{ "cells": [ { "cell_type": "markdown", "metadata": { "vscode": { "languageId": "plaintext" } }, "source": [ "# Leakage pattern prediction\n", "\n", "This code simply generates images of a leakage pattern." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[93m>>> PyXSPEC is not installed, you will no be able to use it.\u001b[0m\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import leakagelib\n", "\n", "SOURCE_SIZE = 53 # pixels\n", "PIXEL_SIZE = 2.8 # arcsec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first need to make an object describing where the source is. Let's do a point source first." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "source = leakagelib.Source.delta(\n", " SOURCE_SIZE, # Number of spatial bins to put in a single row of your image. The image is assumed to be square\n", " PIXEL_SIZE # The size of each pixel in arcsec\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the leakage pattern depends on the PSF, we must specify the detector when loading the PSF. This example uses detector 3.\n", "\n", "Then we need to create a special class, named PSFSourceCombo, which combines one source and one PSF. I.e., it represents the source as seen by a specific detector." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "psf = leakagelib.PSF.sky_cal(\n", " 3, # Detector\n", " source, # Source which the PSF will be applied to. This sets the PSF pixel scale.\n", " 0 # Rotate the source by this amount (radians)\n", ")\n", "combo = leakagelib.PSFSourceCombo(\n", " source,\n", " psf,\n", " use_nn=False # Use Moments leakage parameters.\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Leakage depends on energy and therefore the observed spectrum. Let's assume an unabsorbed spectrum with photon index 2, just for demonstration purposes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ">>> Reading (in memory) /opt/homebrew/anaconda3/lib/python3.12/site-packages/ixpeobssim/caldb/ixpe/gpd/cpf/arf/ixpe_d1_obssim20240101_v013.arf...\n" ] } ], "source": [ "spectrum = leakagelib.DataSpectrum.from_power_law_index(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll compute some leakage patterns. The flux won't be right because we didn't tell LeakageLib how bright the source is, but for this demo that doesn't matter." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "pred_i, pred_q, pred_u = combo.compute_leakage(\n", " spectrum, # Use an example power-law spectrum\n", " normalize=True, # Compute the normalized Stokes coefficients, Q/I, U/I\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we display the patterns and residuals." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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+9au64IILOjFcAAAAAAAwTzqS8fBXf/VX+tGPfqRrr71WhUJB4+Pjav7MV3z27Nmjr33ta7rssstmHjpI0uWXX67+/n599rOf7cRQAQAAAADAPOrIg4c777xTg4OD2rJli5773Oeqv79fg4OD+q3f+i1NTU1Jkh588EHV63WdccYZs/5tpVLR6aefrgceeKATQwUAAAAAAPOoI39qsXHjRtXrdb361a/WlVdeqQ996EO666679PGPf1y7du3SX//1X2tkZESSNDw8nPz74eFh3XPPPft9jzVr1sz6702bNs3fB8ABycJ4qLnLgxAjAAvDrbmrV6+24ZE/rZUz2QXWRWFkLqSvZa2ErUUhdi4wsl71XU1gVLHig6HqwX51+yiOM5v7PmopZ6uFsK5a0LlS6prz+0XhZ003X6LPHIQh2uZ5mFrlYMwuJzOrjtu+Nlyye8D2bQbhku3SUuhklFxmNpIF53V0T9BqwOLBxq+7R+8N+YvWwGgNayH4sGnOz2hNagRpj62E9/WXXXCiF/0GNXcBh8F5YYMk60FYYLPht+HCBZt+X7igxTDMMghwtP0rfX4TZudPRkmNwbk11JXu6SDaVll9Om3LJ/27lXts+1SWXh8n634fTZvP4sJxpXj56SmlL/SYgFJJKkyMpY3BvAjPM3NehmHB5l4hCuu0IZLyAZxxWHf70iU78uBhbGxMExMT+s3f/E39yZ/8iSTpNa95jarVqv70T/9UH/jABzQ5uXdCdnWli1t3d/fM6wAAAAAA4ODRkQcPPT17n2b96q/+6qz2//Sf/pP+9E//VN/+9rfV27u3lNb0dPqUbGpqamYbkUcffXTWf69Zs0aPPfbYgQwbABBwa24rJfUAAK3x6+6Bl2wEgE7oSMbDqlWrJElHHXXUrPYVK1ZIknbu3DnzJxb7/uTip42MjMxsAwAAAAAAHDw68uDhRS96kSRpy5Yts9q3bt0qSVq+fLlOPfVUlUol3X///bP6VKtVbdiwQaeffnonhgoAAAAAAOZRRx48XHrppZKkP//zP5/V/md/9mcqlUp6yUteoqGhIb3sZS/TrbfeqtHR0Zk+n/nMZzQ2NqZLLrmkE0MFAAAAAADzqCMZDy94wQv0G7/xG/r0pz+ter2u8847T3fddZc+97nP6fd+7/dm/ozi2muv1dlnn63zzjtP69at0+bNm/WRj3xEF1xwgS688MJODBUHYD6qV7Rr21TFADorOuNc9QmX7L9X+oKrXCDFge65SY5uZj5p3KVBd0fVFhq+UoWtYBGlktenkrZCkEBfLPpxNE1SdT2oHNEw+6gY/PqhlSohUYq2Uw3+HL275NO8i81032WZv3VxHztM3g/aXRWFVgqmRBU3ovwTmx7e8Gn6LjU/zKUPKxmYyhEujV9SySSsR7siqmzSMMc72kfxOpDiiv5Tcu1Nzg/WtfjfHdhejM4hl54vSTUzF7pNJQFJ6jVVLeIqGnMfXyGooCCzbkdVGKLzJXfteVqNoFVZcK2x60T0+cyZG+3P6Bqbu99VR/kitsJH0Lfi1yo3j1z1CkmaMgtNNL27goWmx8y5rmzulUbC4xRwVa1UCNZtVwEjqvgTHBNfAWvui24LxYj2qyMPHiTpU5/6lI499ljdfPPNWr9+vZ71rGfpox/9qN72trfN9HnhC1+oO++8U1dffbXe/va3a2BgYKb8JgAAAAAAOPh07MFDuVzW+973Pr3vfe/bb79zzjlH3/rWtzo0KgAAAAAA0E4dyXgAAAAAAACHJx48AAAAAACAtunYn1oAC6nVcMp2hVG2M4DTIVQTnfZ0M87lIWXNRtA5fTYeBRyFSVJmG1EYoguzKpd96GGpEFw+8+l0CEEYmWpp3yiAq9Tf7bdhRGFrmTk6xWBNMjlbIR/B5n+zER2m6Jg0TZBkFIo2XU/3XRg8FwzE5Y5FYYhz/feSwoRKt+0sCmwz7WGIZNhuxhG8nzsvi4UgwDBKGDOfL9qdhRZSPKMAvMNZHqxJUZhu5sJDo/BeMxe6gylWKc49vDcKXY0CKu3Ygna7iSCYtlJM28N12wUISyq48NcgKLbZuzTtGxy/LDqXS2k4YR6cnwVzXYn2cU8Q+FlpmGubCUiWJJngxGa3D76czv3nq5sxR9cJF2JbDhaa/orfRwOluQfvuuMX7YvMXecl5eb8c/tNkhStu0Z0vTIfryVR4HTpx4dvrqs333gAAAAAAABtw4MHAAAAAADQNjx4AAAAAAAAbcODBwAAAAAA0DY8eAAAAAAAAG1DVQvA6HT1iXaJPgfVLtAu+2ZcSzMsSO1umvkbpdlHifi+uY3zv4Vk/qw2kbQVqkFFA5O6Lkl9XUNJWyOojDFtor+j3z40g2Rsl5gdpYe71PRmkEo+aSpSSFJPKR1hKXi/zOz7IKBdlTChO22PkvdbCN5XOY8qmwQVXYxmT3qs8+4Bv9li15y3G5077lzLw1T5oCqJaQ72hNx52eqVuFjYW7vl0LiCtyaqaFBrYaJGVVnKpvJJVvP1bLKgOoOriFIr+BT/iZp5v2CeVoJBuzlZDXZFxY05WEezRs1vpJCuVc1g3W4UTEWRYN9n1fQ6IUl5pTdtCz5fl1lHlwSFkqKKRpmr5hFcu12Fjqh6xa4pvwZOmeuVq14h+c9dDD5Hf8W/UNzzRNLW7Fli+1ZLaYWOiqtSIcUVhsw+iiqbuOpFYSEv36yyOU9aKEYUzs9CfV/VjrmtvHzjAQAAAAAAtA0PHgAAAAAAQNvw4AEAAAAAALQNDx4AAAAAAEDb8OABAAAAAAC0DVUtAAAdY5OYg7hkl0oepTBHlVqKiyHfPki1LlTTVPjm7qdsX59XL5W700oHQdEHu+9qwQ6NtuGOSVTRwKWSuyoVkjQWVPPoNknc9WAODHWl266HY/PtroJFlJrvkuKL02N+cLUp3+7GECXFd6UVLJqmTZIma3M/rtk8VHmJtmDP4bBv2hbNw6j6wmEpy6RiWVnTVwfoMtUWWpW731OW02R/SVIwDtdeDMrO2Ooywdii9WfanONh9ZXu9LNkpoqM5KsfSFK+7Udp3yP9bG/2LE3aTCEPSVKhe9C2Z420ykSxGVTcMFVJBkr+R8DRuj8m05V0rYlOQ1eVZCqo4NMTzIGhrnR8O4MKGKPm+lEMr7u+Skg2U53hp/pO7bF9K73p8XOVSiSpVgnmkfnYrmpUpBCUtShGFZRMNZa85Ksf1cz57qpUSVJxpjLN3MbONx4AAAAAAEDb8OABAAAAAAC0DQ8eAAAAAABA2/DgAQAAAAAAtA3hklgYQZjcohYEuVitfr5Wtg0cxJpmrhcWQwBkwI1XikOZ8mIlbYzWAxMOlnV1+76j221zybzf0iWrbd/MhG3Vg8CoLBizC8SKwhdrzbQ9CqiKuMC16P1ckOR4ELJYDn7t4gIjo7wvOzWaPtirOLnTtjfLvel2u31gpB+ET6QrB4N22XrzEQwZcbu5GM0tk6AahUiWgxdKhWwRrybtkxdKyoK550LlQsGxcetaI/ORt1EQblZPxxcFYpaL6cypBWtHFCDr1po9Vf9+jTwd9XDZr8X51Lhtb06MJm3FSR9OWDDrQbFnmd9uJV0jJKk4nZ6NWdWPza0TuQkmlqRGcM17ciLdd9G5NtSV7k9zZZQUXxMmTYpwNTjWvWZBX9Hnf8Qtbt9k2wvT6b6L1rusnh6TWhTWOe3nnLuOueuP5NfzLFj7XUimJGUm+Djv6rd9Ve5LmqLzbN9ZnWtu8ZJ84wEAAAAAALQNDx4AAAAAAEDb8OABAAAAAAC0DQ8eAAAAAABA2/DgAQAAAAAAtA1VLYC5amcljla2TQUMLGKFH8/lKBnbiSpHtGI+zk5XCCAqwlAMqlq4hO4o1frIZcclbZWpNBldkprjPh09c5UxghT7SiHNFY9++5AHx8RVu3CVJySpbtqnGr5zVKlit9l3S7uj3PxUVJFiKjiwDZMU7hLTJaksc1wbQWWBQF7pSTfRPWj7Zi2cJ0E4uk0mj46f20W1INk8rlSRtkeVKtyxKgUHsBTkze+tnDDXfPVDR9ash9UrovT7vGB+BDCVb/Z2TrdRCPra7UrKzXkUja3STD9LJbpPCiZ7I0/bu021DEnaNp6+X7M3TfaXpFXPepEfRt+jaeO4r2ZT2rk5acuqk7Zvs+8I256b/V8Mrh+2ClN0rINzy537UfUcJ6qgtDO4Pjp9wVq8ojedc+Wn/t32ddUrJKkxsCJpawZrsdt3XZnfb8XgelVo4b7f3SM1g6t3MahUkZXTa01e8GNrmmM1Xyvqgn3j4dprr1WWZTr11FOT1+69916dc8456u3t1cqVK3XVVVdpbCwtAwIAAAAAABa3BfnGw+bNm3Xdddepry99mrhhwwa99KUv1UknnaQbbrhBmzdv1oc//GFt3LhRd9xxxwKMFgAAAAAAPFML8uDhHe94h37u535OjUZDTz311KzXrrnmGi1dulR33XWXBgf3fsXluOOO05vf/GZ99atf1QUXXLAQQwYAAAAAAM9Ax//U4u6779Ztt92mG2+8MXltz549+trXvqbLLrts5qGDJF1++eXq7+/XZz/72Q6OFAAAAAAAHKiOfuOh0WjoLW95i970pjfpec97XvL6gw8+qHq9rjPOOGNWe6VS0emnn64HHnigU0PFonOoFGAJUryAQ8S+vKRiEEXkQpKiyLyGCbOKwh7jSD8TytRClmUYshhsw43ZBTJK0h6TBTd01Frbtzj6I/+GJigrm/aZSIXuZUlbtD/Ha0GIYCFtd4GFkg8j2zHhA/CioK2h7vQ2JQondKNwYZ9SHFroQhLdMZWkmgmqqwTBXs0g+K/ZPZS0TQRpj1HIpRMFmrpjEn0+JzpOhWBobsjhvjftWdN/jihAVXnz8MuWzHNl9WkbANnypoLAQRsC2fSrbjMK3m2asMfcb6MwZcJ0g7kQfe6lQ6uStijE1mVOjoylwb2SVG+mIb2S9Kylq9PtTvpQ4Pq2TUlb6Qg/p20wpH4cKPqzTNiw5AMq6+E1zLc70bm8y6w/k8E1pRoMpL8rPShDXT4MsdxMP3de7rV9m8Ecb/YsSdomM3+s3ecu1304aCU4T/Jiuu28WPZjMz8DRdfd6eCa18zTMReiQExzWKMc0Vbup6QOP3j41Kc+pf/4j//QnXfeaV8fGRmRJA0PDyevDQ8P65577gm3vWbNmln/vWlTelIDAOaHW3NXr05vvAAA88Ouu0cfvUCjAYDWdOzXyNu3b9d73/tevec979Hy5cttn8nJvU+LurrSp3vd3d0zrwMAAAAAgINDx77x8O53v1vLli3TW97ylrBPT8/eGqPT09PJa1NTUzOvO48+Ort+7po1a/TYY489w9ECAPbHrbnRnyUAAA6cXXeb/AkngINDRx48bNy4UTfddJNuvPFGbd26daZ9ampKtVpNjz/+uAYHB2f+xGLfn1z8tJGREa1alf69FgAAAAAAWLw68qcWW7ZsUbPZ1FVXXaXjjz9+5n/33XefHnnkER1//PH6wAc+oFNPPVWlUkn333//rH9frVa1YcMGnX766Z0YLgAAAAAAmCcd+cbDqaeeqvXr1yft7373uzU6OqqPfexjevazn62hoSG97GUv06233qr3vOc9GhgYkCR95jOf0djYmC655JJODBcLau7PwrL5eG4WpNtaLSRG52H1ilbGHGzDpYq3+BX3zER+52FtgUOb2xfz6XDbq/tS6LPo3HJVA4L568KZoyoFrQiC+e3ZGX2OPNqIOeJRMYKuUvpCVg/qc4T702wjSPx3Sdy14GvaUWJ2ySRju0oJkjRpqjPsmPRjm6z5xPqJWpr8PWEqXURcFRVJ6q/MfRvFoGSDq8IQppL3pNUrJKlmZt14sC/c3I9S+ifr/ri6ZHmX6C/5Ch8lU9Uk6rt322YbwamT1dM/s40qGdhE/xmH26q7txpFuE8avj2rp5UAsqg8SQsJ/NHtiPtTvLzgz8O8YioSBBUbsqrPfyvu3pq0DQQVIko9adWHPUFlmF1Tfn8e0ZNWtBnqXWL75tu2JG317U/YvsVy8Gfm7poQ3Kdm1YmkrVH0242uxxVzLkcVcbaNp+t8Lbh2l4M1rN5IP19UQSEvpfOzPpQWKpCkQm0q2EY6N4KiD6pMbE+3O7Xb9m32LvXv1zWQtE0F+2jKlBqJ7oWi889dCyvB4t9jFmm3lks/ub+Z6510Rx48HHnkkbr44ouT9htvvFGSZr127bXX6uyzz9Z5552ndevWafPmzfrIRz6iCy64QBdeeGEnhgsAAAAAAOZJx6pazNULX/hC3Xnnnerp6dHb3/523XTTTbryyit12223LfTQAAAAAABAizpW1cK56667bPs555yjb33rW50dDAAAAAAAmHeL7hsPAAAAAADg0MGDBwAAAAAA0DYL+qcWWPzanfg/V7aCRSsVKYJnbK1UxsijeFu73bn33bvxtH+cy93itudosRxrHMzynySrB+dn0bQVgmRzmbTraJZGVRhc8HOU+mwC/5UFydHdTZPAL6nXJL3XorIBRrPS518I0soLJsk+q/vU7nIzTYWvFP2+jxKzXbWEaH+OVdOx7ZzyVS2ixPPB7rR9Iqj64KZAV7Dvo6Ik3WbOuTZJciHfzczNcKkWJMjvmkw/y1jVH2tXwSKqahEdvylz/Jb2+DH3mKorLu187ziC870xX5Uq5igraO756oeITFKhKAWVTGy1EElZUCXCaZoEfgVVLaJzKzMvNIPrRGa2Hc2PvCtYM912TSUPSeoy93dLg+o5UdWHHlO+KKuO2775xJ5oiKnJoG/BnLfBuVUopxUbCr1pJY/9cVUNRqf9nBsZTefctKnMIElH9qYVKSI95Wj9SY9VHlwzm/LvVzGHtRxUcshaqHKn4P7GVbCYrM993a5G9zwtDC2qwFc21W2i6iOuWtb+8I0HAAAAAADQNjx4AAAAAAAAbcODBwAAAAAA0DY8eAAAAAAAAG1DuORhaDGHCIZhjzaAaO6BkVnmp3oWBIG5bUQhLHnuwnyivkF7ZgLigsA22xwFX0apb3hG8v1EfkJ7J+dMuFUQIOfOgcwHDlZMwFix5APNqkGikgvZC3L3WuOCveTDBRvB2Fw2VDMIaqp0D9r2rJYGSRYmdtq+rr2v/yjbtx6FE5rDWg/WGRfCFoVIRmpm300EfbtNGOJAlz9O/SYITpL6K2l7FC6Zm7C8KOTUhXJKPkhy1IRySj7wK8otjfazOyaVIPzMBUn2yJ+r2bQPMJQJ82slmC2PQqRLPiAu7H9Iy5QXSsqKQUhfMQjtdMchODYuoDKbDPoGx6a7bAJWm/547WnOfe3vb/oVIaum7XkQpus+30r/dmFwsjsH8iAsOOtO25tju2zfehCImfWk24j2fWEgDZJsdS12y3y0Vk2aAOBasDZGYcFDXemxKgbJpfk83Ou68MxCw693jb50fxYqvbbvZMnPgR+Np9uOgiHdbUG0L4r+kme3UQnuN1xgZPR+WRBoGjkcV2gAAAAAANAhPHgAAAAAAABtw4MHAAAAAADQNjx4AAAAAAAAbcODBwAAAAAA0DZUteigdlaTcGn7i6Z6hU1CbfWZV9q/kAXpvVkaRVwIqloUC11zHkFU1aLZTJNpo76NZpD8bQJ5XaULyVe7CPN8o2oXBxuqcxw0njax3rwe/Rt31IPAZnUFiefFwtwrK0zW0/aoGkExqIhTr6cJz66yhiRlZm3sCa7KjWA5L3T1p9udHvXvVx1P2rqqvm9/Jd2uJGW19LNMB1HcrgpDq5rm3HdtktRlSjz0V/xxGgqqXXSb+RKldjcL6ZybrM29eoUk7Z5Orx87JqOqFukkKAcVN1yFD0la0p2O2VXykKSePE3TL0ztsX0VJZu3UMHCVQuIilTE17yCFsutTyflhaLykr+fiSp9ZG5/m+oOe9vT6jnZlF878nIwjtJk0tbsGbJ9J+tzT9Xvj4qhBZ/F903ndSFYR/NSt21vdg/MqU2SCgNLkrbG9ids3/qor1JUHEorK2S9vvqRrQsXnCeVYE3ZPZ2e4z/cnc4LSdoxla5rveXo6u11mTUsGJqtEBV9jpYEVS1kKrSMFnxVi617fFWS/9iV7rto3XYVPpZ0+5uFqAqT25/RPnLt0XUwa+67XuWay+LLNx4AAAAAAEDb8OABAAAAAAC0DQ8eAAAAAABA2/DgAQAAAAAAtA3hkm3S6WDHjgdJBiE/B75d/yzMhUNGwZCuvVRIg2AkqasQBKiZZ3JN+ZCZRp4GgdWaE3Pe7t5tpOEzzSCTy4VOusBJaT8BXIua+eDRfGtT6KQLa0WLWgmVC7jQyTwIeyoEwUeVQhpoVQyCKKtmG1UTpihJo0FYYM2kXNVd8pWk3rILzfVzvRgkgT1tmOdPb3s6DZdUwd8G9Az48N5iV7q+7p7yn89kPYbhYBG3P6LAyGU96XFd1u379gdrtOrp+ppX+mzXqgnVdAGlkrR7ys/P3VPp+03UfF8XMHZkrz9OUWCk2x+91V22b2EiDbXLakFgX3DttkGDwZwLZlFL79e2e5NFLs+lPNivYXhvxYThBYGwdhvBdn8SNvcz7dWxtM2EVkrSyr40ODH6fM2Sv79rDqXt0b4ojG9P+0ZzPbg5s2GdDR8sqEoaUJn1+HWmGQQ4Nkd3JW3hb5Pr6XXThQ1K0lTDrz87TejttnG/jxrmmufWL0ka7vf38l2l9FyOrnbu8lhuzD1cVJLyLB1H050jkp6cSPfFv+1Iw1MlacsePw53LRwyc1aSus29QhgKbPab5EMnw/np7nueNjhccwr25RsPAAAAAACgbXjwAAAAAAAA2oYHDwAAAAAAoG148AAAAAAAANqGBw8AAAAAAKBtqGqBveYlCXruz7EyU6VCkrJgSmZZmlYeVbXoKg4mbb2FpbbvQL4sGEf6WWqZT3+tZWli7WS22/ad1C7brkaa9pxnPlnYRX/nWZCyHGYAt2AeqhPYzc7H2OYBFSzmU/6T+RLNmyiN3m4u3UYrVRwkSc30/YpFv42eUlohIBptVL3AFVoZnfbnslt2y0H1imLBv1+hhf2ZT6eVHKJ/HSXIV0zafKXoq4Q4jRZPt6bZob1lX+lgqCv9NH25T4R3FRskqdmVJss3Cv7zTVXT4zpZ93Nr97SvxuKqoETp76sH0yT8FX2+72DRz7nC2I+StuLurbZvc9JUQSn7KhpZl09/zytpSns0t2Qq0OTmnNzfNva2H36VLbJMyhq+mkQkL5l7qOBcbhbNcaj5c6s46c8tje9KxzDq+xYqm5K2bGiF7Vtf/mzbvqeRzqdCFlS56U7vG7Oar1IQVvMwFQKyqt+GWwYL3f4cqu940rZP70nvM7vMORSOLagENVHzn2/beLqNSbMGStJSU2HoaLN+SdJR/X7OVUwVhuh6VTEllLKqPx/yYI5PmGv6k5N+3X5oW7o2/tt2s15KqgTVQ048Ir3WDAfVpFwFi6XFoHpMNG9dJa6ofJ4TVLz5SZWiuV3c+cYDAAAAAABom448ePjHf/xH/df/+l91yimnqK+vT8cee6wuvfRSPfLII0nfhx9+WBdeeKH6+/u1bNky/fqv/7qefNI/7QMAAAAAAItbR/7U4o/+6I/0rW99S5dccome//zn64knntAnPvEJvfCFL9R3vvMdnXrqqZKkzZs369xzz9XQ0JCuu+46jY2N6cMf/rAefPBBffe731Wl4r+CAgAAAAAAFqeOPHj43d/9Xf3VX/3VrAcHr3/96/W85z1Pf/iHf6hbb71VknTddddpfHxc3/ve93TsscdKks4880z94i/+om655RatW7euE8MFAAAAAADzpCN/anH22Wcn31Y44YQTdMopp+jhhx+eafubv/kbvfKVr5x56CBJL3vZy3TiiSfqs5/9bCeGCgAAAAAA5tGCVbXI81w/+tGPdMopp0iStmzZom3btumMM85I+p555pn60pe+1OkhLk7zUn1iPpgE2ZaeY/m+UbWLYpb+mU2lkCbCSlJfIU1dP6K50vZdrgHbXjZJ8dNB+utYnla12F70n6NZ9Am5zTxtj6o+NHOT/G3+/f7krVQGaGHKxZUqXHWCVrdxYKhe0SFPV9XCiSozzEdFFVcZo+nToLtMNYFKV5ASHlSScXO9FqwdW/ak49geJGAPBJUOlnala+PyoaNt31LdrDNTo7ZvMaj64CoPdPUd5bu2cL2K9pHbRlTVorec7rvChK8wFM2tvJwmy48HKe9jJiV8KqhqUQz2xZG9acL68l7/Z6XLe9PP3Vvd5d9v93bbrt3bkqbaU0/YrllXmkJfXOorC9gKCZKaPUOmr0+3t9uNqiwE1QlcFZTDQZ5LebSOBu12Hwb7teDOrWgs036tKphU/CyoWtLcsyP992b9kqRSMahyMzictE2X/H1jIzMVJZastn3V8OMoTO1J2rJaen8oSZkZcx6sgdU9vlrC9K60GlqxO6gC49b+oALGnqACz8ho+lmqwXrnqlq4tU6ShoJrrCvwFF1Rio10bNGatLvuZ+7WsbRqxz894a+PG7el+76/28/7tUf22/bnH5XOxaVBokBhOh1HYcxfo6P7G1sJKFgbbN9gLQ7XncCCPXj43//7f2vLli36wAc+IEkaGRmRJA0PpwvF8PCwduzYoenpaXV1+Ym0Zs2aWf+9aVNaigcAMD/cmrv66FULNBoAOPTZdXd18AMyACwyC1JO81/+5V/0O7/zO/r5n/95/ef//J8lSZOTe+uOugcL3d3ds/oAAAAAAICDQ8e/8fDEE0/ol3/5lzU0NKTbbrtNxR9/3ainp0eSND2dfl1mampqVh/n0UcfnfXfa9as0WOPPTZfwwYA/BS35ubNxgKNBgAOfXbdPUz/vATAwaejDx52796tl7/85dq1a5fuuecerVr1k6/l7vsTi31/cvHTRkZGtGzZsvDPLAAAAAAAwOLUsQcPU1NTetWrXqVHHnlEd955p04++eRZrx999NFavny57r///uTffve739Xpp5/eoZEuEi2HSB7YX820FgwZbSTahgklCkIkC5kPLykV02+7dBfT0CrJB0keU1hi+x7b50Ntuk3zuM9r0ZNT6ZibQeDOdMH/uVCjmG48C/Zno2lCdPIgUKiFoMYocNJtI8+D32znQaiN20YrIZIt/kaHIMlFrJUgItc3CpyM2oPALqdQNSFewXj7u3xglPvtY3/Fr3dPTaTrwcbtE7bvzok0+EqShkyI16krfGjuyctPTNoGp56yfQvjabibJGW1dMxdwSF1IZADFb/mTjX8NW/IBHb1BAGcPSYAL5oXLvRQknY30/25c9qva9P1ua8zy/v8Gt1fSce8zF2AJHWNP5m0FUfTsEhJamzfatubo7uCEaaKA0uStrx/me2bd/k51+hJt1FvHvj63GjE53WuljKRD3q5pFozV7HFkDd3XY0CYW1gXXTPUAwCI/vM3Old4sc2titpagSBqdmEDwDMKv+evt2KY01PqTGU3jc2ev1cbwT3r2UT/hpdP1xrVtri32/Kr/2uPeqretpebfjz8KkJHy45VU/v+4aCwMijB9MA2UGz1kk+RFLy53ApuG90c+6pSX+f+tiuKdv+r0+lgZGPB9fjHnMde8Eqf035udV+bRz40T+njdE5NW3G0TNo+0ZzLjP3QnkQ7uqCJPOyDwX+SRDl3FbdjmQ8NBoNvf71r9e3v/1tfe5zn9PP//zP236vfe1rdfvtt88Khvz617+uRx55RJdcckknhgoAAAAAAOZRR77x8N/+23/TF7/4Rb3qVa/Sjh07dOutt856/bLLLpMkXXPNNfrc5z6n888/X29961s1Njam66+/Xs973vP0xje+sRNDBQAAAAAA86gjDx42bNggSfq7v/s7/d3f/V3y+r4HD8ccc4z+4R/+Qb/7u7+r//E//ocqlYp++Zd/WR/5yEfIdwAAAAAA4CDUkQcPd91115z7nnLKKfrKV77SvsEAAAAAAICO6UjGAwAAAAAAODx1tJwmOiusVGETT1t7BuW2HVVh8FUt5l69QpLKhd6krU9LbN8VSpNeTxjwKeGnLvGJtf3lNAF4x7RPdN04mrZP7u6zfSfzI2272/3Tmf/zonohrWrRyH0KcVypIk37jfq6KhoN+eTkPKhq4ZN6o+oEc088p3rFIpNlyks/Ph+iKhMFfy5azXSe2nT1/b2fEW5jKp3r0XiLjaDKhEn3b/T6S20zT9eOsaCCwr9v8+/3wH/sTNrue9RXpDjj+KVJ23nH+eT2E1YcYduL42myfBbsi15TZWLlgF/XpoNKQEf2pqnbbruSVGik62Be8deU6S6fQL5zLN1GVL2iUkxTvF0lD0nqD8bcV0jneGHsR7ZvcSytQBJVr2hsf8K2Z6X02lsY8se6eeRxaVuf75uX5n5co6IWZbM/I1nLlb8Ofa1WCymacgLhbjVrcZjAH8yFVhQG03WpGVSvaDzpq0FMP5GeA8XuDbZveeUxSVvXcWv9+w2tsu15d7r2N4PqR25/FkwVGUnqWZGu25KUFdM1pWuZX9ey7vSeNKpqERkeSK9XQ13+2nZkUO3Cmaz5eVQy83M6qJzw1GS6bm/c7qvIPb7L3/fvNtU8jjsi/dlDkl4wnO7nk5f7nxG6fvg9217bnFZdKS5dYfu6ii6lZwdrcWHuP9rP3K/9bHsl/dy5qXQh6Sc/U85xTeYbDwAAAAAAoG148AAAAAAAANqGBw8AAAAAAKBtePAAAAAAAADahnDJg45/VmSDJIOwx8wc9izzUyEKgXRBkoUWthFtt1zwQWD9hTREZXnDh7Ac05du++QgRPLMYx+z7YNDe5K2LSMrbd9mngYN7an5cKXauA8J6srT/mMFH6Q0naWBOQ35cMmovZ6nIXq1pt9HTRdcGWQS5UFgZNSOQ032tMFiTRMOFWUSFeom7LGFEElJykzgYNzXhCQ2g3W05oOrNJWet0f0+vO+d0kaRtYXhBOu6Pf79d93pOftJtMmSVt2pGP+fiVd6/aOY4ltP7o7De+1wXOSBivpNWF5XxoWKUnVIMCxq5ROjh7TJklyxzq4DhaDSbesOx1zlHnYW0zHXJj263Y24edLYcrs/0l/TOo7tyVtzdFd/v1MiKQklVYemzb2+5Cy6pKjbbszEQTENUzgoQs1lHzoZNA1bJcUxM8dujLtDTqNQlAj7hzIgnPZnkdBiF0UWJe1snYvf1bSVNyTzn8pDlKtjafn3OgPfXBr4bGRpK3v8TT8T5K6V6dBlJJUOio9twpLltu+eTFdB7Nl/h6z57Sz/TiqU+n7DfhrTWPgqKQtWtdWD/prjWvvCjZSMcGXU0GYZRSK6sIvnxz31/PHd6XH+slxc/8gqSe4xj5vOL22PW+FD4o/WruTtuK/+RDJqgmRlKTysSemjb1LfF8TtlobCtbnKOi/xXunuWolzFLiGw8AAAAAAKCNePAAAAAAAADahgcPAAAAAACgbXjwAAAAAAAA2oYHDwAAAAAAoG2oanFIC9LYTfWJYsGn2JaCKhOVYn/SVs6Cvllv0tat9N9LUl/Tty817at7/JjXDtaTttOO2mr7Hv/Cf7btlaN2JW09D47bvtP1ND18vO7TiYuZT3RfMp1uY3fNp+lOmCoTE1mabixJ4wU/5tFse9JWk0/Cd/I8SL6mesVh7+mC1XNTEiUL/k1mkr/DxOYoWblp5mTBb6NQHfPbcOo+XdtVu8im/XnY15We48/qGbJ9j1nt2087Kt3GWNWfn1WTHl4OygMs7/X7MzNVO7JGuuZKUm9fek3oK0dVJvwkcOOLqiJk0yblPdj3FfM5JKlikr+zqq9IkVXTbTcn/fvlJoFekhpuHgVzK29E665R8tcaFdJE92Z3Wl1F8mnzUcWIIJg+PFaOq4CRB/88C6qSREn9h7pCo6ZSVGUi+jfmOp41/bnsSg9FFYyaRV9Rxc29LKgCo6aZv/1psr8klYePs+19wbno1KfSikaT29PKBZKvliFJlZEtadsRvmJM+Zi0okE+/Bzbt7HCVD+Q1AiqhzjFZrqm9MmvJ8cO+rWjZs5PV3lC8lVuxoPKN+PB9eqpifSYPDXh18aauc4/5wh/D/3cI9OfSSTpWf3p/Cxv22j7NkbSShXTW32VvOKQnwP5UFppJC/7n6NcVZLRWlheznKXXld9RPLzJQ/uvVylsv3hGw8AAAAAAKBtePAAAAAAAADahgcPAAAAAACgbXjwAAAAAAAA2oYHDwAAAAAAoG2oarEYBOnMrW0jfYaUhVUt0uTWqKqFq14hSX2FNKV1KD/S9l3SSBOzlwVpvEf2+DEfabof05sm3krSycueStqOPW6T7dt94k7b3jz+WUnb0sq/2b4nTKb7rlr3p1Z30acyb5lMU6C3mTZJeqpqUsmDUgLj8qnODVMZo55P+77NdD/neZB8bRLh2ykL0nRd5QR0xjPZ89G/cWnJWZCYngWJ7mFKu3u/SrreRf++UAvOl91pxZg8qFJQ6E5Tt4tl//lKFb9mHmGS5ZdFFT6K6doRGg/O5WaaQJ5XfBJ32VTo6Cr6sdXMdiWpaK6PUSWOzByTqKKIq0gh+eoTzXGfvG8rWNT9dSmSdZl9F1RdyUrp3MiCY1roX+LfcCC9dudBJZVKw8xxU5lAkooVP29dpYogCN/2jUTFMgrzcT910MmlZkPlrLXrb1Y3xze4hufmni2PqgO57UqqFdJqCZUgKT+rpVW23BgkKTvqONvevWR50lY+7gnbtzm6K22b8Od9Pr7Httux9Q769r60vRlUNJgMqqHtGE+vK6XgxFjRY6rZ7fH7ohCc4+pfkTTtno4qUqTXzZ1T/jo4VZ/7vD12yM+BowfT6+DqPj+3Kk88bNunv3tf0rbr8cdt34apgtI77KtXdL3oZX4b/en8HCv4ihujE+l+7ikF18FgDXTL62Sw75t5OgeiNbf443UnV1z1aNZ25tAHAAAAAADgGeHBAwAAAAAAaBsePAAAAAAAgLbhwQMAAAAAAGgbwiXnKAqxOxi50MlC5qdCKfOhk91KQ9gGmz6I8igTmHN0r9+fR/f6ILdjetOgodWDu3zflWlgzuCxI7Zvfswq2z619rykrRQEGB2lu5K2es0HbVX/3e/nicaSpG3HtO9bbaZhMGOFdP9I0qjSoDtJmmzsStpqDR+21jShk7k6GyLZKne+EjjZGeWnmRuthD1aeXAco1A0F7xbTwMEJR9o5gILJSmvTvpxmPMzn/bv1wwCFee6XUnKG+k2osBBuXBC07a33QeaZV1puFd0dcym0nC2wV4fwBUFV3UV0xdK1TG/DdPeSoikJOW1NAAt2kfFofSzZD1pYKgkqcu352V/jXWyRnru2IBASfm0vya4+VwY/VEwNhN0FgSXFYKQwKKZR3nRzy0FwbFWcD78+B00t5izQ0QuKW8qi/ZJsOZmrQShRoG1ruuUD1+suLkQBEY2ho6e83abJmBXkhpLj00bV55k+2aTu5O2crB2ZNHa765Bwdphz4HgGtaT+evEkq50nY+CW1sJ/s6DAOCmufZGl+Npc10anfbzsBgs/kcPpHPj2CG/dizd81jSVrvvLtv3yYf+xbaPj6T3y82aH/PgccNJW/fJZ9q+YahzIz3/Jps+YNSFhlbMtbFV1WDCTJrA+ujtesqtfYeBbzwAAAAAAIC24cEDAAAAAABom0X54GF6elpXX321Vq1apZ6eHp111ln62te+ttDDAgAAAAAALVqUDx6uuOIK3XDDDfq1X/s1fexjH1OxWNQrXvEKffOb31zooQEAAAAAgBYsunDJ7373u/o//+f/6Prrr9c73vEOSdLll1+uU089Ve985zt17733LvAIAQAAAADAXC26Bw+33XabisWi1q1bN9PW3d2tK6+8Utdcc402bdqkY445pq1jaFsFiyAN2vNfRnEVKaL+WZAyHbUfqEKw31xgbSlKMC/4hNW+cpo0vrTfJ5sPLt+RtJWP9mnIteHTbHvvUWlVi8aRQXr4kz9M2pY9utX2PfJHK2x7156hpK0ahBDvaabj2FncZvuON3xVi2oj3R+Npk9qbuYm1de1aX/VLlqoguHOkyg6uQXReU21i/mUK2v8+FwNUrQzk+QcJm67tSrabpDQHqX+275u25NBkvroLtvuqiWEVSZcCn2htfU5K7dSCSBNGs+rLVTWkOz4ChW/7wvTaVWLrrJPsS92+apIxUZ6/Fy1DMmnzYfVK0zquuT3Z9a9xPZtdg8kbY0+X7Wj2pWu8ZI0bVLFo7TySjPdz4WJnbZvoZSm9EtS0yXWRxULzPlQmPT7Pkpud1UL8qB6Re7mRqv3K1nhsCtqIf147WqhcoEk5a5yTQv7u9UKRdl0WiUiN1XPJGk8T+dIz4C/f4qS+V17ven7ZuWlSVvBtEmSgsI1rvJAdymo9uIqKFWDSjSm4oYk9Zk1sxpU+Ki5E2JwpX+/4LiWzfqzvNe/n1vDlvUE1YGCn41W9KXrUt+ux23f5n/8c9KWT/t72u4j/Fpc6k3Xn66l6RovSZXj1iZthaEjbd/oHqJg1t0jlgbXiTydR+WoFFQLsiy6/537WtLqKBbdn1o88MADOvHEEzU4ODir/cwz95Yp2bBhwwKMCgAAAAAAPBOL7hsPIyMjGh5O66Pua9u61f8mec2aNbP+e9OmTfM/OACAJL/mrl6d1l4HAMwPu+4ezboL4OCw6L7xMDk5qa6u9Ks73d3dM68DAAAAAICDw6L7xkNPT4+mp9O/6Zyampp53Xn00Udn/feaNWv02GOPzf8AAQB2zc1b/BtjAMDc2XXXZcUAwCK06B48DA8Pa8uWLUn7yMiIJGnVqlUtba9tQZEHMffDQfQDQyNPQx0lqZalD4cmMh/iNVpPA4x2VP3U6y/79iXTaejL+JQPKatPp9+Yyaf9PChtT+eaJI3v+F7SVhzzfbs3b07apvestn0nqj6IZ7yefvlotOZDX/YU0lDNau5DiRomiFLygZHxD41mvrQSFtmqeQiSbIVbIwicfIby/CfhkUFAlQ2BNKGHUhD2GIVLmmBByYdZ5lGYngs/m0zb9ievmc9X8MF7Nsiw4te1zAXBSS2HUSZa/KElK5txRPvTBNXlLYYFumMVhhNW0l9M2DkkSVHopDt+Qd/MBeMF7+eC2SSp7C5N9SDw08yjvNJruzaDddQFxzWDgD+37/KS3/dZ3d8rqGDWgWgONEzfaH5H87ZYkli/Z4v2dyvnp2sPQgHzXh/K6M7lenCoGuaF7RP+mjIZbKRhzoFaEC65azLddi2YY+VgTvaW0/NzqNuv/Uu703W+ry84D4OA5GgddFzQ5lSw7/srQQjkhAlur/twWxezeGRwXmY1v74WRtJQzSjUubQ8/XOjwnHPs33LRX8ttetddD6YtbjZ8GtguA1z/KKw4O4eEzoZLIGtXGPLwb1JwwRXRqtqscWQy0X3pxann366HnnkEe3ZMzsF9L777pt5HQAAAAAAHBwW3YOH173udWo0Grrppptm2qanp3XzzTfrrLPOanspTQAAAAAAMH8W3Z9anHXWWbrkkkv0e7/3e9q2bZue85zn6C/+4i/0+OOP68///M8XengAAAAAAKAFi+7BgyT95V/+pd7znvfoM5/5jHbu3KnnP//5uv3223Xuuecu9NAAAAAAAEALFuWDh+7ubl1//fW6/vrrF3ooAAAAAADgACzKBw+HhCDt15uPqI25V6rIs7Q9qn5Qy3xS/Hj2VLrdgk/irudp++TEgO07UfdVH6YafUlbM19p+7ZiyaQvudq3+X+l7/eUT6zd+YPjk7bHHllj+/77rmW2fetkmiy7x6X/S5oupAnA9eD4tVJ9IuzbUonEQ6OsV1QNh2oXc/Dj1HxbvUI+odtVngg1ggoY9aBKwVRalSKqHJEH1TVakfWka1UheL/CwJJ0DJX030tSXvZrY14y2w6uP5lJ3Y72W1Q1IDfVNZpuDJKaJol7PPN9J0yqvOQTswd6j7B9K+V029nUqO1bmNrj26fTqkHNqLLJrh8lTcVJv90sSMLPXTWIaB72pdUConnRkm5/PZZLmw+rHgT3POb6EVYace2N1q5Lh+UKnWXKS12tVaSQlJtjFhWVchUiggIRIVepYtpUW5CkPdX0HBib9sc8qj6xczI9t1ybJE3U0vcLhqZiMNXLxXQ/D3b5H7OGB9Lz9sheX01iabevwtBnZnslqJ7TyNJtu0oX+2X2c1YLqkmNpj8j1H74iN/szm223V0fy8edZPs2htKKh+76I0l5KVgzzXlSqAZrv7lnabZQzUXyFYYKk2klD0nKRtN9FL1fFlSqsJ/P91RvyVW82f/PtXP9qXfRhUsCAAAAAIBDBw8eAAAAAABA2/DgAQAAAAAAtA0PHgAAAAAAQNvw4AEAAAAAALQNVS3axUUDh4mgLpG3xWdCJuHZVa+QpKargBC8Xd7wCd2NPN1GteDTbScL6TZ2Fwdt3531I337zjSddme11/bdVT0maZuo+hTbEyZ9e8/GNMl2dIdPyP3hE8NJ279sX277/vNun+j+w/E0UXmHJmzfqmlv5j6pOTcVRfbX3lFRfHaHUalinv04wTkvBGn0QfWbOct89YOoMoarYBFVjpAJDy909/u+pjqHJMkkZje7/PvV+9LqDPUuvzaOmZR3SRqvmSpFwZQuV9JrUH+/X/x7y769YCpjNIs+jX33dDrmXUH1iqm6ny8VkxRfC+L0+8vpNaGvP0gwD5K/s2J6W5SVe/w2ptPrRB5UwGiY6iph/2Bs7oYt6/cVPqL7jdx8vrzoU/NdxZTMVDWR4io2drtByrtMVZJ4I8H6khU093z1Q0ezWFY9OC9qppqE5K99jWgbruBIcA2PxtGKSfOGT034ObZt3Le7ShVTdb+OdpfSc64ZfL7xYC12tgdj/rft6Xl/9KCf/6et9NeEZw2l627FrM+SVKmk62AjOIey6N7MrB1hxZTx9L6//uQW23dy207b3rMi/SyFIb/eFXuXpGOIKv4E66tbl6IKGO5+w1XskqQs88fEVQ0Kq4SY98ubvhpRWN3G/KyZBRWUWqk09pN9lGsuay/feAAAAAAAAG3DgwcAAAAAANA2PHgAAAAAAABtw4MHAAAAAADQNoRLdlIU2GJDoHzoSyuRPVnuQ7xc8EijGbxf5qdIwwRUNgo+WKWWjSVtUyZwUpImij5kZk8hDZ0cG1tl+041XBjcCts3z30QSl9XGvry1JgP+HlsNG3fOOoDuP59zAe5jDR3J207Ck/ZvhN5uo/qJuxTkprBHPDzK5pzc+8bWgRBkoRIdkI2E9AULB0+CDcIe4qCq5yGCcKTpKyZngN5EBaYu3C7IIArDuRLw6gmGn6dceGLO0bTtUeKg9XGqunnmw6CGrtK6f4cHvDhWSv6/Ocb6jLHquHfb3Q6bR81n1mSRs3n2Pt+aYBaueD3ZzFLz/Fyl5+IlW6/nttwyUYQaGpCQ7MuHyJZnPTXvKaZ+3kQ+CUT7BgFqEUBqs2u9PpYLfnzwQUNdnf7IFF3nu19Q/NZgvO9GQS5+ffbT8BfGOR9aMolVRt5GAwZhT26ENo4XDJtD5YZ7QnOcRf22G3WJMmHzUahsm67keW9fl3rD9YJJwrCdevu7mkfLPjErnSdH5vy55BbtyWpVEzP5aN6fei6+9T9UdBzLQhDbOF6rFK6ThQHltiuXcHa7zR3b7ftWeWH6RCCNanRu9Rvuz/9OWM89+tdv7n+F0Z/5MdWD8LfzfUjvDcJ7jd85+DEdDUPonXbhQUXoioELYxNfOMBAAAAAAC0EQ8eAAAAAABA2/DgAQAAAAAAtA0PHgAAAAAAQNvw4AEAAAAAALTNYV3VIkq5z9ThRORWEv+zuVcT8NUIJOVzf97UDKsXpNvIo+RvE2/faPqE9npzwrZXi5PpNoo+KbY4eXzS1l30Sb9FUy1DkgZK6WfZVfXptj+cSNsfH/f7bUtzl23fXngiaZto+goftabZF6bKiBRXtcij1NsD1eHqFVSqWGQyzSRft5TCHLDbiCpgRHPaJHGPNf02JmvpNqpBknq1GiTFm7T9qCLF7un0/NwdJJtHmuacawTnoUtHLwdJ1T1BknpfOW2P0uab5vyMEuhrLmJfUs1UXGoGvzMpmuZycA2L0rzzroGkrVH0a3+hkV6DsulR3zeoPlGspJVU8iAFPaukieeuSpUk5aYChiTVTAWLaN+7aghdJX/uRJVp3D7aV/nmZ0UVFZxitA4chvJ8b1WLPDjvzbImyR/faO1wh8ad35JULvp76HIzbXdroBRUVAnWpOOW+EoATm957vPGra2StKJv7vfQ0Xq+zFSHeTK4Tmx8ylfKGauma+nJy11VN+nZS9P1Z2DK32PKnLOSJLcOBtfdrJju58LgEbZvxVTACAXrWnM8XXfz6qO2b6F/iW93jX3Dtq9bd/OgslYzqKLh7k2i+6as6n82sn0bfh7ZYxVUNMxc9bF5+q4C33gAAAAAAABtw4MHAAAAAADQNjx4AAAAAAAAbcODBwAAAAAA0DaHfLjkMwmfa1dg3byEVs5HeF8rAZVBOGHmwiWjkCsXUhKFHoZhlqnxbLtt/1ExDdfpHV9l+xYzH+SypJKG3Uw1/PHbOpEekx/VfRjQ9mIaIilJY41tSZsLkZR8kGQcIulDgux+blfg5DwgRPJgkSkPgvj2ca9HwXRVE3rn2vYKQu/MmvnUpD9fdk2l50sUDlbI/HoQBZI5LkCt14Q37u/9yiZRcaDi98WRvel6t6LP3wYMNcdse7YnXduKfcts36K5JkRBlNMNv/644MOgqyqFdB8VpvfYvlndB3A1y2lQ3USQztdopp+vt9sHiVWC0DAXSFaY9vvehb4VpnyYpQsukyR1DSVNUWjfuPnc0fSO5n1mgiTzYC7Px/1N1uGA48Uiz+O7J3NaSJLsYQh2n9+G33BX8JNFbzld+5f2+M51k0FbCUIro3Z3rZis+700Vk3XeZMTLCk+X3rM2n1kr78erhpMwx4f2+nv+b63aZdt/85jO5K2qeDzHdWfBjsOBCGS4ZpiAiOzWhBs3kh3XnHpcts3O3KlbW+M7kq3a9okKa9OuUH4vhXTV1JWT9v7g8B7dx9TX3K07TtW9cfEBTWXpnbZviqa8yS4hoVckHQQLm2DJINQ4JmfHXJFS8LszTx9FwAAAAAAgGeGBw8AAAAAAKBtePAAAAAAAADapiMPHr7+9a/rN37jN3TiiSeqt7dXa9as0Zve9CaNjIzY/vfee6/OOecc9fb2auXKlbrqqqs0Nhb8zSMAAAAAAFi0OhIuefXVV2vHjh265JJLdMIJJ+jRRx/VJz7xCd1+++3asGGDVq78SbDIhg0b9NKXvlQnnXSSbrjhBm3evFkf/vCHtXHjRt1xxx2dGC4AAAAAAJgnHXnwcMMNN+icc85R4afSMy+88EKdd955+sQnPqEPfvCDM+3XXHONli5dqrvuukuDg4OSpOOOO05vfvOb9dWvflUXXHBBJ4bcFq0k889LBYxwIGYcUcp0kJNsP0lQFaGVChjNIJbZ5cpXG/5bMHuytELE1ixNDpckjfk09qGyOzX88XuqlibL7iimacOSNNHcadurzTQpvt7wCcdNW6kiOE5hpYpWKli4ChgHnhxOpYpD074KFa5ig+TPokaQxD3VUlULz6000fQtm4Tn3rKvELG0x6eVd5XStbQYrK/T9XQgEzWfxN0IBu0S1l26uiQdaRLkB6b9WlUcTdfRSF5JK0FIUrmQVhiK5kUtKFXh0uYHuvwxccn7hcndtm9W88nmeaXXbMS0yc/FZrC29pf7bHvFrNHN4PpoP0uUQN/l36/cTK9XtYKvuOGKBVSD45QFc7wYlVSYo6iaC1LlFve1O76NAzxekhSc4nYN6wvW11aUgjG7agLROtpfSdfGaLvRfnZrf/S18qqpkhbN9egc2jGWVpTYtMNXYRirptV28lJaWUOSsqqvvuYqR7jqFZFsaIVtb3YP2PZiaWs6hnFfpUimMlZhYInf7lBa4UPyd8WFCX/PPjm0OmmbCqpXRFzVoKGGr6KlcvozTFSZKfr5ylY6CipVuG1k0c94zX1jntu9WUf+1OLcc8+d9dBhX9uyZcv08MMPz7Tt2bNHX/va13TZZZfNPHSQpMsvv1z9/f367Gc/24nhAgAAAACAebJg4ZJjY2MaGxvTkUceOdP24IMPql6v64wzzpjVt1Kp6PTTT9cDDzzQ6WECAAAAAIAD0JE/tXBuvPFGVatVvf71r59p2xc2OTw8nPQfHh7WPffcE25vzZo1s/5706ZN8zRSAMDPcmvu6tXp1w8BAPPDrburjmbdBXBwaPnBQ7PZVLXq/67kZ3V1ddm/+7v77rv1+7//+7r00kv1C7/wCzPtk5OTM//uZ3V3d8+8DgAAAAAADg4tP3i4++67df7558+p78MPP6y1a9fOavuXf/kX/cqv/IpOPfVU/dmf/dms13p69gZUTU+ngSlTU1MzrzuPPvrorP9es2aNHnvssTmNEwDQGrfm5vMQOgoA8Ny6GwW2AsBi0/KDh7Vr1+rmm2+eU9+f/ZOJTZs26YILLtDQ0JC+9KUvaWBgwPbf9ycXP21kZESrVq1qdbgHrflI/G+pMkb0A0NL1S58ZEhu+mbB2+VZVJ0hreRQb/pvwEw2diVt20s+OTlKIB+tpim7Zflt7MnSFOFJ+eTdWjDmRjN92NbM0zbJV6pw+/jHW/HNLW3jwFHB4vCQS5r+cYWKVu6Fo0IVLkA/CiaKkr9dcntv2ffNsvQcX2oqQUhSX1A5or8y9+ikSZNqPVYLxhZsw1W16HIfWlKf0m8rFqaCqg9BgnVe9knojkuFj6qEuOoVkq/yEVX+cLKqX3Pz3b5qR8Gkh/csTatzSFKthUkezfG8aCpKVIJ93zDHr+n3W5RsntXS/VEt+QotZTOPoqoyJtC/ZS7VP7oFyaJ7lrCS06Ery/ZfPSR6xf2bA68xETPLXbhWlex9Y3BeBHN9wmy6x6yXkjRQSTtXgrFFD9hdFSbXJknj1XQN2zPlKpbFKuazVIMKUXum03Ui7/fVJPKgvFxj55NJW1bxFeMKfem2o2tH3jNk2111n1JQycGNLa8H+9NUwJD8/MqD9dXNjHqLDwBd92hfWOZ6IMlXr1BwrQkrYKSfMM+Da8rMPprbRaDlBw8rV67UFVdc0eo/0/bt23XBBRdoenpaX//6122Ow6mnnqpSqaT7779fl1566Ux7tVrVhg0bZrUBAAAAAIDFryNVLcbHx/WKV7xCW7Zs0Ze+9CWdcMIJtt/Q0JBe9rKX6dZbb9Xo6E9qU3/mM5/R2NiYLrnkkk4MFwAAAAAAzJOOVLX4tV/7NX33u9/Vb/zGb+jhhx/Www8/PPNaf3+/Lr744pn/vvbaa3X22WfrvPPO07p167R582Z95CMf0QUXXKALL7ywE8MFAAAAAADzpCMPHjZs2CBJ+vSnP61Pf/rTs1571rOeNevBwwtf+ELdeeeduvrqq/X2t79dAwMDuvLKK/WhD32oE0MFAAAAAADzqCMPHh5//PGW+p9zzjn61re+1Z7BHEaiQL/2hU5GoU6t/EWP30YzNwEvJpBRkmoaS9rGo3cr+pCyqeLSpK0sH4xTUzqO6XzU9JTqzTSIUorCJX2ojd1HizxQy805AicPTfsCk+bj6Lq8tGawfrkgQykOCPPS8ygOkfQBVYU8XVOyIKCqWEnXlOhzRNlx3ebzZXW/NmbT6dqogr8NaPSma6Ak5ZW0ulRe6bN9CybbKwqXdMGCkjRpwyX9emeDD4O1sTnh1+jSVNpeqpr9Jqm72Ju0Tdb9zI+Cx5omzDK6YualqbStPLfy5vtkJpytt2vu1/lmi/MT7Zdp7zoXBX9Gc8+Vu5+P4xhl7FXMxst1H/5aHDNhgS4cT9JE/0rbXm2ka0cU/tvKZcLkQv64Pf3g0b53wa2t6u9OQ2Gj93tqIl2Mx5f5YMglUQjkdHqscrOPJSkzgbWFaX8n3gyuNdMD6XGtBGMr1NLP19ieFiqQpEYQplscOiJpy/uPtH3L5v48C4IaXYi0JJXN+TDW9NdHF8JaDq67kdxc66PA1lZ+ppjZ7hyndEcyHgAAAAAAwOGJBw8AAAAAAKBtePAAAAAAAADahgcPAAAAAACgbXjwAAAAAAAA2qYjVS2An5UH1SuyKArfpKXmQdWHetMk7wbvl8sn8tYLaSp8KfNpuk7VjEFqsVJFVCXEpM1Gn681i7syBha/TM8srTtKQXcaQaWd6G1dSntUOcJVquguBSn+pnqFJGWNNF07q/lqNsWu9Nl/T9knbmfB53YVLLJGUOmgkL5fM0jGzitpxYa9/dP2Rpian64p3SX/+45y0bdvn0g/y+i0X0fHTXr4EUGCuZpBNL3bn1V//Ep96b5rBsdpKqh2kZl1t7cUpMqX0lT/vO6T/qM54JLNCxM7fV9zrCvBvIgKYLXS1+27QhCVngftWcGnwh/qSoUsPA+bQcUYJ+rplkxXFWN/7Dam0kotkpTVTAWX4LyYrAf3ky2s/a4aRFQlJNrPbtM9wXrnxtETVPwpBmM+oi8996OxTZjqQKNVv98G+pfb9qwrrWjU2LnN9s2r6fErBedm1pdWk5CkscJAut3CkO175KrnJm3FYI1vjO6y7Xk9vXYXu/ptX3cfXonWnqAyVsPs/uj42dU8qPISVYiyfYN2W+0i2u5MNY+5vS/feAAAAAAAAG3DgwcAAAAAANA2PHgAAAAAAABtw4MHAAAAAADQNoRL4plx6VBhoIkLsPHPvFoJncyzIKjR9G00/XajsMdmMW0vBuGSWZZ+Fhdwub/3y12QS8DvI4IhsTgUWwwck6TMnbSKAiODsMfgbe14ogwo29biuWXO5awehD0WTZChWU/2vtDC7wmi9cRsI+/yYYGNUrdtnzIBjpGKOSilYN9HoZM1E7Y1VvWhYRO1tG+z4sPBMhPUKPmAsWx61PatlNN91FPy7zcZhEtOmwC7KEyuqzsNVstq6RySJDX9tSZz7c3goNTTgLhCEKDmQislKc8OLOwxCuuMRGGUh4NKENBaLPh9WAvCE50uc34WgtDVlgTzprHk6KQtD9bA/mDtqJrPPRGsX26diaZeFFC5xIQFR+fykxPpeRjN9Urw+fq75/4jnNu2C+OVpPqSYdteHlyW9n3ih8Ebpmt0XvUhkoXqmG3vH1iZtG0z+02S9vQelbQNHu0/X3Fko213gZguvFmSCuM7kra+vnT/SFIxuNZMm1DU6Orqwk+js7cYpPS7uViI7ita+BkvWvsjfOMBAAAAAAC0DQ8eAAAAAABA2/DgAQAAAAAAtA0PHgAAAAAAQNvw4AEAAAAAALQNVS2wQKLs1gNPbs+VJsjnwXazYBtprrnUzFyrT6GPq1f4bdhKFS1UumirFlPFcXhrNYVekqJwddecB9svFoJE92Z6zkU5+y41PWv4czZKeLZVA8LkaFMBIzrvW22fo7zoanlIUyZxW/LHKqooUjFlScpB596y30eu/0QtqmphktQHemzfQsVX7XCiyhGF8e1JW39/ULGp5KuHTJpkeZd2Lkky6fY93QO+byBrpNfHrOarMDl5UO2kpVoSQWUMV5Himawnh6tC7s+L6K4qM1Uwor1dqJkKJ9O+GkF4b2bS7/OKPy+apj08L0ziv+RT/Men/D5y3Pq1v/beUto+airtSNJTE+l1pRxUJTlphT/HXSWgaC1+YjRdwzbtTo+pJA11+fNz5dCK9P0Glvg3NFUtmpPjtmu2zVfG6DHV6JYvP9H2fcpUu+gaXGX7+vp0UmFyd9po1ktJKo49mbTZa7+kytJjbHvZrIOT0XXXzPFqcOMUTE91mXuWPJgwmRlbVL0iKNgU4hsPAAAAAACgbXjwAAAAAAAA2oYHDwAAAAAAoG148AAAAAAAANqGBw8AAAAAAKBtqGpxGMpNbnHWWiZ1sOEg2jRIf/eiShUHJotS8zOfQmvCdJVnPg05y0z6a5AuHVW7kGm3lS72biVod9slERyd9XQzzgWQR5UqHJdULkmlKJ152lcksH1Nm6t0IUl50WdjZzLv10JFilrmL8smMH3v+5mqG1HlgbyUjnk8iKR21RYkn+geHRPXGlWv6C37JHXX/uSETxrfPZ2uo/XlS2zf7iCNPS9WkrZmz5Dt61LMCxM7bd/+AV89ROW0Pdr3bu0vV/ptz1KQsJ430vcrTJk0d8leP6KqK3keVI4KxmG5ahdcwuYky/OwAo+rZCJJlajajmOOe16ee2WYvQMxVTTKvuqMS/GfDNaqKN2/lVtPt64NVvz+6TLVJCRptJruoyfG/TFxFXiOHvD788je4Jpg1t3Rqr/3dFUttoz6qharBv21bUXfsqSt0Ddo+zZ2pxV/VPXvZ/vKV5/qGhr2fQtpFZSJYB0t9x1h2+12g/XcCta64tQe39+cD/1BlalqOb0ujZn5JrX0E4LqQUWYpns88DQbzjW36kZ84wEAAAAAALQNDx4AAAAAAEDb8OABAAAAAAC0zYI8eHjzm9+sLMv0yle+0r7+xS9+US984QvV3d2tY489Vu973/tUr7fwd4IAAAAAAGBR6Hi45P33369bbrlF3d0+ROWOO+7QxRdfrJe85CX6+Mc/rgcffFAf/OAHtW3bNn3yk5/s8GgxL1xwXEuBk1JrcSlmCOELwXbN8PKgb2ZCtVoNhoz7zxEhklgEcv0kPDIKjMzMuR+FE7pwqWjlKNSDEMlWwu3c+xWCy2QQ5Ja7/l0+ALBpgtUK4doYhEBV0lCtKDCqasLZooCqWrANF6zWEwTvupTe7iA8q1zwvwcZ7E7775iae2DbeBSS2T1g220AXsUH4KmWzrmsNuE3Wx0PxrE0aQvy6OQ+SrXhP18xCO3LsnTMYRhpOQ2Zy4PjF2qmxySc4S5cEnOSNetxkGfdh0va/sG67c6BaN7YkFD5oN7p4NbHrWEucFKSTC6kJKlq+kd9+0zobbSu5bnfyO7ptP+oCbyVpKGu9Dwa7PL7bVmPb++qjiZt9VJ6PZCkgrnGNmt+f+6aDELXj0rXqkIlmAPmvC8O+VDHrNuP2V0/CmNP2q4rjliTtAUfT7n8GubmeDM6p1zYqgkmlqSs6q8JVnC/Ue5N9300lyPunizKMXbnn7t3k6QgLzrU0QcPeZ7rqquu0uWXX66vf/3rts873vEOPf/5z9dXv/pVlUp7hzc4OKjrrrtOb33rW7V27dpODhkAAAAAAByAjv6pxWc+8xn94Ac/0LXXXmtff+ihh/TQQw9p3bp1Mw8dJOm3f/u3lee5brvttk4NFQAAAAAAzIOOPXgYHR3V1VdfrWuuuUYrV660fR544AFJ0hlnnDGrfdWqVVq9evXM6wAAAAAA4ODQsT+1+MAHPqCenh69/e1vD/uMjIxIkoaHh5PXhoeHtXXr1vDfrlkz++97Nm3a9AxHCgB4Om7NPXr16gUaDQAc+ty6u5p1F8BBouUHD81mU9WqD6r5WV1dXcqyTI888og+9rGP6a//+q/V1ZWGFe0zOTk58+9+Vnd3t/bs2dPqcAEAAAAAwAJq+cHD3XffrfPPP39OfR9++GGtXbtWb33rW3X22Wfrta997X779/TsTRSdnk5Tl6empmZedx599NFZ/71mzRo99thjcxonpDxITM/C/OkDfcMOV2HIoqoR/q+N8jxNss2iviapOeK2u9cBVrUAOsytuc08n0lODgLIbQJyKahqkZl1Ikpuz4KqFplJdA/P2aCywgGrTdnmrJQ+ZM+iZH+T8C35dSlatauNdH+6NknqKfmtDJTS/oXxnbZv0yRxu8oaktRT9u+3rCe9Tdk5OffKCo3oWhPNAVvVos92zbvSyhjZ5G6/2aCyQNms/d1BXLlLFY+SxvOCT1i3VSaiShUuYT3ab2a7kj9fo6u/O9+jKi/RcW0e4hWe3LqbN5tSoxZXHInaa5NJUzRPbQWwoNJXLfO/YJw2a81U3W8jPG+NYjBHes2a0mOq8khSt8w8Deb6eLCGOa56hSQVzaZd236Zcyu6ltbMvo8qF0XjmDI/Mg4c4f90vv6jHyZtzUlf2acYVrUwa8qPHrddy410XzRXPNf23RNM8f6eZUlbKagyUZhIr3lZI6geM+3b83JaESTvDipjmCpa/UFFkajSkatgEZ1nrjm4JZipSjbXnxZbfvCwdu1a3XzzzXPqOzw8rL//+7/Xl7/8ZX3+85/X448/PvNavV7X5OSkHn/8cS1btkyDg4Mzf2IxMjKiY445Zta2RkZGdOaZZ7Y6XAAAAAAAsIBafvCwcuVKXXHFFXPu/8Mf7n3q9ZrXvCZ5bcuWLTr++OP10Y9+VG9729t0+umnS5Luv//+WQ8Ztm7dqs2bN2vdunWtDhcAAAAAACygtodL/sIv/ILWr1+ftK9bt07Petaz9K53vUvPe97zJEmnnHKK1q5dq5tuukn/5b/8FxWLe79y+slPflJZlul1r3tdu4cLAAAAAADmUdsfPBx77LE69thjk/a3ve1tOuqoo3TxxRfPar/++ut10UUX6YILLtAb3vAG/eAHP9AnPvEJvelNb9JJJ53U7uECAAAAAIB51LFymnP1yle+Up///Of1+7//+3rLW96i5cuX65prrtF73/vehR4aDmZhwNjcQx3DOKEgYMlroe9BGJIVhZTi8JDpJ0FDQUSiXIZgFoQvWtH5FrTbgLBicOkzQVJ5EC4VBbnZ8EsTIhUpmBCpvW8YhLAV0yC3ehBE6TILoxDJHpcCKqlQHUsbg2DBRiHdR3uq/vNNB4FtPhTNf76R0TRgdMekDy5bOrTKtufFNNxrtBaEsJld19edBk5KskF+kpSZ49pT9nNu0qSDBTliqgeBe2Uzb/NSEETpBPMzCn2VCSvMojtPs40o+DILLjVRGOWhLVfWrKtZ9gHsURZiee4ZrS2pB6GF42b+RuG2bhtRMGQ5uNj0mjUsCsjLzY9DU8HYdk8H6525Z+uv+DFXzOIxEPWt+VDG6e40vLdQ9QvC0YPpdeLRHf5zbNw+YdtX9KXrxEAQwDnxw01JW2Vwh+3b3ePDe7NSOkEbo7ts3/LAkqQtCpp18zAyFJxTkgmXrAUB11V//HJ3HxLsT7e+Fmv+OHVV/DWvauZt+KOROU+C3NIw3DWyYA8efjpo8mddfPHFyTchAAAAAADAwadNtcMAAAAAAAB48AAAAAAAANqIBw8AAAAAAKBtePAAAAAAAADaZtFVtcDiElUpyLTwydGdH1sr1SsCi6RSBdUn0C6lKPr4x1wFi6weVLUIEp6tVqrLBNt1FSyi6hXNzEepF9ymg/PeVdyoB78P8PnjUqOefu4oVb6FMO/wOJa2PZ60NXvTdHXJp79PmvFK0njVf8Ip03/7eFopQZI270grRxzZ649ff2XQt5fTfReN2Sl3+woRXYWgWokRHD4rCN4PqwUUTSmDgqnkIfmKG2H1iqDd9c+bwXltqqNkQVWZw7N6RSDL9lZjCdbAYsGvVXmWHveoio9bM5vBvVYzOF/KT3Nt+GmtVODprwRrcQv3Oa6Cxc4pvyZFVRFcun9UQGlpV7o/y0887DsHx++JnjVJWy1YPFxVi6P60jZJ+penTOUi+ctYc4+vVFGspOtMeeUxtm+2+rm23c3n8lJfHahhrkHl3K9JS7v9/ozWTDs0t2aaYypJednv59xVzIjOP7cWN/x1sBisA4OVtHpIdA67Ci2RqHpIhG88AAAAAACAtuHBAwAAAAAAaBsePAAAAAAAgLbhwQMAAAAAAGgbHjwAAAAAAIC2oaoFnhFXFaGVahKLpqrCIqky0S6LZj/jsLHvlApD513i8jxUpAjbXXO4jbmvYVHXrHlgnyVakqJKFVP1tL3aiNLt00FXXHy8fKq8JDW7B5K2qPKH20eF4DrhEuEl6Zih7qRt55SvELFyIO377GW9tu9gxc+B7kK6P6OqAO6YRBUpGl39tj0zlRyi3wi54xeJVn435rJLV5eUt1LVIveVMWTS3101l70bN58vWBsKrVS8OeRl4TkoSVmwqJilQ408qKpjEv+j+4voHHBrShYtNKY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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(ncols=3, figsize=(12, 3.5), sharex=True, sharey=True)\n", "\n", "axs[0].pcolormesh(source.pixel_centers, source.pixel_centers, pred_i, vmin=0, cmap=\"inferno\")\n", "axs[1].pcolormesh(source.pixel_centers, source.pixel_centers, pred_q, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "axs[2].pcolormesh(source.pixel_centers, source.pixel_centers, pred_u, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "\n", "axs[0].set_title(\"I\")\n", "axs[1].set_title(\"Q/I\")\n", "axs[2].set_title(\"U/I\")\n", "\n", "for ax in axs:\n", " ax.set_aspect(\"equal\")\n", " ax.set_xlim(source.pixel_centers[-1], source.pixel_centers[0])\n", " ax.set_ylim(source.pixel_centers[0], source.pixel_centers[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extended source prediction\n", "\n", "Let's suppose we know what the extended source's flux image looks like, with very good resolution (e.g. a Chandra observation). Assuming that flux is stored in `data/prediction/pwn-i.fits`, " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "source = leakagelib.Source.load_file(\"data/prediction/pwn-i.fits\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll give it a model polarization map, and ask what the resulting leakage pattern is." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "source.polarize_file((\"data/prediction/pwn-q.fits\", \"data/prediction/pwn-u.fits\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The original I, Q, and U images stored in these files are" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(ncols=3, figsize=(12, 3.5), sharex=True, sharey=True)\n", "\n", "axs[0].pcolormesh(source.pixel_centers, source.pixel_centers, source.source, vmin=0, cmap=\"inferno\")\n", "axs[1].pcolormesh(source.pixel_centers, source.pixel_centers, source.q_map, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "axs[2].pcolormesh(source.pixel_centers, source.pixel_centers, source.u_map, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "\n", "axs[0].set_title(\"I\")\n", "axs[1].set_title(\"Q/I\")\n", "axs[2].set_title(\"U/I\")\n", "\n", "for ax in axs:\n", " ax.set_aspect(\"equal\")\n", " ax.set_xlim(source.pixel_centers[-1], source.pixel_centers[0])\n", " ax.set_ylim(source.pixel_centers[0], source.pixel_centers[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The X and Y units are arcseconds. The leakage patterns are found in the same way as above." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "psf = leakagelib.PSF.sky_cal(3, source, 0)\n", "combo = leakagelib.PSFSourceCombo(source, psf, use_nn=False)\n", "pred_i, pred_q, pred_u = combo.compute_leakage(spectrum, normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code plots the polarization patterns --- that is, predictions for what the detector actually sees." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(ncols=3, figsize=(12, 3.5), sharex=True, sharey=True)\n", "\n", "axs[0].pcolormesh(source.pixel_centers, source.pixel_centers, pred_i, vmin=0, cmap=\"inferno\")\n", "axs[1].pcolormesh(source.pixel_centers, source.pixel_centers, pred_q, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "axs[2].pcolormesh(source.pixel_centers, source.pixel_centers, pred_u, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "\n", "axs[0].set_title(\"I\")\n", "axs[1].set_title(\"Q/I\")\n", "axs[2].set_title(\"U/I\")\n", "\n", "for ax in axs:\n", " ax.set_aspect(\"equal\")\n", " ax.set_xlim(source.pixel_centers[-1], source.pixel_centers[0])\n", " ax.set_ylim(source.pixel_centers[0], source.pixel_centers[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see, the predictions are much lower than the true source polarization. That's because LeakageLib computes the predicted detector output, which is lower than the truth by a factor of mu. You can re-plot after dividing by mu to correct this effect." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "one_over_mu = spectrum.get_avg_one_over_mu(use_nn=False)\n", "\n", "fig, axs = plt.subplots(ncols=3, figsize=(12, 3.5), sharex=True, sharey=True)\n", "\n", "axs[0].pcolormesh(source.pixel_centers, source.pixel_centers, pred_i, vmin=0, cmap=\"inferno\")\n", "axs[1].pcolormesh(source.pixel_centers, source.pixel_centers, pred_q*one_over_mu, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "axs[2].pcolormesh(source.pixel_centers, source.pixel_centers, pred_u*one_over_mu, vmin=-0.3, vmax=0.3, cmap=\"RdBu\")\n", "\n", "axs[0].set_title(\"I\")\n", "axs[1].set_title(\"Q/I\")\n", "axs[2].set_title(\"U/I\")\n", "\n", "for ax in axs:\n", " ax.set_aspect(\"equal\")\n", " ax.set_xlim(source.pixel_centers[-1], source.pixel_centers[0])\n", " ax.set_ylim(source.pixel_centers[0], source.pixel_centers[-1])" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }