PyTICS_Example Usage.ipynb

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   "source": [
    "### This notebook shows example usage of the PyTICS algorithm for NGC 3783 taken with ten LCO 1-m telescopes for the u band \n",
    "### The main github page has attached the paper on the algorithm (Vieliute et al. submitted to MNRAS)\n",
    "### PyTICS will soon be available as a public access package allowing for easy intercalibration of photometric data with a couple lines of code as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a18da715-2d28-4822-a478-cee12cd9c600",
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   "outputs": [],
   "source": [
    "# -------------- Import packages --------------\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import TwoSlopeNorm\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import arx\n",
    "import time\n",
    "from scipy.optimize import curve_fit\n",
    "import math\n",
    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
    "from functools import partial\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# ------- Import star data in pickle format -------\n",
    "lco = pd.read_pickle('lco_latest_stan.pkl')\n",
    "\n",
    "# -------------- Obs info --------------\n",
    "#AGN Name\n",
    "obj_name = 'NGC3783'\n",
    "\n",
    "#FILTERS = pd.unique(lco['Filter']) #not in order\n",
    "FILTERS = ['up', 'B', 'gp', 'V', 'rp', 'ip', 'zs', 'Y'] #in order\n",
    "print('FILTERS:', FILTERS)\n",
    "\n",
    "TEL = list(pd.unique(lco['telid']))\n",
    "print('SCOPES:', TEL)\n",
    "\n",
    "AGN_ID = 2387"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e65c4e85-4139-4a60-a563-f65a6df50995",
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    "# ===================================== Function Library ============================================\n",
    "def Clean(Star_File):\n",
    "    \"\"\" Drop duplicate values, existing zp and zp_err columns, and strange errors (= 99.0).\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Original data file (pd dataframe).\n",
    "    \n",
    "    Output:\n",
    "    File2 - Cleaned up data file (pd dataframe).\n",
    "    \"\"\"\n",
    "    \n",
    "    File2 = Star_File.drop(columns = [\"zp\", \"zp_err\"])\n",
    "    File2 = File2.drop_duplicates(subset=['id_apass', 'Filter', 'MJD', 'corr_aper', 'telid', 'airmass', 'seeing'], keep='first', inplace=False, ignore_index=False)\n",
    "    File2 = File2.drop(File2[(File2['err_aper'] > 20)].index)\n",
    "    return(File2)\n",
    "    \n",
    "def Locate_Star_ID(Star_File, Star_ID):\n",
    "    \"\"\" Locate star(s) in the file by ID(s).\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Star_ID - Star ID(s) (float or list).\n",
    "    \n",
    "    Output:\n",
    "    Stars - pd dataframe containing selected stars by ID.\n",
    "    \"\"\"\n",
    "    if type(Star_ID) == list:\n",
    "        mask = Star_File['id_apass'].isin(Star_ID)\n",
    "        Stars = Star_File[mask]\n",
    "    else:\n",
    "        Stars = Star_File.loc[Star_File['id_apass'] == Star_ID]\n",
    "    return(Stars)\n",
    "\n",
    "def Locate_Star_Filter(Star_File, Fltr):\n",
    "    \"\"\" Locate star(s) in the file by filter(s).\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Fltr - Filter(s) (string or list).\n",
    "    \n",
    "    Output:\n",
    "    Stars - pd dataframe containing selected stars by filter.\n",
    "    \"\"\"\n",
    "    if type(Fltr) == list:\n",
    "        mask = Star_File['Filter'].isin(Fltr)\n",
    "        Stars = Star_File[mask]\n",
    "    else:\n",
    "        Stars = Star_File.loc[Star_File['Filter'] == Fltr] \n",
    "    return(Stars)\n",
    "\n",
    "def Locate_Star_Scope(Star_File, Telescope):\n",
    "    \"\"\" Locate star(s) in the file by telescope(s).\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Telescope - Telescope(s) (string or list).\n",
    "    \n",
    "    Output:\n",
    "    Stars - pd dataframe containing selected stars by telescope.\n",
    "    \"\"\"\n",
    "    if type(Telescope) == list:\n",
    "        mask = Star_File['telid'].isin(Telescope)\n",
    "        Stars = Star_File[mask]\n",
    "    else:\n",
    "        Stars = Star_File.loc[Star_File['telid'] == Telescope]\n",
    "    return(Stars)\n",
    "\n",
    "def Locate_Star_Epoch(Star_File, Epoch):\n",
    "    \"\"\" Locate star(s) in the file by Epoch(s).\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Epoch - MJD(s) (string or list).\n",
    "    \n",
    "    Output:\n",
    "    Stars - pd dataframe containing selected stars by epoch.\n",
    "    \"\"\"\n",
    "    if type(Epoch) == list:\n",
    "        mask = Star_File['MJD'].isin(Epoch)\n",
    "        Stars = Star_File[mask]\n",
    "    else:\n",
    "        Stars = Star_File.loc[Star_File['MJD'] == Epoch]\n",
    "    return(Stars)\n",
    "\n",
    "def Generic_Optimal(Data, Errors):\n",
    "    \"\"\" Compute inverse variance weighted average.\n",
    "    \n",
    "    Input:\n",
    "    Data - Data to average (list/array).\n",
    "    Errors - Errors of data to average (list/array).\n",
    "    \n",
    "    Output:\n",
    "    avg - Optimal average (float).\n",
    "    err - standard deviation of average (float).\n",
    "    \"\"\"\n",
    "    \n",
    "    #errors handled by returning nan values\n",
    "    #ensure data are in arrays\n",
    "    var = np.array(Errors)**2\n",
    "    Data = np.array(Data)\n",
    "    \n",
    "    if len(var) != 0:\n",
    "        try:\n",
    "            w = 1/var\n",
    "            avg = np.nansum(w*Data) / np.nansum(w)\n",
    "            var = 1/np.nansum(w)\n",
    "        except:\n",
    "            avg = np.nan\n",
    "            var = np.nan\n",
    "    else:\n",
    "        avg = np.nan\n",
    "        var = np.nan\n",
    "    \n",
    "    if var**0.5 == np.inf:\n",
    "        var == np.nan\n",
    "        avg == np.nan\n",
    "    \n",
    "    err = var**0.5\n",
    "    return(avg, err)\n",
    "\n",
    "def Brightest(Star_File, Filter, AGN_ID = AGN_ID):\n",
    "    \"\"\" IDs of the brghtest stars in the data for a specific filter, identified using optimal average. \n",
    "    \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Filter - Filter (string).\n",
    "    AGN_ID - ID of AGN (int).\n",
    "    \n",
    "    Output:\n",
    "    Brightest_IDs - IDs of stars sorted from brightest to dimmest (list).\n",
    "    \"\"\"\n",
    "    \n",
    "    #Select specified filter data\n",
    "    Star_Data0 = Locate_Star_Filter(Star_File, Filter)\n",
    "    \n",
    "    #Make sure AGN isn't included\n",
    "    Star_IDs = [k for k in pd.unique(Star_Data0['id_apass']) if k!= AGN_ID]\n",
    "\n",
    "    #Compute optimal average of instrumental magnitudes\n",
    "    MAGS = []\n",
    "    IDS = []\n",
    "    for ID in Star_IDs:\n",
    "        Star_Data = Locate_Star_ID(Star_Data0, ID)\n",
    "        mag = Star_Data['mag_aper'].values\n",
    "        mag_err = Star_Data['err_aper'].values\n",
    "        mag_mean = Generic_Optimal(mag, mag_err)[0]\n",
    "        MAGS.append(mag_mean)\n",
    "        IDS.append(ID)\n",
    "    \n",
    "    #Sort from brightest to dimmest and return IDs\n",
    "    MAGS_ref = MAGS\n",
    "    MAGS = sorted(MAGS)\n",
    "    Brightest_indices = [MAGS_ref.index(i) for i in MAGS]\n",
    "    Brightest_IDs = np.array(IDS)[np.array(Brightest_indices)]\n",
    "    return Brightest_IDs\n",
    "\n",
    "def root_mean_squared_deviation(data, mean):\n",
    "    \"\"\"Compute rms of dataset.\n",
    "    Input:\n",
    "    data - dataset to compute rms for (array/list).\n",
    "    mean - mean of dataset (float).\n",
    "    \n",
    "    Output:\n",
    "    rms - root mean squared deviation (float).\n",
    "    \"\"\"\n",
    "    \n",
    "    rms = (sum((data-mean)**2) / (len(data) - 1))**0.5\n",
    "    return(rms)\n",
    "\n",
    "def Brightest_Reduced(Star_File, Filter, AGN_ID = AGN_ID, frac = 0.5):\n",
    "    \"\"\" IDs of the brightest stars in the data for a specific filter, identified using optimal average. \n",
    "        Only include stars that have more than specified number of data points per star.\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Filter - Filter (string).\n",
    "    AGN_ID - ID of AGN (int).\n",
    "    frac - (Optional) Fraction of max number of epochs to keep (float).\n",
    "    \n",
    "    Output:\n",
    "    Brightest_IDs - IDs of stars sorted from brightest to dimmest (list).\n",
    "    \"\"\"\n",
    "    \n",
    "    #Select specified filter data\n",
    "    Star_Data0 = Locate_Star_Filter(Star_File, Filter)\n",
    "    \n",
    "    #Make sure AGN isn't included\n",
    "    Star_IDs = [k for k in pd.unique(Star_Data0['id_apass']) if k!= AGN_ID]\n",
    "    \n",
    "    Max_length = 0.0  \n",
    "    #Get max number of epochs amongst all stars\n",
    "    for ID in Star_IDs:\n",
    "        Star_Data = Locate_Star_ID(Star_Data0, ID)\n",
    "        if len(Star_Data['MJD'].values) > Max_length:\n",
    "            Max_length = len(Star_Data['MJD'].values)\n",
    "    \n",
    "    #Select all stars with more than specified number of datapoints\n",
    "    keep_dp = frac * Max_length\n",
    "    Reduced_IDs = []\n",
    "    for ID in Star_IDs:     \n",
    "        Star_Data = Locate_Star_ID(Star_Data0, ID)\n",
    "        if len(Star_Data['MJD'].values) > keep_dp:\n",
    "            Reduced_IDs.append(ID)\n",
    "    \n",
    "    #Compute optimal average of instrumental magnitudes\n",
    "    MAGS = []\n",
    "    IDS = []\n",
    "    for ID in Reduced_IDs:\n",
    "        Star_Data = Locate_Star_ID(Star_Data0, ID)\n",
    "        mag = Star_Data['mag_aper'].values\n",
    "        mag_err = Star_Data['err_aper'].values\n",
    "        mag_mean = Generic_Optimal(mag, mag_err)[0]\n",
    "        MAGS.append(mag_mean)\n",
    "        IDS.append(ID)\n",
    "    \n",
    "    #Sort from brightest to dimmest and return IDs\n",
    "    MAGS_ref = MAGS\n",
    "    MAGS = sorted(MAGS)\n",
    "    Brightest_indices = [MAGS_ref.index(i) for i in MAGS]\n",
    "    Brightest_IDs = list(np.array(IDS)[np.array(Brightest_indices)])\n",
    "    return Brightest_IDs\n",
    "\n",
    "def Epoch_Dist(Star_File, Filter, AGN_ID = AGN_ID, frac = 0.0):\n",
    "    \"\"\" Display distribution of stars by number of epochs per star.\n",
    "        Include how many stars are available with at least a certain fraction of the max number of epochs.\n",
    "        Also show the instrumental magnitude of stars as a function of the number of epochs.\n",
    "        \n",
    "    Input:\n",
    "    Star_File - Star data (pd dataframe).\n",
    "    Filter - Filter (string).\n",
    "    frac - (optional) print number of stars available with at least this fraction of the max number of epochs.\n",
    "    \"\"\"\n",
    "    \n",
    "    #Select specified filter data\n",
    "    Star_Data0 = Locate_Star_Filter(Star_File, Filter)\n",
    "    \n",
    "    #Make sure AGN isn't included\n",
    "    Star_IDs = [k for k in pd.unique(Star_Data0['id_apass']) if k!= AGN_ID]\n",
    "    \n",
    "    #Count number of epochs for each star and mean instrumental mags\n",
    "    Lengths = []\n",
    "    Mags = []\n",
    "    for ID in Star_IDs:\n",
    "        Star_Data = Locate_Star_ID(Star_Data0, ID)\n",
    "        Lengths.append(len(Star_Data['MJD'].values))\n",
    "        Mags.append(Generic_Optimal(Star_Data.mag_aper.values, Star_Data.err_aper.values)[0])\n",
    "    Lengths = np.array(Lengths)\n",
    "    \n",
    "    # --------------- Plot data ----------------\n",
    "    fig, (ax, axb) = plt.subplots(1,2, gridspec_kw={'width_ratios': [3, 2]}, figsize = (16, 6))\n",
    "    fig.subplots_adjust(wspace = 0.4)\n",
    "    nbins = 40\n",
    "    hist0 = ax.hist(Lengths, bins = nbins, weights=np.ones(len(Lengths)) / len(Lengths))\n",
    "    ax.cla()\n",
    "    hist = ax.hist(Lengths, bins = nbins, color = 'gray', alpha = 0.7, zorder = 10, edgecolor = None, weights=np.ones(len(Lengths)) / max(hist0[0]) / len(Lengths))\n",
    "\n",
    "    #CDF\n",
    "    X0 = sorted(list(np.arange(min(Lengths), max(Lengths) + math.floor((max(Lengths) - min(Lengths)) / nbins), (max(Lengths) - min(Lengths)) / nbins)) * 2)[:-1]\n",
    "\n",
    "    N = [0.]\n",
    "    for i in hist0[0]:\n",
    "        N.append(i+N[-1])\n",
    "    N = sorted(N*2)[1:]\n",
    "\n",
    "    X = sorted(X0)\n",
    "    ax.plot(X, N, color = 'black', zorder = 20, lw = 1.0)\n",
    "    \n",
    "    #Number of Stars Available\n",
    "    labs = []\n",
    "    X2 = []\n",
    "    N2 = []\n",
    "    for j in np.arange(1.0, -0.1, -0.01):\n",
    "        j = round(j, 3)\n",
    "        X2.append(j*max(Lengths))\n",
    "        N2.append(len([k for k in Lengths if k > j*max(Lengths)]) / len(Lengths))\n",
    "        labs.append(str(len([k for k in Lengths if k > j*max(Lengths)])))\n",
    "    N2 = sorted(N2*2)[1:]\n",
    "    X2 = sorted(X2*2)[1:] ; X2 = sorted(X2, reverse = True)\n",
    "\n",
    "    \n",
    "    ax.plot(X2, N2, color = 'Teal', zorder = 20, lw = 1.0)\n",
    "    \n",
    "    # ------- Formatting -------\n",
    "    ax.set_title(str(Filter) + ' Filter')    \n",
    "    ax.set_xlabel('Number of Epochs', fontsize = 15)\n",
    "    ax.set_ylabel('# of Stars Available with at least M datapoints \\n M = Frac x max # of Epochs', color = 'Teal', fontsize = 15)\n",
    "    ax.set_xlim(0, max(Lengths))\n",
    "\n",
    "    \"\"\"\n",
    "    #nicer ticks test\n",
    "    m = 10**(len(str(max(Lengths))) - 1)\n",
    "    if max(Lengths) < 1000:\n",
    "        ax.set_xticks(np.arange(0, math.ceil(max(Lengths) / m) * m, 100))\n",
    "    elif max(Lengths) < 2500:\n",
    "        ax.set_xticks(np.arange(0, math.ceil(max(Lengths) / m) * m, 250))\n",
    "    else:\n",
    "        ax.set_xticks(np.arange(0, math.ceil(max(Lengths) / m) * m, 500))\n",
    "    \"\"\"\n",
    "    \n",
    "    ax.set_ylim(0, 1.01)\n",
    "    ax.set_yticks(np.arange(0.0, 1.1, 0.1))\n",
    "    ax.set_yticklabels([round(i) for i in np.arange(0, len(Lengths)+len(Lengths)/11, 0.1*len(Lengths))], color = 'Teal')\n",
    "    ax.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax.minorticks_on()\n",
    "    \n",
    "    ax2 = ax.twinx()\n",
    "    ax2.plot(X, N, alpha = 0)\n",
    "    ax2.set_ylim(0, 1.01)\n",
    "    ax2.set_yticks(np.arange(0, 1.1, 0.1))\n",
    "    ax2.set_yticklabels([round(j, 2) for j in np.arange(0, 1.1, 0.1)], color = 'black')\n",
    "    ax2.set_ylabel('CDF', color = 'black', fontsize = 15)\n",
    "    ax2.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax2.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax2.minorticks_on()\n",
    "    \n",
    "    ax3 = ax.twiny()\n",
    "    ax3.vlines(np.arange(0,1.05,0.1), 0, 1,color = 'teal', lw = 0.8, ls = '--', alpha = 0.2, zorder = 0)\n",
    "    ax3.vlines(np.arange(0,1.05,0.05), 0, 1,color = 'teal', lw = 0.5, ls = '--', alpha = 0.2, zorder = 0)\n",
    "    ax3.hlines(np.arange(0,1.05,0.1), 0, 1,color = 'teal', lw = 0.8, ls = '--', alpha = 0.2, zorder = 0)\n",
    "    ax3.hlines(np.arange(0,1.05,0.05), 0, 1,color = 'teal', lw = 0.5, ls = '--', alpha = 0.2, zorder = 0)\n",
    "    ax3.set_xticks(np.arange(0, 1.1, 0.1))\n",
    "    ax3.set_xticklabels([round(j, 2) for j in np.arange(0, 1.1, 0.1)], color = 'Teal')\n",
    "    ax3.plot(X, N, alpha = 0)\n",
    "    ax3.set_xlim(0, 1.01)\n",
    "    ax3.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax3.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax3.minorticks_on()\n",
    "    ax3.set_xlabel('Frac', color = 'Teal', fontsize = 15)\n",
    "        \n",
    "    axb.scatter(Lengths, Mags, color = 'orange')\n",
    "    axb.set_xlabel('Number of Epochs', fontsize = 15)\n",
    "    axb.set_ylabel('Mean Instrumental Mag', fontsize = 15)\n",
    "    axb.invert_yaxis()\n",
    "    axb.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    axb.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    axb.minorticks_on()\n",
    "    \n",
    "    if frac == 0.0:\n",
    "        print('============================== ',str(Filter), 'Filter  ==============================')\n",
    "        print('Total number of stars available: ', len(Lengths))\n",
    "        print('Max number of epochs amongst all stars:', max(Lengths))\n",
    "    else:\n",
    "        print('============================== ',str(Filter), 'Filter  ==============================')\n",
    "        print('Total number of stars available: ', len(Lengths))\n",
    "        print('Max number of epochs amongst all stars:', max(Lengths))\n",
    "        print('Number of stars available with more than ' + str(frac)+' of the max number of epochs: '+str(len([k for k in Lengths if k > frac*max(Lengths)])) )\n",
    "\n",
    "def Detect_Var(Star_File, Filter, Lim = None, High = [], Label = False, Log = True, TEL = TEL, AGN_ID = AGN_ID, ww = 0.2, bww = 1.0):\n",
    "    \"\"\"Display rms vs mag plot to identify variable stars.\n",
    "\n",
    "    Input:\n",
    "    Star_File - Calibrated star dataframe (pd dataframe).\n",
    "    Filter - Filter (string).\n",
    "    Star_File_All (Optional) - If you want to specify your own star IDs to use in the calibration, rather than selecting the brightest ones (list/array)\n",
    "    Lim - (Optional) If doing a fit, select limit below which mag stars are fitted. If no Lim, all stars will be fitted. (float)\n",
    "    High - (Optional) Star IDs to highlight (list)\n",
    "    Label - (Optional) If True, all star IDs will be displayed on the plot, to visually identify certain stars (bool)\n",
    "    Log - (Optional) turn plot into log-log plot (bool)\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    #Make sure correct filter data is selected\n",
    "    Star_File = Locate_Star_Filter(Star_File, Filter)\n",
    "\n",
    "    #Set up figure\n",
    "    fig, (ax, ax2) = plt.subplots(1,2, figsize = (12,6), sharey = True)\n",
    "    fig.subplots_adjust(wspace = 0)\n",
    "    \n",
    "    #Put data on log plot if wanted\n",
    "    if Log == True:\n",
    "        ax.set_yscale('log')\n",
    "        ax2.set_yscale('log')\n",
    "\n",
    "    #Set plot titles\n",
    "    ax.set_title(r'RMS = $(\\frac{ΣV_{i}}{N} + V_{X})^{0.5}$     ' + Filter + ' Filter', size = 20)\n",
    "    ax2.set_title(r'RMS = $(\\frac{Σ(m_{i} - <M(*)>)^{2}}{N - 1})^{0.5}}$     ' + Filter + ' Filter', size = 20)\n",
    "    \n",
    "    Star_IDs = pd.unique(Star_File['id_apass'])\n",
    "    \n",
    "    cond = False ; cond2 = False\n",
    "    Len_Lim = 300\n",
    "    \n",
    "    all_mags = []\n",
    "    all_rms = [] ; all_rms_err = []\n",
    "    \n",
    "    all_rms2 = []\n",
    "    \n",
    "    for ID in Star_IDs:\n",
    "        dat2 = Locate_Star_ID(Star_File, ID)\n",
    "        if len(dat2) > 0:\n",
    "            mags2 = dat2['m_star'].values[0]\n",
    "            mags_err2 = dat2['m_star_err'].values[0]            \n",
    "            \n",
    "            rms = root_mean_squared_deviation(dat2['mag_aper'], dat2['m_star'])\n",
    "            rms_star = ((np.sum(dat2.err_aper.values**2) / len(dat2)) + dat2.rms_star.values[0]**2)**0.5\n",
    "\n",
    "            if ID == Star_IDs[0]:\n",
    "                lab1 = 'using max 100 stars'\n",
    "            else:\n",
    "                lab1 = None\n",
    "\n",
    "            #If ID's were specified in High argument, they will appear as bright pink markers\n",
    "            if ID in High:\n",
    "                ax.scatter(mags2, rms_star, color = 'deeppink', s = 25, marker = 'x', zorder = 10)\n",
    "                ax2.scatter(mags2, rms, color = 'deeppink', s = 25, marker = 'x', zorder = 10)\n",
    "            else:\n",
    "                ax.scatter(mags2, rms_star, color = 'blue', s = 5, label = lab1, zorder = 10, alpha = 0.7)\n",
    "                ax2.scatter(mags2, rms, color = 'blue', s = 5, label = lab1, zorder = 10, alpha = 0.7)\n",
    "\n",
    "            all_mags.append(dat2['m_star'].values[0])\n",
    "            all_rms.append(rms_star)\n",
    "            all_rms_err.append(dat2['rms_star_err'].values[0])\n",
    "\n",
    "        ax.legend(loc = 'upper left')\n",
    "        ax2.legend(loc = 'upper left')\n",
    "\n",
    "        \n",
    "        #Add star IDs next to data points if wanted\n",
    "        if Label == True:\n",
    "            if Log == True:\n",
    "                if ID in High:\n",
    "                    t = ax.text(mags2, rms_star, str(ID), rotation = 0, color = 'deeppink', alpha = 1.0, size = 10, zorder = 10, weight = 'bold')\n",
    "                else:\n",
    "                    ax.text(mags2, rms_star, str(ID), rotation = 90, color = 'black', alpha = 0.5, size = 8)\n",
    "            else:\n",
    "                if ID in High:\n",
    "                    t = ax.text(mags2 - 0.05, rms_star + 0.003, str(ID), rotation = 0, color = 'deeppink', alpha = 1.0, size = 10, zorder = 10, weight = 'bold')                        \n",
    "                else:\n",
    "                    ax.text(mags2 - 0.05, rms_star + 0.003, str(ID), rotation = 90, color = 'black', alpha = 0.5, size = 8)\n",
    "\n",
    "    #ax3.hlines(0, -20, len(Star_IDs)+20, color = 'black', lw = .8, zorder = 0, ls = '--')\n",
    "    #ax3.set_xlim(-5, len(Star_IDs)+5)\n",
    "    all_mags_sorted = np.array(sorted([i for i in all_mags if i > Lim]))\n",
    "    all_mags_ind = np.array([all_mags.index(i) for i in all_mags_sorted])\n",
    "    all_rms_sorted = np.array([all_rms[i] for i in all_mags_ind])\n",
    "    all_rms_sorted_err = np.array([all_rms_err[i] for i in all_mags_ind])\n",
    "\n",
    "    ax.set_xlabel('Mean Instrumental Magnitude')\n",
    "    ax2.set_xlabel('Mean Instrumental Magnitude')\n",
    "    ax.set_ylabel('RMS')\n",
    "    #ax2.set_ylabel('RMS')\n",
    "    ax.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax.minorticks_on()\n",
    "    ax2.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax2.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax2.minorticks_on()\n",
    "    #ax.set_ylim(10**(-3), 10**(0))\n",
    "    ax.legend()\n",
    "\n",
    "\n",
    "def Plot_LC(Star_Data, Filter, TEL = TEL, err_th = 0.05, Stars = []):\n",
    "    \"\"\"Plot calibrated star lightcurves and total error distribution.\n",
    "       Remove outliers based on a double outlier detection method and inspect cleaned lightcurves.\n",
    "    \n",
    "    Input:\n",
    "    Star_Data - Calibrated star dataframe (pd dataframe).\n",
    "    Filter - Filter (string).\n",
    "    err_th - (Optional) Error threshold for clipping outliers.\n",
    "    Stars - (Optional) If you don't wish to plot aLL the lightcurves for all the stars in the file, input IDs of stars you do want to see (list/array).\n",
    "    \"\"\"\n",
    "    #COLORS = ['#E9967A', '#FFB6C1', '#8FBC8F', '#DDA0DD', '#6495ED', '#FFD700', '#008080', '#FF4500', '#8A2BE2', '#008B45']#['#FFA07A', '#FFC0CB', '#98FB98', '#D8BFD8', '#87CEEB', '#FFD700', '#20B2AA', '#FF6347', '#9370DB', '#00FA9A']\n",
    "    import random\n",
    "    COLORS = [\"#{:06x}\".format(random.randint(0, 0xFFFFFF)) for _ in range(len(TEL))]\n",
    "\n",
    "    font = 'Cambria'\n",
    "    if len(Stars) > 0:\n",
    "        #Selecting only inputed stars\n",
    "        Star_IDs = Stars\n",
    "    else:\n",
    "        #All stars in dataframe\n",
    "        Star_IDs = pd.unique(Star_Data['id_apass'])\n",
    "    \n",
    "    for ID in Star_IDs:\n",
    "        #set up figure\n",
    "        T_no = len(TEL)\n",
    "        num_of_plots = (T_no - 1) // 5 + 1\n",
    "        if len(TEL) < 5:\n",
    "            fig0, ax0 = plt.subplots(num_of_plots, len(TEL), sharex = False, figsize = (15, 2.5*num_of_plots))\n",
    "            ax0 = ax0.reshape(1, len(TEL))\n",
    "        else:\n",
    "            fig0, ax0 = plt.subplots(num_of_plots, 5, sharex = False, figsize = (15, 2.5*num_of_plots))\n",
    "        \n",
    "        fig0.subplots_adjust(wspace=0.1, hspace = 0.4)\n",
    "        r = 0\n",
    "        c = 0\n",
    "        \n",
    "        #older MAD method\n",
    "        #scope_mad = []\n",
    "        \n",
    "        for scope in TEL:\n",
    "            #Select data for particular telescope\n",
    "            dat = Locate_Star_ID(Locate_Star_Scope(Star_Data, scope), ID)\n",
    "            err = dat.err_tot\n",
    "                        \n",
    "            if len(dat) > 0:\n",
    "                nbins = int(0.8*len(err))\n",
    "                if nbins == 0:\n",
    "                    nbins = 1\n",
    "                hist = ax0[r, c].hist(err, bins = nbins, alpha = 0.7, density = False, color = COLORS[TEL.index(scope)])\n",
    "                ax0[r, c].vlines(err_th, 0, max(hist[0]), lw = 1.2, ls = '--', color = 'red', label = 'Outlier Threshold')\n",
    "                \n",
    "                #older MAD method\n",
    "                #MAD = np.sum(abs(err - np.median(err))) / len(err)\n",
    "                #ax0[r, c].axvspan(np.median(err) - MAD, np.median(err) + MAD, alpha=0.1, color='red',label = 'MAD')\n",
    "                #ax0[r, c].axvspan(np.median(err) - 2*MAD, np.median(err) + 2*MAD, alpha=0.1, color='orange',label = '2*MAD')\n",
    "                \n",
    "                ax0[r, c].legend()\n",
    "                ax0[r, c].tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "                ax0[r, c].tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "                ax0[r, c].minorticks_on()\n",
    "                ax0[r, c].set_xlabel('Total error')\n",
    "            ax0[r, c].set_title('Star ' + str(ID) + '\\n'+ str(scope) +'\\n No. of datapoints: ' + str(len(dat)), fontsize = 10)\n",
    "\n",
    "            if c < 4:\n",
    "                c = c + 1\n",
    "            else:\n",
    "                r = r + 1\n",
    "                c = 0\n",
    "            #older mad method\n",
    "            #scope_mad.append(MAD)\n",
    "\n",
    "        fig, ax4 = plt.subplots(3,1, figsize = (16, 9), sharex = True)\n",
    "        #fig2, ax4 = plt.subplots(3,1, figsize = (19, 14))\n",
    "        \n",
    "        fig.subplots_adjust(hspace = 0)\n",
    "        #fig2.subplots_adjust(hspace = 0.)\n",
    "\n",
    "        ax4[1].set_ylabel('Inst. Mag', fontproperties = font, size = 15)\n",
    "        ax4[2].set_ylabel('Inst. Mag', fontproperties = font, size = 15)\n",
    "        ax4[2].set_xlabel('MJD', fontproperties = font, size = 15)\n",
    "\n",
    "        tot_dat = 0.\n",
    "        clipped = 0.\n",
    "        for scope in TEL:\n",
    "            #Select data based on particular telescope\n",
    "            dat = Locate_Star_Scope(Locate_Star_Filter(Locate_Star_ID(Star_Data, ID), Filter), scope)\n",
    "            lcodat = Locate_Star_Scope(Locate_Star_Filter(Locate_Star_ID(lco2, ID), Filter), scope)\n",
    "\n",
    "            if len(dat) > 0:\n",
    "                #older mad method\n",
    "                #con1 = (dat['err_tot'] > np.median(dat['err_tot']) + scope_mad[TEL.index(scope)])\n",
    "\n",
    "                #First outlier detection method based on specified threshold\n",
    "                con1 = (dat['err_tot'] > err_th)\n",
    "                #Secondary outlier detection method based on deviation from mean\n",
    "                con2 = (abs(dat['m_star'] - dat['mag_aper']) > 3*dat['err_tot'])\n",
    "                outliers = np.where(np.logical_or(con1,con2))[0]\n",
    "                not_outliers = [j for j in np.arange(0, len(dat), 1) if j not in outliers]\n",
    "\n",
    "                dat2a = dat.iloc[outliers]\n",
    "                dat2b = dat.iloc[not_outliers]\n",
    "                tot_dat = tot_dat + len(dat)\n",
    "                clipped = clipped + len(outliers)\n",
    "\n",
    "                ax4[1].scatter(dat2a['MJD'].values, dat2a['mag_aper'].values, color = 'red', s = 30, marker = 'x', zorder = 10, alpha = 1.0, linewidths=1.3)\n",
    "                label = scope\n",
    "\n",
    "                Data = Locate_Star_Scope(dat, scope)\n",
    "                ax4[2].scatter(dat2b['MJD'].values, dat2b['mag_aper'].values, s = 20, zorder = 10, label = label, color = COLORS[TEL.index(scope)])\n",
    "                ax4[2].errorbar(dat2b['MJD'].values, dat2b['mag_aper'].values, yerr = dat2b['err_tot'].values,ls = 'none', color = COLORS[TEL.index(scope)], lw = 0.5)\n",
    "                ax4[1].errorbar(dat['MJD'].values, dat['mag_aper'].values, yerr = (dat['err_aper'].values**2 + dat['rms_sc'].values**2 + dat['rms_t'].values**2 + dat['rms_star'].values**2)**0.5, ls = 'none', color = COLORS[TEL.index(scope)], lw = 0.5)\n",
    "                ax4[1].scatter(dat['MJD'].values, dat['mag_aper'].values, s = 20, label = label, color = COLORS[TEL.index(scope)])\n",
    "                \n",
    "                ax4[0].errorbar(lcodat.MJD, lcodat.mag_aper,yerr = lcodat.err_aper, ls = 'none', color = COLORS[TEL.index(scope)], lw = 0.5)\n",
    "                ax4[0].scatter(lcodat.MJD, lcodat.mag_aper, s = 14,label = label, color = COLORS[TEL.index(scope)] )\n",
    "                \n",
    "                #ax4[1].set_ylim(-5.8, -6.3)\n",
    "                #ax4[2].set_ylim(max(dat2b['mag_aper'].values) - max(dat2b['mag_aper'].values) / 5, min(dat2b['mag_aper'].values) + min(dat2b['mag_aper'].values) / 5)\n",
    "\n",
    "                #ax5.set_title('Uncalibrated Star Lightcurve', fontproperties = font, size = 20)\n",
    "                if scope == TEL[0]:\n",
    "                    lab = 'm(*) + dmags + dmagt'\n",
    "                else:\n",
    "                    lab = None\n",
    "                #ax5.scatter(dat.MJD, dat.m_star + dat.DMAGS + dat.DMAGT, s = 14, color = 'black' , marker = 'x',lw = 0.8, label = lab)\n",
    "                #ax5.errorbar(dat.MJD, dat.m_star + dat.DMAGS + dat.DMAGT,yerr = dat.err_tot, ls = 'none', color = 'black',lw = 0.8)\n",
    "        ax4[0].legend()\n",
    "\n",
    "                \n",
    "        Data = Locate_Star_ID(Star_Data, ID)\n",
    "        ax4[1].hlines(Data['m_star'].values[0], min(Data['MJD'].values), max(Data['MJD'].values), lw = 1, ls = '--', label = 'Star: ' + str(ID))\n",
    "        ax4[1].legend(loc='center right')\n",
    "        ax4[1].invert_yaxis()\n",
    "        ax4[1].tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "        ax4[1].tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "        ax4[1].minorticks_on()\n",
    "        ax4[1].set_title('Star ' + str(ID) + '  |  Datapoints Clipped: '+str(int(clipped))+' out of '+str(int(tot_dat)))\n",
    "        ax4[2].hlines(Data['m_star'].values[0], min(Data['MJD'].values), max(Data['MJD'].values), lw = 1, ls = '--', label = 'Star: ' + str(ID))\n",
    "        ax4[2].legend(loc='center right')\n",
    "        ax4[2].invert_yaxis()\n",
    "        ax4[2].tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "        ax4[2].tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "        ax4[2].minorticks_on()\n",
    "        #ax4[1].set_ylim(max(Data.mag_aper) + 0.05, min(Data.mag_aper) - 0.05)\n",
    "        #ax4[2].set_ylim(max(Data.mag_aper) + 0.05, min(Data.mag_aper) - 0.05)\n",
    "        ax4[1].set_xticks(np.arange(int(min(Data.MJD)), int(max(Data.MJD)), 100), labels = np.arange(int(min(Data.MJD)), int(max(Data.MJD)), 100), fontproperties = font, size = 15)\n",
    "        ax4[2].set_xticks(np.arange(int(min(Data.MJD)), int(max(Data.MJD)), 100), labels = np.arange(int(min(Data.MJD)), int(max(Data.MJD)), 100), fontproperties = font, size = 15)\n",
    "\n",
    "        #ax4[0].set_title('Example Uncalibrated Star Lightcurve in U band (Star 2344)', fontproperties = font, size = 15)\n",
    "        ax4[0].legend(loc='center right')\n",
    "        ax4[0].invert_yaxis()\n",
    "        ax4[0].tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "        ax4[0].tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "        ax4[0].minorticks_on()\n",
    "        ax4[0].set_ylabel('Inst. Mag', fontproperties = font, size = 15)\n",
    "        ax4[0].set_xlabel('MJD', fontproperties = font, size = 15)\n",
    "        ax4[0].set_xticks(np.arange(int(min(Data.MJD)), int(max(Data.MJD) + 100), 100), labels = np.arange(int(min(Data.MJD)), int(max(Data.MJD) + 100), 100), fontproperties = font, size = 15)\n",
    "        #ax5.set_yticks(np.arange(int(max(Data.mag_aper)), int(min(Data.mag_aper)), 1), labels = np.arange(int(max(Data.mag_aper)), int(min(Data.mag_aper)), 1), fontproperties = font, size = 15)\n",
    "\n",
    "def AGN_LC(Original_Star_File, Star_Data, Filter, unit = 'mag', err_th = 0.05, Plot = True, Rem_out = True, AGN_ID = AGN_ID, TEL = TEL, zp = [0.,0.]):\n",
    "    \"\"\" Return Dataframe with calibrated AGN Lightcurve. Choose whether to leave in outliers.\n",
    "        Can also plot the AGN lightcurve.\n",
    "    \n",
    "    Input:\n",
    "    Original_Star_File - Original lco file (pd dataframe).\n",
    "    Star_Data - Calibrated star dataframe (pd dataframe).\n",
    "    Filter - Filter (string).\n",
    "    err_th - (Optional) Error threshold for clipping outliers.\n",
    "    Plot - (Optional) Choose whether to display the error distributions and lightcurves (Bool).\n",
    "    Rem_out = (Optional) Choose whether to remove outliers from returned AGN dataframe (Bool). \n",
    "    \n",
    "    Output:\n",
    "    AGN_DF - dataframe containing calibrated AGN lightcurve.\n",
    "   \"\"\"\n",
    "    \n",
    "    #Set up dataframe\n",
    "    columns = ['Filter','telid', 'MJD', 'mag', 'err', 'err_sys']\n",
    "    AGN_DF = pd.DataFrame(columns=columns)\n",
    "    font = 'Cambria'\n",
    "    AGN_Data = Locate_Star_ID(Locate_Star_Filter(Original_Star_File, Filter), AGN_ID)\n",
    "    for scope in TEL:\n",
    "        #Select data based on telescope\n",
    "        AGN_Data2 = Locate_Star_Scope(AGN_Data, scope)\n",
    "        for E in pd.unique(AGN_Data2['MJD']):\n",
    "            \n",
    "            #Select data based on Epoch\n",
    "            AGN_Data3 = Locate_Star_Epoch(AGN_Data2, E)\n",
    "            Star_Data2 = Locate_Star_Epoch(Locate_Star_Filter(Locate_Star_Scope(Star_Data, scope), Filter), E).head(1)\n",
    "            \n",
    "            #Assuming the AGN has a datapoint for that epoch...\n",
    "            if len(Star_Data2) != 0:\n",
    "                \n",
    "                #Apply correction parameters\n",
    "                AGN_mag = AGN_Data3['mag_aper'].values - Star_Data2['DMAGT'].values - Star_Data2['DMAGS'].values + zp[0]\n",
    "                AGN_mag_err = (AGN_Data3['err_aper'].values[0]**2 + Star_Data2['rms_sc'].values[0]**2 + Star_Data2['rms_t'].values[0]**2)**0.5\n",
    "                #AGN_DF = AGN_DF.append({'Filter': Filter, 'telid': scope, 'MJD': E, 'mag': AGN_mag[0], 'err': AGN_mag_err}, ignore_index = True)\n",
    "                AGN_DF = pd.concat([AGN_DF, pd.DataFrame([pd.Series({'Filter': Filter, 'telid': scope, 'MJD': E, 'mag': AGN_mag[0], 'err': AGN_mag_err, 'err_sys': AGN_mag_err*0.0+zp[1]})], columns = pd.Series({'Filter': Filter, 'telid': scope, 'MJD': E, 'mag': AGN_mag[0], 'err': AGN_mag_err, 'err_sys': AGN_mag_err*0.0+zp[1]}).index)]).reset_index(drop=True)\n",
    "    \n",
    "\n",
    "    if Plot == True:\n",
    "        print('======================================================== ' + Filter + ' Filter' + ' ========================================================')\n",
    "        #COLORS = ['navy', 'green', 'blue', 'orange', 'purple', 'yellow', 'skyblue', 'violet', 'darkgreen', 'maroon']\n",
    "\n",
    "        #updated 8th August\n",
    "        import random\n",
    "        COLORS = [\"#{:06x}\".format(random.randint(0, 0xFFFFFF)) for _ in range(len(TEL))]\n",
    "        T_no = len(TEL)\n",
    "        num_of_plots = (T_no - 1) // 5 + 1\n",
    "        \n",
    "        if len(TEL) < 5:\n",
    "            fig0, ax0 = plt.subplots(num_of_plots, len(TEL), sharex = False, figsize = (20, 2.5*num_of_plots))\n",
    "            ax0 = ax0.reshape(1, len(TEL))\n",
    "        else:\n",
    "            fig0, ax0 = plt.subplots(num_of_plots, 5, sharex = False, figsize = (20, 2.5*num_of_plots))\n",
    "        fig0.subplots_adjust(wspace=0.1, hspace = 0.4)\n",
    "        \n",
    "        fig, ax = plt.subplots(1,1,figsize=(15, 3.5), sharex = False)\n",
    "        fig.subplots_adjust(wspace=0.1, hspace = 0.25)\n",
    "        ax.set_title('Uncalibrated AGN Lightcurve', fontproperties = font, size = 15)\n",
    "        ax.set_xlabel('MJD',fontproperties = font, size = 15)\n",
    "        ax.set_ylabel('Inst. Mag',fontproperties = font, size = 15)\n",
    "        ax.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6, top = True, right = True)\n",
    "        ax.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4, top = True, right = True)\n",
    "        ax.minorticks_on()\n",
    "        ax.invert_yaxis()\n",
    "\n",
    "        fig2, (ax2, ax3) = plt.subplots(2,1,figsize=(15, 7))\n",
    "        fig2.subplots_adjust(wspace=0.1, hspace = 0.25)\n",
    "        \n",
    "        if unit == 'mag':\n",
    "            lab = 'Mag'\n",
    "        elif unit == 'flux':\n",
    "            lab = 'Flux (mJy)'\n",
    "        else:\n",
    "            print('======= Invalid Unit ======')\n",
    "            print('Unit options are \"mag\" or \"flux\"')\n",
    "        \n",
    "        ax2.set_title('Calibrated AGN Lightcurve',fontproperties = font, size = 15)\n",
    "        ax2.set_ylabel(lab, fontproperties = font, size = 15)\n",
    "        ax2.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6, top = True, right = True)\n",
    "        ax2.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4, top = True, right = True)\n",
    "        ax2.minorticks_on()\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.set_xlabel('MJD',fontproperties = font, size = 15)\n",
    "\n",
    "        \n",
    "        clipped = len(AGN_DF.loc[AGN_DF['err'] > err_th])\n",
    "        #ax3.set_title('Calibrated AGN Lightcurve without outliers  |  '+ 'Datapoints Clipped: ' +str(int(clipped))+' out of '+str(len(AGN_DF))+'  |  Outlier error threshold: '+ str(round(100*err_th, 1)) + '%',fontproperties = font, size = 15)\n",
    "        ax3.set_ylabel(lab, fontproperties = font, size = 15)\n",
    "        ax3.set_xlabel('MJD',fontproperties = font, size = 15)\n",
    "        ax3.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6, top = True, right = True)\n",
    "        ax3.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4, top = True, right = True)\n",
    "        ax3.minorticks_on()\n",
    "        ax3.invert_yaxis()\n",
    "        \n",
    "        \n",
    "        r = 0\n",
    "        c = 0\n",
    "        \n",
    "        MA = 0.\n",
    "        MI = 10000\n",
    "        #Plot error distributions\n",
    "        for scope in TEL:\n",
    "            dat = Locate_Star_Scope(AGN_DF, scope)\n",
    "            dat2 = dat.loc[dat['err'] < err_th]\n",
    "            \n",
    "            if len(dat) > 0:\n",
    "                nbins = int(len(dat) / 2)\n",
    "                if nbins == 0:\n",
    "                    nbins = 1\n",
    "                hist = ax0[r, c].hist(dat['err'].values, bins = nbins, alpha = 0.7, density = False, color = COLORS[TEL.index(scope)], edgecolor = 'black')\n",
    "                ax0[r, c].vlines(err_th, 0, max(hist[0]), lw = 1.2, ls = '--', color = 'red', label = 'Outlier Threshold: '+ str(err_th))\n",
    "\n",
    "                #Old mad selection\n",
    "                #ax0[r, c].vlines(np.median(dat['err'].values), 0, max(hist[0]), lw = 1, ls = '--', color = 'maroon', label = 'Median: '+ str(round(np.median(dat['err'].values), 6)))\n",
    "                #MAD = np.sum(abs(errors[TEL.index(scope)] - np.median(errors[TEL.index(scope)]))) / len(errors[TEL.index(scope)])\n",
    "                #ax0[r, c].axvspan(np.median(dat['err'].values) - MAD, np.median(dat['err'].values) + MAD, alpha=0.1, color='red',label = 'MAD')\n",
    "                #ax0[r, c].axvspan(np.median(dat['err'].values) - 2*MAD, np.median(dat['err'].values) + 2*MAD, alpha=0.1, color='orange',label = '2*MAD')\n",
    "\n",
    "                ax0[r,c].legend()\n",
    "                ax0[r,c].tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "                ax0[r,c].tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "                ax0[r,c].minorticks_on()\n",
    "                ax0[r,c].set_title(str(scope) + '  |  No. of datapoints: '+ str(len(dat['err'].values)))\n",
    "                ax0[r,c].set_xlabel('Total error')\n",
    "    \n",
    "            if c < 4:\n",
    "                c = c + 1\n",
    "            else:\n",
    "                r = r + 1\n",
    "                c = 0\n",
    "                        \n",
    "            #Plot uncalibrated data\n",
    "            ax.scatter(Locate_Star_Scope(AGN_Data, scope)['MJD'], Locate_Star_Scope(AGN_Data, scope)['mag_aper'], s = 5, label = scope, color = COLORS[TEL.index(scope)])\n",
    "            \n",
    "            if unit == 'flux':\n",
    "                F = MagToJansky(dat['mag'].values) * 10**3\n",
    "                F_err = MagToJanskyErr(F, dat['err'].values)\n",
    "                F2 = MagToJansky(dat2['mag'].values) * 10**3\n",
    "                F2_err = MagToJanskyErr(F2, dat2['err'].values)\n",
    "                \n",
    "            else:\n",
    "                F = dat['mag']\n",
    "                F_err = dat['err']\n",
    "                F2 = dat2['mag']\n",
    "                F2_err = dat2['err']\n",
    "                \n",
    "            #Plot calibrated data with errors\n",
    "            if len(F) > 0:\n",
    "                if MA < max(F):\n",
    "                    MA = max(F)\n",
    "                if MI > min(F):\n",
    "                    MI = min(F)\n",
    "                ax2.scatter(dat['MJD'], F, s = 5, color = COLORS[TEL.index(scope)], label = scope)\n",
    "                ax2.errorbar(dat['MJD'], F, yerr = F_err, lw = 0.5, color = COLORS[TEL.index(scope)], ls = 'none')\n",
    "                #Plot calibrated data without errors\n",
    "                ax3.scatter(dat2['MJD'], F2, s = 5, color = COLORS[TEL.index(scope)], label = scope)\n",
    "                ax3.errorbar(dat2['MJD'], F2, yerr = F2_err, lw = 0.5, color = COLORS[TEL.index(scope)], ls = 'none')\n",
    "        #print(MA - MI)\n",
    "        ax.set_xticks(np.arange(int(min(AGN_Data['MJD'])), int(max(AGN_Data['MJD']))+200, 200), labels = np.arange(int(min(AGN_Data['MJD'])), int(max(AGN_Data['MJD']))+200, 200), fontproperties = font, size = 15)\n",
    "        ax2.set_xticks(np.arange(int(min(AGN_Data['MJD'])), int(max(AGN_Data['MJD']))+200, 200), labels = np.arange(int(min(AGN_Data['MJD'])), int(max(AGN_Data['MJD']))+200, 200), fontproperties = font, size = 15)\n",
    "        ax3.set_xticks(np.arange(int(min(AGN_Data['MJD'])), int(max(AGN_Data['MJD']))+200, 200), labels = np.arange(int(min(AGN_Data['MJD'])), int(max(AGN_Data['MJD']))+200, 200), fontproperties = font, size = 15)\n",
    "\n",
    "        ax.legend(loc = 'right')\n",
    "        ax2.legend(loc = 'right')\n",
    "        ax3.legend(loc = 'right')\n",
    "        \n",
    "        if unit == 'mag':\n",
    "            ax2.invert_yaxis()\n",
    "            ax3.invert_yaxis()\n",
    "        \n",
    "    if Rem_out == True:\n",
    "        print('Returning dataframe with outliers taken out')\n",
    "        AGN_DF = AGN_DF.loc[AGN_DF['err'] < err_th]\n",
    "        return AGN_DF\n",
    "    else:\n",
    "        print('Returning dataframe with outliers kept in')\n",
    "        return AGN_DF\n",
    "\n",
    "# -------------------------------------------------------------------------------\n",
    "# NOT FINISHED UPDATING =========================================================\n",
    "# -------------------------------------------------------------------------------\n",
    "def Plot_by_year(AGN_dataframe, year, FILTERS = FILTERS, TEL = TEL):\n",
    "    \"\"\" Plot particular year of AGN data for closer visual inspection.\n",
    "        *Note: MJD boundaries may need to be adjusted here for individual AGNs.\n",
    "    \n",
    "    Input:\n",
    "    AGN_dataframe - dataframe containing calibrated AGN data from AGN_LC function (pd dataframe).\n",
    "    year - year of data to plot (int)\n",
    "    \"\"\"\n",
    "    print('ADJUST YOUR YEAR LIMITS IN THE FUNCTION (TEMPORARY)')\n",
    "    import random\n",
    "    COLORS = [\"#{:06x}\".format(random.randint(0, 0xFFFFFF)) for _ in range(len(TEL))]    \n",
    "    #Ref dates may need to be changed depending on specific AGN monitoring\n",
    "    if year == 1:\n",
    "        ref_date1 = 0\n",
    "        ref_date2 = 59500\n",
    "    elif year == 2:\n",
    "        ref_date1 = 59500\n",
    "        ref_date2 = 59850\n",
    "    elif year == 3:\n",
    "        ref_date1 = 59850\n",
    "        ref_date2 = 10**(10)\n",
    "    # ------->> Add another elif if more years...\n",
    "\n",
    "    fig, ax = plt.subplots(len(FILTERS),1, figsize = (15, 10), sharex = True)\n",
    "    fig.subplots_adjust(wspace=0.1, hspace = 0.0)\n",
    "    \n",
    "    ax[0].set_title('Year ' + str(year), size = 15)\n",
    "    ax[-1].set_xlabel('MJD')\n",
    "    for filter in FILTERS:\n",
    "        dat0 = Locate_Star_Filter(AGN_dataframe, filter)\n",
    "        if len(dat0) > 0:\n",
    "            dat = dat0.loc[(dat0['MJD'] > ref_date1) & (dat0['MJD'] < ref_date2)]\n",
    "            dat = dat.loc[dat['err'] < 0.03]\n",
    "            if len(dat) > 0:\n",
    "                ax[FILTERS.index(filter)].scatter(dat.MJD, dat.mag, s = 10, color = 'Teal', zorder = 0, label = filter)\n",
    "                ax[FILTERS.index(filter)].errorbar(dat.MJD, dat.mag, yerr = dat.err, ls = 'none', color = 'Teal', zorder = 0)\n",
    "                ax[FILTERS.index(filter)].invert_yaxis()\n",
    "                ax[FILTERS.index(filter)].legend()\n",
    "                ax[FILTERS.index(filter)].set_ylim(min(dat.mag) - 0.05, max(dat.mag) + 0.05)\n",
    "                ax[FILTERS.index(filter)].vlines(np.arange(min(dat['MJD'].values), max(dat['MJD'].values) + 10, 10), min(dat['mag'].values) - 0.3, max(dat['mag'].values) + 0.3, color = 'black', ls = '--', lw = 0.5, alpha = 0.8)\n",
    "                ax[FILTERS.index(filter)].tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "                ax[FILTERS.index(filter)].tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "                ax[FILTERS.index(filter)].minorticks_on()\n",
    "                ax[FILTERS.index(filter)].set_ylabel('mag')\n",
    "                ax[FILTERS.index(filter)].invert_yaxis()\n",
    "\n",
    "\n",
    "def Check_RMS_MAG(Star_File, Filter = FILTERS[0]):\n",
    "    \"\"\"Compute the mean magnitude & rms of initial star data, calibrated using zeropoints, and plot rms vs mag to \n",
    "       check AGN ID.\n",
    "    Inputs:\n",
    "    Star_File - Original file with zeropoints (pd dataframe).\n",
    "    Filter - (Optional) Filter (str).\n",
    "    \"\"\"\n",
    "    \n",
    "    #Set up figure\n",
    "    fig, ax = plt.subplots(1,1, figsize = (5, 5))\n",
    "    #Select data based on filter\n",
    "    dat = Locate_Star_Filter(Star_File, Filter)\n",
    "    Star_IDs = pd.unique(dat['id_apass'])\n",
    "    \n",
    "    for ID in Star_IDs:\n",
    "        #Select data based on star ID\n",
    "        dat2 = Locate_Star_ID(dat, ID)\n",
    "        mag = dat2.mag_aper.values + dat2.zp.values\n",
    "        \n",
    "        #Compute mean magnitude\n",
    "        mean = np.mean(mag)\n",
    "        #Compute rms\n",
    "        rms = root_mean_squared_deviation(mag, mean)\n",
    "        ax.scatter(mean, rms, color = 'lightcoral', edgecolor = 'black', linewidth = 1,  s = 25)\n",
    "        #ax.text(mean, rms, str(ID), rotation = 90, size = 10, alpha = 0.7)\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_xlabel('mag', size = 15)\n",
    "    ax.set_ylabel('rms', size = 15)\n",
    "    ax.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax.minorticks_on()\n",
    "    \n",
    "def Check_LC(Star_File, ID, Filter = FILTERS[0], date = None):\n",
    "    \"\"\"Plot lightcurve of chosen ID, using original data with zeropoints, to help identify the AGN.\n",
    "    Inputs:\n",
    "    Star_File - Original file with zeropoints (pd dataframe).\n",
    "    ID - ID of potential AGN (float)\n",
    "    Filter - (Optional) Filter (str).\n",
    "    \"\"\"\n",
    "    #Set up figure\n",
    "    fig, ax = plt.subplots(1,1, figsize = (12, 4))\n",
    "    \n",
    "    #Select data based on ID and Filter\n",
    "    for scope in TEL:\n",
    "        dat = Locate_Star_Scope(Locate_Star_ID(Locate_Star_Filter(Star_File, Filter), ID), scope)\n",
    "        mag = dat.mag_aper.values + dat.zp.values\n",
    "        flux = 10**((mag - 8.9) / -2.5) * 10**3\n",
    "        MJD = dat.MJD\n",
    "        ax.scatter(MJD, mag, s = 15, linewidth = 1)\n",
    "    ax.set_title('Star ID: '+ str(ID))\n",
    "    ax.set_xlabel('MJD', size = 15)\n",
    "    ax.set_ylabel('Mag', size = 15)\n",
    "    ax.tick_params(axis = 'both', which = 'major', direction = 'in', length = 6)\n",
    "    ax.tick_params(axis = 'both', which = 'minor', direction = 'in', length = 4)\n",
    "    ax.minorticks_on()\n",
    "\n",
    "def Convergence_Plot(Traces, mode, parameter, parameter_err = None, lim = 20, zoom = 0, TEL = TEL):\n",
    "    \"\"\" Plots of traces of intercalibration algorithm to visualy check convergence.\n",
    "    \n",
    "    Input:\n",
    "    Traces - Traces output from Corr() (list).\n",
    "    mode - 'double' or 'single' (str): \n",
    "            double: Plots the trace of a parameter AND its error as a shell around the plot.\n",
    "            single: Plots any ONE parameter from the list above. Can be used to check the convergence of the errors themselves for example.\n",
    "    parameter - parameter to check convergence for (str).\n",
    "    parameter_err - (Optional) if mode = 'double', parameter error (str).\n",
    "    lim - (Optional) number of plots to show for epoch & star_mag related parameters as there can be a lot of them (int).\n",
    "    zoom - (Optional) zoom into convergence tail to check finer fluctuations (int).\n",
    "    \"\"\"\n",
    "    \n",
    "    #Possible parameters to check\n",
    "    names = ['dmagt', 'dmagt_err', 'rms_t', 'rms_t_err', 'DMAGT', 'DMAGT_err', 'dmags', 'dmags_err', 'rms_sc',\n",
    "             'rms_sc_err', 'm_star', 'm_star_err', 'rms_star', 'rms_star_err', 'DMAGS', 'DMAGS_err']\n",
    "    scope_pars = ['dmags', 'dmags_err', 'rms_sc','rms_sc_err', 'DMAGS', 'DMAGS_err']\n",
    "    time_pars = ['dmagt', 'dmagt_err', 'rms_t','rms_t_err', 'DMAGT', 'DMAGT_err']\n",
    "    star_pars = ['m_star', 'm_star_err', 'rms_star', 'rms_star_err']\n",
    "    iterations = len(Traces[-1])\n",
    "    \n",
    "    if mode == 'single':\n",
    "        par = Traces[names.index(parameter)]\n",
    "            \n",
    "        if parameter in scope_pars:\n",
    "            import random\n",
    "            COLORS = [\"#{:06x}\".format(random.randint(0, 0xFFFFFF)) for _ in range(len(TEL))]\n",
    "\n",
    "            #Set up plot\n",
    "            T_no = len(TEL)\n",
    "            num_of_plots = (T_no - 1) // 5 + 1\n",
    "            \n",
    "            if len(TEL) < 5:\n",
    "                fig, ax = plt.subplots(num_of_plots, len(TEL), sharex = False, figsize = (20, 2.5*num_of_plots))\n",
    "                ax = ax.reshape(1, len(TEL))\n",
    "            else:\n",
    "                fig, ax = plt.subplots(num_of_plots, 5, sharex = False, figsize = (20, 2.5*num_of_plots))\n",
    "                fig.subplots_adjust(wspace=0.3, hspace = 0)\n",
    "\n",
    "            #Iteration number\n",
    "            IT = np.arange(1, iterations + 1, 1)[zoom:]\n",
    "            \n",
    "            r = 0\n",
    "            c = 0\n",
    "            for j in range(len(TEL)):\n",
    "                D = []\n",
    "                for i in range(len(par)):\n",
    "                    D.append(par[i][j])\n",
    "                D = D[zoom:]\n",
    "\n",
    "                ax[r, c].plot(IT, D, label = 'Scope: ' + str(TEL[j]), color = COLORS[j], lw = 0.8)\n",
    "                ax[r, c].legend()\n",
    "\n",
    "                if r == 1:\n",
    "                    ax[r, c].set_xlabel('Iteration')\n",
    "                if c == 0:\n",
    "                    ax[r,c].set_ylabel(parameter)\n",
    "\n",
    "                if c < 4:\n",
    "                    c = c + 1\n",
    "                else:\n",
    "                    r = r + 1\n",
    "                    c = 0\n",
    "        \n",
    "        elif parameter in time_pars:\n",
    "            SCOPE = 0\n",
    "            IT = np.arange(1, iterations + 1, 1)[zoom:]\n",
    "            r = 0\n",
    "            c = 0            \n",
    "            if lim < 4:\n",
    "                lim = 4\n",
    "            fig, ax = plt.subplots(math.ceil(lim / 4), 4, sharex = True, figsize = (30,20))\n",
    "            fig.subplots_adjust(wspace=0.2, hspace = 0)\n",
    "            for i in range(lim):\n",
    "                D = []\n",
    "                for j in range(len(par)):\n",
    "                    D.append(par[j][SCOPE][i])\n",
    "                D = D[zoom:]\n",
    "                ax[r, c].plot(IT, D, label = 'MJD: ' + str(i), lw = 0.8)\n",
    "                ax[r, c].legend()\n",
    "                ax[r, c].ticklabel_format(useOffset=False)\n",
    "\n",
    "                if r > math.ceil(lim / 4) - 4:\n",
    "                    ax[r, c].set_xlabel('Iteration')\n",
    "                if c == 0:\n",
    "                    ax[r,c].set_ylabel(parameter)\n",
    "\n",
    "                if c < 3:\n",
    "                    c = c + 1\n",
    "                else:\n",
    "                    c = 0\n",
    "                    r = r + 1\n",
    "\n",
    "        elif parameter in star_pars:\n",
    "            IT = np.arange(1, iterations + 1, 1)[zoom:]\n",
    "            r = 0\n",
    "            c = 0\n",
    "\n",
    "            if lim < 5:\n",
    "                lim = 5\n",
    "\n",
    "            fig,ax = plt.subplots(math.ceil(lim / 5), 5, sharex = True, sharey = False, figsize = (20, 10))\n",
    "            fig.subplots_adjust(wspace=0.4, hspace = 0)\n",
    "            for j in range(lim):\n",
    "                D = []\n",
    "                for i in range(len(par)):\n",
    "                    D.append(par[i][j])\n",
    "                D = D[zoom:]\n",
    "                ax[r, c].plot(IT, D, lw = 0.8)\n",
    "                ax[r, c].ticklabel_format(useOffset=False)\n",
    "\n",
    "                if r > math.ceil(lim / 5) - 5:\n",
    "                    ax[r, c].set_xlabel('Iteration')\n",
    "                if c == 0:\n",
    "                    ax[r,c].set_ylabel(parameter)\n",
    "\n",
    "                if c < 4:\n",
    "                    c = c + 1\n",
    "                else:\n",
    "                    c = 0\n",
    "                    r = r + 1         \n",
    "    \n",
    "    elif mode == 'double':\n",
    "        \n",
    "        if parameter_err == None:\n",
    "            raise ValueError(\"With 'double' mode, par and par_err must be specified.\")\n",
    "        \n",
    "        else:\n",
    "            par = Traces[names.index(parameter)]\n",
    "            par_err = Traces[names.index(parameter_err)]\n",
    "            \n",
    "            if parameter in scope_pars:\n",
    "                COLORS = ['red', 'green', 'blue', 'purple', 'black', 'gray', 'violet', 'teal', 'orange', 'yellow']\n",
    "\n",
    "                #Set up plot\n",
    "                fig,ax = plt.subplots(2,5, sharex = True, figsize = (20, 10))\n",
    "                fig.subplots_adjust(wspace=0.3, hspace = 0)\n",
    "\n",
    "                #Iteration number\n",
    "                IT = np.arange(1, iterations + 1, 1)[zoom:]\n",
    "                r = 0\n",
    "                c = 0\n",
    "                for j in range(10):\n",
    "                    D = []\n",
    "                    D_err = []\n",
    "                    for i in range(len(par)):\n",
    "                        D.append(par[i][j])\n",
    "                        D_err.append(par_err[i][j])\n",
    "                    D = D[zoom:]\n",
    "                    D_err = D_err[zoom:]\n",
    "                    \n",
    "                    ax[r, c].plot(IT, D, label = 'Scope: ' + str(TEL[j]), color = COLORS[j], lw = 0.8)\n",
    "                    ax[r, c].legend()\n",
    "\n",
    "                    ax[r, c].plot(IT, np.array(D) + np.array(D_err), color = COLORS[j], lw = 0.8, ls = '--')\n",
    "                    ax[r, c].plot(IT, np.array(D) - np.array(D_err), color = COLORS[j], lw = 0.8, ls = '--')\n",
    "                    ax[r, c].fill_between(IT, np.array(D) - np.array(D_err), np.array(D) + np.array(D_err), color = COLORS[j], alpha = 0.3)\n",
    "\n",
    "                    if r == 1:\n",
    "                        ax[r, c].set_xlabel('Iteration')\n",
    "                    if c == 0:\n",
    "                        ax[r,c].set_ylabel(parameter)\n",
    "\n",
    "                    if c < 4:\n",
    "                        c = c + 1\n",
    "                    else:\n",
    "                        c = 0\n",
    "                        r = r + 1\n",
    "            elif parameter in time_pars:\n",
    "                SCOPE = 0\n",
    "                IT = np.arange(1, iterations + 1, 1)[zoom:]\n",
    "                r = 0\n",
    "                c = 0            \n",
    "                if lim < 4:\n",
    "                    lim = 4\n",
    "                fig, ax = plt.subplots(math.ceil(lim / 4), 4, sharex = True, figsize = (30,20))\n",
    "                fig.subplots_adjust(wspace=0.2, hspace = 0)\n",
    "                for i in range(lim):\n",
    "                    D = []\n",
    "                    D_err = []\n",
    "                    for j in range(len(par)):\n",
    "                        D.append(par[j][SCOPE][i])\n",
    "                        D_err.append(par_err[j][SCOPE][i])\n",
    "                    D = D[zoom:]\n",
    "                    D_err = D_err[zoom:]\n",
    "                    ax[r, c].plot(IT, D, label = 'MJD: ' + str(i), lw = 0.8)\n",
    "                    ax[r, c].legend()\n",
    "                    ax[r, c].ticklabel_format(useOffset=False)\n",
    "\n",
    "                    ax[r, c].plot(IT, np.array(D) + np.array(D_err), lw = 0.8, ls = '--', color = 'blue')\n",
    "                    ax[r, c].plot(IT, np.array(D) - np.array(D_err), lw = 0.8, ls = '--', color = 'blue')\n",
    "                    ax[r, c].fill_between(IT, np.array(D) - np.array(D_err), np.array(D) + np.array(D_err), alpha = 0.3)\n",
    "\n",
    "                    if r > math.ceil(lim / 4) - 4:\n",
    "                        ax[r, c].set_xlabel('Iteration')\n",
    "                    if c == 0:\n",
    "                        ax[r,c].set_ylabel(parameter)\n",
    "\n",
    "                    if c < 3:\n",
    "                        c = c + 1\n",
    "                    else:\n",
    "                        c = 0\n",
    "                        r = r + 1\n",
    "\n",
    "            elif parameter in star_pars:\n",
    "                IT = np.arange(1, iterations + 1, 1)[zoom:]\n",
    "                r = 0\n",
    "                c = 0\n",
    "\n",
    "                if lim < 5:\n",
    "                    lim = 5\n",
    "\n",
    "                fig,ax = plt.subplots(math.ceil(lim / 5), 5, sharex = True, sharey = False, figsize = (20, 10))\n",
    "                fig.subplots_adjust(wspace=0.4, hspace = 0)\n",
    "                for j in range(lim):\n",
    "                    D = []\n",
    "                    D_err = []\n",
    "                    for i in range(len(par)):\n",
    "                        D.append(par[i][j])\n",
    "                        D_err.append(par_err[i][j])\n",
    "                    D = D[zoom:]\n",
    "                    D_err = D_err[zoom:]\n",
    "                    ax[r, c].plot(IT, D, lw = 0.8)\n",
    "                    ax[r, c].ticklabel_format(useOffset=False)\n",
    "\n",
    "                    ax[r, c].plot(IT, np.array(D) + np.array(D_err), lw = 0.8, ls = '--')\n",
    "                    ax[r, c].plot(IT, np.array(D) - np.array(D_err), lw = 0.8, ls = '--')\n",
    "                    ax[r, c].fill_between(IT, np.array(D) - np.array(D_err), np.array(D) + np.array(D_err), alpha = 0.3)\n",
    "\n",
    "                    if r > math.ceil(lim / 5) - 5:\n",
    "                        ax[r, c].set_xlabel('Iteration')\n",
    "                    if c == 0:\n",
    "                        ax[r,c].set_ylabel(parameter)\n",
    "\n",
    "                    if c < 4:\n",
    "                        c = c + 1\n",
    "                    else:\n",
    "                        c = 0\n",
    "                        r = r + 1\n",
    "    else:\n",
    "        raise ValueError(str(mode) + \" is not a valid mode input. Please select either 'single' or 'double'.\")\n",
    "\n",
    "def Phot_Cal(DF, filt, catalogue='apass_updated.csv', AGN_ID=AGN_ID):\n",
    "    df = {}\n",
    "    apass = pd.read_csv(catalogue)\n",
    "    for i,idx in enumerate(np.unique(DF['id_apass'])[:]):\n",
    "        #print(idx)\n",
    "        df_temp = Locate_Star_ID(DF,idx)\n",
    "        #Apply correction parameters\n",
    "        mags = df_temp['mag_aper'].values\n",
    "        #plt.plot(mags,'ko')\n",
    "        errs = (df_temp['err_aper'].values**2 + df_temp['rms_sc'].values**2 + df_temp['rms_t'].values**2)**0.5\n",
    "        mag,err = Generic_Optimal(mags,errs)\n",
    "        ss = apass['id'] == idx\n",
    "        #print(ss.sum())\n",
    "        df[i] = {\n",
    "            'id_apass':idx,\n",
    "            'mag':df_temp['mag_aper'].values[0],\n",
    "            'err':err,\n",
    "            'mag_lco': apass[filt[:1]+'_lco'].values[ss][0],\n",
    "            'err_lco': apass[filt[:1]+'err_lco'].values[ss][0]\n",
    "        }\n",
    "    #print(mag,df_temp['mag_aper'].values[0],apass[filt[:1]+'_lco'].values[ss])\n",
    "    zps = pd.DataFrame.from_dict(df,\"index\")\n",
    "    from astropy.stats import sigma_clip\n",
    "    diffs =  zps['mag_lco'].values - zps['mag'].values\n",
    "    diffs_err = np.sqrt(zps['err'].values**2 + zps['err_lco'].values**2)\n",
    "    pp = sigma_clip(diffs, sigma=3)\n",
    "    zp,aa,zp_err,ee = arx.arx(diffs[~pp.mask],diffs_err[~pp.mask])\n",
    "    return zp, zp_err\n",
    "\n",
    "#Random other functions ---------------\n",
    "def Line_Mod(X, M, Y0):\n",
    "    Y = 10**(M*X + Y0)\n",
    "    return(Y)\n",
    "def linear_func(x, intercept):\n",
    "    #test\n",
    "    return 0.2 * x + intercept\n",
    "#--------------------------------\n",
    "\n",
    "\n",
    "# -------------- Clean up data file --------------\n",
    "lco2 = Clean(lco)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a881b80d-2302-475b-9154-a953eb6e19fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Corr(Star_File, Filter, MAX_LOOPS = 100, bad_IDs = [], safe = 0.5, frac = 0.3, TEL = TEL, AGN_ID = AGN_ID, Star_Lim = None):\n",
    "    \"\"\"Algorithm to compute telescope correction parameters, star magnitudes, and extra variance as well as their errors.\n",
    "    \n",
    "    Input:\n",
    "    Star_File - Uncalibrated Star data (pd dataframe).\n",
    "    Filter - Filter (String).\n",
    "    MAX_LOOPS - (Optional) Max number of loops if convergence isn't reached (int).\n",
    "    bad_IDs - (Optional) Star IDs to omit in the calibration, primarily meant for variable stars (list/array).\n",
    "    safe - (Optional) Safety step size to avoid overstepping (float).\n",
    "    frac - (Optional) Fraction of stars with number of datapoints above max number of epochs (float).\n",
    "    TEL - Telescope list (list/array)\n",
    "    AGN_ID - AGN ID (float)\n",
    "    \n",
    "    Output:\n",
    "    df - dataframe containing final correction parameters, extra variances, their errors and their corrected magnitudes (pd dataframe)\n",
    "    TRACES - Traces of the correction parameters (arrays)\n",
    "    \"\"\"\n",
    "    # ======================= Select stars to use in calibration ========================\n",
    "    \n",
    "    start_time = time.time()\n",
    "\n",
    "    #Select stars based on fraction of datapoints\n",
    "    Star_IDs = Brightest_Reduced(Star_File, Filter, frac = frac)\n",
    "    \n",
    "    #Further select stars based on variability\n",
    "    if len(bad_IDs) > 0:\n",
    "        Star_IDs = [i for i in Star_IDs if i not in bad_IDs]\n",
    "    \n",
    "    #Max number of stars to use\n",
    "    if Star_Lim != None and len(Star_IDs) > Star_Lim:\n",
    "        Star_IDs = Star_IDs[:Star_Lim]\n",
    "    \n",
    "    #Total number of stars used in calibration\n",
    "    Nstars = len(Star_IDs)\n",
    "    print('No. of Stars: '+ str(Nstars))\n",
    "    \n",
    "    #Select star data based on filter and star IDs\n",
    "    Star_Data = Locate_Star_Filter(Locate_Star_ID(Star_File, Star_IDs), Filter)\n",
    "    \n",
    "    # ====================== Dataframes to return at the end ============================\n",
    "    \n",
    "    #Length of correction parameter dataframe\n",
    "    df_length = len(Star_Data['id_apass'].values)\n",
    "    \n",
    "    #'scope_mean' and 'scope_mean_err' are temporary and will not be returned in the end, just used to speed up computation\n",
    "    df = pd.DataFrame({'id_apass': Star_Data['id_apass'].values, 'Filter': Star_Data['Filter'].values,\n",
    "                       'MJD': Star_Data['MJD'].values, 'telid': Star_Data['telid'].values, \n",
    "                       'scope_mean': df_length*[0.], 'scope_mean_err': df_length*[0.],\n",
    "                       'mag_aper': Star_Data['mag_aper'].values, 'err_aper': Star_Data['err_aper'].values,\n",
    "                       'dmagt': df_length*[0.], 'dmagt_err': df_length*[0.], 'rms_t': df_length*[0.],\n",
    "                       'rms_t_err': df_length*[0.], 'dmags': df_length*[0.], 'dmags_err': df_length*[0.],\n",
    "                       'rms_sc':df_length*[0.], 'rms_sc_err': df_length*[0.], 'm_star': df_length*[0.],\n",
    "                       'm_star_err': df_length*[0.], 'rms_star': df_length*[0.], 'rms_star_err': df_length*[0.],\n",
    "                       'err_tot': Star_Data['err_aper'].values, 'DMAGT': df_length*[0.], 'DMAGS': df_length*[0.],\n",
    "                       'DMAGT_err': df_length*[0.], 'DMAGS_err': df_length*[0.],\n",
    "                       'airmass': Star_Data['airmass'].values, 'seeing': Star_Data['seeing'].values}) \n",
    "    \n",
    "    df.set_index(Star_Data.index.values)\n",
    "    \n",
    "    # ==================================== Traces ====================================\n",
    "    \n",
    "    epochs = []\n",
    "    dmagts = [] ; dmagts_err = []\n",
    "    rms_ts = [] ; rms_ts_err = []\n",
    "    dmagss = [] ; dmagss_err = []\n",
    "    rms_scs = [] ; rms_scs_err = []\n",
    "    mstars = [] ; mstars_err = []\n",
    "    rms_stars = [] ; rms_stars_err = []\n",
    "    DMAGTS = [] ; DMAGTS_err = []\n",
    "    DMAGSS = [] ; DMAGSS_err = []\n",
    "    \n",
    "    # ============================== Intercalibration =================================\n",
    "    \n",
    "    star_mag_correction = 0.\n",
    "    loop = 0\n",
    "    \n",
    "    while loop < MAX_LOOPS:\n",
    "        # ---------------------------------  1.dmagt  -------------------------------\n",
    "        if __name__ == \"__main__\":\n",
    "            df = process_dataframe_parallel2(df, loop, safe, Star_IDs)\n",
    " \n",
    "        # ---------------------------------- 2.dmags --------------------------------\n",
    "        #Compute temporary mean of star using optimal average\n",
    "        for ID in Star_IDs:\n",
    "            \n",
    "            #Select data for particular telescope\n",
    "            Star_Data_S = Locate_Star_ID(df, ID)\n",
    "            Mag = Star_Data_S['mag_aper'].values\n",
    "            Err = Star_Data_S['err_tot'].values\n",
    "            Mean, Mean_err = Generic_Optimal(Mag, Err)\n",
    "            \n",
    "            #Update dataframe\n",
    "            df['m_star'] = np.where((df['id_apass'] == ID), Mean, df['m_star'])\n",
    "            df['m_star_err'] = np.where((df['id_apass'] == ID), Mean_err, df['m_star_err'])\n",
    "        \n",
    "        if __name__ == \"__main__\":\n",
    "            df = process_dataframe_parallel3(df, loop, safe, Star_IDs)\n",
    "            \n",
    "        \n",
    "        dat = df\n",
    "        initial_DMAGS = (dat.DMAGS + dat.dmags).values\n",
    "        DMAGS_mean, DMAGS_mean_err = Generic_Optimal(initial_DMAGS, dat['dmags_err'].values)\n",
    "        \n",
    "        Corr1 = dat.dmags - DMAGS_mean\n",
    "        \n",
    "        #Propagate error in new dmagt accordingly\n",
    "        CorrE = (dat.dmags_err**2 + DMAGS_mean_err**2)**0.5\n",
    "        \n",
    "        #Update dataframe\n",
    "        df.loc[Corr1.index, 'dmags'] = Corr1\n",
    "        df.loc[CorrE.index, 'dmags_err'] = CorrE\n",
    "        df.loc[CorrE.index, 'DMAGS_err'] = CorrE\n",
    "        \n",
    "        #Updated dataframe\n",
    "        dat = df\n",
    "        Corr2 = dat.DMAGS + dat.dmags\n",
    "        \n",
    "        #Update dataframe\n",
    "        df.loc[Corr2.index, 'DMAGS'] = Corr2\n",
    "        \n",
    "        # ---------------------------------- 3.m(*) --------------------------------\n",
    "        \n",
    "        if __name__ == \"__main__\":\n",
    "            df = process_dataframe_parallel4(df, loop, safe)\n",
    "        \n",
    "        first_star_corr = Locate_Star_ID(df, Star_IDs[0])['m_star'].values[0]\n",
    "\n",
    "        df['mag_aper'] = df['mag_aper'].values - first_star_corr\n",
    "        df['m_star'] = df['m_star'].values - first_star_corr\n",
    "\n",
    "        #Keep track of how much the data shifts in total\n",
    "        star_mag_correction = star_mag_correction + first_star_corr\n",
    "        \n",
    "        #Update traces\n",
    "        epochs_temp = []\n",
    "        dmagts_temp = [] ; dmagts_err_temp = []\n",
    "        rms_t_temp = [] ; rms_t_err_temp = []\n",
    "        DMAGTS_temp = [] ; DMAGTS_err_temp = []\n",
    "        dmagss_temp = [] ; dmagss_err_temp = []\n",
    "        DMAGSS_temp = [] ; DMAGSS_err_temp = []\n",
    "        rms_sc_temp = [] ; rms_sc_err_temp = []\n",
    "        \n",
    "        for Telescope in TEL:\n",
    "\n",
    "            Star_Data_T = Locate_Star_Scope(df, Telescope)\n",
    "            dat = Star_Data_T.sort_values('MJD')\n",
    "            \n",
    "            unique_epochs = pd.unique(dat['MJD'])\n",
    "            all_epochs = dat['MJD'].values\n",
    "            unique_epoch_ind = np.array([list(all_epochs).index(e) for e in unique_epochs])\n",
    "            \n",
    "            epochs_temp.append(dat['MJD'].values[unique_epoch_ind])\n",
    "            dmagts_temp.append(dat['dmagt'].values[unique_epoch_ind]) ; dmagts_err_temp.append(dat['dmagt_err'].values[unique_epoch_ind])\n",
    "            rms_t_temp.append(dat['rms_t'].values[unique_epoch_ind]) ; rms_t_err_temp.append(dat['rms_t_err'].values[unique_epoch_ind])\n",
    "            DMAGTS_temp.append(dat['DMAGT'].values[unique_epoch_ind]) ; DMAGTS_err_temp.append(dat['DMAGT_err'].values[unique_epoch_ind])\n",
    "            \n",
    "\n",
    "            dmagss_temp.append(dat['dmags'].values[0]) ; dmagss_err_temp.append(dat['dmags_err'].values[0])\n",
    "            DMAGSS_temp.append(dat['DMAGS'].values[0]) ; DMAGSS_err_temp.append(dat['DMAGS_err'].values[0])\n",
    "            rms_sc_temp.append(dat['rms_sc'].values[0]) ; rms_sc_err_temp.append(dat['rms_sc_err'].values[0])\n",
    "            \n",
    "        epochs.append(np.array(epochs_temp, dtype = object))\n",
    "        dmagts.append(np.array(dmagts_temp, dtype = object)) ; dmagts_err.append(np.array(dmagts_err_temp, dtype = object))\n",
    "        rms_ts.append(np.array(rms_t_temp, dtype = object)) ; rms_ts_err.append(np.array(rms_t_err_temp, dtype = object))\n",
    "        DMAGTS.append(np.array(DMAGTS_temp, dtype = object)) ; DMAGTS_err.append(np.array(DMAGTS_err_temp, dtype = object))\n",
    "        dmagss.append(np.array(dmagss_temp, dtype = object)) ; dmagss_err.append(np.array(dmagss_err_temp, dtype = object))\n",
    "        DMAGSS.append(np.array(DMAGSS_temp, dtype = object)) ; DMAGSS_err.append(np.array(DMAGSS_err_temp, dtype = object))\n",
    "        rms_scs.append(np.array(rms_sc_temp, dtype = object)) ; rms_scs_err.append(np.array(rms_sc_err_temp, dtype = object))\n",
    "        \n",
    "        mstars_temp = [] ; mstars_err_temp = []\n",
    "        rms_stars_temp = [] ; rms_stars_err_temp = []\n",
    "        for ID in Star_IDs:\n",
    "            dat = Locate_Star_ID(df, ID)\n",
    "            mstars_temp.append(dat['m_star'].values[0]) ; mstars_err_temp.append(dat['m_star_err'].values[0])\n",
    "            rms_stars_temp.append(dat['rms_star'].values[0]) ; rms_stars_err_temp.append(dat['rms_star_err'].values[0])\n",
    "        mstars.append(np.array(mstars_temp)) ; mstars_err.append(np.array(mstars_err_temp))\n",
    "        rms_stars.append(np.array(rms_stars_temp)) ; rms_stars_err.append(np.array(rms_stars_err_temp))\n",
    "\n",
    "        TRACES = [dmagts, dmagts_err, rms_ts, rms_ts_err, DMAGTS, DMAGTS_err, dmagss, dmagss_err, rms_scs, rms_scs_err, mstars, mstars_err, rms_stars, rms_stars_err, DMAGSS, DMAGSS_err]\n",
    "\n",
    "        #Check convergence\n",
    "        tiny = 1*10**(-4)\n",
    "        All_par_diff = []\n",
    "        if loop > 0:\n",
    "            for i in np.arange(0, len(TRACES) - 1, 2):\n",
    "                tr = TRACES[i]\n",
    "                tr_err = TRACES[i+1]\n",
    "                try:\n",
    "                    All_par_diff.extend((abs((np.concatenate(tr[-1]).ravel()) - (np.concatenate(tr[-2]).ravel())) / np.concatenate(tr_err[-1]).ravel()))\n",
    "                except:\n",
    "                    All_par_diff.extend((abs(np.array(tr[-1]) - np.array(tr[-2])) / np.array(tr_err[-1])))\n",
    "            #print(max(All_par_diff))\n",
    "            if max(All_par_diff) < tiny:\n",
    "                print('Iteration ' + str(loop + 1) + '/' + str(MAX_LOOPS) + ' Finished')\n",
    "                df['m_star'] = df['m_star'] + star_mag_correction\n",
    "                df['mag_aper'] = df['mag_aper'] + star_mag_correction\n",
    "                print('Total run time:', time.time() - start_time)\n",
    "                break\n",
    "        print('Iteration ' + str(loop + 1) + '/' + str(MAX_LOOPS) + ' Finished')\n",
    "        loop = loop + 1\n",
    "    if loop == MAX_LOOPS:\n",
    "        df['m_star'] = df['m_star'] + star_mag_correction\n",
    "        df['mag_aper'] = df['mag_aper'] + star_mag_correction\n",
    "        print('Total run time:', time.time() - start_time)\n",
    "    return(df, TRACES)\n",
    "\n",
    "def dmagt(object_data, loop, safe, Star_IDs):\n",
    "    object_data = object_data\n",
    "    #Compute avg and sig(avg) magnitude of the telescope for each star using optimal average\n",
    "    for ID in Star_IDs:\n",
    "        #Select data for particular star\n",
    "        Star_Data_S = Locate_Star_ID(object_data, ID)\n",
    "        Mag = Star_Data_S['mag_aper'].values\n",
    "        Err = Star_Data_S['err_tot'].values\n",
    "        Mean_Scope_Mag, Mean_Scope_Mag_Err = Generic_Optimal(Mag, Err)\n",
    "        \n",
    "        #Update dataframe\n",
    "        object_data['scope_mean'] = np.where((object_data['id_apass'] == ID), Mean_Scope_Mag, object_data['scope_mean'])\n",
    "        object_data['scope_mean_err'] = np.where((object_data['id_apass'] == ID), Mean_Scope_Mag_Err, object_data['scope_mean_err'])\n",
    "    \n",
    "    Unique_MJDs = pd.unique(object_data['MJD'])     \n",
    "    Telescope = object_data.telid.values[0]\n",
    "    if __name__ == \"__main__\":\n",
    "        # Process the DataFrame in parallel\n",
    "        result_df = process_dataframe_parallel(object_data, loop, safe, Telescope)\n",
    "\n",
    "    #Ensure average of the dmagt's is zero to avoid degeneracy\n",
    "    Star_Data_T = result_df\n",
    "    initial_DMAGT = Star_Data_T['DMAGT'].values + Star_Data_T['dmagt'].values\n",
    "    DMAGT_mean, DMAGT_mean_err = Generic_Optimal(initial_DMAGT, Star_Data_T['dmagt_err'].values)\n",
    "    Corr1 = Star_Data_T.dmagt - DMAGT_mean\n",
    "    \n",
    "    #Propagate error in new dmagt accordingly\n",
    "    CorrE = (Star_Data_T.dmagt_err**2 + DMAGT_mean_err**2)**0.5\n",
    "    \n",
    "    #Update dataframe\n",
    "    result_df.loc[Corr1.index, 'dmagt'] = Corr1\n",
    "    result_df.loc[CorrE.index, 'dmagt_err'] = CorrE\n",
    "    result_df.loc[CorrE.index, 'DMAGT_err'] = CorrE\n",
    "    \n",
    "    #Updated dataframe\n",
    "    Star_Data_T = result_df\n",
    "    Corr2 = Star_Data_T.dmagt + Star_Data_T.DMAGT\n",
    "    \n",
    "    #Update dataframe\n",
    "    result_df.loc[Corr2.index, 'DMAGT'] = Corr2\n",
    "    \n",
    "    return(result_df)\n",
    "    \n",
    "def dmags(object_data, loop, safe, Star_IDs):\n",
    "    #Select data for particular telescope\n",
    "    object_data = object_data\n",
    "    Shifted_Mags = (object_data.mag_aper - object_data.dmagt - object_data.m_star).values\n",
    "    \n",
    "    #Propagate errors accordingly\n",
    "    #Shifted_Mags_Err = ((object_data.err_tot**2 + object_data.dmagt_err**2 + object_data.m_star_err**2)**0.5).values\n",
    "\n",
    "    #Jul12:\n",
    "    Shifted_Mags_Err = (object_data.err_tot.values**2 - object_data.rms_sc.values**2 + object_data.dmagt_err.values**2 + object_data.m_star_err.values**2)**0.5\n",
    "    \n",
    "    #Compute correction parameters for that telescope\n",
    "    dmags, dmags_err, rms_sc, rms_sc_err = arx.arx(Shifted_Mags, Shifted_Mags_Err, 1000)\n",
    "    Telescope = object_data.telid.values[0]\n",
    "    #Take safe size step to prevent overstepping\n",
    "    if loop > 0:\n",
    "        #old_dmags = df.loc[df['telid'] == TEL[i]]['dmags'].values[0]\n",
    "        old_rms_sc = object_data['rms_sc'].values[0]\n",
    "    \n",
    "        #dmags = old_dmags + safe*(dmags - old_dmags)\n",
    "        #12Jul - uncommented this:\n",
    "        #rms_sc = old_rms_sc + safe*(rms_sc - old_rms_sc)\n",
    "    \n",
    "    #Update dataframe\n",
    "    object_data['dmags'] = np.where((object_data['telid'] == Telescope), dmags, object_data['dmags'])\n",
    "    object_data['dmags_err'] = np.where((object_data['telid'] == Telescope), dmags_err, object_data['dmags_err'])\n",
    "    object_data['rms_sc'] = np.where((object_data['telid'] == Telescope), rms_sc, object_data['rms_sc'])             \n",
    "    object_data['rms_sc_err'] = np.where((object_data['telid'] == Telescope), rms_sc_err, object_data['rms_sc_err'])\n",
    "    object_data['DMAGS_err'] = np.where((object_data['telid'] == Telescope), dmags_err, object_data['DMAGS_err'])\n",
    "    \n",
    "    #Update total error of datapoint with new sc_rms\n",
    "    err_tot = (object_data.err_aper**2 + object_data.rms_star**2 + object_data.rms_t**2 + rms_sc**2)**0.5\n",
    "    \n",
    "    #Update dataframe\n",
    "    object_data.loc[err_tot.index, 'err_tot'] = err_tot\n",
    "    \n",
    "    return object_data\n",
    "\n",
    "def star_mags(object_data, loop, safe):\n",
    "    object_data = object_data\n",
    "    \n",
    "    #Apply correction parameters\n",
    "    Shifted_Mag = object_data['mag_aper'].values - object_data['dmagt'].values - object_data['dmags'].values\n",
    "    \n",
    "    #Propagate errors accordingly\n",
    "    #Shifted_Mag_Err = ((object_data['err_tot'].values)**2 + object_data['dmagt_err'].values**2 + object_data['dmags_err'].values**2)**0.5\n",
    "\n",
    "    #Jul12\n",
    "    Shifted_Mag_Err = (object_data.err_tot.values**2 - object_data.rms_star.values**2 + object_data['dmagt_err'].values**2 + object_data['dmags_err'].values**2)**0.5\n",
    "    \n",
    "    #Compute mean of star\n",
    "    m_star, m_star_err, rms_star, rms_star_err = arx.arx(Shifted_Mag, Shifted_Mag_Err, 1000)\n",
    "    \n",
    "    #Take safe size step to prevent overstepping\n",
    "    if loop > 0:\n",
    "        old_mstar = object_data['m_star'].values[0]\n",
    "        #old_rms_star = object_data['rms_star'].values[0]\n",
    "        \n",
    "        m_star = old_mstar + safe*(m_star - old_mstar)\n",
    "        #rms_star = old_rms_star + safe*(rms_star - old_rms_star)\n",
    "    \n",
    "    #Update dataframe\n",
    "    object_data['m_star'] = m_star\n",
    "    object_data['m_star_err'] = m_star_err\n",
    "    object_data['rms_star'] = rms_star           \n",
    "    object_data['rms_star_err'] = rms_star_err\n",
    "    \n",
    "    #Update total error of datapoint with new star_rms\n",
    "    err_tot = (object_data.err_aper**2 + object_data.rms_sc**2 + object_data.rms_t**2 + rms_star**2)**0.5\n",
    "    Shift = object_data.mag_aper - object_data.dmagt - object_data.dmags\n",
    "    \n",
    "    #Update dataframe\n",
    "    object_data.loc[err_tot.index, 'err_tot'] = err_tot\n",
    "    object_data.loc[Shift.index, 'mag_aper'] = Shift\n",
    "    \n",
    "    return object_data\n",
    "def process_object_data(object_data, loop, safe, Telescope):\n",
    "    object_data = object_data\n",
    "    #Shift mags by telescope means\n",
    "    Shifted_Mags = (object_data.mag_aper - object_data.scope_mean).values\n",
    "\n",
    "    #Propagate errors accordingly\n",
    "    #Shifted_Mags_Err = ((object_data.err_tot**2 + object_data.scope_mean_err**2)**0.5).values\n",
    "\n",
    "    #Jul12:\n",
    "    Shifted_Mags_Err = (object_data.err_tot.values**2 - object_data.rms_t.values**2 + object_data.scope_mean_err.values**2)**0.5\n",
    "    \n",
    "    #Compute correction parameters for that MJD\n",
    "    dmagt, dmagt_err, rms_t, rms_t_err = arx.arx(Shifted_Mags, Shifted_Mags_Err, 1000)\n",
    "    \n",
    "    #Take safe size step to prevent overstepping\n",
    "    if loop > 0:\n",
    "        old_dmagt = object_data['dmagt'].values[0]\n",
    "        #old_rms_t = object_data['rms_t'].values[0]\n",
    "        \n",
    "        dmagt = old_dmagt + safe*(dmagt - old_dmagt)\n",
    "        #rms_t = old_rms_t + safe*(rms_t - old_rms_t)\n",
    "    \n",
    "    E = object_data.MJD.values[0]\n",
    "    #Update dataframe\n",
    "    object_data['dmagt'] = np.where((object_data['MJD'] == E), dmagt, object_data['dmagt'])\n",
    "    object_data['dmagt_err'] = np.where((object_data['MJD'] == E), dmagt_err, object_data['dmagt_err'])\n",
    "    object_data['rms_t'] = np.where((object_data['MJD'] == E), rms_t, object_data['rms_t'])\n",
    "    object_data['rms_t_err'] = np.where((object_data['MJD'] == E), rms_t_err, object_data['rms_t_err'])\n",
    "\n",
    "    #Updated dataframe\n",
    "    Star_Data_T = Locate_Star_Scope(object_data, Telescope)\n",
    "    Star_Data_mjd = Locate_Star_Epoch(Star_Data_T, E)\n",
    "    \n",
    "    #Update total error of datapoint with new t_rms\n",
    "    err_tot = (Star_Data_mjd.err_aper**2 + Star_Data_mjd.rms_sc**2 + Star_Data_mjd.rms_star**2 + rms_t**2)**0.5\n",
    "    \n",
    "    #Update dataframe\n",
    "    object_data.loc[err_tot.index, 'err_tot'] = err_tot\n",
    "    \n",
    "    return object_data\n",
    "\n",
    "def process_dataframe_parallel(df, loop, safe, Telescope, num_workers=4):\n",
    "    # Group the DataFrame by the 'MJD' column\n",
    "    grouped_data = df.groupby('MJD')\n",
    "\n",
    "    # Prepare the input for parallel processing\n",
    "    object_data_list = [object_subset for _, object_subset in grouped_data]\n",
    "\n",
    "    # Use ThreadPoolExecutor to parallelize the processing\n",
    "    with ThreadPoolExecutor(max_workers=num_workers) as executor:\n",
    "        futures = {executor.submit(process_object_data, object_subset, loop, safe, Telescope): object_subset for object_subset in object_data_list}\n",
    "\n",
    "    # Collect the results and concatenate into a single DataFrame\n",
    "    results = [future.result() for future in as_completed(futures)]\n",
    "    result_df = pd.concat(results, ignore_index=True)\n",
    "\n",
    "    result_df = pd.concat(results, ignore_index=True)\n",
    "    return result_df\n",
    "\n",
    "\n",
    "def process_dataframe_parallel2(df, loop, safe, Star_IDs, num_workers=4):\n",
    "    # Group the DataFrame by the 'telid' column\n",
    "    grouped_data = df.groupby('telid')\n",
    "\n",
    "    # Prepare the input for parallel processing\n",
    "    object_data_list = [object_subset for _, object_subset in grouped_data]\n",
    "\n",
    "    # Use ThreadPoolExecutor to parallelize the processing\n",
    "    with ThreadPoolExecutor(max_workers=num_workers) as executor:\n",
    "        futures = {executor.submit(dmagt, object_subset, loop, safe, Star_IDs): object_subset for object_subset in object_data_list}\n",
    "\n",
    "    # Collect the results and concatenate into a single DataFrame\n",
    "    results = [future.result() for future in as_completed(futures)]\n",
    "    result_df = pd.concat(results, ignore_index=True)\n",
    "\n",
    "    return result_df\n",
    "\n",
    "def process_dataframe_parallel3(df, loop, safe, Star_IDs, num_workers=4):\n",
    "    # Group the DataFrame by the 'telid' column\n",
    "    grouped_data = df.groupby('telid')\n",
    "\n",
    "    # Prepare the input for parallel processing\n",
    "    object_data_list = [object_subset for _, object_subset in grouped_data]\n",
    "\n",
    "    # Use ThreadPoolExecutor to parallelize the processing\n",
    "    with ThreadPoolExecutor(max_workers=num_workers) as executor:\n",
    "        futures = {executor.submit(dmags, object_subset, loop, safe, Star_IDs): object_subset for object_subset in object_data_list}\n",
    "\n",
    "    # Collect the results and concatenate into a single DataFrame\n",
    "    results = [future.result() for future in as_completed(futures)]\n",
    "    result_df = pd.concat(results, ignore_index=True)\n",
    "\n",
    "    return result_df\n",
    "\n",
    "def process_dataframe_parallel4(df, loop, safe, num_workers=4):\n",
    "    # Group the DataFrame by the 'telid' column\n",
    "    grouped_data = df.groupby('id_apass')\n",
    "\n",
    "    # Prepare the input for parallel processing\n",
    "    object_data_list = [object_subset for _, object_subset in grouped_data]\n",
    "\n",
    "    # Use ThreadPoolExecutor to parallelize the processing\n",
    "    with ThreadPoolExecutor(max_workers=num_workers) as executor:\n",
    "        futures = {executor.submit(star_mags, object_subset, loop, safe): object_subset for object_subset in object_data_list}\n",
    "\n",
    "    # Collect the results and concatenate into a single DataFrame\n",
    "    results = [future.result() for future in as_completed(futures)]\n",
    "    result_df = pd.concat(results, ignore_index=True)\n",
    "\n",
    "    return result_df\n",
    "# -------------- Clean up data file --------------\n",
    "lco2 = Clean(lco)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18cb3ef8-369d-43ce-a3a7-f4dad17dc31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of Stars: 98\n",
      "Iteration 1/100 Finished\n",
      "Iteration 2/100 Finished\n",
      "Iteration 3/100 Finished\n",
      "Iteration 4/100 Finished\n",
      "Iteration 5/100 Finished\n",
      "Iteration 6/100 Finished\n",
      "Iteration 7/100 Finished\n",
      "Iteration 8/100 Finished\n",
      "Iteration 9/100 Finished\n",
      "Iteration 10/100 Finished\n",
      "Iteration 11/100 Finished\n",
      "Iteration 12/100 Finished\n",
      "Iteration 13/100 Finished\n",
      "Iteration 14/100 Finished\n",
      "Iteration 15/100 Finished\n",
      "Iteration 16/100 Finished\n",
      "Iteration 17/100 Finished\n",
      "Iteration 18/100 Finished\n",
      "Iteration 19/100 Finished\n",
      "Iteration 20/100 Finished\n",
      "Iteration 21/100 Finished\n",
      "Iteration 22/100 Finished\n",
      "Iteration 23/100 Finished\n",
      "Iteration 24/100 Finished\n",
      "Iteration 25/100 Finished\n",
      "Iteration 26/100 Finished\n",
      "Iteration 27/100 Finished\n",
      "Iteration 28/100 Finished\n",
      "Iteration 29/100 Finished\n",
      "Iteration 30/100 Finished\n",
      "Iteration 31/100 Finished\n",
      "Iteration 32/100 Finished\n",
      "Iteration 33/100 Finished\n",
      "Iteration 34/100 Finished\n",
      "Iteration 35/100 Finished\n",
      "Iteration 36/100 Finished\n",
      "Iteration 37/100 Finished\n",
      "Iteration 38/100 Finished\n",
      "Iteration 39/100 Finished\n",
      "Iteration 40/100 Finished\n",
      "Iteration 41/100 Finished\n",
      "Iteration 42/100 Finished\n",
      "Iteration 43/100 Finished\n",
      "Iteration 44/100 Finished\n",
      "Iteration 45/100 Finished\n",
      "Iteration 46/100 Finished\n",
      "Iteration 47/100 Finished\n",
      "Iteration 48/100 Finished\n",
      "Total run time: 1175.954091310501\n"
     ]
    }
   ],
   "source": [
    "#Derive correction and extra variance parameters using iterative algorithm\n",
    "DF_up, TR_up = Corr(lco2, 'up', MAX_LOOPS = 100, bad_IDs = [], safe = 0.6, frac = 0.5, TEL = TEL, AGN_ID = AGN_ID, Star_Lim = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a825c976-51c9-4731-bbd4-45f1fcc42ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Input data contains invalid values (NaNs or infs), which were automatically clipped. [astropy.stats.sigma_clipping]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================== up Filter ========================================================\n",
      "Returning dataframe with outliers kept in\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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itFqt7Nu3r0TfwiRZ8eSL2Wymb9++JCcnc+DAgRL7Fa0L1qtXL4ASBfyLF78vy4ULF/jyyy9dxymuR48eAHzzzTeubffffz8BAQGsXLmSY8eOufVPS0sr9ThlvV4mk4l58+YBcPbs2QrFXF7S6aeffsJisbiShqV9v/bs2cPatWsrtJhDWecqa3twcDAXLlwokVjt168fmqaxbt26EvsU7VsYU1JSktu5ylqM4FLvR4Bu3brRtm1bli1bxrx580qMZgPnFFOA1atXl3isonXohBBCCFF5kmgTQgghRJUxm80sWbIELy8vxowZ4zZqavHixfzwww+udpcuXdA0jf/+97+uxMR//vMfEhISAMjKynL1LazbVnx0TlnbC82YMcM1xe/gwYPMmTOHvn37ct9997n6FO5bNBFSVNeuXQFcsVutVqZMmeJKoBSNs1u3bnh6evLll1+Sn5+P3W5nxYoVALzwwguYzWaeeOIJLly4ADhHOb344ouuVSsBnnjiCby8vHjzzTddSa74+HhXgvJSSZJXXnmF3NzcMlf+LEzgvP/++67Xpn79+ixYsAClFCNGjODEiROAM2H073//u9TjFCbRSkum3Xzzza5RVxVR1vfvxIkTzJw5021Bh9K+54VTgW+//XZatWpF27Ztadu2LVFRUTz44INuScqy3jOFz6P49j59+pCbm8vy5csBiIuL4+zZs0RGRvLwww+zefNmFi9e7Or/888/M3DgQNfrXDha8OOPP8bhcKDrOtOnT3fVyyv+/Sw8f2JiYqmvSaGRI0dy6tQpjh07xgMPPFDi8fvvv5+oqCjmzZvnlgxcu3Ytt99+e4kRk0IIIYSoIkoIIYQQoort3btX3X///SosLEy1adNGtWrVSjVq1Ejdcsst6oMPPnD1W7hwoQoLC1ORkZFq+PDhavny5eqjjz5SgGrfvr2KjY1VR44cUW3atFGA0jRNzZgxQyml1Nq1a1W9evUUoPz9/dWiRYtcx7377rvVxIkT1X333afat2+voqKiVMuWLdUzzzyjcnJyXP3y8vJUjx49FKAANWzYMOVwONyei91uV48//rjy9/dXXbp0UQ8//LDavXu3Gj9+vALU0KFD1YULF1z9P/74YxUZGamaN2+u7rrrLrVs2TLXY2vXrlUDBgxQwcHBqkePHmrgwIFur0eh1atXqy5duqgGDRqooUOHqpdeekmdPHlSAcpsNquXXnqp1Nf9xRdfdD0XHx8fNX/+fLfHZ82apSwWi6tPjx493B5ft26dGjZsmAoODlYdOnRQ3bp1U4888oiaMWOGatu2ravfqVOnVLt27RSg6tWrp7799tsSsSQmJip/f3/13HPPlRproT179ri+vwaDQfn4+CgfHx/l5eXlirNZs2au16+s7/kTTzyhDAaDMpvNrv0K/wUHB6vc3Fy1adMmFRYWpgDl4eGhFixYoJRS6scff1T+/v4KUAEBAeqLL75wHffcuXPqrrvuUoGBgWrAgAEqJiZGJSUlKaWUys/PV3/9619V48aNVcuWLVXv3r3VE088oc6cOeP2HN944w0VGhqqwsPD1UMPPaS2bNmipk2bpgDVs2dPtW/fPqWUUv/3f//nijkqKkrFx8eX+bodPXpUAeqVV14ps09qaqqaOHGiCg8PV23atFE333yzmjp1qjp37ly53xMhhBBCXD5NqSsoECGEEEIIIUQN++yzz3j44Yf55Zdf6NOnj2v7qVOnGD9+PKtWreK3336jZ8+eNRilEEIIIa4HMnVUCCGEEELUaf/85z9p06aNW5INICIigrvvvhtN02jUqFENRSeEEEKI64kk2oQQQgghRJ3Wrl074uPj+fXXX922Hz58mPnz5/Pkk0/SuHHjGopOCCGEENcTmToqhBBCCCHqtMzMTP7973/z008/kZ2djZeXF0op/P39GTduHOPGjUPTtJoOUwghhBDXAUm0CSGEEEIIIYQQQghRBWTqqBBCCCGEEEIIIYQQVUASbUIIIYQQQgghhBBCVAFTTQdQG+m6ztmzZ/Hz85N6HkIIIYQQQgghhBDXOaUUWVlZhIeHYzCUPW5NEm2lOHv2LBERETUdhhBCCCGEEEIIIYSoRU6dOlXuauaSaCuFn58f4Hzx/P39S+3To0cPtm3bdjXDuiKZmZlERESU+5xqm7r2Gte1eKFuxVwX38NQt15jkHirW118H9e117iuxQt1K2Z5D18ddS3muhRvXXwPQ916jUHirW518X1c117juhYv1K2Y6+J7GKBr164cPXrUlTMqiyTaSlE4XdTf37/Mb7rRaKxTb4hC5T2n2qauvcZ1LV6omzHXpfcw1L3XWOK9OurS+7iuvcZ1LV6omzHLe7h61bWY61q8ULfew1D3XmOJ9+qoS+/juvYa17V4oW7GXJfew+B8jYFLlhiTxRCEEEIIIYQQQgghhKgCkmgTQgghhBBCCCGEEKIKyNTRcvTo0cM1NLBQTEwMMTExNRSREEIIIYQQQgghhKhO8+bNY968eW7bjh8/XqF9JdFWjm3btpU5X1iSbdWvrr3GdS1eqJsx1zV17TWWeEVxde01rmvxQt2MuS6pi69vXYu5rsVbF9W111jiFcXVtde4rsULdTPm2qy0QVYzZ87kmWeeueS+mlJKVVdgdVVmZiYBAQFkZGTUqcJ85bkWn5O4vsh7WFwL5H0s6jp5D4u6Tt7D4log72NR19XV93BF45YabdcJT09PnnvuOTw9PWs6FCEui7yHxbVA3seirpP3sKjr5D0srgXyPhZ13bX+HpYRbaWoq9lVIYQQQgghhBBCCFH1ZESbqDbLV35Z0yEIIYQQQgghhBBC1DqSaBOV9u2qr2o6BCGEEEIIIYQQ4rqxKbWgpkMQFSSrjpajR48eGI1Gt22lrTxxvdKVIi7dQbJVJ8RioEugEYOm1XRYQgghhBBCCCFEnaOUwm6343A4Sjx2JsOG1bfkdlF1jEYjJpMJTdOYN28e8+bNc3u8tO9LaaRGWymkRlvpbFYrLz41gq/3/8Yf29/I3f9cxq6si4m1rkFGugZJ7lYIIYQQQgghhKgMm81GYmIiubm5pT6eY1f4mC49sCXXWoC3xVzV4V03vL29CQsLw8PDo8RjFc0VSVZEVNjzk+7lo70bCdDhg93rabhrF97Nu7geP5SVRZfAAAyazEgWQgghhBBCCCEqQtd1jh8/jtFoJDw8HA8PD7Ris8XSbDpBHqV/1tZ1xdl0G7k2B0ajDYcRmoX5YzTIZ/OKUkphs9k4d+4cx48fp1WrVhgu8/WTRJuosP/t2wIaWBRkaLA39mduaB4FaFg5Q649h01pSfSt166mQxVCCCGEEEIIIeoEm82GrutERETg7e1dah8PTcfiWXri5/g5K1ZlxmA2o+wKZfbgRIqVdk2DSL6QR0Z2PgG+noTU8yqRwBMXeXl5YTabOXHiBDabDYvFclnHqXXpzXXr1nHnnXcSHh6OpmksX768zL4TJ05E0zTmzJlzyePOmTOHNm3a4OXlRUREBH/605+wWq1VF/h1JOv3d03s9/PRtXR0HORymDyOsS/nZM0GJ4QQQgghhBBC1AKVXcDgckZQ6boi16Zf3KAUmsGAvUAn8WgB9mQztnxF8oVcki+UPi1VXHS5o9jcjlEFcVSpnJwcoqKiePvtt8vtt3z5crZs2UJ4ePglj7l06VKeffZZnnvuOQ4cOMCiRYv4/PPP+etf/1pVYV8XBnfoCYUV/RSMGP8KmgrEgBEDJrxoDkh2XAghhBBCCCGEOJ6jX7rTFTp2NhPd4UC32ZwbNA2l6wQ4fFE2AxoaCkWA3Ze0rPxqj0fUwqmjQ4YMYciQIeX2OXPmDJMmTWLFihUMGzbsksfcvHkzffr0YeTIkQBERkby4IMPsnXr1iqJ+XrxwttfYvx9MYRRbXvjaD6IH77Yxp333YAXzdEw42dMR1e61GkTQgghhBBCCCGqmTXfjq7roGmgzGgGA0a9AA/Ni8I0nxEDJmWs0TivJ3UuG6LrOqNHj+aZZ56hQ4cOFdqnb9++7Nixw5VYO3bsGD/99NMlk3SZmZll/svPv/4ywR4WCy+/8x2vzJjP4YaPsXv3afbGOaeKehCGlZOcyk9lU9qhGo5UCCGEEEIIIYS49lk8fx8/pRSOvFwM6LSPrIfZcnG2mUFp2DUHZmMBSqkyjiTAmXPSdZ3s7OxSc0EVUecSba+99homk4mnnnqqwvs88MADvPTSS/Tt2xez2UyLFi0YMGAAzz77bLn7RUREEBAQUOq/V1999UqfSp1i1+18mrieOQnfs80zmd/2HWXtyr3Y8gvItaWQx2EKSAaQOm1CCCGEEEIIIcRV0CzMF4tmR0Nh0ex4e5pIOp9LhjEbzUNHaQqjMpBhOEtOdiLJqUlVev7K1NmvjD179tCvXz+8vLxo1KgRL774oluSMDExkZEjR9KmTRsMBgNTpkwpcYyvv/6a7t27ExgYiI+PD126dOGjjz4q97ypqamcOnWK7t27l8gDRUREVCj2Wjd1tDw7duxg7ty5xMbGVmqljDVr1vDPf/6T+fPn07NnT44cOcLkyZMJCwvjH//4R5n7nTp1Cn9//1If8/T0rHT8ddkXyZs5aU0FwC8ijBZNFxN/9G50XZGYuYXA4KLDUKVOmxBCCCGEEEKIa8/+7FO0961YwuVqyDtxjJC8bBQaGSY/MpWJrFxnvbZsbBjNOegFaWi/56jSM9MIDQ6rsvMX1tkfN24cw4cPr5JjZmZmMmjQIAYMGMC2bduIj4/n4YcfxsfHh2nTpgGQn59PcHAwf/vb33jjjTdKPU69evX429/+Rtu2bfHw8OCHH35g3LhxNGzYkMGDB5e6T4MGDYiIiGD79u14eHiUiKsiybY6lWhbv349KSkpNGnSxLXN4XAwbdo05syZQ0JCQqn7/eMf/2D06NFMmDABgE6dOpGTk8Njjz3G3/72tzJXlfD39y8z0Xa9ScnPcH2taRpJB3eh0RFlbkr2SQ8Cgx2uxzvUol86QgghhBBCCCFEVTmQc7rWJNqUrmPPzQHAppnJMJXMXziUxX0oTBXPHL1Unf3IyEgmTJhAfHw8X3/9NfXr1+fNN9+kd+/eTJgwgV9++YVmzZqxePFiunfvDjgXtLRarSxZsgRPT086duxIfHw8s2fPZurUqWiaRmRkJHPnzgXg/fffL/Xc/fv3d2tPnjyZDz74gA0bNpSZaDMYDBgMBnx9fbFYLJfxitSxqaOjR49m9+7dxMXFuf6Fh4fzzDPPsGLFijL3y83NLZFMMxqNKKVkfnIFNfQMcH2tlELXHZiyFtIv8ifmDr+DvoHtiLQE0zewHb0D29RgpEIIIYQQQgghxLUvJ+Eo/J7TsBo9UQ476UvfJmn6ONKXvo1y2F2PFzKbzVc9D/LGG2/Qp08fdu7cybBhwxg9ejRjxozhoYceIjY2lpYtWzJmzBhXXJs3b6Zfv35uMwkHDx7M2bNnyxxgdSlKKX755RcOHTrEzTffXBVPq0y1bkRbdnY2R44ccbWPHz9OXFwc9erVo0mTJtSvX9+tv9lsJjQ0lDZtLiZ3xowZQ6NGjVx11O68805mz55NdHS0a+roP/7xD+666y6MRll5oyLuC+nFF8mbSc5P59zJk2DX6d31Jj6YtQyDZqBPUNuaDlEIIYQQQgghhLhuOKxW19cKjYzPFpK+9G1QCmvcZlBWAkc+9vuKpM4kVk5uNimpyYQEh161OIcOHcrEiRMBmDFjBgsWLKBHjx7cd999APzlL3+hV69eJCcnExoaSlJSEpGRkW7HCAkJASApKYlmzZpV+NwZGRk0atSI/Px8jEYj8+fPZ9CgQVXzxMpQ6xJt27dvZ8CAAa721KlTARg7dixLliyp0DFOnjzpNoLt73//O5qm8fe//50zZ84QHBzMnXfeyT//+c8qjf1aZjKYeDDsJgDsEYpGM+5l5d503t+UwWP9PDEZpS6bEEIIIYQQQghxtRgtFuw52QAE2LNw7N92cQSbUlj37QQ9C4PRgO64WO4pJy/7qsbZuXNn19eFCbNOnTqV2JaSkkJoqDMBWLwuf+Fot8rU6wfw8/MjLi6O7OxsfvnlF6ZOnUrz5s1LTCutSrUu0da/f/9KDWMsbdjgmjVr3Nomk4nnnnuO55577gqjEwDvrE1h/q/JKODIr86VRp+8JaRmgxJCCCGEEEIIIa5xSimyUhXWTIXFrzlGjqNb8zBZLIT37cehnVucyTZNw7tjJ3y9fQDnSLaix7iazGaz6+vCRFlp23RdB3CNaisqJSUFuJiUqyiDwUDLli0B6NKlCwcOHODVV1+9vhJtovbbkZDjqp+ofm8LIYQQQgghhBDXO7tDZ+6yXWzZl8TAh27G7jBjMlZdefysVEXWOWdCqiAf/IKbE9DcefzWT04jPduGfmgn9bvfSKsnpmIwmTh28ojbMSo7Kuxq69WrF9OnT8dms7lW/ly5ciXh4eElppRWllKK/Pz8KoiybJJoK0ePHj1K1HCLiYkhJiamhiKqHfRi2e/ibSGEEEIIIYQQ4nr0xmdxzPp0JwC97i3gjc8O8cyorlVybKUU2Vm62zZb7sXP4waTiTb/9wxBfu6rZfp4+ZKdk43SjGAIQmnOBRGqIuF2qTr7l2PkyJG88MILPPzww0yfPp3Dhw/zyiuvMGPGDLeY4+LiXDGcO3eOuLg4PDw8aN++PQCvvvoq3bt3p0WLFthsNn766Sc+/PBDFixYcMkYli5dyqxZs9y2OYpMvy2PJNrKsW3bNvz9Sy6Pe70zaOW3hRBCCCGEEEKI69GXq4+UaFdVou1clp1cpWM0a3gUOEexeXi7fyAvnmQDaNgghCyrRk6eFTRPsq2QfCGX0Po+VxxTVdTZLy4gIIBVq1YRExND9+7dCQoKYurUqa5jF4qOjnZ9vWPHDj755BOaNm3qKjGWk5PDk08+yenTp/Hy8qJt27Z8/PHH3H///ZeMYdSoUYwfP95tW2ZmJgEBAZfcVxJtotK6Rfqy5Zhz+qj2e9tmszJv8WOcTjxI47C2xIx7Bw+Pkj/gQgghhBBCCCGEqLz0XDsFngqD0jDbDegmhW/9S4980TSNAocZtItTWNOy8qsk0XapOvul1dUv3j8yMrLEtk6dOrFu3bpyz32pWnMvv/wyL7/8crl9qkPVTRQW143H+jXkyVtCaNXQkydvCeGxfg2Zt/gx4o9tITcvg/hjW5i3+LGaDlMIIYQQQgghhLiq7h3QEoNBo1FEPby8PXhodK8qLbfkmW8EBZoCY4FG9vnLO3aBvYB98Xs4euKwaxECUTVkRJuoNJNR48lbQtxWGj2deNCtT/G2EEIIIYQQQghxrfvTA13wigjGs1EwmqZhimjIzjQ73eqZS+1vt9v54Mv3aN+sE34BvjQKa1xm7bRAbxN5mQqjfrGWfNEabeUJ8vMk+UKuq60c2Th0Ozm52Rw/dZQWTVtV4lmK8siINlElGoe1LbcthBBCCCGEEEJc60xGA8GRDd2SZUeyyx4x9ubimbz/5XtYbTqpGYqjp1PKnBIZ7GfC0/tifTYoWaOtLCH1vAmp54OvtwcGlQ16lusxa761QscQFSOJNlElYsa9Q+vmPfH2CqB1857EjHunpkOqdstXflnTIQghhBBCCCGEqMO+WvE5yrMXaB6gmcixGtxGnhWlaRohjUz4BRvw9NHwCzbg16BiiTZN0wit702LRgF4e9rRuJjMs3hKffWqJFNHy9GjRw+MRqPbtpiYGGJiYmoootrLw8PCnyZ+WNNhXFXfrvqKe267t6bDEEIIIYQQQghxlelKEZfuINmqE2Ix0CXQiOH3UWwtfY3sTHe4+rb0NZZ1GFCgzB3wUD4YlAEv3avchQo0TcM/uGLJtRIx64pjZzOx2uthMPuCnoqXp4VmES0u63jXsqVLlzJr1iy3bQ6Ho4ze7iTRVo5t27bh7+9f02EIIYQQQgghhBCiFolLdxCb5ky8nMlz4NAV23ZdIPZEDlERXihTAZ6NgilITKVTk/AyjzP89vuJW9EOD2VGofB2WMgvuLxE2qUcO5tJTp7t95YJH+8mtGgcUC3nqutGjRrF+PHj3bZlZmYSEHDp10umjgohhBBCCCGEEEJUQlKe++imRV/tZt4vSWw+ms3CNSm88dE+cnPyefn1lcz/+XiZx3lq3NM0NzsXGvRQHgBYtNIXTrhS1nx7uW1RNSTRJoQQQgghhBBCCFEJiiKjzpRid5LGHbFv/r5Bw7NefdfDeX5+ZR7HZDLRMiIMAE/lTLCZzeVMNb0CFk9juW1RNSTRJoQQQgghhBBCCFEJbpM7f6/N1jR1r2tT4cKhA27vRL1gP2LT7OhlrCZaWBreqAwljq2UIseuUOD8v4xjVISvRx6ofJyF4fKdbVHlJNEmymV36Mz6dCcj/v5fZn26E7uj7GWJhRBCCCGEEEKIa5GudDamHeSM9QLrz+3B9t/PQP/987FS3NzBm/VtRrjatrTzGI0GbhnSGaPJSGyag51ppU/VDG/nXj7fw/tiqi3XAbkOZ6It16HIrVg9/lKlZ6ahOc6h2c+gOc6Rnpl2+QcTZZJEmyjX3GW7mPlJLOvizjLzk1jmLttV0yEJIYQQQgghhBBX1eb0eDakHyBPt7Ep6zCpp1fj9cHroBSeKz+nRZdI/Hr1oV3eESbeXJ8nbw3F4mVG0y4mzY5klz5wpfsfPbD4anh4afgFG/BrcHEfu+4+gq14u1KKr7FQBWsurFu3jjvvvJPw8HA0TWP58uVXflBgz5499OvXDy8vLxo1asSLL75YYjTf2rVr6datGxaLhebNm7Nw4UK3xwsKCnjxxRdp0aIFFouFqKgo/ve//1VJfOWRVUdFuX7bm+ga8qqUsw3RZfZ3OOz8b/VCjiTsoGVkN24f8DhGo7zNhBBCCCGEEELUXaetqRcbmkZehxb4rjoBmkZB9E1omsYNt3chOqg73eqZgMa8dyy/Qsc2mjQsfhpBjYxYLO7joYqn1a4gzUagfxApqUlu7SuVk5NDVFQU48aNY/jw4Vd8PHCu7jlo0CAGDBjAtm3biI+P5+GHH8bHx4dp06YBcPz4cYYOHcqjjz7Kxx9/zMaNG3nyyScJDg52xfH3v/+djz/+mHfffZe2bduyYsUK/vCHP7Bp0yaio8vOa1wpyYCUo0ePHhiN7sUBY2JiiImJqaGIrr7iyfJLJc//t3ohP6x6G1AcPLwZgGEDJ1VPcEIIIYQQQgghyrV/TRLt+4fWdBh1XmNLAxKs59B1WPXfMDKSwull2ERHu46pYSNnJ03jSLbj90QbmDSwF/kM3dK38osPFB90lmfNw9/khcFQ+QmKIQ1C0dDIycvGx8uXhg1CKn2M4oYMGcKQIUPKfDwyMpIJEyYQHx/P119/Tf369XnzzTfp3bs3EyZM4JdffqFZs2YsXryY7t27A7B06VKsVitLlizB09OTjh07Eh8fz+zZs5k6dSqaprFw4UKaNGnCnDlzAGjXrh3bt29n5syZrkTbRx99xN/+9jeGDh0KwBNPPMGKFSuYNWsWH3/8cbnPa+nSpcyaNcttm8NRsXm7MnW0HNu2bWP//v1u/66nJBuAQdPKbRd35Ph2LubY1e9tIYQQQgghhBA1Yf+apEt3EpfUK7A1fQPbcSHVi+Qkb6x4sbr+LXyz8nSZ+5g0xdGNn2O32zi68XPaeZVeo608JoNW9CM2ebkZHD91tNS+drudOYteZ+RTf2DOotex293Pp2kaIcGhNG/SkpDgULdprdXpjTfeoE+fPuzcuZNhw4YxevRoxowZw0MPPURsbCwtW7ZkzJgxrqmhmzdvpl+/fnh6erqOMXjwYM6ePUtCQoKrz2233eZ2nsGDB7N9+3YKCgoAyM/Px2KxuPXx8vJiw4YNl4x51KhRJfJB27Ztq9DzlUSbKNeNHUMLF1BB05zt8ui6Xm5bCCGEEEIIIYSoawyagT5BbSmwFU2jaJxPc58e2tL34uMZWRl8PH8yKacO8PH8yYx7ekSlz+tthJyMFJRSpJ49THZ6CtZ8a6l93/5gNrMX/Yv129Ywe9G/ePuD2ZU+X3UYOnQoEydOpFWrVsyYMYOsrCx69OjBfffdR+vWrfnLX/7CgQMHSE5OBiApKYmQEPfRdoXtpKSkcvvY7XZSU53TfAcPHszs2bM5fPgwuq6zatUqvv32WxITE6v1+UqiTZRr8ogonh7ZlZu7hPP0yK5MHhFVbn/NYEDHxAntj2wyvc+PKSOw2iTZJoQQQgghhBCi7vP1dE+jdAz3omuQkSAzdA0yEh10sUKX3eEcWbX5xwXcPuZl9h/ZW+nzaZqGw5qFcjgoyM8FpbB4Wkrtu3XXZteoMKUUW3dtrvT5qkPnzp1dXxcmxzp16lRiW0pKimtb8dF2hc+r6PZL9Zk7dy6tWrWibdu2eHh4MGnSJMaNG1eiRFhVq3WJtsqsWDFx4kQ0TXPNyS1Peno6MTExhIWFYbFYaNeuHT/99FPVBX6NMhkNTHswmmUvD2Hag9GYjOW/ZVo1684e43RytcbYNT8S8yN44qPjVylaIYQQtcmPu2TJeCGEEKKmZaVWrCB/bbT7f7aaDsGNzWbFT8VTTzuAp5ZH96ZeLBzdjK5BJoZHeNI1yORWbslkNKMZjNQLbU63W0Zzx0PPo6vKL2fQLKIFmqZhNJrw8falWUSLUvvdENXLlWTSNI0bonpd3hOtYmaz2fV1YXylbSucERcaGuoauVaoMAlXmJQrq4/JZKJ+/foABAcHs3z5cnJycjhx4gQHDx7E19eXZs2aVeXTK6HWLYZQ0RUrli9fzpYtWwgPD7/kMW02G4MGDaJhw4Z8+eWXNG7cmFOnTuHn51eVoV9z7A7FO2tTiD2RQ9emPjzWryEmY/lzuG8f8DjPbdhFhr3wh0Zjf0Iaut2OwVTr3m5CCCGq0U970hkWdeWrWQlRk5av/JJ7bru3psMQQojLlnW+7iba9qyw0fl2j5oOw2Xe4sfIbd2Gjra5ALTWe2Lx+LDM/gF+AYx6fDbNe9+Hphlo0WcEcekOugZV7rOxwWDAaIQOrTuV22/S2KmAc2TbDVG9XO2rTSlF8oU8cqwFOHTlGmlWUb169WL69OnYbDY8PJzf/5UrVxIeHk5kZKSrz/fff++238qVK+nevbtbEg/AYrHQqFEjCgoK+OqrrxgxovJTeCuj1mU+LrViBcCZM2eYNGkSK1asYNiwYZc85vvvv8+FCxfYtGmT6wVv2rRplcR7LVu4JpkFq51Z481Hs9GVYtKt5ddoMxpNdIoIZOvxbECjc8LPZHnVI37ez7Sd/OxViFoIIYQQoup8u+orSbQJIYQA4HTiQXxbt3Frl0fTNG669QHOWgsTTRpJeTpU031Ik8nElPF/rp6DlyE7O5sjR4642sePH+eXtb9h1yyEhTfGoSuycgsqdcyRI0fywgsv8PDDDzN9+nQOHz7MK6+8wowZM1yj3x5//HHefvttpk6dyqOPPsrmzZtZtGgRn376qes4W7Zs4cyZM3Tp0oUzZ87w/PPPo+s6f/5z9b5GtW7q6KXous7o0aN55pln6NChQ4X2+e677+jVqxcxMTGEhITQsWNHXnnllUsuzZqZmVnmv/z8untXoKK+j0svt12WBaOb0TL9AF75GXQ6tZqxa//CqeXLqj5AIYQQQlwVulJsTbYxc1wmLw1I5/2JWRRYpQarEEKI60vjsLbltktTfCxX5SeO1m7bt28nOjqa6OhoAKZOncqgAb2ZP/c1Vx+bvfzcS3EBAQGsWrWK06dP0717d5588kmmTp3K1KkXR+g1a9aMn376iTVr1tClSxdeeukl3nzzTbeZkVarlb///e+0b9+eP/zhDzRq1IgNGzYQGBhY5rl1XUfXdbKzs0vNBVVErRvRdimvvfYaJpOJp556qsL7HDt2jF9//ZVRo0bx008/cfjwYWJiYrDb7cyYMaPM/SIiIsp87LnnnuP555+vTOjXDYuHgUn7Xif3VEJNhyKEEJftm7VH+UO/0utfCHG92ZnmYNWfrRgDNQqyIGGHg4+m5PLIQt+aDk0IIYS4amLGvcOcne/j7RVA47C2xIx7p9z+3kYNrVhqrXi7ruvfv3+JqaFHTmeQk+esr/ff1Tvw8XKf/lu8f2RkZIltnTp1Yt26deWeu1+/fsTGxpb7+P79+y/5HIpKTU3l1KlTDBs2jBMnTlRq30J1KtG2Y8cO5s6dS2xsbInVJcqj6zoNGzbknXfewWg00q1bN86ePcu///3vchNtp06dwt/fv9THPD09Kx1/XXNnVCAL1qS4tSsq4p4RHHrrdbf2tcJut/P2B7OJ3buNOYteZ9LYqZik/pwQ15xv1h6TRJsQvzuS7cB2QqfhaE9yNzrvSifH22s4KiGEEOLq8vCw0CIymj8//2SF+vuYNBp4GjhrdaCUIvMrO3lHFKt7KG4e54nRVPG8Rl1SPF1TifRNjWvQoAERERFs377dVR+uUGZmZrkDsgrVqezA+vXrSUlJoUmTJq5tDoeDadOmMWfOHBISEkrdLywsDLPZ7LaEa7t27UhKSnIrrlecv79/mYm268HjA0IwGDS3xRAqqnXM02gGA+e3/0b97jfS6gnnEE+r1crYaSPYf2Qv7Vt25INZy7BYSl+auLZ6+4PZzF70L5RSzF70L4CrPg9eCCGEuNpMxa4rg1vWuQokFWa16Tzx0XEOJVnJO5mD1aZj8bh2n68QQojq0cLXQDMf59+PHccKyFjmvEm1OtaB0hW3TPSqyfCqjY/FTHauza1dVxgMBgwGA76+vpedq6hTibbRo0czcOBAt22DBw9m9OjRjBs3rsz9+vTpwyeffIKu6xgMzjd5fHw8YWFhZSbZBJiMGk/eEnJZ+xpMJpo/+jQb9+WS/I2dkH1WRs/xZuy0EWyKXQ/Aptj1jJ02gs/nfVeVYVcbu0Nn7rJdzP/BjN0yEEPeKlCKrbs213RoQgghRLVq6WvgiKaR9f3FUWxK1xi36CiHkqy0CbWwYHSzayYZ9cRHx9l6PAeA3FwHd7y+lv89e5OMYBdCCFEpLXydg326BpnYesbm9tiun2xlJtp0XXHifD5Wu8Ji0mhav27NqAup53xeOdYCfCxmV/t6UeuuhrKzs4mLiyMuLg5wrlgRFxfHyZMnqV+/Ph07dnT7ZzabCQ0NpU2biyt/jBkzhr/+9a+u9hNPPMH58+eZPHky8fHx/Pjjj7zyyivExMRc7ad3XfloSi4J2+3kZcKnsdN58qYVbN65wfW4wWQi/I6+zE34gU8T12PXa/cUlLnLdjHzk1hyCvzRvW4DQwM0TeOGqF41HZoQopp9s/ZoTYcgRI2KDjKhn1KuaaMAJ+PsNHw3kMBDFjw/9+aJxcdrMMKqdSjJ6tY+k2Xi7Q9m11A0QgghrgUFse6LCCVdyMVqK/0z8Inz+eTYdBy6Isemc+J83VqMUdM0Qut706JRAKH1vStV+utaUOsSbaWtWBEdHV1uLbXiTp48SWJioqsdERHBypUr2bZtG507d+app55i8uTJPPvss1Uev7ioaO2WBMN/ySDBrcDhXX+eQkibllhVASetqXyRXLtHhv22NxFX+JqGySOQqeOfZdLYqeXuJ4So+75Ze6ymQxCiRhk0jUZt3EdzaWh42A0YgOALFny+8amZ4KpBm1ALhgJFWJIFv3MZ3Lw1lK07t9V0WEIIIWqQrnQ2ph3kjPUCG9MOoqvKrb7dPsyI+n0hhA1+O9ljOMao51eW2tdqV+W2Re1W68a/l7ZiRXlKq8u2Zs2aEtt69erFb7/9dgWRicoKaW3i+PYCNJzZ6yPGbykI/BvGnG/RCvbTILKpW2Y7JT+jpkKtEL3Y29LPx19qswkhhLhujJ7j7RqtXkhDw6PAgIZG0HlPVr9rvSaKOy8Y3Yw/33WGgFRfYrV+NMxqQKOTz9d0WEIIIWrQ5vR4NqQfAHD93yeobYX37z/ewryvdhGYHUS81wkcmk7AsdJrl1lMGjk25dau7ZRSZGSmkJVzHqUUnmYvghtEusp3XU+uv2dcCT169KB9+/Zu/+bNm1fTYdUZo+d4k+u3Hytp6NhJ9mkJxiCUwR+71yBsFzJAaRiVP36qB4GGduiVSLJebYZiw12vs9GvQgghrnNmi4FHFvoS2f3ifVqFIiDbA4VCUxqr/2Nl7SJrOUepGyweBsJsfs5EIgFoaJizG9V0WEIIIWrQaWtque1LMZo0tO4X+KneBuwGB0pTdGhev9S+Tet74uNhwGjQ8PEw1FiNtrSMCxXum5GZQkbWOXRdRymF1ZbLufMnqjG66rV06dIS+aAePXpUaF9JtJVj27Zt7N+/3+1fXa/rdjTbcelOVcRsMfDP/3bjeI//w2rywuHlnA6szM3AszfHvv6FUFMnAuiDB8EU2BuzM+3qxVdZN3YMdSXXNA2C/OpWQUohRO20fOWXNR2CEJUyeo43kd1NePqCl5+BiCQf1+h1cBZ3vhaEtHYmFJMNW93aQgghri92h2LOqmw++KYpW7c3dpUTamxpUOljLX3+Nvp0DsPb00ifzmEsff62UvsZDBrNgi20C/OiWbAFg6FmRnmkZ6ZXuG9Obsm+toK6e/Nt1KhRJfJB27ZVrIyEXDFcZ45m666VT64Gi8XCFwt+oNHgSa5tyjMalIOenW/EQmMKivQ/ku2gW73a+bacPCIKgC37kujZIZQ9O36u4YiEEHXd8pVf8u2qr7jntntrOhQhKqxwZFuhN+7OJO1M0To118aQ78KpssY9EBltYvQc75oOSQghRA1485ds3vg5BzBDQmPat0rj9qZN6BXYutLHsniY+OqVoVUfZBXTdZ3jp46Sk5fD0ROHaRbR4rKmgHqYLdUQXe0nI9rEVeEb2NOt7elBnVtEwGQ0cGtAe3p2COX7Dcc4fjYDu6NyBTCFEKKob1d9VdMhiDqmoEDnkzdzmTUhk0/ezKWgoOb/DnUeai63XddYrVbuj7mLrne3ZIVxJM1vNPLIQl/MFrlsFkKI69FXsUVHZWlkZ3vSJ6gtBu3a/btw/NRRcnKzQSlycrM5furoJffx8Q50a5uMZoLrN62mCGu3a/edIWqVR+5o79Z+8o/dMJlMtPR1fwsWb9c23y1JZeYnsRw8kc6RMxnMXbarpkMSQghxHVk2P4+DH9rIiNM5+KGN56dsxWqt2WkZ/cdbGDDRQoueJgZMtNB/fN2+ez122gg2xa4nPTONTbHr2blve02HJIQQQlxV1nxrue2i1q1bx5133kn7dtFERnRi9S8bCfBvSHho6ytaCGHPnj3069cPLy8vGjVqxIsvvlhi4cy1a9fSrVs3LBYLzZs3Z+HChWUe77PPPkPTNO65557LjqmiandWQ1wzpo2M5plRXWnbNJBnRnVl2khnvbboIBNdg4w08tLoGmQkOqh2ThstlJaV75qTj4Lf9ibVaDxCiKsj8XxOTYcgBAAJq5wrfnq0dk7PNOxqwd+Gbmf1u1Yc9upZUKjAqvP+49m8eks67z+eTYHVfRSd0aQx4FELY+f5MuBRS51fcXT/kb1u7aycrBqKRAghrl8Ou2L1u1ZO7XFU69+4ihge7X4DKSzg2k+jWDwt5baLysnJISoqirfffhuAwIAQAv0bol3B6oGZmZkMGjSI8PBwtm3bxltvvcXMmTOZPXu2q8/x48cZOnQoN910Ezt37mT69Ok89dRTfPVVyRkjJ06c4Omnn+amm2667Jgqo3ZnNcQ1w2Q0MO3BaKY9GO223aBpdP09uWazWZn7ziOcTjxI47C2xIx7Bw+P2nFX3GFXrFucj1e6H1092xLrcxCgVq+SKoS4MkWTa0nnc2swEiEuMha5Zv3N8Bw9rc/jY+3I6v9Y0XXFrRO9qvycH03JJWG7M8GXsN3OR1Ny3Wq0XWvat+zIptj1rrafj18NRiOEENendYvzWf2OFRTO/4EBj9bMZ8OnBvpiMGhsTbBxQ6QHtzaNrJE4rqZmES1cNdp8vHxoFtGCs+k2wgM9SvQdMmQIQ4YMKfNYkZGRTJgwgfj4eL7++mvq16/Pm2++Se/evZkwYQK//PILzZo1Y/HixXTv3h1wrvhptVpZsmQJnp6edOzYkfj4eGbPns3UqVPRNI2FCxfSpEkT5syZA0C7du3Yvn07M2fOZPjw4a7zOxwORo0axQsvvMD69etJT0+v0teqNNd+KlbUGfMWP0b8sS3k5mUQf2wL8xY/VtMhuaxdZGX1f6yYCsx0y25PgN2XAIcvIRub8ct/cmv0DosQonpIck3URhG3Om9OmYKdl3BFV/vc+kMOunKONrPb7cx+9xUmPB1NzF87MXvhaGy2y5timhxvL7d9rflg1jJ6d72JAL9AIsKaADBn0evY7df28xZCiNrkZJwdiswkOhlXM7+D7VYrW8beTctne9DjowPkvWpn25+DSozuri3sDp1Zn+5kxN//y6xPd152TXGDwUCLpq2ICGtCi6atMBgMZOY5LjuuN954gz59+rBz506GDRvG6NGjGTNmDA899BCxsbG0bNmSMWPGuKaGbt68mX79+uHp6ek6xuDBgzl79iwJCQmuPrfd5r5q6+DBg9m+fTsFBReXXHzxxRcJDg5m/Pjxlx1/ZUmirRw9evSgffv2bv/mzZtX02Fds04nHiy3XZN2/WRzfa2h4efwIdDuR4OcMNa+m8+aRXV32WIhhKhqdodi/q/J7DqZy/xfk7E75GZEVQm610TACBN+Q4z08nrB7TGrbmNT2iEA3lw8k83b3iE59Ty6buPw8a2XfQMrpLWp3Pa1xmKx8Pm875hw/xOcSTyLIz2APQvb8e//WyM31oQQ4ipp3NFYbvtq2TJhBKm/beCkdThpOe3Jy3SO7v5wcu0sKzJ32S5mfhLLurizzPwk9oprigcF1ANA1xUOXXEgMY/j55yj6Ctj6NChTJw4kVatWjFjxgyysrLo0aMH9913H61bt+Yvf/kLBw4cIDk5GYCkpCRCQkLcjlHYTkpKKreP3W4nNTUVgI0bN7Jo0SLefffdSj/3pUuXlsgH9ejRo0L7XttXSldo27Zt+Pv713QYVUJXirh0BylWndg0O10CjRiuYM50dWgc1pb4Y1vc2rWHxsVbKtDSGsHJIo/t/qmgWqbrCCFEXfTO2hTm/5qMAub/6rxgevKWkPJ3EpekK4UyaATc61zV02+IicwvCwAN8yOHIMuDPdnJvPWPv7B55wYG3VifxBM5NP79pb+cG1gFVh1d1zEYnX8Fm0QZGT3H262P1Wpl7LQR7D+yl/YtO/LBrGVYLLWj9MOV2LprM50dMZzX9tJI3UTuNsXaRVZukb/3QghR7RKKjWAr3r5aMg7sA8Csp0GRz89n9l7+6K7qtGVfkqumuFLOdlU4cT4fBTh0RY5NceJ8Ps2CK/63vnPnzq6vC5NjnTp1KrEtJSWF0NBQgBI13gpHuxXdXl6frKwsHnroId59910aNGhQ4VgLjRo1qsQouMzMTAICAi65r4xou07EpTuITXNg1SE2zUFceu37xRAz7h1aN++Jt1cArZv3JGbcOzUdkkvnoeYiLUVTa/EfLrnDLYQQhWJP5BSd7UHsidp517eu2ZlmJ9F68e9Nm1Fm/B44g6HzBUw9UzEPP0FKajKbYtejlCIzu4AgP+c9VaUu3sAqHHE4YfGxS444/GhKLidjdXQHKAdoBg2zxf3ysfgqnWOnjaiGZ3/13RDVixb6H1xtDc1thLsQQojqYbVaORqX7rYt5XDNfH4NaNcBgByTe63x2vrxr2eHUFc+UNOc7apgLTaiu3j7Uszmi5+nC5NjpW3TdedU19DQUNfItUIpKSnAxaRcWX1MJhP169fn6NGjJCQkcOedd2IymTCZTHz44Yd89913mEwmjh49WqnnUBkyou06YNd19ma4/2JKroVzyj08LPxp4oc1HUap+o+3YDBobPk8nx73ebAl9ijEXXw8amjJopBCCHG96trUh9+OZqNwjgfu2tSnpkO6JhzJdv/bnaug13gjG9MvTgs5tGGz6+utezK4oVMAtgKd7FwHv+2+gNVq5b0N6SxY47xY3Xw0G11XTBpY+oV4ReqzFV+ls3i7LtHtdg4vmM357b9xW9cbWOYXCG7lGmvXbAAhhLgWjZ02Aj/9cU4Y/kcv/UUUipDW5kvvWA16vreMLRNGsOdAT7ftvg1q59+DySOiAOdItp4dQl3tK6GUwqTZcXBx+q5BFaCU5YpWFi1Pr169mD59OjabDQ8P52ftlStXEh4eTmRkpKvP999/77bfypUr6d69O2azmbZt27Jnzx63x//+97+TlZXF3LlziYiIqJbYQRJt14UVSXZsxfJqIRYZzFgZRpPGgEctnN1v59aJ3vS338JvI9+hRbCJJl1M3DzO89IHEUKI68Rj/RoC8MlvqYy8sYGrLape78A2aGictqbS2NKA2Pglrsd0YNv+HByOwuTYRsZOG0F689fdjvH9rvQyE20hrU2uFUcL28UVX6WzfcuOl/18alr8vJkcesv5+pzbuIb2N3dj5b6Lj7uPcBdCCFEd9h/ZS6ZhHI3ohwMb501xzJgzsEL72q1WtkwYQcaBfQS060DP95ZhKqecwf7sU7T3LTvhYrJY6PPxd1j/k8fad/Nd26OG1c6BFiajgWkPRl+6YyVkZJ5D2QvQtEDnBt1Kfs4pks0hhAaHkZ2dzZEjR1z9jx8/TlxcHPXq1aNJkyaXdc6RI0fywgsv8PDDDzN9+nQOHz7MK6+8wowZM1zJvccff5y3336bqVOn8uijj7J582YWLVrEp59+Cjhrrnbs6H5NEhjofA7Ft1c1ybZcBy7Y3Id1GoEugTVTTPJaYTRp1G9qYOw8XwY8asFoqp13NIQQoiaYjBpP3hJCVBNvnrwlBJNRfkdWhZa+xhJtg2agT1Bb7g/rSzevSHSHA5PRhNFoomeXPvh6+7rtU9nRZqPneBPZ3YSXP0R2N5WozwYXV+kM9A+id9eb+GDWsso/uVri1HL32AOP/ZUGTY206GliwEQL/cfX/dpzQoiyHc2ufeV1rkftW3ZEN9g4bfqFnPBD1H81nq158a6VtctTuHhBQUYaqb9tYMuE8ssZHMg5XaGY+o+3MGCipUb/Hhhq6HLqQh7YtCBAodBQ+edAKdIz0gDYvn070dHRREc7E3xTp04lOjqaGTNmXPY5AwICWLVqFadPn6Z79+48+eSTTJ06lalTp7r6NGvWjJ9++ok1a9bQpUsXXnrpJd58802GDx9+Rc+3KsiItutAPQ/NraZLQ4tW6xZCqCs6Da6ddy6EEEJc+6KDjGias/xDiMVQ4qbZ2Gkj+C1uo6ttNBho1yqaXeouCpJWYQ4dRFvtO/p1CWTB6hRXvzbNfZm/L5XkE/uI/+9cFv/7Ezw8PdicHs9payptXm3Aw4GtMWil358tXKXzWqRpuvPG2r99L91ZCFHnHc3WaeErAxJq2gezljF22giMrcMI2h+KJTKVDekHAOgTVP6CeekHDnLW8//QKEBhxnTg0yqJqXCGU02qqUSbTS+8yfZ7HTWHXrRJ//79XYsQlCYhIaHEtuL9IyMjS2zr1KkT69atKze2fv36ERsbW26fopYsWVLhvldCRrRdBwaHmgi1aK4Z1SGezpXLROV1vl0SbUIIIa4+q9XKg5PuZuwDrVny8r2097K73TSzO3S2HQvE7vcourkjCgP7j+zFu8trmIOi8W7/F8xB0XhF/Qvb8Y8Isf5KkDmTQT3q06FjfTy8/GjcpifNb4txLm6Qfoh1KYc48GwjVvyhPnMnnKOgFtZ3vRIOu2L1u1Y+iMlm9btWHHZFxD3uIx+Kt4UQ1w5d6WxMO8jniRvYmHawQqOlarttcT/UdAhVwmKx8Mncb+nlmIojPgD9rBfKoXEq79wl9z3X4FkSLVPIM7Yl0TKFcw2eLbd/lj2vqsK+ZhkMzkyC+j2zpnk4FwYM9A+qsZhqOxnRVo4ePXpgNLrf0YiJiSEmJqaGIro8JoOBcC8DSVbnUOi4DIXB4KBrkHz7r8Tdg2p+SKoQQojrQ+HKnoBrZc+io8je+CyOXOPNYNRwmFtjyFtF+5Z5HE6xUXT5sT2nMln98ytoupHb9A8J+NWH1MgCGs7w4L8f/I3+w5/muyN72Zd9ivxXOmPsmYK+L4i0XYqPpuTyyMJrZ2TXmkVWV72do1sK2Om3i1CfHHy9vDGZPWg2egKtY56Gv46u4UiFENVhU/ohNqYfBCDBeg6FAlrWbFBXaFvcD/TockdNh3HZ7HY7b38wm627NtOpIIa87d1Aadh3BUG2B2p09iWPccHzj6BBmsddF9ul0JXO5vR4ztky2Zh2kF6ljNyubL23a1Wgt4mUrAJX28NkIrBBGA0bhNRgVNVv6dKlzJo1y22bw1Gx6eWSaSnHtm3b8Pf3r+kwqkTxVUZr46qjFWV36MxdtsttJRWT8eoPzrzntnuv+jmFEEJcny61sufXvxxj2IWb2ey3i15ZUayun8cHsx7liaVn2Ho8x9VPz3YWK27MLYSpXqgcyN+nk7fVASiST+7/fTEDhX7CF48xR3BeWmulrjhal+3+qaBISyPPO5dTPaJpcPokDT7+ngs7fsNgkktlIa5V+7JPlWg3M9ftRFtd9+bimbzx/msAeNkfoZFy3igyBOdTsD0YjSJ/z5QiLt3hVk7BoGlomgHnckBOWhllDzanx7umo5Y1LbWw3hvgqvfW5+OaK5XgWUNzR4P9nH8Lz2UVEOxnJtivYbWtNlqbjBo1ivHjx7tty8zMJCAg4JL7ytTR60TxVUbr8qqjc5ftYuYnsayLO8vMT2KZu2xXTYckhBCXJelcYk2HIOqI4it5Fm93PRZNuC2YEFt9wm3BDMkaisViYcHoZtzQzIcgs5Fhu0IZtr4/DxbsRGFH4+JF8vmFNjr1uofsjHXcPf1p/IxeGJpmU/Bd4UpsqtQVR+s29zIaWoANDBrK7rxbnXFgn6tb8SmmQohrQfGfZfnZrmlfrfjc9XWytu33UYZg39AQY9sMIiwNXI/vTLMTm+bgTJ4iNs3BzjTnzaDiq0Pruiq19EHxaailTUt1/R0oo321WWpocSlN02job8ZocP5/PSTZrlTdzbaISukSaKRrkBED0DXIWKKAss1m5Y3/jGHa8zfwxn/GYLNZaybQCvhtbyKFJeaUcraFEKIuSjp3tqZDEHXEpVb2rG8PREMjzBaMhkZ9eyAAFg8Di8e34OEzTfBMNGPCgoVAmut3uz7AADRtZ2ZQz2DC+3ThlO08p/LP0/KF03hm+mLy04nsVvqKo3VZ1FBn3dUNfnGAQqufQ9L375GVdhJdg4B2HQBIPelg9X+sHN1iZ/V/rKxZVHuvkYQQFdfBp0m5bVEDiuQ6dxvmczxwCeYAB/WaavR/xItega0BKLDqrJqax6lH8ig4q6PbdPZmWtGVTv/xFgLDL6Y5MhKdpQ9KnEorvw0X/w6U1RaiLLUu0bZu3TruvPNOwsPD0TSN5cuXl9l34sSJaJrGnDlzKnz8zz77DE3TuOeee6441rrEoGl0DTIR5OH8v/iqo/MWP0b8sS3k5mUQf2wL8xY/VkORXpquym8LIYQQ15rClT33rDjG5/O+w1KsRkzTDh6Awlf3BtTv7YuO7baioXHQkgBAC3U3mtGOlz9Edncm0fblnHTbJ8eYy7OLGzNjdT0e+Y8f5jo8Gr6owgLoiUO3k9c3gWM+J7F2/oKCnLNYzxwh3jeZxIFt6PmeM5mZmex+oeE+5VQIUVf1DmpD38B2RFqC6RPQDi9akmLViU2zy8JxNWT47fe7vlaag67359O8syf/N7UNNzVo66qh9tGUXHL36qhs0K2w7+ks5n6YwL3v7KNAV+RnFyubVKz0ga50Mgrck29FR3kX6vneMhrc2BdzQBANbuzr+rsgxKXUujkAOTk5REVFMW7cOIYPL7vY/PLly9myZQvh4eEVPvaJEyd4+umnuemmm6oi1Dqpc2Dpy1WfTjxYbrs2KZ4kLN4WQgghrjdj5vrw0ZRcvOLrExJVcvTZeXM6De0NOGe+QFtrJABBoR786duidUbc/55m261lFoiujZav/LJC9VOLFkCv/9Q51NM5ZAXPQd8d5eqTGOlTTsHra/cD+P41SbTvH1rTYQhxVRg0A928Innz73/Gt/XN3HhHDJpmIDbNOX1cFo67uuy6Tuuhf+IfNz9OetJRPI6tZtLoKXz6tJVxi45y+LSV3juCCczywJrtAJyfa7O+t1Nv2HHyz3hx6JTiiY+Oc1PrEBK2X0yuFS99sCntEBkO90Rb0WmphUwWS43WZKttzDU0dbUuqnVXTUOGDOHll1/mj38sfXUQgDNnzjBp0iSWLl2K2Wwus19RDoeDUaNG8cILL9C8efMK7ZOZmVnmv/z8/Aodo7Zp4Vt6oi08pHW57drkxo6hRRdQ48aOckEohBDi+ma2GHhkoS9//TWQRxb6lhh9ltrrMFmGHFLMFwBQKNfUyUIdfCPc2nZ0NqQfYFPaoUue32qzM3z6T7R94COGT/8Jq+3qL5zw7aqvKtSvaAF0TdNAOUc+5Bwpvearf4j7B4vir9u1ZP+apJoOQYirqnBF55zcTLei+XV54bi6yGFXLJqTx+6/51NwwoOg8A60GvwUJpOJ3ady2Xo8h4YHvPE8a8aaBUoZXOUPcjbasZ3w+f1IGoeSrIye402TrgYMRtCMoIrVaSs+gttiMLumpYqyma6TRJuu6+i6TnZ2dqm5oIqodYm2S9F1ndGjR/PMM8/QoUPF50i/+OKLBAcHl1g1ojwREREEBASU+u/VV1+9nPBrr+I/M7X4Z2jyiCieHtmVm7uE8/TIrkweEXXpnYQQQojr2NKXb+PULbupZ/Aju146N0/wpN949xFbvQPbEOFZv8S+xT+QlGbU8yvZuDuR9GwbG3cnMur5lVUWe9UrOSKt+CyxntF3ub5u0MRIv0c9CWpkIKiR8wJJFkQQ4tpQuILz+bNHUepiIqYuLxxXF61bnM+ZTwuw7tHBoJH5jZ0LNufv2ex85wjDxIZWdrVLA5zTPI9qy7GbsrF7ONB+bkRYkgWDHdqEWjBbDBgMBnQHKAeciHUUq9Pm/mHXYvCoEyO3xdWRmprKqVOn6N69e4k8UERExKUPQC2cOnopr732GiaTiaeeeqrC+2zcuJFFixYRFxdXqXOdOnUKf3//Uh/z9PSs1LFqu7NJ8eW2axOT0cC0B6NrOgwhhBCizrB4mPjqX0PL7WPQDGQ58kp55NJ33/YdO19uuzbp4NOEjRnOqaNKOf8VrUJRv15jhtz65MUNmvMVSDvj/BC+5t18Np1J4O8vtruKUQshqkP7lh3ZFLueQ7ErWPPVTPoMnciNjeqVWDhOVK8TOwtc90CyvrfjyNSp5+EcPWzo6Bwh7ZNr5HRoLl0O1EOhyNCOcs4RQqi9FxoaveKCsYYX8NKLzimgxeuyFW138I1wlRAAaGcJZeNDd5F+4CCLfVpzR4elNO3qyc3jPDGaavEIlKsswOv6+Llo0KABERERbN++HQ8P91HsmZmZFUq21am07Y4dO5g7dy5Lliyp8JKyWVlZPPTQQ7z77rs0aFBy3nV5/P39y/x3rSXaGoe1Lbd9rTma7aiS4zjsitXvWvkgJpvV71opKHAWUP1vok0KqQohymS325mz6HUyMtM5dvIIdvvVn2YnROlKXl8Vn1Jamg7N65fbri10pfCiJU2M/QkzRtE3sB192hu5+Lw1enX7A0aj+73oXT/ZXF8fsZzif7HHr17QQohLKrDqvP94Nq/eks77j2e7TRMsT+GKzgG+/thPbCPE37/UheNE9dL1i6937kYHHmgMDnX+Hp71YiP8LAaSG1ipl+ZBvslBil8qe7V3CVLtXIsYaGjUy/bA4uFMcRSvy1bY1pUOCgJN3gSafAgy+WKY9g9Sf9tASk43dtkOcWybklWmSxHoXfo4rcosaFkZe/bsoV+/fnh5edGoUSNefPFFVJHP14mJiYwcOZI2bdpgMBiYMmVKiWPs27eP4cOHExkZWeGFNA0G54hIX1/fUnNBFVGnEm3r168nJSWFJk2aYDKZMJlMnDhxgmnTphEZGVnqPkePHiUhIYE777zTtc+HH37Id999h8lk4ujRo1f3SdRSMePeoXXznnh7BdC6eU9ixr1T0yFVq6PZVVN3Yc0iK6v/Y+XoFjur/2Nl2bxcYtMcnMlTxKY52JkmH56FECW9/cFsZr33KgX2Ao6dPMKoyX+UZJuoFYon1SIsDegd2OaS+y19/jb6dA4j0NeDPp3DWPr8bdUV4hXZmeZgZ7pOjt0bm70RXrTk45cmcMegSbRt1Zs7Bk3i9gGPl7LnxQ+BRyynSnlcCFGTPpqSS8J2O3mZkLDdXmyaYNksFgsfz1rOn5rtpsuBD0nLoMJJOnHl7A6dmUt3Er83y217sJeGyeBMVVg8DKz5c3u6t/Iht1kByRPSaDXyW/r07I1vuPv3uWhybfQcbyK7m9xW1wbnQggbMw6Sbs8l3Z4DQOa+vdhQmPVjbseTVaYrpnBBy7fffrvKjpmZmcmgQYMIDw9n27ZtvPXWW8ycOZPZs2e7+uTn5xMcHMzf/vY3oqJKLyeVm5tL8+bN+de//kVo6NWr7V6npo6OHj2agQMHum0bPHgwo0ePZty4caXu07ZtW/bs2eO27e9//ztZWVnMnTu3wnNsr3UeHhb+NPHDmg6jVrI7dOYu28WWfUn07BDK5BFRmIzOX/zFf/keW+Ug7K6LC3QcydbpVu+qhiuEqAO27trs1t4Uu563P5jNlPF/rqGIhHDqHdgGDY3T1lQaWxpUeMVRi4eJr14pf2pqbXCk2Ij2I9kOutXzZNjASWXuc/eg4fi1NLP2XedCWDmGPAz+VTPaxZqey4K7dpKZ0wh/nzM88V00lkDvS+8ohHBT3jTBSylM0gHYC5ztRxb6Vml8onRvfBbH6nfyCOAcrWji2t402n3BQ4uHgcXjWxTZ8jTwNAVWnY+m5HIi1k7Tru4rbmsmncwe89njsRnPTr2Y89WtbDuQQreRDrwsLbGTiQl/suzHCWjXgVkH1vOHvA7AegCOad/R+txdvHRTOo06OI9dfKEh4TRkyBCGDBlS5uORkZFMmDCB+Ph4vv76a+rXr8+bb75J7969mTBhAr/88gvNmjVj8eLFdO/eHYClS5ditVpZsmQJnp6edOzYkfj4eGbPns3UqVPRNI3IyEjmzp0LwPvvv1/quXv06EGPHj0AePbZZ6v4mZet1r1TsrOziYuLc9VTO378OHFxcZw8eZL69evTsWNHt39ms5nQ0FDatLl4t3XMmDH89a9/BZx3KYrvExgYiJ+fHx07diwx51aI4t74LI5/L41lXdxZ/r00ljc+iyvyaPGpoTJVVAhxaTdE9SqxrXjyTYiaYNAM9Alqy/1hfekT1FaKQwP33HYv/cdbuGmCB9n10sk0Z3EmJ6NKVlZdcNdO0nLa49ACScttz5tjzlzVshOnv3dfqTXrfP5VO7cQVamsaYJlsdp0xi06Su9/7uPoHpvbY5VJ0okr8+XqI7SyNmW392HXNoufxs3jKlamqXDF7eEvepdYcfvNxTOZ9d6rrN+2hq8/1Jj16U7WxZ1Fc0TgRSt0rHjRCg0ver63jLMWEyt8Lyb7jhm+w26DgrzKjZKsabrS2Zh2kM8TN7Ax7aBzqmwt8MYbb9CnTx927tzJsGHDGD16NGPGjOGhhx4iNjaWli1bMmbMGNfU0M2bN9OvXz+3kl2DBw/m7NmzJCQk1NCzqLhad/W0fft2oqOjiY52FrufOnUq0dHRzJgxo8LHOHnyJImJidUVorgGKKWY/2syExYfY/6vydgdZV/Ufrn6SJntqKHuidrmg9zvvrT0vT4KRgohKmfS2Kn07nqTq61pWqnJNyFE1Wrpayi3XRajSWPhyTV84vELBZqDAofOTU98hd1x+R9grDadc/mtQNPwse/Au4+JnMzgq1p24kzxRFuqJNpE3VTWNMGy/OH5BWw9nkNGnoPzPvmoIjfLL5WkE1XDarVyNvkcp/iJXOPFhXi8/Kn0AgSdby85eOar/32Opow00QfTyzqSrtntQIEnjdHQ8KULGhreRGKyWOjcpRe7DG+jcKBQhOg3uB2vriRgN6fHsyH9AAnWc2xIP8Dm9NqxyOHQoUOZOHEirVq1YsaMGWRlZdGjRw/uu+8+WrduzV/+8hcOHDhAcnIyAElJSYSEhLgdo7CdlJR01eOvrFr3W6R///5uBe4upbRs5po1a8rdZ8mSJZULSlwzdKWIS3ewLzmfRb86f4h/O5oNwJO3hJS3a6n6jbegGTROxtlp0sVE34c92JOtk2zVCbEYZMUiIUSpTCYTS+d+TYfbmmLx9GLcvY8xaezUmg5LiGtedJAJTXNc1t/p4iupnkrOZu6yXZe9EvoTHx3H09+bM6Hn6bsH/O404khXUnaiDli+8kvuue3emg5DFFE4sqmijuz/Lx4dnTe8NnY9R7+4hoTkWzCYoOMLHuhKyYII1WzstBEUFPRij+Ermln//ftWRdTQKlp0UIPO+pPkk46X8qFbdntOeiRhzdPx8QETzvdLkNE5iu2DWcsYO20EsTv2oaHRVLnXGq0rCdjT1tRy2zWlc+fOrq8LE2adOnUqsS0lJcVVS634ApiFeaKKLoxZk+rGu6WG9OjRA6PR/QIsJiaGmJiYGopIXKm4dAexaQ6y8y7WaFHAjoScMve5d0BLZn26061dyGjSGPCoxa1/16BaN1BUCFELmUwmvL186NK+m9RmE9cEh8PO/1Yv5EjCDlpGduP2AY+XWL2zJtl1nRVJdi7YFPU8NDoHGCr1QbpD8/ps3O2cMeHpBRNeDOTChRPY9U6YDJV/noeSrGR1y8Goa1w43AXHiQKC/3p1S5rkJZ29que7Vny76itJtNUydofinbUpxJ7IoWtTHx7r1xCTseyfb19PA4UTRnUTJP0hmy6DggDYnaMwpTvoGlR7fn9di/Yf2QtaN/I4hcWexVnzQR58OIp+4y2X3rkChg++n33vdCCfNMC5KqkBA7u2Hud2y1Fst/wRlKKlr/P7bLFY+Hzed7Tu3RyFIp2j+NEUTdOJ7OZ5yVGStUVjSwMSrOfc2rWB2Xxx5ldhoqy0bbruHCkeGhpaYuRaSkoKQImRbtVl6dKlzJo1y22bw+Eoo7c7+e1Rjm3btlV4+VZRNyQXriKkFDdtDeZkaC5NkrzJjyh7zv2fHuiCwaC5LYZQnL2ggF9/3sh5kw/17TncMrAPJrO5lKMJIcT1IyxAfg9eT/77y3x+/GUeAAcPb0LpOnfc9lQNR+WkK51lpzNJs1/Ak0YkWhUrkuwMC694Ymvp87fRefSn5Os2jCYND4sBc5jii+TNPBh206UPUEybUAtb83LQUfww8AyN87y4z6PJVS07YU2RUivi2rBwTTILVjs/hG8+mo2uFJNuLbnCoG63c3jBbCLOH8Cr/XlOmoJpG+rFPbc14lyRNc6SZeXRKrM/+xTtfUsuQNi+ZUfWHvdHw8Ye+1j8ut7ErRO/q7LzPjXuaf4du4bcbX0BUCjyDTZWr9jDoNT/kN6gNREHVhP97DS3/QIb+JCYuJlYw0xMuhe5PRfz4ttfVFlc1a1XYGsAt0WN6qJevXoxffp0bDabq67+ypUrCQ8PJzIy8qrEMGrUKMaPH++2LTMzk4CAgEvuK0NvxHUl5PcCmb7nzARfsOCf6/w/+Ee/MvcxGQ1MezCaZS8PYdqD0a4VR4v69eeNnGzVk5wWnTnZqie//ryx2p6DEOLaknSuaj7o/rgrrUqOU5USMwou3UlcM7bs/K7cdk3anB5PVr6BnMQzFCTqZHxZwPm8yn2QtniYiGrZAL8iI9c1DVLyMy4rpgWjm3FDMx/8vYy0i/Dmybsj6BpkJDpIyk7UdknnZCRgbfN9XHq57ULx82ZycO6/KLiQysiPhrPY9wcWj29BhG+xxRRkdckqcyDndKnbP5i1DLPJA28vH3p3vYkPZi2r0vOaTCb+/Nat3DLRC7zs7PDdT4YxG6UgrqAhS+b9TON960qMbB5592hWmh8izXCQleaHuCG6R5XGVd1qYlGj8ha0vFwjR47E09OThx9+mL179/LNN9/wyiuvuFYcLVR43uzsbM6dO0dcXBz79+93PW6z2Vx9bDYbZ86cIS4ujiNHjpR22iojI9rEdaWwFssGux0NjfrpHmhoeKVd2aiLVJMPGAwYUhPRG4Q520IIUQFV9YHtpz3pDIsKKvUxqSckrhrNiHf9toT3fAqVfrTW1Dk6bU0l/at62Fso8lJ0MpbZ8TUAT1fuODd2DOXIpmTI+r2esIKGlkvf2S6NxcPA4vEtLmtfUbOq6gaJqD6ZeQ7sDlVi+ujRrz/jzQDFOSO8GaCY+vVntJ38rOszwv4MB+0DjFJn+SryMFfflPnCMj/Z5x18HncMnKW5+Tz0D4Ro2fR8r2Ry7/FRT7E5dgObd26gV3RfHh9VO0Zm12bbt29nwIABrvbUqc66w2PHjr3s+vgBAQGsWrWKmJgYunfvTlBQEFOnTnUdu1DhIpoAO3bs4JNPPqFp06auWv5nz5516zNz5kxmzpxJv379Llnb/0pIok1cVwyaRtcgE7G+BYCifroFUDRpf4U/CpoGSmE4cxy9fqizLYQQtYTUExJXww3Rd7EzzYFnYFMCGveCRjeyM81Ot3o1O4VYVzoOpchbr2FuAemLnSMtc9Y7Kp1omzwiin0J51mdfBIKNCJ863NfiKwYLERNuzMqkAVrUlztTKuDd9amuBY7K5wyOjvnFPFmCNAh3gxz7MkM4+JnhNR8JbXZrpKx00ZQYB9AemYam2LXM3baCD6fV7UjoR12xbrF+ez72c7odn2Yl7UGpTlvlDz0wE2YLCXrwS1c+iabd25AKcXmnRtYuPRNqaV7CZda0LK0BSyL94+MjCyxrVOnTqxbt67cc19qIc3Sjns1yJhYcV3JzrXy+prt2JvaMbbIw+IHkd3NjJl7hSPQlAJNQ2/UzJV0E0KIitqYdpDPEzewMe0gupK6MKJuGnrrk0REjaJ+q2HODZrGkeyafz9vSjtEqjUITZnxMF2s2aRR+ZtiJqOB9/86EF8PC39pfQ8jw2+6rIUQ7HY7cxa9zsin/sCcRa9jt9srfQwhxEWPDwihcZB7Ur9wsTO7Q/Hy68t54mArGtTrCwYjGUZAgzPXeE5Nd+hsWHoMa3YBG5YeQ3fU/O/kQvuP7C23XRXWLc5n9TtWctMVtt/qManFANo2DeSZUV350wNdSt1n667NrsSMUoqtuzZXeVzi2ieJNnFdmb91L0ERHbFZswl/pR6m547wyEJfzFdYh6GBPQd0Hb1BGOi6sy2EqJPsDp1Zn+4kPTufWZ/uxF7NF6UFuoMN6QdIsJ5jQ/oBNqUdqvJzyDQncTUYjSa8vS9vGmV12pdzEk/C8XroHJ6hnq7tnYde/ki70PpXtvrcm4tnMuu9V1m/bQ2z3nuVNxfPvKLjCXG9Mxk1wgPdpyDqvydLFq5O5vOcViQHtSDSUB+PpqNdfTp26H5V47zaNn2W4Eyw2RUblh5j02cJNR2SS/uWHcttV4WTcXYoHP+goLEjmDXzhpdZdxvghqherhpgmqZxQ5SMWhaVd43n8IVw51WvMZqm4entj6ZpeNVrXCXHvWVgnxKrjgohqs/u/9nofHv11PSYu2wXMz+JRSmY+UlsqX2+WXuUP/SreG0lh8PO/1Yv5PCx7aSm53PsdB43RPUGBXblvkz4lox4NE2jV2DrKitgK4W7xdXS0tfIznSHW7vmaSjAo2cmpvxGWDoZ8Gyn0398ySlDFRVW/8pGwn/1v89LtKc++uwVHVOI651BK739/a50V1mXk8Gd8GnQFkfCEnpWQwH+2ub0vnS3RNPpfek1GI27D2Yto9WIRQT6B9G+Zcdq+V406WLi6Nbfk22as30pk8Y6a4At/OQtHh/5f662EJUhibZy9OjRA6PR/QIxJiaGmJiYGopIVIauFHHpDpKtOiEWA10CjeRdOI3FJxCjyYRSirwLp4GSS39Xlsls5rYh/a/4OOLaVNmkjLi0PSuqL9G2ZV+Sa/a3Us52cd+sPVap7+m3qxbw+rp6eKlupBj6YDbtY/370/D2MKN5uo+qsaOzIf0ACkXfoHaAs8bU5vR4t6Xar8YqUkJUVnSQEU3D7W9vTevgG0Fs2hlMWhB2SxrB/wikgWc6RlPgZR/zD/2aX1lQxWetSmlXIa5Yt0hfthzLKcyp0C3St0Sf3U1vJSjrDG3CW5ZaD6yFr/Nv67a4H+jR5Y5qjrj6Ne4QSELcBVeiqXGHwKt27sJrlzPWC2xMO0ivwNbousZ/1iaz9kgOjUK98PENZNe3R6tt0ZybxzlHMW/5PJ+e93u62uUxmUxMGf9nIiOaS33b69zSpUuZNWuW2zaHw1FGb3eSaCvHtm3b8Pf3r+kwxGXQleKnszaS8p3tM3kOlFI8eUNH5m/dS0B4GzLOHuLJG6p+iLKoHkezHbSoFSMjKm/+13sk0VaH9GgXwrq4s27tAwlpl308m83KrPUNyNLa46k2gWYi8+RqvKJex7prGn4WC7YvIsGog8OAeXgCmgH2ZZ+iu08rRj2/kuNZ5xjxJ380DRKs51BK0bdeuyp4tkJUrcKC4rVJ78A2KP0QB7Js2JSdYM807ghteEXHvNLf6cMH38/y9/Zz3PC9sz3kQX48a+OCTVHPQ2NwqAmTQZLpQlTGY/2cP9exJ3Lo2tTH1b6zSyALVv++UIJSDArNI8Fc8mfYYVec/LSADXF5nGz4HV07DsNoqttZ8N4PRAKw6bPj9H6gmat9NWxKO8TGjIMAzhuIShEXG+RatMJuMpBr06t10ZzCVUfrRxgqfYNWkmxi1KhRjB8/3m1bZmYmAQGXLpNRu66EhKgiO9McriRboSPZOt3qWfhz/+6sTCrgttbXdk2Ga83RbL3OJtqSzufWdAjiCmzem+iq1zZ5RFSZNT3KMm/xY2QzkWC1kTRDlHOjBkZf50W+shooWBbp3IjC0DgbU69UlEMxZfR+Ak61IK3hOdA1MDqH2u3LOSmJNlFrFU6VPpKwg5aR3bh9wOMYjTVzyekc3a6TbWtB9wDnCLuqGDlh1+18kbyZlPwMGnoGcF9Ir0otivDUuKfJ+uo4h1pncUNUL1rePplEq/PnO9GqWJFkZ1h4xT8UWq1Wxk4bwf4je11TsCylrKYnxLXMZNRcq4wWsjuUszZXkPPn6c4ugTzevzOPPvtFif3XLLKy9l3nB4iMzjprFlm5daJX9Qd+jdqXc7JE+/tdF3//tmnuzxaH+v0zWvXGUl2zIIQoi9wqE9ekI9kVG9IpRHWy2uwMn/4Tqel5DJ/+E1abrCpXF2w7kOzW3rQniQK7zsxPYpm7bFelj3c68SA+KoHGju8wqUzQ4bb0f3LX2taYHH7k5zm4OG9Mw7bUmYDz+q49wUcjaGwLgXwjBV83LXLU2n2H3e5QzP81mV0nc5n/a7Lzg464bvz3l/n8sOotDh7exA+r3uK/v8yvsVh2pjmITXNwJk8Rm+ZgZ1rVXB98kbyZk9ZUrKqAk9ZUvkiu3Kp0JpMJi96QT978hinj/0xagfvjF2yV+5kZO20Em2LXk56ZxqbY9YydNqJS+wtxLbI7FM8/s4AFa1I4nWbjdJoNcCbk7h40vET/3T8VlNuui2p2MYTy58hnZtf911eIskiiTVw3akdBZnE9GfX8SjbuTkQBG3cnMur5lTUdkqiAnh1CKW3AS9F6bYnnK76ycOOwtnRyvIKVBuRrIdy0rQHBaRY8HZ4YMKMV+1Ns0oz0DWyHfiAQrchFqePAxWHqCsXGtIPoquIrom5MO8jniRsqvd/leGdtCvN/TSYt18H8X5N5Z21KtZ5P1C5bdn4HuhHsXgTEvsmWpR447DWTbC1+462qbsSl5GeU266seh5aue3yWK1WtuxyT/TtP7L3iuIRoq4qsOq8/3g2r96SzovDjhO+7gO3x7+PSwfKmhZY/PdU3b9JdLUXQ9CVIjbNzn8TbYQYO4G6+Lusg28Ed3YJdLUTU/IwGjX5jCauSZJoE9eklr7ub+1Qi0Z0kPwSF1fXvmPnXV8PuL0T3Yd1IzbN7lpuXtROk0dE8fTIrphNBnp3urhYikdgPXq0c05Jqcx04Jhx79C2eTeaesUzvskcmji83RJoAIqLia9ew/zYtTOIA2fzKLw6bmZtRH6Ghr/ROYUlw57LhvQDbEo/VO657XY7cxa9TlZeDhvS9pNgPefcL638/S6H1Wrl/pi76DS4OUtWbCx6XU/siYonJsW1wfv4ODSHFx5pPWHfCNYssl71GOy6Tm41DSRu6BlQbrs8hYmAnDTF+49nU2DVGRxqIsyi4WmAMIuzRltFjZ02AofD/Ym2byk1aMX16aMpuSRst5OXCSo1iAZZFyq8b9RQj3LbdVHjDoFFB81X+2IIO9PsrlHEqdZ6tPa8ES+DB30D29HTrwVNHF/SLXAzzYLtNK3vgbeHQT6jiWuSJNrENSk6yETXICONvDS6BhkZGmauttVshChLh+b1AfDwMHLLkM4EhwX+PnVJppDWZiajgWkPRhPo60mvjmGu7Z5Bl1dAxMPDwp8mfsis57fyp4kfEtraRNG75Eqzc67Nn1Eh++n/qAcHmmcy/+dkThmtFF4d982Kpk1ogxIrje7LPlXqOQusOosey+T5m85y9pNocNjQHRenaBSvm3KlHHbF1PuX4rvlUeqn9WfA6hu5aUsw6M5n0LWpT5WeT9RuN0TfhWfSEDxTbgFAw1AjU7BWJNkpPn6t+I24y3VfSC+aWBpg0cw0sTTgvpBe5fbX7XYOvfU6m8b+kXfuPUDCdjsoSNhu56MpuZgMBoaFezA60pNh4R6VWgih+Og1k9HEB7OWXdbzEqKuS46/eI2loaHp7tf/d0YFlrlvv/EW+j/qgX/LsxgDk8mNfL9EEruo/WX8Da5Nej8QSd9RzTGYNPqOal7tiyEcyXYfMW9zBNHIUo8+QW1Z8etC/vvLW/icm03E2Qe51esrzAZNPqOJa9IVXW388MMPVRVHrdSjRw/at2/v9m/evHk1HZaogMJVz4aEedA1yFSpX+BFL4YPvfU6ul2SIuLyfPCPgUSE+GIym9CKvAeLX4SI2qt4vbYfNx2/4mOOnuNN024m8MxDaQUocxZa4/WkdnyEvBbv88PedNoe9edEI/dRYE2jzVR0WstHU3I5EevAUOBPQcgaNJsPKf/9EIPyJlD1R3NEVunIyjWLrNQ/8wcacRNpWjyeujdWi4OG2Z48eUuIa+U3cX0YeuuT+HgH4Bf/TJGt1TuSd/nKL0tsK17nzKg5b8RVBZPBxINhNzE58g4eDLvpkgsh7J3zGvN+PstrpmEkJge5PVY0MXA5io9eG3z/SFkIQVy3Qlpf/FlUKPLMwb83FD0ifXh8QEgZezpXqMxp9h5Hm/6BPHM8P/76Jj+VU2PyQM7pKou7uhiMBvqOao7F1+xMuFVyQaeqtGXnd+W2Rd21bt067rzzTsLDw9E0jeXLl1fJcffs2UO/fv3w8vKiUaNGvPjii6gi16+JiYmMHDmSNm3aYDAYmDJlSoljvPvuu9x0000EBQURFBTEwIED2bp1a4XOv3Tp0hL5oB49elRo3yv6SZsxYwYAGRkZ5OXllXg8LS2Nu+66Cx8fH9q2bcuXX5a8CKrNtm3bxv79+93+xcTE1HRYoprFz5vJwbn/4tzGNRyc+y/i582s6ZBEFXHYFavftfJBTDar37VWe82gBV/v5VRyNkqXqaJ1VfF6bQWVfM8ULgowYfEx16IAZouB8f/x48WNYRgtVooWTzl8fDsATc76uE0utfjBzeM86eDTxO34xduFnB/cnUfIb/grKBNBjYZgxBcj3hj0plVWFB6cBaMLa8110h8D4HRYHma7gSdvCcFklLvV1xOj0USv4Q3ctlX3FKxvV31VYlvxOmcNPWtu5MSiTWn80vFh8k1epAZqqCKJx6KJgcvxwaxl9O56E0GB9fljzCTa3TnoqtRiFKI2Gj3HG1P4Gaykkaj9xmmvbAJStnG/z2HeG9f8kn+PthZL/hRvi/IVr7cm9deuDzk5OURFRfH2229X2TEzMzMZNGgQ4eHhbNu2jbfeeouZM2cye/ZsV5/8/HyCg4P529/+RlRUVKnHWbNmDQ8++CCrV69m8+bNNGnShNtuu40zZ85cMoZRo0aVyAdt27atQvFfUaLt3Llz3HTTTdSrV4/AwEBiYmLcMowvvvgiP/zwA/Xr16dz585MmDCBDRs2XMkphbgihQU6U6x6mbWyTi1fVm5bXF0V+Z5V1JpFVlb/x8rRLXZW/8da7TWDvlx9BACbrcDtd6NcdNQdReu1hYYE0jzcv1L7L1ydzLxfk9l8NJt5vyazcHVysR7u72el665Cwa0TLp7Ly9+A0aTRO6gNfQPbEWkJpm9gO3oHtSn1vCFFpqd6ptwCmp2g5kPwJPz3HloVr8588Xk0V3e5vvb0kQTb9arfeAs3TfAgu1463wWvZt7x1dW28rLdbmf3gZ3E7t3GnEWvY/99JPqV1D2ragn12oNmIN0nhI3dzpHqlwkomnYzMnqO9xUd22Kx8Pm871jwyQ+E9+mKVRWwIf0Am9PjqyZ4IeoQs8XACo+H+NTclRWmkeh5J7Eff5m///keuelzFUQHGd3K9xStv3ZD9F1ufYu3Rd01ZMgQXn75Zf74xz+W+nhkZCQvv/wyY8aMwdfXl6ZNm/Ltt99y7tw57r77bnx9fenUqRPbt2937bN06VKsVitLliyhY8eO/PGPf2T69OnMnj3b9bkqMjKSuXPnMmbMGAICSq+VunTpUp588km6dOlC27Zteffdd9F1nV9++aXqX4girijRdubMGTZt2kSnTp1o27YtCxcu5OOPP3Y9vmHDBjRN44cffmDZsmUsW7aMWbNmXXHQQlyuuHQHsWkOrDrEpjmIS3eUGOWklJQurE1K+55drppatt3hUPz6392cPpZS4qJD1G5F67VpJvcpwBXx/a70ctvFl7o3GAw83j+EkN4ajRMvfvjuPNTsfFwz0CeoLfeH9aVPUNsSNdvAuSjBCm0MO01zcJgy8Dx3C7o5Gw+zT5FEW9VN5LPrdow3J7kd0eQJXgEaER3lvX69Mpo0Fp5cwycev5BkvMDGvdW38vLbH8wm5XwyF9LPM3vRv3j7A+fd7iupe1YRdt3Op4nrmZvwA58mrseul51IbJl/ApROlncwDqPih7Dt5BryuXDzYcyWqonrtDW13LYQ141if+DM/oEYTBVLtPcslvzpGX1XpX7Wr3flle8ZeuuT3DHo/2jbqjdDb53EodTepGfnM+vTndgdMgK3qjgcdn78+W3mvjeOH39+u9w6g1fTG2+8QZ8+fdi5cyfDhg1j9OjRjBkzhoceeojY2FhatmzJmDFjXEm0zZs3069fPzw9PV3HGDx4MGfPniUhIeGy48jNzaWgoIB69S6v9nJFXfFf9lWrVhEXF8euXbv4/PPP+eijj1yP5eQ468t07OisHXHbbbdx9OjRco9Xmfm9EydORNM05syZU+4xr2Rerri2JFv1Eu3io5zSm7/q1ifinhFXM0RRTGnfs9JUbFro1V22/d4BLV1fr/7fHnySz1W6ZqC4thmNRS/8NVo2647JqPH0P+tzy0QvWvQ0MWCihf7jK15vaey0EWyKW02c9iYfal043nou/n71KEg6gPp9KplSOqfivqnQxZddt/PnZS+X+QHji+TN5N59AMsrOzB0vkCDkcn8bW0ATaKMaJJnu64VXXm5tHZV2RK30fmF0rit4GNS3nmQRROz+D7BykcJ+fx41oZdr/oPcV8kb+akNRWrKuCkNZUvkjeX2feleU9z+8lvICOVvKSzFGSmk2PIc418rgqNPOuX2xbiejH89vtdX2/3dG9fypDfk0G+PkHcMej/GHLrk5X6WRdlMxpNDBs4ickTFhN/vi+zPt1FgV1n5iexzF22q6bDu2b8b/VCflj1NgcPb+KHVW/zv9ULazokAIYOHcrEiRNp1aoVM2bMICsrix49enDffffRunVr/vKXv3DgwAGSk52zP5KSkggJca+pWNhOSkq67DieffZZGjVqxMCBAy//yVTAFY2hb9q0Kbfccourfe+99/LPf/7T1S7MRhqK3EE0m83lHrNwfu+4ceMYPnx4mf2WL1/Oli1bCA8PL7NPocJ5ub1798ZisfD6669z2223sW/fPho1anTJ/cW1I8Ri4Eyew6299if36YNnzt/A0MnPcn77b9TvfiOtnph6tcMURZT2PSvNmkVW1r6bD8DRLXZ0XXHrRC+3PlFDPVjze5/CdlU5mu2gRbEpoX96oAsGg8aWfUn07BDK5BGl1w4Q1647uwSyYHWKW7soTw/nqLXw0FZ07XQ7tw94HHCOBhrw6OUVMy++AuH+I3sxmUx89p9n6DrwXoJb9ic3/kPSEtbwP+9Uhg2cVO7xvkjezM/ffMH9PTq4PmA8GHYTDrti3eJ8Dm9uzMkWfrR88Dhe/9iF0swYTaVPaRXXlw7N67Nxd6Jbuzrov9fB9FNNCVO90BwaJ3Y4SHpOEfKcJ4lWxYokO8PCq7ZOXEp+hns7L4sfz9q4YFPU83BOVS0cReft78esj15gw4RlnE/OAiDXWLK+8RUpfg9H7umI69RT457GYDCwdddmbojqxaSxFb+WL0wGnTi91/X3scTPerF2TbHadJ746DiHkqy0DvHk7oj/cfxkHD/+/Da3D3gchZF31qbw1fbz3GZzYHeoGpk+286ncYltW/YlUVhVRSlnW1SNIwk7KFr/19mueZ07d3Z9XZgw69SpU4ltKSkphIaGApSYSVKYX6rsDJNCr7/+Op9++ilr1qyp9kWDrijR5u3tjd1ux/T7UNyUlBS3pJpSCqPR/YOnzWYr95hDhgxhyJAh5fY5c+YMkyZNYsWKFQwbNuyScS5dutSt/e677/Lll1/yyy+/MGbMmEvuL64dXQKd78f9GQ7aBxjpEmhkbbFRTZuzjqA3GMTk95/GVIMr8win0r5npSk5LdSGZcQJTltTaWxpQK/A1vQbb0EzaJyMs9Oki4mbx3mWeqzLcTRbL5FoK5x2CGDXdVYk2Uv9ACbqDqUUsz7d6ZrqMHlEVLm/Jx7vH4JB04g9kUPXpj4lVt8MbRhG0rlE/vGny1/F2+5QvLM2hV0nc5n/azLtWnZmc+xa1+PtW3YkPuEgSjk4u2MhPg07kJ7wCxrw64EVZHaI4r6QXmWumpiSn0F2Wrpb22638/qkNeRt74ZGEPt9rFi+DCH07rM08W9Q6nHE9Wfp87cx6vmV7Dt2ng7N67P0+duq5TyF154FWiZakeyS7cTFUWzFVyCtCg09AzhZZHqmn6E7iVbneUpL7hVYdYam9iUn2chun8Mc9TzjNvIZwFpgZ/HeDJSnJ1p+PuM6BmAxV+xy/Yz1fLltIa4XJpOJKeP/fNn7O+yK1BM6Lz94gdQgK9ZBDcnQc6lXL5+0NE+G9awdw7Wf+Og4W487Z5BtS8gh/lgQUY40fljlLEh/XLvXdbMv16azcE0yk24NrdaYdLudwwtmuw1YaO8bUaJfzw6hrN91FqVA05xtUTVaRnbj4OHNOJNtGi0ju9V0SID7gKvCRFlp2/TfR6CHhoaWGLmWkuJ8Pxcf6VYRM2fO5JVXXuHnn392S/pVlytKtHXo0IHhw4fz6KOPkpuby6xZs8jOziY1NRUvLy8SExPRNI1z584RHBxMQkKCq0Dt5dJ1ndGjR/PMM8/QoUOHyzpGReflZmZmlvmYp6en23xhUTcU1g1IzVd0DXK+/YuOclIotnkeYN3SfHRd8cyorjUZrvid3aaTddDBj6/lEdfayOi5PqXUtHH/EGXVncWgARKs51Ao+ga1Y8CjFuwOnbnLdvHC5GPc2bf5JZMlVWFFkr3cD2Cibth9JJWft59CKZj5SSyAK5laGpNR48lbyr4YCA0OJ+lcYpmPV8Q7a1OY/2syCpj/azKP3T0PjSeIOxBLl3Zd+WDWMnoN78zwwffzw89vkXbkv65UhHe7Hm6j1ErT0DOgRPvtD2aTsn0w/r8fyWpx4PF9K37Vs3j2z72u6PmIa4fFw8RXrwyt9vP07NKbDdvXYlb+KNTvyTaFR9OLH4aLr0BaFe4L6cWypM2cyzfjpzVH091/Voon9z6akovjtDcWoEd2B0wRVv70gPvvh8V7MzD5+xC75Shde7Zg8d4Mnoiu2EjAxpYGJFjPAc4RIicPF2BvqMtNQyEqac0iK+dPOAg4bMAPL06db8jBVhnw+++WG/2DoRZMijqUVHRWjkaOFolDN2I0OIg/tp3/ZblPjfs+Lr1aE2123c4HsZ9x4aYwLI2603j6TJSu03bysyX6Fs7ymPN5HFPu7yKzPqpQ4eyIIwk7aBnZzdWua3r16sX06dOx2Wx4eDg/M61cuZLw8HAiIyMrdax///vfvPzyy6xYsYLu3btfsr+u6+i6TnZ2domBYuXliIq6okTbjBkz6N+/Pz/88ANKKQIDA5k3bx7R0dFkZGSQnZ1NdHQ0PXv25O6772bFihX079//Sk7Ja6+9hslk4qmnnrrsY1R0Xm5ERMnse6HnnnuO559//rJjELVH4SinL5aeJoFk8gzOpNuXq49Ioq0WiEt3sOrPVgJHmynIgoQdDj6akssjC33d+hWfFmq6OYmiFXn2ZZ+ib1A7AN74LI5Zn+4E4OCJ2KuSVC3+gas6RleI6nchK7/WTXXYkZBTZIIA7Dqdz+fzvivR76lxT3P+Qhznz/3imlJm8nUmBsqaBmO320n832aUQ4cCOxG+IdwX0ovRcTMJYTDHtO9oru5CGZ131C9k+5c5Mk6I5Su/5J7b7q3y404aO5UFS+di9sgmLWsn3vnNSDMcouE9Zmx5UTQN8q+WFUdNBhNtLDeSa9XRFaB0QEMpyPzajorX+bWHg9zIxRw9tZ30fa8CzqkqGhoeGV4lkmAOsxmzphEeUZ8tcec5dTYHlWbnsX4NS53yZbNZmbf4MU4nHqRxWBtUy1HkBefwzYIszp25AKcC5FpGiEra/VMB1Hf+vJ0Oy6XpWR8Otir8cK3x465Mnhp46fJF1anAqtN3SzDGJBPbOqeSXD8PH0MCdt2MQXMQeywAgt33saYkYrdGYqqmKXNfJG8mtYE3aBq5Xdpy+pUp+L61rNREW+GsjyU/Hij3hqWovMLpz1dTdnY2R45crDl6/Phx4uLiqFevHk2aNLmsY44cOZIXXniBhx9+mOnTp3P48GFeeeUVZsyY4TZ1NC4uzhXDuXPniIuLw8PDg/bt2wPO6aL/+Mc/+OSTT4iMjHSNkvP19cXX17fEeQFSU1M5deoUw4YN48SJE5cV/xVddXTs2JHY2Fi+/vprjEYjw4cPJzw8nIEDB/LZZ5/h4eHBww8/zLRp05g7dy7h4eE888wzl32+HTt2MHfuXGJjY6/KvNxTp07h7+9f6mMymu3aUVgL6dktsZxIyqrpcEQxyVYd2wkdY72LP/OnY9M59NZ8Wj0x1bWKVPFpoYcGnSHDLZd1sVG8+PTVSKrW89BcI9oK26LuMZsMFNgvpnB7tKv80PXiht1y92Xv63DYSTl/CgikMHu2+9BuOg0eSPuWHflg1jLX3zqTyUTD+l5cuHBxf+vpI1hCnBdAG9MO0iuwtdtKpm8unskb778GwNtjH+NPj/wF06M3o+uKI9rX5GtpNFd34cEFToSFEGA8e9nPRVy7bNYCZv1pLcv3vMv+rwPwjI5lx77fXLWTTBVcDbC4wmnTsSdywOBFE/9oCtJt7NeWcEZbQ9dXnuaQ5V0+X/0sRkP1/M49kl3klo5mwHPF5yQnDyHzBxOgsSYun5zIXHJbbCLAcy8eed0o/FkNaV3yeZ9PySS8aQPOnk5j8z5nSYR5yVZ0XTFpYMmRKPMWP0b8sS0AxB/bSubBc/gMGEfKKWdtU7lpKMTluHi9dj4wn7BkL9oe8edg88wqWEqwanw0JRfLWecon15xwaT4Z6N7fYi9rYED5/qQkteVmIGBLFidDGh4FBTQe0MwS+9ZykM/PILS4O1fc9iaYOOGSA8m3eJzxfXbUvIznPNAATQNa/OyB62Ia8v27dsZMGCAqz11qrMu4tixY1myZMllHTMgIIBVq1YRExND9+7dCQoKYurUqa5jF4qOvpio3bFjB5988glNmzZ1rUw6f/58bDYb997rfqOvvIFTDRo0ICIigu3bt7tG0xXKzMwsd0BWoSu+vdekSROmTJniti04OJj/+7//c7XffvttXnzxRfz9/S/7Ygpg/fr1pKSkuGVFHQ4H06ZNY86cOZdc5rWy83L9/f3LTLSJa8+9A1q6RjkVtkXNC7EY8GhqwHHh4pid4JyFHJw73204evHi8eYLTdiYcdDV7uBzeXdTKku32zk0fyZrD/5AWgMznfr+gSG3PsngUFOJGm3i2lY0CVBYn620i9iXp/37ss/x0y/zOZPe9uKFLZBV4ElOZhqbYtczdtoIt9Ft7nU7QPt9eJ5VOadaK6XoW6+dq/9X//vc7Xxf/e9zpj76LAaDgd3GedQzRXLe+xz1s/JIar6Jqb3ca0IN7RR42c9NXDtmTllL3vZuhGu3kre9O5tjNxJnXMP6bWvQdZ2pj5Yc7VARC1cns2CNs16LLU9x/oROtONGGnID+SqNRuomwvP6smaRtcTiONXFYcsk7eckjBRehGt4nhzHiWZ7UVHTCN7/Nt75nQhpbWL0HO8S+3snpnDc5mDD9nPgFeja/v2u9FITbacTD7q1vU1JHNpRfj1kIUT5ooZ6kPjdIAByvOyYdAOZx2YT7vN/nA3N5c6owJoNEEiOv1iOSUOjXqYRr/PZHG8Dh1J6E10vl6iPJjEwK5K9DR7A5LBjzm/BUWsz1iyysivSzhu/OEejrz9sQ9cVUwf7XVFMDT0DOJl3znlNohSWY6eIuGfEFR1T1A39/5+9+46PolobOP6b3U2yKaR3CIQQegkBIl1ApYMNRQVBERQ1FkS9CDb0tV0FRIoiiIAar3qviA0RRHozlNBbIAk1hJJCymazu/P+EbJk0xMSkoXnez/53MzMmTPProfNzjOn9O5tXaigJCXlaYqWDw0NLbavbdu2rF+/vsxrl3Xd0q5dHo1Gg0ajwc3NrcqLJtT4nd5///tf7r///nLnQ6uIUaNGFRvu2b9/f0aNGsWYMWPKPLey43LFzUdWiKyb2ntqMX2oZ8cuE1pHA85ZuwjK/RSAk8tK7o4O0M2rOYqi2CyGUOC+PuGk+vhw8XwGPn7ueF2s+oTRFlUlLs1MisHC9ktG4uL/S7znUVzSTnK2np4Tq+dy+Ng/THhiMQODdGxJO8IpwwW2pfsW6z0kas5P645xT68m11xP4d5sALEHz5VatvDcaVuPZQKUOV9bVfyz6xcUtQVNzAs5phtb7HjBCqSBfvlDXIrO23GyaRjplqtDrvemxxP73/evDENrAYrt60XJH67SMuldQkwu7Lcs5GBwfyL9G3Fb92EM6POUzeIMrYNdam2VM1F3ZOwNwRGFEPU2FBSaWO4hTvsJcDV5WxW/7k6z/q61XP0sVdDQ1fL2ld8V9izPq7FEW7ibll1pJvKHjKqcCMxDLbLcp9aixTepLyeaNMCv91tMnvRXqfW98GB75m06jZu/LxmXy5/XuEFQC2uPNgC3eo05/M/VRJs8NBSi8nqN1bM98XbSVltoeNYVyP8saX3QmUu6GJ7s82YtR5jfIzZxe8FnhIpn3m6OOQUD8dTLTaLtvm7sNN1OfU0j/BIcUf2uTDqvaNizPI8fu+ba1PfjLkOJibbYuN+Iaj+k2P6C+Y4L3zflz1u5meTL53A5fZ6eZ3U0i674iq9C3EiuOdEWGxvLli1byMjIsK4QUeDy5cssWrSI+++/v8L1lTe+18fHdkJYBwcHAgMDad68uXXf6NGjqV+/Pu+//z5QtXG54uZTeIVIUXdoFIVbAhxJa68Q7jSI7JTECp6nobtXixKPtezdhrMGC2HNAlFVlaC2QVWOLy7NzM5U85XfLWT7tsVs/gPPi3mcDcl/AhKfGMuKNfPw6HC7tZddouF8sd5DovoUTaz9tO54sUSb2WxixZp57Ny7gg5tBzCgz5NotSX/WVRRycoxYS70d668VbJ2JtnOnbYzKavKr6cs/up6rMNcVJW85JXWY63C2wAwfkT+XB1F5+34/ORKKJRoO/7r52SePgrAkePb6NwmkBOnT1iPD+v/AF9PyMZ0pj56oIPlRRwMDzHl1ebWHuuf/n3OZnEGqP4Eo7BPKcoOPNUiiZ/rkoOtuTkxI720bN2xFIOTPxf2/0Ba0lq0bdbjtf1L6wqop4Ky8T8xkLRwlVsiy/63oNNq8Av1p1WKhtQMI4eP509pMbS9Z4nlo8fMLzRHWwvGj55Hg2WH5aGhENdAq1OY+I4P7+5IJeRKoq2L5S0yjEmM7KSvEw+PRs104esJ2Zw7YsI/XEt45gISjm4B1ZVmGSpO2i74Zb3HoXq/oGBAQ6F5IyrxmVhaou2TH3Yz7dudqCps2J0/bcSLD0UyIvjW/ALNgdtKr7cgUVfRVdyFsDfXlGj74IMPePXVV63d9RRFKdZ1r7JzqVXH+N4TJ05Yl3qHqo3LFULULU3cNOTdPZzDsz8E4JLDUJrfXbUkVWqeav1sUhSFE+lGpq/fX6U/8ucMhR8wKOjwxtEvGOds2wcP8Yk70DdtRj31FoycwZFg9l/eL4m2GlJSYq2oP1Z/yu+r5wJwJvkoqsXCkH4lL7STbTBhNuRRkGfTaODFhzqUeRPboZErW49lXllcPX+7unWOvJPzqz/jhOZedGo6fsZ1HDv5LVqtjk5tO7Nk+g8ApU5A39othE1pV4eeGS/aroCqkMOL4yYza/E0nn1kIuENXYhdnA5cfcLvkFnfZlqI65VgFPbjsu9mvE/fxUHN1zQ134+h2Ryaml2IP5HNsP4PVKlOg9GCRVXzVyVRFHJcb4fLF0osGzGo5lZ41igKxzdNIzvn6oIi5noHyNFn42xwISk4i1SPXJxyNfh4N2TQ7Q9VqN7O7X04dzGHHIOZxkHOPNm7eILObDaxav0XaLRabusx2vqwQB4aCnHtdFqFHsOcWXdloS0VlbTmjmxLGcLehcf4bFTjWo3PQa8psjDYUqYMi6QZl8nhAgoKyfonrkwtcfV+/GRQFr0HeaM21vDxX1f/Pg/rUPLwuLSMknvub9131mZxqI+/28XmvWeJmdoPvWP5KYbCibqKrOIuhL25pkTb7NmzGTBgAI8++ii+vr488cQTfPHFF1gsFrZv384vv/xCTExMpeosb3xvUSWNuV27dm25ZYQQ9qWJmxZL9EsoGg0Xt28lOX0izaJbl3mOyWIpNi+aTqOxWZhAVVXW/bWf9avyh9hV9o98gF7D6RzzlS0VE5cw52RxNsR2wZTw0I6ctrTDAU806NHiimopf65IUf0Kertt22W7MueWnb9xQveAdU41tdATX4PRRNElcArayrFMM03ctMWu80QvfwCbOdqq28Dbn0bRaIhP3MH38Ykc2TjdGrVWoyl3Xoluns1RUDiRfY60nWtIUTQUnt2pQVALJoz9F39uWE7Thi78vnouHs6tcMjtaO2tU3RC9+uRYBT2JXzIBf7+4hOaOvUhK2ABSv2NRO1aQnda4LHdg7xRFhz0lXvI8dTXCZxJy4MrD3nNji1J0y3ntPYsIx4JRQFO7jHTsL2OW8fU7AJWRYdvanRa1vY8RacdDTkaehmdWSGx/nlu8Q4ptddsYfnDUc0E+bkwbIALkZ7aEnvQLF/9KcuvPCw4dHQzFouFoaU8LBBCVF7vsXo0Vxba2mnJYLtXHmoO/JOQxVNfJzCkeEev2nXlY8IZX1RUcrStQVXxDbhIrqNCqouRvU1T+dSSw7Cm9Xko14WTySY6X1kMoSRp6SUn2ixFbtdNZpVNe84y4s0/Wfr+4HJD3bY/uc6t4i5EdbrmoaO//vqrtfeYi4sLvXr1AqBPnz7ceuut/Pjjj7zwwgvXehkhqlUTN+mabI80Oh3Nn/0XAAkvZFpXHC3Nn8kma0LtrEHlz2QTg4MdrQsTJKXlcurEJdatzE+ybd13Fqhcoq29Z36C5UC6GQeNgsYcSFiH98lpMJqUX8bj5OhAr24jGdDnSZYkGbGgoCP/CaRWlcVWakNpvd3iMnuw7MpQxy3HMjEYLcXKlGRPWsmJNp1WqfEhkwVDQfMMFv73/HYezNvJRu0kTrPOOj9bWQqGWKft+Iv1G7+jYDiJg86Jxg3bEz1mPgCBfkHWxGR65DN47JqDQ1ZzGkd4FZvQvSCh+O3WC4zo4lsjCUZhX54d8wIXLj3MxYu7yFFAkxWCY1p+sjZph5nZwzN5fmk9tLqKj4I4nGyw/q4oClguk67NZG/YWm4f/1RNvIxSFR2+GT1mPuO/Ps0G7Xmbch1DK5Z0jvTSoij5PaYD9Brr35mi/inysOCfXb9Iok2IalR4oa3P301Czbl67HCygbqWZxvW/wH2HviCfcoCgpRuBOpaExRwhnHfNeWjh1NY3iY/mXXwdDZHkw00a+3Cgz1c6OBV+ZSAptCoNeeAYHJSknH2DyR2dxKbHr6Tzl/8gK6Mh32dWweyYfeZgk7JZU7FIYQ9uqZsQ6tWrWyGaAYEBLBnzx7rdufOnVmzZs21XKJWRUVF0apVK5ufuXPn1nZYohqUdFMs7EtGSvlJkEtGtcRtnUbD4GBHVn+zgS/nXJ2UuujTuYrQKAodvHT46zUYLWBRPdAoTrgGtGfgs/8w/a1Y7uw/Aa1Wh79eB4UG1eVvi5pw9mJWqdtnL2axKfUQrk3bW/edSs7F+fA99Ij1Q2tSUCyQm3e1QRQdBlF4O9tcesP5btnbVX0JlfL1hGwcVU/0eOGqBtLXssg6P1tFHE3YTuE5W8JCO/DC+K9wdCzhS7JGJb1jNNw9hsfmuRXriVSQYIxo6MLTtwXUiblsRO3S6XRouGBdHFfVZVp7RAKknbGwdqGhlLNL1jywUNtUVbTGrTg7Wvj707IXx6oJjo56Xhj/FdOn/mP9d/PZqMZEhbrgpFNw12sY38u/wknngr8r/fw1nN0xj9kLH+P3v+ZgNpe/OIIQombYfOaUsF0XPDfmJVyd3ejeuRttntzH1C31if6tNU5ujpiKfMk15uV/jz5rHZVROV3aBKIo4ODuiXNgMGgUnAODcQ4I5sLWjWwbV/Zqo88Pj+ClER1w0Gl4aUTZU3EIUVtiYmKK5YOioqIqdO413eX5+vqyceNGevToAUC3bt145513+OGH/DlhUlNT2bp167VcolbFxsbi7i49ToSoizLOl58VKzxEtGC7ME2ROSRPnsvEZLZUeTLWvCK5v6KJvgFXetLlD2XV0D9QEm01JflidrHtgol3j5y+xBd/xdG8Qxc8zDmkbl/N8WN6BnscQH+xPzqzQlhCPQr/9XLR63DSO5Cp0zDhgfYs+v2AdWhyjhl+P2O0Dk0GMBoNzF30BEeO/8PZc/FEj5lfctKqmpw7YkIJz2/PXS1vY9JeZs6V+dkqQi2ymFHRbYBbIu+0DlMr2BaiKnRp7VBRbZJtlV0Z9LNRjXnq6wQOJxtoHqgnbtcR2rfuiJtL3bj51TtqWDzu2lb8LDyP5KGjm4vNI9k58k7r8YJtIUTNKPqZ89moxvyemlLbYdnQ6XS4OLsw572fih/T2H7ndXAyoOJKuikHik2OUbo8g4WvJ2RjPNKY8e4B/BB4CkVRcA0OIeLE32iM+d3+0g/uLzvWK4vQLf79oMzNJuqskSNHMnbsWJt9GRkZeHh4lHvuNd3l3X333dx22238/vvv9O3bl0cffZSWLVvSvn172rRpw5YtWwgMlG6gQojqYzaprF+UiyFDZc0CA7eOcSp1uFF/m8SWUiyx1aVNoHWlJMWi0PZge97ulUpoGydGzXSp9JxBDhrILZSfKJrYK+hJJ66fwqtaDX/tDzbvTUbvqnB4h5EWnZzw6nQH2acTCTIHkt0wBv25/jgbtDQ848o/viZMJhM6nQ4FBVdnHZ5uTjx/f1vm/m87MzbswqtBaxRFsRmaDFxJsuXP2XTk+DbmLnqCF8Z/VWOvM6CZDgq1vfBIr3LnZyuscO/0krbv6juMQbffjebKfHDhoR0Z0OfJa4pZ3FwKJ4XyfLagaPPAXPjzsOJdii0mE0mfz+Dx7Vvx6dSFpo9MpOPSag64Dig6j+S2Xb/YJNoKz9Eo/yaFqFl6Rw2Lxpa9yFJNMJlMzFkyg392byasgTO+nk40DetU5krpJXF31nJLY1f+SciiQaAz/r6OKChkmirXm/jrCdkkbs/vXatmONMzsz4rGuxE7+sHmVfLebQsex5lIW5015RoGzZsGJs2bcLHxweA0NBQvvrqK5588kn27NlDgwYNmDdvXrUEKoQQAGsXGqwrQK353IDFopbaC6K8xNbzwyOY//M+0jON1LO4EGz0AxQSt5v4ekJ2kdWcSmcyWzh2Op20y7l4euhx9XTDx6l4Yk/UjILFDQBe/ehlvBuMsC4XbzJb+Pi7OAA27y15ol1T+nlu0f6bNKfHAWiW6I5quowxN4NZi6Yx8fFXbMrPWjSN7Bwn9J7BNitrF+7BeOrsIZtzim5Xt1EzXfjpdQVn9/ykW9F508oT3rgTh+K3wpUlDMIbd7I5XrBq6eA7nqmmiMXNpmhSSB/uyrqFedbjlVkZ9MjcadYVqM9vWltiD8wbXf5DJxMX4h6lXftx3NrbCa0M0xbihmI0Gnjlvf6kp5/DJ+kx1H8e4ILZiWSPfZgM87hrSOX+Ji8a24QHYvZyR9cQcjmFjnrkKalAUIXONxgMxO9KRUc9IH/lcY/LjhjTLuHk6c3uhrcRcnw9Du4edP6i4r3qhbgRXdMcbTqdjlmzZtGhQwfrvvvvv59z585x9uxZkpKS6Nat2zUHKYQQBfYsz7PZXv9lLn9/noPZVPkJ1nRaDZ5u+d3ljUqedRhTvP4k545UfC6cT37YzeHES6iOOtZvOsb5rQcYHOxoHUZYlEVVmbH8CH+cNbIz1YSlEist30xMZpVP/z7HuEXH+fTvc5hKmQvtp3XHrb//748f+ShmJ3kmCx/F7OTL3w4UrzdPJTPt6o25VoXcwD+s2w3OOrPGMgZQ+fHP723ODfRxse7b+sd8m1WyC/dgbBDUwua8otvVzUGvoWGElsl/e5Y4b1p5BvR5kiF9n6FF024M6fuM9IwR1a5g4Y7nxy1i8B3P0PtxF3qO1ZHlvo8Dzp/x+e6HMBjK71lhMpmY+7/PmeOhkqhVMaIy93+fk56RxvET8ZhMN848ZkWHZxfeXrvQwJrPDRzbZmLN54Zic9wZDAYeiL6Ttv3DeCD6zgq9tzcDk8XC72eMfJ2Yy+9njJhuwiStsA9ms4m3ZwwiOzsZ55yWHDp7Cq3Jg6yms3FI68j2uVWb06xrtySOb5xENsfI4Sh5HC//JPKT+xMfiMFkNnNM+RkAFZWL2lQ0Do6oFgvKmWO8ovmbfpsPlrkQghA3g0p9E58yZUqFyul0OgICAlAUhddee61KgQkhRMlsky2qGdYuyK30RNoF7usTTp9+benQqQnqlbrj9ScweuRgMlfsC/h5B2dat2+IMdfEbQPbcd6h7HmGdqWa+GX9cU7nqOxMNbMrteQbQ7Mpf3jskuhM1iwwVCmZaE9i434DwGA0MWzKcto+s4q5q5PZciyTuX+fY96akpeYL8xgtH0vs3LyipUxGeFSipmkg3loTnrSt+tIshsvQtXmkOu1jRNen3JRubKwT5G3PMjH1bpv46+zSTywCdViIUh/tQej2WwirFEHtFoHAJo1vsW6emddVTQJUpnhKEJUhVan8MW+EfyQM5Rtpg/ZvGsdj7xY9uTZkN+jdJkpjUOO+V9iP/WAZaY08kx5HD8Rz5wlM2o++Otk0O1PM6Tvs1cS4M8y6PanrceKPnQquv3Ii8PZvHMDaRmpbN65oULv7c2gYDXyXMvV1ciFqIv+WP0pF8+fx3PnXFDM1ofBuowW+b+nhVa6TqPRwKlfPyNt/woyE/4hy3wA/ZXvKkWZzSZ+/2sO2TkZ/P7XHNYuyMbn9L3o8QTARA7nddtJ6xFPPa2Jdm6X2fH5nfSJ+UmSbDeh9evXM3ToUIKD80d7LFu2rFrq3bt3L7169cLZ2Zn69evz9ttv2zzkBli3bh0dO3ZEr9cTFhZWbETl0qVL6dSpE56enri6utK+fXu+/vrraomvLJX6Jv3hhx+yd+9e/Pz8KlQ+JSWFFStW8M4771QpOCGEKCpikCNrrwwdLayyE2kX6N2/DSzIxRRhJOcPBRUVk2Lm89wVuHwXwcsjO5R6bsF8cS7rA8g4bsLtNmcURSGoSUCZ14zPtE3g7cswEOmlRaNcffZhMqtMf/Uimau1KCgc22Yqc5jsjSA27jei2g9h5NSVbNpzFgdPb9x9rvYSm7c2hR1JWXw2qjFwdf617YfOMf0/u3h+eARBvloSC903ueh1ZGRdvQH1cHMkPdOIyQhLP73Mre3rMfGtaLRaLavWf4nujkWsWfYnKICq0D/vG96/LY1MFwvabDOJabnc0/9Bpi0D1WLmq/fu4/UvjzE42Nt6jeWrP2XFms+s2+FhUTW6EIIQ9upA/L4yt4syGQzEfPWJdXu1CyQV+Sb7z+4t1RZfbStIgJdELfrQqch2Zd/bm0XRRYou5lp4IPpODsTvo1V4G5ZM/6FS81sKUR0sqkpcmplzBgsBeg3tPbVs2/ULHrs/Jtd/DS4nRtLF0h8A5+ShqKi4N8i0qcNsNrFizTxrYqykOdzmLnqCrNPxAJxf+Q364DB6ji754UThxVh+WzUbz82DcCD/u04T9S4ySCK142x+/PCXEs8XN5esrCwiIiIYM2YMw4YNq5Y6MzIy6Nu3L3369CE2NpYjR47w6KOP4urqyosvvghAQkICgwYN4vHHH+ebb75h06ZNPP300/j5+Vnj8Pb25tVXX6VFixY4Ojry22+/MWbMGPz9/enfv3+1xFqSSiXaLBYLy5cvByiWSSyNotjvfBFRUVFotVqbfdHR0URHR9dSREKIXmP1bP0uF8Plokeq1tvreLZKzgYLPlEu5JCLgkKOxoBFY+F/a+JLTbQZjCYmPHwAv+MhKCikHzThO6BqvYAMljw2px6mh3fLK3VbGDrrMG03+5IalE3IWVeg6snEwkxmlfnrUtiZlEWHRq480csfXR2Y18dgMLBj7z+07R9GquNLgBOmy+k2ZVTgn4Qsxi85hs/ek7w+ZDHZ2lsBhWnf7gSggb+OxDNXzxk7pDU6nYaZ38cx4YH2mEwWPv4+zno8qmWA9WbW3zeUyDYD8POdwT+7t7DluDemM/UxQf6CXAaFxAu56MNGE+D1C63De3NLRFda+bjaxPlPkQnM/9n1C0MLTWBe2F19q+fLiBD2qFV4Gzbv3GDdtlgsGAyGUhMd28YNx2Iw0NEEO/T5P26Oekx5udbvpbdEdL0usV9PBSscF17YJzvkIuppLzbV2033yxFkh1wCrq6CVvS9bRXephYir3uKrkaenLTP+j4V9Pz7fq4kDkTNMhgtPPJlGgfO5tEqyIEJ97qxN8PCsROX2XMonU6NXLGoGnSZTclsOot6R14udLaKk2cmzy22XZhhxZp5/LZqDqBe+f/i86oWnS/WcimF2J9+5dO4KdwS0ZVnHpmITpf/XbboYizHdKdowdWHinonPUsqsbq5uLENHDiQgQMHlno8NDSUcePGceTIEZYuXYqPjw+zZs2iW7dujBs3jtWrV9O4cWMWLVpEp0758wTHxMRgMBhYvHgxTk5OtGnThiNHjjBjxgwmTpyIoijMmzePhg0bMnPmTABatmzJ9u3bmTZtmjXR1rt3b5tYnn/+eZYsWcLGjRvLTbTFxMQwffp0m31ms7lC70ml7gobN25MQkICPXr0YNSoUTRv3rzUhJuqqpw+fZp//etflblEnRIbG4u7u3tthyGEKESrU+jyoFOxXm2VmUi7KFVR0XpfTTZla4r3mCtq5NSVNEhqx6Z6u+lxuT0A5ksqqquK9nIm4FXqueFuWtLTsgHIZB8anNifddqaaHvq6wTOpOWhbZLO5Xp51kRbZZOJJT0hnbfmHJ+tzV+OfsuxTCwWlWfuqP3VoR95cTjG3Es4XErH7HUSHJqAomA8+xeOQXfYlN2ZcIGO6ZcwOB8D914AqCps3ZeMVqvQyOMgSWlNaeR5lOh7H8LN1YVFvx+gQ2ZL1v9xCQ9TPOnaTCiSX4xqPwSACWPz/26FDfrG5riiKqhA3Mkcpj5xB/f0Gg/kL3X/5ZOZnDtiyl/9M6Tif1oLFhkQ4ma0ZPoPdBzagqx0Ix3Vl2iROoopPeM55bSCEMcuDLi/Pbc97mpdWTr94H6i8sDVkp9kQ4WxI57NnzN48TRCghvxzCMTa/dFVTOz2cS3B0+Q6xxss8Lxbzk78HYLJt75BHrVgUs5Z3iNMOt5S6b/wKgXR7Dd8hiqawvMYZ4YjBb0jvk9py0mE0c/m4ExPY3Dsz+k6VMT0ehu/CHjRVcjn/3RwzbHpeefuB4e+TKNzceNAGw+buTcfzIIb5BL4xA3TpzJ5sSZbO5oFI2z21FcToy8cpaKvp5C1xHO3DrG0/q5WCA+cQdXvyeqV7ZtNQhqYV0RHUCruDFjwfsAbIhdi8VisVkAKkXpjr+6CYCjbgdpfrmtdQhr39ENpfdnHVXS939NHej89PHHH/Pee+/x+uuv8/HHHzNq1Ci6d+/OY489xkcffcSkSZMYPXo0+/fvR1EUtmzZQq9evXBycrLW0b9/fyZPnkxiYiKNGzdmy5Yt9OvXz+Y6/fv3Z+HCheTl5eHgYDs0WlVV/v77bw4fPsy///3vcmMeOXIkY8eOtdmXkZGBh4dHKWdcVak52o4dO8bKlSsJCgri2Wef5dVXX+XkyZN06dKFXr162fz07t2bkSNHsnjx4spcQgghytVrrB4HZ/Cqr8GrvkLvx53oNbZqf+zD3bS49tSRvSX/6YSKSj1z/oqN9/UJL/W8/ccvApCgP2Xdp0vTkXv6PE90Cy7zmpFeWjLSMzFyHhNp5BBP4azP4eT8+ebOBOagAoaAP1FRORuYUOqCACXZlWpiZ6rZOhfcDyeN/Lo7Nf+gBVrEu7P3I5XnnzpDdnbFns7UlANH9wL5f5Q0l79AYzqOaswi98xaLMYMVFXFnHUCVVXJzcxERUGTl5CfYQNAxWyxYDLlceb4QlCzOXN8IWP/9SAAedn5q9SaT7jgYXJDp+b3Vo49WPq8b5oi95yqoqIAHRq5Wlc5hatL3edkQML2PEzbPkMt9Oe1c5EJzW8Wg9p61nYIoo7T6/VoNBrutqxAjw9aHHBV65NqPIFrRhvWL8xjXaH5Nz1atmZANnhaoIUR7vduyISx/2LC2H/h4e5JWMNwa2+MG8Xy1Z+SpXEttsKxqqjsdDuEUZPHTrdDqIrt3wa9Xo+m7ReY6nXCrHFjW6KJR75Msx4/Mncahz75ADUvj0OffMCRudOu10uqVQWrkY8KdWJwsCMtQpvZHG/TrL0sliBKZTQa+Pjz0bw49Ramzr2PUzkX2JR6CItauXZy4KztnIqnTpqwLAfXbB3Byc4oFrisaU33iUdw04ahdTYQ2lHHy3+40+dxfbEkG0B4aEeufpdUrmzbih4zn2ZhnQGFZmGd2bYvzeZ44QWgbom8k1OaoUD+Vy2DryM73A6Q6Z1Gn/F6elfxe7eoeXFpZpvv/3Fptfsdv8CgQYMYP348TZs25Y033uDy5ctERUVx//3306xZMyZNmsTBgwc5dy7/u3lycjIBAbbT8RRsJycnl1nGZDJx4cIF67709HTc3NxwdHRk8ODBzJ49m759+9bky638qqN33HEH33//PadOneLOO+/knXfeITg4mOeff54DB4qv7lbTL0AIcfPR6hR8G2l54Wd3XvjZg9vGO5f4paMiIr209B7nhFsu+HXQoO+WRqtOLrz0UAc6ZLYsdSGC1mE+HNUn0dhQH8hP0PkEaXnm1hAcddqSLmWlURR0ClxWYjErGaCotHYLsR5vHpj/5cWsUXHN0ZEesIsD4eks91GYt7Z4Yqi01TmLzgWXmaeScWWxACejhlbxHpz1z8E91pnJE8tfaKAmhTq7gwq9c+DeKRMY/1FL7n8qFTI2kbnpXvIyzmFKP8jlhHjCd7vgr3Yh3HIno1OG4mhxQGtRMGf8gJOjQtOGLtZ6C3on6I2FvxAqaFUtigKdW5fem09fTyG0kw5nd0AL6FVCfZ14ope/TbnCK9SeCsrGkuKOY/i/8fQIZEjfZxlYaALzm8ngiNJ7dQpRoFV4G9yoTxP1LgCUK/8r+H33cqO1bOcvfiCgS0+inL2Z2rInHy7ddsMl1or6Z9cv5Fw8TNKG9wAwmy2s33maek2a4Rxw9aFO0QdDJpOJnYkZNvsK39yfXGY75Kvo9s1iyfQf6NahJ57uXnTr0JMRk76VxRJEqeYueoIjx7eRnZPOuRN7STt7jI1pB9mSdqTCdRiMJsyGdCxpp/N3qOCVpqH+bhc0KQr6XA3Nj7mjojB00NO88UsUb24I5LHP65W5onjB6uFarUOpq4c7Oup5YfxX9Oo6ghfGfwVqkfoKfdW9o+dj5Gn8UIF4dSRmj6YcbJLBiaiDpSb7qiLQx6X8QqJSzhksZW7Xlnbt2ll/L0iOtW3btti+lJQU676i05AVjKYsvL8iZerVq0dcXByxsbG8++67TJw4kbVr117LyylXpRNtBXx9fXn55Zc5dOgQP/74IxcuXKBjx450796dr776SpYRF0LUqO4PO5VfqAI0ikInXweemOjKs/PdeXVWY354byAdMluwbkEux7aZ+PvzHHoPXsmwKcutq1rGTO2Hc9d0PB2cyfROo+NrenwaVvwj1UGjpYdnS0L1fvTwbEk3z+bWY5+NakxUqAsajZEzAVkcDmzIofAMVI3Cr3Fpxeqat/Ycc/8+d3V1zhKScQAoCp7GzTQPq4f/BT0KCj6pjigoOBys+tDba2U2mxgQ7IafpwO3aJwIbtkMfT036rduyV2vTARLHpePHSIzxRs17TIds1rhjCs9ze+jV53QmNO5N6sd2npa9I4amjVyxUGTP4lfq/A2GAwGHI1X/9uEG0JwVfW8NKIDzw+PKDUuRVF4bJ4bL6/yQOOiFhtqWiCgmc46Efnh0AzS3Y0czmjG+1PWyQqeQpRjyfQfUBXbZEYXy1uFtvL/4RmNBmYveYKl4ckcfqI7UV/+cNOsbHd0eTR5WecwGbPYsGk/m3de5myWBpegYJq3b87LIzvwwoPtbc6ZtWgahktxNr1+WwWVvLpg/lENG2OOY8jMY2PMcSwVXHXb3un1er6f+wt7/zzO93N/Ic1k+0FfdPEEcXMrNseZMf9+95ThQknFSzRy6kou7d+EJuU4jnkQeVxH3z06FBRSvzKS42yh0RnXSg/1K5hv1sXZvdzvHg/e/QYAwwY8YLN/2IAHsJhMHJj1EY+8MQ2jmj8v8FnHu9A46nEODEbvH1SpuMoTVGSeW3HtAookZItu15bCwzgLkmAl7bNc6UkcGBho7blWoCAJV5CUK62MTqfDx8fHuk+j0RAeHk779u158cUXue+++3j//fer66WVqFre9d69exMTE8OZM2cICwtjzJgx1K9fn3Xr1lVH9UIIUUy7ATWbGCrci0JB4SIZbN6dzMipKwHQO+r48YNBLF3ZhRkrQ3HpoqvU4i8KCt29WvBAUA+61gvn6JxpbH7kXg7P/hBHjYXF48J5/9bN6NUUzmt6lFlX0eRbwXa4mxbVrJK9zUT60jzS/puHh2E9zcPcSXPPf32eGfnvo0PZnfBq1Io189jnfgGtTgPmPDJiN2La5kv2Yz3x/uFBnn14Eh7O2aCquFj0Nr1dABwsJuoZ/K3vk6IouOtzCO/2Bq4dZjDw1c+4rJ5miyb/i2UTQwNC69fjxYci0WnL/zM4f10K2UYLeWZIvJDL/HUpNsdHzXQhw99Irs6MSWdhU4fz1fn2VJgM1RT2SK/X0/tRN+u2euV/BdoNcsBksfDlnkPoe35E/duncSRxF3MXPVEb4V53nSPvBIuR9KQ17F7UFeeA8xQeHqb38inxs+zHP7+HvePhwLOQl4pT9m6WPOZpPR5y93Cb8tltns1PsJlUNsYcZ/N3iTX6uuoqb0elzG1xc2sQ1MJmW3NlRfEGet8KnW8xmdhz4DRgQTHmMGKDE+2TdOgs+f9+zckQcja/h1fH0JpPQD035iVeHDeZnlG9eXHcZJ4b8xJH5k7js9Vn2e94D6riACigKGgdHVEUBad69Wo8LnFt2ntq6eClpb6zQgcvLe09a/FL/jXo2rUr69evx2i8ek+2cuVKgoODCQ0NtZZZtWqVzXkrV66kU6dOxeZnK0xVVXJzy5+T+1pUS6ItKSmJd999l+7du/Ptt9+iqir+/v64ukqGWghhr2y/XOdoconMbGGdm62ARVXZmWoixWAhPc+CpYIrMhdWMFfO+U1rOfDJDD7ps433b0sj5ccxdA8xULgvf6cW7uVe49QlA0Of+ob0CQ+i/zWPC9PzSP/OhMZNwT3HgYzMPLRmJX8+umxHVFQadNyP2Xz9h8jkGSzsmNUZn3UrUfLqAQp8cz+mLX6Q6YB60Bvfvc+w+z8v0b1tME2NIYVuwvP/v1PO7Vx2SyVNaQUoWFQFs2MIKc63sy0hmxR9H3I8LhCv+ZHTygYOOs+rVO/DnUlZ1t/VItsADnoNTV9U+e2O02S5mrHoYGh7z2t6X6pChmoKe3XbeBf6jNfTpLOO3o/ruXWMI/p6gNbIXz/+w6AZe1iwXMeOgyYMl8/TdNDcYj1LAv3KnhvTXg28/WmG9H0Wz4at8Ox0B2hsb5jy0tNKPlEFMMKl1bC1GwFnJ1sXQgBoFv0SLZ5/BcXBgRbPv0KOW5vC86hzan8p9dZhBQvTvH9bGl8+mUleFYZL9Q/UEaRXcNJAkD5/hVchCkSPmU+zxrfgpDji4exPPc9gurs3p6tns/JPJv/7XnD6cVBVjEoeZxzPY9Ll4RF09Ttng7MuBHRXik1TURN0Oh3PPDKRWyK68uf+y8xZMoPEn75nV2hf2p34+2rBgqF4QMdQt5IrE3WGRlHo4KVjYJAjHbx012UhhMzMTOLi4oiLiwMgISGBuLg4Tpw4UeU6R4wYgZOTE48++ij79u3jp59+4r333rOuOArw5JNPkpSUxMSJEzl48CBffvklCxcu5KWXXrLW8/7777Nq1SqOHz/OoUOHmDFjBl999RUPP/xwaZeuFlVOtKWmpvL555/Ts2dPwsLCeP3117lw4QJPP/0027Zt4+DBg9alWYUQwt60G1T8KUhTQ0Nah/nY7CuYcNRggfQ8qjThaMHcOCpwuMkqUnNakZMBiTvMRMT14d6ufjQMduHBoSE0au7NrtT8hJjJYGDNw/eSefqE9UtQE9MXoGhwu5zCDKchnPrvGet1nJpr8D43kNxcM7l+Zk5HZJDr9Q8ZbV5hH0+zYs28Ssd+LcwmldnDMzGdCUdj8gCLA3lqI7SXGmLeVDB3msK5Iyb0jjqiG/ehY0Zra082JzcFnAz46R1Y2wksigugcEJ7HzlqodWAFA3ODTtjUrJZqRuNtt0W7u3dpFg8penQ6OpDI6XIdoEnewcQfVsAXZu4EX1bAE/2DihWRghRMq1Ooc/jeh6Z68bt4505uVfFcFkFsyNmh2TCVgZgMFpoHOLG9r2pOPs0L9azJNCveocz1RUFw8FeH/s1avIpTm/YDKi0S1oNqkr7xJUlnlfSkLACFtXClsvxxN3XDerVo+kzLxHSxqtwRzkatPasmRdUgwovTJO43cTs4ZmlzrNamqKLJeg0dWPIlagbHB31DMprS58fz9D1m/047dqP37e/oVEq1k5OLvuBZ04u5MlTS3CyGDju/h/eWO/FhJ/crQ8bbhvvzEvv+KDTXp/elDMWzuDTHf4k6e/h0x3+zPeOQAEaXri6Aq+jxoCni5anbwu4LglAYX+2b99OZGQkkZGRAEycOJHIyEjeeOONKtfp4eHBqlWrOHXqFJ06deLpp59m4sSJTJx4dXXxxo0bs3z5ctauXUv79u35v//7P2bNmsWwYcOsZbKysnj66adp3bo13bp143//+x/ffPMN48aNq/oLroBKPaYxGo38+uuvfPPNN/zxxx8YjUacnJy49957GT16NAMHDryhJqWNiopCq7V9chgdHU10dHQtRSSEuF56j9Wz7btcDPlTfdHY0AC9o5aYqbZLSFfXhKMWBUy+AehTd0H3ULI35SfsUo6aadPWh0Ytryb44jMtdPSGbeOGM935Xnoc+I7fOk24kmzL/2LW8OJ+Vrd5FIvRaL15uvxrfoKuS6QvXdr7kJvwK/tWvWat92jC9irFXlXrFhpIO2OhcO/BPELA8RDkRVn3BzTL/7tSeDgvgIuHwqs/B9J/ehqW1KtfclOUW1GxHb6pd9Lj4O5Fq/A2LJn+Q6WWpH+ilz/T52lw0MKQHo1L/JKp0yo8fZsk14SoDvkLjFz9XPC4MsT9TEoOSbldULLPEj1mfi1FVzs+X/I06acO0ZAjoBhxNWfRZ/8i+qVvBN4sVv65MS+h0WjYvGsbiZl3MGeFM5sSlhMztR/bs46yKS2/R2C4amJz2mG6PZg/T+jm7xLo9mBjuj0Yeh1fXfUovDANQNoZC2lnLBzbZsJiUbl9vHMtRSZuJBdiN4OqYtJAqreOT3ctIvTzfUSPmY+jY/nfLRwxEZm1n8ijr+MSEoreMf/fb5/Ha2fOyZhDDdF5tQSLEZ1XJIc92xF6fg8nfFpZy4zt1YjGfk7Sc16Uqnfv3tZFCEqSmJhYbF/R8qGhocX2tW3blvXr15d57V69erFz585Sj7/zzju88847ZdZRmpiYGKZPn26zz2yuWKeKSj2mCQwMZPjw4fzyyy9ERUXx+eefk5yczH//+1+GDh1aYpLt+eefr8wl6pTY2FgOHDhg8yNJNiFuDlqdQucHry640ONyBPXvymBHVrzNMu7VMeFoyN3DOd7chUv3PERe8G+4dL+a4C9IMpUk/eB+kj2b0OjC/vwdisIx7WMAnPBpTWDaMer7xlrLZ28yYWy8gvRTWzEm/Irx+O829amW6zv5ddHEGYCqzaLJbV8T2jF/tc/QTjpGzSxYkaro092Sn/YaTRbcHWxvuMbd0dI62XVFk2wFK2HptAoujlrcnbV8+nSH6/aUWYiblc43GRUVk3MSTuf6kV4v/7Ni7dYUgn31jOvQrkI3tDeSgqGyChYaqT/i0OAPPLx+JvTu+0osr9PpmDD2X5jdx3LigiPZuWY27TnLyKkr2Z950qbs/syTaLQa0tt4oXdzoMfIMDQVmL+yrinr7+We5XmlHhOiUq58V0ps6kyekwajxsKR49sqNG9k0bkRi27XBpNTiHUYnqIooNGRGBCJITAcHWYU4Mk+AZJkEzelkSNHFssHxcbGln8ilezRlpaWhqIo3HbbbTRq1IitW7eyZcuWUsunpKSwYsUKPvnkk8pcRggh6oTeY/VoNAq7Y9O5HH6G7HsS2ZiW/wSmh3dLAOsEowfSzbTyKH/CUZPZwic/7CYtM5fp/9nF88MjaBb9Et8d/w6la2/OXV5Nk2AFxQ3cGmsYNdOFPdkWdhUakhruln8Nj5b5ybQNLa4MCVJVdEo2isVCip8/3Syf4P3cwzQ85cjONYmcNf9GTt4W4n/fwpC+zxBfJF+kue5DZBRs1pJHxbtJFkNeW1TialntBjmwbkGuzTbkz4f22Zr8BQrOXrTQvl02Q/p15aLGg51JWXRo5FqloQ6yEpYQtUN323TyLj1AdqOv8cl8h+OdM9GnW2iUlcS344aWOpzvrr7DStx/I2gQ1IIjx7dd3aEoZAe40Sz6pdJPgmLziu4/fpF+FP08zP8cXr43jebYr1EzXfh6QjbnjpjIM4DJ5lmOrB4qqsmVeRJzXGy/7xWdN7IkzaJfQtFouLh9Kz6dutD0qYnlnlPTglxzOWNwQ9E6FdqrcEnrTtNAFzSnNfKAUYgqqFSiTVEUBgwYgL+/f7ld5lRVxWw2V2oVPiGEqEsK5g06MmAduXk5aHHHRW3OzrRUThg2MDywKzqNjg5eOi7kqnTwKv8j9ZMfdjPt252oKkz7Nr+b84sPReLRsi0W1UKuNg+HYA0hXzoTrFdw0GuIdFJQVQsHLquYLXDWYMFksdD5ix94cdwIPq53L67mbFqF+TDvkbas+Gsmf677nAvAH+s/pX8vMy/EPMeKNS7EJ3YlPLQjA/o8yQrmcSh+K/k3IArhja/fvJoms4WshhdRT3ta51xzdNEQ6B9a6pL0BYnPE3Em0pIt9B6b36Plyd4BaBSFxRvPc/ws/LroHvlSKIQdaxYewZGOzxAYOZbgKD+GK/6gqjjuS7eZ0L+ou/uV3LvrRhA9Zj6vvNuDnIL5DICGjSPRlDNlS+swHzbtOWuz3dq1IZvSryYFDqzbSPY9t3LpVDaGzDw2xhyn24OhdterzUGv4bF5+RO1//15DmsLPZiJGFSzK5WLm4dvVFcubFlPjpvW5ka66LyRRRmMFp76OonDGYNpfuswPhvVGI2u9v+NLXupB3dP28hZgzdcmWuuXdJqTnq3wOF4HJ5+geXUIIQoSaUSbf/61794//33K3WBV199tVLl169fz0cffcSOHTs4e/YsP/30E3fffXeJZcePH8/8+fP5+OOPmTBhQpn1/vjjj7z++uscO3aMJk2a8O6773LPPfdUKjYhxM1KwZlwTKTjiB9GznHB4MV/z23hoaCelapp2/7kgnULUNX8bQCNVsvlsztxcPW/ckwl0Dn/aalGUTiXC8YrIzuTDSp/JpsYHKynzzdL6VPkGn9vWlxs++5BExl8xzM2+wf0eRKAnXtX0KHtAOv29fDFqzOYe9wbH28PbslsQ1hHB/wCAuBC6ecUJD4BYl7IRKvLT6YVzI/WyMeR17bJk1ch7F3BZ9GpoOFXH9gqCqYmrWsxqtrl6Kjng1c3MnfRE5w6e4gGQS0qNE9dzNR++cNFj1+kdZgPMVP74eig4b/Lv8VUz4lGBgM/ffYpLHfiknd/LCaVjTHHAegxMqymX1aN6TVWj3LlwUzD9jpuHeNU/klCVEBBLzSv7ZvZpNHj4uxR5r/HPIOFrydkc2yvESdXF9I7ZfFPQhZPfZ3AorEVX5ipprg5O/LW4Zl8dzmcv9s8AopCw/N78c04SaD5Iv1G136vOyHsUaUSbZVNsgG8++67lSqflZVFREQEY8aMsVktoqhly5axbds2goPLX859y5YtPPDAA/zf//0f99xzDz/99BPDhw9n48aNdO7cuVLxCSFuPq3dQtifGoCW/KGERpLxoCspuYnWMk3cKvZUMqplAOvjzthsA4Q37sRvf32GZ6NepJ/aQn0XB9qHdbOWu2S0HfZyMdfCpofvJP3gfjxatqbzFz+guzL3mMl2vEyx7QIFq9n1vXUcsxc8zaaPNqDLbEqj1u6MnuWGQxXmm6uoy2t+Q/UbxQXHNJZ7b6RRTj0e8Qig/tC+Va5zcIQXr5VfTAhRxxV8Nv12ykByrgqKAqqKv6dbbYdWqxwd9bww/qtKnaN31PGfqX2ZN+cYqfsc+fiT/XR71IE1Mf8hNe0iKRpQLcAld/C+cpIKp/anVXf411XhBzNCVCeNTkfzZ/9Fc+D8uS1M7vVimeULVsPVosEvV49rjpYsVzOHkw3XJ+AKSD+4nz4ZW2h+dgtLo/6Fu+EinY/9SkDnrkS1H1Lb4Qlhl2q/v2oRAwcO5J133uHee+8ttczp06d55plniImJwcHBodw6Z86cSd++fZk8eTItWrRg8uTJ3H777cycObMaIxdC3Ki6eTZHr3HAifzEviNBqIC/k4e1TBO3sudmK8+APk8y5I6nCHTIoYVxH/dHdkZTaOi9t6NtLy3dsX1c2LqRvPRULmzdyLZxVyfU9fK0fQBRdLuouYue4PxPIzDpUiDXjaSdZr6ekH1Nr6c0FpOJw7M/xDUzpdixqPZDaDD0xp1jSQhROQOCHQly1uCkgSBnDQOCZfhfVXw26zgXvg2AennkfN+QdQtz6TPyIdtC3hlkO1y5LVCgQWvP6x6nEDeiwqvheht/pWW8B5o8leaBdScR7NGyNVrVTMilwzz/51jant1CQOeudP7ih9oOTQi7VecSbeWxWCyMGjWKl19+mdatKzaEYMuWLfTr189mX//+/dm8eXOZ52VkZJT6k5ubW+a5Qogbh0bR0Mb96hciV1qh16Zyf0DXStcVe/BcidsFPTieH7eIwXc8U2yesv6BOoL0Sv4Np17B/dVRNsfTD+63/v7ahF/w8aqPRqPFx6s+r034pcyYTp09hC6zKdkNY67sUWy+GJbHYjKx95Np3PvSz3R+fTuPfhGPwVh8BVOzSeW/T6zhyyXd8TVlMqjZXJr5bkaDifv6hFfoqanZpLJmgYGTe82sWWDAbJIJroW4Uek0GgYHOzIq1InBwY6lLoIgypa6xh1Q0DRPx+Gxo5g3BNDi1u5069CTuwwaunXoyfsLXiTP3QGNTqHHyDC6PRha22ELYdcMBgMPRN9JUvY/qFcW4wgwLKDhWVfu+MeVz0Y1ruUIr+r8xQ/4dumBg4cXvl16MGj3Cbp/84t1pIQQNxuLxYLFYiEzM7PEXFBFVGroaF3w73//G51Ox3PPPVfhc5KTkwkICLDZFxAQQHJycpnnhYSElHrszTffZOrUqRWOQQhh3yK9dCiKmXMGCwF6Le09w2x6nFVU59aBbNh9BvXKaKjOrSs2yWzBDWeBTU2bcuHi1aSdR8urDx6cnd1455W/KxxTg6AWnHM7ikN62yt7VPLczjDiuTHcEtGVZx6ZiK6UCbdNFgtLdyawUBmC97k/yGzUmNiELB55fx3/ebWnzUTdaxca2LenE04o7HSNxCVbQyv/jXRqksoLDz5eoVjXL8plzXwDqOT/P9TY8KB7etnv/ERCCFFAq2iwAJbDHmiapwP5D5C+n/sLK7o05/u5+Q9jHJ216N0c7HpuNiGuN4uqEpeW//3Q30khh2Oczr3A5tV/siVuE4q6jYfZhxYHTjq/ioKCV5ZTmQu7XG86vZ7u35T9UFaIm8mFCxc4efIkgwcPJikpqUp12FWibceOHXzyySfs3Lmz0quZFi2vqmq5dZw8eRJ3d/cSjzk5yaSqQtxMNIpSoVVFy/P88AgAZn4fx4QH2lu3K6vzFz+wbdxwmznaqip6zHxmm57m4i+jwCkTrXcaC8/djuW8kQ2xa7FYLEx8/BUALKqFLWlHOJF9AdXchhyTM6pPCC3C09D/sY89jW4HReFYpo4jc6fR4vn88/IMFjYtybWuMPqXdxQ9ss5j9jiAYj6FroKr252IM0FBJzb1ynYNuafX1UmKA31ckH7MQgh71HWIG+u/MGLeFIB5kz8O9yfS2u3qw2STWWX+uhQycszkGM2YzGqdX1SmcHIjQK+hvae2Sg+/hLhWu1JN7ErL78V/OsdCNnnkKOcJ6hZJpzODif3pV85ZYglSu5Ll0AVQqd/eo+xKhRC1ytfXl5CQELZv346jo+20FRkZGWV2yCpQd1LpFbBhwwZSUlJo2LAhOp0OnU5HUlISL774IqGhoaWeFxgYWKz3WkpKSrFebkW5u7uX+iOJNiFEVei0Gl58KBJPNydefCiywgmmYvVcefo4aMexa+7e7+io58XoL3nvzz68vakBKx1HYdFcXUDhxxXfW3/fcOkQc9ck88c/OlbHZmG25HfNaxDkQkaf/MVl2iX9RY6DKyMOBzNtwTRMJhNfT8im8JoM6bp6uCQ8jqrm96irqIbtdVBwL6Vc2S4k0K/8BXKqIsjHtUbqFUKImtZnnAu9n9Dj0yEXvxEp9B7rTDfP5tbj89el8Onf58gzQ7bRwvx1xefQrGt2pZrYmWrmdI7KzlQzu1Jr7qGLEGWJzyw8VYaCI/Xzf1MUgpo3A2CVZgzZ9Q7g7A6hnRwYPevmXthFiLpOo9Gg0Whwc3MrMRdUoTpqOMZqNWrUKPbs2UNcXJz1Jzg4mJdffpk///yz1PO6du3KqlWrbPatXLmSbt26lXKGEELUTXkGC18+mcn7t6Xx5ZOZ5BmKz4V2zYp2Cii0vXh9Krt3emEmh627LmIy51/f3c2BrPBIFAUaXtgHGh0Grwjm7/Kl5/0dObz9gk2V7S6fICtgFc5urYgeM7/Cod06xok+T+hx8VTo84SeW8fYPvQYP+KZSr1UIcSN4a6+spBKabQ6hduecOb5+QE8O7E5PX1boFGu3gLsSMyk8GyXOxIzr3+QlWSb3Ci+LURtKfjKpKoqGk3+lpu7M+/+0ZHJf3vy2LyaXdXd06PsjiRC1IT169czdOhQgoODURSFZcuWVUu9e/fupVevXjg7O1O/fn3efvttVPXqX6yzZ88yYsQImjdvjkajYcKECSXW8+OPP9KqVSucnJxo1aoVP/30U7XEV5Y6l2jLzMy0JtEAEhISiIuL48SJE/j4+NCmTRubHwcHBwIDA2ne/OqTudGjRzN58mTr9vPPP8/KlSv597//zaFDh/j3v//NX3/9Vep/CCGEqKsKlonPyYDE7aZqXx3UaDTQuY07/br60qWtBxpgWP8HOJB5EoBDR5wBhSMH85/mxCdmAioaLXRpH4yDg5kTvvlzvRmOfIKzX2uCukawT/OFdTJgUBmSlkjIvb356NUfcXSseG88rU6hz+N6Qtpq6fO4Hq3ONit4d7/7rvEdKJnM1yZE3VZT//ZvBha17G0hROnCbVadV8nlDJDfoy1uxV9Afu8Y/XVaWMDT3b4SbfL96saQlZVFREQEc+bMqbY6MzIy6Nu3L8HBwcTGxjJ79mymTZvGjBkzrGVyc3Px8/Pj1VdfJSKi5Ol4tmzZwgMPPMCoUaPYvXs3o0aNYvjw4Wzbtq3aYi1JnUu0bd++ncjISCIjIwGYOHEikZGRvPHGGxWu48SJE5w9e9a63a1bN7777jsWLVpEu3btWLx4Md9//z2dO3eu9viFEKImFV0NtDKrg1bE3EVPkJOTjKOiJ+z4FzxsiSPz16HsSz+BwWAgz5ADQHamAz3/8cNxqpbU/zMS7pyEt/449Z0z2dPwNmt9Pv5mbrnvbjKj/uGssoVc0gjt5MBt//cgL42s+tDZ663wfG1CCHEjyDNYyMlQCf7Si57/+Fnnv9QoCr/vTq3d4Mphm9wovi3E9RLppSXSU4OrLhuLJpEc4oH8Hm3ZqWkAtApvU4sR1m3y/erGMHDgQN555x3uvffeEo+HhobyzjvvMHr0aNzc3GjUqBE///wz58+f56677sLNzY22bduyfft26zkxMTEYDAYWL15MmzZtuPfee5kyZQozZsyw9moLDQ3lk08+YfTo0Xh4lDz34cyZM+nbty+TJ0+mRYsWTJ48mdtvv52ZM2dW+/tQWJ27w+nduzeqqhb7Wbx4cYnlExMTi/VMW7t2bbHy9913H4cOHcJoNHLw4MFSG4EQQtRlAc10ZW5fq1NnDwHgcLErjmkd0Zo9OHx2O/G7c5j0v3dJO7qUvOS/aH68Hn6X9Djmabm810LCWw14MLgn/3u+O0H6izg45NHw1icIDMrh+28aQ/hr/On4KImdn+OxeW40uvf+ao1bCCFE5Xw9IRtzHmhzNfhd0uOQl99DuGOoK4s2na/l6MoW6aWlg5eW+s4KHby0RHpJok3UDo2ikKPEk2Rcx/kt58l5px3Kj+Gc3RhHXuplunXoyZLpVV+wSojKMplVPv37HOMWHefTv89hMteNbsoff/wx3bt3Z9euXQwePJhRo0YxevRoHn74YXbu3El4eDijR4+2JtG2bNlCr169bObG79+/P2fOnCExMbHC192yZQv9+vWz2de/f382b95cLa+rNHUu0VaXREVF0apVK5ufuXPn1nZYQoib2KiZLoR20l2ZUFfHqJku1Vp/wcIEmjwPFBS2at4kQfMrqsGBoG6RtG13EcOJH2ia4GJdQRSu9qzTO2pYMflWHhp2FI0GHB0tGI1aLlz0IKjHfPmyKYQQdUThHtGb6u1GURU0qFhUlfhzhjp1g1ZUwUrgA4Mc6eClkxVHRa3an3mSvB8bofgZsOzxJvO7BtyVM5k9f8Tz/dxfrtuwUYCo9kNK3F/PVxbyu1kULHCz5Vgmn/59rs4scDNo0CDGjx9P06ZNeeONN7h8+TJRUVHcf//9NGvWjEmTJnHw4EHOnTsHQHJycrHFKwu2iy50WZbS6qlIHTExMcXyQVFRURW6bvV2hbjBxMbGVnhVCSGEuB4c9Boem1dzq1VFj5nP3EVPkLyjf6E51UATkIOiKLTs2YXtP/3MRcsOguhqTbYV7lmn0+kI2baf1IvNaZ61B/ADFHTuTa/rl00hhBClC2img/zpN0nQn8IDbywWlc/W5N+Uzf37HBaLyjN3BNZilELYAxXT+gC0nQoWflLYvdzIbeOdr3skpSbafCTRdrPYmZRVaFbk/O26oF27dtbfCxJfbdu2LbYvJSWFwMD8vztKkYcoBb3diu4vT0n1VKSOkSNHMnbsWJt9GRkZpQ5TLUx6tAkhhLBydNTzwviv8Bu2k7PKFjrrp6DUy0MJyp+bzcnJGU93Ly53+oJGHbSl9qzzmPcd/nmncV+zNX+HqtI8UJJsQghRV4ya6YJFY8Gg5OKf502uJg80trcGv+5Oq53ghLAjrV0bApD3S8NCe6WXpagdHRq5WlufcmW7LnBwcLD+XpDkKmmfxZK/inRgYGCxXmcpKfkPgor2UCtLafVUpo6qkB5tQgghipnw+ETmOM4gSb8NzeZ0FAVQVdIOHmfs8Cd55pGJ6HSl/wnxbN6Scd+PZWOLB3DOTaeh5SKfjbrz+r0AIYQQZXLQa6jnqeXY7TtIP34RR10QGo0kB8piMpmY/dUMLudk8eGOb+ga2oHu3i3QKNJ34WbWzas58X1Pc/wbf+u+doMcyjhDiJrzRK/8drgzKYsOjVyt2/ama9euTJkyBaPRiKOjIwArV64kODiY0NDQStWzatUqXnjhBeu+lStX0q1bt+oO2YYk2oQQQhSj0+l4cuRzPPLSA2Qknyc7NZ29f/3N9p9/R7VYsFgsTHz8lVLP7/zFD2wbN5z+B39nuHsinb/4AZ2j3IgIIURdoijw43uDAOj5/n4w54Kq5h8Ahrb3vKb6TRYLfyabuGRU8XZU6B+ow2Q08siLwzkQv49W4W1YMv0Hu5lWYM6SGWxMPYDGUYfq7crm9EMoGoUeXi1rOzRRizSKhlHPNGC9ay4n4kw0bK/j1jF1a6hmq94yBPxmodMqPH1bzfbWKiozM5P4+HjrdkJCAnFxcXh7e9OwYcMyzizdiBEjeOutt3j00UeZMmUKR48e5b333uONN96wGfYZFxdnjeH8+fPExcXh6OhIq1atAHj++ee59dZb+fe//81dd93Fzz//zF9//cXGjRur/oIrQBJtQghRCwJ9qncRg5rwyIvD2bxzA6iw48njBKj3461J4pKynx///L7MRJtOr6f7N79cx2hrnn89eTothLhxpV9Ow5SXRfbe13Fp8yYNzL/S0OyL2fwkWm3FbxkMRgtPfZ3A4WQDPl5ODL6jPjqdhrMGlT+TTXz17pW/LcDmnRt45MXhfD/XPv5e/LN7C80f6s/ev9bk71AU9meelESbQKtT6PN43U0YS6JN1KTt27fTp08f6/bEiRMBeOSRR1i8eHGV6vTw8GDVqlVER0fTqVMnvLy8mDhxorXuApGRkdbfd+zYwbfffkujRo2sK5N269aN7777jtdee43XX3+dJk2a8P3339O5c+cqxVVRkmgTQoha8PS9bcsvVMsOxO8DwFn1I9IyAQUNJ9RVNLD0JlW1j5ui6hTgIYk2IcSNy3x+PdAU15zNuHCCMP7DH6tBwcLQfs9VqA6LqvLwl8c5eCobgPScbH5aeYr7B+X3aLhkVK1/WwoU3a7LbonoyrliE2jXzZVZhRDieundu7d1oYKSFCS9CitaPjQ0tNi+tm3bsn79+jKvXdZ1C9x3333cd9995ZarTjKORwghasE9vZrUdgjlahXeBgBPmqJc+XPR1fI24eq9DBvwQG2GJoQQopq1cdyCAjQKdiHE8qt1/z+7Kv5gZVeqiYQUA83D6ln3XbiUa/3d21Gx/m0pUHS7JBZVZWeqiT/OGtmZasJSgRurmvDMIxPxztJe3aFqqKfewteJufx+xojpyiTeQgghbm6SaCtDVFQUrVq1svmZO3dubYclhBDXxZLpP9CtQ89iC2d5unvy3JiXaicoIYQQNWLJ9B9wdNAQEqDHX91k3Z+WmYvJXLEEUnymBV9vJzq28aZluDsAvt6OOCoQpM+fo63gb4unuxfdOvRkyfQfyq13V6qJnalmTueo7Ew1syvVVLUXWUlmk8qaBQaWRGeyZoEBi0lFl3QJi9GEcimLBtpbyTa5kGvBOjRWCCHEjSEmJqZYPigqKqpC58rQ0TLExsbi7u5e22EIIUSt0Ov1fD/3F4YOHw7Hr+6/9QH/MlccvVENautZ2yEIIUSN0ev1KIpydZJpi8K+I8/QPj2KDyYkMvnjxmh15a9Kek+/BmRk5VHfX8/lzDwGdrbgdfRzBl8Zfqq78relMuIzLcW2O3pXqooqWbvQwLoF+T3yjm0zsWXHdubt+QBVVZnz7HO8tmgImkKzClwyyjDSm0lL1wa1HYIQogaNHDmSsWPH2uzLyMjAw8Oj3HOlR5sQQogy+TbU0me8niaddfQZr6fX2Lo72W9NGhzhVdshCCFEjSq86IFzwlhOGT3xymxO7hYv1i40lHqeRbWwKfUQuZxGp1Pw9nCibQsvbqn3DRkJf1Rq+Gldsmd5ns12xt4Q63xAqqqSlnzM5ri3Y/mJSHHjaOUWUtshCCHqqJuvS4IQQojKUajTK2kJIYSoHi7O7jQL68yB+D04nB0K7tsBUFDYszyP28c7l3je5tTDbEo/BKqCM1noLrthsZjIPr+P7AsH8fUKvqa4wt207Eoz22xfH7Y91PROehSDgqqqKIqC4/E1BDVtyyWjirdj/tBYIYQQQv4aCCGEEEIIIQCFF8Z/xUcxOzm13o3GhsJD40ofFrk/68SV01VyiCfn6FrOxa6wHu8ceWep5+YZLHw9IZtzR0wENNMxaqYLDnrbQTeRXloUBRIyzTR209Le8/ok2iIGObJ2wdXFHHrc74un4yvMWjyN5x59iWdGTUCn02FRVeLSzKw6ZyJAr6G9pxZNsdVJhRBC3Cwk0SaEEKJMd/UdVtshCCGEuI5eeLA9/96aRPctEdZ9EYMcbcoYjBae+jqBo6cMdIxti8dlHUqvPZw3TcYlwJ+Btz1FwsndhId2ZECfJ0u91tcTskncnr+IQOJ2E19PyOaxeW42ZTSKQgcvHR28ru+tS6+xehSNwok4Ew3b67h1jBN36P7FkqVfMGHsv6zldqWa2JWWP4/c6RwzqqrS0duhtGqFEELc4CTRJoQQokx397uvtkMQQghxHem0Gl75OJT1i3JtkkyFPfV1Av8kZNE8vh7uyc6AgmV5R/SNh5FlXsQxvQ8vjP+q3GudO2Iqc7s2aXVKhaZOqK3FGoQQQtRNshiCEEIIIYQQwkZBkmnEdBcSdpj4sF86Xz6ZSZ7BgslgYH/8eQDO+htwfOwokD+Xm8XxIgCnzh6q0HUCmunK3BZCCCHsjSTayhAVFUWrVq1sfubOnVvbYQkhhBBCCHFdFAztzMm4OrRz27jh+F84RLukv3DN1lIwf5uKisboA0CDoBYVqn/UTBdCO+lwdofQTvlztJlMJmYu/JARz93DzIUfYjLVnV5uJSm6OEPRbbPZxO9/zeGTL8bw+19zMJvr9usRQojKWL9+PUOHDiU4OBhFUVi2bFm11Lt371569eqFs7Mz9evX5+2337au/Fxg3bp1dOzYEb1eT1hYGPPmzbM5vnjxYhRFKfZjMJS+knaBmJiYYvmgqKioCsUuj4zKEBsbi7u7e22HIYQQQgghRK0oaWhnYPp+Hrm8jTW9ojkSlMX55Q3wdDViqReP0vwXmoV0JnrM/ArV76DXFJuTbcaCD/n4y38DsCF2LRaLhYmPv1I9L6gGFCzWkJxjRrd7C4bv53C44y00fWoiGp2OP1Z/yu+r8x/WHzq6GdViYUi/52o5aiGEqB5ZWVlEREQwZswYhg2rnrmdMzIy6Nu3L3369CE2NpYjR47w6KOP4urqyosvvghAQkICgwYN4vHHH+ebb75h06ZNPP300/j5+dnE4e7uzuHDh23q1+vLnxZg5MiRjB07tlhcHh4e5Z4riTYhhBBCCCFEiYzu2agZzpxxuEBwni9G9xw8gluTt3UjfTfMoeV7OowPNCbYuwH3B3RFp9l8zdf8ccX3xbbrcqKtYLGGQ19N4/DsD8kBLmz8mxNL/0PI3Q+w4dz/bMpv2/WLJNqEEDeMgQMHMnDgwFKPh4aGMm7cOI4cOcLSpUvx8fFh1qxZdOvWjXHjxrF69WoaN27MokWL6NSpE5Dfm8xgMLB48WKcnJxo06YNR44cYcaMGUycOBFFUZg3bx4NGzZk5syZALRs2ZLt27czbdo0m0SboigEBgbW6HtQlAwdFUIIIYQQQpRohfdmzjieZ7XnVtL8c/m13SXe7jKdtE9X4eDhQ4elW3m+yT08FNQTnaaanuEr5WzXUSeX/WCznXHqFC/tC2QFM9mtfQOL9HEQQtykPv74Y7p3786uXbsYPHgwo0aNYvTo0Tz88MPs3LmT8PBwRo8ebR0aumXLFnr16oWT09WFePr378+ZM2dITEy0lunXr5/Ndfr378/27dvJy8uz7svMzKRRo0Y0aNCAIUOGsGvXrhp/vXUu0Vbe+N6pU6fSokULXF1d8fLy4o477mDbtm3l1jtz5kyaN2+Os7MzISEhvPDCCxUalyuEEEIIIcTNyqKx8Lv3BlQPB/7ukIKXnxPhofX49og7yk+76f7NL+gqMASnMob1f6DMbXtxOLgLCf6RmDTu9Ny5mr3aKaDCLZF31nZoQogblNmksmaBgSXRmaxZYMBsUss/6ToYNGgQ48ePp2nTprzxxhtcvnyZqKgo7r//fpo1a8akSZM4ePAg586dAyA5OZmAgACbOgq2k5OTyyxjMpm4cOECAC1atGDx4sX88ssv/Oc//0Gv19O9e3eOHj1ao6+3zj1WKW98b7NmzZgzZw5hYWHk5OTw8ccf069fP+Lj4/Hz8yuxzpiYGF555RW+/PJLunXrZh3fC/mZVSGEEEIIIURx9/UJZ/p/duEckD/sxtVZR7C/M1t3XuCSsWZu4J4b8xIajYZ/dm/hloiuPPPIxBq5TnULuXs4h2d/aN0+HNQFlCvd8RSFbLURLU87MOj2p2spQiHEjW79olzWzDeACsf+yZ9js8/j1fswpCratWtn/b0gOda2bdti+1JSUqzDPBXFtjtzQW+3wvvLK9OlSxe6dOliPd69e3c6dOjA7NmzmTVr1rW9qDLUuURbeeN7R4wYYbM9Y8YMFi5cyJ49e7j99ttLPGfLli10797dem5oaCgPPfQQ//zzT/UFLoQQQgghxA3mhQfbo9Eo/H5Wz7lLeWg0sGPfJXy9nfB2rJkxnTqdjglj/1UjddekZtEvcfKn78g+dQKAi/Xqg6qCovBbh+donLKLfrc8jFZb527BhBA3iBNxpoKFoEG9sl0HODg4WH8vSIKVtM9isQAQGBho7blWICUlBbialCutjE6nw8fHp8Q4NBoNUVFRNd6jrc4NHa0Mo9HI/Pnz8fDwICIiotRyPXr0YMeOHdbE2vHjx1m+fDmDBw8us/6MjIxSf3Jzc6v1tQghhBBCCHE96f2Dyi2j02p48aFIfoluSliwM0mnssjKNjH+zhD6B0rCqDCNTkefFVvx6dwd9Hp6p35AgHIUPdk0z03g/TYXaBb9Um2HKYS4gTVsr7s6r6VyZdsOde3alfXr12M0Gq37Vq5cSXBwMKGhodYyq1atsjlv5cqVdOrUySaJV5iqqsTFxREUVPrfP4vFgsViITMzs8RcUEXY5bv+22+/8eCDD5KdnU1QUBCrVq3C19e31PIPPvgg58+fp0ePHqiqislk4qmnnuKVV8pevSgkJKTUY2+++SZTp06t6ksQQgghhBCiVjkHBle4rJuTjl+fblqD0dwYdHo9PWJ+5beVszi2ei7N814FYHC/aNr2kySbEKJm3Tomf/GAE3EmGrbXWbdrUmZmJvHx8dbthIQE4uLi8Pb2pmHDhlWqc8SIEbz11ls8+uijTJkyhaNHj/Lee+/xxhtvWHu/Pfnkk8yZM4eJEyfy+OOPs2XLFhYuXMh//vMfaz1vvfUWXbp0oWnTpmRkZDBr1izi4uKYO3duqde+cOECJ0+eZPDgwSQlJVUpfrtMtPXp04e4uDguXLjAggULGD58ONu2bcPf37/E8mvXruXdd9/l008/pXPnzsTHx/P8888TFBTE66+/Xup1Tp48ibu7e4nHCq9+IYQQQgghhL2pP7T4fMiiemzb9Uux7SH9nqulaIQQNwutTrnuc7Jt376dPn36WLcnTsyfV/ORRx5h8eLFVarTw8ODVatWER0dTadOnfDy8mLixInWugEaN27M8uXLeeGFF5g7dy7BwcHMmjXLZq7/tLQ0nnjiCZKTk/Hw8CAyMpL169dzyy23lHptX19fQkJC2L59O46OjjbHMjIyyuyQVcAuE22urq6Eh4cTHh5uzU4uXLiQyZMnl1j+9ddfZ9SoUYwbNw7In3QvKyuLJ554gldffRWNpuQRtO7u7qUm2oQQQgghhLBnDSTRJoQQ4hr17t3bughBSRITE4vtK1o+NDS02L62bduyfv36Mq/dq1cvdu7cWerxjz/+uNILYGo0GjQaDW5ubuiruKq2Xc/RVkBV1TLnTMvOzi6WTNNqtaiqWmaDEEIIIYQQQojKuiXyzjK3hRBC3LjqXI+2ssb3+vj48O6773LnnXcSFBTExYsX+fTTTzl16hT333+/9ZzRo0dTv3593n//fQCGDh3KjBkziIyMtA4dff3117nzzjvRarXX/TUKIYQQQgghblyDbn8ajUZDfOIOwkM7MqDPk7UdkhBCiOukziXayhrfO2/ePA4dOsSSJUu4cOECPj4+REVFsWHDBlq3bm0958SJEzY92F577TUUReG1117j9OnT+Pn5MXToUN59993r98KEEEIIIYQQNwWtVsfgO56p7TCEEELUgjqXaCtvfO/SpUvLrWPt2rU22zqdjjfffJM333zzWsMTQgghhBBCCCGEEKJEdS7RVpdERUUVG1oaHR1NdHR0LUUkhBBCCCGEEEIIIWpSTEwM06dPt9lnNpsrdK4k2soQGxsrq44KIYQQQgghhBBC3ERGjhzJ2LFjbfZlZGTg4eFR7rk3xKqjQgghhBBCCCGEEELUNkm0CSGEEEIIIYQQQghRDSTRJoQQQgghhBBCCCFENZBEmxBCCCGEEIJ6Pk61HYIQQghh9yTRJoQQQgghhKCeryTahBBCVM769esZOnQowcHBKIrCsmXLqqXevXv30qtXL5ydnalfvz5vv/02qqpaj589e5YRI0bQvHlzNBoNEyZMKLO+7777DkVRuPvuu6slvrJIok0IIYQQQgghhBBCVFpWVhYRERHMmTOn2urMyMigb9++BAcHExsby+zZs5k2bRozZsywlsnNzcXPz49XX32ViIiIMutLSkripZdeomfPntUWY1l01+UqdioqKgqtVmuzLzo6mujo6FqKSAghhBBCCCGEEKJuGDhwIAMHDiz1eGhoKOPGjePIkSMsXboUHx8fZs2aRbdu3Rg3bhyrV6+mcePGLFq0iE6dOgEQExODwWBg8eLFODk50aZNG44cOcKMGTOYOHEiiqIQGhrKJ598AsCXX35Z6vXNZjMjR47krbfeYsOGDaSlpVXodcXExDB9+vRidVWE9GgrQ2xsLAcOHLD5kSSbEEIIIYQQQgghRMV8/PHHdO/enV27djF48GBGjRrF6NGjefjhh9m5cyfh4eGMHj3aOjR0y5Yt9OrVCyenq1Ma9O/fnzNnzpCYmFipa7/99tv4+fkxduzYSp03cuTIYvmg2NjYCp0riTYhhBBCCCGEEEIIO2cyq8xclcmIBZeYuSoTk1kt/6TrYNCgQYwfP56mTZvyxhtvcPnyZaKiorj//vtp1qwZkyZN4uDBg5w7dw6A5ORkAgICbOoo2E5OTq7wdTdt2sTChQtZsGBB9b2YCpCho0IIIYQQQgghhBB2bs7fWcxYlYkKbDxqBGBCX7faDQpo166d9feChFnbtm2L7UtJSSEwMBAARVFs6ijo7VZ0f2kuX77Mww8/zIIFC/D19a168FUgiTYhhBB2p21/x9oOQQghbjitegfWdghCCCGuwT+JRgr6sKlXtusCBwcH6+8FibKS9lksFgACAwOL9VxLSUkBKNbTrTTHjh0jMTGRoUOHWvcV1K/T6Th8+DBNmjSp7EupEBk6KoQQwu60GyCJNiGEqG6SaBNCCPt2S6gjBf29lCvb9qhr166sX78eo/FqonDlypUEBwcTGhpaoTpatGjB3r17iYuLs/7ceeed9OnTh7i4OEJCQmooeunRJoQQQgghhBBCCGH3nrnNFcjvyXZLqKN1uyZlZmYSHx9v3U5ISCAuLg5vb28aNmxYpTpHjBjBW2+9xaOPPsqUKVM4evQo7733Hm+88YbN0NG4uDhrDOfPnycuLg5HR0datWqFXq+nTZs2NvV6enoCFNtf3STRJoQQQgghhBBCCGHndFrlus/Jtn37dvr06WPdnjhxIgCPPPIIixcvrlKdHh4erFq1iujoaDp16oSXlxcTJ0601l0gMjLS+vuOHTv49ttvadSoUaVXJq1uilowo5ywysjIwMPDg2bNmqHVam2ORUdHEx0dXUuRCSGEEEIIIWpT5OBm7Pr9SG2HIYS4gRgMBhISEmjcuDF6vb62w7mpFfy32Lx5M9OnT7c5ZjabOXLkCOnp6bi7u5dah/RoK0NsbGyZb54QQgghhBBCCCGEuLGMHDmSsWPH2uwr6JRVHlkMQQghhBBCCCGEEEKIalDnEm3r169n6NChBAcHoygKy5Ytszk+depUWrRogaurK15eXtxxxx1s27at3HrT0tKIjo4mKCgIvV5Py5YtWb58eQ29CiGEEEIIIYQQQghxs6lzibasrCwiIiKYM2dOicebNWvGnDlz2Lt3Lxs3biQ0NJR+/fpx/vz5Uus0Go307duXxMRE/ve//3H48GEWLFhA/fr1a+plCCGEEEIIIYQQQoibTJ2bo23gwIEMHDiw1OMjRoyw2Z4xYwYLFy5kz5493H777SWe8+WXX3Lp0iU2b96Mg4MDAI0aNaq+oIUQQgghhBBCCCHETa/OJdoqw2g0Mn/+fDw8PIiIiCi13C+//ELXrl2Jjo7m559/xs/PjxEjRjBp0qRiq4oWlpGRUeoxJycnnJycril+IYQQQgghhBBCCFE3WCwWLBYLmZmZGI1Gm2Nl5YgKs8tE22+//caDDz5IdnY2QUFBrFq1Cl9f31LLHz9+nL///puRI0eyfPlyjh49SnR0NCaTiTfeeKPU80JCQko99uabbzJ16tRreRlCCCGEEEIIIYQQoo64cOECJ0+eZPDgwSQlJVWpDrtMtPXp04e4uDguXLjAggULGD58ONu2bcPf37/E8haLBX9/f+bPn49Wq6Vjx46cOXOGjz76qMxE28mTJ3F3dy/xmPRmE0IIIYQQQgghhLhx+Pr6EhISwvbt23F0dLQ5lpGRUWaHrAJ2mWhzdXUlPDyc8PBwunTpQtOmTVm4cCGTJ08usXxQUBAODg42w0RbtmxJcnIyRqOx2JtXwN3dvdREmxBCCCGEEEIIIYS4cWg0GjQaDW5ubuj1+qrVUc0x1QpVVcnNzS31ePfu3YmPj8disVj3HTlyhKCgoFKTbEIIIYQQQgghhBBCVEadS7RlZmYSFxdHXFwcAAkJCcTFxXHixAmysrKYMmUKW7duJSkpiZ07dzJu3DhOnTrF/fffb61j9OjRNr3bnnrqKS5evMjzzz/PkSNH+P3333nvvfeIjo6+3i9PCCGEEEIIIYQQ4oawfv16hg4dSnBwMIqisGzZsmqpd+/evfTq1QtnZ2fq16/P22+/jaqqNmXWrVtHx44d0ev1hIWFMW/ePJvj+/fvZ9iwYYSGhqIoCjNnzqyW2MpT5xJt27dvJzIyksjISAAmTpxIZGQkb7zxBlqtlkOHDjFs2DCaNWvGkCFDOH/+PBs2bKB169bWOk6cOMHZs2et2yEhIaxcuZLY2FjatWvHc889x/PPP88rr7xy3V+fEEIIIYQQQgghxI0gKyuLiIgI5syZU211ZmRk0LdvX4KDg4mNjWX27NlMmzaNGTNmWMskJCQwaNAgevbsya5du5gyZQrPPfccP/74o7VMdnY2YWFhfPDBBwQGBlZbfOWpc3O09e7du1iWsrClS5eWW8fatWuL7evatStbt269ltCEEEIIIYQQQgghxBUDBw5k4MCBpR4PDQ1l3LhxHDlyhKVLl+Lj48OsWbPo1q0b48aNY/Xq1TRu3JhFixbRqVMnAGJiYjAYDCxevBgnJyfatGnDkSNHmDFjBhMnTkRRFObNm0fDhg2tvdRatmzJ9u3bmTZtGsOGDQMgKiqKqKgogOva0arO9WgTQgghhBBCCCGEEDeGjz/+mO7du7Nr1y4GDx7MqFGjGD16NA8//DA7d+4kPDyc0aNHWztdbdmyhV69euHk5GSto3///pw5c4bExERrmX79+tlcp3///mzfvp28vLzr9tpKUud6tNUlUVFRNiuVAkRHR8vcbkIIIYQQQgghhKhTLGYLm79L5NT+NBq09qTbg6FotLXfv2rQoEGMHz8egDfeeIPPPvuMqKgo61z7kyZNomvXrpw7d47AwECSk5MJDQ21qSMgIACA5ORkGjduTHJysnVf4TImk4kLFy4QFBR0TTHHxMQwffp0m31ms7lC50qirQyxsbG4u7vXdhhCCCGEEEIIIYQQZdr8XSIbY46DColxlwDoMTKslqOCdu3aWX8vSI61bdu22L6UlBTrXGqKotjUUdDbrfD+ipSpqpEjRzJ27FibfRkZGXh4eJR7bu2nNoUQQgghhBBCCCHENTm1Pw0KprxXr2zXAQ4ODtbfC5JgJe2zWCwA1l5thaWkpABXk3KlldHpdPj4+FTzK6gcSbQJIYQQQgghhBBC2LkGrT2hoDOXcmXbDnXt2pX169djNBqt+1auXElwcLB1SGnXrl1ZtWqVzXkrV66kU6dONkm82iCJNiGEEEIIIYQQQgg71+3BUHqMDCM00pseI8Po9mBojV8zMzOTuLg44uLiAEhISCAuLo4TJ05Uuc4RI0bg5OTEo48+yr59+/jpp5947733rCuOAjz55JMkJSUxceJEDh48yJdffsnChQt56aWXrPUYjUZrbEajkdOnTxMXF0d8fPw1vebyyBxtQgghhBBCCCGEEHZOo9Vc9znZtm/fTp8+fazbEydOBOCRRx5h8eLFVarTw8ODVatWER0dTadOnfDy8mLixInWugEaN27M8uXLeeGFF5g7dy7BwcHMmjWLYcOGWcucOXOGyMhI6/a0adOYNm0avXr1Yu3atVWKrSIk0SaEEEIIIYQQQgghKq13797WRQhKkpiYWGxf0fKhoaHF9rVt25b169eXee1evXqxc+fOUo+XVO/1IENHhRBCCCGEEEIIIYSoBtKjrQxRUVFotVqbfdHR0URHR9dSREIIIYQQQgghhBCiJsXExDB9+nSbfWazuULnSqKtDLGxsbi7u9d2GEIIIYQQQgghhBDiOhk5ciRjx4612ZeRkYGHh0e558rQUSGEEEIIIYSooEC/4NoOQQghRB0miTYhhBBCCCGEqKBAv6DaDkEIcYOqjYn7ha3q+G8giTYhhBBCCCGEEEKIWuLg4ABAdnZ2LUciCv4bFPw3qQqZo00IIYQQQgghhBCilmi1Wjw9PUlJSQHAxcUFRVFqOaqbi6qqZGdnk5KSgqenZ7GFMStDEm1CCCGEEEIIIYQQtSgwMBDAmmwTtcPT09P636KqJNFWhqioqGJZzOjoaKKjo2spIiGEEEIIIYQQQtxoFEUhKCgIf39/8vLyajucm5KDg4M1BzR37lzmzp1rc9xsNleoHkWV2faKKViyNT09HXd399oORwghhBBCCFFHLFv5P+7ud19thyGEEOI6q2iuSBZDEEIIIYQQQogKkiSbEEKIskiiTQghhBBCCCGEEEKIalDnEm3r169n6NChBAcHoygKy5Ytszk+depUWrRogaurK15eXtxxxx1s27atwvV/9913KIrC3XffXb2B13G5ublMnTqV3Nzc2g5FiCqRNixuBNKOhb2TNizsnbRhcSOQdizs3Y3ehuvcHG1//PEHmzZtokOHDgwbNoyffvrJJin27bff4u/vT1hYGDk5OXz88cf897//JT4+Hj8/vzLrTkpKonv37oSFheHt7V0siVfgRpyj7UZ8TeLmIm1Y3AikHQt7J21Y2Dtpw+JGIO1Y2Dt7bcN2O0fbwIEDeeedd7j33ntLPD5ixAjuuOMOwsLCaN26NTNmzCAjI4M9e/aUWa/ZbGbkyJG89dZbhIWFXXOcRVefENXP3t5je4sX7DNme2Nv77HEK4qyt/fY3uIF+4zZntjj+2tvMdtbvPbI3t5jiVcUZW/vsb3FC/YZs72ZP39+hcrVuURbZRiNRubPn4+HhwcRERFlln377bfx8/Nj7NixFa4/IyOj1J/Zs2dfa/iiHPb2QWFv8YJ9xmxv7O09lnhFUfb2HttbvGCfMdsTe3x/7S1me4vXHtnbeyzxiqLs7T22t3jBPmOui3Jzc0vNA82bN69CdehqOMYa8dtvv/Hggw+SnZ1NUFAQq1atwtfXt9TymzZtYuHChcTFxVXqOiEhIaUeK+t6QgghhBBCCCGEEMK+vP/++7z11lvXVIdd9mjr06cPcXFxbN68mQEDBjB8+HBSUlJKLHv58mUefvhhFixYUOnk2MmTJ0lPTy/xx8fHpzpeihBCCCGEEEIIIYSoAyZPnlxqHqhJkyYVqsMue7S5uroSHh5OeHg4Xbp0oWnTpixcuJDJkycXK3vs2DESExMZOnSodZ/FYgFAp9Nx+PDhUt8sd3f3Uie402jsMkcphBBCCCGEEEIIIUrg5OSEk5NTice0Wm2F6rDLRFtRqqqWuixsixYt2Lt3r82+1157jcuXL/PJJ5+UODy0YCHWjIyMUq9pNpvLPF7XFMRqTzHb23tsb/GCfcVsj20Y7Os9Bom3ptljO7a399je4gX7ilna8PVhbzHbU7z22IbBvt5jkHhrmj22Y3t7j+0tXrCvmO2xDUP+ewxXc0alUdTySlxnmZmZxMfHAxAZGcmMGTPo06cP3t7e+Pj48O6773LnnXcSFBTExYsX+fTTT/nmm2/YsWMHrVu3BmD06NHUr1+f999/v8RrPProo6SlpbFs2bISj586darM+dmEEEIIIYQQQgghxM3n5MmTNGjQoNTjda5H2/bt2+nTp491e+LEiQA88sgjzJs3j0OHDrFkyRIuXLiAj48PUVFRbNiwwZpkAzhx4sQ1De0MDg7m5MmT1KtXD0VRqv5ihBBCCCGEEEIIIYTdU1WVy5cvExwcXGa5OtejTQghhBBCCCGEEEIIeyQz+gshhBBCCCGEEEIIUQ0k0SaEEEIIIYQQQgghRDWQRJsQQgghhBBCCCGEENVAEm11lKqqTJo0iTlz5tR2KEJUSU21Yfm3Ia4XaWviRiDtWAghhBB11Y36PUUSbXVAaGgoiqLY/Gg0Gj788EPc3d1tym7atIkhQ4bQsmVLWrRoQZs2bfj000+L1ZmTk0NMTAzDhw+ne/fuRERE4Ofnx4ABA1i/fn2x8vv372fKlCnceuut9OzZk/r169O0aVOmTJlCdnZ2mfGfPXuW119/nfbt29OiRQsOHDhwbW+IsDs10YYrW6+0YXEtaqINf/PNN8XqLPrz008/WctLGxbXqqY+i7Oyspg6dSpt27YlIiKChg0bMnz4cI4fP16srLRjUV3Kaw8ZGRk8++yztGjRgmbNmtGpUyf+97//lVvv999/T8OGDdm3b1+Jx6UNi+pSXW1Y7utEbarOz+Kb6t5OFbWuUaNGqo+Pj9q8efNiP6tWrbKW+/bbb1WtVqtOnTpVtVgsqqqq6jfffKMC6ptvvmlT52effaYC6jPPPKMajUZVVVX1+PHjaqNGjVQHBwd1586dNuUDAgJUPz8/dc+ePaqqqqrFYlGnTp2qAuqwYcNKjf2bb75Rg4KC1Pfff19NTk6ujrdD2KGaaMOVqVdVpQ2La1MTbXjOnDkqoAYEBBSr08vLSw0ICFANBoO1vLRhca1qoh1nZWWpnTp1Uhs3bqzGx8erqqqqly5dUjt37qwGBgaqZ86csSkv7VhUh/LaQ15entqtWze1V69eamZmpqqqqvraa6+pgPrtt9+WWOfu3bvVXr16qYAKqHv37i2xnLRhUR2qsw3LfZ2oLdX9WXwz3dtJoq0OaNSoUYlJhsJSU1PVevXqqe3atSt2rF+/fqqDg4N66tQp677//ve/ardu3Wxu4lRVVd9//30VUKdOnWqz//bbb1cXLFhgs89sNqsuLi6qVqtVTSZTset+/vnnqoeHhxoXF1feSxQ3uJpowxWtt4C0YXEtaqINv/POO+o999xjTWQUsFgsaqtWreRzWFS7mmjHBV+Yly5dalN28+bNKqA+9dRTNvulHYtrVZH28Pnnn6uAum3bNuu+vLw8NSAgQA0ODlbz8vKs+w0GgxodHa1GRkaqs2bNUn19fctMtEkbFtequtuw3NeJ2lDd7VhVb657O0m01QEVaXAxMTEqoP7rX/8qdmz69OkqoM6dO7fca82YMUMF1JkzZ5Zb1mKxqB4eHqqXl1exY7t27VIdHR3Vr776qtx6xI2vptpwZT6MSyJtWFRUTbThuLg49ejRo8XKLlu2THVyclLPnTtXblzShkVl1EQ7btq0qQqo2dnZxcq7u7urfn5+5cYl7VhUVEXbQ9euXVVvb+9i+0ePHq0C6po1a2z2f/3119absoJebaUl2koibVhUVE214ZLIfZ2oKTXVjm+mezuZo81OJCYmAhAQEFDsWJMmTQBKnWuigMFgYNGiRQQHBzNy5Mhyr/ntt9+Snp7Oyy+/XOzY5MmTCQ0NrVA9QkD1tOHKkjYsqlNl23BERATh4eHFyv773//moYcewt/fv9xrShsW1a2y7TgxMZF69erh7OxcrHxYWBjnz58nJSWlzGtKOxYVVZH2kJubyz///FPi52vLli0B2LFjh83+hx9+GK1WW+W4pA2LiqqpNlyU3NeJmnS92nFl2VM7lkSbnXBzcwMgNTW1UscATCYTf//9N7179yYwMJBNmzbh6+tb6rXOnDnDO++8w0svvcScOXOYPHmyzfGEhARWrFhB3759WbhwIXfeeScdO3akZcuWTJgwgcuXL1f1ZYob2LW04cqSNixqQnW04bVr17JlyxYmTJhQZjlpw6KmVLYdu7m5kZmZSV5eXqXqAmnHonIq2h6OHz+O2Wwu8bts/fr1ATh58mS1xCRtWFTG9WjDcl8napp8FlcP3XW/oijG2dmZn376ibVr13Lp0iVMJhPdu3dnypQpNG7cGIDOnTsDsHHjxmLnnz17FgBXV9dixz744AOmT5/OhQsX8PX1ZdKkSaX2orh8+TIdOnQgKSmJvLw87rrrLiIjI4uVW716NQBff/01ERER/PDDDzg4OLBkyRLGjRvHzp07WbduHYqiVO0NEXanptpwReotTNqwqKqa/Bwu7IMPPqB3795ERESUeFzasLgWNdGOO3fuzIoVK9i8eTO9evWy7ldVlXPnzhUrD9KORdVUtD2kpaUBoNfri9VR0PMyKyvrmmKRNiyqoqbbsNzXieuhJtvxTXVvV9tjV4WqXrx4UU1MTLRu79y5Uw0PD1c9PDzUQ4cOWfcPGDBABdR58+apJpNJzc3NVb/55hvVy8tLBdTJkyeXeo20tDR12rRpqpOTk9qiRQs1NTW11LImk0mNjY1Vu3fvrmo0mmLzZr388ssqoH722WfFzi2IcfXq1ZV4B4S9q6k2XNF6i5I2LCrrenwO79q1SwXUZcuWlRuPtGFRFTXRjjds2KDqdDo1MjJSPXnypKqqqhofH6/ee++91tUbc3NzS4xH2rGojIq2hw0bNqiAetdddxUr9/PPP6uA+uSTT5Z6ncrM0SZtWFTG9WrDcl8nalJNtuOb6d5Oho7WAd7e3jRq1Mi6HRkZyaxZs0hPT+fNN9+07v/xxx95+eWXmTVrFh06dOCee+7h1KlTPPfcc8DVsdAl8fDw4MUXX+TNN9/k0KFDvPrqq6WW1Wq1dOrUid9++w0/Pz8mTpxo0+2zYIhIQZfQwm699VYAtm3bVsFXL24ENdWGK1pvUdKGRWVdj8/hDz74gLCwMIYOHVpuPNKGRVXURDvu0aMHq1atwtfXl549e3LHHXfwf//3fzz77LN4enoSFhaGo6NjifFIOxaVUdH2UDBkOTs7u1i53NxcANzd3aslJmnDojKuVxuW+zpRk2qyHd9M93aSaKujevbsCdg2CBcXFz788EP279/P7t27+f3335k0aRLx8fFotVoGDRpUbr333HMPAH///Xe5ZT09Penduze5ubls3rzZur+gK2hJ87X4+PgA195lX9i/mmrDJdVbGmnD4lpUZxs+duwY//vf/3juuefQaCr+p1fasLhW1dGOe/fuzcqVK0lISOCvv/5i8eLFNGjQgLS0tAoljqUdi4qoaHto0KABQImLcFy4cAG4urBHdZE2LCrierdhua8TNeF6t+Mb9d5OEm11lMlkAsDJyanMcpmZmfzyyy/cdddd1kZUFk9PT+DqBMZVKd+0aVOg5MkNC/5RFfzDEzevmmrDFa23gLRhUVXV2YY/+ugjXF1deeyxxyodh7RhcS1q6rM4JiYGjUbDo48+WqE4pB2L8lS0Pfj6+hISEsKRI0cwm8025Y4cOQLk98SsbtKGRXmudxuW+zpRE653O75R7+0k0VbL5s2bx+23316sce7cuRPAZuLhkrz++uvk5eUxbdo0m/2TJk0iIiICVVVt9sfFxQFw2223Wfelp6fj5ubGf/7zn2L1x8XF4eTkRLdu3az7Bg8ejKIoJT49+euvvyrcM0ncGGqqDVemXmnD4lrUVBsucO7cOZYsWcJjjz1GvXr1SiwjbVhcq5pux4UlJSUxc+ZMHn/8cdq3b2/dL+1YXIvKtIdhw4aRk5PDunXrbMqtXLmSDh060KpVq1KvU/BvxGKxFDsmbVhci5pqw3JfJ66nmmrHN9293XWdEU4U8+abb6qAOmnSJDUvL09VVVU9efKkGhkZqQYHB1snHi4qLS1NfeGFF1QXFxf1zz//LHb8+eefVwH1tddeU81ms6qqqpqUlKRGRESoTZo0US9evGgtm5qaqgJqSEiIGh8fb93/ySefqIA6c+bMEuvXaDTqjz/+aN336aefqoqiqG+//XbV3gxhl2qqDVemXmnD4lrUVBsuMGnSJFWj0ajHjh0rtYy0YXGtarodF1i3bp0aGhqqDhkyRM3JybE5Ju1YXKuKtoczZ86o/v7+alRUlJqenq6qqqp+9tlnqoODg7px48ZS6z916pR10Y+PPvqo2HFpw+Ja1UQblvs6cb3VRDu+2e7tJNFWy3bs2KE+8cQTauvWrdWwsDC1devWavPmzdXnnntOPXfuXLHyW7duVbt06aI6OzurQ4YMUffs2VNivZs3b1bHjRuntmnTRm3atKnaunVrtUWLFupLL71UbGUao9Govvvuu+odd9yhNm7cWG3Tpo3apEkTtV+/fury5ctLrN9isaiffPKJ2qZNG7VFixZq69at1Z49e6rff//9Nb8nwr7UVBuuTL3ShsW1qKk2XMDd3V29++67yywjbVhcq5pux08++aQaFham+vv7qzNmzFCNRmOxMtKOxbWqTHs4cuSIetddd6khISFq27Zt1d69e6ubNm0qsd7/+7//U5s2barqdDrrarmA6uPjo0ZERFjLSRsW16om2rDc14nrrSba8c12b6eoapE+qKJOMxqNrFq1iqioKPz9/Ws7HCEqTdqwsHfShsWNoLLtuGBoR7t27dDpdDUdnhBCCCGE3ZJEmxBCCCGEEEIIIYQQ1UAWQxBCCCGEEEIIIYQQohpIok0IIYQQQgghhBBCiGogiTYhhBBCCCGEEEIIIaqBJNqEEEIIIYQQQgghhKgGkmgTQgghhBBCCCGEEKIaSKJNCCGEEEIIIYQQQohqIIk2IYQQQgghhBBCCCGqgSTahBBCCCGEEEIIIYSoBpJoE0IIIYS4Qb3yyitoNBoURcHT0xODwVBiOVVVadKkCYqioNVqeeWVVwB4+OGHURQFRVEICwtj+/btAGzYsIHQ0FBrve3bt6dly5Y0bNiQAQMGMH/+fHJzc6/b6xRCCCGEqCsk0SaEEEIIcYP64IMPGDBgQP8x4wAA7slJREFUAADp6en8+uuvJZb766+/OH78OAADBgzggw8+AOCbb76hefPmACxcuJBOnToB0LNnT6ZPnw7AHXfcQVxcHAcPHmT37t306NGDp59+mqioKE6cOFGjr08IIYQQoq6RRJsQQgghxA3M39+f2267DchPnJVkwYIFdO/eHQA/Pz+bYwEBASXu9/HxKVaPl5cXr732GrNnz2bv3r3cfffdmM3ma34NQgghhBD2QhJtQgghhBA3uEaNGtG9e3f++OMPLl68aHPs/Pnz/Pzzz4waNarEcxVFqfT1xo8fT4sWLdi1axc//fRTlWIWQgghhLBHkmgTQgghhLjBqarKqFGjyMvL44cffrA5tnjxYjp16kSLFi2q7XoajYa77roLgN9++63a6hVCCCGEqOsk0SaEEEIIcYNTVZUHHngAJyenYsNHv/jiC5544glUVa3Wa7Zp0waA/fv3V2u9QgghhBB1mSTahBBCCCFuAp6engwZMoTNmzeTkJAAwNq1a0lJSWH48OHVfj1fX18A0tLSqr1uIYQQQoi6ShJtQgghhBA3idGjRwNXF0WYP38+Dz/8MM7OztV+LYvFAoBer6/2uoUQQggh6ipJtAkhhBBC3CQGDhyIr68vMTExXLp0iaVLl/LEE0/UyLVSU1MBCAwMrJH6hRBCCCHqIkm0CSGEEELcJBwcHHjggQc4fPgwzz33HJGRkbRt27ZGrhUXFwdAp06daqR+IYQQQoi6SBJtQgghhBA3kYLhozExMTXWm01VVX7//XcAHnzwwRq5hhBCCCFEXSSJNiGEEEKIG1jR1URvueUWmjdvjoeHBw888ECp5a7Fl19+ycGDBxk5ciQRERHVVq8QQgghRF2nq+0AhBBCCCFEzTl37hwaje2z1a+++oqsrCxcXFys+86cOQNASkpKheq9ePFisX2ZmZl88cUXvPzyywwcOJD58+dfQ+RCCCGEEPZHerQJIYQQQtygpkyZwqpVq1ixYgXPPvusdf8tt9xCnz59rNsrV67k5ZdfBmDFihVMmTKlWF0ODg7W3zdu3Ggt/+eff9K+fXtat25Nx44diY2N5ccff2T58uU2iTwhhBBCiJuBolbnOAEhhBBCCHFD6dGjB5s2beL8+fP4+vrWdjhCCCGEEHWaJNqEEEIIIUSJ8vLyaNCgAd7e3hw8eLC2wxFCCCGEqPNkjjYhhBBCCFGM0Whk0qRJXLp0iUWLFtV2OEIIIYQQdkESbUIIIYQQopipU6eyb98+Nm7cSOfOnWs7HCGEEEIIuyBDR4UQQgghRDGqqqIoSm2HIYQQQghhV2TVUSGEEEIIUYwk2YQQQgghKk8SbUIIIYQQQgghhBBCVANJtAkhhBBCCCGEEEIIUQ0k0SaEEEIIIYQQQgghRDWQRJsQQgghhBBCCCGEENVAEm1CCCGEEEIIIYQQQlQDSbQJIYQQQgghhBBCCFENJNEmhBBCCCGEEEIIIUQ1kESbEEIIIYQQQgghhBDVQFfbAdRFFouFM2fOUK9ePRRFqe1whBBCCCGEEEIIIUQtUlWVy5cvExwcjEZTer81SbSV4MyZM4SEhNR2GEIIIYQQQgghhBCiDjl58iQNGjQo9bgk2kpQr149IP/Nc3d3L7FMVFQUsbGx1zOsa5KRkUFISEiZr6musbf32N7iBfuK2R7bMNjXewwSb02zx3Zsb++xvcUL9hWztOHrw95itqd47bENg329xyDx1jR7bMf29h7bW7xgXzHbYxsG6NChA8eOHbPmjEojibYSFAwXdXd3L/U/ulartasGUaCs11TX2Nt7bG/xgn3GbE9tGOzvPZZ4rw97asf29h7bW7xgnzFLG65Z9hazvcUL9tWGwf7eY4n3+rCndmxv77G9xQv2GbM9tWHIf4+BcqcYk0RbGaKioqxvZIHo6Giio6NrKSIhhBBCCCGEEEIIUZPmzp3L3LlzbfYlJCRU6FxJtJUhNjbWrrKrQgghhBBCCCGEEOLalNTJqnnz5hw5cqTcc0tfJkEIIYQQQgghhBBCCFFhkmirIhk+WvPs7T22t3jBPmO2N/b2Hku8oih7e4/tLV6wz5jtiT2+v/YWs73Fa4/s7T2WeEVR9vYe21u8YJ8x25vHH3+8QuUUVVXVGo7F7mRkZODh4UF6evoNM3T0RnxN4uYibVjcCKQdC3snbVjYO2nD4kYg7VjYO3ttwxWNW3q03SScnJx48803cXJyqu1QhKgSacPiRiDtWNg7acPC3kkbFjcCacfC3t3obVh6tJXAXrOrQgghhBBCCCGEEKL6SY82IYQQQgghhBBCCCGuI0m0iUpbtvJ/tR2CEEIIIYQQQgghRJ2jq+0AhP35edWP3N3vvtoOQwghhBBCCCGEuKGYzWby8vJqO4ybkoODA1qt9prrkURbGaKiooq9ydHR0bJsrhBCCCGEEEIIIaqNqqokJyeTlpZW26Hc1Dw9PQkMDOTTTz9l7ty5NsfMZnOF6pBEWxliY2NlMYQyWFSVuDQzCZlmGrtpae+pRaMotR2WEEIIIYQQQghhVwqSbP7+/ri4uKBU8d46PSsXD9cbczXPmqSqKtnZ2aSkpAAld7IqWAyhPJJoExVmNBh4+7nhbDuwldeeuJO73v2B3Zfz//GnpuZndjt4SZMSQgghhBBCCCEqymw2W5NsPj4+11TX2UtGAnz01RTZzcXZ2RmAlJQU/P39qzyMVBZDEBU29Zn7WLJnAxpjHkv2bGDL7t02xw9fvoxFtdRSdEIIIYQQQgghhP0pmJPNxcWlSudbLCoJ5w3sO5VNRraRA4mXMFvk3rwqCv4bXMs8eZJoExW2Yv82UECvAgrs2/kXoAJg4DTnTafYnHq4VmMUQgghhBBCCCHsUVWHiyZdzCXLaAEFUMGsceDwibT8ed8uZnM4KZXki9moqlq9Ad+AqvrfoDBJtIlKu3yl1ez4eTYWTSImMsjmIDkcY3/WidoNTgghhBBCCCGEuIkYTLYJNEWjwZRnIfmkEUOKhtxcC+cuZXPuUk4tRXhzkUSbqLD+rTsXdGADFfq1uoX8lDmoFKy+IYshCCGEEEIIIYQQ14PFoqKaLViMRus+1WLBDWcsmVoc1Px51F3MerIMVR8OKSquziXa1q9fz9ChQwkODkZRFJYtW1Zq2fHjx6MoCjNnziy33pkzZ9K8eXOcnZ0JCQnhhRdewGAwVF/gN4G35vyPR9r1ROvgwCPtenLvez+wN1ZFhztaXHGmCa3dQmo7TCGEEEIIIYQQ4qZw/EwGeTnZqGYTqCoooLXk4YLtyqNOqiM1MXK0Mjmcyti7dy+9evXC2dmZ+vXr8/bbb9sMfT179iwjRoygefPmaDQaJkyYUKyOpUuX0qlTJzw9PXF1daV9+/Z8/fXX1RJfWepcoi0rK4uIiAjmzJlTZrlly5axbds2goODy60zJiaGV155hTfffJODBw+ycOFCvv/+eyZPnlxdYd8UHPV63pn/C5273s5rc5ay+uAlNq4+AIAzYfjpGtDNs3ktRymEEEIIIYQQQtwcDLmm/F9UFXNONhqgVag3haca015J/VTD9GPFVDSHUxkZGRn07duX4OBgYmNjmT17NtOmTWPGjBnWMrm5ufj5+fHqq68SERFRYj3e3t68+uqrbNmyhT179jBmzBjGjBnDn3/+WW2xlkRXo7VXwcCBAxk4cOD/s3ff4VGV2QPHv3dmkkwKKRBIAUICkd4CQQyoFEUExHVFcBUDsqCgUcGgq2JZ9efq6gICgrAigiK6YlnWggIinQiBEKoQWuhpkELKZDJz7++PMJNMekJCEjif5+Ex751b3pEw5dzznlPhPufOneOpp55izZo1jBgxotJzxsTE0L9/fx5++GEAgoODeeihh9i5c2eFx2VlZZX7mIuLCy4uLuU+fj3705BRjH19Lc6BvmRlFq7xdtYCSLHG81VSAaP9IjDoGtyvlhBCCCGEEEIIcV0xuhjIyStaNupqdCr8r5eOy6mFnUd1mkK+zkyTK4/VpspiOMHBwUyaNImEhAS+++47mjVrxrx58+jXrx+TJk1i/fr1hISEsHTpUsLDw4HCZCmTycSyZctwcXGha9euJCQkMHv2bKKjo1EUheDgYObOnQvAJ598Uua1Bw4c6DCeOnUqn376KVu3bmXo0KFlHqOqKqqqkp2djbnYclyoOEZUXIPLaKuMqqpERkby/PPP06VLlyodc+utt7J79257YO3EiROsXr260iBd69at8fLyKvPPO++8c9XPpbG6764HOHjiIpvWHsCcX0CuKZNcJQGTdoHTpjS+To6p7ykKIYQQQgghhBDXvbaBnri7OqMoCu6uzrQN9ASgia9Ck+Y6FGcVVadiccoANateOo++//779O/fnz179jBixAgiIyMZN24cjzzyCHFxcYSGhjJu3Dj73GJiYhgwYIBDctPQoUM5f/48iYmJNZqDpmmsX7+eI0eOcPvtt5e7X1paGmfOnCE8PLxUHKh166qVymp0aUfvvvsuBoOBZ555psrH/OUvfyE1NZVbb70VTdOwWCw88cQTvPjiixUed+bMGTw9Pct87EbLZrOoFr5OjiElP5P8i5lYc35F1W5DNWtcsm7BVSmK2abkZ9bjTIUQQgghhBBCiBuDokArpzwsBdnoFCMJ8xdx4fftGLuEEzxpKjqvHCwXs1AsuaTkA2j4Nw+4pnMcPnw4kydPBuC1115j4cKF9OnTh9GjRwPwwgsvEBERQXJyMv7+/iQlJREcHOxwDj8/PwCSkpIICQmp8rUzMzNp2bIl+fn56PV6PvzwQ4YMGVLu/r6+vrRu3Zpdu3bh7Ozs8FhWVlaVgm2NKtC2e/du5s6dS1xcHEo1Fhdv3LiRf/zjH3z44Yf07duXY8eOMXXqVAICAnj11VfLPc7T07PcQNuN5uvkGE6b0gDQfFxxMewgOycQzbkd5kw9rh5FUfEWLl71NU0hhBBCCCGEEOKGYUpJIj8lCYDErxZw6qvPQNPIit2KucBCs0cmoGi59v0zstKveaCte/fu9p9tAbNu3bqV2paSkoK/vz9AqZiPLdutOrEggCZNmhAfH092djbr168nOjqatm3bllpWaqPT6dDpdHh4eGA0Gqt1Lfs5anRUPdmyZQspKSkEBQVhMBgwGAycOnWK6dOnl4p2Fvfqq68SGRnJpEmT6NatG3/+8595++23eeedd1BV9do9gUaseJaaoiiYc3IxXF6Ep24fb/a7hyCjL07oCTL6Mtovoh5nKoQQQgghhBBCXP80TcN8Mc0+zvzjAPbWopqG6eBurFqJYNG1XzmKk1NRbThboKysbbb4jC2rrbiUlBSgKChXVTqdjtDQUHr27Mn06dN54IEH6rwUWKPKaIuMjOTOO+902DZ06FAiIyOZMGFCucfl5uai0znGFPV6PZqm1cv65MaohYtXUUabpqGqVgCaO2/BoDPwUMBt9Tk9IYQQQgghhBDihmJKSUKzWuxjr05dydgXVxhsUxSMXXqXOsbby+daTrFGIiIimDFjBmaz2b58c+3atQQGBlaYZFUVmqaRn59fC7MsX4MLtGVnZ3Ps2DH7+OTJk8THx9O0aVOCgoJo1qyZw/5OTk74+/vToUMH+7Zx48bRsmVLe5Ry5MiRzJ49m7CwMPvS0VdffZV7770XvV5/bZ5YIzfaL6Jw+WhWEucSEsjPKUw9HTX0wXqemRBCCCGEEEIIceMpyEh3GLd5YCxmYxPS98Zh7NIbrzHjQM3ByckZBYUCawFQGGyq7hLM8lQWw6mJhx9+mDfeeINHH32UGTNmcPToUd5++21ee+01h3nHx8fb55Camkp8fDzOzs507twZgHfeeYfw8HDatWuH2Wxm9erVfPbZZyxcuLDmT7gKGlygbdeuXQwaNMg+jo6OBmD8+PEsW7asSuc4ffq0QwbbK6+8gqIovPLKK5w7d47mzZszcuRI/vGPf9Tq3K9ntqw1S3ML87fOpkPbTtwz+D6eGh9d31MTQgghhBBCCCFueDpnF8JnvM7BY6dQcQFrGqgqFhS0K8syU9KSUFDwa+5fK9esjRhOSV5eXqxbt46oqCjCw8Px8fEhOjrafm6bsLAw+8+7d+/miy++oE2bNvbOpDk5OTz55JOcPXsWV1dXOnbsyOeff86DD9ZtwpCiydrJUrKysvDy8iIzM1OaIVTgp73pjOjR8NNOhRBCCCGEEEKIhspkMnHy5ElCQkKqVYA/L/mCvRECgEsLf1z9Ajh+6ig5udn27Xq9HqvVah97uDehbVBo7Uz+OlPR30VVY0WNqhmCaBgsVo0Pf0vmnZ/O8+FvyVisEqsVQgghhBBCCCGuJWMLf4wtAjB4NMHYIgBji8IstZDW7XB380CvN+Du5kFTb1+H49xc3etjujeMBrd0VDR8H21K4cPfktGAD39LBuDJwdXr/CGEEEIIIYQQQojq0TSNy2kapiwNo6dCkxZ+GBXHZaA6nY52bW6yj5NSL1zrad7QJNAmqm13Yo69I7B2ZSyEEEIIIYQQQoi6dTlN43JqYb21glQN0OHZvOLGBrl5ORWORe2SQFsF+vTpU6oraVRUFFFRUfU0o4ZBLVHWr+RYCCGEEEIIIYS4EZnMFsa+vpbYQ0n06ezPitfvwuhcO6EXTdPIvqw6bDPnVv593N3Vg+ycyw5jUbEVK1Ywa9Ysh23F69xVRAJtFYiNjZVmCGXQKRWPhRBCCCGEEEKIG9HY19eybV/hUs1t+y4w9vW1fPv28Fo5d+plC3mais5JwbmgsOS+s1vlX8hb+PqhAamXLqJzaoGmc0PTNBRFvsyXZ+zYsUycONFhm60ZQmWkGYKott7BHtj+OSpXxkIIIYQQQgghxI3u4ImLFY6vRq5ZxeRixWS0YtVrWN1VmvhWHixTFAV0TVCVJliskHwpl+RLubU2L+FIMtpEtT0+oAUAX/yexsO3+PL4gBaYzSYWLH2csxcO0yqgI1ETPsLZueptiYUQQgghhBBCiMauS9tm9ow227i2aFfKNtWkeFP65XxQXB3G/s2k+2hdkIw2UW0GvcKTg/14aUQgTw72w6BXWLD0cRJO7CA3L5OEEztYsPTx+p6mEEIIIYQQQghxTa14/S76dw/A2aDQv3sAK16/q9bOrSjgkq9H0UBvVdDn6LicJjXTGxrJaBM1NqKHj/3nsxcOOzxWciyEEEIIIYQQQlzvjM6GWqvJVpKbs548q4ZVX7RctCrNEAB8mrg4LhdVcziYcA6ji5GQ1u3Q6SQPq7bI/0lRK1oFdKxwLIQQQgghhBBCCEcWi4VPv/mYtEsppF1KtS8PLUvzJgZc3IoaIUDVmiEA+DV1w6+pOx5uzjjrTZjzL2K1WsjJzebkmeNX/TxEEQm0iVoRNeEj2rfti5urF+3b9iVqwkf1PSUhhBBCCCGEEKJBm7d0Jp988zEms0papsbxsynlBtsURcGvpYFm3gacXBSaNNdVqRmC7Vj/Zm60a+mFaslAKVbpzZRvqvH8N2/ezMiRIwkMDERRFFatWlXjcxW3f/9+BgwYgKurKy1btuTNN98s9f9l06ZN9O7dG6PRSNu2bVm0aJHD4wUFBbz55pu0a9cOo9FIjx49+OWXX2plfhWRpaOiVjg7G3l28mf1PQ0hhBBCCCGEEKLR+HbNV2gut4DiDIqBHJOO5Et5+DdzK3N/RVHwbK7g2bzm1zS6GMnJzXYY11ROTg49evRgwoQJjBo1quaTKiYrK4shQ4YwaNAgYmNjSUhI4NFHH8Xd3Z3p06cDcPLkSYYPH85jjz3G559/zrZt23jyySdp3ry5fR6vvPIKn3/+OYsXL6Zjx46sWbOGP//5z2zfvp2wsLBamWtZJKNNCCGEEEIIIYQQoj5ooBpaOWzKMRXU6SVDWrfD3c0Dvd6Au5sHIa3b1fhcw4YN46233uL+++8v8/Hg4GDeeustxo0bh4eHB23atOF///sfqamp/OlPf8LDw4Nu3bqxa9cu+zErVqzAZDKxbNkyunbtyv3338+MGTOYPXu2Patt0aJFBAUFMWfOHDp16sSkSZP461//ysyZM+3nWb58OTNmzGD48OG0bduWJ554gqFDhzJr1qwaP9+qkEBbBfr06UPnzp0d/ixYsKC+pyWEEEIIIYQQQoh6pGoacekWfr5gJi7dglpBbbWKjLr7QXSaHoOmR0HBTXWlhqeqlKpqHDubyYETl9D0LegU2pV2bW6q80YI77//Pv3792fPnj2MGDGCyMhIxo0bxyOPPEJcXByhoaGMGzfOHkSLiYlhwIABuLi42M8xdOhQzp8/T2Jion2fu+5y7Og6dOhQdu3aRUFBYaAyPz8fo9ExW8/V1ZWtW7dWOucVK1aUigf16dOnSs9Xlo5WIDY2Fk9Pz/qehhBCCCGEEEIIIRqQ+AwrcelWAM7lFf63l0/1QyzPTHiOE1tiMGpNcNaccFONWPLNNZqTxWJh/qez2bk3hpt7RPDU+GgMhqI5nTifRU5e4blz8sycOJ9FaCuvGl2rOoYPH87kyZMBeO2111i4cCF9+vRh9OjRALzwwgtERESQnJyMv78/SUlJBAcHO5zDz88PgKSkJEJCQkhKSrJvK76PxWIhLS2NgIAAhg4dyuzZs7n99ttp164d69ev53//+x9Wq7XSOY8dO5aJEyc6bMvKysLLq/L/XxJoE0IIIYQQQgghhKiGpDzHYM3hQ8d4679HOG4MokeQB5cTj7L7UBJ9Ovvz1t/upJO3c5nnMRgMBLu0AS7jrDqBAk5azUI18z+dzewl/0TTNLbu2gTAtIl/sz9uyrc47F9yXFe6d+9u/9kWHOvWrVupbSkpKfj7+wOFteiKs2W7Fd9e2T5z587lscceo2PHjiiKQrt27ZgwYQJLly6tledVHlk6KoQQQgghhBBCCFENGsWCPJrGZ1uycUk6jgU9u0/lciDHE7NFY9u+C6w+klnhufx93K+cszBQ5OSkr9Gcdu6NsQebNE1j594Yh8eNLoYKx3XFycnJ/rMtCFbWNlVVAexZbcWlpKQARUG58vYxGAw0a9YMgObNm7Nq1SpycnI4deoUhw8fxsPDg5CQkNp8eqVIoE0IIYQQQgghhBCiGhxyqRSF1DwDtx3+yj42uLraHzYYnaiI/kpcTX8lRKNUsG9Fbu4RYQ9aKYrCzT0iHB4PCWiCs6EwEOds0AgJaFLDK9WtiIgINm/ejNlctIR27dq1BAYG2peURkREsG7dOofj1q5dS3h4uEMQD8BoNNKyZUssFgvffvstf/rTn+p0/hJoE0IIIYQQQgghhKgGf9di4RRNxc96kVO+Xa6MNSx5eQAMursbRjeXChsmBHYqzCzTaYXndHarWajtqfHRRE98kdv6DCR64os8NT7a4fHUS8kUmM6hWM5SYDpH6qXkGl2nuOzsbOLj44mPjwfg5MmTxMfHc/r06Rqf8+GHH8bFxYVHH32UAwcO8N///pe3336b6OhoeyBxypQpnDp1iujoaP744w8++eQTlixZwnPPPWc/z44dO/juu+84ceIEW7Zs4e6770ZVVf72t7+Vd+laITXahBBCCCGEEEIIIapItVhwW/E+zVz9yezSD9VqZXS7TL5NHoBTQS43ueZicTeR27sNg4d1R1EU4tKtaJpG76als9vC73cm4ZCCs6uCu6eOJr41C7QZDAaHmmwlZWSmlxr7Nw+o0bVsdu3axaBBg+zj6OjC4N748eNZtmxZjc7p5eXFunXriIqKIjw8HB8fH6Kjo+3nBggJCWH16tU8++yzLFiwgMDAQObNm8eoUaPs+5hMJl555RVOnDiBh4cHw4cPZ/ny5Xh7e9doXlWlaFpdNY5tvGydJDIzM2/4rqMWq8rclXv5YesJRt7alqljemDQSyKkEEIIIYQQQogb0+G5/+TIB+9h6j+cy699jMvar8i/60E8Vi4gzMPKTU9EozMY+Op0PpeL9RtoYoAHg1xKnc9kMnHy5ElCQkIwGo11N+/jhzCb8+1jZ2cXOrbrXGfXa4wq+ruoaqxIMtpEheau3MvML+LQNDhyOg6A6Q+F1fOshBBCCCGEEEKI+nFm1UoADGePgaJguak7KAo5wx/hpu4t0Ck1rbJWt7w9fUhJS3IYi9ongTZRod8PXMCW86hphWMoP9BmtVr4ZcMijiXuJjS4N3cPmoJeL79mQgghhBBCCCGuLwWh3QFQPbwA0Dy82JNupXfTwu/AoR569mRY7fuHetSsm2ht8fP1R0EhJy8bd1cPWvj61et8rlcSAalAnz590Osd/yFERUURFRVVTzO69lSt4nFJv2xYxI/r5gMah48WthIecedTdTM5IYQQQgghhBDiGjKZVT4eOJPj2U40N5r5U4EVQ4uWhQ8qCseyiwJtYT56FAXi06309NHT07tmgTZN00hJS3YIkCk1yJpTFAW/5v41msONZsWKFcyaNcthm9VqLWdvRxJoq0BsbOwNX6OtZMprZSmwxxJ3A7ZonHZlLIQQQgghhBBCNH5PLD/JgQJfcIHTqsaqtWd4YERwmfua8/N595Ux9HvsQ96d9SSfzlpZoxpsyWlJ9iWf2TmX0dCuuomBqNjYsWOZOHGiwzZbjbbKSFV7UaFbuvpji60pSuG4Iu2CwiocCyGEEEIIIYQQjdWRJFPRQFG4lFng8HioR1GYZfz0MWyP20LmxfNsj9vC+OljanTNjKz0CseiYZGMNlGhqWN6ALDjYBJ9u/jbx+UpubJUWtoKIYQQQgghhLhedPA3svNkjn3cOcCVXj56DmVa6ezluDz00LEDAMT8tJCuEfdx6OCmml1Uvmg3KhJoExUy6HXV6jJ64vQeVAxcpi0HDS9ydGc6QwaqGJ0leVIIIYQQQgghROO2MDKEJ5afZP/ZXLq1cmNhZAhGZx1eTgrtSjQ76Bzale1xWzgQs4rH3lrDwfyLNbqmt1eJbqFe0i20IZPoh6hVocG92a+fwXnd3ViUJlwwteaJ5Sfre1pCCCGEEEIIIcRVMZtNLFz6KE3PPMRY/3/x78iW9qSSkkE2gE9nraRfr9vw9vShWYs2fDprZY2u6+frj59vAB7uTfDzDcDPVxoaNGQNLtC2efNmRo4cSWBgIIqisGrVqnL3nTx5MoqiMGfOnErPm5GRQVRUFAEBARiNRjp16sTq1atrb+ICgLsHTcHs3JFU/W1XtigcSkxHtVjqdV5CCCGEEEIIIcTVWLD0cRJO7CA3L5OEEztYsPTxCvc3Go18Of9/fPqfBFzdvTmUZ0DVqr/u09YttG1QKH7N/WvUcVRcOw0u0JaTk0OPHj2YP39+hfutWrWKHTt2EBgYWOk5zWYzQ4YMITExkW+++YYjR46wePFiWrZsWVvTvi5ZrBof/pbMpKUn+PC3ZCzWyl8Q9HoD3Vp7Y1s03j3xV5onHeDowtl1O1khhBBCCCGEEKIOnb1wuMJxWeIzrMSlW1GBuHQr8RnWOppdw6FpGkkXczl+LpOki7loNQguNmYNLtA2bNgw3nrrLe6///5y9zl37hxPPfUUK1aswMnJqdJzfvLJJ1y6dIlVq1bRv39/2rRpw6233kqPHhUX9r/RLdqYzILfkok5ns2C35JZtDG5SsctjAyhQ95JnApyiTj6X8ZveoG02O11PFshhBBCCCGEEKLutAroWOG4LEl5aoXjxq6sVYnJl/JIvpRDdq6Z5Es5JF/Kq/Z59+/fz4ABA3B1daVly5a8+eabpQJ2mzZtonfv3hiNRtq2bcuiRYvKPd9//vMfFEXhvvvuq/ZcqqvBBdoqo6oqkZGRPP/883Tp0qVKx3z//fdEREQQFRWFn58fXbt25e2338ZqrTiSnJWVVe6f/Pz82ng6DdoP8RkVjstjdNYxPfkT3vh2OG0uHsRZLQD1+noxEUIIIW40BSaVT6Zk887gDD6Zkk2BSd7bhRBC3FiiJnxE+7Z9cXP1on3bvkRN+KjSY673hqFlrUrMMRU47lNiXJmsrCyGDBlCYGAgsbGxfPDBB8ycOZPZs4tWyp08eZLhw4dz2223sWfPHmbMmMEzzzzDt99+W+p8p06d4rnnnuO2224r9VhJqqqiqirZ2dllxoKqotF1HX333XcxGAw888wzVT7mxIkT/Pbbb4wdO5bVq1dz9OhRoqKisFgsvPbaa+Ue17p163If+/vf/87rr79enanfWHT6isdCCCGEaDRUTWNhVA5pewtvUibusrB8Wi5/XeRRzzMTQgghrh1nZyPPTv6sWscoJUJrJceN3bBhwxg2bJjDtuKJZ8MG9ebBh8ZxMfk03333Hc2aNWPevHn069ePSZMmsX79ekJCQli6dCnh4eEArFixApPJxLJly3BxcaFr164kJCQwe/ZsoqOjURSFRYsWERQUZK/Z36lTJ3bt2sXMmTMZNWqU/fpWq5WxY8fyxhtvsGXLFjIyMip8PmlpaZw5c4YRI0Zw6tSpGv0/aVQZbbt372bu3LksW7asWsX/VFWlRYsWfPTRR/Tu3Zu//OUvvPzyyyxcuLDC486cOUNmZmaZf1566aWrfToN3sge3hWOK+LbJwJsf0eKUjgWQgghRKO0J93KxeNW3PoX3ThLTpBGR0IIIURl/F31FY6vRyXDNcs+/pD+/fuzZ88eRowYQWRkJOPGjeORRx4hLi6O0NBQxo0bZ18aGhMTw4ABA3BxcbGfY+jQoZw/f57ExET7PnfddZfDdYYOHcquXbsoKCjKoHvzzTdp3rw5EydOrNLcfX19ad26Nbt27SoVBzpz5kyVztGoMtq2bNlCSkoKQUFB9m1Wq5Xp06czZ84c+//wkgICAnByckKvL/qF7tSpE0lJSZjNZpydncs8ztPTE09Pz1p9Do3JlEF+6HQKaw9kcFdXbx4f0KLKx970RDQAF3f9TrPwW+xjIYQQQjQ+x7KtOLfR0WSkgdxthVltfu0b1cdIIYQQol709C6MQ+zPsNLNW28f1wWLVWXuyr3sOJhE3y7+TB3TA4P+2udXuRudyM4128d3DBnK5MmTAXjttddYuHAhffr0YfTo0QC88MILREREkJycjL+/P0lJSQQHBzuc08/PD4CkpCRCQkJISkqybyu+j8ViIS0tjYCAALZt28aSJUuIj4+v8tx1Oh06nQ4PDw+MRmMNnn0jC7RFRkZy5513OmwbOnQokZGRTJgwodzj+vfvzxdffIGqquh0hb9kCQkJBAQElBtkE2DQKzw52I8nB/tVvnMJOoOBDk//rdR2i8XC/E9ns3NvDDf3iOCp8dEYDI3q11AIIYS4Ifn+zYA1FdCDUzOFsTNd63tKdcZi1fhoUwpxp3Lo1cadxwe0wKCv+moKIYQQwkanKPTyMZBqUsn8xsLyeBNBPQ3cPsEFvaF231vmrtzLzC/i0DTYsvc8ANMfCqvVa1SFX9PCzwiZ2fnodQo3hxfNwRYc69atW6ltKSkp+Pv7A5RaxWjLdiu+vaJ9Ll++zCOPPMLixYvx9fWtledVVQ0uwpGdnc2xY8fs45MnTxIfH0/Tpk0JCgqiWbNmDvs7OTnh7+9Phw4d7NvGjRtHy5YteeeddwB44okn+OCDD5g6dSpPP/00R48e5e23365WnTdRfQUmleXTcklOsODX3kDkHDc+WD6T9z95F4AtsRtRVZXox16s55kKIYQQoiKhHjo2fFGA5YIGVihI0di2wszgyddnsG3RxmQWbkgBIOZ4Nqqm8dQd/vU8KyGEEI3ZxdMqh/9tAuD4DguaqpX7PqppGqmXLeSaVdycdTRvYqhS+awdB5Ps9dE0rXBcHxRFwb+ZG/7N3NDrFIcEJ9vzcHJyKrVNvdJE0ZbVVlxKSuH7si0oV94+BoOBZs2acfDgQRITExk5cqT9cdv5DQYDR44coV27drXyfEtqcDXadu3aRVhYGGFhhRHP6OhowsLCKmxaUNLp06e5cOGCfdy6dWvWrl1LbGws3bt355lnnmHq1Km8+KIEeOrS8mm5JO6ykJcF/4l7meXTcvl2zVcO+5QcN2QWq8qsL/cwMOpbZn25B4tVuq0JIYS4MYT5GLBss9qXjQLErzYzYclx+v3jIBOWHMdkvn7eF0t2Wv98y0ksFqlJJ4QQouaykh3fJ/euNpezJ6RetpByuYDsfCsplwtIvVy196C+XfyLl0qnb5fGeZMoIiKCzZs3YzYX/T9au3YtgYGB9iWlERERrFu3zuG4tWvXEh4ejpOTEx07dmT//v3Ex8fb/9x7770MGjSI+Pj4CptfXq0Gl9E2cOBAe7pfVZRVl23jxo2ltkVERPD7779fxcxEdRUvknxSt5rDu6aR7F4UAFV0OtrfeTtfXdhKK6MvEd7t0SkNLvZrVzwN98jpOKB+0nCFEEKIa02nKLjoFHKLdUq7dN6KzzJPvEPAZZMbT1hOsnRy3dwZrm8ZWRnM/3Q20yaWLoshhBBCVEVBXFGg7ZjxDK0v+WIyu2N0Lh2Wyci1lBq38HQqtV9JU8f0AHCo0VbXKluVWBMPP/wwb7zxBo8++igzZsywr0p87bXX7NlvU6ZMYf78+URHR/PYY48RExPDkiVL+PLLLwEwGo107drV4bze3t4ApbbXtoYb1RCNnl97A1qxD+QHdUvIN+fbx+H33UPHuwaQaEpla8YfbE8/Uh/TrLLfD1xwSMP9/cCFig8QQgghriPdhzt+wNdpOtzzDOiA5peMuP/XvX4mVgdG9vBGsWp4ZzjRdvtOOhw0sTN+R31PSwghRCPWOUBv/3681z2B/boTjH19ba1ew6DXMf2hMFa+NYzpD4Vdk0YItbEqsSQvLy/WrVvH2bNnCQ8P58knnyQ6Opro6KImiyEhIaxevZqNGzfSs2dP/u///o958+YxatSoq35OV6vBZbSJ60fkHDeeHvgrnpabUClgn24BFrf7MOSuAqDHHYMd+v4ezDnNrU071dNsK6dqFY+FEEKI69nAiUZ0OoUty0xYrtw3U1DwyXRGQcHr8vXTYGrKID+OLD+P90F/UgihBWG4FjSp72kJIYSoR6qmEpORwFlTWo1WZA2caGTBt3vxzvYhU3+ZPR6ZeJ0oO0vN281AyuUCh3FDVdmqxLJWIZbcPzg4uNS2bt26sXnz5gqvPWDAAOLi4qo812XLllV536shGW2izjgZdaT3ns+XTr3IVzJRdb5oxv5YjXegYsBiLbnOvGF389KVKD5ZciyEEEJcz/QGhUGPGWni6/j+p2gKGhruzjo2LDZhtTT+O1EGvULbjHYoKPjRGwUdrhf61Pe0hBBC1KOYjAS2ZvxhX5EVk5FQreP1BgUl/BKrm27ForOiKRpd2jYrc9/mTQy0aOKEh4ueFk2caN6kfgJt6ZmXqryvpmlkZCZz5vwhTp89QHLKCXvzgRuNBNoq0KdPHzp37uzwZ8GCBfU9rUbl01kruaVnf1BcUd3uAkVB0/thbTKJs3H7QFNw1UJpovXBT98NtRr1+a61W7o6Fpa8pWvjLCwphBBCXI0ewx0z10KSPVBQMF2GDf82sWmJqZ5mJoQQQtSds6a0CsdVseL1u+jfPQBng0L/7gGseP2uMvdTFIUWnk4E+7rQwtOpSh1H60JGVkaV9828nErm5VRUVUUDTOZcUi+eqrO51bUVK1aUigf16VO1m24NN/+wAYiNjcXT07O+p9GoGY1Gvl74I61v64Zm7FW4UVHAEERXfSbtjLeQZvIGFNJMsCfdSu+mDfPX0lZI8oetJxh5a9trUlhSCCGEaGgGTDSi6BROx1sI6mkg/sd80s8V3Sjbu9rM4Mmu9TjD2tFjuDMbF+c7jIUQQtx4LFaN+b/lsPZoG1ybuxDe6yw6HbQy+lb7XEZnA9++PbwOZln/8vNzSm0zFzTem29jx45l4sSJDtuysrLw8vKq9NiGGdEQ1x3FY5D9Z80lDINOZeqjz/HteavDfseyG26gzVZYUjqNCiGEuJHZlpDaxP9oBopnpF8fpRVKBhRvn+BS31MSQghRD+atz+b9X3MAJ0hshZfejYmD9ER4t6/vqdUZTdNISUsmNy+H5NQkWvj61SirztnJWPlO16GGGdEQ1x2/gH6cSSmKcAf4NsFgMADW8g8SQgghRIPXfbgTm4plfpXsTtpYlQwoCiGEuDF9G1c8K0vhSEIL+t/fvN7mcy0kpyWRkpZ05ecLaGj4Nw+o1jl0Oh3Nm7Wpi+k1eFKjTVwTY+5oX+Y41MPxV7DkuKGxWFVmfbmHgVHfMuvLPVisN2ZxRyGEEPXDatHYsNjEp1HZDabxwMCJRgZNNtKur4FBk40MnNi4g1Mmk4kHo+6l29C2PBh1LyZT4132IoQQQtRERlZ6heOyuLi4O4ybePii0zXs7/d1RTLaxDXx7F96otMpzFsZzzNjetrrm4X5GFAUK8kmFT+jjp7e+nqeacXmrtzLzC/i0DQ4crqwjbAsJRVCCHGtbPzYxKaP83Hrr+f4v02oVo07ptRvPbTrLfNr/PQxbI/bAsD2uC2Mnz6GrxZ8X8+zEkIIUV9GhRl5f32Ow/i6V/I+XhXu63k1Kczyy8/PwcXF3T6+EUmgTVwTtvpmbQM9+fOAdvbtOkWhl0/j+TXccTAJW2NUTSscCyGEENdK7E9mAPQ+heONy7M4tddMSC8Xbp/ggt5Q+/XRrBaNzUvzHWqV1cV1GopDxw5UOBZCCHFjeeZOD3Q6hR/353FPN1eeGuxe+UGNnLeXj33pqG1cGUVR8PZsUZfTajQaT4RDXBeKB9lKslot/LJhEccSdxMa3Ju7B01Br29Yv6J9OvmxOf68w1gIIYS4VqxXbvYUnFP5Xfd3+ua/TmKsRmKsCVXVuKMOun1uWmKyd988vsOCpmrXRVfR8nQO7WrPaLONhRBC3LgMeoVpQzyYNsSjvqdyzfj5+qOgkJaeiq9Pc1r4yvfe6rgxF8yKBunn9R/y47oPOHx0Oz+u+4Cf139Y31MSQgghGpTgOwtLLOg8Cz/CKcU6fO5bXVAn19y72lzh+Hrz6ayV9Ot1G96ePvTrdRufzlpZ31MSQgghrilFUfBr7o+bqzt+zf1r1HH0RiaBNtFg7NjzfYXj+mQrPn3hs6b0yu4IGvTK7sjJxa6s/3dugyhGLYQQ4vrXfqwLnqMNNBmiJ8L1jRKPFr0XWSwW3v/4n0yIvpWpr/blh7XzsFotNbxqyQ/X1/eHbaPRyFcLvmf/mhN8teB7jMYboBaPEEKIMqkWC0c+eI+t4x7gm0m/suzJyw2mGdG14O3pXek+mzdvZuTIkQQGBqIoCqtWraqVa+/fv58BAwbg6upKy5YtefPNN9G0ov/vFy5c4OGHH6ZDhw7odDqmTZtW6hwHDx5k1KhRBAcHoygKc+bMqZW5VaZhrctrYPr06YNe71icPyoqiqioqHqakagvm5fms+EjEx6aN73x4qxzCr2zO6OgsGlxPqDUyXIdIYQQorg0i4b3aCcAmgwzkPVNAYWBLw2fwZdRtSboFB3zls7kx18/wN1Vj9HJldXrF6AA99z1TLWv2X2405X3uqKxEEIIUZcaSn3QhAUzOfLBe1w03E2iey9QrJzYacVqUbnzCbdrPp9rzcerKQCqqnHqYj4mi4bRoNCmmQs6XeHfR05ODj169GDChAmMGjWqVq6blZXFkCFDGDRoELGxsSQkJPDoo4/i7u7O9OnTAcjPz6d58+a8/PLLvP/++2WeJzc3l7Zt2zJ69GieffbZas1hxYoVzJo1y2Gb1Wqt0rESaKtAbGwsnp6e9T2NG8bNYfeyev0Ch3FDcTreYk8UUFDolBvMSeUH2mr3Agr7VhdIoE0IIUSd8zPqOJdX+CHPa5QBd+c8kg9cxmnIOZL6XGTrxSzi/vcTCz+fS0QPNzIuF2Wx7djzfbUDbbY79j4tdYBGj+HODJh442R4FZhUlk/LJTnBgl97A5Fz3HAyyoIQIYSoaw2lPuiZVYXlA1TFC5Si1/+dKwu484lrPp16c+piPjlmFYAcc2HQLaR54eeBYcOGMWzYsHKPDQ4OZtKkSSQkJPDdd9/RrFkz5s2bR79+/Zg0aRLr168nJCSEpUuXEh4eDhQGuUwmE8uWLcPFxYWuXbuSkJDA7NmziY6ORlEUgoODmTt3LgCffPJJmdfu06cPffr0AeDFF1+s1nMeO3YsEydOdNiWlZWFl5dXpcfKJwXRYAy/40nuGfI0HW/qxz1Dnmb4HU/W95Tsgnoaiq2U0Qg2ley6cmOkDgshhKg/qqahaRpNDNDEAL2aGdA9sAfXV/diuCUNRa8Rk3yAWR+/g8lsqpVrblxiYtPifNLPqaSf09CgVEaByWTiwah76Ta0LQ9G3YvJVDvXbgiWT8slcZeFvCxI3GVh+bTc+p6SEELcEBpafdBsQ2+HscV8Y33/M5VYKltyXJn333+f/v37s2fPHkaMGEFkZCTjxo3jkUceIS4ujtDQUMaNG2dfGhoTE8OAAQNwcXGxn2Po0KGcP3+exMTEq34+dU0CbaLB0OsNjLjzKaZOWsqIO59qUB1Hb5/gwqDHjbTra2DAYy549T5OiDbS/niP4c71ODshhBA3gj3pFvZkqFy2QGGimlaqXFp2brb957PJJodaJrZMcYtV48Pfkpm09AQf/paMxVr+h+WSDRbKargwfvoYtsdtISMrne1xWxg/fUz1n1wDlZxgqXAshBCi9plMJpLTkkpsrZ/6oK3vK3xPyzb0hWLvqe5NG2a9UlVT2ZZ+mK8ubGVb+mFUTa2V8xpL3GQrOa7M8OHDmTx5MjfddBOvvfYaly9fpk+fPowePZr27dvzwgsv8Mcff5CcnAxAUlISfn6OnU5t46Skkr8bDU/DiWSIOqNqGvEZVpJNKn5GHT299eika0i16A0Kgx4rWioz0DK4VM0AIYQQoi4dy1ZLjbs0ac22jMP2bWfj9hU9fjqXjiGeuLkFMjDiz/ZM8Y82pfDhb8lowO/HCwNzTw52/DBbpGQQrnRQ7tCxAxWOGzO/9gYSd1kcxkIIIerW+OljyDGHka9LJ0J9Ew2t3uqDto96DkWn48TKLMx5Rd+hm7ZqmN+nYzIS2JrxBwCJplQA+vt0vOrztmnmwqmL+eQVqLg66WjTrHrff7t3727/2RYw69atW6ltKSkp+Pv7A5TqdGq7edgYOqBKRtsNYE+6hbh0K+fyNOLSrexJl7uxV8sWeBu/wINBjxnrpTCnEEII0c+7A7d6dyLY2JxbvTvRVR9gf0wD7rztMc6mNGHGrDk8/Mz9mEwmdidm28NlGrA7MbusUwOlM7bLyuDuHNq1wnFjYusut338/Rz54D3GznQmONyAqycEhxfWaBNCCFG3Dh07wD79Ak7pfuGcsoU/XBcxsJ7qg+oMBjo8/Td6jutdlFSnQHCvhrmi6awprcJxTWiaRlZ2Km5cwEuXhJZ3ltSLyQ5Z85VxcioKlNoCZWVtU9XCm4r+/v6lMtdSUlIASmW6NURyW+4GUNYd8N5N62kyQgghhKiRUA89ezKsDmOdonO4U9330efQKzp27o3h5h4RbN+9hZg9WwHsyzrdejl25lIr+Jw8YKIRRadUmMH96ayVjJ8+hkPHDtA5tCufzlp5lc+0/ti6ywGkbtuIpqr8dVH1iicLIYS4Op1Du7I9bgt5pLLWMI5+3W+rcmKDxWRix6QxZP5xEK9OXej78UoMxqsP0tne/xr6iqZWRl97JpttfLUyL6eSmZVyZaRhyreSnXMZDQ3/5gEVHltTERERzJgxA7PZjLNzYVBz7dq1BAYGEhwcXCfXrE0SaBNCCCGEaATCfPQoCg6lIIozmUwOAa8pY59hycpFDvscOnaAm3s7flmpqJxEydIJZTEajXy14PtqPpuGydZdrvi441QJtAkhxLVku4Hzx4lDDBr7EH0HDWFb+mEivNujUypelLdj0hjSfi+8wZT2+1Z2TBpD/8+v/j2qKu+HDUGEd3ugMJOtldHXPr4a+fk5xUYKV5LOyMhMx795ANnZ2Rw7dsy+x8mTJ4mPj6dp06YEBQXV6JoPP/wwb7zxBo8++igzZszg6NGjvP3227z22msOS0fj4+MByM7OJjU1lfj4eJydnencuTMAZrOZQ4cO2X8+d+4c8fHxeHh4EBoaWqO5VYUE2m4AZd0BF0IIIUTjolMUevmU/9HN1pQAirLXOob2ZK92L2ruWXRureiofE/vYHd2nChcPqoAgf6u/HzBLHVchRBCNAi2Gzjb0g+zNeMPzpgvcsZ8Eai83ljmHwcrHF/vSma61wYXF3dM9mCbVrRk9MrHhV27djFo0CD7/tHR0QCMHz+eZcuW1eiaXl5erFu3jqioKMLDw/Hx8SE6Otp+bpuwsDD7z7t37+aLL76gTZs29s6k58+fd9hn5syZzJw5kwEDBrBx48Yaza0qJNB2Ayh+B9xJVzgWQgghxPWlrKYEvR5Yg1NiLjTtBZqGW3B/Hh/QAoC1BzLo1q4Jrdt7cy5P42xuAV//+CV/Hz0aZxdntqQkEPOiB+ZEN1q1dyJyrjtOxuu7vG/r+8bYl47axkKI65OqqcRkJDhk/lSWLSWurZrUG2vSsRsH4nugUICGE1077q1w/0PZZ+js0fqq5nm982rSHIDL2enk5pnszVe9PX0AGDhwYIX12mxBr+JK7h8cHFxqW7du3di8eXOFc6usTlxZ570WJNBWgT59+qDXOwaloqKiiIqKqqcZ1YxOUejprSc+Aw5lWonPsModayGEEKIRKbks9NNZKzEWqzljsaq4+d5PmqZDZ4pBKThE59CuHE0xg+39XlFISMln/rJ/sXNvDAN7RODfbSrZV3ok/fzpK9wy7DHGTx/D02+9y4a/GVG8s7Fe9iBxt4Xl03L56yKPenj2daPApLJ8Wi7JCRb82hc2OrB1l7u463eahd/CTU9EV34iIUSjVFfdGUXtsFo0TF+3IS8+EJenDqF4Wmjp0qzS4/L7LefCEQuF6VYaN/WrOOTxR85ZCbRVQlEUvD1b4NWkOSlpyeTkZePu6kEL34bflOBqrFixglmzZjlss1qt5eztSAJtFYiNjcXT07O+p1Er4jOsxKUX/lLY/lvR8hMhhBBCNBxlLQstXhdt7sq9nM7qhOYMVqf2tPE+zKez3uKJFefYebKotoq7msKsj99BUfW4xTxG+me5OAfrafFaUfe0Q8cOcDD7DOqpMIyRx7Bu8wcUkhOur67ly6flkrir8Dkl7ipg1vz9dOoai2/cDnyvBNl0BvmsJMT16kyJ7KiSY3HtWSwW5n86m517Y+hWEEXert6gKWiXXLD82hIeq/wc+9eqFG8Pun+typ1l5MnYMhrPmS6VW/+trhorNFaKouDX3L++p3HNjB07lokTJzpsy8rKwsvLq9JjJTf2BpFsUiscCyGEEKLhKmtZaHG/70si7HJHPC3u9MrpRFDQHRiNRhZGhnBziDtOerg5xJ2cvS8A0IrBBGgRaDkK+QdV8nZaGTb+H+zb+g2dQ7sCGro22VgTbDccNfzaX19BJ8fAoYK1bQaHb2rK4TYeHJ77TxIWzKy3uQkh6p6GVuFYXHvzls5k1sfvsCV2I8d25YFWGDDT0l2wHvbinOmifV9V04hLt/DzBTNx6RZU+/LAkqu2yl7FZctozFPNbM34g5iMhFL72BorFGSm2xsrCFEVEmi7QfiVqKlSctyYWKwqs77cw8Cob5n15R4sVgkaCiGEuL4VBr/KHzc73pLe2Z0pUCz0zu5Ms+MtATA661g6sR3xb3Rn6cR26LTC4JJKPkqxLx9pH+ax46cFNAsw86cZz9FE74rLjH1Yd7QAjwJ8epiJnONWx8/y2nIMHGro/PJAp6BZCjP/S3YgFUJcXxSt4rG49r5d85X952Ql1h78tGxtgb5jJq2MvvbH96RbiEu3ci5PIy7dyp70wve37sOdHM6pqhoFZSSZVKX+243eWEHUXOONtohq6emtp5ePHqMOevno6endeBsizF25l5lfxHH4VAYzv4hj7sqKC1wKIYQQjd2ns1bSr9dteHv60K/XbXw6yzEI5H2hOQoKEVk9UFDwvtDc4fECk8qSyZe54+xaHirYQ0t1oEP2RnAXIxMiRxDYP6ywu1v+RYI9m9Lpn+cY+t+LTP24+XXXCCFyjhvB4Qa2NY1D1yUDJSAPNA2lRA1bq0Vjw2ITn0Zls2GxCatFvo0LcT1o7dq8wrGoB8VeXvfpPuSk9zKa9cqnaRuFgX91JcK7vf3xY9mOwTPbeOBEI96BRe9XmRc0lk/LLXWp4kG7ssYAXp26VDgWojzX1xoAUS6dotDLx0BavlZmbTaz2cSCpY9z9sJhWgV0JGrCRzg7N8z1578fuGDvdKJphWMIq/AYIYQQojEzGo0ONdlKcnHWo+WBr8XbPi7u02dyOBVnIVNvwhtvOmuPom9yGRfF094I4NtLqQ7HKBo8GHhrrT+XhsLJqOOvizx45+HTuL6eeWWrQm6PDvDlT/aOoxuXmNi0OB+A4zssqKrGHZNd62nWQojaYgvaFO86KurXqLsf5P1P3gVAU6z0ejCfqY/5AY5F9wtMKidfyycvUSVwnhHFWQNDYXBNb1DILxGEK6vGqO3ve3fWcXp7tivz77/vxytL1WgToioa3K3JzZs3M3LkSAIDA1EUhVWrVpW77+TJk1EUhTlz5lT5/P/5z39QFIX77rvvqufaGLXzKPuvfMHSx0k4sYPcvEwSTuxgwdLHr/HMqk7VKh4LIYQQN5rbHvAANLytTQDtyrjIiX0mFBR7FpuCgpenNy/95s1fF3ngZNTZSuHYlRxfb2ylKMxmlUs7fyH39BESF7/CuQO/EPr0c7SPeg6AfasLHI4rORZCNE46RUd/n448GHAr/X06liqEL669ZyY8x/RJL9GhbSemT3qJZyY8V+Z+y6flkntARcuGnC1WDj53mfc+PcKjHx/DZFZL1RQtq8ao7e//zmbdy/37NxiN9P/8e4bvPk7/z7+/oRshiOppcBltOTk59OjRgwkTJjBq1Khy91u1ahU7duwgMDCwyuc+deoUzz33HLfddlttTLVRaudR9pLRsxcOVzhuSHQllnSUHAshhBA3mkGPuaI36DgdbyGop4HbJ7g4PH7RKYMWFl8OuB3jtsu9rmx1vFOllCgYXXJ8vbCoFr5OjuH05Yuc9snHYs4nM24j1tB0sFrIzEtlbcBhutg7jpa8oyd3+IS4XhTvcnlzjwieGh+NQboN1wtV09h3GToMn8bt90fT01tf7ve84hlqGUsLaDrxJKZzrsQm5vDE8pN8NCeE5dNySU6w2LO2i7O9D6TkZ9LCxYv2bgEYdPL3LmpPg/ttGjZsGMOGDatwn3PnzvHUU0+xZs0aRowYUaXzWq1Wxo4dyxtvvMGWLVvIyMiohdleP1oFdCThxA6HcUN1S1d/tu47j6aBohSOhRBCiBuZ3qAw6LHy77SnRRzFdaMricbz3Ha5FxoaPYY7BuNaG305ZSpaPpppyWVb+mEivNtfV5keXyfHcNqUBk7Qur0TBr2KAuQcK6r5WvyGY4/hzmy8snTUNhZCXB/mLZ1pX6q4JXYjqqoS/diL9TyrG9OedCt7Mgqb0ZzLs6Jp0LupAZNZ5YnlJzl61kS/3c3xvuxMdm42etyLMrU7ZcI5V0DhSJLJXhqgPPb3AeC0KY2vk2N4KODGTcYRta/RfWpSVZXIyEief/55unSpejHCN998k+bNmzNx4sQqH5OVlVXun/z8/MpP0IhMGfchzXxaotPpaebTkinjPqzvKZVr6pgePPdwLzq28ea5h3sxdUyP+p6SEEII0aCteOsuzgzehxfuZDfN4PZJLgyY6BiYi/BuT3+vjrgohfdhMyw5bM34g+3pRyo9v8lsYdSM1bQdtYxRM1ZjMpeuh9NQpORn2n9WFAUU7LVfbYrfcBww0cigyUba9TUwaLKx1P83IUTjVbzLZVljcW1YLRrbPzGR8n/5WNJVNKvGsezCoNsTy0+y82QOxjNOuJx3wnQZ9FZ3jij/QXXNI9M/l+M/B1w5k0YH/8LX6AKTyidTsnlncAafTMl26Dxa/H2grLG4samqiqqqZGdnlxkLqoqrCrT9+OOPV3N4jbz77rsYDAaeeeaZKh+zbds2lixZwuLFi6t1rdatW+Pl5VXmn3feeae6U2/Qft3yCRfTz6GqVi6mn+PXLZ/U95TKZdDrmP5QGBsXjGL6Q2EY9I0uXiyEEEJcU0ZnA9/+czjxP/+Z2WuDGTLFDb2hZCkGHYqikK85BskO5pyu9PxjX1/Ltn0XyM23sm3fBca+vrZW51+bWrh42X/WNA2uZMjbuLp6EjXhI/vYli04foEHgx4zlvr/JoRoxGRleIOweWk+qV9ZMO1XsaZD1n+L3oeOJJkAuOhjZm+ndKCwtEGaspcVajjuxkyC1rei04km9GnlzsLIEKCwjlviLgt5WZC4y+LQebT4+0BZY1E91amzXx379+9nwIABuLq60rJlS958883C9+0rLly4wMMPP0yHDh3Q6XRMmzat1DkWL17Mbbfdho+PDz4+Ptx5553s3LmzwuumpaVx5swZwsPDS8WBWrduXaW5X1WE4rXXXgMgMzOTvLy8Uo+np6dz77334u7uTseOHfnmm2+u5nLs3r2buXPnsmzZslKt18tz+fJlHnnkERYvXoyvb+mWvRU5c+YMmZmZZf556aWXavIUGqyde76vcCyEEEKI69+ZvNQytlb+mevgiYsVjhuS0X4RBBl9MSpO6LJc0dTi9WsV7rh1fKnO6xVlRgghGq9Rdz9Y4VhcG6f2FNiDnJd/sGD6w0rolSZ+tgy1ZunOnPUvDJZpaLjiyx3mxRgSA9Ch0DnBh9vj/TE6Fx5XstNo8XHx94Egoy+j/SLsj70y6/k6e57XK1ud/fnz59faObOyshgyZAiBgYHExsbywQcfMHPmTGbPnm3fJz8/n+bNm/Pyyy/To0fZq9w2btzIQw89xIYNG4iJiSEoKIi77rqLc+fOlXttX19fWrduza5du0rFgc6cOVOl+V9VjbbU1FRuu+02tm/fjsFgYNKkScyfP98eBHvzzTf58ccfadWqFd27d2fSpEn4+/tz6601axW/ZcsWUlJSCAoKsm+zWq1Mnz6dOXPmkJiYWOqY48ePk5iYyMiRI+3bVLXww5HBYODIkSO0a9euzOt5enri6elZo7kKIYQQQjQ2ZXUa7eJR+d3bLm2bsW3fBYdxQ2XQGRxq8axt81/uGfI0xxJ3Exrcm7sHTSl1jC0zAooyIyqq/yOEaByemfAcOp3OoRmCuPZUtejNJ3ebFd8eesJ8CkMVCyNDGPjeIZJ9TfilGck3WMn2yOKs8g09siajWIuOLR5M82tvsL9u28Y2Jd8HLCYT2ybdT8Yfh/nWJY/QY6/QppcLt09wkSzmKqiszn5wcDCTJk0iISGB7777jmbNmjFv3jz69evHpEmTWL9+PSEhISxdupTw8HAAVqxYgclkYtmyZbi4uNC1a1cSEhKYPXs20dHRKIpCcHAwc+fOBeCTT8pekbdixQqH8eLFi/nmm29Yv34948aNK/MYnU6HTqfDw8MDYw07zV5VRtu5c+fYvn073bp1o2PHjixatIjPP//c/vjWrVtRFIUff/yRlStXsnLlSmbNmlXj60VGRrJv3z7i4+PtfwIDA3n++edZs2ZNmcd07NiR/fv3Oxxz7733MmjQIOLj46uc+ne96xt2b4VjUTarRWPDYhOfRmWzYbGJggKVuHQLP18wE5duQS1Z9EUIIYRowEp2GvUyuNHPu0Olx614/S76dw/A2aDQv3sAK16/q66meFVUTSv1Ph01qjsj7nyKqZOWMuLOp9DrS9+HrigzQghR/2qadWowGJg28W98Me+/TJv4N+k4Wk90OsfvTJ4u2DuOGp11bPxbZ8Jvcic3pICUxzKYtboNe9cdJjTMx+G44sG0yDluBIcbcPWE4PDSnUeL2zFpDGm/byXjciAW1ciJWI0N/zaxcYmpFp/lje3999+nf//+7NmzhxEjRhAZGcm4ceN45JFHiIuLIzQ0lHHjxtmXhsbExDBgwABcXIoaNw0dOpTz58+XmWBVVbm5uRQUFNC0adOrfUoVuupXknXr1jF48GAAvvnmGz766CMiIyOBwhRCgK5duwJw11138dxzz1V4vuzsbI4dO2Yfnzx5kvj4eJo2bUpQUBDNmjneIXVycsLf358OHYo+BI4bN46WLVvyzjvvYDQa7de38fb2dpiXgGF3PImi01V4N1eUtnGJiU1XOpEd32HhfI6FvHudAFu3HI3eTZ3qc4pCCCFElZXsPNrNo02VOo4anQ18+/bwupxarSirq92fB5S9sqG4ijIjhBD172qyTgtMKsun5ZKcYMGvfWFAxskoNaCvFatFc8hoQ4E2YY7fn4zOOpZOLP1aHTnHrfDvfreF4N6OwbTyOo9aVAtfJ8dwwZROgNGH0X4RZP5xEDMamfq2QNGSwn2rC7hjsuvVP8lryGq18MuGRQ7f68u6gXStDR8+nMmTJwOFJcgWLlxInz59GD16NAAvvPACERERJCcn4+/vT1JSEsHBwQ7n8PPzAyApKYmQkJAazePFF1+kZcuW3HnnnTV/MlVwVa8gbdq0sQfZAB544AFSU4s+nNmikTpd0WWcnCoOOuzatYuwsDDCwsIAiI6OJiwszF4PripOnz7NhQsXKt9R2On1hkrv5t6oLFaVWV/uYcwrPzPryz1YrEV3yPatLnDY98Q6q8P4WLbUcBFCCNF4RHi351bvTgQbm3OrdycivNvX95Rqla2LXXnj8hTPjMjsfK7CzIjqUC0WjnzwHtvH38+RD95DtUimnBA1cTVZpxUVzRd1y2JVeefZRNYdOGHf1qaXntsnuFRwVBFbMC2gg56/LvKoUoD06+QYTpvSKMDKaVMaXyfH4NWpC4u8IMF9qsO+eVkq8x/MYsNiE1ZL41ip9MuGRfy4bj6Hj27nx3Xz+WXDovqeEgDdu3e3/2wLmHXr1q3UtpSUFPu2knX5bfGlqtbrL+m9997jyy+/5LvvvqvxktCquqpoipubGxaLxZ5im5KS4hBU0zQNvV7vcIzZbK7wnAMHDnToJFGZstIGN27cWOExy5Ytq/L5hZi7ci8zv4hD02DL3vMATH8o7Mqj0qpICCHE9UOn6Ojv07G+p9HgFM+MGPdmLE7GLrVy3kNzZ7P6s67k6UfjGv8Hw82z6Tr9b7VybiFuJFeTdSpLw+vP+/+JJ3VnC/Z5HeUmU2Ed9qwkrdp10fo/UjowZ7FYmP/pbHbujaG76yPouwfz0/ZEhk014GoIJY9EXAkmxXSa0R+v5PwdrXEr+K/9eA0N02UF02WVlOMmNFVjcCPIbjuWuJui76TalXH9K55wZQuUlbXNVk/fltVWnC0IZwvKVcfMmTN5++23+fXXXx2CfnXlqjLaunTpwqhRo+w12EaOHEl2djZpaWnk5ORw4cIFFEWxZ7klJiZikTt1opH5/UASttivphWObXoMd3bYt+0Qx4zNUA/HQLMQQggh6o+ti11544rYMtx3HU4uleFeU2tWhpNtiMBoPUK2UwQ/rb9X6rsKUQPVqccFYDKrTFhynH7/OEi6h2MiiCwNv3a+2VBYMipXn1dsa/VfA7vf7Vxq27ylM5n18Ttsid3IH5t8mfXlHg6fysCc2QZXbqIJvXHlJrx0HTEYjXTvGcFe3Xws5GIio9Q89q6uOGGooQgN7k1Rt3DlyrjxiYiIYPPmzQ6JWmvXriUwMLDUktLK/Otf/+L//u//+OWXX+zNFuraVb2KvPbaawwcOJAff/wRTdPw9vZmwYIFhIWFkZmZSXZ2NmFhYfTt25c//elPrFmzhoEDB9bS1IW4Nkp+4C0+HjDRiKJTOB1vIaingVsfdWZ/tkqyScXPqKOntwTahBBCiIYizMeAolhr9D5dPMN95hdxQPEM9+ozmVUu0hWDouCkpeLW30DuXn/2pFukvqsQ1VRePa7yPLH8JDtPFtYT/7lzEsPwxyfb2V6jTdQ9k8lEcuoFNupfAoYChVlkPYZXbdloZb795SsUTU9r7U5aqKH0yjYQ534YZ60lCgrOFNZ+NyqtAPh01krGTx/Drt37cMGrVHMgSo0bJlut9WtZe72yOvs18fDDD/PGG2/w6KOPMmPGDI4ePcrbb7/Na6+95rB0ND4+3j6H1NRU4uPjcXZ2pnPnzkDhctFXX32VL774guDgYHuWnIeHBx4eddc9/KoCbV27diUuLo7vvvsOvV7PqFGjCAwM5M477+Q///kPzs7OPProo0yfPp25c+faO4QKUd8sVo2PNqUQdyqHXm3ceXxACwz6sl88dUr5Y71BYdBjjuu7e/lI8VQhhBCiIdIpCr18avbxd8dBxwz3HQeTKj6gEk8sP4mLlxvn/C4yeK9Gk5F6rBkax7JVetdtMzQhbnhHkoq6SaoG2No3le0v186ScFE146ePwXzZlYtsx9t6H2edk2nb24kBE2tW5L4UBbqrT5JPBq6aO72zO3PaOalUwpztq53RaOSrBd/TPuKmMoJs0H1447gBYqu9fi3t2rWLQYMG2cfR0dEAjB8/vsZlu7y8vFi3bh1RUVGEh4fj4+NDdHS0/dw2ttr+ALt37+aLL76gTZs29hJjH374IWazmQceeMDhuL///e+8/vrrNZpbVVx1XmxQUBDTpk1z2Na8eXOefvpp+3j+/Pm8+eabeHp6NqqWyX369ClVYy4qKoqoqKh6mpGoLR9tSuHD35LRgN+PZwPw5OCy13rf0jWArfsuoGmgKIVjIYQQQtxY+nbxZ8ve82ga3DLMSO8BVralHybCu32VOrOWdCTJxOVeOehVhaSjIwjUazR/qXF8kROisevgb7RntNnG4to6dOwAurxMwMBFdrDR6398NGcZ+nKSH6pr1NAHOfhRF/JJB0BBQYeOvbEnGTSse2FWlKYR6uEYn9AZgGL97oxNIOJhY5UbNNyIKquzX1Zd/ZL7BwcHl9rWrVs3Nm/eXOG1K6vvX9a1q2rFihXMmjXLYZvVWrUmSnUe9fr6668ZPXo0TZs2vltzsbGxeHp61vc0RB3YnZhTrERk4bg8U8f0AOCHrScYeWtb+1gIIYQQRaxWC79sWOSwXKUhdTG3qCprkixcMms0dVYY6m/AoKt6gMz2/r9i8wEihrmBUsDWjD/Q0LjVp1O159PB38jOvBxUNH688xwRl5pxSxtfqe8qRA1UZ7UKwMLIEJ5YfpIjSSY6+BtZGFlLWVSiyjqHdmV73BbAjOHyYvr0ug2DvvZWBj0z4Tn+FbeR3NhbgcJlqfk6MxvW7KfP+rn4PDwe9zMJhD3rmEQTHBJIsJuBrYe+49bO9xM5x61K3UzF9Wfs2LFMnDjRYVtWVhZeXl6VHnvVn35iY2OJiYkhKyvL3iHC5vLlyyxdupTRo0df7WWEqFVqgcZtO5uzt2MGPQ57k9+6/DbeBr2O6Q+FVViHRbVYOLpwNhd3/U6z8Fu46YlodI0oe1MIIYS4Wr9sWMSP6+YDGoePxgBc8+Ur5VE1lZVns0i3XMKFllwwaaxJsjAisHQB7fLYPg+cdEl0KNVzMPtMjQJtti/6h5NMtPR1YeTNzWjtoZf6rkLUwKKNySzcUNiRMOZ4Nqqm8dQd/qX2K/6Z/cXwW7jpBfnMXl9sNdF2xG+nb89+fDprZa2e32Aw8LcP7mDz0nw2fJbNbv0RMvXZoIE+YS/bZn7MGLdj6KId36emPPI0993lwZbnf+av/xpXq3MSN46relX55z//ycsvv2xP11MUpVTqXvFCdUI0FM1/aoLLJSfanHen+SUj+T/pYXLNz5ew6H3iLprJj3wVl60/oS16n45PST1CIYQQN45jJ3dBsXzxwnHDEJORQLalJflcwIWWAFwy16y7Z7c+bmRYit+gq9l5jM46lk5sV6NjhRCOfojPKDUuK9CWsGAmRz54D4DUbRvRVJWOU1+8FlMUJdhqoq1a+w333fVA5QfUgK2e9rlDFn7JOgvJhdvXNRvIZZ07re/rVeqYupqLuLFcVQ7kBx98wN13381XX33F+vXradu2LRs2bGD9+vW8++679O/fn5MnT9bWXIWoNa7pTigoBKQYUVBwTb+6miiHXf3JfWQ6mqcPuY9M57Br6Td2IYQQ4npWcmVDyXF9OmtKw2zOIj9B5fKvFpLfyMe7hgGyLu5BFY6FEA3XmVUrKxyLa8disTBnyXus/HEFc5a8h8ViqbNrefvraO3XxD6O9eyJa+tg2kc9V+684g7E1vm8xPXrqvNkf/jhB3RX6lu4ubkxYMAAAAYNGsTtt9/Ot99+y7PPPnu1lxGiVgV1NnBqt4Umuc6ARlDnq/unkNu6A+h0aKoKOl3hWAghhLiBKDodKHpcPFvR+tYZOJtTUTUNXQNY3dDSxZeEd/To7tFI/6iwynXKOwXw7+oXQO/n0wFFUdh7+SQ9moQQ4d2+tqcrhKimkT28WbgxxT4O8HLCYtVK1Wmzaho/u2kEFsB5J7ivkkLqou7MWzqT9z95F4AtsRtRVZXox+omuzAzSUXnVvx3QcEjKLjMZcPzP53N7CX/RNM0Zi/5JwDTJv6tTuYlrl9XldHWuXNne5ANwM/Pj3379tnHffv2ZcOGDVdzCSHqxLi57gSHO+HqCcHhToyb6351J9TrQdNQLAWgaYVjIYQQ4gZyU0g4AWGPE9B7Cl6tInAJGcme9IaRCeBKOyynjDgbijLOU45WrXNYSTpFR3+fjjwZNIz+Ph1r1HFUCFG7pgzy4+aQos/zuxJz+GhTUeBNtVg48sF7/GTN4Cc3iDPCT26w5aZW9TFdAXz7y1cVjmuD1aKxYbGJM/ut9LzcEd2VApuKArd0LXsF0s69MfZyWJqmsXNvTK3PS1z/riqNx9fXl61bt3LrrYWdPPr168dbb73FypWFKbjp6en8/vvvVz9LIWpJbp6J//tmJQafVlj6n+XV98fg5nr17bydPTzJVRRwdgFFwdlDutUKIYS4sdw9aAopRy9hdb7SjUtROJat0rueG8+rmsqRy9k4t9Hj1saTy1e2+7WvvwLoFouF+Z/OZufeGG7uEcFT46MxSEF2IWrMoFccsmc1YHdiDlDYkfTt91ax8dxNBBu7g3Ubu42FgfYTRrk5Xm9KJjvXQfLz5qX5bPjIVPgLEeNDVMQg9noepm8Xf3sn6ZJu7hHB1l2b0DQNRVG4uUdE7U9MXPeu6hbcfffdx+DBg1m3bh0Ajz76KD/88AM9e/bkkUceITw8HH9/qVUlGo7/+/Zr2vYbg4ZC235j+L9vv66V84aEtgFNQ/UNAE0rHAshRBWomsq29MN8dWEr29IPo2oNp66VENWh1xtwc6u85f21tj39CBnWXDxfTkVndEbxALeuEDnHrd7mZFuatCV2I7OX/JP5n86ut7kIcb1QSywDtY0XbUjmq5ybSPZpR3N9M5zbRAKFTfv69ux3zecpCo0a+mCF49pwOt5SvEcPrazNWfnWMKY/FIZBX3Yo5Knx0URPfJGm3s2InvgiT42PrvV5ievfVd06GzVqFNu2baNZs2YABAcH89lnnzFlyhT27dtHq1atWLRoUa1MVIja0Cz0FhRFISC4G4qi0Cz0llo5b1hTJxSdlWSTip9RT09vuTsmhKia7elH2JZ5GIBEUyqapnFr0071PCshaibUQ8+eDKvDuL4dzDkNdMFqyMTJ4EPrT1zRKZk4GetvyeeO+G0OS5N2xG+rt7kIcb3QKWWPf9ibUbhWEPDJTcG742h8T/2HYROflSBKPXpmwnPodDp+/G0V9wy+r07+LoJ6Gji+80qwTSkcV8ZgMDBt4t/Ye3iP1GYTNXZVgTaDwcC8efMcto0ePZo///nPXLx4kRYtWqA0gAK4NdWnTx/0JWptRUVFERUVVU8zElfL2ckZABe3Jg7jq6VTFHr5yJIPIUTZrFYLv2xYxLHE3YQG9+buQVPQ6wtfMwqDAEV2ZCagKAoR3u2l9pNodMJ89CgKV2486RrIjScFC+kY8MZMMnpCaOpSv5mjqqpVOBZCVF/vYA92nMixxVToHexRap+9be7AVYO5D06lowRR6pUtoFWXwazbJ7gAsOOrfPo+6GIfC1EVK1asYNasWQ7brNaq1Xet1if4GTNmVGk/g8GAn58fiqLwyiuvVOcSDUpsbCyHDh1y+CNBtsatb2s/tCvLsjRNpW9rv3qekbgR/HfT8fqegqhn3/+6iH/85sP3x7vyj/U+jJjQlQej7sVkMlGyKIkFla0Zf7A944h9mywvFY2F7cbTsABnevkYGkTH0S4ercnjOCo5mEnC2XCOe/yb1+ucijcTK2sshKi+xwe04MnBfkS08+DJwX48PqAFACN7etv32Rc0mCH+ebSPeq6eZimuJb1BYdBjRlp30zPoMSN6Q9Xfk/40ZFQdzuz6snnzZkaOHElgYCCKorBq1apaOe/+/fsZMGAArq6utGzZkjfffNOeDW6zadMmevfujdFopG3btqVWVH733XeEh4fj7e2Nu7s7PXv2ZPny5VW6/tixY0vFg2JjY6t0bLVScN577z32799P8+ZV+3CSkpLCL7/8wltvvVWdywhRK1RNIz7D6nBXvU9zF5ycbNucGsiddnG9+/C7/fx5QLv6noaoRwt2BpOptEZPPplKV5Jz+mA+vobx08fw9Fvvsi3jcKljDmaf4RbPDsxduZf/7j7EyCfdQJHlpUJUVz/vDigonDWl4aJz494WIfWeLdq3Zz+27d5sL7bdN6w/cekWh88sDSFIKURjYtArPDm49E30KQP90CkKaw9kcFdXbx4f0B2dXv591TVVU4nJSOCsKY1WRl8ivNujqgr/3pTMpmM5dOrgxT1dvejdtO5vynQbWv1VTPfd9UAdzOT6lJOTQ48ePZgwYQKjRtVOgDIrK4shQ4YwaNAgYmNjSUhI4NFHH8Xd3Z3p06cDcPLkSYYPH85jjz3G559/zrZt23jyySdp3ry5fR5Nmzbl5ZdfpmPHjjg7O/Pjjz8yYcIEWrRowdChQ2tlrmWpVqBNVVVWr14NUCqSWJ7GvHRUNF6qprH6vJmk/MLxuTwrmqbRu6mTLPEU11zSxdz6noKoR2azidT8pqAoZNIZFAVNccK1x3scOvYmfT3ac3y5B4nxBeg6ZuL0QOKVUjIa7/8nnllf7sHoroBSVLj9YM5pCbQJUUU6RUd/n471PQ0HtlpEtq6j/e99hrj0wuUoxT+zCCGuni0AV1YQrjirRWPz0nxOx1sI6mng9gku1cqAEo5iMhLYmvEHUHiTEGDPbm8WbkwBoF17bxZtSObJwX51/nrX/e7aKRckyjZs2DCGDRtW7uPBwcFMmjSJhIQEvvvuO5o1a8a8efPo168fkyZNYv369YSEhLB06VLCw8OBwmWbJpOJZcuW4eLiQteuXUlISGD27NlER0ejKAqLFi0iKCiIOXPmANCpUyd27drFzJkz7YG2gQMHOsxl6tSpfPrpp2zdurVOA23Vup0XEhKCpmn079+ff//732zcuJENGzaU+ee3335j+fLl0nVU1Is96VZ7kM3mWLYstRJCXHsLlj6Ou3qSdpaPgcIv0q4dp6H3aEfn0K5s+cTM8c+aYN3XlIKVwVh/9wWgi3sQ32w4Vs5Z5YO/EFWhahpx6RZ+vmAmLt1Sqithzc97dcu5DQYDg1tO44t5/2XaxL9xosT9mOp+ZjGZTDwYdS/dhrYttixdCGGxanz4WzKTlp7gw9+SsVjLfw3YuMTEhn+bOL7DwoZ/m9i4RP4dXY2zprRS4x/2ZtjHHm4G/jh+Wb6j3SDef/99+vfvz549exgxYgSRkZGMGzeORx55hLi4OEJDQxk3bpw9oSsmJoYBAwbg4lJUV2/o0KGcP3+exMRE+z533XWXw3WGDh3Krl27KCgoKDUHTdNYv349R44c4fbbb6+7J0s1M9qOHz/Or7/+yuLFi3n66afp06cPkydPZvTo0Q7/A4pr0aJFrUxUiOo4ll21IoVC1CWT2cLY19eSlpHHqBmrWfH6XRidJaPyRnP2wmG6WXdjwY3jTAQVbottjvflAEK7f078T+ZieyuoX7Tn1mHNudnjJtol5dDlUhN+1cWgWkB35deni0frenkuQlSF2WxiwdLHOX4qjnZtehE14SOcnY31Mpc96VZ7F9TCTDHo3fTqX4dro1vw/jXmWsuyGD99DNvjthTOLW4L46eP4asF39fKuYVozD577xMW5PQBIOZ4Nqqm8dQdZSeC7FtdUGp8x2TXOp/j9aqV0deeyWYblyaNYGpbWeWTGkIpguHDhzN58mQAXnvtNRYuXEifPn0YPXo0AC+88AIREREkJyfj7+9PUlISwcHBDufw8yvMSk1KSiIkJISkpCT7tuL7WCwW0tLSCAgIACAzM5OWLVuSn5+PXq/nww8/ZMiQIXX6fKtdoOLOO+/kq6++4uzZs9x777289dZbBAYGMnXqVA4dOlRq/7p+AkJUVaiH1GMT19bY19eybd8FNGDbvguMfX1tfU9J1INWAR3RYcHAZby0A9y+qynN0404W5w4HaeSneb4IdND70p/n45s+cRMaFI7Wpn90BcYOPnPlpBh5FbvTvTz7lCtOUgzBXEtLVj6OAnHdmHNN5D8zVhmPv0dVkv9fJkqeeOttm7ElewWXHJcXSU/o1T3M8uhYwcqHAtxo1EtFg7N+xfmbx0Lo/8Qn1HBUSVfpyQIdDUivNtzq3cngo3NudW7ExHe7R0aU+w+cIlO7bzkO1oti8+wEpdu5VyeRly6lfiMhpGA0r17d/vPtuBYt27dSm1LSUmxbytZhsyW7VZ8e1X2adKkCfHx8cTGxvKPf/yD6OhoNm7ceDVPp1I1rgTr6+vL888/z+HDh/n2229JS0ujd+/e9O/fn88++0xS1kW9CvVw/NX2NyqE+ciLuLi2Dp64WOFY3BiiJnxE+7Z98XD1ZHSbNbS2eKAUW/pZ8mN89+GFdUr2rjZjWyLqrroSkBjC1K53cNqUypzEH/nywhYsqqVKc7DVSUk0pbI14w9iMhJq46kJUaazFw7jlvgoitUV5/S+5O2+m81L8ys/sFEpmR1Q9WwBq0Vjw2ITZ/Zb2bDYhNWiEeajp5ePnpauCr189NX6zGIymVBVx+B559CuVT5eiOtJgUnlkynZvH17Gp/950488i5V+dgew50rHIvKFV+uH5+hEuHdgQcDbqW/T0d0io4pA/14cnALugS508bXhSmDWsh3tFqWbFIrHNcXJ6eiOny2IFhZ22zvZ7astuJsQThbUK68fQwGA82aNbNv0+l0hIaG0rNnT6ZPn84DDzzAO++8U1tPrUy10nJp4MCBrFixgvPnz9O2bVsmTJhAy5Yt2bRpU22cXohqC/MxOHxgHR7g1CBSZsWNpUvbohf4QXd34/Hpw2q1RpBoHJydjTw7+TNmvb6TZyd/hn97x2VrrbrpGTTZSLu+BgZNNnLboy58+FsyKZeLgmjdc9rj7eHCf9N2cNqURgFWTpvS+Do5pkpzOJOXWuG4NkiNKGHTKqAjTuk90OUXdqlX0HFqT+laKXXNoqqoJV5uS96Iq6mSy7ers5x789J8NnxkIjdDY8NHJjYvzUenKPTyMTAswJlePtXrwDd++hiysjPtY08PLz6dtbLKxwtxPVk+LZfEXRbMZiNO5mCcLY7/5kf28C732AETjQ7vxwMm1s+S98ZsT7rFIZtqT3rRZxmr1cKaDQuwnHiJ8W1/ZuaIFvRpJt/RapufUVfhuLGIiIhg8+bNmM1FJVbWrl1LYGCgfUlpREQE69atczhu7dq1hIeHOwTxStI0jfz8ur0BWCvFgk6dOsXnn3/OihUrOHLkCJqm0aJFC9zd3Wvj9EJUm+0DqxD1acXrdzH29bXEHkll8LDuKIpCXLp0k7vRRc5xY/m0XJITLPi1NxA5x434w6sZ/9g9AMz/NYlFv6XQvkUTumR5o6AQampN9+FOHMjPdDhXSomxjdWiseHjHLb/fIBc9x3QdyNew/+CzuCMsxaAptRu0MNq0Yh+cAUe5x6jme6/tNnxKi8P280/f47AqZF+wBM1FzXhI/754z7U02Pt21T12n+RWpNkIafYihkPQ+GNuNrQz7sDCgpnTWm0MvoS4d2+wv1Vi4WjC2dzcdfv/HHpTdBaFT6gwen4qmWmlqfkMlGdTofRKAECcWNKTij696SgoBR77fE06pkyqPzOo3qDwqDH5N/O1SjZ2OBYtkrvpoU//7z+Q35avwCAw0e3o6kq99z1zLWe4nWvp3dhhmDxGm11LTs7m2PHihp4nTx5kvj4eJo2bUpQUFCNzvnwww/zxhtv8OijjzJjxgyOHj3K22+/zWuvvWbPfpsyZQrz588nOjqaxx57jJiYGJYsWcKXX35pP88777xDeHg47dq1w2w2s3r1aj777DMWLlx4dU+6EjX+tJGens7KlSv5/PPP2b59O5qm4evry5NPPsm4cePo06dPbc6zXvTp0we93vEXMyoqiqioqHqakRCiMTE6G/j27eHc9NAKhzoBxT90iBuPk1HHXxd5AIV3d3/Z8CEbt39OSloidw+awg97M+h43JPzLfLoekxBQyOkt4GBE42kpHpxulgXrxYuXmVeY9MSE5s/LgA6kt/+B3Rb+pGifEbgPc/gTlcKClJQNa3W7iJvXGKi2bk/o6Bjl/IuRnzQLnuzfFqu/bmKG4ezs5Gg1p04sbMo+KTTXftM3ktmx2sWqNTa77xO0dHfp2OV9/9j/iz+veECp3xH0D77KK60BBRQIKjn1QX/Ood2tTdCAOh/z8irOp8QjZlfewOJuwqDbRoaeU6FmbVoGmMjmmHQV/waUPi+vIhjibsJDe7N3YOmoNfLzfvasGPP96XGEmirffWRcLJr1y4GDRpkH0dHRwMwfvx4li1bVqNzenl5sW7dOqKioggPD8fHx4fo6Gj7uQFCQkJYvXo1zz77LAsWLCAwMJB58+YxatQo+z45OTk8+eSTnD17FldXVzp27Mjnn3/Ogw8+WOkcVqxYwaxZsxy2Wa1Vq3lXrb8Bs9nMDz/8wOeff87PP/+M2WzGxcWF+++/n3HjxjFs2DAMhuvnhSg2NhZPT8/6noa4xorfdW4Wfgs3PRGN7jr6vRbXjq3rqKKTlHhRtl82LOLHdfMB7cp/Ae4k6Lw7F1rkAYV35DOTVPQGhdF+EXydHMMFUzoBRh9G+0WUed7i9d3yW/yG967FGFsa0eOBDifMlkDiM6y19kFs3+oClCvVKLqpj9vnXTyzQNxY2oQ5cSLWWliEUCkcX2tNnRUumDSHcX35ZHce67s+Suu0Q/x8Swj37Pkdb0s/+j7owu0TXK7q3J/OWsn46WM4dOwAnUO7MnjUmFqatRCNT+QcNz59JpsTe9K5yCG8jNm0TYrl5nbeTBnYvdLji78vHz5aWJ5hxJ1P1fGsrx+hHnp7p2fbWFz/Bg4caG9CUJbExMRS20ruHxwcXGpbt27d2Lx5c4XXHjBgAHFxceU+/tZbb/HWW29VeI7yjB07lokTJzpsy8rKwsur7BvdxVXrE7a/vz+ZmYXLVPr168e4ceMYM2ZMhReaOnUqc+fOrc5lhKhXCQtmcuSD9wBI3bYRTVXpOPXFep6VqA0FJrXUkr26XNZm6zqqN+jQNM2e1SYfOm4cFqvGR5tSiDuVQ6827jw+oIXD3fSjJ3dR1A5B4+jJXYzs+QAnVkH7xOI3egqPMegMPBRwWxWurNjP65IyGICmrSMwk29/PClPBZ+reHIOij4YtdXutf/s115uUtyobMGj0/EWgnoarjqYVBND/Q2sSbJwyazR1FlhqH/9/T4eM7YBRYdBLUDT6/ijdRJjPPS1skzNaDTy1YLvUTWVmIwEdmcdZ1v6YSK826NTZOm2uLE4GXWsc3qE7YbCLM/JOUDqPF5etBtdJdlsAMcSd1P8fblwLKoqzEePopS9bPHmsHtZfWXpqG0sxPWqWp84MjIyUBSFwYMH06ZNG37//XdiYsovxJySksIvv/wigTbRqJxZtbLUWAJt1wdbgVyAxF2WOl/WZusyarWo/PbzPtrd5Mf9N7e8JrUSRMOwaEMyCzcWdkiKOZ6Nqmo8dae//XGtRKdATVWZMtCPWf0u4r7ezb7d1om0qroPd2Lpx1/TVrsXj4TpGHutoU3LP3M6/0LRtWryhMrRY7gzGxcXFZU1uECrboXBbHFj0hsUIsYbmH/yN2J/SqLPSX9WvH4XRudrF+wy6HSMCGwYXQODLx7kWIswWl88yMkWPbmQ78TxzEwsVjcM+toJhtm6CwP2/1ZneasQ14uSdQsTTZervDolNLj3lUy2wnTc0ODetT/B61hFyxaH3/EkOp3OYVmuENeran3aURSFu+++mxYtWlS6NlXTNKxWq0NdIiGEqE8ll7HV9bK2Lm2bsW1fYWBjwy/7MZ9Po9fQNnV6TdGw/LA3o9S4eKBNp3P8gq3T6TDoFZ77RzM2h+bXKBvIYrGwTzefw+7/o713L/oPa8vAiX9hTYoZFwLt+ym1FGqzqBaSRuzCNcsT3ZGmhN/cjIF/dUVvkPf/G50tqxdg274LjH19Ld++PbyeZ1U/7nc9inZgGbGBA8lNOsfFS/kcT8/k/f8k8/zYXrVyjbPF6jeWNRbiRlGybmHn0K5VPtYW/CkeDLJlixZvfiLZotWn1xtkGa64YVQr0Pa3v/2Nd955p1oXePnll6u1vxDXmtWisXlp0Rfalvc+yNEF79ofb32f1Dq5XhQvkGsb1yVb19E9R1II69CCFa/fVafXE41PaEg4h4/9jv3OeUg4cHWdz+Ytncn7nxS+hi1Ovg0P3QvcYXiRFi4K5/JUFEUp7A7uUrUvCZV9wfg6OYYzBWnoHkgDTpBi9EVvqMryVnG9s2X1ljdu7Cyqha+TY0jJz6SFixej/SIw6Mp+X+m/5CsMk8aw4WRL8gyF3XCOGc9weENmrQXaWhl9STSlOoyFuBEVr1uYFeDHp7NWVn7QFWUFg7am/8G2jMMAJJpS0dC41adTrc5ZCHF9qda3zOoG2QD+8Y9/VPsYIa6ljUtMbLqy5On4Dgu3T3yKjlMVh2YIouErGTC9fYJLqYyayDlupWq01SVb11Fx4xrZ05uFG1IcxsXZ7pxv3P45A/s9UivLKL5d81WpcfRjL7L5v+8Td8lMt/5/Jnn3fBJNWYRN/6zSbmrbM45U+AUjJT/TYf+SY3HjKp7VaxvXBYvFwvxPZ/Pj+h+4PXsRTtktaXGTnhYvOZGBYq/PZtDVbgbK18kx9i7Ap01pfJ0cU24NRYPRSP/Pv8dt0kpIugzAcdeztKFJrc2nr1cop02pnDalEWT0pa9XaK2dW4jGxFa3sLYczD5TatyYAm0/7U1nRI9aK8oqhKiCBpfzunnzZkaOHElgYCCKorBq1apy9508eTKKojBnzpwKz7l48WJuu+02fHx88PHx4c4772Tnzp21O3HRaO1bXeAw3v+LlQ5P/41+n35Hh6f/Jh1HG4mNS0xs+LeJ4zssbPi3iY1LTKX2cTLq+OsiD176zZu/LvKo00YIQgBMGehH1GA/Itp5EDXYjykD/Rwet905H3PvK4y486lKg15VUnJF6JXxtz//h/O7F5GRcpK8cxu4mLabXzYsqvR0B7PPkLD9d4cxFAa3Nyw2YXqrB0e/CEG7Um6uhUvlnZjEjWHF63fRv3sA3h7O9O8eUGdZvfM/nc3sJf/k0nFnCs4HkpcFp3Zb2fd3M/kqXDBprEmq/VIBNQkyPzAotMLx1diRecwh8Lcj81itnVuIG1s5b6z1zGLV+PC3ZCYtPcGHvyVjsZY9r6VbU8vcLoSoOw3uW2ZOTg49evRg/vz5Fe63atUqduzYQWBgYIX7AWzcuJGHHnqIDRs2EBMTQ1BQEHfddRfnzp2rrWmLRs3xTSnm8lFmfbkHi1UtZ3/REJUMmJYc1zeLqvLTeTPLE/P56bwZiyq/XzcCg17hycF+fDyhLU8O9nPoOFpcn5731No1R939YNljBZp6FTVVUBTYdmQ929IPo2oV/T5qJGzf6TAG2HQluJ0T34RDSS4cfqcbgQZfRvtF1NIzEY2dLav38H8i+fbt4XXWCGHn3hg0TcMZDxSK/o2ZTxX9Xl8y1/4X45JBZV8nL346b+aTE2W/zheYVJpuvonHM+6jv6Ur0x/sxbN/6emwj6ppxKVb+PmCmbh0C6pW9XlLjTYh6kZH19bs3ePD/nhv9u7xoaNr6/qeElDYcGnBb8nEHM9mwW/JLNqQ7PC4yawyYclxDl8wMWHJcUzmuv/sqVosHPngPbaPv58jH7yHaqnbeshCNFQNLlVn2LBhDBs2rMJ9zp07x1NPPcWaNWsYMWJEpedcsWKFw3jx4sV88803rF+/nnHjxl3VfEXjV7xbnoZGrMsfbF6Rj6pqtVY3RdScqmnEZ1gd2oTrymyyUvpu47b0w2XWlbJYVeau3MuOg0n07eLP1DE9aq3rW3nWJFm4YCqcoy27oqF0wxPXl2cmPIdOp2Pn3hhu7hHBU+MLl7+PGvogP/76AenHfraHIgwdw9ia8UeF9Wa6uAeVGlssFjZ/lQIUBhpMRitN431ZvwYiJzW4jxbiOndzjwi27tqEmWw0NHuwzblN0et6U+fab84x2i/CoUZbE+3mCl/nl0/L5dRuK6Cni6kDzbYZMEQ6vvfEphWwL0vl9MlUgkKaU2BR6du8au8VUqNNiNpRYFL5bGoOpw9ZyGtaQMpwF/aeaWp//IB3UwYMrscJXlGy4dLn25LZvHGP/bPtE8tPsvNkDgA7T+bwxPKTLJ3Yrk7ndHThbA7Pexc0jdTtmwDo8PTf6vSaQjREje7TsKqqREZG8vzzz9OlS5canSM3N5eCggKaNm1a4X5ZWVnlPubi4oKLS9W7wImGa8BEI4pO4esVZ0kkmTxdYdDtmw3HJNDWAMRnWNmVXMDlH6zErLKwtaOeJxa4l1r2WTxgCuAzOIutGX8ApetKzV25l5lfxKFpsGXveQCmPxRWp8+jZDZFXWRXCAFgMBiYNrH0h9pnJjwHqBw68jVOzk4Y/VrjFlL4PlpevRmLxULsqh/Iv5SJcimHiOBe9PPpwLxPZpKRNZQ0ZRNttXvRdGYADpyV+mzi2rMFk39c/wOGy+fIT/EiXXeENmO9cVa60sxFx1D/2v/Ia9AZeND/VuIzrCTlWbmQXQCGoqzRkq/zVel8/fvZHNy93HB1c0FRFH4/m1PlQFuEd3tUTWN7WgLn9+mIKcil7xi1zm8kCXG9KQyKWwCFFNWM8Vs3uDnH/nhcYm79Te4Kq0Wj5QF3gs97EdvtImZnlYzsfBL/OM/m+POoqsaRJMfvqkeSSpdVqU0W1cKGzh5c/noOTb9dS9Ov15AWu50OdXpVIWqfqqqoqkp2djZms9nhsYpiRMU1unfed999F4PBwDPPPFPjc7z44ou0bNmSO++8s8L9WrdujZeXV5l/atIYQjRMtu5+u0PjiPM4DLV/01tchWSTSuo7BTi10UE+pO21snxa6Q84AyYaGTTZSLu+BgZNNmL60x8OjxcvZPv7gQvYVuNoWuG4rpXMpqiL7AohKmIwGOgQ4omTPg+sBZjOnyA/5eyVR8sO/M7/dDbvf/xPLiSeZP7Tz7B71Y/oFB074rdxTPmOE7rCYtOaPo9TATm4a4nX5smIRmnV2m/q5Ly24PKvX2zhUNAMluu68yOjWfD3Ifw0cwwjAp1rvRGCzZ50C3HpVs6bQNPpKf7m0tRZwWq18NOv85n78QQMvkkOx5bV+dpssQJgvVK+wjYuj9ls4v1/j2P66zcz96NH2f5TJscPm/hqWQozV+zh/f/EX/2TFOIGUxgEL/ycdjYgD68s56J/21CtJd11ZdMSE632e+B30ZXuR7zpeMyT/EtFnZ2/2XCMDv7FupdrGq0tKXW6lPPr5BgyQ/xRvTzQ5eRy9u1pIKVSRCOUlpbGmTNnCA8PLxUHat26akvHG1Wgbffu3cydO5dly5ahlLl0rHLvvfceX375Jd999x1Go7HCfc+cOUNmZmaZf1566aUaXV80XHVZoFjUnJ9Rh/mUSu62oi8bZWUB2AKm4xd4MOgxI4q+/MK1aomHSo7rwlB/AwFGBR0QYFTqJLtC3JhsNVjC39jPox8fY+bimTz8zJ+Zs+Q9LCU+UB9L3E3xfwsZO9cCpZeH2thqXwFomsbOvTEAqKrGPv0CDO7pXGyairOWSVLb7fQzLq6DZygaO4vFwpwl7/H3918s8/eyxuctVgh8wbpk1v87D+/dT9LD8gy+anfusnyGdV8EVkvdvcgfyy72JVKnw33uC3DpEtmLTpP6Vj4fz9jKj2s+5PDR7ZxoPZomIRdw9YTg8LI7X6clpqJpGvt3J/L16tMs//58hbWVFix9nIQTO8jNyyThxA72xb3u8Pg3G6QhghDV5dfegHblvdI1r/DrcsfjXva3T10DuFe6d7UZWzAw6IIHQWec6Xr0J4d9FkaG0NUpDTSVUVtW0/u/uXw7eQNWi4bFqjFnXTYPL77EnHXZ5TZSqI6U/MzCArBAXpebMLVtDTr9VZ9XNHzVaWhZHfv372fAgAG4urrSsmVL3nzzTfvnUoALFy7w8MMP06FDB3Q6HdOmTSvzPN9++y2dO3fGxcWFzp0789///rfC6/r6+tK6dWt27dpVKg505syZCo+1aVSBti1btpCSkkJQUBAGgwGDwcCpU6eYPn06wcHBlR4/c+ZM3n77bdauXUv37t0r3d/T07PcP7Js9Prz7F968vzYXnRs483zY0sXKBb1o6e3nmbt9MUCbRo+qXPY9si9WEzlp8CXVVfKpmSNt7JrvtVMeUVgDTodIwKd+WtblzrNrhA3FrPZxD3vrmbnyWzyCjRiE3P4aI8vW2I3Muvjd5i3dKbD/qHBvSlK21Xwa92FIKMvZ/MvltkU4eYeEfYbW4qicHOPwkYHOp0OTbGSwl6sPUfQWvuG7tob9Os9vK6fsmiEPlj6Pus/yqF12sP8+u9sBoy+mW5D2/Jg1L2YKngdr0zxQuC/fZzHpsUm/Cy3EKZNxVfrQUvtNjrlTSmzC3VdUfJzMY//N5c2+HJip5Wz67vjdnJC4YMGM1nhT1fY+frv94ZyLO4kR8/lczYpD6tWVFupLGcvHHYYuxmSOLLbXOa+QoiqiZzjRn5gARZFJdfViotFj/H4JgKTXEHT6B3sUd9TpOQSHJeCbIZc3Fg40DQGuiUT99gDjP/fJLqdOEGr1CSyDf3Zv7c3m5fmM+/XbGaty2bLUTOz1mUz79fsq55RCxcve+bf5UE3YzxxBt8+0iDpRlDVhpbVkZWVxZAhQwgMDCQ2NpYPPviAmTNnMnv2bPs++fn5NG/enJdffpkePXqUeZ6YmBgefPBBIiMj2bt3L5GRkYwZM4YdO3aUe22dTodOp8PDw6PMWFBVNKpvepGRkezbt4/4+Hj7n8DAQJ5//nnWrFlT4bH/+te/+L//+z9++eUXwsPDr9GMRWNi0OuY/lAYGxeMYvpDYVLTpIHQKQpPLHAnONyAQXcZj4IY/PPmkfb7VnZMGlPucf18OnCrdyeCjc251bsT/XyKKkTc0tXfdsMNRSkc15ajC2dzYP67xKTtYF7iYqJfCeP7NXOwWqXr0o3Glm1z37wjfPhbcq3cLS5pwdLHSTX50FzddmWLgt6jqNDxt2u+ctj/7kFTuGfIU3S8qR/3DHmKfreN47QpjVOmVLZm/MH29CMO+z81PproiS/S1LsZ0RNftNfC6tuzH4qikJNnJeF0LsGuJ7lnyNMMv+PJWn+OovE79lNzelqn0VobTJj6LE3P3UtGVjrb47Ywfnr5r+OVKV4IvM15d2xfPBV0RKhvXvlZqdMu1KEexbI1NI0zt3TknM8obB+xFXS4nJ5ArhaIVoWP3W4uTrzzQEecvJo5bC+vtlKrgI4OY48mIQ6BNsnOF6L6nIw6gqdpmIxWgi64A5CqxNH1D1eMaf/l8QEt6nmG0H24U7GRRqu8L1HREWi6wP3mHfT9+U1St22kICuDXoecyHHqfWVfHaf2FPDtHsfXlJLjmhjtF0Froy9OZgteiUkMvmDgpieir/q8ouEbNmwYb731Fvfff3+ZjwcHB/PWW28xbtw4PDw8aNOmDf/73/9ITU3lT3/6Ex4eHnTr1o1du3bZj1mxYgUmk4lly5bRtWtX7r//fmbMmMHs2bPtWW3BwcHMnTuXcePG4eXlVea158yZw5AhQ3jppZfo2LEjL730EnfccQdz5syp9f8PxTW4tUvZ2dkcO1aU5n7y5Eni4+Np2rQpQUFBNGvm+MHDyckJf39/OnQo+hI9btw4WrZsaa+j9t577/Hqq6/yxRdfEBwcTFJSYY0MDw8PPDwawh0JIURFnIw6/rrIg9W9e1CQk27fnvnHwXKP0Sk6+vt0LPOxqAe64dYxCM3FBSU/nwldy35hri5bEdjDj/Wiy//2oxp05Gtmfv5tIXq9gWF3PElMRkKZnVBF/YmN/5E+Pe+p9fN+tCmFD39LRgOO/ZYMwJOD/Wr1GmcvHMZdS6SV9XtSdbeCpmHNPl60Q4nYnl5vYMSdT9nH/z7jeJNqf+YxYr9+h7MXDtMqoCNREz5i2sS/sffwHnuDBatFo5vlKSa1GE6yEkvoHWk8PeFZDIYG95FCNBC+mYPQUHDCAwWFduqfidfPBeDQsQPXYAZ1t3Q0zEePqlrYfeIQqSc3cf7wEtx93XA7+5B9n/N+JlwTJ3K63R+MCKv8NWBPugUfbxdauRk4cuIygGOtpWKiJnzEgqWP2//NTh63iFarjjh01RZCVN/jA1rwD9d0PK4E2lpovdHMFxkbbsSgr/+1owMnGtHpFE7HW2jVFZy37iHjcDYDnb/mcK6JHR6f0iXvI5JcHgd0pDuNvHKkhqrWzfwNOgMPB95eOOgANIDOrKLheP/993n77bd59dVXef/994mMjKR///789a9/5V//+hcvvPAC48aN4+DBgyiKQkxMDAMGDHBYSTh06FBeeuklEhMTCQkJqdJ1Y2JiePbZZx22DR069MYLtO3atYtBgwbZx9HRhVHw8ePHs2zZsiqd4/Tp0+iKLcv68MMPMZvNPPDAAw77/f3vf+f111+/6jkLIa4Nr05dSPt9q8O4Jtanqjh5XQmyG51Yn6oyIvDq52crAuueF0beOscg4LHE3WxPP8K2zMJlPommwjo8tzYt3elRXBtWq4VfNixi4/bPSUlL5O5BU9Dra+9tMe5Ujv3rvXZlXNtaBXSk24m3uUgYBu0yTXTnObW3qOPoqLsfrOQMjh+2T/zwb7LPHQUg4cQOFix9nGcnf+awz8YlJjYtNgOtaEYruqsuEmQTFVIo/DeQouzGW3PMsOoc2rXG5x3Z05uFG1IAOB2YQ6fjXihldDTqMbxqXTtrQqcobP22sE6ajcl/La5n/2Kfy9mAPHrvG4bTza0Zfkf/Ss95LFvlz3e14nJOAafO5uDXzIWFkWV/oXB2Npb6N1rXXbSFuBEY9Aq3jnJl05WO9iHaSFzDd1/p4F3/bLWJ7Z5YxezF/+TrT96luzUKZ30EBvUdsg0R6JXLFC8bodNpjOpl5P1fiz6XjOpVce3ykixWlbkr9/LD1hOMvLUtU8f0kNVADYTFqvHRphTiTuXQq407jw9o0SCCw8OHD2fy5MkAvPbaayxcuJA+ffowevRoAF544QUiIiJITk7G39+fpKSkUuXB/PwKb1YlJSVVOdCWlJRkP674eWzJV3WlwX0yHjhwoEOBu8okJiaW2rZx48ZK9xFCND59P17JjkljSN+7B58eYfT9eGWNznPJrFU4rilbEVi3kG645jrWugoN7s3BnNMO2w7mnJZAWz36ZcMiflw3H9Cu/BeHbK+r1auNO78fz0aj8ONtrzbutXZum+LZLP0D/s3kyA/5KPg5du6N4eYeEfalnuXp4tGabRlFNZ7MFx078Jas/wSUWoa3b3UBd0x2vYpnIa53qV4baHr5XnbrZ3KTZTSprRbgnedD59CufDqrZq/jAFMG+hGz/wI7T2QTox4k1yOIvk1DuHWoDwpwZp+VoJ4Gbp9Qt3V1S/47sTb5g3xjJi4mL84G5OKapyPT00xTn1ZVDuYbDDp8vFx44pGbaGIAo7N8gRXiWiueNVb4WnIHekP9ByzK8+0vheUizijr6c1zJBkfB0VBU668BmpwJiCHQWHNeOQOF3SKwo/787inmytPDa7eZ5S5K/cy84s4NA0On4pj5fqjjLnjJgm4NQDFV1T8fryw9l5tr6ioieI18m2Br27dupXalpKSgr9/YVmfkg0wbXGi6jbGLOs8NW2uWVUNLtAmhBDlMRiN9P/8+6s+T1NnhQumok6Kv609SEoT3VV/OGjh4sXpvFQsWRe50Lroi11oSB/uHjSFj8/+RhPtZkycwEhbUMtf+irq3rGTu6BYzlnhuPbYargUv6NY24pns5hMJsZPH8OhYwfoHNqVKWOfqTTTrJ93BxQU+3LmnYGdSTix0/64rf7Tn4aMwmw2sWDp41xMfxEdxVNAr0HbXtGohY5I5beP59Jc68npph/i02kH/7wj+qqySE1mlSeWnyQ+RQNFwZqXS5zHYS42PcffJ9e87ltNtAro6JDRpjPo2DrgFN1j23M4JAuDVSGxZSo3K1UrVxLqoWdPhtVhXBZbVu6xxN2EBveu9axcIW50xbPGTGaVSZ+e4EiSiQ7+RhZGhjS8APiVuIFFMaGhkafvApqGr18aXe5rx+l4CztaZHP7hED0eoVpQzyYNqRmZZR+P3CB4rkxp5Iu868VcaiqxvNje9XCkxE1dS1WVNSEk1NRXUFbkKusbapamKxgy2orLiWlMIu9ZIZaRco7T3XOURMN7NVBCCFqj0VV+em8meWJ+fx03ozlygv3UH8DAUYFtcDCyaPJ/PJDPDO/iGPuyr1XdT1bEVgPb3/UgQNpbQxkxB1RTHtsGXq9gSZKH5xohhudcaIZTZQ+tfE0RQ3Z3shtart5gUGv8ORgPz6e0JYnB/vVedr++Olj2B63pVpF5m21DB9ocQsZu38FRUczn5Y4O7nSvm1foiZ8BMB9dz3AgqWFy+MyOr+MViy4VpfL8sT14ekJzzL4MVc633YUt7DlWC35bPlnR94amMYnU7IpMKmVn6SEJ5afZOfJHNAZcPJoAvqyg1HXQtSEj2jfti9url60b9uX99+Io0lTP7bcnEqWZwGXfMyoBugdXLWMkTAfPb189Pg4QS8fPWE+ZT+31es/5Md1H3D46HZ+XPcBq9d/WJtPSwhRjO01JzPPWmEn4Po0amhhuYhs7QwXlBjyDM3wCzjPY1/dRMR4Zzb2SuZcQT4PLjnOV6dMxKVbUKuxkqw4tZzDvv7tWNkPiGumVxv3YguF62ZFxbUQERHB5s2bMZuLGvysXbuWwMDAUktKKzvPunXrHLatXbuWfv361dZUyyS3vYQQ1601SRZ75toFk8aaJAsjAp0x6HSMCHRm9Mu/smVv0VK53w9cAK6mto0OL27B6qrR8Z77GepvwFCsXqTF6oYCGPCwj8W1999Nx/nzgHYoOsd7TRvPdebE2cKmBUd/S0bVNJ66o/KOtMezrbQrJ+PkWikwqfjsfoqHLHP50XA/Odr5ahWZL76MFhTuGfJUqWW0tuVxVs9D5IYsxuVyb4aM6l/ny/JE42cwGAgNciPhaB4AurxWOGX0xopC4i4Ly6fl8tdF1cuqKN6FU1EUXLx8yL+UVi9dNsuqk1a8fhxAn+CqZ7XqFIVePgZ6+VT8MX3nnu9LjUfe9UwVZy2EqI6SnX/L6wRcn56Z8Bw6nY6de2Po2uMAT42/y57ZPmHJ8cKbE8AfZ3I5cj6Py1c+h1b2WlMWXTnL7i4mX+TIB+9x0xPR6KR+a724FisqSqqsoWVNPPzww7zxxhs8+uijzJgxg6NHj/L222/z2muvOSz7jI+Pt88hNTWV+Ph4nJ2d6dy5MwBTp07l9ttv59133+VPf/oT//vf//j111/ZunVrWZetNfLbX4E+ffqgL3GHNCoqiqioqHqakRCiOiqrxVbyblx5d+eqqrzAnk0zl6Ilq7axuPb+u+kEfx7QjptCwjlyLAaAs0n5uOQO59YsX2LC0lB1Gj/EZ1Qx0KaWG2j7z6o3+ct9r9Xq/MuyfFouzS19UFBoqd5GG+1uLocuqvLxR0ssoz1axjJa+/I4BXLbLqFV2wMMeuyO2nkC4rpXPCikGbIdmhac3mOhwKTiZKz6QosO/kb7l0bQaBEUwF+GBTWYLptTBvqhU5QGV4xaCFEzjq855XcCrk8Gg8HeIbykkoFBc0FhJnFSngo+1b/WLV392brvPJoGrn6B5CVfwNUvAOPp/Rye+080VaXj1Berf2Jx1WwrKq6l2mhoWZKXlxfr1q0jKiqK8PBwfHx8iI6Otp/bJiysKEli9+7dfPHFF7Rp08Zep79fv3785z//4ZVXXuHVV1+lXbt2fPXVV/Tt27fSOaxYsYJZs2Y5bLNareXs7UgCbRWIjY3F09OzvqchhKih4rXYbOPiSt6NK+/uXFVVFtgb6m9gTZKFS2aNps4KQ/3lJbg+aGhsSz9MVqduFMSGQloCx465MNL7EMaLQzFYFdqebMLlZld/tzpu/y/XJNCWnGCxBy4i1Dex6C8zvxpF5rUSy2hLjsGx8UKrgI72ZaVCVJchozsamv13VrVS7ay2hZEhPLH8ZLF6Sd0aVL2k2viiU1kNtr5h9/LT+gUOYyFE3Sj9mlO1jocNRclAoWcTJzQ08rkEBFT7fLabGos3p6Br6o+1IJ9b8v+gbd42AM6sWimBthtIZQ0ty2pOWXL/4ODgUtu6devG5s2bK7x2VRppPvDAAzzwwAOV7lfS2LFjmThxosO2rKwsvLy8Kj1WvuUJIa5blQW2bunqz5a95+1jVdOwWNUaN0SoLLBnW7Iq6oetFf3BC8lsTc8BBUIfnMi+L5ZgsKSSG7QCY/JQXE16gs6707ZHxXerVU0jPsNKikklLt1CT2+9PVhraxxwOfsS7/97HFETPsLZue7ufvu1N5C4y2Ifh4b5YDRW/Xq6EstoS46h7OVxQlRV8aBQQbMYFMUKWtFrcnKCpbxDy2R01rF0YrtanWND8/P6D+3/zw4f3Y6mqtxTbGnosDueRNHpHAJxQoi60dhfc2yBwp0nc2jl74qXZ+HnldT8LKoTaCswqSyflktygoXmoaEYPCxcPnca1xYBBB38AaurJKkIAdIMQQhxHbMFtiKDXey12YqbOqYH/bpdaR+tKnhvDOXNWzNqXJzb1mTBRQcBRslYa2hsrej1rqq9M5eiKLQZNIJOyl9QXVIBcM034OWqZ8qgsrNRLBYLc5a8xytffM7uSxZMKsSlW4kv1iXQ1jgANBJO7GDB0sfr9LlFznEjONyAqycEhxuInFO9+n+hIeFQrHRu4ViI2jPsjie5Z8jTdLypH/cMm0xwL8fafn7t5fWypB0larCVHOv1Bkbc+RRTJy1lxJ1PScdRIa5jVquFn36dz9yPJ/DTr/OxWmt2c8LNVWH08CA0xXQlq7h6gbHl03JJ3GUhLwtOxVnovy8Qc8YlQOG0b1fWdivM/ml937Xt/ixEQyPvyP/f3p3HN1WsDRz/nSRtUyhdKNBStrK2rKVAQTbZVDZx4woqAiIIahGRRQW96lVft8smgiKI4EVUvBdRVFQQ2UGgtEVAoBRa9rJ2oUuaJpn3j9JAutGWboHn+354L3POnMkkDifJk5l5hBC3LYNex+kL2dPoq9mqEGCuCTexObfMWKvcdh5IIL/Z5Sp+L3XrNiFnoWjds+7sS1/IsAnb+WLmt3lmhs1dMoPZn7/P4y9/47AZ67nrgrM5iQMKKpc2F6Ou2OP1ejkzYWRmjCgrOUGhHFndrs2K8GtWvOCwzWLhyCezuBTxJ74d7pBNt4UQtySr1cJP6+bx47ovSMtIxctDh6Zlz3C12WwlSn4S1FCPQmEhGT3VcNWlF/lai8VC/L5UIPtzkYaG1xVX8ABlyWJv/d702v8Fvp260ix8SrH7JsStRD6VCCEEYNay7PsFxRpP4h5Tr8jX5ixJ/HHrMQZ1a8TzQ0JKvPxUlJ1OLbOXCl9JsqGUsgfJrCcOkdFwC9hcMfvspJohivWW9yESRk4ewor5jrNIVv62AoA/VrxHo1Z3omnZ/639rtvI3Z444LpyZZY7CCJEWbuZ4HDM/Bkc/ugDAC5s23jLbrrdMfQ+1ly3B1vH6/Zgs1oUm5dkciLaQv22Bu4c5YbeIMkWbiRnyf85kw0/o85hyb8Qlc0v6z/mlz8+wUWncfZwAxqlLSat0ScYz9/FLs8ZJQq0det6kmObPsWn2wBsZGDlDFC0JbFzl8zgjLkNGdoFGqv7USgu6RPRubiibDaqnY8lvE9tgp/9QH78ELc9+SYohHA6Voti/acZzL4/hdn3J/PHpxlYLSVLGfqPXk0AcFXZm8JCdqCtOMuYcpYkHjqexIyvIvnw272F1rcpRWSihV/OmolMtGArwiaetyOT2caoxUfp8n8HGLX4KCZz8ZfzXu/5ISFMeawdOpML+lM+1DfWpJt3c4w6F9BZwZBBcrsJJNdbZF9F+Xfs/rwNXf3PdSYumo0rZ5CRmkg7Hz1tva9lHg0ftRD/Wo0BjWaNOkniACHyYTKZGBp+H637NmJo+H2YTDdOQGKxWJj/v0+Z56VI1hRWFCe/L3riD2cy4Prltnc/x4A+z9rPbVxsYsOnJo7utLDhUxMbFzu+diV5bW8HUYkWIhOtnM5QRCZaiUos3vI7IcqL1Wphw/ZlaBpoWdVxudICvcULncUTl6T2sPWVErWr6TRSjm1FaelkaLFouqIFmq0WReQ3bihl5bS2CROJJLpFk9Qtlmp6C208rrBp/v20mDBVgmxCIDPahBBOaONiE5sWZV4rL8pE02n0eqr4m80/PzQEY0BN4v4yo/ufQpkVVpcsLnU9gsVatJlpOw+co2ff1uyNiCOkQ0N2HjhXaP2oRAtRSdlBo9MZVpRStK/ukqfe7TpjIWeG4DcHskjHDdDYFZfGM8vibmojYoNex+RHQ9l75CJT77yWgvzydbNGlIJT5659IW3RpFWedgb3G8rsz98HYPOqWXTrMZh2Pv4OdVxdjbgY3KjmUV0SCAhRgJGTh7A9cgsA2yO35DuDNLe5S2bwvSUJXOGEC5wwwMPl0NeKUNhM07/WZOUp9xnnbi+X5LW9HcSm2vKU21evoM4IUYhf1n9M+hUT7idGYa6xnc62NwFwvdgVDQ19atNit2k2mzi7eiG2jFSS9qzHM6Q7Lb2LNuN+02ITDZOfQEPDpC5zUPcFl2v/wLYPoordDyFuBxJoE0I4ndxfMACOR2WRs2dEcey/osjc6U1gIz0XM8woFKnKxMwVkaBTTB3W7oZtNAxpQJN2DbFabfTu34bYyLhC6xf1g/71AcWjOy3YbMrhi9StavY30cz8Ogq36jXwqN/QfvxwQunMyHiwRyOH8oA+z6LT6Vi3+XN6dRlB4o/rOX0hirbN2/HFzLwzZSaMmoJOp2PX3h10DOlMo/qOwb+cjKMnzxzEy7MmZrOpTDOOCuGscs8YzXcG6XVsFgvffPupvRzhBscNMOk23HQ7ZwZ2QeXivra3K6UUQ8Pv4+/Y/bRo0irffTmFqAg7o1bjtXc2qU3nUuXEMPtx15QQQFGvRdVitzl/yVjOndgHQNLudbidS6DLs4X/VJHz4+fOFTaCry4xbazuJ0Udp1df+bciREFk6agQwgnlXWpps5VspldChpX0zdcCXxoaOpXd1v82xBZ6bZbJxudPp+L2kT/n3zTTvHU9NE3Dx8+rWH1ItZiw2ByXr1isiq0rMxyO5RdgvBXlvO6ZyYmo65bVppqspbKE9MEejoGxnFkjwx56k/v7TWTF/B85/Mcpvpz5PV9NtPBub8dMtAaDgYmjX+SruauYOPpFmlRz/M3q+oyjySnnyzzjqBDOKveM0WpVq2GxFLyUL2b+DLKSkuzlPUZw8fa+LTfdTq93CYUi1ngShSK93iWH87lf2/xm596OmnjoHcp7t/yX7ZFbSEpJtM/8E6KsWayKOetSeWzRZeasS8VssRGZaGHlyUyHLUUMV2etGc/1dbjeu7aO4R8WP9CWOzHTlQsn0GmFhwNytkeJ1Z927IOnNxNG3X73XiGKSma0FSIsLAy93vENOTw8nPDw8ArqkRACIGSAKxuvWzoKoNOVbJ8zhYbSFPrq1wJ16brMQq7IZjJbmD7oFNGWOLqlh5J5wMbRrQnUbeCLB9ZCr23ioScqyQJomDiNjXT+ey6RR2t3v9q2jUFzD1PDoypGNz31zuZ8mLr5vdwsVsXCTeeJPJ5GuwZVGdujFgZ9xS9HNZlMjJw8hL9j93PF+DzgAVYrpuM/4VZ/IDqdhlVlLyEd98VRTNGTS30GQljbex3KOSnsgUIz0TbO9cWtvDOOCuGsvpj5Lb0e7ciphJMAnDx7gscmPIjBYKBjSGfGj5yE4bq9fk5+/y1hmXDekh1kA3hkyLhbfj8gi83GbwkWLmQqarpp9PU38FPGHqp7BBBrPEmSIYXLGWd4lWuzdb+Y+a39nppznxQQ6qNH07AnQ5i7/E2H8zLzT5SHuetTmf17dtb7LUfMnEq30qylG+kmC5/9eJIODarSvu39/LnuCPrMmlevUhiraXR+zFjirURyJ2qq4x/EnMUf2Gfo577nwrWM7Wn6DBTKnjis+9BaeeoKcatZvnw5M2fOdDhmtRb+PS+H/OsoxO7du/H09Kzobgghcukx2kh8lNUeBEGDBqF59zgrCg2o2t1gD7QpFFYt+waakyghP8PeWEvjpPbE1ThNtyuh2ddudCGz5wXGdgko9DFDffTsTI5Bp2qSzkEUWZzPvHY7fmZZHGeSsrjUKAX3zOxAm0JRJXgv0KPIzy2/7GoLN53n4z/OoYA/j6YC8GxvvyK3WVZy9hOqaQFzte3gfjdoGhkJZ3CrYwJdFXvdyLiLJEVuQQFbDhpp8ejHhA/pxfNDQkDZmPfFrDwfGkuy3925GEuh5RxZJhvLJqZzLsaCXzMDAa1bEXtim/18Zc84KkRFMRqN6HR6dDZXWqmnaGkbzeVdBzmnRbBvR3M+2L2Bl+bd5fBvtV867HOFNB0EV7n1Z1RYrRa+OniCTPcANE3jrEnxW4IFpSkiPbKD+JEeh2igVXO4zmg0yp5s+dBpGu18rr3fNm/U3L6XHcjMP1E+VkY6boWxbp+JJEsqrYO8OXEmnRNn0vHv9Q+6TlrF9q/aoHc3Ua9FVYZ/WBUXY8kXpIWPWnh11v0umjXqiJVmzP7sXQC27N6IzWZj0lPXMjj/vDeRsOZ+bI4+Q0YNV/aY/qZT9Ybc2b86d45yK3E/hHAWw4YNY/To0Q7HUlJS8PK68eolWToqhHA6eoPGyHlV6TXOSONOBnqNNZb4Dd/fXYfXYAPWizaMrXV49DVTq4YbU4e144VH2hZ43YFjl7jkkmwvKxR3NWvE+Dvr4WrQF3gdZH/Q9zFeIlnbitLMoClquV27YefsRZbpZiPDzcq5xj8R0/g06Y2+KNZzy51dbc3ZLCLiU+3z4hSwYOO5UlmOebNyZhEoQJfxOwbT72jmw+jMf2G9cgilFNa0EyilyLxyAYUOtGrY3O8hPcvLnu117pIZzPzsXbbs3sjMz95l7pIZAGxeksmGhVcz9C00sXnJjWct5s48W1Am2pyZbxkpEBeRRfqmD2jS8A4k46gQRaDB3daleKumGPGhNp0xaRepo7qTEdGBTddl06z3wBD0aLQ1a4xP1gj/x7hbfkbFmvUfk6ariqZdCzZeNqs8PwTl98OQyWxj6ILLNJ6WwNAFlyv8Pl8ZfTHzW7q06463pw9d2nVnyYxvJSu4KJDVauHn3+fx4Wej+Pn3eVitpZO11poKp89mkJp+rb2oExkMGvAs7375PK9v8efJT6vdVJANshM1vTDuP/ZETavWrXQ4v/K3FQ7lNfuS7H9386lOpMchqgxOoNdTxtsiOZcous2bNzNo0CACArJ/FPr+++9Lpd19+/bRo0cP3N3dqVOnDm+++abDtjIAmzZton379hiNRho1asSCBQsczi9duhRN0/L8Kets3Lf2pxMhxC1LbyhZltHc2npnB8XOedjwC3OlrXc1XtIeveF1LRv58kvKNjxs7pi0TCzeGQyfU7/Ij/uwX2f+e24H5zOTqeXmxcN+ne3ngvyN7IpLAw0yjTYON0rmiu4MrslhWKyqyEs9cyddSDApUsxX35wUdN9dkxP+6bj9WYVnLHEsGVfyjJ43q3njluyI2kqIGdYbbHjq/iTlym8AZOx9EffQzzAdfhtV8yWykhMxuPWjjakdrc4P4lef7Vw0JLFj3xkyTi93aHflryuY9NTL2ckyroswFiV5xvA5VRxmqg2fUyXfetfPdDtVO51aJ4z4NvuAHv6f8sgDr5X4NRHidjC471AufdoBvcqelaxd/b+cv+9dY6b31SQwzcKnoOl0XIr4E98Od9D0mUkV1u/ysitqNT7ubTlzbD0Nuk9HKUV1Vx2PPtIWnU7jx63HGNStUfaM3lxGfp7E9mNmALYfMzPy8yRWPC0pNq+Xe+bfnssWopKyZ7VnZwWH9tXl65LI9sv6j/n5apbyQ0e2o2w27r1nQrHasFht1HZJ4oTyAE0DBQ1jdbTa5UNVPwO1E4ycq2Gibb38P3OUhnat+2X/JXccOZ+48u6D5wotCwGQlpZGSEgIo0aNYvDgwaXSZkpKCnfffTe9evVi9+7dxMTE8MQTT1C1alUmT54MQFxcHAMGDOCpp57iyy+/ZNu2bTz77LPUrFnToR+enp4cPnzYof2yTnwj7xxCiNta7mUkOXIvNbzc+BSDe1+bMbD8jXsY9sZaog6f52i7PSx/4x5cXIv+S6NBZ7DvyZbbJ8Mb8vR/jhEZn4xNZQfLTJo/kSl+LNh4jvF9/B3qF2ffNVvyH8AdVE9ypeZlI6f806l52UjiKg3GFbn7pe6fTTszcedWgrI0ak2bjE/jRpyJi2P1+7OxWbJIOXoQXVYtbMlJtEtrTqu0e3FTbmhoZKg4+ifeS9TFtTSvlmvfhKsvQ+5kGUVJnuFi1OW7J1tufs0MxEVkoaFxODAFt0wde+LTWfykBNmEuJEJo6bw5sJkhy94d9j+dV2Na/9WdQYDQc+9WH6dqySOrAnHzcMfizmNK+cP891+V95OdCPY35NfZz+IMZ/3HpPJxM7Yy6C7dg/7++ztkVDnZsSmWvOUJdAmcuyMWp2nXNxA2+xvotm6IZrq+rpUqxlC0CkD9S/p0BSkrrKgPKBrZE24uzR77ijnR8DB/YYy+/P37ccH9xuap25YC392J4CmN+DuH0BYC/88dYTo378//fv3L/B8YGAgY8aMISYmhu+++w5fX1/mzp1Lly5dGDNmDOvXr6dhw4YsWbKEDh06ANn7o5lMJpYuXYqbmxutWrUiJiaGWbNmMWnSJDRNY8GCBdSvX585c+YA0Lx5cyIiIpgxY4ZDoE3TNPz9y3fsytJRIYTIx6bFJjZ8enWp4acZTJ+zm5lfR2GxZge+jK4GVr4zgGMrn2DlOwM4bS69KfRGVx1LxzTh3Z47cCMx+xdPADR+jE7KU3/BxnPM/+McO46mMv+PcyzYmP1rY+7sagDGK5sJalQNnTV71ki9M1XR0PC64lpq/S+JK9ER/KOakcDGjfFt1RxdFSN1WwQz/O1/8fwTUzG6GbF5PIhB6Wmf2gKjMtpnvWRlbsQ3qwqZLkZq13TcH29w36GYTCYOxEQ7HC9p8oz8DJ9ThcyALDINVtLdLWxrdwGbrDYSokgMBkOuPTYV6rqoW5sB2efMZhOzPx3B5Dc6MvvTEZjNZbvko7LoFHof2Mxkppxg75LO7Dl5iRNnDKRk2NgVl8Yzy+LyvW7k5CFYU/ZDzhIbpWhRu2R7mQohSk92ZnVFmvkcfaPdCLyoR6eyP5OZY7KTDXiluBJ9Ir3M+zJh1BQmj5lG97CeTB4zjQmjpmCzWPh77r+Z9tyHRB6+yP60alTxD0Dn6kYV/wDc/WqXeb/ErWn27Nl07dqVqKgoBg4cyPDhwxkxYgSPP/44kZGRNGnShBEjRtiXhu7YsYMePXrg5nZte6C+ffty5swZ4uPj7XXuueceh8fp27cvERERZGVd+3EpNTWVBg0aULduXe69916ioqLK/PlKoE0IIfKxd435upJGZpaFGV9GMfub6HzrH00t+d43NouFwx99wPaRD3H4ow+wWbKXIvbr9TRV3L24oOtW6PW5g2855VAfPX4GRfpOC8nfZZH03yxsVkVQI0+SPc0oFDWTjCgUDVtW7Bcw3w53cLa+G7aq7tiy9Fh21iDtye64LhnA8yNfJvzh7Nl/VWzXAmw5mpoCSdT+wt3TD0hl5IP96B7Ww/6hceTkIRxOXcsOXfYvuApV4uQZ+XEx6rj0QCo/3XWaLFeFzZA9U1IIUTQj5noQ2MGAuyc0aG+g5+ir+2+OM9JztBGbUizatBpb8yfxav4IMXERzF8ytqK7XS7693mWe+9+jip1m+Hd4S4aBMH1s/xy9vTM7e/Y/bBvHPz9HGQlYkjdwxdPepdLn51ZEw9doWVxe+sYel+h5RuxWSxkJScBYNWsDj8q5EzrrXvWnWRPM+0aVM3bQCkzGAxMHP0iX81dxcTRL2IwGDjyySwWrD/Das8epCg3Io6nARp6V1dAK5cAoLg5VotiwyITX4SnsmGRCaulcvz6O2DAAMaNG0fTpk157bXXuHLlCmFhYTz88MM0a9aMl156iYMHD3LuXPaEgYSEBPz8HBO25ZQTEhIKrWOxWLh48SIAwcHBLF26lNWrV/P1119jNBrp2rUrR44cKdPnK3OhhRAiXxrXr2XS0AhNC+J/G2KZOqxdqT5SzPwZHP7oAwAubNuIstkIfv5l9HoDQ+9owCcbz9vrDgrxvmF76RnJWK0W9HoDVX9VXFyUvT9Zla56PHUupKRmkWVQXKhuwjfNjcatXQvcf6y8NH1mEtUursOwL43Md1qjeZsh1QXLfi+WTUznhfnZ+xFt+F8yXFTkfNG0aZk01g3iaJfdmHR9uZBuxD19Lw8PfJbVp/pzx/8dJEM/hCuGqehtOuqru0k27ueNUS8X3qFiah9YlZ3HshNNaFfLQoiiyW+ZttVq4dcNC5i3dA+12o7BtdEg3DQdlozL1A59ilMHv66g3pYvvd7AwLvGk9IyhBOmixyPdwH7nUbRzC//REAtmrTKzqZ5eT38uZ6O7bpjdL23PLte7kqSXTq3UB8DmuaYrVuIHAP6PItOpyM2fg9NAtvTr9fTxbo+Zv4M2p2I4kyNu7FoVvZ4ZGfx7Hq3N8ejrJw4aKGWzpX6kxRje9Qqo2eRv5/3JjIwxIeLu3dwvMa9tDmxgb8a9OH6wL4G5RIAFDcnJwEYCo7uyv7xvjT2tb5Zbdq0sf89JzjWunXrPMfOnz9vX+ap5frhOme22/XHb1Tnjjvu4I477rCf79q1K+3ateOjjz5i7ty5N/ekCiGBNiGEyEebAS5sWuSYmdLfXINLnHY4ZlOK6CQr5002IhMttPXWF3s208nvv73WHq789EU7Vv2QhF8zA2Nm1gQNfj+Sil8td+oH+2Cx2TDodNgsFo58Mou28UZOefUANFA2EjN09J3yDa9c+I7jVZfY44XVBhmo8d8BGH3dMBp1nH4glb6NV7L/719Zu7Uf/Xo9jV5fvm8LWSYby55P4+TfaSS29qVZ24bwhyfWAzn90DgXY8Gg1zHx4ba0TTHx15osQBEywJUeo734dHMbtvxRC9DI0AK4oHVl/u5AzprSrj7x1hjbvktG1AusNYygS5vupZ4tK+cD8fX75AkhSibLZGPO8CMknxxElkdzfkr24nLaUR4ZVB+bDao3G0S1pLJf9lGZ5CTQ0WomcMTDhYDoHVyqVodJvueBvPvWfTHzW0ZOHsLfsftp0aQVX8z8Nm+jt5hNi01svPq+fXSnBWVT9kQaRVXQvq1CQHbgu3/PpznyySwuLfuD2IPpNH1mEroiZj8++f23DLh0gjqZZ/mxxj0Euu7klf8uwKCvuJmTpswsxn60msjL9Vix7iStjS1pcG4f7pkp2YE2pejYyIMj50w8dkcN+XzjBE5EWxwSgJ2ILp3suDfLxeXaapKcIFh+x2y27FVC/v7+9plrOc6fz558kBOUK6iOwWDA19c3337odDrCwsJkRpsQQlSEnqON7P05i6QzVnJ+zdPQ+EevJg71opOsRCZmb56c87838yE9wW0MKVoYpEB8hIWvJmfQ8LXq3NvQB4DzZvgtwcLAAFdi5s9g3/zZeNbuiK7rHdg0Fxpbl3DUMIaaF84z0/0++hzcD7S0t1/V1A8/PzeeebwpHpcjWPvfeYDiTEIsAAPvGl/ivheX1aL4aEjq1dfYiDJVZcfmIfjWusiV1Gsblvo1y349Ny025Ql+6g0ae+JTuf4X1yQtGLPluj1ENA0372DcPH3K7AunQa/xbG+/G1cUQtzQsonppMT5o0PDXOsPGv82njMdL2DOUmw+2prB9dwYO2phRXezXNksFhJWL+LU4Z10TDTTISIZHXAuNhBeyBtoy51N06EtZWNHUgynTBepa6xBZ+9m6DTnXyLpuOUD/PlNJif/spZ4dpsQ+TnyySwOzX0flOLC9k0AxUrSosdGu9T9tEvdT5V6gRUaZAMY8+EPRCY3AZVFRGJjDnp6MG3D4xys04UqpkTuMkXx1hPP8/zX8fI5x0nUb2vInsl2dfJz/bbOGfLp3Lkz06dPx2w24+qavZf02rVrCQgIIDAw0F7nxx9/dLhu7dq1dOjQwSGIdz2lFNHR0Q6z6cqC87+rlqGwsDBatGjh8Gf+/PkV3S0hRDnQGzSq19XICeA0NNWlnp8HLzzS1qHeOZOt0HJR1HtgCJD9fuht2UCVbtfeEM/FWLhsdtxbIad88vtv+aLH+zS4+Dc2nZHsW3p2f+tfOkCCd2MsaVfs11350UKa+y+Ykk/icTmChOjFXP+T15G4iGL3/WZsWmwi6YyNa0EyDUNqUwy9Z9r3awrsYLAva839JSqnnDvxgG/1+rSq6+VwrEPjmuz77Rgr5q8u83TeQoibcy7G4rAXo1dK9gfsPfsvc/FyJu0DauPqenv9O56/ZCwxx3aS5QKXa7pwILQqliJ8irdYbcz8Ooqe4SvtCX22Jx1ma9JB4k0X2Jp0kO1Jh8v+CZQLx0Ca6QpXExqZ2Lj49kieIcrexd3bQSlsQGyQO98c/Yaff5+H1XrjWUM5n/cKKleEvRe80DQdKAuapiPdrRYbWzxO89PbqX/pb54IMmPQawxo7V3RXRVFdOcoN3qNvbrX6Vgjd47Kf4uB0pSamkp0dDTR0dEAxMXFER0dzYkTJ0rc5mOPPYabmxtPPPEE+/fvZ9WqVbzzzjv2jKMATz/9NMePH2fSpEkcPHiQzz//nMWLFzNlyhR7O//617/47bffOHbsGNHR0YwePZro6GiefvrGS7+XL1+eJx4UFhZWpP47Z3iznOzevRtPT8+K7oYQooI0CHXh2G4rKOiWGoLvoPPsTElx+PXfz6jjdIbVfo2fsfi/XzQLn0LsfxZyvkt3Uo/8TZWuetK3Zbfp18xAdVeNs6Zr0aTqrte+TCR4N77WkKZxVP8kACd8W+KfdJRaNa1cuQQoSN9mIa3nfzmw4l8EN+mEsl7rN4CylTyhQ0nkDpyBwuJxhGZNQhj4tEc+Vzjum5fzpSr3Ut1a1esx//FAnlkWx75T6bSuW4VPhjcsza4LIcqQoUYCKsUPq/sJPGKmcMEnO0hy+NgVmterQqjP7bdv1qmzh64VNI3TDd3J8DAwvG3hX9RnfxPNzK+zl9keOh6JzabwvPOiQ50DqSfp5tO81Ptc3vLb8iHHX2uy6FPMZaRC5OvqZ6VzAa7EtqgCWho/rfsIZbNx7z0TCr20WfgUNJ2OSxF/4tvhDpo+M6k8elwoZf+t82pYQNOzvvUTuNdviEfNpjR9picAA0N8KqR/ovj0Bq3c92SLiIigV69e9vKkSdlje+TIkSxdurREbXp5ebFu3TrCw8Pp0KEDPj4+TJo0yd42QMOGDVmzZg0vvPAC8+fPJyAggLlz5zJ48GB7naSkJMaOHUtCQgJeXl6EhoayefNmOnbseMM+DBs2jNGjRzscS0lJwcvLq4ArrpFAmxBCFCDnF6C/dieT0uQMafcdZ2tSdqCnq08wgH2j5L+TrbTw0pdo42SdwcDFB8JIv/dVElZPol6AhuYB7oE6hs+pguaavVz0QqaipptGX//sW3e9B4bgv/8o++r1uNqSwlVnQrNaSa1Wi8lXvqPr/5az7WuNPeuPcdb6E2ZtPyhoEtie2Fwz2HS68p7knCtwpim6ToopcHPh3F+i2gzInhJ+fSKCC7pu9AysitFVx5LRjfNtRwhRuRl6zyTr8lDSGyzD/fgIDrY5T1VrIC0a1WDByEa3ZVbfurWDiTm289oBTSPdz4Nm4VMKvgj434bYPOUn78z9BaFyZKS7WT1HG9HpNE5EWzj9twXTlevP3hrPUVQCuuzPeYk1XOC6e9HOqNU3DLTpDIZiLTMtD21rphCZVAtNf/2sJ43d1cNoHuhX5P3nxO2tZ8+e9iQE+YmPj89zLHf9wMDAPMdat27N5s2bC33sHj16EBkZWeD52bNnM3v27ELbKAuydFQIIQqQ84uQ3xsxuD4cj6a/umQz44K9Ts7GybWMOtr5GEr8BfCoS/YMA1MVPS4BOup97k7DN91wMeow6HQMDHDliYZuDAxwxXA1INYsfArvtrrIkVb346GZCQusyo5/hrH7rY48PTiJv3p6se7Pxdz5pIEXljej97gqBAd15t67x9Ov19M0adiB65dtZpfLT06gLIdvfQODBjxbYEKGnqON9Bp3dSr8OCM9R2f/Wje2Ry2e7e2Hix4evmuwbNQrhJNr1iSE5Pbjyaq5G7/XXXjavI3wDumM3vYSRtfb86Nr+KiFuBurORyr3zC0RF+CW1atX2jZWeW8Z4+c78EdjzgulQoZ4FpBvRK3mhphnUHTuFD71hhTn024jw4+saAcVzmYk5Joe3ZLBfVKCOcnIWohhLgBpWX/Pz3VqEIQlzNT2Hr5EF18SnEDaU3j8pGfrz2mUjTxuHaLzslues5kw8+oy85uajDQ+vkpfJerqe/XzOG3TZ8CcOjIdrLMZh4YMClPooOcmWOR+36lXet+xU5TfzMsVhtRHoeIbJBMmGpGt74+HLrBF+iCpsLnJCL442CybNQrxC0g5150TNcGY0BHMntbsbTpTJzBQPcK7ltFcXU18t4rW5m/ZCynzh6ibu1gwouQEOIfvZrYl47mlLv4BKFpmkMyhFtNj9FGtKuz23KSIQhRGnKWe545+AN/cW0ZdsfQ+wq9zmS28cyyOA4nmAjyN/LJ8IaV4ocDo5sLSyfez1v/Xs23qY1B02gT/zs1rpwi8K+N8NLIiu6iEE5JAm1CCHEDGhruNMFCMq7UJFWdIzLJDbTDpbavTafQ+/j5jwW4edYl+dQO6rgbCG3U1X4+KtFKVFL2r42nM6woBe2r538L/2Pb0jzlBwbk3QdErzcw8K7x5ZppNMdnr8xixv7s/T4iOEoVj1B6trz3ptoc1a1maXRNCFHBcu5Nn++/iE3TsIR0AcDSuOUNrry1uboaeWHcf4p1zQuPtEWn09h5IIFOLf15fkgIOk1He/dA5r76In/H7rdnY76VEsVUxB5F4vaQs/yziXUSv25YQGz8HpoEtr/hj5XPLItjV1waALvi0nhmWVyl2eIiZv4MWn8+k0sthrElaAhdY/5LQGIs1K1X0V0TwmlVfBg9l82bNzNo0CACAgLQNI3vv/++wLrjxo1D0zTmzJlzw3ZXrlxJixYtcHNzo0WLFqxatar0Oi2EuKXVM9bAjQDcCADATAJuBHAg9aS9TmOPm7ud9u/zLPfe9Sy+7q4Em/fzcLs7HJahxqY6TunPXb6exWIutFwZXNnwk0P5fxtiCWt7c4E22ahXiFtLLW8PyNmvRanssigWg17H5EdD+fbt/kx+NBSDPvu9auTkIWyP3EJSSiLbI7cwcnLFZz8Uwpnk/CDw/JglDLxrfIHbXmSZbHz+dCp+n3nTfVdN+3aBhxMqTybck99/i15Z6XPgPzwYMYt6lw+jV9ZKkRVVCGdV6Wa0paWlERISwqhRoxyyReT2/fffs3PnTgICAm7Y5o4dOxg6dChvvfUWDz74IKtWrWLIkCFs3bqVTp06lWb3hRC3oM7ezYhNTiPLln2/0WFEAdp1mys39ri5LHjFnV2mlGLb4/eRfPAAXs1b0umzbzFcnY3g4x3A5aTT9ro+3oXfJ81mEx8tepZLq4djSG1Kg5aejJjrgUsJMqgWVbXMxDJrWwhxa+gX4MpvCRYumxXVXXX2RDCieMzmLBbMO0rifldcm1+hyxMuHDz2t0Odv2P3V1DvhLi1LZuYTnyEBVf01LxspGq6nrSqVoL8K+eMy5ATf6BzM9Ls6YmVIiuqEM6q0s1o69+/P2+//TYPPfRQgXVOnz7N+PHjWb58OS4uLgXWyzFnzhzuvvtupk2bRnBwMNOmTaNPnz5FmgknhBA6TUcrTyM5P0MaaYiZ0+W6gXSTXDPm3H7/Lxf/3EpWciIX/9zKzjHXfnV8deJqfH3qoNPp8fWpw6sTVxfa9vwlY7mw6jEshvOQ6cHxSCvLJqaXyfOwWSwc/ugDqpsucn0WuH/0alImjyeEcF45iWCGBzomghHF88ncY1z8yg+qZZGxoj6bFmfSa9ijDnVaNGlVQb0T4tZ2LsZi/3t18480j/UiKC2OT4Y3rMBeOco9c63p2AkEPfeiZBwV4iY43b8em83G8OHDmTp1Ki1bFm2vjh07dvDCCy84HOvbt+8NA20pKSkFnnNzc8PNTTZWFeJ2EeqTfbuMSU3FFWjk4UIXn/LbQDrUx4CmXUuGcHbBW1iuO5988ID97+7uHrz98h9FbvvU2UO4pzYlqelcjOf6AprDB8MbsVksHPp4NsuOuHK8Zmu6dmnOuF7+GPSOGVitFsWqpzdyLKI5DZXGA00/JC4ljLt6j+OFR9oW+fGEEEIUXeIGT0BDF5SMLigZy891Cf6kK1127HXYo00IUfr8mhmI350FmoafaRENzgzC80yVSpEIIUez8CloOh2XIv7Et8MdMpNN3PZsNhs2m43U1FTMZscteAqLEV3P6QJt77//PgaDgQkTJhT5moSEBPz8HDPR+fn5kZCQUOh19eoVvAHk66+/zhtvvFHkPgghnJtO02hf3YX21X2A8t8LTKdptPO5dsveFhTMxT+32stezUu+SXjd2sGc8ziCS3Lrq0cUfs1uPFsYsrOh/r52C//VupCatJO/vGqwd+N5lKYxvo+/Q92Ni038Fd0eDBp7qrbHkKGjke82DKkWDPribfAthBCiaPSaDhtgO+yFLigZyJ6pvWJ+4bOdhRAlZzKZGDl5CIeOxjBI24IOF066vwKaRrouuKK75yAnwYMQItvFixc5efIkAwcO5Pjx4yVqw6kCbXv27OHDDz8kMjISTdNufMF1ctdXSt2wjZMnT+Lp6ZnvOZnNJoSoSJ0++5adY4Y47NFWUuGjFvKRJXuPNtxSqd+iGkmhC3hswjY6hnRm/MhJGApYPhCVaOVEUGcyks4Tcvxd/mpwF6Dx3aY4nu1Rw2HZwV9rsoDs++7v1cPo6L4atOwZdUIIIcpG53s92PyZGes2P6zbauHycDwtPSSboBClwaYU0UnZKw5quWlkcJTTmRfZvv43dkRvQ9lsJGi7qa06k+ZyB6Co09arorsthChEjRo1qFevHhEREbi6ujqcS0lJKXRCVg6nCrRt2bKF8+fPU7/+tX2RrFYrkydPZs6cOcTHx+d7nb+/f57Za+fPn88zyy03T0/PAgNtQghRkQxGI12/LJ3ZCK6uRiaHfw7h2eVZi95j9ufvA7Bl90ZsNhuTnnoZAJuysSMphhPpF1HWVqRb3EHTaNXMm8Sq2ffUNsfXc6p6MDHzZxD8fPZ1WSYbVy7Y7I+ZbKiG68XOmGr/St3aleuXXSGEuJX0GlMFnU7HvogUdMFJtB3hThfv8tv6oCxcH9zwM+po6613yNQtRHmJSrQQlZT9+eZ0ho10ssjQLlC7Sygdzgxk96ofWacbRX9tGQ2qdMSvmQvD51Sp4F4LIQqj0+nQ6XR4eHhgNJYscUnlWRxeBMOHD+evv/4iOjra/icgIICpU6fy22+/FXhd586dWbduncOxtWvX0qVLl7LushBCOJ2Vv64osLw96TBbkw5yPtOTNMu1Nx6jm46UXtlZnEOOr0cBi/aYsFizEx4sm5iOxXGLA1R6ICkZ9QgftbBsnogQQgj0Bo3eY915fqEfz00KonuNYHSaU30FyCMq0UJkopXTGYrIRCtRiUXfV1SI0hSbaruupOFKney/aRq1g7ID2jadmcT285j2hzdPLijbrO5CVITNmzczaNAgAgIC0DSN77//vlTa3bdvHz169MDd3Z06derw5ptvotS1ZGpnz57lscceIygoCJ1Ox8SJEwtt75tvvkHTNB544IFS6V9hKt2MttTUVGJjY+3luLg4oqOjqV69OvXr18fX19ehvouLC/7+/gQFBdmPjRgxgjp16vDuu+8C8Pzzz3PnnXfy/vvvc//99/PDDz/w+++/s3XrVoQQQuSSe1LAdeV9KSfZG+VDw0anOXfUl05tfdE0jSruBrx6tKTaBkisWovEanX4xeNB9kydw8CgDJKihqOnmr2dtlcO862+PS90fA9X18qZ4l4IIUTl5BjcyC63r15BnRHiOjkfmZRSnIs9il5voEPrTpJwRNzS0tLSCAkJYdSoUQwePLhU2kxJSeHuu++mV69e7N69m5iYGJ544gmqVq3K5MmTAcjMzKRmzZq88sorzJ49u9D2jh8/zpQpU+jevXup9O9GKl04PSIigtDQUEJDQwGYNGkSoaGhvPbaa0Vu48SJE5w9e9Ze7tKlC9988w1LliyhTZs2LF26lBUrVtCpU6dS778QQpSlLJONz59O5d3eSXz+dCpZJtuNLyqmwX2H5in/nXoSgF2RVdkb6UP8MSM7oi6RcCEDUKDLolZNDaVlcaJGdlIFU8yHJBDM3KX/5pxtH4qcX6AU9ydGM+Xxdkx6rF2p918IIYQQojw08dBfV1JkcsZeiouMxmq1oNfpSrz8TAhn0L9/f95++20eeuihfM8HBgby9ttvM2LECDw8PGjQoAE//PADFy5c4P7778fDw4PWrVsTERFhv2b58uWYTCaWLl1Kq1ateOihh5g+fTqzZs2yz2oLDAzkww8/ZMSIEXh5Fbz3odVqZdiwYfzrX/+iUaNGpfvkC1DpAm09e/ZEKZXnz9KlS/OtHx8fn2eK4MaNG/PU/8c//sGhQ4cwm80cPHiwwEEghBCV2bKJ6cRHWMhIgfgIC8smppdq+2azCawHuK9nXfp2DeSFJ6cyYdQUDqadAuBYbDVA4+/93gD8suks/kYdzb1OE2C04ppl5a8GfQCwJu+3t/ub7nHOajswa8kEdnChcZcGTH40FIO+0r0NCSGEqOQcgxt5y0KUl1AfPe189NRx13A1nCGD7JVZmqZRxdsbgL9j9xfSghCly2JVzFmXymOLLjNnXap9G5eKNnv2bLp27UpUVBQDBw5k+PDhjBgxgscff5zIyEiaNGnCiBEj7EG0HTt20KNHD4cklH379uXMmTMF7s1fkDfffJOaNWsyevTo0nxKhZJvOEII4UTOxVgKLd+s+UvGciRuFzaLFa/jj5L6U2+2LLGgbGCxWMhMSwPAbNLTfVdNevzgz/k3zdxRtRmPBHRn6B314eobpFv9R2jU+AphDw4CneI3wzC2NHiAJxd4UO/+f5Rqv4UQQhRPYTOk3/7xVAX27MauD26089ET6iOBNlExdJpGW28dnsZjZGhHQMv+DKSUomaD7MyELZq0qsguitvMvD/SmLUulS1HzMxal8q8P9IquksADBgwgHHjxtG0aVNee+01rly5QlhYGA8//DDNmjXjpZde4uDBg5w7dw6AhISEPMkrc8q5E10WZtu2bSxevJhFixaV3pMpAgm0CSGEE/FrZii0fLNOnT0EgP5KEFXinsJyOpg/Ps0g7lgqszcu4eKR/2I+u46gY9WoedmIi1nnMLPu6V5+jOtRg+pVztGhfyjd+7nQ8R8PEPbQfcC1Zal1B5XO/g1CCCFK5voZ0nERWbzz6Dks1uxg29oDyRXcu8LpNI12Pgb613alnY9BMo6KCrUjKYatSQdJsmR/FvLUu3N2exSxG7bTpV132Z9NlKtd8ebrNmvJLlcGbdq0sf89J2DWunXrPMfOnz9vP6blurfnzHbLfbwgV65c4fHHH2fRokXUqFGjZB0voUqXDEEIIUTBhs+pwrKJ6ZyLseDXzFDqKeLr1g4m5thODGn10dBI5hheNMJ8yYC+lS+t21xiz/rtNE1+HO26LAk5M+sMeo0J99TBrfkBLpqSOHmiCju21MLbayzPP9maCaMmlmp/hRBClMz1M6K3VdtLh1PNmf1NNFOHyd6ZQhTHSdNFlFXDutuXrHV1qNIqi1kT+qJ//K2K7pq4DXUMdGXrkexgm3a1XBm4uLjY/54TKMvvmM2W/YOPv79/nplrOUG43DPdCnL06FHi4+MZNGiQ/VhO+waDgcOHD9O4cePiPpUikUBbIcLCwtDrHaeih4eHEx4eXkE9EkLc7lyMOp5c4FFm7YePWsj8JWM5v20MAOmcx4tGaF5mNE0jILgpEd+v5pJtD7XpbA+25Z5Zp5SN9Wtr4+5uxWzWc/6CBwcaPojBIG87QghRGfg1MxAXkYWGRpzxFA0zA9i4IZaqteuQkmHl4z/OMbZHLQx6mS0mRGEUiqyVDaCaGdtf1bnwl2JTVRO9x7lXdNfEbWh876pA9ky2joGu9rKz6dy5M9OnT8dsNuPqmh0sXLt2LQEBAQQGBhapjeDgYPbt2+dw7NVXX+XKlSt8+OGH1KtXr9Drly9fzsyZMx2OWa3WIj22fOMpxO7du/H09KzobgghRLlxdTXywrj/8OzPn+N7pg4Z2nlQCl1NEyjF2ZgjAKzTjWK03x+4pNbJd2ad57ptJF56CL8jG6FBH0DjcIKp/J+QEEKIfA2fU4WJ/eKomuaBVbPxi/c2alWtz/w/svfHmf/HOWw2xfi7/Cu4p0JUbpoCy2Y/XJ+IzTnC3jVmCbSJCmHQa0y8u+x+lM9PamoqsbGx9nJcXBzR0dFUr16d+vXrl6jNxx57jH/961888cQTTJ8+nSNHjvDOO+/w2muvOSwdjY6OtvfhwoULREdH4+rqSosWLTAajbRq5bhHovfVJCW5j+dn2LBheRIopKSkFJrhNIcE2oQQQuQx46tHmTLsK/xtrXHpHgdGG2gandt1x3gyhY4hnRk/MqjAGWpeC76hVr9O1L+4LzsLqVIE+UtqeyGEqCxcjDoCn0pm5tcb7cfcqvuSlXWtzo97kyTQJsQN1HOvyUHAsvX65WwyE1TcPiIiIujVq5e9PGnSJABGjhzJ0qVLS9Sml5cX69atIzw8nA4dOuDj48OkSZPsbecIDQ21/33Pnj189dVXNGjQoNiZSUubBNqEEELkUcXDnY9/GM2Ks1uJN522H6/fIpipfcbc8HrvoOaMWTGarcFDcc9Mpr7tEp8Mv68suyyEEKKYXnikLTqdxs4DCXRq6c/aM26kJmbd+EIhhF1n72Ycufs0x76sZT/WZoBLIVcIcWvp2bOnPVFBfvILeuWuHxgYmOdY69at2bx5c6GPXdjj5qekgb/ikkCbEEKIAtU11iDedCG7oBR//rGOs6btjB85qdD91jp99i07xwyh78GfGeIZT6fPvsXgKomuhRCiMjHodUx+9NpsALf1CXyy4VrGt0FtvSugV5WbTdnYkRTDKdNF6hpr0Nm7GTpN3t9uZzpNx/DxddlcNZMT0RbqtzVw5yi3iu6WEKICSaBNCCFEviwWC7tWrSbBLQ2fVk2I/m0dET/8jLLZsNlsTHrq5QKvNRiNdP1ydTn2VgghxM2wWCyY45bhZ3LhgrEH43r683TPomV2K4hNKaKTrJwz2fAz6mjrrcdmtTLvi1ns2rvj6jYEhf9wU9lsTzrMtqRDAMSbLqBQdPNpXsG9EhVNb9Do9ZRskSGEyOY872pCCCHK1bwvZjF78Xv5Tsle+duKQgNtQgghnMvcJTOY/fn7AHh0W4U57jcMdxX/Pm8y23hmWRyHE0wE+LpxV+86GAw6TmdYUUqxaeVM++Ns2b3xhj/cVDYHUk/mKUugTQghxPVknrMQQoh87dq7A6UUmtITYn2Oeyz/wdfWBk3poXjbIQghhKjkVv62wv53V3WJyL++4Off52G1WorVzjPL4tgVl0ZyhpWDp9JZtfaU/Vxsqs3hcXI/rnPI/QYob4hCCCEcyYw2IYQQ+eoY0pmtEZtoYwknVD2Phg6T7RLJxNG7n6SsF0KIW8rVeJG/rytG1ySq6W38tO4jbDYbg+6ZUKQmbEpx4GyGw7GLlzPzfZwCy5Vcy6r12ZZ8yKEshBBCXE9mtAkhhMjX+JGTmDT6ZVq5PoZ29e2isXqADtWeZMKoKRXcOyGEEKVpcL+h6IC2wZ7Usm2zH98VVfT9NqMSLfh4uxHUqBrentlZF2tUv7YpfBMPPYP7Dc3zuDdiU4rIRAsrT2YSmWjBVswsc6Wpi08Q3bybE2isSRfP5iSZGrMsPpOfz5ix2GwV1i8hhBCVh8xoK0RYWBh6vd7hWHh4OOHh4RXUIyGEKD8Gg4GJo19k9upkEk9f+1LjVc3bqTauFkIIcWMTRk3h1OlfSU8/Sy217cYX5CM21caD99TlSloWFoti487zDO1Xl9rumj0ZQsioKeh0OodkCDcSnWQlMtEKQOLV/23nUzHvQzpNR1efYAB+PmMmwZT9/njWpPgtwcLAANcK6ZcQQojStXz5cmbOnOlwzGq1Fula+aZUiN27d+Pp6VnR3RBCiAoVMsCVjYsyHcpCCCFuLQaDAY10NO3asZNJzUnV6mEyWzC6Fu1rg8Ggw8crexbbwwPqY0k/T5/6NXF1zX7v0F39Eac4EjKsecvlEGizWhSbl2RyItpC/bYG7hzlht5w7QW6bHacWZe7LIQQwnkNGzaM0aNHOxxLSUnBy8vrhtfK0lEhhBCF6jHaSK9xRhp3MtBrnJEeoyV9vRBC3Irq1g62/11luXLs5GBqr3+V6QNPkWW68bLIJh6OK0GUUiT8vZL5S8beVL8UWqHlsrJxsYkNn5o4utPChk9NbFxscjhf3VUrtCyEEOL2JIE2IYQQhdIbNHo9ZWTkfA96PWV0+DVfCCHErSN81EKaNepEltVIlaiPyVAaRuVG1UQvlk1ML/A6m7KxLfEQR0x/4uuWSGbKKUzJJzi2bjJnIxdy6uyhAq8titzvOuX1LvTXmqxCy339DdQ2arjpoLZRo6+/LBYSQgghgTYhhBBCCCEE4Opq5IVx/yHR9V0Mqc3sxzU0zsVYCrxue+JhtiYd5HjmBWLMO7iw7SUOfHMvSXG/g7I6zJQrCX93XaHlslN4ilSDTsfAAFeGB7oxMMAVg06+Wgkhbj+bN29m0KBBBAQEoGka33//fam0u2/fPnr06IG7uzt16tThzTffROVKhrNp0ybat2+P0WikUaNGLFiwwOH8gQMHGDx4MIGBgWiaxpw5c0qlbzci7wZCCCGEEEIIu+Vv3IPFO4OGpjr2Y37NCp6tdSDthEPZb8BImjXqRBV3L5o16kT4qIUFXmu1KDYsMvFFeCobFpmwWvLuc9bWW087Hz0+LtDOR09bb30+LZW+3HuSFrRHaU5W1F/Omis8K6oQQpS3tLQ0QkJCmDdvXqm1mZKSwt13301AQAC7d+/mo48+YsaMGcyaNcteJy4ujgEDBtC9e3eioqKYPn06EyZMYOXKlfY66enpNGrUiPfeew9/f/9S69+NyPxmIYQQQgghhJ3R1cD7P9Zn2cQanIux4NfMwPA5VQq5wnExp87gxgvj/lOkx9q02GRPuHN0pwVlU/Qe5+7YnqbRzsdQ7plGe4w2ouk0h2QI+bk+K+rpjIrNiiqEEOWtf//+9O/fv8DzgYGBjBkzhpiYGL777jt8fX2ZO3cuXbp0YcyYMaxfv56GDRuyZMkSOnToAGRn/DSZTCxduhQ3NzdatWpFTEwMs2bNYtKkSWiaxoIFC6hfv759llrz5s2JiIhgxowZDB48GICwsDDCwsIAePnll8v2hbiOzGgTQgghhBBCOHAx6nhygQfT/vDmyQUeuBgdvzaYzDZGLT5Kt38d4PCzbUh7oivpS6pxfNE7nF29ELPZVEDLjvauMRdarkhF3aM036yoQghRAWxWG1uXH+Ob6ZFsXX4Mm/XGiWzKw+zZs+natStRUVEMHDiQ4cOHM2LECB5//HEiIyNp0qQJI0aMsC8N3bFjBz169MDN7doPHH379uXMmTPEx8fb69xzzz0Oj9O3b18iIiLIynLcU7O8SaBNCCGEEEIIUSzPLItjV1waxpMuVD3rBmmu2Na0x3AmlHMn9hUj02hFpTooPRWVFVUIIXLb/k08W5cfIz7qMluXH2P7N/EV3SUABgwYwLhx42jatCmvvfYaV65cISwsjIcffphmzZrx0ksvcfDgQc6dOwdAQkICfn5+Dm3klBMSEgqtY7FYuHjxYjk8q4JJoE0IIYQQQghRZDaLhb/jEwG45GPG0PU8kJ00wVblNECRM422GeBSaNkZOH+oUAhxqzh1IOla3hZ1tVwJtGnTxv73nOBY69at8xw7f/68/Zim5foR4+pst+uPF6VORZDNAwoRFhaGXu+42Wp4eDjh4eEV1CMhhBBCCCHKT5bJxrKJ6Q57tR39dAY1E/xJ9QvFN9EVl0eOY93mh0LheqE7GdVii5xptOdoI7oi7INWmfm76zhjsjqUhRCiItRt6U189OXsYJuWXa4MXFyu/YiSEwTL75jNlr3U1d/f3z5zLUdOEC4nKFdQHYPBgK+v7033efny5cycOdPhmNVatK0BJNBWiN27d+Pp6VnR3RBCCCGEEKJCLJuYTnyEBYD4CAvLJqZT7/C3jDx9moPBffih7VQyTlVBq2rGVi0WLWg1zeoVnmn0ejn7oF3PYrEw74tZ7Nq7g44hnRk/chIGQ+X92pKTBfWcyYafUZcnK6rVauHXDQuIjd9Dk8D29Ov1NHp95X0+Qgjn1eWRQCB7Jlvdlt72srPp3Lkz06dPx2w24+qanfF57dq1BAQEEBgYaK/z448/Oly3du1aOnTo4BDEK6lhw4YxevRoh2MpKSl4eXnd8Fq5wwshhBBCCCHydS7GkqdcD3C1ZdH64G/4PNGKzKYNqNPXxsN+nTHott/0Y85dMoPZn78PwJbdG7HZbEx6qvyyxRVXTlbUgvyy/mN+Xj8fgENHtqNsNu69Z0J5dU8IcRvR6XV0G9aoXB8zNTWV2NhYezkuLo7o6GiqV69O/fr1S9TmY489xr/+9S+eeOIJpk+fzpEjR3jnnXd47bXX7LPfnn76aebNm8ekSZN46qmn2LFjB4sXL+brr7+2t2M2m/n777/tfz99+jTR0dF4eHjQpEmTm3jWhZN5zUIIIYQQQoh8mT3TUSiS9FdQKMye6dR7YAgAOqWo/+IM7v3jJI/W7o5BVzq/4a/8dUWh5crKZrFw+KMP2D7yIQ5/9AE2S3aQcmfUaod6uctCCOHMIiIiCA0NJTQ0FIBJkyYRGhrKa6+9VuI2vby8WLduHadOnaJDhw48++yzTJo0iUmTJtnrNGzYkDVr1rBx40batm3LW2+9xdy5cxk8eLC9zpkzZ+x9O3v2LDNmzCA0NJQxY8aU/AkXgcxoE0IIIYQQQuTr1+rbaXG+DTuq7eUuXVcOdr/CufrDGfRRZzxXfESN9h1p+sykGzdUHE6aXSBm/gwOf/QBABe2beTEd19T976HMZ1LAOfL8SCEEEXSs2dPexKC/MTHx+c5lrt+YGBgnmOtW7dm8+bNhT52jx49iIyMLPB8fu2WB5nRJoQQQgghhMiXTWfj5+pbSK6Szvr2F1EuGgmJZv6X1hjjrK8Jeu5FdKW8f9rgvkMLLVdWJ7//1qGccuoUU/8O4HfXBZhPDcCGDhR0DL2vgnoohBCiPEigTQghhBBCCJGvf/TK3sNG55qdDdRkstK+VXUOHr1CbKqtTB5zwqgpTB4zje5hPZk8ZhoTRk0pk8cpa4cD7iCuVihmgyftD2/nkPUVgo5ZGdDn2YrumhBCiDJU6QJtmzdvZtCgQQQEBKBpGt9//73D+TfeeIPg4GCqVq2Kj48Pd911Fzt37rxhu3PmzCEoKAh3d3fq1avHCy+8gMlkKqNnIYQQQgghhPN74ZG2TB3WDq86tbMPaDnrOMtuKY7BYGDi6Bf5au4qJo5+sVJnHL1ezt51OQ7XvsP+eh2v2Yo0rQFhxhaScVQIIW5xlS7QlpaWRkhICPPmzcv3fLNmzZg3bx779u1j69atBAYGcs8993DhwoUC21y+fDkvv/wyr7/+OgcPHmTx4sWsWLGCadOmldXTEEIIIYQQwukZ9DomPxrK/b2bAmB007Fn/2WaN/aiiYe+gntXuTQLn0KVutcy7F2qVgeu7g30U7sJ1LddotNn3xZ0uRBCiFuEpipiZ7gi0jSNVatW8cADDxRYJyUlBS8vL37//Xf69OmTb53x48dz8OBB1q9fbz82efJkdu3axZYtWwps8+TJk3h6eubbppubG25ubsV7QkIIIYQQQjghs8XGW78lEH0inXr+7jzRvRYdfA3oNCfJVFBOLCYTf45+mMToPVjdPfj63gWccKlFsL87nwxviNG10s1zEEJUAiaTibi4OAIDA3F3d6/o7tzW0tLSiI+Px8/PD1dXV4dzKSkp1KtXj+Tk5AJjReDkWUfNZjMLFy7Ey8uLkJCQAut169aNL7/8kl27dtGxY0eOHTvGmjVrGDlyZKHt16tXr8Bzr7/+Om+88UZJuy6EEEIIIYTTcDXoeGtgQEV3o9IzGI10W/4jVquFXzcsoEv8MkYEtqdfr6fR6yXIJoTIn4tLdmri9PR0CbRVsLNnzxIbG0vHjh1JT08vURtOGWj76aefeOSRR0hPT6d27dqsW7eOGjVqFFj/kUce4cKFC3Tr1g2lFBaLhWeeeYaXX3650Me50Yw2IYQQQgghhMjtl/Uf8/P6+QAcOrIdZbNx7z0TKrhXQojKSq/X4+3tzfnz5wGoUqUKmswYLldKKdLT0zGbzYSEhHD27Nk8dXJmtN2IUwbaevXqRXR0NBcvXmTRokUMGTKEnTt3UqtWrXzrb9y4kf/7v//j448/plOnTsTGxvL8889Tu3Zt/vnPfxb4OJ6enoVOBxRCCCGEEEKI3HZGrc5TlkCbEKIw/v7+APZgm6gYPj4++Pv731Sg0ykDbVWrVqVJkyY0adKEO+64g6ZNm7J48eICkxv885//ZPjw4YwZMwaA1q1bk5aWxtixY3nllVfQ6WQatxBCCCGEEEIIISqGpmnUrl2bWrVqkZWVVdHduS25uLig1998oh+nDLTlppQiMzOzwPPp6el5gml6vR6lFJU4F4QQQgghhBDCCXUMvY81V5eO5pSFEKIo9Hp9qQR7RMWpdIG21NRUYmNj7eW4uDiio6OpXr06vr6+/N///R/33XcftWvX5tKlS3z88cecOnWKhx9+2H7NiBEjqFOnDu+++y4AgwYNYtasWYSGhtqXjv7zn//kvvvukwEshBBCCCGEKFUD+jyLTqcjNn4PTa4mQxBCCHF7qHSBtoiICHr16mUvT5o0CYCRI0eyYMECDh06xBdffMHFixfx9fUlLCyMLVu20LJlS/s1J06ccJjB9uqrr6JpGq+++iqnT5+mZs2aDBo0iP/7v/8rvycmhBBCCCGEuC3o9QYG3jW+orshhBCiAmhK1k7mkZKSgpeXF8nJybdMMoTMzEzeffddpk2bJhlThVOSMSxuBTKOhbOTMSycnYxhcSuQcSycnbOO4aLGiiTQlo9bMdB2Kz4ncXuRMSxuBTKOhbOTMSycnYxhcSuQcSycnbOO4aL2W9JtltD8+fNvXEncFGd7jZ2tv+CcfXY2zvYaS39Fbs72Gjtbf8E5++xMnPH1dbY+O1t/nZGzvcbSX5Gbs73GztZfcM4+O5uFCxcWqZ7MaMtHTpSyWbNmeZIlhIeHEx4eTosWLfj7778rqIfF54wRY2d7jZ2tv+BcfXbGMQzO9RqD9LesOeM4drbX2Nn6C87VZxnD5cPZ+uxM/XXGMQzO9RqD9LesOeM4drbX2Nn6C87VZ2cYw/Pnz88TvIyNjSUrK+uG/a50yRAqk927d1fa/+hCCCGEEEIIIYQQovTlTLK6XlBQEDExMTe8VpaOCiGEEEIIIYQQQghRCiTQJoQQQgghhBBCCCFEKZClo/nI2bYuJSWlwDpWq7XQ85VNTl+dqc/O9ho7W3/BufrsjGMYnOs1BulvWXPGcexsr7Gz9Recq88yhsuHs/XZmfrrjGMYnOs1BulvWXPGcexsr7Gz9Recq8/OOIYh+zWGazGjgkgyhHycOnWKevXqVXQ3hBBCCCGEEEIIIUQlcvLkSerWrVvgeQm05cNms3HmzBmqVauGpmkV3R0hhBBCCCGEEEIIUYGUUly5coWAgAB0uoJ3YpNAmxBCCCGEEEIIIYQQpUCSIQghhBBCCCGEEEIIUQok0CaEEEIIIYQQQgghRCmQQJsQQgghhBBCCCGEKFdKKV566SXmzZtX0V0pVRJoq6Ru1QEnbh9lNYbl34YoLzLWxK1AxrFwVmfPnuWf//wnbdu2JTg4mL///tvhfEpKCs899xzBwcE0a9aMDh068L///e+G7a5YsYL69euzf//+fM8fOHCA6dOnc+edd9K9e3fq1KlD06ZNmT59Ounp6TfVZ3F7Ka0xnJGRwfLlyxkyZAhdu3YlJCSEmjVr0q9fPzZv3pynvoxhUZpK814cGBiIpmkOf3Q6HR988AGenp4OdZ1+HCtR4Ro0aKCAfP988cUXDnW3bt2qBg4cqIKDg1VQUJBq2bKlmj9/fp4209PT1Zdffqkefvhh1aVLF9WmTRtVo0YN1bdvX7Vp06Y89ffv36+mTZumunfvrrp166YCAgJUkyZN1LRp01RaWlqh/T9z5ox69dVXVUhIiAoKClIHDhy4uRdEOJ2yGMPFbVfGsLgZZTGGly1bVmCbOX++++47e30Zw+JmldW9ODU1Vb3++uuqVatWqk2bNqpevXrq4YcfVkePHs1TV8axKA1ffvmlql27tnr33XdVQkJCnvNZWVmqS5cuqkePHio1NVUppdSrr76qAPXVV1/l2+bevXtVjx497P8m9u3bl289Pz8/VbNmTfXXX38ppZSy2WzqjTfeUIAaPHhwifssbi+lOYY/+eQTBajx48crs9mslFLq2LFjqkGDBsrFxUVFRkY61JcxLEpLad+LGzRooHx9fVVQUFCeP+vWrXOo6+zjWAJtlUBRB9xXX32l9Hq9euONN5TNZlNKZQ8kQL3++usObcoNWZSnshjDxWlXKRnD4uaUxRieN2+eApSfn1+eNn18fJSfn58ymUz2+jKGxc0qi3GclpamOnTooBo2bKhiY2OVUkpdvnxZderUSfn7+6szZ8441JdxLG7Wp59+qry8vFR0dHShdQC1c+dO+7GsrCzl5+enAgICVFZWlv24yWRS4eHhKjQ0VM2dO1fVqFGj0EBbnz591KJFixyOWa1WVaVKFaXX65XFYilRn8Xto7TH8H//+1/VpUsXh88MSin17rvvKkC98cYbDsdlDIvSUNrjWKnszyn5fefLj7OPYwm0VQJFGXCJiYmqWrVqqk2bNnnO3XPPPcrFxUWdOnXKfkxuyKI8lcUYLmq7OWQMi5tRFmP47bffVg8++KA9kJHDZrOpFi1ayH1YlLqyGMc5v0xfP/tSKaW2b9+uAPXMM884HJdxLG5GVFSUcnV1Vf/5z38Krde5c2dVvXr1PMdHjBihALVhwwaH48uWLbOPvZxZbQUF2vJjs9mUl5eX8vHxKXGfxe2hrMZwfmbNmqUANWfOnBvWlTEsiqOsxnFxvtvlx5nGsezR5iTWrFnDlStX6NevX55zffv2JSsrix9++MF+7B//+Afbtm3Dzc3NoW5O2dvb2+H477//zpgxYxyOaZqGi4sLnp6e6PV6h3PR0dE899xzfPTRR4SEhNzMUxO3ieKO4eKSMSzKWnHH8L333ssHH3yApmkOdVevXs3Ro0d55plnHI7LGBblobjjeMWKFQB56nfu3BlPT888+7DIOBY3Y9q0aQQGBjJs2LAC62RmZrJr1y6aNGmS51zz5s0B2LNnj8Pxxx9/PM/YK46vvvqK5ORkpk6dWqI+i9tHWY3h3EwmE0uWLCEgIKBIY0/GsCiO8hrHxeVM41gCbU4iPj4eAD8/vzznGjduDFDgpq455IYsKlJpjOHikjEsSlNxx3BISEi+Hz7ef/99Hn30UWrVqnXDx5QxLEpbccdxfHw81apVw93dPU/9Ro0aceHCBc6fP1/oY8o4FkURFxfHr7/+yt13383ixYu57777aN++Pc2bN2fixIlcuXIFgGPHjmG1WqlRo0aeNurUqQPAyZMnS6VPZ86c4e2332bKlCnMmzePadOmlajP4vZQHmPYYrHwxx9/0LNnT/z9/dm2bVu+7eSQMSyKS+7FpUMCbU7Cw8MDgMTExGKdA7khi8rhZsZwcckYFmWhNMbwxo0b2bFjBxMnTiy0noxhUVaKO449PDxITU0lKyurWG2BjGNRPOvXrwdg2bJlAHz77bfs2rWLqVOnMnfuXAYOHIhSiqSkJACMRmOeNnICwmlpaTfVlytXrtC0aVMCAwP55z//SadOnQgNDS1xn8XtoazH8HvvvUft2rXp06cPR48e5Z577inwRzsZw6KkynIcu7u7s2rVKnr27EmbNm1o0aIFTz31FHFxcfn2xanHcUWuWxXZgoODVZs2bVSPHj1U69atVfPmzdWYMWPUsWPH7HX+/PNPBaiePXvmuT4ns93o0aPznHv33Xftm77WqFFD/fvf/y4w61dKSopq0qSJcnFxUYC6//771bZt2/LUW7RokQKUp6enWrhwocrIyFAWi0UtXrxYaZqmunfvnmdPInFrK6sxXJR2rydjWJRUWd6Hr9e3b998r88hY1jcjLIYx/369VOA2rhxo0Ndm82mmjZtqgB18uRJh3MyjkVJTJ06VQHqk08+yXMuZxyuX79ebdmyxT6ucvvhhx8UoJ5++ukCH6c4e7RZLBa1e/du1bVrV6XT6fJk5i1qn8XtobzGcFJSkpoxY4Zyc3NTwcHBKjExscC6MoZFcZXlOL506ZKKj4+3lyMjI1WTJk2Ul5eXOnToUIF9csZxLIG2SqCoAy5nkCxYsEBZLBaVmZmpvvzyS+Xj46MANW3atAIfQ27IoiyV1Ri+nW7GomKVx304KipKAer777+/YX9kDIuSKItxvGXLFmUwGFRoaKg9oBYbG6seeughBShAZWZm5tsfGceiOMaMGaMAtXr16jzn3nnnHQWod955x34vvfvuu/PU+/bbbxWgXnzxxQIfpyTJEBITE5Wfn59yc3NTJ06cKHafxe2hvMZw7jafffbZG9aVMSyKqrzH8Zo1axSghg4desO6zjSOZeloJVC9enUaNGhgL4eGhjJ37lySk5N5/fXX7cdXrlxpn/7Yrl07HnzwQU6dOsWECROAa5sO5sfLy4vJkyfz+uuvc+jQIV555ZUC6+r1ejp06MBPP/1EzZo1mTRpksP66pwlIjlrr6935513ArBz584iPntxKyirMVzUdnOTMSyKqzzuw++99x6NGjVi0KBBN+yPjGFREmUxjrt168a6deuoUaMG3bt356677uKtt97iueeew9vbm0aNGuHq6ppvf2Qci+LIWWqU3zJlX19fIHsZUt26dQHy3Rvw4sWLwLX9BkuLt7c3PXv2JDMzk+3btxe7z+L2UN5j+MEHHwTgjz/+uGFdGcOiqMp7HHfv3h0o2vu9M41jCbRVUvkNuCpVqvDBBx9w4MAB9u7dy88//8xLL71EbGwser2eAQMG3LBduSGL8lJWY/hWvRmLyqc0x/DRo0f53//+x4QJE9Dpiv7WK2NY3KzSGMc9e/Zk7dq1xMXF8fvvv7N06VLq1q1LUlJSkQLHMo5FUTRt2hTIf/PsnC9tdevWpUaNGtSrV4+YmBisVqtDvZiYGCA7QFzavL29gWt7Exanz+L2UN5jOL8xWdz6MoZFbuU9ji0WCwBubm5F6p+zjGMJtFVSRR1wqamprF69mvvvv9/+obQwckMW5aWsxvCtejMWlU9pjuF///vfVK1alSeffLLY/ZAxLG5GWd2Lly9fjk6n44knnihSP2QcixsZOHAgmqbl+2Pw77//7hAEHjx4MBkZGWzatMmh3tq1a2nXrh0tWrQo8HFyvhDabLY855KTk/Hw8ODrr7/Ocy46Oho3Nze6dOlSoj6LW19ZjeGXXnqJkJCQPJu5R0dHA9C7d2/7MRnD4maV1ThesGABffr0yROUi4yMBKBHjx72Y7fEOC7Xhaoij08++UT17t1bWSwWh+Pr169XgBo7dmyh10+cOFEZjcY8m8O/+OKLqk2bNnk2Ef7ll1/yrJdOSkpSVatWVV999VWe9jt16qTc3NzU5cuX7ceOHj2qNE1T9913X576vXr1Unq9Xh0/frzQfotbR1mN4eK0K2NY3IyyGsM5EhISlNFoVBMnTiywDRnD4maV9Ti+Xnx8vPL29lbjxo1zOC7jWNys559/Xul0OrVy5Ur7sY8//lhpmqbefPNN+7EzZ86oWrVqqbCwMJWcnKyUyv434OLiorZu3Vpg+6dOnbLvRfjvf/87z/nExEQFqHr16qnY2Fj78Q8//FABas6cOSXus7g9lMUYfv755xWgXn31VWW1WpVSSh0/flyFhISoxo0bq0uXLtnryhgWpaEsxvHrr7+uAPXSSy+prKwspZRSJ0+eVKGhoSogIMAhsdKtMI4l0FbBijPgrpeUlKReeOEFVaVKFfXbb7/lOS83ZFFeymoM3243Y1FxymoM53jppZeUTqdTR48eLbCOjGFxs8p6HOfYtGmTCgwMVPfee6/KyMhwOCfjWNwsm82mPvzwQ9WqVSsVHBysWrZsqbp3765WrFiRp25MTIy6//77Vb169VTr1q1Vz549881uq5RSb731lmratKkyGAz2JB6A8vX1VSEhIfZ6ZrNZ/d///Z+66667VMOGDVWrVq1U48aN1T333KPWrFlz030Wt76yGMPbt29XY8aMUa1atVJNmzZVLVu2VMHBwWrKlCl5EtzJGBaloSzG8Z49e9TYsWNVy5YtVaNGjVTLli1VUFCQmjBhgjp37pxD3VthHGtK5ZqDKspVZGQkn376Kdu2bSMjIwN3d3csFgt9+/bllVdeoVatWg71d+7cycSJE9m7dy99+vThnXfeoXXr1nna3bFjB59//jl//vknmZmZuLq6YrVauffee3nllVfsSzgge2+Uf//732zYsIGjR49StWpVMjIyaNy4MRMnTqR///552ldK8dFHH7Fo0SIsFgt6vZ7q1aszfvx4hgwZUuqvk6i8ymoMF6ddGcPiZpTVGM7h5eVF7969WbVqVYF1ZAyLm1XW4/iZZ55h7dq1pKam8vLLLzN+/HhcXFwc6sg4FkIIIYQACbQ5GbPZzLp16wgLC8vzoVkIZyBjWDg7GcPiVlDccZyzh0qbNm0wGAxl3T0hhBBCCKclgTYhhBBCCCGEEEIIIUqBZB0VQgghhBBCCCGEEKIUSKBNCCGEEEIIIYQQQohSIIE2IYQQQgghhBBCCCFKgQTahBBCCCGEEEIIIYQoBRJoE0IIIYQQQgghhBCiFEigTQghhBBCCCGEEEKIUiCBNiGEEEIIIYQQQgghSoEE2oQQQgghhBBCCCGEKAUSaBNCCCGEuEW9/PLL6HQ6NE3D29sbk8mUbz2lFI0bN0bTNPR6PS+//DIAjz/+OJqmoWkajRo1IiIiAoAtW7YQGBhob7dt27Y0b96c+vXr069fPxYuXEhmZma5PU8hhBBCiMpCAm1CCCGEELeo9957j379+gGQnJzMjz/+mG+933//nWPHjgHQr18/3nvvPQC+/PJLgoKCAFi8eDEdOnQAoHv37sycOROAu+66i+joaA4ePMjevXvp1q0bzz77LGFhYZw4caJMn58QQgghRGUjgTYhhBBCiFtYrVq16N27N5AdOMvPokWL6Nq1KwA1a9Z0OOfn55fvcV9f3zzt+Pj48Oqrr/LRRx+xb98+HnjgAaxW600/ByGEEEIIZyGBNiGEEEKIW1yDBg3o2rUrv/zyC5cuXXI4d+HCBX744QeGDx+e77WaphX78caNG0dwcDBRUVGsWrWqRH0WQgghhHBGEmgTQgghhLjFKaUYPnw4WVlZfPvttw7nli5dSocOHQgODi61x9PpdNx///0A/PTTT6XWrhBCCCFEZSeBNiGEEEKIW5xSiqFDh+Lm5pZn+ehnn33G2LFjUUqV6mO2atUKgAMHDpRqu0IIIYQQlZkE2oQQQgghbgPe3t7ce++9bN++nbi4OAA2btzI+fPnGTJkSKk/Xo0aNQBISkoq9baFEEIIISorCbQJIYQQQtwmRowYAVxLirBw4UIef/xx3N3dS/2xbDYbAEajsdTbFkIIIYSorCTQJoQQQghxm+jfvz81atRg+fLlXL58me+++46xY8eWyWMlJiYC4O/vXybtCyGEEEJURhJoE0IIIYS4Tbi4uDB06FAOHz7MhAkTCA0NpXXr1mXyWNHR0QB06NChTNoXQgghhKiMJNAmhBBCCHEbyVk+unz58jKbzaaU4ueffwbgkUceKZPHEEIIIYSojCTQJoQQQghxC8udTbRjx44EBQXh5eXF0KFDC6x3Mz7//HMOHjzIsGHDCAkJKbV2hRBCCCEqO0NFd0AIIYQQQpSdc+fOodM5/rb6n//8h7S0NKpUqWI/dubMGQDOnz9fpHYvXbqU51hqaiqfffYZU6dOpX///ixcuPAmei6EEEII4XxkRpsQQgghxC1q+vTprFu3jl9//ZXnnnvOfrxjx4706tXLXl67di1Tp04F4Ndff2X69Ol52nJxcbH/fevWrfb6v/32G23btqVly5a0b9+e3bt3s3LlStasWeMQyBNCCCGEuB1oqjTXCQghhBBCiFtKt27d2LZtGxcuXKBGjRoV3R0hhBBCiEpNAm1CCCGEECJfWVlZ1K1bl+rVq3Pw4MGK7o4QQgghRKUne7QJIYQQQog8zGYzL730EpcvX2bJkiUV3R0hhBBCCKcggTYhhBBCCJHHG2+8wf79+9m6dSudOnWq6O4IIYQQQjgFWToqhBBCCCHyUEqhaVpFd0MIIYQQwqlI1lEhhBBCCJGHBNmEEEIIIYpPAm1CCCGEEEIIIYQQQpQCCbQJIYQQQgghhBBCCFEKJNAmhBBCCCGEEEIIIUQpkECbEEIIIYQQQgghhBClQAJtQgghhBBCCCGEEEKUAgm0CSGEEEIIIYQQQghRCiTQJoQQQgghhBBCCCFEKfh/5wqxUnCx98gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ZP_up = Phot_Cal(DF_up, 'up', catalogue='apass_updated.csv') #Zeropoint data\n",
    "AGN_DF_up = AGN_LC(lco2, DF_up, 'up', err_th = 0.03, Plot = True, Rem_out = False, zp = ZP_up) #Calibrate AGN Lightcurve "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88e9864-6690-49a1-b55f-71fba6b25a65",
   "metadata": {},
   "source": [
    "### (Top) uncalibrated AGN lightcurve in Instrumental Magnitudes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924ae656-1755-42f7-af2b-76c93a663373",
   "metadata": {},
   "source": [
    "### (Middle) calibrated AGN lightcurve with the PyTICS algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2becfd8-dce3-4e8c-9455-ef80779be0d6",
   "metadata": {},
   "source": [
    "### (Bottom) Same as above but with data clipped below 3% uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c6d6f76-ad16-46c5-b3f1-79c18af15023",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fdfc96-4d0a-4ecb-a21e-7a5a80be204a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}