{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3b - 1st & 3rd Order Aberrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "nbsphinx-toctree"
    ]
   },
   "source": [
    "### June 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial illustrates how various aberration coefficients are computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:19.328502Z",
     "iopub.status.busy": "2026-03-24T10:16:19.328342Z",
     "iopub.status.idle": "2026-03-24T10:16:29.624892Z",
     "shell.execute_reply": "2026-03-24T10:16:29.623904Z"
    }
   },
   "outputs": [],
   "source": [
    "from optiland.samples.objectives import TripletTelescopeObjective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:29.628830Z",
     "iopub.status.busy": "2026-03-24T10:16:29.628295Z",
     "iopub.status.idle": "2026-03-24T10:16:32.446405Z",
     "shell.execute_reply": "2026-03-24T10:16:32.444955Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x400 with 1 Axes>, <Axes: xlabel='Z [mm]', ylabel='Y [mm]'>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B3oBEy7HXQgghhMQTFFWEkLhB6keJW7OQoAiXqJLxVOJsrURUiYMm1xVpdLE3Pj6urnO5JFiMqM9NVFMwI9NutA3X+l0tEVoOdAzOwuiewSZDN86yHUV9wmEkWLswjj4cNU2g7chT+F3fc+g3az8vEvRYaE1HZWo5qnK2oDKjGpXplahMq0SKdc5FI4QQQmIZiipCSFwg45ZEVFVWVq74tSKSjpdoQcL4JLTwpJNOWna7waIqGkiCjpSUFOVUhYOMRDN2JCZjR3FyYJvX58PRcSfahmqUm/XI0Czah2dxeNSBJMyg2tiN1yX3oz6xGxZjB8Z9fTiCLrSO9uAvPbvRbTbD5x9elmtJUWKrMrsBVRk1qEirQFV6FTLtkReghBBCSDihqCKExAV9fX1KvBQVFa34tctxqo4eParGU63EAdJD/qIlqgRxxMSpihRGgwHFaTY1nVOZGtj+uz/8Cb6UXBQ0XIHmngn8umcc+3vGMDbjRiqmsM3Wg8tSB1Bn64bJ0IER91F04AjaR/vwr97ncY/ZAo9fbKWbE1GZUobKrHpUBTlbeYl5EU/4QQghhKwGiipCSFyg14/Kzc2NiKjq7OxUTtBKwvgkA6G8JpqiSkIAxVGLJuLyTU+MYteWBpyzsySwTRyzKa8FzX0TaFIiawL39YyjbXBKJc1IN0zi7LQBvDVlADWeIzB62tDv7EYHRtE2OoCX+/biQYsFTr+OSjLaUJlS6hdbm5TQEsFVmFQIk3HttbYIIYSQ1UJRRQiJG1G13PpRqwn/k1TqUvB3ueOpgt2qmZkZRFNUNTU1IZpItkGHw3FMQWQRq/lpdhSkJ+C82jmxO+vy4FD/pBJaTT0T+EOvzMcxMu1S+4tt03h15hBusPVjk/cw4GpF78xhtPlG0DY2hNb+fXjUasW0X2zZDGZUpJSgIrMWVemblNCqSqtCSWoJ078TQgiJChRVhJCYR8LyDh8+vOz6UQuxlFMlgkvar6urW3G7IqqiHf4nAkfSuEumwmgwNjam5tnZ2cs63m4xYXNRmpqC3+P+CQf294yr8EERWbf0jqN1QHO1JOpvW4YT5yQN4QJ7Lyp9XfA5WtE91YE2nwNtYyNoG2jBU1YbxgyaQDYbjChNKkRVZh0q/EJLBFd5ajnsZm28GyGEEBIOKKoIITGPCJ6V1I9aqVM1ODio3JjVhBZGW1Slp6eruYTe6SnWoyGqxJVaS4ZDeX1eql1Ni7lazb0T2N0zjl92lGJkWksYkmwz4fQcN3alDeAN1h6UujthmD6IjvF2tMGJ1tExtA224QGbDQN+sWWQWluJeSo5RmVGlQojFMEliTKSrXNJOQghhJDlQlFFCIl5JNW5iJfV3tQfb0yViDY5ZrlOzHqH/wmTk5NRFVVy3tWEXobL1bq7NwVfGciDx7tNHVOWmYAzcpw4LXkAl5u7UezqhGesBe1jrWiDC23WMbQOdeHP9ifQbZjr+zx7tkr7LlkI9WyEIrjS7ZpYJYQQQhaCoooQEheiKj8/f9WZ4Y4nqiTxg4iG1YTTiagaHh5GtJAaXZIcQ0RVtBBRFS0BtxJXS+aPNBkxMi1iewuSbW9EXV4yTsuewY7EPrzF0I18RztcQ83oGG1FK1xoF7E1chRP2J/FXQYPPP62M61pWo0tf3IM5W6lVyEnIYcZCQkhhFBUEUJiG5fLhe7ubuzYsWPVbchN8VLhfyKqVuNSrYdTJf+XtLS0qIoqCTUsKdGy/q0nS7laelKM5t5xPNrtxo8HMuDxivvUiLKsq9CQl4ydmdM4096L63yHkT55CK6BJnSOtKLN4EabdRRtYwN4wf4iHjB44IL295JiSUJFWpUSWMGCqzC5EEbDypKaEEIIiV0oqgghMY0IKinMK05VJESVJH0YGBhAdXX1qkWVjMeKJjKuKlqiSt57EVWrFZ3RdLXODXK1HG4PDvZNBhwtEVs/fMGJ4SkZU1WPZNsW1OZfj4aaJJycNonXWHtQ4u6EbfgA3AP70T3ShlajB20WEVsjODDQhIcNXsz4vS27yabCByv94YO64CpJKYHZyJ9eQgiJN/jNTgiJaaR+lITl6WOJwh3+19PTo+bz04UvF7k2cdMkkYbU0YoG4lS1t7dH5Vwi3kSQriVJxXpgMy/sag3oY7X8YuvZjjH8emASHm8CgDqUZZ2MuvwUNFQmYXvKBC4zHUHWdBuMg83w9jehb6QVrSYfWi0WtE9MoHWoDf8wPoIJn1udw2wwozytPDBeS4mttEq1zWbSikUTQgiJPSiqCCExjdSPkqx8axnXspSoEidMxJAIldU6VYKEAKakpCDenKrx8XE1jzVRtdjfQW6qXU3zXS1trNYEmiWMsHccdzx9BN+ZckoP+12tXZrY2paIbckjuApHkDByEBhogq+/CUMjbWjzi622qRm0jRzGvcbHMeSTNgAjjChOKQ4Zr6ULrkRL4jq+K4QQQpYDRRUhJGYRISTjnTZv3rymdpYK/zt69KgSDCst+qujJ7eQEMBoiSoRgFKnShwySVoRSST0T7L+paamIl4RV6uxME1NS7lauztGcM9zh+H2yt+SBaWZO1FfcB7qqlPRkJeIzQmD2OnsgHGgRYkt9DdjbKQTbSYD2iwWtE470Tbejz+ZnkaPdy5kND8pP1BjK1hwpdlWJ/QJIYSEH4oqQkjM0t/fr8TDaupHBSOCaanwv7WM1woWVdFCFzhTU1OBulWRdKrkHCdaBryVuFq/fLoTw8rVApKsdtTmn4X6gtejboeILTvqrAM4afSgElm62JoePop2sxGtVgvapj1onRrHP/pexJ2eaXj9STKy7FnHCC2Zy/YTrT8IIWS9oagihMQs4lKJIFprkoTFwv9ECI2MjKzJCVsPUaWHKkZDVEmYYTyE/oWLpVytJj0pxjGuFlCamY76gtegLv8K1Dekoj7XhnrvUTQONoeILcfIYXSaTGizWtCa7EXbrAN7Bppxn3cKbp/2N5xiTVnQ2RLHixkJCSEkMlBUEUJiFinKKwkq1poAYrHwv97eXjVfi2hYD1GlhxmKqIo0Ev5XWFgY8fPEi6v1qpqcY1wtvYCxhBH+6ulODAVcLRNq8/NRX1CDurK3oeH0FNRmW1Ez0Y6aIKElc/dwJw5bTCqMsC3Fi1ZXK5pG2vFHz4OY9SfJSDAnaAkyggSXTDKWixkJCSFkbcTUt+jzzz+PX/7yl2hqasLg4CC+9a1v4dxzzw3sl5uin/zkJ3jggQfU09Nt27bhP/7jP1BaWrqu100IiZxTtdbQv6WcKgn9k/FCq01SIYjgEzctmrWq5JqTkpIwPT0d0fPId658164l8+KJzKKu1qRjLnywZxx7Oue7Womoy69AfcE21G9JQX1BKkqSDagYPoiK/macHyS2vCOd6JEwQslGmJqLVk832sZ78VjHnzHhdaj2LEaLyj5YmVqJ4sRi1OXUKXerLLUMVtPKC14TQsiJSEyJKrkpkVoxl156KT7xiU8cs/+OO+7A3XffjZtvvhlFRUX40Y9+hA9+8IP4zW9+E8jARQiJD8T5GR4eRkNDQ8REVV9fnxIMq01SobctbpXUu4omMq4q0k6VfCdLnSqKqjC7Wil2Nc13tVr7pwI1tUR0HetqpaCuoAH1BaejfpMspyLZ4ETRYAuK+ptxji62+pvgG+3CgIQRSoKMtFy0YxCtUyN4zvNPDDdpYtxkMKm6WiHp39MrUZFawYyEhBASy6LqzDPPVNNCyNO9u+66CzfddFPAvfrSl76E1772tXjsscfwute9bsHXySB3mXT0Gx9pb7FsYMHnXM5xZGPBfouPPpNU53IDmpWVFZa+XOhvQsL/RDCstX0RVSICo/k3J6JKryEVKaR96QMZtzX/PPychReryYj6AnGlJLSzKLBdjdVSCTE0Z0tcrd8EuVolGQnKyaovkPpa56L+pBSUZCTC6J5CzsAB5Aw04TTlajUDvU0wjB3BqFESZFjRlpaPVsME2h378fvePehzaenzhcKkwpAQQn1KtcVvFsiNCD9nsQn7LbZYbj/FlKhaCrnBGhoawqmnnhrYlpycrAaYv/zyy4uKqttvvx0//elPQ0J1LrroInWzIOmIj/cm6yE9zLQUO7DfYg8JY5MHHvpNvO4iSUFeCXM73mf1eEgb8tkPru0kDoy4VxJeuNb2deEXrdpR+jnlPVvrtS+FfI5kvNn8907g5yw6JBiAkwsT1ARoobBOtxftQ9No6ZvCgf4ptPRP4pf/GsbwtPa3kGg1oTonCTW5SajNOwU1JeeiekeScrtmxwaQNH0EDUMHsGWoBcbBAzD2tcA42YtJgwFtSmwV4JDBgTbXIfyt/xX80jUGnz8jYbY9G2UpZahIqdDmqRUoTylHhi2DfwcRgJ+z2IT9FlssN9IkbkSVCCr9RiIY+cHX9y3EjTfeiGuvvTbkjfvBD36gBNnxQgZ15SrH8kMRO7DfYo/ExMRj+kzGU8lyOOowiYskokDa1xkYGFBjN08++eQ1n0N+PCWhQ3D7kUa+v0R4RrJO1djYmBK8CyXy4OdsfclMT8WOqtBt812tvT0TeOCl3hBXqzonEY3F+WiQ5BjVV6vxWwajAb7ZUSQNtGBLfzO2SBihOFvdzTBM9mLWYECHxYrWzGK0m0xonezDCxNd+D/HMNw+j2o7zZoWCB/UXS0JKcxLzOPfxxrg5yw2Yb/FFsv9HY0bUbWWsBw9O1fwGyd/5Mv5Q9eP44citmC/xW5/6X0mSSQk61y4+lBcqeC2RFTJD5+E/631HHr4XzT/3iQDoIg5+X9J4opIIGO2JMxwsf8XP2cbi0AGwqC6WuJqqQyEveOqkPG+IyP49bNdGJp0BlwtGaulQgj9mQhrN1+LFLv/JmNmBPb+ZtQNNKFOz0jY2QxM9UN8scNWO9oyi9FqTkTb1Aj2TT6JP7T+Hg6vv31z4oJiqyi5CCZjZP5u4w1+zmIT9lvssNw+ihtRpTtU4koF16yRgew1NTXreGWEkHAjAmV0dBRbtmwJS3sLFf+VwsIJCQmw2+1rbl8e1kQ7UYXuisl7JeGNkUDcvbVkRiTrj9VsRENhqpre5A9Rlb+dwUlnSFKM5+eP1cpMQF1+ql9slaO+fCtKT06E0ei/+ZgehqW/CZUDTajsb8YFytnaD0wPQryro9YEtGeVojUlE22OGbT1voC/dj6KKbcWEmU1WlVGQkn/XpHuTwOfVqkyElpMkXNfCSFktcSNqJJsfyKsnnvuOdTW1qpt8uPwyiuv4IorrljvyyOEhJFw1I863lMoEVXhKpwrokrC/6KJXqtKwvMiJaqkbdaoik9yUmzIScnBOUEZCMXVah2YDNTUkvmvn+lUAuxYV0vm9ajdcuqcqyVMDcLU34SSgWaU9DfhHBFbh18CZobVqKx+awJas8rQnpqDVjfQNtSMf3U/gVHXRCAjYWlq6VxyjPRKJbhEgEkdLkIIWS9iSlTJD7gU+wxOTtHS0qKelObn5+Pqq6/Gz372M5SUlARSqstA9uBaVoSQ2EfGCq21ftR85jtVEv4n3x/hYD2dqkjWx9LD/8iJ42ppmQRTFxyrFexq3bv7MFyehVwtEVsno7TsrDlXS8aXTPbDMNCEvP5m5A00YZeEEnbuBWbH1CHD1gS0ZVegLTUHbT47Wse68Lv+F9E/q42ZNsCAwuTCQPhgcEhhilV7wEAIIZEkpkTV/v378d73vjew/p3vfEfNL774YlWb6oYbblChLl/96lfVU+Ht27fj+9//PmtUERKHTpW4SGupHzXfqQpOmSqZ/0ZGRlRdvFgVVXpyD/lOjASSVVDKUTD8jyzf1erC4KQjxNUSsdVQoNXUqis8EymVQQ9B5TM50avGaWX2NyNzoAk7RWy1vwA4tPTuE9YkTWyl5aLNlIC2qUE8OnIAR6f7AxkJcxNyAwIrWHBl2sPjdBNCSMyJqp07d2L37t1L3hiJ6AoWXoSQ+COcoXkLiSoZryXOVbgEg4iq4Hp40UAEpwirSDlVemFhPcyQkOW6WpqjNY7mngm80DWC3+6Zc7WK9bpa/jBCEVtlFefBWPXquUbkszp+VImtlP5mbBtowjaVJONZwKml9p+xJaMju1KrtWVPQJtjCv868k/c03IPPP6MhJLmfX5hY5kzIyEhJO5FFSGEiPiR0Lxt27ZFTFRJKnUhXKFtIqpEpLndblXTKVrIWKpIiSoJxxYY/kdW42qdXX2sq6WHDypX69nDAVcrwRI0VksVQE5FbX4uUjcVAZsumGtcPsNjR1S694T+JtQPNKO+X9K/twAu7SGAy5aKrpwqtKbloS0hCW1eN/b27cHvDv0OTn9GwiRLkpYgY57gkoyERkN43HFCSPxBUUUIiSnERZLQs0g6VSKqRPzoIXRrRS/VIG5VtEVVpML/dLFGp4qE09V600nHulrNfqH14uHRY1yt4PBBeX1ZZjGM6SVA9WvmGpLxkmOHldiSjIRVMqlaWwcAf7ZBjy0N3bmbNGdLxJbRi9bhZjza+Sim3doDBJvJpoRWcOp3mZeklsBiZEZCQk50KKoIITGFuFSC1I+KlKiSUgwS+heuEKBgURUuobYcVGpsv+sWCadKxqtGsrgwObFZzNVqG/SP1eqZULW17nrusBJgoa6WP3wwX0IIU5CaUQbIVPO6ULE12gH0N8M00ITS/maUDjTh3MGDgFt7GOGzp6Mvtxpt6QVoTUhGq9GL9vEuPNn9JMacWhINs8GsMhKKyFLuVlqVWpb073bz2ksyEEJiA4oqQkjMiSq5kQ+3OJkvqsLpwOjuVLTHVcl7FCmnSkSVnmGQkGi6Wkoo5acCQa6WhArqjlZTr7haY/jtniPHcbUSYcysBGSqe/1cY14PMCJiq0llJMzvb0Z+XzN2DR4APNpn2JeQieHcGiW22hJT0Gr0oW16EA/0v4iBmYFARsLilOKQ8Vqq3lZ6pQoxJITEFxRVhJCYQpyXcLpIizlVZWVlcSGqIpV1UML/GPpHNgrZyTacVS1TdmCbyxOUgXAlrpbU1cqq0qb6i+dO4nEDI+1+sdWMrP4mZPU04xRxtrwu7ZikHIzn1KAtoxBtCSloMxnQ6h7Hnzv+jO7J7kBTkgxjfoIMEVzp9vCFNRNCogtFFSEk5kRVuJMjBIsqSSYxPj4eVsEQHP4XTfQxVfJ/C3c2MxFVubm5YW2TkHBiMa3O1SpK92cgDIitFJRlJcFkMgPZ1dqES+ca9LiA4TYltmTcVmp/E7YfeRnbhw4BXrd2THIepnNq0J5RrJwtEVttnkk80f0E7mq+K5CRUNK8hxQ29guvnIQcZiQkZINDUUUIiSmGhoZQV1cXMVEliTCESIT/SYKNaJKQkKD+XyLmwl2vT8SaiDZC4snVmhNbE7h7nqtVk5+ihQ/6CxmLy5WWYAFMFiCnVpuCcTsBEVaSFKO/GYkDTWjs3I1GEWB+EYWUAjiza9GZJWIrDW1mcbYm8cLAC3jg0ANw+R2wFEsKKtK18VrB7lZBUkEU3zlCyFJQVBFCYgZxR2SKRBrvaIiq9Qj/EyQEMNyiSvqBY6pIPLpabzyp6BhXS7IQSvjgS4fHcN+ebjg93iBXay58UJaVq2U0AGYrkNegTcG4HYCEDA40K3fLOtCM6rZ/oVpCC31au0gtgju7Ft3ZpWhNErFlUs5Wy0gLHu54GDP+rIWSCKMsuQybMjaFhBOWpJTAbOQtHiHRhJ84QkhMuVRCJMP/RFTphXNj3akKFlXhxOPxKIFIp4qcqK5W28BUIHxQamst29USzDYgf7M2BeOaBSQZhl9smQeaUXbocZSNdODV8I/5TCuBN7sWvTnlaEtOR6vZhBbHKA5PduMf3f/AhHNCO4XRjPLU8rkwQn/drfK0cpUanhASfiiqCCExgySQEMKdIGG+qBIxIsIqnO2bTKZ1Cf+LhKjSMwpSVJET1dUSkSTTGxEmV0s1bAcKtmpTMM7pELFlHGhG4YFHUTjaibP8h/jSy4CcOgzlVaA1OQNtFhNaPVNonziMe1vuxdCs9kBKihcXJxcHhFaw4Eq0RK/cAyHxCEUVISRmEMFjt9thtVojKqoiEdYmySqiLarkvRIoqgjZOK7WPc8dRv88V6s+fy4phqR8D7hagjURKNyuTcE4p+AbaMHs4RdhH2+Hob8J2U0PIXvsME5TBxiAjHIgtx5jBWeiLTkTrRYJI5xB20Qn/tj+R/RM9QSak/FZIdkI/eGEaba0iL93hMQDFFWEkJhBBE+k03iPjY1FRCysh1MlQk7OG+6xXLqoimYhY0LiydUaEler158Uo2cCe4+M4f7nj3W19PBBSfVeHuxqCdYkoPAkuFOrpdK3PB3StjsmgIGWQDZCmae98gBOGu/WkiAajEBGhRJb04Wnoz0lG60WM9q8M2id6MDjhx/HnU13wusf35Vlz5orbOwXWjKX7cxISMgcFFWEkJhhZGQkIoIn2KmSdOrl5eVhP4eMq4q2qJL/l7hV4RZVuvNFUUXI6shKtuHMTTId62rp4YMSSvib3XOult1iRG2e5mgFXK38FBwTqGxLAYp3alMws2PHiK3Evb9B40QPGmW/waQVQs6tg6PoNHSkZKHdakOrdxpt453Y07cH9x28D25/mvgUa4oKHZwvuPKT8lWYISEnGhRVhJCYcqpKSkoi0raIKq/Xi8nJyYiIBXGMpAZWtJGsf5FwqkQk6vW3CCHhdbUu2764q/Vyd6irVZBqQ0NRGuqXcrUEexpQcqo2BTMzcozYsr3wK9RO9kEliZcsglmb1JgtV/GVOJKagzarDW3eabSOd2D/0H481PYQZj2ag51gTtBEln/Mlu5sFSUXMSMhiWv4100IiQlE9ExMTERkvJMewjI1NaWEVbyE/+nJKiLhVIkDxtAfQtbP1WofnML+o2PY2zWM1qFZ3LvnMPrGj3W1xM3Sk2OkJS7wICQhAyg9XZuCmR4OiCx9btn9c1RMDaACwPlGi1YIOacO3uI3oyctH602G9o9mthqHWvFY4cfw4RLy0hoMVpU9kElsiQ5hr/uVllqGaym8I6TJWQ9oKgihMQE4o6IsIpUbSRdtAVnzQsn8eRUiahi6B8h6+tq1eSloDo3GedvSlPfi/KQQ1ytlt4JLXyw91hXS8ZqBUSWPxPhgq6WkJgJlO3SpmCmBkOElsyNbY+haGZYjRo7RwRSdo0SW76iN2IgvRhtdjtaPdNoF7E12ornep/D8KyWzdVkMKm6WsFJMmRekcqMhCS2oKgihMQEkUyOoI+p0kVVpML/1sOpEkdJxqKFExFpkRCehJC1u1q7Nsl0rKulhw/KfCFXS0/zLtkH6xdztYSkbKDibG3SkTGpUwPHiC3Dob8gd3YUuQBON9v9zlY9UHgpRtJLldhS2QjH29E21obft/4efdN9gWYLkwpD0r/rgivVGv4C8ISsFYoqQkhMMDMzo+aRTFQh46n05A7x5FSFW8yJUxUpx5AQEhlXS6bLgrKyD0850dzjT4rRO4FXjo7hgRfmXK3CNHuIoyWiqyJ7EVdLQoGTc7Wp8lWhYmuy7xixhQOPIMMxhh0AdpgTgBxxtuqBgjdgMrMS7fYEtKkwwja0jbbhb4f/hl/s/wV8/iLIOQk5C6Z/z7RnMiyZrBsUVYSQmEBu5CU5QrhrVAUjokoEVTgL/8ajqKJTRUjsk5lkVY7WUq6WZCL87Z4jAVfLZtaSaYiTpYutJV0tETgp+dpUdV6o2JroOVZsNT+EZOcEtgDYYkkCcmpV6nfkXYjZhip0JiSj1TOJtjHN2Xqm5xlV3Njt075bpaaWXsw4OP17XmIexRaJOBRVhJCYCf+TG/lI/TCKUyWJKiIlFiJRL2o9RZW0Swg5sVytJn8Wwn09Y3jgxW443atwtQT5Hk8t1KZN54eKrfFuoL8ZGGjS5v37gX3/B7trSmUjrLWm+MVWHZD7GrgaqnE4MRVt7km0jrUpsbVvaB/+0PYHODz+8g/mxAWdLclIaDKaIvumkhMGiipCSMw4VZESPMHhf5ESC+vlVImzF25RJe1FIkSSEBI7rpZbz0DoDx8UsbUmV0sXW2nF2lR9wdx2rxcYOxzqavW+ArxyPyyuaVQCqLSl4QJdbOWcB0/9e3A0KRNt7nHlbEmCjPaxdvy166+Yck2pZq1Gq8pIOD/9e2lKKSwmlowgK4OiihASM05Venp6RNrW3a/p6emIOlUejwfr5VSJaAyXyydOFUUVISc2ZpMR1ZKBUFwtrMzVUskwJCmGv7bWkq6WICHZGWXaVPO6ULE12hkqto6+COz9DUzuWUhVwxJ7Gl4l47WU2Dobvrp3oj8lF62uMeVqySSC6189/8KoY1T7vxnMKEn1ZyQMcrZEgEkdLkIWgqKKEIIT3anSkfC/nJyciLQt47TWw6nSC/TKucNRrFfqeElbDP8jhKzF1bpvTzd6x1tDXK3gmloiutITjzOGVsRWZoU21V40t93rAUY6QsXWkT3Ai3fB4HEgD0BeQgZ2KbEl01lA3TsxnFaANudoiNj63aHfoX+mXzVrgAGFyYUBkRUcUpgiYYnkhIaiihASE0QjOYJkGIyUAyNOlQiSaKMn9giXqNJDCSmqCCFhcbV6/Ukx/JkI/+/FowFXq0Afq+UXWyK0pK6WtLckMk4qq0qb6t4wt93j9ost/3gtmXc9DTz/C8DrQqaIwsRs7BShlSNjtk4Dat6OiYwStDlHVCZCXXA92vkojk4eDWQkzE3MDXG1dMElGQnJiQFFFSFkwyOha5EMOdPD4kRURUosiFO1HuF/waIqHFBUEULC6mpVZatpvqulhw+K2JICxsGuliTSEIG1IldLMJmB7E3aVH9JqNgabtOSYujuVsc/gT23A143xIPalpSLbSqEUJytHcCmt2EmsxwdzlG0jrUGBNdTR5/C3c13w+PTvu8zbBmBbIQyybIIrkQfCxvHGxRVhJCYCP0TIj2OR5ykSKVs3wjhf+EUVZFMbU8IOXEJdrUu3VYY2D4y5URTr4gsTWyJuxUWV0sXW6pWVk3odrcTGG4NTf3e9nfgudsAnwcSO1GfnI/6gNjaDlRdDVdWFbpcYyp8UARX+2g7Xhp4SYUSOr1aFtgkc9Ix2QhlXTISGg3hL+tBNoio+u///u8VN3zTTTchLS1tNddECCHHJKmIljsSSadqPcL/dFEVLpdMF2cUVYSQaJKxiKvVMSRjtbTwQRFbUsD41seOdbX0pBjLdrUEs9U/5qo+dLvbAQwdChVbhx4Fnv0J4PNCvnWrUgpRFRBbm4HKK+HJ2oRu94QSW039Teie7VbLEko47Z5WTdtNdpUQY36SDEmcYTEyI2HMi6q77roLW7ZsWXY8/osvvoirrrqKoooQElanKlI38sFZ8SLpVK1H+F+4nSqKKkLIRkFcqE25KWqa72rpCTH0MVvzXa1AUoyCVDSsxNVSJ7YBeY3aFIxrFhg6GFpn68CfgKdvlUB2SEWs0tRilOTW48z0SliKtsJQcTl8WTXo80wpgaUnyJD0708efRJjjjHtlAYzSlNLQ8ZsyXJZahnsZmZjjanwv29961vIzFzeYLtzzjlnLddECCELiqpoOFWREgt6oopwpjZfDmazOSKiKhxJLwghJFKu1hlVWWo6nqvVM8/VmhNbKWgoSF2+qyVY7ED+Fm0KxjUDDB4IEltNMB98GIbdP1G75RchP70U+Tn1OFN3t8ovU2Jr2DsbEFoqScZoG+4/eD8GZgb8rzWgOKU4tLixv+5WkiUpLO8nCaOo+sIXvoDk5ORlNgl85jOfQVbW3B8yIYRsZKcqmEi7YdEWVZEK/6OoIoTEq6v14EtH4fC7WvmpMlZrDa6WYEkACrZpk+DzYWpyEslWAwwitoJTv7/yADD2vYBgysooQ1ZOPU7RxdbWNwDZNRj3uQLJMXTB9Uj7Izg6dTRw2rzEvJDxWrrgSrdHpubjic6yRNXFF1+8okYvvPDC1V4PIYQsmBxBhIi4PZEgWORESixI+J8gbpW+HE2niqKKEEJW5mqJwNLE1sRxXa36/FTV1oqwJgFFJ2tTMI5JYKAl4GopsbX3XmD8iLbfYERqRjm259Rjuy62Nl8IZFVjGl60j7eHCK4nup/Ar5t/Da9PE4qS5l0PH9QzE8p6TkJOVB/6xRtryv43PT19zMDrlThahBCyXKdKxEGkv+yjIdxE3OhCJ1ZFlYjCaApDQghZL1frkiBXa3TaqdXUUo6WJrYWcrXE0VJJMfJTUJG9QldLsCUDxTu0KZjZ8SCx5Q8lfPEuYMLvThmMSMysRGNOHRr1WluNFyix5TQAneOdgWyEMn++/3kVSujyalldUywpIRkJdcFVkFTAjITLYMW/7N3d3fjGN76BPXv2qLoxOnpIy7PPPrvSJgkh5LhOVaTETjDivkRKuAU7VdFEziv/p3CdN9qikBBCNgoyvmphV2s6NCnGC934kd/VsipXK1k5WZrYSkFdXorKELhi7KlAySnaFMzM6LFi6/lfApO92n6jGdbMKlTn1qFaZSOsA+rPU8WR3QYDjkwcCRQ1FoerZaQFD3c8jBn3jHp5gjkB5anlSnDp47VURsKUEpiN/D3QWfE78bnPfU7NP//5z6vEFbQJCSHREFWRdEb077FIioXgMVXRRgRpuJwqiipCCJnvaiWraSWuVm6KFQ0Faagv1GprSVKMVblaQkI6UHqaNgUzPRwqtqS48e6fAVNakgsYLTBnbUJ5bh3Kc+rxahFbdecAmZXwGo3oneo9JknGP478AxPOCe3/bjRrYss/ZqsqTXO3JCW8zXTiFYhf8S/jwYMH8ctf/hLl5eWRuSJCCFknpyqSYmG9nKrgzIPhQNqhqCKEkLW4WmPY2zmE1qFZ/E5crbHZEFcrUFPLP2ZrxWO1dBIzgbIztCmYqaHQ8VoiuNr/AUwPaftNVhizqlGYW4fCnHqcJWKr5kwgswI+gxFDs0PHpH+/t+VetV2QUEFxsVT4oN/Z0gVXoiUR8cqKfxkbGhrQ19dHUUUIiRr6OJ5IEw2nKtZFlThV0RC4hBASr65WVU4Szq1MVXkI5LdBXK1ABkJJjtE7jt8HuVp5qTZ/4eIwuFpCUhaQdBZQflbo9smB0BBCmbf+DZgZ0fabbDBk1yA7tw7ZOXU4TcZtVV8NZJQDRpOqqTXf2fpj+x/RM9UTOIWMzwp2tvRQwjRbWiCaQx6kRjokf8OE/331q1/FwMAAqqqqjrkJqa6uDuf1EUKIElXRcEciKRbWM/xPBGk4nSqKKkIICa+rdXpllpp0PF4f2gen5sIHeyYi62oJyTnaVBFUb1Z+syb7jxVbB/8MzGqFiWFOALKrkZZbj22SJCO7Bu7yS+BKLoAbHhUuKCKrY6wDHeMdODxxGI92PIpfTf8KPmi/iSnWFOQn5iPdnI6y3WVq2yc+9Qkk2mPH2VrxXcrIyIhKVvHFL34x5GaBiSoIIZEUVdEo/BsNURXrTlW0U8ITQsiJgNxHu31uuL1ulY1P5qnJbpxcCWwtT4Tba4XLm4rR6VkcGhxDu0xDh/HS4AQeOjSpZfAzeJGWYEBBuhV5aVbkppqRlWxCWqIRXp8n0L4+6efR54HJ54bL41r8+HQD3Kk1cHuccHsccKvt43CN/wveiX8BbSv//4vwksnkNaEMmqiacc3Et6j60pe+hJqaGnzlK19hogpCSFSQkLNofNdEIxnGejhV4c7+R6eKELJRkO9UjwiGhUSBCADfPMGwgIgIPuYYgbGYAAkSHAu9JuT4eeJEJqfHGXrdPq0G4IpJBMwy+VclL3enTOMG+MZMgE9+18wwGcywmmSyIMFsRaLVCpvZAoskqzCatcngnxvNsFvtgeX5x1hMlmOON/uPs8g2xwTMk30wj/do09hhTI934wjcOGwxo91qR7vNihGD9nuYakpATfom1OVuQ21aLV7qekltT7bGVpmmFYuqnp4e/Pd//zdKSkoic0WEEBLlG3ld8MRz+F+4zivtUFQREh9IMdglHYsFRETwMQsKkCCXY8n2go9f4vzHbJ/Xrl5jKZwEiwi1HCQeQgTGPEEhc0nEsJDYCDneYIbX7UWSPUkTKPOOn/+awLIuaBY5Pvg1kixibNoVkn1Q5i19E5h1zY3VCoQP5kra9yRUZNphMXgBrxvwerTJ5/GvL7bNf7x/27DXjP2mcewzm7Df5sO+RKDPkqLOmWawoNFgxxUuLxqnJ9EwPoAC5ywMaAFMf4HTlomXcLU61uARiWiLX1F1yimn4MCBAxRVhJCoQadqY4kqhv+REx0VqrWAC3FcxyJIGCw7BOs4x886ZwEj5kTPvLCtRUWQz61EVTg5rpjQhYF/e/B+ScGdZEla0DlZ6PhFBYgh9JjQ9kwwwwiLwQgLDOom2KzmBpgNBph9gMnnhUHel+MIhxVvc89t83lm4XTMwGoegSFwHn3yv2bZ24IFUOi2NK8Hp3ndOC3o/+JL8cDrccPrcUk6QqDLA2OXByas7m9h1GjEfpsV+6xWbW6zosc/BjrF40Wj04E3OJxodDjR4HSiyO1B4NfcYJqbyzV7HMD0AAIHuMMvmDeUqDr77LOVU3Xo0CFs2rTpmMHjr3rVq8J5fYQQokLXIi2qpP1oiIVYF1UcU0XCFaq1oADxh2FFJARr3vELhoEFiaSFxpSsOVRrEcRVWCysalFBESQ4QkK11HaTEhAWERGqbaMSE/qyiAolLpSYMAaJC30blMCQ7WrdB1iU6PBpk9rm1V7n88Eiy17AIDfuCwmMhQRBQGTI5AK8M6sSDnPbgs+9yDZZjjZSHFcmEQ5q2ajm0gdSJ0qy5mnbTUHHzd+mvSawzWQBzLbQbTIFzhG0LeTcJhiMJpiMEg4YfA4Tpt0G9Iw70T3uQveYE12jDhwedWLWY4AbRqQm2JCdZYEpfQozCcMYNPSj3XEER2cH1X8zxZyIhrQqXJi+CQ0ZtWjMqEVxcjEMcq3OSS3BRd9+oPcVoHcvMHRI6w+TFchtAAq2AYXbgexG4I6HtffOHufhf1/72tfU/LbbbjtmHxNVEEIigZ4IJx6cqvUinE7Vev9fTlQ83tCB5kuGTM0TBguKhwVckenZaRjMhgXFRIjjMW+cymIiaKFQsHAzFxplgkV3I+YtayLDLyRkuxIOJiSJUAiIDRss5oQ55yLIwbAExIVs00WFzEVQ+Lf5dIHhC5qL8PDBLPXdfPrco0SIRZbV5IFR3fh7AVewIHAAvpklRYIvSGAY1ls4hNzQ+0XAvBv3UIExf5suHPzbQoRDUDshbc7fdhyBcdxt8687aNsCwmhZ2xbC58PU5KRKqY4N8n0q6SCq/JMw7hzHKwP78OThl/BC38t4bqIFk54+wAX4Zm3wzBYBjgbk2TehPqMBJxdWoaEwHQ1pTmSM7QdaHwd6XtKmkY65LIH5W4DKc4EzP6wJqZw6wByUsdApIX9+URVjrFhUPffcc9jo/OY3v1EFioeGhlSK90984hPYvHnzel8WIWQDO1VCNM6xXokqTuTwv+BQrcVCsJYSKUu5IscbU7Jke4uEgi3muOiph8PFnJjQBIc8OTfB4Hc3/GJDresOxlyolN2/rG2bczWUo+EXGJoIMfsnv6gIuBqa4LB4Pf7tusjwBISHtuyBxevWjlOTG2aPvEZ7nQQPRe2WdDGRsORN9fzj5gkCeUpvWYVTESQmHC4PbPYETYQcV3Qs7F4cu+04AmP+NhLTSNa9pqEm7B/aj31D+9S8a6JL7Us0J6I+qx6XF74ODVkNaMxqRJqlAAd6JtDZ2Ybpzj2wdj2I3KZmlKMdGQatAPC0IREDybVw5b4KSSd9FNk1p8KSW6v9rcUpkS/8EmX+/Oc/4zvf+Q4+/elPKyF111134YMf/CDuu+8+la2QEEIWI14dmOWKKhlfIZPcvEuIVvC6vjyJSRhhRNd415JjSkRUTE5Pwmw1Lywu9NeoMSMuuFRqXqd2jCwH2plbdh8zWF2btHVxcfxhZWruX/d54AmzGJFbSG08RvA0L1QqIDT8DoZyNGQCEvQQKhESgfmc0FBTwNHQ3A/Vtt8ZmXNE9Pa1/cHbtHaDzh9wVebaW+iv3ee/WTYc70n+kmJiOa7EUk7FccTEsrYtLxRq2duk/Y34/SCFUicnYdtAjgfZ2Ew6J9E0HCqgOsclXyCQYE5AfWY9zik+RxNQ2Y0oTy2HUb4tRrs012n3L9X8NJmm+rVGEzLgq9qOsfS34nnzJrzgKsXTIynY3zuF7n0zwD7A+kgnNuUO+YsYpwQKGWclx04iioiIqn379mH37t2qZtX8NL0f+9jHsJ7ceeedeOMb34hLL71UrYu4euKJJ/Dggw/i7W9/+7peGyFkdazFZZHXKkEAb4hQkJodavK6Me4axKxxHOOePhwdPwSfT8JqPP7jZTC3ti7LHq+0pIXbSDhWoB05Rm7gA+eQdf08XkxOTWAk+RB+v/d7sNmt8Hi0ECppw6OLAq+079bGnPiFgSxrY1D8AkFN3jnBINfhn1z6svq/ylVK3RMvHBYnPLPADx8zQiSSiAx5R2Uu3+CybdlDlOW+zQv86IEfrbgvDIGb/+Ab/3niIEgIzBcRSX7hMOeGBB8fvP3Y1wZEiVoOHlsi405EFOlhYrpzExxCJuNWjIEQM2NgHMRqbtLnuwgLhD0te1t4Q6F8MGByampDhSQRQlbPtGtaCah9g/uwf3i/mouAkl8AEVC1GbU4q+gsvGfre5SIEgElbjWG24CeF4Fnbp8L4Zsd1RpNztfC9na8XZvLlFasHt6lAzjZP93kv4axGRda/JkH1dQ7gYdePhrIQJibYkOdLrTyU1GdnYATRlT9/Oc/x49+9COUlZUdU6dqvZ/yulwuNDc348YbbwxskzCVU089FXv37l3wNU6nU006DocjcCN2vBs5/ZhIhfPMOD1oHZiMSNsnMtJf0zMzSExwr/vf7KLI35TKQOSZi5X3ebSMRGqub9eXPf4MQvprgo71H+P1ulTGH4+6kdfqY3hVnQy5kXf5BYMICM0NEDGgHStztxIf2g29Pp+/rN/gizOgCQ6Zy029WvLf5Ife9Muyvs2n7V9gPpA8id39Bjz819s0AWBQ9/WQkQNKFBi0ZU0oIGS/b7l9LAlN5UXPR65bDVk+/ONIayBsyeQXAnI7q4sD0xLr8mMnX9ry2gSfAUYZTA6Dtl3W/eM/ZD0wGYxwzbrUoGSrxQSTOko7Vk0GQ8i6IWS70b8895rpySkYDSbkZuWoNqX2iQgNk8ECkxq3YvYPgraoceQ2GUBvsKptRqNVCQOfXyDIXHNGTPAZZFkTDbLN57/p15Y1QRK6TZbNaq67K77gttW+ufPo2xZzHORvR/JMxVauqQXw+adlqWQ5SH7/nLHz3UhCYJ/FJpHot1nPDDonDqJtvAXtEy1oH2/G0ekuJaAsRivKU2pQk3oyLiy+BhUptShMLFW/JbbRViR0vYKE5/+G2aFXYB/aB5NrSrXpTC7GTFYjZhpvwmz2ZrXsTswLPbHcpk6OLXltCRYjTi5NV5Pg8frQOzaL9qEpdAxOoW1wCvfuPoLhKSfM8OA6v64anpxFXob4/uvLcu/zVyyq7r77bnz+85/HJZdcgo3G6OioSr08P8xP1js6/IPk5nH77bfjpz/9aWBdshledNFFmJycVCLteG/yzMyMWo7El9n+ngm85ecvINYwQAbmeiHPxE3+af6yfGiMhuBtcpOpr3tCX2OYW9ZeG9pm8HGLnS+k7aD2tGezHrUOg6T5lNTdHlWVXJZ9RpnLPm2/eqavluVmxKeWtXUVIAWfQW749X2yRd8m+7W5NvndAXUM4JH9BhERfoHgFwkeg2TdAdwyV9vm5i6DXKG2T453+/fJXK0H7XevRFysAhEH2s0/YFQpaQ1zy3Iz7pNluSnX9hn8y2qbrKtj/DfxPqO232eATY7yGZA8I9IByB5PU8cou8T/OplrxQ3liKDlBeY+v2zwqW2SEW9uu2pH/iLUuhxjChwj+5UMCtonf0Vq3b8cOM4nf1UmeNWxWnv6X5zsl2XtL84Ij88IZ2Cb/pcYtN8/yTmjOHJkefRF+4SapxYHsocQQtaGwQmjvQcm+xGY7N0wymTrh0HuJbxmeB0F8MwUwTN7CryzxfA6cjAJLxyGIzAb9yDV8FsUGDtQbeiE3aB9p7Z78/C0rwKveC/BK74K7POWYWQ2FZDkfi36iQ/5p+jQ0j2MJMv6//bphkvYRZWIh23btiFeEFfr2muvDXnjfvCDH6jwB5vNtizlKsdGQlSdNPAr/OucsUANg2DnQXcklCsRcCvmuRT+7XPHzHc5/BmD5jkfgXb87ogeWuRWboOEHGnhU8pl0PcrR8EfemRA6A1/0I29JgCChIIuHIIEQ0AU6Ovz2goVEwb1jDWkreDzqHVtrrepzeWYufXIoQ301hwGo3r6L3NxCyT0SHMWNPdAHAU1qWV58q/PtUmOswWti0Mgc6PfHTAqx0CbyzaT0aIdoxwCS8BJMBmsMJrktVZtuyoSaIHJJG6CRbkJ2lyrwB58Lj0N69x2k3ItIv2E9Ftf/yqKiorwqjNfE5FzSVHz559/HsXFxRH7fpOHPk88+QSuuPyKqI/vvP/++5GUlIStW7euua3de/bAbDKph0/LexKbwCfoMQL7LPZgn8V/vzk9DnROHkL7eAvaJpqVC3VkskM9yDUbLChN2YTKlDNQkVqLypQ6FCWVq+Qu9uEmJAy+goShZ9XcNtICoyS8MRjhSKvCTPYWDGdd7XegGuC1pqIMUNMbwvB/9Hh96BmbQfvgNDqGptA+qE2Dk5ozbjYaUJKZiIrsRJRnJaEiOwkVWYlKRD34C81Q2Faei+Tk9Q8HtFgskRFV11xzDe699158/OMfx0YjPT0dJpMJw8PDIdtlPSsra8HXWK1WNc1/4+SPfDlfUPpxkfgy+78Xf4BZrxNug9EvEIzaZFR+it+90PfpgmIRIWLU3A8lPFRYlV9YKDGkb5ubPL650CsN7cm+Nvw6PIig0MSCjFWYEw5qrqfClZt4v2hQKXGVCJB1TQyYTWYkKOEgKXVFGGg1O/Q21LFB9Tz07W6XW1Uy1+t6BM45b1kLZfK3pcZXBIc4zWvb/38IPkaFTfGHbs1kmJ3ISwRqcxMj8n5ap4zoNE6jKNGrzhEJ+jGJJsM06vOTkZurhUBEi6dsLqQl+cLyfztqd8Nk8mFLcfpxbxomJ80Re+hEwg/7LPZgn8VXvzk8DhwYPhBIIiFT62ireoAt9xk1GTU4vXAHGrKuU1n4NqVvgsU9C/S+rI17ar5Nmw+0aKn1JfQ5tx4o3Q6cpo2BMuQ1wm5NUhk8M8L0/xmbdqGpdxzNatzUBJp7x9HSNxEybkoSU1yxoxgNKkFFKipzkmAxHZs5UobkPOhfTrSZN8Tf9XKvYcWi6rrrrsNHPvIRXHbZZaisrDym+O83v/lNrBciiOrq6lStrHPPPVdtk0Qakgb+qquuQqxxa3YOZtwz6gY9RCgsIADmhEfodtsCwuCY1wYLgeBjgsTCQsIk+JyLHbPUuURwrN+XmVYfYiN8WMnxiVY/zU+8Ey+Ee9zneqSFJ4SQeMLpcQYSSOgi6tDIIZXZVO6lqjOqsSV7C95a91aVRKI6vRpWx6RWOFeE094/avOhVm0gpckG5G8GSk8HTnuPlkBCiupKra8wIe6TuE0imiTpRHOPloDi6Nis2m81GVGdl6xE0yXbCuMyw19YRZWIJsn8t3PnTqSlpW24m1IJ5bv55pvR0NCAxsZG/PrXv1bjnjbiGLDj8dhbHlvvSyBkQxDOOktLEY1zrMd3ZjgL9m6073xCCNnoSImJg6MHAynMRUwdHDkYEFCbMjYp4XRlzZWagMqohm1mzJ9570Xghd9py5LWXLAkAQVbgU0XAGd/XBNQ2TVarbIwu08B8STuU+8EHG7t4WNeqk2Jp0u3FwVSpFdkL+w+nSisWFQ99NBD+MY3voGzzjoLG5HXvva1KtX7j3/8Y1X8t6amBrfccsui4X+EkI2PZPGMhosUSVGltx3rokr6QhICEUIIORaptScheyKcdBF1YOSA2i7ROlXpVWjIbMDrS16PkwpPQm1GDezTw5poOvoisOc+bXmiR2vQlqYJqPpLgYLtmoDKqgpbEV3dfVLiSYmoCRXGN999EtF06bZCFb5XewK5TxEVVampqWow90bmLW95i5oIIfGB3MhH2kVS9awiKNzWM2RO/l/hdKriNUySEEJWggilttG2kEK6LcMtcHqdaohDZVqlGvt0adWlqpBubXoN7BO98PW8BGfnc7C+/CAMIqCmB/2DiLI04bTt6rkaUBnlYasbR/dpg4mqd7/73fjJT36CL3zhC7DbZZgbIYREFklAEw1REg0HJtadKooqQsiJiNRvbBvzC6hBv4AaaVHJJaRUhxJQ2Y14Q+UblJCqSduExPGjmuvUJYV0fw307AUcY6pAhkWK6BZuB05555yASi0Mi4BajfskBXgzk+YSt5GVs6o6Vd3d3SrMrqCg4JhEFXfeeecqLoMQQpYWVdFwkaJxDnHdoo38v8J1Xob/EULiHSle3z7WPjcGamifcqBmPbNKQJWnlSvhdGHFhWpel1aFxNHDmoBqfwl46hdaRj5/EV2kl2mi6cwPKSfKl78FU0hUCbPWKqJGp52BjHuaiJo4xn1S4onu08YTVXpWPUIIiRdRpRNJsbDe4X/hFFV0qggh8SSgOsc7QwRU83Czyr4slKeWq+QRry17rZrXp1YgaaRTE1Cte4Anfg707QM8UiDWAGRt0gRU3Ru0ef4WIHFebUL5PZicXNF1uj1eVe9JBJQunmTeo7tPZiNq/Jn3LhMBlZ9C9ykWwv8IISSaiCMeL6JqPZwq+X+FK/xPBC6dKkJILOL1eQMCSg/haxpuCgiostQylUTi/NLzlYCqSy5FiggoSSBx8BngH/8DDDQBXjcgZWFy6jThtPUqbSyUpDS3paz5OnX3KTh870DfnPuUn2pHXUEK3nhSkUpZ3uB3n8xx4D5ZLBZ8+tOfDizHtagihJD1EFVut5SrjizxKqoY/kcIOREFVNd4V0gSCRFQU/6QvJKUEiWczi05VwvhSypE6nCH5kC1PAn03AoMHgB8XsBoAfIagKKTgJ1SRHe7VgPKuraC6uI+dQ5PY79/zJPuQM13n+rzU5WAkvA9caLi2X0yGAywWmPz/7csUfXqV78a999/P9LT05fV6Bve8AbcdtttaswVIYSsFXla5XK5In6eSAo33Wlbj0QVIoLEYQoHFFWEkI2GPLQ6PHE4REDJNOnSQuyKkouUcHrXlnepZBL19nykDbdpAqrpcaDn+4CsC+YEzXEqPws44980JyqnHjBb1+w+7Q8qmLuvexStg9Mh7pOIJk08parwvXhxn04UliWqJiYm8OSTT2oD6pbB2NgYf3QJIWEVVdFwqiJ5jvUO/wvXeUWcRaMvCCFkse/SI5NH5sTT4H7sH96PCeeE2l+YVKiE001bblJOVIM1G+nD7ZqA2vc3oOc7wNhhrTFrilYDqubCuQx8WdWAafWBXPrYp2D3ScL3eseD3KfcZNTkJuHNO0vUuCdxojLi2H06UVj2X83NN98c2SshhJB1GlOlC554FFVy3nA6VRRVhJBofn8dnToaGP+kC6lx57jan5+UrxyoGxtvRENmPRosacjQQ/j2PgL0fAOY7NMas6drKcw3X+4XUNuBjAr5Ul719Y1MOVWtJ919ktC9+WOfxH1608mh7pPJaMDk5KQyK9YjeoGso6h67rnnInR6Qgg5PhJfHY0beQkxDGdNp2B0URhtURXu81JUEUIigXz39k71hmThk/moY1Ttz0vMU87T9Q3XawLKlISsYcnC9yLw4h+Anq8AMyNaY0k5mmg66TpNSImISitZdfpycZ9U3Sd/xr3mBdyn2jwZ75SCN0nyCEldvoT7tJ7ZYEnkYKIKQkhMiCpxWyIleIIRwRCJjEPr5VTpAihcTpW4huF2vwghJxbyHdI33TeXhW9YC+MbcWiiKDchVwmoa+qvQaMIKNiRrQTUS8DzDwK9XwYcmluF1GJNNJ32vrkQvpT8VQso3X3SC+bK8oG+STj97lNBml2Jp8tPFvGUioaCFJRncewToagihMQAeiYgcZJsNlvY2w9+auh0OiMiqnTHKNpCJBKiSu8LiipCyHK+X/un+0PcJ5kPzw6r/dkJ2SqE7611b0Vjeg0aYEHOcJcmoJ67Xyui6095rsL1RDSd9dE5AZWUvSb3SSWPCDhQx7pPkq788pOKVfieiCmOfSKLQVFFCNnw6EJKBE8kRFUwco6kpKS4yf6nZ00M55gqvV273R6WNgkh8cPA9EBAOOkianBmUO3LtGcqAXVlzZVoTK9Gg8eE3OEOGHr3AofuAfr3Ax6nVkQ3u0YTTQ2XzRXRTVheFuql3Ce99tN890lE0+X62Ce6TySSompgYAA5OTmrOQchhITFqXI4HEhJWXthxeOJqkggomo9nB3dqdIdprWiu3iRep8IIbGDiKWAAyVZ+Ib2o3+mX+3LsGWgIbsBl1dfjobUSjS6Dcgb7tQE1IFfAwPNgM8DGM1aynIRTtuv1eZ5jYBteRmnV+I+2cR98hfLveLkYlXzSQRUeiLdJ7J2lv0re9VVV+FTn/oULrzwwjCclhBClo/uiETqRn5++F88iapwO1W6OKOoIuTEYmhmKFD/SXehJKxPSLelqzFQl226DA0pZWh0+5A/5BdQTXcAQ4fkmxYwWTXBVHIKcOo754roWuyrc5/UmKel3acrdhT5xVMqyrMS6T6R9RdV73//+/HVr34Vf//73/GZz3wGaWlpkbsqQghZQFTNzmpPGyNJJEXVetSo0kVVuJwqiipC4p+R2ZFjxkBJZj4h1ZqqBNQllZegIakYjW4vCoe7NAG1/2fASIfWiCVRC9mrOg846yP+Irp1gMmyYvepTTLv+TPuiXiS5b5xh9pP94lsFJb9K3vllVdi165d+M///E/lWn32s5/FOeecE9mrI4QQf8iZCBIJ/4skIhgidY71cqp08cPwP0LIQow5xuYK6YqAGtynakMJKZYUFcJ3UflFaEwqRIPTjeLhw5qAeuV/gPFurRFbqiaa6i4OKqK7CTCu7DtveMqpMu4Fh+8d7J9znwol815BKt68QxdPdJ/IxmFFv7JFRUX48Y9/jHvuuQef+MQnUFFRccxNwp133hnuaySEnOBIcge5mY+UU6WH/yUkJERMVEkK8nAJm/V0qoLHtxFCYk9ANQ03KeGkC6nuSU0YJVuSlQP12rLXoDEhDw1OF0p0AfXSrcDUgNZIQqZW+2nrVXMCKr18RUV0XXrdJ7/7pIfvzXefNhemKQGlZ96j+0Q2Miv+le3p6VEhgKmpqXjVq17FlLqEkKggWf9mZvxpdSNEYmJixIRbvDhVejsUVYRsbMad42gaagoJ4zs8cVjtSzQnKgF1fsmr0WjLRqMSUF0wioB64VFgdkxrJKVAE0073zEnoFKLVlQDStwnTTzNhe8dlLFPnjn3SUTTnHhKRUV2EkzG6GZKJWStrOhX9oEHHsB3v/tdnHrqqfjNb36DjIyMNV8AIYRsFFElqdQjJarWq1iuiCoJnQzXucU1FLeKooqQjcOkcxLNI82B8D0ppts53qn2JZgTUJ9Zj3OLzkGDNQONDifKhg/DeHQv8PyfAeek1khaKVCwFTjjg5oTlb8VSMlbk/skU//Ese7TlRK+J6nL81ORlhj+uoCEbGhR9cEPfhD79u1TYX8XX3xxZK+KEEKiKKok/E/EgogqKR8Rb6JKQifDWR8rkqGYhJClmXJNKQdKd59eGXgFXZNdAQFVl1mHswt24T0lFwYElOnIXmDPw4Db/7nNrNJcp5oL5xyoxMywu09X7qT7RE4czCu5IbjrrruQl7f8pxaEEBLODICTk/4nqhEaUyWiqqtLuzmJlzFV4ijpySXChThVFFWERJ5p1zSah5tDsvB1jHXABx/sJjtqM2txas4OvKvkAmx2OFAxdBimzr3Ac38EvC7AYASyazXRtPmKuSK69tRlu09tA1NKNKnkEX4HSnef7BYjavPoPhEiLPsX/tZbb+U7RghZV1HV398fsdTkulMVKTdsPUVVuM/L8D9Cws+MewYtwy1zAmpwH9rG2pSAsplsqM2oxWk5J+Ed+WejYXYWlSKg2vcCgw/B4PMCRguQ6y+ie/L1Wg0oqQllTVzW+YcmHYGMe3r43qH+OfepKD1BJYu4amcJ6gpS/Jn36D4RohP9X3hCCFll+J84SuKQSEKJSIT/JScnq3A5t9sddiEiokr+D9FG/j96xr5wIe1FenwbISeCgApOIiECyuvzwmq0KgdqZ/Zm3JBzKhpmZ1A51AVL68vA8B+0Bsx2IG8zfGW74Nh+I2wVp8IgRXTNtmW7T1rhXH/43gLu09biNFwVFL5H94mQpaGoIoTEBJLuXJieng67qNJJSUlRcxEM+nI4RVW4w/CWg4jQSDhVFFWELA+Hx3GMgGodbYXH54HFaEFNRg1OyqjD27JORuPMNKpEQB3cC4w+pDVgSdISSFS/Bij4hOZEZdcAJrM8EYJrchK25OQFM/KJ+6SPedLD9+a7T1IoV9wnJZ4KUug+EbJKKKoIITElqqamppCdnR0RpyrSomo9wv9EVIXbqRLHbXR0NKxtEhIPOD1OHBg5EBBQEsInAsrtc8NsNKM6vRpb06rw1vRGJaCqB7tgaXkRmPiT1oA9TRNNDZdp4XuyLEkljhPyrGXemw5xn2R5INh9yk9V7tNbTilRYXwy/iktge4TIeGCoooQEhOIyyOiJBLJKuaLKnHDws16iiqpKxhr6e0J2ei4PC4cGD2gpTAf2q+mg6MH4fa6YTaYsSl9EzanlOKqlBo0zkyherAT1qbdwPQjWgOJ2Vrq8u3XBBXRLTtuDagQ9+moTKNoG5w5xn16qxJPqWq5jO4TIRGHoooQEhOI6BFxEKkMgNK+JMMQ8SZuWLhZz/C/cDt7uqjSxSghJ4KAOjR6KCQLnzhSIqBMBhOq0qvQmFSMKxLL0TA9iZqhTtj2PQM4/qw1IAVzRTSd+u45ASWFdZf4/Ij71DowGci419S7gPuUl4JGEVCnlvnD9+g+EbJeUFQRQmIGKTgeKadK0N2qeHOqwh3+J+JTTxqih2USEi+4vC60jbYFwvdERLWMtKjtRoMRVWmVaEgsxBvzi9AwPYHawQ7YX34KcPm/NzLKNdF01of9Kcy3Ack5S55zUDLvBcSTFr53qH8CLo9vSfdJzCf5TpQkO3zAQcj6QlFFCIkZ0tPT0draGpG29RuStLS0iDhVklEw2k6VCB9JfR7urIN6eyI+KapILCNOk4x50t0nKaordaGcXqcSUJWp5WhILMDFuWeicUoEVDsSOp8EPOIWGYDsak041V86VwMqIeO47pOIJxFRKnlE78QxY5+2l6QpASXuU21+yqLuk/5AiBCy/lBUEUJiyqmamJgIe9hZcHsiqrq7uxEPokoElfzfwi2qxKnSRVVWVlZY2yYkkgKqfax9LomEhPANH8CsZxYGGFCRWobGhAJclH0aGqfGUTvQjsSOJwCvGzCYgJw6TThtvdovoDYDtpQl3SddPOnhe8e6T6kB8STJIzj2iZDYhaKKEBJTTpWIExnPE8606sFPe0VUtbS0IB5ElZ5MIhLhf0IkHD1CwoHH60HHeEdIIV0J4ZP6UCKgylJK0JiQh9dlnITGqTHU9bchqf2f8m0AmKyA1Hwq2gHsfIe/iG4DYFnYlXW6vWgbDHWfJHxPRJWQYDEpt0ncp6tP1cL3lnKfCCGxCUUVISRmyMzMVHNxq8Jdq0p3qkS4iRgJZwFgr9erpvUSVZEK/6OoIhsBKZirBFRQFr6m4SYloISy5GI02HNxQepmNEyOor6/FcntT2gvNidoIXsVrwJ2fUhzoMSRMltX5T4VZyQo0XSNiKcCGfuUitLMRLpPhJwAUFQRQmJOVI2PjyMvLy+sTpXRXwdGRJUgg7/15bUiAi0SjtHx0BNuhFtUyXslbhVFFVkPAdU13hUI31MCaqgJ027tb700qRAN9hycl1yPhskRJaBS2p/SXmxN0URT7RvmMvDJmCijaUH3SWXeC6r5tLD7lK7cJ33sU6qd7hMhJyoUVYSQmEGcHkmrLqIqkuO2IiWq4sWpEiRBRaTS2xOiC6jDE4cD4Xv7hzUBNenS/u6KE/PRYM/Ge5I2oWFCBNQhpLU/rb1YkkVI2N7mN88JqIyKBYvoSpIITTzNhe+JoFrKfSrLTISR7hMhJAiKKkJITCGJEcbGxsLaZnCiCkmpLk6MhBiGi/V0qkwmU0RSuYtTRVFFwvkZPDJxBPuG92H/oBbCJ9OES/scFiXkosGWhZvspWh0DaOh7yDSpru0FyflakV0T7p+TkCllRxTA0q5T31+8dS7uPt0UmkGrjmtlO4TIWRFUFQRQmIKKWR78ODBsLYZHP4nc3GowumGuVyudRNVIn4iUb9GxrSFU3iSEwf5vHVPdocU0hUHatypfeYK7NlotGXhRlshGh3DqO87iIxZv4ASsSSi6bTXBBXRzV/QfdLE01z43nz3SUSTuE960Vy6T4SQtUBRRQiJOVG1Z88elfhBF0JrZX6tF3HDIuFURTv8T8Y8RSL0Tw//Gx4ejkjbJH6Qz1bPVE9IFj4J4xtzaG5zni0TjbZMXG/OReO0EQ19h5Dp9AuozEpNNFVfMldENynrGPfp0FFdPM05UIOTTrU/0TrnPl17WqkST3SfCCGRgKKKEBJT5ObmKkElTlK4xjzNr3slCTGam5sR606ViCo9/XkkRFUkaoaR2EX+Fvqm+5RwCnahRh2jan+uLR0Nlky8zZCBBqcHDX2tyHZ3AQYjkF2jCaeGt84V0bWnHes+HRg4vvt0Whnq81MCmffoPhFCogFFFSEkpsjJyVHz0dHRsIkqIVgY6E5VuNww3amKlGt0vPC/SCDhf/L/kgLDkToH2fgCShdO+hio4VnNvcy2pqLRkolrkIKG2Vk0DLQjRwSU0Qzk1gMFJwPbpAbUNiCvEbAmhbpP/ZJ57wjdJ0JIzEBRRQiJKZKSktQN/cjICMrLy8PS5ny3RUSVCCpJxCDZBmPVqZLrLygoiEjbep0wcQwpquKf/un+EAElbtTQ7JDal2lJQaMlHVd57GicNqNhsBO5ni7AZNMEU+GZwI73a8kkcuoBy9zfS//ELJo7RDT1BsSTCCq3V3OfSjL9mffoPhFCNjgUVYSQmAwBFKcqXMwXVTJuS5Asg+ESVZKBL1xjwDZC+F+wqJL+IPHD4MygKp4bHMY3MDOg9mWak9FgScMVLjMaJ4GG4W7keTwwWJK0kL2y1wCn+xNI5NQCJkuI+9S0dzAQvifz+e7TyWWa+6Rn3kuh+0QIiREoqgghMYcU/m1qaoqYqJK06uIqiagqKSkJi6iKtkulh+bJ2KdIiyoSuwzNDIW4TzINzGoCKt2ciEZzGt7o9KFx3IOG0V7ki4CypQEFW4FNl2q1oERAZVUFiuiK+6REU0tXIHxvvvtU73efGgpSlBNF94kQEutQVBFCYlJUPfPMM0qshCOj3nxRJcsSAhguN0yuM9rjqcSlEiLlVEn9KxFsFFWxg4x30sc+6S6UjIsSUk0JaDSn4JJZJxrHHNg8MYgCtweGxCxNONWfG1REt1zVgHK4PWjtn0JT1zian21Z0H2q092n07XwPbpPhJB4haKKEBJz5OdrdWlkXFU4Qs+C61QFJ8Q4evQowsF6OFV6SnjdUYrU+DaKqo3J6Oxo6BiooX0qtbmQYrKjwZSMNzidaBiZQuPkKIrcHiClAJ6czTBtfT0MugOVWgjxlwYmpe7TBJpfltC9F49xn8RpEgF1rYx9KtDGPpVk0H0ihJw4xIyo+tnPfoYnn3wSLS0t6sn0Y489dswxvb29+NrXvobdu3erG4mLL74Y//Zv/6bGMhBC4gcRPCKCpE5SuETV/LTg0q6EGIYjZbiIqmgnc5AkFUKkwv8E+Z6VEEmyvkjNp/lZ+KS4rpBitKHelIwLHQ40jE6icWocxW43DOmlmmg6+Y2aE5W/Fb7kXIyNjqN3GlrSiH+OBTLwDU2Fuk87/O6ThO/V5NF9IoSQmFEbMj7g/PPPx5YtW/C73/3umP0ejwcf/vCHVcjOz3/+cwwODuILX/iCElQirAgh8YN8rkVYDQ1p2cciJapEDEkYXXJy8pradzqdEXWMFnOq5P8USTEn74s8zCLRY9w5jqahJiWgVCHdof04MnlE7UsyWtFgTMIFjlk0joyhYWYKJW43jFmbNAFVo9eA2gpfQoZW90nEkxTP3XMUTT3NaB2YOsZ9epuE7tF9IoSQ+BBV73nPe9T897///YL7n376abS3t+PWW29Vwqq2thbvfe97ccstt+Dd7373ouMu5GZHJh0Z2K3fZMm0FPoxxzuObCzYb/HRZ5IqvKurKyz9qIf/Bbclok1EiYQYSphbOJyqaP7NiajSrztS55X29Xpe80UpP2drZ8I5obLw6e6TTF0TXWpfotGCekMiznPMoGFkBI2OGZS6vTDm1AEFJwH1/vFP+Zsxa0rSxj71jKNp/wSa/3pAOVG6+5Tkz7wn7tObt+dhe3nOkmOf2KcbB37OYhP2W2yx3H6KGVF1PF5++WVs2rRJCSqdM844A1//+tfR2tqKurq6BV93++2346c//WnIE/CLLrpIhc7otWWWepNnZmbU8lrDg0j0YL/FHlLEVh54yOdS77PCwkIcOXIEs7OzKmnCWpAQORFVesicIOuSEEOcquN9FyynfXF1gtuPNPKwSHfbIoWkm5dJIgPmhxnyc7YyplxTODB6AM2jzWgeaUbLaAu6JjUBZTeYUQc7zpqdwebxYTQ6ZlHqMcCQXQdP3inw1myBJ28zprLqMOgwoaV/Cgf6p9ByaBIH+p9Hx9BMwH0qTrejNi8JV52cj9rcZNTkJaEo3Q6jwRDos4QECwxu+bxpDxnJxoWfs9iE/RZb6IbLCSOqJAwoMzMzZJsusJYKEbrxxhtx7bXXhrxxP/jBD9QN0PGydenKVY7lhyJ2YL/FHnroXHCfiVMln20RKnpdqdUiwkk++/PD/CS5hIS3ifO9FuQ6i4uL1xxGuBJE6EjYdDiyIy6GCCkZ1yYCTpy9YPg5W5xp13SIAyWhfJ3jnfDBB7vBhDqDHWfNTKNxfAiNDgfKfGaYpAZU4S5gx1blQDkya3BoyInmHq1gbnPzBJp692I4yH2qK0jFaZXZePuZWtpycZ+SbYv/7LPPYg/2WWzCfostlvs7uq6iSkLz7rjjjiWP+e1vf4vy8vKIXYPcNAVn5dLfOPkjX84fun4cPxSxBfstdvtL7zPJAChukoiH+Tf0qw3/m//3IE7VgQMH1vx3IoJNwv+i+fcmWfnkwVIkzyn1vOS9k2QVIhrnw8+ZJqBaRloC459EQLWPtSsBZTOYUAsbzpiZxk0TIqCcqDDYYM7fCpRoKcx9BdswYCvF/r5pLXnEwXE0/WMMrQOPweN3n8qytLFP159RpsRTQ0EqijPEfV35+84+iz3YZ7EJ+y12WG4frauoetvb3oZLLrlkyWOKioqW1ZbcPOzbty9km+5QBYcEEkLiAwnVFdEzMDCA+vr6iHxpinB77rnn1lQPS0SHODnRzv4nY53CUbh4KcTNl4dSMu6MADPuGbQMtwRl4duHttF2eOGFFUbUwopTZqbw9slRNDicqDIlwSzjniq08U/O3C1oduWiuU8b/9S8W9KXd2N4qj3EfTq1IhPXn1GuEkccz30ihBASHdb1mzgjI0NN4UCyAkrWPwlF0cMApTioDKSurKwMyzkIIRsLcUeam5vX3M5CiRb0EMO11sPSxzRFU1SJiJOxZmtNsLFct+pEFFWz7lnlQOmFdGXeNtYKj88LC4yogQUnT0/iuqlxNDidqLKkw1JQDVSL+7QVgyn1eGIqDU29k6pgbtO+cbQOtMLjPRRwn+rzU5X7JOJJllfrPhFCCIk8MfN4S8Y1SIiJzOUGSOpVCfIkVsZbnH766aioqMDnP/95fOhDH1Iu1Y9+9CNcddVVUS+6SQiJnqh69tlnlYBYi2gRN2mhZBd6PSz5PlmtqNIHuEZTVOkFeaMhqmRMQLyLKofHgQPDB4JqQe3DodFDSkCZYUCNz4Jt0xO4ZmZSOVDV9jxYChqBMs19ajNX4f/GEtDcN4mmtnE0PzWB4amDqm26T4QQEh/EzLf2j3/8Y/zhD38IrOvJJWT7zp071Q3Rd7/7XVX8V5JPyABqKf6rp2InhMQfenhbf38/SktLV93OYgV+w1EPSy/ZEE1RpRfkjZZTdfjwYcQLTo8TB0cOBkL49g2+ogSU2+dRAqraZ8LmqUlcNTutkkhUJxXBWrAZvqptGE1rwD5fGW4bsWrhey+I+zRF94kQQk4AYkZU3XzzzWpaCgnV+f73vx+1ayKErC9paWnKKQmHqBJHaiEkdXtHR8eaRdX8lOPRcKqiUXBYUqrL+aQA+1pT20cbl8eFg6NBAmrgFbUuAsoEAzZ5jWicnsCbZ2fR6HChOrUUtoLtcFVtwRF7LV5yl+D+IYMmoFrEfZK+blMukySOEPfphl3lKnmErCfRfSKEkLiF3/CEkJhF3CURUyKq1sJSokqS5bz44osqPbk4V7HgVI2OjipBFQ2Ro2cAlBDAtaa2jyQurwuto61zWfgGX8aBkYNw+dyQd6lSBNTUBN7kcKDR6UZNehVsBdswUdmIQ6Yq/Gu2GP8z4EZz1zha94j75AHQgXKVeS8VN5xRjrqCFJV5ryid7hMhhJxoUFQRQmIaEVWSrGK1omep8D/dqZL9EgIo2QZjYUyVhP9FI/RPd6oESRK0UUSV2+tWAiowBmpgL1pGDsLpc0Okc6XHgIbpSVzqcKDB7UNtRjUsedvQV1KLJlTg91MF2NfvQNPecYxMS6IRL5JtR5XbdFpFlnKf1NinPLpPhBBCNPhrQAiJacrKylTyGkmtrmfrC6dTJQkqRKxJPazViCpxqiT1+GLtx7qoEkdM3p+1jDtbq4BqG2uby8InAmr0IBxeF0QmV3qAhukpvMHpQIPHiJrMWiBrC7oKqrHXU47/Hc/Bvt5ptHWI+yTi2oeyzFHlPr19V0XAfZKxT6wnQwghZDEoqgghMY0IHXGB+vr6IiKqJIRO2hXRtpbCv9FEwv8WKsYbCURoSAhgNESVhNxJ4dz9w5qA2tf/ohJQs34BVe72oWF2Ghc6nGjwmVGTWQ9HWiMOZVbheWcZvjmShqbuKYwc1NLcJ9tMqMt34fTKLLz9TLpPhBBCVg9/OQghcTGuSsotrJalwv8EESgvv/zyqkVVNJNUiGsniSMkgUe0kBDAcIsqEVCd452BFOb7+l5A8+hBzHg1QVTu9qJhdgavEQEFG6rS6zGRXI/mlEo8PVOCXw4mofXgjN99AsoyE1BfYMfbd+WiviBFCSi6T4QQQsIFRRUhJOYpLy/HX//611WPq1qOqPrXv/6F6enpFWfUE1EVjSx8OiKoRFhFU1RJFsa2trZVv97r884JKHGg+p5H8+ghTHu1JB+lbg8aZ2fxahFQpmQUJ9dgxF6Pl61leGKiGN8dsGGk362OTZHMewUpOK0qFW8/K1Ut030ihBASafgrQwiJeaTwt6T0Xu24KhEhS415Cq6HJQJupaIqmgkcJPRPkJC8aIqqyclJ9X+V8WPHE1CHJw5r4XuDL2N/7/NoGjuEKb+AKvYLqFc5nag3pSM/sRr95lo8by3DH2fy8Z+DNnh6NPepPCtJJY+4sVZLWU73iRBCyHpBUUUIiYtxVRJi19PTsypRdTynSgSKCIfViCpJVBHN8D9JbS5EK1GFIO+NIMk8JAV98PvaNd6FpuEm7BvYi/29u7F/rBWTfgFV5PagweHAu6SIrjkLWbZqHDVU41lvCe4Yz0PnjD3EfaqvTsUb81NV+F4N3SdCCCEbCP4iEUJiHhFElZWVSlSFO1GFjozbWk370R5TJaJKBNVq08uvVlT54MP+7v14xfUK9vW/FBBQE34BVeh2o8HhxDucLlSaspFq3oQu7yY8MVWE74/mYcybEHCfRDRd0SDiSXOg6D4RQgjZ6FBUEULiAhFV+/fvX1YI2kqdKl1UvfLKK3C5XLBYLMtud3Z2NqpjqqReVKTHU0kh3a6pLrRNtqFjrBntY804WHoY9zXdBzQB+X4BdYPTg3JTNmyGTWh1VeKx8UJ8e6YQs7DNuU+1qfiU332qzU9BopU/S4QQQmIP/noRQuKCqqoqJWLETVppiN7xxlTp9bCkfRm3JQWBl4MkzpCxXtEUVeJUhXM81ZhzDO2T7WibPITOkVfUcqdrGB74YPD5UOp2o9bhxFlOL3LcaUi01KN5VgRUAf7kLYHLYAm4T6dsTcX1dJ8IIYTEIRRVhJC4QELQJCFEd3f3ikXVcsL/pG0J45PU7csVVeJSCdF2qlZTpNjj86BnpkeJpvaxJnSMNqFt5ggGvTNqv93rQ43TiR1OJ67w2JDsy8e0oxwvTJfiRVcJHvAVwGLwYVtZDuoqUvDW/BSUpZmxvSIXSbblOXuEEEJIrEJRRQiJGzZt2qTqSS0nnG+lokraE7EmRYaXi4QiRlNUzczMKCEndaOWPM49g46pDrRPtKJj5GW0TxxCu3MAs/Cq/bniPjlduMzpQa43DSZ3MbqmK/G8oxj3e0sxaUxFaboVlTl2VGba8a4sOwzjPWja/RQ+e9NnVHikvKeSEZDhfIQQQk4E+GtHCIkrUfX000+rtOIZGRlhDf8TRFS1tLQsux5WtJ0qcakEXVSJsBlyDKmxTzLuqWP0FbRPH0a3ZwI++QHw+VDhcqHO4cL5HjNS3LmYcpRh31Q5XvGW4hFfAXJS7KjItKOy1IYLsux4d6YdJelWWEyh71dX1wiaDVq2w+WOOSOEEELiBYoqQkjcIOOe5Ib+yJEjKxJVy3GqdFElAkxSqy8nBFAXVdFKb97R14HOpA7cf/Tn6DrYilZHH8Z9LrUvxeNFndOJc1we5HlSYHAW4uhkBfa5y/AnbynctgxUZtpQmWdHVaYdr8kSMWVDktUUlWsnhBBCYhmKKkJI3CDukRQCFlG1ZcuWZb9uueGCOTk5ynWSZBjLFVVWqzXi6c17p3pxx7Pfxr2dj8CR40NJrwt1Thd2ukxIdmdharoUB2cq0Owrx78MhSjKTEJlth2VNTa8McuOj2XakZ1kZuIIQgghZJVQVBFC4oqamho89NBDStDY7Vrx2HA5VSI6RLQtt15VpNOpd4y14+dPfx2/730KiR4Prhz3wjF8Og646/APbymeTs1WY54qS+04KdOOK7JsKE6zwWykeCKEEELCCUUVISTuRNUf/vAHlQVQ0qwvBwnpM5lMYa+HJaIqEqF/TYP7cNu/voJHh15GtseNt42b0Tp4MR4yn4JNtglsr0jF6+xe2Eyj2gtkANUQ0CcTIsPExESEWiaEEEI2PhRVhJC4Qmo0SWheV1fXskXVSrIFiqiS4yW1uozhiqao2tPzDH76r6/iyYk2FLtcePtYEl4aeTMO1Lwe7768Co7uZrz00kvA7AAmZ4FJRBcJuZS084QQQsiJBkUVISTuqKurwz//+c9lZ+lbbvY/IT09HZmZmTh69OiyRJWMw1oLIuCe6PwLbnv2m3h+pgebnE5cP5KJ58ffjKEdr8XNZ1eiItsv3Mp2YdeuXWs6HyGEEEJWDkUVISQuRdXf/vY3NfappKQkrKJKEAesubn5uA7XWpwqj9eDRw8+gJ/t+T6aXSPYOuvANcNF2OO8GuZd5+KWM8qQnbx0+CEhhBBCogNFFSEk7sjOzkZWVhY6OzuXJaqWm6giuB7Wc889h/HxcaSlpS3aphTjTU5OXtG1uzwu/P6VX+DnL/8UnZ4pnDbjwFVDm/Cy+ToUn7cLHzm5GAlMc04IIYRsKCiqCCFxh7hH9fX1Svgcz4US8bNSUSX1qiSxhSTDWExUuVwueDyeZTtV065p3PfCj3FH853o8zlx7pQDJw1tRVf6jdh56cn4TGM+TMzaRwghhGxIKKoIIXGJiKonnnhCJZRYqqaUCCphJaJKak+VlpYqUdXQ0LDgMeJSCcdzqsYcY7jr2W/jzrYHMeFz44JJN4xDp2Gq9CZcet1m7CzLYP0oQgghZINDUUUIiUsKCgpUUomOjo4lRZU4WSsVVUJ1dTX++te/LpoM43iianB6AL946iu458jf4PF5cMGED46R82BvvBE3vqUOVTkrCxskhBBCyPpBUUUIiUvE3WlsbMTu3btx+umnLyqaVuNU6fWw/vznPy+aDGMxUXVkrAv/++TNeKD/OVh8XlwwZsL4xKXIPfU6XLerCjkpTD5BCCGExBoUVYSQuEVE1ZNPPqnSnxcXF4fVqZJEGJJa/fDhw4uKKhl3pRcIPjS4Hz974mb8abQJqV4PLhxNwKjzStSc/RZceUoJEq38OiaEEEJiFf6KE0Lilvz8fCV82tvbFxVVq3WqdLdq7969C6ZW1zP/vXz0afz0qS/jseku5LvduHgkA5OmG7DrgkvxusY8mE0rPy8hhBBCNhYUVYSQuEWEzpYtW/DUU08tOvZptU6VUFtbi6effhpDQ0MqjbvOuGsc+8f/ib1JT+Jnf/kZypwipgqA9PfgkssvwKkVmUw+QQghhMQRFFWEkLhGRNXjjz+OI0eOqFToi4kqCdVbKZIB0G63o6WzBe2OPXil93G8OHEQbb5p+AwG1PpceENPFZJLP4y3XnYGNuWmhOX/RAghhJCNBUUVISSukbFPkv2vtbV1SVG1XKdKQv0Oj3XgpbaH8eKRJ/FM9n4cHnfCO2FQ4X11M0bUThVgerYR5sxd+Nj7L0Nuqj3s/y9CCCGEbBwoqgghcc/WrVtVpr7Z2VnlLK1kTJUU5d135Am82PZn7Ol7Cfsc/Rg1akKswulCxawF9TN5cM02YtKyBbaEBBSluzAxeBSn1GZQUBFCCCEnABRVhJC4Z/PmzXjkkUdUwgopCrxY+J8IrCOjbXip9Y/Y3fUE9o63oQ0z8BgMSPR6UTfrxc7ZVFg9lUhPOxtZFWehtjgX7Xseh92kiTMNM/LSN2HHjh1R/p8SQgghZD2gqCKExD1JSUmqWK+EAOqiatYzi77xVrQfeQ5DqU/g5r/8DQd8IxgxauKo1OlGicOKSkcx0hJPRn7xa1G1qQ5bijOOqSX1qrq3rMv/ixBCCCEbA4qqDczfmvvg8gQ//SZhwefDzOwsEuyTkh5uva8m9vF5YfB5YAjMtUm2w+uGz+eGz+uE1+tUyx6PA16fG16vS1v3b5d1jxzrc8HjdcPrc8Hr86hjW1u64PY40DTykNomx3nln7zef4zX51XbPf65tl+OVUuYcc5iwjeBxx+9Hf1GB4aC8lIkpntRNevDdkcG0s21KMg9DxXbz8GWslwUptmZqY8QQgghS0JRtYH58N0vYmLWvd6XEbcY4IXJPxnnzU3wwQSPts1w7HHmhV5jCH699lptv7Q1/1gPjNAmyLLBA8ADg0Emb2A7/OuyHfACBpm0Zdnuk3V44VNjfLR1H3zqOJ9BlrS5Vx3nCyyruexVc2jzecvqLAbAE9gOuPW5QV2NmntgUHO3waD2e4LnYRIjk0OTMFqNSBxPnOs/n099gZlk7gNEI5l98l7LNv+kL8uYKZ8BJjOQPGtBrisVZmc6DK4s+Fw5cHiz8e4brse2ykIYjRRQhBBCCFkZFFUbmH9+8jwxVY5FNooT4JXJHXAK1HrAIfC7Bf79+j6DmnsDrzf43DKoZK49v+MQaEtvP9C2J3C8ti3o9cHtB84Xen5tm34+d6A9zdnQr8ELr1ccB3ExPHD7ZHLD7RVHwgsPPGrZLcdAXAmvOkbN4VPOhTuwXSYRCPI6H1xBy9qNvwgFg5q7DAYlJEQIBISCfx4iFALH+IVEUBtO/1x/XchxQftkLm1FCiUmYIAZhqBlozY3mGGCEWaDf5vBqI4zG7Rlk8GktltkuywbjDAaTNpxal2WTTAZZdmsHW+UbWaYjfq62b9ugVEdZ4HJqK+btWWTde4Yk0XNTUaZW2EyybLMrTCbLPjE7z+HN191BV71qvNgsdjVPnkNDKYVuY0dnR1wT0/ANE84paeno7KyKAI9QQghhJATAYqqDUz6T04GZsc08eEXJT6vSApfyI27CADXIjf5C93cy1w/XnMTQtsKOWahttS6AR6jES6DMeBIeAzGgHjQ1oOvb87R0K5Dcz3UsroOTeRok/JaNIyrlxSLoQkJERHBQkGbB0SFXyjoAkJf1sWCxWiGXbaJOPALAyUylCjwr4swkH1KMPhFhOz3CwhNVPjbNJpgUcJDP4e/7RCRYoLFuMAxupgJ2m40rOqN25C0tbXhwf97CI4ZN66+8ro1heJlNNSE9doIIYQQQgSKqg3M6/PSMO6x+10Xn+a6qKCryKHdoPsdCONCN/P+uf9GXt+vHyvbbUYzEoNEwXKFwYLnC9oeIh7mnX/+dtMS5xcmJyeRnJzMsTIxwFe+8hV4PB788Y9/xPPPP8+MeoQQQgjZcFBUbWCu2vouFcq2kDCYv201wmT+MeJunAgiQ69LRGLDpbrjjjsC61/60pfwu9/9bl2viRBCCCFkPhRVG5i3b377el8CIRvCpdJ58MEHlVt18sknr+t1EUIIIYQEQ1FFCNmw3HjjjbjuuuuUuzgzM4OEhARkZGSs92URQgghhIRAUUUI2bCcddZZai6iiuPgCCGEELJRiQlRdfToUdx2223YvXs3hoaGkJ2djde//vV4xzveAYvFEjju4MGD+K//+i/s379fPc2+6qqrcMMNN6zrtRNCCCGEEELim5gQVR0dHepJ9Wc+8xkUFxejtbVVjbWQcKCPfOQj6hh5iv2BD3wAp556Kj796U/j0KFDalB7SkoKLr/88vX+LxBCCCGEEELilJgQVbt27VKTjgirzs5O3HfffQFR9fDDD8PlcuHzn/+8cq+qqqpw4MAB3HnnnRRVhBBCCCGEkBNbVC2EOFOpqamB9b179+Kkk04KCQc844wzVDrm8fHxkGODcTqdatJxOBxqLs7Y8VJv68cwRXdswX6LPdhnsQf7LPZgn8Ue7LPYhP0WWyy3n2JSVB0+fBj33HNPwKUSZKxVYWFhyHGZmZmBfYuJqttvvx0//elPA+tmsxkXXXSREm3ifC2FnpFM4OD52IH9Fnuwz2IP9lnswT6LPdhnsQn7LbbQDZcNLapuueWWkMKeC/Hb3/4W5eXlgfX+/n588IMfxAUXXIA3velNYUnZfO211wbWZ2dn8cMf/lA5XsGu12IfCnmj5Th+KGIH9lvswT6LPdhnsQf7LPZgn8Um7LfYwuv1LsuxWldR9ba3vQ2XXHLJkscUFRUFlgcGBvDe974XW7duxWc/+9mQ47KysjA8PByyTV+XfYthtVrVNP+N++53v7vC/w0hhBBCCCEkHpHhQna7fWOKKkl7vtxCnuJQiaCqq6vDF77wBRiNxpD9IrRuvfVWuN1uFcInPPPMMygrK1s09G8hpA7ORz/6USW0jvf0QEIE3/CGN+Chhx5SryOxAfst9mCfxR7ss9iDfRZ7sM9iE/ZbbCEOlQiq4/VVTIypEkH1nve8BwUFBWoc1cjISGCf1KwSLrzwQjU2StKoS20qSbt+11134WMf+9iKziVibbkiTMZciYiz2WxqIrEB+y32YJ/FHuyz2IN9Fnuwz2IT9lvssZRDFVOiShwnSU4hkxT9DUYKAguiHn/wgx+o4r/XXXcd0tPT8c53vpPp1AkhhBBCCCERJSZElYy7Ot7YK6G6uhq33XZbVK6JEEIIIYQQQoTQgUlkRci4q3e9610hiS7Ixof9Fnuwz2IP9lnswT6LPdhnsQn7LT4x+Fh5jBBCCCGEEEJWDZ0qQgghhBBCCFkDFFWEEEIIIYQQsgYoqgghhBBCCCFkDVBUEUIIIYQQQsgaoKhaI1Jh+ZprrsHOnTvR0tISsu/gwYOqVtauXbtU5ew77rhj3a7zROfo0aOqMPSll16KM888E5dddhl+8pOfqAJ8wbDPNh6/+c1vVEkF6RMp7P3KK6+s9yURP7fffjuuv/56nHPOOXjNa16Dj3/84+jo6Ag5xuFwqPqB559/Ps4++2x84hOfwNDQ0LpdMwnlf//3f9Xv17e//e3ANvbZxqS/vx+f+9znVL/I79hb3vIW7N+/P7Bf8o79+Mc/xute9zq1//3vfz+6urrW9ZpPZDweD370ox+F3HdI2Z/g/HDss/iComqNfP/730d2dvYx2ycnJ/GBD3wABQUF+OUvf4kPfehD+J//+R/cf//963KdJzpyoydfXp/5zGdwzz334GMf+xjuu+8+/PCHPwwcwz7bePz5z3/Gd77zHZV69le/+hVqamrwwQ9+EMPDw+t9aQTA888/jyuvvFKJK/ksud1u9RmamZkJHPPf//3f+Mc//oGvf/3r6vM0ODiobtLJ+rNv3z71/SY1HoNhn208xsfHcdNNN8FsNuN73/ueetj00Y9+FKmpqYFj5CHg3XffjU9/+tNKLNvtdvV9KSKZRB/pj9/+9rf45Cc/iXvvvVf1xS9+8Qt1DxJ8DPssjpCU6mR1PPHEE74rrrjC19ra6tuxY4evubk5sO/ee+/1nXfeeT6n0xnY9v3vf993+eWXr9PVkvnccccdvksvvTSwzj7beFx//fW+r3/964F1j8fju/DCC3233377ul4XWZjh4WH1Xbhnzx61PjEx4TvttNN8jz76aOCY9vZ2dczevXvX8UrJ1NSU701vepPv6aef9r3rXe/yfetb31Lb2WcbE/ktuummmxbd7/V6fa997Wt9v/jFLwLbpC/POOMM38MPPxylqyTBfPjDH/Z98YtfDNn27//+777/9//+n1pmn8UfdKpWiYRCfOUrX1EhZfJkYT579+7FSSedBIvFEth2xhlnoLOzUz1xIuuPOFPBT/nYZxsLCc1sbm7GaaedFthmNBpx6qmnqr4iG/MzJeifq6amJuVeBfdheXk58vPz2YfrjIT3SbhRcN8I7LONiTiH9fX1+NSnPqVCbWXYwQMPPBDY393dre5L5PtRJzk5GZs3b8bLL7+8Tld9YrN161Y899xz6h5COHDgAF566SUVyi6wz+IP83pfQCwiYWRf/OIXcfnll6OhoUGN15mPfFAKCwtDtmVmZgb2Bd/Mk+hz+PBhZcF/5CMfCWxjn20sRkdHVUy63gc6sj5/3A5Zf7xerxqXs23bNmzatCnwuZGHFCkpKcf0IcforB+PPPKIemAhoUjzYZ9tTOQGXELWr732Wtx4441qLNW3vvUt1VcXX3xxoG+ysrJCXsd+Wz/e/va3Y2pqCm9+85vVA0H5jpQxUxdddJHazz6LPyiqgrjllluOm5hA4mOffvpp9UGRLzYSG30mT1qDB/tKzPIFF1yAN73pTVG4SkJODOejtbVVDcQmG5fe3l4lfmUMnM1mW+/LIctEbsjlIe6//du/qfW6ujr1eROhJaKKbDweffRRPPzww/jyl7+MqqoqlcxMxivm5OSwz+IUiqog3va2t6ksY0tRVFSk7FyxZnULV0eyYF144YXKxZInD/MH0+vr859KkMj3mc7AwADe+973Klv+s5/9bMhx7LONRXp6Okwm04J9wv7YeILqiSeeUEkN8vLyAtulnySMc2JiIsT5YB+uH+JQyfsv35064gi/8MILKvmBPKhin208JCFWRUVFyDZZ/9vf/qaW9b4RhyM4eZb0myT4IeuTyEwy1kpmP0Ec/J6eHpXYR0QV+yz+oKgKIiMjQ03HQ7Igve997wusS2YkyXj11a9+VcXCCnLTfuutt6rYdMnWIzzzzDMoKytjGNk69JnuUImgkid8X/jCF5QdHwz7bGMhYS3SV88++yzOPffcwNNaeahx1VVXrfflEX8o9De+8Q089thjqkRB8AMMQcaAyGdJ+lDSQAsSuiluiXzeSPQ55ZRTVLaxYGRssHzPyQ2gjJ1in208JKxWH5ujI+uSrVaQz57cpMv3Y21tbWCMo5SguOKKK9blmk90Zmdnj7nPkAeFekp19ln8QVG1CuRHJ5jExEQ1Ly4uDjylFcfqpz/9qfqxkh8qsenvuusulcqbRB8RVO95z3vUD5CMoxoZGQns058Qsc82HjJ+4Oabb1ZhL42Njfj1r3+t0nUfz50k0XOoJLxFwsnke1AeMOmDrSWBj8ylNoukxU9LS0NSUhK++c1vqpvzLVu2rPfln5BIH+hj3nSkr8QZ1rezzzYekpjiHe94B37+85+rRBWSDl8SVegRFwaDAVdffTV+9rOfoaSkRN2wS40kCTXTH0qR6CI13qS/5J6xsrJShf/deeedqm6VwD6LPwySAnC9LyLWkUQV8iGRD4v+tEEvJCs3HTKgVH6w5Om6DFwk0ef3v/+9CstciN27dweW2WcbD0koInXDJERCQiLEKdYdYbK+SNHYhRAnWBe+Um/lu9/9rkqOIMXSJaOmZDBbqL4fWR/e/e53q98uKd4ssM82Jv/85z/xgx/8QCVakqRK8tApeFyw3M6JYyxiS8I3t2/frvpNXEgSfWTsvRT2/fvf/64e5MrnR0IBpe6inmWYfRZfUFQRQgghhBBCyBpgnSpCCCGEEEIIWQMUVYQQQgghhBCyBiiqCCGEEEIIIWQNUFQRQgghhBBCyBqgqCKEEEIIIYSQNUBRRQghhBBCCCFrgKKKEEIIIYQQQtYARRUhhBBCCCGErAGKKkIIIScMl1xyCXbu3KmmiYmJqJ9/9+7dgfN//OMfj/r5CSGERAZzhNolhBBCIiZM3vve9y66f8eOHfjJT36y6H557Rvf+EYkJycj2mzbtg0PP/wwvv3tb8PpdEb9/IQQQiIDRRUhhJCYQhcm8/nHP/6Br33ta7jyyiuXfH1iYiKys7OxHlgsFnVum81GUUUIIXEEw/8IIYTEFLowCZ4klO973/sebrzxRlxwwQUrau/3v/89zj33XPzzn//E5ZdfjjPPPBOf/OQnMTs7iz/84Q8qZPC8887DN7/5TXg8nsDrZPttt92Gz3/+8zj77LNx8cUX4/HHH8fIyAg+9rGPqW1vfetbsX///gi8C4QQQjYSFFWEEEJiGhFUMj7p5JNPxvve975VtSEC6u6778ZXv/pV3HLLLdizZw/+/d//HU8++aQSa1/60pdw//33469//WvI6+666y7lnN15550466yzlMD6whe+gNe//vX41a9+heLiYrXu8/nC9L8lhBCyEaGoIoQQErN4vV589rOfhclkwpe//GUYDIZVteN2u/HpT38adXV1Spydf/75ePHFF/G5z30OlZWVynWS5BIyniuYXbt24YorrkBpaSne+c53YmpqCg0NDcotKysrww033ID29nYMDQ2F6X9MCCFkI0JRRQghJGb54Q9/iJdfflklfkhKSlp1O3a7XblKOllZWSgsLFTjr3QyMzNVaF8w1dXVIa8RNm3aFPIaYf7rCCGExBdMVEEIISQmeeSRR1SI3Xe/+13lFK0Fs9l83G3igokzttgxuku20Lb5ryOEEBJf0KkihBASc7S0tOA///M/8YEPfABnnHHGel8OIYSQExw6VYQQQmKK0dFRlURC6lFddNFFGBwcDNkv46syMjLW7foIIYSceFBUEUIIiSmeeOIJ9PT0qOnCCy88Zn9BQYFKk04IIYREC4OPeV4JIYScIEhtqauvvhrXXHPNul7HzTffrFLBS4INQgghsQ/HVBFCCDmhkDpUkiJ9cnIy6ud+4YUX1Ln/9Kc/Rf3chBBCIgedKkIIIScMEjIoNamEoqIiGI3RfbYoRYYHBgbUckJCArKzs6N6fkIIIZGBoooQQgghhBBC1gDD/wghhBBCCCFkDVBUEUIIIYQQQsgaoKgihBBCCCGEkDVAUUUIIYQQQggha4CiihBCCCGEEELWAEUVIYQQQgghhKwBiipCCCGEEEIIWQMUVYQQQgghhBCC1fP/ASPn9YW//ftRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lens = TripletTelescopeObjective()\n",
    "lens.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.449827Z",
     "iopub.status.busy": "2026-03-24T10:16:32.449491Z",
     "iopub.status.idle": "2026-03-24T10:16:32.471472Z",
     "shell.execute_reply": "2026-03-24T10:16:32.469721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seidel Aberrations:\n",
      "\tS1: -2.707e-03\n",
      "\tS2: -1.412e-03\n",
      "\tS3: -1.479e-03\n",
      "\tS4: -5.568e-04\n",
      "\tS5: 3.606e-05\n"
     ]
    }
   ],
   "source": [
    "print(\"Seidel Aberrations:\")\n",
    "for k, seidel in enumerate(lens.aberrations.seidels()):\n",
    "    print(f\"\\tS{k + 1}: {seidel:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.474143Z",
     "iopub.status.busy": "2026-03-24T10:16:32.473771Z",
     "iopub.status.idle": "2026-03-24T10:16:32.491217Z",
     "shell.execute_reply": "2026-03-24T10:16:32.490485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order transverse spherical aberration:\n",
      "\tSurface 1: -5.087e-01\n",
      "\tSurface 2: -2.442e-01\n",
      "\tSurface 3: 2.466e-02\n",
      "\tSurface 4: -2.759e+00\n",
      "\tSurface 5: 3.481e+00\n",
      "\tSurface 6: -5.623e-04\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order transverse spherical aberration:\")\n",
    "for k, value in enumerate(lens.aberrations.TSC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.493815Z",
     "iopub.status.busy": "2026-03-24T10:16:32.493412Z",
     "iopub.status.idle": "2026-03-24T10:16:32.510528Z",
     "shell.execute_reply": "2026-03-24T10:16:32.509754Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order longitudinal spherical aberration:\n",
      "\tSurface 1: -2.849e+00\n",
      "\tSurface 2: -1.367e+00\n",
      "\tSurface 3: 1.381e-01\n",
      "\tSurface 4: -1.545e+01\n",
      "\tSurface 5: 1.949e+01\n",
      "\tSurface 6: -3.149e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order longitudinal spherical aberration:\")\n",
    "for k, value in enumerate(lens.aberrations.SC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.513059Z",
     "iopub.status.busy": "2026-03-24T10:16:32.512710Z",
     "iopub.status.idle": "2026-03-24T10:16:32.529830Z",
     "shell.execute_reply": "2026-03-24T10:16:32.529054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order sagittal coma:\n",
      "\tSurface 1: -2.491e-02\n",
      "\tSurface 2: 2.011e-02\n",
      "\tSurface 3: 4.053e-03\n",
      "\tSurface 4: 8.930e-02\n",
      "\tSurface 5: -9.345e-02\n",
      "\tSurface 6: 9.330e-04\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order sagittal coma:\")\n",
    "for k, value in enumerate(lens.aberrations.CC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.532429Z",
     "iopub.status.busy": "2026-03-24T10:16:32.532072Z",
     "iopub.status.idle": "2026-03-24T10:16:32.549240Z",
     "shell.execute_reply": "2026-03-24T10:16:32.548512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order tangential coma:\n",
      "\tSurface 1: -7.473e-02\n",
      "\tSurface 2: 6.034e-02\n",
      "\tSurface 3: 1.216e-02\n",
      "\tSurface 4: 2.679e-01\n",
      "\tSurface 5: -2.803e-01\n",
      "\tSurface 6: 2.799e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order tangential coma:\")\n",
    "for k, value in enumerate(lens.aberrations.TCC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.551984Z",
     "iopub.status.busy": "2026-03-24T10:16:32.551502Z",
     "iopub.status.idle": "2026-03-24T10:16:32.567371Z",
     "shell.execute_reply": "2026-03-24T10:16:32.566754Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order transverse astigmatism:\n",
      "\tSurface 1: -1.220e-03\n",
      "\tSurface 2: -1.657e-03\n",
      "\tSurface 3: 6.659e-04\n",
      "\tSurface 4: -2.890e-03\n",
      "\tSurface 5: 2.509e-03\n",
      "\tSurface 6: -1.548e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order transverse astigmatism:\")\n",
    "for k, value in enumerate(lens.aberrations.TAC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.570173Z",
     "iopub.status.busy": "2026-03-24T10:16:32.569893Z",
     "iopub.status.idle": "2026-03-24T10:16:32.588313Z",
     "shell.execute_reply": "2026-03-24T10:16:32.587525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order longitudinal astigmatism:\n",
      "\tSurface 1: -6.831e-03\n",
      "\tSurface 2: -9.280e-03\n",
      "\tSurface 3: 3.729e-03\n",
      "\tSurface 4: -1.618e-02\n",
      "\tSurface 5: 1.405e-02\n",
      "\tSurface 6: -8.670e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order longitudinal astigmatism:\")\n",
    "for k, value in enumerate(lens.aberrations.AC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.590870Z",
     "iopub.status.busy": "2026-03-24T10:16:32.590555Z",
     "iopub.status.idle": "2026-03-24T10:16:32.608041Z",
     "shell.execute_reply": "2026-03-24T10:16:32.607193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order transverse Petzval sum:\n",
      "\tSurface 1: -1.850e-03\n",
      "\tSurface 2: -9.425e-05\n",
      "\tSurface 3: -1.636e-03\n",
      "\tSurface 4: -5.416e-04\n",
      "\tSurface 5: 1.167e-03\n",
      "\tSurface 6: 1.395e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order transverse Petzval sum:\")\n",
    "for k, value in enumerate(lens.aberrations.TPC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.610648Z",
     "iopub.status.busy": "2026-03-24T10:16:32.610406Z",
     "iopub.status.idle": "2026-03-24T10:16:32.631015Z",
     "shell.execute_reply": "2026-03-24T10:16:32.630153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order longitudinal Petzval sum:\n",
      "\tSurface 1: -1.036e-02\n",
      "\tSurface 2: -5.278e-04\n",
      "\tSurface 3: -9.159e-03\n",
      "\tSurface 4: -3.033e-03\n",
      "\tSurface 5: 6.537e-03\n",
      "\tSurface 6: 7.812e-03\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order longitudinal Petzval sum:\")\n",
    "for k, value in enumerate(lens.aberrations.PC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.633688Z",
     "iopub.status.busy": "2026-03-24T10:16:32.633478Z",
     "iopub.status.idle": "2026-03-24T10:16:32.652641Z",
     "shell.execute_reply": "2026-03-24T10:16:32.651477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third-order distortion:\n",
      "\tSurface 1: -1.503e-04\n",
      "\tSurface 2: 1.443e-04\n",
      "\tSurface 3: -1.593e-04\n",
      "\tSurface 4: 1.111e-04\n",
      "\tSurface 5: -9.870e-05\n",
      "\tSurface 6: 2.540e-04\n"
     ]
    }
   ],
   "source": [
    "print(\"Third-order distortion:\")\n",
    "for k, value in enumerate(lens.aberrations.DC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.656241Z",
     "iopub.status.busy": "2026-03-24T10:16:32.655918Z",
     "iopub.status.idle": "2026-03-24T10:16:32.672612Z",
     "shell.execute_reply": "2026-03-24T10:16:32.671884Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First-order transverse axial color:\n",
      "\tSurface 1: -1.893e-01\n",
      "\tSurface 2: -1.120e-01\n",
      "\tSurface 3: -5.758e-02\n",
      "\tSurface 4: -2.546e-01\n",
      "\tSurface 5: 7.011e-01\n",
      "\tSurface 6: -1.541e-02\n"
     ]
    }
   ],
   "source": [
    "print(\"First-order transverse axial color:\")\n",
    "for k, value in enumerate(lens.aberrations.TAchC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.675209Z",
     "iopub.status.busy": "2026-03-24T10:16:32.674854Z",
     "iopub.status.idle": "2026-03-24T10:16:32.691525Z",
     "shell.execute_reply": "2026-03-24T10:16:32.690881Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First-order longitudinal axial color:\n",
      "\tSurface 1: -1.060e+00\n",
      "\tSurface 2: -6.274e-01\n",
      "\tSurface 3: -3.224e-01\n",
      "\tSurface 4: -1.426e+00\n",
      "\tSurface 5: 3.926e+00\n",
      "\tSurface 6: -8.627e-02\n"
     ]
    }
   ],
   "source": [
    "print(\"First-order longitudinal axial color:\")\n",
    "for k, value in enumerate(lens.aberrations.LchC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T10:16:32.693959Z",
     "iopub.status.busy": "2026-03-24T10:16:32.693555Z",
     "iopub.status.idle": "2026-03-24T10:16:32.713797Z",
     "shell.execute_reply": "2026-03-24T10:16:32.713096Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First-order lateral color:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\tSurface 1: -9.272e-03\n",
      "\tSurface 2: 9.230e-03\n",
      "\tSurface 3: -9.461e-03\n",
      "\tSurface 4: 8.240e-03\n",
      "\tSurface 5: -1.882e-02\n",
      "\tSurface 6: 2.556e-02\n"
     ]
    }
   ],
   "source": [
    "print(\"First-order lateral color:\")\n",
    "for k, value in enumerate(lens.aberrations.TchC()):\n",
    "    print(f\"\\tSurface {k + 1}: {value:.3e}\")"
   ]
  }
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