Chemfit_from_research.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "incorporated-japan",
   "metadata": {},
   "source": [
    "A lot of the code here will be hard to follow, but it serves just to illustrate the perfromance of the algorithm for the\n",
    "research in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "choice-garage",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import integrate\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import minimize, Parameters, Parameter, report_fit\n",
    "from sklearn.metrics import r2_score\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "binding-membership",
   "metadata": {},
   "source": [
    "Again lots of functions that will be explained later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "composed-alaska",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_coord(data, init):\n",
    "\n",
    "    rate_list_E = []\n",
    "    rate_list_A1 = []\n",
    "    rate_list_A2 = []\n",
    "    rate_list_tE = []\n",
    "    for i in range(len(data[0])-1):\n",
    "        rate_list_E.append((data[0][i+1]-data[0][i])/(timespan[i+1]-timespan[i]))\n",
    "        rate_list_A1.append((data[1][i+1]-data[1][i])/(timespan[i+1]-timespan[i]))\n",
    "        rate_list_A2.append((data[2][i+1]-data[2][i])/(timespan[i+1]-timespan[i]))\n",
    "        rate_list_tE.append((data[3][i+1]-data[3][i])/(timespan[i+1]-timespan[i]))\n",
    "    rate_lists = [rate_list_E,rate_list_A1,rate_list_A2,rate_list_tE]\n",
    "\n",
    "    return rate_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "answering-belfast",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Needed parameters\n",
    "T = [323,363,393]\n",
    "P = 50\n",
    "KH= 1282\n",
    "CH2 = P/KH\n",
    "R= 8.314\n",
    "timespan = [0, 1, 3, 5, 7, 9, 11, 13, 15, 19, 23, 27, 31, 40, 50, 60, 90, 120, 150, 180, 240, 300, 360, 440, 530, 620, 810, 900, 955, 1246] #the time points measured at, can be extended or cut to see effects of data richness\n",
    "time = timespan[-1]\n",
    "init = [[1,0,0,0,1,0,0,0], [4,0,0,0,2,0,0,0], [8,0,0,0,1.5,0,0,0]] #initial conditions, you can add or remove one later and see how the data changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "genetic-aruba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Eact(A, ktrue):\n",
    "    T=363\n",
    "    E = (np.log(ktrue/A))*R*T\n",
    "    return E"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intended-platinum",
   "metadata": {},
   "source": [
    "In the cell below you can later change the noise values to maybe 0.1, and see how the data changes later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "congressional-reverse",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_noise():\n",
    "    static_noise = np.random.normal(0,0.01,30)\n",
    "    variable_noise = np.random.normal(0,0.05,30)\n",
    "    return static_noise, variable_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "spectacular-french",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_system(diff,pre_consts, E_acts,T):\n",
    "    rate_consts = pre_consts*np.exp(E_acts/(R*T))\n",
    "    print(rate_consts)\n",
    "    sol_1 = integrate.solve_ivp(diff, [0,time], init[0], t_eval = timespan, args=[rate_consts])\n",
    "    sol_2 = integrate.solve_ivp(diff, [0,time], init[1], t_eval = timespan, args=[rate_consts])\n",
    "    sol_3 = integrate.solve_ivp(diff, [0,time], init[2], t_eval = timespan, args=[rate_consts]) \n",
    "    \n",
    "    noisy_data_list_1 = []\n",
    "    noisy_data_list_2 = []\n",
    "    noisy_data_list_3 = []\n",
    "    i = 0\n",
    "    for res in range(len(sol_1.y)):\n",
    "        static_noise,variable_noise = get_noise()\n",
    "        noisy_data_1 = sol_1.y[i] + static_noise + np.multiply(sol_1.y[i],variable_noise)\n",
    "        noisy_data_2 = sol_2.y[i] + static_noise + np.multiply(sol_2.y[i],variable_noise)\n",
    "        noisy_data_3 = sol_3.y[i] + static_noise + np.multiply(sol_3.y[i],variable_noise)\n",
    "        noisy_data_list_1.append(noisy_data_1)\n",
    "        noisy_data_list_2.append(noisy_data_2)\n",
    "        noisy_data_list_3.append(noisy_data_3)\n",
    "        i += 1\n",
    "    \n",
    "    rate_lists_1  = change_coord(noisy_data_list_1,init[0])\n",
    "    rate_lists_2  = change_coord(noisy_data_list_2,init[1])\n",
    "    rate_lists_3  = change_coord(noisy_data_list_3,init[2])\n",
    "    \n",
    "    return [sol_1.y,sol_2.y,sol_3.y],[noisy_data_list_1,noisy_data_list_2,noisy_data_list_3],[rate_lists_1,rate_lists_2,rate_lists_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "sunset-hollow",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_r2(real,simul):\n",
    "    return r2_score(real[0], simul[0]) + (r2_score(real[1], simul[1])) + (r2_score(real[2], simul[2])) + (r2_score(real[3], simul[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "explicit-iceland",
   "metadata": {},
   "outputs": [],
   "source": [
    "def true_r2(diff,pre_consts,E_acts,T, noisy_data, rate_data):\n",
    "    #test R squared comparison for random values\n",
    "    rate_consts = pre_consts*np.exp(E_acts/(R*T))\n",
    "    k1,k2f,k2b,k3,k4,k5 = rate_consts\n",
    "    ind = {'k1': k1, 'k2f': k2f, 'k2b': k2b, 'k3': k3, 'k4': k4, 'k5': k5}\n",
    "    best_1 = integrate.solve_ivp(diff, [0,time], init[0], args = (ind,), t_eval = timespan)\n",
    "    best_2 = integrate.solve_ivp(diff, [0,time], init[1], args = (ind,), t_eval = timespan)\n",
    "    best_3 = integrate.solve_ivp(diff, [0,time], init[2], args = (ind,), t_eval = timespan)\n",
    "    changed_rates_1 = change_coord(best_1.y,init[0])\n",
    "    changed_rates_2 = change_coord(best_2.y,init[1])\n",
    "    changed_rates_3 = change_coord(best_3.y,init[2])\n",
    "    score = (get_r2(noisy_data[0], best_1.y)+(get_r2(noisy_data[1],best_2.y))+(get_r2(noisy_data[2],best_3.y)))\n",
    "    score += (get_r2(rate_data[0],changed_rates_1)+get_r2(rate_data[1],changed_rates_2)+get_r2(rate_data[2],changed_rates_3))\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "studied-romantic",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_sim(diff, noisy_data, rate_data, max_score):\n",
    "    def residual(paras):\n",
    "\n",
    "        \"\"\"\n",
    "        compute the residual between actual data and fitted data\n",
    "        \"\"\"\n",
    "        subject = {'k1': paras['k1'].value, 'k2f': paras['k2f'].value, 'k2b': paras['k2b'].value, 'k3': paras['k3'].value, 'k4': paras['k4'].value, 'k5': paras['k5'].value}\n",
    "\n",
    "        sol_1 = integrate.solve_ivp(diff, [0,time], init[0], args = (subject,), t_eval = timespan)\n",
    "        sol_2 = integrate.solve_ivp(diff, [0,time], init[1], args = (subject,), t_eval = timespan)\n",
    "        sol_3 = integrate.solve_ivp(diff, [0,time], init[2], args = (subject,), t_eval = timespan)\n",
    "\n",
    "        changed_rates_1 = change_coord(sol_1.y,init[0])\n",
    "        changed_rates_2 = change_coord(sol_2.y,init[1])\n",
    "        changed_rates_3 = change_coord(sol_3.y,init[2])\n",
    "\n",
    "        score = (get_r2(noisy_data[0], sol_1.y)+(get_r2(noisy_data[1],sol_2.y))+(get_r2(noisy_data[2],sol_3.y)))\n",
    "        score += (get_r2(rate_data[0],changed_rates_1)+get_r2(rate_data[1],changed_rates_2)+get_r2(rate_data[2],changed_rates_3))\n",
    "        print(score)\n",
    "        return max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score\n",
    "\n",
    "\n",
    "\n",
    "    # measured data (timespan, and noisy data list [0])\n",
    "    plt.figure()\n",
    "    \n",
    "\n",
    "    # set parameters including bounds; you can also fix parameters (use vary=False)\n",
    "    params = Parameters()\n",
    "    params.add('k1', value=0.01, min=1e-10, max=1)\n",
    "    params.add('k2f', value=0.01, min=1e-10, max=1)\n",
    "    params.add('k2b', value=0.01, min=1e-10, max=1)\n",
    "    params.add('k3', value=0.01, min=1e-10, max=1)\n",
    "    params.add('k4', value=0.01, min=1e-10, max=1)\n",
    "    params.add('k5', value=0.01, min=1e-10, max=1)\n",
    "\n",
    "\n",
    "    # fit model\n",
    "    results = minimize(residual, params, method='leastsq', max_nfev = 1000)  # leastsq nelder\n",
    "    # check results of the fit\n",
    "    fin_sub = {'k1': results.params['k1'].value, 'k2f': results.params['k2f'].value, 'k2b': results.params['k2b'].value, 'k3': results.params['k3'].value, 'k4': results.params['k4'].value, 'k5': results.params['k5'].value}\n",
    "    data_fitted = integrate.solve_ivp(diff, [0,time], init[0], args = (fin_sub,), t_eval = timespan)\n",
    "    print(residual(results.params)[0])\n",
    "\n",
    "    # plot fitted data\n",
    "    plt.figure()\n",
    "    plt.scatter(timespan, noisy_data[0][0], marker='o', label='measured data CE')\n",
    "    plt.scatter(timespan, noisy_data[0][1], marker='o', label='measured data CA1')\n",
    "    plt.scatter(timespan, noisy_data[0][2], marker='o', label='measured data CA2')\n",
    "    plt.scatter(timespan, noisy_data[0][3], marker='o', label='measured data CtE')\n",
    "    plt.plot(timespan, data_fitted.y[0], '-', linewidth=2, label='fitted data CE')\n",
    "    plt.plot(timespan, data_fitted.y[1], '-', linewidth=2, label='fitted data CA1')\n",
    "    plt.plot(timespan, data_fitted.y[2], '-', linewidth=2, label='fitted data CA2')\n",
    "    plt.plot(timespan, data_fitted.y[3], '-', linewidth=2, label='fitted data CtE')\n",
    "    plt.legend()\n",
    "    # display fitted statistics\n",
    "    report_fit(results)\n",
    "    \n",
    "    return results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "french-commodity",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lines(T,constants):\n",
    "    A1, A2f, A2b, A3, A4, A5 = constants['A1'],constants['A2f'],constants['A2b'],constants['A3'],constants['A4'],constants['A5']\n",
    "    E1, E2f, E2b, E3, E4, E5 = constants['E1'],constants['E2f'],constants['E2b'],constants['E3'],constants['E4'],constants['E5']\n",
    "    tk1 = A1*np.exp(-E1/(R*T))\n",
    "    tk2f = A2f*np.exp(-E2f/(R*T))\n",
    "    tk2b = A2b*np.exp(-E2b/(R*T))\n",
    "    tk3 = A3*np.exp(-E3/(R*T))\n",
    "    tk4 = A4*np.exp(-E4/(R*T))\n",
    "    tk5 = A5*np.exp(-E5/(R*T))\n",
    "    ind_const= {'k1': tk1, 'k2f': tk2f,'k2b': tk2b, 'k3': tk3, 'k4': tk4, 'k5': tk5}\n",
    "    lines_1 = integrate.solve_ivp(diff, [0,time], init[0], args = (ind_const,), t_eval = timespan)\n",
    "    lines_2 = integrate.solve_ivp(diff, [0,time], init[1], args = (ind_const,), t_eval = timespan)\n",
    "    lines_3 = integrate.solve_ivp(diff, [0,time], init[2], args = (ind_const,), t_eval = timespan)\n",
    "    return lines_1.y, lines_2.y, lines_3.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "representative-snowboard",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_sim_full(diff, noisy_data, rate_data, max_score, temperatures,guesses):\n",
    "    def residual(paras):\n",
    "\n",
    "        \"\"\"\n",
    "        compute the residual between actual data and fitted data\n",
    "        \"\"\"\n",
    "        subject = {'A1': paras['A1'].value, 'A2f': paras['A2f'].value, 'A2b': paras['A2b'].value, 'A3': paras['A3'].value, 'A4': paras['A4'].value, 'A5': paras['A5'].value, 'E1': paras['E1'].value, 'E2f': paras['E2f'].value, 'E2b': paras['E2b'].value,  'E3': paras['E3'].value, 'E4': paras['E4'].value, 'E5': paras['E5'].value}\n",
    "        \n",
    "        A1, A2f, A2b, A3, A4, A5 = subject['A1'],subject['A2f'],subject['A2b'],subject['A3'],subject['A4'],subject['A5']\n",
    "        E1, E2f, E2b, E3, E4, E5 = subject['E1'],subject['E2f'],subject['E2b'],subject['E3'],subject['E4'],subject['E5']\n",
    "        \n",
    "        tk1 = A1*np.exp(-E1/(R*T[-1]))\n",
    "        tk2f = A2f*np.exp(-E2f/(R*T[-1]))\n",
    "        tk2b = A2b*np.exp(-E2b/(R*T[-1]))\n",
    "        tk3 = A3*np.exp(-E3/(R*T[-1]))\n",
    "        tk4 = A4*np.exp(-E4/(R*T[-1]))\n",
    "        tk5 = A5*np.exp(-E5/(R*T[-1]))\n",
    "        tks = [tk1,tk2f,tk2b,tk3,tk4,tk5]\n",
    "        \n",
    "        for tk in tks:\n",
    "            if tk>1:\n",
    "                return max_score,max_score,max_score,max_score,max_score,max_score,max_score,max_score,max_score,max_score,max_score,max_score,\n",
    "        \n",
    "        lines_list = []\n",
    "        rates_list = []\n",
    "\n",
    "        for temp in temperatures:\n",
    "            rates_int = []\n",
    "            lines = get_lines(temp,subject)\n",
    "            lines_list.append(lines)\n",
    "            for i in range(len(lines)):\n",
    "                rates_int.append(change_coord(lines[i],init[i]))\n",
    "            rates_list.append(rates_int)\n",
    "\n",
    "\n",
    "        score = (get_r2(noisy_data[0][0], lines_list[0][0])+(get_r2(noisy_data[0][1],lines_list[0][1]))+(get_r2(noisy_data[0][2],lines_list[0][2])))\n",
    "        score += (get_r2(noisy_data[1][0], lines_list[1][0])+(get_r2(noisy_data[1][1],lines_list[1][1]))+(get_r2(noisy_data[1][2],lines_list[1][2])))\n",
    "        score += (get_r2(noisy_data[2][0], lines_list[2][0])+(get_r2(noisy_data[2][1],lines_list[2][1]))+(get_r2(noisy_data[2][2],lines_list[2][2])))\n",
    "        score += (get_r2(rate_data[0][0],rates_list[0][0])+(get_r2(rate_data[0][1],rates_list[0][1]))+(get_r2(rate_data[0][2],rates_list[0][2])))\n",
    "        score += (get_r2(rate_data[1][0],rates_list[1][0])+(get_r2(rate_data[1][1],rates_list[1][1]))+(get_r2(rate_data[1][2],rates_list[1][2])))\n",
    "        score += (get_r2(rate_data[2][0],rates_list[2][0])+(get_r2(rate_data[2][1],rates_list[2][1]))+(get_r2(rate_data[2][2],rates_list[2][2])))                                                                                            \n",
    "        print(score)\n",
    "        return max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score,max_score-score\n",
    "\n",
    "\n",
    "\n",
    "    # measured data (timespan, and noisy data list [0])\n",
    "\n",
    "    plt.figure(0)    \n",
    "    plt.scatter(timespan, noisy_data[0][0][0], marker='o', label='measured data CE')\n",
    "    plt.scatter(timespan, noisy_data[0][0][1], marker='o', label='measured data CA1')\n",
    "    plt.scatter(timespan, noisy_data[0][0][2], marker='o', label='measured data CA2')\n",
    "    plt.scatter(timespan, noisy_data[0][0][3], marker='o', label='measured data CtE')\n",
    "    plt.figure(1)    \n",
    "    plt.scatter(timespan, noisy_data[1][0][0], marker='o', label='measured data CE')\n",
    "    plt.scatter(timespan, noisy_data[1][0][1], marker='o', label='measured data CA1')\n",
    "    plt.scatter(timespan, noisy_data[1][0][2], marker='o', label='measured data CA2')\n",
    "    plt.scatter(timespan, noisy_data[1][0][3], marker='o', label='measured data CtE')\n",
    "    plt.figure(2)    \n",
    "    plt.scatter(timespan, noisy_data[2][0][0], marker='o', label='measured data CE')\n",
    "    plt.scatter(timespan, noisy_data[2][0][1], marker='o', label='measured data CA1')\n",
    "    plt.scatter(timespan, noisy_data[2][0][2], marker='o', label='measured data CA2')\n",
    "    plt.scatter(timespan, noisy_data[2][0][3], marker='o', label='measured data CtE')\n",
    "    \n",
    "    # set parameters including bounds; you can also fix parameters (use vary=False)\n",
    "    params = Parameters()\n",
    "    params.add('A1', value=guesses[1,0], min=1e-5, max=1e5)\n",
    "    params.add('A2f', value=guesses[1,1], min=1e-5, max=1e5)\n",
    "    params.add('A2b', value=guesses[1,2], min=1e-5, max=1e5)\n",
    "    params.add('A3', value=guesses[1,3], min=1e-5, max=1e5)\n",
    "    params.add('A4', value=guesses[1,4], min=1e-5, max=1e5)\n",
    "    params.add('A5', value=guesses[1,5], min=1e-5, max=1e5)\n",
    "    params.add('E1', value=guesses[0,0], min=1000, max=100000)\n",
    "    params.add('E2f', value=guesses[0,1], min=1000, max=100000)\n",
    "    params.add('E2b', value=guesses[0,2], min=1000, max=100000)\n",
    "    params.add('E3', value=guesses[0,3], min=1000, max=100000)\n",
    "    params.add('E4', value=guesses[0,4], min=1000, max=100000)\n",
    "    params.add('E5', value=guesses[0,5], min=1000, max=100000)\n",
    "\n",
    "\n",
    "    # fit model\n",
    "    results = minimize(residual, params, method='leastsq', max_nfev = 1000)  # leastsq nelder\n",
    "    report_fit(results)\n",
    "    # check results of the fit\n",
    "    fin_sub = {'A1': results.params['A1'].value, 'A2f': results.params['A2f'].value, 'A2b': results.params['A2b'].value, 'A3': results.params['A3'].value, 'A4': results.params['A4'].value, 'A5': results.params['A5'].value, 'E1': results.params['E1'].value, 'E2f': results.params['E2f'].value, 'E2b': results.params['E2b'].value,  'E3': results.params['E3'].value, 'E4': results.params['E4'].value, 'E5': results.params['E5'].value}\n",
    "    for i in range(len(temperatures)):\n",
    "        data_fitted = get_lines(temperatures[i],fin_sub)\n",
    "        plt.figure(i)\n",
    "        plt.plot(timespan, data_fitted[0][0], '-', linewidth=2, label='fitted data CE')\n",
    "        plt.plot(timespan, data_fitted[0][1], '-', linewidth=2, label='fitted data CA1')\n",
    "        plt.plot(timespan, data_fitted[0][2], '-', linewidth=2, label='fitted data CA2')\n",
    "        plt.plot(timespan, data_fitted[0][3], '-', linewidth=2, label='fitted data CtE')\n",
    "        plt.legend()\n",
    "    print(residual(results.params)[0])\n",
    "\n",
    "    # display fitted statistics\n",
    "    \n",
    "    return results.params\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "killing-individual",
   "metadata": {},
   "outputs": [],
   "source": [
    "class fitting_set:\n",
    "    def __init__(self,true_system, sim_system, pre_constants, E_acts, temperatures):\n",
    "        self.true_system = true_system\n",
    "        self.sim_system = sim_system\n",
    "        self.pre_constants = pre_constants\n",
    "        self.E_acts = E_acts\n",
    "        self.temperatures = temperatures\n",
    "        self.rates_dict = {'temp': [], 'k1': [], 'k2f': [], 'k2b': [], 'k3': [], 'k4': [], 'k5': []}\n",
    "        \n",
    "    def get_true_data(self):\n",
    "        self.true = []\n",
    "        self.noisy = []\n",
    "        self.rates = []\n",
    "        for temperature in self.temperatures:\n",
    "            sim = simulate_system(self.true_system,self.pre_constants,self.E_acts,temperature)\n",
    "            self.true.append(sim[0])\n",
    "            self.noisy.append(sim[1])\n",
    "            self.rates.append(sim[2])\n",
    "        \n",
    "    def get_true_r2(self):\n",
    "        for i in range(len(self.temperatures)):\n",
    "            print(true_r2(self.true_system,self.pre_constants,self.E_acts,self.temperatures[i],self.noisy[i],self.rates[i]))\n",
    "        \n",
    "    def fit_sys(self,temp_num,max_score):\n",
    "        fit_sim(self.sim_system, self.noisy[temp_num], self.rates[temp_num], max_score)\n",
    "        \n",
    "    def fit_sys_all(self,max_score):\n",
    "        for i in range(len(self.temperatures)):\n",
    "            fits = fit_sim(self.sim_system, self.noisy[i], self.rates[i], max_score)\n",
    "            self.rates_dict['temp'].append(self.temperatures[i])\n",
    "            self.rates_dict['k1'].append(fits['k1'].value)\n",
    "            self.rates_dict['k2f'].append(fits['k2f'].value)\n",
    "            self.rates_dict['k2b'].append(fits['k2b'].value)\n",
    "            self.rates_dict['k3'].append(fits['k3'].value)\n",
    "            self.rates_dict['k4'].append(fits['k4'].value)\n",
    "            self.rates_dict['k5'].append(fits['k5'].value)\n",
    "        \n",
    "    def kin_params_guesses(self):\n",
    "        self.Eact_list = []\n",
    "        self.A_list = []\n",
    "        for variable,lis in self.rates_dict.items():\n",
    "            log_list = np.log(lis)\n",
    "            reciprocal_list = 1/np.array(self.rates_dict['temp'])\n",
    "            lin_model = np.polyfit(reciprocal_list,log_list,1)\n",
    "            slope = lin_model[0]\n",
    "            intercept = lin_model[1]\n",
    "            self.Eact_list.append(slope*8.314)\n",
    "            self.A_list.append(np.exp(intercept))\n",
    "        del self.Eact_list[0]\n",
    "        del self.A_list[0]\n",
    "        \n",
    "    def fit_sys_full(self,max_score):\n",
    "        guesses = np.zeros([2,6])\n",
    "        guesses[0,:] = self.Eact_list\n",
    "        guesses[0,:] *= -1\n",
    "        guesses[1,:] = self.A_list\n",
    "        for guess in guesses[0,:]:\n",
    "            if guess <1000:\n",
    "                guess = 1000\n",
    "        fit_sim_full(self.sim_system, self.noisy, self.rates, max_score, self.temperatures,guesses)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "colonial-regulation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here i set up my differential, you can set up different ones, but mind that this means you need to possibly change\n",
    "# Some more things like initial conditions to fit the system\n",
    "def diff(x, init, rate_const):\n",
    "    CE, CA1, CA2, CtE,Cprecat, Ccat, Ccatdead, H2uptake = init\n",
    "    try:\n",
    "        k1, k2f, k2b, k3, k4, k5= rate_const['k1'],rate_const['k2f'],rate_const['k2b'],rate_const['k3'],rate_const['k4'],rate_const['k5']\n",
    "    except:\n",
    "        k1,k2f,k2b,k3,k4,k5 = rate_const\n",
    "    dEdt = -k1*CE*Cprecat - k2f*CE*CA1 + k2b*CtE*CA2\n",
    "    dA1dt = k1*CE*Cprecat - k2f*CE*CA1 + k2b*CtE*CA2 + 2*k3*CtE*Cprecat\n",
    "    dA2dt = k1*CE*Cprecat + k2f*CE*CA1 - k2b*CtE*CA2\n",
    "    dtEdt = k2f*CE*CA1 - k2b*CtE*CA2 - k3*CtE*Cprecat\n",
    "    dprecatdt = -k1*CE*Cprecat - k3*CtE*Cprecat + k4*Ccat*CH2 - k5*Cprecat\n",
    "    dcatdt = k1*CE*Cprecat + k3*CtE*Cprecat - k4*Ccat*CH2\n",
    "    dcatdeaddt = k5*Cprecat\n",
    "    dH2uptakedt = k4*Ccat*CH2\n",
    "    return dEdt, dA1dt, dA2dt, dtEdt, dprecatdt, dcatdt,dcatdeaddt, dH2uptakedt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "polar-anime",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in this cell are all the parameters needed to make data for the algorithm to later estimate\n",
    "exp_1_const = np.array([0.01209668296564242, 0.01517495478255645, 0.0042608145032644508, 0.0897487647678990227, 0.032891811706016034, 0.012690318715024702]) #rate constants\n",
    "exp_1_pre_consts = np.array([4, 1.6, 1, 5, 2, 40]) #Arrhenius pre constants, to change the constants for different temperatures\n",
    "exp_1_temps = np.array([323,363,393]) # temperatures, you can add or remove one later and see the program adapt\n",
    "exp_1_E_acts = get_Eact(exp_1_pre_consts,exp_1_const) # generate activation energies from the rate constants and Arrhenius pre-factors\n",
    "exp_1 = fitting_set(diff,diff,exp_1_pre_consts,exp_1_E_acts, exp_1_temps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "measured-laptop",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00589749 0.00852319 0.00216736 0.05455227 0.01977726 0.00467962]\n",
      "[0.01209668 0.01517495 0.00426081 0.08974876 0.03289181 0.01269032]\n",
      "[0.0188359  0.02165481 0.0064632  0.12198501 0.0450056  0.02347147]\n"
     ]
    }
   ],
   "source": [
    "# This displays the values of the constants for each temperature\n",
    "exp_1.get_true_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "overhead-posting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2466e268520>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check to see if the data got generated right before noise is added\n",
    "plt.figure()\n",
    "plt.plot(timespan,exp_1.true[0][0][0], label = 'CE')\n",
    "plt.plot(timespan,exp_1.true[0][0][1], label = 'CA1')\n",
    "plt.plot(timespan,exp_1.true[0][0][2], label = 'CA2')\n",
    "plt.plot(timespan,exp_1.true[0][0][3], label = 'CtE')\n",
    "plt.plot(timespan,exp_1.true[0][0][4], label = 'Cprecat')\n",
    "plt.plot(timespan,exp_1.true[0][0][5], label = 'Ccat')\n",
    "plt.plot(timespan,exp_1.true[0][0][6], label = 'Ccatdead')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(timespan,exp_1.true[1][0][0], label = 'CE')\n",
    "plt.plot(timespan,exp_1.true[1][0][1], label = 'CA1')\n",
    "plt.plot(timespan,exp_1.true[1][0][2], label = 'CA2')\n",
    "plt.plot(timespan,exp_1.true[1][0][3], label = 'CtE')\n",
    "plt.plot(timespan,exp_1.true[1][0][4], label = 'Cprecat')\n",
    "plt.plot(timespan,exp_1.true[1][0][5], label = 'Ccat')\n",
    "plt.plot(timespan,exp_1.true[1][0][6], label = 'Ccatdead')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(timespan,exp_1.true[2][0][0], label = 'CE')\n",
    "plt.plot(timespan,exp_1.true[2][0][1], label = 'CA1')\n",
    "plt.plot(timespan,exp_1.true[2][0][2], label = 'CA2')\n",
    "plt.plot(timespan,exp_1.true[2][0][3], label = 'CtE')\n",
    "plt.plot(timespan,exp_1.true[2][0][4], label = 'Cprecat')\n",
    "plt.plot(timespan,exp_1.true[2][0][5], label = 'Ccat')\n",
    "plt.plot(timespan,exp_1.true[2][0][6], label = 'Ccatdead')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "abroad-frontier",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.707867043019768\n",
      "17.44994903322814\n",
      "19.228393485763803\n"
     ]
    }
   ],
   "source": [
    "# Check the R-squareds for the true rate constants for each temperature so we can see how well our estimate are later on\n",
    "exp_1.get_true_r2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "hydraulic-quarterly",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.41461252445878\n",
      "11.41461252445878\n",
      "11.41461252445878\n",
      "11.414611138723021\n",
      "11.414612196870475\n",
      "11.414612158051588\n",
      "11.414612623573987\n",
      "11.41461263016006\n",
      "11.414612176254177\n",
      "-28.008706360655232\n",
      "11.67021907629909\n",
      "11.670217360380208\n",
      "11.670220648206179\n",
      "11.670217489769003\n",
      "11.670219032629063\n",
      "11.670218367674082\n",
      "11.670219598716432\n",
      "12.854963881574756\n",
      "12.854962581432256\n",
      "12.854964888583016\n",
      "12.854962677137932\n",
      "12.85496389151003\n",
      "12.854963480091392\n",
      "12.854964016817105\n",
      "14.116926974475822\n",
      "14.116926345957278\n",
      "14.11692731365126\n",
      "14.11692629171567\n",
      "14.116927040778819\n",
      "14.116926893629433\n",
      "14.116926682152199\n",
      "15.087036178281094\n",
      "15.087035341102595\n",
      "15.087036908718225\n",
      "15.087035253890258\n",
      "15.087036164076286\n",
      "15.087035889714521\n",
      "15.08703632658397\n",
      "15.567606133632355\n",
      "15.567605980993427\n",
      "15.567606193516374\n",
      "15.56760583190059\n",
      "15.567606194209567\n",
      "15.567606227246877\n",
      "15.567605674464913\n",
      "15.624658648221564\n",
      "15.624657833134952\n",
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    {
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      "7.272003555336596\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 1000\n",
      "    # data points      = 6\n",
      "    # variables        = 6\n",
      "    chi-square         = 317.292214\n",
      "    reduced chi-square = 317.292214\n",
      "    Akaike info crit   = 35.8083821\n",
      "    Bayesian info crit = 34.5589390\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "[[Variables]]\n",
      "    k1:   0.00611163 (init = 0.01)\n",
      "    k2f:  0.00817806 (init = 0.01)\n",
      "    k2b:  0.00215709 (init = 0.01)\n",
      "    k3:   0.04147154 (init = 0.01)\n",
      "    k4:   0.01967116 (init = 0.01)\n",
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      "6.537674227811316\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 1000\n",
      "    # data points      = 6\n",
      "    # variables        = 6\n",
      "    chi-square         = 256.447106\n",
      "    reduced chi-square = 256.447106\n",
      "    Akaike info crit   = 34.5309778\n",
      "    Bayesian info crit = 33.2815346\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "[[Variables]]\n",
      "    k1:   0.01184097 (init = 0.01)\n",
      "    k2f:  0.01502496 (init = 0.01)\n",
      "    k2b:  0.00404774 (init = 0.01)\n",
      "    k3:   0.09219103 (init = 0.01)\n",
      "    k4:   0.03259325 (init = 0.01)\n",
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      "4.719579597915885\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 1000\n",
      "    # data points      = 6\n",
      "    # variables        = 6\n",
      "    chi-square         = 133.646589\n",
      "    reduced chi-square = 133.646589\n",
      "    Akaike info crit   = 30.6206367\n",
      "    Bayesian info crit = 29.3711935\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "[[Variables]]\n",
      "    k1:   0.02075628 (init = 0.01)\n",
      "    k2f:  0.02127635 (init = 0.01)\n",
      "    k2b:  0.00618341 (init = 0.01)\n",
      "    k3:   0.11665291 (init = 0.01)\n",
      "    k4:   0.04486859 (init = 0.01)\n",
      "    k5:   0.02468419 (init = 0.01)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and now we fit the data we generated and see, with the right differential if the program can find our constants for every temperature\n",
    "exp_1.fit_sys_all(24) # if temperatures of initial conditions were changed, the max score to fill in here is amount of initial conditions*8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "usual-northwest",
   "metadata": {},
   "source": [
    "Good results, the r-squareds are close to optimal, and the constants are on average only a few percent points off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "vocational-creek",
   "metadata": {},
   "outputs": [],
   "source": [
    "#construct kinetic parameters form the results at individual temperatures\n",
    "exp_1.kin_params_guesses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "mature-denmark",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-18211.15690968874, -14454.674822159392, -15825.67318735262, -15964.651637776691, -12419.89996374545, -26760.11702581118]\n",
      "[5.260425234094435, 1.7869700303849272, 0.7777629870448576, 16.475488556276286, 2.0037887548489652, 89.83701426960472]\n"
     ]
    }
   ],
   "source": [
    "# These are our guesses for the optimization across different temperatures\n",
    "print(exp_1.Eact_list)\n",
    "print(exp_1.A_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "published-participation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.   1.6  1.   5.   2.  40. ]\n",
      "[-17507.67079081 -14058.09983295 -16473.03587668 -12132.82763899\n",
      " -12396.90042988 -24312.24532045]\n"
     ]
    }
   ],
   "source": [
    "# These are the real values, you can see we're already close\n",
    "print(exp_1.pre_constants)\n",
    "print(exp_1.E_acts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "considerable-original",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "53.42736651120437\n",
      "53.427366524241826\n",
      "53.42736650501465\n",
      "53.42736652244237\n",
      "53.42736652675523\n",
      "53.4273665262356\n",
      "53.42736652659275\n",
      "53.427366526910646\n",
      "53.427366527023864\n",
      "53.427366525682224\n",
      "53.4273670646007\n",
      "53.4273670646007\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 871\n",
      "    # data points      = 12\n",
      "    # variables        = 12\n",
      "    chi-square         = 4139.31233\n",
      "    reduced chi-square = 4139.31233\n",
      "    Akaike info crit   = 94.1205396\n",
      "    Bayesian info crit = 99.9394194\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "[[Variables]]\n",
      "    A1:   5.26872242 (init = 5.260425)\n",
      "    A2f:  1.78045968 (init = 1.78697)\n",
      "    A2b:  0.77400910 (init = 0.777763)\n",
      "    A3:   16.4743916 (init = 16.47549)\n",
      "    A4:   2.00534805 (init = 2.003789)\n",
      "    A5:   89.5447819 (init = 89.83701)\n",
      "    E1:   18208.3601 (init = 18211.16)\n",
      "    E2f:  14464.9873 (init = 14454.67)\n",
      "    E2b:  15845.0247 (init = 15825.67)\n",
      "    E3:   15984.9260 (init = 15964.65)\n",
      "    E4:   12423.0831 (init = 12419.9)\n",
      "    E5:   26759.0573 (init = 26760.12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53.4273670646007\n",
      "18.572632935399298\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mLS2NRx55hOLiYmJiYli6dCmxsbFs27aNGTNmADBhwgSnx5VS8sADD/DVV1/RrVs3/P397duefPJJVq9eTVlZGaNGjeKNN97gs88+q3X8RYsW1apX01XxzDPP8MILL9hvAgEBAcycOdNDV7N1EdJFp2K1SkJcA7yEFpL4jpTyaSHEk0CqlHKVNbLlLSAUTUv/IqXcUNc+R4wYIVNTU5vbfgC+3JPJ/R9ud7otPjKInx4b65bjtDXMFjPfnv6WDw98yNazW+3ll8Vfxm0DbmNU3CgVD+4F7N+/n4EDBwLQ87G1HjnGiWfr7gvp2bMnqampdlFOTU3llVdeqeUGueKKK3jhhRcYMWIERqORMWPG8MUXX9CxY0c++eQT1q9fzzvvvMOQIUN45ZVXGD16NHPmzGHdunW1XC4rV67k3//+N//73//Iyspi0KBBvP3229x8883k5uYSHR0NwB133MEtt9zCddddV+34gMt6jkRHR3P8+HG7W8WRmi6XhIQEPvzwwyZcYc/g+NuwIYTYJqUc4ax+g3zo1pjyL2uU/c1hOQ24tNGtdRN9OoW63OaLHaMFFQWsPLySjw98bE9wFWwI5vo+1zNtwDR6RTQ8zEmhaCoHDx5k7969jB8/HgCz2UxsbCz5+fnk5+czevRoQBPadevW1fr8999/z6233operycuLo6xY6sMr02bNvH8889TWlpKbm4uCQkJtYS6MfXqor25XNo8PTuEuNzmSx2jh/MO89GBj1hzdI09zrx7WHduG3gbUy+YSqi/6xubwjuoz5JuS0gpSUhI4Oeff65Wnp+f36z9lpeXc//995Oamkq3bt1YsGCB00FXDa2XkJDAtm3bqt0wfBWfSCTtb9DRJTywVrm3Z10Eza2y8dRG/rD+D9y46kZWHFpBubmcS+Mu5dWrXmX1Dau5feDtSswVHiEsLIyioiKn6/379yc7O9su6EajkX379hEZGUlkZCQ//vgjgEsXxujRo/nkk08wm81kZmayadMmALsox8TEUFxczIoVK5wev656jsydO5c5c+Zw9qw2GrqyspK33367aRekjeMTFjrAhb2iWb3rDJFBfhSUGb1+blGzxczHBz/mg7QP7NkLgwxBTL1gKrcOvJXeEb1buYWK9sC0adOYOXMmS5YsYcWKFUyfPp17773X3im5YsUKHnzwQQoKCjCZTDz00EMkJCTw7rvvMmPGDIQQLjtFb7jhBjZu3MigQYPo3r07I0eOBCAyMpKZM2eSmJhIly5duPDCC+2fqXl8V/Ucueaaa8jKymLcuHFIKRFC2DtsAebMmcPChQvt67/++mu1DlpvokGdop7AnZ2iAK9sPMwLGw4x8/JezLt2kNv2Wx+eyD1+rOAY83+cb48d7xraldsG3sb1fa5vUzPOK9yDs44vhQI81CnqDfTrrAndwaziemq6D3cn8zdbzHyQ9gEv73iZSkslnYM789hFj3FltytVtIpCoagXnxH0AV20VDIHMgtb7JjuTOZ/ouAEj//0OLuytUyRN/S5gTkXzlEWuUKhaDA+I+hdo4IIDTBwrqiCnOIKYkIDPH5MdyTzN1vMfLj/Q5bsWEKFuYJOQZ14YtQTjO462l3NVCgU7QSfiHIB0OkEA7po1uz+FrLSXeUBb2h+8JOFJ7l7/d0sSl1EhbmCKRdMYeXUlUrMFQpFk/AZQQcYFKe5XdLOtIygzx42m0B99XDJhswsZJEW/pv2X25edTM7zu2gY1BHXhn7Ck9f9jQRAbVHsykUCkVD8BmXC8CgWKugt5CFXl/ucWcRMENihjB/83y2ZW0DYHLvyTx20WNKyBUKRbPxKQt9oFXQ97WQhQ6aqG+4eQO779rNhps3VBPzBZsXkFmSiUSSWZLJ4z8+ztQvprItaxsdAjuw+MrFPHP5M0rMFa3OkiVLGDhwILfffjurVq3i2We1WSZTUlJIS6uay2bp0qWcOdO4+WtOnDhBYmJivfWmT5/ucnBQc44PWvrcxMREBg8eTHJyMi+88IJ9m8lkomPHjjz22GPVPvPKK6/Qp4+WWjonJ6fRx2wNfErQ+3cJw6ATHM0uprjC1KptcRYBY5ImjBYj1/S6hpSpKYzt7vtDkRXewWuvvcZXX33Fhx9+yJQpU+zi5g5BdydNOb5j+tw9e/awZcuWaom6vvrqK/r168fy5ctxHJdz6aWX8vXXX9OjRw+3td/T+JSgB/rpGRgbjpSwJ72gVdtSV6TLc6OfIzIwsuUao1DUgWP63BdffJGlS5cya9YsNm/ezKpVq5gzZw5JSUk899xz9vS1SUlJlJWVsW3bNsaMGcPw4cOZOHEimZnaBOXbtm1j6NChDB06lFdffdXpcaWUzJo1i/79+zNu3DjOnauaPuHJJ5/kwgsvJDExkXvuuQcpJStWrKh1fGf1alJf+txly5Yxe/ZsunfvXi0vTXJyMj179nTHJW4xfMqHDjCkawR7MgrYnZ7PyAs6tFo7uoR0IbMks1Z5bEhsK7RG4TUs8JD7bYFrA+f111/nf//7H5s2bbKnzwUYNWoUU6ZMqZY+d926ddXS5z7wwAPV0ufOmzePd955h7vvvrta+lxnfP755xw8eJC0tDR7+lzbkPxZs2bxt79pCV3vuOMO1qxZw80338wrr7xSLX2us3o1sy3u3buX4cOHO21DeXk5X3/9NW+88Qb5+fksW7aMUaNGNfCitj18ykIHGNotEoBd6fmt2o5LYi+pVdaQCBiFwltwTJ+blJTEwoULSU9Pd5o+1xn1pc+9+OKLGTx4MBs3bmTfvn1O99HQeq5Ys2YNV155JUFBQdx0002kpKRgNpsbtY+2hM9Z6Ek2QT/dei6XTw9+yudHPgcgzD+M4spiNemyomHUYUm3NXwhfe6yZcv48ccf7a6V8+fPs3HjRnuOd2/D5yz0CzqG4m/QkZFfRs/H1nLpsxtJ2VFzClTP8fGBj3lqy1MAzBkxh823bq4VAaNQeAu+nD63sLCQH374gVOnTnHixAlOnDjBq6++yrJly5p2sdoAPifoq3edwWS22Ncz8suYu3JPi4j6R/s/4ulfngbgsYse486EOz1+TIXCk0ybNo1FixaRnJzM0aNH7elrk5KSMJvNrFixgr/+9a8MHTqUpKQkNm/eDMC7777Ln/70J5KSkpx2VIKWPrdv374MGjSIO++802n63IkTJzpNn5uUlGTv3HRWz5FrrrmGWbNmMW7cOBISEhg2bBiFhYV8/vnnjB07loCAqjQhU6dOZfXq1VRUVLBkyRK6du1Keno6Q4YM4Q9/+IO7LqvH8Jn0uTYufXYjGU6mnfP03KL/Tfsvz219DoC5F83ltoE159FWKJyj0ucqXNFu0+facDWHqCfnFv0g7QOe3/o8APMunse0AdM8diyFQuGlWCxgLAVjCegDIcj9EU0+J+hxkUFOLXRPzS36ffr3djGff8l8bul/i0eOo1AovAgpwVwJlSWaiFeWgLEMcPCIFPpDWCwER7vtsD4n6HMm9uexz3ZTbqryo3tqbtGzJWf5vx//D9ASdSkxVyjaKY7Wd2UJVJaCxVj3Z8yVUHBaW3aTqPucoNvmEH1s5W7KjRYig/xYMCXB7XOLGi1G5nw3h4KKAi6Lv4wZiTPq/5BCofB+GmJ9Awg9+IdoL79gyD9VW+SlBYoylaDXxfXJ8RSUGXli1T6u6N+x2WLuLGviwbyD7MzeSafgTvzjsn+gEz4XMKRQKKDh1rch0EHAQ8AQAEI47MeFxW6udFtTfVLQAS7urd3xfjmea5/puyk4mzd0/k/zMVqM6IWeRaMXERUY5bZ2KxSKVqSp1rd/CNQ376/e37l46/3d1nyfNSv7dQojMtiPzIJy0vOaHuHiLGui0XqnnZU8i2GdhzWrnQpFW6Ddps+1WDCV5NMxpgOPzb4XsvbBuTTIP8ntd99D/0smkjj2Zmb8+SmMfuEQ2R06DoQug6HDBRDWBQLD6xdz0DpAaz7JC51W7iZ8VtB1OsGFPTUrfcux803eT11ZE5XfXOErtIv0uevXs2dHKls2fkmEvwWyD8LZ3Xz1+X/p16sby79YizRXatZ3QDi333Y7B/buYs++A5QRwNsrNkBwB/ALrO5KaSjB0RDRrcoi1/tr626McvFZQQe4uFeV26WpuJoftFNwJ+U3V/gEPps+12KBimKeWfgkL/ztEeL0uXAujYCys8y8ebzmUkGybNVXzL7/Hrr36MHPRwvs1vc1N9+OCIpA6A1cdNFFpKenN/9iB0dD5wSIS9be3Sjm4MM+dIBLemvpc3853nQLffaw2dV86AD+On8eGf5Is9unUNRk8HuDPbLfPXftcbnNJ9LnSskdv7udNZ8t47rxo6v5vvempTF8QHewmKr7vv1DKDfr+PrHrbzx3sfkGw0sW76SUaOvrNZOo9HIBx98wOLFi5vzFbQIDTIxhRCThBAHhRBHhBCPuahzixAiTQixTwjxkXub2TQGxoYTFmDgdG5Zk0eKXtv7Wh4e/jAC7RErzC+MJy99UiXaUrR7WjV9rtX63rTucy4ensTgQf3YuPFr9m3fAiXZdusbQxAgNNdGTd93QBhrvlxXb/rc+++/n9GjR3P55Ze78/J5hHotdCGEHngVGA+kA1uFEKuklGkOdfoCc4FLpZR5QohOnmpwY9DrBCN6RrHpYDa/Hs9tcvjivvP7kEgujbuU18a9plwtCo9RlyXd1mix9LlPPEF5aTGU5oKpAvJOwFmDVu+hv5D65X/pFt+FBf98k3ITWiejLfpEpychcTDb9h9n7NhetY5VX/rcv//972RnZ/PGG28065xaioYo00XAESnlMSllJfAxMLVGnZnAq1LKPAAp5TnaCLZZi3443LRJXg/mHmT10dUYdAbmXTJPibmiXdEq6XNLNes6JtBM8ak9rPjkQ83qzj9JWJAfRfm5gKTcrAOhI6b3YIqDurFi/Q+aT9pqfdsiT5qaPvftt99m/fr1LFu2DJ3OO/73DWllPHDaYT3dWuZIP6CfEOInIcQWIcQkdzWwuYzppz0sfHcoG4ul8ZklX9r+EhLJb/v/lm5h3dzdPIWiTePx9Ll9+jBo0EDuvP1WRl6YDIUZRJafYua0KSReeDkTb7qDC4cOAnQQEM70u+7k3nmLSLrmbgK6DmbmPX8k8aIxTJw81e3pc++9916ysrIYOXIkSUlJPPnkk26/vu6m3vS5QoibgUlSyj9Y1+8ALpZSznKoswYwArcAXYHvgcFSyvwa+7oHuAege/fuw0+ePOm+M3GBlJJRz24ks6CcNQ9cRmJ8wzOc/Zr5K7/f8HtC/EL48sYviQ50b4+0QgHtLH2ubdRlZUnVyEuLqXY9QxD4B7seddlO8ET63AzA0TTtai1zJB34RUppBI4LIQ4BfYGtjpWklG8Cb4KWD70Bx242X+w8Q0GZNhDotre28OTUxAb50qWUvLjtRQDuTrhbiblC0ViaMurSwfetaDwNEfStQF8hRC80IZ8G1Jy9IQW4FXhXCBGD5oI55sZ2NomUHRnMXbmHMqPWa11YbuKhT3by99X7eOK6uhN2bTi5gb3n9xITFMMdg5z30isUCgekBFMZVBRDZXE91neIZoG3Y+vbE9Qr6FJKkxBiFrAe0APvSCn3CSGeBFKllKus2yYIIdIAMzBHStn04G83sWj9QbuYO5JXamTuSi2awJmoGy1GlmxfAsB9Q+8j2C/Ysw1VKJpDaa6Wsc9cqY0+dHOObZdICaZyqCjSBLyiGGSN/5uyvluUBg0sklJ+CXxZo+xvDssSeMT6ajPUFXteZjSzaP1Bp4K+8tBKThWdomd4T27oe4Mnm6hQNI/SXC2ntrTm//dAjm07DRFwvT/o/cBYrm0TOgiKapkbjMK3R4q6mr3IhjPBLzWW8u9d/wbgwWEP4qfz81j7FIpmU5RZJeY23JVj2y7gxVBZ5FzAdX5aiGBAKPiHam4WxxuMxei5G4yiFj4t6HMm9q/mQ6+Js2np3kt7j/Pl5xkSM4Rx3cd5uokKRfNwlUu7qTm2TRUOAl5U2wdeU8D1/tX93+ePeO4Go6gX74iWbyLXJ8fzzI2DiQyqbWU7m5bufNl5lu5dCsBDwx9qcg51haLFcJVLu6E5ts0mKMtjyXN/Z2C/C7j9lhtYtfwDnl30L7CYSFn/PWkncyCiO3QaxNJ1WzlT5qdlHXTWmenkRnLi9BkSx9Qci1ib1kifW3N/V1xxBf379ycpKYmkpCR7DhtvwactdNBE/frkeFJ2ZPC3L/ZSWG4iyE/PMzcOruU/f2P3G5SaShnddTQXdnE+SEGhaFOExVZ3cUDdObYtZs0tUmG1wE2a2/G1t5by9cf/pmt8HASEMsU/DALCSPluMZPD4hk0UhtxvfS990gcPJi4uDjn+3c1iQPuMY6WLl1KYmKi6+M7wZ4+d8MG4uLiqKio4P3333e5vw8//NCe/Mvb8HlBt3F9cjyX9O7AqGe/wWyRXNG/Y7XtpwtPs/zgcgSCh4Y91DqNVCgai82N4SrKRVq0KdNsPvDKEqrHgQvu/b/nOHYqg6un/5kZM35PVHQ0qamp3HbbbaxatYrvvvuOhQsXcuutt9rT1wYFBfHzzz+TlpbGI488QnFxsZap8bUXiQ3WsW3XXmY88ncAJowZCbraUiOl5IEHHuCrr76iW7du+PtXPVU8+eSTrF69mrKyMkaNGsUbb7zBZ599Vuv4ixYtqlWv5pP1M888wwsvvGAX7YCAAGbOnFktHa9tf16PlLJVXsOHD5etwR3/+UX2+Osa+f7m49XK534/VyYuTZTzfpjXKu1StF/S0tKqlvsP8MhLZmyvep07IGVBhpTlhVKazVJKKXv06CGzs7OllFK+++678k9/+pOUUsq77rpLLl++3N6+MWPGyK1bt0oppaysrJQjR46U586dk1JK+fHHH8u7775bypLzcvDAvvK7z96S8uxe+ejsWTIhIaHWeX/22Wdy3Lhx0mQyyYyMDBkREWE/1vnz5+31fve738lVq1bVOn5d9RyJioqS+fn5Tq99zf2NGTNG9uvXTw4dOlQOHTpUPvroo04/11I4/jZsoIWLO9XVdmOh27hpWDzfH8pmxbZ07hjZE4DCykI2nNwAwL1D723F1ikUHiI4pqoz04m13BQc0+cCmM1mYmNjya/UkV9czugb/wDAHTNmsu7rTbU+X1/63Oeff57S0lJyc3NJSEjguuuuq7WPhtZrDMrl4kVMTOhCWICBXekFHMoqol/nMF7Y+gIV5goAZqyfwexhs1W+c0WrMPDA/vorSYvmOikvhIpCLbTQEcdIlICwpk9CXJoLZfmQe1yba7OGX162VPrcBQsoLy9vcr2EhAS2bdtW7Ybhq/h0lIszAv30TB6q+dI+25bO2mNrSTmSYt+eWZLJgs0LWHtsbSu1UKFwgqlCSyF7/hic3aOFB5ac08RcaJkICe+qTeLQOQGiemiRKE0Vc1M5FJwmLCSQopIS+4ClsOBAz6fPtYpyTEwMxcXF1SJfHNP31lXPEVfpc2vuzxdodxY6wM3Du7Ls11Os3JHBhqJ/IWskCyo3l7N4+2JlpStaD3s0SqFmiVufIO0YAjURDwzXhtS7O09/ZSlIC9OmTmTmnKdY8p+PWfHm80y/aRL33nuvvRNxxYoVPPjggxQUFGAymXjooYdISEjg3XffZcaMGQghmDBhgtND3HDDDWzcuJFBgwbRvXt3Ro4cCUBkZCQzZ84kMTGRLl26VEuLa0vfazu+q3qOXHPNNWRlZTFu3DiklAgh7FPd1dwfYO8kBe1m8fXXX7vtsnqaetPneooRI0bI1NTUVjm2lJKr/vkdx3JKCLlgETr/2mlnBILdd+1uhdYp2hv79+9n4IAB1kE9VjdKRTHVolGE3upGCdOE3NBEy7uhnNnheltcsmePrbDjifS5PocQgpuGd2XR+v0IvfPUAF1CurRwqxTtjvICOP49lEbDubTa8dt+wVUC7h/SshkJXcWTN9WFo2gR2qWgA9w4LJ4Xf1yN0JeC1IGoGpgRqA9k9rDZrdg6hU9isUDWHjjyNRz5Bk7/og2tn/gpmIO16BObgAeEaUmuWovGDlhStAnaraDHRgQR23Uv+UDf4Kso1u3lbMlZuoR0UVEuCvdRch6ObaoS8RKH6XaFHrqPhMAIiOkPfkFtJy94fQOWFG2SdivoRZVFFOt3goTy7DFs+NO/WrtJCl/AbIIz260C/jVkbKeaLzw8HvpcBX3GQ6/REBQJ+/drkz20NYKjlYB7Ge1W0NefWI9JVkJZH9JOG+wx6QpFoynMhKPfaAJ+dBOU51dt0/tDj1HQZ5z26jig7VjhCp+j3Qr6F0e+ACApahw/nNBi0ude004m6lU0D1MlnN5S5UbJ2lt9e3TvKgHveZnWoalQtADtUtBPFJxgZ/ZOgg3B3Hfh9fywYycrd2QwZ2J/DPp2N9bK+9j9KXzzJBSkQ0RXuOpvMOQWzx4z70SVgB/7Tpux3oZfsOY+6TMOLhgLHS7wbFsU1QgNDaW4uLjOOgsWLCA0NJRHH33UZZ2UlBT69evHoEGDGnX8devWMX/+fEpLSwkICGDs2LH885//ZMGCBbz11lt07FiVCPDbb78lMjKyUftvDO1S0FcdXQXAhJ4TGNkrjo6h+8kuqqDPvHXERwYxZ2L/OieQVrQiuz+F1Q9aZ49Hi8RY/aC27E5RryyFkz9V+cLPH6m+vdMgqy98nNaxaQhw37EVmEwmDIaWlaeUlBQmT57cKEHfu3cvs2bNYu3atQwYMACz2cybb75p3/7www/XeRNxN+3OHDVbzHZBn3rBVL7YeYa80qp424z8Muau3EPKjozWaqKiLr55skrMbRjLtPLmICXkHIafX4UPboDnesKHN8Mvr2tiHhABg6bClJfh4TS4/2eYsBB6X9HiYp6yI4NLn91Ir8fWcumzG5v9Wz1x4gQDBgxg+vTp9OvXj9tvv52vv/6aSy+9lL59+/Lrr78CUFJSwowZM7joootITk7miy++sH/+8ssvZ9iwYQwbNozNmzcDkJmZyejRo0lKSiIxMZEffvgB0CxqGytWrGD69OlA1ajNiy++mL/85S8cPXqUSZMmMXz4cC6//HIOHDgAwPHjxxk5ciSDBw/m8ccfd3leTz/9NP369eOyyy7j4MGD9vK33nqLCy+8kKFDh3LTTTdRWlrK5s2bWbVqFXPmzCEpKYmjR486rVeT559/nnnz5jFgwAAA9Ho99913X1O/imbT7iz0X87+QlZpFl1DuzKs8zBmL/0Wk6X6aNm6JpBWtDIF6Y0rrwtTBZz4EQ5vgEPrIe949e1xyVW+8PgRoG/9v0vKjoxq0yraDBCgWb/XI0eOsHz5ct555x0uvPBCPvroI3788UdWrVrFP/7xD1JSUnj66acZO3Ys77zzDvn5+Vx00UWMGzeOTp068dVXXxEYGMjhw4ftedM/+ugjJk6cyLx58zCbzU4FsSbp6els3rwZvV7PVVddxeuvv07fvn355ZdfuP/++9m4cSOzZ8/mvvvu48477+TVV191up9t27bx8ccfs3PnTkwmE8OGDWP48OEA3HjjjcycOROAxx9/nP/85z888MADTJkyhcmTJ9tnKbKlIKhZz5G9e/fy5z//2eX5vPjii/z3v/8FICoqyp6vxlO0/i+0hbF1hk7pMwWd0DmdKBqcTyCtaANEdK2adLhmeUMoPGMV8A1w7NvqvvCgKKuAj9d84aEdXe6mtVi0/mCtOXLdYYD06tWLwYMHA1p2wquuugohBIMHD+bEiRMAbNiwgVWrVtmnbysvL+fUqVPExcUxa9Ysdu7ciV6v59ChQwBceOGFzJgxA6PRyPXXX09SUlK97fjNb36DXq+nuLiYzZs385vf/Ma+raJCy2fz008/8dlnnwFwxx138Ne//rXWfn744QduuOEGgoO1cNApU6bYt+3du5fHH3+c/Px8iouLmThxotO2NLReXbS0y6VdCXpRZRHfnPoGgCkXaF9wXGQQGU7E29kE0oo2wFV/q+5DB21AzlV/c17fYoaMbZoFfni9lqnQkc6J0HcC9JsIXS8End5zbXcDnjJAAgKq3EY6nc6+rtPpMJm0iaKllHz22Wf07199Lt4FCxbQuXNndu3ahcViITAwENCyKX7//fesXbuW6dOn88gjj3DnnXdWm1GoZrrbkBAtIshisRAZGcnOnTudtrc58/1Onz6dlJQUhg4dytKlS/n222+bXM+Wmnfo0KFNbo87aVc+9A0nNlBhruCiLhcRH6pZM3Mm9ifIr/qfWK8TtSaQVrQRhtwC1y2BiG6A0N6vW1K9Q7QsD/asgJX3wAt94T/j4YcXNDH3C4Z+V8PkF+HhfXDfTzDuCeh+SZsXc3BtaLSEATJx4kRefvllbAn9duzQEngVFBQQGxuLTqfjgw8+wGzWniBOnjxJ586dmTlzJn/4wx/Yvn07AJ07d2b//v1YLBY+//xzp8cKDw+nV69eLF++HNBuJrt27QLg0ksv5eOPPwbqTs2bkpJCWVkZRUVFrF692r6tqKiI2NhYjEZjtc/XTKXrqp4jc+bM4R//+If9qcRisfD666/XdRk9Sruy0L84qrlbpvapmoHc9pi6aP1Bu6UupWRw14iWb6CiYQy5pbqASwlZaZoFfmiDliNFOrglonpC34nQbwL0uAz8Alu8ye5izsT+1XzoAEF++hYxQObPn89DDz3EkCFDsFgs9OrVizVr1nD//fdz00038f777zNp0iS7lf3tt9+yaNEi/Pz8CA0NtU/M/OyzzzJ58mQ6duzIiBEjXIYcfvjhh9x3330sXLgQo9HItGnTGDp0KIsXL+a2227jueeeY+rUqU4/O2zYMH77298ydOhQOnXqVC217lNPPcXFF19Mx44dufjii+0iPm3aNGbOnMmSJUtYsWKFy3qODBkyhJdeeolbb72V0tJShBBMnjzZvt3Rhw5aJE3Pnj0bd+EbQbtJn3u25CzjV4wnyBDEt7d8S7Cf86HWc1fuZtmvp7k6sQv//t3wFmufopEYy7RMhYfWw+GvoOBU1TadQQsl7DdRE/KYvm16dKazFKl1kbIjg0XrD3Imv4w4FWbr06j0uS74MUObPWVk7EiXYg4w+6p+fL4jg3V7z7L9VB7Duke1VBMV9ZF3QhPvwxs0MXecei2ko+YL7zsBLrhSS3jVWFpjwFITuD45Xgm4wintRtB/SNdiYC/relmd9bpEBDLj0l689u1Rnl13gE/uuaRZHTCKZmCqhFM/awJ+eAPkHKq+PS65ypUSmwy6ZnQJtdSAJYXCg7QLQTeajWzJ3ALA5fGX11v/3isu4KNfT/Hr8Vw2HTzH2AGdPd1EhY3CTDhitcKPfguVDn7LgHAtnLDvBOg7HkI7ue+4dQ1YUoKu8BIaJOhCiEnAYkAPvC2lfNZFvZuAFcCFUsrWmV/OCTvO7aDUVEqfyD4NmokoPNCPWVf2YeHa/Ty37iBj+nVCr1NWukewmCE9tcoKP1tj2r9OgzTx7jsBul3suUkf3DlgSaFoJeoVdCGEHngVGA+kA1uFEKuklGk16oUBs4FfPNHQ5vBDhuZuaYh1buOOkT1496cTHMwqYuX2dH4zopunmtf+KDmvpZs9tF57L8ur2uYXDL3GWEV8PER2b5k2NXfAkkLRBmiIhX4RcERKeQxACPExMBVIq1HvKeA5YI5bW+gGbB2il8XX7T93JMCg59GJ/Xj4k13866tDXDc0jkC/th+n3CaxWDTL+/BXWmhheirVJn2I6mWNSBnfemGFjR2wpFC0QRrSixQPOJou6dYyO0KIYUA3KeVaN7bNLWQWZ3Ik/wj+en/m/TiPIe8NYcKKCaw9Vn9Tpw6NZ2BsOJkF5bz/8wnPN9aXKM7WBvek/An+NQDeHAObFkL6Vs1t0vtKmPgMzNoGs3fC1c9pw+5bK0a8IQOWFG0Sx2RfrliwYIE9ZYErUlJSSEuraafWz7p16xgxYgSDBg0iOTm5Vm6XpKQkpk2bVq1s+fLlJCQkoNPpcGf4drM7RYUQOuBfwPQG1L0HuAege/eWeZT+8YxmnZvMJs6WngUgsySTBZsXANQ5d6hOJ3js6gHc9c6vvLrpKL8d0Z2I4FacuLctU1kKpzZr+VGOfqtNhuxIeHyVL7zXGAio/0/Y4tQcsKRoNXwlfe7+/fsxm8388MMPlJSU2AddJSYmsnLlSv74xz+69RwaYqFnAI4O5K7WMhthQCLwrRDiBHAJsEoIUSvwXUr5ppRyhJRyhGPSd0+ScjgFAAuWauXl5nIWb19c7+dH941h1AUdKCgz8sKGg/XWbzfYcqT88E9YOhme6wH/vQk2v6yJuSFQi0gZ/yTc+5M2zP66xTDg2rYp5t7E7k/hxURYEKm97/60WbtT6XM9lz532bJl3HHHHUyYMMF+vQAGDhxYKyeOW5BS1vlCs+KPAb0Af2AXkFBH/W+BEfXtd/jw4dLTrD6yWiYuTXT5Grx0cIP2szcjX/b5v7Wyx1/XyHV7zni41W2Y80el3PofKT/+nZTPdJfyiXCHV4SUr4+W8qsnpDz6rZSVZa3dWq8hLS2t4ZV3fSLlws7Vr/3Czlp5Ezl+/LjU6/Vy9+7d0mw2y2HDhsm7775bWiwWmZKSIqdOnSqllHLu3Lnygw8+kFJKmZeXJ/v27SuLi4tlSUmJLCvTvu9Dhw5J23/7hRdekAsXLpRSSmkymWRhYaGUUsqQkBD7sZcvXy7vuusuKaWUd911l7z22mulyWSSUko5duxYeejQISmllFu2bJFXXnmllFLK6667Tr733ntSSilfeeWVavuzkZqaKhMTE2VJSYksKCiQF1xwgVy0aJGUUsqcnBx7vXnz5sklS5bYj798+XL7Nlf1HElOTpY7d+50eW379esnT548KdevXy8nT55ca/uYMWPk1q1bXX7e2W8DSJUudLXeZxoppUkIMQtYjxa2+I6Ucp8Q4knrjle5/zbTPNYeW8vi7YvJLMmss15DQhgBEuIimHv1QJ5ck8acFbsZFBtB9w5tcJZ2d1OaC8e/0yY+PvYt5J+svj2yhzYqs/cVmhtFzRDveTwUL6/S59amuelzU1NTiYmJoXv37sTHxzNjxgxyc3OJjvbc/6RBTiop5ZfAlzXKnHb/SymvaH6zms7aY2tZsHkB5ebyOusF6gOZPWx2g/aZsiODt384BkBRuYnb397C138eQ4DBx6JeSnM1N8rJnzQRz9xFtWiUwEjoPUYT8N5XQnSvuvfnJUPpvQoPxcur9LlNq1dX+txly5Zx4MABezKuwsJCPvvsM/ukGZ7A59LnLt6+uF4x1wkdC0YtqLND1IZthpgzBVX7PJ1Xxsz32sy4qaZhqoSM7fDrW1qa2SXD4Ple2rRrP74ImTu1aJSel2tCPHMj/OUY3PI+jJjRMDFf/aA1tltWDaVvpr+33eMqLr4F4uVV+tzauEqfa7FY+PTTT9mzZw8nTpzgxIkTfPHFFyxbtqyuS9xsfG7o/9mSs3VuD9QHNljMwfkMMQDfH87hf3szmZQY26R2tihSahZc+lbNAk/fqlnfpho3PkOglh+l64WaJd59FPg30bWkhtJ7hlaMl1fpcxuePveHH34gPj6euLg4e93Ro0eTlpZGZmYmW7Zs4YEHHiA7O5trr72WpKQk1q9f37QvxgGfS587YcUEl77z2JBYZg+b3WAxB+j12FrAQrw4T4U0UEAolWihi2GBBr588HK6RXvAn95Ud4XZBEWZkHsMzmzXBvGkp0Kxkxtdh77QdYT2ih8BnRPcN7R+QSTV3DV2BCzId88xfITGps9Vrqz2Q7tPnzt72OxaPvRGWeUWizZZ8JkdcGYHK4M20tdyjFBRtb8SGUABoeRbQsn7dwTxF/REF9ZZ+3NFdNUGpUR2g9DOTZsFp67Mfwk3QGEG5J+C/NPW91NanfyT2pyZFlPtfQZFaaJtF/DhWpmnUEPpPYeKl1e4wOcE3Sbaf//575SZygjzD2PexfPqF/MDX8Iv/4Yzu6CiwF6cDCDgnIxEYCGSEkJEBSFUECfOgxE4sNv5PnUGCI+D0C5gCNCsX72/w7uL5V/ecO6u+Pxe+PyPIC3Oj+cKQwBMeg6G/rZxn2sOaii9QtHi+Jyggybqr+58ldNFp3lrwlskdEhwXVlKbTDMV/OrysJiITZJ8yfHJbPufGcWfndemyEmIpC5V3Vjcr9ADn6/nKe3VBBGGbMMnzNQdxqETksoVVkCJdlVFrQ7kGZAaKMuI7tbnwS6W1/dtDDC96ZAYY2IB1MFbHyqfkF356O87XPKNaBQtBg+Kei55bmcLjpNkCGI/lF1jMaymGHdX2HrW1VlYXEw/u/VhOfq3Z9ydcCTEJgOgV0h8G8QeQv9j77LaF0iC013sKkyiXf9n+di3QFtvxP/AaseqN7xqPeHi+6BbheB2QjmSuurxvLml6GisHZ7w2Jh9m4w+Ls+p8IM5+X1hbV5YoIH5RpQKFoUnxT0fTn7ABgYPRCDzsUpVpbCZ7+Hg19WLy86U13I6hA6WZDO7/WnSbP0YKVlNNMr/8I7fou4pOAA4psna0eRmCsh7QuY+HTdJxDV07m7YvyTdYs5NN13raJSFAqvx+fi0AH25uwFYHDMYOcVirPhvcmamAsnl8AmZFCn0GURgxCwyO8NbtJ9RxmB3G38C2vNI5s3AKQ5mf+u+psm/o40xHetJnhQKLwenxT0PTlapr/EmMSqQntCowj4Z38tHjuyu+sORpuQ1SF0z1T+hlLpj15Invd7k9/ov6WcAGYb7+OnwDHOP9fQKI8ht8DDe7UQv4f3NtxKburNoBUHrCgUzcEb0+fOmTOHAQMGMGTIEG644Qby8/MbfVxn+JygSyntFrpd0KuNWkTrXBQCRj1oFT4n2ISsDqFLDR/PY8Y/kG6JQQAP6D6nO2cxo2dG0Ux+EMOrf6alojyacjNoqmWvUHgAW7qBlqQpgm5Ln/vf//6XtLQ0UlNT6dOnj317zfS5NsaPH8/evXvZvXs3/fr145lnnnHLOficoJ8pOUNeRR5RAVHEh1rn4XDmNpESflpcv5DVsX3OxP58pR/DZZVL6F3xIaONizlniGNk7w5UmAW/r3iE7wKvwismTFATPHgNa4+tZcKKCY2arKUuVPrclk+fO2HCBHu+90suuYT0dDe5Nl2lYfT0y1Ppc9cdXycTlybKe7+6t6rwiYgaqV4dUr5KqaUe/VeCtv6vhNqpSOvY/vn2dDnqmW9kz7+ukaOe+UZ+vj1dms0W+X8rd8sef10j+877Um48kOWRc1X4Bo1Jn7vm6Bo54oMR1dJAj/hghFxzdE2Tj6/S57Ze+lwppZw8ebL9utbE7elzvYm1x9by1M9PAbDz3E7WHlurDSgK7gClObU/YHOn1BdeV8f265PjuT45vlb5wusTOXm+lB+P5HD3u1sJDzSw4LoEbhyufNKKpuMs+ZxtspbGpLSoiUqfW5uWSJ/79NNPYzAYuP322xu1b1f4jKDXTJtbbCxmweYF6MyVXO0s1aaH/cNf7DxD6olc+3phuYk/r9hFdnEFfxxzgceOq/BtXCWfqy8pXX2o9LlNq9ec9LlLly5lzZo1fPPNN806H0d8xofuynJJ/3q+NmIzPA7Cu9JS/uFF6w9SbqoeQSMlPLPuAG99fwyzpXWSoim8G1eTsjR0spbmoNLn1qap6XP/97//8fzzz7Nq1Sr7U4Q78BlBd2ahGKRkSs4ZbaXwjBbZcuObjQsDbCJn8stcbnv6y/3c8sbPHMt2njZUoXDF7GGzCdQHVitrzGQtzWH+/PkYjUaGDBlCQkIC8+dr6TLuv/9+3nvvPYYOHcqBAweqpc8dOnQoycnJfPLJJ8yerbXRlj531KhRxMa6Tj/94Ycf8p///IehQ4eSkJBg71RcvHgxr776KoMHDyYjw/nIaMf0uVdffbXT9LmXXnqpvTMTtPS5ixYtIjk5maNHj7qs54hj+tyBAweSmJjIsWPH6k2fO2vWLIqKihg/fjxJSUnce++9DfkK6sVn0uc6S5s7uaiEZ3LOV6/oF9Qi0RuXPruRDCei3iHEH71OcK6oggCDjr9MGsDdo3qi07nnkUvhfTQ2fa5tisWzJWfpEtKl0SmhFd5DY9Pn+oyFPrrr6OoFUnJ3gZN8KI6jQD3InIn9CfKrnjo3yE/P/MmD+OrhMdyYHE+FycJTa9KY9uYWTuSUuNiTQlGda3tfy4abN7D7rt1suHmDEnOFHZ8Q9LXH1vLFkS+qlV1cXkE/o9H5B1pgOPv1yfE8c+Ng4iODEEB8ZBDP3DiY65PjiQj241+/TeKtO0fQMSyAX0/kcvXiH1j603EsyreuUCiaiE9EuTjrEL3TmXVuo4WGs7sKabQxflBnRvSIYsHqfXyx8wwLVqexbu9ZFt08lO4dPDALkkKh8Gl8wkKv2SEaYzJxWVk5laDNk+lIGxvOHhXiz+Jpybz+u+HEhPrzy/FcJi3+ng9+PqGsdYVC0Sh8QtBrhmxNLClFB2wNDAS/YAiKpq0PZ5+U2IUND49h8pBYSivNzP9iH7e//QunztcebqxQKBTO8AlBnz1sNgZR5T2aVKKJYJC0QFkumMpaLFyxOUSH+PPKbcN47fZhRIf48/Ox81z5z2+5/8Nt/Ho8l9aKSFIoFN6BTwj6tb2vZWgnbaRWhMlMUkUlJmBYRaVWoYUiW9zFNYNjeWRcP4L89Jgtki/3nOWWN37m2iU/8mnqacqN5tZuokLRZmir6XNr7m/69On06tWLpKQkkpKSGDVqVKOPVR8+IegA5dbZgRaf03K21Ort9aKJGlJ2ZPD0l/spqyHcaZmF/GXFbkY9u5FF6w+QWeB68JJC4c34QvpcZ/tbtGgRO3fuZOfOnfaslO7EJwTdIi0czdXSY/YxVjqv5EUTNSxaf7CWmANEBfuRGB9Obkklr246ymXPbeJPH20n9UR1d8w3L7/PD8NHsW/AQH4YPopvXn6/JZtvp2D1ag6PvYr9AwdxeOxVFDgMv26LeEt73d1OlT7X/elzne2vJfCJsMX/fv0o5dKETkoiLBIj4OdYoY1FttSHq7QB+aVGts8fz/ZTebz70wnW7T3L2t2ZrN2dSWJ8ONNH9SL0p410fuMFAs1aDH5MSR7lr7/AN8DYWXcgy8uxlJZiKSvDUlqKdFi2lFrLjEaEQY8wGMBgQBj8nKwbHMr8EH4GrUyvB4Mfxd9uImvRC2DNkGc6c4bM+dp3EHHddc26PgWrV3PuxZcwZWZiiI2l08MPuWWfmfP/hrQmi3Jne92Jp9p55MgRli9fzjvvvMOFF17IRx99xI8//siqVav4xz/+QUpKCk8//TRjx47lnXfeIT8/n4suuohx48bRqVMnvvrqKwIDAzl8+DC33norqampfPTRR0ycOJF58+ZhNpudCmJN0tPT2bx5M3q9nquuuorXX3+dvn378ssvv3D//fezceNGZs+ezX333cedd97Jq6++6nQ/27Zt4+OPP2bnzp2YTCaGDRvG8OHahDM33nijPUHW448/zn/+8x8eeOABpkyZwuTJk7n55psBiIyMdFrPkb1799aaoQhg1KhRtfYHWu6XhQsXAlpiL1c5YpqK1wv62mNreSljAwhBR2tSoG+Dg6gUgmtLSrUIl6ufa9OdoTWJiwxymjYgLjIIIQTDe0QzvEc0mQVlfLjlFJ/8fIyCA4dZufUnehdkcHFoR3oUZuEntesRaDbS5dVnOPDas1qGsFZClpdz5rG5nH/3XfQhoehCtZc+LBSdfT0EvbXcVqYPDbHXLdywgbN/e8LtgnbuxZfs+3Rs77kXX2pTgu6pdqr0ubVpbvpcZyxatKiawLubBgm6EGISsBjQA29LKZ+tsf0R4A+ACcgGZkgpT7q5rU5ZvH0xRmvqyQHWTtDvgoP4NTBQE3T/EK8Sc9DSBnz54rvctmctHcvyyQ6K5KPEa7hu4g2Ubt1KxbHjVB4/jun4ca4/cZxr0zPAXHdHqQ5ASkRAALqgIHTBwYjgIHTBIeiCgsjNOo/fyaPoHQTfJASVfQfRpU8PpMlkfRnBZEIaTUizuarMaN1uNiNNRkxnMp03xGymIm2/+y4WmqBlPrGAiqNH0UdEoo+MRB8Rob1HWt/Dw7WnCReYMp2311V5a+Gpdqr0uU2rV1f63NagXkEXQuiBV4HxQDqwVQixSkrp6O3fAYyQUpYKIe4Dngd+64kG18RxUNHF1h/HT0FBnNdbuwe8qDPUxpXp2+m7Yzk6a39A57J8Htr6EWLrRzi9SwqBX7du+PfsyeHtaeyK6cMPcUMoCAil3OBPmSGATqV5TLrpSsYnxpEQF17rD3Fq+ChialjvBinJzzhD/KoVjT6Hw2OvwnTmTK1yfadOdHvtNSzFxVhKirEUF2MuLsZSXIKluMhh2bqtpPq6rHTeRyJLSzn/+ht1tkkXFlYl9DXedWFhWAprjy421JENsDUwxMY6va4t0U5b+tyXX34ZIQQ7duwgOTmZgoICunbtik6n47333quWPrdr167MnDmTiooKtm/fzp133mlPn9u/f38+//xzwsLCah3LMX3ub37zG6SU7N69m6FDh9rT5/7ud7+rM33u9OnTmTt3LiaTidWrV/PHP/4RqJ0WNz5eG81dX/pcWz1H5syZw4033shll11Gv379sFgsvPnmm9x777219tcSNMRCvwg4IqU8BiCE+BiYCtgFXUq5yaH+FuB37mxkXXQJ6WLPsjig0shBfz9yDHpijdZeci/qDDXl5VG0fj1n//GMXcxtCAAhCBw8mIBevfC3v3ri36MHOqtFdfjl97ny9RcYlbmHrZ0HsiU2gW2d+nMwugcHNx1j8aZjxEYEctXATowf1IVLekcTYNATXZLntE2uyuuj08MPVfP1AojAQDrPeZSgxIQm7RPg0JVjMTuxRnUREUTfeQfmggLM+fn2d0u+db2wEEtREZaiIoyNmL/RnJ3N4bFjMUR3QB8dhSEqGn10tLZsK4u2lkVFowsJdttkBc5wdV07PfyQx45pY/78+Tz00EMMGTIEi8VCr169WLNmDffffz833XQT77//PpMmTaqWPnfRokX4+fkRGhrK++9rnfO29LkdO3ZkxIgRFBc7TyP94Ycfct9997Fw4UKMRiPTpk1j6NChLF68mNtuu43nnnuOqVOnOv2sY/rcTp06OU2f27FjRy6++GK76E6bNo2ZM2eyZMkSVqxY4bKeI47pc0tLSxFCMHnyZKf7g+o+dIBff/0Vf3//xn4VLqk3fa4Q4mZgkpTyD9b1O4CLpZSzXNR/BTgrpVzobLsNd6XPfernp/j00KcAbDqZzrrQEJZERbAgJ5drK2WbHRlqw1xcTNHXX1P45ZeUbP4Z6gnXGnigfnfFNy+/j//S14kuySM3JIqSu+4lYOIkvkrL4uu0LM4VVdjrhgYYGNOvI90+ep2xp1IJqzGZdk5IFJdva1p4VUt0XoImaLFPPVnnvqXFgqWwsJrY29+tol+elkb5/v3IigrQ6bRXI8PnREAA+uhoDFFRNYQ/GkO0tSzKehPo0AFdSAgHDhxoVPpcT1xXRdukselz3SroQojfAbOAMVLKCifb7wHuAejevfvwkyeb52Zfe2wtT/z0BBWWCoItFracTOfBTjF0Npl53ByqRba0QTG3lJdT/N33FK5dS/F332kCAqDXEzJqFGV79mDJz6/1OUNcHH03ftO8Y1ske88U8FVaFl+lZXHgbJXVobOYSTx/nAuz9jM45xjxxeco/OMjXPXAnc06prtpKUGTUmIpKcWcl4v5/HlMuXmY83Ix5eZizs3DnGsty83FlKeV1eywrA/h50flksX079ZNiyDSa9FD6B2iiByXdTqPPgEo2haNFfSGuFwygG4O612tZTUPMg6YhwsxB5BSvgm8CZqF3oBj18ni7YupsGiH6l1pxAJsCwwkNDwebt7Q3N27FWk0UvLzzxSuXUvR199gKanKfx40YjgR115L2MSJGKKjXVqh7nis1ukEQ7pGMqRrJH+e0J/TuaV8vT+Lr/dnseVIDrs79mF3R21ghL+QJPt3YPv6A4zoEc2w7lFEBPvVcwTPs6nrMBZNmMeZ/DLiIoOY07U/13vgOEII9KEh6ENDoFu3+j8AWEpLrYLvIPx5uZjO51YTfm05D1laChYLlobeCIRA6B1CSPV6rSO6tAxpMSP0eu0pIDKy2TcAU34+pqwsLYzVzw9D584YIiM99jlF82mIoG8F+goheqEJ+TTgNscKQohk4A00S/6c21vpAscO0V5GE4f8/SjS6yhu5oS57qT84CHyln1E0f/WY3awugMTEgi/9lrCr56EX40OLZu12RJWaLfoYO6+tBd3X9qLgjIj3x48x09Hckg9mcex7BJ+OZ7LL8dzAW1gRL/OoQzvEc2IHlGM6BlF92jP+oxrkrIjg7kr99gHXmXklzF35R6AOlMVtxS64GD8g4Oha8P6bixlZRw4coSA3hcgzVqkEPboITOYq6KHMJmQFos94sgZ0mzGlJ2NKTtbKxDCYXyAdayAk3WsNwmh04IJTPn5GDPOgNTmxZVGo7YOdYpzUz+ncA/1CrqU0iSEmAWsRwtbfEdKuU8I8SSQKqVcBSwCQoHl1j/3KSnlFJc7dROOHaI9TEZ2WjsGu/iFe/rQ9WLMyuLMo3Mo3brVXmbo1InI395C+DXXENCrV52fj7juuhb3i0YE+TE1KZ6pSZow5pZUsu1kHqknc9l2Io/d6QUcyirmUFYxy349BUBMaIBd3If3iCIhLgJ/g+cGIDsbRVtmNLNo/cE2IeiNRRcUpAlpUCC6BtwYbYJuE31jerom9rUQoBNgsSCNRqSryV5qfkqnA4NBq1/THSstmM6eRRcYWHVDqNFmU1aWXcyrfS4rSwl6I2lKMr4GxaFLKb8EvqxR9jeH5XGNPrIbmD1sNv/3w1wsSHoYTXwbHESgxcLss6dh96ct4j+v6c+Nue9eTJmZnH/r7Vp/IlNBAf7du9cr5m2F6BB/xg/qzPhBnQGoMJnZm1FA6ok8Uk/mse1kHjnFFfxv31n+t097Kgow6BjaLbJK5LtHu9VN42oUbV2Tcrd1AgMDOX/+PB06dKj3aUfodAiHqIhKl+MPJEGDEqpuACbHcQPV17E+DUiz9gSAi9BQAGkyUXHkSFV7bMJutfBd3Tik0Yi0WOxPAIq6kVJy/vx5e0x/Q/H6SaKveieBc3odyzMy+Ud0FL8tKtYGFEV009LlehBnvu76cEfHZltBSsnxnBJN3E9olvzR7Npzo/bsEMyALuEMiA1jQJdwBsaG0S0quEkTY7uafDs+MoifHhvbpPNobYxGI+np6bUG2TTos1lZzgeV6fX4de7c6P1JiwUsFkw5OWCxOK0jDAZ7vUYjhObXt0YR2d7R6bSbQ43y9nwDCAwMpGvXrvj5VTeImtsp2mYx7/qYPKsodDeaeP+sg/u+BQYUORuGDYCfH7iwVNrayMPmIISgd8dQencM5ZYRWqdhnt1Nk8eGfWc5llPCifOlnDhfarfiAUL89fTvEsaA2HAGWt/7dwkjPLBua37OxP7VfOigTb49Z2L/Oj7VtvHz86NXE5/aCo4ccR3GecUVTW5TQ8JDpdmshX7aIoByz1P83XcUrF5T/SYjBLqQECxlZfWOaK6FwYA+MlILA7W9oqO09UhbGGhkVZhoVJR9TEZ7xKsFfdlPCzGGCDqaTOwJ8CdHr9esc2iRAUXORuwBYDRiiItrtRF9rUlUiD/jBnWmuMLEe5tPVNtm0An6dAolr7SSrMIKtp/KZ/up/Gp1ukYF2a14m1Xfs0MIeuuN2+YnX7T+YFWUy8T+bvGfp+zI8Mh+PYmnOtAbsl+h12OIjsYQHY1NQsOvvpqQSy91+jlpsWApKsKcl6fdAPKron3MefmYc3O1bfl51uigPG20cE4O5pycBrddFxxcp/jbxwHY6kRE+MyTgNe6XBZuWcgnBz4GIRhWXs6osnLejgjXBhSVlMKNb3nUh162axcnbrvdqcVhiItzOaKvvgEwvkJ9rpHckkoOZBay/2wRBzILOXC2iINZRVSaaj/GB/rp6N85rJbbJjLYfSPsakbPgGb5P3Pj4DYv6r6MrKzElJdvF39zXp52A7AKvjnfenPIs94c8vNdPh27RKfTUkA4WvtRtgFgDk8GUdEYrDcEXXDTJnF3xxgKn3O5rD22lk8OfqL544BuRhNp/v6U63QsjorkWkugx8RcmkzkvPEGOa/9WxNzIapFA9jixVsy9LAtUl/nZXSIP6P6xDCqT4x9m8ls4cT5EvZnFnHgbCEHMos4cLaIjPwydqUXsCu9oNq+YkID6B0TQq+YEHp11N57x4TQvUMwAQZ9o9rra9EzvoLw98evcyf8OndqUH1tMFhJHeKfW+NpIB9LQYG2Pa/haS5EYKBV5CO1dBCOTwP2G4KDKygigsJ16zyeotkrBX3x9sXagpQgBN1MJpaHaUnzzxr0WrpcD1B56hRn5vyFsl27AIieMYOAvn3IfvkVp6LdGqGHbYW6UgC7wqDX0adTGH06hXHd0Dh7eUGpURP4s5rQ788s4uDZInKKK8gpruDXE7nV9iOE5rrpFRNqF/yeVrGPiwyyu28c8cXomfaINhgsFH1oKHTv3qDPSKNR6wvIc7D2nYp/nn1QmCwvx5SZiSkzE6ejKJ03rlYoqLtTNHuloNsGFAVISYUQRJnMZOk1i6yLPsjt1rmUkoKVK8l6+h9YSksxdO5M3HPPEnLJJQBE3nCDW4/nC7iz8zIi2I+Le3fg4t4d7GUWi+RsYTnHc0o4llPC8ewSjucUczynhNN5ZZzO1V7fH8quti9/vY4eHYLtVr0m+KF0Dg/kbGHtDu66bkAK30D4+WGIicEQE0NDulOllMiysmrib+8LcOYKysvTBhW6cG+7M1DCKwXdNqDID6gATAL73W92oXstKktlJWefWEDB558DEH7N1XR54gn0ERFuPY6v4cnOS9BSGMRFBhEXGcSlDm4bgEqThdN5pVaRtwq+VeyzCis4fK6Yw+ecZ/hzxE8nmJjQmd3p+cRFBtEhxF/lUVEghEDYRwQ37PcszWYOj70Kc1ZWrW3uDJTwSkEf3XU0nxz4mArrn6tQpwcpuaSsjGuzz7vtOKacHNIfeJCyHTu0Ds2/LyB8yhT1p24g1yfHt4r/2d+g44KOoVzQsfZs8CUVJo7nlNR6HcsuprC8+nB6o0Xyzk8neOenE4A2aCo2ItB+I4lzXI4MJDYiiJAAr/xLKTyM0Ovp/OifPZ762Ct/feuPrAIhMALBFgvpfgYQgpN+/m4LVyxPS+P0n2bZfePdXn2FwEGD3LJvResREmAgMT6CxPjqT1hSSvJKjRzPKSY9r4wz+eVkFpRxJr+MDOtyfqnRHlPvishgP2IjgoiP1MQ+NkITe5vwdw4LwKD3jRA5ReNoiUAJrxP0tcfWkm8qtUe4xJtMHPXXBqOcNejdMhl04f/Wc2buXGRZGUHJyXR9eQmGmJj6P6jwWoQQRIf4Ex0SzfAezuuUVJisIl/OmXxN7M8UVF/OLzWSX2pkf2bt2Y9AS6/SOdy5lR8bEUh8ZBCRwX7qKdBH8XSghNcJ+uLti7WRZ1JiEYJ4o4lfg7R8B11M5mZ1iEqLhZxXXyPHOpN4xI030mXBExStX99uww8VVYQEGOxROM6QUnK+pNIq8FVCn1lQTkZ+GZkFZZwrqiCzoJzMgnK2nXQeJhfkpyc2UhP36i6eKms/0K9xYZmK9oHXCbotwiXcYiFfryfSYqFUp9M6RPPym7TPgtWryfrnvzCftQ5NF4JOf/0L0XfdReGaNR6PHVX4BkIIYkIDiAkNYIgLz1+lyUJWoVXsa1r7+eWcKSijqNzEsewSjjnJi2MjOsRfE/eIIMqMZnadzqew3ERMiD9/GN2baRd2IyJIWfrtDa8T9C5+4WQaCwi2CrrBGgoUYbZoA4oaScHq1Zx5fD5UOESTGgwYrJnvnOVrcXfsqKL94G/Q0S06mG7RrkcaFpYbybSKu93Kz9es/DMFZZwtKCe3pJLckkr2ZlR37eSUVPLsugM8u+4AfnrtBtMxLMB6o/G3L9d8Dw+snQpX4X14naDPzstnQbDE1q1kFhBosTA3Nw+uXtzo/WUteqG6mAMYjXbBdhUj6ktJthRti/BAP8K7+NG/i3PXjsUiySmu4ExBOTPe3Upuae10twIwmqXdvVMf/gYdHV2IfkxoANEh/nQI9Sc6xJ+oYH+ng7MUrY/XCfq12achJJiXoiIBEJKq/C11+M+d5VDw79kL8znnEyzZBNsQG9suk2wp2i46naBTeCCdwgPJcyLmNg48NYnsIm00rfZe6bBc/b2k0kxGfpnT0b01EUKbDCU6xJ8OIf7WzuQA+/LxnBK+3JPJ+ZJKOocF8MBVfZl2YTcV3dMCeJ2gI/RcU1LKwphoAB7OKyDSYgHhupOoZipQ05kznPm/eXU+YtoE21WSLXfGjioUTaWuFAuBfvp63Ts2SitN5BRVku0g9DaxP1+suXfOl1SQW1JJfpnRHs1Tl58fIKuogsdT9vJ4yl5C/PWEB/lpTyBBBuu7H+GBBiKC/Fxs09bDAv3UU0ED8D5Bl2Y+Cw2hWKcjyGLhT51iuM02qYULnOYtNxqRQNCwYZTt21fN7eIo2O09yZaibeOuFAvB/ga6dzDQvUP94m8yW8gvM2oiX1xJXmkl50sqyS2u5M3vj1JS6TzneUmlmZJKc4NcQM4ICzAQHuRHWKChlvhX3RBqbwsP8iMswNCkCVW8Da8T9LUdu/GvQO0H08VkZm9gAAsC/CG4A9e6+Exd/u4eH/6XwjVr6hTs9pxkS9G2aY388Aa9zu5bp8akSC99fcjl/vb+fSKFZUYKy40UlBopLDfZ1wvLTNb3qvUC+7KRogqT/dUUhKi6ITh7CtBuCNWfGsIdbhKhAd7Raex1gr44KpLQ0hyKgEiLmePCj3IhWBwV4VLQXfrB4+IQQijBVng1nkixUDM/fEZ+GXNX7rEfzxWuXEDxkUGEBmjCGEfjE55ZLJLiSusNwCr+BWW2G4CrG4OtvnZDKCw3WdM7ND7fk05Q+2bg7AnBibsoPNCPYH99i9wQvE7QzxoL6GuRZALBFlmt3BUdH5hF5rzHq82BqPzgCoVrmpof3lNTBOp0wi6iRDX+82aLpLjc4UZgE3+HpwBXN4aCMiMllWZ7v0FTMOgE4UF+6AQUlBkxmiXxHpgVy+sEPcIi8bPGnuuoEvQuZuepKS2lpRR+uU4Tc2tGRtuMQsoqVyic09T88J7OstlU9DpBRLAfEcF+dGvC501mC0XlJqfuoYIyF08GDnVLK83kllSPSGroU09j8CpBX3tsLcVILcgWMFsX9FIy+3xurfrm/HyO/3YaxpMntXodOtD5r39RQq5Q1ENTJiix0VpZNj2JQa8jKsSfqJCmTXtYabIw+vlNtXLuu3tWLK8KDF28fTEmnY5yqy/K9m4GCI621ytYvZpDY67g0CUj7WIOYM7JIXP+3yhYvbolm61QeB1zJvYnqEa+GHe4Ttor/gYdWU4mUAH3zorlVYJuy+NSbJ2hu8A2UEEIFlsHGhWsXs2ZeY87TSQPVcP2FQqFa65PjueZGwcTHxmEQOvUVBNmNw9XTzfunBXLq1wutjwuudbp5nL0VRbEWaOW0yLr+UVQ6Xr0HKhh+wpFQ/BF10lr4qkOY0e8ykKfnZ1FiNmMUQjCzBYKdFXN7xLShdLtOzBnZ9exBw01bF+hULQ0LfHU41UWOpXFdDaGc0yvJ9pipkjvx6X7zNy9QRJWfpqT3FbvLlS4okKhaC08/dTjNYK+9thaFsRE06tSiwONrLQw/xUTYeX2oJd6UeGKCoXCl2mQoAshJgGLAT3wtpTy2RrbA4D3geHAeeC3UsoT7mzo4u2LKdfpCLTGoHctsBBeXv+sLfrISDrP+z8l4gqFwuep14cuhNADrwJXA4OAW4UQNWdL/j2QJ6XsA7wIPOfuhtoiXPpkaoLeuXbYuVNEcLASc4VC0S5oSKfoRcARKeUxKWUl8DEwtUadqcB71uUVwFXCzYkLuoR04dJ9Zjqe1XH7JjNj1zfMW6QiWhQKRXuhIYIeD5x2WE+3ljmtI6U0AQVAB3c00MbsYbO5+ytJj2M6pm6R+Fc27H6hIloUCkV7oUXDFoUQ9wghUoUQqdkNCC905Nre1xJWBjLYTFqCmcJo5zmXqx1PRbQoFIp2REP8FhlQLZ9NV2uZszrpQggDEIHWOVoNKeWbwJsAI0aMcJ5NywW24fq9D9bRZJ0OfXg45oICNRGFQqFodzRE0LcCfYUQvdCEexrUCvheBdwF/AzcDGyUUjZKsOvj3HNP1xmeqKJZFApFe6deQZdSmoQQs4D1aGGL70gp9wkhngRSpZSrgP8AHwghjgC5aKLvVkw5rvOdxy16Xgm5QqFo9zQoVERK+SXwZY2yvzkslwO/cW/TqmMINmEqrd1c4WdWYq5QKBR4US6XTpf4IfSWamVCbyH2cq8Z7KpQKBQexWsEPeLeBcReUooh2ARIDMEmYi8pJeLeBa3dNIVCoWgTeI95O+QWImZDxDdPQkE6RHSFq56GIbe0dssUCoWiTeA9gg6aeCsBVygUCqd4jctFoVAoFHWjBF2hUCh8BCXoCoVC4SMoQVcoFAofQQm6QqFQ+AjCzSlXGn5gIbKBk038eAyQ48bmtAbqHNoG6hzaBt5+Di3Z/h5Syo7ONrSaoDcHIUSqlHJEa7ejOahzaBuoc2gbePs5tJX2K5eLQqFQ+AhK0BUKhcJH8FZBf7O1G+AG1Dm0DdQ5tA28/RzaRPu90oeuUCgUitp4q4WuUCgUihooQVcoFAofwesEXQgxSQhxUAhxRAjxWGu3xxlCiG5CiE1CiDQhxD4hxGxrebQQ4ishxGHre5S1XAghlljPabcQYljrnkEVQgi9EGKHEGKNdb2XEOIXa1s/EUL4W8sDrOtHrNt7tmrDrQghIoUQK4QQB4QQ+4UQI73texBCPGz9He0VQiwTQgS29e9BCPGOEOKcEGKvQ1mjr7sQ4i5r/cNCiLvawDkssv6WdgshPhdCRDpsm2s9h4NCiIkO5S2nWVJKr3mhzWl6FOgN+AO7gEGt3S4n7YwFhlmXw4BDwCDgeeAxa/ljwHPW5WuAdYAALgF+ae1zcDiXR4CPgDXW9U+Badbl14H7rMv3A69bl6cBn7R2261teQ/4g3XZH4j0pu8BiAeOA0EO1396W/8egNHAMGCvQ1mjrjsQDRyzvkdZl6Na+RwmAAbr8nMO5zDIqkcBQC+rTulbWrNa9cfahAs8EljvsD4XmNva7WpAu78AxgMHgVhrWSxw0Lr8BnCrQ317vVZud1fgG2AssMb6h8tx+EHbvw+0ScRHWpcN1nqildsfYRVDUaPca74Hq6Cftoqawfo9TPSG7wHoWUMMG3XdgVuBNxzKq9VrjXOose0G4EPrcjUtsn0PLa1Z3uZysf24baRby9os1kfeZOAXoLOUMtO66SzQ2brcVs/rJeAvgG0y1w5AvpTSZF13bKf9HKzbC6z1W5NeQDbwrtVt9LYQIgQv+h6klBnAC8ApIBPtum7Du74HG4297m3u+6jBDLQnC2gj5+Btgu5VCCFCgc+Ah6SUhY7bpHa7brMxo0KIycA5KeW21m5LMzCgPTL/W0qZDJSgPerb8YLvIQqYinZzigNCgEmt2ig30Nave30IIeYBJuDD1m6LI94m6BlAN4f1rtayNocQwg9NzD+UUq60FmcJIWKt22OBc9bytnhelwJThBAngI/R3C6LgUghhG3qQsd22s/Buj0CON+SDXZCOpAupfzFur4CTeC96XsYBxyXUmZLKY3ASrTvxpu+BxuNve5t8ftACDEdmAzcbr0xQRs5B28T9K1AX2sPvz9ap8+qVm5TLYQQAvgPsF9K+S+HTasAW0/9XWi+dVv5ndbe/kuAAodH01ZBSjlXStlVStkT7TpvlFLeDmwCbrZWq3kOtnO72Vq/VS0wKeVZ4LQQor+16CogDS/6HtBcLZcIIYKtvyvbOXjN9+BAY6/7emCCECLK+qQywVrWagghJqG5IadIKUsdNq0CplmjjHoBfYFfaWnNaskOBjd1UlyDFjVyFJjX2u1x0cbL0B4ndwM7ra9r0HyZ3wCHga+BaGt9AbxqPac9wIjWPoca53MFVVEuva0/1CPAciDAWh5oXT9i3d67tdttbVcSkGr9LlLQoiW86nsA/g4cAPYCH6BFUrTp7wFYhubzN6I9Kf2+KdcdzU99xPq6uw2cwxE0n7jtf/26Q/151nM4CFztUN5imqWG/isUCoWP4G0uF4VCoVC4QAm6QqFQ+AhK0BUKhcJHUIKuUCgUPoISdIVCofARlKArFAqFj6AEXaFQKHyE/wel8eTBBG/Y+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's fit all the data at once, regardless of temperature\n",
    "exp_1.fit_sys_full(72) # The number to fill in here is number of temperatures*the number for the previous simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tough-rainbow",
   "metadata": {},
   "source": [
    "With good information from the prior fitting this step does very little, it just makes it consistent with the arrhenius law. \n",
    "With worse guesses this optimizer can have significant impact on improving the fit for data that must be consistent with Arrhenius law."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "blocked-wells",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}