{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b307888-39ee-40e5-9950-f6eabba9ba3f",
   "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": 2,
   "id": "a068b19f-a17b-479a-9a3b-6632541f5a86",
   "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": 3,
   "id": "69f11549-198a-44de-b92f-8821c2331e63",
   "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": 4,
   "id": "a8403542-1fc5-452d-8204-2d209627a6d5",
   "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": 5,
   "id": "cf4fe375-50ce-449f-a684-81226c0179ec",
   "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": 6,
   "id": "e40a7457-7c85-4d12-ab43-906b35dfe5be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1060\n",
      "Test Mathematica point in Python: 1032.8152046282437\n",
      "1026.259730262297\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "81cb9b6d-ac47-4520-94ec-6676c3a7a5cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.790623469142\n",
      "Mlcdm = -19.428583685268908\n",
      "omlcdm = 0.3165893176224527\n",
      "wolcdm = -1\n",
      "walcdm = 0\n",
      "hlcdm = 0.6725515837865229\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": 8,
   "id": "5297ca97-bab8-430f-96e9-0661e601225e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#χ^2 for the wCDM model using all three likelihoods\n",
    "def obj_wwcdm(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_wwcdm = [-19.05, 0.3205, -1.0, 0.6705]\n",
    "\n",
    "minimizer_kwargs_wwcdm = {\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",
    "minwwcdm = basinhopping(\n",
    "    obj_wwcdm,\n",
    "    x0=initial_guess_wwcdm,\n",
    "    minimizer_kwargs=minimizer_kwargs_wwcdm,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mwwcdm = minwwcdm.x[0]\n",
    "omwwcdm = minwwcdm.x[1]\n",
    "wowwcdm = minwwcdm.x[2]\n",
    "wawwcdm = 0\n",
    "hwwcdm = minwwcdm.x[3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22316e51-72d2-442c-b087-cfd57df45c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.6923784195606\n",
      "Mwwcdm = -19.423397527158578\n",
      "omwwcdm = 0.31448391244009194\n",
      "wowwcdm = -1.010353662710515\n",
      "wawwcdm = 0\n",
      "hwwcdm = 0.6751045297764388\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum chi2:\", minwwcdm.fun)\n",
    "print(\"Mwwcdm =\", Mwwcdm)\n",
    "print(\"omwwcdm =\", omwwcdm)\n",
    "print(\"wowwcdm =\", wowwcdm)\n",
    "print(\"wawwcdm =\", wawwcdm)\n",
    "print(\"hwwcdm =\", hwwcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d001561-b995-49e2-9472-9822797cb0c6",
   "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": 7,
   "id": "e9583fb3-1a6c-4bb6-9e79-0e74403f2621",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Riess point \n",
    "def chi2riessh(h):\n",
    "    return (h - h0riess)**2/(0.0142)**2\n",
    "\n",
    "def chi2total5(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) + chi2riessh(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ea97f67-17c3-4f99-bb48-2d90614e7777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1047.8763051813364\n",
      "Mwcdm = -19.385712873484604\n",
      "omwcdm = 0.2983761994240545\n",
      "wowcdm = -1.074074768570873\n",
      "wawcdm = 0\n",
      "hwcdm = 0.6934669355181287\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the wCDM model using all three likelihoods and adding the Riess point\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 = chi2total5(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": 16,
   "id": "7117e4d9-c823-4fde-9a8f-13bcb3a9106e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1047.343501344547\n",
      "Mcplriess = -19.393001034158825\n",
      "omwcplriess = 0.29825785029821916\n",
      "wocplriess = -1.1250300172029628\n",
      "wacplriess = 0.20672207438886622\n",
      "hcplriess = 0.6931120500377138\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the wCDM model using all three likelihoods and adding the Riess point\n",
    "def obj_cplriess(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 w0 >= 3.0 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total5(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_cplriess = [-19.05, 0.3205, -1.0, 0.0, 0.6705]\n",
    "\n",
    "minimizer_kwargs_cplriess = {\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",
    "mincplriess = basinhopping(\n",
    "    obj_cplriess,\n",
    "    x0=initial_guess_cplriess,\n",
    "    minimizer_kwargs=minimizer_kwargs_cplriess,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mcplriess = mincplriess.x[0]\n",
    "omcplriess = mincplriess.x[1]\n",
    "wocplriess = mincplriess.x[2]\n",
    "wacplriess = mincplriess.x[3]\n",
    "hcplriess = mincplriess.x[4]\n",
    "\n",
    "print(\"Minimum chi2:\", mincplriess.fun)\n",
    "print(\"Mcplriess =\", Mcplriess)\n",
    "print(\"omwcplriess =\", omcplriess)\n",
    "print(\"wocplriess =\", wocplriess)\n",
    "print(\"wacplriess =\", wacplriess)\n",
    "print(\"hcplriess =\", hcplriess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93de3c36-410a-41a5-a9e5-21b275c10e1c",
   "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_wwcdm = 1032.6923784195612\n",
    "Mwwcdm = -19.423397513557912\n",
    "omwwcdm = 0.31448390671613147\n",
    "wowwcdm = -1.0103536947470049\n",
    "wawwcdm = 0\n",
    "hwwcdm = 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",
    "#με Riess\n",
    "chi2_wcdm = 1047.8763051813364\n",
    "Mwcdm = -19.385712873484604\n",
    "omwcdm = 0.2983761994240545\n",
    "wowcdm = -1.074074768570873\n",
    "wawcdm = 0\n",
    "hwcdm = 0.6934669355181287\n",
    "\n",
    "chi2_cplriess = 1047.343501344547\n",
    "Mcplriess = -19.393001034158825\n",
    "omcplriess = 0.29825785029821916\n",
    "wocplriess = -1.1250300172029628\n",
    "wacplriess = 0.20672207438886622\n",
    "hcplriess = 0.6931120500377138\n",
    "\n",
    "\n",
    "np.savez('cosmology_parameters_22.npz',\n",
    "         chi2_lcdm=chi2_lcdm, Mlcdm=Mlcdm, omlcdm=omlcdm, wolcdm=wolcdm, walcdm=walcdm, hlcdm=hlcdm,\n",
    "         chi2_wwcdm=chi2_wwcdm, Mwwcdm=Mwwcdm, omwwcdm=omwwcdm, wowwcdm=wowwcdm, wawwcdm=wawwcdm, hwwcdm=hwwcdm,\n",
    "         chi2_cpl=chi2_cpl, Mcpl=Mcpl, omcpl=omcpl, wocpl=wocpl, wacpl=wacpl, hcpl=hcpl,\n",
    "         chi2_wcdm=chi2_wcdm, Mwcdm=Mwcdm, omwcdm=omwcdm, wowcdm=wowcdm, wawcdm=wawcdm, hwcdm=hwcdm,\n",
    "         chi2_cplriess=chi2_cplriess, Mcplriess=Mcplriess, omcplriess=omcplriess, wocplriess=wocplriess, wacplriess=wacplriess, hcplriess=hcplriess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52f0f64a-9aad-4a1f-95fc-cfdf3da6da7b",
   "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": 39,
   "id": "c3d7440f-3ee2-49a2-b632-252049b35ed1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pathos\n",
      "  Downloading pathos-0.3.5-py3-none-any.whl.metadata (11 kB)\n",
      "Collecting ppft>=1.7.8 (from pathos)\n",
      "  Downloading ppft-1.7.8-py3-none-any.whl.metadata (12 kB)\n",
      "Collecting dill>=0.4.1 (from pathos)\n",
      "  Downloading dill-0.4.1-py3-none-any.whl.metadata (10 kB)\n",
      "Collecting pox>=0.3.7 (from pathos)\n",
      "  Downloading pox-0.3.7-py3-none-any.whl.metadata (8.0 kB)\n",
      "Collecting multiprocess>=0.70.19 (from pathos)\n",
      "  Downloading multiprocess-0.70.19-py312-none-any.whl.metadata (7.5 kB)\n",
      "Downloading pathos-0.3.5-py3-none-any.whl (82 kB)\n",
      "Downloading dill-0.4.1-py3-none-any.whl (120 kB)\n",
      "Downloading multiprocess-0.70.19-py312-none-any.whl (150 kB)\n",
      "Downloading pox-0.3.7-py3-none-any.whl (29 kB)\n",
      "Downloading ppft-1.7.8-py3-none-any.whl (56 kB)\n",
      "Installing collected packages: ppft, pox, dill, multiprocess, pathos\n",
      "  Attempting uninstall: dill\n",
      "    Found existing installation: dill 0.3.8\n",
      "    Uninstalling dill-0.3.8:\n",
      "      Successfully uninstalled dill-0.3.8\n",
      "Successfully installed dill-0.4.1 multiprocess-0.70.19 pathos-0.3.5 pox-0.3.7 ppft-1.7.8\n"
     ]
    }
   ],
   "source": [
    "!pip install pathos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12ef2032-675f-4631-8367-a7a7679f3368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Contours without Riess point-CPL\n",
    "#1)Contour om - Μ\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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_vals1 = np.linspace(0.29, 0.34, 150) \n",
    "M_vals1 = np.linspace(-19.4, -19.5, 150)\n",
    "OM_grid1, M_grid1 = np.meshgrid(om_vals1, M_vals1)\n",
    "\n",
    "params1 = [(om, M) for om, M in zip(OM_grid1.ravel(), M_grid1.ravel())]\n",
    "results1 = Parallel(n_jobs=-1, prefer=\"threads\" )(delayed(chi2total)(M, om, wocpl, wacpl, hcpl) for om, M in params1)\n",
    "Z_1 = np.array(results1).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl\n",
    "levels1 = [\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",
    "contour1 = ax.contour(OM_grid1, M_grid1, Z_1, levels=levels1, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omcpl, Mcpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$M$')\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": 11,
   "id": "5f74c576-a0f7-4f80-96e5-b000ee60bb74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2)Contour om - h\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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_vals2 = np.linspace(0.29, 0.34, 150) \n",
    "h_vals2 = np.linspace(0.63, 0.7, 150)\n",
    "OM_grid2, h_grid2 = np.meshgrid(om_vals2, h_vals2)\n",
    "\n",
    "params2 = [(om, h) for om, h in zip(OM_grid2.ravel(), h_grid2.ravel())]\n",
    "results2 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mcpl, om, wocpl, wacpl, h) for om, h in params2)\n",
    "Z_2 = np.array(results2).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl\n",
    "levels2 = [\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",
    "contour2 = ax.contour(OM_grid2, h_grid2, Z_2, levels=levels2, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omcpl, hcpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$h$')\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": 12,
   "id": "1e143ccb-0492-4cf5-8c23-cba024c27eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Αποθήκευση σε ξεχωριστό κελί\n",
    "np.savez('contour_data_cpl_22.npz', OM_grid1=OM_grid1, M_grid1=M_grid1, Z_1=Z_1,\n",
    "                                  OM_grid2=OM_grid2, h_grid2=h_grid2, Z_2=Z_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8f2a9bf-2924-4fdc-958b-e419e8ce9d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3)Contour M - h\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "M_vals3 = np.linspace(-19.4, -19.45, 150) \n",
    "h_vals3 = np.linspace(0.63, 0.70, 150)\n",
    "M_grid3, h_grid3 = np.meshgrid(M_vals3, h_vals3)\n",
    "\n",
    "params3 = [(M, h) for M, h in zip(M_grid3.ravel(), h_grid3.ravel())]\n",
    "results3 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(M, omcpl, wocpl, wacpl, h) for M, h in params3)\n",
    "Z_3 = np.array(results3).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels3 = [\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",
    "contour3 = ax.contour(M_grid3, h_grid3, Z_3, levels=levels3, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mcpl, hcpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$h$')\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": 14,
   "id": "59157676-401c-415b-9a8d-474709d749bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4)Contour om - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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(0.29, 0.34, 150) \n",
    "wo_vals4 = np.linspace(-1.20, -0.95, 150)\n",
    "OM_grid4, wo_grid4 = np.meshgrid(om_vals4, wo_vals4)\n",
    "\n",
    "params4 = [(om, wo) for om, wo in zip(OM_grid4.ravel(), wo_grid4.ravel())]\n",
    "results4 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mcpl, om, wo, wacpl, hcpl) for om, wo in params4)\n",
    "Z_4 = np.array(results4).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels4 = [\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",
    "contour4 = ax.contour(OM_grid4, wo_grid4, Z_4, levels=levels4, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omcpl, wocpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$w_{0}$')\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": 15,
   "id": "6466e49a-a905-4ce9-9613-3baea974461f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5)Contour Μ - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "M_vals5 = np.linspace(-19.4, -19.5, 150) \n",
    "wo_vals5 = np.linspace(-1.20, -0.95, 150)\n",
    "M_grid5, wo_grid5 = np.meshgrid(M_vals5, wo_vals5)\n",
    "\n",
    "params5 = [(M, wo) for M, wo in zip(M_grid5.ravel(), wo_grid5.ravel())]\n",
    "results5 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(M, omcpl, wo, wacpl, hcpl) for M, wo in params5)\n",
    "Z_5 = np.array(results5).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels5 = [\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",
    "contour5 = ax.contour(M_grid5, wo_grid5, Z_5, levels=levels5, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mcpl, wocpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$w_{0}$')\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": 16,
   "id": "c4912dd3-83f9-4964-aeef-abb2f79709f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#6)Contour h - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "h_vals6 = np.linspace(0.66, 0.69, 150) \n",
    "wo_vals6 = np.linspace(-1.20, -0.95, 150)\n",
    "h_grid6, wo_grid6 = np.meshgrid(h_vals6, wo_vals6)\n",
    "\n",
    "params6 = [(h, wo) for h, wo in zip(h_grid6.ravel(), wo_grid6.ravel())]\n",
    "results6 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mcpl, omcpl, wo, wacpl, h) for h, wo in params6)\n",
    "Z_6 = np.array(results6).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels6 = [\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",
    "contour6 = ax.contour(h_grid6, wo_grid6, Z_6, levels=levels6, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(hcpl, wocpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$h$') \n",
    "ax.set_ylabel(r'$w_{0}$')\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": 17,
   "id": "7db27fa0-d67c-4da0-9c5a-d0fd72dc5005",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('contour_data_cpl_22.npz', OM_grid1=OM_grid1, M_grid1=M_grid1, Z_1=Z_1,\n",
    "                                  OM_grid2=OM_grid2, h_grid2=h_grid2, Z_2=Z_2,\n",
    "                                  M_grid3=M_grid3, h_grid3=h_grid3, Z_3=Z_3,\n",
    "                                  OM_grid4=OM_grid4, wo_grid4=wo_grid4, Z_4=Z_4,\n",
    "                                  M_grid5=M_grid5, wo_grid5=wo_grid5, Z_5=Z_5,\n",
    "                                  h_grid6=h_grid6, wo_grid6=wo_grid6, Z_6=Z_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20663a06-dc07-4763-a56e-5f03dd364068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#7)Contour om - wa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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_vals7 = np.linspace(0.29, 0.34, 150) \n",
    "wa_vals7 = np.linspace(-0.2, 0.5, 150)\n",
    "OM_grid7, wa_grid7 = np.meshgrid(om_vals7, wa_vals7)\n",
    "\n",
    "params7 = [(om, wa) for om, wa in zip(OM_grid7.ravel(), wa_grid7.ravel())]\n",
    "results7 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mcpl, om, wocpl, wa, hcpl) for om, wa in params7)\n",
    "Z_7 = np.array(results7).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels7 = [\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",
    "contour7 = ax.contour(OM_grid7, wa_grid7, Z_7, levels=levels7, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omcpl, wacpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$w_{a}$')\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": 19,
   "id": "4a902ec7-dae6-4822-a775-453ab564d3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#8)Contour M - wa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "M_vals8 = np.linspace(-19.4, -19.5, 150) \n",
    "wa_vals8 = np.linspace(-0.2, 0.5, 150)\n",
    "M_grid8, wa_grid8 = np.meshgrid(M_vals8, wa_vals8)\n",
    "\n",
    "params8 = [(M, wa) for M, wa in zip(M_grid8.ravel(), wa_grid8.ravel())]\n",
    "results8 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(M, omcpl, wocpl, wa, hcpl) for M, wa in params8)\n",
    "Z_8 = np.array(results8).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels8 = [\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",
    "contour8 = ax.contour(M_grid8, wa_grid8, Z_8, levels=levels8, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mcpl, wacpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$w_{a}$')\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": 20,
   "id": "b247c23f-cb09-482c-9749-a2837e819bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#9)Contour h - wa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "h_vals9 = np.linspace(0.66, 0.69, 150) \n",
    "wa_vals9 = np.linspace(-0.2, 0.5, 150)\n",
    "h_grid9, wa_grid9 = np.meshgrid(h_vals9, wa_vals9)\n",
    "\n",
    "params9 = [(h, wa) for h, wa in zip(h_grid9.ravel(), wa_grid9.ravel())]\n",
    "results9 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mcpl, omcpl, wocpl, wa, h) for h, wa in params9)\n",
    "Z_9 = np.array(results9).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels9 = [\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",
    "contour9 = ax.contour(h_grid9, wa_grid9, Z_9, levels=levels9, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(hcpl, wacpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$h$') \n",
    "ax.set_ylabel(r'$w_{a}$')\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": 21,
   "id": "d10e24ea-d150-4351-a470-037e1e0f7bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10)Contour w0 - wa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "wo_vals10 = np.linspace(-1.2, -0.9, 150) \n",
    "wa_vals10 = np.linspace(-0.4, 0.7, 150)\n",
    "wo_grid10, wa_grid10 = np.meshgrid(wo_vals10, wa_vals10)\n",
    "\n",
    "params10 = [(wo, wa) for wo, wa in zip(wo_grid10.ravel(), wa_grid10.ravel())]\n",
    "results10 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mcpl, omcpl, wo, wa, hcpl) for wo, wa in params10)\n",
    "Z_10 = np.array(results10).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_cpl \n",
    "levels10 = [\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(wo_grid10, wa_grid10, Z_10, levels=levels10, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(wocpl, wacpl, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$w_{0}$') \n",
    "ax.set_ylabel(r'$w_{a}$')\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": 22,
   "id": "ddfc25b3-6ba6-4d90-bcd7-62bcb77cb270",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('contour_data_cpl_22a.npz', OM_grid1=OM_grid1, M_grid1=M_grid1, Z_1=Z_1,\n",
    "                                     OM_grid2=OM_grid2, h_grid2=h_grid2, Z_2=Z_2,\n",
    "                                     M_grid3=M_grid3, h_grid3=h_grid3, Z_3=Z_3,\n",
    "                                     OM_grid4=OM_grid4, wo_grid4=wo_grid4, Z_4=Z_4,\n",
    "                                     M_grid5=M_grid5, wo_grid5=wo_grid5, Z_5=Z_5,\n",
    "                                     h_grid6=h_grid6, wo_grid6=wo_grid6, Z_6=Z_6,\n",
    "                                     OM_grid7=OM_grid7, wa_grid7=wa_grid7, Z_7=Z_7,\n",
    "                                     M_grid8=M_grid8, wa_grid8=wa_grid8, Z_8=Z_8,\n",
    "                                     h_grid9=h_grid9, wa_grid9=wa_grid9, Z_9=Z_9,\n",
    "                                     wo_grid10=wo_grid10, wa_grid10=wa_grid10, Z_10=Z_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3abe26a0-4c7a-48bb-b098-f7dd0e050817",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 1. Φόρτωση του αρχείου\n",
    "data = np.load('contour_data_cpl_22a.npz')\n",
    "\n",
    "# 2. Επαναφορά των μεταβλητών για το κάθε contour ξεχωριστά\n",
    "\n",
    "# Contour 1\n",
    "OM_grid1 = data['OM_grid1']\n",
    "M_grid1 = data['M_grid1']\n",
    "Z_1 = data['Z_1']\n",
    "\n",
    "# Contour 2\n",
    "OM_grid2 = data['OM_grid2']\n",
    "h_grid2 = data['h_grid2']\n",
    "Z_2 = data['Z_2']\n",
    "\n",
    "# Contour 3\n",
    "M_grid3 = data['M_grid3']\n",
    "h_grid3 = data['h_grid3']\n",
    "Z_3 = data['Z_3']\n",
    "\n",
    "# Contour 4\n",
    "OM_grid4 = data['OM_grid4']\n",
    "wo_grid4 = data['wo_grid4']\n",
    "Z_4 = data['Z_4']\n",
    "\n",
    "# Contour 5\n",
    "M_grid5 = data['M_grid5']\n",
    "wo_grid5 = data['wo_grid5']\n",
    "Z_5 = data['Z_5']\n",
    "\n",
    "# Contour 6\n",
    "h_grid6 = data['h_grid6']\n",
    "wo_grid6 = data['wo_grid6']\n",
    "Z_6 = data['Z_6']\n",
    "\n",
    "# Contour 7\n",
    "OM_grid7 = data['OM_grid7']\n",
    "wa_grid7 = data['wa_grid7']\n",
    "Z_7 = data['Z_7']\n",
    "\n",
    "# Contour 8\n",
    "M_grid8 = data['M_grid8']\n",
    "wa_grid8 = data['wa_grid8']\n",
    "Z_8 = data['Z_8']\n",
    "\n",
    "# Contour 9\n",
    "h_grid9 = data['h_grid9']\n",
    "wa_grid9 = data['wa_grid9']\n",
    "Z_9 = data['Z_9']\n",
    "\n",
    "# Contour 10\n",
    "wo_grid10 = data['wo_grid10']\n",
    "wa_grid10 = data['wa_grid10']\n",
    "Z_10 = data['Z_10']\n",
    "\n",
    "data = np.load('contour_data_cpl_22b.npz')\n",
    "\n",
    "# Contour 11\n",
    "OM_grid11 = data['OM_grid11']\n",
    "M_grid11 = data['M_grid11']\n",
    "Z_11 = data['Z_11']\n",
    "\n",
    "# Contour 12\n",
    "OM_grid12 = data['OM_grid12']\n",
    "h_grid12 = data['h_grid12']\n",
    "Z_12 = data['Z_12']\n",
    "\n",
    "# Contour 13\n",
    "M_grid13 = data['M_grid13']\n",
    "h_grid13 = data['h_grid13']\n",
    "Z_13 = data['Z_13']\n",
    "\n",
    "# Contour 14\n",
    "OM_grid14 = data['OM_grid14']\n",
    "wo_grid14 = data['wo_grid14']\n",
    "Z_14 = data['Z_14']\n",
    "\n",
    "# Contour 15\n",
    "M_grid15 = data['M_grid15']\n",
    "wo_grid15 = data['wo_grid15']\n",
    "Z_15 = data['Z_15']\n",
    "\n",
    "# Contour 16\n",
    "h_grid16 = data['h_grid16']\n",
    "wo_grid16 = data['wo_grid16']\n",
    "Z_16 = data['Z_16']\n",
    "\n",
    "# Contour 17\n",
    "OM_grid17 = data['OM_grid17']\n",
    "wa_grid17 = data['wa_grid17']\n",
    "Z_17 = data['Z_17']\n",
    "\n",
    "# Contour 18\n",
    "M_grid18 = data['M_grid18']\n",
    "wa_grid18 = data['wa_grid18']\n",
    "Z_18 = data['Z_18']\n",
    "\n",
    "# Contour 19\n",
    "h_grid19 = data['h_grid19']\n",
    "wa_grid19 = data['wa_grid19']\n",
    "Z_19 = data['Z_19']\n",
    "\n",
    "# Contour 20\n",
    "wo_grid20 = data['wo_grid20']\n",
    "wa_grid20 = data['wa_grid20']\n",
    "Z_20 = data['Z_20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "324a4731-c79a-41bf-96a9-f5ca24a38ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "dataa = np.load('contour_data_cpl_22a.npz')\n",
    "datab = np.load('contour_data_cpl_22b.npz')\n",
    "\n",
    "OM_grid1 = dataa['OM_grid1']\n",
    "M_grid1 = dataa['M_grid1']\n",
    "Z_1 = dataa['Z_1']\n",
    "\n",
    "\n",
    "OM_grid11 = datab['OM_grid11']\n",
    "M_grid11 = datab['M_grid11']\n",
    "Z_11 = datab['Z_11']\n",
    "\n",
    "\n",
    "levels1 = [\n",
    "    chi2_cpl + dchi(1,2),  # 1 sigma \n",
    "    chi2_cpl + dchi(2,2),  # 2 sigma \n",
    "    chi2_cpl + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels11 = [\n",
    "    chi2_cplriess + dchi(1,2),  # 1 sigma \n",
    "    chi2_cplriess + dchi(2,2),  # 2 sigma \n",
    "    chi2_cplriess + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour1 = ax.contour(OM_grid1, M_grid1, Z_1, levels=levels1, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour11 = ax.contour(OM_grid11, M_grid11, Z_11, levels=levels11, colors='red', linewidths=0.7)\n",
    "\n",
    "ax.plot(omcpl, Mcpl, 'ko', markersize=8) \n",
    "ax.plot(omcplriess, Mcplriess, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'M')\n",
    "\n",
    "ax.axhline(y=-19.4, color='black', linestyle='--')\n",
    "\n",
    "\n",
    "ax.axhline(y=-19.244, color='black', linestyle='--')\n",
    "ax.text(0.95, -19.4, r'$M_B^{P18} = -19.4$', color='black', \n",
    "        transform=ax.get_yaxis_transform(), va='bottom', ha='right', fontsize=12)\n",
    "\n",
    "ax.text(0.95, -19.244, r'$M_B^{R20} = -19.244$', color='black', \n",
    "        transform=ax.get_yaxis_transform(), va='bottom', ha='right', fontsize=12)\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "ax.set_ylim(-19.45, -19.2)\n",
    "ax.set_xlim(0.29, 0.33)\n",
    "custom_lines = [Line2D([0], [0], color='red',  lw=2),\n",
    "                Line2D([0], [0], color='blue',  lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89cccd2d-b281-436a-acef-2737651bfa5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ενωση αρχειων\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import MaxNLocator, FormatStrFormatter\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "# Φόρτωση δεδομένων\n",
    "dataa = np.load('contour_data_cpl_22a.npz')\n",
    "datab = np.load('contour_data_cpl_22b.npz')\n",
    "# Δεδομένα για το 1ο γράφημα (Ω_0m vs h)\n",
    "OM_grid2 = dataa['OM_grid2']\n",
    "h_grid2 = dataa['h_grid2']\n",
    "Z_2 = dataa['Z_2']\n",
    "\n",
    "OM_grid12 = datab['OM_grid12']\n",
    "h_grid12 = datab['h_grid12']\n",
    "Z_12 = datab['Z_12']\n",
    "\n",
    "# Δεδομένα για το 2ο γράφημα (M vs h)\n",
    "M_grid3 = dataa['M_grid3']\n",
    "h_grid3 = dataa['h_grid3']\n",
    "Z_3 = dataa['Z_3']\n",
    "\n",
    "M_grid13 = datab['M_grid13']\n",
    "h_grid13 = datab['h_grid13']\n",
    "Z_13 = datab['Z_13']\n",
    "\n",
    "# Επίπεδα (Σημείωση: τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "levels_cpl= [\n",
    "    chi2_cpl + dchi(1,2),  # 1 sigma \n",
    "    chi2_cpl + dchi(2,2),  # 2 sigma \n",
    "    chi2_cpl + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels_cplriess = [\n",
    "    chi2_cplriess + dchi(1,2),  # 1 sigma \n",
    "    chi2_cplriess + dchi(2,2),  # 2 sigma \n",
    "    chi2_cplriess + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 2 υπογραφήματα\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs h) ---\n",
    "contour2 = ax1.contour(OM_grid2, h_grid2, Z_2, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour12 = ax1.contour(OM_grid12, h_grid12, Z_12, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax1.plot(omcpl, hcpl, 'ko', markersize=8) \n",
    "ax1.plot(omcplriess, hcplriess, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'h')\n",
    "\n",
    "# Περιορισμός σε 2 δεκαδικά ψηφία και αφαίρεση του τελευταίου αριθμού δεξιά\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs h) ---\n",
    "contour3 = ax2.contour(M_grid3, h_grid3, Z_3, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour13 = ax2.contour(M_grid13, h_grid13, Z_13, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax2.plot(Mcpl, hcpl, 'ko', markersize=8) \n",
    "ax2.plot(Mcplriess, hcplriess, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "\n",
    "# Περιορισμός σε 2 δεκαδικά ψηφία και αφαίρεση του τελευταίου αριθμού δεξιά\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα δύο γραφήματα\n",
    "for ax in (ax1, ax2):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "# Προσθήκη υπομνήματος (legend) στο πρώτο γράφημα\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "\n",
    "ax2.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=12)\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο γράφημα\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax2.set_xlim(-19.45, -19.35)\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f8d50531-8765-4850-92b8-588e9ebb8be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataa = np.load('contour_data_cpl_22a.npz')\n",
    "datab = np.load('contour_data_cpl_22b.npz')\n",
    "# Δεδομένα 1ου γραφήματος (Ω_0m vs w_0)\n",
    "OM_grid4 = dataa['OM_grid4']\n",
    "wo_grid4 = dataa['wo_grid4']\n",
    "Z_4 = dataa['Z_4']\n",
    "\n",
    "OM_grid14 = datab['OM_grid14']\n",
    "wo_grid14 = datab['wo_grid14']\n",
    "Z_14 = datab['Z_14']\n",
    "\n",
    "# Δεδομένα 2ου γραφήματος (M vs w_0)\n",
    "M_grid5 = dataa['M_grid5']\n",
    "wo_grid5 = dataa['wo_grid5']\n",
    "Z_5 = dataa['Z_5']\n",
    "\n",
    "M_grid15 = datab['M_grid15']\n",
    "wo_grid15 = datab['wo_grid15']\n",
    "Z_15 = datab['Z_15']\n",
    "\n",
    "# Δεδομένα 3ου γραφήματος (h vs w_0)\n",
    "h_grid6 = dataa['h_grid6']\n",
    "wo_grid6 = dataa['wo_grid6']\n",
    "Z_6 = dataa['Z_6']\n",
    "\n",
    "h_grid16 = datab['h_grid16']\n",
    "wo_grid16 = datab['wo_grid16']\n",
    "Z_16 = datab['Z_16']\n",
    "\n",
    "# Επίπεδα (τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "levels_cpl = [\n",
    "    chi2_cpl + dchi(1,2),  # 1 sigma \n",
    "    chi2_cpl + dchi(2,2),  # 2 sigma \n",
    "    chi2_cpl + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels_cplriess = [\n",
    "    chi2_cplriess + dchi(1,2),  # 1 sigma \n",
    "    chi2_cplriess + dchi(2,2),  # 2 sigma \n",
    "    chi2_cplriess + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 3 υπογραφήματα\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(16, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs w_0) ---\n",
    "contour4 = ax1.contour(OM_grid4, wo_grid4, Z_4, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour14 = ax1.contour(OM_grid14, wo_grid14, Z_14, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax1.plot(omcpl, wocpl, 'ko', markersize=8) \n",
    "ax1.plot(omcplriess, wocplriess, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs w_0) ---\n",
    "contour5 = ax2.contour(M_grid5, wo_grid5, Z_5, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour15 = ax2.contour(M_grid15, wo_grid15, Z_15, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax2.plot(Mcpl, wocpl, 'ko', markersize=8) \n",
    "ax2.plot(Mcplriess, wocplriess, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 3ο Γράφημα (h vs w_0) ---\n",
    "contour6 = ax3.contour(h_grid6, wo_grid6, Z_6, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour16 = ax3.contour(h_grid16, wo_grid16, Z_16, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax3.plot(hcpl, wocpl, 'ko', markersize=8) \n",
    "ax3.plot(hcplriess, wocplriess, 'ko', markersize=10) \n",
    "\n",
    "ax3.set_xlabel(r'h') \n",
    "\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax3.xaxis.set_major_locator(MaxNLocator(nbins=5)) # Εδώ δεν χρειάζεται prune γιατί είναι το τελευταίο δεξιά\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα τρία γραφήματα\n",
    "for ax in (ax1, ax2, ax3):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "# Προσθήκη υπομνήματος (legend) στο πρώτο γράφημα\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax3.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=12)\n",
    "\n",
    "ax.set_ylim(-1.2, -1)\n",
    "ax2.set_xlim(-19.45, -19.35)\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο και τρίτο γράφημα\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax3.tick_params(labelleft=False)\n",
    "\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d9f00647-0d85-42bf-b165-e00d37210809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataa = np.load('contour_data_cpl_22a.npz')\n",
    "datab = np.load('contour_data_cpl_22b.npz')\n",
    "# Δεδομένα 1ου γραφήματος (Ω_0m vs w_a)\n",
    "OM_grid7 = dataa['OM_grid7']\n",
    "wa_grid7 = dataa['wa_grid7']\n",
    "Z_7 = dataa['Z_7']\n",
    "\n",
    "OM_grid17 = datab['OM_grid17']\n",
    "wa_grid17 = datab['wa_grid17']\n",
    "Z_17 = datab['Z_17']\n",
    "\n",
    "# Δεδομένα 2ου γραφήματος (M vs w_a)\n",
    "M_grid8 = dataa['M_grid8']\n",
    "wa_grid8 = dataa['wa_grid8']\n",
    "Z_8 = dataa['Z_8']\n",
    "\n",
    "M_grid18 = datab['M_grid18']\n",
    "wa_grid18 = datab['wa_grid18']\n",
    "Z_18 = datab['Z_18']\n",
    "\n",
    "# Δεδομένα 3ου γραφήματος (h vs w_a)\n",
    "h_grid9 = dataa['h_grid9']\n",
    "wa_grid9 = dataa['wa_grid9']\n",
    "Z_9 = dataa['Z_9']\n",
    "\n",
    "h_grid19 = datab['h_grid19']\n",
    "wa_grid19 = datab['wa_grid19']\n",
    "Z_19 = datab['Z_19']\n",
    "\n",
    "# Δεδομένα 4ου γραφήματος (h vs w_0)\n",
    "wo_grid10 = dataa['wo_grid10']\n",
    "wa_grid10 = dataa['wa_grid10']\n",
    "Z_10 = dataa['Z_10']\n",
    "\n",
    "wo_grid20 = datab['wo_grid20']\n",
    "wa_grid20 = datab['wa_grid20']\n",
    "Z_20 = datab['Z_20']\n",
    "\n",
    "# Επίπεδα (τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "levels_cpl = [\n",
    "    chi2_cpl + dchi(1,2),  # 1 sigma \n",
    "    chi2_cpl + dchi(2,2),  # 2 sigma \n",
    "    chi2_cpl + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels_cplriess = [\n",
    "    chi2_cplriess + dchi(1,2),  # 1 sigma \n",
    "    chi2_cplriess + dchi(2,2),  # 2 sigma \n",
    "    chi2_cplriess + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 3 υπογραφήματα\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1, ncols=4, figsize=(16, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs w_a) ---\n",
    "contour7 = ax1.contour(OM_grid7, wa_grid7, Z_7, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour17 = ax1.contour(OM_grid17, wa_grid17, Z_17, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax1.plot(omcpl, wacpl, 'ko', markersize=8) \n",
    "ax1.plot(omcplriess, wacplriess, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'$w_{a}$')\n",
    "\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=4))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs w_a) ---\n",
    "contour8 = ax2.contour(M_grid8, wa_grid8, Z_8, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour18 = ax2.contour(M_grid18, wa_grid18, Z_18, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax2.plot(Mcpl, wacpl, 'ko', markersize=8) \n",
    "ax2.plot(Mcplriess, wacplriess, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='both', nbins=4))\n",
    "\n",
    "# --- 3ο Γράφημα (h vs w_a) ---\n",
    "contour9 = ax3.contour(h_grid9, wa_grid9, Z_9, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour19 = ax3.contour(h_grid19, wa_grid19, Z_19, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax3.plot(hcpl, wacpl, 'ko', markersize=8) \n",
    "ax3.plot(hcplriess, wacplriess, 'ko', markersize=10) \n",
    "\n",
    "ax3.set_xlabel(r'h') \n",
    "\n",
    "\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax3.xaxis.set_major_locator(MaxNLocator(prune='both', nbins=4))\n",
    "\n",
    "# --- 4ο Γράφημα (w_0 vs w_a) ---\n",
    "contour10 = ax4.contour(wo_grid10, wa_grid10, Z_10, levels=levels_cpl, colors='blue', linewidths=0.7)\n",
    "contour20 = ax4.contour(wo_grid20, wa_grid20, Z_20, levels=levels_cplriess, colors='red', linewidths=0.7)\n",
    "\n",
    "ax4.plot(wocpl, wacpl, 'ko', markersize=8) \n",
    "ax4.plot(wocplriess, wacplriess, 'ko', markersize=10) \n",
    "\n",
    "ax4.set_xlabel(r'$w_{0}$') \n",
    "\n",
    "\n",
    "ax4.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax4.xaxis.set_major_locator(MaxNLocator(prune='lower', nbins=4))\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα τρία γραφήματα\n",
    "for ax in (ax1, ax2, ax3, ax4):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "# Προσθήκη υπομνήματος (legend) στο πρώτο γράφημα\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax4.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=12)\n",
    "ax.set_ylim(-0.8, 0.8)\n",
    "ax2.set_xlim(-19.45, -19.35)\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο και τρίτο γράφημα\n",
    "ax2.tick_params(left=False)\n",
    "ax3.tick_params(left=False)\n",
    "ax4.tick_params(left=False)\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1321df2a-8617-4acc-ac93-d26bf3eb272a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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