReld08oW8mUXTvLp8jZe8haTzoP2hL8FwqUYkWy8/vN4fV0znhcTQCBxDue1Dr6dPI/EReaKZdVg1yBK1XPn6uvporWm03lUdUFONWuA0F08mPF3vg0LxoaTiUxE4IYiuIFc1fxFXzrfPoXOv/gtwHFH9lAWAMTf/u5pjwg6xxqFnH2liUay81VGsPsasY7uhCdOTAzkWS6ydL5sO3bnYu7fh2XuCwoMJaQVSHJ2hTTVHJShfoQtAaASFh4r1qg0iYE7WFSsoUvS1VonrWfroE5ulfjkKAUQcU1HN61hG1gofTjXT6I4dGnlcoTIroGGgQEMKTCG8xoFiGCDgIh7cqeYQ/gYvzGxI16CyogVKVAI/ymqOcEiupKhzU4FCpFBhPIIDmF7ACH9hoYixKV8GXmWkToHbNLYMwTNBBZsajSL9KBERm4PO4j72WH31zmAjQKKDHBnLCjEE4z9o6CQLmv0W9XjBpU8LdLgj3vxbNhLGc0wEHfg2KLZ2YhWBBeyct9uTLo8J4TFR+x9AMuMXtKTz0bxUQBCvN75FYvgflaq7DfY24VPuwz0tUtn0ynpaO9QbnGVs+fBPW36BDZNV0NtElDgYtSsw6NdSzRww3zUIKEosNbm8S5nMr/1Ip8LbyUUfokm2mmy2kFT2WWzns5E3YO8gbGJnj+V/QhUFseJBrsC5ZvMMYJpzz//vHJpJFjT09MzbXWZ22e+z7jd1BT8Iku50xJ5DqUPzQLhYMym5KyS3p4m6W59R2aC/bKm0CV/OTEszx3rk87+EdlW0y+9gugjI+Fy8OyQ7G21SbSrX9aURMknSwG84Bm2+eyIOKAleN88hMqdF415AtywXoGYdR2e0Xxhg2iWjvBov65ldCt7r954qUBwqAzg0BX5FokDRefJkyL7/ASHXIGACl9Zmi0L0mxSf6pBNuyMlJcPzpTTtmxxRgYAhRC1MwKu8tQTqs7rkHuv7ZGls0fkYG2vPPQOhaYdAglqRPDEfAELxSPQMypvh5sYJhrFYN3fOxv9Qrt2d1pkcz2imOFxW5xMF7JwxS6qbx2R3MKF0HFaMG3aQnrQeDN/ghlMXMgTlNDAw3iCpd4g0qUUkcyfPcj82fHBQU+ktYkwrRh2/nzTeO5Q51v+hTxfRwxinePpiExlu7xDXHuXPVmXMpZbqDRlfKctW/fBhX3BpHSGfJcY2nsxWIBupa1tnfL5e28JyAbT2kNk5vBvwXbo+gTS65lIJLvJ2MEsCH+xCS6b20a2XaMp4MFk+joV4NJk6r2Yz7mgwFA4KOAZBZXSdHo/NGSOS86MhZJftkTikjKgDwSxVrdbmH0IUUnOHJLOlpPS21Ervc0NEg8qehhYO/09LdLXFXixQPemufMvk7MtCA3bVC+vKw8SAhxuUWk3CGI4lAEEQkB2avc4AQw4MVEz4CDDbSxMsYVwbpgBApGFQHCIYJADH8UNIliEw/1wJ7PHQFuAlGhkIVuosx9RfPi6z5wAMKm1P041QB93+4jd6HwK4HEf93y7QR8cMCKR8QQyhXReEwMIfXQBlFFMIQUy4VsxhNzb+FbgkaqaFegG6d/uhrhtxV9r4EJWNTNeSmbM8riQuXON+RqChtRAVw1co2ziGMbbX6zVBvop4p0uhZVLpWjOSknPmSFR0IVicozYVb4RfHe11ktz7REARR0AhaqR5/wpxGQ8MNLU8uXLx42ERVCguLjYo8OjxWgJApFJonVqpnoBStc0ChBfCNclX9eMfdPsjPMR4L1YQRRffQ52H+0ylSBgsPVOJN9UAHIEg3ifUEuLDKtbb71Vvve97yn21FNPPaWiiE13oqYX3cP0fTYRkDQY17Ppbn+ofJHE5HzJm3Gd1I+0i6PvEBgvDtmb6JA3awYkDsEfqnMi5c0Gu/xkd696cWB3hssKuJjNK4+RYnhpRUEDMAraP1sQFayxzyFfmjco186OlpzkQXnoZbim7YVLOZ4V1kgb4k0AHAqL8gkQaXBoX3MKwJV2WT/nhCwFQBSBJ/zj+OwFQ0cnuo7dXIbIY6VJkguG0s5d7fLYphTZ0VokQxHJAIX0K6SxV9hCQMhhE8uwDfz/AVlQ2A09oQFZWOGQPSd65EGAQtvOgPEDMIgAzyfnWOEeDaFs6LdbEI5+BwCqxG6nXF0+Il/Esc21FgBlFjmBFyh351ulMpWC09Boi4ELWfqcaQeF2LvMrBTJzkqVU4gwE2wyi3nynGVL5yJipf8wyR/FiGS7AIy9ByFTDY4FcqUz29XbrW6y7hHaHUpFrzWJ2gZ7DS+WfGa2ENs0ER2RqXDLC8TuYXt8ufmMZztem8VwJ1u2pNJnBDGWuW3bXuWmNlFx6/HqDh3/8CwwMDAob23ciXDq5eO6H3KMFBflIdhJjmeM1Nc14UV0qwKVzFpSOxBk6bJ9x6D7g8iY05C41tZsz0AA1TRUPW6RU9E2b3DpfFw5x23wJZjhggJDtE9ieoHMX/VZuJT1gyWUAk2aVAUKeaeElCy4Gy2RZrCLju78u/QgnH0C2EMR0KLpbDoGd7RTkpo9w/s0z++M7CJZtqhaerqapacPYtT1oMEB5FB6Q4oIg2+CQW4aD7V0uJtOTQouYV4AQk6AOKSvkzFE5hAjlJGVRDCIHyrkGCdaIAw5JF1RVsmIZUQ04kmGWLRa3BB78ST3D2I0alP/NmUaAwyRdUOWkHGCh1mE9mPKaBSCYx63MDKKdN8UM8jIM4aRhPI41R3TLrKp3DsMyAjnql0WWT0TLmSL4mVR9Ty48QWOFtbXeRLX54DSzKG80OAAIgSllMusxeukoHwx3AjP1YbSpmHZWYUVAIqGISg+vuaJx6QBNihyzDDqjOIVTIh0M2OEi1SGZOeimYARE8VxV6+GzhUWzFOVdJ28xiyfIrrBtHWq6je7IlFEmgLX5uhXgeoxg0rTYZtAdQdzzOwaxvwTAVEINhAY4vUxpw8LwDO3geOPkcLo7kjBb2rt+BJeDsZGHN8/+9nPhFHYOOa///3vy+WXX64Emzk2AkURC6b8YPNM1DWM7WakNqZgXM+CbUco3+QtQKZtSgaYniOtUnOoWdJimwG4hEkj3MjfOt0v+ZEu+bf5sfL2iRF57OigFOXGypfmR0sWfLv/vH1ATkIzOg9h3MvxTuBwt0t+sNUhB5r75bMLouX/fsEi1x1skcfeiJU9ZxIlDICMJTJKwqwxMhIehecz7lM+zN2J4FA3mKpbzmRiwR6mXMsWAhyalxUuRwYiwGQCgzUiUr5YFSnzoAPksiEc/dZweWRbnpweKYTrmG89IQuYaeEAp8LBErLYhyQnsU/uXdcrN66EgDYCLuw60iUPbe2XrWA4MfrYnHSr3FAAraVYiyTEhcl8kAc6ob/U2Ad9oY4I2Xg2TJZlOiQSbndNA1ZZWxwml+Ex2wK3N1t4hcyac7fkFlwY9qy3WG4w4bR1+GCafbzw9t6L92CZNZMfkdN/JsGM51/YKDWnvSI28iXhOMlsO2Y9nzfY2i3lLCK+BdIbGqdJH9ph2vG3//u0vAsGzWTSeG55wTAMgmFeTSaE/VJoCH31Hz+Bbj3tExzaDP0iPv+8o1FNxg4f5jmTBTY/zDZPV90Ec2y2YVzvA4oNNp6GlBn0YJuyIPickZGqmmfWkuL4C6ZM3k8trR1CweTx6jbbIDsrHcFtZuM+3Kt0sQh4r1g+b1xwa7L1TcT+bBtD2usUbNteeW2LtLZ0yg03rBQyVPNzDVCNwNdW/L1ZhvtzPO2wC9G/idhiuvJecGCIYtFJGQXj9icyOg4uZHFgjMQpIObI+y8BHKqT6Nhw6Wo5IqcPbAawlKpc0HwlnjNz9jLp7myQ/sHdUtsOl66BKDcQ5J44KqCI0BAnkwBB8OZSgTnwJ3Piga5cyrS7GEEZpS9E5guo7G5gSCEn7u3+AUQni8fbTAgm8y1QJPZTS6Ad59rUWxydCNaMgjAGFmOAPyqHGwBSMJF72wPq4LfhPmaARAR9zMdYinYjGwsIoX7lQuc+T5etf7Meo3JW6saHjH3LcgfkxgXxctnlq2RmxRIDGHPn9v4a7G2Qvvbj0gfXsH4AcnQdi0kul8oV66WkcuW4DCBDxNr4Q+hd9mR/c2HLxeOuXbsUuDMRwEUvVM3ACUNjP/744wowCFQWF608Lxi2CUEYLsSPHj2qQtaTPUQWUTDnTtYu5vPM4AlZPwQaGLY8mP5NxjZT0eZgyyCoQ0HtYEEUMsy2bdumivcGG8xgTCDWGMcbNXmCSZqhFUxecx4zgOkdrn2iZZEl9Itf/EKBuRyHFGPn2L/QaaL6TmZ9K16rqQRrL3TfP0r1WcIgJh07RyIhltzR0CylCWHyhTkiDx9ElLBjA/LKiX7phAZQWXYMGEFRUgLXsKf3DshfEcY9I9opK7KsCuQ50OWUHhtc0Q47pa67Vz4/L0Iun5cgiyuHZPuBAXn8zXjZV4uIpBHDEKYGMIT5hTMsAiwinM8XOG6QyJs9dMfck1Kd1SNZlZE41y75UX3SWDciT7+TKH8/MVMGozLBQhrLElJgkBMC2BCXtuAjwwCE4ntlzWUQmV7ulLz0YcVSfnVfv7x82C4ne1ySEIO5Bco5CsbsA0cBavVb5OpMgEdwk8vA3HZ5qVVaT4fL3mN2OX1yRHa7YqUU+28stEhBYpgcPG2XDLhTXwgXMj3+zJNm7hsvnLaZ4UFQ6CuIILUIiwp/6cOMSMa2frAbYeqmIBHgeuW1rfLS3zcjEECj9PfDfX7MPG/8SrzZMTzjfMWIg9HKGb9lgXOw77t2H1aC11ORtC2fe+4tOXrsjGLl6DQR4JBukIHc8oJhP2jm1U6wMujS4yuxnImGsNegnVHeueCQjkbF48GAQ7TZHxC17emn35B+MFMuVBrP1fRicz26UHbxVQ/HEhdTwQIP3uPXzDpcUF0BRtlsOVPbpP7OPPnUBszRrHLf52/zCfpokHXxojly7TXLfTXP7z62m+7ABfmZChgKRlvrfOrz2xAfB9i2vPxsKcgLrm36b8uZM2flRoBCjJhGNtREymAzLlT/fHT5gu+64MDQRHtIIeMCgBHEUY7u+LsMD9bhDd+wtNXtkdb8WVJYscJvkXQpm7/8VjlbcxhRyprlgfdMTBeyaAjoUDsIvk4k1vDljnIlY6Qs7sAANPSEDDCFaC4ZRGxMmBsMUuwhJvwewad/0CZDMQixjP2EfhIirZIQFYYAJV4hQgHEEHahM5oGhowNA5QxtEMI8iCTGxzit8MM5HhAISOP1h1SbmEeQElvE5gy+mF8s9GqcG64mUHGpgF2Ga3LjR2S5UUdkpMSj+gQeQFdyKgp1N20T9qbDoERNgDNJYfEpUIwc1lwoJCufaq/udgkwPLb3/42KCYOQ52//vrrKkIYw6ozsYw777xTLfYZFvvZZ59VoM93vvMdn+AJwYXt27er6GLBgDsErMzaRg888IBiKX3rW986p3yW/fOf/1z1hWHGpyIRZGCYdjKh2D+CUwQGgukfwSOyTN544w3FXglkG61dQ2D19ttvD8o27J+ZGTIZtg6v3fHjx5U71Hggio72RbbQpz/96THMKTMY4495xLaaxa7Huz4EmCbiMqXLM4OJ/HsRKFy72X6+2kOhdYJC7DPH4vmAQv7s4qte7320L4XDOY4efvjhcZlQ5nv1E5/4xHkDQ5MF6bz7EfqNZx+ilCVmXSU93Vg0249JZVq4rJ05In886JB2MEnX5lgQOSxcsvBuZwNAnldq4XoNaen5ieEyMyVMOhCUIhbP5n68vkFAUtlY65IdDTZZkWOTzy2Kkavmx8vSygHZcbBPHn/LAIgsAIas4XBLx7PfhW8XQKIRgkQAmYYBRHU7IzzsoburjwOE6ZT2rjB5ekuEvHm8BCyhArEh8pmacOARaIHrmxkMsjCCKkLRZycOABDqlxuXOwAIOaSptVv++GavvApAqAYsJ+oJVWdHyKfoOoa/dX85LrIH9bzcDL1EsJ5nAih79YhLMmvxbElG/oRo2dwJZlGqSz5T7pJZ0B2CZrUUld8Alu21F8SFTI9ZTrzNbi9c6O384JByc/F+66wny2R4cCGxHG+Vi4vzEZjC/9TS23VK/U0NgllzvveUua3+yiIo87sHn1XRsPylhrOI/tXQCt1IvAjkfG/Idg4gRFvwzXag5K89etHXhrfb//Cl9ZNyLQqG9RKobYGOcaH1v394Vp555s1z+m0+j+4unf/1BzVP8pW0HXkskC2Ddclju/72902yDeyGQIkMg8hIuI6uvdKvbTl+6Q65bMf+gODQxk27VP9uvfXqoFgZwYJD23cclMsvq5abbrpcsrJG2RHsV1Njm+zee0R2IA9BtEHom/pKwYxBX+cF2scx++JLb3t0tHzl5fjdsX2/YmCE3OIMCwUDrDCn+W8jQfZFCys9fxu99a8GB+3y6GMvSyP+Fq1bd6Vn/JlB1tSUBFyDskn9fWXdN998FYDqFgUOcay/v/OwZ1yShcR5ZxM0fnbvOSoEdYOpbyoYZetuulKxM3/526fV3yB9Hy7FPbtwQYUad7QD2/biS5sUePqVf7xjjD19lcH8y5dXKXbXZPvn65641Pb5f3pfRD0hc6hwFhbomKwd3fmSOOz10KVplfqjW4QuZylZM/y2dsTWCbZAppQ1HJH1FY3y7BGAQ4odQ9iG3mQGQMQXix42jVtPiNHJLAocwoONx3EaGUQEhYx92On+bRRnka6+IemEAHU6FNxZA6amYiUIdI4oqgEMKVhG4TNugAY/FBiEb5UD+73BoVGXMOM8DQgZ7Ud/qC2kgCEzQKQqcddj2tbVqkYY9Rpfxu9bys/IzKQusQ44pPHE+yocvRYJd5/g+RroOg3AbicYXTXQgeqToQFEV6mcHxRTyLusqfytGSMlJSVKM4WCtgyp7guw0eGyCZB897vfVYAHE8sgcMMFPz9cuDJSExeSZFcQACJ4xERA6M9//rNa4NINTSfz4tx7AaqBGR16mwt1Cv8SNCAARCYEy9dlZ2RkSGVlpafsYDfM4r7e5xAYuueee3z2TzM5zG3Q/fNuO9vszzbs08mTJ+Wb3/ym0MUv2GRmhkwmUhjFqHXfCDj4A1F4/amnw9D2N9xwg2IMmUES9r+wsFAxuwg0styvf/3rnrHEa0yXrAcffNDTta1bt8rVV189BuAbD6gxjxleM1+JAM6nPvUpFbae/SHgdd999ylg7hvf+IanPtZFAIX5mLzHHvdpJhTBM55fVlamztdAHm3CRFCOZZFV5Ov+YR5vtzaW4Y855wvko831tdq4caNfdt5zzz0njz32mGLXMZogxy/Hok6B7jdPJmyY800WpDOXF9o2LECXsryihXiEOaXh+NPSM3hUroDODp9nfz7kkK0Ivz4r1SaHW4blCbBlahxWWQIXr08BLAoDa3cf9IX4DP3kDIsSsa5pcUGc2SVv1Ljkvbp+WZk/IJ9bEiNXL0qUZVWDsuvwkOyDa9iek1GyvzYez24w3gAU8QURQ14qBpH6WGTnqTg5WI/gBnHdCEffIXW9STKMoAh0DY9E/C8+dy0Ux+PHAVQKgFBO4qBcM69X5pc6pDA3THLTR6S1o08e2dgrfz84ZABC0EZSTCN+8C7IBbbTPLiQxaE/z0Nc+r1ui2xos8r1cKebW+iSNri5vYPIqfvgMjYbRFmCQtVZEQDCrIjQNht099WSnGo8Vy7kuKLby223rvK4I/l6462Bjbc3fSCLF1bIP/7DHXiRUoXIsYGZhhcyIhndB37/0PMKyLFB65GsHjMbxdummgly7Hit9yHPb7M+kr9MgcS3KUj74O+fC9geLvoITm3dth8ghhUuwlePK2BrbotmvUyV3hCv9UNgp+zcdViBOP19A34BCd0OLoZrAuhUBWNHlhUIODRfX92uQNeX5RnRnprwnNuobJsPVsSXAcBdd+3Yl8ze94Cva61ZPlsRij4CZVnxN2+8axUMONTRBebhhm2y6Z3d5wBrtBtf4gyDVeWLocaxd+vNVytQaUbx+f3t4HV/dcO76rqb7yFf9ZrtQ7e4D3Yf89h3EZguN625/GMLFJnBC38gIsfyb377VwX28Rrefvs1Y5iXvKdvWXc13ksMy29+94wCa/TfiXfe3esZfxpknV9dJl+673af7E26B2u2nz+gxpt5yLFuHpcc70wOROJmgJJA9ZndZSfiKuuPPRmFZ8yKFfPk/V0HFXCr70PaIRIsKt7XtAPbxrXUgupyBQqZX1iwjPXrV0s9bPH8i5tUGQcPnZJjJ2rliSdfV/YMtn++/jZcyvvCf4B0KXSAwtTxyRlKe6arhQ9tG7QeOzHHi5BERDuzqoghY9PwYLv0tR2Q/vYj4rR3AKSBQCSwktNd7ryYdWL65oaIeK4BhvBfTkiVt5eaKo6mUfjEAG6U+DSBH34h2zBdyPBwSMCgw/RWxut2WAAAIABJREFUeagxTs4gJiV20MeNjO7MPAmzZII4XOx6PmikRQFCxr4xAI+qj80yzmUrCG6p0PXujzcopCa57If+dp/OXhraSkYfWazZGquKWmVuVodk4u1ouMsmLrQ/ISVf4lPO1Rga6mmQttp3pKV2p3S3t8GVzCmxqbOkfOH1kp47c9SAH9JWV1eX7N+/Xy34KbBLVx8CRIygRf0hRhrjQvNHP/qR0lP513/9VwUEmUEBTlBmz56tPjzv1KlTalHJ8l5++WV54okn1CKWwMctt9yiGEYEcJjnkUcekR//+MdKP4jXubW1VX0zuhnLpdsYw7wzKll/f7/s2bNHHefCmQtt6v6wfNZDwIIaMNXV1co97pe//KVaIB84YLwp27dvn6qTfwRZZkJCgqcNv/71r4XHmbzzMa+//r355pueNnj3j2Xx3GuuvUYtzmlXf7YhkPK1r31NAQuB3iqzTPaBgMef/vQnBcJs2bJF2YSJ9jFfQ91PddBPSk5OlsTERKUxRHBjw4YNcvDgQTXx4hjQ15/X9itf+Yp8+9vflqqqqjFgA68TgUFekxEIz+7cuVNd27/97W9Kn4eA2Pz58xXLiGOqo6NDscxYNscf66ItzWOhvb1dtdg8FvibINUf//hHZYPm5mbxzkeQiu6M/GZdBH4GBwdVn1gfxwVBKrLfOKZtNpu6Lhx7zMt2E+AkwEhAhO3jtSXoxfM55gjk8ZqxT7Qb+7N792559913haAN749Zs2ZJbGyssguPsa207SuvvKL6ynuObeSHfWK7XnzxRU/Esbi4OGlra/PcB7SR7hfHwKuvvqpAL7ad10lfK7LqCC6SBbZ27VpMBiKVHYO933zl87Yxr3coTd4CvJYJSdlY3aWAOdSMqKLtUp4aJsnRItB3lm1NeNb1gQWU7pKbyiPk9lnhkgx3rbf2DcjWRoAqmRZZkeGUPRBptgPUWVmAFzXDLqnrdMpxgEQtHcPS2tkvUdAnWjDTIkvmRsgNYPGsquqR4rRe6euxSUsbnrsjQxCIRtQwahJBG8iFYAfDQyPS3Rcu3UOxeJeCABQjFJFGPjvcMmwQOmLY+fgeuX1pu3x7fZd8ce2wrKxySX7GIMZgrzy/o1MeACi04fgwdIEsYkNfc5LC4T5mlV64pY/g7zrnAnyhn4k4C6uKLJINQKgOmnv8lMe6EJltRN5rA4CWHCaf9QKF8srukrzC+cC0xuqbTf5qBH8mr1tZKdxvZ+bjPm2FGPwp2YxF6ptvbZct7+6BC9W7SlNn1wdHFChE97EVKxAsIgpAHIC3QOnNjdvlNSx6OU+pmjtTrl29HH8bgn9JEKhs72Ovv7FNXsCkvwdABpk9DhX1I3Biu5jP30fNr8ZJJUU5ij3lK9rau1t3y+tv7hi3Pax/EG3u6x+UivIisLgqJSE+eN1FXkPale09dvSM9PRiTHuleVWlAPPmjFsuRXA3vbNLDh2pkQFoaY5wLjtOmgo7coF8HdxgKmYV+6zNfH0n0i62X9u2paVD5mAcLlow1v1R/e2CvRvONsth9Ntf4nWy2ezKLsFeK31t1qy5DPfPbDwz21DP2Mh0ulyOW/OHdbH95nFIO917zzr5wf/9svzDfetl+bJ5eDamQpP1/N7973z/oDz519cxJ6iZ0D1kHru0b3d3v8yuKJHysiJ/Zrxw+x2D4mrbBZG3A2KJKxBL+mKR2HPXNFPRILobvgf2VFJinMTFxsj+gyeFQPqTT2/A3O4F/B3cqrZ/9vPH5NXXtgnD0OfkpMs/feUuBe55M7etVjw3yoo9f5c5ZrzHH8ch/ybTHZHjwDzPJvj0wK+eEAKZrW1GdO/eHuqvngULDMxeuCQmJMRKfBy19cD6TYiTy1fOx71R4Rmj5vpYF8fmePVRq6iltVOV2QeJkS0AcPgsIeMyG/1lfbptL/5tk+zbf0L1i2P86LEavBDfr+4xnZfl0E7ZYNPRdVTbwQ58QN/Xum233XKVfPtbn0WU6xnngKyJCfF4CVogvfjbeAR/I1kf7y19P4/XP9Whj2A6v78aF9ggZA7llS5Q0cq6GndJRBSo540fSHNSmuSWQyg12ohypZs11NcoXc2HVBj0OOhBl2Yjgog0K1DlrdN4PUcsxQ3+KECESJASmwbYolzGDE0hh2IFGW8b6QLjVGiK8aGrGcEl9RtlkDbeiUlIUlQ4WEMxClSKR+zZWJzTTV0CU2IVTMo9TNWvJxysm9uaPaQ2jX0Af1yqbThGIEjlG6szpPah7Z4Hh7ssVQizm76NaZLez2PGcYJClxU1Sn5yn0RZ+eZ2UHo6jsupvS9ByyEC0eWqdGYZ6qmDTsIb0l67XQb7OvA2Y0SSMiukbPH6Cxpu3tMgHxtk/lBHheACF6f8kBEUFRWl3GfIAiHqvW7dOgXoUI/G+48yi+UimcLMBHMIbhCcINjDBz3LIDBCl5aSkhJVNhMXzAR7brrpJlmzZo2ndSw/NTUVPq8G5Zxlkqnxiwd+odrANprLJztk/Xq45c0okdiYWNUGtplgB9vN8nVi2UTKtQ6ObsN4+SbTP9bJtiTEJ8j111+v7ObPNnfffbdf23oa795g2wl0sH/etmMW1kO7mvvpXYb5N9tIsI+Rr/Q4IIjxwx/+UD08yYChfZSN3deP55gT6yRgRDbNM88847n2BEfIGuOH55J1w3M5tnjdCBTpMglsePfHeywQtKBYekpKimqPFpXWfeYbQ20DzVjTfeI3QRzzeGRb/ud//kedwzFGJhvbyvFKcIVjkGAomUN6PLNPd9xxh6xYuUL9/WMffvKTn6iyCebwWvPDVFxssJf4NlOfz7FEoJFlkAWk+8TryYhnN998s6cPvI66T776RQCU1+rRRx9V91p2drYC73hfe9+rwd5vHF8EC8e7FqqRoTRpC9CtK78Yk28kMof6ho7KVXkI0hDtAnPIKS93R8pZq0Xmu4ZBix+UPQBrdvVFgjEjci2imVVnOqUFWnWbGlxSluCSVXkWhHK3yuE2p7zb4JD38Yl83y5LcvoQ8SwMkcGiZU5BjJTi+7arbWBmDEgz3LRaEQq+qd2CbTyn8SbRgednSzfE1fHozUwakSxEAMtKgeIgdAH5nZkKtUC4nEREkIU7JI0tA4hwClYS6qvvcUhNF1zAeAj3xpxMq9xYHI7vMGkbdMkzNWCOdoXLAGjuZ4Ez9bZZ5JpCh9wy1ypF+S7ZX+/ECyoX+holWUmG+5iZKaRAoQsQmj7QReUb1SsuXyDUeNkJ8VRq8zSrRV6vOm31qiXy4//3dcW4iImJHhfo13WZmSITiTgVqK3+jtEd4H4wmS50KijMPscFSLdhIQCIfwaQNpHEt+OZiEw00aTf+lOIeifernsnLq6CSYzisw6uV/Pn+Y80F0w5E82Th7G1AItSf2mqru+8uWU+q9AueTnjuAWaTw72WvHacGF61ZWLFDg3PIxIhmDo7IFbDgGCRgiI17vdeMzlEwTKcwvnUj9mPtgQvEeLCnIUW28q3TIZofDG61dOytXd3GZexyo/NvZp+I/QzmW4tmtvBFsK984uuOSar+/ZxnYFpJeU5MJVd/RakulFd0dfSf9dDseDa8miQ56xovOynJvXXSElKMMMCtG1yg4h7NIZeVJSlCvXrV7qKZ5zqojIcElNTlQaPDqRpRQfH6vGaARAKe/6CHwvBAi1BBH3JlIfy2ediUnxamx5t+2qKxaO6bo5rz7Avq0EaDUffx/eB4BptivzaHsWF+fCayjX533B/pXA9fk/f/BVWXfTFeeUEah/Yxr4EfsBEokHjbgkuuaAf3/DyV1yas9L0BuqhZhxpKSkl0h2yQpJzpknETHGg26wp1ZaajZLa+1u6e9tAyN8BHiJU7p77XIG1O3XjmfJW6cADhG5Uf/g49429IMIBHEfWD/qGx8MZM4iCQ7RzUzNKLnfdC43o5AvLS5GMpMhno1JcR8Q1aYuiG26F3MeQyuQhokbo+CMAms0gKO28QM7PYCQyosdChjiFw9y21QOgSEzEGQq3124qQ5VmZFQxqrCVlk9ox5vP/skNgI0etVHfuENKOj2mQWLpGD2KknLr5IRe4+0nnwNoNBW6exABLjuAentgajo4rtk4ep7xhWbHq34wmwRSNEfvRDlolkDAFx4E9DxBgS8W6fLcMDVYNhuAH48x9f5Oq93Gfyt/iCjTu/6fJXPhbZ324ItO9h8uo2+6vfXP+9+nc+55rK4cGdZ4+nv+LOhd7vMv8+3jTx/yDbkufYRXEAiuqK+lmTeENQi44TXjfvZTiZ/fTL3Y7y+szx+fPWJ5fN88/ViPrqJccwT/PFur26Xd5+io4y26+MDAwOesvV41G3wton3eJlMn3S9qk/UgnOLu4bhgW62ty87mPfp7WBtPJkx5au+0L5RCzjwDK6v2anAIYv9qCSCuHuofRiaQ0451I3ryccpnlp2PFsT4M6dDeAoJ8Yht810SWkiBKj3AUwBJnEzmEG50Q56TEs9vL7eOjosuxvB/sGLnQg89yJRTmGiRaoBPl03K0qq8hm1DO/ACPIyahmeY+H4pj4gh5N6gjPiqAuBI6ApRJ0HJ+YZdCNr6hpRYNDrR+0AgwAEoVI73ibxGc2XQJcBiLoVTKdhuJBtOuNCoAmLJALkOg036hroGa0vdsrKDLi+NVognu2Su+eALRVlkacOuWRnb7jMgJbSZ8qdY9zHLgZQyDxuGemJb1F5Dyq3dXdioIiJLkS5APjlr/4ijz2xQZXy7W9+Wu7/8ieCBpXM7Qpmm6xOLrgvdOLfD2uEb+2kybQpAmWNx7D110dePzvmKCMc014p2Guox8B4z2Lv8s/3dyA7suzJ2NJXmwLZd6J1BCrLV93mfbouPit5rznw94bMCXMi8BOOl81MvH6sj267UwkI6fom2nd//RvvOvo7b1r2I/Kk8/DvxHXqL2LJxEuvCuh0po9KPkxlnYxINoy/QdEA7Hj/+rq+rI/Xc6LX0rss3W5/93Qw93CgseurPgueo2Sl+fpbF0x9elxweapYcAEYnYHGkK+2TYU9A/VvKsfJxVbWJQcM0YDDoHmfPrBRGo6+BmwGjJboWEmCO1l8arFExRkh6Po6a6Wj+Yj0dzdhkqcjHBiuWaPgUKYBDnkBOwbQYwA+Y0Eiul5pwMjYJj5EAWkVpYzlkHWEfVFYuEQBYaVgNW+QQU6uTPiLMRC4g+e4AR09OtRPOom5E0EeNwhkoDk4guOKEeQBg0z7FAjkOZuFGb9HC9Qln1P3vfNb8Na1RRIjOiU2khHW2ET20Ei8USKjUuBSBnsnZUm4xQ5h7XoAQk1qIUxQKDKxQhatvldK5qwcrSe0FbLAx8gCmqnlDfh9mCa4GNv0YdojVPeFt0AgcGhvl37KiCwDmFIdOyL74G6Wmxwu98wakTPQInr4EESpVfRQp9wB8sI1uU5p7XLIqV6XvHDCJR80AQTA5BKKQtAKAsMT2j4EnPgIzIFOUWY8xK7xnRWPFz7AiLIhAk1wqLnHKc19YCahnCZ888NE8AkeZ+oD7AdaQRapSAuTuRlweQPA026zyNzsMKmAaPSjCHbFaUBCRJi82BQBkAjAD+qaH2OTWkQla7VESjEDZ+CcQbwwWl0ARhQ+WXHhHk2hiw0UmuoRsuuDw/LALx+XTdAgobjq17/2aYj8jrKPp7q+UHkhC4QsELLAGAtcQGAoZPmQBS5FC1xSrmTawBFRcZKcUSRt9Zky1NuLtyED0tF6Sgb6O/EmPA5AjF3sg73wX+yG6DPADY29oAACOEkJEVIkdlkX3iAp0cPyzGFoIKhEAMXQ3VHAintC6abrqFmfcj3DtyFAbeRXCj3cp8oAiIIyGJ7epv2wPfNdz4ZRHeszfNlwGoEgN3LDLwBBxtSUdfDjbh82DBcxvd/9rQ7DDU5nc9egvtzFanSHGJNnp2oS3sAi+tjaslqpyOyGC5wdInpuUMhdDk9R5sCE3A5B7+62AenrPKVewjqhCTEM7QYSoux2hAYvrr5oXMjczQ99hSxwQS1wMQFCuuMXY5su6EUJVfahW8Dbray956CUgBX0rSqnbKgF+6fZKo1DIhlOu5TGQJA6LlrernfJbEQuK0oGmxMRy+ogxbEYxODSNIcgSJm83hgmmWAY5UN+xZKLaEMzXFLT7pTNZ5zSBfHnFhsZtQKNLjwf20cEhB5F/uU+kn6Z+Ezkhy9v+NKSIJCaOOhvkPNKAfKsLbZK90iYQPtaquDSVgWtpOY+kX5nmNwwA99wI2OZNSNgEHVAu8yOtpdFylrrsPwZzKb3+iJkHqKQ3Q/m0NzMCJCS0D4AW7bwMplV/eG7j033ADFH3WGo5cmyYKa7naHyQxYIWSBkgZAFQhb4OFrgkgSGeKFiEzPwyRJGwnKFO+DSNCT9Iy2Yy0GcEnRwzu5cjCiCpHER/c3ZXlx0uKTEOGXVzC4pzRyRP7+fIY39hniph7ipwCHM8pT2EAviN/SG8MMIY8/fauZofEwTSRWsnvNRMop4XoBEFzE2eTSxpVpfCNueDhj7DaCIwJE+xu/R3qkT9G+123yMP93luCssxwT71nn9koe3qBF0i2JIXuoYqb4Z549yhlg2qeXUr4FYJ7IYVVDzKExScsoACkGEMibefW7oK2SBkAVCFghZIGQBwwJjwSG4ZtmOCoJ4yqfnWmRR3rD8CbpDb3RGShuilFUnQfB5LlzDEpyy97RT6tqj8Ly3SmUyIoNFOeVtsIS21NB1G/vwHLu7xCULssJkAMDMKmj+XFUswuAizX0uOQVNnxZ8N/e7pJsSXabHIjfJEuLz7IoCKyJxWiQZ04H9rS55CQBTxzCEfJME7bTIPrizbenAHALuYXeVC9i1LukFIJQLicOnGiPkSE+YDPKFDwpLj3RCmwgC07YI2Q73sjWFEJmuDJfsuDDphRvckCtDUguvlJySVYg+VoAXLZfslCyo4W2OTkMx5ep5vrVdgioslClkgZAFQhYIWSBkgZAFptQCl+wsJCY+TVIyZ0DU8RBcxaiuDnAFQBB1AphMjlgK4+DEjzAHvxk6sAczw/iEXJlTvVbmAAvJynlfXnp/SN46QZPoGaMb9dAFqJKZWKAx8TPeKhoAitO9j28MMcV0Z1Vxy8aklFgDgOocMARkxxwEaDPKFNJH3B3QgI4qkPt0yfx2b3tXpjqte+4+TzWNfXPJNaUjsn7xiFRWXSkFM+dLV/1BqTm4QXrbj8J3dARvVo0C+a/RI3e/aGGcb+w39o0MQ2TbmghQaKwIOGsLpZAFQhYIWSBkgZAFaAENDiXAHfnsqTelp2ULIne2ydz0CPkCHsjUHdrVbZWTCOOeF47nEJzDakeipBNUn8VpLlkGF7KOIYSv7wPDCGBKosUhaYgPPzIA8Kcf7mLQGEKwM3nhuFOKAPKsLg4TzgwKAe50dkIvB9SgJTPBPsLU4RRErOfkhkPvyKUYPxFg8Tx3cEQKwegpR7SwWanhshVR0cqyRMqzEeoe7tJ2uIrvADhU1eWUkjgwl3rBRAbYMyvFJbvgEncKQBCfySeGrFKLkPQZYBZ9pswi61BnFvJBik8GEY4+r+wOyUXksfCwSKUT8lFIOrQ5NSHu++JtnhDV3L99x34VFphuZEsWzw0xhj4KFzzUh5AFQhYIWSBkgY+MBS5ZYCicwoeIUmaNiEIYeA3CGNfFDApxjxkrsdvo9hQtWSVLpXj2VZJZWKHKSElKlNjozdgelA2HzWeMQiJGSYbwpIcl5DlMVzK0A/8rUEgziTSO4h4yJWlxUlmYIR19dtl2/KzvgaSq5z/44P+xzCDvHrnbOoYxxFNNffCUh/0EudxFfHJJlNy4MEnmL1whZXOWIxpLpGRmFUKnKVGO7ngB4NARiYwgK8hUlrtlXt0CW8gCFhEYQ9kzJS2nxKgj9G/IAiELhCwQskDIAj4sQHAoJbVIEpPvkYaaSmk48Qye5UdlYU6EVKWPyM6GYfnLiTA50KWZvBaIUYusTLGDLQQWTh3Fq8PEBS2/qnSH5MVYpAP6PQj6Je0IafZiHXR94Mp1XxoZrk55rwGhcjFXiAdgkYi8dJY+AWBncwu+ASiBeCwrChlNFAWAUXyw0yUvnxHp4YsV+J8NDDslAc/ColiLJOB3KzSEaqFNlAE85wBALBci/F6Rj0Lg1vYUWEaH+lAHnsNVcIP7PFzHKpKhLQS3thNnUU7W1TJr3iclKSX/I8cS2oEIZhve2K6ueDUiJs2rAq0KaQSA0DA+TIzctAhhukMpZIGQBUIWCFkgZIGQBS4eC1yywBBNGAlmSlRcEqjoEB0gzYZoBeZ0dHsawxjCfuIkdhsja8RIfsV1UjrvWolJTMUbK2PSOWvuSqXqnxS/Q8pB935uR580dCHOrC4UWwr4IUaCLw3WKI0CDyMHBz1uZ+6MPG5KdCuLBKjlcuJ1piH24z5qAl/Upvs3vsI8jKXRPKp+pLAxbCBdlGYduX+PAXYsMgvCnjdVR8rlC4tl8RKEq84phhCnMRToAlZccRnqtMqxXX8DOHRIwl0DAIc8DnbuQsd+aRwqmtck5Ebm00ahnSELhCwQskDIAqMWIEvGGhYt+XhRwwigZ088Lbaeo8hglQW5+GQMIRz9iDxVGyF7we4Z6RuWMx0uORJrlTZoEdkREWVtjlNS8Hza0RkhCxDaPj0K0U3wXByCunR8pEuS4OrVDVZw6zCYPwCSyMtZW0hXbZwXbYGItUVaBizyl+MIbIHpwF2lIrdXhsnNeOSdAJvobyctsg2h51vA/OmDnl5FolNmxAI4GkFEliiXQCJIHJgUtHc75aEWq2ztipBGe5gsQMj7T5U64R4H9hDK6sN0omMoXRJzrpRZc9fAdSz/I8MS0leUUcfq6hoVK4jpTG0jQty3SxZCrWs3sjvXr0Zo4KtCbKHQH4KQBUIWCFkgZIGQBS4yC1zSwBDfOIaHg4KtgCAkNzrhizGkmEKOGCmovF7KF6yR2ASEqjclAiOllcslB8LJM47tlsrcLfL8DpENCCury/W4eCmwSFVoYDseLR7uI3iEz2gWTy35SfEyuyBD8tPipb9/AOfqaGmqKP+JxzwMIFNGbBoAkdfJ3uwhd+GVmS5ZWzUIxlKMzF60SkorFkl8XCxAn7EU9kgAO4UVy2V4qFeO7+4S+0A9ugPuu0qU2vZCu/y3PHQkZIGQBUIWCFkgZIGAFtCuZbkFc+Vs7V6wh56VnsGjeNJEyeKiKFmSZwNANCx/rbXKhk6rbII7V77VIZ/JtcvSbIdsbgqTdwAMAZOR3NRhnOUUEHskORFuXDGIZAaNoC5o4A3DBYzJFW48ObPjnAglDMAGTKNhAElvwz2sYS/Cx6e7oGPklLJERg5DxDKcv7UnUpJrXHJj9ojcDRLM8SGHJMGFbVtHuLyLisM6wSbGs7cS7mr/stgiS4ugswetw64eh7R0gp8UM1sqFt0teXQd47zlI+I6Zr6wGempYAlVyOZ39khdQ4u8t/2AFBfnIotFnnvhbVm8sEJuv3WVFBTqgB8Bh0XoYMgCIQuELBCyQMgCIQtcQAtc0sAQI4wxHLxHAFltGMCFt8aQDfFmE9NnSmH58nNAIW1vgkMJCQkyb/5KSU3LlBmFu2XNsaPy8s4heeO4UpFGVoIiGohxAyQEYjzgkPuYF1aTHBUp84rTZUZWEkK8YxIZBhp9VIR0DvnQGWKDvM432mjsLE6NU6043Y7QKGMyuk8y4zZukOjaCqesn49ILzPLpKz6OsnOm+FhCen+m7/pppeWO0Na6gqkvaFJiWMbxQYGhQZ6O2Wwr0ti4sGfD6WQBUIWCFkgZIGQBYKwgPGiJx7soWV42IRLY91ecQ4dk96hUYDocrB73q8flkcOO+Ug3LfONIbLqw12scBVLC3WpYAcBC+DcLVT4ggORUcA+AEjCC5jXQB/LHA7Uy9Z8Dh3AdQ53G2RPW1hYsfzLR9C0WUAiviEe681Ql5qi5K1eU7JhtbeMFi+NgBLr3RFSw2Yx/mRDmmHTlGtPUpawEZKRx3XADC6vsQqRWkGC7m7ZxiAkEN6hpIlt3iVlM+57iMvMB0O4G0ptIOWLp0jdc+1yKlT9fI/P31c8vMzZc31K2TtTZdLSXGeYmeHUsgCIQuELBCyQMgCIQtcXBa4pIGhwb4WGehtVCLI5uSNqfA35yHR8YlwPxs/WhYBooKicsnNK5Hs3O2SG/2o3FzRKc/uSZZNdRmeqgjQ5KQmSnI84uSyEjJvABB19PbDFQvh6u0j0tw9KDGREVKclSo5KakIT4uoaaCjF6QnyvDMHHn/WIN0MRKYVweSARoVpCdJaW4qRC050XSJbZihyxgONwJUbacsKwdFvt8mXf390tk7KEeaO8eWg1/LcjplVUmDFBfkyYpr7pXSWXhbCU0lb5bQOSdix2Bfh9j6W5Qrm9mRTHXV1wnYN2IfVB9Mz/3kCO0OWSBkgZAFQhYIWcC3BTR7iMyahto9YwAiPO6kLNYh/70S0cHwuNvdOCKHuqzYBugD962uUyNyGOHrS6KgO0TGT4pN8qxOcUZapAAxEerANOLTK8oJVtGIQ6yIS88IYgvyw6Af5JTabriAZTjkcriI1fXhZRLeiGwHQ+m0HYAS0hDYwPsHI+TwYDjcxxC0gqLSxQhjPysKzKQY6ei0SWO7A1pCLumxpShAaEHlNZKcAjDkI8oS8r6K+flZ8oPv3y9f+Nyt0tzcrg7n5mVKUUGORGJeEwKFvC0W+h2yQMgCIQuELBCywMVhgUsWGOrvrpOe9mPiGOkHMDRW/4ZTPzM4RDKPNSJc+jtOS1fTKYlPyhzX+gROKEo9s6xKYsPWyP7tz8s9808CIDojLx0rkaOdhVKSRbewZKUZxBTmpqlnJsQr/YIR0MhnInoKJ0KxkZESgePDmIwyWRBlJS81RSJmR0hrdy90FlwyBL/89MRoyUtLAAgELYSwCHVqObnnAAAZE0lEQVReeLgBw0QgD/8LQ9viMHFliomIllQAUxbQ6QszU+RATYuc7euX5YUOTFYb8Pa0A3pGw1KckyEZqM8aCfXOIFJfV6P0tB2TYeg3OR0DBu7l4zwNEDF6mRVaDl2tJ6QdNk5IzfGRO7QrZIGQBUIWCFkgZIHAFlBh2/HJL16sXK80QOQAg8jhPAIhYwBEEHNekB0BrRpRLKLdjcPQHbIAKIqQ3fQpQ7JCAPrRMw4F+rS7jOlOngUMoD6HvFsHcWg8jhcljEhEn8gHQ5HywYBVjoDEm2R1yQDAnW5HBBhBjGhmPG9zAQRdkz2M6GkWKcmOlZwEK15ODUpP7xDC3VuVjlBYTMX/396dPbd1X3cA/xL7xn0DSZEUKVMUZW2WLFuWXTeexGmbdDpdJi996Utn+tB/qu+dadNJOpM2znSmTZPUciLLWmwtFCWRFCku4AKAWC/Q77kgLJAECaWWKhP83ilEEgDvBT6XDtAvzu8cTF7+Eatyz7gDHY5KIFQ9o1Y1FItFcGpyDJMnR92rbdmcAqGD/+Z1qwQkIAEJSOB1CxzaYCibWkEmySVOHFH/df+d7cqhej2G/AEfQ44lzHzxU4YjAcTHLr6QvYUipcIG2mJsPe1EEUyX8NcXWank5PCMpemJYq/75tKinhLL1+2rj70K+K6WgZEta6vcZv8WWeXzvNamxEEnHo7VjaEzGuLvlvmGt+SGS56yH1E/K4t4b4f7tKdoWzXsKtlnlds/2Cy0oAVT/J3xjgTeuvCQJfUJltCXEfZscbgKewOVvCiml+m1UtlRg3+3Np/h2cyvsLZ4C04+SV6b31KNgHb2GKpWD1n4FmApfoaB3cKjm+60t0isu8GRdLMEJCABCUigvsDugMhei6o9iFYTtzm+voWvn2WMsaHQxKTfnTZmSdFv2Y+oGhQtMSxa5th4C41sW2RA9DOOtOfL446NA8xQYEXQg7S9qgLxECeOcd89rBq60OfBR0PAAHsWeTwR8OUZW5kiFhaLaG/n67e3iK3SBKbe/REGhs8xEAo13bSx+mdo/2stIHLX7GmTgAQkIAEJSOBQCBzKYCi9MYeNpVtc5rTMiiF+dGjv9/imrsg1/7lcAblMgSGMwzdnrPrhxb4Ggz74uSIrs/kQ07/7Z7clUP/xxuGQU0hzf2so8Tj2RqettYUNJ9mo0smic+sG8vkvkOaks+XCBJbyUwyJqvFN7fmvXFd5mM8Dlso9OKWMlUGV90+V+5X55tRhyLUj4Krudvu5VoMhX0saHYFl9AQeIup9ilDLBgOpInycvVty8m6o1AK/G6JlUpWy7oP+MrNcnrc6dw0rTz5lRdY0HSvvnh2W29tIenOzryX2W/D5S/DzUt18DMKKxSxmv/xPdPWPYvLSnxx0KN0mAQlIQAISaChQDYjsjtaDaHDkPOYff46nc7e5fOs2yqwkslc6a9kX8hcxFmU10VsRfmhSGf5pn63YEFCHVbkLyRJWOGFskV+fJjnmvs2Lfn7wY1svK3HjfI33+n2szLWPdypbhj0KN5IMfzL2noJL0taLSOY62MS6B/6e8zj9/keItcUVCG176YsEJCABCUhAAodP4NAFQ4XsJitZvsDas1scP19ZRuYwBNpK5ZDcyPKNWYzNpeOIdXchGGInSvbHWV96zE8ZHyHIEbetbUFsbUzjq//+Byw/vo7hqe+gve/EvmfOYTCSYXVS3u2bU9nsU0tvS4GfTrLKh+NufRtp91PLt7rLSOUDWNn0YX6zB2vpCAOUekFRNRzafVjed3v8/HZEVM28tg9sy9WArugWxjtn0BkpoqN7AEG/H5b5pFa4bIwVTrZVq4zse8Y1rDzKIJtc5YSxTQQidKmzZTbnaPIrLD36DIlnX/K+6/y9MgoFH/sl+fh95XFbcOX27+TyMevd5GcPh1DYYUhURjBQpNUyHt/8BG2d/RgYbxy+1XkoukoCEpCABCSwR6DapHp4/Iq71Mx69sEqiWZvYWH+Nqdp3kU5d4+99yqfGbH/NEL8UMhhzz/bBtv8iKW59DrsRWQ0wLDI4esmP0yxMIivuMlNfs+eRK1RL5IMmvhZE0Mh/qIvjnSxmxVCb+Iip3rGWvt5AH7wVJ2OqobKe86VrpCABCQgAQlI4PAIHKpgyEKh5dnfYGXuU1bxbDD8KCKTzmOD07ki7UM4+8F30Ttymm/wuK7fLqzEscbUuewWFmZuYvrGJ1h6+hAhBkSxWJZNkq0SaAu9w+fR2jOGWNfwjjOX31rBxsoDhk4Jftq4vZ6r9h7MScqchtLZexwnLvwQ8fFLyBW9WHxyGxvPrBdCDmsMjRJrKWQyeaRyUY7FjWMjx8CIu7PRtu7qt5rsyCpy7P2ltRVqC6UZPvESzKArtIxIOIhwJIBWC1zG/oqhUByRSAhOdhWzd36Gh9lZpDd31ce7j5cH4ASWjSW+WV56gL5dlVIODdKJ+1hbuIGn059h5emXyGzl+JhZZRXsQaitBwPxCRybeJuh2/Om0lvJdawuPORo4WvsR3QXoZDDyiyGQ6E80uv3ce/av7hVVn2jFw7PfxF6pBKQgAQk8K0XqK0isgdbDYrsg5vkxgKbVt9iyLMEW0G2zK9wVlHihxb2Ncr3APZam2Eza3bHQ5I9hlJcGtbWHmcVUDdS6w5aNsEPkuIYOHYG8VgvYh1x/tzvThQ9an2DvvV/DHqAEpCABF5UoIX/r6+P/Vb5v+XaJCCBnQKH6r+KxMJ9PLr1X8hn5t3/nrdSWWS3vBh84w9w/M3vuH1tgmGOHqmztXfFMTh+DvPT1/Hg859jc2OeE3FbWH10HdmNJ+jim7+uwTOIdBzj9LJ+LqFKIbn2gG8Qn1SWq9XZp11VdHj8kYs8/ocIhCoTz7q73uenmBy5y9QnzUAonc4itbHEsOgrhjMP2Zz5Pqt3+EaVb0y5KIuPI8xqG/YUKiTZnJodhEp84+mLon/8MgZOvINItIfVN92sxvEzeGETa/ZLsqaW1clipWCIVU+n+cb1Fj2SXM7Fd7ncauuSSlz6llx7xGqga4i08U0uQ7Ail8ll+NxTDHWW5m5gZf5L3meZzTRzLMn38/jvYeryD90gKsRpbuG2LpbRV0bxus+dy+uGTpyj63lMf/EfePrgU6SSDLA48tdxkgyMPoc3wElwkU509I7uI6irJSABCUhAAt9MoDYo6uw+wddMvuZsD6YouSW0VpFrS5/5mstUyEKj5AaDIm4xviZGW3sZ+tjisUpFrF1v9/N4/e5rrQ19sCbK2iQgAQlI4JAKBDrgOfP3wKm/ZTDEclIvJwpok4AEvhY4VMGQLflq730TM7dmWe2TRLh1EJPvfoxjb7yzJ7TYfY5tTH0PLxZydPaN4s7//ASJhet8o7fJ6p0MSnMMmZJzvH2A++pjsFFkKLTAKVuPWJ3Ejw7rbCUurWrvmUD8+PmvQyG7m427t4tt7ZwC1t7ejjKngpUmJpFMzOLep/+IuXuP3abObEHNyqY8gx72FUKOTS1zXLrFMnZO9Zo6ewXDk1fcN6MHjZe3Y/Wy58LG8mNWBD1ikGVVQzsntZVKeeSzCazO32CVEyusGPIw2kFyfY7h2H1WGrGhNgMs66HQ1v0mzrz1Rxg++TYfR3xHGFTL4GPTa19rFwZPtLJqagizrNb68tOfsnroK7cSqq13kNVC5/mGW02oa930vQQkIAEJvDoBe830eZ5/iFHvSJ2c/tneNebepNCnnpCuk4AEJNBkAgz44Y1ULk321PR0JPAyBA5VMBSKdmD8wvcZ2jhYYpn4KKt0Rk59sG+VUD2gIMOhwRMX3MBj/v41hky/wGpiDu0OS87ZZHptZdoNdWwJmjW2LuQ4lcwp7Oz1s73jEieB9Ry7gP6RqXqH2nGdBTtW5ROKdCAcq1TeWKNmD8OhsvXx4cWOaVuZjZ09vjb42SOpGjA1OoDdN9Y1gkC4j89jnUESa+RrNmtkXWBFUmLpDkOgWVYeRViZlHcrjHLZDPs1WeNOH5fDXcWZq3+JuFt9VamAanRsC4jM841zH7pL+G7+8p/cxtlnrv4FRqeu8Hm82H4aHUe3S0ACEpCABF6GgDtCXVOzXgal9iEBCUhAAhKQQBMIHKpgyLwtVJngtKuR0x+43++3dOygc2NBRmfvMMepd7qhxd1rP+FyrznEWkoMT6zpwHZIw8bV1bBm9/7KZS8ra06g99gU+/BEd9+878+BcDuXfA3wuGEUUzsrkWzpVyUasulfXlYK1S4G23eXX98QokeE1U6biQd77uzum+X0RTbRdopcKsYleGWW1TtcupbN+VlRGcf4uXdx8uLH6B+d2rdKaM+Oa66wqqwxBkHhaKcbDP0+4dJB+9VtEpCABCQgAQlIQAISkIAEJCABCbwagUMXDBmDBUJ2+aabVcSMnb7qlpFP3/hXBiRPWElT00Ogpil0zbeVw/J3rIIpFKnf02i/x2YVQIFwhJO8Ang+56xy7+oxymUPQ6tu9urp3G83da+3HgsttmaWm+2rNlZ6/vj5HRtpW9WVTUDLs5eQxzeAU5f/jOPlv9dwSV7dA9dcaeHQIPsO2SOo7UfU6Pd0uwQkIAEJSEACEpCABCQgAQlIQAL//wJHvpOiNYy25U4jpz5kktKJPEfTurEKq4UO2qyYJ7fF5pWrMyjmKs2eD7p/7W0+NrP0ccT8flsLG2D6WFHkCwT3u8ue69OcwvJs5jO3wTRY+bRfrdHz61vYONvD5+vD8NQHOH3lBwf2E9pzwAOucHsP1TSpPuCuukkCEpCABCQgAQlIQAISkIAEJCCB1yhwKCuGXraXhUNjZz7i8qo1zN/9N46rbRwMsYMzihwTv7bwW04Ls0bLkwhympnXz6ZmB2z59DJ7/DzhRK/d9ULPf8nLwSh2seVYjbbKqPlHWHjwa04F+zUbaM/yV9x0q+7mVhLxHwuFsrkAJ49dxam3v88x9N+8AqvuAXWlBCQgAQlIQAISkIAEJCABCUhAAt9aAQVD26cmHOvAwPELnBo2zfH106y4sXBl/2DGbebMyWgrc1/Awp7ugRkEI92cTtYNX7jLbRwd4MU2G3nL/+NUsFWkE/dZZXSPzZ5tctjOrdpjyAaa5TghbWt9HrH2vh13chgoOYUt5DMJZDfnkE0945j5r7D45KY7WcxxG2XvnEjmPoav91L5rlj0ItY5gYkL30X34Pjuh6KfJSABCUhAAhKQgAQkIAEJSEACEjgCAgqGak5yz9Akx9Ofx2xqllU1Dpszsw/PAVuJzZxz+RQSKzNIbSxy6VcYwVAnp49FWDlk37eihaU/Xh+nn7CZNKMh3u8pVp4+YEiU3rPnagxVLmWRXn+ApelfMCF6wsbQUTeicjjFLMffK2RTSK0vIrF4161yKmQZFOXSKJW2A6HtFKh2OdnziMsaansYIHnYuLuDwVOPln3tORO6QgISkIAEJCABCUhAAhKQgAQkcDQEFAzVnGebLtbWNeBO9sqnF1FkZU69zSnaKHsLYexrmcvC8vw+x4Blk42lV7kMzCKZFreptW1WLWQ/2xe7b4HVQjYhbP+thExqCTN3PsHc9C8RCHLcO3eSyyZRYtNod7pYgaPmM1usHiqhxNSnxBH3dlyPrRPjgTyeMhtR7614ssfgOC0cVe9hpdAJVjqd2P9h6BYJSEACEpCABCQgAQlIQAISkIAEmlpAwdCu02sNn4PhKPsHkaamVU8l1Ckjs+VgK8VghsFQiOPuw1EuG2OPIdsyqQTW1xNuAOQPtMDvt8lcZX5lhGQZDf+xEfGVHw7+u7IlYVvcXzlZZshj1UYWKnG8vONDoeBhjyD7mSPn4eX0MvYHYnCU3uSxGQzZmHsvM6kAjx8IFhkYPQ+h3Idhj5ATyYLhVl4YOmmTgAQkIAEJSEACEpCABCQgAQlI4EgKKBjaddoDXP4Vbe9FjhVD+Rxv3C66yaRLSK4X4Qm0YfjU2zh28jJaO9lsmhO4qptTLLCCKIfVhUdYezaNtaVpfn/PbU7t87fA5y0xKOK93QoibrsKeuzq3TU+tuwrl/Own5HPrQoKR3swPvUOOuMnEOI4+0hrJ/ddSaasisi2xOIMVp9Oc1ncNDaX7zIgsuPmGRpZQFQ9uHtXbRKQgAQkIAEJSEACEpCABCQgAQkcYQEFQ7tOvo1a93p825U9lRutSihXiOLY1CWOtr+KHi7BirR17dubp3foDeQy7/GSwtryLEOih5if5ij5pbvsO2RVRLbUa+9f3e5QyGFFUD7vhz/Yh/Fz72KIYVQbwygLgwKs9PEyEKo3Fr7v2ASbU6fcfkSLM9fx6NbPkUrc4/PK8XkV9x5Y10hAAhKQgAQkIAEJSEACEpCABCRwJAUUDO067dnMKhtEz6GYz7q3ZNPWYDqKE+d/gJMXOdb9gECouqtAKMaJZDG3oqijbwRD4+cwduYP8ezRDTy48e9ILt9HKGwj6ev3GSqXOBOtwFAo50d8/D1Mvfun6GEvoPALHNsegy0Ps0srv7cgySauTf/ux0iu3Oa8shL7EJW43KyEreQal6utIcIlcdokIAEJSEACEpCABCQgAQlIQAISOHoCCoZqznmRE8bSnPaV2kgwGCowmHGQ3CigZ/QURk+/z8bU8d/7L8QqkHytXazy6UI7QxobY3/nNz9GavUrhFg95GFAs3sr5NlTyNOP8bPv4OTbf4y+0am6lUG7f6/ez1ZZNDJ5hZPL1vHw+ioyyTmUvTlWD5X4HDNwtgOwer+r6yQgAQlIQAISkIAEJCABCUhAAhJoboE6C5qa+wkf9OwyqVVsrM67499tyZUtIQu3j+GNcx+hs2/4oF99odsspBk7fQVvXvlztPWeYlVQ0B0dX92s+4+FQtmcD8NTH+LSx3/zjUKh6n79rF4aGL+EnqGzDKKCvNrLYKiMxLMHWFl4+EKPXXeSgAQkIAEJSEACEpCABCQgAQlIoPkEFAzVnNO15Rksz97hGPicGwpleYkfP4vhk5f+zxU7u/9kbInZcYZDA2MX2Uw6wMvz5tXFIkfSMxTq6J3EyCk2t2aFUr0eQrv3+SI/xzqPoefYOfgjvQyj/DbEDMnVJ3g6c9NdTqZNAhKQgAQkIAEJSEACEpCABCQggaMnoGCo5pwHgm3svRNDcjOP9UQBkc4xDI6ff+kj3S0cGmDg1BU/xSlkz4OhEkfIl9hfaGD8Ao977qX+Ndr0tJ7hM+gaOMPJaWHki0H4OWHN5w3AKRRe6rG0MwlIQAISkIAEJCABCUhAAhKQgAQOh4B6DNWcp+6Bk7j4vb/DwswtLM3eY7XQmZce0FQP194zhM7+40iv3eMEefYU4tK1UtGD9p4JHvPsSw+j7LjRjiH0Dp/npLRFtHYfxzB7D7lNrVs7Dsdfqx6lBCQgAQlIQAISkIAEJCABCUhAAi9VQMFQDac/GGMvoRiibb0YnrjsjoS36V6vYou293JiWJyTyUJoQRZFx4HjeDjFbJSB0cirOCSsamh48n10D03BnmuYY+9f1lK1V/KAtVMJSEACEpCABCQgAQlIQAISkIAEXqmAgqE6vNVx83VuemlXWUhj4VC0rR/Z1BZDIU4Ic1o44j6OGC+vagtxNL1dtElAAhKQgAQkIAEJSEACEpCABCQgAfUYeo1/Az5/AB6vj4FQiQ2huZqMj8XnC6qK5zWeEx1aAhKQgAQkIAEJSEACEpCABCRwlARUMfSaz3ahWEQ2z2VkRS9CEavmaX/Nj0iHl4AEJCABCUhAAhKQgAQkIAEJSOCoCKhi6DWeaadUQj5fQCYLZPI+9qAOwxsIvsZHpENLQAISkIAEJCABCUhAAhKQgAQkcJQEWsrcjtIT/jY912xqHZtrz1AsZLmUrAyfP8j+Qv2ItnZ9mx6mHosEJCABCUhAAhKQgAQkIAEJSEACTSqgYKhJT6yelgQkIAEJSEACEpCABCQgAQlIQAISaCSgpWSNhHS7BCQgAQlIQAISkIAEJCABCUhAAhJoUgEFQ016YvW0JCABCUhAAhKQgAQkIAEJSEACEpBAIwEFQ42EdLsEJCABCUhAAhKQgAQkIAEJSEACEmhSAQVDTXpi9bQkIAEJSEACEpCABCQgAQlIQAISkEAjAQVDjYR0uwQkIAEJSEACEpCABCQgAQlIQAISaFIBBUNNemL1tCQgAQlIQAISkIAEJCABCUhAAhKQQCMBBUONhHS7BCQgAQlIQAISkIAEJCABCUhAAhJoUgEFQ016YvW0JCABCUhAAhKQgAQkIAEJSEACEpBAIwEFQ42EdLsEJCABCUhAAhKQgAQkIAEJSEACEmhSAQVDTXpi9bQkIAEJSEACEpCABCQgAQlIQAISkEAjAQVDjYR0uwQkIAEJSEACEpCABCQgAQlIQAISaFIBBUNNemL1tCQgAQlIQAISkIAEJCABCUhAAhKQQCMBBUONhHS7BCQgAQlIQAISkIAEJCABCUhAAhJoUgEFQ016YvW0JCABCUhAAhKQgAQkIAEJSEACEpBAIwEFQ42EdLsEJCABCUhAAhKQgAQkIAEJSEACEmhSAQVDTXpi9bQkIAEJSEACEpCABCQgAQlIQAISkEAjgf8FBGq54DPbK6sAAAAASUVORK5CYII=" + } + }, + "cell_type": "markdown", + "id": "3a787a26-6646-4fc3-9f15-8739e4ed47fe", + "metadata": {}, + "source": [ + "# 312.1: Accessing and visualising faint features in coadd images with the Butler\n", + "\n", + "
\n", + "\n", + "![image.png](attachment:1e2050a2-4e1c-4956-84ad-cc5f196353c8.png)\n", + "\n", + "
\n", + "\n", + "Deployment: For the Rubin Science Platform at data.lsst.cloud. \\\n", + "Data Release: [Data Preview 1](https://dp1.lsst.io/) \\\n", + "Container Size: large \\\n", + "LSST Science Pipelines version: r29.2.0 \\\n", + "Last verified to run: 2026-06-11 \\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks) \\" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-info", + "metadata": {}, + "source": [ + "**Learning objective:** Use the Butler to load a `deep_coadd` patch from DP1, display it with a stretch suited to faint extended emission, and understand how the choice of stretch function and display range determines what is visible in the data.\n", + "\n", + "**LSST data products:** `deep_coadd`, `skyMap`\n", + "\n", + "**Packages:** `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`, `astropy.visualization`\n", + "\n", + "**Credit:** Originally developed by the Rubin Community Science team.\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the\n", + "[Support Category](https://community.lsst.org/c/support/6)\n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualise the data at the signal levels where LSB structures live.\n", + "\n", + "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 ECDFS field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", + "\n", + "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 202.6 describes the pixel mask planes in detail. lsb-203 covers mask plane usage for LSB science. lsb-202 covers background subtraction; lsb-301 characterises systematics affecting LSB science." + ] + }, + { + "cell_type": "markdown", + "id": "b334cfae-3f6f-458e-a3e4-89a1265f8578", + "metadata": {}, + "source": [ + "### 1.1. Import packages\n", + "\n", + "Import common scientific analysis packages `numpy`, `matplotlib`, and `astropy`.\n", + "\n", + "Import LSST Science Pipelines packages for sky geometry (`lsst.geom`), image display (`lsst.afw.display`), and data access via the Butler (`lsst.daf.butler`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.visualization import AsinhStretch, ImageNormalize, LinearStretch\n", + "\n", + "import lsst.geom as geom\n", + "import lsst.afw.display as afwDisplay\n", + "from lsst.daf.butler import Butler" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1-2", + "metadata": {}, + "source": [ + "### 1.2. Define parameters and functions\n", + "\n", + "Set the LSST plate scale and define unit-conversion helpers. Two functions are used throughout:\n", + "\n", + "- `build_sky_arr` — masks detected sources and detector artefacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See lsb-203 for a detailed treatment of these mask planes.\n", + "- `sky_stats` — estimates the sky level and per-pixel noise from the masked array. The sky level is the robust median; the noise is 1.4826 × MAD (median absolute deviation). Returns `(vmin, vmax)` where `vmin = sky − lo_sigma × σ̂` and `vmax = sky + hi_sigma × σ̂`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-params", + "metadata": {}, + "outputs": [], + "source": [ + "PIXEL_SCALE = 0.2\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "\n", + "afwDisplay.setDefaultBackend(\"matplotlib\")\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.dpi\": 150,\n", + " \"image.origin\": \"lower\",\n", + " \"axes.titlesize\": 10,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ac181cbb", + "metadata": {}, + "source": [ + "Define `build_sky_arr` and `sky_stats`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-helpers", + "metadata": {}, + "outputs": [], + "source": [ + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel⁻¹ to AB mag arcsec^-2 for LSST pixel scale.\"\"\"\n", + " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec^-2 to nJy pixel⁻¹ for LSST pixel scale.\"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", + "\n", + "\n", + "def build_sky_arr(\n", + " masked_image,\n", + " arr,\n", + " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", + " source_planes=(\"DETECTED\",),\n", + "):\n", + " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + "\n", + " mask_arr = masked_image.getMask().getArray()\n", + " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", + " exclude = np.zeros(arr.shape, dtype=bool)\n", + " for plane in list(bad_planes) + list(source_planes):\n", + " if plane in mask_pd:\n", + " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", + " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", + "\n", + "\n", + "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", + "\n", + " Uses the robust median as sky level and 1.4826 × MAD as pixel noise.\n", + " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", + " sources do not bias the estimates.\n", + " \"\"\"\n", + " flat = arr[np.isfinite(arr)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s2", + "metadata": {}, + "source": [ + "## 2. Load a coadd patch\n", + "\n", + "Open the DP1 butler, locate the tract and patch containing the ECDFS field centre, and load the `deep_coadd` for the i-band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-butler", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", + "skymap_name = \"lsst_cells_v1\"\n", + "\n", + "target_ra = 53.218\n", + "target_dec = -28.095\n", + "band = \"i\"\n", + "\n", + "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", + "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "tract_info = skymap.findTract(sky_point)\n", + "patch_info = tract_info.findPatch(sky_point)\n", + "tract_id = tract_info.getId()\n", + "patch_id = patch_info.getSequentialIndex()\n", + "patch_wcs = tract_info.getWcs()\n", + "patch_bbox = patch_info.getOuterBBox()\n", + "\n", + "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", + "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", + "coadd_arr = coadd.image.array.copy()\n", + "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "\n", + "print(\n", + " f\"Shape: {coadd_arr.shape} \"\n", + " f\"({coadd_arr.shape[1] * PIXEL_SCALE / 60:.1f} × \"\n", + " f\"{coadd_arr.shape[0] * PIXEL_SCALE / 60:.1f} arcmin)\"\n", + ")\n", + "print(\n", + " f\"Sky pixels for statistics: {np.isfinite(coadd_sky).sum():,} / {coadd_sky.size:,}\"\n", + " f\" ({100 * np.isfinite(coadd_sky).mean():.1f}%)\"\n", + ")\n", + "sky_val = np.nanmedian(coadd_sky)\n", + "print(\n", + " f\"Sky level: {sky_val:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_val):.1f} mag/arcsec^2\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s3", + "metadata": {}, + "source": [ + "## 3. Full-patch display\n", + "\n", + "Display the full coadd patch with a sky-noise anchored stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-display-compare", + "metadata": {}, + "outputs": [], + "source": [ + "vmin, vmax = sky_stats(coadd_sky)\n", + "fig = plt.figure(figsize=(4, 4))\n", + "disp = afwDisplay.Display(frame=fig)\n", + "disp.scale(\"asinh\", min=vmin, max=vmax)\n", + "disp.image(coadd.image)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cfced9ea", + "metadata": {}, + "source": [ + "> Figure 1: The full i-band deep_coadd patch (tract 5063, patch 14) displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky minus 2 sigma (black) to sky plus 8 sigma (white), emphasising low-surface-brightness structure over the 11.3 by 11.3 arcminute field." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s4", + "metadata": {}, + "source": [ + "## 4. Postage stamp and stretch comparison\n", + "\n", + "The Butler supports lazy loading: `parameters={\"bbox\": ...}` fetches only the pixels within the requested bounding box from the object store, making sub-region access efficient regardless of the full patch size.\n", + "\n", + "**Display tools.** Two display approaches are used in this section.\n", + "\n", + "**`lsst.afw.display`** is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", + "\n", + "```python\n", + "disp.scale('asinh', 'zscale') # ZScale: automatic range\n", + "disp.scale('asinh', min=vmin, max=vmax) # explicit range from sky_stats()\n", + "```\n", + "\n", + "*ZScale* fits a linear function to the core of the pixel histogram to set the display range automatically. The range adapts to each image so the same surface brightness will appear at a different gray level in images of different depth or background. Setting `min`/`max` explicitly from `sky_stats()` anchors the display to the physical noise floor. This approach is used in section 3.\n", + "\n", + "**`ImageNormalize + imshow`** (astropy) is used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." + ] + }, + { + "cell_type": "markdown", + "id": "79ec6be5", + "metadata": {}, + "source": [ + "Define the four co-ordinates used as demonstration targets throughout sections 4.1 and 4.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b3ec972", + "metadata": {}, + "outputs": [], + "source": [ + "_demo_targets = [\n", + " (53.3375, -27.810, \"(5,0)\"),\n", + " (53.010, -28.350, \"(5,1)\"),\n", + " (52.965, -28.184, \"(4,1)\"),\n", + " (53.220, -28.095, \"(3,3)\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "2a028501", + "metadata": {}, + "source": [ + "Load a 4′ × 4′ stamp centred on each target galaxy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-bbox", + "metadata": {}, + "outputs": [], + "source": [ + "half_width = 600\n", + "\n", + "demo_stamps = []\n", + "for ra, dec, label in _demo_targets:\n", + " sp = geom.SpherePoint(ra * geom.degrees, dec * geom.degrees)\n", + " ti = skymap.findTract(sp)\n", + " pi = ti.findPatch(sp)\n", + " wcs = ti.getWcs()\n", + " cen = wcs.skyToPixel(sp)\n", + " did = {\"band\": band, \"tract\": ti.getId(), \"patch\": pi.getSequentialIndex()}\n", + " bbox = geom.Box2I(\n", + " geom.Point2I(int(cen.getX()) - half_width, int(cen.getY()) - half_width),\n", + " geom.Extent2I(2 * half_width, 2 * half_width),\n", + " )\n", + " bbox.clip(pi.getOuterBBox())\n", + " stamp = butler.get(\"deep_coadd\", dataId=did, parameters={\"bbox\": bbox})\n", + " sky_a = build_sky_arr(stamp.getMaskedImage(), stamp.image.array)\n", + " demo_stamps.append((sky_a, stamp, f\"RA={ra} Dec={dec}\"))\n", + "\n", + "stamp_arcmin = 2 * half_width * PIXEL_SCALE / 60\n", + "fig, axes = plt.subplots(4, 1, figsize=(4, 4 * 4), squeeze=False)\n", + "for idx, (sky_a, stamp, label) in enumerate(demo_stamps):\n", + " plt.sca(axes[idx][0])\n", + " disp = afwDisplay.Display(frame=fig)\n", + " vmin, vmax = sky_stats(sky_a)\n", + " disp.scale(\"asinh\", min=vmin, max=vmax)\n", + " disp.image(stamp.image)\n", + " axes[idx][0].set_title(label, fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "b7409ecb", + "metadata": {}, + "source": [ + "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centred on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise floor using an arcsinh stretch." + ] + }, + { + "cell_type": "markdown", + "id": "4ec932ba", + "metadata": {}, + "source": [ + "### 4.1. Stretch functions\n", + "\n", + "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalised pixel value. The two transfer functions used here are:\n", + "\n", + "| Stretch | Transfer function | Behaviour |\n", + "|---------|-------------------|-----------|\n", + "| **Linear** | $f(t) = t$ | Uniform mapping; preserves noise texture near sky level but saturates bright sources unless $v_\\mathrm{max}$ is set conservatively |\n", + "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; preserves sky noise texture while compressing bright sources |\n", + "\n", + "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behaviour. Smaller values compress more aggressively; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", + "\n", + "```python\n", + "AsinhStretch(a=0.1) # default — stronger compression\n", + "AsinhStretch(a=0.5) # gentler — brighter features retain more structure\n", + "AsinhStretch(a=2.0) # close to linear\n", + "```\n", + "\n", + "Both panels below use the same display range (sky $-$ 2σ to sky $+$ 30σ) so that any visual difference is due solely to the transfer function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6a51c6", + "metadata": {}, + "outputs": [], + "source": [ + "stretches = [\n", + " (\"Linear\", LinearStretch(), 30),\n", + " (\"Asinh\", AsinhStretch(a=0.05), 30),\n", + "]\n", + "\n", + "lo = 2.0\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps), 2, figsize=(6, 2 * len(demo_stamps)), squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, (name, stretch_fn, hi) in enumerate(stretches):\n", + " norm = ImageNormalize(\n", + " vmin=sky - lo * sigma,\n", + " vmax=sky + hi * sigma,\n", + " stretch=stretch_fn,\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " if row == 0:\n", + " axes[row][col].set_title(f\"{name} (-{int(lo)}σ to +{hi}σ)\")\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "id": "3d68d84d", + "metadata": {}, + "source": [ + "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky minus 2 sigma to sky plus 30 sigma). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." + ] + }, + { + "cell_type": "markdown", + "id": "92410f2d", + "metadata": {}, + "source": [ + "### 4.2. White point, black point, and contrast\n", + "\n", + "The display range $[v_\\mathrm{min},\\, v_\\mathrm{max}]$ determines which flux levels map to black and white:\n", + "\n", + "- **Black point** $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", + "- **White point** $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", + "\n", + "Expressing both endpoints in multiples of $\\hat{\\sigma}$ gives display ranges that are reproducible and physically comparable across images of different depth.\n", + "\n", + "> **Note:** `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", + "\n", + "The grid below holds the black point fixed at sky $- 2\\hat{\\sigma}$ and varies the white point, showing how the choice of white point affects what is visible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95338f39", + "metadata": {}, + "outputs": [], + "source": [ + "hi_sigmas = [4, 8, 20, 50]\n", + "col_labels = [f\"sky + {h}σ\" for h in hi_sigmas]\n", + "\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps),\n", + " len(hi_sigmas),\n", + " figsize=(1.5 * len(hi_sigmas), 1.5 * len(demo_stamps)),\n", + " squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " vmin = sky - 2.0 * sigma\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, hi in enumerate(hi_sigmas):\n", + " norm = ImageNormalize(\n", + " vmin=vmin,\n", + " vmax=sky + hi * sigma,\n", + " stretch=AsinhStretch(),\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])\n", + " if row == 0:\n", + " axes[row][col].set_title(col_labels[col], fontsize=9)" + ] + }, + { + "cell_type": "markdown", + "id": "47f64152", + "metadata": {}, + "source": [ + "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky minus 2 sigma and arcsinh stretch applied. From left to right the white point increases from sky plus 4 sigma to sky plus 50 sigma, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-summary", + "metadata": {}, + "source": [ + "## 5. Summary\n", + "\n", + "- `Butler.get('deep_coadd', parameters={'bbox': ...})` fetches only the requested sub-region, making postage stamp access efficient for large patches.\n", + "- The **stretch function** determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", + "- The **white point** ($v_\\mathrm{max}$) and **black point** ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5072fe-2cba-48f2-b780-5fccaa15eec3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb new file mode 100644 index 0000000..4e4fce1 --- /dev/null +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb @@ -0,0 +1,543 @@ +{ + "cells": [ + { + "attachments": { + "1e2050a2-4e1c-4956-84ad-cc5f196353c8.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "3a787a26-6646-4fc3-9f15-8739e4ed47fe", + "metadata": {}, + "source": [ + "# 312.1: Accessing and visualising faint features in coadd images with the Butler\n", + "\n", + "
\n", + "\n", + "![image.png](attachment:1e2050a2-4e1c-4956-84ad-cc5f196353c8.png)\n", + "\n", + "
\n", + "\n", + "Deployment: For the Rubin Science Platform at data.lsst.cloud. \\\n", + "Data Release: [Data Preview 1](https://dp1.lsst.io/) \\\n", + "Container Size: large \\\n", + "LSST Science Pipelines version: r29.2.0 \\\n", + "Last verified to run: 2026-06-11 \\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks) \\" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-info", + "metadata": {}, + "source": [ + "**Learning objective:** Use the Butler to load a `deep_coadd` patch from DP1, display it with a stretch suited to faint extended emission, and understand how the choice of stretch function and display range determines what is visible in the data.\n", + "\n", + "**LSST data products:** `deep_coadd`, `skyMap`\n", + "\n", + "**Packages:** `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`, `astropy.visualization`\n", + "\n", + "**Credit:** Originally developed by the Rubin Community Science team.\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the\n", + "[Support Category](https://community.lsst.org/c/support/6)\n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualise the data at the signal levels where LSB structures live.\n", + "\n", + "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 ECDFS field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", + "\n", + "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 202.6 describes the pixel mask planes in detail. lsb-203 covers mask plane usage for LSB science. lsb-202 covers background subtraction; lsb-301 characterises systematics affecting LSB science." + ] + }, + { + "cell_type": "markdown", + "id": "b334cfae-3f6f-458e-a3e4-89a1265f8578", + "metadata": {}, + "source": [ + "### 1.1. Import packages\n", + "\n", + "Import common scientific analysis packages `numpy`, `matplotlib`, and `astropy`.\n", + "\n", + "Import LSST Science Pipelines packages for sky geometry (`lsst.geom`), image display (`lsst.afw.display`), and data access via the Butler (`lsst.daf.butler`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.visualization import AsinhStretch, ImageNormalize, LinearStretch\n", + "\n", + "import lsst.geom as geom\n", + "import lsst.afw.display as afwDisplay\n", + "from lsst.daf.butler import Butler" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s1-2", + "metadata": {}, + "source": [ + "### 1.2. Define parameters and functions\n", + "\n", + "Set the LSST plate scale and define unit-conversion helpers. Two functions are used throughout:\n", + "\n", + "- `build_sky_arr` — masks detected sources and detector artefacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See lsb-203 for a detailed treatment of these mask planes.\n", + "- `sky_stats` — estimates the sky level and per-pixel noise from the masked array. The sky level is the robust median; the noise is 1.4826 × MAD (median absolute deviation). Returns `(vmin, vmax)` where `vmin = sky − lo_sigma × σ̂` and `vmax = sky + hi_sigma × σ̂`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-params", + "metadata": {}, + "outputs": [], + "source": [ + "PIXEL_SCALE = 0.2\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "\n", + "afwDisplay.setDefaultBackend(\"matplotlib\")\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.dpi\": 150,\n", + " \"image.origin\": \"lower\",\n", + " \"axes.titlesize\": 10,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ac181cbb", + "metadata": {}, + "source": [ + "Define `build_sky_arr` and `sky_stats`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-helpers", + "metadata": {}, + "outputs": [], + "source": [ + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel⁻¹ to AB mag arcsec^-2 for LSST pixel scale.\"\"\"\n", + " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec^-2 to nJy pixel⁻¹ for LSST pixel scale.\"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", + "\n", + "\n", + "def build_sky_arr(\n", + " masked_image,\n", + " arr,\n", + " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", + " source_planes=(\"DETECTED\",),\n", + "):\n", + " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + "\n", + " mask_arr = masked_image.getMask().getArray()\n", + " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", + " exclude = np.zeros(arr.shape, dtype=bool)\n", + " for plane in list(bad_planes) + list(source_planes):\n", + " if plane in mask_pd:\n", + " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", + " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", + "\n", + "\n", + "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", + "\n", + " Uses the robust median as sky level and 1.4826 × MAD as pixel noise.\n", + " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", + " sources do not bias the estimates.\n", + " \"\"\"\n", + " flat = arr[np.isfinite(arr)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s2", + "metadata": {}, + "source": [ + "## 2. Load a coadd patch\n", + "\n", + "Open the DP1 butler, locate the tract and patch containing the ECDFS field centre, and load the `deep_coadd` for the i-band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-butler", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", + "skymap_name = \"lsst_cells_v1\"\n", + "\n", + "target_ra = 53.218\n", + "target_dec = -28.095\n", + "band = \"i\"\n", + "\n", + "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", + "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "tract_info = skymap.findTract(sky_point)\n", + "patch_info = tract_info.findPatch(sky_point)\n", + "tract_id = tract_info.getId()\n", + "patch_id = patch_info.getSequentialIndex()\n", + "patch_wcs = tract_info.getWcs()\n", + "patch_bbox = patch_info.getOuterBBox()\n", + "\n", + "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", + "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", + "coadd_arr = coadd.image.array.copy()\n", + "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "\n", + "print(\n", + " f\"Shape: {coadd_arr.shape} \"\n", + " f\"({coadd_arr.shape[1] * PIXEL_SCALE / 60:.1f} × \"\n", + " f\"{coadd_arr.shape[0] * PIXEL_SCALE / 60:.1f} arcmin)\"\n", + ")\n", + "print(\n", + " f\"Sky pixels for statistics: {np.isfinite(coadd_sky).sum():,} / {coadd_sky.size:,}\"\n", + " f\" ({100 * np.isfinite(coadd_sky).mean():.1f}%)\"\n", + ")\n", + "sky_val = np.nanmedian(coadd_sky)\n", + "print(\n", + " f\"Sky level: {sky_val:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_val):.1f} mag/arcsec^2\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s3", + "metadata": {}, + "source": [ + "## 3. Full-patch display\n", + "\n", + "Display the full coadd patch with a sky-noise anchored stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-display-compare", + "metadata": {}, + "outputs": [], + "source": [ + "vmin, vmax = sky_stats(coadd_sky)\n", + "fig = plt.figure(figsize=(4, 4))\n", + "disp = afwDisplay.Display(frame=fig)\n", + "disp.scale(\"asinh\", min=vmin, max=vmax)\n", + "disp.image(coadd.image)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cfced9ea", + "metadata": {}, + "source": [ + "> Figure 1: The full i-band deep_coadd patch (tract 5063, patch 14) displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky minus 2 sigma (black) to sky plus 8 sigma (white), emphasising low-surface-brightness structure over the 11.3 by 11.3 arcminute field." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-s4", + "metadata": {}, + "source": [ + "## 4. Postage stamp and stretch comparison\n", + "\n", + "The Butler supports lazy loading: `parameters={\"bbox\": ...}` fetches only the pixels within the requested bounding box from the object store, making sub-region access efficient regardless of the full patch size.\n", + "\n", + "**Display tools.** Two display approaches are used in this section.\n", + "\n", + "**`lsst.afw.display`** is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", + "\n", + "```python\n", + "disp.scale('asinh', 'zscale') # ZScale: automatic range\n", + "disp.scale('asinh', min=vmin, max=vmax) # explicit range from sky_stats()\n", + "```\n", + "\n", + "*ZScale* fits a linear function to the core of the pixel histogram to set the display range automatically. The range adapts to each image so the same surface brightness will appear at a different gray level in images of different depth or background. Setting `min`/`max` explicitly from `sky_stats()` anchors the display to the physical noise floor. This approach is used in section 3.\n", + "\n", + "**`ImageNormalize + imshow`** (astropy) is used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." + ] + }, + { + "cell_type": "markdown", + "id": "79ec6be5", + "metadata": {}, + "source": [ + "Define the four co-ordinates used as demonstration targets throughout sections 4.1 and 4.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b3ec972", + "metadata": {}, + "outputs": [], + "source": [ + "_demo_targets = [\n", + " (53.3375, -27.810, \"(5,0)\"),\n", + " (53.010, -28.350, \"(5,1)\"),\n", + " (52.965, -28.184, \"(4,1)\"),\n", + " (53.220, -28.095, \"(3,3)\"),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "2a028501", + "metadata": {}, + "source": [ + "Load a 4′ × 4′ stamp centred on each target galaxy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb201-bbox", + "metadata": {}, + "outputs": [], + "source": [ + "half_width = 600\n", + "\n", + "demo_stamps = []\n", + "for ra, dec, label in _demo_targets:\n", + " sp = geom.SpherePoint(ra * geom.degrees, dec * geom.degrees)\n", + " ti = skymap.findTract(sp)\n", + " pi = ti.findPatch(sp)\n", + " wcs = ti.getWcs()\n", + " cen = wcs.skyToPixel(sp)\n", + " did = {\"band\": band, \"tract\": ti.getId(), \"patch\": pi.getSequentialIndex()}\n", + " bbox = geom.Box2I(\n", + " geom.Point2I(int(cen.getX()) - half_width, int(cen.getY()) - half_width),\n", + " geom.Extent2I(2 * half_width, 2 * half_width),\n", + " )\n", + " bbox.clip(pi.getOuterBBox())\n", + " stamp = butler.get(\"deep_coadd\", dataId=did, parameters={\"bbox\": bbox})\n", + " sky_a = build_sky_arr(stamp.getMaskedImage(), stamp.image.array)\n", + " demo_stamps.append((sky_a, stamp, f\"RA={ra} Dec={dec}\"))\n", + "\n", + "stamp_arcmin = 2 * half_width * PIXEL_SCALE / 60\n", + "fig, axes = plt.subplots(4, 1, figsize=(4, 4 * 4), squeeze=False)\n", + "for idx, (sky_a, stamp, label) in enumerate(demo_stamps):\n", + " plt.sca(axes[idx][0])\n", + " disp = afwDisplay.Display(frame=fig)\n", + " vmin, vmax = sky_stats(sky_a)\n", + " disp.scale(\"asinh\", min=vmin, max=vmax)\n", + " disp.image(stamp.image)\n", + " axes[idx][0].set_title(label, fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "b7409ecb", + "metadata": {}, + "source": [ + "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centred on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise floor using an arcsinh stretch." + ] + }, + { + "cell_type": "markdown", + "id": "4ec932ba", + "metadata": {}, + "source": [ + "### 4.1. Stretch functions\n", + "\n", + "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalised pixel value. The two transfer functions used here are:\n", + "\n", + "| Stretch | Transfer function | Behaviour |\n", + "|---------|-------------------|-----------|\n", + "| **Linear** | $f(t) = t$ | Uniform mapping; preserves noise texture near sky level but saturates bright sources unless $v_\\mathrm{max}$ is set conservatively |\n", + "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; preserves sky noise texture while compressing bright sources |\n", + "\n", + "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behaviour. Smaller values compress more aggressively; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", + "\n", + "```python\n", + "AsinhStretch(a=0.1) # default — stronger compression\n", + "AsinhStretch(a=0.5) # gentler — brighter features retain more structure\n", + "AsinhStretch(a=2.0) # close to linear\n", + "```\n", + "\n", + "Both panels below use the same display range (sky $-$ 2σ to sky $+$ 30σ) so that any visual difference is due solely to the transfer function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c6a51c6", + "metadata": {}, + "outputs": [], + "source": [ + "stretches = [\n", + " (\"Linear\", LinearStretch(), 30),\n", + " (\"Asinh\", AsinhStretch(a=0.05), 30),\n", + "]\n", + "\n", + "lo = 2.0\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps), 2, figsize=(6, 2 * len(demo_stamps)), squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, (name, stretch_fn, hi) in enumerate(stretches):\n", + " norm = ImageNormalize(\n", + " vmin=sky - lo * sigma,\n", + " vmax=sky + hi * sigma,\n", + " stretch=stretch_fn,\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " if row == 0:\n", + " axes[row][col].set_title(f\"{name} (-{int(lo)}σ to +{hi}σ)\")\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])" + ] + }, + { + "cell_type": "markdown", + "id": "3d68d84d", + "metadata": {}, + "source": [ + "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky minus 2 sigma to sky plus 30 sigma). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." + ] + }, + { + "cell_type": "markdown", + "id": "92410f2d", + "metadata": {}, + "source": [ + "### 4.2. White point, black point, and contrast\n", + "\n", + "The display range $[v_\\mathrm{min},\\, v_\\mathrm{max}]$ determines which flux levels map to black and white:\n", + "\n", + "- **Black point** $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", + "- **White point** $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", + "\n", + "Expressing both endpoints in multiples of $\\hat{\\sigma}$ gives display ranges that are reproducible and physically comparable across images of different depth.\n", + "\n", + "> **Note:** `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", + "\n", + "The grid below holds the black point fixed at sky $- 2\\hat{\\sigma}$ and varies the white point, showing how the choice of white point affects what is visible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95338f39", + "metadata": {}, + "outputs": [], + "source": [ + "hi_sigmas = [4, 8, 20, 50]\n", + "col_labels = [f\"sky + {h}σ\" for h in hi_sigmas]\n", + "\n", + "fig, axes = plt.subplots(\n", + " len(demo_stamps),\n", + " len(hi_sigmas),\n", + " figsize=(1.5 * len(hi_sigmas), 1.5 * len(demo_stamps)),\n", + " squeeze=False,\n", + ")\n", + "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", + " flat = sky_a[np.isfinite(sky_a)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " vmin = sky - 2.0 * sigma\n", + " bbox = stamp.getBBox()\n", + " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", + " for col, hi in enumerate(hi_sigmas):\n", + " norm = ImageNormalize(\n", + " vmin=vmin,\n", + " vmax=sky + hi * sigma,\n", + " stretch=AsinhStretch(),\n", + " )\n", + " axes[row][col].imshow(\n", + " stamp.image.array,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=stamp_extent,\n", + " )\n", + " axes[row][col].set_xticks([])\n", + " axes[row][col].set_yticks([])\n", + " if row == 0:\n", + " axes[row][col].set_title(col_labels[col], fontsize=9)" + ] + }, + { + "cell_type": "markdown", + "id": "47f64152", + "metadata": {}, + "source": [ + "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky minus 2 sigma and arcsinh stretch applied. From left to right the white point increases from sky plus 4 sigma to sky plus 50 sigma, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." + ] + }, + { + "cell_type": "markdown", + "id": "lsb201-summary", + "metadata": {}, + "source": [ + "## 5. Summary\n", + "\n", + "- `Butler.get('deep_coadd', parameters={'bbox': ...})` fetches only the requested sub-region, making postage stamp access efficient for large patches.\n", + "- The **stretch function** determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", + "- The **white point** ($v_\\mathrm{max}$) and **black point** ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d38726146d19d20a8ca697f2df5dc58895d6babf Mon Sep 17 00:00:00 2001 From: MelissaGraham Date: Tue, 16 Jun 2026 16:14:15 +0000 Subject: [PATCH 2/5] rm duplicate --- .../312_1_visualisation.ipynb | 551 ------------------ 1 file changed, 551 deletions(-) delete mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb deleted file mode 100644 index b29554c..0000000 --- a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualisation.ipynb +++ /dev/null @@ -1,551 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "1e2050a2-4e1c-4956-84ad-cc5f196353c8.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "3a787a26-6646-4fc3-9f15-8739e4ed47fe", - "metadata": {}, - "source": [ - "# 312.1: Accessing and visualising faint features in coadd images with the Butler\n", - "\n", - "
\n", - "\n", - "![image.png](attachment:1e2050a2-4e1c-4956-84ad-cc5f196353c8.png)\n", - "\n", - "
\n", - "\n", - "Deployment: For the Rubin Science Platform at data.lsst.cloud. \\\n", - "Data Release: [Data Preview 1](https://dp1.lsst.io/) \\\n", - "Container Size: large \\\n", - "LSST Science Pipelines version: r29.2.0 \\\n", - "Last verified to run: 2026-06-11 \\\n", - "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks) \\" - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-info", - "metadata": {}, - "source": [ - "**Learning objective:** Use the Butler to load a `deep_coadd` patch from DP1, display it with a stretch suited to faint extended emission, and understand how the choice of stretch function and display range determines what is visible in the data.\n", - "\n", - "**LSST data products:** `deep_coadd`, `skyMap`\n", - "\n", - "**Packages:** `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`, `astropy.visualization`\n", - "\n", - "**Credit:** Originally developed by the Rubin Community Science team.\n", - "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", - "\n", - "**Get Support:**\n", - "Everyone is encouraged to ask questions or raise issues in the\n", - "[Support Category](https://community.lsst.org/c/support/6)\n", - "of the Rubin Community Forum.\n", - "Rubin staff will respond to all questions posted there." - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-s1", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualise the data at the signal levels where LSB structures live.\n", - "\n", - "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 ECDFS field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", - "\n", - "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 202.6 describes the pixel mask planes in detail. lsb-203 covers mask plane usage for LSB science. lsb-202 covers background subtraction; lsb-301 characterises systematics affecting LSB science." - ] - }, - { - "cell_type": "markdown", - "id": "b334cfae-3f6f-458e-a3e4-89a1265f8578", - "metadata": {}, - "source": [ - "### 1.1. Import packages\n", - "\n", - "Import common scientific analysis packages `numpy`, `matplotlib`, and `astropy`.\n", - "\n", - "Import LSST Science Pipelines packages for sky geometry (`lsst.geom`), image display (`lsst.afw.display`), and data access via the Butler (`lsst.daf.butler`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-imports", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from astropy.visualization import AsinhStretch, ImageNormalize, LinearStretch\n", - "\n", - "import lsst.geom as geom\n", - "import lsst.afw.display as afwDisplay\n", - "from lsst.daf.butler import Butler" - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-s1-2", - "metadata": {}, - "source": [ - "### 1.2. Define parameters and functions\n", - "\n", - "Set the LSST plate scale and define unit-conversion helpers. Two functions are used throughout:\n", - "\n", - "- `build_sky_arr` — masks detected sources and detector artefacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See lsb-203 for a detailed treatment of these mask planes.\n", - "- `sky_stats` — estimates the sky level and per-pixel noise from the masked array. The sky level is the robust median; the noise is 1.4826 × MAD (median absolute deviation). Returns `(vmin, vmax)` where `vmin = sky − lo_sigma × σ̂` and `vmax = sky + hi_sigma × σ̂`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-params", - "metadata": {}, - "outputs": [], - "source": [ - "PIXEL_SCALE = 0.2\n", - "PIXEL_AREA = PIXEL_SCALE**2\n", - "\n", - "afwDisplay.setDefaultBackend(\"matplotlib\")\n", - "\n", - "plt.rcParams.update(\n", - " {\n", - " \"figure.dpi\": 150,\n", - " \"image.origin\": \"lower\",\n", - " \"axes.titlesize\": 10,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ac181cbb", - "metadata": {}, - "source": [ - "Define `build_sky_arr` and `sky_stats`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-helpers", - "metadata": {}, - "outputs": [], - "source": [ - "def nJy_per_pix_to_mu(flux_nJy):\n", - " \"\"\"Convert nJy pixel⁻¹ to AB mag arcsec^-2 for LSST pixel scale.\"\"\"\n", - " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", - "\n", - "\n", - "def mu_to_nJy_per_pix(mu):\n", - " \"\"\"Convert AB mag arcsec^-2 to nJy pixel⁻¹ for LSST pixel scale.\"\"\"\n", - " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", - "\n", - "\n", - "def build_sky_arr(\n", - " masked_image,\n", - " arr,\n", - " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", - " source_planes=(\"DETECTED\",),\n", - "):\n", - " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", - "\n", - " mask_arr = masked_image.getMask().getArray()\n", - " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", - " exclude = np.zeros(arr.shape, dtype=bool)\n", - " for plane in list(bad_planes) + list(source_planes):\n", - " if plane in mask_pd:\n", - " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", - " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", - "\n", - "\n", - "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", - " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", - "\n", - " Uses the robust median as sky level and 1.4826 × MAD as pixel noise.\n", - " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", - " sources do not bias the estimates.\n", - " \"\"\"\n", - " flat = arr[np.isfinite(arr)]\n", - " sky = np.median(flat)\n", - " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", - " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-s2", - "metadata": {}, - "source": [ - "## 2. Load a coadd patch\n", - "\n", - "Open the DP1 butler, locate the tract and patch containing the ECDFS field centre, and load the `deep_coadd` for the i-band." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-butler", - "metadata": {}, - "outputs": [], - "source": [ - "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", - "skymap_name = \"lsst_cells_v1\"\n", - "\n", - "target_ra = 53.218\n", - "target_dec = -28.095\n", - "band = \"i\"\n", - "\n", - "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", - "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", - "tract_info = skymap.findTract(sky_point)\n", - "patch_info = tract_info.findPatch(sky_point)\n", - "tract_id = tract_info.getId()\n", - "patch_id = patch_info.getSequentialIndex()\n", - "patch_wcs = tract_info.getWcs()\n", - "patch_bbox = patch_info.getOuterBBox()\n", - "\n", - "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", - "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", - "coadd_arr = coadd.image.array.copy()\n", - "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", - "\n", - "print(\n", - " f\"Shape: {coadd_arr.shape} \"\n", - " f\"({coadd_arr.shape[1] * PIXEL_SCALE / 60:.1f} × \"\n", - " f\"{coadd_arr.shape[0] * PIXEL_SCALE / 60:.1f} arcmin)\"\n", - ")\n", - "print(\n", - " f\"Sky pixels for statistics: {np.isfinite(coadd_sky).sum():,} / {coadd_sky.size:,}\"\n", - " f\" ({100 * np.isfinite(coadd_sky).mean():.1f}%)\"\n", - ")\n", - "sky_val = np.nanmedian(coadd_sky)\n", - "print(\n", - " f\"Sky level: {sky_val:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_val):.1f} mag/arcsec^2\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-s3", - "metadata": {}, - "source": [ - "## 3. Full-patch display\n", - "\n", - "Display the full coadd patch with a sky-noise anchored stretch." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-display-compare", - "metadata": {}, - "outputs": [], - "source": [ - "vmin, vmax = sky_stats(coadd_sky)\n", - "fig = plt.figure(figsize=(4, 4))\n", - "disp = afwDisplay.Display(frame=fig)\n", - "disp.scale(\"asinh\", min=vmin, max=vmax)\n", - "disp.image(coadd.image)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cfced9ea", - "metadata": {}, - "source": [ - "> Figure 1: The full i-band deep_coadd patch (tract 5063, patch 14) displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky minus 2 sigma (black) to sky plus 8 sigma (white), emphasising low-surface-brightness structure over the 11.3 by 11.3 arcminute field." - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-s4", - "metadata": {}, - "source": [ - "## 4. Postage stamp and stretch comparison\n", - "\n", - "The Butler supports lazy loading: `parameters={\"bbox\": ...}` fetches only the pixels within the requested bounding box from the object store, making sub-region access efficient regardless of the full patch size.\n", - "\n", - "**Display tools.** Two display approaches are used in this section.\n", - "\n", - "**`lsst.afw.display`** is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", - "\n", - "```python\n", - "disp.scale('asinh', 'zscale') # ZScale: automatic range\n", - "disp.scale('asinh', min=vmin, max=vmax) # explicit range from sky_stats()\n", - "```\n", - "\n", - "*ZScale* fits a linear function to the core of the pixel histogram to set the display range automatically. The range adapts to each image so the same surface brightness will appear at a different gray level in images of different depth or background. Setting `min`/`max` explicitly from `sky_stats()` anchors the display to the physical noise floor. This approach is used in section 3.\n", - "\n", - "**`ImageNormalize + imshow`** (astropy) is used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." - ] - }, - { - "cell_type": "markdown", - "id": "79ec6be5", - "metadata": {}, - "source": [ - "Define the four co-ordinates used as demonstration targets throughout sections 4.1 and 4.2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b3ec972", - "metadata": {}, - "outputs": [], - "source": [ - "_demo_targets = [\n", - " (53.3375, -27.810, \"(5,0)\"),\n", - " (53.010, -28.350, \"(5,1)\"),\n", - " (52.965, -28.184, \"(4,1)\"),\n", - " (53.220, -28.095, \"(3,3)\"),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "2a028501", - "metadata": {}, - "source": [ - "Load a 4′ × 4′ stamp centred on each target galaxy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-bbox", - "metadata": {}, - "outputs": [], - "source": [ - "half_width = 600\n", - "\n", - "demo_stamps = []\n", - "for ra, dec, label in _demo_targets:\n", - " sp = geom.SpherePoint(ra * geom.degrees, dec * geom.degrees)\n", - " ti = skymap.findTract(sp)\n", - " pi = ti.findPatch(sp)\n", - " wcs = ti.getWcs()\n", - " cen = wcs.skyToPixel(sp)\n", - " did = {\"band\": band, \"tract\": ti.getId(), \"patch\": pi.getSequentialIndex()}\n", - " bbox = geom.Box2I(\n", - " geom.Point2I(int(cen.getX()) - half_width, int(cen.getY()) - half_width),\n", - " geom.Extent2I(2 * half_width, 2 * half_width),\n", - " )\n", - " bbox.clip(pi.getOuterBBox())\n", - " stamp = butler.get(\"deep_coadd\", dataId=did, parameters={\"bbox\": bbox})\n", - " sky_a = build_sky_arr(stamp.getMaskedImage(), stamp.image.array)\n", - " demo_stamps.append((sky_a, stamp, f\"RA={ra} Dec={dec}\"))\n", - "\n", - "stamp_arcmin = 2 * half_width * PIXEL_SCALE / 60\n", - "fig, axes = plt.subplots(4, 1, figsize=(4, 4 * 4), squeeze=False)\n", - "for idx, (sky_a, stamp, label) in enumerate(demo_stamps):\n", - " plt.sca(axes[idx][0])\n", - " disp = afwDisplay.Display(frame=fig)\n", - " vmin, vmax = sky_stats(sky_a)\n", - " disp.scale(\"asinh\", min=vmin, max=vmax)\n", - " disp.image(stamp.image)\n", - " axes[idx][0].set_title(label, fontsize=8)" - ] - }, - { - "cell_type": "markdown", - "id": "b7409ecb", - "metadata": {}, - "source": [ - "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centred on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise floor using an arcsinh stretch." - ] - }, - { - "cell_type": "markdown", - "id": "4ec932ba", - "metadata": {}, - "source": [ - "### 4.1. Stretch functions\n", - "\n", - "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalised pixel value. The two transfer functions used here are:\n", - "\n", - "| Stretch | Transfer function | Behaviour |\n", - "|---------|-------------------|-----------|\n", - "| **Linear** | $f(t) = t$ | Uniform mapping; preserves noise texture near sky level but saturates bright sources unless $v_\\mathrm{max}$ is set conservatively |\n", - "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; preserves sky noise texture while compressing bright sources |\n", - "\n", - "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behaviour. Smaller values compress more aggressively; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", - "\n", - "```python\n", - "AsinhStretch(a=0.1) # default — stronger compression\n", - "AsinhStretch(a=0.5) # gentler — brighter features retain more structure\n", - "AsinhStretch(a=2.0) # close to linear\n", - "```\n", - "\n", - "Both panels below use the same display range (sky $-$ 2σ to sky $+$ 30σ) so that any visual difference is due solely to the transfer function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c6a51c6", - "metadata": {}, - "outputs": [], - "source": [ - "stretches = [\n", - " (\"Linear\", LinearStretch(), 30),\n", - " (\"Asinh\", AsinhStretch(a=0.05), 30),\n", - "]\n", - "\n", - "lo = 2.0\n", - "fig, axes = plt.subplots(\n", - " len(demo_stamps), 2, figsize=(6, 2 * len(demo_stamps)), squeeze=False,\n", - ")\n", - "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", - " flat = sky_a[np.isfinite(sky_a)]\n", - " sky = np.median(flat)\n", - " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", - " bbox = stamp.getBBox()\n", - " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", - " for col, (name, stretch_fn, hi) in enumerate(stretches):\n", - " norm = ImageNormalize(\n", - " vmin=sky - lo * sigma,\n", - " vmax=sky + hi * sigma,\n", - " stretch=stretch_fn,\n", - " )\n", - " axes[row][col].imshow(\n", - " stamp.image.array,\n", - " norm=norm,\n", - " cmap=\"Greys_r\",\n", - " origin=\"lower\",\n", - " extent=stamp_extent,\n", - " )\n", - " if row == 0:\n", - " axes[row][col].set_title(f\"{name} (-{int(lo)}σ to +{hi}σ)\")\n", - " axes[row][col].set_xticks([])\n", - " axes[row][col].set_yticks([])" - ] - }, - { - "cell_type": "markdown", - "id": "3d68d84d", - "metadata": {}, - "source": [ - "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky minus 2 sigma to sky plus 30 sigma). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." - ] - }, - { - "cell_type": "markdown", - "id": "92410f2d", - "metadata": {}, - "source": [ - "### 4.2. White point, black point, and contrast\n", - "\n", - "The display range $[v_\\mathrm{min},\\, v_\\mathrm{max}]$ determines which flux levels map to black and white:\n", - "\n", - "- **Black point** $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", - "- **White point** $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", - "\n", - "Expressing both endpoints in multiples of $\\hat{\\sigma}$ gives display ranges that are reproducible and physically comparable across images of different depth.\n", - "\n", - "> **Note:** `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", - "\n", - "The grid below holds the black point fixed at sky $- 2\\hat{\\sigma}$ and varies the white point, showing how the choice of white point affects what is visible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95338f39", - "metadata": {}, - "outputs": [], - "source": [ - "hi_sigmas = [4, 8, 20, 50]\n", - "col_labels = [f\"sky + {h}σ\" for h in hi_sigmas]\n", - "\n", - "fig, axes = plt.subplots(\n", - " len(demo_stamps),\n", - " len(hi_sigmas),\n", - " figsize=(1.5 * len(hi_sigmas), 1.5 * len(demo_stamps)),\n", - " squeeze=False,\n", - ")\n", - "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", - " flat = sky_a[np.isfinite(sky_a)]\n", - " sky = np.median(flat)\n", - " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", - " vmin = sky - 2.0 * sigma\n", - " bbox = stamp.getBBox()\n", - " stamp_extent = (bbox.beginX, bbox.endX, bbox.beginY, bbox.endY)\n", - " for col, hi in enumerate(hi_sigmas):\n", - " norm = ImageNormalize(\n", - " vmin=vmin,\n", - " vmax=sky + hi * sigma,\n", - " stretch=AsinhStretch(),\n", - " )\n", - " axes[row][col].imshow(\n", - " stamp.image.array,\n", - " norm=norm,\n", - " cmap=\"Greys_r\",\n", - " origin=\"lower\",\n", - " extent=stamp_extent,\n", - " )\n", - " axes[row][col].set_xticks([])\n", - " axes[row][col].set_yticks([])\n", - " if row == 0:\n", - " axes[row][col].set_title(col_labels[col], fontsize=9)" - ] - }, - { - "cell_type": "markdown", - "id": "47f64152", - "metadata": {}, - "source": [ - "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky minus 2 sigma and arcsinh stretch applied. From left to right the white point increases from sky plus 4 sigma to sky plus 50 sigma, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-summary", - "metadata": {}, - "source": [ - "## 5. Summary\n", - "\n", - "- `Butler.get('deep_coadd', parameters={'bbox': ...})` fetches only the requested sub-region, making postage stamp access efficient for large patches.\n", - "- The **stretch function** determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", - "- The **white point** ($v_\\mathrm{max}$) and **black point** ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c5072fe-2cba-48f2-b780-5fccaa15eec3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "LSST", - "language": "python", - "name": "lsst" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From fc9791fc290752ec36219286887963c8c802d975 Mon Sep 17 00:00:00 2001 From: garrethmartin Date: Wed, 17 Jun 2026 09:59:27 +0000 Subject: [PATCH 3/5] SP-2645: add notebooks 312.2 and 312.3 --- .../312_2_lsb_detection.ipynb | 980 ++++++++++++++++++ .../312_3_tidal_features.ipynb | 462 +++++++++ 2 files changed, 1442 insertions(+) create mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb create mode 100644 DP1/300_Science_Demos/312_Low_surface_brightness/312_3_tidal_features.ipynb diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb new file mode 100644 index 0000000..dfa0204 --- /dev/null +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb @@ -0,0 +1,980 @@ +{ + "cells": [ + { + "attachments": { + "2bf47867-c587-448a-860b-9833ea5d5586.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "lsb302-header", + "metadata": {}, + "source": [ + "# 312.2. Detecting LSB structures\n", + "\n", + "
\n", + "\n", + "![logo.png](attachment:2bf47867-c587-448a-860b-9833ea5d5586.png)\n", + "\n", + "
\n", + "\n", + "For the Rubin Science Platform at data.lsst.cloud.\\\n", + "Data Release: [Data Preview 1](https://dp1.lsst.io/)\\\n", + "Container Size: large\\\n", + "LSST Science Pipelines version: r29.2.0\\\n", + "Last verified to run: 2026-06-17\\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-info", + "metadata": {}, + "source": [ + "**Learning objective:** Apply pixel binning and adaptive smoothing to reveal low surface brightness structures in DP1 coadd images, and compare how each technique affects the detectable extent of galaxy outskirts through radial surface brightness profiles.\n", + "\n", + "**LSST data products:** `deep_coadd`, `skyMap`\n", + "\n", + "**Packages:** `lsst.daf.butler`, `lsst.geom`, `lsst.afw.display`, `numpy`, `matplotlib`, `scipy`, `astropy`\n", + "\n", + "**Credit:** Originally developed by the Rubin Community Science team.\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the\n", + "[Support Category](https://community.lsst.org/c/support/6)\n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-s1", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Low surface brightness structures span a wide range of angular scales and physical origins: dwarf satellite galaxies subtend tens to hundreds of arcseconds; tidal streams and shells extend over arcminutes; and intracluster light fills degrees around nearby rich clusters. All share the property that their surface brightness is comparable to, or fainter than, the per-pixel noise in a single epoch. Detecting them requires suppressing pixel-to-pixel noise while preserving diffuse flux.\n", + "\n", + "This notebook applies two complementary noise-suppression techniques to the DP1 ECDFS coadd. Pixel binning block-averages $N\\times N$ pixels to reduce noise by $\\sqrt{N}$ while exactly preserving surface brightness — faint diffuse emission is revealed without any blurring of the underlying sky level. Adaptive smoothing adjusts the kernel size pixel-by-pixel so that bright regions retain resolution while faint diffuse regions are smoothed only as much as needed to achieve a target signal-to-noise ratio. The closing section compares radial surface brightness profiles measured from each technique to show where each method hits the noise floor.\n", + "\n", + "**Related tutorials:** Tutorial 312.1 introduces the display stretches used throughout this series; 312.3 covers interactive surface brightness measurement of tidal features.\n", + "\n", + "### 1.1. Import packages\n", + "\n", + "`lsst.daf.butler` and `lsst.geom` access the data; `lsst.afw.display` renders images with an astronomically appropriate stretch. `numpy` and `scipy.ndimage` handle the numerical operations; `astropy.visualization` provides the `ImageNormalize` and `AsinhStretch` classes; `matplotlib` produces all figures." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.ndimage import uniform_filter, binary_dilation\n", + "from astropy.visualization import AsinhStretch, ImageNormalize\n", + "\n", + "import lsst.geom as geom\n", + "import lsst.afw.display as afwDisplay\n", + "from lsst.daf.butler import Butler" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-s1-2", + "metadata": {}, + "source": [ + "### 1.2. Define parameters and functions\n", + "\n", + "`TARGET` sets the sky coordinate of the target galaxy.\n", + "\n", + "`nJy_per_pix_to_mu`, `mu_to_nJy_per_pix`, `build_sky_arr`, `sky_stats`, and `sky_norm` are unit-conversion and sky-statistics helpers used throughout. `show_lsb` wraps them into a multi-panel display helper. `bin_image` performs block averaging with NaN propagation. `adaptive_smooth` applies a spatially varying kernel via normalised convolution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-params", + "metadata": {}, + "outputs": [], + "source": [ + "afwDisplay.setDefaultBackend(\"matplotlib\")\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.dpi\": 150,\n", + " \"image.origin\": \"lower\",\n", + " \"axes.titlesize\": 10,\n", + " }\n", + ")\n", + "\n", + "TARGET = (53.010, -28.350) # (RA, Dec)\n", + "\n", + "BLUE = \"#648FFF\"\n", + "PURPLE = \"#785EF0\"\n", + "MAGENTA = \"#DC267F\"\n", + "ORANGE = \"#FE6100\"\n", + "YELLOW = \"#FFB000\"\n", + "\n", + "\n", + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel**-1 to AB mag arcsec**-2 for LSST pixel scale.\"\"\"\n", + " return -2.5 * np.log10(np.abs(flux_nJy) * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec**-2 to nJy pixel**-1 for LSST pixel scale.\"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", + "\n", + "\n", + "def build_sky_arr(\n", + " masked_image,\n", + " arr,\n", + " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", + " source_planes=(\"DETECTED\",),\n", + "):\n", + " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + " mask_arr = masked_image.getMask().getArray()\n", + " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", + " exclude = np.zeros(arr.shape, dtype=bool)\n", + " for plane in list(bad_planes) + list(source_planes):\n", + " if plane in mask_pd:\n", + " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", + " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", + "\n", + "\n", + "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", + "\n", + " Uses the robust median as sky level and 1.4826 x MAD as pixel noise.\n", + " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", + " sources do not bias the estimates.\n", + " \"\"\"\n", + " flat = arr[np.isfinite(arr)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma\n", + "\n", + "\n", + "def sky_norm(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return an ImageNormalize anchored to the sky background.\n", + "\n", + " Wraps sky_stats. Pass a source-masked array so that detected objects do\n", + " not bias the sky median or noise estimate.\n", + " \"\"\"\n", + " vmin, vmax = sky_stats(arr, lo_sigma=lo_sigma, hi_sigma=hi_sigma)\n", + " return ImageNormalize(vmin=vmin, vmax=vmax, stretch=AsinhStretch())\n", + "\n", + "\n", + "def show_lsb(\n", + " arrays,\n", + " titles,\n", + " lo_sigma=2.0,\n", + " hi_sigma=8.0,\n", + " cmap=\"Greys_r\",\n", + " n_cols=None,\n", + " norm=None,\n", + " sky_arr=None,\n", + "):\n", + " \"\"\"Display one or more images with a MAD-based LSB stretch.\n", + "\n", + " Parameters\n", + " ----------\n", + " arrays : list of ndarray (displayed as-is)\n", + " sky_arr : ndarray, optional\n", + " Source-masked array used only for computing the display stretch.\n", + " If not supplied, arrays[0] is used -- biased upward when sources are present.\n", + " norm : ImageNormalize, optional\n", + " Pre-computed normalisation; overrides sky_arr.\n", + " \"\"\"\n", + " if norm is None:\n", + " ref = sky_arr if sky_arr is not None else arrays[0]\n", + " norm = sky_norm(ref, lo_sigma=lo_sigma, hi_sigma=hi_sigma)\n", + " n_panels = len(arrays)\n", + " n_cols = n_cols or min(n_panels, 3)\n", + " n_rows = int(np.ceil(n_panels / n_cols))\n", + " fig, axes = plt.subplots(\n", + " n_rows, n_cols, figsize=(5 * n_cols, 5 * n_rows), squeeze=False\n", + " )\n", + " for idx, (arr, title) in enumerate(zip(arrays, titles)):\n", + " row, col = divmod(idx, n_cols)\n", + " axes[row][col].imshow(arr, norm=norm, cmap=cmap, origin=\"lower\")\n", + " axes[row][col].set_title(title)\n", + " axes[row][col].set_xlabel(\"x (pixels)\")\n", + " axes[row][col].set_ylabel(\"y (pixels)\")\n", + " for idx in range(n_panels, n_rows * n_cols):\n", + " row, col = divmod(idx, n_cols)\n", + " axes[row][col].set_visible(False)\n", + "\n", + "\n", + "def bin_image(arr, bin_factor):\n", + " \"\"\"Block-average arr by bin_factor in each dimension, ignoring NaN (masked) pixels.\"\"\"\n", + " ny, nx = arr.shape\n", + " ny_trim = (ny // bin_factor) * bin_factor\n", + " nx_trim = (nx // bin_factor) * bin_factor\n", + " trimmed = arr[:ny_trim, :nx_trim]\n", + " with np.errstate(invalid=\"ignore\"):\n", + " return np.nanmean(\n", + " trimmed.reshape(\n", + " ny_trim // bin_factor, bin_factor, nx_trim // bin_factor, bin_factor\n", + " ),\n", + " axis=(1, 3),\n", + " )\n", + "\n", + "\n", + "def adaptive_smooth(arr, noise_sigma, target_snr=3.0, kernel_sizes=None):\n", + " \"\"\"Adaptive kernel smoothing using normalised convolution to handle masked pixels.\n", + "\n", + " At each pixel, selects the smallest kernel for which |smoothed / noise_eff| >= target_snr.\n", + " Masked (NaN) pixels are excluded from each kernel window via normalised convolution, so\n", + " they do not bleed into adjacent valid pixels.\n", + "\n", + " Parameters\n", + " ----------\n", + " arr : 2-D ndarray, NaN only at bad pixels (SAT, CR, NO_DATA, ...)\n", + " noise_sigma : float, per-pixel sky noise from clean (source-masked) pixels\n", + " target_snr : float, target |signal/noise| ratio\n", + " kernel_sizes : list of int, uniform-filter widths in pixels\n", + " \"\"\"\n", + " if kernel_sizes is None:\n", + " kernel_sizes = [3, 7, 15, 31, 61]\n", + "\n", + " valid = np.isfinite(arr)\n", + " arr_z = np.where(valid, arr, 0.0)\n", + " valid_f = valid.astype(np.float64)\n", + "\n", + " def _nconv(k):\n", + " num = uniform_filter(arr_z, size=k)\n", + " den = uniform_filter(valid_f, size=k)\n", + " return np.where(den > 0.1, num / den, np.nan)\n", + "\n", + " result = _nconv(kernel_sizes[-1])\n", + " kernel_map = np.full(arr.shape, float(kernel_sizes[-1]))\n", + "\n", + " for k in reversed(kernel_sizes[:-1]):\n", + " sm = _nconv(k)\n", + " noise_eff = noise_sigma / k\n", + " snr = np.abs(sm) / noise_eff\n", + " meets = (snr >= target_snr) & np.isfinite(sm)\n", + " result = np.where(meets, sm, result)\n", + " kernel_map = np.where(meets, float(k), kernel_map)\n", + "\n", + " return np.where(valid, result, np.nan), np.where(valid, kernel_map, np.nan)" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-s2", + "metadata": {}, + "source": [ + "## 2. Load a coadd patch\n", + "\n", + "Open the DP1 butler and load the $i$-band `deep_coadd` for the ECDFS field centred on the target galaxy. Display the full patch with a sky-noise anchored stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-butler", + "metadata": {}, + "outputs": [], + "source": [ + "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", + "skymap_name = \"lsst_cells_v1\"\n", + "\n", + "target_ra, target_dec = TARGET\n", + "band = \"i\"\n", + "\n", + "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", + "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "tract_info = skymap.findTract(sky_point)\n", + "patch_info = tract_info.findPatch(sky_point)\n", + "tract_id = tract_info.getId()\n", + "patch_id = patch_info.getSequentialIndex()\n", + "patch_wcs = tract_info.getWcs()\n", + "patch_bbox = patch_info.getOuterBBox()\n", + "\n", + "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", + "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", + "PIXEL_SCALE = coadd.getWcs().getPixelScale().asArcseconds()\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "print(f\"Pixel scale: {PIXEL_SCALE:.4f} arcsec/pixel\")\n", + "coadd_arr = coadd.image.array.copy()\n", + "print(f\"Loaded {band}-band coadd: {coadd_arr.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "55c16928", + "metadata": {}, + "source": [ + "Display the full coadd patch with a sky-noise anchored asinh stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d94ac6c", + "metadata": {}, + "outputs": [], + "source": [ + "coadd_sky_full = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "vmin_patch, vmax_patch = sky_stats(coadd_sky_full)\n", + "\n", + "fig = plt.figure(figsize=(5, 5))\n", + "disp = afwDisplay.Display(frame=fig)\n", + "disp.scale(\"asinh\", min=vmin_patch, max=vmax_patch)\n", + "disp.image(coadd.image)\n", + "plt.title(f\"{band}-band deep_coadd — tract {tract_id}, patch {patch_id}\")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap1", + "metadata": {}, + "source": [ + "> Figure 1: Full $i$-band `deep_coadd` patch containing the target galaxy at RA = 53.010°, Dec = −28.350°. The asinh stretch is anchored to the per-pixel sky noise." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-mask-md", + "metadata": {}, + "source": [ + "Build display and analysis masks from the pipeline mask planes. `coadd_display` retains detected sources so they remain visible; `coadd_lsb` additionally masks `DETECTED` with a 30-pixel dilation to suppress PSF wings for sky statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-mask-code", + "metadata": {}, + "outputs": [], + "source": [ + "coadd_display = build_sky_arr(coadd.getMaskedImage(), coadd_arr, source_planes=())\n", + "\n", + "coadd_sky_prelim = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "dilation_pixels = 30\n", + "dilated_mask = binary_dilation(\n", + " ~np.isfinite(coadd_sky_prelim), iterations=dilation_pixels\n", + ")\n", + "coadd_lsb = np.where(dilated_mask, np.nan, coadd_arr)\n", + "\n", + "sky_pixels_full = coadd_lsb[np.isfinite(coadd_lsb)]\n", + "sky_sigma = 1.4826 * np.median(np.abs(sky_pixels_full - np.median(sky_pixels_full)))\n", + "print(\n", + " f\"After {dilation_pixels}-pixel dilation: {100*(1 - dilated_mask.sum()/dilated_mask.size):.1f}% of pixels available for LSB analysis\"\n", + ")\n", + "print(\n", + " f\"Per-pixel 1-sigma noise: {sky_sigma:.1f} nJy/pix -> {nJy_per_pix_to_mu(sky_sigma):.1f} mag/arcsec^2\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-s4", + "metadata": {}, + "source": [ + "## 3. Pixel binning to reveal diffuse structure\n", + "\n", + "Block-averaging $N\\times N$ pixels reduces pixel-to-pixel noise by $\\sqrt{N}$ while exactly preserving surface brightness — the mean of $N$ sky pixels estimates the sky level equally well regardless of bin size, and the surface brightness units (nJy per original pixel area) remain unchanged. This is the fundamental advantage of binning over smoothing for LSB work: there is no ambiguity about whether diffuse signal has been diluted or redistributed.\n", + "\n", + "Masked (NaN) pixels are excluded from each block average automatically." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-depth-estimate-intro", + "metadata": {}, + "source": [ + "### 3.1. Noise scaling\n", + "\n", + "The table below converts the $\\sqrt{N}$ noise scaling to surface brightness depth, assuming uncorrelated pixel noise. Surface brightness is an intensive quantity, so the sky level is unchanged by binning — only the noise floor drops." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-depth-estimate", + "metadata": {}, + "outputs": [], + "source": [ + "print(\n", + " f\"Per-pixel 1-sigma noise: {sky_sigma:.1f} nJy/pix -> {nJy_per_pix_to_mu(sky_sigma):.1f} mag/arcsec^2\\n\"\n", + ")\n", + "print(\n", + " f\"{'Bin factor':>10} {'Resolution':>12} {'sigma_eff (nJy/pix)':>20} {'1-sigma depth (mag/arcsec^2)':>28} {'Gain (mag)':>10}\"\n", + ")\n", + "print(\"-\" * 85)\n", + "mu_1x = nJy_per_pix_to_mu(sky_sigma)\n", + "for bf in [1, 2, 4, 8, 16, 32]:\n", + " sigma_eff = sky_sigma / bf\n", + " mu = nJy_per_pix_to_mu(sigma_eff)\n", + " gain = mu - mu_1x\n", + " print(\n", + " f'{bf:>10} {bf * PIXEL_SCALE:>11.1f}\" {sigma_eff:>20.1f} {mu:>28.1f} {gain:>+10.2f}'\n", + " )\n", + "\n", + "bin_factors = [1, 4, 8, 16]\n", + "binned_arrays = [bin_image(coadd_display, bf) for bf in bin_factors]\n", + "shared_norm = sky_norm(bin_image(coadd_lsb, 1), lo_sigma=2.0, hi_sigma=8.0)" + ] + }, + { + "cell_type": "markdown", + "id": "b13f7051", + "metadata": {}, + "source": [ + "Quantify the noise reduction as a function of bin factor. Measured $\\sigma$ (from the negative-half pixel mirror, unaffected by real emission) is compared to the $\\sqrt{N}$ expectation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20a6ba62", + "metadata": {}, + "outputs": [], + "source": [ + "x_range = 4.0 * sky_sigma\n", + "measured_sigmas = []\n", + "colors = [YELLOW, ORANGE, MAGENTA, BLUE]\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "for bf, color in zip(bin_factors, colors):\n", + " binned_lsb = bin_image(coadd_lsb, bf)\n", + " pixels = binned_lsb[np.isfinite(binned_lsb)].ravel()\n", + " neg = pixels[pixels < 0]\n", + " meas_sigma = 1.4826 * np.median(np.abs(neg))\n", + " measured_sigmas.append(meas_sigma)\n", + "\n", + " axes[0].hist(\n", + " pixels,\n", + " bins=120,\n", + " range=(-x_range, x_range),\n", + " density=True,\n", + " histtype=\"step\",\n", + " color=color,\n", + " lw=1.5,\n", + " label=f\"Bin {bf}x | sigma = {meas_sigma:.2f} nJy\",\n", + " )\n", + "\n", + "axes[0].set_xlabel(\"Pixel value (nJy)\")\n", + "axes[0].set_ylabel(\"Probability density\")\n", + "axes[0].set_title(\"Sky pixel distributions at each bin factor\")\n", + "axes[0].legend(fontsize=8)\n", + "axes[0].axvline(0, color=\"k\", ls=\":\", lw=0.8)\n", + "\n", + "expected_sigmas = [sky_sigma / bf for bf in bin_factors]\n", + "axes[1].plot(\n", + " bin_factors, expected_sigmas, \"k--o\", ms=6, label=\"Expected: sigma / bin_factor\"\n", + ")\n", + "axes[1].plot(\n", + " bin_factors,\n", + " measured_sigmas,\n", + " \"s-\",\n", + " ms=6,\n", + " color=ORANGE,\n", + " label=\"Measured (negative-half MAD)\",\n", + ")\n", + "axes[1].set_xlabel(\"Bin factor\")\n", + "axes[1].set_ylabel(\"sigma (nJy)\")\n", + "axes[1].set_title(\"Noise reduction: expected vs measured\")\n", + "axes[1].set_xscale(\"log\", base=2)\n", + "axes[1].set_yscale(\"log\")\n", + "axes[1].set_xticks(bin_factors)\n", + "axes[1].set_xticklabels([str(b) for b in bin_factors])\n", + "axes[1].legend()" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap4", + "metadata": {}, + "source": [ + "> Figure 2: Left — sky pixel histograms at each bin factor; the distribution narrows as noise decreases. Right — measured $\\sigma$ versus the $\\sqrt{N}$ expectation. The ratio exceeds 1 at large bin factors, indicating spatially correlated background residuals at arcminute scales." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-interp", + "metadata": {}, + "source": [ + "At small bin factors the ratio is close to 1, showing that pixel noise is largely uncorrelated at sub-arcsecond scales. At large bin factors the measured $\\sigma$ exceeds the $\\sqrt{N}$ prediction, reflecting spatially correlated background residuals left by the pipeline sky subtraction. In practice, binning beyond 8–16× yields less depth improvement than expected." + ] + }, + { + "cell_type": "markdown", + "id": "41b0f09d", + "metadata": {}, + "source": [ + "### 3.2. Visual comparison\n", + "\n", + "Zoom in on the target galaxy at each bin factor to show how resolution and noise trade off at the scale of the galaxy outskirts (field of view $\\pm$120 arcsec around the target)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c81a11c4", + "metadata": {}, + "outputs": [], + "source": [ + "pixel_target = patch_wcs.skyToPixel(sky_point)\n", + "target_px = int(pixel_target.getX()) - patch_bbox.getMinX()\n", + "target_py = int(pixel_target.getY()) - patch_bbox.getMinY()\n", + "print(f\"Target galaxy patch-relative position: ({target_px}, {target_py})\")\n", + "\n", + "zoom_half_arcsec = 120.0\n", + "zoom_half_pix = int(zoom_half_arcsec / PIXEL_SCALE)\n", + "\n", + "fig, axes = plt.subplots(\n", + " 1, len(bin_factors), figsize=(5 * len(bin_factors), 5), squeeze=False\n", + ")\n", + "for col, (bf, barr) in enumerate(zip(bin_factors, binned_arrays)):\n", + " bx = target_px // bf\n", + " by = target_py // bf\n", + " bh = max(1, zoom_half_pix // bf)\n", + "\n", + " x0 = max(0, bx - bh)\n", + " x1 = min(barr.shape[1], bx + bh)\n", + " y0 = max(0, by - bh)\n", + " y1 = min(barr.shape[0], by + bh)\n", + "\n", + " extent = [\n", + " (x0 - bx) * bf * PIXEL_SCALE,\n", + " (x1 - bx) * bf * PIXEL_SCALE,\n", + " (y0 - by) * bf * PIXEL_SCALE,\n", + " (y1 - by) * bf * PIXEL_SCALE,\n", + " ]\n", + " axes[0][col].imshow(\n", + " barr[y0:y1, x0:x1],\n", + " norm=shared_norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=extent,\n", + " )\n", + " axes[0][col].set_title(\n", + " f'Bin {bf}x ({bf * PIXEL_SCALE:.1f}\" res.)\\nsigma = {sky_sigma / bf:.0f} nJy'\n", + " )\n", + " axes[0][col].set_xlabel(\"Delta arcsec\")\n", + " if col == 0:\n", + " axes[0][col].set_ylabel(\"Delta arcsec\")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap5", + "metadata": {}, + "source": [ + "> Figure 3: Zoom-in ($\\pm$120 arcsec) around the target galaxy at each bin factor on a shared stretch." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-s5", + "metadata": {}, + "source": [ + "## 4. Adaptive smoothing\n", + "\n", + "Pixel binning treats every location in the image identically. Adaptive smoothing instead applies a spatially varying kernel: small where the signal is bright enough to be detected at fine resolution, large where the signal is faint and noise dominates. The result preserves compact structure while revealing diffuse emission in regions that pure binning would show as blank noise.\n", + "\n", + "The algorithm selects the smallest kernel (from a discrete set of 3, 7, 15, 31, and 61 pixels) at which $|\\text{smoothed value}| / (\\sigma_{\\rm sky} / k) \\geq 3$. Masked pixels are excluded via normalised convolution — zeroing bad pixels in both numerator and denominator — so they do not bleed into adjacent valid pixels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-find-galaxy", + "metadata": {}, + "outputs": [], + "source": [ + "host_sky_point = sky_point\n", + "host_pix = patch_wcs.skyToPixel(host_sky_point)\n", + "\n", + "host_x = int(host_pix.getX()) - patch_bbox.getMinX()\n", + "host_y = int(host_pix.getY()) - patch_bbox.getMinY()\n", + "print(f\"Host galaxy pixel position (patch-relative): ({host_x}, {host_y})\")\n", + "\n", + "coadd_ny, coadd_nx = coadd_arr.shape\n", + "if 0 <= host_x < coadd_nx and 0 <= host_y < coadd_ny:\n", + " print(f\"Pixel value at host: {coadd_arr[host_y, host_x]:.1f} nJy/pix\")\n", + "else:\n", + " print(\"WARNING: position outside patch\")" + ] + }, + { + "cell_type": "markdown", + "id": "7f3ab63f", + "metadata": {}, + "source": [ + "Load the postage stamp centred on the host galaxy and build display and sky-analysis masks. `stamp_sigma` — the per-pixel noise on source-free sky pixels — is used as the noise threshold in the adaptive smoothing and surface brightness slice sections." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-postage-stamp", + "metadata": {}, + "outputs": [], + "source": [ + "stamp_half = 600\n", + "stamp_bbox = geom.Box2I(\n", + " geom.Point2I(int(host_pix.getX()) - stamp_half, int(host_pix.getY()) - stamp_half),\n", + " geom.Extent2I(2 * stamp_half, 2 * stamp_half),\n", + ")\n", + "stamp_bbox.clip(patch_bbox)\n", + "\n", + "stamp = butler.get(\"deep_coadd\", dataId=data_id, parameters={\"bbox\": stamp_bbox})\n", + "stamp_arr = stamp.image.array.copy()\n", + "\n", + "stamp_display = build_sky_arr(stamp.getMaskedImage(), stamp_arr, source_planes=())\n", + "stamp_bad = ~np.isfinite(stamp_display)\n", + "\n", + "stamp_sky_prelim = build_sky_arr(stamp.getMaskedImage(), stamp_arr)\n", + "stamp_dilated_mask = binary_dilation(~np.isfinite(stamp_sky_prelim), iterations=15)\n", + "stamp_lsb = np.where(stamp_dilated_mask, np.nan, stamp_arr)\n", + "\n", + "stamp_sky_pixels = stamp_lsb[np.isfinite(stamp_lsb)]\n", + "stamp_sigma = 1.4826 * np.median(np.abs(stamp_sky_pixels - np.median(stamp_sky_pixels)))\n", + "print(\n", + " f\"Stamp per-pixel 1-sigma: {stamp_sigma:.1f} nJy/pix -> {nJy_per_pix_to_mu(stamp_sigma):.1f} mag/arcsec^2\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-multiscale-intro", + "metadata": {}, + "source": [ + "### 4.1. Algorithm demonstration\n", + "\n", + "Apply adaptive smoothing to the stamp and display the result alongside the original and the kernel size map, which shows which smoothing scale was selected at each location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-multiscale-a", + "metadata": {}, + "outputs": [], + "source": [ + "kernel_sizes = [1, 3, 7, 15, 31, 61]\n", + "smooth_result, kernel_map = adaptive_smooth(\n", + " stamp_display, stamp_sigma, target_snr=3.0, kernel_sizes=kernel_sizes\n", + ")\n", + "\n", + "norm = sky_norm(stamp_lsb, lo_sigma=2.0, hi_sigma=8.0)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(11, 5))\n", + "axes[0].imshow(stamp_display, norm=norm, cmap=\"Greys_r\", origin=\"lower\")\n", + "axes[0].set_title(\"Original (bad pixels masked)\")\n", + "axes[0].set_xlabel(\"x (pixels)\")\n", + "axes[0].set_ylabel(\"y (pixels)\")\n", + "\n", + "im = axes[1].imshow(kernel_map, cmap=\"cividis\", origin=\"lower\", vmin=0, vmax=61)\n", + "axes[1].set_title(\"Kernel size map (pixels)\")\n", + "axes[1].set_xlabel(\"x (pixels)\")\n", + "axes[1].set_ylabel(\"y (pixels)\")\n", + "plt.colorbar(im, ax=axes[1], label=\"Kernel size (pixels)\")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap6", + "metadata": {}, + "source": [ + "> Figure 4: Left — original stamp with bad pixels masked. Right — kernel size map: small values (bright) indicate compact sources resolved at fine scale; large values (dark) indicate noise-dominated diffuse regions where the algorithm applies the largest kernel." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-binvadapt", + "metadata": {}, + "source": [ + "Compare pixel binning at 8× with the adaptive result on the same stretch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-multiscale-b", + "metadata": {}, + "outputs": [], + "source": [ + "bin_factor_ref = 8\n", + "stamp_binned = bin_image(stamp_display, bin_factor_ref)\n", + "ny_full, nx_full = stamp_display.shape\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", + "axes[0].imshow(\n", + " stamp_binned,\n", + " norm=norm,\n", + " cmap=\"Greys_r\",\n", + " origin=\"lower\",\n", + " extent=[0, nx_full, 0, ny_full],\n", + ")\n", + "axes[0].set_title(\n", + " f'Pixel binning {bin_factor_ref}x ({bin_factor_ref * PIXEL_SCALE:.1f}\" res.)'\n", + ")\n", + "axes[0].set_xlabel(\"x (pixels, native scale)\")\n", + "axes[0].set_ylabel(\"y (pixels, native scale)\")\n", + "\n", + "axes[1].imshow(smooth_result, norm=norm, cmap=\"Greys_r\", origin=\"lower\")\n", + "axes[1].set_title(\"Adaptive smoothing (variable resolution)\")\n", + "axes[1].set_xlabel(\"x (pixels, native scale)\")\n", + "axes[1].set_ylabel(\"y (pixels, native scale)\")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap7", + "metadata": {}, + "source": [ + "> Figure 5: Pixel binning at 8× (left) versus adaptive smoothing (right) on the same stretch. Binning achieves uniform noise reduction at the cost of spatial resolution everywhere; adaptive smoothing retains compact source morphology while suppressing noise in the diffuse outskirts." + ] + }, + { + "cell_type": "markdown", + "id": "c47c4c5c", + "metadata": {}, + "source": [ + "### 4.2. Noise quantification\n", + "\n", + "Quantify the noise improvement from adaptive smoothing. Apply the same algorithm to the full coadd patch (for consistent sky statistics with the binning comparison), then inspect the kernel size distribution from the stamp." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1685d71d", + "metadata": {}, + "outputs": [], + "source": [ + "kernel_sizes = [1, 3, 7, 15, 31, 61]\n", + "coadd_smooth, _ = adaptive_smooth(\n", + " coadd_display, sky_sigma, target_snr=3.0, kernel_sizes=kernel_sizes\n", + ")\n", + "\n", + "sky_valid = np.isfinite(coadd_lsb)\n", + "sky_raw = coadd_lsb[sky_valid]\n", + "sky_smoothed = coadd_smooth[sky_valid]\n", + "\n", + "sky_sigma_raw = 1.4826 * np.median(np.abs(sky_raw - np.median(sky_raw)))\n", + "sky_sigma_smooth = 1.4826 * np.median(np.abs(sky_smoothed - np.median(sky_smoothed)))\n", + "noise_reduction = sky_sigma_raw / sky_sigma_smooth\n", + "depth_gain_mag = nJy_per_pix_to_mu(sky_sigma_smooth) - nJy_per_pix_to_mu(sky_sigma_raw)\n", + "\n", + "print(f\"Sky noise in inter-source regions (full patch):\")\n", + "print(\n", + " f\" Before adaptive smoothing: {sky_sigma_raw:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_sigma_raw):.1f} mag/arcsec^2\"\n", + ")\n", + "print(\n", + " f\" After adaptive smoothing: {sky_sigma_smooth:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_sigma_smooth):.1f} mag/arcsec^2\"\n", + ")\n", + "print(\n", + " f\" Noise reduction: {noise_reduction:.1f}x (equivalent to binning ~{noise_reduction**2:.0f} pixels)\"\n", + ")\n", + "print(f\" Surface brightness gain: {depth_gain_mag:+.2f} mag/arcsec^2\")\n", + "\n", + "kvals = kernel_map[np.isfinite(kernel_map)].ravel()\n", + "counts = [np.sum(kvals == float(k)) for k in kernel_sizes]\n", + "fracs = [c / len(kvals) for c in counts]\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "axes[0].bar(\n", + " range(len(kernel_sizes)),\n", + " [100 * f for f in fracs],\n", + " tick_label=[f'{k} px\\n({k * PIXEL_SCALE:.1f}\")' for k in kernel_sizes],\n", + " color=[\"gray\", YELLOW, ORANGE, MAGENTA, PURPLE, BLUE],\n", + ")\n", + "axes[0].set_xlabel(\"Kernel size selected\")\n", + "axes[0].set_ylabel(\"Fraction of pixels (%)\")\n", + "axes[0].set_title(\"Kernel size distribution\")\n", + "for i, f in enumerate(fracs):\n", + " axes[0].text(i, 100 * f + 0.5, f\"{100*f:.1f}%\", ha=\"center\", fontsize=8)\n", + "\n", + "x_range = 4.0 * sky_sigma\n", + "axes[1].hist(\n", + " sky_raw,\n", + " bins=100,\n", + " range=(-x_range, x_range),\n", + " density=True,\n", + " histtype=\"step\",\n", + " color=\"gray\",\n", + " lw=1.5,\n", + " label=\"Unsmoothed (sky pixels)\",\n", + ")\n", + "axes[1].hist(\n", + " sky_smoothed,\n", + " bins=100,\n", + " range=(-x_range, x_range),\n", + " density=True,\n", + " histtype=\"step\",\n", + " color=ORANGE,\n", + " lw=1.5,\n", + " label=\"Adaptive smooth (sky pixels)\",\n", + ")\n", + "axes[1].axvline(0, color=\"k\", ls=\":\", lw=0.8)\n", + "axes[1].set_xlabel(\"Pixel value (nJy)\")\n", + "axes[1].set_ylabel(\"Probability density\")\n", + "axes[1].set_title(\"Sky pixel distributions before/after adaptive smoothing\")\n", + "axes[1].legend(fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap8", + "metadata": {}, + "source": [ + "> Figure 6: Left — fraction of stamp pixels assigned to each smoothing kernel; the dominant kernel indicates the typical angular scale at which the field becomes noise-dominated. Right — full-patch sky pixel distributions before (grey) and after (orange) adaptive smoothing, using the same sky region as the binning comparison." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-s6", + "metadata": {}, + "source": [ + "## 5. Signal-to-noise profile\n", + "\n", + "A radial SNR profile — median flux in each annulus divided by the effective per-pixel noise — shows directly at what radius each noise-reduction method drops below a detection threshold." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-radial-profile-intro", + "metadata": {}, + "source": [ + "Compute the galaxy centre position in the stamp, build circular annuli out to 50 arcsec, and measure the median flux in each. For binned methods, divide by the uniform effective noise $\\sigma_{\\rm pixel}/N$. For adaptive smoothing, use a pixel-by-pixel effective noise derived from `kernel_map` ($\\sigma_{\\rm pixel}/k_{i,j}$) and take the median per-pixel SNR in each annulus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-radial-profile", + "metadata": {}, + "outputs": [], + "source": [ + "stamp_ny, stamp_nx = stamp_arr.shape\n", + "centre_x = int(host_pix.getX()) - stamp_bbox.getMinX()\n", + "centre_y = int(host_pix.getY()) - stamp_bbox.getMinY()\n", + "print(f\"Host position in stamp: ({centre_x}, {centre_y})\")\n", + "\n", + "if not (5 < centre_x < stamp_nx - 5 and 5 < centre_y < stamp_ny - 5):\n", + " print(\"WARNING: host near edge -- using stamp centre as fallback.\")\n", + " centre_x, centre_y = stamp_nx // 2, stamp_ny // 2\n", + "\n", + "\n", + "def expand_binned(arr_binned, bf):\n", + " \"\"\"Repeat block-averaged values back to native pixel resolution.\"\"\"\n", + " expanded = np.repeat(np.repeat(arr_binned, bf, axis=0), bf, axis=1)\n", + " out = np.full((stamp_ny, stamp_nx), np.nan)\n", + " h = min(expanded.shape[0], stamp_ny)\n", + " w = min(expanded.shape[1], stamp_nx)\n", + " out[:h, :w] = expanded[:h, :w]\n", + " return out\n", + "\n", + "\n", + "grid_y, grid_x = np.mgrid[:stamp_ny, :stamp_nx]\n", + "r_arcsec = np.sqrt((grid_x - centre_x) ** 2 + (grid_y - centre_y) ** 2) * PIXEL_SCALE\n", + "\n", + "r_max = 50.0\n", + "r_bins = np.linspace(0, r_max, 40)\n", + "r_centres = 0.5 * (r_bins[:-1] + r_bins[1:])\n", + "\n", + "\n", + "def snr_profile(arr, sigma_eff):\n", + " \"\"\"Median flux in each annulus divided by scalar sigma_eff.\"\"\"\n", + " prof = []\n", + " for r_lo, r_hi in zip(r_bins[:-1], r_bins[1:]):\n", + " ann = (r_arcsec >= r_lo) & (r_arcsec < r_hi) & ~stamp_bad & np.isfinite(arr)\n", + " prof.append(np.median(arr[ann]) / sigma_eff if ann.sum() > 10 else np.nan)\n", + " return np.array(prof)\n", + "\n", + "\n", + "def snr_profile_adaptive(smooth, kmap, sigma):\n", + " \"\"\"Median per-pixel SNR in each annulus using spatially varying sigma = sigma/k.\"\"\"\n", + " prof = []\n", + " for r_lo, r_hi in zip(r_bins[:-1], r_bins[1:]):\n", + " ann = (\n", + " (r_arcsec >= r_lo)\n", + " & (r_arcsec < r_hi)\n", + " & ~stamp_bad\n", + " & np.isfinite(smooth)\n", + " & np.isfinite(kmap)\n", + " )\n", + " if ann.sum() > 10:\n", + " prof.append(np.median(smooth[ann] * kmap[ann] / sigma))\n", + " else:\n", + " prof.append(np.nan)\n", + " return np.array(prof)\n", + "\n", + "\n", + "snr_unbinned = snr_profile(stamp_arr, stamp_sigma)\n", + "snr_bin4 = snr_profile(expand_binned(bin_image(stamp_display, 4), 4), stamp_sigma / 4)\n", + "snr_bin8 = snr_profile(expand_binned(bin_image(stamp_display, 8), 8), stamp_sigma / 8)\n", + "snr_bin16 = snr_profile(\n", + " expand_binned(bin_image(stamp_display, 16), 16), stamp_sigma / 16\n", + ")\n", + "snr_adaptive = snr_profile_adaptive(smooth_result, kernel_map, stamp_sigma)\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "ax.plot(r_centres, snr_unbinned, \"-\", lw=1.5, color=\"gray\", alpha=0.8, label=\"Unbinned\")\n", + "ax.plot(r_centres, snr_bin4, \"-\", lw=1.5, color=YELLOW, label=\"Bin 4x\")\n", + "ax.plot(r_centres, snr_bin8, \"-\", lw=1.5, color=ORANGE, label=\"Bin 8x\")\n", + "ax.plot(r_centres, snr_bin16, \"-\", lw=1.5, color=MAGENTA, label=\"Bin 16x\")\n", + "ax.plot(r_centres, snr_adaptive, \"-\", lw=2.0, color=BLUE, label=\"Adaptive smooth\")\n", + "ax.set_xlim(0, r_max)\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"Radius (arcsec)\")\n", + "ax.set_ylabel(r\"SNR (median flux / $\\sigma_{\\rm eff}$)\")\n", + "ax.legend(fontsize=9)" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-cap10", + "metadata": {}, + "source": [ + "> Figure 7: Radial SNR profile (azimuthal median flux divided by the effective per-pixel noise $\\sigma_{\\rm eff}$) for five noise-reduction methods, out to 50 arcsec. Fixed binning at 16$\\times$ (magenta) achieves far higher SNR than necessary in the bright inner regions, sacrificing spatial resolution everywhere for a gain that is redundant where the signal is already strong. Adaptive smoothing (blue) instead applies only the smallest kernel that reaches the target SNR of 3 at each pixel, tracking a noise floor rather than exceeding it: resolution is preserved in bright regions and smoothing is applied only in the faint outskirts where it is genuinely needed." + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-summary", + "metadata": {}, + "source": [ + "## 6. Summary\n", + "\n", + "- Pixel binning reduces noise by $\\sqrt{N}$ while exactly preserving surface brightness. At large bin factors (beyond 8–16x) correlated background residuals cause the measured $\\sigma$ to exceed the $\\sqrt{N}$ prediction.\n", + "- Adaptive smoothing via normalised convolution selects the finest kernel that meets a target SNR at each pixel, combining resolution preservation in bright regions with aggressive noise suppression in diffuse outskirts.\n", + "- A radial SNR profile, dividing the azimuthal median by the effective noise of each method, shows directly at what radius the galaxy signal drops below detection — and how far each technique extends that limit." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_3_tidal_features.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_3_tidal_features.ipynb new file mode 100644 index 0000000..fbccd2f --- /dev/null +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_3_tidal_features.ipynb @@ -0,0 +1,462 @@ +{ + "cells": [ + { + "attachments": { + "2bf47867-c587-448a-860b-9833ea5d5586.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "lsb303-header", + "metadata": {}, + "source": [ + "# 312.3. Painting and measuring a tidal feature\n", + "\n", + "
\n", + "\n", + "![logo.png](attachment:2bf47867-c587-448a-860b-9833ea5d5586.png)\n", + "\n", + "
\n", + "\n", + "For the Rubin Science Platform at data.lsst.cloud.\\\n", + "Data Release: [Data Preview 1](https://dp1.lsst.io/)\\\n", + "Container Size: large\\\n", + "LSST Science Pipelines version: r29.2.0\\\n", + "Last verified to run: 2026-06-17\\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)" + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-info", + "metadata": {}, + "source": [ + "**Learning objective:** Load a DP1 coadd stamp, interactively paint a mask over a visible tidal feature using the `%matplotlib widget` backend, and measure the mean and median surface brightness of the painted region in mag arcsec$^{-2}$.\n", + "\n", + "**LSST data products:** `deep_coadd`, `skyMap`\n", + "\n", + "**Packages:** `lsst.daf.butler`, `lsst.geom`, `numpy`, `matplotlib`, `scipy`, `astropy`\n", + "\n", + "**Credit:** Originally developed by the Rubin Community Science team.\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles,\n", + "software releases, or other notebooks.\n", + "\n", + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the\n", + "[Support Category](https://community.lsst.org/c/support/6)\n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-s1", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "Tidal features — streams, shells, and fans of stars stripped from interacting galaxies — are often too irregular in shape for automated aperture photometry. Visual identification and manual masking is a common first step, and is the basis of dedicated inspection tools such as [JAFAR](https://jafar.astro.unistra.fr/), which uses expert and citizen-science classifications to build catalogues of low-surface-brightness features in wide-field survey images.\n", + "\n", + "The `TidalMaskPainter` class defined in Section 1.2 provides a simple interactive brush using the `%matplotlib widget` backend. Clicking and dragging marks a region; the resulting boolean mask is used in Section 5 to measure the mean and median surface brightness in mag arcsec$^{-2}$.\n", + "\n", + "**Related tutorials:** Tutorial 312.1 introduces image display stretches; 312.2 covers noise-suppression techniques including pixel binning and adaptive smoothing.\n", + "\n", + "### 1.1. Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb303-imports", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.ndimage import binary_dilation\n", + "from astropy.visualization import AsinhStretch, ImageNormalize\n", + "\n", + "import lsst.geom as geom\n", + "from lsst.daf.butler import Butler" + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-s1-2", + "metadata": {}, + "source": [ + "### 1.2. Define parameters and functions\n", + "\n", + "`nJy_per_pix_to_mu`, `build_sky_arr`, `sky_stats`, and `sky_norm` are identical to the implementations in tutorials 312.1 and 312.2. `PIXEL_SCALE` (arcsec/pixel) and `PIXEL_AREA` (arcsec$^2$/pixel) are derived from the coadd WCS in Section 2.\n", + "\n", + "`TidalMaskPainter` creates an interactive figure: pass in the image array and its `ImageNormalize`, then click and drag to paint with a circular brush. The brush radius (in native pixels) is set via the `brush` argument at instantiation. The painted boolean mask at native pixel resolution is accessed via `painter.mask`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb303-params", + "metadata": {}, + "outputs": [], + "source": [ + "TARGET = (53.010, -28.350) # (RA, Dec)\n", + "BAND = \"i\"\n", + "STAMP_HALF = 450 # pixels; +/- 90 arcsec, +/- 1.5 arcmin\n", + "\n", + "plt.rcParams.update({\"figure.dpi\": 150, \"image.origin\": \"lower\", \"axes.titlesize\": 10})\n", + "\n", + "\n", + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy/pix to AB mag/arcsec2 for the LSST pixel scale.\"\"\"\n", + " return -2.5 * np.log10(np.abs(flux_nJy) * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def build_sky_arr(\n", + " masked_image,\n", + " arr,\n", + " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", + " source_planes=(\"DETECTED\",),\n", + "):\n", + " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + " mask_arr = masked_image.getMask().getArray()\n", + " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", + " exclude = np.zeros(arr.shape, dtype=bool)\n", + " for plane in list(bad_planes) + list(source_planes):\n", + " if plane in mask_pd:\n", + " exclude |= (mask_arr & (1 << mask_pd[plane])) != 0\n", + " return np.where(exclude | ~np.isfinite(arr), np.nan, arr)\n", + "\n", + "\n", + "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return (vmin, vmax) anchored to sky via median + 1.4826 * MAD.\"\"\"\n", + " flat = arr[np.isfinite(arr)]\n", + " sky = np.median(flat)\n", + " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma\n", + "\n", + "\n", + "def sky_norm(arr, lo_sigma=2.0, hi_sigma=8.0):\n", + " \"\"\"Return ImageNormalize anchored to the sky background.\"\"\"\n", + " vmin, vmax = sky_stats(arr, lo_sigma=lo_sigma, hi_sigma=hi_sigma)\n", + " return ImageNormalize(vmin=vmin, vmax=vmax, stretch=AsinhStretch())\n", + "\n", + "\n", + "_ORANGE_RGB = np.array([254.0, 97.0, 0.0])\n", + "\n", + "\n", + "class TidalMaskPainter:\n", + " \"\"\"Interactive brush painter. Click and drag to paint; retrieve mask via .mask.\"\"\"\n", + "\n", + " def __init__(self, arr, norm, brush=8):\n", + " ny, nx = arr.shape\n", + " self.mask = np.zeros((ny, nx), dtype=bool)\n", + " self._brush = brush\n", + " self._active = False\n", + " self._last_drawn = (-1, -1)\n", + "\n", + " arr_disp = np.where(np.isfinite(arr), arr, 0.0)\n", + " normed = np.clip(np.asarray(norm(arr_disp)), 0.0, 1.0)\n", + " self._base = (plt.cm.Greys_r(normed) * 255).astype(np.uint8)\n", + " self._canvas = self._base.copy()\n", + "\n", + " self.fig, self.ax = plt.subplots(figsize=(5, 5))\n", + " self._im = self.ax.imshow(\n", + " self._canvas,\n", + " origin=\"lower\",\n", + " extent=[-0.5, nx - 0.5, -0.5, ny - 0.5],\n", + " )\n", + " self.ax.set_title(f\"Click and drag to paint -- brush = {brush} px\")\n", + " self.fig.canvas.mpl_connect(\"button_press_event\", self._press)\n", + " self.fig.canvas.mpl_connect(\"motion_notify_event\", self._motion)\n", + " self.fig.canvas.mpl_connect(\"button_release_event\", self._release)\n", + "\n", + " def _paint(self, bx, by, force=False):\n", + " r = self._brush\n", + " ny, nx = self.mask.shape\n", + " y0, y1 = max(0, by - r), min(ny, by + r + 1)\n", + " x0, x1 = max(0, bx - r), min(nx, bx + r + 1)\n", + " yy, xx = np.ogrid[y0:y1, x0:x1]\n", + " circle = (yy - by) ** 2 + (xx - bx) ** 2 <= r**2\n", + " self.mask[y0:y1, x0:x1] |= circle\n", + " base_s = self._base[y0:y1, x0:x1, :3].astype(np.float32)\n", + " canvas_s = self._canvas[y0:y1, x0:x1]\n", + " blended = np.clip(0.55 * base_s + 0.45 * _ORANGE_RGB, 0, 255).astype(np.uint8)\n", + " canvas_s[circle, :3] = blended[circle]\n", + " ldx = bx - self._last_drawn[0]\n", + " ldy = by - self._last_drawn[1]\n", + " if force or ldx * ldx + ldy * ldy >= r * r:\n", + " self._im.set_data(self._canvas)\n", + " self.fig.canvas.draw_idle()\n", + " self._last_drawn = (bx, by)\n", + "\n", + " def _press(self, ev):\n", + " if ev.inaxes == self.ax and ev.button == 1:\n", + " self._active = True\n", + " self._paint(int(round(ev.xdata)), int(round(ev.ydata)), force=True)\n", + "\n", + " def _motion(self, ev):\n", + " if self._active and ev.inaxes == self.ax:\n", + " self._paint(int(round(ev.xdata)), int(round(ev.ydata)))\n", + "\n", + " def _release(self, ev):\n", + " self._active = False\n", + " self._im.set_data(self._canvas)\n", + " self.fig.canvas.draw_idle()" + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-s2", + "metadata": {}, + "source": [ + "## 2. Load a coadd postage stamp\n", + "\n", + "Open the DP1 butler and load a $\\pm$1.5 arcmin stamp centred on the target at RA = 53.010, Dec = −28.350." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb303-butler", + "metadata": {}, + "outputs": [], + "source": [ + "TARGET_RA, TARGET_DEC = TARGET\n", + "\n", + "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", + "skymap_name = \"lsst_cells_v1\"\n", + "\n", + "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", + "sky_point = geom.SpherePoint(TARGET_RA * geom.degrees, TARGET_DEC * geom.degrees)\n", + "tract_info = skymap.findTract(sky_point)\n", + "patch_info = tract_info.findPatch(sky_point)\n", + "tract_id = tract_info.getId()\n", + "patch_id = patch_info.getSequentialIndex()\n", + "patch_wcs = tract_info.getWcs()\n", + "patch_bbox = patch_info.getOuterBBox()\n", + "data_id = {\"band\": BAND, \"tract\": tract_id, \"patch\": patch_id}\n", + "\n", + "host_pix = patch_wcs.skyToPixel(sky_point)\n", + "stamp_bbox = geom.Box2I(\n", + " geom.Point2I(int(host_pix.getX()) - STAMP_HALF, int(host_pix.getY()) - STAMP_HALF),\n", + " geom.Extent2I(2 * STAMP_HALF, 2 * STAMP_HALF),\n", + ")\n", + "stamp_bbox.clip(patch_bbox)\n", + "\n", + "stamp = butler.get(\"deep_coadd\", dataId=data_id, parameters={\"bbox\": stamp_bbox})\n", + "stamp_arr = stamp.image.array.copy()\n", + "host_x = int(host_pix.getX()) - stamp_bbox.getMinX()\n", + "host_y = int(host_pix.getY()) - stamp_bbox.getMinY()\n", + "\n", + "PIXEL_SCALE = stamp.getWcs().getPixelScale().asArcseconds()\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "\n", + "arcmin = 2 * STAMP_HALF * PIXEL_SCALE / 60\n", + "print(\n", + " f\"Loaded {BAND}-band stamp: {stamp_arr.shape} \" f\"({arcmin:.1f}' x {arcmin:.1f}')\"\n", + ")\n", + "print(f\"Host galaxy at stamp pixel: ({host_x}, {host_y})\")\n", + "print(f\"Pixel scale: {PIXEL_SCALE:.4f} arcsec/pixel\")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-s3", + "metadata": {}, + "source": [ + "## 3. Inspect the image and build the sky mask\n", + "\n", + "Compute a robust sky noise estimate from pixels uncontaminated by sources or artefacts. The stamp is displayed at native resolution with raw pixel values and an arcsinh stretch from $-2$ to $+40\\,\\sigma$; the orange cross marks the target position." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb303-mask", + "metadata": {}, + "outputs": [], + "source": [ + "stamp_sky = build_sky_arr(stamp.getMaskedImage(), stamp_arr)\n", + "stamp_artefacts = build_sky_arr(stamp.getMaskedImage(), stamp_arr, source_planes=())\n", + "stamp_dilated = binary_dilation(~np.isfinite(stamp_sky), iterations=25)\n", + "stamp_lsb = np.where(stamp_dilated, np.nan, stamp_arr)\n", + "\n", + "sky_pix = stamp_lsb[np.isfinite(stamp_lsb)]\n", + "sky_sigma = 1.4826 * np.median(np.abs(sky_pix - np.median(sky_pix)))\n", + "\n", + "print(\n", + " f\"1-sigma sky noise: {sky_sigma:.1f} nJy/pix -> \"\n", + " f\"{nJy_per_pix_to_mu(sky_sigma):.1f} mag/arcsec2\"\n", + ")\n", + "\n", + "vmin, vmax = sky_stats(stamp_lsb, lo_sigma=2, hi_sigma=40)\n", + "norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=AsinhStretch())\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.imshow(stamp_arr, norm=norm, cmap=\"Greys_r\", origin=\"lower\")\n", + "ax.plot(host_x, host_y, \"+\", color=\"#FE6100\", ms=14, mew=2.5, label=\"target galaxy\")\n", + "ax.legend(fontsize=9)" + ] + }, + { + "cell_type": "markdown", + "id": "0d69630d", + "metadata": {}, + "source": [ + "> Figure 1: Native-resolution i-band stamp centred on the target galaxy (orange cross), displayed with an arcsinh stretch from $-2$ to $+40\\,\\sigma$. The stretch is tuned to reveal faint diffuse emission around the central source." + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-s4", + "metadata": {}, + "source": [ + "## 4. Paint the tidal feature\n", + "\n", + "Run the cell below to open the interactive painting figure. Click and drag to paint over a candidate tidal feature; the painted region is shown in orange. The brush radius is set to 15 pixels ($3''$). When finished painting, run Section 5 to compute the photometry.\n", + "\n", + "To start over, re-run this cell to reset the painter, then run Section 5 again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb303-paint", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "\n", + "painter = TidalMaskPainter(stamp_arr, norm, brush=15)" + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-s5", + "metadata": {}, + "source": [ + "## 5. Measure surface brightness\n", + "\n", + "Run the cell below after painting. It retrieves the painted mask, excludes artefact pixels (NaN in `stamp_display`), and reports the mean and median surface brightness in mag arcsec$^{-2}$. The histogram shows the surface brightness distribution of above-sky pixels in the painted region." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb303-photometry", + "metadata": {}, + "outputs": [], + "source": [ + "plt.close(\"all\")\n", + "\n", + "native_mask = painter.mask\n", + "\n", + "if native_mask.sum() == 0:\n", + " print(\"No pixels painted. Run the painting cell and draw over a feature first.\")\n", + "else:\n", + " valid = native_mask & np.isfinite(stamp_artefacts)\n", + " n_total = int(native_mask.sum())\n", + " n_valid = int(valid.sum())\n", + " print(f\"Painted region: {n_total} pixels ({n_total * PIXEL_AREA:.0f} arcsec2)\")\n", + " print(\n", + " f\"After masking: {n_valid} usable pixels \"\n", + " f\"({100 * n_valid / max(n_total, 1):.0f}%)\"\n", + " )\n", + " print()\n", + "\n", + " if n_valid < 10:\n", + " print(\"Too few usable pixels -- try a larger aperture.\")\n", + " else:\n", + " pixels = stamp_arr[valid]\n", + " mean_flux = float(np.nanmean(pixels))\n", + " med_flux = float(np.nanmedian(pixels))\n", + " depth_1s = nJy_per_pix_to_mu(sky_sigma)\n", + " depth_3s = nJy_per_pix_to_mu(3 * sky_sigma)\n", + "\n", + " if mean_flux > 0:\n", + " print(f\"Mean SB: {nJy_per_pix_to_mu(mean_flux):8.2f} mag/arcsec2\")\n", + " if med_flux > 0:\n", + " print(f\"Median SB: {nJy_per_pix_to_mu(med_flux):8.2f} mag/arcsec2\")\n", + " print()\n", + " print(f\"1-sigma depth: {depth_1s:8.2f} mag/arcsec2\")\n", + " print(f\"3-sigma depth: {depth_3s:8.2f} mag/arcsec2\")\n", + " print(f\"SNR (median): {med_flux / sky_sigma:8.1f}\")\n", + "\n", + " ny_m, nx_m = native_mask.shape\n", + " fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "\n", + " axes[0].imshow(stamp_arr, norm=norm, cmap=\"Greys_r\", origin=\"lower\")\n", + " rgba = np.zeros((ny_m, nx_m, 4), dtype=float)\n", + " rgba[native_mask] = [0.996, 0.380, 0.0, 0.5]\n", + " axes[0].imshow(\n", + " rgba, origin=\"lower\", extent=[-0.5, nx_m - 0.5, -0.5, ny_m - 0.5]\n", + " )\n", + " axes[0].set_title(\"Painted region\")\n", + "\n", + " pos_pix = pixels[pixels > 0]\n", + " mu_pos = nJy_per_pix_to_mu(pos_pix)\n", + " axes[1].hist(\n", + " mu_pos, bins=60, color=\"#FE6100\", edgecolor=\"k\", linewidth=0.4, alpha=0.85\n", + " )\n", + " if mean_flux > 0:\n", + " mu_mean = nJy_per_pix_to_mu(mean_flux)\n", + " axes[1].axvline(\n", + " mu_mean, color=\"k\", ls=\"--\", lw=2, label=f\"mean = {mu_mean:.1f}\"\n", + " )\n", + " if med_flux > 0:\n", + " mu_med = nJy_per_pix_to_mu(med_flux)\n", + " axes[1].axvline(\n", + " mu_med, color=\"k\", ls=\"-\", lw=2, label=f\"median = {mu_med:.1f}\"\n", + " )\n", + " axes[1].axvline(\n", + " depth_1s, color=\"0.5\", ls=\":\", lw=1.5, label=f\"1-sigma = {depth_1s:.1f}\"\n", + " )\n", + " axes[1].set_xlabel(\"Surface brightness (mag arcsec$^{-2}$)\")\n", + " axes[1].set_ylabel(\"Count\")\n", + " axes[1].set_title(\"Surface brightness distribution\")\n", + " axes[1].legend(fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "2f583e56", + "metadata": {}, + "source": [ + "> Figure 2: Left -- painted region (orange overlay) on the native i-band image. Right -- surface brightness distribution (mag arcsec$^{-2}$) of above-sky pixels in the painted region; vertical lines mark the mean (black dashed), median (black solid), and 1-sigma depth (grey dotted)." + ] + }, + { + "cell_type": "markdown", + "id": "lsb303-summary", + "metadata": {}, + "source": [ + "## 6. Summary\n", + "\n", + "- Tidal features and other irregular structures may be best measured by defining a freehand region directly on the image; the resulting pixel sample gives direct access to the flux distribution of the selected area without assumptions about shape or symmetry.\n", + "- The median is the preferred surface brightness estimator for diffuse emission because it is robust to unmasked compact sources within the aperture, unlike the mean which is pulled upward by any residual point sources.\n", + "- Comparing the measured surface brightness against the per-pixel sky noise floor shows directly whether a candidate feature is a genuine detection or consistent with sky noise.." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f4268d287e086e555a182558bbd653c4b59cab4b Mon Sep 17 00:00:00 2001 From: garrethmartin Date: Fri, 19 Jun 2026 12:37:05 +0000 Subject: [PATCH 4/5] SP-2645: update 312.1 visualization notebook --- .../312_1_visualization.ipynb | 274 +++++++++++------- 1 file changed, 172 insertions(+), 102 deletions(-) diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb index 4e4fce1..05387fa 100644 --- a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb @@ -10,7 +10,7 @@ "id": "3a787a26-6646-4fc3-9f15-8739e4ed47fe", "metadata": {}, "source": [ - "# 312.1: Accessing and visualising faint features in coadd images with the Butler\n", + "# 312.1. Accessing and visualizing faint features in coadd images with the Butler\n", "\n", "
\n", "\n", @@ -18,12 +18,12 @@ "\n", "
\n", "\n", - "Deployment: For the Rubin Science Platform at data.lsst.cloud. \\\n", + "For the Rubin Science Platform at data.lsst.cloud. \\\n", "Data Release: [Data Preview 1](https://dp1.lsst.io/) \\\n", "Container Size: large \\\n", "LSST Science Pipelines version: r29.2.0 \\\n", - "Last verified to run: 2026-06-11 \\\n", - "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks) \\" + "Last verified to run: 2026-06-17 \\\n", + "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)" ] }, { @@ -54,11 +54,11 @@ "source": [ "## 1. Introduction\n", "\n", - "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualise the data at the signal levels where LSB structures live.\n", + "Low surface brightness (LSB) science depends on detecting faint signals including diffuse galaxy outskirts, tidal debris, and intracluster light. These features can be 100 to 10,000 times fainter than the night sky background. Reaching these depths requires both deep imaging and careful image handling. The first step is knowing how to access and visualize the data at the signal levels where LSB structures live.\n", "\n", - "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 ECDFS field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", + "This notebook demonstrates how to use the Butler to load a `deep_coadd` patch from the DP1 Extended Chandra Deep Field South (ECDFS) field, display it with a stretch calibrated for faint extended emission, and understand how stretch function and display range choices interact.\n", "\n", - "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 202.6 describes the pixel mask planes in detail. lsb-203 covers mask plane usage for LSB science. lsb-202 covers background subtraction; lsb-301 characterises systematics affecting LSB science." + "**Related tutorials:** Tutorial 103.5 demonstrates interactive image display with Firefly; 103.8 covers image display with matplotlib; 103.9 covers image stamps and bounding-box access. Tutorial 312.2 applies noise-suppression techniques to reveal faint structure; 312.3 covers interactive surface brightness measurement of tidal features." ] }, { @@ -86,41 +86,11 @@ "\n", "import lsst.geom as geom\n", "import lsst.afw.display as afwDisplay\n", - "from lsst.daf.butler import Butler" - ] - }, - { - "cell_type": "markdown", - "id": "lsb201-s1-2", - "metadata": {}, - "source": [ - "### 1.2. Define parameters and functions\n", - "\n", - "Set the LSST plate scale and define unit-conversion helpers. Two functions are used throughout:\n", - "\n", - "- `build_sky_arr` — masks detected sources and detector artefacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See lsb-203 for a detailed treatment of these mask planes.\n", - "- `sky_stats` — estimates the sky level and per-pixel noise from the masked array. The sky level is the robust median; the noise is 1.4826 × MAD (median absolute deviation). Returns `(vmin, vmax)` where `vmin = sky − lo_sigma × σ̂` and `vmax = sky + hi_sigma × σ̂`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lsb201-params", - "metadata": {}, - "outputs": [], - "source": [ - "PIXEL_SCALE = 0.2\n", - "PIXEL_AREA = PIXEL_SCALE**2\n", + "from lsst.daf.butler import Butler\n", "\n", "afwDisplay.setDefaultBackend(\"matplotlib\")\n", - "\n", "plt.rcParams.update(\n", - " {\n", - " \"figure.dpi\": 150,\n", - " \"image.origin\": \"lower\",\n", - " \"axes.titlesize\": 10,\n", - " }\n", - ")" + " {\"figure.dpi\": 150, \"image.origin\": \"lower\", \"axes.titlesize\": 10})" ] }, { @@ -128,7 +98,14 @@ "id": "ac181cbb", "metadata": {}, "source": [ - "Define `build_sky_arr` and `sky_stats`." + "### 1.2. Define parameters and functions\n", + "\n", + "Two functions are used throughout:\n", + "\n", + "- `build_sky_arr` — masks detected sources and detector artifacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See tutorial lsb-203 for a detailed treatment of these mask planes.\n", + "- `sky_stats` — estimates the sky level and per-pixel noise from the source-masked array, returning display limits `(vmin, vmax)`.\n", + "\n", + "Sky noise is the pixel-to-pixel variation in flux on blank patches of sky, measured after masking detected sources and instrument artifacts. It sets the fundamental detection floor: any structure fainter than a few sky noise units per pixel is invisible without additional noise suppression. Here sky noise is estimated robustly: the sky level is the median of the source-free pixel array, and the noise is $\\hat{\\sigma} = 1.4826 \\times \\mathrm{MAD}$, where MAD is the median absolute deviation and the factor 1.4826 makes $\\hat{\\sigma}$ consistent with the standard deviation of a Gaussian distribution. The display limits are $v_\\mathrm{min} = \\mathrm{sky} - \\mathrm{lo\\_sigma} \\times \\hat{\\sigma}$ and $v_\\mathrm{max} = \\mathrm{sky} + \\mathrm{hi\\_sigma} \\times \\hat{\\sigma}$, defaulting to $2\\hat{\\sigma}$ below and $8\\hat{\\sigma}$ above sky.\n" ] }, { @@ -138,23 +115,30 @@ "metadata": {}, "outputs": [], "source": [ - "def nJy_per_pix_to_mu(flux_nJy):\n", - " \"\"\"Convert nJy pixel⁻¹ to AB mag arcsec^-2 for LSST pixel scale.\"\"\"\n", - " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", - "\n", - "\n", - "def mu_to_nJy_per_pix(mu):\n", - " \"\"\"Convert AB mag arcsec^-2 to nJy pixel⁻¹ for LSST pixel scale.\"\"\"\n", - " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", - "\n", - "\n", "def build_sky_arr(\n", " masked_image,\n", " arr,\n", " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", " source_planes=(\"DETECTED\",),\n", "):\n", - " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + " \"\"\"Return arr with artifact and source pixels set to NaN.\n", + "\n", + " Parameters\n", + " ----------\n", + " masked_image : lsst.afw.image.MaskedImage\n", + " Masked image whose mask plane is used to identify bad pixels.\n", + " arr : ndarray, shape (ny, nx)\n", + " Pixel array to mask, typically the image or variance plane.\n", + " bad_planes : tuple of str, optional\n", + " Mask plane names treated as artifacts.\n", + " source_planes : tuple of str, optional\n", + " Mask plane names treated as detected sources.\n", + "\n", + " Returns\n", + " -------\n", + " masked_arr : ndarray, shape (ny, nx)\n", + " Copy of arr with artifact, source, and non-finite pixels set to NaN.\n", + " \"\"\"\n", "\n", " mask_arr = masked_image.getMask().getArray()\n", " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", @@ -168,9 +152,25 @@ "def sky_stats(arr, lo_sigma=2.0, hi_sigma=8.0):\n", " \"\"\"Return (vmin, vmax) anchored to the sky background.\n", "\n", - " Uses the robust median as sky level and 1.4826 × MAD as pixel noise.\n", + " Uses the robust median as sky level and 1.4826 x MAD as pixel noise.\n", " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", " sources do not bias the estimates.\n", + "\n", + " Parameters\n", + " ----------\n", + " arr : ndarray\n", + " Pixel array, typically source-masked (NaN at bad/source pixels).\n", + " lo_sigma : float, optional\n", + " Lower stretch limit in units of sky noise below the sky median.\n", + " hi_sigma : float, optional\n", + " Upper stretch limit in units of sky noise above the sky median.\n", + "\n", + " Returns\n", + " -------\n", + " vmin : float\n", + " Lower display limit (sky - lo_sigma * noise).\n", + " vmax : float\n", + " Upper display limit (sky + hi_sigma * noise).\n", " \"\"\"\n", " flat = arr[np.isfinite(arr)]\n", " sky = np.median(flat)\n", @@ -178,6 +178,55 @@ " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" ] }, + { + "cell_type": "markdown", + "id": "6960f99f", + "metadata": {}, + "source": [ + "Define unit-conversion helpers.\n", + "\n", + "`PIXEL_SCALE` and `PIXEL_AREA` are derived from the coadd WCS in Section 2, immediately after the data are loaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "162f29c5", + "metadata": {}, + "outputs": [], + "source": [ + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel**-1 to AB mag arcsec**-2 for the LSST pixel scale.\n", + "\n", + " Parameters\n", + " ----------\n", + " flux_nJy : float or array_like\n", + " Pixel flux in nJy pixel**-1.\n", + "\n", + " Returns\n", + " -------\n", + " mu : float or array_like\n", + " Surface brightness in AB mag arcsec**-2.\n", + " \"\"\"\n", + " return -2.5 * np.log10(flux_nJy * 1e-9 / PIXEL_AREA / 3631.0)\n", + "\n", + "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec**-2 to nJy pixel**-1 for the LSST pixel scale.\n", + "\n", + " Parameters\n", + " ----------\n", + " mu : float or array_like\n", + " Surface brightness in AB mag arcsec**-2.\n", + "\n", + " Returns\n", + " -------\n", + " flux_nJy : float or array_like\n", + " Pixel flux in nJy pixel**-1.\n", + " \"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9" + ] + }, { "cell_type": "markdown", "id": "lsb201-s2", @@ -185,7 +234,7 @@ "source": [ "## 2. Load a coadd patch\n", "\n", - "Open the DP1 butler, locate the tract and patch containing the ECDFS field centre, and load the `deep_coadd` for the i-band." + "Instantiate the Butler with the DP1 repository, locate the tract and patch containing the ECDFS field center, and load the `deep_coadd` for the i-band." ] }, { @@ -197,13 +246,13 @@ "source": [ "butler = Butler(\"dp1\", collections=\"LSSTComCam/DP1\")\n", "skymap_name = \"lsst_cells_v1\"\n", - "\n", "target_ra = 53.218\n", "target_dec = -28.095\n", "band = \"i\"\n", "\n", "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", - "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "sky_point = geom.SpherePoint(\n", + " target_ra * geom.degrees, target_dec * geom.degrees)\n", "tract_info = skymap.findTract(sky_point)\n", "patch_info = tract_info.findPatch(sky_point)\n", "tract_id = tract_info.getId()\n", @@ -213,22 +262,41 @@ "\n", "data_id = {\"band\": band, \"tract\": tract_id, \"patch\": patch_id}\n", "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", - "coadd_arr = coadd.image.array.copy()\n", + "PIXEL_SCALE = coadd.getWcs().getPixelScale().asArcseconds()\n", + "PIXEL_AREA = PIXEL_SCALE**2\n", + "print(f\"Pixel scale: {PIXEL_SCALE: .4f} arcsec/pixel\")\n", + "coadd_arr = coadd.image.array.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "lsb312-sky-noise", + "metadata": {}, + "source": [ + "Mask detected sources and artefacts using `build_sky_arr` and estimate the sky noise from the remaining pixels. Sky noise is the pixel-to-pixel flux variation remaining after the pipeline subtracts the large-scale background; it sets the fundamental detection floor for low-surface-brightness features. Expressing display limits in multiples of the sky noise gives a stretch that is physically motivated and reproducible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb312-sky-stats", + "metadata": {}, + "outputs": [], + "source": [ "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", "\n", + "width_arcmin = coadd_arr.shape[1] * PIXEL_SCALE / 60\n", + "height_arcmin = coadd_arr.shape[0] * PIXEL_SCALE / 60\n", + "print(f\"Shape: {coadd_arr.shape} ({width_arcmin: .1f} x {height_arcmin: .1f} arcmin)\")\n", + "\n", + "n_sky = np.isfinite(coadd_sky).sum()\n", + "n_tot = coadd_sky.size\n", "print(\n", - " f\"Shape: {coadd_arr.shape} \"\n", - " f\"({coadd_arr.shape[1] * PIXEL_SCALE / 60:.1f} × \"\n", - " f\"{coadd_arr.shape[0] * PIXEL_SCALE / 60:.1f} arcmin)\"\n", - ")\n", - "print(\n", - " f\"Sky pixels for statistics: {np.isfinite(coadd_sky).sum():,} / {coadd_sky.size:,}\"\n", - " f\" ({100 * np.isfinite(coadd_sky).mean():.1f}%)\"\n", + " f\"Sky pixels for statistics: {format(n_sky, ',')} / {format(n_tot, ',')}\"\n", + " f\" ({100 * np.isfinite(coadd_sky).mean(): .1f}%)\"\n", ")\n", "sky_val = np.nanmedian(coadd_sky)\n", - "print(\n", - " f\"Sky level: {sky_val:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_val):.1f} mag/arcsec^2\"\n", - ")" + "print(f\"Sky level: {sky_val: .1f} nJy/pix = {nJy_per_pix_to_mu(sky_val): .1f} mag/arcsec^2\")" ] }, { @@ -261,7 +329,7 @@ "id": "cfced9ea", "metadata": {}, "source": [ - "> Figure 1: The full i-band deep_coadd patch (tract 5063, patch 14) displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky minus 2 sigma (black) to sky plus 8 sigma (white), emphasising low-surface-brightness structure over the 11.3 by 11.3 arcminute field." + "> Figure 1: The full i-band deep_coadd patch displayed with an arcsinh stretch anchored to the sky noise floor. The display range runs from sky $- 2\\hat{\\sigma}$ (black) to sky $+ 8\\hat{\\sigma}$ (white), emphasizing low-surface-brightness structure." ] }, { @@ -271,11 +339,9 @@ "source": [ "## 4. Postage stamp and stretch comparison\n", "\n", - "The Butler supports lazy loading: `parameters={\"bbox\": ...}` fetches only the pixels within the requested bounding box from the object store, making sub-region access efficient regardless of the full patch size.\n", - "\n", - "**Display tools.** Two display approaches are used in this section.\n", + "Two display approaches are used in this section.\n", "\n", - "**`lsst.afw.display`** is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", + "`lsst.afw.display` is the Science Pipelines library for image display. It supports a Firefly backend (the RSP's interactive viewer, covered in tutorial 103.5) and a matplotlib backend (tutorial 103.8). This notebook uses the matplotlib backend. Its `scale()` method takes a stretch name and a display range:\n", "\n", "```python\n", "disp.scale('asinh', 'zscale') # ZScale: automatic range\n", @@ -284,7 +350,7 @@ "\n", "*ZScale* fits a linear function to the core of the pixel histogram to set the display range automatically. The range adapts to each image so the same surface brightness will appear at a different gray level in images of different depth or background. Setting `min`/`max` explicitly from `sky_stats()` anchors the display to the physical noise floor. This approach is used in section 3.\n", "\n", - "**`ImageNormalize + imshow`** (astropy) is used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." + "`ImageNormalize` (astropy) and `imshow` (matplotlib) are used for the comparison grids in sections 4.1 and 4.2. `lsst.afw.display` interprets its `min`/`max` arguments differently for different stretches — for asinh it uses the Lupton et al. (2004) parameterisation in which the arguments control the softening, not the black and white points. `ImageNormalize` always treats `vmin` and `vmax` as pixel flux cutoffs, which is helpful for a controlled comparison." ] }, { @@ -292,7 +358,7 @@ "id": "79ec6be5", "metadata": {}, "source": [ - "Define the four co-ordinates used as demonstration targets throughout sections 4.1 and 4.2." + "Define the four coordinates used as demonstration targets throughout sections 4.1 and 4.2." ] }, { @@ -302,11 +368,11 @@ "metadata": {}, "outputs": [], "source": [ - "_demo_targets = [\n", - " (53.3375, -27.810, \"(5,0)\"),\n", - " (53.010, -28.350, \"(5,1)\"),\n", - " (52.965, -28.184, \"(4,1)\"),\n", - " (53.220, -28.095, \"(3,3)\"),\n", + "demo_targets = [\n", + " (53.3375, -27.810),\n", + " (53.010, -28.350),\n", + " (52.965, -28.184),\n", + " (53.220, -28.095),\n", "]" ] }, @@ -315,7 +381,7 @@ "id": "2a028501", "metadata": {}, "source": [ - "Load a 4′ × 4′ stamp centred on each target galaxy." + "Load a 4′ × 4′ stamp centered on each target galaxy. The third stamp may appear smaller or off-center because the requested bounding box extends beyond the patch boundary and is clipped; `bbox.clip()` handles this silently." ] }, { @@ -328,7 +394,7 @@ "half_width = 600\n", "\n", "demo_stamps = []\n", - "for ra, dec, label in _demo_targets:\n", + "for ra, dec in demo_targets:\n", " sp = geom.SpherePoint(ra * geom.degrees, dec * geom.degrees)\n", " ti = skymap.findTract(sp)\n", " pi = ti.findPatch(sp)\n", @@ -336,7 +402,8 @@ " cen = wcs.skyToPixel(sp)\n", " did = {\"band\": band, \"tract\": ti.getId(), \"patch\": pi.getSequentialIndex()}\n", " bbox = geom.Box2I(\n", - " geom.Point2I(int(cen.getX()) - half_width, int(cen.getY()) - half_width),\n", + " geom.Point2I(int(cen.getX()) - half_width,\n", + " int(cen.getY()) - half_width),\n", " geom.Extent2I(2 * half_width, 2 * half_width),\n", " )\n", " bbox.clip(pi.getOuterBBox())\n", @@ -360,7 +427,7 @@ "id": "b7409ecb", "metadata": {}, "source": [ - "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centred on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise floor using an arcsinh stretch." + "> Figure 2: Four 4-arcminute by 4-arcminute i-band postage stamps centered on target galaxies in the ECDFS field, loaded via bounding-box sub-region access. Each stamp is independently scaled to its local sky noise using an arcsinh stretch. The third stamp is smaller because its bounding box was clipped to the patch boundary." ] }, { @@ -370,22 +437,22 @@ "source": [ "### 4.1. Stretch functions\n", "\n", - "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalised pixel value. The two transfer functions used here are:\n", + "Every image display maps pixel flux values onto a $[0,\\,1]$ grey-scale range. Let $t = (x - v_\\mathrm{min})\\,/\\,(v_\\mathrm{max} - v_\\mathrm{min})$ be the normalized pixel value. The two transfer functions used here are:\n", "\n", - "| Stretch | Transfer function | Behaviour |\n", + "| Stretch | Transfer function | Behavior |\n", "|---------|-------------------|-----------|\n", "| **Linear** | $f(t) = t$ | Uniform mapping; preserves noise texture near sky level but saturates bright sources unless $v_\\mathrm{max}$ is set conservatively |\n", - "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; preserves sky noise texture while compressing bright sources |\n", + "| **Arcsinh** | $f(t) = \\mathrm{arcsinh}(t/a)\\,/\\,\\mathrm{arcsinh}(1/a)$ | Near-linear at small $t$, logarithmic at large $t$; allocates more of the grey-scale range to faint emission while keeping bright sources visible |\n", "\n", - "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behaviour. Smaller values compress more aggressively; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", + "The `a` parameter in `AsinhStretch` controls how quickly the function transitions from near-linear to logarithmic behavior. Smaller values devote more grey-scale contrast to faint structure; larger values approach linear. `AsinhStretch(a=0.1)` is the default (as used by `afwDisplay` internally). Changing it is straightforward:\n", "\n", "```python\n", - "AsinhStretch(a=0.1) # default — stronger compression\n", + "AsinhStretch(a=0.1) # default — greatest contrast in the faint regime\n", "AsinhStretch(a=0.5) # gentler — brighter features retain more structure\n", "AsinhStretch(a=2.0) # close to linear\n", "```\n", "\n", - "Both panels below use the same display range (sky $-$ 2σ to sky $+$ 30σ) so that any visual difference is due solely to the transfer function." + "Both panels below use the same display range (sky $-$ $2\\hat{\\sigma}$ to sky $+$ $30\\hat{\\sigma}$) so that any visual difference is due solely to the transfer function." ] }, { @@ -402,7 +469,10 @@ "\n", "lo = 2.0\n", "fig, axes = plt.subplots(\n", - " len(demo_stamps), 2, figsize=(6, 2 * len(demo_stamps)), squeeze=False,\n", + " len(demo_stamps),\n", + " 2,\n", + " figsize=(6, 2 * len(demo_stamps)),\n", + " squeeze=False,\n", ")\n", "for row, (sky_a, stamp, gal_label) in enumerate(demo_stamps):\n", " flat = sky_a[np.isfinite(sky_a)]\n", @@ -424,7 +494,8 @@ " extent=stamp_extent,\n", " )\n", " if row == 0:\n", - " axes[row][col].set_title(f\"{name} (-{int(lo)}σ to +{hi}σ)\")\n", + " axes[row][col].set_title(\n", + " f\"{name} (-{int(lo)}$\\\\hat{{\\\\sigma}}$ to +{hi}$\\\\hat{{\\\\sigma}}$)\")\n", " axes[row][col].set_xticks([])\n", " axes[row][col].set_yticks([])" ] @@ -434,7 +505,7 @@ "id": "3d68d84d", "metadata": {}, "source": [ - "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky minus 2 sigma to sky plus 30 sigma). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." + "> Figure 3: Comparison of linear (left) and arcsinh (right) stretch functions applied to four postage stamps with the same display range (sky $- 2\\hat{\\sigma}$ to sky $+ 30\\hat{\\sigma}$). The arcsinh stretch preserves noise texture near the sky level while compressing bright sources into a smaller range of grey levels." ] }, { @@ -446,12 +517,12 @@ "\n", "The display range $[v_\\mathrm{min},\\, v_\\mathrm{max}]$ determines which flux levels map to black and white:\n", "\n", - "- **Black point** $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", - "- **White point** $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", + "- Black point $v_\\mathrm{min}$: pixels below this map to black. Setting this slightly below sky (sky $- 2\\hat{\\sigma}$) keeps the noise floor visible rather than clipping it to black.\n", + "- White point $v_\\mathrm{max}$: pixels above this saturate to white. A lower white point suppresses bright sources and reveals faint diffuse structure; a higher white point shows the full dynamic range but compresses sky-level emission into a narrow band of grey.\n", "\n", "Expressing both endpoints in multiples of $\\hat{\\sigma}$ gives display ranges that are reproducible and physically comparable across images of different depth.\n", "\n", - "> **Note:** `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", + "***Note***: `lsst.afw.display` uses ZScale by default. ZScale fits a linear function to the core of the pixel histogram to set the display range automatically. It is well-suited to source inspection but the range adapts to each image, so the same surface brightness does not map to the same grey level across different images.\n", "\n", "The grid below holds the black point fixed at sky $- 2\\hat{\\sigma}$ and varies the white point, showing how the choice of white point affects what is visible." ] @@ -464,7 +535,7 @@ "outputs": [], "source": [ "hi_sigmas = [4, 8, 20, 50]\n", - "col_labels = [f\"sky + {h}σ\" for h in hi_sigmas]\n", + "col_labels = [f\"sky + {h}$\\\\hat{{\\\\sigma}}$\" for h in hi_sigmas]\n", "\n", "fig, axes = plt.subplots(\n", " len(demo_stamps),\n", @@ -503,7 +574,7 @@ "id": "47f64152", "metadata": {}, "source": [ - "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky minus 2 sigma and arcsinh stretch applied. From left to right the white point increases from sky plus 4 sigma to sky plus 50 sigma, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." + "> Figure 4: Effect of varying the white point on displayed contrast, with the black point fixed at sky $- 2\\hat{\\sigma}$ and arcsinh stretch applied. From left to right the white point increases from sky $+ 4\\hat{\\sigma}$ to sky $+ 50\\hat{\\sigma}$, revealing progressively more of the dynamic range at the cost of compressing faint diffuse structure toward black." ] }, { @@ -513,17 +584,16 @@ "source": [ "## 5. Summary\n", "\n", - "- `Butler.get('deep_coadd', parameters={'bbox': ...})` fetches only the requested sub-region, making postage stamp access efficient for large patches.\n", - "- The **stretch function** determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", - "- The **white point** ($v_\\mathrm{max}$) and **black point** ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." + "- The stretch function determines how pixel flux values map to the display grey-scale. The arcsinh stretch preserves the noise texture at low flux levels, making it well-suited to assessing faint diffuse emission.\n", + "- The white point ($v_\\mathrm{max}$) and black point ($v_\\mathrm{min}$) determine the contrast. Anchoring both to the measured sky noise $\\hat{\\sigma}$ gives results that are physically motivated and directly comparable across fields, bands, and depths — unlike ZScale, which adapts automatically to each image's pixel distribution." ] } ], "metadata": { "kernelspec": { - "display_name": "LSST", + "display_name": "general", "language": "python", - "name": "lsst" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -535,7 +605,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.11.9" } }, "nbformat": 4, From 635e88baae0a82be36ad6e511e2d2c43c368ffd4 Mon Sep 17 00:00:00 2001 From: garrethmartin Date: Fri, 19 Jun 2026 15:05:45 +0000 Subject: [PATCH 5/5] SP-2645: update 312.1 visualization notebook and update 312.2 binning notebook --- .../312_1_visualization.ipynb | 21 +- .../312_2_lsb_detection.ipynb | 710 ++++++++++++------ 2 files changed, 506 insertions(+), 225 deletions(-) diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb index 05387fa..8631f45 100644 --- a/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_1_visualization.ipynb @@ -103,7 +103,7 @@ "Two functions are used throughout:\n", "\n", "- `build_sky_arr` — masks detected sources and detector artifacts (`DETECTED`, `SAT`, `BRIGHT_OBJECT` etc.) by setting those pixels to `NaN`, so that sky statistics are not biased by bright compact objects. See tutorial lsb-203 for a detailed treatment of these mask planes.\n", - "- `sky_stats` — estimates the sky level and per-pixel noise from the source-masked array, returning display limits `(vmin, vmax)`.\n", + "- `sky_stats` — estimates the sky level and per-pixel noise, returning display limits `(vmin, vmax)`.\n", "\n", "Sky noise is the pixel-to-pixel variation in flux on blank patches of sky, measured after masking detected sources and instrument artifacts. It sets the fundamental detection floor: any structure fainter than a few sky noise units per pixel is invisible without additional noise suppression. Here sky noise is estimated robustly: the sky level is the median of the source-free pixel array, and the noise is $\\hat{\\sigma} = 1.4826 \\times \\mathrm{MAD}$, where MAD is the median absolute deviation and the factor 1.4826 makes $\\hat{\\sigma}$ consistent with the standard deviation of a Gaussian distribution. The display limits are $v_\\mathrm{min} = \\mathrm{sky} - \\mathrm{lo\\_sigma} \\times \\hat{\\sigma}$ and $v_\\mathrm{max} = \\mathrm{sky} + \\mathrm{hi\\_sigma} \\times \\hat{\\sigma}$, defaulting to $2\\hat{\\sigma}$ below and $8\\hat{\\sigma}$ above sky.\n" ] @@ -171,11 +171,13 @@ " Lower display limit (sky - lo_sigma * noise).\n", " vmax : float\n", " Upper display limit (sky + hi_sigma * noise).\n", + " sigma : float\n", + " Per-pixel noise estimate (1.4826 * MAD).\n", " \"\"\"\n", " flat = arr[np.isfinite(arr)]\n", " sky = np.median(flat)\n", " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", - " return sky - lo_sigma * sigma, sky + hi_sigma * sigma" + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma, sigma\n" ] }, { @@ -296,7 +298,10 @@ " f\" ({100 * np.isfinite(coadd_sky).mean(): .1f}%)\"\n", ")\n", "sky_val = np.nanmedian(coadd_sky)\n", - "print(f\"Sky level: {sky_val: .1f} nJy/pix = {nJy_per_pix_to_mu(sky_val): .1f} mag/arcsec^2\")" + "print(f\"Sky level: {sky_val: .1f} nJy/pix = {nJy_per_pix_to_mu(sky_val): .1f} mag/arcsec^2\")\n", + "_, _, sky_sigma = sky_stats(coadd_sky)\n", + "mu_sigma = nJy_per_pix_to_mu(sky_sigma)\n", + "print(f\"Per-pixel 1-sigma noise: {sky_sigma: .1f} nJy/pix = {mu_sigma: .1f} mag/arcsec^2\")" ] }, { @@ -316,7 +321,7 @@ "metadata": {}, "outputs": [], "source": [ - "vmin, vmax = sky_stats(coadd_sky)\n", + "vmin, vmax, _ = sky_stats(coadd_sky)\n", "fig = plt.figure(figsize=(4, 4))\n", "disp = afwDisplay.Display(frame=fig)\n", "disp.scale(\"asinh\", min=vmin, max=vmax)\n", @@ -416,7 +421,7 @@ "for idx, (sky_a, stamp, label) in enumerate(demo_stamps):\n", " plt.sca(axes[idx][0])\n", " disp = afwDisplay.Display(frame=fig)\n", - " vmin, vmax = sky_stats(sky_a)\n", + " vmin, vmax, _ = sky_stats(sky_a)\n", " disp.scale(\"asinh\", min=vmin, max=vmax)\n", " disp.image(stamp.image)\n", " axes[idx][0].set_title(label, fontsize=8)" @@ -591,9 +596,9 @@ ], "metadata": { "kernelspec": { - "display_name": "general", + "display_name": "LSST", "language": "python", - "name": "python3" + "name": "lsst" }, "language_info": { "codemirror_mode": { @@ -605,7 +610,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb b/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb index dfa0204..6cf066f 100644 --- a/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb +++ b/DP1/300_Science_Demos/312_Low_surface_brightness/312_2_lsb_detection.ipynb @@ -56,13 +56,13 @@ "\n", "Low surface brightness structures span a wide range of angular scales and physical origins: dwarf satellite galaxies subtend tens to hundreds of arcseconds; tidal streams and shells extend over arcminutes; and intracluster light fills degrees around nearby rich clusters. All share the property that their surface brightness is comparable to, or fainter than, the per-pixel noise in a single epoch. Detecting them requires suppressing pixel-to-pixel noise while preserving diffuse flux.\n", "\n", - "This notebook applies two complementary noise-suppression techniques to the DP1 ECDFS coadd. Pixel binning block-averages $N\\times N$ pixels to reduce noise by $\\sqrt{N}$ while exactly preserving surface brightness — faint diffuse emission is revealed without any blurring of the underlying sky level. Adaptive smoothing adjusts the kernel size pixel-by-pixel so that bright regions retain resolution while faint diffuse regions are smoothed only as much as needed to achieve a target signal-to-noise ratio. The closing section compares radial surface brightness profiles measured from each technique to show where each method hits the noise floor.\n", + "This notebook applies two complementary noise-suppression techniques to the DP1 Extended Chandra Deep Field South (ECDFS) coadd. Pixel binning block-averages $N\\times N$ pixels to reduce noise by $\\sqrt{N}$ while exactly preserving surface brightness; faint diffuse emission is revealed without any blurring of the underlying sky level. Adaptive smoothing adjusts the kernel size pixel-by-pixel so that bright regions retain resolution while faint diffuse regions are smoothed only as much as needed to achieve a target signal-to-noise ratio. The closing section compares radial SNR profiles to show at what radius the azimuthal-median galaxy flux falls to the residual noise level of each technique — the point at which the signal becomes undetectable with that approach.\n", "\n", "**Related tutorials:** Tutorial 312.1 introduces the display stretches used throughout this series; 312.3 covers interactive surface brightness measurement of tidal features.\n", "\n", "### 1.1. Import packages\n", "\n", - "`lsst.daf.butler` and `lsst.geom` access the data; `lsst.afw.display` renders images with an astronomically appropriate stretch. `numpy` and `scipy.ndimage` handle the numerical operations; `astropy.visualization` provides the `ImageNormalize` and `AsinhStretch` classes; `matplotlib` produces all figures." + "`lsst.daf.butler` and `lsst.geom` access the data; `lsst.afw.display` renders images with an astronomically appropriate stretch. `numpy` handles array operations; `scipy.ndimage.uniform_filter` powers the normalized convolution in `adaptive_smooth`; `astropy.visualization` provides the `ImageNormalize` and `AsinhStretch` classes; `matplotlib` produces all figures." ] }, { @@ -74,12 +74,16 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from scipy.ndimage import uniform_filter, binary_dilation\n", + "from scipy.ndimage import uniform_filter\n", "from astropy.visualization import AsinhStretch, ImageNormalize\n", "\n", "import lsst.geom as geom\n", "import lsst.afw.display as afwDisplay\n", - "from lsst.daf.butler import Butler" + "from lsst.daf.butler import Butler\n", + "\n", + "afwDisplay.setDefaultBackend(\"matplotlib\")\n", + "plt.rcParams.update(\n", + " {\"figure.dpi\": 150, \"image.origin\": \"lower\", \"axes.titlesize\": 10})" ] }, { @@ -89,9 +93,7 @@ "source": [ "### 1.2. Define parameters and functions\n", "\n", - "`TARGET` sets the sky coordinate of the target galaxy.\n", - "\n", - "`nJy_per_pix_to_mu`, `mu_to_nJy_per_pix`, `build_sky_arr`, `sky_stats`, and `sky_norm` are unit-conversion and sky-statistics helpers used throughout. `show_lsb` wraps them into a multi-panel display helper. `bin_image` performs block averaging with NaN propagation. `adaptive_smooth` applies a spatially varying kernel via normalised convolution." + "`TARGET` is the sky coordinate of the target galaxy." ] }, { @@ -101,42 +103,56 @@ "metadata": {}, "outputs": [], "source": [ - "afwDisplay.setDefaultBackend(\"matplotlib\")\n", - "\n", - "plt.rcParams.update(\n", - " {\n", - " \"figure.dpi\": 150,\n", - " \"image.origin\": \"lower\",\n", - " \"axes.titlesize\": 10,\n", - " }\n", - ")\n", - "\n", "TARGET = (53.010, -28.350) # (RA, Dec)\n", "\n", "BLUE = \"#648FFF\"\n", "PURPLE = \"#785EF0\"\n", "MAGENTA = \"#DC267F\"\n", "ORANGE = \"#FE6100\"\n", - "YELLOW = \"#FFB000\"\n", - "\n", - "\n", - "def nJy_per_pix_to_mu(flux_nJy):\n", - " \"\"\"Convert nJy pixel**-1 to AB mag arcsec**-2 for LSST pixel scale.\"\"\"\n", - " return -2.5 * np.log10(np.abs(flux_nJy) * 1e-9 / PIXEL_AREA / 3631.0)\n", - "\n", - "\n", - "def mu_to_nJy_per_pix(mu):\n", - " \"\"\"Convert AB mag arcsec**-2 to nJy pixel**-1 for LSST pixel scale.\"\"\"\n", - " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9\n", - "\n", + "YELLOW = \"#FFB000\"" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-sky-helpers-md", + "metadata": {}, + "source": [ + "`build_sky_arr` masks detected sources and detector artefacts by setting flagged pixels to `NaN`, preventing bright compact objects from biasing sky statistics. `sky_stats` returns display limits and the per-pixel sky noise $\\hat{\\sigma}$ (the same quantity used throughout Tutorial 312.1).\n", "\n", + "Sky noise is the pixel-to-pixel flux variation on blank patches of sky, measured after masking sources and artefacts. It sets the fundamental detection floor: any structure fainter than a few $\\hat{\\sigma}$ per pixel is invisible without additional noise suppression. The noise is estimated robustly as $\\sigma_\\mathrm{sky} = 1.4826 \\times \\mathrm{MAD}$, where the factor 1.4826 makes the MAD-based estimate consistent with the standard deviation of a Gaussian distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-sky-helpers", + "metadata": {}, + "outputs": [], + "source": [ "def build_sky_arr(\n", " masked_image,\n", " arr,\n", " bad_planes=(\"SAT\", \"NO_DATA\", \"BAD\", \"CR\", \"BRIGHT_OBJECT\"),\n", " source_planes=(\"DETECTED\",),\n", "):\n", - " \"\"\"Return arr with artefact and source pixels set to NaN.\"\"\"\n", + " \"\"\"Return arr with artifact and source pixels set to NaN.\n", + "\n", + " Parameters\n", + " ----------\n", + " masked_image : lsst.afw.image.MaskedImage\n", + " Masked image whose mask plane is used to identify bad pixels.\n", + " arr : ndarray, shape (ny, nx)\n", + " Pixel array to mask, typically the image or variance plane.\n", + " bad_planes : tuple of str, optional\n", + " Mask plane names treated as artifacts.\n", + " source_planes : tuple of str, optional\n", + " Mask plane names treated as detected sources.\n", + "\n", + " Returns\n", + " -------\n", + " masked_arr : ndarray, shape (ny, nx)\n", + " Copy of arr with artifact, source, and non-finite pixels set to NaN.\n", + " \"\"\"\n", " mask_arr = masked_image.getMask().getArray()\n", " mask_pd = masked_image.getMask().getMaskPlaneDict()\n", " exclude = np.zeros(arr.shape, dtype=bool)\n", @@ -152,66 +168,132 @@ " Uses the robust median as sky level and 1.4826 x MAD as pixel noise.\n", " Pass a source-masked array (e.g. from build_sky_arr) so that detected\n", " sources do not bias the estimates.\n", + "\n", + " Parameters\n", + " ----------\n", + " arr : ndarray\n", + " Pixel array, typically source-masked (NaN at bad/source pixels).\n", + " lo_sigma : float, optional\n", + " Lower stretch limit in units of sky noise below the sky median.\n", + " hi_sigma : float, optional\n", + " Upper stretch limit in units of sky noise above the sky median.\n", + "\n", + " Returns\n", + " -------\n", + " vmin : float\n", + " Lower display limit (sky - lo_sigma * noise).\n", + " vmax : float\n", + " Upper display limit (sky + hi_sigma * noise).\n", + " sigma : float\n", + " Per-pixel noise estimate (1.4826 * MAD).\n", " \"\"\"\n", " flat = arr[np.isfinite(arr)]\n", " sky = np.median(flat)\n", " sigma = 1.4826 * np.median(np.abs(flat - sky))\n", - " return sky - lo_sigma * sigma, sky + hi_sigma * sigma\n", + " return sky - lo_sigma * sigma, sky + hi_sigma * sigma, sigma" + ] + }, + { + "cell_type": "markdown", + "id": "f0f28cee", + "metadata": {}, + "source": [ + "Define unit-conversion helpers. Both functions use `PIXEL_SCALE` and `PIXEL_AREA`, which are derived from the coadd WCS in Section 2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-unit-conv", + "metadata": {}, + "outputs": [], + "source": [ + "def nJy_per_pix_to_mu(flux_nJy):\n", + " \"\"\"Convert nJy pixel**-1 to AB mag arcsec**-2 for the LSST pixel scale.\n", + "\n", + " Parameters\n", + " ----------\n", + " flux_nJy : float or array_like\n", + " Pixel flux in nJy pixel**-1.\n", + "\n", + " Returns\n", + " -------\n", + " mu : float or array_like\n", + " Surface brightness in AB mag arcsec**-2.\n", + " \"\"\"\n", + " return -2.5 * np.log10(np.abs(flux_nJy) * 1e-9 / PIXEL_AREA / 3631.0)\n", "\n", "\n", + "def mu_to_nJy_per_pix(mu):\n", + " \"\"\"Convert AB mag arcsec**-2 to nJy pixel**-1 for the LSST pixel scale.\n", + "\n", + " Parameters\n", + " ----------\n", + " mu : float or array_like\n", + " Surface brightness in AB mag arcsec**-2.\n", + "\n", + " Returns\n", + " -------\n", + " flux_nJy : float or array_like\n", + " Pixel flux in nJy pixel**-1.\n", + " \"\"\"\n", + " return 3631.0 * 10 ** (-mu / 2.5) * PIXEL_AREA * 1e9" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-display-helpers-md", + "metadata": {}, + "source": [ + "`sky_norm` wraps `sky_stats` into an `ImageNormalize` object for use with `imshow`. `bin_image` performs $N\\times N$ block averaging with NaN propagation. `adaptive_smooth` applies a spatially varying kernel via normalised convolution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-display-helpers", + "metadata": {}, + "outputs": [], + "source": [ "def sky_norm(arr, lo_sigma=2.0, hi_sigma=8.0):\n", " \"\"\"Return an ImageNormalize anchored to the sky background.\n", "\n", " Wraps sky_stats. Pass a source-masked array so that detected objects do\n", " not bias the sky median or noise estimate.\n", + "\n", + " Parameters\n", + " ----------\n", + " arr : ndarray\n", + " Pixel array, typically source-masked (NaN at bad/source pixels).\n", + " lo_sigma : float, optional\n", + " Lower stretch limit in units of sky noise below the sky median.\n", + " hi_sigma : float, optional\n", + " Upper stretch limit in units of sky noise above the sky median.\n", + "\n", + " Returns\n", + " -------\n", + " norm : astropy.visualization.ImageNormalize\n", + " Normalization with asinh stretch and sky-anchored limits.\n", " \"\"\"\n", - " vmin, vmax = sky_stats(arr, lo_sigma=lo_sigma, hi_sigma=hi_sigma)\n", + " vmin, vmax, _ = sky_stats(arr, lo_sigma=lo_sigma, hi_sigma=hi_sigma)\n", " return ImageNormalize(vmin=vmin, vmax=vmax, stretch=AsinhStretch())\n", "\n", "\n", - "def show_lsb(\n", - " arrays,\n", - " titles,\n", - " lo_sigma=2.0,\n", - " hi_sigma=8.0,\n", - " cmap=\"Greys_r\",\n", - " n_cols=None,\n", - " norm=None,\n", - " sky_arr=None,\n", - "):\n", - " \"\"\"Display one or more images with a MAD-based LSB stretch.\n", + "def bin_image(arr, bin_factor):\n", + " \"\"\"Block-average arr by bin_factor in each dimension, ignoring NaN (masked) pixels.\n", "\n", " Parameters\n", " ----------\n", - " arrays : list of ndarray (displayed as-is)\n", - " sky_arr : ndarray, optional\n", - " Source-masked array used only for computing the display stretch.\n", - " If not supplied, arrays[0] is used -- biased upward when sources are present.\n", - " norm : ImageNormalize, optional\n", - " Pre-computed normalisation; overrides sky_arr.\n", + " arr : ndarray, shape (ny, nx)\n", + " Input pixel array; NaN at masked pixels.\n", + " bin_factor : int\n", + " Downsampling factor; output shape is (ny // bin_factor, nx // bin_factor).\n", + "\n", + " Returns\n", + " -------\n", + " binned : ndarray, shape (ny // bin_factor, nx // bin_factor)\n", + " Block-averaged array.\n", " \"\"\"\n", - " if norm is None:\n", - " ref = sky_arr if sky_arr is not None else arrays[0]\n", - " norm = sky_norm(ref, lo_sigma=lo_sigma, hi_sigma=hi_sigma)\n", - " n_panels = len(arrays)\n", - " n_cols = n_cols or min(n_panels, 3)\n", - " n_rows = int(np.ceil(n_panels / n_cols))\n", - " fig, axes = plt.subplots(\n", - " n_rows, n_cols, figsize=(5 * n_cols, 5 * n_rows), squeeze=False\n", - " )\n", - " for idx, (arr, title) in enumerate(zip(arrays, titles)):\n", - " row, col = divmod(idx, n_cols)\n", - " axes[row][col].imshow(arr, norm=norm, cmap=cmap, origin=\"lower\")\n", - " axes[row][col].set_title(title)\n", - " axes[row][col].set_xlabel(\"x (pixels)\")\n", - " axes[row][col].set_ylabel(\"y (pixels)\")\n", - " for idx in range(n_panels, n_rows * n_cols):\n", - " row, col = divmod(idx, n_cols)\n", - " axes[row][col].set_visible(False)\n", - "\n", - "\n", - "def bin_image(arr, bin_factor):\n", - " \"\"\"Block-average arr by bin_factor in each dimension, ignoring NaN (masked) pixels.\"\"\"\n", " ny, nx = arr.shape\n", " ny_trim = (ny // bin_factor) * bin_factor\n", " nx_trim = (nx // bin_factor) * bin_factor\n", @@ -219,25 +301,42 @@ " with np.errstate(invalid=\"ignore\"):\n", " return np.nanmean(\n", " trimmed.reshape(\n", - " ny_trim // bin_factor, bin_factor, nx_trim // bin_factor, bin_factor\n", - " ),\n", - " axis=(1, 3),\n", + " ny_trim\n", + " // bin_factor,\n", + " bin_factor,\n", + " nx_trim\n", + " // bin_factor,\n", + " bin_factor),\n", + " axis=(\n", + " 1,\n", + " 3),\n", " )\n", "\n", "\n", "def adaptive_smooth(arr, noise_sigma, target_snr=3.0, kernel_sizes=None):\n", - " \"\"\"Adaptive kernel smoothing using normalised convolution to handle masked pixels.\n", + " \"\"\"Adaptive kernel smoothing using normalized convolution to handle masked pixels.\n", "\n", " At each pixel, selects the smallest kernel for which |smoothed / noise_eff| >= target_snr.\n", - " Masked (NaN) pixels are excluded from each kernel window via normalised convolution, so\n", + " Masked (NaN) pixels are excluded from each kernel window via normalized convolution, so\n", " they do not bleed into adjacent valid pixels.\n", "\n", " Parameters\n", " ----------\n", - " arr : 2-D ndarray, NaN only at bad pixels (SAT, CR, NO_DATA, ...)\n", - " noise_sigma : float, per-pixel sky noise from clean (source-masked) pixels\n", - " target_snr : float, target |signal/noise| ratio\n", - " kernel_sizes : list of int, uniform-filter widths in pixels\n", + " arr : ndarray, shape (ny, nx)\n", + " Input pixel array; NaN at bad or source-masked pixels.\n", + " noise_sigma : float\n", + " Per-pixel sky noise from clean (source-masked) pixels, in nJy pixel**-1.\n", + " target_snr : float, optional\n", + " Target |signal/noise| ratio used to select the smoothing kernel.\n", + " kernel_sizes : list of int, optional\n", + " Uniform-filter widths in pixels; defaults to [3, 7, 15, 31, 61].\n", + "\n", + " Returns\n", + " -------\n", + " result : ndarray, shape (ny, nx)\n", + " Adaptively smoothed array; NaN at bad pixels.\n", + " kernel_map : ndarray, shape (ny, nx)\n", + " Kernel width applied at each pixel; NaN at bad pixels.\n", " \"\"\"\n", " if kernel_sizes is None:\n", " kernel_sizes = [3, 7, 15, 31, 61]\n", @@ -249,7 +348,8 @@ " def _nconv(k):\n", " num = uniform_filter(arr_z, size=k)\n", " den = uniform_filter(valid_f, size=k)\n", - " return np.where(den > 0.1, num / den, np.nan)\n", + " with np.errstate(invalid=\"ignore\", divide=\"ignore\"):\n", + " return np.where(den > 0.1, num / den, np.nan)\n", "\n", " result = _nconv(kernel_sizes[-1])\n", " kernel_map = np.full(arr.shape, float(kernel_sizes[-1]))\n", @@ -272,7 +372,7 @@ "source": [ "## 2. Load a coadd patch\n", "\n", - "Open the DP1 butler and load the $i$-band `deep_coadd` for the ECDFS field centred on the target galaxy. Display the full patch with a sky-noise anchored stretch." + "Instantiate the Butler with the DP1 repository and load the $i$-band `deep_coadd` for the ECDFS field centered on the target galaxy. Display the full patch with a sky-noise anchored stretch." ] }, { @@ -289,7 +389,8 @@ "band = \"i\"\n", "\n", "skymap = butler.get(\"skyMap\", skymap=skymap_name)\n", - "sky_point = geom.SpherePoint(target_ra * geom.degrees, target_dec * geom.degrees)\n", + "sky_point = geom.SpherePoint(\n", + " target_ra * geom.degrees, target_dec * geom.degrees)\n", "tract_info = skymap.findTract(sky_point)\n", "patch_info = tract_info.findPatch(sky_point)\n", "tract_id = tract_info.getId()\n", @@ -301,7 +402,7 @@ "coadd = butler.get(\"deep_coadd\", dataId=data_id)\n", "PIXEL_SCALE = coadd.getWcs().getPixelScale().asArcseconds()\n", "PIXEL_AREA = PIXEL_SCALE**2\n", - "print(f\"Pixel scale: {PIXEL_SCALE:.4f} arcsec/pixel\")\n", + "print(f\"Pixel scale: {PIXEL_SCALE: .4f} arcsec/pixel\")\n", "coadd_arr = coadd.image.array.copy()\n", "print(f\"Loaded {band}-band coadd: {coadd_arr.shape}\")" ] @@ -322,13 +423,13 @@ "outputs": [], "source": [ "coadd_sky_full = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", - "vmin_patch, vmax_patch = sky_stats(coadd_sky_full)\n", + "norm_patch = sky_norm(coadd_sky_full)\n", "\n", - "fig = plt.figure(figsize=(5, 5))\n", - "disp = afwDisplay.Display(frame=fig)\n", - "disp.scale(\"asinh\", min=vmin_patch, max=vmax_patch)\n", - "disp.image(coadd.image)\n", - "plt.title(f\"{band}-band deep_coadd — tract {tract_id}, patch {patch_id}\")" + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "ax.imshow(coadd_arr, norm=norm_patch, cmap=\"Greys_r\", origin=\"lower\")\n", + "ax.set_title(f\"{band}-band deep_coadd: tract {tract_id}, patch {patch_id}\")\n", + "ax.set_xlabel(\"x (pixels)\")\n", + "ax.set_ylabel(\"y (pixels)\")" ] }, { @@ -336,7 +437,7 @@ "id": "lsb302-cap1", "metadata": {}, "source": [ - "> Figure 1: Full $i$-band `deep_coadd` patch containing the target galaxy at RA = 53.010°, Dec = −28.350°. The asinh stretch is anchored to the per-pixel sky noise." + "> Figure 1: Full $i$-band `deep_coadd` patch containing the target galaxy (RA = 53.010°, Dec = −28.350°), displayed with an asinh stretch anchored to the sky noise estimated from source-masked pixels." ] }, { @@ -344,7 +445,7 @@ "id": "lsb302-mask-md", "metadata": {}, "source": [ - "Build display and analysis masks from the pipeline mask planes. `coadd_display` retains detected sources so they remain visible; `coadd_lsb` additionally masks `DETECTED` with a 30-pixel dilation to suppress PSF wings for sky statistics." + "Build display and analysis masks from the pipeline mask planes. `coadd_display` retains detected-source pixels for a natural-looking image; `coadd_sky` masks detected sources and artefacts, leaving only blank sky pixels. `sky_stats` estimates the per-pixel noise `sky_sigma` from `coadd_sky`." ] }, { @@ -354,23 +455,17 @@ "metadata": {}, "outputs": [], "source": [ - "coadd_display = build_sky_arr(coadd.getMaskedImage(), coadd_arr, source_planes=())\n", + "coadd_display = build_sky_arr(\n", + " coadd.getMaskedImage(), coadd_arr, source_planes=())\n", "\n", - "coadd_sky_prelim = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", - "dilation_pixels = 30\n", - "dilated_mask = binary_dilation(\n", - " ~np.isfinite(coadd_sky_prelim), iterations=dilation_pixels\n", - ")\n", - "coadd_lsb = np.where(dilated_mask, np.nan, coadd_arr)\n", + "coadd_sky = build_sky_arr(coadd.getMaskedImage(), coadd_arr)\n", + "_, _, sky_sigma = sky_stats(coadd_sky)\n", "\n", - "sky_pixels_full = coadd_lsb[np.isfinite(coadd_lsb)]\n", - "sky_sigma = 1.4826 * np.median(np.abs(sky_pixels_full - np.median(sky_pixels_full)))\n", - "print(\n", - " f\"After {dilation_pixels}-pixel dilation: {100*(1 - dilated_mask.sum()/dilated_mask.size):.1f}% of pixels available for LSB analysis\"\n", - ")\n", - "print(\n", - " f\"Per-pixel 1-sigma noise: {sky_sigma:.1f} nJy/pix -> {nJy_per_pix_to_mu(sky_sigma):.1f} mag/arcsec^2\"\n", - ")" + "sky_val = np.nanmedian(coadd_sky)\n", + "mu_sigma = nJy_per_pix_to_mu(sky_sigma)\n", + "mu_val = nJy_per_pix_to_mu(sky_val)\n", + "print(f\"Sky level: {sky_val: .1f} nJy/pix = {mu_val: .1f} mag/arcsec^2\")\n", + "print(f\"Per-pixel 1-sigma noise: {sky_sigma: .1f} nJy/pix = {mu_sigma: .1f} mag/arcsec^2\")" ] }, { @@ -392,7 +487,9 @@ "source": [ "### 3.1. Noise scaling\n", "\n", - "The table below converts the $\\sqrt{N}$ noise scaling to surface brightness depth, assuming uncorrelated pixel noise. Surface brightness is an intensive quantity, so the sky level is unchanged by binning — only the noise floor drops." + "The table below converts the $\\sqrt{N}$ noise scaling to surface brightness depth, assuming uncorrelated pixel noise. Surface brightness is an intensive quantity: it measures flux per unit area and does not depend on how many pixels are averaged, so the sky level in mag arcsec$^{-2}$ is unchanged by binning. Only the noise floor — the minimum surface brightness detectable at $1\\hat{\\sigma}$ per binned pixel — drops by $\\sqrt{N}$ with each factor-of-$N$ binning step.\n", + "\n", + "> **Warning:** This cell may produce a `RuntimeWarning: Mean of empty slice` from `numpy`. This is expected: bin blocks that fall entirely within masked regions (detector edges, saturated columns) contain only `NaN` pixels, so `np.nanmean` has nothing to average and returns `NaN` for those blocks. The warning can be safely ignored — the output array is correct." ] }, { @@ -402,25 +499,34 @@ "metadata": {}, "outputs": [], "source": [ - "print(\n", - " f\"Per-pixel 1-sigma noise: {sky_sigma:.1f} nJy/pix -> {nJy_per_pix_to_mu(sky_sigma):.1f} mag/arcsec^2\\n\"\n", - ")\n", - "print(\n", - " f\"{'Bin factor':>10} {'Resolution':>12} {'sigma_eff (nJy/pix)':>20} {'1-sigma depth (mag/arcsec^2)':>28} {'Gain (mag)':>10}\"\n", - ")\n", - "print(\"-\" * 85)\n", "mu_1x = nJy_per_pix_to_mu(sky_sigma)\n", + "print(f\"Per-pixel 1-sigma noise: {sky_sigma: .1f} nJy/pix -> {mu_1x: .1f} mag/arcsec^2\\n\")\n", + "cols = [\n", + " f\"{'Bin factor': >10}\",\n", + " f\"{'Resolution': >12}\",\n", + " f\"{'sigma_eff (nJy/pix)': >20}\",\n", + " f\"{'1-sigma depth (mag/arcsec^2)': >28}\",\n", + " f\"{'Gain (mag)': >10}\",\n", + "]\n", + "print(\" \".join(cols))\n", + "print(\"-\" * 85)\n", "for bf in [1, 2, 4, 8, 16, 32]:\n", " sigma_eff = sky_sigma / bf\n", " mu = nJy_per_pix_to_mu(sigma_eff)\n", " gain = mu - mu_1x\n", - " print(\n", - " f'{bf:>10} {bf * PIXEL_SCALE:>11.1f}\" {sigma_eff:>20.1f} {mu:>28.1f} {gain:>+10.2f}'\n", - " )\n", + " res_str = f'{bf * PIXEL_SCALE: .1f}\"'\n", + " row = [\n", + " f\"{bf: >10}\",\n", + " f\"{res_str: >12}\",\n", + " f\"{sigma_eff: >20.1f}\",\n", + " f\"{mu: >28.1f}\",\n", + " f\"{gain: >+10.2f}\",\n", + " ]\n", + " print(\" \".join(row))\n", "\n", "bin_factors = [1, 4, 8, 16]\n", "binned_arrays = [bin_image(coadd_display, bf) for bf in bin_factors]\n", - "shared_norm = sky_norm(bin_image(coadd_lsb, 1), lo_sigma=2.0, hi_sigma=8.0)" + "shared_norm = sky_norm(bin_image(coadd_sky, 1), lo_sigma=2.0, hi_sigma=8.0)" ] }, { @@ -428,7 +534,7 @@ "id": "b13f7051", "metadata": {}, "source": [ - "Quantify the noise reduction as a function of bin factor. Measured $\\sigma$ (from the negative-half pixel mirror, unaffected by real emission) is compared to the $\\sqrt{N}$ expectation." + "Quantify the noise reduction as a function of bin factor. Since all real astrophysical emission contributes positive flux, pixels with negative values can only arise from noise. Taking the MAD of these negative-valued pixels (and mirroring the result to recover the full noise width) gives a noise estimate uncontaminated by faint galaxy outskirts, unmasked PSF wings, or diffuse emission. This ‘negative-half’ estimator is compared to the $\\sqrt{N}$ expectation in Figure 2 right." ] }, { @@ -445,7 +551,7 @@ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", "\n", "for bf, color in zip(bin_factors, colors):\n", - " binned_lsb = bin_image(coadd_lsb, bf)\n", + " binned_lsb = bin_image(coadd_sky, bf)\n", " pixels = binned_lsb[np.isfinite(binned_lsb)].ravel()\n", " neg = pixels[pixels < 0]\n", " meas_sigma = 1.4826 * np.median(np.abs(neg))\n", @@ -459,7 +565,7 @@ " histtype=\"step\",\n", " color=color,\n", " lw=1.5,\n", - " label=f\"Bin {bf}x | sigma = {meas_sigma:.2f} nJy\",\n", + " label=f\"Bin {bf}x | sigma = {meas_sigma: .2f} nJy\",\n", " )\n", "\n", "axes[0].set_xlabel(\"Pixel value (nJy)\")\n", @@ -469,9 +575,8 @@ "axes[0].axvline(0, color=\"k\", ls=\":\", lw=0.8)\n", "\n", "expected_sigmas = [sky_sigma / bf for bf in bin_factors]\n", - "axes[1].plot(\n", - " bin_factors, expected_sigmas, \"k--o\", ms=6, label=\"Expected: sigma / bin_factor\"\n", - ")\n", + "axes[1].plot(bin_factors, expected_sigmas, \"k--o\",\n", + " ms=6, label=\"Expected: sigma / bin_factor\")\n", "axes[1].plot(\n", " bin_factors,\n", " measured_sigmas,\n", @@ -503,7 +608,7 @@ "id": "lsb302-interp", "metadata": {}, "source": [ - "At small bin factors the ratio is close to 1, showing that pixel noise is largely uncorrelated at sub-arcsecond scales. At large bin factors the measured $\\sigma$ exceeds the $\\sqrt{N}$ prediction, reflecting spatially correlated background residuals left by the pipeline sky subtraction. In practice, binning beyond 8–16× yields less depth improvement than expected." + "At small bin factors the ratio of measured to expected $\\sigma$ is close to 1, showing that pixel noise is largely uncorrelated at sub-arcsecond scales. At large bin factors the measured $\\sigma$ exceeds the $\\sqrt{N}$ prediction, reflecting spatially correlated background residuals left by the pipeline sky subtraction. The orange curve in the right panel shows this directly: beyond bin factors of 8--16, the measured noise falls more slowly than the dashed expected line, so binning yields less depth improvement than ideal $\\sqrt{N}$ scaling predicts." ] }, { @@ -558,8 +663,11 @@ " extent=extent,\n", " )\n", " axes[0][col].set_title(\n", - " f'Bin {bf}x ({bf * PIXEL_SCALE:.1f}\" res.)\\nsigma = {sky_sigma / bf:.0f} nJy'\n", - " )\n", + " f'Bin {bf}x ({\n", + " bf\n", + " * PIXEL_SCALE: .1f}\" res.)\\nsigma = {\n", + " sky_sigma\n", + " / bf: .0f} nJy')\n", " axes[0][col].set_xlabel(\"Delta arcsec\")\n", " if col == 0:\n", " axes[0][col].set_ylabel(\"Delta arcsec\")" @@ -570,7 +678,7 @@ "id": "lsb302-cap5", "metadata": {}, "source": [ - "> Figure 3: Zoom-in ($\\pm$120 arcsec) around the target galaxy at each bin factor on a shared stretch." + "> Figure 3: Zoom-in ($\\pm120$ arcsec) around the target galaxy at each bin factor on a shared stretch. Cutouts are clipped to the patch boundary, so a panel may not reach $\\pm120$ arcsec on all sides if the target lies near the patch edge." ] }, { @@ -582,7 +690,9 @@ "\n", "Pixel binning treats every location in the image identically. Adaptive smoothing instead applies a spatially varying kernel: small where the signal is bright enough to be detected at fine resolution, large where the signal is faint and noise dominates. The result preserves compact structure while revealing diffuse emission in regions that pure binning would show as blank noise.\n", "\n", - "The algorithm selects the smallest kernel (from a discrete set of 3, 7, 15, 31, and 61 pixels) at which $|\\text{smoothed value}| / (\\sigma_{\\rm sky} / k) \\geq 3$. Masked pixels are excluded via normalised convolution — zeroing bad pixels in both numerator and denominator — so they do not bleed into adjacent valid pixels." + "The `adaptive_smooth` helper (defined in Section 1.2) implements this approach. It accepts a list of candidate kernel sizes as the `kernel_sizes` parameter; here `kernel_sizes = [1, 3, 7, 15, 31, 61]` pixels, chosen to span roughly 0.3 arcsec to 20 arcsec in steps of approximately a factor of two. At each pixel it selects the smallest kernel for which $|\\text{smoothed value}| / (\\sigma_{\\rm sky} / k) \\geq 3$. Masked pixels are excluded via normalised convolution (zeroing masked pixels in both numerator and denominator), so they do not bleed into adjacent valid pixels.\n", + "\n", + "First, locate the host galaxy within the patch and extract a postage stamp centred on it; the stamp is used for the algorithm demonstration in Section 4.1." ] }, { @@ -601,7 +711,7 @@ "\n", "coadd_ny, coadd_nx = coadd_arr.shape\n", "if 0 <= host_x < coadd_nx and 0 <= host_y < coadd_ny:\n", - " print(f\"Pixel value at host: {coadd_arr[host_y, host_x]:.1f} nJy/pix\")\n", + " print(f\"Pixel value at host: {coadd_arr[host_y, host_x]: .1f} nJy/pix\")\n", "else:\n", " print(\"WARNING: position outside patch\")" ] @@ -611,7 +721,7 @@ "id": "7f3ab63f", "metadata": {}, "source": [ - "Load the postage stamp centred on the host galaxy and build display and sky-analysis masks. `stamp_sigma` — the per-pixel noise on source-free sky pixels — is used as the noise threshold in the adaptive smoothing and surface brightness slice sections." + "Load the postage stamp centred on the host galaxy. Two versions of the stamp array are built with `build_sky_arr`: `stamp_display` masks only genuinely bad pixels (saturated columns, cosmic rays, missing data) and is used for display; `stamp_sky` additionally masks pixels flagged as detected sources, leaving only blank sky for noise estimation. `stamp_sigma` is the per-pixel sky noise derived from `stamp_sky` and is used as the noise threshold in both adaptive smoothing and the SNR profile." ] }, { @@ -623,26 +733,24 @@ "source": [ "stamp_half = 600\n", "stamp_bbox = geom.Box2I(\n", - " geom.Point2I(int(host_pix.getX()) - stamp_half, int(host_pix.getY()) - stamp_half),\n", + " geom.Point2I(int(host_pix.getX()) - stamp_half,\n", + " int(host_pix.getY()) - stamp_half),\n", " geom.Extent2I(2 * stamp_half, 2 * stamp_half),\n", ")\n", "stamp_bbox.clip(patch_bbox)\n", "\n", - "stamp = butler.get(\"deep_coadd\", dataId=data_id, parameters={\"bbox\": stamp_bbox})\n", + "stamp = butler.get(\"deep_coadd\", dataId=data_id,\n", + " parameters={\"bbox\": stamp_bbox})\n", "stamp_arr = stamp.image.array.copy()\n", "\n", - "stamp_display = build_sky_arr(stamp.getMaskedImage(), stamp_arr, source_planes=())\n", + "stamp_display = build_sky_arr(\n", + " stamp.getMaskedImage(), stamp_arr, source_planes=())\n", "stamp_bad = ~np.isfinite(stamp_display)\n", "\n", - "stamp_sky_prelim = build_sky_arr(stamp.getMaskedImage(), stamp_arr)\n", - "stamp_dilated_mask = binary_dilation(~np.isfinite(stamp_sky_prelim), iterations=15)\n", - "stamp_lsb = np.where(stamp_dilated_mask, np.nan, stamp_arr)\n", - "\n", - "stamp_sky_pixels = stamp_lsb[np.isfinite(stamp_lsb)]\n", - "stamp_sigma = 1.4826 * np.median(np.abs(stamp_sky_pixels - np.median(stamp_sky_pixels)))\n", - "print(\n", - " f\"Stamp per-pixel 1-sigma: {stamp_sigma:.1f} nJy/pix -> {nJy_per_pix_to_mu(stamp_sigma):.1f} mag/arcsec^2\"\n", - ")" + "stamp_sky = build_sky_arr(stamp.getMaskedImage(), stamp_arr)\n", + "_, _, stamp_sigma = sky_stats(stamp_sky)\n", + "mu_stamp = nJy_per_pix_to_mu(stamp_sigma)\n", + "print(f\"Stamp per-pixel 1-sigma: {stamp_sigma: .1f} nJy/pix -> {mu_stamp: .1f} mag/arcsec^2\")" ] }, { @@ -652,7 +760,7 @@ "source": [ "### 4.1. Algorithm demonstration\n", "\n", - "Apply adaptive smoothing to the stamp and display the result alongside the original and the kernel size map, which shows which smoothing scale was selected at each location." + "Apply `adaptive_smooth` (Section 1.2) to the postage stamp centred on the host galaxy. The stamp is used here rather than the full patch so the kernel size map can be inspected at the scale of an individual galaxy. Print the fraction of stamp pixels assigned to each kernel size, then display the kernel size map alongside the original stamp to show spatially where fine or coarse smoothing was applied. A direct comparison with fixed 8x pixel binning follows." ] }, { @@ -667,7 +775,28 @@ " stamp_display, stamp_sigma, target_snr=3.0, kernel_sizes=kernel_sizes\n", ")\n", "\n", - "norm = sky_norm(stamp_lsb, lo_sigma=2.0, hi_sigma=8.0)\n", + "kvals = kernel_map[np.isfinite(kernel_map)].ravel()\n", + "for ks in kernel_sizes:\n", + " frac = 100 * np.mean(kvals == float(ks))\n", + " print(f\" {ks: 2d} px ({ks * PIXEL_SCALE: .1f}\\\"): {frac: .1f}% of stamp pixels\")" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-multiscale-a-md", + "metadata": {}, + "source": [ + "Display the original stamp alongside the kernel size map: bright compact sources receive small kernels; noise-dominated regions receive large kernels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-multiscale-plot", + "metadata": {}, + "outputs": [], + "source": [ + "norm = sky_norm(stamp_sky, lo_sigma=2.0, hi_sigma=8.0)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(11, 5))\n", "axes[0].imshow(stamp_display, norm=norm, cmap=\"Greys_r\", origin=\"lower\")\n", @@ -675,7 +804,8 @@ "axes[0].set_xlabel(\"x (pixels)\")\n", "axes[0].set_ylabel(\"y (pixels)\")\n", "\n", - "im = axes[1].imshow(kernel_map, cmap=\"cividis\", origin=\"lower\", vmin=0, vmax=61)\n", + "im = axes[1].imshow(kernel_map, cmap=\"cividis\",\n", + " origin=\"lower\", vmin=0, vmax=61)\n", "axes[1].set_title(\"Kernel size map (pixels)\")\n", "axes[1].set_xlabel(\"x (pixels)\")\n", "axes[1].set_ylabel(\"y (pixels)\")\n", @@ -687,7 +817,7 @@ "id": "lsb302-cap6", "metadata": {}, "source": [ - "> Figure 4: Left — original stamp with bad pixels masked. Right — kernel size map: small values (bright) indicate compact sources resolved at fine scale; large values (dark) indicate noise-dominated diffuse regions where the algorithm applies the largest kernel." + "> Figure 4: Left -- original stamp with bad pixels masked. Right -- kernel size map in pixels; blue indicates small kernels applied to compact, well-detected sources; yellow indicates large kernels applied to noise-dominated and low-surface-brightness regions." ] }, { @@ -695,7 +825,7 @@ "id": "lsb302-binvadapt", "metadata": {}, "source": [ - "Compare pixel binning at 8× with the adaptive result on the same stretch." + "Compare a fixed 8x pixel binning (uniform across the entire stamp) with the adaptive result on the same stretch." ] }, { @@ -718,8 +848,9 @@ " extent=[0, nx_full, 0, ny_full],\n", ")\n", "axes[0].set_title(\n", - " f'Pixel binning {bin_factor_ref}x ({bin_factor_ref * PIXEL_SCALE:.1f}\" res.)'\n", - ")\n", + " f'Pixel binning {bin_factor_ref}x ({\n", + " bin_factor_ref\n", + " * PIXEL_SCALE: .1f}\" res.)')\n", "axes[0].set_xlabel(\"x (pixels, native scale)\")\n", "axes[0].set_ylabel(\"y (pixels, native scale)\")\n", "\n", @@ -734,7 +865,7 @@ "id": "lsb302-cap7", "metadata": {}, "source": [ - "> Figure 5: Pixel binning at 8× (left) versus adaptive smoothing (right) on the same stretch. Binning achieves uniform noise reduction at the cost of spatial resolution everywhere; adaptive smoothing retains compact source morphology while suppressing noise in the diffuse outskirts." + "> Figure 5: Pixel binning at 8× (left) versus adaptive smoothing (right) on the same stretch. Binning achieves uniform noise reduction at the cost of spatial resolution everywhere; adaptive smoothing retains compact source morphology while suppressing noise in the diffuse outskirts. Ring-like artifacts visible around bright sources in the adaptive-smoothed panel are a known side effect: the kernel size changes abruptly at source boundaries, creating a visible discontinuity between the finely-smoothed core and the more heavily smoothed surrounding halo." ] }, { @@ -744,7 +875,7 @@ "source": [ "### 4.2. Noise quantification\n", "\n", - "Quantify the noise improvement from adaptive smoothing. Apply the same algorithm to the full coadd patch (for consistent sky statistics with the binning comparison), then inspect the kernel size distribution from the stamp." + "Apply `adaptive_smooth` to the full coadd patch rather than the stamp, so that the sky pixel sample is large enough for robust noise statistics. Print the before and after sky noise, then produce two diagnostic plots: a bar chart showing the fraction of stamp pixels assigned to each kernel size (indicating the typical angular scale at which the field becomes noise-dominated), and a histogram comparing the sky pixel distributions before and after smoothing (a narrower distribution after smoothing confirms genuine noise reduction)." ] }, { @@ -756,30 +887,50 @@ "source": [ "kernel_sizes = [1, 3, 7, 15, 31, 61]\n", "coadd_smooth, _ = adaptive_smooth(\n", - " coadd_display, sky_sigma, target_snr=3.0, kernel_sizes=kernel_sizes\n", + " coadd_sky, sky_sigma, target_snr=3.0, kernel_sizes=kernel_sizes\n", ")\n", "\n", - "sky_valid = np.isfinite(coadd_lsb)\n", - "sky_raw = coadd_lsb[sky_valid]\n", + "sky_valid = np.isfinite(coadd_sky)\n", + "sky_raw = coadd_sky[sky_valid]\n", "sky_smoothed = coadd_smooth[sky_valid]\n", "\n", - "sky_sigma_raw = 1.4826 * np.median(np.abs(sky_raw - np.median(sky_raw)))\n", - "sky_sigma_smooth = 1.4826 * np.median(np.abs(sky_smoothed - np.median(sky_smoothed)))\n", + "_, _, sky_sigma_raw = sky_stats(sky_raw)\n", + "_, _, sky_sigma_smooth = sky_stats(sky_smoothed)\n", "noise_reduction = sky_sigma_raw / sky_sigma_smooth\n", - "depth_gain_mag = nJy_per_pix_to_mu(sky_sigma_smooth) - nJy_per_pix_to_mu(sky_sigma_raw)\n", - "\n", - "print(f\"Sky noise in inter-source regions (full patch):\")\n", - "print(\n", - " f\" Before adaptive smoothing: {sky_sigma_raw:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_sigma_raw):.1f} mag/arcsec^2\"\n", - ")\n", - "print(\n", - " f\" After adaptive smoothing: {sky_sigma_smooth:.1f} nJy/pix = {nJy_per_pix_to_mu(sky_sigma_smooth):.1f} mag/arcsec^2\"\n", - ")\n", - "print(\n", - " f\" Noise reduction: {noise_reduction:.1f}x (equivalent to binning ~{noise_reduction**2:.0f} pixels)\"\n", + "depth_gain_mag = (\n", + " nJy_per_pix_to_mu(sky_sigma_smooth) - nJy_per_pix_to_mu(sky_sigma_raw)\n", ")\n", - "print(f\" Surface brightness gain: {depth_gain_mag:+.2f} mag/arcsec^2\")\n", "\n", + "mu_raw = nJy_per_pix_to_mu(sky_sigma_raw)\n", + "mu_smooth = nJy_per_pix_to_mu(sky_sigma_smooth)\n", + "gain_str = format(depth_gain_mag, '+.2f')\n", + "noise_equiv = noise_reduction**2\n", + "print(\"Sky noise in inter-source regions (full patch):\")\n", + "print(f\" Before adaptive smoothing: {sky_sigma_raw: .1f} nJy/pix = {mu_raw: .1f} mag/arcsec^2\")\n", + "smooth_str = f\"{sky_sigma_smooth: .1f}\"\n", + "mu_smooth_str = f\"{mu_smooth: .1f}\"\n", + "print(f\" After adaptive smoothing: {smooth_str} nJy/pix = {mu_smooth_str} mag/arcsec^2\")\n", + "nr_str = f\"{noise_reduction: .1f}\"\n", + "ne_str = f\"{noise_equiv: .0f}\"\n", + "print(f\" Noise reduction: {nr_str}x (equivalent to binning ~{ne_str} pixels)\")\n", + "print(f\" Surface brightness gain: {gain_str} mag/arcsec^2\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-noise-plot-md", + "metadata": {}, + "source": [ + "Plot the kernel size distribution from the stamp and the sky pixel histograms before and after smoothing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-noise-plot", + "metadata": {}, + "outputs": [], + "source": [ "kvals = kernel_map[np.isfinite(kernel_map)].ravel()\n", "counts = [np.sum(kvals == float(k)) for k in kernel_sizes]\n", "fracs = [c / len(kvals) for c in counts]\n", @@ -789,14 +940,14 @@ "axes[0].bar(\n", " range(len(kernel_sizes)),\n", " [100 * f for f in fracs],\n", - " tick_label=[f'{k} px\\n({k * PIXEL_SCALE:.1f}\")' for k in kernel_sizes],\n", + " tick_label=[f'{k} px\\n({k * PIXEL_SCALE: .1f}\")' for k in kernel_sizes],\n", " color=[\"gray\", YELLOW, ORANGE, MAGENTA, PURPLE, BLUE],\n", ")\n", "axes[0].set_xlabel(\"Kernel size selected\")\n", "axes[0].set_ylabel(\"Fraction of pixels (%)\")\n", "axes[0].set_title(\"Kernel size distribution\")\n", "for i, f in enumerate(fracs):\n", - " axes[0].text(i, 100 * f + 0.5, f\"{100*f:.1f}%\", ha=\"center\", fontsize=8)\n", + " axes[0].text(i, 100 * f + 0.5, f\"{100 * f: .1f}%\", ha=\"center\", fontsize=8)\n", "\n", "x_range = 4.0 * sky_sigma\n", "axes[1].hist(\n", @@ -807,7 +958,7 @@ " histtype=\"step\",\n", " color=\"gray\",\n", " lw=1.5,\n", - " label=\"Unsmoothed (sky pixels)\",\n", + " label=\"Unsmoothed (source-masked)\",\n", ")\n", "axes[1].hist(\n", " sky_smoothed,\n", @@ -817,13 +968,13 @@ " histtype=\"step\",\n", " color=ORANGE,\n", " lw=1.5,\n", - " label=\"Adaptive smooth (sky pixels)\",\n", + " label=\"Adaptive smooth (source-masked)\",\n", ")\n", "axes[1].axvline(0, color=\"k\", ls=\":\", lw=0.8)\n", "axes[1].set_xlabel(\"Pixel value (nJy)\")\n", "axes[1].set_ylabel(\"Probability density\")\n", "axes[1].set_title(\"Sky pixel distributions before/after adaptive smoothing\")\n", - "axes[1].legend(fontsize=8)" + "axes[1].legend(fontsize=8)\n" ] }, { @@ -831,7 +982,7 @@ "id": "lsb302-cap8", "metadata": {}, "source": [ - "> Figure 6: Left — fraction of stamp pixels assigned to each smoothing kernel; the dominant kernel indicates the typical angular scale at which the field becomes noise-dominated. Right — full-patch sky pixel distributions before (grey) and after (orange) adaptive smoothing, using the same sky region as the binning comparison." + "> Figure 6: Left -- fraction of stamp pixels assigned to each smoothing kernel; the dominant kernel indicates the typical angular scale at which the field becomes noise-dominated. Right -- pixel value distributions from the full patch, restricted to source-masked pixels (i.e., locations with no detected sources), before (grey) and after (orange) adaptive smoothing. These distributions describe the noise: a narrower peak after smoothing confirms genuine noise reduction." ] }, { @@ -841,7 +992,9 @@ "source": [ "## 5. Signal-to-noise profile\n", "\n", - "A radial SNR profile — median flux in each annulus divided by the effective per-pixel noise — shows directly at what radius each noise-reduction method drops below a detection threshold." + "A radial SNR profile shows how the azimuthal-median galaxy flux compares to the residual noise level of each technique as a function of radius. Where SNR approaches or falls below 1, the surface brightness in that annulus is comparable to the noise floor. The signal-to-noise ratio in each annulus is the azimuthal median flux divided by the effective per-pixel noise $\\sigma_\\mathrm{eff}$.\n", + "\n", + "For pixel binning at factor $N$, averaging $N^2$ independent pixels reduces noise by $N$, so $\\sigma_\\mathrm{eff} = \\hat{\\sigma} / N$. For adaptive smoothing, the kernel size $k$ varies pixel by pixel, giving a spatially varying $\\sigma_\\mathrm{eff}(i,j) = \\hat{\\sigma} / k_{i,j}$, where $(i,j)$ are pixel coordinates and $k_{i,j}$ is the value of `kernel_map` at that pixel." ] }, { @@ -849,7 +1002,7 @@ "id": "lsb302-radial-profile-intro", "metadata": {}, "source": [ - "Compute the galaxy centre position in the stamp, build circular annuli out to 50 arcsec, and measure the median flux in each. For binned methods, divide by the uniform effective noise $\\sigma_{\\rm pixel}/N$. For adaptive smoothing, use a pixel-by-pixel effective noise derived from `kernel_map` ($\\sigma_{\\rm pixel}/k_{i,j}$) and take the median per-pixel SNR in each annulus." + "Compute the galaxy center position in the stamp, build circular annuli out to 50 arcsec, and measure the median flux in each. For binned methods, $\\sigma_\\mathrm{eff} = \\hat{\\sigma} / N$ is uniform across the annulus. For adaptive smoothing, the per-pixel SNR is computed using the local $\\sigma_\\mathrm{eff}(i,j) = \\hat{\\sigma} / k_{i,j}$, and the annulus value is the median of those per-pixel SNRs." ] }, { @@ -860,44 +1013,132 @@ "outputs": [], "source": [ "stamp_ny, stamp_nx = stamp_arr.shape\n", - "centre_x = int(host_pix.getX()) - stamp_bbox.getMinX()\n", - "centre_y = int(host_pix.getY()) - stamp_bbox.getMinY()\n", - "print(f\"Host position in stamp: ({centre_x}, {centre_y})\")\n", + "center_x = int(host_pix.getX()) - stamp_bbox.getMinX()\n", + "center_y = int(host_pix.getY()) - stamp_bbox.getMinY()\n", + "print(f\"Host position in stamp: ({center_x}, {center_y})\")\n", "\n", - "if not (5 < centre_x < stamp_nx - 5 and 5 < centre_y < stamp_ny - 5):\n", - " print(\"WARNING: host near edge -- using stamp centre as fallback.\")\n", - " centre_x, centre_y = stamp_nx // 2, stamp_ny // 2\n", + "if not (5 < center_x < stamp_nx - 5 and 5 < center_y < stamp_ny - 5):\n", + " print(\"WARNING: host near edge -- using stamp center as fallback.\")\n", + " center_x, center_y = stamp_nx // 2, stamp_ny // 2\n", "\n", + "grid_y, grid_x = np.mgrid[:stamp_ny, :stamp_nx]\n", + "r_arcsec = np.sqrt((grid_x - center_x) ** 2\n", + " + (grid_y - center_y) ** 2) * PIXEL_SCALE\n", "\n", + "r_max = 50.0\n", + "r_bins = np.linspace(0, r_max, 40)\n", + "r_centers = 0.5 * (r_bins[:-1] + r_bins[1:])\n" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-expand-md", + "metadata": {}, + "source": [ + "`expand_binned` tiles each block-averaged pixel back to its original `bf x bf` footprint, returning an array at native resolution so binned and unbinned images can be compared pixel-for-pixel in the same annuli." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-expand-fn", + "metadata": {}, + "outputs": [], + "source": [ "def expand_binned(arr_binned, bf):\n", - " \"\"\"Repeat block-averaged values back to native pixel resolution.\"\"\"\n", + " \"\"\"Repeat block-averaged values back to native pixel resolution.\n", + "\n", + " Parameters\n", + " ----------\n", + " arr_binned : ndarray, shape (ny // bf, nx // bf)\n", + " Block-averaged array from bin_image.\n", + " bf : int\n", + " Bin factor used to create arr_binned.\n", + "\n", + " Returns\n", + " -------\n", + " out : ndarray, shape (stamp_ny, stamp_nx)\n", + " Array expanded to native pixel resolution by pixel repetition.\n", + " \"\"\"\n", " expanded = np.repeat(np.repeat(arr_binned, bf, axis=0), bf, axis=1)\n", " out = np.full((stamp_ny, stamp_nx), np.nan)\n", " h = min(expanded.shape[0], stamp_ny)\n", " w = min(expanded.shape[1], stamp_nx)\n", " out[:h, :w] = expanded[:h, :w]\n", - " return out\n", - "\n", - "\n", - "grid_y, grid_x = np.mgrid[:stamp_ny, :stamp_nx]\n", - "r_arcsec = np.sqrt((grid_x - centre_x) ** 2 + (grid_y - centre_y) ** 2) * PIXEL_SCALE\n", - "\n", - "r_max = 50.0\n", - "r_bins = np.linspace(0, r_max, 40)\n", - "r_centres = 0.5 * (r_bins[:-1] + r_bins[1:])\n", - "\n", - "\n", + " return out\n" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-snrprofile-md", + "metadata": {}, + "source": [ + "`snr_profile` steps through each radial annulus and divides the median flux by a scalar `sigma_eff`, giving the SNR assuming uniform noise. Used for the unbinned image and each fixed bin factor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-snrprofile-fn", + "metadata": {}, + "outputs": [], + "source": [ "def snr_profile(arr, sigma_eff):\n", - " \"\"\"Median flux in each annulus divided by scalar sigma_eff.\"\"\"\n", + " \"\"\"Median flux in each annulus divided by scalar sigma_eff.\n", + "\n", + " Parameters\n", + " ----------\n", + " arr : ndarray, shape (stamp_ny, stamp_nx)\n", + " Pixel array at native or expanded-binned resolution.\n", + " sigma_eff : float\n", + " Effective noise per pixel in the same units as arr.\n", + "\n", + " Returns\n", + " -------\n", + " prof : ndarray, shape (n_bins,)\n", + " Median SNR in each radial annulus; NaN where fewer than 10 pixels.\n", + " \"\"\"\n", " prof = []\n", " for r_lo, r_hi in zip(r_bins[:-1], r_bins[1:]):\n", - " ann = (r_arcsec >= r_lo) & (r_arcsec < r_hi) & ~stamp_bad & np.isfinite(arr)\n", - " prof.append(np.median(arr[ann]) / sigma_eff if ann.sum() > 10 else np.nan)\n", - " return np.array(prof)\n", - "\n", - "\n", + " ann = (r_arcsec >= r_lo) & (\n", + " r_arcsec < r_hi) & ~stamp_bad & np.isfinite(arr)\n", + " prof.append(np.median(arr[ann])\n", + " / sigma_eff if ann.sum() > 10 else np.nan)\n", + " return np.array(prof)\n" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-snradapt-md", + "metadata": {}, + "source": [ + "`snr_profile_adaptive` uses the spatially varying effective noise $\\hat{\\sigma} / k_{i,j}$ at each pixel rather than a scalar, then takes the annulus median of those per-pixel SNRs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-snradapt-fn", + "metadata": {}, + "outputs": [], + "source": [ "def snr_profile_adaptive(smooth, kmap, sigma):\n", - " \"\"\"Median per-pixel SNR in each annulus using spatially varying sigma = sigma/k.\"\"\"\n", + " \"\"\"Median per-pixel SNR in each annulus using spatially varying sigma = sigma / k.\n", + "\n", + " Parameters\n", + " ----------\n", + " smooth : ndarray, shape (stamp_ny, stamp_nx)\n", + " Adaptively smoothed image from adaptive_smooth.\n", + " kmap : ndarray, shape (stamp_ny, stamp_nx)\n", + " Kernel-size map from adaptive_smooth.\n", + " sigma : float\n", + " Per-pixel sky noise before smoothing, in nJy pixel**-1.\n", + "\n", + " Returns\n", + " -------\n", + " prof : ndarray, shape (n_bins,)\n", + " Median SNR in each radial annulus; NaN where fewer than 10 pixels.\n", + " \"\"\"\n", " prof = []\n", " for r_lo, r_hi in zip(r_bins[:-1], r_bins[1:]):\n", " ann = (\n", @@ -911,28 +1152,63 @@ " prof.append(np.median(smooth[ann] * kmap[ann] / sigma))\n", " else:\n", " prof.append(np.nan)\n", - " return np.array(prof)\n", - "\n", - "\n", + " return np.array(prof)\n" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-radial-compute-md", + "metadata": {}, + "source": [ + "Compute the radial SNR profile for the unbinned image, three bin factors, and adaptive smoothing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-radial-compute", + "metadata": {}, + "outputs": [], + "source": [ "snr_unbinned = snr_profile(stamp_arr, stamp_sigma)\n", - "snr_bin4 = snr_profile(expand_binned(bin_image(stamp_display, 4), 4), stamp_sigma / 4)\n", - "snr_bin8 = snr_profile(expand_binned(bin_image(stamp_display, 8), 8), stamp_sigma / 8)\n", + "snr_bin4 = snr_profile(expand_binned(\n", + " bin_image(stamp_display, 4), 4), stamp_sigma / 4)\n", + "snr_bin8 = snr_profile(expand_binned(\n", + " bin_image(stamp_display, 8), 8), stamp_sigma / 8)\n", "snr_bin16 = snr_profile(\n", " expand_binned(bin_image(stamp_display, 16), 16), stamp_sigma / 16\n", ")\n", - "snr_adaptive = snr_profile_adaptive(smooth_result, kernel_map, stamp_sigma)\n", - "\n", + "snr_adaptive = snr_profile_adaptive(smooth_result, kernel_map, stamp_sigma)\n" + ] + }, + { + "cell_type": "markdown", + "id": "lsb302-radial-plot-md", + "metadata": {}, + "source": [ + "Plot the radial SNR profiles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "lsb302-radial-plot", + "metadata": {}, + "outputs": [], + "source": [ "fig, ax = plt.subplots(figsize=(8, 5))\n", - "ax.plot(r_centres, snr_unbinned, \"-\", lw=1.5, color=\"gray\", alpha=0.8, label=\"Unbinned\")\n", - "ax.plot(r_centres, snr_bin4, \"-\", lw=1.5, color=YELLOW, label=\"Bin 4x\")\n", - "ax.plot(r_centres, snr_bin8, \"-\", lw=1.5, color=ORANGE, label=\"Bin 8x\")\n", - "ax.plot(r_centres, snr_bin16, \"-\", lw=1.5, color=MAGENTA, label=\"Bin 16x\")\n", - "ax.plot(r_centres, snr_adaptive, \"-\", lw=2.0, color=BLUE, label=\"Adaptive smooth\")\n", + "ax.plot(r_centers, snr_unbinned, \"-\", lw=1.5,\n", + " color=\"gray\", alpha=0.8, label=\"Unbinned\")\n", + "ax.plot(r_centers, snr_bin4, \"-\", lw=1.5, color=YELLOW, label=\"Bin 4x\")\n", + "ax.plot(r_centers, snr_bin8, \"-\", lw=1.5, color=ORANGE, label=\"Bin 8x\")\n", + "ax.plot(r_centers, snr_bin16, \"-\", lw=1.5, color=MAGENTA, label=\"Bin 16x\")\n", + "ax.plot(r_centers, snr_adaptive, \"-\", lw=2.0,\n", + " color=BLUE, label=\"Adaptive smooth\")\n", "ax.set_xlim(0, r_max)\n", "ax.set_yscale(\"log\")\n", "ax.set_xlabel(\"Radius (arcsec)\")\n", "ax.set_ylabel(r\"SNR (median flux / $\\sigma_{\\rm eff}$)\")\n", - "ax.legend(fontsize=9)" + "ax.legend(fontsize=9)\n" ] }, { @@ -951,7 +1227,7 @@ "## 6. Summary\n", "\n", "- Pixel binning reduces noise by $\\sqrt{N}$ while exactly preserving surface brightness. At large bin factors (beyond 8–16x) correlated background residuals cause the measured $\\sigma$ to exceed the $\\sqrt{N}$ prediction.\n", - "- Adaptive smoothing via normalised convolution selects the finest kernel that meets a target SNR at each pixel, combining resolution preservation in bright regions with aggressive noise suppression in diffuse outskirts.\n", + "- Adaptive smoothing via normalized convolution selects the finest kernel that meets a target SNR at each pixel, combining resolution preservation in bright regions with aggressive noise suppression in diffuse outskirts.\n", "- A radial SNR profile, dividing the azimuthal median by the effective noise of each method, shows directly at what radius the galaxy signal drops below detection — and how far each technique extends that limit." ] }