{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age Wizard\n",
    "---\n",
    "\n",
    "Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials\n",
    "\n",
    "#  Initial imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hoki.age_utils import AgeWizard\n",
    "import hoki\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('tuto.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A systematic ageing method \n",
    "\n",
    "### Motivation\n",
    "One of the classic methods for ageing clusters is to fit isochrones to observational data in Colour-Magnitude Diagrams (CMDs)  or in Hertzsprung-Russel Diagrams (HRDs).\n",
    "\n",
    "There are a number of issues with this ubiquitous technique though:\n",
    "\n",
    "* Fitting is usually done by eye\n",
    "* They only take into account single stars\n",
    "* It cannot be used if your sample only contains a few sources\n",
    "\n",
    "**Our goal is to provide a method that answers all of the issues metioned above**\n",
    "\n",
    "### How it works\n",
    "\n",
    "By matching the observed properties of your sources to the BPASS models you can find the **most likely age** of a **location on the HRD/CMD**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"age_method.png\" alt=\"Drawing\" style=\"width: 1000px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all your sources belong to the same region, you can then combine the PDFs of your individual sources to find the age PDF of the whole cluster.\n",
    "\n",
    "All of this can be done step by step using the chain `find_coordinates()` -> `calculate_pdfs` -> `multiply_pdfs()`, or you could simply use `AgeWizard()` which handles this pipeline and facilitates loading in the models. \n",
    "\n",
    "Note: In this tutorial we will focus on `AgeWizard()`.\n",
    "\n",
    "\n",
    "### Remember to exercise caution in your interpretation\n",
    "\n",
    "This is true of all methods but it is worth repeating. This technique helps you get to the answer, but it doesn't give you the answer. \n",
    "\n",
    "The **most likely age** of the **matching location** is not necessarily the age of your source - as we'll see below Helium Stars created through binary interactions and envelope stripping are found at similar locations as WR stars but at different ages.\n",
    "\n",
    "# Ages from Hertzsprung-Russell Diagram\n",
    "\n",
    "First, we need some observational data to compare to our models. \n",
    "\n",
    "`AgeWizard()` expects a `pandas.DataFrame`. If you want to use the HRD capabilities you will need to provide a `logT` and a `logL` column:\n",
    "* logT: log10 of the stellar effective temperature\n",
    "* logL: log10 of the stellar luminosity **in units of Solar Luminosities**\n",
    "\n",
    "Additionally, your DataFrame will ideally contain a `name` column  with the name of the sources you're working on. If you don't provide one, `AgeWizard()` will make it's own sources names: source1, source2, etc...\n",
    "\n",
    "_Data source: McLeod et al. 2019, Stevance et al. in prep._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stars = pd.DataFrame.from_dict({'name':np.array(['Star1','Star2','Star3','Star4', \n",
    "                                                 'Star5','Star6','Star7','Star8','Star9',\n",
    "                                                 'Star10','Star11','Star12','Star13','Star14', \n",
    "                                                 'WR1', 'WR2']),\n",
    "                                'logL':np.array([5.0, 5.1, 4.9, 5.9, 5.0, 5.4, 4.3, 5.7, 4.5, 4.5, \n",
    "                                                 4.9, 4.5, 4.3, 4.5, 5.3, 5.3]), \n",
    "                                'logT':np.array([4.48, 4.45, 4.46, 4.47, 4.48, 4.53, 4.52, 4.52, 4.52,\n",
    "                                                 4.56, 4.46, 4.52, 4.52, 4.52, 4.9, 4.65])})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialise our `AgeWizard()`. Each instance corresponds to one model and one model only: i.e. 1 IMF and 1 metalicity.\n",
    "\n",
    "If we want to have a look at 2 different metallicities for example, we can just instanciate `AgeWizard` twice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "agewiz006 = AgeWizard(obs_df=stars, model='./data/hrs-bin-imf135_300.z006.dat')\n",
    "agewiz008 = AgeWizard(obs_df=stars, model='./data/hrs-bin-imf135_300.z008.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During instanciation, the observations and models are matched and the age PDFs are calculated. They are summarised in a `pandas.DataFrame` that you can access as follows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Star1</th>\n",
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       "      <th>Star4</th>\n",
       "      <th>Star5</th>\n",
       "      <th>Star6</th>\n",
       "      <th>Star7</th>\n",
       "      <th>Star8</th>\n",
       "      <th>Star9</th>\n",
       "      <th>Star10</th>\n",
       "      <th>Star11</th>\n",
       "      <th>Star12</th>\n",
       "      <th>Star13</th>\n",
       "      <th>Star14</th>\n",
       "      <th>WR1</th>\n",
       "      <th>WR2</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>5.594794e-11</td>\n",
       "      <td>0.021803</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021803</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>0.021803</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.107142</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036928</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.030149</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030149</td>\n",
       "      <td>0.036928</td>\n",
       "      <td>0.030149</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.004603</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046310</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.035243</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035243</td>\n",
       "      <td>0.046310</td>\n",
       "      <td>0.035243</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.006676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058174</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.052005</td>\n",
       "      <td>7.820472e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052005</td>\n",
       "      <td>0.058174</td>\n",
       "      <td>0.052005</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.012032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081692</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>1.012331e-04</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>0.081692</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>7.629112e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.063921</td>\n",
       "      <td>4.216232e-03</td>\n",
       "      <td>0.074740</td>\n",
       "      <td>9.941176e-07</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>0.074740</td>\n",
       "      <td>0.063921</td>\n",
       "      <td>0.074740</td>\n",
       "      <td>4.525980e-08</td>\n",
       "      <td>0.009378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.075321</td>\n",
       "      <td>0.011572</td>\n",
       "      <td>0.142812</td>\n",
       "      <td>1.100859e-01</td>\n",
       "      <td>0.075321</td>\n",
       "      <td>0.003387</td>\n",
       "      <td>0.078904</td>\n",
       "      <td>2.914504e-01</td>\n",
       "      <td>0.086366</td>\n",
       "      <td>1.476180e-04</td>\n",
       "      <td>0.142812</td>\n",
       "      <td>0.086366</td>\n",
       "      <td>0.078904</td>\n",
       "      <td>0.086366</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.238335</td>\n",
       "      <td>0.430026</td>\n",
       "      <td>0.233166</td>\n",
       "      <td>8.879082e-02</td>\n",
       "      <td>0.238335</td>\n",
       "      <td>0.558455</td>\n",
       "      <td>0.135883</td>\n",
       "      <td>4.335802e-01</td>\n",
       "      <td>0.093930</td>\n",
       "      <td>1.763084e-03</td>\n",
       "      <td>0.233166</td>\n",
       "      <td>0.093930</td>\n",
       "      <td>0.135883</td>\n",
       "      <td>0.093930</td>\n",
       "      <td>2.155362e-14</td>\n",
       "      <td>0.015148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.295138</td>\n",
       "      <td>0.348300</td>\n",
       "      <td>0.279178</td>\n",
       "      <td>2.892240e-02</td>\n",
       "      <td>0.295138</td>\n",
       "      <td>0.345761</td>\n",
       "      <td>0.187286</td>\n",
       "      <td>1.529185e-01</td>\n",
       "      <td>0.147844</td>\n",
       "      <td>4.403392e-03</td>\n",
       "      <td>0.279178</td>\n",
       "      <td>0.147844</td>\n",
       "      <td>0.187286</td>\n",
       "      <td>0.147844</td>\n",
       "      <td>3.207127e-01</td>\n",
       "      <td>0.023512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.324156</td>\n",
       "      <td>0.153717</td>\n",
       "      <td>0.267386</td>\n",
       "      <td>9.289685e-03</td>\n",
       "      <td>0.324156</td>\n",
       "      <td>0.044554</td>\n",
       "      <td>0.176904</td>\n",
       "      <td>8.005393e-02</td>\n",
       "      <td>0.204892</td>\n",
       "      <td>1.732683e-02</td>\n",
       "      <td>0.267386</td>\n",
       "      <td>0.204892</td>\n",
       "      <td>0.176904</td>\n",
       "      <td>0.204892</td>\n",
       "      <td>5.301331e-01</td>\n",
       "      <td>0.745544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.023544</td>\n",
       "      <td>0.025031</td>\n",
       "      <td>0.023663</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.023544</td>\n",
       "      <td>0.032551</td>\n",
       "      <td>0.072708</td>\n",
       "      <td>3.722405e-02</td>\n",
       "      <td>0.165809</td>\n",
       "      <td>1.310383e-02</td>\n",
       "      <td>0.023663</td>\n",
       "      <td>0.165809</td>\n",
       "      <td>0.072708</td>\n",
       "      <td>0.165809</td>\n",
       "      <td>2.254815e-02</td>\n",
       "      <td>0.060667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.016194</td>\n",
       "      <td>0.017432</td>\n",
       "      <td>0.015374</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>0.011823</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>5.566887e-04</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>6.358691e-03</td>\n",
       "      <td>0.015374</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>9.453766e-03</td>\n",
       "      <td>0.002344</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.017234</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>0.014033</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.017234</td>\n",
       "      <td>0.003118</td>\n",
       "      <td>0.003887</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.006177</td>\n",
       "      <td>6.590932e-02</td>\n",
       "      <td>0.014033</td>\n",
       "      <td>0.006177</td>\n",
       "      <td>0.003887</td>\n",
       "      <td>0.006177</td>\n",
       "      <td>3.787228e-06</td>\n",
       "      <td>0.001189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.008077</td>\n",
       "      <td>0.001189</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.008077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004152</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>6.874719e-01</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>0.004152</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>6.385924e-05</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.007283</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>4.532046e-02</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.007283</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>1.007283e-03</td>\n",
       "      <td>0.000000</td>\n",
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       "name     Star1     Star2     Star3         Star4     Star5     Star6  \\\n",
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       "4     0.000000  0.000000  0.000654  0.000000e+00  0.000000  0.000000   \n",
       "5     0.000000  0.000000  0.015330  7.629112e-01  0.000000  0.000000   \n",
       "6     0.075321  0.011572  0.142812  1.100859e-01  0.075321  0.003387   \n",
       "7     0.238335  0.430026  0.233166  8.879082e-02  0.238335  0.558455   \n",
       "8     0.295138  0.348300  0.279178  2.892240e-02  0.295138  0.345761   \n",
       "9     0.324156  0.153717  0.267386  9.289685e-03  0.324156  0.044554   \n",
       "10    0.023544  0.025031  0.023663  0.000000e+00  0.023544  0.032551   \n",
       "11    0.016194  0.017432  0.015374  0.000000e+00  0.016194  0.011823   \n",
       "12    0.017234  0.011523  0.014033  0.000000e+00  0.017234  0.003118   \n",
       "13    0.008077  0.001189  0.006150  0.000000e+00  0.008077  0.000000   \n",
       "14    0.000061  0.000322  0.001203  0.000000e+00  0.000061  0.000134   \n",
       "\n",
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       "2     0.046310  0.000000e+00  0.035243  0.000000e+00  0.000000  0.035243   \n",
       "3     0.058174  0.000000e+00  0.052005  7.820472e-08  0.000000  0.052005   \n",
       "4     0.081692  0.000000e+00  0.057408  1.012331e-04  0.000654  0.057408   \n",
       "5     0.063921  4.216232e-03  0.074740  9.941176e-07  0.015330  0.074740   \n",
       "6     0.078904  2.914504e-01  0.086366  1.476180e-04  0.142812  0.086366   \n",
       "7     0.135883  4.335802e-01  0.093930  1.763084e-03  0.233166  0.093930   \n",
       "8     0.187286  1.529185e-01  0.147844  4.403392e-03  0.279178  0.147844   \n",
       "9     0.176904  8.005393e-02  0.204892  1.732683e-02  0.267386  0.204892   \n",
       "10    0.072708  3.722405e-02  0.165809  1.310383e-02  0.023663  0.165809   \n",
       "11    0.004711  5.566887e-04  0.006944  6.358691e-03  0.015374  0.006944   \n",
       "12    0.003887  0.000000e+00  0.006177  6.590932e-02  0.014033  0.006177   \n",
       "13    0.004152  0.000000e+00  0.006443  6.874719e-01  0.006150  0.006443   \n",
       "14    0.007283  0.000000e+00  0.003896  4.532046e-02  0.001203  0.003896   \n",
       "\n",
       "name    Star13    Star14           WR1       WR2  \n",
       "0     0.025706  0.021803  0.000000e+00  0.107142  \n",
       "1     0.036928  0.030149  0.000000e+00  0.004603  \n",
       "2     0.046310  0.035243  0.000000e+00  0.006676  \n",
       "3     0.058174  0.052005  0.000000e+00  0.012032  \n",
       "4     0.081692  0.057408  0.000000e+00  0.003365  \n",
       "5     0.063921  0.074740  4.525980e-08  0.009378  \n",
       "6     0.078904  0.086366  0.000000e+00  0.000145  \n",
       "7     0.135883  0.093930  2.155362e-14  0.015148  \n",
       "8     0.187286  0.147844  3.207127e-01  0.023512  \n",
       "9     0.176904  0.204892  5.301331e-01  0.745544  \n",
       "10    0.072708  0.165809  2.254815e-02  0.060667  \n",
       "11    0.004711  0.006944  9.453766e-03  0.002344  \n",
       "12    0.003887  0.006177  3.787228e-06  0.001189  \n",
       "13    0.004152  0.006443  6.385924e-05  0.000039  \n",
       "14    0.007283  0.003896  1.007283e-03  0.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agewiz006.pdfs.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But those are just numbers... PDFs are meant to be plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all_pdfs(agewiz):\n",
    "    # creating the figure\n",
    "    f, ax = plt.subplots(4, 4, figsize=(15,15))\n",
    "    plt.subplots_adjust(hspace=0.4)\n",
    "    axes = ax.reshape(16)\n",
    "\n",
    "    # We plot each star an individual axis \n",
    "    for source, axis in zip(agewiz.sources, axes):\n",
    "        \n",
    "        # Plotting one PDF\n",
    "        axis.step(hoki.BPASS_TIME_BINS, agewiz.pdfs[source],where='mid')\n",
    "        axis.fill_between(hoki.BPASS_TIME_BINS, agewiz.pdfs[source], step='mid', alpha=0.3)\n",
    "        \n",
    "        # Labels and limits\n",
    "        axis.set_title(source)\n",
    "        axis.set_ylabel('Probability (%)')\n",
    "        axis.set_xlabel('log(years)')\n",
    "        axis.set_ylim([0,0.6])    \n",
    "        axis.set_xlim([6,8.5])     \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age PDFs at z=0.006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_pdfs(agewiz006)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age PDFs at z=0.008"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_pdfs(agewiz008)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Likely Ages\n",
    "\n",
    "The most likely age for **each source** is summarised by `AgeWizard.most_likely_ages`. This is returned as a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.9, 6.7, 6.8, 6.5, 6.9, 6.7, 6.8, 6.7, 6.9, 7.3, 6.8, 6.9, 6.8,\n",
       "       6.9, 6.9, 6.9])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agewiz006.most_likely_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Different metallicities will give different results. In this example we summarise our results into a new DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_likely_ages = pd.DataFrame.from_dict({'name': agewiz006.sources, \n",
    "                                           'z006': agewiz006.most_likely_ages, \n",
    "                                           'z008': agewiz008.most_likely_ages}) \n",
    "\n",
    "# Note that the attribute AgeWizard.sources is the list of names you provided, or if you didn't the ones it created"
   ]
  },
  {
   "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",
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       "    }\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>name</th>\n",
       "      <th>z006</th>\n",
       "      <th>z008</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Star1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Star2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Star3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Star4</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Star6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Star7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Star8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Star9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Star10</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Star11</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Star12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Star13</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Star14</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>WR1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>WR2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  z006  z008\n",
       "0    Star1   6.9   6.8\n",
       "1    Star2   6.7   6.7\n",
       "2    Star3   6.8   6.9\n",
       "3    Star4   6.5   6.5\n",
       "4    Star5   6.9   6.8\n",
       "5    Star6   6.7   6.7\n",
       "6    Star7   6.8   6.8\n",
       "7    Star8   6.7   6.5\n",
       "8    Star9   6.9   6.9\n",
       "9   Star10   7.3   7.3\n",
       "10  Star11   6.8   6.9\n",
       "11  Star12   6.9   6.9\n",
       "12  Star13   6.8   6.8\n",
       "13  Star14   6.9   6.9\n",
       "14     WR1   6.9   6.8\n",
       "15     WR2   6.9   6.9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_likely_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTANT\n",
    "The `most_likely_ages` (and `most_likley_age`, see below) tools **do not give you a direct answer** - they are a way to summarise the data and be used in your interpretation but **please do not use these as a black box**.\n",
    "\n",
    "Looking at the PDFs plotted above and the table of most likely ages, we can see that a few stars stand out:\n",
    "\n",
    "* Star 4 has a lower (most likely) age than the rest of the sample\n",
    "* Star 10 has a higher (most likely) age than the rest of the sample\n",
    "* The WR PDFs show peaks beyond log(age/years) - which souldn't be possible for WR stars.\n",
    "\n",
    "All of these are discussed an interpreted in details in Stevance et al. (in prep). Star 4 is an example of a star that is probably the result of a merger and rejuvination, Star 10 is more likely to be on the young tail of the distribution and not as old as the most likley age implies, and the WR stars share their HRD locations with helium stars which appear at later times, which populate the later peak. \n",
    "\n",
    "\n",
    "# Plotting Aggregate Ages\n",
    "\n",
    "If you are looking for the age of a whole population then you can ask `AgeWizard` to combine the PDFs it calcualted on initialisation. This is done with the method `AgeWizard.calcualte_sample_pdfs()`. \n",
    "\n",
    "It can be called as such and it will multiply all the PDFs, or you can provide a list of columns to ignore in the paramter `not_you`. You can also ask it to return the resulting PDF by setting `return_df` to True. If you don't, it will store the result in `AgeWizard.sample_pdf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns None\n",
    "agewiz006.calculate_sample_pdf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pdf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.018170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.030998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.042060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.052826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.057152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.085512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.152308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.196053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.206209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pdf\n",
       "0  0.018170\n",
       "1  0.025543\n",
       "2  0.030998\n",
       "3  0.042060\n",
       "4  0.052826\n",
       "5  0.057152\n",
       "6  0.085512\n",
       "7  0.152308\n",
       "8  0.196053\n",
       "9  0.206209"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agewiz006.sample_pdf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also quickly retrieve the **most likely age of the whole sample** from the PDF you just calculated using `AgeWizard.most_likely_age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.9])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agewiz006.most_likely_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>pdf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.024695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.019613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.034380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.058792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.072591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.132265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.174126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.211425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.190401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pdf\n",
       "0  0.024695\n",
       "1  0.025288\n",
       "2  0.019613\n",
       "3  0.034380\n",
       "4  0.058792\n",
       "5  0.072591\n",
       "6  0.132265\n",
       "7  0.174126\n",
       "8  0.211425\n",
       "9  0.190401"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or return directly\n",
    "agewiz008.calculate_sample_pdf(return_df=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.8])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agewiz008.most_likely_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Down below we create a quick function to plot our aggregate ages for z=0.006 and z=0.008. \n",
    "\n",
    "As an example of the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster1_sources = ['Star1', 'Star2', 'Star3', 'Star4', 'WR1']\n",
    "cluster2_sources = [item for item in agewiz006.sources if item not in cluster1_sources]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_aggregate_age(agewiz, ax):\n",
    "    \n",
    "    # Aggregate age PDF for all sources\n",
    "    cluster12 = agewiz.calculate_sample_pdf(return_df=True).pdf\n",
    "    \n",
    "    # Aggregate age PDF for cluster1\n",
    "    cluster1 = agewiz.calculate_sample_pdf(not_you=cluster2_sources, return_df=True).pdf\n",
    "    \n",
    "    # Aggregate age PDF for cluster2\n",
    "    cluster2 = agewiz.calculate_sample_pdf(not_you=cluster1_sources, return_df=True).pdf\n",
    "\n",
    "\n",
    "    \n",
    "    ax.step(hoki.BPASS_TIME_BINS, cluster1, where='mid', alpha=1, lw=3)\n",
    "    ax.fill_between(hoki.BPASS_TIME_BINS, cluster1, step='mid', alpha=0.1,  label='Cluster1')\n",
    "    \n",
    "    ax.step(hoki.BPASS_TIME_BINS, cluster2, where='mid', alpha=1, lw=3)\n",
    "    ax.fill_between(hoki.BPASS_TIME_BINS,  cluster2, step='mid', alpha=0.3,  label='Cluster2')\n",
    "    \n",
    "    ax.step(hoki.BPASS_TIME_BINS, cluster12, where='mid',alpha=0.5, c='k', lw=3)\n",
    "    ax.fill_between(hoki.BPASS_TIME_BINS,  cluster12, step='mid', alpha=0.2, label='All', color='k')\n",
    "\n",
    "\n",
    "    ax.set_ylabel('Probability')\n",
    "    ax.set_xlabel('log(ages)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6b320e16d8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(ncols=2, nrows=1, figsize=(7,5))\n",
    "\n",
    "plot_aggregate_age(agewiz006, ax[0])\n",
    "plot_aggregate_age(agewiz008, ax[1])\n",
    "\n",
    "for axis in ax:\n",
    "    axis.set_ylim([0,0.4])\n",
    "    axis.set_xlim([6,8.5])\n",
    "    \n",
    "# Cool legend\n",
    "ax[0].legend(fontsize=14, loc='center right', bbox_to_anchor=(1.9, 1.1), ncol=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probability given an age range\n",
    "\n",
    "The BPASS time bins are separated by 0.1 dex in log space (log(age/years) = 6.0, 6.1, 6.2, 6.3 etc..) and the PDFs for your individual sources will cover a range of BPASS time bins.\n",
    "\n",
    "Consequently, giving a single age may not make a lot of sense. Also the PDFs will rarely, if ever, resemble a Gaussian distribution: that means that you can't give an error bar in the \"classical\" sense. \n",
    "\n",
    "Another interesting measure is the probability that the age of your star falls into a chosen age range. \n",
    "In this case our aggregate age is most likely to be between 6.7-6.9, so let's investigate how likely it is for each individual star to fall in that range. \n",
    "\n",
    "For that we use `AgeWizard.calculate_p_given_age_range()` which returns a Series containing the probabilities corresponding to each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Star1     0.857630\n",
       "Star2     0.932043\n",
       "Star3     0.779729\n",
       "Star4     0.127003\n",
       "Star5     0.857630\n",
       "Star6     0.948770\n",
       "Star7     0.500073\n",
       "Star8     0.666553\n",
       "Star9     0.446666\n",
       "Star10    0.023493\n",
       "Star11    0.779729\n",
       "Star12    0.446666\n",
       "Star13    0.500073\n",
       "Star14    0.446666\n",
       "WR1       0.850846\n",
       "WR2       0.784204\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agewiz006.calculate_p_given_age_range([6.7, 6.9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can collate those in one single DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_ages_679 = pd.DataFrame.from_dict({'name': agewiz006.sources, \n",
    "                                     'z006': agewiz006.calculate_p_given_age_range([6.7,6.9]), \n",
    "                                     'z008': agewiz008.calculate_p_given_age_range([6.7,6.9])}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>name</th>\n",
       "      <th>z006</th>\n",
       "      <th>z008</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Star1</th>\n",
       "      <td>Star1</td>\n",
       "      <td>0.857630</td>\n",
       "      <td>0.784335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star2</th>\n",
       "      <td>Star2</td>\n",
       "      <td>0.932043</td>\n",
       "      <td>0.737951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star3</th>\n",
       "      <td>Star3</td>\n",
       "      <td>0.779729</td>\n",
       "      <td>0.659319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star4</th>\n",
       "      <td>Star4</td>\n",
       "      <td>0.127003</td>\n",
       "      <td>0.134967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star5</th>\n",
       "      <td>Star5</td>\n",
       "      <td>0.857630</td>\n",
       "      <td>0.784335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star6</th>\n",
       "      <td>Star6</td>\n",
       "      <td>0.948770</td>\n",
       "      <td>0.738479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star7</th>\n",
       "      <td>Star7</td>\n",
       "      <td>0.500073</td>\n",
       "      <td>0.469008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star8</th>\n",
       "      <td>Star8</td>\n",
       "      <td>0.666553</td>\n",
       "      <td>0.278005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star9</th>\n",
       "      <td>Star9</td>\n",
       "      <td>0.446666</td>\n",
       "      <td>0.549145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star10</th>\n",
       "      <td>Star10</td>\n",
       "      <td>0.023493</td>\n",
       "      <td>0.023109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star11</th>\n",
       "      <td>Star11</td>\n",
       "      <td>0.779729</td>\n",
       "      <td>0.659319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star12</th>\n",
       "      <td>Star12</td>\n",
       "      <td>0.446666</td>\n",
       "      <td>0.549145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star13</th>\n",
       "      <td>Star13</td>\n",
       "      <td>0.500073</td>\n",
       "      <td>0.469008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star14</th>\n",
       "      <td>Star14</td>\n",
       "      <td>0.446666</td>\n",
       "      <td>0.549145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WR1</th>\n",
       "      <td>WR1</td>\n",
       "      <td>0.850846</td>\n",
       "      <td>0.721919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WR2</th>\n",
       "      <td>WR2</td>\n",
       "      <td>0.784204</td>\n",
       "      <td>0.789553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name      z006      z008\n",
       "name                              \n",
       "Star1    Star1  0.857630  0.784335\n",
       "Star2    Star2  0.932043  0.737951\n",
       "Star3    Star3  0.779729  0.659319\n",
       "Star4    Star4  0.127003  0.134967\n",
       "Star5    Star5  0.857630  0.784335\n",
       "Star6    Star6  0.948770  0.738479\n",
       "Star7    Star7  0.500073  0.469008\n",
       "Star8    Star8  0.666553  0.278005\n",
       "Star9    Star9  0.446666  0.549145\n",
       "Star10  Star10  0.023493  0.023109\n",
       "Star11  Star11  0.779729  0.659319\n",
       "Star12  Star12  0.446666  0.549145\n",
       "Star13  Star13  0.500073  0.469008\n",
       "Star14  Star14  0.446666  0.549145\n",
       "WR1        WR1  0.850846  0.721919\n",
       "WR2        WR2  0.784204  0.789553"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_ages_679"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To learn more about how we use and interpret this statistic, you can checkout Stevance et al. in prep an the associated Jupyter Notebook (link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ages from Colour-Magnitude Diagram \n",
    "\n",
    "[TO WRITE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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