Binary pathways to SLSNe I - SN 2017gci.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def df_last_step(df):\n",
    "    \"\"\" Returns a dataframe with only the rows corresponding to the last time steps of each model \"\"\"\n",
    "    max_age=df.groupby('filenames').max().age\n",
    "\n",
    "    temp_dfs = [] \n",
    "    for age, model in zip(max_age.values, max_age.index.values):\n",
    "        temp_dfs.append(df[(df.filenames==model) & (df.age==age)])\n",
    "\n",
    "    last_step = pd.concat(temp_dfs)\n",
    "    del temp_dfs\n",
    "    return last_step\n",
    "\n",
    "\n",
    "plt.style.use('hfs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Search\n",
    "\n",
    "The most time consuming part of the model search is to find and record all the models with M1 ZAMS at least 10 solar masses, and create the data frame containing the last step of teach of these models. \n",
    "\n",
    "These steps have been carried out and pre-cropped data is provided alongside this jupyter notebook to make the recreation of this analysis manageable to all. You will need the following files:\n",
    "\n",
    "* `Models_M1zams_10msol_ALL.pkl`\n",
    "* `Models_M1zams_10msol_LAST.pkl`\n",
    "\n",
    "Note that these files are too large to be stored on GitHub: Send en email to `hfstevance@gmail.com` if you need them. \n",
    "\n",
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9476, 52114157)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_massive = pd.read_pickle('Models_M1zams_10msol_ALL.pkl')\n",
    "last_step = pd.read_pickle('Models_M1zams_10msol_LAST.pkl')\n",
    "\n",
    "data_massive.filenames.unique().size, data_massive.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neutron Stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models that match\n",
    "ns_sn_models = last_step[last_step.Mrem_SN < 2.43].filenames.unique()\n",
    "ns_supersn_models = last_step[last_step.Mrem_superSN < 2.43].filenames.unique()\n",
    "\n",
    "# cropping the data frame\n",
    "data_ns_SN=data_massive[data_massive.filenames.isin(ns_sn_models)]\n",
    "data_ns_superSN=data_massive[data_massive.filenames.isin(ns_supersn_models)]\n",
    "\n",
    "# clean up \n",
    "del data_massive\n",
    "gc.garbage.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5155, 9233)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ns_SN.filenames.unique().size, data_ns_superSN.filenames.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejecta Mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models that match\n",
    "mej_sn_models = last_step[(last_step.Mej_SN>8) & (last_step.Mej_SN<13)].filenames.unique()\n",
    "mej_supersn_models = last_step[(last_step.Mej_superSN>8) & (last_step.Mej_superSN<13)].filenames.unique()\n",
    "\n",
    "# cropping the data frame\n",
    "data_nsmej_SN = data_ns_SN[data_ns_SN.filenames.isin(mej_sn_models)]\n",
    "data_nsmej_superSN = data_ns_superSN[data_ns_superSN.filenames.isin(mej_supersn_models)]\n",
    "\n",
    "# clean up \n",
    "del data_ns_SN, data_ns_superSN\n",
    "gc.garbage.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(774, 1863)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nsmej_SN.filenames.unique().size, data_nsmej_superSN.filenames.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No hydrogen at the end of the life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the last steps takes a few seconds because it is iterative\n",
    "\n",
    "last_step_SN, last_step_superSN = df_last_step(data_nsmej_SN), df_last_step(data_nsmej_superSN)\n",
    "\n",
    "last_step_SN_grouped=last_step_SN.groupby('filenames').mean()\n",
    "last_step_superSN_grouped=last_step_superSN.groupby('filenames').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "I_SN = last_step_SN_grouped[(last_step_SN_grouped.X<0.01)&(last_step_SN_grouped.MH1<0.001)]\n",
    "I_superSN = last_step_superSN_grouped[(last_step_superSN_grouped.X<0.01)&(last_step_superSN_grouped.MH1<0.001)]\n",
    "\n",
    "last_step_ISN = last_step_SN[last_step_SN.filenames.isin(I_SN.index.unique())]\n",
    "last_step_IsuperSN = last_step_superSN[last_step_superSN.filenames.isin(I_superSN.index.unique())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ISN = data_nsmej_SN[data_nsmej_SN.filenames.isin(I_SN.index.unique().values)]\n",
    "data_IsuperSN = data_nsmej_superSN[data_nsmej_superSN.filenames.isin(I_superSN.index.unique().values)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(774, 1863)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of models for the super SN\n",
    "last_step_SN_grouped.index.unique().size, last_step_superSN_grouped.index.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 761)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I_SN.index.unique().size, I_superSN.index.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the sizes of the dataframes grouped by filenames or of the unique indices tell us the number of models found by each search criterion. These are reported in Table 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When do they lose their H?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def when_lose_H_years(tejecta_to_csm=150, vphot=8000, vhalpha=1000):\n",
    "    t_ej_to_csm_sec = tejecta_to_csm*24*3600 # days to seconds\n",
    "    r_csm = t_ej_to_csm_sec*vphot\n",
    "    return r_csm/vhalpha/3600/24/365 #seconds to years\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSM velocity of order 100 km/s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(21.91780821917808, 32.87671232876713)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"CSM velocity of order 100 km/s\")\n",
    "when_lose_H_years(tejecta_to_csm=100, vhalpha=100), when_lose_H_years(tejecta_to_csm=150, vhalpha=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSM velocity of order 1000 km/s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.191780821917808, 3.287671232876712)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"CSM velocity of order 1000 km/s\")\n",
    "when_lose_H_years(tejecta_to_csm=100, vhalpha=1000), when_lose_H_years(tejecta_to_csm=150, vhalpha=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models with Normal SN energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEWBINMODS/NEWBINMODS/z006/sneplot-z006-35-0.7-0.6',\n",
       "       'NEWBINMODS/NEWBINMODS/z006/sneplot-z006-12-0.9-0.8'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ISN.filenames.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "filenames\n",
       "NEWBINMODS/NEWBINMODS/z006/sneplot-z006-12-0.9-0.8     54420.0\n",
       "NEWBINMODS/NEWBINMODS/z006/sneplot-z006-35-0.7-0.6    317434.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ISN[(data_ISN.X<0.01)&(data_ISN.MH1<0.001)].groupby('filenames').max().age - data_ISN[(data_ISN.X<0.01)&(data_ISN.MH1<0.001)].groupby('filenames').min().age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So they lose they Hydrogen tens or hundreds of thousands of years before epxlosion. They are therefore not suitable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models with Super SN energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_superSN=data_IsuperSN.filenames.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.0 NEWBINMODS/NEWBINMODS/z006/sneplot-z006-30-0.4-2.2\n",
      "31.0 NEWBINMODS/NEWSECMODS/z006_2/sneplot_2-z006-30-25.1189-1.80000\n"
     ]
    }
   ],
   "source": [
    "for model in matches_superSN:\n",
    "    temp_df = data_IsuperSN[data_IsuperSN.filenames == model]\n",
    "    t_Hlost = temp_df.age.max()-temp_df[(temp_df.X<0.01)&(temp_df.MH1<0.001)].age.min()\n",
    "    if t_Hlost < 150:\n",
    "        print(t_Hlost, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two models! one is a primary star, the other is a secondary. Let's make the data, save it and then move on to futher analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_match = data_IsuperSN[data_IsuperSN.filenames.isin(['NEWBINMODS/NEWBINMODS/z006/sneplot-z006-30-0.4-2.2',\n",
    "                                                           'NEWBINMODS/NEWSECMODS/z006_2/sneplot_2-z006-30-25.1189-1.80000'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_ISN\n",
    "del data_IsuperSN\n",
    "gc.garbage.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data for Table 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "primary_model = data_match.filenames.unique()[0]\n",
    "secondary_model = data_match.filenames.unique()[1]\n",
    "\n",
    "data_prim = data_match[data_match.filenames == primary_model]\n",
    "data_sec = data_match[data_match.filenames == secondary_model]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mass ZAMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZAMS -- Model 1\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325657</th>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          M1    M2\n",
       "325657  30.0  12.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('ZAMS -- Model 1')\n",
    "data_prim[data_prim.age==0.0][['M1', 'M2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZAMS -- Model 2\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1142778</th>\n",
       "      <td>30.0</td>\n",
       "      <td>25.1189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           M1       M2\n",
       "1142778  30.0  25.1189"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('ZAMS -- Model 2')\n",
    "data_sec[data_sec.age==0.0][['M1', 'M2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Characteristics at the end of the life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>X</th>\n",
       "      <th>MH1</th>\n",
       "      <th>MHe1</th>\n",
       "      <th>MC1</th>\n",
       "      <th>MO1</th>\n",
       "      <th>MNe1</th>\n",
       "      <th>Mej_superSN</th>\n",
       "      <th>Mrem_superSN</th>\n",
       "      <th>P_bin</th>\n",
       "      <th>modelimf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325781</th>\n",
       "      <td>11.42328</td>\n",
       "      <td>12.07601</td>\n",
       "      <td>0.009843</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>1.54064</td>\n",
       "      <td>1.30368</td>\n",
       "      <td>7.78203</td>\n",
       "      <td>0.75115</td>\n",
       "      <td>9.96994</td>\n",
       "      <td>1.45334</td>\n",
       "      <td>0.176182</td>\n",
       "      <td>1.582918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M1        M2         X     MH1     MHe1      MC1      MO1  \\\n",
       "325781  11.42328  12.07601  0.009843  0.0004  1.54064  1.30368  7.78203   \n",
       "\n",
       "           MNe1  Mej_superSN  Mrem_superSN     P_bin  modelimf  \n",
       "325781  0.75115      9.96994       1.45334  0.176182  1.582918  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prim.tail(1)[['M1', 'M2', 'X', 'MH1','MHe1', 'MC1', 'MO1', 'MNe1', 'Mej_superSN','Mrem_superSN', 'P_bin', 'modelimf']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period in years at death:  64.3063935\n"
     ]
    }
   ],
   "source": [
    "print(\"period in years at death: \", data_prim.tail(1).P_bin.values[0]*365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>X</th>\n",
       "      <th>MH1</th>\n",
       "      <th>MHe1</th>\n",
       "      <th>MC1</th>\n",
       "      <th>MO1</th>\n",
       "      <th>MNe1</th>\n",
       "      <th>Mej_superSN</th>\n",
       "      <th>Mrem_superSN</th>\n",
       "      <th>P_bin</th>\n",
       "      <th>modelimf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1142896</th>\n",
       "      <td>11.4469</td>\n",
       "      <td>26.48166</td>\n",
       "      <td>0.009891</td>\n",
       "      <td>0.00039</td>\n",
       "      <td>1.54683</td>\n",
       "      <td>1.3192</td>\n",
       "      <td>7.79297</td>\n",
       "      <td>0.74307</td>\n",
       "      <td>9.9918</td>\n",
       "      <td>1.4551</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.014344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              M1        M2         X      MH1     MHe1     MC1      MO1  \\\n",
       "1142896  11.4469  26.48166  0.009891  0.00039  1.54683  1.3192  7.79297   \n",
       "\n",
       "            MNe1  Mej_superSN  Mrem_superSN     P_bin  modelimf  \n",
       "1142896  0.74307       9.9918        1.4551  0.157336  0.014344  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sec.tail(1)[['M1', 'M2', 'X', 'MH1', 'MHe1', 'MC1', 'MO1', 'MNe1','Mej_superSN','Mrem_superSN', 'P_bin', 'modelimf']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period in years  at death:  57.4278225\n"
     ]
    }
   ],
   "source": [
    "print(\"period in years  at death: \", data_sec.tail(1).P_bin.values[0]*365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 1\n",
    "\n",
    "### Main Sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_all =  pd.read_pickle('Models_M1zams_10msol_ALL.pkl')\n",
    "zams = data_all[data_all.age==0.0][['log(T1)','log(L1)']]\n",
    "zams.sort_values('log(L1)', inplace=True)\n",
    "MS_T, MS_L = zams['log(T1)'].values, zams['log(L1)'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-43be556213e7>:12: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  L_dot_1=dL_1/dt_1\n",
      "<ipython-input-56-43be556213e7>:12: RuntimeWarning: invalid value encountered in true_divide\n",
      "  L_dot_1=dL_1/dt_1\n",
      "<ipython-input-56-43be556213e7>:13: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  last_loss_1 = data_after_mt_1[L_dot_1>=1e-6]\n"
     ]
    }
   ],
   "source": [
    "# Mass transfer phases\n",
    "mt_1 = data_prim[data_prim.DM1R<0.0][['age', 'log(T1)','log(L1)']]\n",
    "\n",
    "### Mass loss\n",
    "data_after_mt_1 = data_prim[data_prim.age > mt_1.age.max()]\n",
    "\n",
    "# Common envelope phase\n",
    "CEE_1 = data_prim[data_prim['log(a)']<data_prim['log(R1)']][['age', 'log(T1)','log(L1)']]\n",
    "\n",
    "# Finding the phase where Luminosity cahnges very rapidly\n",
    "dL_1=np.gradient(data_after_mt_1['log(L1)'].values)\n",
    "dt_1 = np.gradient(data_after_mt_1.age.values)\n",
    "L_dot_1=dL_1/dt_1\n",
    "last_loss_1 = data_after_mt_1[L_dot_1>=1e-6]\n",
    "\n",
    "# When H poor\n",
    "Hpoor_1 = data_prim[(data_prim.MH1<0.001) & (data_prim.X <0.01)].head(1)[['age', 'log(T1)','log(L1)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CO_core growth\n",
    "\n",
    "We check where the core growth starts - obviously it's not a clear divide but we pick that age at which the growth really picks up "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>CO_core1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325732</th>\n",
       "      <td>6591750.0</td>\n",
       "      <td>0.00006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325733</th>\n",
       "      <td>6605524.0</td>\n",
       "      <td>0.00006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325734</th>\n",
       "      <td>6619487.0</td>\n",
       "      <td>0.00006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325735</th>\n",
       "      <td>6633788.0</td>\n",
       "      <td>0.00007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325736</th>\n",
       "      <td>6648632.0</td>\n",
       "      <td>0.00007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325737</th>\n",
       "      <td>6664217.0</td>\n",
       "      <td>0.00008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325738</th>\n",
       "      <td>6680809.0</td>\n",
       "      <td>0.00009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325739</th>\n",
       "      <td>6698854.0</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325740</th>\n",
       "      <td>6718973.0</td>\n",
       "      <td>0.00011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325741</th>\n",
       "      <td>6741643.0</td>\n",
       "      <td>0.00013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325742</th>\n",
       "      <td>6766173.0</td>\n",
       "      <td>0.00017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325743</th>\n",
       "      <td>6773698.0</td>\n",
       "      <td>6.97610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325744</th>\n",
       "      <td>6774378.0</td>\n",
       "      <td>7.82659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325745</th>\n",
       "      <td>6776216.0</td>\n",
       "      <td>8.46123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325746</th>\n",
       "      <td>6781188.0</td>\n",
       "      <td>8.88386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325747</th>\n",
       "      <td>6793526.0</td>\n",
       "      <td>9.23852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325748</th>\n",
       "      <td>6806241.0</td>\n",
       "      <td>9.40742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325749</th>\n",
       "      <td>6816292.0</td>\n",
       "      <td>9.46222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325750</th>\n",
       "      <td>6824162.0</td>\n",
       "      <td>9.48064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325751</th>\n",
       "      <td>6830643.0</td>\n",
       "      <td>9.50068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325752</th>\n",
       "      <td>6836222.0</td>\n",
       "      <td>9.51840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325753</th>\n",
       "      <td>6841201.0</td>\n",
       "      <td>9.52767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325754</th>\n",
       "      <td>6845790.0</td>\n",
       "      <td>9.53361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325755</th>\n",
       "      <td>6850125.0</td>\n",
       "      <td>9.53382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325756</th>\n",
       "      <td>6854281.0</td>\n",
       "      <td>9.52466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325757</th>\n",
       "      <td>6858108.0</td>\n",
       "      <td>9.52014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325758</th>\n",
       "      <td>6861009.0</td>\n",
       "      <td>9.51920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325759</th>\n",
       "      <td>6862501.0</td>\n",
       "      <td>9.51478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325760</th>\n",
       "      <td>6863165.0</td>\n",
       "      <td>9.51231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325761</th>\n",
       "      <td>6863829.0</td>\n",
       "      <td>9.50960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325762</th>\n",
       "      <td>6864528.0</td>\n",
       "      <td>9.50418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325763</th>\n",
       "      <td>6865260.0</td>\n",
       "      <td>9.50182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325764</th>\n",
       "      <td>6866018.0</td>\n",
       "      <td>9.50118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325765</th>\n",
       "      <td>6866802.0</td>\n",
       "      <td>9.50018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325766</th>\n",
       "      <td>6867613.0</td>\n",
       "      <td>9.49680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325767</th>\n",
       "      <td>6868402.0</td>\n",
       "      <td>9.49182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325768</th>\n",
       "      <td>6869006.0</td>\n",
       "      <td>9.47861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325769</th>\n",
       "      <td>6869321.0</td>\n",
       "      <td>9.46491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325770</th>\n",
       "      <td>6869451.0</td>\n",
       "      <td>9.45079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325771</th>\n",
       "      <td>6869507.0</td>\n",
       "      <td>9.43445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325772</th>\n",
       "      <td>6869535.0</td>\n",
       "      <td>9.41841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325773</th>\n",
       "      <td>6869549.0</td>\n",
       "      <td>9.31320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325774</th>\n",
       "      <td>6869555.0</td>\n",
       "      <td>9.20183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325775</th>\n",
       "      <td>6869558.0</td>\n",
       "      <td>9.11425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325776</th>\n",
       "      <td>6869560.0</td>\n",
       "      <td>9.03893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325777</th>\n",
       "      <td>6869560.0</td>\n",
       "      <td>8.98388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325778</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>8.93173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325779</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>8.88967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325780</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>8.88208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325781</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>8.88208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  CO_core1\n",
       "325732  6591750.0   0.00006\n",
       "325733  6605524.0   0.00006\n",
       "325734  6619487.0   0.00006\n",
       "325735  6633788.0   0.00007\n",
       "325736  6648632.0   0.00007\n",
       "325737  6664217.0   0.00008\n",
       "325738  6680809.0   0.00009\n",
       "325739  6698854.0   0.00010\n",
       "325740  6718973.0   0.00011\n",
       "325741  6741643.0   0.00013\n",
       "325742  6766173.0   0.00017\n",
       "325743  6773698.0   6.97610\n",
       "325744  6774378.0   7.82659\n",
       "325745  6776216.0   8.46123\n",
       "325746  6781188.0   8.88386\n",
       "325747  6793526.0   9.23852\n",
       "325748  6806241.0   9.40742\n",
       "325749  6816292.0   9.46222\n",
       "325750  6824162.0   9.48064\n",
       "325751  6830643.0   9.50068\n",
       "325752  6836222.0   9.51840\n",
       "325753  6841201.0   9.52767\n",
       "325754  6845790.0   9.53361\n",
       "325755  6850125.0   9.53382\n",
       "325756  6854281.0   9.52466\n",
       "325757  6858108.0   9.52014\n",
       "325758  6861009.0   9.51920\n",
       "325759  6862501.0   9.51478\n",
       "325760  6863165.0   9.51231\n",
       "325761  6863829.0   9.50960\n",
       "325762  6864528.0   9.50418\n",
       "325763  6865260.0   9.50182\n",
       "325764  6866018.0   9.50118\n",
       "325765  6866802.0   9.50018\n",
       "325766  6867613.0   9.49680\n",
       "325767  6868402.0   9.49182\n",
       "325768  6869006.0   9.47861\n",
       "325769  6869321.0   9.46491\n",
       "325770  6869451.0   9.45079\n",
       "325771  6869507.0   9.43445\n",
       "325772  6869535.0   9.41841\n",
       "325773  6869549.0   9.31320\n",
       "325774  6869555.0   9.20183\n",
       "325775  6869558.0   9.11425\n",
       "325776  6869560.0   9.03893\n",
       "325777  6869560.0   8.98388\n",
       "325778  6869561.0   8.93173\n",
       "325779  6869561.0   8.88967\n",
       "325780  6869561.0   8.88208\n",
       "325781  6869561.0   8.88208"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prim[(data_prim.CO_core1>0.0) &(data_prim.age!=0.0) ][['age', 'CO_core1']].tail(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ONe_Core growth\n",
    "\n",
    "Same as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>ONe_core1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325752</th>\n",
       "      <td>6836222.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325753</th>\n",
       "      <td>6841201.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325754</th>\n",
       "      <td>6845790.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325755</th>\n",
       "      <td>6850125.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325756</th>\n",
       "      <td>6854281.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325757</th>\n",
       "      <td>6858108.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325758</th>\n",
       "      <td>6861009.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325759</th>\n",
       "      <td>6862501.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325760</th>\n",
       "      <td>6863165.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325761</th>\n",
       "      <td>6863829.0</td>\n",
       "      <td>0.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325762</th>\n",
       "      <td>6864528.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325763</th>\n",
       "      <td>6865260.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325764</th>\n",
       "      <td>6866018.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325765</th>\n",
       "      <td>6866802.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325766</th>\n",
       "      <td>6867613.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325767</th>\n",
       "      <td>6868402.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325768</th>\n",
       "      <td>6869006.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325769</th>\n",
       "      <td>6869321.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325770</th>\n",
       "      <td>6869451.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325771</th>\n",
       "      <td>6869507.0</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325772</th>\n",
       "      <td>6869535.0</td>\n",
       "      <td>0.00004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325773</th>\n",
       "      <td>6869549.0</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325774</th>\n",
       "      <td>6869555.0</td>\n",
       "      <td>0.00025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325775</th>\n",
       "      <td>6869558.0</td>\n",
       "      <td>0.25678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325776</th>\n",
       "      <td>6869560.0</td>\n",
       "      <td>0.68873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325777</th>\n",
       "      <td>6869560.0</td>\n",
       "      <td>1.11484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325778</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>1.49340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325779</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>1.82470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325780</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>1.88716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325781</th>\n",
       "      <td>6869561.0</td>\n",
       "      <td>1.88716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  ONe_core1\n",
       "325752  6836222.0    0.00001\n",
       "325753  6841201.0    0.00001\n",
       "325754  6845790.0    0.00001\n",
       "325755  6850125.0    0.00001\n",
       "325756  6854281.0    0.00001\n",
       "325757  6858108.0    0.00001\n",
       "325758  6861009.0    0.00001\n",
       "325759  6862501.0    0.00001\n",
       "325760  6863165.0    0.00001\n",
       "325761  6863829.0    0.00001\n",
       "325762  6864528.0    0.00002\n",
       "325763  6865260.0    0.00002\n",
       "325764  6866018.0    0.00002\n",
       "325765  6866802.0    0.00002\n",
       "325766  6867613.0    0.00002\n",
       "325767  6868402.0    0.00002\n",
       "325768  6869006.0    0.00002\n",
       "325769  6869321.0    0.00002\n",
       "325770  6869451.0    0.00002\n",
       "325771  6869507.0    0.00002\n",
       "325772  6869535.0    0.00004\n",
       "325773  6869549.0    0.00010\n",
       "325774  6869555.0    0.00025\n",
       "325775  6869558.0    0.25678\n",
       "325776  6869560.0    0.68873\n",
       "325777  6869560.0    1.11484\n",
       "325778  6869561.0    1.49340\n",
       "325779  6869561.0    1.82470\n",
       "325780  6869561.0    1.88716\n",
       "325781  6869561.0    1.88716"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prim[(data_prim.ONe_core1>0.0) &(data_prim.age!=0.0) ][['age', 'ONe_core1']].tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time (years)')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 750x1500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2,figsize=(5,10))\n",
    "\n",
    "\n",
    "ax[0].plot(MS_T, MS_L, ls='--', c='grey', label='Zero Age Main Sequence')\n",
    "ax[0].plot(data_prim['log(T1)'], data_prim['log(L1)'], label='ZAMS=30', zorder=15, c='k')\n",
    "\n",
    "ax[0].plot(CEE_1['log(T1)'], CEE_1['log(L1)'], c='orange', lw=4, alpha=1, label='CEE phase', zorder=10)\n",
    "ax[0].plot(mt_1['log(T1)'], mt_1['log(L1)'], c='green', lw=4, alpha=0.5,\n",
    "         label='mass transfer phase')\n",
    "\n",
    "ax[0].plot(last_loss_1['log(T1)'], last_loss_1['log(L1)'], c='cornflowerblue', lw=4, alpha=0.5,\n",
    "         label='Rapid mass loss')\n",
    "ax[0].scatter(Hpoor_1['log(T1)'],Hpoor_1['log(L1)'], marker='x', s=10, c='cornflowerblue', zorder=20, label='H poor')\n",
    "\n",
    "ax[0].text(4.4, 5.25, r'ZAMS=30$M_{\\odot}$', color='k')\n",
    "ax[0].text(5.2, 5.6, 'H-poor', color='cornflowerblue')\n",
    "\n",
    "ax[0].set_ylim([4.75,5.8])\n",
    "ax[0].set_xlim([5.5, 3.5])\n",
    "ax[0].set_xlabel('log(T)')\n",
    "ax[0].set_ylabel('log(L)')\n",
    "\n",
    "\n",
    "######################\n",
    "ax[1].plot(data_prim.age, data_prim.X, color='k', zorder=100, alpha=0.5, \n",
    "           label='X (hydrogen mass fraction)')\n",
    "\n",
    "ax[1].axvspan(mt_1.age.min(), mt_1.age.max(), color='green',label='Mass transfer', alpha=0.5)\n",
    "ax[1].axvspan(CEE_1.age.min(), CEE_1.age.max(), color='orange', label='CEE', alpha=0.8)\n",
    "ax[1].axvspan(last_loss_1.age.min(), last_loss_1.age.max(), color='cornflowerblue', \n",
    "              label='Strong winds', alpha=0.8)\n",
    "\n",
    "ax[1].axvline(6773698.0, ls='--', color='midnightblue', lw=1, label='Onset Core He burning')\n",
    "ax[1].axvline(6864528.0, ls=':', color='darkred', lw=1, label='Onset Core C burning')\n",
    "\n",
    "ax[1].plot(data_prim.age, data_prim['log(R1)'], label=r'log(R/R$_{\\odot}$)',)\n",
    "ax[1].plot(data_prim.age, data_prim.MH1, label=r'M$_{H}/M_{\\odot}$')\n",
    "\n",
    "\n",
    "\n",
    "ax[1].set_xlim([6.1e6,6.9e6])\n",
    "ax[1].set_ylim([-0.3,3])\n",
    "ax[1].legend(loc=1, fontsize=10, ncol=2, bbox_to_anchor=(1.05, -0.2))\n",
    "ax[1].set_xlabel('Time (years)')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}