{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Transit Timing Example" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "using NbodyGradient" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ElementsIC{Float64}\n", "Orbital Elements: \n", "3×7 Array{Float64,2}:\n", " 0.82 0.0 0.0 0.0 0.0 0.0 0.0\n", " 0.000318 221.717 0.0 0.0069 0.0 1.5708 0.0\n", " 3.0e-6 228.774 -38.129 0.0054 0.0 1.5708 0.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Specify the initial conditions:\n", "\n", "# Fill in system body parameters\n", "# Default values are zeros\n", "# First, the star \n", "a = Elements(m = 0.82)\n", "\n", "# Next, the planets\n", "b = Elements(\n", " m = 3.18e-4,\n", " P = 221.717,\n", " ecosϖ = 0.0069,\n", " I = pi/2,\n", ")\n", "c = Elements(\n", " m = 3e-6,\n", " P = 228.774,\n", " t0 = -228.774/6, # < We want mean anomaly to be +60 deg, so its\n", " ecosϖ = 0.0054, # transit should have about occurred 1/6 of an orbit \n", " I = pi/2 # prior to the initial time.\n", ")\n", "\n", "# Generate the initial conditions\n", "ic = ElementsIC(0.0,3,a,b,c)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3×3 Array{Float64,2}:\n", " -2.16714e-6 -2.16714e-6 0.592581\n", " 1.60063e-20 -4.1079e-17 -2.06854e-17\n", " 0.000261403 -0.670871 -0.337818" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now, pass the ICs to the State struct, which keeps track of the current state of the system\n", "s = State(ic)\n", "s.x # positions " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Integrator{Float64}(NbodyGradient.ah18!, 1.0, 0.0, 9837.282)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set up the integrator.\n", "t0 = 0.0 # Initial time\n", "h = 1.0 # Time step\n", "tmax = 9837.282 # Time to integrate to.\n", "\n", "intr = Integrator(h, t0, tmax)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# If we want to calculate transit times,\n", "# simply pass a TransitTiming structure to\n", "# the integrator.\n", "\n", "# Pass tmax and the initial conditions. \n", "# This allocates arrays to hold the transit times\n", "# and derivatives.\n", "tt = TransitTiming(tmax,ic);" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Now, run the integrator!\n", "intr(s,tt)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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GOUJy27vQLS0wz+dwwKivwQPrYeRXwPDCPx9VHYyNlLxIRGh+4f6G6yMA3vJcTA3xQHRcuF1OB3PnjADM/97XPTMAuNq5jh5UAzB3zgj1e5HwdqIYfn8VbH8TnDFmXcqXfgkxcRf+t53Vtac5AjNwhrkM+x93Qv2ZwD+vnEXJi0QE68KdQhUznesBfKturEt1NFy4Z+VmMv+W8WS4E9hiDGC7N5t4Rz23d12jZdIS/hrq4NUb4chW6NoLbv+nOeoSTE4nXP+8+fxl283eMBJ0Sl4kYszKzeSvkwqJc3jY4s1hh9EfgAx3QlRduGflZrLi+zN47Z58GsbeBsBDKauZNVItzyXMrXzGTBgSe8I9H0F2vj1xJKXDl583j9e/CDv/aU8cUUwddiVyGAbDDr8NQNf8u/hV5tiobY3vcjrIH9gT+twDO36O4+hnULwW+k22OzSRjin7DJb9r3k8+3+he5a98Qy6wuwls+o3Zkfe3mPB3dfemKKIRl4kchSvhWO7IDaRgZfdznVj+5A/sGfUJS4tJLjNPZ0ANv7J3lhEOsrrgXcfNJcrD5nd9Dtttxk/Nlc31ZyAN79lxilBoeRFIsemP5u3I78MCdoawifvdvN2+1vmzrki4WbdC2ajuPhkuOYXobNhYkycWcAb1w0OrIRPfm53RFFDyYtEhppK8+IMMP52e2MJNX0nQtoIc3XE1r/bHY1I+xzf37gkGrhyntl3JZT0HAjXNG5JsOxncGC1vfFECSUvEhm2/sPchyR1KGRNsjua0OJwNCV0G/5gNvcSCQeGAQsfMt/b2VNh/B12R9S6MTfC6G+Y/V/euBtOR24n71Ch5EUigzVlNP620BlSDiWjbzD3fCnbrtbmEj4K/gqff2z+7l77a3OZcqi65ueQMgCqDsLC7+pDQoCF8G+CSBuVfAolBeCMhTE32R1NaEpMgRHXm8cb/2hnJCJtU10KH/zQPL7sh+b0TCiLT4KvvWz+Hdq5EDa8bHdEEU3Ji4Q/a9Rl+JfMDpjSOqtwd9sbUFNlbywiF/Le/2fWsmWOhckP2B1N2/QeB1f8xDz+4IdwZIet4UQyJS8S3upOw5bGItTxt9kbS6jrlw+pQ8z6gW3/sDsakXPb8Q7sfNds/3/db8EVRi3JJt9v9oBpqDG3D6g7bXdEEUnJi4S3ne9CbSV07wc5l9odTWhrXrirqSMJVWeOw7/+0zye+ghkjLI3nvbybR+QBkd3wuLH7Y4oIil5kfBmTRmNuy20i/lCxZibwBVn1gkd3mx3NCJn++BHcKrMHCW85P+zO5qO6dYLvvI783jDy1D4ib3xRCD9tZfwdWyP2RjK4YSxN9sdTXjo2hOGX2seq+OuhJp9S6HgFcAB1/4WYuLtjqjjBs5o2jTyk/9nbywRSMmLhC9r1GXwzNBrXBXKrMLdrX+H2pP2xiJiqT1p9nQBuOjb0O8ie+Pxh6mPmnU7hZ9A8Xq7o4koSl4kPDXUwaevmccq1G2f/tPMfhR1J2H7m3ZHI2Ja+j9wogjc/WDGf9kdjX90zzKb1wGseNreWCKMkhcJT7vfh1NHoVu6OfIibedwQN4d5rEKdyUUHNwAa583j+c8A/HdbA3Hr6Y+DDhg13twZLvd0UQMJS8Snqwpo7HfBFesvbGEozE3m820Dm2E0q12RyPRbtn/AoY5SjHocruj8a/UwTDiOvN4uUZf/EXJi4SfE8Ww90PzeNwt9sYSrrr1gmHXAODd+CdW7yvnnYJDrN5XjsertuYSRMf2wp4PzONwXV10IdP+j3m7/U0o32dvLBEijDr/SLTzeA3WFVbQY91zDMPA6D8NR6i3DA9lebfDjrc5tf6v/MfyKdRgruzIdCcwd84IZuVm2hygRIW1883bIbMgdZC9sQRK5mhzenvPYlj5DFz7G7sjCnsaeZGwsGhbCVOfWso3X1xF0mcLAJh7cAKLtpXYHFn4WnR6KEXeXiRxmmuca333l1bWcN8rm/T/VgLvdAUUvGoeT77f3lgCzRp9KXgNKg/ZG0sEUPIiIW/RthLue2UTJZU1THNupY+jnBNGVxacHKOLbAd5vAbz/vkZr3tmAHBTzFLfz6xJo3kLd2gKSQJr05/M7SrScyHnErujCax+kyF7KnjrYfVv7Y4m7Cl5kZDm8RrMW7jDd0H9hsu8yL7lmUotcYAush2xrrCCksoa/u65hAbDyQTnbgY7Dvp+bgAllTWsK6ywL0iJbJ56WPuCeTz5PnMVXKSb9qh5u+EPcOqYvbGEOSUvEtKsiyxAKpVc4dwEwOueywBdZDuqrNr8f3qUHvzbmwfA113LznmeiN/teAeqD0PXXpD7NbujCY6BM8xdshvOwJr5dkcT1pS8SEhrfvGc41pFrMNDgXcgu4x+5zxPLiwtKcF3/JbnYgCucq6nadLo7PNE/MYwYM1z5vHEuyE2Sn7PHA64pHHTyXUvQk2lvfGEMSUvEtKaXzxnOjcCsNCTf97z5MIm5aSQ6U7AAXziHU2NEUu2s4yhjmIAHJirjiblpNgap0So4nVmjyFXPEy4y+5ogmvoNZA6FGorYf1LdkcTtpS8SEizLrLdOclE52cALG6c5gBdZDvK5XQwd84IAGpIYLl3FAAznRuwKg/mzhmByxkFdQgSfGueNW9Hf93sORRNnM6m2pfVz0HdaXvjCVNKXiSkWRfZGc7NxDi87PRmUWykA+gi20mzcjOZf8t4MtwJLPZOAGCmawMZ7gTm3zJefV4kMI4fgJ0LzeNIXx59Lrlfg+794PQx2PwXu6MJS0peJOTNys3kezlmV8olzUZddJHtvFm5maz4/gxuuPkeDJyMcu5nxb2D9f9UAmfdC2B4IWc6pI+0Oxp7uGLg4ofN45W/NjealXZRh10JffU1ZJStBODSa+8gO24IaUnmVJFGXDrP5XQwceQQWDsZilbh2v0+XPRtu8OSSFRb3bQvWf4D9sZit7HfNPd0qjoIWxbA+FvtjiisaORFQl/hMqg/BUm9GT3xUq4b24f8gT2VuPhb415HfPZPe+OQyLX5r1BbBT0Hw6Ar7Y7GXrEJMOVB83jFL8HrsTeeMKPkRULfZ/8yb4ddHR2NrOwy7Grzdv9Ks227iD95PU37GE2+1yxcjXZ5/wEJ3aFin9n3RtpMvz0S2rxe2L3IPB56tb2xRLqUAZA2EgyPuYGciD/teh+O7zcv1mNusjua0BDfzewuDLD8abP/jbSJkhcJbYc2wskjEJ8M/afZHU3k09SRBIrVUTbvDojramsoIWXStyCuGxzZqg8N7aDkRULbrsYpo0FXQEycvbFEAyt52fsh1J+xNxaJHCWfwoEV4IwxL9bSJDEFJtxpHn/yc3tjCSNKXiS0+epdrrE3jmiROQaS+5o7/X7+sd3RSKRY3bgVwIjrwd3H1lBCUv6D4IyFg+vgyHa7owkLSl4kdB3bC8d2m2/qwVG+MiFYHA5NHYl/VZfCtjfM4/wobUp3IUnpMOQq83jL3+yNJUwoeZHQZU0Z9Z8KCW57Y4kmVvKy630t35TOW/8SeOshazL0ybvw+dFq1NfN261/NxcqyHkpeZHQ9dl75q2mjIIre4q5IuR0ORSvtTsaCWf1Z2DDy+axRl3Ob8gsc2FC1SE4sNLuaEKekhcJTSePNl04h862N5Zo44o1/5BCU82RSEdsWWAmwe5+5m7Kcm6xCTDiOvN4q6aOLkTJi4Sm3e8DBmSOBXdfu6OJPs3rXtR7QjrCMJqWR1/0bXM/Hzm/0TeYt9vfgfoae2MJcUpeJDRpyshegy6HmASzqVjZDrujkXB0YBUc/czsYaJ9e9omeyok94HaSvV8uQAlLxJ66k7B5x+Zx+qqa4+4rjDgMvNYU0fSEdvfNG9HXK+C+7ZyOmHU18zjLQvsjSXEKXmR0LPvI2ioge79IH2k3dFELy2Zlo7yepr26hn5ZXtjCTejGqeO9iyGM8ftjSWEKXmR0LOrccpo6DXaiNFOQ2eDw2l2Rz1RbHc0Ek4OrIRTR81VawOm2x1NeMnINfcY89Rps8bzUPIiocXTYPYXAdW72K1rqtmbA5oSSpG22NY4ZTR8jrl6TdpndGPPFzWsOyclLxJaitfCmQro0gP65dsdjWjqSNrL0wA73zWPNWXUMVbDugMrNep5DkpeJLRYn/AHX6WllaFgWGPB9P6VcLrC3lgkPOxfbvZ26ZICOZfYHU14cveF/tPM461/tzeWEBWU5OW5554jJyeHhIQE8vLyWL58+TnPffPNN7nyyivp1asXycnJ5Ofn88EHHwQjTLGbYTTbiFGrjEJCygBz/t3waOmmtM32t8zbEddqyqgzRjWbOlKvpbMEPHlZsGABDz/8MI8//jibN29m2rRpzJ49m6KiolbP/+STT7jyyit577332LhxI5dddhlz5sxh8+bNgQ5V7Hb0MzheCK54GHi53dGIRVNH0laeeti50DzWlFHnjLgOXHFwdCcc2WZ3NCEn4MnL008/zV133cXdd9/N8OHDeeaZZ8jKymL+/Pmtnv/MM8/wve99j4kTJzJ48GB++tOfMnjwYBYuXBjoUMVu1sVxwKUQ383WUKQZK3nZ+6G5V43IuRQuM2vWElPNhmvScV26N9tpWj1fviigyUtdXR0bN25k5syZLe6fOXMmq1atatNjeL1eqqurSUlJafXntbW1VFVVtfiSMOXrqqspo5CSOQaS+0L9afj8Y7ujkVDWYspINWudNvpG83brG9rh/QsCmrwcO3YMj8dDenp6i/vT09MpLS1t02P84he/4NSpU9xwww2t/vzJJ5/E7Xb7vrKysjodt9igqgQObwIcMEQbMYYUh0NTR3JhDXWws/H3Q1NG/jF4ptmduPow7F9hdzQhJSgFu44vNBozDOOs+1rz2muv8ZOf/IQFCxaQlpbW6jmPPfYYlZWVvq/iYi0rC0vWKqO+EyAp/fznSvA1Ji/1O97jnc1FrN5XjserIkJppnAZ1JyArmmQfbHd0USGmHhzewXQTtNfENDkJTU1FZfLddYoS1lZ2VmjMV+0YMEC7rrrLv72t79xxRVXnPO8+Ph4kpOTW3xJ+PB4DVbvK+fIerOplXeIpoxC0Qcnc6iiG7G1Fbzyt79z04trmPrUUhZtK7E7NAkVvimj68DpsjeWSGJNHe14VzVnzQQ0eYmLiyMvL48lS5a0uH/JkiVMmTLlnP/utdde44477uDVV1/lmmvUZTVSLdpWwtSnlnLPi0vpcWQ1ADcv76kLYohZtK2Ee1/dyhLPWABmujYAUFpZw32vbNLrJdBQqymjQOmXb9ac1VbB7kV2RxMyAj5t9Oijj/LSSy/x8ssvs3PnTh555BGKioq49957AXPa57bbbvOd/9prr3Hbbbfxi1/8gsmTJ1NaWkppaSmVlZWBDlWCaNG2Eu57ZRMllTVMd24hzuFhnzeTtdWpuiCGEI/XYN7CHRjAYs8EAGY6NwAG1qTRvIU7NIUU7fZ9BLWV0C0D+k22O5rI4nQ22y5ADessAU9ebrzxRp555hmeeOIJxo4dyyeffMJ7771HdnY2ACUlJS16vvzud7+joaGBBx54gMzMTN/XQw89FOhQJUiaXxABrmz8JL/Em6cLYohZV1hBSWUNAJ94R1NjxJLtLGOYw6wtM4CSyhrWFar7blTTlFFgWVNHexar03WjoKxlu//++7n//vtb/dkf//jHFt9//PHHgQ9IbNX8ghhDA5c5CwBY4skDWl4Q8wf2tClKASirrvEdnyGBVd6RzHAVMM25hc88/Vo9T6JMfU1Twb2mjAIjbTikj4IjW2HH2zDhTrsjsp32NpKga36hG+fYi9txmnIjic3G4HOeJ/ZIS0po8f1Kby4AU53bznueRJF9S816jKTekHWR3dFErtGN7UK00zSg5EVs0PxCd7HLvAiu8o7E+4VfR10Q7TcpJ4VMdwJWY4MVjcnLROcuYmnAAWS6E5iU03oTSYkC282Vgoy83qzPkMAY9TXAAUWr4fgBu6OxnX7TJOiaXxCnOLcDTZ/oAV0QQ4jL6WDunBGA+brsMrI4aiST6KhlvGMPAHPnjMDlvHDfJolA9Wdg1/vmsaaMAiu5N+Rop2mLkhcJOuuCmEgN4xx7AVjpHQng+4SvC2LomJWbyfxbxpPhTgAcrG58rWYm7mL+LeOZlZtpb4Bin73/hrqT5lLePhPsjibyWYW72mk6OAW7Il80KzeT1MsbiF3podjbi2LDbFqY4U5g7pwRuiCGmFm5mVw5IoN1hRUkbN0LBau5M/MADr1O0c1aZaQpo+AYPgf++Sgc2wWlW8x9x6KUkhexzQTvFgDihlzGr3LHkpZkThVpxCU0uZwOc/VXyvVQ8GMchzdCbTXEJ9kdmtih7jTsamyaNvIr9sYSLRLcMHS2ueJoy9+iOnlRqiz2KVwGQPqYq7hubB/yB/ZU4hIOemRDj/7gbYADbdsdXiLQnsVQfwrc/aDPeLujiR6jGhvW7Xw3qqeOlLyIPU6VQ+lW8zjnEntjkfbLmW7efr7M3jjEPs2njNqw0a74ycDLwBUHJ4rg2G67o7GNkhexx/5PzNu0EdCt9R3DJYQNsJKXj20NQ2xSdwp2f2Aea5VRcMV1hf5TzeM9i+2NxUZKXsQe1id26xO8hBfrdSvbDieP2huLBN/uD6DhDHTPht7j7I4m+gy+yrxV8iISZI31Lr5P8BJeuqaa7cqh6bWU6OGbMvqypozsMPhK8/bAaqipsjcWmyh5keA7UQwVn4PDBdkX2x2NdJSVeCp5iS611U2f+HO1ysgWPQdCykDw1kft1K2SFwk+62LXZzwkJNsbi3RcjupeotLuD6ChBlIGQMZou6OJXoNnmrdROnWk5EWCT/UukSF7CjhjzFUPFYV2RyPBoimj0GBNHe1ZEpVLppW8SHAZhupdIkV8N+g70TzW1FF0qD9jbgkAMOJ6W0OJetkXQ2winCxtajsRRZS8SHAd3QUnj0BMAvSdZHc00lnq9xJdDqw0p4ySekPGKLujiW6xCTDgUvM4CqeOlLxIcFmf0PtNNt98Et6aF+16vfbGIoG3d6l5O2iGpoxCQfOpoyij5EWCS/UukaXPBHPo+nS52fNFItu+D83bgZfbG4eYBjUmLwfXwekKe2MJMiUvEjyeBti/wjxWvUtkiIlrWu6uqaPIVnkQjn4GDmfTdIXYq3uW2aXc8MK+pXZHE1RKXiR4Sj6F2kpzZ9TMsXZHI/6ifi/RYW/jqEvv8ZCYYm8s0sQ3dRRddS9KXiR4Cj82b/tPA6fL1lDEj6wpwP0roaHO3lgkcKwpo0GaMgopVr+Xvf8Gr8feWIJIyYsEj+pdIlN6LiT2hPpTcGij3dFIIHgampoRqt4ltGRdBPFus+7s8Ga7owkaJS8SHPU1ULzWPFa9S2RxOiHnEvNYU0eR6fAmqKk0L5J98uyORppzxcLAy8zjKJo6UvIiwVG81uwP0S0DUofYHY34m/q9RDar3mXAdHDF2BuLnC0KtwpQ8iLB0byrrvpDRB5rNO3gOqg9aW8s4n+qdwltg64wbw9vhuoj9sYSJEpeJDgKPzFvVe8SmXrkgLsfeBugaLXd0Yg/nTneVMukepfQlJTetILT2r4hwil5kcCrqYJDm8xj1btEJoej6bXVLtOR5fOPzT4iqUPNviISmqJs6kjJiwTegZVgeCBlILj72h2NBIrVuExFu5Flr6aMwsKQq8zbfR+Bp97eWIJAyYsE3ufaRToqWCuOSrfCqWP2xiL+YRhNnVs1ZRTaeo8zWxbUVkLxOrujCTglLxJ4hervEhW6pZmtyqGpxknC29FdUHUIXPGQPcXuaOR8nK6mwt0omDpS8iKBdbIMynYAjqZP5hK5crRVQESxVhllT4G4RHtjkQuLoroXJS8SWNYn8IxR2g8lGlh1L+r3EhlU7xJeBs4wN84s2wEniu2OJqCUvEhgWStPNOoSHbKngMMFxwvh+AG7o5HOqD9jFtuD6l3CRWIK9J1oHu9dYm8sAabkRQLL15zuUlvDkCBJSG5qH6+po/B2YJXZFTupN6QNtzsaaSvf1JGSF5GOqSiEE0XgjIF++XZHI8EyQFsFRATfKqMZ6oodTqzk5fOPoaHW1lACScmLBI71ybvvRIjvZm8sEjyNo2x1ez/inc0HWb2vHI/XsDcmaT9fvcsMe+OQ9skYZe4hV3+6adovAil5Eb/zeA1W7yvn4KZFAHj7q94lmnxQmUUNccTVlPPc3xZy04trmPrUUhZtK7E7NGmrykNwdCfggAGX2R2NtIfDAYOvNI8jeOpIyYv41aJtJUx9aik3v7iKhINm1n//6iRduKLEom0l3PvaNtZ5hgJwsXM7AKWVNdz3yib9HoQLa8qoz3itEgxH1tTR7g/sjSOAlLyI3yzaVsJ9r2yipLKGoY6DpDqqOG3Es7S6ny5cUcDjNZi3cAcGsNKbC8AU5zYArEmjeQt3aAopHFj9XbTKKDwNuBScsVCxD8r32R1NQCh5Eb9ofuECuLjxorXOO4w6YgBduCLdusIKSiprAFjRmLxc5PwMFx7ATGBKKmtYV1hhV4jSFl6PuT8OqL9LuEpIhuzGRRIROnWk5EX8ovmFC2BK43TBSu9IQBeuaFBW3fT67zCyqTQSSXKcYbjjwDnPkxB0eDPUnIB4N/SZYHc00lER3m1XyYv4RfMLkhMvE52fAbCqMXlp7TyJLGlJCb5jAycbvUMAmOjcdc7zJARZq4wGXAKuGHtjkY6zkpf9K6DulL2xBICSF/GL5hek4Y4ikh1nqDK6sNPIPud5Elkm5aSQ6U7A6giywWsW7U5oTF4cQKY7gUk5KgANaap3iQypQ6B7P/DUQuFyu6PxOyUv4hfNL1zWxWqTdwjexl8xXbgin8vpYO4cc1dpB7C+MXmZ6NyNo7Eaau6cEbicangWss6cgIMbzGPVu4Q3hwMGNS6ZthLSCKLkRfyi+YVrUuOU0brGi5d1qdKFK/LNys1k/i3jyXAnsMUYQK0RQ5rjBHnJlcy/ZTyzcjPtDlHOp3AZGJ6mT+0S3qw95fZHXrM6TWiK38zKzWT+N8eR98ZuoGnaIMOdwNw5I3ThihKzcjO5ckQG6worOPWvXOIrCvjbbHDq9Q99ezVlFFGyLzZvy7bDqXLo2tPeePxIIy/iV7P61NCL43idcdz6ta/w2j2TWfH9GUpcoozL6SB/YE9Shpmf/JzFa22OSC7IMJqa02nKKDJ06wW9hpnHRavsjcXPlLyIfx1YDYCzzzjm5A0gf2BPTRVFM2tDzqI19sYhF3ZsD1QWgyu+6RO7hL/+U83b/SvsjcPPlLyIfxWZyYt2kRYAsi4yb4/tMoetJXRZRZ3Z+RCXaG8s4j9KXkTaQMmLNNe1J6SatU9o6ii0qd4lMmU3Ji9HtsHpyGkSquRF/OfkUSjfCzig30V2RyOhot9k89ZKbCX01Nc0fTJXvUtk6dar6QPEgcipe1HyIv5jXZzSRkCXHvbGIqFDdS+hr2g1NJyBpEzz/SuRJQKnjpS8iP/4powm2xuHhBbr9+HwZqg/Y28s0jpfV90ZZnMziSxKXkTOw0pesqfYG4eElh79oVsGeOvNBEZCT+En5u2Ay+yNQwKjf+TVvSh5Ef+oPQklW8xjjbxIc45mNVCqewk9NZVQutU8ti5yElm6pZldkzEi5j2o5EX84+A6s624ux+4+9odjYQa1b2ErqK1YHghZQAkq5lkxIqwqSMlL+If1kUpW0ukpRW+FUdrweu1NxZp6UDjxUzTvZHNl7xExg7TSl7EP6wleJoyktakj4LYrlBbCUd32h2NNGe9d7M1ZRTRrNe3dBucOW5vLH6g5EU6z1MPBzeYx/306U1a4YqBrInmcYTMuUeEulNNRdQaeYlsSenQczBg+LZxCWdBSV6ee+45cnJySEhIIC8vj+XLzz1sVVJSws0338zQoUNxOp08/PDDwQhROqPkU7NHRJcejUVhIq1Q3UvoKV4H3gZwZ0GPbLujkUCLoLqXgCcvCxYs4OGHH+bxxx9n8+bNTJs2jdmzZ1NUVNTq+bW1tfTq1YvHH3+cMWPGBDo88QfflFE+ODWYJ+fQvO5FQsOBleatRl2iQwTVvQT8SvP0009z1113cffddzN8+HCeeeYZsrKymD9/fqvn9+/fn1/96lfcdtttuN3uQIcn/mB9kla9i5xPnwngcEFlEVQetDsagWb1LtpFOipYyUvp1rCvewlo8lJXV8fGjRuZOXNmi/tnzpzJqlX+2WOhtraWqqqqFl8SRF5vs866+vQm5xHfDTJHm8eaOrJffU1TrZqSl+iQlNFU9xLm78GAJi/Hjh3D4/GQnp7e4v709HRKS0v98hxPPvkkbrfb95WVleWXx5U2Kt8DZyogpgtkappPLkB1L6Hj0Ebw1EK3dOg50O5oJFj6NyaqYV73EpQCBccX9sowDOOs+zrqscceo7Ky0vdVXFzsl8eVNrKGnftOgJg4e2OR0JdlddpV8mK75vUu2s8oevSfZt6Ged1LTCAfPDU1FZfLddYoS1lZ2VmjMR0VHx9PfHy8Xx5LOsA3ZaTmdNIGVl3UkW1mW/oE1bXZxpe8aMooqlivd8kWOHMCunS3M5oOC+jIS1xcHHl5eSxZsqTF/UuWLGHKFNVHRATtJC3tkZQBPXIAA4rX2x1N9PLUm8ukQclLtEnOhJ6DCPe6l4BPGz366KO89NJLvPzyy+zcuZNHHnmEoqIi7r33XsCc9rntttta/JuCggIKCgo4efIkR48epaCggB07dgQ6VGmvykNwoggcTsiaZHc0Ei6sUbri8P3DGfYOF0D9aeiSAr2G2R2NBJuVsIbx1FFAp40AbrzxRsrLy3niiScoKSkhNzeX9957j+xssyFSSUnJWT1fxo0b5zveuHEjr776KtnZ2ezfvz/Q4Up7WKMuGaMhPsneWCR89JsMn74a1p/6wl7z/YzUmyn69J8Gm/4U1kW7AU9eAO6//37uv//+Vn/2xz/+8az7DMMIcETiF6p3kY6wfl8OboCGOhV620H9XaKbteKodEvY1p4p5ZaOs/bH0E7S0h6pg83pioYz5h9PCS6vp9ku8Ko9jErJvSFlIBjesB0BVfIiHXPmOJQ11iFp5EXaw+Fo1u8l/DeICzulW6G2CuKTIWOU3dGIXfqHd92LkhfpmOJ1gGFm793S7I5Gwo1vn6Pw/NQX1qwl0v0mg9NlbyxiH1+/l/Cse1HyIh3TfDNGkfZqPvKiGrfgUr2LQLN+L5+adS9hRsmLdIxvzlzJi3RA5hiISYDT5VC+1+5ooofXq+Z0YnL3gZQBjXUv4bfTu5IXab/6Gji8yTzWyIt0REwc9MkzjzV1FDxHPzPr1WITofdYu6MRu4VxvxclL9J+hzeBpw66ppmZu0hHqO4l+KxRl6xJ4Iq1NxaxXxjXvSh5kfbzzZnna0M36TitOAo+35TRVHvjkNBgrTgqKYCaKltDaS8lL9J+ak4n/tB3IuCAin1wsszuaCKfYcD+ZjtJi7j7mnuNGV4oDq+6FyUv0j5eT9OGbkpepDO6dIf0keaxpo4Cr3wvnCoDV3xTvZFImPZ7UfIi7XNku9ngKi4J0nPtjkbCXWPdy+GtH/FOwSFW7yvH49XS6YCwpoz6ToTYBHtjkdARpnUvQdnbSCKI9Qk5ayK49OsjnfOpYxhjgLLtH/PQ5isAyHQnMHfOCGblZtobXKTRlJG0xlpxdLjArHtJSLY1nLbSyIu0icdrsHpfOYe2fAiAN0tTRtI5i7aVcN8n8QCMdBygCzUAlFbWcN8rm1i0rcTO8CKLYTSNvPRXfxdppnsW9OgPhies6l6UvMgFLdpWwtSnlnLTi6uJOWiOvDy4Ml4XF+kwj9dg3sIdHKYnh4yexDo8jHXuA8CaNJq3cIemkPzlxAGoOgTOmMZCaZFmrNVnYTR1pORFzmvRthLue2UTJZU1ZDnKSHecoM5w8VF1lj4dS4etK6ygpNIcadngHQrABMcu388NoKSyhnWFFXaEF3ms9ga9x0NcV3tjkdDTX8mLRBDr07H12XdS48Vlm5HDGczhfn06lo4oq67xHa9vTF4mOned9zzpBNW7yPlYU4mHN0Nttb2xtJGSFzmn5p+OASY0XlzWeYcB+nQsHZeW1LTaZZN3MABjnXtx4D3nedIJvnoXNaeTVnTvZ34ZzVphhDglL3JOX/zUa30ytob5z3WeyIVMykkh052AA9hlZHHKiCfZcYbBjkMAODBXHU3KSbE1zohQdRiOF4LDCVkX2R2NhCqrb5eSFwl3zT/19qCKQc7DAGxs/KTc2nkibeFyOpg7ZwQAXlx86h0IwHjnHqwNJ+bOGYHLqe0nOs2qd8kYHTbLYMUGWZPM2zBZcaTkRc6p+afjPOceAPZ6e3Mc8w+gPh1LZ8zKzWT+LePJcCewyTAT4vGOPWS4E5h/y3j1efEXqwgzW0uk5TysUbmDG8xO6iFOyYucU/NPx9aUkVVcqU/H4g+zcjNZ8f0ZXH7llwD4UspBVnx/hhIXf7JGXtTfRc4nbYTZOb2uGsp22B3NBSl5kfOyPh3nx5ojL1a9iz4di7+4nA6GT7gcgMSqfbhqjtscUQQ5eRSONa7i0l5kcj5OF/SdYB6HwdSR+rvLBc0a2h3D8TkYMPvq6/ha5lAm5aRoxEX8p2tPSBlo7jB9aCMMvtLuiCJDUeOoS9pISNT0rlxA1kXw+Udm0e7Eu+2O5rw08iIXdngzDm89dE3jiovzyR/YU4mL+J+vYDA8VjuEBfV3kfYIo6JdJS9yYdZmjP0mg0NJiwSI1bb+oJIXv1G9i7RH3wmAA47vh+ojdkdzXkpe5MKaJy8igWJ96ju4MSxWO4S8M8fhyDbzuJ9GXqQNEtxm4S6E/OiLkhc5P6+36ZdYyYsEUtoIiOtmrnY4+pnd0YS/ojWAAT0HQ1K63dFIuOjXuGRayYuEtWO7oOYExCaaTa5EAsXpgj7jzWPVvXSeNWWUrVVG0g5Wv5cQfw8qeZHzK1pt3vbJA1esvbFI5OtrTR2ttzeOSOAbMVXyIu1gTd+WFEB96G79ouRFzq9IfwAliLTiyD/qa8wdgkH7GUn79MiBrr3AUwcln9odzTkpeZHzs0ZeVO8iwWCtOCrfA6e1W3mHHd5sXny69oKUAXZHI+HE4Wg2dRS6dS9KXuTcqkrgxAFzN1rroiISSIkp0HOQeXxwg72xhLNitTeQTgiDfi9KXuTcrD+A6SO1G60Ej6/uRVNHHWZN92ZpxFQ6wPq9KV4LhmFvLOeg5EXOzdffRfUuEkRZjaN8qnvpGK+35ciLSHtljgFXHJw6CscL7Y6mVUpe5Nys5EUFfxJM1sjLITWr65DyPWaDupguam8gHRObAJljzeMQ/RCh5EVaV1sNpVvMY428SDClDYe4JKg7CWU77Y4m/FgfOvrkQUycvbFI+ArxuhclL9K6gxvA8IK7H7j72B2NRJPmzepU99J+vv4uGjGVTrBG3IuUvEg40R9AsZPvU5+a1bWb1d5AxbrSGVbyUrYDairtjaUVSl6kdervInbSiqOOOVkGFZ8DjqbCZ5GOSEqHHv0BIyTbFih5kbN5Gpo+8erTm9ih7wTztnyvmtW1hzVimjYcuvSwNxYJfyG8z5GSFznbkW1Qfwri3eYfQZFgS0wxd0MG7XPUHlohKP4UwkW7Sl7kbL4/gJPM4kkRO2ifo/YrUn8X8SMrCT64IeTaFih5kbP5Glzp05vYyNqSQnUvbVN/pmkjPSUv4g9pIxrbFlSbhbshRMmLtGQY6qwrocEaeTm0KeQ+9YWkQ5vAWw/dMqB7tt3RSCRwuprqz0Js6kjJi7R04gBUl4AzFnqPtzsaiWa9hjVrVhdan/pCUvMRU23GKP4SokW7Sl6kJashUeYYiEu0NxaJbk4X9M0zj0PsD2dI8tWqacpI/ChEi3aVvEhL6u8iocTX70Urjs7L623WWFLvXfGjvhMABxzfD9VH7I7GR8mLtKR6FwklWnHUNsd2mV1QYxMhY5Td0UgkSXBD+kjzOIRGX5S8SJMzx+Fo40Z46hMhocAqFqzYB6fK7Y0llDXfjNEVa28sEnlCcOpIyYs0sT7d9hwE3XrZG4sImF1iU4eYx5o6Ojf1d5FACsGiXSUv0kR/ACUUaZ+jCyvWe1cCyBp5KSmA+hpbQ7EoeZEmWq0gocjaYDCEPvWFlOpSs5gSR1NjPxF/6pEDXXuBp85MYEKAkhcxNdTCoY3msYp1JZRYIy+HNpqbhkpL1oeO9JFmcaWIvzkczaaOQqPuRcmLmEo+BU8tJKZCz4F2RyPSpNcwiE+G+tNQtt3uaEKPdTFRkb0EUojVvSh5EVPz/i7qzimhxOk0V9FAyPzhDClqbyDB0HzkxTDsjQUlL2JRsa6Esiw1q2tV3Sko3WIeayNVCaTMMeCKg1NH4Xih3dEoeRG0GaOEvr5qVteqQxvB2wBJvcGdZXc0EsliEyBzrHkcAu9DJS8Cx/bAmQqISYCM0XZHI3I2a4+j44Vw8qi9sYSSomZbAmi6VwLNGgG1PuzaSMlLlPN4DfZt/DcAlT3H4HGqO6eEoC49IHWoeaypoybq7yLBZP2eRcvIy3PPPUdOTg4JCQnk5eWxfPny856/bNky8vLySEhIYMCAATz//PPBCPO8PF6D1fvKeafgEKv3lePx2l+w1FmLtpUw9amlbFrxPgB/OZTB1KeWsmhbic2RiZzN29jDZPfGDyPmPdgpXg8UNyZyWmkkwWBN35btMPfSslHAk5cFCxbw8MMP8/jjj7N582amTZvG7NmzKSoqavX8wsJCrr76aqZNm8bmzZv54Q9/yHe/+13eeOONQId6TtZF/qYX1/DQ6wXc9OKasL/IL9pWwn2vbKKksoYJzl0AbPAOpbSyhvte2RTW/20SeRZtK+Fn25IAqNi1MiLeg51WthNqKyG2K6Tn2h2NRIOkdOjRHzDg4AZbQwl48vL0009z1113cffddzN8+HCeeeYZsrKymD9/fqvnP//88/Tr149nnnmG4cOHc/fdd3PnnXfy85//PNChtqr5Rb65cL7Ie7wG8xbuwABSqSTHeQSv4WCTdzDWZ9l5C3fok62EBOs9+NGpHABGOz7HhSes34N+YU0ZZU0EV4y9sUj0CJF+LwFNXurq6ti4cSMzZ85scf/MmTNZtWpVq/9m9erVZ51/1VVXsWHDBurr6wMWa2uaX+TjqeNSZwF3ud4DCOuL/LrCCl8yZo267DL6UkVXwPxvK6msYV1hRYef463NB/nXlhJO16kjqnRc8/fgXqM3VUYiiY5ahjmKwvo96BdWsa6285Bg8u0wbW/RbkCTl2PHjuHxeEhPT29xf3p6OqWlpa3+m9LS0lbPb2ho4NixY2edX1tbS1VVVYsvf2l+ke/OSf4Y9788HvNXkjkJ+Ocib4ey6qZRpIud2wBY4x1x3vPawzAMfv7Bbh54dROf7NbKEOm45u9BAycFXrP783jnnsb7wvM96Be+Yl3Vu0gQWcnywQ1m3ZVNglKw6/jCEj7DMM6670Lnt3Y/wJNPPonb7fZ9ZWX5r9dB84v3EVLY583E6TDId+4853nhIC0pwXc8xWm2W1/pPXvOvPl57fHpwUoOnThDYpyLS4emdSxIEc5+b23wmiuO8py7z3texKs6DCeKwOHUZowSXGnDzd+5UV+HupO2hRHQ5CU1NRWXy3XWKEtZWdlZoyuWjIyMVs+PiYmhZ8+eZ53/2GOPUVlZ6fsqLi72W/xfvHivaLzAT2kcrTjXeaFuUk4Kme4E+nCMgc4SGgwna73DfT93AJnuBCblpHTo8f+15TAAlw9PJyHW5Y+QJUp98b21wRgCwIQvJC/h9h7sNN9mjLkQn2RvLBJdnC64+98w5xlbNwINaPISFxdHXl4eS5YsaXH/kiVLmDJlSqv/Jj8//6zzFy9ezIQJE4iNPbsHSXx8PMnJyS2+/MW6yFvjPasak5eLG0crOnuRt4vL6WDunBFMcZlJ2BZjANUkAvj+W+fOGYHL2f6mV4Zh8N5WM/m8ZlSGX+KV6PXF92CBdxANhpO+jmP05ljYvgc7rbhZczqRKBTwaaNHH32Ul156iZdffpmdO3fyyCOPUFRUxL333guYIye33Xab7/x7772XAwcO8Oijj7Jz505efvllfv/73/Of//mfgQ71LNZFHsyL+mrvcDyGg0HOw2RgzrF39CJvt1m5mTwy0FylsaLZlFGGO4H5t4xnVm5mhx63oPiEpozEb774HjxNAtuN/kDT6Eu4vgc7xdpIVf1dJEoFfH3djTfeSHl5OU888QQlJSXk5uby3nvvkZ2dDUBJSUmLni85OTm89957PPLIIzz77LP07t2bX//613z1q18NdKitmpWbyfxbxjNv4Q5KKmGbkcMYx+fM6rqLyV9+oMMXedsZBr3LzU9vl199I9mJY0hLMj/BduZC8N5WMyHSlJH4S8v3YA0bvEMZ4/yc6Ql7uforD4bve7Cjak9CaePUtUZeJEo5DCME9rb2o6qqKtxuN5WVlX6dQvJ4DdYVVrDzL49wJ+9wdOBX6XXry357/KA7sgPm50NMF/jBAYiJ7/RDGobB1Kc+4tCJMzx/Sx6zcjVtJP5jvQddn73LpPUPY6SPxHFf6y0XItrnH8OfrzM3Ynxk2wVPFwkX7bl+a2+jNnI5HeQP7MmJDLNWp8vBFeZuzOGqcJl5mz3FL4kLNE0ZdY1zcenQXn55TBGL9R6cdMnVADiO7IAzJ+wNyg6+/i6aMpLopeSlneJyplBrxNCt9giU77M7nI77/GPzdsClfntITRlJUCSlQ8oAzBblUbhJo1XvoikjiWJKXtppeL90NnnN5ZoUfmxrLB3mqYf9K8zjAdP98pDNVxldPSrKahAk+Prlm7fWhTxaeD1Ne8po5EWimJKXdhrVx81K70gAGvZ+bG8wHXVoo9lcqEsKpI/yy0NqykiCyhp1KLK3RXnQHdkGddUQlwTpI+2ORsQ2Sl7aKS05gZ0J48xv9i+3tT1yh33eWO8yYDo4/fMr8K8tmjKSILJGXg5thIY6e2MJpv0rzdt+k81mYSJRSslLBzj65lFtdCGm9gSUbrE7nPaz6l1y/DllZCYv14zWlJEEQc9BkNgTGmqg5FO7owmeA43JS/+L7Y1DxGZKXjpgRN8U1nqHmd9YoxjhovYkHGzcytxPxbqbi09wuLKGrnEupg/RlJEEgcMRfXUvXm9T8pKt5EWim5KXDhjVx+3bKsC35DhcFK0GbwN0z4aUHL885HuaMhI7WAWr0VL3cnQnnDkOsYnQe5zd0YjYSslLB4zq4/a11DcOrIaGWpsjagffEmn/TBl5vZoyEps0H3kJ555LbXWgsSFf1iRwnb3Pm0g0UfLSAenJ8VQkDuCo4cbRcCa8ek34ub9LwUFNGYlNMsdATAKcqYBje+yOJvCs9gbZU+2NQyQEKHnpAIfDQW7f7qxqXDIdNnUvJ4+aSy3Bb8W61pTRFSM0ZSRBFhMHfSaYx5Fe92IYKtYVaUbJSwc17/cSNnUvVpzpo6BraqcfrvmUkRrTiS2ipd/LsT1w6qg50tQnz+5oRGyn5KWDcpsX7R7aCLXV9gbUFn6ud9GUkdguWlYcHWicMuo70W97kYmEMyUvHZTbx81BoxdFRpq5eudAiO9uaxjNmtNd5peH/JemjMRuWRMBBxwvhOpSu6MJHKs5XfYUe+MQCRFKXjqotzuBlK5xrPCESd3L8UKoLAJnLGTnd/rhvF6D961VRpoyErskuCG9cQQ0UqeODKPpw5H6u4gASl46zOFwtJw6CvW6F2vKKGsSxHXt9MNZjem6xcdwiaaMxE6RXvdyvBCqD5sfPPpOtDsakZCg5KUTcnsnN604OrLNXM0Tqvy8RNoq1L1ieJqmjMRevuQlQuterCmjPnkQl2hvLCIhQslLJ4zq46aCZD539TfvCNXRF68XCj8xj/2wRFqrjCSkWEW7pVvN7S8ijZZIi5xFyUsn5PZxA/Bx3QjzjlBNXkq3mG3F45Kgz/hOP9zm4hOUaMpIQoW7D7j7geGBQxvsjsb/9ms/I5EvUvLSCX17dKF7YizLQ71o15oy6j/VL23FNWUkIadfhO5zdKLILLR3uJr2chIRJS+d4XA4yO3tZp13GF5HDJw4AMf32x3W2awRIT/0d9GUkYSkSK17sUZdeo+D+G72xiISQpS8dFJuHzen6EJRl+HmHaE2+lJfAwca/6D7oVhXU0YSkqy6l+L14GmwNxZ/OqD+LiKtUfLSSaMa615WGyG6ZPrgOmg4A93SodewTj+crzGdpowklPQaDvFuqD8FR7baHY3/+Ip1tRmjSHNKXjrJSl4WVg8x7yj8xGwqFSqskaCc6eBwdOqhvF6D97c1NqYb3buzkYn4j9MZeXUvVSVQ8Tk4nE3TYiICKHnptKyULiQnxLC+YSDemC7m5mllO+wOq4kf+7tsLj7umzKaNrjzGzuK+FWk1b1Yoy4Zo8xOwiLio+Slk6xOu/XEcKRH4zLkUKl7OXMCDm8yj/1QrPuvLebeMVdqLyMJRb5NGteE1uhnR+1v3IwxW1NGIl+k5MUPrKmjT2PHmHeESt3LgZVgeKHnIHD37dRDGYbBosYpo9m5Gf6ITsS/eo8HVxycPGK21A93ak4nck5KXvzAalb3wenGgtj9K0NjxYMfp4w+PVjJ4coausa5tMpIQlNsgrmkGMK/7uVkGRzbbR736/xGqiKRRsmLH1jJy3vHUjG69IC66qbpGht4vAar95VTvePf5vd+2BLA2kF6xnBNGUkIi5S6F2sX6bSRkJhibywiIUjJix9kpySSFB9DbQNUZzR+SrKp7mXRthKmPrWUh198j6STn+MxHMx+u2nKpyMMw+D9bWa9i6aMJKQ1r3sJZ5oyEjkvJS9+4HQ6GNknGYA9Xc2i3codS3in4BCr95Xj8QaneHDRthLue2UTJZU1XOzcBsBWI4c9VTHc98qmDicw2w9XUVRxmoRYJ5cO1ZSRhDCrhf6x3XCq3N5YOkP7GYmcl5IXP8ntbU4dLSgfCEBC6Ua+//pabnpxDVOfWtqpkY+28HgN5i3cgZUmXewyk5eV3lzfffMW7uhQImX1drl0SBqJcTF+iFYkQBJTIHWoeVy81t5YOup0BZRtN4+VvIi0SsmLn4zqayYvfyuM47CRQryjgYnOXQCUVtZ0auSjLdYVVlBSWdP4ncHUxpGXFd5RjfdASWUN6wor2vW4hmHw/tbGKaNRmjKSMBDudS9WvUvqUOimkU6R1ih58ZMRmcmNRw5Wec2tAi52mp+eOjvy0RZl1TW+44mOXaQ7TnDaiGeTd/A5z2uL3UdO8vmxU8TFOJkxLM0vsYoEVLjXvajeReSClLz4SVl1re94hcdMXq5wbsRKXTo68tFWaUkJvuPbYxYD8LZnCrXEnfO8trCmjC4ZnEpSQmwnoxQJAmvk5fBmqD9jbywdcUD1LiIXouTFT46dbEpePvSO55QRzyDnYaY0jr5Y2jvy0VaTclLIdCeQznGucq4H4C+emb6fO4BMdwKTctq37NI3ZZSb6bdYRQKqR3/olgHeejhkX8uCDqmphNLGjSWVvIick5IXP2k+olFNIv/wXALAf7gWnfM8f3I5HcydM4KbYj4k1uFhnXcoO41swExcAObOGYHL2fbNGfcdPcmuI9XEOB1cMTw9AFGLBIDDEb51L0VrzK7YKQMgWR8YRM5FyYufTMpJwd2laVrlT56rALjcuZl+jiMdHvloj1nDenJvt+UA/KXhSt/9Ge4E5t8ynlntHD1Z1Njb5eJBqbgTNWUkYSRc6158+xlp1EXkfLTu1U9cTgcPXzGIeQt3AvC50ZuPPWO41PUpt7sW8z8Nt7Z75KPdPltIQs1RjG7pfPPaB7nitJe0JDNh6sjzvrdVexlJmLJGXorXgdcDzjDpCu0r1tVmjCLno5EXP7otP4f4mKb/pX/wzALgxphlvHDjkHaPfLTbuhcBcOT9B5OHZHLd2D7kD+zZocSlqPw02w9X4XI6mDlSyYuEmfRciOsGtZVwZPuFzw8FtdVwuMA81siLyHkpefEjl9Ph22H6/ksH8pWv38qZ5AF04zRX1n0U2Ccv3WrO7ztjIO+OTj+ctcroopwUUrrGXeBskRDjioHsKebx3n/bG0tbFa8FwwPd+0H3LLujEQlpSl78zNqksabey3Xjsugy9X7zB+t+B15v4J64cdSF4XP8Uujn28tolIoGJUwNMevO2L3o/OeFCqs5XbamjEQuRMmLn1kjL9sOVZp3jLkJ4pOhfC/s+zAwT3rmOGz9u3k88Z5OP9yhE2coKD6BwwFXjdQqIwlTQ8xpW4rXhcc+R/vVnE6krZS8+Jk18rL9cCVerwHx3WDcreYP1z4fmCcteBXqT0PayKah8k6wVhlNzE4J2NJukYBz94X0UYABexbbHc351Z2GQxvNYz+8h0UinZIXPxvYqysJsU5O1XkoLD9l3jnpHsBhzr0f3e3fJ/R6m6aMJt1t9rjoJGsPJu1lJGFvaOPoy+737Y3jQg6uN5vqJfWGHjl2RyMS8pS8+FmMy+nb58g3dZSSA0Nnm8frXvDvE+5bCscLId4No27o9MOVVdWw4cBxAGZpibSEuyGN77u9S6Ghzt5Yzqf5fkZ++AAiEumUvASANXW09WBl050X3WveFrwKZ07478msZGjszeYUVSd9sL0Uw4Bx/bqT6e7S6ccTsVXvcdA1DeqqmxKEULRf+xmJtIeSlwCwkpctzZOXnEsgbQTUn4KCv/rniSoKm+byJ97tl4d8z7eXkUZdJAI4nTCkcY+v3R/YG8u5nDluLpMG8++EiFyQkpcAmNTf3AJgc/FxTtY2mHc6HHDRt83jtb8zu3521obfAwYMnAGpgzr9cOUna1lbaK7K0EaMEjGGNKt7MQx7Y2nNrkVmvUvaCOg50O5oRMKCkpcA6J/aleyeidR7DFbva7ZEc9QN0KUHnDjQ+d4Tdadh01/M40nf6txjNVq84wheA3L7JJOVkuiXxxSx3YDLwBUHx/fDMT8XzPvDznfN2+HX2huHSBhR8hIg04f0AmDZ7rKmO+MSYfzt5nFnl01vewNqTpjdOAfP7NxjNWray0ijLhJB4rtB/2nm8a4QW3VUUwV7G/s/jbjO3lhEwoiSlwCxkpePdx3FaD5UPfFucLig8JOO77liGE2FuhPu8sumcydO1/lGiVTvIhHHWu0Xat129ywGTy30HAxpw+2ORiRsKHkJkPyBPYlzOTl4/AyfHzvV9IPuWTD8S+bx2t917MEProfSLRCTAONv63ywwJIdR2jwGgzLSGJAr86vWhIJKdZWAcVr4XSFvbE0t+Md83bEtVoiLdIOSl4CJDEuhkk5ZuHusl1HW/7wovvM2y0LOvaH1Bp1yf0qJKZ0Isomvr2MNGUkkah7P7MDteGFPUvsjsZUd6opFk0ZibSLkpcAaqp7+ULy0m8yZIyGhhoOLJnPOwWHWL2vHI/33CshPF6z+HfRmk/xbn/bvHNS5/cxAqiqqWfFnmOAuupKBPN12w2RqaM9S6DhDPTob/49EJE2U/ISQNOHmsnLms/LqalvtjTa4WBL35sAiNn0ex59fSM3vbiGqU8t9bXmb27RthKmPrWUm15cw7aFv8HprWebYzCLKvyTaCzdWUadx8vAXl0ZnKYpI4lQ1pLpvR+Cp97eWKDlKiNNGYm0i5KXABqc1o1MdwK1DV7WFjZNDy3aVsLXV/TmqJFMH0c5M50bACitrOG+Vza1SGAWbSvhvlc2UVJZgwsP34wxVyb8vvbKs87tqPe3Na0ycuiPqESqPnmQmAq1lXBglb2x1J9papo34npbQxEJR0peAsjhcHDpUGvVkblk2uM1mLdwB7XE8qrncgD+I8YcxrYmjeYt3IHHa/jOBS9JnObLrhVkOio4ZiTzL+9FLc7tqFO1DXzcWJOjKSOJaE5XU+Gu3d129y2FupOQ3Bf6jLc3FpEwFGN3AJFu+pBevLau2Ff3sq6wgpLKGgBeabiC+13vMsm5i6djnyOOepI5TbczZ6j9lQNXfTWLaypJSjjT4jFf91xGHbEAlFTWsK6wgvyBPTsU3793HqG2wUu/lETfhpIiEWvIVeb2HLvfh6v+r33TNTsap4y0ykikQ5S8BNiUQam4nA4+P3qK4orTlFXX+H52lB780zuZL7tW8hXXipb/sHFbpPhmf9fqDBf7jQz+1NCyKV3zx2yvv20oBuD6sb01ZSSRb+AMcMZCxedQvhdSBwc/hobapmZ5WmUk0iEBTV6OHz/Od7/7Xd591/yUce211/Kb3/yG7t27n/PfvPnmm/zud79j48aNlJeXs3nzZsaOHRvIMAMqOSGWvH49WLe/gmW7jzLwCz1U/qf+Fgq9mXhwcpIuVBtdOEkXHrpmAp7YbnznrX2cNLpQTaJvtOWL0pISOhRbUflpVu4tx+GAr0/I6tBjiISV+CToPxU+/8hMIOxIXj5fZtbddMuAvpOC//wiESCgNS8333wzBQUFLFq0iEWLFlFQUMCtt9563n9z6tQpLr74Yn72s58FMrSgmj60qdvupJwUMt0JWGMc5bj5tecrPOu5nj95ruIt7yVsTZrGsPxrGDlhOnXJ/anA3Wri4gAy3Qm+fjLtZY26TB2Uqr2MJHr4uu3aVPeys7Ex3fA55q7XItJuAXvn7Ny5k0WLFvHSSy+Rn59Pfn4+L774Iv/85z/ZtWvXOf/drbfeyo9//GOuuOKKQIUWdFa/l1X7juHxGsydMwKAL07SWN/PnTMCl9OBy+lo87nt1eDx8veNZvLyjYn92v3vRcKWVbRbtBrOHA/uc3vq4bN/mceaMhLpsIAlL6tXr8btdnPRRRf57ps8eTJut5tVq/y3TLG2tpaqqqoWX6FmRGYyqd3iOV3nYcOBCmblZjL/lvFkuFtO92S4E5h/y3hmNety255z2+PjXUc5UlVLStc4rhiR1qHHEAlLPfpDr+FgeGDPv4P73PuXmwlTYipkTwnuc4tEkIDVvJSWlpKWdvZFMS0tjdLSUr89z5NPPsm8efP89niB4HQ6uGRIKm9uOsSy3UeZMjCVWbmZXDkig3WFFZRV15CWZE7/tDaK0p5z2+r19eaoy1fH9yE+pvMbO4qElaGz4OhOs9vu6K8H73mtVUbDv+SXDVVFolW7R15+8pOf4HA4zvu1YYPZdK211SuGYfh1Vctjjz1GZWWl76u4uNhvj+1Pvq0Cmu1z5HI6yB/Yk+vG9iF/YM/zJiPtOfdCjlTV8FFj35kbJ6pQV6KQr9vukuB12/V6YOdC81hTRiKd0u6RlwcffJBvfOMb5z2nf//+bNmyhSNHjpz1s6NHj5Kent7epz2n+Ph44uPj/fZ4gXLJ4F44HPBZaTWllTVnTQMF0z82HsTjNZiQ3YNBaUm2xSFim74ToUsKnKkwd5ruPzXwz3lgFZw+Bgndof+0wD+fSARrd/KSmppKamrqBc/Lz8+nsrKSdevWMWmSuRxw7dq1VFZWMmVK9M319ugax5i+3SkoPsEnu49yg00jHl6vwYLGKaNvTFKhrkQppwsGz4Qtr5tLpoORvFh7GQ37Erhab3sgIm0TsILd4cOHM2vWLO655x7WrFnDmjVruOeee/jSl77E0KFDfecNGzaMt956y/d9RUUFBQUF7NixA4Bdu3ZRUFDg1zoZu5xzl+kgWvN5OUUVp0mKj+FqbQcg0cy3y3QQlkx7vc266mrKSKSzAtpk4K9//SujRo1i5syZzJw5k9GjR/OXv/ylxTm7du2isrLS9/27777LuHHjuOaaawD4xje+wbhx43j++ecDGWpQWP1elu85SoPHa0sMVqHutWN7kxinBssSxQbOAGcMlO+B8n2Bfa6D6+FkKcQnw4DpgX0ukSgQ0KtXSkoKr7zyynnPMYyWmwrecccd3HHHHQGMyj5j+nane2IsJ07XU1B8ggn9O9ZcrqOOn6pj0TZzBEu9XSTqJbgh+2IoXGZOHU15MHDPtaOxMd3Q2RAT+jV6IqFO7R2DyOV0MG2wfVNHb20+RJ3Hy8jeyYzq6w7684uEHF+33UWBew7DaKp30ZSRiF8oeQkyu+peDKNZoa6WR4uYWnTbPRGY5zi8CSqLIbarOVUlIp2m5CXILhlirtTacrCSYydrg/a8BcUn2HWkmoRYJ9eO7RO05xUJaSkDIHUoeBtg34eBeQ5rymjIVRDbJTDPIRJllLwEWVpSAiN7JwNm4W6wWKMuV4/KxN1FyzRFfKzRl10BmDoyjKbkRVNGIn6j5MUGrXXbDaSTtQ28++lhQIW6Imdpvsv0ST+/J0u3wvH9ENMFBl/p38cWiWJKXmxgJS+f7DmG12tc4OzO++enhzld52FAalcm9u8R8OcTCStZF0F6LtRWwsKHzNESf7FGXQZfAXFd/fe4IlFOyYsNxmf3oFt8DBWn6th2uPLC/6CTrN4uN07M8uu+UiIRwemCLz8PzljY9S8o+Kt/HrfFlNH1/nlMEQGUvNgi1uXk4kE9gcBPHX1WWkVB8QlinA6+mtc3oM8lErYyRsFlPzSP3/8BHD/Q+cc8+pnZAM8VZ25FICJ+o+TFJpcOTQPg4wAvmbYKda8ckU5qNzXHEjmnix+CrMlQVw1v32e29O+MjX80bwdeDgnJnQ5PRJooebHJJY11L5uLjlN5uj4gz1FT7+GtzYcAc8pIRM7D6YIvzzf7sRxYCWue7fhjrX4O1jZuaTLuFv/EJyI+Sl5s0qd7FwandcNrwIq9xwLyHB9sL+XE6Xr6dO/i6+wrIueRMgBm/dQ8/vAJOLKj/Y+x8U/wwWPm8aU/hOFf8l98IgIoebFVU7fdsoA8vjVl9PUJfXE5Vagr0ibjb4fBV4GnDt76FjTUtf3fbv2HuWIJYMp3YPr3AhOjSJRT8mIjq+5l2e6jZ21Q2VkHyk+xal85Dgd8fYKmjETazOGAa38DXVLMPi3Lfta2f/fZe/DmtwADJtwJV/63+Vgi4ndKXmw0oX8PusS6OFJVy2el1X59bGvU5ZLBvejTXS3JRdolKR3mPGMer/glFK87//n7PoK/3w6GB0Z/A67+hRIXkQBS8mKjhFgX+QPNJdNWsuEPVTX1/H3jQQBumqRRF5EOGXEdjL4RDC+89W2oO9X6eQdWw+s3m9NMw+fAdc+CU39aRQJJ7zCbWTs8/3HVfv7mhwSm3uPlgb9u4mh1Lb3dCcwYlt7pxxSJWrP/F5L7QMXnsPi/zv754c3w6g1QfxoGXQFf/T24YoIfp0iUUfJis5kjM/jujEEA/PCtrazqxMojwzB4/K2tLN9zjMQ4Fy/cNoG4GL3EIh3WpTtc/5x5vOH3sOffTT8r2wl/+QrUVkH2xXDDXyBGvZREgkFXthDwyJVDmDOmNw1eg3tf2cjespMdepzfLt3L3zYcxOmAZ28eT24ft58jFYlCAy6Fi+41j995AE5XQPk++PP1cKYC+uTBzQsgLtHOKEWiipKXEOBwOPh/XxvN+H7dqapp4M4/rqfiVDuWZwJvbT7IL5bsBuCJ63K5bFhaIEIViU5X/ARSh8DJUrP+5c/Xm8fpufDNf0B8kt0RikQVJS8hIiHWxYu3TSArpQtFFaf51p83UFPvadO/Xb2vnO/9YwsA354+gFsmZwcyVJHoE9sFvvw7cLhgz2KoLIKeg+DWtyAxxe7oRKKOkpcQ0rNbPH+4YyJJCTFsOHCc77+x5YL9X/aWVfPtv2yg3mNwzahMvn/VsCBFKxJl+oyH6d83j9394LZ3oJtGOEXsoOQlxAxKS+L5W/KIcTp4p+Awv/pwzznPPVpdyx1/WE9VTQN52T34xQ1jcKqTrkjgTP8e3PImfOtjcGuXdhG7KHkJQRcPSuW/r88F4Jl/7+Htxs0Vmztd18Bdf1rPweNn6N8zkRdvm0BCrCvYoYpEF4cDBl0OXXvaHYlIVFPyEqJumtSPb18yAIDv/WML6/dX+H7m8Rp897UCthyspEdiLH/8j0mkdI2zK1QREZGgUvISwr4/axizRmZQ5/HyrT9v4EC52eHzv/+5g3/vPEJcjJOXbp9A/9SuNkcqIiISPEpeQpjT6eCXN45ldF83x0/X8x9/XM+vP9zDH1ftB+CXN4wlL1srHUREJLooeQlxXeJcvHTbBHq7E/j86Cmebuzl8sOrh3HN6EyboxMREQk+JS9hIC05gd/fMZGucWZB7i2T+3HPtAE2RyUiImIP7SAWJoZnJvPm/Rez9VAl14/tjcOhJdEiIhKdlLyEkaEZSQzNUBtyERGJbpo2EhERkbCi5EVERETCipIXERERCStKXkRERCSsKHkRERGRsKLkRURERMKKkhcREREJK0peREREJKwoeREREZGwouRFREREwoqSFxEREQkrSl5EREQkrCh5ERERkbAScbtKG4YBQFVVlc2RiIiISFtZ123rOn4+EZe8VFdXA5CVlWVzJCIiItJe1dXVuN3u857jMNqS4oQRr9fL4cOHSUpKwuFw+PWxq6qqyMrKori4mOTkZL8+tviXXqvwotcrfOi1Ch/h9loZhkF1dTW9e/fG6Tx/VUvEjbw4nU769u0b0OdITk4Oi18E0WsVbvR6hQ+9VuEjnF6rC424WFSwKyIiImFFyYuIiIiEFSUv7RAfH8/cuXOJj4+3OxS5AL1W4UWvV/jQaxU+Ivm1iriCXREREYlsGnkRERGRsKLkRURERMKKkhcREREJK0peREREJKwoeWmj5557jpycHBISEsjLy2P58uV2hyTAJ598wpw5c+jduzcOh4O33367xc8Nw+AnP/kJvXv3pkuXLlx66aVs377dnmCj3JNPPsnEiRNJSkoiLS2N66+/nl27drU4R69XaJg/fz6jR4/2NTfLz8/n/fff9/1cr1PoevLJJ3E4HDz88MO++yLx9VLy0gYLFizg4Ycf5vHHH2fz5s1MmzaN2bNnU1RUZHdoUe/UqVOMGTOG3/72t63+/H//9395+umn+e1vf8v69evJyMjgyiuv9O2BJcGzbNkyHnjgAdasWcOSJUtoaGhg5syZnDp1yneOXq/Q0LdvX372s5+xYcMGNmzYwIwZM7juuut8Fzy9TqFp/fr1vPDCC4wePbrF/RH5ehlyQZMmTTLuvffeFvcNGzbM+MEPfmBTRNIawHjrrbd833u9XiMjI8P42c9+5ruvpqbGcLvdxvPPP29DhNJcWVmZARjLli0zDEOvV6jr0aOH8dJLL+l1ClHV1dXG4MGDjSVLlhjTp083HnroIcMwIvd9pZGXC6irq2Pjxo3MnDmzxf0zZ85k1apVNkUlbVFYWEhpaWmL1y4+Pp7p06frtQsBlZWVAKSkpAB6vUKVx+Ph9ddf59SpU+Tn5+t1ClEPPPAA11xzDVdccUWL+yP19Yq4jRn97dixY3g8HtLT01vcn56eTmlpqU1RSVtYr09rr92BAwfsCEkaGYbBo48+ytSpU8nNzQX0eoWarVu3kp+fT01NDd26deOtt95ixIgRvgueXqfQ8frrr7Np0ybWr19/1s8i9X2l5KWNHA5Hi+8NwzjrPglNeu1Cz4MPPsiWLVtYsWLFWT/T6xUahg4dSkFBASdOnOCNN97g9ttvZ9myZb6f63UKDcXFxTz00EMsXryYhISEc54Xaa+Xpo0uIDU1FZfLddYoS1lZ2VmZrISWjIwMAL12IeY73/kO7777Lh999BF9+/b13a/XK7TExcUxaNAgJkyYwJNPPsmYMWP41a9+pdcpxGzcuJGysjLy8vKIiYkhJiaGZcuW8etf/5qYmBjfaxJpr5eSlwuIi4sjLy+PJUuWtLh/yZIlTJkyxaaopC1ycnLIyMho8drV1dWxbNkyvXY2MAyDBx98kDfffJOlS5eSk5PT4ud6vUKbYRjU1tbqdQoxl19+OVu3bqWgoMD3NWHCBL75zW9SUFDAgAEDIvL10rRRGzz66KPceuutTJgwgfz8fF544QWKioq499577Q4t6p08eZK9e/f6vi8sLKSgoICUlBT69evHww8/zE9/+lMGDx7M4MGD+elPf0piYiI333yzjVFHpwceeIBXX32Vd955h6SkJN8nQbfbTZcuXXy9KfR62e+HP/whs2fPJisri+rqal5//XU+/vhjFi1apNcpxCQlJfnqxixdu3alZ8+evvsj8vWyb6FTeHn22WeN7OxsIy4uzhg/frxveafY66OPPjKAs75uv/12wzDMZYJz5841MjIyjPj4eOOSSy4xtm7dam/QUaq11wkw/vCHP/jO0esVGu68807f37tevXoZl19+ubF48WLfz/U6hbbmS6UNIzJfL4dhGIZNeZOIiIhIu6nmRURERMKKkhcREREJK0peREREJKwoeREREZGwouRFREREwoqSFxEREQkrSl5EREQkrCh5ERERkbCi5EVERETCipIXERERCStKXkRERCSsKHkRERGRsPL/A03C61TI1bAtAAAAAElFTkSuQmCC", "text/plain": [ "Figure(PyObject
)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "221.80266718923252\n", "221.78389784838888\n", "221.78392439692522\n" ] } ], "source": [ "using PyPlot, Statistics\n", "\n", "# plot the transit timing variations\n", "t1 = tt.tt[2,1:tt.count[2]]\n", "nplot = [8,22,43]\n", "for iplot=1:3\n", " pavg = mean(t1[2:nplot[iplot]] - t1[1:nplot[iplot]-1])\n", " it = collect(0:1:nplot[iplot]-1)\n", " ttv1 = t1[1:nplot[iplot]] .- it .* pavg .- t1[1]\n", " if iplot == 2\n", " scatter(it,ttv1)\n", " else\n", " plot(it,ttv1)\n", " end\n", " println(pavg)\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Julia 1.5.2", "language": "julia", "name": "julia-1.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }