Script_NBs/00_Getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook shows how to access/view training set stored in the database (zarr files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select Kernel\n",
    "In the ['Setup.ipynb'](../Setup/Setup.ipynb) notebook we have alreay created a `LSST_Train` which includes all packages neccessary to access the explore the training data, but it is not guaranteed that when you open a new notebook (for example, this one), the `LSST_Train` will be selected automatically. So we recommend first making sure that the correct kernel is used by checking the top right corner ([observe kernel currently used](../Setup/figs/kernel.jpg)) of the notebook that you are in. If it displays something other than `LSST_Train` (e.g. `Python 3.7 (py37)` [screenshot](../Setup/figs/py37.jpg), you should click on it and change the kernel to `LSST_Train` ([openup window](../Setup/figs/kernel_window.jpg) -> [select 'LSST_Train'](../Setup/figs/select_kernel.jpg))\n",
    "\n",
    "\n",
    "#### Setup Connection\n",
    "Before running this notebook (and all other notebooks that will use the 'utils.py' module), you would need to set the path to the data files as appropriate depending on whether you are working in SciServer or offline, and call the `init()` function to build the connection to the data files.\n",
    "\n",
    "- **In SciServer**\n",
    "The path to the data files are hard coded in the `utils.py` module, you would only need to call the `init` function without any arguments. (_**Run the 2nd code cell**_)\n",
    "\n",
    "- **Offline**\n",
    "The path to the directory hosting the data files needs to be provided by assigning the path string to variables `data_dir`, and then call the `init` function with the paths as the arguments, like `init(data_dir)`. (_**Run the 3rd code cell**_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import zarr\n",
    "from utils import *\n",
    "pd.set_option('display.max_columns', 999)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['text.usetex'] = False\n",
    "plt.rcParams['font.size'] = 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **In SciServer**: Run this cell to setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell if on SciServer\n",
    "init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Offline**: Run this cell to setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # assign the data directory to data_dir\n",
    "# data_dir = 'path/to/training/data/directory'\n",
    "\n",
    "# # Initiate connection with data directory\n",
    "# init(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quasars/AGNs\n",
    "#### Note:\n",
    "We start building the QSO catalog by selecting sources that are in the area of Stripe 82 from the SDSS DR7 Quasar catalog and the [DR14](https://www.sdss.org/dr14/algorithms/qso_catalog/) Quasar catalog separately, then find the union of these two samples to form the source catalog, which ends up containing ~27k objects. Next, we query the Stripe 82 database on CAS to retrive the multi-epoch PSF magnitudes and errors from [Stripe 82 DR7 Coadd](https://iopscience.iop.org/article/10.1088/0004-637X/794/2/120), including extinctions in all five bands. The exact query used is shown below:\n",
    "\n",
    "```sql\n",
    "select q.*, p.objid, p.psfmag_u, p.psfmag_g, p.psfmag_r, p.psfmag_i, p.psfmag_z,\n",
    "  p.psfmagerr_u, p.psfmagerr_g, p.psfmagerr_r, p.psfmagerr_i, p.psfmagerr_z, p.extinction_u,\n",
    "  p.extinction_g, p.extinction_r, p.extinction_i, p.extinction_z, b.distance\n",
    "into mydb.qsoPhoto --ra, dec\n",
    "from mydb.s82specqso as q\n",
    "  cross apply fgetnearbyobjeq(q.ra, q.dec, 1/60.0) b\n",
    "  join photoprimary as p on p.objid = b.objid\n",
    "where ((p.flags & 0x10000000) != 0) \n",
    "  AND ((p.flags & 0x8100000c00a4) = 0) \n",
    "  AND (((p.flags & 0x400000000000) = 0) or (psfmagerr_g<=0.2))\n",
    "  AND (((p.flags & 0x100000000000) = 0) or (p.flags & 0x1000) = 0)\n",
    "  and (psfmagerr_g <= 1 and psfmagerr_r<= 1 and psfmagerr_i<=1 and psfmagerr_u<= 1 and psfmagerr_z<= 1)\n",
    "  and (p.run = 106 or p.run = 206)\n",
    "```\n",
    "\n",
    "Lastly, we remove the objects don't have reliable detection in the Coadd. We are left with ~25k spectroscopically confirmed AGNs in the final catalog. Later, this catalog is matched to Gaia DR2 for proper motin and parallax measurments and SpIES for MIR detections.\n",
    "\n",
    "**Missing data**   \n",
    "All missing data in the catalog is marked using `NaN`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AGN master catalog\n",
    "\n",
    "The full AGN catalog can be extracted with the `get_qso_cat()` convenience function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>train_id</th>\n",
       "      <th>ra</th>\n",
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       "      <th>extinction_g</th>\n",
       "      <th>extinction_r</th>\n",
       "      <th>extinction_i</th>\n",
       "      <th>extinction_z</th>\n",
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       "      <th>hapermag3</th>\n",
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       "      <th>kapermag3</th>\n",
       "      <th>kapermag3err</th>\n",
       "      <th>ukidss_pstar</th>\n",
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       "      <th>fuv_magerr</th>\n",
       "      <th>nuv_mag</th>\n",
       "      <th>nuv_magerr</th>\n",
       "      <th>src2galex</th>\n",
       "      <th>mpstype</th>\n",
       "      <th>des_coadd_id</th>\n",
       "      <th>extended_class_coadd</th>\n",
       "      <th>extended_class_wavg</th>\n",
       "      <th>flags_g</th>\n",
       "      <th>flags_i</th>\n",
       "      <th>flags_r</th>\n",
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       "      <th>mag_auto_z</th>\n",
       "      <th>magerr_auto_g</th>\n",
       "      <th>magerr_auto_i</th>\n",
       "      <th>magerr_auto_r</th>\n",
       "      <th>magerr_auto_y</th>\n",
       "      <th>magerr_auto_z</th>\n",
       "      <th>wavg_mag_psf_g</th>\n",
       "      <th>wavg_mag_psf_g_dered</th>\n",
       "      <th>wavg_mag_psf_i</th>\n",
       "      <th>wavg_mag_psf_i_dered</th>\n",
       "      <th>wavg_mag_psf_r</th>\n",
       "      <th>wavg_mag_psf_r_dered</th>\n",
       "      <th>wavg_mag_psf_y</th>\n",
       "      <th>wavg_mag_psf_y_dered</th>\n",
       "      <th>wavg_mag_psf_z</th>\n",
       "      <th>wavg_mag_psf_z_dered</th>\n",
       "      <th>wavg_magerr_psf_g</th>\n",
       "      <th>wavg_magerr_psf_i</th>\n",
       "      <th>wavg_magerr_psf_r</th>\n",
       "      <th>wavg_magerr_psf_y</th>\n",
       "      <th>wavg_magerr_psf_z</th>\n",
       "      <th>src2des</th>\n",
       "      <th>psPm[0]</th>\n",
       "      <th>psPm[1]</th>\n",
       "      <th>psParallax</th>\n",
       "      <th>stdColor[0]</th>\n",
       "      <th>stdColor[1]</th>\n",
       "      <th>stdColor[2]</th>\n",
       "      <th>stdColor[3]</th>\n",
       "      <th>psFlux[u]</th>\n",
       "      <th>psFlux[g]</th>\n",
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       "      <td>0.147035</td>\n",
       "      <td>0.107939</td>\n",
       "      <td>0.070178</td>\n",
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       "      <td>0.063209</td>\n",
       "      <td>16.787697</td>\n",
       "      <td>0.054704</td>\n",
       "      <td>5.262303e-02</td>\n",
       "      <td>0.947215</td>\n",
       "      <td>0.050483</td>\n",
       "      <td>6379430435810709504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.093075</td>\n",
       "      <td>0.240537</td>\n",
       "      <td>1.100549</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036148</td>\n",
       "      <td>-0.022135</td>\n",
       "      <td>-0.220993</td>\n",
       "      <td>0.35088</td>\n",
       "      <td>0.26291</td>\n",
       "      <td>-0.01495</td>\n",
       "      <td>0.23360</td>\n",
       "      <td>17663.977528</td>\n",
       "      <td>25333.897183</td>\n",
       "      <td>32274.469694</td>\n",
       "      <td>31825.497722</td>\n",
       "      <td>40029.683101</td>\n",
       "      <td>s82Qso</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>309.2220</td>\n",
       "      <td>-0.160658</td>\n",
       "      <td>1117</td>\n",
       "      <td>52885</td>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>1.775506</td>\n",
       "      <td>DR6Q_HW</td>\n",
       "      <td>1.257713e+18</td>\n",
       "      <td>8647475121151149056</td>\n",
       "      <td>20.22258</td>\n",
       "      <td>20.10596</td>\n",
       "      <td>19.98231</td>\n",
       "      <td>19.65110</td>\n",
       "      <td>19.64488</td>\n",
       "      <td>0.014701</td>\n",
       "      <td>0.005218</td>\n",
       "      <td>0.006074</td>\n",
       "      <td>0.006600</td>\n",
       "      <td>0.023517</td>\n",
       "      <td>20.22721</td>\n",
       "      <td>20.09055</td>\n",
       "      <td>19.98604</td>\n",
       "      <td>19.65119</td>\n",
       "      <td>19.64618</td>\n",
       "      <td>0.016463</td>\n",
       "      <td>0.006677</td>\n",
       "      <td>0.007932</td>\n",
       "      <td>0.008536</td>\n",
       "      <td>0.025621</td>\n",
       "      <td>0.483065</td>\n",
       "      <td>0.355435</td>\n",
       "      <td>0.257791</td>\n",
       "      <td>0.195475</td>\n",
       "      <td>0.138594</td>\n",
       "      <td>6</td>\n",
       "      <td>206</td>\n",
       "      <td>0.023886</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.758934</td>\n",
       "      <td>0.461401</td>\n",
       "      <td>-1.460134</td>\n",
       "      <td>0.653973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.066321</td>\n",
       "      <td>4226625226631362560</td>\n",
       "      <td>0.396559</td>\n",
       "      <td>0.495796</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433841002105</td>\n",
       "      <td>0.113455</td>\n",
       "      <td>0.083288</td>\n",
       "      <td>0.054151</td>\n",
       "      <td>0.033727</td>\n",
       "      <td>19.169900</td>\n",
       "      <td>0.102561</td>\n",
       "      <td>18.693764</td>\n",
       "      <td>0.095908</td>\n",
       "      <td>18.334024</td>\n",
       "      <td>0.177777</td>\n",
       "      <td>17.441616</td>\n",
       "      <td>0.108151</td>\n",
       "      <td>1.714368e-04</td>\n",
       "      <td>0.999819</td>\n",
       "      <td>0.043858</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.460134</td>\n",
       "      <td>-0.758934</td>\n",
       "      <td>0.396559</td>\n",
       "      <td>0.11662</td>\n",
       "      <td>0.12365</td>\n",
       "      <td>0.33121</td>\n",
       "      <td>0.00622</td>\n",
       "      <td>28501.385092</td>\n",
       "      <td>32930.588107</td>\n",
       "      <td>36901.303832</td>\n",
       "      <td>50062.466916</td>\n",
       "      <td>51148.877388</td>\n",
       "      <td>s82Qso</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>309.2296</td>\n",
       "      <td>0.171063</td>\n",
       "      <td>1117</td>\n",
       "      <td>52885</td>\n",
       "      <td>354</td>\n",
       "      <td>1</td>\n",
       "      <td>1.110641</td>\n",
       "      <td>DR6Q_HW</td>\n",
       "      <td>1.257728e+18</td>\n",
       "      <td>8647474692191290368</td>\n",
       "      <td>19.44578</td>\n",
       "      <td>19.42037</td>\n",
       "      <td>19.31383</td>\n",
       "      <td>19.41686</td>\n",
       "      <td>19.44050</td>\n",
       "      <td>0.018865</td>\n",
       "      <td>0.006770</td>\n",
       "      <td>0.008106</td>\n",
       "      <td>0.011149</td>\n",
       "      <td>0.050139</td>\n",
       "      <td>19.45775</td>\n",
       "      <td>19.41872</td>\n",
       "      <td>19.31060</td>\n",
       "      <td>19.41246</td>\n",
       "      <td>19.43800</td>\n",
       "      <td>0.021031</td>\n",
       "      <td>0.008076</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.012674</td>\n",
       "      <td>0.052438</td>\n",
       "      <td>0.426967</td>\n",
       "      <td>0.314159</td>\n",
       "      <td>0.227854</td>\n",
       "      <td>0.172775</td>\n",
       "      <td>0.122499</td>\n",
       "      <td>6</td>\n",
       "      <td>106</td>\n",
       "      <td>0.073788</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.023733</td>\n",
       "      <td>0.561890</td>\n",
       "      <td>0.532933</td>\n",
       "      <td>0.756200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.020108</td>\n",
       "      <td>4231137828510030336</td>\n",
       "      <td>0.517200</td>\n",
       "      <td>0.529416</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433816504832</td>\n",
       "      <td>0.100299</td>\n",
       "      <td>0.073630</td>\n",
       "      <td>0.047872</td>\n",
       "      <td>0.029816</td>\n",
       "      <td>19.053074</td>\n",
       "      <td>0.107903</td>\n",
       "      <td>19.285357</td>\n",
       "      <td>0.198457</td>\n",
       "      <td>18.067146</td>\n",
       "      <td>0.156423</td>\n",
       "      <td>17.129730</td>\n",
       "      <td>0.093723</td>\n",
       "      <td>9.472145e-01</td>\n",
       "      <td>0.052623</td>\n",
       "      <td>0.116025</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.532933</td>\n",
       "      <td>-0.023733</td>\n",
       "      <td>0.517200</td>\n",
       "      <td>0.02541</td>\n",
       "      <td>0.10654</td>\n",
       "      <td>-0.10303</td>\n",
       "      <td>-0.02364</td>\n",
       "      <td>58302.017211</td>\n",
       "      <td>61925.019907</td>\n",
       "      <td>68308.787979</td>\n",
       "      <td>62120.391275</td>\n",
       "      <td>61798.356728</td>\n",
       "      <td>s82Qso</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>309.2387</td>\n",
       "      <td>0.028991</td>\n",
       "      <td>1117</td>\n",
       "      <td>52885</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>0.442569</td>\n",
       "      <td>DR6Q_HW</td>\n",
       "      <td>1.257713e+18</td>\n",
       "      <td>8647474692191291392</td>\n",
       "      <td>20.72143</td>\n",
       "      <td>20.31074</td>\n",
       "      <td>20.15662</td>\n",
       "      <td>19.78815</td>\n",
       "      <td>19.46793</td>\n",
       "      <td>0.050663</td>\n",
       "      <td>0.013162</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>0.015634</td>\n",
       "      <td>0.051420</td>\n",
       "      <td>20.59918</td>\n",
       "      <td>20.14651</td>\n",
       "      <td>19.92018</td>\n",
       "      <td>19.50606</td>\n",
       "      <td>19.18451</td>\n",
       "      <td>0.055630</td>\n",
       "      <td>0.014413</td>\n",
       "      <td>0.016459</td>\n",
       "      <td>0.015766</td>\n",
       "      <td>0.050661</td>\n",
       "      <td>0.440081</td>\n",
       "      <td>0.323808</td>\n",
       "      <td>0.234852</td>\n",
       "      <td>0.178081</td>\n",
       "      <td>0.126262</td>\n",
       "      <td>3</td>\n",
       "      <td>106</td>\n",
       "      <td>0.016343</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020786</td>\n",
       "      <td>0.669640</td>\n",
       "      <td>-0.662283</td>\n",
       "      <td>1.054638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011677</td>\n",
       "      <td>4229631531940209664</td>\n",
       "      <td>0.442488</td>\n",
       "      <td>0.672959</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433877410599</td>\n",
       "      <td>0.103388</td>\n",
       "      <td>0.075897</td>\n",
       "      <td>0.049346</td>\n",
       "      <td>0.030735</td>\n",
       "      <td>18.931684</td>\n",
       "      <td>0.079098</td>\n",
       "      <td>18.281277</td>\n",
       "      <td>0.067206</td>\n",
       "      <td>17.412958</td>\n",
       "      <td>0.077741</td>\n",
       "      <td>16.409594</td>\n",
       "      <td>0.043707</td>\n",
       "      <td>5.292210e-07</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.073863</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.662283</td>\n",
       "      <td>0.020786</td>\n",
       "      <td>0.442488</td>\n",
       "      <td>0.41069</td>\n",
       "      <td>0.15412</td>\n",
       "      <td>0.36847</td>\n",
       "      <td>0.32022</td>\n",
       "      <td>17994.203485</td>\n",
       "      <td>27268.917052</td>\n",
       "      <td>31426.411126</td>\n",
       "      <td>44123.605389</td>\n",
       "      <td>60250.529019</td>\n",
       "      <td>s82Qso</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_id        ra       dec  spec_plate  spec_mjd  spec_fiberid  \\\n",
       "0         0  309.2164  0.516741        1117     52885           356   \n",
       "1         1  309.2194 -0.479245        1117     52885           304   \n",
       "2         2  309.2220 -0.160658        1117     52885           302   \n",
       "3         3  309.2296  0.171063        1117     52885           354   \n",
       "4         4  309.2387  0.028991        1117     52885           301   \n",
       "\n",
       "   is_qso_final         z source_z     specobjid           sdss_objid  \\\n",
       "0             1  1.641482  DR6Q_HW  1.257728e+18  8647474692728161280   \n",
       "1             1  0.930411  DR6Q_HW  1.257714e+18  8647475120614278144   \n",
       "2             1  1.775506  DR6Q_HW  1.257713e+18  8647475121151149056   \n",
       "3             1  1.110641  DR6Q_HW  1.257728e+18  8647474692191290368   \n",
       "4             1  0.442569  DR6Q_HW  1.257713e+18  8647474692191291392   \n",
       "\n",
       "   psfmag_u  psfmag_g  psfmag_r  psfmag_i  psfmag_z  psfmagerr_u  psfmagerr_g  \\\n",
       "0  20.32299  20.16837  20.07710  19.77096  19.69925     0.031342     0.011038   \n",
       "1  20.74151  20.39063  20.12772  20.14267  19.90907     0.024440     0.006830   \n",
       "2  20.22258  20.10596  19.98231  19.65110  19.64488     0.014701     0.005218   \n",
       "3  19.44578  19.42037  19.31383  19.41686  19.44050     0.018865     0.006770   \n",
       "4  20.72143  20.31074  20.15662  19.78815  19.46793     0.050663     0.013162   \n",
       "\n",
       "   psfmagerr_r  psfmagerr_i  psfmagerr_z  modelmag_u  modelmag_g  modelmag_r  \\\n",
       "0     0.014122     0.015842     0.048335    20.32215    20.16104    20.08157   \n",
       "1     0.007038     0.010528     0.030004    20.73599    20.37212    20.11103   \n",
       "2     0.006074     0.006600     0.023517    20.22721    20.09055    19.98604   \n",
       "3     0.008106     0.011149     0.050139    19.45775    19.41872    19.31060   \n",
       "4     0.015673     0.015634     0.051420    20.59918    20.14651    19.92018   \n",
       "\n",
       "   modelmag_i  modelmag_z  modelmagerr_u  modelmagerr_g  modelmagerr_r  \\\n",
       "0    19.77601    19.70080       0.034087       0.012585       0.016045   \n",
       "1    20.10914    19.89181       0.026254       0.008130       0.008713   \n",
       "2    19.65119    19.64618       0.016463       0.006677       0.007932   \n",
       "3    19.41246    19.43800       0.021031       0.008076       0.009459   \n",
       "4    19.50606    19.18451       0.055630       0.014413       0.016459   \n",
       "\n",
       "   modelmagerr_i  modelmagerr_z  extinction_u  extinction_g  extinction_r  \\\n",
       "0       0.017909       0.050748      0.381036      0.280362      0.203342   \n",
       "1       0.011976       0.031653      0.685644      0.504490      0.365898   \n",
       "2       0.008536       0.025621      0.483065      0.355435      0.257791   \n",
       "3       0.012674       0.052438      0.426967      0.314159      0.227854   \n",
       "4       0.015766       0.050661      0.440081      0.323808      0.234852   \n",
       "\n",
       "   extinction_i  extinction_z  type  run  src2photo nsc_id  madvar  nsigvar  \\\n",
       "0      0.154188      0.109321     6  106   0.072612   None     NaN      NaN   \n",
       "1      0.277450      0.196715     6  206   0.050114   None     NaN      NaN   \n",
       "2      0.195475      0.138594     6  206   0.023886   None     NaN      NaN   \n",
       "3      0.172775      0.122499     6  106   0.073788   None     NaN      NaN   \n",
       "4      0.178081      0.126262     3  106   0.016343   None     NaN      NaN   \n",
       "\n",
       "      pmdec  pmdec_error      pmra  pmra_error  src2nsc  gaia_astrometry  \\\n",
       "0 -0.511302     0.819070 -0.306719    1.147903      NaN                1   \n",
       "1 -0.022135     0.313026  0.036148    0.336771      NaN                1   \n",
       "2 -0.758934     0.461401 -1.460134    0.653973      NaN                1   \n",
       "3 -0.023733     0.561890  0.532933    0.756200      NaN                1   \n",
       "4  0.020786     0.669640 -0.662283    1.054638      NaN                1   \n",
       "\n",
       "   src2gaia          gaiaedr3_id  parallax  parallax_error  spies_id  \\\n",
       "0  0.041320  4231211839387175424  1.150066        1.069362      <NA>   \n",
       "1  0.059419  4226575164493552128 -0.220993        0.337915      <NA>   \n",
       "2  0.066321  4226625226631362560  0.396559        0.495796      <NA>   \n",
       "3  0.020108  4231137828510030336  0.517200        0.529416      <NA>   \n",
       "4  0.011677  4229631531940209664  0.442488        0.672959      <NA>   \n",
       "\n",
       "   spies_ra  spies_dec  flux_auto_ch1  fluxerr_auto_ch1  class_star_ch1  \\\n",
       "0       NaN        NaN            NaN               NaN             NaN   \n",
       "1       NaN        NaN            NaN               NaN             NaN   \n",
       "2       NaN        NaN            NaN               NaN             NaN   \n",
       "3       NaN        NaN            NaN               NaN             NaN   \n",
       "4       NaN        NaN            NaN               NaN             NaN   \n",
       "\n",
       "   flux_auto_ch2  fluxerr_auto_ch2  class_star_ch2  src2spies     ukidss_id  \\\n",
       "0            NaN               NaN             NaN        NaN  433796596990   \n",
       "1            NaN               NaN             NaN        NaN  433863658375   \n",
       "2            NaN               NaN             NaN        NaN  433841002105   \n",
       "3            NaN               NaN             NaN        NaN  433816504832   \n",
       "4            NaN               NaN             NaN        NaN  433877410599   \n",
       "\n",
       "         ay        aj        ah        ak  yapermag3  yapermag3err  japermag3  \\\n",
       "0  0.089510  0.065709  0.042722  0.026609  19.325617      0.098304  18.882778   \n",
       "1  0.147035  0.107939  0.070178  0.043710  18.807596      0.060388  17.898998   \n",
       "2  0.113455  0.083288  0.054151  0.033727  19.169900      0.102561  18.693764   \n",
       "3  0.100299  0.073630  0.047872  0.029816  19.053074      0.107903  19.285357   \n",
       "4  0.103388  0.075897  0.049346  0.030735  18.931684      0.079098  18.281277   \n",
       "\n",
       "   japermag3err  hapermag3  hapermag3err  kapermag3  kapermag3err  \\\n",
       "0      0.115129  18.259062      0.178485  17.800070      0.148309   \n",
       "1      0.048343  17.426260      0.063209  16.787697      0.054704   \n",
       "2      0.095908  18.334024      0.177777  17.441616      0.108151   \n",
       "3      0.198457  18.067146      0.156423  17.129730      0.093723   \n",
       "4      0.067206  17.412958      0.077741  16.409594      0.043707   \n",
       "\n",
       "   ukidss_pstar  ukidss_pgalaxy  src2ukidss             galex_id  fuv_mag  \\\n",
       "0  5.262303e-02        0.947215    0.237267                 <NA>      NaN   \n",
       "1  5.262303e-02        0.947215    0.050483  6379430435810709504      NaN   \n",
       "2  1.714368e-04        0.999819    0.043858                 <NA>      NaN   \n",
       "3  9.472145e-01        0.052623    0.116025                 <NA>      NaN   \n",
       "4  5.292210e-07        0.999999    0.073863                 <NA>      NaN   \n",
       "\n",
       "   fuv_magerr    nuv_mag  nuv_magerr  src2galex  mpstype  des_coadd_id  \\\n",
       "0         NaN        NaN         NaN        NaN     <NA>          <NA>   \n",
       "1         NaN  21.093075    0.240537   1.100549        1          <NA>   \n",
       "2         NaN        NaN         NaN        NaN     <NA>          <NA>   \n",
       "3         NaN        NaN         NaN        NaN     <NA>          <NA>   \n",
       "4         NaN        NaN         NaN        NaN     <NA>          <NA>   \n",
       "\n",
       "   extended_class_coadd  extended_class_wavg  flags_g  flags_i  flags_r  \\\n",
       "0                   NaN                  NaN      NaN      NaN      NaN   \n",
       "1                   NaN                  NaN      NaN      NaN      NaN   \n",
       "2                   NaN                  NaN      NaN      NaN      NaN   \n",
       "3                   NaN                  NaN      NaN      NaN      NaN   \n",
       "4                   NaN                  NaN      NaN      NaN      NaN   \n",
       "\n",
       "   flags_y  flags_z  mag_auto_g  mag_auto_i  mag_auto_r  mag_auto_y  \\\n",
       "0      NaN      NaN         NaN         NaN         NaN         NaN   \n",
       "1      NaN      NaN         NaN         NaN         NaN         NaN   \n",
       "2      NaN      NaN         NaN         NaN         NaN         NaN   \n",
       "3      NaN      NaN         NaN         NaN         NaN         NaN   \n",
       "4      NaN      NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   mag_auto_z  magerr_auto_g  magerr_auto_i  magerr_auto_r  magerr_auto_y  \\\n",
       "0         NaN            NaN            NaN            NaN            NaN   \n",
       "1         NaN            NaN            NaN            NaN            NaN   \n",
       "2         NaN            NaN            NaN            NaN            NaN   \n",
       "3         NaN            NaN            NaN            NaN            NaN   \n",
       "4         NaN            NaN            NaN            NaN            NaN   \n",
       "\n",
       "   magerr_auto_z  wavg_mag_psf_g  wavg_mag_psf_g_dered  wavg_mag_psf_i  \\\n",
       "0            NaN             NaN                   NaN             NaN   \n",
       "1            NaN             NaN                   NaN             NaN   \n",
       "2            NaN             NaN                   NaN             NaN   \n",
       "3            NaN             NaN                   NaN             NaN   \n",
       "4            NaN             NaN                   NaN             NaN   \n",
       "\n",
       "   wavg_mag_psf_i_dered  wavg_mag_psf_r  wavg_mag_psf_r_dered  wavg_mag_psf_y  \\\n",
       "0                   NaN             NaN                   NaN             NaN   \n",
       "1                   NaN             NaN                   NaN             NaN   \n",
       "2                   NaN             NaN                   NaN             NaN   \n",
       "3                   NaN             NaN                   NaN             NaN   \n",
       "4                   NaN             NaN                   NaN             NaN   \n",
       "\n",
       "   wavg_mag_psf_y_dered  wavg_mag_psf_z  wavg_mag_psf_z_dered  \\\n",
       "0                   NaN             NaN                   NaN   \n",
       "1                   NaN             NaN                   NaN   \n",
       "2                   NaN             NaN                   NaN   \n",
       "3                   NaN             NaN                   NaN   \n",
       "4                   NaN             NaN                   NaN   \n",
       "\n",
       "   wavg_magerr_psf_g  wavg_magerr_psf_i  wavg_magerr_psf_r  wavg_magerr_psf_y  \\\n",
       "0                NaN                NaN                NaN                NaN   \n",
       "1                NaN                NaN                NaN                NaN   \n",
       "2                NaN                NaN                NaN                NaN   \n",
       "3                NaN                NaN                NaN                NaN   \n",
       "4                NaN                NaN                NaN                NaN   \n",
       "\n",
       "   wavg_magerr_psf_z  src2des   psPm[0]   psPm[1]  psParallax  stdColor[0]  \\\n",
       "0                NaN      NaN -0.306719 -0.511302    1.150066      0.15462   \n",
       "1                NaN      NaN  0.036148 -0.022135   -0.220993      0.35088   \n",
       "2                NaN      NaN -1.460134 -0.758934    0.396559      0.11662   \n",
       "3                NaN      NaN  0.532933 -0.023733    0.517200      0.02541   \n",
       "4                NaN      NaN -0.662283  0.020786    0.442488      0.41069   \n",
       "\n",
       "   stdColor[1]  stdColor[2]  stdColor[3]     psFlux[u]     psFlux[g]  \\\n",
       "0      0.09127      0.30614      0.07171  25982.198589  31090.684004   \n",
       "1      0.26291     -0.01495      0.23360  17663.977528  25333.897183   \n",
       "2      0.12365      0.33121      0.00622  28501.385092  32930.588107   \n",
       "3      0.10654     -0.10303     -0.02364  58302.017211  61925.019907   \n",
       "4      0.15412      0.36847      0.32022  17994.203485  27268.917052   \n",
       "\n",
       "      psFlux[r]     psFlux[i]     psFlux[z]   class subclass  s82LSST  \\\n",
       "0  33815.371339  44828.061729  48635.957944  s82Qso     None        1   \n",
       "1  32274.469694  31825.497722  40029.683101  s82Qso     None        1   \n",
       "2  36901.303832  50062.466916  51148.877388  s82Qso     None        1   \n",
       "3  68308.787979  62120.391275  61798.356728  s82Qso     None        1   \n",
       "4  31426.411126  44123.605389  60250.529019  s82Qso     None        1   \n",
       "\n",
       "   s82SDSS  sdss_lcN  \n",
       "0        1      54.0  \n",
       "1        1      59.0  \n",
       "2        1      49.0  \n",
       "3        1      50.0  \n",
       "4        1      46.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qso_df = get_cat(\"s82Qso\")\n",
    "qso_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** \\\n",
    "You might wonder why the `train_id` has missing values (e.g., object with a `train_id` of 1 does not exist), there is no very particular reason to that, mostly caused by some quality cuts I made in the process of building this training set, for example, sources having extremely large photometric errors will be removed and thus does not appear in the final training set. In addition, each object class only occupy a certain range of `train_id` (no AGN has a `train_id` greater than 100000) and this is purely a desgin choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column definitions\n",
    "\n",
    "`qso_cat_meta()` will display the column definitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_id': 'Unique ID for each source in the training set',\n",
       " 'ra': 'Source RA in degrees (J2000 degree)',\n",
       " 'dec': 'Source DEC in degrees (J2000 degree)',\n",
       " 'spec_plate': 'SDSS spectrum plate number',\n",
       " 'spec_mjd': 'MJD for the spectroscopic observation',\n",
       " 'spec_fiberid': 'SDSS spectrum fiberid',\n",
       " 'is_qso_final': 'IS_QSO_FINAL from SDSS DR16Q catalog',\n",
       " 'z': 'SDSS DR16 spectroscopic redshift',\n",
       " 'source_z': 'Origin of the spectrum (or redshift) for spectroscopically confirmed sources',\n",
       " 'specobjid': 'SDSS spectrum ID',\n",
       " 'sdss_objid': 'SDSS photo object id',\n",
       " 'psfmag_{band}': 'SDSS PSF magnitude in u,g,r,i,z',\n",
       " 'psfmagerr_{band}': 'SDSS PSF magnitude error in u,g,r,i,z',\n",
       " 'modelmag_{band}': 'SDSS model magnitude in u,g,r,i,z',\n",
       " 'modelmagerr_{band}': 'SDSS model magnitude error in u,g,r,i,z',\n",
       " 'extinction_{band}': 'Extinction magnitude in u,g,r,i,z',\n",
       " 'type': 'SDSS morphology classification, 3 for extended and 6 for point-like',\n",
       " 'run': 'SDSS run number',\n",
       " 'src2photo': 'Distance from source to SDSS photo object (arcsec)',\n",
       " 'nsc_id': 'NOIRLab Source Catalog (NSC) DR2 unique ID',\n",
       " 'madvar': 'NSC DR2 MAD variability metric value (mag)',\n",
       " 'nsigvar': 'Number of sigma from the median MAD variability metric in NSC DR2',\n",
       " 'src2nsc': 'Distance from source to NSC match (arcsec)',\n",
       " 'src2gaia': 'Distance from source to Gaia match (arcsec)',\n",
       " 'gaiaedr3_id': 'Gaia EDR3 source id',\n",
       " 'parallax': 'Gaia EDR3 parallax (mas)',\n",
       " 'parallax_error': 'Gaia EDR3 parallax error (mas)',\n",
       " 'pmra': 'Proper motion measurement in RA direction (mas/yr)',\n",
       " 'pmra_error': 'Proper motion measurement uncertainty in RA direction (mas/yr)',\n",
       " 'pmdec': 'Proper motion measurement in DEC direction (mas/yr)',\n",
       " 'pmdec_error': 'Proper motion measurement uncertainty in DEC direction (mas/yr)',\n",
       " 'gaia_astrometry': 'If proper motion measurements from Gaia, 1 for Yes and 0 for No (from NSC)',\n",
       " 'spies_id': 'Unique ID assigned to each source in the two channel SpIES catalog',\n",
       " 'spies_ra': 'RA (average of two channels) from SpIES in degrees (J2000)',\n",
       " 'spies_dec': 'DEC (average of two channels) from SpIES in degrees (J2000)',\n",
       " 'flux_auto_ch1': 'SpIES 3.6 microns flux value (microjansky) automatically extracted using SExtractor',\n",
       " 'fluxerr_auto_ch1': 'SpIES 3.6 microns flux error given by SExtractor',\n",
       " 'class_star_ch1': 'SpIES 3.6 micrometer morphology classification, > 0.5 for resolved source',\n",
       " 'flux_auto_ch2': 'SpIES4.5 micrometer flux value (microjansky) automatically extracted using SExtractor',\n",
       " 'fluxerr_auto_ch2': 'SpIES 4.5 microns flux error given by SExtractor',\n",
       " 'class_star_ch2': 'SpIES 4.5 microns morphology classification, > 0.5 for resolved source',\n",
       " 'src2spies': 'Distance from source to SpIES match (arcsec)',\n",
       " 'ukidss_id': 'Unique WSA ID of this merged source detection',\n",
       " 'a{band}': 'Galactic extinction correction in Y, J H, K',\n",
       " '{band}_apermag3': 'Default point source aperture mag (Vega) in Y, J H, K',\n",
       " '{band}_apermag3err': 'Error in the associated Y, J H, K magnitude',\n",
       " 'ukidss_pstar': 'Probability that the source is a point source',\n",
       " 'ukidss_pgalaxy': 'Probability that the source is extended',\n",
       " 'galex_id': 'GALEX ID for matched object',\n",
       " 'fuv_mag': 'GALEX FUV magnitude (AB)',\n",
       " 'fuv_magerr': 'GALEX FUV magnitude error',\n",
       " 'nuv_mag': 'GALEX NUV magnitude (AB)',\n",
       " 'nuv_magerr': 'GALEX NUV magnitude error',\n",
       " 'mpstype': 'GALEX survey mode, 0 for MIS and 1 for AIS',\n",
       " 'src2galex': 'Distance from source to GALEX match(arcsec)',\n",
       " 'des_coadd_id': 'Dark Energy Survey (DES) DR2 coadd unique ID',\n",
       " 'extended_class_coadd': 'DES morphology classification based on coadd data',\n",
       " 'extended_class_wavg': 'DES morphology classification based on the weighted average of single epoch data',\n",
       " 'flags_{band}': 'DES photometry flags for g, r, i, z, y bands, use less than 4 for well behaved objects',\n",
       " 'mag_auto_{band}': 'DES model photometry in g, r, i, z, y bands for an elliptical model based on the kron raidus',\n",
       " 'magerr_auto_{band}': 'Error in DES model photometry',\n",
       " 'wavg_mag_psf_{band}': 'DES weighted average PSF mag in g, r, i, z, y',\n",
       " 'wavg_magerr_psf_{band}': 'Error in DES weighted average PSF mag',\n",
       " 'wavg_mag_psf_{band}_dered': 'Extinction corrected DES weighted average PSF mag',\n",
       " 'src2des': 'Distance from source to the DES coadd match (arcsec)',\n",
       " 'psPm[0]': 'LSST nomenclature: point source model proper motin in RA. Same value as pmra.',\n",
       " 'psPm[1]': 'LSST nomenclature: point source model proper motin in Dec. Same value as pmdec',\n",
       " 'psParallax': 'LSST nomenclature: point source model parallax. Same value as parallax',\n",
       " 'stdColor[0-3]': 'LSST nomenclature: SDSS color (not corrected for extinction), u-g, g-r, r-i, i-z, from low to high',\n",
       " 'psFlux[band]': 'LSST nomenclature: point source model flux (nJy), converted from SDSS \"psfmag\"',\n",
       " 'class': 'Source label',\n",
       " 'subclass': 'Spectroscopic subclass if avaiable',\n",
       " 's82LSST': 'If in s82 region defined by LSST training data, 1 for Yes, 0 for No.',\n",
       " 's82SDSS': 'If in s82 region defined by SDSS, 1 for Yes, 0 for No.',\n",
       " 'sdss_lcN': 'Number of data points in SDSS light curve, NaN for no light curve exists'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_cat_meta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P.S.\n",
    "We added a few more columns to best mimic the LSST data model, specifically **psPm[]**, **psParallax**, **psFlux[]** and **stdColor[]**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get/Plot light curves\n",
    "\n",
    "The light curves were downloaded from SDSS CAS using the following query:\n",
    "\n",
    "```sql\n",
    "select q.train_id, p.run, p.psfmag_u, p.psfmag_g, p.psfmag_r, p.psfmag_i, p.psfmag_z,\n",
    "  p.psfmagerr_u, p.psfmagerr_g, p.psfmagerr_r, p.psfmagerr_i, p.psfmagerr_z, p.offsetRa_u, \n",
    "  p.offsetRa_g, p.offsetRa_i, p.offsetRa_z, p.offsetDec_u, p.offsetDec_g, p.offsetDec_i, \n",
    "  p.offsetDec_z, f.airmass_u, f.airmass_g, f.airmass_r, f.airmass_i, f.airmass_z, \n",
    "  LTRIM(STR(mjd_u,20,2)) as mjd_u,\n",
    "  LTRIM(STR(mjd_g,20,2)) as mjd_g,\n",
    "  LTRIM(STR(mjd_r,20,2)) as mjd_r,\n",
    "  LTRIM(STR(mjd_i,20,2)) as mjd_i,\n",
    "  LTRIM(STR(mjd_z,20,2)) as mjd_z\n",
    "into mydb.s82qsoLC\n",
    "from mydb.s82qsoFinal as q -- ra, dec\n",
    "  cross apply fgetnearbyobjalleq(q.ra, q.dec, 1/60.0) b\n",
    "  join photoobj as p on p.objid = b.objid --photoobj for both primary+secondary\n",
    "  join field as f on f.fieldid = p.fieldid\n",
    "where ((p.flags & 0x10000000) != 0) \n",
    "  AND ((p.flags & 0x8100000c00a4) = 0) \n",
    "  AND (((p.flags & 0x400000000000) = 0) or (p.psfmagerr_r <= 0.2))\n",
    "  AND (((p.flags & 0x100000000000) = 0) or (p.flags & 0x1000) = 0)\n",
    "  and (p.psfmagerr_g <= 1 and p.psfmagerr_r<= 1 and p.psfmagerr_i<=1 and p.psfmagerr_u<= 1 and p.psfmagerr_z<= 1)\n",
    "  and (p.run !=106)\n",
    "  and (p.run !=206)\n",
    "  and f.quality in (2,3)\n",
    "order by train_id\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To retrieve the light curve for any object, use functions ``get_sdss_lc()``.  It is necessary to provide the `train_id` as a required argument.  This is an integer index for each object in the catalog. One can convert `train_id` to SDSS run-rerun-camcol-field-obj `objID` using `train2sdss` and vice versa, using `sdss2train`.    \n",
    "- To plot the light curves, use the function `plot_sdss_lc()` for original data, or use the function `plot_merge_gri()` for merged light curves that are normalized by mean or median of each band. \n",
    "\n",
    "**!!Note that not all integers are valid `train_id`s.**\n",
    "For example, 0 is valid and 1 is not. To obtain a list of valid `train_id`s, you can use the function `valid_ids()` or look at the `train_id` column in the static catalog dataframe (returned by `get_qso_cat()`, `get_var_cat()` or `get_gal_cat`). Convenience functions in `utils.py` will check for the provided `train_id`, if not valid, an error will be thrown!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>train_id</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>s82Qso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>s82Qso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>s82Qso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>s82Qso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>s82Qso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_id   class\n",
       "0         0  s82Qso\n",
       "1         1  s82Qso\n",
       "2         2  s82Qso\n",
       "3         3  s82Qso\n",
       "4         4  s82Qso"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_ids().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what arguments can be passed to each function, use the help method, `help(plot_sdss_lc)` for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot_sdss_lc in module utils:\n",
      "\n",
      "plot_sdss_lc(train_id, bands=['u', 'g', 'r', 'i', 'z'], clip=True)\n",
      "    Plot SDSS light curves without merging.\n",
      "    \n",
      "    Args:\n",
      "        train_id (int): Unique ID for an object in training sample.\n",
      "        bands (list): Optional, a list specifying light curves in which bands to plot.\n",
      "        clip (bool): Optional, indicates whether to run filter removing outliers, default to True.\n",
      "    \n",
      "    Return:\n",
      "        bands (list): A list specifying the band filters in which to plot the light curves.\n",
      "        x (list): A list of numpy arrays of mjd for observations in each band.\n",
      "        y (list): A list of numpy arrays of magnitude in each band (psf mags)\n",
      "        err (list): A list of numpy arrays of magnitude errors in each band\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plot_sdss_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plot_sdss_lc` function will return lists of arrays that can be used to make plots. Please use `help` to see the definition for each returned list. The size of each list will be determined by how many bands one wants to plot the light curves in. The argument `clip` specifies whether to clean up the light curves using a three point median filter.  The example below demonstrates how to make a plot using the returned values, where a clean light curve has been plotted on top of the orginal light curve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f27d423fb50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot light curves in the u band\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "qso_train_id = 0\n",
    "bands1, x1, y1, err1 = plot_sdss_lc(qso_train_id, bands=['u'])\n",
    "bands2, x2, y2, err2 = plot_sdss_lc(qso_train_id, bands=['u'], clip=False) # return light curves with bad observations\n",
    "\n",
    "for i in range(len(bands1)):\n",
    "    plt.errorbar(x1[i], y1[i], err1[i], label='clean LC in {} band'.format(bands1[i]), fmt='.')\n",
    "    plt.errorbar(x2[i], y2[i], err2[i], label='original LC in {} band'.format(bands2[i]),\\\n",
    "                 fmt='s', markerfacecolor='none')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Light curve for object {}'.format(0))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**  \n",
    "From the plot above, we can see the flux in the original light curve suddenly drop around MJD=53650. We suspect that this is caused some kind of calibration failure, since such behavior is observed across many light curves and all occured around that time.  So, we implemented a method to remove artifacts by first applying a three-point median filter to the light curve, then removing outliers that deviate more than 0.5 mag from the median-filtered light curve.  Further cleaning beyond this demonstration is likely to needed for optimal use of the light curves. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Currently, we only normalize and merge light curve by subtracting the mean or median (can be specified while calling the plot function). More sphoiscated light curve merging methods should be implemented, for example, [CREAM](https://academic.oup.com/mnras/article/456/2/1960/1066664), but computing cost will need to be considered. For now, this should be okay to get started. Any comments/suggestions for better light curve merging methods are welcome.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f26d6284d90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# merged lc using sdss g, r, i\n",
    "# plot light curves in the u band\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "bands1, x1, y1, err1 = plot_merged_lc(66, ['g', 'r', 'i'])\n",
    "\n",
    "for i in range(len(bands1)):\n",
    "    plt.errorbar(x1[i], y1[i], err1[i], label='LC in {} band'.format(bands1[i]), fmt='.')\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Merged Light curve for object {}'.format(66))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advanced Plots\n",
    "One can use `get_sdss_qso` to place more strict cuts on the data or even implement your own outlier removal algorithm (set clip=False). The code below demonstrates how to make a light curve plot for the object 66, but request `psfmagerr_z < 0.2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>run</th>\n",
       "      <th>objid</th>\n",
       "      <th>psfmag_u</th>\n",
       "      <th>psfmag_g</th>\n",
       "      <th>psfmag_r</th>\n",
       "      <th>psfmag_i</th>\n",
       "      <th>psfmag_z</th>\n",
       "      <th>psfmagerr_u</th>\n",
       "      <th>psfmagerr_g</th>\n",
       "      <th>psfmagerr_r</th>\n",
       "      <th>psfmagerr_i</th>\n",
       "      <th>psfmagerr_z</th>\n",
       "      <th>modelmag_u</th>\n",
       "      <th>modelmag_g</th>\n",
       "      <th>modelmag_r</th>\n",
       "      <th>modelmag_i</th>\n",
       "      <th>modelmag_z</th>\n",
       "      <th>modelmagerr_u</th>\n",
       "      <th>modelmagerr_g</th>\n",
       "      <th>modelmagerr_r</th>\n",
       "      <th>modelmagerr_i</th>\n",
       "      <th>modelmagerr_z</th>\n",
       "      <th>mjd_u</th>\n",
       "      <th>mjd_g</th>\n",
       "      <th>mjd_r</th>\n",
       "      <th>mjd_i</th>\n",
       "      <th>mjd_z</th>\n",
       "      <th>offsetRa_u</th>\n",
       "      <th>offsetRa_g</th>\n",
       "      <th>offsetDec_u</th>\n",
       "      <th>offsetDec_g</th>\n",
       "      <th>airmass_u</th>\n",
       "      <th>airmass_g</th>\n",
       "      <th>airmass_r</th>\n",
       "      <th>airmass_i</th>\n",
       "      <th>airmass_z</th>\n",
       "      <th>datetime_u</th>\n",
       "      <th>datetime_g</th>\n",
       "      <th>datetime_r</th>\n",
       "      <th>datetime_i</th>\n",
       "      <th>datetime_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2583</td>\n",
       "      <td>8658181379134129317</td>\n",
       "      <td>20.752705</td>\n",
       "      <td>20.302303</td>\n",
       "      <td>20.139132</td>\n",
       "      <td>19.678621</td>\n",
       "      <td>19.405819</td>\n",
       "      <td>0.067002</td>\n",
       "      <td>0.021101</td>\n",
       "      <td>0.020923</td>\n",
       "      <td>0.022196</td>\n",
       "      <td>0.056890</td>\n",
       "      <td>20.575941</td>\n",
       "      <td>20.096352</td>\n",
       "      <td>19.796225</td>\n",
       "      <td>19.284721</td>\n",
       "      <td>19.013844</td>\n",
       "      <td>0.072567</td>\n",
       "      <td>0.021756</td>\n",
       "      <td>0.021300</td>\n",
       "      <td>0.018897</td>\n",
       "      <td>0.054752</td>\n",
       "      <td>52172.23</td>\n",
       "      <td>52172.23</td>\n",
       "      <td>52172.23</td>\n",
       "      <td>52172.23</td>\n",
       "      <td>52172.23</td>\n",
       "      <td>0.050017</td>\n",
       "      <td>0.063208</td>\n",
       "      <td>-0.026299</td>\n",
       "      <td>0.017641</td>\n",
       "      <td>1.334659</td>\n",
       "      <td>1.341772</td>\n",
       "      <td>1.327766</td>\n",
       "      <td>1.331184</td>\n",
       "      <td>1.338186</td>\n",
       "      <td>2001-09-20 05:31:12</td>\n",
       "      <td>2001-09-20 05:31:12</td>\n",
       "      <td>2001-09-20 05:31:12</td>\n",
       "      <td>2001-09-20 05:31:12</td>\n",
       "      <td>2001-09-20 05:31:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3322</td>\n",
       "      <td>8658184553116140540</td>\n",
       "      <td>20.404827</td>\n",
       "      <td>19.967112</td>\n",
       "      <td>19.821909</td>\n",
       "      <td>19.461239</td>\n",
       "      <td>19.222466</td>\n",
       "      <td>0.063279</td>\n",
       "      <td>0.019551</td>\n",
       "      <td>0.019281</td>\n",
       "      <td>0.019913</td>\n",
       "      <td>0.060033</td>\n",
       "      <td>20.342876</td>\n",
       "      <td>19.857126</td>\n",
       "      <td>19.616943</td>\n",
       "      <td>19.212286</td>\n",
       "      <td>18.980976</td>\n",
       "      <td>0.070101</td>\n",
       "      <td>0.020362</td>\n",
       "      <td>0.019971</td>\n",
       "      <td>0.019752</td>\n",
       "      <td>0.055417</td>\n",
       "      <td>52522.19</td>\n",
       "      <td>52522.19</td>\n",
       "      <td>52522.19</td>\n",
       "      <td>52522.19</td>\n",
       "      <td>52522.19</td>\n",
       "      <td>0.088002</td>\n",
       "      <td>0.034941</td>\n",
       "      <td>0.040476</td>\n",
       "      <td>0.034664</td>\n",
       "      <td>1.193908</td>\n",
       "      <td>1.193192</td>\n",
       "      <td>1.194755</td>\n",
       "      <td>1.194313</td>\n",
       "      <td>1.193533</td>\n",
       "      <td>2002-09-05 04:33:36</td>\n",
       "      <td>2002-09-05 04:33:36</td>\n",
       "      <td>2002-09-05 04:33:36</td>\n",
       "      <td>2002-09-05 04:33:36</td>\n",
       "      <td>2002-09-05 04:33:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    run                objid   psfmag_u   psfmag_g   psfmag_r   psfmag_i  \\\n",
       "0  2583  8658181379134129317  20.752705  20.302303  20.139132  19.678621   \n",
       "1  3322  8658184553116140540  20.404827  19.967112  19.821909  19.461239   \n",
       "\n",
       "    psfmag_z  psfmagerr_u  psfmagerr_g  psfmagerr_r  psfmagerr_i  psfmagerr_z  \\\n",
       "0  19.405819     0.067002     0.021101     0.020923     0.022196     0.056890   \n",
       "1  19.222466     0.063279     0.019551     0.019281     0.019913     0.060033   \n",
       "\n",
       "   modelmag_u  modelmag_g  modelmag_r  modelmag_i  modelmag_z  modelmagerr_u  \\\n",
       "0   20.575941   20.096352   19.796225   19.284721   19.013844       0.072567   \n",
       "1   20.342876   19.857126   19.616943   19.212286   18.980976       0.070101   \n",
       "\n",
       "   modelmagerr_g  modelmagerr_r  modelmagerr_i  modelmagerr_z     mjd_u  \\\n",
       "0       0.021756       0.021300       0.018897       0.054752  52172.23   \n",
       "1       0.020362       0.019971       0.019752       0.055417  52522.19   \n",
       "\n",
       "      mjd_g     mjd_r     mjd_i     mjd_z  offsetRa_u  offsetRa_g  \\\n",
       "0  52172.23  52172.23  52172.23  52172.23    0.050017    0.063208   \n",
       "1  52522.19  52522.19  52522.19  52522.19    0.088002    0.034941   \n",
       "\n",
       "   offsetDec_u  offsetDec_g  airmass_u  airmass_g  airmass_r  airmass_i  \\\n",
       "0    -0.026299     0.017641   1.334659   1.341772   1.327766   1.331184   \n",
       "1     0.040476     0.034664   1.193908   1.193192   1.194755   1.194313   \n",
       "\n",
       "   airmass_z          datetime_u          datetime_g          datetime_r  \\\n",
       "0   1.338186 2001-09-20 05:31:12 2001-09-20 05:31:12 2001-09-20 05:31:12   \n",
       "1   1.193533 2002-09-05 04:33:36 2002-09-05 04:33:36 2002-09-05 04:33:36   \n",
       "\n",
       "           datetime_i          datetime_z  \n",
       "0 2001-09-20 05:31:12 2001-09-20 05:31:12  \n",
       "1 2002-09-05 04:33:36 2002-09-05 04:33:36  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first get the light curve data\n",
    "qso_66 = get_sdss_lc(66, clip=False)\n",
    "qso_66.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply custom cuts to the light curves\n",
    "# require observations having psfmagerr_z < 0.2\n",
    "x = qso_66.mjd_z[qso_66.psfmagerr_z < 0.2]\n",
    "y = qso_66.psfmag_z[qso_66.psfmagerr_z < 0.2]\n",
    "err = qso_66.psfmagerr_z[qso_66.psfmagerr_z < 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f26d4e507d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a custom LC plot\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "plt.errorbar(x, y, err, fmt='.' , label='psfmagerr_z < 0.2')\n",
    "plt.errorbar(qso_66.mjd_z, qso_66.psfmag_z, qso_66.psfmagerr_z, \\\n",
    "             fmt='s', markerfacecolor='none', label='No cut' )\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Custom Light curve for object {}'.format(66))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15, loc=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Non-AGN variables\n",
    "\n",
    "### Note:\n",
    "The non-AGN variables catalog consists of all objects in [Ivezic et al. 2007](http://faculty.washington.edu/ivezic/sdss/catalogs/S82variables.html)--except those have been spectroscopically identified as AGN. Thus we are **NOT** confident that all sources in this catalog are non-AGN. This catalog could contains AGNs that were missed by target selection algorithms in the first place.  \n",
    "\n",
    "All SDSS light curves all queried directly from CAS using the following query:\n",
    "\n",
    "```sql\n",
    "select q.train_d, q.ivz_id, p.run, p.psfmag_u, p.psfmag_g, p.psfmag_r, p.psfmag_i, p.psfmag_z,\n",
    "  p.psfmagerr_u, p.psfmagerr_g, p.psfmagerr_r, p.psfmagerr_i, p.psfmagerr_z, p.offsetRa_u, \n",
    "  p.offsetRa_g, p.offsetRa_i, p.offsetRa_z, p.offsetDec_u, p.offsetDec_g, p.offsetDec_i, \n",
    "  p.offsetDec_z, f.airmass_u, f.airmass_g, f.airmass_r, f.airmass_i, f.airmass_z, \n",
    "  LTRIM(STR(mjd_u,20,2)) as mjd_u,\n",
    "  LTRIM(STR(mjd_g,20,2)) as mjd_g,\n",
    "  LTRIM(STR(mjd_r,20,2)) as mjd_r,\n",
    "  LTRIM(STR(mjd_i,20,2)) as mjd_i,\n",
    "  LTRIM(STR(mjd_z,20,2)) as mjd_z\n",
    "into mydb.vstarLC\n",
    "from mydb.vstarFinal as q --catalog including ra, dec\n",
    "  cross apply fgetnearbyobjalleq(q.ra, q.dec, 1/60.0) b\n",
    "  -- join photoobj below to get both primary+secondary object\n",
    "  join photoobj as p on p.objid = b.objid\n",
    "  join field as f on f.fieldid = p.fieldid\n",
    "where ((p.flags & 0x10000000) != 0) \n",
    "  AND ((p.flags & 0x8100000c00a4) = 0) \n",
    "  AND (((p.flags & 0x400000000000) = 0) or (p.psfmagerr_r <= 0.2))\n",
    "  AND (((p.flags & 0x100000000000) = 0) or (p.flags & 0x1000) = 0)\n",
    "  and (p.psfmagerr_g <= 1 and p.psfmagerr_r<= 1 and p.psfmagerr_i<=1 and p.psfmagerr_u<= 1 and p.psfmagerr_z<= 1)\n",
    "  and (p.run !=106)\n",
    "  and (p.run !=206)\n",
    "  and f.quality in (2,3)\n",
    "  and p.type = 6\n",
    "order by train_d\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-AGN Variables master catalog\n",
    "\n",
    "The full non-AGN catalog can be extracted with the `get_var_cat()` convenience function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>train_id</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>spec_plate</th>\n",
       "      <th>spec_mjd</th>\n",
       "      <th>spec_fiberid</th>\n",
       "      <th>is_qso_final</th>\n",
       "      <th>z</th>\n",
       "      <th>source_z</th>\n",
       "      <th>specobjid</th>\n",
       "      <th>sdss_objid</th>\n",
       "      <th>psfmag_u</th>\n",
       "      <th>psfmag_g</th>\n",
       "      <th>psfmag_r</th>\n",
       "      <th>psfmag_i</th>\n",
       "      <th>psfmag_z</th>\n",
       "      <th>psfmagerr_u</th>\n",
       "      <th>psfmagerr_g</th>\n",
       "      <th>psfmagerr_r</th>\n",
       "      <th>psfmagerr_i</th>\n",
       "      <th>psfmagerr_z</th>\n",
       "      <th>modelmag_u</th>\n",
       "      <th>modelmag_g</th>\n",
       "      <th>modelmag_r</th>\n",
       "      <th>modelmag_i</th>\n",
       "      <th>modelmag_z</th>\n",
       "      <th>modelmagerr_u</th>\n",
       "      <th>modelmagerr_g</th>\n",
       "      <th>modelmagerr_r</th>\n",
       "      <th>modelmagerr_i</th>\n",
       "      <th>modelmagerr_z</th>\n",
       "      <th>extinction_u</th>\n",
       "      <th>extinction_g</th>\n",
       "      <th>extinction_r</th>\n",
       "      <th>extinction_i</th>\n",
       "      <th>extinction_z</th>\n",
       "      <th>type</th>\n",
       "      <th>run</th>\n",
       "      <th>src2photo</th>\n",
       "      <th>nsc_id</th>\n",
       "      <th>madvar</th>\n",
       "      <th>nsigvar</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>pmdec_error</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmra_error</th>\n",
       "      <th>src2nsc</th>\n",
       "      <th>gaia_astrometry</th>\n",
       "      <th>src2gaia</th>\n",
       "      <th>gaiaedr3_id</th>\n",
       "      <th>parallax</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>spies_id</th>\n",
       "      <th>spies_ra</th>\n",
       "      <th>spies_dec</th>\n",
       "      <th>flux_auto_ch1</th>\n",
       "      <th>fluxerr_auto_ch1</th>\n",
       "      <th>class_star_ch1</th>\n",
       "      <th>flux_auto_ch2</th>\n",
       "      <th>fluxerr_auto_ch2</th>\n",
       "      <th>class_star_ch2</th>\n",
       "      <th>src2spies</th>\n",
       "      <th>ukidss_id</th>\n",
       "      <th>ay</th>\n",
       "      <th>aj</th>\n",
       "      <th>ah</th>\n",
       "      <th>ak</th>\n",
       "      <th>yapermag3</th>\n",
       "      <th>yapermag3err</th>\n",
       "      <th>japermag3</th>\n",
       "      <th>japermag3err</th>\n",
       "      <th>hapermag3</th>\n",
       "      <th>hapermag3err</th>\n",
       "      <th>kapermag3</th>\n",
       "      <th>kapermag3err</th>\n",
       "      <th>ukidss_pstar</th>\n",
       "      <th>ukidss_pgalaxy</th>\n",
       "      <th>src2ukidss</th>\n",
       "      <th>galex_id</th>\n",
       "      <th>fuv_mag</th>\n",
       "      <th>fuv_magerr</th>\n",
       "      <th>nuv_mag</th>\n",
       "      <th>nuv_magerr</th>\n",
       "      <th>src2galex</th>\n",
       "      <th>mpstype</th>\n",
       "      <th>des_coadd_id</th>\n",
       "      <th>extended_class_coadd</th>\n",
       "      <th>extended_class_wavg</th>\n",
       "      <th>flags_g</th>\n",
       "      <th>flags_i</th>\n",
       "      <th>flags_r</th>\n",
       "      <th>flags_y</th>\n",
       "      <th>flags_z</th>\n",
       "      <th>mag_auto_g</th>\n",
       "      <th>mag_auto_i</th>\n",
       "      <th>mag_auto_r</th>\n",
       "      <th>mag_auto_y</th>\n",
       "      <th>mag_auto_z</th>\n",
       "      <th>magerr_auto_g</th>\n",
       "      <th>magerr_auto_i</th>\n",
       "      <th>magerr_auto_r</th>\n",
       "      <th>magerr_auto_y</th>\n",
       "      <th>magerr_auto_z</th>\n",
       "      <th>wavg_mag_psf_g</th>\n",
       "      <th>wavg_mag_psf_g_dered</th>\n",
       "      <th>wavg_mag_psf_i</th>\n",
       "      <th>wavg_mag_psf_i_dered</th>\n",
       "      <th>wavg_mag_psf_r</th>\n",
       "      <th>wavg_mag_psf_r_dered</th>\n",
       "      <th>wavg_mag_psf_y</th>\n",
       "      <th>wavg_mag_psf_y_dered</th>\n",
       "      <th>wavg_mag_psf_z</th>\n",
       "      <th>wavg_mag_psf_z_dered</th>\n",
       "      <th>wavg_magerr_psf_g</th>\n",
       "      <th>wavg_magerr_psf_i</th>\n",
       "      <th>wavg_magerr_psf_r</th>\n",
       "      <th>wavg_magerr_psf_y</th>\n",
       "      <th>wavg_magerr_psf_z</th>\n",
       "      <th>src2des</th>\n",
       "      <th>psPm[0]</th>\n",
       "      <th>psPm[1]</th>\n",
       "      <th>psParallax</th>\n",
       "      <th>stdColor[0]</th>\n",
       "      <th>stdColor[1]</th>\n",
       "      <th>stdColor[2]</th>\n",
       "      <th>stdColor[3]</th>\n",
       "      <th>psFlux[u]</th>\n",
       "      <th>psFlux[g]</th>\n",
       "      <th>psFlux[r]</th>\n",
       "      <th>psFlux[i]</th>\n",
       "      <th>psFlux[z]</th>\n",
       "      <th>class</th>\n",
       "      <th>subclass</th>\n",
       "      <th>s82LSST</th>\n",
       "      <th>s82SDSS</th>\n",
       "      <th>sdss_lcN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83881</th>\n",
       "      <td>1000000</td>\n",
       "      <td>359.999343</td>\n",
       "      <td>0.725718</td>\n",
       "      <td>1091</td>\n",
       "      <td>52902</td>\n",
       "      <td>544</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SDSS</td>\n",
       "      <td>1.228506e+18</td>\n",
       "      <td>8658200952398872576</td>\n",
       "      <td>20.63010</td>\n",
       "      <td>18.07784</td>\n",
       "      <td>16.74953</td>\n",
       "      <td>16.03611</td>\n",
       "      <td>15.68609</td>\n",
       "      <td>0.104239</td>\n",
       "      <td>0.032567</td>\n",
       "      <td>0.015689</td>\n",
       "      <td>0.013916</td>\n",
       "      <td>0.018462</td>\n",
       "      <td>20.57722</td>\n",
       "      <td>17.95785</td>\n",
       "      <td>16.61120</td>\n",
       "      <td>15.89028</td>\n",
       "      <td>15.53031</td>\n",
       "      <td>0.108315</td>\n",
       "      <td>0.007582</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.003655</td>\n",
       "      <td>0.005988</td>\n",
       "      <td>0.141884</td>\n",
       "      <td>0.104397</td>\n",
       "      <td>0.075717</td>\n",
       "      <td>0.057414</td>\n",
       "      <td>0.040707</td>\n",
       "      <td>6</td>\n",
       "      <td>7140</td>\n",
       "      <td>0.053434</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.744705</td>\n",
       "      <td>0.173767</td>\n",
       "      <td>-24.270859</td>\n",
       "      <td>0.544733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.258033</td>\n",
       "      <td>2738296192438195200</td>\n",
       "      <td>0.816008</td>\n",
       "      <td>0.323039</td>\n",
       "      <td>2876928</td>\n",
       "      <td>359.999364</td>\n",
       "      <td>0.725725</td>\n",
       "      <td>1278.187215</td>\n",
       "      <td>4.307095</td>\n",
       "      <td>0.541796</td>\n",
       "      <td>779.172023</td>\n",
       "      <td>3.812894</td>\n",
       "      <td>0.881300</td>\n",
       "      <td>0.078942</td>\n",
       "      <td>433846114748</td>\n",
       "      <td>0.033332</td>\n",
       "      <td>0.024469</td>\n",
       "      <td>0.015909</td>\n",
       "      <td>0.009909</td>\n",
       "      <td>14.820878</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>14.392549</td>\n",
       "      <td>0.002656</td>\n",
       "      <td>1.377001e+01</td>\n",
       "      <td>3.242632e-03</td>\n",
       "      <td>1.357631e+01</td>\n",
       "      <td>4.105477e-03</td>\n",
       "      <td>5.292210e-07</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1034022270</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.8708</td>\n",
       "      <td>15.8128</td>\n",
       "      <td>16.4993</td>\n",
       "      <td>15.4281</td>\n",
       "      <td>15.4895</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.001115</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>18.0795</td>\n",
       "      <td>17.9918</td>\n",
       "      <td>16.1474</td>\n",
       "      <td>16.1042</td>\n",
       "      <td>16.7755</td>\n",
       "      <td>16.7166</td>\n",
       "      <td>15.8844</td>\n",
       "      <td>15.8556</td>\n",
       "      <td>15.8809</td>\n",
       "      <td>15.8480</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.001161</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.221471</td>\n",
       "      <td>-24.270859</td>\n",
       "      <td>-20.744705</td>\n",
       "      <td>0.816008</td>\n",
       "      <td>2.55226</td>\n",
       "      <td>1.32831</td>\n",
       "      <td>0.71342</td>\n",
       "      <td>0.35002</td>\n",
       "      <td>19575.571261</td>\n",
       "      <td>213250.092907</td>\n",
       "      <td>724793.169654</td>\n",
       "      <td>1.398241e+06</td>\n",
       "      <td>1.966027e+06</td>\n",
       "      <td>s82vStar</td>\n",
       "      <td>M0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83882</th>\n",
       "      <td>1000001</td>\n",
       "      <td>19.998863</td>\n",
       "      <td>-0.825449</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8658177822394548224</td>\n",
       "      <td>20.90837</td>\n",
       "      <td>19.63553</td>\n",
       "      <td>19.29971</td>\n",
       "      <td>19.17203</td>\n",
       "      <td>18.95996</td>\n",
       "      <td>0.113219</td>\n",
       "      <td>0.030709</td>\n",
       "      <td>0.022807</td>\n",
       "      <td>0.034107</td>\n",
       "      <td>0.049033</td>\n",
       "      <td>20.95527</td>\n",
       "      <td>19.63853</td>\n",
       "      <td>19.33470</td>\n",
       "      <td>19.19503</td>\n",
       "      <td>18.94798</td>\n",
       "      <td>0.118751</td>\n",
       "      <td>0.015704</td>\n",
       "      <td>0.014495</td>\n",
       "      <td>0.016428</td>\n",
       "      <td>0.047043</td>\n",
       "      <td>0.172420</td>\n",
       "      <td>0.126865</td>\n",
       "      <td>0.092013</td>\n",
       "      <td>0.069771</td>\n",
       "      <td>0.049468</td>\n",
       "      <td>6</td>\n",
       "      <td>1755</td>\n",
       "      <td>0.060815</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.022021</td>\n",
       "      <td>0.270250</td>\n",
       "      <td>6.718658</td>\n",
       "      <td>0.349345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.103441</td>\n",
       "      <td>2533733389823945216</td>\n",
       "      <td>0.048009</td>\n",
       "      <td>0.310091</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433805575720</td>\n",
       "      <td>0.040504</td>\n",
       "      <td>0.029734</td>\n",
       "      <td>0.019332</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>18.431671</td>\n",
       "      <td>0.031510</td>\n",
       "      <td>18.100578</td>\n",
       "      <td>0.040077</td>\n",
       "      <td>1.807033e+01</td>\n",
       "      <td>1.102390e-01</td>\n",
       "      <td>1.790219e+01</td>\n",
       "      <td>1.681832e-01</td>\n",
       "      <td>1.714368e-04</td>\n",
       "      <td>0.999819</td>\n",
       "      <td>0.084281</td>\n",
       "      <td>3784433398400949248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.417549</td>\n",
       "      <td>0.468055</td>\n",
       "      <td>1.921361</td>\n",
       "      <td>0</td>\n",
       "      <td>1161009124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.4934</td>\n",
       "      <td>18.8956</td>\n",
       "      <td>19.0401</td>\n",
       "      <td>17.4848</td>\n",
       "      <td>18.5105</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>0.003850</td>\n",
       "      <td>0.002955</td>\n",
       "      <td>0.006595</td>\n",
       "      <td>0.004753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102956</td>\n",
       "      <td>6.718658</td>\n",
       "      <td>-7.022021</td>\n",
       "      <td>0.048009</td>\n",
       "      <td>1.27284</td>\n",
       "      <td>0.33582</td>\n",
       "      <td>0.12768</td>\n",
       "      <td>0.21207</td>\n",
       "      <td>15143.433182</td>\n",
       "      <td>50792.112095</td>\n",
       "      <td>69203.017754</td>\n",
       "      <td>7.783656e+04</td>\n",
       "      <td>9.631457e+04</td>\n",
       "      <td>s82vStar</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83883</th>\n",
       "      <td>1000002</td>\n",
       "      <td>37.499205</td>\n",
       "      <td>1.248179</td>\n",
       "      <td>3127</td>\n",
       "      <td>54835</td>\n",
       "      <td>562</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SDSS</td>\n",
       "      <td>3.520844e+18</td>\n",
       "      <td>8647475122531991552</td>\n",
       "      <td>20.99784</td>\n",
       "      <td>19.94525</td>\n",
       "      <td>19.86542</td>\n",
       "      <td>19.86857</td>\n",
       "      <td>19.88928</td>\n",
       "      <td>0.018714</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>0.005687</td>\n",
       "      <td>0.019610</td>\n",
       "      <td>21.00677</td>\n",
       "      <td>19.94220</td>\n",
       "      <td>19.86969</td>\n",
       "      <td>19.86950</td>\n",
       "      <td>19.88751</td>\n",
       "      <td>0.020314</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>0.005323</td>\n",
       "      <td>0.006864</td>\n",
       "      <td>0.020284</td>\n",
       "      <td>0.125279</td>\n",
       "      <td>0.092179</td>\n",
       "      <td>0.066856</td>\n",
       "      <td>0.050695</td>\n",
       "      <td>0.035943</td>\n",
       "      <td>6</td>\n",
       "      <td>206</td>\n",
       "      <td>0.080803</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.414729</td>\n",
       "      <td>0.478468</td>\n",
       "      <td>-1.206271</td>\n",
       "      <td>0.659762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.081420</td>\n",
       "      <td>2501387338283191808</td>\n",
       "      <td>-0.880141</td>\n",
       "      <td>0.439543</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433874549052</td>\n",
       "      <td>0.029433</td>\n",
       "      <td>0.021607</td>\n",
       "      <td>0.014048</td>\n",
       "      <td>0.008750</td>\n",
       "      <td>19.217644</td>\n",
       "      <td>0.090923</td>\n",
       "      <td>19.434920</td>\n",
       "      <td>0.194439</td>\n",
       "      <td>-9.999995e+08</td>\n",
       "      <td>-9.999995e+08</td>\n",
       "      <td>-9.999995e+08</td>\n",
       "      <td>-9.999995e+08</td>\n",
       "      <td>9.996572e-01</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.196114</td>\n",
       "      <td>3785207454586900992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.384655</td>\n",
       "      <td>0.173528</td>\n",
       "      <td>0.940821</td>\n",
       "      <td>0</td>\n",
       "      <td>1288405865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.9674</td>\n",
       "      <td>19.9229</td>\n",
       "      <td>19.9032</td>\n",
       "      <td>20.0843</td>\n",
       "      <td>19.9652</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.006420</td>\n",
       "      <td>0.004048</td>\n",
       "      <td>0.044331</td>\n",
       "      <td>0.012351</td>\n",
       "      <td>19.9445</td>\n",
       "      <td>19.8670</td>\n",
       "      <td>19.9015</td>\n",
       "      <td>19.8634</td>\n",
       "      <td>19.8807</td>\n",
       "      <td>19.8287</td>\n",
       "      <td>19.9743</td>\n",
       "      <td>19.9489</td>\n",
       "      <td>19.9349</td>\n",
       "      <td>19.9059</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>0.020785</td>\n",
       "      <td>0.005515</td>\n",
       "      <td>0.082800</td>\n",
       "      <td>-1.206271</td>\n",
       "      <td>-1.414729</td>\n",
       "      <td>-0.880141</td>\n",
       "      <td>1.05259</td>\n",
       "      <td>0.07983</td>\n",
       "      <td>-0.00315</td>\n",
       "      <td>-0.02071</td>\n",
       "      <td>13942.959638</td>\n",
       "      <td>38185.145051</td>\n",
       "      <td>41096.935529</td>\n",
       "      <td>4.097206e+04</td>\n",
       "      <td>4.077280e+04</td>\n",
       "      <td>s82vStar</td>\n",
       "      <td>A0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83884</th>\n",
       "      <td>1000003</td>\n",
       "      <td>347.500129</td>\n",
       "      <td>0.327693</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8658194479339798528</td>\n",
       "      <td>20.85255</td>\n",
       "      <td>18.36844</td>\n",
       "      <td>17.26255</td>\n",
       "      <td>16.81212</td>\n",
       "      <td>16.61782</td>\n",
       "      <td>0.145147</td>\n",
       "      <td>0.015377</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.011034</td>\n",
       "      <td>0.018605</td>\n",
       "      <td>20.78941</td>\n",
       "      <td>18.26851</td>\n",
       "      <td>17.14023</td>\n",
       "      <td>16.66936</td>\n",
       "      <td>16.46701</td>\n",
       "      <td>0.151814</td>\n",
       "      <td>0.008468</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.004666</td>\n",
       "      <td>0.011174</td>\n",
       "      <td>0.229234</td>\n",
       "      <td>0.168668</td>\n",
       "      <td>0.122332</td>\n",
       "      <td>0.092761</td>\n",
       "      <td>0.065769</td>\n",
       "      <td>6</td>\n",
       "      <td>5633</td>\n",
       "      <td>0.065740</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.021509</td>\n",
       "      <td>0.081061</td>\n",
       "      <td>19.651280</td>\n",
       "      <td>0.104584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.248731</td>\n",
       "      <td>2651369215462505472</td>\n",
       "      <td>1.069016</td>\n",
       "      <td>0.084396</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433877193002</td>\n",
       "      <td>0.053852</td>\n",
       "      <td>0.039533</td>\n",
       "      <td>0.025703</td>\n",
       "      <td>0.016009</td>\n",
       "      <td>15.809603</td>\n",
       "      <td>0.006072</td>\n",
       "      <td>15.358065</td>\n",
       "      <td>0.003907</td>\n",
       "      <td>1.473854e+01</td>\n",
       "      <td>5.391538e-03</td>\n",
       "      <td>1.460933e+01</td>\n",
       "      <td>9.656386e-03</td>\n",
       "      <td>5.292210e-07</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.198510</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>996509225</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.1601</td>\n",
       "      <td>16.5640</td>\n",
       "      <td>17.0251</td>\n",
       "      <td>16.2619</td>\n",
       "      <td>16.2920</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.002254</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>18.2531</td>\n",
       "      <td>18.1114</td>\n",
       "      <td>16.7911</td>\n",
       "      <td>16.7213</td>\n",
       "      <td>17.1827</td>\n",
       "      <td>17.0875</td>\n",
       "      <td>16.5319</td>\n",
       "      <td>16.4853</td>\n",
       "      <td>16.5921</td>\n",
       "      <td>16.5389</td>\n",
       "      <td>0.001082</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>0.129798</td>\n",
       "      <td>19.651280</td>\n",
       "      <td>2.021509</td>\n",
       "      <td>1.069016</td>\n",
       "      <td>2.48411</td>\n",
       "      <td>1.10589</td>\n",
       "      <td>0.45043</td>\n",
       "      <td>0.19430</td>\n",
       "      <td>15943.979213</td>\n",
       "      <td>163173.078425</td>\n",
       "      <td>451862.025866</td>\n",
       "      <td>6.841918e+05</td>\n",
       "      <td>8.334769e+05</td>\n",
       "      <td>s82vStar</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83885</th>\n",
       "      <td>1000004</td>\n",
       "      <td>347.499157</td>\n",
       "      <td>0.933601</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8647474693013372928</td>\n",
       "      <td>20.73130</td>\n",
       "      <td>19.97220</td>\n",
       "      <td>19.80912</td>\n",
       "      <td>19.79414</td>\n",
       "      <td>19.58842</td>\n",
       "      <td>0.017597</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>0.005980</td>\n",
       "      <td>0.010104</td>\n",
       "      <td>0.018352</td>\n",
       "      <td>20.72877</td>\n",
       "      <td>19.95681</td>\n",
       "      <td>19.80609</td>\n",
       "      <td>19.79865</td>\n",
       "      <td>19.58788</td>\n",
       "      <td>0.019146</td>\n",
       "      <td>0.005147</td>\n",
       "      <td>0.005753</td>\n",
       "      <td>0.007430</td>\n",
       "      <td>0.018400</td>\n",
       "      <td>0.217905</td>\n",
       "      <td>0.160332</td>\n",
       "      <td>0.116286</td>\n",
       "      <td>0.088176</td>\n",
       "      <td>0.062518</td>\n",
       "      <td>6</td>\n",
       "      <td>106</td>\n",
       "      <td>0.056469</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.481785</td>\n",
       "      <td>0.404122</td>\n",
       "      <td>0.426086</td>\n",
       "      <td>0.556542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019289</td>\n",
       "      <td>2651474798643293184</td>\n",
       "      <td>-0.146853</td>\n",
       "      <td>0.521256</td>\n",
       "      <td>1652608</td>\n",
       "      <td>347.499152</td>\n",
       "      <td>0.933628</td>\n",
       "      <td>67.998082</td>\n",
       "      <td>2.627400</td>\n",
       "      <td>0.108509</td>\n",
       "      <td>83.477272</td>\n",
       "      <td>2.961925</td>\n",
       "      <td>0.843248</td>\n",
       "      <td>0.098264</td>\n",
       "      <td>433824144605</td>\n",
       "      <td>0.051195</td>\n",
       "      <td>0.037582</td>\n",
       "      <td>0.024435</td>\n",
       "      <td>0.015219</td>\n",
       "      <td>18.858759</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>18.515894</td>\n",
       "      <td>0.056679</td>\n",
       "      <td>-9.999995e+08</td>\n",
       "      <td>-9.999995e+08</td>\n",
       "      <td>1.759643e+01</td>\n",
       "      <td>1.401911e-01</td>\n",
       "      <td>9.967527e-01</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>0.046304</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>992703425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.9508</td>\n",
       "      <td>19.7803</td>\n",
       "      <td>19.8109</td>\n",
       "      <td>19.4944</td>\n",
       "      <td>19.5608</td>\n",
       "      <td>0.003812</td>\n",
       "      <td>0.006136</td>\n",
       "      <td>0.004214</td>\n",
       "      <td>0.034874</td>\n",
       "      <td>0.009057</td>\n",
       "      <td>19.9318</td>\n",
       "      <td>19.7971</td>\n",
       "      <td>19.7833</td>\n",
       "      <td>19.7170</td>\n",
       "      <td>19.8019</td>\n",
       "      <td>19.7115</td>\n",
       "      <td>19.4745</td>\n",
       "      <td>19.4302</td>\n",
       "      <td>19.5460</td>\n",
       "      <td>19.4954</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.003134</td>\n",
       "      <td>0.002617</td>\n",
       "      <td>0.018721</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>0.014843</td>\n",
       "      <td>0.426086</td>\n",
       "      <td>0.481785</td>\n",
       "      <td>-0.146853</td>\n",
       "      <td>0.75910</td>\n",
       "      <td>0.16308</td>\n",
       "      <td>0.01498</td>\n",
       "      <td>0.20572</td>\n",
       "      <td>17831.124164</td>\n",
       "      <td>37248.846289</td>\n",
       "      <td>43284.683640</td>\n",
       "      <td>4.388074e+04</td>\n",
       "      <td>5.389427e+04</td>\n",
       "      <td>s82vStar</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       train_id          ra       dec  spec_plate  spec_mjd  spec_fiberid  \\\n",
       "83881   1000000  359.999343  0.725718        1091     52902           544   \n",
       "83882   1000001   19.998863 -0.825449        <NA>      <NA>          <NA>   \n",
       "83883   1000002   37.499205  1.248179        3127     54835           562   \n",
       "83884   1000003  347.500129  0.327693        <NA>      <NA>          <NA>   \n",
       "83885   1000004  347.499157  0.933601        <NA>      <NA>          <NA>   \n",
       "\n",
       "       is_qso_final   z source_z     specobjid           sdss_objid  psfmag_u  \\\n",
       "83881          <NA> NaN     SDSS  1.228506e+18  8658200952398872576  20.63010   \n",
       "83882          <NA> NaN     None           NaN  8658177822394548224  20.90837   \n",
       "83883          <NA> NaN     SDSS  3.520844e+18  8647475122531991552  20.99784   \n",
       "83884          <NA> NaN     None           NaN  8658194479339798528  20.85255   \n",
       "83885          <NA> NaN     None           NaN  8647474693013372928  20.73130   \n",
       "\n",
       "       psfmag_g  psfmag_r  psfmag_i  psfmag_z  psfmagerr_u  psfmagerr_g  \\\n",
       "83881  18.07784  16.74953  16.03611  15.68609     0.104239     0.032567   \n",
       "83882  19.63553  19.29971  19.17203  18.95996     0.113219     0.030709   \n",
       "83883  19.94525  19.86542  19.86857  19.88928     0.018714     0.003049   \n",
       "83884  18.36844  17.26255  16.81212  16.61782     0.145147     0.015377   \n",
       "83885  19.97220  19.80912  19.79414  19.58842     0.017597     0.003996   \n",
       "\n",
       "       psfmagerr_r  psfmagerr_i  psfmagerr_z  modelmag_u  modelmag_g  \\\n",
       "83881     0.015689     0.013916     0.018462    20.57722    17.95785   \n",
       "83882     0.022807     0.034107     0.049033    20.95527    19.63853   \n",
       "83883     0.003790     0.005687     0.019610    21.00677    19.94220   \n",
       "83884     0.008606     0.011034     0.018605    20.78941    18.26851   \n",
       "83885     0.005980     0.010104     0.018352    20.72877    19.95681   \n",
       "\n",
       "       modelmag_r  modelmag_i  modelmag_z  modelmagerr_u  modelmagerr_g  \\\n",
       "83881    16.61120    15.89028    15.53031       0.108315       0.007582   \n",
       "83882    19.33470    19.19503    18.94798       0.118751       0.015704   \n",
       "83883    19.86969    19.86950    19.88751       0.020314       0.004680   \n",
       "83884    17.14023    16.66936    16.46701       0.151814       0.008468   \n",
       "83885    19.80609    19.79865    19.58788       0.019146       0.005147   \n",
       "\n",
       "       modelmagerr_r  modelmagerr_i  modelmagerr_z  extinction_u  \\\n",
       "83881       0.004511       0.003655       0.005988      0.141884   \n",
       "83882       0.014495       0.016428       0.047043      0.172420   \n",
       "83883       0.005323       0.006864       0.020284      0.125279   \n",
       "83884       0.005031       0.004666       0.011174      0.229234   \n",
       "83885       0.005753       0.007430       0.018400      0.217905   \n",
       "\n",
       "       extinction_g  extinction_r  extinction_i  extinction_z  type   run  \\\n",
       "83881      0.104397      0.075717      0.057414      0.040707     6  7140   \n",
       "83882      0.126865      0.092013      0.069771      0.049468     6  1755   \n",
       "83883      0.092179      0.066856      0.050695      0.035943     6   206   \n",
       "83884      0.168668      0.122332      0.092761      0.065769     6  5633   \n",
       "83885      0.160332      0.116286      0.088176      0.062518     6   106   \n",
       "\n",
       "       src2photo nsc_id  madvar  nsigvar      pmdec  pmdec_error       pmra  \\\n",
       "83881   0.053434   None     NaN      NaN -20.744705     0.173767 -24.270859   \n",
       "83882   0.060815   None     NaN      NaN  -7.022021     0.270250   6.718658   \n",
       "83883   0.080803   None     NaN      NaN  -1.414729     0.478468  -1.206271   \n",
       "83884   0.065740   None     NaN      NaN   2.021509     0.081061  19.651280   \n",
       "83885   0.056469   None     NaN      NaN   0.481785     0.404122   0.426086   \n",
       "\n",
       "       pmra_error  src2nsc  gaia_astrometry  src2gaia          gaiaedr3_id  \\\n",
       "83881    0.544733      NaN                1  0.258033  2738296192438195200   \n",
       "83882    0.349345      NaN                1  0.103441  2533733389823945216   \n",
       "83883    0.659762      NaN                1  0.081420  2501387338283191808   \n",
       "83884    0.104584      NaN                1  0.248731  2651369215462505472   \n",
       "83885    0.556542      NaN                1  0.019289  2651474798643293184   \n",
       "\n",
       "       parallax  parallax_error  spies_id    spies_ra  spies_dec  \\\n",
       "83881  0.816008        0.323039   2876928  359.999364   0.725725   \n",
       "83882  0.048009        0.310091      <NA>         NaN        NaN   \n",
       "83883 -0.880141        0.439543      <NA>         NaN        NaN   \n",
       "83884  1.069016        0.084396      <NA>         NaN        NaN   \n",
       "83885 -0.146853        0.521256   1652608  347.499152   0.933628   \n",
       "\n",
       "       flux_auto_ch1  fluxerr_auto_ch1  class_star_ch1  flux_auto_ch2  \\\n",
       "83881    1278.187215          4.307095        0.541796     779.172023   \n",
       "83882            NaN               NaN             NaN            NaN   \n",
       "83883            NaN               NaN             NaN            NaN   \n",
       "83884            NaN               NaN             NaN            NaN   \n",
       "83885      67.998082          2.627400        0.108509      83.477272   \n",
       "\n",
       "       fluxerr_auto_ch2  class_star_ch2  src2spies     ukidss_id        ay  \\\n",
       "83881          3.812894        0.881300   0.078942  433846114748  0.033332   \n",
       "83882               NaN             NaN        NaN  433805575720  0.040504   \n",
       "83883               NaN             NaN        NaN  433874549052  0.029433   \n",
       "83884               NaN             NaN        NaN  433877193002  0.053852   \n",
       "83885          2.961925        0.843248   0.098264  433824144605  0.051195   \n",
       "\n",
       "             aj        ah        ak  yapermag3  yapermag3err  japermag3  \\\n",
       "83881  0.024469  0.015909  0.009909  14.820878      0.003358  14.392549   \n",
       "83882  0.029734  0.019332  0.012041  18.431671      0.031510  18.100578   \n",
       "83883  0.021607  0.014048  0.008750  19.217644      0.090923  19.434920   \n",
       "83884  0.039533  0.025703  0.016009  15.809603      0.006072  15.358065   \n",
       "83885  0.037582  0.024435  0.015219  18.858759      0.069088  18.515894   \n",
       "\n",
       "       japermag3err     hapermag3  hapermag3err     kapermag3  kapermag3err  \\\n",
       "83881      0.002656  1.377001e+01  3.242632e-03  1.357631e+01  4.105477e-03   \n",
       "83882      0.040077  1.807033e+01  1.102390e-01  1.790219e+01  1.681832e-01   \n",
       "83883      0.194439 -9.999995e+08 -9.999995e+08 -9.999995e+08 -9.999995e+08   \n",
       "83884      0.003907  1.473854e+01  5.391538e-03  1.460933e+01  9.656386e-03   \n",
       "83885      0.056679 -9.999995e+08 -9.999995e+08  1.759643e+01  1.401911e-01   \n",
       "\n",
       "       ukidss_pstar  ukidss_pgalaxy  src2ukidss             galex_id  fuv_mag  \\\n",
       "83881  5.292210e-07        0.999999    0.156500                 <NA>      NaN   \n",
       "83882  1.714368e-04        0.999819    0.084281  3784433398400949248      NaN   \n",
       "83883  9.996572e-01        0.000171    0.196114  3785207454586900992      NaN   \n",
       "83884  5.292210e-07        0.999999    0.198510                 <NA>      NaN   \n",
       "83885  9.967527e-01        0.003076    0.046304                 <NA>      NaN   \n",
       "\n",
       "       fuv_magerr    nuv_mag  nuv_magerr  src2galex  mpstype  des_coadd_id  \\\n",
       "83881         NaN        NaN         NaN        NaN     <NA>    1034022270   \n",
       "83882         NaN  23.417549    0.468055   1.921361        0    1161009124   \n",
       "83883         NaN  22.384655    0.173528   0.940821        0    1288405865   \n",
       "83884         NaN        NaN         NaN        NaN     <NA>     996509225   \n",
       "83885         NaN        NaN         NaN        NaN     <NA>     992703425   \n",
       "\n",
       "       extended_class_coadd  extended_class_wavg  flags_g  flags_i  flags_r  \\\n",
       "83881                   3.0                  3.0      0.0      0.0      0.0   \n",
       "83882                   0.0                 -9.0      3.0      3.0      3.0   \n",
       "83883                   0.0                  0.0      0.0      0.0      0.0   \n",
       "83884                   2.0                  2.0      2.0      2.0      2.0   \n",
       "83885                   0.0                  0.0      3.0      3.0      3.0   \n",
       "\n",
       "       flags_y  flags_z  mag_auto_g  mag_auto_i  mag_auto_r  mag_auto_y  \\\n",
       "83881      0.0      0.0     17.8708     15.8128     16.4993     15.4281   \n",
       "83882      3.0      3.0     19.4934     18.8956     19.0401     17.4848   \n",
       "83883      0.0      0.0     19.9674     19.9229     19.9032     20.0843   \n",
       "83884      2.0      2.0     18.1601     16.5640     17.0251     16.2619   \n",
       "83885      3.0      3.0     19.9508     19.7803     19.8109     19.4944   \n",
       "\n",
       "       mag_auto_z  magerr_auto_g  magerr_auto_i  magerr_auto_r  magerr_auto_y  \\\n",
       "83881     15.4895       0.000818       0.000286       0.000351       0.001115   \n",
       "83882     18.5105       0.002817       0.003850       0.002955       0.006595   \n",
       "83883     19.9652       0.003611       0.006420       0.004048       0.044331   \n",
       "83884     16.2920       0.001105       0.000476       0.000541       0.002254   \n",
       "83885     19.5608       0.003812       0.006136       0.004214       0.034874   \n",
       "\n",
       "       magerr_auto_z  wavg_mag_psf_g  wavg_mag_psf_g_dered  wavg_mag_psf_i  \\\n",
       "83881       0.000336         18.0795               17.9918         16.1474   \n",
       "83882       0.004753             NaN                   NaN             NaN   \n",
       "83883       0.012351         19.9445               19.8670         19.9015   \n",
       "83884       0.000594         18.2531               18.1114         16.7911   \n",
       "83885       0.009057         19.9318               19.7971         19.7833   \n",
       "\n",
       "       wavg_mag_psf_i_dered  wavg_mag_psf_r  wavg_mag_psf_r_dered  \\\n",
       "83881               16.1042         16.7755               16.7166   \n",
       "83882                   NaN             NaN                   NaN   \n",
       "83883               19.8634         19.8807               19.8287   \n",
       "83884               16.7213         17.1827               17.0875   \n",
       "83885               19.7170         19.8019               19.7115   \n",
       "\n",
       "       wavg_mag_psf_y  wavg_mag_psf_y_dered  wavg_mag_psf_z  \\\n",
       "83881         15.8844               15.8556         15.8809   \n",
       "83882             NaN                   NaN             NaN   \n",
       "83883         19.9743               19.9489         19.9349   \n",
       "83884         16.5319               16.4853         16.5921   \n",
       "83885         19.4745               19.4302         19.5460   \n",
       "\n",
       "       wavg_mag_psf_z_dered  wavg_magerr_psf_g  wavg_magerr_psf_i  \\\n",
       "83881               15.8480           0.000930           0.000358   \n",
       "83882                   NaN                NaN                NaN   \n",
       "83883               19.9059           0.002637           0.003202   \n",
       "83884               16.5389           0.001082           0.000438   \n",
       "83885               19.4954           0.002888           0.003134   \n",
       "\n",
       "       wavg_magerr_psf_r  wavg_magerr_psf_y  wavg_magerr_psf_z   src2des  \\\n",
       "83881           0.000460           0.001161           0.000406  0.221471   \n",
       "83882                NaN                NaN                NaN  0.102956   \n",
       "83883           0.002498           0.020785           0.005515  0.082800   \n",
       "83884           0.000532           0.001526           0.000478  0.129798   \n",
       "83885           0.002617           0.018721           0.004500  0.014843   \n",
       "\n",
       "         psPm[0]    psPm[1]  psParallax  stdColor[0]  stdColor[1]  \\\n",
       "83881 -24.270859 -20.744705    0.816008      2.55226      1.32831   \n",
       "83882   6.718658  -7.022021    0.048009      1.27284      0.33582   \n",
       "83883  -1.206271  -1.414729   -0.880141      1.05259      0.07983   \n",
       "83884  19.651280   2.021509    1.069016      2.48411      1.10589   \n",
       "83885   0.426086   0.481785   -0.146853      0.75910      0.16308   \n",
       "\n",
       "       stdColor[2]  stdColor[3]     psFlux[u]      psFlux[g]      psFlux[r]  \\\n",
       "83881      0.71342      0.35002  19575.571261  213250.092907  724793.169654   \n",
       "83882      0.12768      0.21207  15143.433182   50792.112095   69203.017754   \n",
       "83883     -0.00315     -0.02071  13942.959638   38185.145051   41096.935529   \n",
       "83884      0.45043      0.19430  15943.979213  163173.078425  451862.025866   \n",
       "83885      0.01498      0.20572  17831.124164   37248.846289   43284.683640   \n",
       "\n",
       "          psFlux[i]     psFlux[z]     class subclass  s82LSST  s82SDSS  \\\n",
       "83881  1.398241e+06  1.966027e+06  s82vStar       M0        1        1   \n",
       "83882  7.783656e+04  9.631457e+04  s82vStar     None        1        1   \n",
       "83883  4.097206e+04  4.077280e+04  s82vStar       A0        1        1   \n",
       "83884  6.841918e+05  8.334769e+05  s82vStar     None        1        1   \n",
       "83885  4.388074e+04  5.389427e+04  s82vStar     None        1        1   \n",
       "\n",
       "       sdss_lcN  \n",
       "83881      69.0  \n",
       "83882      36.0  \n",
       "83883      65.0  \n",
       "83884      66.0  \n",
       "83885      49.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstar_df = get_cat('s82vStar')\n",
    "vstar_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column definitions (output will be the same as qso_cat_meta())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_cat_meta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P.S.\n",
    "As above, we added a few more columns to best mimc the LSST data models, specifically **psPm[]**, **psParallax**, **psFlux[]** and **stdColor[]**. Detailed column definition can be obtain using function ``var_cat_meta()``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get/Plot light curves\n",
    "- To retrieve the light curves, use the function `get_sdss_lc()` with `train_id` at the input.  \n",
    "- To plot the light curves, use function `plot_sdss_lc()` or `plot_merged_lc()`.\n",
    "\n",
    "The `train_id` for non-AGN variable starts at 100012. As for the QSO light curves, `clip` is set to `True` as the default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f26d4dfdc50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot var\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "var_train_id = 1000015\n",
    "bands1, x1, y1, err1 = plot_sdss_lc(var_train_id)\n",
    "\n",
    "for i in range(len(bands1)):\n",
    "    plt.errorbar(x1[i], y1[i], err1[i], label='LC in {} band'.format(bands1[i]), fmt='.')\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Light curve for object {}'.format(var_train_id))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f26d4c8a6d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot gri merged light curve\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "bands1, x1, y1, err1 = plot_merged_lc(var_train_id, ['g', 'r', 'i'])\n",
    "\n",
    "for i in range(len(bands1)):\n",
    "    plt.errorbar(x1[i], y1[i], err1[i], label='LC in {} band'.format(bands1[i]), fmt='.')\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Merged Light curve for object {}'.format(var_train_id))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Galaxy master catalog\n",
    "\n",
    "Our training set also includes ~110k spectroscopically confirmed galaxies. Similar convenience functions as demonstrated for AGNs and non-AGN variables are also implemented for galaxies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_id</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>spec_plate</th>\n",
       "      <th>spec_mjd</th>\n",
       "      <th>spec_fiberid</th>\n",
       "      <th>is_qso_final</th>\n",
       "      <th>z</th>\n",
       "      <th>source_z</th>\n",
       "      <th>specobjid</th>\n",
       "      <th>sdss_objid</th>\n",
       "      <th>psfmag_u</th>\n",
       "      <th>psfmag_g</th>\n",
       "      <th>psfmag_r</th>\n",
       "      <th>psfmag_i</th>\n",
       "      <th>psfmag_z</th>\n",
       "      <th>psfmagerr_u</th>\n",
       "      <th>psfmagerr_g</th>\n",
       "      <th>psfmagerr_r</th>\n",
       "      <th>psfmagerr_i</th>\n",
       "      <th>psfmagerr_z</th>\n",
       "      <th>modelmag_u</th>\n",
       "      <th>modelmag_g</th>\n",
       "      <th>modelmag_r</th>\n",
       "      <th>modelmag_i</th>\n",
       "      <th>modelmag_z</th>\n",
       "      <th>modelmagerr_u</th>\n",
       "      <th>modelmagerr_g</th>\n",
       "      <th>modelmagerr_r</th>\n",
       "      <th>modelmagerr_i</th>\n",
       "      <th>modelmagerr_z</th>\n",
       "      <th>extinction_u</th>\n",
       "      <th>extinction_g</th>\n",
       "      <th>extinction_r</th>\n",
       "      <th>extinction_i</th>\n",
       "      <th>extinction_z</th>\n",
       "      <th>type</th>\n",
       "      <th>run</th>\n",
       "      <th>src2photo</th>\n",
       "      <th>nsc_id</th>\n",
       "      <th>madvar</th>\n",
       "      <th>nsigvar</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>pmdec_error</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmra_error</th>\n",
       "      <th>src2nsc</th>\n",
       "      <th>gaia_astrometry</th>\n",
       "      <th>src2gaia</th>\n",
       "      <th>gaiaedr3_id</th>\n",
       "      <th>parallax</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>spies_id</th>\n",
       "      <th>spies_ra</th>\n",
       "      <th>spies_dec</th>\n",
       "      <th>flux_auto_ch1</th>\n",
       "      <th>fluxerr_auto_ch1</th>\n",
       "      <th>class_star_ch1</th>\n",
       "      <th>flux_auto_ch2</th>\n",
       "      <th>fluxerr_auto_ch2</th>\n",
       "      <th>class_star_ch2</th>\n",
       "      <th>src2spies</th>\n",
       "      <th>ukidss_id</th>\n",
       "      <th>ay</th>\n",
       "      <th>aj</th>\n",
       "      <th>ah</th>\n",
       "      <th>ak</th>\n",
       "      <th>yapermag3</th>\n",
       "      <th>yapermag3err</th>\n",
       "      <th>japermag3</th>\n",
       "      <th>japermag3err</th>\n",
       "      <th>hapermag3</th>\n",
       "      <th>hapermag3err</th>\n",
       "      <th>kapermag3</th>\n",
       "      <th>kapermag3err</th>\n",
       "      <th>ukidss_pstar</th>\n",
       "      <th>ukidss_pgalaxy</th>\n",
       "      <th>src2ukidss</th>\n",
       "      <th>galex_id</th>\n",
       "      <th>fuv_mag</th>\n",
       "      <th>fuv_magerr</th>\n",
       "      <th>nuv_mag</th>\n",
       "      <th>nuv_magerr</th>\n",
       "      <th>src2galex</th>\n",
       "      <th>mpstype</th>\n",
       "      <th>des_coadd_id</th>\n",
       "      <th>extended_class_coadd</th>\n",
       "      <th>extended_class_wavg</th>\n",
       "      <th>flags_g</th>\n",
       "      <th>flags_i</th>\n",
       "      <th>flags_r</th>\n",
       "      <th>flags_y</th>\n",
       "      <th>flags_z</th>\n",
       "      <th>mag_auto_g</th>\n",
       "      <th>mag_auto_i</th>\n",
       "      <th>mag_auto_r</th>\n",
       "      <th>mag_auto_y</th>\n",
       "      <th>mag_auto_z</th>\n",
       "      <th>magerr_auto_g</th>\n",
       "      <th>magerr_auto_i</th>\n",
       "      <th>magerr_auto_r</th>\n",
       "      <th>magerr_auto_y</th>\n",
       "      <th>magerr_auto_z</th>\n",
       "      <th>wavg_mag_psf_g</th>\n",
       "      <th>wavg_mag_psf_g_dered</th>\n",
       "      <th>wavg_mag_psf_i</th>\n",
       "      <th>wavg_mag_psf_i_dered</th>\n",
       "      <th>wavg_mag_psf_r</th>\n",
       "      <th>wavg_mag_psf_r_dered</th>\n",
       "      <th>wavg_mag_psf_y</th>\n",
       "      <th>wavg_mag_psf_y_dered</th>\n",
       "      <th>wavg_mag_psf_z</th>\n",
       "      <th>wavg_mag_psf_z_dered</th>\n",
       "      <th>wavg_magerr_psf_g</th>\n",
       "      <th>wavg_magerr_psf_i</th>\n",
       "      <th>wavg_magerr_psf_r</th>\n",
       "      <th>wavg_magerr_psf_y</th>\n",
       "      <th>wavg_magerr_psf_z</th>\n",
       "      <th>src2des</th>\n",
       "      <th>psPm[0]</th>\n",
       "      <th>psPm[1]</th>\n",
       "      <th>psParallax</th>\n",
       "      <th>stdColor[0]</th>\n",
       "      <th>stdColor[1]</th>\n",
       "      <th>stdColor[2]</th>\n",
       "      <th>stdColor[3]</th>\n",
       "      <th>psFlux[u]</th>\n",
       "      <th>psFlux[g]</th>\n",
       "      <th>psFlux[r]</th>\n",
       "      <th>psFlux[i]</th>\n",
       "      <th>psFlux[z]</th>\n",
       "      <th>class</th>\n",
       "      <th>subclass</th>\n",
       "      <th>s82LSST</th>\n",
       "      <th>s82SDSS</th>\n",
       "      <th>sdss_lcN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101080</th>\n",
       "      <td>500001</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>-6.491940</td>\n",
       "      <td>7147</td>\n",
       "      <td>56574</td>\n",
       "      <td>831</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.514150</td>\n",
       "      <td>BOSS</td>\n",
       "      <td>8.047035e+18</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108800_6175</td>\n",
       "      <td>0.222555</td>\n",
       "      <td>7.294460</td>\n",
       "      <td>-15.980300</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2909222142693348352</td>\n",
       "      <td>21.554506</td>\n",
       "      <td>0.20671</td>\n",
       "      <td>22.486982</td>\n",
       "      <td>0.200167</td>\n",
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       "      <td>0</td>\n",
       "      <td>1031564897</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>20.9030</td>\n",
       "      <td>20.8437</td>\n",
       "      <td>21.6482</td>\n",
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       "      <td>0.015199</td>\n",
       "      <td>0.073861</td>\n",
       "      <td>0.015117</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>s82Gal</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>101081</th>\n",
       "      <td>500002</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.978464</td>\n",
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       "      <td>1.058699e+19</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.118270</td>\n",
       "      <td>-7.999970</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1033886220</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>22.7506</td>\n",
       "      <td>21.6170</td>\n",
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       "      <td>21.2157</td>\n",
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       "      <td>0.045237</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.048553</td>\n",
       "      <td>23.3665</td>\n",
       "      <td>23.2803</td>\n",
       "      <td>22.2557</td>\n",
       "      <td>22.2133</td>\n",
       "      <td>22.9258</td>\n",
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       "      <td>21.9948</td>\n",
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       "      <td>0.028034</td>\n",
       "      <td>0.033293</td>\n",
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       "      <td>0.034718</td>\n",
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       "      <td>s82Gal</td>\n",
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       "    <tr>\n",
       "      <th>101082</th>\n",
       "      <td>500003</td>\n",
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       "      <td>22.80974</td>\n",
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       "      <td>3</td>\n",
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       "      <td>-9.999995e+08</td>\n",
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       "      <td>9.525807e-06</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284.456000</td>\n",
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       "      <td>0.70785</td>\n",
       "      <td>0.99590</td>\n",
       "      <td>0.43494</td>\n",
       "      <td>2539.818413</td>\n",
       "      <td>3679.772079</td>\n",
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       "      <td>17787.753651</td>\n",
       "      <td>26805.506258</td>\n",
       "      <td>s82Gal</td>\n",
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       "    <tr>\n",
       "      <th>101083</th>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>8647474692481942528</td>\n",
       "      <td>24.71674</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.001692</td>\n",
       "      <td>0.55922</td>\n",
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       "      <td>8.388326e-02</td>\n",
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       "      <td>1.714368e-04</td>\n",
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       "      <td>23.0008</td>\n",
       "      <td>20.5150</td>\n",
       "      <td>20.4739</td>\n",
       "      <td>21.4232</td>\n",
       "      <td>21.3672</td>\n",
       "      <td>19.9936</td>\n",
       "      <td>19.9662</td>\n",
       "      <td>20.1175</td>\n",
       "      <td>20.0862</td>\n",
       "      <td>0.031630</td>\n",
       "      <td>0.005699</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.021535</td>\n",
       "      <td>0.006910</td>\n",
       "      <td>0.097263</td>\n",
       "      <td>-0.960028</td>\n",
       "      <td>0.411506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.29373</td>\n",
       "      <td>1.71472</td>\n",
       "      <td>1.12221</td>\n",
       "      <td>0.48608</td>\n",
       "      <td>-74.131936</td>\n",
       "      <td>1482.931673</td>\n",
       "      <td>7503.313445</td>\n",
       "      <td>21143.645432</td>\n",
       "      <td>33508.699608</td>\n",
       "      <td>s82Gal</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101084</th>\n",
       "      <td>500005</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>4.457334</td>\n",
       "      <td>4415</td>\n",
       "      <td>55831</td>\n",
       "      <td>441</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.695770</td>\n",
       "      <td>BOSS</td>\n",
       "      <td>4.970969e+18</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90368_4493</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>0.235583</td>\n",
       "      <td>-17.641800</td>\n",
       "      <td>13.7766</td>\n",
       "      <td>12.578000</td>\n",
       "      <td>13.73180</td>\n",
       "      <td>0.150176</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433873520623</td>\n",
       "      <td>0.036372</td>\n",
       "      <td>0.026701</td>\n",
       "      <td>0.017360</td>\n",
       "      <td>0.010812</td>\n",
       "      <td>1.937090e+01</td>\n",
       "      <td>8.535977e-02</td>\n",
       "      <td>18.637129</td>\n",
       "      <td>0.085933</td>\n",
       "      <td>17.906187</td>\n",
       "      <td>0.095595</td>\n",
       "      <td>16.822720</td>\n",
       "      <td>0.061396</td>\n",
       "      <td>5.292210e-07</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.044003</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1032299883</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.9895</td>\n",
       "      <td>19.8152</td>\n",
       "      <td>20.9624</td>\n",
       "      <td>19.3741</td>\n",
       "      <td>19.3780</td>\n",
       "      <td>0.158938</td>\n",
       "      <td>0.019517</td>\n",
       "      <td>0.030547</td>\n",
       "      <td>0.083053</td>\n",
       "      <td>0.022946</td>\n",
       "      <td>23.8012</td>\n",
       "      <td>23.7055</td>\n",
       "      <td>20.7087</td>\n",
       "      <td>20.6616</td>\n",
       "      <td>21.9363</td>\n",
       "      <td>21.8721</td>\n",
       "      <td>20.1715</td>\n",
       "      <td>20.1400</td>\n",
       "      <td>20.4155</td>\n",
       "      <td>20.3796</td>\n",
       "      <td>0.121448</td>\n",
       "      <td>0.013281</td>\n",
       "      <td>0.021995</td>\n",
       "      <td>0.050056</td>\n",
       "      <td>0.015221</td>\n",
       "      <td>0.125353</td>\n",
       "      <td>12.578000</td>\n",
       "      <td>-17.641800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>s82Gal</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        train_id        ra       dec  spec_plate  spec_mjd  spec_fiberid  \\\n",
       "101080    500001  0.000346 -6.491940        7147     56574           831   \n",
       "101081    500002  0.000731  1.368325        9403     58018           554   \n",
       "101082    500003  0.001230  4.795821        4277     55506           869   \n",
       "101083    500004  0.001720  0.559179        4216     55477           698   \n",
       "101084    500005  0.001809  4.457334        4415     55831           441   \n",
       "\n",
       "        is_qso_final         z source_z     specobjid           sdss_objid  \\\n",
       "101080          <NA>  0.514150     BOSS  8.047035e+18                 <NA>   \n",
       "101081          <NA>  0.978464     BOSS  1.058699e+19                 <NA>   \n",
       "101082          <NA>  0.732226     BOSS  4.815713e+18  1237678777404359168   \n",
       "101083          <NA>  0.611246     BOSS  4.746986e+18  8647474692481942528   \n",
       "101084          <NA>  0.695770     BOSS  4.970969e+18                 <NA>   \n",
       "\n",
       "        psfmag_u  psfmag_g  psfmag_r  psfmag_i  psfmag_z  psfmagerr_u  \\\n",
       "101080       NaN       NaN       NaN       NaN       NaN          NaN   \n",
       "101081       NaN       NaN       NaN       NaN       NaN          NaN   \n",
       "101082  22.80974  22.47705  21.76920  20.77330  20.33836     0.434497   \n",
       "101083  24.71674  23.42301  21.70829  20.58608  20.10000     0.510836   \n",
       "101084       NaN       NaN       NaN       NaN       NaN          NaN   \n",
       "\n",
       "        psfmagerr_g  psfmagerr_r  psfmagerr_i  psfmagerr_z  modelmag_u  \\\n",
       "101080          NaN          NaN          NaN          NaN         NaN   \n",
       "101081          NaN          NaN          NaN          NaN         NaN   \n",
       "101082     0.135595     0.105466     0.076008     0.189769    22.31176   \n",
       "101083     0.081610     0.036526     0.017451     0.024800    24.33854   \n",
       "101084          NaN          NaN          NaN          NaN         NaN   \n",
       "\n",
       "        modelmag_g  modelmag_r  modelmag_i  modelmag_z  modelmagerr_u  \\\n",
       "101080         NaN         NaN         NaN         NaN            NaN   \n",
       "101081         NaN         NaN         NaN         NaN            NaN   \n",
       "101082    22.03083    20.94204    19.95612    19.61465       0.447796   \n",
       "101083    23.05808    21.30822    20.14845    19.68445       0.485633   \n",
       "101084         NaN         NaN         NaN         NaN            NaN   \n",
       "\n",
       "        modelmagerr_g  modelmagerr_r  modelmagerr_i  modelmagerr_z  \\\n",
       "101080            NaN            NaN            NaN            NaN   \n",
       "101081            NaN            NaN            NaN            NaN   \n",
       "101082       0.146669       0.093080       0.063656       0.184519   \n",
       "101083       0.063109       0.019274       0.011790       0.023917   \n",
       "101084            NaN            NaN            NaN            NaN   \n",
       "\n",
       "        extinction_u  extinction_g  extinction_r  extinction_i  extinction_z  \\\n",
       "101080           NaN           NaN           NaN           NaN           NaN   \n",
       "101081           NaN           NaN           NaN           NaN           NaN   \n",
       "101082      0.140829      0.109733      0.075913      0.056411      0.041960   \n",
       "101083      0.134839      0.099213      0.071957      0.054563      0.038686   \n",
       "101084           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "        type   run  src2photo       nsc_id    madvar   nsigvar       pmdec  \\\n",
       "101080  <NA>  <NA>        NaN  108800_6175  0.222555  7.294460  -15.980300   \n",
       "101081  <NA>  <NA>        NaN   95488_4186  0.149413  0.118270   -7.999970   \n",
       "101082     3  7754   0.023660   89344_2982  0.084278  0.782384  976.243000   \n",
       "101083     3   106   0.155915    97536_361  0.218418  0.803961    0.411506   \n",
       "101084  <NA>  <NA>        NaN   90368_4493  0.081080  0.235583  -17.641800   \n",
       "\n",
       "        pmdec_error        pmra  pmra_error   src2nsc  gaia_astrometry  \\\n",
       "101080      10.2419  -25.782700    10.10530  0.166958                0   \n",
       "101081      18.2898  -26.260200    18.27130  0.162893                0   \n",
       "101082      21.4925  284.456000    21.40500  0.236604                0   \n",
       "101083       1.8521   -0.960028     1.85241  0.113410                0   \n",
       "101084      13.7766   12.578000    13.73180  0.150176                0   \n",
       "\n",
       "        src2gaia  gaiaedr3_id  parallax  parallax_error  spies_id  spies_ra  \\\n",
       "101080       NaN         <NA>       NaN             NaN      <NA>       NaN   \n",
       "101081       NaN         <NA>       NaN             NaN      <NA>       NaN   \n",
       "101082       NaN         <NA>       NaN             NaN      <NA>       NaN   \n",
       "101083       NaN         <NA>       NaN             NaN   2869319  0.001692   \n",
       "101084       NaN         <NA>       NaN             NaN      <NA>       NaN   \n",
       "\n",
       "        spies_dec  flux_auto_ch1  fluxerr_auto_ch1  class_star_ch1  \\\n",
       "101080        NaN            NaN               NaN             NaN   \n",
       "101081        NaN            NaN               NaN             NaN   \n",
       "101082        NaN            NaN               NaN             NaN   \n",
       "101083    0.55922     142.128934          3.620633        0.062941   \n",
       "101084        NaN            NaN               NaN             NaN   \n",
       "\n",
       "        flux_auto_ch2  fluxerr_auto_ch2  class_star_ch2  src2spies  \\\n",
       "101080            NaN               NaN             NaN        NaN   \n",
       "101081            NaN               NaN             NaN        NaN   \n",
       "101082            NaN               NaN             NaN        NaN   \n",
       "101083      99.074101          3.396904        0.302029   0.180994   \n",
       "101084            NaN               NaN             NaN        NaN   \n",
       "\n",
       "           ukidss_id        ay        aj        ah        ak     yapermag3  \\\n",
       "101080          <NA>       NaN       NaN       NaN       NaN           NaN   \n",
       "101081          <NA>       NaN       NaN       NaN       NaN           NaN   \n",
       "101082  433831605075  0.040232  0.029534  0.019202  0.011960 -9.999995e+08   \n",
       "101083  433839925918  0.031673  0.023252  0.015117  0.009416  1.929710e+01   \n",
       "101084  433873520623  0.036372  0.026701  0.017360  0.010812  1.937090e+01   \n",
       "\n",
       "        yapermag3err  japermag3  japermag3err  hapermag3  hapermag3err  \\\n",
       "101080           NaN        NaN           NaN        NaN           NaN   \n",
       "101081           NaN        NaN           NaN        NaN           NaN   \n",
       "101082 -9.999995e+08  18.706715      0.097293  17.944977      0.075971   \n",
       "101083  8.388326e-02  18.403646      0.062566  17.668932      0.088194   \n",
       "101084  8.535977e-02  18.637129      0.085933  17.906187      0.095595   \n",
       "\n",
       "        kapermag3  kapermag3err  ukidss_pstar  ukidss_pgalaxy  src2ukidss  \\\n",
       "101080        NaN           NaN           NaN             NaN         NaN   \n",
       "101081        NaN           NaN           NaN             NaN         NaN   \n",
       "101082  17.232878      0.075128  9.525807e-06        0.999981    0.073235   \n",
       "101083  16.814308      0.070725  1.714368e-04        0.999819    0.098707   \n",
       "101084  16.822720      0.061396  5.292210e-07        0.999999    0.044003   \n",
       "\n",
       "                   galex_id    fuv_mag  fuv_magerr    nuv_mag  nuv_magerr  \\\n",
       "101080  2909222142693348352  21.554506     0.20671  22.486982    0.200167   \n",
       "101081                 <NA>        NaN         NaN        NaN         NaN   \n",
       "101082                 <NA>        NaN         NaN        NaN         NaN   \n",
       "101083                 <NA>        NaN         NaN        NaN         NaN   \n",
       "101084                 <NA>        NaN         NaN        NaN         NaN   \n",
       "\n",
       "        src2galex  mpstype  des_coadd_id  extended_class_coadd  \\\n",
       "101080    2.79006        0    1031564897                   3.0   \n",
       "101081        NaN     <NA>    1033886220                   3.0   \n",
       "101082        NaN     <NA>          <NA>                   NaN   \n",
       "101083        NaN     <NA>    1034039632                   3.0   \n",
       "101084        NaN     <NA>    1032299883                   3.0   \n",
       "\n",
       "        extended_class_wavg  flags_g  flags_i  flags_r  flags_y  flags_z  \\\n",
       "101080                  3.0      3.0      3.0      3.0      3.0      3.0   \n",
       "101081                  3.0      0.0      0.0      0.0      0.0      0.0   \n",
       "101082                  NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "101083                  3.0      3.0      3.0      3.0      3.0      3.0   \n",
       "101084                  3.0      2.0      2.0      2.0      2.0      2.0   \n",
       "\n",
       "        mag_auto_g  mag_auto_i  mag_auto_r  mag_auto_y  mag_auto_z  \\\n",
       "101080     21.5277     19.5937     20.1767     19.0910     19.2155   \n",
       "101081     22.7506     21.6170     22.2994     20.9129     21.2157   \n",
       "101082         NaN         NaN         NaN         NaN         NaN   \n",
       "101083     22.4439     19.9949     20.8421     19.4713     19.6089   \n",
       "101084     22.9895     19.8152     20.9624     19.3741     19.3780   \n",
       "\n",
       "        magerr_auto_g  magerr_auto_i  magerr_auto_r  magerr_auto_y  \\\n",
       "101080       0.047576       0.015734       0.016823       0.103315   \n",
       "101081       0.052702       0.040667       0.045237       0.138179   \n",
       "101082            NaN            NaN            NaN            NaN   \n",
       "101083       0.041935       0.009697       0.012691       0.039937   \n",
       "101084       0.158938       0.019517       0.030547       0.083053   \n",
       "\n",
       "        magerr_auto_z  wavg_mag_psf_g  wavg_mag_psf_g_dered  wavg_mag_psf_i  \\\n",
       "101080       0.020973             NaN                   NaN         20.9030   \n",
       "101081       0.048553         23.3665               23.2803         22.2557   \n",
       "101082            NaN             NaN                   NaN             NaN   \n",
       "101083       0.013090         23.0841               23.0008         20.5150   \n",
       "101084       0.022946         23.8012               23.7055         20.7087   \n",
       "\n",
       "        wavg_mag_psf_i_dered  wavg_mag_psf_r  wavg_mag_psf_r_dered  \\\n",
       "101080               20.8437         21.6482               21.5673   \n",
       "101081               22.2133         22.9258               22.8679   \n",
       "101082                   NaN             NaN                   NaN   \n",
       "101083               20.4739         21.4232               21.3672   \n",
       "101084               20.6616         21.9363               21.8721   \n",
       "\n",
       "        wavg_mag_psf_y  wavg_mag_psf_y_dered  wavg_mag_psf_z  \\\n",
       "101080         20.0825               20.0429         20.5229   \n",
       "101081         22.0960               22.0676         21.9948   \n",
       "101082             NaN                   NaN             NaN   \n",
       "101083         19.9936               19.9662         20.1175   \n",
       "101084         20.1715               20.1400         20.4155   \n",
       "\n",
       "        wavg_mag_psf_z_dered  wavg_magerr_psf_g  wavg_magerr_psf_i  \\\n",
       "101080               20.4777                NaN           0.010867   \n",
       "101081               21.9624           0.044900           0.028034   \n",
       "101082                   NaN                NaN                NaN   \n",
       "101083               20.0862           0.031630           0.005699   \n",
       "101084               20.3796           0.121448           0.013281   \n",
       "\n",
       "        wavg_magerr_psf_r  wavg_magerr_psf_y  wavg_magerr_psf_z   src2des  \\\n",
       "101080           0.015199           0.073861           0.015117  0.187965   \n",
       "101081           0.033293           0.222756           0.034718  0.199227   \n",
       "101082                NaN                NaN                NaN       NaN   \n",
       "101083           0.007902           0.021535           0.006910  0.097263   \n",
       "101084           0.021995           0.050056           0.015221  0.125353   \n",
       "\n",
       "           psPm[0]     psPm[1]  psParallax  stdColor[0]  stdColor[1]  \\\n",
       "101080  -25.782700  -15.980300         NaN          NaN          NaN   \n",
       "101081  -26.260200   -7.999970         NaN          NaN          NaN   \n",
       "101082  284.456000  976.243000         NaN      0.33269      0.70785   \n",
       "101083   -0.960028    0.411506         NaN      1.29373      1.71472   \n",
       "101084   12.578000  -17.641800         NaN          NaN          NaN   \n",
       "\n",
       "        stdColor[2]  stdColor[3]    psFlux[u]    psFlux[g]    psFlux[r]  \\\n",
       "101080          NaN          NaN          NaN          NaN          NaN   \n",
       "101081          NaN          NaN          NaN          NaN          NaN   \n",
       "101082      0.99590      0.43494  2539.818413  3679.772079  7091.134864   \n",
       "101083      1.12221      0.48608   -74.131936  1482.931673  7503.313445   \n",
       "101084          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "           psFlux[i]     psFlux[z]   class subclass  s82LSST  s82SDSS  \\\n",
       "101080           NaN           NaN  s82Gal     None        1        0   \n",
       "101081           NaN           NaN  s82Gal     None        1        0   \n",
       "101082  17787.753651  26805.506258  s82Gal     None        1        0   \n",
       "101083  21143.645432  33508.699608  s82Gal     None        1        1   \n",
       "101084           NaN           NaN  s82Gal     None        1        0   \n",
       "\n",
       "        sdss_lcN  \n",
       "101080       NaN  \n",
       "101081       NaN  \n",
       "101082       NaN  \n",
       "101083      34.0  \n",
       "101084       NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gal_df = get_cat('s82Gal')\n",
    "gal_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column definitions (output will be the same as qso_cat_meta())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gal_cat_meta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get/Plot light curves\n",
    "- To retrieve the light curves, use the function `get_sdss_lc()` with `train_id` at the input.  \n",
    "- To plot the light curves, use function `plot_sdss_lc()` or `plot_merged_lc()`.\n",
    "\n",
    "The `train_id` for galaxies starts at 1000001. As for the QSO light curves, `clip` is set to `True` as the default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f26d4ba1a90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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anqJ+gzKav7hLvnOUDu6r4TjFiMAa7PYaK1LTU5TsSmqzhTqjcpdyP/23qjdvdixOdA/fj6SMvqktpluruB88iAQ7wmjhig/ttVBzIysCcVUjtvm+uGu/bOA4xZDONFb4+jMP1qKZtue/OvLth3XYzpf1yZxLSbJjWFv50OiiIa1XiPPBg0iwI4gWrvgRzkmfQSngj6sasSmwXI/jFFs62ljh35/5vrJDrZLsjN3vyddXn21oUPWGNyIWOzqvvXxocEFfpaQmKSm55XrxPHgQdwVEULAWLpKz2OR/w8zpl43t9HHy1f1Rj5/4nHrPIDL8P4Mcp9iS6r0pMZxjMXJirvbtOuR5YqVKbwuob93qIWM8o81YK+NyKWPyJOn/uKk11oTKh/w/p0nJRknJyUpxJTd3s+kbPCgeP7e0YEcQLSfxpb0Wam5kRSCuasQHjlP8atEdn6T66sYWLaC1g0ap1+jRcuXladhDDyqjsDBKkfZc4dxU2pF8KCPrq3rseM6daMGOIFq44gvJM5CY+GzHL9/3aOXntaqv9pSHBNbSFzyzMtTqiBHh5EOX336SJE/CnpKapPTM1LjOnWjBjjBaTnoWbmoFgLZ19GpganqKsvqnhd0Cynk4NoWbD01feKxyh2fFfe5Egg04hJtaex7KhoDukZqeEtbNkZyHux8/aIIjwY4wvoB7DrptAwDn+b5Hw2kB5TzcvZz8QVN68fdUevH3HIwuuqjBBhziP+xrPN+YAQDx4Ngtd3gf/bl5Wt6R2c3d9iUnJ3MejrCO9JbWXmNj06FDcldWqnrz5oRomKQFG3AIg9EAQHQNLuirTPfnSreHOA93g7wjs2Vsk2TdSjLq8A8aX6t19ebNqtu+XQ1lZQkzYBAt2ICDOtK/KwAgfOG2avat/Fh9Du5U1sFhkui2L5KyDu7QhC2/18G+R6h/9U5lHbxBndnn1RveaDVgULx3uUgLdjdKtPoiAIhVnG97purNmzXynUc1ZOfahGkJjWXVG95Qv4MfacQn/1DWgQ/bHEkz2Gey6dAhNXz2mZL79fUMGCR9NWBQnCPBDiESJ2ffG4kPfOLiplYgNrR3vg1ncAxExo7p5+vDU7/V4e/CwPWCHePqDW/IuJtkZBk6vRtkTJ4km5QsK9NuYhx4vPzLQvb85mYNXnyDchYsSJgBg0iwQ3A6GU7E+iIAiEWcb2NX9ebNqtu2TQ1lZSq95PthH5vAY3pgxYqgx7gjCR+6LqOwUB+Ov1i7R5S0mRgH+0wGloU0fXFQA6+YlxDJtUSCHVQkTs7B6osQX7jkDMQHzrex6+Czq756Ul/f8nkA/3Nu4DGtevGloMc43IQPzpm24ic69YV72tzXQWusJ08KWhZSevH39P6kyXH/fUuCHUQkTs6h3kiIH5T4APEhnPMtg2PEPv9zbuAxzTz9tJDHuDorXxVDTyC5jiHBPpMZhYXqNXq0XHl5LX4MNR06JHdNjep27ozr71sS7CA6kwy3V1OWUVio4Y8tT6j6op5ix/Tz9cEJJ3LJGYgTob64fRjtL3r6njet+bFJTVXf86YF/f70v5Jc+p3ZktTimGbPmhXyGF90/3d00f3f6b4/Cu0K9ZlM7tNHrsMOa37uKyFSY6Oa9u7VJx0oI4o1dNMXhC8Z9l3CaC8Z9p0IZK0+mXNpyAQ6o7CQxDqG7Zh+vtyVlTrsd7e2/LB7j61PonQhBCSygmdWNj/2XWoe/qhnQBL/wTEa6xvbHBwDzsooLNTwvz72VYmAFPT70/9KsqzVwWdXtTimkic5a9i1S3tvu7352CJ2BR4/Sa2OW2DFQDx/39KCHUJGYWHYxfZtlZRQtxsfQtXdtzjJe1HiA8Q+/1bRwPKur0ZdtUqyTRpg90Y32B7G//s11Pdn4Dn24DPPBG3JtE1NlO4lkMDjHs/ftyTYDmirpIS63fjQ5knee2yVmqp+s2ZR4gPEsMCSrtJLvt/qx/Pggr46LP2ADt/5dxVuuVO1P5/HOTpKQn1/ZhQWKjknp3k529TUqnWz6dAh2epqSvcSiO8KR/LAgUrOydGwRx6O2+9bSkQcEKqkJNzSEURf80ne2lYn+V6jR7cqHQGk4GVFiJ5gJV2qr29+6H+5uf/et5VT+g9Pf8nJyXF7GTretVWSmTJwoJr2eq8uNDV5BiPx466sbH4cz6UEaCmjsFC9Dj+8+XG8IsF2SLD66kQc+jNRtXWSD1Y3FgoJV8/BD+jYE6yky5//j+fqIWNkk5Jl3I1xfRk6EYS6P6ngmZX68Iwz1VBaKkna85ub1evII5uXPex3t3pugAxoGEH8S4SaekpEIoiu+eJLR+rug2Fwi56FvpZjT4uSLn9JSepdXNziR1DtoFH65Jzr6NkpxvU7//zmx4Gfs/Z6i0F86ewIn7GKFuwIorygZ+GKRc8SqqwI0eN/zh0w73KVL77Rc3xSUzXwyvktPo/TFx4r6djoBYuwtPc568gVRsSuRLwiSIIdYXz4ew4Srp6FH9Cxyf+ce+DxFRyfONfRbnMRnxKxgYoEG3AICVfPww/o2MbxSQyMIZH4ErGBigQbcBBf6AAAdEwiXqkgwQYAAEBUJdqVCnoRAQAAABxEgg0AAIBuN+vedZp177pohxERJNgAAACAg0iwAQAAAAeRYAMAAAAOIsEGAAAAHESCDQAAADiIBBsAAABwEAk2AAAA4CASbAAAAMBBJNgAAACAg0iwAQAAAAeRYAMAAAAOIsEGAAAAHESCDQAAADiIBBsAAABwEAk2AAAA4CASbAAAAHS7qtoGlX1Ro42lB6IdiuNIsAEAQNya88IczXlhTrTDQAdtLD2g7eVV2nWgRrOXrU+4JJsEGwAAAN1q/Y79clvP44ZGt9bv2B/dgBxGgg0AAOLWofpD2v3lbm2p2BLtUNABRQUDlGQ8j10pSSoqGBDdgBxGgg0ASDiUDfQMWyq26P0D76vsUJkuf/Fykuw4ctzwbI0enKn87HQtn1uk44ZnRzskR6VEOwAAAICOmvPCHO3+cresPHUGDe4GvbnnTU3InRDdwBC2zDSXMtNcCZdcS7RgAwASEGUDPUOmK1NGnjoDV5JLEwdN5OoFYgIJNgAgoVA20HP0Se2jo7KPUl6fPN1/+v20XiNmkGADABLKm3vebFU2gMTVJ7WPhvQeQnIdh3am/k47U38X7TAighpsAEBCmThoooyMrGxz2QBih69846EzH3Jke05tB3ASCTYAIKFMyJ2go7KPUlVDlW4+8WZaNgF0OxJsAEDC6ZPaR31S+5BcxzGnW7qB7kSCDQBIOCRlsetQ/SFVNVRpS8WWLv0Acmo7QCRwkyMAAOgWTvXw0tZ26KIRsYAEGwAAdIuO9PDSVqIcajt00RhfmlSjBn2ekMeJBDsEOqoHgPjGeTz2+Pfo0lYPL/6J8vee/562VGxpcTx9PcUEbocuGuPHlootqjO71GD2JeSPIRLsCOMEDwDdb84Lc7T98+3RDgMBJuROUGpSqpJNsn4y6Scha6f9E2Ur2ypR9vUUEzjATKjEG7HHc0ytZBLzxxAJdghO1XBRCwYkLn5Ax7YmdxPn3xizpWKL6t31arJNuuWNW0IeG/9EWZL6pvZttUywAWZCJd6InM6eBz0/foxkE/PHEAl2EN1xEwYAIHL2Vu9VTVMN598Y499K2Var5YTcCRqWOaz5+S1v3KK91XvD+sHEyI7xYULuBPWy+XLZgS1+DCVKwwUJdhBO1XBRCwYkNq5QxaYtFVtUWlXa/Ly+qT7o+TdRvsjjSUdKOBptY/Pj+qZ6fVL1CT+YYlBXzoPJSpdL/TUhd0LCfR5JsINwqoaLWjAgcXGFKnYFJtNJJonzb4zoSAlHpiuz+XGSSaLBKgY5eR7cum+r3t77tvZW79Xbe9/WzOdmOhdoFJBgB+FUDRe1YPEl0X49I7K4QhW7Aut3r/36tZx/Y0i4JRxPTn1So7NHK69Pnq79+rU0WMWgrp4HxwzJ0pghWdpSsUU1TTWqd9ertKpU9e56vX/g/bhuuGAkxxCenPqkI9thuN744cSoYAzt23P4kjgryxd+jPE1buw4uEMFfQs086jgLWGMBBgdHTk/+r5DZx41U0+8/4SqGqp084k3c7xihFPnwWCJua/3mHg91iTYDgmVWPk/J/mKXb7LXFZWl794easbLqTwjhtf2D2HL4njCz829Unto2Nyjgn5uW3rM4/YRINV7OnqedD3+QzWUm1k4rrhggTbISRW8S3YZa7A49heos0Xds/j1JUuOK+9H8ThfOYRff7Hsa0GrHCmIzKcOA9OyJ2g9OR0NdkmDek9RGWHypTdK9uB6KKHGmwHcLNT/HPihlRqcoH4wU3osaO9+1+4P6ZnGJ41XDkZOZI8Pcjsrd0b1zkVCbYDwk2s6NIrdrV1Q6rvuLXXBytf2ED84Cb02MF3I/wbKssOlTVPj+fGKhJsB4STWNHKHfuC3dnuf9xKq0rbPH58YQPRF6y1M1QLKAOSRF84340k4InPv6HSbd0J0VhFDbYDwinyp94v9gWr2/M/bj5tHT9qcoH4Qa1u9LX33ci9LT2Df28kqcmpGpQxSI22Ma5vIKcF2yHttYRQPhCfAvvTlTh+QCyjtTO+tPfdyL0tPUPgFeCcjJy4v7pEC7ZD2msJoUuv+OR/3FJMStz/ogYSGa2d8ae970b6m+85Eq0bRhLsbpRob56ewnfcfDh+QGwKVW5AN6qxra3vRhqnEK9IsIF2UKcJxIdgrZ20ase+9s6xNE71PInwvUuC3Y0S4Q0DALEqWGvnsneWcYN5AmOE5MSRaMeQBBsAkDACWzup4Y1/iZZ4oWcgwQYAJCxqeIHY0NOuNkQswTbGDJX0Z0mDJbkl3WetvdMYc6ukcyXVS/pI0hxr7ReRigMA0HME+/KmhhdAd4tkP9iNkhZaa4+WVCRpvjFmjKSXJI2z1h4j6QNJP49gDAAAIEHR7zliVcRasK21uyXt9j6uMsZsk5RnrX3Rb7H1kmZEKgYAAHrKJemehh5iEMu6ZSRHY8wISYWSXg+Ydamk50OsM88Y86Yx5s29e/dGOEIAABBPGOURsSziCbYxpo+kpyVdba2t9Ju+SJ4ykuXB1rPW3metnWitnZiTkxPpMAEAQBxpb5h1IJoimmAbY1zyJNfLrbUr/aZfIukcSbOttTaSMQAAgMTj6yEmr08e5SFxoKfVy0cswTbGGEkPSNpmrb3Nb/qZkn4qaaq1tjpSrw8AABJbn9Q+GtJ7CMl1jPPVy5cdKtPlL17eI5LsSLZgF0u6WNIpxpgt3n9nSbpLUqakl7zT/hTBGAAAABBFPbFePpK9iPxb8hZHtbQmUq8JAACA2NITR1RlJEcAABCX6IIxPvTEEVVJsAEAABBRPW1E1W7pBxsAAAAIZda96zTr3nXRDsMxJNgAAACAg0iwAQA9zpwX5mjOC3OiHQYckGgtn0gM1GADAAAgooLekPrQ2Z7/56zW9fuv8U78d7fFFEm0YAMAEg6tmgCiiQQbAAAAcBAJNgAAAOAgEmwAAADAQSTYAIAe51D9Ie3+cre2VGyJdihw2kNnf3XzHOJGuvtLDWyqkD7dEO1QHEGCDQDoUbZUbNH7B95X2aEyXf7i5STZQLR9ukEjGj9WbtMe6ZGpCZFkk2ADAHqUN/e8KSsrSWpwN+jNPW9GOSIg8bXZs8/OV2VkZSSpqV7a+Wp3hhYRJNgAgB5l4qCJMp6vcrmSXJo4aGKUI0IwdLXYg4w4UVbG87M3OVUacWK0I+oyEmwAQI8yIXeCjso+Snl98nT/6fdrQu6EaIcE9GxDJ2tnyuGqSB4kXfKcNHRytCPqMkZyBAD0OH1S+6hPah+SayBG1CT1Vo16a1ACJNcSLdgAAACAo0iwAQAJ6b3dldTwAogKSkQAAAAQVUsG3CpJWhHlOJxCgg0A6HEeOvOhaIcAIIFRIgIAAAA4iAQbAAAAcBAJdgh0cA8AAIDOIMEGAAAAHESCDQDocbhKCSCSSLABAAAAB5FgAwAAIKqqahtU9kWNNpYeiHYojiDBBoDOeuhszz8AQKdtLD2g7eVV2nWgRrOXrU+IJJuBZgAAQNy6fv813kf/jmoc6Lz1O/bLbT2PGxrdWr9jv44bnh3doLqIFmwAAAB0v7pK6eCnOrXPTiUZzyRXSpKKCgZENy4H0IINhMtXCjBndXTjANAhvt5CVlxxfJQjQUfQMh0fOv35+nSDtOddybo1+h/f1XkDl2hD40jdeWFh3LdeSyTYQPuosQUAwFk7X5Ws2/O4qV6TtFVl/cYnRHItUSICAJ3nvbypTzdEOxIE0eS2KvuiRlW1DdEOBUCgESdKxpuGJqdqa+ox0Y3HYSTYTqE3AaBn8V3e/KJUemQqSXas8J6Lq2obVF3fpF0HarS9vIokO06NaNjBd2uiGjpZGjRO6jdcuuQ5/Td1zFfzEiCnIsEGgM4IuLypna9GNx60UFnb2PzYbVs+BxAjemVJfYd6ku0EQ4INOCkBfnUjTAGXNzXixOjGgxY+/7K++XGSkbLSWt9ydP3+a/i8ArHmobOl8rejHUWXkWAD4aLeFv4CLm8mYgtMPEtOMkqSlJ+drtGDM5WZ5op2SPBxuCFiW2Oenqg7PiEGJ0HioBeRbkRXUXGsrrK5OyE9MpWECh69sjz/eC/EnAe1WE2pVrf3uz3aoSCCNpYe0LXVF6lRyVqxbL2Wzy3ScdEOqoehO8XgaMEGwlF7kHpbII4cpZ0aY0qjHQYibP2O/Rpvduh/kv+mcU3btX7H/miHBEiiBRsIT1pfT72tdVNvC8SJDNXq+v3XaMmAW6MdCiLk1D47dWnqb+RSoxqUotI+46MdEkLoaS3dtGAD4eiVFV69LXXaAOCIdPeXSrW10v4PQ55TR9e+pV6mXinGrbSkJo2ufaubo0S4elqtPAk20B5f0iy13Z0Q/SL3PHNWe/4BcNanGzSicYdS1CQdKpcePif4OXXEiZKMrCTD1cWY5auVf7Tum5q9bH2PSLJJsCONbtvim3/SvOddT7IdCv0iA4Azdr4qI8n4noc6pw6drJ0ph6sieRA3n8ew9Tv2q1HJcitJDY3utmvl6yqlxrq2v2/jADXYIXS4Vqiu0nMj3Kcb+IAnEv+k2bo9xzgUX7/I1GkDQNeMOFHW+9D4TQumJqm3atRbg/jujVlFBQOUoiY1ysqV4lJRwYCgy42qf0/63Ntj15534zqnogXbCWGWBly//xq/xB1xwX8wEZPkudkxFPpFBgBnDJ2snSkFalSylOSSBo/nnBrHjhuerZsy/qqLe73q6UpxeHbQ5cbWv92yUSuOrwSTYDuB0oDE5Zc0X9f/d5plbml7+QQe9hWIC957JpLV1O6i6e4vuSk5htUk9Va9SZNc6Z5zK2JSuvtLDWyqaPdzdHRKmb7da13I5FqStqYe07JRK46vBJNgO4EhkxObN2n+b+qYaEcCoC1+VxMzVCc1Fxm0Nqr+PY1o/JibkmMB9yrFr083aETjx8pt2tPlz9H1+6/RRVUPeRq1knt5/o/jxioSbCdQGgAA0ed3NdFaqcwO1J6GjKCLjq1/29v3hLjyCHTWzldlZD118k59jnplSSm94v6qBQl2CB3ur5HSAACILu/VRCvPjXH5Zp+Od29W3qF3Wi26NfUYX1rAlUegs0acKOv7qcrnqAUS7CAc7a+RgUd6FvpFBqLHezXxk+Rhcsso2VilqkEnVv+/Vov+N3WMdqYczpXHaOjo96K7MfTyD52tEQ07nI0P4aObxJBIsIPoUH+NbQnoXSTd/aWzgQIAWuqVpZosT+8T1kpJsjq76f883X8FqEnqzZXH7vbpBqn87bBr35Nsk9RQTa18DKtJ6q19ybl8jgKQYAfh668xSU1ypSSF7K+xXQG9i/S2JNgAEGmjsxpVadMlScZIyWrydP+F6POv0Q2jZrcy2a9rVGrl41q4vY0kChLsIMLtr7FdAb2LfGl6OxcknMed7EBiqKvUwKSq5hFKmpSstXVH9YjhmWOef41uGDW7X5re7fbStdNVoCUDbnUySjgtjN5GmhPwOB/B0YcEO4Rw+mtsV2DvIlKP+vUGAFHhHXHVyNNR35ON39Q/Kod3/Z4adN3QyZIrw9MNm69mt42a7Jqk3vTSlQja623EPwHf866n7j7OkWCH4NilDF/vIpJjfUUCANrgHXHVSmpUip5u8rR6+t9Tc/3+a7g5LlqSUjzdsA2dHN5IyPTSFf/a623EPwGXkXrndHuITiPBDsbBjtObRaKvSABAa72yVK1ecsvoo+wTtEVHSlLX7qlBZLQxEvKSAbdS+pEo2uttZMSJMv6lQGl9W28jzpBgBxOJZHjEiZ7bJq2RO8lFX5HxhK73gLjTpGRZGY3OatTowZnKz07v2j01iIw2RkKuqm1Q2Rc12lgzKErBIRzh/hBqs7eRICW18d7FcUq0A4hJ3ksZkpVxqOP0je5R+m3dIk1Kel8b3eN0jXuUjut6pACAdmSmuZSZ5iK5jkW+xKr2oHTBsubka2PpAW0vr5LbSrO/mKrlQ5/jOzMR+Tde9cr6avTGPe96rmw8MjVua+9JsIPxXsrobb/UoEv+Et6BbaeFc/2O/XrTHqUNTUcr2e15zskeAKJnp6tAY7k6FX2+xMrvu3b9jv1ye0eyb7DJWl+dR4LdUwQrGyLBThw1Sb1Vo94a1MWDunW35272olM8fWs3ysqV4qIOEACAEIoKBijJSG4ruVwpKpr5o6+6UeVHUWLzlQ1Zd1wPv06CHWG+uqQV3r6132kapqmXXkvrNQAAIRw3PFujB2eqsrZRd15YyHdmDKuqbVBlbaM2lh5w5jiFKBuKN9zk2I0c6VsbABCa96bk9zVC1UoLudi2xjw9UXc8/WLHsMw0l/L6pfOdGcN8tfK7DtQ42898AnTNSIIN+LQx2AGAxLGx9ICurb5Ij9Z9k8FnosHdKDXWOXOuravUwKYKjap/r+vbQoe1qJX362ceJNiARziDHQCIK+9rRNB63fU79qtRyXIriaSgu326QWqolprqvjrXdrYrVO95O7dpj36x/+ect6PAVysvOdDPfIJ1iRtWgm08vmuMud77fJgxJn7b7YFAbQx2ACCxFBV4bjpPUhODz3Q3/3NrV8+13vO2kZSiBs7bUeCrlaef+dbCvcnxHkluSadIWiKpStLTkiZFKC6geyXIXcsA2nfc8Gz9Kf0eud1u5Z/zM40mKeg+Tp5rvduy1q1GuZTs21ZdpecGuU83xHUNb7ygn/ngwi0R+bq1dr6kWkmy1h6QlBqxqIDuFjiKFCdlIHF9ukEldoNONW9o9D++S2lBd+rCuXbr7oPNXd/6b6sieZB+OeA3nueU+yFGhNuC3WCMSZZkJckYkyNPizY6oLnLvijHgRCCDHYAIAHtfFXGO15vPA9kEbecPNf2ytK+ZKv/po7xPE+QQUoSUXs50Kx713nmX3F8N0UUWeG2YP9e0jOSco0xv5b0b0k3RSwqAAAiZcSJsjKeFiNKwhKLrwRF4tgiqsJqwbbWLjfGbJR0qiQj6Txr7baIRhZltDYDQPy6VIslSe8Emzl0snamHK7e9ksNuuQvtHAmkgQZpATxr80E2xjT3+9phaS/+s+z1n4eqcAAAIiUmqTeqlFvDSIBSzyU+yEGtNeCvVGeumsjaZikA97H/SR9IunwSAYHAADQlrFD+mrFnMSo20XiaLMG21p7uLW2QNI/JJ1rrR1orR0g6RxJK7sjQAAAOqrJbVXX6GaURgBREW4vIpOstf/je2Ktfd4Y88sIxQQAQKdtLD2g6vomSdLsZetVMLC3MtNcUY4KXZXu/lK97Zf0bx2nEqV3kHCF24vIPmPMdcaYEcaY4caYRZIYWxYAEHP8hz5vaHSrsrYxitHAEZ9u0IjGj5XbtIf+rREXwk2wL5KUI09Xfc9KyvVOAwAgpvgPfe5KSdLVdffq+v3XRDEitGXWveua+0AOKVjf5UAMCyvBttZ+bq29ylpb6P13FT2IoKepqm1Q2Rc11HQCMe644dnKSE1WakqSls8t0tEpZdEOCV0VpO/yWfeuazmyIxBDwqrBNsa8LO8ojv6stac4HlGM6Gm1QpA0Z3XIWRtLD2h7eZXc1lPTuXxukY4bnt2NwQHoiOQko+Qk4/mcDukb7XDQVUH7Lm+n1RuIonBvcvyx3+M0SRdIoqgNPcb6Hfvl9v7EbGh0a/2O/STYANAZbTRmtIW+yxFPwh3JcWPApNeMMa9EIJ6ERqt4/CoqGKAkI7mtp6bTv8YTABAdSwbcSh/YiEnhloj4j+iYJOk4SYMjEhEQg44bnq3RgzNVWduoOy8spPUaiHNLBtwqSVoR5TgAJKZwS0T8R3RslPSxpMsiFRQQizLTXMpMc5FcAwCANoWbYB9tra31n2CM6dXWCsaYoZL+LE9Lt1vSfdbaO/3m/1jSrZJyrLX7OhQ1EAG+bqIo5QEAAF0RboL9H0nHBkxbF2Sav0ZJC621m4wxmZI2GmNesta+502+T5P0SYcjBgAAQOLq5I2wsaTNBNsYM1hSnqR0Y0yhPCUikpQlKaOtda21uyXt9j6uMsZs827rPUm3S/qJpFVdih4AAACIMe21YJ8h6fuS8iXd5je9StK14b6IMWaEpEJJrxtjpkoqs9a+ZYxpe0UAAAAgzrSZYFtrH5H0iDHmAmvt0515AWNMH0lPS7panrKRRZJOD2O9eZLmSdKwYcM689IAAABAt2uvROS71tq/SBphjPlR4Hxr7W1BVvNf3yVPcr3cWrvSGDNe0uGSfK3X+ZI2GWMmW2vLA7Z9n6T7JGnixImtRpEEAAAAYlF7JSK9vf/36eiGjSeDfkDSNl8ibq19R1Ku3zI7JU2kFxEAAAAkSo9e7ZWI3Ov9/8ZObLtY0sWS3jHGbPFOu9Zau6YT2wIAAADiQrgjOeZIulzSCP91rLWXhlrHWvtvfdXrSKhlRoTz+gAAAEC8CLcf7FWSXpX0/yQ1RS4cAACA1q7pfZMqaxt1Z+kBRtRFzAs3wc6w1v40opEAAOCQMUOyoh0CHLSx9IC2l1fJbaXZy9Zr+dyiaIcEtCkpzOX+bow5K6KRAAAABLF+x365vf2JNTS6tX7H/ugGBLQj3AT7KnmS7BpjTKUxpsoYUxnJwAAAcERdpXTwU+nTDdGOBJ1UVDBASd67ulwpSSoqGBDdgIB2hFUiYq3NjHQgAAA47tMN0p53JeuWHpkqXfKcNHRytKNCBx03PFujB2d6arAvLKQGO4Y41Z1eVW2DKmsblZWWosw0lyPbjKZwexE5Nsjkg5JKrbWNzoYEAIBDdr7qSa4lqane85wEOy5lprmUmeYiuU5A/jX2SUYaPTj+23XDvcnxHknHSnrH+3y8pLckDTDG/I+19sVIBAcAQJeMOFEySZ4kOznV8xxATPGvsXdbqbI2/ttuw63B3imp0Fp7nLX2OEkTJL0r6VuSbolMaAAAdNHQydKgcVK/4ZSHADHKv8Y+yUhZaeG2/8aucP+C0dbarb4n1tr3jDGF1todnhHRAQCIUb2yPP9IroGY5F9j36NqsCW9b4z5o6THvc9nSfrAGNNLUkNEIgMAAECP4KuxTxThloh8X9KHkq6WtEDSDu+0BkknRyCuhFFV26CyL2q0sfRAtENBN5h17zrNunddtMMAgB7hvd2VnHMRk8Ltpq9G0lLvv0CHHI0ogQQbeYq7nwEg+pzqWgwAggm3m75Rkn4jaYykNN90a21BhOJKCMFGniLBBgCgffwIQjwLt0TkIUl/lNQoT0nInyU9GqmgEgUjTwEAAPQ84SbY6dbaf0oy1tpSa+1iSadELqzE4LsrNj87nfIQAADEvSroGcLtRaTWGJMk6b/GmCsllUnKjVxYiYORpwAAAHqWcFuwr5aUIel/JR0n6WJJl0QoJgAAACBuhduLyBveh4ckzYlcOAAAAEB8azPBNsY819Z8a+1UZ8MBAAD4CvXaiEfttWAfL+lTSX+V9LokxkUHAAAA2tBegj1Y0mmSLpL0HUmrJf3VWrs10oEBsYY+WQEAQDjavMnRWttkrX3BWnuJpCJ5hktfa4z5YbdEBwAAAMSZdm9yNMb0knS2PK3YIyT9XtLKyIYFAEDnccUJQDS1d5PjI5LGSXpe0o3W2ne7JSoAANAjVNU2qLK2URtLDzBmBBJGe/1gXyzpSElXSfqPMabS+6/KGFMZ+fAAAECi2lh6QNvLq7TrQI1mL1uvjaUHoh0S4Ig2W7CtteEORAMAANAh63fsl9t6Hjc0urV+x35asZEQwh0qHUh4XKYEEtSc1dGOACEUFQxQkpHcVnKlJKmoYEC0QwIcQQs1IC5TAkA0HDc8W6MHZyo/O13L5xbRuIGEQYINKPhlSqA9s+5dxyhzQBdlprmU1y+d5BoJhQQb0FeXKSUuUwIAgK6hBtshvlYs+l6NT77LlJW1jbrzwkJaUoA4xbk4cXFMEU9IsAGvzDSXMtNcJNcAAKBLKBEBAAAAHEQLNgAASBx0y4gYQAs2AAAA4CASbKAddMUGAAA6ggQbAAAAUbXiiuMTqqcYEmzAQVW1DSr7ooaRIAEA6MFIsAGHMNw6AACQSLABxzDcOgAAkEiwAccw3DoAAF2TKKWWJNiAQ3zDrednp2v53CJGhAQAoAMSqdSSBBtwUGaaS3n90kmuAQDooEQqtSTBBgAAQNQlUqklQ6UDAAAg6nyllpW1jbrzwsK4vhpMgg0AAICYkJnmUmaaK66Ta4kSEQAAAMBRJNgAAACAg0iwAQBATJp17zq9t7sy2mEAHUaCDQAAus17uysdS5qb3DYhBiVB4iHBBgAAMStUEl1V26Dq+qaEGJQEiYcEGwAAdJsmt1VdozushLitJLqytrH5cbwPSoLEQ4INAAC6xcbSA6qub1J9ozusVue2kuistK96Go73QUmQeOgHGwDQI826d50kacUVx0c5kp7DP0EOp9W5rSQ6M82ljNRk9e+dGveDkiDx0IINAAC6hX+CHE6rsy+Jzs9O1/K5Ra2S6OQko7x+6STXiDm0YAMAgG5x3PBsZaQmq9FtgybMwZBEIx7Rgg0AnVRV20AXYUAHJScZ9UpJImFGQqMFO8Ko7QMS08bSA9peXiW3lWYvWx92axwAIPHRgg0AnbB+x365recxXYQBAPyRYANAJxQVDFCS8TymizAAgD9KRACgE44bnq3RgzNVWdtIF2EAgBZIsAGgkzLTXMpMc5FcAwBaoEQEAAAAcBAJNgAAAOAgEmwAAADAQdRgA170WQ4A3Y9zLxIRLdgOYUQ3AIg+zsU9R1Vtg+oa3aqqbYh2KEArJNgO8I3otutAjWYvW8+JvQdbccXxtMYAUcK5uOfwHev6Rre2l1dxrBFzSLAdwIhuABB9nIt7Dv9j7bbiWCPmkGA7gBHdACD6OBf3HP7HOsmIY42YQ4LtAN+IbvnZ6Vo+t4hBJxIMNZ1AfOBc3HP4jnVqSpJGD87kWCPmkGA7JDPNpbx+6XzIEww1nUB84Vzcc2SmudQrJUmZaa5ohwK0QoINtIGaTgAA0FEk2EAbqOkEAAAdxUAzQBt8dX6VtY2688JCLjsDAIB2kWAD7chMcykzzUVyDQAAwkKJCAAAAOAgEmwAAADAQSTYAAAAgINIsAEAAAAHkWADAAAADiLBBgAAABwUsQTbGDPUGPOyMWabMWarMeYqv3k/NMa8751+S6RiAAAAALpbJPvBbpS00Fq7yRiTKWmjMeYlSYMkTZN0jLW2zhiTG8EYAAAAgG4VsRZsa+1ua+0m7+MqSdsk5Un6gaSbrbV13nkVkYoBAAAA8eW93ZWade+6aIfRJd1Sg22MGSGpUNLrko6UdKIx5nVjzCvGmEndEQMAAIg/Y4ZkacUVx0c7DKBDIj5UujGmj6SnJV1tra00xqRIypZUJGmSpCeMMQXWWhuw3jxJ8yRp2LBhkQ4TAAAAcEREW7CNMS55kuvl1tqV3sm7JK20HhskuSUNDFzXWnuftXaitXZiTk5OJMMEAAAAHBOxFmxjjJH0gKRt1trb/GY9K+kUSWuNMUdKSpW0L1JxAECkcNkaAJy14orj477+WopsC3axpIslnWKM2eL9d5akByUVGGPelfS4pEsCy0MAAACqahtU9kWNNpYeiHYoQIdErAXbWvtvSSbE7O86+VqVlZWqqKhQQ0ODk5vtkPmF6ZKkbdu2RS0GdI3L5VJubq6ysrKiHQoA9HgbSw9oe3mV3FaavWy9ls8t0nHDs6MdFhCWiN/kGGmVlZXas2eP8vLylJ6eLk9lSvdL3XtIknRETp+ovD66xlqrmpoalZWVSRJJNgBEyNDsdFXWNmpj6YE2E+b1O/bL7b2+3dDo1vod+0mwETfifqj0iooK5eXlKSMjI2rJNeKfMUYZGRnKy8tTRQVdswNAJPhapXcdqNHsZevbLP0oKhigJO/XuislSUUFA1otQxd+iFVxn2A3NDQoPT092mEgQaSnp0e11AgAElmwVulQjhuerdGDM5WfnU55COJO3JeISKLlGo7hvQQAkeNrlXbb0K3S/jLTXMpMc5FcI+4kRIINAABin69VurK2UXdeWEjijIQV9yUiTpt177qE6H8RANA2uoCLjsw0l/L6pZNcI6GRYMeAxYsXa+DAVoNZtvLRRx/psssu09ChQ5WamqqcnBzNmDFD69evb3M9Y4zuuusup8LtsJ07d8oYo7///e9Ri0GS3n33XRljtHbt2qjGASD6OnKzHeILDWWIBZSIxInXXntNZ511lkaNGqUlS5boiCOO0N69e7Vy5UoVFxfr888/V9++fYOuu27dOh1++OHdHDEAxC66gAMQSSTYcaCmpkazZs3SpEmTtGbNGqWmpjbPu+CCCzR37ly5XK6Q6xcVFXVHmAAQNzp6sx0AdAQlInHgySefVFlZmW6//fYWybXPySefrIyMjJDrB5aIlJSUaMaMGXrsscc0cuRIZWVlacqUKdq1a1ebcYQqZQm3BKWyslIXX3yxMjMzlZubqxtvvLHF/O3bt+vCCy/U0KFDlZGRobFjx+qOO+6Q2+1uXmbt2rXNZR4zZ85Unz59VFBQoHvuuafV691zzz0aOnSoevfurXPPPVe7d+9uN0YAPQNdwAGIJBLsALF408srr7yiww47TOPHj3dsm6+//rruuusuLV26VPfdd582bdqkefPmObb9YK655hplZGToqaee0uWXX64bb7xRd999d/P8srIyHXXUUbrnnnu0Zs0aXX755brhhhv029/+ttW2Lr/8cn3ta1/TM888o5KSEs2fP18bNmxonr9q1SrNnz9f55xzjlauXKnx48fr0ksvjejfByC+cLMdgEihRMSP76YXt5VmL1sfM60aZWVlGjZsmKPbrKys1OrVq5Wd7fn7ysvLtWDBAtXU1ERs4J6xY8fq3nvvlSSdccYZqqio0E033aQf/OAHSkpK0qmnnqpTTz1Vkmfo8hNOOEHV1dW6//779fOf/7zFti666CJdd911kjwt8n/729+0cuVKTZ48WZL061//Wmeeeab++Mc/Nr/e3r17tWzZsoj8bQAAAD60YPvpyAhT3c3pAVAmTZrUnFxL0pgxYyR5kvlImT59eovn559/vj777LPm0pTa2lrdcMMNGjlypHr16iWXy6VFixbp448/VmNjY4t1Tz/99ObHLpdLo0aNat5OU1OTNm/erGnTprV6PQAAgEgjwfbju+lFiq2bXvLy8vTJJ584us1+/fq1eO6r7a6trXX0dfzl5uYGfe6rjf7pT3+q3/3ud5o3b57WrFmjN954o7mVOjCuYPH7ltm7d68aGxtDvh4AAEAkkWD76cpNL0fk9NEROX0iEldJSYnKysq0devWiGw/XGlpaaqvr28x7cCB8GvVKyoqgj4fMmSIJM/NnD/84Q/1k5/8RN/61rc0ceJEpaR0vIopJydHKSkpIV8PAAAgkkiwA8TiTS8zZsxQXl6eFixYoIaGhlbz165dq+rq6ojHkZ+fr6qqqhZlJC+++GLY6z/zzDMtnq9cuVJDhgxRfn6+JE93hL169Wqe39TUpMcff7zDcSYnJ2vChAlatWpVq9cDkNhWXHG8VlxxfLTDANDDcZNjjKivr9dTTz3VavpJJ52knJwcrVixQlOmTFFxcbHmz5+vgoIC7du3T88++6yWL1+u/fsjXy9+5plnKj09XZdeeqkWLlyojz/+WH/605/CXn/r1q264oordMEFF+hf//qXHnjgAd15551KSvL8zjvttNN09913a+TIkerfv7/uvvtu1dXVdSrWa6+9Vueff75+8IMfaPr06XrllVf0wgsvdGpbAAAAHUELdoyoqqrSzJkzW/3zlYUUFxdr06ZNGjdunBYtWqRTTjlF8+bNU2VlpV566aWQozg6aeDAgXr66ae1a9cunXfeefrLX/6ixx57LOz1b7nlFlVWVuqCCy7Qvffeq1/84he68sorm+f/4Q9/0Iknnqj58+fr0ksv1bhx41r1HhKu6dOn6w9/+IP+9re/6bzzztPmzZv1wAMPdGpbAAAAHUELdgxYvHixFi9e3O5yI0eO1IMPPtjh7VtrWzxfu3Ztq2VKSkpaLRfMlClTNGXKlDa3H2jEiBHNy8yePTvkcoMGDWpVRiJ5+rxuL85gf9OVV17ZIoEPJ9ZguNwMAAA6ghZsAAAAwEG0YAegtRIAAABdQQs2AAAAYkZVbYPKvqjRxtLwuwKONSTYAAAAiAkbSw9oe3mVdh2o0exl6+M2ySbBBgAAQExYv2O/3N7+CBoa3Vq/I/LdEEcCCTYAAABiQlHBACUZz2NXSpKKCgZEN6BO4iZHAAAAxITjhmdr9OBMVdY26s4LC2NqZO2OIMEGAABAzMhMcykzzRW3ybVEiUhrD53t+QcAAAB0Agl2DFi8eLEGDhzY7nIfffSRLrvsMg0dOlSpqanKycnRjBkztH79+jbXM8borrvucircTomFGA4dOiRjjB5++OGoxgEAABIbJSJx4rXXXtNZZ52lUaNGacmSJTriiCO0d+9erVy5UsXFxfr888/Vt2/foOuuW7dOhx9+eDdHDAAA0DORYMeBmpoazZo1S5MmTdKaNWuUmpraPO+CCy7Q3Llz5XK5Qq5fVFTUHWECAABAlIjEhSeffFJlZWW6/fbbWyTXPieffLIyMjJCrh9YnlFSUqIZM2boscce08iRI5WVlaUpU6Zo165d7caydu1aHXPMMUpLS9OkSZO0YcMGDRw4UIsXL2533fr6el111VXq37+/+vXrpx/+8Ieqr69vnr97925deumlKigoUHp6uo488khdd911LZbZuXOnjDF64okndMUVV6hv377Kz8/XDTfcILfb3eL1nn76aR155JFKT0/XN7/5TW3fvr3dGAEAALqKFuw48Morr+iwww7T+PHjHdvm66+/rs8++0xLly5VTU2NrrrqKs2bN09r1qwJuU5ZWZnOOussfeMb39BNN92k8vJyzZ49WzU1NWG95tKlS1VUVKTly5dr69atWrRokdLS0nTrrbdKkvbt26f+/fvrtttuU3Z2tj744AMtXrxYe/fu1b333ttiWz/5yU90wQUX6KmnntI///lPLVmyRGPHjtW3v/1tSdKmTZs0a9YsTZ8+XXfeeae2bt3aPA8AACCSSLAD1VVKtQelTzdIQydHOxpJnsR22LBhjm6zsrJSq1evVna2pwuc8vJyLViwQDU1NUpPTw+6zh133KGMjAz97W9/a14mKytLs2bNCus1MzMz9eSTTyopKUlTpkxRXV2dfv3rX+vnP/+5+vfvr/Hjx+t3v/td8/LFxcXq3bu3Lr30Uv3hD39o0Xr/zW9+U0uXLpUknXbaaXrhhRe0cuXK5iT65ptv1pFHHqknnnhCxpjm17vuuus6vrMAAAA6gBIRf59ukPa8K31RKj0y1fM8RhhjHN3epEmTmpNrSRozZowkTzIfyhtvvKHTTjutRQI+derUsF9z2rRpSkr66i13/vnnq6amRu+++64kyVqrO+64Q2PGjFF6erpcLpdmz56turo6ffLJJy22dfrpp7d4PmbMmBYlLhs2bNDUqVNb7Lfzzz8/7FgBAAA6iwTb385XJeut422q9zyPAXl5ea0SzK7q169fi+e+1uHa2tqQ65SXlysnJ6fFtLS0NPXp0yes18zNzQ36fPfu3ZI8LeQLFy7U9OnTtWrVKm3YsEF333130LiCxe+/THl5ecjXAwAAiCQSbH8jTpSMd5ckp3qex4CSkhKVlZVp69atUY1j8ODB2rt3b4tptbW1OnToUFjrV1RUBH0+ZMgQSZ6bOWfOnKlf//rXOv300zVp0iT17t2707GGej0AAIBIIsH2N3SyNGic1G+4dMlzMVODPWPGDOXl5WnBggVqaGhoNX/t2rWqrq6OeByTJk3SSy+91OKmxueeey7s9VetWtWip4+VK1cqPT1d48aNk+TpjrBXr14t1lm+fHmnY33uuedkrW3xegAAAJHGTY6BemV5/nVzcl1fX6+nnnqq1fSTTjpJOTk5WrFihaZMmaLi4mLNnz9fBQUF2rdvn5599lktX75c+/fvj3iMV199te6++26de+65WrBggcrLy3XzzTcrIyOjRW11KFVVVZo5c6Yuv/xybd26VUuWLNGVV16p/v37S/LcrPj73/9eX//613XEEUdo+fLl+vDDDzsV609/+lN9/etf17e//W1ddtllevfdd/XAAw90alsAAAAdQYIdI3zJZ6CXX35ZJSUlKi4u1qZNm3TTTTdp0aJF2rNnj/r166cTTjhBL730UshRHJ2Ul5en1atX66qrrtL555+vo48+Wg8++KBOO+00ZWVltbv+woULtWPHDl100UVyu92aO3eubrrppub5119/vfbu3dvc08f555+v3//+9zr33HM7HOvEiRP1+OOP6+c//7nOO+88TZw4UStWrNDkybFxVQIAACQuEuwYsHjx4rAGahk5cqQefPDBDm/fv0xC8pSUBCopKWm1XDAnn3yy3n777ebn//73v1VXV6evfe1rYcfgP+iNvz59+uihhx5qc90RI0YEjfPhhx9uNW3mzJmtfrSE8zcCAAB0BQk2OuSnP/2pCgsLNXjwYL3//vv65S9/qWOOOUYnnXRStEMDAACICSTYgeasjnYEMa2urk7XXHON9uzZo8zMTJ1++um67bbbwqrBBgAA6AlIsNEhd9xxh+64445ohwEAABCzSLABAEBMWnHF8dEOAegUrusDAAAADqIFGwAAdBtapdET0IINAAAAOIgEGwAAAHAQCTYAAADgIBLsAHNemKM5L8yJdhgAAACIUyTYMWDx4sUaOHBgu8t99NFHuuyyyzR06FClpqYqJydHM2bM0Pr169tczxgTcnjySHn44YdljNGhQ4e69XUD3XXXXTLGRDUGALFpxRXHc8MdgIigF5E48dprr+mss87SqFGjtGTJEh1xxBHau3evVq5cqeLiYn3++efq27dv0HXXrVunww8/vJsjBgAA6JlIsONATU2NZs2apUmTJmnNmjVKTU1tnnfBBRdo7ty5crlcIdcvKiqKWGy1tbVKS0uL2PYBAOiIqtoGVdY2amPpAR03PDva4aATEuHKEiUiceDJJ59UWVmZbr/99hbJtc/JJ5+sjIyMkOsHloiUlJRoxowZeuyxxzRy5EhlZWVpypQp2rVrV5tx+Mo+NmzYoJKSEqWnp+vWW29tc51t27bpxBNPVHp6uo488kg988wzLeavXr1ap512mnJzc5WVlaWioiK9+OKLLZbxldBs3rxZRUVFysjIUGFhoV599dUWy9XV1enKK69Uv3791L9/fy1YsEANDQ1txgcASBwbSw9oe3mVdh2o0exl67Wx9EC0Q0IPRYId4FD9Ie3+cre2VGyJdijNXnnlFR122GEaP368Y9t8/fXXddddd2np0qW67777tGnTJs2bNy+sdS+66CKdc845WrNmjc4555w2l501a5amTZumlStXavz48Zo5c6beeuut5vkff/yxzj33XD366KN6+umn9Y1vfENTpkzRa6+91mI71dXVuuSSS3TFFVfo6aefVq9evTR9+nRVV1c3L/Ozn/1My5Yt0y9+8QstX75cpaWlWrp0aQf2CgAgnq3fsV9u63nc0OjW+h37oxsQeixKRPxsqdii9w+8Lyury1+8XPeffr8m5E6IdlgqKyvTsGHDHN1mZWWlVq9erexsz+Wz8vJyLViwQDU1NUpPT29z3f/93//VVVddFdbrzJ07Vz/+8Y8lSWeccYbGjBmj3/zmN3r88cclSVdeeWXzsm63WyeffLK2bt2qBx54QMXFxc3zampqdMcdd+iUU06RJA0ZMkSFhYX617/+pTPPPFP79+/Xn/70J914441auHBhi9cDAPQMRQUDlGQkt5VcKUkqKhgQ7ZDQQ9GC7efNPW/KyvPTt8HdoDf3vBnliL7idE8YkyZNak6uJTUnomVlZe2ue/bZZ4f9OtOnT29+nJSUpGnTpmnDhg3N03bt2qVLLrlEeXl5SklJkcvl0osvvqgPPvigxXZcLpdKSkpaxesra3nnnXdUW1uradOmtXo9AEDPcNzwbI0enKn87HQtn1tEDTaihhZsPxMHTZSRkZWVK8mliYMmRjskSVJeXp7effddR7fZr1+/Fs99td21tbXtrjto0KCwXyc3N7fV8927d0vytFhPnTpVVVVVWrJkiUaOHKnevXvr+uuvV0VFRYv1srKylJT01e/BwHjLy8tDvh4AoOfITHMpM81Fco2oIsH2MyF3go7KPkpVDVW6+cSbY6I8RPLclPjggw9q69atGjt2bLTD6VBrekVFhQYMGNDi+ZAhQyRJH374oTZv3qznn39eZ555ZvMyNTU1HY5p8ODBzdvv379/i9cDAADoTpSIBOiT2kdDeg+JmeRakmbMmKG8vLyQvWKsXbu2xc1+scS/1xC3261Vq1Zp8uTJkr5KpHv16tW8TGlpaasbHMMxfvx4paWladWqVa1eDwCQeBgoCLGMFuwYUV9fr6eeeqrV9JNOOkk5OTlasWKFpkyZouLiYs2fP18FBQXat2+fnn32WS1fvlz798fmndLLli1Tamqqxo0bp/vvv18ffvih/vrXv0qSRo8erfz8fC1cuFC//OUvVVVVpRtuuEF5eXkdfp0BAwZo3rx5uuGGG5SSkqKxY8fq/vvvj/pIkgAAoOchwY4RVVVVmjlzZqvpL7/8skpKSlRcXKxNmzbppptu0qJFi7Rnzx7169dPJ5xwgl566aWQozhG2+OPP64FCxbouuuuU35+vlasWKHCwkJJnpbrlStXav78+ZoxY4by8/O1aNEirV27tlM157fccosaGhq0ZMkSJSUl6bvf/a5+9KMfNfcqAgAA0B2MtTbaMbRr4sSJ9s03g/fosW3bNh199NGOvdacF+ZIkh468yHHton44vR7CgDQfWbdu05SYowGiOgwxmy01nappwtqsAEAAAAHUSISgJZrAAAAdAUt2AAAAICDSLABAAAAB5FgAwAAAA4iwQYAAAAcRIINAAAAOIgEGwAAAHAQCXaA0ou/p9KLvxftMAAAABCnSLBjwOLFizVw4MB2l/voo4902WWXaejQoUpNTVVOTo5mzJih9evXt7meMUZ33XVXl+N8+OGHZYzRoUOHurRMd7jrrrtkjIlqDAAAoGdioJk48dprr+mss87SqFGjtGTJEh1xxBHau3evVq5cqeLiYn3++efq27dv0HXXrVunww8/vMsxnH322Vq3bp0yMjK6vC0AAIBERYIdB2pqajRr1ixNmjRJa9asUWpqavO8Cy64QHPnzpXL5Qq5flFRkSNx5OTkKCcnx5FtAQAAJCpKROLAk08+qbKyMt1+++0tkmufk08+uc1W5cASkZKSEs2YMUOPPfaYRo4cqaysLE2ZMkW7du1qM46OlH9s27ZNJ554otLT03XkkUfqmWeeaTF/9erVOu2005Sbm6usrCwVFRXpxRdfbLGMr3Rm8+bNKioqUkZGhgoLC/Xqq6+2WK6urk5XXnml+vXrp/79+2vBggVqaGhoN0YAAIBIIMGOA6+88ooOO+wwjR8/3rFtvv7667rrrru0dOlS3Xfffdq0aZPmzZvn2PZnzZqladOmaeXKlRo/frxmzpypt956q3n+xx9/rHPPPVePPvqonn76aX3jG9/QlClT9Nprr7XYTnV1tS655BJdccUVevrpp9WrVy9Nnz5d1dXVzcv87Gc/07Jly/SLX/xCy5cvV2lpqZYuXerY3wIAANARlIgEaDp0SO7KSlVv3qyMwsJohyNJKisr07BhwxzdZmVlpVavXq3s7GxJUnl5uRYsWKCamhqlp6d3eftz587Vj3/8Y0nSGWecoTFjxug3v/mNHn/8cUnSlVde2bys2+3WySefrK1bt+qBBx5QcXFx87yamhrdcccdOuWUUyRJQ4YMUWFhof71r3/pzDPP1P79+/WnP/1JN954oxYuXNji9QAAAKKBFmw/1Zs3q277djWUlemTOZeqevPmaIfUzOkeMSZNmtScXEtqTkjLysoc2f706dObHyclJWnatGnasGFD87Rdu3bpkksuUV5enlJSUuRyufTiiy/qgw8+aLEdl8ulkpKSVnH6ylneeecd1dbWatq0aa1eDwAAIBpIsP1Ub3hDslaSZBsaPM9jQF5enj755BNHt9mvX78Wz3213bW1tY5sPzc3t9Xz3bt3S/K0WE+dOlX/+c9/tGTJEr388st64403NGXKlFavn5WVpaSkr96mgXGWl5eHfD0AAIBooETET8bkSZIxkrUyLpfneQwoKSnRgw8+qK1bt2rs2LHRDicsFRUVGjBgQIvnQ4YMkSR9+OGH2rx5s55//nmdeeaZzcvU1NR0+HUGDx7cvP3+/fu3eD0AAIBooAXbT0ZhoXqNHi1XXp6GPfRgzNRgz5gxQ3l5eSF7x1i7dm2Lm/5igX+vIW63W6tWrdLkyZMlfZVI9+rVq3mZ0tLSVjc4hmP8+PFKS0vTqlWrWr0eAABANESsBdsYM1TSnyUNluSWdJ+19k5jzARJf5KUJqlR0v9nrd0QckPdLLlPHyX36dPtyXV9fb2eeuqpVtNPOukk5eTkaMWKFZoyZYqKi4s1f/58FRQUaN++fXr22We1fPly7d+/v1vjbc+yZcuUmpqqcePG6f7779eHH36ov/71r5Kk0aNHKz8/XwsXLtQvf/lLVVVV6YYbblBeXl6HX2fAgAGaN2+ebrjhBqWkpGjs2LG6//77oz6SJAAA6LkiWSLSKGmhtXaTMSZT0kZjzEuSbpF0o7X2eWPMWd7nJRGMIy5UVVVp5syZraa//PLLKikpUXFxsTZt2qSbbrpJixYt0p49e9SvXz+dcMIJeumll0KO4hgtjz/+uBYsWKDrrrtO+fn5WrFihQq9P1p69eqllStXav78+ZoxY4by8/O1aNEirV27Vu+++26HX+uWW25RQ0ODlixZoqSkJH33u9/Vj370o+ZeRQAAALqTsd6b+iL+QsasknSXpB9LetBau8IYc5Gkc62132lr3YkTJ9o333wz6Lxt27bp6KOPdizO0ou/J0ka/uifHdsm4ovT7ykAQPeZde86SdKKK46PciSIV8aYjdbaiV3ZRrfc5GiMGSGpUNLrkq6W9A9jzO/kqQH/RnfEAAAAAHSHiCfYxpg+kp6WdLW1ttIY8ytJC6y1Txtjvi3pAUnfCrLePEnzJDk+yEpbaLkGAABAV0S0FxFjjEue5Hq5tXald/IlknyPn5Q0Odi61tr7rLUTrbUTc3JyIhkmAAAA4JiIJdjGM/TgA5K2WWtv85v1maSTvI9PkfTfSMUAAAAAdLdIlogUS7pY0jvGmC3eaddKulzSncaYFEm18paBAAAAAIkgYgm2tfbfkkyI2cdF6nUBAACAaGIkRwAAAMBBJNgAAACAg0iwAQAAAAeRYAd4ZukmPbN0U7TDAAAAQJwiwY4Bixcv1sCBA9td7qOPPtJll12moUOHKjU1VTk5OZoxY4bWr1/f5nrGGN11111OhdshJSUlmjFjRlRe29/EiRP1/e9/P9phAACAHqBbhkpH17322ms666yzNGrUKC1ZskRHHHGE9u7dq5UrV6q4uFiff/65+vbtG3TddevW6fDDD+/miAEAAHomEuw4UFNTo1mzZmnSpElas2aNUlNTm+ddcMEFmjt3rlwuV8j1i4qKuiNMAAAAiBKRuPDkk0+qrKxMt99+e4vk2ufkk09WRkZGyPUDS0R8ZRuPPfaYRo4cqaysLE2ZMkW7du1qM44RI0bIGNPq3+LFi9v9G+677z6NGDFC6enpOvvss1VWVtZi/s9+9jONHz9effr0UX5+vmbPnq3y8vJWr//jH/9Yt99+u/Lz85Wdna0LL7xQX3zxRYvl3n33XRUXFystLU1HH320nnvuuXbjAwAAcAot2AHqaxpVV9Oo8h0HNbggeMlFd3vllVd02GGHafz48Y5t8/XXX9dnn32mpUuXqqamRldddZXmzZunNWvWhFznmWeeUV1dXfPzl19+Wddee62OPPLINl9r3bp1ev/993XbbbeptrZWP/3pT3XeeefpjTfeaF6moqJC1157rQ477DDt3btXS5cu1SmnnKJ33nlHycnJzcs98cQTOuaYY3Tfffdp165d+tGPfqRrr71W99xzjyRPa/8ZZ5yhgQMH6rHHHlNNTY2uvvpqHTp0SOPGjevs7gIAAAgbCbaf8h0Hta/skGSlVbdv1rQFhTGRZJeVlWnYsGGObrOyslKrV69Wdna2JKm8vFwLFixQTU2N0tPTg65TWFjY/PiTTz7Rbbfdpu9///v6zne+0+ZrVVRU6D//+Y+GDx8uSRo+fLhOOOEEvfDCCzrzzDMlSQ8++GDz8k1NTTr++OOVn5+v1157Td/85jeb57lcLj377LNKSfG8dd977z09/vjjzQn2Qw89pIqKCr3++uvKz8+X5Gn5PuGEEzq0fwAAADqLEhE/ZR8ckKzncVOT2/M8RhgTatT5zpk0aVJzci1JY8aMkaRWpRvB1NTUaPr06Ro2bJj++Mc/trv8scce25xcS1JxcbFyc3O1YcOG5mnPP/+8vvGNb6hv375KSUlpTo4/+OCDFts6+eSTm5NrX9wVFRWqr6+XJG3YsEHHHXdc8/r+rwcAANAdSLD95B2ZLXnz2OTkJM/zGJCXl6dPPvnE0W3269evxXNfbXdtbW27686bN0+lpaVauXKl0tLS2l0+WHKbm5ur3bt3S5LeeOMNTZ06Vfn5+Xr00Ue1bt265q4HA+MJFre1tjnBLi8vD/l6AAAA3YESET+DC/pqYF4f1dU06vTLxsZEeYjkuSnxwQcf1NatWzV27NioxnLHHXfor3/9q1544YUWrdJtqaioCDptyJAhkjy13Tk5OVqxYkVzS31paWmn4hs8eLC2b98eVgwAgMSz4orjox0CQAt2oNT0FGX2T4uZ5FqSZsyYoby8PC1YsEANDQ2t5q9du1bV1dURj+Pll1/WNddco5tuuknf+ta3wl5v06ZNLVrgX3vtNVVUVGjy5MmSPCUnLperRRnM8uXLOxXjpEmTtHHjxhY9ovheDwAAoDvQgh0j6uvr9dRTT7WaftJJJzW37k6ZMkXFxcWaP3++CgoKtG/fPj377LNavny59u/fH9H4Dh48qG9/+9saN26cvvnNb7YYPTI/P79FzXOg3NxcnXPOOVq8eHFzLyLHHnts8w2Op512mu644w5dffXVOvfcc/Wf//xHf/nLXzoV55w5c/SrX/1KZ599thYvXqyamhr94he/CGukTAAAACeQYMeIqqoqzZw5s9X0l19+WSUlJSouLtamTZt00003adGiRdqzZ4/69eunE044QS+99FLIURydcuDAAe3bt0/79u3T8ce3vPx2ww03tNkX9vHHH69vfetbuvrqq7V3716VlJTovvvua55/1lln6be//a3+8Ic/6P7779fxxx+vv//97+12/xdMRkaG/vGPf+h//ud/dOGFF2rEiBFaunSpfvWrX3V4WwAAAJ1hrLXRjqFdEydOtG+++WbQedu2bdPRRx/t2Gs9s3STJGn6wmMd2ybii9PvKQAAED+MMRuttRO7sg1qsAEAAAAHUSISgJZrAAAAdAUt2AAAAICDSLABAAAAByVEgh0PN2oiPvBeAgAAXRX3CbbL5VJNTU20w0CC8A16AwAA0Flxn2Dn5uaqrKxM1dXVtD6i06y1qq6uVllZmXJzc6MdDgAAiGNx34tIVlaWJOmzzz4LOow4EC6Xy6VBgwY1v6cAAAA6I+4TbMmTZJMUAQAAIBbEfYkIAAAAEEtIsAEAAAAHkWADAAAADiLBBgAAABxEgg0AAAA4iAQbAAAAcJCJh8FZjDF7JZVGaPMDJe2L0LZ7AvZf17D/uo592DXsv65h/3UN+69r2H9dE2r/DbfW5nRlw3GRYEeSMeZNa+3EaMcRr9h/XcP+6zr2Ydew/7qG/dc17L+uYf91TST3HyUiAAAAgINIsAEAAAAHkWBL90U7gDjH/usa9l/XsQ+7hv3XNey/rmH/dQ37r2sitv96fA02AAAA4CRasAEAAAAHJUyCbYzZaYx5xxizxRjzpnfarcaY7caYt40xzxhj+nmnjzDG1HiX3WKM+ZPfdo7zbudDY8zvjTHGO72XMWaFd/rrxpgR0fg7I6WD+2+2377bYoxxG2MmeOetNca87zcv1zu9J+6/X3r33RZjzIvGmMP8lv+5d1+8b4w5w28677929p8x5jRjzEbv8huNMaf4badHvv+kDu9DzoEBOrj/OAcGCLb//Ob92BhjjTED/aZxDvTTkf3HObC1Du6/7jn/WWsT4p+knZIGBkw7XVKK9/FvJf3W+3iEpHdDbGeDpOMlGUnPS5rinf7/SfqT9/GFklZE+2+O1v4LWGa8pB1+z9dKmhhkuZ64/7L8Hv+v398/RtJbknpJOlzSR5KSef+Fvf8KJR3mfTxOUllPf/91Yh9yDuzC/gtYhnNgiP3nnT5U0j/kGctioHca58Cu7T/OgV3bf91y/kuYFuxgrLUvWmsbvU/XS8pva3ljzBB5TqjrrGcv/lnSed7Z0yQ94n38lKRTfb9sElWY++8iSX8NY3M9cf9V+j3tLcl3w8M0SY9ba+ustR9L+lDSZN5/LYXaf9bazdbaz7zTt0pKM8b0amdzPW7/SW2+B4PiPdhSmPuPc2Dbbpf0E7Xcd5wDw9dq/3EO7JBg77+gnH7/JVKCbSW96L1cMi/I/Evl+TXic7gxZrMx5hVjzIneaXmSdvkts8s7zTfvU0nyJp0HJQ1w8g+Iso7uP59Zav3l8pD3sssv/N6APXL/GWN+bYz5VNJsSdd7JzfvCy/f+4z3X3j7z98FkjZba+v8pvXE95/U8X3IObClzr4HOQd6tNp/xpip8rSuvhWwLOfA1jqy//xxDvTo6P6L+PkvpdN/SuwpttZ+5q03eskYs91a+y9JMsYsktQoabl32d2Shllr9xtjjpP0rDFmrDyXBAL5fvW0NS8RdGT/yTv965KqrbXv+k2eba0tM8ZkSnpa0sXy/ArskfvPWrtI0iJjzM8lXSnpBoXeF7z/wtt/kiTvZ/a38pQy+fTU95/UsX3IObC1zrwHOQd+pdX+k7RILT+fPpwDW+vI/pPEOTBAR/Zft5z/EqYF23e5xFpbIekZSZMlyRhziaRz5HnT+S4x11lr93sfb5Sn/utIeX6t+JdB5EvyXYbZJU8tj4wxKZL6Svo8sn9V9+nI/vNzoQJabqy1Zd7/qyQ95tuOeuj+8/OYPC0Nkt++8PK9z3j/hbf/ZIzJ9y73PWvtR37b6ZHvP6lj+5BzYGsdfQ96cQ70CrL/TpKnvvotY8xOed5Lm4wxg8U5sJUO7j/OgQE6sv+66/yXEAm2Maa399eajDG95fnF8q4x5kxJP5U01Vpb7bd8jjEm2fu4QNIoeW5S2S2pyhhT5L2s8j1Jq7yrPSfpEu/jGZL+L0jCGZc6uv+8yyVJminpcb9pKearu3Rd8iTmvpadnrj/RvktNlXSdu/j5yRdaDx3JR8uz/tvA++/8Paf8fRms1rSz621r/ltp0e+/6RO7UPOgX468RnmHOgnxP57w1qba60dYa0dIU+Ccqy1tlycA1vo6P7jHNhSJ/Zf95z/bAzc/dnVf5IK5Lkj+S15Cv4Xead/KE/NzBbvP98doBd4l3tL0iZJ5/pta6I8b8iPJN0lNQ/GkybpSe82N0gqiPbfHa39551XIml9wHZ6S9oo6W3vdu7UV3eG98T997T3vfS2pL9JyvNbZ5H3Pfa+vHcp8/4Lb/9Juk7Sl37vyy2Scnvq+6+T+5BzYBf2n3ce58B29l/AMjvl18sD58DO7z/OgV3ef91y/mMkRwAAAMBBCVEiAgAAAMQKEmwAAADAQSTYAAAAgINIsAEAAAAHkWADAAAADiLBBoA4ZIyxxphH/Z6nGGP2GmP+7n3+fWPMXd7Hi40xZcYzfPJ/jTErjTFjohU7ACQ6EmwAiE9fShpnjEn3Pj9NUlkby99urZ1grR0laYWk/zPG5EQ6SADoiUiwASB+PS/pbO/jixQwbHco1toVkl6U9J0IxQUAPRoJNgDEr8flGXI6TdIxkl7vwLqbJI2OSFQA0MORYANAnLLWvi1phDyt12s6uLpxPCAAgCQSbACId89J+p3CLA/xUyhpm/PhAABSoh0AAKBLHpR00Fr7jjGmJJwVjDEXSDpd0sIIxgUAPRYJNgDEMWvtLkl3BpmVIqnO7/kCY8x3JfWW9K6kU6y1e7shRADocYy1NtoxAAAcZoy5XdJ/rbX3RDsWAOhpSLABIMEYY56XlCrpfGvtwWjHAwA9DQk2AAAA4CB6EQEAAAAcRIINAAAAOIgEGwAAAHAQCTYAAADgIBJsAAAAwEEk2AAAAICD/n/pOuBSk2Uy4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot var\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "gal_train_id = 500004\n",
    "bands1, x1, y1, err1 = plot_sdss_lc(gal_train_id)\n",
    "\n",
    "for i in range(len(bands1)):\n",
    "    plt.errorbar(x1[i], y1[i], err1[i], label='LC in {} band'.format(bands1[i]), fmt='.')\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Light curve for object {}'.format(var_train_id))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f26d4a6ac50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot gri merged light curve\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "bands1, x1, y1, err1 = plot_merged_lc(gal_train_id, ['g', 'r', 'i'])\n",
    "\n",
    "for i in range(len(bands1)):\n",
    "    plt.errorbar(x1[i], y1[i], err1[i], label='LC in {} band'.format(bands1[i]), fmt='.')\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.title('Merged Light curve for object {}'.format(var_train_id))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Magnitude')\n",
    "plt.legend(fontsize=15)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "LSST_Train",
   "language": "python",
   "name": "lsst_train"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}