diff --git a/analysis/pr409-intruders-at-search-box-edge.ipynb b/analysis/pr409-intruders-at-search-box-edge.ipynb new file mode 100644 index 00000000..f22103ba --- /dev/null +++ b/analysis/pr409-intruders-at-search-box-edge.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d0e4c016", + "metadata": {}, + "source": [ + "## Calibrate magnitude offset to reflect spoiler or imposter star partly in search box\n", + "\n", + "If a spoiler or imposter star is within 4 pixels of the effective search box edge, then\n", + "some pixels are outside the search box and the spoiler is effectively fainter.\n", + "\n", + "This notebook computes a positive magnitude offset to reflect the fraction of the star /\n", + "imposter light that is actually within the box. This offset is empirically determined\n", + "from the ACA star PSF.\n", + "\n", + "In reality the PEA does not work exactly like that, and imposters (hot pixels) do not\n", + "necessarily have a star-like PSF. This ad hoc method is mostly a handy and performant\n", + "way to ensure that small changes in T_ccd result in small changes in the probabibility\n", + "that a candidate acquisition star is the brightest in the search box." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "753e9319", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from chandra_aca.aca_image import AcaPsfLibrary\n", + "from chandra_aca.transform import count_rate_to_mag, mag_to_count_rate\n", + "\n", + "matplotlib.style.use(\"bmh\")\n", + "%matplotlib inline\n", + "\n", + "psf_lib = AcaPsfLibrary()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9bab8f5b", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dmags(offsets=None, mag0=10.0):\n", + " if offsets is None:\n", + " offsets = np.arange(-4, 4.01, 0.01)\n", + " norm = mag_to_count_rate(mag0)\n", + " dmags = {\"row\": [], \"col\": []}\n", + " for axis in \"row\", \"col\":\n", + " for offset in offsets:\n", + " rc = (offset, 0) if axis == \"row\" else (0, offset)\n", + " img = psf_lib.get_psf_image(*rc, pix_zero_loc=\"edge\", norm=norm)\n", + " img_clip = img.aca[:0, :] if axis == \"row\" else img.aca[:, :0]\n", + " norm_clip = float(np.sum(img_clip))\n", + " mag_clip = count_rate_to_mag(norm_clip) if norm_clip > 0 else mag0 + 10\n", + " dmags[axis].append(mag_clip - mag0)\n", + " dmags[\"mean\"] = np.mean([dmags[\"row\"], dmags[\"col\"]], axis=0)\n", + " return offsets, dmags" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f109fff4", + "metadata": {}, + "outputs": [], + "source": [ + "offsets_int, dmags_int = get_dmags(offsets=[-4, -2.0, -1.0, 0, 1, 2.5, 3.4, 4])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7e068c31", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NGoSVK1di5cqV0Gg0XV+Ljo7GsWPHkJmZid27dyMzMxOHDx9GamoqUlNTsXr1auzcuRPu7u69HqPz9+6PmpoaAJD9uf2VkJDQ63NBQUEAgLq6uq7PHT16tGuaN7kpaTo6OgAAZ8+e7fH57OxsvP7669i7dy/Kysp61fOSkpJeP0un02HkyJF9+j2WLVuGJ554AsOGDcPNN9+MCRMmIDk5Gd7e3j22O3z4MABgxowZVzR1UkdHB95//3189dVXOHPmDBoaGnrM3SmX/78dOnQIAFBQUCA7dhcuXAAgjd2cOXN6fM3DwwMArun5JKmRlFFVVYX169cDAG6++WbcfPPNstutXr26q5EcN24cACA1NRUmkwkqleqKH69zvshVq1Zh1apVstt89tlnePXVV3s1mldTYmIi3nnnHUydOhW7du2y+s/38fEB8EvxvBKurq4AYHEezrKysh7bubi4AIDFeeouN3+dHHd3d7z55pt48803kZubi7S0NLz//vv497//jfr6+q4XKjc3NwBSERoyZMhlf+Ybb7yBmpoarFmzpteBy9q1a3u8+F2Oq6srzGYzamtr+/Q7Ab/8DTr/JoT8Nzq4FvPg+td01sLuOpv97vMDd9aAo0eP4ujRoxZ/XnNzc9f/Hz58uGte4ilTpuCGG26Ai4sLlEolTpw4gQ0bNsgeBPv6+vb5AGPlypXw8vLCu+++i3/961948803oVAoMGnSJLz++uuIi4sD0LeDeABYunQp1q1bh/DwcMyfPx9+fn7Q6XQAgLfeeuuKD+IB4Ntvv73sdt3HrpMtHMRTIynjk08+gcFgQHx8vMWJtTds2IDt27fj0qVLCAkJQUpKCoYMGYKzZ89izZo1uOeeeyz+fLPZDJPJBI1Gg4yMDBw/fhwBAQGYNWuW7Pbp6ek4deoUvvvuO9x2223W+BWvmLOzM4DfdpRtybBhw6BSqXod4V5ObGwsAKlhv/vuu3t8zWg0Yv/+/QDQVVQ6tz906BDMZnOvI9TO7ftj4MCBGDhwIG699Vb4+Phg3bp1XU1fUlISMjIysG3btl9tJHNzcwEAixcv7vW1tLS0K86TlJSETZs2ITs7G8OGDevDbwKcO3cOnp6eGDBgQJ++j1wf6OBaIuLBtbV0NpyPP/443njjjSv6nn/84x9oa2uTbdBffvllbNiwQfb7+ru86/Lly7F8+XLU19fj4MGDWLduHf7zn/9g+vTpyMnJgbe3d4+D+F+TkZGBdevWYcqUKdiyZUuPs9JmsxmvvfbaFeXqHLsNGzbghhtu6NPvZAsH8dRIyuhcvebdd99FYmKi7Dbe3t545ZVX8NFHH+GFF16AUqnEqlWrMHXqVDzyyCOws7PDsmXLej1hzpw5g0ceeQQffvghQkNDu47yH3300a63Sv/btm3bMHPmTKxevdrqjWR6ejrOnDmDpUuX9joi6ujowKuvvgoAmDBhglUfF5CefDExMcjKykJbW9sVHZEtWLAAHh4eWLt2LR566CEkJSV1fe2tt95CXl4epk6diuDgYADSWzgTJ05Eamoq3n//fTzwwANd22/duhU7d+684rz5+flobW3t1aTV1dVBr9d3FTAAeOCBB7Bq1Sq88MILmDFjRq9msri4uKtp61zabM+ePT2K0LZt2351JaXuHn/8cWzatAn33nsvvvvuux5vPwJAS0sLTp061WPMOn+viooKLF68+Dev301sEx1c/0K0g2trSUxMhFKpxL59+674e3Jzc+Hh4SF7lrcvB8F95ebmhtmzZ2P27Nkwm834z3/+g3379mHRokVd9W3Hjh14+eWXL1vTOg/i58+f36OJBKR9rPNs4a/pfMx9+/b1uZHs/Ftf6WpwQuJ7iaZ49uzZwwCw4cOHX3a73NxcplAoek1t8eOPP3at4hAZGcnuu+8+9qc//Yk9/PDDLCkpiSkUCubk5MTKy8tZU1MTc3JyYmq1mpWXl1t8LJPJ1HWB8pXcvHG5qS7+27p167puxpkxYwZ7/PHH2XPPPcfuuOMO5ufn13XH83/ngxVutmHslyl75C5CvpJ5JJctW8aee+65rrkS/fz8et09mp2d3TUdxdy5c9nzzz/Pbr755h7zSFqa+Lu7zrGKj49nK1asYM899xy75557mLe3t+ydjqtXr2ZKpZLpdDq2ZMkS9vzzz7P77ruPxcbGsokTJ3Ztd/LkSabVaplOp2O33nore+qpp9isWbOYQqHomlPuv+dYszSP5CuvvMIUCgVzcHBgN954I3vqqafYAw88wGbPnt31N/5vnXcnys0TRwhjrGs+yCNHjljcpvNO4P/5n//p+lznhOT29vbss88+u6IJye+9914GSHNHWtI5Kfr48eOvKH9fauKRI0fYmjVret1tzJg0M8SiRYsYAPbggw/2+Jq1amJ8fDzTaDSyj9/feSTlQOYmoNtvv50BYC+88ILsXeq5ubksLy+v698zZsxgAHpN3P7hhx92jceVZL0SW7Zskc00d+5cBqDH9Dqdd23LTepeXV3ddVNr58wAixYt6rFNRUVF142uVzKuBoOBRUREMHt7e4vT/Bw8eFD2RtcJEyYwlUolO8fvtYIayf/Seffqv/71r1/ddtKkSQwA+/HHH3t8vrq6mr3wwgtszJgxzMPDo2s+wTFjxrC///3vXdMXrF69mgFgCxcu/NXH6pxKY+XKlb+6bV+KZmNjI/vyyy/ZHXfcwYYPH848PT2ZSqXqyvvyyy/Lzi1oraJZUVHBtFotu+mmm3p97XKFMD09nS1YsIB5eXkxjUbDgoKCLruyTU5ODlu4cCFzdXVlDg4OLCkpiW3cuJG9/vrrDABbv379r2YtKipizz33HBs7dmzXhLiBgYFs5syZbPPmzbLfc/DgQbZo0SLm7e3NNBoN8/f3ZzNmzGDffvttj+0OHDjAJk2axNzc3JiTkxNLTk5m69at6zqwudJGkjHG9u3bx5YsWcL8/f2ZRqNhXl5ebOTIkezxxx9nR48e7bX9mDFjLrs6Cbm+0cG1+AfX1mokGxoauhZdGDRoELvzzjvZs88+y5YvX85GjRrFALC1a9d2bb9lyxYGgDk7O7O7776brVy5kk2YMIEplUp24403WrWRdHV1Zb6+vmzx4sXsiSeeYCtXruzKFBcX16N+5eXlseDgYAaAJSQkdG0/b948ptPpug5ajEZj15yRY8aMYU899RRbvnw58/b2ZuPGjWMBAQFXPK4nT57s2j/Gjh3LHnzwQfbkk0+ypUuXsvDwcAag1zKg9fX1zM7Ojk2fPr3P4yESaiQJd/fddx/T6XQW19q9mjoPHM6ePfu7P7YIOidZ//vf/847ChEUHVyLf3BtrUaSsV9WqxkzZgxzcXFhWq2WBQUFscmTJ7M333yz1/RhP/30Exs9ejRzcnJirq6ubNq0aSwtLa1PWa/Ee++9xxYsWMDCwsKYvb09c3d3ZzExMezVV1+V/XtUV1ezp59+mkVGRjKdTsdcXV3ZyJEj2fPPP9/jzGBNTQ174IEHWEhICNPpdCw8PJw999xzrKWlpc/jWlFRwZ555pmu1XQcHR3ZwIED2eLFi9lnn33W64zq+++/z4Bfpqu7VikYuwoXehDSBxUVFRg0aBBWrFiBt99+2+o/32w2o7KysteUQbt27eq6fvH06dNWf9xrwYIFC5CZmYnz589f03cNEmJL/vCHP+CTTz5BQUGBxanOyLUvISEBzc3NyM7O7tPNaKKhm20Id76+vvj888+RnZ0te2f1b2UwGBAUFIRJkyZhyJAhUKvVyM7Oxo4dO6DT6fDuu+9a9fGuFa2trYiNjcVjjz1GTSQhAnnhhRewdu1avPjii1fl4Jrwt379emRmZuKnn366pptIAKAzksTmmUwmrFy5Env27EFRURGam5vh5eWFCRMm4Pnnn+/zxLiEEHK1/fjjj8jOzsYzzzxj9YNrwt/atWtRUVGBxx57jHeU36xPjaTRaMTf/vY3fPHFFygvL4e/vz/uuOMO/PnPf6YdnRBCrgDVUUKILenTW9uvvvoqVq1ahU8++QTDhg1DRkYG7rzzTri6uuLRRx+9WhkJIcRmUB0lhNiSPjWShw4dwvz587vWigwNDcXatWuRkZFxVcIRQoitoTpKCLElfWokx40bh1WrVuH8+fOIjIzEyZMnsX//frz11luy25vNZjQ1NcFkMvWYXV6r1XatZUkIIb8FYwwmkwlarfaaeGuY6ighRDS/pY72qZF85pln0NDQgCFDhkClUsFkMuHFF1/ELbfcIru9wWCgo2xCyO8iOTn5d113ub+ojhJCRNWfOtqnRvLrr7/G559/ji+//BLDhg3DiRMn8NhjjyEgIAArVqzotX3nLe1vLf0IhraOrs8roYRS8fvf7q7ycP/dH9OSaSvHYstPhbxjdGkZIM4L8Pxx/vgus5x3DACAY7mRd4QuN0zww7Yf8nnH6KKqa+EdAQCgtVPh/tcmXzNTaFzrdVQkM5+cgK3/u5d3jF8ItFb9zCcmYOsb+3nHkDAz7wRdZj4xXqx9RhBaew0e/mp5v+ponxrJp556Cs8++yxuvvlmAMDw4cNx6dIlvPzyy7IFsPNtGHM7YO629rkZZgC//46lshenKaipbodBb+Ido4u+Q5wnemWTAXqjGHk0BjFyAEBtVZtQ+4yqXZznE4Aeb/uK7FqvoyKpKayHobXj1zf8vQi0D9YUCTQ2AjWSwu0zgulPHe3TG+Gtra293jtXqVQwm8XZSa4VGUcqeUcQ1oHcWt4RhJR5QIyztOS3oTpqPZnfX58rUl2JzB/O8I4gJNpnrK9PjeS8efPw4osvYtOmTSgoKMC6devwxhtvYOHChVcrn82aNiuIdwRhzY/x5R1BSFNuCOUdgVgB1VHrmfrIWN4RhDX14STeEYRE+4z19emt7bfffhv/8z//gwcffBCVlZUICAjAH/7wB/zlL3+5WvkIIcSmUB0lhNiSPjWSzs7OeOuttyxOU0Gu3JnTdbwjCOtEUSPvCEI6e7KGdwRiBVRHrSdn10XeEYSVsyePdwQh0T5jfeJPumajzCZa4twSE10qJstkpn2GkO7MVCwsMgtyw6JoaJ+xPmokOYke6cE7grDiQ1x4RxDSsFgv3hEIEcqw6YN4RxDWsGkDeUcQEu0z1keNJCGEEEII6RdqJDnZvb2YdwRhbcyiqZHkpG4SZwJ7QkSw573DvCMIa8+qdN4RhET7jPVRI8nJyDh6m9KSxDA33hGENCLRm3cEQoQyYvYQ3hGENWJ2JO8IQqJ9xvqokeTE00ucJQlF4+Os5R1BSB7e9rwjECIUzxA33hGE5RnsxjuCkGifsT5qJDlpbKQlmiypbxNr6T1RNDUYeEcgRChNlWKs9y6ipioaGzm0z1gfNZKcHNxbxjuCsHblVPOOIKRDu0t4RyBEKAc/P8Y7grAOfn6CdwQh0T5jfdRIcjJzbjDvCMJaHOfHO4KQpi8M4x2BEKHMWDmedwRhzXg8mXcEIdE+Y33USBJCCCGEkH6hRpKTczn1vCMI61RJE+8IQjp/upZ3BEKEcn5vPu8Iwjq/r4B3BCHRPmN91Ehy0k43lFjUZqAlrOTQPkNIT+1Net4RhEVjI4/GxfqokeSE5pG0LDHMlXcEIY0Y5cM7AiFCGTGH5gS0ZMTswbwjCIn2GeujRpIQQgghhPQLNZKcpO6kqVws2XKqincEIe3dWsQ7AiFCSfuAlgG0JO3DDN4RhET7jPVRI8lJVLQH7wjCGhnkwjuCkIaM9OQdgRChRE2K4B1BWFGTaLowObTPWB81kpz4+tFyd5YEuOl4RxCSj78D7wiECMVnIB1cWeITQWMjh/YZ66NGkpOWFroD15ImvYl3BCG1NtOymoR011LbxjuCsFrqaGzk0D5jfdRIcpK6o5h3BGFtzqrkHUFIaVsKeUcgRChpq4/wjiCstA+O8o4gJNpnrI8aSU7mLAjlHUFYS0f5844gpFlL6NoeQrqb/exE3hGENfvpCbwjCIn2GeujRpIQQgghhPQLNZKcXLzQwDuCsHLKW3hHEFLe2XreEQgRysXDdLmHJReP0HRhcmifsT5qJDmprzPwjiCsuhYaGzkNdbS0FyHdNZQ18Y4grIbyZt4RhET7jPVRI8lJfKI37wjCGhvhzjuCkGLH+PKOQIhQ4hYO4x1BWHHzo3hHEBLtM9bXp0YyNDQUCoWi18dDDz10tfIRQohNoTpKCLEl6r5sfPToUZhMv8zxd/r0aUybNg1LliyxejBbtz+1jHcEYe04U807gpAO7qQpo2wB1VHrOfBxJu8Iwjrw6XHeEYRE+4z19emMpLe3N/z8/Lo+Nm7ciIiICKSkpFytfDYrfBAtA2jJYD8n3hGEFDbYjXcEYgVUR60nfHQQ7wjCCk8cwDuCkGifsb5+XyNpMBjw+eef46677oJCobBmputCQKAj7wjCCvaw4x1BSP5B1GDbGqqjv41/lA/vCMLyH0LX4cuhfcb6+vTWdnfr169HfX097rjjjl/dlnm4gNn/siSgUqGCUqHq70P3mzlEnJsVmjRAY7g4621XJjLeEbrUuxtRPVaM5QBbL2p5R+jSqlCgzV+cAxDHRjGWGmPqa/eewb7UUSPrgJH98rxQQsmljopE39IBKMUZA5WTOM9PQweDyt2VdwwAgLlZnCnd9M0GQKSDNibOa29/9buR/OijjzBr1iwEBAT86rZpNZ+htbW1698zJ87D6MGTkbX7EibeLt1BdWZfMZQqBYaMDQQA7P74FOJmhsPNzxENla3I2HQRU+4cDgA4d6gURoMJw1KkU9RpX5zBsAlB8ApyRnNtOw5+dxbT74sBAORmlKO1Xo8RU0PAHHTYt70YkcM84BvogLYWI/ZsKsTsm8IBAPnnGlBX0464sVLDeXBXKUIHuSAg2AkGvRk71hdg9pJwKJRA4cVGVJS0YtQEPwBAeloZ/IOcEBTuDJORYev3+Zi+MBQarRKll5pRmNeIpEnSWGUeqEBRdTuWJkv//vpAKeYl+MJBp0JxTRtyipsxbaR0NHn4fB1cHdSIGuAMAPjuUBmmx3jDxV6N8jo9juc3YFacdISVkVsPnUaJ4SHS2+brj5QjZZgn3J00qG404PD5OsxNkH63E/nSPJYxYVKh+dhUjkku7vDT6FBtNGBzfQ2We0krzGS0NKLNbMJ4Z+lu6m9rKzDGyRUDtHaoNxnxQ20l7vKWfpcTrU2oNXZgsosHAGBdXSViHJwRprNHi9mEL2rKcZ+39Dc+3daMsg49prl4AgB+qq9ClJ0jHBQq3OEciI+bSnCXywBooMBZQzMuGtswx0Ealy2tVQhV2yNK6wQTGD5sLMZy5wDYK1TI7WhFtqEJ8x2l33VHazV81TqM0EpjuLqxCLc4+cNZqUaBsQ2Z7Q1Y7CT9Hfe01cJVqUacThrDr5SVmBPsCTetBqWtehypbMDCUGm8D1c2QKNUIN5L2vbrixWYMsADXjoNqtoN2FNah5vCpQwZVY0wMYbRPtJ4/5BfibF+rvCz16HO0IGthTW4ZaCU4URNE1qMJiT7ugEANlyqQryXM+zMSkwf54udByqwYJo0hucLmlHfaEDiCGm8U49UYWCoEwb42qNdb8bmtDIsnBYIhQLIK2pBRXU7xsRK470/oxpBAQ4ICXCA0cjw4+5S3DA5AGq1ApdKW1FU2opxCV4AgEPHa+DrZYfwIEcwBqzbUYIpNw+Czl6NsoJG5J+uxdi5oQCA42klcPW0Q3i09DhbPzmLCQvD4eCiRWVxM85lVGL8Auk5d+pAGewc1BgUK/1dd3x5HkmzQuDsrkNteSuyDpRh4mJpRZ8zRyqgVCowZJQ0/ru/voC4yQPg4euAa1Vf6uh+bEYrfqmjM8bPRmJoMrI2n8WkB5IAANnbL0CpUiJqijRmO/99EPGLo+Ee6IKGsiakf5OFaY8mAwDO7smDUW9E9MxIAEDqqiOInhEJrzB3NFe3YP+aTMx8Slod5cL+ArTUtSFmnnQn8L6PjiJyfBh8I73Q1tCO3e8expznJgIA8o4Uoa6kEfGLpNp+8NNjCE0YgIChPjC0dmD7m/sx+9kUKFVKFB4vRfn5aiQuHQEAOLL2JPyjfBAc4w9ThwlbXtuLGSvHQ2OvRsnpChSeKMWY22IBABnfn0ZpThXmPiddErDx5TRMfTgJds46lJ+rxoVDhRh/RxwA4PiPOXD2dsTAMcEAgC3/uw/j74qHk4cDqvJrkb3zIibeOwoAcGrreWgdtBg8IRQAsP1fBzH65hFw9XVCbXEDjv94FlMeHA0AOLPrIgBg6M/jnfrJKcTMioB7gDMaK1uR/kMOpt4fDwA4f7AYhtYORE8NAwDs/TQLQ1NC4BXiipa6duz/4hRmPCxluJheiqaaNsTMkn7ugS9PIyIxAH4DPdDebMDuD45j9uNShoIT5agtbkLc3EEAgMPf5iB4uA90DhpMuy8WO1Yfx4wH4qBSK1F0phplF2qROF/6m2f8dAE+4W4IHuYNs5lh6/9lYuq9MdDaqVF2oRYFWZUYs3iINIZbL8LNzwlhMVJN2/JOBiauGAF7Zy0q8utx4Ugpxt08FACQtTMfDq52GPjzKmVb/7kfybePhJOnA6ov1eP0jouYeI80Ltk7LkKtU3WN987/S8eoxUPh6ueE+tImZG7IwZQHEqV9NjUfJhPDsClS/Uj9IAMjZg6CR5ArmqpacWhtFqY/Ij0Xzh8oRHuTHiNmSuOyd80xDJkQCp2TFpMeGI201emY/Yy071w8XISG8ibELZDyH/jkGMITg+Af5Q19swE7/n0Qc56bCIUCKMgsQVVeHUYtiZbGe+1JDIj2xYDhfjAaTNj6v/sw88nxUGtVKD5VjuLTFUi6ZSQA4Oi3p+Ed7o7Q+EAwBmx6ORXTHk2GzkmLspxK5B0pQvId0rgcW5cNV39nRCRJ++zmV1KRct9oOHrYozK3Bjl7LiLlXmlcsjadhZ2zDpETpH1r2xv7MPa2ODj7OKLmUv0V1QjPYDf0l4KxvrfDly5dQnh4OH744QfMnz/f4nZGoxFpaWn4553fwtBGZyS7m35XJL45WMo7RheRzkje4+ePDxrFuLHEQaAzkvf6+WLdjhLeMbo4nhPjpiitnQqPvjEFKSkpUKv7fWz8u+trHX3thlUwtNIZye7mPD8Jm17ZyztGF5HOSM56PAlb3sngHQOAWGckZz8xFpteTuUd4xeCnJHUOmiwcsPd/aqj/aq6a9asgY+PD+bMmXNlD6LUwqy8dt9+uhpEOrMuGiVocOTQPmNb+lxHFRqYaR/oga4rtYyGRh6Ni/X1ubszm81Ys2YNVqxYcU0d/YvmQpk4R2iiyTbQigxy8opon7EVVEeto+CYOO/qiObSqUreEYRUkCnOuzq2os+N5M6dO1FYWIi77rrrauS5bpTX03J3lhQZ23lHEFJFNY2LraA6ah1VebW8IwirurCRdwQhVeXV8Y5gc/rcSE6fPh2MMURGRl6NPNeN8VEevCMIa6aDF+8IQuq8SYZc+6iOWseoG6N5RxBW/JyBvCMIqfMmGWI9dOEiIYQQQgjpF2okOUk9XcM7grA2ttC1PXL2Z4hxlzQhoji8Not3BGGlrz/PO4KQDq89yTuCzaFGkpMQH3EmIxfNIK04U2iIJCjg2p0vkZCrYUA0rVJiScAQuhRGzoBocaYBtBXUSHIS5kNNgSWDNdRIygmhRpKQHgYM9+MdQVgDqJGURfuM9VEjyUmHSYxJSEVkYGbeEYRkNNI+Q0h3RoOJdwRhGTuojsqhfcb6qJHk5IfDZbwjCGtNE83zJefH3TRnHiHdbf3nft4RhLV91THeEYS09X/38Y5gc6iR5GRRkj/vCMK60zmQdwQh3TD519djJuR6MvOJcbwjCGv6/XG8Iwhp5pPjeUewOdRIcqJR0TpNlmgVtFvKUatpnyGkO7X2+l5r/HLUGqqjcmifsT7a0zjJr2zlHUFY5zpoKUA5l0ppnyGku+JT5bwjCKv4LE0xJ4f2GeujRpKTS5VtvCMI64KBGkk5RdRIEtJD8Wmac9aSUmokZRWfruAdweZQI8nJxGiamsGSuY40N5yccQm0dCQh3SXdMoJ3BGElLqDlN+Uk3TKSdwSbQ40kIYQQQgjpF2okOdmXU8s7grC2ttJSgHIOHae3qgjp7uh3p3lHEFbmplzeEYR09FvaZ6yNGklO/Nx0vCMIK0htxzuCkHy9aFwI6c473IN3BGF5BbvwjiAk73B33hFsDjWSnAzyp2UALRmmdeIdQUjhQbTPENJdaBzNrWpJyHC61lxOaDzNU2xt1Ehywmi1O4vMoMGRQ/sMIT0xelJYREMjj8bF+qiR5OSbg7TcnSUfNBbzjiCkdTto6UhCutv0yl7eEYS15Z0M3hGEtOnlVN4RbA41kpzcMMqXdwRh3e5Mb1fJmZ1Cy2oS0t20R8bwjiCsKXfTNDdypj0ylncEm0ONJCf2tEyTRQ4KGhs5djp6uhLSnc5RyzuCsHQOGt4RhKRzon3G2uiViZOialrZxpKLHbSCi5ziCtpnCOmu7GwV7wjCKsut4x1BSGU5tM9YGzWSnJwrbeYdQVinDE28Iwgpt4D2GUK6y0un66ktyT9Oa0rLyUsv4h3B5lAjycnUEd68IwhrgSNdPypn4mjaZwjpLnl5LO8Iwhq7JIp3BCElr4jjHcHmUCNJCCGEEEL6hRpJTg6do+tXLNnZRksByknPomU1Cenu2IYc3hGEdXxbHu8IQjq2/gzvCDanz41kSUkJbrvtNnh6esLBwQExMTHIzMy8GtlsmrsT3VFnibeSxkaOmwvdbWgrqI5ah6sfrYJliauPA+8IQnL1c+Ydweb0qZGsq6tDcnIyNBoNtmzZgjNnzuCf//wn3NzcrlI82zUkkAqgJSN1tEasnMhQ2mdsAdVR64kYHcQ7grDCY/14RxBSRBLtM9am7svGr776KoKCgrBmzZquz4WGhlo7EyGE2Cyqo4QQW9KnM5I//vgjEhISsGTJEvj4+CA2NhYffPDB1cpm076lJRIt+qCRpmeQs56WSLQJVEetZ/NrtESiJVvfpUsl5Gx+NY13BJvTp0YyLy8P7733HgYNGoRt27bh/vvvxyOPPIJPP/30st9nNBt6fJiZ6TeFtgUzY314RxDWTU60FKCcqck0LZIt6HcdZR09PqiOAin3juIdQVgTlkXzjiCklPsSeUewOX16a9tsNiMhIQEvvfQSACA2NhbZ2dl47733sHz5covfl1bzGVpbf1mtZMa0BRgdMwtZ6VWYODcYAJCdWQ2VSoEhMZ4AgF0bChCf7Ac3Lzs01OpxdG8Zpi4IBQCcO1kDY4cZwxKkefVSNxUiOt4bXn72aG4w4MCOYsy4MRwAkJtdh5amDoxM8oHBTY0tZ6oxIsAJgW52aNGb8OOpStySIDUuZytaUN1swLgIdwDAjrM1iPRxQIiHPfRGM74/UYGb4/2hVAC5Va0orm/HxEEeAIA952sR7GGHCC8HGM0M3xwrx40xftCqFSioaUNudSumDpZ+t30X6+AbbIcFA6THfb+hGLc5+8NRqUJ+RxuO6xuxyElqGna31sJdpUGsTrpA+KOGEix28oWbSo0iYzsOtdXjJmfpWpi9bXWwVygxys4VAPBJYynmOHrDS6VBuVGP3a21uNVFesxDbfUAgDH2bgCAtvBNCGgcBgejK9rVzSh0PY7ImvEAgCqHPBiVHfBvHgwAuOh+BL7Ng+DU4QG9qhX57kcxpDoFAFBtfwl6dQsCm4YCAPLdjsKzNRQuBm90KPXI9TyAqKrJAIBa+2K0auowoHE4AOCS6zG4tQcirCkA4/0MUA74GqaiWwGmgsIxFwqHSzBXTQEAKL33gLUOAGsZBMAMVfAXMBffBGbWQeFQAIXTOZgrZ0jbeu4FM3iDNUnzqimDPgcrXQhmcoTCvhgK15Mwl8+RvuZxEMzoDPZzphe86zADwXCFFuVoxVFUYh6k/fAoKqGBEjHwAgD8gItIQSA8YYdqtGEfyrAQ0n54HFUwgSEB0gHEj8jHaPjCFw6ohx47UIQlGAgAyEINWmFEEqR9YBMKEANvROidMclZiZ/OVuGWkdLfMaeyGbWtHUgOlfbZbeerEeXjiGA3e7R3mPB9diVuHekHhUKB89UtKGvUIyVc2md35dYgzMMe4R4OMJrM+PpUBZYO94VapURebSvya9swZaC0z6bl1cLfRYdIL0cwxvDlyXLMGx0FnZ0aZUXNyD/fgLFTAqXf9XAFXN11CB8s7VtbvstDyswgODhpUFnWirNZtZgwY4D0u2ZUwc5ejchhUv7t6wswZlIAnF21qK1qQ9bRKkyc/XONOF4NlVKBISN/rhE/XUL8WF94eNvjWtLfOrofm9GKbnV0/GwkhiYja/NZTHogCQCQvf0ClColoqZEAAB2/vsg4hdHwz3QBQ1lTUj/JgvTHk0GAJzdkwej3ojomZEAgNRVRxA9IxJeYe5orm7B/jWZmPnUBADAhf0FaKlrQ8y8KAAK7FuTichxofAd5Im2hnbsXpWOOc9I2+alF6OutBHxC6QacPCz4whNCERAlA8MbR3Y/tZBzH5mPJRKJQpPlKH8QjUSl0jPtyNfZ8F/iDeCR/rDZDRhy+v7MePxZGjs1CjJrkDhiXKMWSatIZ3xQzb8o3ww7y9STdj8dgYm3zUSdo4alF+sw8WMciQvlZ7zJ7bnw9nTDhHx0vNm23vHMO6WoXB0s0N1YSPO7CvsarxO77kErb0akUnS/rzzwxNIXBAJFy8H1JU148T2PExaMQIAkLNfevckapx03d2PF5qRONQdXq461DcZsO9kDeaNkx4zO78ReoMJcYN/3tfTKzByoCt8PezQ1GrEzqOVWJgSIP1tLjWhsaUDiUN/fq5mVGJIiDMCve3Rpjdh08Fy3DhJypdb3Iyqej3GRP/8XD1ehbAARwTFeCAp3h2flpfjDj9/qBUKnGttRX57G2Z6SNtur61BsJ0dhjg4wgyG/5SV4XZfP+iUSuS1tSG7tQXzPKX6truuFj5aLaIdpWu1PyorxVIfXzipVChsb0dmcxMWekmvyXvr6+CiViPGSXrdOviPkxg7LwxObjpUl7Yg+1AZUhZL9S77UBnUGhUGJ0i1cdfa80iYFgRXL3vUV7bh2J4iTF4q7aNnj1bAbGIYmiS95qV+l4sRyf7w8HdEU50ehzcXYNoy6XXqwvEqtLd0YPg4aUz3rbuIwQm+CE/0gUKhQNqHGZj1lPQal3ekGA0VTYi9IernffYEwkYFwn+IN/TNBux85zDmPDMeUChw6VgpqvLrkLB4mLTPfpWFwGG+GDDcFyaDCVvfOICZK5Oh0qpQfKoCJdkVGH2ztL9kfJ8N7zB3hMQFAIxh06v7MPWh0dA5aVGWU4m8I0VIviMeAHBsXTZc/Z0RkSTVv82vpCLlvtFw9LBHZW4NcvZcRMq9UlOcteks7Jx1iJwQJu3fb+zD2Nvi4OzjiJpL9VdUIzyD3dBfCsYYu9KNQ0JCMG3aNHz44Yddn3vvvffwj3/8AyUlvd92MxqNSEtLw+vP74FB/8vRs1KhglLZpx7WKqpGiXMTR2KKG7a2VvOO0eWWhCO8I3QZ1xYIpc9u3jEAAM+fWcA7QpeJuQOxJ0+caaN8j4ixApFWp8Ljf01CSkoK1Orfv670VX/r6Gs3rIKhtaPr80oooeSxLr2Sw2NaMHp5AjJ+usA7RpfGiYN4R+gyco4nttWKMWVY5KoK3hG6xI9yxdFvT/OO0YUZjbwjAAC0Dhqs3HB3v+pon7ZOTk7GuXPnenzu/PnzCAkJufyDqHQwq+htmO7S2xt4RxCWwu0Y7whCOl4qRuNGfpt+11GFBmbF1Ux27Tl3kJZItORoYyPvCEI6m5rPO4LN6dM1ko8//jgOHz6Ml156Cbm5ufjyyy+xevVqPPTQQ1crn81a4kzXu1liLp/HO4KQ5gyhJRJtAdVR6xl/6zDeEYS1yJuuw5cz4e543hFsTp8ayVGjRmHdunVYu3YtoqOj8fe//x1vvfUWli1bdrXyEUKITaE6SgixJX2+oGju3LmYO3fu1chyXUlrE+daN9EoPQ7zjiCkI4X1vCMQK6E6ah2ndhfwjiCsfQ31vCMIKWvLed4RbA6ttc2JI4+L5K8RzERLe8lx0NI+Q0h3dk60bKgljgLdFCUSO2cd7wg2hxpJThLsxLmDXDSsYQTvCEIaTmvEEtLDoMQA3hGEFedM9UJO5LjL39RG+o4aSUIIIYQQ0i/USHLycSMtkWiJMvBr3hGE9O2pct4RCBHKjg+O844grM/Ky3hHENL2tw7yjmBzqJHk5AZHmsrFEvbzijSkp2k/rzJDCJEkLRrCO4Kw5np58Y4gpM6VkYj1UCPJiYdKwzuCsFiHG+8IQnKzp32GkO6cPa+t5TF/T+5qqhdynL0deUewOdRIclJq1POOICyFTpzltERS2Uz7DCHd1ZbQak+WlBuoXsipLaRV5ayNGklO9tI8khYpPA7xjiCkw0VUAAnpLovmkbRob3097whConkkrY8aSU5udvbjHUFY5rIFvCMI6YYoWvKMkO4m3j6cdwRh3eRDy/DKmfiHUbwj2BxqJAkhhBBCSL9QI8nJgbZ63hGEpXQ/yjuCkDKK6a1tQro7s7eQdwRhHWqkeiEne+dF3hFsDjWSnKgUCt4RxMVoaS85KiXtM4R0p1TRS5glKlC9kKNS0bhYGz0LOUmyc+UdQVjm+jjeEYQUG0DLahLS3ZDkAbwjCCvRheqFnCGTwnlHsDnUSBJCCCGEkH6hRpKTzxtp+SpLlAHf844gpHXZNL8mId3tXnOSdwRhra2gJVXl7Pq/I7wj2BxqJDmZ5kDL3VnCqlN4RxDS+FB33hEIEUrcrIG8IwhriocH7whCil8YxTuCzaFGkhNftZZ3BGExA60RK8fLkfYZQrpz86Pl7izx0VC9kONG15pbHTWSnFSZDLwjCEuhreUdQUi1rR28IxAilIbKVt4RhFXdQfVCTkN5M+8INocaSU62tlTzjiAshddu3hGElJpHDTYh3WVsvMA7grC219bwjiCko9+d5h3B5lAjycntLgG8IwjLXHoj7whCWhRNS54R0t2Uu0byjiCsW31pGV45Ux9O4h3B5lAjSQghhBBC+oUaSU7S22n5KkuUrsd5RxDSidJG3hEIEcq5QyW8Iwgro4nqhZxzafm8I9gcaiQ5MTDGO4K4lHSRuJwOM+0zhHRnNJh4RxCWgeqFLKOe9hlro0aSk3H2brwjCMtcl8g7gpBGDaBlNQnpblhKMO8IwhrrSvVCzrDpNPeotVEjSQghhBBC+qVPjeTf/vY3KBSKHh9+fnRnWH983UTLV1mi9N/AO4KQfsqp5B2BWAHVUetJ+5ymcrHk2yqqF3JSVx/lHcHm9PmM5LBhw1BWVtb1cerUqauRy+Yl29Nyd5aw2tG8IwiJ3tq2HVRHrWPYRHpr25JkF6oXcqKnD+Idweao+/wNajUdPVvBALWOdwRhMT3tX3L8nGmfsRVUR63DawAtd2dJgI7qhRyvUDfeEWxOn89IXrhwAQEBAQgLC8PNN9+MvLy8X/0eo0nf48NsNvYrrC2pM9GdyZYo1DQ1kpyGdtpnbEW/6ijr6PFhZnT3aXNdO+8Iwqo30uusnOZqWlbT2vp0RnL06NH49NNPERkZiYqKCvzjH//A2LFjkZ2dDU9PT4vft+/0m2ht/eWPN33WIsSPnYtjOfWYPlZarePk+QaolED0QOl0/Jb95Rg93AMerlrUN3bg4MlqzB7vDwDIvtiIDqMZMYPdAAA7DlUgZrAbvD10aGoxYs/RStwwUVo55mx+E1rajIgf6o52LwV+qK1CvKMzQnR2aDKZ8FVNBe71kbbNam1GZYcBU109AAA/1lVjmL0jIuzs0W4249Pqctzj7Q+lQoGcthZc0rdjppv0e2+ur0G4zh5D7B1gZAz/qSrDCi8/6JRK5La3IaetBfPcvaS8DbUYHXYYY1siAAD6oK+gLb0BCpMDzPbFMLqcgbZiOgCgw+MQlB2uUDUNlbYN/BbaiplQGJ1htiuH0e0YtOWzAQBG96OA2Q7qhuHStgHroK2aCEWHO8y6anR4HIKubJ60rZs0V6O6PhYAEOZ0Bg1VU9Ch94daWw13v59QVXgnAMDJLR0KVRuaalIAAJ6BX6GpNhmGtiCoNfVwD/gWVZfuBQA4uh6DSluLxqqpAACPgO/QUh8HfWs4VKpmeAV/hor8BwAADi6noLErRUPlDACAu98GtDVFQWFygrp0EaLC1yD74j1gTA13l7NwcbqIS6VzAADB/lvQ1BKCusahUCjMGBaxGjn5d8BksoOrUy48XE8jv2QBACDIbwda231RUz8CADAs4n2cv3QrOozOcHYsgI9HBi4WSavpBPrsgaHDFVV1cQCAvwzeAGXFdCg6XMHsymF2z4CqbC4AwOyeATANlPXS6hqmwHVQVqVAYfAA01XD7LkfqlIpg9ntOKAwQ1kXL23r/xOUtaOh0PuAaeph9tkJVYmUwex6ClC1QvnzW/wmv81QNsTgbJMGs0d547uKStwVKD0XsppaUN1hwGQP6VKJH6uqMdzJCWH2dmg1mfFleQXuDvCHQgGcaW5Fib4d0zyl/XtLdQ0GOjhgkIM9OswMn5SVY4W/HzRKBS60tiG3tRWzvKT9e0dNLQJ1dhjq5ADGgI9Ky3BzSDjsdEoUV7Qht6AZE0d7AwDSs2rh5qJFZKgTAGD9jhJMTfaFk4Ma5VXtOH2hEVPH+gAAjmXXwd5OhagI6czSxj1lGD/KG65OalTV6nH8TD2mj/u5RpxtgFIJDI/8uUaklWP0SA94eVxbZ176W0f3YzNa8UsdnTF+NhLDkpG16RwmPSDtK9k7cqFUKRA1WaotO98+hPhFw+Ae6IKGsiakf3sK0x4ZCwA4m5oHo96E6BnSW32p76cjesYgeIW6o7m6Ffs/zsTMJ8cDAC4cuISWujbEzB0ChVqN/V+cwqAxA+Ab7o62Rj1S/3MCsx6TMuQfK0d9WRNi50g/99DXZxAa4wv/wZ4wtBmxc1UmZj6SCKVKgcJTlai8WIeEBYMBAOk/nIV/pCeCor1hMpqx7e2jmPZAPDR2apSerUHhqUokLYkCABzbeAG5EQ6Y+M9JAICPc8twU6gPHNQqXGppx6naZswNkmru3op6uGvVGO4u7ZOfXyzHDcFecNGoUdqqR3p1IxYES/vvocoG2KmViPVwBgB8lV+B6QEe8NBpUNluwN7yetwYKu2/R6uluRpHeUn77w8uuRiv8IWPwg61TI8drBRLlWEAgOOsFu3MhDFK6XE2mAsxSuGFAIUDGlgHNrIiLFOGAwBOsTrUMwPGK6V9f6O5GMMV7ghROKKVGfEtu4QVSulvnMMaUMHaMFEpneHeZi7BIIULxjhXISnYgPaAdbArWQwFU8PokAeTQxF01VIt13vuhao9EOqWCDCY0T7gW9iVLoDCrIPRvhAmpwvQVU0BABg8DkJp8IS6WfpbtQV+A135HChNjjDZlaDDJRt2ldLrlsH9CBRGZ2h+ft368/IAzAvwhJtWg9I2PQ7VNGDxAGkMD9U0QKNQIMFDGsO1hRWY5usBL50GlXoD9lTUYWmwNA5HaxthYgxJnlIN+K6oEsnervC306HO0IHNZTVYFiKNw/G6JrSYTBjn5QYAWFdShQR3Z7iUGzDpwSTs/fgEZj0mrXKTl1GKhoqWrn32wNrTCI/3h3+kJ/QtHdi5KgOzV46BQgEUnChHdUE9EhYMAQAc+e4MAqO8MWCYN4wGE7a9nY4Zf0yEWqtCcXYVSnKqMPpGaRwy1p+FV6gbQmP8wBiw+Y1DmPrQaOictCjLqUJeehGSV0ivPcfWn4GrnzMikoIAAJtfTUPKfYlwdLdHZW4NclLzkHLPKABA1uZzsHPWIXJ8qLQPvLEfY2+PhbO3I2oK66+oRngGS+PUHwrG+j+hYUtLCyIiIvD0009j5cqVvb5uNBqRlpaGF949An23+b6USjWUqj6/q/6bFU8S5yb1N6MZ9EFf8Y7RZZhWnJt/mgoeQPTAVbxjAAB2tnnxjtDlXPrT+LCkjHeMLgF7xTgjptMq8dwDMUhJSYFa/fvXld/qSuvoa/Pfh6H1l7PSSiihVKh+z6gAAJWz8+/+mJZM/Pc0fJwrznNCGSfOuykvujqjbcDXvGMAAP721S28I3R52OSATf88xDtGF3OrGGdItQ4arFx/V7/q6G+quo6Ojhg+fDguXLhw+QdR62Bi5t/yUIQQYpOuuI4qNDArfqdQhBByhX7TKTq9Xo+cnBz4+/tbK891w+iSzTuCsLzdj/GOIKTjTc28I5CrgOpo/2XVNfGOIKwO5zO8IwjpwuFi3hFsTp8aySeffBJpaWnIz8/HkSNHcOONN6KxsRErVqy4WvlsFlNTU2CJViPO20MiaaSL520C1VHraaQlEi1iamqy5bTW0w1a1tanRrK4uBi33HILBg8ejEWLFkGr1eLw4cMICQm5WvlslobmSrSopHIS7whCSnF34x2BWAHVUesZ5+vGO4KwtHX0GiNn5ExaItHa+nSN5FdfiXNzCCGEXIuojhJCbIk4tzFfZwy+23hHEFZE0He8IwhpXWU17wiECOWnoireEYTV7rOddwQh7fssi3cEm0ONJCfqxmjeEYRVWZvAO4KQ4pydeEcgRCgxHuJMRSQaTeMw3hGEFDk2iHcEm0ONJCfKtkDeEYTV1BLKO4KQQuzteEcgRChBjvScsETVTq8xcnwj3HlHsDnUSHLCVC28IwhLQ3cbymoy0h2qhHTXTDMZWGSm1xhZrY163hFsDjWSnBj8N/KOIKzIkC95RxDSNxWVvCMQIpTvC+gaSUv0fpt4RxBS6oc0T7G1USPJia54Ke8Iwsq++AfeEYR0dyBNWE1IdysG0nPCEvuSm3hHENLslWN4R7A51EgSQgghhJB+oUaSE5PzOd4RhOXpRtMzyDnVTNc8EdJddj2tEGaJ0YleY+TkZZbxjmBzqJHkxKylOQEtcbCr4B1BSJUGA+8IhAilqr2DdwRhmbU1vCMIqb6Mbua0NmokOdHUJPOOIKyi8mm8IwhpigdNW0FIdxP96DlhibZ2LO8IQoqbG8k7gs2hRpIQQgghhPQLNZKcGHx28o4grLDA9bwjCOmnKnqripDuNhfTJUKW6L138Y4gpINfneYdweZQI8mJuplOr1tS20DLR8oZ6ujAOwIhQolyc+QdQViq5kG8IwgpNJamjLI2aiQ5UbYG844grIbmgbwjCCnCwZ53BEKEEuZEzwlL1G30GiMnYLAn7wg2hxpJXpS0TJMlKlU77whCajebeUcgRCh6ek5YxOg1RpahjZbVtDZqJDnRB67jHUFYUWEf844gpM/LaFokQrpbm0fPCUvaA9bzjiCkHe8e5R3B5lAjyYmumJavsiT74n28IwjpzgC6toeQ7pYP9OMdQVh2xUt4RxDSrMeSeEewOdRI8sJo6C1hNDayVAreCQgRixL0pLBEQS/vspRUSK2O9jROTI4XeUcQlrvLGd4RhHS2pZV3BEKEcr6RnhOWGOk1RlZhFl0OYW3USHJiti/hHUFYzo6XeEcQ0qV2ugmJkO4KW+g5YYnJjl5j5FRcrOMdweZQI8mJpnoC7wjCKiybxTuCkGZ4evCOQIhQpvrTc8ISXQ29xsgZtXAI7wg2hxpJQgghhBDSL9RIctLhlco7grBCAjbxjiCkrdW1vCMQIpQdpfScsETvlcY7gpDSv8/hHcHmUCPJiZJWHbCosTmCdwQhhdnb8Y5AiFBCneg5YYmqNYh3BCH5R9LKNtb2mxrJl19+GQqFAo899piV4lw/VC3hvCMIq66RrmGRM5jW2rZJVEf7b5ALPScsUbfSa4ycoOE+vCPYnH43kkePHsXq1asxYsQIa+a5fihomSZLFDQ2soyM8Y5ArIzq6G9DzwnLGNVRWSYjLatpbf1qJJubm7Fs2TJ88MEHcHd3t3am64J+wHe8IwhrWMSHvCMI6ePSct4RiBVRHf3tPr9IzwlL2gO/5x1BSFv/dYR3BJvTr0byoYcewpw5czB16tQr2t5o1MPY0d71YTbRkZKuZBHvCMLKybuTdwQh3e5Py8HZkj7XUdbR48PMTFc5ofhuDfflHUFYdqULeUcQ0vSHRvGOYHPUff2Gr776CseOHcPRo1e+8HnG1hfR2vrLCgTT5i5CzIQbkJ5fj7kjpOsVMi81QqUEYoJcAAAbTlQgeaAHvJw0qG3pwN7ztVgQKxWNk8VN6DCZkRDiCgDYlFWF+FAX+Lno0NBuxI7satwYL73oZpc2o6ndiKRwNxjcGb5vqES8vQtCtXZoMpmwtqEc93kEAgCy2ptRYTRgmpM0N9mGxioM0zlhoM4ebWYzPq0vwz3ugVApgJz2FhR0tGOWs3Th7qamakRoHTBE54AOxvCfulLc4eYPnVKJC/o2nNW3YJ6LFwBge1Mt1HpnqDvX2w7+DChZDJgcAPtCwOU0UDFb+prnfqDDDWiMlv49YC1QPgcwugB2pYBbBlB+g/Q198OA2Q5oiJH+HfgNUDkV6PAAtJWA1wGgs7i4ZUj/rU8AAISEf4FLFRPR0uYLe10NIgK24HT+bQAAf89MqFVtKKocBwAYEvw9SqqT0NQaCJ22AYOD1iHr4h0AAF/3k7DT1uFSxUQAQGTQBlTUjURDcyg06lZEh32J4xfuAQB4u2XDyb4M+WXSC+nAwE2oaRyC0qbBuJB/D2IjPkdm7nKYmRpeLufh4ZSP86UzAACD/HegviUYVY2DoYAZCYM+xvGLy2A06+DhlA8ftzM4WzwHABDhtwfN7T6oqB8GAEgYuAanCpZAb3SCm2MhAjyO40zRfABAmO8+tBtcUFY3EgCQ774f3jVjoTE6o11XjQaX0/Ctkn63epfTUDA1XJukazrLfHbCs24UtB2uMGjqUOt+DH6VUwAADc45YAoz3BqlDOXeqXBvGA6dwRMd6iZUeR5CQMV0AECj03mYVO1wb5De7qzw2gvXpiEY5GiPGwO98W1dBe7xkvbZk21NqDZ2YIrzz/tsfRWG2zshXGePVrMJn9eW417PACgUCmS3NaO4Q48ZLj/vsw3VGKRzQKSdAzqYGWtqynCnpz80CiXOt7figr4Vc1ylfXZbYw0GaHQYZu8Exhg+qCnFzKn+sNOqUFTdhvMlzZgy0hsAcPhcHdydNBgc6AQA+O5AKWbG+8DJTo2y2nZkFTRiRpz0vM+4UA97nQrDgp0BAOsPl2HSCC+4OmhQ1aDH0Qv1mJ0gPe+P5zVAqVBgZJhUI35KL8fYKA94u+pwrelPHd3PNqGV/VJHZ4yfjcSwccjafB6T7k8EAGTvyIVSrUTUJOn6uJ3vHEb8oqFwD3BBQ3kT0r89jWl/HAMAOJuWD6PeiOjpgwAAqavTET19ELxC3dFc04r9Hx/DzCek5/yFg5fQUtuGmLlDoNCosP/L0xiUNAC+4W5oazQgdc0JzHpUypB/rBz1Zc2InTMQAHDomxyEjvSF/2APGNqM2Pn+Mcz84ygoVQoUnqpCZV4dEuZHAgDS152D/yAPBEV7w9Rhxrb/y8C0++OhsVOh9GwNCk9XIunGKADAsY0X4OOowYohUq3/sLgMt/j7wlGlREFbO040NmOBr7T/ptbWw12jxkhnaZ/8uKQcC3294KpWo7hdj8P1jbjRT9p/99c1wF6pRLyrtE9+XlqBWd4e8NRoUKE3YE9tPW72l/bfI/WNAIDRbtI+OX34B2iqmoIOvR/U2mq4+m1ETeEdAABHt6NQqNrQ/PP8ju6BX6OldiwMbUFQaerhHvAdqi9JtdHB9ThU2lo0VUn1wy3ge7TVx0HfGgalqgWewZ+hKv9+AIC9yylo7MrQWCnVD1e/H9HeFIW2qmkIqZwjRB11LwWmR3nBxV6NikY9Mi41Ys5wabwzLjVAo1Ji5ICfa8CJCkwY5AEPRw1qmjuw/2It5o+UasCJokaYGBAfLI33xqxKJIa5wcdZi4Y2I3bmVGNxnLQ/nCppQluHGYmhUp+w5XQVRg5wQWCoIybdE4e9n2Zh5iPSPpuXUYaGyhbEzpb22YNfZSMszh/+kR7Qt3Rg1+pjmPXYaCgUwKWTFai+1ID4G37eZ78/i4AoLwwY6gWjwYTt/5eB6Q8lQK1VofhMNUpzqpG4WHqNyPzxPLxCXBEy0heMAVveOoJpjyZD56hF2dkq5KUXI3l5rLR/b8iBq58TIkZLN01tfm0vUu4dBUd3e1RerEHOnnyk3CO9fmdtPgc7Zx0ix4cCALa9eQBjb4uBs7cjagrrr6hGeAa5ob8UjF35RSZFRUVISEjA9u3bMXKktINMnDgRMTExeOutt3ptbzQakZaWhue/zIC+45ejZ4VKDaWqzz3sb1Y9Spwj+Hdi8wCv/bxjdJlu38I7QpeiiskI9xNj6oo3asW5YP1M5k3Y3STOqgy+B8VYs1anUeKvt41ASkoK1Orfv670VX/r6GsLPoChtaPr80oooVSofq/YXVQuTr/7Y1oS+NpYpNbW847R5fub3+QdoYtT7Qxh6mjCXx7gHaHL9CY9TmzJ5R2ji6lZjNderYMGK9fd0a862qetMzMzUVlZifj4+K7PmUwm7N27F++88w70ej1Uqt6FTaXRQaWgC1x7cLrAO4GwvFzO844gpJx2MQoO+W36W0fVCg3MYvTuwjhH689bRHVUXuGpSt4RbE6frpGcMmUKTp06hRMnTnR9JCQkYNmyZThx4oRs8SMW/Pw2BOntXAktkShnnqs37wjECqiOWs8cb5oT0BKqo/KSlkTxjmBz+nRG0tnZGdHR0T0+5+joCE9Pz16fJ4QQ0hvVUUKILaGVbXih5assivDbzTuCkHY01vCOQIhQdtaIc82waKiOyju2kS4rs7bffGV6amqqFWJch9p9AYdC3imE1NzmBw/nAt4xhOOv0SHf0M47BrkKqI72j79Oi4I2ek7IoToqz2OAM8ov0Brt1kRnJHlppmUALaloGMo7gpCi7cW5W5YQEQxzcuQdQVhUR+WFxtB8vNZGjSQhhBBCCOkXaiR5Cf6MdwJhjRr0H94RhLS6uoR3BEKE8mFxGe8IwqI6Km/zm7REorVRI8lLyWLeCYR1Mv9m3hGEtMyD3pIhpLtb/GmJREuojsqbfG8s7wg2hxpJXkwOvBMIy2CksZHjqKT5BQnpzlFFL2GWUB2VZ+ek5R3B5tCzkBd7umPbEnfHS7wjCClf38Y7AiFCoTu2LaM6Kq88l+7YtjZqJHlxOc07gbD8PU7yjiCkE21NvCMQIpQTjc28IwiL6qi8i+mlvCPYHGokeamYzTuBsM4U3cA7gpAWuvnwjkCIUBb4evGOICyqo/KSb6XVo6yNGklCCCGEENIv1Ejy4rmfdwJhhfvS8pFydjfRtT2EdJdaW887grCojso7seUi7wg2hxpJXjrceCcQVpvBnXcEIXmoNLwjECIUd81vXuXXZlEdlefsac87gs2hRpKXRrpOw5KyuhG8IwgpxsGZdwRChDLSmZYNtYTqqLyIxADeEWwONZKEEEIIIaRfqJHkZcBa3gmEFRfxKe8IQvpPNU1bQUh3H5eU844gLKqj8ra9c5R3BJtDjSQv5XN4JxDWmcL5vCMIaZE7Tf9DSHcLafofi6iOyhu3bDjvCDaHGklejC68EwirvcOVdwQhuanoxgJCunNV03PCEqqj8hzd7XhHsDnUSPJiR29TWuLqUMI7gpCKDbQcHCHdFbfreUcQFtVRedWXGnhHsDnUSPLilsE7gbCCvI7wjiCkQy1UAAnp7nB9I+8IwqI6Ku9MGq1Bbm3USPJSTstXWXK6cBHvCEJa4u7LOwIhQrnRz5t3BGFRHZU3YTlNi2Rt1EgSQgghhJB+oUaSF/fDvBMIK8TnIO8IQtrXXMc7AiFC2V9Hl3tYQnVU3umd+bwj2BxqJHkx051jlhiNNDZy7BUq3hEIEYq9kl7CLKE6Kk/rQEvNWhs9C3lpiOGdQFgltXG8IwgpwZGmjCKku3hXWjbUEqqj8iLHDuAdweZQI0kIIYQQQvqlT43ke++9hxEjRsDFxQUuLi4YM2YMtmzZcrWy2bbAb3gnEFZM2Je8Iwjp05oy3hGIFVAdtZ7PSyt4RxAW1VF5O1dl8o5gc/rUSA4YMACvvPIKMjIykJGRgcmTJ2P+/PnIzs6+WvlsV+VU3gmEdb50Ju8IQprt6sk7ArECqqPWM8vbg3cEYVEdlZe4KIp3BJvTp/Wl5s2b1+PfL774It577z0cPnwYw4YNs2owm9dBBdCSVj2NjRwvtZZ3BGIFVEetx1NDN05YQnVUnouPA+8INqffC5WaTCZ8++23aGlpwZgxYy6/bYcepg5T178VKjWU1/u6wdpK3gmE5WRHYyOnvIOWg7M1famjRtYBI+vo+rcSSiiv8zv5K/QG3hGERXVUXl1pE+8INqfP3dypU6cwZswYtLe3w8nJCevWrcPQoUMv+z2nv3oBra2tXf+ePnsR4pLnIfNsHWYm+QEATlyoh1KpwIgIaaH5TQfLkRTtAU8XLeqaDDiQVYO5yf7Sz8trQIeRITbSDQCw7UgFYiNd4eNuh8aWDuzOrMKCCQEAgJyCJjS3GTEqyh0GRwU2lFQh1t0ZwQ52aDYa8W1RFe4M+/nnNjSjSt+BST7uUobSakS5OCLcyR7tJjO+LKzAilA/qBQKnGtqRWFLO6b5SUd928prEeZoh0hnBxgZw6cF5VgW4gudUomLzW0419SK2f7SW5O7K+qwpcYeYTVPAQC+UJzHQhYOB6hRhGacRg1mIQQAcABlcIMOwyA9zte4gFkIgQu0KEMLMlGFuQgFABxBBeygwkh4AQC+w0VMRiA8YIcqtOEAyrAA4QCAY6gCAMRBWhmieeAaaGqToDJ4waypg94rDfZlCwAAHS6nwJR6aOsTAADtvlugqY+FSu8Hs7oRep/tsC+9UdrWOQdM3QBtXZK0rc8OaJqioGobAKZqRbv/T7AvXgoAMDqdh0lbBV1tMgBA77UHqpZw6JsicbTpQeR7pyK8cjKUTIVG+xI06yoQUC/diVjqdhyOem+4tg0AUzBc9NmJsKqJUJk1aLIrR4N9EQbUjQIAlLtmwa7DFW6t0pjm+uxESM04aEx2aNFVodbxIoJqpbwVLtnQmBzg0RIGAFhd2YQFOn+4KTUoMbXjYEcNltgFSn8bQy20CgVGaaT95Yu2YszQ+cBLqUWlWY+dhircaifdIXikow5mxjBG+/Pfsb0EEzSe8FfZodZswEZ9BZbbBwEAMjvq0cpMGK+V9pfv20uRqHHHQJMdbtP4YX1+FW4fLO2z2bXNqGnvwIQAKcPmS9UY6uGIUGd7tBlN+Ca3EssH+0GhUOBsXQtKW/SYPEDKsL2wBhGu9ohwdUCH2Ywvz1fg1khfaJRKXGxoxcWGNkwP/nmfLa5FgKMOQ9wdwRjDp+fKsSDGC3Y6FUrK25Bb0ISUJB8AwNGTNXBz0WJQmHRH7YbtxZg6zg+ODmqUV7Uj+3wDpiRLq/QcP10HOzsVogZKd6Rv3FWKCYnecHHWoLpWj2On6zB9glQjsnKkGhE9WKoRW1PLkBjjCS8PHa41/amj+80/odX8Sx2dMX42EsOSkbXpHCY9MBoAkL0jF0qVAlGTIwAAO98+hPhFw+Ae6IKGsiakf3sK0x4ZCwA4m5oHo96E6BmDAACp76cjesYgeIW6o7m6Ffs/zsTMJ8cDAC4cuISWujbEzB0ChVKJfZ+eQGRyMHwjPNDaqEfq6kzMflL6uXkZpagvbULcDYMBAAe/PIXQOH8EDPGCoc2IHe8cwawnxkKpVKDwZAUqLtZg1CLpd0//Lhv+g70QNNwXJqMZW988hOl/HA2NnRolOVUoOlmBpJujAQCZG87C7kAdHo2Q9uctH2Vj0s2RsHPUoKKgERdPVmHsfGkcTqYWw9ldh/CRUr3b/skZJC+IgKOrDtUlzcg5XI7xiwcCAE4fKIXOTo1B8dL+vOuLsxg1MwQunvaoq2jFydRiTFwaKY3hkXIAwJDR0j5673+ewnh/N/jaaVGj78CO4lrcHPHzvl7ThDajGWN9pf13fUEVRvm4INBBh4YOI34qqMZtg37e12ubUW8wYoKfGwBgY2E1hns4IcTJDi1GE77Nq8QdkVINyKlvQXmrAZN+rgFbi2oQ6eaAKK09fjySgPWHyrForD/UKgXyyltRXN2GCdHS83pfdg0CPOwQ4e8Is5nhuwNlmJ/kB51GicKqNuSWtmDySOn15FBOLTxdtIgMdAIAfLu/FLMTfOFop0JpTTuyC5swLVYa3/TzdXCyU2NosFQDArcdwpgbB8PJ3Q41xU3I3luECbdKf/Mze4ug1qoQmSS9Zu/++DTi54TD1dsB9RUtOL41H5NWSH/zc4dKYDIxDB0n1dW0L85g+KRgeAQ4oam2DUfWXcDUu6WVay4cLYO+uQPRk4Kl58/XORicFAjPICek3B6NtI8yMevJcdI+m16MhvJmxN4wRNpnPzuBsMRA+A/2hr7FgJ3vHMGcp8cBCgUuHS9FVX4dEhZJ7x4c+foUAof5YEC0L0wGE7a+eRAzHx8LlVaF4tMVKMmuxOilwwEAGT9kwzvMHSGxAQBj2PTafkz7YxJ0TlqU5VQhL70IySuk17hj68/A1c8ZEUnS68LmV9OQcl8iHN3tUZlbg5zUPKTcI73GZW0+BztnHSLHhwIAtr2xH2Nvj4WztyNqCuuvqEZ4Bkv7Wn8oGGOsL99gMBhQWFiI+vp6fP/99/jwww+RlpYmWwSNRiPS0tLwt1Xp0Hc7I6lU8jkjWT1CnKP3e2M88YXiPO8YXZ4evJ13hC6mS8uR67uDdwwAwKenRvOO0OX+jnB8clacG268sjp+faPfgVarxHMPxyIlJQVq9bXxTkd/6uhr89+HoZX/GUmlTpzGfc7/TMGWj8S5trRgaQDvCF3uCfLDN/tKeccAAHjsEWcS8Jn3RGPTq/t4x+jCjILUUQcNVq6/q191tM9VV6vVYuBA6cgtISEBR48exb/+9S+8//77lh9Eo4MJ5r4+FCGE2KR+1VGFBmbF75WQEEKuzG+eR5IxBr2ert3qq863lklv1U7inKkVydHKRt4RyFVCdbR/Ot9aJr2dyKPlI+Xk7M7jHcHm9OmM5PPPP49Zs2YhKCgITU1N+Oqrr5CamoqtW7derXyEEGJTqI4SQmxJn85IVlRU4Pbbb8fgwYMxZcoUHDlyBFu3bsW0adOuVj6b1XmTC+nNqzmSdwQhjfKhJRJtAdVR6+m8yYX0FhPuyjuCkKImh/OOYHP6dEbyo48+ulo5CCHkukB1lBBiS2itbU7Wg67TsKTAaz/vCEL6/iLNC0dId6lf0/XUlmxKp+Uj5exelc47gs2hRpKTZPjzjiAs34Zo3hGEND7AjXcEQoQycuIA3hGENXqIO+8IQoqdN4R3BJtDjSQn3rDnHUFY9h1uvCMIyceelkgkpDt3X1ruzhIvF6oXctwD6Vpza6NGkpNatPOOICy9mpawklPbLsbEtYSIorGmjXcEYdU1U72Q01jRzDuCzaFGkpPdKOEdQVil7pm8IwhpR1Et7wiECOXo1ku8Iwhr7+ka3hGEdOSb07wj2BxqJDm5ERG8IwgrrGoi7whCWjrIl3cEQoQyZRld72bJ/CSaGknOtD8m8Y5gc6iRJIQQQggh/UKNJCcnUc07grBqnC7yjiCk41V07Sgh3V3IpCmxLDldQEuqyjm/r4B3BJtDjSQn7TDxjiAsk8LAO4KQ2k1m3hEIEYq+3cg7grDaO6heyNG30k1I1kaNJCejQde7WeLTFMU7gpDG+NGSZ4R0F50cwDuCsBIGufGOIKThMwbxjmBzqJEkhBBCCCH9Qo0kJxtRwDuCsAo9D/KOIKQNeVW8IxAilH3f5/KOIKytdP2orLSPaHo5a6NGkpN4ePOOICyvpsG8IwhplC+tyEBId1E0xY1FMeF0KYycYZPDeUewOdRIcuIPR94RhOVg8OQdQUgBjjreEQgRilegE+8IwvJzp3ohxyuM1iC3NmokOWkE3ZlsiUHdyjuCkBoMdIcqId21NOh5RxBWUyvVCzkttfT6Ym3USHKyBbS0lyVF7od5RxDSxnyae5SQ7g6spzlnLdl+nK6plrPv4+O8I9gcaiQ5WQqagsCSiKrJvCMIadlguh6MkO6mrxjKO4KwFif7844gpJkrk3lHsDnUSBJCCCGEkH6hRpKTbNTyjiCsOsd83hGEdKqmmXcEQoSSd5LevrUkp4iWVJVz8XAR7wg2hxpJTupBF4lbYlC18I4gpHo9XTxPSHdNdVRHLWmkm21kNVXTzTbWRo0kJ8mg61cs8W2M5h1BSOMD3HhHIEQoIycO4B1BWKMH0zQ3cmLm0jzF1kaNJCGEEEII6RdqJDmh6X8sK/I4wjuCkDYW0PQ/hHR3cANN/2PJDpr+R9b+T2j6H2ujRpKTaNDqLZa4t4TxjiCk4Z60igch3UWMpKVmLYkKonohZ+CYIN4RbE6fGsmXX34Zo0aNgrOzM3x8fLBgwQKcO3fuamWzaUGgJ7klTnof3hGEFOJsxzsCsQKqo9bjG0rrz1sywMuedwQh+UV68Y5gc/rUSKalpeGhhx7C4cOHsWPHDhiNRkyfPh0tLXSXbV+1gu6os8Soojsx5bQaTbwjECugOmo97S0dvCMIq01P9UJOexO9vlibui8bb926tce/16xZAx8fH2RmZmLChAlWDWbr1inyeEcQVoHXXt4RhPRtbiXvCMQKqI5az56vzvOOIKyf0it4RxDSrnfTeUewOb/pGsmGhgYAgIeHh1XCXE+WsUjeEYQ1sGIa7whCWjGEpoyyRVRH+2/W3cN4RxDWTeMDeEcQ0pxnxvOOYHP6dEayO8YYVq5ciXHjxiE6+vLz/hk79DB2/HKaXalUQ6nq90MTQohN6FMdZR0wsl/eylVCCaVCdbUjEkLIZfW7m3v44YeRlZWF/fv3/+q2xzb/A62tv8wmP2P6QiTGzUZWZhUmzZTuoMo+UQOlUoGoEdJR+c5NhYhP8oW7pw4NdXqkH6jAtLnBAICzp2th7GCIjpXufE7dVozoWE94+dijudGA/btLMXNBKADgQk49Wpo7EDPKG0Z7FXYcq8KwEGcEeNqhtd2EzUcrcOPPR27ni5tR02TAmCgpw54T1RgY6Iggb3voO8z48VA5Fo/3h1KhQF5ZC0pr2jEuWsqw71QNBnjbI8zPASYzww/7yzB/rB+0aiUKK9uQV9aCiSOli3wPnqlFW7kKt6qlF47PTpdh8WAfOGhUKGxsx+mqZsyOkLbdX1wPN50a0d7SzTlrz5RjToQXXHRqlDbrkVHWiBsGSXcuHi5tgJ1KiRhfZwDANzkVmBrmAQ87DSpbDThQVI+Fg6UbWTLKGwEACX7SxepvZvhgvL8bfO21qGnvwI6SWtwc4QsAOF7dhDaTGWN9XQEA6wuqMMrbBYGOOjQYjPjpUjVuG+QHAMiqbUa93ogJ/m4AgI2XqjHc0wkhTnZoMZrwbV4l7oiUzqzl1LWgvM2ASQHSxLlbi2oQ6eqAeE9naAqW4uucCtwy1A9qpQK5da241NCOKaHS32b3pVoEOdthkIcDzIzhi+xyLInyhZ1KiYKGNpyracGMcGkM9xbVwcteg6Fe0hh+froMCwZ7w0mjRnFTO05WNGHOQGkMDxbXw1mnwnBvaQwH7CjBlDhvODtqUFnXjuMXGjAj8edxuVAPjUqB6HBpXDYeLEPycE+4O2tR22jAoexazBkjjcupiw0wmRliBknjsvVIOeIHu8PbTYfG5g6knqjCDeOk/fBMQSPa9CbE/zyh8I6jFRge7gqvQBVudfXErn1luGGG9LzJzW9EfUMHEmKk/XDvoQoMDHNGgJ8D9HoTtuwuxfyZA6BQKJBf2IyKqnYkxUvjciC9EkGBjggOdITRZMbG7SWYOz0QapUShSUtKCppQXKitL8czqyGr7cdwoKdwBjDhq3FmDvGBzp7FcoKm5GXU4fknzMd318OVw8dwodK+beszcWEuSFwdNagsqQFZ09UY8KcEGl/OVwBOwc1IkdI+bd/exFjpgXB2U2Lmoo2nDpSgYk3SM/l7IwqKFUKRMVK+Xety0fceH94+ly7NxX0pY7uZ5vQyrrV0fGzkRiajKzNZzHpgSQAQPb2C1CqlIiaEgEA2Pnvg4hfHA33QBc0lDUh/ZssTHs0GQBwdk8ejHojomdK746krjqC6BmR8ApzR3N1C/avycTMp6S32i/sL0BLXRti5kUBAPb9JwOR48PgO8gTbQ3t2P3eEcx5NgUAkJdejLqSBsQvlM4WHvz0OEITAhEw1AeG1g5sf+sAZj+TAqVKgcLjpSi/UIPEm4YDAI58lQX/KG8Ej/SHqcOMLa/vxYyV46CxU6MkuwKFJ8owZlkMACDj+2zoa5ow81bpd9306j5MeTARds46lJ+vRu6hIoxbEQsAOLHxHJy9HBCRJO2jW984gPF3xMLRwwHV+XXI3p2HlLvjAQCntl2AzkGDyPGhAIAdbx/G6Jui4eLrhLqSRhz/6Swm358IAMjZLV2iFDU5HACwe/VRxM4dDPdAFzRWtODIN6cx7Y+jAQDn912CvrUDw2cMBACkfXQMwyaHwSvMHS21bdj3yXHMfHwsAODi4WI0Vbd0TZ69/9MTGJgUBL9IT7Q3GbDrvXTMeXocAKAgsxQ1RQ2IXyD9bQ6vPYXgkX5w9+vAkkAdtv/rEGY+MRYqtQpFWeUoO1uNxJuk156j32XDd5AHgkf6g5nN2Pz6AUx7JAlaew1Kc6pQcKwUY5eNBAAc25AD9wAXhI0KBABsfn0/Jt2XAHtXO1Tk1uD8/kKMv0Ma75Obz8PRzQ4Dx0qv2Vt/qMG4O+Lh5OWA6oI6nN52ARP/II3h6W0XoNapMGSiNIY7/n0QiUuGw9XfGXUljcj8IRtT/zjm5/G+CLOJYdg0aQz3vHcEI+YMhmewG5qqWnDws+OYsXLcz+NdgPYmPUbMlsYw7cOjiJoYDmcve0y8Nw5pq9Mx+5mUn8e7CA3lTYhbMBQAcOCTYwhPDIJ/lDf0zQbs+PdBzHluIhQKoCCzBFV5dRi1JPrn8T6JAdG+GDDcD0aDCVv/dx9mPjkeaq0KxafKUXy6Akm3SGN49NvT8A53R2h8IBgDNr2cimmPjIXOSYuynErkHSlC8h3SfnhsXTZc/Z0RkSSN4eZXUpFy32g4etijMrcGOXsuIuVeaQyzNp2FnbMOkROkGU+2vbEPY2+Lg7OPI2ou1V9RjfAMdkN/KRhjrK/f9Mc//hHr16/H3r17ERZmeaoWo9GItLQ0vPa3NBj0/3VGUvn7n5FsCnH43R/TEs8hDihsbOcdo4tRoNficI2dMGPjVCzOBeuhDlqUlrfxjtHFrkyMtb+1OhUee2k8UlJSoFZfO+909LmO3rAKhlYBzkgqFL//Y1rgP8wP5edqeMf4hVKcsfEb5CHM2DCjODdF+Q32Rvk5gebY7HsLdlVoHTRYueHuftXRPl0jyRjDww8/jB9++AG7d+++bPHrTq226/HBo4kUTUowLV9lCY2NvMRYmrbCFvS7jio0PT7obW0g/uczSKQ3Ght5CYvpulpr61NH99BDD+HLL7/Ehg0b4OzsjPLycgCAq6sr7O0FOqVFCCGCojpKCLElfToj+d5776GhoQETJ06Ev79/18fXX399tfLZrO35YrzlICIaG3n7j9D0P7aA6qj1HF6bxTuCsGhs5B364gTvCDanT2ck+3E5JbFgkLsDKloMvGMIicZGXmiQI6praTLdax3VUesJHumHmsIG3jGERGMjLzjGHzWX6nnHsCm01jYnYW70FpYlNDbyBgQ48o5AiFAChtJyqpbQ2MgLHObLO4LNoUaSE4PJzDuCsGhs5HV00LgQ0l1HOy01awmNjTwaF+ujRpKTr3No+SpLaGzkbdpZwjsCIULZ/q9DvCMIi8ZG3rY3fn3OVtI31EhycstQP94RhEVjI2/e9AG8IxAilJlPjOUdQVg0NvJmPUXr2VsbNZKcqAWauFY0NDbyVCoaF0K6U6lpLk1LaGzkqTTU9lgbjSgnuXWtv77RdYrGRt6lYjFWkiFEFEVZ5bwjCIvGRl7hyTLeEWwONZKcXGoQYwlAEdHYyCspE2d5REJEUHa2mncEYdHYyCvLEWh5RBtBjSQnU0I9eEcQFo2NvLGjvHlHIEQoiTdF844gLBobeaNvHsE7gs2hRpIQQgghhPQLNZKc7L5UyzuCsGhs5B3OpLdkCOnu6HfZvCMIi8ZGXvo3p3hHsDnUSHIS5GzHO4KwaGzk+fnQij+EdOc7iC6DsYTGRp7fIE/eEWwONZKcDPJw4B1BWDQ28kKDnHhHIEQowSP9eUcQFo2NvODYAN4RbA41kpyYGeMdQVg0NvJoXAjpiZlp2VBLaGzkmU1UR62NGklOvsimOb4sobGR9+PWYt4RCBHK5tcP8I4gLBobeZtfTeMdweZQI8nJkihf3hGERWMjb/ZUekuGkO6mPZLEO4KwaGzkTX8smXcEm0ONJCd2Khp6S2hs5Gk1tOQZId1p7TW8IwiLxkae1oHGxdroFZuTggZapcQSGht5JWW0dCQh3ZXSKiUW0djIKz1TyTuCzaFGkpNzNS28IwiLxkZe3qUm3hEIEUrBsVLeEYRFYyOvIKOEdwSbQ40kJzPCvXhHEBaNjbzxSXTtKCHdjV02kncEYdHYyBu7PJZ3BJtDjSQhhBBCCOkXaiQ52VtUxzuCsGhs5B09Xs07AiFCObYhh3cEYdHYyMtcR0tHWhs1kpx40R11FtHYyHN31/GOQIhQ3ANceEcQFo2NPPdAV94RbA41kpwM9aLl7iyhsZE3MNSZdwRChBI2KpB3BGHR2MgLTxzAO4LNoUaSEEIIIYT0S58byb1792LevHkICAiAQqHA+vXrr0Is2/f56TLeEYRFYyNvw9Yi3hGIlVAdtY7Nr+/nHUFYNDbyNr1CSyRaW58byZaWFowcORLvvPPO1chz3Vgw2Jt3BGHR2MibluLPOwKxEqqj1jHpvgTeEYRFYyNv8gOjeUewOeq+fsOsWbMwa9asq5HluuKk6fPQXzdobOQ52NO42Aqqo9Zh72rHO4KwaGzk0bhYH10jyUlxUzvvCMKisZFXXklLRxLSXUVuDe8IwqKxkVdxgcbF2n6XUxxGYzuMRlPXv5VKNZTK6/vsyskKWu7OEhobeTkXGnhHIBwZWQeMrKPr30oooVSoOCbi7/z+Qt4RhEVjI+/8vnzeEWzO79LN7T/2T7S2tnb9e+akeRg9ZApOpRYh5dYoAMCZ/SVQqRQYPCYAALDn02zEzgiDm68DGqpakbk5D5NXRAMAzh8pg9FgwtDx0m38e9fmYNj4AfAc4IzmunYc+v48pt0zAgBwMbMCLQ16jJgcDOi02Le1CJHDPeAb6IjWlg6k/nQJs28eCADIO1eP+up2xCX7AQAO7ixGaKQrAoKdYdCbsOOHfMxaGgGlUoHCiw2oKG7BqBQpb3pqKfyDnRAU7gKTyYyt3+Rh+uIwaLQqlFxqQlFuI5KmSNMxZO4vQ0qYD5oapReFjd/lY+qcINjZq1Fe0oILZ+sx/udtj6dXwdlFg4FD3AAAW9YVYPzUQDg5a1BV0YbskzWYOF0ah1PHqqHVqTB4mDsAYPtPlzB6vD9c3bSorW7H8fQqTJkdJI33yVoAwNCRHgCAXZuLEJvoDQ8vOzTUG3BkXxmmzwsBAJzLroNBb8LwOGnpwtTtxRg20hPevvZoburAvp0lmLUwFACQe7YeTY0diE2UrnPct6sEg4a4wS/QEe1tRuzcVIS5N4YBAPJzG1FT1YaEMdLSf4fSyhAc5oy4RG+U5DVix9cXMeOWgVCplSjKbUDZpSYkTpF+14zdJfAJckLwIFeYzQxbv7iAqUsioLVToaygCQXn6jFmhvS7Ht9bCjcve4QNlcZly+fnMXFBGOydNKgobsaFkzUYN0f6XbMOlsPBWYOBwz0BADs2nsKY+RFwctOhprQZ2QfKMGHJIGkMD5ZCrVUhMkHKv/vLs4ifHgJXL3vUV7bi+K4iTLplsDSG6eUwmRiGjpGuc0z75jyGTwiEh58jmuracWRjPqbeLj0XLhyrhL6lA9HjpX1g/w+5GDzKF8OSA1BwshL71uZgxv0x0hieqEBDVRtipknjf+j78wiL8YFfhBv0rR3Y/fFpzHwgBgqFAoWnq1BV2IT42eHSPvtjLgIHeyBwsAdMHSZs/yAL0+8dAZVGhZJztSg5V4vEG6TnRubmPHgHOyM42huMMWx97wQm3x4FnaMGZedrkX+8HGOXDpXGe8tFuPo4Ijxeeh5tffsoJtw+Ag5uOlTm1+PcgSKMv224tM/uzIedkxaDkqTfdceqY0i6MQrOXvaoLW5C1s58TLxDei6fSb0EpUqJIeOlv+vuD08gbs5AeAy4PqZF2o/NaMUvdXTG+NlIDE1G1uazmPRAEgAge/sFKFVKRE2JAADs/PdBxC+OhnugCxrKmpD+TRamPZoMADi7Jw9GvRHRMyMBAKmrjiB6RiS8wtzRXN2C/WsyMfOpCQCAC/sL0FLXhph50j6676OjiBwfBt9IL7Q1tGP3u4cx57mJAIC8I0WoK2lE/KJhAICDnx5DaMIABAz1gaG1A9vf3I/Zz6ZAqVKi8Hgpys9XI3Gp9Dc+svYk/KN8EBzjD1OHCVte24sZK8dDY69GyekKFJ4oxZjbpCXuMr4/jUn3j0ZTVQsAYOOLezD1kbGwc9ah/GwVLhy8hPF3SdcJHt9wBs7ejhg4Vnqeb3ktDePvHgUnTwdU5dUie0cuJv4hEQBwavM5aB21GJwi1antb+7H6FtGwtXPGbVFDTi+4QymPDwGAHBmZy4AYOhU6Xmy651DiJ0/FB5Brmgob8KRtScx/fFxAIBzafkwtBgwfLZUE1LfT8ewaQPhHe6B5ppW7PvoKGY9nQIAyD14CU1VLYidLz2n9v0nA4PGhsBviDfam/TY+e+DmPunSQCA/KPFqCmsR8Ji6fXx0OfHERwTgLiFw1CcVY5tb+zDrKcnQKVRofBEGcpyKjH6Fmn5xPSvs+AX6YXg2ACYTWZsfiUN0x8fB62DBqVnKlGQUYyxy+MAAJk/ZMM90AXho6Xn36aXUzH5wSTYu9qh4nw1zu/Lx/i7RwEATvyUA0d3ewwaFwoA2Pr6Xoy7Mx5OXo6ozq/D6W3nMfF+6VrF01vPQ61TY8gkqS7t+NcBJN40Aq7+zqgraUTm96cx9ZGxAICcXRdhNpkxbLpUg/e8dxgjZg+BZ4gbmipbcPDzY5ixcjwA4PzefLQ36TFizhAAQNoH6YiaFIHhswYj70gR0lYfwexnpX324uFCNJQ1IW6htM8e+DgT4aOD4B/lA32zATv+dQBznpsIhVKBgswSVOXVYtQSqYYd/vIEBkT7YcAIPxgNJmx9fS9mPjUBaq0KxVnlKD5djqRbYwAAR789Be9wD4TGB4KZGTa9nIppjyZD56RFWU4l8o4UIfmOeADAsXXZcPV3RkRSMABg8yupSLlvNBw97FGZW4OcPReRcq+0z2ZtOgs7Zx0iJ0j77LY39mHsbXFw9nFEzaX6K6oRnsFu6C8FY4z1+5sVCqxbtw4LFiyQ/brRaERaWhreuPcHGNqMXZ9XKlRcjqTNvh6/+2NaMvu2Qdj4HR0ZyZm3IASbPzvPOwYAQNEiztvJs+4aii3vHucdowtrF+MSBK29Go9+ugQpKSlQq6+9dzqutI6+dsMqGFrpjGR3c/80CRtf3MM7hpBobOTRuMjTOmiwcsPd/aqjv0vVVSu0MCvpcszuThyt4h1BWFkHy3lHEFLW7ku8IxCO1AoNzAreKcRy4idaBtASGht5NC7W1+dGsrm5Gbm5uV3/zs/Px4kTJ+Dh4YHg4GCrhrNljk60DKAlDs40NnIcXWmJRFtBddQ6HN3teUcQFo2NPBoX6+vzacKMjAzExsYiNla6TmXlypWIjY3FX/7yF6uHs2WDotx4RxBW5/WJpKeIn685JNc+qqPW0XkNHumNxkYejYv19fmM5MSJE/EbLqskhJDrHtVRQoitoAsXOdm6voB3BGFt/yr31ze6Du344CTvCIQIZevre3lHEBaNjTwaF+ujRpKTcZMDeEcQ1thZdI2YnDE3DuYdgRChjLsznncEYdHYyKNxsT5qJDlxctHyjiAsJ1caGzlO7rS0FyHdOXk58o4gLBobeTQu1keNJCfVtNydRdVlrb++0XWopphW/CGku+r8Ot4RhEVjI4/GxfqokeTk9HFa79OS7PRK3hGElL23iHcEQoRyepsYCxeIiMZGHo2L9VEjycnEGQN4RxBWyvxQ3hGENOHWobwjECKUzmX2SG80NvJoXKyPGklCCCGEENIv1EhyQm9tW0Zvbcs7Q29tE9LD6a30NqUlNDbyaFysjxpJTtQaWjTXErWGdks5aq2KdwRChKLW9XlNjesGjY08Ghfro1dsToZEe/COIKzBsV68IwgpMonmHiWkuyGTwnlHEBaNjTwaF+ujRpIQQgghhPQLNZKc7NhYyDuCsHZ9d5F3BCHt/vg07wiECGXHvw7wjiAsGht5NC7WR40kJ4nJvrwjCCthUiDvCEKKn0NvyRDSXeJNI3hHEBaNjTwaF+ujRpITV3cd7wjCcvWkpQDluHo78I5AiFBc/Z15RxAWjY08Ghfro0aSk7oaPe8IwqqvouUj5dRXtPCOQIhQ6koaeUcQFo2NPBoX66NGkpPMwxW8Iwjr2N4y3hGEdHxrPu8IhAgl83u6btgSGht5NC7WR40kJ1PnBPOOIKzJi+laQDmTVkTzjkCIUKY+MpZ3BGHR2MijcbE+aiQJIYQQQki/UCPJSU5WLe8Iwjp7rIp3BCGdO1TCOwIhQsnZRVOFWUJjI4/GxfqokeTEbGa8IwjLbKKxkWOicSGkB7PJzDuCsGhs5NG4WB81kpwMi/HkHUFYQ0f58I4gpKHjBvCOQIhQhk0fxDuCsGhs5NG4WB81koQQQgghpF+okeRkz9Yi3hGElbqeprmRk/bFGd4RCBHKnvcO844gLBobeTQu1keNJCcj4r15RxDWiDG0fKSc4ZNoyihCuhsxewjvCMKisZFH42J9V7WR1Oul1VvMzHQ1H+aaYzYbkX5sM8xmI+8owjGbjTiStY3G5r+YmRFHzu6i55IMM5Munu+sN7aG6qg8MzMhveAAjYsMGht5NC6WdY5Jf+povxrJd999F2FhYbCzs0N8fDz27dsnu53BYJACgv5o3ZmZCdu2r6NmSYbZbMTWnevpif5fzMyErXt+onGR0VlfOuvNtaLvdZTuNu3ODDO27dtM4yKDxkYejYtlnWPSnzra50by66+/xmOPPYY//elPOH78OMaPH49Zs2ahsLCwzw9OCCHXI6qjhBBb0edG8o033sDdd9+Ne+65B1FRUXjrrbcQFBSE995772rkI4QQm0N1lBBiK9R92dhgMCAzMxPPPvtsj89Pnz4dBw8e7LU9Y9IEykotA7qdSlZCBaVC1Y+4v41Z9/s/phylSQUHBwdodSqo1WJkEoVS9fPY2KmgVvIfG4WpT0+Rq0ZpNknjYq+GWiFGJiZIDqVOqjOd9UZ0/a2jKnsFgF9+RyWUXOqoKJQMcIADtNBArdDwjiMUGht5NC6WKe2k//anjvbplaC6uhomkwm+vj3vqvX19UV5eXmv7VUqqcg99n+L+xzM1j2JybwjCOtppPCOIKSVmMc7gtA6643o+ltHn/j6D79LvmvJ07ifdwRh0djIo3G5vP7U0X6dUlAoFD3+zRjr9TkAcHZ2RkJCAkwmU4+va7Va6HS6/jw0IYT0wBiDyWSCVqvlHaVPqI4SQkTxW+ponxpJLy8vqFSqXkfNlZWVvY6uAUCpVMLV1bXPoQghpC80mmvnbSqqo4QQEfW3jvbpZhutVov4+Hjs2LGjx+d37NiBsWPH9isAIYRcT6iOEkJsSZ/f2l65ciVuv/12JCQkYMyYMVi9ejUKCwtx//103QEhhFwJqqOEEFvR50Zy6dKlqKmpwQsvvICysjJER0dj8+bNCAkJuRr5CCHE5lAdJYTYin6tbPPggw+ioKAAer0emZmZmDBhQq9trnTVhuvJyy+/jFGjRsHZ2Rk+Pj5YsGABzp07xzuWcF5++WUoFAo89thjvKMIoaSkBLfddhs8PT3h4OCAmJgYZGZm8o7FldFoxJ///GeEhYXB3t4e4eHheOGFF2A2XzsrVlAd7R+qo1eG6mhPVEd7s1YdvSprbdOqDfLS0tLw0EMP4fDhw9ixYweMRiOmT5+OlpYW3tGEcfToUaxevRojRozgHUUIdXV1SE5OhkajwZYtW3DmzBn885//hJubG+9oXL366qtYtWoV3nnnHeTk5OC1117D66+/jrfffpt3NKuhOiqP6uivozraE9VReVaro+wqSExMZPfff3+Pzw0ZMoQ9++yzV+PhrlmVlZUMAEtLS+MdRQhNTU1s0KBBbMeOHSwlJYU9+uijvCNx98wzz7Bx48bxjiGcOXPmsLvuuqvH5xYtWsRuu+02Tomsj+rolaE62hPV0d6ojsqzVh21+hnJzlUbpk+f3uPzllZtuJ41NDQAADw8PDgnEcNDDz2EOXPmYOrUqbyjCOPHH39EQkIClixZAh8fH8TGxuKDDz7gHYu7cePGYdeuXTh//jwA4OTJk9i/fz9mz57NOZl1UB29clRHe6I62hvVUXnWqqNWX+Osr6s2XK8YY1i5ciXGjRuH6Oho3nG4++qrr3Ds2DEcPXqUdxSh5OXl4b333sPKlSvx/PPPIz09HY888gh0Oh2WL1/OOx43zzzzDBoaGjBkyBCoVCqYTCa8+OKLuOWWW3hHswqqo1eG6mhPVEflUR2VZ606etUWy73SVRuuVw8//DCysrKwf/9+3lG4KyoqwqOPPort27fDzs6OdxyhmM1mJCQk4KWXXgIAxMbGIjs7G++99951XQC//vprfP755/jyyy8xbNgwnDhxAo899hgCAgKwYsUK3vGshuro5VEd/QXVUcuojsqzVh21eiPZ11Ubrkd//OMf8eOPP2Lv3r0YMGAA7zjcZWZmorKyEvHx8V2fM5lM2Lt3L9555x3o9fprZh1la/P398fQoUN7fC4qKgrff/89p0RieOqpp/Dss8/i5ptvBgAMHz4cly5dwssvv2wTjSTV0V9HdbQnqqOWUR2VZ606avVrJGnVBssYY3j44Yfxww8/YPfu3QgLC+MdSQhTpkzBqVOncOLEia6PhIQELFu2DCdOnLhuix8AJCcn95ra5Pz589f9fIOtra1QKnuWL5VKdU1N/3M5VEctozoqj+qoZVRH5Vmtjlrn3p+evvrqK6bRaNhHH33Ezpw5wx577DHm6OjICgoKrsbDXTMeeOAB5urqylJTU1lZWVnXR2trK+9owqG7DSXp6elMrVazF198kV24cIF98cUXzMHBgX3++ee8o3G1YsUKFhgYyDZu3Mjy8/PZDz/8wLy8vNjTTz/NO5rVUB2VR3X0ylEdlVAdlWetOnpVGknGGPu///s/FhISwrRaLYuLi6OpGRhjAGQ/1qxZwzuacKgA/uKnn35i0dHRTKfTsSFDhrDVq1fzjsRdY2Mje/TRR1lwcDCzs7Nj4eHh7E9/+hPT6/W8o1kV1dHeqI5eOaqjv6A62pu16qiCMcZ+49lRQgghhBByHboqK9sQQgghhBDbR40kIYQQQgjpF2okCSGEEEJIv1AjSQghhBBC+oUaSUIIIYQQ0i/USBJCCCGEkH6hRpIQQgghhPQLNZKEEEIIIaRfqJEkhBBCCCH9Qo0kIYQQQgjpF2okCSGEEEJIv1AjSQghhBBC+uX/A5CXYBgtmc6XAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = psf_lib.get_psf_image(0, 0, pix_zero_loc=\"edge\", norm=10000)\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "axes[0].imshow(np.log10(img), origin=\"lower\", extent=(0, 8, 0, 8))\n", + "axes[0].set_title(\"ACA PSF (log scale)\")\n", + "axes[1].imshow(img, origin=\"lower\", extent=(0, 8, 0, 8))\n", + "axes[1].set_title(\"ACA PSF (linear scale)\");" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1467208e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(offsets_int, dmags_int[\"mean\"])\n", + "plt.xlabel(\"Image center offset from search box edge (pixels)\")\n", + "plt.title(\"Effective magnitude delta vs. offset from box edge\")\n", + "plt.ylabel(\"Magnitude delta (mag)\");" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "30b6df44", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-20. -10. -5. 0. 5. 12.5 17. 20. ]\n", + "[0.0, 0.03, 0.18, 0.75, 2.03, 5.12, 6.28, 10.0]\n" + ] + } + ], + "source": [ + "print(np.array(offsets_int) * 5)\n", + "print([np.round(x, 2) for x in dmags_int[\"mean\"]])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ska3-jupiter", + "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": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/index.rst b/docs/index.rst index 7821e7ea..0edb370d 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -311,6 +311,9 @@ The following environment variables are used by proseco: If this is a relative path then it is relative to ````. - ``AGASC_SUPPLEMENT_ENABLED``: set to ``"False"`` to disable using the AGASC supplement. This is for testing and should not be used in production. +- ``PROSECO_DISABLE_BOX_EDGE_DMAG``: If set to ``"True"`` then disable the + box edge dmag offset in the acq star selection. This is for testing and should not + be used in production. - ``PROSECO_DISABLE_OVERLAP_PENALTY``: if set to ``"True"`` then disable the overlap penalty in the acq star selection. This is for testing and should not be used in production. diff --git a/proseco/acq.py b/proseco/acq.py index 41698fff..ced2f940 100644 --- a/proseco/acq.py +++ b/proseco/acq.py @@ -45,6 +45,10 @@ OVERLAP_MAG_DEADBAND = 0.2 # overlap penalty applies for mag difference > deadband OVERLAP_PAD = 20 # arcsec, extra padding for overlap check +# Constants in get_box_edge_dmag() function +BOX_EDGE_DMAG_OFFSETS = np.array([-20.0, -10.0, -5.0, 0.0, 5.0, 12.5, 17.0, 20.0]) +BOX_EDGE_DMAG_VALUES = np.array([0.0, 0.03, 0.18, 0.75, 2.03, 5.12, 6.28, 10.0]) + def load_maxmags() -> dict: """ @@ -450,7 +454,7 @@ def update_idxs_halfws(self, idxs, halfws): """ Update the rows of self to match the specified ``agasc_ids`` and half widths. These two input lists must match the length - of self and correspond to stars in self.cand_acqs. + of acqs and correspond to stars in self.cand_acqs. :param agasc_ids: list of AGASC IDs :param halfws: list of search box half widths @@ -1282,6 +1286,22 @@ def get_spoiler_stars(stars, acq, box_size): return spoilers +def force_even_and_on_ccd(val: int, offset: int) -> int: + """Force val to be even and on the CCD (0-1024). + + Parameters + ---------- + val : int + Input value. + offset : int + If ``val`` is odd then add ``sign(offset)`` to make even. + """ + if val % 2 == 1: + val += np.sign(offset) + val = np.clip(val, 0, 1024) + return val + + def get_imposter_stars( dark, star_row, @@ -1320,26 +1340,10 @@ def get_imposter_stars( box_col = int(box_size.col) # Make sure box is within CCD - box_r0 = np.clip(acq_row - box_row, 0, 1024) - box_r1 = np.clip(acq_row + box_row, 0, 1024) - box_c0 = np.clip(acq_col - box_col, 0, 1024) - box_c1 = np.clip(acq_col + box_col, 0, 1024) - - # Make sure box has even number of pixels on each edge. Increase - # box by one if needed. - # - # TO DO: Test the clipping and shrinking code - # - if (box_r1 - box_r0) % 2 == 1: - if box_r1 == 1024: - box_r0 -= 1 - else: - box_r1 += 1 - if (box_c1 - box_c0) % 2 == 1: - if box_c1 == 1024: - box_c0 -= 1 - else: - box_c1 += 1 + box_r0 = force_even_and_on_ccd(acq_row - box_row, -1) + box_r1 = force_even_and_on_ccd(acq_row + box_row, 1) + box_c0 = force_even_and_on_ccd(acq_col - box_col, -1) + box_c1 = force_even_and_on_ccd(acq_col + box_col, 1) # Get bgd-subtracted dark current image corresponding to the search box # and bin in 2x2 blocks. @@ -1517,14 +1521,59 @@ def get_intruders(acq, box_size, name, n_sigma, get_func, kwargs): colnames = ["yang", "zang", "mag", "mag_err"] if len(intruders) == 0: - intruders = {name: np.array([], dtype=np.float64) for name in colnames} + out = {name: np.array([], dtype=np.float64) for name in colnames} else: - ok = (np.abs(intruders["yang"] - acq["yang"]) < box_size.y) & ( - np.abs(intruders["zang"] - acq["zang"]) < box_size.z + dys = intruders["yang"] - acq["yang"] + dzs = intruders["zang"] - acq["zang"] + box_margin = ( + 0.0 if os.environ.get("PROSECO_DISABLE_BOX_EDGE_DMAG") == "True" else 20.0 ) - intruders = {name: intruders[name][ok] for name in ["mag", "mag_err"]} + ok = (np.abs(dys) < box_size.y + box_margin) & ( + np.abs(dzs) < box_size.z + box_margin + ) + intruders = intruders[ok] + mags = [] + for dy, dz, mag in zip(dys, dzs, intruders["mag"]): + mag += get_box_edge_dmag(dy, box_size.y) # noqa: PLW2901 + mag += get_box_edge_dmag(dz, box_size.z) # noqa: PLW2901 + mags.append(mag) + out = { + "yang": intruders["yang"], + "zang": intruders["zang"], + "mag": np.array(mags), + "mag_err": intruders["mag_err"], + } + + return out + + +def get_box_edge_dmag(dyz, box_size): + """ + Calculate the magnitude penalty for an intruder near the edge of a search box. + + This uses linear interpolation of an empirical relationship determined in the + ``pr409-intruders-at-search-box-edge.ipynb`` notebook. - return intruders + Parameters + ---------- + dyz : float + Offset from the box center to the intruder (arcsec). + box_size : float + Size of the box (arcsec). + + Returns + ------- + dmag : float + Magnitude penalty for being near the box edge. + """ + if os.environ.get("PROSECO_DISABLE_BOX_EDGE_DMAG") == "True": + return 0.0 + + d_box_edge = abs(dyz) - box_size + # Note: np.interp is at least 5x faster than scipy.interpolate.interp1d (even with + # that returning a lambda func). + dmag = np.interp(x=d_box_edge, xp=BOX_EDGE_DMAG_OFFSETS, fp=BOX_EDGE_DMAG_VALUES) + return dmag def calc_p_on_ccd(row, col, box_size): diff --git a/proseco/tests/conftest.py b/proseco/tests/conftest.py index 2252606d..0fb50031 100644 --- a/proseco/tests/conftest.py +++ b/proseco/tests/conftest.py @@ -17,6 +17,11 @@ def disable_fid_offsets(monkeypatch): monkeypatch.setenv("PROSECO_ENABLE_FID_OFFSET", "False") +@pytest.fixture() +def disable_box_edge_dmag(monkeypatch): + monkeypatch.setenv("PROSECO_DISABLE_BOX_EDGE_DMAG", "True") + + @pytest.fixture(autouse=True) def use_fixed_chandra_models(monkeypatch): monkeypatch.setenv("CHANDRA_MODELS_DEFAULT_VERSION", "3.48") diff --git a/proseco/tests/test_acq.py b/proseco/tests/test_acq.py index 489695f6..e90e6679 100644 --- a/proseco/tests/test_acq.py +++ b/proseco/tests/test_acq.py @@ -207,7 +207,7 @@ def get_test_cand_acqs(): return CACHE["cand_acqs"].copy() -def test_calc_p_brightest_same_bright(): +def test_calc_p_brightest_same_bright(disable_box_edge_dmag): """ Test for an easy situation of three spoiler/imposters with exactly the same brightness as acq star so that p_brighter is always 0.5. @@ -239,7 +239,7 @@ def test_calc_p_brightest_same_bright(): assert np.allclose(probs, [0.25, 0.25, 0.25, 0.3334, 0.5, 1.0], rtol=0, atol=0.01) -def test_calc_p_brightest_same_bright_asymm_dither(): +def test_calc_p_brightest_same_bright_asymm_dither(disable_box_edge_dmag): """ Test for an easy situation of three spoiler/imposters with exactly the same brightness as acq star so that p_brighter is always 0.5. @@ -308,7 +308,7 @@ def test_calc_p_brightest_same_bright_asymm_dither(): assert np.allclose(probs, [0.333, 0.333, 0.333, 0.5, 1.0, 1.0], rtol=0, atol=0.01) -def test_calc_p_brightest_1mag_brighter(): +def test_calc_p_brightest_1mag_brighter(disable_box_edge_dmag): """ Test for the situation of spoiler/imposter that is 1 mag brighter. """ @@ -541,7 +541,9 @@ def test_get_acq_catalog_21007(proseco_agasc_1p7, disable_overlap_penalty): assert info == exp -def test_box_strategy_20603(proseco_agasc_1p7, disable_overlap_penalty): +def test_box_strategy_20603( + proseco_agasc_1p7, disable_overlap_penalty, disable_box_edge_dmag +): """Test for PR #32 that doesn't allow p_acq to be reduced below 0.1. The idx=8 (mag=10.50) star was previously selected with 160 arsec box. diff --git a/proseco/tests/test_catalog.py b/proseco/tests/test_catalog.py index 1531c60d..8e65670e 100644 --- a/proseco/tests/test_catalog.py +++ b/proseco/tests/test_catalog.py @@ -109,12 +109,12 @@ def test_get_aca_catalog_20603(proseco_agasc_1p7): " 1 2 5 FID 8x8 -1829.63 156.96 1 1 25", " 2 3 116791824 BOT 6x6 622.00 -953.60 28 1 160", " 3 4 40114416 BOT 6x6 394.22 1204.43 24 1 140", - " 4 5 40112304 BOT 6x6 -1644.35 2032.47 12 1 80", + " 4 5 40112304 BOT 6x6 -1644.35 2032.47 16 1 100", " 5 6 40113544 GUI 6x6 102.74 1133.37 1 1 25", " 0 7 116923496 ACQ 6x6 -1337.79 1049.27 20 1 120", " 1 8 116923528 ACQ 6x6 -2418.65 1088.40 28 1 160", " 5 9 116791744 ACQ 6x6 985.38 -1210.19 28 1 160", - " 6 10 40108048 ACQ 6x6 2.21 1619.17 24 1 140", + " 6 10 40108048 ACQ 6x6 2.21 1619.17 16 1 100", ] assert aca[TEST_COLS].pformat(max_width=-1) == exp @@ -653,16 +653,22 @@ def test_bad_obsid(): assert "ValueError: text does not have OBSID" in aca.exception -def test_bad_pixel_dark_current(): +@pytest.mark.parametrize("no_box_edge_dmag", [True, False]) +def test_bad_pixel_dark_current(monkeypatch, no_box_edge_dmag): """ Test avoidance of bad_pixels = [[-245, 0, 454, 454]] - Put a bright star near this bad column and confirm it is not picked at all. - Put a bright star at col = 454 - 20 / 5 - 105 / 5 (dither and search box - size) and confirm it is picked with search box = 100 arcsec. + size) and confirm it is picked with appropriate search box size: + - 100 arcsec if the box edge dmag offset is disabled + - 80 arcsec if the box edge dmag offset is enabled (default). This means that + the bad pixel imposter is leaking into the search box as intended. """ + if no_box_edge_dmag: + monkeypatch.setenv("PROSECO_DISABLE_BOX_EDGE_DMAG", "True") dark = DARK40.copy() stars = StarsTable.empty() stars.add_fake_constellation(mag=np.linspace(8.0, 8.1, 4), n_stars=4) @@ -687,7 +693,8 @@ def test_bad_pixel_dark_current(): exp_ids = [2, 100, 101, 102, 103] assert sorted(aca.guides["id"]) == sorted(exp_ids + [4]) assert aca.acqs["id"].tolist() == exp_ids - assert aca.acqs["halfw"].tolist() == [100, 160, 160, 160, 160] + exp_halfw = 100 if no_box_edge_dmag else 80 + assert aca.acqs["halfw"].tolist() == [exp_halfw, 160, 160, 160, 160] def test_fid_trap_effect(): diff --git a/proseco/tests/test_jupiter.py b/proseco/tests/test_jupiter.py index d7f7c5b8..0f9885bf 100644 --- a/proseco/tests/test_jupiter.py +++ b/proseco/tests/test_jupiter.py @@ -272,7 +272,7 @@ def test_jupiter_midline(): @pytest.mark.parametrize("col_dist_arcsec", np.arange(-300, 305, 20)) -def test_jupiter_acquisition(col_dist_arcsec): +def test_jupiter_acquisition(col_dist_arcsec, disable_box_edge_dmag): """ Test how jupiter is handled during acquisition. diff --git a/ruff.toml b/ruff.toml index cad49f4e..f025714f 100644 --- a/ruff.toml +++ b/ruff.toml @@ -2,7 +2,11 @@ extend = "ruff-base.toml" # These are files to exclude for this project. extend-exclude = [ - "**/*.ipynb", # commonly not ruff-compliant + "analysis/pr388-backstop-vs-proseco-star-positions.ipynb", + "analysis/pr388-validate-drift-model-fid-positions.ipynb", + "analysis/maxmag/interpolate-maxmags-contour-plot.ipynb", + "analysis/maxmag/maxmags-demo.ipynb", + "analysis/maxmag/validate-maxmags-recent-loads.ipynb", ] # These are rules that commonly cause many ruff warnings. Code will be improved by