{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 2: Generating Age Probability Distributions for Stars with Gaia Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous tutorial, we've calibrated the age--action spline model using a calibration sample of red giant branch stars with independent, asteroseismic ages. \n",
    "\n",
    "Now, let's use that spline model to generate age probabilities for other stars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import zoomies\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query Gaia\n",
    "\n",
    "You will need to download the Gaia DR3 data for any stars you're interested in. You can perform a mass query at https://gea.esac.esa.int/archive/ and read in the resulting table, or you can use ``astroquery`` to download the data right from your notebook, as we'll do here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install astroquery if you haven't already!\n",
    "from astroquery.gaia import Gaia\n",
    "Gaia.MAIN_GAIA_TABLE = \"gaiadr3.gaia_source\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Query finished. [astroquery.utils.tap.core]\n"
     ]
    }
   ],
   "source": [
    "# Do your Gaia query or input a table of Gaia data\n",
    "\n",
    "import astropy.units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "coord = SkyCoord(ra=280, dec=60, unit=(u.degree, u.degree), frame='icrs')\n",
    "width = u.Quantity(0.5, u.deg)\n",
    "height = u.Quantity(0.5, u.deg)\n",
    "\n",
    "r = Gaia.query_object_async(coordinate=coord, width=width, height=height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering out stars without radial velocity\n",
    "r = r[~r['radial_velocity'].mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the Gaia data for the couple of stars we're interested in, as an astropy table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=2</i>\n",
       "<table id=\"table4829929872\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>dist</th><th>solution_id</th><th>designation</th><th>source_id</th><th>random_index</th><th>ref_epoch</th><th>ra</th><th>ra_error</th><th>dec</th><th>dec_error</th><th>parallax</th><th>parallax_error</th><th>parallax_over_error</th><th>pm</th><th>pmra</th><th>pmra_error</th><th>pmdec</th><th>pmdec_error</th><th>ra_dec_corr</th><th>ra_parallax_corr</th><th>ra_pmra_corr</th><th>ra_pmdec_corr</th><th>dec_parallax_corr</th><th>dec_pmra_corr</th><th>dec_pmdec_corr</th><th>parallax_pmra_corr</th><th>parallax_pmdec_corr</th><th>pmra_pmdec_corr</th><th>astrometric_n_obs_al</th><th>astrometric_n_obs_ac</th><th>astrometric_n_good_obs_al</th><th>astrometric_n_bad_obs_al</th><th>astrometric_gof_al</th><th>astrometric_chi2_al</th><th>astrometric_excess_noise</th><th>astrometric_excess_noise_sig</th><th>astrometric_params_solved</th><th>astrometric_primary_flag</th><th>nu_eff_used_in_astrometry</th><th>pseudocolour</th><th>pseudocolour_error</th><th>ra_pseudocolour_corr</th><th>dec_pseudocolour_corr</th><th>parallax_pseudocolour_corr</th><th>pmra_pseudocolour_corr</th><th>pmdec_pseudocolour_corr</th><th>astrometric_matched_transits</th><th>visibility_periods_used</th><th>astrometric_sigma5d_max</th><th>matched_transits</th><th>new_matched_transits</th><th>matched_transits_removed</th><th>ipd_gof_harmonic_amplitude</th><th>ipd_gof_harmonic_phase</th><th>ipd_frac_multi_peak</th><th>ipd_frac_odd_win</th><th>ruwe</th><th>scan_direction_strength_k1</th><th>scan_direction_strength_k2</th><th>scan_direction_strength_k3</th><th>scan_direction_strength_k4</th><th>scan_direction_mean_k1</th><th>scan_direction_mean_k2</th><th>scan_direction_mean_k3</th><th>scan_direction_mean_k4</th><th>duplicated_source</th><th>phot_g_n_obs</th><th>phot_g_mean_flux</th><th>phot_g_mean_flux_error</th><th>phot_g_mean_flux_over_error</th><th>phot_g_mean_mag</th><th>phot_bp_n_obs</th><th>phot_bp_mean_flux</th><th>phot_bp_mean_flux_error</th><th>phot_bp_mean_flux_over_error</th><th>phot_bp_mean_mag</th><th>phot_rp_n_obs</th><th>phot_rp_mean_flux</th><th>phot_rp_mean_flux_error</th><th>phot_rp_mean_flux_over_error</th><th>phot_rp_mean_mag</th><th>phot_bp_rp_excess_factor</th><th>phot_bp_n_contaminated_transits</th><th>phot_bp_n_blended_transits</th><th>phot_rp_n_contaminated_transits</th><th>phot_rp_n_blended_transits</th><th>phot_proc_mode</th><th>bp_rp</th><th>bp_g</th><th>g_rp</th><th>radial_velocity</th><th>radial_velocity_error</th><th>rv_method_used</th><th>rv_nb_transits</th><th>rv_nb_deblended_transits</th><th>rv_visibility_periods_used</th><th>rv_expected_sig_to_noise</th><th>rv_renormalised_gof</th><th>rv_chisq_pvalue</th><th>rv_time_duration</th><th>rv_amplitude_robust</th><th>rv_template_teff</th><th>rv_template_logg</th><th>rv_template_fe_h</th><th>rv_atm_param_origin</th><th>vbroad</th><th>vbroad_error</th><th>vbroad_nb_transits</th><th>grvs_mag</th><th>grvs_mag_error</th><th>grvs_mag_nb_transits</th><th>rvs_spec_sig_to_noise</th><th>phot_variable_flag</th><th>l</th><th>b</th><th>ecl_lon</th><th>ecl_lat</th><th>in_qso_candidates</th><th>in_galaxy_candidates</th><th>non_single_star</th><th>has_xp_continuous</th><th>has_xp_sampled</th><th>has_rvs</th><th>has_epoch_photometry</th><th>has_epoch_rv</th><th>has_mcmc_gspphot</th><th>has_mcmc_msc</th><th>in_andromeda_survey</th><th>classprob_dsc_combmod_quasar</th><th>classprob_dsc_combmod_galaxy</th><th>classprob_dsc_combmod_star</th><th>teff_gspphot</th><th>teff_gspphot_lower</th><th>teff_gspphot_upper</th><th>logg_gspphot</th><th>logg_gspphot_lower</th><th>logg_gspphot_upper</th><th>mh_gspphot</th><th>mh_gspphot_lower</th><th>mh_gspphot_upper</th><th>distance_gspphot</th><th>distance_gspphot_lower</th><th>distance_gspphot_upper</th><th>azero_gspphot</th><th>azero_gspphot_lower</th><th>azero_gspphot_upper</th><th>ag_gspphot</th><th>ag_gspphot_lower</th><th>ag_gspphot_upper</th><th>ebpminrp_gspphot</th><th>ebpminrp_gspphot_lower</th><th>ebpminrp_gspphot_upper</th><th>libname_gspphot</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th>yr</th><th>deg</th><th>mas</th><th>deg</th><th>mas</th><th>mas</th><th>mas</th><th></th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mas</th><th></th><th></th><th></th><th>1 / um</th><th>1 / um</th><th>1 / um</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mas</th><th></th><th></th><th></th><th></th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th>electron / s</th><th>electron / s</th><th></th><th>mag</th><th></th><th>electron / s</th><th>electron / s</th><th></th><th>mag</th><th></th><th>electron / s</th><th>electron / s</th><th></th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>km / s</th><th>km / s</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>d</th><th>km / s</th><th>K</th><th>log(cm.s**-2)</th><th>dex</th><th></th><th>km / s</th><th>km / s</th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>K</th><th>K</th><th>K</th><th>log(cm.s**-2)</th><th>log(cm.s**-2)</th><th>log(cm.s**-2)</th><th>dex</th><th>dex</th><th>dex</th><th>pc</th><th>pc</th><th>pc</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>int64</th><th>object</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>bool</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>bool</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>bool</th><th>int16</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>object</th></tr></thead>\n",
       "<tr><td>0.03294202574628697</td><td>1636148068921376768</td><td>Gaia DR3 2154901610573844864</td><td>2154901610573844864</td><td>241023460</td><td>2016.0</td><td>280.0656900806957</td><td>0.016397906</td><td>60.00257229273729</td><td>0.01832858</td><td>1.4395973611007848</td><td>0.017703537</td><td>81.31694</td><td>4.034647</td><td>-3.781376392246355</td><td>0.020791428</td><td>-1.4069714152136137</td><td>0.021559382</td><td>-0.13987689</td><td>-0.17148478</td><td>-0.16272342</td><td>0.06535884</td><td>0.28755578</td><td>0.00486031</td><td>-0.20837094</td><td>-0.15121393</td><td>-0.09816643</td><td>-0.008488968</td><td>313</td><td>9</td><td>312</td><td>1</td><td>1.5664704</td><td>355.76276</td><td>0.071118824</td><td>1.6831243</td><td>31</td><td>False</td><td>1.517273</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>36</td><td>26</td><td>0.03028327</td><td>39</td><td>16</td><td>0</td><td>0.0017206654</td><td>14.2530775</td><td>0</td><td>0</td><td>1.0627708</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>False</td><td>332</td><td>26596.06625197267</td><td>6.273805</td><td>4239.224</td><td>14.625323</td><td>34</td><td>13216.772791953359</td><td>14.825079</td><td>891.51447</td><td>15.035728</td><td>32</td><td>18934.12460635135</td><td>23.005768</td><td>823.0164</td><td>14.054783</td><td>1.2088591</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0.9809456</td><td>0.41040516</td><td>0.5705404</td><td>-58.9981</td><td>3.5211964</td><td>2</td><td>20</td><td>1</td><td>16</td><td>5.3412848</td><td>--</td><td>--</td><td>848.42834</td><td>--</td><td>5250.0</td><td>4.0</td><td>0.0</td><td>111</td><td>--</td><td>--</td><td>--</td><td>13.703053</td><td>0.11823523</td><td>21</td><td>--</td><td>NOT_AVAILABLE</td><td>89.61278656239459</td><td>24.619597593406176</td><td>309.19731087572416</td><td>82.05259222492064</td><td>False</td><td>False</td><td>0</td><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>1.0310119e-13</td><td>5.149859e-13</td><td>0.9998932</td><td>5379.0903</td><td>5362.152</td><td>5393.7485</td><td>4.5733</td><td>4.5648</td><td>4.5806</td><td>0.0485</td><td>0.0305</td><td>0.0648</td><td>652.7858</td><td>646.5188</td><td>660.3986</td><td>0.1392</td><td>0.1265</td><td>0.15</td><td>0.1137</td><td>0.1033</td><td>0.1226</td><td>0.0603</td><td>0.0548</td><td>0.065</td><td>MARCS</td></tr>\n",
       "<tr><td>0.034378304381720556</td><td>1636148068921376768</td><td>Gaia DR3 2154901988530967552</td><td>2154901988530967552</td><td>1404810341</td><td>2016.0</td><td>280.0194041542316</td><td>0.015531355</td><td>60.03298871744937</td><td>0.01771612</td><td>0.5369007191849855</td><td>0.017558102</td><td>30.578518</td><td>4.8870983</td><td>-4.8768787205310264</td><td>0.019893862</td><td>-0.31588292766908044</td><td>0.021310652</td><td>-0.10731452</td><td>-0.23620193</td><td>-0.11497756</td><td>0.05939605</td><td>0.29391325</td><td>-0.023874374</td><td>-0.18965203</td><td>-0.24747391</td><td>-0.037051436</td><td>0.10042425</td><td>280</td><td>0</td><td>277</td><td>3</td><td>0.33902383</td><td>288.02304</td><td>0.03911611</td><td>0.55264676</td><td>31</td><td>False</td><td>1.5930966</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>32</td><td>25</td><td>0.030482063</td><td>36</td><td>15</td><td>0</td><td>0.00040681817</td><td>32.448666</td><td>1</td><td>0</td><td>1.0133395</td><td>0.20663822</td><td>0.0930223</td><td>0.08154183</td><td>0.031603187</td><td>-136.6344</td><td>84.516</td><td>8.542825</td><td>8.555851</td><td>False</td><td>300</td><td>30990.814155148175</td><td>9.8583145</td><td>3143.622</td><td>14.459285</td><td>32</td><td>17775.656355814244</td><td>20.999525</td><td>846.47894</td><td>14.713978</td><td>33</td><td>19015.991510360494</td><td>19.759075</td><td>962.3928</td><td>14.050098</td><td>1.1871791</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.6638794</td><td>0.25469303</td><td>0.40918636</td><td>29.453352</td><td>10.561475</td><td>2</td><td>14</td><td>3</td><td>13</td><td>3.9535046</td><td>--</td><td>--</td><td>788.70197</td><td>--</td><td>6000.0</td><td>2.5</td><td>-0.75</td><td>111</td><td>--</td><td>--</td><td>--</td><td>14.051752</td><td>0.13647689</td><td>12</td><td>--</td><td>NOT_AVAILABLE</td><td>89.6401909216361</td><td>24.648574475550223</td><td>309.16325479158144</td><td>82.09051468241744</td><td>False</td><td>False</td><td>0</td><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>1.0282279e-13</td><td>5.135947e-13</td><td>0.9999208</td><td>6483.586</td><td>6460.9736</td><td>6514.4473</td><td>4.0976</td><td>4.0761</td><td>4.1173</td><td>-0.4975</td><td>-0.517</td><td>-0.479</td><td>1753.8871</td><td>1703.02</td><td>1810.7133</td><td>0.1891</td><td>0.1782</td><td>0.2041</td><td>0.1644</td><td>0.1548</td><td>0.1776</td><td>0.089</td><td>0.0838</td><td>0.0961</td><td>MARCS</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "        dist             solution_id     ... libname_gspphot\n",
       "                                         ...                \n",
       "      float64               int64        ...      object    \n",
       "-------------------- ------------------- ... ---------------\n",
       " 0.03294202574628697 1636148068921376768 ...           MARCS\n",
       "0.034378304381720556 1636148068921376768 ...           MARCS"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Jz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now need to calculate ln(Jz) (log of vertical action) for each Gaia star. The star must have a radial velocity. (Make sure you check the number of stars that have radial velocities from Gaia for the sample you're interested in.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: You must input a table with at least two rows. It doesn't work if you just input one row of Gaia data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/zoomies_test_3/lib/python3.14/site-packages/gala/potential/potential/builtin/special.py:257: GalaFutureWarning: The MilkyWayPotential2022 class will be deprecated soon. Instead, use: MilkyWayPotential(version=\"v2\") to get what is currently the MilkyWayPotential2022 class. Or, to always use the latest Milky Way model in Gala, you can call the class with no arguments MilkyWayPotential() or specify MilkyWayPotential(version=\"latest\")\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating actions with galpy...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><i>Table length=2</i>\n",
       "<table id=\"table4829929872\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>dist</th><th>solution_id</th><th>designation</th><th>source_id</th><th>random_index</th><th>ref_epoch</th><th>ra</th><th>ra_error</th><th>dec</th><th>dec_error</th><th>parallax</th><th>parallax_error</th><th>parallax_over_error</th><th>pm</th><th>pmra</th><th>pmra_error</th><th>pmdec</th><th>pmdec_error</th><th>ra_dec_corr</th><th>ra_parallax_corr</th><th>ra_pmra_corr</th><th>ra_pmdec_corr</th><th>dec_parallax_corr</th><th>dec_pmra_corr</th><th>dec_pmdec_corr</th><th>parallax_pmra_corr</th><th>parallax_pmdec_corr</th><th>pmra_pmdec_corr</th><th>astrometric_n_obs_al</th><th>astrometric_n_obs_ac</th><th>astrometric_n_good_obs_al</th><th>astrometric_n_bad_obs_al</th><th>astrometric_gof_al</th><th>astrometric_chi2_al</th><th>astrometric_excess_noise</th><th>astrometric_excess_noise_sig</th><th>astrometric_params_solved</th><th>astrometric_primary_flag</th><th>nu_eff_used_in_astrometry</th><th>pseudocolour</th><th>pseudocolour_error</th><th>ra_pseudocolour_corr</th><th>dec_pseudocolour_corr</th><th>parallax_pseudocolour_corr</th><th>pmra_pseudocolour_corr</th><th>pmdec_pseudocolour_corr</th><th>astrometric_matched_transits</th><th>visibility_periods_used</th><th>astrometric_sigma5d_max</th><th>matched_transits</th><th>new_matched_transits</th><th>matched_transits_removed</th><th>ipd_gof_harmonic_amplitude</th><th>ipd_gof_harmonic_phase</th><th>ipd_frac_multi_peak</th><th>ipd_frac_odd_win</th><th>ruwe</th><th>scan_direction_strength_k1</th><th>scan_direction_strength_k2</th><th>scan_direction_strength_k3</th><th>scan_direction_strength_k4</th><th>scan_direction_mean_k1</th><th>scan_direction_mean_k2</th><th>scan_direction_mean_k3</th><th>scan_direction_mean_k4</th><th>duplicated_source</th><th>phot_g_n_obs</th><th>phot_g_mean_flux</th><th>phot_g_mean_flux_error</th><th>phot_g_mean_flux_over_error</th><th>phot_g_mean_mag</th><th>phot_bp_n_obs</th><th>phot_bp_mean_flux</th><th>phot_bp_mean_flux_error</th><th>phot_bp_mean_flux_over_error</th><th>phot_bp_mean_mag</th><th>phot_rp_n_obs</th><th>phot_rp_mean_flux</th><th>phot_rp_mean_flux_error</th><th>phot_rp_mean_flux_over_error</th><th>phot_rp_mean_mag</th><th>phot_bp_rp_excess_factor</th><th>phot_bp_n_contaminated_transits</th><th>phot_bp_n_blended_transits</th><th>phot_rp_n_contaminated_transits</th><th>phot_rp_n_blended_transits</th><th>phot_proc_mode</th><th>bp_rp</th><th>bp_g</th><th>g_rp</th><th>radial_velocity</th><th>radial_velocity_error</th><th>rv_method_used</th><th>rv_nb_transits</th><th>rv_nb_deblended_transits</th><th>rv_visibility_periods_used</th><th>rv_expected_sig_to_noise</th><th>rv_renormalised_gof</th><th>rv_chisq_pvalue</th><th>rv_time_duration</th><th>rv_amplitude_robust</th><th>rv_template_teff</th><th>rv_template_logg</th><th>rv_template_fe_h</th><th>rv_atm_param_origin</th><th>vbroad</th><th>vbroad_error</th><th>vbroad_nb_transits</th><th>grvs_mag</th><th>grvs_mag_error</th><th>grvs_mag_nb_transits</th><th>rvs_spec_sig_to_noise</th><th>phot_variable_flag</th><th>l</th><th>b</th><th>ecl_lon</th><th>ecl_lat</th><th>in_qso_candidates</th><th>in_galaxy_candidates</th><th>non_single_star</th><th>has_xp_continuous</th><th>has_xp_sampled</th><th>has_rvs</th><th>has_epoch_photometry</th><th>has_epoch_rv</th><th>has_mcmc_gspphot</th><th>has_mcmc_msc</th><th>in_andromeda_survey</th><th>classprob_dsc_combmod_quasar</th><th>classprob_dsc_combmod_galaxy</th><th>classprob_dsc_combmod_star</th><th>teff_gspphot</th><th>teff_gspphot_lower</th><th>teff_gspphot_upper</th><th>logg_gspphot</th><th>logg_gspphot_lower</th><th>logg_gspphot_upper</th><th>mh_gspphot</th><th>mh_gspphot_lower</th><th>mh_gspphot_upper</th><th>distance_gspphot</th><th>distance_gspphot_lower</th><th>distance_gspphot_upper</th><th>azero_gspphot</th><th>azero_gspphot_lower</th><th>azero_gspphot_upper</th><th>ag_gspphot</th><th>ag_gspphot_lower</th><th>ag_gspphot_upper</th><th>ebpminrp_gspphot</th><th>ebpminrp_gspphot_lower</th><th>ebpminrp_gspphot_upper</th><th>libname_gspphot</th><th>Jz</th><th>Jphi</th><th>Jr</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th>yr</th><th>deg</th><th>mas</th><th>deg</th><th>mas</th><th>mas</th><th>mas</th><th></th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th>mas / yr</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mas</th><th></th><th></th><th></th><th>1 / um</th><th>1 / um</th><th>1 / um</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mas</th><th></th><th></th><th></th><th></th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th>electron / s</th><th>electron / s</th><th></th><th>mag</th><th></th><th>electron / s</th><th>electron / s</th><th></th><th>mag</th><th></th><th>electron / s</th><th>electron / s</th><th></th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>km / s</th><th>km / s</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>d</th><th>km / s</th><th>K</th><th>log(cm.s**-2)</th><th>dex</th><th></th><th>km / s</th><th>km / s</th><th></th><th>mag</th><th>mag</th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>K</th><th>K</th><th>K</th><th>log(cm.s**-2)</th><th>log(cm.s**-2)</th><th>log(cm.s**-2)</th><th>dex</th><th>dex</th><th>dex</th><th>pc</th><th>pc</th><th>pc</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>km kpc / s</th><th>km kpc / s</th><th>km kpc / s</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>int64</th><th>object</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float64</th><th>float32</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>bool</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>bool</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>int16</th><th>int16</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>float32</th><th>int16</th><th>float32</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>bool</th><th>int16</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>bool</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>object</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>0.03294202574628697</td><td>1636148068921376768</td><td>Gaia DR3 2154901610573844864</td><td>2154901610573844864</td><td>241023460</td><td>2016.0</td><td>280.0656900806957</td><td>0.016397906</td><td>60.00257229273729</td><td>0.01832858</td><td>1.4395973611007848</td><td>0.017703537</td><td>81.31694</td><td>4.034647</td><td>-3.781376392246355</td><td>0.020791428</td><td>-1.4069714152136137</td><td>0.021559382</td><td>-0.13987689</td><td>-0.17148478</td><td>-0.16272342</td><td>0.06535884</td><td>0.28755578</td><td>0.00486031</td><td>-0.20837094</td><td>-0.15121393</td><td>-0.09816643</td><td>-0.008488968</td><td>313</td><td>9</td><td>312</td><td>1</td><td>1.5664704</td><td>355.76276</td><td>0.071118824</td><td>1.6831243</td><td>31</td><td>False</td><td>1.517273</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>36</td><td>26</td><td>0.03028327</td><td>39</td><td>16</td><td>0</td><td>0.0017206654</td><td>14.2530775</td><td>0</td><td>0</td><td>1.0627708</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>False</td><td>332</td><td>26596.06625197267</td><td>6.273805</td><td>4239.224</td><td>14.625323</td><td>34</td><td>13216.772791953359</td><td>14.825079</td><td>891.51447</td><td>15.035728</td><td>32</td><td>18934.12460635135</td><td>23.005768</td><td>823.0164</td><td>14.054783</td><td>1.2088591</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0.9809456</td><td>0.41040516</td><td>0.5705404</td><td>-58.9981</td><td>3.5211964</td><td>2</td><td>20</td><td>1</td><td>16</td><td>5.3412848</td><td>--</td><td>--</td><td>848.42834</td><td>--</td><td>5250.0</td><td>4.0</td><td>0.0</td><td>111</td><td>--</td><td>--</td><td>--</td><td>13.703053</td><td>0.11823523</td><td>21</td><td>--</td><td>NOT_AVAILABLE</td><td>89.61278656239459</td><td>24.619597593406176</td><td>309.19731087572416</td><td>82.05259222492064</td><td>False</td><td>False</td><td>0</td><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>1.0310119e-13</td><td>5.149859e-13</td><td>0.9998932</td><td>5379.0903</td><td>5362.152</td><td>5393.7485</td><td>4.5733</td><td>4.5648</td><td>4.5806</td><td>0.0485</td><td>0.0305</td><td>0.0648</td><td>652.7858</td><td>646.5188</td><td>660.3986</td><td>0.1392</td><td>0.1265</td><td>0.15</td><td>0.1137</td><td>0.1033</td><td>0.1226</td><td>0.0603</td><td>0.0548</td><td>0.065</td><td>MARCS</td><td>3.530247057416689</td><td>-1627.3115601530285</td><td>24.777721413012102</td></tr>\n",
       "<tr><td>0.034378304381720556</td><td>1636148068921376768</td><td>Gaia DR3 2154901988530967552</td><td>2154901988530967552</td><td>1404810341</td><td>2016.0</td><td>280.0194041542316</td><td>0.015531355</td><td>60.03298871744937</td><td>0.01771612</td><td>0.5369007191849855</td><td>0.017558102</td><td>30.578518</td><td>4.8870983</td><td>-4.8768787205310264</td><td>0.019893862</td><td>-0.31588292766908044</td><td>0.021310652</td><td>-0.10731452</td><td>-0.23620193</td><td>-0.11497756</td><td>0.05939605</td><td>0.29391325</td><td>-0.023874374</td><td>-0.18965203</td><td>-0.24747391</td><td>-0.037051436</td><td>0.10042425</td><td>280</td><td>0</td><td>277</td><td>3</td><td>0.33902383</td><td>288.02304</td><td>0.03911611</td><td>0.55264676</td><td>31</td><td>False</td><td>1.5930966</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>32</td><td>25</td><td>0.030482063</td><td>36</td><td>15</td><td>0</td><td>0.00040681817</td><td>32.448666</td><td>1</td><td>0</td><td>1.0133395</td><td>0.20663822</td><td>0.0930223</td><td>0.08154183</td><td>0.031603187</td><td>-136.6344</td><td>84.516</td><td>8.542825</td><td>8.555851</td><td>False</td><td>300</td><td>30990.814155148175</td><td>9.8583145</td><td>3143.622</td><td>14.459285</td><td>32</td><td>17775.656355814244</td><td>20.999525</td><td>846.47894</td><td>14.713978</td><td>33</td><td>19015.991510360494</td><td>19.759075</td><td>962.3928</td><td>14.050098</td><td>1.1871791</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.6638794</td><td>0.25469303</td><td>0.40918636</td><td>29.453352</td><td>10.561475</td><td>2</td><td>14</td><td>3</td><td>13</td><td>3.9535046</td><td>--</td><td>--</td><td>788.70197</td><td>--</td><td>6000.0</td><td>2.5</td><td>-0.75</td><td>111</td><td>--</td><td>--</td><td>--</td><td>14.051752</td><td>0.13647689</td><td>12</td><td>--</td><td>NOT_AVAILABLE</td><td>89.6401909216361</td><td>24.648574475550223</td><td>309.16325479158144</td><td>82.09051468241744</td><td>False</td><td>False</td><td>0</td><td>True</td><td>True</td><td>False</td><td>False</td><td>False</td><td>True</td><td>True</td><td>False</td><td>1.0282279e-13</td><td>5.135947e-13</td><td>0.9999208</td><td>6483.586</td><td>6460.9736</td><td>6514.4473</td><td>4.0976</td><td>4.0761</td><td>4.1173</td><td>-0.4975</td><td>-0.517</td><td>-0.479</td><td>1753.8871</td><td>1703.02</td><td>1810.7133</td><td>0.1891</td><td>0.1782</td><td>0.2041</td><td>0.1644</td><td>0.1548</td><td>0.1776</td><td>0.089</td><td>0.0838</td><td>0.0961</td><td>MARCS</td><td>49.70225373350188</td><td>-2209.7850153513127</td><td>83.49405970242807</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "        dist             solution_id     ...         Jr        \n",
       "                                         ...     km kpc / s    \n",
       "      float64               int64        ...      float64      \n",
       "-------------------- ------------------- ... ------------------\n",
       " 0.03294202574628697 1636148068921376768 ... 24.777721413012102\n",
       "0.034378304381720556 1636148068921376768 ...  83.49405970242807"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function returns a table with three new \n",
    "# columns for the action angles at the end: Jz, Jphi, and Jtheta\n",
    "\n",
    "# This line may take a while for very large tables.\n",
    "\n",
    "r_actions = zoomies.calc_jz(r)\n",
    "r_actions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age-Jz Relation Calibrated on RGB ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We previously calibrated an age--vertical action spline model in Tutorial 1, and saved it in the ``\"RGB_spline_model\"`` directory. Let's read that model back in here.\n",
    "\n",
    "Alternatively, you can download the folder from Zenodo: 10.5281/zenodo.10048927"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "spline_direct = \"../RGB_spline_model/\"\n",
    "\n",
    "# Reading the pre-calibrated spline model back in\n",
    "RGB_spline = zoomies.read(directory=spline_direct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``KinematicAgeSpline.evaluate_ages()`` function evaluates dynamical ages for the ln(Jz) values in our table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1515.01it/s]\n"
     ]
    }
   ],
   "source": [
    "# this function evaluates age predictions for a set of ln(Jz) values.\n",
    "\n",
    "eval_grid, starRGB_eval_pdf = RGB_spline.evaluate_ages(np.log(r_actions['Jz']), eval_grid=np.linspace(0, 14, 1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the age probabiltiy distribution for the first star in our Gaia table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'lnJz = 1.2613678564329032')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yyWBlefzFixdnuEZWoB4+fLihVq1a6v0h9STX9+vX75HlHIzvxdy8R4hyw0b+k308IqLHJX9VS1eAjKnIzYJuRERkOpwFRvSUyGaU4t8GlBIRkelxDBBRPpPBq9u3b1cLHMq4BOM+UUREZD7YBUaUz2S6ugx4lVV/ZXBs5hlLRESkPQYgIiIi0h2OASIiIiLdYQAiIiIi3eEg6GxWN5Ul6GWZftmhmYiIiMyfrOwjq4vLVjz/ttI8A1AWJPzI6qRERERkeWQbGVmRPCcMQFkwbtAoFSg7PBMREZH5i4mJUQ0YudlomQEoC8ZuLwk/DEBERESWJTfDVzgImoiIiHSHAYiIiIh0hwGIiIiIdIcBiIiIiHSHAYiIiIh0hwGIiIiIdIcBiIiIiHSHAYiIiIh0hwGIiIiIdIcBiIiIiHSHAYiIiIh0hwGIiIiIdIcByISSUlKx62wUDAaDKR+WiIiIMmEAMqFVB6/g9Tn70Gn6Hmw9fZNBiIiISCMMQCYUfT8Jzg62CA2/iz7z9qPjtN3YcuoGgxAREZGJ2RjYH/OImJgYuLu7Izo6Gm5ubvla4ZH3EjB75wUsCrqM+0kp6lzNUu4Y0boSnvUrDhsbm3x9PCIiIr2IycPnNwPQE1bg44qKTcDsHRewMF0QCixXFGOe90P9skWeymMSERFZsxgGINNV4JO6FZuAWTsvYP7uS0hITlXn2lb3wui2fqhYvOBTfWwiIiJrwgBkwgrML9ej72PyxrNYGRKOVANgZ2uDLv4+GPlcZRQr5GSSMhAREVkyBiATVmB+O3PjHr7ccBqbTt5Qx4Wc7DG8dSX0alwODnYcs05ERJQdBiALDkBG+y/dxqfrTuDIlWh1XKGYK8Z3qI7mlYtpUh4iIiJzxwBkwgp8mlJTDapLTFqEbsUlqnPPVfPCh+2roYyHi2blIiIisvTPb/apmDFbWxt0DSiDLe8+g35Ny6txQRtP3EDr77ZjyuazSPxn0DQRERHlDafBm3ELUGZnb9zDJ7+fwK5zUeq4UvGCmNi5JuqXLap10YiIiDTHFiArVcmrEBb1C8T33erAw9URZ2/GovOMILy/+qhaZZqIiIhyh11gFkZWin65TilsHtUCXfxLq3NL9oXhuW+348+j17UuHhERkUVgALJQhV0c8eWrtbH0rQYo7+mKm/cSMGjJQQxeehC3/xkwTURERFljALJwjSt44s/hzTCkZUU1SPqPI9fR5rsd+Pt4hNZFIyIiMlsMQFbA2cEO77atgtVvN1bbZ8g+Y/0XhWDkilCODSIiIsoCA5AVqVW6MNYNbYoBzX0hm8qvOngVbb/bge1nIrUuGhERkVlhALLC1qBxL1TFLwMboZyHCyJiHqDX3GD83+8nkJD8cNd5IiIivWMAslKyNtCfw5ujV6Oy6nju7ovoOG0Pzt28p3XRiIiINMcAZMUKONrhk5drYE4vfxR1dcTJ6zF4ccou/BwcBoPBoHXxiIiI9BuApk2bhnLlysHZ2RkNGjRAcHBwttceP34cnTt3VtfLejiTJ09+5JoJEyYgICAAhQoVQvHixdGxY0ecPn0aetaqqhc2DG+GphU98SApFeNWHcXbSw4iOp6LJxIRkT5pGoCWL1+OkSNHYvz48Th48CBq166Ntm3b4ubNm1leHx8fD19fX0ycOBHe3t5ZXrN9+3YMHjwYe/fuxcaNG5GUlIQ2bdogLi4OelbczRkL+wZi3PN+sLe1wZ/HIvD89zsQcvm21kUjIiLS115g0uIjrTVTp05Vx6mpqfDx8cHQoUMxduzYHH9WWoFGjBihbjmJjIxULUESjJo3b27Re4HllyNX7mL4slBcjIpTYUgGTfdt8rBVjYiIyFJZxF5giYmJCAkJQevWrf9XGFtbdRwUFJRvjyOVIIoWzX7D0ISEBFVp6W/WPl3+96FN8WKtEkhONeDTdSfUCtL3HrBLjIiI9EGzABQVFYWUlBR4eXllOC/HERH5s4qxtChJC1GTJk1Qo0aNbK+TcUOSGI03aYWydgWd7DGle1188lJ1ONjZYP3RCLw0dTdORVh3+CMiIjKLQdBPk4wFOnbsGJYtW5bjdePGjVMtRcZbeHg49EC6vHo1LocVAxqhpLuz6hLrOG03fgm5onXRiIiIrDMAeXp6ws7ODjdu3MhwXo6zG+CcF0OGDMG6deuwdetWlC79cNf07Dg5Oam+wvQ3PalbpgjWDWuG5pWLqVli7648jPdWH0VicqrWRSMiIrKuAOTo6Ij69etj8+bNGbqs5LhRo0aPfb8yplvCz+rVq7FlyxaUL18+n0ps3WSdoPm9AzDyucpqG42l+8LQ46e9iLyXoHXRiIiIrKsLTKbAz549GwsWLMDJkycxaNAgNV29T58+6vs9e/ZU3VPpB06Hhoaqm3x99epV9fW5c+cydHstXrwYS5cuVWsByXgiud2/f1+T52hJbG1tMKxVJcztFYBCzvbYf+kOXpq6S80aIyIisiaaToMXMgX+q6++UiGlTp06+OGHH9T0ePHMM8+o6e7z589Xx5cuXcqyRadFixbYtm2b+jq7qdzz5s1D7969c1Uma58GnxvnI2Px1sIDuBAZByd7W0zqXAsd65bSulhERET58vmteQAyRwxA/9TDgySMWBaKLaceLkzZv7kvxrTzg50t1wsiIiLzYxHrAJH5c3N2wOye/hjcsoI6nrXjAvrM36+CERERkSVjAKIcSWvP6LZ+mPafeijgYIcdZyLx6ow9CL8dz5ojIiKLxQBEudK+VgmsHNgIXm5OOHMjFp2m78ahsDusPSIiskgMQJRrNUq5Y83gJqhWwg1RsYnoNmsv/jhynTVIREQWhwGI8qSEewHVEtS6anEkJKeqPcSmbT2n1l8iIiKyFAxAlGeuTvb48Q1/9G3ycEmCr/46jf/+coQrRxMRkcVgAKLHHhz9UYdq+PTl6pBZ8StDrqD3vGDuKE9ERBaBAYieyBuNymFu7wC4Otphz/lb6PLjXtyMecBaJSIis8YARE/smSrFsXxAI3gWdMLJ6zHoNH2PWkmaiIjIXDEAUb7NEFs1qDHKe7ri6t376DxjD0Iuc5o8ERGZJwYgyjdlPFzwy8BGqO1TGHfjk9Ru8ptO3GANExGR2WEAonzlUdAJP7/VAC2rFMODpFT0X3QAy4LDWMtERGRWGIAo37k42qs9xLr4l0aqARi76iimbD7LtYKIiMhsMADRU2FvZ4tJnWth6LMV1fE3G8/gi/UnGYKIiMgsMADRU2NjY4NRbargwxerqePZOy9i7K9HkSLNQkRERBpiAKKnrl/T8vjy1VpqwcTlB8Ix7OdDXDWaiIg0xQBEJtHF3wfTe9SDg50N/jh6HW8tPID7iSmsfSIi0gQDEJlMuxolMKdXAAo42GH7mUj0nLsPMQ+S+AoQEZHJMQCRSTWvXAyL3wxEIWd77L90B91n7UVUbAJfBSIiMikGIDK5+mWLYln/hvAs6Ijj12LQ9ccg7h9GREQmxQBEmqhe0h0rBjRCCXdnnI+MQ9dZe3E9+j5fDSIiMgkGINKMb7GCKgSVKlwAF6Pi0PXHvWofMSIioqeNAYg05VPUBcsHNESZoi4Iux2vusPCb8fzVSEioqeKAYg0V7rIwxAkO8lfuXNfhaBLUXFaF4uIiKwYAxCZhRLuBdTA6ArFXHEt+gG6zgrC+chYrYtFRERWigGIzIaXmzOW9W+Eyl4FcSMmQY0JOnvjntbFIiIiK8QARGalWCEn/PxWQ/h5F1LrA3WbtRenIxiCiIgofzEAkdnxKPgwBFUv6YZbcYn4z2y2BBERUf5iACKzVMTVEUvf/F8I6j57H8cEERFRvmEAIrPl7uKAxf0apHWHSUsQZ4cREVF+YAAis28JWvJmg7SB0d1n70XYLa4TRERET4YBiCxiTNCSNx9Okb8e/UCFIC6WSERET4IBiCxqdpivp6vaLuM/P+3FNW6bQUREj4kBiCxGcTdnLH2rIcp6uCD89n3VEhQR/UDrYhERkQViACKL4u3urFqCfIoWwOVb8Wpg9M0YhiAiIsobBiCyOCULF1AhSHaRvxAVh9fn7MOduESti0VERBaEAYgsdgNVCUFebk44cyMWveYF496DJK2LRUREFoIBiCxWGQ8XtU5QUVdHHLkSjX4LDuB+YorWxSIiIgvAAEQWrZJXISzsG4hCTvYIvngbAxeHIDE5VetiERGRmWMAIotXo5Q75vUJQAEHO2w/E4kRyw8hOYUhiIiIsscARFbBv1xRzOpZH452tlh/NAJjVx1FaqpB62IREZGZYgAiq9GsUjH80L0u7Gxt8EvIFfzfuhMwGBiCiIjoUQxAZFXa1fDG16/VUl/P33MJ3248o3WRiIjIDDEAkdXpVLc0Pu1YQ309Zcs5zNx+XusiERGRmWEAIqv0RsOyGNPOT3098c9TWBYcpnWRiIjIjDAAkdUa9EwFdRPvrT6KDccitC4SERGZCQYgsmr/bVsFXf19IBPChi07hKDzt7QuEhERmQEGILJqNjY2+LxTDbSp5qUWSHxr4QEcuxqtdbGIiEhjDEBk9eztbNX0+AbliyI2IRm95wXjUlSc1sUiIiINMQCRLjg72GF2L39UK+GGqNhEvDF3H27GPNC6WEREpBEGININN2cHLOgbiLIeLgi/fR895wYj+j53kCci0iMGINKVYoWcsKhvA/X/UxH38NaCA3iQxB3kiYj0hgGIdKeMhwsW9PlnB/lLtzFkKTdPJSLSGwYg0qVqJd3wUy9/ONrbYtPJGxi36ij3DSMi0hEGINKtBr4emNq9LmxtgJUhV9SK0UREpA8MQKRrbap7Y2Lnh5un/rjjAn7aeUHrIhERkQkwAJHudfH3wX/bVVH18NkfJ/Fb6FXd1wkRkbVjACKSfcNaVEDvxuVUXby78jB2nY1ivRARWTEGIKJ/tsz46MVqaF+rBJJSDBiwiFtmEBFZM80D0LRp01CuXDk4OzujQYMGCA4Ozvba48ePo3Pnzup6+cCaPHnyE98nkZGtrQ2+7VIbjXw9EJeYgt7z9iPsVjwriIjICmkagJYvX46RI0di/PjxOHjwIGrXro22bdvi5s2bWV4fHx8PX19fTJw4Ed7e3vlyn0TpOdnb4cee9VFVbZmRgJ5z96n/ExGRdbExGAwGrR5cWmcCAgIwdepUdZyamgofHx8MHToUY8eOzfFnpYVnxIgR6pZf92kUExMDd3d3REdHw83N7bGfH1ku2SfslRl7cOXOfdQq7Y6f32oIVyd7rYtFRET59PmtWQtQYmIiQkJC0Lp16/8VxtZWHQcFBZn0PhMSElSlpb+RvhV3c8bCvoEo6uqII1eiMWjJQSQmp2pdLCIiyieaBaCoqCikpKTAy8srw3k5joiIMOl9TpgwQSVG401ajIh8ixXEnF7+KOBghx1nIjHm1yNITdWswZSIiKxpELQ5GDdunGouM97Cw8O1LhKZibplimD66/VgZ2uD1YeuYtJfXC2aiMgaaBaAPD09YWdnhxs3bmQ4L8fZDXB+Wvfp5OSk+grT34iMWlYpjknG1aK3X8CcXRdZOUREFk6zAOTo6Ij69etj8+bNaedkwLIcN2rUyGzuk0i8Wr902mrRn647gbWHr7FiiIgsmKbTWmS6eq9eveDv74/AwEC1rk9cXBz69Omjvt+zZ0+UKlVKjdExDnI+ceJE2tdXr15FaGgoChYsiIoVK+bqPomeZLXomzEJmL/nEkatCIWHqyOaVPRkhRIRWSBNA1DXrl0RGRmJjz76SA1SrlOnDjZs2JA2iDksLEzN4jK6du0a6tatm3b89ddfq1uLFi2wbdu2XN0n0ZOuFh0Zm4A/jlzHgEUhWNa/IWqUcmelEhFZGE3XATJXXAeIcpKQnIJec4Ox98JteBZ0wqpBjVHGw4WVRkSkMYtYB4jIkleLntXTn6tFExFZMAYgosfg5uyABX0CUKpwAVy6FY9+8/cjLiGZdUlEZCEYgIieZLXofoEo4uKAw/+sFp2UwtWiiYgsAQMQ0ROoUKwg5vYO+N9q0b8cAYfVERGZPwYgovxYLbrHw9WiV8lq0RtOs06JiMwcAxBRPmjpVxwTX6mpvp65/TzmcrVoIiKzxgBElE9e8/fB6LYPV4v+P64WTURk1hiAiPLR289UQO/G5dTXslr07nNRrF8iIjPEAESUz6tFf/hiNbSvWQJJKQa1WvSxq9GsYyIiM8MARJTPZDD0t11ro5GvB2ITktF73n6E3YpnPRMRmREGIKKntFr0jz3rc7VoIiIzxQBE9JRXiy5d5OFq0X3m7VctQkREpD0GIKKnvVp030AUdXXE0avRGLQ4BInJXC2aiEhrDEBET5lvsYKY1zsALo522Hk2CqN/OYzUVAPrnYhIQwxARCZQ26cwZrxeH/a2Nvgt9Bq+WH+S9U5EpCEGICITaVG5GL56rZb6+qddFzFrx3nWPRGRRhiAiEyoU93SeO8FP/X1F+tPYdXBK6x/IiINMAARmVj/5hXwZtPy6uv//nIE207f5GtARGRiDEBEGnjvharoWKckklMNeHvJQRwOv8vXgYjIhBiAiDRga2uDL1+tjWaVPBGfmII+8/fjQmQsXwsiIhNhACLSiKO9rZoZVrOUO27HJaLn3GDcjHnA14OIyAQYgIg0VNDJHvP6BKCchwuu3LmPXvP2I+ZBEl8TIqKnjAGISGOeBZ2wsG8D9f+T12PQf+EBJCSnaF0sIiKrxgBEZAbKeLhgfp8A1SK098JtjFx+GClcLZqI6KlhACIyEzVKuWPWG/XhYGeDP45ex//9fhwGA7fMICJ6GhiAiMxI44qe+LZLHdjYAAuCLmP6Nq4WTUT0NDAAEZmZDrVL4qMXq6mvv/rrNFbsD9e6SEREVocBiMgM9WlSHoOeqaC+Hrf6KDafvKF1kYiIrAoDEJGZ+m/bKni1fmk1GHrw0oMIuXxH6yIREVkNBiAiM2VjY4MJr9REyyrF8CApFf0W7Me5m/e0LhYRkVVgACIyYw52tpjWox7q+BTG3fgk9JwTjOvR97UuFhGRxWMAIjJzLo72mNs7AL7FXHEt+gF6z92P6HiuFk1E9CQYgIgsQFFXRyzsGwgvNyecvnEPby08gAdJXC2aiOhxMQARWYjSRVywoG8gCjnbI/jSbQz7+RBXiyYiekwMQEQWxM/bDT/19Fc7yf994gbeX32Uq0UTET0GBiAiC9PA1wM/dKsLWxtg2f5wfPnXaa2LRERkcRiAiCxQuxreaoq8mLHtPGbt4JYZRER5wQBEZKG6BpTB2Of91NdfrD+FFQe4ZQYRUW4xABFZsIEtKmBAc1/19dhfj+Cv4xFaF4mIyCIwABFZOGkF6uJfGqkGYOjPhxB0/pbWRSIiMnsMQERWsGXGF51qok01LyQmp6o1go5djda6WEREZo0BiMgK2NvZ4ofuddHQtyhiE5LRa24wLkTGal0sIiKzxQBEZCWcHewwu6c/apRyw624RLzBfcOIiLLFAERkRQo5O2B+n0D4erri6t37avPUO3GJWheLiMjsMAARWRnPgk5Y2C8Q3m7OOHszFn3m70dcQrLWxSIiMisMQERWum/Yon6BKOzigNDwuxi4OEQNkCYioocYgIisVCWvQpjXOwAujnbYeTYKI1eEcvNUIqJ/MAARWbG6ZYrgxzfqw8HOBuuOXMdHvx3j5qlERAxARNavWaVimNy1LmxsgCX7wvDdxjNaF4mISHNsASLSgfa1SuCzjjXU1z9sOYc5uy5qXSQiIk0xABHpRI8GZTG6bRX19afrTmDFfm6eSkT6xQBEpCNvP1MB/Y2bp646gnVHrmldJCIiTTAAEels37Bxz/uhe2AZtXnqiGWh2HrqptbFIiIyOQYgIh2GIBkP9HKdkkhONag1griDPBHpDQMQkQ7Z2drg69dqo3XV4khITsWbC/arBROJiPSCAYhIpxzsbDH1P/XQuIIH4hJT1A7ypyJitC4WEZFJMAAR6ZhxB/m6ZQoj+n4SXv8pGBej4rQuFhHRU8cARKRzrk72mN87EFVLuCEqNgGv/7QP1+7e17pYRETmE4C2bNmC5GTuKk1kbdxdHNTmqb7FXHH17n0VgiLvJWhdLCIi8whAzz33HG7fvp123LBhQ1y9evWJCjBt2jSUK1cOzs7OaNCgAYKDg3O8fuXKlfDz81PX16xZE+vXr8/w/djYWAwZMgSlS5dGgQIFUK1aNcycOfOJykikB54FnbC4XwOUKlwAF6Li8MacfYiOT9K6WERE2gcgg8GQ4fj48eNISHj8vxKXL1+OkSNHYvz48Th48CBq166Ntm3b4ubNrNcl2bNnD7p3745+/frh0KFD6Nixo7odO3Ys7Rq5vw0bNmDx4sU4efIkRowYoQLR2rVrH7ucRHpRsnABLHmzAYoVcsKpiHvoNS8YsQls9SUi62NjyJxqcmBra4uIiAgUL15cHRcqVAiHDx+Gr+/DlWXzSlp8AgICMHXqVHWcmpoKHx8fDB06FGPHjn3k+q5duyIuLg7r1q3L0ApVp06dtFaeGjVqqOs+/PDDtGvq16+P559/Hp999lmuyhUTEwN3d3dER0fDzc3tsZ4bkSU7HXEPXWcF4W58Ehr5emBenwA1YJqIyJzl5fPbNq8LqMktu+O8SExMREhICFq3bv2/wtjaquOgoKAsf0bOp79eSItR+usbN26sWnuka06y3datW3HmzBm0adPmscpJpEdVvAthQZ9AuDraIejCLQxechCJyalaF4uIKN/Y5+ViCRStWrWCvf3DH4uPj0eHDh3g6OiY4Trpzvo3UVFRSElJgZeXV4bzcnzq1Kksf0Zan7K6Xs4bTZkyBf3791djgKScEqpmz56N5s2bZ1sW6cZL35UnCZJI72r7FMac3gFqfaDNp25i+LJDmNK9LuztOHmUiHQWgGSsTnovv/wyzI0EoL1796pWoLJly2LHjh0YPHgwSpYs+UjrkdGECRPwySefmLysROauoa8HZvX0x1sLDuDPYxEYueIwvutaR60kTUSk2wD0JDw9PWFnZ4cbN25kOC/H3t7eWf6MnM/p+vv37+O9997D6tWr0b59e3WuVq1aCA0Nxddff51tABo3bpwaPJ2+BUjGIhER0KJyMUzvUU/tGbb28DU42tviy861YMsQREQW7LHbsqUL68CBA2ocz61bt/L889JtJoOTN2/enHZOBkHLcaNGjbL8GTmf/nqxcePGtOuTkpLUTbq90pOgJfedHScnJzVYKv2NiP6ndTUv1f0lLT+/hFzB+2uOPTIrlIjIqgOQTH2X8TQy9kZmcQUGBqpZYc8++yxOnz6dp/uSVhcZn7NgwQI1ZX3QoEFqllefPn3U93v27KlaZ4yGDx+uprh/8803apzQxx9/rEKYTHMXElxatGiB0aNHY9u2bbh48SLmz5+PhQsXolOnTnl9qkSUzvM1S+DbLrUh8x5+Dg7DJ7+fYAgiIn10gclgYwkYxYoVw7fffqsWJJS/Ak+cOKGCTLNmzdSaPMZp8v9GpqtHRkbio48+Uvct09kl4BgHOoeFhWVozZEZXkuXLsUHH3yguroqVaqENWvWqKnvRsuWLVOhqUePHmrRRhkH9Pnnn2PgwIF5eapElIWX65RSs8FG/3IE8/dcUt1h4573e+zZoEREFrEO0JgxY7Bp0ybs3r1brcScnoy/adq0qZpuLoOKLRnXASLK2dJ9YXhv9VH19dBnK2JUmyqsMiKy3nWAZLyNhKDM4UfIthPS9fTXX3/lvcREZFH+06AMPu5QTX09Zcs5TNl8VusiERHlSZ4C0IULF1CvXr1sv+/v76+uISLr17tJebz3gp/6+puNZzBrx3mti0RE9HQC0L1793JsUpKtMWQzUiLSh/7NK2DUc5XV11+sP4X5uy9qXSQiovwfBG0MQVl1gRn73jg1lkhfhraqhITkVEzdeg4f/34CjvZ2qouMiMiqtsKoXLlyjt/nbBAi/RnVpjISU1Ixa8cFNThadsvoGsAQRERWEoBkY1EioszkDx+ZDi9T5GV6/NhVR2EDG3QJ4IrqRGQFAUimucuWErLPluzmLhujyvYYMgOMiPRNQtD4DtVUS/CCoMsYs+qIOs8QREQWPwj6iy++UAsQFixYEKVKlcL333+vNholIjKGoI9fqo5ejcpCVhiTELRifzgrh4gsOwDJlhLTp09Xa/3ICsy///47lixZkuM+W0SkLwxBRGR1AUi2pnjhhRfSjmV3dflld+3atadRNiKyUAxBRGRVY4CSk5MfmQLv4OCgdmAnIsoqBAmOCSIii58G37t3bzg5OaWde/Dggdpo1NXVNe3cqlWr8reURGTRIUg2HFzIgdFEZKkBqFevXo+ce/311/OzPERkhSHok39aghiCiMgiA9C8efOeXkmIyGoxBBGRRQ+CJiJ60hDUM90U+eX7w1ihRKQJBiAi0i4E/XoUi/Ze5itARCbHAEREmoSgPk3KqeMP1xzDnF3cRZ6ITIsBiIg0CUEfvVgNA1tUUMefrjuBGdvO85UgIpNhACIizULQmHZVMLxVJXU8acMpTN50Ri23QUT0tDEAEZGmIeid5ypjdNsq6njyprP46q/TDEFE9NQxABGR5ga3rIgP2ldVX0/fdh6f/3GSIYiInioGICIyC28288X/vfxwwcSfdl3ER78dR2oqu8OI6OlgACIis9GzUTlMfKUmbGygpse/t/ooQxARPRUMQERkVroFlsE3r9WGrQ2wbH843l15GMkpqVoXi4isDAMQEZmdV+qVxvfd6sLO1garDl3FsGWHkJjMEERE+YcBiIjMUofaJTG9Rz042tli/dEIvLXwAO4npmhdLCKyEgxARGS22lb3xpze/ijgYIftZyLRc+4+xDxI0rpYRGQFGICIyKw1q1QMi98MRCFne+y/dAfdZ+3FrdgErYtFRBaOAYiIzF79skWxrH9DeBZ0xPFrMejyYxCuR9/XulhEZMEYgIjIIlQv6Y4VAxqhpLszzkfG4dUZQbgUFad1sYjIQjEAEZHF8C1WECsHNUZ5T1dcvXsfr/0YhFMRMVoXi4gsEAMQEVmUUoULqJYgP+9CiLyXgK4/7sWhsDtaF4uILAwDEBFZnGKFnLC8fyPUK1MY0feT0OOnfdhzPkrrYhGRBWEAIiKL5O7igEX9GqBpRU/EJ6ag97z9+Ot4hNbFIiILwQBERBbL1clerRPUtrqXWil60OIQLAsO07pYRGQBGICIyKI52dth2n/qoau/D2Tz+LGrjmLqlrMwGLiTPBFljwGIiCyevZ0tJnauiSEtK6rjr/8+g4/XHudO8kSULQYgIrIKNjY2eLdtFXzcoRpsbIAFQZcxdNkhJCRz/zAiehQDEBFZld5Nyqud5B3sbPDHkevoO38/YhOStS4WEZkZBiAisjov1S6Jeb0D4epoh93nbqHbrCBEcf8wIkqHAYiIrFLTSp74uX9DeLg64tjVGLw6Yw/CbsVrXSwiMhMMQERktWqVLoxfBjVG6SIFcOlWPF6ZsQfHr0VrXSwiMgMMQERk1WTfsFWDGqutM6QbTLbO2Hk2UutiEZHGGICIyOoVd3PGioGN0NC3qBoQ3Wfefqw8EK51sYhIQwxARKQLbs4OWNA3EC/XKYnkVANG/3IEkzed4YKJRDrFAEREulo1+rsudfD2MxXU8eRNZ1UQSkpJ1bpoRGRiDEBEpCu2tjb4bzs/fN6pBmxtgF9Crqi1gu49SNK6aERkQgxARKRLPRqUxU+9/FHAwQ47z0bhtZlBiIh+oHWxiMhEGICISLee9fPC8gEN4VnQCaci7qHT9N04FRGjdbGIyAQYgIgIel8raPXbjVGhmCuuRz/AazOCsPtclNbFIqKnjAGIiHTPp6gLfh3UGIHliuJeQjJ6zQ1WY4OIyHoxABERASjs4oiF/QLxYq0Sapr8uysP4+u/TiM11cD6IbJCDEBERP9wdrDDD93qpk2Tn7r1HIb8fBD3E1NYR0RWhgGIiCiLafJfvVoLDnY2WH80Qu0mfzOGM8SIrAkDEBFRFl7z98Hifg1Q2MUBh69Eo+O03ThxjTPEiKwFAxARUTYa+HpgzdtN4FvMFddkhtjMPdh88gbri8gKMAAREeWgnKcrVg9qgiYVPRCXmII3Fx7ATzsvcA8xIgvHAERE9C/cXRwwv08gugeWgcEAfPbHSby3+hj3ECOyYAxARES54GBniy861cAH7avCxgb4OTgMfebtR/R97iFGZIkYgIiIcsnGxgZvNvPF7Df84eJoh13notBp2m6cj4xlHRJZGM0D0LRp01CuXDk4OzujQYMGCA4OzvH6lStXws/PT11fs2ZNrF+//pFrTp48iZdeegnu7u5wdXVFQEAAwsLCnuKzICI9aV3NC78MbIyS7s64EBWnZohtPX1T62IRkaUEoOXLl2PkyJEYP348Dh48iNq1a6Nt27a4eTPrXyR79uxB9+7d0a9fPxw6dAgdO3ZUt2PHjqVdc/78eTRt2lSFpG3btuHIkSP48MMPVWAiIsov1Uq64bchTeFftgjuPUhG3/n78eP28xwcTWQhbAwGGdKnDWnxkdaZqVOnquPU1FT4+Phg6NChGDt27CPXd+3aFXFxcVi3bl3auYYNG6JOnTqYOXOmOu7WrRscHBywaNGixy5XTEyMaj2Kjo6Gm5vbY98PEVm/xORUjF97DD8Hh6vjjnVKYmLnWmpVaSIyrbx8fmvWApSYmIiQkBC0bt36f4WxtVXHQUFBWf6MnE9/vZAWI+P1EqD++OMPVK5cWZ0vXry4Cllr1qzJsSwJCQmq0tLfiIhyw9FeBkfXxP+9XB12tjZYE3oNXX4MwvXo+6xAIjOmWQCKiopCSkoKvLy8MpyX44iIiCx/Rs7ndL10ncXGxmLixIlo164d/v77b3Tq1AmvvPIKtm/fnm1ZJkyYoBKj8SatUEREeRkc3bNROSzqF4giLg44ciUaHabsRsjl26xEIjOl+SDo/CQtQOLll1/GO++8o7rGpCvtxRdfTOsiy8q4ceNUc5nxFh7+sCmbiCgvGlfwxNohTeHnXQhRsQnoPmsfVuzn7xMic6RZAPL09ISdnR1u3Mi4rLwce3t7Z/kzcj6n6+U+7e3tUa1atQzXVK1aNcdZYE5OTqqvMP2NiOhx+BR1wa+DGqNtdS8kpqTiv78ewcdrj3PRRCIzo1kAcnR0RP369bF58+YMLThy3KhRoyx/Rs6nv15s3Lgx7Xq5TxlUffr06QzXnDlzBmXLln0qz4OIKDNXJ3vM6FEfI1pXUsfz91xCj5/2IfJeAiuLyEzYa/ngMgW+V69e8Pf3R2BgICZPnqxmefXp00d9v2fPnihVqpQaoyOGDx+OFi1a4JtvvkH79u2xbNkyHDhwALNmzUq7z9GjR6vZYs2bN0fLli2xYcMG/P7772pKPBGRqdja2mBE68rw83bDqBWhCL54Gy9O2YkZr9dHvTJF+EIQ6XkMkASVr7/+Gh999JEarxMaGqoCi3Ggs3RbXb9+Pe36xo0bY+nSpSrwyJpBv/zyi5rhVaNGjbRrZNCzjPf58ssv1UKJP/30E3799Ve1NhARkam1q+Gt1guqUMwVN2IS0PXHICzae5nrBRHpeR0gc8V1gIgov8UmJGP0ysP489jDWaud65XG551qcL0gIr2tA0REpCcFnewxvUc9jH3eD7Y2wK8Hr6DzjD0Ivx2vddGIdIkBiIjIhOsFDWxRAYv6NUBRV0ccvxaDDlN3YceZSL4GRCbGAEREZGJNKnri96FNUbu0O+7GJ6HXvGBM23oOqakckUBkKgxAREQaKFW4AJYPaITugT6QkZhf/XUa/ReFIDo+ia8HkQkwABERaUQ2TJ3wSi1M6lxT7Sm26eQNtJ+yE4fD7/I1IXrKGICIiDTWNaAMVg1qjDJFXXDlzn28OnMP5u++yKnyRE8RAxARkRmoUcod64Y1xfM1vJGUYsDHv5/A4KUHEfOAXWJETwMDEBGRmXBzdlBT5cd3qAYHOxusPxqBDlN24djVaK2LRmR1GICIiMxsqnyfJuWxcmBjNVD68q14vDJjD5bs4+rRRPmJAYiIyAzV8SmMP4Y1ReuqxZGYnIr3Vx/DiOWhiEtI1rpoRFaBAYiIyEwVdnHE7J7+eO8FP9jZ2uC30Gtq4cQT12K0LhqRxWMAIiIy8y6x/s0rYHn/hvB2c8aFyDh0nLabs8SInhADEBGRBfAvVxTrhzd72CWWkqpmib254ABuxyVqXTQii8QARERkIWT/MOkS++Sl6mrhxM2nbqLd5B3Ycy5K66IRWRwGICIiC+sS69W4HNa83QQVirni5r0E9JizD19uOIWklFSti0dkMRiAiIgsULWSbmpDVeNeYtO3ncdrM4MQfjte66IRWQQGICIiC+XiaK/2Epv2n3oo5GyP0PC7eOH7nVh7+JrWRSMyewxAREQWrn2tEvhzeDPUL1sE9xKSMeznQxi14jDucRsNomwxABERWYHSRVzUVPlhz1aEjQ3w68EraDd5J4Iv3ta6aERmiQGIiMhK2NvZYmSbKlgxoBFKFymAq3fvo+usIEz88xQSklO0Lh6RWWEAIiKyMgHliqousS7+pdUA6Znbz6PjtD04HXFP66IRmQ0GICIiK1TI2QFfvlobM1+vr9YPOnk9Rm2j8dPOC0hNNWhdPCLNMQAREVmxdjW8sWFEMzzr93BT1c/+OInX5+zDtbv3tS4akaYYgIiIrFzxQs6Y08sfX3SqiQIOdthz/hbaTt6B30KvwiB9ZEQ6xABERKSTFaT/06CM2k+sjk9h3HuQjOHLQjFo8UFE3kvQunhEJscARESkI+U9XfHLwEYY+Vxl2NvaYMPxCLT5brtaPJGtQaQnDEBERDqcLj+sVSWsHdIU1Uq44U58klo8ka1BpCcMQEREOt5P7LchTTCidaUMrUG/szWIdIABiIhIxxzsbDGideUMrUFD/2kNiorl2CCyXgxARESkWoPWDM7YGvTct2wNIuvFAERERIqj/cPWIOkWq5quNWjAohBERD9gLZFVYQAiIqIMqpd0x2+Dm2B4q4etQX+fuKFag5bsu8xVpMlqMAAREVGWrUHvPFcZ64Y1RW1ZNyghGe+vPoZus/bi3M1Y1hhZPAYgIiLKlp+3G1YNaozxHarBxdEOwZdu44Xvd+KHzWfV1hpElooBiIiIcmRna4M+Tcrj73eao2WVYkhMScW3G8/gxSk7cTDsDmuPLBIDEBER5UrpIi6Y2zsA33erAw9XR5y5EYvOM/bg47XHEZuQzFoki8IAREREedpT7OU6pbBpZAu8Wr80ZC/V+XsuqUHSG45FcDsNshgMQERElGdFXB3x9Wu1sbhfA5Qp6oLr0Q8wcHEI+s7fj7Bb8axRMnsMQERE9NiaVvLEXyOaY+izFeFgZ4OtpyPx3HfbMWXzWSQkp7BmyWwxABER0RMp4GiHUW2qYMOI5mhS0QMJyan4ZuMZtJu8E7vORrF2ySwxABERUb6oUKyg6hL7oXtdFCvkhItRcXh9zj4MWXoQN2K4kjSZFwYgIiLK10HSL9Uuic2jWqB343KwtQHWHbmOVt9sx9xdF5GcwrWDyDzYGAwyhp/Si4mJgbu7O6Kjo+Hm5sbKISJ6TMeuRuODNccQGn5XHVfxKoTxL1VD4wqerFPS9PObAegJK5CIiHKWmmrA8gPhmLThFO7GJ6lzz9fwxnsvVIVPURdWH+UbBiATViAREeXO3fhEtYL04r2XkWoAnOxtMaC5LwY+UwEujvasRnpiDEAmrEAiIsqbUxEx+GTtCQRduKWOS7g7Y9wLVdGhVgk1hojocTEAPSEGICKip0uGn8rK0Z/9cRJX795X5wLLFcVHHaqhRil3Vj89FgagJ8QARERkGg+SUjB7xwVM33Ye95NSIA1A3QLKYORzldVUeqK8YAB6QgxARESmde3ufUz48xR+P3xNHbs62mFgiwp4s5mvWmiRKDcYgJ4QAxARkTb2X7qtusUO/zNt3tvNGaPaVMYr9UrDThYVIsoBA9ATYgAiItJ22vy6o9cx6c9TaeODqpVwU9PmZe8xouwwAD0hBiAiIvMYH7Qw6BKmbDmHew+S1blnqhRTQaiyVyGti0dmiAHIhBVIRERP1524RHy/+axaPyg51aC21+ga4IN3WldGcTdnVj+lYQB6QgxARETmRzZXlW6xDccj1LGzgy36NCmPgc0rwN3FQevikRlgADJhBRIRkekHSk9YfxIHwx4OlHZztseAFhXQp0k5riitczHcC8x0FUhERNospLjp5E18/ddpnL5xT52TdYOGPVsRXQPKwNHeli+LDsUwAJmuAomISDspqQasPXxV7TEWfvvhjLEyRV3UQoov1S4JW06d15UYBiDTVSAREWkvMTkVy/aH4YfN5xAVm6DO+XkXwrttqqBV1eLcY0wnYhiATFeBRERkPuITkzFv9yXM3H4+bep8rdLuGNG6ElpWYRCydjF5+Pw2i07SadOmoVy5cnB2dkaDBg0QHByc4/UrV66En5+fur5mzZpYv359ttcOHDhQJf/Jkyc/hZITEZE5cXG0x+CWFbHzvy0x6JkKcHG0w5Er0eg7/wA6TtuNraduqvFDRJoHoOXLl2PkyJEYP348Dh48iNq1a6Nt27a4efNmltfv2bMH3bt3R79+/XDo0CF07NhR3Y4dO/bItatXr8bevXtRsmRJEzwTIiIyF4VdHDGmnZ8KQgNa+KKAgx0OX4lGn/n70XH6Hmw9zSCkdzYGjaOwtPgEBARg6tSp6jg1NRU+Pj4YOnQoxo4d+8j1Xbt2RVxcHNatW5d2rmHDhqhTpw5mzpyZdu7q1avqvv/66y+0b98eI0aMULfcYBcYEZF1kXFBs3ZcUCtLP0hKVefq+BRWXWMtKhfjGCErYTFdYImJiQgJCUHr1q3/VyBbW3UcFBSU5c/I+fTXC2kxSn+9hKg33ngDo0ePRvXq1f+1HAkJCarS0t+IiMh6eBZ0Ulto7Pzvs3irWXm1iGJo+F30nrcfr8zYgy2nbrBrTGc0DUBRUVFISUmBl5dXhvNyHBHxcKXPzOT8v10/adIk2NvbY9iwYbkqx4QJE1RiNN6kBYqIiKyPrBX0fvtqKgi92bQ8nOxtcSjsrhoj9Pz3O/Fb6FUkpzxsISLrpvkYoPwmLUrff/895s+fn+smzXHjxqnmMuMtPDz8qZeTiIi0DUIfvFgNO8e0RP/mvnB1tMOpiHsYviwUz36zXe07JpuxkvXSNAB5enrCzs4ON27cyHBejr29vbP8GTmf0/U7d+5UA6jLlCmjWoHkdvnyZYwaNUrNNMuKk5OT6itMfyMiIutXvJCz6hrbM7YVRj1XGUVdHRF2Ox4frDmGppO2YsY2mU6fpHUxydoCkKOjI+rXr4/NmzdnGL8jx40aNcryZ+R8+uvFxo0b066XsT9HjhxBaGho2k1mgcl4IBkQTURElJlspjq0VSXsGtMS4ztUQ0l3ZzVwetKGU2g8cQu+3HAKN2IesOKsiL3WBZAp8L169YK/vz8CAwPVej0yy6tPnz7q+z179kSpUqXUOB0xfPhwtGjRAt98842a3bVs2TIcOHAAs2bNUt/38PBQt/QcHBxUC1GVKlU0eIZERGRJ6wjJDvOvNyyL30KvqQUVz92MxfRt5zF75wW8WKsk+jUtjxql3LUuKll6AJJp7ZGRkfjoo4/UQGaZzr5hw4a0gc5hYWFqZphR48aNsXTpUnzwwQd47733UKlSJaxZswY1atTQ8FkQEZE1cbCzxav1S+OVuqWw8eQN/LTzAvZfuoPVh66qW2D5oioIta7qBTvuN2aRNF8HyBxxHSAiIsrsyJW7mLPrIv44ch3JqQ8/Ost6uKB343J4zd8HBZ00b1PQvRjuBfZkGICIiCg716PvY2HQZSzdF4bo+w8HSBdytkcXfx/0aFAGvsUKsvI0wgBkwgokIiL9brz668GrmLfrIi5ExaWdb1bJEz0alEXrqsVhb2d1q82YNQYgE1YgERHpW2qqAdvPRmLJ3svYrDZbfXje280Z/2lQBt0CfFDczVnrYupCDLvATFeBRERERuG34/FzcBiW7w/HrbhEdc7e1gZtq3ujR8MyaOTrwX3HniIGIBNWIBERUWYJySnYcCwCi4Iu48DlO2nnZdC0jBXqXK80vN3ZKpTfGIBMWIFEREQ5OXk9Bov2Xsba0GuITUhW52TmvOxC3zXAB8/6ecHRnmOF8gMDkAkrkIiIKLeDptcfjcCK/eEIvnQ77byHqyM61S2lwlAlr0KszCfAAPSEGICIiOhpuhAZi5UhV/BryBXcvJeQdr52aXd0rFtKrTgtG7ZS3jAAPSEGICIiMoXklFRsOx2JFQfCseXUzbQFFmV16aYVPVXLUJvqXmqLDvp3DEBPiAGIiIhMTTZfXXf4GtaEXkNo+N208y6OdmhTzUu1DEko4tpC2WMAekIMQEREpKWLUXFYc+gq1oRexeVb8WnnPQs6on3NEnihZgn4lyvKfcgyYQB6QgxARERkDmS7zkPhd/Hboav4/ch13P5nbSEhY4TaVfdWYUg2Z+WmrGAAelIMQEREZG6SUlKx62wU/jh6HX8fj0DMg4dT6o0tQ23/CUMNyhfVbTdZDFeCNl0FEhERmVpicip2n4/C+iPX8feJG2mbshqn1T9XzQutq3qhSUVPFHC0080LFMMAZLoKJCIi0rplaM/5WyoM/XUiAnfj/xeGnB1s0bRiMTxXrbhacNHap9bHMACZrgKJiIjMKQztvXALm07cwKaTN3H17v2079nYAHV8CqvWoeeqeqFi8YJWty8ZA5AJK5CIiMhcB1CfvH4Pm05KGLqBI1eiM3y/dJECajuO5pWLoXEFDxRydoClYwAyYQUSERFZgojoB9h86oZqHdp9/pYaR2QkO9bXK1tEBSK5VSvhBlvZsMzCMACZsAKJiIgsTVxCsuoq23EmEtvPROJSurWGjLPKmlUqhmaVPNGoggdKuBeAJWAAMmEFEhERWbrLt+L+CUNR2HM+CvGJKRm+X87DRQWhRhU80dC3KIoXcoY5YgAyYQUSERFZk8TkVIRcvoMdZyOx51wUjl6Nxj9blKWRAdSNfCUQeaChrweKujrCHDAAmbACiYiIrFnMgyTsv3gbQedvIejCLZy4HgNDpkBUoZgr/MsWRf2yRVC/XBH4erpqMsOMAciEFUhERKQnd+MTsffCbTWGSELR6Rv3HrlGWoTqlSkC/3JFVCiqWcodzg5Pf0FGBiATViAREZGe3YlLVF1mIWF3EHLpDg5fuYuEdDPMhKOdLaqVdFPrEBlvZT1c8r2ViAHIhBVIREREGccQHbsWrcLQgcu3VTiKiv3fJq5G3QJ8MLFzLWj1+W2fr49MREREuuZob6u6v+T2FnzVgoyXb8WrlqHQ8Ie349di4OddSNNyMgARERHRUyPdXOU8XdXt5Tql0lqJUjJPLTMxBiAiIiIyeSuR1rQvAREREZGJMQARERGR7jAAERERke4wABEREZHuMAARERGR7jAAERERke4wABEREZHuMAARERGR7jAAERERke4wABEREZHuMAARERGR7jAAERERke4wABEREZHucDf4LBgMBvX/mJgYU78eRERE9JiMn9vGz/GcMABl4d69e+r/Pj4+j/saEBERkYaf4+7u7jleY2PITUzSmdTUVFy7dg2FChWCjY1NvqdTCVbh4eFwc3OD3vD56/v1F3wP8D3A94C+3wMxT/H5S6SR8FOyZEnY2uY8yoctQFmQSitdujSeJnnR9fjGN+Lz1/frL/ge4HuA7wF9vwfcntLz/7eWHyMOgiYiIiLdYQAiIiIi3WEAMjEnJyeMHz9e/V+P+Pz1/foLvgf4HuB7QN/vASczef4cBE1ERES6wxYgIiIi0h0GICIiItIdBiAiIiLSHQYgIiIi0h0GIBOaNm0aypUrB2dnZzRo0ADBwcHQiwkTJiAgIECtrl28eHF07NgRp0+fhl5NnDhRrTI+YsQI6MXVq1fx+uuvw8PDAwUKFEDNmjVx4MAB6EVKSgo+/PBDlC9fXj3/ChUq4NNPP83VnkWWaMeOHejQoYNakVfe62vWrMnwfXneH330EUqUKKHqo3Xr1jh79iz0UgdJSUkYM2aM+nfg6uqqrunZs6fahUAv74H0Bg4cqK6ZPHkyTIUByESWL1+OkSNHqql/Bw8eRO3atdG2bVvcvHkTerB9+3YMHjwYe/fuxcaNG9U//jZt2iAuLg56s3//fvz444+oVasW9OLOnTto0qQJHBwc8Oeff+LEiRP45ptvUKRIEejFpEmTMGPGDEydOhUnT55Ux19++SWmTJkCayT/tuX3nPzhlxV57j/88ANmzpyJffv2qRAgvxMfPHgAPdRBfHy8+iyQUCz/X7Vqlfqj8KWXXoJe3gNGq1evVp8NEpRMSvYCo6cvMDDQMHjw4LTjlJQUQ8mSJQ0TJkzQZfXfvHlT/uw1bN++3aAn9+7dM1SqVMmwceNGQ4sWLQzDhw836MGYMWMMTZs2NehZ+/btDX379s1w7pVXXjH06NHDYO3k3/rq1avTjlNTUw3e3t6Gr776Ku3c3bt3DU5OToaff/7ZoIc6yEpwcLC67vLlywa9PP8rV64YSpUqZTh27JihbNmyhu+++85kZWILkAkkJiYiJCRENfGm329MjoOCgqBH0dHR6v9FixaFnkgrWPv27TO8F/Rg7dq18Pf3x2uvvaa6QOvWrYvZs2dDTxo3bozNmzfjzJkz6vjw4cPYtWsXnn/+eejNxYsXERERkeHfgezfJEMD9Po70fh7UbqBChcuDL1sPP7GG29g9OjRqF69uskfn5uhmkBUVJTq//fy8spwXo5PnToFvZE3vYx9kS6RGjVqQC+WLVummrqlC0xvLly4oLp/pBv4vffeU3UwbNgwODo6olevXtCDsWPHql2w/fz8YGdnp34nfP755+jRowf0RsKPyOp3ovF7eiNdfzImqHv37rrZIHXSpEmwt7dXvwu0wABEmrSCHDt2TP31qxfh4eEYPny4Gv8kg+D1GHqlBeiLL75Qx9ICJO8BGf+hlwC0YsUKLFmyBEuXLlV/7YaGhqo/BGTcg17qgLImYyK7dOmiBobLHwp6EBISgu+//179USitXlpgF5gJeHp6qr/4bty4keG8HHt7e0NPhgwZgnXr1mHr1q0oXbo09EL+scuA93r16qm/eOQmA8NlEKh8La0B1kxm+lSrVi3DuapVqyIsLAx6Ic380grUrVs3NfNHmv7feecdNUNSb4y/9/g78X/h5/Lly+oPJL20/uzcuVP9TixTpkza70Spg1GjRqnZ0qbAAGQC0sxfv3591f+f/i9iOW7UqBH0QP6ykfAjo/23bNmipgLrSatWrXD06FH1V7/xJi0i0v0hX0tAtmbS3Zl52QMZC1O2bFnohcz6kbF/6cnrLr8L9Eb+/UsISv87UboHZTaYXn4npg8/Mv1/06ZNaokIvXjjjTdw5MiRDL8TpTVU/lD466+/TFIGdoGZiIx9kGZu+dALDAxUax3IFME+ffpAL91e0vT/22+/qbWAjP38MvBR1gCxdvKcM493kmm/8gtPD+OgpKVDBgFLF5j8wpc1sGbNmqVueiHrociYH/mLV7rADh06hG+//RZ9+/aFNYqNjcW5c+cyDHyWDzmZ+CB1IN1/n332GSpVqqQCkUwHlw9AWSNMD3UgraKvvvqq6gKSVnFpBTb+XpTvyx/O1v4e8MgU+GSZDAnGVapUMU0BTTbfjAxTpkwxlClTxuDo6Kimxe/du1c3tSJvtaxu8+bNM+iVnqbBi99//91Qo0YNNdXZz8/PMGvWLIOexMTEqNdbfgc4OzsbfH19De+//74hISHBYI22bt2a5b/5Xr16pU2F//DDDw1eXl7qPdGqVSvD6dOnDXqpg4sXL2b7e1F+Tg/vgcxMPQ3eRv5jmqhFREREZB44BoiIiIh0hwGIiIiIdIcBiIiIiHSHAYiIiIh0hwGIiIiIdIcBiIiIiHSHAYiIiIh0hwGIiIiIdIcBiIjMTlBQkNonq3379loXhYisFFeCJiKz8+abb6JgwYKYM2eO2kRV9oiyRLK/k42NzSOboBKR9vivkojMbgPF5cuXY9CgQaoFaP78+Y9cs3btWrWJprOzM1q2bIkFCxaooHH37t20a3bt2oVmzZqpzXZ9fHwwbNgwtQFxTmRzzuLFi6vNayWEjR07FnXq1FHf27Fjh9qs0bhhpZFs6imPI6SshQsXVuWrVq0anJycEBYWlk81Q0T5iQGIiMzKihUr4Ofnp3aEfv311zF37lzZtDnDjtKyi7bsGn748GEMGDAA77//fob7OH/+PNq1a4fOnTvjyJEjKlBJIBoyZEi2j7tkyRK1W/ukSZMQEhKidqueMWNG2vebN28OX19fLFq0KO1cUlKS+rn0O7rHx8er+/jpp59w/PhxFaiIyAyZbNtVIqJcaNy4sWHy5Mnq66SkJIOnp2eG3bHHjBmjdpVPT3ZVl19nd+7cUcf9+vUz9O/fP8M1O3fuNNja2hru37+f5eM2aNDAMHjw4AznmjRpYqhdu3ba8aRJkwxVq1ZNO/71118NBQsWNMTGxqrjefPmqXKEhobytSYyc2wBIiKzIeN9goOD0b17d3Vsb2+Prl27qrFA6a8JCAjI8HOBgYEZjqVlSLqjZByR8da2bVukpqaqFqTsHjvz/WQ+7t27N86dO4e9e/eqY3mMLl26wNXVNe0aR0dH1KpV67HrgIhMw95Ej0NE9K8k6CQnJ2cY9CzdXzKWZurUqXB3d8/1OCLpGpNxP5lJ19bjku6sDh06YN68eShfvjz+/PNPbNu2LcM1MuZIxiMRkXljACIisyDBZ+HChfjmm2/Qpk2bDN+T8T4///wzBg4cqMYGrV+/PsP39+/fn+G4Xr16OHHiBCpWrJjrx5f7lfvp2bNntvcrZHC0tFCVLl0aFSpUQJMmTfLwLInIXLALjIjMwrp163Dnzh3069cPNWrUyHCTwczGbjBp2Tl16hTGjBmDM2fOqEHTxplixpYX+d6ePXvUoOfQ0FCcPXsWv/32W46DoIcOHaoeQ2aUyfUyI0wGUGduzZGuNDc3N/X9Pn36PNU6IaKnhwGIiMyChI/WrVtn2c0lAejAgQMqkEjX0y+//IJVq1apsTYyU8s4C0y6yoSc3759uwpIMkW9bt26+Oijj3JcT6hHjx4YN24c3n33XdWCJGOFZMyPTLVPT9b0kfOyxk/61iIisixcCJGILJ5MX585cybCw8Pz9X6fe+45eHt7Z5j6LqSVKjIyUq33Q0SWiWOAiMjiTJ8+Xc0E8/DwwO7du/HVV1/l2L2VG7J+j4Qo6eKSbThkzNGmTZuwcePGtGuio6Nx9OhRLF26lOGHyMIxABGRxTGO0bl9+7aa1TVq1CjVffUkZKyPDK6W1qQHDx6oQdG//vqr6pYzevnll9U0fRmMLa1DRGS52AVGREREusNB0ERERKQ7DEBERESkOwxAREREpDsMQERERKQ7DEBERESkOwxAREREpDsMQERERKQ7DEBERESkOwxAREREpDv/DyQ/tIMU5jWUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(eval_grid, starRGB_eval_pdf[0])\n",
    "\n",
    "plt.xlabel('Age gyr')\n",
    "plt.ylabel('PDF')\n",
    "plt.title('lnJz = ' + str((np.log(r_actions['Jz'][0]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here it is for the second star:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'lnJz = 3.906050278824991')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is the age prediction for the second star in the table\n",
    "\n",
    "plt.plot(eval_grid, starRGB_eval_pdf[1])\n",
    "\n",
    "plt.xlabel('Age gyr')\n",
    "plt.ylabel('PDF')\n",
    "\n",
    "plt.title('lnJz = ' + str((np.log(r_actions['Jz'][1]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}