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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-0",
+   "metadata": {},
+   "source": [
+    "<img src=\"../../Fink_PrimaryLogo_WEB.jpg\" width=400 />\n",
+    "\n",
+    "# Fink case study: extragalactic candidates with LSST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-1",
+   "metadata": {},
+   "source": [
+    "## Goal\n",
+    "\n",
+    "This notebook illustrates how to use the Fink API to explore extragalactic transient candidates from the LSST/Rubin alert stream. We focus on two Fink-defined tags:\n",
+    "\n",
+    "- **`extragalactic_lt20mag_candidate`**: bright extragalactic transient candidates with magnitude < 20.\n",
+    "- **`sn_near_galaxy_candidate`**: supernova candidates spatially coincident with a known galaxy.\n",
+    "\n",
+    "For each tag, we show how to query the API, inspect the data, and leverage Fink's science modules outputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-2",
+   "metadata": {},
+   "source": [
+    "## What is behind?\n",
+    "\n",
+    "This notebook uses:\n",
+    "- **Fink tags**: candidate lists built nightly by Fink from quality cuts, cross-matches, and ML scores. See https://doc.lsst.fink-broker.org/science/classification/\n",
+    "- **Fink's object API** to retrieve full multi-band photometry for individual `diaObject`s.\n",
+    "\n",
+    "API reference: https://api.lsst.fink-portal.org  \n",
+    "Field definitions: https://doc.lsst.fink-broker.org/services/api/definitions/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-3",
+   "metadata": {},
+   "source": [
+    "## Environment set up\n",
+    "\n",
+    "Standard libraries only — all available in Colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "cell-4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 13})\n",
+    "\n",
+    "APIURL = 'https://api.lsst.fink-portal.org'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-schema-intro",
+   "metadata": {},
+   "source": [
+    "## Field names and schema\n",
+    "\n",
+    "Fink/LSST alert fields are prefixed by their origin (see https://doc.lsst.fink-broker.org/services/api/definitions/):\n",
+    "- `r:` — original LSST fields\n",
+    "- `f:` — Fink science module outputs (ML scores, cross-matches, ...)\n",
+    "- `v:` — values computed at query runtime\n",
+    "- `b:` — cutout data\n",
+    "\n",
+    "You can programmatically retrieve the full schema for any endpoint:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "cell-schema-query",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fink cross-matching fields (f:):\n",
+      "  f:xm_gaiadr3_DR3Name  —  Unique source designation of closest source from Gaia catalog; if exists within 1 arcsec.\n",
+      "  f:xm_gaiadr3_Plx  —  Absolute stellar parallax (in milli-arcsecond) of the closest source from Gaia catalog; if\n",
+      "  f:xm_gaiadr3_VarFlag  —  Photometric variability flag from Gaia DR3. 1 if the source is variable, 0 otherwise.\n",
+      "  f:xm_gaiadr3_e_Plx  —  Standard error of the stellar parallax (in milli-arcsecond) of the closest source from Gai\n",
+      "  f:xm_gcvs_type  —  Object type of the closest source from GCVS catalog; if exists within 1 arcsec.\n",
+      "  f:xm_legacydr8_e_zphot  —  Uncertainty on zphot from Legacy Surveys DR8 South Photometric Redshifts catalog - standar\n",
+      "  f:xm_legacydr8_fqual  —  Photo-z reliability flag from Legacy Surveys DR8 South Photometric Redshifts catalog. =1 f\n",
+      "  f:xm_legacydr8_pstar  —  Star likelihood based on colours from GMM star-QSO classification (Legacy Surveys DR8 Sout\n",
+      "  f:xm_legacydr8_zphot  —  Photo-z estimate from Legacy Surveys DR8 South Photometric Redshifts catalog - mean of the\n",
+      "  f:xm_mangrove_2MASS_name  —  2MASS source designation of closest source from Mangrove catalog; if exists within 1 arcmi\n",
+      "  f:xm_mangrove_HyperLEDA_name  —  HyperLEDA source designation of closest source from Mangrove catalog; if exists within 1 a\n",
+      "  f:xm_mangrove_ang_dist  —  Angular distance of closest source from Mangrove catalog; if exists within 1 arcmin.\n",
+      "  f:xm_mangrove_lum_dist  —  Luminosity distance of closest source from Mangrove catalog; if exists within 1 arcmin.\n",
+      "  f:xm_simbad_otype  —  Object type of the closest source from SIMBAD database; if exists within 1 arcsec. See htt\n",
+      "  f:xm_spicy_class  —  Class name of closest source from SPICY catalog; if exists within 1.2 arcsec.\n",
+      "  f:xm_tns_fullname  —  TNS name, if it exists.\n",
+      "  f:xm_tns_redshift  —  Redshift from TNS, if it exists.\n",
+      "  f:xm_tns_type  —  TNS label, if it exists.\n",
+      "  f:xm_vsx_Type  —  Object type of the closest source from VSX catalog; if exists within 1 arcsec.\n",
+      "  f:xm_x3hsp_type  —  Counterpart (cross-match) to the 3HSP catalog if exists within 1 arcminute.\n",
+      "  f:xm_x4lac_type  —  Counterpart (cross-match) to the 4LAC DR3 catalog if exists within 1 arcminute.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Retrieve the schema for the /api/v1/sources endpoint\n",
+    "r_schema = requests.get(\n",
+    "    '{}/api/v1/schema'.format(APIURL),\n",
+    "    params={'endpoint': '/api/v1/sources'}\n",
+    ")\n",
+    "schema = r_schema.json()\n",
+    "\n",
+    "# Print the Fink science module outputs (v: fields) relevant to cross-matching\n",
+    "fink_fields = schema.get('Fink science module outputs (f:)', {})\n",
+    "xm_fields = {k: v for k, v in fink_fields.items() if k.startswith('xm_')}\n",
+    "\n",
+    "print('Fink cross-matching fields (f:):')\n",
+    "for name, meta in xm_fields.items():\n",
+    "    print(f\"  f:{name}  —  {meta['doc'][:90]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-5",
+   "metadata": {},
+   "source": [
+    "# 1. Bright extragalactic candidates\n",
+    "\n",
+    "The tag `extragalactic_lt20mag_candidate` is assigned by Fink to alerts that pass extragalactic selection criteria and have a measured PSF magnitude below 20. This is a useful starting point to build a bright, actionable follow-up list.\n",
+    "\n",
+    "The full list of available Fink tags is at https://api.lsst.fink-portal.org/api/v1/tags."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-6",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>API query:</font> <font color='orange'>latest alerts</font> tagged as bright extragalactic candidates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "cell-7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieved 10 alerts\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = requests.post(\n",
+    "    '{}/api/v1/tags'.format(APIURL),\n",
+    "    json={\n",
+    "        'tag': 'extragalactic_lt20mag_candidate',\n",
+    "        'n': '10',\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "pdf = pd.read_json(io.BytesIO(r.content))\n",
+    "print(f'Retrieved {len(pdf)} alerts')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-8",
+   "metadata": {},
+   "source": [
+    "The table contains per-alert information including the latest photometry point and Fink-added values. The full schema is at https://doc.lsst.fink-broker.org/services/api/definitions/ or programmatically via `/api/v1/schema`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "cell-9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>f:clf_cats_class</th>\n",
+       "      <th>f:clf_cats_score</th>\n",
+       "      <th>f:clf_earlySNIa_score</th>\n",
+       "      <th>f:clf_elephant_kstest_science</th>\n",
+       "      <th>f:clf_elephant_kstest_template</th>\n",
+       "      <th>f:clf_snnSnVsOthers_score</th>\n",
+       "      <th>f:fink_broker_version</th>\n",
+       "      <th>f:fink_science_version</th>\n",
+       "      <th>f:lsst_schema_version</th>\n",
+       "      <th>f:xm_gaiadr3_DR3Name</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r:trailLengthErr</th>\n",
+       "      <th>r:trailNdata</th>\n",
+       "      <th>r:trailRa</th>\n",
+       "      <th>r:trailRaErr</th>\n",
+       "      <th>r:trail_flag_edge</th>\n",
+       "      <th>r:visit</th>\n",
+       "      <th>r:x</th>\n",
+       "      <th>r:xErr</th>\n",
+       "      <th>r:y</th>\n",
+       "      <th>r:yErr</th>\n",
+       "    </tr>\n",
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+       "    <tr>\n",
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+       "      <td>8.49.0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>Gaia DR3 3833530685621513728</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.084054</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>2026060400135</td>\n",
+       "      <td>1126.8638</td>\n",
+       "      <td>0.031527</td>\n",
+       "      <td>1239.3448</td>\n",
+       "      <td>0.028275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.819196</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.49.0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>nan</td>\n",
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+       "      <td>0.111874</td>\n",
+       "      <td>0</td>\n",
+       "      <td>151.163540</td>\n",
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+       "      <td>False</td>\n",
+       "      <td>2026060400135</td>\n",
+       "      <td>2691.6174</td>\n",
+       "      <td>0.055161</td>\n",
+       "      <td>2517.2760</td>\n",
+       "      <td>0.048951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.999297</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.033899</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.49.0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>Fail</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.226865</td>\n",
+       "      <td>0</td>\n",
+       "      <td>149.463352</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026060300112</td>\n",
+       "      <td>3418.9355</td>\n",
+       "      <td>0.176035</td>\n",
+       "      <td>2660.6720</td>\n",
+       "      <td>0.136641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>0.675439</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.867563</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.49.0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>Fail</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.162789</td>\n",
+       "      <td>0</td>\n",
+       "      <td>151.163537</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026060300112</td>\n",
+       "      <td>4025.1600</td>\n",
+       "      <td>0.122306</td>\n",
+       "      <td>1174.8976</td>\n",
+       "      <td>0.099664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.961704</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.142086</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.49.0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>Fail</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.179412</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>3392.4763</td>\n",
+       "      <td>0.131214</td>\n",
+       "      <td>485.7441</td>\n",
+       "      <td>0.110056</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 131 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   f:clf_cats_class  f:clf_cats_score  f:clf_earlySNIa_score  \\\n",
+       "0                12          0.999954              -1.000000   \n",
+       "1                11          0.999992              -1.000000   \n",
+       "2                11          0.999297              -1.000000   \n",
+       "3                11          0.999996               0.675439   \n",
+       "4                11          0.961704              -1.000000   \n",
+       "\n",
+       "   f:clf_elephant_kstest_science  f:clf_elephant_kstest_template  \\\n",
+       "0                            NaN                             NaN   \n",
+       "1                            NaN                             NaN   \n",
+       "2                            NaN                             NaN   \n",
+       "3                            NaN                             NaN   \n",
+       "4                            NaN                             NaN   \n",
+       "\n",
+       "   f:clf_snnSnVsOthers_score f:fink_broker_version f:fink_science_version  \\\n",
+       "0                   0.198571                5.0rc0                 8.49.0   \n",
+       "1                   0.819196                5.0rc0                 8.49.0   \n",
+       "2                   0.033899                5.0rc0                 8.49.0   \n",
+       "3                   0.867563                5.0rc0                 8.49.0   \n",
+       "4                   0.142086                5.0rc0                 8.49.0   \n",
+       "\n",
+       "  f:lsst_schema_version          f:xm_gaiadr3_DR3Name  ...  r:trailLengthErr  \\\n",
+       "0            lsst.v10_0  Gaia DR3 3833530685621513728  ...          0.084054   \n",
+       "1            lsst.v10_0                           nan  ...          0.111874   \n",
+       "2            lsst.v10_0                          Fail  ...          0.226865   \n",
+       "3            lsst.v10_0                          Fail  ...          0.162789   \n",
+       "4            lsst.v10_0                          Fail  ...          0.179412   \n",
+       "\n",
+       "   r:trailNdata   r:trailRa  r:trailRaErr  r:trail_flag_edge        r:visit  \\\n",
+       "0             0  149.928637           NaN              False  2026060400135   \n",
+       "1             0  151.163540           NaN              False  2026060400135   \n",
+       "2             0  149.463352           NaN              False  2026060300112   \n",
+       "3             0  151.163537           NaN              False  2026060300112   \n",
+       "4             0  150.114333           NaN              False  2026060300112   \n",
+       "\n",
+       "         r:x    r:xErr        r:y    r:yErr  \n",
+       "0  1126.8638  0.031527  1239.3448  0.028275  \n",
+       "1  2691.6174  0.055161  2517.2760  0.048951  \n",
+       "2  3418.9355  0.176035  2660.6720  0.136641  \n",
+       "3  4025.1600  0.122306  1174.8976  0.099664  \n",
+       "4  3392.4763  0.131214   485.7441  0.110056  \n",
+       "\n",
+       "[5 rows x 131 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-columns-tip",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Tip:</font> <font color='orange'>select only the columns you need</font>\n",
+    "\n",
+    "By default the API returns all ~130 fields. If you only need a subset — for example to build a target list or cross-match — pass the `columns` parameter to reduce transfer size and speed up the query. Column names follow the `prefix:field` convention described in the schema section above.\n",
+    "\n",
+    "Useful columns for a lightweight object list:\n",
+    "\n",
+    "| Column | Description |\n",
+    "|---|---|\n",
+    "| `r:diaObjectId` | Unique object identifier |\n",
+    "| `r:ra`, `r:dec` | Sky position |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "cell-columns-example",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns returned: ['r:dec', 'r:diaObjectId', 'r:ra']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r:dec</th>\n",
+       "      <th>r:diaObjectId</th>\n",
+       "      <th>r:ra</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.997570</td>\n",
+       "      <td>170028500588167170</td>\n",
+       "      <td>150.114315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.997535</td>\n",
+       "      <td>170028500588167170</td>\n",
+       "      <td>150.114301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.507443</td>\n",
+       "      <td>170411112000913487</td>\n",
+       "      <td>151.163543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.997573</td>\n",
+       "      <td>170028500588167170</td>\n",
+       "      <td>150.114332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.507433</td>\n",
+       "      <td>170411112000913487</td>\n",
+       "      <td>151.163541</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      r:dec       r:diaObjectId        r:ra\n",
+       "0  2.997570  170028500588167170  150.114315\n",
+       "1  2.997535  170028500588167170  150.114301\n",
+       "2  2.507443  170411112000913487  151.163543\n",
+       "3  2.997573  170028500588167170  150.114332\n",
+       "4  2.507433  170411112000913487  151.163541"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Request only the columns needed — here just the object ID\n",
+    "r_slim = requests.post(\n",
+    "    '{}/api/v1/tags'.format(APIURL),\n",
+    "    json={\n",
+    "        'tag': 'extragalactic_lt20mag_candidate',\n",
+    "        'n': '10',\n",
+    "        'columns': 'r:diaObjectId,r:ra,r:dec',\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "pdf_slim = pd.read_json(io.BytesIO(r_slim.content))\n",
+    "print(f'Columns returned: {list(pdf_slim.columns)}')\n",
+    "pdf_slim.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-10",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Inspect:</font> <font color='orange'>alerts per diaObject</font>\n",
+    "\n",
+    "Multiple alerts can correspond to the same astrophysical object (`r:diaObjectId`). Grouping by `r:diaObjectId` shows how many times each object has been observed within this sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "cell-11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     r:diaObjectId  n_alerts\n",
+      "170028500588167170         4\n",
+      "170411112000913487         4\n",
+      "170028510474666056         1\n",
+      "314003014107006318         1\n"
+     ]
+    }
+   ],
+   "source": [
+    "alerts_per_object = (\n",
+    "    pdf.groupby('r:diaObjectId')\n",
+    "    .size()\n",
+    "    .reset_index(name='n_alerts')\n",
+    "    .sort_values('n_alerts', ascending=False)\n",
+    ")\n",
+    "print(alerts_per_object.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-12",
+   "metadata": {},
+   "source": [
+    "Objects with multiple alerts have been detected on several separate visits. We pick the one with the most alerts for the light-curve inspection below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-13",
+   "metadata": {},
+   "source": [
+    "## 1.1 Plot light-curve\n",
+    "\n",
+    "We select the `diaObjectId` with the most alerts and query all its photometry via the `/api/v1/sources` endpoint (see the `lsst/photometry` tutorial for a full walkthrough)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-14",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>API query:</font> <font color='orange'>photometry</font> for one bright extragalactic candidate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "cell-15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected diaObjectId: 170028500588167170\n",
+      "Retrieved 429 photometry points\n"
+     ]
+    }
+   ],
+   "source": [
+    "selected_id = alerts_per_object['r:diaObjectId'].iloc[0]\n",
+    "print(f'Selected diaObjectId: {selected_id}')\n",
+    "\n",
+    "columns = [\n",
+    "    'r:midpointMjdTai',\n",
+    "    'r:psfFlux',\n",
+    "    'r:psfFluxErr',\n",
+    "    'r:band',\n",
+    "]\n",
+    "\n",
+    "r = requests.post(\n",
+    "    '{}/api/v1/sources'.format(APIURL),\n",
+    "    json={\n",
+    "        'diaObjectId': str(selected_id),\n",
+    "        'columns': ','.join(columns),\n",
+    "        'output-format': 'json',\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "pdf_obj = pd.read_json(io.BytesIO(r.content))\n",
+    "print(f'Retrieved {len(pdf_obj)} photometry points')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-16",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Inspect:</font> first rows of the photometry table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "cell-17",
+   "metadata": {},
+   "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>r:band</th>\n",
+       "      <th>r:midpointMjdTai</th>\n",
+       "      <th>r:psfFlux</th>\n",
+       "      <th>r:psfFluxErr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>z</td>\n",
+       "      <td>61196.044028</td>\n",
+       "      <td>63313.832</td>\n",
+       "      <td>1063.96040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>z</td>\n",
+       "      <td>61196.043532</td>\n",
+       "      <td>63456.457</td>\n",
+       "      <td>1159.86120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>y</td>\n",
+       "      <td>61196.041480</td>\n",
+       "      <td>52922.996</td>\n",
+       "      <td>2751.31980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>y</td>\n",
+       "      <td>61196.040962</td>\n",
+       "      <td>60658.527</td>\n",
+       "      <td>2924.17200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>r</td>\n",
+       "      <td>61196.038951</td>\n",
+       "      <td>39914.180</td>\n",
+       "      <td>490.75644</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  r:band  r:midpointMjdTai  r:psfFlux  r:psfFluxErr\n",
+       "0      z      61196.044028  63313.832    1063.96040\n",
+       "1      z      61196.043532  63456.457    1159.86120\n",
+       "2      y      61196.041480  52922.996    2751.31980\n",
+       "3      y      61196.040962  60658.527    2924.17200\n",
+       "4      r      61196.038951  39914.180     490.75644"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf_obj.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-18",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Plot:</font> <font color='orange'>multi-band light-curve</font>\n",
+    "\n",
+    "We use a colour and marker scheme consistent with the LSST filter set and plot the difference-image PSF flux as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "cell-19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "UNIQUE_BANDS = ['u', 'g', 'r', 'i', 'z', 'y']\n",
+    "MARKERS = {'u': 'o', 'g': '<', 'r': '>', 'i': 's', 'z': '*', 'y': 'p'}\n",
+    "COLORS  = {\n",
+    "    'u': '#15284f', 'g': '#626d84', 'r': '#afb2b9',\n",
+    "    'i': '#dbbeb2', 'z': '#e89070', 'y': '#f5622e'\n",
+    "}\n",
+    "\n",
+    "def plot_lightcurve(pdf_phot, title=''):\n",
+    "    \"\"\"Plot multi-band difference-image PSF flux light-curve.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    pdf_phot : pd.DataFrame\n",
+    "        DataFrame with columns r:midpointMjdTai, r:psfFlux, r:psfFluxErr, r:band.\n",
+    "    title : str\n",
+    "        Plot title (e.g. diaObjectId).\n",
+    "    \"\"\"\n",
+    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "\n",
+    "    for band in UNIQUE_BANDS:\n",
+    "        mask = pdf_phot['r:band'] == band\n",
+    "        if mask.sum() == 0:\n",
+    "            continue\n",
+    "        ax.errorbar(\n",
+    "            pdf_phot.loc[mask, 'r:midpointMjdTai'],\n",
+    "            pdf_phot.loc[mask, 'r:psfFlux'],\n",
+    "            pdf_phot.loc[mask, 'r:psfFluxErr'],\n",
+    "            color=COLORS[band],\n",
+    "            marker=MARKERS[band],\n",
+    "            label=f'{band} band',\n",
+    "            ls='',\n",
+    "        )\n",
+    "\n",
+    "    ax.axhline(0, ls='--', color='grey', lw=0.8)\n",
+    "    ax.set_xlabel('Time (MJD)')\n",
+    "    ax.set_ylabel('PSF Flux (nJy)')\n",
+    "    ax.set_title(title or str(selected_id))\n",
+    "    ax.legend()\n",
+    "    ax.grid(alpha=0.4)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "cell-20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_lightcurve(pdf_obj, title=f'diaObjectId: {selected_id}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-21",
+   "metadata": {},
+   "source": [
+    "# 2. SN candidates near a galaxy\n",
+    "\n",
+    "The tag `sn_near_galaxy_candidate` selects alerts that are spatially consistent with a known galaxy. This tag is particularly useful to build a sample of hosted transients.\n",
+    "\n",
+    "We query the latest alerts for this tag."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-22",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>API query:</font> <font color='orange'>latest alerts</font> tagged as SN candidates near a galaxy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "cell-23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Retrieved 100 alerts, 78 unique objects\n"
+     ]
+    },
+    {
+     "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>f:xm_legacydr8_e_zphot</th>\n",
+       "      <th>f:xm_legacydr8_fqual</th>\n",
+       "      <th>f:xm_legacydr8_zphot</th>\n",
+       "      <th>r:band</th>\n",
+       "      <th>r:dec</th>\n",
+       "      <th>r:diaObjectId</th>\n",
+       "      <th>r:psfFlux</th>\n",
+       "      <th>r:psfFluxErr</th>\n",
+       "      <th>r:ra</th>\n",
+       "      <th>f:xm_tns_redshift</th>\n",
+       "      <th>f:xm_mangrove_2MASS_name</th>\n",
+       "      <th>f:xm_mangrove_HyperLEDA_name</th>\n",
+       "      <th>f:xm_mangrove_ang_dist</th>\n",
+       "      <th>f:xm_mangrove_lum_dist</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.278</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.241</td>\n",
+       "      <td>g</td>\n",
+       "      <td>2.947896</td>\n",
+       "      <td>313875416771199127</td>\n",
+       "      <td>2638.1125</td>\n",
+       "      <td>345.46980</td>\n",
+       "      <td>148.873207</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.235</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.705</td>\n",
+       "      <td>g</td>\n",
+       "      <td>2.998947</td>\n",
+       "      <td>313875416773296195</td>\n",
+       "      <td>2426.3890</td>\n",
+       "      <td>368.19778</td>\n",
+       "      <td>149.221265</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.346</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.236</td>\n",
+       "      <td>g</td>\n",
+       "      <td>1.865042</td>\n",
+       "      <td>313875416786927752</td>\n",
+       "      <td>5028.1655</td>\n",
+       "      <td>385.28064</td>\n",
+       "      <td>148.857682</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   f:xm_legacydr8_e_zphot  f:xm_legacydr8_fqual  f:xm_legacydr8_zphot r:band  \\\n",
+       "0                   0.278                     1                 1.241      g   \n",
+       "1                   2.235                     0                 0.705      g   \n",
+       "2                   0.346                     1                 1.236      g   \n",
+       "\n",
+       "      r:dec       r:diaObjectId  r:psfFlux  r:psfFluxErr        r:ra  \\\n",
+       "0  2.947896  313875416771199127  2638.1125     345.46980  148.873207   \n",
+       "1  2.998947  313875416773296195  2426.3890     368.19778  149.221265   \n",
+       "2  1.865042  313875416786927752  5028.1655     385.28064  148.857682   \n",
+       "\n",
+       "   f:xm_tns_redshift f:xm_mangrove_2MASS_name  f:xm_mangrove_HyperLEDA_name  \\\n",
+       "0                NaN                      nan                           NaN   \n",
+       "1                NaN                      nan                           NaN   \n",
+       "2                NaN                      nan                           NaN   \n",
+       "\n",
+       "   f:xm_mangrove_ang_dist  f:xm_mangrove_lum_dist  \n",
+       "0                     NaN                     NaN  \n",
+       "1                     NaN                     NaN  \n",
+       "2                     NaN                     NaN  "
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GALAXY_COLS = ','.join([\n",
+    "    'r:diaObjectId', 'r:ra', 'r:dec',\n",
+    "    'r:psfFlux', 'r:psfFluxErr', 'r:band',\n",
+    "    'f:xm_mangrove_ang_dist', 'f:xm_mangrove_lum_dist',\n",
+    "    'f:xm_mangrove_HyperLEDA_name', 'f:xm_mangrove_2MASS_name',\n",
+    "    'f:xm_legacydr8_zphot', 'f:xm_legacydr8_e_zphot', 'f:xm_legacydr8_fqual',\n",
+    "    'f:xm_tns_redshift',\n",
+    "])\n",
+    "\n",
+    "r = requests.post(\n",
+    "    '{}/api/v1/tags'.format(APIURL),\n",
+    "    json={\n",
+    "        'tag': 'sn_near_galaxy_candidate',\n",
+    "        'n': '100',\n",
+    "        'columns': GALAXY_COLS,\n",
+    "        'output-format': 'json',\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "pdf_sn = pd.read_json(io.BytesIO(r.content))\n",
+    "print(f'Retrieved {len(pdf_sn)} alerts, {pdf_sn[\"r:diaObjectId\"].nunique()} unique objects')\n",
+    "pdf_sn.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-galaxy-intro",
+   "metadata": {},
+   "source": [
+    "## 2.1 Host galaxy cross-match analysis\n",
+    "\n",
+    "Fink cross-matches every alert against two galaxy catalogues at query time (`f:` prefix):\n",
+    "\n",
+    "| Field | Catalogue | Description |\n",
+    "|---|---|---|\n",
+    "| `f:xm_mangrove_ang_dist` | Mangrove | Angular distance to nearest galaxy within 1 arcmin (arcsec) |\n",
+    "| `f:xm_mangrove_lum_dist` | Mangrove | Luminosity distance of the matched galaxy (Mpc) |\n",
+    "| `f:xm_mangrove_HyperLEDA_name` | Mangrove / HyperLEDA | HyperLEDA identifier of the host |\n",
+    "| `f:xm_legacydr8_zphot` | Legacy Surveys DR8 | Photo-*z* of the nearest galaxy in the field |\n",
+    "| `f:xm_legacydr8_e_zphot` | Legacy Surveys DR8 | Photo-*z* uncertainty |\n",
+    "| `f:xm_legacydr8_fqual` | Legacy Surveys DR8 | Quality flag (1 = reliable photo-*z*) |\n",
+    "| `f:xm_tns_redshift` | TNS | Spectroscopic redshift, if already reported |\n",
+    "\n",
+    "We use these to characterise the host-galaxy population and assess how bright the transients are relative to their distance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-galaxy-stats-md",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Inspect:</font> <font color='orange'>host-galaxy match statistics</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "cell-galaxy-stats",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total alerts              : 100\n",
+      "Mangrove/HyperLEDA match  :   1  (1%)\n",
+      "Mangrove/2MASS match      :   0  (0%)\n",
+      "Legacy DR8 photo-z        : 100  (100%)\n",
+      "TNS spectroscopic z       :   4  (4%)\n",
+      "\n",
+      "Total unique objects       : 78\n",
+      "Mangrove/HyperLEDA match   :   1  (1%)\n",
+      "Mangrove/2MASS match       :   0  (0%)\n",
+      "Legacy DR8 photo-z         :  78  (100%)\n",
+      "TNS spectroscopic z        :   1  (1%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def _is_matched(col,pdf):\n",
+    "    \"\"\"True where the cross-match succeeded (not NaN, not 'Fail', not 'nan').\"\"\"\n",
+    "    s = pdf[col].astype(str)\n",
+    "    return ~s.isin(['nan', 'Fail', 'None', '']) & pdf[col].notna()\n",
+    "\n",
+    "n_tot = len(pdf_sn)\n",
+    "n_mangrove = _is_matched('f:xm_mangrove_HyperLEDA_name', pdf_sn).sum()\n",
+    "n_2mass    = _is_matched('f:xm_mangrove_2MASS_name', pdf_sn).sum()\n",
+    "n_legacy   = _is_matched('f:xm_legacydr8_zphot', pdf_sn).sum()\n",
+    "n_tns      = _is_matched('f:xm_tns_redshift', pdf_sn).sum()\n",
+    "\n",
+    "print(f'Total alerts              : {n_tot}')\n",
+    "print(f'Mangrove/HyperLEDA match  : {n_mangrove:3d}  ({100*n_mangrove/n_tot:.0f}%)')\n",
+    "print(f'Mangrove/2MASS match      : {n_2mass:3d}  ({100*n_2mass/n_tot:.0f}%)')\n",
+    "print(f'Legacy DR8 photo-z        : {n_legacy:3d}  ({100*n_legacy/n_tot:.0f}%)')\n",
+    "print(f'TNS spectroscopic z       : {n_tns:3d}  ({100*n_tns/n_tot:.0f}%)')\n",
+    "\n",
+    "# One row per unique object (keep first alert per object)\n",
+    "pdf_sn_unique = pdf_sn.groupby(\"r:diaObjectId\", as_index=False).first()\n",
+    "\n",
+    "n_tot      = len(pdf_sn_unique)\n",
+    "n_mangrove = _is_matched('f:xm_mangrove_HyperLEDA_name', pdf_sn_unique).sum()\n",
+    "n_2mass    = _is_matched('f:xm_mangrove_2MASS_name',     pdf_sn_unique).sum()\n",
+    "n_legacy   = _is_matched('f:xm_legacydr8_zphot',         pdf_sn_unique).sum()\n",
+    "n_tns      = _is_matched('f:xm_tns_redshift',            pdf_sn_unique).sum()\n",
+    "\n",
+    "print(\"\")\n",
+    "print(f'Total unique objects       : {n_tot}')\n",
+    "print(f'Mangrove/HyperLEDA match   : {n_mangrove:3d}  ({100*n_mangrove/n_tot:.0f}%)')\n",
+    "print(f'Mangrove/2MASS match       : {n_2mass:3d}  ({100*n_2mass/n_tot:.0f}%)')\n",
+    "print(f'Legacy DR8 photo-z         : {n_legacy:3d}  ({100*n_legacy/n_tot:.0f}%)')\n",
+    "print(f'TNS spectroscopic z        : {n_tns:3d}  ({100*n_tns/n_tot:.0f}%)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-galaxy-ang-md",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Plot:</font> <font color='orange'>angular separation from host and photo-*z* distribution</font>\n",
+    "\n",
+    "A small angular offset from the galaxy nucleus is expected for a hosted SN. Very large separations (> 30 arcsec) may indicate a chance coincidence or a misidentification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "cell-galaxy-ang",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Clean numeric columns\n",
+    "ang  = pd.to_numeric(pdf_sn['f:xm_mangrove_ang_dist'], errors='coerce')\n",
+    "zphot = pd.to_numeric(pdf_sn['f:xm_legacydr8_zphot'],  errors='coerce')\n",
+    "fqual = pd.to_numeric(pdf_sn['f:xm_legacydr8_fqual'],  errors='coerce')\n",
+    "\n",
+    "ang_ok   = ang.dropna()\n",
+    "z_ok     = zphot[fqual == 1].dropna()\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Left: angular separation\n",
+    "axes[0].hist(ang_ok, bins=2, color='steelblue', edgecolor='white')\n",
+    "axes[0].axvline(30, ls='--', color='grey', lw=1, label='30 arcsec')\n",
+    "axes[0].set_xlabel('Angular separation from Mangrove galaxy (arcsec)')\n",
+    "axes[0].set_ylabel('Number of alerts')\n",
+    "axes[0].set_title(f'Host offset (n = {len(ang_ok)})')\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(alpha=0.4)\n",
+    "\n",
+    "# Right: photo-z distribution (reliable only)\n",
+    "axes[1].hist(z_ok, bins=20, color='darkorange', edgecolor='white')\n",
+    "axes[1].set_xlabel('Photo-z (Legacy DR8, fqual = 1)')\n",
+    "axes[1].set_ylabel('Number of alerts')\n",
+    "axes[1].set_title(f'Redshift distribution (n = {len(z_ok)})')\n",
+    "axes[1].grid(alpha=0.4)\n",
+    "\n",
+    "plt.suptitle('Host galaxy cross-match — SN candidates near a galaxy', y=1.02)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-galaxy-bright-md",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>Plot:</font> <font color='orange'>transient PSF flux vs. host distance</font>\n",
+    "\n",
+    "We compare the PSF flux of each alert to the distance of its host:\n",
+    "\n",
+    "- **Left**: PSF flux max distribution per band — how bright these transients appear on sky at maximum light measurement.\n",
+    "- **Right**: PSF flux max vs. photo-*z*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "cell-galaxy-bright",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6AAAAoIAHZAcPHmzFihVzZ/CDM2EVrG3btq075q233rJoUa3YG2+80WUEKNugTJkybrumiU2ZMsUOP/xwV7sLAAAABYPKVClrdH9rDASWsgIAAAASUURFvqZPn279+vULWZbg6KOPtr59+9rXX39t0aRasVr0QKvctmjRwtXP0gIP+n3v3r1uuhoAAAAKjmbNmtlzzz1n//zzT45969ats+eff96VNAAAAAAKXIbszp07Q9ZuDazhqmOi7eqrr7bKlSvbyJEj3Yq7WkRCU8JeffVVt7gCAAAACg6N90499VS3aGufPn1cfVWvbNbYsWNdhuykSZPi3UwAAGJm585dtm37DqtapZJ/QUkABTQgq9Vr33//fZclG4r26ZhY0KIJgQsnAAAAoGBSWay3337brr/+erd4VqA6deq4mq5aywAAgMJi3fqNtmTZcvt7+SqrXau6VTu0CoFZoKCWLLjsssvsk08+sZ49e7qMhMzMTHdR/diLL77YPv3006ivbgsAAIDC55xzzrGlS5fa999/b6+//rq7zJ4925YsWWJnn312vJsHAEDMZWX5bOeuXbbwj8U2Z+7Ptmr1WhejAVDAMmRvvfVW+/HHH90A+I033nClAiQrK8t8Pp91797dBgwYkN9tBQAAQCGiDJ8JEya4JAC54oor7JprrnF1YlVPVhcAAAq7lJQUK1WqpIvJeIHZFSvXkDELFLSArN7MCsReeeWVNmXKFJelICpT0LVrV+vYsWN+txMAAACFjE76B2b4jBs3zo0zWbgLAJCX+qqVK1W0wvJ/k8AsUIADsp5OnTq5CwAAAJDfVBf2m2++sUsvvTRbFhAAAHmpr1qqZEkrmVbUatbM9M/wLQyB2YyMDNuyZZulp6e7bdWrVY130wDkR0AWAAAAiBYFYocNG2b/+c9/rEKFCm7bTTfdZHfffXeu11HAdvHixTFsJQAgGeqrbtmSbrvTM6xO7RoFPltUGbJ79qSbz2dWoUI5lyFbtUrleDcLQAACsgAAAEhIQ4YMsbp169rnn39ua9eutb///tsqVapkhx56aLybBgBIqvqqJWy3L7PAT+MPDMSWKV2qQD5GoKAgIAsAAICE/RLdu3dvdxFNt7znnnv8i3wBABCulNQUK1mypFuIvKAFZvWYdu3aTSAWSCIEZAEAAJAUvvrqKzvqqKPi3QwAQBIriPVVU1NTrHQpArFAMiEgCwAAgKTQrl27eDcBAJDkClp91apVKlmJEmnuJ4FYoAAHZHfv3m2TJ0+2I444wpo3bx6dVgEAAAAAAOQTX9Z/p/WbpRSoaf2lS5dyl0jt3LnLtm3fQUAXiLHUvF4hLS3NrrrqKvvpp5+i0yIAAAAAAIB8rK+6d1+Gm9bf+IiG1uzEY61mjWoEIM1s3fqN9tvvf9mcuT/bqtVrLTMzM95NAgqFPGfIqrZK7dq1bdu2bdFpEQAAAAAAQD7VVy1VsqSVrFjOmjRpbMWKFYt3kxJOVlbBW+gMKHAZstKrVy+bMGGCK34NAAAAAACQaDQN/6gjD7OmJxzj6sUSYAwtJSXFLXRWsmSaPzBLxiyQgIt6tWrVyt5++207/vjjrW/fvnbYYYdZqVI5a5a0bds2P9oIAAAAAAAQUX1VLeSF8GZEKzCr/vICs8tXrLaSaUWtZs1Mtx9AHAOynTp18v9+4403urMpwTVatI0zKQAAAMgvQ4cOtSFDhuQYe3o2bdpkV1xxhU2ZMiXmbQMAoKAFZjMyMmzLlm22PTXLqqzf6OruAohjQHbs2LH5dPcAAABAeIYNG2bTpk2zSZMmWc2aNbPt+/rrr+2SSy6xdevWxa19QCisYA4g2ShDds+edPP5zJV6UIZs1SqV490soEApGmkNWQAAACCWxowZYzfffLMdd9xx9tJLL1mXLl3cl0ZlzY4YMcJq1arlArNAoq1gvmTZcvt7+SoWygGQNIHYMqVLuc8snUxatWqVFSlCuQIg7gFZAAAAINauvvpqO/nkk+3CCy+0bt262VVXXWULFiywGTNm2Pnnn28vvPCClS9fPt7NBHJgBXMAiUxlJ3ft2p0tEOt9RlF/F4iOiE9xrFixwtXoUiZC8eLF7csvv3Tb169f77bPmTMnP9sJAAAAWJMmTeyHH36wli1bugDszJkz7YEHHrA333yTYCwSFiuYA0hkqakpVrpUKWt8RENrduKxrlYsJ4yABAzILl261Jo2bWpvvfWWGxQHDiKqVKniBskvvvhifrYTAAAAsH379tntt9/usmIbNGhgRYsWtdGjR1OqAEm1UA6BWQCJQiUJjjryMAKxQDKULLj77rvdYEJTxEqWLGlVq1bNtv/MM8+0qVOn5lcbESOZ6Xts6YzPwj6+aPE0q920bVTbBAAA4Pnzzz/toosusnnz5tl1111njz32mPu9R48e1rFjR7vzzjtt6NChbpwKJNMK5unp6W5b9WrZv1cBQLSVLl3KXQAkQUD2888/t/79+1vt2rVt48aNOfbXrVvXVq5cmR/tQxyCsgAAAInohBNOcKWyNEvrvPPOc9uaN29u8+fPd/Vkhw8fbtOmTbPp06fHu6lAnlYw/+/COaxgDgBAYRFRQHbbtm1WvXr1XPfv3bvXne1FclCma14QtAUAAPFw/PHH26uvvmp16tTJtr1s2bL2+uuvW6dOnezGG2+MW/uASFYwZ3EvAAAKn4gCssqM/fXXX3PdP2vWLGvUqNHBtAsxlNeyAyprQFAWAADEmurE7i9w1adPH2vdunVM2wQc7ArmAACg8ImowFa3bt3s5ZdfdjVkA1cOFU0hmzx5snXv3j3/WgkAAIBCL5zg1RFHHBGTtgB5wQrmAAAgXxb1ev/9913NrrZt27pg7IgRI+yuu+6y2bNnu+lkAwYMiOSmAQAAgP36559/7IcffrDNmze7KeDBLrvssri0C8htBfMSJdLcT4KwAAAg4oBsuXLlbObMmTZo0CBXx0tTcD777DOrUKGC9e3b1+6//34rUaIEPQwAAIB8o+Brv3797MUXXwwZiPUQkEUiYQVzAACQLyULvKDsE088YevXr3dZCmvXrrWNGzfaU0895fYBAAAA+emRRx6x5557znr06GHjx493SQGapfX000/bYYcdZk2bNnVJAgAAAECBDMgGqlKlilWtWtVfRxYAAADIbwrCdu7c2V555RU744wz3LYTTzzRrr32Wps7d65t2LDB/QQAAAAKbED2zTffdBkKqiWri37XNgAAACC/LVmyxAVkJTX1v8PYffv2uZ+lS5e23r17u3IGAAAAQIGrIbtz507r2rWrffnll26qmGrHypw5c1xAVlPJ3nvvPTcwBgAAAPJDyZIlrVixYu73MmXKuNlZ69at8++vVq2arVixIo4tBAAAAKKUIXv33XfbF198Yf3797fVq1fbpk2b3EW/a9tXX33ljgEAAADyS926dW3x4sXudwVmGzVqZB9//LF//+eff26HHnpoHFsIAAAARCkg+8Ybb9gFF1xgo0aNcpkIHv2ubf/+97/dMQAAAEB+OeWUU+ydd97x/33ppZfaa6+9Zh06dLD27dvb5MmTrXv37nFtIwAAABCVkgXbtm1zA9/9DZY//PDDSG4aAAAACOnWW2+10047zdLT0y0tLc3uvPNOV7Jg4sSJVqRIEbv66qvt3nvvjXczAQAAgPzPkD322GPtr7/+ynW/9h1zzDGR3DQAAAAQUvXq1e300093wVhREPbJJ590pbPWr19vzz77rJUoUSJm7dH9Kkis0gm63ypVqrikhW+++Sbbcd9//7117NjRypYta+XKlXMLk82bNy/kbaoE2GWXXeZuSzVzmzZt6jJ/Q1FgevDgwVa/fn3XJw0bNrThw4f7FzoDAABAAcqQ1UDvvPPOc1PDzjnnnGz73n33Xbe67ZQpU/KrjQAAAEBC+fvvv91YeMeOHdanTx87/PDDbevWrfbzzz/bqlWr/MfNmjXLHVezZk0bNmyY2zZ69Ghr06aNzZgxI1sSgwK8rVu3dlm/t9xyi9WqVcteffVVV4bh5Zdftt69e2drw4UXXujG3ldccYW1bNnSZs6caYMGDbJFixbZuHHjYtgbAAAAyPeArAZ5wXQmvmvXrnbEEUdY48aN3baFCxfaH3/84QaWkyZNcqULAAAAgPyk2Vi6bNy40Xw+X479yjCNtksuucQyMjJcAFaZu7m54YYbrHjx4jZ9+nQXlBUFWDV+HjBggH366af+Y0eMGGFLly619957z5/0oGCvgq3KxNUaDmXKlHHbVR5MwVgFbh999FG37corr7QKFSrYY4895so3tGrVKsq9AAAAgKgFZPd3hv333393l0AamP7yyy/20ksvRdQoAAAAINiaNWusV69e9sUXX7i/QwVjU1JSoh6QVXD122+/deUSFIxViQBdSpUqle04ZarOmTPHJTd4wVjR7wqujh071tauXetfJFfZsCo7EDgDTWUZ+vfv7x6TgrDeomU6Vm666aZs96m/FZBVXV0CsgAAAElcQzYrKyvPl8zMzOi3HgAAAIWGsj6/+uorF3R866233O/Bly+//DLq7fAWr61Tp44LnqrWa+nSpV3ZAgVCPQrGijJcg7Vo0cIFlOfOnesPNqvUgbaHOjbw9rzfFditXbt2tmP1d40aNbIdCwAAgAJQQxYAAACINQVbb7zxRnvkkUfi2g6V6JKrrrrKDjvsMBs/frzt3bvXlQ649NJLXbas6r1qgS4JzI71eNu8erN5OdY7/qijjgrZPh2/cuXKXNuvxcB08Wzbts399BIrEJ7AZBTkDX0XGfotcvRd5Oi7yNF3hbPvssJsMwFZAAAAJAXVT23UqFG8m2Hbt293P8uWLeuyclUjVrS+QoMGDeyuu+5ypRV27drltqelpeW4jRIlSrif3jF5Odb7PdSx3vGBxwZ78MEH7d57782xfcWKFe4xITzKcN68ebMrk6ELwkffRYZ+ixx9Fzn6LnL0XeHsu+3/GydGLSCrVWGffvrpXBdUUIctXrw40psHAAAAsjn77LPt888/t2uvvTau7VCJAunRo4c/GCsVK1a0c88911555RWXRevVlA3MRvXs2bPH/fSOycux3u+hjvWOD65nG+jOO+90i4EFZsiq1IEu5cqVO+Djx/9nwOg7kPotNTWsSnD4H/ouMvRb5Oi7yNF3kaPvCmffbfvfzKOoBGRfeOEFNxDWAPSII45w9bMAAACAaFJJgFNPPdVuvvlmt9BV/fr145I1UatWLffTW4wrkBb5EmV1qJZrcKkBj7fNK0eQl2O940Md6x0fqvSBR5m1obJr9YUn2b70xJvXZ/Rb3tF3kaHfIkffRY6+ixx9V/j6LjXM9kYUkH3ggQfs+OOPt08++cQqV64cyU0AAAAAeVKhQgVXCkAB2SeffDLkMQrQZmRkRLUdJ510ko0ZMyZknVZvW9WqVd1FZs6caVdeeWW242bNmuXaeuKJJ/oDuQqianswb1vTpk3925o1a2aTJk1yZQYCF/bS36ovq0xdAAAAJKaIwsz//POP9enTh2AsAAAAYmbkyJEuGHvooYfaOeecY5dddlmOixbVijbVilWt1YkTJ9qOHTv829esWWNTpkyxww8/3NW61UVB1MmTJ/sX7RL9rm2nnHJKtixblUBQya+pU6f6t2VmZtpTTz3lgtFnnnlmtmNl1KhR2drm/X3xxRdH6dEDAADgYEWUIdu4cWM3DSvRaPGCo48+2pYuXWr9+vWz0aNHx7tJAAAAyCcKTLZv394+/vhjK1asWNzaoVqxjzzyiF1zzTXWokULu+KKK2zv3r327LPPup9qp+eJJ56wDh06WJs2bVyZBe9xqDaaSjAEGjhwoAvU9uzZ09V4Vcbsa6+9ZnPmzLEXX3wx24JbZ511lqup+9hjj9nWrVutZcuWLhP3pZdesksuucRat24dwx4BAABA1DNk7777bnvmmWeynelPBIMHD7b169fHuxkAAACIgk2bNln37t3jGoz1XH311fbWW29ZmTJlbNCgQXb//fe7tRW++uorO+200/zHtWrVyqZNm2b16tWze+65xx2rzNnp06fbcccdl+02K1WqZN99953LwNXiuTfccIMLtr7++utudlowBW81LtdCZ3379rUvv/zShg0bZi+//HJM+gAAAAAxzJDt1q2by0Y96qijrEuXLm6AWaRIkWzHqCaWBpyx8uOPP7opWprKNmDAgJjdLwAAAGJDAczly5dbotCYWJcDUfbqF198EdZtKit2woQJYR1bokQJGz58uLsAAACggAdk//zzT5eNum3btlwHjLEMyKq21lVXXWWdO3d2g2ICsgAAAAWPslCVIXveeedlW+AKAAAAKPABWU2JWrdunauJpXpYqqMVT48//rj9/vvvbtoYAAAACiYlAiiDVHVblXXaoEGDkLO0VEcVAAAAKFABWS0YcNttt/kXJognLeA1ZMgQl7Gr0gnLli074HXS09PdxaNMX9HiCrpEle//f0Z6X147o97WKD6GZBP3Pi9k6O/Yo89ji/6OLfo7tqLZz+PGjfP/rlqrugQjIAsAAIACGZAtX768ValSxRLBtdde67IjtBJtuB588EG79957c2xfsWJFttVroyEzM8P/M9IaaD6fzzZv3uy+cOgSa/nxGJJNvPu8sKG/Y48+jy36O7bo79javn171G6boDoAAAAKbUBWtbvefvtt69evn8XTxIkT7bPPPnOr1OZltd0777wzWwBXGbK1a9d2l3Llylk0/b3qLxfILFKkqNWpUyfiLyP6cqn2pqamWqzlx2NINvHu88KG/o49+jy26O/Yor9jy5t5BAAAACAfA7LXXHON9erVy7p27Wo33HCD1a9fP0f9LolmsE4lBxRUPfPMM61atWq2aNEit33VqlXu59atW922ypUrW4UKFbJdNy0tzV2C6Uta1L+oeYk5Kf+9v0h5bY3LF8t8egzJJq59XgjR37FHn8cW/R1b9Hfs0McAAABAFAKyTZo0cVP+fvjhB5s6dWqux2VmZlq07N6929avX28ffPCBu4TKntXl4YcftltvvTVq7QAAAAAAAACAqAZktYBWvGuwlS5d2iZPnpxju4K0ffv2tc6dO1ufPn3s2GOPjUv7AAAAAAAAACBfArJDhw61eFPN2PPPPz/H9mXLlrmfDRs2DLkfAAAAAAAAAOKFIl8AAAAAAAAAkMgZstOnTw/ruLZt21qs1atXz62kDAAAAAAAAAAFIiDbvn37sGrIRnNRLwAAABQuKps1ZMiQXMehmzZtsiuuuMKmTJkS87YBAAAAUQ3Ijh07Nse2jIwMW7x4sY0bN85lqV5zzTWR3DQAAAAQ0rBhw2zatGk2adIkq1mzZrZ9X3/9tV1yySW2bt26uLUPAAAAiFpAtlevXrnuu+222+yEE06I5GYBAACAXI0ZM8ZuvvlmO+644+yll16yLl26WFZWlsuaHTFihNWqVcsFZgEAAIBCtahXxYoV7corr7SRI0fm900DAACgELv66qtt9uzZVq1aNevWrZtde+21bs2C+++/38477zybN2+etWjRIt7NBAAAAPI/QzacoOySJUuicdMAAAAoxJo0aWI//PCDdezY0V544QW37YEHHrCBAwfGu2kAAABAfDJk9+zZYxMmTHCZCwAAAEB+2rdvn91+++02Y8YMa9CggRUtWtRGjx5NqQIAAAAU7AxZrV6b28q2M2fOtPXr19vDDz98sG0DAAAA/P7880+76KKLXGmC6667zh577DH3e48ePVzG7J133mlDhw611NR8zzkAAAAA4huQHTduXMjthxxyiB1++OH2+OOPW8+ePQ+2bQAAAICfFo4tXry4vfXWW65mrDRv3tzmz59vV111lQ0fPtymTZtm06dPj3dTAQAAgPwNyGo1WwAAACCWjj/+eHv11VetTp062baXLVvWXn/9devUqZPdeOONcWsfACSinTt32bbtO6xqlUpWpEiReDcHABCtRb0AAACA/KY6sfsLJvTp08dat24d0zYBQKJbt36jLVm23P5evspq16pu1Q6tQmAWAOKMAlsAAABICuEEEI444oiYtAUAkklWls927tplC/9YbHPm/myrVq+1zMzMeDcLAAqtsDNkzz333DzdcEpKir377ruRtAkAAADI4ZVXXgnruMsuuyzqbQGAZKLv56VKlXTlB73A7IqVa8iYBYBED8i+//77ef7ABwAAAPLL5Zdf7saYPp9vv+NOArIAEFpqaiqBWQBIpoBsOAt5qa7X7bffbnPmzLHq1asfbNsAAAAAv6+++irHtoyMDFu8eLE988wzVqpUKbv//vvj0jYASMbArD5Dt2zZZunp6W5b9WpV4900ACgU8mVRrwULFtgdd9xhH3/8sVvl9r777rNbbrklP24aAAAAcNq1axdy+6mnnmq9evWyk046yX788Ufr0KFDzNsGAMlECVd79qSbJhxUqFDOZchWrVI53s0CgELjoBb1WrFihZs69q9//cu++OILu+GGG1yGwt13320lS5bMv1YCAAAA+5GWlmaXXHKJy5QFAOQeiN21a7ft3p1upUuVssZHNLRmJx5rNWtUsyJFWPMbABI6Q3bz5s1uOpgGvJra0KNHDxs+fLjVq1cv/1sIAAAAhBmUXbVqVbybAQAJR7W3FYhVRmyZ0qWoGQsAyRSQVfB11KhR9tBDD9mWLVusU6dO7vfjjz8+ei0EAAAADmDNmjU2ZswYq1+/frybAgAJJzU1xWXEEogFgCQLyL700ks2dOhQW716tZ1wwgk2YsQIV68LAAAAiIVTTjkl5PZNmzbZ77//bnv37rXx48fHvF0AkMiqVqlkJUqkuZ8EYgEgyQKyV111laWkpFjTpk2te/fuNn/+fHfJjY69+eab86udAAAAKOSWLFnixpiB9Pchhxxi3bp1s+uvv95atWoVt/YBQCIqXbqUuwAAkrRkgerOzJkzx10OhIAsAAAA8tOyZcvi3QQAAAAgdgHZr7766uDvDQlp2MMv2bZtO8M+/pJWta1MiaK2fXv41/ntq48s1ZeZ98alpFhaWvE8XaVo8TSr3bRt3u+rkJg2fZbtSU/P8/VKpKVZ+7YtotImAAAAAACAwiLsgGy7du2i2xLEjYKxm7duD/v4LF/Wf39m+cK+joKxRVPCP/7/+SwzfU8E10NuFIzdsyfvAVkAAAAUPHv37rPf/lhqNatXsSqVK8a7OQAAFAp5KlmAgk1lJiqUKxPOkRHfh89nlhnG9VUew2tTWom0sG6bwG3eqbj/gRC8BQDES4MGDfJ8HY0dFi9eHJX2AAUtEDv/10U2c/YvtmHTFuvQ+kTr0ObEeDcLAIBCgYAs/BSMfeS+Gw543LyP3on4PhSMPfKUsw943Meffe0CgQoYdm4VXnb20hmfEZTNA9e3ndqF/VwAABBrderUybGIF4D8C8Su27DJUlNTLSMjgtJiAAAgYgRkAQAAkJCmTZsW7yYABToQe0iFcla0aBFbt2FLvJsHFAo7d+6ybdt3WNUqlaxIkSLxbg6AOCIgCwAAAAAF1L59GTZvwV8hA7EAYmvd+o22ZNly+3v5Kqtdq7pVO7QKgVmgkEqNdwMAAACA3IwcOdIWLlwY72YASevX35fYJ1/OsmUr1li5sqWt8iHlCcYCcaTFsXfu2mUL/1hsc+b+bKtWr7XMTMqGAIUNAVkAAAAkrIEDB9pPP/3k/3vz5s1Wo0YN++677yxR7Nq1yy1Apnq3119/fY79f/zxh3Xt2tUqVqxopUuXtjZt2tiXX34Z8ra2bt1q/fv3t5o1a1qJEiWsSZMm9uyzz/oXPA2UlZVljz/+uB155JHu2Nq1a9uAAQNs586dUXmcSE5Njmxgp5/SwurVrm7btu+0DZu2UjMWiCP9ryhVqqSVLJlGYBYoxChZAAAAgKShIOTatWstPT1xFpwcPHiwrV+/PuS+xYsXW6tWraxo0aJ2++23W/ny5e2FF16w008/3T766CPr2LGj/9i9e/dap06dXABaQdnGjRu7Y/r27Wv//POPDR06NNtt33zzzfbkk0/aeeed5wKxyiTW37r+559/7qamA8WKFbVm/2psxzVplKOGrBb1JVsWiA+9BxWY1f81LzC7YuUaShkAhQQBWQAAACBCP/74o40aNcqVVlBQNNidd95pW7Zssblz59rxxx/vtl122WUu87Vfv372+++/u2wpefHFF23OnDkuqKqArFx11VX273//2x544AHr3bu31a1b123/9ddf7amnnrJu3brZW2+95b+/+vXr2w033GCvv/669ezZM0a9gGRQvHixXAOzCggBiG9gNiMjw7Zs2eZOOGpb9WpV4900AFHEaXMAAAAgAppaqoBp586dXWA0mEoHvPfee9a+fXt/MFbKlCljV155pf35558uAOt59dVXrVSpUu42A9100022b98+e+ONN/zbXnvtNVfGQPsC6bq6jYkTJ+bzo0VBC8xe2/s863JmO6t8SAWyZIE40gmRXbt2W3r6PqtQoZw1aljPqlapHO9mAYgyMmQBAACQdLys0nhS/VZluAZmqAb6+eefXaZTy5Ytc+xr0aKF+6mA7EknneS+kCvb9oQTTnD1YANpvx5vYPBWvyuDSvsC6boK/gYeG0xtCiz5sG3bNvdTbSBTMnxefyVrnykIe+JxR9gxjRvYwj+XWY1qlWP2WJK97+KFfkv+vtP962SaLvp9z550U4nwMqVLWa2a1bKVKoh3WxOt75IRfVc4+y4rzDYTkAUAAEBCe/TRR90UfFGmqIKTd999t1WunDODSPvefffdqLdp6dKlNmTIEFc/tl69erZs2bIcx6xevdr91AJdwbxtq1at8i9Wtnv37pDHpqWlucfqHevdtrZpX6jbnjFjhqtJW7x48Rz7H3zwQbv33ntzbF+xYoWVLVs2jEcPUUBFz5tec4lwguBgVCyXZrt3bbfly7fH5P4KUt/FEv2W/H2nNmTs3WPbMv4biE1LK26HVCxv5cqVscyMvdk+5xNFovRdMqLvCmffbd8e3v9SArIAAABIaFqkSpdAs2bNCnlsrAbt1157rTVo0MBuueWWXI/ZtWuX+xkqaOplwXrH7O9Y73jvGO/4/R3rHRMqIKu6toHtVoZs7dq13aVcuXK5Ph6EznRTv7GAWt7Qd5Gh35K/7zKzUmzLtp1WulTOjNhElSh9l4zou8LZd9v+N/PoQAjIAgAAIGEl4lQ11Wf97LPPbPr06VasWLFcj1MtVwksD+DZs2dPtmP2d6x3vHeMd/y6detyPTbwNoMpkBsqmKsvPMn2pSfevD6j3/KOvosM/ZbcfacArBbwqlqlUsIHYhOt75IVfVf4+i41zPYm16MCAAAA4kgBU2WXnnnmmVatWjVbtGiRu/z9999u/9atW93fW7ZssRo1arhtoaagetu8EgUVK1a0kiVLhjxW97lhw4Zs5Qx029oWKoCr21A5g1DZsQCA+CldupRVr1Y1qYKxAKKDgCwAAAAQJtV5Xb9+vX3wwQd22GGH+S/t27f3Z8/q7xdffNGOOeYYl4k6c+bMHLfjlVxo2rSpP5tCC3qpNENwkHX27Nlu2p53rDRr1sxlD2tfcHbsvHnzsh0LAIXNzp27bM3adZaZmRnvpgBASARkAQAAgDCVLl3aJk+enOPyzDPPuP2dO3d2f5977rlWpkwZO+ecc2zatGk2f/58/23s2LHDBWwVuD3ppJP823v06OHqvj7//PPZ7nPUqFFWtGhRu/DCC/3b9Lvq5WpfoBdeeMHdxsUXXxzFXgAKnr1799m8X/609Rs2x7spyAfr1m+0337/y+bM/dlWrV5LYBZAwqGGLAAAABAm1Yw9//zzc2xftmyZ+9mwYcNs+x988EH74osv7LTTTrObb77ZLZqloKnKCijLNnARsquuusrGjh3rSiLo9ho3bmwffvihvfPOO3bPPfdYvXr1/Mcq+7Zfv342evRo69atmyuhsHDhQnvyySetXbt21rNnz6j3BVBQArHzf11kM2f/Yhs2bbEOrU+0Dm1OjHezkA+ysny2c9cuW/jHYlu+YrWVTCtqNWtmJl09SgAFEwFZAAAAIEoaNWpk3333nQ0cONBGjBhhe/fudaUJPv74Y+vYsWO2Y1Xz9fPPP3fB19dee802btzoArxPPfWUC74GU3asgrTKqFVwV3Vj+/fvb8OGDSPgAOQhELtuwyb3nsnIIIuyINEJLy2gpfIuCsxu2ZJuu9MzrE7tGm5xLeq4AognArIAAADAQVJgVHVeQ1Gm67vvvhvW7VSoUMFlvepyIAomDBgwwF0ARB6IPaRCOStatIit27Al3s1DFOg5VmB2ty/TnzG7YuUaq12rOoHZJJeRvsd8WbmfSElJLWJF00rEtE1AuAjIAgAAAAAKtH37Mmzegr9CBmJROKSkpljJkiVdPdktW7a5BRT1OqherWq8m4YIg7Frf51rWZkZuR6TWqSoVWtyIkFZJCQCsgAAAEhYI0eOdAtjKcsUACL16+9L7JMvZ9mmzdusWtVDrERa8Xg3CTHmy/LZrl27FZq1ChXKuQzZqlUqW0FXULNI9ZgUjFX7U0KU6fFlZbn9+3vsQDwRkAUAAEDCUu3VWrVq+QOymzdvtiZNmtjkyZPt5JNPjnfzACSJJv/X3n2AOVWlfQB/M52BYYYy9N4EkaKUVRQRF+ygouAKKqJYUT+KrmCjqdiwUFalL2uvq2sFFVkVKSKodET6IL1On8z5nv+Bm71JbmaSTHr+v2ezIzc3NzcnN8m5733Pe1o3k+ISuyND9kRuvmRVrcIM2TiAGrL5+QU6SzorKyOuasjGQxYpgrF4Da5KhcFYimwMyBIRERFRVJ1Y//nnn3qoKRGRt5KTk6TLmW2kQ9sWbjVkGZiNTajrjYxYlPeunJ4ulapVlbZt20hycrLEC2aREkWuqA3Ibtq0SV577TVZsGCBbNmyRQoKCvQstP3795fhw4dL5cqVw72LREREREREFEFSUpI9BmZxwYdiR0KCTQdiT5YmqCG7d++Oi6xYK8wiJYo8URuQnTNnjkyfPl369u0rgwYN0le5Fi1aJI888oi88847snTpUl2wm4iIiIiIiKi8wOyBQ0fCvVtUQUVFxbJu41apUS1DTm/dUgdiEYRlsJ2IIk3UBmSvvfZaGTNmjGRmZjqW3XnnndKyZUt54oknZPbs2XLPPfeEdR+JiIiIKDhsNlu4d4GIYiwwi0Be/brZ4d4l8jMQaw6s9zyvk/Ts3incu0VRJFYnP6PIFbUB2c6dO1suv+6663RAds2aNSHfJyIiIiIKvMmTJ8tbb72l/7u4uFgHYx9++GGpWdN9dmzc99FHH4VhL4ko2gOzHdu1CvduUAUCsUbpiZISDsGPJ7oOrpRYLvdWPEx+RpEnagOynuzatUv/rV27drh3hYiIiIgCYNWqVfpmhvJUVpg5S0QUn4HY6llV9eRs+w6w9EQ8QMYqgqRlTUqG+7FeeTj5GYVDTAVk7Xa7TJw4UZKSkmTgwIEe18OsvOaZeY8dO6b/oq5M0GvLqP/99fe5jP0M1L6adsmnbaanJsrWHxZ6tW6i41l8f91erx+Ati1rH0JyfIRY0N6LOGzvxd8vk8LCIp8fl5qaIj3O+4uEWyS3+a6V34u9yPfZ1BNTUqVBp/MkEkVye8citndoBeM3mIiICIqLS2T1ms2WgdhAyM3Nk2PHTzhqz8aKQGSRRhpkqiJjNZBlBjj5GYVSTAVkhw8fLj/++KM8+eSTctppp3lcb9KkSTJ+/Hi35Tt37pSMjIyg7qP9VAo8/u7YscOvbSil5PDhwzoDJBBZIL7v08nIZ4LNJvaiAq+ew9hNPNKb57CfGmaCv962UyDaNlRtHk6+tq0/70U8tndebr4Ul3ge4uJJKNs1Wtu8JD8XDeXz44LxXRAP7R2L2N6hdfz48XDvAhERxai1G/6QL79ZKocOH5M6tapLWmpKQLe/b/9B+WPbDtm+Y7c0bFBX6tTOjurAbCCzSCMRywdQNIuZgOyjjz4q06ZNk9tvv11P9lUW3D9y5EinDNmGDRvqW9WqVYO6n9t3b9ZBgsTEJGnUqJHfmSI4ucT+4opgRWFfjL/e7FPOLz9JqSqSBFuCZFat4tVzICMZ+2y32bx6jg2bt+vgVmJSotftFIi2DVWbh5OvbevPexGP7W20E6SlpZa7fkHByYzPULZrtLa58dmGxJTyO13GhaJgfBfEQ3vHIrZ3aBkjj0IlJydHdu/erSd2zcrKCulzExFRaLVt3UyKS+yODNkTufmSVbVKwDJkobRUSW5enqzfuEV27toT1YHZYGSRElFgxERAdty4cfL444/LkCFD5JVXXil3/dTUVH1zhZO0oJ+oGYk5tpPP5y9jXwOxv6Zd8mp7H/yUI4ePHpdqmRny3MT7vHqOLxYu1gEoBKrO8HGfvX6NAWrbsvYjJMdICPn6WkL52qO1vXGMX9K7h9efCYiU1xixbX7qs52YmiZNu/Uud/WtSxaKvbAgaN8FMd/eMYrtHTqBbuPVq1fLN998I4MHD5YaNWo4lh84cEBuvPFGWbBggf43SlZhoq/HHnssoM9PRESRIzk5Sbqc2UY6tG3hVkM2UIFZjKZJT6+kL+hWJDCLiaIiIRDKYKv/JTFLSkokJSWwWdhEMROQRTAW5QfQSZ81axaHIhIRERHFEFxs/+yzz5xGN8HQoUPlyy+/lGbNmknHjh3l+++/133C9u3by1VXXRW2/SUiouDXcU1JSfYYmA1U7XFsy9/ALIKxf65dqUsFeNx+YpLOXg1lwBSToa3buFXq182W7JrVQva80Qjzg+Tl50tSYoGkpiRJcpAv6kdKAJ9CJ6oDshMmTNAdb2RHzJkzh1kvRERERDEG8wNceumlTsu2b98uH3/8sXTo0EGWLl2qRz7t379fOnXqJDNnzmRAlogoivlSx9UqMHvg0JGA7o8RmEW25JEjx3Q5PiyrW6eWx8cgsIZgLIJomCjK/f7SMuu6BiMQa26fnud1kp7dO4XkuaN28rPSUrGh7KK9RPLzi6U4IUH+3Ltf6jVMC3j5imAE8BngjXxRG5CdPn26jB07VtcH7NWrl7zxxhtO99euXVt69y5/aCsRERERRS7UiG3VqpXTMpQwgLvvvttRhio7O1tuuOEGmTt3blj2k4iIJGx1XM2BWSMDNHD7UqpLjSklkpVVVe9LreyaXj0WwVgE0ty2KaEJxpoDsUYGccmpSZupnMnPlF0SbOpk4p8SKSlV8vvWnbJ73+GA1xUOdAA/UjO0KUYCsitWrNB/MYM2yhW46tGjBwOyRERERFHuxIkTbpN1LV++XJep6tmzp9Py5s2by6FDh0K8h0REFGj+1nFFYLZjO+eLeIEIxFapnB5Vk3tZBWKrZ1XVNXb3HQhsBnEssJr8DMfb/l05UskRsLRJitiCOuFboAL4kZahTTEWkJ03b56+EREREVHsatCggfz+++9Oy5YsWaKDtC1atHBajuGkVapUCfEeEhFRsFSkjqu/lFKSl5cflYHY4uISWb1ms2UglsrmmilqS06VEkmU0oT/hc0Q2vS1fEU4hTtDm8rGoqtEREREFLE6d+4s8+fPlz179jhqyv7222+6ZJWrdevWSb169cKwl0REFIrAbGpqsg6E/b5lm641G5znsknl9HRpc1pz6dKpvdSvVycqgrGwdsMf8uU3S2Xbzj1SNaOy1KyeyWBsAOHCAIL1hYXFunxFi+ZNvC5fQRQzGbJEREREFPtGjx4t7733nrRu3VpOO+00Wbt2rT4x/7//+z+3dT/55BO3MgZERBT9KlLH1Re1smtIWlqq/hstQViztq2bSXGJ3ZEheyI3X7KqVmFQNo7LV1DkYkCWiIiIiCJWhw4d5MMPP5QxY8bozFjUiR0/frx069bNab0vv/xS9u3bJ5deemnY9pWIiKI7EFa5crq+BYqu1amHh5eiFoLTcqVKpbggL6Az3icnJzkmN3OtIcvAbHyVr6DIx4AsEREREUW0K664Qt/KcvHFF8vx48dDtk9ERBQ80R4IQ4AVtTsxcRJuJfl5okQ5ryM22b9pjaPOZyBnvMfkZp4Cswhyk2/lK0J1/BkBfKvlFHsYkCUiIiKiiLd//375448/pGbNmjpLloiIYleoA2GBhsAqAqyYOAlZsEbgVWz/m8bHZrPpZcGc8d4qMHvg0BGft1NSYpdf1myWBvVqSXbNahIPQlm+whzA93Qc4H6s5wsGeCMbA7JEREREFLGQyXP33XfLrFmzdMYUnHPOObqMQXZ2drh3j4goLpQUFpQZMAzUkPtYqONqMLeHzoJNSg7bjPfmwOy6jVulfl3vfj+Liopl9ZrNsmbNOtm++5BccF4n6dm9k8SDQJev8DaAH4jPWLACvBRYDMgSERERUcSaNm2azJgxQ+rVq6cDsZs3b5YlS5bIHXfcIR988EG4d4+IKC6CsX+uXamDO54Ecsh9KANh8QaB2Y7tWnkViDUyavcfPCTZ1SpLiT24QeN4F6gLGsEI8Ibj4k9paanYiwulOD9Xl9qI9P32BwOyRERERBSx5s+fL23atJGlS5dKRkaGXnbbbbfJvHnz5MiRI5KVlRXuXSQiimk6OGIv0YEQPeze7f7gDbmn0DIHYo2as9Uyq0qVyimy//DJCcgoOkRj0LLEdPGnVCk5mlckiYf3SILN5lgn0PWWw8n925SIiIiIKEJs3LhRbr75ZkcwFu69916x2+2yadOmsO4bEVE8MSafcr1ZBWkpuhQXl8iKVevllbkfykefLdZ1ZqtnVZWa1TMlKYnD2in0F38SkpLFlnjyr3HD8li6+MMMWSIiIiKKWLm5ubpcgZnxb9xHFK4amq7DKWNpGCURxZe1G/6QL79ZKocOH5M6tapLWmpKuHeJ4pjt1G+qzXayFq6RIRuKesuhxIAsEREREUU0zERt9W9jki+icNTQdB1OGUvDKCkyJ9EyXwRITErmsRZlInnG+7atm0lxid1RquBEbr5kVa0S8uzYUE4eF0nPTfGJAVkiIiIiimifffaZ/Pnnn45/5+Xl6aDsu+++K6tXr3ZaF8tHjBgRhr2kuKuhqZTYEu0nh1EqFVPDKCkyJ9EyXwRISkrmBYAoEQ0z3icnJ0mXM9tIh7Yt3GrIIjArkhhzk8dFynNT/GJAloiIiIgi2htvvKFvrl599VW3ZQzIUihqaDoCsqeGU0qpncFYCv4kWqcuAsRaHcVYF00z3qekJLsFZvcfPCRJCZV1hnasTh7HiesoHBiQJSIiIqKItWjRonDvAhFR+C8AmC4C6ICRPfTD3CN5yH2ki4Rgq7+B2dVrNsuaNevk4JH88Bz3p4Sifmg4n5viDwOyRERERBSxevToEe5dICKKa9Ew5J6CF5jt3LG1VK+aKsfzSqRBvVrh3iWKA6q0VM8ToNTJzGUx5g6IsYs/DMgSERERERERUdQPuafgwOReHc5oqmvKEoXi4k8pArJ2u5SWFDsCsrF28YcBWSIiIiIiIiLyiMFWIs8Tgnm6WIG6uzqgSD5f/CktLRV7To7UrVfP6UJALF38YUCWiIiIiIjIxxqapabhlDalwr1rREQRGZQ0B9Fyc/Pk2PETUiu7hiQmJsbE6/5z7cqTw+ot4HfiaIFdSurXl5RK6SHfv2iUdCrYioBsYnKqJFeqHLOZ2QzIEhERERER+VhD0zycMsFmi6lhlBQZXCfRMi4CqNJE+d8AXqLIDkoCvh+R+bhv/0H5Y9sO2b5jtzRsUFfq1M52C8yGc/I4X59bZ3LiolzCqcn2XO+34/NaxAnByBIDskRERERERD7W0HQdThlLwygpMifRMi4CYFliUjIvAFBEKDcoiSCn+WJWqZLcvDxZv3GL7Ny1xxGYDefkcRV9brxu3O8Kn1kiTxiQJSIiIiIiKodrsDUehlNSZE2iZb4IgIAsLwBQqGAkQHF+ruV3XXFBniil9H2WQUlxDnLabDZJT6+kj2fXwGx2646SUEb6d7AufHHiOgoHBmQj3Lf/XSoFhYVuy+0ldtmweXuZj01LTZULzj+73Ofo0LaxJCUmSkKCTb5YuLjc9bt0bK6vanm7PuTnF+gv3qPHTsj9j07x6jlSU5K9Xh+G9GgiqYk2KSkokHVf/0e8VWwX6XBRn3LX+2HZr7Js5Vrxh92uZOCAK/x+v8vj7XvtjwaJJyQxvVRstiLZumShBFNSSqo07Hy+RAJf34uCAt/fN+Nx3n6OQvF+71r5vdiLC4P23vlzjDdJLJQkm0hhYZFPj7MXFvCYjRDBPGZjga/v3Wl/fCtJJSd/V1NTU3x6roT0DKlx/Sg/9pLMNm3aJK+99posWLBAtmzZIgUFBdK8eXPp37+/DB8+XCpXruy0/saNG+XBBx+UxYsXS1FRkZx11lkyfvx4ufDCC922ffToUXnkkUfkgw8+kIMHD+rt3nPPPXLnnXfq99wMJ7MvvfSSvPrqq7Jt2zbJzs6WAQMGyIQJE9z2gYiii1XghxcBKFwlCY7m7JCEQ7t1eRarDNiSgvyTwVgfkldxDLsGZqtUTvdYyiDU9W6Jgo0B2QiHEzRPgZ7iEs81WnyRkpykg5/eBpWMdb1dH4wTCARyDx89Xu76WM+X9fVznKqkhKdKCUJRJQStza/dF4VFxRV+v8MlSZQk6f6e0gGueBHK9yKS3nN7UaHYiwoiql1VusIHXMSPIT88ZikW37vE4nxJsZ9cv7Q4P4h7Rp7MmTNHpk+fLn379pVBgwZJcnKyLFq0SAdS33nnHVm6dKlUqlRJr4uAbbdu3SQpKUn+/ve/S2ZmpsycOVMuvvhi+fzzz6VXr16O7SJY27t3b1m1apXce++90qZNG73O3XffLXv37pVx48Y57ceIESNkypQpcvXVV8uoUaNk/fr1+t94/FdffcWADRERVRiCl7jZElIkwSJIiuxZJcqvvro5MFtSUiJHjhyTwsJCvaxunVoSznq3DMpGv5IID74zIBtF0tJSnTJkE5Osrxj5e0KOYQaVKpV/MCJr1ciQzaxaxattG49BYLJaZka562Pbxl9v1odixGxO1dr25qcg2XYyeOtPOxUVl3gd7HbNZvHn/fYklMEX/L4mpQXnyyrSg2bevBeOdVNTA7qeWaiDbYle/DhV5L3zvl0Lfc5aDbZYOmaDiQHi4L53+K1LrJLp1bqlucf8PlEid9dee62MGTNGB1cNyGBt2bKlPPHEEzJ79myd1QpY78iRI7Jy5Urp2LGjXnbTTTdJ27ZtZdiwYbJhwwZHX2HWrFmyYsUKHVRFQBZuu+02ueaaa+TJJ5+UIUOGSOPGjfXytWvXytSpU6Vfv37y/vvvO/ajadOmct9998lbb70lAwcODGm7EBFFi0gP1EQij3VS7RWbsAoZsugzopuSlVVVZ8jWyq4pkVDvNp5F+2ekJAqC7wzIRtEJ2iW9ezi+sHbs2CGNGjWyzHzA8Gd/ToIRZLy678nnKAtKCCBrFYHS5ybe59W2fX2M8RoQ8PX2OfRj8gqd2qosKGvgTyYt2um6ay73at233//Ur6xab1+Dv++1P+xik5bdegdl2xhWHqkBLm/fC1/5M3w7lO83grFNvXi//X3vfGnXDd984uVllpNCUUIgHo9Zf4TymI0F3r53Ozd9of8WJ6VJnVudMyY92T97nJSeOFrhfaSTOnfubLn8uuuu0wHZNWvW6H/n5ubKxx9/LBdccIEjGAtVqlSRoUOHymOPPaYDsF27dtXL33jjDUlPT9dBWDOUQUAJg7fffltn2cKbb76pLxLjPjM8dvTo0bqkAgOyREQnAzP2kmJdlgt1UJHReWDz2v8Famw2tySaigZqoj2YFQrmQGwoShV4FVx2qXfrLR3IlRLL5dEoGoKZsRB8Z0CWiIiIiCgAdu3apf/Wrl1b//3111/10MtzzjnHbd2zzz55Yc4IyOLE9Oeff9b1ZdNcRqTgfgQLsK4B/40L80Yw14DHIvhrXtcV9gk3w7Fjx/Rf7ANu5B2jvdhmvmPb+Yft5mdgad3PUlJSLEfyisR2KEdsSumJqE46GYxNqZzhCNromqgI4JYUS0Jyit/PWW4w6/SzIjaYZYbjDRcAS0/d3O4/tdxut4sqcS/Vp4zHm47f3Nw8RyC2Qf06ToHYYB7f+vnxxMbNw2vx9nOmUFctIVHseK8t4nr6uWwJer1o+tzi2MdnoKxgZkU+I6H4vis99V6j7jFehyvHMR2E71Rvt8eALBERERFRBeFEdOLEibpWrJGZmpOTo//Wr1/fbX1j2e7du/Xfw4cPS35+vuW6qampUrNmTce6xraxDPdZbXvJkiW6Jm1KivuJ0qRJk/SkYq527twpGRnelYqikydzeN8QzPG3RFW8Ytv5h+3mO2TFHj2eJ8pmk7wSJbbCUkSTxI65YlHCTgfVSiU5oUjEdjJoo5RdlN0u9pwcPYmav89psyWInCrF56RUiVJFUrxrp1/bDzUEmHOL7WLLRW1XiyxQe4mUFIkkqEI9EbQVBMRK9/wph4+ekFJ7kSQlpkj1aplStWoVsZcUOf2+BZN+bxCYT7SL7dT7bebPe1+aWVuUsg7AqVIlpUePyZ59+6PqM/u/dkoMWDuF+vvOHoT32lvHj3s3DxIDskREREREFYTSAT/++KOu9XraaafpZXl5JzOwrIKmRhassU5Z6xrrG+sY65e1rrGOVUAWdW1HjhzplCHbsGFDfatataoPrzq+GVljaDdOoOYbtp1/2G6+Q4mCxMN7RBKTJCGvWGpkVELRUymU4pOlCk4FftIqp4nt1BB2ZLairEHdevUkuVJlv58zISnZQ83Vim0/VIyyC4V5J+TI7gSplpakLzo6nMo8tBcXiT1ZpHqTlpYZv1gnOS1d35edmyd1j5+QWtk1QlKaINzvjVFuMto+s2inhEM5kpCQaD2Rmz1BSksTpF4Qj+HSCn7fhfNzaIw8Kg8DskREREREFfDoo4/KtGnT5Pbbb9fBTgPqwYK5PIChoKDAaZ2y1jXWN9Yx1t+3b5/Hdc3bdIVArlUwFyc80XTCGAmMNmO7+Y5t5x+2m290WyG77lSWnfHfjmWnZipIMGfhGev42c7Gcxo3NxXcfqBZ1bstKSp01Nm1Y0h3SbEU55WIHVm/p6C9EMgqyTshSpQc3fmH5fB2o9YoXmtGRhV9C5dwvDfR+JnF+11akK+rMFhlpyJQaju1XjBfV0IF2i6cn0Nvt8eALBERERGRn8aNGyePP/64DBkyRF555RWn+5A5AlZDMY1lRomCatWqSaVKlSzXRZD2wIED0qNHD6dtr1u3Tt/nGlzFNlDOwCo7loiIqLzJm1Aj1KizW4oyqDp4fTKA7VhHYVKkYh2MRXkGZCK6BmQjYeKkWJuEKxQTxmH7xvvqqVwA3v9Ie1+jDQOyRERERER+BmNRi3Xw4MEya9Yst5OWdu3a6WApShm4Wrp0qf7buXNnRzYFJvRatWqVW5B1+fLlOhvFWBe6dOkiCxYs0Pd1797dKTt29erVcv755wflNRMRUezwNBN9qd1+MvyqA7FKklJSJTWjkiPTEPejdmy1xi3k8PbfdTA20WJyJwQ9Iyloh9eJjN2ygsS432oSqEgOoFtlJAdswjirgKzFhGiRSkVw8J0BWSIiIiIiH02YMEEHY2+88UaZM2eO5fC0KlWqSJ8+feSDDz6QX375RTp06KCXnzhxQgdwW7ZsKV27dnWsf/3118sPP/wgM2bMkHvvvdex/MUXX9R1+6677jrHMvw36tXiPnNAdubMmbp27KBBg4L46omIoj8LEEElZcy2rgOQkROoCTUEY93qbJoCcbYEm77fPPS7VJXq9wCPtSpVEImwvwhWBv3YCnEAPWgZyfiMWAVfoyAga4uC4DsDskREREREPpg+fbqMHTtWGjVqJL169ZI33njD6f7atWtL79699X9PmjRJvv76a7noootkxIgRetIsBE1RVuDTTz91yqq97bbbZO7cuXrCrW3btkmbNm3ks88+kw8//FAeeeQRadKkiVP27bBhw3Tt2n79+slll10m69evlylTpujSBgMHDgxhixARRXYWIAJVmFVdB7OMYJIjqKROLS8NaKAmkjPz4lmkBlsrHEAPYEayDvgalyg8BF91CYsIzSSOluA7A7JERERERD5YsWKF/ouZk1GuwBUCokZAtkWLFjrrdfTo0fLUU09JUVGRLk3wxRdf6GCuGWq+fvXVVzr4+uabb8rBgwelefPmMnXqVB18dYXsWARpkVGL4C7qxiKzFtm70TR5CBFRsLIAjSy5kpJiUXb7yUmIbDanIAzWyW51hh6WH4hATTRk5hGVBZ+FpLRKYkNWdKL7cWqUrDB/ZiJRUoQH3xmQJSIiIiLywbx58/TNW8h0/eijj7xaNysrS2e94laexMREGTVqlL4REcWaQGQBGlly9pJisefkSN169dwuWAU6Sy4aMvOIvPv8nby4YAUlK6hiGJAlIiIiIiIiopiEwGdCcookJqdKcqXKIRlBEDPB1lM1RI2sZKOurGvZBZZniE18X4OLAVkiIiIiIiIiojjlGnhzDrgpKcX9JcVOE33pSb6SklmeIQax7EZoMCBLRERERERERBRnygq8GVm+ibYEqZpRU+o2auSUXWyUXWB5htjLXOX7GhoMyBIRERERERERxRlvAm9KbJKzd5/Hcg8MysVm5irf1+BjQJaIiIiIiIiIIgrrV4ZGeYE3lCugyMDM1djCgCwRERERERERRQTWryTyjMHW2MGALBERERERERFFBGYBElE8YECWiIiIiIiIiCIGg61EFOvcKzITERERERERERERUVAwIEtEREREREREREQUIgzIEhEREREREREREYVIVAdkS0tL5YUXXpDWrVtLWlqaNGzYUEaNGiW5ubnh3jUiIiIiIiIiIiKi2ArIjhgxQkaOHCmnn366TJ06Vfr37y9TpkyRPn366GAtERERERERERERUSRJkii1du1aHYTt16+fvP/++47lTZs2lfvuu0/eeustGThwYFj3kYiIiIiIiIiIiCgmMmTffPNNUUrJ8OHDnZbfdtttkp6eLq+99lrY9o2IiIiIiIiIiIgopgKyK1askISEBOnatavTctSS7dixo76fiIiIiIiIiIiIKJLYFNJMo1C7du1k3759snfvXrf7BgwYIO+++64UFhZKSkqK2/1Yjpvh6NGj0qhRI9m+fbtUrVo1qPu9Y/m3kpDzuyQf2C0lPrZ8UXGJV+ulJPtfiaJSWlq56xw9fkJnJ4PNZvNqu+b1MzOqlLt+QWFhhZ4jLTW13PWLCwtEb1rp/5XL2Avl4z6ZH+fLc3j7CF+2789z6Gc41U7FQfq2SMJT+PBehILv70Uo+P5++/4MJ/8PHydvvqN8fe/8addQHIO+4jEbOcdsLPD1vUuwF+nHlCamypFWZ3v1HJmblkqivVAS0jOl+o0PSjAdO3ZMGjduLEeOHJHMzMygPhcFBvrCWVlZsnPnzqD3hWMJ5qxAm2FiYSSKkPfYdv5hu/mPbec/tp3/2Hbx2XbHjh3T+11eXzhqa8jm5eVJqoegG7JkjXWsArKTJk2S8ePHuy3HyQMRERFRTLvvqZA8zfHjxxmQjRJ4rwAnD0REREQU/L4wM2RPRd4PHTokNWrU8DrzMRKi7cxiCB22eWixvUOPbR5abO/QYnuHFrqW6IDWq1cv6jIa4hX6wjk5OZKRkREVfeFIwe8W/7Ht/MN28x/bzn9sO/+x7eKz7ZSXfeGozZDFC1u3bp0OrLpmyu7evVtq1qxpGYwFrO/6GAzTijY4KKPtwIx2bPPQYnuHHts8tNjeocX2Dh1mxkYXnCw0aNAg3LsRtfjd4j+2nX/Ybv5j2/mPbec/tl38tV2mF33hqE1b6NKli76av3z5cqflBQUFsnr1auncuXPY9o2IiIiIiIiIiIgopgKy1113nR5S9eKLLzotnzlzpq4dO2jQoLDtGxEREREREREREVFMlSxADdlhw4bJtGnTpF+/fnLZZZfJ+vXrZcqUKdKjRw8ZOHCgxCqUWxg7dqzHSc0o8NjmocX2Dj22eWixvUOL7U1EwcDvFv+x7fzDdvMf285/bDv/se38lxoHbRe1k3qB3W7XGbIzZsyQbdu26bqxyJydMGGCVKlSJdy7R0RERERERERERBQ7AVkiIiIiIiIiIiKiaBK1NWSJiIiIiIiIiIiIog0DskREREREREREREQhwoAsERERERERERERUYgwIBsCmzZtkscee0zOPvtsyc7OloyMDOnYsaM88cQTkpub67b+xo0b5aqrrpJq1apJ5cqVpXv37vLNN99Ybvvo0aNy7733Sv369SUtLU3atm0rL7/8sliVBi4tLZUXXnhBWrdurddt2LChjBo1ynIfYk1eXp40a9ZMbDab3HPPPW73s80D49ChQ3L//fdLixYt9OvF8d6zZ0/57rvvnNZbtmyZ9OrVS38WqlatKpdccomsXr3acps5OTly00036W1VqlRJOnfuLO+++67luoWFhfqz1rRpUz0bY/PmzeXxxx+X4uJiiTUnTpyQJ598Utq1a6fbEZMaduvWTebNm+d2LLK9fTNp0iTp37+/4zujSZMmZa4fKe07f/58OfPMM/V2a9euLUOHDpX9+/dLrLR3QUGBzJw5U6688kq9Dl4nHnP99dfL+vXrQ96GvrzvRBR5KtJH87VvP27cOP39ZnV77rnnJNpUtH/rqS08TcrsSz89ltuurOMIt+Tk5Jg97nztm3kSj7/zFW273bt362306NFD6tatqz+DOP994IEH5ODBg27r41zE03FndR4e68cdHuOpPQ4cOFChc4VYbrt5ZRxHxg3HZtQfd5jUi4LrwQcfVFWqVFEDBw5UU6ZMUS+//LIaMGAAIiaqffv2Ki8vz7Hu77//rqpXr65q1aqlnnzySTV9+nTVsWNHlZSUpBYuXOi03cLCQtWlSxd934gRI9SMGTPU1Vdfrbc7duxYt/2477779H1YB+viMXhsz549ld1uV7Fs1KhR+j3A6x82bJjTfWzzwNi2bZtq0qSJqlmzpj7mZ8+erZ5//nl18803qzfffNOx3o8//qhSU1NVs2bN9P244b/x/vz6669O2zx48KBq2rSpqly5snr00UfVq6++qnr06KHbdM6cOW77cOWVV+r7brnlFjVz5kz9F/8ePHiwiiU4ds477zyVkJCghgwZotvlhRdeUF27dtWv9+9//7tjXba37/Aa8J3Qq1cvVa1aNdW4cWOP60ZK++J5cR+2h+1i+3ie008/XZ04cULFQnuvX79er4tjf8KECWrWrFnqoYce0o9JSUlR33zzTcja0Jf3nYgiU0X6aL707QF9RNyH3+p//etfTrd169apaFPR/i0e2717d7e2eOutt9zW9aWfHutt98svv7i1GW4PPPCAY5uxetz50jfzJF5/5yvadvh+Qz8Lx9fkyZP1cTt06FB93DZo0EDt2bPHaf25c+fq50QfzfW4W758uYq34w6Pad26teVnt6CgoELnCrHcdlu2bLFss2eeeUZv+8wzz4yJ444B2RBYsWKFOnLkiNvyhx9+WB80U6dOdSzr37+/DrKsWrXKsez48eOqUaNGqlWrVqq0tNSxHB0SPB4dQbN+/fqp5ORkHSAzrFmzRtlsNn2fGR6Lbbz++usqVq1cuVIlJibqHxCrgCzbPDAQJMGPck5OTpnrIaCdkZGhdu3a5ViG/8ay3r17O61rdDA//vhjx7KSkhK9DXzB430yfPrpp3rdkSNHOm0D/8byH374QcWKJUuW6Nc0fPhwtwsG+BHPzMx0LGN7+w4dAEPbtm3L7EBEQvvu379fpaen6+1gewY8D9Z94oknVCy094EDB5y+pw1r167VJwqdOnVyWh7MNvTlfSeiyFPRPpovfXtzYGzr1q0q2gWif+vLxVtf+umRLljnBrfffrt+/CeffBKzx50vfTMr8fw7X9G2w3HrGnQFXOhG2yHxySowtmjRIhXtKtp2gMcgqOoNX84V4qHtrODCHNpo2rRpMXHcMSAbRri6hoPmjjvu0P/GlTlcibvwwgvd1kU2ENZdtmyZY9m5556rf1jy8/Od1v3vf/+r13366afdOoi4zwyPxTYuvfRSFYvwBXbWWWepyy+/XHdGXAOybPPAWLx4sVOguqioSOXm5rqtt3nzZke2missQyfV/INfv3591bx5c7d158+fr7fz9ttvO5YNGjRIL9uxY4fTuvg3lt91110qVnzxxRf6NeEKoSv8YNerV0//N9u74srqQERK+xodYmzHFbI52rRpo6KFvx02fM/ju9wsWG3o6/tORJEnWH001769VWDs6NGjqri4WMVz2xkBWVxILivA4Gs/PR6PO7RR1apVdVKEOdAYa8ddRfsK/J0PfGDs2LFjup0uvvhij4ExrIPPeSyoaEAWnz98Dsviy7lCPB53paWlqkWLFqpSpUrq8OHDMXHcsYZsGO3atUv/Rf0a+PXXX3W9u3POOcdtXdSoghUrVjjqD/3888+6Bg7qD5l17dpV18ow1jUel5CQoO8zw2NR88q8bixBjaYNGzbItGnTLO9nmwfGZ599pv82atRI+vTpo+vdoL5Qq1at5LXXXnOsZ7xmT+2NPvrKlSv1v/fs2aPrwhjvg+u65u0Z/426vqjFZYZ/16tXL6baG8dUVlaWPPPMM7qm0I4dO/RxPmbMGN1+qBkGbO/gipT2LW8/cGyg5nCswncz2tf4LQ12G/ryvhNRZApWH821b++qffv2kpmZqZ8Hdd8///xzide2e++99yQ9PV3X56xVq5aenwHzNPjbT4/X4w79wGPHjsnNN98siYmJMXvcVRR/5wOvvO+7vn376tq7OO46dOjgdE4Yb1CPGN93+BziHG7w4MG6VqyZr+cK8Wjx4sXy+++/yzXXXKPbMRaOOwZkw8Rut8vEiRMlKSlJBg4cqJcZH0qcQLoylhmFiw8fPiz5+fmW62LiEkzwYy5yjG1jGe6z2jYKShcVFUks2bp1q4wdO1ZPuuCpiDTbPDAw2QLcdtttemKvf/7znzJnzhxJSUmRG2+8UebOnetze/uyrrG+1brG+uZ1ox0mtfj444+levXqMmDAAGncuLG0adNGpk+fLu+//75+H4DtHVyR0r7lbRsnDq6dvljyyiuv6E4sOrdmwWpDX99LIoo8weijWfXtDThxvP3222Xq1Kny0Ucf6clOtm/fLpdffrmeiCTe2g4BSVw8RlAWfcYLL7xQJ09gsi7zBcRY+74NxnE3e/ZsnRRyyy23uN0XS8ddRfF3PvBwng2u/S8EHvEdiMQonK+8+OKLemJWnBOOHz9e4g0mQHv44YflzTff1Le//e1v8vrrr+vvQXP/nMedd993gMn4XEXrcZcU7h2IV8OHD5cff/xRz5J+2mmn6WV5eXn6r9WPtJGRaaxT1rrG+sY6xvplrWusgwBarLjzzjv1rH4jR470uA7bPDCOHz+u/yLLYdGiRY7XhBlx8R489NBD+sc6UO3tuq7x396+N7EAMxGfccYZ+iogsh0QCEdAFj9E6HT37t2b7R1kkdK+vm47lixZskR/x+MKOL5nzILVhvHc3kSxIhh9NKu+vfk+Vwig4Xd8xIgRcu211+rf9XhpO2SLmWFGcWRxImjx0ksv6b/GdmLp+zbQxx0SIr7//nv561//Kk2bNnW7P5aOu4ri73xgTZ48WWdnI+CPCypmSBbBzeyOO+6Qzp07y+OPP67PCT0lS8WiTz/91OnfCMief/75MmjQIB3Unjlzpl7O465sR44c0YlHLVq0kB49erjdH63HHTNkw+DRRx/VV4HxBYYhxuaoPmBojitE983rlLWusb6xjrF+WeuatxkLkJq+cOFCefnllyU5OdnjemzzwECJArj++uudOpHI5ETA8M8//9SdxkC1t1X7ldfesdLW8Ntvv+kgLIKuzz77rFx99dVy66236k55nTp1dIYsMnXY3sEVKe3r67ZjBYYLIssH5QfQ2XUtJROsNozX9iaKJYHuo3nq25elRo0aOnkAJ5m4uBQtgtW/feCBB3Qf0hy8iLXv20C3XVnZYrF23FUUf+cDZ9asWfrzij6Yp7KArhBkvP/++6WkpEQWLFgg8Q4JNAgOxvL3XaC98cYberQyznm9FQ3HHQOyIYbhOYjQDxkyRA+zNMNJpadUdGOZka6OQBeCYFbr4kOMIS/mdHdsG8usPuDYBobPxEKmJuA1ImPqsssu08Ep1BnBDUN0APWp8G90RNjmgdGgQQP9F+3tqm7duo6SD760ty/rGut7GsaB5Z6GLkcjDMXAD3P//v2dluNHGp0jHOvbtm1jewdZpLRvedvGUEZjnViBet64IIFaXMjKtzregtWGvr6XRBR5AtlHK6tvXx4jWwf7Ei2C1b9FAoWxbfNzGdu1eq5o+74NZNshwDB//nwdYMWF+Vg/7iqKv/OBgZJ0uPB00UUX6WzFshKfXMXjcVdee8Ty912gzZ49W5cEQr1sX0T6cceAbAihw4b6FUiXxpUlfPGbtWvXTkfxMdzJ1dKlS/VfpFwDCsKfddZZsmrVKrcf9eXLl+s6OMa60KVLFz3xCe4zQ1Bn9erVTutGO1w52b9/v77i1LJlS8ftggsucGTP4t94D9jmgWFMTmAUdzczlmHCBrQJeGpvfCY6derkCOTiR8d4H1zXBdf2xo/Vzp07ndbFv1GTJ5ba2/hRRhasVQfd+Mv2Dq5Iad/y9gNDZ2NpSCKCsb169XKUSEENZSvBakNf3nciikyB6qOV17cvz+bNm8ucFCcSBat/i8ejz2huC1/66fHWdv/5z39k7969csMNN3gsgxBLx11F8Xc+MMFYZGOjD/bvf/+bx10FIUHM3Ba+nivEk9WrV+v+PxKPrBLAovq4UxQS48ePV2juG2+8Udntdo/rXXvttSohIUGtXr3asez48eOqUaNGqmXLlqq0tNSxfNq0aXqbU6ZMcdpGv379VFJSktq6datj2a+//qpsNpu+zwyPxTb+9a9/qVhRVFSk3n33XbfbP/7xD/1aL7nkEv3vjRs36vXZ5hV36NAhlZGRoerXr6/bzpCTk6MqV66sWrVq5VjWuXNnve7u3bsdy/DfWPbXv/7Vabv333+/bquPP/7YsaykpER16dJFZWVlqWPHjjmWf/LJJ3rdkSNHOm0D/8by7777TsWK4cOH69f09NNPOy0/fPiwqlu3rqpWrZpuJ2B7V0zbtm1V48aNPd4fCe27b98+ValSJdW1a1fH+w54Hqw7ceJEFSvt/fPPP6vq1aurhg0bqi1btpS5rWC2oS/vOxFFHl/6aL///rtav36933374uJideTIEbflO3bs0N9nNWrUUHl5eSpe2u7AgQOW2zV+I137Nr700+PhuDNcfvnl+jHYZjwcd770FbZv367bDueEBv7O+992MHfuXP05xGsv77ix+ozjWGzRooVKSUlRO3fuVPHSdgcPHrRc14gp3HXXXX6fK8TDcWcYNmyYW7vEynHHgGwIGB84dBz++c9/6h9b823BggWOdTdv3qyDKbVq1VKTJk1S06dPVx07dlSJiYnqiy++cNpuYWGh6tSpkw4E4gRz5syZ6uqrr9bP9cgjj7jtxz333KPvwzpYF4/BY3v06FFmRzJWIFiK148PtBnbPDBeffVV/VrxhTt58mTdljjmk5OT1ZdffulY74cfftBfis2aNVMvvPCCvuG/Ebg1d7aNL1Z8eVepUkU99thj+jkuuOAC/TyzZs1y24crrrhC33frrbfq+/EX/77hhhtULNm2bZvuTKNTj9f28ssvqyeeeEI1adJEv14cwwa2t+/mz5+vO+a44XsBHSDj37jPLFLa97nnntP3YXvYLraPfWjdurXTRZJobm8c9ziBxHE/btw4t99S3E6cOBGSNvTlfSeiyORtHw3f2645LL707XGxFN9rN998sw42zpgxQ40aNUplZmbqvuY777yj4qntcFH57LPPVmPGjNH9l2effVb17NlTr/eXv/zFLdjjSz891tvOHBjE60dw0ZNYO+586ZuhLdF25kSZeP6dr2jbffTRRzoYi8fhOHL9vvvwww+dtoHkkL/97W/63ATH+MMPP6yXYbs4R4yntsNxc8YZZ+hAK343XnzxRXXVVVfp9Zo3b64vFFTkXCHWP7OQn5+vH1uvXj2niymuovW4Y0A2BAYPHqwPBE83HIBm69atU3379tU/mLiSd+6556qFCxd6/LFFgBEHG3402rRpo6ZOnWp5tRgHMH6IkK2IdXFQjxgxIuJP1oMdkAW2eWC8//77ujOdnp6uf0h69+6tvv/+e7f1lixZoi688ELdqcF6F110kVq5cqXlNnft2qWDJwjEpKamqjPPPFO99dZbluviCxtfvvghQ3s3bdpUTZgwwePVtmiGzImbbrpJZyWjI48r9t27d9fvgSu2t2+MToE339eR1L7IXmjfvr3ebnZ2thoyZIjau3evipX2XrRoUZm/pVYduWC2oS/vOxFFHm/7aFaBMV/69gUFBfpiEE7KcVKJ3+w6deqoa665Ri1btkzFW9v9+9//1t+XeAy+a9Fn7NChgz6Jxne2FV/66bHcdga0Fe5DcMyTWDvufOmblRXcicff+Yq23dixY8v8vnPNfMRFhrPOOksnj+C4Q5/30ksvjcoLKBVtO5wH9+nTR4/sSktL08cdLgA8+OCDOqZQ0XOFePjMvv766/q+hx56qMzni9bjzob/C3fZBCIiIiIiIiIiIqJ4wEm9iIiIiIiIiIiIiEKEAVkiIiIiIiIiIiKiEGFAloiIiIiIiIiIiChEGJAlIiIiIiIiIiIiChEGZImIiIiIiIiIiIhChAFZIiIiIiIiIiIiohBhQJaIiIiIiIiIiIgoRBiQJSIiIiIiIiIiIgoRBmSJiIiIiIiIiIiIQoQBWSIKmgsuuECaNGkS7t2IOqWlpTJu3Dhp1qyZJCUlic1mk0iD/cN+bdu2zav1cRzgeIh2Dz74oDRt2lSKiorC8vwvvfSS1KhRQw4fPhyW5yciIqLg9ZdiTUFBge4DPvzww+HeFfKDUkrOOussGTJkSLh3hSgmMSBLFKG+/fZb3YEz36pUqSKdOnXSQRm73e60/i+//CLXX3+9tGjRQtLS0qRmzZrSvn17ueOOO2TVqlVO67pu13x76qmnyt23ij6eyvbPf/5Txo8fLz179pTZs2fLv/71r3DvUtR48cUXZd68eUHZ9tatW/Vn77HHHpOUlJQKfa6fe+45vx6Pz3NqaqpMnDjRr8cTERFRaPvowXDkyBEd7MW+RLLnn39e7+v999/vtBxtdsUVV4RtvyI5eG/c0NfMzs6Ws88+W0aMGCG//vqr5eNuvvlmp8clJiZKrVq1pE+fPvL9999bPqa4uFj+8Y9/6GM2KytL3xB4xfFrTjrA9rBf8+fPl9WrVwfttRPFq6Rw7wARlQ1B1ssuu0xfoczJydHBpuHDh8vatWtlxowZep1PPvlErrrqKv2jfdNNN+mgLDo/mzZtkk8//VRatmwpZ555ptN2O3bsKKNGjXJ7Ptf1PKno48mzhQsXSmZmpsyaNSsis2P9sXHjxpC8FgRkkYmBzmmg4WJD1apV5YYbbpBwwcWWO++8U5588kmdbYJsWSIiIorMPnqwoJ+Pi/cQqSOQ8vPz5dlnn9XZldWqVQv37kSNCRMm6NFYCOxjRBQCoUjQQLB05MiRHi/qv/zyy/rCAAKqxjH4xRdfyNdffy3nn3++07roJ7/xxhtyzTXXyNChQ/Vz/ec//9HH75IlS+Ttt992rNu3b1/dt37iiSfk3XffDfrrJ4onDMgSRThcrTQHgO666y5p06aNDtYhS6527doyZswYqVSpkqxYsUIaNGjgNvz94MGDbtutX79+hQJLFX08efbnn3/qK9WBDmAeP35cMjIyJByQ1RnNjh07Jq+//rrceuutkpycHNZ9wedu7Nix+sTP6qIIERERRUYfPZ4h4IfAMZJFyHuXXnqpdO7c2S3TuH///jJ58mSdgIMSWq6uvfZaPULS0KNHD7nyyit1UNwckMXFA7w3SOZ57733HMuHDRum10PQ9ZVXXnEKouM4nzRpkj5HqVOnThBeNVF8YskCoiiDDL1zzjlHX43/448/9LLNmzfLaaed5haMhYSEBP3DHSk81RI1hn8Zw81LSkrk3HPPlcqVK8uGDRuc1sUVX6yLoePlwXq4CvzNN9/odktPT9ft9PTTT+v7ceUZQTYM7cF9GD6FjooZ/o3AF7KC0TlBluLpp5+ut2EellbRfTbaYNGiRbJ9+3bH0CNztud///tf6d27t86gRRAeJwO4au6pfi+OEXTQqlevro+d8qDG2Y033qhPIhBEbd68uTz00EOSl5dnuX5ubq7cd999unOG/fnLX/6ir8R7+77/9NNPcvXVV+sOJJ4PxzGuwKMtXf3+++86ywLvH4Zx1atXT3c0V65cqe9HW6HdFi9e7DR0y6jbhiv+6ORiX/Ee4qICMluWLl1abrt89tln+rVifU9tjeME2TI4RnAsXXzxxTpLvSz79u3Tr2XQoEGW96NzjM+wufYcagujnZilQEREFNl9dENhYaHuT6EPg/5Ohw4ddN/CFfo/6F+in4m+CkbCoJ/022+/OfUXkUEJyJI1+jvmeRu82U55sD1PJcq8ycpFPwV9roqMnjt69KgOPmL0H9oN5zToa7m2L6CvhIxPvA+4oY+IclNWfVBkgCLzs1GjRnq76IciQOmpLADKvyEgavSPGzZsqPdjy5YtOiMV+4VzACsIiKLN0If3F/rxaE+8LgRG0Sctz1//+lfHeaJrggagH22Gfaxbt67ud+KYMUP/GWUO/v3vf/v9GojIHTNkiaIMOnkITIFxFRRBMwxNQcCpW7duXm0HP6oHDhxwWoYfYPzgh+Lx5cFkVrh6iyDo3/72N1m2bJnuAOF1YjjNeeedp7MEvYFOFIbh3H777foq/TvvvCOjR4/WnQ3Ua0VHDfWR0K5TpkzR63z11VeOx6Nz9sEHH+iOLNoarx1DgLANdAhfffXVgOwzsipQLxYBSbTtCy+8oJfjOQGvAfuAzi0CxMh2feutt/RQI+wHHmd24sQJfXUcHUTch+BfWRDM7Nq1q+783n333brUBTr96Pj98MMPOtCK12iGtkKtKnSW0cFDW1xyySXy+eefS69evcp8PpTT6Nevn+5k4/Xg2Pnxxx910BrDs8wBRwRu0bFE2yOAfsYZZ8ihQ4d08BXHPWpgoe1QYwufC/PkEegko2QCAtlou//7v//THeq9e/fq2lqov4z6XGXB80CXLl0s70fHGFkF2A7KCRj1ZnEysGbNGt1GVnAhACcEOL6QRYLMaPNEGDie0I6uk+PhhO+1117T7zGGpxEREVHk9dENgwcP1iNsUEsVATyUWEIAEBduzb/xuECLfir6LMi4RUbi9OnT9e/+d999p4Ob6C+ij4g+D/qF6EuBuT/gzXbKg31EP8MMfdtp06aVm/2LhAX0HS+88ELxF/qjOK/ZsWOH3HLLLdK2bVvZs2ePrn2KBAD0DRs3bqzXxWjA7t27674dSjuhjfA6MR+DVfASrwFBapwboG+IwCqSJ9Bn/vnnn3Uf2IDScAj0IuECfW70W9GeX375pe7joZ+O9xfZq+hv4qK52Zw5c6RVq1ZuZQN8hX4y3m+cu6D/igv/ZcFrMh5nhv3FDfuF4wD9TLxfH330ke6PGiMvzZAAgnManBegfYkoQBQRRaRFixYpfETHjx+v9u/fr/bt26d++eUXNXToUL387LPPdqz77rvvKpvNppe3a9dO3XHHHWr27Nlq69atltvGela32rVre7Vv3j6+R48eqnHjxk7L8G8s9/R6586d67T8/fff18uHDRum8vLyVNu2bVW1atXU9u3bvd5XtM3SpUsdywoLC1WdOnX08nvvvddp/REjRujHbNiwwbEMz1taWuq27RtuuEElJCSonJycgO6zVbuVlJSoRo0aqczMTLV7926n19KtWze9H5s2bXLaBvbh4YcfVt4aOHCgfsynn37qtPz+++/Xy2fNmuVYNnbsWL2sa9eueh8MO3fuVJUrV1atW7cu833Pz8/Xx0v37t1VcXGx07rPP/+83jaOCUDbow1TU1P1Z8CV3W73+DyGl156SW9z2bJlyh/nn3++fg+tGG399NNPOy1/5pln9PIvvvjC7Th/9tlnHcu+/PJLvWz69OlOj3/ttdf08rffftvtOSdOnKjv++mnn/x6PURERBT8PrrRX7r88sud+pLLly/Xy0ePHu1YtmDBAr1swIABTuuuXr1aJSYmqvPOO8+xDH18rIvtu/JlO77Ac9aqVUs1b95cHThwoMx1//jjD70P6FdbMdqkLPfdd59KS0vT+222bds2lZGRoQYPHuxY9sADD+htou9kZix37RueOHHC7fnWrVunUlJS1F133eVYlpubq2rWrKmys7PVrl27PPZBN27cqJ8Hz2f2/fffW/YRrRjHyooVKzyuM3nyZL3OlClTHMvQDliGfcDxiPOEhQsXqvbt21v2L2Ht2rWqc+fOTudxycnJ6h//+IfH58b7fsYZZ5T7OojIeyxZQBThkFGJDD9k0mF4E65mIqPOPGQEQ9IxDAZ/d+7cqbMUkUWI4UzI0Nu/f7/bdnFlGZNHmW++DIGu6OO9hav+uLKPq/q4gotsU9TmwhAjbyEbAPtrwBBxZIKiP4jh9ma4uu46vAdXiY16rshqQGYmMlhxZRo1enGFPtD77ArD8o0MAfMQI7yWv//973o/cGXbleustp7g8R9//LG+Uu46LB9XypH9/OGHH7o9DtkZ2AcDhuIhKwMlG9avX+/x+XC8IIsBJQiQGYr2NG7G8y9YsED/RbYs2hDrtm/f3m1b2LfyoMQDoI2QeeorfIbKyv7GPrgeS0ZWiOtQMVfIXsFn1bX0BP6N7A1k0LgyJvMqL+uZiIiIwtdHN2B0jnluAIy4QUaruY9g9LMwyse8Lrbdp08fnRVp1ad3FajtuGaroqwX+sEY4VTepKLG9v0dOYc+Omr3I6sUJabM/URkqmJEktFPNEaRYbg9ygh40w/GNoznwTwB2C7eS2S3IgvYgCxY3IeRXNgPT31QZMBiVNr8+fOdym6hL4fRZcigDQSj/Bj22RX2Ha8B+4m+JUa+oVwCRr25wrkNsoBxvoLzN2Td4hzonnvu0a/BCt5z9juJAoslC4giHIbSoGYROlToPOAH36pzg+HwuKFjgc4d6pBiSA+CbCjEjg6FGYZSlTekvCwVfbwvUMgenS4MTb/tttscQ7O8hZqbroxC9UYNLtfl5onQ0LF66qmndAcFQ9FOXtj/H9ShDfQ+u8IQeMBwLVfGMtd6WuiUmYfAl9dxxrA0q+3jeEMn16peF4aEuUK9MmN/rO4HI1iLALMnCNiCcbJSkRpkKCGBIf4oJ4BhfujII6CO5cZwt7Lg8+f6vpshSO5ab8s4WbGaVM912xgChxMnBJ9R8gJth2FhOIEzB7wNxr4EeuI3IiIiCmwf3VNfFP0Ecx8BfT0E+Kz6TuifIdCLdcqbG8KX7aDv51qWAK/B3PdAPxivE+UVULLLdUi+FaN/Ulbfqbx+KdoGfWlPr9d8QR6vB8kWrhfpESy36gujnNmjjz6q+1quJQ3M5wa+9EFxPCApASUOcDEdpbxQNgKB7EBN8GYEYq3mhXj//ff1cjwv3mP0e62SEFBuARcE0PfE+Y0B54so2YCgLAL35km9jPeS/U6iwGJAlijC4eqlL4FP/FCiQ4gbrsai44XOzK5duywn/Qo1Tz/kVpM4GVDjE9mhgFpNWNe1lmlZPNXvLOs+cwdy5MiRMnXqVLnuuut00AydO9QBQ40p1E5Fdmmg9zkQMLFUpDLaF1fuEYC04jrZQEWg7hWycpcvX64vTiCjHLVqUTsYdVpRk6ssOBnAe+rPMebNyQgC08i0QSYFjjVk2eBx6CxbQZa2sV9EREQU2X10b/qb4fDcc8/picHMkNRhngQLGZboQ2G0l7c1YY3+idFf8ZXRLmhf9LUDCf1zZN4ieImgLALMCKjjHAVzPrgGqL2FOrMYLYW+HAKymDgMwV5PfTl/GJOOWQXF8ZqM2sXo1yILFq8P8yxgUi4DauUi2I0guxmC2RhticlucY5jTApmwHvJfidRYDEgSxTDkLFnZNvt3r07IgKyuOpu1Tmzyr40rgRj+BE6GLhii4AoAleuE1gFEyaLQicHE2iZGRM3hGKfjcwKDN13tW7dOqd1/IEOFiYJs9o+MoAxiYJV4BSZrhgC5+v+GJMloANc3skMLi4AskfLU96Ve2RP4AYo74GMh0ceeaTcgCwmEcPEXhi25jpRRyBgQglkI2B4HrIV5s2bp8tsWGUsG8ceAvzeZKkQERFR5EO/CRf50bdyLdFk9K2M7M2y+ju+bAeTs2KEnZm5X4cL5zNnztTlsVAOzVsNGzbUAc/yyjZ5YozyQp/am6A3JkZD3wiv25wliyH2KI3lWtIBQVeMIsSkX2YIVOIivlUf9KKLLipzH/A4tCcmCM7JydGBWZQPwGS3gYDzJ+w7ynC5vmdWMCkvgsJILMG+GxcFcE4ImMjLU4KMa6JMYWGh7jdXdMQfETljDVmiGIDhQ1ZX2DHcBzOcInBjni00nNCxQX1RozNg/Mij3qqn4T+ogYRhNw899JC+couAFa7ehwo6MK7tiyveGPoeqn3G7KaoQTt37lw91MhQXFysO8vomKNesL/QeUVAEEO4cDyZYd/RwbUKWqINUE/MgExsZJwiUOipXAGgXAAyjbFtqwB9fn6+HnJlnBggMImsUauAsfm9QT02q+0hkOoKFyjQ4fcme8PIFEHWQLCgtAWC35i9Fp+PsjIqsB/IeDDPqExERETRy6gZj0CauW+DkVYIHiIIZ2QoGr//Vn0YX7aD4C0CnuabMVQdwT9kp6L/Zx7a7m3fGTVJzfVYfe2XYvg/Rja99957luuY65miD4vkgTfffNMtA9hq38C1b4/As7mPDQhk4kL85MmT9fZduW4DfTkEOtFu6KvdfPPNZY6i8hbeZ2S0IkCNRA9vRsHhfUTGLs67zO1ilBbDxX8znFOgD4/zRtcSDTg/QH8fdXKJKHCYIUsUAxDwQ3ALNYrwI4sfUmScIrMTdTgxNNvfovqBhoxRZJqiw4fAE37csZ9WHQtcWcaVXQQ1jSFS6CytWLFC1znCsJ3yJhUIVPtiojSULMB+o00RHLR67mDtMzpz06ZN051i1H1C0BcZrXgudPjwfBUNuqO+KoakoSOP4WktWrTQQ/vxHMgQtpqQAFfQ0eFGRjACqK+88ooOpiI7oCzIjEVNXjwXgrcYso/nQxYDOo4ffPCBPhFAIBTBZgSiMXQK2a3I0EDGKtZF1ioyD+699169XdSGxXuAIVoICBuB5scff1yX7sBnBFkh6EBjAgg8F7I+yoPnQHt/9tlnehvBgCA16tkikI8TLdS3tbJlyxbZuHGj5UkGERERRSdMxDRgwADdT8YFWvQ3ECBE0gJGvZn7VuhLot+EdZs3b65rlKJvhT6PL9vxBJmi6Leif44+EEbwmOH58DxlQQARE4AhqGqMTjJDRiv6Z1YwaSxGliGxBK8FN/TxUNsWSQ/oj+HCtBFURAAUwURMAIvna926tXz33Xd6LgcEVM0ZxRi+j9d144036vMSBC7xPNgm2tKcHYr10K/EuQD6nrhYjnZH0gtKYCH71JwQgb4nAt7oy+E5y5orwZPPP/9c90+RDIH3D8FQ9InRz37ggQf0zVuYiwDJExMnTtR9dZxPoI1eeuklefnll3UiBfqfeXl5ep9xnoLt47zSDG2Dcm1WE80SUQUoIopIixYtwiVX9eyzz5a77jvvvKOGDBmiTj/9dJWVlaWSkpJUrVq11CWXXKLee+89t/Wx3csvv9zvffP28T169FCNGzd2Wz5v3jzVqlUrlZycrJo0aaKefvpp9fXXX+vtzp07V6+zfv16lZ6errp166aKi4udHr9kyRL9Gvv06ePVvg4ePNhtOZZZfQUa7W7sB+Tm5qr7779fNWrUSKWmpqoWLVqoSZMmqa+++ioo++yp3eDbb79VvXr1UhkZGXpfOnbsqGbNmuXTNsryxx9/qBtuuEFlZ2fr96dp06ZqzJgxug3Mxo4dq1/7mjVr1D333KNq166t96dLly5qwYIFbtvFvmCfXP32229q0KBBql69evr5cNyec845asKECergwYNO627YsEGvi+fCunXr1lVXXnmlWrlypWOdvXv3qn79+qlq1aopm82m93Hr1q36fR0wYIDej7S0NH1/165d1cyZM1VpaalXbXPXXXep6tWrq8LCQq/aGs+L50dbGb755hu97Pnnn7d8Drxu3H/LLbd43I9x48bptj5w4IBX+01ERETh6aMb/SX0CbzpG6H/+NRTT6nWrVurlJQU3V9BX+fXX391e/yyZct0nxN9TzyHuS/iy3asGH0YTzerPp2r/Px83W9CP9FVWdvGbc+ePXo99D/RNzrjjDN0/61KlSr6NQ0dOlQtXbrUrQ979dVX63XQT+7bt69eVqNGDXXppZc6rbt48WJ17rnn6nUzMzPVZZddpvuknvp0aGu0H7aF9mzYsKEaOHCg2rJli9u68+fP16/hwgsvVL4wjhXjhr4ung996+HDh6tffvnF8nHGOc3+/fst7x89erS+H+df5v7ysGHD9HkYngfHEJ5nxowZlv1inA9ce+21Pr0eIiqfDf9XkYAuERGRN7XEkAn71VdfSbTatm2bzrhAprK/EzR89NFHOrsAGdbIUHD1zDPP6CwPZHScc845bvdjtlwML0T27PPPP+/XPhARERGFAkodoHTC1q1bwzJaD5m+yJC944479CiuUHjnnXf0qDpk7CIrNdqh74rasStXrvQ4ES8R+Yc1ZImIKKhQlgI1XF2HP0UbTBiB2XcxvM5cN9cXRg3adu3aud2HIXIojYH7rIKxgJMJBGVRkoGIiIgokqHfhJIAoSizhJJZrozat+WVVwgklIZAEDgWJsBC7t64ceP0ZGUMxhIFHjNkiYgoaDARA66soy4V6nxZ1aGNB5hM4aefftJ12xBw/fnnnx33IWvkxx9/1O2ErAqs66l+LBERERG569mzp67Fj4lwUX/166+/lk8++US6deum50QIxORanmCCMTwf6taiNiuygkePHh205yOi2MCALBERBQ2G1yPzE7PM4go7JtmKR8gOweQOyNBAqYH69es77kOgGuULkE2BydTGjx8f1n0lIiIiijaTJ0/WE8aixBSyZRs0aKCzVMeOHasnZg2mb7/9VgeEs7Ky9EV1XIDHJFhERGVhQJaIiIiIiIiIiIgoROIzVYmIiIiIiIiIiIgoDBiQJSIiIiIiIiIiIgoRBmSJiIiIiIiIiIiIQoQBWSIiIiIiIiIiIqIQYUCWiIiIiIiIiIiIKEQYkCUiIiIiIiIiIiIKEQZkiYiIiIiIiIiIiEKEAVkiIiIiIiIiIiIiCY3/B14AZEeDYdd/AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pdf_sn_grouped = pdf_sn.groupby('r:diaObjectId').agg('max').reset_index()\n",
+    "flux = pd.to_numeric(pdf_sn_grouped['r:psfFlux'], errors='coerce')\n",
+    "band = pdf_sn_grouped['r:band']\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Left: PSF flux per band\n",
+    "mybins = np.arange(pdf_sn_grouped['r:psfFlux'].min(), pdf_sn_grouped['r:psfFlux'].max(), pdf_sn_grouped['r:psfFlux'].min()-pdf_sn_grouped['r:psfFlux'].max()/10)\n",
+    "for b in UNIQUE_BANDS:\n",
+    "    mask = (band == b) & flux.notna()\n",
+    "    if mask.sum() == 0:\n",
+    "        continue\n",
+    "    axes[0].hist(\n",
+    "        flux[mask], bins=mybins, histtype='step',\n",
+    "        lw=2, color=COLORS[b], label=f'{b} band',\n",
+    "    )\n",
+    "axes[0].set_xlabel('PSF Flux max for objects (nJy)')\n",
+    "axes[0].set_ylabel('Number of objects')\n",
+    "axes[0].set_title('PSF Flux max distribution')\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(alpha=0.4)\n",
+    "\n",
+    "# Right: PSF flux vs. photo-z, coloured by band\n",
+    "zphot = pd.to_numeric(pdf_sn_grouped['f:xm_legacydr8_zphot'], errors='coerce')\n",
+    "fqual = pd.to_numeric(pdf_sn_grouped['f:xm_legacydr8_fqual'], errors='coerce')\n",
+    "\n",
+    "for b in UNIQUE_BANDS:\n",
+    "    mask = (band == b) & flux.notna() & zphot.notna() & (fqual == 1)\n",
+    "    if mask.sum() == 0:\n",
+    "        continue\n",
+    "    axes[1].scatter(\n",
+    "        zphot[mask], flux[mask],\n",
+    "        color=COLORS[b], marker=MARKERS[b],\n",
+    "        s=40, alpha=0.7, label=f'{b} band',\n",
+    "    )\n",
+    "axes[1].set_xlabel('Photo-z (Legacy DR8)')\n",
+    "axes[1].set_ylabel('PSF Flux max for objects (nJy)')\n",
+    "axes[1].set_title('PSF Flux max vs. redshift')\n",
+    "axes[1].legend()\n",
+    "axes[1].grid(alpha=0.4)\n",
+    "\n",
+    "plt.suptitle('Transient PSF flux — SN candidates near a galaxy', y=1.02)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-31",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "\n",
+    "In this notebook we demonstrated how to work with Fink extragalactic candidate tags for LSST/Rubin:\n",
+    "\n",
+    "- Queried the `/api/v1/schema` endpoint to inspect available field names and their origin prefix (`r:`, `f:`, ...).\n",
+    "- Queried the `extragalactic_lt20mag_candidate` tag to retrieve bright extragalactic alerts and grouped them by `r:diaObjectId`.\n",
+    "- Used the `columns` parameter to request only the fields needed, reducing transfer size.\n",
+    "- Fetched full multi-band photometry for a selected object via `/api/v1/sources` and plotted its difference-image light-curve.\n",
+    "- Queried the `sn_near_galaxy_candidate` tag with galaxy cross-match columns (`f:xm_mangrove_*`, `f:xm_legacydr8_*`, `f:xm_tns_redshift`).\n",
+    "- Computed host-galaxy match statistics: fraction of alerts with a Mangrove and/or Legacy DR8 counterpart.\n",
+    "- Plotted the angular separation from the Mangrove host galaxy and the photo-*z* distribution from Legacy DR8.\n",
+    "- Showed apparent magnitude distributions per band and apparent magnitude vs. redshift.\n",
+    "- Estimated indicative absolute magnitudes from Mangrove luminosity distances."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-32",
+   "metadata": {},
+   "source": [
+    "## An issue to report, or a question to ask: contact@fink-broker.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8087aa9e",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fink-client (3.12.0)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}