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{
 "cells": [
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  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Write something to the notebook, who wrote it, to which purpose ...\n",
    "Move functions to another file, which gets imported -> better arranged at all\n",
    "Need to redefein paths to the potentials?"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 46,
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   "metadata": {},
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fixes    
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   "outputs": [],
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   "source": [
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    "### Install python modules\n",
    "#!pip3 install -U matplotlib\n",
    "#!pip3 install -U pandas\n",
    "#!pip3 install -U ase\n",
    "\n",
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    "### Import python modules\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ase\n",
    "\n",
    "### Varibales form RuNNer UC\n",
    "Bohr2Ang = 0.5291772109030   # CODATA 2018\n",
    "Ang2Bohr = 1/Bohr2Ang\n",
    "Eh2eV    = 27.211386245988   # CODATA 2018\n",
    "eV2Eh    = 1/Eh2eV\n",
    "f_conversion = eV2Eh/Ang2Bohr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotData(dataset):\n",
    "    '''\n",
    "    Use this function to get an E/atom over V/atom curve of the workshop's\n",
    "    data sets in the pandas data frame.\n",
    "    '''\n",
    "    fig1 = plt.figure(figsize=[51.2,28.8])\n",
    "    # Define plot limits\n",
    "    limits = [(7.5,25),(-4,-2.5)]\n",
    "    # Define plot data\n",
    "    atomic_volume = [dataset['atoms'][i].get_volume()/dataset['number_of_atoms'][i] for i in range(dataset.shape[0])]\n",
    "    atomic_energy = dataset.energy/dataset.number_of_atoms\n",
    "    # Count plot data\n",
    "    counter = 0\n",
    "    data_indices = []\n",
    "    for point in range(len(atomic_volume)):\n",
    "        if limits[0][0] <= atomic_volume[point] <= limits[0][1]:\n",
    "            if limits[1][0] <= atomic_energy[point] <= limits[1][1]:\n",
    "                counter += 1\n",
    "                data_indices.append(point)\n",
    "    print('Number of points in plot: {}'.format(counter))\n",
    "    # Creating the plot E/atom vs. V/atom\n",
    "    plt.text((limits[0][1]-limits[0][0])/2+limits[0][0], (limits[1][1]-limits[1][0])*0.95+limits[1][0], 'Number of points in plot: {}'.format(counter), fontsize = 30)\n",
    "    plt.plot(atomic_volume, atomic_energy, '*')\n",
    "    plt.xlabel('atomic volume in $\\AA{}^3$', fontsize = 30)\n",
    "    plt.ylabel('atomic energy in eV', fontsize = 30)\n",
    "    plt.xticks([i for i in range(0,250,10)], fontsize = 30)\n",
    "    plt.yticks([i for i in np.arange(0.0,-4.0,-0.5)], fontsize = 30)\n",
    "    plt.xlim(limits[0])\n",
    "    plt.ylim(limits[1])\n",
    "    plt.grid()\n",
    "    plt.show()\n",
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    "    return fig1\n",
    "\n",
    "def radialSF(distance, eta=0.0, shift=0.0, cutoff=12.0, type_fct=2, type_cutoff=1, inner_cutoff=0.0):\n",
    "    '''\n",
    "    '''\n",
    "    d = distance\n",
    "    e = eta\n",
    "    s = shift\n",
    "    t = type_fct\n",
    "    c = cutoff\n",
    "    tc = type_cutoff\n",
    "    ic = inner_cutoff\n",
    "    if t == 0:\n",
    "        pass\n",
    "    elif t == 2:\n",
    "        radial_part = np.exp(-e * (d - s)**2) \n",
    "        cutoff_part = cutofffct(d, c, tc, ic)\n",
    "    else:\n",
    "        pass\n",
    "    return radial_part * cutoff_part, radial_part, cutoff_part\n",
    "\n",
    "def angularSF(angle123, distance12, distance13, distance23, eta=0.0, Lambda=1.0, zeta=1.0, cutoff=12.0, type_fct=3, type_cutoff=1, inner_cutoff=0.0):\n",
    "    '''\n",
    "    '''\n",
    "    a123 = angle123/360 * (np.pi * 2)\n",
    "    d12 = distance12\n",
    "    d13 = distance13\n",
    "    d23 = distance23\n",
    "    e = eta\n",
    "    z = zeta\n",
    "    l = Lambda\n",
    "    t = type_fct\n",
    "    c = cutoff\n",
    "    tc = type_cutoff\n",
    "    ic = inner_cutoff\n",
    "    if t == 3:\n",
    "        angular_part = 2**(1 - z) * (1 + l * np.cos(a123))**z \n",
    "        radial_part = np.exp(-e * (d12**2 + d13**2 + d23**2))\n",
    "        cutoff_part = cutofffct(d12, c, tc, ic) * cutofffct(d13, c, tc, ic) * cutofffct(d23, c, tc, ic)\n",
    "    else:\n",
    "        pass\n",
    "    return angular_part * radial_part * cutoff_part, angular_part, radial_part, cutoff_part\n",
    "\n",
    "def cutofffct(distance, cutoff=12.0, type_fct=1, inner_cutoff=0.0):\n",
    "    '''\n",
    "    '''\n",
    "    d = distance\n",
    "    c = cutoff\n",
    "    t = type_fct\n",
    "    ic = inner_cutoff\n",
    "    x = (d - ic) / (c-ic)\n",
    "    if t == 0:\n",
    "        pass\n",
    "    elif t == 1:\n",
    "        if d < ic:\n",
    "            return 1.0\n",
    "        elif ic <= d <= c:\n",
    "            return 0.5 * (np.cos(np.pi * x) + 1)\n",
    "        elif c < d:\n",
    "            return 0.0\n",
    "    else:\n",
    "        pass\n",
    "    \n",
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    "### Define RuNNer Fit Class\n",
    "class RuNFit:\n",
    "    '''\n",
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    "    This ia a class for a RuNNer fit. It reads the output of mode1 and mode2, reads the train-/testpoints and -forces\n",
    "    of the best epoch given by RuNNer.\n",
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    "    '''\n",
    "    def __init__(self, dir_fit = None, epoch = 0):\n",
    "        self.dir_fit = dir_fit\n",
    "        self.epoch = epoch\n",
    "        self.train2data, self.test2data = self.read_mode1()\n",
    "        self.rmseEtr, self.rmseFtr, self.rmseEte, self.rmseFte, self.rmseEs, self.rmseFs = self.read_mode2()\n",
    "        self.trainpoints, self.testpoints, self.trainforces, self.testforces = self.read_epoch()\n",
    "        self.datapoints = self.reconstr_data()\n",
    "    \n",
    "    def read_mode1(self):\n",
    "        dir_fit = self.dir_fit\n",
    "        mode1_in = open(dir_fit +'/'+ 'mode1.out', 'r')\n",
    "        train2data = {}\n",
    "        test2data = {}\n",
    "        for line_in in mode1_in:\n",
    "            splitline = line_in.split()\n",
    "            if len(splitline) >= 2 and splitline[1] == 'Point':\n",
    "                if splitline[5] == 'training':\n",
    "                    train2data[splitline[6]] = splitline[0]\n",
    "                elif splitline[5] == 'testing':\n",
    "                    test2data[splitline[6]] = splitline[0]\n",
    "                else:\n",
    "                    print('Problem with data!')\n",
    "                    print(line_in)\n",
    "                    exit()\n",
    "        return train2data, test2data\n",
    "    \n",
    "    def read_mode2(self):\n",
    "        '''\n",
    "        Read RMSEs, usually in eV/atom an eV/bohr.\n",
    "        '''\n",
    "        dir_fit = self.dir_fit\n",
    "        rmseEs = []\n",
    "        rmseFs = []\n",
    "        try:\n",
    "            mode2_in = open(dir_fit +'/'+ 'fit.out', 'r')\n",
    "        except:\n",
    "            mode2_in = open(dir_fit +'/'+ 'mode2.out', 'r')\n",
    "        for line_in in mode2_in:\n",
    "            splitline = line_in.split()\n",
    "            if len(splitline) == 0:\n",
    "                continue\n",
    "            elif splitline[0] == 'ENERGY':\n",
    "                rmseEtr = float(splitline[2])\n",
    "                rmseEte = float(splitline[3])\n",
    "                rmseEs.append([rmseEtr, rmseEte])\n",
    "            elif splitline[0] == 'FORCES':\n",
    "                rmseFtr = float(splitline[2])/Bohr2Ang\n",
    "                rmseFte = float(splitline[3])/Bohr2Ang\n",
    "                rmseFs.append([rmseFtr, rmseFte])\n",
    "            elif splitline[0] == 'OPTSHORT':\n",
    "                rmseEtr = float(splitline[1])\n",
    "                rmseFtr = float(splitline[3])/Bohr2Ang\n",
    "                rmseEte = float(splitline[2])\n",
    "                rmseFte = float(splitline[4])/Bohr2Ang\n",
    "        rmseEs = np.array(rmseEs)\n",
    "        rmseFs = np.array(rmseFs)\n",
    "        return rmseEtr, rmseFtr, rmseEte, rmseFte, rmseEs, rmseFs\n",
    "\n",
    "    def read_epoch(self):\n",
    "        dir_fit = self.dir_fit\n",
    "        epoch = self.epoch\n",
    "        # Trainpoints\n",
    "        trainpoints_file = dir_fit +'/'+ 'trainpoints.{:06d}.out'.format(epoch)\n",
    "        trainpoints = np.loadtxt(trainpoints_file, dtype = 'float', skiprows = 1)\n",
    "        trainpoints = pd.DataFrame(trainpoints, columns = ['Conf.', 'atoms',\n",
    "                                                           'Ref. total energy(Ha)',\n",
    "                                                           'NN total energy(Ha)',\n",
    "                                                           'E(Ref.) - E(NN) (Ha/atom)'])\n",
    "        \n",
    "        # Testpoints\n",
    "        testpoints_file = dir_fit +'/'+ 'testpoints.{:06d}.out'.format(epoch)\n",
    "        testpoints = np.loadtxt(testpoints_file, dtype = 'float', skiprows = 1)\n",
    "        testpoints = pd.DataFrame(testpoints, columns = ['Conf.', 'atoms',\n",
    "                                                         'Ref. total energy(Ha)',\n",
    "                                                         'NN total energy(Ha)',\n",
    "                                                         'E(Ref.) - E(NN) (Ha/atom)'])\n",
    "\n",
    "        # Trainforces\n",
    "        trainforces_file = dir_fit +'/'+ 'trainforces.{:06d}.out'.format(epoch)\n",
    "        trainforces = np.loadtxt(trainforces_file, dtype = 'float', skiprows = 1)\n",
    "        trainforces = pd.DataFrame(trainforces, columns = ['Conf.', 'atom',\n",
    "                                                           'Ref. Fx(Ha/Bohr)',\n",
    "                                                           'Ref. Fy(Ha/Bohr)',\n",
    "                                                           'Ref. Fz(Ha/Bohr)',\n",
    "                                                           'NN Fx(Ha/Bohr)',\n",
    "                                                           'NN Fy(Ha/Bohr)',\n",
    "                                                           'NN Fz(Ha/Bohr)'])\n",
    "\n",
    "        # Testforces\n",
    "        testforces_file = dir_fit +'/'+ 'testforces.{:06d}.out'.format(epoch)\n",
    "        testforces = np.loadtxt(testforces_file, dtype = 'float', skiprows = 1)\n",
    "        testforces = pd.DataFrame(testforces, columns = ['Conf.', 'atom',\n",
    "                                                         'Ref. Fx(Ha/Bohr)',\n",
    "                                                         'Ref. Fy(Ha/Bohr)',\n",
    "                                                         'Ref. Fz(Ha/Bohr)',\n",
    "                                                         'NN Fx(Ha/Bohr)',\n",
    "                                                         'NN Fy(Ha/Bohr)',\n",
    "                                                         'NN Fz(Ha/Bohr)'])\n",
    "\n",
    "        return trainpoints, testpoints, trainforces, testforces\n",
    "\n",
    "    def reconstr_data(self):\n",
    "        len_data_set = len(self.trainpoints) + len(self.testpoints)\n",
    "        datapoints = [None] * len_data_set\n",
    "        for point in range(len(self.trainpoints)):\n",
    "            datapoints[int(self.train2data[str(point+1)])-1] = np.array(self.trainpoints.iloc[point])\n",
    "        for point in range(len(self.testpoints)):\n",
    "            datapoints[int(self.test2data[str(point+1)])-1] = np.array(self.testpoints.iloc[point])\n",
    "        return pd.DataFrame(np.array(datapoints), columns = ['Conf.', 'atoms',\n",
    "                                                           'Ref. total energy(Ha)',\n",
    "                                                           'NN total energy(Ha)',\n",
    "                                                           'E(Ref.) - E(NN) (Ha/atom)'])\n",
    "\n",
    "    def plot_points(self):\n",
    "        rmseE = self.rmseEtr\n",
    "        trainpoints = self.trainpoints\n",
    "        testpoints = self.testpoints\n",
    "        \n",
    "        #Atomic energy difference over the number\n",
    "        fig1 = plt.figure('Energy Difference 1')\n",
    "        fig1.suptitle('Energy Difference DFT vs. NNP')\n",
    "        plt.plot(trainpoints['Conf.'],(trainpoints['Ref. total energy(Ha)']\\\n",
    "                                       - trainpoints['NN total energy(Ha)'])\\\n",
    "                                       / trainpoints['atoms'] * Eh2eV,\n",
    "                 'r+', label='Trainpoints')\n",
    "        plt.plot(testpoints['Conf.'] + len(trainpoints),(testpoints['Ref. total energy(Ha)']\\\n",
    "                                                         - testpoints['NN total energy(Ha)'])\\\n",
    "                                                         / testpoints['atoms']  * Eh2eV,\n",
    "                 'g+', label='Testpoints')\n",
    "        plt.xlabel('# of Structure')\n",
    "        plt.ylabel('(E(DFT)-E(NNP))/atom in eV')\n",
    "        rmse_line = np.array([[0, rmseE], [len(trainpoints)+len(testpoints), rmseE]])\n",
    "        plt.plot(rmse_line[:,0], rmse_line[:,1]*2, 'b-', label='+-2RMSE')\n",
    "        plt.plot(rmse_line[:,0], -rmse_line[:,1]*2, 'b-')\n",
    "        plt.legend()\n",
    "\n",
    "        #Atomic energy NNP over atomic energy DFT\n",
    "        fig2 = plt.figure('Energy Difference 2')\n",
    "        fig2.suptitle('Energy Difference DFT vs. NNP')\n",
    "        plt.plot(trainpoints['Ref. total energy(Ha)'] / trainpoints['atoms'] * Eh2eV,\n",
    "                 trainpoints['NN total energy(Ha)'] / trainpoints['atoms'] * Eh2eV,\n",
    "                 'r+', label='Trainpoints')\n",
    "        plt.plot(testpoints['Ref. total energy(Ha)'] / testpoints['atoms'] * Eh2eV,\n",
    "                 testpoints['NN total energy(Ha)'] / testpoints['atoms'] * Eh2eV,\n",
    "                 'g+', label='Testpoints')\n",
    "        min_Eatom_pos = trainpoints['Ref. total energy(Ha)'].argmin()\n",
    "        max_Eatom_pos = trainpoints['Ref. total energy(Ha)'].argmax()\n",
    "        ref_line = np.array([[trainpoints['Ref. total energy(Ha)'][min_Eatom_pos] /\n",
    "                              trainpoints['atoms'][min_Eatom_pos],\n",
    "                              trainpoints['Ref. total energy(Ha)'][min_Eatom_pos] /\n",
    "                              trainpoints['atoms'][min_Eatom_pos]],\n",
    "                             [trainpoints['Ref. total energy(Ha)'][max_Eatom_pos] /\n",
    "                              trainpoints['atoms'][max_Eatom_pos],\n",
    "                              trainpoints['Ref. total energy(Ha)'][max_Eatom_pos] /\n",
    "                              trainpoints['atoms'][max_Eatom_pos]]])\n",
    "        plt.plot(ref_line[:,0] * Eh2eV, ref_line[:,1] * Eh2eV, 'b-', label='Ref.')\n",
    "        plt.xlabel('E(DFT)/atom in eV')\n",
    "        plt.ylabel('E(NNP)/atom in eV')\n",
    "        plt.legend()\n",
    "        \n",
    "        #Energy difference over atomic energy DFT\n",
    "        fig3 = plt.figure('')\n",
    "        fig3. suptitle('')\n",
    "        plt.plot(trainpoints['Ref. total energy(Ha)'] / trainpoints['atoms'] * Eh2eV,\n",
    "                 trainpoints['E(Ref.) - E(NN) (Ha/atom)'] * Eh2eV,\n",
    "                 'r+', label='Trainpoints')\n",
    "        plt.plot(testpoints['Ref. total energy(Ha)'] / testpoints['atoms'] * Eh2eV,\n",
    "                 testpoints['E(Ref.) - E(NN) (Ha/atom)'] * Eh2eV,\n",
    "                 'g+', label='Testpoints')\n",
    "        min_Eatom_pos = trainpoints['Ref. total energy(Ha)'].argmin()\n",
    "        max_Eatom_pos = trainpoints['Ref. total energy(Ha)'].argmax()\n",
    "        rmse_line = np.array([[trainpoints['Ref. total energy(Ha)'][min_Eatom_pos] /\n",
    "                               trainpoints['atoms'][min_Eatom_pos], rmseE],\n",
    "                              [trainpoints['Ref. total energy(Ha)'][max_Eatom_pos] /\n",
    "                              trainpoints['atoms'][max_Eatom_pos], rmseE]])\n",
    "        plt.plot(rmse_line[:,0] * Eh2eV, rmse_line[:,1]*2, 'b-', label='+-2RMSE')\n",
    "        plt.plot(rmse_line[:,0] * Eh2eV, -rmse_line[:,1]*2, 'b-')\n",
    "        plt.xlabel('E(DFT)/atom in eV')\n",
    "        plt.ylabel('(E(DFT)-E(NNP))/atom in eV')\n",
    "        plt.legend()\n",
    "        \n",
    "        return fig1, fig2, fig3\n",
    "\n",
    "    def plot_forces(self):\n",
    "        rmseF = self.rmseFtr\n",
    "        trainpoints = self.trainpoints\n",
    "        testpoints = self.testpoints\n",
    "        trainforces = self.trainforces\n",
    "        testforces = self.testforces\n",
    "        fig1 = plt.figure('Force Difference 1')\n",
    "        fig1.suptitle('Force Difference DFT vs. NNP')\n",
    "        plt.plot(trainforces['Conf.'], (trainforces['Ref. Fx(Ha/Bohr)'] - trainforces['NN Fx(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'r1', label='TrainFx')\n",
    "        plt.plot(trainforces['Conf.'], (trainforces['Ref. Fy(Ha/Bohr)'] - trainforces['NN Fy(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'r2', label='TrainFy')\n",
    "        plt.plot(trainforces['Conf.'], (trainforces['Ref. Fz(Ha/Bohr)'] - trainforces['NN Fz(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'r3', label='TrainFz')\n",
    "        plt.plot(testforces['Conf.'] + len(trainpoints), (testforces['Ref. Fx(Ha/Bohr)'] - testforces['NN Fx(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'g1', label='TestFx')\n",
    "        plt.plot(testforces['Conf.'] + len(trainpoints), (testforces['Ref. Fy(Ha/Bohr)'] - testforces['NN Fy(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'g2', label='TestFy')\n",
    "        plt.plot(testforces['Conf.'] + len(trainpoints), (testforces['Ref. Fz(Ha/Bohr)'] - testforces['NN Fz(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'g3', label='TestFz')\n",
    "        plt.xlabel('# of Structure')\n",
    "        plt.ylabel('F(DFT)-F(NNP) in eV/Ang')\n",
    "        rmse_line = np.array([[0, rmseF / Bohr2Ang], [len(trainpoints) + len(testpoints), rmseF / Bohr2Ang]])\n",
    "        plt.plot(rmse_line[:,0], rmse_line[:,1]*2, 'b-', label='+-2RMSE')\n",
    "        plt.plot(rmse_line[:,0], -rmse_line[:,1]*2, 'b-')\n",
    "        plt.legend(ncol = 3)\n",
    "\n",
    "        fig2 = plt.figure('Force Difference 2')\n",
    "        fig2.suptitle('Force Difference DFT vs. NNP')\n",
    "        plt.plot(trainforces['Ref. Fx(Ha/Bohr)'] * Eh2eV / Bohr2Ang, trainforces['NN Fx(Ha/Bohr)'] * Eh2eV / Bohr2Ang, 'r1', label='TrainFx')\n",
    "        plt.plot(trainforces['Ref. Fy(Ha/Bohr)'] * Eh2eV / Bohr2Ang, trainforces['NN Fy(Ha/Bohr)'] * Eh2eV / Bohr2Ang, 'r2', label='TrainFy')\n",
    "        plt.plot(trainforces['Ref. Fz(Ha/Bohr)'] * Eh2eV / Bohr2Ang, trainforces['NN Fz(Ha/Bohr)'] * Eh2eV / Bohr2Ang, 'r3', label='TrainFz')\n",
    "        plt.plot(testforces['Ref. Fx(Ha/Bohr)'] * Eh2eV / Bohr2Ang, testforces['Ref. Fx(Ha/Bohr)'] * Eh2eV / Bohr2Ang, 'g1', label='TestFx')\n",
    "        plt.plot(testforces['Ref. Fy(Ha/Bohr)'] * Eh2eV / Bohr2Ang, testforces['Ref. Fy(Ha/Bohr)'] * Eh2eV / Bohr2Ang, 'g2', label='TestFy')\n",
    "        plt.plot(testforces['Ref. Fz(Ha/Bohr)'] * Eh2eV / Bohr2Ang, testforces['Ref. Fz(Ha/Bohr)'] * Eh2eV / Bohr2Ang, 'g3', label='TestFz')\n",
    "        min_FXYZatom = trainforces[['Ref. Fx(Ha/Bohr)', 'Ref. Fy(Ha/Bohr)', 'Ref. Fz(Ha/Bohr)']].min().min()\n",
    "        max_FXYZatom = trainforces[['Ref. Fx(Ha/Bohr)', 'Ref. Fy(Ha/Bohr)', 'Ref. Fz(Ha/Bohr)']].max().max()\n",
    "        ref_line = np.array([[min_FXYZatom, min_FXYZatom],\n",
    "                             [max_FXYZatom, max_FXYZatom]])\n",
    "        plt.plot(ref_line[:,0] * Eh2eV / Bohr2Ang, ref_line[:,1] * Eh2eV / Bohr2Ang, 'b-', label='Ref.')\n",
    "        plt.xlabel('F(DFT) in eV/Ang')\n",
    "        plt.ylabel('F(NNP) in eV/Ang')\n",
    "        plt.legend(ncol = 3)\n",
    "\n",
    "        fig3 = plt.figure('Force Difference 3')\n",
    "        fig3.suptitle('Force Difference DFT vs. NNP')\n",
    "        plt.plot(trainforces['Ref. Fx(Ha/Bohr)'] * Eh2eV / Bohr2Ang, (trainforces['Ref. Fx(Ha/Bohr)'] - trainforces['NN Fx(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'r1', label='TrainFx')\n",
    "        plt.plot(trainforces['Ref. Fy(Ha/Bohr)'] * Eh2eV / Bohr2Ang, (trainforces['Ref. Fy(Ha/Bohr)'] - trainforces['NN Fy(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'r2', label='TrainFy')\n",
    "        plt.plot(trainforces['Ref. Fz(Ha/Bohr)'] * Eh2eV / Bohr2Ang, (trainforces['Ref. Fz(Ha/Bohr)'] - trainforces['NN Fz(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'r3', label='TrainFz')\n",
    "        plt.plot(testforces['Ref. Fx(Ha/Bohr)'] * Eh2eV / Bohr2Ang, (testforces['Ref. Fx(Ha/Bohr)'] - testforces['NN Fx(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'g1', label='TestFx')\n",
    "        plt.plot(testforces['Ref. Fy(Ha/Bohr)'] * Eh2eV / Bohr2Ang, (testforces['Ref. Fy(Ha/Bohr)'] - testforces['NN Fy(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'g2', label='TestFy')\n",
    "        plt.plot(testforces['Ref. Fz(Ha/Bohr)'] * Eh2eV / Bohr2Ang, (testforces['Ref. Fz(Ha/Bohr)'] - testforces['NN Fz(Ha/Bohr)']) * Eh2eV / Bohr2Ang, 'g3', label='TestFz')\n",
    "        plt.xlabel('F(DFT) in eV/Ang')\n",
    "        plt.ylabel('$\\Delta F(NNP)$ in $\\mathrm{eV}\\,\\mathrm{\\AA{}}^{-1}$')\n",
    "        min_FXYZatom = trainforces[['Ref. Fx(Ha/Bohr)', 'Ref. Fy(Ha/Bohr)', 'Ref. Fz(Ha/Bohr)']].min().min()\n",
    "        max_FXYZatom = trainforces[['Ref. Fx(Ha/Bohr)', 'Ref. Fy(Ha/Bohr)', 'Ref. Fz(Ha/Bohr)']].max().max()\n",
    "        rmse_line = np.array([[min_FXYZatom, rmseF],\n",
    "                             [max_FXYZatom, rmseF]])\n",
    "        plt.plot(rmse_line[:,0] * Eh2eV / Bohr2Ang, rmse_line[:,1] * 2 / Bohr2Ang, 'b-', label='+-2RMSE')\n",
    "        plt.plot(rmse_line[:,0] * Eh2eV / Bohr2Ang, -rmse_line[:,1] * 2 / Bohr2Ang, 'b-')\n",
    "        plt.legend(ncol = 3)\n",
    "        return fig1, fig2, fig3\n",
    "\n",
    "    def plot_rmse(self):\n",
    "        rmseEs = self.rmseEs\n",
    "        rmseFs = self.rmseFs\n",
    "        fig1 = plt.figure('RMSEs over Epochs')\n",
    "        fig1.suptitle('')\n",
    "        plt.plot(rmseEs[:,0], 'r-', label='RMSE Etr')\n",
    "        plt.plot(rmseEs[:,1], 'r:', label='RMSE Ete')\n",
    "        plt.plot(rmseFs[:,0], 'g-', label='RMSE Ftr')\n",
    "        plt.plot(rmseFs[:,1], 'g:', label='RMSE Fte')\n",
    "        plt.xlabel('# epoch')\n",
    "        plt.ylabel('RMSE in eV and eV/Ang')\n",
    "        plt.legend()\n",
    "        return fig1\n",
    "\n",
    "    def plot_data(self):\n",
    "        '''\n",
    "        Plot of pandas dataset!\n",
    "        '''\n",
    "        dataset = self.RuNNer2data()\n",
    "        fig1 = plt.figure(figsize=[51.2,28.8])\n",
    "        # Define plot limits\n",
    "        limits = [(7.5,25),(-4,-2.5)]\n",
    "        # Define plot data\n",
    "        atomic_volume = [dataset['atoms'][i].get_volume()/dataset['number_of_atoms'][i] for i in range(dataset.shape[0])]\n",
    "        atomic_energy = dataset.energy/dataset.number_of_atoms\n",
    "        # Count plot data\n",
    "        counter = 0\n",
    "        data_indices = []\n",
    "        for point in range(len(atomic_volume)):\n",
    "            if limits[0][0] <= atomic_volume[point] <= limits[0][1]:\n",
    "                if limits[1][0] <= atomic_energy[point] <= limits[1][1]:\n",
    "                    counter += 1\n",
    "                    data_indices.append(point)\n",
    "        print('Number of points in plot: {}'.format(counter))\n",
    "        # Creating the plot E/atom vs. V/atom\n",
    "        plt.text((limits[0][1]-limits[0][0])/2+limits[0][0], (limits[1][1]-limits[1][0])*0.95+limits[1][0], 'Number of points in plot: {}'.format(counter), fontsize = 30)\n",
    "        plt.plot(atomic_volume, atomic_energy, '*')\n",
    "        plt.xlabel('atomic volume in $\\AA{}^3$', fontsize = 30)\n",
    "        plt.ylabel('atomic energy in eV', fontsize = 30)\n",
    "        plt.xticks([i for i in range(0,250,10)], fontsize = 30)\n",
    "        plt.yticks([i for i in np.arange(0.0,-4.0,-0.5)], fontsize = 30)\n",
    "        plt.xlim(limits[0])\n",
    "        plt.ylim(limits[1])\n",
    "        plt.grid()\n",
    "        return fig1\n",
    "\n",
    "        n2, bins2, patches2 = plt.hist(dataset.energy/dataset.number_of_atoms, 20)\n",
    "        plt.xlabel('atomic energy in eV')\n",
    "        plt.ylabel('amount of structures')\n",
    "\n",
    "    def NN_over_data(self):\n",
    "        '''\n",
    "        NN predictions over pandas dataset!\n",
    "        '''\n",
    "        dataset = self.RuNNer2data()\n",
    "        datapoints = self.datapoints\n",
    "        fig1 = plt.figure(figsize=[25.6,14.4])\n",
    "        # Define plot limits\n",
    "        limits = [(7.5,25),(-4,-2.5)]\n",
    "        # Define plot data\n",
    "        atomic_volume = [dataset['atoms'][i].get_volume()/dataset['number_of_atoms'][i] for i in range(dataset.shape[0])]\n",
    "        atomic_energy = dataset.energy/dataset.number_of_atoms\n",
    "        # Creating the plot E/atom vs. V/atom\n",
    "        plt.plot(atomic_volume, atomic_energy, '+', label='ref')\n",
    "        plt.plot(atomic_volume, datapoints['NN total energy(Ha)']/self.datapoints['atoms'] * Eh2eV, 'x', label='NN')\n",
    "        plt.plot(atomic_volume, datapoints['E(Ref.) - E(NN) (Ha/atom)'] * Eh2eV - 3.8, '+', label='ref-NN')\n",
    "        plt.plot(limits[0], [-3.8, -3.8], color='black', label='ref. line')\n",
    "        plt.xlabel('atomic volume in $\\AA{}^3$', fontsize = 30)\n",
    "        plt.ylabel('atomic energy in eV', fontsize = 30)\n",
    "        plt.xticks([i for i in range(0,250,10)], fontsize = 30)\n",
    "        plt.yticks([i for i in np.arange(0.0,-4.0,-0.5)], fontsize = 30)\n",
    "        plt.xlim(limits[0])\n",
    "        plt.ylim(limits[1])\n",
    "        plt.grid()\n",
    "        plt.legend()\n",
    "        return fig1\n",
    "\n",
    "    def RuNNer2data(self):\n",
    "        '''\n",
    "        Reading RuNNer's input.data file and create a pandas data frame as used for/in the workshop\n",
    "        '''\n",
    "        RuNNer_input = open(self.dir_fit +'/'+ 'input.data', 'r')\n",
    "        atom_counter = 0\n",
    "        found_data_point = False\n",
    "        lattice_constants = []\n",
    "        atomic_positions = []\n",
    "        elements = []\n",
    "        forces = []\n",
    "        pandas_data_frame = []\n",
    "        for line in RuNNer_input:\n",
    "            if len(line.strip().split()) == 0:\n",
    "                continue\n",
    "            elif line.strip().split()[0] == 'begin':\n",
    "                found_data_point = True\n",
    "            elif found_data_point:\n",
    "                if line.strip().split()[0] == 'end':\n",
    "                    atomic_positions = np.array(atomic_positions, dtype = float) * Bohr2Ang\n",
    "                    elements = ''.join(elements)\n",
    "                    forces = np.array(forces, dtype = 'float') * 1/f_conversion\n",
    "                    pandas_data_frame.append([len(pandas_data_frame)+1, ase.Atoms(elements, positions = atomic_positions, cell = lattice_constants, pbc = True), data_point_energy, forces, atom_counter])\n",
    "                    # Here add to pandas data frame ...\n",
    "                    #print(atomic_positions, elements, forces, lattice_constants)\n",
    "                    found_data_point = False\n",
    "                    atom_counter = 0\n",
    "                    lattice_constants = []\n",
    "                    atomic_positions = []\n",
    "                    elements = []\n",
    "                    forces = []\n",
    "                elif line.strip().split()[0] == 'energy':\n",
    "                    data_point_energy = float(line.strip().split()[1]) * Eh2eV\n",
    "                elif line.strip().split()[0] == 'charge':\n",
    "                    data_point_charge = float(line.strip().split()[1])\n",
    "                elif line.strip().split()[0] == 'atom':\n",
    "                    atom_counter += 1\n",
    "                    atomic_positions.append([line.strip().split()[1], line.strip().split()[2], line.strip().split()[3]])\n",
    "                    elements.append(line.strip().split()[4])\n",
    "                    forces.append([line.strip().split()[7], line.strip().split()[8], line.strip().split()[9]])\n",
    "                elif line.strip().split()[0] == 'lattice':\n",
    "                    lattice_constants.append((float(line.strip().split()[1]) * Bohr2Ang, float(line.strip().split()[2]) * Bohr2Ang, float(line.strip().split()[3]) * Bohr2Ang))\n",
    "                elif line.strip().split()[0] == 'comment':\n",
    "                    pass\n",
    "        RuNNer_input.close()\n",
    "        return pd.DataFrame(np.array(pandas_data_frame), columns = ['name', 'atoms', 'energy', 'forces', 'number_of_atoms'])\n",
    "\n",
    "    def plot_all(self):\n",
    "        '''\n",
    "        '''\n",
    "        figRMSE = self.plot_rmse()\n",
    "        figE1, figE2, figE3 = self.plot_points()\n",
    "        figF1, figF2, figF3 = self.plot_forces()\n",
    "        return figRMSE, figE1, figE2, figE3, figF1, figF2, figF3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "# About RuNNer\n",
    "**RuNNer** is a stand-alone Fortran program for the construction of high-dimensional neural network potentials (HDNNP) written mainly by Jörg Behler. It relates the local environment of an atom to its atomic energy $E_\\mathrm{s}$, which contributes to the sum of all $N$ atomic energies resulting in the total energy of the system $E_\\mathrm{s}$\n",
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    "\n",
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    "$E_\\mathrm{s} = \\sum_{a}^{N}E_\\mathrm{a}$.\n",
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    "\n",
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    "The atomic energy is described by an atomic neural network (NN), which is element specific. This gives the oppurtunity to describe different numbers of atoms with the same NN, which would be no the case if there is only one NN for the whole system. To feed information to the NN, the local environment up to a certain cutoff radius $r_\\mathrm{c}$ is described by so-called symmetry functions (SF) (more details are shown in a few moments) forming the SF vector $G$, which are the input layer of the NN. In the next layers of the NN - the hidden layers - this information will be processed and in the final layer - the output layer - the atomic NN will provide the atomic energy $E_\\mathrm{a}$. In each layer, there are a certain number of nodes $y$ which are connected by the weights $a$ and can be biased by the biases $b$. For the NN training the wheights and biases are optimized to represent best the data in the training data set.\n",
    "\n",
    "![test](NNP1.png)\n",
    "\n",
    "In general **RuNNer** can be separated into three different stages - so-called modes, in which different steps are performed.\n",
    "- mode 1: SF calculation, data set splitting in training and test set\n",
    "- mode 2: training of the NN to construct the NNP\n",
    "- mode 3: prediction of energy, forces, stress, charges\n",
    "\n",
    "All these steps are performed consecutively beginning with mode 1. Needed input files are:\n",
    "* ``input.nn``: \n",
    "  - main control file needed in all modes\n",
    "  - contains all control parameters (NN architecture, symmetry functios, ...)\n",
    "* ``input.data``:\n",
    "  - needed in mode 1 and 3\n",
    "  - contains structural information (lattice vectors, atomic positions, forces, charges, total energy)\n",
    "  - output of electronic structure code muts be converted to ``input.data`` format\n",
    "  - RuNNer repository provides the RuNNerUC (universial converter) to convert from several formats (FHI-aims, VASP, xyz, LAMMPS) to input.data format and vice-versa"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "# Getting the First Data Set\n",
    "\n",
    "Before we are gettinger deeper into **RuNNer**, we will go one step back. At the beginning of each NNP, there is your data set. For sure, the data set does not have to be good/perfect/large, because you can increase your data set step by step and train different generations of your NN, ending up with an accurate potential. For getting you first data set there are several ways like:\n",
    "- small random displacements,\n",
    "- thermal displacements by a simple potential - like force fields - in MD,\n",
    "- experimental data.\n",
    "\n",
    "The question \"What is a good data set?\" is not that simple to answer and it strongly depends on the purpose your potential. But one important point is for sure the distribution of your data over the configurational space you like to handle with your potential. If some configurations are missing, the NNP will provide inaccurate results, because you make the NNP predict energies and forces for an unknown configuration. In **RuNNer**, this is called an ``extrapolation``, that means the NNP is not trained to such a configuration.\n",
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    "\n",
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    "Here in the workshop, we are dealing with bulk-Cu. So, a first apllication of your NNP could be to predict the equilibrium lattice constant of bulk-Cu and you will calculate the energy of a bulk-Cu unit cell with different lattice constants to give a energy-volume curve, which provides the equilibrium lattice constant at its minimum. Thus, your data set should contain information of different cell volumes."
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in plot: 8073\n"
     ]
    },
    {
     "data": {
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      "image/png": 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lx3asRZJTk8r7BKmPGdSBNBJJZ1yiZ5MONWIqkeUNNRIR6YnZ09IDoy4TK5Ez3QEAAMSKtiIAbywrm1zdmWks1rgfAxMAAOmKOC0AAEBoVueDxDPGUDZ7ic/vGxqaPY+538ufw1DYhOVTh/TVxu379PXuFrV2OOUwpHOPK9Wcc76pkp75SYuZJzovLdG6+/ncx1hbpymHITlNKcuQ2jpjO9Yi6SdJ930CeyNBHUiweAeVgnXG2XXJ20SV13sk4r9+fppl5UfyBatTdFIC0ePGBAAAAECqYtBKckXTMWxl3I+BCQCAdEScFgAAILR45IPEK8aw9Ibxuubxt7Vp137PYwN65euRK8olU5r+SK127muT0zz4mgtGHR72szx61RjNqVqrRbWumEqsic6xindeWrIHcVrx+dzH2GeNzdrW3Ko+hbn6RnFPy+NZ7u9qTf1OtXUePLDskiuIzEKCOpBg0QSVunNxDdYZZ9fZpONd3lAjETf++pwYSg67CFWn6KSElPwblVRg10FMAAAAAIDUEE3HsJVxSgYmAADSCXFaAACAyFmdDxKvGMPw0l7qkZvl89je1g4NH9BLlVVrtX1vmyTXbNpOUzq6pFDNbR0RbdsuOTENTS0aPqBIxUV5en19Y8h4T3fzN5I9iNOKeJb3MRZP7u/qwuMPV7vTtF2uIDILCepAgiRjmVtvdppNOpLGhlXlDTQSceChBfrjD78d02dA8kVSp+ikhJT8G5VUYNdBTEguBncAAAAAiEaknaJ2ilMCQLojvpNaiNMCAABELpXyQTY0NPv83tTS0WXCzU5TyjIMHVV8SMSJzHb5DuYt36A1X+3S0cWFYeM90eZv2GUQZyrEs/y/q8XvbPL8bNcyI/05kl0AIFPUzJyg80aVKj/HVe3ycxw6f1SpamZNUENTi6bOX6mGA502wyqXqWz2Ei1cVS/TdF1cy2Yv0bDKZTGVwd1JVHXdOE0bM1iNza0xf67u8G5shGJFeQONRCzIzdLwAb2i3hbsJVSdAqT4nUvTUSrcTCHxIr1eAwAAAIDk6hSdO2WkhpcWae6UkSE7U+0SpwS6yz+mn67vidRHfCe1EKcFAABIT7U3T/LJb8nLNnT4oQXKyz6Y73LmiH469oheusPmyfbe/HMyNjQ0yzQlp2l2ifd0N3+jZuYElfXp4fndPzcokffKscazPti8W9/61Uv64OvdcSlfoDyqAb3yddEJA4nBIWmYQR1IkFBBpcqqtT6jw+I1Q0KyR85FO6rNqvLu3t+uof0KdcPEIZr36gbt2tfe7W3BPgjUIhxmm4mOXZb/QvLZZRQ6AAAAgPSV7DilN2YXRnckY8U+VglENIjvpC7itAAAAOnDO+bgnd/S1ulUj9wstXU6lZtlqKXdqfVbmrVxx96UuucLlZPhH2PpTv6G/32NJLW0O/XCu5v1u0uOl5TYe+VY41k3PrVGe1o7dOOTa/Svn59mZdEkBc6jmnRMied7SXYMDpnJME0z2WVAHJWXl5t1dXXJLgYOmLGgTsU98z1BpUWrvpAzQBXMy3boe98eqEW19crNcjVMpo0elDINkGAamloibpgAkfCvU417WiJe6gixqa6uVkVFRbKLEdacqrVpdy4F4o3rNQAAAIBMUlm1Vk/U1hMzQEQCdY5LimvSbzLeE6mP+A6Q2lKlDwYAAATX0NSiyQ+uUMOeVl0+ZpAam1t98ltefn+LzhjRP2TuWCrc80WTkxFt/ob7vuaFdzfLabq+kwG9ClTWp0ArP9uRMvfKZbOXBP3bxl+fY+l7kUeFZDAM423TNAMeaMygDiSQ/0iqGyYeHTRA+Mtn16XdDAnMeA2r2Wm2LdgTs80A0eN6DQAAACATpOrswlbN+M7M8d2TjBX7WCUQ3UF8BwAAALBepPfSgWIOkit5eu6UkZo7ZaT+XvelFr75RcDXnz+qNKn3fNHEDKLJyYg2f8N9X2NKnpnnxx/dR3Mv+FbIQbl2s/SG8brm8be1add+z2MDDy3QH3/4bcvfizwq2A0J6kAShQoQpusFg2RRAImUrudSIN64XgMAAABId6ma9GvV0tWJXAI7nSQj6ZdEY3QX8R0AAADAWpHcSwdbBcthSDWzJnh+949LuJ9jSgm75wuWiB5NzCCanIzu5G8Eu69JpXvl4aW91CM3y+exgtwsDR/QK0klAhKHBHUgyeIVILTrDDgkiwIAYH9crwEAAKxh1/gMkCriWYdSqSNTsm7G91SdOd5OkpH0S6IxuoP4DgAAAGCNaO6la2ZO0Ni7l8tp+m7DaUqn3PNvz/O94xIOw/X3s0f0V+/CPDXuaUlIXNE/Ed0OMQP/zx3qviaSe2W7xGd372/X0H6FumHiEM17dYN27WtPWlmARDJM0wz/LKSs8vJys66uLtnFQBJUVq3VE7X1mjZ6EDPgALBcdXW1Kioqkl0MAAAAIK3YJVgO6xCfAWIT7zo0Y0Gdinvm+3Rkend8Wi2W83yopauj2ZZV2wEAAPFDHwwAAPYS6F66YmixtjS16q4LRurW5z/wudf/+dNr9MzqTT7bGNArX89dP87n3jtUXCKeMZFgs7znZjl01rf6dztmYEV8O9Tn7s72ic8C8WcYxtumaQYMqjKDOpCigl107TCaDQAAAAAARC+apVNhb8RngNgkqg4lenbhWM7zVs34nmozxwMAAAAAkoPJNA4KdC/98dZmfb5tr36yaLU2bt+rea9s0A2Thuj6J1erR45DQ0oKtaGh2TM7+qRjSrp8j4HiEomIidTMnBB08PrvXtnQ7ZhBLHGPSD53pNtvaGrRmLuXy3ve5njGZ6krQHAkqAMpKthFN1QjAgAAAAAA2A/JzOmH+AwQm3SrQ1ad5yNZujqR2wEAAAAApC8m0/Dlvpd++q16mab0+ba9kg7+777Xl6SLTjhceTlZGnNUn6jvvauuO1mXP1Krva0dau2IT0wk1OD17sQMrIh7hIoFRbv9ecs3yDSlsj49tKWpJe6xJeoKEBwJ6kCKCXfRTcQMOIz8AgAAsM4Hm3fr4vlv6ulrx2r4gF7JLg4AIAnSLRETzFAMxCrd6pBV53mrZnxP9MzxEjFlAAAAAEgVTKYRmPte+unaL8I+d/E7myRJedkOzZ0yMqp770Wr6rVjb5vn9fGKiQRLRO9OzMCKuEeoWFCk2/c/djdu3+f5OR7fI3UFCM+R7AIAiE7NzAk6b1Sp8nNc1Tc/x6HzR5WqZtYEz3PcjYiq68Zp2pjBamxutbQM3iO/AAAAEJsbn1qjPa0duvHJNckuCgAgSdItERMu8Y7PwKWhqUVT569UAzNAp510qkPxPM/btQ74l4uYMgAAAACkhkjyktJZuPvs80YdHvG2TNOM+P2GzlmqstlLPLOwS66kakOKOiYSSaxg/vRyzZ0yUsNLizR3ykifxPRoWRX3CBYLinT7/seuw3DNor7wR6PjEluquu5k9TkkV3nZhqTMqytAJJhBHUgxkVx04zUDDiO/AAAArFM2e4nP7xsamj2Pbfz1OckoEgAgibqzdGo6SqcZdpMxQ3EmuufFj1T7+Q7ds+wj3T91VLKLAwulWx2K13nerstIu8s19q7lcnr1xxNTBgAAAAB7S+XJNKyILQa7z/bPmQqnrE8P/e3ak8KWd/KDK9TY3KoLjz9c7U4z4Czh0X6WZMQKrIh7hIoFRbJ9/2O3rdOp8Uf31fijizX+6OLoP1QYi1bVa3sCZrsHUpkRyUgdpK7y8nKzrq4u2cWAxWYsqFNxz3yfi24sI9ki1dDUEnTJFC6uQHoKdgNXXV2tioqK5BUMANLAB5t365rH39amXfs9jw08tEB//OG3NXxArySWDACA5KmsWqsnaus1bfQgWyVawn6CdQqS9IpMYdc6EK7DnpgyAADh0QcDAEi2ZOUlxco7tnjDpCFRJauHu8/2z5lyGFLfwjxtb25Vp1f6ZbZD6nBKR/QuUM2siVG/n/s92zqdUcdIh85ZqrbOrrmgyY4VJFIijt1g+y7LMPSd4SUpUVcAKxmG8bZpmgEPfEeiCwMgdpEssxKvpV1XfrotJUdJAugell8GgPgZXtpLPXKzfB4ryM0iOd2m4tW+BgC4DKtc5lnC1jRdM+yWzV6iYZXLkl002FSwiVeYkAVSZrTd7LrkeqBylfXpIYnZxAAAAAAgVUSSl2QngWKLo+9artrPg+c6+McOamZO8Ny/Sl3vs/1n5zYl9SrIkVOu+11JGlJSqH9ef4ouHztYw0uLQpY3WHL6RScMVNV14zRtzGA1NrcGLGsw5x5XKknKMgJ/hkwQ7Ni1MlYULCaz8uaJCZtgNt3jXkgfJKgDaSoeSaXzlm9Q4542DSku7NIYApBeSA4BgMTYvb9dQ/sV6qFLj9fQfoXavb892UVKa7EEbBi0BQDxZddES9jXilkTNdir01ByLZ28YnbwmamQOTKh7WbXJdcDlavTaerysYOJKQMAAAAA4sI/tugtWK6Dd+xgWOUyjb5ruTZu3+f5e0u7Uy+8u9nnPntbc6umjTl4f7tzX5uKC/P0yBXlunzsYB1VfEhESf3u8roTyd0uOuFw3T/1uC7bCBfncOd3LH5nkyR5ZnRvabdHrCDewvX/NTS1aPKDK0IOWIhGsmMymRD3QvrITnYBAFjLf5TdwlX1WriqPqblWvy3ub6hWd+dV2PJEjANTS1RLakDIDFqZk7wWZ7Ke/llAIB1aud8x/Pz5AOzGiB+vAM2kS6JGI/2NQCgq2QH9ZF6Sory1el09bblZBlq7zTV6TQ5ZjJcprXd3B3j3stW20Ggcs2dMlKSPP8DAAAAAGAV79hibpahtk5TWQ5DnU6zS65DoNiBJDkOJIs7TdeM6AN6FaisT4HP+3gnnd8w8Wi9/P4WNexp1Ytrt0Tc7+Rd3k7T9b5OUxpaUqjm1g6f50Ua5/DP78hyGDplSF/1OSQvIwaJ/3rZR6r9fIfuWfqR7r94lM/foo0VRZrHloyYTKbFvZAeDJY8TW/l5eVmXV1dsouBBGpoagmaVNrdDrp4bNOtsmqtnqit17TRg6JqrAGIvzlVa7Wotl65WQ61dTp96ml1dbUqKiqSW0AAACIUbKnESAI28WwLAwB8zVhQp+Ke+T5BfbsvH4zk4piBP9puAAAgHdAHAwBA9LzjRDc+tVobGpo9E2GU9MzTCzeMV0nP/KCxgyzDUNWaTQHzI/xF2u8UKtk5kriWf1nzsg0V5uVowY9Ga/iAXj7PDZXfEU48JhdNxISl4fZDsL87DOnNmycFLJed89iIe8GuDMN42zTNgIF5ZlAH0kw8ZhyLxzYZ1QXYn11n4QIAIFqxrAzCjL4AkDjeHTDMsItIcMzAXyLbbqwMCdgLdRIAAABIvmS2y73jREcVH6IxR/XRWSP660ePv6WGPa2e1XWDxQ4aI8yPCJX0XDNrgs93EGpl30jiWoHK2trRpkVv1nfZXiz5Hd1ZgTgZ2/QXbF5m98Pu/sEl733tWYlRki4YdXiX4zMV8tjos0QqIkEdSEPxSCq1epuxJAkBSAw6+gEA6SLWgA2DtgAAAFJHotpuiehoBTKFFUks1EkAAAAg+ezSLp8/vVzDKpdp4ZtfeB7zTjiuGFbcJXYQaX5EuKTnyqq1qv18h0bfuTzge0eb7LytuVUOGWGTp7uT3xFrUnage7lEJnqvmDVBU+ev1Mbt+zyPlfXpob9de5Kkg/2DnU5TWYbkNKWjSwrV3NbRZVupksdGnyVSjWEGG0qCtFBeXm7W1dUluxhAQLEsL5OOmOEFqYTlJQEAqSaSpRIBAACAcCJdxhtA5GJZQp06CSAd0QcDAEg1dmuXByuPIWnVnEkx5+QcddMSOaNIufROdu7Oezc0tQRNno7ls8S63UD3cvEqazDj73lVX+3cr9wsQ22dpo7oXaCaWRM9f4+mf5A8NqSTROYhGobxtmmaASuWI67vDAAhuEd1VV03TtPGDFZjc2uyi5RU97z4kWo/36F7ln2U7KIAAACknfnTyzV3ykgNLy3S3CkjSU5HRmloatHU+SvVwEwaAADErGbmBJ03qlT5Oa7ulfwch84fVaqaWRMsfy+u4amF/RW9YZXLVDZ7iRauqpdpumbWK5u9RMMql0W8jUTWSQAAAACB2a1d7i6PP1PS6DuXa2gU9xyBnDqkWGV9eig325AkOQzpzBH9VDG0WA7XQ8o68H9uiJV9I72PjHWlYKu3G+peLl5lDWZEaZEuHztYz/5kvC4fO1jDS4t8/h5N/yB5bEgn3itaJFN2Ut8dQEbrzvIy6ch/5ObidzZp8TubmOEFAAAAgCXssqwqAADpIJEdrVzDUwv7K3pWLKGe6OQHAAAAAF11p10ez9l93eUJ5jvHlGjq/JXdfu9HrxrtmW07L9s123ZxYZ5Wfb5DTtOVnN5pSkNLCvXbS473zNztL5L7SPf31CPHoWljBvvMBG4Fd1J2NNsNdy/XnW12l5W5Z+SxIR345yEuXFWvhavqk5aHSII6YGOJXGoByWOagdf9CfY4AAAAAETCbkEoAADSRbw7WrmGpxb2V/dZlVyeyOQHAAAAIJUkMu8o2nZ5vAf5bmtu1eVjB2tHc6uWrtvi8zf372PvWq7P7j6n29t3f95zH1yhhavqPX/rPJDus76hWTdXrdUff/Btn+8/2H1kbpZDowYd6rO/3N/TtNGDPEnTgZKnu7uvu5OUHe5eLpUTvcnVQ6qzYjIAKxkkQKa38vJys66uLtnFQDdVVq3VE7X1rkYGM66krYamFn1//kp9sX2f57GyPj30t2tPSovGDo239FRdXa2KiopkFyPtfLB5ty6e/6aevnashg/oleziAACAFNfQ1BI0CEXbHAAA++IaHj/xiFWyv2IzY0Gdinvm+ySxhFpyHQAyAX0wAACr2DHvyD852y1eg3xnLKhTYV62tu1t02sfNwZ8TqzvHei+sH9RvjYeyAO6fIzv9x/sPjLbMPTMmk2aNnqQ/v72V1F9T4ne1+l6L2fHOgNEy73CQ26Wa4WHeB/PhmG8bZpmwBMAM6gDNsSMK5mlpChfnU7XYKGcLEPtnaY6nWbadF6wtC0QuRufWqM9rR268ck1+tfPT0t2cQAAQIqzakZKAACQWFzD4ycesUr2V2xSeWY9AAAAwK7snHeU6Nl93fccc6rWSpIchnQgRSfq9w426Nn7vlCSWtqdnuR0qev3738f2dLu1HNrNvs8313W3AN/D1bWZO3rdLuXs3OdAaJlp5XmSFAHbMhuSy0g/kaUFqliWIktLgxWofEGRK5s9hKf3zc0NHse2/jr7i0pBgAAINkrCAUAACLHNdxa8Y5Vsr8AAAAA2Imd846SNch3W3OrLh87WJt37derHzXIYSjoewdLRA816Nl9X3jWiP6a/cx7+mrnfs/fHIZ0+vB+usMrkdv7PvKRFZ/pjU+3a9e+Np/9leUwVLV6U8jvyc77OpXwPdpDPFa+y0R2GkBCgjpgQ8y4knnsdGGwCo03IHJLbxivax5/W5t2HbxJHXhogf74w28nsVQAACAdpOO9BgAAmYBruLXiHatkfwEAALshwQvIbHbPO0rGIF/3fdsp97wqSTp7RH/1LswL+N7uRPR7ln2kL3fu15ovd6ktzKBn7/vC04YW64kDs6BLrhnbiwvzfL7/O84fqeufXK2+PXN1/9RRmlO1Votq6332V2ME35PV+zpTrx92rzOZIh4r3yG5SFAHbML/As+MK0h1NN6AyA0v7aUeuVk+jxXkZmn4gF5JKhEAAAAAAED6IFYJAAAyDQleAOycd5SMQb7+K2stWbdFkpSX7Qj6nMXvbJLkmgH9vFGlPoOeK4YWa0tTqxr2tHS5t9zW3KojehdoaL+eWl2/U/m5WWpsbvV5jv95etPOfSouzNMDU0epavVXWrL2a73036d6th3qe9q0c7/rtRcfpxfXbY1pX2fy9cPOdSbdxXvlOySPYZpmssuAOCovLzfr6uqSXQxEoLJqrZ6orde00YPS9gKfqaPsMtmMBXUq7pnv03jzvtHIVOlQF6qrq1VRUZHsYqSV0Xe+okN75OiGiUM079UN2rWvXbVzvpPsYgEAAAAAkNLSIQ4DaxCrBACkCvpgEAv/BC83ErwAZLqGppagK2u54wXu5/xzzeag28nLdqit06mjiwu1oaFZJT3z9MIN4wPGHALlggU7TzsMyZQ0bfQgSYoqh8yKnDOuH0imSOon7MswjLdN0wwYZCNBPc2RoG5/drjAJ6qTJhOS8IFIpENdIDgKAAAAAABSQTrEYQAAQGahDwaxIMELQDqyKq9pTtVaLaqtV26WK8k8UKzA/RxDkvNAWmV+jkNF+TnqNE3t3t+ujs6u+ZbeeV6hcsFqZk7wOU9HIjfbofUBcsiszDnj+gEpuRM9RFI/YU+hEtQdgR4EkDg1MyfovFGlys9xVcf8HIfOH1WqmlkTElYG7+VZ/DU0tWjq/JVqiGHZkmGVy1Q2e4kWrqqXabqW4SibvUTDKpfFUmzAFj7YvFvf+tVL+uDr3WGfS10AAAAAAABIDOIwAAAAyEQlRfnqmZet1g6n8rIdau1wqmdeNsmFACxhRQ5Rd4TKa4rGtuZWTRszWFXXjdO0MYPV2Nwa9DlnjegvyTWzeWuHU70KcrRjb5vOO7ZUDqPrtls7nJ6YQ6hcMP/ztCSV9enheW6WIc/2sw78cO63BgT8PFbmnHH9gGRdXeuOSOonUk92sgsAZLpkXuD9R9ItXFWvhavqfUbSeV94ujsqyX/0n/coO0SPZYnt5can1mhPa4dufHKN/vXz00I+l7oAAAAAAACQGMRhAAAAkKncCV6XjR6kRbX1akxwIimA9BVpDpFVeS2R5DVFqqGpRTv3teuOKSNV0jNfc6eMDPi8+dNdkwDPWFCny8cO1tNv1au909SGhmZJ0jOrN3V5TZbD0ORjB3hiDuFywQ4mwffTz//2rlo7nD7Pdes8MIX74tWbtHj1pi6f2+qcM64fmcvKutZd7ronKWj9ROohQR2wge5c4K1ozIXqpLHywsMoO2t1Z9CA1UntJMlLZbOX+Py+oaHZ89jGX58T8DXUBQAAAAAAgMQgDgMAAIBMRYIXgGhEkv8RbQ6RFZNhStYOPo+2TO5z6Q0Tj+5Sht49cpWTZah+x345DFciuX/MYdPO/SouzNMDFx+nF9dt9ckFc2+7smqtGptbNfDQAk/e2IwFdep0mhrSv6dWrG9Up6mQn9vKpHKuH5mLiR4QLySoAzbQnQt8JA2ncI3IUJ00Vl94GGUXu1gGDVjV+I/X9lLR0hvG65rH39amXfs9jw08tEB//OG3Q76OuhBfDJ4AAAAAAABuxGEAAAAAAAgtkvyPSHOIupPXEqqP34rB58HKlJvl0KhBh4bNLfAug2FIrR1OTTqmRK+tb5QknT2iv3oX5nWJOQzsXaDXNjTqxbVbunyv/mX6cud+LXzzC/297kvP9zSnaq2cUtjPTVI5rMBED9EjPykyJKgDKSaaxlwkjchgnTRWX3hoEMXO3eBf8u5mdZpSliFNPq405KABq5dgscOSLnYxvLSXeuRm+TxWkJul4QN6hXwddSE60TboGDwBAAAAAADciMMAALqDRAMAAFIX1/HIRZP/EWkOUXcmwwzXxx/r4HNPrs17X6vTaSrLYWjysQOUbRh6Zs2miHILtjW3akhxodY3NEtyfVduS9ZtkeRKJJci+14j+Z4YdI9E45iLTqLzk1L1+maYppnsMiCOysvLzbq6umQXAxGK5ETS0NQStJHifo1/Y8ct2iTiGQvqVNwz3+fC492pg8Tqzn6N5HiJhtXbS3Wj73xFh/bI0Q0Th2jeqxu0a1+7aud8J9nFSojq6mpVVFTE/X0qq9bqidp6TRs9KGSDzqrzHgAAAAAASLxU7WABAKSnSOPSQLwkqg8GANIR1/HIRZv/EWkO0ZyqtVpU65qhvK3TGXRfxNrHH2ksIdj7RPq+4V6fl+1QYV62FvxotIYP6BXx9xrp9wTAXpKVn2Tn65thGG+bphkwqZQZ1AEbiWRkTSSjErszItHNuwHXnRmG6EyKn5qZE3TewyvUuKfNM6qzpGeenrt+XNDXWD0TPku6+PJORp98XGkSS5J+op2tP5bzHgAAAAAASC5WRAMA2AGryAIAokV+hH1wHY9epPkfgY7zUDlEkc7CXHXdyZr+SK2aW9vV2mEqL9uhHjlZOvywAjXsaQlbpyKNJbhybd5QY1OLOk3JYUi5WQ6Zklo7wucW+OciGIZ0+KEF+mrnfs/31trRpkVv1nvK8ean29XSHvp7ZbZqIDUlOj8p1a9vjmQXAIDrRFI2e4kWrqqXabpOJGWzl2hY5bKAz3c3UqquG6dpYwarsbnV5++xJBF7N+C6I9bXI7iSonxNOqafnKarYe40TU06piTsfg13vETL6u0BgdTMnKDzRpUqP8fVVMnPcej8UaWqmTUh4PMZPAEAAABktoamFk2dv1INdOQAKSXauCg43wFAPEUblwYAgPwI++A63j2R5H9Ee5zPn16uuVNGanhpkeZOGRlwlnVJWrSqXtv3tnmS01s7nNq5v13rNjWFfK9wsQT/+2ZXrk2JnHLNcGxKOuKwHmrrDJ5b4L0N71wEhyGZprRzb5schrokjZbNXqKxdy9Xw55WDSkpDPm9Rvo9AbCXROcnpfr1jRnUARuIdmRNJDObRzvSLtbRNqk+WidVdGcEZXdmwk/k9oBAutOgY4QxkoXZMQAAAJLvnhc/Uu3nO3TPso90/9RRyS4OgAil6opot956q2677TZJ0r///W9VVFQk7L3TYbb5HTt26P7779fSpUv1ySefaO/evTJNU7169dKuXbuSXbyYVVRU6LXXXpMkmaaZ5NIk3qOPPqorr7xSkvTXv/5VV1xxRXILZLHq6mpNmODqBP7Vr36lW2+9NbkFgqWYCAUAEKlI8yPSpQ8pFT4H1/HuCZX/Ea88IP/tSurye6j3cscSXlr39YHkdkNnjRzgiSUEum/2zyV4+f0tIXML/LfxZK0rGd59h7e3rVPSgdnYsx1qaT9YfueBJ21oaNZ359WQNwWkoUTmJ6X69Y0EdcAG4nEiiTaJONbOoFTtTEo1JIcjk0TboIt3/UiFwAuSIx2SAwAAAFKVf4fW4nc2afE7m+j4ASxiGIbP72+++abGjBkT8jVPPfWULr30UknhkzdTvYMlkdJlgpCtW7dq7Nix2rhxY7KLkhbWrFmjZ599VpI0ZcoUjRo1KqnlgbWqq6tVXV0tSbriiitUVlaWlHJ88skn+tOf/qTq6mpt2LBBe/bsUUFBgUpLS3XiiSfq0ksv1Xe/+92It7dy5Ur96U9/0muvvaavv/5a+fn5OvLII3XBBRfo2muvVd++fYO+duPGjTryyCO79TkCDRjZsmWLXn/9db311lt6++23tWnTJn2xaavaW/aqsPAQ5ffur2dqR+rskv/WuHHjuvW+qYhYOACEF2l+RLr0IaXK52BCM2vFKw/If7t52Q717pGrhj0tnuTuLEM6fXg/3R6g3/9gLMH15NYOUz3zsnXKPf8Oe9/c0NSi9Vv36IUbxnvaOd65BcHuvXOzDJUeWqCN2/dJkue7yDIMVa3Z5IlplPXpoS1NLeRNAQmW6HuYROfvpfL1jQR1wCaSfSKJtTOIziQAVrPbgIxUCbwgcdIlOQAAACCVBZuZNhNnrAUS4eabb9by5cst3Way46KpIl0mCLnzzjs9yenjxo3T5Zdfrn79+skwDOXk5CS3cClozZo1ntn8y8rKSFBPM9XV1Z79W1FRkZQE9V//+te65ZZb1N7e7vP4nj179PHHH+vjjz/WwoULNXHiRP3973/XYYcdFnRbpmnqf/7nf/Tb3/7Wp622f/9+7dy5U++8844eeughLVq0SBMnTrT0cwRLav/tb3+re+65J+DfmnbvVtPu3WrY+LHGv7pYF154oR577DEVFhZaWjY7SuVYOMn1ABIlXH5EuvQhpdrnsFv/cqrrbh5QuOux/3bbOp3qmZ+tLU0Hn9NpSn0L8wK+PtAM7K4kcofOG1Ua8r45XDsn0L13W4dTbZ2mJzldklranXr+3c06fXg/n5hG9ccN5E0BSZDK9zCRSOXrGwnqgE1EeyKJR4Al1s4gOpMApKNUC7wgcdIlOQAAACCVrZg1Ud+fv1JfeHUQlfXpob9de1ISSwWkr1dffVWvvPKKvvOd71i2zVTuYEmkdJkgZOnSpZKk3r176+WXX1aPHj2SXCLruWe8zlRXXHGFrrjiimQXAxb47W9/q5tuusnz+6mnnqpzzjlHRxxxhHbu3KnVq1drwYIFam1t1auvvqpzzjlHK1asUFZWVsDt3XTTTfrNb34jSTrkkEN09dVXa/To0WpubtbixYv1r3/9S1u3btX555+vmpqagAMuSkpKVFVVFVH577jjDr3zzjuSpCuvvDLo83JycvTtb39bJ5xwgo444gj1799fhxxyiLZu3aqamho988wz6ujo0DPPPKPt27fr1VdflcPhiKgMqSYdYuHpnpgCwF5C5UekSx9SunwOdF938oAiuR77b/fl97foiN4FOnbgoZKk977apcbm1oCvrZk5QVPnr+wym/mcc76p372yQS3tThlyJZG775tDzYy+/s6DqwEFuve+8PjD1e409cK7m+U0pdxsQ6W9ClTW95AuMY0ZC+pUMayEvCkgRpHmRabDPUy6I0EdSFHxCLDE2hlEZxKAdETgBcGkS3IAAABAKispylfngbV/c7IMtXea6nSatMkAi/Xo0UP79rk6fm+66SZLE9QRObtOEBLNZCpffvmlJGnYsGFpmZwOpIt9+/bpl7/8pef3Rx55RFdddVWX591888065ZRTtGnTJr355pt6/vnnNWXKlC7PW716te69915JUq9evfT666/r2GOP9fx9xowZuvXWW3XbbbepublZ//Vf/6VVq1bJMAyf7fTo0SPg9v3t2rVLl156qSTJ4XAEHTRx7bXXqrKyMuis6Ndff73effddfec739G2bdv02muv6e9//7suvvjisGVIRakcCycxBYBVomnbhsqPSJc+pHT5HOi+aPKAwl2PvevXHeeP1PVPrlbfnrmaO2Vk2G27X7umfqfaOn1XTnTPZv67S47XtuZWDSkp1CcNzRpSUuhJcvdv52QZrlnazz2utMt7Bbr37luYJ1PyzPg+/ui+AXO1yJsCrBFpXmQq38NkivQc3g2ksWGVy1Q2e4kWrqqXaboadGWzl2hY5bJkFw0A0hKBF4TiDlBUXTdO08YMDjqSHwAAAPEzorRIl48drOd+Ml6Xjx2s4aVFyS4SkHaOOOIIXXDBBZKkuro6PfPMM0kuUWaaP71cc6eM1PDSIs2dMtKn4ztZGppaNPnBFar93NVpGE5bW5skKS8vL95FAxCD//znP2pubpYknXjiiQGT0yXpyCOP1OzZsz2/19TUBHze7bffLtN0JRLdddddPsnpbr/61a80evRoSdJbb73lWXGhOxYtWqSWFtcgnkmTJumII44I+LyysrKgyeluxx13nG6++WbP70uWLOl2uewulWPhNTMn6LxRpcrPcaU/5Oc4dP6oUtXMmpDkkgFINd4JcbFKlz6kdPkc4TQ0tWjq/JVqsMlA4FQU7nrsXb+irWvu5597XKnOG1Uqx4FxjLnZhsr69NCpQ4s1rHKZXnp/qzY0NMuUtKGhWS+9v1XDKpd52jkt7a4EeneO++J3NnXJuQp07+2uB4/88EQVF+bpq537BMB60eZFpvI9TKYgQR1IMXYLsNBIB5AJ7B544VycPHZMDgAAAMg0tMmA2EVyXzl37lw5HK6YZGVlpTo7O7v9foZhyDAMVVRUxPzciooKz3Mkyel06i9/+YsqKipUUlKiQw45RN/61rd05513as+ePT6v3bJli375y1/q2GOPVVFRkXr16qVTTz1VTz/9dNSfaenSpTr//PM1cOBA5eXlaeDAgbr00ku1cuXKiLexbds23XnnnTrllFPUv39/5ebmqri4WKeccoruvffeLuX3V1ZWJsMwVFZWJklqaWnRvHnzNH78ePXr108OhyOi7zyYffv26Te/+Y0mTJig/v37y5GdowED+uu939+o3Sv/psdf/zBgp+Gtt97qs48k6bXXXvM85v5XXV0dVXkeffRRz2sfffRRSdIbb7yhyy67TGVlZcrPz1f//v11/vnna9myyCd4+fDDD3XjjTdq5MiR6tWrlwoKCjR48GBNnTpVVVVVYV/vf0xGUu4PPvhAM2bM0De+8Q0VFBSoT58+mjRpkp588klPUm+gbVx55ZWex6688sou36n7WPC2efNm3XLLLTrppJN02GGHKScnR71799aQIUN02mmn6bbbbtNbb70V2ZcV4efz51+v9+3bp/vuu0/l5eXq3bu3DjnkEI0YMUI33XSTdu7c2e2yuHkfg+7jzIo6G45/ncnLy1NJSYnGjx+vu+++W7t37w5Z3ttuu83z2IQJE7rs31jqczgNDQ2en4cMGRLyuUOHDvX8vHfv3i5/37Nnj6cOFhUVBZ3N3DAM/fSnP/X83p1zsdtf//pXz8/BkuujMXz4cM/PW7ZsiXl7dmb3WHgwJKYAiFU8JgpMl3hFunyOcKwcnJBodukrDnY9PuWef3epX/517cjZSwKW379uLn5nk/65ZrOcpms28/ZOU+OP7qtHrxwdNp9qW3OrLjzhcFUMLVbWgQz3SHOu3PXgxXVfq7G5VQN7syIYEA/dyYtM1XuYTJGd7AIACCzY0lF2C7BEuqQGgMwUzTJ4dmb3pbg4FwMAAAAAYhHJfeXw4cM1ffp0PfbYY/rwww+1YMGCoEmGydLc3KwpU6Zo+fLlPo+vW7dOlZWVWrx4sZYvX67evXtr5cqVOv/889XY2Ojz3JqaGtXU1Oitt97SfffdF9H7/uQnP9H/+3//z+exTZs26amnntLf/vY33XLLLfrVr34VchuPPvqobrjhhi5J6Nu2bdOKFSu0YsUKPfDAA6qqqtJJJ50Utkyff/65zj33XL3//vsRfYZw3nzzTV100UXavHmzz+Pmvt1q3bdbrZs+UNNbz6r4vP9V7Z9+Ycl7RuvXv/615syZI6fz4JLyW7du1T//+U/985//1DXXXKM//OEPnoEWgfzqV7/SnXfe2WUARn19verr6/X3v/9dp512mhYvXqw+ffpYUu5HH31U1157rVpbD3agtrS06NVXX9Wrr76ql156KWiid7SWLFmiSy65xDMzttuuXbu0a9cuffLJJ3r99df1m9/8Rrt27bLkPcP57LPPdO655+qDDz7wefyDDz7QBx98oCeffFLV1dUBk+27y4o6G06wOtPY2KjGxka98cYbuv/++7Vo0SKdccYZMb2XN++BEZ9//nm3v7d+/fp5ft6wIXSSlvffv/nNrsuov/baa57j+9RTT1WPHsGTec4880zPz9EMLPG2bt061dXVSZJ69+7tWQEkFp9++qnn5/79+8e8PTuzeyw8FHdiymWjB2lRbb0amdAFQBRqZk7Q3KUf6uX3t6il3an8HIfOHNFfc87pem1DehlWuUytHQfvIdzJ03nZDn089+wklixyduorDnQ99q9fDkMy5JrFPD/Hof5F+fpix76A5Q9UN3v3yNXJ3+irq8cf6XPND5RPtfLT7Z5tuds5c6rWymmaMgxFnHOVDscJkAq6kxeZyvcwmYAEdcCmQjUg7RBgofEFIBJ2uhlOR5yLAQAAAACxiPa+8rbbbtOTTz6ptrY23XrrrbrsssuUm5ubyCKHdOWVV2r58uUaN26cpk6dqv79++uLL77Qww8/rC+++EKrV6/Wz372M912220688wz1dbWph/96EcaP368cnNzVVNToz/96U/q6OjQ/fffrzPPPFOnn356yPf83e9+p2effVZ9+/bVj370Ix177LHat2+fXnzxRS1evFhOp1O33nqr+vTpo+uvvz7oNn72s59JkvLy8nTRRRfplFNOUZ8+fbRjxw69+OKLeu6557R161Z95zvf0VtvveUzm6+/1tZWXXjhhXr//fc1fvx4XXTRRSotLVVjY6O2bt0a9fe6Zs0aTZw4Ufv375ckHX/88Trvwu9rVaNDb33wuZo/rFHrpg/k3N+kxn/crg+uPUUlXjM7X3LJJRo1apQkeRJFR4wYoblz5/q8z8iR3e9EfPbZZ/Xcc8/pkEMO0dVXX60TTzxRnZ2dev311/X444+ro6NDf/rTn1RUVBR04MFNN92kX//615KkrKwsXXLJJZo4caIKCgq0du1a/eUvf9HWrVv12muvaeLEiXrzzTdVUFDQ7TJL0osvvqi///3v6tWrl37yk5/o+OOPl2EYev311/XXv/5V7e3teuyxx3Tqqaf6zAI9ceJEVVVV6dVXX9WDDz4oSfrpT3+qiRMn+mzfOxF406ZNPsnp55xzjk4//XSVlpbK6XSqoaFB7777rv71r38Fndnbak1NTTrnnHP00Ucf6bzzztPZZ5+tww47TJ999pl+//vfq76+Xl988YV+8IMf6PXXX7fkPa2os+EEqjOXXXaZBg0apC1btuhvf/ub3njjDW3fvl2TJ0/Wyy+/7DMburvOPPXUU55ZxO+4444udaRv377d+xIiMG7cOPXt21fbtm3TW2+9pb/+9a8+M/a7bdy4UXfffbckqU+fPrr88su7PGfdunWen7/97W+HfN/i4mINHjxYX3zxhbZt26aGhgaVlJREVfa//OUvnp8vu+wy5eXlRfV6f59++qnuuusuz+8XXnhhTNtD/JCYAiAWdpsoEImTyoMT7NhXHOx67F+/3Frandq4fZ+kwOUPVDcnHVPiyT3wv+a786nOGtFf1zz+lhr2tHbJVdjW3KohxYVa39CsoSWFEc22nMrHCZBq7JAXCeuQoA7YTCQNSDsEWGh8AQjFjjfD6YhzMQAAAAAgFtHeVw4ePFgzZszQgw8+qC+++EJ/+MMfdMMNNyS41MH94x//0J133qmbb77Z5/ErrrhCo0aN0ubNm/XEE0/o3XffVX5+vlasWKFjjz3W87xLL71UJ510kn7wgx9Ikn7zm9+ETVB/9tlnNWLECL366qs+SZRXX321nn32WX3/+99XR0eHZs2apfPOO0+DBg3yef3bb7+t//3f/5UkDRs2TM8//7yGDBni85wZM2ZoyZIluuCCC7Rv3z5deeWVWrVqVdAybdmyRVu2bNEDDzyg//7v/w5Z/nCcTqcuv/xyT6LtjTfeqAceeEAOh0Nzqtbqw4J69So/VztWPKldK56Qs7NDP/jBD7R+/Xrl57uSeY455hgdc8wxPtvt27evpkyZElPZvD333HMqLS1VdXW1z/f3wx/+UNdcc43OOOMM7dmzR7/5zW908cUX68QTT/R5/cqVK3XPPfdIkg455BAtXbpUp556qufvl156qf73f/9XZ555purq6vTee+/plltu0f/93//FVO6nn35ao0aN0ksvveRz/EybNk1nnXWWJxH2vvvu80lQHzRokAYNGuQzy/kJJ5wQ8jt98sknPcnp99xzj2bOnBnweaZpasWKFTF8qsitXr1aubm5ev755zV58mSfv11zzTU68cQT9fnnn6umpka1tbUaPXp0zO8Za50NJ1Sdcbvhhht0xx136JZbblF7e3vQOrNmzRrPa8aPH++TxB5v+fn5+sMf/qBLLrlEHR0duuqqq/Too49q8uTJOuKII7Rz50698847WrBggVpbW3X44YfrmWeeCbiywPr16z0/RzKjuztB3f3aaBLU29vbtXDhQs/v3vUmnI0bN3q+887OTm3btk1vvvmmnn76ac/+vOKKKyw9dwEA7IWEuMyUyoMTktVX3J1V1L3r14wFrtVu7r7wWFU+u1b1O/bJeWA29Yqhxfpq535Nnlej7CyH/viDb0dVN+dPL9ewymVa+OYXnse8cxUk+eQxrG9o1vqGZg2rXBYyjyGVjxMg1dghLxLWCb6OIYCkqJk5QeeNKlV+jqt65uc4dP6oUtXMmpDkkvmi8QUglFQ5l6U6zsUAAAAAgFh0576ysrJShxxyiCTpzjvv9CS82sGZZ57ZJTldcs3I654JubOzU++++64eeughn+R0t+nTp3sSnJcvX66Ojo6Q75mdna2nn346YALllClT9D//8z+SpH379un3v/99l+fcdttt6ujoUF5enl544YUuyelu55xzjmbPni1Jqq2t1X/+85+Q5brgggtiTk6XpBdeeEHvv/++JGns2LH6zW9+40m03dbcqsvHDtbzPz1FP/mf2Rp03HhJ0pdffumTIGqFhqYWTZ2/Ug0hEhH+8pe/BPz+xo4dq3vvvVeSK3n4gQce6PKc//u//5Npmp6fvZPT3Q477DD94x//8MxK/vvf/94nQbw7cnJy9I9//CPg8XPBBRdo3LhxkqQPP/xQX375ZUzv9cknn3h+vuaaa4I+zzAMnXLKKTG9VzQqKyu7JKdLrtm4vevzSy+9ZMn7xVpnwwlVZ7z98pe/1DnnnCMpPnUmUqHq1kUXXaQXX3xR3/ymK8Hp9ddf18yZM3XppZfquuuu05///GdlZ2frvvvu03vvvRd0AIF3PYlk1nfvJPdo69gLL7ygxsZGSdJxxx2nE044IeLXvvjii7rgggt0wQUX6Hvf+56uvfZaPfroo9q/f7+OPvpoPfzww/rrX/8aVXkAAKll/vRyzZ0yUsNLizR3ykifBDmkN3cCdNV14zRtzOCIZtS2g2T1FXuvoh4p7/pVM2uiFv/4ZM17dYNOGNxbpuQp/6eNe7Vuc5PWbW7Smi93ad4rG6Kqm/4T6bkZkoaXFqnqupO7nceQqscJACQTCeqAzaRSsiGNLwDBpNK5LNVxLgYAAAAAxCLa+8qSkhL97Gc/kyQ1NDTot7/9bfwLGSF3Enog7kRfSerXr5++973vBX3u+PGuROu2tjZ9+umnId/zzDPP1IgRI4L+/Wc/+5mysrIkSVVVVT5/27lzp5YsWSJJOv/883X00UeHfK/LL7/c8/PLL78c8rk//elPQ/49Us8884zn51/84hcyDMPzu3+SwBMP3R3wdcFEknTuFi4BYsSIETrzzDODvv6qq65S7969JUn//Oc/1dnZ6flba2urli5dKsmVGHv11VcH3c7gwYN16aWXSpL27t0bdj+EM3nyZH3jG98I+veJEyd6fv7ggw9iei93Yr0kTwJ1smVlZXnqbaDjwcrP7xZLnY1EqDrjzz3oxP91sTBN0/MvktnKw9WtCRMmaN68eRo+fHjAv+/du1cPPPCA/vznP3sGefjzHsjkniU+lIKCAs/Pe/bsCft8b3/5y188P0cze3ooOTk5Ov300zVmzBhLtgcAAOwnlQcnJLKveFjlMpXNXqKFq+plmq6ZyctmL9GRs5f4tOPdbfsPNu8Oes/nboe+9fkOTRszWJ1Op0xT2tDgOwje/R7DKpdFVEb3RHpZDt92eFmfQ7Tmy11atKq+23kMqXycAECyZCe7AAC6SpWlo1hSA0AoqXIuS3WciwEAAAAAsejOfeUvfvEL/f73v9eOHTt033336brrrtNhhx0WryJGLFTyYL9+/Tw/f/vb3w44o3Gg5+7cuTPke06aNCnk3/v3769vfvObWrdundavX6/du3erV69ekqQ33nhDTqdrZrf8/Hw9++yzIbfV3t7u+fnDDz8M+rysrCyddNJJIbcVqdraWkmuWbVPP/30kM89+eSTVVhYqObmZq1atSrstr0TY+de8K2Az/Gf/c57afabvPL5w+2H3NxcjRs3Ti+88IL27dunDz74QN/6lus93333XbW2upI4KioqlJubG3JbZ5xxhh555BFJ0qpVqzR16tSwnzWYsWPHhvz74Ycf7vk53LEYzumnn67f/OY3kqQLL7xQN910k77//e9r4MCBMW03FkOHDvUMHAh0PFj5+d1iqbORiGedsVKouvXx3LMlSY2NjbroootUU1Oj4uJiPfzww5o8ebIGDBig3bt36/XXX9ftt9+ud999V7NmzdJ7772nxx9/POT5NVTCfqy2bNmiF198UZKrzk+bNi2q11977bW69tprJbkGKH399df697//rXvvvVe///3vNX/+fM2dO1c33XST5WUHAADorkT2FdfMnKC5Sz/Uy+9vUUu7U/k5DvUtzNNXO/frtn++r8bmNj102fGetv2NT63RJ43NPm18/3bolzv3a+GbX4ScYffMEf10R4SfzT2RXqfTVJYhdR4YQ/n59r2SXO1eScoyDFVdN448BgCIMxLUARsi2RBAOuBcBgAAAABAeurVq5dmzZqlWbNmaffu3fr1r3+te++9N9nFUp8+fYL+LS8vL6Ln+T+3pSV0R3W4Wc/dz1m3bp1M09SWLVs8ya4bN270POfxxx/X448/HnZbbqGSdfv06RPRLMWR+PrrryW5knZ79uwZ8rkOh0Pf+MY39O6772rHjh1qa2sLmuz95mfbtfFAYkCgxFi3QAkQZ47orznnfFNLFz/leV6k+8Ft8+bNngR192eUXAnT4Xg/x/u13dG3b9+Qf4/mWAzn7LPP1mWXXaZFixapsbFRP//5z/Xzn/9cQ4YM0cknn6xTTz1VkydPVklJSUzvE42+fftGlCgtxf753WKps5GIV52xWqi6JblmRh8/frzWr1+vPn36aNWqVTryyCM9r+/bt68uvPBCffe739XEiRO1cuVKPfHEEzrppJP0k5/8xOe9CgsLPT/v378/bNm8nxPuO/T2+OOPq6OjQ5JrVYpw5/pQcnNzNXjwYF1xxRW65JJLNHnyZC1fvlw333yzevXqpeuuu67b2wYAu2poatH1T67WQ5cdz4rI6DaOo/TmTv5uaXe131vanfpqp6vttmTtFknS6DuXe57vng3du43v3w51O/hTVy+9v1XVHzd2uV8MZltzqy4f65pI788rPtN/Pt2mXfvau7R7S3rmk8cAAHEWagASAAAAEJNolusGAAAAAKSOn/70pyotLZUkPfTQQ9q8eXOSS6SQs/Z253mR6NGjR9jnHHLIIZ6fm5sPLle+e/fubr9vW1tb0L8VFBR0e7v+9uzZI8n3M4TinYjqfm0gfQvzlJ/j2g/5OQ6dP6pUNbMmdHmeOwEi3PLrsewH73JG8jkj/YyRsPJYjMTChQv15z//WSNGjPA8tmHDBj322GO6+uqrVVpaqssuuyzmxPtIORyuBJXzRpVGdDxYIZZjJRLB6kywGJmVx1M0wtWthx9+WOvXr5fkWjXDOzndW35+vh544AHP7w8++GCX5xx66KGen7dv3x62bN7P8X5tOI8++qjn56uuuiri14WTn5+vv/zlL576escdd3hWvwAAu4umj8Z7NROgu+x2HNFPab1NO/d52u6Ryss2PG18/3aoJJX16eGzTYffojsDeuX73B+E26/zp5dr7pSRGl5apAemjtKkY/qFvacEAMQHCeoAokYjHgAQKbsFogAAAAAA1igoKNAvf/lLSa7Zbm+//XbL3yMVEgD37dsX9jl79+71/OydjOr986OPPirTNCP+V11dbennCMY9e7H3ZwjFO5k31MzHWQ4j4gSBbc2tmjZmsKquG6dpYwarsbm1y3Ni2Q/e5Yzkc0b6Ge3IMAxdffXVWrdunT799FM99thjmjFjhoYMGSJJ6uzs1JNPPqkxY8Zo69atCSlTpIMQrBLLsRKJYHUmWIwsmcdTqLq1ZMkSz8/f+c53Qm5nzJgxnu/p448/7jL4xnvVAe+VI4L54osvAr42lJUrV+rDDz+UJA0cOFBnnHFGRK+L1KBBg/TNb7pml9+yZYs+/vhjS7cPAPESSR/NsMplKpu9RAtX1cs0XTMdl81eomGVyxJYUqQ6ux5H9FNaa1jlMlWv3+Yz83k4DkNq6zR92vje7dDLxw5Wh9P03A8YhvSN4kIZhjy/TzqmxOf+INr9Gsk9JQAgPrKTXQAAqce7sTf3gm8luzgAABuKdHlmAAAAAEDquvrqq3Xffffp008/1SOPPKL//d//jeh1ubm5amtrCzkLuCRt27bNimLG1SeffBLxcwzDUP/+/T2PH3744Z6f33//fesLZ4EBAwZo165d2rJli/bs2RMygdY0TX366aeSpD59+ig3Nzfoc9s7nbpqjGvJ9UW19WoMMRnK/Onlnp+DLb8ezX6Q5Jn9X3J9RrcNG8InOHg/x3s7qeaoo47SUUcdpR/84AeSpHfeeUc/+tGPtHr1an355Zf6v//7P913330JKYs7YSSS4yFWsdTZSPjXmfJ7VgSNkX10x1kR15l4CFW3vFfFKCoqCrkdwzBUVFTkSbbft2+fevXq5fn7yJEHt11XVxdyW42NjZ4E9b59+6qkpCTMp3D561//6vn5hz/8YVxWJ/A+/+3atcvy7QOAlaLpo6mZOUFzl36ol9/fopZ2p/JzHDpzRH/NOeebiS425Jos8PonV+uhy45PqRme7XYc0U/Zff7HoPv3NV/uUltHZInp2Q5p1BG91dTSrr6FeTqquNCnje/fDp2xoE4Vw0p02ehB+vOKz/T8u5t14fEDdfX4I33uD7q7XyO5pwQAxAczqAOImF1HvQIA7CfRyzMDAAAAABIvJyfHM3N6R0eHbrnllohed+ihh0ryTYAMZNWqVTGVLxFeffXVkH/fsmWLZ1bfoUOH+iRtnnrqqTIM19rlzz33nC1njB89erQkV/L58uXLQz73P//5jydB1f26YIb26+lZcn3ulJE+CQPdEa5sbW1teuONNyRJhxxyiIYPH+7523HHHae8vDxJUnV1tdrb20Nu6+WXX/b8HO5zxlNDU4v+32ufeX43TTOm7Z1wwglasGCB5/cVK1bEtL1ozJ9ebunxEEosdTYS/nUmVIwsXJ3xTrKOdf9Gyzsp/csvvwz53P3796uxsdHz+2GHHebz94qKCk8de/3117V///6g23rppZc8P599dmTJY/v27dPTTz8tyZUsf+WVV0b0umiYpqnPPjtY3/r27Wv5ewCAlaLpo0n0aiYILVVn/LbbcUQ/Zfe5j8FfL/1IU+ev1D0vfqS3Nu7Q5G8N0HmjSpXlMMJuo8Mp1X2xU19s36dF14wN28b3vh/okZOlDqepghxHl/sD9isApB4S1AFEzI6NvYamFk2dv1INcZxRBQAQPbsFogAAAAAA8XHppZfq2GOPlSQ99dRTevfdd8O+xp0c/MUXX/gk/PmbN2+eNYWMoxdffNGTzBrIvHnz1NnZKUm68MILff5WUlKis846S5K0fv16PfLII/EraDdddNFFnp/vu+++kEmy99xzT8DXJcL777+vf/3rX0H//uijj2rnzp2SpPPOO09ZWVmev+Xl5emcc86R5Jq1/9FHHw26nS+//FJPPvmkJFei+xlnnGFB6btn3vIN2ri70/P73r17Y95mWVmZ5+eOjo6Yt2dHsdTZSPjXmeKeeUFjZOHqTGFhoednK/ZvNLxnPX/qqadCPnfx4sWegR3f+ta3PMnoboWFhfrud78rSWpqagpax0zT1EMPPeT5/eKLL46orIsXL1ZTU5Mk18Cfb3zjGxG9LhrPPvusGhoaJEn9+vWLy3sAyFzx6O+Oto/GvZpJ1XXjNG3MYDU2t1pWFkQmHSYLTMZxFKz+ZEI/pdXnDv9j8JnVm1T7+Q4tfmeT5/d/rtmsTmf4gZMOQzp/VKmqrjs54jJGUgcyYb8CQLohQR1AxOzY2EvVEcQAkAkIaAJA+mBgKAAACMYwDN15552SXMmFDz74YNjXuJOyJWnWrFkBk55vueUWvfLKK9YVNE46Ojp08cUX+8we7Pb888/rvvvukyT16NFDP/7xj7s8Z+7cucrJyZEk/fSnP9XChQtDvl99fb1+8YtfeBIl4+2cc87RiBEjJElvvPGGfvGLXwSc6f2uu+7S888/L0k64ogjNG3atISUz9tVV12lTz/9tMvjtbW1+sUvfiHJNSP1f//3f3d5zi9+8QvPbNX/8z//45lt3dvOnTv1ve99z5Mo/OMf/9izGkAieSduZPfq53l8zp/+GfJ1t99+u/71r3+FnKn///2//+f5+bjjjou9sDbU0dGhk04/Vx983nVW8EjqbDiB6kxj0/4uMbJI6syRRx7p+fmdd96J6P0Nw/D827hxY9Tld7v00ks9Pz/yyCNBz03vvvuufvazn3l+nz59esDn/fKXv/SsGHHTTTfpvffe6/Kc22+/3bNyxoknnuhJag/nL3/5i+fnq666KqLXSNInn3yie++915PcHswrr7zis91rr73WZ3Z7AIhVvPq7o+mjSeRqJgjMjpMFRisZx1Go+pMu/ZTBYvPdOXcE21ZDU4uGDyjSGSP6BXmly4Be+frusf1VMbTYM5O6+1i98PjDZRhSXrZDpqSeedlatKo+4jJGWgfSZb8iOejrAhIvO9kFAJBa3I29y0YP0qLaejUm6aI9rHKZWjsOBtIXrqrXwlX1yst26OO5kS37CACIL+/A09wpI0M8EwBgd97B7rkXfCvZxQEAADYzefJknXzyyfrPf/4T0Sy/V111le69917t2LFD//jHP3TKKado2rRp6tu3r+rr6/XUU0+prq5Ol1xySdiZe5NtypQpevbZZzVixAhdc801+ta3vqV9+/bppZde0t///ndP8v0999yjI444osvrTzjhBP3+97/XNddco9bWVk2fPl3333+/zj//fB199NHKy8vTrl279NFHH+mNN95QbW2tTNPUjTfemJDP53A4tGDBAo0bN0779+/X/fffr3//+9+aNm2aBg4cqK1bt+pvf/ubVqxYIUnKycnR448/rvz8xE5q4t4Po0aN0tVXX60TTzxRnZ2dev311/X44497Znf+7//+b5144oldXj927FjNmjVLd999t/bs2aPTTjtNl156qSZOnKiCggKtW7dOf/7zn7V161ZJ0rHHHqvbb789oZ/RrWbmBM1d+qFefn+LzOIyZR1yqDr37tL+D6s1a9YsjR07VgUFBZKkgoICnXbaaZKkV199Vb/61a/Uv39/nXnmmRo1apT69+8vp9OpzZs365///KdqamokuWaV//nPf56UzxdvQ0dP1PraV3XiqOP0s+t/HHWdDSdQnTnhBFedWdc2UP22btVrf/ub/hhBnTnllFOUk5Oj9vZ2/d///Z8Mw9Cxxx7rmaH8sMMO0+jRo2P4NoI766yzPPXK6XRq+vTpWrBggSZPnqwBAwaoqalJr732mp5++mm1trqSgo477jhdf/31Abd3/PHHa+bMmbrnnnu0e/dunXzyyfrRj36k0aNHq7m5WYsXL9bLL78syTXj+h//+EdPQnson3/+uV577TVJUlFRkb73ve9F/Bmbm5s1a9Ys/epXv9KkSZN04oknavDgwerZs6f27dunzz77TC+//LL+85//eF4zbtw4zZ49O+L3AIBQ4t3fTR9NaollssCGphZd/+RqPXTZ8Rkzk3Qk9ScV6kAk+84/Nh/LuSNYnH/e8g1a89UuHV3sWsHHYUjeE6UbkkxJJx/VR/dfPEpzqtbKaZo+x2qjVy7R5AdrtHBVfVRljLQOpMJ+hX3R1wUkHgnqAKJiRWPPihsk7yB8S7tT+TkOnTmiv+ac881ubQ8AAABAVwwMBRIrEzsUgUSgbiXG3Xff7UmEDae4uFgLFy7UhRdeqJaWFr3xxhtdZqyePHmyHnnkEdsnqN944406/PDD9fDDD+uuu+7q8nfDMHTLLbcETdqUpKuvvlolJSW65pprtHXrVq1Zs0Zr1qwJ+vw+ffokNAH8+OOP1/Lly3XRRRfp66+/1jvvvBNwNufDDjtMixYtUkVFRcLK5nb++edr7Nixuvnmm/W73/0u4HOuvvpq3XvvvUG3cddddyk7O1t33XWXOjs7tXDhwoCzRp922mlavHixJwk80bwTN/Jzc3ToKZdr+4sPqb29vcvnGzx4sGcWbfeMz1u2bNFjjz2mxx57LOD2+/btqyeeeMIzC3i62f2N76hnR4H2vLOk23U2HKvqTN++ffW///u/uvvuu9Xc3KxbbrnF5++nnXaaqquru13OcBYtWqT/+q//8tSDl19+2ZNE7m/ChAlatGhRyHpx9913q62tTb/97W+1d+/egHW1pKRETz75pEaNGhVRGf/61796BhVcfPHF6tGjR0Sv89bS0qIlS5ZoyZIlQZ/jcDj0ox/9SA888EDCB+AASF/0d8NfdycLzMSEy0TVn3jHE0Ltu2Cx+dwsh84bVRrVZw+2LX8bGpoDvv6IwwpUv2O/ajfukBT4WPXOJXrzpkk++8cwpDOG99MdYXKM7DJhJtIPfV1A8pCgDqQoqxvCieyos+IGKZYRxAAAAAAiQ0cZkFiZ2KEIJAJ1KzFOPfVUnXXWWXrxxRcjev7ZZ5+tNWvW6J577tHy5cu1ZcsW9erVSyNHjtRVV12ladOmRTR7rh089NBD+u53v6s//OEPevvtt7Vt2zb17dtXp5xyim688UaddNJJYbdx7rnn6vPPP9fjjz+upUuXavXq1dq2bZs6OzvVq1cvHX300SovL9cZZ5yhM844Q7m5uQn4ZAeddNJJ2rBhg/74xz/queee0wcffKBdu3apqKhIQ4cO1eTJk3Xdddfp0EMPTWi5vM2aNUvjx4/XQw89pP/85z+eY2rMmDG67rrrdPbZ4Tudb7/9dl1yySX6wx/+oOXLl+vLL79UW1ubiouLNWbMGF122WW68MILE/BpQvNN3DhC74wcqrxPXlVdXZ0aGxs9M1p7e+GFF1RTU6Ply5dr5cqV+uSTT7R9+3YZhqHDDjtMI0aM0Nlnn62rr746qfsx3nKzHMo//ccqGjpaueuXa//XG7Rj+/ao62w4VtWZu+66S8cee6wee+wxrVmzRjt27FBbW1vM5YtEQUGBFixYoJ/+9Kd67LHH9J///EcbN27Unj17VFBQoNLSUo0ZM0aXXnqpzjrrrLDnbMMw9MADD+j73/++/vjHP+r111/X5s2blZ+fr6OOOkpTpkzRj3/8Y/Xt2zei8jmdTp+BFldddVVUn++4447T2rVrVV1drddff10bNmzQ1q1btW3bNuXk5Kh3794aPny4xo8fr2nTpukb3/hGVNsHgHDo74a/aCcLzOSEy3jWH+/cmXjFEyLZd6Fi8797ZUNUnz3YtmacepT+8PpnWvLe1+p0mspyGJp87ADt3temgYcdoqffqld7p6n6HfslSV/u3K+y2Ut8yjl3ykg1NLVo6vyVnnwj7/3jno39s8a9Xcron6fE7OiIF/q6gOQx3KPKkZ7Ky8vNurq6ZBcDcVBZtVZP1NZr2uhBXRrC3Uk2D7U9q/g3st26e4M0Y0GdinvmBx2VCSB+qqurkzIbFgAASLw5VWu1qNY1M0tbpzOu9wxAprL6fhmAC3ULSG+PPvqorrzySkmuWZSvuOKK5BYItnXrrbfqtttukyT1u/Qu9TpqFPc2AGyNPpjMQX+3faXCSlwNTS1BEy7tWmYrxav+VFatDTi7uGRdPCHSfRcsNt+dzx5oW39/+6uQcZNIyxko3+iom5bIGSAlMTfLoVGDDnUNAHhlQ9zzlAA3+rqA+DEM423TNANeiJhBHUgxkYykjGYUZyJH1UY6Is19s3frucN16/MfBL3pY/QkAAAAEH8sqwnEHzO4APHhrltL3t2sTlPKMqTJx5VStwAgg50+vL9uumoc9zZIaamQNAkgMvR321ciV+Lq7nk902fht7r+BBvkLsnyWF2k+y5YbL47n917W4+s+ExL1n6tqutO1lWP1amxqUWdpuQwpCyHoaqfnNylnIahLuUMlW/05k2TAsY7sw1Di1dv0ug7lwd8HRMKIF7o6wKSgwR1IMWE6rTuTrJ5IjvBI21ku2/2bnxqjT5pbI76po/AHAAAAGAdOsqA+Mv0DkUgXk65998+sbJOU3puzWa9uG4LHZ4AkKGuHn+khpcWcW+DlJbIpElkJvpakckSOcGfWyzndRIureOfO5PlMNTpNJWbZcQlVhfJvrMyNu+9rYKcLO3a365Fq+o16ZgSLaqt98QknZ2mHqn5XPdPHeUp55DiQq1vaNbQkkI1Nrd6thMq36ikp2+8s6XdqefWbA5YNibrQCLQ1wUkBwnqQIoJ1WndnWTzRHeCh2pk+9/sbWholhT9TR+BOQAAAABAqqFDEcGQHNJ9NTMn6LyHV6hxT5s6naayHIZKeubpuevHJbtoAAAAUUtG0iQyE32tyGSJnODPivM6CZfWCZQ7M6SkUL+75Pi4xOqC7bt4xoGGzlmqtk7T8/vCVfWen72PxcXvbNLidzZ1ef36hmatb2jWsMpl+nju2SHzjRqaWrR07de68PiBunr8kXro3xv0rw+2ymFIrR2msgzXRAK5TNYBwGLE0+2FBHUgBQXrtO5usnkiO8FD3SC5b/ZeWrfFp/Gbl23orJEDwt70JSowx4UMAAAAAGA1OhQRDMkh3VdSlK9Jx/TzzATW1unUpGNKiOcAAICUlMikSWQmBkEAiZ3gzy7ndfIfDgqUO5Po1XfiGQc697hSLX5nkyc53H3MLX1vs9qdXZ+f45DOPrY05DEaLN9o3vIN2rW/XQU5Dg0vLdJhPXLVfiA53l23hpYU6rdxGgAAIHMRT7cXEtSBFBSq07o7yeZ26QR33+y1dTo9DWKHIbV1mhHd9CXqBo4LGQAAAAAAiDeSQ6zB6gQA7IxkIPjjmEAoiV4VGZnHLsmyQLIl6j7SLud18h8OSmbuTDzjQP7bdk+i3tLuOubemD1J35+/Ul9s3+d5TlmfHvrbtSfpd69sCHmM+n9nwyqXqWz2ki6fw1trh1NZhqEjiw9J+AAAhMb9CFIZ8XR7MkzTDP8spKzy8nKzrq4u2cUAIjZjQZ2Ke+br08Y92t7cpr6FeTqquFCNe1p8GrbBzKlaq0W19crNcs2KNW30IMtuovwvZG5cyJCJqqurVVFRkexiAICtEcQBAACxaGhqCZocQtsCANJDZdVaPVFbb2kcG6mNYwLhuPvRvJMmI+k/Q2pJZh9MPPtaER3iy5khmed18h/sJZ5xIP9tZzkMnTKkr/ockqfm1nZVf9wY9FioGFYc1THqfq+X1m1Ra4dTuVmGinvma1tzq1o7iG/ZHfcjSGXE05PHMIy3TdMMeHFgBnUAthLrzVY8RzMzawAAAIgGs44AAIBY2GUmNQCA9ZjVKzqZkKDHMYFI2WVVZKQvViCyD+LLmSER5/VgbSnyH+wlnnEg/223dTo18NACz7mloalF5z28Qo172tTpNOUwpCyHoaqfnKzhA3p5tuN9jAY7rrzfS5LaOk31yM1SWyfxLTvjfgTpgHi6PTmSXQAAsNL86eWaO2WkZxkgK0cXcyEDAACRcC9fuHBVvUzTFcQpm71EwyqXJbtoAAAgxbiTQ6quG6dpYwarsbk12UUCAFigZuYEnTeqVPk5rm66/ByHzh9VqppZE5JcMnvyTtBLVxwTSDcNTS2aOn+lGkhuTjnx7GtFZIgvp4ZUOs8Fa0uR/xBauH0cj2MgnnGgbc2tuuD4wzWkpFAXHj/Qs213ovnJR/WV0zSVl+2Q05TaO00terM+6PaCHVfDKpfpiVW+r9vQ0CxDIr5lY9yPIF0QT7cfZlAHgCgwawAAAAiHWUcAAIBVmCETANITyUCRyaRZ/DgmkG6Y+RnoPuLL8WfF6iypcJ4bOmep2jpNz++B2lLkPwR3z4sfqfbzHbpn2Ue6f+qoqP/eHfGMA82fXq7KqrV6/+smjTriUN0/1fVe7mP560ML5JARtP1dM3OCrn9ytdbU7wx5XIU6h5X0zCe+ZVPcjyBdEE+3HxLUASAKXMgAAEA4BHEAALAXKzqeAQCwGslA4WVagh7HBNJBJg0sAeKF+HL8xZJcnkrnuXOPK9XidzYpy5A6TQVsS5H/0JX/Pl78ziYtfmeTZx+H+3skrIxVRbKtYMetty937pckOQwpN9vRpf097xVXvbnw+MPV7jSDttE5h6Uu7kcAxAMJ6kCKonMVAADAvgjiAOCeDbCPVJjVDACQeUgGCi/Tkls4JpAOMm1gCRAvxJfjw4rk8lQ4z/l/Tvdk1y3t6d2WsoppmiEfD/f3SFgZq4pkW/7HrcOQTh1arNxsh15f3+hzLGc5DFWt3uRpf7/w7mY9t2azZ1uL39nk+TlYGz3QOYx4uf1xPwIgHhzJLgCA7vFuZAbT0NSiqfNXqoEbVgAZjvMhgESbP71cc6eM1PDSIs2dMtInqAMgM0RyzwYgvoZVLlPZ7CVauKpepunqeC6bvUTDKpclu2gAAMBLqNidO7ml6rpxmjZmsBqbW5NQQiA2mRSfzrSBJUC8EF+Oj5qZE3TeqFLl57hSpfJzHDp/VKlqZk2IeBt2Oc+Furb4f84sh6GKYcW66ISBQdtSmXStCmfFrIka3KeHz2NlfXpoxeyJEf09FCtjVeG25b1PvY/bLENymtKbn21XYW7XY3lva4dP+/uUIX19jidJ6pGbpYtOGBi0jR7oHEa8HMgcXFPgjRnUgRQTzaheZggDABfOhwAAIFFSaZlfIN2lwqxmAAAgdOyOWfyQDjItPp2smZ+ZmRVAOFYll9thhvtQ1xb/z9nW6dTAQwtCXoMy7VoVSklRvjqdrtnQc7IMtXea6nSanuMk3N9DiTVW5X2tC7ct/336ZK0rkb3zwLZa2p16ZvUmZRmGqq4b5zmWA7W/j7ppiZxeE8Tva+vU4ne+0gvvbQ4b8yZeDmQerinwZkSzxAhST3l5uVlXV5fsYsBCDU0tQRuZ7gavfwPPjQYeAKtUV1eroqIi2cUIi/MhAABItEju2RBfJCXA25yqtVpUW6/cLFeH7LTRgwiKAwBgE8TukO44xhOrsmqtnqitT4s2f6r0wQCpaMaCOhX3zPdJLk+lGeojvbZE+jm5VgUW7Ptzxx175Dg08LBDunUcdTdW1dDUoskPrlBjc6vnNe5t5TgMtXWauuj4w/XC2q8D7tPcLIc6nE6fRHO3cPv7ir+s0sbt+/T17ha1djjlMKRzjyuNKOZNvBzIHFxTMpdhGG+bphnwQugI9CAA+4pkVK8VS1OlC5YNATIb50MAAJBodlnmN5OxXCy8uWc1C7bkMAAAdpQpcW1id0h3HOOJMaxymcpmL9HCVa6ZYReuqlfZ7CUaVrks2UVDGsqUa3Q6mz+9XHOnjNTw0iLNnTIypZLTpciuLQ1NLdq5r103TDo67OcMtr2q607O6GM92HHijjsO7N2j28dRd2JVQ+cs1ei7lqthT6vPte7J2npNGzNY3xneX5JUu3GHZ59mOQzP6/OyHXr2+pMVbA7bcJPbPnrVGI07uq/aOl0xb1OKOOZNvBzIHNz/IBAS1IEUFK7BSgPvIBITgMwWy/kwliAjAUoAADIbCbHJQVICAkn1jmcAQGbKlLg2fRlIdxzjiUEiDBIpU67RsK9Iri3RHKfBtrdoVX1cjvVU7UONJe7o/5mjjVUNq1ymts6uCeQOQ8rOcmjhm19o6dqvJUlf7tyv0Xct1z/XbFan11TprR1Offd3K2QYUmFels92yvr00LM/GRd2v8QS8yZeDmQG7n8QiBFuFBRSW3l5uVlXV5fsYiAJUn1pqnDCLdnOsiFAfKXS8pLdPR/GshxoOi0lGm8fbN6ti+e/qaevHavhA3oluzgAAMCGwt3/eT+P5WIBAEAqy8S4drr3ZQAc44kxp2qtFtXWKzfLobZOZ8rH5lOpD8ZKkd7/J0MmXqMle++TTBbs2tLd49R7e5MfrJEzQBqZVcd6qvahxhJ3jOUzB9unknTRCYdr1lnHdClXxdBi1W7cqR1720JuOyfLUHunqSN6F+i0ocUpuV8A2E+q3//Q9ukewzDeNk0z4I4mQT3NkaCefjgRuoRrxJOYAMRXOgdHYwkyZmqAMhanP/CaNjQ0a0hJof7189OSXRwAAGBD0XTipFtSAgAAyCzEtQGge1I9EcZfOvfBePPv97Zz4mqmXqPtvE/QVbjj1F3nbj13uG59/oOAOSfxOtbToQ812rhjqM9cM3NCxBNyjL17ecBBAw5D+uzucwKWy5T0xKp6n+cbks4bVard+9o08LBDdNnoQTr3wRXqDJA3mEr7BQCsRNune0IlqGcnujAAYuO9HFMmngj9G/ELV9Vr4ar6Lg1klg0B0F01MycEDbzE87WZpmz2Ep/fNzQ0ex7b+OtzklEkAABgM5He/3lzLxfrnZQAAACQKohrA0D3eCejz50yMoklQTTc/d5j7/JNvozk/j/RMu0a3Z2YDJIv3HHqrnM3PrVGnzQ2B8w5idexng59qNHGHUN95nmvRJb3U1KULwWZdzY7yxFVuUxJz63Z7KrHV42RJK28aWLK7xcAsAJtn/ghQR1IEZwIXaNDvzmgSP2K8vTa+sawDWQSEwB0RyyBl0wLUMZi6Q3jdc3jb2vTrv2exwYeWqA//vDbSSwVAACwk+50XJGUAAAAUh1xbSCxWLkYSDz/fm//3Eu7Jkhm0jU6HZKJM9W25lZdcPzhWr9lj4b1L1Jjc2uXOrehoVlS8JyTeBzr6dCHesf5I3X9k6vVt2duRHHHQJ/5hXc367k1mz3PiSTv59Shxar7YoeaWzs9j5X16aG/XXuSpODx0IamFp338BtqbGpRpyllGa4yPXf9uJBlTLX9AvuinY1UQtsnfkhQB1KE/4nQMKQzhvfTHRnU4T5v+Qa9+9UuHV1cGFEDmcQEAN0VS+Bl0859Ki7M0wNTR+nF97ekdYAyFsNLe6lHbpbPYwW5WRo+oFeSSgQAAOyGDhIAAJCJiGsDiZXpKxcD3hKVSBYoAah/Ub42bt9n6/v/TLpGE5NJXfOnl6uyaq3e/7pJo444VPdPLVdDU4vmLv1QL637Wq0dB4eE5GU7dNbIrsl38TrWU32QR3faDP6f+asde1XUIzeqBMhHrxqt8fe8qubW/crNMtTWaaq1o1PXLwp9vi4pytekY0q0qNaVAN/W6dSkY0q6PD/V9wvsi3Y2Ugltn/ghQR1IEd4nQochOU2p+uNG/dfjb+uPP/h2Wp8Qg43odZqmpo0ZTAMZaeODzbt18fw39fS1Y0nSTbJYAi8De/fQaxu26cV1X3OjFcbu/e0a2q9QN0wconmvbtCufe3JLhIAALAZOkgAAAAAxAMrFwNdJSqRLFACUKfT1OVjuf+3E2IyqSfUte173x6otk7Tk2uSZUhtnYlNvrPjII9IBubE0mYI9JnnVK2NOgFyRGmRKoaVeOrjax83RHS+jqQeW71fmDUbtLORqmj7xIdhmv4LJiGdlJeXm3V1dckuBixy1E1L5AxQZbMM6dO7z0l8gRLEPaI30ChSd4OWRi7SwekPvKYNDc0aUlKof/38tGQXJ6Tq6mpVVFQkuxi24n+j5caNFgAAAAAAwEHEcpHuOMZTQyR9T0CyJLoPJhn9GzMW1Km4Z75PApB3kiSQzuLVVgh1bfvls+tU3DNfnzU2a1tzq/oU5uobxT0zvu5VVq3VE7X1mjZ6UNBEb6vbDLGc/8Kdr+3QDo3kO0V6o50NZB7DMN42TTPgxYwZ1IEU8uZNkzT27uVdktQ7Tals9pK0TYKMZBmNeI/ot0NDHumrbPYSn983NDR7Htv46/QdfJJuAi1JGW5JNgAAAAAAgEzDMt9Id6l4jGdiHwhLuAMHJaN/w44zKSP1pcr1LF5thVDXtkxOQg8kmhmerW4zxHL+C3e+DnRsJapeMGs23GhnA/DmSHYBAESupChfU0Yd3uVxhyGdOaKfamZNiHqbDU0tmjp/pRpsviyFexmNquvGadqYwWpsbpXkauSWzV6ihavqZZquRm7Z7CUaVrnM0vf3bsgDVlt6w3gdfmiBz2MDDy3Q0hvHJ6lEiJb7xj7bMLjRQtRS5VoMAAAAAEAsEhXLBZIllY/xTO0DCdb3BGQaEslgF7H2l9j9ehasrTB0zrKIPnck3w/XtsjUzJyg80aVKj/HlTaXn+PQ+aNKg+bcWPG9WtEfGOx8fco9/w7aDk1UvYj2O0VkUrUfmXMRADfDNM3wz0LKKi8vN+vq6pJdDFhoxoI6fda4Vxsamn0ev3xM95bHSfXldeK9NEwylrRDZjr9gdd86vWQkkL96+enJbFEoSV6eUm7c59LBx5aoNOGlbAkJaKS6tdiAAAAAAAiwTLfSHepeIzTBwLYU6g+mHjNhDtjQZ2Ke+bTv4Gk6m5/Sapcz4K1FbINQ8+s2RT2c9OfZK05VWu1qLZeuVkOtXU6o/5eoz0fu/ffhccfri937g/7umDb9z5f/3nFZ/r3Rw168NLjdcNTa7RrX5ucB9IAHYY8P3tz14t4XE9i/U7RFfUeQCowDONt0zQD3jxkJ7owAGIzf3q5ZiyoU0t7p44deKgk6b2vdkU92iwey+skY8mseI/oT8aSdshMu/e3a2i/Qt0wcYjmvbpBu/a1J7tIiID/ufTLnfu18M0v9Pe6L20V8II9sdQdAAAAACCTMDsr0l0qHuP0gdhXMvrckBq8Z8K1MlHNOxl97pSRlm0XyZNK55FY+0tS5Xrm31ZoaXfquTWbPX8P9rnpT4oP9wzP3gNzouE+H/966Uf6alfghPOGphaNuXu5vOeOXfzOJknSSXct16d3nxN2+/7ne+/zdY+cLO3a367pj9TKPxfdabqS1HMPHGv+9SIe15NYv1McRL0HkC5IUAdSkBWj1eNxkxavgEg48Wzk2jmgnEpBBYRXO+c7np8nH1eaxJIgGqkS8II9cfwAAAAAADINCQtId6l2jNu5DyTTJavPDfEVS98eiWqIViqdR2LtL0nm9Szaeu3dVnhkxWd649Pt2rWvLeTnpj8pPro7MMf/fPzMalfC+di7luszv4Tzecs3yDSlsj49tHH7Pp+/dZpS2ewl3RqQEGzVAH9OU2pp960Xp9zz77hdTxjsZB3qPYB0QYI6kKGsvElLdkAk3o1cuwaUUymoAKQrOnAQC44fAAAAAECmIWEBqS5cEloqHuN27QPJVMnuc0N8xdK3R6IaIpWK5xEr+kuSdT2Ltl57txXunzpKc6rWalFtfcjPTX+SvbjPx//0mv1eciWDuxPOJfnUQ//kdElhByS8tO5rtXaYyss2dNbIAZ7nNTS1aPiAIhUX5em1jxuDJqpnGa5j5+Sj++jqcUd56gXXk9RAvQeQLkhQBzKYVTdp6d6AjSWgHI9ZzlMxqACkMzpwEAuOHwAAAAAAgNSRjhPHpGJSfTpL9z63TGVF3x6JaohUqp5HYu0vSfT1zKo++0g/N/1J9uE+H0uSw3Alpkt+CeemNHfph1ry7mZ1Hvh7WZ8eOrx3gd74ZLsMuRLYV3663bPdhqYW/deCt2UY0pF9DlFrh+uFrR2mz/l+3vINWvPVLh1dXKi2TqeyDHnew81x4LFJx5R42qze9YLrSWqg3gNIB4ZpmuGfhZRVXl5u1tXVJbsYyADukb25WQ61dTo1bfSgtAnOxqKyaq2eqK239PtoaGoJGlTgpgGJUl1drYqKimQXAwAAAAAAABkuHpOEAP78k9DcmDgmNOpn9BqaWnTOgzXa1txGn1saibZvL1gfzIwFdSrume+TqOZOyqW+wRt99y7xrBeJ6LOnXtuL9/745bPrVNwzXzuaW7V03RY5DMmUPHVt6JxlausMPLO5JH33WwP0ydY9Wt/QrMvHuF5TWbVWC1fVR10uw5CGlBSqb2Gevtzhmql9/vTyLtcJb6GuJwAARMswjLdN0wx4IWEGdQARCXfzw8g9X/Gc5ZwZEgAAAAAAAADAJR1ntIb9pOpstMlG/YzevOUb1LinTUNLCvXbS47PuD63dE3GtKpvL9QM0dQ3eKPv3iVUvYj1fJOIPnvqtb2498fkeSv0wg3jVdIzXzMW1Onysb51raGpRT3zs7V9b5sMuRLX/S1d+7XnZ3cuSTDudueMU4/SH17/LOJBEaFWEmAFHQBAopCgDiAi4W5+aMD6inewmqAC/KVr0BYAAAAAAAAIJJJJQoiZwSpMHBOdeE7ik678v7P1Dc367ryajPvOrE7GtMt1oKGpRUvXfq0Ljx+oq8cfaWnfHvUNgYTru7dL3YiXSOqFFeebePXZhyt/uu8/u/HfHw17WjX6zuUB98cp9/xbo+9a7nmud3L6Eb0LtGnXfjlNKevAjOtOU8rLNtS7R54a9rTIeeAF7sT23CzD0+4cXtqL9igAIOU4kl0AAPY2rHKZymYv0cJV9TJN181P2ewlGla5LNlFs7V4B6vnTy/X3CkjNby0SHOnjGS5JfgEUQAAAAAAAIB0VzNzgs4bVar8HFdXV36OQ+ePKlXNrAme5xAzg5XcSWhV143TtDGD1djcmuwi2VYk9dMuGppaNHX+SjUkeSKgVPrO4iFe/ZF2uQ7MW75Bu/a3qyDHYXnfnt2PHbvUMTvy/m4S/T11p26k0r4MVS+sPN/Eq88+XL22y7ktU9TMnCCH0fXx1g6nhlUu8+yPk+5a7pPI7u2cY/trx942T3J6p+lOTneordNUz/xsT3K65EpOH1JSqGd/Mt6n3Ul7FACQaphBHUhhiRgZy7KV3ccs50gEZsZIHmYnAAAAAAAASJ5Qk4QQM0M8sJJs5FJpxvlYZ9C1Kk6cSt9ZPFjdH2mX60AiymH3Y8fqWfHTiX+ScSK+p1iOyVTal6HqRSrkPwQr/yn3/NsW57ZMU1KUrymjDtczqzd5HnPPgN7a4dTCVfWSpM4Q21jy3hbPz51eieiP/PBE/fxva7Rjb5uO6F2gYwceKkl676tdOqr4EM/gBzfaowCAVEOCOpDCvG8Cb5g0JC6JknYPatgZNwdIhFQIoqSrVArEAQAA+GOwHQAASAfek4T8ecVnWrL2a93wnSHEzAAbsPskPlYlDlsZJ7b7dxZPVvdH2uU6kKhy2PHYSfYgATvHPQJ9N94/x/N76s4xmex92V3B6kWq5D8EKr9dzm2ZaG9bh4aUFGpDQ7McB2ZAv/D4w9Vhmj77I9thqLk1VKq6fPbbvFc2qLG5VdNGD6K/GQCQlkhQB1JQsJtASXFJlLRjUAOAS6oEUdJJqgbiAAAAvDHYDgAApAPvSUJ65GRp1/52T/uGmBmQXHafxCfWJL94xInt/p3Fm5X9kXbpO0lUOex47CQ7kdbOcQ//78ZhSIZcCa/x/p4CHZPZDkPXLwqezG/Vvkz0oIFQ9SIV8h+Cld+9/wxDtHETaP70cs1YUKcxR/XxOW76FuZ52gMt7c4wW5EcB/bb8+9u1nNrNnset0t/s50H9wAAUhMJ6kAKqpk5Qb98bp3+9cFWOU3fv8Wj4WrHoAaAg1IhiJJOkh1UBQAA8ZXuQXgG2wEAgGSzur0VrH3jMETMDEBQsSYOEye2ntX9kXbpO7FLOawW7npeUpSvbMNQS7tTOQ5X4ma2YcQ91pIKcY9A5x9JCRtM4X9MvvZxg77atT9oMr9VAy2CDRpIRiwulfMftjW3akhxodY3NGtoSaEam1uTXaS0Ee5YDHTczFhQp2ljBuusEf01+5n39NXO/ZJcg036F+Vr4/Z9Ptvwzu85b1Sp7doRdh7cAwBITSSoAymopChfnzXuldN0jbB0mlLWgWWEJKmsTw/97dqTkltIAAmTykGUVBRNIC7dE9wAAEhH6R6EJ4kCAAAkm9XtrVDtG3c8hpgZkJnCxWdjSRy2ywzd6SzW+Lpd+k5iLYdd+xkiuZ6/tXGHJGnAoT1Uv2Ofag/8Hq1ovoNUiXt4n39mLKiT5DpWEjGIwX1MRpPMH8v5Mtz7pHsszkr+3+X6hmatb2jWsMplthmAkcq6cyx6n+MLcrIkHZwlvdNp6qITDte2vW1asb6xyyoJv3tlg23aEakwuAcAkJoM0zTDPwspq7y83Kyrq0t2MWAh/4ZhKDQWAcRLdXW1Kioqkl2MpJmxoE7FPfN9AnHeAQi3yqq1eqK2XtNGDyKoBgAIyK6djJko2L1WOt5Xzalaq0W19crNcqit00lbBQAAJEQ821u0bwAEEu/4bKRxYnRPuP2XzjEV7z4Yu/UzRHI9D9efHe21P9rvgHZBZBqaWsIO8ovn+yxb+7XaOrvmK6VjLM4qidpnmSbW+5Rgr3cY0ps3TdLZv6vR9r1tys0y1O40PeckO7UjOLYAALEwDONt0zQDXsSYQR1IMYFGfRfl5ygv26GGPa1q7XDKYUjnHleqGacepanzV6ZlYAYAkincjCeMMgcARIoZguwjVWbYskK6Li8OAADsLZ7tLdo3qSWdk0phD4mKz9plhu50E+n+S/eYil37GSK5nruf89K6r9XacTABOS/bobNGRn7t7+53QLvAJdz1NlErQQR7nxWzJmZMLM4qrN4RH925T/GuX6FeP++VDdq+t02S9J3h/XXYIbmec5IV7Qir2tUcWwCAeCFBHUgxgRqGknTikYepavUm5WW7RoL3zMvWolX1KReYSVZgmoA4ACtlUoIbAKB77NrJmMkyKQhPEgUAAEiGeLa3aN+klnRPKo0GfRPxQXw2tYXbf5kSU7HrcRzJ9dz9nLZOUw5DcppSliG1dTq18tPtEb9Xd78D2gUukVxvE5XMH+h9MikWZyUGYFgv2LEoU0EnhPSvX/6vf/7dzXpuzWaf1yxd+7Uk12Adq1jZrubYAgDEAwnqQAra1tyqC48/XB9v2aN9bZ36bNtevfX5Dk9jcfKDNVq4qt7z/FQKzCQrME1AHICVCKoBAMKxaydjpiMIDwAAEF+0t+IjVZKcMyWpNBr0TcQH8dnUFm7/ZUpMxc7HcSTXc/dzPmts1rbmVvUpzNX2PW1a39Ac8TnPzt+BnUVzvU1UMn+w96FtGD0GYMRHoGMxUDstWP0yJBX3zNMDFx+nF9dt1Vc79ik3x6FXPtgq54GFJLIM6fTh/XS7BfstHu1qji0AQDwYpmmGfxZSVnl5uVlXV5fsYiAOvnHTEnUGqL552Y6QgRm73rD7N6Dd4h2YTtb7AqmuurpaFRUVyS6Grc1YUKfinvk+gQzvG3sAAOZUrdWi2nrlZrlWQZo2ehAJCQAAAACiVlm1Vk/U1tv+nqKhqSXl+i7ihb6J+CM+m9rC7b90j6m4+2BS4TiOZJBULOe8VPgO7IbrLRCbUOesYLk42Q5Dz6ze5HM9mlO1Vk94TSwpSZePseZ6RT0HANiJYRhvm6YZsJHODOpAignWGJak80eVehqcqTaaPFmzHWTKLAsAEo9R5gCAcJghCAAAAEAsIp050S4zrDMT7kH0TcQf8dno2eVcIYXff4mOqSTruwn2PdhpX0WyEkQs5zzqcvSivd7a6XgKxO7lQ2oKdVz5n7MMQzpjeD/dMWVkl1yclnannluz2fNa7/Z4xbBiHdG7QMcOPFSS9N5Xu9TY3GpJ+WlXAwBShSPZBQAQnZqZE3TeqFLl5xysvg5DMgz5NDjdgZmq68Zp2pjBljV04yVZDWga7gAAAEiW+dPLNXfKSA0vLdLcKSOZ/Qkpq6GpRVPnr1QDgywAAAASyr+/ID/HofNHlapm1gSf53knDyZbqvVdxAt9E7AjO50rwkl0TCXcd9PQ1KIpD7+hC/7fGwm5N7bDvhpWuUxls5do4ap6maYrKbNs9hINq1zW5bnRnPOIMVjzHURzvbXD8RTqM9uhfEg/oY4r73OWw5BMU1q/pVnXL1qthj0tPvXrwhMOV/9eeQHb4/Onl6tm1kQ9PO0EPTztBNXMmmjp9Yp2NQAgFRimaSa7DIij8vJys66uLtnFgMXcy9YZkpymdM7I/updmJfyS5ola4k2loYDoudeXhIAAACorFqrJ2rr025JdQAAgFTg7i/IzXKordPp0yYLtiKr/wzrSA76JmAXwc4VhiGtunlSRAMn0nWG40jPo5VVa7VwVb0k6fIxsd8bB+uDsdN5vaGpJeis6IGOgUjPecQYIvsOrKhzdjqe3J/5wuMP15c79+uhy47XKff82zblQ/oIddzXzJzgqVdj71ouZ4B0uixD+vTuc3weC9UeB1JZurbvAFjPMIy3TdMMGNAgQT3NkaCeniK9gU+VxkIs5UyVzwikGxLUgczFtRcA4GanjkwAAIBM1NDUojN/+7omfrNEV487qkt/QbTJgwAyU6BzRf+ifG3cvi/iZOt0TSoOdx4Ndl8sxXZvHKwPxm7n9e4kZQaLLxNjiO47sKLO2eF4CvaZswxp5U2Tkl4+pJ9Qx/28VzZ46tUNk4Zo7tIP9c81mwNuxzuhvUeOQwMPO4RBh0g76dq+A2C9UAnqjkQXBkDsgi1b57/0VaosdxVLOVPlMwIAkC649gIA3GpmTtB5o0oDLmELAACA+Ju3fIN27W9XQXZWl/4CSSopylfPvGy1djiVl+1Qa4dTPfOySeoCDvDvV8tU3ucKSWppd2rj9n2SpIWr6lU2e4mGVS4L+NphlctUNnuJFq6ql2mGf74V33ki91u482jNzAk6Y0Q/OYyDr8kypLNG9IvLvbHdzuvbmls1bcxgVV03TtPGDFZjc2vY1wSLLxNj6PodSFJZnx4+30G0dS4UOxxP7s/sr9OURt+1XC+8u9k2xzvSQ6Dj/oV3N2v0nct96pX7+DMM39fn5zh01oh++uaAIt3z4kd6a+MODezdQ3OnjFTfwlyt37pHd0wZmZwPh4jQ/gvPymsNAGQnuwAArOO+ofdfbmjhqnotXFVvuxHm/iOioylnLK8FAADR49oLAPBnh45MAACATBTNPbo7edB7RkccxEpxmc07UTbTZ4V0nyu+3rVfyz9q8DzuPbNsIDUzJwSdiTYQK77zRO+3UOfRkqJ8FRfm+fTLdppS38K8uJ1T7HRe9x4UNTdMQma4a1eiYwx2PP+XFOXrhXc3+xxPG7fv0+g7l3u+p5qZE1T53Dr964OtMs3wdTScZB9P7v1uGJIheT67+3Pt3t+ugb172OJ4R/rwP+6/2rFXRT1yu1zL/rlms0y/17a0O/Xi+1slSWu+3CXp4Pksy5CcEu0Km6P9F1607TsACIUEdSAN+N/Q+zeS7dpYiKVRQ4MIAIDE4toLAAgk2R2ZAAAAmSiae/RokgczEQkqmYmJGLqq/rjR5ztxa2kPnSQcaVKxFd95svZbuPPotuZWHdG7QMcOPFSS9N5XuyKaSTxe5YlEMpKzI7l2JTLGEOz8n+zE9VOHFGvj9r3avHu/2jpMOQzp3ONKPd9TSVG+Pm/cK9OUHIZiTuS/4/yRuv7J1erbMzdp7QT3ft/R3Kql67b4fK7fXXK853m0Y2AV7/PoDROP1vVPrtZhDqPLtWzVzZM0d+mHXQaOBNN54Dm0K+yJ9l/kmJgGgJUc4Z8CwO4CLXlW1qeHJNm6sRBLo4YGEQAAicW1F8gsLHOJSM2fXq65U0ZqeGmR5k4Z6dPBAwAAgPjgHj12LFufOuJxfxqoX+38UaWqmTUhKeWxA//vJMthqGJYsS46YWDYZGt3cmnVdeM0bczggM+P5TsPt42q605O6j6ZP71cNbMm6uFpJ+jhaSeoZtZE298beydnWy1YHYnk2pWIGEO48388v5tIPHrVaI07uq/aO03lZTtkSp7vyV32DQ3NklyzjZumtKi2vtvvl+zPKx3c752mqcvHDtYLPz0l6LkEqcuq66fV12F3HXjr8x1drmXu85YpV96NYUhlfXp4rkOSa6CIId/Hu3ONQ/xZ0RbJJJG07wAgEsygDqSBQDf0nU7XDZzdZ7GLZSQ8M/UBAJBYXHuBzMEsggAAAIC9cY8eG/dMvi+t+1qtHabysg2dNXKAz0y+yZ5F1w7s8B3E4/40VKJsuM+crvfL/t9JW6dTAw8tiOgzRjKjtxUDa4JtY9Gq+rTcJ/GQiNljQ9URO1y7gs3kvmzt1yqbvcTzvGTOrBvse7JylVM7ziTMqi/pzarrp1Xb8a8DX+7cr4VvfqG/133pUwf862P1xw1q7XDKYbgGiZw9or96F+Z5HmfwaHxY0SZlkG90OCcDsIphmhGsQ4KUVV5ebtbV1SW7GEiAGQvqVNwz3+dG1b0kVyYHTwHER3V1tSoqKpJdDAAAYDH/wLwby1wCAAAASDdzqtbqiVUHZ529fMwgn0Snyqq1eqK2XtNGD8rYpNdkfgfxvj8N1K82f3p50M+cjPvlRA8QCPad2Gn73ts498EV6gyQ65AOMYx49cE0NLUET3A2FdPxlkoxpTlVa7Wotl65Wa7BGNNGD9INk4YE/W7s1M8eqOzdOT+HOhbs9HmR+iI9N4S75kV7jgm3vUB1oGJosbY0teqPP/h20HoQ7FoW72toprOqTZqK+8kOA0YBIBzDMN42TTPgCZUE9TRHgnpmI3gKhEZjvvtIUAcAID3ROQUAAAAgE4RKcpKUMkmW/qyKedsh0TTR96fhPnMy7pczrZ8v2uM3nWMY8eyDCZbgHMnxFmofpdL+CJagaFXyd3dFUgesTK5M9udFZoj03BDuHBRoO6cNLVbDnlbNn941odx/e4Hql38dOLq4UJ80NlMXbMQObdJky7T2IIDUFCpBPTvRhQEQf3Zckguwo3RdihMAAKC7WOYSAAAAQCaomTkh5CzCQf8WpURPkmJVzDvk95Mgib4/DfeZE1meTO3ni/b4LSnKV7ZhqKXdqdwsw/YxDCvPB7Fsa1tzq6aNGexJcF606gst9FpNItTxFmofpVJMyTuhe+6UkZ6f/b+bxj0tCS1XJHUgWNm7I9mfF5kh3Lkh0mteoO181rhXnzQ2+9SZYNvLMiSn5PNcdx14+q16maa0oaE5ZBmQeHZokyZLprYHAaQfEtSBNJTJjTQgEjTmAQAAgqNzCgAAAEC6C5csZVWSZaImSbE65m2XRNNA96fxSvqP5DMn6n450/r5Yjl+39q4Q5L0neH9ddghubaOYVh5PohlW/4JzjdMPDrs8RbpPkr1mJKVyd/RSFa/ZbI+LzJPqHND1XUna/ojtWpubVdrhxnymhdJQnnNzAmaOn+lNm7f5/PaTlNdnuuuX5GcB5EcdmmTJkOmtQcBpC8S1IE0lMmNNCASNOYB+0j0LFIAgPDonAIAAABgNTvGgEIlS8WaZJnoZMN4xLxDfQeJ2p+B7k8rq9bGnOQbrPzh9nui7pczrZ+vO8evfx1buvZrSVJetiPu5Y1WJOeDSOtUPM4tkRxvke4jYkrdQ78l0l2oc8OiVfXavrdNksJe8+ZPL1dDU4vWbdqtfkV5em19o0+dWbb2a42+a3nAMuTnOILWr0y77qaaVB/81F0clwDSBQnqQJrK1EYaEAka84B9JGoWKQAAAAAAACSPHWNAoZKl7jh/pK5/crX69sztVpJlopMN4xHzDvX9JGN/WpmYG6z8dkquzaR+vu4cv92tY8kYLBNJWSOtU/E6t4Q73uhXiy++X2Qi/+u6JLV2OJVlGGpsbg36unnLN+jdr3bp6OLCLnVmxayJmrv0Qz2/ZrNMSTlZ0uGH9lBbh1NfN7WErF+ZdN1NNXZqnyUaxyWAdECCOpCmMrmRBkSCxjyQXMlashIAAAAAAACJE68YULyTTGNNwE5GsmEiYt7JjOlFmpgb6thIpZhkpvXzRXv8dreORVq3rTzHhCprsGMyxyFtqIhuW7GI5Hizw6oK6SxZ/ZbsOyRLyOu6KU2dv9LnuPQ/X25oaJYkOU1T08YMVuOeFs850jzwnPZOafzRfdXY3KqJPfuFrF+Zdt1FauC4BJAODNM0wz8LKau8vNysq6tLdjEAAGmmurpaFRUVyS4GUlhDU0vQwBNBUABArOhcAwAAAOwhXjGgyqq1eqK2XtNGD7J0Bu9As3lK6lYC84wFdSrume+TDOWdZJKKkh3Tm1O1Votq65Wb5VBbpzPg/g91bCS7/N3FPW5g0dSxaOu21eeYYGUNdkxO6L1TU86cGNW2kile5+R4oD75SqV9h/Tjvq7nZDnU1uHURSccrvunjupyXDY0tWjGgrdVVJCt1zdsk2kq4DU80nM95wEAAA6y4rpoGMbbpmkGvClhBnUAAAAkHEtWAgDiKRnLzQMAAADoyuoYULxnwI50lu5IpOOMh8mO6YWaYTiSYyPZ5e8uO97j2iG5L5o6Fmndjtc5JlhZgx2Th+Y5ot5Wonjv+1Pu+XfKrErgZsf6lAyptKIE0pf7ur6zuVVL1m3R4nc2afE7mzx/dx+XWYbklFyzo5uSw5BaO5xa+el2bdvTqusXuc5Joc713ucuzgMAABwU7+siCeoAbM0OAS4ASIRMPN8la8lKAED6onMNAAAAsB8rY0BWJpAHkqoJzImUzJheqMTcSI+NVIpJ2vkeN9WS+yKt2/E+xwQS8JjsHbe3i5n3vo/X9xWP/hI716dkSMaxDvir/rgx4Izn/jpN1/9NLR2SJOeB3xv2tOonT6zWxh17PdejYOf6yqq1qv18h0bfudyz3Uw/D8BXJvbVA8hsiWofk6AOpJl0aDRZNXo1Hb4LAPFjt3NEqgX0rZDsmV4AAOmHzjUAAADAfqyMASUigTyaBGa7xRgTwa4xvUiPDbuWPxA73uNGksRg13oRSd1OxiCVQMdkdXV13N6vu4Lte4chmZKl31c8+kvsWJ+SiQFZsAP/epllHExGdx+Xknwe9/f59r2SfM9JFxx/uNZv2aNh/Yu0qNb1eCCZfh6Ar0zsq0dy2LWtjMyTqPZx8LWhAKQk70ZTqpq3fINn9OrCVfUyTdcNRdnsJRpWuSzo6xqaWjR1/ko1HAgopcN3ASB+7HKOGFa5TGWzl0R1vgMAAIHRuQYAAJCZ/GPDSG/uJNOq68Zp2pjBamxutXT786eXa+6UkRpeWqS5U0b6JI/6iyXGyHFrvXgfG4lmx3vcmpkTdN6oUuVlG5KkvGxD548qVc2sCZ7nWBl7t7KeRFq30+04sop73+fnuFJM8nMcOn9UqU4d0tey7yue/SV2rE/JxrFuL5nYLnDXy5Z2VyK6dxK694CYYMnp/s4fVao3b56kHjlZev/rJhXkOPTmTZN8zl1ZDtf1KzfL4DwASfTVI/HskqcCJKp9bJhmhFdypKTy8nKzrq4u2cVAAviPWnfLzTI0alDvlBh5FewzSL6jV4N9jsqqtXqitl4OBb5JYWkmwDrV1dWqqKhIdjG6Jdi5xopzRHdGuzY0tQQdlWj38zYSjxHVABDejAV1Ku6Z7zMjWqiEEgAAAKQ+d2x42uhBzHaHhLAixshxi0jY8R53TtVaPeE1G+3lY1zHcDxi75laT+zUB+Mdk/7dKxu0qLZeuVkOtXU6Ld8v8e4vsWN9Atwy9Xw3Y0Gd1m/Zo8+37/M8lpftUN/CPG1rblFrh6ksQ+p9SK6K8nN02CG5WvPlTgVKKwk207p7pYfcLFfy3ZCSQv3ukuM5D0ASffVInHjmqQDdZVX72DCMt03TDPhCEtTTHAnqmSNYo6m906mla7foohMO1/1TRwV8nV0S3dyfYcl7X6vTefDclJvtUHuIIEeoxHYpsuR2ANGxU3A0WvG8yexu8GhO1VpLgrp2OqcjPjI1QBkL6gUAAACsRhsTsA86eJEsscQYOW6RykIdv6GWiI+2zZTp9cROfTDeMenG5ta4J3hb1V8CpIpUP9/Fcn8cLs/DMORzLvj7218Fff7lYwfrqx17VdQjt8t1aPf+dg3s3YPBKQiKaw8SgcEQSGehEtQdiS4MgPjwX3ahpd2p59Zs1tK1WyRJi9/ZFHAZGjstHXLKvf/WP9ds9klOlyTTNEMuKxZoSbmyPj1kGGKJNgBdxGOZmliX/rJqGUU7ndNhLZaX6z7qBQAAAKxGGxOwj0Cx4fNHlapm1oQklwzpLpYYYyzHbUNTi6bOX6mGPS0xf4ZMw3dnjVDHr7tetLQ7ZUhqae9+7J3ze/IFikm/9P5W/b3uSw0vLdLcKSPjkuBpVX8JkCpS/XwXy/1xzcwJOmNEPzmMg485JB3Ru0B9C3O7nAvc35XnuYZU1qeHKoYVa+6UkXr0qjEB22ePXjlac6eMjOu5C6mNaw8SIR55KkAqyE52AQBYx91oumz0IJ0zr0aB1kdwr5rgPxp14ap6LVxVn9SRuDUzJ+i8h99QY1OLOk3XEkwlRfl67vpxIS/IgS7inU7T8124R8ECgJv3+dKKc0SomWEi4Q6ENDS1aP3WPXrosuOjen87ntNhrViPMSulymyR1AsAAABYjTYmYD908CKZuhtjjOW49U4CY1bH6PDdWSPc8butuVVDSgr1SUOzhpQUdjvBi/N78uOwyYpJeyeOzp0yMq7vBdhBqp7vIrk/DnceKynKV3FhnrznL3RKOm1osc+12n0u8H9Ppylt3L5PX+8+2AaLpn2W7PMs7INrDxLF6jwVIBWQoA6kEXejaVjlsoDJ6ZK0YvZESfZKdPPmdJrqNF0zn7d1OjXpmJKIbgYCXcTdDUcakAD8WX2TaVXwqLudJFXXnazLH6nV3tYOtXZYd04nMGMfdgpQpkpnnl3bOgAAAEhdtDEBe6KDF8kSS4wx2uM2EYOk0jUWyAAz6wU7fv2/6w0NzdrQ0Kxhlcu69V2n4vndynqU7DisnWLSQLpLxfNdJPfHkZzHtjW36ojeBTp24KGSpPe+2hV0cJNrwsMVatzTpk6nqSyHoZKeeXru+nGe50TTPkv2eRZA5mEwBDIRCepAGnLfDPxzzWafx0t75XuCBnYMKsxbvkENe1wzK/zukuOjuvmK10X8g827dfH8N/X0tWM1fEAvy7YLIP3EEjyKtZNk0ap67djbJkmWntMJzNhLsgOUqdaZ593WMQzZoq0DAACA1GbHeBoAOniRmqI9bhMxSCpdY4EMMLNesOPX6u/a/30amlo0df5KWw+isKIeHYzD7pWU3DhssmPSQKZIxfZsqPvjaPqTvD97JO856Zh+WlRbH/WEh96s6u9K18F9AABYiQR1IA2dcu+/fRrUbluafIMGsQYVrGpwB5pR4bvzamyR8HbjU2u0p7VDNz65Rv/6+WlJLQsAe4sleNTdwL3/+VNyJeE6DHV76dRA27V7InKmSHaAMhU787Y1t2pIcaHWNzRraAxLCgMAAABuJOkAAJIhnoOkUiUW2N0+KQaYJU48vmvv/W7nQRRW1qOamRN0w1+rtWabmfQ4bLJj0gDsLdj9sX9/ksOQzhjeT7dbcB6x4p7cqv4uO1+XAACwCxLUgTQUydJGUuxBBasa3HZMeCubvcTn9w0NzZ7HNv76nGQUCUAa627gPtT5M5agvx3Py0i+VOvM8+8UWt/QrPUxLCkMAAAASCTpAACSJ16DpFIlFhhLn1S6DDBLhZlarf6u5y3foNrPd2j0ncs9j9lxEIWV9aikKF8F2YZaOzpTIg4LIHMFuz/27k/KMqROU/q0cW/E57FQ1zsr7slj6e9qaGrRmLuXyzQPPmbH6xIAAHZBgjqQhqxa2igYq2fTsGPC29Ibxuuax9/Wpl37PY8NPLRAf/zht5NWJgDprTuB+3idP+14XoY9pFJnXqp0rgIAAAAAAEQiXoOk7B4LtKJPKtUHmLkT9foW5qr28x26Z9lHun/qqGQXKyCrvutAq4e62THOZ3U92t1qpkwcFkB6i3ZwlPv5b23cIdOUOg887p4QMDfL0Po7vxtyG/Gcmdxdvh45Dk0bM1hnjeivn/9tjb7auS+i189bvkGmKZX16aEtTS30PwEAEAYJ6kCaimcCWTwSvuyW8Da8tJd65Gb5PFaQm6XhA3olqUSIVSrMLILM1t3AfbzOn3Y7L8MeUqkzz+6dqwDQHbRpAQAAAMSDnWOBTEIgnXT3cnV6zdS6+J1NWvzOprSeqdV/v2c5DHU6TeVmGbaN81lZj244IV8VFa74q93jsADSW7TJ4u7nXzDqcHWY5sHz+IGZ1M89rjToa62eKDFU+aaNHqS5U0aqsmqtGptbNbB3v5Cv8y/bxu0HE9rtel0CAMAODNN73RGknfLycrOuri7ZxYCNWJXQMKdqrRbV1is3yzVD+7TRg7o1etXOCRaj73xFh/bI0Q0Th2jeqxu0a1+7aud8J9nFQjdVVq3VE7X13T5W4au6uloVFRXJLgYAhDRjQZ2Ke+b7dAp5J9kDQKqhTQsAAAAgE1nVJ5VqQs0iLimiWWhTmfd+b+1wakhJoX53yfEZEeejDwaxsHP/O1JHsGtQoGTxhqYWjbl7uaJJPwu2nV8+t07/+mCrnKbvihmxHsvhrqmhyuUum/fAKYchDTqsh+ZeMFIvrtua9tclAABCMQzjbdM0A14ImUEdyDBWLYdk1SwAocqT7Jtn72T0ySFG8sLeEjHSGgBgT3aa8T3Z7RoAqY02LQAAAIBMZucZ3uPJPYv4P9dsTnZRkiLQfh9eWpT0OB9gd1blAyCz+a9k4TCkM4b30+0BzsHzlm+QaUplfXpoS1OLZ8WT3j1ydfzgQ7WvpVM1n2xTp9MMuRJKSVG+PmvcK6f5/9n7+/goz/ve9/3eM3oYsJDtgIQtE1BSgxKQY7nRAlzjRrLTxq1dW252SBrMStduu/B2s+y9ss4GstF6ta9GSUz3dl87ND1nKXt1ne6CcZIerHg1QnYSErJEX1iyMKTYjmvVMZYNxiPAIASMHmbu84cYMTOaGc3TPffT5/2PzUiauWbmfriu3/W7fpcUNK5VJpcpbew+nPP8Srr5mNT3U11haElNSGfGJzQxPf8OLam79k5GY9pw6xJtuLVOG26ty/PTBQDAP0hQB3wgPBbRum8cUOKC1WITGopN+MolwcLJyetwD7b/BAA4wZN9r2vwrXPauf91PfX5FrubUxb01+Aldh/P9GkBAAAA+JmTihCUUzwZLp0FlQH9fGt7mVtUXn793oFCUeAApZSYkB00pKgpvTl6KSk2mnrMnTh7efb/J6Zjuvdj9Xr83pV64K8PKRozVV0RmE06T42xrtqxX5PRaxkt8f/dOzgiSXktukiXZ5IuwXxhVVCT0VjWdiXy64I5AACKQYI64AO7DgxLhtT4oeQVq3YmNGRLsCg2eR1IlDrYzGVwCQBAqaT2a/YdPal9R0/6YlKA/hrsYkUyud3HM31aAAAAAPCX+Nh2YVVQj6xfoR+/elrvX5yQIcmUtKSmmjEhgCQUOECpPTM4ItOUolf/PRweV+P23tn5jXRV1pd/aKG6Hm7W86+8r9GLEe06MKzwxQmtrK/Rt75wR8bE7t+7vUH7Xj45mwwfqgxocjqmmDmTLyLNv+hivjyT1ATzH716Oq+EcxZOAQCQPxLUAQ+bb8WqnQkN2RIs0g2e21bV6fTYhFZ19mmSld/IE6uZAQB2Mc0Mj5e3GWVFpR7YrZTJ5E46nunTAgAAAIB/xHfj++wdt6iro1mjFyOqWxRiTAggIwocoNRe/Oq9SXkbVUEpZhr62z+cSdROV5V8w61LtOHWOv3R3w0lxVWHw+P63V39c+KqqfHXeOX0yFRMv3/HLZo2zZwXXcy3SCM1wTwxyZyEcwAArEGCOuBhqR1wSWq4PqS//J9u1/OvnrY9eJUpwSLd4PnN0Uv619FxPdyS3yAEkFjNDPtYUcEVgLsc2taujd2HkxYKNi5eqO8/eqeNrbIWlXpgFyuSyZ10PNOnBQAAAADvy2U3PsaEADKhwAFKKV3ehmTqP/e8op/9b+2SMh9zucZV+7e2q/O5V/Tj196XaUrBgKG7Vy7R4uuqNT4xpSU11TkvumCRBgDAacgZIkEd8LTEDnjc+MS0Nqxcog0rl9jYshnZEiziA5nvvTSzbdRweFyS9OzRk7O/w6ACgNOVsoIrAHeqrw1pOjZT8qMqaGgyaioaMz3ddyEIDLtYkUzO8QwAAAAAKKdsu/G9duqCPt/9or736Hqtvvn6srbLCUjuAOZHgQOU2skPLss0lZRz8tbZy2rc3itJOvHk/bOPJx5zucZV62tDemv0kkxTChhSzDS17IYFs/OqW3YP5bXogkUaAAAnIWeIBHXA854eGEn691hkOu1gwWnig+fH77l1TpLJjQur9Bu3LtYf3fVRBhUASqLUgW0rKrgCcK81DbVqa6r3VUCUIDDsYFUyOcczAAAACkEiJYBCZNuNb9P/PaCLE9N64plj+vFXPmVjK+1BcgcAlN+yGxdm/Fll0JCUvt8bHoto//H39Pu/fkvGvI7U+dSrtX60d3Bk9jqf76ILFmkAAJyAnKFrDDPTMmx4Qmtrqzk0NGR3M2Cj105d0J/8/RGdPH9l9rFlNyzQd770Sdd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      "text/plain": [
       "<Figure size 3686.4x2073.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in workshop's data set in pandas data frame\n",
    "data = pd.read_pickle('Cu_df4_2.5eV_25A3_8K.pckl.gzip', compression='gzip')\n",
    "fig1 = PlotData(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "As already mentioned, your data needs to be stored in the ``input.data`` file. This file is used for ``mode 1`` to generate all the needed files for the NN training in ``mode 2``. In this case the ``input.data`` stores all the information of the electronic structure code like the total energy and charge, the structure (lattice constants, atomic positions), atomic forces and may the atomic charges. If used in ``mode 3``, only the structural paramteres are necessary, since ``mode 3`` is the prediction mode and we may do not know the outcome of an electronic structure calculation.\n",
    "\n",
    "The ``input.data`` follows a certain format with certain keywords. Each structure is emebedded between the keywords ``begin`` and ``end``, to separate different structures from each other. For periodic structures the 3 lattice vectors are introduced by the keyword ``lattice``, for non-periodic structures these keyword is just missing. Information about the atoms is given line by line, thus each atom in one line, beginning with the ``atom`` keyword, followed by the Cartesian coordinates (x, y and z), the element, the atomic charge, an unused column and the atomic force components (fx, fy and fz). The ``energy`` keyword is followed by the total energy of the current structure, equivalent the overall charge is marked by the ``charge`` keyword. Comments can also be added with the ``comment`` keyword. Important aspect: the data is given in special units. A length is given in ``bohr``, an energy in ``Hartree``, thus forces in ``Hartree/bohr`` and charges in the elementary charge ``e``. In general, periodic and non-periodic structures can be mixed, as well as different numbers of atoms per structure can be combined. Information can be given in a free format (number of digits), but it is recommended to use at least six digits and the order of the keywords is arbitrary in general."
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   ]
  },
  {
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   "cell_type": "raw",
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   "metadata": {},
   "source": [
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    "begin\n",
    "lattice   2.34735543   -4.06574009    0.00000000\n",
    "lattice   2.34735543    4.06574009    0.00000000\n",
    "lattice   0.00000000    0.00000000   13.45504276\n",
    "comment x             y             z         element atomic charge   unused      fx            fy               fz\n",
    "atom    0.00000000    0.00000000    0.00000000   Cu    0.00000000   0.00000000   -0.00000000   -0.00000000    0.00000002\n",
    "atom    2.34735543    1.35524733   10.09128112   Cu    0.00000000   0.00000000   -0.00000000    0.00000134   -0.00000002\n",
    "atom    0.00000000    0.00000000    6.72752138   Cu    0.00000000   0.00000000    0.00000000    0.00000000   -0.00000004\n",
    "atom    2.34735543   -1.35524733    3.36375974   Cu    0.00000000   0.00000000    0.00000000   -0.00000134    0.00000003\n",
    "energy -0.4746414926841609\n",
    "charge 0.0\n",
    "end"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "**pyiron RuNNer Fit**"
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   ]
  },
  {
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   "cell_type": "code",
   "execution_count": 12,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from pyiron import Project"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "pr = Project(\"runner_fit\")"
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   ]
  },
  {
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   "cell_type": "code",
   "execution_count": 14,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "!cp ../../datasets/Cu_training_archive.tar.gz .\n",
    "!cp ../../datasets/export.csv ."
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_fit = Project(\"import_database\")\n",
    "if len(pr_fit.job_table()) == 0:\n",
    "    pr_fit.unpack(\"Cu_training_archive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df1_A1_A2_A3_EV_elast_phon</td>\n",
       "      <td>/df1_A1_A2_A3_EV_elast_phon</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/pyiron/workshop-material/day_2/runner/import_database/Cu_database/</td>\n",
       "      <td>2021-02-08 10:33:52.341472</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df3_10k</td>\n",
       "      <td>/df3_10k</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/pyiron/workshop-material/day_2/runner/import_database/Cu_database/</td>\n",
       "      <td>2021-02-08 10:33:53.993230</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>df2_1k</td>\n",
       "      <td>/df2_1k</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/pyiron/workshop-material/day_2/runner/import_database/Cu_database/</td>\n",
       "      <td>2021-02-08 10:33:54.435308</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>zora@cmti001#1</td>\n",
       "      <td>TrainingContainer</td>\n",
       "      <td>0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "   id    status chemicalformula                         job  \\\n",
       "0   1  finished            None  df1_A1_A2_A3_EV_elast_phon   \n",
       "1   2  finished            None                     df3_10k   \n",
       "2   3  finished            None                      df2_1k   \n",
       "\n",
       "                        subjob projectpath  \\\n",
       "0  /df1_A1_A2_A3_EV_elast_phon        None   \n",
       "1                     /df3_10k        None   \n",
       "2                      /df2_1k        None   \n",
       "\n",
       "                                                                    project  \\\n",
       "0  /home/pyiron/workshop-material/day_2/runner/import_database/Cu_database/   \n",
       "1  /home/pyiron/workshop-material/day_2/runner/import_database/Cu_database/   \n",
       "2  /home/pyiron/workshop-material/day_2/runner/import_database/Cu_database/   \n",
       "\n",
       "                   timestart timestop totalcputime        computer  \\\n",
       "0 2021-02-08 10:33:52.341472     None         None  zora@cmti001#1   \n",
       "1 2021-02-08 10:33:53.993230     None         None  zora@cmti001#1   \n",
       "2 2021-02-08 10:33:54.435308     None         None  zora@cmti001#1   \n",
       "\n",
       "            hamilton hamversion parentid masterid  \n",
       "0  TrainingContainer        0.4     None     None  \n",
       "1  TrainingContainer        0.4     None     None  \n",
       "2  TrainingContainer        0.4     None     None  "
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      ]
     },
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     "execution_count": 16,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "pr_fit.job_table()"
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   ]
  },
  {
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   "cell_type": "code",
   "execution_count": 17,
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "import pyiron_contrib"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 18,
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   "metadata": {},
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   "outputs": [],
   "source": [
    "j = pr.create.job.RunnerFit('fit', delete_existing_job=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "j.add_job_to_fitting(pr_fit.load('df1_A1_A2_A3_EV_elast_phon'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job fit was saved and received the ID: 4\n"
     ]
    }
   ],
   "source": [
    "j.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Model</th>\n",
       "      <th>Species</th>\n",
       "      <th>Config</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RuNNer-Cu</td>\n",
       "      <td>[/home/pyiron/workshop-material/day_2/runner/runner_fit/fit_hdf5/fit/input.nn, /home/pyiron/workshop-material/day_2/runner/runner_fit/fit_hdf5/fit/weights.029.data, /home/pyiron/workshop-material/...</td>\n",
       "      <td>RuNNer</td>\n",
       "      <td>[Cu]</td>\n",
       "      <td>[pair_style nnp dir \"./\" showew no showewsum 0 resetew no maxew 100 cflength 1.8897261328 cfenergy 0.0367493254 emap \"1:Cu\"\\n, pair_coeff * * 12\\n]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  \\\n",
       "0  RuNNer-Cu   \n",
       "\n",
       "                                                                                                                                                                                                  Filename  \\\n",
       "0  [/home/pyiron/workshop-material/day_2/runner/runner_fit/fit_hdf5/fit/input.nn, /home/pyiron/workshop-material/day_2/runner/runner_fit/fit_hdf5/fit/weights.029.data, /home/pyiron/workshop-material/...   \n",
       "\n",
       "    Model Species  \\\n",
       "0  RuNNer    [Cu]   \n",
       "\n",
       "                                                                                                                                                Config  \n",
       "0  [pair_style nnp dir \"./\" showew no showewsum 0 resetew no maxew 100 cflength 1.8897261328 cfenergy 0.0367493254 emap \"1:Cu\"\\n, pair_coeff * * 12\\n]  "
      ]
     },
     "execution_count": 29,
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     "metadata": {},
     "output_type": "execute_result"
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    }
   ],
   "source": [
    "j.lammps_potential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The RuNNer Workflow\n",
    "\n",
    "We discussed the ``input.data`` and will have a look at the next steps. Here, the ``input.nn`` is explained. Keywords can be given in an arbitrary order, but grouping keywords by the modes is useful for the general structure. The units are the same as for the ``input.data``, see above. If a keyword is not specified, **RuNNer** uses default values, if possible. A summary in the output files gives more detailed information in the specific case. Most keywords can only be specified ones, to avoid contradictions, otherwise an error will be printed. Also comments can be added to the file, which are indicated by a hash ``#``. In principle, it is possible to change the ``input.nn`` for every mode, however it is highly recommended not to do that. Anyway here, you will not have the opportunity to explicitly change the ``input.nn``, since **RuNNer** is called via the pyiron environment. At the moment, the implementation of **RuNNer** to pyiron is on a very early stage, thus no changes are possible for the input.\n",
    "\n",
    "In the following we will discuss a subset, but the most important keywords. Beginning with some general keywords of the ``input.nn``. I think the first keywords are self-explanatory together with the given comments. The data set splitting in ``mode 1`` and the initial weights in ``mode 2`` rely on random numbers. For the reproducibility, a random number seed (keyword ``random_seed``) has to be defined, which will give the same results, if the run is repeated later. Together with this, the generator for the random numbers can also be defined (keyword ``random_number_type``). The second group of keywords describe the architecture of the NN and the activation functions of the nodes via the keywords ``global_...``. Usually, we use 2-3 hidden layers with 10-40 nodes each.\n",
    "\n",
    "**Is there another option than ``use_short_nn``?**\n",
    "Charges be used for fitting, which needs different keywords."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### general keywords\n",
    "nn_type_short 1                           # 1=Behler-Parrinello energy is a sum of atomic energies\n",
    "runner_mode 1                             # 1=calculate symmetry functions, 2=fitting mode, 3=predicition mode\n",
    "number_of_elements 1                      # number of elements\n",
    "elements Cu                               # specification of elements\n",
    "random_seed 20                            # integer seed for random number generator\n",
    "random_number_type 6                      # 6 recommended\n",
    "\n",
    "\n",
    "### NN structure of the short-range NN\n",
    "use_short_nn                              # use NN for short range interactions\n",
    "global_hidden_layers_short 2              # number of hidden layers\n",
    "global_nodes_short 15 15                  # number of nodes in hidden layers\n",
    "global_activation_short t t l             # activation functions  (t = hyperbolic tangent, l = linear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RuNNer Mode 1\n",
    "\n",
    "In **RuNNer**'s mode 1 the following steps are performed:\n",
    "- calculation of SF values,\n",
    "- splitting of data set in train and test data set.\n",
    "\n",
    "The amount of test structures is defined by the keyword ``test_fraction``. Here, ``test_fraction 0.10`` means 10% of the data set will be used for testing and is not part of the training data. ``use_short_forces`` keyword states to use also the atomic forces for the fitting process in ``mode 2``, but it recommended to use it also in ``mode 1`` to create the necessary force files."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### symmetry function generation ( mode 1):\n",
    "test_fraction 0.10000                     # threshold for splitting between fitting and test set\n",
    "use_short_forces                          # use forces and prepare the files in mode 1 for fitting in mode 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next group of keywords, the SFs are defined. There are two different types of SFs: the radial SFs ``symfunction_short XX type XX ...`` to describe the atomic distances and angular SFs ``symfunction_short XX type XX XX ...`` to describe the spatial distribution of the neighboring atoms. ``cutoff_type`` keyword describes the cutoff function type. All mentioned SF types are shown in the next section."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### symmetry function definitions (all modes):\n",
    "cutoff_type 1\n",
    "symfunction_short Cu  2 Cu     0.000000      0.000000     12.000000\n",
    "symfunction_short Cu  2 Cu     0.006000      0.000000     12.000000\n",
    "symfunction_short Cu  2 Cu     0.016000      0.000000     12.000000\n",
    "symfunction_short Cu  2 Cu     0.040000      0.000000     12.000000\n",
    "symfunction_short Cu  2 Cu     0.109000      0.000000     12.000000\n",
    "\n",
    "symfunction_short Cu  3 Cu Cu     0.00000       1.000000      1.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000       1.000000      2.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000       1.000000      4.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000       1.000000     16.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000      -1.000000      1.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000      -1.000000      2.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000      -1.000000      4.000000     12.000000\n",
    "symfunction_short Cu  3 Cu Cu     0.00000      -1.000000     16.000000     12.000000"
   ]
  },
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