{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XID+ Example Output Analysis\n", "\n", "(This is based on a Jupyter notebook, available in the [XID+ package](https://github.com/H-E-L-P/XID_plus/tree/master/docs/notebooks/examples/) and can be interactively run and edited)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook provides some example code for basic analysis of the XID+ outputs, including:\n", "\n", "1. Loading up output\n", "2. Creating Posterior replicated maps and animations\n", "3. Creating marginalised posterior plots\n", "4. Creating Bayesian p-value maps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", " data = yaml.load(f.read()) or {}\n", "WARNING: AstropyDeprecationWarning: block_reduce was moved to the astropy.nddata.blocks module. Please update your import statement. [astropy.nddata.utils]\n" ] } ], "source": [ "import pylab as plt\n", "%matplotlib inline\n", "\n", "\n", "import numpy as np\n", "import xidplus\n", "from xidplus import moc_routines\n", "output_folder='./'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load up posterior output from XID+" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "priors,posterior=xidplus.load('test.pkl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to compare how good our fit is, its often useful to look at original map. There is a routine within XID+ that makes the original fits map from the data stored within the prior class. Lets use that to make the SPIRE maps for the region we have fit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets use the [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) plotting package and [APLpy](http://aplpy.readthedocs.io/en/stable/) package to view those maps, plotting the sources we have fit on top of those maps." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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qox/9qDZt2qRf/vKXkqRt27Zp8+bN2rZt24w/DgAAAAAAgNmqZSvFvvrVr2p0dFQPPPCAHnjgAUnSbbfdJt/3tWnTJt1zzz268847NTExoY0bN2rJkiX6whe+oCiK9KUvfUnS5Iqyv/mbv2lVimhTcRAqGg/klDzZnpt3OpklUaQk8GV5RVkOs3MAYL576KGH1NXVpS9/+csaHh7WLbfcove+972SJgtm9913n771rW+pWCzqIx/5iK677jr90z/9k6666irdcccdev755/XZz35WW7du1RNPPKEHH3xQX/ziF2lBDQAAAAAAcFTLimL33nuv7r333pP+/Prrr9f1119/3H0UwPBWDr+0X7/74aDqSSrDMvX2P16rM5b35p3WaRsd2q+Xdj6miXqqgmFqxZqr1bG0J++0AAA5uummm3TjjTc2bpum2fj/PXv2aMWKFers7JQkrV69Wo8//rjuuOMOOUcnVqRpKtednCxy+eWXa/Pmzdq8efMMPgIAAAAAAIDZraV7igHNFAehfvfDQVmuI7vTnbz9g0Fd8ZENbbViLIkivbTzMZmOLcupKIlCvfTzR/X2mz7IijEAmMeO7a9WrVb1qU99Sp/+9KcbP6tWq439V4/9brVaVUdHhyTpwIED2rJliz7/+c9Lkvr6+k65Qoz9WAEAaD/sxQoAADB9LdtTDGi2aDxQPUkbBTDbc1VPUkXjQc6ZnZ4k8DVRT2U5k4/DclxN1FMlgZ9zZgCAvA0NDeljH/uYPvShD2nDhg2N+yuVimq1WuN2rVZrFMl27dqlO+64Q5/5zGe0Zs2aKY3DfqwAALQf9mIFAACYPopiaBtOyZNhmYqDUNLkyjHDMuWUvJwzOz2WV1TBMJVEk48jiUIVDFOWV8w5MwBAng4ePKhPfOIT2rJli2699dbjfnbuuefqxRdf1MjIiKIo0uOPP67LLrtMu3fv1t133637779f11xzTU6ZAwAAAAAAtAfaJ6Jt2J6rt//xWv3uB4OKan5jT7F2ap0oSZbjaMWaq/XSzx9VEgSNPcVonQgA89tXv/pVjY6O6oEHHtADDzwgSbrtttvk+742bdqke+65R3feeacmJia0ceNGLVmyRF/4whcURZG+9KUvSZpcUcYerfNTmkRKokCW48m02v+aIk1jJXEoy3Zlmnbe6QAAZqE4jvX5z39e+/btUxRF+vM//3O9973vbfz8kUce0Ve+8hVZlqWNGzfqT//0TzU2NqYtW7aoWq0qjmPdc889uuyyy7Rt2zY9+OCD2rx58ylbUAMAgPY3J4tiX/7gp9/6l07g/9ye/UukCxZlX+XzcrWaOXbn7uytAzsWFDLH2lb22In6RKa4+oTUdVavVn9kg+JaILvsyXZdTeVwpYVmpjElyV/SlTl2fM+Jz62lBTr7bdf+/surmvOm362d25l5XMvM9jeWpHd0Z38uv1oLM8c+dyB77HCQZI51rewLZn935GDm2Ol4bP/hzLFLF2T/YnGBkz322SPDmeIWedlXgh6spZlj37mknDl295GxzLGLK9n/Wd5/JM4ci9nh3nvv1b333nvSn19//fW6/vrrj7uPAhgkqTr8moae+4Xq9VSGYarn/NWqdC/JO63MamMH9eorT2lioq5CwdDSsy5RecGivNMCAMwyDz30kLq6uvTlL39Zw8PDuuWWWxpFsTiOdd999+lb3/qWisWiPvKRj+i6667TP/3TP+mqq67SHXfcoeeff16f/exntXXrVj3xxBN68MEH9cUvfpGiGAAAc9ycLIphbrNdV7bbXqvDTsS0HZl2+8/kBgAA+UmTSEPP/UKGZcu2yo3bqy7/47ZcMZamsV595SkZpi3TtBu3V75tfd6pAQBmmZtuukk33nhj47Zp/n4y7J49e7RixQp1dk5ONl29erUef/xx3XHHHXKOdmlJ01Tu0e8WLr/8cm3evFmbN2+ewUcAAADyQFEMAABgnnv+SK2px/OmsQr3VB7ZM9L0Y9pO9tXvp1Ja1fy9Qg98a9eb7ouiccXDI3LskhJNnsc4Hlf1t8/KcUpTOm40eqipeUpS13uuzhRXr41Kr9hyypNfYpqSgtoRFRaXlQbZV8KfyuEj9aYfc2Fna14DQ4eyr4I+mWKxNa+B8Tj7Sv6TGQ6b+14lSUu8qb1OTtcLY6NNP+aL1ezdAk7FMpr/fL14aWv+roO7m/8c8LzmvwY+dsWdTT/mx/Ufmn7MdlcuT3Z3qFar+tSnPqVPf/r3XYOq1aoWLFhw3O9Wq1V1dHRIkg4cOKAtW7bo85//vCSpr6/vlCvESqXWPKcBAMDMoygGAAAAtCnTdFQoGErTRKZpKU0TFQqGTLP9VolJkuV4MgxTaRzJtB2lcSTDMGU52dvpAgDmrqGhId111126/fbbtWHDhsb9lUpFtdrvi6i1Wq1RJNu1a5f+4i/+Qp/73Oe0Zs2aKY0zPj7e+P9CoaCyk32LhulIWjDhYKq8Fc2f8DMV8ZH8HvPIU4/nNnb3ZVfmNvb4b7Jv1TJdZ5ydTwvwQ2PNn3g0VXY+byeSpLU9i3Mb+4XqkdzGjuvNnxQ3VaN+PmM/80zzJxZN1UN/+Ze5jFvYsqUxgWZiYuK4f8tbM4URmMfSOFI4Pqo0jvJOBQAAzHGmaWnxovNVn0gUxeOqTyRavOh8mWZ7zn0zbUe9F65RmkQKakeUJpF6L1xDy2kAwJscPHhQn/jEJ7Rlyxbdeuutx/3s3HPP1YsvvqiRkRFFUaTHH39cl112mXbv3q27775b999/v6655pqcMgcAADOhVqupVqsdVxCTWCkGNFV1+DUN7dqper0uwzDU8/YrVDmjfTe6BwAAs1+p1K3lyy5XmkYyTadtC2LHVM5YonPX3KgkCmQ5HgUxAMAJffWrX9Xo6KgeeOABPfDAA5Kk2267Tb7va9OmTbrnnnt05513amJiQhs3btSSJUv0hS98QVEU6Utf+pKkyRVlf/M3f5Pnw8AskMaR4sCX7RXb/rojTSIlcSjLdttyf1kAmAnt/YkZmEXSONLQrp0yLEf20XY/Q7t2atUf3dD2F1UAAGB2M02r7Ythb2TaDtdPAIBTuvfee3Xvvfee9OfXX3+9rr/++uPuowCGPzR2YEivPPmYJup1FQxDZ116tRacuTTvtDKpHnldr+59UvV6KsMwtXTlpap05tceDwBmK9onAk2SxIHq9XrjCxzTdlSv15VE+fViBgAAAAAAwJulcaRXnnxMlu3KW9Apy3b1ypOPtuV2GGkS6dW9T8owbbnFDhmmrVf3Pqk0ab/HAgCtRlEMaBLL9mQYRuPiaXJjeION4QEAAAAAAGaZOPA1Ua/LdI5ObnYcTdTrigM/58xOXxKHqtfTRstE03JUr6dK4jDnzABg9qEoBkxRGkcKx0dPOmPItB31XHCF6kmksDaqehKp54IraP0DAACgo9dStZNfSwEAAMwk2yuqYBhKo6OTm6NIBcOQ7RVzzuz0WbYrwzAbK8PSJJJhmLJsN+fMAGD2mTMbD/T392vr1q15p4E5qjr8moZ27VS9XpdhGOq54ApVupe86fcq3Uu0avUNSuJAlu3JdCmIAcCplMtl9fX1aWBgIO9UALRQ9fCr2v/MzsYeF70XrlHljDdfSwEAAMwU03Z01qVX65UnH1Uc+o09xdpxcrNpOVq68lK9uvdJJXHQ2FPs2MoxAMDvzZmi2LEv0wqFQs6ZYK5J40hDu3bKsBzZttO4vWr1DSe8UGJjeACYulqtlncKAFosjSPtf2anTMuRc/Raav8zP9e5a27kmgkAAORqwZlL9bZrP6A48GV7xba+Nql0LtY5F12rJA5l2S4FMQA4iTlTFANaJYkD1et12UcvjEzbURIFSuKgrS+WAAAAZkISBarXUzlvuJaKI19JxLUUAADI31ya3GxaDsUwAHgL7CkGvAXL9mQYRmP/izSOZBiGLNvLOTMAAIDZz3K8yT0ujruWMmU5XEsBAAAAAGYWRTHgLZi2o54LrlA9mdwcvp5E6rngijkziwgAAGA60nqiOAmU1pMT/ty0HfVeuEZpEimoHVGaROq9cA3XUgAAAACAGUf7xDfYNxpljn3HmZXMsYeDMHPspee4mWP3Hcn+eMdqE5ljV600M8XVwuxjjrxezxxrLymqe8lKdazqVRz6st2iTGdqX+KUl2Z/iR0cyp7z1auyPx9tI3utfGVHKXPs/pHR7LHDaeZYZxrvguNR9nO0uGJnjl3Wkf1LxOWV7M+Npw6OZI51rGz7PXpWnHnMS5cuyBx7JMz+/riomP38DNWCzLH/o+8zmWMxP/X392vr1q15p4E25wejOjj6siYm6ioUDC3qWK6i1/Gm36ucsUTnrrlRSRTIcjwKYgCQQblcVl9fX2NPdQAAAJw+imLAFJmOM+ViGAAAs92xL9QKhWxFayCtJzo4+rKMginTdBu3e50LTvj7c2m/DgDIQ61WyzsFADlJk0hJHMqyXfYMA4BpoigGAAAA4LTV64kmJuoyzcnOBaZhKU1i1U/SRhEAAACnr3rkdb2690nV66kMw9TSlZeq0rk477QAoG2xpxgAAACA02YYlgoFo7GXWFpPVCgYMgzm3QEAADRDmkR6de+TMkxbbrFDhmnr1b1PKk2yt/wHgPmOotgURUGosYMjiqax/xeA1omDULXDI4p5jQIAMCNMw9KijuWqT6SKEl/1iVSLOpbLpCgGAADQFEkcql5PGy0TTctRvZ4qifnuAwCy4hPrFLz+wj798t92KE1SmZapyz6wXovP6c07LQBHHXpxn377vUHV01SGaeodN67TwhW8RgEAaLWi16Fe5wLV64kMw6IgBgAA0ESW7cowTKVJJNNylCaRDMOUZbt5pwYAbYuVYm8hCkL98t92yPYcdZzZJdtz9Mt/286KMWCWiINQv/3eoGzPUWXh5Gv0t9/dwYoxAABmiGlYsi2PghgAYE7r7+9XqVTKOw3MMfV6qjiZXA12IqblaOnKS1VPY4X+qOpprKUrL22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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figs,fig=xidplus.plot_map(priors)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Posterior replicated data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use each sample we have from the posterior, and use it to make a replicated map, including simulating the instrumental noise, and the estimated confusion noise. You can think of these maps as all the possible maps that are allowed by the data. \n", "\n", "> NOTE: You will require the `FFmpeg` library installed to run the movie" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xidplus.replicated_map_movie(priors,posterior,50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Joint Posterior Analysis of sources" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Select source you want to plot joint distribution\n", "s1=2\n", "s2=18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sources 2 and 18 are close together, lets look at their joint posterior probabiity distribution. The code below is an example of how you can plot the joint posterior probability density function for the $250 \\mathrm{\\mu m}$ flux, and an inset of the real map." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Auto-setting vmin to -1.522e+01 [aplpy.core]\n", "INFO: Auto-setting vmax to 3.332e+01 [aplpy.core]\n" ] }, { "data": { "image/png": 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2dYGbs53S4dyQIAjoGeqJkop6FJVxEyEiIiKyHkzKCQB0XU3MrRXiH7GEhYiIyMrZYI9y\ngEk5XZGcbp79yf+Iiz2JiIisF7uvkM0z90WebbqHeEAlcKaciIiIrAsXehIAIC2zFC6OWgT5uCgd\nyi052mvQ1d8N5y6Vobm5BWoz7KdOREREBuJCT7Jl5VX1yCuuRo9QT6gs4A92z1BPNDQ2IyOvQulQ\niIiIyJik1pNbUV05k3LSlYJEXanXNneRIa1xnuPOnkRERGQlmJQT0jJbk/KeZtqf/I8igtwBABdz\nyxWOhIiIiMg4WFNOuqS8R1cPhSPRT2iAK9QqARdzmJQTERFZFRuuKWdSbuOaW0SkZbVuGuTiZJ6b\nBv2RVqNGiJ8rMvIq0NwiQm0lv4xERES2zpAWh2yJSFYhu6AStfVNuk15LEV4kDvqG5qRW1ildChE\nRERkNIYs8rSOpNxkM+XNzc1YvHgx0tPTIQgCli5dCnt7eyxYsACCICAyMhLx8fFQqfg+wZTaSlei\nQi0vKd97/BIu5pQjxM9V6XCIiIiIOsVkGfBPP/0EANi6dSvi4uKwZs0aJCQkIC4uDps3b4Yoitiz\nZ4+pwqErLG2RZ5vwtsWerCsnIiKyHm015VIPK2CypHzs2LFYtmwZACA3Nxdubm5ISkrC0KFDAQAj\nR47EoUOHTBUOXZGWWQJ7OzVC/S1rtjk8kEk5ERGR1RFgQJ9ypYM2DpPWimg0GsyfPx/Lli3D5MmT\nIYqirjjf2dkZlZWVpgzH5tXUNSLrciUiQzwsbmdMZ0ct/LyccCGnHKIoKh0OERERUaeYPBNbsWIF\nvv/+e7zyyiuor6/Xna+uroabm5upw7Fp57LKIIpATwtb5NkmPMgdlTUNKC6vUzoUIiIiok4xWVL+\n5Zdf4r333gMAODo6QhAE9OnTB0ePHgUA7N+/HzExMaYKhwCkZpUAaN223hJFsK6ciIjIqrS1RJR6\nWAOTdV+5++67sXDhQsyaNQtNTU1YtGgRIiIi8Morr+DNN99EeHg4xo8fb6pwCJa7yLNN22LPCznl\nGNrbX+FoiIiIqNO4eZD8nJycsHbt2uvOf/rpp6YKga4hiiLOZpXC19MRXm4OSodjkKsdWMoUjoSI\niIiocyxrdR8ZzeWSGpRXNVjsLDkAeLk5wN3FDhdzK5QOhYiIiIxBcueVtg2ELB+TchuVeqV0xdJ2\n8ryWIAgID3RHQUkNqmoalA6HiIiIOkswoEc5k3KyZGmZrYs8LW0nzz/qdqVfeWY+22kSERFZPEMW\neTIpJ0uWllkKjVrQ1WVbqrDA1jaaGbnswEJERESWy2QLPcl8NDQ2Iz23HOFB7rDTqpUOp1PCAlqT\n8vQ81pUT6SvIx3i/96b4K+RcVrOs4zvb1cg6PgDZW7Y52ss6PMprWuS9AYC6Rnn3nNCo5Z9NlbsJ\nSKiXnazjXyqTvxT0vu7B7b5fJ/sdLQeTcht0MaccTc2iRS/ybBPs6wq1SkAGk3IiIiLLJ0NLxMbG\nRixatAg5OTloaGjAU089he7du2PBggUQBAGRkZGIj4+HSqVsAYmkpLyyshJZWVlQqVQIDg6Gq6ur\nXHGRjNoWeVrqTp7X0mpUCPFzRWZeBVpaRKispFcpERGRTTKkRryD5+/cuRMeHh5YtWoVysrKMHXq\nVERFRSEuLg6xsbFYsmQJ9uzZg3HjxnUi8M7TKynft28fPvzwQ5w/fx7+/v7QaDTIy8tDREQE5syZ\ng1GjRskdJxlR2yJPS93J84/CAtyQkVeByyU1CPB2VjocIiIiMiMTJkzQbVApiiLUajWSkpIwdOhQ\nAMDIkSNx8OBB80/KFyxYAG9vbyxZsgSRkZHtHjt37hy2b9+OXbt2YfXq1bIFScaVllUKDxd7+Hk5\nKR2KUejqynPLmZQTERFZMuHKIfWaW3B2bs0Nqqqq8MwzzyAuLg4rVqzQrfVwdnZGZaXyXdw6LJ55\n9tln8cILL6C2tva6xyIjI7Fw4UK88MILsgRHxldcXovC0lr06Oop+8IjU9F1YGFdORERkUUTBEBQ\nCdIOPdKZvLw8PPLII5gyZQomT57crn68uroabm5uMr4q/XSYlPv5+QEAVq9ejcmTJ+PDDz9EYWFh\nu+f4+/vLEx0ZXVs9eVSYdZSuAFdnypmUExER0R8VFRVhzpw5ePHFFzF9+nQAQHR0NI4ePQoA2L9/\nP2JiYpQMEYCEhZ4bN25ETk4OvvrqK8ydOxcBAQGYNm0a7rrrLmi1WjljJCNKzbiyaVCY5XdeaePl\n5gBXJztk5DIpJyIiovbeffddVFRUYP369Vi/fj0A4OWXX8by5cvx5ptvIjw8XFdzriRJ3VeCgoIw\ndepUaDQabN26FRs3bsSaNWvwwgsvKF4cT/pJzSiBSiUgMsRD6VCMRhAEdAt0w5nzRaitb4KjPTt9\nEhERWSQZuq8sXrwYixcvvu78p59+Ku0+MtM7e9m2bRu++uorFBYWYurUqdi8eTP8/f1x+fJlTJs2\njUm5BWhsasb57HKEB7rBwc66EtewgNakPDO/AlFW0H+diIjIJsnQp9xS6J2ZHTt2DPPmzUNsbGy7\n835+foiPjzd6YGR8F7LL0dTcYlWlK210deW5TMqJiCxZZWEpcpIuorayGo6uzgjqHQ5XH+tZB0Ud\nkKH7iqXQOylfuXLlTR8zhzoc6lhKWz25FSat7MBCRGT5KgtLkfTjrwBEAEB9VQ3K8orQe+xQJuZk\n9TpMyhcuXHj9RRoNQkJCMHPmTO7qaUFSr2wa1MsKZ8q7+rtBJbT2KiciIsuUk3QRbQn5VSJyki4i\n6s7BSoREJiYIguSWzdbS4rnDpLxtt6NriaKItLQ0xMXF4X//939lCYyMSxRFpGaUwMvNHj6ejkqH\nY3T2WjUCvF2QmVcBURSt5heUiMiW1FZWSzpPVog15Tc3bdq0mz42adIkowZD8iksrUVJRT1u6xdg\ntQlrt0A3HDhdhcLSWvhayW6lRES2xNHVGfVVNTc8T2TtOtw86EYKCgqwefNm3balZP6suZ68DevK\niYgsW1DvcFy/ak+4cp7IuhmUlGdlZSExMRGrVq0ydjwkk7ZNg6yxnrxNt0B3AEB6HuvKiYgskauP\nJ3qPHQqPAB/YuzjBI8CHizxtkSDxsBJ6d18pKSnBN998g/Ly1oQnMDAQu3btwtNPPy1bcGQ8KZkl\n0KhViAh2VzoU2bS1RUznzp5ERBbL1ceTizptmKASIEisEZf6fHOl90z5Y489huTkZDljIZnU1DUi\nPacckSEe0GrUSocjGx8PRzg7apHBpJyIiMgySZ0lt6LZcknbOiYkJMgVB8nobFYpWkQgupv1lq4A\nrS2RwgLckJJejLqGJqvbtZSIiIisl94z5WPHjsW2bdtw6dIl5Obm6g4yfynprfXk0d26KByJ/LoF\nuKFFBLLyK5UOhYiIiKRqa4ko9bACek8lVlZW4v3334en59XFFoIgYM+ePbIERsaTfCUpj7LiRZ5t\nwq4s9szIq0CPrlwYREREZEkEofWQeo010Dsp3717Nw4fPgwHBwc54yEja25uQVpWCUL8XODmbKd0\nOLLrxraIREREZIH0Ll8JCQnRdV4hy5GeV4Ha+mb0CrP+0hUA6OrnCkEA0nP5Z5WIiIgsh94z5YIg\nYNKkSYiMjIRWq9Wd37hxoyyBkXFcrSe3/tIVAHCw1yDQ2xkZuRUQRdFqdy8lIiKySjZcv9JhUl5f\nXw97e3s8+eSTHT6HzE9yejEA21jk2SYswB0Hz+SiqKwOPp6OSodDZHZyCpuNNtaf+vsYbaybKa8t\nknX86gZR1vEBwMXeoL369PbbL3Wyji+K8v+MPEK1HT+pE7x95P1/AABBnvJ2/XLQyPsa+vnLv1P7\ngby8Wz9BgPStLc0kJ6+srERWVhZUKhWCg4Ph6uoq6foO//S88MILGDFiBCZOnAgXF5d2j1VVVWHT\npk04dOgQ3nnnHWmRk+xEUURyegk8XO3h38VJ6XBMplugGw6eyUVGXjmTciIiIpLVvn378OGHH+L8\n+fPw9/eHRqNBXl4eIiIiMGfOHIwaNUqvcTpMyteuXYstW7Zg+vTpcHNzg7+/P9RqNXJzc1FaWopH\nHnkEa9euveUYjY2NWLRoEXJyctDQ0ICnnnoKAQEBeOKJJxAWFgYAmDlzJiZOnKhX0KSfgtJalFTU\n4bZ+ATZVxtG2s+fF3HIMifZXOBoiIiLSV2v1isQdPRVMcRYsWABvb28sWbIEkZGR7R47d+4ctm/f\njl27dmH16tUdjtVhUq5SqTBr1izMmjULqampyMjIgEqlQteuXREVFaVXwDt37oSHhwdWrVqFsrIy\nTJ06FX//+9/x6KOPYs6cOXqNQdKlXCldsZVFnm3CgzwAABdzuNiTiIjIoqggvXxF/sqkm3r22Wfh\n5+eHM2fOXPdYZGQkFi5ciPz8fL3GklT8FBUVpXcifq0JEyZg/PjxAFpLKtRqNRITE5Geno49e/Yg\nNDQUixYtuq48hjonycYWebbx9nCAm7Mdk3IiIiILIwiCATPlyk2V+/n5AQBWr16N0tJSTJkyBVOm\nTIGPz9X1Nv7++n1qb5L3Fs7OznBxcUFVVRWeeeYZxMXFoV+/fnjppZewadMmhISEsCZdBkkXi+Fg\np0ZEkLvSoZiUIAiICHJHfnENqmoalA6HiIiIrNzGjRvx7rvvoqGhAXPnzsUTTzyB7777Do2NjXqP\nYbIJ/7y8PDzyyCOYMmUKJk+ejHHjxqFPnz4AgHHjxiE5OdlUodiEiuoGXLpciZ6hnlCrFfxcRyHh\nV96IXGS/ciIiIjKBoKAgTJ06Fffeey/OnTuHjRs34t5778UPP/yg1/UmydaKioowZ84cvPjii5g+\nfToAYO7cubr6m8OHD6N3796mCMVmtNWT97ahVojXighmXTkREZHFURl4KGzbtm14+OGH8eijj6K5\nuRmbN2/G5s2bsXHjRsTHx+s1hrwNNa949913UVFRgfXr12P9+vUAWlervv7669BqtfD29sayZctM\nEYrN0NWTh9toUn5lpvxCNpNyIiIiy2HA5kFm0Kj82LFjmDdvHmJjY9ud9/PzM6+kfPHixVi8ePF1\n57du3WqK29uk5IvFUKsE9OzqqXQoivDv4gxHew0u5JQpHQoRERFZuZUrV970sbZmJx3ROymvqamB\nk1P7DWhycnIQFBSk7xBkInUNTTifXYaIYHc42JvkfZfZUakEhAe5IyW9GHX1TTb7cyAiIrIkggET\n5Ur2KV+4cOF15zQaDUJCQjBz5kxJu3rqXYUzbdo0nDp1Svf95s2b8eCDD+p9IzKds1mlaG4REW2j\n9eRtIoLc0SICGXkVSodCRERE+rCwmvKhQ4dedwwcOBDFxcWIi4uTNJbe04f//Oc/sXDhQowZMwbJ\nyclwcHDA559/Ljl4kl/Sxbb+5DaelAe31ZWXISrMtnq1ExERkfymTZt208cmTZokaSy9k/KYmBg8\n/PDDWL16NVxcXLBhwwYEBgZKuhmZRvKVziu2tmnQH0Vc2dnzAjuwEBERWQYDNg9StH7lBgoKCvDj\njz/C2dlZ0nV6J+UPP/ww1Go1du3ahZycHDz//PMYPXo0FixYIDlYkk9zcwvSMksQ4ucCdxd7pcNR\nVLCvC+w0KiblREREZDJZWVlITEzEqlWrJF2nd1I+fvx4zJ49GwAQHByML774QvLNSH4Xc8tRW99s\n86UrAKBWqxAW6IaLOeVobGqBVmMGjUyJiIjo5gRI73BoBhPlJSUl+Oabb1Be3joRGBgYiF27duHp\np5/Wewy9k3JXV1d8+eWX7c71799f7xuRabCevL2IIA+czSpDRl45IkNssz0kSVddXY2jR48iMzMT\ngiAgNDQUt912G+ztbfvTJyIiuQmq1kPqNUp77LHH0KNHj051JdQ7KT969Kju68bGRpw4cQIxMTGY\nOnWqwTcn42urJ+9to5sG/VGPrp74v8MZOJtZyqScOlRbW4v/+Z//wQ8//ICePXsiMDAQGo0GJ0+e\nREJCAsaNG4e//e1vkusEiYjI+iUkJHTqer2T8j/eqKysDM8++2ynbk7GJYoiktOL4e3uAF9PR6XD\nMQs9Q1sT8bSsUkhbA0226MUXX8QDDzyA559/HipV+6mXlpYW/PTTT3jxxRd1OxMTEZGxWeaOnmPH\njsW2bdvZ09HJAAAgAElEQVQwbNgwqNVq3XkpTVEM3lHFyckJOTk5hl5OMsguqEJ5VQNGDgySvnLZ\nSgX5uMDZQYO0zFKlQyELsG7dupv+7qhUKtx1110YM2aMiaMiIrIdllq+UllZiffffx+enlc/lRcE\nAXv27NF7DL2T8tmzZ+v+sRJFEdnZ2Rg1apSEcEluLF25nkolILKrJ06dLURlTQNcneyUDonMmCAI\n+OWXX/Ddd98hPz8fKpUKvr6+GDlypG6bZL7hJSKiP9q9ezcOHz4MBwcHg8fQOymfN2+e7mtBEODp\n6Ynu3bsbfGMyvqSLV5JyLvJsp2doa1J+NqsUg6P8lA6HzNjatWtx5swZ3HffffD19QXQ2m92+/bt\nOHXqFObPn69whEREVs5Cu6+EhISgvLzcNEn50KFDDb4JmUZSeglcHLUI8XNVOhSz0rPrlbryTCbl\ndGvffvst/u///u+6evJ7770X9957L5NyIiK6IUEQMGnSJERGRkKr1erOb9y4Ue8xOkzKry1buREp\nNyP5FJXVoqCkBkOj/aFSmcFbRjPSo+vVxZ5Et2Jvb4/8/PzrFubk5ubCzo6lT0REchMMWOdpDlWF\nTz75ZKfH6DApnzFjBnx8fFBcXIwuXVgWYa50pSvhXgpHYn7cXewR0MUZZzNLIYoia4LpphYsWIBZ\ns2YhLCwMPj4+AIDCwkJkZGR0utWVOfHvYrxVUZ8eKjDaWDdzR195u0lllzfIOj4ApOc1yTp+c02z\nrOOLLaKs4wOAoNJ2/KRO8HNXd/ykTurq5iTr+NmVNbKOb6+Wf8VkiJt1dYerr6+Hvb39LStK2p7T\nkQ6T8nXr1uHrr7/Gn//8Z3zxxRfSIiWTSbqyyDOaizxvqGeoJ37+LRu5RdUI8nFROhwyU7fddhu+\n++47nDlzBgUFBRBFEX5+fujfvz9nyomITEBQCRAkfuIv9fnG9MILL2DEiBGYOHEiXFza5xdVVVX4\n6quvcOjQIbzzzjsdjtVhUj5w4ED07dsXoiiiV69euvNtM44pKSkGvAQytuSLxbDTqhER5KF0KGap\nR9fWpDwts4RJOd1SdnY2jh071q77iqOjI/r06aN0aERE1s/CFnquXbsWW7ZswfTp0+Hm5gZ/f3+o\n1Wrk5OSgrKwMjzzyCNauXavXWB0m5QkJCUhISMBTTz2FDRs2dDp4Mr7KmgZk5leiX3dvaDVm0KzT\nDOk2EcosxZiYrgpHQ+Zq06ZN+PzzzzF+/Hj07dsXQGv5yuLFi3Hfffdhzpw5CkdIRGTdBBhQUy5L\nJPpRqVSYNWsWZs2ahdTUVGRkZEClUqFr166IioqSNJbe3VeYkJuvlPQSAOxPfivdAt2h1aiQyk2E\n6BY2btyIL7/8Eo6O7WseH330UUybNo1JORER3VRUVJTkRPxanFa1AuxP3jGtRoUeXT2RkVuO6tpG\npcMhM6XRaNDUdP2CvLq6unYtrohsXRc3F+xc+Tz+dOcQpUMha6My8LACes+Uk/lKulgMtUrQlWjQ\njfUJ74Kki8VITi/GkGh/pcMhM/Tkk09i6tSpGD58eLvuK0eOHMGzzz6rcHRE5sHBToulf50OF0fD\nN0khouvpnZRfvHgRGzZswKVLl9rNJG3fvl2WwEg/dfVNOJ9dhohgdzjY8z3WrfSN8MZnP55F4gUm\n5XRjkydPxtChQ3H48GFd95WYmBjMmzcPfn7ceIrIz9MNS/86HT26BigdClkpS+1Tbgx6Z3H/+Mc/\nMGXKFNx///1Qq+Xv9Un6ScsqRXOLiGiWrnSoZ5gnNGoBv18oUjoUMmOOjo66XvYqlQqCILC3PRGA\nP905BH+ZOAoOdlr8lpaOQT27KR0SkVXROynXaDT461//KmcsZIBk3aZBTMo74mCnQWSIJ9KySlFT\n1wgnB9YIU3s//PADVq5cidjYWHh7ewMAfv31V7z99tuIi4vD5MmTJY1XV1eHF198EcXFxXB2dsaK\nFSvg5eWFw4cP46233oJGo0GXLl2wYsWKdotLb3bd8ePHsWLFCgiCgCFDhuDFF19EbW0t/vGPf6Cy\nshJLly5Fjx49jPozIWrzpzuH4nJpOdZs/RbBvl2YlJMsDJkIMYeJk5qaGjg5td88KicnB0FBQXqP\noXdp/IgRI7Bv3z79oyOT0G0axJlyvfSJ6IKWFhHJVzrWEF3rX//6Fz777DMsX74ccXFxiIuLwz//\n+U9s27YN69evlzzeli1b0KNHD2zevBlTp07VjfHqq6/inXfewaZNmxAaGopt27bpdd3rr7+ON998\nE59//jnOnDmD5ORkXLx4EaNHj8Zrr72GgwcPdv6HQHQTb279Pzz+xodISs9ROhSyZipAkHiYw0LP\nadOm4dSpU7rvN2/ejAcffFDSGHq/jOHDhyMuLg6DBw/G8OHDMWzYMAwfPlzSzci4mppbkJpZihA/\nV7g5c7dBffSNaJ39TGQJC92AIAhwdXW97ryzs7PeZXvr1q3Dli1bAAAnTpzAiBEjAAAjR47E4cOH\nAQCffPKJbia+qanpuu2Xb3bd559/jpCQEFRXV6OqqgpOTk6Ijo5Gbm4u3nvvPUydOtWAV02kn+Op\nF9EiikqHQdZOMPBQ2D//+U8sXLgQq1atwqOPPopffvkFn3/+uaQx9C5fWbJkCRISEtC7d2+oVGbw\nloRwMacc9Q3NLF2RoFeYF9QqAYkXipUOhczQn//8Zzz44IMYN26crvtKUVERdu/ejenTp9/y2m+/\n/RZbtmxBTk4OtFotvv32Wxw/flzXtcXZ2RmVlZUAAF9fXwDA7t27cfToUcTFxbUbq6qqSvfm4Nrr\nNBoNTp06heeeew4RERHw9/eHIAh4/vnnjfdDICKyUqdPn8bq1avxySefIDMzEwsWLIAgCIiMjER8\nfHyn8tuYmBg8/PDDWL16NVxcXLBhwwYEBgZKGkPvpNzd3R0TJkyQHCTJJ4n15JI52GsQGeKBs5fK\nUFvfBEd2rKFrzJkzB0OGDMG+fftw5swZAK0J9Kuvvop+/frd8tqJEydi4sSJWLduHby9vTFz5kw8\n/fTTqK6uBgBUV1fDzc1N9/yPP/4Y3333HT788MPrZspdXFxuet2AAQOwd+9erFmzBu+//z6eeeYZ\no7x2IiJzINeOnh988AF27typW7+TkJCAuLg4xMbGYsmSJdizZw/GjRsnOd42Dz/8MNRqNXbt2oWc\nnBw8//zzGD16NBYsWKD3GHq/JRg7diy2bNmCsrIy1NbW6g5STttsLzcNkqZPhDdaWkTdTqhE1woK\nCsK4cePw6quv4rXXXsPTTz+Nfv36ISkpSfJYgwYN0q3F2b9/PwYPHgygdYfk48eP4+OPP4aXl5de\n14miiIceegjl5eUAWmfQ+aklEZF+unbtinXr1um+T0pKwtChQwG0lgkeOnSoU+OPHz8e//nPfxAc\nHIzY2Fh88cUXqK+vlzSG3tOEb731FgBg6dKlEAQBoihCEASkpKRIi5qMorlFRFJ6Mfy8nODj6djx\nBaTTN8Ib2/eew5nzhRgU5at0OGRGvv32WyQkJMDDwwMNDQ1Yt26drpvJ4sWL8cUXX3Q4xrx583Rf\nz5w5E/Pnz8fMmTOh1Wrxr3/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WFhG39w9UOhSbpFYJuKN/IL4+kI7T5woxOIqfVtiCZcuW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\nupW/3OUQibqvbzQGJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import aplpy\n", "import seaborn as sns\n", "sns.set(color_codes=True)\n", "import pandas as pd\n", "sns.set_style(\"white\")\n", "import xidplus.posterior_maps as postmaps\n", "\n", "\n", "\n", "labels=[r'Source 1 $250\\mathrm{\\mu m}$ flux (mJy)',r'Source 2 $250\\mathrm{\\mu m}$ flux (mJy)']\n", "df = pd.DataFrame(posterior.samples['src_f'][:,0,[s1,s2]],columns=labels)\n", "g = sns.PairGrid(df,size=5)\n", "g.map_diag(sns.kdeplot,c='Red')\n", "g.map_lower(sns.kdeplot, cmap=\"Reds\",alpha=0.8,n_levels=10,normed=True, shade=True,shade_lowest=False)\n", "g.set(ylim=(0,40))\n", "g.set(xlim=(0,40))\n", "\n", "g.axes[0,1].spines['bottom'].set_color('white')\n", "g.axes[0,1].spines['left'].set_color('white')\n", "cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,as_cmap=True)\n", "\n", "real_250 = aplpy.FITSFigure(postmaps.make_fits_image(priors[0],priors[0].sim)[1],figure=g.fig,subplot=(2,2,2))\n", "real_250.show_colorscale(cmap=cmap)\n", "\n", "real_250.show_markers(priors[0].sra, priors[0].sdec, edgecolor='black', facecolor='black',\n", " marker='o', s=40, alpha=0.5)\n", "real_250.recenter(priors[0].sra[s1], priors[0].sdec[s1], radius=0.01)\n", "\n", "real_250.add_label(priors[0].sra[s1], priors[0].sdec[s1]+0.0005, 1, relative=False,size=20,color='white')\n", "real_250.add_label(priors[0].sra[s2], priors[0].sdec[s2]-0.0010, 2, relative=False,size=20,color='white')\n", "real_250.tick_labels.set_xformat('dd.dd')\n", "real_250.tick_labels.set_yformat('dd.dd')\n", "\n", "\n", "real_250.add_colorbar(axis_label_text=r'$250\\mathrm{\\mu m}$ flux (mJy)') \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "### Posterior Predictive checking and Bayesian P-value maps" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "When examining goodness of fits, the typical method is to look at the residuals. i.e. $\\frac{data - model}{\\sigma}$. Because we have distribution of $y^{rep}$, we can do this in a more probabilisitic way using posterior predictive checks. For more information on posterior predictive checks, [Gelman et al. 1996](http://www.stat.columbia.edu/~gelman/research/published/A6n41.pdf) is a good starting point.\n", "\n", "\n", "\n", "For our case, the best way to carry out posterior predictive checks is to think about one pixel. We can look at where the real flux value for our pixel is in relation to the distribution from $y^{rep}$. \n", "\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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zczFhwgTs2bOnipETEYm7cD1HdHzJkiVIS0tDQkICPD09IZPJcP78eSxbtgyW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\nL774AnXq1NFXmFSOC5dzAQCebRsYOBIiIt1IzSo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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from xidplus import posterior_maps as postmaps\n", "\n", "rep_maps=postmaps.replicated_maps(priors,posterior)\n", "\n", "import matplotlib as mpl\n", "sns.set_style(\"white\")\n", "\n", "fig=plt.figure(figsize=(10,5))\n", "\n", "\n", "# This is the colormap I'd like to use.\n", "cm = sns.diverging_palette(220, 20, as_cmap=True)\n", "\n", "# Get the histogramp\n", "Y,X = np.histogram(rep_maps[0][20,:], 25, normed=1)\n", "#C = [cm(((x-X.min())/x_span)) for x in X]\n", "C = [cm(((((x-np.mean(rep_maps[0][20,:]))/np.std(rep_maps[0][20,:]))+6)/12.0)) for x in X]\n", "\n", "\n", "plt.bar(X[:-1],Y,color=C,width=X[1]-X[0])\n", "plt.xlabel('Pixel Flux mJy')\n", "plt.ylabel('p(pixel flux)')\n", "plt.axvline(3.9, linestyle='--')\n", "plt.axvline(-10.1,linestyle=':')\n", "plt.annotate('flux in map that \\n model cannot explain',xy=(4, 0.01), xycoords='data',\n", " xytext=(4, 0.1), textcoords='data',rotation='vertical',size='large')\n", "plt.annotate('too much flux in model \\n compared to map',xy=(-10, 0.01), xycoords='data',\n", " xytext=(-10, 0.1), textcoords='data',rotation='vertical',size='large')\n", "\n", "#ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])\n", "ax1 = fig.add_axes([0.82, 0.15, 0.02, 0.7])\n", "\n", "norm = mpl.colors.Normalize(vmin=-6, vmax=6)\n", "cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cm,\n", " norm=norm,\n", " orientation='vertical')\n", "cb1.set_label('$\\sigma$')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "We can calculate fraction of $y^{rep}$ samples above and below real map value. This is often referred to as the Bayesian p-value and is telling us the probability of drawing the real pixel value, from our model which has been inferred on the data. This is tells us if the model is inconsistent with the data, given the uncertianties in parameters and data.\n", "\n", "* $\\sim 0.5$ means our model is consistent with the data \n", "* 0.99 or 0.01 means model is missing something.\n", "\n", "We can convert this to a typical '$\\sigma$' level and create map versions of these Bayesian p-values:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figs, fig=xidplus.plot_Bayes_pval_map(priors, posterior)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Red indicates the flux value in the real map is higher than our model thinks is possible. This could be indicating there is a source there that is not in our model.\n", "Blue indicates the flux in the real map is lower than in our model. This is either indicating a very low density region or that too much flux has been assigned to one of the sources." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Creating Catalogues\n", "\n", "We can also create catalogues from the posterior probability density function" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xidplus.catalogue as cat" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "SPIRE_cat=cat.create_SPIRE_cat(posterior,priors[0],priors[1],priors[2])\n", "SPIRE_cat.writeto('test.fits',overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check table\n", "Lets read in table with Astropy table" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from astropy.table import Table" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "catalogue=Table.read('test.fits')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<Table length=51>\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
HELP_IDRADecF_SPIRE_250FErr_SPIRE_250_uFErr_SPIRE_250_lF_SPIRE_350FErr_SPIRE_350_uFErr_SPIRE_350_lF_SPIRE_500FErr_SPIRE_500_uFErr_SPIRE_500_lBkg_SPIRE_250Bkg_SPIRE_350Bkg_SPIRE_500Sig_conf_SPIRE_250Sig_conf_SPIRE_350Sig_conf_SPIRE_500Rhat_SPIRE_250Rhat_SPIRE_350Rhat_SPIRE_500n_eff_SPIRE_250n_eff_SPIRE_500n_eff_SPIRE_350Pval_res_250Pval_res_350Pval_res_500
degreesdegreesmJymJymJymJymJymJymJymJymJymJy/BeammJy/BeammJy/BeammJy/BeammJy/BeammJy/Beam
str27float64float64float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32
1891150.747114622.019372291493.74847.663191.181642.143744.784480.6541443.956588.780071.13176-2.55132-4.50904-6.236811.575671.541542.358091.001250.9996510.9996942000.02000.02000.00.00.0010.0
1896150.746556442.013817344354.600056.36972.842673.658665.486891.850561.928264.578170.519253-2.55132-4.50904-6.236811.575671.541542.358091.001530.9992811.000562000.02000.02000.00.00.0030.0
2640150.7426721672.0293733060413.998418.88439.1746312.898115.48719.418061.869514.19930.517889-2.55132-4.50904-6.236811.575671.541542.358091.001921.001161.000282000.02000.02000.00.00.0080.0
5128150.7526802822.036591142423.179355.001121.515381.175022.57950.3161554.843748.411981.72842-2.55132-4.50904-6.236811.575671.541542.358090.9992810.9997250.9990162000.02000.02000.00.00.0170.001
5129150.7410062882.03326257014.140865.904872.338281.572663.093270.5151442.126924.432630.572698-2.55132-4.50904-6.236811.575671.541542.358090.9996270.9993960.9994932000.02000.02000.00.00.0180.0
5130150.7471233192.0399264171415.16316.773413.559918.350819.890316.837814.38617.360211.346-2.55132-4.50904-6.236811.575671.541542.358090.9989790.9985970.9990772000.02000.02000.00.00.0030.001
5159150.7543466032.0338129052415.013916.807713.05777.175889.162444.820384.098727.768031.15535-2.55132-4.50904-6.236811.575671.541542.358090.999181.001120.9993272000.02000.02000.00.0020.0040.001
6067150.7554566052.029923855242.47554.139461.05965.077327.130543.021582.4075.264340.697328-2.55132-4.50904-6.236811.575671.541542.358091.001530.9998710.9992672000.02000.02000.00.0020.0230.002
6728150.7315579422.035488248892.275464.036990.8145941.282562.656320.3771021.257022.829280.311752-2.55132-4.50904-6.236811.575671.541542.358090.9991940.9990040.9992942000.02000.02000.00.0010.00.002
.................................................................................
54557150.7471160312.022705389674.166086.520532.010111.809363.885620.4812753.853728.231031.19911-2.55132-4.50904-6.236811.575671.541542.358090.9991270.9989050.9995052000.02000.02000.00.00.0030.0
56696150.7215561732.043824810930.884122.16440.2407980.9493732.360940.2554321.200043.066910.313454-2.55132-4.50904-6.236811.575671.541542.358090.9993510.9989611.001712000.02000.02000.00.0140.0020.008
56700150.7293370912.041599802171.482472.863670.4490761.045822.223370.3227391.391983.187430.412141-2.55132-4.50904-6.236811.575671.541542.358090.9999420.9998220.998372000.02000.02000.00.0010.00.004
57200150.7404581262.0515948904110.47412.7447.88944.536386.913962.202412.684145.658350.824703-2.55132-4.50904-6.236811.575671.541542.358090.9986460.998650.9990782000.02000.02000.00.00.0010.005
58792150.7393341292.022152857781.535842.89060.5367871.040912.183790.3118711.339512.953360.337598-2.55132-4.50904-6.236811.575671.541542.358090.9994911.000850.9995122000.02000.02000.00.00.0020.0
62679150.7510158472.043813518312.448074.682180.7555752.667075.367110.9643113.189247.197390.878154-2.55132-4.50904-6.236811.575671.541542.358090.9998910.9996941.000342000.02000.02000.00.00.00.004
62696150.7165472932.028272101750.9001131.877450.2733191.051672.089770.3232882.65564.829450.958473-2.55132-4.50904-6.236811.575671.541542.358090.9986411.000140.9988872000.02000.02000.00.0010.0680.226
64788150.7571178952.015479799417.40538.999435.699526.234988.095414.302851.509783.650690.413473-2.55132-4.50904-6.236811.575671.541542.358090.9991240.998980.9992342000.02000.02000.00.00.0030.004
64789150.7226685432.045490955680.6934141.731350.1719591.009552.435270.2540881.504513.613530.414402-2.55132-4.50904-6.236811.575671.541542.358090.9995811.000641.000242000.02000.02000.00.3190.0010.002
64790150.7460060082.026594435443.666455.708631.700929.7518511.8367.742272.950045.881840.874799-2.55132-4.50904-6.236811.575671.541542.358091.000891.000120.9996322000.02000.02000.00.00.0040.0
" ], "text/plain": [ "\n", "HELP_ID RA Dec ... Pval_res_250 Pval_res_350 Pval_res_500\n", " degrees degrees ... \n", " str27 float64 float64 ... float32 float32 float32 \n", "------- ------------- ------------- ... ------------ ------------ ------------\n", " 1891 150.74711462 2.01937229149 ... 0.0 0.001 0.0\n", " 1896 150.74655644 2.01381734435 ... 0.0 0.003 0.0\n", " 2640 150.742672167 2.02937330604 ... 0.0 0.008 0.0\n", " 5128 150.752680282 2.03659114242 ... 0.0 0.017 0.001\n", " 5129 150.741006288 2.0332625701 ... 0.0 0.018 0.0\n", " 5130 150.747123319 2.03992641714 ... 0.0 0.003 0.001\n", " 5159 150.754346603 2.03381290524 ... 0.002 0.004 0.001\n", " 6067 150.755456605 2.02992385524 ... 0.002 0.023 0.002\n", " 6728 150.731557942 2.03548824889 ... 0.001 0.0 0.002\n", " ... ... ... ... ... ... ...\n", " 54557 150.747116031 2.02270538967 ... 0.0 0.003 0.0\n", " 56696 150.721556173 2.04382481093 ... 0.014 0.002 0.008\n", " 56700 150.729337091 2.04159980217 ... 0.001 0.0 0.004\n", " 57200 150.740458126 2.05159489041 ... 0.0 0.001 0.005\n", " 58792 150.739334129 2.02215285778 ... 0.0 0.002 0.0\n", " 62679 150.751015847 2.04381351831 ... 0.0 0.0 0.004\n", " 62696 150.716547293 2.02827210175 ... 0.001 0.068 0.226\n", " 64788 150.757117895 2.01547979941 ... 0.0 0.003 0.004\n", " 64789 150.722668543 2.04549095568 ... 0.319 0.001 0.002\n", " 64790 150.746006008 2.02659443544 ... 0.0 0.004 0.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "catalogue" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 1 }