Lista2_c.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7af35d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "  import gi\n",
    "  gi.require_version('NumCosmo', '1.0')\n",
    "  gi.require_version('NumCosmoMath', '1.0')\n",
    "except:\n",
    "  pass\n",
    "\n",
    "import math\n",
    "from gi.repository import NumCosmo as Nc\n",
    "from gi.repository import NumCosmoMath as Ncm\n",
    "import interpolation \n",
    "import numpy as np\n",
    "import scipy as sc\n",
    "import scipy.stats as scs\n",
    "import scipy.integrate as integrate\n",
    "import scipy.optimize as opt\n",
    "from scipy.special import erf\n",
    "import random as rd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "ser = Ncm.Serialize.new(0)\n",
    "Ncm.cfg_init()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7079fde2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2689/201909576.py:20: Warning: unable to set property 'w-mean' of type 'gboolean' from value of type 'gchararray'\n",
      "  snia = Nc.DataDistMu.new_from_id (dist, Nc.DataSNIAId.SIMPLE_LEGACY)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#----------------------------------------------------------------------------------\n",
      "# Model fitting. Interating using:\n",
      "#  - solver:            NLOpt:ln-neldermead\n",
      "#  - differentiation:   Numerical differentiantion (forward)\n",
      "#....................\n",
      "#  Minimum found with precision: |df|/f =  1.00000e-08 and |dx| =  1.00000e-05\n",
      "#  Elapsed time: 00 days, 00:00:00.3552910\n",
      "#  iteration            [000116]\n",
      "#  function evaluations [000118]\n",
      "#  gradient evaluations [000000]\n",
      "#  degrees of freedom   [000112]\n",
      "#  m2lnL     =     110.950177682567 (     110.95018 )\n",
      "#  Fit parameters:\n",
      "#     70.1266932058548     0.264955145171665    0.804953011622957   \n",
      "#----------------------------------------------------------------------------------\n",
      "# Data used:\n",
      "#   - Legacy sample\n",
      "#----------------------------------------------------------------------------------\n",
      "# Model[03000]:\n",
      "#   - NcHICosmo : \\Lambda{}CDM\n",
      "#----------------------------------------------------------------------------------\n",
      "# Model parameters\n",
      "#   -      H0[00]:  70.1266932058548    [FREE]\n",
      "#   -  Omegac[01]:  0.264955145171665   [FREE]\n",
      "#   -  Omegax[02]:  0.804953011622957   [FREE]\n",
      "#   - Tgamma0[03]:  2.7245              [FIXED]\n",
      "#   -      Yp[04]:  0.24                [FIXED]\n",
      "#   -    ENnu[05]:  3.046               [FIXED]\n",
      "#   -  Omegab[06]:  0.0432              [FIXED]\n",
      "#----------------------------------------------------------------------------------\n",
      "# NcmMSet parameters covariance matrix\n",
      "#                                                  ----------------------------------------------\n",
      "#     H0[03000:00] =  70.13       +/-  0.9295      |  1           |  0.4144      |  0.6111      |\n",
      "# Omegac[03000:01] =  0.265       +/-  0.2084      |  0.4144      |  1           |  0.9583      |\n",
      "# Omegax[03000:02] =  0.805       +/-  0.3036      |  0.6111      |  0.9583      |  1           |\n",
      "#                                                  ----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, 'NcHICosmoLCDM')\n",
    "mset = Ncm.MSet ()\n",
    "mset.set (cosmo)\n",
    "\n",
    "#\n",
    "#  Setting parameters Omega_c and w to be fitted.\n",
    "#\n",
    "cosmo.props.Omegac_fit = True\n",
    "cosmo.props.Omegax_fit = True\n",
    "cosmo.props.H0_fit = True\n",
    "\n",
    "#\n",
    "#  Creating a new Distance object optimized to redshift 2.\n",
    "#\n",
    "dist = Nc.Distance (zf = 2.0)\n",
    "\n",
    "#\n",
    "#  Creating a new Data object from distance modulus catalogs.\n",
    "#\n",
    "snia = Nc.DataDistMu.new_from_id (dist, Nc.DataSNIAId.SIMPLE_LEGACY)\n",
    "\n",
    "#\n",
    "#  Creating a new Dataset and add snia to it.\n",
    "#\n",
    "dset = Ncm.Dataset ()\n",
    "dset.append_data (snia)\n",
    "\n",
    "#\n",
    "#  Creating a Likelihood from the Dataset.\n",
    "#\n",
    "lh = Ncm.Likelihood (dataset = dset)\n",
    "\n",
    "#\n",
    "#  Creating a Fit object of type NLOPT using the fitting algorithm ln-neldermead to\n",
    "#  fit the Modelset mset using the Likelihood lh and using a numerical differentiation\n",
    "#  algorithm (NUMDIFF_FORWARD) to obtain the gradient (if needed).\n",
    "#\n",
    "fit = Ncm.Fit.new (Ncm.FitType.NLOPT, \"ln-neldermead\", lh, mset, Ncm.FitGradType.NUMDIFF_FORWARD)\n",
    "\n",
    "#\n",
    "#  Running the fitter printing messages.\n",
    "##\n",
    "fit.run (Ncm.FitRunMsgs.SIMPLE)\n",
    "\n",
    "#\n",
    "#  Printing fitting informations.\n",
    "#\n",
    "fit.log_info ()\n",
    "\n",
    "#\n",
    "#  Calculating the parameters covariance using numerical differentiation.\n",
    "#\n",
    "fit.numdiff_m2lnL_covar ()\n",
    "\n",
    "#\n",
    "#  Printing the covariance matrix.\n",
    "#\n",
    "fit.log_covar ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d71236e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#----------------------------------------------------------------------------------\n",
      "# Likelihood ratio confidence region at 68.270%, bestfit [  0.26495515    70.126693]:\n",
      "#\n",
      "#  looking root in interval [           0    1.5151948]:\n",
      "#..\n",
      "#  root found at    1.5076229 with precision 1.00000000e-02.\n",
      "#  border found at    1.5076229.\n",
      "#.\n",
      "#  looking root in interval [   7.0669608    8.6377572]:\n",
      "#..\n",
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     ]
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    {
     "name": "stdout",
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     "text": [
      "#..\n",
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      "#..\n",
      "#  root found at     9.859985 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.0745869    10.645383]:\n",
      "#..\n",
      "#  root found at    9.8869395 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.1015413    10.672338]:\n",
      "#..\n",
      "#  root found at    9.9138178 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.1284196    10.699216]:\n",
      "#..\n",
      "#  root found at    9.9407241 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.1553259    10.726122]:\n",
      "#..\n",
      "#  root found at    9.9675451 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.1821469    10.752943]:\n",
      "#..\n",
      "#  root found at     9.994213 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.2088148    10.779611]:\n",
      "#..\n",
      "#  root found at    10.020893 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.2354952    10.806292]:\n",
      "#..\n",
      "#  root found at    10.047105 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.261707    10.832503]:\n",
      "#..\n",
      "#  root found at    10.073418 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.2880199    10.858816]:\n",
      "#..\n",
      "#  root found at    10.099402 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.3140041      10.8848]:\n",
      "#..\n",
      "#  root found at    10.125168 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.3397695    10.910566]:\n",
      "#..\n",
      "#  root found at    10.150654 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.365256    10.936052]:\n",
      "#..\n",
      "#  root found at    10.175876 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.3904783    10.961275]:\n",
      "#..\n",
      "#  root found at    10.200786 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.4153878    10.986184]:\n",
      "#..\n",
      "#  root found at    10.225401 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.4400024    11.010799]:\n",
      "#..\n",
      "#  root found at    10.249686 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.4642876    11.035084]:\n",
      "#..\n",
      "#  root found at    10.273637 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.488239    11.059035]:\n",
      "#..\n",
      "#  root found at    10.297249 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.5118508    11.082647]:\n",
      "#..\n",
      "#  root found at    10.320532 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.535134     11.10593]:\n",
      "#..\n",
      "#  root found at    10.343447 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.5580488    11.128845]:\n",
      "#..\n",
      "#  root found at    10.366049 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.580651    11.151447]:\n",
      "#..\n",
      "#  root found at    10.388237 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.6028388    11.173635]:\n",
      "#..\n",
      "#  root found at    10.410145 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.6247473    11.195544]:\n",
      "#..\n",
      "#  root found at    10.431681 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.6462832     11.21708]:\n",
      "#..\n",
      "#  root found at    10.452894 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.667496    11.238292]:\n",
      "#..\n",
      "#  root found at    10.473763 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.688365    11.259161]:\n",
      "#..\n",
      "#  root found at     10.49432 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.7089219    11.279718]:\n",
      "#..\n",
      "#  root found at    10.514551 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.7291526    11.299949]:\n",
      "#..\n",
      "#  root found at    10.534482 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.7490843    11.319881]:\n",
      "#..\n",
      "#  root found at    10.554103 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.7687052    11.339502]:\n",
      "#..\n",
      "#  root found at    10.573485 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.7880866    11.358883]:\n",
      "#..\n",
      "#  root found at    10.592502 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.8071041      11.3779]:\n",
      "#..\n",
      "#  root found at     10.61133 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.8259314    11.396728]:\n",
      "#..\n",
      "#  root found at    10.629879 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.8444812    11.415277]:\n",
      "#..\n",
      "#  root found at    10.648181 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.8627833     11.43358]:\n",
      "#..\n",
      "#  root found at    10.666261 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.8808624    11.451659]:\n",
      "#..\n",
      "#  root found at    10.684114 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    9.898716    11.469512]:\n",
      "#..\n",
      "#  root found at    10.701769 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.9163706    11.487167]:\n",
      "#..\n",
      "#  root found at    10.719232 with precision 1.00000000e-02.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#  looking root in interval [   9.9338334     11.50463]:\n",
      "#..\n",
      "#  root found at    10.736503 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.9511048    11.521901]:\n",
      "#..\n",
      "#  root found at    10.753615 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.9682173    11.539014]:\n",
      "#..\n",
      "#  root found at    10.770573 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   9.9851749    11.555971]:\n",
      "#..\n",
      "#  root found at    10.787371 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.001973    11.572769]:\n",
      "#..\n",
      "#  root found at    10.804057 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.018659    11.589455]:\n",
      "#..\n",
      "#  root found at    10.820616 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.035218    11.606014]:\n",
      "#..\n",
      "#  root found at    10.837064 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.051666    11.622462]:\n",
      "#..\n",
      "#  root found at    10.853427 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.068029    11.638825]:\n",
      "#..\n",
      "#  root found at      10.8697 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.084302    11.655098]:\n",
      "#..\n",
      "#  root found at     10.88593 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.100532    11.671328]:\n",
      "#..\n",
      "#  root found at    10.902061 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.116663    11.687459]:\n",
      "#..\n",
      "#  root found at    10.918238 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.132839    11.703636]:\n",
      "#..\n",
      "#  root found at    10.934145 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.148746    11.719543]:\n",
      "#..\n",
      "#  root found at    10.950292 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.164894     11.73569]:\n",
      "#..\n",
      "#  root found at    10.966338 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    10.18094    11.751737]:\n",
      "#..\n",
      "#  root found at    10.982385 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.196987    11.767783]:\n",
      "#..\n",
      "#  root found at    10.998445 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.213047    11.783843]:\n",
      "#..\n",
      "#  root found at    11.014547 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.229149    11.799945]:\n",
      "#..\n",
      "#  root found at    11.030677 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.245279    11.816075]:\n",
      "#..\n",
      "#  root found at    11.046865 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.261467    11.832264]:\n",
      "#..\n",
      "#  root found at    11.063125 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.277727    11.848524]:\n",
      "#..\n",
      "#  root found at    11.079455 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.294057    11.864853]:\n",
      "#..\n",
      "#  root found at    11.095853 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.310455    11.881252]:\n",
      "#..\n",
      "#  root found at    11.112427 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.327029    11.897826]:\n",
      "#..\n",
      "#  root found at    11.129051 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.343652    11.914449]:\n",
      "#..\n",
      "#  root found at    11.145818 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.360419    11.931216]:\n",
      "#..\n",
      "#  root found at    11.162723 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.377325    11.948121]:\n",
      "#..\n",
      "#  root found at    11.179798 with precision 1.00000000e-02.\n",
      "#  looking root in interval [     10.3944    11.965196]:\n",
      "#..\n",
      "#  root found at    11.197017 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.411619    11.982415]:\n",
      "#..\n",
      "#  root found at    11.214436 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.429038    11.999834]:\n",
      "#..\n",
      "#  root found at    11.232041 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.446643    12.017439]:\n",
      "#..\n",
      "#  root found at    11.249846 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.464448    12.035245]:\n",
      "#..\n",
      "#  root found at    11.267866 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.482468    12.053264]:\n",
      "#......\n",
      "#  root found at     11.36604 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   10.580642    12.151438]:\n",
      "#........\n",
      "#  root found at    12.053262 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267864     12.83866]:\n",
      "#......\n",
      "#  root found at    12.151435 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.366037    12.936833]:\n",
      "#.......\n",
      "#  root found at    12.053258 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    11.26786    12.838656]:\n",
      "#......\n",
      "#  root found at     12.15143 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.366032    12.936828]:\n",
      "#.......\n",
      "#  root found at    12.053252 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267854     12.83865]:\n",
      "#......\n",
      "#  root found at    12.151423 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.366025    12.936821]:\n",
      "#......\n",
      "#  root found at    12.249593 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.464195    13.034992]:\n",
      "#......\n",
      "#  root found at    11.955066 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.169668    12.740464]:\n",
      "#.......\n",
      "#  root found at    12.249583 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.464185    13.034981]:\n",
      "#.......\n",
      "#  root found at    12.151402 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.366004      12.9368]:\n",
      "#.......\n",
      "#  root found at     12.05322 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267822    12.838619]:\n",
      "#......\n",
      "#  root found at    12.151389 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    11.36599    12.936787]:\n",
      "#.......\n",
      "#  root found at    12.053206 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267808    12.838604]:\n",
      "#......\n",
      "#  root found at    12.151374 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365976    12.936772]:\n",
      "#.......\n",
      "#  root found at    12.053191 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267793    12.838589]:\n",
      "#......\n",
      "#  root found at    12.151358 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365959    12.936756]:\n",
      "#.......\n",
      "#  root found at    12.053174 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267776    12.838572]:\n",
      "#......\n",
      "#  root found at     12.15134 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365942    12.936738]:\n",
      "#......\n",
      "#  root found at    12.249506 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.464108    13.034904]:\n",
      "#......\n",
      "#  root found at    11.954976 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.169578    12.740375]:\n",
      "#.......\n",
      "#  root found at    12.249487 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.464089    13.034885]:\n",
      "#.......\n",
      "#  root found at    12.151302 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365904      12.9367]:\n",
      "#.......\n",
      "#  root found at    12.053117 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267718    12.838515]:\n",
      "#......\n",
      "#  root found at    12.151282 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365883     12.93668]:\n",
      "#.......\n",
      "#  root found at    12.053096 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267698    12.838494]:\n",
      "#......\n",
      "#  root found at    12.151261 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365862    12.936659]:\n",
      "#.......\n",
      "#  root found at    12.053075 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267677    12.838473]:\n",
      "#......\n",
      "#  root found at    12.151239 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365841    12.936638]:\n",
      "#.......\n",
      "#  root found at    12.053053 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267655    12.838452]:\n",
      "#......\n",
      "#  root found at    12.151218 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    11.36582    12.936616]:\n",
      "#......\n",
      "#  root found at    12.249382 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.463984    13.034781]:\n",
      "#......\n",
      "#  root found at    11.954852 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.169454     12.74025]:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#.......\n",
      "#  root found at    12.249361 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.463963    13.034759]:\n",
      "#.......\n",
      "#  root found at    12.151175 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365777    12.936573]:\n",
      "#.......\n",
      "#  root found at    12.052989 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267591    12.838387]:\n",
      "#......\n",
      "#  root found at    12.151154 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365755    12.936552]:\n",
      "#.......\n",
      "#  root found at    12.052968 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    11.26757    12.838366]:\n",
      "#......\n",
      "#  root found at    12.151133 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365735    12.936531]:\n",
      "#.......\n",
      "#  root found at    12.052947 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267549    12.838346]:\n",
      "#......\n",
      "#  root found at    12.151113 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365715    12.936511]:\n",
      "#......\n",
      "#  root found at    12.249278 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    11.46388    13.034676]:\n",
      "#......\n",
      "#  root found at    11.954748 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    11.16935    12.740146]:\n",
      "#.......\n",
      "#  root found at    12.249259 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.463861    13.034657]:\n",
      "#.......\n",
      "#  root found at    12.151075 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365677    12.936473]:\n",
      "#.......\n",
      "#  root found at    12.052891 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267493    12.838289]:\n",
      "#......\n",
      "#  root found at    12.151058 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365659    12.936456]:\n",
      "#.......\n",
      "#  root found at    12.052874 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267476    12.838272]:\n",
      "#......\n",
      "#  root found at    12.151042 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365643     12.93644]:\n",
      "#.......\n",
      "#  root found at    12.052859 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267461    12.838257]:\n",
      "#......\n",
      "#  root found at    12.151027 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365629    12.936425]:\n",
      "#.......\n",
      "#  root found at    12.052845 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267447    12.838244]:\n",
      "#......\n",
      "#  root found at    12.151014 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365616    12.936412]:\n",
      "#......\n",
      "#  root found at    12.249183 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.463785    13.034582]:\n",
      "#......\n",
      "#  root found at    11.954656 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.169258    12.740054]:\n",
      "#.......\n",
      "#  root found at    12.249174 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.463775    13.034572]:\n",
      "#.......\n",
      "#  root found at    12.150994 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365596    12.936393]:\n",
      "#.......\n",
      "#  root found at    12.052816 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267418    12.838214]:\n",
      "#......\n",
      "#  root found at    12.150987 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365589    12.936386]:\n",
      "#.......\n",
      "#  root found at     12.05281 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267412    12.838208]:\n",
      "#......\n",
      "#  root found at    12.150983 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365585    12.936381]:\n",
      "#.......\n",
      "#  root found at    12.052807 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.267409    12.838205]:\n",
      "#......\n",
      "#  root found at    12.150981 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.365583    12.936379]:\n",
      "#.....\n",
      "#  root found at    12.725571 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   11.940173     13.51097]:\n",
      "#..\n",
      "#  root found at    12.927969 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.142571    13.713367]:\n",
      "#..\n",
      "#  root found at    12.939579 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.15418    13.724977]:\n",
      "#..\n",
      "#  root found at    12.958299 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.172901    13.743697]:\n",
      "#..\n",
      "#  root found at    12.974365 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.188966    13.759763]:\n",
      "#..\n",
      "#  root found at    12.990536 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.205137    13.775934]:\n",
      "#..\n",
      "#  root found at     13.00652 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.221121    13.791918]:\n",
      "#..\n",
      "#  root found at    13.022435 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.237037    13.807833]:\n",
      "#..\n",
      "#  root found at    13.038269 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.252871    13.823667]:\n",
      "#..\n",
      "#  root found at    13.054053 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.268655    13.839451]:\n",
      "#..\n",
      "#  root found at    13.069796 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.284398    13.855194]:\n",
      "#..\n",
      "#  root found at    13.085499 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.300101    13.870897]:\n",
      "#..\n",
      "#  root found at    13.101191 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.315793    13.886589]:\n",
      "#..\n",
      "#  root found at    13.116889 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.33149    13.902287]:\n",
      "#..\n",
      "#  root found at    13.132626 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.347228    13.918024]:\n",
      "#..\n",
      "#  root found at    13.148308 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.36291    13.933706]:\n",
      "#..\n",
      "#  root found at    13.164116 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.378717    13.949514]:\n",
      "#..\n",
      "#  root found at    13.179952 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.394554     13.96535]:\n",
      "#..\n",
      "#  root found at    13.195872 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.410474     13.98127]:\n",
      "#..\n",
      "#  root found at    13.211889 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.426491    13.997287]:\n",
      "#..\n",
      "#  root found at    13.227972 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.442573     14.01337]:\n",
      "#..\n",
      "#  root found at    13.244169 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.458771    14.029567]:\n",
      "#..\n",
      "#  root found at     13.26055 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.475152    14.045948]:\n",
      "#..\n",
      "#  root found at    13.277181 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.491783     14.06258]:\n",
      "#..\n",
      "#  root found at      13.2936 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.508202    14.078999]:\n",
      "#..\n",
      "#  root found at    13.310572 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.525174     14.09597]:\n",
      "#..\n",
      "#  root found at    13.327619 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.542221    14.113017]:\n",
      "#..\n",
      "#  root found at    13.344878 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.55948    14.130276]:\n",
      "#..\n",
      "#  root found at    13.362353 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.576955    14.147752]:\n",
      "#..\n",
      "#  root found at    13.380075 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.594677    14.165473]:\n",
      "#..\n",
      "#  root found at    13.398073 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.612675    14.183471]:\n",
      "#..\n",
      "#  root found at    13.416319 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.630921    14.201717]:\n",
      "#..\n",
      "#  root found at    13.434855 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.649457    14.220253]:\n",
      "#..\n",
      "#  root found at    13.453706 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.668308    14.239104]:\n",
      "#..\n",
      "#  root found at    13.472868 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.68747    14.258267]:\n",
      "#..\n",
      "#  root found at    13.492365 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.706967    14.277763]:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#..\n",
      "#  root found at    13.512197 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.726798    14.297595]:\n",
      "#..\n",
      "#  root found at    13.532379 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.746981    14.317777]:\n",
      "#..\n",
      "#  root found at    13.552978 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.76758    14.338376]:\n",
      "#..\n",
      "#  root found at    13.573982 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.788583     14.35938]:\n",
      "#..\n",
      "#  root found at    13.595083 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.809685    14.380481]:\n",
      "#..\n",
      "#  root found at    13.617049 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.831651    14.402447]:\n",
      "#..\n",
      "#  root found at    13.638925 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.853527    14.424323]:\n",
      "#..\n",
      "#  root found at    13.661568 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    12.87617    14.446966]:\n",
      "#..\n",
      "#  root found at    13.684502 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.899104      14.4699]:\n",
      "#..\n",
      "#  root found at    13.707835 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.922437    14.493233]:\n",
      "#..\n",
      "#  root found at     13.73158 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.946182    14.516978]:\n",
      "#..\n",
      "#  root found at    13.755715 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.970317    14.541113]:\n",
      "#..\n",
      "#  root found at    13.780246 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   12.994847    14.565644]:\n",
      "#..\n",
      "#  root found at    13.805152 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.019754     14.59055]:\n",
      "#..\n",
      "#  root found at    13.830423 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.045024    14.615821]:\n",
      "#..\n",
      "#  root found at    13.856039 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.070641    14.641437]:\n",
      "#..\n",
      "#  root found at    13.881979 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.096581    14.667377]:\n",
      "#..\n",
      "#  root found at    13.908231 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.122833    14.693629]:\n",
      "#..\n",
      "#  root found at    13.934764 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.149366    14.720162]:\n",
      "#..\n",
      "#  root found at    13.961475 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.176076    14.746873]:\n",
      "#..\n",
      "#  root found at    13.988469 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.203071    14.773867]:\n",
      "#..\n",
      "#  root found at    14.015618 with precision 1.00000000e-02.\n",
      "#  looking root in interval [    13.23022    14.801016]:\n",
      "#..\n",
      "#  root found at    14.042904 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.257505    14.828302]:\n",
      "#..\n",
      "#  root found at    14.070289 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.284891    14.855687]:\n",
      "#..\n",
      "#  root found at    14.097731 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.312333     14.88313]:\n",
      "#..\n",
      "#  root found at    14.125214 with precision 1.00000000e-02.\n",
      "#  looking root in interval [   13.339816    14.910613]:\n",
      "#..\n",
      "#  root found at    14.152599 with precision 1.00000000e-02.\n",
      "#  looking root in interval [     13.3672    14.937997]:\n",
      "#..\n",
      "#  root found at    14.179995 with precision 1.00000000e-02.\n",
      "#  Start found at [291], ending...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f09699bc9a0>"
      ]
     },
     "execution_count": 6,
     "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": [
    "#\n",
    "#  Creating a new Likelihood ratio test object.\n",
    "#  First we create two PIndex indicating which parameter\n",
    "#    we are going to study.\n",
    "#\n",
    "p1 = Ncm.MSetPIndex.new (cosmo.id (), Nc.HICosmoDESParams.OMEGA_C)\n",
    "\n",
    "#p2 = Ncm.MSetPIndex.new (cosmo.id (), Nc.HICosmoDESParams.OMEGA_X)\n",
    "p2 = Ncm.MSetPIndex.new (cosmo.id (), Nc.HICosmoDESParams.H0)\n",
    "lhr2d = Ncm.LHRatio2d.new (fit, p1, p2, 1.0e-2)\n",
    "\n",
    "\n",
    "\n",
    "cr_rg = lhr2d.conf_region (0.6827, 300.0, Ncm.FitRunMsgs.SIMPLE)\n",
    "\n",
    "\n",
    "fisher_rg = lhr2d.fisher_border (0.6827, 300.0, Ncm.FitRunMsgs.SIMPLE)\n",
    "\n",
    "cr_p1array = cr_rg.p1.dup_array ()\n",
    "cr_p2array = cr_rg.p2.dup_array ()\n",
    "\n",
    "fisher_p1array = fisher_rg.p1.dup_array ()\n",
    "fisher_p2array = fisher_rg.p2.dup_array ()\n",
    "\n",
    "#\n",
    "#  Ploting the confidence regions obtained from both methods.\n",
    "#\n",
    "\n",
    "plt.title (\"Confidence regions (%.2f)\" % (cr_rg.clevel * 100.0))\n",
    "plt.plot (cr_p1array, cr_p2array, 'r', label=\"Likelihood Ratio\")\n",
    "plt.plot (fisher_p1array, fisher_p2array, 'b-', label=\"Fisher Matrix\")\n",
    "plt.xlabel(r'$\\Omega_c$')\n",
    "plt.ylabel(r'$w$')\n",
    "\n",
    "plt.legend(loc=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e711a89",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}