diff --git a/docs/source.rst b/docs/source.rst index 749a043..1901b38 100644 --- a/docs/source.rst +++ b/docs/source.rst @@ -31,7 +31,7 @@ Source Documentation :members: :inherited-members: -.. autoclass:: syncfit.models.BaseModel +.. autoclass:: syncfit.models.SyncfitModel :members: `syncfit.analysis` diff --git a/docs/tutorials/2_fit_custom_model.ipynb b/docs/tutorials/2_fit_custom_model.ipynb index 46bf96e..da604fd 100644 --- a/docs/tutorials/2_fit_custom_model.ipynb +++ b/docs/tutorials/2_fit_custom_model.ipynb @@ -82,7 +82,7 @@ " 86.0\n", " 3914\n", " 30\n", - " False\n", + " True\n", " \n", " \n", " 1\n", @@ -152,7 +152,7 @@ " 37.5\n", " 2360\n", " 40\n", - " False\n", + " True\n", " \n", " \n", " 8\n", @@ -222,7 +222,7 @@ " 15.9\n", " 801\n", " 14\n", - " False\n", + " True\n", " \n", " \n", " 15\n", @@ -240,21 +240,21 @@ ], "text/plain": [ " facility date dt nu F_nu F_err upperlimit\n", - "0 NOEMA 2022-03-24 22:14 41.48 86.0 3914 30 False\n", + "0 NOEMA 2022-03-24 22:14 41.48 86.0 3914 30 True\n", "1 NOEMA 2022-03-24 22:14 41.48 102.0 3609 34 False\n", "2 NOEMA 2022-03-25 00:45 41.58 136.0 3045 41 False\n", "3 NOEMA 2022-03-25 00:45 41.58 152.0 2750 51 False\n", "4 VLA 2022-03-31 04:08 47.73 31.4 2130 30 False\n", "5 VLA 2022-03-31 04:08 47.73 33.5 2260 30 False\n", "6 VLA 2022-03-31 04:08 47.73 35.6 2350 40 False\n", - "7 VLA 2022-03-31 04:08 47.73 37.5 2360 40 False\n", + "7 VLA 2022-03-31 04:08 47.73 37.5 2360 40 True\n", "8 VLA 2022-03-31 04:13 47.73 8.5 270 12 False\n", "9 VLA 2022-03-31 04:13 47.73 9.5 336 11 False\n", "10 VLA 2022-03-31 04:13 47.73 10.5 385 12 False\n", "11 VLA 2022-03-31 04:13 47.73 11.5 438 14 False\n", "12 VLA 2022-03-31 04:23 47.74 12.8 583 12 False\n", "13 VLA 2022-03-31 04:23 47.74 14.3 724 12 False\n", - "14 VLA 2022-03-31 04:23 47.74 15.9 801 14 False\n", + "14 VLA 2022-03-31 04:23 47.74 15.9 801 14 True\n", "15 VLA 2022-03-31 04:23 47.74 17.4 935 15 False" ] }, @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "1e28d045-addf-4966-a707-d7ba9d845e8e", "metadata": { "tags": [] @@ -332,12 +332,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [00:03<00:00, 161.31it/s]\n" + "100%|███████████████████████████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 2602.04it/s]\n" ] }, { "data": { - "image/png": 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", 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qT8eOoV7nyJxI5gEAgM+WLFkS9DGjfVnENm3aaNWqVUErubFLypN0jaTn9f3GUuMCGHNbgDEFypfrjx07VhaLRZ9++mmowzEVknkAAOCz5taPNyZWlkVMTk7W3//+d+Xm5vq0yo0v9snx4PAVku6XIyHu18yx1gYloubz5/o//vGPZbFYnF+xjmQeAAD4xG63a9u24M7hxtKyiFarVbNmzVJRUZFP69D7qkZSvhwJfXP/frBY38/wh9vxC9dvSufOnRt9zzWxP3jwYBAjMweSeQAA4JN9+/bp2LFjQR0zFpdF7NWrlzZs2KC8vDylpaUFbdxAnJb0mkHX/qu818tPmjRJdrvd+dWYjh07OhP7//gPXxYeNT+SeQAA4JOioqKgjxmryyJarVY9/PDDOnDggBYtWqThw4cHdfzm+JO8J9XBdlqOun9v7rnnHre2L4n9Cy+8EBPlOCTzAADAJwcOHAj6mLG+LGJ8fLwmTJigdevWafv27frtb3+rG264QW3bNnfV+ObbJ2lumK8598J1mzJ8+HD17t270fddE/vKyspG+7km9qH4YGoUo8vUAACASQSjbrzemHIsi3hN0Ef2XaQsi9i7d29n0mq321VWVqbCwkJt3bpVb7zxRsAP4friOUl3Sxoa8is51sv/kw/9HnjgAZ/HvPzyy91m6xubke/Xz/1R4ebsIxApmJkHAAA+qasLTVGMGZZF9Ez+Qs1isahLly66++679dRTT2nfvn1hqbG3S7pXje80GyzlkiZeuF5T0tLSdNdddzX7Oq6z9mPHjm20n5nLcEjmAQCAT4K1pKKnSF8WsW3btk2uphIO4ayx/1rS9QpdQn/gwvhf+9B39uzZQbvv3n77bZ9q7c2GZD5GZGdnKzMz0+gwAAAmFqq68UhfFnHAgAERM3Mbrhr7LyQNk6MUJpjWS8q+ML43gwYN0owZM4IcwfdcE/tQJ/eZmZnKzs4OydjUzAMAAJ906tQpJONeXBbxFyEZvWm+LIs4ePDgcITit8Zq7MvLy1VTU6MzZ84oISFBdrtd+fn52rNnj1/jfy1puKSHJf1e0qUBxHpajodd/yTvpTWS40PLwoULZbWSqnpjsUfT3xkgSaqsrKxXU1dRUaHU1FSDIgIARIOvvvpK3bp1C8nYXSQVy/tOrMF0Wo4Hb72tprJ9+/YmV1Mxg1OnTunuu+/WRx991Kzzu0j6D0mTJbXx47zjcnxgel7e/zu7ys3N1axZs/w4I/KFKj+jzAYAAPikS5cuIVsy0azLIppFcnKy/v73vys3N7dZNej7JD0k6Qo5dprNl7RT9ZcVtUnaoe93pL3iwnn+JPI333yzfvnLX/odY6wimQcAAD6xWCzq379/yMZ/TtKGkI3uLhTLIkY6q9WqWbNmqaioSAMHDmzWGDX6PlHvLSlFjln7H1z4N0VSH32f8Pu7mOnQoUP1zjvvUF7jB5J5AADgs1DWj0fbsoiRqlevXtqwYYPy8vJ0+eWXBzRWjaQySXsu/BvITgRDhgzRihUrlJycHFBMsYZkHgAA+GzcuHEhHT9al0WMNBeXujx48KAWLlxoyI6zrm6++WatWrVKbdq0MTQOMyKZBwAAPuvdu3fIlti7KBaWRYwU8fHxuu+++1RRUaGHHnpILVq0CPv1c3NztXz5cmbkm4nVbKIQq9kAAOx2u/bt26eioiIdOHBANTU1qqurU3x8vJKSktSpUydlZWWpS5cufq+hvmjRIk2cODFEkX/PIuOWRSwqKlKvXr0CuKo5lZSU6L777tOWLVtCfq2BAwcqPz8/Zv47hyo/4+kCAACixI4dO7RkyRJt2rRJ27Zt07Fjx7ye07ZtW/Xv31+DBw/WuHHjfFq55a677lJaWpoqKiqCEXaj7JLyJL2n8C6L+Pjjj8dMgunpYj39ggULlJubG5L/jdPS0jR79mzNmDGDB12DgJn5KMTMPADEjjNnzmjp0qV66aWXVFBQEPB42dnZeuCBB3T33Xc3WS9uxI6oSZLGSxohaYCkHnKflbRJ2iVpm6S1cuzs6u8DmTfffLOWL19Okimprq7OeW/985//DHi84cOH64EHHtBdd90Vtc8iNCVU+RnJfBQimQeA6Gez2QybPTUikW9IkqR0SfGS6iQdUWCrqQwdOlQff/wxtdsN2LFjh95++21t2rRJW7du9fmvPgMGDNDgwYN1zz33RMV6/YEgmY9ya9eu1UsvvaQNGzaooqJC7dq1U//+/TV16lSNGTPGr7FI5gEguhlZ1+yZyMfHx6uuri7kcYTakCFDtHLlSlZT8YHdbldZWZkKCwtVXl6umpoanTlzRgkJCUpKSlLHjh3Vr18/de7cOWI++EUCkvkoNmfOHOXm5qqx/ykmTZqk/Px8XXKJb4sPkcwDQHSy2Wx65plnNG/evLAm0PHx8Zo/f74eeeQRt9ftdrtyc3PrvW42N998s9555x1m5BFSocrPWJrSYC+99JKeeuop2e12DRgwQKtXr1ZFRYWKioqcKwW8/vrrpv9BCQAITHV1tW677TY98sgjYZ8Jr6urazCRl6Rf/epXuummm8IaT7CwLCKiATPzBjp27Ji6du2q48ePq3v37ioqKqr3w2TixIlatGiRrFarSkpK1L17d6/jMjMPANHl2LFjuvXWW7Vp0yajQ5HkiMe1HOXUqVO64YYbtHHjRuOC8lOsLYsI4zEzH0GOHj2q5cuX6z//8z/1hz/8Qa+99poKCgp07tw5v8Z5/fXXdfz4cUmOZbAamhV4+umn1aJFC9lsNr388svBCB8AYCLV1dUaPXp0QIl8kqQukn5w4d+kAGO69dZbderUKWc7OTlZK1eu1JAhQwIcOfTS0tKUl5enDRs2kMgjKkTNzHx1dbUKCwu1efNmbd68WVu2bFFZWZnz/auuusqt3Rx79uzR7NmztWLFigb/xJmRkaGpU6dqzpw5Pi25NGrUKK1Zs0YJCQk6fvy4EhMTG+w3YsQIrVu3Tl27dlVpaanXcZmZB4DoYLPZdNttt+mjjz7y67xLVX8JR9d9PW2Sdst9CcfTfsZ200036e9//7vbSjenTp3S3Xff7Xe84RDryyLCeCHLz+wm9+yzz9qvueYa+yWXXGKXY3+JBr+uuuqqgK7z+uuv25OTk5u8xsWvrKws+759+7yO2bp1a7sk+5AhQ5rsN3v2bOfYx44d8zpuRUVFvZgqKip8/E4BAJHiqaee8un3zsWvLpL9ecl+TLLb/fg6duG8Ln5cS5I9Nze3Xsxnz5615+bm2uPj4/0aK9hfbdu2td9www323/72t/bt27cb8L8e4C5U+Znpd0RYt26ddu7cGdJrrFy5UpMnT9b58+edr3Xv3l0jR45Uu3btVFpaquXLl6umxrG6bVFRkUaPHq2NGzcqJSWlwTEPHz6sEydOSJK6devW5PW7du3qPN61a5cp/owJAAhMcXGxHnvsMZ/6WiQ9LClHzSuhaSPpF5L+n6TfSnpOjkzDm8cee0z/+q//6lauYrVaNWvWLP3rv/5r2JbPlKQrrrhC119/vW699VYNGjSIZRERM6KyZr5ly5a69tprg/Jk+uHDhzVhwgRnIm+xWPTss89q9+7devnll/Xkk09q8eLF2r9/v6677jrneSUlJZo2bVqj41ZWVjqP09PTm4zB9f1vv/22md8JAMAstmzZoiFDhujs2bNe+14pqUDSswq8Fv7SC+MUXBjXm7q6Ot13332y2Wz13uvVq5c2bNigvLy8eqUFwRIXF6cbbrhBhYWFOnjwoP76179q/Pjx6tKlC4k8Yobpk/nExEQNGjRIDz74oBYuXKidO3fqxIkTWrt2rS6//PKAx8/JydHJkyed7fnz52vmzJn11nxPTU3Vhx9+qJ49ezpfW7x4sYqKihoct7q62u17aEpS0vc/nl3PAwBEjzNnzmjRokUaOnSoBg0a5Pa7pzE/kLRe0tAgxzL0wrg/8KHvli1btGDBggbfs1qtevjhh3XgwAEtWrRIw4cPD0p8AwcO1Jtvvqnq6mp9/PHHysrKCsq4gBmZvszmrbfeCtnYR44c0auvvupsd+vWTbNnz260f2Jiol588UWNGjVKkmMN3pycHL377rtNXsfb7AGzCwAQvWw2mxYsWKDc3FxVVFT4fN6VklZL6hiiuDpeGH+YpK+99M3NzdWDDz7Y6IOl8fHxmjBhgiZMmKAdO3bo7bff1qZNm7R161YdO3bMayxt27bVgAEDNHjwYN1zzz3q3bu3v98OELVMn8yH0gcffOC2as3UqVMVFxfX5DkjR45Ujx49tHv3bkmOevtTp07VK/lp2bKl8/hirX1jXN93PQ8AYG4lJSXNqiu3SFqk0CXyF3WU9Kak4Wq6hr6iokJLly7VhAkTvI7Zu3dvZzJut9tVVlamwsJClZeXq6amRmfOnFFCQoKSkpLUsWNH9evXj/p3oAkk801YtmyZW3vs2LE+nTd27Fjl5ORIkmpra/Xxxx/rzjvvdOvjWgLkbSbmyJEjzuPLLrvMpxgAAJHLZrPpmWee0bx585q1m+vDCn5pTWOGSXpIjodim/LSSy/5lMy7slgs6tKli7p06dLM6ACYvmY+lAoKCpzH6enpbqvKNGXoUPcfsevWravX54orrnCudONt7fivvvrKeexakw8AMJ/q6mrddttteuSRR5qVyHeRY9WacMq5cN2m/POf/9SOHTvCEQ4AFyTzjTh06JBz6UhJfj1c49l3165dDfbr37+/JKmwsFBnzpxpdLwNGzZIcixR2bZtW5/jAABElmPHjumGG24IaFOlhxT4qjX+ulTSf/jQ7+233w51KAA8kMw34mLN+0VXXunLIl0O6enpbg8BeY510R133CHJUYrz/vvvN9jn4MGDzr8QXOwPADCf6upqjR49Wps2bWr2GJdK+mnwQvLLZHn/EBHI9wageUjmG3Hw4EG3dqdOnXw+12KxqEOHDo2OddGkSZPUpk0bSY6NN06frr+Z9qxZs3Tu3DlZrVZNnz7d5xgAAJHDZrNp7Nix2rhxY0DjjJdjgycjtLlw/aZs3bpVdrsv200BCBaS+UZ4rufu7yoyrVq1ch7bbDbV1tbW69O2bVs98cQTkqQ9e/bouuuu0z/+8Q99++232r59u/7t3/5Nf/vb3yRJM2fOVPfu3f39Npz69Omjjh071vvKy8tr9pgAAN8888wzAZXWXDQiCLGE8vrHjh1TWVlZOEIBIlpeXl69nKtPnz4huRar2TTCM5n3trGTJ8/+1dXVDY7xwAMPqLy8XLm5udqyZYtGjhxZr8+kSZP01FNP+XV9T998802Dr1dVVQU0LgCgacXFxZo3b15QxuoflFFCe/3CwkJWp0HMq6qqarQyI9hI5hvhOZPe2EYYjUlISHBrN7WW/JNPPqmbbrpJf/7zn7VhwwZVVlY6N8iYMmWKbr/9dr+u3ZD27durRYsW9V6/uKIOACD4bDab7rvvvmatWuMpSVKPwEMKSM8LcTS1O0p5eXmYogEiV0pKilvJtSSdO3eu0cnVQJDMN8JzFt3fH8Seq9MkJTX92NCIESM0YkTo/oC6fft2paamhmx8AEB9jz76qLZu3RqUsdrL+F/aVknpksqa6ONtI0QgFsycOVMzZ850e62yslJpaWlBv5bRPxcilmeNfEM1703x7M/OrQAQO2w2m3Jzc/X0008HbUz//j4cOt7iaGqpZQDBRzLfCM/k++TJk36d79rfarX6XXMfbNnZ2bJarSouLjY0DgCIdtXV1Ro7dmxQHnh1FXihTnB4i8OzzBSAlJmZKZvNFpKxSeYb4Vnn5E8NoN1ud3vowXMsAEB0OnbsmG699daQrLf+jSSbjP3FbZN0xEsfb2WlAIKLZL4RPXv2dGvv37/f53OPHDniVmPvOZYRCgoKqJkHgBAKxqZQTamRtFvSNSEZ3Te71PTDr5LUsWPHcIQCmEpxcXHIauZZZ74RGRkZat26tbNdVFTk87mFhYVu7R49jF5/AAAQSsHaFMqbbSEdPTjX79evX8jjAPA9kvkmZGdnO48rKipUWlrq03kbNmxwa1977bVBjQsAEFmCtSmUN2tDfoXArt+2bVt17tw5HKEAuIBkvgme67u/8847Pp3n2i8xMVE33nhjUONqjuzsbGVmZhodBgBEnWBuCuXNYknHw3Kl+o5fuH5TBgwYIIvFEoZoAHPJzMx0myQOJpL5JowZM8Zts6hXXnlFZ8+ebfKcNWvW6IsvvnC2b731ViUnJ4csRgCAcWw2m+6///6gbArli9OSXgvLler7q7zXyw8ePDgcoQBwQTLfhPT0dE2ZMsXZLi0tVW5ubqP9a2trNWPGDGfbYrFo7ty5IY3RVwUFBSxLCQBBtmDBAm3ZsiWs1/yTvCfVwXZa0vM+9LvnnntCHQpgSsXFxSooKAjJ2CTzXsyZM0etWrVytufNm6e8vDydP3/erV9lZaVuueUWlZSUOF8bP368srKywhYrACB8zpw50+QET2PivLzv7RfzPknhniaae+G6TRk+fLh69+4djnAAuLDY7Xa70UEEYv/+/erWrVuD7507d86t3aJFiwb7ffLJJxoxYkSj11ixYoXGjBnjlsB3795do0aNUrt27fTll19q+fLlbltY9+rVSxs3blRKSoo/305QNLT0UUVFBUtTAkAQLVq0SBMnTvT7vDRJFQFe2yKpQNLQAMfxxXpJwyV5SxYWLVqkCRMmhCEiwJxClZ+Zfp15u91eL2lvTGP9vH2eGT16tPLz8zV9+nSdPn1akrR3717t3bu3wf59+/bVe++9Z0giDwAIj5deeqlZ5/nyFFWKpKom3rdLuleORDuUq7qXS5oo74l8Wlqa7rrrrhBGAqAxlNn4aNKkSSosLNQdd9yhuLiG/0iakZGhxx57TJ999lnELc3FajYAEDw7duxodv2rXVJbL3182Z3ka0nXy5Fwh8KBC+N/7UPf2bNnuy0YAcBdKFezMf3MfOfOnb3OrAfLD37wA7333nv67rvvtH79epWXl6uqqkrt27dX165dNWzYsEZLeQAA0WPJkiXNPveIHOUxnzTRZ4ekfpIKm+gjSV9IGibpzQv/Bst6OWbkfUnkBw0a5Lb4A4DwMn0yb4TLLrtMY8aMMToMvxQUFFAzDwBBsn79+mafWyOpm5pO5msk3SRppyRvi15+LUdN+8OSfi/p0mZH5li1Zq4cK+b4Mk0WHx+vhQsXymolnQCaUlxc3GDNfDBQZgMAgB+OHj2qdevWBTRGJx/6vCXfV62xS8qTdI0cS0ge9zOe4xfOu0bSc/ItkZekxx9/XL169fLzagCCiWQeAAAfHT9+XH369PG68IK3h1y/kuStenafpKNyzND7ap+khyRdIel+SflyzO7bPPrZ5Cjlyb/Q74oL53lbftLVzTffrF/+8pd+nAEgFPi7GAAAPti+fbuGDx+uqqqm1plx6C1pUxPvL5ZjJtzbI7TPS1olx8o2G32MU3KU6eRf+JKkJEnpkuLlKNs5osA2nho6dKjeeecdymuACMDMfIxgNRsAaB6bzabc3Fz169fPp0Q+SdJIL31Oy5Gge6uetUv6maRXJA3xIdbG1Egqk7Tnwr+BJPKDBw/WihUrlJzsyyKbAKTQrmZDMg8AQCOqq6t122236ZFHHvF5T5P2knzZOulFSb4UqXwt6W45Ztn9KbkJhR49emj16tVq06aNwZEAuMj0O8CiPnaABYDAHTt2TLfeeqs2bWqqYKa+H0jaLccKM97KaB6SYxnILT6Me6Wk1+Qo33lM3le5CbaOHTtq7969SkxMDPOVgegQqvyMmXkAADxUV1c3K5GXvk+yH/Ch7/OS/kOOWnZvvpb0Y0kVkjZIGuh3ZM0XFxenjz76iEQeiEAk8wAAuKitrdWAAQOalchL0jdyrBZzl3yriZ8jaaaPY19cgvIeOUp5nvDhGsHw+9//niUogQhFMg8AwAUlJSXq3r27vvjii2aPUSNHmU2CpNk+9P9a0lJJI/y4xj456u1z5Kijny7HjrGhwBKUQGQjmQcAxLyLK9b88Ic/VHl5ecDjbbvw7wz5Vg6zR1KpHJs2+aNG0uuSXpZUKMcHiNZy7AIbjF/wLEEJRD7+3wkAiGnV1dUaO3asPvroo6CNuVbSZDl+yeZLypL3B1bLL3xlSDrczOueufAVDEOGDGEJSsAEmJmPEawzDwD1HTt2TDfccENQE3nJsSnU8QvHvSTN9+Pcw3KsVW+km2++WatWrWIJSiBIWGceAIAgq66u1ujRo5v9oGtTTsuxjORFv5J/a8QHsqlTIOLj45Wbm6vly5czIw+YBOvMRyHWmQeAptlsNt12221Bn5F31UVSsb6fZT8l6QZJG0N2xcAMHDhQ+fn5rFoDhAjrzAMAECTPPPNMwIn81V7e3ydprks7WdJKSUMCumrwpaWlKS8vTxs2bCCRB0yIZB4AEFOKi4s1b968gMf5dx/6PCfHBk8XtZG0Sv6V3ITK0KFDtWjRIh04cEAPP/wwK9YAJkUyDwCIGTabTffdd5/q6rytLdO0JEkPyrdNoe6VY5Wai5Il/V1Srnzb+TXYfvzjH6uwsFDr16/XhAkTFB9vRBQAgoVkHgAQMx599FFt3bo14HHay5GU+7op1PVyT+itkmZJKpJv69AHy29+8xutWbNGWVlZYbwqgFAimQcARD2bzaacnBw9/fTTQRnv4ly2r5tCfSFpmKT1Hq/3kqMMJ0/eZ/kDNXDgQD3xxBMhvgqAcCOZBwBEterqat1222367W9/G7QxLxbpXNwUypdCla8lDZf0SzmWrrzIKulhSQckLbrQJ9ji4+OVn59PXTwQhUjmYwSbRgGIRaHaFOobSbYLx/5sCmWXYxb+GknP6/uNpSTHB4IJktZJ2i7pt3IsZdk28HD1+OOPs1INYCA2jQIAwE+h3BSqRtJul7a/m0Ltk/SQpCsk3S/H7P5Off8Bobekx+VYyvJTSU9Luk5SXDNivfnmm/XLX/6yGWcCMAM2jYpCbBoFINaFY1OofEmTXdrB2BQqSVK6HLP0dZKOKLDdYPv376+1a9eymysQAdg0CgAAHwVjUyhv1nq0g7EpVI2kMkl7LvwbSCLfokULrVq1ikQeiHIk8wCAqBKsTaG8WSz3mncpsjaFGjFihNq2DUbFPYBIRjIPAIgaNptN999/f8CbQvnitKTXGnjd6E2hLho2bJiBVwcQLiTzAICosWDBAm3ZsiVs1/uTGi6FMWpTKFf33HOPQVcGEE4k8wCAqHDmzBnl5uaG9Zr7JM1t4v1wbgrlavjw4erdu3cYrwjAKCTzAICosHTpUlVUVPh9XqC/CJ+TI2FvTDg2hfL0wAMPhOEqACIByTwAICq89NJLzTovMcDr2iXdK6ncS79AN4VqLd9iTUtL01133eXjqADMjn2dAQCmt2PHDhUUFDTr3Hg5HmYNxNeSrpe0WlJHH/r3vvAlOT4MlEkqlOMDQY2kM5IS5Fh3vqOk9pJ+JseSld7Mnj1b8fFGPnoLIJxI5gEAprdkyZJmn+vLWu4XN3FqyheShkl688K/vrJI6nLhqyHrJU2U4wODN4MGDdKMGTP8uDoAs6PMBgBgeps2bWr2uWck9fPSp05SCx/G+lqOmvhfKvDZ/tOSZl4Yz5dEPj4+XgsXLpTVyjwdEEv4f3yMyM7OltVqVXFxsdGhAEBQ2e12bdu2LaAxBslR5tKUc77GI8fqNe9J+g9Jk+XYTMpXxyX9VdLzcqyW46vHH39cvXr18uMMAOGSmZkpm80WkrFJ5gEAprZv3z4dO3asyT7Jkk418f5pOZaO9LYWTqKkWl/jkvSQpEckjZc0QtIAST3k/svXJmmXpG2S1sqxs6wvpT+ubr75Zv3yl7/08ywA0YBkPkYUFBQoNTXV6DAAIOiKioq89uktqalCnHckPXrhqym18i+hlxyJef6FL8nxUGu6vq/DPyL/k3dXQ4cO1TvvvEN5DRDBiouLVVlZqbS04O84Qc08AMDUDhw40OT7SZJGehnj9IV+vuzWWitHIt5cNXKsXrPnwr+BJPJDhgzRihUrlJycHMAoAMyMZB4AYGo1NU2nw+3lWN/dmwWSXpZvibq3lW3C4eabb9aqVavUpk0bo0MBYCCSeQCAqdXVNZ1ax8tRZpPtZZx9kv4maX5wwgqZFi1aKDc3V8uXL2dGHgA18wAAc/O2QdLFVP8BSd62lXpO0qeSbpL0UaCBhUDr1q21bt069enTx+hQAEQIZuYBAKaWlJTU5PvfyLFizF1yrFjTFLukSZJekDQkGMEFUefOnbV//34SeQBuSOYBAKbWqVOnJt+vkbRbUoKk2T6M97WkMZJeUeQk9D/60Y9UVFSk1q1bGx0KgAhDMg8AMLWsrCyvfS5uKTVDvq1Y84WkWyU9LkfJjZFuvvlmffLJJzzoCqBBJPMAAFPr0qWL2rZt22SftRf+tcqx3rsvK9Z8LelGST3lSOoDWY6yOeLi4njQFYBXJPMAAFOzWCzq379/k30WSzp+4biXfF+xxi7pT5IWSpopqV+zIvRfx44d9fnnn2vWrFlsBgWgSSTzAADTGzx4cJPvn5b0mkv7V/KvfGafpFxJJXKU6aT4GZ8/evToob1796pXr14hvAqAaEEyDwAwvXHjxnnt8yd9v9uqVdK78v8B11pJWyRVXWgH+5fo4MGDtXXrViUmJgZ5ZADRimQeAGB6vXv3VnZ209tC7ZM016WdLGmlAlux5nwA53oaMmSIPvzwQ+rjAfiFZB4AEBUeeOABr32ek7TBpd1G0ipFxoo1q1atYsUaAH4jmQcARIW77rpLaWlNbwtll3SvpHKX15Il/V2Omvhwr1gjSU888QQr1gBoNpL5GJGdna3MzEyjwwCAkElISNDs2d63hfpa0vVyT+itkmZJKpJv69AHy29+8xvNmTOHFWuAKJeZmem1FLC5SOYBAFFjxowZGjjQezr+haRhktZ7vN5LjjKcPElNz/EHbuDAgXriiSdCfBUA0c5it9vtRgeB4KqsrKz3p+aKigqlpqYaFBEAhE9JSYmysrJUV1fnta9F0sOSfi/pUo/36iQtlfSSpH8GOcb4+HgVFRWx/CQQQ0KVnzEzDwCIKr169dL8+b5tC2WXYxb+GknP6/uNpSRH/fwESeskbZf0W0k3SGp6r1nfPP744yTyAIKCmfkoxMw8gFhns9l022236aOPPvLrvCRJ4yWNkDRAUg856umd48qxcdRHcixruV7SWT9ju/nmm7V8+XLq5IEYE6r8jJ8kAICoY7Va9e677+qGG27Qxo0bfT6vRlL+hS/JkdynyzFLXyfpiL7feKo5hg4dqnfeeYdEHkDQUGYDAIhKycnJWrlypYYMaf62UDWSyiTtufBvIIn8kCFDtGLFCpagBBBUJPMAgKjVpk0brVq1SjfdZOy2UGwKBSBUSOYBAFEtOTlZf//735Wbm6v4+PBuCxUfH6/c3Fw2hQIQMiTzAICoZ7VaNWvWLBUVFfm0Dn0wDBw4UEVFRZo1axY18gBChmQeABAzevXqpQ0bNigvL6/eqhLBkpaWpry8PG3YsIHlJwGEHMl8BPjqq6+0ZMkS/frXv9aPf/xjpaSkyGKxyGKxKD8/3+jwACCqWK1WPfzwwzpw4IAWLVqk4cOHB2Xc4cOHa9GiRTpw4IAefvhhZuMBhAU/aSJAt27djA4BAGJOfHy8JkyYoAkTJmjHjh16++23tWnTJq1atcqn89u2basBAwZo8ODBuueee9S7d+8QRwwA9ZHMR5B27dqpf//+atu2rZYsWWJ0OAAQM3r37q3evXvLYrG4vf6nP/1JNTU1OnPmjBISEpSUlKSOHTuqX79+6ty5c73+ABBuJPMRYPHixRowYIC6du0qSfr0009J5gHAYGyQDsAMSOYjwLhx44wOAQBinussu81mMzASAPBdVCfzR48e1fr161VeXq6qqiplZGSoa9euGjJkiFq0aGF0eACACOFZLsPvCABmEfZkvrq6WoWFhdq8ebM2b96sLVu2qKyszPn+VVdd5dZujj179mj27NlasWKF6urq6r2fkZGhqVOnas6cOWHfQAQAENkorwFgJmFL5vPy8rRw4UKVlJTo/PnzIbvOG2+8oenTp+vUqVON9jl8+LDmz5+vZcuWaenSpercuXPI4gEARDbXWXkSeQBmE7Zkft26ddq5c2dIr7Fy5UpNnjzZ7cNC9+7dNXLkSLVr106lpaVavny5ampqJElFRUUaPXq0Nm7cqJSUlJDGBgCIPKxGA8DsDK2Zb9mypfr166dt27Y1OZPui8OHD2vChAnORN5iseiZZ57RQw89pEsu+X5vrMrKSo0bN06ffvqpJKmkpETTpk3TokWLAro+AMDcmJUHYEZh2wE2MTFRgwYN0oMPPqiFCxdq586dOnHihNauXavLL7884PFzcnJ08uRJZ3v+/PmaOXOmWyIvSampqfrwww/Vs2dP52uLFy9WUVFRo2OXlZU5d2Rt7lf79u0D/h4BAMFDeQ2AaBC2mfm33norZGMfOXJEr776qrPdrVs3zZ49u9H+iYmJevHFFzVq1ChJjh/iOTk5evfdd0MWIwAgclBeAyBaRMXSlB988IHbqjVTp05VXFxck+eMHDlSPXr00O7duyU56u1PnTql5OTken07dOigXbt2BRSj1RoV/6kBIOowKw/AzKIiw1y2bJlbe+zYsT6dN3bsWOXk5EiSamtr9fHHH+vOO++s1y8uLk49evQIPFAAgOEorwEQTcJWMx9KBQUFzuP09HR17drVp/OGDh3q1l63bl1Q4wIARBbKawBEG9Mn84cOHdKJEyec7aysLJ/P9ewbaCkNAMA8mJUHEA1Mn8xfrHm/6Morr/T53PT0dLcdYD3HAgBED8prAEQj09fMHzx40K3dqVMnn8+1WCzq0KGD9u3b1+BY4VJaWqrKykpnu6SkxO29TZs2OdspKSnq1atXWOMDALOjvAZAtDJ9Ml9dXe3WbtmypV/nt2rVynlss9lUW1urxMTEoMTmq9///vf661//2uB7OTk5zod0JWnEiBHODa/80adPH7Vo0aLe6zNnztTMmTP9Hg8AzIpZeQChlpeXp7y8PLfXzp07F5JrRV0y728i7tm/uro67Ml8OHzzzTcNvl5VVRXmSAAgvCivARBuVVVVYav4MH0yX1tb69Z2rYH3RUJCglu7pqYm4Jj8lZ+fr/z8/JBeo3379g3OzKekpIT0ugBgJMprABghJSVFHTp0cHvt3LlzjU6uBsL0ybznLLrr5lG+OHPmjFs7KSkp4Jgi0fbt25Wammp0GABgGGblAYRLQ2XMlZWVSktLC/q1TL+ajWeNvOdMvTee/f2tuQcARCbKawDEAtPPzHsm3ydPnvTrfNf+Vqs1KuvlJSk7O1tWq1XFxcVGhwIAIUd5DYBIkpmZKZvNFpKxTT8z71mPVF5e7vO5drvd7eEEz7EAAObHrDyAaGb6mfmePXu6tffv3+/zuUeOHHGrsfccK5oUFBRQMw8gJlBeAyDSFBcXUzPfmIyMDLVu3drZLioq8vncwsJCt3aPHj2CFhcAIPworwEQa0yfzEuOevCLKioqVFpa6tN5GzZscGtfe+21QY0LAGAcZuUBxIKoSOZvv/12t/Y777zj03mu/RITE3XjjTcGNa5Ikp2drczMTKPDAICQobwGQKTKzMx0m3wOpqhI5seMGeO2WdQrr7yis2fPNnnOmjVr9MUXXzjbt956q5KTk0MWIwAgdCivARCroiKZT09P15QpU5zt0tJS5ebmNtq/trZWM2bMcLYtFovmzp0b0hiNVlBQwLKUAGICs/IAIk1xcbEKCgpCMnZUJPOSNGfOHLVq1crZnjdvnvLy8nT+/Hm3fpWVlbrllltUUlLifG38+PHKysoKW6wAgOChvAZALLPYw/STb//+/erWrVuD7507d86t3aJFiwb7ffLJJxoxYkSj11ixYoXGjBnjlsB3795do0aNUrt27fTll19q+fLlqqmpcb7fq1cvbdy4USkpKf58OxGtoaWPKioqWJoSQNTxLK8hmQcQqUKVn4VtnXm73V4vaW9MY/28/ZAePXq08vPzNX36dJ0+fVqStHfvXu3du7fB/n379tV7770XVYk8AMQqEnkAsShqymwumjRpkgoLC3XHHXcoLi6uwT4ZGRl67LHH9Nlnn6lz587hDdAgrGYDINpQXgPALEK5mk3YZuY7d+4cth+2P/jBD/Tee+/pu+++0/r161VeXq6qqiq1b99eXbt21bBhwxot5QEARD5WrwEAh7DVzCN8qJkHEO2YlQdgNqHKz6KuzAYAEN1cE3mbzWZgJABgPJJ5AIBpeJbXUDIJINaRzAMATInyGgAI4wOwMFZ2drasViu7wAIwLcprAJhVZmZmyH5uMTMPAIh4lNcAQMOYmY8RBQUFrGYDICpQXgPAbIqLixtczSYYmJkHAEQ011n5s2fPGhgJAEQeknkAQMTyLK+xWvmDMgC4IpkHAJgC5TUAUB/JPAAgIrnOytfW1hoYCQBELpJ5AEDE8SyvSUhIMCgSAIhsFB/GCNaZB2BWlNcAMDvWmQcAxAzKawDAd8zMxwjWmQdgBpTXAIhGrDMPAIg5lNcAgHck8wCAiOA6K3/69GkDIwEA8yCZBwAYzrO8JikpyaBIAMBcSOYBABGF8hoA8B3JPADAUK6z8uXl5QZGAgDmQzIPADBMp06d3NodOnQwKBIAMCeSeQCAYVxn4imvAQD/sc58jGAHWACRxrW85quvvjIwEgAILXaABQBElZtuusmt3aVLF4MiAQBzs9j5u2bUaWiHsYqKCnaABRAxXGfl+TUEIBaEKj9jZh4AEFauifznn39uXCAAEAVI5gEAYTN16lS39g9/+EODIgGA6EAyDwAIm1deecV5THkNAASOZB4AEBau5TX/+7//a2AkABA9SOYBACG3aNEit7bnajYAgOYhmQcAhNzEiROdx5TXAEDwkMwDAELKtbzmiSeeMDASAIg+JPMAgJDZuHGjW3vOnDkGRQIA0YlkHgAQMkOHDnUeU14DAMFnNToAhEd2drasVquKi4uNDgVAjHAtr7n++usNjAQAjJWZmSmbzRaSsZmZBwAE3aFDh9zaq1atMigSAIhuFjt/94w6lZWVSktLc3utoqJCqampBkUEINa4zsrzawYAQpefMTMPAAgq10QeABBaJPMAgKCpq6tzazMrDwChRTIPAAiahIQE5zGJPACEHsk8ACAoKK8BgPAjmQcABB2z8gAQHiTzAICAsXoNABiDZB4AEBDKawDAOCTzAICgYVYeAMKLZB4A0GyU1wCAsUjmAQDNQnkNABiPZB4AEDBm5QHAGCTzAAC/UV4DAJGBZB4A4BfKawAgcpDMAwCajVl5ADCW1egAEB7Z2dmyWq0qLi42OhQAJuY6K2+z2QyMBADMIzMzM2Q/M5mZBwD4xLO8pkWLFgZFAgC4iJn5GFFQUKDU1FSjwwAQJSivAQDfFRcXq7KyUmlpaUEfm5l5AIBXrrPyZ8+eNTASAIArknkAQJM8y2usVv6oCwCRgmQeAOAzymsAILKQzAMAGuU6K3/69GkDIwEANIRkHgDQoE6dOrm1k5KSDIoEANAYknkAQIPKy8udx5TXAEBkIpkHANTjWl7z5ZdfGhgJAKApJPMAADczZsxwa3fr1s2gSAAA3pDMAwDcvPjii85jymsAILKRzBvs3Llz+sc//qFHHnlEI0aMUPv27RUfH6+UlBRlZmbq5z//uT7//HOjwwQQI1zLa1asWGFgJAAAX1jsTLsYql+/fioqKmqyj8Vi0a9//Wvl5ubW27ylIQ1tF1xRUaHU1NSAYgUQ3RYtWqSJEyc62/x6AIDgCVV+xsy8waqqqmSxWDRq1Ci9/PLL2r59u7799lvt379fCxcuVKdOnWS32/X000/rd7/7ndHhAohiJPIAYD4k8wa7++67VVJSotWrV2vatGnq3bu3LrvsMl155ZW67777tHnzZl1xxRWSpNzcXB05csTgiAFEI9e/+j3xxBMGRgIA8EdUl9kcPXpU69evV3l5uaqqqpSRkaGuXbtqyJAhatGihdHh+ezZZ5/Vr371K0nSa6+9pkmTJjXZnzIbAP7YvHmzfvSjHznbUfxrAQAME6r8zBrQ2c1QXV2twsJCbd68WZs3b9aWLVtUVlbmfP+qq65yazfHnj17NHv2bK1YsUJ1dXX13s/IyNDUqVM1Z84cxcfHB3StcLjmmmucxwcPHjQwEgDRiEQeAMwrbMl8Xl6eFi5cqJKSEp0/fz5k13njjTc0ffp0nTp1qtE+hw8f1vz587Vs2TItXbpUnTt3Dlk8wfDNN984j1NSUgyMBEC0cS2vuf766w2MBADQHGFL5tetW6edO3eG9BorV67U5MmT3T4sdO/eXSNHjlS7du1UWlqq5cuXq6amRpJUVFSk0aNHa+PGjRGdJC9evNh5PGzYMAMjARBNDh065NZetWqVQZEAAJor7GU2rlq2bKl+/fpp27ZtTc6k++Lw4cOaMGGCM5G3WCx65pln9NBDD+mSS75/zreyslLjxo3Tp59+KkkqKSnRtGnTtGjRooCuHyofffSRPvzwQ0nS8OHD9cMf/tDgiABEiw4dOjiPKa8BAHMK22o2iYmJGjRokB588EEtXLhQO3fu1IkTJ7R27VpdfvnlAY+fk5OjkydPOtvz58/XzJkz3RJ5SUpNTdWHH36onj17Ol9bvHix17XejVBeXq7JkydLkhISErRgwQKDIwIQLXzZswIAEPnCNjP/1ltvhWzsI0eO6NVXX3W2u3XrptmzZzfaPzExUS+++KJGjRolyTEjlZOTo3fffbfB/mVlZerSpUtAMaanp7vVvntz8uRJjRkzxrkU5fPPP8+sPICg8FwYgFl5ADCvqFhn/oMPPnD75TR16lTFxcU1ec7IkSPVo0cPZ3vlypUBl/oES01Njf71X//V+deCefPmadq0aQZHBSBaJCQkOI9J5AHA3AytmQ+WZcuWubXHjh3r03ljx45VTk6OJKm2tlYff/yx7rzzznr9OnTooF27dgUUo9Xq23/quro63XXXXVq7dq0k6Te/+Q07vwIIGsprACC6REUyX1BQ4DxOT09X165dfTpv6NChbu1169Y1mMzHxcW5zeKHis1m07hx4/S///u/kqSHHnpIf/jDH0J+XQCxiVl5ADA/05fZHDp0SCdOnHC2s7KyfD7Xs2+gs++BOHfunH7yk5/ogw8+kCQ98MADeu655wyLB0D0cZ2VJ5EHgOhg+mR+9+7dbu0rr7zS53PT09PddoD1HCtczp8/r/vvv19LliyRJP2///f/9OKLLxoSC4DoRHkNAEQn0yfzBw8edGt36tTJ53MtFovbOsueY4WD3W7X9OnT9frrr0uSJk+erL/85S/84gUQMszKA0D0MH3NfHV1tVu7ZcuWfp3fqlUr57HNZlNtba0SExODEpsvHn74Yb3yyiuSpDvuuEMvvPBCk6vqxMXFua1EAQDeUF4DANEr6pJ5fxNxz/7V1dVhTeaff/555/H777+v1q1bN9l/8uTJys/P9/s6ffr0UYsWLeq9PnPmTM2cOdPv8QCYA3/lA4Dwy8vLU15enttr586dC8m1TJ/M19bWurVda+B94TnLXVNTE3BMkaixDauqqqrCHAkAozArDwDhUVVVFbbybdMn856z6J47G3pz5swZt3ZSUlLAMfkjXL9c27dv3+DMfEpKSliuDyD8KK8BAGOkpKS4PZcpOWbmG5tcDYTpk3nPGnnPmXpvPPv7W3NvFtu3b1dqaqrRYQAIE8prAMA4DZUxV1ZWKi0tLejXirpk/uTJk36d79rfarWGtV4+nLKzs2W1WlVcXGx0KADCjFl5ADBWZmambDZbSMY2/dKUnn/CKC8v9/lcu93uVs/kORYAmJHrrHyofnkAACKD6Wfme/bs6dbev3+/z+ceOXLErcbec6xoUlBQQJkNEAM8y2saelYGABBexcXFISuzMf3MfEZGhttyjkVFRT6fW1hY6Nbu0aNH0OICAKNRXgMA0c/0ybzkqAe/qKKiQqWlpT6dt2HDBrf2tddeG9S4ACCcXGflz549a2AkAIBwiYpk/vbbb3drv/POOz6d59ovMTFRN954Y1DjiiTZ2dnKzMw0OgwAIeJZXmO1mr6KEgCiRmZmptvkczBFRTI/ZswYt82iXnnlFa+zUmvWrNEXX3zhbN96661KTk4OWYwAEC6U1wBA7IiKZD49PV1TpkxxtktLS5Wbm9to/9raWs2YMcPZtlgsmjt3bkhjNFpBQQHLUgJRynVW/vTp0wZGAgBoSHFxsQoKCkIydlQk85I0Z84ctWrVytmeN2+e8vLydP78ebd+lZWVuuWWW1RSUuJ8bfz48crKygpbrAAQLN26dXNrh3sXawCAsSz2MP09dv/+/fV+6Vx07tw5t3ZjS6l98sknGjFiRKPXWLFihcaMGeOWwHfv3l2jRo1Su3bt9OWXX2r58uWqqalxvt+rVy9t3LhRKSkp/nw7Ea2hpY8qKipYmhKIQq6z8pTXAEDkClV+FrYnpOx2e72kvTGN9fP2i2r06NHKz8/X9OnTnX9q3rt3r/bu3dtg/759++q9996LqkQeQOxwTeQ///xz4wIBABgmaspsLpo0aZIKCwt1xx13KC4ursE+GRkZeuyxx/TZZ5+pc+fO4Q3QIKxmA0QX1+d+JOmHP/yhQZEAALwJ5Wo2YSuzMcJ3332n9evXq7y8XFVVVWrfvr26du2qYcOGRfWuiA39Gefqq6+W1WrlIVggSlBeAwDmkZmZKZvNpj179ri9Howym6hO5mMVNfNAdHNN5N9///16e20AACJPqPKzqCuzAYBo9vbbb7u1SeQBILaRzAOAiYwbN855zB9WAQAk8wBgEq7lNb/73e+MCwQAEDHCtjQljJWdnc0DsICJbd682a09b948gyIBAPjr4gOwocDMPACYwI9+9CPnMeU1AICLmJmPEQUFBaxmA5iUa3nNqFGjDIwEANAcxcXFDa5mEwzMzANABDt06JBbe/Xq1QZFAgCIRCTzABDBOnTo4DymvAYA4IlkHgAilGt5DQAADSGZB4AIVFdX59ZmVh4A0BCSeQCIQAkJCc5jEnkAQGNYzSZGsM48YB6U1wBAdGGdeQCIUczKAwCawsx8jGCdecAcXGflSeQBIDqwzjwAxADKawAA/iKZB4AIxKw8AMAXJPMAEAEorwEANAfJPAAYjPIaAEBzkcwDQARhVh4A4A+SeQAwEOU1AIBAkMwDgEEorwEABIp15mMEO8ACkY1ZeQCIXuwACwBRxnVWPlQ/4AEA0Y+Z+RjBDrBA5PAsr2nRooVBkQAAwoEdYAEgSlFeAwAIBMk8AISR66z82bNnDYwEABANSOYBIEw8y2usViodAQCBIZkHAANQXgMACAaSeQAIA9dZ+dOnTxsYCQAgmpDMA0CI9enTx62dlJRkUCQAgGhDMg8AIbZjxw7nMeU1AIBgIpkHgBByLa/5/PPPjQsEABCVSOYBIEQeeught/YPf/hDYwIBAEQti52/+UadhnYYu/rqq2W1WlVcXGxQVEDscZ2V50ctAMSuzMxM2Ww27dmzx+31iooKpaamBjQ2M/MAEAKuifz7779vXCAAgKjGjiUxoqCgIOBPfgB88/bbb7u1b7/9doMiAQBEguLi4gYrJ4KBmXkACLJx48Y5jymvAQCEEsk8AASRa3nNr3/9awMjAQDEApJ5AAiSzZs3u7WffvppgyIBAMQKknkACJIf/ehHzmPKawAA4UAyDwBB4FpeM2TIEAMjAQDEEpJ5AAjQoUOH3NobNmwwKBIAQKwhmQeAAHXo0MF5THkNACCcSOYBIACu5TUAAIQbyTwANFNdXZ1bm1l5AEC4kcwDQDMlJCQ4j0nkAQBGIJkHgGagvAYAEAlI5gEgQMzKAwCMQjIPAH5ynZUnkQcAGIlkHgD8QHkNACCSWI0OAOGRnZ0tq9Wq4uJio0MBogaz8gAAX2RmZspms4VkbGbmAcBHlNcAACINM/MxoqCgQKmpqUaHAZgW5TUAgOYqLi5WZWWl0tLSgj42M/MA4Cdm5QEAkYJkHgC8oLwGABCpSOYBoAmU1wAAIhnJPAD4iFl5AECkIZkHgEa4zsqHakkxAAACQTIPAA3wLK9p0aKFQZEAANA4knkA8ILyGgBApCKZBwAPrrPyZ8+eNTASAACaRjIPAC48y2usVvbWAwBELn5LGezIkSN6//33tXXrVn3++ec6cuSIvvvuO507d06XX365+vbtq7Fjx+onP/mJ4uLijA4XiCmU1wAAIp3Fzm8rQ7311lu69957vfa75pprtHz5cnXu3Nlr34a2C66oqFBqampzwwRiguus/OnTp5WUlGRgNACAaBKq/IwyG4PFx8dryJAhmjNnjlasWKHt27eroqJC+/bt0yeffKJ7771XFotFO3fu1E033aSamhqjQwai0rBhw9zaJPIAADNgZt4EnnzyST366KOSpL/+9a/66U9/2mR/ZuYB/7nOyvNjEQAQbKHKz6K6Zv7o0aNav369ysvLVVVVpYyMDHXt2lVDhgwx1ZrR06ZNcybzW7du9ZrMA/CPayJfVFRkYCQAAPgn7Ml8dXW1CgsLtXnzZm3evFlbtmxRWVmZ8/2rrrrKrd0ce/bs0ezZs7VixQrV1dXVez8jI0NTp07VnDlzFB8fH9C1wsE1xsTERAMjAaLPI4884tbu27evMYEAANAMYUvm8/LytHDhQpWUlOj8+fMhu84bb7yh6dOn69SpU432OXz4sObPn69ly5Zp6dKlPj1UaqRFixY5j3/0ox8ZGAkQfXJzc53HlNcAAMwmbMn8unXrtHPnzpBeY+XKlZo8ebLbh4Xu3btr5MiRateunUpLS7V8+XLnQ6RFRUUaPXq0Nm7cqJSUlJDG5q8TJ06orKxMr7/+ul544QVJjgf07rrrLoMjA6KHa3nN0qVLDYwEAIDmMbRmvmXLlurXr5+2bdvW5Ey6Lw4fPqwJEyY4E3mLxaJnnnlGDz30kC655PtFeyorKzVu3Dh9+umnkqSSkhJNmzbNbfbbKL/73e80f/78eq/Hx8frZz/7mV544YV6G9oAaJ5ly5a5te+8806DIgEAoPnCtjRlYmKiBg0apAcffFALFy7Uzp07deLECa1du1aXX355wOPn5OTo5MmTzvb8+fM1c+ZMt0ReklJTU/Xhhx+qZ8+eztcWL14c0Q+93XXXXZo2bZqSk5ONDgWIGrfffrvzmPIaAIBZRcTSlJ07d9b+/fslNe8B2CNHjujKK690PuzarVs37dq1q8kdU9esWaNRo0Y523fddZfefffdBvuWlZWpS5cufsXkKT09Xd98802Tferq6pzfw6lTp7Rr1y69/vrrWrhwoex2u+bMmaOcnByvs/MsTQk0zfX/Q7/+9a/19NNPGxgNACAWsGlUEz744AO3VWumTp3aZCIvSSNHjlSPHj2c7ZUrVwZc6hOo+Ph4tWzZUi1btlR6erquu+46/fd//7feeOMNSY715hcsWGBojIDZbd682a1NIg8AMLOoWGfes/Z17NixPp03duxY5eTkSJJqa2v18ccfN1g326FDB+3atSugGK3W5v+nnjhxol5++WX985//1DPPPKNf/OIXAcUCxDLXFaEi4A+TAAAEJCqS+YKCAudxenq6unbt6tN5Q4cOdWuvW7euwWQ+Li7ObRbfCIMHD9Y///lPHThwQN9++21QnjMAYo1rec3gwYMNjAQAgOAwfZnNoUOHdOLECWc7KyvL53M9+wY6+x5KNpvN6BAAUzt8+LBbe+PGjQZFAgBA8Jg+md+9e7db+8orr/T53PT0dLfdVT3HiiRr166VJLVp00aXXXaZwdEA5nPFFVc4jymvAQBEC9Mn8wcPHnRrd+rUyedzLRaLOnTo0OhY4VBSUuK1zwsvvKDCwkJJjjp/1poH/MP/ZwAA0cr0NfPV1dVu7ZYtW/p1fqtWrZzHNptNtbW1SkxMDEpsvsjOzlb//v111113aeDAgerYsaOSkpJ07Ngxff7558rPz9cHH3wgSWrfvr1+//vfhy02IBq4rnQlMSsPAIguUZfM+5uIe/avrq4OazJ//vx5rV69WqtXr26yX//+/fXmm2+qffv2zbpOnz591KJFi3qvz5w5UzNnzmzWmIAZJCQkOI9J5AEA4ZCXl6e8vDy3186dOxeSa5k+ma+trXVru9bA+8L1F70k1dTUBByTPz7++GP94x//0Lp161RaWqqKigqdPHlSycnJ6tixo/r376+xY8dq9OjR9Xaz9UdjG1ZVVVU1e0wg0lFeAwAwQlVVVdjKt02fzHvOonv+Sd2bM2fOuLWTkpICjskfgwYN0qBBgzRr1qyQXqd9+/YNzsynpKSE9LpApGBWHgAQLikpKW7PZUqOmfnGJlcDYfpk3rNG3nOm3hvP/v7W3JvF9u3bA94uGDAT11l5EnkAQDg1VMZcWVmptLS0oF8r6pL5kydP+nW+a3+r1RrWevlwys7OltVqVXFxsdGhACFHeQ0AIJJkZmaGbM8g0y9N6fknjPLycp/PtdvtbvVMnmMBMD9m5QEA0cz0M/M9e/Z0a+/fv9/nc48cOeJWY+85VjQpKCigzAYxgfIaAECkKS4uDlmZjeln5jMyMtS6dWtnu6ioyOdzL27EdFGPHj2CFheA8KO8BgAQa0yfzEuOevCLKioqVFpa6tN5GzZscGtfe+21QY0LgHGYlQcAxIKoSOZvv/12t/Y777zj03mu/RITE3XjjTcGNa5Ikp2drczMTKPDAEKG8hoAQKTKzMx0m3wOpqhI5seMGeO2WdQrr7yis2fPNnnOmjVr9MUXXzjbt956q5KTk0MWI4DQobwGABCroiKZT09P15QpU5zt0tJS5ebmNtq/trZWM2bMcLYtFovmzp0b0hiNVlBQwLKUiAnMygMAIk1xcbEKCgpCMnZUJPOSNGfOHLVq1crZnjdvnvLy8nT+/Hm3fpWVlbrllltUUlLifG38+PHKysoKW6wAgsd1Vj5Ua/gCABCpLPYwTWPt379f3bp1a/C9c+fOubVbtGjRYL9PPvlEI0aMaPQaK1as0JgxY9wS+O7du2vUqFFq166dvvzySy1fvlw1NTXO93v16qWNGzcqJSXFn28nojW09FFFRQVLUyLqeJbXMCsPAIhUocrPwrbOvN1ur5e0N6axft5+UY8ePVr5+fmaPn26Tp8+LUnau3ev9u7d22D/vn376r333ouqRB6IVSTyAIBYFDVlNhdNmjRJhYWFuuOOOxQXF9dgn4yMDD322GP67LPP1Llz5/AGaBBWs0G0cZ2Vr62tNTASAACaFsrVbMI2M9+5c+ewzZz94Ac/0HvvvafvvvtO69evV3l5uaqqqtS+fXt17dpVw4YNa7SUB0Dk8yyvSUhIMCgSAACMFbaaeYQPNfOIdqwpDwAwm1DlZ1FXZgMgurkm8idOnDAwEgAAjEcyD8A0hg8f7tbm4XUAQKwjmQdgGq4bblBeAwBAGB+AhbGys7NltVrZBRam5VpeU1RUZGAkAAD4JzMzM2QbGzIzDyDiPfroo27tvn37GhMIAAARhtVsohCr2SDasHoNAMDsWM0GQExyTeSXLFliYCQAAEQeknkAEWv58uVu7XvuucegSAAAiEwk8wAi1pgxY5zHlNcAAFAfyTyAiORaXvPQQw8ZFwgAABGMZB5AxNm6datb+7nnnjMoEgAAIhvrzMcI1pmHmQwcONB5THkNAMDsWGceQMxwLa/p16+fgZEAABD5WGc+CrHOPMzqm2++UUZGhrPNjycAQLRgnXkAUY9EHgAA/5DMA4gIruU1AADANyTzAAxXV1fn1mZWHgAA35DMAzBcQkKC85hEHgAA35HMAzAU5TUAADQfyTyAiMGsPAAA/iGZB2AY11l5EnkAAPzHDrAxgh1gEWkorwEAxAp2gAUQ1ZiVBwCgeZiZjxEFBQXsAIuIQXkNACCWFBcXN7gDbDAwMw8grCivAQAgeEjmARiGWXkAAAJDMg8gbCivAQAguEjmAYQF5TUAAAQfyTyAsGNWHgCA4CCZBxByrrPyoVpnFwCAWEQyDyCkPMtrWrRoYVAkAABEH5J5AGFDeQ0AAMFFMg8gZFxn5Wtraw2MBACA6MQOsDEiOztbVqtVxcXFRoeCGOFZXpOQkGBQJAAAGCszMzNkz4wxMw8g5CivAQAgNJiZjxEFBQVKTU01OgzECNdZ+W+++cbASAAAMF5xcbEqKyuVlpYW9LGZmQcQVNdff71bOz093aBIAACIfiTzAILqk08+cR5TXgMAQGiRzAMIGtfymqKiIgMjAQAgNpDMAwiKP/zhD27tvn37GhMIAAAxhGQeQFDMnj3beUx5DQAA4UEyDyBgruU1f/vb3wyMBACA2EIyDyAgy5cvd2tPnDjRoEgAAIg9JPMAAjJmzBjnMeU1AACEF8k8gGZzLa+ZMWOGgZEAABCbSOYBNMvevXvd2i+88IJBkQAAELtI5gE0y9VXX+08prwGAABjkMwD8Jtrec0111xjYCQAAMQ2knkAfvnmm2/c2jt27DAoEgAAQDIPwC8ZGRnOY8prAAAwltXoABAe2dnZslqtKi4uNjoUmJhreQ0AAPBNZmambDZbSMZmZh6AT+rq6tzazMoDAGA8ZuZjREFBgVJTU40OAyaWkJDgPCaRBwDAd8XFxaqsrFRaWlrQx2ZmHoBXlNcAABCZSOYB+IVZeQAAIgfJPIAmuc7Kk8gDABBZSOYBNIryGgAAIhvJPACfMCsPAEDkIZkH0CDKawAAiHwk8wDqobwGAABzIJkH0CRm5QEAiFwk8wDcUF4DAIB5kMxHoPPnz2vYsGGyWCyyWCzq3Lmz0SEhRlBeAwCAuZDMR6AXX3xRGzZsMDoMxDhm5QEAiHwk8xGmrKxMc+bMUVxcnDp16mR0OIghrrPyNpvNwEgAAICvSOYjzJQpU3Tq1CnNmjVLXbt2NTocxAjP8poWLVoYFAkAAPAHyXwE+e///m+tXr1aV199tebOnWt0OIhRlNcAAGAeVqMDCJWjR49q/fr1Ki8vV1VVlTIyMtS1a1cNGTIkImcdDx8+rF/96leyWCz6r//6LyUkJBgdEmKE66x8bW2tgZEAAAB/hTWZr66uVmFhoTZv3qzNmzdry5YtKisrc75/1VVXubWbY8+ePZo9e7ZWrFihurq6eu9nZGRo6tSpmjNnjuLj4wO6VjD9/Oc/1/Hjx/Xv//7vuu6664wOBzHCs7yGD5EAAJhLWJL5vLw8LVy4UCUlJTp//nzIrvPGG29o+vTpOnXqVKN9Dh8+rPnz52vZsmVaunRpRCz7uHjxYn3wwQdKT0/XH//4R6PDQYyivAYAAPMJSzK/bt067dy5M6TXWLlypSZPnuz2YaF79+4aOXKk2rVrp9LSUi1fvlw1NTWSpKKiIo0ePVobN25USkpKSGNrynfffacZM2ZIkp5//nm1bdvWsFgQW1xn5cvLyw2MBAAANJdhNfMtW7ZUv379tG3btiZn0n1x+PBhTZgwwZnIWywWPfPMM3rooYd0ySXfP+NbWVmpcePG6dNPP5UklZSUaNq0aVq0aFFA1w/Ef/zHf6iyslKjR4/W+PHjDYsDseW2225za3fo0MGgSAAAQCDCsppNYmKiBg0apAcffFALFy7Uzp07deLECa1du1aXX355wOPn5OTo5MmTzvb8+fM1c+ZMt0ReklJTU/Xhhx+qZ8+eztcWL16soqKiRscuKytz7sTa3K/27ds3OPaKFSv0t7/9TS1bttRLL70U4H8FwHcrVqxwHlNeAwCAeYVlZv6tt94K2dhHjhzRq6++6mx369ZNs2fPbrR/YmKiXnzxRY0aNUqSI5HJycnRu+++G7IYG1JVVaXp06dLcnwYufLKK8N6fcQu1/Kapj7IAgCAyGf6pSk/+OADt1Vrpk6dqri4uCbPGTlypHr06KHdu3dLctTbnzp1SsnJyfX6dujQQbt27QooRqu1/n/m3/3udyovL9fAgQOdNfNAqD377LNu7b59+xoTCAAACArTJ/PLli1za48dO9an88aOHaucnBxJjrW1P/74Y9155531+sXFxalHjx6BB+rhq6++kiRt2bLF67r3+/fvd86mjhgxwlnzD/jrV7/6lfOY8hoAAMzP9DvAFhQUOI/T09PVtWtXn84bOnSoW3vdunVBjQuINK7lNW+88YaBkQAAgGAx9cz8oUOHdOLECWc7KyvL53M9+wZaSuOvZ599VnPnzm2yz7Rp01RYWKiMjAznXyBatWoVjvAQZT755BO39k9+8hODIgEAAMFk6mT+Ys37Rf48RJqenq74+Hhnvb3nWKHWrVs3r30uJu7x8fEaMGBAqENCFLv++uudx5TXAAAQPUxdZnPw4EG3dqdOnXw+12KxuK2t7TkWEC1cy2umTZtmYCQAACDYTD0zX11d7dZu2bKlX+e7lqzYbDbV1tYqMTExKLFFmj59+jT4oO3MmTM1c+ZMAyJCOOzdu9et/fLLLxsUCQAAsSMvL095eXlur507dy4k14qqZN7fRNyzf3V1ddQm8998802Dr1dVVYU5EoTT1Vdf7TymvAYAgPCoqqoKW9WHqZP52tpat3Z8fLxf5yckJLi1a2pqAo4pmIK5BGX79u0bnJlPSUkJ2jUQWVzLa1x3PQYAAKGVkpLiVs4tOWbmG5tcDYSpk3nPWXTXzaN8cebMGbd2UlJSwDFFqu3btys1NdXoMBAmx48fd2uXlJQYEwgAADGooTLmyspKpaWlBf1apk7mPWvkPWfqvfHs72/NvZlkZ2fLarWquLjY6FAQBm3btnUeU14DAICxMjMzZbPZQjK2qVez8Uy+T5486df5rv2tVmvU1ssjtriW1wAAgOhm6pl5z1qk8vJyn8+12+1uDyZ4jhVtCgoKKLOJAZ6lZszKAwBgvOLi4pCV2Zh6Zt7zob79+/f7fO6RI0fcEh8eEEQ0cH2om0QeAIDoZ+pkPiMjQ61bt3a2i4qKfD63sLDQrd2jR4+gxQUYgfIaAABij6mTecnxYOdFFRUVKi0t9em8DRs2uLWvvfbaoMYVabKzs5WZmWl0GAgTZuUBAIgcmZmZbjlrMJk+mb/99tvd2u+8845P57n2S0xM1I033hjUuIBwcp2VJ5EHACB2mD6ZHzNmjNtmUa+88orOnj3b5Dlr1qzRF1984WzfeuutSk5ODlmMkaCgoIBlKaMU5TUAAES24uJiFRQUhGRs0yfz6enpmjJlirNdWlqq3NzcRvvX1tZqxowZzrbFYtHcuXNDGiMQLszKAwAQW0yfzEvSnDlz1KpVK2d73rx5ysvL0/nz5936VVZW6pZbbnHbDXP8+PHKysoKW6xAMFFeAwBAbLPYw5AB7N+/X926dWvwvXPnzrm1W7Ro0WC/Tz75RCNGjGj0GitWrNCYMWPcEvju3btr1KhRateunb788kstX75cNTU1zvd79eqljRs3KiUlxZ9vJ+I1tI5pRUUF68xHGc/yGpJ5AAAiV6jys7BsGmW32+sl7Y1prJ+3RGX06NHKz8/X9OnTdfr0aUnS3r17tXfv3gb79+3bV++9917UJfKNyc7OltVqpW4+SpHIAwAQuTIzM2Wz2UIydlSU2Vw0adIkFRYW6o477lBcXFyDfTIyMvTYY4/ps88+U+fOncMbIBAklNcAAAApTGU2Rvjuu++0fv16lZeXq6qqSu3bt1fXrl01bNiwRkt5ogVlNtGN8hoAAMzH1GU2Rrjssss0ZswYo8MAQopEHgCA2BZVZTZAtHOdlQ9V7R0AADAPknnAJDzLa6K9XAwAAHgXtWU2cMdqNtGF8hoAAMyD1WyAGOc6K19bW2tgJAAAIJIwMx8jCgoKWM3GpDzLaxISEgyKBAAANEdxcXGDq9kEAzPzgIlQXgMAAFyRzAMRzHVWvry83MBIAABAJCKZByLU7bff7tbu0KGDQZEAAIBIRTIPRKhly5Y5jymvAQAADSGZByKQa3lNUVGRgZEAAIBIxmo2MYJ15s1jwYIFbu2+ffsaEwgAAAgK1pkHYsgvfvEL5zHlNQAAoCnMzMcI1pk3B9fymtdff93ASAAAQLCwzjwQA9asWePW/rd/+zeDIgEAAGZBMg9EiFGjRjmPKa8BAAC+IJkHIoBrec2///u/GxgJAAAwE5J5wGB79+51a7/66qsGRQIAAMyGZB4w2NVXX+08prwGAAD4g2QeMJBreU2XLl0MjAQAAJgRyTxgkKqqKrf2V199ZVAkAADArFhnPkawA2zkad26tfOY8hoAAKIXO8ACUca1vAYAAKC5mJmPEewAGznq6urc2szKAwAQ3dgBFogiCQkJzmMSeQAAEAiSeSCMKK8BAADBRDIPGIRZeQAAECiSeSBMXGflSeQBAEAwkMwDYUB5DQAACAWSeSDMmJUHAADBQjIPhBjlNQAAIFRI5oEQorwGAACEEsk8ECbMygMAgGBjB9gYkZ2dLavVquLiYqNDiRmU1wAAAEnKzMyUzWYLydjMzAMhQHkNAAAIB2bmY0RBQYFSU1ONDiMmMSsPAEBsKy4uVmVlpdLS0oI+NjPzQJC5zsqfPXvWwEgAAEC0I5kHgsizvMZq5Y9fAAAgdEjmgRChvAYAAIQayTwQJK6z8rW1tQZGAgAAYgXJPBAEnuU1CQkJBkUCAABiCck8EGSU1wAAgHAhmQcC5Dorf+DAAQMjAQAAsYZkHgjAhAkT3NodO3Y0KBIAABCLSOaBACxevNh5THkNAAAIN5J5oJlcy2uKiooMjAQAAMQqknmgGRYsWODW7tu3rzGBAACAmEYyDzTDL37xC+cx5TUAAMAoJPOAn1zLa/7yl78YGAkAAIh1JPOAH9asWePWnjJlikGRAAAASFajA0B4ZGdny2q1qri42OhQTG3UqFHOY8prAACALzIzM2Wz2UIyNjPzgI9cy2vuu+8+4wIBAAC4wGJnejHqVFZWKi0tze21iooKpaamGhSR+e3du1dXX321s83/bQAAgD9ClZ8xMw/4gEQeAABEIpJ5wAvX8pqrrrrKwEgAAADckcwDTaiqqnJrl5WVGRMIAABAA0jmgSa0bt3aeUx5DQAAiDQk80AjXMtrAAAAIhHJPNCAuro6tzaz8gAAIBKRzAMNSEhIcB6TyAMAgEhFMg94oLwGAACYBck80ARm5QEAQCQjmTdYWVmZLBaLT19/+tOfjA436rnOypPIAwCASEcyD1xAeQ0AADAbq9EB4Hsvv/yyfvKTnzT6vutDmQgtZuUBAIAZkMxHkISEBLVs2dLoMGIS5TUAAMCMKLNBzKO8BgAAmFVUz8wfPXpU69evV3l5uaqqqpSRkaGuXbtqyJAhatGihdHhIQIxKw8AAMwk7Ml8dXW1CgsLtXnzZm3evFlbtmxRWVmZ8/2rrrrKrd0ce/bs0ezZs7VixYp6O3lKUkZGhqZOnao5c+YoPj4+oGuFQl1dneLi4pgxDgPKawAAgJlZ7GHKYPLy8rRw4UKVlJTo/PnzjfYLNJl/4403NH36dJ06dcpr36ysLC1dulSdO3du9vUCVVZWpi5dukiSrr76an377bc6evSorFarOnbsqGuvvVbTp0/XkCFDfB6zsrJSaWlpbq9VVFQoNTU1qLGbneeHJZJ5AAAQKqHKz8I2M79u3Trt3LkzpNdYuXKlJk+e7PZhoXv37ho5cqTatWun0tJSLV++XDU1NZKkoqIijR49Whs3blRKSkpIY/PFnj17nMc2m01lZWUqKyvTa6+9pqlTp+rPf/6zrNaorowyDIk8AAAwI0MfgG3ZsqWuvfZaJScnBzzW4cOHNWHCBGcib7FY9Oyzz2r37t16+eWX9eSTT2rx4sXav3+/rrvuOud5JSUlmjZtWsDXb65LLrlEgwcP1rPPPqutW7fqyJEjOnPmjPbt26eXX35ZV111lSTpL3/5i6ZPn25YnNHGdVb+7NmzBkYCAADQfGErs5kwYYL27dungQMHasCAARo4cKB69uypSy65RJ07d9b+/fslNb/M5sEHH9RLL73kbD/++OP67W9/22Df2tpa9evXT7t27ZLkSOy2bdumrKysBvu7lsI0V3p6ur755hu/zzt+/LhGjhypoqIiSdL69es1dOjQJs+hzKZplNcAAIBwM32ZzVtvvRWysY8cOaJXX33V2e7WrZtmz57daP/ExES9+OKLGjVqlCRHMpeTk6N33303ZDE2V5s2bbRw4UL17dtXkvT66697TebhOxJ5AABgZlFRgP3BBx+4rVozdepUxcXFNXnOyJEj1aNHD+3evVuSo97+1KlTDZb8dOjQwTmL31yB1Lr/8Ic/VLdu3VRaWqpt27YFFEesc52Vr62tNTASAACAwEVFMr9s2TK39tixY306b+zYscrJyZHkSOw+/vhj3XnnnfX6xcXFqUePHoEHGoD09HSVlpbq+PHjhsZhZp7lNQkJCQZFAgAAEBxRsQNsQUGB8zg9PV1du3b16TzPcpV169YFNa5gOnz4sCSpbdu2BkcSHSivAQAA0cD0yfyhQ4d04sQJZ7uxh1gb4tk30FKaUCkqKtK+ffskSf379zc4GnNynZU/cOCAgZEAAAAEj+mT+Ys17xddeeWVPp+bnp7utgOs51jh4C2xPHr0qO6//35n+6c//WmoQ4o6kydPdmt37NjRoEgAAACCy/Q18wcPHnRrd+rUyedzLRaLOnTo4Jz19hwrHLKysjR06FDdfvvt6t+/v6644grFxcXp8OHDWrVqlf74xz864/r5z3+uwYMHhz1Gs3vttdecx5TXAACAaGL6ZL66utqt3bJlS7/Ob9WqlfPYZrOptrZWiYmJQYnNFzabTcuXL9fy5csb7XPJJZfo4Ycf1h/+8IdmX6dPnz5q0aJFvddnzpypmTNnNnvcSOdaXsNKQAAAIBzy8vKUl5fn9tq5c+dCcq2oS+b9TcQ9+1dXV4c1mc/Pz1dBQYE2b96sAwcO6OjRozp9+rRat26tbt266dprr9W///u/B7yaTmMbVlVVVQU0biRz3XtAkvr162dQJAAAIJZUVVWFreLD9Mm851rhrjXwvvBcnrCmpibgmPxxxx136I477gj5ddq3b9/gzHxKSkrIr22UKVOmOI8prwEAAOGSkpKiDh06uL127ty5RidXA2H6ZN5zFt118yhfnDlzxq2dlJQUcEyRaPv27QFvF2wmruU1f/nLXwyMBAAAxJqGypgrKyuVlpYW9GuZPpn3rJH3d1dPz/7+1tybRXZ2tqxWq4qLi40OJeTWrFnj1nadoQcAAAi3zMxM2Wy2kIxt+qUpPZPvkydP+nW+a3+r1RrWenmExqhRo5zHlNcAAIBoZvqZec96pPLycp/Ptdvtbg8neI4VTQoKCmKizMa1vObee+81MBIAAACH4uLikJXZmH5mvmfPnm7t/fv3+3zukSNH3GrsPceCuezdu9et/eabbxoUCQAAQHiYPpnPyMhQ69atne2ioiKfzy0sLHRrB7r8I4x19dVXO48prwEAALHA9Mm85Hi486KKigqVlpb6dN6GDRvc2tdee21Q44ok2dnZyszMNDqMkHEtr7nssssMjAQAAMBdZmamW74aTFGRzN9+++1u7Xfeecen81z7JSYm6sYbbwxqXAgPz42vvv32W4MiAQAACK+oSObHjBnjtlnUK6+8orNnzzZ5zpo1a/TFF18427feequSk5NDFqPRCgoKonZZStcyK8prAABApCkuLlZBQUFIxo6KZD49Pd1tLfHS0lLl5uY22r+2tlYzZsxwti0Wi+bOnRvSGBEaruU1AAAAsSYqknlJmjNnjlq1auVsz5s3T3l5eTp//rxbv8rKSt1yyy0qKSlxvjZ+/HhlZWWFLVYEh+duv8zKAwCAWGOxhykD2r9/v7p169bge+fOnXNrt2jRosF+n3zyiUaMGNHoNVasWKExY8a4JfDdu3fXqFGj1K5dO3355Zdavny5ampqnO/36tVLGzduVEpKij/fTkRraB3TioqKqFtn3nVWnkQeAABEslDlZ2HbNMput9dL2hvTWD9vCdvo0aOVn5+v6dOn6/Tp05Ica497rj9+Ud++ffXee+9FVSLfmOzsbFmt1qipm6e8BgAAmEVmZqZsNltIxo6aMpuLJk2apMLCQt1xxx2Ki4trsE9GRoYee+wxffbZZ+rcuXN4A0TQMSsPAABiVdjKbIzw3Xffaf369SovL1dVVZXat2+vrl27atiwYY2W8kSDaC+zobwGAACYjenLbIxw2WWXacyYMUaHgSCivAYAAOB7UVdmg9jBrDwAAIh1JPMwDcprAAAA3JHMwxQorwEAAKgvqmvm8b1oWpqSWXkAAGAmLE2JmEZ5DQAAQMOYmY8RBQUFplyakvIaAABgdsXFxQ0uTRkMzMzDNJiVBwAAcEcyj4jlOit/9uxZAyMBAACITCTziEie5TVWKxVhAAAAnkjmEfEorwEAAGgY050xwkxLU7rOytfW1hoYCQAAQOBYmhIxw7O8JiEhwaBIAAAAIh8z8zHCjEtTUl4DAACiAUtTIia4zsp//fXXBkYCAABgDiTziAj333+/W7tTp04GRQIAAGAeJPOICPn5+c5jymsAAAB8QzIPw7mW12zYsMHASAAAAMyFZB6GevXVV93aQ4YMMSgSAAAA8yGZh6GmTJniPKa8BgAAwD8k8zCMa3nNf/3XfxkYCQAAgDmxznyMiLQdYNesWePWnjp1qkGRAAAAhBY7wCLqjBo1ynlMeQ0AAEDzMDMfIyJpB1jX8pp7773XwEgAAABCjx1gETVKS0vd2m+++aZBkQAAAJgfyTzC6l/+5V+cx5TXAAAABIZkHmHjWl6TkJBgYCQAAADRgWQeYVFVVeXWrq2tNSgSAACA6EEyj7Bo3bq185jyGgAAgOAgmUfIuZbXAAAAIHhI5hFSdXV1bm1m5QEAAIKHZB4h5fqgK4k8AABAcJHMI2QorwEAAAgtdoCNEdnZ2bJarSouLjbk+szKAwCAWJWZmSmbzRaSsZmZR0i4zsqTyAMAAIQGM/MxoqCgQKmpqWG5FuU1AAAA3ysuLlZlZaXS0tKCPjYz8wgpZuUBAABCh2QeQUV5DQAAQPiQzCNoKK8BAAAIL5J5hASz8gAAAKFHMo+gI5EHAAAID1azQdDY7XaVlZUZHQYAAEDMYGYeQdW5c2ejQwAAAIgZJPMAAACASZHMAwAAACZFMg8AAACYFMk8AAAAYFIk8wAAAIBJkcwDAAAAJkUyDwAAAJgUm0bFiOzsbFmtVhUXFxsdCgAAQEzJzMyUzWYLydjMzAMAAAAmxcx8jCgoKFBqaqrRYQAAAMSc4uJiVVZWKi0tLehjMzMPAAAAmBTJPAAAAGBSJPMAAACASZHMAwAAACZFMg8AAACYFMk8AAAAYFIk8wAAAIBJkcwDAAAAJkUyDwAAAJgUyTwAAABgUlajA0D0yMvLU1VVlVJSUjRz5kyjw0EM4h6E0bgHYTTuwdhjsdvtdqODQHBVVlYqLS3N7bWKigqlpqaG9LodO3bUwYMH1aFDB5WXl4f0WkBDuAdhNO5BGI17MHKFKj+jzAYAAAAwKZJ5AAAAwKRI5mNEdna2MjMzvfbLzMxssp+3980u3N9fsK8X6Hj+nu9Pf1/7cg9yD3IPGot7kHvQaLF+D/qLB2Cj0Pnz5+u9ZrPZJDnqtZrirV9T7587d875r7frRCpf/ztF6vUCHc/f8/3pzz3oG+5B7kGjcQ9yDxotWu/Bb7/9tl7fhnI2f/EAbBTatWuXevXqZXQYAAAAaEJJSYl69uwZ0BiU2QAAAAAmRTIPAAAAmBTJPAAAAGBS1MxHIZvNpr1797q91q5dO11yCZ/dAAAAjHD+/HkdPXrU7bXu3bvLag1sPRqSeQAAAMCkmKoFAAAATIpkHgAAADApknlEtOrqaj333HO67rrrlJqaqri4OLVq1UqZmZmaNm2atm3bZnSIiAEnT57U008/rWHDhqldu3ZKSEhQp06dNG7cOK1atcro8GBiX331lZYsWaJf//rX+vGPf6yUlBRZLBZZLBbl5+f7Pd7atWs1fvx4derUSQkJCcrIyNBtt92mZcuWBT94RIVg3YPBvpfhO2rmEbF27Nih0aNH68CBA432sVgsmj17tp588skwRoZYsm3bNt15551N3odTpkzRf/3Xf8lisYQxMkSDpu6ZhQsX6r777vN5rDlz5ig3N1eN/VqfNGmS8vPzWQwBboJ1DwbzXoZ/+H80IlJ1dbUzkW/RooVmzJihLVu2qKKiQrt379Zf/vIXdezYUXa7XU899ZQWLlxodMiIQgcOHNAtt9zivA/nzJmjkpISfffdd9qyZYt+8pOfSJJeeeUVzZ492+BoYWbt2rXTDTfcoHHjxjXr/JdeeklPPfWU7Ha7BgwYoNWrV6uiokJFRUWaOHGiJOn111/XI488EsywEUUCvQeDPQ78YAci0KuvvmqXZJdkf+qppxrs88UXX9iTk5Ptkux9+/YNc4SIBffff7/zPvzv//7vBvv8/Oc/t0uyW61W+65du8IcIcxu8eLF9tLSUmf7H//4h/OeW7hwoU9jHD161N6mTRu7JHv37t3t1dXV9frce++9zvt0z549wQofUSAY92Awx4H/AlvYEvBw9OhRrV+/XuXl5aqqqlJGRoa6du2qIUOGqEWLFj6PU1RU5DyePHlyg32uvvpqDR06VKtWrdLu3bsDjh3RIxj34dmzZ7VkyRJJUo8ePfSzn/2swX5PPPGE/ud//kdnzpzRggUL9Oc//zlo3wciU7B+zkkKyuzl66+/ruPHj0uSHn/8cSUnJ9fr8/TTT2vJkiWy2Wx6+eWX9eyzzwZ8XUSHYM2gMxNvHJL5GFBdXa3CwkJt3rxZmzdv1pYtW1RWVuZ8/6qrrnJrN8eePXs0e/ZsrVixQnV1dfXez8jI0NSpUzVnzhzFx8d7HS8hIcF53FR958X30tLSmhE1wsls9+GePXt06tQpSdKPf/zjRvu1bdtWWVlZ2rRpk5YuXUoybxCz3V/B9MEHH0hy/Ny84447GuzTsWNHDRs2TOvWrdP7779PMm9ywfxAiShg9J8GEDrPPvus/ZprrrFfcsklzj91NfR11VVXBXSd119/3Vnu4u0rKyvLvm/fPq9jvvnmm85znnvuuQb7lJWV2Vu1amWXZJ8+fXpA3wNCx6z3YUFBgbP/o48+2mTf0aNHO/t+/fXXAX0f8I9Z76/GNKc0oXXr1nZJ9iFDhjTZb/bs2c6xjx071qz40LiTJ0/a165da//jH/9ov+eee+ydO3cO6j1otzvKS++88057fHx8g/deRkaGfd68efYzZ840+xrBKo+hzCZ8mJmPYuvWrdPOnTtDeo2VK1dq8uTJOn/+vPO17t27a+TIkWrXrp1KS0u1fPly1dTUSHKUz4wePVobN25USkpKo+OOGzdOTz31lHbs2KFZs2bpu+++06RJk9SpUycdO3ZM//znPzV37lydPHlSPXv21O9///uQfp9oPrPeh66vHzt2rMnrf/fdd87jkpISderUKZBvB34w6/0VLIcPH9aJEyckSd26dWuyb9euXZ3Hu3bt0pAhQ0IaW6zIy8vTwoULVVJS4naPBNsbb7yh6dOnO/9i2JDDhw9r/vz5WrZsmZYuXarOnTuHLB5EEKM/TSB0br/99nqf2lu2bGm/9tpr3WaYmjtbcOjQIefMuCS7xWKxP/vss/Zz58659auoqLBfd911bnFMmDDB6/gVFRX2G264odHZryuuuMI+Z84ce1VVVbPiR3iY9T6sqamxJyYm2iXZe/Xq1Wi/Y8eOOftJsr/++uvN+j7QPGa9vxrj72zm//3f/zn7//KXv2yy7wcffODsu2zZMr9jQ8Maugcb+gpkZn7FihX1/vrUvXt3+7Rp0+yPPPKIfdy4cfakpCS393v16mU/ceKE39diZt58WJoyiiUmJmrQoEF68MEHtXDhQu3cuVMnTpzQ2rVrdfnllwc8fk5Ojk6ePOlsz58/XzNnzqxX456amqoPP/xQPXv2dL62ePFit4dcG5Kamqo333xT06ZNa/D948ePq7y8XJWVlQF8Fwg1s96HiYmJuu222yQ5Zttfe+21Bvs99thjqq2tdbZdY0HomfX+Cpbq6mrncWJiYpN9k5KSGjwPwdeyZUtde+21DT6M7K/Dhw9rwoQJzll/i8WiZ599Vrt379bLL7+sJ598UosXL9b+/ft13XXXOc8rKSlp9PcnoozRnyZgjKuuuiqg2YJvvvnGrWavW7du9rq6uibP+eSTT9xmDe66664m+7/11lv2Sy+91G6xWOwPPvigfevWrfbvvvvOfuDAAfvf/vY3e/fu3e2S7Jdffrn9s88+8/t7gPEi/T4sKSlxzu5arVb73Llz7bt27bIfPXrUvm3bNvtPf/pTuyS3mfmXX37Z7+8DoRHp91dD/J3NXL9+vbP/3Llzm+y7atUqZ98333zTr7jQuPHjx9sHDRpkf/DBB+0LFy6079y50/mXm0DvQbvdbn/ggQfc7qnHH3+80b41NTX2nj17uv0lqbCw0K/rMTNvPszMo1k++OADt9Ucpk6dqri4uCbPGTlypHr06OFsr1y5stHavw8++EATJkzQ6dOn9ec//1kvvvii+vfvr3bt2qljx46aOHGiPvvsM/3Lv/yLvv32W40fP15nz54NzjcH0wj1fdizZ0+99957at26tWw2m3JyctSzZ0+1a9dO/fv312uvvaYOHTooJyfHeU7btm0D/K4QKUJ9fwVDy5YtnccXa/Yb4/q+63kIzFtvvaXPPvtML774ou677z5lZmYGbZfdI0eO6NVXX3W2u3Xr1uQGdYmJiXrxxRedbbvd7vbzCdGJZB7NsmzZMrf22LFjfTrPtV9tba0+/vjjBvv99re/lSR16dJF06dPb7BP27ZtNWfOHElSWVmZVq1a5VMMiB6hvg8l6YYbbtDu3bv1yCOPqE+fPmrZsqWSkpLUs2dPPfroo9q5c6dbeQMPv0aPcNxfgXItJaqoqGiy75EjR5zHl112WchiQvCY4QMljEcyj2YpKChwHqenp7utktCUoUOHurXXrVtXr8/x48e1Y8cOSdKAAQNksVgaHW/QoEHO41CvaIHIE8r70FX79u315JNP6v/+7/908uRJnT59WiUlJcrJyVGbNm20ZcsWSVJcXJyysrL8/C4QqcJ1fwXiiiuucK6YU1pa2mTfr776ynnsWtuPyGWGD5QwHsk8/Hbo0CHnUmiS/EpePPvu2rWrXh/XPwU3lch7vh+sP2vCHEJ9H/rq7NmzWrFihSTHjJi3hxBhDpFyf/mif//+kqTCwkKdOXOm0X4bNmyQ5FiiknIwczDDB0oYj+wHftu9e7db+8orr/T53PT0dLedET3Hkhx/Nr7YZ+vWrbLb7Y2Ot3nzZufxVVdd5XMcML9Q34e+evXVV/Xtt99Kkh544IFmj4PIEin3ly8u7vpaW1ur999/v8E+Bw8edCaGje0Si8hipg+UMBbJPPx28OBBt7Y/NcIWi0UdOnRodCzJUapwcXmtr776yu3hH1cnTpzQk08+KcmxjfnIkSN9jgPmF+r70BebN2/WrFmzJDlq68eMGdOscRB5IuH+8tWkSZPUpk0bSY6lUk+fPl2vz6xZs3Tu3DlZrdZGn0NCZDHTB0oYix1g4TfP9Yn9XRWhVatWzmObzaba2tp6pQm/+93vtGbNGtlsNk2fPl07d+7U5MmT1aVLF50+fVrr1q3T/PnztXfvXknSL3/5Sx7oijHhuA8lqXfv3rrjjjt0/fXXq3PnzkpOTta+ffu0ZMkSLViwQGfOnFHnzp31P//zP837RhCRwnV/lZaWuu2VUVJS4vbepk2bnO2UlBT16tWr3hht27bVE088oQcffFB79uzRddddpz/84Q/q3bu3Dh06pKefflp/+9vfJEkzZ85U9+7d/fpeYIxgfKDct29fg2O5CsY9GMxx0AxGr40JYwSy9u3TTz/ttubtf/7nf/p1/qBBg9zOr6ysbLDfW2+9ZW/ZsqXXXfWmTJlSbzdGmIMZ7kPXXUQb+hoyZIh9//79fl0b4WGG+2vy5Mk+7R4qyT5ixIgmr/nII4/YLRZLo+dPmjSJn5VhFsg9+NJLL7n97/fcc8/5dX6fPn3czq+pqWmwX7DuwWDey/APM/Pwm+tul5Lc/pTni4SEBLd2Y2sjjx8/XtnZ2frLX/6iVatW6YsvvlBVVZUSEhLUsWNHDRs2TD/72c80bNgw/74BRIVw3YcvvPCCVq9erW3btunIkSOqra1VWlqaBgwYoAkTJuiee+7x+qA2zCdc91cwPfnkk7rpppv05z//WRs2bFBlZaXatm2rAQMGaMqUKbr99ttDHgOCx/OvQ/4+XO/Zv7q6mgf0oxTJPPzm+cPAdQ1cX3iutuC6xbinDh06aP78+Zo/f75f10D0C9d9+LOf/Uw/+9nP/AsOpheu+ys/P1/5+fl+jd2UESNGaMSIEUEbD8YJ1wfKYN2Dwb6X4TsegIXfPGtHPX/geOPZn50I0Rzchwgl7i8YLZwTZzA3knn4zfOX0smTJ/0637W/1Wrlz35oFu5DhBL3F4zGB0r4imQefnNdck2SysvLfT7Xbre7PVXvORbgK+5DhBL3F4zGB0r4imQefvPcBnz//v0+n3vkyBG3PxWypTiai/sQocT9BaPxgRK+IpmH3zIyMtS6dWtnu6ioyOdzCwsL3do9evQIWlyILdyHCCXuLxiND5TwFck8miU7O9t5XFFRodLSUp/O27Bhg1v72muvDWpciC3chwgl7i8YiQ+U8BXJPJrFc73id955x6fzXPslJibqxhtvDGpciC3chwgl7i8YjQ+U8AXJPJplzJgxbmvevvLKKzp79myT56xZs0ZffPGFs33rrbcqOTk5ZDEi+nEfIpS4v2A0PlDCFyTzaJb09HRNmTLF2S4tLVVubm6j/WtrazVjxgxn22KxaO7cuSGNEdGP+xChxP0Fo/GBEr4gmUezzZkzR61atXK2582bp7y8PJ0/f96tX2VlpW655RaVlJQ4Xxs/fryysrLCFiuiF/chQon7C0biAyV8YbHb7Xajg0Bo7N+/X926dWvwvXPnzrm1W7Ro0WC/Tz75pMmtwVesWKExY8a4/WLr3r27Ro0apXbt2unLL7/U8uXL3baR7tWrlzZu3KiUlBR/vh2YFPchQon7C5Gsc+fOzlVorrrqKpWVlfk9xqFDh9SjRw/nuvEWi0XPPPOMHnroIV1yyfdzspWVlRo3bpw+/fRT52sTJkzQokWLAvoeYAJ2RK19+/bZJQX09Y9//MPrdV577TX7pZde6tN4ffv2te/bty/k3zsiB/chQon7C0YrKyuzt2jRosEvz3ujsX6ffvppk9f4+9//br/kkkvcxurevbt9+vTp9jlz5tjHjRtnT0pKcnu/V69e9hMnToTpvwKMRJkNAjZp0iQVFhbqjjvuUFxcXIN9MjIy9Nhjj+mzzz5T586dwxsgYgL3IUKJ+wuNsdvtOnfuXINfnhrrZ/dSJDF69Gjl5+fr0ksvdb62d+9evfzyy3ryySe1ZMkSt78M9e3bVytWrOAvQzGCMhsE1Xfffaf169ervLxcVVVVat++vbp27aphw4Y1+iduINi4DxFK3F9wVVZWpi5dugQ0xj/+8Q9dd911Xvt98cUXmj17tlasWNHgg7AZGRmaMmWKHn30UbcHZxHdSOYBAABMhA+UcEUyDwAAAJgUNfMAAACASZHMAwAAACZFMg8AAACYFMk8AAAAYFIk8wAAAIBJkcwDAAAAJkUyDwAAAJgUyTwAAABgUiTzAAAAgEmRzAMAAAAmRTIPAAAAmBTJPAAAAGBSJPMAAACASZHMAwAAACZFMg8AAACYFMk8AAAAYFIk8wAAAIBJkcwDAAAAJkUyDwAAAJgUyTwAAABgUiTzAAAAgEmRzAMAAAAmRTIPAAAAmBTJPAAAAGBSJPMAAACASZHMAwAAACb1/wH/WHsLAbnljAAAAABJRU5ErkJggg==", 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" ] @@ -349,9 +349,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\\mathrm{p} = 2.01e+00_{-0.014}^{1.772}\n", - "\\mathrm{log F_v} = 7.89e-01_{-0.339}^{0.354}\n", - "\\mathrm{log nu_a} = 1.05e+01_{-1.297}^{0.143}\n" + "\\mathrm{p} = 2.01e+00_{-0.005}^{0.487}\n", + "\\mathrm{log F_v} = 7.87e-01_{-0.119}^{0.053}\n", + "\\mathrm{log nu_a} = 1.05e+01_{-1.051}^{0.007}\n" ] } ], @@ -396,7 +396,7 @@ ")\n", "\n", "# print out the constraints we get\n", - "labels = syncfit.models.B5B3.get_labels() \n", + "labels = syncfit.models.B5.get_labels() \n", "constraints = syncfit.analysis.get_bounds(sampler, labels, toprint=True)" ] }, @@ -405,20 +405,129 @@ "id": "7109af3d-54ed-4840-b5de-aa697a40571e", "metadata": {}, "source": [ - "Which has a pretty good looking fit! But, let's see if we can make it better by customizing the B5 model a little more!\n", + "Which has a pretty good looking fit! But, let's see if we can make it better by customizing the B5 model a little more! For this tutorial, we are going to modify the prior function, called `lnprior`, but some other methods that can be customized are `SED` (the model itself) and `loglik` (the likelihood function which, by default, is just gaussian).\n", "\n", - "To do this, we need to subclass the existing B5 model and modify the functions we want to improve upon for this data. The best way to do this, is go look at the source code documentation on readthedocs for the B5 model and see which models you want to improve on. You can also use the github for this code to go and copy the original code, then modify it as you see fit.\n", + "To do this, we use the decorator `@syncfit.models.B5.override`. All of the models, including the generic `SyncfitModel` model, have this override decorator which allows you to customize that specific method of the model class. If you want to use the generic `SyncfitModel` instead of one of the other builtin models please note that you will need to define `lnprior`, `SED`, and `get_labels` using this decorator." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5e8d65bd-ac2e-4cb0-a4ee-f105bb1b5c5f", + "metadata": {}, + "outputs": [], + "source": [ + "@syncfit.models.B5.override\n", + "def lnprior(theta, p=None, **kwargs):\n", + " '''\n", + " Our customized lnprior function. This needs the arguments\n", + " that it has and no more or less! Otherwise it won't work \n", + " when we actually try to run the MCMC.\n", + " '''\n", "\n", - "For this tutorial, we are going to modify the prior function, called `lnprior`, but some other methods that can be customized are `SED` (the model itself) and `loglik` (the likelihood function which, by default, is just gaussian).\n", + " # All of this is copied from the source code and is fine to leave\n", + " # since it is just unpacking the theta value\n", + " if p is None:\n", + " p, log_F_nu, log_nu_a= theta\n", + " else:\n", + " log_F_nu, log_nu_a = theta\n", "\n", - "I know subclassing and learning what that means can be somewhat of a steep learning curve. But, it is the best way to reduce code redundancy for this type of software! If you need to refresh your memory on subclassing in python, I recommend this tutorial for a quick overview: https://www.geeksforgeeks.org/create-a-python-subclass/. The basic idea though, is that when you subclass you get all of the methods from the superclass that you are using. \n", + " # However, here we want to change things to narrow the\n", + " # priors on all three parameters\n", + " if 2.9 < p < 3.1 and -1 < log_F_nu < 1 and 9 < log_nu_a < 11:\n", + " return 0.0\n", "\n", - "So, now let's actually do this customization! In this case, we use the superclass `syncfit.models.B5` which itsels subclasses the `syncfit.models.base_model` superclass. " + " else:\n", + " return -np.inf" + ] + }, + { + "cell_type": "markdown", + "id": "8174a192-3cb3-4289-8312-59ea20a048e8", + "metadata": {}, + "source": [ + "In this case, since we've just overwritten a method of the B5 model class directly, we can just pass in the B5 model again." ] }, { "cell_type": "code", "execution_count": 6, + "id": "891ecb2d-67b0-409e-96f6-4d2abe917f12", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/500 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\mathrm{p} = 2.91e+00_{-0.007}^{0.063}\n", + "\\mathrm{log F_v} = 9.58e-01_{-0.014}^{0.004}\n", + "\\mathrm{log nu_a} = 1.06e+01_{-0.110}^{0.003}\n" + ] + } + ], + "source": [ + "# now we can fit the data\n", + "sampler = syncfit.do_emcee(\n", + " theta_init = theta_init,\n", + " nu = cmc.nu,\n", + " F_muJy = cmc.F_nu,\n", + " F_error = cmc.F_err,\n", + " model = syncfit.models.B5, # use the same model here!\n", + " niter = niter,\n", + " nwalkers = nwalkers\n", + ")\n", + "\n", + "# Then let's just check how the fit looks\n", + "syncfit.analysis.plot_best_fit(\n", + " model = syncfit.models.B5,\n", + " sampler = sampler,\n", + " \n", + " # be careful with units for the data\n", + " # the frequency must be in GHz space\n", + " # and the flux densities must be in mJy space\n", + " nu = cmc.nu*1e9,\n", + " F = cmc.F_nu*1e-3\n", + ")\n", + "\n", + "# print out the constraints we get\n", + "labels = syncfit.models.B5B3.get_labels() \n", + "constraints = syncfit.analysis.get_bounds(sampler, labels, toprint=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1eafa4c9-aeef-48fb-b88e-39b0a9611f47", + "metadata": {}, + "source": [ + "You can also do this customization by subclassing. I know subclassing and learning what that means can be somewhat of a steep learning curve. But, it is the best way to reduce code redundancy for this type of software! If you need to refresh your memory on subclassing in python, I recommend this tutorial for a quick overview: https://www.geeksforgeeks.org/create-a-python-subclass/. The basic idea though, is that when you subclass you get all of the methods from the superclass that you are using. \n", + "\n", + "So, now let's actually do this customization! In this case, we use the superclass `syncfit.models.B5` which itsels subclasses the `syncfit.models.SyncfitModel` superclass. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, "id": "ed8d243f-1ff1-4f63-aec2-26c1cbfee0fb", "metadata": { "tags": [] @@ -461,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "eaade645-d5d1-4751-bdcb-12c6c66bfcae", "metadata": { "tags": [] @@ -471,12 +580,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [00:03<00:00, 136.99it/s]\n" + "100%|███████████████████████████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 2642.76it/s]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -488,9 +597,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\\mathrm{p} = 2.91e+00_{-0.008}^{0.058}\n", - "\\mathrm{log F_v} = 9.57e-01_{-0.142}^{0.004}\n", - "\\mathrm{log nu_a} = 1.06e+01_{-0.685}^{0.002}\n" + "\\mathrm{p} = 2.91e+00_{-0.009}^{0.065}\n", + "\\mathrm{log F_v} = 9.58e-01_{-0.318}^{0.004}\n", + "\\mathrm{log nu_a} = 1.06e+01_{-1.299}^{0.003}\n" ] } ], diff --git a/src/syncfit/_version.py b/src/syncfit/_version.py index f102a9c..3dc1f76 100644 --- a/src/syncfit/_version.py +++ b/src/syncfit/_version.py @@ -1 +1 @@ -__version__ = "0.0.1" +__version__ = "0.1.0" diff --git a/src/syncfit/mcmc.py b/src/syncfit/mcmc.py index 14ab35f..521ef01 100644 --- a/src/syncfit/mcmc.py +++ b/src/syncfit/mcmc.py @@ -7,10 +7,10 @@ import emcee from .analysis import * from .models.b5_model import B5 -from .models.base_model import BaseModel +from .models.syncfit_model import SyncfitModel def do_emcee(theta_init:list[float], nu:list[float], F_muJy:list[float], - F_error:list[float], model:BaseModel=B5, niter:int=2000, + F_error:list[float], model:SyncfitModel=SyncfitModel, niter:int=2000, nwalkers:int=100, fix_p:float=None, upperlimits:list[bool]=None, day:str=None, plot:bool=False ) -> tuple[list[float],list[float]]: @@ -22,8 +22,8 @@ def do_emcee(theta_init:list[float], nu:list[float], F_muJy:list[float], nu (list): list of frequencies in GHz F_muJy (list): list of fluxes in micro janskies F_error (list): list of flux error in micro janskies - model (BaseModel): Model class to use from syncfit.fitter.models. Can also be a custom model - but it must be a subclass of BaseModel! + model (SyncfitModel): Model class to use from syncfit.fitter.models. Can also be a custom model + but it must be a subclass of SyncfitModel! niter (int): The number of iterations to run on. nwalkers (int): The number of walkers to use for emcee fix_p (float): Will fix the p value to whatever you give, do not provide p in theta_init diff --git a/src/syncfit/models/__init__.py b/src/syncfit/models/__init__.py index 7466430..4de3eb0 100644 --- a/src/syncfit/models/__init__.py +++ b/src/syncfit/models/__init__.py @@ -4,4 +4,4 @@ from syncfit.models.b1b2_model import B1B2 from syncfit.models.b1b2_b3b4_weighted_model import B1B2_B3B4_Weighted from syncfit.models.b5b3_model import B5B3 -from syncfit.models.base_model import BaseModel +from syncfit.models.syncfit_model import SyncfitModel diff --git a/src/syncfit/models/b1b2_b3b4_weighted_model.py b/src/syncfit/models/b1b2_b3b4_weighted_model.py index 821cd3a..56db5c9 100644 --- a/src/syncfit/models/b1b2_b3b4_weighted_model.py +++ b/src/syncfit/models/b1b2_b3b4_weighted_model.py @@ -2,9 +2,9 @@ Various models to use in MCMC fitting ''' import numpy as np -from .base_model import BaseModel +from .syncfit_model import SyncfitModel -class B1B2_B3B4_Weighted(BaseModel): +class B1B2_B3B4_Weighted(SyncfitModel): ''' This is a specialized model that uses a weighted combination of the B1B2 model and the B3B4 model. The idea of this model is from XXXYXYXYX et al. (YYYY). @@ -59,7 +59,7 @@ def SED(nu, p, log_F_nu, log_nu_a, log_nu_m): def lnprior(theta, nu, F, upperlimit, p=None, **kwargs): ''' Priors: ''' - uppertest = BaseModel._is_below_upperlimits( + uppertest = SyncfitModel._is_below_upperlimits( nu, F, upperlimit, theta, B1B2_B3B4_Weighted.SED, p=p ) diff --git a/src/syncfit/models/b1b2_model.py b/src/syncfit/models/b1b2_model.py index c1c4c1f..da338a0 100644 --- a/src/syncfit/models/b1b2_model.py +++ b/src/syncfit/models/b1b2_model.py @@ -2,9 +2,9 @@ Various models to use in MCMC fitting ''' import numpy as np -from .base_model import BaseModel +from .syncfit_model import SyncfitModel -class B1B2(BaseModel): +class B1B2(SyncfitModel): ''' Two-break model for the self-absorption break (nu_a) and the minimal energy break (nu_m). This model uses nu_m > nu_a, the opposite of the B4B5 model. @@ -36,7 +36,7 @@ def SED(nu, p, log_F_nu, log_nu_a, log_nu_m): def lnprior(theta, nu, F, upperlimit, p=None, **kwargs): ''' Priors: ''' - uppertest = BaseModel._is_below_upperlimits( + uppertest = SyncfitModel._is_below_upperlimits( nu, F, upperlimit, theta, B1B2.SED, p=p ) diff --git a/src/syncfit/models/b4b5_model.py b/src/syncfit/models/b4b5_model.py index d931203..fd8f542 100644 --- a/src/syncfit/models/b4b5_model.py +++ b/src/syncfit/models/b4b5_model.py @@ -2,9 +2,9 @@ Various models to use in MCMC fitting ''' import numpy as np -from .base_model import BaseModel +from .syncfit_model import SyncfitModel -class B4B5(BaseModel): +class B4B5(SyncfitModel): ''' Two-break model for a combination of the self-absorption break (nu_a) and the minimal energy break (nu_m). This model requires that nu_m < nu_a, you should @@ -37,7 +37,7 @@ def SED(nu, p, log_F_nu, log_nu_a, log_nu_m): def lnprior(theta, nu, F, upperlimit, p=None, **kwargs): ''' Priors: ''' - uppertest = BaseModel._is_below_upperlimits( + uppertest = SyncfitModel._is_below_upperlimits( nu, F, upperlimit, theta, B4B5.SED, p=p ) diff --git a/src/syncfit/models/b4b5b3_model.py b/src/syncfit/models/b4b5b3_model.py index 583f4b0..9d3bfc9 100644 --- a/src/syncfit/models/b4b5b3_model.py +++ b/src/syncfit/models/b4b5b3_model.py @@ -2,9 +2,9 @@ Various models to use in MCMC fitting ''' import numpy as np -from .base_model import BaseModel +from .syncfit_model import SyncfitModel -class B4B5B3(BaseModel): +class B4B5B3(SyncfitModel): ''' Three-break model using the self-absorption break (nu_a), cooling break (nu_c), and minimum energy break (nu_m). This model always requires that nu_m < nu_a < nu_c. @@ -41,7 +41,7 @@ def SED(nu, p, log_F_nu, log_nu_a, log_nu_m, log_nu_c): def lnprior(theta, nu, F, upperlimit, p=None, **kwargs): ''' Priors: ''' - uppertest = BaseModel._is_below_upperlimits( + uppertest = SyncfitModel._is_below_upperlimits( nu, F, upperlimit, theta, B4B5B3.SED, p=p ) diff --git a/src/syncfit/models/b5_model.py b/src/syncfit/models/b5_model.py index bd739d3..48e1b19 100644 --- a/src/syncfit/models/b5_model.py +++ b/src/syncfit/models/b5_model.py @@ -2,9 +2,9 @@ Various models to use in MCMC fitting ''' import numpy as np -from .base_model import BaseModel +from .syncfit_model import SyncfitModel -class B5(BaseModel): +class B5(SyncfitModel): ''' Single break model for just the self-absorption break. ''' @@ -30,7 +30,7 @@ def SED(nu, p, log_F_nu, log_nu_a): def lnprior(theta, nu, F, upperlimit, p=None, **kwargs): ''' Priors: ''' - uppertest = BaseModel._is_below_upperlimits( + uppertest = SyncfitModel._is_below_upperlimits( nu, F, upperlimit, theta, B5.SED, p=p ) diff --git a/src/syncfit/models/b5b3_model.py b/src/syncfit/models/b5b3_model.py index 50dab5f..b046da9 100644 --- a/src/syncfit/models/b5b3_model.py +++ b/src/syncfit/models/b5b3_model.py @@ -2,9 +2,9 @@ Various models to use in MCMC fitting ''' import numpy as np -from .base_model import BaseModel +from .syncfit_model import SyncfitModel -class B5B3(BaseModel): +class B5B3(SyncfitModel): ''' Two-break model that uses both the self-absorption break and the cooling break. This model forces the cooling break to always be larger than the self-absorption @@ -38,7 +38,7 @@ def SED(nu, p, log_F_nu, log_nu_a, log_nu_c): def lnprior(theta, nu, F, upperlimit, p=None, **kwargs): ''' Priors: ''' - uppertest = BaseModel._is_below_upperlimits( + uppertest = SyncfitModel._is_below_upperlimits( nu, F, upperlimit, theta, B5B3.SED, p=p ) diff --git a/src/syncfit/models/base_model.py b/src/syncfit/models/syncfit_model.py similarity index 94% rename from src/syncfit/models/base_model.py rename to src/syncfit/models/syncfit_model.py index 50bded0..214b23e 100644 --- a/src/syncfit/models/base_model.py +++ b/src/syncfit/models/syncfit_model.py @@ -6,7 +6,7 @@ from abc import ABC, abstractmethod import numpy as np -class _BaseModelMeta(type): +class _SyncfitModelMeta(type): ''' This just gives all the subclasses for BaseModel the same docstrings for the inherited abstract methods @@ -18,7 +18,7 @@ def __new__(mcls, classname, bases, cls_dict): member.__doc__ = getattr(bases[-1], name).__doc__ return cls -class BaseModel(object, metaclass=_BaseModelMeta): +class SyncfitModel(object, metaclass=_SyncfitModelMeta): ''' An Abstract Base Class to define the basic methods that all syncfit models must contain. This will help maintain some level of standard for the models @@ -184,3 +184,11 @@ def __subclasshook__(cls, C): if all(any(arg in B.__dict__ for B in C.__mro__) for arg in reqs): return True return NotImplemented + + # add a register method so users don't have to create a new class + @classmethod + def override(cls,func): + ''' + This method should be used as a decorator to override other methods + ''' + exec(f'cls.{func.__name__} = func')