diff --git a/dmu1/dmu1_ml_XMM-LSS/2_Merging_low_memory.ipynb b/dmu1/dmu1_ml_XMM-LSS/2_Merging_low_memory.ipynb
index 8a1fbfc3..2ff1a8da 100644
--- a/dmu1/dmu1_ml_XMM-LSS/2_Merging_low_memory.ipynb
+++ b/dmu1/dmu1_ml_XMM-LSS/2_Merging_low_memory.ipynb
@@ -19,7 +19,7 @@
      "output_type": "stream",
      "text": [
       "This notebook was run with herschelhelp_internal version: \n",
-      "04829ed (Thu Nov 2 16:57:19 2017 +0000) [with local modifications]\n"
+      "44f1ae0 (Thu Nov 30 18:27:54 2017 +0000)\n"
      ]
     }
    ],
@@ -54,7 +54,7 @@
     "\n",
     "from astropy import units as u\n",
     "from astropy.coordinates import SkyCoord\n",
-    "from astropy.table import Column, Table\n",
+    "from astropy.table import Column, Table, join\n",
     "import numpy as np\n",
     "from pymoc import MOC\n",
     "\n",
@@ -95,27 +95,101 @@
    },
    "outputs": [],
    "source": [
-    "candels = Table.read(\"{}/CANDELS.fits\".format(TMP_DIR))[\"candels_id\",\"candels_ra\",\"candels_dec\",\"candels_stellarity\"]           # 1.1\n",
-    "cfht_wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR))[\"wirds_intid\",\"wirds_ra\",\"wirds_dec\",\"wirds_stellarity\", 'flag_merged']     # 1.3\n",
-    "cfhtls_wide = Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR))[\"cfhtls-wide_id\",\"cfhtls-wide_ra\",\"cfhtls-wide_dec\",\"cfhtls-wide_stellarity\"]   # 1.4a\n",
-    "cfhtls_deep = Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR))[\"cfhtls-deep_id\",\"cfhtls-deep_ra\",\"cfhtls-deep_dec\",\"cfhtls-deep_stellarity\"]   # 1.4b\n",
+    "candels = Table.read(\"{}/CANDELS.fits\".format(TMP_DIR)\n",
+    "                    )[\"candels_id\",\n",
+    "                      \"candels_ra\",\n",
+    "                      \"candels_dec\",\n",
+    "                      \"candels_stellarity\"]           # 1.1\n",
+    "cfht_wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR)\n",
+    "                       )[\"wirds_intid\",\n",
+    "                         \"wirds_ra\",\n",
+    "                         \"wirds_dec\",\n",
+    "                         \"wirds_stellarity\", \n",
+    "                         'flag_merged']     # 1.3\n",
+    "cfhtls_wide = Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR)\n",
+    "                        )[\"cfhtls-wide_id\",\n",
+    "                          \"cfhtls-wide_ra\",\n",
+    "                          \"cfhtls-wide_dec\",\n",
+    "                          \"cfhtls-wide_stellarity\"]   # 1.4a\n",
+    "cfhtls_deep = Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR)\n",
+    "                        )[\"cfhtls-deep_id\",\n",
+    "                          \"cfhtls-deep_ra\",\n",
+    "                          \"cfhtls-deep_dec\",\n",
+    "                          \"cfhtls-deep_stellarity\"]   # 1.4b\n",
     "#We no longer use CFHTLenS as it is the same raw data set as CFHTLS-WIDE\n",
     "# cfhtlens = Table.read(\"{}/CFHTLENS.fits\".format(TMP_DIR))         # 1.5\n",
-    "decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR))[\"decals_id\",\"decals_ra\",\"decals_dec\"]             # 1.6\n",
-    "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR))[\"servs_intid\",\"servs_ra\",\"servs_dec\"]               # 1.8\n",
-    "swire = Table.read(\"{}/SWIRE.fits\".format(TMP_DIR))[\"swire_intid\",\"swire_ra\",\"swire_dec\"]               # 1.7\n",
-    "hsc_wide = Table.read(\"{}/HSC-WIDE.fits\".format(TMP_DIR))[\"hsc-wide_id\",\"hsc-wide_ra\",\"hsc-wide_dec\", \"hsc-wide_stellarity\"]         # 1.9a\n",
-    "hsc_deep = Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR))[\"hsc-deep_id\",\"hsc-deep_ra\",\"hsc-deep_dec\", \"hsc-deep_stellarity\"]         # 1.9b\n",
-    "hsc_udeep = Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR))[\"hsc-udeep_id\",\"hsc-udeep_ra\",\"hsc-udeep_dec\", \"hsc-udeep_stellarity\"]       # 1.9c\n",
-    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))[\"ps1_id\",\"ps1_ra\",\"ps1_dec\"]                   # 1.10\n",
-    "sxds = Table.read(\"{}/SXDS.fits\".format(TMP_DIR))[\"sxds_b_id\", \"sxds_ra\", \"sxds_dec\",'flag_merged']                 # 1.11\n",
-    "sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR))['sparcs_intid', 'sparcs_ra', 'sparcs_dec', 'sparcs_stellarity']             # 1.12\n",
-    "dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR))['dxs_id','dxs_ra','dxs_dec', 'dxs_stellarity']            # 1.13\n",
-    "uds = Table.read(\"{}/UKIDSS-UDS.fits\".format(TMP_DIR))['uds_id','uds_ra','uds_dec','uds_stellarity']            # 1.14\n",
-    "vipers = Table.read(\"{}/VIPERS.fits\".format(TMP_DIR))[\"vipers_id\",\"vipers_ra\",\"vipers_dec\"]             # 1.15\n",
-    "vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))[\"vhs_id\",\"vhs_ra\",\"vhs_dec\",\"vhs_stellarity\"]             # 1.16\n",
-    "video = Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR))['video_id','video_ra','video_dec','video_stellarity','video_flag_gaia']         # 1.17\n",
-    "viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR))[\"viking_id\",\"viking_ra\",\"viking_dec\",\"viking_stellarity\",\"viking_flag_gaia\"]       # 1.18"
+    "decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR)\n",
+    "                   )[\"decals_id\",\n",
+    "                     \"decals_ra\",\n",
+    "                     \"decals_dec\"]             # 1.6\n",
+    "servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR)\n",
+    "                  )[\"servs_intid\",\n",
+    "                    \"servs_ra\",\n",
+    "                    \"servs_dec\"]               # 1.8\n",
+    "swire = Table.read(\"{}/SWIRE.fits\".format(TMP_DIR)\n",
+    "                  )[\"swire_intid\",\n",
+    "                    \"swire_ra\",\n",
+    "                    \"swire_dec\"]               # 1.7\n",
+    "hsc_wide = Table.read(\"{}/HSC-WIDE.fits\".format(TMP_DIR)\n",
+    "                     )[\"hsc-wide_id\",\n",
+    "                       \"hsc-wide_ra\",\n",
+    "                       \"hsc-wide_dec\", \n",
+    "                       \"hsc-wide_stellarity\"]         # 1.9a\n",
+    "hsc_deep = Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR)\n",
+    "                     )[\"hsc-deep_id\",\n",
+    "                       \"hsc-deep_ra\",\n",
+    "                       \"hsc-deep_dec\", \n",
+    "                       \"hsc-deep_stellarity\"]         # 1.9b\n",
+    "hsc_udeep = Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR)\n",
+    "                      )[\"hsc-udeep_id\",\n",
+    "                        \"hsc-udeep_ra\",\n",
+    "                        \"hsc-udeep_dec\", \n",
+    "                        \"hsc-udeep_stellarity\"]       # 1.9c\n",
+    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR)\n",
+    "                )[\"ps1_id\",\n",
+    "                  \"ps1_ra\",\n",
+    "                  \"ps1_dec\"]                   # 1.10\n",
+    "sxds = Table.read(\"{}/SXDS.fits\".format(TMP_DIR)\n",
+    "                 )[\"sxds_b_id\", \n",
+    "                   \"sxds_ra\", \n",
+    "                   \"sxds_dec\",\n",
+    "                   'flag_merged']                 # 1.11\n",
+    "sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR)\n",
+    "                   )['sparcs_intid', \n",
+    "                     'sparcs_ra', \n",
+    "                     'sparcs_dec', \n",
+    "                     'sparcs_stellarity']             # 1.12\n",
+    "dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR)\n",
+    "                )['dxs_id',\n",
+    "                  'dxs_ra',\n",
+    "                  'dxs_dec', \n",
+    "                  'dxs_stellarity']            # 1.13\n",
+    "uds = Table.read(\"{}/UKIDSS-UDS.fits\".format(TMP_DIR)\n",
+    "                )['uds_id',\n",
+    "                  'uds_ra',\n",
+    "                  'uds_dec',\n",
+    "                  'uds_stellarity']            # 1.14\n",
+    "vipers = Table.read(\"{}/VIPERS.fits\".format(TMP_DIR)\n",
+    "                   )[\"vipers_id\",\n",
+    "                     \"vipers_ra\",\n",
+    "                     \"vipers_dec\"]             # 1.15\n",
+    "vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR)\n",
+    "                )[\"vhs_id\",\n",
+    "                  \"vhs_ra\",\n",
+    "                  \"vhs_dec\",\n",
+    "                  \"vhs_stellarity\"]             # 1.16\n",
+    "video = Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR)\n",
+    "                  )['video_id',\n",
+    "                    'video_ra',\n",
+    "                    'video_dec',\n",
+    "                    'video_stellarity',\n",
+    "                    'video_flag_gaia']         # 1.17\n",
+    "viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR)\n",
+    "                   )[\"viking_id\",\n",
+    "                     \"viking_ra\",\n",
+    "                     \"viking_dec\",\n",
+    "                     \"viking_stellarity\",\n",
+    "                     \"viking_flag_gaia\"]       # 1.18"
    ]
   },
   {
@@ -225,7 +299,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cfht_wirds['flag_merged'].name = 'flag_merged_wirds'"
@@ -268,7 +344,7 @@
      "data": {
       "image/png": 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vTBpTBicSG6FiL84qyEvi4301bNIidp/nv7+FA4i/brNwIk0HSn/r7OpmW1k9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\niYiIiDhExUpERETEIf8f7Yg0wGcoYFYAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10b2e5710>"
+       "<matplotlib.figure.Figure at 0x116ded9e8>"
       ]
      },
      "metadata": {},
@@ -378,7 +454,7 @@
      "data": {
       "image/png": 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ulkzM4uOzC5yOI8PE43YxsyiV8tp2jvTpCrmcmkqX+GX13kN09g5w2dQcp6OM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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116315128>"
+       "<matplotlib.figure.Figure at 0x10d7d2e80>"
       ]
      },
      "metadata": {},
@@ -431,7 +507,7 @@
      "data": {
       "image/png": 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Li0czfUSCtj0ZBowxXDM5kxOdPaw/qI7s/kZ7F/i41w/UMTIlirzkKLdLGVLJ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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1162de4e0>"
+       "<matplotlib.figure.Figure at 0x1181ec320>"
       ]
      },
      "metadata": {},
@@ -542,7 +618,9 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "sxds['flag_merged'].name = 'flag_merged_sxds'"
@@ -585,7 +663,7 @@
      "data": {
       "image/png": 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iXFA3zijUjTMK1djaqRc/OKQXPqjWw2/s1r+9vltlOSn6xNR8XTVp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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10af00c18>"
+       "<matplotlib.figure.Figure at 0x10b8bd898>"
       ]
      },
      "metadata": {},
@@ -636,7 +714,7 @@
      "data": {
       "image/png": 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Yf0VxcbE752rHiTf2NwCwZFq6nysZsCA/mayEKFbvqlWwEvGpa+tiR2ULOypb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\n/pUaF8nH5+fy1JYqvnHt1HF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z6/P1rSozsx1m9i0zC/HdV2xmP/uAxxaZ2e2jV+2fHPuU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\nQIhgBQAQsgA6T9YGw5nBsgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1156f0780>"
+       "<matplotlib.figure.Figure at 0x10b8e9a58>"
       ]
      },
      "metadata": {},
@@ -685,7 +763,7 @@
      "data": {
       "image/png": 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Q43U6jgig0iUi4vestbyyo57q9l5umJtHTlK005ECwgUT0+gbGmZnbYfTUUQAlS4REb+3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\nRNTgL7ZGh7aiQlPkAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1156e0fd0>"
+       "<matplotlib.figure.Figure at 0x11a80b5c0>"
       ]
      },
      "metadata": {},
@@ -782,7 +860,7 @@
      "data": {
       "image/png": 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mBKsQUNnYoT9vq9Lt505koONHcPbkLE0ek6yH3jmgT87PZ+oFhLzG9m5tK2/S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\nUSDKFGUmM6nPObV29qq5o0c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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1162b8f60>"
+       "<matplotlib.figure.Figure at 0x1181ec668>"
       ]
      },
      "metadata": {},
@@ -831,7 +909,7 @@
      "data": {
       "image/png": 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r99YC4bmMzbtZWpxNVISxsqxGxUqC1rnGOlU3dbDtcCPbjzTS2tVLXHQE08em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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ceedf98>"
+       "<matplotlib.figure.Figure at 0x126c70470>"
       ]
      },
      "metadata": {},
@@ -880,7 +958,7 @@
      "data": {
       "image/png": 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LXf1DvLCjhkmJUawoTvvI+ybHhPPw5+fT3T/EnY9uoW9weJxSiojbmjr7+dnbR6ho7uEz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\nC9V0cDrPww1FAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x125f9b710>"
+       "<matplotlib.figure.Figure at 0x109dc3a58>"
       ]
      },
      "metadata": {},
@@ -922,7 +1000,7 @@
      "data": {
       "image/png": 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ReX16uAIN7T24b2bCsD1nWqQfArxM+Dqvftiek4iGTmVzJ1Zvy8OWkzUYG+2HH1+TivGx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\nMzMriRMrMzMzs5I4sTIzMzMryW+Q8ZG9f+ruDgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115618358>"
+       "<matplotlib.figure.Figure at 0x11a7f8cc0>"
       ]
      },
      "metadata": {},
@@ -986,7 +1064,7 @@
      "data": {
       "text/html": [
        "&lt;Table length=10&gt;\n",
-       "<table id=\"table4667397064-968557\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<table id=\"table4726900608-222054\" class=\"table-striped table-bordered table-condensed\">\n",
        "<thead><tr><th>idx</th><th>ps1_id</th><th>ra</th><th>dec</th><th>flag_merged</th><th>candels_id</th><th>candels_stellarity</th><th>wirds_intid</th><th>wirds_stellarity</th><th>flag_merged_wirds</th><th>cfhtls-wide_id</th><th>cfhtls-wide_stellarity</th><th>cfhtls-deep_id</th><th>cfhtls-deep_stellarity</th><th>hsc-wide_id</th><th>hsc-wide_stellarity</th><th>hsc-deep_id</th><th>hsc-deep_stellarity</th><th>hsc-udeep_id</th><th>hsc-udeep_stellarity</th><th>decals_id</th><th>sparcs_intid</th><th>sparcs_stellarity</th><th>sxds_b_id</th><th>flag_merged_sxds</th><th>dxs_id</th><th>dxs_stellarity</th><th>uds_id</th><th>uds_stellarity</th><th>vipers_id</th><th>video_id</th><th>video_stellarity</th><th>video_flag_gaia</th><th>vhs_id</th><th>vhs_stellarity</th><th>viking_id</th><th>viking_stellarity</th><th>viking_flag_gaia</th><th>servs_intid</th><th>swire_intid</th></tr></thead>\n",
        "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
        "<tr><td>0</td><td>103040354349392544</td><td>35.4349721923</td><td>-4.13153415525</td><td>False</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>-1</td><td>-1</td><td>nan</td><td>-1</td><td>False</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
@@ -1022,14 +1100,14 @@
        "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
        "}});\n",
        "require([\"datatables\"], function(){\n",
-       "    console.log(\"$('#table4667397064-968557').dataTable()\");\n",
+       "    console.log(\"$('#table4726900608-222054').dataTable()\");\n",
        "    \n",
        "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
        "    \"optionalnum-asc\": astropy_sort_num,\n",
        "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
        "});\n",
        "\n",
-       "    $('#table4667397064-968557').dataTable({\n",
+       "    $('#table4726900608-222054').dataTable({\n",
        "        order: [],\n",
        "        pageLength: 50,\n",
        "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
@@ -1117,7 +1195,9 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
@@ -1223,7 +1303,7 @@
    "source": [
     "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
     "                                   name=\"help_id\"))\n",
-    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"ELAIS-N1\", dtype='<U18'),\n",
+    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"XMM-LSS\", dtype='<U18'),\n",
     "                                   name=\"field\"))"
    ]
   },
@@ -1286,7 +1366,7 @@
      "data": {
       "image/png": 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YqQhWcWC8cT1g5ncpkqSizBTNzknVdoIVAGCGIVjFuJBzOtE1GBWN6xOtLs/RjhqCFQBg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\nPEKwAgAA8AjBCgAAwCP/P0aJ2w1g+8soAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12d0a7358>"
+       "<matplotlib.figure.Figure at 0x13ac17978>"
       ]
      },
      "metadata": {},
@@ -2000,7 +2080,9 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#megacam_stats.show_in_notebook()"
@@ -2294,7 +2376,9 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#vista_stats.show_in_notebook()"
@@ -2635,7 +2719,9 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#suprime_stats.show_in_notebook()"
@@ -2893,9 +2979,9 @@
    "outputs": [],
    "source": [
     "master_catalogue[id_names].write(\n",
-    "    \"{}/master_list_cross_ident_xmm-lss_low-memory{}.fits\".format(OUT_DIR, SUFFIX))\n",
+    "    \"{}/master_list_cross_ident_xmm-lss_low-memory{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
     "id_names.remove('help_id')\n",
-    "master_catalogue.remove_columns(id_names)"
+    "#master_catalogue.remove_columns(id_names)"
    ]
   },
   {
@@ -2959,7 +3045,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Missing columns: set()\n"
+      "Missing columns: {'candels_id', 'cfhtls-wide_id', 'sdss_id', 'hsc-deep_id', 'viking_id', 'wirds_intid', 'ps1_id', 'uds_id', 'sxds_b_id', 'vhs_id', 'video_id', 'sparcs_intid', 'hsc-udeep_id', 'swire_intid', 'dxs_id', 'specz_id', 'hsc-wide_id', 'cfhtls-deep_id', 'vipers_id', 'servs_intid', 'decals_id'}\n"
      ]
     }
    ],
@@ -2976,7 +3062,93 @@
    },
    "outputs": [],
    "source": [
-    "master_catalogue[columns].write(\"{}/master_catalogue_xmm-lss_low-memory{}.fits\".format(OUT_DIR, SUFFIX))"
+    "master_catalogue[columns].write(\"{}/master_catalogue_xmm-lss_low-memory{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## XII - folding in the photometry\n",
+    "On XMM-LSS there is too much data to load all in to memory at once so we perform the cross matching without photometry columns. Only now do we fold in the photometry data by first cutting the catalogue up in to manageable sizes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19\n"
+     ]
+    }
+   ],
+   "source": [
+    "split_length = 10000 #number of sources to include in every sub catalogue\n",
+    "num_files = int(np.ceil(len(master_catalogue)/split_length)\n",
+    "print(num_files)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "surveys = [\n",
+    "    ['candels',      \"CANDELS.fits\"]\n",
+    "    ['cfht_wirds',   \"CFHT-WIRDS.fits\"],\n",
+    "    ['cfhtls_wide',  \"CFHTLS-WIDE.fits\"],\n",
+    "    ['cfhtls_deep',  \"CFHTLS-DEEP.fits\"],\n",
+    "    ['decals',       \"DECaLS.fits\"],\n",
+    "    ['servs',        \"SERVS.fits\"],\n",
+    "    ['swire',        \"SWIRE.fits\"],             \n",
+    "    ['hsc_wide ',    \"HSC-WIDE.fits\"],\n",
+    "    ['hsc_deep',     \"HSC-DEEP.fits\"],\n",
+    "    ['hsc_udeep',    \"HSC-UDEEP.fits\"],      \n",
+    "    ['ps1',          \"PS1.fits\"],                  \n",
+    "    ['sxds',         \"SXDS.fits\"],               \n",
+    "    ['sparcs',       \"SpARCS.fits\"],           \n",
+    "    ['dxs',          \"UKIDSS-DXS.fits\"],          \n",
+    "    ['uds',          \"UKIDSS-UDS.fits\"],          \n",
+    "    ['vipers',       \"VIPERS.fits\"],           \n",
+    "    ['vhs',          \"VISTA-VHS.fits\"],           \n",
+    "    ['video',        \"VISTA-VIDEO.fits\"],       \n",
+    "    ['viking',       \"VISTA-VIKING.fits\"]]     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n=0\n",
+    "for sub_file in range(num_files):\n",
+    "    \n",
+    "    sub_catalogue = master_catalogue[n*split_length:(n+1)*split_length-1]\n",
+    "    \n",
+    "    for survey in surveys:\n",
+    "        sub_catalogue = join(sub_catalogue, \n",
+    "                               Table.read(\"{}/{}\".format(TMP_DIR, survey[1])),\n",
+    "                               join_type='left')\n",
+    "    #sub_catalogue.remove_columns(id_names)\n",
+    "    sub_catalogue.write(\"{}/master_catalogue_xmm-lss_low-memory{}_{}.fits\".format(OUT_DIR, SUFFIX, n), overwrite=True)\n",
+    "    n += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sub_catalogue[:10].show_in_notebook()"
    ]
   }
  ],