GBDES_residual_analysis_demo.ipynb

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   "source": [
    "from lsst.daf.butler import Butler\n",
    "from lsst.meas.algorithms import ReferenceObjectLoader\n",
    "import lsst.geom\n",
    "import lsst.afw.geom as afwgeom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb75e82d-1b32-4dc6-9b73-396c4a0e2a19",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from astropy.time import Time\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ceda98d-b251-47bf-8aa2-b3f31a411d30",
   "metadata": {},
   "source": [
    "## Set up the `butler` that will fetch the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a27a303b-5d99-4a0f-8b60-4aac693be700",
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   },
   "outputs": [
    {
     "data": {
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       "<pre style=\"margin: 0.1em;\n",
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       "border-left-style: solid;\n",
       "font-family: var(--jp-code-font-family);\n",
       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">botocore.credentials</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Found credentials in shared credentials file: /home/c/csaunder/.lsst/aws-credentials.ini</pre>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "butler = Butler('/repo/embargo', collections='u/csaunder/DM-41130/dev3_vis1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "14b6935d-771b-4800-b942-cbee946c6d82",
   "metadata": {
    "execution": {
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   "source": [
    "# The sky is split into \"tracts\", which are further split into \"patches\". The \"skymap\" determines how this is done. I'm not using it\n",
    "# anywhere in this analysis, but it has some helpful functions.\n",
    "skymap = butler.get('skyMap', skymap='latiss_v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d348bd-83d8-48d5-8d9c-32c73ef4ce26",
   "metadata": {},
   "source": [
    "## There are a couple ways to get information about the visits (aka exposures)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d47a438-e6d3-42e8-bd1a-747dbb9dd265",
   "metadata": {},
   "source": [
    "### The `visitTable` gives an overview of the conditions for a given visit:"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit</th>\n",
       "      <th>physical_filter</th>\n",
       "      <th>band</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>decl</th>\n",
       "      <th>skyRotation</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>altitude</th>\n",
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       "      <th>airmass</th>\n",
       "      <th>expTime</th>\n",
       "      <th>expMidpt</th>\n",
       "      <th>expMidptMJD</th>\n",
       "      <th>obsStart</th>\n",
       "      <th>obsStartMJD</th>\n",
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       "    <tr>\n",
       "      <th>visitId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2023051100262</th>\n",
       "      <td>2023051100262</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>239.941279</td>\n",
       "      <td>-24.050861</td>\n",
       "      <td>-24.050861</td>\n",
       "      <td>322.911701</td>\n",
       "      <td>100.652699</td>\n",
       "      <td>33.079204</td>\n",
       "      <td>56.920796</td>\n",
       "      <td>1.826588</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-05-12 01:08:56.302993</td>\n",
       "      <td>60076.047874</td>\n",
       "      <td>2023-05-12 01:08:41.302993</td>\n",
       "      <td>60076.047700</td>\n",
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       "    <tr>\n",
       "      <th>2023051100263</th>\n",
       "      <td>2023051100263</td>\n",
       "      <td>SDSSg_65mm~empty</td>\n",
       "      <td>g</td>\n",
       "      <td>239.943099</td>\n",
       "      <td>-24.042851</td>\n",
       "      <td>-24.042851</td>\n",
       "      <td>170.005077</td>\n",
       "      <td>100.427273</td>\n",
       "      <td>33.535722</td>\n",
       "      <td>56.464278</td>\n",
       "      <td>1.804276</td>\n",
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       "      <td>2023-05-12 01:11:06.209005</td>\n",
       "      <td>60076.049377</td>\n",
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       "      <td>60076.049204</td>\n",
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       "      <th>2023051100264</th>\n",
       "      <td>2023051100264</td>\n",
       "      <td>SDSSi_65mm~empty</td>\n",
       "      <td>i</td>\n",
       "      <td>239.940919</td>\n",
       "      <td>-24.040927</td>\n",
       "      <td>-24.040927</td>\n",
       "      <td>170.004955</td>\n",
       "      <td>100.024337</td>\n",
       "      <td>34.385246</td>\n",
       "      <td>55.614754</td>\n",
       "      <td>1.765315</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-05-12 01:15:04.912000</td>\n",
       "      <td>60076.052140</td>\n",
       "      <td>2023-05-12 01:14:49.912000</td>\n",
       "      <td>60076.051967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023051100268</th>\n",
       "      <td>2023051100268</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>239.960079</td>\n",
       "      <td>-24.042010</td>\n",
       "      <td>-24.042010</td>\n",
       "      <td>323.867206</td>\n",
       "      <td>99.573769</td>\n",
       "      <td>35.343223</td>\n",
       "      <td>54.656777</td>\n",
       "      <td>1.724092</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-05-12 01:19:39.311494</td>\n",
       "      <td>60076.055316</td>\n",
       "      <td>2023-05-12 01:19:24.311494</td>\n",
       "      <td>60076.055142</td>\n",
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       "      <th>2023051100269</th>\n",
       "      <td>2023051100269</td>\n",
       "      <td>SDSSg_65mm~empty</td>\n",
       "      <td>g</td>\n",
       "      <td>239.959845</td>\n",
       "      <td>-24.048026</td>\n",
       "      <td>-24.048026</td>\n",
       "      <td>170.004092</td>\n",
       "      <td>99.360495</td>\n",
       "      <td>35.811664</td>\n",
       "      <td>54.188336</td>\n",
       "      <td>1.704281</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-05-12 01:21:49.752999</td>\n",
       "      <td>60076.056826</td>\n",
       "      <td>2023-05-12 01:21:34.752999</td>\n",
       "      <td>60076.056652</td>\n",
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       "      <th>2023091400082</th>\n",
       "      <td>2023091400082</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>270.194112</td>\n",
       "      <td>-24.950260</td>\n",
       "      <td>-24.950260</td>\n",
       "      <td>359.998802</td>\n",
       "      <td>263.701292</td>\n",
       "      <td>44.750180</td>\n",
       "      <td>45.249820</td>\n",
       "      <td>1.417337</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-15 02:35:28.260509</td>\n",
       "      <td>60202.107966</td>\n",
       "      <td>2023-09-15 02:35:13.260509</td>\n",
       "      <td>60202.107792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023091400083</th>\n",
       "      <td>2023091400083</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>270.210518</td>\n",
       "      <td>-24.950264</td>\n",
       "      <td>-24.950264</td>\n",
       "      <td>359.998614</td>\n",
       "      <td>263.622517</td>\n",
       "      <td>44.584958</td>\n",
       "      <td>45.415042</td>\n",
       "      <td>1.421490</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-15 02:36:18.753500</td>\n",
       "      <td>60202.108550</td>\n",
       "      <td>2023-09-15 02:36:03.753500</td>\n",
       "      <td>60202.108377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023091400084</th>\n",
       "      <td>2023091400084</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>270.226810</td>\n",
       "      <td>-24.950250</td>\n",
       "      <td>-24.950250</td>\n",
       "      <td>359.998689</td>\n",
       "      <td>263.542128</td>\n",
       "      <td>44.416073</td>\n",
       "      <td>45.583927</td>\n",
       "      <td>1.425701</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-15 02:37:09.293010</td>\n",
       "      <td>60202.109135</td>\n",
       "      <td>2023-09-15 02:36:54.293010</td>\n",
       "      <td>60202.108962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023091400085</th>\n",
       "      <td>2023091400085</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>270.243077</td>\n",
       "      <td>-24.950268</td>\n",
       "      <td>-24.950268</td>\n",
       "      <td>359.998634</td>\n",
       "      <td>263.461810</td>\n",
       "      <td>44.247203</td>\n",
       "      <td>45.752797</td>\n",
       "      <td>1.429949</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-15 02:37:59.895505</td>\n",
       "      <td>60202.109721</td>\n",
       "      <td>2023-09-15 02:37:44.895505</td>\n",
       "      <td>60202.109547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023091400086</th>\n",
       "      <td>2023091400086</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "      <td>r</td>\n",
       "      <td>270.259439</td>\n",
       "      <td>-24.950265</td>\n",
       "      <td>-24.950265</td>\n",
       "      <td>359.998582</td>\n",
       "      <td>263.383379</td>\n",
       "      <td>44.082019</td>\n",
       "      <td>45.917981</td>\n",
       "      <td>1.434210</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2023-09-15 02:38:50.500006</td>\n",
       "      <td>60202.110307</td>\n",
       "      <td>2023-09-15 02:38:35.500006</td>\n",
       "      <td>60202.110133</td>\n",
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       "<p>1698 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "                       visit   physical_filter band          ra        dec  \\\n",
       "visitId                                                                      \n",
       "2023051100262  2023051100262  SDSSr_65mm~empty    r  239.941279 -24.050861   \n",
       "2023051100263  2023051100263  SDSSg_65mm~empty    g  239.943099 -24.042851   \n",
       "2023051100264  2023051100264  SDSSi_65mm~empty    i  239.940919 -24.040927   \n",
       "2023051100268  2023051100268  SDSSr_65mm~empty    r  239.960079 -24.042010   \n",
       "2023051100269  2023051100269  SDSSg_65mm~empty    g  239.959845 -24.048026   \n",
       "...                      ...               ...  ...         ...        ...   \n",
       "2023091400082  2023091400082  SDSSr_65mm~empty    r  270.194112 -24.950260   \n",
       "2023091400083  2023091400083  SDSSr_65mm~empty    r  270.210518 -24.950264   \n",
       "2023091400084  2023091400084  SDSSr_65mm~empty    r  270.226810 -24.950250   \n",
       "2023091400085  2023091400085  SDSSr_65mm~empty    r  270.243077 -24.950268   \n",
       "2023091400086  2023091400086  SDSSr_65mm~empty    r  270.259439 -24.950265   \n",
       "\n",
       "                    decl  skyRotation     azimuth   altitude  zenithDistance  \\\n",
       "visitId                                                                        \n",
       "2023051100262 -24.050861   322.911701  100.652699  33.079204       56.920796   \n",
       "2023051100263 -24.042851   170.005077  100.427273  33.535722       56.464278   \n",
       "2023051100264 -24.040927   170.004955  100.024337  34.385246       55.614754   \n",
       "2023051100268 -24.042010   323.867206   99.573769  35.343223       54.656777   \n",
       "2023051100269 -24.048026   170.004092   99.360495  35.811664       54.188336   \n",
       "...                  ...          ...         ...        ...             ...   \n",
       "2023091400082 -24.950260   359.998802  263.701292  44.750180       45.249820   \n",
       "2023091400083 -24.950264   359.998614  263.622517  44.584958       45.415042   \n",
       "2023091400084 -24.950250   359.998689  263.542128  44.416073       45.583927   \n",
       "2023091400085 -24.950268   359.998634  263.461810  44.247203       45.752797   \n",
       "2023091400086 -24.950265   359.998582  263.383379  44.082019       45.917981   \n",
       "\n",
       "                airmass  expTime                   expMidpt   expMidptMJD  \\\n",
       "visitId                                                                     \n",
       "2023051100262  1.826588     30.0 2023-05-12 01:08:56.302993  60076.047874   \n",
       "2023051100263  1.804276     30.0 2023-05-12 01:11:06.209005  60076.049377   \n",
       "2023051100264  1.765315     30.0 2023-05-12 01:15:04.912000  60076.052140   \n",
       "2023051100268  1.724092     30.0 2023-05-12 01:19:39.311494  60076.055316   \n",
       "2023051100269  1.704281     30.0 2023-05-12 01:21:49.752999  60076.056826   \n",
       "...                 ...      ...                        ...           ...   \n",
       "2023091400082  1.417337     30.0 2023-09-15 02:35:28.260509  60202.107966   \n",
       "2023091400083  1.421490     30.0 2023-09-15 02:36:18.753500  60202.108550   \n",
       "2023091400084  1.425701     30.0 2023-09-15 02:37:09.293010  60202.109135   \n",
       "2023091400085  1.429949     30.0 2023-09-15 02:37:59.895505  60202.109721   \n",
       "2023091400086  1.434210     30.0 2023-09-15 02:38:50.500006  60202.110307   \n",
       "\n",
       "                                obsStart   obsStartMJD  \n",
       "visitId                                                 \n",
       "2023051100262 2023-05-12 01:08:41.302993  60076.047700  \n",
       "2023051100263 2023-05-12 01:10:51.209005  60076.049204  \n",
       "2023051100264 2023-05-12 01:14:49.912000  60076.051967  \n",
       "2023051100268 2023-05-12 01:19:24.311494  60076.055142  \n",
       "2023051100269 2023-05-12 01:21:34.752999  60076.056652  \n",
       "...                                  ...           ...  \n",
       "2023091400082 2023-09-15 02:35:13.260509  60202.107792  \n",
       "2023091400083 2023-09-15 02:36:03.753500  60202.108377  \n",
       "2023091400084 2023-09-15 02:36:54.293010  60202.108962  \n",
       "2023091400085 2023-09-15 02:37:44.895505  60202.109547  \n",
       "2023091400086 2023-09-15 02:38:35.500006  60202.110133  \n",
       "\n",
       "[1698 rows x 16 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitTable = butler.get('visitTable')\n",
    "visitTable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0662a02-beb8-4d5f-a032-70720b33db08",
   "metadata": {},
   "source": [
    "### The visit summaries have more in-depth info:\n",
    "I'm going to use the visit summaries below, mostly because I can be sure I am grabbing the same visits that went into the astrometric fit for the band and tract that I am looking at here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8212aca9-0b99-45c6-905e-069c55079f98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T17:23:32.469475Z",
     "iopub.status.busy": "2023-11-17T17:23:32.469201Z",
     "iopub.status.idle": "2023-11-17T17:23:54.978936Z",
     "shell.execute_reply": "2023-11-17T17:23:54.978594Z",
     "shell.execute_reply.started": "2023-11-17T17:23:32.469462Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tract 5634 has 374 visits (aka exposures) in r-band\n"
     ]
    }
   ],
   "source": [
    "visitSummaryRefs = list(butler.registry.queryDatasets('visitSummary', tract=5634, band='r', skymap='latiss_v1'))\n",
    "visits = np.array([visRef.dataId['visit'] for visRef in visitSummaryRefs])\n",
    "visitSummaries = [butler.get(visitSummaryRefs[v]) for v in visits.argsort()]\n",
    "print(f'Tract 5634 has {len(visits)} visits (aka exposures) in r-band')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5de27360-ae99-48d6-8f35-a52d4f9d7fb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-15T21:47:13.397296Z",
     "iopub.status.busy": "2023-11-15T21:47:13.397106Z",
     "iopub.status.idle": "2023-11-15T21:47:13.403953Z",
     "shell.execute_reply": "2023-11-15T21:47:13.403592Z",
     "shell.execute_reply.started": "2023-11-15T21:47:13.397279Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'lsst.afw.table.ExposureCatalog'>\n",
       " id bbox_min_x bbox_min_y ... psfTraceRadiusDelta maxDistToNearestPsf\n",
       "       pix        pix     ...                                        \n",
       "--- ---------- ---------- ... ------------------- -------------------\n",
       "  0          0          0 ...          0.05838635             768.489"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's an example visitSummary. There's one row per detector--LATISS is only one detector, so this is a one-row table.\n",
    "visitSummaries[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a6b5a73c-f8ca-4af9-88c5-273f836e6c9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-15T21:50:23.665916Z",
     "iopub.status.busy": "2023-11-15T21:50:23.665359Z",
     "iopub.status.idle": "2023-11-15T21:50:23.675901Z",
     "shell.execute_reply": "2023-11-15T21:50:23.675511Z",
     "shell.execute_reply.started": "2023-11-15T21:50:23.665900Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Build a list that we can use to go from the extension index to the visit and detector. I have this sorted in the same way that it\n",
    "# is done in gbdesAstrometricFit, so this list will give us a key to what the \"catalogNumber\" indices below refer to.\n",
    "extensionVisits = []\n",
    "extensionDetectors = []\n",
    "for visSum in visitSummaries:\n",
    "    for row in visSum:\n",
    "        extensionVisits.append(row['visit'])\n",
    "        extensionDetectors.append(row['id'])\n",
    "\n",
    "extensionInfo = pd.DataFrame({'visit': extensionVisits,\n",
    "                              'detector': extensionDetectors})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f811e76d-1c27-434e-996a-b6de7b8a08b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T20:46:45.121112Z",
     "iopub.status.busy": "2023-11-14T20:46:45.120925Z",
     "iopub.status.idle": "2023-11-14T20:46:45.124458Z",
     "shell.execute_reply": "2023-11-14T20:46:45.124095Z",
     "shell.execute_reply.started": "2023-11-14T20:46:45.121099Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AUXTEL_PHOTO_IMAGING'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For a given row of a visitSummary, you can access the \"visitInfo\", which is the same for each row because they all correspond to\n",
    "# the same visit. The visitInfo has lots of helpful information.\n",
    "visSum = visitSummaries[0]\n",
    "visInfo = visSum[0].getVisitInfo()\n",
    "visInfo.getScienceProgram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e202c599-ad8a-405b-a359-feb5aa2b9f6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-16T15:18:11.821014Z",
     "iopub.status.busy": "2023-11-16T15:18:11.820458Z",
     "iopub.status.idle": "2023-11-16T15:18:11.837026Z",
     "shell.execute_reply": "2023-11-16T15:18:11.836668Z",
     "shell.execute_reply.started": "2023-11-16T15:18:11.820997Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit</th>\n",
       "      <th>altitude</th>\n",
       "      <th>airmass</th>\n",
       "      <th>mjd</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023052300621</td>\n",
       "      <td>69.638090</td>\n",
       "      <td>1.066195</td>\n",
       "      <td>60088.215718</td>\n",
       "      <td>270.144118</td>\n",
       "      <td>-25.049109</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023052300626</td>\n",
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       "      <td>1.060891</td>\n",
       "      <td>60088.218336</td>\n",
       "      <td>270.160268</td>\n",
       "      <td>-25.049147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023052300632</td>\n",
       "      <td>71.915991</td>\n",
       "      <td>1.051593</td>\n",
       "      <td>60088.223235</td>\n",
       "      <td>270.176475</td>\n",
       "      <td>-25.049132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023052300645</td>\n",
       "      <td>74.520799</td>\n",
       "      <td>1.037341</td>\n",
       "      <td>60088.231879</td>\n",
       "      <td>270.192944</td>\n",
       "      <td>-25.049119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023052300655</td>\n",
       "      <td>76.330875</td>\n",
       "      <td>1.028910</td>\n",
       "      <td>60088.238001</td>\n",
       "      <td>270.209295</td>\n",
       "      <td>-25.049111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>2023091400082</td>\n",
       "      <td>44.750180</td>\n",
       "      <td>1.417337</td>\n",
       "      <td>60202.107966</td>\n",
       "      <td>270.194112</td>\n",
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       "      <td>1.421490</td>\n",
       "      <td>60202.108550</td>\n",
       "      <td>270.210518</td>\n",
       "      <td>-24.950264</td>\n",
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       "      <th>371</th>\n",
       "      <td>2023091400084</td>\n",
       "      <td>44.416073</td>\n",
       "      <td>1.425701</td>\n",
       "      <td>60202.109135</td>\n",
       "      <td>270.226810</td>\n",
       "      <td>-24.950250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>2023091400085</td>\n",
       "      <td>44.247203</td>\n",
       "      <td>1.429949</td>\n",
       "      <td>60202.109721</td>\n",
       "      <td>270.243077</td>\n",
       "      <td>-24.950268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>2023091400086</td>\n",
       "      <td>44.082019</td>\n",
       "      <td>1.434210</td>\n",
       "      <td>60202.110307</td>\n",
       "      <td>270.259439</td>\n",
       "      <td>-24.950265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             visit   altitude   airmass           mjd          ra        dec\n",
       "0    2023052300621  69.638090  1.066195  60088.215718  270.144118 -25.049109\n",
       "1    2023052300626  70.431618  1.060891  60088.218336  270.160268 -25.049147\n",
       "2    2023052300632  71.915991  1.051593  60088.223235  270.176475 -25.049132\n",
       "3    2023052300645  74.520799  1.037341  60088.231879  270.192944 -25.049119\n",
       "4    2023052300655  76.330875  1.028910  60088.238001  270.209295 -25.049111\n",
       "..             ...        ...       ...           ...         ...        ...\n",
       "369  2023091400082  44.750180  1.417337  60202.107966  270.194112 -24.950260\n",
       "370  2023091400083  44.584958  1.421490  60202.108550  270.210518 -24.950264\n",
       "371  2023091400084  44.416073  1.425701  60202.109135  270.226810 -24.950250\n",
       "372  2023091400085  44.247203  1.429949  60202.109721  270.243077 -24.950268\n",
       "373  2023091400086  44.082019  1.434210  60202.110307  270.259439 -24.950265\n",
       "\n",
       "[374 rows x 6 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build another table of some helpful information about the visits that went into the astrometric fit.\n",
    "visits = []\n",
    "altitudes = []\n",
    "airmasses = []\n",
    "mjds = []\n",
    "ras = []\n",
    "decs = []\n",
    "for visSum in visitSummaries:\n",
    "    visits.append(visSum['visit'][0])\n",
    "    visInfo = visSum[0].getVisitInfo()\n",
    "    alt = visInfo.boresightAzAlt.getLatitude().asDegrees()\n",
    "    altitudes.append(alt)\n",
    "    airmass = visInfo.boresightAirmass\n",
    "    airmasses.append(airmass)\n",
    "    mjd = visInfo.date.get(visInfo.date.MJD)\n",
    "    mjds.append(mjd)\n",
    "    raDec = visInfo.boresightRaDec\n",
    "    ras.append(raDec.getRa().asDegrees())\n",
    "    decs.append(raDec.getDec().asDegrees())\n",
    "visitInfo = pd.DataFrame({'visit': visits, 'altitude': altitudes, 'airmass': airmasses,\n",
    "                          'mjd': mjds, 'ra': ras, 'dec': decs}) \n",
    "visitInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5ba6baf8-2e14-4b2c-9521-03e19d8fbfe6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-16T15:18:16.197935Z",
     "iopub.status.busy": "2023-11-16T15:18:16.197758Z",
     "iopub.status.idle": "2023-11-16T15:18:16.332804Z",
     "shell.execute_reply": "2023-11-16T15:18:16.332423Z",
     "shell.execute_reply.started": "2023-11-16T15:18:16.197921Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Altitudes for all r-band exposures in tract 5634')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t6tixo9kxrF1UWPv/w7itoqICV69eBQD87W9/w3PPPYcxY8Zg69atOHDgADIyMtCjR4862177/9NYpGqMre+zUN+22i5evAghBEJDQy0+I+np6RZJ7c0334z+/fubEqj6/t9sHYvCwkIIIWR9Rl5//XU8/fTT+OyzzzB48GAEBQVhzJgxjbrIqP2+Fy9eBADMmTPH4jjMmjULwB9/K45uj6+vr1niB9T8P5eVlTVofzeq6/guXboUM2fORFxcHD755BOkp6cjIyMDCQkJZp/B33//HW3atIGbW9Of3hITEwEA6enppm3Gz/6f/vQns1jjhdiRI0cs9hMbG4v4+HhMmDABe/bsgRDCrO7v0UcfRUxMDO677z7T97yxLvLq1asoKipy7C+mQq4vD6cGW716NYQQ+Pjjj/Hxxx9bPL9u3To8//zzdV6BOpLxC628vNxsZEXtE4rxjzwvLw/h4eGm7VVVVRZJQuvWrdG9e3e88MILdb6n8cQBAFOmTMGUKVNw7do1fPvtt0hKSsKoUaPw888/y7rKr93G3Nxci+3GIZCtW7c2215X4XNdPvvsM1y7dg2bN282a9OxY8fsap8ctdu0detWs6LMG48dUPP/UVteXh68vLxMvQ/r16/Hww8/jEWLFpnFXb58Ga1atbK7jTd+Fup679pTBdTWunVrSJKEffv21Tmap/a2t99+G9u2bUPv3r2xbNky/N///V+dI/5sHQsPDw+4ubnJ+oy0aNECCxYswIIFC3Dx4kVTr8jo0aPx448/Aqj526nrRFRfD2Pt/1vje82bNw9jx46t8zXG4b1y2qMUdf1drV+/HoMGDcKKFSvMtpeUlJj9fNNNN+G7776DwWBo8oRGCAEAZu/TvXt3s+TGWmxdjIMYbuytzczMxNmzZ816XI0GDx6MgIAAXLlypSG/gmawZ0alqqursW7dOnTs2BF79uyxeMyePRu5ublWR4fUvlq2RafT1RlrPPH897//Ndu+detWs5+NowXef/99s+0ffvihxQilUaNGITMzEx07dkSvXr0sHrVPyEDNl/Xw4cMxf/58VFRU2Jx4qi533XUXsrKyLK6e3n33XUiSVOfwSjmMX843nmSFEFi1alWD9mePbt26WT12mzdvNruaLikpwdatW9G/f39TIixJkkWCsG3bNpw/f75BbYqPj4e3t7fFZ2H//v2yblOMGjUKQgicP3++zs9Ht27dTLHHjx/H448/jocffhj79u1D9+7d8X//938oLCy02K+tY9GiRQvExcVh8+bNZn8LBoMB69evR9u2bXHLLbdY7Dc0NBSTJ0/GuHHj8NNPP5muqiMjI/Hzzz+bJZv5+fmmUTe2dO7cGZ06dcIPP/xQ53Ho1asX/Pz8ZLfHWer7LrGmrs/gf//7X6SlpZltGz58OMrKymxOyteQNtT27rvvAoDZcG3jLbLa373bt2+3iK3L5cuXcfz4cdx8882mbZs2bbL4jjeOllq5ciW++OKLRv0eWsCeGZXasWMHLly4gBdffLHOIYUxMTFYtmwZ3nnnHYwaNarOfRhnC37rrbfg5+cHb29vREVFWdwiMOrWrRu++eYbbN26FWFhYfDz80Pnzp0xYsQIBAUF4ZFHHsHChQvh4eGBtWvX4ty5c2av79q1KxITE/Haa6/B09MTd999NzIzM/Hyyy9bDDNcuHAhdu3ahb59++Lxxx9H586dUVZWhpycHGzfvh0rV65E27ZtMW3aNPj4+KBfv34ICwtDXl4eUlJSEBAQgDvuuMPu4/rUU0/h3XffxciRI7Fw4UJERERg27ZtWL58OWbOnFnniUqOoUOHwsvLC+PGjcPcuXNRVlaGFStW1HlCdTZ3d3cMHToUf/vb32AwGPDiiy+iuLjYbA6MUaNGYe3atejSpQu6d++Ow4cP46WXXkLbtm0b9J6BgYGYM2cOnn/+efzlL3/BAw88gHPnziE5OVnWbaZ+/frhr3/9K6ZMmYJDhw5hwIABaNGiBXJzc/Hdd9+hW7dumDlzJq5du4YHH3wQUVFRWL58Oby8vPDhhx8iNjYWU6ZMsZgBVs6xSElJwdChQzF48GDMmTMHXl5eWL58OTIzM7Fx40ZT4hoXF4dRo0ahe/fuCAwMxMmTJ/Hee++hT58+8PX1BVBzm/TNN99EYmIipk2bhvz8fCxZssSuYbdvvvkmhg8fjj/96U+YPHkywsPDUVBQgJMnT+LIkSP46KOPZLfHWbp164ZNmzbhgw8+QIcOHeDt7W2WgNZl1KhR+Ne//oWkpCQMHDgQP/30ExYuXIioqCizi6Fx48ZhzZo1mDFjBn766ScMHjwYBoMBBw4cQNeuXU31hfV9n9Vlw4YN2Lx5M0aOHImIiAhcuXIFH330ETZt2oTJkyejR48epthhw4Zh9OjRWLhwIQwGA+Lj43Ho0CEsWLAAo0aNwp133gkAKCoqwtChQzF+/Hh06tQJPj4++Pnnn/Hvf/8b5eXlSEpKMu2zrgTIOJXE7bffjl69esk78FrmosJjaqQxY8YILy8v01Dqujz00EPCw8PDVP2OWqOZhBDitddeE1FRUcLd3d1sKGddI2qOHTsm+vXrJ3x9fS1GnBw8eFD07dtXtGjRQoSHh4ukpCTTMPEbhyuXl5eL2bNni5CQEOHt7S3i4+NFWlpanaN1fv/9d/H444+LqKgo4enpKYKCgsTtt98u5s+fL65evSqEEGLdunVi8ODBIjQ0VHh5eYk2bdqIBx98UPz3v/+1eQzrGs0khBBnz54V48ePF8HBwcLT01N07txZvPTSS6ZRIUL8MbrkpZdesvk+Rlu3bhU9evQQ3t7eIjw8XPz9738XO3bsqHOkSGNGMz366KOy2mP8HV588UWxYMEC0bZtW+Hl5SV69uwpvvzyS7PYwsJC8cgjj4iQkBDh6+sr7rzzTrFv3z6L0TjG0SgfffRRne9141Bhg8EgUlJSRLt27YSXl5fo3r272Lp1a70jfOqyevVqERcXJ1q0aCF8fHxEx44dxcMPPywOHTokhBAiMTFR+Pr6Wgy7NY7ke/XVV+0+FkIIsW/fPjFkyBDT+8bHx4utW7eaxTzzzDOiV69eIjAwUOh0OtGhQwfx1FNPicuXL5vFrVu3TnTt2lV4e3uL6Oho8cEHH9Q7mqm+z9sPP/wgHnzwQRESEiI8PT2FXq8XQ4YMMY1ytKc9tdU3mqlFixYWsfWNbKwtJydHDBs2TPj5+QkApt+1vs+PEDXfHXPmzBHh4eHC29tbxMbGis8++6zOv5fr16+Lf/7zn6JTp07Cy8tLBAcHiyFDhpgNsbf2fVZbWlqauOuuu4Rerxeenp7C19dX3HHHHWL58uVm3wtGpaWl4umnnxbt2rUTHh4eon379mLevHmirKzMFFNWVib+8pe/iK5du5qGe7dt21YkJibaHCYuBEcz1SYJ8b8beUREzVROTg6ioqLw0ksvYc6cOa5uDhHZiTUzREREpGpMZoiIiEjVeJuJiIiIVI09M0RERKRqTGaIiIhI1ZjMEBERkappftI8g8GACxcuwM/PT/bU80RERORaQgiUlJTIWmtL88nMhQsX0K5dO1c3g4iIiBrg3LlzNmcb13wyY1yX5Ny5c3ZNEU5ERESuU1xcjHbt2tW5vlhtmk9mjLeW/P39mcwQERGpjJwSERYAExERkaoxmSEiIiJVYzJDREREqsZkhoiIiFSNyQwRERGpGpMZIiIiUjUmM0RERKRqTGaIiIhI1ZjMEBERkappfgZgIvpDRZUB76Xl4GxBKSKCfDGxTyS8PHhNo3TVBoGD2QW4VFKGED9v9I4KgrsbF84lMmIyQ9RMpGzPwqp92TCIP7a9sP0kpvWPwrwR0a5rGFmVmpmL5C0nkFdcbtqm99ch+Z5bkRAT5sKWESkHL8mImoGU7Vl481vzRAYADAJ489tspGzPck3DyKrUzFzMWH/ELJEBgLzicsxYfwSpmbkuahmRsjCZIdK4iioDVu3Lthqzal82KqoMTmoRyVFtEHhm83GrMc9sPo7q2hkqUTPEZIZI495Ly7HokanNIGriSDnST+fjSmml1ZgrpZVIP53vpBY1XLVBIO10Pj4/dh5pp/OZgJHDsWaGSOOy8685NI6c4/vTv8uO69epdRO3puFSM3OxYGsWcovKTNvCAryRNDqaNT/kMOyZIdI4uWNeODZGWS5cKbMdZEecK6Rm5mLm+iNmiQwA5BWVYSZrfsiBmMwQaVyPtq0cGkfOER7o49A4Z6s2CCzYmoW6bigZty3YmsVbTuQQTGaINK7ouvW6C3vjyDl6RwQ5NM7ZDmYXWPTI3EgAyC0qw8HsAuc1ijSLyQyRxn1y5JysuFY+nk3cErLHjxdLHBrnbJdK5N3+khtHZA2TGSINu15Rjazcq7Jir7BnRlEOny10aJyztW6hc2gckTVMZog0bJEdk+G18vVqwpaQvVp4uTs0ztkMQl4tjNw4ImuYzBBpWE5+qezYgmvltoPIacbcFu7QOGc7kC1v/hu5cUTWMJkh0rDIYF/ZsbzNpCweMhcAlRvnfJwUgJxHqX8FROQAz9qxgKTEk4qiXL4qr6dMbpyzxUXJG2UlN47IGiYzRBrm4+WO2PYBsmL7dAxu4taQPUL8vB0aR6RlTGaINKzaIPDE4Ftkxca2D2zi1pA9ekcFoZWv9eHyrXw90VuhPRusmSFnYjJDpFGpmbm488XdmLQuQ1b8hgNnm7hF5GjKvjHImhlyHiYzRBpU35o41pwtkD/yiZrewewCm6tmF5ZWKnYGXbm3LXl7kxyByQyRxlhbE8eaiCD5I5+o6eUVy0tE5cY5W3yHYJu3yQJ9PRHfgckMNR6TGSKNsbUmTl3cJGBin8imaRA1yGWZ0/zLjXM2dzcJi8d2sxqTMrYb3N14m4kaj8kMkcY0ZK2buMggeCl2vpLmydYtJnvjXCEhJgwrE2Oh9zcfcRUW4I2VibFIiAlzUctIazxc3QAicqyGDNXtpPdrgpZQY8i9Taj0xQASYsIwNFqPg9kFuFRShhA/b/SOCmKPDDkUkxkijTEO6bXnip31MsoTKHOtLLlxruTuJrHQl5oU+5WJNKiiyiA7lvUyytTaT+aq0zLjiLSMyQyRxqSfzkdpRbXs+Gn9o1gvo0AhLeUlKXLjiLSMt5mINCbtzGVZcRKAvw6Iwjw71m8iJ+Kcc0SyMZkh0hx5Z7fpAzvgmeFdm7gt1FBqX2iSyJnYt0ykMXILLYNb6FBtUPpYmOaLC00SyefSZKaqqgr/+Mc/EBUVBR8fH3To0AELFy6EwfBH8aIQAsnJyWjTpg18fHwwaNAgnDhxwoWtJlK2OyKDIMnonHlh+0nc+eJupGbmNn2jyG5qX2iSyJlcmsy8+OKLWLlyJZYtW4aTJ09iyZIleOmll/DGG2+YYpYsWYKlS5di2bJlyMjIgF6vx9ChQ1FSUuLClluqNgiknc7H58fOI+10Pq94yWUOny2EkPnxyysqw8z1R5jQKJStUWmVdoxaI9Iyl9bMpKWl4d5778XIkSMBAJGRkdi4cSMOHToEoKZX5rXXXsP8+fMxduxYAMC6desQGhqKDRs2YPr06S5r+41SM3OxYGuW2RTyYQHeSBodzRkuyensmQFYoKbCZsHWLAyN1nMiMwWRMyrtWkU10k/no1+n1k5qFZEyubRn5s4778TXX3+Nn3/+GQDwww8/4LvvvsOIESMAANnZ2cjLy8OwYcNMr9HpdBg4cCD2799f5z7Ly8tRXFxs9mhK9a1OzCtechV7aygEgNyiMsWuvtxcyR2VJjeOSMtcmsw8/fTTGDduHLp06QJPT0/07NkTTz75JMaNGwcAyMvLAwCEhoaavS40NNT0XG0pKSkICAgwPdq1a9dk7be2OrFx24KtWbzlRE7VOyoIYQHedo/YbciaTtSUODabSC6XJjMffPAB1q9fjw0bNuDIkSNYt24dXn75Zaxbt84sTqpVzSiEsNhmNG/ePBQVFZke586da7L221qdmFe85ArubhKSRtfMHWPPaY6jYpRF7qg0LhNA5OKamb///e945pln8NBDDwEAunXrhrNnzyIlJQWTJk2CXq8HUNNDExb2R+3JpUuXLHprjHQ6HXQ658yIKfdKlle85GwJMWFYkRiL5C0nkFdsex6SsABvjopRmDsi5f1/yI0j0jKX9syUlpbCzc28Ce7u7qah2VFRUdDr9di1a5fp+YqKCuzduxd9+/Z1alvrwnkgSPnk9c3c0yOMxb8KkyGzR1duHJGWuTSZGT16NF544QVs27YNOTk5+PTTT7F06VL8+c9/BlBze+nJJ5/EokWL8OmnnyIzMxOTJ0+Gr68vxo8f78qmA7BdmyCBV7zkGsbC9Lxieb2CW37IZW2XwrAAmEg+l95meuONN/Dcc89h1qxZuHTpEtq0aYPp06fjn//8pylm7ty5uH79OmbNmoXCwkLExcVh586d8PPzc2HLaxhrE2auPwIJMCsENiY4SaOjecVLTmWtML0+xtou1l8oCQuAieSShJA7vZY6FRcXIyAgAEVFRfD392+S9+A8M6QkaafzMW5Vut2v+/dDt+He28KboEXUEN+fuowJbx+wGff+X+LQ72bOM0PaY8/5mwtNOkBCTBiGRutxMLsAl0rKEOJXc2uJPTLkCg0tOGdtl7KwAJhIPiYzDuLuJrGLnhShdQv7R/MFtfBibZfC2FMAzBmAqbnjqtlEWtOADsHn741hT6LCsACYSD72zDhIRZUB76Xl4GxBKSKCfDGxTyS8PJgrkvNdvmp7XpkbTR8QhRHdWdulPCwAJpKLyYwDpGzPwqp92bhxZOsL209iWv8ozBsR7bqGUbNkz22maf0j+RlVqD4dg7FszylZcUTNHbsOGillexbe/NY8kQEAgwDe/DYbKduzXNMwar7suFB/e18OF0NVqPgOwWjl62k1JtDXE/EdmMwQMZlphIoqA1bty7Yas2pfNiqqDE5qEZF9t5kEuBiqUrm7SVg8tpvVmJSx3VjrRAQmM43yXlqORY9MbQZRE0fkLPYOseZiqMqVEBOGlYmx0Pub/5+GBXhjZWIs57Ei+h8mM41wtqDUoXFEjmBrmY267MrKa7L2OEu1QSDtdD4+P3YeaafzNdbbZP67aHyuUyK7MZlpoGqDkP2FEhHk28StIfqDcZkNe3x+7IKqT/6pmbm488XdGLcqHU9sOoZxq9Jx54u7VV8P9McaW+a3Di8Wl2Pm+iOq//2IHIXJTAMYvzjfS//VZqybBEzsE9n0jSK6QUJMGFYkxkLvL29kU/61CtXeajKe8G9cTgQA8orKVH3Ct7bGlnEb652IajCZsVN9X5z1mdY/ivPNkEskxITh27lDoJP5+WvoMgiupOUT/sHsAqvfMwKsdyIy4lnWDvasRuwm1UxGxjk8yJUOny1EuczRdGpcm0nLJ3y5yaUak1AiR+OkeXaw9cVpNDG+PZ4bdSt7ZMjl7DnR3R4R2IQtaRpaPuHLTS7VmIQSORrPtnaQ+4XYKzKIiQwpgj0nOrkLGyqJlk/4tkalSagZos0FQomYzNhFy1+cpE29o4LQwstdVqwaFyzU8gn/xlFptX8/489Jo6M5aR4RmMzYRc78Ha18PGEQQpUFh6Q97m4SBtxyk8xo9Z0UtX7CN41KCzC/QNIHeGMFJ80jMpGExmdfKi4uRkBAAIqKiuDv79/o/RlHMwG1p7EyFxbgjaTR0fyyIZf7/tRlTHj7gM249/8Sh343t3ZCixwvNTMXyVuykFf8x61gLf0NVlQZ8F5aDs4WlCIiyBcT+0Sq9la2ln4Xalr2nL9ZAGwn45XSgq1ZVouBjXNc8OqJXK2otMJmjBYWLKx9XWbQSO9oamauxffN299lqzJRS9mehVX7zBfmfWH7SUzrz5Gf1DhMhxsgISYM3z09BO8/EodWPnWvaqv2OS5IG6oNAv/adtJm3Atj1LtgYWpmLmasP4KLJbVmyS0pxwwVT5oHaGtCwJTtWXjz22yL9ewMAnjz22ykbM9yTcNIE5jMNMKPecW4cr2y3ufVPMcFaYPc6QQCW3g5oTWOV20QeGbzcasx8zYfV+UFhZYmBKyoMuCtfdlWY97al40KmXMiEdXGZKYBjMsZyLniBdQ5xwVpg5bnYQGA9DP5uFJa/wUFABSWViL9TL6TWuQ4WpoQcN3+HNiqzhSiJo6oIZjM2Mne5QwADtUm19H6dAJpp+UlKXLjlERLiWhGjryES24cUW1MZuxgz3IGgLrnuCBt0PI8LDXk/jUq/1ZMbVpKRH1lznUkN46oNiYzdpBbfwBoY44LUj+tz8PSp4O8oeRy45RES4nofT3bOjSOqDYmM3awpzuXk1qRUhinEwj1197Ea3fIPJHLjVMSLSWifTu1ttnr4uvljr6d1Jd0kjIwmbGD3O7coV1DsHv2IFWfJEh7tDgPi9ZrMbQyA7C7m4SlD/awGrP0wR6qSMxImTgDsB2qDQJ3vrgbeUVlNu/Au0ngRFCkCMZ5WOqzUkUnxdpe/vInLNtzymbcY4Nvxpw/dXZCi5qGVmbNTc3MRdLnmbhY8sdEjqF+Xlhwb4xqP4PUdDgDcBMxdvvOXH8EEqyXFBonggLAhIZcRu48LEOj9Sq9KtZuAbBRXUs1rNqXjeR71DcDcEJMGIZG63EwuwCXSsoQ4ldT86POzx4pifpSexerr9u3Pqs4ERS5kJbnYQG0XQAM/NGrdmMiAwB5xWWqnd3Y3U1Cn47BuPe2cPTpGMxEhhyCyUwDGJczmBjf3masQQDvpeU0faOI6qDleVgAIL5jsM3C0hZe7ojvqL51p7Q8uzGRozGZaSB3NwmSJO+K4mxBaRO3hqg+2rkNU20QSDudj8+PnUfa6XzTSdxW7YinCmtLAO33qhE5EmtmGiEiyNehcUSO1qdDayzbc9pmnIebsk/4ddWN6P29Ma53O5sn/CullTiYXYA+KuudsadXrd/N6ryNRuQoyv4GU7iJfSJh63avm1QTR+QK8R2DEeBj+5rlg0PnFHu7wlrdyKtf/SJrH2qY8t+SdnrViJoak5lG8PJww7T+UVZjpvWPUuUQStIGdzcJU/tZ/4wCyl2wUE7diBxqmPK/Nq0XNxM5Es+yDWS8fx/dJgCjuodZ9NC4ScD0AZxnhlwvsnULWXFK7L2QUzdijZqm/K9Ny7MbEzkaa2YaIDUzFwu2Zpmt0xTq54X+nW6Cr85D1ZNakfaoecFCe0ZZ1Z77SW1T/tdmz+zGrJmh5o5nWzulZuZi5vojFgtOXiqpwCdHzqNvx2A80r8DExlSjNsjAutdrNBI+l+c8sirBxkeE6r6Kf9r+/7UZYfGEWkZe2bsUG0QWLA1q86vV4GaE8KCrVkqnk2VtKbaILBuf7bNlEAAyMguQD+FLfQndzRWYlwklo0P1tTMsheuXHdoHJGWMZmxw8HsAosemRsJ/FFIqbZhoKQ9dd0OtSbtzGXFJTN3RAXZXDpE+l+ccWZZrQhv5ePQOCIt470QO8gtkPwg41cuYUAuVd/tUOuU14tx+GyhrF6lw2cLndEcp+orsw5GbhyRljGZsYPcAsnPjl1Al+d2IGV7VhO3iMiStduh1iixV0PuBYQSR2I11h2RMkczyYwj0jImM3boHRWEsABvWdevxlWzmdCQs9m6HVqXQF9PxHdQXjKj5pFYjXVA5jIFcuOItIzJjB3c3SQkja6ZN0Zuh/xbXDWbnKwhvRQpY7spsljW1gXEjfPI1Ld2k1p9fPicQ+OItIzJjJ0SYsKwIjEWof7yrgSFANbtz2naRhHdwJ5eirAAb6xU8PBlaxcQN84jsysrD3e+uBvjVqXjiU3HMG5VOu58cTdSM3Od2l5HOi9zlJLcOCItYzLTYPKv+uROfkXkCL2jgtDK19NqjM5DwntTe+O7p4coNpExMl5A1DePDIA6i53zisowc/0R1SY0bQPlLVArN45Iyzg0207GUSL2dGD7erk3WXuI6mLr1mZ5lcDfP/4vku+JVnwyA9QkNEOj9RbzyADAnS/u1uTcT/fFtsVnxy7IiiNq7lzaMxMZGQlJkiwejz76KABACIHk5GS0adMGPj4+GDRoEE6cOOGy9jZ0lMh9PfllQ86TfjofpRXVNuPyitXVc2GcR+be28LRp2Mw3N0ku+Z+Ups4mQXZcuOItMylyUxGRgZyc3NNj127dgEAHnjgAQDAkiVLsHTpUixbtgwZGRnQ6/UYOnQoSkpKXNLehowSAYA4BQ55Je1KO2Pf9PYLtmaptlhWy0O3M2QmYHLjiLTMpcnMTTfdBL1eb3p88cUX6NixIwYOHAghBF577TXMnz8fY8eORUxMDNatW4fS0lJs2LDBJe1t6BeiFif0IiWTfztFzT0XgLaHbstNSu1NXom0SDEFwBUVFVi/fj2mTp0KSZKQnZ2NvLw8DBs2zBSj0+kwcOBA7N+/v979lJeXo7i42OzhKA39QlTjVSGpV0Mmv1PrZ1ROsXOgr6epvkZd5Cal6qoFImoKiklmPvvsM1y5cgWTJ08GAOTl5QEAQkNDzeJCQ0NNz9UlJSUFAQEBpke7du0c1sbbIwLRkBpCNV4VknrFdwi2eYKvTc2fUVvFzmqd50luUqrEmZuJnE0xycw777yD4cOHo02bNmbbJck8exBCWGy70bx581BUVGR6nDvnuAmlDp8thD2lBTdO6EXkLO5uEhaP7SYrVu2f0fQztoudr1VUI12Fs+TKSUqVOnMzkbMpIpk5e/YsvvrqK/zlL38xbdPr9QBg0Qtz6dIli96aG+l0Ovj7+5s9HMWervgbJ/RS25BQUr+EmDCsTIyF3sbkjgLq/oymnZaXpMiNUxJ3Nwl3RAZajekVGaja/zsiR1JEMrNmzRqEhIRg5MiRpm1RUVHQ6/WmEU5ATV3N3r170bdvX1c0066ueOOEXmqYw4O0KSEmDN8/MwQbp8Vjar9ItNRZTitl7+0o5ZHbVaq+0VoVVQZ8ffKS1ZivT15S7W00IkdyeTJjMBiwZs0aTJo0CR4ef3zZSpKEJ598EosWLcKnn36KzMxMTJ48Gb6+vhg/frxL2ipnoclWPp54/y9xqphZlZqXq+VVFtuKSitVNddMbX06tHZonJK8l5Zj87a2QdTEETV3Lp8B+KuvvsKvv/6KqVOnWjw3d+5cXL9+HbNmzUJhYSHi4uKwc+dO+Pn5uaClf6wTM3P9EUgwv9YzJjiL7+uGfjer74uTtCc1MxcLtmbZnFROzbPkxnesqSu5UlpZb0wrX0/Eq7BI9mxBqUPjiLTM5T0zw4YNgxACt9xyi8VzkiQhOTkZubm5KCsrw969exETE+OCVv7B1jox7I0hJTAuuyFnkkc1zzUjp9h5sUJXBLclIkjemkty44i0zOU9M2pU3zoxavzCJO1p6LIbap1rxljsnLzlBPKKy03b9f46JN9zq2ovMCb2icS/tp2UFUfU3DGZaSDjOjFEStPQZTfUPNcMLzCImjcmM0Qa05AeFl8vd9XONWOktQuMNd+fkR03feDNTdwaImVzec0METlWQ3pY3KxMREmusfOE9WHZ9sYRaRmTGSKNkTOFQG1Xy6tUWQCsZVfL6x+h1ZA4Ii1jMkOkMcYpBJpLAbBWdQ6VNwWF3DgiLWMyQ0QA1F0ArEUP3C5vkVy5cURaxgJgIo2pNgg8s/m4Xa9Rw2KT1QbRrEYrxcksZpYbR6RlTGaINCb9dL7VGXHrck+PMEUnBnXNZhwW4I2k0dGqnUfGlowceTVMGTkFnHWcmj3eZiLSmLQzl+1+zZYfclFtayEgF6lvNuO8ojJVrytly/7T8v4f5cYRaRmTGSLNsb+HRanLGVibzdi4bcHWLMUmYo1xvvC6Q+OItIzJDJHGNHTiOCWOZrI1m7Ga15WypU0reQXZcuOItIzJDJHGxHeoWUnaXkoczSQ3wVJiItZY/Tre5NA4Ii1jMkOkMXJWkq5NqaOZ5CZYSkzEGiu+YzB8vdytxrTwckc8RzMRMZkh0iLjStJ6f3kn+aTR0YoczdQ7KshmL1Ogr6ciEzEich4mM0QalRAThu+fGYKN0+IxpEvdtyIkANMHRKl6eLP2Sn9rpJ/JR2lFtdWYaxXVSD+T76QWESkXkxkiDXN3k1B0vQK7f/y9zucFgDe/zVbs8OaD2QU258y5UlqpyQLgtNPykhS5cURaxmSGSMOqDQLJW07YjFPq8ObmXAAsv89Jef9vRM7GZIZIw9LP5COvuNxmnFKHNzfnAuA+HeTN6is3jkjLuJwBkUalZubimU/kr9GkxN6N3lFBCAvwRl5RWZ39DxIA/f9GYmlt7ab4jjVD7K3dZmvl68nRTERgMkOkScYlAOy5AaHE3g13NwlJo6Mxc/0RSDC/oWJMU5JGR2NXVp7m1m4yDrGfsf5IvTGLx3ZTdcJG5Ci8zUSkMdaWAKiPUueZAWpGZa1IjIU+wDzZ0gd4Y0ViLADUuXZTrgbWbjIOsQ/105lt1/vrsDIxVrWJGpGjsWeGSGNsLQFQF6XOM2OUEBOGodF6i9tIAHDni7vrTdwEaoqbh0brFf37WXP010JcKjGve7pYXI6jvxYymSH6HyYzRBpjT+1LoK8nUsZ2U8VJ0d1Nslh3Ku10vs3EzVjc3NA1q1wpZXsW3vw222K7cUg9AMwbEe3kVhEpD28zEWmM3NqX+SO64tA/hqoikalPXpG8FaPlxilJRZUBq/ZZJjI3WrUvGxVVBie1iEi5mMwQaYycJQBa+Xpi6p1Rqr31YlRwrcKhcUryXloObE39YxA1cUTNHZMZIg2ydbV+vbwK6WfyFTlRnj1+KyyVFRfUUmc7SGHOFsj73eTGEWkZkxkijUk/bXtNn/JqgQlvH8CdL+5W7WifaoPA5z9ckBUrd8FNJYkI8nVoHJGWMZkh0pi0M5dlx+apePjywewCFFyzvm4TALTUeSh22Lk1E/tEwtZdQDepJo6ouWMyQ6Q58utgjDeZlLo2kzVyR21VVquzQNbLww3T+kdZjZnWPwpeHvwaJ+JfAZHG2DsEWUC5azNZI3fUVnmVAekqXVl63ohoTB8QZdFD4yYB0wdEcVg20f9wnhkijbkjsmG3VJS4NpM1vaOC0MLLHdds1AcBNbfe+nVS54KM80ZEY/awLngvLQdnC0oREeSLiX0i2SNDdAMmM0Qak9HAHhYlrs1kjbubhAG33IQdmXkyotU9BN3Lww2P9O/g6mYQKRZTeyKNsacAGKg5zSt5bSZrEuMjZMWpcfZfIpKPyQyR5tjXCyGg/LWZ6hPfIdjmBIGBvp6I78BkhkjLmMwQaUxDeiGO/lrYBC1peu5uEhaP7WY1JmVsN1UmakQkH5MZIo2R01tRm5rX+EmICcPKxFiLifHCAryxMjFW1WtPEZE8LAAm0hhjb8WM9Udkv8a4xo9ai0wTYsIwNFqPg9kFuFRShhC/mhog9sgQNQ9MZog0yNhbkbwlC3nF8oZcq32NH3c3iYW+RM0UbzMRaVRCTBi+f2YIJsa3lxXPNX6ISK2YzBBpmLubhOdG3co1fohI05jMEGkc1/ghIq3jtxdRM9CzfWCjniciUjImM0QaV20QWLA1q97nJahz1WwiIiMmM0QadzC7ALlF9Y9oUsuq2dUGgbTT+fj82Hmknc5n8kVEJhyaTaRxclfDVvKq2amZuViwNcssKQsL8EbS6GjTpHjVBsF5ZoiaKSYzRBondzVspa6anZqZi5nrj6B2P0xeURlmrj+CFYmxAGAz2VEzJmpE1jGZIdK43lFBCAvwRl5RmUVCANTUzOgVumq2sd6nrnYL1LT9mc3HUVRaaTXZUXNCk5qZi+QtJ5BXXG7apvfXIfmeW1X9exE5kstrZs6fP4/ExEQEBwfD19cXt912Gw4fPmx6XgiB5ORktGnTBj4+Phg0aBBOnDjhwhYTqYu7m4Sk0dEALNfTNv6s1FWz5dT7XKkjkTE+B6i7uDk1Mxcz1h8xS2QAIK+4HDPWH0FqZq6LWkakLC5NZgoLC9GvXz94enpix44dyMrKwiuvvIJWrVqZYpYsWYKlS5di2bJlyMjIgF6vx9ChQ1FSUuK6hhOpTEJMGFYkxkIfYH4rSR/greiei8bW8ailuLku1QaBZzYftxrzzObjqk3UiBypQbeZTp8+jTVr1uD06dP497//jZCQEKSmpqJdu3a49dZbZe/nxRdfRLt27bBmzRrTtsjISNO/hRB47bXXMH/+fIwdOxYAsG7dOoSGhmLDhg2YPn16Q5pP1CypcTFGR9XxKLm4uT7pp/NxpbTSasyV0kqkn85Hv06tndQqImWyu2dm79696NatGw4cOIDNmzfj6tWrAID//ve/SEpKsmtfW7ZsQa9evfDAAw8gJCQEPXv2xKpVq0zPZ2dnIy8vD8OGDTNt0+l0GDhwIPbv31/nPsvLy1FcXGz2IKIaxsUY770tHH06Bis6kQH+qPdpbCuVWtxsTdqZyw6NI9Iyu5OZZ555Bs8//zx27doFLy8v0/bBgwcjLS3Nrn2dOXMGK1asQKdOnfDll19ixowZePzxx/Huu+8CAPLy8gAAoaGhZq8LDQ01PVdbSkoKAgICTI927drZ1SYiUg459T6+Xu5W99HK11ORxc22yU3hlJ2QEjmD3cnM8ePH8ec//9li+0033YT8/Hy79mUwGBAbG4tFixahZ8+emD59OqZNm4YVK1aYxUmS+R+rEMJim9G8efNQVFRkepw7d86uNhGRslir91k+vqfN11dWGZqqaU2qT8dgh8YRaZndNTOtWrVCbm4uoqLMF647evQowsPD7dpXWFgYoqOjzbZ17doVn3zyCQBAr9cDqOmhCQv7o0Dx0qVLFr01RjqdDjqdzq52EJGy1Vfvk346H6UV1VZfe62iWpV1JfEdguHr5W7192vh5Y74DkxmiOzumRk/fjyefvpp5OXlQZIkGAwGfP/995gzZw4efvhhu/bVr18//PTTT2bbfv75Z0RERAAAoqKioNfrsWvXLtPzFRUV2Lt3L/r27Wtv04lIxeqq92FdCREBDUhmXnjhBbRv3x7h4eG4evUqoqOjMWDAAPTt2xf/+Mc/7NrXU089hfT0dCxatAinTp3Chg0b8NZbb+HRRx8FUHN76cknn8SiRYvw6aefIjMzE5MnT4avry/Gjx9vb9OJSHO0W1diT68TUXNn920mT09PvP/++1i4cCGOHj0Kg8GAnj17olOnTna/+R133IFPP/0U8+bNw8KFCxEVFYXXXnsNEyZMMMXMnTsX169fx6xZs1BYWIi4uDjs3LkTfn5+dr8fEWlLn47BWLbnlKw4tbGn10ltt9CIHK3Byxl07NgRHTt2bHQDRo0ahVGjRtX7vCRJSE5ORnJycqPfi4i05Y7IIEgSIKzMGydJNXHqo91eJyJHk5XM/O1vf5O9w6VLlza4MURE9jh8ttBqIgPUJDqHzxaqrncmLioIy/bIiyNq7mQlM0ePHjX7+fDhw6iurkbnzp0B1BTturu74/bbb3d8C4mI6iF3Zl81zgDsVs/0Ew2NI9IyWcnMnj1/XB4sXboUfn5+WLduHQIDAwHUrLE0ZcoU9O/fv2laSUTNXrVBWAzNljuzrxpnAM4ruu7QOCIts7tm5pVXXsHOnTtNiQwABAYG4vnnn8ewYcMwe/ZshzaQiCg1MxfJW7KQV/xHD4ve3xv/HNUVYQHeyCsqq3PlbAk1k+upcQbgY79dkR13Xy/OdE7Nm91Ds4uLi3Hx4kWL7ZcuXeJK1kTkcKmZuZix/ohZIgMAecVlmLXhKO7pEVZnIgPUrJqdNDpa8WtQ1Y0FwERy2Z3M/PnPf8aUKVPw8ccf47fffsNvv/2Gjz/+GI888ohpZWsiIkeoNgg8s/m41Zi1+89aff7or4WObJLTtA/ydWgckZbZncysXLkSI0eORGJiIiIiIhAREYEJEyZg+PDhWL58eVO0kYiaqfQz+bhSWmk1ptzG2kur9mWjQoXrM3UJlTeXltw4Ii2zO5nx9fXF8uXLkZ+fj6NHj+LIkSMoKCjA8uXL0aJFi6ZoIxE1U2kOmN3WIID30nIa3xgnu3yt3KFxRFrW4EnzWrRoge7duzuyLUREtdiYREamswWlDtmPM12+KjOZkRlHpGV2JzODBw+GZGVeg927dzeqQURERn06tMayPacbvZ8IFdaVXLlu/faavXFEWmZ3MnPbbbeZ/VxZWYljx44hMzMTkyZNclS7iIgQ3zEYrXw9bdbN2DKxT6RjGuREksxRSnLjiLTM7mTm1VdfrXN7cnIyrl692ugGEREZubtJuCMyELuyLjV4H2o91Wt5EU0iR7O7ALg+iYmJWL16taN2R0SEiioDvj7Z8EQGqKm6UWMBcHyHml4pW4oa2WtFpAUOS2bS0tLg7a2+KcOJSLneS8uBwQE1wGosAHZ3k7BoTIzNuGc/O45qRxwkIhWz+zZT7YnxhBDIzc3FoUOH8NxzzzmsYUREjkpC1FgADAABPl42Y66UViL9dD76dWrthBY1jbrW3VLnrM3kKnYnM/7+/majmdzc3NC5c2csXLgQw4YNc2jjiKh5c0QS4iapswAYANLOXJYdp9ZkJjUzFwu2ZiG36I/lKsICvJE0OhoJMWEubBmpid3JzNq1a5ugGURElib2icQL20826lbTtP5R8PJw2B11J9P2+kypmbmYuf6IxWxCeUVlmLn+CFYkxjKhIVns/gvv0KED8vMtZ+W8cuUKOnTo4JBGEREBgJeHG6b1j7IaMzQ6xOrzPdsHOrJJTiU3B1PjiKZqg8CCrVl1Toto3LZgaxbrgUgWu5OZnJwcVFdXW2wvLy/H+fPnHdIoIiKjeSOiMX1AFGqXULhJwLT+kcg8X1zvayWo94RYbRBYsz/HZlygryfiO6gvmTmYXWB2a6k2ASC3qAwHswuc1yhSLdm3mbZs2WL695dffomAgADTz9XV1fj6668RGRnp0MYREQE1Cc3sYV3wXloOzhaUIiLIFxP7ROLw2UKs2pdT7+tuPCGqrfci/Uw+iq5X2Yyb1CdSlcWyl0rqT2QaEkfNm+xkZsyYMQAASZIsZvr19PREZGQkXnnlFYc2jojIyMvDDY/0N7+VreUTotxFNqtU2OsEACF+8qbykBvnKhyJpQyykxmDwQAAiIqKQkZGBlq3VmflPBGpT0WVwaJXxsvDTTMnxLrJTVLUmczcHhEICdZbL/0vTqk4Eks57B7NlJ2d3RTtICKqU8r2LKzal202oumF7ScxrX8U5iZ0RViAt9Xai7CAmqtltZG7yGafDuq8sMzILrCZhon/xSlx2DlHYimLrGTm9ddfx1//+ld4e3vj9ddftxr7+OOPO6RhREQp27Pw5reWF1AGAdP2e3qE1RljdE+PMFV2+8d3DIbOww3lVYZ6Y3QebohXWS2Q0fenf5cdp7RkxtZILGPh+dBovSo/e2okK5l59dVXMWHCBHh7e9e70CRQU0/DZIaIHKGiyoC39lnvCX7z22zo/a3fQtryQy7mJnRV3Uml2iBQYSWRAWqOUbVBqO53A4ALV+TVMcmNcyZ7RmKprfBcrWQlMzfeWuJtJiJyhnX7cyBklIPkFVs/2an1pPJeWo6s2zDvpeVYFEarQXigj0PjnEnLhedqZfc8MwsXLkRpqeV6KdevX8fChQsd0igioowcx80vosaTitx1qdS4iCYA9O0o79aR3Dhn0nbhuTrZncwsWLAAV69etdheWlqKBQsWOKRRRES+Xu4O25caTypy16VS6yKa8R2Cbf4ft/ByV+SEgMaRWNYofSSW1tidzAghzBaaNPrhhx8QFKS+EQNEpEz39WwrKy7I16veE4sE9Y5mGh8XgTq+as2oeRFNADbXzFLqmlr2jMQi55D9SQkMDERQUBAkScItt9yCoKAg0yMgIABDhw7Fgw8+2JRtJaJmpG+n1jZPZjoPNzw/JgaA5VKLxp+TRkerrkA2NTMXQ175xmbNkJoX0TyYXYArpZVWYwpLKxW5nIE9q5mTc8ieZ+a1116DEAJTp07FggULzJYz8PLyQmRkJPr06dMkjSSi5snDTUKFjef/FKPHisRYi8nL9CqdvKy++UtuVLMuVRTmjYh2WrscTd1FtNpezVyNZCczxiUMoqKi0LdvX3h6ejZZo4iI0s/ko7TCclHbG12rqEb6mXwkxIRhaLRe9dPKW5u/xMjP2x0Hnx0KHwfWFLmCmoto+3QMxrI9p2TFkXPI6p8sLi42PXr27Inr16+bbbvxQUTkCHLXJpIbp3TVBoG132dbnb8EAErKqnHs3BXnNKoJ9Y4KQliAtyrrndRcvKxVsnpmWrVqVWfR742MhcHV1davpIiI5JG/NlFqZi6St5xAXnG5aaveX4fke25VxW2mutb4sUaZt17s4+4mIWl0NGauP2KxRpMa6p1sLfCp1gVA1UpWMrNnzx5ZOzt69GijGkNEZCR3bSIPNzfMWH/EYntecTlmrD+ClQpfI0dOjUxtSrz10hAJMWGqrHfa/8tlm7Mzl1cZsP+Xy+jf+SYntap5k5XMDBw4sN7nioqK8P777+Ptt9/GDz/8gCeffNJRbSOiZuyOqCCbqyoDwNr9OVaff2bzccWukSOnRqY2X093ZJ4vwu0RgaodyXQjNdY7fXL0N9lxTGaco8F/Cbt370ZiYiLCwsLwxhtvYMSIETh06JAj20ZEzdjhs4WyTvJXrlsf3nultBLpCq2rsbXGT11KK6vxwvaT6PLcDqRsz2qiljmXu5uEPh2Dce9t4ejTMVjRiQwAm4Xp9sZR48kezQQAv/32G9auXYvVq1fj2rVrePDBB1FZWYlPPvkE0dHqHSJIRMpja80le6Sduay4lZeBxtW+3LhyuJqHaKvRHZFB2Jl1UVYcOYfsnpkRI0YgOjoaWVlZeOONN3DhwgW88cYbTdk2ImrGCq6W2w6STZlX+o6ofVm1L9tm/YbSVRsE0k7n4/Nj55F2Oh/VCi+endQ30qFx1Hiye2Z27tyJxx9/HDNnzkSnTp2ask1ERAhq4eWwfSl1vg/j8GR7bzXdyCDUu3I2UPdIrjCFFwC7u0nQebih3EoSqfNwU/ztMi2R3TOzb98+lJSUoFevXoiLi8OyZcvw+++/N2XbiKgZ0wf4yIprqbN+TRbo66nY+T7c3STc06PxJ2y1rpxtHMlVO5nLKyrDzPVHkJqZ66KWWZd+Ot9qIgPUjGZSaq2WFslOZvr06YNVq1YhNzcX06dPx6ZNmxAeHg6DwYBdu3ahpKSkKdtJRM3M7RGBsHVh6yYBKWO7WY1JGdtNsVfI1QaBDw7JGxljjRpXzrY2ksu4bcHWLEXecuLaTMpj92gmX19fTJ06Fd999x2OHz+O2bNnY/HixQgJCcE999zTFG0kombo8NlC2DqPGQTQuqUOKxNjofc3rz8JC/BW/Bwz6afzbS62aIsEda6cbWsklwCQW1SmyIUmuTaT8tg1mqm2zp07Y8mSJUhJScHWrVuxevVqR7WLiJo5exYivPe2cNXNVQI45srdS6W1GWpeaJJrMylPo5IZI3d3d4wZMwZjxoxxxO6IiOxeiNA4V4m6ND4JMdZmKHHouTVqXmjSuDaTtXlkuDaTc6l/+kgi0iQ1L0Qol6OSLzXWZvSOCrK5WKOvl7uq/3/JeZjMEJEiGRciBCz7L9SwEKEcclZflkd9x6DaIGzOkFtaUa3IAuD00/k2236topqjmZzIpclMcnIyJEkye+j1etPzQggkJyejTZs28PHxwaBBg3DixAkXtpiInMm4EKE+wPxWgz7AGysUXtzrTOq7vQas25/t0Dhn4mgm5XFIzUxj3Hrrrfjqq69MP7u7/3GVsmTJEixduhRr167FLbfcgueffx5Dhw7FTz/9BD8/P1c0l4icTI0LEcol5wrfFrXWZmTkFMqOmzagiRtjN45mUhqXJzMeHh5mvTFGQgi89tprmD9/PsaOHQsAWLduHUJDQ7FhwwZMnz7d2U0lIhdRZ3GvbY4azaRGLWTeXpMb50wczVSj2iAUc5Hh8r+CX375BW3atEFUVBQeeughnDlzBgCQnZ2NvLw8DBs2zBSr0+kwcOBA7N+/v979lZeXo7i42OxBRKRMjf/iLyytVOhcLNaNjW3r0DhnklPrpNYeM7lSM3PRb/HXGLcqHU9sOoZxq9LRb/HXLpu12aXJTFxcHN599118+eWXWLVqFfLy8tC3b1/k5+cjLy8PABAaGmr2mtDQUNNzdUlJSUFAQIDp0a5duyb9HYiIGspRV+5KnIvFlr43t7bZ69JC546+N6tryHlzkJqZixnrjyCv2Hwx2Lzicsxw0TIULk1mhg8fjvvuuw/dunXD3XffjW3btgGouZ1kJEnmVy5CCIttN5o3bx6KiopMj3PnzjVN44mIGslRo5mUOBeLLe5uEhLj21uNSYxrr8jaqOY8mqnaIPDM5uNWY57ZfNzpo9BcfpvpRi1atEC3bt3wyy+/mOpoavfCXLp0yaK35kY6nQ7+/v5mDyIipbJV82Ll2g2AeufakbMu1QeHflPk0OzmPJpJzhIcV0ornZ7IKSqZKS8vx8mTJxEWFoaoqCjo9Xrs2rXL9HxFRQX27t2Lvn37urCVRESOcTC7wOaJQdg4l9/TI0yRvRe2KPWkKE/zHc30/enfHRrnKC5NZubMmYO9e/ciOzsbBw4cwP3334/i4mJMmjQJkiThySefxKJFi/Dpp58iMzMTkydPhq+vL8aPH+/KZhMROYQjal2U2nthi5p7N+TWOmlxNNP5wusOjXMUlw7N/u233zBu3DhcvnwZN910E+Lj45Geno6IiAgAwNy5c3H9+nXMmjULhYWFiIuLw86dOznHDBFpgiNqXYy9F2pbm0nNvRtcm0l5XJrMbNq0yerzkiQhOTkZycnJzmkQEZETGdefyisqQ2P6VtLOXFZdMqP2uVq8PNysJjNqnf9HrXi0iYhcxNr6U/ZRXu+FLXdEyitalhvnTHJqndQ6/48twlYRl51xjsJkhojIhepbf8oeSu29sCZD5olebpwzya11UuP8P7b8XlJuO8iOOEdhMkNE5GIJMWH47ukh+PNtbRr0eiX2Xtii5gJgubVOapz/x5bL1+QlKXLjHIXJDBGRAri7Sbi/V8NmLFdi74Vt6i0ANtY61dcyCeqd/0etmMwQESlEfIdgtPL1tPt1Suy9sEXNw5ut1ToZf04aHa3K+X9suamlvN4muXGOwmSGiEgh3N0k3BEZ2IBXqu+kqfbFGuurddIHeGNFYiwSYsJc1LKm1bqlzqFxjuLSodlERPSHiioDvj55ye7XKbH3ojlIiAnD0Gg9DmYX4FJJGUL8am4tabFHxsjW8hr2xjkKkxkiIoV4Ly0H9k7mG+jrqdjeC2vsWaxRyXPouLtJTCYVgLeZiIgU4mxBqd2vSRnbTZU9AWoezUTKw2SGiEghIoJ8ZceGBXhjpaprM9Q7momUh8kMEZFCTOwTKevU/d7U3vju6SEqTmTUPZqpOZNkFsPIjXMUJjNERArh7ibZXNNH5+GGvje3VuWtpRupfTRTcxXWSt6Qa7lxjsJkhohIIdJP56O8ymA1przKgPTT+U5qEdlSbRBIO52Pz4+dR9rpfFTbW8GtMq18vBwa5ygczUREpBD2FMUqeYSPHFoYzZSamYsFW7OQW/THGkxhAd5IGh2t6luA1ly5XuHQOEdhzwwRkWLIu3X0W+F11fcAqH00U2pmLmauP2KWyABAXlEZZq4/gtTMXBe1rGldKLzu0DhHYTJDRKQQcotdPzt2AXe+uFvlJ0z1jmaqNggs2JqFutJJ47YFW7NUn3CqCZMZIiKFkFMUa5Sr8h4ANY9mOphdYNEjcyOBmv+fg6pcANQ6IeQlaHLjHIXJDBGRSgmotwdAzqKaSp3d+FJJ/YlMQ+LU5PJVebUwcuMchckMEZFCyCmKrU2tPQByFtXsFRmoyCHoIX7yhh3LjVOT8mrro+3sjXMUJjNERArR0GLXvCLnFls6gpxFNb8+eQkVNoaqu0LvqCCbvUqtfD3ROyrISS1ynratfBwa5yhMZoiIFKNhvRAF15zbpe8IchbVNIiaOCWylWRVKjAJc4SuYf4OjXMUJjNERArR0GLXoJY6B7ek6cldVLMhi282NXvmyNGaorJKh8Y5CpMZIiKFsGc00430/uqrzZC7qKY9i286i9rnyGkMzjNDRET1qjYIpJ/OR5WdI5PUWpshZ1FN6X9xyqPeOXK0iskMEZGLpWbm4s4Xd2PCOwfsLnhV6+lSzqKaXh5uihzNpOY5crSKyQwRkQvVNy2+XIWllaocmq3mRTW54rfyMJkhInIRa9Pi20ONk7M157oTcjwmM0RELmJrWny51Dk5m3rrTprzaCZJkvf/ITfOUZjMEBG5iCN6VNRaAKzmupPm3KsUHihvMjy5cY7CZIaIyEUc0aOi1snZ1F13ot5epcbq27G1Q+MchckMEZGL9I4KQliAd6NOeVq9naFkau5VaiylLhDKZIaIyEXc3SQkjY4G0LhreDXezlBz3Ym6e5Uax91NwuKx3azGpIzt5vQh9UxmiIhcKCEmDCsSY6EPaMwtJ/XdzmjOdSdqlxAThpWJsRYzT4cFeGNlYiwSYsKc3iYPp78jERGZSYgJw9BoPdJP5+PRDUdw5bp969qo83aGeutO7OlV6tfJubUjzmL8zB7MLsClkjKE+Hmjd1SQyyY5ZM8MEZECuLtJ6NepNRbf1w0S5J/C1Xo7Q811J+xVquHuJqFPx2Dce1s4+nQMdulszUxmiIgUxDG3nZRPqYWk8qi3V0mrmMwQESlMQkwYvnt6COaP6GIzVqlFsra4u0n4v15trcY82Kst12YiWZjMEBEpkLubhCKZtTNqvJ1RbRD44NBvVmM+OPQbqu1cRdwZmvNoJqViMkNEpFjavZ2RfjofV0qtJ2tXSitV2etEzsdkhohIobR8O0PNRbRqniNHq5jMEBEplLZvZ6i310nNiZhWMZkhIlIwLw/rX9O2nlcqdfc6qTcR0yp1/hUQEWlMtUEg7XQ+Pj92Hmmn81FtEDiYXWCzrqSwtBIHswuc1ErHUfPQbHUnYtrEGYCJiFwsNTMXC7ZmIbeozLQtLMAbCbeGynp9XtH1pmpakzEOzX7z2+x6Y5Q6NPuOyCCHxlHjsWeGiMiFUjNzMXP9EbNEBgDyisqwZv9ZWfsouFbRFE1rUtUGgS0/5FqN2fJDriKHZh+QWdgrN44aj8kMEZGLVBsEFmzNQl2na3tO4UEtdY5qktMczC6wSOBqyy0qU+QttE+OWp8fx944ajwmM0RELiLnhC5H7dWL1eBSibzfW26cM9kalm1vHDUekxkiIhdxxIm6la8nekeprzYjxE9eAiY3zplYM6M8iklmUlJSIEkSnnzySdM2IQSSk5PRpk0b+Pj4YNCgQThx4oTrGklE5ECOOFFXVhkc0BLn6x0VZHM0k1ITtUl9Ix0aR42niGQmIyMDb731Frp37262fcmSJVi6dCmWLVuGjIwM6PV6DB06FCUlJS5qKRGR48g5odui5plmK2wkYkpN1NzdJOhszO+j83BT5EgsrXJ5MnP16lVMmDABq1atQmBgoGm7EAKvvfYa5s+fj7FjxyImJgbr1q1DaWkpNmzY4MIWExE5jq0TuhxqnGlWzUsCpJ/OR7mN/7fyKoMi265VLk9mHn30UYwcORJ333232fbs7Gzk5eVh2LBhpm06nQ4DBw7E/v37691feXk5iouLzR5EREok54Quj/p6ANS8JICa265VLp00b9OmTThy5AgyMjIsnsvLywMAhIaaTxoVGhqKs2frn3shJSUFCxYscGxDiYiagKNOduqcaVbNSwKoue3a5LKemXPnzuGJJ57A+vXr4e1dfxGcJJl/GIQQFttuNG/ePBQVFZke586dc1ibiYgcq/EnO6VO+W+LmpcEUHPbtcplyczhw4dx6dIl3H777fDw8ICHhwf27t2L119/HR4eHqYeGWMPjdGlS5csemtupNPp4O/vb/YgIlIiR5zslDrlvy1qXhE8vkMwPGwccw83SZFt1yqXJTN33XUXjh8/jmPHjpkevXr1woQJE3Ds2DF06NABer0eu3btMr2moqICe/fuRd++fV3VbCIih5FzQrdFqVP+a1lFlQFVNo55lUE4pLib5HFZMuPn54eYmBizR4sWLRAcHIyYmBjTnDOLFi3Cp59+iszMTEyePBm+vr4YP368q5pNRKQoSp3y3xY1j2ZatD3LoXHUeIpeNXvu3Lm4fv06Zs2ahcLCQsTFxWHnzp3w8/NzddOIiBrNUaOZlDjlvy32jAjq16l1E7fGPjn5pQ6No8ZTVDLzzTffmP0sSRKSk5ORnJzskvYQETUlR41mUuKU/7apd0RQZLAv9v0iL46cw+XzzBARNV+NP1FLAG6PCLQZpzRqHhH07Ihoh8ZR4zGZISJyEUecqAWADBXWzKh5NJOXh5us5Qy8bMSQ4/BIExG5SHyHYIec8DjTrHMdzC6QtZyBGguz1YrJDBGRi1QbBCqrHTF8V3l1JbaoeTRTXtF1h8ZR4zGZISJykffSciAcMEWMEutKbFHz+kYF1yocGkeNx2SGiMhFzhY0fuiuzsNNkXUltql3NFNQS51D46jxmMwQEblIRFDjh+6WVxlUOQOwmkcz6f3lDYWXG0eNx2SGiMhFJvaJhCOWVVrz/ZnG78TJ5BQ/K7XX6faIQJv9RWodMq9WTGaIiFzEy8MN0/pHNXo/u7IuOaA1zlUtY+0ipfY6ZWQXwFar1DpkXq2YzBARudC8EdGYPiCqkT00yjvh27Juf7ZD45xJzcXLWqWo5QyIiJqjeSOiMXtYF7yXloOzBaUoLa/Cd6cuI6+4XNbrh0Xrm7iFjpeRUyg7btqAJm6M3dRbvKxVTGaIiBTAy8MNj/TvYPq52iCw/5fLmLjmoNXXSQAm92v8rSpna2Fj9l9745ypT8dgLNtzSlYcOQdvMxERKZC7m4T+nW/C9AHWE5W/DohS5bT5Y2PbOjTOmdS8FINWqe8vgIioGamvpsZNAqYPiMI8lS5m2Pfm1rLWN+p7c2sntYjUjLeZiIgUrnZNTUSQLyb2iVRlj8yN3G1UPXs4Ytx6E7BnKYZ+nZiMOQOTGSIiFahdU6N2ak4I7BnNpLS2a5W603oiIlIldQ9v5mgmpWEyQ0RELqDehEDNSzFoFZMZIiJyOjUnBBzNpDxMZoiIyOli28tbt0huHDVvTGaIiMjpNhw469A4Z7KneJmcg8kMERE5XXb+NYfGOZO6i5e1ickMERE5nXrLfwG1t16LmMwQEZHT9WwnrxZGbpwzqbl4WauYzBARkdOFtfJxaJwz3REZBMlGp4sk1cSRczCZISIip+sdFYSwAG+rMWEB3ugdpbyE4PDZQghhPUaImjhyDiYzRETkdO5uEpJGR0OCZWWJcVvS6Gib6ze5Ql7RdYfGUeMxmSEiIpdIiAnDisRY6Gv10OgDvLEiMRYJMWEuapl1BdcqHBpHjceFJomIyGUSYsIwNFqPg9kFuFRShhC/mltLSuyRMQpqqXNoHDUekxkiInIpdzdJVSN/9P7Wa33sjaPG420mIiIiO/SOCkIrX0+rMa18PRVZvKxVTGaIiIjsVFFlsPp8pY3nybGYzBAREdmBazMpD5MZIiIiO3BtJuVhMkNERGQXrs2kNExmiIiI7MC1mZSHyQwREZEd5K65xLWZnIfJDBERkR0ysgscGkeNx2SGiIjIDiwAVh4mM0RERHZhAbDSMJkhIiKyAwuAlYfJDBERkR3iOwTD18vdakwLL3fEd2Ay4yxMZoiIiEjVmMwQERHZgcsZKA+TGSIiIjtwNJPyMJkhIiKyC0czKQ2TGSIiIjtwNJPyuDSZWbFiBbp37w5/f3/4+/ujT58+2LFjh+l5IQSSk5PRpk0b+Pj4YNCgQThx4oQLW0xERM0dRzMpj0uTmbZt22Lx4sU4dOgQDh06hCFDhuDee+81JSxLlizB0qVLsWzZMmRkZECv12Po0KEoKSlxZbOJiIhIQVyazIwePRojRozALbfcgltuuQUvvPACWrZsifT0dAgh8Nprr2H+/PkYO3YsYmJisG7dOpSWlmLDhg2ubDYRETVjHM2kPIqpmamursamTZtw7do19OnTB9nZ2cjLy8OwYcNMMTqdDgMHDsT+/fvr3U95eTmKi4vNHkRERI7C0UzK4/Jk5vjx42jZsiV0Oh1mzJiBTz/9FNHR0cjLywMAhIaGmsWHhoaanqtLSkoKAgICTI927do1afuJiKi54WgmpXF5MtO5c2ccO3YM6enpmDlzJiZNmoSsrCzT85Jk/mEQQlhsu9G8efNQVFRkepw7d67J2k5ERM0PRzMpj4erG+Dl5YWbb74ZANCrVy9kZGTg3//+N55++mkAQF5eHsLCwkzxly5dsuituZFOp4NOp2vaRhMRUbMV3yEYrXw9caW00mpckY3nyXFc3jNTmxAC5eXliIqKgl6vx65du0zPVVRUYO/evejbt68LW0hERM2Zu5uERWNibMY9+9lxVBuEE1pELu2ZefbZZzF8+HC0a9cOJSUl2LRpE7755hukpqZCkiQ8+eSTWLRoETp16oROnTph0aJF8PX1xfjx413ZbCIiauYCfLxsxlwprUT66Xz069TaCS1q3lyazFy8eBETJ05Ebm4uAgIC0L17d6SmpmLo0KEAgLlz5+L69euYNWsWCgsLERcXh507d8LPz8+VzSYiombOnhFNTGaankuTmXfeecfq85IkITk5GcnJyc5pEBERkSwc0aQkiquZISIiUjqOaFIWJjNERER2iu8QDC8P66dQnYcb12dyEiYzREREdqo2CFRWG6zGVFQbOJrJSZjMEBER2em9tBwIG3mKEDVx1PSYzBAREdnpbEGpQ+OocZjMEBER2SkiyNehcdQ4TGaIiIjsNLFPpM1B19L/4qjpMZkhIiKyk7ubBF8vd6sxvjp3uLtxnhlnYDJDRERkp4PZBbhWUW015lp5NQ5mFzipRc0bkxkiIiI7XSopc2gcNQ6TGSIiIjuF+Hk7NI4ah8kMERGRnXpHBSEswLveImAJQFiAN3pHBTmzWc0WkxkiIiI7ubtJSBodDcByKUnjz0mjo1kA7CRMZoiIiBogISYMKxJjoQ8wv5WkD/DGisRYJMSEuahlzY+HqxtARESkVgkxYRgarcfB7AJcKilDiF/NrSX2yDgXkxkiIqJGcHeT0KcjV8d2Jd5mIiIiIlVjMkNERESqxmSGiIiIVI3JDBEREakakxkiIiJSNSYzREREpGpMZoiIiEjVmMwQERGRqjGZISIiIlXT/AzAQggAQHFxsYtbQkRERHIZz9vG87g1mk9mSkpKAADt2rVzcUuIiIjIXiUlJQgICLAaIwk5KY+KGQwGXLhwAX5+fpAkLvwF1GS77dq1w7lz5+Dv7+/q5qgKj13j8Pg1HI9dw/HYNZwrj50QAiUlJWjTpg3c3KxXxWi+Z8bNzQ1t27Z1dTMUyd/fn3/YDcRj1zg8fg3HY9dwPHYN56pjZ6tHxogFwERERKRqTGaIiIhI1ZjMNEM6nQ5JSUnQ6XSuborq8Ng1Do9fw/HYNRyPXcOp5dhpvgCYiIiItI09M0RERKRqTGaIiIhI1ZjMEBERkaoxmSEiIiJVYzKjEufPn0diYiKCg4Ph6+uL2267DYcPHzY9L4RAcnIy2rRpAx8fHwwaNAgnTpww28dbb72FQYMGwd/fH5Ik4cqVKxbvU1hYiIkTJyIgIAABAQGYOHGiRdyvv/6K0aNHo0WLFmjdujUef/xxVFRUNMWv7RDOOHY5OTl45JFHEBUVBR8fH3Ts2BFJSUkWx4XHru7PnVF5eTluu+02SJKEY8eOmT2ntmMHOPf4bdu2DXFxcfDx8UHr1q0xduxYs+fVdvycdex+/vln3HvvvWjdujX8/f3Rr18/7NmzxyymuR27goIC/L//9//QuXNn+Pr6on379nj88cdRVFRk9j5KOl8wmVGBwsJC9OvXD56entixYweysrLwyiuvoFWrVqaYJUuWYOnSpVi2bBkyMjKg1+sxdOhQ09pUAFBaWoqEhAQ8++yz9b7X+PHjcezYMaSmpiI1NRXHjh3DxIkTTc9XV1dj5MiRuHbtGr777jts2rQJn3zyCWbPnt0kv3tjOevY/fjjjzAYDHjzzTdx4sQJvPrqq1i5cqVZPI9d/Z87o7lz56JNmzYW29V27ADnHr9PPvkEEydOxJQpU/DDDz/g+++/x/jx403Pq+34OfPYjRw5ElVVVdi9ezcOHz6M2267DaNGjUJeXh6A5nnsLly4gAsXLuDll1/G8ePHsXbtWqSmpuKRRx4xey9FnS8EKd7TTz8t7rzzznqfNxgMQq/Xi8WLF5u2lZWViYCAALFy5UqL+D179ggAorCw0Gx7VlaWACDS09NN29LS0gQA8eOPPwohhNi+fbtwc3MT58+fN8Vs3LhR6HQ6UVRU1NBfsck469jVZcmSJSIqKsr0M4+d9WO3fft20aVLF3HixAkBQBw9etTsOTUdOyGcd/wqKytFeHi4ePvtt+t9L7UdP2cdu99//10AEN9++61pW3FxsQAgvvrqKyEEj53Rhx9+KLy8vERlZaUQQnnnC/bMqMCWLVvQq1cvPPDAAwgJCUHPnj2xatUq0/PZ2dnIy8vDsGHDTNt0Oh0GDhyI/fv3y36ftLQ0BAQEIC4uzrQtPj4eAQEBpv2kpaUhJibG7Or5T3/6E8rLy826MZXCWceuLkVFRQgKCjL9zGNXv4sXL2LatGl477334Ovra/G82o4d4Lzjd+TIEZw/fx5ubm7o2bMnwsLCMHz4cLPbBmo7fs46dsHBwejatSveffddXLt2DVVVVXjzzTcRGhqK22+/HQCPnVFRURH8/f3h4VGzpKPSzhdMZlTgzJkzWLFiBTp16oQvv/wSM2bMwOOPP453330XAEzdoaGhoWavCw0NNT0nR15eHkJCQiy2h4SEmPaTl5dn8T6BgYHw8vKy672cxVnHrrbTp0/jjTfewIwZM0zbeOzqJoTA5MmTMWPGDPTq1avOGLUdO8B5x+/MmTMAgOTkZPzjH//AF198gcDAQAwcOBAFBQWm91LT8XPWsZMkCbt27cLRo0fh5+cHb29vvPrqq0hNTTXdluGxA/Lz8/Gvf/0L06dPN21T2vlC86tma4HBYECvXr2waNEiAEDPnj1x4sQJrFixAg8//LApTpIks9cJISy22VJXfO39yIlRCmceO6MLFy4gISEBDzzwAP7yl7+YPcdjZ+mNN95AcXEx5s2bZzVOTccOcN7xMxgMAID58+fjvvvuAwCsWbMGbdu2xUcffWQ6Aanp+Dnr2AkhMGvWLISEhGDfvn3w8fHB22+/jVGjRiEjIwNhYWF1vk9D3stZHH3siouLMXLkSERHRyMpKcnsOSWdL9gzowJhYWGIjo4229a1a1f8+uuvAAC9Xg8AFpnupUuXLLJia/R6PS5evGix/ffffzftR6/XW7xPYWEhKisr7XovZ3HWsTO6cOECBg8ejD59+uCtt94ye47Hrm67d+9Geno6dDodPDw8cPPNNwMAevXqhUmTJpneS03HDnDe8TOecG98L51Ohw4dOpi9l5qOnzM/e1988QU2bdqEfv36ITY2FsuXL4ePjw/WrVtneq/meuxKSkqQkJCAli1b4tNPP4Wnp6fpOaWdL5jMqEC/fv3w008/mW37+eefERERAQCIioqCXq/Hrl27TM9XVFRg79696Nu3r+z36dOnD4qKinDw4EHTtgMHDqCoqMi0nz59+iAzMxO5ubmmmJ07d0Kn05nuMSuJs44dUDMcctCgQYiNjcWaNWvg5mb+58VjV7fXX38dP/zwA44dO4Zjx45h+/btAIAPPvgAL7zwAgD1HTvAecfv9ttvh06nM3uvyspK5OTkmN5LbcfPWceutLQUACz+Vt3c3Ew9Xs312BUXF2PYsGHw8vLCli1b4O3tbbZPxZ0vHFpOTE3i4MGDwsPDQ7zwwgvil19+Ee+//77w9fUV69evN8UsXrxYBAQEiM2bN4vjx4+LcePGibCwMFFcXGyKyc3NFUePHhWrVq0yVfAfPXpU5Ofnm2ISEhJE9+7dRVpamkhLSxPdunUTo0aNMj1fVVUlYmJixF133SWOHDkivvrqK9G2bVvx2GOPOedg2MlZx+78+fPi5ptvFkOGDBG//fabyM3NNT2MeOzq/9zdKDs722I0k9qOnRDOPX5PPPGECA8PF19++aX48ccfxSOPPCJCQkJEQUGBEEJ9x89Zx+73338XwcHBYuzYseLYsWPip59+EnPmzBGenp7i2LFjQojmeeyKi4tFXFyc6Natmzh16pTZ91lVVZVpP0o6XzCZUYmtW7eKmJgYodPpRJcuXcRbb71l9rzBYBBJSUlCr9cLnU4nBgwYII4fP24Wk5SUJABYPNasWWOKyc/PFxMmTBB+fn7Cz89PTJgwwWI449mzZ8XIkSOFj4+PCAoKEo899pgoKytrql+90Zxx7NasWVPn87WvF3js6v7c3aiuZEYI9R07IZx3/CoqKsTs2bNFSEiI8PPzE3fffbfIzMw024/ajp+zjl1GRoYYNmyYCAoKEn5+fiI+Pl5s377dbD/N7dgZh7LX9cjOzjbFKel8IQkhhGP7eoiIiIichzUzREREpGpMZoiIiEjVmMwQERGRqjGZISIiIlVjMkNERESqxmSGiIiIVI3JDBEREakakxkiIiJSNSYzRKQokydPhiRJmDFjhsVzs2bNgiRJmDx5sil2zJgxFq+VJAmenp4IDQ3F0KFDsXr1atNaO0SkPUxmiEhx2rVrh02bNuH69eumbWVlZdi4cSPat29v9bUJCQnIzc1FTk4OduzYgcGDB+OJJ57AqFGjUFVV1dRNJyIXYDJDRIoTGxuL9u3bY/PmzaZtmzdvRrt27dCzZ0+rr9XpdNDr9QgPD0dsbCyeffZZfP7559ixYwfWrl3bxC0nIldgMkNEijRlyhSsWbPG9PPq1asxderUBu1ryJAh6NGjh1lyRETawWSGiBRp4sSJ+O6775CTk4OzZ8/i+++/R2JiYoP316VLF+Tk5DiugUSkGB6ubgARUV1at26NkSNHYt26dRBCYOTIkWjdunWD9yeEgCRJDmwhESkFkxkiUqypU6fiscceAwD85z//adS+Tp48iaioKEc0i4gUhreZiEixEhISUFFRgYqKCvzpT39q8H52796N48eP47777nNg64hIKdgzQ0SK5e7ujpMnT5r+LUd5eTny8vJQXV2NixcvIjU1FSkpKRg1ahQefvjhpmwuEbkIkxkiUjR/f/96nzMYDPDwMP8aS01NRVhYGDw8PBAYGIgePXrg9ddfx6RJk+Dmxs5oIi2ShBDC1Y0gImqIhIQE3HzzzVi2bJmrm0JELsTLFCJSncLCQmzbtg3ffPMN7r77blc3h4hcjLeZiEh1pk6dioyMDMyePRv33nuvq5tDRC7G20xERESkarzNRERERKrGZIaIiIhUjckMERERqRqTGSIiIlI1JjNERESkakxmiIiISNWYzBAREZGqMZkhIiIiVWMyQ0RERKr2/wFz8m3MVazV1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mjds, altitudes)\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Altitude')\n",
    "plt.title('Altitudes for all r-band exposures in tract 5634')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "db153f58-ccac-430d-b15a-61dd984aaf3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-16T15:18:31.058130Z",
     "iopub.status.busy": "2023-11-16T15:18:31.057931Z",
     "iopub.status.idle": "2023-11-16T15:18:31.190656Z",
     "shell.execute_reply": "2023-11-16T15:18:31.190273Z",
     "shell.execute_reply.started": "2023-11-16T15:18:31.058117Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Airmasses for all r-band exposures in tract 5634')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mjds, airmasses)\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Airmass')\n",
    "plt.title('Airmasses for all r-band exposures in tract 5634')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc2d44f-b5c0-4072-8091-e05121315190",
   "metadata": {},
   "source": [
    "## Now let's look at the output of the astrometric fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f60fca5e-efeb-409c-98f8-3fbfddcf539e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T14:01:05.213322Z",
     "iopub.status.busy": "2023-11-14T14:01:05.212779Z",
     "iopub.status.idle": "2023-11-14T14:01:05.221143Z",
     "shell.execute_reply": "2023-11-14T14:01:05.220838Z",
     "shell.execute_reply.started": "2023-11-14T14:01:05.213307Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"margin: 0.1em;\n",
       "padding-left: 0.25em;\n",
       "border-left-style: solid;\n",
       "font-family: var(--jp-code-font-family);\n",
       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.daf.butler.registry.datasets.byDimensions._manager</span> <span style=\"color: var(--jp-warn-color0)\">WARNING</span>: Could not load storage class &#x27;Spectraction&#x27; for spectraction; if it has components they will not be included in query results.</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "[DatasetType('gbdesAstrometricFit_config', {}, Config),\n",
       " DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict),\n",
       " DatasetType('gbdesAstrometricFitSkyWcsCatalog', {band, instrument, skymap, physical_filter, tract, visit}, ExposureCatalog),\n",
       " DatasetType('gbdesAstrometricFit_fitStars', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict),\n",
       " DatasetType('gbdesAstrometricFit_metadata', {band, instrument, skymap, physical_filter, tract}, TaskMetadata),\n",
       " DatasetType('gbdesAstrometricFit_log', {band, instrument, skymap, physical_filter, tract}, ButlerLogRecords)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# N.B. you can use butler.registry.queryDatasetTypes() to get information about dataset types:\n",
    "butler.registry.queryDatasetTypes('gbdes*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8484c373-a8d6-4d5a-bc79-4cd6e66db249",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-15T21:58:02.598641Z",
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     "iopub.status.idle": "2023-11-15T21:58:02.607864Z",
     "shell.execute_reply": "2023-11-15T21:58:02.607501Z",
     "shell.execute_reply.started": "2023-11-15T21:58:02.598627Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DatasetRef(DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict), {instrument: 'LATISS', skymap: 'latiss_v1', physical_filter: 'SDSSr_65mm~empty', tract: 5615, ...}, run='u/csaunder/DM-41130/dev3_vis1/20231027T161450Z', id=5abfc877-b057-441d-b995-2d33a321e552),\n",
       " DatasetRef(DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict), {instrument: 'LATISS', skymap: 'latiss_v1', physical_filter: 'SDSSr_65mm~empty', tract: 5634, ...}, run='u/csaunder/DM-41130/dev3_vis1/20231027T161450Z', id=25548cdb-17a4-440d-92c8-4d985d53dd02),\n",
       " DatasetRef(DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict), {instrument: 'LATISS', skymap: 'latiss_v1', physical_filter: 'SDSSr_65mm~empty', tract: 5615, ...}, run='u/csaunder/DM-41130/dev3_vis1/20231031T203849Z', id=d65e97e6-3bc4-4438-ba20-84779b3444e2),\n",
       " DatasetRef(DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict), {instrument: 'LATISS', skymap: 'latiss_v1', physical_filter: 'SDSSr_65mm~empty', tract: 5634, ...}, run='u/csaunder/DM-41130/dev3_vis1/20231031T203849Z', id=ed4b2ac5-dfbd-44f0-956c-f66f333d475f),\n",
       " DatasetRef(DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict), {instrument: 'LATISS', skymap: 'latiss_v1', physical_filter: 'SDSSr_65mm~empty', tract: 5615, ...}, run='u/csaunder/DM-41130/dev3_vis1/20231101T143758Z', id=9a692040-ff5c-44ec-b07f-c1f43d7e09b8),\n",
       " DatasetRef(DatasetType('gbdesAstrometricFit_starCatalog', {band, instrument, skymap, physical_filter, tract}, ArrowNumpyDict), {instrument: 'LATISS', skymap: 'latiss_v1', physical_filter: 'SDSSr_65mm~empty', tract: 5634, ...}, run='u/csaunder/DM-41130/dev3_vis1/20231101T143758Z', id=fc3beea7-b61f-4c1b-b227-ab119bfde806)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use queryDatasets to see what gbdesAstrometricFit_starCatalog outputs exist. What you see below is that I ran the astrometric fitter\n",
    "# on the r band of tracts 5615 and 5634 multiple times. The butler stored the output of all these runs with a timestamp on the end of\n",
    "# the collection name. When you run butler.get(), you will automatically get the latest run, unless you specifically request another.\n",
    "list(butler.registry.queryDatasets('gbdesAstrometricFit_starCatalog'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22235eae-d418-4230-8c78-04d5fb8a7e9f",
   "metadata": {},
   "source": [
    "## Get the catalog of the best fit of the objects used in the astrometric fit:\n",
    "Here's a description of the columns:\n",
    "\n",
    "•\tstarMatchID - the id of this source\n",
    "\n",
    "•\tstarReserve - if True, this star was reserved for validation and was not used in the fit.\n",
    "\n",
    "•\tstarColor - color, will be filled in when we actually use color in the fit.\n",
    "\n",
    "•\tstarPMCount - number of sources for this star that had proper motion/parallax information (this will always be zero when proper motion fitting is turned off, even though the Gaia stars do have proper motion information)\n",
    "\n",
    "•\tstarDetCount - number of sources for this star not counting those with proper motion/parallax information\n",
    "\n",
    "•\tstarClipCount - how many of this object's sources were clipped\n",
    "\n",
    "•\tstarDOF - degrees of freedom of the position fit\n",
    "\n",
    "•\tstarChisq - chi-squared value for the position fit\n",
    "\n",
    "•\tstarX - best-fit RA in degrees\n",
    "\n",
    "•\tstarY - best-fit Dec in degrees\n",
    "\n",
    "•\tstarPMx - best-fit proper motion in RA in milliarcseconds / year\n",
    "\n",
    "•\tstarPMy - best-fit proper motion in Dec in milliarcseconds / year\n",
    "\n",
    "•\tstarParallax - best-fit parallax in milliarcseconds\n",
    "\n",
    "•\tstarInvCov - inverse covaraiance matrix -- stored flat, so you need to reshape the arrays to (2,2) for fits without PM or to (5,5) for fits with PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3c15e86-ea28-4732-aa7c-bcc67898979c",
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    "execution": {
     "iopub.execute_input": "2023-11-17T16:12:31.907358Z",
     "iopub.status.busy": "2023-11-17T16:12:31.907140Z",
     "iopub.status.idle": "2023-11-17T16:12:32.127290Z",
     "shell.execute_reply": "2023-11-17T16:12:32.126893Z",
     "shell.execute_reply.started": "2023-11-17T16:12:31.907343Z"
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starMatchID</th>\n",
       "      <th>starReserve</th>\n",
       "      <th>starColor</th>\n",
       "      <th>starPMCount</th>\n",
       "      <th>starDetCount</th>\n",
       "      <th>starClipCount</th>\n",
       "      <th>starDOF</th>\n",
       "      <th>starChisq</th>\n",
       "      <th>starX</th>\n",
       "      <th>starY</th>\n",
       "      <th>starPMx</th>\n",
       "      <th>starPMy</th>\n",
       "      <th>starParallax</th>\n",
       "      <th>starInvCov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>4.761145</td>\n",
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       "      <td>-25.054878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[1.8087929221032, -0.010619721657492457, -0.01...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.084361</td>\n",
       "      <td>-89.925627</td>\n",
       "      <td>-25.022477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[15.356696943370924, -0.0717498846590493, -0.0...</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4623</th>\n",
       "      <td>4623</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.483348</td>\n",
       "      <td>-89.678526</td>\n",
       "      <td>-25.014572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.768501828378629, -0.0052839259527495954, -0...</td>\n",
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       "    <tr>\n",
       "      <th>4624</th>\n",
       "      <td>4624</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.342775</td>\n",
       "      <td>-89.678551</td>\n",
       "      <td>-24.991095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.8415519987096797, -0.005598865929983926, -0...</td>\n",
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       "    <tr>\n",
       "      <th>4625</th>\n",
       "      <td>4625</td>\n",
       "      <td>True</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.572888</td>\n",
       "      <td>-89.678303</td>\n",
       "      <td>-25.055037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9406979550948308, -0.007149478499763768, -0...</td>\n",
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       "    <tr>\n",
       "      <th>4626</th>\n",
       "      <td>4626</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.401067</td>\n",
       "      <td>-89.678008</td>\n",
       "      <td>-25.058063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[1.6673737329229685, -0.009711436875619893, -0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4627</th>\n",
       "      <td>4627</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.396862</td>\n",
       "      <td>-89.677771</td>\n",
       "      <td>-25.017940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[9.971359617506732, -0.019542163097260267, -0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4628 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      starMatchID  starReserve  starColor  starPMCount  starDetCount  \\\n",
       "0               0        False     -888.0            0             2   \n",
       "1               1        False     -888.0            0             2   \n",
       "2               2        False     -888.0            0             2   \n",
       "3               3        False     -888.0            0             2   \n",
       "4               4        False     -888.0            0             2   \n",
       "...           ...          ...        ...          ...           ...   \n",
       "4623         4623        False     -888.0            0             2   \n",
       "4624         4624        False     -888.0            0             2   \n",
       "4625         4625         True     -888.0            0             3   \n",
       "4626         4626        False     -888.0            0             2   \n",
       "4627         4627        False     -888.0            0             2   \n",
       "\n",
       "      starClipCount  starDOF  starChisq      starX      starY  starPMx  \\\n",
       "0                 0        2   4.386844 -89.929544 -24.994483      0.0   \n",
       "1                 0        2   1.449038 -89.926920 -25.020756      0.0   \n",
       "2                 0        2   0.046936 -89.926137 -24.935999      0.0   \n",
       "3                 0        2   4.761145 -89.926204 -25.054878      0.0   \n",
       "4                 0        2   1.084361 -89.925627 -25.022477      0.0   \n",
       "...             ...      ...        ...        ...        ...      ...   \n",
       "4623              0        2   3.483348 -89.678526 -25.014572      0.0   \n",
       "4624              0        2   1.342775 -89.678551 -24.991095      0.0   \n",
       "4625              0        4   4.572888 -89.678303 -25.055037      0.0   \n",
       "4626              0        2   0.401067 -89.678008 -25.058063      0.0   \n",
       "4627              0        2   2.396862 -89.677771 -25.017940      0.0   \n",
       "\n",
       "      starPMy  starParallax                                         starInvCov  \n",
       "0         0.0           0.0  [0.5606299583452715, -0.00287514863800916, -0....  \n",
       "1         0.0           0.0  [1.1770725331311531, -0.00831149871846034, -0....  \n",
       "2         0.0           0.0  [3.2371116852659445, -0.020032877183524308, -0...  \n",
       "3         0.0           0.0  [1.8087929221032, -0.010619721657492457, -0.01...  \n",
       "4         0.0           0.0  [15.356696943370924, -0.0717498846590493, -0.0...  \n",
       "...       ...           ...                                                ...  \n",
       "4623      0.0           0.0  [0.768501828378629, -0.0052839259527495954, -0...  \n",
       "4624      0.0           0.0  [0.8415519987096797, -0.005598865929983926, -0...  \n",
       "4625      0.0           0.0  [0.9406979550948308, -0.007149478499763768, -0...  \n",
       "4626      0.0           0.0  [1.6673737329229685, -0.009711436875619893, -0...  \n",
       "4627      0.0           0.0  [9.971359617506732, -0.019542163097260267, -0....  \n",
       "\n",
       "[4628 rows x 14 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starCatDict = butler.get('gbdesAstrometricFit_starCatalog', skymap='latiss_v1', tract=5634, physical_filter='SDSSr_65mm~empty')\n",
    "starCatDict['starInvCov'] = starCatDict['starInvCov'].tolist()\n",
    "starCat = pd.DataFrame(starCatDict)\n",
    "starCat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397c791c-c2dc-442d-b45c-f4c6ba58f705",
   "metadata": {},
   "source": [
    "## Get the table of all the source measurements\n",
    "Here's a description of the columns:\n",
    "\n",
    "•\tmatchId – The id of the object (i.e. the star) corresponding to this source (this corresponds to \"starMatchID\" in the starCatalog table)\n",
    "\n",
    "•\tcatalogNumber – The index of the extension (a single detector from one exposure) of the source\n",
    "\n",
    "•\treserve – Was this source used in the fit or reserved for validation\n",
    "\n",
    "•\tclip – Was this source clipped from the fit (if True, probably shouldn’t use this source for analysis)\n",
    "\n",
    "•\txpix, ypix – x and y pixel coordinates of the source\n",
    "\n",
    "•\txrespix, yrespix – residuals in pixel coordinates\n",
    "\n",
    "•\txworld, yworld – x and y coordinates in the plane tangent to the field center\n",
    "\n",
    "•\txresw, yresw – residuals in the plane tangent to the field center. This is very close to RA and Dec, so you can use these in place of residuals in RA/Dec\n",
    "\n",
    "•\tchisq – chi-squared value of the fit\n",
    "\n",
    "•\tcolor – color of the source. We don’t have this information currently, but it will be interesting to look at once we do.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e07dfe26-d9f3-44fd-aa78-20ee2053625c",
   "metadata": {
    "execution": {
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      ],
      "text/plain": [
       "       matchID  catalogNumber  objectNumber   clip  reserve  hasPM  color  \\\n",
       "0            0            351         11934  False    False  False -888.0   \n",
       "1            0            232           195  False    False  False -888.0   \n",
       "2            1            351         11709  False    False  False -888.0   \n",
       "3            1            232           130  False    False  False -888.0   \n",
       "4            2            351         13162  False    False  False -888.0   \n",
       "...        ...            ...           ...    ...      ...    ...    ...   \n",
       "87070     4625             51            20  False     True  False -888.0   \n",
       "87071     4626            351          2239  False    False  False -888.0   \n",
       "87072     4626             51            13  False    False  False -888.0   \n",
       "87073     4627            351          2859  False    False  False -888.0   \n",
       "87074     4627             51           115  False    False  False -888.0   \n",
       "\n",
       "            xresw      yresw         xpix  ...        sigpix     xrespix  \\\n",
       "0       -0.043829   0.006742   270.070456  ...  3.344196e-07  360.000000   \n",
       "1      156.533523 -75.743863    53.088194  ...  8.124678e-01    1.624860   \n",
       "2       -0.019069   0.000122   270.073080  ...  2.363627e-07  360.000000   \n",
       "3       64.044084 -28.814364   129.341398  ...  5.847506e-01    0.665100   \n",
       "4        0.001060  -0.002823   270.073863  ...  1.426319e-07  360.000000   \n",
       "...           ...        ...          ...  ...           ...         ...   \n",
       "87070  -69.863689 -30.391452  3956.538712  ...  5.242865e-01   -0.734229   \n",
       "87071    0.001049  -0.001988   270.321992  ...  1.950407e-07  360.000000   \n",
       "87072  -34.475793  56.818422  3965.410349  ...  9.456592e-01   -0.352820   \n",
       "87073    0.016453   0.006740   270.322229  ...  8.254302e-08  360.000000   \n",
       "87074  -11.109723  -5.460085  3994.288173  ...  9.430412e-02   -0.116841   \n",
       "\n",
       "            yrespix    xworld    yworld     chisq  chisqExpected  \\\n",
       "0      1.884157e-09 -0.114716  0.005034  0.001125       0.000469   \n",
       "1     -8.126504e-01 -0.114673  0.005013  4.385719       1.999531   \n",
       "2      3.871037e-11 -0.112313 -0.021237  0.000428       0.000492   \n",
       "3     -3.097465e-01 -0.112296 -0.021245  1.448610       1.999508   \n",
       "4     -7.842971e-10 -0.111680  0.063520  0.000047       0.002587   \n",
       "...             ...       ...       ...       ...            ...   \n",
       "87070 -3.082736e-01  0.112922 -0.055527  3.646678       1.999182   \n",
       "87071 -5.518999e-10  0.113205 -0.058545  0.000014       0.000121   \n",
       "87072  5.981946e-01  0.113196 -0.058529  0.401053       1.999879   \n",
       "87073  1.876387e-09  0.113457 -0.018422  0.003403       0.002103   \n",
       "87074 -5.557858e-02  0.113454 -0.018423  2.393459       1.997897   \n",
       "\n",
       "       covTotalW_00  covTotalW_11  covTotalW_01  \n",
       "0          1.784286      0.967478      0.005022  \n",
       "1       6006.533080   6343.915098  -1249.814082  \n",
       "2          0.849885      0.506651      0.003632  \n",
       "3       2832.525112   3981.424919  -1212.919252  \n",
       "4          0.309414      0.184799      0.001165  \n",
       "...             ...           ...           ...  \n",
       "87070   1813.817143   3770.791711   -710.840919  \n",
       "87071      0.599803      0.332676      0.001947  \n",
       "87072   8952.814953   8572.326778  -3114.976318  \n",
       "87073      0.100419      0.063785      0.000147  \n",
       "87074     82.275497     86.681493    -22.397730  \n",
       "\n",
       "[87075 rows x 21 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starResiduals = pd.DataFrame(butler.get('gbdesAstrometricFit_fitStars', skymap='latiss_v1', tract=5634, physical_filter='SDSSr_65mm~empty'))\n",
    "starResiduals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2599a956-6ab1-4c55-9171-d12bd0dcf042",
   "metadata": {},
   "source": [
    "#### Get all the reference sources that went into the fit (note proper motion and parallax values are not included in this table but are available elsewhere):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "60ab5ee5-713b-48d8-b245-0dff6dd4221e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T17:31:59.661047Z",
     "iopub.status.busy": "2023-11-17T17:31:59.660807Z",
     "iopub.status.idle": "2023-11-17T17:31:59.677241Z",
     "shell.execute_reply": "2023-11-17T17:31:59.676865Z",
     "shell.execute_reply.started": "2023-11-17T17:31:59.661032Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>matchID</th>\n",
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       "      <th>covTotalW_01</th>\n",
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       "      <td>-0.019069</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>270.073080</td>\n",
       "      <td>...</td>\n",
       "      <td>2.363627e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.871037e-11</td>\n",
       "      <td>-0.112313</td>\n",
       "      <td>-0.021237</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.000492</td>\n",
       "      <td>0.849885</td>\n",
       "      <td>0.506651</td>\n",
       "      <td>0.003632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>351</td>\n",
       "      <td>13162</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>-0.002823</td>\n",
       "      <td>270.073863</td>\n",
       "      <td>...</td>\n",
       "      <td>1.426319e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-7.842971e-10</td>\n",
       "      <td>-0.111680</td>\n",
       "      <td>0.063520</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.002587</td>\n",
       "      <td>0.309414</td>\n",
       "      <td>0.184799</td>\n",
       "      <td>0.001165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>351</td>\n",
       "      <td>5069</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.020903</td>\n",
       "      <td>-0.004087</td>\n",
       "      <td>270.073796</td>\n",
       "      <td>...</td>\n",
       "      <td>1.942291e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-1.140499e-09</td>\n",
       "      <td>-0.111633</td>\n",
       "      <td>-0.055359</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.552999</td>\n",
       "      <td>0.354840</td>\n",
       "      <td>0.002091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>351</td>\n",
       "      <td>11669</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.002955</td>\n",
       "      <td>0.002904</td>\n",
       "      <td>270.074373</td>\n",
       "      <td>...</td>\n",
       "      <td>6.590681e-08</td>\n",
       "      <td>360.0</td>\n",
       "      <td>8.058159e-10</td>\n",
       "      <td>-0.111140</td>\n",
       "      <td>-0.022957</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.065141</td>\n",
       "      <td>0.039958</td>\n",
       "      <td>0.000189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87064</th>\n",
       "      <td>4623</td>\n",
       "      <td>351</td>\n",
       "      <td>2825</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.036258</td>\n",
       "      <td>-0.011743</td>\n",
       "      <td>270.321474</td>\n",
       "      <td>...</td>\n",
       "      <td>2.921481e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-3.252719e-09</td>\n",
       "      <td>0.112776</td>\n",
       "      <td>-0.015054</td>\n",
       "      <td>0.001193</td>\n",
       "      <td>0.000562</td>\n",
       "      <td>1.301662</td>\n",
       "      <td>0.772262</td>\n",
       "      <td>0.005137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87066</th>\n",
       "      <td>4624</td>\n",
       "      <td>351</td>\n",
       "      <td>3370</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.005899</td>\n",
       "      <td>-0.008822</td>\n",
       "      <td>270.321449</td>\n",
       "      <td>...</td>\n",
       "      <td>2.793063e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-2.449038e-09</td>\n",
       "      <td>0.112775</td>\n",
       "      <td>0.008424</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>1.188487</td>\n",
       "      <td>0.706890</td>\n",
       "      <td>0.004711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87068</th>\n",
       "      <td>4625</td>\n",
       "      <td>351</td>\n",
       "      <td>2282</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.054638</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>270.321697</td>\n",
       "      <td>...</td>\n",
       "      <td>2.652190e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>9.791066e-10</td>\n",
       "      <td>0.112941</td>\n",
       "      <td>-0.055519</td>\n",
       "      <td>0.002822</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>1.063870</td>\n",
       "      <td>0.641341</td>\n",
       "      <td>0.004914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87071</th>\n",
       "      <td>4626</td>\n",
       "      <td>351</td>\n",
       "      <td>2239</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>-0.001988</td>\n",
       "      <td>270.321992</td>\n",
       "      <td>...</td>\n",
       "      <td>1.950407e-07</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-5.518999e-10</td>\n",
       "      <td>0.113205</td>\n",
       "      <td>-0.058545</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.599803</td>\n",
       "      <td>0.332676</td>\n",
       "      <td>0.001947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87073</th>\n",
       "      <td>4627</td>\n",
       "      <td>351</td>\n",
       "      <td>2859</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>0.016453</td>\n",
       "      <td>0.006740</td>\n",
       "      <td>270.322229</td>\n",
       "      <td>...</td>\n",
       "      <td>8.254302e-08</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.876387e-09</td>\n",
       "      <td>0.113457</td>\n",
       "      <td>-0.018422</td>\n",
       "      <td>0.003403</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.100419</td>\n",
       "      <td>0.063785</td>\n",
       "      <td>0.000147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4586 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       matchID  catalogNumber  objectNumber   clip  reserve  hasPM  color  \\\n",
       "0            0            351         11934  False    False  False -888.0   \n",
       "2            1            351         11709  False    False  False -888.0   \n",
       "4            2            351         13162  False    False  False -888.0   \n",
       "6            3            351          5069  False    False  False -888.0   \n",
       "8            4            351         11669  False    False  False -888.0   \n",
       "...        ...            ...           ...    ...      ...    ...    ...   \n",
       "87064     4623            351          2825  False    False  False -888.0   \n",
       "87066     4624            351          3370  False    False  False -888.0   \n",
       "87068     4625            351          2282  False     True  False -888.0   \n",
       "87071     4626            351          2239  False    False  False -888.0   \n",
       "87073     4627            351          2859  False    False  False -888.0   \n",
       "\n",
       "          xresw     yresw        xpix  ...        sigpix  xrespix  \\\n",
       "0     -0.043829  0.006742  270.070456  ...  3.344196e-07    360.0   \n",
       "2     -0.019069  0.000122  270.073080  ...  2.363627e-07    360.0   \n",
       "4      0.001060 -0.002823  270.073863  ...  1.426319e-07    360.0   \n",
       "6      0.020903 -0.004087  270.073796  ...  1.942291e-07    360.0   \n",
       "8      0.002955  0.002904  270.074373  ...  6.590681e-08    360.0   \n",
       "...         ...       ...         ...  ...           ...      ...   \n",
       "87064  0.036258 -0.011743  270.321474  ...  2.921481e-07    360.0   \n",
       "87066  0.005899 -0.008822  270.321449  ...  2.793063e-07    360.0   \n",
       "87068  0.054638  0.003474  270.321697  ...  2.652190e-07    360.0   \n",
       "87071  0.001049 -0.001988  270.321992  ...  1.950407e-07    360.0   \n",
       "87073  0.016453  0.006740  270.322229  ...  8.254302e-08    360.0   \n",
       "\n",
       "            yrespix    xworld    yworld     chisq  chisqExpected  \\\n",
       "0      1.884157e-09 -0.114716  0.005034  0.001125       0.000469   \n",
       "2      3.871037e-11 -0.112313 -0.021237  0.000428       0.000492   \n",
       "4     -7.842971e-10 -0.111680  0.063520  0.000047       0.002587   \n",
       "6     -1.140499e-09 -0.111633 -0.055359  0.000839       0.000297   \n",
       "8      8.058159e-10 -0.111140 -0.022957  0.000344       0.000595   \n",
       "...             ...       ...       ...       ...            ...   \n",
       "87064 -3.252719e-09  0.112776 -0.015054  0.001193       0.000562   \n",
       "87066 -2.449038e-09  0.112775  0.008424  0.000140       0.000250   \n",
       "87068  9.791066e-10  0.112941 -0.055519  0.002822       0.001025   \n",
       "87071 -5.518999e-10  0.113205 -0.058545  0.000014       0.000121   \n",
       "87073  1.876387e-09  0.113457 -0.018422  0.003403       0.002103   \n",
       "\n",
       "       covTotalW_00  covTotalW_11  covTotalW_01  \n",
       "0          1.784286      0.967478      0.005022  \n",
       "2          0.849885      0.506651      0.003632  \n",
       "4          0.309414      0.184799      0.001165  \n",
       "6          0.552999      0.354840      0.002091  \n",
       "8          0.065141      0.039958      0.000189  \n",
       "...             ...           ...           ...  \n",
       "87064      1.301662      0.772262      0.005137  \n",
       "87066      1.188487      0.706890      0.004711  \n",
       "87068      1.063870      0.641341      0.004914  \n",
       "87071      0.599803      0.332676      0.001947  \n",
       "87073      0.100419      0.063785      0.000147  \n",
       "\n",
       "[4586 rows x 21 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The highest \"catalogNumber\" always refers to the reference catalog, so we can use that to find which sources come from \n",
    "# the reference catalog\n",
    "refId = starResiduals['catalogNumber'].max()\n",
    "starResiduals[starResiduals['catalogNumber'] == refId]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456003c4-5236-4b54-83a8-b30477137a04",
   "metadata": {},
   "source": [
    "### To get the visit corresponding to a given source in the starResiduals table:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f4c7e0c9-855f-44cc-a0a6-a3d7c452f9b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T17:55:00.159366Z",
     "iopub.status.busy": "2023-11-14T17:55:00.158887Z",
     "iopub.status.idle": "2023-11-14T17:55:00.163233Z",
     "shell.execute_reply": "2023-11-14T17:55:00.162836Z",
     "shell.execute_reply.started": "2023-11-14T17:55:00.159351Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The source at index 0 is from visit 2023091400064 and detector 0 at airmass 1.344\n"
     ]
    }
   ],
   "source": [
    "exampleStarResidual = starResiduals.iloc[0]\n",
    "correspondingExtension = extensionInfo.iloc[exampleStarResidual['catalogNumber']]\n",
    "visInfo = visitInfo[visitInfo['visit'] == correspondingExtension['visit']].iloc[0]\n",
    "print(f\"The source at index 0 is from visit {correspondingExtension['visit']} and detector {correspondingExtension['detector']}\",\n",
    "     f\"at airmass {visInfo['airmass']:.3f}\")\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e746c1-1bd6-4834-9474-1cf78800e54e",
   "metadata": {},
   "source": [
    "### To get the star that corresponds to a given source measurement and vice versa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2cbfd7e1-f609-4d50-88af-d0f2be272486",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T21:51:53.455121Z",
     "iopub.status.busy": "2023-11-14T21:51:53.454937Z",
     "iopub.status.idle": "2023-11-14T21:51:53.463504Z",
     "shell.execute_reply": "2023-11-14T21:51:53.463146Z",
     "shell.execute_reply.started": "2023-11-14T21:51:53.455108Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starMatchID</th>\n",
       "      <th>starReserve</th>\n",
       "      <th>starColor</th>\n",
       "      <th>starPMCount</th>\n",
       "      <th>starDetCount</th>\n",
       "      <th>starClipCount</th>\n",
       "      <th>starDOF</th>\n",
       "      <th>starChisq</th>\n",
       "      <th>starX</th>\n",
       "      <th>starY</th>\n",
       "      <th>starPMx</th>\n",
       "      <th>starPMy</th>\n",
       "      <th>starParallax</th>\n",
       "      <th>starInvCov</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "      <td>[0.5606299583452715, -0.00287514863800916, -0....</td>\n",
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      ],
      "text/plain": [
       "   starMatchID  starReserve  starColor  starPMCount  starDetCount  \\\n",
       "0            0        False     -888.0            0             2   \n",
       "\n",
       "   starClipCount  starDOF  starChisq      starX      starY  starPMx  starPMy  \\\n",
       "0              0        2   4.386844 -89.929544 -24.994483      0.0      0.0   \n",
       "\n",
       "   starParallax                                         starInvCov  \n",
       "0           0.0  [0.5606299583452715, -0.00287514863800916, -0....  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exampleStarResidual = starResiduals.iloc[0]\n",
    "correspondingStar = starCat[starCat['starMatchID'] == exampleStarResidual['matchID']]\n",
    "correspondingStar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d6837fd-36ef-41f4-b217-8c6206c9299d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T21:52:57.185255Z",
     "iopub.status.busy": "2023-11-14T21:52:57.184979Z",
     "iopub.status.idle": "2023-11-14T21:52:57.197430Z",
     "shell.execute_reply": "2023-11-14T21:52:57.197055Z",
     "shell.execute_reply.started": "2023-11-14T21:52:57.185241Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matchID</th>\n",
       "      <th>catalogNumber</th>\n",
       "      <th>objectNumber</th>\n",
       "      <th>clip</th>\n",
       "      <th>reserve</th>\n",
       "      <th>hasPM</th>\n",
       "      <th>color</th>\n",
       "      <th>xresw</th>\n",
       "      <th>yresw</th>\n",
       "      <th>xpix</th>\n",
       "      <th>...</th>\n",
       "      <th>sigpix</th>\n",
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       "      <th>chisqExpected</th>\n",
       "      <th>covTotalW_00</th>\n",
       "      <th>covTotalW_11</th>\n",
       "      <th>covTotalW_01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>100</td>\n",
       "      <td>351</td>\n",
       "      <td>5083</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>-0.043112</td>\n",
       "      <td>0.466457</td>\n",
       "      <td>270.088420</td>\n",
       "      <td>...</td>\n",
       "      <td>7.816954e-07</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.295807e-07</td>\n",
       "      <td>-0.098387</td>\n",
       "      <td>-0.052954</td>\n",
       "      <td>0.038981</td>\n",
       "      <td>0.009913</td>\n",
       "      <td>9.167839</td>\n",
       "      <td>5.615742</td>\n",
       "      <td>0.040616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>1.311784</td>\n",
       "      <td>-108.680123</td>\n",
       "      <td>3874.441833</td>\n",
       "      <td>...</td>\n",
       "      <td>1.230175e+00</td>\n",
       "      <td>0.100367</td>\n",
       "      <td>1.131267e+00</td>\n",
       "      <td>-0.098387</td>\n",
       "      <td>-0.052985</td>\n",
       "      <td>2.375183</td>\n",
       "      <td>1.998584</td>\n",
       "      <td>38432.108440</td>\n",
       "      <td>6330.105512</td>\n",
       "      <td>7162.318131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>100</td>\n",
       "      <td>9</td>\n",
       "      <td>458</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>-27.342269</td>\n",
       "      <td>-0.595576</td>\n",
       "      <td>3924.054872</td>\n",
       "      <td>...</td>\n",
       "      <td>7.300376e-01</td>\n",
       "      <td>0.285822</td>\n",
       "      <td>2.401485e-03</td>\n",
       "      <td>-0.098395</td>\n",
       "      <td>-0.052955</td>\n",
       "      <td>0.146780</td>\n",
       "      <td>1.996982</td>\n",
       "      <td>5277.145516</td>\n",
       "      <td>4636.904199</td>\n",
       "      <td>-812.050804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>100</td>\n",
       "      <td>192</td>\n",
       "      <td>71</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>-5.384135</td>\n",
       "      <td>-151.439884</td>\n",
       "      <td>208.826979</td>\n",
       "      <td>...</td>\n",
       "      <td>6.802789e-01</td>\n",
       "      <td>-0.076974</td>\n",
       "      <td>-1.581499e+00</td>\n",
       "      <td>-0.098389</td>\n",
       "      <td>-0.052997</td>\n",
       "      <td>9.079173</td>\n",
       "      <td>1.996475</td>\n",
       "      <td>7059.489679</td>\n",
       "      <td>2611.172733</td>\n",
       "      <td>-685.413209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>100</td>\n",
       "      <td>327</td>\n",
       "      <td>37</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-888.0</td>\n",
       "      <td>95.414544</td>\n",
       "      <td>-27.064210</td>\n",
       "      <td>299.756573</td>\n",
       "      <td>...</td>\n",
       "      <td>9.318923e-01</td>\n",
       "      <td>0.993643</td>\n",
       "      <td>-2.958390e-01</td>\n",
       "      <td>-0.098361</td>\n",
       "      <td>-0.052962</td>\n",
       "      <td>1.242630</td>\n",
       "      <td>1.998046</td>\n",
       "      <td>7339.573012</td>\n",
       "      <td>9021.272848</td>\n",
       "      <td>-1744.114532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     matchID  catalogNumber  objectNumber   clip  reserve  hasPM  color  \\\n",
       "390      100            351          5083  False    False  False -888.0   \n",
       "391      100              0            89  False    False  False -888.0   \n",
       "392      100              9           458  False    False  False -888.0   \n",
       "393      100            192            71  False    False  False -888.0   \n",
       "394      100            327            37  False    False  False -888.0   \n",
       "\n",
       "         xresw       yresw         xpix  ...        sigpix     xrespix  \\\n",
       "390  -0.043112    0.466457   270.088420  ...  7.816954e-07  360.000000   \n",
       "391   1.311784 -108.680123  3874.441833  ...  1.230175e+00    0.100367   \n",
       "392 -27.342269   -0.595576  3924.054872  ...  7.300376e-01    0.285822   \n",
       "393  -5.384135 -151.439884   208.826979  ...  6.802789e-01   -0.076974   \n",
       "394  95.414544  -27.064210   299.756573  ...  9.318923e-01    0.993643   \n",
       "\n",
       "          yrespix    xworld    yworld     chisq  chisqExpected  covTotalW_00  \\\n",
       "390  1.295807e-07 -0.098387 -0.052954  0.038981       0.009913      9.167839   \n",
       "391  1.131267e+00 -0.098387 -0.052985  2.375183       1.998584  38432.108440   \n",
       "392  2.401485e-03 -0.098395 -0.052955  0.146780       1.996982   5277.145516   \n",
       "393 -1.581499e+00 -0.098389 -0.052997  9.079173       1.996475   7059.489679   \n",
       "394 -2.958390e-01 -0.098361 -0.052962  1.242630       1.998046   7339.573012   \n",
       "\n",
       "     covTotalW_11  covTotalW_01  \n",
       "390      5.615742      0.040616  \n",
       "391   6330.105512   7162.318131  \n",
       "392   4636.904199   -812.050804  \n",
       "393   2611.172733   -685.413209  \n",
       "394   9021.272848  -1744.114532  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exampleStar = starCat.iloc[100]\n",
    "correspondingResiduals = starResiduals[starResiduals['matchID'] == exampleStar['starMatchID']]\n",
    "correspondingResiduals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde5dcfd-8961-439a-b03d-a2ce67bd44a1",
   "metadata": {},
   "source": [
    "### To go from the starResiduals positions in tangent-plane coordinates to sky coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "56d4719b-b1ed-442f-8716-d49d16404e49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T17:29:02.104315Z",
     "iopub.status.busy": "2023-11-17T17:29:02.104084Z",
     "iopub.status.idle": "2023-11-17T17:29:02.131306Z",
     "shell.execute_reply": "2023-11-17T17:29:02.130884Z",
     "shell.execute_reply.started": "2023-11-17T17:29:02.104298Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Make a WCS mapping that goes from the tangent plane to sky coordinates. (Feel free to ignore the details.)\n",
    "allDetectorCorners = []\n",
    "for visSum in visitSummaries:\n",
    "    detectorCorners = [lsst.geom.SpherePoint(ra, dec, lsst.geom.degrees).getVector() for (ra, dec)\n",
    "                       in zip(visSum['raCorners'].ravel(), visSum['decCorners'].ravel())]\n",
    "    allDetectorCorners.extend(detectorCorners)\n",
    "boundingCircle = lsst.sphgeom.ConvexPolygon.convexHull(allDetectorCorners).getBoundingCircle()\n",
    "fieldCenter = lsst.geom.SpherePoint(boundingCircle.getCenter())\n",
    "\n",
    "cdMatrix = afwgeom.makeCdMatrix(1.0 * lsst.geom.degrees, 0 * lsst.geom.degrees, True)\n",
    "tangentToSkyWcs = afwgeom.makeSkyWcs(lsst.geom.Point2D(0, 0), fieldCenter, cdMatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f9b1bbbc-852e-433f-ba3f-7af62f153654",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T17:33:52.335007Z",
     "iopub.status.busy": "2023-11-17T17:33:52.334433Z",
     "iopub.status.idle": "2023-11-17T17:33:52.355161Z",
     "shell.execute_reply": "2023-11-17T17:33:52.354773Z",
     "shell.execute_reply.started": "2023-11-17T17:33:52.334990Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use the WCS `pixelToSkyArray` method to convert from the tangent plane to the sky:\n",
    "starResidualsRA, starResidualsDec = tangentToSkyWcs.pixelToSkyArray(starResiduals['xworld'], starResiduals['yworld'], degrees=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd5e4a4a-c8bb-46ae-91cc-83ca7da5503b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gaiaInd = starResiduals['catalogNumber'] == refId\n",
    "print(starResidualsRA[gaiaInd][:5])\n",
    "print(starResiduals[gaiaInd]['xpix'][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290239af-7443-4596-af6b-a3799395447c",
   "metadata": {},
   "source": [
    "### Get the reference catalog data (from Gaia):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f73c16e8-7171-434f-b103-5fe62c712b33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-16T15:20:53.481499Z",
     "iopub.status.busy": "2023-11-16T15:20:53.480921Z",
     "iopub.status.idle": "2023-11-16T15:20:53.575407Z",
     "shell.execute_reply": "2023-11-16T15:20:53.575025Z",
     "shell.execute_reply.started": "2023-11-16T15:20:53.481483Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "gaiaRefs = butler.registry.queryDatasets('gaia_dr3_20230707', tract=5634, skymap='latiss_v1').expanded()\n",
    "dataIds = [ref.dataId for ref in gaiaRefs]\n",
    "refCats = [butler.getDeferred(ref) for ref in gaiaRefs]\n",
    "gaiaLoader = ReferenceObjectLoader(dataIds=dataIds,\n",
    "                                   refCats=refCats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dca3cc3d-bbc4-423f-955a-372c10f89de5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-16T15:20:56.728565Z",
     "iopub.status.busy": "2023-11-16T15:20:56.728442Z",
     "iopub.status.idle": "2023-11-16T15:20:57.983170Z",
     "shell.execute_reply": "2023-11-16T15:20:57.982689Z",
     "shell.execute_reply.started": "2023-11-16T15:20:56.728553Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"margin: 0.1em;\n",
       "padding-left: 0.25em;\n",
       "border-left-style: solid;\n",
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       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loading reference objects from None in region bounded by [270.03449052, 270.36550948], [-25.15000172, -24.84999828] RA Dec</pre>"
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       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Correcting reference catalog for proper motion to &lt;Time object: scale=&#x27;utc&#x27; format=&#x27;mjd&#x27; value=60160.11046011866&gt;</pre>"
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   ],
   "source": [
    "ctr = lsst.geom.SpherePoint(-89.8, -25, lsst.geom.degrees)\n",
    "radius = 0.15 * lsst.geom.degrees\n",
    "epoch = Time(np.median(mjds), format='mjd')\n",
    "gaiaCat = gaiaLoader.loadSkyCircle(ctr, radius, 'phot_g_mean', epoch=epoch).refCat.asAstropy()"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5e1646e110>"
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     },
     "execution_count": 122,
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gaiaCat['coord_ra'].to(u.degree) - 360 * u.degree, gaiaCat['coord_dec'].to(u.degree), label='Gaia stars', marker='+')\n",
    "plt.scatter(starCat['starX'], starCat['starY'], label='fit positions')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "96613cf4-4697-4da1-876f-f9487b8c1ebe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T19:55:06.541319Z",
     "iopub.status.busy": "2023-11-14T19:55:06.540969Z",
     "iopub.status.idle": "2023-11-14T19:55:07.141087Z",
     "shell.execute_reply": "2023-11-14T19:55:07.140676Z",
     "shell.execute_reply.started": "2023-11-14T19:55:06.541302Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5e2a45af10>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(starCat['starX'], starCat['starY'], label='fit positions')\n",
    "plt.scatter(gaiaCat['coord_ra'].to(u.degree) - 360 * u.degree, gaiaCat['coord_dec'].to(u.degree), label='Gaia stars', marker='+')\n",
    "plt.xlim(-89.85, -89.9)\n",
    "plt.ylim(-25.05, -25.0)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "68e41220-5ebb-438b-aee8-b764334f3440",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-16T15:21:05.715741Z",
     "iopub.status.busy": "2023-11-16T15:21:05.715246Z",
     "iopub.status.idle": "2023-11-16T15:21:05.718365Z",
     "shell.execute_reply": "2023-11-16T15:21:05.718091Z",
     "shell.execute_reply.started": "2023-11-16T15:21:05.715728Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'coord_ra',\n",
       " 'coord_dec',\n",
       " 'phot_g_mean_flux',\n",
       " 'phot_bp_mean_flux',\n",
       " 'phot_rp_mean_flux',\n",
       " 'phot_g_mean_fluxErr',\n",
       " 'phot_bp_mean_fluxErr',\n",
       " 'phot_rp_mean_fluxErr',\n",
       " 'coord_raErr',\n",
       " 'coord_decErr',\n",
       " 'epoch',\n",
       " 'pm_ra',\n",
       " 'pm_dec',\n",
       " 'pm_raErr',\n",
       " 'pm_decErr',\n",
       " 'pm_flag',\n",
       " 'parallax',\n",
       " 'parallaxErr',\n",
       " 'parallax_flag',\n",
       " 'coord_ra_coord_dec_Cov',\n",
       " 'coord_ra_pm_ra_Cov',\n",
       " 'coord_ra_pm_dec_Cov',\n",
       " 'coord_ra_parallax_Cov',\n",
       " 'coord_dec_pm_ra_Cov',\n",
       " 'coord_dec_pm_dec_Cov',\n",
       " 'coord_dec_parallax_Cov',\n",
       " 'pm_ra_pm_dec_Cov',\n",
       " 'pm_ra_parallax_Cov',\n",
       " 'pm_dec_parallax_Cov',\n",
       " 'astrometric_excess_noise']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is a list of all the information in the Gaia catalog:\n",
    "gaiaCat.colnames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a52dc9-6f6e-4cff-aaa8-816191f93f56",
   "metadata": {},
   "source": [
    "## Accessing the original source catalogs (RA and Decs come from fit between individual detector and the reference catalog):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2a37a2c6-0ade-4249-ad2f-3a96322c6afe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-14T21:24:09.996245Z",
     "iopub.status.busy": "2023-11-14T21:24:09.996116Z",
     "iopub.status.idle": "2023-11-14T21:24:10.050947Z",
     "shell.execute_reply": "2023-11-14T21:24:10.050520Z",
     "shell.execute_reply.started": "2023-11-14T21:24:09.996233Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coord_ra</th>\n",
       "      <th>coord_dec</th>\n",
       "      <th>ccdVisitId</th>\n",
       "      <th>parentSourceId</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>xErr</th>\n",
       "      <th>yErr</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>...</th>\n",
       "      <th>hsmShapeRegauss_flag_galsim</th>\n",
       "      <th>hsmShapeRegauss_flag_no_pixels</th>\n",
       "      <th>hsmShapeRegauss_flag_not_contained</th>\n",
       "      <th>hsmShapeRegauss_flag_parent_source</th>\n",
       "      <th>sky_source</th>\n",
       "      <th>detect_isPrimary</th>\n",
       "      <th>visit</th>\n",
       "      <th>detector</th>\n",
       "      <th>band</th>\n",
       "      <th>physical_filter</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sourceId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172052246308061185</th>\n",
       "      <td>270.183142</td>\n",
       "      <td>-24.994208</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>0</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.183142</td>\n",
       "      <td>-24.994208</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061186</th>\n",
       "      <td>270.178182</td>\n",
       "      <td>-24.994660</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>0</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.178182</td>\n",
       "      <td>-24.994660</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061187</th>\n",
       "      <td>270.164994</td>\n",
       "      <td>-24.995862</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>0</td>\n",
       "      <td>1064.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.164994</td>\n",
       "      <td>-24.995862</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061188</th>\n",
       "      <td>270.161026</td>\n",
       "      <td>-24.996223</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>0</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.161026</td>\n",
       "      <td>-24.996223</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061189</th>\n",
       "      <td>270.130284</td>\n",
       "      <td>-24.999331</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>0</td>\n",
       "      <td>2254.761596</td>\n",
       "      <td>37.718036</td>\n",
       "      <td>3.180406</td>\n",
       "      <td>0.803548</td>\n",
       "      <td>270.130284</td>\n",
       "      <td>-24.999331</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061957</th>\n",
       "      <td>270.100261</td>\n",
       "      <td>-25.104296</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>172052246308061701</td>\n",
       "      <td>3666.701111</td>\n",
       "      <td>3864.776073</td>\n",
       "      <td>0.731167</td>\n",
       "      <td>0.628457</td>\n",
       "      <td>270.100261</td>\n",
       "      <td>-25.104296</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061958</th>\n",
       "      <td>270.198590</td>\n",
       "      <td>-25.097850</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>172052246308061713</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>3958.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.198590</td>\n",
       "      <td>-25.097850</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061959</th>\n",
       "      <td>270.197135</td>\n",
       "      <td>-25.098036</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>172052246308061713</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.197135</td>\n",
       "      <td>-25.098036</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061960</th>\n",
       "      <td>270.108705</td>\n",
       "      <td>-25.105776</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>172052246308061721</td>\n",
       "      <td>3386.004777</td>\n",
       "      <td>3948.830751</td>\n",
       "      <td>1.721271</td>\n",
       "      <td>0.562424</td>\n",
       "      <td>270.108705</td>\n",
       "      <td>-25.105776</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172052246308061961</th>\n",
       "      <td>270.110411</td>\n",
       "      <td>-25.106268</td>\n",
       "      <td>41020452096</td>\n",
       "      <td>172052246308061721</td>\n",
       "      <td>3330.000000</td>\n",
       "      <td>3973.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270.110411</td>\n",
       "      <td>-25.106268</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2023052300621</td>\n",
       "      <td>0</td>\n",
       "      <td>r</td>\n",
       "      <td>SDSSr_65mm~empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      coord_ra  coord_dec   ccdVisitId      parentSourceId  \\\n",
       "sourceId                                                                     \n",
       "172052246308061185  270.183142 -24.994208  41020452096                   0   \n",
       "172052246308061186  270.178182 -24.994660  41020452096                   0   \n",
       "172052246308061187  270.164994 -24.995862  41020452096                   0   \n",
       "172052246308061188  270.161026 -24.996223  41020452096                   0   \n",
       "172052246308061189  270.130284 -24.999331  41020452096                   0   \n",
       "...                        ...        ...          ...                 ...   \n",
       "172052246308061957  270.100261 -25.104296  41020452096  172052246308061701   \n",
       "172052246308061958  270.198590 -25.097850  41020452096  172052246308061713   \n",
       "172052246308061959  270.197135 -25.098036  41020452096  172052246308061713   \n",
       "172052246308061960  270.108705 -25.105776  41020452096  172052246308061721   \n",
       "172052246308061961  270.110411 -25.106268  41020452096  172052246308061721   \n",
       "\n",
       "                              x            y      xErr      yErr          ra  \\\n",
       "sourceId                                                                       \n",
       "172052246308061185   442.000000    26.000000       NaN       NaN  270.183142   \n",
       "172052246308061186   612.000000    26.000000       NaN       NaN  270.178182   \n",
       "172052246308061187  1064.000000    26.000000       NaN       NaN  270.164994   \n",
       "172052246308061188  1200.000000    26.000000       NaN       NaN  270.161026   \n",
       "172052246308061189  2254.761596    37.718036  3.180406  0.803548  270.130284   \n",
       "...                         ...          ...       ...       ...         ...   \n",
       "172052246308061957  3666.701111  3864.776073  0.731167  0.628457  270.100261   \n",
       "172052246308061958   309.000000  3958.000000       NaN       NaN  270.198590   \n",
       "172052246308061959   359.000000  3960.000000       NaN       NaN  270.197135   \n",
       "172052246308061960  3386.004777  3948.830751  1.721271  0.562424  270.108705   \n",
       "172052246308061961  3330.000000  3973.000000       NaN       NaN  270.110411   \n",
       "\n",
       "                          dec  ...  hsmShapeRegauss_flag_galsim  \\\n",
       "sourceId                       ...                                \n",
       "172052246308061185 -24.994208  ...                        False   \n",
       "172052246308061186 -24.994660  ...                        False   \n",
       "172052246308061187 -24.995862  ...                        False   \n",
       "172052246308061188 -24.996223  ...                        False   \n",
       "172052246308061189 -24.999331  ...                        False   \n",
       "...                       ...  ...                          ...   \n",
       "172052246308061957 -25.104296  ...                         True   \n",
       "172052246308061958 -25.097850  ...                        False   \n",
       "172052246308061959 -25.098036  ...                        False   \n",
       "172052246308061960 -25.105776  ...                        False   \n",
       "172052246308061961 -25.106268  ...                         True   \n",
       "\n",
       "                    hsmShapeRegauss_flag_no_pixels  \\\n",
       "sourceId                                             \n",
       "172052246308061185                           False   \n",
       "172052246308061186                           False   \n",
       "172052246308061187                           False   \n",
       "172052246308061188                           False   \n",
       "172052246308061189                           False   \n",
       "...                                            ...   \n",
       "172052246308061957                           False   \n",
       "172052246308061958                           False   \n",
       "172052246308061959                           False   \n",
       "172052246308061960                           False   \n",
       "172052246308061961                           False   \n",
       "\n",
       "                    hsmShapeRegauss_flag_not_contained  \\\n",
       "sourceId                                                 \n",
       "172052246308061185                               False   \n",
       "172052246308061186                               False   \n",
       "172052246308061187                               False   \n",
       "172052246308061188                               False   \n",
       "172052246308061189                               False   \n",
       "...                                                ...   \n",
       "172052246308061957                               False   \n",
       "172052246308061958                               False   \n",
       "172052246308061959                               False   \n",
       "172052246308061960                               False   \n",
       "172052246308061961                               False   \n",
       "\n",
       "                    hsmShapeRegauss_flag_parent_source  sky_source  \\\n",
       "sourceId                                                             \n",
       "172052246308061185                               False       False   \n",
       "172052246308061186                               False       False   \n",
       "172052246308061187                               False       False   \n",
       "172052246308061188                                True       False   \n",
       "172052246308061189                               False       False   \n",
       "...                                                ...         ...   \n",
       "172052246308061957                               False       False   \n",
       "172052246308061958                               False       False   \n",
       "172052246308061959                               False       False   \n",
       "172052246308061960                               False       False   \n",
       "172052246308061961                               False       False   \n",
       "\n",
       "                    detect_isPrimary          visit  detector  band  \\\n",
       "sourceId                                                              \n",
       "172052246308061185              True  2023052300621         0     r   \n",
       "172052246308061186              True  2023052300621         0     r   \n",
       "172052246308061187              True  2023052300621         0     r   \n",
       "172052246308061188             False  2023052300621         0     r   \n",
       "172052246308061189              True  2023052300621         0     r   \n",
       "...                              ...            ...       ...   ...   \n",
       "172052246308061957              True  2023052300621         0     r   \n",
       "172052246308061958              True  2023052300621         0     r   \n",
       "172052246308061959              True  2023052300621         0     r   \n",
       "172052246308061960              True  2023052300621         0     r   \n",
       "172052246308061961              True  2023052300621         0     r   \n",
       "\n",
       "                     physical_filter  \n",
       "sourceId                              \n",
       "172052246308061185  SDSSr_65mm~empty  \n",
       "172052246308061186  SDSSr_65mm~empty  \n",
       "172052246308061187  SDSSr_65mm~empty  \n",
       "172052246308061188  SDSSr_65mm~empty  \n",
       "172052246308061189  SDSSr_65mm~empty  \n",
       "...                              ...  \n",
       "172052246308061957  SDSSr_65mm~empty  \n",
       "172052246308061958  SDSSr_65mm~empty  \n",
       "172052246308061959  SDSSr_65mm~empty  \n",
       "172052246308061960  SDSSr_65mm~empty  \n",
       "172052246308061961  SDSSr_65mm~empty  \n",
       "\n",
       "[777 rows x 160 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srcs = list(butler.registry.queryDatasets('sourceTable_visit', tract=5634, skymap='latiss_v1', band='r'))\n",
    "src = butler.get(srcs[0])\n",
    "src"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f93080-9944-4c7d-9e0e-0e1b975bc8b2",
   "metadata": {},
   "source": [
    "## Use the magnitudes from the ATLAS catalog to get colors:\n",
    "This is the catalog being used as the starting point for photometric calibration for LATISS data. We'll use it to get colors, but the `objectTable` produced from the stacked images could also be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "201b0b05-26bc-4108-a32d-3fb4e276115f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T15:34:42.105984Z",
     "iopub.status.busy": "2023-11-17T15:34:42.105530Z",
     "iopub.status.idle": "2023-11-17T15:36:03.329280Z",
     "shell.execute_reply": "2023-11-17T15:36:03.328846Z",
     "shell.execute_reply.started": "2023-11-17T15:34:42.105968Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "atlasRefs = butler.registry.queryDatasets('atlas_refcat2_20220201', tract=5634, skymap='latiss_v1').expanded()\n",
    "dataIds = [ref.dataId for ref in atlasRefs]\n",
    "refCats = [butler.getDeferred(ref) for ref in atlasRefs]\n",
    "atlasLoader = ReferenceObjectLoader(dataIds=dataIds,\n",
    "                                    refCats=refCats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d765233f-1965-400c-bfe2-ab75f15258b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T15:44:00.804432Z",
     "iopub.status.busy": "2023-11-17T15:44:00.804083Z",
     "iopub.status.idle": "2023-11-17T15:44:00.935703Z",
     "shell.execute_reply": "2023-11-17T15:44:00.935331Z",
     "shell.execute_reply.started": "2023-11-17T15:44:00.804419Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Schema(\n",
       "    (Field['L'](name=\"id\", doc=\"unique ID\"), Key<L>(offset=0, nElements=1)),\n",
       "    (Field['Angle'](name=\"coord_ra\", doc=\"position in ra/dec\"), Key<Angle>(offset=8, nElements=1)),\n",
       "    (Field['Angle'](name=\"coord_dec\", doc=\"position in ra/dec\"), Key<Angle>(offset=16, nElements=1)),\n",
       "    (Field['D'](name=\"g_flux\", doc=\"flux in filter g\", units=\"nJy\"), Key<D>(offset=24, nElements=1)),\n",
       "    (Field['D'](name=\"r_flux\", doc=\"flux in filter r\", units=\"nJy\"), Key<D>(offset=32, nElements=1)),\n",
       "    (Field['D'](name=\"i_flux\", doc=\"flux in filter i\", units=\"nJy\"), Key<D>(offset=40, nElements=1)),\n",
       "    (Field['D'](name=\"z_flux\", doc=\"flux in filter z\", units=\"nJy\"), Key<D>(offset=48, nElements=1)),\n",
       "    (Field['D'](name=\"g_fluxErr\", doc=\"flux uncertainty in filter g\", units=\"nJy\"), Key<D>(offset=56, nElements=1)),\n",
       "    (Field['D'](name=\"r_fluxErr\", doc=\"flux uncertainty in filter r\", units=\"nJy\"), Key<D>(offset=64, nElements=1)),\n",
       "    (Field['D'](name=\"i_fluxErr\", doc=\"flux uncertainty in filter i\", units=\"nJy\"), Key<D>(offset=72, nElements=1)),\n",
       "    (Field['D'](name=\"z_fluxErr\", doc=\"flux uncertainty in filter z\", units=\"nJy\"), Key<D>(offset=80, nElements=1)),\n",
       "    (Field['Flag'](name=\"variable\", doc=\"set if the object has variable brightness\"), Key['Flag'](offset=88, bit=0)),\n",
       "    (Field['D'](name=\"epoch\", doc=\"date of observation (TAI, MJD)\", units=\"day\"), Key<D>(offset=96, nElements=1)),\n",
       "    (Field['Angle'](name=\"pm_ra\", doc=\"proper motion in the right ascension direction = dra/dt * cos(dec)\"), Key<Angle>(offset=104, nElements=1)),\n",
       "    (Field['Angle'](name=\"pm_dec\", doc=\"proper motion in the declination direction\"), Key<Angle>(offset=112, nElements=1)),\n",
       "    (Field['F'](name=\"pm_raErr\", doc=\"1-sigma uncertainty on ra\", units=\"rad/year\"), Key<F>(offset=120, nElements=1)),\n",
       "    (Field['F'](name=\"pm_decErr\", doc=\"1-sigma uncertainty on dec\", units=\"rad/year\"), Key<F>(offset=124, nElements=1)),\n",
       "    (Field['Flag'](name=\"pm_flag\", doc=\"Set if proper motion or proper motion error is bad\"), Key['Flag'](offset=88, bit=1)),\n",
       "    (Field['Angle'](name=\"parallax\", doc=\"parallax\"), Key<Angle>(offset=128, nElements=1)),\n",
       "    (Field['Angle'](name=\"parallaxErr\", doc=\"uncertainty in parallax\"), Key<Angle>(offset=136, nElements=1)),\n",
       "    (Field['Flag'](name=\"parallax_flag\", doc=\"Set if parallax or parallax error is bad\"), Key['Flag'](offset=88, bit=2)),\n",
       "    (Field['D'](name=\"Ag\"), Key<D>(offset=144, nElements=1)),\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atlasCat = butler.get(list(atlasRefs)[0])\n",
    "atlasCat.getSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1835ac24-7bae-4f55-9160-2a20fb1ad722",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T16:33:20.791956Z",
     "iopub.status.busy": "2023-11-17T16:33:20.791736Z",
     "iopub.status.idle": "2023-11-17T16:33:22.045122Z",
     "shell.execute_reply": "2023-11-17T16:33:22.044657Z",
     "shell.execute_reply.started": "2023-11-17T16:33:20.791940Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"margin: 0.1em;\n",
       "padding-left: 0.25em;\n",
       "border-left-style: solid;\n",
       "font-family: var(--jp-code-font-family);\n",
       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loading reference objects from None in region bounded by [270.03449052, 270.36550948], [-25.15000172, -24.84999828] RA Dec</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"margin: 0.1em;\n",
       "padding-left: 0.25em;\n",
       "border-left-style: solid;\n",
       "font-family: var(--jp-code-font-family);\n",
       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loaded 12384 reference objects</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loading reference objects from None in region bounded by [270.03449052, 270.36550948], [-25.15000172, -24.84999828] RA Dec</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
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       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loaded 12384 reference objects</pre>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<pre style=\"margin: 0.1em;\n",
       "padding-left: 0.25em;\n",
       "border-left-style: solid;\n",
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       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loading reference objects from None in region bounded by [270.03449052, 270.36550948], [-25.15000172, -24.84999828] RA Dec</pre>"
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       "<IPython.core.display.HTML object>"
      ]
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    },
    {
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       "<pre style=\"margin: 0.1em;\n",
       "padding-left: 0.25em;\n",
       "border-left-style: solid;\n",
       "font-family: var(--jp-code-font-family);\n",
       "font-size: var(--jp-code-font-size);\n",
       "line-height: var(--jp-code-line-height);\n",
       "\"><span style=\"color: var(--jp-warn-color2)\">lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader</span> <span style=\"color: var(--jp-info-color0)\">INFO</span>: Loaded 12384 reference objects</pre>"
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      ]
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   ],
   "source": [
    "ctr = lsst.geom.SpherePoint(-89.8, -25, lsst.geom.degrees)\n",
    "radius = 0.15 * lsst.geom.degrees\n",
    "#epoch = Time(np.median(mjds), format='mjd')\n",
    "atlasCatG = atlasLoader.loadSkyCircle(ctr, radius, filterName='g').refCat.asAstropy()\n",
    "atlasCatR = atlasLoader.loadSkyCircle(ctr, radius, filterName='r').refCat.asAstropy()\n",
    "atlasCatI = atlasLoader.loadSkyCircle(ctr, radius, filterName='i').refCat.asAstropy()\n",
    "atlasGR = atlasCatG['g_flux'].to(u.ABmag) - atlasCatR['r_flux'].to(u.ABmag)\n",
    "atlasRI = atlasCatR['r_flux'].to(u.ABmag) - atlasCatI['i_flux'].to(u.ABmag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fa6ef1a5-482c-4547-8174-2df9d5c1142c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T16:35:43.950747Z",
     "iopub.status.busy": "2023-11-17T16:35:43.950546Z",
     "iopub.status.idle": "2023-11-17T16:35:43.964540Z",
     "shell.execute_reply": "2023-11-17T16:35:43.964121Z",
     "shell.execute_reply.started": "2023-11-17T16:35:43.950734Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4628 4541\n"
     ]
    }
   ],
   "source": [
    "# Use astropy.coordinates.SkyCoord to match the objects in the objectTable with the starCatalog:\n",
    "atlasCoords = SkyCoord(ra=atlasCatR['coord_ra'], dec=atlasCatR['coord_dec'])\n",
    "starCoords = SkyCoord(ra=starCat['starX'].to_numpy() * u.degree, dec=starCat['starY'].to_numpy() * u.degree)\n",
    "\n",
    "id, d2d, d3d = starCoords.match_to_catalog_sky(atlasCoords)\n",
    "goodInds = d2d.arcsecond < 1\n",
    "print(len(starCoords), goodInds.sum())\n",
    "\n",
    "# We will denote missing color information with NaNs\n",
    "starCatGR = np.ones(len(starCat)) * np.nan\n",
    "starCatGR[goodInds] = atlasGR[id[goodInds]]\n",
    "starCatRI = np.ones(len(starCat)) * np.nan\n",
    "starCatRI[goodInds] = atlasRI[id[goodInds]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1733f5c3-f09c-4d21-9028-1b8c8fcd8f6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-17T16:42:22.438371Z",
     "iopub.status.busy": "2023-11-17T16:42:22.438067Z",
     "iopub.status.idle": "2023-11-17T16:42:22.737592Z",
     "shell.execute_reply": "2023-11-17T16:42:22.737137Z",
     "shell.execute_reply.started": "2023-11-17T16:42:22.438354Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'R - I')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's a color-color plot for the objects in the starCat:\n",
    "plt.hexbin(starCatGR, starCatRI, extent=[-0.5, 3.5, -0.5, 3.5], bins='log')\n",
    "plt.colorbar()\n",
    "plt.xlabel('G - R')\n",
    "plt.ylabel('R - I')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LSST",
   "language": "python",
   "name": "lsst"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}