events/2022/rest_api_how_to.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c80ad04",
   "metadata": {},
   "source": [
    "# Fink REST API - How-to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f399fba4",
   "metadata": {},
   "source": [
    "## HBase in Fink\n",
    "\n",
    "- Used as the backend for our web services (incl. REST API)\n",
    "- Updated once a night, at the end of the observing night\n",
    "- Support concurrency & can deal simultaneously with hundreds of requests without problems\n",
    "\n",
    "![](images/rest_api.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a95a98",
   "metadata": {},
   "source": [
    "## Retrieve object data\n",
    "\n",
    "The list of arguments for retrieving object data can be found at https://fink-portal.org/api/v1/objects.\n",
    "\n",
    "In a unix shell, you would simply use\n",
    "\n",
    "```bash\n",
    "# Get data for ZTF21aaxtctv and save it in a CSV file\n",
    "curl -H \"Content-Type: application/json\" -X POST -d '{\"objectId\":\"ZTF21aaxtctv\", \"output-format\":\"csv\"}' https://fink-portal.org/api/v1/objects -o ZTF21aaxtctv.csv\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/objects?objectId=ZTF21aaxtctv&output-format=json\" -O ZTF21aaxtctv.json\n",
    "```\n",
    "\n",
    "In python, you would use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86f50778",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/objects',\n",
    "  json={\n",
    "    'objectId': 'ZTF21aaxtctv',\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c46bda2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        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>b:cutoutDifference_stampData</th>\n",
       "      <th>b:cutoutScience_stampData</th>\n",
       "      <th>b:cutoutTemplate_stampData</th>\n",
       "      <th>d:cdsxmatch</th>\n",
       "      <th>d:mulens</th>\n",
       "      <th>d:nalerthist</th>\n",
       "      <th>d:rf_kn_vs_nonkn</th>\n",
       "      <th>d:rf_snia_vs_nonia</th>\n",
       "      <th>d:roid</th>\n",
       "      <th>d:snn_sn_vs_all</th>\n",
       "      <th>...</th>\n",
       "      <th>d:tracklet</th>\n",
       "      <th>v:classification</th>\n",
       "      <th>v:g-r</th>\n",
       "      <th>v:rate(g-r)</th>\n",
       "      <th>v:dg</th>\n",
       "      <th>v:rate(dg)</th>\n",
       "      <th>v:dr</th>\n",
       "      <th>v:rate(dr)</th>\n",
       "      <th>v:lastdate</th>\n",
       "      <th>v:constellation</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023330</td>\n",
       "      <td>9.880839</td>\n",
       "      <td>2021-11-18 13:14:59.997</td>\n",
       "      <td>Virgo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>binary:ZTF21aaxtctv_2459537.0497222:cutoutDiff...</td>\n",
       "      <td>binary:ZTF21aaxtctv_2459537.0497222:cutoutScie...</td>\n",
       "      <td>binary:ZTF21aaxtctv_2459537.0497222:cutoutTemp...</td>\n",
       "      <td>Unknown</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.036741</td>\n",
       "      <td>-15.560908</td>\n",
       "      <td>2021-11-18 13:11:35.998</td>\n",
       "      <td>Virgo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 129 columns</p>\n",
       "</div>"
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       "                        b:cutoutDifference_stampData  \\\n",
       "0  binary:ZTF21aaxtctv_2459537.0520833:cutoutDiff...   \n",
       "1  binary:ZTF21aaxtctv_2459537.0497222:cutoutDiff...   \n",
       "\n",
       "                           b:cutoutScience_stampData  \\\n",
       "0  binary:ZTF21aaxtctv_2459537.0520833:cutoutScie...   \n",
       "1  binary:ZTF21aaxtctv_2459537.0497222:cutoutScie...   \n",
       "\n",
       "                          b:cutoutTemplate_stampData d:cdsxmatch  d:mulens  \\\n",
       "0  binary:ZTF21aaxtctv_2459537.0520833:cutoutTemp...     Unknown       0.0   \n",
       "1  binary:ZTF21aaxtctv_2459537.0497222:cutoutTemp...     Unknown       0.0   \n",
       "\n",
       "   d:nalerthist  d:rf_kn_vs_nonkn  d:rf_snia_vs_nonia  d:roid  \\\n",
       "0             5                 0                 0.0       0   \n",
       "1             4                 0                 0.0       0   \n",
       "\n",
       "   d:snn_sn_vs_all  ...  d:tracklet  v:classification  v:g-r  v:rate(g-r)  \\\n",
       "0              0.0  ...                       Unknown    NaN          NaN   \n",
       "1              0.0  ...                       Unknown    NaN          NaN   \n",
       "\n",
       "   v:dg  v:rate(dg)      v:dr  v:rate(dr)               v:lastdate  \\\n",
       "0   0.0         0.0  0.023330    9.880839  2021-11-18 13:14:59.997   \n",
       "1   0.0         0.0 -0.036741  -15.560908  2021-11-18 13:11:35.998   \n",
       "\n",
       "   v:constellation  \n",
       "0            Virgo  \n",
       "1            Virgo  \n",
       "\n",
       "[2 rows x 129 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698dc002",
   "metadata": {},
   "source": [
    "You can also retrieve the data for several objects at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01040c68",
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = ['ZTF21aaxtctv', 'ZTF21abfmbix', 'ZTF21abfaohe']\n",
    "\n",
    "# get data for many objects\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/objects',\n",
    "  json={\n",
    "    'objectId': ','.join(mylist),\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fea30f1",
   "metadata": {},
   "source": [
    "Note that for `csv` output, you need to use"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e1a62e",
   "metadata": {},
   "source": [
    "```python\n",
    "# get data for ZTF21aaxtctv in CSV format...\n",
    "r = ...\n",
    "\n",
    "pd.read_csv(io.BytesIO(r.content))\n",
    "```\n",
    "\n",
    "You can also get a votable:\n",
    "\n",
    "```python\n",
    "import io\n",
    "from astropy.io import votable\n",
    "\n",
    "# get data for ZTF21aaxtctv in JSON format...\n",
    "r = ...\n",
    "\n",
    "vt = votable.parse(io.BytesIO(r.content))\n",
    "```\n",
    "\n",
    "By default, we transfer all available data fields (original ZTF fields and Fink science module outputs).\n",
    "But you can also choose to transfer only a subset of the fields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41c1d85a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_all_fields():\n",
    "    r = requests.post(\n",
    "      'https://fink-portal.org/api/v1/objects',\n",
    "      json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "      }\n",
    "    )\n",
    "    return r\n",
    "    \n",
    "def load_some_fields():\n",
    "    \"\"\" Select only jd, and magpsf\n",
    "    \n",
    "    Note that the fields should be comma-separated. \n",
    "    Unknown field names are ignored. \n",
    "    This will speed-up dramatically results in some cases:    \n",
    "    \"\"\"\n",
    "    r = requests.post(\n",
    "      'https://fink-portal.org/api/v1/objects',\n",
    "      json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'columns': 'i:jd,i:magpsf'\n",
    "      }\n",
    "    )\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d17d99df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "545 ms ± 94.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit load_all_fields()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7613ec81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83 ms ± 3.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit load_some_fields()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad68c0f9",
   "metadata": {},
   "outputs": [
    {
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       "           i:jd   i:magpsf\n",
       "0  2.459537e+06  19.149723\n",
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   "source": [
    "pdf = pd.read_json(load_some_fields().content)\n",
    "pdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce80ea1d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1d4ccab0",
   "metadata": {},
   "source": [
    "### Upper limits and bad quality data\n",
    "\n",
    "You can also retrieve upper limits and bad quality data (as defined by Fink quality cuts)\n",
    "alongside valid measurements. For this you would use `withupperlim` (see usage below).\n",
    "Note that the returned data will contained a new column, `d:tag`, to easily check data type:\n",
    "`valid` (valid alert measurements), `upperlim` (upper limits), `badquality` (alert measurements that did not pass quality cuts).\n",
    "Here is an example that query the data, and plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a602c728",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/objects',\n",
    "  json={\n",
    "    'objectId': 'ZTF21aaxtctv',\n",
    "    'withupperlim': 'True'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)\n",
    "\n",
    "def plot_photometry(pdf):\n",
    "    fig = plt.figure(figsize=(15, 6))\n",
    "\n",
    "    colordic = {1: 'C0', 2: 'C1'}\n",
    "\n",
    "    for filt in np.unique(pdf['i:fid']):\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]\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='^', 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='v', color=colordic[filt]\n",
    "        )\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.xlabel('Modified Julian Date')\n",
    "    plt.ylabel('Magnitude')\n",
    "    plt.show()\n",
    "plot_photometry(pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c789a",
   "metadata": {},
   "source": [
    "### Cutouts\n",
    "\n",
    "Finally, you can also request data from cutouts stored in alerts (science, template and difference).\n",
    "Simply set `withcutouts` in the json payload (string):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a879db7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "columns = [\n",
    "    'b:cutoutScience_stampData',\n",
    "    'b:cutoutTemplate_stampData',\n",
    "    'b:cutoutDifference_stampData'\n",
    "]\n",
    "\n",
    "# transfer cutout data\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/objects',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'withcutouts': 'True',\n",
    "        'cols': ','.join(columns)\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "for col in columns:\n",
    "    # 2D array\n",
    "    data = pdf[col].values[0]\n",
    "\n",
    "    axes[columns.index(col)].imshow(np.arcsinh(data))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa9c631",
   "metadata": {},
   "source": [
    "## Retrieve cutout data from the Fink database\n",
    "\n",
    "The list of arguments for retrieving cutout data can be found at https://fink-portal.org/api/v1/cutouts.\n",
    "\n",
    "### PNG\n",
    "\n",
    "In a unix shell, you can retrieve the last cutout of an object by simply using\n",
    "\n",
    "```bash\n",
    "curl -H \"Content-Type: application/json\" \\\\\n",
    "    -X POST -d \\\\\n",
    "    '{\"objectId\":\"ZTF21aaxtctv\", \"kind\":\"Science\"}' \\\\\n",
    "    https://fink-portal.org/api/v1/cutouts -o cutoutScience.png\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/cutouts?objectId=ZTF21aaxtctv&kind=Science\" -O ZTF21aaxtctv_Science.png\n",
    "```\n",
    "\n",
    "This will retrieve the `Science` image and save it on `cutoutScience.png`.\n",
    "In Python, the equivalent script would be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cebfc81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=300x300 at 0x7F9B95776310>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io\n",
    "import requests\n",
    "from PIL import Image as im\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/cutouts',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'kind': 'Science',\n",
    "    }\n",
    ")\n",
    "\n",
    "image = im.open(io.BytesIO(r.content))\n",
    "image.resize((300, 300))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6f2714",
   "metadata": {},
   "source": [
    "Note you can choose between the `Science`, `Template`, or `Difference` images.\n",
    "You can also customise the image treatment by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78efc286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=300x300 at 0x7F9B95744F90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io\n",
    "import requests\n",
    "from PIL import Image as im\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/cutouts',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'kind': 'Science', # Science, Template, Difference\n",
    "        'stretch': 'sigmoid', # sigmoid[default], linear, sqrt, power, log\n",
    "        'colormap': 'viridis', # Valid matplotlib colormap name (see matplotlib.cm). Default is grayscale.\n",
    "        'pmin': 0.5, # The percentile value used to determine the pixel value of minimum cut level. Default is 0.5. No effect for sigmoid.\n",
    "        'pmax': 99.5, # The percentile value used to determine the pixel value of maximum cut level. Default is 99.5. No effect for sigmoid.\n",
    "        'convolution_kernel': 'gauss' # Convolve the image with a kernel (gauss or box). Default is None (not specified).\n",
    "    }\n",
    ")\n",
    "\n",
    "image = im.open(io.BytesIO(r.content))\n",
    "# image.save('mysupercutout.png')\n",
    "image.resize((300, 300))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae8313a",
   "metadata": {},
   "source": [
    "By default, you will retrieve the cutout of the last alert emitted for the object `objectId`.\n",
    "You can also access cutouts of other alerts from this object by specifying their candidate ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3d08a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=300x300 at 0x7F9B953D0C90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io\n",
    "import requests\n",
    "import pandas as pd\n",
    "from PIL import Image as im\n",
    "\n",
    "# Get all candidate ID with JD for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/objects',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'columns': 'i:candid,i:jd'\n",
    "    }\n",
    ")\n",
    "\n",
    "pdf_candid = pd.read_json(r.content)\n",
    "# Get the first alert\n",
    "first_alert = pdf_candid['i:candid'].values[-1]\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/cutouts',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'kind': 'Science',\n",
    "        'candid': str(first_alert)\n",
    "    }\n",
    ")\n",
    "\n",
    "image = im.open(io.BytesIO(r.content))\n",
    "# image.save('mysupercutout_firstalert.png')\n",
    "image.resize((300, 300))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f844cd5d",
   "metadata": {},
   "source": [
    "### FITS\n",
    "\n",
    "You can also retrieve the original FITS file stored in the alert:\n",
    "\n",
    "```bash\n",
    "curl -H \"Content-Type: application/json\" \\\\\n",
    "    -X POST -d \\\\\n",
    "    '{\"objectId\":\"ZTF21aaxtctv\", \"kind\":\"Science\", \"output-format\": \"FITS\"}' \\\\\n",
    "    https://fink-portal.org/api/v1/cutouts -o cutoutScience.fits\n",
    "```\n",
    "\n",
    "or equivalently in Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "207da0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x7f9b96f7f3d0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io\n",
    "from astropy.io import fits\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/cutouts',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'kind': 'Science',\n",
    "        'output-format': 'FITS'\n",
    "    }\n",
    ")\n",
    "\n",
    "data = fits.open(io.BytesIO(r.content), ignore_missing_simple=True)\n",
    "# data.writeto('cutoutScience.fits')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ef290c",
   "metadata": {},
   "source": [
    "### Numpy array\n",
    "\n",
    "You can also retrieve only the data block stored in the alert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09dcd3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 63)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/cutouts',\n",
    "    json={\n",
    "        'objectId': 'ZTF21aaxtctv',\n",
    "        'kind': 'Science',\n",
    "        'output-format': 'array'\n",
    "    }\n",
    ")\n",
    "\n",
    "pdf = pd.read_json(r.content)\n",
    "array = pdf['b:cutoutScience_stampData'].values[0]\n",
    "np.shape(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb25168",
   "metadata": {},
   "source": [
    "## Conesearch\n",
    "\n",
    "Perform a conesearch around a position on the sky given by (RA, Dec, radius).\n",
    "The initializer for RA/Dec is very flexible and supports inputs provided in a number of convenient formats.\n",
    "The following ways of initializing a conesearch are all equivalent (radius in arcsecond):\n",
    "\n",
    "* 193.822, 2.89732, 5\n",
    "* 193d49m18.267s, 2d53m50.35s, 5\n",
    "* 12h55m17.218s, +02d53m50.35s, 5\n",
    "* 12 55 17.218, +02 53 50.35, 5\n",
    "* 12:55:17.218, 02:53:50.35, 5\n",
    "\n",
    "In a unix shell, you would simply use\n",
    "\n",
    "```bash\n",
    "# Get all objects falling within (center, radius) = ((ra, dec), radius)\n",
    "curl -H \"Content-Type: application/json\" -X POST -d '{\"ra\":\"193.822\", \"dec\":\"2.89732\", \"radius\":\"5\"}' https://fink-portal.org/api/v1/explorer -o conesearch.json\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/explorer?ra=193.822&dec=2.89732&radius=5&startdate_conesearch=2021-06-25 05:59:37.000&window_days_conesearch=7&output-format=json\" -O conesearch.json\n",
    "```\n",
    "\n",
    "In python, you would use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8134d42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Get all objects falling within (center, radius) = ((ra, dec), radius)\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/explorer',\n",
    "  json={\n",
    "    'ra': '193.822',\n",
    "    'dec': '2.89732',\n",
    "    'radius': '5'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877c64b8",
   "metadata": {},
   "source": [
    "Maximum radius length is 18,000 arcseconds (5 degrees). Note that in case of\n",
    "several objects matching, the results will be sorted according to the column\n",
    "`v:separation_degree`, which is the angular separation in degree between\n",
    "the input (ra, dec) and the objects found. Also note this focuses on ALERTS, not OBJECTS, because the classification in Fink is done for alerts.\n",
    "\n",
    "In addition, you can specify time boundaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83ad847b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Get all objects falling within (center, radius) = ((ra, dec), radius)\n",
    "# between 2021-06-25 05:59:37.000 (included) and 2021-07-01 05:59:37.000 (excluded)\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/explorer',\n",
    "  json={\n",
    "    'ra': '193.822',\n",
    "    'dec': '2.89732',\n",
    "    'radius': '5',\n",
    "    'startdate_conesearch': '2021-06-25 05:59:37.000',\n",
    "    'window_days_conesearch': 7\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e4cde0",
   "metadata": {},
   "source": [
    "Here is the performance of the service for querying a\n",
    "single object (database of 1.3TB, about 40 million alerts):\n",
    "\n",
    "![conesearch](https://user-images.githubusercontent.com/20426972/123047697-e493a500-d3fd-11eb-9f30-216dce9cbf43.png)\n",
    "\n",
    "_circle marks with dashed lines are results for a full scan search\n",
    "(~2 years of data, 40 million alerts), while the upper triangles with\n",
    "dotted lines are results when restraining to 7 days search.\n",
    "The numbers close to markers show the number of objects returned by the conesearch._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "085bfaba",
   "metadata": {},
   "source": [
    "## Search by Date\n",
    "\n",
    "Choose a starting date and a time window to see all alerts in this period.\n",
    "Dates are in UTC, and the time window in minutes.\n",
    "Example of valid search:\n",
    "\n",
    "* 2021-07-01 05:59:37.000\n",
    "\n",
    "In a unix shell, you would simply use\n",
    "\n",
    "```bash\n",
    "# Get all objects between 2021-07-01 05:59:37.000 and 2021-07-01 06:09:37.000 UTC\n",
    "curl -H \"Content-Type: application/json\" -X POST -d '{\"startdate\":\"2021-07-01 05:59:37.000\", \"window\":\"10\"}' https://fink-portal.org/api/v1/explorer -o datesearch.json\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/explorer?startdate=2021-07-01 05:59:37.000&window=10&output-format=json\" -O datesearch.json\n",
    "```\n",
    "\n",
    "In python, you would use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91547f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Get all objects between 2021-07-01 05:59:37.000 and 2021-07-01 06:09:37.000 UTC\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/explorer',\n",
    "  json={\n",
    "    'startdate': '2021-07-01 05:59:37.000',\n",
    "    'window': '10'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5673e9",
   "metadata": {},
   "source": [
    "You can see it takes some time to load the data, because we currently transfer all columns... Modifications will be done soon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8034c43d",
   "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>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",
       "      <th>d:roid</th>\n",
       "      <th>d:snn_sn_vs_all</th>\n",
       "      <th>d:snn_snia_vs_nonia</th>\n",
       "      <th>d:tracklet</th>\n",
       "      <th>i:candid</th>\n",
       "      <th>i:chipsf</th>\n",
       "      <th>...</th>\n",
       "      <th>i:sigmapsf</th>\n",
       "      <th>i:ssdistnr</th>\n",
       "      <th>i:ssmagnr</th>\n",
       "      <th>i:ssnamenr</th>\n",
       "      <th>i:tooflag</th>\n",
       "      <th>i:xpos</th>\n",
       "      <th>i:ypos</th>\n",
       "      <th>v:classification</th>\n",
       "      <th>v:lastdate</th>\n",
       "      <th>v:constellation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009939</td>\n",
       "      <td>0.868732</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642249732315015013</td>\n",
       "      <td>53.030850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028584</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>855.9870</td>\n",
       "      <td>2451.6245</td>\n",
       "      <td>SN candidate</td>\n",
       "      <td>2021-07-01 05:59:37.000</td>\n",
       "      <td>Virgo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642250831115015010</td>\n",
       "      <td>1.783729</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107317</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>1361.1305</td>\n",
       "      <td>1611.5293</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2021-07-01 06:01:11.997</td>\n",
       "      <td>Libra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642250830315015024</td>\n",
       "      <td>1.368616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132419</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>2018.6060</td>\n",
       "      <td>1784.6932</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2021-07-01 06:01:11.997</td>\n",
       "      <td>Scorpius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642250831715015009</td>\n",
       "      <td>15.777208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097214</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>1106.7772</td>\n",
       "      <td>1492.5022</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2021-07-01 06:01:11.997</td>\n",
       "      <td>Scorpius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642251786315010005</td>\n",
       "      <td>10.625117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156160</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>259.8762</td>\n",
       "      <td>1471.4307</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2021-07-01 06:02:33.999</td>\n",
       "      <td>Ophiucus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642251300915015019</td>\n",
       "      <td>1.544776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208598</td>\n",
       "      <td>0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>118311</td>\n",
       "      <td>0</td>\n",
       "      <td>299.8589</td>\n",
       "      <td>2342.6077</td>\n",
       "      <td>Solar System MPC</td>\n",
       "      <td>2021-07-01 06:01:53.003</td>\n",
       "      <td>Libra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642251300915015018</td>\n",
       "      <td>0.986492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169158</td>\n",
       "      <td>0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>141633</td>\n",
       "      <td>0</td>\n",
       "      <td>1017.9193</td>\n",
       "      <td>2897.4468</td>\n",
       "      <td>Solar System MPC</td>\n",
       "      <td>2021-07-01 06:01:53.003</td>\n",
       "      <td>Libra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642251300915015013</td>\n",
       "      <td>1.365358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146369</td>\n",
       "      <td>0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>35424</td>\n",
       "      <td>0</td>\n",
       "      <td>2461.6987</td>\n",
       "      <td>1223.7521</td>\n",
       "      <td>Solar System MPC</td>\n",
       "      <td>2021-07-01 06:01:53.003</td>\n",
       "      <td>Libra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>V*</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642251301615010001</td>\n",
       "      <td>9.958154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167798</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>625.7769</td>\n",
       "      <td>491.2318</td>\n",
       "      <td>V*</td>\n",
       "      <td>2021-07-01 06:01:53.003</td>\n",
       "      <td>Scorpius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>RRLyr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1642249732515015003</td>\n",
       "      <td>1.330981</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165761</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>null</td>\n",
       "      <td>0</td>\n",
       "      <td>2349.0515</td>\n",
       "      <td>1792.7096</td>\n",
       "      <td>RRLyr</td>\n",
       "      <td>2021-07-01 05:59:37.000</td>\n",
       "      <td>Virgo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2498 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     d:cdsxmatch  d:mulens  d:rf_kn_vs_nonkn  d:rf_snia_vs_nonia  d:roid  \\\n",
       "2464     Unknown       0.0                 0               0.135       0   \n",
       "2023     Unknown       0.0                 0               0.106       0   \n",
       "2084     Unknown       0.0                 0               0.087       0   \n",
       "2074     Unknown       0.0                 0               0.046       0   \n",
       "141      Unknown       0.0                 0               0.046       0   \n",
       "...          ...       ...               ...                 ...     ...   \n",
       "837      Unknown       0.0                 0               0.000       3   \n",
       "838      Unknown       0.0                 0               0.000       3   \n",
       "839      Unknown       0.0                 0               0.000       3   \n",
       "840           V*       0.0                 0               0.000       0   \n",
       "2497       RRLyr       0.0                 0               0.000       0   \n",
       "\n",
       "      d:snn_sn_vs_all  d:snn_snia_vs_nonia d:tracklet             i:candid  \\\n",
       "2464         0.009939             0.868732        nan  1642249732315015013   \n",
       "2023         0.000000             0.000000        nan  1642250831115015010   \n",
       "2084         0.000000             0.000000        nan  1642250830315015024   \n",
       "2074         0.000000             0.000000        nan  1642250831715015009   \n",
       "141          0.000000             0.000000        nan  1642251786315010005   \n",
       "...               ...                  ...        ...                  ...   \n",
       "837          0.000000             0.000000        nan  1642251300915015019   \n",
       "838          0.000000             0.000000        nan  1642251300915015018   \n",
       "839          0.000000             0.000000        nan  1642251300915015013   \n",
       "840          0.000000             0.000000        nan  1642251301615010001   \n",
       "2497         0.000000             0.000000        nan  1642249732515015003   \n",
       "\n",
       "       i:chipsf  ...  i:sigmapsf  i:ssdistnr  i:ssmagnr  i:ssnamenr  \\\n",
       "2464  53.030850  ...    0.028584        -999     -999.0        null   \n",
       "2023   1.783729  ...    0.107317        -999     -999.0        null   \n",
       "2084   1.368616  ...    0.132419        -999     -999.0        null   \n",
       "2074  15.777208  ...    0.097214        -999     -999.0        null   \n",
       "141   10.625117  ...    0.156160        -999     -999.0        null   \n",
       "...         ...  ...         ...         ...        ...         ...   \n",
       "837    1.544776  ...    0.208598           0       19.7      118311   \n",
       "838    0.986492  ...    0.169158           0       19.4      141633   \n",
       "839    1.365358  ...    0.146369           0       19.5       35424   \n",
       "840    9.958154  ...    0.167798        -999     -999.0        null   \n",
       "2497   1.330981  ...    0.165761        -999     -999.0        null   \n",
       "\n",
       "      i:tooflag     i:xpos     i:ypos  v:classification  \\\n",
       "2464          0   855.9870  2451.6245      SN candidate   \n",
       "2023          0  1361.1305  1611.5293           Unknown   \n",
       "2084          0  2018.6060  1784.6932           Unknown   \n",
       "2074          0  1106.7772  1492.5022           Unknown   \n",
       "141           0   259.8762  1471.4307           Unknown   \n",
       "...         ...        ...        ...               ...   \n",
       "837           0   299.8589  2342.6077  Solar System MPC   \n",
       "838           0  1017.9193  2897.4468  Solar System MPC   \n",
       "839           0  2461.6987  1223.7521  Solar System MPC   \n",
       "840           0   625.7769   491.2318                V*   \n",
       "2497          0  2349.0515  1792.7096             RRLyr   \n",
       "\n",
       "                   v:lastdate  v:constellation  \n",
       "2464  2021-07-01 05:59:37.000            Virgo  \n",
       "2023  2021-07-01 06:01:11.997            Libra  \n",
       "2084  2021-07-01 06:01:11.997         Scorpius  \n",
       "2074  2021-07-01 06:01:11.997         Scorpius  \n",
       "141   2021-07-01 06:02:33.999         Ophiucus  \n",
       "...                       ...              ...  \n",
       "837   2021-07-01 06:01:53.003            Libra  \n",
       "838   2021-07-01 06:01:53.003            Libra  \n",
       "839   2021-07-01 06:01:53.003            Libra  \n",
       "840   2021-07-01 06:01:53.003         Scorpius  \n",
       "2497  2021-07-01 05:59:37.000            Virgo  \n",
       "\n",
       "[2498 rows x 47 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.sort_values('d:rf_snia_vs_nonia', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a651df",
   "metadata": {},
   "source": [
    "Please, note this focuses on ALERTS, not OBJECTS, because the classification in Fink is done for alerts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ada4ae",
   "metadata": {},
   "source": [
    "## Get latest alerts by class\n",
    "\n",
    "The list of arguments for getting latest alerts by class can be found at https://fink-portal.org/api/v1/latests.\n",
    "\n",
    "The list of Fink class can be found at https://fink-portal.org/api/v1/classes\n",
    "\n",
    "```bash\n",
    "# Get list of available class in Fink\n",
    "curl -H \"Content-Type: application/json\" -X GET https://fink-portal.org/api/v1/classes -o finkclass.json\n",
    "```\n",
    "\n",
    "To get the last 5 candidates of the class `Early SN Ia candidate`, you would simply use in a unix shell:\n",
    "\n",
    "```bash\n",
    "# Get latests 5 Early SN Ia candidates\n",
    "curl -H \"Content-Type: application/json\" -X POST -d '{\"class\":\"Early SN Ia candidate\", \"n\":\"5\"}' https://fink-portal.org/api/v1/latests -o latest_five_sn_candidates.json\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/latests?class=Early SN Ia candidate&n=5&output-format=json\" -O latest_five_sn_candidates.json\n",
    "```\n",
    "\n",
    "In python, you would use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5b359b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Get latests 5 Early SN Ia candidates\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/latests',\n",
    "  json={\n",
    "    'class': 'Early SN Ia candidate',\n",
    "    'n': '5'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cf8500",
   "metadata": {},
   "source": [
    "Note that for `csv` output, you need to use\n",
    "\n",
    "```python\n",
    "# get latests in CSV format...\n",
    "r = ...\n",
    "\n",
    "pd.read_csv(io.BytesIO(r.content))\n",
    "```\n",
    "\n",
    "You can also specify `startdate` and `stopdate` for your search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "64ef6e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Get all classified SN Ia from TNS between March 1st 2021 and March 5th 2021\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/latests',\n",
    "  json={\n",
    "    'class': '(TNS) SN Ia',\n",
    "    'n': '100',\n",
    "    'startdate': '2021-03-01',\n",
    "    'stopdate': '2021-03-05'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "178c5dad",
   "metadata": {},
   "source": [
    "Please, note this focuses on ALERTS, not OBJECTS, because the classification in Fink is done for alerts. If you then want to get the photometry for all objects, you need to query again the database using the `api/v1/objects` endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "896ebe0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ZTF21aanyfqu'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf = pdf.sort_values('i:ndethist', ascending=True)\n",
    "my_alert = pdf['i:objectId'].values[0]\n",
    "my_alert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb364bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data for ZTF21aaxtctv\n",
    "r = requests.post(\n",
    "    'https://fink-portal.org/api/v1/objects',\n",
    "    json={\n",
    "        'objectId': my_alert,\n",
    "        'withupperlim': 'True',\n",
    "        'output-format': 'json'\n",
    "    }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3c89bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_photometry(pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7abe0bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = pdf['d:tag'] == 'valid'\n",
    "plt.hist(pdf['v:classification'][mask].values);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "175394c4",
   "metadata": {},
   "source": [
    "## Retrieve Solar System Object data\n",
    "\n",
    "The list of arguments for retrieving SSO data can be found at https://fink-portal.org/api/v1/sso.\n",
    "The numbers or designations are taken from the MPC archive.\n",
    "When searching for a particular asteroid or comet, it is best to use the IAU number,\n",
    "as in 8467 for asteroid \"8467 Benoitcarry\". You can also try for numbered comet (e.g. 10P),\n",
    "or interstellar object (none so far...). If the number does not yet exist, you can search for designation.\n",
    "Here are some examples of valid queries:\n",
    "\n",
    "* Asteroids by number (default)\n",
    "  * Asteroids (Main Belt): 8467, 1922\n",
    "  * Asteroids (Hungarians): 18582, 77799\n",
    "  * Asteroids (Jupiter Trojans): 4501, 1583\n",
    "  * Asteroids (Mars Crossers): 302530\n",
    "* Asteroids by designation (if number does not exist yet)\n",
    "  * 2010JO69, 2017AD19, 2012XK111\n",
    "* Comets by number (default)\n",
    "  * 10P, 249P, 124P\n",
    "* Comets by designation (if number does no exist yet)\n",
    "  * C/2020V2, C/2020R2\n",
    "\n",
    "Note for designation, you can also use space (2010 JO69 or C/2020 V2).\n",
    "\n",
    "In a unix shell, you would simply use\n",
    "\n",
    "```bash\n",
    "# Get data for the asteroid 8467 and save it in a CSV file\n",
    "curl -H \"Content-Type: application/json\" -X POST -d '{\"n_or_d\":\"8467\", \"output-format\":\"csv\"}' https://fink-portal.org/api/v1/sso -o 8467.csv\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/sso?n_or_d=8467&output-format=json\" -O 8467.json\n",
    "```\n",
    "\n",
    "In python, you would use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13e5f890",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# get data for object 8467\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/sso',\n",
    "  json={\n",
    "    'n_or_d': '8467',\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37924af",
   "metadata": {},
   "source": [
    "You can also attach the ephemerides provided by the [Miriade ephemeride service](https://ssp.imcce.fr/webservices/miriade/api/ephemcc/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e7a1ad64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'Date', 'LAST', 'HA', 'Az', 'H', 'Dobs', 'Dhelio', 'VMag',\n",
      "       'SDSS:g', 'SDSS:r', 'Phase', 'Elong.', 'AM', 'dRAcosDEC', 'dDEC', 'RV',\n",
      "       'RA', 'Dec', 'Longitude', 'Latitude', 'd:cdsxmatch', 'd:mulens',\n",
      "       'd:rf_kn_vs_nonkn', 'd:rf_snia_vs_nonia', 'd:roid', 'd:snn_sn_vs_all',\n",
      "       'd:snn_snia_vs_nonia', 'i:candid', 'i:chipsf', 'i:classtar', 'i:dec',\n",
      "       'i:diffmaglim', 'i:distnr', 'i:distpsnr1', 'i:drb', 'i:fid', 'i:field',\n",
      "       'i:isdiffpos', 'i:jd', 'i:jdendhist', 'i:jdstarthist', 'i:maggaia',\n",
      "       'i:magnr', 'i:magpsf', 'i:magzpsci', 'i:ndethist', 'i:neargaia',\n",
      "       'i:nid', 'i:nmtchps', 'i:objectId', 'i:publisher', 'i:ra', 'i:rb',\n",
      "       'i:rcid', 'i:sgscore1', 'i:sigmagnr', 'i:sigmapsf', 'i:ssdistnr',\n",
      "       'i:ssmagnr', 'i:ssnamenr', 'i:tooflag', 'i:xpos', 'i:ypos',\n",
      "       'd:tracklet', 'v:classification', 'v:lastdate', 'v:constellation',\n",
      "       'i:magpsf_red'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# get data for object 8467\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/sso',\n",
    "  json={\n",
    "    'n_or_d': '8467',\n",
    "    'withEphem': True,\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)\n",
    "print(pdf.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3740674",
   "metadata": {},
   "source": [
    "Where first columns are fields returned from Miriade (beware it adds few seconds delay).\n",
    "There are some limitations:\n",
    "\n",
    "    - Color ephemerides are returned only for asteroids\n",
    "    - Temporary designations (C/... or YYYY...) do not have ephemerides available\n",
    "\n",
    "See e.g. what we display at https://fink-portal.org/?query_type=SSO&n_or_d=8467\n",
    "\n",
    "By default, we transfer all available data fields (original ZTF fields and Fink science module outputs).\n",
    "But you can also choose to transfer only a subset of the fields to speed-up the query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d1829760",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only jd, and magpsf\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/sso',\n",
    "  json={\n",
    "    'n_or_d': '8467',\n",
    "    'columns': 'i:jd,i:magpsf'\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993eabf3",
   "metadata": {},
   "source": [
    "## Retrieve tracklet data\n",
    "\n",
    "The list of arguments for retrieving tracklet data can be found at https://fink-portal.org/api/v1/tracklet.\n",
    "\n",
    "Each night there are a lot of fast moving objects seen in single exposures (or a few).\n",
    "These objects usually leave discrete tracks (several connected dots), that we call tracklets.\n",
    "The magnitude is rather low, and their magnitude can oscillate (e.g. rotating objects).\n",
    "This is somehow similar to solar system object, expect that these objects\n",
    "seem mainly man-made, they are fast moving, and they typically orbit around the Earth (this\n",
    "is also tighted to the detection method we use).\n",
    "\n",
    "In order to get tracklet data, you need to specify the date in the format `YYYY-MM-DD hh:mm:ss`.\n",
    "Note you can also specify bigger interval, e.g. `YYYY-MM-DD` to get all tracklets for one day,\n",
    "or `YYYY-MM-DD hh` to get all tracklets for one hour.\n",
    "\n",
    "In a unix shell, you would simply use\n",
    "\n",
    "```bash\n",
    "# Get tracklet data for the night 2021-08-10\n",
    "curl -H \"Content-Type: application/json\" -X POST -d '{\"date\":\"2021-08-10\", \"output-format\":\"csv\"}' https://fink-portal.org/api/v1/tracklet -o trck_20210810.csv\n",
    "\n",
    "# you can also specify parameters in the URL, e.g. with wget:\n",
    "wget \"https://fink-portal.org/api/v1/tracklet?date=2021-08-10&output-format=json\" -O trck_20210810.json\n",
    "```\n",
    "\n",
    "In python, you would use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45cbb733",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Get all tracklet data for the night 2021-08-10\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/tracklet',\n",
    "  json={\n",
    "    'date': '2021-08-10',\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abbb6dba",
   "metadata": {},
   "source": [
    "You can also specify up to the second if you know the exposure time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e8390421",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get tracklet data TRCK_20211022_091949\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/tracklet',\n",
    "  json={\n",
    "    'id': '2021-10-22 09:19:49',\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e303b4",
   "metadata": {},
   "source": [
    "Finally if there are several tracklets in one exposure, you can select the one you want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cb7ef5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get first tracklet TRCK_20211022_091949_00\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/tracklet',\n",
    "  json={\n",
    "    'id': '2021-10-22 09:19:49 00',\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b624a3ca",
   "metadata": {},
   "source": [
    "They are ordered by two digits 00, 01, 02, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483ff418",
   "metadata": {},
   "source": [
    "## Fink data statistics\n",
    "\n",
    "The [statistics](https://fink-portal.org/stats) page makes use of the REST API.\n",
    "If you want to further explore Fink statistics, or create your own dashboard based on Fink data,\n",
    "you can do also all of these yourself using the REST API. Here is an example using Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20174722",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# get stats for all the year 2021\n",
    "r = requests.post(\n",
    "  'https://fink-portal.org/api/v1/statistics',\n",
    "  json={\n",
    "    'date': '2021',\n",
    "    'output-format': 'json'\n",
    "  }\n",
    ")\n",
    "\n",
    "# Format output in a DataFrame\n",
    "pdf = pd.read_json(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1525e2c0",
   "metadata": {},
   "source": [
    "Note `date` can be either a given night (YYYYMMDD), month (YYYYMM), year (YYYY), or eveything (empty string).\n",
    "The schema of the dataframe is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "54f75fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_names = {\n",
    "    'basic:night': 'Observation date',\n",
    "    'basic:raw': 'Number of alerts received',\n",
    "    'basic:sci': 'Number of alerts processed (passing quality cuts)',\n",
    "    'basic:n_g': 'Number of measurements in the g band',\n",
    "    'basic:n_r': 'Number of measurements in the r band',\n",
    "    'basic:exposures': 'Number of exposures (30 seconds)',\n",
    "    'basic:fields': 'Number of fields visited',\n",
    "    'class:simbad_tot': 'Number of alerts with a counterpart in SIMBAD',\n",
    "    'class:simbad_gal': 'Number of alerts with a close-by candidate host-galaxy in SIMBAD',\n",
    "    'class:Solar System MPC': 'Number of alerts with a counterpart in MPC (SSO)',\n",
    "    'class:SN candidate': 'Number of alerts classified as SN by Fink',\n",
    "    'class:Early SN Ia candidate': 'Number of alerts classified as early SN Ia by Fink',\n",
    "    'class:Kilonova candidate': 'Number of alerts classified as Kilonova by Fink',\n",
    "    'class:Microlensing candidate': 'Number of alerts classified as Microlensing by Fink',\n",
    "    'class:SN candidate': 'Number of alerts classified as SN by Fink',\n",
    "    'class:Solar System candidate': 'Number of alerts classified as SSO candidates by Fink',\n",
    "    'class:Tracklet': 'Number of alerts classified as satelitte glints or space debris by Fink',\n",
    "    'class:Unknown': 'Number of alerts without classification'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c3aa5d",
   "metadata": {},
   "source": [
    "All other fields starting with `class:` are crossmatch from the SIMBAD database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d07f682",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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