diff --git a/DP2/300_Science_demos/310_Photometric_redshifts/310_2_Photoz_analysis.ipynb b/DP2/300_Science_demos/310_Photometric_redshifts/310_2_Photoz_analysis.ipynb
new file mode 100644
index 00000000..748e6365
--- /dev/null
+++ b/DP2/300_Science_demos/310_Photometric_redshifts/310_2_Photoz_analysis.ipynb
@@ -0,0 +1,1345 @@
+{
+ "cells": [
+ {
+ "attachments": {
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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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FVQu7UAlzv7v/u7vztjcWtBkx/84AfQAvfqq69GTU3NIGgiGvPnz8cXvvAFY2cl\nLS1tELiorq42KjMCLWx5Su8OWgwLPCkoKDDqR7Foa3Gsej733HMGKNJCV7aT7r77biMlI9BHEkMC\nTgSMCAhTOydMmIDs7Gzq8Z4YtLGkukSHn/70p1D7li5dive9731ntE8SJAICqqqqDG3xwQYt4gXI\nSC1Iklkq2wbxXHk1mRGPo9XsbLpEzqKRTyPuM2fONMCCO49cn9fX1xvAxR2va/FXUi9SIZRHuuuu\nu25IHVW/G264wdid+vnPf274oPem9qgterfiv0I8QMOmU9+QdIreR3RQGj2XZzyVo/ZY8EP1tyDc\nzTffTLFg2eFwpNuigTjRVZvUh9UfVE+Bmm4aSqN6S+pMwYI35sb1x01HKlfu9yfX8uqzOtT3rPSZ\nsos/4oX6lW3vrFmzcNNNN7moa5B2gFO1XcCsgBqp0SnePnNnUF9SP1m+fHnMfqg2fehDHzLfquoV\nDXSqXn/7t39r6qo2J8pjN5jrrk/yOj4HYkkN1eQHsbkujFePdeNQ3WE8sKMNR2hvSF6/0ura8TM+\nWzQtD++a5cNjNAr0na2UIE0J4JoFXpxs6kbDc6lo7KMRZo67AU8Gx9zYUxWjWkaarxzhTmBwOu5e\nEMLs4ma8YzY9gFL0Z2uLRhoeEhFyhWNtlFLJ82AJQaxX6jxGUkgpJDFUTtf1Aoi2UHLJhpump+O2\nafSa1tGE37zQj6f3TiMoRB01TS6GCZpL+CkVK1Do+sXtePsaeSHrwbde6MAmAmITqEJ2fZUPV8/0\n41XaP9rDTaYsSiBdMiGElqAX87nhtYnVeJpA1arJPiMt1Nzpx9TJazCJamTjFfopsWhBoeEmzu7y\npcJwioZPbYg3qR98zvmH0ihoAaOJfDIkzgEtWtzSAfFy9vb2U4L4FSxfOs8YmY2X7s2KjwYUz2c9\n1OckfWWlv2JJ30hSQGqD6qPxbE8NV+dJ5SVG4iEWbeUbCUB101Z9JbFmg1SHJDEWT2pMfUTSFDn0\nBnmugnuRH4umytSRDEM5INBD70sSfBs2bB20U6bfWQsQDadepnmX/U0ezh6b+ohAY4EzGp0EtIw2\nSErmyNFaU95IoH0lPahNEuBCFbFjLrA/XpnqG8NJn8qbWDk9q0lS6AQldWJ9N/Kmpj4vD2zia7z+\nJpBsUvmNeMcdbzPzTBmQV3CDbCP1V9nmEi9Vn0TaF6/dF1P8m/b19nWdQlv9NgMMdbadpOexE+in\nfaGhQd1aot8RbCLyMEgr7J3tfejs6IU/NQdl1StRWDqdswsaf355O3Y1NtCoo1yua1LIiQcBEt4Q\nyCGgQ9Uy87UwWrEmkL5KMjGMHMRBFE+ASECQiRQdY5U5QkuZSLOf8R09/ejN4A9i1ATUkI38aaf6\n2EnWu41p+wloiZwBZVQTe82zAWkMWKNrpXHAHgfAYYSTkQ8F5gg0ctoY/dzEm2dqLA8SU5pB9TPR\nNQXouQpWElOgLsx/E6d2Rh47jOLEdWoAK+dOwKpL1yC3qIrvrw6NJ3bj+L5NaGs4xI81TNE+WnQg\nv5RVJHRh5tH2rDg+8PkIkAUb0HDkFQR7WzCxch5Kpq1EdqEjiaJk5zpogSpgQACFFqFS47ELX6nu\n/PM//7Mp8q677hosWotZ2a4RaKLFswCZ6EWt7gXALFy40OSzz3UWkDF16tS4wJDqoUN2XBoaGgxA\nYQsXKCV7Kc8884yJkhSIBXbUFjcYMH36dKP6JTDGBlv+2rVrDZggsOEw1Yx0rnBJ/Kh8tU8Ag4Al\nW39LR2cLrOlaZSsIWJDajkC1N954wwA35gH/qO6KF3ih8rRIl3SLu2ybNpGzgIjPfe5zBmiLrp9o\nC1yT2pc7WOBDdRR/9e5s3d3pxDs3j75PCR7Z11E/UFm2PPe1O7+u9UySUjZt9HPdq2ylUVB7BDgJ\nmHIHpXEDSHpfUpFTn7VB70t8FYCi9xVNQ+lUD8Wrj8QLbjrRvFE9pk2bNih5pndpg+WDu72aOMbi\nrc2j/uPumzbenm1fEgglia5Y/VDlCrzS+9R3q+9h3bp1hjfqz9Ft1juPxZ9oHtu+qneeDIlzQFJD\nMoJ8dOcuTAyfMFJD2+ms8tc7mpFOAODYgBe9GkcZDvd6saIiA2+bX4DZtKmjceH+nV14ZOcAyrJT\ncefaAL2etOKn6zwEhwgY8XkgheCQL/50JUAPZa8co4v0nF6U5vRj8aROdAToCXNnGAfkF4E0zNjm\nVAFB7upsruW34ac0Ea+DfH4sIjUUrUJ26+xsfGJVNsp87Vi/pQOP7yjH8X46S/APD2R4QgH46TjD\n29+FxZPbcMcaXnMT5OGN7dh4LGS8kPk4X9jbQjCK0kKLJgCzqIb32AEvHj/pw5xy/q63BbGPxzwa\nw15YRACLdpJagrNRkzuffTw+P8TnsQYrOm/zjzRxtun02yup40RCdBnlF4kqmbttb+dO9bW0s6GQ\nRTuTb0YYrb2hkOaOF1jQwvPL//JJfOEfPj6s0d3xqrYW0pIsWP/y68ZWSnQ5Ai9l7+RB2jORRFy0\nNER0+uh7t+2t6Ge6P8oFpyTnZAtmtGGkb/Ni/K5Gy4OLKb3el6TUplVXUgqnbNB+jdog0MetXhYt\nPeRWvTXzLs6z4gWVk38W3h3djgEEwHz0f30hLvii3/zurh5TFX0rI9mWi1dnG+8GHkOkF62O7Aat\nygkiCRyKFyyQKvDIHdwgm2weffij/5hw+1Snt3oYn5lFHK4FBrohtbGu1iPo72lGb2cjOlpr0d3R\nQG/tfQQsJD3iDs4gZjELPemlXZ6+3lQUVSxBzfTlyMkvRVZuIb185XKSl4JTLe1YQXe524+Lll6g\n1L4o7ROh7VDk7FDiPq6gMoztAl4YMMgAQpIeihxENMKSFiKYYmwXCeGQ0WhNdqma1tcfMIdfPm8t\nadcY3EU7RLUUnW/s6o/skokWK6A/zn/nWnVinAPq6FLXShA5IqCQnjvxDqhzBuCjCZpJq3S6ds4O\nXeURKOTkFW0nnmU4M+jTZ0XZOF0bsI1qZPPKsXLNMsxcciXF/vworpyDyhlLeE+994Ov4uiO36Gn\nbT+BIHpZM/lF53QwpAZvyVP6renrOUwPZc3ooyHyhmM7kFlUjYpZawj6TR1MeS4vtCjUIckMLWyt\n9JAmuDLUqwW3VIO00NSCUbZJpLaiRaYW2sMtfvXDHR3Mj3mMeKVzL4a18FZZ+oG0QXXSj5nUxgoL\nCw2Y4JbWsel0jleO4rXA16LXAjSia4PAAQucjNQ+d9vFG6nPKdk6aHUAAEAASURBVK/s5YiO2mOD\nyrCqWlJfU9BZBpHHEtQOgW9SzYsVpKYlEM0dBETt2bPHSLxMJ/gjgC5WEO21a9cakEHSPJIg0TGe\noaqqyryTWGW462r5aNO5+4w7nX0+mrOMaH/sYx8zfToeuCTe6P2Wl5ePhvSo09p+qIz6Lt19zU1M\n8RawUn9eR2BI705SdNFBaS0QN9wz8dj9TUSnTd7H5oCkhvxZNfBkzEGgpx7XVXMnvDuEn+3l+Mnx\nNxQBhZT7ysnp+MvVhSigtOjjL56Ap30A84v82EQD0d96tR+fWJ6Gd11Ji0ChZtz/ItDYG6ZPM9oI\nCmdQiJdTFrO7cGY9AnQ08dvdEq8P453z9+OyyVSt5Vj8011U8aXqWHQYoPrzAAElS07gkII9l+f6\nccscGsyem4Yybxs2EBT64e+L8HrTJITShgcCPMEBBxTq68SiyhZ87MZeLKQnsm8924YndhL44tzB\nqJBRYujVRg9eawzh6pIQ1ek8+PByLxYRLGpoDhij2dks6s9qvJQW8qOFTSqevGpcpYXciwKHI2P7\nO9yiNLqM4XbAx1b6+OfSYiN6waFSpdrzne89jLPxApVo7bXwGY29IS2q+vojdiITLWSc06kNRqqF\nki3uoF19uaHWwlTqUB/7yDvcj8/ptVv9Jpb0jRa8Y7XX5JaoiFXpWIvfWOnixQ0nkXQxflfx2vlW\nibdghezXSEV3/frX4bbnJoBI6mXRtq3cqrfjzQszD4qA/FLXko2qaDs/8epw2aVLUFJSFO/xWce7\nx45zIWk62vaNFhg+6wa/CQTOKzDUcOQNHN/9LBe8RBc9BFJ6uigl1MGJeMR9pGZoAkAGA+85IbTR\nfT10NOubiJnL34bJs1YjK28Cxc/TBlPrYtHSK3Di6CHUUuLiqR0caEhO0JC5EBhiDFITEGGwE0IV\nGcE7TLxSq2QFBz8itMGJnAxLD0oQKYUykZ7o9FBct7vXj0x6KosO/RxUTgkUorRQwOzYMJ/AGFOA\nCjFXrrjIc6XRs6jDxOmjNfECfETDdR8NCqnd5jnTDAJLEbrm/nR7nUo5pFU9y3tdO0zx4NJZtAkx\nswLzFl9hQCE98qekwp9HY7U88iaUIz2NyPG2NtqNqj89EbFMtaR4Fi4hOS4ft4XD4T5KUrWgjbPf\n/voD6N7xKuW76NlinIAh1VtBi0ZJD0lyw9oH0kJTNkm04JaKjqSFZBRYUgxKJ1sv5zII9BFtgU6S\nRnJLhcQqR4v04SRAYuVRnPLpiA5ukGG07XPzRh64xK+RgiRGYtVjpHyJPNc7E0AgVT4bVEdJ2yhU\nV1fHBWL03A0iWAkSxY9XEB/cIKC7nHjvS2kEdh08eNAkH6lNbpqxrgW06RgpDFefkfIm8ny0/VDS\nTfpWBOJFq7klUl4yzbnjQF5BJfIm0kNZ43b+ojfimoo+nGiXm3U/JtGmUBmPFXRV/475eSjy9OEH\n6xvxk90BrJ6SgZsn+eE/0o8NkuLZMoCPL0vBu98GlBQ24sfPD2A33bj7aaQ6lJJpvJVFu7K3reij\nlNBjOytxso3gUs0+XEZQhiaN8NPdBN/rbKqRzwKF/mJ1Bd4+OwWBtqMEhbrwg5cmYUtbNQKpUstw\nDWZuchyMZWjaT0PT6OvAdYtace/NvZhU0I1fb2rBr7dxY4tAWU25D++jF7IMkmnd48GWVj9+3uTD\nIX7O9+QANM2ERw/2G1tHty1Kx4zcEPafDMKfMx8FOeNnW0hNcS8KdD8cwKPnNrh3tBU33KI0ugzz\nu0Jw8WIPAjMEIMhrVKJeoKKlp0bLAy00E7U3JMmXiyXIBshzL2w0bqbzzkL6IZH2JsJDLdjHYm9I\nElGrViygBPgbMdViTtAW12YakZbUUqKLb3ebVHdrk8Udr+u3yncV3a63wn1KRKpH3r10bb1fqW2x\n+pokcwRQnu+g/itD9yuW1yRUtNoiwGa8gnvsiKdqNpqyx9K+0dC/GNOeV2AoLbOAxqWDlArZTQAh\nnaADRdCoDtbX60gtGICDXNSPXGa2H2npzgLWxuujSE3LQgFt0ORNmBST38XlU7Fq9VVo7l2P3aea\ncZjeSISbGOCE35Smc+be+N7SDe+kHmZiBaD4DPhDuXUjZaOdULm8h5fPDDgUJTFEglJRC9EegOoX\nopqbgA6nDKCLkkQn2cYmSgoN6KOOPDAn2zBF6r/uI3Hm2ok0YI59Zs9qj6MSZsEj1V35VYYOxfNM\n4EeSWIMSQa7nVopIaU0w50gdItfmiY1nohVV/bh1STZWLqg2YJCTcejftIxcFEwoQTN3gHq6h/5A\nCAjq7fOwH1B8n7u1hjR5qA1leorlojxINZMeevuimmBGCbLzaMfhPASBAbJbcg+9aFk7PDKiLLsu\nAokk8aIfJIWzXYTHao4ACJUjgEA2cGRn6HwGC0ypzNEu/t28EVh1vusezSfV/wMf+AB/X/S1O6ps\nAoXs+xOPlSZecANjyhOtchcv3/mOd4NdI7XpbOomsOYXv/iF6Z/izXgGdz+sqoovSWXrcL5BPFtu\n8nwmBzRWTplF7yv9tIu150FU5Kfhtpm0p9cb4vcWxkcW5eDq2Xl4dW8LJXuaMbMkHf+03IODHZS4\nJViyujKN6mZe/PF4P39v+3EvPZVds0ISY234n6dC2ElgxKvxCwKH0s14fGYtiMcQHNp4rJg0PHjP\nwr2YNbEFn1rGDQ3aEnr0EKiaFSvX6bhlBK8+vjIPl5R3I9BCm0LraHB/QyWOBqoQ8ElSyPldOZ3D\nuZI9IS83ueR9rDi9BR+4vgW3X9aHprZufPvZdjy2PUAbRhxRJSFBNTQvx8HJlIK/c44XPfu92NHm\nw4YGD7J3MT6F3v2a/ATGYAxO91HIowuzMX/2e8dVWii6TbqXJyctPkcK7h1t7XZXcEERb4HgBpHs\nzvhI9C+G50eO1FK6+OSoqqp549ku+LQYS8Te0NmWM6qGnUVitw2QRPvfWRRnsibCw7F4JtK3M5Kq\nWjzvS2fbpmT+C58D6neSHhIAL1tW1gi6+poMHccKApNle2csQGIsesPF1dFuXDM1cTIz45siGC7/\neD47W2k71e1Cbt948m442ucVGMqbWIXC8rmoPbgdPV00QMCFm9eXiZT0ImRk5yM9K9/UteXUUZw6\ncQwZmTScVpBmACIBCP4ULwb6KIXS0Ry3TR6CN7MXXkbpgAO4YrYDDBkwRnM5A4rwgot8yv4M0jCP\nIvcSAnIAFlaPk1TBSsYFvYxR86FAINXbOThJZXm6lsRQL1XcwumyG6JcTmjtpgve9h5HUshG6pHK\nsakMOmLLVSI9dA7zyPkTqRfjBfaYtuisdBYUcq6V1wGFToNHirOHyUMa5t6pZuTaFWeY4nDGYQ2v\nSWPJnDKslgrZvFU2Z8xzKNDNd9VJWxFOK6XJ10fjoz0EhPxpVIWauwwTJ80kGuRFd3sLj0a+10Y0\nnTyIJu62CUOrmbcIlTPHz8BmdMUFFsgO0Nq1p+3wGHSa4IAFbpRHwJG8fknF7FwFu8jXwjtap/Zc\nlXE+6EgNTgDXueTNWOrttmFj32GidKKBsdHmT7Scs03nBuTOllZ0fvV368VOXh06OiTZ6QCj0WnP\n5b37O6utraVhToqQJBgu1PeUYPXfEsl8lJgtqrgO7a0n0Vr/ImbSwPMNU8L42b4BPLy7E02dAbx0\nqAud4RRkDPhoC7AXW0/1Iz3cjeun0nPLzAw8wPFsfS29YnKT5WOLgSsW0026rxPf+22I6Qkycfz2\ncAMh6KOzB19s1TKplW0+4dgeEDg0p7iFKmy0BUNc5+f7QPf2sdm9jLaPPrG6FCsm9uLI/lo8sTEV\nv905DUf7yyitNAwoFKQ9oYDsCXVjflkTPvy2dqyZT1WwwzSsvY6Gpo8GDfiVRkBIwNBe2jPaQXtB\n2dyUmkbw50PzgvjJPvKjxYuXjofwMucUk/PDVCGTwWk/jtQHUFG9DJNpe8+nNo9jcIM2iRajhbxs\nNVjgQbuww3kZc4NII6VNtA5vdjot1l7evG3Q01riklaOO+Szrf9I6lBnS/985rceg1TmSHZEzmW9\nxEO3++1o2urfY1EpE90pNNIbL2ygNJEMD4/FzlA8msn4N58DUis91dCMez9617AgjsAh2R6SZI5c\no0udUX0tnr0ePRvPdYLbIPyFZnDZXTdJ28UyTj2aN38uwKXRlHcxpB3fGUYUB3xU+yqpWoATB7ai\n4fheTJq+FFNmX4LsghKqEtF7EtWRFPqpYnby4BtortuH9qYjaK87gZy8VGIIHnS2nUJHS2wU1Rbn\npRHqRcvX4nh9G/bVHcf6vXpC0IMgjgAT48nEwDKcpBlwRlJCAlPkhUyQEdPquewLGWkh3lNiSPkd\nb2QOMOQYs1Y8DVhyd7Kb4FB/XwoNUAsscqSFWrt6jViganA6iDbvzKE/Cjyb/5F7A/wo2qbVQ5al\ne/2zwA7jHJBIae21k3bQMLUKU14BYvba0GWcguKMPSadGcy9c+lcO/FSIVs0PQfTZ84joBe/6/TQ\nhlRn8wGqdlB1jipSAoW6umTEuwhV81di6oLLUVwxA+m0CyVGBWlfSjamdDTXH8XJwzsIFDWhgjaL\n0jMpV3+WQRIP8pgklRPZDBouCBRw2+HRQlVgh2yryCbOwYMHzQL5XC+SLX1b3nB1HI9nbjfotg5j\nMcD7VlycC6x7s4Gukd75uQDk9N5lm0lqWaJ32223GW9mav/9999vvIiNVI+zfb5q1Srj8c9+Z6OZ\n/Kie51rF82zb86eYP79wCibPvAFHAo0IduykxEuQKlFB/P5QN7LCQSymUeVnj/Xja7TZo42DAXr/\nXF6ehiWzMzCNal9pAQ8eoDTpy/QKdvIPBIcW9eLG+emYVEhvXo+F8Nw2qpQH+qhaRoPU9AQa9KbF\nBIgsOLT9VCEWlTXiLtodWk3poRRKJ91PEGYnJXRskOrYzbMycNusXFSktmLDxmb8aF0BtjRWoS8l\nn6CQ5graKBkaPAKEgn3wcOwCjUwvqGijPaFuOmYIYPPednyLoNDmoyEMcANE6mPvqfFTPdqDDcdI\nLYVtbKJL+tYQrpkdxEdqQlh3xEOgjAa6e7y4q9KHBfROdqKRNtoyqUJWvHDcQSG1rrS0CCXFRXF3\nq4dywLmTpJ/UH2xYtXLBsItct1rEubATYct9M88vc3Gvw4Jjw6nSuetpxkzOzWwYq3qEVYfSQset\nlmLpXixnt7SQ6jya/nEu1PKGk+5RfdTPR6tSpnejb0JuyWPZMBLNl9a/ZtL8KdgwER//FEJXdw+e\nf2ETliyeg9tuuXLYJquPTJtKJw40Sm37iAFlaJxckkFuW1ICEZVmOPB92MJGeGgk0DlmKQwHUI1A\nZlwen4u6RYNLZ6PKOS6NfJOJxl/dj1PF8oursPy6j9C+UCcycwqNnSCBQtEht6gcfd2rUUsQacfL\nv0Zb83HkUnoohbZopIrWeGIvJkjiJE4onTQNl65civaWerR3dWJ7LcXgCHgY0EfzO4EfkrwxgAjv\nGedM+xzgxaQlSCSD0w4g49gYclzVC/hRBlGzGT1o7+5FW5ofE7JYFqO9Kk9laNCPYC5OdSM3Im1A\nGHvvSmRAnkg9I2kcUIf0dK/D1j9y7wBEincAoMF7gUJMM5jPVMbUfGi9jEElpw6Df8UUxq+sHsBt\ny6hCtnQx1fiGl5TpaNrDd7SNEkIEymg+qrubXpEKZmDuqttQNZdA4Bm2oU6DP6JdXj2fIFE/DYqf\njnf4Nra/paWl6OzsNKDOSMCQSnBLjCi9jDxr0SygRMandQhoGgtwEq8FtkxNEmXEWR6UEqlrPHqj\njbflK5/KVxtHsnNky3CDSuPBG1vOWM/RgMFoQBTl1XsWf9zBgmfuuPN9bfkuCS29MwGgsQwvJ1Iv\ntefLX/6yAYDkle4LX/iC8QhmvY25JbASoTfWNKNVDVO91X6FqqqRVc/GWq9kvsQ5IEnbotJlXEGd\nwoE36jExqx63zPSijirj6w52E3gJ4x+XZeKZPQE8sLsXk8szcO/SdHr9CuC7L3VjfyddyWd50US3\n8VL7+qc/hvBGXRfuWZaOf/kocP0bp/DjZzKx7VguvAO9lB6i5JA/gxJEadx84HeqsTkSBA51UFJ1\nw9ESglBeo1q2guDQklIfdnT5cf9eSrByQ+qji1KxpLAb4b5m/OYPPvx4wyQCW1MQ8Me2JyRnFt4A\n1cYoJeTp70FJTic+eFMHbrsshBTaT3p5Ryu++8cebKL0T4D8mDnBh+vobn5qlge5PJaVAk3tYZzo\n9GB9YwqeOe7BmtIgUmlo+1S3H9dWUVKK2vJ1FKzu9c3GvEXvQ2X1+ZGejTaWm4g7bes+WGwfyb19\n9OI9UcmayCu9IE8CMx755TOcJ5yWcDRjamRxNVyl3bxTurPZwbZqKZbHZ7ubPly9x+OZ+Pj//ff9\nBmAbC30tYi0wFyt/IhIGiUhejcWF/ZrViw2oFA+0E2ik8TbaG1WsdlzIcWMFNi/kNo21bgJzZOx9\n/ctbjTH6kVS/3KCHyiwpnTBoyFnAkbUlpf6XCE19T3X1jVi2NHHj0Sp3Unmxqa8k9/QbIsBbYNRI\n4NZYy1OZiQbVzc3HROv2q988j3qqxd1y81pISrVyUokpUr8XMgC+etXCC6J9ifJhPNOdd2BIxqIL\nSqpGbFNaRjbMQXBA6mHbN/yK4JDUy3xoqduF/a8/h8zcCQZcikVMeWYtuBStTcfQ2fMKTjT3oaWH\nE8eIpzKTh2AJrQMxjpNJgUDcubSeySQ5JGBFdIy6mIAV2kSSBFGY0kPGfomZgHISqjOP7p4QerNp\nO4nEZWsolXHZFB1vIR0ZoD4dCLu4QBgHNCJ958KAOAbIUYZB0Ed5nGMQ4DH3UVJAoiIgyEgUuQEh\nxamtETqWtr0XLRN0VhrdOHHzS7tww+JsrL3ybZhVs8bhiUl75p9uGupsb9iDDkr8dHZ2GdWxzPwZ\nWHDZOzF94doRJYAcI9YTziR8FjGaoGkB+corrxhvVKMBXKxXJDdwItfYMlItwGA4WipTP/SJSJvI\n6LRoyRaOvDJJasku/M+i6QlndYMnMuKrOsiwbyLtGwtvEq7YOUio+lVVVZn3kAiIYiXMVLTyuQFA\n+05EZzipMTdgMVITLFA1Urro59F8X7futLv26LQj3f/0pz/FD37wA+OBTYCgjLELpDnfwd0Pxd+R\n7Du51emqq4c3Kn6+2/KnXJ7HS2PS2QuRmlODpmP1mEW39PfUAN/fRk9le3rw5L5utFHKdlppOu6l\nqtj0tCDuf7UbvzwSRlF6iCAJRx+CPHtaA+jsC+MXO0M42tqJjyz24yqORZfU9OKPW7vxk2ezseN4\nDqWN++Gl9FCYm0whbwoPP4fYyAYOX0S09NA7aw5gaVk7SmvoNIF5J6d34vgRAlW/z8VTB6ahJ62E\nUkjaMdVo7gQDBtEFvYxLy+sY6Iq+JKsD11/SidvWhGgvqB/H6jvw2GvdVD8bwBECP1myk0g6Bygx\n+83dHlxJIOhqts3P+UEJh7nLZvpwimPE63sGcHB/AFvDmZhaFMbN1R5Myffijf39KJ6x6LyokNl2\nuifNihvJnbYWAxs2bjWLXoFCWthKOiJecHuVUZpEJWvi0RtNvOpqd+NHky9WWoEvv35sHX7z+Asc\nt09yA6xnWFAiFg037+zzszVGnIitHFvWWM9qu3bZpfZyLoLlpcC1/QeODpE+Gw1wWMt6DVenRKQf\nrOSVFpzx+orojNaFvQXtxK9Y4JCkhuSNSiERcEg8++a3HsTDj/yOa49ek+98/BlJ1fRCUz06HzyJ\nV4b6ktZRiQIP0f3X/dsoL4eSJjtOCSL1v4ceftoYrf7ze981BCixddFvi0DWFctqcNONl9vohM6q\nd9WUMiORJGAoEdtaZ1NeQpWKJFLdtHkhcCiRutnflsOHT+CWmy43QJukEEdDQ0Wfr/aNhhfjlfa8\nA0OjbUhaRg7Vj9YYHGXn+l+jv/sYJ3I0aHnkVdQfnovqmivikpRK2bLL34ljtGn09poj+MFml5tl\ngSceTvpkO8gYZ+YtLwUICVQx6mbsPEZiyDE8xOeUEDLXzKe8PAxwpBrwOsiju6cPvRl0jcy8mlJm\np/qRleqjlDknku5AIMaBXVQHPdCfyMGTAX94b/AaAwCZSMXoYeSwoFDkmdpkMkTOvB4iNWTzmrO7\nTF6rsiTjBLVNNx4UZvRi5eRmivFnU9e7clgVsoG+drSc2ILG2u00Lt3NRWYI2YWzMH9NYqCQLf1c\nn7XYlIttuYAfCexQ2QIGXn75ZeMhzAIjoiFpDHkm07Nnn5V3PS8+//nPxwRPREPSMwJ8EgGGpk+f\njrVrT9s20iJdtozkoczWwfJFtL/yla/giiuugNyMn4sgkEG8ef755037BAwJFEukfaqf2qk8AkyG\n440AE9muEe/e9a53JcQbtc8NtOhaYMpowvve9z5jSFx8E7A3HIhivX3pnb/3ve81bbNlucEYC+hE\nv1/Vz23s2uaNdxYAMhqVKUvHDSaKhtqkPhRLasjNP5vffe7t7TWgkNo8derUswKF4vHFXV68a/FX\n70rf2UMPPTSiJJT7W5VHPDeIF6+M4eJt3YdLk3yWGAfkpSy//Gq0tdYS3NmLmok+3DwjgB9sC6Kl\nn7aHOBy/Z6EPZbSx8zhBnqeOBNEGPxbk0gV3ESWG+oJI59hMyz00KM3f5aNhbDnRjxVlzbhnRTre\ntiwLa2q68dIbnfjJcw5A5CEw5PfR1p+PwBDH/zBBoqBAIoJMAaqndYRSBqWH7sY+zKb0UEMrVcte\nTMVz+6spJTQZ/dbrGIdAD1XfvC4wyCMPqpQUKs6hTaRLunDrmiABoQBOnGrH/3u6E08REDrWFjb2\nhOaV+nG3VMc4L3hgD7CD5TxTT3V1Sj1PZxuf2BFGyWHQSDfT56RhfUsmZheG8P7ZYcwjT9romr56\nzvWomnfjeVEhs29VE2+32osWehtpO0dx7p1apbeTZS2iZUT6EnpWmjZtslmwWHrRZ/1WSZXMBvOb\nyvFgvIO7rvHKEiijxfaTT78ULwmU5mRtg2lDb2+fkYzWQs0dEjGoHa8+dtHXQOcpctM+FtWiRKRe\n3PUdzbUWWvd982d49NFnhwXCJGHwD1/4bzPWx6Jv+ahn6g/xeOleHMeiY+NUr1/+6jlj68rGxTqr\nr8r2yx1vvzoubwXirGFfHgkckr0hzWXuvOOahKQyEgWHxDuptN16y1qaMHBspdm21J44hVde3YGN\nm7ZxLnOUzlz4mxQjJNIHY2QbNkp99tFfPzdoRytWYvXflykhI/6Npe/GonmxxyUCrKiN7t9Ggezy\nBGYN+Efbv+ql6ZIHfvYE1B/efttVg/3PAiECWQsLcrF40exBGqPh46qVCw3d4yccOz7q6wJKbb+U\nFJLWqQJnJPWYaHnnQqLsztuvoQbIMfy/7/zC/AbZ71BjlOx0qd+JD6rbI/ydEnj68Y+9k2MY1bEj\nY00sGpKuuoSSQ2rbWNs3Gh5fqGkveGBIjJPkUPW8NQQuPNj58q8Q6ONu/UADjuxch9yiMhSVz4zL\n30BvE6ZWl6D2+G5cO6MWv9vH2agBT4SE6JIDugF4hIs4AMugBzLZGGInsuBQmOXLk5pjZ4gTGeYz\nUkQ8OxuLHk7metCaloIiWnBXCZyawi+AhpOhocGU5kQJgzF10q3AHOesa92Ye/2JHI7EUGQiYp7z\nWmeTntdSMTNpnXjzTPcReuZsr01ZtjzX2aQHbpx+GDPyOF3vCtLTzHrjjj49q0AJzwhdzfsJ2G2k\nnaDD6ODurjySTZm/OCFJoTOIncMITTyrqqro9rQY//Iv/4K6ujp86lOfiglKaKEptZzly5cbUERg\niYJoXHPNNYMSDAJAfve73xmAQgDN6tWrB+2bCBCSRNFdd91l1NBsU9yL8+gFqOiv5aJei3vZeJFH\ntCeffNIMFKqDFuySVrG0J0yYgJoabsGPMrjrEJ1V6msCRdS26PYJ/Iiug21fdN0lcRSPN5JMURmf\n+cxnjM2m6DrEu3dLhmjwHEmSJJqOVKE+/OEPG8BGgEM8EEXv/2c/+5lJJ+BlxowZQ0AS8UC8EPCj\ntsgL22c/+9nBviT+SiVL79AGq17nBviGew82n85Kp74SKwhMfP/73294IbBSgNe9995rAJVPf/rT\ng4CiaKhdqrNCdN9TnJWEEhgp/syaNcvkV14BeYpT0L3ao74SDYiZBPwTrdamPPEk5/Qsun0yAP+h\nD33IxG/evDmudJ7ek8BeqYrKC53qpL5og2irHyvEanOsdOpbYwHpLK3k+TQHNHbKWHKIY+yxXQ+g\nrXsPrqQhZQmz/nh7EOvrQphd1IftdQM0Th3EsaAfi0u8dOPu4wZEGG90yBoPvXZNA2jaDodOhfFa\ncxi/PxzG5mO041PRg4+sSse1K3Nw6aIevLy9F6/v8+C1A2nYeTwbHgJDNEREWjwTGHIkiJxx+7VD\n2dh1YiHKMltpB6mZKl15GEibwFHRwzGbruY5/klCyBjIk3QQAaGSnB5cXdOBJTOCqK7gjuPEAdQ1\ndeG7z3Tiie19DiBEKagQ2y0pITBbmKphS6Z4kc1u+cg+4JU2D55v5LQrNYSaKWE0Us3txTpgO+Nn\nFoYNKLS0jDUI+NHjnUODtdeicMLk00w9T1dSezl8pNZIX2hyHWvH2wIbsreyeOFsA2IoX1r68JKG\nbomD8Vi8ulkk9YFvffchA+T0UyJDUj0DPMcLWtRKEuQAFx3xgjMWcW41TBjOoLZAie9872EMVx8t\n+uSqXcCEwAQt/EYyYOuujsA9ubA/V/aG9K6/xUWYFoYCcbo5z40HSNh6aDEsI+PxQiJ8VF79ptvF\nXDQt9/u19Rru/Sq/6iVQ6lGCSOKt+qCkLm6+6Yoh5KO/gSEPIzcqS1I+L29y3pPqOdK7SgQcam3r\nwDPPbcAfXnr1DGBNfBugetIAnUJEA5Kqltpz2y1XsR5XYgZB2rMJeu+PPbHOkQ4jGGr7bKxy3eVI\nLe71rXsG+atFttSQ/lSBIvHLghfxQET15fv++2dGYk7v8B13XjtE8lLf9F2Mkz03gdf6Xba/E44a\nouMm3oKsC2pm4uP3Cgw5U3rzBMEeK1kXD6hRP3V7OlRfd/dLPVdQ21Sn4cpzq8uORlVW32ks+z/p\nHGMuu3SJAYH1m2S/Q/EhlfVS3cQH1a2P4L3qJpDN1ln1Fo13v+t6HCMv5AlONHbvOWR++x98yJHG\nSrR9SvdWCj7akvjCxdAgqRjlFNJOAA0at9QdZpX7KD3UwmmcD/kTq5CSJlsAQ0N/dwM66l9DZ8MO\nBGk7wM88A5zrnWiLpOWsUxNPHU4YREmcOPNAU0Ub9Nymca6NzSKBKJHoABcWkhbKTk2Bj4ARp4c0\nNgn0yDgjxcc14XQOkeL1IIhj40WIkw4+c8AdxTv3Jk4FDdJwwKDTUkHOs2hQSOmNNSR7ZglOfQV0\nqf3uNp5u7YqKU5hf0ozSvB74wn0I8cPJLaxATtGZNoZ6Wo+i4dDzqDu8GS2NjfRiRDH6wpmYs+Im\nlFTOUolvamhpacHrr79uQImtW7diz549g4tFuYfXwlGL+S996UsQiCBJHQFBbnUaTVDk0l4LVy2y\ndTSyraL329/+1uTXQlUL9FtuuQV33303xRZLzGL6O9/5jgEMXnvtNQP2tLW1GfBHXpe0aNbCXG7e\n58yZY2ju3CmvBA4A8oc//AFPPPGEoS+JnrVr1+KLX/wiZAtGto4kBaNyDx48aPrM7t27jcSFAMz8\n/Hzk5uYO1uGb3/ym4YH6SHQ6lR+vfVrs2zpEt08vVnmlfiRATQvyeLwRPyXlpEW8JJKGC6IjcEy8\n+9a3vmX4LJ4o7Nq1a8g7tO0cjl5hYSFycnIMn7Zv346nn37aqBcqj/qAff/19fUGRPrc5z6HJUuW\nDAEbKioqDP8Ejshb17Zt2/D9738fDz/8ML797W8bGosXL8aiRYtMn7J9S8DKpk2bTPXUJoFHti/I\n45ckdtx9QQkF5qjd6wgWNjc3G89g7nQCqSQhI2k4ebVTH+7r6zP1U3n/9V//ZfqG+qYALD1TGvU9\n9X/VW3zUOxdf9X3Yfq38jzzyiLE5lJaWZtqjZ8qrvimpMPFPfXfevHl0ZZppyhVfW1tbzbNHH33U\nvD/xSHxTfdUm9dfHHnvM9HP1Q/UDefmz34HOtl3q0+L1xo0bTV3FT/uu7rvvPkycONGAvXfccQdU\nTwX1m0S+t1jp4r0LQzj5Z9Qc8BKQySsop/ROAdroNKKfGzWzKQ2USzN8rzYCG05yDOvow5qJYVw/\ny4+75vpQGA7gd6/3YENtGPOLPVhdHGJaj5HCWT2Zjh4GaJunJYSDLdytbArgZFM30rwBrJjhwSUL\n/bhpdQBXzm/H5IIOtLf1oaGJvxkB2iKi6pexSUTD1WHasBugGFJ7lw/tfZkI0vGEJ5LGQ89i6KO4\njtzOZ7Xh7cub8Nk7W3HvLf24fFEIU4p70M5v9sH1rfjGc514bt8AGuhtc4AixxNzaU8vw4cuqpwH\nIgvaHrqlLyWwdU01MJGYyTGWeZybJjMyw8jgnGBjgxdl+R58kJJCblBo8pz3YvLUFcS0ToOdo34B\nY8ygRcjsWVMpPVhhpAM0YV734it48qk/4Pl1m6k+tc7sEL++dbcBhaT6csXly8xk26tJxTDhqaf/\niN89u8GMVfPmTsP1167B5Erq141DeOK3L+Ix1rWTQIZsHiYC+uo3SeniHWZ+NUJdp9CArICFigrH\njoU7uYAEGaQdqT4qX+BLN21XzpxRZRZ4ubm0zp5g0DucTJWLAPvYrt0H0dHJfh0V5s+bYXbIR6Ir\nMOWF32/G3n1HOG+ho5DIOBxFbsjtueCjFsjXvW0N5s+bPoS2vXG/39HUSzyxvG2gF9y57IcrVwzd\naBP/cnKyaYS9luPlYVvkGWe9p34CNSo/0Xdl343AKIGqkm6wC3VbgKWrfuI+VJb47+6H4tP733sL\nvviFv8Bf/K93m0VzKe3TuBfClu5ozrIx8/NfPGXA0tF8Q+6+K/62tnZwbjsVc2ZPHU3x45M2QFty\np14GmrfDk10BT/FKIGvSuJS1fcd+o2abk5OFzIx07Nx1AC/w9/OBn/8W3yZgLdDtJw88jv/86g+h\n30VJrJSUFOEv//xuvPMd13L9MXSO7Pf7yMNpg7/L6jO2n9j+p7P61F/+xXsMsOPuAwKf/vNrP8L6\nDVvR1EQDdgwdHV2cux0zgIh+I1RXHQp5/L1Zy9/1RQtmDfZRd3kqS31zpPJkWL2R/UChs6vbGG/X\nWNLJ8sppM0jl2bo9+uvnsWPnftMu9fG9ew+b9AL1bVrRkQFpOUk4drzOfDuql8Ad+13but188xX4\n/Oc+YsAhfXfukJ+Xi1kzp6Cpuc38tqk8/TbY79nSiNc+N6230rUD+V0kLZLk0ORZy423suYTr3AS\nEoDOtXsnoHLuVUjNyBvSkp7242g+uQ1dHU2g6R/MLAtwN7DeACsbjxYZcMSY+jEoCSczuqEKlfFQ\nZlTGCMhwsudIBbFD8dqRFlK80kpiiOCKoCMzGXLUyVrZ2XOpQlaUmaEnyPJ5kEE1tC7tPLqDMCAG\nwTskYOplInTPDmqemDS6163ODhhkzpF7AUlmkLD35hyJE0HdOwSck6FvHphYE6kkgyEMgUJrppxE\nZX4X0vzauaVh7aZ92LflV/BxcV9StWQwdU/rYdTv+y0aDm+gy/lmflRB5BfPwpxV7zyv7uYHKxTj\nQlI3WoxK3Ulgihbp//RP/2QW/dXV1WbhKtT7pptuglRSZs+ePQQUsiQFGt14o0T7fUYyQotLeXLS\nYlaLX0mZCBCSpIkW4wpatHd10U7TDTcY8MTS0oK4qIgeYAgeKYimgKHvfe97Rh1I9XTTl+qVDtHO\nysoy6VXnvLw8U2/Rt0G0BSJYIMXWQe0bLt1Y2mfrLgDq5ptvNgt9AUnuulveSE0oHm9t3e1ZdZfk\nlNoXzTulURvFV3c7bd5YZ/FXYJ8kd2w/kPSYQDa/32/eg/hjeaz3pzzuIGDr7//+740kmCRp7LsX\nWGhV6vR+BNQpr8AQ0dMz2Y0STYEP0e2J7gvinUARgVm33367GSRVD9tmSWUpqD4C5GQXyLZJ4ItA\nJstz9Ue1T6CnQBZ5wFu4cKGpk0BGeduTNJ3AUElBudukuksiTv1bbfj3f/93Q1v97/rrrzcAqOox\nfbojvaTB2eZXW+fOnWu+J/HEtknv89ZbbzV9RXmj26S46Hbpe9W7+tGPfmTqIimhj3/847jsssvO\n6E+2r4/EY/UvAZojpVN9kmHsHPBRYqdquuMNUpJDHT17cHWlH8XpYfxwewi/a0vFSc5EFoUHKNlL\nqZ8OH17vTAWFaXB1XgBLS8Ko55r298c5hucCV1V4UJXvx67GEDaeCOJVurD//qYBLCnrwqJyL5ZV\nEcickoHZ1Wl45zVUkSTAc7LJi/pmD88e1PEcDHA3kZsy9W30Ucaht4TllFKNS4fPT6BG1xM8SOc4\nnprC8T7YSy+ntB1EyaCtx4M40U4pqFaqgPFRiPOCmVSTu6GaXsQo8XSqJ4yHDxL4avWhmzuWJ3oI\nPDd4cF1WEO9Y6EF1ZQhbj3kIbAFvsJ3FeWdKChlQ6Dy4ph/urWpH9eorV3LTopRj5la8Qpsy9VRv\namvrMNmuvmoV/usrn4FAkEzOddwLkOHo2p1cpZGERfRkfbi8o30mCYWP3fOO0WY76/RTppSfoQJk\niUpFRGoNownaHY9WKUokv96Jdv2PU91E6knRQa7gEwlahEniQwuk8xnU94Zz4X6u3m+8dlmVvPLS\nxPgk3iT6rvRu8vNy8LZrLqHE+SKzqN1MCR1JQBigqK6R5gTOdMctEKi8rNi8BsfuzBzzjU6tqjDS\nevGkq8by3uSh8IbrLh2cf4yFhvLoPUql6U8x6Hu30lJWNdG+35N8xwLSq6omGWPPS5c471KSXmlp\nsSUv7e+y3rPoWVqWt+oTd95+NaZHqfQqnUCcGdMqMZXlve3qVTaLmVOlaM1amGeAKfvAAUezTB9V\nf40uT6rFy5bNM4abR1Oe6GtOmcf+rzljdN0ERrmDO62NV33WXrGcm9HzOed0vP25eWG/DXl6m8Y2\nx/ou1L4Z06fgq//+Kbydv2/ub0/lDNc+W4+34pkYiEENLpq2Banff3T3y9j7yqO0/XgU6bTnU1g8\nFZOmX4qCiqVIzXR+MLtbD+Hk/mdph2gLutrpNjcYID4SQmt7Pw7VhfH0vhJsPEJwSMiN+cOLyLXA\nH4FADvjjgEHmmh1ZZz03skAmnTKdzqvn6oAFWRmYkEcD2rR10ElRtlNtneiMLOYGmT0IxkSAm8i9\nOQ2COUxtgB7BN3zCa+fMGamNZ7ucaxcdxjnpVJor3hRuSnCi7TMTr6QEhSadwlVTj6M8r4v2kSh2\nrXYyeDw+pKYVonTKMlTVvA0TpyymWl8b6vc8RlDoj2hqrOekkbup7R7MXvFOrLzhoyMamzaEz+Mf\nLagFpuhsF9dawFsAQAvSWIBAdBUtHS0u3XRi5bdpo2noXj94ymPLt2lsHjd9gTbRdbPpbD732U07\n0XQ2v03vLl91jNU+m8eezyavpaGzLVsDx3DB3c7h0rmfnW0dlV+AlM4K4osADvsuJZHT3d1tJMH0\n3vTMvmPlidUmdztGarstK1abRF/53e9L6SQdJKkhSeNE11fPY7VJ/U1p7XMBcZa27Y/mYYz87vJ1\nPZY22XL1zSq/DgXRszzQtTuoHUofKyTKY3e6WHSScaPnQJAbI4f3bzBqZZ6+Pcij4O62hgH8Dw1S\n723zwm+GUbqv5wZNFm39FdMIdVlGCHfMCGEW7fD8+LUwDrUDt86gXR7GE3PBUQJGz+4awBsng+hg\nPj/HvRTSqcj1YOEkH66fm4rFk9PgJTDqUT8xY7jzey/7gKKh0c3DjaAwbQnJppBE3YMB9h+qktW2\nBAwY9MxuglY0Jt0XZP2kLsY8IY6LK8qoOkJJpwHaL3rhcBjNrEMOG3KEEkHHac/o5qoQLi0hWHqC\n97Q99J4aDwrSaHOIa/TXCIBVFXioPhY6U1LoTQaF3G/XURUImG/K/bul709qY7Em3O787mtN3L/y\ntR9CovoK//uv3oe//qv3JwwquWklcq0dZB3nO+j3QwutWLwZS520CNIxlmDUKbggjPWbmOg7tH3A\n/f7HUpfR5hmOj6I1Fl7GqsNw/B1tGcPRilW2O86WpXFOvBbfBaS6g/qUBVP1/lSevFXF6mvufGO5\ntvUZS153npHeozvtuF/3NiK07esI7/85PCWr4J3/V0DxinEpVqpeUvlLJ8ij92T56X6/Kljvc7Tv\nMpqWbUC8bzqRb3i4vhurPEmzpnBsjfVbl0h5tl9oGSr6w/2+2LSx+nmsup0Lfg7XPsvvt+L5ogOG\n9BL6ezux/7WncHTnb4m2dhIcykQ+9fBzJ1QjPbvEQB0dTYfRdHInOlvrOMmzEwNO/gh8nAaHih1w\naAiwoxI0QzXTRZ50bUEigULR1056k86ZYprsZlFEsT8vtyP1gcgrWUhAzpCge9I38a5numTcYIxA\nHqUxEeah89zEnb43eSwtc7aFKa8O1/3gZSR/5P6OmnosKjuFvJRWZKVxoqx48sCcdUnVgLQ0qicV\nViInv4TqclwgdxyngdE64xlBoFBa7iysvO4ezFi0VrmTIcmBPzkOuAGMC6XxF2KdLhTeJOtxfjgw\nHDi0k8aZbVgyMYSFWQG8QRWysnwv/mxuAIfqA/j+dnr5pPdQL8fEO6h6dX1FCPVUK9tP0OZX+8LY\nRrtFnF1ShZsqiiSWRltFAoo09pXkeDAx20u1Lg+v5foXKKMRaA7NqGsjnQ4dlCLqpMv4TmcxRhzI\nGL6mHWwDBmVQ+leGsecXe5FPgKep14MagkPzJoTxgx1mqKS0sBePn0xBK9XTJlOFbEEGvZXRK1mj\nJwVTUgK0K0T5W06or5wMXD+FdaABImtT6EKQFLLvYDzO2nH+6n/9CLIFoZ30T/31B42ExXiUlaSZ\n5ECSA0kOnMGB8wgMnVF2MiLJgYuAA2PbgniTG5aank2X91Nx6mgJeto70N/Xjab6A+jubKatoSwC\nQdTJ7aHdju42bvoR3LDYC+stACc/NwXV6MctvhPITx/A03vLIi3iLJA7fkpvgJTIhNJMCRmvBzrp\nLDqOUWoTEVEpExnnmVD+HgtIKYkJgxeRe6c850ZAEO8VdCKQ49wpjY7BB7zVjY2PnM3jiK0gk1YR\nkWDvI8Wb7BHqEeTHeB+7btoRzCluR3FmP/y02eCurUjoXlJXfX2taGnoRkfzQSLdmocHaJROEjiS\nOvBR/3XxBaNCZlmQPCc5cD45EC3Fcj7LjlfWhVineHVNxr81ORCtVtbYtgPTM4P4DG33PHk4iBfq\n/Ggg2DIhNEAbPASDstLwB6qQzaO/g+pCPzcdfDhJtayFE6hWNiGAY/TS/BSlcYp9IUyiWQRPuR+3\nTqNkUVMIfzgcRhtd3TdJ34sDWEsT7RXQ3hCxHSM4pDipkSlQq8wcAoLMtSI1EdBznTlTqiKgdCPV\nxdoCXhztJOBDlbZF1BY+Sc2qzqAXN03nmWpkonl4wIP1zV509dPD2MwU3EZPqj/YFcArnSmYmx/C\nn9d4sag0hfOTMOpaaO/ENwPzltGm0AUkKSQWnOsgcNpKQAy3O32uy03SS3IgyYEkB5IcSHIgyYGR\nOXBRAkNqVjalVbLyStHVchAhH8Uu+2lPJXCKczgvJ3Y0zEZgJiyvIgyc65lgz5rtZdNAZEFmCFdP\na8WM4gDu31KMlm5HZWIwnRAUTQoNKCRCvJZXMv6TbSFn4sgz780RmUgO2h1SqXrk/NFFnEAQaLBQ\nJdGNjeO1fRYFCBnoyDxTGpsokt/e2+eKtsHSidxXFARxa00nKribmkLDdvLAIltLpn0mjQAn0xDe\nqawQDXT1URKq32GP0pBmiAY3C8umo2LGkgtOhcw0I/knyYEkB5IcSHLgTeXAUHDofnoN2YNM2g3/\nIO3vLK8ggEK7Q+taUtEU9GFhfgCXLggTlKEB6gNhHKetoDB3I+YWBFBNVbNn9gIvHea4k+XH3KIg\n3k1QaHkp7fkFfFhbDVzFQ/Y76ygJtJ/Gqo1EUFcY1Cg3Q5llhIZJSQppCF9T6cN0GoMuYJ3eoCe0\n3x4mwESgp4wmDDMoJbSV6mwbaaco64QHd9NsRl4q6REQqqD9owdqU7CbanE9VDcTsUJ6H6s9FcSG\n3hRs6fbhahrP/nBNKso51rZTDa4nPBFF1WtRMZ3exyZWc6Plop2SWVYOe3Z7p5FtiD9VL0XDMin5\nMMmBJAeSHEhyIMmBN4kDF+0sJDOXE6rS6Wg/tZ2AkKyrC5ggEESPJgpW3kbXxo60ziaeDkooO97W\n2k9L6OVYuPZWLOAksbR8Ix7b3IuNB8USg6Y4OXRpCfDSCQJHnImfmUlqNskgd/a6121EdsfcO3lO\n/82iXSSFrh7NTqMCARandJVhn0WuLaBj4hVnE+gcubZRNquqpnrZ5zrrVn+Yf9XUAO5cNoAaqn1V\nzVyG5qNv4MDWJ9HRtJe6owHurDoE9ddki/wVPUkuOfHOkwFOnjNTcgkKcYacDEkOJDmQ5ECSA0kO\nxOCABYfy8stwbN9TaKt7Ed19jVhYnIJ7ahy7Q1vb/DhEN+5lPo5D/HecLtzbaF9jYVEIl0wKoZHS\nQq92UMKIYEqOJ4giD731EPSpo9evUoI4nX10Eb8nREPVNPw81Svv8ZiST8mhZtqFC4RxyQwfjtAA\n9P4Guo6nPaLtjWGOyVQ9oxTPo9uDqCz2Y1YBjVMWApubKaFEx1lzyjzYzDoN0CbEFoJDC6nGNjU7\niONUoc7Ppm29ghC2tPhwtM+ZWh3u9eFEvRdFNLT9rpnA22f6qDrmRSudnnXTHf3kOe9GZfVyAkJp\nVNM+rUoXg2UXTZR1bS6bEHKXbMEfxW/YuNXYkpAa2SUrF47Zbs5Fw4xkRZMcSHIgyYEkB5IcuIg4\ncNECQz5/Kr2QZcOfmkZgyAEmLPZhoRX7Hhxow7nr75XaUzrKKLI9reZqlFbPp+GsdBQV5CEz/QV6\n7+nBS3vdOU5DIg64QmkhQ0pl8hh8LJka3psonXlE7p2Snb9lhVmYVVmCFs5atx444X50+toUoD88\nzH/nbO+dQm1ypWMYBImc2yH3JkkknUAuBZ5uXpKGm5flYdmKSzFn0eX0xpJGgIx2mrLysGP9IwSH\n9nC3tY9NieRxcp5ucuRepxB3SAMUsS+ixNDECloHTYYkB5IcSHIgyYEkB+JwQOBQ0cRpyC/8CI4c\nqMGx3T8nYLOHzg9SsJhSvBuODeCBvV7sbnU2UiSrW5wRxurCAUoLBfDHIx7slgQRbfnNnxBERSZt\n/tB+z2RqjjX2ePHYUS+aqcr1wQl0Y0svZOuPESziXCGbgEUe00pZei+BnRfrPdhHQImCx/TE6cNE\nupFHug87msNUb6M7X22s0KB0d38Iudwoqcry4BXeN9OG0BF6JyumOvUbrX6Eme/KKSRCQg8e8mJf\nJ+0XkdQcGs7+MFXH5hWSRm8Ie46TTumVmLf0AyiYUPWWkxKSi+vnXtho3ro8EVlgSMaPZSRUQV6Y\nVq1cYK6Tf5IcSHIgyYEkB5IcSHLgwuDARQsMiX1plExJz8qnMeoGaTcZHEaohQAaNzhkJHgY308L\nkv0DGZgy7zrMWnojVdEmwJ9CeXGGeYvX0qq/D/k56zFjihe/2dyJZnrYGiRqrgzqY6IMfZUl/GdQ\nIocRujZxvNaFrt2Bz1P9KawvtztlzGAwuK7NZeSeJwPMuOOYx7bPM1j2ICE+tE8jcUOAHQ8qi2i0\nc2EqrlhWjUtW0111xXQa4nS6gqR9ptWspaQQ3QFvfBTtjTvhC/ewDmJw/GBxKeVPz6TFzWRIciDJ\ngSQHkhxIcmAYDkhKxu/NQNWMNRzoKBW0+wH0tu1hDj/VyniU9BqA6MHDKdjZSjCnM4DDTcAOqo7J\nFtEAPaK8rSyIQm8ILzen0LU9gZr0ILZxXOzjeJ5JNa+8FDqcoHBu44APmwgkSS7n2slhgjYhFKZ7\n8I7ZHtR3e/DgXnpGIQZ1NyV73jmfZXGzY0+DjFpTSoiu5+v7vOigzcI5eSFMyfRhL9XVUtJCoOMy\nBDnQN9Lr2LdPpWBDix+n+mmgmtJD750ZwspK6rJx+JTqWFPvBORNWsv5xi0oJCj0VpESsq9YXseO\nHK0dBIAOH6lF7ckG4/bXqpG9/barcMfbr0lKC1mmJc9JDiQ5kORAkgNJDlwgHLiogSHtOEpyyAGC\nyNEIOhEFixgJl/4+SgoFOAGtuR5zV9xmQCH3O/CS1uyFl6Fi2lLM2LkJ88t/j0e56fXSHiIyg3SV\nQwiNRXtYkrm193rOawsO6dYAOroAinKzMauiBBUTciC3z+GI2pt56Epn7t1/9MwiL26CjHfaGpV5\nMK0l4jyvoueUG+d3Y96UDNSsvAazay5BTnYWQZ+hIuwC3KrnX47+nnbs3tyK/u7jbJVAMgW3vSEn\nJvk3yYEkB5IcSHIgyYGxcsCqllVWLcaxQ69QeuhBtHXv4biThlXVabikso8AUR8eOuzH8wRe/kg7\nP+X+EN4zqR+XEBh64aQX61tS0EYQqKJoAOkcpzI4FOfR61iJJIkG/GilN7MAVcAUwjRWrbGzLFt2\ngwjYUNIoQCDpJaqH1b5Gj2gcK+cR2JlN1bNrKYJ0asCLTe2pyD8Yxi3lAbybHtH29gSQxw2Tl5p8\n2NiaAi/V0rTXMysvjP+zwoM1U7O4ARRGS1sQdc2UT8qYg3mr3ofJVB3zUzr3rQYKia+lJROwZPFc\n/OGPrxpASF7IfvWb5/nEg1/+6jksXjgb77zzWlRXT1LyZEhyIMmBJAeSHEhyIMmBC4gDFzUwJM9g\ncgdvpHbE1Iho0BkSQ5ys9WoSVzwd1XMvOwMUsu9D4FBuXh6WrliLCRPLML16E27YtRtPvMJdywOc\nUBrJG4FAFoiJAEICYgYrEXlmk0SIZ6SlEpCZiKml+fSKwkkkJXIyaRWzuy+GnSHlicrvkHEiSwoI\n5jCirpmGCoYkjGSKVMvkiYBEl8wI4c7FfZg5YybmLLsJkybPHJQScmgP/ZtGNT2phNUdrkTj8TrQ\nnrcp0/4dmvr0XXd7M3Rk5tIwQzIkOZDkQJIDSQ4kOZAAB8xGD8fgqhmXcpihbZ7DryLUs5uGnU8D\nRFfO8WDD0QH8aAdVstp8OF7rxVPHB+ChqlghVblmEJQpoNTPKdoRzKSYTma6H+k0GF1Pw9NtBH88\nVDszmyxUCdNouYNSSK83eNHP8a2MhqKnZ0lhjcalG1LwZGMarq8IotQfpGRSCgboXOGZ1nQcoeTx\npNQgiPXgWD+NZNO2ngxVX1UWwA3T/Jg6kTck3tIW4BgdRFtPPjecrsbchTe+5Q1M+wi8rb5kEda/\n/Dp+/ZsXcPjICXzt6z/BpPJiXH/dpbj9tisxfdpkSmcP3YxKoHskkyQ5kORAkgNJDiQ5kOTAOHPg\nogaGujvq0NVey3leBBCJMGvonQOdaB6SkZ1L9bOR1ZwEEFVNm4uKydMxqeIPmJT+Q9w6qwWPvJ6P\nV2qLB1+JAJoS2ibKo9SNmWVK8oYAUUtHF13W0q07PXw1tPcgnQDQ5OIilBUW0T0tvabRvXvlxDyK\nW5fj9f3H0SNPYO7ABmSkpaC8KB9Ty4qQlyUbC2H0DXD2yrOXwFL/QAi+GZzsdvXy6EJrZzeOnuKW\nZVSYX9KMK6tOoHryJFx+/T2YNY+7lbSpFC0lFJXN3PZ0NKKv6xTLo2FvVwLx1409uR5hoL+HB2Xm\nkQSG3HxJXic5kORAkgNJDozMASs9JMmao4c2DwGIOqmBPTsriK9f7sFrVCnbUhvAThp73klpIU+/\nB48dCGJXrY82iID5dAs/q7COfbhgAAAaj0lEQVQflSkhhAgOVXDoP0kj0Rq90sOUKiJ45A9SFpYq\nY4srPTRgHcaRVi+WFgdxOT2eHe0YQC7VzV5u9hMAIqDE0E9p4F09fkoL+cyYOEFGpauB2+amUTIp\nA41NfTjREOSGD8fm3gIDCK1YcD0Kiia/ZaWEDGNcf6qmlOM//u2vce9H78JJqpEpVFaWYmpVBdLS\nU5OgkItXycskB5IcSHIgyYEkBy4kDly0wFBny2G0NexCcEAqWW7YwgEt3OCQhHlSUnzobDqI5tp9\nyCkoG/EdCDiRUeqZc5Ygy9uAV//4CN6/6CBumX0Uj++twv62KZhSXIxJRQU00EwbAgzeiJh6cW4O\nwR+6dOcxlYd2xzIpMZRCN7sDAYE7qqMfkwoLkTIrhbYJOmhnIYxeGmcsys0gzVyCQLSF4E0x+bQL\np5ASEX/3cjc1ixNShQzWsTA7G55SGt+cWIhdR+rRQqCohjudN808joKUZoqzBzC1fCJKJrC8tEyT\nb6Q/Hc3H0XpqNwZovykU7HFwrxiZLEAk72UpFONvqd+HhhP7kDehIkbqZFSSA0kOJDmQ5ECSA8Nz\nYFB6aPolRvXKAkRBShAFezkuUdB2No05Ly9PIeACI0W0pZYbMT00SE1D0G8QKFJ4vhP46eEQsrlR\n00Jj0QolHqqVdwTx+yNUH+NwvDQ3gBSme7UnFVu7/dhDg9V5/jDBHdoFInDUyLHYGbVB+0VhIxm0\nYKIH08ozMSnXx80pbs7wqKWHtA5qXHszZmPu6rsplbvIOHR4q6qNGWbG+KP5Sk5OFmrmzcC8OdNM\nCs2NklJCMZiVjEpyIMmBJAeSHEhy4ALiwEULDPV0nKLKEqWF6KJ+UGIoIjnkCImf5rIglJQ0Pwb6\nTmH/q49SYiYVk2asOp1gmKtQoAvB/lYapSZQE8xEehcNSi5l2cE+1FE0vSlYbCaXgnpC3IE0kA9t\nFRAlQip3IB3gxPkbMBI/FkoJEwyi/YO8HBRkZTAvjWESRNIEipAUslIpWURqQdJ06DpCSaoqIafB\nGyrTIU3AVDgFU/ObsCTvAIoymw1wlOHrRoaPM1XZVuhqQHebs3snGsOFrlYajNz3e4JobyDQ30H+\nyn/L6XqfvnaqoScC31Ipit/ddgwnDryGMnp7y8qdOFwxyWdJDiQ5kORAkgNJDsTlQDRApLHI2iBq\naNqBJqqSF2WFMY2ewmbNSUE+7QkJKdpIj2YWKJKR6ka6jW/udTZYGsJ+PEuX9l4JtrrCACcKAUoE\nHe7SqApMpNTQBNIuoh71ohIvrqkEKvK0YZOJFCboonr6CUos5eenc4gNoCs0A/MvvRsVVUvp1CLj\nLedtzMWqhC4FENlNrYQyJBMlOZDkQJIDSQ4kOZDkwJvKgYsSGOpsOYKWk6+jt7OBruppX4BzQWFC\ngQDVrXoHaE9oAAJhfH56PIkcabQ1kEKNrO62g9i76RfM40H59JUjMj840Im+7mZTjiY5eTke5DNv\nf7AXhd2vU11sK7ro6ezUwEzUD8wjSORI8gwl7MQJQLFQkQOpKBW9lFF1DWYz00kX5uQ0yAYNAbgs\nWYfIYHY/OpGfegoTUw/QDW8t0r3tSPMH4KfL3FCwn4dKoC2j9v+/vTt7buu+7gD+JQFiIwmQBHdK\nXESKlkRRoiU5oupYthIn6ThJx55pp50+9LGP/YP63j40mXYyfYjbvMR1Y8d2FSuyLYmbTHETF3ED\nQOxLv+dSEEGKFJyItkTweycQQGwX+FzPAPni/M5ZRDxaPhhKRB5i5cFHWJ7+GJuPpui43XQ6x3J7\nG0lv1naeZ78FN0v0a3gqbm7+yprNpvDgqw/Q3HEKQ9feLd6kcwlIQAISkMCfJVAMiOzB1oPoZN8V\nzNz/DHMPbnP51hcoJDlSjJ90SVYS+Wuy6K9jNdGVAH802W4IbR/L1hg6y3Hp8xwxb2HRAs8XWDlk\nVT8d/OHHNqsI6mTDaneNm0uo+bFsn7fc4gyBNiIMf+J2GzDNUWSRZAMK7mbUtL6KCzd+jPqGLgVC\n21z6VwISkIAEJCCBIyhw5IKhdGIDq/M3sfbwNsfUby8jsxBoK5pCdDPJMKiOjY/bUc9lUz5/iN8V\nC1hbnGbYMc0x6m4Eg15sbUzhy9/+M5b4xbJ3+Edo7HjlwEOXTW85gUo6tR2Q2B3tV0t3VQYhP4MQ\njrt1b26hib8sXuYkkxibUS5vujEXacF6vJYBSjHRKd3F42+bpVc5l3lfm2j2JBLajpGePAMvWDFS\nU2ALA433uc8MGps74WPFENsBIbqyzibR2z+DWiBU3PgqWXmUQCJiPYM24K3lqJV9tvjGA5p8gIdT\nn2D14V0GYpt8XIH9jNxIpdy8vP26LbiyIM6Wj1nvJguH/P6cs5TM62GZfnoF92+9j1C4AycGy4dv\n+7wUXSUBCUhAAhJ4SqAYEvUNvoGe/mv8jGWPPqskmr6F+Rl+L0jcdYKiNS4Ps08sP38QslM2zUpb\nbidDNajnDye9HDlfe8rD5eisOs7yM93DMIiPiLBpdA17EgVrXYgwaLKwaSPKB7rbEcuG0dA4jKuj\nDIJCXJLOJef2eo7bcjEHUv9IQAISkIAEJFBRAkcqGLJQaOnrD7H84GMkGVrkc1nEY2lsrG4hEOrC\nyFs/RlvvMCuDvHDV8MQvbLbMLJXYwtzk5xi/+V9YXLgPPwOi+rokvyiuw5aKta1f4sSy0wyTencd\nXGu8vLE8xtBpjb82liQtxXvxW2eB01DCrb0YvPxX6BwcRSrrxvz9W9h4eI89hlJY24hhld9QE4k0\noqlajsXt4HQUC4zsF0zWBFnq8yT54fdMPqeFLfZLZdAbY/i0xfM4wr5l1AZ8CAQ8CDa1cync33HC\nSRdquQwtl1jG13/8T0wmZhGL7KmPd14rd1CVw8biPQZqY+jYUyllfZqij+5ide4m5iY+xfLcXSTi\nacTZb6HGF4Y/2MypIoPoOXOVy8N2mkpvcfrYyvwkZsc+Yb+ncScc8noLbDCZQWx9Anc/+oVTSt7R\n/1pRTOcSkIAEJCCB5xYoBkSMfZznKgZF9sNNZH0OcwyKIptLYNshLG0uMhlaRZ4/WiC3ijp+B7DP\n2jibWXNRNyIMkaJxTi5taOco+zCi6zlUbfIzOMTP2p6LrCJqQ7CxE8GGDn4+Kwh67oOnJ5CABCTw\nogQ4wAdufm5wOrQ2CUhgt0AVg5OSWGL3jS/bXwsTH2P8/37BSpZ5VgZxjT8nfiXiLrScHEH/xbfR\nzr42vgCrhPbZUvEoNlcXMDP2GcZuvs9laPNobPKirj7EsCWM5u4LaD55EbWNPQxCOp3eOusPP8fi\n1P9gaeZLhkMsyaFUkcs5J12CfQv6ht/DpR/+A7z+oLPnfC7jhFZ2nxg7WMZiCX7RXOTyt6+wsTSJ\nRwvjsB5J9nMmF2VxMq+fDazZ4DITZXPqPHsNufn+atHR/z10DV5DbX0rQk0tHLvrgY9TPTyccmbB\nV3GymO1vfuwD3P39vzKoucPlXPyWy41P/2RzVXv5PJ3oGfohBi6/64Rg2XQM8fX7iCzfweKDPziB\nUGRthe81jWSqhgHSKM5zOZhV/vg4za021MxSeY7ifbxl2QE0FY/wcRMY+8NvMDf+CfsxPWJYxffA\n6WvZbD36zr+JkRt/j6b27SaUxcfqXAISkIAEJPBtCBT4y0uOn4vFwRR5p4SWv8Y4gyr4mctUKLKx\nyNOSs/v6UBvqeKq2klx+cha/FdlnrAVQVfy1xoY+2Lk2CUhAAhI4ogL2GcAVFMjyVwGG/NsBEc+1\nSUACjsCRikttyVdD2zCmbs1yKkmUS8Y6MfT6X6L7zOtPhRZ7j6+NqW8NvIIgQ45wRx9u/++/Y3Xh\nc37RizDESSA/k+RSqxlW43Sx+qiNXyqzDHPmsbb8NXsWRfY+nfO3La1qaDmNzv5LT0Ihu8HG3dvJ\ntkZOAWtsbEThRDvy54ZYWTONO7/7F8zce8Avn+wDxOZCruo0gyD2FUIanuoUx9hzdG5TB86/+n0G\nK2/wNfILKb+gHrTZvtrYc2F96WueptkPyaqGdnr/2OPyeYY4yTU8mv2cVU4xeoV5LX9ZXZvhsrEJ\nxDbZUHsr5fRQCDWfw8hr76D37DVOF+vcFQbZcxU3N5s2uRkWnQwE0dTWjene8/jyo1/xPY45X6xD\nrZ1oP3WJX7hbiw/RuQQkIAEJSOBbFbAAx80fQ561hVtDaGwZcO6i0OdZUrpNAhKQQIUI2P+X4g/v\nzqlC3pLehgQOU+BIBUP+uiYMXvk5g48cK1xu49SFH7Ba58aBVUL7QfkYYpwcfI0BURfDmY8w+cf/\nZvPKOTTklll9tM4gaIqhjpvBBqeEsbF1OhXncjM2uOaT7S2tynMSWGv3JU7gurDfrnZd5/zy6PbC\n3kOgPsyeBB5W1ERRzbL3Avv4ZHgqViNZY2dXTRAe9kgqBky7nmyfPzz+Br6nXngDrQyyNhkkcdZu\nyWaNrDNspP1o6S5DoFku9wow/ErzPUcZGCXZQ4iNO9lHqJPjgUfe/Ft09g07VUIlT3HgRQuIQs1d\nOHP5R06I9Plv/417K2Dk+t/g1PB1vo/6Ax+rGyQgAQlIQALftYCFR5xN9l3vVvuTgAQkIAEJSEAC\nL6XAkQqGTDDAfjdnR99F34UbzuWDlo49S9uCjHB7r9Mvx+Orx1e//w8u95pDfX2e4QkDFYZC2yFN\n8fzpZysUXAxD+tHWcx4eX93TdzjgGm+ggdVAnXyMH9mYdbTc2XbCJ5v+ZWXrpYvBdu530CXrBRQI\ntnLJ3NRTd3Gem+X02XQCW5wclohv8D2y3J5L1xJJVjh52jDw6ijOfe8ddJ4aPrBK6KknLrnCqrIG\nLlxn8NXkBEN/SrhU8jS6KAEJSEACEpCABCQgAQlIQAISkMB3JHDkgiFzsXDITs+7WfXQwMW32Feg\nmo2pf4VkcoaVNCW/IJaUCJVc3N4tyxH9dY3w1+7f0+ig12YVQF5/LSd4cUzKnq24j0KhmuFKM3sC\n2XKvb75ZL4RihZE9V2msVHxup+6JS9hyOTbT5nSxVJqPqWnHEMO2odF3yi7JK/dqLBw6OXiZd2NL\nz5J+ROUep9slIAEJSEACEpCABCQgAQlIQAIS+O4FSlKQ737nL8MevVzmZMudeoduMCdp5Kh1e1UW\no+xEKfu9TivmSW0tIbIyiUxyd+XPfvcvvc7t5lQTjpg/aLOeQjWsKLLpat90i3EKy8Lkx4isTjMR\nYnPNAx64c30VsmwQnU67+d7fwMU33nOWgx1GmOP0HlIodMAR0NUSkIAEJCABCUhAAhKQgAQkIIGX\nR+BIVgwdNp+FQ6df/QmSW2uYufNrFNzlgyF2cGYgtMoR75+Aw8LQ0H4OPk79cnnY1OwZWyq2hNjG\nNPsLsSv+AZubg1FcPFmfnnLb9qj5SU4l+xBzY7/jtLM5PiRz4MPsGav4j4VCiaSHk8eusVLop06l\n0IEP0g0SkIAEJCABCUhAAhKQgAQkIAEJVKSAgqHHh9X64nT1X0bk0QQSm1Ps8WPhysHBjIU2mVQU\ny7O3WTm0gpa1Sfhqm9louQU1gWZW/IS4ZIzLzDgW10bj8n9IJ1YQW7nLKqN7SCdtctjuzap5bI/s\nfY1kZBaxtVnUN3bsulMuE0c2s4V0/BESGw8YBD3E8swdLEx/gVhkxRnRW7VnIpk9QWmlkP2dybjY\n62gAZ678BK0nB+0qbRKQgAQkIAEJSEACEpCABCQgAQkcMwEFQyUHvLX7PMe9X8L0l7OsqsmxOTP7\n8Dxjy7OZcyoVwypH2kc3Frn0y88wqJHBToBLxQK8XM+ePxxH7+b0Ey4PYzSE6Po8lucnOQns6WCo\nGEMV8klsrU9icfx9JkRfswqpzgmMspkEHxdjwBTj8yzg0cIYq5zWnZDJmZ6Wfzyi/nEKtBMGlUZc\n1lC7mgFSNSeYsRF2Q6t6AT3jGOsmCUhAAhKQgAQkIAEJSEACEpBAJQsoGCo5ujZdzMau14ZakYot\ncoLX0+GN3T2XLXApmIUwNtLeLltjohRH0EcYBq1yGZhFMlWoZoNq26xayP6282wmDQtxCgyVDt7y\niMdWMPnFbzAz/uH21DM+OJWIIs+m0RZI2fMkEwme5/k3eGL4xP1WVxec/TjnvLx3s5eSy1Uhx/u3\ndJ1Gy4lX9t5Ff0tAAhKQgAQkIAEJSEACEpCABCRwTAQUDO050G42fLapYZnEbhoLVLIMgeLxHLZi\nFswU4K9v4FSyJlYKbT9JPLaG9fV1J5ix6zw1nMzFfkXWZ7pgGQ3/KdgF5489O97zZy6XYTi0jq1o\ngWGPVRvZ/vM8ubkMrJo9guxvF6MpFwKcjsYUClsR2zeDIV52MZOy/Xt9WQZGOyGU8zJsYRknktkE\nMR9P2iQgAQlIQAISkIAEJCABCUhAAhI4ngK704/jabDrXXv9QdRxeZVVDKWTvOlx0U18K4/IRhbV\nniB6zr2GnrOjCDZ1cBnW41SId7Uqnlw2hZX5Kaw+nMDa4iSXe03Ay+FiFg65XRYU8Y6WMtn2+Lm3\n/9i+es9VzJCqkUxWI8PpYVYV5K8LY+D8VYQ7TvNyE6ubwk9eg+3ftkcLk1iZm+CyuElsLo/D48mj\nxp1maGQBUXHnzl31jwQkIAEJSEACEpCABCQgAQlIQALHWEDB0J6D73Z7GOC4GchwfVYxFGKVUCpT\ni+6hKzh1/rrTrLku1Hxgb5627jNIxqNI8bS6NM2QaBKz458iwpDG59sOh6q3V5nt2vveUCjHiqBU\nqoaNrFswMDSKboZRIYZRFgZ5A0EnENpvvHx7z1nuO8KlZzHMT36GqVvvs5H1BKuIUnxf2V371B8S\nkIAEJCABCUhAAhKQgAQkIAEJHF8BBUN7jn0yvsLGzrOs0LFyISCxxVAoXYvTl36Gc1d/hmcFQsWn\nsqbTdkIYaGrvQ/fpyzg98jYWpm5i7OavOZVsEn6/jaTfWeJVfKydF/JVSKdd7EVUg86BUZx//T20\nnhh0RsrvFwSVPtYu2/Kw4hKxULgDgfpGjH/6S+73DueV5bnqLM/lZuxjFFnj8rNV1Ab5QrVJQAIS\nkIAEJCABCUhAAhKQgAQkcOwEFAyVHHIbP2/TvqIba2w8nWG1Tg6RzQxae8+i/8JbaGBj6j91s6Vm\nblYX1fLUEO7kOPsQbn/4S0RXx+Bn9VA1A5q9m4VCqG7FwMhVnBv9OTpODR9YnbT3sXv/tsqivqHr\n7Jm0jonPVhGPzqPgSrF6KM/wi82redImAQlIQAISkIAEJCABCUhAAhKQwPEU2GdB0/GEsHcdjy5j\nY2XWGf9uS64SXEIWCPXhlUtvI9ze+9ww1ux54MJ1XHzjrxFqeQXpjMfpIVR8Yuv+k2EolEy60Tv0\nJkZ/+o/PFQoVn9fD6qWu01fRcvICgyjrieRitVLB6YO0zF5E2iQgAQlIQAISkIAEJCABCUhAAhI4\nngIKhkqOuzWLXnrwFQOhFOJcQmbBUOepi+g9d+3PrtgpeXrnoi0x62c41DVwhc2kPTztFG1ls1VO\nKNTYOshg6BpCrFD6JkvH9u5jv7+DzT1o7RmBJ9DCMKrGmZwWWZ3B3OQtZznZfo/RdRKQgAQkIAEJ\nSEACEpCABCQgAQlUtoCCoZLj6/GF2HunDtFIGutrGdQ29uHE6UtP+vWU3PW5Llo4dKJ/BM0dZ9jf\nmuPKHm95jpDPs79Q18AlnBy8XLz6UM5dbKptwVC4a5hj7n2sVvKihhPWrNl2cZrZoexITyIBCUhA\nAhKQgAQkIAEJSEACEpDAkRHYKVc5Mi/523uhLSeGcPWdf3KqaBYf3EMXq4UOO6ApvvqG1m6nMXVs\nbZwT5NlTiEvXctlqNLQMMIwaOfQwyvZbH+5GW8+rWFt+iGBzH3rOfX+7qXWwqfiydC4BCUhAAhKQ\ngAQkIAEJSEACEpDAMRJQMFRysD2+eoQ76lHX0I7es3/BkfA7071K7nYoF+sb2xBiM2qXywcuIEM2\nl3OCoca2Xr6GvkPZx94nsaqh3vM30NI9DHuvAU4jO6ylanv3pb8lIAEJSEACEpCABCQgAQlIQAIS\nePkFFAztc4yejJvf57bDuspCmrqGVtSF2pCIxpHLJXmqQpDj5YMMjL6tzV8fhp20SUACEpCABCQg\nAQlIQAISkIAEJCAB9Rh6gf8N1Hi8qHa5GQjl2RCaq8n4Wtxur6p4XuAx0a4lIAEJSEACEpCABCQg\nAQlIQALHSUAVQy/4aGeyWSTTOTaEdsFX28BqnoYX/Iq0ewlIQAISkIAEJCABCUhAAhKQgASOi4Aq\nhl7gkbZKoVQqg3iiComUmz2o/XB7fC/wFWnXEpCABCQgAQlIQAISkIAEJCABCRwngaoCt+P0hl+m\n95qIrmFz9SEy6QSXkhWcJWTWX6gu1PwyvUy9FglIQAISkIAEJCABCUhAAhKQgAQqVEDBUIUeWL0t\nCUhAAhKQgAQkIAEJSEACEpCABCRQTkBLycoJ6XYJSEACEpCABCQgAQlIQAISkIAEJFChAgqGKvTA\n6m1JQAISkIAEJCABCUhAAhKQgAQkIIFyAgqGygnpdglIQAISkIAEJCABCUhAAhKQgAQkUKECCoYq\n9MDqbUlAAhKQgAQkIAEJSEACEpCABCQggXICCobKCel2CUhAAhKQgAQkIAEJSEACEpCABCRQoQIK\nhir0wOptSUACEpCABCQgAQlIQAISkIAEJCCBcgIKhsoJ6XYJSEACEpCABCQgAQlIQAISkIAEJFCh\nAgqGKvTA6m1JQAISkIAEJCABCUhAAhKQgAQkIIFyAgqGygnpdglIQAISkIAEJCABCUhAAhKQgAQk\nUKECCoYq9MDqbUlAAhKQgAQkIAEJSEACEpCABCQggXICCobKCel2CUhAAhKQgAQkIAEJSEACEpCA\nBCRQoQIKhir0wOptSUACEpCABCQgAQlIQAISkIAEJCCBcgIKhsoJ6XYJSEACEpCABCQgAQlIQAIS\nkIAEJFChAgqGKvTA6m1JQAISkIAEJCABCUhAAhKQgAQkIIFyAgqGygnpdglIQAISkIAEJCABCUhA\nAhKQgAQkUKECCoYq9MDqbUlAAhKQgAQkIAEJSEACEpCABCQggXIC/w/0zoxX9FG74gAAAABJRU5E\nrkJggg==\n"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "37cff7e5-f47d-4d6a-b3f6-e558ffef52da",
+ "metadata": {},
+ "source": [
+ "# 310.2. Photo-z analysis\n",
+ "\n",
+ "
\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "For the Rubin Science Platform at data.lsst.cloud.\\\n",
+ "Data Release: [Data Preview 2](https://dp2.lsst.io)\\\n",
+ "Container Size: Large\\\n",
+ "LSST Science Pipelines version: v30.0.11\\\n",
+ "Last verified to run: 2026-09-02\\\n",
+ "Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
+ "DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d34b984f-da78-416f-b14c-6a1ed4284a3f",
+ "metadata": {},
+ "source": [
+ "**Learning objective:** Understand and apply the photo-z errors in analyses.\n",
+ "\n",
+ "**LSST data products:** LSDB `object_photoz` table\n",
+ "\n",
+ "**Packages:** `lsdb`\n",
+ "\n",
+ "**Credit:** Originally developed by the Rubin Community Science team with Bryce Kalmbach. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n",
+ "\n",
+ "**Get Support:**\n",
+ "Everyone is encouraged to ask questions or raise issues in the [Support Category](https://community.lsst.org/c/support) of the Rubin Community Forum.\n",
+ "Rubin staff will respond to all questions posted there."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21862001-071f-40b9-8a1f-75a74bbc55c4",
+ "metadata": {},
+ "source": [
+ "## 1. Introduction\n",
+ "\n",
+ "Photometric redshift (photo-z) estimates for Data Preview 2 were generated by the Rubin Commissioning Photo-z Science Unit and are available as [LSDB](https://docs.lsdb.io/)-formatted files, as documented in \"Photometric Redshifts for Data Preview 2\" ([RTN-124](https://rtn-124.lsst.io/)).\n",
+ "\n",
+ "**Algorithms:**\n",
+ " * `bpz`: Bayesian Photometric Redshifts (BPZ; [BenÃtez 2000](http://arxiv.org/abs/astro-ph/9811189))\n",
+ " * `dnf`: Directional Neighbourhood Fitting (DNF; [De Vicente et al. 2016](http://arxiv.org/abs/1511.07623))\n",
+ " * `fzboost`: FlexZBoost ([Izbicki & Lee 2017](https://doi.org/10.1214/17-EJS1302))\n",
+ " * `gpz`: Gaussian processes for photometric redshifts (GPz; [Almosallam et al. 2016](http://doi.org/10.1093/mnras/stw1618))\n",
+ " * `knn`: k-Nearest Neighbors (kNN; [The RAIL Team et al. 2025](https://arxiv.org/abs/2505.02928))\n",
+ " * `tpz`: Trees for Photo-Z ([Carrasco Kind & Brunner 2013](https://academic.oup.com/mnras/article/432/2/1483/1029454))\n",
+ "\n",
+ "**Measurements:** For each algorithm these tables carry four point estimates, `z_best`, `z_mode`, `z_mean`, and `z_median` plus $1\\sigma$ and $2\\sigma$ credible intervals (`z_err68_low`/`high`, `z_err95_low`/`high`). \n",
+ "\n",
+ " * `z_best`: Minimal risk point estimate described in Section 4.2 of [Tanaka et al. 2018](https://arxiv.org/abs/1704.05988) with $\\gamma = 0.15$\n",
+ " * `z_mode`, `z_mean`, and `z_median`: point-estimates based on statistics derived from the posterior distribution function (PDF)\n",
+ " * `z_err68_low`/`high`, `z_err95_low`/`high`: the $1\\sigma$ and $2\\sigma$ credible intervals derived from the PDF\n",
+ "\n",
+ "**Caveats:** These estimates and their error bars are only very lightly validated.\n",
+ "Treat them as provisional and use the results with caution.\n",
+ "\n",
+ "**Guidance and recommendations:** This tutorial characterizes the photo-z and their errors through a series of data visualizations, in order to help guide scientific analyses that incorporate the photo-z.\n",
+ "At this time, there are no flag values or strict, quantitative guidance to assist users in understanding photo-z estimate quality and deciding when a photo-z is \"good enough\", and that ultimately depends on the needs of the analysis.\n",
+ "Providing photo-z flags is a work in progress.\n",
+ "\n",
+ "For scientific analyses that use the photo-z, it is recommended to:\n",
+ " * use the results of more than one estimator, as they can differ (Section 3.1)\n",
+ " * use the photo-z errors, as the errors can be large (Section 3.1)\n",
+ " * consider the photometry (fluxes and errors), as uncertain photometry leads to uncertain photo-z (Sections 3.2 and 4)\n",
+ "\n",
+ "\\\n",
+ "**Related tutorials:** The 100-level tutorial on catalog access (for LSDB and TAP) and the Butler."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f81924f-b53d-41d2-9d37-ed379158d726",
+ "metadata": {},
+ "source": [
+ "### 1.1. Import packages\n",
+ "\n",
+ "Import the `lsdb` package and a variety of other LSST, astronomy, and data visualization modules."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc4aec92-f4d4-435f-861e-83c211c30110",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import lsdb\n",
+ "from dask.distributed import Client\n",
+ "from lsst.daf.butler import Butler\n",
+ "from lsst.rsp import RSPDiscovery\n",
+ "from bokeh.io import output_notebook\n",
+ "from tqdm import tqdm\n",
+ "import holoviews as hv\n",
+ "from holoviews.operation.datashader import datashade, dynspread\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import healsparse as hsp\n",
+ "import hpgeom as hpg\n",
+ "import skyproj\n",
+ "from datetime import datetime\n",
+ "import logging\n",
+ "import warnings\n",
+ "import os\n",
+ "import gc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "069acbc4-c69a-4feb-aa88-f68f80d9e081",
+ "metadata": {},
+ "source": [
+ "### 1.2. Define parameters and functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d409c88-d774-4d6e-a760-1ae4b06210e8",
+ "metadata": {},
+ "source": [
+ "It is [recommended by the LSDB developers to use a Dask client](https://docs.lsdb.io/en/latest/tutorials/pre_executed/rubin_dp2.html#1.2-Create-a-Dask-client) when accessing the LSDB data sets.\n",
+ "Establish the client as per the guidance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a2d60c6-19dd-4174-a01b-4635fca267c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client = Client(\n",
+ " n_workers=4,\n",
+ " threads_per_worker=1,\n",
+ " memory_limit=\"auto\",\n",
+ " local_directory=f\"/deleted-sundays/{os.environ.get('USER', 'dask_scratch')}\",\n",
+ ")\n",
+ "print(f\"Dask dashboard: {client.dashboard_link}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28fc9a15-00e7-45c2-8036-3d373c47b563",
+ "metadata": {},
+ "source": [
+ "Quiet down the output of the client going forward by suppressing all logging output except warnings."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0e348bc1-b1e4-4d30-ba0b-7c8d307897bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logging.getLogger(\"distributed\").setLevel(logging.WARNING)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7da492aa-86ee-445b-94e2-862ad6cc57d4",
+ "metadata": {},
+ "source": [
+ "Instantiate the Butler and TAP services."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab89144a-8c6c-45aa-8840-44479579a09f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "butler = Butler('dp2', collections='dp2')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1901c0be-16a9-4758-9c7e-4021c3d45f78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "discovery = RSPDiscovery(\"dp2\")\n",
+ "tap_service = discovery.get_tap_client()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9a9861d-141c-401d-bd79-538219e18236",
+ "metadata": {},
+ "source": [
+ "Set up for interactive plotting with `holoviews`. Order is important here: the `output_notebook()` command must be run after the `hv.extension('bokeh')` command or plots will not display."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8fa1a84e-d8e6-4011-a1aa-14805050b16a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hv.extension('bokeh')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d9d2f2b-b18b-4231-b80d-aeac0d66ffe0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3783eb65-5f36-43ef-8556-7a120b21e6a2",
+ "metadata": {},
+ "source": [
+ "## 2. Retrieve a dataset\n",
+ "\n",
+ "### 2.1. Load photo-z in a small region\n",
+ "\n",
+ "Load the photo-z for objects within a 2 degree radius of random central coordinates within the DP2 WFD-region."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff719147-f9fa-41d0-9834-caa9f4eb950a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ra_cen = 310.0\n",
+ "dec_cen = -20.0\n",
+ "radius = 2.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d6e5e2e-7489-4ce5-bd76-5cf1d482cd1f",
+ "metadata": {},
+ "source": [
+ "All columns (aside from the coordinates, `ra` and `dec`, and the identifier `objectId`) are named as `_z_`.\n",
+ "\n",
+ "Choose to return only the \"best\" and $1\\sigma$ error intervals for the FlexZBoost and BPZ algorithms, as examples.\n",
+ "In [RTN-124](https://rtn-124.lsst.io/) these two algorithms were identified as the most and least performant with DP2, respectively (see their Fig 10)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3be33642-233b-4448-b53f-f2fdefa3e71a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pz_cat = lsdb.open_catalog(\n",
+ " \"/rubin/lsdb_data/dp2/object_photoz\",\n",
+ " search_filter=lsdb.ConeSearch(ra=ra_cen, dec=dec_cen, radius_arcsec=radius * 3600),\n",
+ " columns=[\"objectId\", \"ra\", \"dec\",\n",
+ " \"fzboost_z_best\", \"fzboost_z_err68_low\", \"fzboost_z_err68_high\",\n",
+ " \"bpz_z_best\", \"bpz_z_err68_low\", \"bpz_z_err68_high\"],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "479b37b3-aa8f-448f-9012-de445a8bd9b8",
+ "metadata": {},
+ "source": [
+ "Option to display the lazily-loaded table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d541fa1-c074-44c7-8911-00078b8743c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pz_cat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de9a98e0-9ebf-4138-a671-970f017d4b78",
+ "metadata": {},
+ "source": [
+ "Choose to limit this exploration to photo-z results between 0.1 and 3.0, the range over which the LSST was designed to optimize photo-z estimates (the LSST System Science Requirements Document, [LPM-17](https://docushare.lsst.org/docushare/dsweb/Get/LPM-17))."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c66e358-23c9-47c3-9b72-ad904b5e56ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "z1 = \"0.01\"\n",
+ "z2 = \"3.0\"\n",
+ "query = \"\"\"fzboost_z_best > {} and fzboost_z_best < {} and bpz_z_best > {} and bpz_z_best < {}\n",
+ " \"\"\".format(z1, z2, z1, z2)\n",
+ "pz_cat_zlim = pz_cat.query(query)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "113a46aa-b4c4-4881-89d2-d16dcb155c49",
+ "metadata": {},
+ "source": [
+ "### 2.2. Load photometry and join\n",
+ "\n",
+ "Load the LSDB-formatted `Object` table, and retrieve the Gaap (Gaussian Aperture) fluxes with a 1 pixel aperture, and their errors.\n",
+ "As described in [RTN-124](https://rtn-124.lsst.io/), the Gaap 1-pixel photometry were used as inputs to the photo-z algorithms."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a198fc09-2a84-4d34-a02d-38478c8b9c0d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obj_cat = lsdb.open_catalog(\n",
+ " \"/rubin/lsdb_data/dp2/object_collection\",\n",
+ " search_filter=lsdb.ConeSearch(ra=ra_cen, dec=dec_cen, radius_arcsec=radius * 3600),\n",
+ " columns=[\"objectId\", \"coord_ra\", \"coord_dec\",\n",
+ " \"u_gaap1p0Flux\", \"g_gaap1p0Flux\", \"r_gaap1p0Flux\",\n",
+ " \"i_gaap1p0Flux\", \"z_gaap1p0Flux\", \"y_gaap1p0Flux\",\n",
+ " \"u_gaap1p0FluxErr\", \"g_gaap1p0FluxErr\", \"r_gaap1p0FluxErr\",\n",
+ " \"i_gaap1p0FluxErr\", \"z_gaap1p0FluxErr\", \"y_gaap1p0FluxErr\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f3eecab-71fa-4946-94da-537dae0614e6",
+ "metadata": {},
+ "source": [
+ "Option to display the lazily-loaded table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e89f00f8-ff84-4a29-9ec9-6f057e6b9f80",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# obj_cat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3bc80a0e-32b0-41d5-b776-fb675c4f8275",
+ "metadata": {},
+ "source": [
+ "Join the photo-z and object photometry tables together."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6bfa4d31-ff12-478b-8dde-7ef217740eaf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "join_table = pz_cat_zlim.join(obj_cat, left_on=\"objectId\", right_on=\"objectId\",\n",
+ " suffixes=('', '_obj'), suffix_method=\"overlapping_columns\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c72fd21-6a94-4ffc-84c8-9e63b76c276a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# join_table"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8fbfa219-eec9-4277-bd3e-a28d4a81b32d",
+ "metadata": {},
+ "source": [
+ "### 2.3. Add photo-z error columns\n",
+ "\n",
+ "Follow the [LSDB instructions for generating new columns](https://docs.lsdb.io/en/latest/tutorials/pre_executed/map_partitions.html#2.-Generating-New-Columns) to add _approximate_ errors from the $1\\sigma$ intervals. \n",
+ "\n",
+ "> **Notice:** The intervals are not necessarily symmetric, and using this error value as, e.g., $z \\pm \\delta$, would be inappropriate. The `z_err68` columns are only used in this tutorial to represent the size of the error. In analyses, use the low and high values as reported in the photo-z table.\n",
+ "\n",
+ "Also calculate the absolute value of the difference between the BPZ and FlexZBoost redshift estimates: $\\Delta = |z1 - z2|$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ccb76879-07d6-4924-9d03-9e314df7e948",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pz_err68(df, pixel):\n",
+ " df[\"fzboost_z_err68\"] = 0.5 * (df[\"fzboost_z_err68_high\"] - df[\"fzboost_z_err68_low\"])\n",
+ " df[\"bpz_z_err68\"] = 0.5 * (df[\"bpz_z_err68_high\"] - df[\"bpz_z_err68_low\"])\n",
+ " df[\"abs_diff_zbest\"] = np.abs(df[\"bpz_z_best\"] - df[\"fzboost_z_best\"])\n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ffd1c4a-2014-45c8-81dd-8289024b09b1",
+ "metadata": {},
+ "source": [
+ "Apply the `pz_err68` function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26f652c5-fc54-4116-b6fb-2cbff7d8d708",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "unrealized = join_table.map_partitions(pz_err68, include_pixel=True)\n",
+ "unrealized"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da094426-6c24-4e52-9431-6a6327aa8e40",
+ "metadata": {},
+ "source": [
+ "Compute the values for the entire table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b2a97a75-2169-4a45-80e4-4b3ac7cedb6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = unrealized.compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fdb1bb50-5e08-4232-ace6-dfa4ebdf16c7",
+ "metadata": {},
+ "source": [
+ "View the first five rows of the table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c27a168-d9de-470c-857e-493a2260e1fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result.head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46cbe4b2-952c-4dd6-8fc8-de9f5b2c4a5a",
+ "metadata": {},
+ "source": [
+ "Restart client workers to free up memory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "778e28c0-93ae-4c71-a94b-ceed0de166cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client.restart()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e9e6fe8-77c1-45bb-ab94-650ab72f271d",
+ "metadata": {},
+ "source": [
+ "Delete what won't be used in the next section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6fc1a61-043f-4a50-9bc0-7fd97485e8f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del pz_cat, pz_cat_zlim, obj_cat\n",
+ "del join_table, unrealized\n",
+ "gc.collect()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9ede71b-0ec1-499f-acfa-67749fbe1bb4",
+ "metadata": {},
+ "source": [
+ "## 3. Explore photo-z error relations\n",
+ "\n",
+ "### 3.1. Between algorithms\n",
+ "\n",
+ "To visualize and characterize the photo-z values and their errors, create a few interactive plots.\n",
+ "\n",
+ "Define a function to make all the plots with the same design."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4aa2370c-ff93-473a-a2a4-8422b7da1cbf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_z_dyn_plot(col1, col2, data, x1x2=(0, 3), y1y2=(0, 3)):\n",
+ " \"\"\"\n",
+ " Create a datashader plot.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " col1, col2: string\n",
+ " The x and y axes columns.\n",
+ " data: dataframe\n",
+ " The dataframe containing the columns.\n",
+ " x1x2, y1y2 : tuple\n",
+ " The x- and y-axis limits\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " p, bounds: DynamicMap\n",
+ " Generate the plot with `p * bounds`.\n",
+ " \"\"\"\n",
+ "\n",
+ " w = int(600)\n",
+ " h = int(np.floor(w * y1y2[1] / x1x2[1]))\n",
+ " if h > w:\n",
+ " h = w\n",
+ " points = hv.Points((data[col1], data[col2]))\n",
+ " boundsxy = (0, 0, 0, 0)\n",
+ " box = hv.streams.BoundsXY(source=points, bounds=boundsxy)\n",
+ " bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])\n",
+ " p = dynspread(datashade(points, cmap=\"Viridis\"))\n",
+ " p = p.opts(width=w, height=h, padding=0.05, show_grid=True,\n",
+ " xlim=x1x2, ylim=y1y2, xlabel=col1, ylabel=col2,\n",
+ " tools=['box_select'])\n",
+ " return p, bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e3de7f63-2dc9-4359-bb9d-6dd3f83c19f4",
+ "metadata": {},
+ "source": [
+ "Plot the FlexZBoost vs. the BPZ best redshift estimates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "66e51e29-3330-4fe9-b76f-a9401b052548",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p, bounds = make_z_dyn_plot(\"fzboost_z_best\", \"bpz_z_best\", result)\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f368841-7c5d-4371-b555-1c56dffa23a4",
+ "metadata": {},
+ "source": [
+ ">**Figure 1:** For all objects in the retrieved subsample, the FlexZBoost vs. the BPZ \"best\" photo-z point estimates. This plot shows a general locus of agreement along x=y, but also many clumps of differing results. Notice that the FlexZBoost estimates show a more narrow quantization in the off-locus regions, where as the BPZ clumps are broader."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ce30688-70b6-4a60-b009-c1968be1bf9b",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Plot the FlexZBoost vs. the BPZ best redshift $1\\sigma$ error estimates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4b941bd-5bab-4793-ba25-638f18339283",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p, bounds = make_z_dyn_plot(\"fzboost_z_err68\", \"bpz_z_err68\", result, y1y2=(0, 1.5))\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59901bd6-87b6-4dda-9fec-c36784840118",
+ "metadata": {},
+ "source": [
+ ">**Figure 2:** For all objects in the retrieved subsample, the FlexZBoost vs. the BPZ $1\\sigma$ error estimates. This plot shows that the errors for BPZ are quite different from those of FlexZBoost. The BPZ errors appear to have an upper limit, for example, and the the FlexZBoost errors exhibit more narrow quantization. Most notably, there does not appear to be much of a locus along x=y beyond $\\delta z \\sim 0.3$; the two algorithms do not agree on the error in the photo-z estimates."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c31b1107-f58c-4fe9-8730-82500e1f698e",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "A natural expectation is that the photo-z errors are larger for objects with very different photo-z estimates from the two algorithms.\n",
+ "Make a few plots to characterize whether this is the case.\n",
+ "\n",
+ "Plot the difference between the BPZ and FlexZBoost redshift estimates vs. the BPZ $1\\sigma$ error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e96e816d-d565-47c9-b7f2-a758ad8be7c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p, bounds = make_z_dyn_plot(\"abs_diff_zbest\", \"bpz_z_err68\", result, y1y2=(0, 1.5))\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e5bb0f-7061-4a19-ab61-c7a16ee890d8",
+ "metadata": {},
+ "source": [
+ ">**Figure 3:** For all objects in the retrieved subsample, the absolute difference between the BPZ and FlexZBoost point estimates vs. the BPZ $1\\sigma$ error. Whereas the expectation is that objects with very different photo-z from different estimators (large x-value) would also have larger photo-z errors (large y-values), this plot does not show a very strong correlation along x=y. This indicates that at least some uncertainty in the photo-z estimates is not represented in the error intervals."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e3e08486-0e71-4aff-938b-96fda64d2dc9",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Same as above, but use the FlexZBoost error values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "752d6d15-9121-4dec-8dbb-fb49830d9f38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p, bounds = make_z_dyn_plot(\"abs_diff_zbest\", \"fzboost_z_err68\", result, y1y2=(0, 3))\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01329fd3-4dbd-4217-b43c-499e1052f2c6",
+ "metadata": {},
+ "source": [
+ ">**Figure 4:** Similar to Figure 3, but vs. the FlexZBoost $1\\sigma$ error. This plot also shows a weaker-than-expected correlation between the estimated error and the uncertainty between algorithms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ba44f36-3243-4168-ab08-170d5825036f",
+ "metadata": {},
+ "source": [
+ "### 3.2. With photometry \n",
+ "\n",
+ "To visualize and characterize the photo-z values and their errors, together with the photometry from which the photo-z were derived, create a few interactive plots.\n",
+ "\n",
+ "Plot the $i$-band vs. the $g$-band magnitudes, to get a sense of the values.\n",
+ "\n",
+ "\n",
+ "> **Warning:** The following cell produces a pink RuntimeWarning error message due to failures when attempting to convert negative forced fluxes to magnitudes. Forced fluxes are measured on sky-subtracted images regardless of whether the object has any detectable flux in the image, so forced fluxes can be slightly negative due to small fluctuations in the background. Negative fluxes cannot be converted to magnitudes, and will be omitted from the plot. For this particular demonstration that is ok, but analyses in which non-detections are meaningful or need to be accounted for should take care when converting forced fluxes to magnitudes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e91a70db-8127-4ad2-90a5-a06a8ad43214",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xvals = -2.5 * np.log10(result[\"i_gaap1p0Flux\"]) + 31.4\n",
+ "yvals = -2.5 * np.log10(result[\"g_gaap1p0Flux\"]) + 31.4\n",
+ "xlabel = \"i-band Gaap Mag (forced)\"\n",
+ "ylabel = \"g-band Gaap Mag (forced)\"\n",
+ "points = hv.Points((xvals, yvals))\n",
+ "boundsxy = (0, 0, 0, 0)\n",
+ "box = hv.streams.BoundsXY(source=points, bounds=boundsxy)\n",
+ "bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])\n",
+ "p = dynspread(datashade(points, cmap=\"Viridis\"))\n",
+ "p = p.opts(width=400, height=400, padding=0.05, show_grid=True,\n",
+ " xlim=(16, 30), ylim=(16, 30), xlabel=xlabel, ylabel=ylabel, tools=['box_select'])\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d51b94f7-6e09-4cf5-9ccc-ae8949af66ba",
+ "metadata": {},
+ "source": [
+ ">**Figure 5:** The $i$-band vs. $g$-band magnitudes. Notice that with forced photometry, magnitudes can be very faint (fainter than the detection threshold of the images, which is $\\sim24$ mag around the selected coordinates, as will be shown in Section 4)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73134aa3-eb4d-4a6d-91b2-f7a209564173",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Now that the `RuntimeWarning` is understood, it is ok to ignore it for the rest of this tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "387bbe35-5d53-473e-bba7-ffff34925db6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2cca5fb-3ca7-473b-a53e-d68e4607a73c",
+ "metadata": {},
+ "source": [
+ "Plot the magnitude vs. its error, for the $i$-band, also to get a sense of the values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a951ca2f-b4c7-4805-b59d-d189ec71543f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xvals = -2.5 * np.log10(result[\"i_gaap1p0Flux\"]) + 31.4\n",
+ "yvals = xvals - (-2.5 * np.log10(result[\"i_gaap1p0Flux\"]+result[\"i_gaap1p0FluxErr\"]) + 31.4)\n",
+ "xlabel = \"i-band Gaap Mag (forced)\"\n",
+ "ylabel = \"i-band Gaap Mag Error\"\n",
+ "points = hv.Points((xvals, yvals))\n",
+ "boundsxy = (0, 0, 0, 0)\n",
+ "box = hv.streams.BoundsXY(source=points, bounds=boundsxy)\n",
+ "bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])\n",
+ "p = dynspread(datashade(points, cmap=\"Viridis\"))\n",
+ "p = p.opts(width=600, height=300, padding=0.05, show_grid=True,\n",
+ " xlim=(16, 30), ylim=(0, 3), xlabel=xlabel, ylabel=ylabel, tools=['box_select'])\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2ca30d5-49f6-43de-9f78-7286a3b29839",
+ "metadata": {},
+ "source": [
+ ">**Figure 6:** The $i$-band magnitude vs. its error. Objects detected with a signal-to-noise ratio of 5 will have an uncertainty of $\\delta m \\sim 0.24$ mag, and this is at $\\sim 24$ mag in this plot. But notice that with forced photometry, the magnitude errors can be quite large for faint (undetected) objects."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57137776-8c57-4dd1-933d-d539e456932b",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Plot the $i$-band magnitude error vs. the FlexZBoost photo-z error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e6bd4fd4-6849-45f8-938b-38f70e1cb9ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mags = -2.5 * np.log10(result[\"i_gaap1p0Flux\"]) + 31.4\n",
+ "xvals = mags - (-2.5 * np.log10(result[\"i_gaap1p0Flux\"]+result[\"i_gaap1p0FluxErr\"]) + 31.4)\n",
+ "yvals = result[\"fzboost_z_err68\"]\n",
+ "xlabel = \"i-band Gaap Mag Error\"\n",
+ "ylabel = \"FlexZBoost Photo-z Error\"\n",
+ "points = hv.Points((xvals, yvals))\n",
+ "boundsxy = (0, 0, 0, 0)\n",
+ "box = hv.streams.BoundsXY(source=points, bounds=boundsxy)\n",
+ "bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])\n",
+ "p = dynspread(datashade(points, cmap=\"Viridis\"))\n",
+ "p = p.opts(width=600, height=300, padding=0.05, show_grid=True,\n",
+ " xlim=(0, 0.5), ylim=(0, 2), xlabel=xlabel, ylabel=ylabel, tools=['box_select'])\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "754ce209-c6fd-4144-ae0f-20ade0dfcf75",
+ "metadata": {},
+ "source": [
+ ">**Figure 7:** The $i$-band magnitude error vs. the FlexZBoost photo-z error. This plot shows the correlation: larger photometry errors yield larger photo-z errors."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "126bdbe4-016b-40a8-9807-ad5a7c95559c",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Similar to the above plot, but with the BPZ photo-z error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12e5f683-805d-4b66-af4a-dee8c9ef4feb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mags = -2.5 * np.log10(result[\"i_gaap1p0Flux\"]) + 31.4\n",
+ "xvals = mags - (-2.5 * np.log10(result[\"i_gaap1p0Flux\"]+result[\"i_gaap1p0FluxErr\"]) + 31.4)\n",
+ "yvals = result[\"bpz_z_err68\"]\n",
+ "xlabel = \"i-band Gaap Mag Error\"\n",
+ "ylabel = \"BPZ Photo-z Error\"\n",
+ "points = hv.Points((xvals, yvals))\n",
+ "boundsxy = (0, 0, 0, 0)\n",
+ "box = hv.streams.BoundsXY(source=points, bounds=boundsxy)\n",
+ "bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])\n",
+ "p = dynspread(datashade(points, cmap=\"Viridis\"))\n",
+ "p = p.opts(width=600, height=300, padding=0.05, show_grid=True,\n",
+ " xlim=(0, 1), ylim=(0, 1.5), xlabel=xlabel, ylabel=ylabel, tools=['box_select'])\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e49bddb-dc4a-4e46-9b0b-64b05125aaf5",
+ "metadata": {},
+ "source": [
+ ">**Figure 8:** Similar to Fig 7, but with the BPZ photo-z error, also showing how larger photometry errors yield larger photo-z errors."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee07e40b-82ee-4ffa-bbb5-e844a552acb7",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Photometric redshift estimates are based on the object colors, and in some regions of color-space the relationship is less well-constrained. Explore this with a plot of the $g$-$i$ color vs. the BPZ photo-z error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61fab207-8837-4bd0-970b-518fb77d908a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "band1 = \"g\"\n",
+ "band2 = \"i\"\n",
+ "mags1 = -2.5 * np.log10(result[band1 + \"_gaap1p0Flux\"]) + 31.4\n",
+ "mags2 = -2.5 * np.log10(result[band2 + \"_gaap1p0Flux\"]) + 31.4\n",
+ "xvals = mags1 - mags2\n",
+ "yvals = result[\"bpz_z_err68\"]\n",
+ "xlabel = band1 + \"-\" + band2 + \" Gaap color (forced)\"\n",
+ "ylabel = \"BPZ Photo-z Error\"\n",
+ "points = hv.Points((xvals, yvals))\n",
+ "boundsxy = (0, 0, 0, 0)\n",
+ "box = hv.streams.BoundsXY(source=points, bounds=boundsxy)\n",
+ "bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])\n",
+ "p = dynspread(datashade(points, cmap=\"Viridis\"))\n",
+ "p = p.opts(width=600, height=300, padding=0.05, show_grid=True,\n",
+ " xlim=(-4, 5), ylim=(0, 1.5), xlabel=xlabel, ylabel=ylabel, tools=['box_select'])\n",
+ "p * bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cd944a3b-0d3d-4f75-a497-291b6405bb73",
+ "metadata": {},
+ "source": [
+ ">**Figure 9:** The $g$-$i$ Gaap color vs. the BPZ photo-z error. This plot shows the relationship beween galaxy color and photo-z error."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54de3df3-26b9-4f9b-a034-01dfacc5f12e",
+ "metadata": {},
+ "source": [
+ "\\\n",
+ "\\\n",
+ "Delete what won't been needed in Section 4."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12a4832f-3367-4cdd-bb0e-5f6b16a5ecda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del p, bounds\n",
+ "del result\n",
+ "gc.collect()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35fcf6f5-8087-4427-b7e5-dfdf23c1721d",
+ "metadata": {},
+ "source": [
+ "## 4. Explore photo-z spatial variation\n",
+ "\n",
+ "As demonstrated above, the photometric quaily -- the brightness of the object, the depth of the deep coadd images, and the number of filters in which forced fluxes are measured -- affect the photo-z quality.\n",
+ "\n",
+ "For Data Preview 2, the number of filters and image depth is not uniform across the surved area (see the maps on, e.g., the [observatons page](https://dp2.lsst.io/overview/observations.html)).\n",
+ "\n",
+ "This section creates sky maps of the photo-z error and the photometric depth, and compares them.\n",
+ "\n",
+ "### 4.1. Load photo-z in a larger region\n",
+ "\n",
+ "Similar to Section 2.1., but with a larger radius so that the maps cover a larger region of sky."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "defc0aa8-d6af-4c3a-b14b-928a8114691f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ra_cen = 310.0\n",
+ "dec_cen = -20.0\n",
+ "radius = 20.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3e0f733-547a-4aa9-9fb1-bd0ee1c032af",
+ "metadata": {},
+ "source": [
+ "For this section, only use the FlexZBoost redshifts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3eafe070-ab65-4195-9e87-7fdbc70d33b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pz_cat = lsdb.open_catalog(\n",
+ " \"/rubin/lsdb_data/dp2/object_photoz\",\n",
+ " search_filter=lsdb.ConeSearch(ra=ra_cen, dec=dec_cen,\n",
+ " radius_arcsec=radius * 3600),\n",
+ " columns=[\"objectId\", \"ra\", \"dec\", \"fzboost_z_best\",\n",
+ " \"fzboost_z_err68_low\", \"fzboost_z_err68_high\"],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b428143-4b24-4fe0-83e2-172c30265a6a",
+ "metadata": {},
+ "source": [
+ "As before, limit to redshifts 0.01 - 3.0 and calculate the _approximate_ error, then compute the full table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da927bb4-54c0-450a-a23f-936a92ae4c42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "z1 = \"0.01\"\n",
+ "z2 = \"3.0\"\n",
+ "query = \"\"\"fzboost_z_best > {} and fzboost_z_best < {}\n",
+ " \"\"\".format(z1, z2)\n",
+ "pz_cat_zlim = pz_cat.query(query)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a5a7e453-2170-4481-84f0-e517bc703517",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pz_err68_v2(df, pixel):\n",
+ " df[\"fzboost_z_err68\"] = 0.5 * (df[\"fzboost_z_err68_high\"] - df[\"fzboost_z_err68_low\"])\n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3025a72-6e7a-49bd-895f-7a56dc38f234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "unrealized = pz_cat_zlim.map_partitions(pz_err68_v2, include_pixel=True)\n",
+ "unrealized"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "301bf1a2-8e2f-4044-9293-3da846df5228",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = unrealized.compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4408d4d0-7e49-475a-932f-f4972114518c",
+ "metadata": {},
+ "source": [
+ "### 4.2. Plot the sky map of photo-z error\n",
+ "\n",
+ "Set up a HealSparse map with relatively low resolution that covers the sky region over which photo-z were returned."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "70cf50cd-b3a1-4f8c-a1fc-231c0ca58ecf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nside_coverage = 8\n",
+ "nside_sparse = 64\n",
+ "hspmap = hsp.HealSparseMap.make_empty(nside_coverage=nside_coverage,\n",
+ " nside_sparse=nside_sparse,\n",
+ " dtype=np.float32, sentinel=hpg.UNSEEN)\n",
+ "pixels = hpg.angle_to_pixel(nside_sparse, np.array(result.ra.values),\n",
+ " np.array(result.dec.values), lonlat=True)\n",
+ "unique_pixels = np.unique(pixels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6d1711b-36bc-444f-956a-e6e18225d679",
+ "metadata": {},
+ "source": [
+ "Calculate two values for every HealPix: the average photo-z and the average photo-z error.\n",
+ "Because this processing takes a few minutes, progress is written out."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76a5aed5-fde7-4851-a5b0-5572c09de600",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "values_meanz = np.array(result.fzboost_z_best.values)\n",
+ "values_zerr = np.array(result.fzboost_z_err68.values)\n",
+ "unique_values_meanz = np.zeros(len(unique_pixels), dtype='float32')\n",
+ "unique_values_zerr = np.zeros(len(unique_pixels), dtype='float32')\n",
+ "for i in tqdm(range(len(unique_pixels))):\n",
+ " tx = np.where(pixels == unique_pixels[i])[0]\n",
+ " unique_values_meanz[i] = np.mean(values_meanz[tx])\n",
+ " unique_values_zerr[i] = np.mean(values_zerr[tx])\n",
+ " del tx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79375521-aae3-4dad-b8f5-48f72e1e726f",
+ "metadata": {},
+ "source": [
+ "Plot the sky map of the average photo-z and the average photo-z error."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "764bf190-926e-47db-bcd7-4b70c4ca6aa3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
+ "hspmap.update_values_pix(unique_pixels, unique_values_meanz)\n",
+ "sp1 = skyproj.McBrydeSkyproj(ax=axs[0])\n",
+ "sp1.draw_hspmap(hspmap)\n",
+ "sp1.draw_colorbar(label=\"mean redshift\", shrink=0.6, pad=0.01)\n",
+ "sp1.ax.set_xlabel('RA', fontsize=14)\n",
+ "sp1.ax.set_ylabel('Dec', fontsize=14)\n",
+ "hspmap.update_values_pix(unique_pixels, unique_values_zerr)\n",
+ "sp2 = skyproj.McBrydeSkyproj(ax=axs[1])\n",
+ "sp2.draw_hspmap(hspmap)\n",
+ "sp2.draw_colorbar(label=\"mean error\", shrink=0.6, pad=0.01)\n",
+ "sp2.ax.set_xlabel('RA', fontsize=14)\n",
+ "sp2.ax.set_ylabel(\" \")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53c26171-984e-4d52-9cc9-224bc5542339",
+ "metadata": {},
+ "source": [
+ ">**Figure 10:** The average FlexZBoost redshift (left) and its error (right) over the region, showing the spatial variation of photo-z quality across the sky. Darker (bluer) regions have lower photo-z (left) or lower photo-z error (right). Notice that photo-z and its error are correlated: photo-z estimates are less uncertain for brighter, lower-redshift galaxies. Notice also some features in the left- and right-hand plots do not correlate, or even anti-correlate. In general, the point is that the spatial variation in the photo-z quality must be taken into consideration for scientific analyses using the photo-z estimates.\n",
+ "\n",
+ "\\\n",
+ "Restart client workers and delete what is not needed below.\n",
+ "\n",
+ "> **Warning**: the cell below might produce a pink UserWarning that is OK to ignore."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "273dda03-2846-4dc2-a425-53f5fd42ca15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client.restart()\n",
+ "del hspmap, pixels, unique_pixels\n",
+ "del values_zerr, values_meanz, unique_values_zerr, unique_values_meanz\n",
+ "del result\n",
+ "gc.collect()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72a8b4aa-2e3b-45e7-b081-ebb56ccafcaf",
+ "metadata": {},
+ "source": [
+ "### 4.3. Compare with the sky map of image depth \n",
+ "\n",
+ "The expectation is that in regions of the LSST deep coadd images that are deeper (i.e., have a higher magnitude limit) and covered by more filters, the photo-z errors will be lower.\n",
+ "\n",
+ "The deep coadd images, in which the object forced fluxes are measured, are divided into tracts and patches (the LSST \"Skymap\").\n",
+ "Use the TAP service to retrieve all patches within the sky region defined above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e4dd67cc-afbb-4ea0-974f-b16dbbf47f77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = \"\"\"SELECT lsst_patch, lsst_tract, s_dec, s_ra, s_region\n",
+ " FROM dp2.CoaddPatches\n",
+ " WHERE CONTAINS(POINT('ICRS', s_ra, s_dec), CIRCLE('ICRS', {}, {}, {})) = 1\n",
+ " \"\"\".format(ra_cen, dec_cen, radius)\n",
+ "job = tap_service.submit_job(query)\n",
+ "job.run()\n",
+ "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+ "assert job.phase == 'COMPLETED'\n",
+ "patches_table = job.fetch_result().to_table()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cdfb65d-2aab-4547-8222-3df99a21a59d",
+ "metadata": {},
+ "source": [
+ "Set up HealSparse map pixels using the same resolution as used above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c5afec7e-bcce-46a2-93ec-0b14cb6d9d24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pixels = np.unique(np.array(hpg.angle_to_pixel(32,\n",
+ " patches_table['s_ra'],\n",
+ " patches_table['s_dec'])))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "942a672c-1c5d-4b22-a44c-9b7ed5a40313",
+ "metadata": {},
+ "source": [
+ "Define the survey property map name to be retrieved from the Butler.\n",
+ "This one is the PSF $5\\sigma$ limiting magnitude."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abc702d3-f65e-493c-b822-c1dedcbefd9b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "map_name = \"deepCoadd_psf_maglim_consolidated_map_weighted_mean\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd51943c-e1c2-4024-9537-f61c0456811a",
+ "metadata": {},
+ "source": [
+ "For each of filters $u$, $i$, and $y$, load the depth map, create a HealSparse map from it, and display them side-by-side.\n",
+ "This plot is limited to three filters (intead of all six) because the maps are large and take time to load."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f8a47adc-33a6-4b06-8996-2fc50d81cc7f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+ "for f, filt in enumerate([\"u\", \"i\", \"y\"]):\n",
+ " print(f, filt, 'loading', datetime.now())\n",
+ " map_band = filt\n",
+ " hspmap_cov = butler.get('deepCoadd_psf_maglim_consolidated_map_weighted_mean.coverage',\n",
+ " band=map_band, skymap='lsst_cells_v2')\n",
+ " cov_pixels, = np.where(hspmap_cov.coverage_mask)\n",
+ " temp = []\n",
+ " for p, pix in enumerate(pixels):\n",
+ " tx = np.where(cov_pixels == pix)[0]\n",
+ " if len(tx) == 0:\n",
+ " temp.append(p)\n",
+ " subset_pixels = np.delete(pixels, temp, axis=0)\n",
+ " hspmap_maglim = butler.get(map_name, band=map_band, skymap='lsst_cells_v2',\n",
+ " parameters={'pixels': list(subset_pixels), 'degrade_nside': 64})\n",
+ " sp = skyproj.McBrydeSkyproj(ax=axs[f])\n",
+ " sp.draw_hspmap(hspmap_maglim, cmap=\"viridis_r\")\n",
+ " sp.draw_colorbar(label=map_band + '-band depth', shrink=0.6, pad=0.05)\n",
+ " sp.ax.set_xlabel(\"RA\", fontsize=14)\n",
+ " sp.ax.set_ylabel(\"Dec\", fontsize=14)\n",
+ " sp.ax.tick_params(labeltop=False)\n",
+ " if f > 0:\n",
+ " sp.ax.set_ylabel(\" \")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e50d8286-bca4-4868-b251-f7a66ea7a203",
+ "metadata": {},
+ "source": [
+ ">**Figure 11:** The depth in the $u$, $i$, and $y$-bands (left to right). Comparing with Fig 10, notice that regions without overlap in the $u$- and $y$-bands (at RA$\\sim320$, Dec$\\sim-20$ deg) have larger photo-z errors. Note that the above depth maps are just the $5\\sigma$ point-source limits, and do not incorporate factors such as Galactic extinction and reddening, field stellar crowdedness and deblending, or the intrinsic distribution of low- or high-redshift galaxies along this line-of-sight."
+ ]
+ },
+ {
+ "attachments": {
+ "3b1b0ccd-2911-43f8-a2e6-132b4c5e64ff.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "3aa9ffcd-bcbd-4e72-a5cc-2f47b646b1b8",
+ "metadata": {},
+ "source": [
+ "For convenience, a small version of Fig 10, to more easily compare with Fig 11.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5bd0a9e5-43de-4073-ac85-91b66a836641",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del hspmap_maglim, hspmap_cov, cov_pixels, pixels\n",
+ "gc.collect()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15acaa80-b840-47d3-bed5-3c742b00cec2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client.close()\n",
+ "del client\n",
+ "gc.collect()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "LSST",
+ "language": "python",
+ "name": "lsst"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}