NGC1850_2be_or_not_2be.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from hoki import load\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import gc\n",
    "\n",
    "plt.style.use('hfs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Searching for NGC 1850-BH1 in BPASS \n",
    "\n",
    "This jupyter notebook provides all of the code required to reproduce the analysis of [Stevance et al. 2022](https://arxiv.org/abs/2112.00015). We provide these resources openly to promote reproducibility and as a learnign tool for our peers - if you use this code as a starting point for your own scientific analysis, we would be grateful if you could **acknowledge this notebook** in the acknowledgment section of your paper and **cite any relevant references** mentioned along the way. \n",
    "\n",
    "## 1. Loading in data\n",
    "\n",
    "### 1.1 Observations\n",
    "\n",
    "The NGC 1850 was obtained through private communications with the authors of [Saracino et al. 2021](https://arxiv.org/abs/2111.06506). If you make use of this workflow and data for your work please cite their paper as well and for the full data please contact the authors of the original paper.\n",
    "\n",
    "- We first load in the photometric data of our system of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting the system with the BH candidate (ID = 224, private communication with Chris Usher)\n",
    "phot_system =  pd.read_csv('ngc1850bh1.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The BPASS synthetic photometry provides aboslute magnitudes, so we need to take the distance modulus into account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fste075/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1745: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "# Distance modulus \n",
    "mu = 18.45\n",
    "\n",
    "# Getting absolute magnitudes \n",
    "phot_system.loc[:, 'f336w'] -= mu\n",
    "phot_system.loc[:, 'f438w'] -= mu\n",
    "phot_system.loc[:, 'f814w'] -= mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      id     snr  f336w  f336w_err  f438w  f438w_err  f814w  f814w_err\n",
       "205  224  1183.1 -2.537      0.002 -1.749      0.004  -1.88      0.003"
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "phot_system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 BPASS models\n",
    "\n",
    "The full BPASS stellar library is 50GB so we provide a cropped data set which has been reduced using hoki ([Stevance et al. 2020](https://ui.adsabs.harvard.edu/abs/2020JOSS....5.1987S/abstract)).\n",
    "The pickle file contains a pandas DataFrame with models which fit the following criteria:\n",
    "- Z = 0.010 \n",
    "- M1$_{\\rm zams}$ > 0.5 M$_{\\odot}$ (mass of the bright component)\n",
    "- age < 500 Myrs \n",
    "\n",
    "The full dataframe (without the above conditions) is `models_z010_bin_v221.pkl` and was released with the dataset available on [Zenodo](https://zenodo.org/record/4064300#.YdIRf3VBzmE). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_g(M, logR):\n",
    "    return np.log10((6.67259 * 10 ** (-8)) * (1.989 * 10 ** 33) * M / (((10 ** logR * 6.9598 * (10 ** 10)) ** 2)))\n",
    "\n",
    "## Loading in the dataframe\n",
    "data_crop = pd.read_pickle('models_z010_bin_M1zams_gt_05_age_less_5e8.pkl')\n",
    "\n",
    "## The periods in BPASS are given in years, we turn them into days\n",
    "data_crop.P_bin = data_crop.P_bin.astype(float)*365.25\n",
    "\n",
    "data_crop['log_g1']=log_g(data_crop.M1, data_crop['log(R1)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Isolating the columns we need\n",
    "data_crop = data_crop[['age',\n",
    "                       'M1', \n",
    "                       'M2', \n",
    "                       'P_bin', \n",
    "                       'f336w', \n",
    "                       'f814w', \n",
    "                       'log(T1)',  \n",
    "                       'log(R1)', \n",
    "                       'log(T2)', \n",
    "                       'log(R2)',\n",
    "                       'modelimf', \n",
    "                       'mixedimf', \n",
    "                       'types', \n",
    "                       'log_g1',\n",
    "                       'B',\n",
    "                       'V',\n",
    "                       'R',\n",
    "                       'filenames'\n",
    "                      ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model search\n",
    "\n",
    "## Search 1: Looking for NGC1850-BH1 as in Saracino et al. 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fste075/.local/lib/python3.8/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "data_search1 = data_crop[  (data_crop.age>=0.8e8) # Wide age range\n",
    "                         & (data_crop.age<=3.0e8)\n",
    "                         & (data_crop.M1>=3) # Bright component mass\n",
    "                         & (data_crop.M1<=7)\n",
    "                         & (data_crop.P_bin>3)# Periods\n",
    "                         & (data_crop.P_bin<7)\n",
    "                         & (data_crop.types==2) # secondary star evolution (primary already \"died\")\n",
    "                         & (data_crop.M2>=5) # BH mass\n",
    "                        ]\n",
    "data_search1.age/=1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "              age       M1        M2     P_bin     f336w     f814w  log(T1)  \\\n",
       "2060573  98.19045  3.30256  5.672905  3.012423 -0.374835 -1.662275  3.78358   \n",
       "2060574  98.19305  3.20946  5.765906  3.125814 -0.148935 -1.574548  3.76839   \n",
       "2060575  98.19564  3.11659  5.858851  3.253879  0.007787 -1.524926  3.75873   \n",
       "2060576  98.19870  3.00786  5.967791  3.425380  0.121966 -1.495575  3.75103   \n",
       "2060731  98.28766  3.96183  5.013867  4.006204 -1.408296 -2.486675  3.82044   \n",
       "...           ...      ...       ...       ...       ...       ...      ...   \n",
       "2155510  98.32927  4.79478  6.509142  6.513269 -2.612597 -3.230995  3.90447   \n",
       "2155511  98.33641  4.72849  6.575454  6.587726 -2.469956 -3.221777  3.88569   \n",
       "2155512  98.34236  4.65721  6.646802  6.675404 -2.335055 -3.203084  3.86695   \n",
       "2155513  98.34769  4.57769  6.726403  6.782974 -2.195944 -3.174644  3.84715   \n",
       "2155514  98.35257  4.48859  6.815595  6.916465 -2.030832 -3.125850  3.82584   \n",
       "\n",
       "         log(R1)  log(T2)  log(R2)  modelimf  mixedimf types    log_g1  \\\n",
       "2060573  1.12186  3.44493      0.0  0.028298       0.0     2  2.712866   \n",
       "2060574  1.12907  3.44493      0.0  0.028298       0.0     2  2.686028   \n",
       "2060575  1.13498  3.44493      0.0  0.028298       0.0     2  2.661455   \n",
       "2060576  1.14256  3.44493      0.0  0.028298       0.0     2  2.630873   \n",
       "2060731  1.23025  3.44493      0.0  0.048002       0.0     2  2.575132   \n",
       "...          ...      ...      ...       ...       ...   ...       ...   \n",
       "2155510  1.27134  3.44493      0.0  0.206297       0.0     2  2.575824   \n",
       "2155511  1.29197  3.44493      0.0  0.206297       0.0     2  2.528518   \n",
       "2155512  1.31132  3.44493      0.0  0.206297       0.0     2  2.483222   \n",
       "2155513  1.33059  3.44493      0.0  0.206297       0.0     2  2.437202   \n",
       "2155514  1.35013  3.44493      0.0  0.206297       0.0     2  2.389586   \n",
       "\n",
       "                B         V         R  \\\n",
       "2060573 -0.592221 -1.087646 -1.461609   \n",
       "2060574 -0.386121 -0.943620 -1.354744   \n",
       "2060575 -0.253530 -0.855897 -1.292327   \n",
       "2060576 -0.167497 -0.800210 -1.254083   \n",
       "2060731 -1.706965 -2.058240 -2.335828   \n",
       "...           ...       ...       ...   \n",
       "2155510 -3.051607 -3.112895 -3.184526   \n",
       "2155511 -2.939062 -3.047636 -3.155597   \n",
       "2155512 -2.794557 -2.963633 -3.114915   \n",
       "2155513 -2.610783 -2.857766 -3.061010   \n",
       "2155514 -2.398473 -2.724613 -2.983805   \n",
       "\n",
       "                                                 filenames  \n",
       "2060573  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2060574  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2060575  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2060576  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2060731  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "...                                                    ...  \n",
       "2155510  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2155511  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2155512  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2155513  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "2155514  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-5-...  \n",
       "\n",
       "[340 rows x 18 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search1.filenames.unique().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing our models to NGC1850-BH1\n",
    "\n",
    "There are 13 matching models in total, the number size of the dataframe (340) includes each tiemstep at which these models match our criteria - since they might gain and lose mass over time they might come in or out of the matching range. Some systems will spend longer within the range than others, and some systems will be much more likely to occur than others due to the IMF. \n",
    "\n",
    "A first order visualisation is to use the `modelimf` column to apply a grey scale to our plot of M1 Vs M2 - the darker the point, the more likely this combination of M1-M2. As we can see below NGC1850-BH1 is not found in our search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(4.36, 11, 'NGC1850-BH1')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cb = plt.scatter(data_search1.M1, data_search1.M2, \n",
    "                 c=data_search1.modelimf, cmap='Greys', alpha=0.2, marker='o')\n",
    "\n",
    "plt.xlabel('M$_{bright}$')\n",
    "plt.ylabel('M$_{BH}$')\n",
    "\n",
    "plt.plot([5], [11], ls=' ', marker='o', color='purple')\n",
    "plt.text(4.36, 11, \"NGC1850-BH1\", color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can groub by M1-M2 and sort by modelimf to find the best combination of masses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M1       M2     \n",
       "4.99375  6.30957    0.705961\n",
       "4.99376  6.30957    0.705961\n",
       "4.99372  6.30957    0.412594\n",
       "4.99569  6.30957    0.352980\n",
       "4.99383  6.30957    0.352980\n",
       "Name: modelimf, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search1.groupby(['M1', 'M2']).sum().sort_values('modelimf', ascending=False)['modelimf'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search 2: Looking for NGC1850-BH1 with El-Bardy constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_current_age = data_crop[(data_crop.age>=0.8e8) & (data_crop.age<=1.2e8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "FUDGE = 1 # error bar window for the absolute magnitude\n",
    "f336_lo = (phot_system.f336w-phot_system.f336w_err-FUDGE).values[0]\n",
    "f336_hi = (phot_system.f336w+phot_system.f336w_err+FUDGE).values[0]\n",
    "\n",
    "f814_lo = (phot_system.f814w-phot_system.f814w_err-FUDGE).values[0]\n",
    "f814_hi = (phot_system.f814w+phot_system.f814w_err+FUDGE).values[0]\n",
    "\n",
    "FUDGE_P = 2 # error bar window for the period\n",
    "P_lo = 5.04 - FUDGE\n",
    "P_hi = 5.04 + FUDGE \n",
    "\n",
    "# magnitude constraints\n",
    "condition1 = (data_current_age.f336w.values > f336_lo) & (data_current_age.f336w.values < f336_hi)\n",
    "condition2 = (data_current_age.f814w.values > f814_lo) & (data_current_age.f814w.values < f814_hi)\n",
    "# period contraints\n",
    "condition3 = (data_current_age.P_bin > P_lo) & (data_current_age.P_bin < P_hi)\n",
    "# mass constraints (El Bardy & Brudge)\n",
    "condition4 = (data_current_age.M2 >=2)&(data_current_age.M2 <6)\n",
    "condition5 = (data_current_age.M1 <=2) & (data_current_age.M1 >0.5)\n",
    "condition6 = data_current_age.M1 <data_current_age.M2 #  otherwise RV definitely wrong\n",
    "condition7 = data_current_age.types==2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fste075/.local/lib/python3.8/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "data_search2 = data_current_age[  condition1 \n",
    "                                & condition2 \n",
    "                                & condition3 \n",
    "                                & condition4\n",
    "                                & condition5\n",
    "                                & condition6\n",
    "                                & condition7\n",
    "                               ]\n",
    "data_search2.age/=1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>age</th>\n",
       "      <th>M1</th>\n",
       "      <th>M2</th>\n",
       "      <th>P_bin</th>\n",
       "      <th>f336w</th>\n",
       "      <th>f814w</th>\n",
       "      <th>log(T1)</th>\n",
       "      <th>log(R1)</th>\n",
       "      <th>log(T2)</th>\n",
       "      <th>log(R2)</th>\n",
       "      <th>modelimf</th>\n",
       "      <th>mixedimf</th>\n",
       "      <th>types</th>\n",
       "      <th>log_g1</th>\n",
       "      <th>B</th>\n",
       "      <th>V</th>\n",
       "      <th>R</th>\n",
       "      <th>filenames</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1942485</th>\n",
       "      <td>81.10234</td>\n",
       "      <td>0.89635</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.899321</td>\n",
       "      <td>-3.382708</td>\n",
       "      <td>-1.335633</td>\n",
       "      <td>4.80418</td>\n",
       "      <td>0.07925</td>\n",
       "      <td>3.44493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.231713</td>\n",
       "      <td>-1.731224</td>\n",
       "      <td>-1.399315</td>\n",
       "      <td>-1.384676</td>\n",
       "      <td>NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942486</th>\n",
       "      <td>81.10239</td>\n",
       "      <td>0.89634</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.899347</td>\n",
       "      <td>-3.369800</td>\n",
       "      <td>-1.324285</td>\n",
       "      <td>4.80694</td>\n",
       "      <td>0.07378</td>\n",
       "      <td>3.44493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.242648</td>\n",
       "      <td>-1.718281</td>\n",
       "      <td>-1.386421</td>\n",
       "      <td>-1.372717</td>\n",
       "      <td>NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942487</th>\n",
       "      <td>81.10239</td>\n",
       "      <td>0.89634</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.899347</td>\n",
       "      <td>-3.369800</td>\n",
       "      <td>-1.324285</td>\n",
       "      <td>4.80694</td>\n",
       "      <td>0.07378</td>\n",
       "      <td>3.44493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.242648</td>\n",
       "      <td>-1.718281</td>\n",
       "      <td>-1.386421</td>\n",
       "      <td>-1.372717</td>\n",
       "      <td>NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942657</th>\n",
       "      <td>80.30528</td>\n",
       "      <td>1.15594</td>\n",
       "      <td>2.259553</td>\n",
       "      <td>5.426077</td>\n",
       "      <td>-3.427124</td>\n",
       "      <td>-1.182224</td>\n",
       "      <td>4.54519</td>\n",
       "      <td>0.30828</td>\n",
       "      <td>3.44493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.884111</td>\n",
       "      <td>-1.712385</td>\n",
       "      <td>-1.448577</td>\n",
       "      <td>-1.312300</td>\n",
       "      <td>NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age       M1        M2     P_bin     f336w     f814w  log(T1)  \\\n",
       "1942485  81.10234  0.89635  2.554929  4.899321 -3.382708 -1.335633  4.80418   \n",
       "1942486  81.10239  0.89634  2.554929  4.899347 -3.369800 -1.324285  4.80694   \n",
       "1942487  81.10239  0.89634  2.554929  4.899347 -3.369800 -1.324285  4.80694   \n",
       "1942657  80.30528  1.15594  2.259553  5.426077 -3.427124 -1.182224  4.54519   \n",
       "\n",
       "         log(R1)  log(T2)  log(R2)  modelimf  mixedimf types    log_g1  \\\n",
       "1942485  0.07925  3.44493      0.0  0.047072       0.0     2  4.231713   \n",
       "1942486  0.07378  3.44493      0.0  0.047072       0.0     2  4.242648   \n",
       "1942487  0.07378  3.44493      0.0  0.047072       0.0     2  4.242648   \n",
       "1942657  0.30828  3.44493      0.0  0.048631       0.0     2  3.884111   \n",
       "\n",
       "                B         V         R  \\\n",
       "1942485 -1.731224 -1.399315 -1.384676   \n",
       "1942486 -1.718281 -1.386421 -1.372717   \n",
       "1942487 -1.718281 -1.386421 -1.372717   \n",
       "1942657 -1.712385 -1.448577 -1.312300   \n",
       "\n",
       "                                                 filenames  \n",
       "1942485  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...  \n",
       "1942486  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...  \n",
       "1942487  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...  \n",
       "1942657  NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-1.99526-1.00000',\n",
       "       'NEWBINMODS/NEWSECMODS/z010_2/sneplot_2-z010-6-1.99526-1.20000'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search2.filenames.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are therefore 2 matching models, the first 3 rows are very similar ages and essentially the same stellar properties. This table corresponds to Table 1 in the paper (although the first 3 rwos have been collapsed to 1)\n",
    "\n",
    "Note that the MODEL ID is a new way to identify the BPASS models and the hoki features to which it is relevant are not publically available yet, but the tables in this notebook and in the paper are in the same order so the MODEL ID is not relevant at this time. \n",
    "\n",
    "### Lifetime of the phases which match observations - ONLY RUN IF YOU HAVE THE STELLAR LIBRARIES\n",
    "\n",
    "#### **/!\\ WARNING /!\\**\n",
    "\n",
    "You will only be able to run the following code if you have the original stellar libraries downloaded - you can download the file `bpass-v2.2-newmodels.tar.gz` [here](https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl).\n",
    "You can set up your `MODELS_PATH` in hoki using the function `load.set_models_path()`, or jsut replace `load.MODELS_PATH` below by the path to your local BPASS stellar library.\n",
    "\n",
    "\n",
    "**Note the ages here are in years, not Myrs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>M1</th>\n",
       "      <th>M2</th>\n",
       "      <th>log(T1)</th>\n",
       "      <th>P_bin</th>\n",
       "      <th>f336w</th>\n",
       "      <th>f814w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>81101310.0</td>\n",
       "      <td>0.89655</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.74273</td>\n",
       "      <td>0.013412</td>\n",
       "      <td>-3.726676</td>\n",
       "      <td>-1.620189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>81101840.0</td>\n",
       "      <td>0.89645</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.77342</td>\n",
       "      <td>0.013413</td>\n",
       "      <td>-3.565104</td>\n",
       "      <td>-1.488226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>81102340.0</td>\n",
       "      <td>0.89635</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.80418</td>\n",
       "      <td>0.013414</td>\n",
       "      <td>-3.382708</td>\n",
       "      <td>-1.335633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>81102390.0</td>\n",
       "      <td>0.89634</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.80694</td>\n",
       "      <td>0.013414</td>\n",
       "      <td>-3.369800</td>\n",
       "      <td>-1.324285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>81102390.0</td>\n",
       "      <td>0.89634</td>\n",
       "      <td>2.554929</td>\n",
       "      <td>4.80694</td>\n",
       "      <td>0.013414</td>\n",
       "      <td>-3.369800</td>\n",
       "      <td>-1.324285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age       M1        M2  log(T1)     P_bin     f336w     f814w\n",
       "199  81101310.0  0.89655  2.554929  4.74273  0.013412 -3.726676 -1.620189\n",
       "200  81101840.0  0.89645  2.554929  4.77342  0.013413 -3.565104 -1.488226\n",
       "201  81102340.0  0.89635  2.554929  4.80418  0.013414 -3.382708 -1.335633\n",
       "202  81102390.0  0.89634  2.554929  4.80694  0.013414 -3.369800 -1.324285\n",
       "203  81102390.0  0.89634  2.554929  4.80694  0.013414 -3.369800 -1.324285"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1 = load.dummy_to_dataframe(load.MODELS_PATH+data_search2.filenames.iloc[0])\n",
    "dat1[['age', 'M1', 'M2', 'log(T1)', 'P_bin',  'f336w', 'f814w']].tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This system only matches our criteria for the last 40 years of its life. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>M1</th>\n",
       "      <th>M2</th>\n",
       "      <th>log(T1)</th>\n",
       "      <th>P_bin</th>\n",
       "      <th>f336w</th>\n",
       "      <th>f814w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>80270880.0</td>\n",
       "      <td>1.15636</td>\n",
       "      <td>2.259553</td>\n",
       "      <td>4.61028</td>\n",
       "      <td>0.014852</td>\n",
       "      <td>-2.833263</td>\n",
       "      <td>-0.537147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>80288630.0</td>\n",
       "      <td>1.15615</td>\n",
       "      <td>2.259553</td>\n",
       "      <td>4.58004</td>\n",
       "      <td>0.014854</td>\n",
       "      <td>-3.121964</td>\n",
       "      <td>-0.850704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>80305280.0</td>\n",
       "      <td>1.15594</td>\n",
       "      <td>2.259553</td>\n",
       "      <td>4.54519</td>\n",
       "      <td>0.014856</td>\n",
       "      <td>-3.427124</td>\n",
       "      <td>-1.182224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>80320480.0</td>\n",
       "      <td>1.15572</td>\n",
       "      <td>2.259553</td>\n",
       "      <td>4.50542</td>\n",
       "      <td>0.014858</td>\n",
       "      <td>-3.741417</td>\n",
       "      <td>-1.521360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>80332550.0</td>\n",
       "      <td>1.15553</td>\n",
       "      <td>2.259553</td>\n",
       "      <td>4.46595</td>\n",
       "      <td>0.014859</td>\n",
       "      <td>-3.994971</td>\n",
       "      <td>-1.827803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age       M1        M2  log(T1)     P_bin     f336w     f814w\n",
       "167  80270880.0  1.15636  2.259553  4.61028  0.014852 -2.833263 -0.537147\n",
       "168  80288630.0  1.15615  2.259553  4.58004  0.014854 -3.121964 -0.850704\n",
       "169  80305280.0  1.15594  2.259553  4.54519  0.014856 -3.427124 -1.182224\n",
       "170  80320480.0  1.15572  2.259553  4.50542  0.014858 -3.741417 -1.521360\n",
       "171  80332550.0  1.15553  2.259553  4.46595  0.014859 -3.994971 -1.827803"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat2 = load.dummy_to_dataframe(load.MODELS_PATH+data_search2.filenames.iloc[-1])\n",
    "dat2[['age', 'M1', 'M2', 'log(T1)', 'P_bin',  'f336w', 'f814w']].iloc[167:172]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15200.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "80320480.0-80305280 # take the delta t between matching bins and the next one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This system only matches the criteria for 15,200 years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search 3: Looking for best match to observations\n",
    "\n",
    "This is section 2.2 of the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "FUDGE=0.8\n",
    "f336_lo = (phot_system.f336w-phot_system.f336w_err-FUDGE).values[0]\n",
    "f336_hi = (phot_system.f336w+phot_system.f336w_err+FUDGE).values[0]\n",
    "\n",
    "f814_lo = (phot_system.f814w-phot_system.f814w_err-FUDGE).values[0]\n",
    "f814_hi = (phot_system.f814w+phot_system.f814w_err+FUDGE).values[0]\n",
    "\n",
    "FUDGE_P = 2 #1.5 days\n",
    "P_lo = 5.04- FUDGE\n",
    "P_hi = 5.04 + FUDGE \n",
    "\n",
    "T_lo = np.log10(10000)\n",
    "T_hi = np.log10(20000)\n",
    "\n",
    "# magnitude constraints\n",
    "condition1 = (data_current_age.f336w.values > f336_lo) & (data_current_age.f336w.values < f336_hi)\n",
    "condition2 = (data_current_age.f814w.values > f814_lo) & (data_current_age.f814w.values < f814_hi)\n",
    "# period contraints\n",
    "condition3 = (data_current_age.P_bin > P_lo) & (data_current_age.P_bin < P_hi) \n",
    "# mass constraints (El Bardy & Brudge)\n",
    "condition4 = (data_current_age.M2 >=2)&(data_current_age.M2 <6)\n",
    "condition5 = (data_current_age.M1 <=2) & (data_current_age.M1 >0.5)\n",
    "condition6 = (data_current_age.M1 < data_current_age.M2) # otherwise RV definitely wrong\n",
    "# Temperature constraints - form original paper\n",
    "condition7 = (data_current_age['log(T1)']>T_lo) & (data_current_age['log(T1)']<T_hi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fste075/.local/lib/python3.8/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "data_search3 = data_current_age[  condition1 \n",
    "                                & condition2 \n",
    "                                & condition3 \n",
    "                                & condition4\n",
    "                                & condition5\n",
    "                                & condition6\n",
    "                                & condition7\n",
    "                               ]\n",
    "data_search3.age/=1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>age</th>\n",
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       "      <th>f814w</th>\n",
       "      <th>log(T1)</th>\n",
       "      <th>log(R1)</th>\n",
       "      <th>log(T2)</th>\n",
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       "      <th>modelimf</th>\n",
       "      <th>mixedimf</th>\n",
       "      <th>types</th>\n",
       "      <th>log_g1</th>\n",
       "      <th>B</th>\n",
       "      <th>V</th>\n",
       "      <th>R</th>\n",
       "      <th>filenames</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>785107</th>\n",
       "      <td>84.17926</td>\n",
       "      <td>1.62442</td>\n",
       "      <td>3.175703</td>\n",
       "      <td>4.451199</td>\n",
       "      <td>-2.285956</td>\n",
       "      <td>-1.876484</td>\n",
       "      <td>4.03156</td>\n",
       "      <td>0.86722</td>\n",
       "      <td>3.90344</td>\n",
       "      <td>0.27211</td>\n",
       "      <td>2.875204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.913994</td>\n",
       "      <td>-1.990652</td>\n",
       "      <td>-1.917188</td>\n",
       "      <td>-1.898039</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785108</th>\n",
       "      <td>84.41889</td>\n",
       "      <td>1.51924</td>\n",
       "      <td>3.236406</td>\n",
       "      <td>4.829167</td>\n",
       "      <td>-2.344947</td>\n",
       "      <td>-1.956485</td>\n",
       "      <td>4.02810</td>\n",
       "      <td>0.88649</td>\n",
       "      <td>3.90117</td>\n",
       "      <td>0.27666</td>\n",
       "      <td>2.875204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.846382</td>\n",
       "      <td>-2.065517</td>\n",
       "      <td>-1.993789</td>\n",
       "      <td>-1.976508</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785109</th>\n",
       "      <td>84.60834</td>\n",
       "      <td>1.40573</td>\n",
       "      <td>3.284027</td>\n",
       "      <td>5.240260</td>\n",
       "      <td>-2.385274</td>\n",
       "      <td>-2.038569</td>\n",
       "      <td>4.02236</td>\n",
       "      <td>0.90782</td>\n",
       "      <td>3.89942</td>\n",
       "      <td>0.28018</td>\n",
       "      <td>2.875204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.769998</td>\n",
       "      <td>-2.139755</td>\n",
       "      <td>-2.070948</td>\n",
       "      <td>-2.056251</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785110</th>\n",
       "      <td>84.76026</td>\n",
       "      <td>1.27463</td>\n",
       "      <td>3.321985</td>\n",
       "      <td>5.736701</td>\n",
       "      <td>-2.393425</td>\n",
       "      <td>-2.111510</td>\n",
       "      <td>4.01309</td>\n",
       "      <td>0.93154</td>\n",
       "      <td>3.89804</td>\n",
       "      <td>0.28295</td>\n",
       "      <td>2.875204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.680040</td>\n",
       "      <td>-2.200572</td>\n",
       "      <td>-2.136524</td>\n",
       "      <td>-2.125552</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785269</th>\n",
       "      <td>85.98281</td>\n",
       "      <td>1.69785</td>\n",
       "      <td>4.329369</td>\n",
       "      <td>4.709687</td>\n",
       "      <td>-2.269250</td>\n",
       "      <td>-1.950836</td>\n",
       "      <td>4.01925</td>\n",
       "      <td>0.88435</td>\n",
       "      <td>3.99148</td>\n",
       "      <td>0.35145</td>\n",
       "      <td>2.784498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.898935</td>\n",
       "      <td>-2.058397</td>\n",
       "      <td>-1.990123</td>\n",
       "      <td>-1.970666</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.4-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785270</th>\n",
       "      <td>86.25593</td>\n",
       "      <td>1.59652</td>\n",
       "      <td>4.430678</td>\n",
       "      <td>5.303751</td>\n",
       "      <td>-2.320971</td>\n",
       "      <td>-2.051461</td>\n",
       "      <td>4.01182</td>\n",
       "      <td>0.91273</td>\n",
       "      <td>3.98868</td>\n",
       "      <td>0.35710</td>\n",
       "      <td>2.784498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.815450</td>\n",
       "      <td>-2.148516</td>\n",
       "      <td>-2.084264</td>\n",
       "      <td>-2.068196</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.4-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785437</th>\n",
       "      <td>85.79303</td>\n",
       "      <td>1.84741</td>\n",
       "      <td>5.467959</td>\n",
       "      <td>4.542479</td>\n",
       "      <td>-2.296344</td>\n",
       "      <td>-2.020316</td>\n",
       "      <td>4.00668</td>\n",
       "      <td>0.89953</td>\n",
       "      <td>4.05662</td>\n",
       "      <td>0.41684</td>\n",
       "      <td>2.716116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.905239</td>\n",
       "      <td>-2.124570</td>\n",
       "      <td>-2.058572</td>\n",
       "      <td>-2.039006</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.5-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785438</th>\n",
       "      <td>86.04238</td>\n",
       "      <td>1.73832</td>\n",
       "      <td>5.576845</td>\n",
       "      <td>5.179625</td>\n",
       "      <td>-2.351501</td>\n",
       "      <td>-2.083837</td>\n",
       "      <td>4.00579</td>\n",
       "      <td>0.91412</td>\n",
       "      <td>4.05422</td>\n",
       "      <td>0.42174</td>\n",
       "      <td>2.716116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.849625</td>\n",
       "      <td>-2.185328</td>\n",
       "      <td>-2.120004</td>\n",
       "      <td>-2.101651</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.5-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832761</th>\n",
       "      <td>98.61051</td>\n",
       "      <td>0.97558</td>\n",
       "      <td>2.045098</td>\n",
       "      <td>5.769679</td>\n",
       "      <td>-3.272404</td>\n",
       "      <td>-2.298059</td>\n",
       "      <td>4.11472</td>\n",
       "      <td>0.88575</td>\n",
       "      <td>3.99855</td>\n",
       "      <td>0.17748</td>\n",
       "      <td>5.438956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.655499</td>\n",
       "      <td>-2.500075</td>\n",
       "      <td>-2.386222</td>\n",
       "      <td>-2.344202</td>\n",
       "      <td>NEWBINMODS/NEWBINMODS/z010/sneplot-z010-5-0.4-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age       M1        M2     P_bin     f336w     f814w  log(T1)  \\\n",
       "785107  84.17926  1.62442  3.175703  4.451199 -2.285956 -1.876484  4.03156   \n",
       "785108  84.41889  1.51924  3.236406  4.829167 -2.344947 -1.956485  4.02810   \n",
       "785109  84.60834  1.40573  3.284027  5.240260 -2.385274 -2.038569  4.02236   \n",
       "785110  84.76026  1.27463  3.321985  5.736701 -2.393425 -2.111510  4.01309   \n",
       "785269  85.98281  1.69785  4.329369  4.709687 -2.269250 -1.950836  4.01925   \n",
       "785270  86.25593  1.59652  4.430678  5.303751 -2.320971 -2.051461  4.01182   \n",
       "785437  85.79303  1.84741  5.467959  4.542479 -2.296344 -2.020316  4.00668   \n",
       "785438  86.04238  1.73832  5.576845  5.179625 -2.351501 -2.083837  4.00579   \n",
       "832761  98.61051  0.97558  2.045098  5.769679 -3.272404 -2.298059  4.11472   \n",
       "\n",
       "        log(R1)  log(T2)  log(R2)  modelimf  mixedimf types    log_g1  \\\n",
       "785107  0.86722  3.90344  0.27211  2.875204       0.0     1  2.913994   \n",
       "785108  0.88649  3.90117  0.27666  2.875204       0.0     1  2.846382   \n",
       "785109  0.90782  3.89942  0.28018  2.875204       0.0     1  2.769998   \n",
       "785110  0.93154  3.89804  0.28295  2.875204       0.0     1  2.680040   \n",
       "785269  0.88435  3.99148  0.35145  2.784498       0.0     1  2.898935   \n",
       "785270  0.91273  3.98868  0.35710  2.784498       0.0     1  2.815450   \n",
       "785437  0.89953  4.05662  0.41684  2.716116       0.0     1  2.905239   \n",
       "785438  0.91412  4.05422  0.42174  2.716116       0.0     1  2.849625   \n",
       "832761  0.88575  3.99855  0.17748  5.438956       0.0     1  2.655499   \n",
       "\n",
       "               B         V         R  \\\n",
       "785107 -1.990652 -1.917188 -1.898039   \n",
       "785108 -2.065517 -1.993789 -1.976508   \n",
       "785109 -2.139755 -2.070948 -2.056251   \n",
       "785110 -2.200572 -2.136524 -2.125552   \n",
       "785269 -2.058397 -1.990123 -1.970666   \n",
       "785270 -2.148516 -2.084264 -2.068196   \n",
       "785437 -2.124570 -2.058572 -2.039006   \n",
       "785438 -2.185328 -2.120004 -2.101651   \n",
       "832761 -2.500075 -2.386222 -2.344202   \n",
       "\n",
       "                                                filenames  \n",
       "785107  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2  \n",
       "785108  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2  \n",
       "785109  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2  \n",
       "785110  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2  \n",
       "785269  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.4-0.2  \n",
       "785270  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.4-0.2  \n",
       "785437  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.5-0.2  \n",
       "785438  NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.5-0.2  \n",
       "832761    NEWBINMODS/NEWBINMODS/z010/sneplot-z010-5-0.4-1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first and last row, although they match our criteria, would give a mass ratio that is unphysical for the RV of the system (see Figure 2, right panel of El Bardy & Brudge 2021), so in Table 2 we do not present these matches. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|     age |      M1 |      M2 |   log(T1) |   log(R1) |   log(T2) |   log(R2) |   P_bin |   log_g1 |\n",
      "|--------:|--------:|--------:|----------:|----------:|----------:|----------:|--------:|---------:|\n",
      "| 84.4189 | 1.51924 | 3.23641 |   4.0281  |   0.88649 |   3.90117 |   0.27666 | 4.82917 |  2.84638 |\n",
      "| 84.6083 | 1.40573 | 3.28403 |   4.02236 |   0.90782 |   3.89942 |   0.28018 | 5.24026 |  2.77    |\n",
      "| 84.7603 | 1.27463 | 3.32198 |   4.01309 |   0.93154 |   3.89804 |   0.28295 | 5.7367  |  2.68004 |\n",
      "| 85.9828 | 1.69785 | 4.32937 |   4.01925 |   0.88435 |   3.99148 |   0.35145 | 4.70969 |  2.89894 |\n",
      "| 86.2559 | 1.59652 | 4.43068 |   4.01182 |   0.91273 |   3.98868 |   0.3571  | 5.30375 |  2.81545 |\n",
      "| 85.793  | 1.84741 | 5.46796 |   4.00668 |   0.89953 |   4.05662 |   0.41684 | 4.54248 |  2.90524 |\n",
      "| 86.0424 | 1.73832 | 5.57684 |   4.00579 |   0.91412 |   4.05422 |   0.42174 | 5.17962 |  2.84963 |\n"
     ]
    }
   ],
   "source": [
    "print(data_search3.iloc[1:-1][['age','M1', 'M2', 'log(T1)', 'log(R1)', 'log(T2)', 'log(R2)','P_bin', 'log_g1']].to_markdown(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create Figure 1 we find the unique models that match our criteria (and physical inclinations) and we plor their evolution in a grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_all = data_search3.filenames.unique()[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.3-0.2',\n",
       "       'NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.4-0.2',\n",
       "       'NEWBINMODS/NEWBINMODS/z010/sneplot-z010-6-0.5-0.2'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2250x1500 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(nrows=3, ncols=3, figsize=(15,10))\n",
    "ax=ax.reshape(9)\n",
    "IDs=[166726, 163570,168485]\n",
    "i=0\n",
    "for j, match in enumerate(matches_all):\n",
    "    ax4=ax[i]\n",
    "    ages = data_search3[data_search3.filenames == match].age.values\n",
    "    dummy_df=load.dummy_to_dataframe(load.MODELS_PATH+match)\n",
    "    dummy_df.P_bin*= 365.25\n",
    "    mt = dummy_df[-dummy_df.DM1R>1e-16] # note this is in msol/year/1.989 (because of STARS code units)\n",
    "    L = dummy_df['log(L1)'].values\n",
    "    T = dummy_df['log(T1)'].values\n",
    "    \n",
    "    L_match = dummy_df[np.isin(dummy_df.age/1e6, ages)]['log(L1)'].values\n",
    "    T_match = dummy_df[np.isin(dummy_df.age/1e6, ages)]['log(T1)'].values\n",
    "    \n",
    "\n",
    "    ax4.plot(T, L, c='grey', alpha=0.5)\n",
    "\n",
    "    ax4.scatter(mt['log(T1)'], mt['log(L1)'], color='orange', lw=2)\n",
    "    ax4.scatter(T_match, L_match, c='midnightblue', alpha=1, marker='*', s=20)\n",
    "    ax4.set_ylim([ 1.5,4.5])\n",
    "    ax4.set_xlim([5.0,3.5])\n",
    "\n",
    "    ax4.set_ylabel('log(L1)')\n",
    "    ax4.text(3.95, 4.1, f'MODEL ID\\n{str(IDs[j])}')\n",
    "    \n",
    "    age = dummy_df.age.values\n",
    "    i+=1\n",
    "    ax1=ax[i]\n",
    "    ax1.plot(age, dummy_df.M1, c='crimson', label='M$_{bright}$')\n",
    "    ax1.plot(age, dummy_df.M2, c='k', lw = 1, label='M$_{dark}$')\n",
    "    ax1.plot(age, dummy_df['He_core1'], color='purple', ls='--', lw=1, label='He core$_{bright}$')\n",
    "    ax1.set_ylabel('M/M$_{\\odot}$')\n",
    "    ax1.set_xlim([0.25e8, 1.25e8])\n",
    "    \n",
    "    i+=1\n",
    "    ax2=ax[i]\n",
    "    ax2.plot(age, dummy_df['log(R1)'], c='crimson', label='R$_{bright}$')\n",
    "    ax2.plot(age, dummy_df['log(R2)'], c='grey', label='R$_{dark}$')\n",
    "    ax2.plot(age, dummy_df['log(a)'], c='blue', label='seperation')\n",
    "    #ax2.plot(age, np.log10(evewiz.dummy_df['P_bin']), c='limegreen', label='Period')\n",
    "    ax2.set_ylabel('log(R$_{\\odot}$)')\n",
    "    ax2.set_xlim([0.2e8, 1.25e8])\n",
    "    i+=1\n",
    "    for axis in [ax1, ax2]:\n",
    "        axis.axvspan(mt.age.iloc[0],mt.age.iloc[-1], color='orange', alpha=0.2)\n",
    "        axis.axvline(ages[0]*1e6, color='midnightblue', lw=2,ls='-.', alpha=2)\n",
    "\n",
    "ax[-3].set_xlabel('log(T1)')\n",
    "ax[-2].set_xlabel(\"Age (years)\")\n",
    "ax[-1].set_xlabel(\"Age (years)\")\n",
    "ax[1].legend(ncol=3, bbox_to_anchor=[0,1])\n",
    "ax[2].legend()\n",
    "plt.savefig('best_models_evol.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, for table 3 we once again laod the stellar models in a loop, only recording M1, M2 and the `modelimf` (which is the number of such systems we would expect to encounter in a 1 million solar mass cluster with a single star formation burst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>M1</th>\n",
       "      <th>M2</th>\n",
       "      <th>modelimf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.875204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.784498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.716116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    M1   M2  modelimf\n",
       "0  6.0  1.8  2.875204\n",
       "0  6.0  2.4  2.784498\n",
       "0  6.0  3.0  2.716116"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list = []\n",
    "for j, match in enumerate(matches_all):\n",
    "    dummy_df=load.dummy_to_dataframe(load.MODELS_PATH+match)\n",
    "    df_list.append(dummy_df[['M1', 'M2', 'modelimf']].iloc[0])\n",
    "    \n",
    "pd.DataFrame(df_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating the brightness of the \"dark\" component \n",
    "\n",
    "The luminosities of the second star sometimes have odd edge cases resulting form the fact that the second star is not fully evolved in detailed by STARS (this will be addressed in BPASSv3) - in this case the secondary has just undergone significant mass transfer and gained a lot of mass rapidly - this means it will have been rejuvinated and have and resamble a ZAMS of its new mass rather than a 100Myr old star. So we check single star models corresponding ot the mass of the secondary and we look at the V band as a proxy (we don't have synthetic spectra for the individual stellar models, they are calculated by BPASS but then discarded after being turned into broad band photometry to make the data size manageable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M2</th>\n",
       "      <th>B</th>\n",
       "      <th>V</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>785108</th>\n",
       "      <td>3.236406</td>\n",
       "      <td>-2.065517</td>\n",
       "      <td>-1.993789</td>\n",
       "      <td>-1.976508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785109</th>\n",
       "      <td>3.284027</td>\n",
       "      <td>-2.139755</td>\n",
       "      <td>-2.070948</td>\n",
       "      <td>-2.056251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785110</th>\n",
       "      <td>3.321985</td>\n",
       "      <td>-2.200572</td>\n",
       "      <td>-2.136524</td>\n",
       "      <td>-2.125552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M2         B         V         R\n",
       "785108  3.236406 -2.065517 -1.993789 -1.976508\n",
       "785109  3.284027 -2.139755 -2.070948 -2.056251\n",
       "785110  3.321985 -2.200572 -2.136524 -2.125552"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3.iloc[1:4][['M2', 'B', 'V','R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    0.167593\n",
       "V    0.254482\n",
       "R    0.303433\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin_166726 = load.dummy_to_dataframe(load.MODELS_PATH+'NEWSINMODS/z010/sneplot-z010-3.3').iloc[0]\n",
    "sin_166726[['B', 'V','R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B   -2.30735\n",
       "V   -2.32543\n",
       "R   -2.35968\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3.iloc[2][['B', 'V','R']] - sin_166726[['B', 'V','R']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the bright component has V=-2.07 and the dark component has V mag = 0.25. It's not dark but it is >2.3 mag fainter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>M2</th>\n",
       "      <th>B</th>\n",
       "      <th>V</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>785269</th>\n",
       "      <td>4.329369</td>\n",
       "      <td>-2.058397</td>\n",
       "      <td>-1.990123</td>\n",
       "      <td>-1.970666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785270</th>\n",
       "      <td>4.430678</td>\n",
       "      <td>-2.148516</td>\n",
       "      <td>-2.084264</td>\n",
       "      <td>-2.068196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M2         B         V         R\n",
       "785269  4.329369 -2.058397 -1.990123 -1.970666\n",
       "785270  4.430678 -2.148516 -2.084264 -2.068196"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3.iloc[4:6][['M2', 'B', 'V','R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B   -0.556777\n",
       "V   -0.428030\n",
       "R   -0.357843\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin_163570 = load.dummy_to_dataframe(load.MODELS_PATH+'NEWSINMODS/z010/sneplot-z010-4.4').iloc[0]\n",
    "sin_163570[['B', 'V','R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B   -1.59174\n",
       "V   -1.65623\n",
       "R   -1.71035\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3.iloc[5][['B', 'V','R']] - sin_163570[['B', 'V','R']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we're looking at a 1.5 to 1.7 mag difference (used 4.4 Msol because it's the limiting case) - not qutie as significant but still about 5 times fainter, wouldn't be visible in the spectrum reported. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>M2</th>\n",
       "      <th>B</th>\n",
       "      <th>V</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>785437</th>\n",
       "      <td>5.467959</td>\n",
       "      <td>-2.124570</td>\n",
       "      <td>-2.058572</td>\n",
       "      <td>-2.039006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785438</th>\n",
       "      <td>5.576845</td>\n",
       "      <td>-2.185328</td>\n",
       "      <td>-2.120004</td>\n",
       "      <td>-2.101651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M2         B         V         R\n",
       "785437  5.467959 -2.124570 -2.058572 -2.039006\n",
       "785438  5.576845 -2.185328 -2.120004 -2.101651"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3.iloc[6:-1][['M2', 'B', 'V','R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B   -1.089789\n",
       "V   -0.933029\n",
       "R   -0.845331\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin_168485 = load.dummy_to_dataframe(load.MODELS_PATH+'NEWSINMODS/z010/sneplot-z010-5.5').iloc[0]\n",
    "sin_168485[['B', 'V','R']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B   -1.03478\n",
       "V   -1.12554\n",
       "R   -1.19368\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_search3.iloc[6][['B', 'V','R']] - sin_168485[['B', 'V','R']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the smallest flux difference with a mag difference of 1.125 (about 3 times fainter)"
   ]
  }
 ],
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