extragalactic/extragalactic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../Fink_PrimaryLogo_WEB.jpg\" width=400 />\n",
    "\n",
    "# Fink case study: extragalactic science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal\n",
    "\n",
    "The goal of this notebook is to show how Fink can contribute to extragalactic science. This notebook includes queries for **Active Galactic Nucleii** and **Supernova** science. \n",
    "\n",
    "Real-time analysis for follow-up coordination will be better done through the Fink livestreams which processes and makes available the alert data within minutes of reception. Access through this notebook uses the API access which is not real-time based. We aggregate the data into our API and Science Portal database at the end of each observing night. API access can be useful to analyze last night and older alerts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is behind?\n",
    "\n",
    "This notebook will use:\n",
    "- **Fink cross-match** with close by sources in the  [SIMBAD (Wenger et al. 2020)](https://aas.aanda.org/articles/aas/abs/2000/07/ds1821/ds1821.html) catalogue (within 1'') using [xmatch service](http://cdsxmatch.u-strasbg.fr) provided by the Centre de Données astronomiques de Strasbourg (CDS) .\n",
    "- **Fink's machine learning classification** scores \n",
    "    - [SuperNNova](https://github.com/supernnova/SuperNNova) [(Möller & de Boissière 2019)](https://academic.oup.com/mnras/article-abstract/491/3/4277/5651173) to classify SNe at all light-curve epochs\n",
    "    - Random Forest (Leoni et al. in prep) and [(Ishida et al. 2019b)](https://ui.adsabs.harvard.edu/abs/2019MNRAS.483....2I/abstract) to classify early (pre-max) SN candidates\n",
    "- **Fink's extra features** such as color `g-r` or `rate` when possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment set up\n",
    "\n",
    "To run this notebook, you need to import the following libraries (already installed in colab):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import io\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "\n",
    "APIURL = 'https://api.fink-portal.org'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1st case study: known AGNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>API query:</font> <font color='orange'> latest alerts</font> associated with known AGNs\n",
    "\n",
    "Each night, alerts are cross-matched in real-time against the CDS SIMBAD database. About 30% of the stream (after applying quality cuts) ends-up with a counter-part. Classified alerts can be then queried using the Fink API service. To know all available classes in SIMBAD, see https://api.fink-portal.org/api/v1/classes. We will first focus on alerts spatially coincident with an AGN featured in the CDS Simbad catalogue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get latests 5 alerts linked to AGNs in a time window\n",
    "r = requests.post(\n",
    "  '{}/api/v1/latests'.format(APIURL),\n",
    "  json={\n",
    "    'class': '(SIMBAD) AGN',    # tag from CDSxmatch service\n",
    "    'n': '5' ,                  # number of alerts\n",
    "    'startdate': '2021-03-01',  # use a start date\n",
    "    'stopdate': '2021-03-05',   # use a stop date\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can also use the `startdate` and `stopdate` arguments in the query. See https://api.fink-portal.org/api/v1/latests to check all the available arguments. This table contains all alerts linked to a CDS catalogue AGN. This includes\n",
    "- Alert information from ZTF processing (just latest photometry points).\n",
    "- Fink's added values.\n",
    "\n",
    "The table schema is described at https://api.fink-portal.org/api/v1/schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d:cdsxmatch</th>\n",
       "      <th>d:mulens</th>\n",
       "      <th>d:rf_kn_vs_nonkn</th>\n",
       "      <th>d:rf_snia_vs_nonia</th>\n",
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       "      <th>d:snn_snia_vs_nonia</th>\n",
       "      <th>i:candid</th>\n",
       "      <th>i:chipsf</th>\n",
       "      <th>i:classtar</th>\n",
       "      <th>...</th>\n",
       "      <th>i:tooflag</th>\n",
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       "      <th>i:ypos</th>\n",
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       "      <th>v:classification</th>\n",
       "      <th>v:lastdate</th>\n",
       "      <th>v:firstdate</th>\n",
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       "      <th>v:constellation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1522554983115015003</td>\n",
       "      <td>2.126123</td>\n",
       "      <td>0.992</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1112.7437</td>\n",
       "      <td>1651.1133</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AGN</td>\n",
       "      <td>2021-03-03 13:19:10.998</td>\n",
       "      <td>2018-09-06 06:36:04.000</td>\n",
       "      <td>909.279942</td>\n",
       "      <td>Delphinus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1522552633115015000</td>\n",
       "      <td>1.774720</td>\n",
       "      <td>0.663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1112.0876</td>\n",
       "      <td>1650.0837</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AGN</td>\n",
       "      <td>2021-03-03 13:15:48.001</td>\n",
       "      <td>2018-09-06 06:36:04.000</td>\n",
       "      <td>909.277593</td>\n",
       "      <td>Delphinus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1522550283115015003</td>\n",
       "      <td>2.139355</td>\n",
       "      <td>0.660</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1111.1361</td>\n",
       "      <td>1649.2211</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AGN</td>\n",
       "      <td>2021-03-03 13:12:25.004</td>\n",
       "      <td>2018-09-06 06:36:04.000</td>\n",
       "      <td>909.275243</td>\n",
       "      <td>Delphinus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1522547923115015002</td>\n",
       "      <td>2.369038</td>\n",
       "      <td>0.948</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1110.7671</td>\n",
       "      <td>1648.0677</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AGN</td>\n",
       "      <td>2021-03-03 13:09:00.996</td>\n",
       "      <td>2018-09-06 06:36:04.000</td>\n",
       "      <td>909.272882</td>\n",
       "      <td>Delphinus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1522544521815015007</td>\n",
       "      <td>1.376921</td>\n",
       "      <td>0.669</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1824.8479</td>\n",
       "      <td>1103.0751</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AGN</td>\n",
       "      <td>2021-03-03 13:04:07.003</td>\n",
       "      <td>2018-08-02 05:36:57.997</td>\n",
       "      <td>944.310521</td>\n",
       "      <td>Aquila</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
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      "text/plain": [
       "  d:cdsxmatch  d:mulens  d:rf_kn_vs_nonkn  d:rf_snia_vs_nonia  d:roid  \\\n",
       "0         AGN         0                 0                   0       0   \n",
       "1         AGN         0                 0                   0       0   \n",
       "2         AGN         0                 0                   0       0   \n",
       "3         AGN         0                 0                   0       0   \n",
       "4         AGN         0                 0                   0       0   \n",
       "\n",
       "   d:snn_sn_vs_all  d:snn_snia_vs_nonia             i:candid  i:chipsf  \\\n",
       "0                0                    0  1522554983115015003  2.126123   \n",
       "1                0                    0  1522552633115015000  1.774720   \n",
       "2                0                    0  1522550283115015003  2.139355   \n",
       "3                0                    0  1522547923115015002  2.369038   \n",
       "4                0                    0  1522544521815015007  1.376921   \n",
       "\n",
       "   i:classtar  ...  i:tooflag     i:xpos     i:ypos  d:tracklet  d:tns  \\\n",
       "0       0.992  ...          0  1112.7437  1651.1133                      \n",
       "1       0.663  ...          0  1112.0876  1650.0837                      \n",
       "2       0.660  ...          0  1111.1361  1649.2211                      \n",
       "3       0.948  ...          0  1110.7671  1648.0677                      \n",
       "4       0.669  ...          0  1824.8479  1103.0751                      \n",
       "\n",
       "   v:classification               v:lastdate              v:firstdate  \\\n",
       "0               AGN  2021-03-03 13:19:10.998  2018-09-06 06:36:04.000   \n",
       "1               AGN  2021-03-03 13:15:48.001  2018-09-06 06:36:04.000   \n",
       "2               AGN  2021-03-03 13:12:25.004  2018-09-06 06:36:04.000   \n",
       "3               AGN  2021-03-03 13:09:00.996  2018-09-06 06:36:04.000   \n",
       "4               AGN  2021-03-03 13:04:07.003  2018-08-02 05:36:57.997   \n",
       "\n",
       "      v:lapse  v:constellation  \n",
       "0  909.279942        Delphinus  \n",
       "1  909.277593        Delphinus  \n",
       "2  909.275243        Delphinus  \n",
       "3  909.272882        Delphinus  \n",
       "4  944.310521           Aquila  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get objects Ids associated to these alerts (AGNs in ZTF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_AGN_objectIds = pdf['i:objectId'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>API query:</font> get <font color='orange'> photometry </font> for one of these AGNs\n",
    "\n",
    "You can then use the `objects` API service to get all the photometry about a particular object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(\n",
    "  '{}/api/v1/objects'.format(APIURL),\n",
    "  json={\n",
    "    'objectId': recent_AGN_objectIds[0],\n",
    "    'output-format': 'json',\n",
    "    'withupperlim': 'True',\n",
    "    'startdate': '2021-03-01', # use a start date\n",
    "    'stopdate': '2021-03-05', # use a stop date\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "single_object = pd.read_json(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This query can include upper limits and bad  quality  data associated to this object using the argument `withupperlim`.\n",
    "\n",
    "See the `/api/v1/objects` endpoint on https://api.fink-portal.org to get the list of available arguments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>Plot:</font> light-curve for visualization\n",
    "\n",
    "See the example on https://fink-broker.readthedocs.io/en/latest/services/search/objectid to plot full lightcurve with layout template.  We implement here an useful function for plotting light-curves in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lc(pdf):\n",
    "    \"\"\" Plot photometry contains in an alert\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    pdf: pandas DataFrame\n",
    "        Pandas DataFrame with single alert row\n",
    "    \"\"\"\n",
    "    fig = plt.figure(figsize=(15, 6))\n",
    "\n",
    "    # Colors to plot\n",
    "    colordic = {1: 'C0', 2: 'C1'}\n",
    "\n",
    "    # Labels of ZTF filters\n",
    "    filtdic = {1: 'g', 2: 'r'}\n",
    "\n",
    "    for filt in [1, 2]:\n",
    "        # select data from one filter at a time\n",
    "        maskFilt = pdf['i:fid'] == filt\n",
    "\n",
    "        # The column `d:tag` is used to check data type\n",
    "        maskValid = pdf['d:tag'] == 'valid'\n",
    "        plt.errorbar(\n",
    "            pdf[maskValid & maskFilt]['i:jd'].apply(lambda x: x - 2400000.5),\n",
    "            pdf[maskValid & maskFilt]['i:magpsf'],\n",
    "            pdf[maskValid & maskFilt]['i:sigmapsf'],\n",
    "            ls = '', marker='o', color=colordic[filt], label=filtdic[filt]\n",
    "        )\n",
    "\n",
    "        maskUpper = pdf['d:tag'] == 'upperlim'\n",
    "        plt.plot(\n",
    "            pdf[maskUpper & maskFilt]['i:jd'].apply(lambda x: x - 2400000.5),\n",
    "            pdf[maskUpper & maskFilt]['i:diffmaglim'],\n",
    "            ls='', marker='v', color=colordic[filt], markerfacecolor='none'\n",
    "        )\n",
    "\n",
    "        maskBadquality = pdf['d:tag'] == 'badquality'\n",
    "        plt.errorbar(\n",
    "            pdf[maskBadquality & maskFilt]['i:jd'].apply(lambda x: x - 2400000.5),\n",
    "            pdf[maskBadquality & maskFilt]['i:magpsf'],\n",
    "            pdf[maskBadquality & maskFilt]['i:sigmapsf'],\n",
    "            ls='', marker='^', color=colordic[filt]\n",
    "        )\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.legend()\n",
    "    plt.title(pdf['i:objectId'][0])\n",
    "    plt.xlabel('Modified Julian Date')\n",
    "    plt.ylabel('Magnitude')\n",
    "    plt.show()\n",
    "    msg = \"\"\"\n",
    "    - Circles (●) with error bars show valid alerts that pass the Fink quality cuts. \n",
    "    - Upper triangles with errors (▲), represent alert measurements that do not satisfy Fink quality cuts, but are nevetheless contained in the history of valid alerts and used by classifiers.\n",
    "    - Lower triangles (▽), represent 5-sigma mag limit in difference image based on PSF-fit photometry contained in the history of valid alerts.\n",
    "    \"\"\"\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    - Circles (●) with error bars show valid alerts that pass the Fink quality cuts. \n",
      "    - Upper triangles with errors (▲), represent alert measurements that do not satisfy Fink quality cuts, but are nevetheless contained in the history of valid alerts and used by classifiers.\n",
      "    - Lower triangles (▽), represent 5-sigma mag limit in difference image based on PSF-fit photometry contained in the history of valid alerts.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "plot_lc(single_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2nd case study: early supernovae candidates\n",
    "**Early supernova candidates is a tag defined and provided by Fink.** \n",
    "It requires that the alert has no known transient association (from catalogues), it is relatively new (number of previous detections < 20) and when both a [SuperNNova](https://github.com/supernnova/SuperNNova) [(Möller & de Boissière 2019)](https://academic.oup.com/mnras/article-abstract/491/3/4277/5651173) trained to identify SNe Ia or SNe and a Random Forest (Leoni et al. in prep.) based on [(Ishida et al. 2019b)](https://ui.adsabs.harvard.edu/abs/2019MNRAS.483....2I/abstract) trained to select early supernovae have a probability higher than 50% of this object being a SN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>API query:</font>   <font color='orange'>latest alerts </font>  tagged as early supernovae candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get latests Early SN candidates (200 max)\n",
    "r = requests.post(\n",
    "  '{}/api/v1/latests'.format(APIURL),\n",
    "  json={\n",
    "    'class': 'Early SN Ia candidate',\n",
    "    'n': '200',\n",
    "    'color': 'True',\n",
    "    'startdate': '2021-04-01', # use a start date\n",
    "    'stopdate': '2021-04-13', # use a stop date\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>Inspect:</font> <font Color='orange'>color</font>  of these alerts (if there are g and r measurements in the same night)\n",
    "Color `v:g-r` is an added value provided by Fink. It is computed as the subtraction of the `magpsf` in the `g` filter minus its corresponding `magpsf` in the `r` filter for an object in a given night. If no same-night different filter measurements are available, the value of color is `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>v:constellation</th>\n",
       "      <th>v:lapse</th>\n",
       "      <th>v:firstdate</th>\n",
       "      <th>v:lastdate</th>\n",
       "      <th>v:sigma(rate(g-r))</th>\n",
       "      <th>v:rate(g-r)</th>\n",
       "      <th>v:sigma(g-r)</th>\n",
       "      <th>v:g-r</th>\n",
       "      <th>v:sigma(rate)</th>\n",
       "      <th>v:rate</th>\n",
       "      <th>...</th>\n",
       "      <th>i:classtar</th>\n",
       "      <th>i:chipsf</th>\n",
       "      <th>i:candid</th>\n",
       "      <th>d:snn_snia_vs_nonia</th>\n",
       "      <th>d:snn_sn_vs_all</th>\n",
       "      <th>d:roid</th>\n",
       "      <th>d:rf_snia_vs_nonia</th>\n",
       "      <th>d:rf_kn_vs_nonkn</th>\n",
       "      <th>d:mulens</th>\n",
       "      <th>d:cdsxmatch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ophiucus</td>\n",
       "      <td>9.019630</td>\n",
       "      <td>2021-04-03 11:06:14.999</td>\n",
       "      <td>2021-04-12 11:34:30.996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094092</td>\n",
       "      <td>-0.022103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983</td>\n",
       "      <td>6.674384</td>\n",
       "      <td>1562482304415015011</td>\n",
       "      <td>0.787034</td>\n",
       "      <td>0.677686</td>\n",
       "      <td>0</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Draco</td>\n",
       "      <td>10.044410</td>\n",
       "      <td>2021-04-02 08:38:52.996</td>\n",
       "      <td>2021-04-12 09:42:50.003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104643</td>\n",
       "      <td>-0.106969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.986</td>\n",
       "      <td>3.563279</td>\n",
       "      <td>1562404740215015010</td>\n",
       "      <td>0.818026</td>\n",
       "      <td>0.835022</td>\n",
       "      <td>0</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Draco</td>\n",
       "      <td>10.033437</td>\n",
       "      <td>2021-04-02 08:38:52.996</td>\n",
       "      <td>2021-04-12 09:27:01.996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104643</td>\n",
       "      <td>-0.106969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.981</td>\n",
       "      <td>1.512014</td>\n",
       "      <td>1562393770215015003</td>\n",
       "      <td>0.692514</td>\n",
       "      <td>0.751971</td>\n",
       "      <td>0</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ophiucus</td>\n",
       "      <td>8.918762</td>\n",
       "      <td>2021-04-03 11:06:14.999</td>\n",
       "      <td>2021-04-12 09:09:16.001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094092</td>\n",
       "      <td>-0.022103</td>\n",
       "      <td>0.053127</td>\n",
       "      <td>-0.084717</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.216743</td>\n",
       "      <td>1562381434415015007</td>\n",
       "      <td>0.706066</td>\n",
       "      <td>0.874443</td>\n",
       "      <td>0</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Auriga</td>\n",
       "      <td>13.061331</td>\n",
       "      <td>2021-03-30 03:52:44.003</td>\n",
       "      <td>2021-04-12 05:21:03.001</td>\n",
       "      <td>0.076465</td>\n",
       "      <td>-0.004334</td>\n",
       "      <td>0.110112</td>\n",
       "      <td>0.083787</td>\n",
       "      <td>0.060909</td>\n",
       "      <td>-0.023112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983</td>\n",
       "      <td>4.450665</td>\n",
       "      <td>1562222953415015014</td>\n",
       "      <td>0.872538</td>\n",
       "      <td>0.984186</td>\n",
       "      <td>0</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  v:constellation    v:lapse              v:firstdate  \\\n",
       "0        Ophiucus   9.019630  2021-04-03 11:06:14.999   \n",
       "1           Draco  10.044410  2021-04-02 08:38:52.996   \n",
       "3           Draco  10.033437  2021-04-02 08:38:52.996   \n",
       "4        Ophiucus   8.918762  2021-04-03 11:06:14.999   \n",
       "7          Auriga  13.061331  2021-03-30 03:52:44.003   \n",
       "\n",
       "                v:lastdate  v:sigma(rate(g-r))  v:rate(g-r)  v:sigma(g-r)  \\\n",
       "0  2021-04-12 11:34:30.996                 NaN          NaN      0.094092   \n",
       "1  2021-04-12 09:42:50.003                 NaN          NaN      0.104643   \n",
       "3  2021-04-12 09:27:01.996                 NaN          NaN      0.104643   \n",
       "4  2021-04-12 09:09:16.001                 NaN          NaN      0.094092   \n",
       "7  2021-04-12 05:21:03.001            0.076465    -0.004334      0.110112   \n",
       "\n",
       "      v:g-r  v:sigma(rate)    v:rate  ... i:classtar   i:chipsf  \\\n",
       "0 -0.022103            NaN       NaN  ...      0.983   6.674384   \n",
       "1 -0.106969            NaN       NaN  ...      0.986   3.563279   \n",
       "3 -0.106969            NaN       NaN  ...      0.981   1.512014   \n",
       "4 -0.022103       0.053127 -0.084717  ...      1.000  17.216743   \n",
       "7  0.083787       0.060909 -0.023112  ...      0.983   4.450665   \n",
       "\n",
       "              i:candid  d:snn_snia_vs_nonia  d:snn_sn_vs_all  d:roid  \\\n",
       "0  1562482304415015011             0.787034         0.677686       0   \n",
       "1  1562404740215015010             0.818026         0.835022       0   \n",
       "3  1562393770215015003             0.692514         0.751971       0   \n",
       "4  1562381434415015007             0.706066         0.874443       0   \n",
       "7  1562222953415015014             0.872538         0.984186       0   \n",
       "\n",
       "  d:rf_snia_vs_nonia  d:rf_kn_vs_nonkn  d:mulens  d:cdsxmatch  \n",
       "0              0.973                 0         0      Unknown  \n",
       "1              0.698                 0         0      Unknown  \n",
       "3              0.622                 0         0      Unknown  \n",
       "4              0.922                 0         0      Unknown  \n",
       "7              0.941                 0         0      Unknown  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only valid color measurements\n",
    "candSNe_w_color = pdf[~pdf[\"v:g-r\"].isna()]\n",
    "\n",
    "# display 5 of these alerts with inverted column order\n",
    "candSNe_w_color[candSNe_w_color.columns[::-1]][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table schema is described at https://api.fink-portal.org/api/v1/schema."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may want now to see the distribution of the latest colors of alerts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of objects')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.hist(candSNe_w_color[\"v:g-r\"], histtype='step')\n",
    "plt.xlabel(\"Color (g-r)\")\n",
    "plt.ylabel('number of objects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>Selecting </font>   <font color='orange'>\"bluer\" color</font>  (e.g. g-r >0)\n",
    "\n",
    "An example for a possible query: searching alerts which have positive `v:g-r`. Which is equivalent to loosely saying they are \"bluer\" events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>i:objectId</th>\n",
       "      <th>v:g-r</th>\n",
       "      <th>v:lastdate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>0.083787</td>\n",
       "      <td>2021-04-12 05:21:03.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>0.170128</td>\n",
       "      <td>2021-04-12 04:37:05.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>0.083787</td>\n",
       "      <td>2021-04-12 04:22:15.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>0.170128</td>\n",
       "      <td>2021-04-12 03:46:19.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>0.217138</td>\n",
       "      <td>2021-04-11 11:16:46.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>0.217138</td>\n",
       "      <td>2021-04-11 10:50:15.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ZTF21aaqkqwq</td>\n",
       "      <td>0.064339</td>\n",
       "      <td>2021-04-10 10:54:12.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ZTF21aaqkqwq</td>\n",
       "      <td>0.064339</td>\n",
       "      <td>2021-04-10 09:50:37.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>0.092554</td>\n",
       "      <td>2021-04-10 04:48:20.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>0.108023</td>\n",
       "      <td>2021-04-10 04:36:47.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>0.108023</td>\n",
       "      <td>2021-04-10 04:27:31.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>0.092554</td>\n",
       "      <td>2021-04-10 03:54:09.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>0.197639</td>\n",
       "      <td>2021-04-09 11:45:37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>0.197639</td>\n",
       "      <td>2021-04-09 10:48:52.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>0.077716</td>\n",
       "      <td>2021-04-09 07:11:46.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>0.077716</td>\n",
       "      <td>2021-04-09 06:32:37.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>0.064455</td>\n",
       "      <td>2021-04-09 06:04:04.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ZTF21aaqzmdg</td>\n",
       "      <td>0.163885</td>\n",
       "      <td>2021-04-09 05:37:58.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ZTF21aaqzmdg</td>\n",
       "      <td>0.163885</td>\n",
       "      <td>2021-04-09 04:57:13.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ZTF21aaqvuyf</td>\n",
       "      <td>0.033592</td>\n",
       "      <td>2021-04-08 06:50:49.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ZTF21aaqvuyf</td>\n",
       "      <td>0.033592</td>\n",
       "      <td>2021-04-08 05:17:51.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>0.105565</td>\n",
       "      <td>2021-04-08 05:09:37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>0.182505</td>\n",
       "      <td>2021-04-08 04:44:05.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>0.106981</td>\n",
       "      <td>2021-04-08 04:37:21.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>0.106981</td>\n",
       "      <td>2021-04-08 04:29:34.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>0.182505</td>\n",
       "      <td>2021-04-08 04:22:08.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>0.105565</td>\n",
       "      <td>2021-04-08 04:14:01.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>ZTF21aapxbsk</td>\n",
       "      <td>0.138881</td>\n",
       "      <td>2021-04-06 04:27:26.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ZTF21aapxbsk</td>\n",
       "      <td>0.138881</td>\n",
       "      <td>2021-04-06 03:44:53.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>0.033759</td>\n",
       "      <td>2021-04-05 07:06:10.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>0.033759</td>\n",
       "      <td>2021-04-05 06:34:36.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>0.033759</td>\n",
       "      <td>2021-04-05 06:33:15.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>ZTF21aaqzmdg</td>\n",
       "      <td>0.034651</td>\n",
       "      <td>2021-04-05 05:59:11.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ZTF21aapfdqw</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>2021-04-05 05:03:50.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>ZTF21aaqzmdg</td>\n",
       "      <td>0.034651</td>\n",
       "      <td>2021-04-05 04:06:53.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ZTF21aapfdqw</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>2021-04-05 04:02:07.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>ZTF21aaqkqwq</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>2021-04-04 11:44:46.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>ZTF21aaqkqwq</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>2021-04-04 11:20:59.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>ZTF21aareacl</td>\n",
       "      <td>0.193971</td>\n",
       "      <td>2021-04-04 06:14:40.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>ZTF21aapfdis</td>\n",
       "      <td>0.242660</td>\n",
       "      <td>2021-04-04 06:02:51.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>ZTF21aaokuyo</td>\n",
       "      <td>0.252012</td>\n",
       "      <td>2021-04-04 05:40:13.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>ZTF21aareacl</td>\n",
       "      <td>0.193971</td>\n",
       "      <td>2021-04-04 05:12:14.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>ZTF21aapfdis</td>\n",
       "      <td>0.242660</td>\n",
       "      <td>2021-04-04 05:04:40.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>ZTF21aaokuyo</td>\n",
       "      <td>0.252012</td>\n",
       "      <td>2021-04-04 04:38:15.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>0.031613</td>\n",
       "      <td>2021-04-04 04:27:47.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>0.031613</td>\n",
       "      <td>2021-04-04 03:50:22.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>ZTF21aaqzmdg</td>\n",
       "      <td>0.027128</td>\n",
       "      <td>2021-04-03 06:41:18.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>ZTF21aaqzmdg</td>\n",
       "      <td>0.027128</td>\n",
       "      <td>2021-04-03 05:59:31.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>ZTF21aappktx</td>\n",
       "      <td>0.016641</td>\n",
       "      <td>2021-04-02 08:59:00.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>ZTF21aappktx</td>\n",
       "      <td>0.016641</td>\n",
       "      <td>2021-04-02 07:28:48.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>ZTF21aaostqr</td>\n",
       "      <td>0.518420</td>\n",
       "      <td>2021-04-02 06:59:41.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>ZTF21aaokuyo</td>\n",
       "      <td>0.199553</td>\n",
       "      <td>2021-04-02 06:57:34.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>ZTF21aaostqr</td>\n",
       "      <td>0.518420</td>\n",
       "      <td>2021-04-02 05:37:19.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>ZTF21aaokuyo</td>\n",
       "      <td>0.199553</td>\n",
       "      <td>2021-04-02 05:08:13.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>0.091274</td>\n",
       "      <td>2021-04-02 04:41:50.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>0.091274</td>\n",
       "      <td>2021-04-02 04:06:53.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>ZTF21aapjcqb</td>\n",
       "      <td>0.057493</td>\n",
       "      <td>2021-04-01 07:39:09.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>ZTF21aapjcqb</td>\n",
       "      <td>0.057493</td>\n",
       "      <td>2021-04-01 07:10:52.003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       i:objectId     v:g-r               v:lastdate\n",
       "7    ZTF21aaqxiaj  0.083787  2021-04-12 05:21:03.001\n",
       "14   ZTF21aaqvmix  0.170128  2021-04-12 04:37:05.002\n",
       "16   ZTF21aaqxiaj  0.083787  2021-04-12 04:22:15.004\n",
       "21   ZTF21aaqvmix  0.170128  2021-04-12 03:46:19.998\n",
       "22   ZTF21aarcobt  0.217138  2021-04-11 11:16:46.004\n",
       "23   ZTF21aarcobt  0.217138  2021-04-11 10:50:15.000\n",
       "29   ZTF21aaqkqwq  0.064339  2021-04-10 10:54:12.004\n",
       "30   ZTF21aaqkqwq  0.064339  2021-04-10 09:50:37.997\n",
       "39   ZTF21aaqxiaj  0.092554  2021-04-10 04:48:20.002\n",
       "41   ZTF21aaqvmix  0.108023  2021-04-10 04:36:47.998\n",
       "42   ZTF21aaqvmix  0.108023  2021-04-10 04:27:31.997\n",
       "44   ZTF21aaqxiaj  0.092554  2021-04-10 03:54:09.003\n",
       "46   ZTF21aarcobt  0.197639  2021-04-09 11:45:37.002\n",
       "47   ZTF21aarcobt  0.197639  2021-04-09 10:48:52.998\n",
       "50   ZTF21aaquhqr  0.077716  2021-04-09 07:11:46.003\n",
       "51   ZTF21aaquhqr  0.077716  2021-04-09 06:32:37.003\n",
       "52   ZTF21aaquhqr  0.064455  2021-04-09 06:04:04.002\n",
       "53   ZTF21aaqzmdg  0.163885  2021-04-09 05:37:58.996\n",
       "55   ZTF21aaqzmdg  0.163885  2021-04-09 04:57:13.003\n",
       "59   ZTF21aaqvuyf  0.033592  2021-04-08 06:50:49.004\n",
       "64   ZTF21aaqvuyf  0.033592  2021-04-08 05:17:51.996\n",
       "65   ZTF21aaqxiaj  0.105565  2021-04-08 05:09:37.002\n",
       "68   ZTF21aapdvea  0.182505  2021-04-08 04:44:05.001\n",
       "69   ZTF21aaqvmix  0.106981  2021-04-08 04:37:21.003\n",
       "70   ZTF21aaqvmix  0.106981  2021-04-08 04:29:34.002\n",
       "72   ZTF21aapdvea  0.182505  2021-04-08 04:22:08.999\n",
       "73   ZTF21aaqxiaj  0.105565  2021-04-08 04:14:01.997\n",
       "80   ZTF21aapxbsk  0.138881  2021-04-06 04:27:26.001\n",
       "83   ZTF21aapxbsk  0.138881  2021-04-06 03:44:53.002\n",
       "87   ZTF21aaquhqr  0.033759  2021-04-05 07:06:10.996\n",
       "88   ZTF21aaquhqr  0.033759  2021-04-05 06:34:36.002\n",
       "89   ZTF21aaquhqr  0.033759  2021-04-05 06:33:15.002\n",
       "90   ZTF21aaqzmdg  0.034651  2021-04-05 05:59:11.003\n",
       "94   ZTF21aapfdqw  0.009636  2021-04-05 05:03:50.003\n",
       "95   ZTF21aaqzmdg  0.034651  2021-04-05 04:06:53.004\n",
       "98   ZTF21aapfdqw  0.009636  2021-04-05 04:02:07.002\n",
       "99   ZTF21aaqkqwq  0.009964  2021-04-04 11:44:46.000\n",
       "100  ZTF21aaqkqwq  0.009964  2021-04-04 11:20:59.001\n",
       "103  ZTF21aareacl  0.193971  2021-04-04 06:14:40.001\n",
       "104  ZTF21aapfdis  0.242660  2021-04-04 06:02:51.996\n",
       "106  ZTF21aaokuyo  0.252012  2021-04-04 05:40:13.996\n",
       "108  ZTF21aareacl  0.193971  2021-04-04 05:12:14.000\n",
       "111  ZTF21aapfdis  0.242660  2021-04-04 05:04:40.002\n",
       "114  ZTF21aaokuyo  0.252012  2021-04-04 04:38:15.996\n",
       "117  ZTF21aapdvea  0.031613  2021-04-04 04:27:47.998\n",
       "119  ZTF21aapdvea  0.031613  2021-04-04 03:50:22.998\n",
       "123  ZTF21aaqzmdg  0.027128  2021-04-03 06:41:18.004\n",
       "124  ZTF21aaqzmdg  0.027128  2021-04-03 05:59:31.004\n",
       "131  ZTF21aappktx  0.016641  2021-04-02 08:59:00.997\n",
       "135  ZTF21aappktx  0.016641  2021-04-02 07:28:48.003\n",
       "137  ZTF21aaostqr  0.518420  2021-04-02 06:59:41.004\n",
       "138  ZTF21aaokuyo  0.199553  2021-04-02 06:57:34.998\n",
       "140  ZTF21aaostqr  0.518420  2021-04-02 05:37:19.001\n",
       "143  ZTF21aaokuyo  0.199553  2021-04-02 05:08:13.004\n",
       "147  ZTF21aapdvea  0.091274  2021-04-02 04:41:50.001\n",
       "150  ZTF21aapdvea  0.091274  2021-04-02 04:06:53.997\n",
       "160  ZTF21aapjcqb  0.057493  2021-04-01 07:39:09.996\n",
       "162  ZTF21aapjcqb  0.057493  2021-04-01 07:10:52.003"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_selected = candSNe_w_color[candSNe_w_color[\"v:g-r\"] > 0]\n",
    "\n",
    "color_selected[['i:objectId', \"v:g-r\", \"v:lastdate\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>Selecting </font>   <font color='orange'>\"bluer\" brighter</font>    select alerts with given color and brighter than magpsf 19 (either filter)\n",
    "An example for a possible query: searching alerts which have negative `v:g-r` and are brighter than a given magnitude.\n",
    "Please note that this is the latest measured magnitude, not a projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>i:objectId</th>\n",
       "      <th>i:magpsf</th>\n",
       "      <th>v:g-r</th>\n",
       "      <th>v:rate(g-r)</th>\n",
       "      <th>v:lastdate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>18.376510</td>\n",
       "      <td>0.083787</td>\n",
       "      <td>-0.004334</td>\n",
       "      <td>2021-04-12 05:21:03.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>17.434948</td>\n",
       "      <td>0.170128</td>\n",
       "      <td>0.031049</td>\n",
       "      <td>2021-04-12 04:37:05.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>18.292723</td>\n",
       "      <td>0.083787</td>\n",
       "      <td>-0.004334</td>\n",
       "      <td>2021-04-12 04:22:15.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>17.264820</td>\n",
       "      <td>0.170128</td>\n",
       "      <td>0.031049</td>\n",
       "      <td>2021-04-12 03:46:19.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>18.708616</td>\n",
       "      <td>0.217138</td>\n",
       "      <td>0.009848</td>\n",
       "      <td>2021-04-11 11:16:46.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>18.491478</td>\n",
       "      <td>0.217138</td>\n",
       "      <td>0.009848</td>\n",
       "      <td>2021-04-11 10:50:15.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>18.423260</td>\n",
       "      <td>0.092554</td>\n",
       "      <td>-0.006554</td>\n",
       "      <td>2021-04-10 04:48:20.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>17.316248</td>\n",
       "      <td>0.108023</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>2021-04-10 04:36:47.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>17.208225</td>\n",
       "      <td>0.108023</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>2021-04-10 04:27:31.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>18.330706</td>\n",
       "      <td>0.092554</td>\n",
       "      <td>-0.006554</td>\n",
       "      <td>2021-04-10 03:54:09.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>18.618427</td>\n",
       "      <td>0.197639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-09 11:45:37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ZTF21aarcobt</td>\n",
       "      <td>18.420788</td>\n",
       "      <td>0.197639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-09 10:48:52.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>17.866528</td>\n",
       "      <td>0.077716</td>\n",
       "      <td>0.668856</td>\n",
       "      <td>2021-04-09 07:11:46.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>17.944244</td>\n",
       "      <td>0.077716</td>\n",
       "      <td>0.668856</td>\n",
       "      <td>2021-04-09 06:32:37.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>17.930983</td>\n",
       "      <td>0.064455</td>\n",
       "      <td>0.007758</td>\n",
       "      <td>2021-04-09 06:04:04.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ZTF21aaqvuyf</td>\n",
       "      <td>18.185312</td>\n",
       "      <td>0.033592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-08 06:50:49.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ZTF21aaqvuyf</td>\n",
       "      <td>18.218904</td>\n",
       "      <td>0.033592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-08 05:17:51.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>18.511090</td>\n",
       "      <td>0.105565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-08 05:09:37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>17.277006</td>\n",
       "      <td>0.106981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-08 04:37:21.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ZTF21aaqvmix</td>\n",
       "      <td>17.170025</td>\n",
       "      <td>0.106981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-08 04:29:34.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>18.868633</td>\n",
       "      <td>0.182505</td>\n",
       "      <td>0.037375</td>\n",
       "      <td>2021-04-08 04:22:08.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ZTF21aaqxiaj</td>\n",
       "      <td>18.405525</td>\n",
       "      <td>0.105565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-08 04:14:01.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>17.883242</td>\n",
       "      <td>0.033759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-05 07:06:10.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>17.849483</td>\n",
       "      <td>0.033759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-05 06:34:36.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>ZTF21aaquhqr</td>\n",
       "      <td>17.879236</td>\n",
       "      <td>0.033759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-05 06:33:15.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>ZTF21aapfdqw</td>\n",
       "      <td>16.882912</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-05 05:03:50.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ZTF21aapfdqw</td>\n",
       "      <td>16.892548</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-05 04:02:07.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>ZTF21aaqkqwq</td>\n",
       "      <td>18.903156</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>0.061759</td>\n",
       "      <td>2021-04-04 11:44:46.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>ZTF21aaqkqwq</td>\n",
       "      <td>18.913120</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>0.061759</td>\n",
       "      <td>2021-04-04 11:20:59.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>18.834597</td>\n",
       "      <td>0.031613</td>\n",
       "      <td>-0.030003</td>\n",
       "      <td>2021-04-04 04:27:47.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>18.866210</td>\n",
       "      <td>0.031613</td>\n",
       "      <td>-0.030003</td>\n",
       "      <td>2021-04-04 03:50:22.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>ZTF21aappktx</td>\n",
       "      <td>18.388426</td>\n",
       "      <td>0.016641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-02 08:59:00.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>ZTF21aappktx</td>\n",
       "      <td>18.405067</td>\n",
       "      <td>0.016641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-02 07:28:48.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>18.822920</td>\n",
       "      <td>0.091274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-02 04:41:50.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ZTF21aapdvea</td>\n",
       "      <td>18.914194</td>\n",
       "      <td>0.091274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-02 04:06:53.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>ZTF21aapjcqb</td>\n",
       "      <td>18.410028</td>\n",
       "      <td>0.057493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-01 07:39:09.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>ZTF21aapjcqb</td>\n",
       "      <td>18.352535</td>\n",
       "      <td>0.057493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-04-01 07:10:52.003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       i:objectId   i:magpsf     v:g-r  v:rate(g-r)               v:lastdate\n",
       "7    ZTF21aaqxiaj  18.376510  0.083787    -0.004334  2021-04-12 05:21:03.001\n",
       "14   ZTF21aaqvmix  17.434948  0.170128     0.031049  2021-04-12 04:37:05.002\n",
       "16   ZTF21aaqxiaj  18.292723  0.083787    -0.004334  2021-04-12 04:22:15.004\n",
       "21   ZTF21aaqvmix  17.264820  0.170128     0.031049  2021-04-12 03:46:19.998\n",
       "22   ZTF21aarcobt  18.708616  0.217138     0.009848  2021-04-11 11:16:46.004\n",
       "23   ZTF21aarcobt  18.491478  0.217138     0.009848  2021-04-11 10:50:15.000\n",
       "39   ZTF21aaqxiaj  18.423260  0.092554    -0.006554  2021-04-10 04:48:20.002\n",
       "41   ZTF21aaqvmix  17.316248  0.108023     0.000521  2021-04-10 04:36:47.998\n",
       "42   ZTF21aaqvmix  17.208225  0.108023     0.000521  2021-04-10 04:27:31.997\n",
       "44   ZTF21aaqxiaj  18.330706  0.092554    -0.006554  2021-04-10 03:54:09.003\n",
       "46   ZTF21aarcobt  18.618427  0.197639          NaN  2021-04-09 11:45:37.002\n",
       "47   ZTF21aarcobt  18.420788  0.197639          NaN  2021-04-09 10:48:52.998\n",
       "50   ZTF21aaquhqr  17.866528  0.077716     0.668856  2021-04-09 07:11:46.003\n",
       "51   ZTF21aaquhqr  17.944244  0.077716     0.668856  2021-04-09 06:32:37.003\n",
       "52   ZTF21aaquhqr  17.930983  0.064455     0.007758  2021-04-09 06:04:04.002\n",
       "59   ZTF21aaqvuyf  18.185312  0.033592          NaN  2021-04-08 06:50:49.004\n",
       "64   ZTF21aaqvuyf  18.218904  0.033592          NaN  2021-04-08 05:17:51.996\n",
       "65   ZTF21aaqxiaj  18.511090  0.105565          NaN  2021-04-08 05:09:37.002\n",
       "69   ZTF21aaqvmix  17.277006  0.106981          NaN  2021-04-08 04:37:21.003\n",
       "70   ZTF21aaqvmix  17.170025  0.106981          NaN  2021-04-08 04:29:34.002\n",
       "72   ZTF21aapdvea  18.868633  0.182505     0.037375  2021-04-08 04:22:08.999\n",
       "73   ZTF21aaqxiaj  18.405525  0.105565          NaN  2021-04-08 04:14:01.997\n",
       "87   ZTF21aaquhqr  17.883242  0.033759          NaN  2021-04-05 07:06:10.996\n",
       "88   ZTF21aaquhqr  17.849483  0.033759          NaN  2021-04-05 06:34:36.002\n",
       "89   ZTF21aaquhqr  17.879236  0.033759          NaN  2021-04-05 06:33:15.002\n",
       "94   ZTF21aapfdqw  16.882912  0.009636          NaN  2021-04-05 05:03:50.003\n",
       "98   ZTF21aapfdqw  16.892548  0.009636          NaN  2021-04-05 04:02:07.002\n",
       "99   ZTF21aaqkqwq  18.903156  0.009964     0.061759  2021-04-04 11:44:46.000\n",
       "100  ZTF21aaqkqwq  18.913120  0.009964     0.061759  2021-04-04 11:20:59.001\n",
       "117  ZTF21aapdvea  18.834597  0.031613    -0.030003  2021-04-04 04:27:47.998\n",
       "119  ZTF21aapdvea  18.866210  0.031613    -0.030003  2021-04-04 03:50:22.998\n",
       "131  ZTF21aappktx  18.388426  0.016641          NaN  2021-04-02 08:59:00.997\n",
       "135  ZTF21aappktx  18.405067  0.016641          NaN  2021-04-02 07:28:48.003\n",
       "147  ZTF21aapdvea  18.822920  0.091274          NaN  2021-04-02 04:41:50.001\n",
       "150  ZTF21aapdvea  18.914194  0.091274          NaN  2021-04-02 04:06:53.997\n",
       "160  ZTF21aapjcqb  18.410028  0.057493          NaN  2021-04-01 07:39:09.996\n",
       "162  ZTF21aapjcqb  18.352535  0.057493          NaN  2021-04-01 07:10:52.003"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bright_candSNe_w_color = color_selected[color_selected[\"i:magpsf\"] < 19]\n",
    "\n",
    "bright_candSNe_w_color[[\"i:objectId\", \"i:magpsf\", \"v:g-r\", \"v:rate(g-r)\", \"v:lastdate\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there has been two consecutive nights with color measurement, the `v:rate(g-r)` would be different from `NaN`. This could be an useful added value to inspect the color evolution of the transient and filter candidates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> API query:</font>   <font color='orange'>photometry</font>  for one high-probability alert with given color and brighter than magpsf 19 (either filter)\n",
    "An example for a possible query: searching alerts with high-classification probability which have positive `v:g-r` and are brighter than a given magnitude.\n",
    "\n",
    "[SuperNNova](https://github.com/supernnova/SuperNNova) [(Möller & de Boissière 2019)](https://academic.oup.com/mnras/article-abstract/491/3/4277/5651173) can be trained in different ways. Currently we have models trained to disentangle `SNe Ia vs non Ia` `(snn_snia_vs_nonia)` and `SNe vs other transients` `(snn_sn_vs_all)`. These algorithms have been trained using simulations from [(Muthukrishna et al. 2019)](https://arxiv.org/abs/1904.00014)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_threshold = 0.8 # 80% confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select alerts that fit criteria\n",
    "f1 = bright_candSNe_w_color['d:snn_sn_vs_all'] > prob_threshold\n",
    "f2 = bright_candSNe_w_color['d:snn_snia_vs_nonia'] > prob_threshold\n",
    "high_prob_objs = bright_candSNe_w_color[f1 | f2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the latests alert and object\n",
    "high_prob_obj = high_prob_objs.sort_values(by=['i:ndethist'])['i:objectId'].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(\n",
    "  '{}/api/v1/objects'.format(APIURL),\n",
    "  json={\n",
    "    'objectId': high_prob_obj,\n",
    "    'output-format': 'json',\n",
    "    'withupperlim': 'True',\n",
    "    'startdate': '2021-03-01', # use a start date\n",
    "    'stopdate': '2021-03-05', # use a stop date\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "single_object = pd.read_json(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    - Circles (●) with error bars show valid alerts that pass the Fink quality cuts. \n",
      "    - Upper triangles with errors (▲), represent alert measurements that do not satisfy Fink quality cuts, but are nevetheless contained in the history of valid alerts and used by classifiers.\n",
      "    - Lower triangles (▽), represent 5-sigma mag limit in difference image based on PSF-fit photometry contained in the history of valid alerts.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "plot_lc(single_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <font color='orange'>Color evolution </font>  for one high-probability alert with given color and brighter than magpsf 19 (either filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xKCoqyqF9AADgyn755RdNnDhRPXv2VJ06deTt7a3AwEB16dJF77zzjnJzc112rGXLltnudS73d3dRm7J+1K1bt8y+eXl5euedd3TLLbeoZs2a8vPzU4sWLfT888/r/PnzLjuXPzIMQ/PmzVOfPn1Up04dWa1WhYeHq1evXnr99dedGtPLxTUCAABcNV588UVNnDjR9ut58+bp7rvvvmyffv36admyZbZfHzx4sMoEQvn5+erVq5d+/vlnSZK/v78CAwMlSR4eVfvfgu+//359/PHHioyM1KFDhyq7nKveoUOH1LBhwxLbrVaratSooeDgYN14443q0KGDRowYoXr16rmtljlz5ujQoUPq3r27unfv7rbjAACuPklJSbrppptsv46IiNBNN92ko0ePav369Vq/fr3mzJmj7777znbP4qz09HQ9+uijDvVp166drFZrie21a9cutf3Fixd155136scff5QkNWvWTNWrV9euXbv08ssv67PPPtO6desUHh7u+AlcRnp6uu666y6tXLlSktSoUSNFRkbqxIkT+vHHH/XLL7/oH//4h8PjVu27QwAAAAfMnj37svuPHz+u7777roKqcU7jxo3VrFkz1axZs8S+FStW6Oeff5a3t7fWrVunCxcu6LffftNvv/2mGjVqSCq8eW3WrJmqVatW0aVXSTVr1lSzZs3UuHHjyi7FaTVq1FCdOnVUp04dBQQE6OzZs0pISNDnn3+uv//974qMjNT//d//6fTp0245/pw5czRx4sRKeToVAOB6RU/Rl/ZGwx8ZhqHQ0FBNnjxZhw4dUnJysrZs2aKUlBQtXbpUtWvX1pYtW/TII4+Uu67x48crOTlZgwYNsrvPggULbOHkpT+++uqrUts/9dRT+vHHH1W3bl1t2bJFe/bsUXx8vI4eParbbrtNBw4c0MiRI8t9LpcyDENDhgzRypUr1adPH+3fv19JSUnavHmzDh8+rNOnT+ujjz5yamxCQQAAcM0LDg5W9erVtXLlSh09erTMdp988ony8/Ov6icDf/jhB+3Zs0d33XVXiX2//vqrJOnGG2/UrbfeWmr/PXv2aM+ePerQoYNb67xW3HXXXdqzZ49++OGHyi7FaTNmzLCFw6dOnVJOTo6OHz+uRYsWqW/fvsrPz9enn36q1q1b8xQmAMCl6tevr4MHD+r5559XZGRksX133nmn3nrrLUnSokWLdObMGaePs3HjRr399tsaNGiQBg8eXJ6Sy5SWlqYPP/xQkjRt2jS1a9fOti84OFhz586Vv7+/1q5dq++//95lx50zZ45WrFihjh07asmSJSX+obJWrVoaOHCgU2MTCgIAgGte9erVFRMTo4KCgjK/xSf9/iTh1fbNO3tdvHhRUuFrw8DlhIWFaciQIVq2bJk+//xzeXt76/jx4+rXr5/y8vIquzwAwDXC19f3sm8n9O3bV1LhJ1D279/v1DFyc3P18MMPq1q1avr3v//t1Bj22Lhxo/Ly8uTh4aGhQ4eW2F+3bl117dpVUuEna0qTnp6uV155Re3bt7d9j7B58+Z69tlnlZqaWmqfadOmSZL++c9/ysvLtV8BJBQEAACm8MADD0hSmaHg+vXrtW/fPjVq1Mh2Q3c5WVlZmj59um655RYFBgbK19dXkZGRuvfee23f9CtLfn6+Zs6cqbZt26p69eoKCgpS9+7dtXDhwiset7SFRooW8Ch6jefHH38s9rHsS1/vsWehkaVLl2ro0KGqV6+erFarAgMD1bVrV7377rvKycm5bH3//e9/1blzZwUEBKhmzZrq2LGj4uLiZBjGFc/NWS+++KIsFstlv1e3Zs0a27k74nILjeTm5urrr7/W6NGj1a5dO4WFhcnHx0ehoaHq3bu35s2bV+Z5/7Ge/fv368EHH1RERISsVqvq16+vhx9+WMeOHXOoXmcMHz5cL7/8siRp9+7d+vjjj0u0OXjwoKZMmaI+ffqoadOmql69uvz9/XX99ddr7NixSk5OLtGn6Peu6LtLEydOLPEh99KeTNywYYP+9Kc/KTIyUr6+vqpZs6Y6dOigKVOmKD093bUnDwCoVJmZmbafO/tpk1deeUU7d+7UpEmTVL9+fYf6Tpo0SX379tXtt9+u+++/X5988omys7NLbVsU2gUHB8vHx6fUNkXH/+mnn0rs279/v2666SaNHz9eP//8s0JCQtSoUSMdOHBAU6ZMUXR0dIm/F5OSkrRz5055eHioR48e2rRpkx555BHddtttGjx4sF599dXyff7DAAAAuEZNmDDBkGRERkYaBQUFRuPGjQ1Jxo8//lii7YMPPmhIMl566SVj9erVhiRDknHw4MESbY8ePWrccMMNtjbe3t5GzZo1bb/28PAw3nrrrVJrysrKMnr37l2sba1atQyLxWJIMp555hmjW7duhiRjwoQJJfpHRkYakozZs2fbtj3xxBNGnTp1jOrVq9vqqVOnju3Ha6+9ZmtbdNzVq1eXGPvixYtGTEyMrY0ko0aNGrbaJBk333yzkZqaWqJvQUGB8cADD9jaWSwWIzAw0PDw8DAkGXfffbdx3333GZKM++67r9Tfm8sp6hsZGVliX9Gfc7du3crsf+mf6R/Nnj27zLEvt+/SMYt+rwICAoptGzZsmJGfn3/ZvqtWrTL8/f0NSUZAQIDh5eVl2xceHm4cPXr0cr81pTp48KBtjEvnSlkyMzON4OBgQ5LRpUuXEvuL5qQkw8fHx6hdu7btz1aSUbNmTWPdunXF+nz22WdGnTp1DG9vb0OSUb169WLzsk6dOkZycrKtfX5+vvHEE08U+/3z9/c3PD09bb9u1qyZcejQIYd/PwAArlP0d2Np9ymOev311w1JRu3atY28vDyH++/evduwWq1G27Ztbf0v93d3kUv/rvnjj6ioKCM+Pr5En2+++cZ275adnV3quHfeeachyfD09DRyc3Nt2zMzM40WLVoYkoxRo0YZJ06csO07deqUMXDgQEOS0blz52LjffbZZ4YkIyQkxJgyZUqxe7KiH7Vq1TJWrVrlyG+bDU8KAgAAU7BYLLbXgv/4MeaMjAzNnz9fHh4eV3x1OD8/X0OHDtXOnTtVs2ZN/ec//1F6errOnj2rpKQk9e/fXwUFBXryySf17bffluj/3HPP6bvvvpPFYtHkyZOVlpamtLQ0/fbbb3r00Uc1ZcqUKz5p+EdF34wrWnXulltusX1D7tLtVzJ69GgtXLhQjRo10n//+1+dO3dO586d08WLF/XVV1+pUaNG2rhxox588MESfWfOnGl7/frxxx/XyZMnlZqaqtTUVL344ov6/PPPy/xod1VVrVo1jRkzRitWrLD9Xp0/f15nzpzRjBkzVKNGDS1YsOCKrzINHTpUPXv2VEJCgs6fP6+MjAx9/vnnCggI0PHjx/Xcc8+5/Vx8fX3Vs2dPSdKmTZuUlZVVbP9NN92kt99+W/v27VNmZqZOnz6t7Oxsbdq0SX369NG5c+c0YsSIYk98jBgxQr/99ptuueUWSdI//vGPYvPyt99+U0REhK39hAkT9NZbbyk0NFRvv/22zpw5owsXLigzM1OrV69WmzZttHfvXg0ZMkQFBQVu/z0BALhXcnKyJk2aJKnw7whPT0+H+huGoYcffli5ubmaNWuWQ/0HDRqkhQsXKjExUZmZmTp16pTmzZunRo0a6dChQ7rjjjt05MiRYn3atWsni8WigoICffHFFyXGPHHihNatWyep8H7x/Pnztn2zZ89WQkKCunfvrjlz5ig0NNS2Lzg4WJ9++qnq16+vDRs2FHvKMCUlRVLh9wyfeeYZ3Xnnndq1a5eys7O1Y8cO9ezZU2fPntWQIUMu+93sMjkVJQIAAFQBlz4paBiGkZycbHh4eBjVq1c3Lly4YGv30UcfGZKM22+/3TAM47JPChb9i60k47vvvitxzNzcXKNjx46GJOOGG24otu/YsWO2p8BiY2NLrXnkyJG28e19UvCP53u5J+aKxv7jk4Jr1641JBmhoaHFnt661JEjR2xPI27fvt22PTMz0wgKCrL963dpnn32Wduxr5UnBa9kwYIFhiSjcePGl62nR48epT5N+NZbbxmSDD8/v2JPG9jD0ScFDcMw/vWvf9n6JCYm2n2svLw848YbbzQkGXPnzi2x/3JPvl5ar6enp+Hn52f8/PPPpbY5f/68Ub9+fUOS8cUXX9hdHwDAtVzxpGBGRobRpk0bQ5LRoUMHIycnx+Ex3n77bUOS8cQTT5RanzN/d586dcpo0KCBIcl46KGHSuwfNmyYIcmoW7eusXbtWtv2lJQUo3v37sWe4Dty5Ihtf8+ePQ1Jxn/+858yj130xsW//vUv27ZJkybZxmvcuHGJ36eMjAyjbt26hiTj73//u8Pny5OCAADANCIiInTbbbfZngwsUvSEW2lPwP3R559/Lknq1KmT7rjjjhL7vby8NGHCBEnSzp07bSsCS9LChQuVl5cnPz+/Mp/eu/T7fxWpaDW9//u//yv29Nal6tevrx49ekiSvvvuO9v277//3vadnRdeeKHUvs8++6x8fX1dWfJVr1+/fpIKvwf022+/ldlu/Pjx8vAoeVs+aNAgSYXfW0pMTHRPkZcICgqy/bysj52XxtPTU3369JFU+G1OZ8yZM0f5+fnq06ePWrduXWqbgIAA24qSl84/AIB7FH2z+I8/ir7TXNq3Yq/0jV9JysnJ0ZAhQ7R9+3ZFRUVp8eLF8vb2dqi2Y8eO6bnnnlO9evU0efJkZ0+xhODgYNsT+l988UWJbwO/8847uv766/Xbb7+pa9euatCggVq2bKmIiAitXbtWDz30kK1tQECA7ec7duyQJL322mu69dZbS/2xYsUKSSr2xN+l906PP/54id+natWq6dFHH5UkLV++3OHzde2yJQAAAFe5Bx54QN9//70++ugjPfjgg9q/f7/WrVunwMBAW+BwOVu3bpUk3XbbbWW26dGjhzw9PZWfn6+tW7eqVatWxfq2a9dONWrUKLVv06ZNVa9evQpZYOJSGzZskFQYDn766adltjt37pwk6fDhw7ZtRecVERGh6667rtR+NWvWVHR0tO0414oLFy7ovffe0zfffKOEhASdPXtWubm5JdodPXpUdevWLXWMjh07lro9PDzc9nNHQjp3WbdunT788ENt3LhRR48eVUZGRok2Tr26pN/n3/fff1/m75Mk20Ijl84/AIB7NG3aVJ07dy6x/eTJk0pMTFRERIQaNGhQYn/RfU9p8vLyNGLECH333XcKDw/XDz/8oHr16jlc21//+ledP39es2fPLha+uULRZy+KPoNSu3Zt277g4GBt3LhR06ZN08KFC5WUlKRz586pV69eio2N1dGjR/Xhhx+qWrVqxe71zp49K0n65Zdfrnj8ixcv2n4eGBho+3mLFi1KbV+0/eDBg/af5P9HKAgAAEzlrrvuUmBgoDZs2KDExETbKr4jR46060m2kydPStJlb2B9fX0VHBysEydO2Nrb21cqfCKvokPB48ePS5LOnz9f7Bs4Zbn0htWR87qW7Nu3T7169SoWhFWrVk21atWyPfl34sQJSSo1QCtS1v+Z8fL6/Va9tKDR1S4NHi/9P0CS9Mwzz2jq1Km2X3t6eiowMNC2+mJ6eroyMjIue56XUzT/7B3j0vkHAHCP8ePHa/z48SW2z5kzRw888IAefPBBh95wyM/P1//93//pyy+/VEhIiFauXKlGjRo5Vdu2bdskSY899pgee+yxYvuKvm975MgR2z80LV682Bb2XcmlKwvn5eWV2B8QEKAJEybY3gy5VNEbE9HR0bJYLLbt/v7+Onv2rDZv3qz27dvbVYckNW/evNS6LlV0/+rM93Z5fRgAAJiK1WrVyJEjJUkffPCBPvnkE0myvQpjVvn/r737j4m6jOMA/oa7Q44M76aEzbljI9ALnOhYoYvBomYN8x9UbMnPoalYaSzxj5QNNisjg5XDDcuk5g/c3EQnpihMUWhA+IM/WkWId8sfSfEjQPCOpz/Y94kv94MfIlL3fm234X2f577f5+7LOD/P83w+djsAoKioCEKIER9KMNWTpaWlwWq1IigoCMeOHUNbWxu6u7tx79493LlzRxXYHb79aCpSVi9MmzZNFeA9d+6cDAhu2rQJN27cQF9fH/78809ZMGTr1q0Axj9O5f7Lzs4e1f1XVVX1CCMlIqLJNjAwgLS0NJSWlsJoNOLcuXMuV76Nxd27dx0eyuTmwMCAfK6/v3/Ur9nU1ARgMNg2fJJsJGVlZQD+TQGiCA8PV732aC1atAh6vR4A8Ntvvzlt09zcDGB8k68MChIREZHHUQKABQUFsFqtCA8PR2Rk5Kj6KtXi3G2TfPDgAdra2lTth/480irAyV4lCEDOpI9nW+aTHpeyom54xdyhlG3PE8ViscjqgIcPH8bKlStVOfkAuM0jONU8ePAAFy5cAABERUWpVs0eOXIEALBs2TLs3bsX4eHhDhUeH3Wsj3L/ERHR1LdhwwZ8++238Pf3x/fff+8yf+xo3bx50+XEkZIr2mQyyedGynOosNls+OyzzwAAL7/8smrV/kiOHDmCa9euwWAwIDU1VXVs1apVAIAvvvhiTKv//fz88MYbbwAADh486HB86ERtXFzcqF9XwaAgEREReZzIyEgsWLBAzhqPpsDI0L4AcP78eZdtqqqq5HaToVtElL719fUyN9pwv/zyy7jzsj0KJWfQqVOnxtxXGZfFYpGz1cN1dnaioaFh/BfohpJvx2KxuGzzww8/TOg5h55r0aJFTttUVFRM6Dkfpy+//BL3798HAIf/yChjdTVOIYQMKDqjbKV2t4pQuf8qKircBneJiOi/5/3330dxcTGmT5+O8vLyMW2ffemllxAUFISCgoIJu57t27fj4MGD6OrqUj1vsViwcuVK1NbWQqvVOi2eduPGDRw/fly18vDhw4fYv3+/nHQuLCx0WGG4bt06mM1mNDY2YsWKFfj1119Vx+12O6qrq5GRkeEwiZqTkwOtVotLly4hLy9Prq632WzIzs7GtWvX4OvrK1ftjwWDgkREROSRPvnkE2RlZSErKwtr164ddb81a9YAAGpqanD27FmH4zabDbm5uQAGt4oo20UAICEhARqNBr29vcjPz3f6+krfybZ+/XoAg9taioqK3Lbt7u5WfRl+9dVXZWAuLy/PaZ/du3fLHD8TTVlt8PvvvzsN/t27dw/FxcUTes4ZM2bIn50lDe/q6prQaoiPU2lpqcwZFR4e7vD7oIzVVXL0ffv2udzSBEAmWleSrDuTnp4OrVaL+/fvO83RNFR/f7/LoDoREU0tNTU1+PzzzwEM5uLbtm2by+q75eXlDv2tVitaW1vd/g0Zq59++gmpqakwGo0IDQ1FVFQUzGYzTCYTTpw4AV9fX3zzzTdOC4E1NzcjISEB/v7+mD9/PiIjIxEQEIB169bBZrOhoKAAycnJDv30ej1Onz6NsLAwnDlzBiEhIQgODsaSJUuwYMECPP3004iOjsZXX33lsJLw+eefx/79+6HRaLBz5048++yzePHFFzF79mx8+umn0Ol0+PrrrzFv3rwxvxcMChIREZFHev3115Gfn4/8/HwEBASMul9CQoL8krh69WocOnRIfnlraWlBQkICampqAEBVmAEYLMSRmZkJYDB49tFHH8lZ6j/++AObN2/Gd999pwo4TZaYmBg5w52ZmYmtW7eqAj19fX2ora3Ftm3bYDKZVAVU9Ho9duzYAWBwa8uWLVvk9unOzk7k5eVh165dMBgMj+Xaly5dCpPJBABISUlBfX09hBAYGBhAVVUVYmNjx5V82x2z2SwrLqanp6tWQdbU1CA2NhZ//fXXhJ5zIt25cwfHjx9HfHw8EhMT8fDhQ8yZMwenTp1y2Cr12muvAQDKy8uRl5cnC4G0t7dj165deOedd9zmXFIC46dPn3a5hTw4OFjeQ7t370ZycrIq75LNZsPVq1eRm5uL5557DlevXh332ImIaPL09fXJn2/fvo3Lly+7fCjFuR63jRs34u2330ZERAS6urrw448/wmq1IiwsDFu2bEFTUxPeeustp30XLlyIjRs3IjQ0FHfv3kVTUxOMRiMyMjLQ2NiI9957z+V5g4KCUF9fj7179yI2Nhbt7e2oq6vDrVu3MH/+fGRlZaG6ulp+pxkqJSUFtbW1WLVqFby9vdHY2AidToc333wTdXV1Ml/2mAkiIiKi/6mcnBwBQJhMpjH1q6ysFAAEANHS0uJw3Gq1irCwMNnGx8dHGAwG+W9vb29RWFjo9LV7e3vFK6+8IttqNBphNBqFl5eXACCys7NFTEyMACBycnIc+ptMJgFAHDhwwOV4Y2JiXI5NOW9lZaXDsb6+PpGRkSHbABDTp08XRqNReHt7q563Wq2qvna7XSQlJaneA6PRKDQajQAg1qxZI1JSUgQAkZKS4vL6XFH6BgUFOT1+5swZodPp5Pn9/PyEr6+vACBCQkLE4cOH5bHhDhw44PI+cXfs5MmTQqvVqs7p5+cnAIinnnpKVFRUuHy/h95j7rj7vNxpaWmRff39/UVgYKAIDAwUAQEBwsfHR/VZajQakZSUJNra2py+Vn9/v4iOjpbtvby8VPdEfHy8+PDDD13eez///LP8LLy9vUVgYKAwmUzCZDIJi8Ui2w0MDIgdO3bI3wUAQq/Xi5kzZ8r7SHlUV1eP6f0gIiIiR1wpSERERDRGc+bMQX19Pfbs2YOoqCjo9Xr09PRg7ty5SEpKQkNDA959912nfX19fVFeXo7CwkJERETAx8cHQghER0ejtLQUH3/88SSP5l8+Pj4oLi7GlStXkJqaiuDgYNjtdvz999945plnEBsbi507d+L69euq6rTAYN64kpISlJSUyPfEZrNh8eLF2LdvHw4dOvRI16ZsV1Yq8A23bNkyXLp0CcuXL4fRaITdbsfcuXOxfft2NDQ0yEIWE2n58uW4ePEi4uPjYTAYYLPZMGvWLKSlpaGhoWFcCb8fh87OTll9saOjA/7+/jCbzUhMTMSePXtw69YtlJSUOBRKUeh0Opw9exY5OTkIDQ2FTqeDEAIvvPACioqKUFZW5lB4ZKiQkBBUVlZixYoVCAgIQFtbG1pbW9Ha2ipzbwKAl5cXcnNzcf36dWzatAlmsxkajQYdHR0wGo1YunQpPvjgA1y5ckXmICQiIqLx8xLCTcZfIiIiIqIpIC4uDhcuXEBMTAyqqqqe9OUQERER/edxpSARERERTWk9PT2oq6sDAERERDzZiyEiIiL6n2BQkIiIiIimrNu3b2Pt2rWyIMu4E2kTERERkYp25CZERERERJPr6NGj2LBhA9rb2+VzmzdvlpWfiYiIiOjRMChIRERERFNOb28vOjo6YDAYsHDhQqSnpyM5OflJXxYRERHR/wYLjRAREREREREREXkY5hQkIiIiIiIiIiLyMAwKEhEREREREREReRgGBYmIiIiIiIiIiDwMg4JEREREREREREQehkFBIiIiIiIiIiIiD8OgIBERERERERERkYdhUJCIiIiIiIiIiMjDMChIRERERERERETkYf4BT9BymVI46jcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig = plt.figure(figsize=(15, 6))\n",
    "sel = single_object[~single_object['v:g-r'].isna()]\n",
    "plt.scatter(sel['i:jd'],sel['v:g-r'],label='color')\n",
    "xarray= np.arange(sel['i:jd'].min(),sel['i:jd'].max(),0.5)\n",
    "plt.plot(xarray, np.zeros(len(xarray)),linestyle='--')\n",
    "plt.xlabel('Modified Julian Date')\n",
    "plt.ylabel('Color g-r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3rd case study: spectroscopically classified type Ia supernovae in Fink\n",
    "\n",
    "In Fink, we cross-match objects against the [TNS database](https://www.wis-tns.org/). This database provides spectroscopically classified transients with a strong bias for SNe. This is an interesting sample to analyze since many of these SNe have been detected as well by ZTF.\n",
    "\n",
    "To know all available classes available in TNS see https://api.fink-portal.org/api/v1/classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> API query:</font>   <font color='orange'>latest alerts</font> associated with known type Ia SNe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can query recent spectroscopically classified SNe in TNS.\n",
    "For example, query all objects classified as `SN Ia` in TNS between `2020/12/01` and `2021/04/05`, with a maximum of `n alerts` search inside the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      i:objectId  i:ndethist\n",
      "0   ZTF18abcoxmc        1074\n",
      "1   ZTF18abiinnl         531\n",
      "2   ZTF18abncdhd         548\n",
      "3   ZTF18abvuceu         715\n",
      "4   ZTF21aaskyvp           2\n",
      "5   ZTF18acjxyak          16\n",
      "6   ZTF21aasctwo           7\n",
      "7   ZTF21aamwzfi          94\n",
      "8   ZTF21aaopqqk          40\n",
      "9   ZTF21aaobcrz          49\n",
      "10  ZTF21aarskup           4\n",
      "11  ZTF21aafdxca          80\n",
      "12  ZTF18aalcxig         717\n",
      "13  ZTF18abmstte         490\n",
      "14  ZTF21aaihala          24\n",
      "15  ZTF21aaqwjlz          24\n",
      "16  ZTF21aaqztcg          20\n",
      "17  ZTF21aaplooj          27\n",
      "18  ZTF21aarjczf          15\n",
      "19  ZTF18aaibcxu        2340\n",
      "20  ZTF19aaxqsbn         245\n",
      "21  ZTF18abltebf          95\n",
      "22  ZTF18abcscfv         138\n",
      "23  ZTF21aapvsxv          33\n",
      "24  ZTF21aakijia          61\n",
      "25  ZTF18aagrebu         706\n",
      "26  ZTF21aaplnxw          36\n",
      "27  ZTF21aaqqlfw          28\n",
      "28  ZTF18aamlhee        1895\n",
      "29  ZTF21aanuyro          52\n",
      "30  ZTF21aaovhxe          97\n",
      "31  ZTF18acyymoy         168\n",
      "32  ZTF18aaylbjm          64\n"
     ]
    }
   ],
   "source": [
    "r = requests.post(\n",
    "  '{}/api/v1/latests'.format(APIURL),\n",
    "  json={\n",
    "    'class': '(TNS) SN Ia',        # TNS classification\n",
    "    'n': '50',                     # n alerts to query in database\n",
    "    'startdate': '2020-12-01',     # start TNS classification\n",
    "    'stopdate': '2021-04-05',      # end TNS classification\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(io.BytesIO(r.content))\n",
    "\n",
    "print(pdf[['i:objectId', 'i:ndethist']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> API query:</font>   <font color='orange'>photometry</font> of one known type Ia SNe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can then use the `objects` API service to get all the photometry about a particular object and plot it with limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_SNeIa = 'ZTF21aasctwo'\n",
    "#pdf['i:objectId'].iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(\n",
    "  '{}/api/v1/objects'.format(APIURL),\n",
    "  json={\n",
    "    'objectId': selected_SNeIa,\n",
    "    'output-format': 'json',\n",
    "    'withupperlim': 'True'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "single_object = pd.read_json(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A/BgJQAAAAAAAAMCPkQAEAAAAAAAA/Nj/A6gqL6SN1TrXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    - Circles (●) with error bars show valid alerts that pass the Fink quality cuts. \n",
      "    - Upper triangles with errors (▲), represent alert measurements that do not satisfy Fink quality cuts, but are nevetheless contained in the history of valid alerts and used by classifiers.\n",
      "    - Lower triangles (▽), represent 5-sigma mag limit in difference image based on PSF-fit photometry contained in the history of valid alerts.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "plot_lc(single_object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> Visualizing:</font> <font color='orange'>Latest ML scores</font> associated with known type Ia SNe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more general query could be to analyze the ML classification algorithm scores for these recent SNe Ia. To do this we plot the last classification score obtained for the SNe Ia in our list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 6))\n",
    "\n",
    "for score in ['d:snn_sn_vs_all', 'd:snn_snia_vs_nonia', 'd:rf_snia_vs_nonia']:\n",
    "    plt.hist(pdf[score], histtype='step', label=score)\n",
    "plt.xlabel('sn score probability')\n",
    "plt.ylabel('number of objects')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of these SNe Ia are post-peak since spectroscopic follow-up is usually done around maximum brightness. Note that:\n",
    "- **'rf_snia_vs_nonia' is designed only for early SNe classification**, therefore it should not provide high scores for post-peak brightness SNe.\n",
    "- While SuperNNova scores should be valid post-maximum, some of these light-curves may be on the detection tail of the object and/or not have the SN light anymore (e.g. spurious detetions). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> Selecting:</font>   <font color='orange'>SNe with close-by galaxies</font> (not necessarily their host-galaxies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For your science case, it may be useful to check if there is a close-by galaxy to the spectroscopically classified SNe Ia.\n",
    "\n",
    "Fink cross-matches each alert to the closest source (within 1'' radius) in the SIMBAD xmatch service providing `d:cdsxmatch` tag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_simbad_galaxies = [\n",
    "    \"galaxy\",\n",
    "    \"Galaxy\",\n",
    "    \"EmG\",\n",
    "    \"Seyfert\",\n",
    "    \"Seyfert_1\",\n",
    "    \"Seyfert_2\",\n",
    "    \"BlueCompG\",\n",
    "    \"StarburstG\",\n",
    "    \"LSB_G\",\n",
    "    \"HII_G\",\n",
    "    \"High_z_G\",\n",
    "    \"GinPair\",\n",
    "    \"GinGroup\",\n",
    "    \"BClG\",\n",
    "    \"GinCl\",\n",
    "    \"PartofG\",\n",
    "]\n",
    "\n",
    "# select alerts that have a cross-matched Galaxy\n",
    "with_closeby_gal = pdf[pdf['d:cdsxmatch'].isin(list_simbad_galaxies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can expand this query to any type of galaxy present in the SIMBAD catalogue, see http://simbad.u-strasbg.fr/simbad/sim-display?data=otypes  like clusters, Seyfert, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ZTF alert stream also provides useful information of close neighbours:\n",
    "\n",
    "-`i:distnr`: distance to nearest source in reference image PSF-catalog within 30 arcsec [pixels]\n",
    "\n",
    "-`i:magnr`: magnitude of nearest source in reference image PSF-catalog within 30 arcsec [mag]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>i:objectId</th>\n",
       "      <th>i:distnr</th>\n",
       "      <th>i:magnr</th>\n",
       "      <th>d:cdsxmatch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ZTF18acjxyak</td>\n",
       "      <td>0.669415</td>\n",
       "      <td>15.710999</td>\n",
       "      <td>Galaxy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ZTF21aaplooj</td>\n",
       "      <td>0.928013</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>Galaxy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      i:objectId  i:distnr    i:magnr d:cdsxmatch\n",
       "5   ZTF18acjxyak  0.669415  15.710999      Galaxy\n",
       "17  ZTF21aaplooj  0.928013  16.910000      Galaxy"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_closeby_gal[['i:objectId', 'i:distnr', 'i:magnr', 'd:cdsxmatch']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'> API query:</font>   <font color='orange'>thumbnails</font> of one known type Ia SNe\n",
    "It may be useful to visualize in the ZTF provided stamps or thumbnails centered on the transient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transfer cutout data\n",
    "r = requests.post(\n",
    "  '{}/api/v1/objects'.format(APIURL),\n",
    "  json={\n",
    "    'objectId': with_closeby_gal['i:objectId'].iloc[0],\n",
    "    'withcutouts': 'True'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(io.BytesIO(r.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "columns = [\n",
    "    'b:cutoutScience_stampData',\n",
    "    'b:cutoutTemplate_stampData',\n",
    "    'b:cutoutDifference_stampData'\n",
    "]\n",
    "for index, col in enumerate(columns):\n",
    "    # 2D array\n",
    "    data = pdf[col].values[0]\n",
    "    data = np.array(data, dtype=float)\n",
    "    mask = np.isnan(data)\n",
    "    data[mask] = 0.0\n",
    "\n",
    "    # You could optimize the color range to highlight the galaxy\n",
    "    axes[index].imshow(data)\n",
    "    axes[index].set_title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively for displaying images, you can use our cutout service (see `/api/v1/cutouts` on https://api.fink-portal.org for the list of available arguments):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=400x400>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io\n",
    "from PIL import Image as im\n",
    "\n",
    "# get processed image data\n",
    "r = requests.post(\n",
    "    '{}/api/v1/cutouts'.format(APIURL),\n",
    "    json={\n",
    "        'objectId': with_closeby_gal['i:objectId'].iloc[0],\n",
    "        'kind': 'Science', # Science, Template, Difference\n",
    "    }\n",
    ")\n",
    "\n",
    "image = im.open(io.BytesIO(r.content))\n",
    "image.resize((400, 400))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "In this notebook we have searched for AGNs and SNe:\n",
    "- Querying Fink's API for latest alerts using known catalogue types, e.g. from SIMBAD or TNS.\n",
    "- Querying Fink's API for candidate SNe selected using our ML algorithms.\n",
    "- Filtering all of these alerts according to your science case.\n",
    "- Querying Fink's API for photometry and thumbnails for a given object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An issue to report, or a question to ask: contact@fink-broker.org!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}