{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to conduct a simple name search\n",
    "\n",
    "After installing the clusterpluck package, this tutorial demonstrates how to search for an open cluster using just its name.\n",
    "\n",
    "First, import the module and classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import clusterpluck as cp\n",
    "from clusterpluck.gaia import Refine, Info, Plotting\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, perform the search, downloading the basic cluster data from the [SIMBAD Astronomical Database](http://simbad.u-strasbg.fr/simbad/) and the individual star data from the [Gaia archive](https://gea.esac.esa.int/archive/). The default search is processed using Gaia DR2.\n",
    "\n",
    "The name of the cluster must be in a string and must also be in a recognised format, i.e. M.. for Messier catalogue, NGC.. for New General Catalogue, etc. It is possible that lesser known catalogues can be used but they may cause an error if the format doesn't match SIMBAD's.\n",
    "\n",
    "The results will be downloaded and stored as a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stars: 992\n",
      "RA: 07 36 35 Dec: -14 29.3 Rad: 0.5\n",
      "PM_RA: -7.02 PM_Dec: 0.9592 PM_Rad: 2\n",
      "Distance range: 242 pc to 725 pc\n"
     ]
    }
   ],
   "source": [
    "cp.search_name('M47')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Messier 47 is an open cluster in Cancer. The output of the search contains the following information:\n",
    "\n",
    "- The number of stars downloaded from the Gaia database.\n",
    "- The right ascention (RA), declination (Dec) and search radius of the cluster. This is its position in the sky and it's size.\n",
    "- The proper motion RA, Dec and proper motion search radius. This is the rate of cluster's apparent movement across the sky. Cluster stars will all share approximately the same apparent movement and so will form a tight group when these data are plotted.\n",
    "- Finally, the distance range of the search. This helps filter out lots of stars that are unrelated but can also cause cluster stars to be lost. In particular any objects further than 1 kpc (1000 pc) away can suffer from this.\n",
    "\n",
    "Any or all of these can be amended by using the general `search()` method but that is for another tutorial.\n",
    "\n",
    "Now let's use the `load()` method to load the data into a Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = cp.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the contents of the dataframe using simple pandas commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 992 entries, 0 to 991\n",
      "Data columns (total 20 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   name             992 non-null    object \n",
      " 1   otypes           992 non-null    object \n",
      " 2   parallax         992 non-null    float64\n",
      " 3   parallax_error   992 non-null    float64\n",
      " 4   pmra             992 non-null    float64\n",
      " 5   pmra_error       992 non-null    float64\n",
      " 6   pmdec            992 non-null    float64\n",
      " 7   pmdec_error      992 non-null    float64\n",
      " 8   bp_rp            992 non-null    float64\n",
      " 9   phot_g_mean_mag  992 non-null    float64\n",
      " 10  ra               992 non-null    float64\n",
      " 11  dec              992 non-null    float64\n",
      " 12  distance         992 non-null    float64\n",
      " 13  distance_error   992 non-null    float64\n",
      " 14  m_v_tycho        992 non-null    float64\n",
      " 15  b_v              992 non-null    float64\n",
      " 16  abs              992 non-null    float64\n",
      " 17  t_k              992 non-null    float64\n",
      " 18  lum_s            992 non-null    float64\n",
      " 19  prob             992 non-null    float64\n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 155.1+ KB\n"
     ]
    }
   ],
   "source": [
    "t.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the `load()` method takes some time to complete. That's because it's doing a lot of work. The parallax is converted into a distance in parsecs, the gaia g magnitude and colour index is converted into a more standardized Tycho magnitude and b - v colour index. From these the approximate stellar absolute magnitude, effective temperature and luminosity is calculated.\n",
    "\n",
    "Then a simple cluster probability algorithm is run to help classify stars by giving them a percentage style 'rating' based on their proximity to the centeroids of the position, distance and proper motion of the cluster.\n",
    "\n",
    "Finally, a matching algorithm runs through all the objects and compares their RA and DEC to a list of objects in the SIMBAD catalogue. If these match the name of the object and its [object type](https://simbad.u-strasbg.fr/simbad/sim-display?data=otypes) is added to the dataframe and saved in CSV format.\n",
    "\n",
    "The dataframe is in descending g (green) magnitude order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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>otypes</th>\n",
       "      <th>parallax</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmra_error</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>pmdec_error</th>\n",
       "      <th>bp_rp</th>\n",
       "      <th>phot_g_mean_mag</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>distance</th>\n",
       "      <th>distance_error</th>\n",
       "      <th>m_v_tycho</th>\n",
       "      <th>b_v</th>\n",
       "      <th>abs</th>\n",
       "      <th>t_k</th>\n",
       "      <th>lum_s</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HD  60855</td>\n",
       "      <td>**|*|Em*|Be*|V*|IR|UV|X</td>\n",
       "      <td>2.077637</td>\n",
       "      <td>0.207615</td>\n",
       "      <td>-7.109151</td>\n",
       "      <td>0.155182</td>\n",
       "      <td>0.780030</td>\n",
       "      <td>0.133099</td>\n",
       "      <td>-0.118344</td>\n",
       "      <td>5.627999</td>\n",
       "      <td>114.016190</td>\n",
       "      <td>-14.492770</td>\n",
       "      <td>481.315959</td>\n",
       "      <td>43.727498</td>\n",
       "      <td>5.641895</td>\n",
       "      <td>-0.150834</td>\n",
       "      <td>-2.770256</td>\n",
       "      <td>11890.825947</td>\n",
       "      <td>1182.078739</td>\n",
       "      <td>79.900015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HD  61224</td>\n",
       "      <td>**|*|Em*|Be*|IR|UV</td>\n",
       "      <td>2.012069</td>\n",
       "      <td>0.106906</td>\n",
       "      <td>-7.138949</td>\n",
       "      <td>0.080708</td>\n",
       "      <td>1.145077</td>\n",
       "      <td>0.083712</td>\n",
       "      <td>0.081778</td>\n",
       "      <td>6.473912</td>\n",
       "      <td>114.411640</td>\n",
       "      <td>-14.440757</td>\n",
       "      <td>497.000887</td>\n",
       "      <td>25.074589</td>\n",
       "      <td>6.499310</td>\n",
       "      <td>-0.011235</td>\n",
       "      <td>-1.982476</td>\n",
       "      <td>9207.670048</td>\n",
       "      <td>572.182144</td>\n",
       "      <td>38.532447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HD  61017</td>\n",
       "      <td>X|**|*|IR</td>\n",
       "      <td>2.021288</td>\n",
       "      <td>0.100697</td>\n",
       "      <td>-7.310861</td>\n",
       "      <td>0.086194</td>\n",
       "      <td>0.785335</td>\n",
       "      <td>0.074871</td>\n",
       "      <td>-0.002212</td>\n",
       "      <td>6.655011</td>\n",
       "      <td>114.171852</td>\n",
       "      <td>-14.443610</td>\n",
       "      <td>494.734135</td>\n",
       "      <td>23.477293</td>\n",
       "      <td>6.673285</td>\n",
       "      <td>-0.069918</td>\n",
       "      <td>-1.798574</td>\n",
       "      <td>10152.913748</td>\n",
       "      <td>483.029546</td>\n",
       "      <td>80.101179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HD  61114</td>\n",
       "      <td>*|IR</td>\n",
       "      <td>1.743823</td>\n",
       "      <td>0.067370</td>\n",
       "      <td>-7.792587</td>\n",
       "      <td>0.054587</td>\n",
       "      <td>2.608254</td>\n",
       "      <td>0.045973</td>\n",
       "      <td>2.395475</td>\n",
       "      <td>6.861200</td>\n",
       "      <td>114.285469</td>\n",
       "      <td>-14.324596</td>\n",
       "      <td>573.452773</td>\n",
       "      <td>21.330566</td>\n",
       "      <td>8.088121</td>\n",
       "      <td>1.639623</td>\n",
       "      <td>-0.704367</td>\n",
       "      <td>2807.316771</td>\n",
       "      <td>176.315618</td>\n",
       "      <td>0.522240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HD  60998</td>\n",
       "      <td>*|UV|**|IR</td>\n",
       "      <td>1.865338</td>\n",
       "      <td>0.091669</td>\n",
       "      <td>-7.078137</td>\n",
       "      <td>0.070441</td>\n",
       "      <td>1.084258</td>\n",
       "      <td>0.063201</td>\n",
       "      <td>-0.031709</td>\n",
       "      <td>6.872384</td>\n",
       "      <td>114.150463</td>\n",
       "      <td>-14.484610</td>\n",
       "      <td>536.095954</td>\n",
       "      <td>25.111499</td>\n",
       "      <td>6.888938</td>\n",
       "      <td>-0.090495</td>\n",
       "      <td>-1.757274</td>\n",
       "      <td>10538.810106</td>\n",
       "      <td>465.000918</td>\n",
       "      <td>69.543404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2.101019</td>\n",
       "      <td>2.697285</td>\n",
       "      <td>-5.451604</td>\n",
       "      <td>1.943831</td>\n",
       "      <td>-0.055180</td>\n",
       "      <td>2.318828</td>\n",
       "      <td>1.512276</td>\n",
       "      <td>20.853565</td>\n",
       "      <td>114.108059</td>\n",
       "      <td>-14.141460</td>\n",
       "      <td>475.959477</td>\n",
       "      <td>267.552516</td>\n",
       "      <td>21.434158</td>\n",
       "      <td>1.004453</td>\n",
       "      <td>13.046308</td>\n",
       "      <td>3775.135396</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.759856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3.006464</td>\n",
       "      <td>3.076210</td>\n",
       "      <td>-5.971694</td>\n",
       "      <td>2.067749</td>\n",
       "      <td>-0.149494</td>\n",
       "      <td>2.225318</td>\n",
       "      <td>1.086000</td>\n",
       "      <td>20.883945</td>\n",
       "      <td>114.298768</td>\n",
       "      <td>-14.845272</td>\n",
       "      <td>332.616656</td>\n",
       "      <td>168.215278</td>\n",
       "      <td>21.223415</td>\n",
       "      <td>0.699234</td>\n",
       "      <td>13.613695</td>\n",
       "      <td>4545.307747</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.329157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3.919975</td>\n",
       "      <td>4.900724</td>\n",
       "      <td>-5.411766</td>\n",
       "      <td>3.079569</td>\n",
       "      <td>-0.184064</td>\n",
       "      <td>4.386642</td>\n",
       "      <td>1.416664</td>\n",
       "      <td>20.885458</td>\n",
       "      <td>114.524786</td>\n",
       "      <td>-14.436349</td>\n",
       "      <td>255.103698</td>\n",
       "      <td>141.733991</td>\n",
       "      <td>21.407289</td>\n",
       "      <td>0.935868</td>\n",
       "      <td>14.373705</td>\n",
       "      <td>3923.316770</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.007395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>4.023713</td>\n",
       "      <td>3.740082</td>\n",
       "      <td>-6.316390</td>\n",
       "      <td>3.189567</td>\n",
       "      <td>1.229143</td>\n",
       "      <td>2.255890</td>\n",
       "      <td>1.818579</td>\n",
       "      <td>20.899426</td>\n",
       "      <td>113.957239</td>\n",
       "      <td>-14.413898</td>\n",
       "      <td>248.526653</td>\n",
       "      <td>119.723675</td>\n",
       "      <td>21.684544</td>\n",
       "      <td>1.224519</td>\n",
       "      <td>14.707679</td>\n",
       "      <td>3369.758463</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.539806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1.840975</td>\n",
       "      <td>3.254297</td>\n",
       "      <td>-5.187855</td>\n",
       "      <td>2.186801</td>\n",
       "      <td>2.466972</td>\n",
       "      <td>2.374998</td>\n",
       "      <td>3.004002</td>\n",
       "      <td>20.947468</td>\n",
       "      <td>114.296388</td>\n",
       "      <td>-14.796842</td>\n",
       "      <td>543.190356</td>\n",
       "      <td>346.929973</td>\n",
       "      <td>22.697336</td>\n",
       "      <td>2.076619</td>\n",
       "      <td>14.022576</td>\n",
       "      <td>2390.005509</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.045637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>992 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name                   otypes  parallax  parallax_error      pmra  \\\n",
       "0    HD  60855  **|*|Em*|Be*|V*|IR|UV|X  2.077637        0.207615 -7.109151   \n",
       "1    HD  61224       **|*|Em*|Be*|IR|UV  2.012069        0.106906 -7.138949   \n",
       "2    HD  61017                X|**|*|IR  2.021288        0.100697 -7.310861   \n",
       "3    HD  61114                     *|IR  1.743823        0.067370 -7.792587   \n",
       "4    HD  60998               *|UV|**|IR  1.865338        0.091669 -7.078137   \n",
       "..         ...                      ...       ...             ...       ...   \n",
       "987                                      2.101019        2.697285 -5.451604   \n",
       "988                                      3.006464        3.076210 -5.971694   \n",
       "989                                      3.919975        4.900724 -5.411766   \n",
       "990                                      4.023713        3.740082 -6.316390   \n",
       "991                                      1.840975        3.254297 -5.187855   \n",
       "\n",
       "     pmra_error     pmdec  pmdec_error     bp_rp  phot_g_mean_mag          ra  \\\n",
       "0      0.155182  0.780030     0.133099 -0.118344         5.627999  114.016190   \n",
       "1      0.080708  1.145077     0.083712  0.081778         6.473912  114.411640   \n",
       "2      0.086194  0.785335     0.074871 -0.002212         6.655011  114.171852   \n",
       "3      0.054587  2.608254     0.045973  2.395475         6.861200  114.285469   \n",
       "4      0.070441  1.084258     0.063201 -0.031709         6.872384  114.150463   \n",
       "..          ...       ...          ...       ...              ...         ...   \n",
       "987    1.943831 -0.055180     2.318828  1.512276        20.853565  114.108059   \n",
       "988    2.067749 -0.149494     2.225318  1.086000        20.883945  114.298768   \n",
       "989    3.079569 -0.184064     4.386642  1.416664        20.885458  114.524786   \n",
       "990    3.189567  1.229143     2.255890  1.818579        20.899426  113.957239   \n",
       "991    2.186801  2.466972     2.374998  3.004002        20.947468  114.296388   \n",
       "\n",
       "           dec    distance  distance_error  m_v_tycho       b_v        abs  \\\n",
       "0   -14.492770  481.315959       43.727498   5.641895 -0.150834  -2.770256   \n",
       "1   -14.440757  497.000887       25.074589   6.499310 -0.011235  -1.982476   \n",
       "2   -14.443610  494.734135       23.477293   6.673285 -0.069918  -1.798574   \n",
       "3   -14.324596  573.452773       21.330566   8.088121  1.639623  -0.704367   \n",
       "4   -14.484610  536.095954       25.111499   6.888938 -0.090495  -1.757274   \n",
       "..         ...         ...             ...        ...       ...        ...   \n",
       "987 -14.141460  475.959477      267.552516  21.434158  1.004453  13.046308   \n",
       "988 -14.845272  332.616656      168.215278  21.223415  0.699234  13.613695   \n",
       "989 -14.436349  255.103698      141.733991  21.407289  0.935868  14.373705   \n",
       "990 -14.413898  248.526653      119.723675  21.684544  1.224519  14.707679   \n",
       "991 -14.796842  543.190356      346.929973  22.697336  2.076619  14.022576   \n",
       "\n",
       "              t_k        lum_s       prob  \n",
       "0    11890.825947  1182.078739  79.900015  \n",
       "1     9207.670048   572.182144  38.532447  \n",
       "2    10152.913748   483.029546  80.101179  \n",
       "3     2807.316771   176.315618   0.522240  \n",
       "4    10538.810106   465.000918  69.543404  \n",
       "..            ...          ...        ...  \n",
       "987   3775.135396     0.000557   0.759856  \n",
       "988   4545.307747     0.000330   0.329157  \n",
       "989   3923.316770     0.000164   0.007395  \n",
       "990   3369.758463     0.000121   0.539806  \n",
       "991   2390.005509     0.000227   0.045637  \n",
       "\n",
       "[992 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the data are loaded to a variable we can check to see if we have a cluster. Using the `Refine` class, let's see how the proper motion plot, `pm_plot()`, looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Refine.pm_plot(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the cluster's stars are forming a group right in the middle of the plot. That means SIMBAD has given us good proper motion data and the default proper motion radius is acceptable. Any closer and we would lose relevent star data. Further away and there would be mnore chance of unrelated stars being included.\n",
    "\n",
    "Now let's look at a plot of the cluster as a star map, `map()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Refine.map(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows a star map of the search with the star size proportional to their magnitude. It can help show us if our search radius is too wide or narrow. This looks pretty good as the cluster appears fully contained.\n",
    "\n",
    "Now let's see if the distance filter has correct values using the `d_hist()`. This produces a simple histogram of the stellar distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Afd36YWBPkguTXAnsBB5c06LHoKpurartVbWDwdPPL1TV+5iyfkhyUZJXPb8O/DrwKFPWD1X1beDpJK/tmnYBjzFl/TDkJn46JQOT7IdJv7K8Ub6ANwAPA19j8Ev8p137zwFHgRPdcsvQOR9i8Cr4E8C7J/1vGEOfvJ2fvltmqvqBwVzzV7uvY8CHprEfun/XG4G57nfjH4DNU9oPrwC+C/zsUNvE+sHbD0hSg5yWkaQGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQf8He91h2StRCbsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Refine.d_hist(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a very clear, tall peak in the middle of our graph that tells us the cluster stars are clearly outnumbering the unrelated stars. We can also see roughly how far away the cluster is in parsecs just by looking. The distance filter doesn't need refining either. So that is all the parameters involved with the search.\n",
    "\n",
    "However let's have a look at two of the features we can draw from this data; a colour magnitude diagram and a more precise measurement of the distance.\n",
    "\n",
    "Using the `Plotting` class, we can call the `cmd()` instance which uses values of g magnitude and the gaia bp-rp colour index..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plotting.cmd(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and we have a beautiful CMD plot with the classic *main sequence* of stars running from top left to bottom right. These stars are in the middle of their lives, burning Helium in their cores in a relatively stable way just like our sun. The stars above the main sequence are mainly multiple star systems that have a slightly higher luminosity.\n",
    "\n",
    "Other features are the brightest stars at the top which appear to just be 'curling' upwards. This is called the *main sequence turn off*. The stars here are running low on core Helium and starting to evolve into *red giants*. They're not quite at that point but the position of the turn off is a major method of ageing clusters. At the other end are the red and white dwarfs.\n",
    "\n",
    "Finally, use the `Info` class `dist()` instance to extract a calculated distance from the parallax data including a 2-sigma range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance: 474 pc\n",
      "5%: 341 pc - 95%: 632\n"
     ]
    }
   ],
   "source": [
    "Info.dist(t)"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}