class_sz_planck_and_choietal_cib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a5d421f2-9fef-44bd-b447-dd850cedca9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "font = {'size'   : 16, 'family':'STIXGeneral'}\n",
    "axislabelfontsize='large'\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams[\"figure.figsize\"] = [3.0,2.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efef1047-21b9-4f0d-aa4a-10eb158de324",
   "metadata": {},
   "source": [
    "The goal of this notebook is to provide a synthetic picture of the origin of the CIB model in Choi et al. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3694b232-3ea1-4da6-a163-752bc7f04d43",
   "metadata": {},
   "source": [
    "Let us first define several important quantities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "859fc986-a88a-437e-93ea-636985d0b65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "choi_params_dict = {\n",
    "# Amplitude of clustered CIB\n",
    "'a_c':    3.15, # see table 7 of choi et al, the uncertainty is 0.72\n",
    "# # Amplitude of Poisson CIB\n",
    "'a_d':   6.58, # see table 7 of choi et al, the uncertainty is 0.37\n",
    "'Tdust_CIB' : 9.7, # same as f90 act code\n",
    "'nu0_CIB_ghz' : 150., # pivot frequency for CIB in choi et al, same as f90 act code \n",
    "'beta_CIB': 2.87, # see table 7 of choi et al, 2.87+0.34-0.54\n",
    "    \n",
    "    \n",
    "# We note that some other values may be explored. As below.\n",
    "# Tdust_CIB: 24.0       #CIB effective dust temperature [K] (Table 9 of http://www.aanda.org/articles/aa/pdf/2014/11/aa22093-13.pdf)\n",
    "# beta_CIB: 1.2         #CIB modified blackbody spectral index (Table 9 of http://www.aanda.org/articles/aa/pdf/2014/11/aa22093-13.pdf ; Table 10 of that paper contains CIB monopoles)\n",
    "# nu0_CIB_ghz: 353.0    #CIB pivot frequency [GHz]\n",
    "    \n",
    "# The power law indsex in multipole space for clustered CIB\n",
    "# cib ~  l(l+1) l^n\n",
    "# note that we do not use that explicitely in choi at el \n",
    "# 'cibc_n' : -1.2, # this is related to the 0.8 exponent, since \n",
    "\n",
    "'cib_temp_file' : '/Users/boris/Work/CLASS-SZ/SO-SZ/bplike/data/actpolfull_dr4.01/data/Fg/cib_extra.dat',\n",
    "# pivot multipole, so the template is such that Dl = 1 and this pivot scale \n",
    "'high_ell0' : 3000\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "deb8e6c9-1329-4bc9-8bf6-359cd7cc025d",
   "metadata": {},
   "outputs": [],
   "source": [
    "TCMB = 2.725 #Kelvin # 2.725 value in choi et al\n",
    "TCMB_uK = 2.725e6 #micro-Kelvin # 2.725 value in choi et al \n",
    "\n",
    "\n",
    "hplanck=6.626068e-34 #MKS\n",
    "kboltz=1.3806503e-23 #MKS\n",
    "clight=299792458.0 #MKS\n",
    "m_elec = 510.999 #keV\n",
    "\n",
    "# function needed for Planck bandpass integration/conversion following approach in Sec. 3.2 of https://arxiv.org/pdf/1303.5070.pdf\n",
    "# blackbody derivative\n",
    "# units are 1e-26 Jy/sr/uK_CMB\n",
    "def dBnudT(nu_ghz):\n",
    "    nu = 1.e9*np.asarray(nu_ghz)\n",
    "    X = hplanck*nu/(kboltz*TCMB)\n",
    "    return (2.*hplanck*nu**3.)/clight**2. * (np.exp(X))/(np.exp(X)-1.)**2. * X/TCMB_uK\n",
    "\n",
    "# conversion from specific intensity to Delta T units (i.e., 1/dBdT|T_CMB)\n",
    "#   i.e., from W/m^2/Hz/sr (1e-26 Jy/sr) --> uK_CMB\n",
    "#   i.e., you would multiply a map in 1e-26 Jy/sr by this factor to get an output map in uK_CMB\n",
    "def ItoDeltaT(nu_ghz):\n",
    "    return 1./dBnudT(nu_ghz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69852364-fda2-4fb6-8475-ce05761d1ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_mu(p,nu_ghz):\n",
    "        nu = 1.e9*np.asarray(nu_ghz).astype(float)\n",
    "        X_CIB = hplanck*nu/(kboltz*(p['Tdust_CIB']))\n",
    "        nu0_CIB = p['nu0_CIB_ghz']*1.e9\n",
    "        dust_beta_param_name = 'beta_CIB'\n",
    "        X0_CIB = hplanck*nu0_CIB/(kboltz*(p['Tdust_CIB']))\n",
    "        resp = (nu/nu0_CIB)**(3.0+(p[dust_beta_param_name])) * ((np.exp(X0_CIB) - 1.0) / (np.exp(X_CIB) - 1.0)) * (ItoDeltaT(np.asarray(nu_ghz).astype(float))/ItoDeltaT(p['nu0_CIB_ghz']))\n",
    "        resp[np.where(nu_ghz == None)] = 0. #this case is appropriate for HI or other maps that contain no CMB-relevant signals (and also no CIB); they're assumed to be denoted by None in nu_ghz\n",
    "        return resp\n",
    "    \n",
    "from scipy.interpolate import interp1d\n",
    "def get_template(ells,template_file,ell_pivot=None):\n",
    "    ls,pow = np.loadtxt(template_file,unpack=True)\n",
    "    powfunc = interp1d(ls,pow)\n",
    "    if ell_pivot is not None:\n",
    "        pow_pivot = powfunc(ell_pivot)\n",
    "    else:\n",
    "        pow_pivot = 1.\n",
    "    return powfunc(ells)/pow_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "97cd4c5d-8708-4b46-a1aa-54bd483ccd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "planck_2013 ={\n",
    "'217x217':{\n",
    "'cl' : np.asarray([\n",
    "1.78e3, # < this is an upper bound\n",
    "8.47e2, # < this is an upper bound\n",
    "4.17e2, \n",
    "2.62e2, \n",
    "1.75e2, \n",
    "1.17e2, \n",
    "8.82e1, \n",
    "6.42e1, \n",
    "3.34e1, \n",
    "4.74e1\n",
    "]),\n",
    "'ecl' : np.asarray([\n",
    "0.,\n",
    "0., \n",
    "0.47e2, \n",
    "0.20e2, \n",
    "0.13e2, \n",
    "0.10e2, \n",
    "0.89e1, \n",
    "1.61e1, \n",
    "2.15e1, \n",
    "0.65e1\n",
    "]),\n",
    "'ell': np.asarray([\n",
    "53,\n",
    "114,\n",
    "187,\n",
    "320, \n",
    "502, \n",
    "684, \n",
    "890,\n",
    "1158, \n",
    "1505,\n",
    "1956\n",
    "])\n",
    "},\n",
    "\n",
    "    '143x143':{'cl':np.asarray([1.55e2, 6.41e1, 3.64e1, 3.23e1, 2.81e1, 2.27e1, 1.84e1, 1.58e1, 1.25e1]),\n",
    "               'ecl':np.asarray([0.,0., 0.73e1, 0.35e1, 0.30e1, 0.29e1, 0.35e1, 0.91e1, 1.28e1]),\n",
    "               'ell':np.asarray([53,114,187,320, 502, 684, 890,1158, 1505])},\n",
    "    '353x353':{'cl':np.asarray([3.68e4, 1.66e4, 7.88e3, 4.35e3, 2.60e3, 1.74e3, 1.29e3, 9.35e2, 7.75e2, 6.08e2]),\n",
    "               'ecl':np.asarray([0.,0., 0.53e3, 0.18e3, 0.10e3, 0.07e3, 0.05e3, 0.33e2, 0.22e2, 0.16e2]),\n",
    "               'ell':np.asarray([53,114,187,320, 502, 684, 890,1158, 1505, 1956])},\n",
    "    '545x545':{'cl':np.asarray([3.74e5, 1.45e5, 6.63e4, 3.34e4, 1.91e4, 1.25e4, 9.17e3, 6.83e3, 5.34e3, 4.24e3, 3.42e3]),\n",
    "               'ecl':np.asarray([0.,0., 0.51e4, 0.12e4, 0.04e4, 0.03e4, 0.17e3, 0.10e3, 0.06e3, 0.04e3, 0.04e3]),\n",
    "               'ell':np.asarray([53,114,187,320, 502, 684, 890,1158, 1505, 1956, 2649])},\n",
    "    '857x857':{'cl':np.asarray([2.76e6, 7.99e5, 2.87e5, 1.34e5, 7.20e4, 4.38e4, 3.23e4, 2.40e4, 1.83e4, 1.46e4, 1.16e4]),\n",
    "               'ecl':np.asarray([0.,0., 0.37e5, 0.08e5, 0.26e4, 0.18e4, 0.09e4, 0.05e4, 0.03e4, 0.02e4, 0.01e4]),\n",
    "               'ell':np.asarray([53,114,187,320, 502, 684, 890,1158, 1505, 1956, 2649])}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3713957c-4250-4e27-8123-e16e1653031f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################\n",
    "##################################################################################\n",
    "# Here i borrow codes from Emmanuel Schaan \n",
    "# see https://github.com/EmmanuelSchaan/LensQuEst/blob/master/pn_2d.py\n",
    "class Planck13CIBData(object):\n",
    "    \"\"\"Measured CIB power spectra from Planck13 XXX\n",
    "    \"\"\"\n",
    "\n",
    "    # here name has to be the frequency of the CIB map\n",
    "    def __init__(self):\n",
    "\n",
    "      # Shot noise on CIB power spectra\n",
    "      # table 9 from Planck13 XXX on CIB\n",
    "      # unit is Jy^2 / sr\n",
    "      self.PlanckPCIBShot = {}\n",
    "      self.PlanckPCIBShot['857'] = 5364.\n",
    "      self.PlanckPCIBShot['857_545'] = 2701.\n",
    "      self.PlanckPCIBShot['857_353'] = 953.\n",
    "      self.PlanckPCIBShot['857_217'] = 181.\n",
    "      self.PlanckPCIBShot['545'] = 1690.\n",
    "      self.PlanckPCIBShot['545_353'] = 626.\n",
    "      self.PlanckPCIBShot['545_217'] = 121.\n",
    "      self.PlanckPCIBShot['353'] = 262.\n",
    "      self.PlanckPCIBShot['353_217'] = 54.\n",
    "      self.PlanckPCIBShot['217'] = 21.\n",
    "\n",
    "      # Measured CIB power spectra\n",
    "      # table D2 from Planck13 XXX on CIB\n",
    "      # unit is Jy^2/sr\n",
    "      self.PlanckPCIB = {}\n",
    "\n",
    "      # ell values\n",
    "      self.PlanckPCIB['ell'] = np.array([53., 114., 187., 320., 502., 684., 890., 1158., 1505., 1956., 2649.])\n",
    "\n",
    "      # Auto and cross spectra of the CIB: these maps are CMB-free, Galactic dust-free, corrected for SZ contamination and CIB contamination from CMB template\n",
    "      # The values of the spectra at the first two ell values are only upper limits\n",
    "      self.PlanckPCIB['857'] = np.array([0., 0., 2.87e5, 1.34e5, 7.20e4, 4.38e4, 3.23e4, 2.40e4, 1.83e4, 1.46e4, 1.16e4])\n",
    "      self.PlanckPCIB['857_545'] = np.array([0., 0., 1.30e5, 6.36e4, 3.53e4, 2.21e4, 1.63e4, 1.22e4, 9.31e3, 7.38e3, 5.91e3])\n",
    "      self.PlanckPCIB['857_353'] = np.array([0., 0., 4.30e4, 2.20e4, 1.25e4, 7.99e3, 5.88e3, 4.25e3, 3.24e3, 2.54e3, 0.])\n",
    "      self.PlanckPCIB['857_217'] = np.array([0., 0., 9.70e3, 5.26e3, 3.03e3, 1.88e3, 1.31e3, 9.18e2, 7.00e2, 5.38e2, 0.])\n",
    "      self.PlanckPCIB['857_143'] = np.array([0., 0., 1.84e3, 1.06e3, 6.52e2, 3.86e2, 2.55e2, 1.76e2, 1.23e2, 1.03e2, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['545'] = np.array([0., 0., 6.63e4, 3.34e4, 1.91e4, 1.25e4, 9.17e3, 6.83e3, 5.34e3, 4.24e3, 3.42e3])\n",
    "      self.PlanckPCIB['545_353'] = np.array([0., 0., 2.22e4, 1.19e4, 6.93e3, 4.61e3, 3.39e3, 2.50e3, 1.93e3, 1.52e3, 0.])\n",
    "      self.PlanckPCIB['545_217'] = np.array([0., 0., 4.97e3, 2.79e3, 1.65e3, 1.06e3, 7.41e2, 5.38e2, 4.30e2, 3.30e2, 0.])\n",
    "      self.PlanckPCIB['545_143'] = np.array([0., 0., 1.01e3, 5.98e2, 3.77e2, 2.29e2, 1.54e2, 1.03e2, 7.09e1, 5.89e1, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['353'] = np.array([0., 0., 7.88e3, 4.35e3, 2.60e3, 1.74e3, 1.29e3, 9.35e2, 7.45e2, 6.08e2, 0.])\n",
    "      self.PlanckPCIB['353_217'] = np.array([0., 0., 1.75e3, 1.02e3, 6.21e2, 3.97e2, 2.87e2, 1.99e2, 1.59e2, 1.35e2, 0.])\n",
    "      self.PlanckPCIB['353_143'] = np.array([0., 0., 3.61e2, 2.32e2, 1.48e2, 9.42e1, 6.33e1, 4.56e1, 2.77e1, 3.53e1, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['217'] = np.array([0., 0., 4.17e2, 2.62e2, 1.75e2, 1.17e2, 8.82e1, 6.42e1, 3.34e1, 4.74e1, 0.])\n",
    "      self.PlanckPCIB['217_143'] = np.array([0., 0., 1.04e2, 7.49e1, 5.87e1, 3.93e1, 2.64e1, 2.21e1, 1.07e1, 1.45e1, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['143'] = np.array([0., 0., 3.64e1, 3.23e1, 2.81e1, 2.27e1, 1.84e1, 1.58e1, 1.25e1, 0., 0.])\n",
    "\n",
    "      # Error bars on the power spectra:\n",
    "      self.PlanckPCIB['857_error'] = np.array([2.76e6, 7.99e5, 0.37e5, 0.08e5, 0.26e4, 0.18e4, 0.09e4, 0.05e4, 0.03e4, 0.02e4, 0.01e4])\n",
    "      self.PlanckPCIB['857_545_error'] = np.array([9.73e5, 3.23e5, 0.13e5, 0.30e4, 0.10e4, 0.07e4, 0.04e4, 0.02e4, 0.11e3, 0.07e3, 0.06e3])\n",
    "      self.PlanckPCIB['857_353_error'] = np.array([2.91e5, 1.05e5, 0.41e4, 0.11e4, 0.06e4, 0.39e3, 0.27e3, 0.17e3, 0.10e3, 0.07e3, 0.])\n",
    "      self.PlanckPCIB['857_217_error'] = np.array([6.43e4, 2.49e4, 1.22e3, 0.53e3, 0.32e3, 0.22e3, 0.16e3, 0.87e2, 0.23e2, 0.12e2, 0.])\n",
    "      self.PlanckPCIB['857_143_error'] = np.array([1.81e4, 5.12e3, 0.45e3, 0.12e3, 0.59e2, 0.41e2, 0.30e2, 0.23e2, 0.16e2, 0.15e2, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['545_error'] = np.array([3.74e5, 1.45e5, 0.51e4, 0.12e4, 0.04e4, 0.03e4, 0.17e3, 0.10e3, 0.06e3, 0.04e3, 0.04e3])\n",
    "      self.PlanckPCIB['545_353_error'] = np.array([1.15e5, 4.84e4, 0.16e4, 0.05e4, 0.23e3, 0.16e3, 0.11e3, 0.07e3, 0.04e3, 0.03e3, 0.])\n",
    "      self.PlanckPCIB['545_217_error'] = np.array([2.58e4, 1.16e4, 0.48e3, 0.21e3, 0.12e3, 0.09e3, 0.63e2, 0.35e2, 0.12e2, 0.07e2, 0.])\n",
    "      self.PlanckPCIB['545_143_error'] = np.array([7.04e3, 2.53e3, 0.19e3, 0.67e2, 0.39e2, 0.27e2, 0.19e2, 0.14e2, 1.80e1, 1.73e1, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['353_error'] = np.array([3.68e4, 1.66e4, 0.53e3, 0.18e3, 0.10e3, 0.07e3, 0.05e3, 0.33e2, 0.22e2, 0.16e2, 0.])\n",
    "      self.PlanckPCIB['353_217_error'] = np.array([8.01e3, 3.82e3, 0.15e3, 0.06e3, 0.38e2, 0.27e2, 0.20e2, 0.14e2, 0.10e2, 0.05e2, 0.])\n",
    "      self.PlanckPCIB['353_143_error'] = np.array([2.05e3, 8.26e2, 0.62e2, 0.24e2, 0.14e2, 1.06e1, 0.83e1, 0.91e1, 1.11e1, 0.69e1, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['217_error'] = np.array([1.78e3, 8.47e2, 0.47e2, 0.20e2, 0.13e2, 0.10e2, 0.89e1, 1.61e1, 2.15e1, 0.65e1, 0.])\n",
    "      self.PlanckPCIB['217_143_error'] = np.array([4.74e2, 1.89e2, 0.19e2, 0.81e1, 0.58e1, 0.50e1, 0.52e1, 1.19e1, 1.65e1, 0.54e1, 0.])\n",
    "      #\n",
    "      self.PlanckPCIB['143_error'] = np.array([1.55e2, 6.41e1, 0.73e1, 0.35e1, 0.30e1, 0.29e1, 0.35e1, 0.91e1, 1.28e1, 0., 0.])\n",
    "\n",
    "\n",
    "\n",
    "    ##################################################################################\n",
    "\n",
    "    def plotPCIB(self, name='353'):\n",
    "      L = self.PlanckPCIB['ell']\n",
    "      P = self.PlanckPCIB[name]\n",
    "      sP = self.PlanckPCIB[name+'_error']\n",
    "      Pshot = self.PlanckPCIBShot[name]\n",
    "\n",
    "      # P\n",
    "      fig = plt.figure(figsize = (10,6))\n",
    "      ax = plt.subplot(111)\n",
    "      #\n",
    "      ax.errorbar(L, P, yerr=sP, fmt='.', c='k', label=name)\n",
    "      ax.plot(L, Pshot*np.ones_like(L), 'b--', label=r'quoted shot noise')\n",
    "      #\n",
    "      ax.set_xscale('log')\n",
    "      ax.set_yscale('log')\n",
    "      ax.legend(loc=1, numpoints=1)\n",
    "      ax.set_xlabel(r'\\ell')\n",
    "      ax.set_ylabel(r'$C_\\ell^\\mathrm{CIB}$')\n",
    "      plt.show()\n",
    "        \n",
    "    def plotcib_line(self, name = '353'):\n",
    "      L = self.PlanckPCIB['ell']\n",
    "      P = self.PlanckPCIB[name]\n",
    "      sP = self.PlanckPCIB[name+'_error']\n",
    "      Pshot = self.PlanckPCIBShot[name]\n",
    "      ax.errorbar(L, P, yerr=sP, fmt='.', c='k', label='Planck '+name)\n",
    "      ax.plot(L, Pshot*np.ones_like(L), 'b--', label=r'Planck quoted shot noise')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ee605e1a-cb59-42d1-b2b4-eaab99e56bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for example we can plot the planck cib data like:\n",
    "plcib = Planck13CIBData()\n",
    "plcib.plotPCIB('217')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b225eba0-d88d-46b7-aa57-47af9fcbac5f",
   "metadata": {},
   "source": [
    "Plot at 217GHZ, comparison with Choi et al "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "180f3a05-7e4d-49c9-aabb-e7c04b1a16a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# factors for units\n",
    "# the conversion factor 483.690 appears in table 1 of \n",
    "# Planck Collaboration: P. A. R. Ade, N. Aghanim, C. Armitage-Caplan, et al. \n",
    "# \"Planck 2013 results. XXX. Cosmic infrared background measurements and implications for star formation.\" \n",
    "# Astronomy & Astrophysics, 571, A30 (2014). DOI: 10.1051/0004-6361/201322093\n",
    "# it is the conversion factor from MJy*sr^-1 to Kcmb at 217GHz\n",
    "\n",
    "\n",
    "convfac = 483.690"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0815b266-f071-4718-9270-7151f4901d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.0, 10000.0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax) = plt.subplots(1,1,figsize=(12,8))\n",
    "freq = '217x217'\n",
    "freq1 = float(freq.split('x')[0])\n",
    "\n",
    "\n",
    "\n",
    "plcib = Planck13CIBData()\n",
    "plcib.plotcib_line('217')\n",
    "\n",
    "\n",
    "\n",
    "l = np.arange(2,5000)\n",
    "l0 = choi_params_dict['high_ell0']\n",
    "cib_temp = get_template(ells,choi_params_dict['cib_temp_file'],ell_pivot=3000)\n",
    "plt.plot(l,choi_params_dict['a_c']*cib_temp/(l*(l+1.)/2./np.pi)*(convfac)**2.*get_mu(choi_params_dict,[217])**2.,label='choi et al clustered',c='k',lw=0.3)\n",
    "\n",
    "\n",
    "cibp = choi_params_dict['a_d']*(l/l0)**(2)*get_mu(choi_params_dict,[freq1])**2.\n",
    "plt.plot(l,(l*(l+1)/2./np.pi)**-1.*cibp*(convfac)**2.,label='choi et al poisson',lw=0.9,c='r',ls=':')\n",
    "\n",
    "plt.plot(l,(l*(l+1)/2./np.pi)**-1.*cibp*(convfac)**2.+choi_params_dict['a_c']*cib_temp/(l*(l+1.)/2./np.pi)*(convfac)**2.*get_mu(choi_params_dict,[freq1])**2.,\n",
    "         label='choi et al total',c='k',ls='-.')\n",
    "\n",
    "lpow = l[l>1000]\n",
    "clpow = (l[l>1000]/l0)**0.8*choi_params_dict['a_c']/(lpow*(lpow+1.)/2./np.pi)*(convfac)**2.*get_mu(choi_params_dict,[217])**2.\n",
    "plt.plot(lpow,clpow,label=r'$\\propto l^{0.8}$')\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.legend(fontsize=12,ncol=3)\n",
    "plt.ylabel('Cl in Jy2/sr')\n",
    "plt.xlabel('l')\n",
    "plt.title(freq)\n",
    "plt.xlim(1e1,1e4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47cf3d09-a188-45e4-a08b-9c3294893948",
   "metadata": {},
   "source": [
    "Adding Maniyar model (parameters not adjusted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6b1ff36c-5ccd-4cc8-b65a-82e555e8b121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 46s, sys: 1.21 s, total: 5min 47s\n",
      "Wall time: 42.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import classy_sz\n",
    "from classy_sz import Class\n",
    "# a simple conversion from cl's to dl's\n",
    "def l_to_dl(lp):\n",
    "    return lp*(lp+1.)/2./np.pi\n",
    "\n",
    "\n",
    "Omegam0 = 0.3075\n",
    "H0 = 67.74\n",
    "Omegab = 0.0486\n",
    "Omegac = 0.2589\n",
    "Omegac + Omegab\n",
    "omega_b = Omegab*(H0/100.)**2\n",
    "omega_c = Omegac*(H0/100.)**2\n",
    "hparam = H0/100.\n",
    "maniyar_cosmo = {\n",
    "'omega_b': omega_b,\n",
    "'omega_cdm':  omega_c,\n",
    "'h': H0/100.,\n",
    "# 'tau_reio': 0.0561,\n",
    "'ln10^{10}A_s': 3.048,\n",
    "'n_s': 0.9665,\n",
    "# 'sigma8':0.830,\n",
    "'k_pivot': 0.05,\n",
    "'N_ncdm': 1,\n",
    "'N_ur': 2.0328,\n",
    "'m_ncdm': 0.0\n",
    "}\n",
    "\n",
    "\n",
    "M = Class()\n",
    "M.set({'output':'dndlnM,cib_cib_1h,cib_cib_2h'})\n",
    "M.set(maniyar_cosmo)\n",
    "M.set({\n",
    "\n",
    "'mass function' : 'T08M200c',\n",
    "'use_maniyar_cib_model':1,\n",
    "\n",
    "'maniyar_cib_etamax' : 5.12572945e-01,\n",
    "\n",
    "'maniyar_cib_zc' : 1.5,\n",
    "'maniyar_cib_tau' : 8.25475287e-01,\n",
    "'maniyar_cib_fsub' : 0.134*np.log(10.),\n",
    "'Most_efficient_halo_mass_in_Msun' : 5.34372069e+12,\n",
    "'Size_of_halo_masses_sourcing_CIB_emission' :  1.5583436676980493,\n",
    "#for the Lsat tabulation:\n",
    "'freq_min': 9e1,\n",
    "'freq_max': 8.57e2,\n",
    "'dlogfreq' : 0.1,\n",
    "\n",
    "'concentration parameter':'fixed', # this sets it to 5\n",
    "\n",
    "'n_z_L_sat' :100,\n",
    "'n_m_L_sat' :100,\n",
    "'n_nu_L_sat':100,\n",
    "\n",
    "'use_nc_1_for_all_halos_cib_HOD': 1,\n",
    "\n",
    "'sub_halo_mass_function' : 'TW10',#'JvdB14',\n",
    "'M_min_subhalo_in_Msun' : 1e5, # 1e5 see https://github.com/abhimaniyar/halomodel_cib_tsz_cibxtsz/blob/master/Cell_cib.py\n",
    "'use_redshift_dependent_M_min': 0,\n",
    "'M_min' : 1e8*hparam,\n",
    "'M_max' : 1e15*hparam,\n",
    "'z_min' : 0.012,\n",
    "'z_max' : 10.,\n",
    "'ell_min': 10.,\n",
    "'ell_max':5e4,\n",
    "'dlogell':0.3,\n",
    "\n",
    "\n",
    "'ndim_redshifts': 210,\n",
    "'ndim_masses':150,\n",
    "\n",
    "'has_cib_flux_cut': 0,\n",
    "'hm_consistency':0,\n",
    "\n",
    "'epsabs_L_sat': 1e-40,\n",
    "'epsrel_L_sat': 1e-9,\n",
    "    \n",
    "'damping_1h_term':0,\n",
    "\n",
    "# \"P_k_max_1/Mpc\": 50.,\n",
    "# 'k_max_for_pk_class_sz':50.\n",
    "})\n",
    "\n",
    "M.set({\n",
    "       'cib_frequency_list_num' : 6,\n",
    "       'cib_frequency_list_in_GHz' : '100,143,217,353,545,857',\n",
    "      })\n",
    "M.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5cf21fb4-b836-47c3-a5d3-5a1395884113",
   "metadata": {},
   "outputs": [],
   "source": [
    "lmaniyar, clmaniyar1h, clmaniyar2h = np.asarray(M.cl_cib_cib()['217x217']['ell']),np.asarray(M.cl_cib_cib()['217x217']['1h']),np.asarray(M.cl_cib_cib()['217x217']['2h'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Planck = {'name': 'Planck',\n",
    "          'do_cib': 1, 'do_tsz': 1, 'do_cibxtsz': 1,\n",
    "          'freq_cib': [100., 143., 217., 353., 545., 857.],\n",
    "          'cc': np.array([1.076, 1.017, 1.119, 1.097, 1.068, 0.995, 0.960]),\n",
    "          'cc_cibmean': np.array([1.076, 1.017, 1.119, 1.097, 1.068, 0.995, 0.960]),\n",
    "          'freq_cibmean': np.array([100., 143., 217., 353., 545., 857.]),\n",
    "          'fc': np.ones(7),\n",
    "          }\n",
    "\n",
    "i = 2\n",
    "j = 2\n",
    "faccib = Planck['cc'][i]*Planck['cc'][j]\n",
    "\n",
    "\n",
    "clmaniyar1h /= lmaniyar*(lmaniyar+1.)/2./np.pi/faccib\n",
    "clmaniyar2h /= lmaniyar*(lmaniyar+1.)/2./np.pi/faccib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3c92e80b-979f-4cb5-babf-fb69f1eab6a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.0, 10000.0)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax) = plt.subplots(1,1,figsize=(12,8))\n",
    "freq = '217x217'\n",
    "freq1 = float(freq.split('x')[0])\n",
    "\n",
    "\n",
    "\n",
    "plcib = Planck13CIBData()\n",
    "plcib.plotcib_line('217')\n",
    "\n",
    "\n",
    "\n",
    "l = np.arange(2,5000)\n",
    "l0 = choi_params_dict['high_ell0']\n",
    "cib_temp = get_template(ells,choi_params_dict['cib_temp_file'],ell_pivot=3000)\n",
    "plt.plot(l,choi_params_dict['a_c']*cib_temp/(l*(l+1.)/2./np.pi)*(convfac)**2.*get_mu(choi_params_dict,[217])**2.,label='choi et al clustered',c='k',lw=0.3)\n",
    "\n",
    "\n",
    "cibp = choi_params_dict['a_d']*(l/l0)**(2)*get_mu(choi_params_dict,[freq1])**2.\n",
    "plt.plot(l,(l*(l+1)/2./np.pi)**-1.*cibp*(convfac)**2.,label='choi et al poisson',lw=0.9,c='r',ls=':')\n",
    "\n",
    "plt.plot(l,(l*(l+1)/2./np.pi)**-1.*cibp*(convfac)**2.+choi_params_dict['a_c']*cib_temp/(l*(l+1.)/2./np.pi)*(convfac)**2.*get_mu(choi_params_dict,[freq1])**2.,\n",
    "         label='choi et al total',c='k',ls='-.')\n",
    "\n",
    "lpow = l[l>1000]\n",
    "clpow = (l[l>1000]/l0)**0.8*choi_params_dict['a_c']/(lpow*(lpow+1.)/2./np.pi)*(convfac)**2.*get_mu(choi_params_dict,[217])**2.\n",
    "plt.plot(lpow,clpow,label=r'$\\propto l^{0.8}$')\n",
    "\n",
    "plt.plot(lmaniyar,clmaniyar1h,label=r'maniyar 1h')\n",
    "plt.plot(lmaniyar,clmaniyar2h,label=r'maniyar 2h')\n",
    "\n",
    "\n",
    "plt.grid(which = 'both',alpha=0.3)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.legend(fontsize=12,ncol=3)\n",
    "plt.ylabel('Cl in Jy2/sr')\n",
    "plt.xlabel('l')\n",
    "plt.title(freq)\n",
    "plt.xlim(1e1,1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea078b85-5fc7-44eb-b1bd-a9ea6694ebfc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}