{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a276c947",
   "metadata": {},
   "source": [
    "# More Realistic Galaxy Populations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d5d209",
   "metadata": {},
   "source": [
    "Most global 21-cm examples in the documentation tie the volume-averaged emissivity of galaxies to the rate at which mass collapses into dark matter halos (this is the default option in *ARES*). Because of this, they are referred to as $f_{\\text{coll}}$ models throughout, and are selected by setting ``pop_sfr_model='fcoll'``. In the code, they are represented by ``GalaxyAggregate`` objects, named as such because galaxies are only modeled in aggregate, i.e., there is no distinction in the properties of galaxies as a function of mass, luminosity, etc.\n",
    "\n",
    "However, we can also run more detailed models in which the properties of galaxies are allowed to change as a function of halo mass, redshift, and/or potentially other quantities.\n",
    "\n",
    "A few usual imports before we begin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d931f71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import ares\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b43d58",
   "metadata": {},
   "source": [
    "## Double Power-Law Star Formation Efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab3a930f",
   "metadata": {},
   "source": [
    "The most common extension to simple models is to allow the star formation efficiency (SFE) to vary as a function of halo mass. This is motivated observationally by the mismatch in the shape of the galaxy luminosity function (LF) and dark matter halo mass function (HMF). In [Mirocha, Furlanetto, & Sun (2017)](http://adsabs.harvard.edu/abs/2017MNRAS.464.1365M>), we adopted a double power-law form for the SFE, i.e., \n",
    "\n",
    "\\begin{equation}\n",
    "f_{\\ast}(M_h) = \\frac{2 f_{\\ast,p}} {\\left(\\frac{M_h}{M_{\\text{p}}} \\right)^{\\gamma_{\\text{lo}}} + \\left(\\frac{M_h}{M_{\\text{p}}}  \\right)^{\\gamma_{\\text{hi}}}}\n",
    "\\end{equation}\n",
    "\n",
    "where the free parameters are the normalization, $f_{\\ast,p}$, the peak mass, $M_p$, and the power-law indices in the low-mass and high-mass limits, $\\gamma_{\\text{lo}}$ and $\\gamma_{\\text{hi}}$, respectively. Combined with a model for the mass accretion rate onto dark matter halos ($\\dot{M}_h$; see next section), the star formation rate as computed as\n",
    "\n",
    "\\begin{equation}\n",
    "\\dot{M}_{\\ast} = f_{\\ast} \\left(\\frac{\\Omega_{b,0}}{\\Omega_{m,0}} \\right) \\dot{M}_h\n",
    "\\end{equation}\n",
    "\n",
    "In general, the SFE curve must be calibrated to an observational dataset (see [Fitting to UVLFs](example_mcmc_lf)), but you can also just grab our best-fitting parameters for a redshift-independent SFE curve as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "795ff5c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = ares.util.ParameterBundle('mirocha2017:base')\n",
    "pars = p.pars_by_pop(0, strip_id=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2a0724",
   "metadata": {},
   "source": [
    "The second command extracts only the parameters associated with population #0, which is the stellar population in this calculation (population #1 is responsible for X-ray emission only; see [Models with Multiple Source Populations](example_gs_multipop) for more info on the approach to populations in *ARES*). Passing ``strip_id=True`` removes all ID numbers from parameter names, e.g., ``pop_sfr_model{0}`` becomes ``pop_sfr_model``. The reason for doing that is so we can generate a single ``GalaxyPopulation`` instance, e.g.,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd548585",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = ares.populations.GalaxyPopulation(**pars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba99f5d",
   "metadata": {},
   "source": [
    "If you glance at the contents of ``pars``, you'll notice that the parameters that define the double power-law share a ``pq`` prefix. This is short for \"parameterized quantity\", and is discussed more generally on the page about the [ParameterizedQuantity object](../uth_pq.html).\n",
    "\n",
    "**NOTE:** You can access population objects used in a simulation via the ``pops`` attribute, which is a list of population objects that belongs to instances of  common simulation classes like ``Global21cm``, ``MetaGalacticBackground``, etc.\n",
    "\n",
    "Now, to generate a model for the luminosity function, simply define your redshift of interest and array of magnitudes (assumed to be rest-frame $1600 \\unicode{x212B}$ AB magnitudes), and pass them to the aptly named ``LuminosityFunction`` function,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5da4de31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Loaded $ARES/input/hmf/hmf_ST_planck_TTTEEE_lowl_lowE_best_logM_1400_4-18_z_1201_0-60.hdf5.\n",
      "# Loaded $ARES/input/bpass_v1/SEDS/sed.bpass.constant.nocont.sin.z020\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d6fac90>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = 6\n",
    "MUV = np.linspace(-24, -10)\n",
    "_bins, lf = pop.get_lf(z, MUV)\n",
    "\n",
    "pl.semilogy(MUV, lf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13c200c",
   "metadata": {},
   "source": [
    "To compare to the observed galaxy luminosity function, we can use some convenience routines setup to easily access and plot measurements stored in the *ARES* ``litdata`` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "687f5c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/ma/core.py:2826: UserWarning: Warning: converting a masked element to nan.\n",
      "  order=order, subok=True, ndmin=ndmin)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# WARNING: finkelstein2015 wavelength=1500.0A, not 1600.0A!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18f924510>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "obslf = ares.analysis.GalaxyPopulation()\n",
    "ax = obslf.Plot(z=z, round_z=0.2)\n",
    "ax.semilogy(MUV, lf)\n",
    "ax.set_ylim(1e-8, 10)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ceba72f",
   "metadata": {},
   "source": [
    "The ``round_z`` keyword argument makes it so that any dataset available in the range $5.8 \\leq z \\leq 6.2$ gets included in the plot. To do this for multiple redshifts at the same time, you could do something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53fc9702",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/ma/core.py:2826: UserWarning: Warning: converting a masked element to nan.\n",
      "  order=order, subok=True, ndmin=ndmin)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# WARNING: finkelstein2015 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: weisz2014 wavelength=1700.0A, not 1600.0A!\n",
      "# WARNING: vanderburg2010 wavelength=1500.0A, not 1600.0A!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/ma/core.py:2826: UserWarning: Warning: converting a masked element to nan.\n",
      "  order=order, subok=True, ndmin=ndmin)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# WARNING: finkelstein2015 wavelength=1500.0A, not 1600.0A!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/ma/core.py:2826: UserWarning: Warning: converting a masked element to nan.\n",
      "  order=order, subok=True, ndmin=ndmin)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# WARNING: finkelstein2015 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: mclure2013 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: atek2015 wavelength=1500.0A, not 1600.0A!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/ma/core.py:2826: UserWarning: Warning: converting a masked element to nan.\n",
      "  order=order, subok=True, ndmin=ndmin)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n",
      "/Users/jordanmirocha/Dropbox/work/soft/miniconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# WARNING: finkelstein2015 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: bowler2020 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: stefanon2019 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: mclure2013 wavelength=1500.0A, not 1600.0A!\n",
      "# WARNING: rojasruiz2020 wavelength=1500.0A, not 1600.0A!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "redshifts = [5,6,7,8]\n",
    "MUV = np.linspace(-24, -10)\n",
    "\n",
    "\n",
    "# Create a 1x4 panel plot, include all available data sources\n",
    "fig, axes = pl.subplots(1, len(redshifts), figsize=(4*len(redshifts), 4))\n",
    "\n",
    "for i, z in enumerate(redshifts):\n",
    "    obslf.Plot(z=z, round_z=0.3, ax=axes[i])\n",
    "    \n",
    "    _bins, lf = pop.get_lf(z, MUV)\n",
    "    axes[i].semilogy(MUV, lf)\n",
    "    axes[i].annotate(r'$z \\simeq %.1f$' % z, (-24, 1e-1))\n",
    "    axes[i].legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528558ac",
   "metadata": {},
   "source": [
    "To create the ``GalaxyPopulation`` used above from scratch (i.e., without using parameter bundles), we could have just done:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84fd419e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pars = \\\n",
    "{\n",
    " 'pop_sfr_model': 'sfe-func',\n",
    " 'pop_sed': 'eldridge2009',\n",
    "\n",
    "\n",
    " 'pop_fstar': 'pq',\n",
    " 'pq_func': 'dpl',\n",
    " 'pq_func_par0': 0.05,\n",
    " 'pq_func_par1': 2.8e11,\n",
    " 'pq_func_par2': 0.51,\n",
    " 'pq_func_par3': -0.61,\n",
    " 'pq_func_par4': 1e10,  # halo mass at which SFE is normalized\n",
    "}\n",
    "    \n",
    "\n",
    "pop = ares.populations.GalaxyPopulation(**pars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e944b424",
   "metadata": {},
   "source": [
    "**NOTE:** Beware that by default, the double power-law is normalized at $M_h = 10^{10} \\ M_{\\odot}$ (see ``ps_func_par4`` above), whereas the Equation above for $f_{\\ast}$ is defined such that ``pq_func_par0`` refers to the SFE at the peak mass. If you prefer a peak-normalized SFE, you can set ``pq_func='dpl_normP'`` instead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf0edbe",
   "metadata": {},
   "source": [
    "### Accretion Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3786fe23",
   "metadata": {},
   "source": [
    "By default, *ARES* will derive the mass accretion rate (MAR) onto halos from the HMF itself (see Section 2.2 of [Furlanetto et al. 2017](http://adsabs.harvard.edu/abs/2017MNRAS.472.1576F>) for details). That is, ``pop_MAR='hmf'`` by default. There are also two other options:\n",
    "\n",
    "* Plug-in your favorite mass accretion model as a lambda function, e.g., ``pop_MAR=lambda z, M: 1. * (M / 1e12)**1.1 * (1. + z)**2.5``.\n",
    "* Grab a model from ``litdata``. The median MAR from McBride et al. (2009) is included (same as above equation), and can used as ``pop_MAR='mcbride2009'``. If you'd like to add more options, use ``ares/input/litdata/mcbride2009.py`` as a guide.\n",
    "\n",
    "**WARNING:** Note that the MAR formulae determined from numerical simulations may not have been calibrated at the redshifts most often targeted in *ARES* calculations, nor are they guaranteed to be self-consistent with the HMF used in *ARES*. One approach used in [Sun \\& Furlanetto (2016)](http://adsabs.harvard.edu/abs/2016MNRAS.460..417S>) is to re-normalize the MAR by requiring its integral to match that predicted by $f_{\\text{coll}}(z)$, which can boost the accretion rate at high redshifts by a factor of few. Setting ``pop_MAR_conserve_norm=True`` will enforce this condition in *ARES*.\n",
    "\n",
    "See [this page](../uth_pop_halo.html) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e492c2b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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