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    "nbpresent": {
     "id": "bb43365d-32e1-4b4b-9bb5-678e32db901e"
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   "source": [
    "# Astrohack 2017\n",
    "\n",
    "## Quick & Dirty Solution\n",
    "\n",
    "### Team AstroWhack\n",
    "* Ken Bastiaensen\n",
    "* (Benjamin Vandermarliere)\n",
    "* ((Jannes Nys))"
   ]
  },
  {
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   "metadata": {
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     "id": "ea658e75-f8f4-421b-98af-49f1ca0fc5f3"
    }
   },
   "source": [
    "# Quick & Dirty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8afa4d7a-f50e-4f9b-9d86-246349c029f8"
    }
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   "source": [
    "# Quick → Julia on my laptop\n",
    "\n",
    "# Dirty  → Simplest plan of attack\n",
    "* Minimal preprocess to select galaxy\n",
    "* Get some features\n",
    "* Run XGBoost\n",
    "* ...\n",
    "* Profits!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f54df491-5c03-4afa-a0fd-a33ca5dc9488"
    }
   },
   "source": [
    "# Preprocess\n",
    "* smooth image and threshold to create binary image \n",
    "* use `Images.label_components` to label connected pixels (blobs) \n",
    "* central blob = galaxy \n",
    "* crop central galaxy by using symmetry: **minimum** of deviations around center for each axis\n",
    "* set non-label components in crop to zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2f3dc41e-5beb-4f22-affb-05b906b9665e"
    }
   },
   "source": [
    "Some features:\n",
    "* Distance\n",
    "* Gert: intensity of light \n",
    "  * some sums \n",
    "* Benjamin: symmetry\n",
    "  * eigenvalues\n",
    "* prof Maarten Baes: lighter galaxies are more bumpy\n",
    "  * entropy\n",
    "* Some guy on internet: different noise levels per image\n",
    "  * some low percentiles of full image\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "066e0cf3-fd84-49f3-8632-cd9ab1dac988"
    }
   },
   "source": [
    "I want to thank:\n",
    "* Gert for great organisation!\n",
    "* Julia for fast and beautiful language!\n",
    "* XGBoost for amazing regressions!\n",
    "* Benjamin and everyone else for fun vibe!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "2e179963-8ff2-4f55-aab0-ad7c4df9069d"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "using Images, ImageDraw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbpresent": {
     "id": "206da2d6-188c-4267-85fc-a47f646f6f53"
    }
   },
   "outputs": [
    {
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       "<div id=\"interact-js-shim\">\n",
       "    <script charset=\"utf-8\">\n",
       "(function (IPython, $, _, MathJax) {\n",
       "    $.event.special.destroyed = {\n",
       "\tremove: function(o) {\n",
       "\t    if (o.handler) {\n",
       "\t\to.handler.apply(this, arguments)\n",
       "\t    }\n",
       "\t}\n",
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       "    var OutputArea = IPython.version >= \"4.0.0\" ? require(\"notebook/js/outputarea\").OutputArea : IPython.OutputArea;\n",
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       "\t    prompt_area: true,\n",
       "\t    events: IPython.events,\n",
       "\t    keyboard_manager: IPython.keyboard_manager\n",
       "\t})); // Hack to work with IPython 2.1.0\n",
       "\n",
       "\tswitch (type) {\n",
       "\tcase \"image/png\":\n",
       "            var _src = 'data:' + type + ';base64,' + val;\n",
       "\t    $(container).find(\"img\").attr('src', _src);\n",
       "\t    break;\n",
       "\tcase \"text/latex\":\n",
       "\t\tif (MathJax){\n",
       "\t\t\tvar math = MathJax.Hub.getAllJax(container)[0];\n",
       "\t\t\tMathJax.Hub.Queue([\"Text\", math, val.replace(/^\\${1,2}|\\${1,2}$/g, '')]);\n",
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       "\t    comm_manager.register_target(\"Signal\", function (comm) {\n",
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       "            });\n",
       "\t    });\n",
       "\n",
       "\t    // coordingate with Comm and redraw Signals\n",
       "\t    // XXX: Test using Reactive here to improve performance\n",
       "\t    $([IPython.events]).on(\n",
       "\t\t'output_appended.OutputArea', function (event, type, value, md, toinsert) {\n",
       "\t\t    if (md && md.reactive) {\n",
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       "\t    });\n",
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       "\t    initComm(undefined, IPython.notebook);\n",
       "\t} catch (e) {\n",
       "\t    $([IPython.events]).on('kernel_created.Kernel kernel_created.Session', initComm);\n",
       "\t}\n",
       "    });\n",
       "})(IPython, jQuery, _, MathJax);\n",
       "</script>\n",
       "    <script>\n",
       "        window.interactLoadedFlag = true\n",
       "       $(\"#interact-js-shim\").bind(\"destroyed\", function () {\n",
       "           if (window.interactLoadedFlag) {\n",
       "               console.warn(\"JavaScript required by Interact will be removed if you remove this cell or run using Interact more than once.\")\n",
       "           }\n",
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       "       $([IPython.events]).on(\"kernel_starting.Kernel kernel_restarting.Kernel\", function () { window.interactLoadedFlag = false })\n",
       "   </script>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Interact, BenchmarkTools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "090b4532-17d3-4dac-96a2-0770b7d960b0"
    }
   },
   "source": [
    "# Load a file and preprocess a bit"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "ee6cc557-cd74-4f55-b7d2-3625a2c9b9b7"
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   },
   "outputs": [],
   "source": [
    "sampledir = \"/Users/ken/Coding/astrohack/Astrohack/Sample_Data/SAMPLE/\"\n",
    "\n",
    "files = readdir(sampledir)\n",
    "\n",
    "ids = String[first(split(f, \"-\")) for f in files if f[end-4]=='g'];"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "id": "9d0369c7-e6cb-4626-907c-e1720a097aed"
    }
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     "name": "stdout",
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     "text": [
      "  1.183841 seconds (2.03 M allocations: 70.628 MB, 1.49% gc time)\n"
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",
      "text/plain": [
       "151×151 Array{Gray{Float32},2}:\n",
       " Gray{Float32}(0.0150205)     …  Gray{Float32}(0.0095342)  \n",
       " Gray{Float32}(-0.000353095)     Gray{Float32}(0.00256769) \n",
       " Gray{Float32}(0.0111624)        Gray{Float32}(0.0200024)  \n",
       " Gray{Float32}(-0.0103494)       Gray{Float32}(0.0127407)  \n",
       " Gray{Float32}(0.00579856)       Gray{Float32}(0.0117925)  \n",
       " Gray{Float32}(-0.0160354)    …  Gray{Float32}(0.0280175)  \n",
       " Gray{Float32}(-0.0121117)       Gray{Float32}(0.00108888) \n",
       " Gray{Float32}(0.0254494)        Gray{Float32}(-0.00304037)\n",
       " Gray{Float32}(0.0184246)        Gray{Float32}(0.00672054) \n",
       " Gray{Float32}(0.0135947)        Gray{Float32}(0.00205906) \n",
       " Gray{Float32}(0.00403642)    …  Gray{Float32}(0.00718782) \n",
       " Gray{Float32}(-0.000664608)     Gray{Float32}(0.0190579)  \n",
       " Gray{Float32}(0.00148154)       Gray{Float32}(0.0190346)  \n",
       " ⋮                            ⋱  ⋮                         \n",
       " Gray{Float32}(0.00524544)       Gray{Float32}(-0.0277895) \n",
       " Gray{Float32}(0.0192283)     …  Gray{Float32}(0.00261766) \n",
       " Gray{Float32}(0.0126522)        Gray{Float32}(0.0275832)  \n",
       " Gray{Float32}(-0.0058023)       Gray{Float32}(0.027682)   \n",
       " Gray{Float32}(-0.0128325)       Gray{Float32}(0.00797125) \n",
       " Gray{Float32}(-0.00395247)      Gray{Float32}(-0.0216017) \n",
       " Gray{Float32}(0.00780504)    …  Gray{Float32}(-0.0135278) \n",
       " Gray{Float32}(0.00608584)       Gray{Float32}(-0.0137204) \n",
       " Gray{Float32}(0.00585259)       Gray{Float32}(0.0089281)  \n",
       " Gray{Float32}(-0.00349419)      Gray{Float32}(0.00821044) \n",
       " Gray{Float32}(0.019344)         Gray{Float32}(-0.0322906) \n",
       " Gray{Float32}(0.0187973)     …  Gray{Float32}(-0.00174969)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id = ids[27]\n",
    "@time img_raw = readcsv(joinpath(sampledir, id * \"-g.csv\"), Float32)\n",
    "img_gray = colorview(Gray, img_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "nbpresent": {
     "id": "2514ab1e-1851-4df5-8090-f958090b55de"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "151×151 ColorView{RGB}(::ImageCore.StackedView{Float32,3,Tuple{Array{Float32,2},Array{Float32,2},Array{Float32,2}}}) with element type ColorTypes.RGB{Float32}:\n",
       " RGB{Float32}(0.0150205,0.0150205,0.0150205)           …  RGB{Float32}(0.0095342,0.0095342,0.0095342)      \n",
       " RGB{Float32}(-0.000353095,-0.000353095,-0.000353095)     RGB{Float32}(0.00256769,0.00256769,0.00256769)   \n",
       " RGB{Float32}(0.0111624,0.0111624,0.0111624)              RGB{Float32}(0.0200024,0.0200024,0.0200024)      \n",
       " RGB{Float32}(-0.0103494,-0.0103494,-0.0103494)           RGB{Float32}(0.0127407,0.0127407,0.0127407)      \n",
       " RGB{Float32}(0.00579856,0.00579856,0.00579856)           RGB{Float32}(0.0117925,0.0117925,0.0117925)      \n",
       " RGB{Float32}(-0.0160354,-0.0160354,-0.0160354)        …  RGB{Float32}(0.0280175,0.0280175,0.0280175)      \n",
       " RGB{Float32}(-0.0121117,-0.0121117,-0.0121117)           RGB{Float32}(0.00108888,0.00108888,0.00108888)   \n",
       " RGB{Float32}(0.0254494,0.0254494,0.0254494)              RGB{Float32}(-0.00304037,-0.00304037,-0.00304037)\n",
       " RGB{Float32}(0.0184246,0.0184246,0.0184246)              RGB{Float32}(0.00672054,0.00672054,0.00672054)   \n",
       " RGB{Float32}(0.0135947,0.0135947,0.0135947)              RGB{Float32}(0.00205906,0.00205906,0.00205906)   \n",
       " RGB{Float32}(0.00403642,0.00403642,0.00403642)        …  RGB{Float32}(0.00718782,0.00718782,0.00718782)   \n",
       " RGB{Float32}(-0.000664608,-0.000664608,-0.000664608)     RGB{Float32}(0.0190579,0.0190579,0.0190579)      \n",
       " RGB{Float32}(0.00148154,0.00148154,0.00148154)           RGB{Float32}(0.0190346,0.0190346,0.0190346)      \n",
       " ⋮                                                     ⋱  ⋮                                                \n",
       " RGB{Float32}(0.00524544,0.00524544,0.00524544)           RGB{Float32}(-0.0277895,-0.0277895,-0.0277895)   \n",
       " RGB{Float32}(0.0192283,0.0192283,0.0192283)           …  RGB{Float32}(0.00261766,0.00261766,0.00261766)   \n",
       " RGB{Float32}(0.0126522,0.0126522,0.0126522)              RGB{Float32}(0.0275832,0.0275832,0.0275832)      \n",
       " RGB{Float32}(-0.0058023,-0.0058023,-0.0058023)           RGB{Float32}(0.027682,0.027682,0.027682)         \n",
       " RGB{Float32}(-0.0128325,-0.0128325,-0.0128325)           RGB{Float32}(0.00797125,0.00797125,0.00797125)   \n",
       " RGB{Float32}(-0.00395247,-0.00395247,-0.00395247)        RGB{Float32}(-0.0216017,-0.0216017,-0.0216017)   \n",
       " RGB{Float32}(0.00780504,0.00780504,0.00780504)        …  RGB{Float32}(-0.0135278,-0.0135278,-0.0135278)   \n",
       " RGB{Float32}(0.00608584,0.00608584,0.00608584)           RGB{Float32}(-0.0137204,-0.0137204,-0.0137204)   \n",
       " RGB{Float32}(0.00585259,0.00585259,0.00585259)           RGB{Float32}(0.0089281,0.0089281,0.0089281)      \n",
       " RGB{Float32}(-0.00349419,-0.00349419,-0.00349419)        RGB{Float32}(0.00821044,0.00821044,0.00821044)   \n",
       " RGB{Float32}(0.019344,0.019344,0.019344)                 RGB{Float32}(-0.0322906,-0.0322906,-0.0322906)   \n",
       " RGB{Float32}(0.0187973,0.0187973,0.0187973)           …  RGB{Float32}(-0.00174969,-0.00174969,-0.00174969)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_peaks = copy(img_raw)\n",
    "cutoff = min(1, img_raw[fld.(size(img_raw), 2)...])\n",
    "img_peaks[img_peaks .> 1] = 0\n",
    "img = colorview(RGB, img_peaks,img_peaks,img_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbpresent": {
     "id": "2435570d-4866-47ff-8eeb-6917d24541fe"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"151×151 ColorView{RGB}(::ImageCore.StackedView{Float32,3,Tuple{Array{Float32,2},Array{Float32,2},Array{Float32,2}}}) with element type ColorTypes.RGB{Float32}\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "nbpresent": {
     "id": "52234635-88d4-4808-aec4-b7ff60373f40"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "151×151 ColorView{Gray}(::BitArray{2}) with element type ColorTypes.Gray{Bool}:\n",
       " Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " ⋮                                     ⋱  ⋮                \n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n",
       " Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_bin = falses(img_raw)\n",
    "img_smooth = imfilter(img_raw, Kernel.gaussian(2))\n",
    "bin_th = 0.05\n",
    "img_bin[img_smooth .> bin_th] = true\n",
    "colorview(Gray, img_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "78d05a7d-e6da-4940-ac0d-fe3cb8b8d4bc"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labs = label_components(img_bin);\n",
    "\n",
    "centre = fld.(size(labs), 2)\n",
    "labs_blob = zeros(img_raw)\n",
    "labs_ind = labs .== labs[centre...]\n",
    "labs_blob[labs_ind] = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbpresent": {
     "id": "c2239b0f-5201-4c91-87b5-ef7156b34e2d"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "151×151 ColorView{RGB}(::ImageCore.StackedView{Float32,3,Tuple{Array{Float32,2},Array{Float32,2},Array{Float32,2}}}) with element type ColorTypes.RGB{Float32}:\n",
       " RGB{Float32}(0.0,0.0150205,0.0150205)        …  RGB{Float32}(0.0,0.0095342,0.0095342)    \n",
       " RGB{Float32}(0.0,-0.000353095,-0.000353095)     RGB{Float32}(0.0,0.00256769,0.00256769)  \n",
       " RGB{Float32}(0.0,0.0111624,0.0111624)           RGB{Float32}(0.0,0.0200024,0.0200024)    \n",
       " RGB{Float32}(0.0,-0.0103494,-0.0103494)         RGB{Float32}(0.0,0.0127407,0.0127407)    \n",
       " RGB{Float32}(0.0,0.00579856,0.00579856)         RGB{Float32}(0.0,0.0117925,0.0117925)    \n",
       " RGB{Float32}(0.0,-0.0160354,-0.0160354)      …  RGB{Float32}(0.0,0.0280175,0.0280175)    \n",
       " RGB{Float32}(0.0,-0.0121117,-0.0121117)         RGB{Float32}(0.0,0.00108888,0.00108888)  \n",
       " RGB{Float32}(0.0,0.0254494,0.0254494)           RGB{Float32}(0.0,-0.00304037,-0.00304037)\n",
       " RGB{Float32}(0.0,0.0184246,0.0184246)           RGB{Float32}(0.0,0.00672054,0.00672054)  \n",
       " RGB{Float32}(0.0,0.0135947,0.0135947)           RGB{Float32}(0.0,0.00205906,0.00205906)  \n",
       " RGB{Float32}(0.0,0.00403642,0.00403642)      …  RGB{Float32}(0.0,0.00718782,0.00718782)  \n",
       " RGB{Float32}(0.0,-0.000664608,-0.000664608)     RGB{Float32}(0.0,0.0190579,0.0190579)    \n",
       " RGB{Float32}(0.0,0.00148154,0.00148154)         RGB{Float32}(0.0,0.0190346,0.0190346)    \n",
       " ⋮                                            ⋱  ⋮                                        \n",
       " RGB{Float32}(0.0,0.00524544,0.00524544)         RGB{Float32}(0.0,-0.0277895,-0.0277895)  \n",
       " RGB{Float32}(0.0,0.0192283,0.0192283)        …  RGB{Float32}(0.0,0.00261766,0.00261766)  \n",
       " RGB{Float32}(0.0,0.0126522,0.0126522)           RGB{Float32}(0.0,0.0275832,0.0275832)    \n",
       " RGB{Float32}(0.0,-0.0058023,-0.0058023)         RGB{Float32}(0.0,0.027682,0.027682)      \n",
       " RGB{Float32}(0.0,-0.0128325,-0.0128325)         RGB{Float32}(0.0,0.00797125,0.00797125)  \n",
       " RGB{Float32}(0.0,-0.00395247,-0.00395247)       RGB{Float32}(0.0,-0.0216017,-0.0216017)  \n",
       " RGB{Float32}(0.0,0.00780504,0.00780504)      …  RGB{Float32}(0.0,-0.0135278,-0.0135278)  \n",
       " RGB{Float32}(0.0,0.00608584,0.00608584)         RGB{Float32}(0.0,-0.0137204,-0.0137204)  \n",
       " RGB{Float32}(0.0,0.00585259,0.00585259)         RGB{Float32}(0.0,0.0089281,0.0089281)    \n",
       " RGB{Float32}(0.0,-0.00349419,-0.00349419)       RGB{Float32}(0.0,0.00821044,0.00821044)  \n",
       " RGB{Float32}(0.0,0.019344,0.019344)             RGB{Float32}(0.0,-0.0322906,-0.0322906)  \n",
       " RGB{Float32}(0.0,0.0187973,0.0187973)        …  RGB{Float32}(0.0,-0.00174969,-0.00174969)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_labs = colorview(RGB, labs_blob,img_peaks,copy(img_raw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "e33ea53f-8d9c-4703-bb3a-8d271ab16e3a"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24,24)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find box around center that contains the galaxy based on the center label. \n",
    "#It's robust by using the galaxy symmetry so we take minimum of opposite directions\n",
    "@everywhere function findbox(inds)\n",
    "    ci, cj = fld.(size(inds), 2)\n",
    "    north = east = south = west = 0\n",
    "    for j = 1:size(inds, 2)\n",
    "        for i = 1:size(inds, 1)\n",
    "            if inds[i,j]\n",
    "                if i < ci #north\n",
    "                    ci - i > north && (north = ci - i)\n",
    "                else #south\n",
    "                    i - ci > south && (south = i - ci)\n",
    "                end\n",
    "                if j < cj\n",
    "                    cj - j > west  && (west  = cj - j)\n",
    "                else\n",
    "                    j - cj > east  && (east  = j - cj)\n",
    "                end\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "    min_width  = min(east, west) \n",
    "    min_heigth = min(north, south)\n",
    "    return min_width, min_heigth\n",
    "end\n",
    "findbox(labs_ind)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbpresent": {
     "id": "ee161e6e-79cb-4969-bb06-e1d989a8dcdb"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BenchmarkTools.Trial: \n",
       "  memory estimate:  0 bytes\n",
       "  allocs estimate:  0\n",
       "  --------------\n",
       "  minimum time:     291.487 μs (0.00% GC)\n",
       "  median time:      321.457 μs (0.00% GC)\n",
       "  mean time:        339.062 μs (0.00% GC)\n",
       "  maximum time:     1.559 ms (0.00% GC)\n",
       "  --------------\n",
       "  samples:          10000\n",
       "  evals/sample:     1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@benchmark findbox($labs_ind) #it's fast, thanks Julia!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbpresent": {
     "id": "b587653b-d83e-4398-8313-f2578a9516dd"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drawcbox! (generic function with 1 method)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# draw the box on the image\n",
    "function drawcbox!(img, w, h) #width, height\n",
    "    ci, cj = fld.(size(img), 2)\n",
    "    T = eltype(img)\n",
    "    # left ,right\n",
    "    for i = ci-h:ci+h\n",
    "        img[i, cj-w] = one(T)\n",
    "        img[i, cj+w] = one(T)\n",
    "    end\n",
    "    # up,down\n",
    "    for j = cj-w:cj+w\n",
    "        img[ci+h, j] = one(T)\n",
    "        img[ci-h, j] = one(T)\n",
    "    end\n",
    "    img\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "be37055c-e622-48a1-8904-e4752eac202b"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "151×151 ColorView{RGB}(::ImageCore.StackedView{Float32,3,Tuple{Array{Float32,2},Array{Float32,2},Array{Float32,2}}}) with element type ColorTypes.RGB{Float32}:\n",
       " RGB{Float32}(0.0,0.0150205,0.0150205)        …  RGB{Float32}(0.0,0.0095342,0.0095342)    \n",
       " RGB{Float32}(0.0,-0.000353095,-0.000353095)     RGB{Float32}(0.0,0.00256769,0.00256769)  \n",
       " RGB{Float32}(0.0,0.0111624,0.0111624)           RGB{Float32}(0.0,0.0200024,0.0200024)    \n",
       " RGB{Float32}(0.0,-0.0103494,-0.0103494)         RGB{Float32}(0.0,0.0127407,0.0127407)    \n",
       " RGB{Float32}(0.0,0.00579856,0.00579856)         RGB{Float32}(0.0,0.0117925,0.0117925)    \n",
       " RGB{Float32}(0.0,-0.0160354,-0.0160354)      …  RGB{Float32}(0.0,0.0280175,0.0280175)    \n",
       " RGB{Float32}(0.0,-0.0121117,-0.0121117)         RGB{Float32}(0.0,0.00108888,0.00108888)  \n",
       " RGB{Float32}(0.0,0.0254494,0.0254494)           RGB{Float32}(0.0,-0.00304037,-0.00304037)\n",
       " RGB{Float32}(0.0,0.0184246,0.0184246)           RGB{Float32}(0.0,0.00672054,0.00672054)  \n",
       " RGB{Float32}(0.0,0.0135947,0.0135947)           RGB{Float32}(0.0,0.00205906,0.00205906)  \n",
       " RGB{Float32}(0.0,0.00403642,0.00403642)      …  RGB{Float32}(0.0,0.00718782,0.00718782)  \n",
       " RGB{Float32}(0.0,-0.000664608,-0.000664608)     RGB{Float32}(0.0,0.0190579,0.0190579)    \n",
       " RGB{Float32}(0.0,0.00148154,0.00148154)         RGB{Float32}(0.0,0.0190346,0.0190346)    \n",
       " ⋮                                            ⋱  ⋮                                        \n",
       " RGB{Float32}(0.0,0.00524544,0.00524544)         RGB{Float32}(0.0,-0.0277895,-0.0277895)  \n",
       " RGB{Float32}(0.0,0.0192283,0.0192283)        …  RGB{Float32}(0.0,0.00261766,0.00261766)  \n",
       " RGB{Float32}(0.0,0.0126522,0.0126522)           RGB{Float32}(0.0,0.0275832,0.0275832)    \n",
       " RGB{Float32}(0.0,-0.0058023,-0.0058023)         RGB{Float32}(0.0,0.027682,0.027682)      \n",
       " RGB{Float32}(0.0,-0.0128325,-0.0128325)         RGB{Float32}(0.0,0.00797125,0.00797125)  \n",
       " RGB{Float32}(0.0,-0.00395247,-0.00395247)       RGB{Float32}(0.0,-0.0216017,-0.0216017)  \n",
       " RGB{Float32}(0.0,0.00780504,0.00780504)      …  RGB{Float32}(0.0,-0.0135278,-0.0135278)  \n",
       " RGB{Float32}(0.0,0.00608584,0.00608584)         RGB{Float32}(0.0,-0.0137204,-0.0137204)  \n",
       " RGB{Float32}(0.0,0.00585259,0.00585259)         RGB{Float32}(0.0,0.0089281,0.0089281)    \n",
       " RGB{Float32}(0.0,-0.00349419,-0.00349419)       RGB{Float32}(0.0,0.00821044,0.00821044)  \n",
       " RGB{Float32}(0.0,0.019344,0.019344)             RGB{Float32}(0.0,-0.0322906,-0.0322906)  \n",
       " RGB{Float32}(0.0,0.0187973,0.0187973)        …  RGB{Float32}(0.0,-0.00174969,-0.00174969)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawcbox!(img_labs, findbox(labs_ind)...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7dfd47eb-f11b-48a4-8ee3-62e3eaa46139"
    }
   },
   "source": [
    "# interactively look at preprocessing result of sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbpresent": {
     "id": "f8d5a8cf-c64b-4895-a238-b088f496f6a8"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Method definition idplot(Any) in module Main at In[17]:2 overwritten at In[18]:2.\n",
      "WARNING: Method definition idplot(Any, Any) in module Main at In[17]:2 overwritten at In[18]:2.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "idplot (generic function with 2 methods)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function idplot(i, binth=0.05)\n",
    "    sampledir = \"/Users/ken/Coding/astrohack/Astrohack/Sample_Data/SAMPLE/\"\n",
    "    img_raw = readcsv(joinpath(sampledir, ids[i]*\"-g.csv\"))#, Float32)\n",
    "    ci, cj = fld.(size(img_raw), 2)\n",
    "    \n",
    "    img_peaks = copy(img_raw)\n",
    "    cutoff = min(1, img_raw[ci, cj])\n",
    "    img_peaks[img_peaks .> cutoff] = 0\n",
    "    \n",
    "    img_bin = falses(img_raw)\n",
    "    img_smooth = imfilter(img_raw, Kernel.gaussian(2))\n",
    "    img_bin[img_smooth .> binth] = true\n",
    "    labels = label_components(img_bin)\n",
    "    img_cblob = zeros(img_raw)\n",
    "    labs_ind = labels .== labels[ci, cj]\n",
    "    img_cblob[labs_ind] = 0.5\n",
    "    \n",
    "    img_labs = colorview(RGB, img_cblob, img_peaks, img_raw)\n",
    "    \n",
    "    #img_circles = copy(img_labs)\n",
    "    blobs = blob_LoG(img_raw, 1:4) #TODO issue for float32?\n",
    "    blobth = 0.1\n",
    "    bigblobs = [blob for blob in blobs if blob.amplitude > blobth]\n",
    "    for blob in bigblobs\n",
    "        circle = CirclePointRadius(blob.location, sqrt(2)*blob.σ)\n",
    "        draw!(img_labs, circle, RGB(one(eltype(img_raw)),0,0))\n",
    "    end\n",
    "                \n",
    "    drawcbox!(img_labs, findbox(labs_ind)...)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbpresent": {
     "id": "43c71227-5237-4d23-93d1-d0c5e63dfd6f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Int64}(input-7: Signal{Int64}(27, nactions=1),\"i\",27,\"27\",Interact.OptionDict(DataStructures.OrderedDict(\"1\"=>1,\"2\"=>2,\"3\"=>3,\"4\"=>4,\"5\"=>5,\"6\"=>6,\"7\"=>7,\"8\"=>8,\"9\"=>9,\"10\"=>10…),Dict(2=>\"2\",11=>\"11\",39=>\"39\",46=>\"46\",25=>\"25\",42=>\"42\",29=>\"29\",8=>\"8\",20=>\"20\",14=>\"14\"…)),Any[],Any[],true,\"horizontal\")"
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       "Interact.Options{:SelectionSlider,Float64}(input-8: Signal{Float64}(0.045, nactions=1),\"binth\",0.045,\"0.045\",Interact.OptionDict(DataStructures.OrderedDict(\"0.01\"=>0.01,\"0.015\"=>0.015,\"0.02\"=>0.02,\"0.025\"=>0.025,\"0.03\"=>0.03,\"0.035\"=>0.035,\"0.04\"=>0.04,\"0.045\"=>0.045,\"0.05\"=>0.05,\"0.055\"=>0.055…),Dict(0.055=>\"0.055\",0.015=>\"0.015\",0.04=>\"0.04\",0.08=>\"0.08\",0.045=>\"0.045\",0.01=>\"0.01\",0.07=>\"0.07\",0.06=>\"0.06\",0.05=>\"0.05\",0.075=>\"0.075\"…)),Any[],Any[],true,\"horizontal\")"
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",
      "text/plain": [
       "151×151 ColorView{RGB}(::ImageCore.StackedView{Float64,3,Tuple{Array{Float64,2},Array{Float64,2},Array{Float64,2}}}) with element type ColorTypes.RGB{Float64}:\n",
       " RGB{Float64}(0.0,0.0150205,0.0150205)        …  RGB{Float64}(0.0,0.0095342,0.0095342)    \n",
       " RGB{Float64}(0.0,-0.000353095,-0.000353095)     RGB{Float64}(0.0,0.00256769,0.00256769)  \n",
       " RGB{Float64}(0.0,0.0111624,0.0111624)           RGB{Float64}(0.0,0.0200024,0.0200024)    \n",
       " RGB{Float64}(0.0,-0.0103494,-0.0103494)         RGB{Float64}(0.0,0.0127407,0.0127407)    \n",
       " RGB{Float64}(0.0,0.00579856,0.00579856)         RGB{Float64}(0.0,0.0117925,0.0117925)    \n",
       " RGB{Float64}(0.0,-0.0160354,-0.0160354)      …  RGB{Float64}(0.0,0.0280175,0.0280175)    \n",
       " RGB{Float64}(0.0,-0.0121117,-0.0121117)         RGB{Float64}(0.0,0.00108888,0.00108888)  \n",
       " RGB{Float64}(0.0,0.0254494,0.0254494)           RGB{Float64}(0.0,-0.00304037,-0.00304037)\n",
       " RGB{Float64}(0.0,0.0184246,0.0184246)           RGB{Float64}(0.0,0.00672054,0.00672054)  \n",
       " RGB{Float64}(0.0,0.0135947,0.0135947)           RGB{Float64}(0.0,0.00205906,0.00205906)  \n",
       " RGB{Float64}(0.0,0.00403642,0.00403642)      …  RGB{Float64}(0.0,0.00718782,0.00718782)  \n",
       " RGB{Float64}(0.0,-0.000664608,-0.000664608)     RGB{Float64}(0.0,0.0190579,0.0190579)    \n",
       " RGB{Float64}(0.0,0.00148154,0.00148154)         RGB{Float64}(0.0,0.0190346,0.0190346)    \n",
       " ⋮                                            ⋱  ⋮                                        \n",
       " RGB{Float64}(0.0,0.00524544,0.00524544)         RGB{Float64}(0.0,-0.0277895,-0.0277895)  \n",
       " RGB{Float64}(0.0,0.0192283,0.0192283)        …  RGB{Float64}(0.0,0.00261766,0.00261766)  \n",
       " RGB{Float64}(0.0,0.0126522,0.0126522)           RGB{Float64}(0.0,0.0275832,0.0275832)    \n",
       " RGB{Float64}(0.0,-0.0058023,-0.0058023)         RGB{Float64}(0.0,0.027682,0.027682)      \n",
       " RGB{Float64}(0.0,-0.0128325,-0.0128325)         RGB{Float64}(0.0,0.00797125,0.00797125)  \n",
       " RGB{Float64}(0.0,-0.00395247,-0.00395247)       RGB{Float64}(0.0,-0.0216017,-0.0216017)  \n",
       " RGB{Float64}(0.0,0.00780504,0.00780504)      …  RGB{Float64}(0.0,-0.0135278,-0.0135278)  \n",
       " RGB{Float64}(0.0,0.00608584,0.00608584)         RGB{Float64}(0.0,-0.0137204,-0.0137204)  \n",
       " RGB{Float64}(0.0,0.00585259,0.00585259)         RGB{Float64}(0.0,0.0089281,0.0089281)    \n",
       " RGB{Float64}(0.0,-0.00349419,-0.00349419)       RGB{Float64}(0.0,0.00821044,0.00821044)  \n",
       " RGB{Float64}(0.0,0.019344,0.019344)             RGB{Float64}(0.0,-0.0322906,-0.0322906)  \n",
       " RGB{Float64}(0.0,0.0187973,0.0187973)        …  RGB{Float64}(0.0,-0.00174969,-0.00174969)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "comm_id": "57b2624e-3d87-424f-9f6f-809167edff01",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@manipulate for i = 1:length(ids), \n",
    "    binth = 0.01:0.005:0.08\n",
    "        idplot(i, binth)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "782a1464-d0a5-4e55-946b-4d0290b2a914"
    }
   },
   "source": [
    "# quick and dirty approach\n",
    "(= only entropy and symmetrie)\n",
    "## Preprocess\n",
    "- smooth image and threshold to create binary image and use `label_components` to find central galaxy. \n",
    "- crop central galaxy by using symmetry: minimum of deviations around center for each axis\n",
    "- set non-label components in crop to zero\n",
    "## Features\n",
    "- distances (given)\n",
    "- lower percentiles for full image for noise level (eg lower 1 and 5 percentile)\n",
    "- entropy of crop,\n",
    "- add sum of values and number of galaxy (nonzero) pixels\n",
    "- some symmetry measures from eigenvalues\n",
    "\n",
    "Then run good ol' XGBoost on features\n",
    "\n",
    "## further ideas\n",
    "- entropy after fitting and removing (two consecutive?) gaussian fits\n",
    "- use `imROF` for denoising\n",
    "- cut off peaks higher than center value? \n",
    "- remove remaining galaxies/stars in the crop instead of crude non-labels to zero by fitting gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "nbpresent": {
     "id": "741671d0-1d3f-49ca-8fa9-c172ef1176dd"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "49×49 Array{Gray{Float32},2}:\n",
       " Gray{Float32}(0.00675163)   …  Gray{Float32}(0.0479072)  \n",
       " Gray{Float32}(-0.00338378)     Gray{Float32}(0.0491828)  \n",
       " Gray{Float32}(0.0176451)       Gray{Float32}(0.0266497)  \n",
       " Gray{Float32}(0.0321021)       Gray{Float32}(0.0329973)  \n",
       " Gray{Float32}(0.0211408)       Gray{Float32}(0.0299969)  \n",
       " Gray{Float32}(0.0305206)    …  Gray{Float32}(0.0172947)  \n",
       " Gray{Float32}(0.0176071)       Gray{Float32}(0.0347831)  \n",
       " Gray{Float32}(0.0325029)       Gray{Float32}(0.0245)     \n",
       " Gray{Float32}(0.0324689)       Gray{Float32}(0.0230031)  \n",
       " Gray{Float32}(0.0239669)       Gray{Float32}(0.049489)   \n",
       " Gray{Float32}(0.0414707)    …  Gray{Float32}(0.0279436)  \n",
       " Gray{Float32}(0.0269767)       Gray{Float32}(0.0605018)  \n",
       " Gray{Float32}(0.0266628)       Gray{Float32}(0.0560693)  \n",
       " ⋮                           ⋱                            \n",
       " Gray{Float32}(0.0451207)       Gray{Float32}(0.0328975)  \n",
       " Gray{Float32}(0.0261321)       Gray{Float32}(0.0661133)  \n",
       " Gray{Float32}(0.0503749)       Gray{Float32}(0.0295547)  \n",
       " Gray{Float32}(0.0425482)    …  Gray{Float32}(-0.00443279)\n",
       " Gray{Float32}(0.0307534)       Gray{Float32}(0.011413)   \n",
       " Gray{Float32}(0.0500444)       Gray{Float32}(0.0207767)  \n",
       " Gray{Float32}(0.0364699)       Gray{Float32}(0.0156666)  \n",
       " Gray{Float32}(0.0059102)       Gray{Float32}(0.0188135)  \n",
       " Gray{Float32}(0.0194895)    …  Gray{Float32}(0.0360846)  \n",
       " Gray{Float32}(0.0381789)       Gray{Float32}(0.0272379)  \n",
       " Gray{Float32}(0.0203887)       Gray{Float32}(0.0356493)  \n",
       " Gray{Float32}(0.0205004)       Gray{Float32}(0.0218199)  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci, cj = fld.(size(img_raw), 2)\n",
    "BOXMIN = 10\n",
    "cwidth, cheight = max(findbox(labs_ind), (BOXMIN,BOXMIN))\n",
    "\n",
    "img_crop = img_raw[ci-cheight:ci+cheight, cj-cwidth:cj+cwidth]\n",
    "colorview(Gray, img_crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "nbpresent": {
     "id": "9ebc61c2-3b69-4ddb-8acc-fdbb1bb716bc"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "49×49 Array{Gray{Float32},2}:\n",
       " Gray{Float32}(0.00675163)   …  Gray{Float32}(0.0479072)  \n",
       " Gray{Float32}(-0.00338378)     Gray{Float32}(0.0491828)  \n",
       " Gray{Float32}(0.0176451)       Gray{Float32}(0.0266497)  \n",
       " Gray{Float32}(0.0321021)       Gray{Float32}(0.0329973)  \n",
       " Gray{Float32}(0.0211408)       Gray{Float32}(0.0299969)  \n",
       " Gray{Float32}(0.0305206)    …  Gray{Float32}(0.0172947)  \n",
       " Gray{Float32}(0.0176071)       Gray{Float32}(0.0347831)  \n",
       " Gray{Float32}(0.0325029)       Gray{Float32}(0.0245)     \n",
       " Gray{Float32}(0.0324689)       Gray{Float32}(0.0230031)  \n",
       " Gray{Float32}(0.0239669)       Gray{Float32}(0.049489)   \n",
       " Gray{Float32}(0.0414707)    …  Gray{Float32}(0.0279436)  \n",
       " Gray{Float32}(0.0269767)       Gray{Float32}(0.0605018)  \n",
       " Gray{Float32}(0.0266628)       Gray{Float32}(0.0560693)  \n",
       " ⋮                           ⋱                            \n",
       " Gray{Float32}(0.0451207)       Gray{Float32}(0.0328975)  \n",
       " Gray{Float32}(0.0261321)       Gray{Float32}(0.0661133)  \n",
       " Gray{Float32}(0.0503749)       Gray{Float32}(0.0295547)  \n",
       " Gray{Float32}(0.0425482)    …  Gray{Float32}(-0.00443279)\n",
       " Gray{Float32}(0.0307534)       Gray{Float32}(0.011413)   \n",
       " Gray{Float32}(0.0500444)       Gray{Float32}(0.0207767)  \n",
       " Gray{Float32}(0.0364699)       Gray{Float32}(0.0156666)  \n",
       " Gray{Float32}(0.0059102)       Gray{Float32}(0.0188135)  \n",
       " Gray{Float32}(0.0194895)    …  Gray{Float32}(0.0360846)  \n",
       " Gray{Float32}(0.0381789)       Gray{Float32}(0.0272379)  \n",
       " Gray{Float32}(0.0203887)       Gray{Float32}(0.0356493)  \n",
       " Gray{Float32}(0.0205004)       Gray{Float32}(0.0218199)  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_onlycblob = copy(img_raw)\n",
    "img_onlycblob[!labs_ind] = 0\n",
    "boxsize = max(cwidth, cheight)\n",
    "img_box = img_raw[ci-boxsize:ci+boxsize, cj-boxsize:cj+boxsize]\n",
    "colorview(Gray, img_box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "nbpresent": {
     "id": "822d33d7-569f-4308-8a8f-d802d9decb67"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.002158 seconds (738 allocations: 61.313 KB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10-element Array{Float32,1}:\n",
       " 645.704    \n",
       "  17.6002   \n",
       "   0.942475 \n",
       "   0.887299 \n",
       "   0.0973208\n",
       "   0.093082 \n",
       "   0.0130057\n",
       "   0.0130057\n",
       "   0.0113171\n",
       "   0.0113171"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@time reseigs = eigs(img_box, nev=10)\n",
    "norm.(reseigs[1]).^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "nbpresent": {
     "id": "c0ec3dcb-c406-4f96-809b-5bd41c4ec8c8"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.001327 seconds (286 allocations: 31.688 KB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10-element Array{Float32,1}:\n",
       " 647.59     \n",
       "  17.7356   \n",
       "   1.0503   \n",
       "   0.901244 \n",
       "   0.144213 \n",
       "   0.129747 \n",
       "   0.0570758\n",
       "   0.0509714\n",
       "   0.0372561\n",
       "   0.0361984"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@time reseigs = eigs(img_crop'*img_crop, nev=10)\n",
    "norm.(reseigs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "nbpresent": {
     "id": "1ef4950a-1bcf-468a-8d48-db87b8999db4"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.054558771384226"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(img_crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "nbpresent": {
     "id": "d5301cb9-db2e-4f07-a37d-65b2fabd222f"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5881191265860775"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise = img_crop - imfilter(img_crop, Kernel.gaussian(1))\n",
    "\n",
    "maximum(noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "nbpresent": {
     "id": "32f6dad9-757e-4359-87f0-1bddfe41b769"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7846815517236028"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(img_crop - imfilter(img_crop, Kernel.gaussian(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "cf958059-bd99-49c6-a414-047a5d4268e1"
    }
   },
   "source": [
    "# Process function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "ff2295c0-cdea-4d6e-9443-beccc08d264a"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "using StatsBase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "730c237d-91a3-4b08-a401-0ee09f50016b"
    }
   },
   "outputs": [],
   "source": [
    "addprocs();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "d0f0dbb4-1d4f-4d2a-8168-3ac00185b8ba"
    }
   },
   "outputs": [],
   "source": [
    "@everywhere using Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "0797a4ec-37fc-40f8-9d46-90d7fb8acd0c"
    }
   },
   "outputs": [],
   "source": [
    "traindir = \"/Users/ken/Coding/astrohack/Train/\"\n",
    "trainlist = readdir(traindir);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "e4cc6805-c8c9-4e15-9334-3a15ac5a21e3"
    }
   },
   "outputs": [],
   "source": [
    "# remove i-pics once\n",
    "# i_files = filter(x->x[end-4]=='i', trainlist)\n",
    "# cd(traindir)\n",
    "# for file in i_files\n",
    "#     rm(file)\n",
    "# end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "fe834357-5128-4be0-92a5-49847be1d4bd"
    }
   },
   "outputs": [],
   "source": [
    "using DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "nbpresent": {
     "id": "32e6c9bb-39d5-449a-a3d9-321f6b4ec3e0"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>SDSS_ID</th><th>logMstar</th><th>err_logMstar</th><th>Distance</th></tr></thead><tbody><tr><th>1</th><td>1237645879578460255</td><td>11.100999999999999</td><td>0.028999999999999998</td><td>518.0174523561253</td></tr><tr><th>2</th><td>1237645941824356443</td><td>10.539000000000001</td><td>0.032</td><td>172.10307774074076</td></tr><tr><th>3</th><td>1237645943974396134</td><td>11.318</td><td>0.015</td><td>654.2479282849002</td></tr><tr><th>4</th><td>1237645943975837722</td><td>10.862</td><td>0.026000000000000002</td><td>285.27259536182333</td></tr><tr><th>5</th><td>1237645943978328289</td><td>9.485</td><td>0.042</td><td>165.69725598860398</td></tr><tr><th>6</th><td>1237645943978393694</td><td>10.755999999999998</td><td>0.028999999999999998</td><td>173.81129687464386</td></tr><tr><th>7</th><td>1237645943978983579</td><td>10.168</td><td>0.024</td><td>207.54862476923074</td></tr><tr><th>8</th><td>1237645943978983683</td><td>11.792</td><td>0.017</td><td>794.3218972649573</td></tr><tr><th>9</th><td>1237645943979114622</td><td>10.932</td><td>0.044000000000000004</td><td>168.25958468945865</td></tr><tr><th>10</th><td>1237645943979507954</td><td>10.82</td><td>0.012</td><td>158.43732466951565</td></tr><tr><th>11</th><td>1237645943979638945</td><td>10.09</td><td>0.04</td><td>163.56198207122506</td></tr><tr><th>12</th><td>1237646796526715482</td><td>10.455</td><td>0.033</td><td>222.49554219088319</td></tr><tr><th>13</th><td>1237646797600522764</td><td>11.238</td><td>0.013999999999999999</td><td>390.32807209686604</td></tr><tr><th>14</th><td>1237648672921813985</td><td>9.585</td><td>0.057999999999999996</td><td>187.90410472934474</td></tr><tr><th>15</th><td>1237648672922337955</td><td>10.74</td><td>0.031</td><td>196.0181456153846</td></tr><tr><th>16</th><td>1237648673458684355</td><td>10.677999999999999</td><td>0.028999999999999998</td><td>215.66266565527064</td></tr><tr><th>17</th><td>1237648673458881081</td><td>10.315</td><td>0.032</td><td>253.67054138461535</td></tr><tr><th>18</th><td>1237648673459077169</td><td>11.09</td><td>0.011000000000000001</td><td>252.38937703418802</td></tr><tr><th>19</th><td>1237648673459274344</td><td>11.027999999999999</td><td>0.022000000000000002</td><td>400.5773869002848</td></tr><tr><th>20</th><td>1237648673459339331</td><td>11.205</td><td>0.027999999999999997</td><td>399.72327733333333</td></tr><tr><th>21</th><td>1237648673459667002</td><td>9.934</td><td>0.043</td><td>190.03937864672363</td></tr><tr><th>22</th><td>1237648673459667234</td><td>10.974</td><td>0.024</td><td>197.29930996581194</td></tr><tr><th>23</th><td>1237648673460585255</td><td>10.957</td><td>0.016</td><td>299.79245799999995</td></tr><tr><th>24</th><td>1237648673971437623</td><td>10.227</td><td>0.015</td><td>53.38184793447294</td></tr><tr><th>25</th><td>1237648673995162093</td><td>10.449000000000002</td><td>0.032</td><td>131.5328733105413</td></tr><tr><th>26</th><td>1237648673995686549</td><td>11.024000000000001</td><td>0.019</td><td>338.22738851282054</td></tr><tr><th>27</th><td>1237648673995948107</td><td>10.299000000000001</td><td>0.045</td><td>116.58595588888888</td></tr><tr><th>28</th><td>1237648673996997574</td><td>10.603</td><td>0.048</td><td>252.8164318176638</td></tr><tr><th>29</th><td>1237648673997127724</td><td>10.638</td><td>0.026000000000000002</td><td>187.90410472934474</td></tr><tr><th>30</th><td>1237648674510995594</td><td>11.522</td><td>0.013999999999999999</td><td>289.5431431965812</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/plain": [
       "76265×4 DataFrames.DataFrame\n",
       "│ Row   │ SDSS_ID             │ logMstar │ err_logMstar │ Distance │\n",
       "├───────┼─────────────────────┼──────────┼──────────────┼──────────┤\n",
       "│ 1     │ 1237645879578460255 │ 11.101   │ 0.029        │ 518.017  │\n",
       "│ 2     │ 1237645941824356443 │ 10.539   │ 0.032        │ 172.103  │\n",
       "│ 3     │ 1237645943974396134 │ 11.318   │ 0.015        │ 654.248  │\n",
       "│ 4     │ 1237645943975837722 │ 10.862   │ 0.026        │ 285.273  │\n",
       "│ 5     │ 1237645943978328289 │ 9.485    │ 0.042        │ 165.697  │\n",
       "│ 6     │ 1237645943978393694 │ 10.756   │ 0.029        │ 173.811  │\n",
       "│ 7     │ 1237645943978983579 │ 10.168   │ 0.024        │ 207.549  │\n",
       "│ 8     │ 1237645943978983683 │ 11.792   │ 0.017        │ 794.322  │\n",
       "│ 9     │ 1237645943979114622 │ 10.932   │ 0.044        │ 168.26   │\n",
       "│ 10    │ 1237645943979507954 │ 10.82    │ 0.012        │ 158.437  │\n",
       "│ 11    │ 1237645943979638945 │ 10.09    │ 0.04         │ 163.562  │\n",
       "⋮\n",
       "│ 76254 │ 1237680269351059849 │ 10.452   │ 0.032        │ 216.944  │\n",
       "│ 76255 │ 1237680269354074542 │ 10.931   │ 0.015        │ 361.288  │\n",
       "│ 76256 │ 1237680272573596177 │ 11.683   │ 0.01         │ 1027.92  │\n",
       "│ 76257 │ 1237680296734032113 │ 11.227   │ 0.023        │ 482.999  │\n",
       "│ 76258 │ 1237680297272476017 │ 10.806   │ 0.02         │ 263.066  │\n",
       "│ 76259 │ 1237680297805283668 │ 10.995   │ 0.041        │ 316.875  │\n",
       "│ 76260 │ 1237680298342285780 │ 11.044   │ 0.03         │ 412.962  │\n",
       "│ 76261 │ 1237680298344775943 │ 11.628   │ 0.026        │ 673.038  │\n",
       "│ 76262 │ 1237680305857626739 │ 11.297   │ 0.015        │ 342.498  │\n",
       "│ 76263 │ 1237680306394628150 │ 11.002   │ 0.025        │ 419.795  │\n",
       "│ 76264 │ 1237680331091607873 │ 10.417   │ 0.039        │ 175.52   │\n",
       "│ 76265 │ 1237680331628347642 │ 10.765   │ 0.05         │ 299.792  │"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainmeta = readtable(\"/Users/ken/Coding/astrohack/Train.csv\",separator=';')\n",
    "sort!(trainmeta) #sort on ID to match directory listing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "nbpresent": {
     "id": "a6599d5a-0437-4c54-9c79-abac7b934282"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(trainmeta,1) == length(trainlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "nbpresent": {
     "id": "18722c84-91da-42c6-a03b-74b907647cc9"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n",
      "WARNING: Method definition process(Any) in module Main at In[189]:3 overwritten at In[191]:3WARNING: Method definition process.\n",
      "(Any) in module Main at In[189]:3 overwritten at In[191]:3.\n"
     ]
    }
   ],
   "source": [
    "@everywhere function process(file)\n",
    "    \n",
    "    BINTH  = 0.05 # chosen by eye\n",
    "    BOXMIN = 10   # minimum crop of 100 pixels\n",
    "    T = Float32   # typical premature optimization\n",
    "    \n",
    "    img_raw = readcsv(file, T) \n",
    "    ci, cj = fld.(size(img_raw), 2)\n",
    "    \n",
    "    # get percentiles for noise levels\n",
    "    img_vec = reshape(img_raw, length(img_raw))\n",
    "    p1 = StatsBase.percentile(img_vec, 1)\n",
    "    p2 = StatsBase.percentile(img_vec, 5)\n",
    "    \n",
    "    # select the galaxy by thresholding smoothed image and labeling blobs\n",
    "    img_smooth = imfilter(T, img_raw, Kernel.gaussian(2))\n",
    "    img_bin = img_smooth .> BINTH\n",
    "    labels = label_components(img_bin)\n",
    "    labs_ind = labels .== labels[ci, cj]\n",
    "    \n",
    "    # crop galaxy\n",
    "    cwidth, cheight = max(findbox(labs_ind), (BOXMIN,BOXMIN))\n",
    "    crop_ind = ci-cheight:ci+cheight, cj-cwidth:cj+cwidth\n",
    "    img_crop = img_raw[crop_ind...]\n",
    "\n",
    "    #Crudely set non galaxy values in crop to zero to remove possible other galaxy\n",
    "    labels_ind_crop = labs_ind[crop_ind...]\n",
    "    img_crop[!labels_ind_crop] = 0\n",
    "    \n",
    "    # get top eigenvalues (spectra) for symmetry features\n",
    "    reseigs = eigs(img_crop'*img_crop, nev=10)\n",
    "    spect = norm.(reseigs[1])\n",
    "    \n",
    "    # get sum of intensities and (squared) noise\n",
    "    s1 = sum(img_crop)\n",
    "    img_crop_smooth = view(img_smooth, crop_ind...)\n",
    "    s2 = sum(img_crop - img_crop_smooth)\n",
    "    s3 = sum((img_crop - img_crop_smooth).^2)\n",
    "    s4 = sum(labs_ind) #number of galaxy pixels \n",
    "    s5 = norm(img_crop) #why not..\n",
    "    \n",
    "    # get entropy \n",
    "    e1 = Images.entropy(img_crop)\n",
    "    e2 = Images.entropy(img_crop - imfilter(T, img_crop, Kernel.gaussian(2)))\n",
    "    \n",
    "    return T[p1,p2, s1, s2, s3, s4, s5, e1, e2, spect...]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "nbpresent": {
     "id": "9133804e-fa45-4e7c-a048-4e604fb0af64"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.011020 seconds (35.67 k allocations: 2.102 MB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "19-element Array{Float32,1}:\n",
       "   -0.019007 \n",
       "   -0.0117548\n",
       "  309.332    \n",
       "  -11.8464   \n",
       "   39.1035   \n",
       " 1283.0      \n",
       "   19.6011   \n",
       "    4.088    \n",
       "    3.27034  \n",
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       "    0.0271679\n",
       "    0.0230588"
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     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test the function\n",
    "@time process(joinpath(traindir, trainlist[10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "nbpresent": {
     "id": "1d25d85c-166a-44a9-b2eb-3a9111056476"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.009558 seconds (162 allocations: 8.563 KB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "19-element Array{Float32,1}:\n",
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       " 1283.0      \n",
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       "    8.92826  \n",
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       "    0.174709 \n",
       "    0.0511882\n",
       "    0.0380461\n",
       "    0.0332357\n",
       "    0.0284385\n",
       "    0.0271685\n",
       "    0.0230591"
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     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test on other worker thread\n",
    "@time remotecall_fetch(process, 2, joinpath(traindir, trainlist[10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "6546dc11-d802-4707-ae62-32d2c4680cba"
    }
   },
   "outputs": [],
   "source": [
    "# @time begin\n",
    "#     N = 100\n",
    "#     fs = zeros(N, 15)\n",
    "#     fs[:, 1] = trainmeta[:Distance][1:N]\n",
    "#     @sync @parallel for i=1:N\n",
    "#         file = joinpath(traindir, trainlist[i])\n",
    "#         fs[i, 2:end] = process(file)\n",
    "#     end\n",
    "# end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "nbpresent": {
     "id": "b7dc863b-6955-4e1c-8669-e6b966c4d5ac"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "387.255984 seconds (11.21 M allocations: 448.294 MB, 0.18% gc time)\n"
     ]
    }
   ],
   "source": [
    "# process all train images in parallel and extract features\n",
    "# longest step, about 6 minutes on my 2015 macbook pro\n",
    "@time resmap = pmap(process, String[joinpath(traindir, f) for f in trainlist]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "96db7311-4ad6-4554-aeeb-70e5363dc7a2"
    }
   },
   "outputs": [],
   "source": [
    "feats = zeros(eltype(resmap[1]), length(resmap), 1+length(resmap[1]))\n",
    "feats[:, 1] = trainmeta[:Distance]\n",
    "for i = 1:length(resmap)\n",
    "    feats[i, 2:end] = resmap[i]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "df2647cc-d0d0-4bbc-a8f2-61a5ed3ad0a8"
    }
   },
   "outputs": [],
   "source": [
    "values = convert(Array{Float32}, trainmeta[:logMstar]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0f27f5d3-d233-4df2-82b8-0199e3ae5ed0"
    }
   },
   "source": [
    "# Regression\n",
    "XGBoost has a known [compile issue](https://xgboost.readthedocs.io/en/latest/build.html#building-on-osx) on mac because clang does not come with multithreaded OpenMP installed.\n",
    "So I installed gcc with homebrew `brew install gcc --without-multilib`, but you still have to link gcc-6 and g++-6 locally! \n",
    "I installed `Pkg.add(\"XGBoost\")` and in the `deps` folder I copied the config.mk to main xgboost folder and changed the gcc and g++ to local /usr/local/bin/gcc-6 paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "1ccd7d0d-f5e1-4f1d-b723-2a16fa50292f"
    }
   },
   "outputs": [],
   "source": [
    "using XGBoost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "nbpresent": {
     "id": "26ab6edb-4de3-4372-9675-c2b7134c8134"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1374"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(values .== -99) #missing values!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "nbpresent": {
     "id": "a7d5668f-56bc-4bc7-a93b-c0b5de41d2cc"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74891,20)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with removing missing values (god damnit)\n",
    "nrows = length(resmap) - sum(values.==-99)\n",
    "T = eltype(resmap[1])\n",
    "feats2 = zeros(T, nrows, size(feats, 2))\n",
    "values2 = zeros(T, nrows)\n",
    "err2 = zeros(T, nrows)\n",
    "currenti = 1\n",
    "for i = 1:length(resmap)\n",
    "    if values[i] != -99\n",
    "        feats2[currenti, 1] = trainmeta[:Distance][i]\n",
    "        feats2[currenti, 2:end] = resmap[i]\n",
    "        values2[currenti] = values[i]\n",
    "        err2[currenti] = trainmeta[:err_logMstar][i]\n",
    "        currenti += 1\n",
    "    end\n",
    "end\n",
    "size(feats2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "nbpresent": {
     "id": "f2483363-9f57-4d76-93be-19270f418bbc"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plots.GRBackend()"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "gr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "nbpresent": {
     "id": "6bedc75b-4e69-4553-abce-4eab140b42e2"
    }
   },
   "outputs": [
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       "  \" fill=\"#0099ff\" fill-opacity=\"1\"/>\n",
       "<polyline clip-path=\"url(#clip02)\" style=\"stroke:#00002d; stroke-width:0.8; stroke-opacity:1; fill:none\" points=\"\n",
       "  562.809,384.707 562.809,384.952 580.311,384.952 580.311,384.707 562.809,384.707 \n",
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       "<polyline clip-path=\"url(#clip00)\" style=\"stroke:#00002d; stroke-width:0.8; stroke-opacity:1; fill:none\" points=\"\n",
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       "  \"/>\n",
       "<polygon clip-path=\"url(#clip00)\" points=\"\n",
       "511.547,45.985 547.547,45.985 547.547,33.889 511.547,33.889 511.547,45.985 \n",
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       "<polyline clip-path=\"url(#clip00)\" style=\"stroke:#00002d; stroke-width:0.8; stroke-opacity:1; fill:none\" points=\"\n",
       "  511.547,45.985 547.547,45.985 547.547,33.889 511.547,33.889 511.547,45.985 \n",
       "  \"/>\n",
       "<g clip-path=\"url(#clip00)\">\n",
       "<text style=\"fill:#00002d; fill-opacity:1; font-family:Arial,Helvetica Neue,Helvetica,sans-serif; font-size:12; text-anchor:start;\" transform=\"rotate(0, 553.547, 44.437)\" x=\"553.547\" y=\"44.437\">y1</text>\n",
       "</g>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "histogram(values2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "932f1ae2-1799-4d01-8e9c-cfd8346f2e25"
    }
   },
   "outputs": [],
   "source": [
    "rand_ids = shuffle(1:length(values2));\n",
    "\n",
    "train_n = length(values2) - 1734\n",
    "train_ids = rand_ids[1:train_n]\n",
    "\n",
    "#train_sample = sample(1:length(feats), train_n, replace=false)\n",
    "train_x = feats2[train_ids, :]\n",
    "train_y = values2[train_ids]\n",
    "\n",
    "test_n  = 1734\n",
    "test_ids= rand_ids[train_n:train_n+test_n]\n",
    "test_x  = feats2[test_ids, :]\n",
    "test_y  = values2[test_ids]\n",
    "test_err= err2[test_ids];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "nbpresent": {
     "id": "86abb0d6-1795-4a00-b4b1-53717b9443af"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[1]\ttrain-rmse:7.053658\n",
      "[2]\ttrain-rmse:4.942040\n",
      "[3]\ttrain-rmse:3.464624\n",
      "[4]\ttrain-rmse:2.431568\n",
      "[5]\ttrain-rmse:1.710097\n",
      "[6]\ttrain-rmse:1.207397\n",
      "[7]\ttrain-rmse:0.858015\n",
      "[8]\ttrain-rmse:0.615704\n",
      "[9]\ttrain-rmse:0.447069\n",
      "[10]\ttrain-rmse:0.328264\n",
      "[11]\ttrain-rmse:0.243532\n",
      "[12]\ttrain-rmse:0.182273\n",
      "[13]\ttrain-rmse:0.137520\n",
      "[14]\ttrain-rmse:0.104366\n",
      "[15]\ttrain-rmse:0.079667\n",
      "[16]\ttrain-rmse:0.061113\n",
      "[17]\ttrain-rmse:0.047157\n",
      "[18]\ttrain-rmse:0.036617\n",
      "[19]\ttrain-rmse:0.028589\n",
      "[20]\ttrain-rmse:0.022415\n",
      "[21]\ttrain-rmse:0.017671\n",
      "[22]\ttrain-rmse:0.014006\n",
      "[23]\ttrain-rmse:0.011178\n",
      "[24]\ttrain-rmse:0.008958\n",
      "[25]\ttrain-rmse:0.007220\n",
      "[26]\ttrain-rmse:0.005842\n",
      "[27]\ttrain-rmse:0.004757\n",
      "[28]\ttrain-rmse:0.003882\n",
      "[29]\ttrain-rmse:0.003181\n",
      "[30]\ttrain-rmse:0.002616\n",
      "[31]\ttrain-rmse:0.002161\n",
      "[32]\ttrain-rmse:0.001797\n",
      "[33]\ttrain-rmse:0.001501\n",
      "[34]\ttrain-rmse:0.001260\n",
      "[35]\ttrain-rmse:0.001060\n",
      "[36]\ttrain-rmse:0.000909\n",
      "[37]\ttrain-rmse:0.000785\n",
      "[38]\ttrain-rmse:0.000686\n",
      "[39]\ttrain-rmse:0.000604\n",
      "[40]\ttrain-rmse:0.000551\n",
      "[41]\ttrain-rmse:0.000512\n",
      "[42]\ttrain-rmse:0.000505\n",
      "[43]\ttrain-rmse:0.000468\n",
      "[44]\ttrain-rmse:0.000467\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 15.151809 seconds (7.82 k allocations: 6.371 MB)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[45]\ttrain-rmse:0.000464\n",
      "[46]\ttrain-rmse:0.000464\n",
      "[47]\ttrain-rmse:0.000464\n",
      "[48]\ttrain-rmse:0.000464\n",
      "[49]\ttrain-rmse:0.000464\n",
      "[50]\ttrain-rmse:0.000464\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBoost.Booster(Ptr{Void} @0x00007f9bcb14c100)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@time boost = xgboost(train_x, 50, label=train_y, max_depth=50, learning_rate=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "9c368a20-d758-4a2d-a890-b7278bb673ba"
    }
   },
   "outputs": [],
   "source": [
    "test_pred = XGBoost.predict(boost, test_x);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "nbpresent": {
     "id": "1c11f619-57d1-4c2c-bd96-97999ad143b6"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1735×3 Array{Float32,2}:\n",
       " 11.182  11.182    0.009\n",
       " 11.51   11.5777   0.026\n",
       " 10.907  10.8396   0.037\n",
       " 10.59   10.5571   0.041\n",
       " 11.42   11.2049   0.031\n",
       " 10.256  10.011    0.024\n",
       " 11.171  10.6016   0.025\n",
       "  9.349   9.34016  0.055\n",
       " 10.296  10.2444   0.014\n",
       " 10.941  10.942    0.01 \n",
       " 10.71   10.7143   0.042\n",
       " 11.307  11.2312   0.034\n",
       "  9.463   9.29443  0.045\n",
       "  ⋮                     \n",
       "  9.971  10.0718   0.03 \n",
       " 10.808  10.8301   0.022\n",
       " 10.317  10.4416   0.056\n",
       "  9.23    9.22179  0.056\n",
       "  9.746   9.84914  0.033\n",
       " 11.514  11.4574   0.029\n",
       " 10.371  10.3303   0.021\n",
       " 10.349  10.3235   0.033\n",
       " 10.192   9.96918  0.063\n",
       " 10.101  10.2186   0.028\n",
       " 10.792  11.0878   0.023\n",
       " 10.557  10.4991   0.034"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hcat(test_y, test_pred, test_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "nbpresent": {
     "id": "fcb7a3dd-f7f7-44c4-8e63-b4535755515d"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133952.8f0"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum((test_y - test_pred).^2 ./ (test_err.^2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e6a9aa95-b2ed-42b4-9344-3cc49bd099b8"
    }
   },
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "da3d84bc-3fd8-4252-938a-53c2ab0ed314"
    }
   },
   "outputs": [],
   "source": [
    "sub_ids = readcsv(\"validationdata_SDSSID.csv\", Int, header=false)\n",
    "# sort!\n",
    "sub_ids = sort!(reshape(sub_ids, length(sub_ids))) \n",
    "sub_dir = \"/Users/ken/Coding/astrohack/Test/\"\n",
    "sub_files = [joinpath(sub_dir, string(id) * \"-g.csv\") for id in sub_ids];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "nbpresent": {
     "id": "cc6f0670-c136-48e6-862b-d5fb93b083af"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1734"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "BoundsError",
     "evalue": "BoundsError: attempt to access 13-element Array{UInt8,1} at index [-1]",
     "output_type": "error",
     "traceback": [
      "KERNEL EXCEPTION",
      "BoundsError: attempt to access 13-element Array{UInt8,1} at index [-1]",
      "",
      " in next at ./strings/string.jl:92 [inlined]",
      " in getindex(::String, ::Int64) at ./strings/basic.jl:70",
      " in ind2chr(::String, ::Int64) at ./strings/basic.jl:227",
      " in complete_request(::ZMQ.Socket, ::IJulia.Msg) at /Users/ken/.julia/v0.5/IJulia/src/handlers.jl:40",
      " in eventloop(::ZMQ.Socket) at /Users/ken/.julia/v0.5/IJulia/src/eventloop.jl:8",
      " in (::IJulia.##13#19)() at ./task.jl:360"
     ]
    }
   ],
   "source": [
    "length(sub_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "1aa1f283-f257-49b0-908f-61d7eb525296"
    }
   },
   "outputs": [],
   "source": [
    "testfiles = readdir(sub_dir);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "ffdf888e-cfca-48d7-b5cf-8298011ac63c"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# remove i-pics once\n",
    "# i_files = filter(x->x[end-4]=='i', testfiles)\n",
    "\n",
    "# cd(sub_dir)\n",
    "# for file in i_files\n",
    "#     rm(file)\n",
    "# end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "nbpresent": {
     "id": "641eed1a-1199-41b8-a769-7229ac2ce018"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  7.182995 seconds (234.93 k allocations: 9.468 MB)\n"
     ]
    }
   ],
   "source": [
    "@time subresmap = pmap(process, sub_files);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "nbpresent": {
     "id": "80ce5706-bcf0-48a0-9b36-67ad8943b4ae"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>SDSS_ID</th><th>Distance</th></tr></thead><tbody><tr><th>1</th><td>1237645943978590386</td><td>160.57259858689457</td></tr><tr><th>2</th><td>1237645943979114582</td><td>169.9678038233618</td></tr><tr><th>3</th><td>1237645943979311221</td><td>152.88561248433047</td></tr><tr><th>4</th><td>1237648672922468973</td><td>198.58047431623933</td></tr><tr><th>5</th><td>1237648673992671592</td><td>232.3178022108262</td></tr><tr><th>6</th><td>1237648674529476993</td><td>137.08458549572646</td></tr><tr><th>7</th><td>1237648674532753564</td><td>371.5376616239317</td></tr><tr><th>8</th><td>1237648675603874096</td><td>140.07396898005695</td></tr><tr><th>9</th><td>1237648702972887077</td><td>367.6941685726495</td></tr><tr><th>10</th><td>1237648702978654353</td><td>212.67328217094013</td></tr><tr><th>11</th><td>1237648702979244208</td><td>231.03663786039883</td></tr><tr><th>12</th><td>1237648702983897351</td><td>287.83492406267806</td></tr><tr><th>13</th><td>1237648702985797942</td><td>299.7924579999999</td></tr><tr><th>14</th><td>1237648702986125622</td><td>518.0174523561253</td></tr><tr><th>15</th><td>1237648702992023572</td><td>144.7715715982906</td></tr><tr><th>16</th><td>1237648703503794279</td><td>300.64656756695155</td></tr><tr><th>17</th><td>1237648703504515190</td><td>108.0448602193732</td></tr><tr><th>18</th><td>1237648703505432738</td><td>102.49314803418802</td></tr><tr><th>19</th><td>1237648703505629237</td><td>477.4472479259259</td></tr><tr><th>20</th><td>1237648703508119586</td><td>98.64965498290596</td></tr><tr><th>21</th><td>1237648703509758235</td><td>366.41300422222224</td></tr><tr><th>22</th><td>1237648703512772638</td><td>374.9540998917379</td></tr><tr><th>23</th><td>1237648703516508315</td><td>510.33046625356116</td></tr><tr><th>24</th><td>1237648703527649617</td><td>102.9202028176638</td></tr><tr><th>25</th><td>1237648703531123239</td><td>396.7338938490028</td></tr><tr><th>26</th><td>1237648704045908141</td><td>132.38698287749284</td></tr><tr><th>27</th><td>1237648704046563336</td><td>54.66301228490028</td></tr><tr><th>28</th><td>1237648704054231257</td><td>222.06848740740742</td></tr><tr><th>29</th><td>1237648704055017848</td><td>149.46917421652418</td></tr><tr><th>30</th><td>1237648704057573661</td><td>184.48766646153842</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/plain": [
       "8447×2 DataFrames.DataFrame\n",
       "│ Row  │ SDSS_ID             │ Distance │\n",
       "├──────┼─────────────────────┼──────────┤\n",
       "│ 1    │ 1237645943978590386 │ 160.573  │\n",
       "│ 2    │ 1237645943979114582 │ 169.968  │\n",
       "│ 3    │ 1237645943979311221 │ 152.886  │\n",
       "│ 4    │ 1237648672922468973 │ 198.58   │\n",
       "│ 5    │ 1237648673992671592 │ 232.318  │\n",
       "│ 6    │ 1237648674529476993 │ 137.085  │\n",
       "│ 7    │ 1237648674532753564 │ 371.538  │\n",
       "│ 8    │ 1237648675603874096 │ 140.074  │\n",
       "│ 9    │ 1237648702972887077 │ 367.694  │\n",
       "│ 10   │ 1237648702978654353 │ 212.673  │\n",
       "│ 11   │ 1237648702979244208 │ 231.037  │\n",
       "⋮\n",
       "│ 8436 │ 1237679477481341013 │ 189.612  │\n",
       "│ 8437 │ 1237679503792341122 │ 296.803  │\n",
       "│ 8438 │ 1237679543509909678 │ 252.389  │\n",
       "│ 8439 │ 1237680000914227483 │ 183.207  │\n",
       "│ 8440 │ 1237680071248838902 │ 614.959  │\n",
       "│ 8441 │ 1237680090576781616 │ 370.684  │\n",
       "│ 8442 │ 1237680092721643956 │ 247.265  │\n",
       "│ 8443 │ 1237680240907255947 │ 447.126  │\n",
       "│ 8444 │ 1237680240911319122 │ 145.626  │\n",
       "│ 8445 │ 1237680240920297607 │ 232.745  │\n",
       "│ 8446 │ 1237680240923508872 │ 173.384  │\n",
       "│ 8447 │ 1237680241434689718 │ 257.941  │"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submeta = readtable(\"/Users/ken/Coding/astrohack/Test_Distance.csv\",separator=';')\n",
    "sort!(submeta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "nbpresent": {
     "id": "0b6bf9d0-caa9-4200-a913-a18c8bd21661"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8447,2)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(submeta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "nbpresent": {
     "id": "d952396d-4042-4776-8438-0fa1e7922fa2"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>SDSS_ID</th><th>Distance</th></tr></thead><tbody><tr><th>1</th><td>1237645943979114582</td><td>169.9678038233618</td></tr><tr><th>2</th><td>1237648672922468973</td><td>198.58047431623933</td></tr><tr><th>3</th><td>1237648702986125622</td><td>518.0174523561253</td></tr><tr><th>4</th><td>1237648703505432738</td><td>102.49314803418802</td></tr><tr><th>5</th><td>1237648703508119586</td><td>98.64965498290596</td></tr><tr><th>6</th><td>1237648703509758235</td><td>366.41300422222224</td></tr><tr><th>7</th><td>1237648703516508315</td><td>510.33046625356116</td></tr><tr><th>8</th><td>1237648704045908141</td><td>132.38698287749284</td></tr><tr><th>9</th><td>1237648704046563336</td><td>54.66301228490028</td></tr><tr><th>10</th><td>1237648704057966842</td><td>357.01779898575495</td></tr><tr><th>11</th><td>1237648704061505776</td><td>272.46095185754984</td></tr><tr><th>12</th><td>1237648704596279527</td><td>230.60958307692312</td></tr><tr><th>13</th><td>1237648704600736312</td><td>354.882525068376</td></tr><tr><th>14</th><td>1237648704602702523</td><td>209.2568439031339</td></tr><tr><th>15</th><td>1237648704602767946</td><td>210.11095347008546</td></tr><tr><th>16</th><td>1237648705122074764</td><td>155.8749959686609</td></tr><tr><th>17</th><td>1237648705135641098</td><td>308.3335536695156</td></tr><tr><th>18</th><td>1237648705137607019</td><td>142.6362976809117</td></tr><tr><th>19</th><td>1237648705656783006</td><td>179.36300905982904</td></tr><tr><th>20</th><td>1237648720157540452</td><td>349.75786766666664</td></tr><tr><th>21</th><td>1237648720163045463</td><td>368.1212233561253</td></tr><tr><th>22</th><td>1237648720163111061</td><td>204.98629606837605</td></tr><tr><th>23</th><td>1237648720164225051</td><td>96.94143584900284</td></tr><tr><th>24</th><td>1237648720165470336</td><td>317.72875890598283</td></tr><tr><th>25</th><td>1237648720175235225</td><td>177.6547899259259</td></tr><tr><th>26</th><td>1237648720691134685</td><td>503.4975897179487</td></tr><tr><th>27</th><td>1237648720712237155</td><td>146.9068455156695</td></tr><tr><th>28</th><td>1237648720713679086</td><td>313.45821107122504</td></tr><tr><th>29</th><td>1237648720718201265</td><td>166.55136555555555</td></tr><tr><th>30</th><td>1237648721219158109</td><td>386.05752426210813</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/plain": [
       "1734×2 DataFrames.DataFrame\n",
       "│ Row  │ SDSS_ID             │ Distance │\n",
       "├──────┼─────────────────────┼──────────┤\n",
       "│ 1    │ 1237645943979114582 │ 169.968  │\n",
       "│ 2    │ 1237648672922468973 │ 198.58   │\n",
       "│ 3    │ 1237648702986125622 │ 518.017  │\n",
       "│ 4    │ 1237648703505432738 │ 102.493  │\n",
       "│ 5    │ 1237648703508119586 │ 98.6497  │\n",
       "│ 6    │ 1237648703509758235 │ 366.413  │\n",
       "│ 7    │ 1237648703516508315 │ 510.33   │\n",
       "│ 8    │ 1237648704045908141 │ 132.387  │\n",
       "│ 9    │ 1237648704046563336 │ 54.663   │\n",
       "│ 10   │ 1237648704057966842 │ 357.018  │\n",
       "│ 11   │ 1237648704061505776 │ 272.461  │\n",
       "⋮\n",
       "│ 1723 │ 1237678622233919544 │ 377.089  │\n",
       "│ 1724 │ 1237678623306088660 │ 507.768  │\n",
       "│ 1725 │ 1237678723164471586 │ 121.711  │\n",
       "│ 1726 │ 1237678860092309623 │ 559.442  │\n",
       "│ 1727 │ 1237678905180946539 │ 401.859  │\n",
       "│ 1728 │ 1237678919142342820 │ 284.846  │\n",
       "│ 1729 │ 1237679169840087160 │ 281.429  │\n",
       "│ 1730 │ 1237679434525114569 │ 376.235  │\n",
       "│ 1731 │ 1237679437736968315 │ 322.426  │\n",
       "│ 1732 │ 1237679437737754841 │ 181.498  │\n",
       "│ 1733 │ 1237679503792341122 │ 296.803  │\n",
       "│ 1734 │ 1237680000914227483 │ 183.207  │"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_ids_df = DataFrame(SDSS_ID=sub_ids)\n",
    "submeta2 = join(submeta, sub_ids_df, on=:SDSS_ID, kind=:inner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "nbpresent": {
     "id": "c5a9608c-4257-4e8b-863f-1a949466f04c"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issorted(submeta2[:SDSS_ID])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "nbpresent": {
     "id": "f80edfbc-599e-40c5-9e46-0cc12c9f10f2"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1734,2)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(submeta2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "nbpresent": {
     "id": "8ce1af6c-dacc-476a-a1b7-b49c21b1c825"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(submeta2[:SDSS_ID] .== sub_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "b2b6edaf-de0d-49da-bfc3-846b91b412a9"
    }
   },
   "outputs": [],
   "source": [
    "subfeats = zeros(eltype(subresmap[1]), length(subresmap), 1+length(subresmap[1]))\n",
    "subfeats[:, 1] = submeta2[:Distance]\n",
    "for i = 1:length(subresmap)\n",
    "    subfeats[i, 2:end] = subresmap[i]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "nbpresent": {
     "id": "4d10d2bf-4feb-4f85-873e-852c8ab547d4"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>pssid</th><th>mass</th></tr></thead><tbody><tr><th>1</th><td>1237645943979114582</td><td>10.253867</td></tr><tr><th>2</th><td>1237648672922468973</td><td>10.499608</td></tr><tr><th>3</th><td>1237648702986125622</td><td>11.209326</td></tr><tr><th>4</th><td>1237648703505432738</td><td>9.918384</td></tr><tr><th>5</th><td>1237648703508119586</td><td>9.894254</td></tr><tr><th>6</th><td>1237648703509758235</td><td>10.147396</td></tr><tr><th>7</th><td>1237648703516508315</td><td>11.507912</td></tr><tr><th>8</th><td>1237648704045908141</td><td>10.090234</td></tr><tr><th>9</th><td>1237648704046563336</td><td>10.331856</td></tr><tr><th>10</th><td>1237648704057966842</td><td>11.38165</td></tr><tr><th>11</th><td>1237648704061505776</td><td>10.500261</td></tr><tr><th>12</th><td>1237648704596279527</td><td>11.028665</td></tr><tr><th>13</th><td>1237648704600736312</td><td>10.517279</td></tr><tr><th>14</th><td>1237648704602702523</td><td>9.920931</td></tr><tr><th>15</th><td>1237648704602767946</td><td>9.871428</td></tr><tr><th>16</th><td>1237648705122074764</td><td>10.525749</td></tr><tr><th>17</th><td>1237648705135641098</td><td>10.855779</td></tr><tr><th>18</th><td>1237648705137607019</td><td>9.664072</td></tr><tr><th>19</th><td>1237648705656783006</td><td>10.777236</td></tr><tr><th>20</th><td>1237648720157540452</td><td>11.080576</td></tr><tr><th>21</th><td>1237648720163045463</td><td>11.019354</td></tr><tr><th>22</th><td>1237648720163111061</td><td>10.698677</td></tr><tr><th>23</th><td>1237648720164225051</td><td>9.682376</td></tr><tr><th>24</th><td>1237648720165470336</td><td>11.08739</td></tr><tr><th>25</th><td>1237648720175235225</td><td>10.604492</td></tr><tr><th>26</th><td>1237648720691134685</td><td>11.087707</td></tr><tr><th>27</th><td>1237648720712237155</td><td>10.511227</td></tr><tr><th>28</th><td>1237648720713679086</td><td>10.898017</td></tr><tr><th>29</th><td>1237648720718201265</td><td>10.513243</td></tr><tr><th>30</th><td>1237648721219158109</td><td>11.407637</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
      ],
      "text/plain": [
       "1734×2 DataFrames.DataFrame\n",
       "│ Row  │ pssid               │ mass    │\n",
       "├──────┼─────────────────────┼─────────┤\n",
       "│ 1    │ 1237645943979114582 │ 10.2539 │\n",
       "│ 2    │ 1237648672922468973 │ 10.4996 │\n",
       "│ 3    │ 1237648702986125622 │ 11.2093 │\n",
       "│ 4    │ 1237648703505432738 │ 9.91838 │\n",
       "│ 5    │ 1237648703508119586 │ 9.89425 │\n",
       "│ 6    │ 1237648703509758235 │ 10.1474 │\n",
       "│ 7    │ 1237648703516508315 │ 11.5079 │\n",
       "│ 8    │ 1237648704045908141 │ 10.0902 │\n",
       "│ 9    │ 1237648704046563336 │ 10.3319 │\n",
       "│ 10   │ 1237648704057966842 │ 11.3816 │\n",
       "│ 11   │ 1237648704061505776 │ 10.5003 │\n",
       "⋮\n",
       "│ 1723 │ 1237678622233919544 │ 10.885  │\n",
       "│ 1724 │ 1237678623306088660 │ 11.2099 │\n",
       "│ 1725 │ 1237678723164471586 │ 10.8189 │\n",
       "│ 1726 │ 1237678860092309623 │ 11.3288 │\n",
       "│ 1727 │ 1237678905180946539 │ 10.9814 │\n",
       "│ 1728 │ 1237678919142342820 │ 10.2505 │\n",
       "│ 1729 │ 1237679169840087160 │ 10.869  │\n",
       "│ 1730 │ 1237679434525114569 │ 10.9291 │\n",
       "│ 1731 │ 1237679437736968315 │ 11.3565 │\n",
       "│ 1732 │ 1237679437737754841 │ 11.0959 │\n",
       "│ 1733 │ 1237679503792341122 │ 10.8611 │\n",
       "│ 1734 │ 1237680000914227483 │ 10.2471 │"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subpred = XGBoost.predict(boost, subfeats)\n",
    "submission = DataFrame(pssid=sub_ids,mass=subpred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "nbpresent": {
     "id": "9c0d4b02-6684-4dc0-af8e-3285b41a074c"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"/Users/ken/Coding/astrohack/Test\""
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "c6e784fd-67af-4ca8-b079-629b09e0f03c"
    }
   },
   "outputs": [],
   "source": [
    "writetable(\"/Users/ken/Coding/astrohack/AstroWhack_20170510_0515\", submission, separator=',') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ef7dac22-4c4d-479c-83f2-5bd4cee4f4a5"
    }
   },
   "source": [
    "# Final submission on full test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "34159fa4-cabd-4fc9-ab1c-8ec00d414e56"
    }
   },
   "outputs": [],
   "source": [
    "finalfiles = [joinpath(sub_dir, testfile) for testfile in testfiles];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "nbpresent": {
     "id": "ce8d7f2b-6f8f-46c9-b090-7f532b28bd61"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "string((submeta[:SDSS_ID])[i]) = \"1237680241434689718\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# i=8447\n",
    "# @show string(submeta[:SDSS_ID][i])\n",
    "# string(submeta[:SDSS_ID][i]) == testfiles[i][1:end-6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "nbpresent": {
     "id": "37426630-db6b-4cc3-8af8-3f01e33713f7"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 36.799306 seconds (1.17 M allocations: 46.200 MB, 0.09% gc time)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8447-element Array{Any,1}:\n",
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     "execution_count": 290,
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    "@time finalresmap = pmap(process, finalfiles)"
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    "res = finalresmap\n",
    "finalfeats = zeros(eltype(res[1]), length(res), 1+length(res[1]))\n",
    "finalfeats[:, 1] = submeta[:Distance]\n",
    "for i = 1:length(res)\n",
    "    finalfeats[i, 2:end] = res[i]\n",
    "end"
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       "<table class=\"data-frame\"><thead><tr><th></th><th>pssid</th><th>mass</th></tr></thead><tbody><tr><th>1</th><td>1237645943978590386</td><td>10.0421915</td></tr><tr><th>2</th><td>1237645943979114582</td><td>10.253867</td></tr><tr><th>3</th><td>1237645943979311221</td><td>10.291842</td></tr><tr><th>4</th><td>1237648672922468973</td><td>10.499608</td></tr><tr><th>5</th><td>1237648673992671592</td><td>11.1321535</td></tr><tr><th>6</th><td>1237648674529476993</td><td>10.320119</td></tr><tr><th>7</th><td>1237648674532753564</td><td>11.115282</td></tr><tr><th>8</th><td>1237648675603874096</td><td>10.217378</td></tr><tr><th>9</th><td>1237648702972887077</td><td>10.962672</td></tr><tr><th>10</th><td>1237648702978654353</td><td>10.688571</td></tr><tr><th>11</th><td>1237648702979244208</td><td>10.409277</td></tr><tr><th>12</th><td>1237648702983897351</td><td>10.241099</td></tr><tr><th>13</th><td>1237648702985797942</td><td>10.920508</td></tr><tr><th>14</th><td>1237648702986125622</td><td>11.209326</td></tr><tr><th>15</th><td>1237648702992023572</td><td>10.736224</td></tr><tr><th>16</th><td>1237648703503794279</td><td>11.2124</td></tr><tr><th>17</th><td>1237648703504515190</td><td>10.264055</td></tr><tr><th>18</th><td>1237648703505432738</td><td>9.918384</td></tr><tr><th>19</th><td>1237648703505629237</td><td>11.054634</td></tr><tr><th>20</th><td>1237648703508119586</td><td>9.894254</td></tr><tr><th>21</th><td>1237648703509758235</td><td>10.147396</td></tr><tr><th>22</th><td>1237648703512772638</td><td>11.356924</td></tr><tr><th>23</th><td>1237648703516508315</td><td>11.507912</td></tr><tr><th>24</th><td>1237648703527649617</td><td>10.6849375</td></tr><tr><th>25</th><td>1237648703531123239</td><td>10.986324</td></tr><tr><th>26</th><td>1237648704045908141</td><td>10.090234</td></tr><tr><th>27</th><td>1237648704046563336</td><td>10.331856</td></tr><tr><th>28</th><td>1237648704054231257</td><td>10.622571</td></tr><tr><th>29</th><td>1237648704055017848</td><td>9.959886</td></tr><tr><th>30</th><td>1237648704057573661</td><td>11.089472</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
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       "8447×2 DataFrames.DataFrame\n",
       "│ Row  │ pssid               │ mass    │\n",
       "├──────┼─────────────────────┼─────────┤\n",
       "│ 1    │ 1237645943978590386 │ 10.0422 │\n",
       "│ 2    │ 1237645943979114582 │ 10.2539 │\n",
       "│ 3    │ 1237645943979311221 │ 10.2918 │\n",
       "│ 4    │ 1237648672922468973 │ 10.4996 │\n",
       "│ 5    │ 1237648673992671592 │ 11.1322 │\n",
       "│ 6    │ 1237648674529476993 │ 10.3201 │\n",
       "│ 7    │ 1237648674532753564 │ 11.1153 │\n",
       "│ 8    │ 1237648675603874096 │ 10.2174 │\n",
       "│ 9    │ 1237648702972887077 │ 10.9627 │\n",
       "│ 10   │ 1237648702978654353 │ 10.6886 │\n",
       "│ 11   │ 1237648702979244208 │ 10.4093 │\n",
       "⋮\n",
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       "│ 8437 │ 1237679503792341122 │ 10.8611 │\n",
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       "│ 8439 │ 1237680000914227483 │ 10.2471 │\n",
       "│ 8440 │ 1237680071248838902 │ 11.346  │\n",
       "│ 8441 │ 1237680090576781616 │ 10.9764 │\n",
       "│ 8442 │ 1237680092721643956 │ 10.7604 │\n",
       "│ 8443 │ 1237680240907255947 │ 11.2593 │\n",
       "│ 8444 │ 1237680240911319122 │ 10.3873 │\n",
       "│ 8445 │ 1237680240920297607 │ 10.5414 │\n",
       "│ 8446 │ 1237680240923508872 │ 10.1785 │\n",
       "│ 8447 │ 1237680241434689718 │ 10.646  │"
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    "finaldf   = DataFrame(pssid=submeta[:SDSS_ID],mass=finalpred)"
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    "writetable(\"/Users/ken/Coding/astrohack/AstroWhack_final_predictions_20170501\", finaldf, separator=',') "
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