{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c382ba9f-1365-4fd2-b2ec-0a414f55859a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from scipy.integrate import IntegrationWarning\n",
    "\n",
    "# Ignore integration warnings\n",
    "warnings.simplefilter(\"ignore\", IntegrationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fe5c3fd9-96c0-4000-a9fb-b2ffed497c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Physical baryon density parameter (\\Omega_b h^2) from Planck\n",
    "obh2pl = 0.02236\n",
    "\n",
    "# Matter density parameter (\\Omega_m) from Planck\n",
    "ompl = 0.3166\n",
    "\n",
    "# Hubble constant (H_0) in km s^-1 Mpc^-1 from Planck\n",
    "H0pl = 67.27\n",
    "\n",
    "# Dimensionless Hubble parameter (h) from Planck\n",
    "h0pl = H0pl / 100\n",
    "\n",
    "# Dimensionless Hubble parameter (h) from Type Ia Supernovae\n",
    "h0riess = 0.7403\n",
    "\n",
    "# Cosmic Microwave Background (CMB) temperature in Kelvin\n",
    "Tcmb = 2.7255\n",
    "\n",
    "# Speed of light in vacuum in km s^-1\n",
    "c = 299792.458\n",
    "\n",
    "# Hubble distance scale (c / 100) in Mpc\n",
    "cH0 = 2997.92458"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "af4e9d79-7583-46c4-875e-94434eef9f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mpmath\n",
    "import numpy as np\n",
    "# w(a) = w_0 + w_a * (1 - a)^n, i.e., nCPL model\n",
    "# For n=1 we get the usual CPL model\n",
    "def w(a, w0, wa, n):\n",
    "    return w0 + wa * (1 - a)**n\n",
    "\n",
    "# f_DE(a) for the nCPL model\n",
    "def f(a, w0, wa, n):\n",
    "    if wa == 0:\n",
    "        return a**(-3 * (1 + w0))\n",
    "    \n",
    "    harmonic_n = mpmath.harmonic(n)\n",
    "    hyper_term = mpmath.hyper([1, 1, 1 - n], [2, 2], a)\n",
    "    term1 = a**(-3 * (1 + w0 + wa))\n",
    "    term2 = mpmath.exp(-3 * wa * harmonic_n + 3 * a * n * wa * hyper_term)\n",
    "    return float(term1 * term2)\n",
    "\n",
    "# Define z_equality and a_equality in order to avoid some problems.\n",
    "# Tcmb is added as a parameter with a default value of 2.725 K.\n",
    "def zeq(om, h, Tcmb=2.725):\n",
    "    return 2.5*10**4 * om * (h**2) * (Tcmb / 2.7)**(-4)\n",
    "\n",
    "def aeq(om, h, Tcmb=2.725):\n",
    "    return 1.0 / (1.0 + zeq(om, h, Tcmb))\n",
    "\n",
    "#humble parameter\n",
    "def H(a, om, w0, wa, n, h):\n",
    "    # matter and radiation term\n",
    "    term_m_r = (a**-3) * om * (1 + aeq(om, h) / a)\n",
    "    # dark energy term\n",
    "    term_de = (1 - om * (1 + aeq(om, h))) * f(a, w0, wa, n)\n",
    "    \n",
    "    return 100 * h * np.sqrt(term_m_r + term_de)\n",
    "\n",
    "#sound speed\n",
    "def cs(a, obh2):\n",
    "    R = 31500 * obh2 * (Tcmb / 2.7)**-4 * a\n",
    "    return c / np.sqrt(3 * (1 + R))\n",
    "\n",
    "#redshift at decoupling\n",
    "def zcmb(om, obh2, h):\n",
    "    omh2 = om * h**2\n",
    "    g1 = (0.0783 * obh2**-0.238) / (1 + 39.5 * obh2**0.763)\n",
    "    g2 = 0.560 / (1 + 21.1 * obh2**1.81)\n",
    "    return 1048 * (1 + 0.00124 * obh2**-0.738) * (1 + g1 * omh2**g2)\n",
    "\n",
    "#drag redshift\n",
    "def zdrag(om, obh2, h):\n",
    "    omh2 = om * h**2\n",
    "    b1 = 0.313 * omh2**-0.419 * (1 + 0.607 * omh2**0.674)\n",
    "    b2 = 0.238 * omh2**0.223\n",
    "    \n",
    "    term1 = 1291 * omh2**0.251 / (1 + 0.659 * omh2**0.828)\n",
    "    term2 = 1 + b1 * obh2**b2\n",
    "    return term1 * term2\n",
    "\n",
    "from scipy.integrate import quad\n",
    "\n",
    "#comoving sound horizon at drag redshift\n",
    "def rs(ze, om, obh2, w0, wa, n, h):\n",
    "    lower_limit = 10**-11\n",
    "    upper_limit = 1.0 / (1.0 + ze)\n",
    "    \n",
    "    def integrand(x):\n",
    "        return cs(x, obh2) / (x**2 * H(x, om, w0, wa, n, h))\n",
    "    \n",
    "    \n",
    "    result, error = quad(integrand, lower_limit, upper_limit)\n",
    "    \n",
    "    return result\n",
    "    \n",
    "#luminosity distance\n",
    "from scipy.integrate import solve_ivp\n",
    "from functools import lru_cache\n",
    "\n",
    "@lru_cache(maxsize=None)\n",
    "def DLsol(om, w0, wa, n, h):\n",
    "    def dy_dz(zz, y):\n",
    "        a = 1.0 / (1.0 + zz)\n",
    "        return 1.0 / (H(a, om, w0, wa, n, h) / (100.0 * h))\n",
    "    sol = solve_ivp(dy_dz, [0, 1300], [0.0], dense_output=True)\n",
    "    return sol.sol\n",
    "\n",
    "def DL(z, om, w0, wa, n, h):\n",
    "    sol_func = DLsol(om, w0, wa, n, h)\n",
    "    y_val = sol_func(z)[0]\n",
    "    dL_val = y_val * (1.0 + z)\n",
    "    return (c / (100.0 * h)) * dL_val\n",
    "    \n",
    "#angular diameter distance\n",
    "def DA(z, om, w0, wa, n, h):\n",
    "    return DL(z, om, w0, wa, n, h) / (1.0 + z)**2\n",
    "    \n",
    "#volume averaged distance\n",
    "def Dv(zbao, om, w0, wa, n, h):\n",
    "    dl_term = DL(zbao, om, w0, wa, n, h) / (1.0 + zbao)\n",
    "    h_val = H(1.0 / (1.0 + zbao), om, w0, wa, n, h)\n",
    "    \n",
    "    return ((dl_term**2) * (c * zbao) / h_val)**(1.0 / 3.0)\n",
    "\n",
    "#BAO dz ratio\n",
    "def dz(zbao, om, obh2, w0, wa, n, h):\n",
    "    term1 = rs(zdrag(om, obh2, h), om, obh2, w0, wa, n, h)\n",
    "    term2 = Dv(zbao, om, w0, wa, n, h)\n",
    "    return term1/term2\n",
    "\n",
    "#DH distance\n",
    "def DH(z, om, w0, wa, n, h):\n",
    "    return c/H(1/(1+z), om, w0, wa, n, h)\n",
    "\n",
    "def hz(z, om, w0, wa, n, h):\n",
    "    return H(1/(1+z), om, w0, wa, n, h)\n",
    "\n",
    "#shift parameter\n",
    "def R(om, obh2, w0, wa, n, h):\n",
    "    term1 = np.sqrt(om*(100*h)**2) * DA(zcmb(om, obh2, h), om, w0, wa, n, h)\n",
    "    term2 = 1 + zcmb(om, obh2, h)\n",
    "    return term1*term2/c\n",
    "\n",
    "#angular scale of sound horizon at recombination\n",
    "def la(om, obh2, w0, wa, n, h):\n",
    "    term1 = np.pi * DA(zcmb(om, obh2, h), om, w0, wa, n, h)\n",
    "    term2 = 1 + zcmb(om, obh2, h)\n",
    "    term3 = rs(zcmb(om, obh2, h), om, obh2, w0, wa, n, h)\n",
    "    return term1*term2/term3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f3e619af-189b-41c9-b05c-9acbdf9735f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.integrate import quad\n",
    "\n",
    "def E_z(z, om):\n",
    "    return np.sqrt(om * (1 + z)**3 + (1 - om))\n",
    "\n",
    "def DLh(z, om): \n",
    "    integrand = lambda z_prime: 1.0 / E_z(z_prime, om)  \n",
    "    integral_result = quad(integrand, 0, z)[0] \n",
    "    return (1 + z) * integral_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b920102d-cf46-4490-9472-c040d718f8b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data combination\n",
    "#CMB data\n",
    "datacmb = np.array([1.74963, 301.80845, 0.02237])\n",
    "\n",
    "# Covariance Matrix for {R, l_a, Omega_b h^2}\n",
    "covcmb = 10**-8 * np.array([\n",
    "    [1598.9554,  17112.007,  -36.311179],\n",
    "    [17112.007,  811208.45,  -494.79813],\n",
    "    [-36.311179, -494.79813,  2.1242182]\n",
    "])\n",
    "\n",
    "# Inverse Covariance Matrix\n",
    "invcov = np.linalg.inv(covcmb)\n",
    "\n",
    "# Number of CMB data points\n",
    "ndatCMB = len(datacmb)\n",
    "\n",
    "# Vector of residuals (Model - Data)\n",
    "def vec(om, obh2, w0, wa, n, h):\n",
    "    return np.array([\n",
    "        R(om, obh2, w0, wa, n, h) - datacmb[0],\n",
    "        la(om, obh2, w0, wa, n, h) - datacmb[1],\n",
    "        obh2 - datacmb[2]\n",
    "    ])\n",
    "\n",
    "# chi^2 function for CMB shift parameters\n",
    "def chi2R(om, obh2, w0, wa, n, h):\n",
    "    v = vec(om, obh2, w0, wa, n, h)\n",
    "    return v.T @ invcov @ v\n",
    "\n",
    "#ΒΑΟ data\n",
    "databao = np.array([\n",
    "    [0.106, 0.336, 0.015],\n",
    "    [0.44, 0.073, 0.031],\n",
    "    [0.6, 0.0726, 0.0164],\n",
    "    [0.73, 0.0592, 0.0185]\n",
    "])\n",
    "\n",
    "\n",
    "Cijbaoinv = np.array([\n",
    "    [1 / (databao[0, 2]**2), 0, 0, 0],\n",
    "    [0, 1040.3, -807.5, 336.8],\n",
    "    [0, -807.5, 3720.3, -1551.9],\n",
    "    [0, 336.8, -1551.9, 2914.9]\n",
    "])\n",
    "\n",
    "def vecbao(om, obh2, w0, wa, n, h):\n",
    "    return np.array([\n",
    "        databao[i, 1] - dz(databao[i, 0], om, obh2, w0, wa, n, h)\n",
    "        for i in range(len(databao))\n",
    "    ])\n",
    "\n",
    "#SDSS\n",
    "dataSDSS = np.array([\n",
    "    [0.15, 4.465666824, 0.1681350461],\n",
    "    [0.32, 8.4673, 0.167],\n",
    "    [0.57, 13.7728, 0.134],\n",
    "])\n",
    "\n",
    "#Ly-a\n",
    "dataLya = np.array([\n",
    "    [2.34, 11.2005988024, 0.56],\n",
    "    [2.34, 8.86, 0.29],\n",
    "])\n",
    "\n",
    "CijLyainv = np.array([\n",
    "    [1 / (dataLya[0, 2]**2), 0 ],\n",
    "    [0, 1 / (dataLya[1, 2]**2)]\n",
    "])\n",
    "\n",
    "def vecLya(om, obh2, w0, wa, n, h):\n",
    "    return np.array([\n",
    "        dataLya[0, 1] - DA(dataLya[0, 0], om, w0, wa, n, h) / rs(zdrag(om, obh2, h), om, obh2, w0, wa, n, h),\n",
    "        dataLya[1, 1] - DH(dataLya[1, 0], om, w0, wa, n, h) / rs(zdrag(om, obh2, h), om, obh2, w0, wa, n, h)\n",
    "    ])\n",
    "\n",
    "ndatBAO = len(dataSDSS) + len(databao) + len(dataLya)\n",
    "\n",
    "#total BAO χ^2\n",
    "def chi2bao(om, obh2, w0, wa, n, h):\n",
    "    v_bao = vecbao(om, obh2, w0, wa, n, h)\n",
    "    v_lya = vecLya(om, obh2, w0, wa, n, h)\n",
    "    \n",
    "    term_bao = v_bao.T @ Cijbaoinv @ v_bao\n",
    "    \n",
    "    term_sdss = sum(\n",
    "        ((dataSDSS[i, 1] - 1 / dz(dataSDSS[i, 0], om, obh2, w0, wa, n, h))**2) / (dataSDSS[i, 2]**2)\n",
    "        for i in range(len(dataSDSS))\n",
    "    )\n",
    "    \n",
    "    term_lya = v_lya.T @ CijLyainv @ v_lya\n",
    "    \n",
    "    return term_bao + term_sdss + term_lya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5c0e382f-2c66-4671-bd79-0a9378899fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#full pantheon data\n",
    "import pandas as pd\n",
    "dataPanth = pd.read_csv(\"lcparam_full_long_zhel.txt\", sep=r'\\s+')\n",
    "ndatPanth = len(dataPanth)\n",
    "Dij0 = np.loadtxt(\"sys_full_long.txt\")\n",
    "Dij = Dij0[1:].reshape((1048,1048))\n",
    "Id = np.ones(ndatPanth)\n",
    "#Construct of the covariance using only the statistical uncertainties\n",
    "Covstat = np.diag(dataPanth.iloc[:, 5]**2)\n",
    "#Construct of the covariance using only the systematic uncertainties\n",
    "Covsys = Dij\n",
    "Cijtot = Covsys + Covstat\n",
    "InvCovTotal = np.linalg.inv(Cijtot)\n",
    "InvCovstat = np.linalg.inv(Covstat)\n",
    "\n",
    "# χ^2 for the pantheon data\n",
    "def chi2Panth(M, om, w0, wa, n, h):\n",
    "    zcmb = dataPanth.iloc[:, 1].values  \n",
    "    zhel = dataPanth.iloc[:, 2].values  \n",
    "    mb = dataPanth.iloc[:, 4].values\n",
    "    dl_vals = DL(zcmb, om, w0, wa, n, h)\n",
    "    delta_m = mb - (M + 5 * np.log10(((1 + zhel) / (1 + zcmb)) * dl_vals) + 25)\n",
    "    chi2 = delta_m.T @ InvCovTotal @ delta_m\n",
    "    return chi2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a55866f5-baf7-4fa0-8b88-74f5b208c2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#χ^2 formula ignoring the Csys non diagonal matrix\n",
    "def chi2Panthstat2(Mcal, om):\n",
    "    zcmb = dataPanth.iloc[:, 1].values  \n",
    "    zhel = dataPanth.iloc[:, 2].values  \n",
    "    mb = dataPanth.iloc[:, 4].values    \n",
    "    \n",
    "\n",
    "    dl_vals = np.array([DLh(z, om) for z in zcmb])\n",
    "    \n",
    "    delta_m = mb - (5 * np.log10(((1 + zhel) / (1 + zcmb)) * dl_vals) + Mcal)\n",
    "    \n",
    "    chi2 = delta_m.T @ InvCovstat @ delta_m\n",
    "    \n",
    "    return chi2\n",
    "\n",
    "def chi2Panthstat3(Mcal, om):\n",
    "    zcmb = dataPanth.iloc[:, 1].values  \n",
    "    zhel = dataPanth.iloc[:, 2].values  \n",
    "    mb = dataPanth.iloc[:, 4].values    \n",
    "    \n",
    "\n",
    "    dl_vals = np.array([DLh(z, om) for z in zcmb])\n",
    "    \n",
    "    delta_m = mb - (5 * np.log10(((1 + zhel) / (1 + zcmb)) * dl_vals) + Mcal)\n",
    "    \n",
    "    chi2 = delta_m.T @ InvCovTotal @ delta_m\n",
    "    \n",
    "    return chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b53ffc70-3585-4646-b1fd-a683f197e293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1060\n",
      "Test Mathematica point in Python: 1032.8152046282435\n",
      "1026.2597302622967\n",
      "0.12038195926556611\n",
      "6.43509240668118\n"
     ]
    }
   ],
   "source": [
    "#total χ^2\n",
    "def chi2total(M, om, w0, wa, h):\n",
    "    return chi2R(om, obh2pl, w0, wa, 1.0, h) + chi2bao(om, obh2pl, w0, wa, 1.0, h) + chi2Panth(M, om, w0, wa, 1.0, h)\n",
    "ndatot = ndatCMB + ndatBAO + ndatPanth\n",
    "print(ndatot)\n",
    "test_val = chi2total(-19.4294, 0.316911, -1, 0, 0.672379)\n",
    "print(\"Test Mathematica point in Python:\", test_val)\n",
    "x_val = chi2Panth(-19.4294, 0.316911, -1.0, 0.0, 1.0, 0.672379)\n",
    "print(x_val)\n",
    "y_val = chi2R(0.316911, obh2pl, -1.0, 0.0, 1, 0.672379)\n",
    "print(y_val)\n",
    "z_val = chi2bao(0.316911, obh2pl, -1.0, 0.0, 1, 0.672379)\n",
    "print(z_val)\n",
    "def chi2total2(Mcal, om, w0, wa, h):\n",
    "    return chi2R(om, obh2pl, w0, wa, 1.0, h) + chi2bao(om, obh2pl, w0, wa, 1.0, h) + chi2Panthstat3(Mcal, om)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37a73f57-2727-4aa7-bd03-afc2cd138186",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.7906234691413\n",
      "Mlcdm = -19.42858367933054\n",
      "omlcdm = 0.3165893157830777\n",
      "wolcdm = -1\n",
      "walcdm = 0\n",
      "hlcdm = 0.6725515850401479\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import basinhopping\n",
    "\n",
    "def obj_lcdm(params):\n",
    "    M, om, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total(M, om, -1, 0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess = [-19.05, 0.3205, 0.6705]\n",
    "\n",
    "minimizer_kwargs = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minlcdm = basinhopping(\n",
    "    obj_lcdm,\n",
    "    x0=initial_guess,\n",
    "    minimizer_kwargs=minimizer_kwargs,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mlcdm = minlcdm.x[0]\n",
    "omlcdm = minlcdm.x[1]\n",
    "wolcdm = -1\n",
    "walcdm = 0\n",
    "hlcdm = minlcdm.x[2]\n",
    "\n",
    "print(\"Minimum chi2:\", minlcdm.fun)\n",
    "print(\"Mlcdm =\", Mlcdm)\n",
    "print(\"omlcdm =\", omlcdm)\n",
    "print(\"wolcdm =\", wolcdm)\n",
    "print(\"walcdm =\", walcdm)\n",
    "print(\"hlcdm =\", hlcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a7a3d49-60da-4ed5-9c71-83d9bbccc414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.6923784195608\n",
      "Mwcdm = -19.423397526144203\n",
      "omwcdm = 0.3144839135179227\n",
      "wowcdm = -1.0103536691742716\n",
      "wawcdm = 0\n",
      "hwcdm = 0.6751045296873595\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the wCDM model using all three likelihoods\n",
    "def obj_wcdm(params):\n",
    "    M, om, w0, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or w0 <= -3.0 or w0 >= 1.0 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total(M, om, w0, 0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_wcdm = [-19.05, 0.3205, -1.0, 0.6705]\n",
    "\n",
    "minimizer_kwargs_wcdm = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (-3.0, 1.0), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minwcdm = basinhopping(\n",
    "    obj_wcdm,\n",
    "    x0=initial_guess_wcdm,\n",
    "    minimizer_kwargs=minimizer_kwargs_wcdm,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mwcdm = minwcdm.x[0]\n",
    "omwcdm = minwcdm.x[1]\n",
    "wowcdm = minwcdm.x[2]\n",
    "wawcdm = 0\n",
    "hwcdm = minwcdm.x[3]\n",
    "\n",
    "print(\"Minimum chi2:\", minwcdm.fun)\n",
    "print(\"Mwcdm =\", Mwcdm)\n",
    "print(\"omwcdm =\", omwcdm)\n",
    "print(\"wowcdm =\", wowcdm)\n",
    "print(\"wawcdm =\", wawcdm)\n",
    "print(\"hwcdm =\", hwcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f5145fe-ac54-4429-9ab0-39a85111ac05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1031.8200001076448\n",
      "Mcpl = -19.434704767652953\n",
      "omcpl = 0.31459788748449363\n",
      "wocpl = -1.0810461575469668\n",
      "wacpl = 0.2798726755788088\n",
      "hcpl = 0.6742663679712206\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the CPL model using all three likelihoods\n",
    "def obj_cpl(params):\n",
    "    M, om, w0, wa, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or w0 <= -3.0 or w0 >= 1.0 or wa <= -3.0 or wa >= 3.0 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total(M, om, w0, wa, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_cpl = [-19.05, 0.3205, -1.0, 0.0, 0.6705]\n",
    "\n",
    "minimizer_kwargs_cpl = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (-3.0, 1.0), (-3.0, 3.0), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "mincpl = basinhopping(\n",
    "    obj_cpl,\n",
    "    x0=initial_guess_cpl,\n",
    "    minimizer_kwargs=minimizer_kwargs_cpl,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mcpl = mincpl.x[0]\n",
    "omcpl = mincpl.x[1]\n",
    "wocpl = mincpl.x[2]\n",
    "wacpl = mincpl.x[3]\n",
    "hcpl = mincpl.x[4]\n",
    "\n",
    "print(\"Minimum chi2:\", mincpl.fun)\n",
    "print(\"Mcpl =\", Mcpl)\n",
    "print(\"omcpl =\", omcpl)\n",
    "print(\"wocpl =\", wocpl)\n",
    "print(\"wacpl =\", wacpl)\n",
    "print(\"hcpl =\", hcpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a688236-ba9e-42db-a81b-1837ee29d942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1033.950082945731\n",
      "Mcal_2 = 23.802957178375667\n",
      "om_2 = 0.28500810397981957\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def obj_panth2(params):\n",
    "    Mcal, om = params\n",
    "    \n",
    "\n",
    "    if om <= 0.01 or om >= 0.99:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2Panthstat2(Mcal, om)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_panth2 = [23.0, 0.28]\n",
    "\n",
    "\n",
    "minimizer_kwargs_panth2 = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "\n",
    "min2 = basinhopping(\n",
    "    obj_panth2,\n",
    "    x0=initial_guess_panth2,\n",
    "    minimizer_kwargs=minimizer_kwargs_panth2,\n",
    "    niter=10,        \n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mcal_min2 = min2.x[0]\n",
    "om_min2 = min2.x[1]\n",
    "\n",
    "\n",
    "print(\"Minimum chi2:\", min2.fun)\n",
    "print(\"Mcal_2 =\", Mcal_min2)\n",
    "print(\"om_2 =\", om_min2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "feaefbd6-ca05-4261-bfa1-f4e756355e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1025.626391694376\n",
      "mlcdm3 = 23.808577698201713\n",
      "omlcdm3 = 0.29903566744217974\n"
     ]
    }
   ],
   "source": [
    "def obj_panth3(params):\n",
    "    Mcal, om = params\n",
    "    \n",
    "\n",
    "    if om <= 0.01 or om >= 0.99:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2Panthstat3(Mcal, om)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_panth3 = [23.0, 0.28]\n",
    "\n",
    "\n",
    "minimizer_kwargs_panth3 = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "\n",
    "min3 = basinhopping(\n",
    "    obj_panth3,\n",
    "    x0=initial_guess_panth3,\n",
    "    minimizer_kwargs=minimizer_kwargs_panth3,\n",
    "    niter=10,        \n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "mlcdm3 = min3.x[0]\n",
    "omlcdm3 = min3.x[1]\n",
    "\n",
    "print(\"Minimum chi2:\", min3.fun)\n",
    "print(\"mlcdm3 =\", mlcdm3)\n",
    "print(\"omlcdm3 =\", omlcdm3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b28bdafc-69bb-4932-b8a0-1aed4585ba96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.8150143496775\n",
      "Mtot = 23.816440571160058\n",
      "omtot = 0.3166270010247627\n",
      "wotot = -1\n",
      "watot = 0\n",
      "htot = 0.6725268710367047\n"
     ]
    }
   ],
   "source": [
    "def obj_lcdm2(params):\n",
    "    Mcal, om, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total2(Mcal, om, -1, 0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_lcdm2 = [23.0, 0.3205, 0.6705]\n",
    "\n",
    "minimizer_kwargs_lcdm2 = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minlcdm2 = basinhopping(\n",
    "    obj_lcdm2,\n",
    "    x0=initial_guess_lcdm2,\n",
    "    minimizer_kwargs=minimizer_kwargs_lcdm2,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mtot = minlcdm2.x[0]\n",
    "omtot = minlcdm2.x[1]\n",
    "wotot = -1\n",
    "watot = 0\n",
    "htot = minlcdm2.x[2]\n",
    "\n",
    "print(\"Minimum chi2:\", minlcdm2.fun)\n",
    "print(\"Mtot =\", Mtot)\n",
    "print(\"omtot =\", omtot)\n",
    "print(\"wotot =\", wotot)\n",
    "print(\"watot =\", watot)\n",
    "print(\"htot =\", htot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c85eba5d-5316-4b09-9a56-3f8a6f96dda9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Δχ^2 για 1σ, 2 παράμετροι: 2.295748928898636\n"
     ]
    }
   ],
   "source": [
    "#dchi\n",
    "import scipy.stats as stats\n",
    "import scipy.special as special\n",
    "\n",
    "def dchi(nsig, M):\n",
    "    p = special.erf(nsig / np.sqrt(2))\n",
    "    return stats.chi2.ppf(p, M)\n",
    "\n",
    "print(\"Δχ^2 για 1σ, 2 παράμετροι:\", dchi(1, 2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "55c268a9-c7e2-44f0-9f33-3f0c24bd07d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "chi2_lcdm = 1032.7906234691416\n",
    "Mlcdm = -19.428583683289137\n",
    "omlcdm = 0.3165893182993782\n",
    "wolcdm = -1\n",
    "walcdm = 0\n",
    "hlcdm = 0.6725515834715047\n",
    "\n",
    "chi2_wcdm = 1032.6923784195612\n",
    "Mwcdm = -19.423397513557912\n",
    "omwcdm = 0.31448390671613147\n",
    "wowcdm = -1.0103536947470049\n",
    "wawcdm = 0\n",
    "hwcdm = 0.6751045370485488\n",
    "\n",
    "chi2_cpl = 1031.8200001076264\n",
    "Mcpl = -19.434704769334335\n",
    "omcpl = 0.3145979001968007\n",
    "wocpl = -1.081045814578108\n",
    "wacpl = 0.27987150856828413\n",
    "hcpl = 0.6742663592877898\n",
    "\n",
    "chi2_Panthstat2 = 1033.950082945731\n",
    "Mcal_2 = 23.802957178375667 \n",
    "om_2 = 0.28500810397981957\n",
    "\n",
    "chi2_Panthstat3 = 1025.626391694376\n",
    "mlcdm3 = 23.808577698201713\n",
    "omlcdm3 = 0.29903566744217974\n",
    "\n",
    "minlcdm2 = 1032.8150143496775\n",
    "Mtot = 23.816440571160058\n",
    "omtot = 0.3166270010247627\n",
    "wotot = -1\n",
    "watot = 0\n",
    "htot = 0.6725268710367047\n",
    "\n",
    "#με Riess\n",
    "chi2_lcdmriess = 1054.496172098454\n",
    "mtot1 = 23.814274266875557\n",
    "omtot1 = 0.31175009037307555\n",
    "wotot1 = -1.0\n",
    "watot1 = 0.0\n",
    "htot1 = 0.6757990899578967\n",
    "\n",
    "np.savez('cosmology_parameters16.npz',\n",
    "         chi2_lcdm=chi2_lcdm, Mlcdm=Mlcdm, omlcdm=omlcdm, wolcdm=wolcdm, walcdm=walcdm, hlcdm=hlcdm,\n",
    "         chi2_wcdm=chi2_wcdm, Mwcdm=Mwcdm, omwcdm=omwcdm, wowcdm=wowcdm, wawcdm=wawcdm, hwcdm=hwcdm,\n",
    "         chi2_cpl=chi2_cpl, Mcpl=Mcpl, omcpl=omcpl, wocpl=wocpl, wacpl=wacpl, hcpl=hcpl,\n",
    "         chi2_Panthstat2 = chi2_Panthstat2, Mcal_2 = Mcal_2, om_2 = om_2,\n",
    "         chi2_Panthstat3 = chi2_Panthstat3, mlcdm3 = mlcdm3, omlcdm3 = omlcdm3,\n",
    "         minlcdm2 = minlcdm2, Mtot = Mtot, omtot = omtot, wotot = wotot, watot = watot, htot = htot,\n",
    "         chi2_lcdmriess = chi2_lcdmriess, mtot1 = mtot1, omtot1 = omtot1, wotot1 = wotot1, watot1 = watot1, htot1 = htot1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3068aa6c-e03b-4b75-8fdf-bf1d14a84a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best fit parameters for M, Ω_0m are: Ω_0m = 0.2990 ± 0.0219 and M = 23.8086 ± 0.0107 respectively.\n",
      "\n",
      "Covariance Matrix (cij):\n",
      "[[0.00011512 0.00021619]\n",
      " [0.00021619 0.00047942]]\n",
      "\n",
      "Errors:\n",
      "[0.01072928 0.02189562]\n"
     ]
    }
   ],
   "source": [
    "# ERRORS\n",
    "#SNIa\n",
    "def chi2_local3(Mcal, om):\n",
    "    return chi2Panthstat3(Mcal, om)\n",
    "\n",
    "d_Mcal = 0.002\n",
    "d_om = 0.02\n",
    "\n",
    "Mcal0 = mlcdm3\n",
    "om0 = omlcdm3\n",
    "\n",
    "f_0_0 = chi2_local3(Mcal0, om0)\n",
    "\n",
    "f_p_0 = chi2_local3(Mcal0 + d_Mcal, om0)\n",
    "f_m_0 = chi2_local3(Mcal0 - d_Mcal, om0)\n",
    "\n",
    "f_0_p = chi2_local3(Mcal0, om0 + d_om)\n",
    "f_0_m = chi2_local3(Mcal0, om0 - d_om)\n",
    "\n",
    "# f(x+dx, y+dy), f(x+dx, y-dy), f(x-dx, y+dy), f(x-dx, y-dy)\n",
    "f_p_p = chi2_local3(Mcal0 + d_Mcal, om0 + d_om)\n",
    "f_p_m = chi2_local3(Mcal0 + d_Mcal, om0 - d_om)\n",
    "f_m_p = chi2_local3(Mcal0 - d_Mcal, om0 + d_om)\n",
    "f_m_m = chi2_local3(Mcal0 - d_Mcal, om0 - d_om)\n",
    "\n",
    "d2_dMcal2 = (f_p_0 - 2*f_0_0 + f_m_0) / (d_Mcal**2)\n",
    "d2_dom2 = (f_0_p - 2*f_0_0 + f_0_m) / (d_om**2)\n",
    "d2_dMcal_dom = (f_p_p - f_p_m - f_m_p + f_m_m) / (4 * d_Mcal * d_om)\n",
    "\n",
    "# Fisher matrix\n",
    "Fij = 0.5 * np.array([\n",
    "    [d2_dMcal2, d2_dMcal_dom],\n",
    "    [d2_dMcal_dom, d2_dom2]\n",
    "])\n",
    "\n",
    "# Covariance Matrix\n",
    "cij = np.linalg.inv(Fij)\n",
    "\n",
    "# 1σ errors\n",
    "errors = np.sqrt(np.diag(cij))\n",
    "\n",
    "print(f\"The best fit parameters for M, Ω_0m are: \"\n",
    "      f\"Ω_0m = {om0:.4f} ± {errors[1]:.4f} and \"\n",
    "      f\"M = {Mcal0:.4f} ± {errors[0]:.4f} respectively.\\n\")\n",
    "\n",
    "print(\"Covariance Matrix (cij):\")\n",
    "print(cij)\n",
    "print(\"\\nErrors:\")\n",
    "print(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "53fac50f-df43-48d8-9604-4a3dbf87979d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fij = cij = errors = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "500f29b0-092e-43e6-a17c-cec2ec454e30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best fit parameters for M, Ω_0m, h are:\n",
      "M    = 23.81644 ± 0.00490\n",
      "Ω_0m = 0.3166 ± 0.0057\n",
      "h    = 0.6725 ± 0.0038\n",
      "\n",
      "Covariance Matrix (cij):\n",
      "[[ 2.40209707e-05  1.44273908e-05 -9.51598640e-06]\n",
      " [ 1.44273908e-05  3.25509563e-05 -2.14698876e-05]\n",
      " [-9.51598640e-06 -2.14698876e-05  1.42941368e-05]]\n",
      "\n",
      "Errors:\n",
      "[0.00490112 0.00570534 0.00378076]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def chi2_local_3D(Mcal, om, h):\n",
    "    return chi2total2(Mcal, om, -1, 0, h)\n",
    "\n",
    "\n",
    "d_Mcal = 0.002\n",
    "d_om = 0.02\n",
    "d_h = 0.002\n",
    "\n",
    "\n",
    "Mcal0 = Mtot\n",
    "om0 = omtot\n",
    "h0 = htot\n",
    "\n",
    "\n",
    "f_0_0_0 = chi2_local_3D(Mcal0, om0, h0)\n",
    "\n",
    "\n",
    "f_p_0_0 = chi2_local_3D(Mcal0 + d_Mcal, om0, h0)\n",
    "f_m_0_0 = chi2_local_3D(Mcal0 - d_Mcal, om0, h0)\n",
    "\n",
    "f_0_p_0 = chi2_local_3D(Mcal0, om0 + d_om, h0)\n",
    "f_0_m_0 = chi2_local_3D(Mcal0, om0 - d_om, h0)\n",
    "\n",
    "f_0_0_p = chi2_local_3D(Mcal0, om0, h0 + d_h)\n",
    "f_0_0_m = chi2_local_3D(Mcal0, om0, h0 - d_h)\n",
    "\n",
    "\n",
    "f_p_p_0 = chi2_local_3D(Mcal0 + d_Mcal, om0 + d_om, h0)\n",
    "f_p_m_0 = chi2_local_3D(Mcal0 + d_Mcal, om0 - d_om, h0)\n",
    "f_m_p_0 = chi2_local_3D(Mcal0 - d_Mcal, om0 + d_om, h0)\n",
    "f_m_m_0 = chi2_local_3D(Mcal0 - d_Mcal, om0 - d_om, h0)\n",
    "\n",
    "\n",
    "f_p_0_p = chi2_local_3D(Mcal0 + d_Mcal, om0, h0 + d_h)\n",
    "f_p_0_m = chi2_local_3D(Mcal0 + d_Mcal, om0, h0 - d_h)\n",
    "f_m_0_p = chi2_local_3D(Mcal0 - d_Mcal, om0, h0 + d_h)\n",
    "f_m_0_m = chi2_local_3D(Mcal0 - d_Mcal, om0, h0 - d_h)\n",
    "\n",
    "\n",
    "f_0_p_p = chi2_local_3D(Mcal0, om0 + d_om, h0 + d_h)\n",
    "f_0_p_m = chi2_local_3D(Mcal0, om0 + d_om, h0 - d_h)\n",
    "f_0_m_p = chi2_local_3D(Mcal0, om0 - d_om, h0 + d_h)\n",
    "f_0_m_m = chi2_local_3D(Mcal0, om0 - d_om, h0 - d_h)\n",
    "\n",
    "\n",
    "d2_dMcal2 = (f_p_0_0 - 2*f_0_0_0 + f_m_0_0) / (d_Mcal**2)\n",
    "d2_dom2 = (f_0_p_0 - 2*f_0_0_0 + f_0_m_0) / (d_om**2)\n",
    "d2_dh2 = (f_0_0_p - 2*f_0_0_0 + f_0_0_m) / (d_h**2)\n",
    "\n",
    "\n",
    "d2_dMcal_dom = (f_p_p_0 - f_p_m_0 - f_m_p_0 + f_m_m_0) / (4 * d_Mcal * d_om)\n",
    "d2_dMcal_dh = (f_p_0_p - f_p_0_m - f_m_0_p + f_m_0_m) / (4 * d_Mcal * d_h)\n",
    "d2_dom_dh = (f_0_p_p - f_0_p_m - f_0_m_p + f_0_m_m) / (4 * d_om * d_h)\n",
    "\n",
    "# Fisher matrix (3x3)\n",
    "Fij = 0.5 * np.array([\n",
    "    [d2_dMcal2,    d2_dMcal_dom, d2_dMcal_dh],\n",
    "    [d2_dMcal_dom, d2_dom2,      d2_dom_dh],\n",
    "    [d2_dMcal_dh,  d2_dom_dh,    d2_dh2]\n",
    "])\n",
    "\n",
    "\n",
    "cij = np.linalg.inv(Fij)\n",
    "\n",
    "errors = np.sqrt(np.diag(cij))\n",
    "\n",
    "\n",
    "print(\"The best fit parameters for M, Ω_0m, h are:\")\n",
    "print(f\"M    = {Mcal0:.5f} ± {errors[0]:.5f}\")\n",
    "print(f\"Ω_0m = {om0:.4f} ± {errors[1]:.4f}\")\n",
    "print(f\"h    = {h0:.4f} ± {errors[2]:.4f}\\n\")\n",
    "\n",
    "print(\"Covariance Matrix (cij):\")\n",
    "print(cij)\n",
    "print(\"\\nErrors:\")\n",
    "print(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "808eff2f-0010-4189-a694-55453264484a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in c:\\users\\user\\anaconda3\\lib\\site-packages (1.5.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "82f2814d-41ea-4ca5-8232-bf2c4dcfff37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals = np.linspace(0.24, 0.35, 100) \n",
    "M_vals = np.linspace(23.7, 23.9, 100)\n",
    "OM_grid2, M_grid2 = np.meshgrid(om_vals, M_vals)\n",
    "\n",
    "params2 = [(om, M) for om, M in zip(OM_grid2.ravel(), M_grid2.ravel())]\n",
    "results2 = Parallel(n_jobs=-1)(delayed(chi2Panthstat2)(M, om) for om, M in params2)\n",
    "Z_Panthstat2 = np.array(results2).reshape(100, 100)\n",
    "\n",
    "chi2_min = chi2Panthstat2(Mcal_2, om_2) \n",
    "\n",
    "levels_Panthstat2 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour10 = ax.contour(OM_grid2, M_grid2, Z_Panthstat2, levels=levels_Panthstat2, colors='blue', linewidths=0.6)\n",
    "\n",
    "ax.plot(om_2, Mcal_2, 'ko', markersize=8, label='Best fit')\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4ab6c90e-dedf-41e9-a35b-dc08debeaac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "\n",
    "om_vals_Panthstat3 = np.linspace(0.20, 0.40, 100) \n",
    "M_vals_Panthstat3 = np.linspace(23.7, 23.9, 100)\n",
    "OM_grid3, M_grid3 = np.meshgrid(om_vals_Panthstat3, M_vals_Panthstat3)\n",
    "\n",
    "params3 = [(om, M) for om, M in zip(OM_grid3.ravel(), M_grid3.ravel())]\n",
    "results3 = Parallel(n_jobs=-1)(delayed(chi2Panthstat3)(M, om) for om, M in params3)\n",
    "Z_Panthstat3 = np.array(results3).reshape(100, 100)\n",
    "\n",
    "chi2_min = chi2Panthstat3(mlcdm3, omlcdm3) \n",
    "\n",
    "levels_Panthstat3 = [\n",
    "    chi2_min + dchi(1, 2),\n",
    "    chi2_min + dchi(2, 2),\n",
    "    chi2_min + dchi(3, 2)\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour11 = ax.contour(OM_grid3, M_grid3, Z_Panthstat3, levels=levels_Panthstat3, colors='red', linewidths=1.5)\n",
    "\n",
    "\n",
    "ax.plot([], [], color='red', linewidth=1.5, label='Pantheon')\n",
    "ax.plot(omlcdm3, mlcdm3, 'ko', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b952cbf0-ce47-442a-ba6f-a26f8beece1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "\n",
    "contour10 = ax.contour(OM_grid2, M_grid2, Z_Panthstat2, levels=levels_Panthstat2, colors='blue', linewidths=0.6)\n",
    "contour11 = ax.contour(OM_grid3, M_grid3, Z_Panthstat3, levels=levels_Panthstat3, colors='red', linewidths=1.5)\n",
    "\n",
    "ax.plot(om_2, Mcal_2, 'bo', markersize=8, label='Best fit')\n",
    "ax.plot([], [], color='red', linewidth=1.5, label='Pantheon')\n",
    "ax.plot(omlcdm3, mlcdm3, 'ro', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$')\n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "86a0201d-c56b-4d51-9c04-b7f385988447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals4 = np.linspace(0.24, 0.35, 150) \n",
    "M_vals4 = np.linspace(23.7, 23.9, 150)\n",
    "OM_grid4, M_grid4 = np.meshgrid(om_vals4, M_vals4)\n",
    "\n",
    "params4 = [(om, M) for om, M in zip(OM_grid4.ravel(), M_grid4.ravel())]\n",
    "\n",
    "results4 = Parallel(n_jobs=-1,  prefer=\"threads\")(\n",
    "    delayed(chi2total2)(M, om, -1, 0, htot) for om, M in params4\n",
    ")\n",
    "Z_total = np.array(results4).reshape(150, 150)\n",
    "\n",
    "levels_total = [\n",
    "    minlcdm2 + dchi(1, 2),\n",
    "    minlcdm2 + dchi(2, 2),\n",
    "    minlcdm2 + dchi(3, 2)\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour12 = ax.contour(OM_grid4, M_grid4, Z_total, levels=levels_total, colors='blue', linewidths=1.5)\n",
    "\n",
    "ax.plot([], [], color='blue', linewidth=1.5, label='Pantheon+BAO+CMB')\n",
    "\n",
    "ax.plot(omtot, Mtot, 'ko', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b0f8de52-c590-4c3c-a96a-e2936376815b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "\n",
    "contour11 = ax.contour(OM_grid3, M_grid3, Z_Panthstat3, levels=levels_Panthstat3, colors='red', linewidths=1.5)\n",
    "contour12 = ax.contour(OM_grid4, M_grid4, Z_total, levels=levels_total, colors='blue', linewidths=1.5)\n",
    "\n",
    "ax.plot([], [], color='red', linewidth=1.5, label='Pantheon')\n",
    "ax.plot(omlcdm3, mlcdm3, 'ko', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "ax.plot([], [], color='blue', linewidth=1.5, label='Pantheon+BAO+CMB')\n",
    "\n",
    "ax.plot(omtot, Mtot, 'ko', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$')\n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "fe4ba3be-e9d0-432a-ba95-eaf7403ca8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oRo0aAIDKlSsX+ho3b95EYmIiJk+eDEMxJsN069YNKSkpGD16dJ77N2/eDFtbW0yePLlIrxMTE4OBAweiZs2aRW6biKi0uX4d+PRTtXL7iSeAH39UIbN2bWDaNCAiAjh8GHjvPRXWrClkFsTVFejZE/j+exUy160D+vdXq9iPHgXGjlVzSUNC1PsmsjZWsxjoTtq3b489e/YgJiYGnp6edzxu1qxZOHnyJObNm1es14+JiUGzZs0QFRWFl19+GbNmzUJsbCz69OmDGTNm4OGHH77ra/z9998YPXo0Nm/enGdU9E5u3bqFW7duAVCTfMuXLw/AQif5EhE9AMeOAZ99poKlseyQpyfw7LPAc8+pldqlacFMQgKwdCkwfz5w5Eju/e3aAW+9BXTqVLq+HnRnlr4YyKr/FkxLS8PevXsxfPjwO4bMrKwszJo1CxMmTEC1atWQfvv5lyIICAjA9u3bUaVKFcyZMwd9+vTB22+/jbVr1xYpZP7666946qmn8NBDDyE1NbVIbX788cfw8PCAh4eHKWQSEZVEe/aoU+B166rT5BkZKlR+/z1w+TLwv/8BDz9c+kKVpycwejTwzz/A3r3AgAGArS2wbZsa6X34YWD9+sLLKhFZBL0nid6PL7/8Uvz8/OTGjRsFPr5hwwZp3bq1aZIsAGnVqpUkJycXu609e/bI2LFjxdPTUwDIuHHj8i36ud2xY8fkmWeeETs7O1PbAQEBcvz48bu2lZ6eLomJiZKYmCjR0dEWPcmXiOhe7Nol0rZt7iIYg0Gkd2+Rffv07pnlunhR5LXXRFxccr9uDz8ssnEjFw6VZpa+GMhqg2ZsbKz4+fnJpk2b7npsRESEDBkyxPSNmDhxYrHa2rJli0yaNElERI4ePSrly5cXANK/f/9Cw6aISFxcnHz00Ufi5OQkAOSRRx4pVtuW/gEiIiqOP/9Uq8eNQcneXmT4cJFTp/TumfW4fl3k7bdFnJ1zv47t2okcPqx3z0gPlp4TrDZo9uzZUz777LNiPWfcuHECQIKCgor8nN27d4uHh4fEx8eb7ouIiBB/f38BINOmTSvS6/zyyy+mD8LFixeL3L6lf4CIiIoiMlLk2Wdzg5GdncgLL6hROro3V6+KvP56bokkg0FkxAh1P5Uelp4TrHIx0LRp03Dt2jXMmjWrWM9LSEhAQEAAABRprmZOTg7q1auHgIAAhIeH53ls3759aN26NZydnXH16lU4OTnd9fWaN2+OAwcOYO/evWjRokWR+mzpk3yJiAqTmgp88gnw+ee5JYoGDwamTAH+s6ka3aOLF4G33waWL1f/9vRUK/eHD7f+Vfl0d5aeE6zuI7hkyRKcOnUKM2fOLPZzPT09ERwcjHLlyhXp+JMnT+L48ePw8/PL91iLFi3QtWtXJCUl4eTJk0V6vVatWgFAkdsnIrJma9eqskQffaRCZps2av/xRYsYMh+kypWBn35Se7w3aqRWrI8cCbRqpUokEenJqoLm6tWrERYWhgULFuSrhZmdnY2oqKi7vkZiYiK6d+9epPYy/r/GxqVLlwp83FjD08HBoUivl5iYiAYNGty15icRkTWLigJ69FCXqCigShVg1Srg99+BJk307VtJ9uijwIEDqti7q6tard64MfDxx/n3gCfSitUEzbCwMISGhmLp0qWws8u7oVFMTAyGDh2Kc+fOFfoae/bsQUpKCsaPH5/n/uzsbISEhKBDhw6Ijo423V+/fn1UrVoVBw4cKPC19+/fj9q1a6NOnTp37X9cXBw2btyIGTNm3PVYIiJrJAJ8+y0QHKxGM+3sgPHjVY3MXr1KX4kiPdjZAa+8Apw4AXTrBmRmAhMnqtHks2f17h2VSnpPEi2KJUuWiJ2dnXh6eoq3t3eei5ubmwCQSpUqSU5OjmzdulX8/Pykffv2snXrVsn5/5oP4eHh0rx5c/nnn3/yvf6ff/5pmkj7+eef53ls165d4uLiIo0bN5bTp0+LiCo/NH78eHF3d5c///zTdOz169elatWqUrduXfn+++8lPT1dRETOnDkj7du3l2XLlhX7vVv6JF8iIhG12KdTp9zFPi1bihw5onevSrecHJGFC0Xc3NT3xMVFZMEClkIqaSw9J1h80Fy/fr0YDIY8tTALurz11lsiIhIVFSVt27YVV1dXsbW1laCgIOnWrZt8/vnnd/wGpKWlSYsWLaR8+fJy9OjRfI8ba2L6+flJhQoVJDAwUJ577jk5c+ZMnuMyMzMlJCREvL29xcbGRnx9faVTp07yxhtvSGRk5D29f0v/ABER/fSTiIeHCjNOTiIzZohkZendKzI6f17kscdy/wh47jmR1FSdO0UPjKXnBKtcdV6aWPpqMiIqvdLS1Gna775T/27WTC30qVlT335Rfjk5aiX6pEnqdv36wMqVwP8vNSArZuk5wWrmaBIRkeU4elRtg/jdd2ru5TvvqO0kGTItk40NMGECsHUr4OcH/PuvWpi1fr3ePaOSjkGTiIiK5aef1Ojl8eNAQACwZYsqYfSfdZpkgdq2BQ4dUqWPkpPVgqGZM7lnOpkPgyYRERVJdrYqDD5gAHDzJtCpE/DPP0D79nr3jIqjfHkgPBx44QUVMF9/HXjpJSArS++eUUnEoElERHcVHw889RQwfbr699tvAxs3qtOwZH0cHIB584AZM9TUh3nz1OhmaqrePaOShkGTiIgKdf480LIlsHkzUKaMOnX+ySeAra3ePaP7YTCo0czVq9X3ddMmoEMH9UcF0YPCoElERHd08KAKmadOARUrAn/8AfTvr3ev6EHq0UOdSi9bFti3D3j8ceDqVb17RSUFgyYRERVowwbgscdU6GjQQIWQhg317hWZQ8uWwI4dgL+/WpHepg1wh92XiYqFQZOIiPJZsgTo3l3VyuzYEdi5E6hQQe9ekTnVqwfs3g1UrgycPq1WqN+2KzPRPWHQJCKiPObPB4YMUavMhwxRI5vu7nr3irRQvboa2axSBThzRs3ZvH5d716RNWPQJCIik5kzgVGjVNmbMWOAH34A7O317hVpqXJlYNs2NYJ94oQa0eYCIbpXDJpERARAbVH4+uvq9ltvAbNnqx1lqPQJClILhPz8VK3ULl3UNAqi4uKPECIiwpdfAuPHq9vvv6/KFxkM+vZJLzk5QHq6upTmHXNq1lRbVhpXoz/7rJpOQVQcBpHS/N/I8qWmpsLV1RUAkJKSAhcXF517REQlzTffACNHqtuTJwNTpujaHbO5eRM4dgw4e1Zdzp0DLl8GYmPVPMQbN1S4/O8OOY6Oqs6kt7dale3vr04rV68O1KgBPPSQGgEsqXVFd+1SczUzMoBXXgFmzdK7R3Q7S88JDJoWztI/QERk3ZYsUQt+RNTp8pIykimiQuXvvwP796v9vU+eNN+InKsr0KgR8PDDqlRQu3aAl5d52tLD8uXAM8+o27NmqcBJlsHScwKDpoWz9A8QEVmvTZuAp59W4WvMGDUn05pD5o0baoX8hg3A9u0Fr5b28VGnhKtVA6pWBQIDAV9fdfH2Bpyd1faMDg4qrN66pUY509LU6127puqKRkYCERG5l/T0vO0YDECTJmo/+G7dgGbNrPtrC6jtR99+W83b3bgR6NxZ7x4RYPk5gUHTwln6B4iIrNNff6li7KmpwODBwMKF1rnwJzZWbYm5apU6xXv7iGWZMkCrVkDr1kDjxmrEsVy5Bx/4srLUaOnBg8Cff6ryQMeO5T2mUiWgTx+1q5K1hk4RYMQIYMECNW/zr7/UlAHSl6XnBAZNC2fpHyAisj7GvcuvXlVz7zZsUCN41iIzE1i3Dli0SI2s3T6nsn59VWi+UycV6PR6X5cvq4U0mzYB69erQG8UHAwMH66mLFjb6fVbt9SuQQcOqN2i/vhDjQKTfiw9JzBoWjhL/wARkXVJSFAh8+RJFRR27rSeYuw3bgDffgv8738qyBk1bQoMHKj27LbEEbabN4HNm4EVK4A1a9S/AcDJCXjuOTU3tmpVfftYHJcuqRHi69eBoUNVrVXSj6XnBAZNC2fpHyAish5ZWcBTTwG//aZWTe/fbx3bSkZGqkVKCxfmhjR/fyAkRI0K1q6ta/eKJTERWLZM7b70zz/qPhsbdUp9wgS1DaQ12L5djYbn5KigOXSo3j0qvSw9J1jhjBwiIroXb7+tQqazM/DLL5YfMi9cAF54QZUR+vprFTIbNQJCQ4GLF4GPP7aukAkAHh7Aiy+qVfA7dgBPPKHC2o8/qtP+zz6rpjZYurZtVb1VABg9Gjh1St/+kOVi0CQiKgWWLQO++ELdXrhQBTZLFR8PvPaaqk/57bdqTma7dmoU7a+/1Cimo6Pevbw/BoOa67hpE/D330Dfvur+ZcuAWrXU6fTERH37eDcTJ6rAmZamAnJmpt49IkvEoElEVML9+69afAKocGAMNZYmK0uNXNaooWo1ZmYC7dur1eTh4cDjj1vnau27adQI+PlnFaKNhdE/+0yN1q5aZbm7E9nYAIsXA56equ/TpundI7JEDJpERCVYYiLQq5c67dy5M/DBB3r3qGAHDqi6ky+9pBb91K4N/PqrWrndqpXevdNG48ZqasPGjSpsX7miSiJ17w5ERendu4JVqAB89ZW6/dFHKnAS3Y5Bk4iohBJRcxzPngUqVwaWLrW8bRLT0oBx44AWLdTIa9mywJw5aqFMaSwIbjAATz6pvhbvvgvY26v5tHXqqO+fJXrmGTVKnpWlpjXcuqV3j8iSMGgSEZVQP/ygTsna2aktBL299e5RXnv3qhJLX3yhQvGgQWpRyZgxKmCVZk5OavT58GHgkUeAlBT19QkJUbcticGgpjz4+wPHjwOffqp3j8iSMGgSEZVAp04BL7+sbn/0EdC8ub79uV12tupT69bAmTPq9OuGDWq+n6+v3r2zLHXqqFqnU6aoOZELF6q6of/+q3fP8vL2VvNqATVXMyJC1+6QBWHQJCIqYTIygAED1Gnp9u2BN9/Uu0e5oqNVn959VwXOAQOAo0eBLl307pnlsrUFJk8Gtm1TofzUKTXVYNUqvXuWV//+akemW7fUXFtLXcRE2mLQJCIqYT74QNVp9PZW2zRayh7me/aoBS87dgCurqoe5tKlatUy3d1jj6lT6Z06qcVdffqoWqKWEugMBrUwyNFRLeJavVrvHpElsJAfP0RE9CD89ZfaRQcA5s0DypfXtz9G332nai5evap2vzHWwyyJ5YrMycdHTTMYM0b9e+JEYNgwy6lhWa2aqgEKAK+/rkbVqXRj0CQiKiEyMtRWgNnZ6jRmnz5690itRH7lFWDECBWG+vRRi4AeekjvnlkvOzu1Mn/OnNx5mz165G7Pqbfx44FKldTWodOn690b0huDJhFRCfHpp2q+o68v8L//6d0bID1dlb2ZPVv9+4MP1Cp4C9uK2WqNGQOsWweUKaNqbz79tGWMIDo7AzNmqNuffQbExOjbH9IXgyYRUQlw4oRayQ2oYOfjo29/EhPVPt5hYWrO3sqVagEQT5U/WE89pQrbu7io3ZO6dLGM8kd9+qhKB2lpuZ9LKp0YNImIrFxODjBypDp13qWLOm2up2vX1HzMHTsAd3dg82agd299+1SStWmjdhRyd1df86ee0v80usGQO1d4/ny1aQCVTgyaRERWbskStR+4i4ta9avnqOHVq2pP8kOHAD8/4Pff1WppMq9HHlErvd3dVd3NAQPU/Fg9Pf642t0pKwv48EN9+0L6YdAkIrJiycnA22+r2+++q7aa1EtsLNChgzqNX7GiCr+NGunXn9Lm4YeB9evVVIW1a9UcTr1LHxkD5uLFLOJeWjFoEhFZsY8+UostqlcHXn1Vv34kJKj6jkePAuXKqeLiXFmuvdatgWXL1Kj2/Pn6jyQ+/LA6lZ+Tw7mapRWDJhGRlTp7Fpg5U92eOVONZOkhLQ3o2lWdLvf1VYtSatTQpy8E9OqVW3Vg8mRgxQp9+zNlirpesoRzNUsjBk0iIis1fryqTdmpkxo10kN2NjBwoNr1x8MD2LIFqF1bn75QrpdeAt54Q90OCVHTGfTStKmqQJCTk7sfOpUeDJpERFZozx5VMsjGBvj8c/0WAI0bp+YDOjqq+YENGujTD8rv44/V6v/UVKBnTyApSb++GEPv998DcXH69YO0x6BJRGRlRNRoJqC2H6xXT59+fP018OWX6vaiRUCrVvr0gwpmZwf89JNamHXqlNqdSa/FQe3aAQ0bqmkW8+fr0wfSB4MmEZGV+e03YPduwMkJeP99ffqwfTswdqy6PW0a0K+fPv2gwvn5qZFvOzu1K9N33+nTD4MBeO01dfvrr/UvvUTaYdAkIrIiIqqMEQC8+CJQvrz2fbh4UW0tmZWl5mcaR1fJMjVvrv4YAFRlgjNn9OlHv35qx6qoKOCXX/TpA2mPQZOIyIps3Aj8+afaT9pYP1NLGRkqMNy4ATRpokbIuK2k5Rs3ThVQT0tTi4NycrTvg5OTOn0P5K6Kp5KPQZOIyEqI5NZFHD0a8PfXvg+TJgEHDgCenuqUbJky2veBis/GBli4EHB1VdMuvvlGn36MGqX+MNm2Tb+RVdKWQUTvfQOoMKmpqXB1dQUApKSkwMXFReceEZFetm4FOnZUI0MXLmgfNDdvVmVqAGD1arWS2dpkZqqFMUeOqMv582pv9mvX1M5G2dnqOINBjRoHBKhLuXKqbFPDhkD9+qqUkzWaM0fNrXVzUyWPKlTQvg9PPgn8+quacvHxx9q3X9JYek5g0LRwlv4BIiLtPP44sGOHCgrG1d5auXpVBaxr19RoqrWc+hRRgWrTJhVudu0Cbt26/9etVUvVLn36aeDRR9ViG2uQna2qA+zbB3TvDoSFad+H1auB3r1VeI+MtJ6vnaWy9JzAoGnhLP0DRETa+PNPoFkzwN4eOHdOlazRUu/eKiDUq6dOnTs5adt+cUVGqpJLoaH5T9G6uQF166r38tBDasTS318tVLGzyy0BlJKiAvaVK8ClS2p7zcOH1WvfrmxZoH9/YORINeJp6Y4eVXvQZ2WpUepOnbRtPyNDhcy4OFXgv0MHbdsvaSw9J/DvCCIiK2DcanLAAO1D5sqVKmTa2QGLF1tuyMzJUYulZs1ScwCNgdHRUY0GP/EE0LmzGo28nwVMN26o1//lF9XejRvAvHnq0qyZ2pVn4ED1R4ElqlsXGDNGfZ3eeENtHWprq137Dg6qasH8+WpfdgbNko0jmhbO0v9SISLzu3QJCApSI1CHDmk7anbjBlCnjjpl/t57+tXtLEx2ttrPe+pUNVpn1LYtMHSo2vv7/3+MmqXt339Xi2vWrFFzQAGgalXggw/UHwY2FrjsNi4OqF4diI8Hvv0WGD5c2/Z37FDh38MDiImx3D9erIGl5wQL/PgTEdHt5s1TIfOxx7Q/NTt+vAqZwcHAxInatl0Uv/+uviYDBqiQ6eYGvPmmWuSzbRswZIj5QiagRgLbtweWL1d/EHz8sSqSfu4cMGiQ2ud7927ztX+vvLxy67FOmQKkp2vbfuvWaiFSYqJa5EYlF4MmEZEFu3VLjTgBwMsva9v2nj25O8nMn69OQVuKy5dVuGzbVgVMT081ghgZCUyfDlSpon2f/PxUMD93ThVId3dXI9Bt2qgFXCkp2vepMC++qKZhXL6s/baQNjZqpBkAVq3Stm3SFoMmEZEFW7VKjSiWLw9066Zdu1lZKogAwPPPq5XVlkBEjfDWrKn28TYYVD/PnFEjdJ6eevcQcHEBJkwAzp5Ve9GLqLJC9eqprTsthZNT7qjmtGlAaqq27RuD5rp1uVMOqORh0CQismDGkaaRI7VdXLJggaoz6e0NfPqpdu0WJiFBLSJ58UUVilq2BA4eBL76SvXT0vj4qK/jb78BlSur2qcdOgBffJG7UElvISFqPum1a9oXcW/dGvD1VfNFLXF6AT0YDJpERBbq9Glg5051mvH557VrNyUFmDxZ3Z482TJC3IEDqiTPqlUqcM+YocJJ48Z69+zuOnZUp/eNWz+OG6cW32Rk6N0z9bU07lU/c6a2I4u2tqp4OwBs2KBdu6QtBk0iIgu1YIG67tJF2x1cZsxQ9SOrVVMjqXpbulSNfl24oFbf79kDvP66Za7mvhNXV/X9nDVL9fv779XoZmys3j0DBg9W80ujorSfL9m1q7pm0Cy5rOi/KRFR6ZGdrWpWAmqen1ZiY4HPP1e3p01TNQ/1IqIW9gwapEb/evRQi2sefli/Pt0PgwF45RUVqtzd1S5F7durU8d6cnJSuz0B2u841amTqs968qRaREUlD4MmEZEF2r5d7Ujj5aW2OtTK9Onq1Hnjxmo+pF5yctSo5dtvq3+PG6dG26x1j/HbPfEEsHev2pHo33/Vv5OS9O3TCy+owLdvH/DPP9q16+EBtGihbm/bpl27pB0GTSIiC7Rsmbru21e7UcWrV3P3MP/gg/vbPed+5OSo4DNrlvr3jBlqlNWaTpXfTZ06qn6kt7faXvSpp7Rf9X27gACgZ091W+tSR23bqmsGzZKpBP23JSIqGW7dyp0rN3Cgdu1+/jlw8ybQvLmaF6oHETWSuWCBCpaLF6t/l0TBwWqvbw8PtbCpXz81ZUIvo0ap68WLtQ297dqp6+3bLWc1Pj04DJpERBZmyxZ1KrVCBaBVK23aTErKLW/z7rv6jWZ+8knuPMGFC9X8zJKsUSNg0yY1T3LjxtzV/npo21aVOkpJAcLCtGu3eXO1+j0mhvM0SyKrCZoigvnz56NBgwZwcnKCl5cXunfvjoMHDxZ4fHx8PMaOHYugoCA4OTmhfv36WLhw4T21ffHiRYSEhKBixYoIDAxEpUqVMGzYMERFRRV4fEpKCt566y0EBQXBwcEBFStWxKhRo3DlypV7ap+ISpeVK9V1797anS5esECFzVq1ckvOaO2773K3uZw5U62GLg1atlSr0AG1X7teK7ANhtxgb1yIpoUyZYAmTdTtP/7Qrl3SiFiJESNGCAABILa2tqbb9vb2smrVqjzHJiUlSb169aRMmTJSsWJFMRgMpuPnzZtXrHZPnz4t3t7e0q1bN7l+/bqIiFy9elXatWsnfn5+cvbs2TzHJycnS6NGjUz9vL3tgIAAOX36dLHaT0lJMT0/JSWlWM8lIuuTkSHi6SkCiOzcqU2bWVkilSurNr/5Rps2/2vHDhFbW9WHCRP06YPeRo9W779sWZGoKH36EBGh+mBjIxIdrV27r7+u2h05Urs2SwpLzwlWETQ3btwoPj4+EhoaKklJSZKZmSlhYWHi6+srAMTd3d0UAkVERo8eLR9++KGkpaWJiMiFCxekWbNmAkC8vLwkJyenyG137dpVypYtm++bFxkZKQCkX79+ee5/6623pGHDhrJt2za5deuWJCcny/Tp08XOzk4ASMuWLYv13i39A0RED9bWreoXrp+fCoBaWLtWtenlJfL/PzY1FRMjUq6c6sOzz4oU40d0iZKeLtK0qfo6dOwokp2tTz+aNVN9KOa4zH1ZsUK12bixdm2WFJaeE6wiaPbr108OHTqU7/6tW7eavrgLFiwQEZHU1FSZM2dOvmPPnj1rOvbatWtFbtvZ2VmaNWtW4GM+Pj5Su3Zt07+zsrKkRYsWEh8fn+/Yd99919T+f0dBC2PpHyAierDGjlW/cJ9/Xrs2O3dWbb75pnZtGmVliXTooNqvU0ektP+YO3FCxMlJfT3+9z99+jBtmmr/ySe1a/P8edWmvb3IzZvatVsSWHpOsIo5mq1bt0bDhg3z3d++fXs0atQIAHD9+nUAgLOzM8aMGZPv2KpVq8LX1xcVKlSAj49Pkdt2cXHB8ePHkfSfImc5OTlIS0vL06+YmBi8/fbb8PT0zPc648aNM9029pWI6HYiwLp16na3btq0eeYMsHmzmp9nXHWspU8+UWV+nJ2BFSsAFxft+2BJatVStUwBYMIEVUtVa927q+vwcCA5WZs2K1dWe8NnZqraolRyWEXQLCg4GtWoUQMAULly5UJf4+bNm0hMTMTkyZNhKMZyym7duiElJQWjjdsm/L/NmzfD1tYWk29bIlihQgX06NGjwNfx8PCAn58fACAwMLDI7RNR6XHqlNpm0dFR7RijBeMilM6d1YpjLf39NzBlirr91VeqtiSpXXqaNVMhT49V6LVrq+1HMzKA33/Xpk2DIXffei0LxpP5WUXQLExsbCwcHR3xxBNPFHrc/PnzERISghEjRhTr9T/66CNUqlQJS5YswdixY5GTk4Nr167h448/Rnh4OGrWrFmk18nKykJCQgKaNWuGcuXKFXrsrVu3kJSUZLoQUemwZYu6btVKm5G97Gxg0SJ1W8ttLgEgKwt4/nl13acPMGSItu1bMhsb4Isv1O0FC4Bjx7Rt32BQW0MCwG+/addu/frqmiOaJYtVB820tDTs3bsXw4cPL/B0NaAC3qxZszBhwgRUq1YN6enpxWojICAA27dvR5UqVTBnzhz06dMHb7/9NtauXYuHi7Hh7q5du5CRkYE333zzrsd+/PHH8PDwgIeHB8qXL1+s/hKR9TIGzY4dtWlv2zbg8mWgbFntTtUbzZ4NHD6s2p47V7+6nZbq0UfVTj05ObnbcGpJj6BZr566PnJEuzZJA3pPEr0fX375pfj5+cmNGzcKfHzDhg3SunVr0yRZANKqVStJTk4udlt79uyRsWPHiqenpwCQcePGSXYxlgT27NlTOnToUKRj09PTJTExURITEyU6OtqiJ/kS0YORmSni7q4WRBw8qE2bzz+vT0mZq1dF3NxU299+q23b1uTUqdySTwcOaNt2QoIqcQSIXL6sTZt//63a8/HRpr2SwtIXA1lt0IyNjRU/Pz/ZtGnTXY+NiIiQIUOGmL4REydOLFZbW7ZskUmTJomIyNGjR6V8+fICQPr371+ksLl9+3bx9fWVS5cuFatdEcv/ABHRg/Hnn+qXrIeHNmWNMjNFvL1Vm+Hh5m/vdiNHqnabNNGvhI+1GDxYfa169tS+7YYNVds//6xNe8nJqj1A5A7jR1QAS88JVnvqfMSIEXjzzTfvOjcTAKpXr47Q0FDTyu8ff/yxyO3s2bMHffr0MT03ODgYO3bsgL+/P5YvX45PP/200OfHx8fjpZdewurVq1GhQoUit0tEpcuOHeq6VSvA1lab9m7cUCt927Qxf3tGR48C336rbn/xhXY7H1mrCRPUtII1a4ATJ7Rtu3Vrdb17tzbtubqqbVcBtTCOSgar/C8+bdo0BAYG4o033ijW8yZNmgRHR0dER0cX6ficnBy88MILaNKkSZ45oNWrV0dYWBjs7OzwySef3HHeZ3Z2NoYMGYIPP/wQrbTasJiIrJLxl7lWoc+4l3WPHoCdnTZtAsCkSWreYa9e2gZca1W7du782fnztW370UfVtZbbQj70kLqOiNCuTTIvqwuaS5YswalTpzBz5sxiP9fT0xPBwcF3XfVtdPLkSRw/ftxUluh2LVq0QNeuXZGUlISTJ08W+PwXX3wR3bt3R+/evYvdVyIqPUSAvXvV7Uce0aY9437aTz9t/vaMjhwB1q5VI3QffaRdu9bOWCxl2TJVZ1IrTZuq6yNHtGs3KEhdX7yoTXtkflYVNFevXo2wsDAsWLAgXy3M7OxsREVF3fU1EhMT0d1YjfYuMjIyAACXLl0q8HFjDU8HB4d8j40bNw41atTA8OHD8z1248YNli0iIpMLF4CrV9XIYpMm5m/vxAng/Hlt63UCwOefq+s+fdRIHRVN586Avz9w/TqwaZN27VatCnh4ALduaVdiyRg0L1zQpj0yP6sJmmFhYQgNDcXSpUth95/zPDExMRg6dCjOnTtX6Gvs2bMHKSkpGD9+fJ77s7OzERISgg4dOuQ5rV6/fn1UrVoVBw4cKPC19+/fj9q1a6POf6oMv/nmm/D09CywlNGRI0fQs2dP2GoxCYuIrML+/eq6USOgTBnzt2cMK48/rt1OPDExakQOAIpQ5Y1uY2cHPPusur10qXbt3l5E/dAhbdo07r3CoFlyWEXQXLp0Kfr27YudO3eatpA0Xtzd3VGuXDns2LEDbdq0QXh4OPz9/dGhQweEh4dDRAAA27Ztw7hx4/Dbb78hICAgz+sfOnQICxcuRHh4eJ6FQjY2NggNDYW9vT369u2LiP+fNHLr1i1MmDABhw8fxiJjtWMAIoLRo0djxowZ+PLLL/P009vbG87Ozqhfvz4CAwPhUtr3WSMik4MH1XUxSvPel/BwdW2slaiFBQtUcfZHHtHufZYk/fqp602b1AijVurWVddaLUQyLgbSY+tNMg8Np4Dfmw0bNmDw4MEQESQkJNzxuAEDBsBgMKBmzZoIDg7G/v370blzZwQGBqJevXqmEFpQwAsODkaLFi0QGRmZbxV7q1atcODAAdOCHnt7e9ja2qJt27b4+++/Ua1aNdOx48ePx1dffQVAnR6/k2eNf5oSEUFtxQhoc9o8IwPYuVPd1uq0eXY28M036rYe+6mXBA8/DJQrpwLY9u1AEQquPBDGKQ7Hj2vTnnEJBYNmyWEQ45AfWaTU1FS4uroCAFJSUjgSSlTCiKjdcRIT1U45DRqYt70//lCriX181LxQLcoL/for8OSTgJcXcOmSNtMDSqJRo9TK85deUrspaWH7dqBdO6B6dW1WgickqP8PAHDzJuDkZP42rZ2l5wSrOHVORFRSRUWpkGlnp80CmT171HXr1trVsDTOzRwwgCHzfhhHMbdv167NqlXVdWSkKktlbh4euXVkCzkxSFaEQZOISEdHj6rrWrWAAgpYPHD79qnrli3N3xYApKWpYuMAMHCgNm2WVMYC6idOANeuadNm+fLqD5KMDDUCbm4Ggxr5BoC4OPO3R+bHoElEpKMjR9S1cdGFuRlXuLdooU17GzcCKSlAlSrahduSytsbqF9f3TbOszU3e3sVNgHtalsagyZHNEsGBk0iIh0Zt9r7T5U0s7h+Hbh8WY0aNWpk/vaA3MLwvXurdun+GMO6cQGZFoxBU4sRTUCdPgcAlpsuGRg0iYh0dPq0ujZuvWdO//yjrqtVU/tKm5sIsGWLuq1lKaWSzLhY7PBh7do0bo6n1el642czJUWb9si8GDSJiHRkXMn7/xuNmZUxaJp7ZbvRqVNqBNXRMXd+Id2fevXUtVY79QD6Bc3UVG3aI/Ni0CQi0klqau4vb+PqXnMyFt0ODjZ/WwCwe7e6btGCq80flJo11XVkpHaF241zJuPjtWnP+FlJS9OmPTIvBk0iIp0YF1d4eACenuZvz3iavlYt87cF5C484iKgB8fHJ7e25OXL2rTp5qauk5O1ac9YfSEzU5v2yLwYNImIdGIMmoGB2rRnXHikxXxQIDdoNm+uTXulgcEAVKyobkdFadOm1kHT3l5dZ2Ro0x6ZF4MmEZFOjEFBi6CZkpJ7ml6L+aC3buVuW9i0qfnbK02Mq8C12qbRuNGMVqey7f5/c+ysLG3aI/Ni0CQi0klMjLo2BgdzioxU156egLu7+duLiFB7nLu7AxUqmL+90sS4RWNiojbtGUcYtQ5+LIdVMtjp3QEiotLKOCJVrpz52zIGTa1O0xsXHtWubdmBIT09HStWrEBYWBhu3LgBb29v9OjRA3379oWThW60bawzqVXQ1GuE0ZI/N1R0DJpERDoxFsAOCDB/W5cuqWvj/D5zM5ZtMq6StkTr1q3D0KFDER8fDxsbG+Tk5MDGxgarV6/GK6+8gtDQUDz99NN6dzMf45xJrepM2vz/uc/sbG3aM+6pzqBZMvDUORGRTmJj1bW3t/nbMs7P9Pc3f1tAbrDVagS1uNatW4cePXogISEBAJDz/+nGeJ2QkIDu3btj3bp1enXxjozBzxjIzM04kmmn0dCUsWyTo6M27ZF5MWgSEenEuJezFkHz+nV17etr/raA3NI7ljg/Mz09HUOHDgUAiEiBxxjvHzp0KNLT07XqWpEYR/q0DprGuZrmxqBZsjBoEhHpJC5OXRsLYpuTcfRUq6AZHa2utVjoVFwrVqxAfHz8HUOmkYggPj4eK1eu1KhnlslYZogjmnQvGDSJiHSSlKSujYs7SkpbQO5CFeMKaUsSFhYGG5ui/fqzsbHBmjVrzNyj4jFuzWgsO2Ruxrmgxq0hzc1Yr1Or9si8GDSJiHSQnZ1bl9C4uMOcjL+8tWhLj/aK48aNG6a5mHeTk5ODOOPQs4XQ+mtrDJr87NC9YNAkItKBcVQK0GbkRutRIq1HwYrD29u7WCOaXlrMbSgGrb+XerXHoFkyMGgSEeng9vUlWpRrNM6z06o0pLEUjq2tNu0VR48ePYo1otmzZ08z96h4jIX+taogYJzf6+OjTXvGAWRLnHZBxcegSUSkA+OCBwcHbeoFZmaqa60WdBjf013W2+iib9++KFu2LAx3+cIbDAaULVsWffr00ahnRaP1in5jaSwtFpKJ5AZNLaoxkPkxaBIR6cA4wujgoE17WtdCNJ6ZtsSg6eTkhNDQUAC4Y9g03h8aGmpROwRlZOQGv5IYNJOScj+rDJolA4MmEZEOjKeWtR5h1EqZMurauODJ0jz99NMICwuDp6cnAJjmbBqvPT09sXbtWovbGejcOXXt4qLdqWwtR1CN0wLc3HI/Q2TduAUlEZEOtB7p03q/ak9PVSReq/2470W3bt0QHR2NlStXYs2aNYiLi4OXlxd69uyJPn36WNRIptHJk+q6Vi1t/ni4eTN3RFOLXZ6uXFHXllh/le4NgyYRUSmgddA01uv8/x0eLZaTkxMGDRqEQYMG6d2VIjl2TF3XqqVNe8atRJ2dtVmcYyz0X66c+dsibfDUORGRDoxzGI2n0M3NODin1alsPz91ffWqNu2VFgcPquuGDbVp7+xZdV2lijYjqJGR6rpiRfO3Rdpg0CQi0oFxEZBxNbi5GWsSGmsUmpvxNOvFi9q0VxqIAH/8oW4/8og2bZ46pa5r1tSmvQsX1HVQkDbtkfkxaBIR6cDeXl2X9KBpHKGi+3funJov6eAANG6sTZvGOaG1a2vT3vnz6ppBs+Rg0CQi0oHxVHZ2tjZhU+s5k8agEBGhTXulwbZt6rppU+0K7x85oq61CprGU/UMmiUHgyYRkQ5cXHJv374dpbkY50xev27+tgCgbl11ffSoZdbStEa//qqun3hCm/ZycoB//lG3GzUyf3u3buWOaGp1qp7Mj0GTiEgHDg65K8GN+4Kbk7HYtrFUjbk99JB6f0lJPH3+IGRmAlu3qttaBc0zZ9Rn08lJm+B39qwKt25uQECA+dsjbTBoEhHpwGDInTeZlGT+9oz7YhvrFJqbgwNQp466/ddf2rRZku3YoT4nvr5AkybatGlc4d6ggTYbC5w4oa5r1tR+gwEyHwZNIiKdeHmp6/h487dlXJwTFWX+toyMK6P37NGuzZJq+XJ13atXbmksc9u7V123aKFNe8b5oMZpF1QyMGgSEenEGDTj4szf1u2rwLWaM9mqlbrevVub9kqqzExg9Wp1u18/7do1Bk2tSikZg2a9etq0R9pg0CQi0okxaN64Yf62KlZUI2Hp6doVUTcGzb//1mZ6QEm1ebP6Y8TPD2jTRps2k5OBw4fV7ZYttWnz33/VNYNmycKgSUSkE+OCh5gY87fl4KB2dwFyi3CbW+XKalFQVhawZYs2bZZE336rrgcP1mauJADs2qVKb1WtClSqZP72EhLU4iNAuxqhpA0GTSIinWgZNIHclcNaBU0AeOopdb1unXZtliQXLwLr16vbw4dr166xZme7dtq09/ff6rpyZcDbW5s2SRsMmkREOilXTl1HR2vTXq1a6tq4ulcLPXqo63Xr1Gl7Kp7Zs1XJnw4dcr9/WjCWUmrbVpv2jCvctVpRT9ph0CQi0knFiupaq5XgxrlvxiLcWnj0UXXqNSEBWLtWu3ZLguvXgXnz1O1x47Rr9/Jl9RkxGICOHbVpc98+da3VCnfSDoMmEZFOKldW11oVNG/YUF0fPqzdynNbW2DoUHV7wQJt2iwpvvgCSEtTW0527qxduxs3qutmzXIL/ZuTCPDHH+q2VguPSDsMmkREOjGWHLpyRW2/Z2516qjFJPHx2u7WExKirrduBc6d065da3b9OjB3rrr97rvaFjDfsEFdG+fXmtuFC6oSgr09T52XRAyaREQ68fUFXF3ViI5xj2dzcnQE6tdXt/fvN397RkFBakROBPjyS+3atWYffaRKDDVqBHTtql27SUm5e6p366ZNmzt2qOsmTYAyZbRpk7TDoElEpBODAaheXd2OiNCmTeOpSa136zHOMVywQJudkKzZmTPA11+r2599pt1OQICaR3vrllp4ZPyjxNyMQVOrhUekLQZNIiId1aihrrUKmsYi6rt2adOeUYcOKrikpuaGKMpPBHj1VbUbUOfOQPv22rb/00/qun9/bU7XiwDbt6vbjz1m/vZIewyaREQ6MpasOXZMm/aMv8wPHwZiY7VpE1Ch5a231O3PP+eo5p0sXarmSNrbAzNnatt2TIzahQgAnnlGmzYjIlStUAeH3D+CqGRh0CQi0pGx5JBxn2dzK1dOtSkChIdr06bRM88AwcEqZH7wgbZtW4OYGGDsWHV78mSgdm1t21+yRO0G1KKFdjU7N21S161bAy4u2rRJ2mLQJCLSkTFoHjumCnNroVMndW0cvdKKrW3uKN3//qdt4XhLJwK8+KIK4Y0a5Y7+atn+woXqtrFKgBaMC4+efFK7NklbDJpERDqqXl2ttE1L026eprEm46ZN2oVbo44d1WrmrCzghRe0b99SffMNEBamTpkvXKiutfTHH+qPnTJlgH79tGkzJSV3fiaDZsnFoElEpCM7u9xC6n/9pU2bjz0GuLurU7UHDmjT5u3mzFFlnXbvzt35pjT780/glVfU7Y8/1m619+2MC7QGDAA8PbVp87ff1Ar3atW0nyZA2mHQJCLSWdOm6tq437O5OTgAXbqo22Fh2rR5u8BA4JNP1O2331aLQUqrq1eBXr1U4Hr6aeC117Tvw/XrwIoV6vaLL2rX7rp16rpbN20L0pO2GDSJiHRmDJpaFlHv0UNdr1yp3XaUt3vxRbXKOCUF6NsXSE/Xvg96S0tT4fLSJaBmTWDxYm1rZhp9/TWQkaE+h8bPorllZgK//KJud++uTZukDwZNIiKdGYuo//WXNltRAmq3GWdn4OxZ7UZSb2djAyxaBHh5qVPHo0bpE3j1kp0NDByo3ruXlxrd8/DQvh+pqcDs2eq2sai+FrZtA+LiAD8/ljUq6Rg0iYh0Vr262o7y1i3g0CFt2nRxyd1icNkybdr8r6AgYPlyFTpDQ3MDT0mXkwOMHKl24XF0VCHzoYf06cuCBcCNG2qeZJ8+2rVrPFXfq5eqRkAlF4MmEZHODAbgkUfUbS137Bk4UF3/+KM6lamHDh1UAXcAeP11Fb5KsqwsVT5owQIVsJcuBR59VJ++3LqltrgEgDfeUAvTtJCRAaxerW737atNm6QfBk0iIgtg3LHHWO5FC088Afj7qwUpega8V18Fhg9XI339+2tf31MrGRkq3C9apEbxliwBevfWrz/ffafmh1asqG3tzE2bVL3QcuW47WRpoFnQTC+NM72JiIqobVt1vWuXdqOL9vYq4AH67j9uMKj2e/RQo2zduqltGEuS5GSgZ091ytjeXl0PGKBff9LTgWnT1O2JE9UpfK2EhqrrgQN52rw00CRoXrx4EU888QQy9To3Q0Rk4erXV4tCUlLUAhGtvPCCOoW7bRtw/Lh27f6XnZ2ar9mzpxr569FDnVYuCU6dApo3BzZuBJyc1JzMnj317dPXXwPR0UClSsCwYdq1e/06sH69uv3cc9q1S/opctB87733MG7cOMycORMrVqzA3r17ERUVhezs7Ls+t3LlynBwcMC33357X50lIiqpbGzUfEVAFbLWSmBgbnkZ4/aQenFwUGHzmWfUXMZBg4APP7Tu3YPCwoCHH1bbbZYvD+zYoaYs6CkuDvjoI3X7vfe0Hc1ctEiN2Ddpkrv9KpVsBpGiFZTYs2cPevbsiRs3buS538bGBn5+fqhYsSIqVaqEihUr5rkEBATg+vXr6Nu3L5o2bYowPaoDW7HU1FS4uroCAFJSUuDi4qJzj4jIXL7/Hnj+eTX6tW+fdu3u2aNKzDg6quLp/v7atV2QnBxVamfWLPXvrl1VjUmtdqx5ENLSgPHj1S5IANCmjQrRAQH69gtQuxDNng3UrauqHGi1CEgEqFMHOHlS7Qg1cqQ27ZZ0lp4Tihw0AeDcuXPo2rUrTp48WfCL3aW0f9u2bbF169bi9bCUs/QPEBE9OJcvq4UZBoNaoOPrq027ImrV+759wDvv5I526e3774GXXlLzNitXVotnrKHm4vbtwIgRqkYpoBY7TZ+u/f7lBTl+XE3TyM5WI+cdO2rX9q5dKnA7OwNXrqhtUOn+WXpOKNYczapVq2Lfvn3o0KEDypcvj08//RRvvPEGnnnmGTz66KMIDAyEnZ0dRKTAS6dOncz1PoiIrF6FCkDjxir4Gbfn04LBALz1lro9Zw6QkKBd24UZNgzYuxeoWlWNtLZpo+aU/ufEmsW4dAkYOhRo106FzAoVgF9/VVMSLCFkiqjgnp2tFlxpGTIBYO5cdT1wIENmqSL3ICsrS0aNGiUtW7aUa9eu5Xv8ypUr8ueff8rq1atlzpw58s4770hoaKjk5OTcS3MiIpKTkyPz5s2T+vXri6Ojo5QtW1a6desmf/75Z4HHx8XFycsvvyxVqlQRR0dHqVevnvzwww/31PaFCxdk6NChUqFCBalUqZJUrFhRQkJCJDIystDn3bx5U+bOnSuVK1eW8+fP31PbKSkpAkAASEpKyj29BhFZj48+EgFEunTRtt3sbJG6dVXbkydr2/bdJCaKDBum+gaIeHmJfPON6rMlSEgQmTBBxMkpt48vvqjutyRLlqi+lSkjco+/ku5ZdLSInZ1q/9Ahbdsu6Sw9J9xT0DSaOXOmVKtWTY4ePfqg+nNHI0aMMH0hbW1tTbft7e1l1apVeY5NSkqSevXqSZkyZaRixYpiMBhMx8+bN69Y7Z4+fVq8vb2lW7ducv36dRERuXr1qrRr1078/Pzk7Nmz+Z6Tmpoqn3/+uVSqVMnULoMmERXFsWPql7GDg/ZB5eefVdtubiIFjCHobudOkXr1csNczZoqcN68qU9/IiNFJk5UwdfYp1atRPbu1ac/hYmLE/H3V32cOlX79qdMUW0/+qj2bZd0lp4T7itoioisX79eypcvLxs3bnwQ/SnQxo0bxcfHR0JDQyUpKUkyMzMlLCxMfH19BYC4u7ubQqCIyOjRo+XDDz+UtLQ0EVEjks2aNRMA4uXlVayR1a5du0rZsmXzffMiIyMFgPTr1y/fc5KSkuTatWty5swZsbGxYdAkoiLLyRGpXVv9Ug4N1bbt7GyRxo1zR+QsUWamyKxZIp6eueHOz0+NwkZEmL/9jAyRTZtEevUSsbHJ7UOtWiJr16rvnyUaNCg3nKena9t2WpqIr69q/8cftW27NLD0nHDfQVNE5J9//pGgoCCZNWvWg3i5fPr16yeHChhr37p1q+mLu2DBAhFRo4lz5szJd+zZs2dNxxZ0uv9OnJ2dpVmzZgU+5uPjI7Vr1y70+X5+fgyaRFQs77+vfik/8YT2bf/+u2rbxkZEg5NV9ywpSeSLL0QqVcoNe4BIs2YiM2eKnDr14EJfYqLIL7+IDB0qUrZs3vbatRNZvVoFYEu1alXu9/SPP7Rv/+uvVfuVK1v218laWXpOeCBBU0TNy2zevLmMGjVKsrKyHtTLiogUGByNGjVqJADkk08+uevr+Pr6SoUKFYo1ounr6yuurq6SmJiY5/7s7GxxdnaWAQMGFPr8wMBABk0iKpZTp9QvZltbfU5h9+yp2u/c2XJH6IwyMkSWLVN9vX2EERApX16kXz+RDz4QWbNG5MgRkdTUO79WZqbIuXMiW7eKzJ8v8tJLIo0a5X9df3+RMWPUNAdLFxOTO5o4frz27WdliVSrptqfPVv79ksDS88JDyxoiqjFL/369ZMOHTrkC2bm0q9fPwEgP95lPD4tLU0cHBzkm2++KdbrP//88wJABg0alOf+jRs3ipubm5w8ebLQ51euXJlBk4iKrUkT/X45R0SI2Nur9lev1r79exUTIzJnjshjj6k5rreHw9svjo4i3t5qhC0oSJ16d3ERMRju/JygIBUud+xQ4ckaZGerUXFApH597U+Zi4gsXZq7gIu/wszD0nPCAw2aRu+8847UqlWrwIUyD1q7du3E0dFR4uPjCz1u5syZMnLkyGK//pUrV0yLel5++WXJzs6Wq1evSuvWreXAgQN3ff69BM309HRJTEyUxMREiY6OtugPEBGZx+zZ6hd048b6tP/OO6r9ihXVaWprk5Ymsn27Or3ev78K7h4edw6St4fQWrXUqv/XXlMLpKKi9H439+aLL9R7cnLSZxpEdrZInTqqDx9+qH37pUWpDJoiIosWLZIKFSrIzp07zdWEpKamSpkyZWT06NF3PCYzM1NmzpwpTk5OMn36dLl5D8sTz5w5I1WqVBEA0rNnTxk6dKjExcUV6bn3EjQnT55s+tDcfrHEDxARmcf167mjiv/8o337aWkiVata9sKg4srJEYmPF7lwQQWvfftE9uxRX98zZ9SIqKWUTLpfe/fmfn6++kqfPqxcqdr38LC8Uk8lSakNmtevX5devXqJo6PjPdevvJsvv/xS/Pz85MaNGwU+vmHDBmndunWesNaqVStJTk4udlt79uyRsWPHiqenpwCQcePGSXYRfiJxRJOI7lWvXuoX9Zgx+rQfHp470rd5sz59oOKLjhYpV0593/r00WeebXZ2bimqd9/Vvv3SpNQFzV27dsnQoUOlTJkyYmNjIwaDQWxsbOT9999/oO3ExsaKn5+fbNq06a7HRkREyJAhQ0zfiIkTJxarrS1btsikSZNEROTo0aNSvnx5ASD9+/e/a9jkHE0iule//ZZb11Kv09ejR6s+lCsnEhurTx+o6NLTRVq2VN+zOnX0+9wsW5Y7mlnEE4B0jyw9J9x30MzMzJTt27fLK6+8IhUrVhQbGxtTwDQYDGJvby8DBgyQgwcPPoj+mvTs2VM+++yzYj1n3LhxAkCCgoKK/Jzdu3eLh4dHnjmgERER4u/vLwBk2rRphT6fQZOI7lV2tqp7CIjMnatPH1JT1ZxFQKRvX8tfhV6a5eSIDB+uvleentrUFS3IrVu50y44N9P8LD0nGEREirVnJYCjR49i27Zt2Lp1K3bs2IGUlBQAwO0vVa1aNTz33HMICQlBhQoVittEoaZNm4Zr165h1qxZxXpeQkICAgICAADp6el3PT4nJwf16tVDQEAAwsPD8zy2b98+tG7dGs7Ozrh69SqcnJwKfI0qVarg4sWLOH/+PKpUqVKs/gJAamoqXF1dAQApKSlwcXEp9msQkfWaPRt45RWgZk3g+HHAxkb7Pvz1F9CiBZCVBcyfr/YbJ8vz6afA+PHqM7JhA/DEE/r0Y84cYOxYwN8fOHMG+P9fYWQmlp4Tivwja8mSJejduzf8/PzQoEEDvPbaa9iwYQOSk5MhamQU1atXx7hx47Bnzx5ERERg0qRJDzxkLlmyBKdOncLMmTOL/VxPT08EBwejXLlyRTr+5MmTOH78OPz8/PI91qJFC3Tt2hVJSUk4efJksftCRFQUQ4cC7u7AqVPAxo369KFJE2DqVHX75ZeBffv06QfdWWioCpkA8Pnn+oXM+HhgyhR1e8oUhkwqRtC0tbWFm5sbfHx8AMAULsuUKYOxY8fizJkzOH36ND777DO0bNnSLJ1dvXo1wsLCsGDBAhgMhjyPZWdnIyoq6q6vkZiYiO7duxepvYyMDADApUuXCny8Ro0aAAAHB4civR4RUXG5u+eOIM6YoV8/3nwT6N0byMgAevUCrlzRry+U18aNwPPPq9tvvgm89pp+fZk6FYiLA+rUAYYP168fZEHu5Xx7UlKSbN++XT755BN5+umnxcvLSzw9PaVr164yc+ZMs9TPXLNmjXTr1k3SC6g4e+XKFRk0aJD8/vvvhb7G7t27xd/fX65cuZLn/qysLBk6dKi0b99eLl++bLo/OztbqlatKg4ODgW+pzZt2tx1C8oKFSoIADlz5kyhx92Jpc+9ICLzi4wUsbNTc97+/FO/fiQniwQHq340b65KIJG+du4UKVNGfU+GDNG3PFNERG6h/I0b9etHaWPpOeGBrDrPycmRQ4cOyWeffSbt2rUTe3t7adWqlSxevFgyMjLu+/WXLFkidnZ24unpKd7e3nkubm5uAkAqVaokOTk5snXrVvHz85P27dvL1q1bTdtNhoeHS/PmzeWfAgrS/fnnn6Zv0ueff57nsV27domLi4s0btxYTp8+LSKq/ND48ePF3d1d/izkp/65c+fE3t5eAMiSJUvu6b1b+geIiLQxeLD6Bd6zp779iIhQu7wYFweVlLqT1mjnThFXV/W9eOoptR2nXnJyVB+sZevSksTSc4JZ6mjGxsbKggULpE2bNlKuXDmZP39+sfYXv9369evFYDAUWMD89stbb70lIiJRUVHStm1bcXV1FVtbWwkKCpJu3brJ559/fsdvQFpamrRo0ULKly8vRwvYPuHYsWPyzDPPiJ+fn1SoUEECAwPlueeeK3SUMjAwUOzs7PL0sVy5cnLo0KFivX9L/wARkTaOHcvdIvHff/Xty++/5xYDHzOGoUIP27eLODur70H79vqPLq9Zo/piby9yl52Z6QGz9JxwT6vOi+PAgQMYNmwY/P39ERYWBjc3N3M2V+JY+moyItJO//7Azz+rOZKrVunblx9/BJ59VpVzf+cd4KOP9O1PabJ5M9CzJ3DzJtCpExAWBpQpo19/kpOB4GAgKgqYODF34Rhpw9JzgtkLZTRr1gyrVq3C9u3bMXToUHM3R0RUYr33HmAwAKtXA3/+qW9fBgxQZWwAFSwYNLXx00/A00+rkPnkk8DatfqGTACYNEmFzKAg9UcH0e3MHjQzMzMx5/9/GoWFhSE1NdXcTRIRlUjBwcCQIer2xIn69gUARo8GPvtM3X73XY5kmZMIMH26CviZmUDfvmok8w4lnDWzf3/uHxzz5wPOzvr2hyyP2YPmkiVL8NVXX8FgMMDNzQ1l9P7Ti4jIik2ZAjg4AFu3Alu26N0b4I03ckczJ01S5XVycvTtU0mTmQm89BLw9tvq36+8oqYu6F1ZLyNDlTASAQYPBjp21Lc/ZJnMHjSbNm2K8uXLIyAgAD/88ANs9NjWgoiohKhSBXjxRXX7jTeA7GxduwNAnS79/HN1+/PP1ajr/5chpvt07Zoqvj5vnpo28cUXwKxZgK2t3j0D3n8fOHoU8PFR/SIqiNkXA9H9sfRJvkSkvRs3gOrVgYQE4NtvLacw9qJFqnB4VhbQoYNauFS2rN69sl779gF9+gCXLwMuLsCyZUC3bnr3SvnjD6B1azV6vXKlKuZP+rD0nMDhRSIiK+PtrRYGAcCECWrbP0swZAjwyy8qFG3dCjRvDhw7pnevrI+ImvfYpo0KmbVqAQcOWE7ITE5W3+ucHHXNkEmFYdAkIrJCY8aobf5iY1XYtBRPPAHs3g0EBgIREUCzZmqlNBVNdDTw1FPA2LFqbmafPipk1qmjd89yjRkDnD2rvsezZ+vdG7J0DJpERFbI3h74+mt1e/58YO9efftzu4YNgYMHgfbtgbQ0tVJ61CiARUfuTEQt8KlbF9i0CXB0BL78Uk0/sKTy08uWqSkSNjbA0qWAh4fePSJLx6BJRGSl2rQBjOWJR45UI2CWwtdXFRY3jrbOnw80aaJG5yivS5dUuaKBA9U0iCZNgL//VqOaBoPevct1+rT6nAGqnFWrVvr2h6wDgyYRkRX77DM1Z/PIkdyalpbC1haYNk2VYSpfHjh1CmjZUoXPtDS9e6e/jAxVG7NWLbXTk62tKl+1d69lnSoH1Gh0795ASor6A2fSJL17RNaCQZOIyIr5+KhyN4AqN3PkiK7dKVCHDqpfAweqBSSffKKC1Nq16pRxaSMCrF8PNGigamOmpgKPPgr89RcwebKaFmFJRIARI1Qpo4AANefWzk7vXpG1YNAkIrJyzz6rtiXMyFCFsy2xhqWXl5rTFxamFpFcvAj06AF07QqcOKF377Szdy/w+OPq+3XyJODnB4SGArt2qeBpiebMUfNH7eyAFSuAcuX07hFZEwZNIiIrZzCoepo+PsA//1j2ac3u3YHjx9UWmvb2wMaNamvNQYPUKvWSSATYsQPo0gV45BFg5061deTbb6vpBEOGWNZczNuFhwOvv65uf/4552VS8TFoEhGVAP7+wHffqduffabqWFoqFxe1L/qRI0DPniqILV0K1K4NPPcc8O+/evfwwcjOBlavVvNSH39crSa3tVVF7SMi1BQCT0+9e3lnJ0+qeZnZ2eoPgbFj9e4RWSPuDGThLL3iPxFZlhdfVNsVBgSowObrq3eP7u7vv1UB+g0bcu9r316NpHXubBnbLRZHdDSwYIEaZY6KUvc5OQEhIcC4cUC1avr2ryhiY1XB/XPn1ChseLh6D2R5LD0nMGhaOEv/ABGRZUlLAx5+WJ2e7tgxdxTNGhw4oE7PrlqlFg0BQMWKag7q4MHqFLulSkhQi5uWL1dlnYz99/ZWNUTHjlXzMa3BzZtqAdcffwBBQcD+/dbxB0tpZek5gUHTwln6B4iILM/Ro2o0Ki1NzQP85BO9e1Q8Fy6oBSjff68CnFH9+mobxm7dVK1JG50nf124APz6qxqJ/e23vIuwHn1UBcw+faxrJDA7G+jXT53y9/QE9uyxvFJLlJel5wQGTQtn6R8gIrJMy5cDzzyjbq9YoQKPtbl1S5UBWrxYLRq6vSB9QADQrp2q6di6tapFac7gKaKC5d69aqQvPFzNYbxdcLAKaf37AzVrmq8v5iICvPCCmuvr4KDC82OP6d0ruhtLzwkMmhbO0j9ARGS53nxTnYp2cVEjU5ZaPqcobtxQI4e//KJOTScn533czU1t31ivnroOClJllAID1TaJRVnVLQIkJqqdei5fVvt5Hz+uLkePAtev5z3e1lYt9OncWS1qsuRT+3cjArz1lvq82NioP0569dK7V1QUlp4TGDQtnKV/gIjIcmVlAU8+qVagBwYC+/aVjBqIt26p4LxzpyobtH+/mld4J46O6jSwhwfg7p5bbNxgUKe7k5OBpCQVMgt7HXt7oFEjtTimVSu1YMmSV40Xx/vvq12JADWi+fzzunaHisHScwKDpoWz9A8QEVm2+HigRQu1T3WjRiqc/f+PlBIjM1PVozx6VJVMOn5cFYSPjFQjocXl5aUWIVWurOYn1q6truvVs675lkX1ySe5e9LPmgW88oqu3aFisvScwKBp4Sz9A0RElu/sWXWK9/p14IkngHXrLG+bQ3NJTVXvOzFRXZKS1IIX428+Ozs1ymm8BAQAzs769llL06YB77yjbk+dqgrpk3Wx9JzAoGnhLP0DRETWYf9+oG1bdWr42WeBRYv0X7VN+hFRtUs/+kj9+6OPcgMnWRdLzwn8MUNEVAo0b67qU9rZqV14xo7NHdWj0iUnB3jjjdyQ+dlnDJlkPgyaRESlxJNPAgsXqkUwc+eqXWoYNkuXzEy1Q9EXX6h/z5mjQieRudjp3QEiItLOs8+qQu4vvADMnKmC5hdfFK38D1m35GSgb19VHsrWVq0uHzpU715RScegSURUyowYoU6fjhqlVhnn5Khrhs2S68oVoGtXta+8s7Oqk9mli969otKAp86JiEqhkSOBb75Rt2fPBoYNU3U3qeQ5dAho1kyFTF9f4PffGTJJOwyaRESl1IgRQGioOo26cCHQo4cqB0Qlx/Llat/1S5fUNp179wIPP6x3r6g0YdAkIirFhgwB1qwBypRRWzy2b59/q0WyPtnZqgj7M8+oklZPPKFCZrVqeveMShsGTSKiUu7pp4HwcLUjzv79qhTSsWN694ru1dWrQKdOascfQO15v359ydkuk6wLgyYREaFlS7V/eNWqwPnzatvKtWv17hUV1/btQMOGwLZtgIsL8OOPwPTpanoEkR4YNImICICaw2fcQSglRc3ZnDJFrUony5aZCUyaBHToAMTEAMHBwIED6tQ5kZ4YNImIyMTHR9VZfPll9e/331eF3jlv03KdOgU88ojaqzwnR1UQOHAAqFNH754RMWgSEdF/2NurkkehoWqR0G+/AQ0aqHmcZDmys4EvvwQaNQIOHgTKllX1MRcsULUyiSwBgyYRERVoyJDckbErV4COHdV2henpeveMjh5VZYtefVWtKu/QAfj3X6BPH717RpQXgyYREd1R3brAn3+qLStFgBkzgMaN1X2kvfR04L331Pdg/37A3R2YN0+NOlesqHfviPJj0CQiokI5OwPz5wO//AIEBAAnTqhV6m+/zQLvWhEBVq8GatcGPvxQLf7p3h04flzt8sTtQ8lSMWgSEVGRdO2qTtkOGKDmB06froLPqlUqCJF5HDmiTo337g1cuABUqKDmYq5Zo24TWTIGTSIiKjJvb2DZMmDdOqByZSAqSs0LfPJJ4ORJvXtXskRGAkOHqoVY27YBjo6qhNGpU+przlFMsgYMmkREVGxPP61O206aBDg4qJJIdesCo0aphUN072JigNdeAx56SK38F1HB8sQJddrcxUXvHhIVHYMmERHdE2dnFXyOHlXBMztbzeWsVk3tsx0bq3cPrUtkJDBmDFClCjBrFnDrFvD442rRz4oVQFCQzh0kugcMmkREdF9q1FCn0nfuVIuEbt5U+2xXrqz22Y6J0buHlu3ff4GQEBXQ585VAbNlSzVKvG0b0KyZ3j0kuncMmkRE9EC0bq32S1+7VpXfSUsDPv9cjcS98IJa1EJKVpYK5+3bqzmYCxeq+9q1U+Fyzx6gUyfOwyTrZxDhWkFLlpqaCldXVwBASkoKXDg5h4isgAiwaZM6tb5vX+79jz8OjB4NdOum5naWNufOAd9/D/zwAxAdre6zsVFzMF9/HWjeXN/+kfWx9JzAoGnhLP0DRERUGBFg925gzhxVBzI7W93v4wM8+6w6Zdyggb59NLcbN1QJqGXLgB07cu/38VH7kr/0kppmQHQvLD0nMGhaOEv/ABERFVVUlNrFZuHC3NE8AAgOViN6ffqo2yXhdPGVK8CGDUBYmJprmZWl7jcY1Cnx4cNL76guPViWnhMYNC2cpX+AiIiKKytLbZn4ww9qPmdmZu5j1aurmpxPPKFOszs769bNYsnIUFMEwsPVlIH/btHZoAEwcCDwzDNAYKA+faSSydJzAoOmhbP0DxAR0f1ISFBhc9UqFT5v3cp9zNFRrbhu3VpdWrYEPDx062oeiYmq7NC+fWrhzu7davHT7R5+WJV96t0bqFNHn35SyWfpOYFB08JZ+geIiOhBSU5WI4K//qpGBSMj8x9Tvbpa0d6wodr+skYNVRbIyck8fUpLUwt4zpxRq+aPHFHliE6fzr/tpp+fWjXeoQPQpQtQrpx5+kR0O0vPCQyaFs7SP0BEROYgAkREALt25V7OnSv4WINB7fldsaK6Ll9ebZVZtqy6uLmpuZAODoC9PZCTo07fZ2Wpmp+JiUBSkhpdvXpVza+8ckUF3cJ2OapaFWjRQo20PvaY2hmpJMwvJeti6TmBQdPCWfoHiIhIKzduAIcOAX/9BfzzjxpVPH1ajYSak6enGjWtUweoV0/Nt2zQAPD3N2+7REVh6TmBQdPCWfoHiIhITyJqFPLiReDyZXWJjgbi4tQIZXw8kJKiFusYL7a2amTTzk7NA/XwANzd1bW/PxAQoE57V6yoAqaXl97vkujOLD0n2OndASIiontlMKhgGBCgd0+IqCDcgpKIiIiIzIJBk4iIiIjMgkGTiIiIiMyCQZOIiIiIzIJBk4iIiIjMgkGTiIiIiMzCaoKmiGD+/Plo0KABnJyc4OXlhe7du+PgwYMFHh8fH4+xY8ciKCgITk5OqF+/PhYuXHhPbV+8eBEhISGoWLEiAgMDUalSJQwbNgxRUVF3fE5MTAxGjhyJqlWrIigoCP3790dkQfupEREREZVQVhM0R44ciVGjRuHff/9FVlYW4uPjsW7dOjzyyCNYvXp1nmOTk5Px2GOP4bvvvkNWVhYyMjJw5MgRhISEYP78+cVqNyIiAk2aNEFcXBwOHz6MyMhI/PXXX7h48SKaNm2KcwXsiXb+/Hk0bdoU8fHxOHbsGM6cOYPy5cujadOmOHXq1H19HYiIiIishVXsDLRp0yYMGTIEM2bMQM+ePVGmTBls2LABI0aMwPXr1+Hu7o6zZ8/Cx8cHADBmzBgEBARg3LhxKFOmDC5evIh+/frhwIED8PLyQmxsLAxF3JD26aefxp49exAVFZWn2n5UVBQCAwPRr18/LF++3HR/dnY2mjdvjsjISJw/f970nOzsbAQFBaFs2bI4ePAg7O3ti9S+pVf8JyIiIv1Yek6wihHNhQsXYsuWLRgyZAjc3NxgZ2eH7t2748cffwQAJCUlYd26dQCAtLQ01KpVC5MmTUKZMmUAAJUrVzYdGxcXh9jY2CK3vW3bNtSoUSPfN65SpUrw8fHBkSNH8tz/448/4q+//kLfvn3zPMfW1hYDBgzAv//+iwULFhT/i0BERERkZawiaLZu3RoNGzbMd3/79u3RqFEjAMD169cBAM7OzhgzZky+Y6tWrQpfX19UqFDBNPJZFC4uLjh+/DiSkpLy3J+Tk4O0tLR8/Vq6dCkA4JFHHsn3Wi1atAAAfPvtt0Vun4iIiMhaWUXQLCg4GtWoUQOAGrUszM2bN5GYmIjJkycX+bQ5AHTr1g0pKSkYPXp0nvs3b94MW1tbTJ482XRfWloafv/9dwAq2P5XvXr1AACHDh1CQkJCkftAREREZI2sImgWJjY2Fo6OjnjiiScKPW7+/PkICQnBiBEjivX6H330ESpVqoQlS5Zg7NixyMnJwbVr1/Dxxx8jPDwcNWvWNB174sQJpKenAwAqVqyY77U8PT0BqBX0//777x3bvHXrFpKSkkwXIiIiImtk1UEzLS0Ne/fuxfDhw00h7r+ysrIwa9YsTJgwAdWqVTMFwaIKCAjA9u3bUaVKFcyZMwd9+vTB22+/jbVr1+Lhhx/Oc6zx9D2AAvvj4eFhul3YPNGPP/4YHh4e8PDwQPny5YvVXyIiIiJLYdVB87vvvoObmxs++OCDAh/fuHEj2rVrh9deew3p6el466230LFjR6SkpBSrnWrVqmHp0qUYO3Ystm/fjoULF2Lq1KnIycnJc9yNGzdMt52dnfO9jo1N7pe7sMA7YcIEJCYmIjExEdHR0cXqKxEREZGlsNqgeePGDUydOhWhoaHw8vIq8JguXbpg586diIiIwJAhQwAAu3fvxscff1ystrZu3YpNmzbhyy+/xO7du1G+fHnMmDEDAwcOzBM2HRwcTLcLqhqVkZFhun2nPgOAo6Mj3N3dTRciIiIia2S1QXPEiBF488037zo3EwCqV6+O0NBQjBs3DgBMpY6KYs+ePejTp4/pucHBwdixYwf8/f2xfPlyfPrpp6ZjAwICTLdTU1PzvdbtC4CKs/KdiIiIyBpZRcH2/5o2bRquXbuGWbNmFet5CQkJpjBYlLmaOTk5qFevHgICAhAeHp7nsX379qF169ZwdnbG1atX4eTkhPj4eHh7e0NEcPz4cdSuXTvPcw4fPoxGjRrBwcEBcXFxRSqqaumFWImIiEg/lp4TrG5Ec8mSJTh16hRmzpxZ7Od6enoiODgY5cqVK9LxJ0+exPHjx+Hn55fvsRYtWqBr165ISkrCyZMnAQBly5ZFs2bNAADHjh3L95wzZ84AANq0aWNxHwQiIiKiB82qgubq1asRFhaGBQsW5KuFmZ2djaioqLu+RmJiIrp3716k9oxzKi9dulTg48YanrfPzXzhhRcAADt37sx3/N69ewEAAwcOLFL7RERERNbMaoJmWFgYQkNDsXTpUtjZ2eV5LCYmBkOHDsW5c+cKfY09e/YgJSUF48ePz3N/dnY2QkJC0KFDhzyrvOvXr4+qVaviwIEDBb72/v37Ubt2bdSpU8d03+DBg1GvXj38/PPPeU7PZ2Rk4KeffkLdunUxaNCgYr13IiIiImtkFUFz6dKl6Nu3L3bu3GnaQtJ4cXd3R7ly5bBjxw60adMG4eHh8Pf3R4cOHRAeHm5a/b1t2zaMGzcOv/32W55FO4DaqWfhwoUIDw/Ps1DIxsYGoaGhsLe3R9++fREREQFAFVSfMGECDh8+jEWLFuV5LXt7eyxbtgxZWVl4/fXXkZWVhbS0NAwbNgw5OTlYuXIl7O3tzfwVIyIiItKf3d0P0deGDRswePBgiEih2zYOGDAABoMBNWvWRHBwMPbv34/OnTsjMDAQ9erVM4XQguZGBgcHo0WLFoiMjMy3ir1Vq1Y4cOAAPvzwQ7Rq1Qr29vawtbVF27Zt8ffff6NatWr5Xq9u3brYu3cvxo8fjxo1asDe3h6dOnXCP//8U+B8TyIiIqKSyCpXnZcmlr6ajIiIiPRj6TnBKk6dExEREZH1YdAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrNg0CQiIiIis2DQJCIiIiKzYNAkIiIiIrOw07sDRERFISLIzMxETk6O3l0hInqgbGxsYG9vD4PBoHdXHjgGTSKyaNnZ2YiNjUVycjIyMzP17g4RkVnY29vDzc0NPj4+sLW11bs7DwyDJhFZrOzsbERFReHWrVvw8PCAq6srbG1tS+Rf/URUOokIsrOzkZKSgoSEBNy8eROVKlUqMWGTQZOILFZsbCxu3bqFwMBAlClTRu/uEBGZjaurKzw8PBAZGYnY2Fj4+/vr3aUHgouBiMgiiQiSk5Ph4eHBkElEpUKZMmXg7u6O5ORkiIje3XkgGDSJyCJlZmYiMzMTrq6ueneFiEgzbm5upp9/JQGDJhFZJOPq8pIyT4mIqCiMP/NKSoUNBk0ismhc+ENEpUlJ+5lnNUFTRDB//nw0aNAATk5O8PLyQvfu3XHw4MECj4+Pj8fYsWMRFBQEJycn1K9fHwsXLix2uzNmzIDBYCj0MmbMmHzPW7hwIZo3b46goCD4+fmhb9++OHXqVLHbJyIiIrJWVhM0R44ciVGjRuHff/9FVlYW4uPjsW7dOjzyyCNYvXp1nmOTk5Px2GOP4bvvvkNWVhYyMjJw5MgRhISEYP78+cVqd8GCBXc9pmvXrqbbIoKhQ4diypQp+Prrr3H+/HkcPXoUCQkJePjhh/HHH38Uq30iIiIia2UVQXPTpk1Ys2YNQkNDkZSUhPT0dISFhcHX1xeZmZkICQlBbGys6fgJEyagX79+uHHjBqKionD+/Hk0a9YMADBx4sQir+TavXs3bt26hbCwMJw/fx7Xr1/Pc3n33XdRtmxZtG/f3vScr776CqGhofj666/RuHFjAICfnx9WrlwJBwcH9O/fHwkJCQ/ui0NERERkoawiaC5cuBBbtmzBkCFD4ObmBjs7O3Tv3h0//vgjACApKQnr1q0DAKSlpaFWrVqYNGmSqSRK5cqVTcfGxcXlCaWF2bBhA/bt24fu3bujSpUq8PHxyXPZvHkzevbsCXt7ewBqNHPatGmws7NDp06d8ryWh4cHQkJCcOnSJfzvf/97IF8XIiIiIktmFUGzdevWaNiwYb7727dvj0aNGgEArl+/DgBwdnYucM5k1apV4evriwoVKsDHx6dI7b733nvw9fUt8LHIyEgcOHAAffv2Nd138uRJREdHw8/Pr8CVsh06dAAA/PTTT0Vqn4ioNElMTMSsWbPw0EMP3dOceiKyPFYRNAsKjkY1atQAoEYtC3Pz5k0kJiZi8uTJRV7RVViR6BUrVsDLyyvPafO4uDgAaoS1IMY+Hj9+HGlpaUXqAxHRvZo4cSI8PT3zLWC0tbWFp6cnatSogWeffRZbtmzRu6vYv38/Ro4ciTfeeAMRERF6d0dTu3btwttvvw1fX9883yc/Pz8EBQUhICAA5cuXR7t27TBz5swin5W7XZcuXXDmzJl76t++ffvwwgsvIDg4GLVq1UKVKlVQp04dvPrqqzh58uQ9vWZxXLt2DVOnTsUjjzwCHx8fVK9eHY0bN0b//v2xfv16ZGdnY+zYsab317ZtW9jZ2Zm+jnZ2djh//nyR25s2bVq+78PatWuxZMkSjBgxAvb29qbH7O3tUb58edNZz6pVq6JHjx746aefcOvWLXN9SayLWLl27dqJo6OjxMfHF3rczJkzZeTIkQ+s3ebNm8uwYcPy3Hf+/HkBIADk+PHj+Z5z/Phx0+ORkZF3fO309HRJTEyUxMREiY6ONj0nJSXlgfWfyNLdvHlTjh8/Ljdv3tS7K1YtOztbXnjhBdPPkXHjxskXX3wh48ePl4ceesh0//DhwyU7O1vTfu3evTvf/Z06dRIA8sMPP2jWF3Pbt2+fLF68+K7HrV+/3vT9OHTokOn+nJwcOXz4sPTu3VsAiI+Pj+zcubPI7Z84cUIMBoO8+uqrxep3QkKC9O3bV1xcXGTWrFmSkJBgeuzMmTMyfPhwsbOzk/Hjxxf5s1PUr4XRokWLxMXFRRo3bixr166VzMxM02N///239OrVS9zc3ASA7N+/3/RYdHS0VKtWzfT1HDNmTJHau3XrlpQrV870vPnz5+c75o033hAAEhAQIGlpaab7MzIyZPPmzdKgQQMBIE2aNJFLly4V+b0aFfdnX0pKikXnBKsOmqmpqVKmTBkZPXr0HY/JzMyUmTNnipOTk0yfPv2B/NK6ePGiGAwG+fXXX/M91qhRIwEgL730Ur7HtmzZYvowxMTE3PH1J0+ebDru9oslfoCIzIVB88HZsGGD6efI7b+oMzIypH///qbH5syZo1mffv75Z5k8eXK++5999tkSFzR/+OEHee655+563OnTp03fi4iIiAKPCQkJEQDi5eUlsbGxRWp/1KhRAkA8PDyK/HskLi5OgoODxc7OrtBQ+8EHHwgA6devn+Tk5Nz1dYv6tRAReeuttwSAdOnSJU+g+68JEyYIANm0aVOe+1999VXT19PZ2blIX68ffvghz+/d28O10TfffCMApEKFCgW+RnJystSrV08AyGOPPXbXNv+rpAVNqzh1fiffffcd3Nzc8MEHHxT4+MaNG9GuXTu89tprSE9Px1tvvYWOHTsiJSXlvtpduXIlypYti3bt2hXYJ3d3d8ybNw/Tp0/HzZs3cfPmTWzYsAFvvfUWADWPtLB5ohMmTEBiYiISExMRHR19X30lInJ2di7wfnt7e8ydOxeOjo4AVNUMLcTExGDcuHEFPmZnZ6dJHyyRcWFpYT799FPY2toiLi4Oixcvvuvx8fHxWLZsGZydnZGYmIhFixYVqS99+vTBsWPH8Nprr6F169Z3PO6dd95B48aN8fPPP+P9998v0msXxerVqzF9+nSULVsWixYtKnQq20cffYQWLVqYpq8ZeXh4oGXLlrCxsUFaWhrmzp1713a/+OILPProo6Z/u7i45Dvmbt8nV1dXTJkyBQCwY8cOHD58+K7tlmRWGzRv3LiBqVOnIjQ0FF5eXgUe06VLF+zcuRMREREYMmQIAFWy6OOPP76vtlesWJFntfntGjdujIMHD2LgwIH46quv8PDDD2Pw4MFITk6Gm5sbAKBZs2aFbqvn6OgId3d304WIyFy8vb1Rt25dACjWPLZ7FRsbi65duyIqKsrsbZVEvr6+pkWqRZlz+e2336Jz58547rnnAKBIVU9+/vlnbNu2DTY2Nnj55ZcLPdbGxgYvvfQSAOCTTz5BZGTkXV//bpKSkjBixAgAwKuvvgpvb++79uHdd9/NFzQB4KGHHsLTTz8NQL33mzdv3vF1fvvtN5w+ffqu77kogoODTbfvdW5sSWG1QXPEiBF488038cQTT9z12OrVqyM0NNT0F7Sx1NG9iIqKwv79+/OsNv+vGjVqYPHixbhw4QKOHj2KlStXokOHDqZi7f3797/n9okolwiQmlqyLkUs8/tAxcfHAwDKlStnuu/QoUPo0aMHGjRoAB8fHwQFBeHVV19FcnJyvue+99578PDwAKAWbgwaNAgeHh6oVq0aVq1aZTr2woUL6NGjB86dOwcAmD17NqpXr47q1asjJiYmX79EBNOnT0elSpXg6emJMWPGIDs7u8D3sGjRIrRq1QpBQUHw8PBA9+7dcezYsQKPPX78OJ577jk0aNAAAQEBqF27NqZMmVLgIs1t27ahTZs2phGqn376CbVr14arqyv69Olzx8Wf5pKWloYbN24AAAIDAws9Njs7G3PnzsXrr7+OV155BQaDAcePH0d4eHihzzOO/NWrVw+VKlW6a5+Mm5bcunUL33zzTVHeRqFCQ0NNodEYEu+mc+fOqFOnToGPvfnmmwBUdZrQ0NA7vsaMGTMwePBg+Pv7F7PH+d0euO/2fSrx9D53fy+mTp0qr7zySrGfFx8fL46OjuLo6HjPbX/xxRfi5eWVZ55TUbzzzjsCQPz9/Quda/Jflj73gshcijJPKSVFREWzknMxx3/z7du3FzhHU0Rk9+7dYjAYBIBMmjRJRER27Ngh9vb2Mnr0aMnOzpasrCzTgqKBAweanjtnzhypWLFinkWOQUFBUq5cOXF2dhYAYmdnJ2fOnMnTpnEeekFzNJ977jnTHM3nn39e3Nzc8izOmD59er7nDBs2TLp27SpxcXEiIrJq1SpxcHAQd3d3+ffff/Mcu3HjRnF3d5eFCxdKTk6OZGZmymeffSYApF69eqZ5fGfOnDEtvjH29aOPPhInJyepUKGC6f6QkJC7fv2LOi/x9gWld5qjOX36dAEgbm5ucvXq1UJf7+eff5ZHHnnE9O8nn3xSAEj37t3v+JykpCSxtbUVADJkyJC79tnI399fAMjDDz9c6HFF+Vp07NhRAIi7u3uR5n3eyeTJk01ttWzZUgBI9erVC1y49O+//4qNjY2cOHGi0P8vxveAQuZoiog89dRTAkCaN29e7H5zjqbOlixZglOnTmHmzJnFfq6npyeCg4Pz/NVeXMbT5sWZR3ThwgXMmDEDAPDll18WOteEiMicjKOXGRkZWLt2Lfr06QMRweOPP46JEycCAGbOnInMzEz07t0bNjY2sLW1NT22ceNG02u98MILeUbH3nrrLSxevBjR0dG4evUqgoODkZWVhRUrVhS7n3PnzkVwcDBiY2MRHR2NDz/8EADyzUv84YcfsH79eixduhRly5YFAPTq1QvDhw9HUlJSnvJ4V69exaBBg9CjRw8899xzptI3b7zxBoYMGWLaqhhQo1ArV67EgAEDAAC//PILUlJScO3aNVy6dAnff/89AHWa+U6jrPcjNTU1z7/Pnz+PyZMnY+LEiahQoQI2b94MPz+/Ql9j1qxZeP31103/fu211wCo93LhwoUCnxMVFWV6P3eqI10Q4yigcbT6fvzzzz8A1K56RS1HeDdvvPEGAHUae82aNfkenzFjBrp06YJatWoV+TVzcnKQnp6e599///03+vfvjw0bNqBly5ZYu3bt/XfeylnVrOvVq1cjLCwMP/30U74PX3Z2NqKjo+86zJ+YmIju3bvfU/uXLl3Cvn37TKdQiiIjIwODBg1Ceno6xowZw9PmRA+QszNwn2v7LM4d1u08MGPHjsWlS5cQFRUFEUHdunXx/vvvY9iwYaY/oKtXrw5XV1dTnWIAqFixIgD1M9TIwcEBVapUMf17zpw5poWOrq6u6N+/P9577717mrfXr18/UzAC1HSpd999N19A+vzzz9GpU6d889nr168PANi5cyeuXbsGPz8/zJgxA3FxcejVq1e+9saPH49Fixbhl19+wV9//YUmTZoAgOn9tWzZMs/8/sGDB+PFF19Eamoqbty4AT8/P8yePRuzZ8/O99rJycm4efMmdu/ene+x3r1749NPP813f4sWLUxBKzo6GpmZmQAANzc3TJw40TSv9k4OHjyImJgY9OzZ03Rfx44dERwcjGPHjuGrr77C9OnT8z3v9i2SizMoYlyDYJxKcD9fC+Np86JurlIUPXr0QPXq1XHmzBl89tln6N27t+mxK1eu4Mcff8Rvv/1WrNe8cuUK/P394e3tjfT0dMTExJi2uH7ooYdMtVFLPZ1HVItszZo10q1bN0lPT8/32JUrV2TQoEHy+++/F/oau3fvFn9/f7ly5Uqe+7OysmTo0KHSvn17uXz58h2fP3PmTPH29i7yafO0tDTp0aOHAJAXX3zxnmrUWfqQOJG5sLzRg3O3U4EFycjIMN3euXOnDB061PQat8vJySnwfhGRb7/9VgDI0KFD89xf1FPnt8vMzBQAYmNjY7ovJiZGAIi3t7fUrFkzz6VKlSri7e0t3t7e8tdff4mImE55Hz58uMD3HBQUJADk/fffN9333nvv3bGvxte7cOFCga9ndL+nzjMyMmTv3r0ycuRIsbGxEQASGBgoBw8evONrPfvsszJ79ux89xtL83h5eRU4jevo0aOmPhRnilrTpk0FULUlC1OUr4WxLmbjxo2L3H5Bbj91LiIyd+5c03u7vWTThAkTpGnTpqZ/3+up8+TkZFm/fr106NDB9PxWrVrJjRs3itVvnjrXwdKlS9G3b1/s3LnTtIWk8eLu7o5y5cphx44daNOmDcLDw+Hv748OHTogPDzc9NfFtm3bMG7cOPz2228ICAjI8/qHDh3CwoULER4eXuhCoaKeNk9OTsbChQvRqFEjbN++HT/88AO++uor2NhYxZebiAh2dnZYvnw5OnfujO3bt5tOXf9XYac2jT8rjT+H77c/gDo9aWQcKX3++edx8uTJPJfz588jNjYWsbGxaNy4MZKTk3H58uVC+1y7du08r1vYsbf36UG8v8LY29ujRYsWmDdvnmkL48jISPTu3RsZGRn5jr9y5QrWrFmD2bNno1atWnku06dPh52dHeLi4rB06dJ8z61cubLpfRW0QOtOjFMyHnrooXt5i/n6AKiFZQ9SSEiIaZTUOJqbmpqKefPmmRYM3Q9XV1c89dRT2LJli2kl/u7duzF69Oj7fm1rZvHJZ8OGDRg8eDCysrKQkJCAGzdu5LkYV0AOGDAABoMBNWvWRHBwMPbv34/OnTujWrVq6N69Ow4dOoTw8HDT6ZTbBQcHo0WLFihfvvwdV7FfvnwZe/fuLXS1OQDUqVMHFStWxFdffYVBgwbhzJkzGDp06H1/HYiItBIbG4vOnTtj9erVWLFiBd577z3TqXNLkpWVBQA4ffr0XY+9fc6jMXD+l3GOpyWXlevbty8ee+wxAMDFixexa9eufMd89dVXGDlyJCIiIvIF8IiICFPwmTNnTr7nurq6olmzZgCAv//+u0h9Sk1NNZXG6ty58z29r9sZ39/ly5eLFXbvpkyZMqYAuGHDBpw4cQI//PADPDw88pxKfxA++eQTuLq6AgBWrVpVaFmlks7i52g+9dRTef6CvZuKFSti27ZtxWqjTJky2Lt3b6HHVKhQoUj9OH78eLHaJiKyNL169UJERAQuXrwIBwcHvbtzR8aFnVu2bEF8fLwpKN7u2LFjptqTbm5uSE5OxqlTp/Dkk0/mO9YYXOvVq2fejt+nZs2aYceOHQCQr3Zkeno6vv322wIDqNHIkSPx5Zdf4t9//8WOHTtMwc5o1KhR+OOPPxAREYETJ06YRnrvJDw8HDk5OXBxccHw4cPv8V3lev755zF37lyICNauXYuRI0cW6XnGubiFGTNmDD777DPcvHkTn376KXbv3o3XXnut0NrW98LNzQ21a9fGn3/+iczMTKSkpJTahcAWP6JJRETaOXr0KHbt2oVy5crdMWQW54//gjyolcRVqlRBpUqVkJqaipEjR+Zb/Z2dnY1JkybBw8MDtra26NKlC4A711KOioqCg4MDevTo8UD6Zy63n1K+vTA4oKaa1a9fP89Crv+qXbs2WrVqBQAFLtgZOHCgaVTziy++uGt/vvzySwDABx98cNegVxSNGjXCoEGDAABTp04t0m5+xtHJu/H19TVt4GKs1/n888/fX4fvwPh98vPzK9WLghg0iYhKuNt/Ud/tl7YxRP7zzz/YvHkzADXn7/Z5ZrGxsVi+fDkA5CnvYhwR/C/jimkj48jO7c/977GFlQy6/fWMK9NXrFiBjh07Ijw8HJcvX8Yff/yBnj17onr16qYtNidNmgQ7OzscOHAA+/bty/OasbGxOHjwIN544408I6PGPt7pvRX0/u7VrVu37npMREQEVq5cCSB/kfKsrCxMnz7dVJKpMMZTxWFhYThx4kSex2xtbbFq1SpUqlQJCxYsyFPS6r8++eQTbNu2DSEhIXlKKd2vuXPnokmTJoiKisLAgQMLLKZvdPbsWXz++ef5tjVNTU0t8DM2btw405qJUaNG5dtm8vZpFgU9vyjfp2XLluHixYsAkKd6Qqmk71okuhtLX01GZC5cdf5gZGdnm1ZyA5C5c+cWevytW7ckMDBQAIjBYJBKlSqJm5ubrFq1Stzd3U0rlqdNmyYiIuHh4abX3rFjR57XevnllwWAPPTQQ3lWOK9fv14ASJs2bSQnJ0eWLl0q//77ryQnJ0uNGjUEgAwfPjzPa/3999+mdrZv357n/Rmre/z30rhx43yVShYsWCA2NjZSuXJl0+rz69evS7t27aRLly55Pm9ZWVny+OOPCwBp3759nsohkZGRpqL0CxYsKPRreuLECdm6dWuhx4iIbNiwwdT3Xbt25XksLS1Nli1bZipeX7duXbl27Vqevo4bN+6ORe3/66OPPjK11bx5c4mJicl3zJUrV+Sxxx4TR0dH+fTTTyU+Pt702MmTJ2XIkCHi6OgoH3744V3bMyrq10JEJDY21vS9DQ4OlkWLFuX5PXj9+nX54osv5MUXX8y3gj4pKUkaNmwolStXzvN1MurRo4c4ODhIdHR0nvtzcnJk+PDhpq/N4sWL8z33zTffFEBtwHL9+vU8j129elWmTp0qDg4OAkAGDBhQ7IozJW3VOYOmhbP0DxCRuTBo3r8JEyaYSsXcfvHz85Pvvvvujs87ePCgNG3aVMqUKSMtW7Y0lQcaN26ceHh4mMr/hISEmHaRASC2trbSsWNHOX36tAQEBORp083NTTZu3Cgi6pf5iy++KM7OzvL444/Lxo0bZePGjabgZrx4e3vLkSNH5OmnnxZ7e3vT/XZ2djJmzBhTf7OysmTmzJlSp04dcXBwkHLlysnYsWMlISGhwPe3c+dOefLJJ6Vs2bJSo0YNadasmXz11VeSlZVlOubIkSPi5eWVpz9ly5aVTZs2yUsvvSROTk6m+w0Gg7Rv3/6ev0/79u2TKVOm5NkBCYA4OztLpUqVpFKlSuLq6iqVKlWSjh07yty5c/OUn0pLSxNPT8983+M77S5k3MXn9oudnZ3pj4f/Wr9+vQwYMECqVasm1apVk6pVq0r9+vXlrbfeknPnzt3z+y6qrVu3SkhIiNSoUUPc3NykWrVq0qZNGxk2bJjs3bs33/EdO3YUR0dH03tzcHCQ4ODgPMfs2bMn365OU6ZMMf0xdfslICBAwsLCZOXKlfLKK6/k+SwCEE9PT6lcubKUK1dOXF1dpXr16tKnTx9Zv379Pb3fkhY0DSJ67KxLRZWammpauZaSkpJviJ+opEpPT8f58+cRFBQEJycnvbtDRKSJ4v7ss/ScwDmaRERERGQWDJpEREREZBYMmkRERERkFgyaRERERGQWDJpEREREZBYMmkRERERkFgyaRERERGQWDJpEREREZBYMmkRERERkFgyaRERERGQWDJpEREREZBYMmkRERERkFgyaRERERGQWDJpEREREZBYMmkRERERkFgyaRERERGQWDJpEREREZBYMmkREZHXi4uLw9ttvo3nz5nBxcUGTJk2wYsUK/Pbbbyhbtiw++OADvbtIRGDQJCIqsdavX49XXnkFLi4uMBgMMBgMsLGxQUBAAIKCguDn54fAwEA8+eST+O6775CSkqJ3l4vkypUraN68Obp27Yr9+/dj+fLl+Oeff9CvXz+MHDkSCQkJ+Pnnn/XuJhGBQZOI6I7S09OxePFi9O7dG48//jh69+6NxYsXIz09Xe+uFUnXrl3x5ZdfYvr06ab74uLiEBMTg/PnzyMmJgY//fQT0tPTMWLECDRo0ADHjh3TrH85OTnYs2dPsZ/32muvwWAwoHXr1gDU+5w9eza8vLwwatQotGjRAm+99VaBz925c+d99ZmIiscgIqJ3J+jOUlNT4erqCgBISUmBi4uLzj0i0kZ6ejrOnz+PoKAgODk5ad7+unXrMHToUMTHx8PGxgY5OTmm67JlyyI0NBRPP/205v26F1u2bEGnTp0AAJmZmbCzs8vzeFZWFrp06YItW7agRo0aOHr0KBwcHMzerxUrVuDYsWOYMmVKkZ+TkpICb29vNG7cGHv37i1Wezk5OWjQoAGOHDlSzJ4Saae4P/ssPSdwRJOI6D/WrVuHHj16ICEhAYAKKLdfJyQkoHv37li3bp1eXSwWe3v7Qh+3s7PDJ598AgCIiIjAxo0bzd6nmJgYjBs3rtjPO3fuHDIyMu4pCH/88cc4evRosZ9HRPeOQZOI6Dbp6ekYOnQoAOBOJ3yM9w8dOtRqTqPfTXBwsOn2mTNnzNpWbGwsunbtiqioqGI/Nz4+HgBgMBiK9bzvv/8e7777brHbI6L7w6BJRHSbFStWID4+/o4h00hEEB8fj5UrV2rUM/OKjIw03Q4MDMz3eFpaGj744AM0adIE/v7+KFeuHF566SXExcXlOU5E8PXXX6N+/fqoVKkSbGxsYDAY0LBhQwDAhQsX0KNHD5w7dw4AMHv2bFSvXh3Vq1dHTEzMHfu3ZMkSVK9eHQMHDgQA7N+/3/S8V199FQCQkZGBn376CY899hhCQkJMz50+fTo+/vhj0/fU+LyJEycW/wtFRMUjZNFSUlIEgACQlJQUvbtDpJmbN2/K8ePH5ebNm5q226tXL7GxsTH9vyvsYmNjI7169dK0f/di+/btpj5nZmYWeMxLL70kACQwMFDS09PzPBYfHy+NGzeWyZMnS2ZmpmRkZMiECRMEgNStWzfPz6ZZs2ZJ7dq1JTo6WkRELly4IC1atJAGDRrkec3JkycLAJk8efI9vZfHHnssz/1XrlyR5557TsqWLSsA5Lnnnsv3XOPXgMiSFfdnn6XnBI5oEhHd5saNG6a5mHeTk5OTb0TP0qWmpppuiwhOnDiBMWPG4KuvvkKtWrWwZcsWODo65nnOK6+8Aj8/P0yZMgV2dnawt7fH1KlTERwcjKNHj+ZZ1T579mx06dIF5cqVAwBUrlwZS5cuzbcA6UELCAjAwoUL8frrr5u1HSIqHgZNIqLbeHt7w8amaD8abWxs4OXlZeYePVgVKlRAUFAQAgMDYW9vjzp16mDu3Lnw9/fHO++8g8qVK+c5/sqVK1i6dCn69OmT536DwYB69eoBQJ7pA9euXcPKlStx9epV031Vq1ZFu3btzPiucvn7+2vSDhEVDYMmEdFtevToUawRzZ49e5q5Rw9WQkICzp8/j8jISKSkpCA8PBz9+vXD1atXMXjwYDRp0gTnz583Hb9z505kZ2fj/fffR61atfJcdu3aBW9vbyQlJZmOb9u2LS5evIj69evj66+/RkZGBgDkGfU0p7utsCcibTFoEhHdpm/fvihbtuxdVzUbDAaULVs230ifNXFyckK7du2wfPlyUxA8duyYacENkLtIaP78+Th58mSey6VLlxAbG5tn9fj8+fPRokULXLt2DS+99BKqV6+O0NDQuy6uIqKSiUGTiOg2Tk5OCA0NBXDnEjrG+0NDQ3UpJm8O48aNQ1BQEABg3759OHv2LABVzB0ATp8+XaTXKVeuHPbs2YMFCxYgMDAQUVFRGDp0KJ555pkijxQTUcnBoElE9B9PP/00wsLC4OnpCQCmOZvGa09PT6xdu9ZqdgYqChsbGzRt2tT0b+MiJ+OinsL2Dt++fXu+1xo2bBhOnz6NqVOnwt7eHj///DOWLVtmhp4TkSVj0CQiKkC3bt0QHR2NxYsXo0ePHnj88cfRo0cPLF68GNHR0SUqZBpdu3YNgJrn+NBDDwGAaT/xP/74A3PmzMn3nOvXr2PBggWmf7/wwgum246Ojpg4cSJmzJhheg2j4hZcN+IpeCLrwqBJRHQHTk5OGDRoEFatWoXt27dj1apVGDRokNWdLr9169Zdj9m9ezd27doFABg2bBg8PDwAANWqVUO3bt0AAGPHjsWoUaPw999/IyoqCr/88gvatm2LZ555xvQ6YWFh+XYWevzxxwGoFe9GZcqUAYBi76yUkpICAEhOTi7wcePio8zMzHyP3WubRHTvGDSJiEo443xLQJUrul1iYiK+/vprPPXUU8jJyUHbtm0xc+bMPMd88803qFGjBgC12KdJkyYIDAxEt27d8Nhjj6Fr166mY9PS0tClSxfs3r0bgJrj+fXXX6NixYp5RjuNW17u3bsXIoJly5bhyJEjhb6PrKwsrF69GoBatHTy5Ml8x/z+++8AgD///BNpaWl5HjO2uWfPHqSlpWHq1KmFtkdED4C+9eLpbiy94j+Ruei1M1BJ8ttvv8mECRPEzc0tz45Gbm5uEhgYKBUrVhRXV1epUqWKdO3aVZYsWSI5OTkFvtaNGzfklVdekYoVK4q9vb3UrFlTZs2ale94FxcXUzuenp5Sq1Ytef755+XKlSt5jsvJyZEXX3xRnJ2d5fHHH5eNGzcW+l7Wr18v7u7ued6Hra2tBAUFyaZNm+T69evi5+eX732GhYWZXmP//v1So0YN8fX1lddee02Sk5Pv8StLZD4lbWcggwgnvFiy1NRUuLq6AlCnjFxcXHTuEZE20tPTcf78eQQFBVndqWoiontV3J99lp4TeOqciIiIiMyCQZOIiIiIzIJBk4iIiIjMgkGTiIiIiMyCQZOIiIiIzIJBk4iIiIjMgkGTiIiIiMyCQZOIiIiIzIJBk4iIiIjMgkGTiCwaNy8jotKkpP3MY9AkIotkY6N+PGVnZ+vcEyIi7Rh/5hl/Blq7kvEuiKjEsbe3h729PVJSUvTuChGRZpKTk00//0oCBk0iskgGgwFubm5ITEzEzZs39e4OEZHZ3bx5E0lJSXBzc4PBYNC7Ow+End4dICK6Ex8fH9y8eRORkZFwd3eHm5sbbG1tS8wPYCIiEUF2djaSk5ORlJQER0dH+Pj46N2tB4ZBk4gslq2tLSpVqoTY2FgkJycjISFB7y4REZmFvb09PD094ePjA1tbW72788AwaBKRRbO1tYW/vz/8/PyQmZmJnJwcvbtERPRA2djYwN7evkSerWHQJCKrYDAY4ODgoHc3iIioGLgYiIiIiIjMgkGTiIiIiMyCQZOIiIiIzMJqgqaIYP78+WjQoAGcnJzg5eWF7t274+DBgwUeHx8fj7FjxyIoKAhOTk6oX78+Fi5cWOx2Z8yYAYPBUOhlzJgx+Z73ww8/oFmzZihXrhzKlSuH5s2bY9GiRcVun4iIiMhqiZUYMWKEABAAYmtra7ptb28vq1atynNsUlKS1KtXT8qUKSMVK1YUg8FgOn7evHnFard27dqm597psmnTpjzPefnll8XFxUXCwsJM9y1fvlzs7Oxk3LhxxWo/JSXF1E5KSkqxnktEREQlm6XnBKsY0dy0aRPWrFmD0NBQJCUlIT09HWFhYfD19UVmZiZCQkIQGxtrOn7ChAno168fbty4gaioKJw/fx7NmjUDAEycOLHIG9bv3r0bt27dQlhYGM6fP4/r16/nubz77rsoW7Ys2rdvb3rOX3/9hTlz5uCdd95B9+7dTff369cPQ4YMwYwZM3D8+PEH9JUhIiIislxWETQXLlyILVu2YMiQIXBzc4OdnR26d++OH3/8EQCQlJSEdevWAQDS0tJQq1YtTJo0CWXKlAEAVK5c2XRsXFxcnlBamA0bNmDfvn3o3r07qlSpAh8fnzyXzZs3o2fPnnn2I922bRsAoGHDhvler3HjxgCAo0eP3tsXgoiIiMiKWEXQbN26dYHBrX379mjUqBEA4Pr16wAAZ2fnAudMVq1aFb6+vqhQoUKRt3Z677334OvrW+BjkZGROHDgAPr27ZvnfhcXFwDAvn378j0nOTkZBoMBDRo0KFL7RERERNbMKgq2FxQcjWrUqIFDhw6hcuXKhb7GzZs3kZiYiP/9739FrrxvHBEtyIoVK+Dl5ZXntDkAPPXUUxg7diymT5+Ovn37om7duqbH1qxZg+HDh6NmzZpFah9AntP8qampRX4eERERlXy3Z4OiTg3UlM5zRO9bu3btxNHRUeLj4ws9bubMmTJy5MgH1m7z5s1l2LBhBT42depUASDly5eXw4cPi4jI9OnTZfTo0ZKVlXXX105PT5fExERJTEyUM2fO3HUxEi+88MILL7zwwsvVq1cfWM55UKw6aKampkqZMmVk9OjRdzwmMzNTZs6cKU5OTjJ9+nS5efP/2rvzqCiutA3gTwOCyiLgBiKigjJRcSNBB5VsjuYc9eBOkESioBE1cQEx6jhuieMyEXV0opOMmhgV0VEznqjxiImY6JgQAROjYNwVAUWkRVm73+8Pv+6x081OSxd5fufUEe6tuvdtXoSXqupbhbWe9/r166JSqeTIkSPl7rNw4UIBIE5OTjJr1izZtm1blcdftGhRvX+zcuPGjRs3btyUtd24caPWNU5dU4lY4nnWqlm/fj0++OADXLhwAa6urkb9hw4dwooVK3Dy5El9W//+/XH48GE4ODjUeN41a9bggw8+QFZWlsEbgZ4mIoiOjsbNmzexd+9eeHl54T//+Q+6d+9e6fjFxcUoLi4GAGi1Wly7dg29evXC7du30axZsxrHTeahVqvRpk0bZGZmwsnJqb7Dod9gfiwfc2TZmB/LJiLIzs6Gj48P8vLy4OzsXN8hGarfOrfm7t27J61atTJaw9KUS5cuyfjx4/UV//z582s1d9++fSUiIqLc/sLCQgkPD5dr166JVquVmTNnCvDk7OapU6eqPV9+fr4AkPz8/NqETWbC/Fg25sfyMUeWjfmxfJacI0W869yUSZMmYc6cOXjttdcq3dfHxweffvopoqOjAUC/1FFN3Lx5E2fOnDF6t7mOiGDs2LFwc3ODl5cXVCoV4uLiEB0dDbVajeDgYOTn59d4fiIiIiKlUGShuXz5crRr1w4xMTHVOu7Pf/4z7OzskJmZWeO59+7da7RI+9N2796NgwcPYsiQIQbtq1evxrBhw3D37l1s3LixxvMTERERKYXiCs3PP/8c6enpiIuLq/axzs7O6Nq1K9zd3Ws8/549ezBixAjY2JheGWrfvn0AgFatWhm0q1QqLFu2DADw/fffV2tOOzs7LFq0CHZ2djWImMyN+bFszI/lY44sG/Nj+Sw5R4p6M9C+ffuwc+dOxMfHGxV6Go0GmZmZ8PT0rHAMHx8fDB06FGvXrq32/Ldu3UK7du1w5MgRDBo0yOQ+w4cPxxdffIFjx44ZnfV8/Pgx7O3tMWbMGCQkJFR7fiIiIiIlUcwZzQMHDuDTTz/Fjh07jIrMrKwsvPXWW7hy5UqFY3z33XcoKCjAe++9Z9Cu0WgwYcIEDBw4sMLL6nv37oWrqyteeeWVcvcZPnw4ANP3geqeFjRq1KgK4yQiIiJqCBRRaO7YsQNjxoxBUlKS/hGSus3JyQnu7u44ceIEgoKCkJiYiNatW2PgwIFITEzUr5J//PhxREdH4+jRo3BzczMYPyUlBdu2bUNiYmKFbxSq7LI5AIwfPx4jRozAtm3bsHbtWpSWlgIAzp49i8mTJyMsLAxjx46tg68KERERkWWz+EvnX375JYYNG1bpY5ViY2OxcuVK3Lp1C+PHj8cPP/yAwsJCtGvXDn5+fggKCsKUKVP0zyJ/WmFhIV555RXcuHEDR48eRdeuXY32uX37Njw9PSu8bK6j0WiwceNGbN26FdeuXYOjoyPc3NwwadIkREZGVvkRmERERERKZvGFJhEREREpkyIunVu6kpISrFixAr6+vvD29saLL76IpKSkao2h0Wiwfv16dO3aFU2aNIGXlxfmzZunf0JQRc6dO4fQ0FBERERUum9WVhbefvttdOzYER06dEBISAhu3LhRrViVSCk5KigoQGxsLDp06ABbW1u0bdsWU6ZMwZ07d6oVq9IoJT+mxMTEQKVS4dq1a9U+VkmUmqO8vDysWbMGw4cPx+TJk7F48WL9LU0NiZLys3XrVgQEBMDd3R3u7u7o06cPPvvss2rFqkT1laOvv/4ar776KhwdHWFvb4/AwEDEx8dXOE+d1gr1tlR8A1FUVCQvv/yydOnSRa5fvy4iIgkJCdKoUSNJSEio8jgTJkwQa2tr8fT0lEaNGumfYvT666+Xe8yZM2dk9OjRolKpBICEh4dXOMeVK1fEw8NDxowZI48fP5aysjKZOXOmtGzZUi5evFjlWJVGKTl6+PCh9OrVSwCItbW1/hgA4ubmJhkZGVWOVUmUkh9TkpKSxMrKSgDI1atXq3Wskig1Rzt27JCWLVvKggUL5OHDh1U+TmmUlJ933nlH7O3t5cCBA/q23bt3i42NjURHR1c5VqWprxxt375d/zPKxsbG4Lnos2fPNnlMXdcKLDRracaMGQJAzpw5Y9AeGhoqTZs2lStXrlQ6Rnx8vIwZM0bu3LkjIiIFBQUyceJE/TdDWlqayeOuXLkiGo1GwsPDK/0PXlZWJv7+/tKyZUspKCgwaPf09JTu3btLSUlJFV6x8iglR7GxsdKzZ085fvy4FBcXy8OHD2XVqlX6Hw5//OMfq/6iFUQp+fmthw8fio+PjzRp0qTBF5pKzNG8efOkcePGcuTIkSrtr2RKyU9ycrIAkOXLlxv16eY6f/58pbEqUX3kKCcnR5o1ayYLFy7UH/Pjjz/K888/rz/mq6++MjjGHLUCC81auHr1qtjY2EiXLl2M+g4dOiQAJCQkpNJxVq1aJRqNxqCttLRUOnbsKAAq/WvnH//4R6X/wbdv3y4AZOrUqUZ9sbGxAkA++uijSmNVGqXkqKysTPr27St5eXlGfQsXLtT/ULh8+XKlsSqJUvJjSmRkpCxYsEC8vLwadKGpxBz99a9/FQCyZ8+eSvdVOiXlZ9WqVQJADh06ZNS3YcMGASC7d++uNFalqa8crV27VjZs2GA0Tk5Ojri6ugoAefPNNw36zFEr8B7NWti9ezfKysoQGBho1NenTx8AwP79+5Gbm1vhOHPmzIGVlWEqbGxs4O/vDwDo3r17hcc3bty40lh37NgBACZj7du3LwDg448/rnQcpVFKjrKysjB37lw4Ozsb9UVHR+s/vnv3boXjKI1S8vNbhw4dwtmzZ7Fo0aJqHadESsvRV199hfnz5yMkJASjR4+u0jFKpqT86FZ90a0p/bSHDx9CpVKhR48elY6jNPWVI1tbW0RFRRmN07JlS4SHhwMw/p1ijlqBhWYtfPnllwCAjh07GvW5urrCw8MDJSUl+O6772o0flZWFsaNGwdfX98K96tsuaTHjx/jm2++KTdWPz8/AE/WE33w4EGNYrVUSsmRh4eHfrH/32rWrJn+kabt2rWrUZyWSin5edr9+/cxffp0bN++HY0aNapRXEqipByVlpZixowZEJHfxR8BgLLyM2TIEFhbW2PVqlX4+eefDfr279+PyMjISudRovrKUVRUlFFhqtOpUycAgJeXl77NXLUCC81aSElJAQC0bdvWZL/u7FRqamq1xz579ixKS0vx0Ucf1TQ8vQsXLqCoqAiA6Vh1cYoIzp07V+v5LIlSclSRsrIyPHjwQP8uzYZEifmJiorCu+++iy5dutTpuJZKSTlKSEhAeno6AgICcOnSJYSGhqJ3797w8vJCWFhYpU+PUyIl5cfLywtLly5FUVERBg8ejLS0NADA6tWr8cILL5j9Z2l9scQc3bt3DwAQHBysbzNXrcBCs4aKiopQUFAAACYvdwJPzkQB/0toVR05cgRDhgxB586d8ejRo1rFCRieGjcVqy5OoPqxWjIl5agiJ0+eRElJCebMmWPWeZ41JeZn165duHv3LmbMmFFnY1oypeVoz549AJ78zCsoKMCWLVvw3//+F9OnT8fOnTsREBCAX375pU7msgRKyw8AzJ8/HwsXLkRmZiaCgoIwe/ZstGrVChs2bIC1tXWdzWMpLDVHx44dg5+fHwYPHqxvM1etwEKzhp6+l6Jp06Ym99Gdstb9hVCZX375BaGhoRg2bBiysrLw2WefoXfv3rhw4YJZY3361HpVY1UCJeWoIn//+98xcODABne/mdLyk5mZiQULFmDbtm2/m6d7KS1HJ06cAACsWbMG48aNQ5MmTWBra4s5c+YgLCwMubm5eOutt2o9j6VQWn50lixZglmzZmHQoEGIi4vDokWLGtzVNB1LzFFaWhq+/fZbbNy40eD3v7lqBRaaNWRra6v/WMp5uFJJSQmAJ/dgVEWXLl2wa9cu5OTk4P3330fjxo2RlZWFyMhIs8aqi7M6sSqBknJUnm+++Qbffvsttm3bZpbx65PS8hMZGYklS5Y0uPtkK6KkHD169Eh/35iHh4dR/9SpUwEAP/zwA86fP1+ruSyFkvKjU1RUhAkTJmDGjBlISEjAzJkzcf36dQwYMACnT5+ukzksiSXmaNasWZg7dy4GDBhQrVhrWiuw0KwhV1dXfVLKO2Wt+6HXokWLao3t4uKCBQsW6C8DnTp1qlZP73Fzc9N/bCrWp2/qrW6slkxJOTIlLy8PU6dOxb59+0z+4lQ6JeVn06ZNaNq0Kd58880aj6FESsqRWq3Wf+zk5GTUHxgYqL8caM4rEM+SkvIDPClexo4dCzc3N3h5eUGlUiEuLg7R0dFQq9UIDg5Gfn5+reawNJaWo3Xr1sHBwQHLli0z6jNXrcBCs4asra31bwbIzMw0uU92djYAoGfPnjWaY+jQoQgICKhwjqro1q2b/lKfqXF0cdra2uK5556r8TyWRkk5+i2NRoPx48dj2bJl6N+/f52Na0mUlJ/Vq1fj3//+N1QqldF2/fp1AECHDh2gUqka1NlnJeWoRYsW+p9zTxedT9O9wUGr1dZ4HkuipPwAT5b5OXjwIIYMGWLQvnr1agwbNgx3797Fxo0bazWHpbGkHJ04cQL79+9HfHy8yfthzVUrsNCsBd1NtKYuw9y7dw/5+fmwt7c3Oj1dHboiozbvNnZxcdF/E5qK9ddffwUABAUF6dc5ayiUkqPfioqKQnBwMEaNGlVnY1oipeSnffv28PX1NbnZ2NgAeLIciK+vr8EN8w2BUnLUqFEj/TqC5V0a16312Llz5xrPY2mUkh8A2LdvHwDol2vTUalU+jNs33//fa3msESWkKPz589j4cKF2L9/f7n3ipqrVmChWQsRERGwsrJCUlKSUZ/uXpNRo0bBzs6uxnPk5+ejR48eBmtd1cTkyZMBoMJYx40bV6s5LJGScqQTHR2NTp06mbzfJjc3t9yzNUqklPwkJibi4sWLJjfdbQ26fUaMGFHjeSyRUnIEAK+//jqAJwvqm3Lt2jV4e3s3qEXBlZQf3T1+t27dMurTrev49H2CDUV95ygjIwNRUVFISEiAi4uLUf/Vq1f1H5ulVqjWc4TIyJQpUwSApKSkGLSPGjVKmjRpYvDIwOPHj0tAQICsW7euSmPn5uaKu7u7HDt2rML9Pv74YwEgb7zxRrn7lJSUiJ+fn7Ru3VoKCwv17cXFxdKmTRvp1q1bg33WuVJyJCISExMjS5cuNdl37tw5GTBggMHzZxsCJeXHlIb+CEoR5eSooKBA2rZtKzY2NnLp0iWDvoMHDwoAiY+Pr1JcSqKU/GzdulUASEREhFFfYmJig82PSP3lKD09Xfr16yfXr1836nv8+LF8+OGHsmTJEn2bOWoFFpq1VFBQIP7+/tKnTx/Jzc0VrVYr69evF1tbW6Pn7A4ZMkQAiIODg77t7t270rFjR+nWrZts2bJFioqKRETk119/lVdffVV27txZaQyRkZECQPz8/Ay+MX7rp59+kubNm0tUVJSUlpbKo0ePJCwsTNzc3OTixYs1/ApYPiXkSKvVytSpU0WlUknz5s0NNldXV2nSpIkAkLCwsFp+NSyPEvJTkd9DoamkHKWkpIijo6N0795d/8v1559/lvbt20tMTExNXr7FU0p+NBqNjBgxQqytrSUuLk5fsPz444/i7e0tYWFhotVqa/plsGj1kaO0tDRp3bq12NvbG/1ecXZ2FisrKwFg9EdZXdcKLDTrgFqtlhkzZkiHDh3E29tbgoODJS0tzWi/zz//XBwdHWXatGn6ttLSUpkwYYI0b95crKyspGXLljJo0CCJiYmRGzduVDjv2rVrxdXVVQDoN3t7exk/fny5x2RkZMjIkSOlffv20qlTJ5k2bZpkZ2fX/MUrhKXnKDY21mCf8rZDhw7VzRfEwlh6firyeyg0RZSVo/Pnz0twcLA4OztL586dJTAwsMGeKdNRSn7Kyspk3bp10rNnT3F2dhZPT0954YUX5J///GeDLTJ1nmWObt++LS4uLpX+TgkICDAZa13WCiqRchZ2IiIiIiKqBb4ZiIiIiIjMgoUmEREREZkFC00iIiIiMgsWmkRERERkFiw0iYiIiMgsWGgSERERkVmw0CQiIiIis2ChSURERERmwUKTiIiIiMyChSYRERERmQULTSIiIiIyCxaaRERERGQWLDSJiJ6h+/fvY+XKlXjppZfg7u4OOzs7tGjRAv7+/oiNjUVGRkZ9h0hEVGdUIiL1HQQR0e/Bv/71L8yePRtqtRoAoFKp4OzsjIKCApSWlgIArK2tMX36dPztb3+DjY1NfYZLRFRrPKNJRPQMLF26FJGRkVCr1ejfvz8OHz6MwsJC3L9/H8XFxUhOTsbEiROh1Wqxbt06DB06FGVlZfUdNhFRrfCMJhGRmR08eBDBwcEQEUybNg3r16+HlZXpv/MTEhIQGhoKrVaLmJgYrF69+hlHS0RUd1hoEhGZkUajQdeuXZGeno5+/fohKSmp3CJTZ8GCBVi+fDlsbGyQnp6Ojh07PqNoiYjqFi+dExGZ0ddff4309HQAwOLFiystMgEgNjYW9vb2KCsrwyeffGLuEImIzIaFJhGRGSUmJgIAHBwc8NJLL1XpmGbNmmHAgAEGxxMRKRELTSIiM7p8+TIAwMfHp1rvIvf19QUAXL161SxxERE9Cyw0iYjM6MGDBwCenNGsDt3++fn5dR0SEdEzw0KTiMiMXF1dAQB5eXnVOk63f4sWLQzaNRoNVq5cCR8fH9jZ2cHb2xvvv/8+l0IiIovEQpOIyIw8PT0BABkZGSgqKqrycefOnQMAtG3b1qD9nXfewXvvvQd/f39s3LgRf/rTn/CXv/wFb7/9dt0FTURUR1hoEhGZ0WuvvQYAKC0txeHDh6t0TE5ODk6fPg0AGDx4sL79p59+wqZNmxASEoLdu3cjMjISmzZtwpw5c7BlyxYkJyfX/QsgIqoFFppERGb08ssv689qVnXx9bi4OGg0GqhUKoSGhurb4+PjISJ49913DfbXfR4fH2/QHhERgaZNm+LmzZsIDw9H8+bN4eLigilTpqCsrAwFBQWIjo6Gp6cnHB0dMXz4cOTm5tbm5RIRGWChSURkRlZWVli5ciUA4PTp01ixYkWF+588eRIffvghAGDixIl47rnn9H3JycmwsrLC888/b3CMh4cHPDw8jM5opqamolmzZggKCoKDgwOWL18Of39/bN68GStXrkS/fv1w8+ZNzJ8/H6NHj8YXX3yBRYsW1cXLJiICwCcDERE9ExEREdiyZQusrKywb98+BAcHG+1z+fJlBAYGIicnB926dcOpU6fg6Oio7/fz80NOTg6ys7ONjg0ICEBeXh4uXboEACgrK4ODgwM0Gg2OHTuGF198EQCgVqvh4uICrVaLDRs2YNq0afoxfH194ejoyEvwRFRneEaTiOgZ2LRpE0aOHAmtVouRI0di7ty5KC0t1ffv2LEDvXv3Rk5ODnx9fXHkyBGDIhMAHj9+DDs7O5PjN27cGIWFhfrPL1y4gOLiYkyaNElfZAKAo6MjGjVqhL59+xoUmQDg5OQEa2vruni5REQAWGgSEZmVm5sb3Nzc4OnpiaSkJACAVqvFqlWrsGvXLgDAjRs38MYbb0CtVgMA7ty5A39/f/2xp06dAvCkmCwuLjY5T1FRERo3bqz/PDU1FQAwZswYg/0uXbqE4uJijBo1yqBdRJCRkYE//OEPtX/RRET/r+qPqSAiomozdZlbR6vVGvyro1ar9UUnAJSUlAB4stTRxYsXUVJSAltbW4NjMjMz4e3trf88NTUVKpXK6H7OlJQUAE8utT/typUrUKvV6NWrV1VfGhFRpVhoEhGZUVVug2/fvn2V9vP398fRo0eRnJyMwMBAffvt27dx+/ZthISE6NtSU1Ph4+NjdPldV4D27NnTqB2AUTsRUW3w0jkRkUKEhIRApVJh/fr1Bu26z58uNNPS0kyenUxNTYW3tzecnJyM2gEWmkRUt3hGk4hIIXr06IHJkydj8+bNEBEMGjQIycnJ2Lx5M8LDw/WXw2/evInc3FyThWZKSgqCgoJMtnt5ecHZ2dncL4OIfkdYaBIRKciGDRvg5eWFTz75BAcOHECbNm2wePFizJs3T79PWloaAOOzk9nZ2cjOzkbv3r2Nxk1NTTW6n5OIqLa4jiYRERERmQXv0SQiIiIis2ChSURERERmwUKTiIiIiMyChSYRERERmQULTSIiIiIyCxaaRERERGQWLDSJiIiIyCxYaBIRERGRWbDQJCIiIiKzYKFJRERERGbBQpOIiIiIzOL/AGU8xwqigXwEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from joblib import Parallel, delayed\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals4 = np.linspace(0.31, 0.32, 150) \n",
    "M_vals4 = np.linspace(23.78, 23.84, 150)\n",
    "OM_grid4, M_grid4 = np.meshgrid(om_vals4, M_vals4)\n",
    "\n",
    "params4 = [(om, M) for om, M in zip(OM_grid4.ravel(), M_grid4.ravel())]\n",
    "\n",
    "results4 = Parallel(n_jobs=-1,  prefer=\"threads\")(\n",
    "    delayed(chi2total2)(M, om, -1, 0, htot) for om, M in params4\n",
    ")\n",
    "Z_total = np.array(results4).reshape(150, 150)\n",
    "\n",
    "levels_total = [\n",
    "    minlcdm2 + dchi(1, 2),\n",
    "    minlcdm2 + dchi(2, 2),\n",
    "    minlcdm2 + dchi(3, 2)\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour12 = ax.contour(OM_grid4, M_grid4, Z_total, levels=levels_total, colors='blue', linewidths=1.5)\n",
    "\n",
    "ax.plot([], [], color='blue', linewidth=1.5, label='Pantheon+BAO+CMB')\n",
    "\n",
    "ax.plot(omtot, Mtot, 'ko', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1baed3f3-6308-411b-9951-5bae4fbdf404",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Riess point \n",
    "def chi2riessh(h):\n",
    "    return (h - h0riess)**2/(0.0142)**2\n",
    "\n",
    "def chi2total3(Mcal, om, w0, wa, h):\n",
    "    return chi2total2(Mcal, om, w0, wa, h) + chi2riessh(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "678fba4f-dcdc-4df5-92ec-c422bdf5f532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1054.496172098455\n",
      "mtot1 = 23.814274266816184\n",
      "omtot1 = 0.3117500910515616\n",
      "wotot1 = -1.0\n",
      "watot1 = 0.0\n",
      "htot1 = 0.6757990892326802\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the ΛCDM model adding the Riess point\n",
    "import numpy as np\n",
    "from scipy.optimize import basinhopping\n",
    "def obj_lcdmriess(params):\n",
    "    Mcal, om, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total3(Mcal, om, -1.0, 0.0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_lcdmriess = [23.05, 0.3205, 0.6705]\n",
    "\n",
    "minimizer_kwargs_lcdmriess = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minlcdmriess = basinhopping(\n",
    "    obj_lcdmriess,\n",
    "    x0=initial_guess_lcdmriess,\n",
    "    minimizer_kwargs=minimizer_kwargs_lcdmriess,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "mtot1 = minlcdmriess.x[0]\n",
    "omtot1 = minlcdmriess.x[1]\n",
    "wotot1 = -1.0\n",
    "watot1 = 0.0\n",
    "htot1 = minlcdmriess.x[2]\n",
    "\n",
    "print(\"Minimum chi2:\", minlcdmriess.fun)\n",
    "print(\"mtot1 =\", mtot1)\n",
    "print(\"omtot1 =\", omtot1)\n",
    "print(\"wotot1 =\", wotot1)\n",
    "print(\"watot1 =\", watot1)\n",
    "print(\"htot1 =\", htot1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7421d3ad-4033-4051-af96-d26a1e2cbfec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from joblib import Parallel, delayed\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals5 = np.linspace(0.30, 0.34, 150) \n",
    "M_vals5 = np.linspace(23.70, 23.85, 150)\n",
    "OM_grid5, M_grid5 = np.meshgrid(om_vals5, M_vals5)\n",
    "\n",
    "params5 = [(om, M) for om, M in zip(OM_grid5.ravel(), M_grid5.ravel())]\n",
    "\n",
    "results5 = Parallel(n_jobs=-1,  prefer=\"threads\")(\n",
    "    delayed(chi2total3)(M, om, -1, 0, htot1) for om, M in params5\n",
    ")\n",
    "Z_lcdmriess = np.array(results5).reshape(150, 150)\n",
    "\n",
    "levels_lcdmriess = [\n",
    "    minlcdmriess.fun + dchi(1, 2),\n",
    "    minlcdmriess.fun + dchi(2, 2),\n",
    "    minlcdmriess.fun + dchi(3, 2)\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour13 = ax.contour(OM_grid5, M_grid5, Z_lcdmriess, levels=levels_lcdmriess, colors='blue', linewidths=1.5)\n",
    "\n",
    "ax.plot([], [], color='blue', linewidth=1.5, label='Pantheon+BAO+CMB+Riess')\n",
    "\n",
    "ax.plot(omtot1, mtot1, 'ko', markersize=8, label='Best fit', zorder=5)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cec4abef-2418-4f92-a60e-9ad5618d6ff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "\n",
    "contour11 = ax.contour(OM_grid3, M_grid3, Z_Panthstat3, levels=levels_Panthstat3, colors='red', linewidths=1.5)\n",
    "contour12 = ax.contour(OM_grid4, M_grid4, Z_total, levels=levels_total, colors='blue', linewidths=1.5)\n",
    "contour13 = ax.contour(OM_grid5, M_grid5, Z_lcdmriess, levels=levels_lcdmriess, colors='green', linewidths=1.5)\n",
    "\n",
    "ax.plot([], [], color='green', linewidth=1.5, label='Pantheon+BAO+CMB+Riess')\n",
    "\n",
    "ax.plot(omtot1, mtot1, 'ko', markersize=8,  zorder=5)\n",
    "ax.plot([], [], color='red', linewidth=1.5, label='Pantheon')\n",
    "ax.plot(omlcdm3, mlcdm3, 'ko', markersize=8, zorder=5)\n",
    "\n",
    "ax.plot([], [], color='blue', linewidth=1.5, label='Pantheon+BAO+CMB')\n",
    "\n",
    "ax.plot(omtot, Mtot, 'ko', markersize=8,  zorder=5)\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$')\n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b224a7e7-7c26-4809-8ff5-1f8ec90cbed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "\n",
    "\n",
    "contour12 = ax.contour(OM_grid4, M_grid4, Z_total, levels=levels_total, colors='blue', linewidths=1.5)\n",
    "contour13 = ax.contour(OM_grid5, M_grid5, Z_lcdmriess, levels=levels_lcdmriess, colors='green', linewidths=1.5)\n",
    "\n",
    "ax.plot([], [], color='green', linewidth=1.5, label='Pantheon+BAO+CMB+Riess')\n",
    "\n",
    "ax.plot(omtot1, mtot1, 'ko', markersize=8,  zorder=5)\n",
    "\n",
    "\n",
    "ax.plot([], [], color='blue', linewidth=1.5, label='Pantheon+BAO+CMB')\n",
    "\n",
    "ax.plot(omtot, Mtot, 'ko', markersize=8,  zorder=5)\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$')\n",
    "ax.set_ylabel(r\"$\\mathcal{M}$\")\n",
    "ax.set_xlim(0.308, 0.32)  \n",
    "ax.set_ylim(23.76, 23.85) \n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0133710f-4d72-4e9c-bff3-27bed7ed6bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fij = cij = errors = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fd38552e-7e5a-471a-9b0b-0d6a7d2abdc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best fit parameters for M, Ω_0m, h adding the Riess point are:\n",
      "M    = 23.81427 ± 0.00490\n",
      "Ω_0m = 0.3118 ± 0.0057\n",
      "h    = 0.6758 ± 0.0038\n",
      "\n",
      "Covariance Matrix (cij):\n",
      "[[ 2.39813762e-05  1.42703284e-05 -9.60225309e-06]\n",
      " [ 1.42703284e-05  3.20445049e-05 -2.15621840e-05]\n",
      " [-9.60225309e-06 -2.15621840e-05  1.46426570e-05]]\n",
      "\n",
      "Errors:\n",
      "[0.00489708 0.00566079 0.00382657]\n"
     ]
    }
   ],
   "source": [
    "def chi2_local_3D_riess(Mcal, om, h):\n",
    "    return chi2total2(Mcal, om, -1, 0, h)\n",
    "\n",
    "\n",
    "d_Mcal = 0.002\n",
    "d_om = 0.02\n",
    "d_h = 0.002\n",
    "\n",
    "\n",
    "Mcal0 = mtot1\n",
    "om0 = omtot1\n",
    "h0 = htot1\n",
    "\n",
    "\n",
    "f_0_0_0 = chi2_local_3D_riess(Mcal0, om0, h0)\n",
    "\n",
    "\n",
    "f_p_0_0 = chi2_local_3D_riess(Mcal0 + d_Mcal, om0, h0)\n",
    "f_m_0_0 = chi2_local_3D_riess(Mcal0 - d_Mcal, om0, h0)\n",
    "\n",
    "f_0_p_0 = chi2_local_3D_riess(Mcal0, om0 + d_om, h0)\n",
    "f_0_m_0 = chi2_local_3D_riess(Mcal0, om0 - d_om, h0)\n",
    "\n",
    "f_0_0_p = chi2_local_3D_riess(Mcal0, om0, h0 + d_h)\n",
    "f_0_0_m = chi2_local_3D_riess(Mcal0, om0, h0 - d_h)\n",
    "\n",
    "\n",
    "f_p_p_0 = chi2_local_3D_riess(Mcal0 + d_Mcal, om0 + d_om, h0)\n",
    "f_p_m_0 = chi2_local_3D_riess(Mcal0 + d_Mcal, om0 - d_om, h0)\n",
    "f_m_p_0 = chi2_local_3D_riess(Mcal0 - d_Mcal, om0 + d_om, h0)\n",
    "f_m_m_0 = chi2_local_3D_riess(Mcal0 - d_Mcal, om0 - d_om, h0)\n",
    "\n",
    "\n",
    "f_p_0_p = chi2_local_3D_riess(Mcal0 + d_Mcal, om0, h0 + d_h)\n",
    "f_p_0_m = chi2_local_3D_riess(Mcal0 + d_Mcal, om0, h0 - d_h)\n",
    "f_m_0_p = chi2_local_3D_riess(Mcal0 - d_Mcal, om0, h0 + d_h)\n",
    "f_m_0_m = chi2_local_3D_riess(Mcal0 - d_Mcal, om0, h0 - d_h)\n",
    "\n",
    "\n",
    "f_0_p_p = chi2_local_3D_riess(Mcal0, om0 + d_om, h0 + d_h)\n",
    "f_0_p_m = chi2_local_3D_riess(Mcal0, om0 + d_om, h0 - d_h)\n",
    "f_0_m_p = chi2_local_3D_riess(Mcal0, om0 - d_om, h0 + d_h)\n",
    "f_0_m_m = chi2_local_3D_riess(Mcal0, om0 - d_om, h0 - d_h)\n",
    "\n",
    "\n",
    "d2_dMcal2 = (f_p_0_0 - 2*f_0_0_0 + f_m_0_0) / (d_Mcal**2)\n",
    "d2_dom2 = (f_0_p_0 - 2*f_0_0_0 + f_0_m_0) / (d_om**2)\n",
    "d2_dh2 = (f_0_0_p - 2*f_0_0_0 + f_0_0_m) / (d_h**2)\n",
    "\n",
    "\n",
    "d2_dMcal_dom = (f_p_p_0 - f_p_m_0 - f_m_p_0 + f_m_m_0) / (4 * d_Mcal * d_om)\n",
    "d2_dMcal_dh = (f_p_0_p - f_p_0_m - f_m_0_p + f_m_0_m) / (4 * d_Mcal * d_h)\n",
    "d2_dom_dh = (f_0_p_p - f_0_p_m - f_0_m_p + f_0_m_m) / (4 * d_om * d_h)\n",
    "\n",
    "# Fisher matrix (3x3)\n",
    "Fij = 0.5 * np.array([\n",
    "    [d2_dMcal2,    d2_dMcal_dom, d2_dMcal_dh],\n",
    "    [d2_dMcal_dom, d2_dom2,      d2_dom_dh],\n",
    "    [d2_dMcal_dh,  d2_dom_dh,    d2_dh2]\n",
    "])\n",
    "\n",
    "\n",
    "cij = np.linalg.inv(Fij)\n",
    "\n",
    "errors = np.sqrt(np.diag(cij))\n",
    "\n",
    "\n",
    "print(\"The best fit parameters for M, Ω_0m, h adding the Riess point are:\")\n",
    "print(f\"M    = {Mcal0:.5f} ± {errors[0]:.5f}\")\n",
    "print(f\"Ω_0m = {om0:.4f} ± {errors[1]:.4f}\")\n",
    "print(f\"h    = {h0:.4f} ± {errors[2]:.4f}\\n\")\n",
    "\n",
    "print(\"Covariance Matrix (cij):\")\n",
    "print(cij)\n",
    "print(\"\\nErrors:\")\n",
    "print(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ad06c235-b535-446e-a83a-1a75ceb6c8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Αποθήκευση σε ξεχωριστό κελί\n",
    "np.savez('contour_data_wcdm_16.npz', OM_grid2=OM_grid2, M_grid2=M_grid2, Z_Panthstat2=Z_Panthstat2,\n",
    "                                  OM_grid3=OM_grid3, M_grid3=M_grid3, Z_Panthstat3=Z_Panthstat3,\n",
    "                                  OM_grid4=OM_grid4, M_grid4=M_grid4, Z_total=Z_total,\n",
    "                                  OM_grid5=OM_grid5, M_grid5=M_grid5, Z_lcdmriess=Z_lcdmriess\n",
    "                                 )"
   ]
  }
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