docs/Quick_Start_Make_Cluster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPISEA Quick Start: Making A Cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a quick start guide to making a synthetic cluster using the SPISEA package. The cluster is constructed using a user-specified isochrone and initial mass function (IMF). Detailed documentation is provided in the ReadtheDocs page (https://spisea.readthedocs.io/en/latest/index.html).\n",
    "\n",
    "Before starting this tutorial, it is assumed that SPISEA has been installed and the user's python path has been altered to include the SPISEA top-level directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mwhosek/anaconda3/envs/astroconda2/lib/python3.6/site-packages/pysynphot/locations.py:346: UserWarning: Extinction files not found in /Users/mwhosek/models/models_2019_07/cdbs/extinction\n",
      "  warnings.warn('Extinction files not found in %s' % (extdir, ))\n"
     ]
    }
   ],
   "source": [
    "# Import necessary packages. \n",
    "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n",
    "from spisea.imf import imf, multiplicity\n",
    "import numpy as np\n",
    "import pylab as py\n",
    "import pdb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Make a SPISEA isochrone object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster is made from a theoretical isochrone at a given age, extinction, and distance from Earth. These parameters MUST be specified by the user. Other inputs (e.g. stellar evolution/atmosphere models, extinction law, and photometric filters used) are optional keywords. See documentation for all keywords and their default values.\n",
    "\n",
    "Important Note: The IsochronePhot class saves its output as a FITS table, which it will read on subsequent calls for the same isochrone rather than regenerating it from scratch. We highly recommend reading the \"Tips and Tricks: The IsochronePhot Object\" section of the Isochrone object documentation for details on how this process works.\n",
    "\n",
    "Here, we create a 5 Myr cluster isochrone at an extinction of 0.8 mags and distance of 4000 pc from Earth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define isochrone parameters\n",
    "logAge = np.log10(5*10**6.) # Age in log(years)\n",
    "AKs = 0.8 # extinction in mags\n",
    "dist = 4000 # distance in parsec\n",
    "metallicity = 0 # Metallicity in [M/H]\n",
    "\n",
    "# Define evolution/atmosphere models and extinction law\n",
    "evo_model = evolution.MISTv1() \n",
    "atm_func = atmospheres.get_merged_atmosphere\n",
    "red_law = reddening.RedLawHosek18b()\n",
    "\n",
    "# Also specify filters for synthetic photometry (optional). Here we use \n",
    "# the HST WFC3-IR F127M, F139M, and F153M filters\n",
    "filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n",
    "\n",
    "# Specify the directory we want the output isochrone\n",
    "# table saved in. If the directory does not already exist,\n",
    "# SPISEA will create it.\n",
    "iso_dir = 'isochrones/'\n",
    "\n",
    "# Make IsochronePhot object. Note that this will take a minute or two, \n",
    "# unless the isochrone has been generated previously.\n",
    "#\n",
    "# Note that this is not show all of the user options \n",
    "# for IsochronePhot. See docs for complete list of options.\n",
    "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n",
    "                            evo_model=evo_model, atm_func=atm_func,\n",
    "                            red_law=red_law, filters=filt_list,\n",
    "                                iso_dir=iso_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once calculated, the isochrone will be written as a fits file to a location set by the \"iso_dir\" keyword (note: if iso_dir is not defined, the isochrone is saved in the current working directory by default). In the future, the IsochronePhot function will read this file directly rather than recalculating the isochrone again. \n",
    "\n",
    "The output file is named as: \"iso_logAge_AKs_distance_metallicity.fits, using the specified values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          L                   Teff        ...    m_hst_f153m    \n",
      "          W                    K          ...                   \n",
      "---------------------- ------------------ ... ------------------\n",
      " 8.660109049222469e+24  2997.482453641835 ...   21.1520214464892\n",
      " 9.046193280874383e+24 3008.7858260184175 ...  21.10504146353302\n",
      " 9.449142476745298e+24 3020.1687795233192 ... 21.058402938607138\n",
      " 9.869958611246552e+24  3031.631609685951 ... 21.011969614391397\n",
      " 1.076707529684324e+25 3054.6500837349886 ... 20.919821469819336\n",
      "1.0829426036763875e+25 3065.3879429582494 ...  20.91549975563932\n",
      "1.1480726894593663e+25  3078.908328291189 ... 20.853895984633194\n",
      " 1.221834686575575e+25  3093.002145149403 ...  20.78833906517428\n",
      "1.3043887291289771e+25 3107.5858797888227 ...  20.71737738946115\n",
      "1.3956010100862058e+25 3122.5861578385725 ...  20.64218049228478\n",
      "                   ...                ... ...                ...\n",
      " 3.146902975957394e+32 13997.739388490047 ...  5.930259429682409\n",
      "3.1460717791216517e+32 13998.892300822487 ...  5.930802534623409\n",
      "3.1452525359273068e+32 14000.139431008332 ...  5.931362397671123\n",
      " 3.144405489181849e+32 14001.378759406476 ...  5.931929284479498\n",
      "3.1435076384266162e+32   14002.5320654016 ...  5.932494726985555\n",
      "3.1426231762052773e+32 14003.694937388533 ...  5.933057730634233\n",
      "3.1418572729129726e+32 14004.747427385868 ...  5.933555391434915\n",
      "3.1412514101696487e+32 14005.539485757616 ... 5.9339401294227105\n",
      "  3.14065875101774e+32 14006.427618268113 ...  5.934341601415309\n",
      " 3.140072730828415e+32  14007.35269293023 ... 5.9347489819733115\n",
      "Length = 694 rows\n"
     ]
    }
   ],
   "source": [
    "# The stars in the isochrone and associated properties  \n",
    "# are stored in an astropy table called \"points\" \n",
    "# within the IsochronePhot object. \n",
    "print(my_iso.points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('REDLAW', 'H18b'), ('ATMFUNC', 'get_merged_atmosphere'), ('EVOMODEL', 'MISTv1'), ('LOGAGE', 6.698970004336019), ('AKS', 0.8), ('DISTANCE', 4000), ('METAL_IN', 0), ('METAL_ACT', 0.02380291467262473), ('WAVEMIN', 3000), ('WAVEMAX', 52000)])\n"
     ]
    }
   ],
   "source": [
    "# The isochrone table has meta keywords describing its input properties:\n",
    "# REDLAW: which redlaw object was used\n",
    "# ATMFUNC: atmosphere grid was used\n",
    "# EVOMODEL: evolution model grid used\n",
    "# LOGAGE: log(Age) of isochrone\n",
    "# AKS: total extinction used\n",
    "# DISTANCE: distance used\n",
    "# METAL_IN: metallicity requested by user, in [M/H]\n",
    "# METAL_ACT: actual metallicity of model, in [M/H] \n",
    "# (only relevant if user chooses metallicity other than defined grid-points)\n",
    "# WAVEMIN, WAVEMAX: the minimum and maximum wavelengths of the stellar spectra (angstroms)\n",
    "print(my_iso.points.meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The columns in the isochrone table are: ['L', 'Teff', 'R', 'mass', 'logg', 'isWR', 'mass_current', 'phase', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m']\n"
     ]
    }
   ],
   "source": [
    "# See Isochrone Object documentation for column definitions\n",
    "print('The columns in the isochrone table are: {0}'.format(my_iso.points.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 M_sun: F127M = 19.047 mag, F139M = 18.446 mag, F153M = 17.773 mag\n"
     ]
    }
   ],
   "source": [
    "# Example case:\n",
    "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n",
    "idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n",
    "f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n",
    "f139m = np.round(my_iso.points[idx[0]]['m_hst_f139m'], decimals=3)\n",
    "f153m = np.round(my_iso.points[idx[0]]['m_hst_f153m'], decimals=3)\n",
    "print('1 M_sun: F127M = {0} mag, F139M = {1} mag, F153M = {2} mag'.format(f127m, f139m, f153m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa15b648f60>"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a color-magnitude diagram from the isochrone\n",
    "py.figure(1, figsize=(10,10))\n",
    "py.clf()\n",
    "py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n",
    "       my_iso.points['m_hst_f153m'], 'r-', label='_nolegend_')\n",
    "py.plot(my_iso.points['m_hst_f127m'][idx] - my_iso.points['m_hst_f153m'][idx], \n",
    "       my_iso.points['m_hst_f153m'][idx], 'b*', ms=15, label='1 $M_\\odot$')\n",
    "py.xlabel('F127M - F153M')\n",
    "py.ylabel('F153M')\n",
    "py.gca().invert_yaxis()\n",
    "py.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Make an Initial Mass Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SPISEA offers a range of initial mass functions (IMFs) a user can use from to make the cluster. In addition to the parameters defining the IMF, the user can input a SPISEA multiplicity object, which defines the multiplicity properties of the population. The default multiplicity is None (e.g. all stars are single).\n",
    "\n",
    "Here we define a Kroupa IMF using the Multiplicity properties defined in Lu+13. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n",
    "# based on Lu+13. This means that star systems will be unresolved, i.e., \n",
    "# that all components of a star system are combined into a single \"star\" in the cluster\n",
    "imf_multi = multiplicity.MultiplicityUnresolved()\n",
    "\n",
    "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n",
    "\n",
    "# NOTE: when defining the power law slope for each segment of the IMF, we define\n",
    "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n",
    "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n",
    "\n",
    "massLimits = np.array([0.2, 0.5, 1, 120]) # Define boundaries of each mass segement\n",
    "powers = np.array([-1.3, -2.3, -2.3]) # Power law slope associated with each mass segment\n",
    "my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Make the Cluster  \n",
    "#### Option 1: Resolved Cluster without compact objects\n",
    "\n",
    "Here we make a resolved cluster using the ResolvedCluster object. \n",
    "\n",
    "To create the cluster, the user passes in an isochrone object, and imf object, and specifies the total cluster mass. Here we will make a 10^5 M_sun cluster using the isochrone and imf we have defined. \n",
    "\n",
    "###### Some Notes\n",
    "1. Unless an IFMR object is defined, no compact objects are included. Stars that have evolved into compact objects will be dropped from the cluster. \n",
    "2. Stars generated by the IMF object that have masses below the lowest mass in the evolution model are dropped from the cluster.\n",
    "2. If you wish to create a cluster with differential extinction included in the output photometry, then you can use the ResolvedClusterDiffRedden object. It is the same as ResolvedCluster, but with an additional parameter to define dAKs, which characterizes the spread of extinction within the cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 74 stars out of mass range\n"
     ]
    }
   ],
   "source": [
    "# Define total cluster mass\n",
    "mass = 10**5.\n",
    "\n",
    "# Make cluster object\n",
    "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The individual stars (or star systems) in the cluster are stored in an astropy table called \"star_systems\" within the cluster object. If a multiplicity object is used, then an additional \"companions\" table is created that contains the properties of the companions to the primary star within each star system. See cluster object documentation for a description of the columns in these tables. \n",
    "\n",
    "If a multiplicity object is used, then the photometry in the star_systems table is the COMBINED photometry of the system; it includes the contributions from all companions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        mass        isMultiple ...    m_hst_f153m     N_companions\n",
      "------------------- ---------- ... ------------------ ------------\n",
      " 0.6876030977679913      False ... 18.389044207067464            0\n",
      " 0.5955047093772027      False ...  18.59784842254386            0\n",
      "0.22888954345052595      False ... 20.023421078238613            0\n",
      " 0.7674862431783736       True ...   18.0051258246746            1\n",
      " 0.5642222892161842      False ... 18.674063270037458            0\n",
      "  1.089763617194751      False ... 17.646195710306777            0\n",
      " 0.5764417363236184      False ... 18.644066247034154            0\n",
      " 0.6945802953923282      False ... 18.373593985664552            0\n",
      " 0.2918079653214152      False ... 19.646020445469127            0\n",
      " 0.2654043135986742      False ... 19.784918937378045            0\n",
      "                ...        ... ...                ...          ...\n",
      "0.21788083183588838      False ...  20.09740516281386            0\n",
      " 0.2084958320428418      False ... 20.160477086462425            0\n",
      "  7.177783813711204       True ...  13.76389690674836            1\n",
      " 0.6970980816560024       True ...  18.15184809900597            1\n",
      " 0.6631502158305624      False ... 18.441648615714623            0\n",
      " 1.2947809690914678      False ...   17.3222437955907            0\n",
      "0.25029053901467957      False ...  19.87836996594056            0\n",
      " 0.9500007704623843      False ...  17.88252985999676            0\n",
      " 0.4392328921597474      False ...  19.02554009274596            0\n",
      " 0.2605330473087063      False ... 19.813906355345033            0\n",
      " 0.2341168935953943      False ... 19.990680958036442            0\n",
      "Length = 78379 rows\n"
     ]
    }
   ],
   "source": [
    "# Look at star systems table\n",
    "print(cluster.star_systems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cluster table contains these columns: ['mass', 'isMultiple', 'systemMass', 'Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase', 'metallicity', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m', 'N_companions']\n"
     ]
    }
   ],
   "source": [
    "print('The cluster table contains these columns: {0}'.format(cluster.star_systems.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "system_idx         mass        ...    m_hst_f139m        m_hst_f153m    \n",
      "---------- ------------------- ... ------------------ ------------------\n",
      "         3 0.24675997749084191 ... 20.563701959287826  19.90251395648469\n",
      "        13 0.37427415165641575 ... 19.935549695054327  19.26089149163212\n",
      "        21  1.1031866473217342 ...  18.27788436660256  17.62478259245194\n",
      "        27  0.2261763927189317 ... 20.700050305132947 20.041654817137246\n",
      "        30 0.24351327466283715 ... 20.585549714235427 19.924810664357523\n",
      "        33  1.3739797199235617 ... 17.787945415289006 17.192454221341634\n",
      "        36  0.8853706000102676 ...  18.68802065076431 17.993905280315655\n",
      "        36  0.5642335448205728 ...  19.37010845835326 18.674035221483344\n",
      "        47  0.2966822705750629 ...  20.28632611434612  19.61948187143378\n",
      "        54 0.24975150440679514 ... 20.543655971454946  19.88205619159839\n",
      "       ...                 ... ...                ...                ...\n",
      "     78351  1.2376751887661364 ... 18.035126418759774 17.412404186653344\n",
      "     78354 0.31213069455188613 ... 20.204014011448535 19.535371598871034\n",
      "     78357 0.27679441089161594 ...  20.38923702443474  19.72453916098156\n",
      "     78360  0.9014988021676168 ... 18.657742310702165 17.965414404445802\n",
      "     78364  0.6888881335136626 ...  19.08900955643712 18.386198639570274\n",
      "     78366  0.6980230969223282 ... 19.068990255125353 18.365970287906695\n",
      "     78366   4.727838039253875 ... 15.020104863055376   14.6509524800243\n",
      "     78367   1.653298517897867 ... 17.061733897360025  16.51120386646886\n",
      "     78367  3.3438186255283364 ...  15.73063278758436 15.355422018083411\n",
      "     78370  0.9370632968186573 ...  18.59248602156636 17.905121399833412\n",
      "     78371   0.230895129296845 ...  20.66949566003222 20.010460212653776\n",
      "Length = 16156 rows\n"
     ]
    }
   ],
   "source": [
    "# The companions table is accessed in a similar way\n",
    "print(cluster.companions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa15c48c2b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n",
    "# multiple systems on the photometry\n",
    "clust = cluster.star_systems\n",
    "iso = my_iso.points\n",
    "\n",
    "py.figure(2, figsize=(10,10))\n",
    "py.clf()\n",
    "py.plot(clust['m_hst_f127m'] - clust['m_hst_f153m'], clust['m_hst_f153m'],\n",
    "       'k.', ms=5, alpha=0.1, label='__nolegend__')\n",
    "py.plot(iso['m_hst_f127m'] - iso['m_hst_f153m'], iso['m_hst_f153m'],\n",
    "       'r-', label='Isochrone')\n",
    "py.xlabel('F127M - F153M')\n",
    "py.ylabel('F153M')\n",
    "py.gca().invert_yaxis()\n",
    "py.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option 2: Resolved cluster with compact objects (white dwarfs, neutron stars, and black holes)\n",
    "This is quite similar to the above, but includes compact objects. The additional piece of information required is to choose an initial-final mass relation (IFMR.)\n",
    "\n",
    "The output is the same as if we were making a cluster without using an IFMR. However, you can tell that compact objects are made by looking at the 'phase' keyword. Black holes have 'phase' = 103, neutron stars have 'phase' = 102, and white dwarfs have 'phase' = 101. For these compact objects, the luminosity and temperature will return values of zero, and photometry will return nan, since we are assuming they are totally dark.\n",
    "\n",
    "Here, we make 4 different clusters, each of mass $10^6 M_\\odot$, by taking different combinations of age (either 100 Myr or 10 Gyr) and IMF (top-heavy or Kroupa). We then look at the different distributions of BH and WD masses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create isochrone object  \n",
    "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n",
    "my_ifmr = ifmr.IFMR_Raithel18()\n",
    "my_iso_young = synthetic.IsochronePhot(8, 0, 10,\n",
    "                                 evo_model = evolution.MISTv1(),\n",
    "                                      filters=filt_list)\n",
    "\n",
    "my_iso_old = synthetic.IsochronePhot(10, 0, 10,\n",
    "                                 evo_model = evolution.MISTv1(),\n",
    "                                    filters=filt_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create IMF objects                                                                                                                                                     \n",
    "massLimits = np.array([0.1, 0.5, 120])\n",
    "powers_kroupa = np.array([-1.3, -2.3])\n",
    "powers_theavy = np.array([-1.3, -1.3]) # top heavy\n",
    "trunc_kroupa = imf.IMF_broken_powerlaw(massLimits, powers_kroupa)\n",
    "trunc_theavy = imf.IMF_broken_powerlaw(massLimits, powers_theavy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5586 stars out of mass range\n",
      "Found 4106 stars out of mass range\n",
      "Found 99584 stars out of mass range\n",
      "Found 73734 stars out of mass range\n"
     ]
    }
   ],
   "source": [
    "# Make clusters                                                                                                                                                          \n",
    "cluster_mass = 10**6\n",
    "cluster_young_theavy = synthetic.ResolvedCluster(my_iso_young, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n",
    "cluster_old_theavy = synthetic.ResolvedCluster(my_iso_old, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n",
    "cluster_young_kroupa = synthetic.ResolvedCluster(my_iso_young, trunc_kroupa, cluster_mass, ifmr=my_ifmr)\n",
    "cluster_old_kroupa = synthetic.ResolvedCluster(my_iso_old, trunc_kroupa, cluster_mass, ifmr=my_ifmr)\n",
    "\n",
    "# Get the outputs\n",
    "young_theavy = cluster_young_theavy.star_systems\n",
    "old_theavy = cluster_old_theavy.star_systems\n",
    "young_kroupa = cluster_young_kroupa.star_systems\n",
    "old_kroupa = cluster_old_kroupa.star_systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa15ba0bf60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "young_theavy_bh_idx = np.where(young_theavy['phase'] == 103)[0]\n",
    "old_theavy_bh_idx = np.where(old_theavy['phase'] == 103)[0]\n",
    "young_kroupa_bh_idx = np.where(young_kroupa['phase'] == 103)[0]\n",
    "old_kroupa_bh_idx = np.where(old_kroupa['phase'] == 103)[0]\n",
    "\n",
    "bh_bins = np.linspace(5, 16, 16)\n",
    "wd_bins = np.linspace(0.4, 1.4, 16)\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(young_theavy[young_theavy_bh_idx]['mass_current'], histtype = 'step',\n",
    "        bins = bh_bins, label = '100 Myr, TH IMF', color = 'red', linestyle = ':', lw = 2)\n",
    "plt.hist(old_theavy[old_theavy_bh_idx]['mass_current'], histtype = 'step',\n",
    "        bins = bh_bins, label = '10 Gyr, TH IMF', color = 'gray', linestyle = ':', lw = 2)\n",
    "plt.hist(young_kroupa[young_kroupa_bh_idx]['mass_current'], histtype = 'step',\n",
    "        bins = bh_bins, label = '100 Myr, Kr IMF', color = 'red', lw = 2)\n",
    "plt.hist(old_kroupa[old_kroupa_bh_idx]['mass_current'], histtype = 'step',\n",
    "        bins = bh_bins, label = '10 Gyr, Kr IMF', color = 'gray', lw = 2)\n",
    "plt.title('BH Mass Function')\n",
    "plt.xlabel('Mass ($M_\\odot$)')\n",
    "plt.ylabel('Number')\n",
    "plt.legend()\n",
    "\n",
    "young_theavy_wd_idx = np.where(young_theavy['phase'] == 101)[0]\n",
    "old_theavy_wd_idx = np.where(old_theavy['phase'] == 101)[0]\n",
    "young_kroupa_wd_idx = np.where(young_kroupa['phase'] == 101)[0]\n",
    "old_kroupa_wd_idx = np.where(old_kroupa['phase'] == 101)[0]\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(young_theavy[young_theavy_wd_idx]['mass_current'], histtype = 'step',\n",
    "        bins = wd_bins, label = '100 Myr, TH IMF', color = 'red', linestyle = ':', lw = 2)\n",
    "plt.hist(old_theavy[old_theavy_wd_idx]['mass_current'], histtype = 'step',\n",
    "        bins = wd_bins, label = '10 Gyr, TH IMF', color = 'gray', linestyle = ':', lw = 2)\n",
    "plt.hist(young_kroupa[young_kroupa_wd_idx]['mass_current'], histtype = 'step',\n",
    "        bins = wd_bins, label = '100 Myr, Kr IMF', color = 'red', lw = 2)\n",
    "plt.hist(old_kroupa[old_kroupa_wd_idx]['mass_current'], histtype = 'step',\n",
    "        bins = wd_bins, label = '10 Gyr, Kr IMF', color = 'gray', lw = 2)\n",
    "plt.yscale('log')\n",
    "plt.title('WD Mass Function')\n",
    "plt.xlabel('Mass ($M_\\odot$)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotted above are the distributions of BH and WD masses for clusters of different ages (100 Myr or 10 Gyr), with either a top-heavy or Kroupa IMF. For BHs, since those are formed relatively early on, the age of the cluster does not significantly change the mass distribution as most BHs have already formed by 100 Myr. However, the top heavy IMF allows the creation of many more massive compact objects. For WDs, both the age and IMF make significant differences in the distribution (note y-axis is logscaled)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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