angular-cls-justNumcosmo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "137b8913",
   "metadata": {},
   "source": [
    "# Benchmarks of already working functions - Numcosmo Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba44e1e9",
   "metadata": {},
   "source": [
    "First of all, let us import some useful packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "846f1de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "######## GENERAL ########\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5e422b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "######## NUM COSMO ########\n",
    "try:\n",
    "  import gi\n",
    "  gi.require_version('NumCosmo', '1.0')\n",
    "  gi.require_version('NumCosmoMath', '1.0')\n",
    "except:\n",
    "  pass\n",
    "\n",
    "from gi.repository import GObject\n",
    "from gi.repository import NumCosmo as Nc\n",
    "from gi.repository import NumCosmoMath as Ncm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ee5dcf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "######## CCL ########\n",
    "#import pyccl as ccl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d11df8",
   "metadata": {},
   "source": [
    "Now, let us initialize the NumCosmo library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00c3e53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Initializing the library objects, this must be called before \n",
    "#  any other library function.\n",
    "#\n",
    "Ncm.cfg_init ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd483bb",
   "metadata": {},
   "source": [
    "### Defining the cosmological parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7def4d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Omega_c = 0.25   # The cold dark matter density fraction.\n",
    "\n",
    "Omega_b = 0.05   # The baryonic matter density fraction.\n",
    "\n",
    "Omega_k = 0.0    # The curvature density fraction.\n",
    "        \n",
    "h       = 0.7    # The hubble constant divided by 100 km/s/Mpc; unitless.\n",
    "\n",
    "A_s     = 2.1e-9 # The power spectrum normalization.\n",
    "\n",
    "n_s     = 0.96   # The primordial scalar perturbation spectral index.\n",
    "\n",
    "Neff    = 3.046  # The effective number of massless neutrinos present.\n",
    "\n",
    "w0 = -1.0        # The first order term of dark energy equation of state.\n",
    "\n",
    "wa = 0.0         # The second order term of dark energy equation of state."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cc6b3e",
   "metadata": {},
   "source": [
    "### Defining the cosmological model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6634b8bb",
   "metadata": {},
   "source": [
    "Here, I define the cosmological model, accordingly to the parameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52784df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "### NcHICosmo — Abstract class for implementing homogeneous and isotropic cosmological models\n",
    "### NcHICosmoDECpl — Dark Energy -- Chevallier–Polarski–Linder equation of state\n",
    "### nc_hicosmo_new_from_name (GType parent_type, gchar *cosmo_name): Creates a new instance of \"cosmo_name\", \n",
    "#                                                                    asserting that it descends from \"parent_type\".\n",
    "nc_cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, \"NcHICosmoDECpl{'massnu-length':<0>}\")\n",
    "\n",
    "### Source: https://numcosmo.github.io/manual/NcHICosmo.html#nc-hicosmo-new-from-name\n",
    "### Source: https://numcosmo.github.io/manual/NcHICosmoDECpl.html (Theory)\n",
    "\n",
    "### massnu-length: this depends on how many neutrinos types we are considering to be massive or massless.\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "nc_cosmo.omega_x2omega_k ()\n",
    "\n",
    "### This line is for us to work with Omega_k as a parameter instead of Omega_x (dark energy). If we don't have this\n",
    "### line, we will automatically be working with Omega_x.\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "nc_cosmo.param_set_by_name (\"Omegac\",    Omega_c)\n",
    "nc_cosmo.param_set_by_name (\"Omegab\",    Omega_b)\n",
    "#nc_cosmo.param_set_by_name (\"Omegax\",       0.70)\n",
    "nc_cosmo.param_set_by_name (\"Omegak\",    Omega_k)\n",
    "nc_cosmo.param_set_by_name (\"H0\",        h * 100)\n",
    "nc_cosmo.param_set_by_name (\"ENnu\",      Neff)\n",
    "nc_cosmo.param_set_by_name (\"w0\",        w0)\n",
    "nc_cosmo.param_set_by_name (\"w1\",        wa)\n",
    "\n",
    "########################################################################\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13222f7",
   "metadata": {},
   "source": [
    "Now, I will define the function to perform distance calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2566f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "### NcDistance — Cosmological distance and time related quantities.\n",
    "### nc_distance_new (gdouble zf): Creates a new NcDistance object optimized to perform\n",
    "#                                 distance calculations to redshift up to zf.\n",
    "dist = Nc.Distance.new (5.0)\n",
    "\n",
    "### Source: https://numcosmo.github.io/manual/NcDistance.html (Theory)\n",
    "### Source: https://numcosmo.github.io/manual/NcDistance.html#nc-distance-new\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "### nc_distance_prepare (NcDistance *dist, NcHICosmo *cosmo):This function prepares the object \"dist\" using \"cosmo\",\n",
    "#                                                            such that all the available distances functions can be\n",
    "#                                                            evaluated, e.g. nc_distance_comoving().\n",
    "dist.prepare (nc_cosmo)\n",
    "\n",
    "### Source: https://numcosmo.github.io/manual/NcDistance.html#nc-distance-prepare\n",
    "########################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbb2378",
   "metadata": {},
   "source": [
    "Here, I will define the Transfer Function and the Power Spectrum to be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e35ecd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "### THIS IS NECESSARY FOR Nc.PowspecMLTransfer.\n",
    "### NcHIPrim — Abstract class for implementing homogeneous and isotropic primordial cosmological models.\n",
    "### NcHIPrimPowerLaw — Power law implementation for primordial spectra.\n",
    "\n",
    "hiprim = Nc.HIPrimPowerLaw.new ()\n",
    "hiprim.param_set_by_name (\"ln10e10ASA\", math.log (1.0e10 * A_s))\n",
    "hiprim.param_set_by_name (\"n_SA\",       n_s)\n",
    "    \n",
    "nc_cosmo.add_submodel (hiprim)\n",
    "\n",
    "### Source: https://numcosmo.github.io/manual/NcHIPrimPowerLaw.html (Theory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfb7ce88",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "### NcTransferFunc — Abstrac class for perturbation transfer function.\n",
    "### NcTransferFuncEH — Eisenstein-Hu fitting function for the transfer function.\n",
    "### nc_transfer_func_eh_new (void): Creates a new NcTransferFunc of the NcTransferFuncEH type.\n",
    "\n",
    "tf_eh = Nc.TransferFuncEH.new ()\n",
    "\n",
    "### Source: https://numcosmo.github.io/manual/NcTransferFuncEH.html (Theory)\n",
    "########################################################################\n",
    "\n",
    "########################################################################\n",
    "#tf_eh.props.CCL_comp = True\n",
    "\n",
    "##### DÚVIDA 1 ####\n",
    "### Eu esqueci se aqui tem a ver com comparação, compatibilidade ou conta (computation).\n",
    "########################################################################\n",
    "########################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95f9fc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "### NcPowspecML — Abstract class for linear matter power spectrum implementation.\n",
    "### NcPowspecMLTransfer — Class for linear matter power spectrum from a transfer function.\n",
    "### nc_powspec_ml_transfer_new (NcTransferFunc *tf):Creates a new NcPowspecMLTransfer from the transfer function tf .\n",
    "\n",
    "ps_eh = Nc.PowspecMLTransfer.new (tf_eh)\n",
    "\n",
    "### Source: https://numcosmo.github.io/manual/NcPowspecMLTransfer.html (Theory)\n",
    "########################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a6b79c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "ps_eh.prepare (nc_cosmo)\n",
    "########################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2974fb4",
   "metadata": {},
   "source": [
    "### Trying to define the kernel and run the cross correlation computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63284dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################\n",
    "#NcmMSet — A set of different NcmModel objects.\n",
    "#NcmModel — Abstract class for implementing models.\n",
    "mset = Ncm.MSet.empty_new ()\n",
    "mset.set (nc_cosmo)\n",
    "\n",
    "# No mset, \"guardamos\" um conjunto de modelos para cálculos posteriores.\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "# Ell-dependent correction factors\n",
    "# Set up array of ells\n",
    "lmax = 10000\n",
    "ells = np.arange(lmax+1)\n",
    "\n",
    "### O que estava antes:\n",
    "#nls = (lmax - 400) // 20 + 141\n",
    "#ells = np.zeros(nls)\n",
    "#ells[:101] = np.arange(101)\n",
    "#ells[101:121] = ells[100] + (np.arange(20) + 1) * 5\n",
    "#ells[121:141] = ells[120] + (np.arange(20) + 1) * 10\n",
    "#ells[141:] = ells[140] + (np.arange(nls - 141) + 1) * 20\n",
    "\n",
    "##### DÚVIDA 2 ####\n",
    "#Qual a motivação para fazer isso aqui? Em qual equação esses fatores de correção aparecem? Por que o \"nls\"\n",
    "#é definido da forma acima?\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "\n",
    "# Histogram case\n",
    "z1 = [0.51125,0.51875,0.52625,0.5337500000000001,0.54125,0.5487500000000001,0.55625,0.5637500000000001,0.57125,0.57875,0.58625,0.59375,0.6012500000000001,0.60875,0.6162500000000001,0.62375,0.63125,0.63875,0.64625,0.65375,0.66125,0.6687500000000001,0.67625,0.6837500000000001,0.69125,0.69875,0.70625,0.71375,0.7212500000000001,0.72875,0.7362500000000001,0.74375,0.7512500000000001,0.75875,0.76625,0.77375,0.78125,0.7887500000000001,0.79625,0.8037500000000001,0.81125,0.81875,0.82625,0.83375,0.84125,0.84875,0.8562500000000001,0.86375,0.8712500000000001,0.87875,0.88625,0.89375,0.90125,0.9087500000000001,0.91625,0.9237500000000001,0.93125,0.9387500000000001,0.94625,0.95375,0.96125,0.96875,0.9762500000000001,0.98375,0.9912500000000001,0.99875,1.0062499999999999,1.01375,1.02125,1.0287499999999998,1.03625,1.04375,1.05125,1.0587499999999999,1.06625,1.07375,1.0812499999999998,1.0887499999999999,1.09625,1.10375,1.1112499999999998,1.11875,1.12625,1.1337499999999998,1.1412499999999999,1.14875,1.15625,1.1637499999999998,1.17125,1.17875,1.18625,1.1937499999999999,1.20125,1.20875,1.2162499999999998,1.22375,1.23125,1.23875,1.2462499999999999,1.25375,1.26125,1.2687499999999998,1.2762499999999999,1.28375,1.29125,1.2987499999999998,1.30625,1.31375,1.3212499999999998,1.3287499999999999,1.33625,1.34375,1.3512499999999998,1.35875,1.36625,1.37375,1.3812499999999999,1.38875,1.39625,1.4037499999999998,1.41125,1.41875,1.42625,1.4337499999999999,1.44125,1.44875,1.4562499999999998,1.4637499999999999,1.47125,1.47875,1.4862499999999998,1.49375,1.50125,1.5087499999999998,1.5162499999999999,1.52375,1.53125,1.5387499999999998,1.54625,1.55375,1.56125,1.5687499999999999,1.57625,1.58375,1.5912499999999998,1.59875,1.60625,1.61375,1.6212499999999999,1.62875,1.63625,1.6437499999999998,1.6512499999999999,1.65875,1.66625,1.6737499999999998,1.68125,1.68875,1.6962499999999998,1.7037499999999999,1.71125,1.71875,1.7262499999999998,1.73375,1.74125,1.74875,1.7562499999999999,1.76375,1.77125,1.7787499999999998,1.78625,1.79375,1.80125,1.8087499999999999,1.81625,1.82375,1.8312499999999998,1.8387499999999999,1.84625,1.85375,1.8612499999999998,1.86875,1.87625,1.8837499999999998,1.8912499999999999,1.89875,1.90625,1.9137499999999998,1.92125,1.92875,1.93625,1.9437499999999999,1.95125,1.95875,1.9662499999999998,1.97375,1.98125,1.98875,1.9962499999999999]\n",
    "pz1 = [0.03042845,0.03414402,0.03799253,0.04299767,0.05033486,0.06090954,0.07214195,0.08122409,0.09440498,0.1069006,0.1193231,0.1345755,0.1574345,0.1814089,0.2129027,0.2362395,0.2668045,0.3002025,0.3352957,0.3745351,0.4194854,0.4623063,0.5050753,0.5454309,0.5963121,0.6618579,0.7288298,0.8091178,0.898688,0.9962031,1.104119,1.216311,1.32999,1.435197,1.512844,1.58501,1.652862,1.703158,1.741018,1.772948,1.774767,1.763834,1.737957,1.726589,1.737972,1.753971,1.763453,1.802562,1.856402,1.900287,1.9703,2.067721,2.174633,2.281516,2.373034,2.444509,2.524466,2.565968,2.591811,2.636548,2.668677,2.667052,2.662519,2.650229,2.636122,2.642808,2.631578,2.617414,2.585296,2.516713,2.433994,2.371482,2.312537,2.243322,2.161209,2.053728,1.949206,1.849159,1.756659,1.680961,1.607236,1.512338,1.403549,1.296255,1.194675,1.099351,1.006349,0.9175507,0.8346777,0.7518989,0.6750844,0.6090778,0.5471522,0.4921195,0.4383578,0.3932878,0.3505944,0.3069157,0.2660076,0.2260665,0.1912351,0.1602497,0.1342543,0.1105429,0.09371526,0.07828563,0.06450086,0.05691218,0.05188841,0.04927132,0.04626028,0.04556604,0.04560567,0.04429971,0.04462289,0.04330885,0.04531251,0.04556658,0.04682577,0.04804921,0.05014561,0.05017483,0.04971365,0.0498952,0.05009804,0.05045293,0.05051581,0.05173462,0.05239803,0.05211333,0.05046592,0.04824938,0.04685445,0.04832953,0.04773316,0.04719018,0.04581053,0.04330763,0.04180981,0.0409335,0.03975677,0.03999396,0.04020634,0.04043189,0.03879762,0.03973875,0.03834657,0.03841882,0.03693463,0.03597824,0.03656528,0.03627733,0.0368738,0.03706237,0.03943152,0.03926759,0.04037174,0.04044818,0.04122869,0.04159501,0.0407858,0.04100093,0.03983945,0.03702105,0.035423,0.03340422,0.03194788,0.0302819,0.02866295,0.0283969,0.02863473,0.02794187,0.02742471,0.02713259,0.02723712,0.02731734,0.02779379,0.02876089,0.02951878,0.0293301,0.02866049,0.02870192,0.02811025,0.02743557,0.0259726,0.02577944,0.02464586,0.02338112,0.0217144,0.02120506,0.02094031,0.01955321,0.01917961,0.01865226,0.01782826,0.01645653,0.01553533,0.01498327,0.01385961]\n",
    "\n",
    "z2 = [0.51125,0.51875,0.52625,0.5337500000000001,0.54125,0.5487500000000001,0.55625,0.5637500000000001,0.57125,0.57875,0.58625,0.59375,0.6012500000000001,0.60875,0.6162500000000001,0.62375,0.63125,0.63875,0.64625,0.65375,0.66125,0.6687500000000001,0.67625,0.6837500000000001,0.69125,0.69875,0.70625,0.71375,0.7212500000000001,0.72875,0.7362500000000001,0.74375,0.7512500000000001,0.75875,0.76625,0.77375,0.78125,0.7887500000000001,0.79625,0.8037500000000001,0.81125,0.81875,0.82625,0.83375,0.84125,0.84875,0.8562500000000001,0.86375,0.8712500000000001,0.87875,0.88625,0.89375,0.90125,0.9087500000000001,0.91625,0.9237500000000001,0.93125,0.9387500000000001,0.94625,0.95375,0.96125,0.96875,0.9762500000000001,0.98375,0.9912500000000001,0.99875,1.0062499999999999,1.01375,1.02125,1.0287499999999998,1.03625,1.04375,1.05125,1.0587499999999999,1.06625,1.07375,1.0812499999999998,1.0887499999999999,1.09625,1.10375,1.1112499999999998,1.11875,1.12625,1.1337499999999998,1.1412499999999999,1.14875,1.15625,1.1637499999999998,1.17125,1.17875,1.18625,1.1937499999999999,1.20125,1.20875,1.2162499999999998,1.22375,1.23125,1.23875,1.2462499999999999,1.25375,1.26125,1.2687499999999998,1.2762499999999999,1.28375,1.29125,1.2987499999999998,1.30625,1.31375,1.3212499999999998,1.3287499999999999,1.33625,1.34375,1.3512499999999998,1.35875,1.36625,1.37375,1.3812499999999999,1.38875,1.39625,1.4037499999999998,1.41125,1.41875,1.42625,1.4337499999999999,1.44125,1.44875,1.4562499999999998,1.4637499999999999,1.47125,1.47875,1.4862499999999998,1.49375,1.50125,1.5087499999999998,1.5162499999999999,1.52375,1.53125,1.5387499999999998,1.54625,1.55375,1.56125,1.5687499999999999,1.57625,1.58375,1.5912499999999998,1.59875,1.60625,1.61375,1.6212499999999999,1.62875,1.63625,1.6437499999999998,1.6512499999999999,1.65875,1.66625,1.6737499999999998,1.68125,1.68875,1.6962499999999998,1.7037499999999999,1.71125,1.71875,1.7262499999999998,1.73375,1.74125,1.74875,1.7562499999999999,1.76375,1.77125,1.7787499999999998,1.78625,1.79375,1.80125,1.8087499999999999,1.81625,1.82375,1.8312499999999998,1.8387499999999999,1.84625,1.85375,1.8612499999999998,1.86875,1.87625,1.8837499999999998,1.8912499999999999,1.89875,1.90625,1.9137499999999998,1.92125,1.92875,1.93625,1.9437499999999999,1.95125,1.95875,1.9662499999999998,1.97375,1.98125,1.98875,1.9962499999999999]\n",
    "pz2 = [0.02052227,0.02128476,0.02239694,0.02465136,0.02586701,0.03179429,0.03392884,0.0367469,0.04154996,0.04243694,0.04518474,0.04727245,0.05213725,0.06062088,0.06824402,0.07278402,0.08242115,0.09059329,0.09545925,0.1037852,0.1127547,0.1194203,0.1214864,0.1178928,0.1201941,0.1196488,0.1201862,0.1167615,0.1193688,0.1208544,0.1229058,0.1242607,0.1239687,0.1270272,0.1243528,0.1200976,0.1130248,0.1113359,0.1040341,0.09660113,0.08767327,0.08330837,0.07821314,0.07217775,0.06684813,0.06545177,0.06539616,0.06688858,0.06832995,0.06796344,0.07096691,0.07170753,0.07706039,0.08479357,0.09471338,0.1036854,0.1107307,0.1190918,0.1282902,0.1383972,0.1526025,0.1626396,0.1764611,0.1898772,0.1983843,0.2129817,0.2242674,0.2358476,0.2481126,0.2667389,0.2783668,0.3034779,0.3362693,0.3685216,0.4165157,0.4673775,0.521112,0.5931014,0.6609077,0.7264559,0.7962301,0.8749996,0.9607969,1.056802,1.156963,1.256299,1.378734,1.510344,1.672523,1.851898,2.039046,2.210871,2.363395,2.487797,2.587954,2.669776,2.745778,2.766685,2.737778,2.669233,2.582152,2.480724,2.370255,2.269433,2.185606,2.102805,1.983777,1.888873,1.804964,1.731546,1.668124,1.623094,1.573137,1.527214,1.473416,1.422128,1.393125,1.351983,1.336813,1.346803,1.351484,1.340388,1.352429,1.367506,1.368783,1.370403,1.377519,1.385641,1.385694,1.353968,1.30617,1.262473,1.210346,1.161107,1.115958,1.081432,1.041388,1.007879,0.9826291,0.9673974,0.9631128,0.9654323,0.9540624,0.9355531,0.9179233,0.9064376,0.8738709,0.8401182,0.7926394,0.7452493,0.7113778,0.6816197,0.665836,0.6483249,0.6367261,0.6235866,0.6165185,0.6122504,0.6033517,0.5971109,0.5756226,0.54784,0.5118714,0.4759495,0.4347915,0.3872084,0.348376,0.3066147,0.2736164,0.2427426,0.22066,0.2012315,0.1880263,0.1787777,0.1752531,0.1687745,0.1658762,0.165598,0.1634433,0.1612935,0.1563185,0.1494878,0.1408261,0.1306753,0.1201645,0.1131836,0.1053328,0.09754543,0.08818486,0.07864627,0.07041585,0.06252,0.05618527,0.05119691,0.04662244,0.0394575,0.03291768,0.02839416,0.02382886]\n",
    "\n",
    "z1 = np.array(z1)\n",
    "z2 = np.array(z2)\n",
    "\n",
    "### dn/dz is the (automatically normalized) redshift distribution of galaxies.\n",
    "#   Source: https://numcosmo.github.io/manual/NcXcorLimberKernelGal.html.\n",
    "#   Vamos fazer um spline com esses dados para obter dn/dz.\n",
    "\n",
    "##### DÚVIDA 3 ####\n",
    "### De onde vem esses dados??\n",
    "########################################################################\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "### NcmVector *\n",
    "#   ncm_vector_new_array (GArray *a);\n",
    "#   This function saves \"a\" internally and frees it when it is no longer necessary. The \"a\" array must not be freed.\n",
    "\n",
    "v_z1  = Ncm.Vector.new_array (z1)\n",
    "v_pz1 = Ncm.Vector.new_array (pz1)\n",
    "v_pz2 = Ncm.Vector.new_array (pz2)\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "### NcmSplineCubicNotaknot — Cubic spline implementation with 'not a knot' boundary conditions.\n",
    "### NcmSpline *\n",
    "#   ncm_spline_cubic_notaknot_new_full (NcmVector *xv,NcmVector *yv,gboolean init);\n",
    "#   This function returns a new NcmSpline setting all its members.\n",
    "### Parameters\n",
    "#   xv: NcmVector of knots\n",
    "#   yv: NcmVector of the values of the function, to be interpolated, computed at xv\n",
    "#   init: TRUE to prepare the new NcmSpline or FALSE to not prepare it\n",
    "\n",
    "s_pz1 = Ncm.SplineCubicNotaknot.new_full (v_z1, v_pz1, True)\n",
    "s_pz2 = Ncm.SplineCubicNotaknot.new_full (v_z1, v_pz2, True)\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "nc_g1 = None\n",
    "nc_g2 = None\n",
    "##### NcXcorLimberKernelGal *\n",
    "#nc_xcor_limber_kernel_gal_new (gdouble zmin,\n",
    "#                               gdouble zmax,\n",
    "#                               gsize np,\n",
    "#                               gdouble nbarm1,\n",
    "#                               NcmSpline *dn_dz,\n",
    "#                               NcDistance *dist,\n",
    "#                               gboolean domagbias);\n",
    "##### Parameters\n",
    "#zmin: a gdouble.\n",
    "#zmax: a gdouble.\n",
    "#np: number of points in the interpolation.\n",
    "#nbarm1: a gdouble, noise spectrum.\n",
    "#dn_dz: a NcmSpline.\n",
    "#dist: a NcDistance.\n",
    "#domagbias: whether to do magnification bias.\n",
    "\n",
    "\n",
    "nc_g1 = Nc.XcorLimberKernelGal.new (z1[0], z1[-1], 1, 0.0, s_pz1, dist, False)\n",
    "nc_g2 = Nc.XcorLimberKernelGal.new (z1[0], z1[-1], 1, 0.0, s_pz2, dist, False)\n",
    "\n",
    "##### DÚVIDA 5 ####\n",
    "### zmin e zmax eu creio que seja o intervalo para que a interpolação seja feita no Kernel.\n",
    "### Porque np = 1? \n",
    "### O que é \"noise spectrum\"? Quando isso seria usado?\n",
    "### O dn_dz acho que está ok, tirando as dúvidas anteriores.\n",
    "### dist também está ok.\n",
    "### domagbias também está ok.\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "mset.push (nc_g1)\n",
    "mset.push (nc_g2)\n",
    "\n",
    "# Aqui, estamos \"empurrando\" os XcorLimberKernelGal no mset.\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "### NcXcor *\n",
    "#   nc_xcor_new (NcDistance *dist, NcmPowspec *ps, NcXcorLimberMethod meth);\n",
    "#   Two methods are available to compute Limber-approximated integrals: independent GSL numerical integration or \n",
    "# vector integration using Sundials's CVode algorithm.\n",
    "xcor = Nc.Xcor.new (dist, ps_eh, Nc.XcorLimberMethod.GSL)\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "### NcmVector *\n",
    "#   ncm_vector_new (gsize n);\n",
    "#   This function allocates memory for a new NcmVector of doubles with n components.\n",
    "vp_cross = Ncm.Vector.new (lmax + 1)\n",
    "vp_auto1 = Ncm.Vector.new (lmax + 1)\n",
    "vp_auto2 = Ncm.Vector.new (lmax + 1)\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "xcor.prepare (nc_cosmo)\n",
    "########################################################################\n",
    "\n",
    "\n",
    "\n",
    "########################################################################\n",
    "### Reference: https://numcosmo.github.io/manual/NcXcor.html#nc-xcor-limber\n",
    "if nc_g1 and nc_g2:\n",
    "    nc_g1.prepare (nc_cosmo)\n",
    "    nc_g2.prepare (nc_cosmo)\n",
    "    xcor.limber (nc_g1, nc_g2, nc_cosmo, 0, lmax, vp_cross)\n",
    "    xcor.limber (nc_g1, nc_g1, nc_cosmo, 0, lmax, vp_auto1)\n",
    "    xcor.limber (nc_g2, nc_g2, nc_cosmo, 0, lmax, vp_auto2)\n",
    "########################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b13f114",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_cross = [Ncm.Vector.get (vp_cross, i) for i in np.arange(0,lmax+1)]\n",
    "lista_auto1 = [Ncm.Vector.get (vp_auto1, i) for i in np.arange(0,lmax+1)]\n",
    "lista_auto2 = [Ncm.Vector.get (vp_auto2, i) for i in np.arange(0,lmax+1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6fa7ffb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ells, lista_cross, label='cl_cross')\n",
    "#plt.plot(ells, lista_auto1, label='cl_auto1' )\n",
    "#plt.plot(ells, lista_auto2, label='cl_auto2' )\n",
    "plt.legend()\n",
    "plt.xscale('log')\n",
    "plt.xlabel('l')\n",
    "plt.ylabel('C_l')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84310711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4899519074388405e-10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### DÚVIDA\n",
    "# Eu modifiquei o array dos \"ells\". Deixei ele como um simples np.arange(lmax+1), onde lmax=10000. \n",
    "# Mais para a frente, define-se vp = Ncm.Vector.new (lmax + 1). Depois, calcula-se a auto-correlação de \n",
    "# lmin=0 a lmax=lmax. O resultado dos Cls é guardado no vetor Vp. Até aí tudo bem. O problema é que, se eu chamo \n",
    "# Ncm.Vector.get (vp, 20000), ele me dá a resposta! Como assim? Não era para Vp estar definido só até lmax?\n",
    "Ncm.Vector.get (vp_cross, 20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "238d817e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the vector: 10001\n",
      "Element 7001: 7.51200618570892e-11\n",
      "Element 7051: 7.381020883332688e-11\n",
      "Element 7101: 7.25317052626737e-11\n",
      "Element 7151: 7.128359816917301e-11\n",
      "Element 7201: 7.006496952742955e-11\n",
      "Element 7251: 6.887493476210453e-11\n",
      "Element 7301: 6.77126417252692e-11\n",
      "Element 7351: 6.657726894513506e-11\n",
      "Element 7401: 6.546802420387277e-11\n",
      "Element 7451: 6.438414375150549e-11\n",
      "Element 7501: 6.332489092371536e-11\n",
      "Element 7551: 6.22895547492723e-11\n",
      "Element 7601: 6.127744921633609e-11\n",
      "Element 7651: 6.028791225521534e-11\n",
      "Element 7701: 5.932030458046477e-11\n",
      "Element 7751: 5.837400874688375e-11\n",
      "Element 7801: 5.7448428453057384e-11\n",
      "Element 7851: 5.654298772808867e-11\n",
      "Element 7901: 5.565712981935322e-11\n",
      "Element 7951: 5.4790316611438965e-11\n",
      "Element 8001: 5.3942028076680164e-11\n",
      "Element 8051: 5.3111761299987195e-11\n",
      "Element 8101: 5.229902982329422e-11\n",
      "Element 8151: 5.150336320896762e-11\n",
      "Element 8201: 5.0724306290381985e-11\n",
      "Element 8251: 4.9961418566611875e-11\n",
      "Element 8301: 4.921427374961069e-11\n",
      "Element 8351: 4.848245909627818e-11\n",
      "Element 8401: 4.776557501588877e-11\n",
      "Element 8451: 4.7063234590231666e-11\n",
      "Element 8501: 4.6375062939190954e-11\n",
      "Element 8551: 4.5700696941229266e-11\n",
      "Element 8601: 4.5039784794114874e-11\n",
      "Element 8651: 4.4391985501245644e-11\n",
      "Element 8701: 4.375696854880898e-11\n",
      "Element 8751: 4.313441351648152e-11\n",
      "Element 8801: 4.252400974258658e-11\n",
      "Element 8851: 4.1925455937459806e-11\n",
      "Element 8901: 4.1338459810167154e-11\n",
      "Element 8951: 4.076273788777082e-11\n",
      "Element 9001: 4.019801510711377e-11\n",
      "Element 9051: 3.964402444857122e-11\n",
      "Element 9101: 3.910050685552117e-11\n",
      "Element 9151: 3.856721083692602e-11\n",
      "Element 9201: 3.8043892118786984e-11\n",
      "Element 9251: 3.7530313607636155e-11\n",
      "Element 9301: 3.7026245032455366e-11\n",
      "Element 9351: 3.6531462629271544e-11\n",
      "Element 9401: 3.604574909805687e-11\n",
      "Element 9451: 3.556889329891114e-11\n",
      "Element 9501: 3.510068998299148e-11\n",
      "Element 9551: 3.4640939727756536e-11\n",
      "Element 9601: 3.4189448674547196e-11\n",
      "Element 9651: 3.374602830254295e-11\n",
      "Element 9701: 3.331049536049895e-11\n",
      "Element 9751: 3.288267163352883e-11\n",
      "Element 9801: 3.246238374048057e-11\n",
      "Element 9851: 3.2049463069139687e-11\n",
      "Element 9901: 3.164374558486466e-11\n",
      "Element 9951: 3.124507164266184e-11\n",
      "Element 10001: 3.95337e-319\n",
      "Element 10051: 2.096549177296498e-06\n",
      "Element 10101: 1.2215728539168382e-06\n",
      "Element 10151: 8.834809211884117e-07\n",
      "Element 10201: 6.050214961852464e-07\n",
      "Element 10251: 4.2034656290602095e-07\n",
      "Element 10301: 3.31667617084465e-07\n",
      "Element 10351: 2.511461965586024e-07\n",
      "Element 10401: 1.974382837283137e-07\n",
      "Element 10451: 1.6291491186677739e-07\n",
      "Element 10501: 1.3273845919481774e-07\n",
      "Element 10551: 1.1064494486780984e-07\n",
      "Element 10601: 9.42384687998926e-08\n",
      "Element 10651: 8.022289119274233e-08\n",
      "Element 10701: 6.926926893607099e-08\n",
      "Element 10751: 6.040455451668176e-08\n",
      "Element 10801: 5.2920804835679925e-08\n",
      "Element 10851: 4.6741968938531086e-08\n",
      "Element 10901: 4.154047051334052e-08\n",
      "Element 10951: 3.710389867579642e-08\n",
      "Element 11001: 3.3313236487799e-08\n",
      "Element 11051: 3.0049121687464436e-08\n",
      "Element 11101: 2.721633256366622e-08\n",
      "Element 11151: 2.474582600846695e-08\n",
      "Element 11201: 2.2580801476792442e-08\n",
      "Element 11251: 2.0672826104960872e-08\n",
      "Element 11301: 1.8984089833435552e-08\n",
      "Element 11351: 1.7483358981097034e-08\n",
      "Element 11401: 1.614413821692368e-08\n",
      "Element 11451: 1.4944740283193497e-08\n",
      "Element 11501: 1.3866887718142225e-08\n",
      "Element 11551: 1.2895076548017699e-08\n",
      "Element 11601: 1.2016233545387048e-08\n",
      "Element 11651: 1.1219175583263793e-08\n",
      "Element 11701: 1.0494335667414066e-08\n",
      "Element 11751: 9.833488795553654e-09\n",
      "Element 11801: 9.229517499331419e-09\n",
      "Element 11851: 8.676266590700599e-09\n",
      "Element 11901: 8.16837047823563e-09\n",
      "Element 11951: 7.701140459796383e-09\n",
      "Element 12001: 7.270478623159656e-09\n",
      "Element 12051: 6.872773565343406e-09\n",
      "Element 12101: 6.504843269856925e-09\n",
      "Element 12151: 6.1638793969422585e-09\n",
      "Element 12201: 5.847384693667824e-09\n",
      "Element 12251: 5.553143298948557e-09\n",
      "Element 12301: 5.279181166975092e-09\n",
      "Element 12351: 5.0237314541122255e-09\n",
      "Element 12401: 4.7852148852154975e-09\n",
      "Element 12451: 4.562213563502094e-09\n",
      "Element 12501: 4.35345161027987e-09\n",
      "Element 12551: 4.157779979299067e-09\n",
      "Element 12601: 3.9741613379012625e-09\n",
      "Element 12651: 3.801656694463495e-09\n",
      "Element 12701: 3.639415290601704e-09\n",
      "Element 12751: 3.486665218031079e-09\n",
      "Element 12801: 3.3427037833663355e-09\n",
      "Element 12851: 3.206891316139903e-09\n",
      "Element 12901: 3.0786443290631502e-09\n",
      "Element 12951: 2.9574293073672924e-09\n"
     ]
    }
   ],
   "source": [
    "len_vp = Ncm.Vector.len (vp_cross)\n",
    "variation = 3000\n",
    "n_min = len_vp - variation\n",
    "n_max = len_vp + variation\n",
    "step = 50\n",
    "\n",
    "print(\"Length of the vector: \"+str(len_vp))\n",
    "for i in np.arange(n_min,n_max,step):\n",
    "    print(\"Element %i: \" % i + str(Ncm.Vector.get (vp_cross, i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6a31aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(n_min, n_max, 1), [Ncm.Vector.get (vp_cross, i) for i in np.arange(n_min, n_max, 1)])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}