{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "981687d8",
   "metadata": {},
   "source": [
    "# Fit Pure Fluid Parameters\n",
    "\n",
    "This example shows how to use the new fitting class of version 0.21 of teqp \n",
    "to fit the model parameters for the SAFT-VR-Mie model based on fitting\n",
    "pseudo-experimental data obtained from a reference equation\n",
    "of state.\n",
    "\n",
    "In version 0.23, the parameter optimization objects were moved into their own submodule called paramopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bace5e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-15T23:05:33.734647Z",
     "iopub.status.busy": "2025-10-15T23:05:33.734528Z",
     "iopub.status.idle": "2025-10-15T23:05:36.976049Z",
     "shell.execute_reply": "2025-10-15T23:05:36.975334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.23.1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import teqp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import CoolProp.CoolProp as CP\n",
    "import scipy.optimize\n",
    "display(teqp.__version__)\n",
    "\n",
    "nonpolar = {\n",
    "    \"kind\": \"SAFT-VR-Mie\",\n",
    "    \"model\": {\n",
    "        \"coeffs\": [\n",
    "        {\n",
    "          \"name\": \"R32\",\n",
    "          \"BibTeXKey\": \"Bell\",\n",
    "          \"m\": 1.2476268271391935,\n",
    "          \"sigma_m\": 3.6080717234117107e-10,\n",
    "          \"epsilon_over_k\": 172.53065054286867,\n",
    "          \"lambda_r\": 14.634722358167384,\n",
    "          \"lambda_a\": 6\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "}\n",
    "template = {  \n",
    "  'kind': 'genericSAFT', \n",
    "  'model': {\n",
    "      'nonpolar': nonpolar\n",
    "  }\n",
    "}\n",
    "\n",
    "# NOTE: '/' inside field names MUST be escaped as ~1; see https://datatracker.ietf.org/doc/html/rfc6901#section-3\n",
    "pointers = [\n",
    "    '/model/nonpolar/model/coeffs/0/m',\n",
    "    '/model/nonpolar/model/coeffs/0/sigma_m',\n",
    "    '/model/nonpolar/model/coeffs/0/epsilon_over_k',\n",
    "    '/model/nonpolar/model/coeffs/0/lambda_r',\n",
    "    '/model/nonpolar/model/coeffs/0/lambda_a'\n",
    "]\n",
    "x0 = [1.5, 3e-10, 150, 19, 5.7]\n",
    "bounds = [(1,5), (2e-10,5e-10), (100,400), (12,50), (5.1, 6.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5463428",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-15T23:05:36.977547Z",
     "iopub.status.busy": "2025-10-15T23:05:36.977330Z",
     "iopub.status.idle": "2025-10-15T23:05:54.281374Z",
     "shell.execute_reply": "2025-10-15T23:05:54.280915Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 1: f(x)= 2640.2565791642164\n",
      "differential_evolution step 2: f(x)= 962.3756948786976\n",
      "differential_evolution step 3: f(x)= 962.3756948786976\n",
      "differential_evolution step 4: f(x)= 962.3756948786976\n",
      "differential_evolution step 5: f(x)= 317.8225079750391\n",
      "differential_evolution step 6: f(x)= 317.8225079750391\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 7: f(x)= 317.8225079750391\n",
      "differential_evolution step 8: f(x)= 317.8225079750391\n",
      "differential_evolution step 9: f(x)= 317.8225079750391\n",
      "differential_evolution step 10: f(x)= 265.3026660890965\n",
      "differential_evolution step 11: f(x)= 265.3026660890965\n",
      "differential_evolution step 12: f(x)= 265.3026660890965\n",
      "differential_evolution step 13: f(x)= 265.3026660890965\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 14: f(x)= 265.3026660890965\n",
      "differential_evolution step 15: f(x)= 265.3026660890965\n",
      "differential_evolution step 16: f(x)= 153.92786840948762\n",
      "differential_evolution step 17: f(x)= 153.92786840948762\n",
      "differential_evolution step 18: f(x)= 153.92786840948762\n",
      "differential_evolution step 19: f(x)= 153.92786840948762\n",
      "differential_evolution step 20: f(x)= 153.92786840948762\n",
      "differential_evolution step 21: f(x)= 153.92786840948762\n",
      "differential_evolution step 22: f(x)= 153.92786840948762\n",
      "differential_evolution step 23: f(x)= 153.92786840948762\n",
      "differential_evolution step 24: f(x)= 143.4728945770965\n",
      "differential_evolution step 25: f(x)= 115.17668169040033\n",
      "differential_evolution step 26: f(x)= 115.17668169040033\n",
      "differential_evolution step 27: f(x)= 115.17668169040033\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 28: f(x)= 115.17668169040033\n",
      "differential_evolution step 29: f(x)= 115.17668169040033\n",
      "differential_evolution step 30: f(x)= 115.1166132737544\n",
      "differential_evolution step 31: f(x)= 115.1166132737544\n",
      "differential_evolution step 32: f(x)= 98.88614368389673\n",
      "differential_evolution step 33: f(x)= 98.88614368389673\n",
      "differential_evolution step 34: f(x)= 98.88614368389673\n",
      "differential_evolution step 35: f(x)= 98.88614368389673\n",
      "differential_evolution step 36: f(x)= 74.45782154462813\n",
      "differential_evolution step 37: f(x)= 74.45782154462813\n",
      "differential_evolution step 38: f(x)= 73.0782298154598\n",
      "differential_evolution step 39: f(x)= 54.50814101608117\n",
      "differential_evolution step 40: f(x)= 28.236972307787255\n",
      "differential_evolution step 41: f(x)= 28.236972307787255\n",
      "differential_evolution step 42: f(x)= 28.236972307787255\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 43: f(x)= 28.236972307787255\n",
      "differential_evolution step 44: f(x)= 28.236972307787255\n",
      "differential_evolution step 45: f(x)= 28.236972307787255\n",
      "differential_evolution step 46: f(x)= 28.236972307787255\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 47: f(x)= 28.236972307787255\n",
      "differential_evolution step 48: f(x)= 28.236972307787255\n",
      "differential_evolution step 49: f(x)= 28.236972307787255\n",
      "differential_evolution step 50: f(x)= 27.807109540500456\n",
      "differential_evolution step 51: f(x)= 27.807109540500456\n",
      "differential_evolution step 52: f(x)= 27.807109540500456\n",
      "differential_evolution step 53: f(x)= 27.807109540500456\n",
      "differential_evolution step 54: f(x)= 27.807109540500456\n",
      "differential_evolution step 55: f(x)= 27.807109540500456\n",
      "differential_evolution step 56: f(x)= 27.807109540500456\n",
      "differential_evolution step 57: f(x)= 27.807109540500456\n",
      "differential_evolution step 58: f(x)= 27.807109540500456\n",
      "differential_evolution step 59: f(x)= 27.807109540500456\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 60: f(x)= 27.807109540500456\n",
      "differential_evolution step 61: f(x)= 23.498644693715875\n",
      "differential_evolution step 62: f(x)= 23.498644693715875\n",
      "differential_evolution step 63: f(x)= 23.498644693715875\n",
      "differential_evolution step 64: f(x)= 23.498644693715875\n",
      "differential_evolution step 65: f(x)= 23.498644693715875\n",
      "differential_evolution step 66: f(x)= 23.498644693715875\n",
      "differential_evolution step 67: f(x)= 23.498644693715875\n",
      "differential_evolution step 68: f(x)= 21.16019457623664\n",
      "differential_evolution step 69: f(x)= 21.16019457623664\n",
      "differential_evolution step 70: f(x)= 19.620489270537846\n",
      "differential_evolution step 71: f(x)= 19.286816629629598\n",
      "differential_evolution step 72: f(x)= 19.286816629629598\n",
      "differential_evolution step 73: f(x)= 19.286816629629598\n",
      "differential_evolution step 74: f(x)= 19.286816629629598\n",
      "differential_evolution step 75: f(x)= 19.286816629629598\n",
      "differential_evolution step 76: f(x)= 19.286816629629598\n",
      "differential_evolution step 77: f(x)= 19.286816629629598\n",
      "differential_evolution step 78: f(x)= 19.286816629629598\n",
      "differential_evolution step 79: f(x)= 19.286816629629598\n",
      "differential_evolution step 80: f(x)= 19.286816629629598\n",
      "differential_evolution step 81: f(x)= 19.055993887333994\n",
      "differential_evolution step 82: f(x)= 19.055993887333994\n",
      "differential_evolution step 83: f(x)= 19.055993887333994\n",
      "differential_evolution step 84: f(x)= 19.055993887333994\n",
      "differential_evolution step 85: f(x)= 19.055993887333994\n",
      "differential_evolution step 86: f(x)= 19.055993887333994\n",
      "differential_evolution step 87: f(x)= 19.055993887333994\n",
      "differential_evolution step 88: f(x)= 19.055993887333994\n",
      "differential_evolution step 89: f(x)= 19.055993887333994\n",
      "differential_evolution step 90: f(x)= 19.055993887333994\n",
      "differential_evolution step 91: f(x)= 19.055993887333994\n",
      "differential_evolution step 92: f(x)= 19.055993887333994\n",
      "differential_evolution step 93: f(x)= 19.055993887333994\n",
      "differential_evolution step 94: f(x)= 19.055993887333994\n",
      "differential_evolution step 95: f(x)= 19.055993887333994\n",
      "differential_evolution step 96: f(x)= 19.055993887333994\n",
      "differential_evolution step 97: f(x)= 18.987681202407188\n",
      "differential_evolution step 98: f(x)= 18.987681202407188\n",
      "differential_evolution step 99: f(x)= 18.355792749571748\n",
      "differential_evolution step 100: f(x)= 18.355792749571748\n",
      "differential_evolution step 101: f(x)= 18.355792749571748\n",
      "differential_evolution step 102: f(x)= 18.355792749571748\n",
      "differential_evolution step 103: f(x)= 18.355792749571748\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 104: f(x)= 17.732999573616617\n",
      "differential_evolution step 105: f(x)= 17.732999573616617\n",
      "differential_evolution step 106: f(x)= 17.732999573616617\n",
      "differential_evolution step 107: f(x)= 17.396950587217354\n",
      "differential_evolution step 108: f(x)= 17.396950587217354\n",
      "differential_evolution step 109: f(x)= 17.396950587217354\n",
      "differential_evolution step 110: f(x)= 17.396950587217354\n",
      "differential_evolution step 111: f(x)= 17.283336737521402\n",
      "differential_evolution step 112: f(x)= 17.047863176037033\n",
      "differential_evolution step 113: f(x)= 17.047863176037033\n",
      "differential_evolution step 114: f(x)= 17.047863176037033\n",
      "differential_evolution step 115: f(x)= 16.70189296244538\n",
      "differential_evolution step 116: f(x)= 16.70189296244538\n",
      "differential_evolution step 117: f(x)= 16.70189296244538\n",
      "differential_evolution step 118: f(x)= 16.679215439754024\n",
      "differential_evolution step 119: f(x)= 16.585527215317267\n",
      "differential_evolution step 120: f(x)= 16.585527215317267\n",
      "differential_evolution step 121: f(x)= 16.440880985703856\n",
      "differential_evolution step 122: f(x)= 16.440880985703856\n",
      "differential_evolution step 123: f(x)= 16.440880985703856\n",
      "differential_evolution step 124: f(x)= 16.440356282043112\n",
      "differential_evolution step 125: f(x)= 16.440356282043112\n",
      "differential_evolution step 126: f(x)= 16.440356282043112\n",
      "differential_evolution step 127: f(x)= 16.440356282043112\n",
      "differential_evolution step 128: f(x)= 16.440356282043112\n",
      "differential_evolution step 129: f(x)= 16.440356282043112\n",
      "differential_evolution step 130: f(x)= 16.3860525355149\n",
      "differential_evolution step 131: f(x)= 16.255845102047942\n",
      "differential_evolution step 132: f(x)= 16.255845102047942\n",
      "differential_evolution step 133: f(x)= 16.237798228248426\n",
      "differential_evolution step 134: f(x)= 16.04149141698661\n",
      "differential_evolution step 135: f(x)= 16.04149141698661\n",
      "differential_evolution step 136: f(x)= 16.04149141698661\n",
      "differential_evolution step 137: f(x)= 15.861265882923531\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 138: f(x)= 15.683342142054734\n",
      "differential_evolution step 139: f(x)= 15.631350161630701\n",
      "differential_evolution step 140: f(x)= 15.241571118774463\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 141: f(x)= 14.907975717145758\n",
      "differential_evolution step 142: f(x)= 14.907975717145758\n",
      "differential_evolution step 143: f(x)= 14.683725820963293\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 144: f(x)= 14.274009967668707\n",
      "differential_evolution step 145: f(x)= 14.274009967668707\n",
      "differential_evolution step 146: f(x)= 13.945513832128142\n",
      "differential_evolution step 147: f(x)= 13.059689802523561\n",
      "differential_evolution step 148: f(x)= 12.887628269047887\n",
      "differential_evolution step 149: f(x)= 12.887628269047887\n",
      "differential_evolution step 150: f(x)= 12.887628269047887\n",
      "differential_evolution step 151: f(x)= 12.887628269047887\n",
      "differential_evolution step 152: f(x)= 12.887628269047887\n",
      "differential_evolution step 153: f(x)= 12.887628269047887\n",
      "differential_evolution step 154: f(x)= 12.887628269047887\n",
      "differential_evolution step 155: f(x)= 12.887628269047887\n",
      "differential_evolution step 156: f(x)= 12.887628269047887\n",
      "differential_evolution step 157: f(x)= 12.887628269047887\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 158: f(x)= 12.78991787613433\n",
      "differential_evolution step 159: f(x)= 12.78991787613433\n",
      "differential_evolution step 160: f(x)= 12.78991787613433\n",
      "differential_evolution step 161: f(x)= 12.78991787613433\n",
      "differential_evolution step 162: f(x)= 12.78991787613433\n",
      "differential_evolution step 163: f(x)= 12.78991787613433\n",
      "differential_evolution step 164: f(x)= 12.78991787613433\n",
      "differential_evolution step 165: f(x)= 12.78991787613433\n",
      "differential_evolution step 166: f(x)= 12.78991787613433\n",
      "differential_evolution step 167: f(x)= 12.78991787613433\n",
      "differential_evolution step 168: f(x)= 12.78991787613433\n",
      "differential_evolution step 169: f(x)= 12.78991787613433\n",
      "differential_evolution step 170: f(x)= 12.78991787613433\n",
      "differential_evolution step 171: f(x)= 12.78991787613433\n",
      "Polishing solution with 'L-BFGS-B'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             message: Optimization terminated successfully.\n",
      "             success: True\n",
      "                 fun: 12.78991787613433\n",
      "                   x: [ 1.185e+00  3.519e-10  1.145e+02  1.205e+01\n",
      "                        5.303e+00]\n",
      "                 nit: 171\n",
      "                nfev: 7006\n",
      "          population: [[ 1.185e+00  3.519e-10 ...  1.205e+01  5.303e+00]\n",
      "                       [ 1.187e+00  3.515e-10 ...  1.201e+01  5.277e+00]\n",
      "                       ...\n",
      "                       [ 1.185e+00  3.518e-10 ...  1.206e+01  5.300e+00]\n",
      "                       [ 1.184e+00  3.519e-10 ...  1.204e+01  5.305e+00]]\n",
      " population_energies: [ 1.279e+01  1.291e+01 ...  1.279e+01  1.279e+01]\n",
      "194.8172792201637 9203.132811337495\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, '$(w_{fit}/w_{\\\\rm pexp}-1)\\\\times 100$'),\n",
       " Text(0.5, 0, '$T$ / K')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FLD = 'Methane'\n",
    "ppo = teqp.paramopt.PureParameterOptimizer(template, pointers)\n",
    "\n",
    "Ts = np.linspace(100, 170, 10)\n",
    "\n",
    "# Generate some artificial pseudo-experimental data from \n",
    "# the reference EOS as implemented in CoolProp\n",
    "for T in Ts:\n",
    "    pt = teqp.paramopt.SatRhoLPWPoint()\n",
    "    rhoL, rhoV = [CP.PropsSI('Dmolar','Q',Q,'T',T,FLD) for Q in [0,1]]\n",
    "    p = CP.PropsSI('P','Q',0,'T',T,FLD)\n",
    "    w = CP.PropsSI('speed_of_sound','Q',0,'T',T,FLD)\n",
    "    pt.T = T\n",
    "    # Measurands (here, pseudo-experimental values)\n",
    "    pt.p_exp = p\n",
    "    pt.rhoL_exp = rhoL\n",
    "    pt.w_exp = w\n",
    "    \n",
    "    # Additional parameters\n",
    "    pt.rhoL_guess = rhoL\n",
    "    pt.rhoV_guess = rhoV\n",
    "    pt.R = 8.31446261815324\n",
    "    pt.M = CP.PropsSI('molemass',FLD)\n",
    "    AS = CP.AbstractState('HEOS',FLD)\n",
    "    AS.update(CP.DmolarT_INPUTS, 1e-10, T)\n",
    "    pt.Ao20 = AS.tau()**2*AS.d2alpha0_dTau2() # -cv0/R\n",
    "    \n",
    "    # Weights (multiplied by 100 to put on a percentage basis)\n",
    "    pt.weight_p = 1*100\n",
    "    pt.weight_rho = 1*100\n",
    "    pt.weight_w = 0.25*100\n",
    "    ppo.add_one_contribution(pt)\n",
    "\n",
    "def cost_function(x):\n",
    "#     return ppo.cost_function_threaded(x, 10) # This is an option if you have lots of threads\n",
    "    return ppo.cost_function(x)\n",
    "\n",
    "r = scipy.optimize.differential_evolution(cost_function, bounds=bounds, disp=True, maxiter=10000, popsize=8)\n",
    "print(r)\n",
    "x = r.x\n",
    "model = teqp.make_model(ppo.build_JSON(x))\n",
    "\n",
    "Tc, rhoc = model.solve_pure_critical(400, 5000)\n",
    "print(Tc, rhoc)\n",
    "anc = teqp.build_ancillaries(model, Tc, rhoc, 0.5*Tc)\n",
    "\n",
    "def _get_SOS(model, T, rho, z, *, R, M, Ao20):\n",
    "    \"\"\" Helper function to calculate speed of sound \"\"\"\n",
    "    Ar0n = model.get_Ar02n(T, rho, z)\n",
    "    Ar01 = Ar0n[1]; Ar02 = Ar0n[2]\n",
    "    Ar11 = model.get_Ar11(T, rho, z)\n",
    "    Ar20 = model.get_Ar20(T, rho, z)\n",
    "\n",
    "    # M*w^2/(R*T) where w is the speed of sound\n",
    "    # from the definition w = sqrt(dp/drho|s)\n",
    "    Mw2RT = 1 + 2*Ar01 + Ar02 - (1 + Ar01 - Ar11)**2/(Ao20 + Ar20)\n",
    "    if Mw2RT < 0:\n",
    "        return 1e6\n",
    "    w = (Mw2RT*R*T/M)**0.5\n",
    "    return w\n",
    "\n",
    "TcREF = CP.PropsSI('Tcrit', FLD)\n",
    "Tsverify = np.linspace(100, TcREF*0.99999, 1000)\n",
    "RHOL, RHOV, PPP, WWW = [],[],[],[]\n",
    "z = np.array([1.0])\n",
    "for T in Tsverify:\n",
    "    rhoL, rhoV = model.pure_VLE_T(T, anc.rhoL(T)*1.01, anc.rhoV(T)*0.9, 10)\n",
    "    pL = rhoL*model.get_R(z)*T*(1+model.get_Ar01(T, rhoL, z))\n",
    "    pV = rhoV*model.get_R(z)*T*(1+model.get_Ar01(T, rhoV, z))\n",
    "    RHOL.append(rhoL)\n",
    "    RHOV.append(rhoV)\n",
    "    PPP.append(pL)\n",
    "    AS = CP.AbstractState('HEOS',FLD)\n",
    "    AS.update(CP.DmolarT_INPUTS, 1e-10, T)\n",
    "    Ao20 = AS.d2alpha0_dTau2()*AS.tau()**2\n",
    "    WWW.append(_get_SOS(model, T, rhoL, z, R=8.314462618,M=CP.PropsSI('molemass',FLD),Ao20=Ao20))\n",
    "    \n",
    "# Plot the T-rho for VLE\n",
    "line, = plt.plot(np.array(RHOL), Tsverify)\n",
    "plt.plot(np.array(RHOV), Tsverify, color=line.get_color())\n",
    "for Q in [0, 1]:\n",
    "    D = CP.PropsSI('Dmolar','T',Tsverify,'Q',Q,FLD)\n",
    "    plt.plot(D, Tsverify, lw=2, color='k')\n",
    "plt.gca().set(xlabel=r'$\\rho$ / mol/m$^3$', ylabel='$T$ / K')\n",
    "\n",
    "# And a deviation plot, much closer to the critical point\n",
    "# than the fitting region\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(10,5), sharex=True)\n",
    "\n",
    "ax1.plot(Tsverify, (np.array(PPP)/CP.PropsSI('P','T',Tsverify,'Q',0,FLD)-1)*100)\n",
    "ax1.set(ylabel=r'$(p_{fit}/p_{\\rm pexp}-1)\\times 100$')\n",
    "\n",
    "ax2.plot(Tsverify, (np.array(RHOL)/CP.PropsSI('Dmolar','T',Tsverify,'Q',0,FLD)-1)*100)\n",
    "ax2.set(ylabel=r'$(\\rho_{fit}/\\rho_{\\rm pexp}-1)\\times 100$')\n",
    "\n",
    "ax3.plot(Tsverify, (np.array(WWW)/CP.PropsSI('speed_of_sound','T',Tsverify,'Q',0,FLD)-1)*100)\n",
    "ax3.set(ylabel=r'$(w_{fit}/w_{\\rm pexp}-1)\\times 100$', xlabel='$T$ / K')"
   ]
  }
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