This gist shows that in the patch of sky used by @SimonKrughoff contains a small number of galaxies, and the galtileid % (2^32 -1) values are unique

test.ipynb.json

{
 "cells": [
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   "metadata": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/manual/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n",
      "Duplicate object type id 25 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n"
     ]
    }
   ],
   "source": [
    "from lsst.sims.catUtils.mixins import CosmologyMixin\n",
    "from lsst.sims.utils import ObservationMetaData\n",
    "from lsst.sims.catUtils.utils import ObservationMetaDataGenerator\n",
    "from lsst.sims.catalogs.generation.db import CatalogDBObject\n",
    "from lsst.sims.catalogs.measures.instance import InstanceCatalog\n",
    "import eups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/usr/local/manual/anaconda/lib/python2.7/site-packages/sqlalchemy/dialects/mssql/base.py:1294: SAWarning: Did not recognize type 'geography' of column 'geopoint'\n",
      "  return fn(*arg, **kw)\n"
     ]
    }
   ],
   "source": [
    "galDB = CatalogDBObject.from_objid('galaxyTiled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class galCopy(InstanceCatalog):\n",
    "    column_outputs = ['galtileid', 'raJ2000', 'decJ2000', 'redshift']\n",
    "    override_formats = {'raJ2000': '%8e', 'decJ2000': '%8e'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "opsimPath = os.path.join(os.getenv('HOME'), 'data', 'LSST', 'OpSimData')\n",
    "opsimDB = os.path.join(opsimPath,'enigma_1189_sqlite.db')\n",
    "generator = ObservationMetaDataGenerator(database=opsimDB, driver='sqlite')\n",
    "obsMetaDataResults = generator.getObservationMetaData(limit=1, fieldRA=(53, 54), fieldDec=(-29, -27), boundLength=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
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       "49353.051334000003"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "obsMetaDataResults[0].mjd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "galphot = galCopy(db_obj=galDB, obs_metadata=obsMetaDataResults[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "galphot.write_catalog('Twinkles_galphot_2.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/manual/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py:644: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n",
      "  ParserWarning)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('Twinkles_galphot_2.dat',sep=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.rename(columns={'#galtileid':'galtileid'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   },
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    {
     "data": {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>galtileid</th>\n",
       "      <th>raJ2000</th>\n",
       "      <th>decJ2000</th>\n",
       "      <th>redshift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60016401894</td>\n",
       "      <td>0.921689</td>\n",
       "      <td>-0.482484</td>\n",
       "      <td>0.2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60015814101</td>\n",
       "      <td>0.921661</td>\n",
       "      <td>-0.482505</td>\n",
       "      <td>1.3331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60012610249</td>\n",
       "      <td>0.921696</td>\n",
       "      <td>-0.482492</td>\n",
       "      <td>0.8434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60006127897</td>\n",
       "      <td>0.921689</td>\n",
       "      <td>-0.482503</td>\n",
       "      <td>1.3539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60000415840</td>\n",
       "      <td>0.921686</td>\n",
       "      <td>-0.482507</td>\n",
       "      <td>0.4542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     galtileid   raJ2000  decJ2000  redshift\n",
       "0  60016401894  0.921689 -0.482484    0.2575\n",
       "1  60015814101  0.921661 -0.482505    1.3331\n",
       "2  60012610249  0.921696 -0.482492    0.8434\n",
       "3  60006127897  0.921689 -0.482503    1.3539\n",
       "4  60000415840  0.921686 -0.482507    0.4542"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "outputs": [
    {
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       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['galtileid'].apply(lambda x: x % 4294967295).unique()) - len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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    "len(df)"
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       "243475    4166789026\n",
       "Name: galtileid, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['galtileid'].apply(lambda x: x % 4294967295)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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       "55722460950"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['galtileid'].max() - 2**32"
   ]
  }
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