Leviathan.ipynb

{
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    {
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      "text/plain": [
       "'LC08_L1GT_172070_20181125_20181210_01_T2_B9.TIF'"
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   "source": [
    "'LC08_L1GT_172070_20181125_20181210_01_T2_B9.TIF'"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'init': 'epsg:32735'}\n",
      "{}\n",
      "11-03-2019 0.47592296923803734 S-2\n",
      "24-02-2019 0.7823914232144973 S-2\n",
      "26-03-2019 0.4162232271547338 S-2\n",
      "21-12-2018 0.28841656122876475 S-2\n",
      "19-02-2019 0.6759802809755366 S-2\n",
      "05-04-2019 0.2719390057333665 S-2\n",
      "25-04-2019 0.20755606083932718 S-2\n",
      "05-05-2019 0.1837319412507259 S-2\n",
      "21-11-2018 0.14842089457946597 S-2\n",
      "11-12-2018 0.1978571833910274 S-2\n",
      "21-03-2019 0.5211041944477728 S-2\n",
      "31-03-2019 0.3247695200700212 S-2\n",
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      "10-05-2019 0.19068507863090844 S-2\n",
      "20-05-2019 0.18089008535406553 S-2\n",
      "20-05-2019 0.18089008535406553 S-2\n",
      "20-11-2018 0.17741078399230858 D\n",
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      "04-02-2019 0.43506890144224397 D\n",
      "11-02-2019 0.634287847924404 D\n"
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     "text": [
      "/home/emmanuel/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan.\n",
      "  dv = (np.float64(self.norm.vmax) -\n",
      "/home/emmanuel/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan.\n",
      "  np.float64(self.norm.vmin))\n",
      "/home/emmanuel/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan.\n",
      "  a_min = np.float64(newmin)\n",
      "/home/emmanuel/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan.\n",
      "  a_max = np.float64(newmax)\n",
      "/home/emmanuel/anaconda3/lib/python3.7/site-packages/matplotlib/colors.py:885: UserWarning: Warning: converting a masked element to nan.\n",
      "  dtype = np.min_scalar_type(value)\n",
      "/home/emmanuel/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12-02-2019 0.6556467918893737 D\n",
      "17-02-2019 0.7733615391425639 D\n",
      "24-02-2019 0.8337678201429687 D\n",
      "29-03-2019 0.4565520920524881 D\n",
      "30-03-2019 0.4204280948595433 D\n",
      "05-04-2019 0.3479289098096074 D\n",
      "06-04-2019 0.29659463873021136 D\n",
      "06-04-2019 0.28177681755340744 D\n",
      "06-04-2019 0.3138963207873976 D\n",
      "26-04-2019 0.33973681107844894 D\n",
      "27-02-2019 0.8167442592678057 D\n",
      "28-04-2019 0.32797464140034227 D\n",
      "30-04-2019 0.2730134441567958 D\n",
      "30-04-2019 0.27508368937547445 D\n",
      "04-05-2019 0.2632495207918866 D\n",
      "08-05-2019 0.2796725645943032 D\n",
      "09-05-2019 0.2917610237187017 D\n",
      "10-05-2019 0.2865360991898439 D\n",
      "14-05-2019 0.22100988132355143 D\n",
      "19-05-2019 0.2977872316365173 D\n",
      "22-05-2019 0.26087119779429135 D\n",
      "19-01-2019 0.6965195376325299 D\n",
      "24-01-2019 0.6861635434895627 D\n",
      "24-01-2019 0.5433317716139121 D\n",
      "30-01-2019 0.5410903819697717 D\n",
      "11-12-2018 0.16103982584479268 L8\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import csv\n",
    "import rasterio\n",
    "import ogr, gdal\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import earthpy as et\n",
    "import cartopy as cp\n",
    "import geopandas as gpd\n",
    "from rasterio import plot\n",
    "from matplotlib import colors\n",
    "from rasterio.mask import mask\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry import mapping\n",
    "\n",
    "\n",
    "from glob import glob \n",
    "from rasterio.plot import plotting_extent\n",
    "import earthpy.spatial as es\n",
    "import earthpy.plot as ep\n",
    "#----------------------------modules for cloud masking --------------------------\n",
    "import numpy.ma as ma\n",
    "import earthpy.mask as em\n",
    "#------------------------ Modules for changing the crs of a shape file ----------\n",
    "from osgeo import ogr, osr\n",
    "import os\n",
    "\n",
    "class MidpointNormalize(colors.Normalize):\n",
    "    def __init__(self, vmin = None, midpoint = None, vmax = None, clip = False):\n",
    "        self.midpoint = midpoint\n",
    "        colors.Normalize.__init__(self, vmin, vmax, clip)\n",
    "    def __call__(self, value, clip = None):\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0.0, 0.5, 1.0]\n",
    "        return np.ma.masked_array(np.interp(value, x, y), np.isnan(value))\n",
    "\n",
    "'''============================== Entering the Name of the field to be Analysed ======================================'''  \n",
    "#crop_extent = 'Chartonel/Chartonel'\n",
    "crop_extent = 'Gaulunia/GauluniaP1'\n",
    "\n",
    "#==================================== Files containing the data(images) to be analysed ================================ \n",
    "#rootdir = '/media/emmanuel/Transcend/Gaulunia2018'\n",
    "rootdir = '/media/emmanuel/Transcend/SOYBEAN PROJECT/DATA COLLECTION/Gaulunia'\n",
    "\n",
    "min = -1.0\n",
    "mid = 0.0\n",
    "max = 1.0\n",
    "\n",
    "\n",
    "for subdir1, dirs1, files1 in os.walk(crop_extent):\n",
    "    for file1 in files1:\n",
    "        if file1.endswith('.shp') and  file1[-6:] != '1u.shp':\n",
    "            cropping_path = os.path.join(subdir1, file1)\n",
    "            #Changcrs(cropping_path, in_epsg, out_epsg)\n",
    "            croperfile = file1[:-4]\n",
    "            croper1 = crop_extent + '/' + file1[:-4]\n",
    "            crop_field = gpd.read_file(os.path.join(subdir1, file1))\n",
    "            #if not os.path.exists(crop_extent):\n",
    "                #os.mkdir(crop_extent)\n",
    "\n",
    "print(crop_field.crs)\n",
    "\n",
    "try:\n",
    "    os.remove(crop_extent + '/' + croperfile + '.txt')\n",
    "except:\n",
    "    pass\n",
    "\n",
    "\n",
    "#============================ Changing CRS of shapefile from epsg:32735 to epsg:32735==================================\n",
    "in_epsg = 32735\n",
    "out_epsg = 32635\n",
    "in_shp = cropping_path\n",
    "out_shp = cropping_path[:-4] + '1u.shp'\n",
    "\n",
    "driver = ogr.GetDriverByName('ESRI Shapefile')\n",
    "\n",
    "# input SpatialReference\n",
    "inSpatialRef = osr.SpatialReference()\n",
    "inSpatialRef.ImportFromEPSG(in_epsg)\n",
    "\n",
    "# output SpatialReference\n",
    "outSpatialRef = osr.SpatialReference()\n",
    "outSpatialRef.ImportFromEPSG(out_epsg)\n",
    "\n",
    "# create the CoordinateTransformation\n",
    "coordTrans = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)\n",
    "\n",
    "# get the input layer\n",
    "inDataSet = driver.Open(in_shp)\n",
    "inLayer = inDataSet.GetLayer()\n",
    "\n",
    "# create the output layer\n",
    "if os.path.exists(out_shp):\n",
    "    driver.DeleteDataSource(out_shp)\n",
    "outDataSet = driver.CreateDataSource(out_shp)\n",
    "outLayer = outDataSet.CreateLayer(\"reproject\", geom_type=ogr.wkbMultiPolygon)\n",
    "\n",
    "# add fields\n",
    "inLayerDefn = inLayer.GetLayerDefn()\n",
    "for i in range(0, inLayerDefn.GetFieldCount()):\n",
    "    fieldDefn = inLayerDefn.GetFieldDefn(i)\n",
    "    outLayer.CreateField(fieldDefn)\n",
    "\n",
    "# get the output layer's feature definition\n",
    "outLayerDefn = outLayer.GetLayerDefn()\n",
    "\n",
    "# loop through the input features\n",
    "inFeature = inLayer.GetNextFeature()\n",
    "while inFeature:\n",
    "    # get the input geometry\n",
    "    geom = inFeature.GetGeometryRef()\n",
    "    # reproject the geometry\n",
    "    geom.Transform(coordTrans)\n",
    "    # create a new feature\n",
    "    outFeature = ogr.Feature(outLayerDefn)\n",
    "    # set the geometry and attribute\n",
    "    outFeature.SetGeometry(geom)\n",
    "    for i in range(0, outLayerDefn.GetFieldCount()):\n",
    "        outFeature.SetField(outLayerDefn.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n",
    "    # add the feature to the shapefile\n",
    "    outLayer.CreateFeature(outFeature)\n",
    "    # dereference the features and get the next input feature\n",
    "    outFeature = None\n",
    "    inFeature = inLayer.GetNextFeature()\n",
    "\n",
    "# Save and close the shapefiles\n",
    "inDataSet = None\n",
    "outDataSet = None\n",
    "#====================================================================================================================\n",
    "\n",
    "\n",
    "for subdir1, dirs1, files1 in os.walk(crop_extent):\n",
    "    for file1 in files1:\n",
    "        if file1.endswith('1u.shp'):\n",
    "            cropping_path = os.path.join(subdir1, file1)\n",
    "            crop_field2 = gpd.read_file(os.path.join(subdir1, file1))\n",
    "            #if not os.path.exists(crop_extent):\n",
    "                #os.mkdir(crop_extent)\n",
    "print(crop_field2.crs)\n",
    "#===================================================================================================================\n",
    "#                               SENTINEL-2\n",
    "#===================================================================================================================\n",
    "\n",
    "\n",
    "# cd, ce  and cv are used to make sure that the code finds the red and nir bands of the same image       \n",
    "# cd, ce  and cv are used to make sure that the code finds the red and nir bands of the same image       \n",
    "cv = 1\n",
    "cd = 9\n",
    "ce = 21\n",
    "for subdir, dirs, files in os.walk(rootdir):\n",
    "    try:\n",
    "        for file in files:\n",
    "            if file[23:26] == 'B03':\n",
    "                date = file[13:15] + '-' + file[11:13]+ '-' + file[7:11]\n",
    "                cv = 8\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field.total_bounds\n",
    "                greencrop = ndvi_crop[0]\n",
    "                \n",
    "            if file[23:26] == 'B04':\n",
    "                date = file[13:15] + '-' + file[11:13]+ '-' + file[7:11]\n",
    "                ce = 8\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field.total_bounds\n",
    "                redcrop = ndvi_crop[0]\n",
    "               \n",
    "            \n",
    "\n",
    "\n",
    "            if file[23:26] == 'B08':\n",
    "                date1 = file[13:15] + '-' + file[11:13]+ '-' + file[7:11]\n",
    "                cd = 8\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field.total_bounds\n",
    "                nircrop1 = ndvi_crop[0]\n",
    "                               \n",
    "                \n",
    "        if cv == cd and cd == ce:\n",
    "            if int(date[3:5]) == 11:\n",
    "                maskred = 1800\n",
    "                masknir = 1750\n",
    "                maskgreen = 2150\n",
    "            elif int(date[3:5]) == 12:\n",
    "                maskred = 2900\n",
    "                masknir = 1800\n",
    "                maskgreen = 1700\n",
    "            elif int(date[3:5]) == 1:\n",
    "                maskred = 2000\n",
    "                masknir = 3200\n",
    "                maskgreen = 850\n",
    "            elif int(date[3:5]) == 2:\n",
    "                maskred = 2000\n",
    "                masknir = 3000\n",
    "                maskgreen = 1150\n",
    "            elif int(date[3:5]) == 3:\n",
    "                maskred = 2000\n",
    "                masknir = 1500\n",
    "                maskgreen = 1200\n",
    "            elif int(date[3:5]) == 4:\n",
    "                maskred = 2000\n",
    "                masknir = 1190\n",
    "                maskgreen = 1600\n",
    "            elif int(date[3:5]) == 5:\n",
    "                maskred = 2000\n",
    "                masknir = 1200\n",
    "                maskgreen = 2000\n",
    "                  \n",
    "            \n",
    "            masked_red = ma.masked_array(redcrop, mask = (redcrop >= maskred))\n",
    "            masked_nir = ma.masked_array(nircrop1, mask = (nircrop1 <= masknir))\n",
    "            masked_green = ma.masked_array(greencrop, mask = (greencrop >= maskgreen))\n",
    "            \n",
    "            meangreen = np.nanmean(masked_green)\n",
    "            meannir = np.nanmean(masked_nir)\n",
    "            if meannir != 0.0 and meangreen != 0.0:\n",
    "                \n",
    "                mx1 = pd.DataFrame(masked_green)\n",
    "                mx1r = pd.DataFrame(masked_nir)\n",
    "                mx1 = mx1.replace(np.NaN, 0)\n",
    "                mx1r = mx1r.replace(np.NaN, 0)\n",
    "                mx1 = np.array(mx1)\n",
    "                mx1r = np.array(mx1r)\n",
    "\n",
    "                mx2 = mx1 == 0\n",
    "                mx2r = mx1r == 0\n",
    "                \n",
    "                #--------------------Applying the masks to the images---------------------\n",
    "                nircrop2 = em.mask_pixels(nircrop1, mx2)\n",
    "                nircrop3 = em.mask_pixels(nircrop2, mx2r)\n",
    "                redcrop = em.mask_pixels(redcrop, mx2)\n",
    "                redcrop = em.mask_pixels(redcrop, mx2r)\n",
    "                meanredcrop = np.mean(redcrop)\n",
    "                meannircrop = np.mean(nircrop3)\n",
    "\n",
    "                if meanredcrop != np.NaN and meannircrop != np.NaN:\n",
    "                    \n",
    "                    masked_green = pd.DataFrame(masked_green)\n",
    "                    masked_green = masked_green.replace(0, np.NaN)\n",
    "                    masked_green = np.array(masked_green)\n",
    "                    \n",
    "                    nircrop1 = pd.DataFrame(nircrop1)\n",
    "                    nircrop1 = nircrop1.replace(0, np.NaN)\n",
    "                    nircrop1 = np.array(nircrop1)\n",
    "                    \n",
    "                    greencrop = pd.DataFrame(greencrop)\n",
    "                    greencrop = greencrop.replace(0, np.NaN)\n",
    "                    greencrop = np.array(greencrop)\n",
    "\n",
    "                    fig4 = plt.figure(figsize = (16,16))\n",
    "                    plt.rc('xtick', labelsize = 20)\n",
    "                    plt.rc('ytick', labelsize = 20)\n",
    "                    plt.subplot(221)\n",
    "                    plt.title('Green band', fontweight = 'bold', fontsize = 30)  \n",
    "                    plt.imshow(greencrop)\n",
    "                    plt.grid()\n",
    "                    plt.subplot(222)\n",
    "                    plt.title('Near-infrared band', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(nircrop1)\n",
    "                    plt.grid()\n",
    "                    plt.subplot(223)\n",
    "                    plt.title('Green: Cloud Mask', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(masked_green)\n",
    "                    plt.grid()\n",
    "                    plt.subplot(224)\n",
    "                    plt.title('Near-infrared: Shadow Mask', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(masked_nir)\n",
    "                    plt.grid()\n",
    "                    plt.savefig(crop_extent +'/' + date + 'MaskS-2.png')\n",
    "                    plt.close(fig4)\n",
    "\n",
    "                    np.seterr(divide = 'ignore', invalid = 'ignore')\n",
    "                    NDVI = (nircrop3 - redcrop)/(nircrop3 + redcrop)\n",
    "\n",
    "                    NDVI = pd.DataFrame(NDVI)\n",
    "                    NDVI = NDVI.replace(0, np.NaN)\n",
    "                    NDVI = np.array(NDVI)\n",
    "                    Ave_NDVI = np.nanmean(NDVI)\n",
    "                    print(date, Ave_NDVI, 'S-2')\n",
    "                    \n",
    "                    #----------------------------------Plotting NDVI of field---------------------\n",
    "                    min = -1.0\n",
    "                    mid = 0.0\n",
    "                    max = 1.0\n",
    "                    norm = MidpointNormalize(vmin = min, vmax = max, midpoint = mid)\n",
    "                    colormap = plt.cm.nipy_spectral\n",
    "                    fig3 = plt.figure(figsize = (20, 20))\n",
    "                    plt.rc('xtick', labelsize = 30)\n",
    "                    plt.rc('ytick', labelsize = 30)\n",
    "                    plt.title('NDVI Image of Field on\\n' + date + '\\n', fontsize = 30, fontweight = 'bold')\n",
    "                    plt.imshow(NDVI, cmap = colormap, vmin = min, vmax = max, norm = norm)\n",
    "                    plt.colorbar(orientation = 'horizontal', shrink = 0.7)\n",
    "                    plt.grid()\n",
    "                    plt.savefig(crop_extent + '/' + date + 'NDVI-S2.png')\n",
    "                    plt.close(fig3)\n",
    "                \n",
    "\n",
    "                    #-------------------------Plotting Histogram of NDVI for Sentinel-2 raster------------------\n",
    "                    fig4 = plt.figure(figsize = (15, 10))\n",
    "                    ax = fig4.add_subplot(111)\n",
    "                    plt.title('NDVI Histogram of Field on\\n' + date + '\\n', fontsize = 30, fontweight = 'bold')\n",
    "                    plt.rc('xtick', labelsize = 30)\n",
    "                    plt.rc('ytick', labelsize = 30)\n",
    "                    plt.xlabel('NDVI Value', fontsize = 30)\n",
    "                    plt.ylabel('Number of Pixels', fontsize = 30)\n",
    "                    x = NDVI[~np.isnan(NDVI)]\n",
    "                    color = 'blue'\n",
    "                    ax.hist(x, bins = 30, color = color, histtype = 'bar', ec = 'black')\n",
    "                    plt.grid()\n",
    "                    fig4.savefig(crop_extent + '/' + date + 'Histogram' + 'S2.png', dpi = 200, bbox_inches = 'tight', pad_inches = 0.6)\n",
    "                    plt.close(fig4)\n",
    "                \n",
    "                    month = int(date[3:5])\n",
    "                    date1 = int(date[0:2])\n",
    "\n",
    "                    if int(month) == 11:\n",
    "                        date1 = date1 - 19\n",
    "                    elif int(month) == 12:\n",
    "                        date1 = 11 + date1\n",
    "                    elif int(month) == 1:\n",
    "                        date1 = 42 + date1\n",
    "                    elif int(month) == 2:\n",
    "                        date1 = 73 + date1\n",
    "                    elif int(month) == 3:\n",
    "                        date1 = 101 + date1\n",
    "                    elif int(month) == 4:\n",
    "                        date1 = 132 + date1\n",
    "                    elif int(month) == 5:\n",
    "                        date1 = 162 + date1\n",
    "\n",
    "                    datafile = crop_extent + '/' + croperfile + '.txt'\n",
    "                    f = open(datafile, 'a+')\n",
    "                    yes = os.stat(datafile).st_size  == 0\n",
    "                    if yes is False:\n",
    "                        f.write(str(date1) + '        ' + str(Ave_NDVI) + '\\n')\n",
    "                    else:\n",
    "                        f.write('Date           Average_NDVI\\n')\n",
    "                        f.write(str(date1) + '        ' + str(Ave_NDVI) + '\\n')\n",
    "                    f.close()\n",
    "                cd = 3\n",
    "                cv = 6\n",
    "                ce = 23\n",
    "                \n",
    "    except:\n",
    "        pass\n",
    "\n",
    "#========================================================================================================================\n",
    "#                                          PLANETSCOPE\n",
    "#========================================================================================================================\n",
    "\n",
    "'''\n",
    "This part of the code operates of Planetscope images:\n",
    "'''\n",
    "\n",
    "for subdir, dirs, files in os.walk(rootdir):\n",
    "    for file in files:\n",
    "        if file[24:37] == 'AnalyticMS_SR':\n",
    "            try:\n",
    "                date = file[6:8] + '-' + file[4:6] + '-' + file[0:4]\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0, 2, 1, 3]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field.total_bounds\n",
    "                redcrop = ndvi_crop[2]\n",
    "                nircrop = ndvi_crop[3] \n",
    "                greencrop = ndvi_crop[1]\n",
    "                meanred = np.nanmean(redcrop)\n",
    "                meannir = np.nanmean(nircrop)\n",
    "                \n",
    "\n",
    "                if int(date[3:5]) == 11 :\n",
    "                    maskred = 2700\n",
    "                    masknir = 1650\n",
    "                    maskgreen = 2220\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                elif int(date[3:5]) == 12:\n",
    "                    maskred = 2700\n",
    "                    masknir = 2000\n",
    "                    maskgreen = 1800\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                elif int(date[3:5]) == 1 :\n",
    "                    maskred = 2100\n",
    "                    masknir = 2500\n",
    "                    maskgreen = 1900\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                elif int(date[3:5]) == 2:\n",
    "                    maskred = 1800\n",
    "                    masknir = 2900\n",
    "                    maskgreen = 1310\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                elif int(date[3:5]) == 3 :\n",
    "                    maskred = 1200\n",
    "                    masknir = 2000\n",
    "                    maskgreen = 1380\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                elif int(date[3:5]) == 4:\n",
    "                    maskred = 2800\n",
    "                    masknir = 1300\n",
    "                    maskgreen = 2000\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                elif int(date[3:5]) == 5:\n",
    "                    maskred = 3500\n",
    "                    masknir = 1300\n",
    "                    maskgreen = 1700\n",
    "                    #print(date, maskred, np.max(redcrop))\n",
    "                    \n",
    "                masked_red = ma.masked_array(redcrop, mask = (redcrop >= maskred))\n",
    "                masked_nir = ma.masked_array(nircrop, mask = (nircrop <= masknir))\n",
    "                masked_green = ma.masked_array(greencrop, mask = (greencrop >= maskgreen))\n",
    "\n",
    "                if meanred != 0.0 and meannir != 0.0:\n",
    "                    \n",
    "                    greencrop = pd.DataFrame(greencrop)\n",
    "                    greencrop = greencrop.replace(0, np.NaN)\n",
    "                    greencrop = np.array(greencrop)\n",
    "                    \n",
    "                    masked_green = pd.DataFrame(masked_green)\n",
    "                    masked_green = masked_green.replace(0, np.NaN)\n",
    "                    masked_green = np.array(masked_green)\n",
    "                    \n",
    "                    nircropp = pd.DataFrame(nircrop)\n",
    "                    nircropp = nircropp.replace(0, np.NaN)\n",
    "                    nircropp = np.array(nircropp)\n",
    "        \n",
    "                    fig4 = plt.figure(figsize = (16,16))\n",
    "                    plt.rc('xtick', labelsize = 20)\n",
    "                    plt.rc('ytick', labelsize = 20)\n",
    "                    fig4.add_subplot(221)\n",
    "                    plt.title('Green band', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(greencrop)\n",
    "                    plt.grid()\n",
    "                    fig4.add_subplot(222)\n",
    "                    plt.title('Near-infrared band', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(nircropp)\n",
    "                    plt.grid()\n",
    "                    fig4.add_subplot(223)\n",
    "                    plt.title('Green: Cloud Mask', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(masked_green)\n",
    "                    plt.grid()\n",
    "                    fig4.add_subplot(224)\n",
    "                    plt.title('Near-infrared: Shadow Mask', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(masked_nir)\n",
    "                    plt.grid()\n",
    "                    plt.savefig(crop_extent +'/' + date + 'MaskDove.png')\n",
    "                    plt.close(fig4)\n",
    "                    \n",
    "                    mx1 = pd.DataFrame(masked_green)\n",
    "                    mx1r = pd.DataFrame(masked_nir)\n",
    "                    mx1 = mx1.replace(np.NaN, 0)\n",
    "                    mx1r = mx1r.replace(np.NaN, 0)\n",
    "                    mx1 = np.array(mx1)\n",
    "                    mx1r = np.array(mx1r)\n",
    "                    \n",
    "                    mx2 = mx1 == 0\n",
    "                    mx2r = mx1r == 0\n",
    "                    #--------------------Applying the masks to the images---------------------\n",
    "                    nircrop = em.mask_pixels(nircrop, mx2)\n",
    "                    nircrop = em.mask_pixels(nircrop, mx2r)\n",
    "                    redcrop = em.mask_pixels(redcrop, mx2)\n",
    "                    redcrop = em.mask_pixels(redcrop, mx2r)\n",
    "                    meanredcrop = np.nanmean(redcrop)\n",
    "                    meannircrop = np.nanmean(nircrop)\n",
    "                    if meannircrop != np.NaN and meanredcrop != np.NaN:\n",
    "\n",
    "\n",
    "                        np.seterr(divide = 'ignore', invalid = 'ignore')\n",
    "                        NDVI = (nircrop - redcrop)/(nircrop + redcrop)\n",
    "\n",
    "\n",
    "                        NDVI = pd.DataFrame(NDVI)\n",
    "                        NDVI = NDVI.replace(0, np.NaN)\n",
    "                        NDVI = np.array(NDVI)\n",
    "                        Ave_NDVI = np.nanmean(NDVI)\n",
    "                        min = -1.0\n",
    "                        mid = 0.0\n",
    "                        max = 1.0\n",
    "                        norm = MidpointNormalize(vmin = min, vmax = max, midpoint = mid)\n",
    "                        colormap = plt.cm.nipy_spectral\n",
    "                        fig3 = plt.figure(figsize = (20,20))\n",
    "                        plt.rc('xtick', labelsize = 30)\n",
    "                        plt.rc('ytick', labelsize = 30)\n",
    "                        plt.title('NDVI Image of Field on\\n' + date + '\\n', fontsize = 30, fontweight = 'bold')\n",
    "                        plt.imshow(NDVI, cmap = colormap, vmin = min, vmax = max, norm = norm)\n",
    "                        plt.colorbar(orientation = 'horizontal', shrink = 0.7)\n",
    "                        plt.grid()\n",
    "                        plt.savefig(crop_extent + '/' + date + 'NDVI-Dove.png')\n",
    "                        plt.close(fig3)\n",
    "\n",
    "                        #-------------------- Plotting Histogram of NDVI for Sentinel-2 raster---------------------------------------\n",
    "                        fig6t = plt.figure(figsize = (15, 10))\n",
    "                        ax = fig6t.add_subplot(111)\n",
    "                        plt.title('NDVI Histogram of Field on\\n' + date + '\\n', fontsize = 30, fontweight = 'bold')\n",
    "                        plt.rc('xtick', labelsize = 30)\n",
    "                        plt.rc('ytick', labelsize = 30)\n",
    "                        plt.xlabel('NDVI Value', fontsize = 30)\n",
    "                        plt.ylabel('Number of Pixels', fontsize = 30)\n",
    "\n",
    "                        x = NDVI[~np.isnan(NDVI)]\n",
    "                        color = 'brown'\n",
    "                        ax.hist(x, bins = 30, color = color, histtype = 'bar', ec = 'black')\n",
    "                        plt.grid()\n",
    "                        fig6t.savefig(crop_extent + '/' + date + 'Histogram' + 'Dove.png', dpi = 200, bbox_inches = 'tight', pad_inches = 0.5)\n",
    "                        plt.close(fig6t)\n",
    "                        #-------------------------------------------------------------------------------------------------------------\n",
    "                        print(date, Ave_NDVI, 'D')\n",
    "                        month = int(file[4:6])\n",
    "                        date1 = int(file[6:8])\n",
    "                        if int(month) == 11:\n",
    "                            date1 = date1 - 19\n",
    "                        elif int(month) == 12:\n",
    "                            date1 = 11 + date1\n",
    "                        elif int(month) == 1:\n",
    "                            date1 = 42 + date1\n",
    "                        elif int(month) == 2:\n",
    "                            date1 = 73 + date1\n",
    "                        elif int(month) == 3:\n",
    "                            date1 = 101 + date1\n",
    "                        elif int(month) == 4:\n",
    "                            date1 = 132 + date1\n",
    "                        elif int(month) == 5:\n",
    "                            date1 = 162 + date1\n",
    "\n",
    "                        datafile = crop_extent + \"/\" + croperfile + '.txt'\n",
    "                        f = open(datafile, 'a+')\n",
    "                        yes = os.stat(datafile).st_size  == 0\n",
    "                        if yes is False:\n",
    "                            f.write(str(date1) + '        ' + str(Ave_NDVI) + '\\n')\n",
    "                        else:\n",
    "                            f.write('Date           Average_NDVI\\n')\n",
    "                            f.write(str(date1) + '        ' + str(Ave_NDVI) + '\\n')\n",
    "                        f.close()\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "#===================================================================================================================\n",
    "#                               LANDSAT-8\n",
    "#===================================================================================================================\n",
    "\n",
    "\n",
    "# cd, ce  and cv are used to make sure that the code finds the red and nir bands of the same image       \n",
    "# cd, ce  and cv are used to make sure that the code finds the red and nir bands of the same image       \n",
    "dv = 1\n",
    "dd = 9\n",
    "de = 21\n",
    "for subdir, dirs, files in os.walk(rootdir):\n",
    "    try:\n",
    "        for file in files:\n",
    "            if file[41:43] == 'B3':\n",
    "                date = file[23:25] + '-' + file[21:23]+ '-' + file[17:21]\n",
    "                dv = 8\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field2['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field2.total_bounds\n",
    "                greencrop = ndvi_crop[0]\n",
    "                \n",
    "            if file[41:43] == 'B4':\n",
    "                date = file[23:25] + '-' + file[21:23]+ '-' + file[17:21]\n",
    "                dd = 8\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field2['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field2.total_bounds\n",
    "                redcrop = ndvi_crop[0]\n",
    "                \n",
    "            if file[41:43] == 'B5':\n",
    "                date = file[23:25] + '-' + file[21:23]+ '-' + file[17:21]\n",
    "                de = 8\n",
    "                with rasterio.open(os.path.join(subdir, file)) as src:\n",
    "                    extent_geojson = mapping(crop_field2['geometry'][0])\n",
    "                    ndvi_crop, crop_affine = mask(src, shapes = [extent_geojson], crop = True)\n",
    "                    soap_lidar_meta = src.meta.copy()\n",
    "\n",
    "                soap_lidar_meta.update({'driver':'GTiff', 'height':ndvi_crop.shape[1], 'width':ndvi_crop.shape[2], 'transform':crop_affine})\n",
    "                cr_ext = rasterio.transform.array_bounds(soap_lidar_meta['height'], soap_lidar_meta['width'], soap_lidar_meta['transform'])\n",
    "                bound_order = [0]\n",
    "                cr_extent = [cr_ext[b] for b in bound_order]\n",
    "                cr_extent, crop_field2.total_bounds\n",
    "                nircrop1 = ndvi_crop[0]\n",
    "                \n",
    "        if dv == dd and dd == de:\n",
    "            maskred = 12000\n",
    "            masknir = 13500\n",
    "            maskgreen = 10000\n",
    "            # Masking Clouds and Shadows of the Satellite image using numpys 'ma' module\n",
    "            masked_red = ma.masked_array(redcrop, mask = (redcrop >= maskred))\n",
    "            masked_nir = ma.masked_array(nircrop1, mask = (nircrop1 <= masknir))\n",
    "            masked_green = ma.masked_array(greencrop, mask = (greencrop >= maskgreen))\n",
    "            \n",
    "            meangreen = np.nanmean(masked_green)\n",
    "            meannir = np.nanmean(masked_nir)\n",
    "            if meannir != 0.0 and meangreen != 0.0:\n",
    "                \n",
    "                mx1 = pd.DataFrame(masked_green)\n",
    "                mx1r = pd.DataFrame(masked_nir)\n",
    "                mx1 = mx1.replace(np.NaN, 0)\n",
    "                mx1r = mx1r.replace(np.NaN, 0)\n",
    "                mx1 = np.array(mx1)\n",
    "                mx1r = np.array(mx1r)\n",
    "\n",
    "                mx2 = mx1 == 0\n",
    "                mx2r = mx1r == 0\n",
    "                \n",
    "                #--------------------Applying the masks to the images---------------------\n",
    "                nircrop2 = em.mask_pixels(nircrop1, mx2)\n",
    "                nircrop3 = em.mask_pixels(nircrop2, mx2r)\n",
    "                redcrop = em.mask_pixels(redcrop, mx2)\n",
    "                redcrop = em.mask_pixels(redcrop, mx2r)\n",
    "                meanredcrop = np.mean(redcrop)\n",
    "                meannircrop = np.mean(nircrop3)\n",
    "\n",
    "                if meanredcrop != np.NaN and meannircrop != np.NaN:\n",
    "                    \n",
    "                    masked_green = pd.DataFrame(masked_green)\n",
    "                    masked_green = masked_green.replace(0, np.NaN)\n",
    "                    masked_green = np.array(masked_green)\n",
    "                    \n",
    "                    nircrop1 = pd.DataFrame(nircrop1)\n",
    "                    nircrop1 = nircrop1.replace(0, np.NaN)\n",
    "                    nircrop1 = np.array(nircrop1)\n",
    "                    \n",
    "                    greencrop = pd.DataFrame(greencrop)\n",
    "                    greencrop = greencrop.replace(0, np.NaN)\n",
    "                    greencrop = np.array(greencrop)\n",
    "\n",
    "                    fig4 = plt.figure(figsize = (16,16))\n",
    "                    plt.rc('xtick', labelsize = 20)\n",
    "                    plt.rc('ytick', labelsize = 20)\n",
    "                    plt.subplot(221)\n",
    "                    plt.title('Green band', fontweight = 'bold', fontsize = 30)  \n",
    "                    plt.imshow(greencrop)\n",
    "                    plt.grid()\n",
    "                    plt.subplot(222)\n",
    "                    plt.title('Near-infrared band', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(nircrop1)\n",
    "                    plt.grid()\n",
    "                    plt.subplot(223)\n",
    "                    plt.title('Green: Cloud Mask', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(masked_green)\n",
    "                    plt.grid()\n",
    "                    plt.subplot(224)\n",
    "                    plt.title('Near-infrared: Shadow Mask', fontweight = 'bold', fontsize = 30)\n",
    "                    plt.imshow(masked_nir)\n",
    "                    plt.grid()\n",
    "                    plt.savefig(crop_extent +'/' + date + 'Mask-L8.png')\n",
    "                    plt.close(fig4)\n",
    "\n",
    "                    np.seterr(divide = 'ignore', invalid = 'ignore')\n",
    "                    NDVI = (nircrop3 - redcrop)/(nircrop3 + redcrop)\n",
    "\n",
    "                    NDVI = pd.DataFrame(NDVI)\n",
    "                    NDVI = NDVI.replace(0, np.NaN)\n",
    "                    NDVI = np.array(NDVI)\n",
    "                    Ave_NDVI = np.nanmean(NDVI)\n",
    "                    print(date, Ave_NDVI,'L8')\n",
    "                    \n",
    "                    #----------------------------------Plotting NDVI of field---------------------\n",
    "                    min = -1.0\n",
    "                    mid = 0.0\n",
    "                    max = 1.0\n",
    "                    norm = MidpointNormalize(vmin = min, vmax = max, midpoint = mid)\n",
    "                    colormap = plt.cm.nipy_spectral\n",
    "                    fig3 = plt.figure(figsize = (20, 20))\n",
    "                    plt.rc('xtick', labelsize = 30)\n",
    "                    plt.rc('ytick', labelsize = 30)\n",
    "                    plt.title('NDVI Image of Field on\\n' + date + '\\n', fontsize = 30, fontweight = 'bold')\n",
    "                    plt.imshow(NDVI, cmap = colormap, vmin = min, vmax = max, norm = norm)\n",
    "                    plt.colorbar(orientation = 'horizontal', shrink = 0.7)\n",
    "                    plt.grid()\n",
    "                    plt.savefig(crop_extent + '/' + date + 'NDVI-L8.png')\n",
    "                    plt.close(fig3)\n",
    "                \n",
    "\n",
    "                    #-------------------------Plotting Histogram of NDVI for Sentinel-2 raster------------------\n",
    "                    fig4 = plt.figure(figsize = (15, 10))\n",
    "                    ax = fig4.add_subplot(111)\n",
    "                    plt.title('NDVI Histogram of field on\\n' + date + '\\n', fontsize = 30, fontweight = 'bold')\n",
    "                    plt.rc('xtick', labelsize = 30)\n",
    "                    plt.rc('ytick', labelsize = 30)\n",
    "                    plt.xlabel('NDVI Value', fontsize = 30)\n",
    "                    plt.ylabel('Number of Pixels', fontsize = 30)\n",
    "                    x = NDVI[~np.isnan(NDVI)]\n",
    "                    color = 'magenta'\n",
    "                    ax.hist(x, bins = 30, color = color, histtype = 'bar', ec = 'black')\n",
    "                    plt.grid()\n",
    "                    fig4.savefig(crop_extent + '/' + date + 'Histogram' + 'L8.png', dpi = 200, bbox_inches = 'tight', pad_inches = 0.6)\n",
    "                    plt.close(fig4)\n",
    "                \n",
    "                    month = int(date[3:5])\n",
    "                    date1 = int(date[0:2])\n",
    "\n",
    "                    if int(month) == 11:\n",
    "                        date1 = date1 - 19\n",
    "                    elif int(month) == 12:\n",
    "                        date1 = 11 + date1\n",
    "                    elif int(month) == 1:\n",
    "                        date1 = 42 + date1\n",
    "                    elif int(month) == 2:\n",
    "                        date1 = 73 + date1\n",
    "                    elif int(month) == 3:\n",
    "                        date1 = 101 + date1\n",
    "                    elif int(month) == 4:\n",
    "                        date1 = 132 + date1\n",
    "                    elif int(month) == 5:\n",
    "                        date1 = 162 + date1\n",
    "\n",
    "                    datafile = crop_extent + '/' + croperfile + '.txt'\n",
    "                    f = open(datafile, 'a+')\n",
    "                    yes = os.stat(datafile).st_size  == 0\n",
    "                    if yes is False:\n",
    "                        f.write(str(date1) + '        ' + str(Ave_NDVI) + '\\n')\n",
    "                    else:\n",
    "                        f.write('Date           Average_NDVI\\n')\n",
    "                        f.write(str(date1) + '        ' + str(Ave_NDVI) + '\\n')\n",
    "                    f.close()\n",
    "            dd = 3\n",
    "            dv = 6\n",
    "            de = 23\n",
    "                             \n",
    "    except:\n",
    "        pass\n",
    "datafile = crop_extent + \"/\" + croperfile + '.txt'\n",
    "datafilew = crop_extent + \"/\" + croperfile + 'w.txt'\n",
    "datafilew2 = crop_extent + \"/\" + croperfile + 'w2.txt'\n",
    "\n",
    "data = pd.read_csv(datafile, delim_whitespace = True, skipinitialspace = True)\n",
    "data.columns = ['TIME', 'Average_ndvi']\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "data = data.sort_values('TIME').drop_duplicates('TIME')\n",
    "data = data.dropna(how='any')    #to drop if any value in the row has a NaN\n",
    "\n",
    "\n",
    "\n",
    "tfile2 = open(datafilew, 'w')\n",
    "tfile2.write(data.to_string(index=False, header = None))\n",
    "tfile2.close()\n",
    "\n",
    "\n",
    "\n",
    "try:\n",
    "    os.remove(datafilew2)\n",
    "except:\n",
    "    pass\n",
    "with open(datafilew, 'r') as f:\n",
    "    filelines = f.readlines()\n",
    "    for line in filelines:\n",
    "        words = line.split()\n",
    "        #if  int(words[0]) > 60 and int(words[0]) < 75 and float(words[1]) < 0.6:\n",
    "            #continue\n",
    "        #if  int(words[0]) > 75 and int(words[0]) < 80 and float(words[1]) < 0.6:\n",
    "            #continue\n",
    "        #if  int(words[0]) > 80 and int(words[0]) < 90 and float(words[1]) < 0.6:\n",
    "            #continue\n",
    "        #if  int(words[0]) > 60 and int(words[0]) < 100 and float(words[1]) < 0.55:\n",
    "        #    continue\n",
    "        #elif int(words[0]) > 80 and int(words[0]) < 117 and float(words[1]) < 0.75:\n",
    "        #    continue\n",
    "        if int(words[0]) > 80 and int(words[0]) < 120 and float(words[1]) < 0.7:\n",
    "            continue\n",
    "        elif int(words[0]) > 60 and int(words[0]) < 80 and float(words[1]) < 0.55:\n",
    "            continue\n",
    "        elif int(words[0]) > 0 and int(words[0]) < 190 and float(words[1]) > 1.0:\n",
    "            continue\n",
    "        #elif int(words[0]) > 100 and int(words[0]) < 117 and float(words[1]) < 0.6:\n",
    "            #continue\n",
    "        else:\n",
    "            f = open(datafilew2, 'a+')\n",
    "            yes = os.stat(datafilew2).st_size  == 0\n",
    "            if yes is False:\n",
    "                f.write(words[0] + '        ' + words[1] + '\\n')\n",
    "            else:\n",
    "                f.write('Day    Average_NDVI\\n')\n",
    "                f.write( words[0]+ '        ' + words[1] + '\\n')\n",
    "            f.close()\n",
    "            \n",
    "            \n",
    "from numpy import *\n",
    "from scipy.interpolate import *\n",
    "from scipy import interpolate\n",
    "from scipy.integrate import trapz, simps\n",
    "from matplotlib.pyplot import *\n",
    "\n",
    "data = pd.read_csv(datafilew2, delim_whitespace = True, skipinitialspace = False)\n",
    "data.columns = ['TIME', 'Average_ndvi']\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "data = data.sort_values('TIME').drop_duplicates('TIME')\n",
    "\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "\n",
    "\n",
    "list_x = list(x)\n",
    "#list_x = sorted(list_x)\n",
    "list_y = list(y)\n",
    "#list_y = sorted(list_y)\n",
    "y = np.array(list_y)\n",
    "x = np.array(list_x)\n",
    "xp = np.linspace(x.min(), x.max(), 1000)\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------\n",
    "#             Making a polynomial Best Fit (Machine Learning)\n",
    "#-----------------------------------------------------------------------------------------\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import gaussian_process\n",
    "from sklearn.gaussian_process.kernels import Matern, WhiteKernel, ConstantKernel\n",
    "\n",
    "polynomial_features = PolynomialFeatures(degree= 6, include_bias=False)\n",
    "linear_regression = LinearRegression()\n",
    "pipeline = Pipeline([(\"polynomial_features\", polynomial_features),(\"linear_regression\", linear_regression)])\n",
    "pipeline.fit(x[:, np.newaxis], y)\n",
    "\n",
    "# Evaluating  the model using crossvalidation\n",
    "scores = cross_val_score(pipeline, x[:, np.newaxis], y, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "\n",
    "y_poly = pipeline.predict(xp[:, np.newaxis])\n",
    "yp_max = y_poly.max()\n",
    "xp_max_yp = xp[y_poly.argmax()]\n",
    "Areap = simps(y_poly, xp)\n",
    "\n",
    "print('plotting Poly fit')\n",
    "figt = plt.figure(figsize = (20,20))\n",
    "plt.rc('xtick', labelsize = 30)\n",
    "plt.rc('ytick', labelsize = 30)\n",
    "plt.xlabel(\"Time (days)\", fontsize = 30)\n",
    "plt.ylabel(\"Average NDVI\", fontsize = 30)\n",
    "plt.setp(ax, xticks=(), yticks=())\n",
    "plt.plot(x, y, 'o', label= \"Data points\")\n",
    "plt.plot(xp, y_poly,'r-', label=\"Polynomial best fit\", linewidth = 2.5)\n",
    "plt.title('Average NDVI versus Time: Polynomial best fit\\n', fontsize = 30, fontweight = 'bold' )\n",
    "plt.fill_between(xp, y_poly, color = 'brown', alpha=.5, label = 'Area under graph')\n",
    "plt.grid()\n",
    "plt.legend(loc=\"best\", fontsize = 30)\n",
    "plt.savefig(crop_extent + '/' + croperfile + 'Polynomialfit.png')\n",
    "plt.close(figt)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#---------------------------------------------------------------------------------------------\n",
    "#                  Cubic Spline Interpolation fit\n",
    "#---------------------------------------------------------------------------------------------\n",
    "s1rev = interpolate.InterpolatedUnivariateSpline (x, y)\n",
    "# Use a smallish value for s\n",
    "if len(x) < 33:\n",
    "    s2 = interpolate.UnivariateSpline (x, y, s = 0.04)\n",
    "else:\n",
    "    s2 = interpolate.UnivariateSpline (x, y, s = 0.1)\n",
    "\n",
    "\n",
    "yc = s2(xp)\n",
    "Area1c = simps(yc, xp)\n",
    "yc_max = yc.max()\n",
    "xc_max = xp[yc.argmax()] \n",
    "\n",
    "print('Plotting Cubic Spline Curve')\n",
    "figr = plt.figure(figsize = (20, 20))\n",
    "plt.rc('xtick', labelsize = 30)\n",
    "plt.rc('ytick', labelsize = 30)\n",
    "plt.xlabel('Time (days)', fontsize = 30)\n",
    "plt.ylabel('Average NDVI', fontsize = 30)\n",
    "plt.plot(x, y, 'o', label='Data points')\n",
    "plt.plot (xp, yc, 'r-', label='Cubic Spline best fit', linewidth = 2.5)\n",
    "#plt.minorticks_on()\n",
    "plt.title('Average NDVI vs Time: Cubic Spline Best fit\\n',fontsize = 30, fontweight = 'bold')\n",
    "\n",
    "#plt.rc('xtick', labelsize = 40, fontweight = 'bold')\n",
    "#plt.rc('ytick', labelsize = 40, fontweight = 'bold')\n",
    "plt.fill_between(xp, yc, color = 'yellow', alpha=.5, label = 'Area under graph')\n",
    "plt.grid()\n",
    "plt.legend(loc = 'best', fontsize = 30)\n",
    "plt.savefig(crop_extent + '/' + croperfile + 'Cubicspline.png')\n",
    "plt.close(figr)\n",
    "\n",
    "#--------------------------------------------------------------------------------------------------\n",
    "#                      CONVOLUTION  (Moving Average box)\n",
    "#--------------------------------------------------------------------------------------------------\n",
    "def smooth(y, box_pts):\n",
    "    box = np.ones(box_pts)/box_pts\n",
    "    y_smooth = np.convolve(y, box, mode='same')\n",
    "    return y_smooth\n",
    "\n",
    "y_smooth = smooth(y, 2)\n",
    "y_max = y_smooth.max()\n",
    "x_max = x[y_smooth.argmax()]\n",
    "Area = simps(y_smooth, x)\n",
    "\n",
    "print('Plotting Convolve')\n",
    "figu = plt.figure(figsize = (20, 20))\n",
    "plt.rc('xtick', labelsize = 30)\n",
    "plt.rc('ytick', labelsize = 30)\n",
    "plt.xlabel('Time (days)', fontsize = 30)\n",
    "plt.ylabel('Average NDVI', fontsize = 30)\n",
    "plt.title('Average NDVI vs Time: Convolution best fit\\n',fontsize = 30, fontweight = 'bold')\n",
    "plt.plot(x, y, 'o', label='Data points')\n",
    "#plt.plot (x, y, 'r-', label='Spline, fit', linewidth = 2.5)\n",
    "plot(x, y_smooth, 'r-', label=' Convolution best fit', linewidth = 2.5)\n",
    "#plt.minorticks_on()\n",
    "plt.fill_between(x,y_smooth, color = 'green', alpha=.5, label = 'Area under graph')\n",
    "plt.grid()\n",
    "plt.legend(loc = 'best', fontsize = 30)\n",
    "plt.savefig(crop_extent + '/' + croperfile + 'Convolution.png')\n",
    "plt.close(figu)\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------------\n",
    "#   Gaussian Process Modelling\n",
    "#-----------------------------------------------------------------------------------------------\n",
    "\n",
    "kernel = ConstantKernel() + Matern(length_scale=2, nu=3/2) + WhiteKernel(noise_level=1)\n",
    "X = x.reshape(-1, 1)\n",
    "\n",
    "gp = gaussian_process.GaussianProcessRegressor(kernel=kernel)\n",
    "gp.fit(X, y)\n",
    "\n",
    "x_pred = np.linspace(x.min(), x.max(), 1000).reshape(-1, 1)\n",
    "y_pred, sigma = gp.predict(x_pred, return_std=True)\n",
    "x_pred1 = np.reshape(x_pred, x_pred.size)\n",
    "ygp_max = y_pred.max()\n",
    "xgp_max = int(x_pred[y_pred.argmax()])\n",
    "Areagp = simps(y_pred, x_pred1)\n",
    "\n",
    "print('plotting Gaussian Process')\n",
    "figv = plt.figure(figsize = (20, 20))\n",
    "plt.rc('xtick', labelsize = 30)\n",
    "plt.rc('ytick', labelsize = 30)\n",
    "plt.xlabel('Time (Days)', fontsize = 30)\n",
    "plt.ylabel('Average NDVI', fontsize = 30)\n",
    "plt.title('Average NDVI versus Time: Gaussian Process fit\\n', fontsize = 30, fontweight = 'bold')\n",
    "plt.plot(x, y, 'o', label = 'Data Sample')\n",
    "plt.plot(x_pred, y_pred, 'g', label = 'Gaussian Process best fit')\n",
    "\n",
    "plt.fill_between(x_pred1, y_pred, color = 'silver', alpha = 0.5, label = 'Area under graph')\n",
    "plt.grid()\n",
    "plt.legend(loc = 'best', fontsize = 30)\n",
    "plt.savefig(crop_extent + '/' + croperfile + 'GaussianProcess.png')\n",
    "plt.close(figv)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#------------------------------------------------------------------------------------------------\n",
    "#           Writting important data to files\n",
    "#------------------------------------------------------------------------------------------------\n",
    "\n",
    "datafile2 = 'Polynomial data.txt'\n",
    "f = open(datafile2, 'a+')\n",
    "yes = os.stat(datafile2).st_size  == 0\n",
    "if yes is False:\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, yp_max, xp_max_yp, Areap))\n",
    "else:\n",
    "    f.write('%5s                %16s               %16s         %17s\\n'%('Field', 'Max NDVI', 'Date of Max NDVI', 'Area under curve'))\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, yp_max, xp_max_yp, Areap))\n",
    "f.close()  \n",
    "\n",
    "\n",
    "\n",
    "datafile3 = 'Cubic Spline data.txt'\n",
    "f = open(datafile3, 'a+')\n",
    "yes = os.stat(datafile3).st_size  == 0\n",
    "if yes is False:\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, yc_max, xc_max, Area1c))\n",
    "else:\n",
    "    f.write('%5s                %16s               %16s         %17s\\n'%('Field', 'Max NDVI', 'Date of Max NDVI', 'Area under curve'))\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, yc_max, xc_max, Area1c))\n",
    "f.close()            \n",
    "\n",
    "\n",
    "datafile4 = 'Convolution.txt'\n",
    "f = open(datafile4, 'a+')\n",
    "yes = os.stat(datafile4).st_size  == 0\n",
    "if yes is False:\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, y_max, x_max, Area))\n",
    "else:\n",
    "    f.write('%5s                %16s               %16s         %17s\\n'%('Field', 'Max NDVI', 'Date of Max NDVI', 'Area under curve'))\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, y_max, x_max, Area))\n",
    "f.close() \n",
    "\n",
    "\n",
    "datafile5 = 'Gaussian Process.txt'\n",
    "f = open(datafile5, 'a+')\n",
    "yes = os.stat(datafile5).st_size  == 0\n",
    "if yes is False:\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, ygp_max, xgp_max, Areagp))\n",
    "else:\n",
    "    f.write('%5s                %16s               %16s         %17s\\n'%('Field', 'Max NDVI', 'Date of Max NDVI', 'Area under curve'))\n",
    "    f.write('%-20s          %3.6f                  %3.2f                  %3.6f\\n'%(croperfile, ygp_max, xgp_max, Areagp))\n",
    "f.close() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Cubic Spline Curve\n",
      "0.8320793548957901\n"
     ]
    },
    {
     "data": {
      "image/png": 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suermONe73a7y8disVHWP2z7+ZymyxrKPXwKsO9ut7pctRfZY6g64fqJzHQNhRBQqtmGO61X1NwBQk/TY1osmGMMjbcs8P6A8AO9r5Geq+px7wEzN9NZ9AGR5lG1v643mw3cA7lLVg271ZqrqMrf/p6jq86q6TFW9fgF3fCCaA+BTS/25eqc5hti1s5T7UFXfdzz3zjY8AmB1gI9joGXZaFUd495mVd0Ce1DndhHx/JBcaKqaCuBtj8WPiIjfZL5BbkM2vIOUgPkiamP7YnepiHgOtbENi/ylmD/ozoJ38OVaEfGcScx23k1SjwTnFl+paq7zS1X/VNWlqvqi48vTkvw1ucS7Vx0zijqOnTE+yiUDGOLsfaGqm2EPmuWaBMTRY8Sz154CuFlVV7oWGJ/62P8DgTyQIPDVqye1mPYfLFsA3K9uM8Sp6hcAXrKUvQhAvhPYO4am25Lu/wSTRy9X7y1VPaqq4+BxfBTAQNh7u9hUANATwDwRWSIiNfO5r5dUdbqqKmCuBTDH4hZL2VAOj5ykqu86e+aq6g7Yf+xp4EjD4M72Xvqkqn6iqq5en6q6FvZeUcV1bnr6Gfa0FsV1zfnD8xh31LlHVT9T1fthctASEcGaM4KIqCg5hurZhk595vH/dJiZvtw1EZFWqurVG0BVV4rIVgDus0Y1EpEWqrresW9nrwl3tuFdnS3tu1rcZnL03L1lWSeYnh15+ZfzQ70/IlIGpnfXjQAuh+mNFgXfQ2CcPAMSvoZtes2I5fAxgDY+1rnrZFl2m4h0C2BbwAypa4eiyW31b5hjyTkksiJMfiFbfpOikp8fnz6HGZrr+dreBTOsCSJyHnwPrSw2qpopIp8h91DbMgBuhyMA6Th2bTNbTvT43xbAmyMin8AMi9wKM918ruEuWgzDQIvRdlX1DD77GgL4qSWQmGIp5zlUqiPsvTk+8dERyTaU2Ot8d3xJD/bsdb7qK23DYye7BzHc/A/23hxXAViTz310hD0H2Yuqaut5BgDwty4QqrpLRFoBeB+ml2qgOgL4RkRaq2UonQ9evWpU9YyITIHpDeeusYjEaBHOnuuHLZBjC9YJgBiYoYEQM+Pz5ZZyj4qILUBmy49VS0QaqerWQBsbJK1h8nk9oaqej78jgn/N+QVm0iWnS0RkKYCvYJ7rrTAzmLoHD8+l9woiKgQGwogoFG6FCeB48hzauAJmSKBnz53+8D0sZgKA1zyW3QEzNA3w3SvF80uVbdhmPR/LfQmk7GmYWd/8EpFmMIHBgvxyH+vxvy3viMIehACApLx24OiJEGNZ1TCvbT3k5/kNmKqmi8jrAN5wW/yAiIwuiv15cuR8sT0/h23lVTVDRBIB9PNY1U9EhjuCH3fC+0tvNuy9AovaBHjnnLsDZ3vidYRJ3O5umXrneFoIM9TR/TivDeAZ90Ii8gfMeZMIYHY+vkSXBhssy2wJyQETHPRk63nomZPHdp5FwAx5ClRNEalQ2CBKAA75WB6q/E8FZXtdoar7ReQozHB4d77yYfrj6/pZ5D0mVXUfgFtFpDFMj99uMDns8vqucTnMe3ogk7Kkqqqvnme2c0FgPj8UdyDsDEz+LE++zmP38/MC2IO/nkMl81IPJhAULItUtavzHxGJhrlOj4J5rd29LSIbVXWRR3s8Ffaa8w7Mj6ruAbYOjptTlohsBLAUwCfOH0WJiDg0kohCwTa88Uc1uUZcHMGpzy1l73AEFmwmwzsX0x1u+VY8h0oovHulAPYcK/kVSEL2Q57Dvjw5Ev4uQcGHr3gGS2xByL/8tMPXh3d3tiBPQRRlEvv3YZLoOpWDyTFVHFrC/p5rS3DrNMmyrC7Ofsi3DYtcoKq+knAXGUcPps0ei1u7TYAQyHBkZ4+iG5B3cPh8mHP5MwAbRSS/AdeSzBbI8nVu2soGIljnq2fwpijs9rG8VTHs211hZ6bzdx21JfS2Xafz4vmjB2Cu7b4mUwk6Vd3iGMbfBuZ99BoA/4A976HTjQFWn9/nECjY81hYB3z0PLL1CPRUGt5LoarpqroKZpirLffZMx7/B/2ao6o/wLy3HPFTPgLm/fdJAD+LyEciEsiM1kR0jmMgjIiKlSMvk20411Uiop43AI9bylaHPQE8HL8Wew6tuwBAW8fQDc8vzEsduTs8+ZtpKFCBfNgKpDfF4/Du/fAXgGEwwyMrqKqoqsAk88+LbcagCiLi65f7QHK4BOsX9yL7gOrIU+aZj+dOBD6Ne2H4mkXRc0iuu++QO3DndJcj/5wtEFCswyI92HL0OYPWno8/A8AXtkpUdbeqtocZwvsazMQZB21lHS4GMKMQycVLmrxyphW0rLsSf746OfJG7rGsalFEM836+pHl/ELW6y//oS1YU5CZ3ay9AW0zBxcHR57JFar6uqp2AHAt7DMie06i4Ut+n0MgNDPk+XpfD2Q4b6k5NwHX+6qtx6FnHtIieVyqOgPmM95dMD9q/gLfs2gDJs/YY0FqCxGVYgyEEVFxuxvBufYUJGl+QL1SHGy9EJ51BpwCvNlmnSwIW+DwTVV9S1V3eQwL8zXDmjvbYxMATX2Ub5xXhaqaBnvw8PZ8Pme2XDnB9DFy51sSADcX5Q5FJAH2XGQKYK6v7RzDH21TxveGPWnwcQBz8t/CoJkC7x4Pd8LMiuaZo2qGqp7wV5mqrlLV4araUVVrAqgMM4zqKXh/0WyO4glonits14DjAMrn83y1BaiKgu08KQN7ovn8sA2p9RVQKezx5ZmbEgAgIrVg773zRwH24auHaccC1BV0qrocwDeWVYEGdKs7ni8b2+ujMOkVSpM/YA+YtcnnuflJMbbZFvir7Mh35lRk1xxVPamqn6rqfWpmi6wM4DyYnGK25P3+Pj8SUZhgIIyIits9Qaqnh4j46vo/F95d5fvAJO92lw4fMxwBWGRZ1j+QX9ZFpIKIXJ9XuXyItyzzyi3l6G3XM4D6VvlY7uu1CXQKelvvpocCGYYgIlEi0jWvcoXlGP75YlHvx8nxmsyB/cv1bFW15bVxZxseGQPgCcvyGapq621RLBxDMj2/5DpzyHiyBashIpGOxPq2+k+q6gZV/TdMbzlPDfLT3jC3FN7Bhxh456SzEpEmjlxQnsvL2Xr2ikj7Qrb3Ddi/bN8pIp5DsGztLSsigy2zrtp6UDX0nMXPkQOxsF+e7/FxLRzgo/yPBdjHUninBgCAkZaZCV1EpGIB9uXctqWIzBKRQGe5tL2f+evx6ek+SxvKwv7+tVlDkyi/wFT1COz5OgN6HxaRGkE43wLm6InrawIe9xmulyLI1xwRqeyrvKoeUtWl8M5dCdjfK2xB8ZD0pCyg0t5+omLHQBgRFRvH0ERbnqtNMNO7+7t5frgvDx9Tozt6SHn2pImHd9L96X5yp0yA96+yTQF8LSJXeRYWkWoi0kNExsH8Av2ej3oLwvZB/hFHTyPn/i+BCUL4GzoCwEwlDjMRgafHRWSAc4iZIyjxLgLvCfGxZVkXmGFrXq+7iNQSkd4iMhlmCOA/AtxPYU2DPbFy0IhIPRF5FmaiAVtPkDQAz+ZVj6pugpmS3pNt+FYoh0U62XpYtvD4/zdHbhebdgB2isjrItJZRLwCiCJyNYCrLdv6zbVHZ6nqUQCzLav+KyJPOBJhuzgCXM0c65YBSEYx5uhS1V3IPdGFu9dFJFFE2ngOjxWRBiLyJMwMch/AO/djsqW+KgDGOAOyIhIPM5FLYXNNNQEwwf2YFpFbAbxgKbsNvieE8cnRM9eWV/NKAAtFpLn7QhGJEZEH4CORf4DKwMwUuUZE1ovI047gWK6h9o73yNdgD5osz8f+XhAR1wy0jmDIeJjn15PnTNSlhe29dICIjBMRr6Tzjvebe0TkC5geZQOKuoGO/UbBJKxvblm90zFsEkCRXXPGishSEXnMUdbzmKsA4FHLPm3vFccsy9qLiGdv5pLK1v6a+QhQE4UdzhpJRMXJ9ot6OoArNI+Zx0RkEYDOlvr+42OTCQCG5NEeX8MioaobRORjAJ7DGzsAWCUi6Tg75KIavH/lLmgSa5tl8P6g2RBAioj8DpMzI7+5cv4FYL7HsnIwz9sbInIAJm9LnoE1J1WdJyLfAfDs2XUrzGxix2CeswiYnGfFkWjbi6rmiMj/wcw4GAxlRcT5pbUizGOz9XpwygLQR1W3BVj/ZJhkv/78rqq24GZxmwfTW9HfjH7W3mBuzodJtPwMADiOxaMwwy7jYc9Zpzg7MywFZgTMsFX3XhUVALwFYLSI7IfpWRsNM3On58yTxe1FmGBHb8u6no5bmojsgxnyfB7yvsashHkP8gyQDQZwm4gchslB5ytvWH7dA6C3iGyDyXXpa5jfa6peMxkHagTMhBOej6kDgPWO8+kgTGAvASaQZetFVhDNcfa96i/Hvo7D9PypA/v3jr9ghlUHqgKA6SLyDsy15iLYe74cAzAuH/WWJB/CHIOe6QoGwfSyTgVwAOb95jwEZ3KfvLR2e5+DY5/14HsSCdvsxcG+5pRB7lkis0RkD0xeuPIw7yW2XmM/WZbZfhw7H8AeEdmBs7ntJqrq+3m0KxR8zZr6o4hsx9nUFRtV1atXJVE4Yo8wIioWYhJm23pwzc0rCOYw07KstW14DgCoahJMbxxfNqtqXkNPHoXvZObRMF/KmsB/0CMYxsCe/LUczJc0ZxDsCExvpzyp6jcA/utjdQ2YXmDOIJgtpwtgz2PSF/Zp4wGTJ+oSAI0QoiCYk6rOQcGGHvlyhePWDP6Phz8AdFBV29A+Xz5F3r2dSkJvMOfQU1teM6ds5L+tNWG+EF4G3xM3TC7GfFXnBFXdCjNc3Hb9LQszQ+nlMAHxUAfBnLMI3wX/vW1jYY6VJgjgGuPoEezrS208zPnsDIJ5zoqaX2sdfyNhrq++gmDz4edHmryo6u8wPbR89XauCfO6Xoii/R5QASZQcpnjr68f35919PgLxH6cDXjXhHkebUEwBfCwqnqlECgNHL3ae8Ce30xw9j26IYonCAaYwOkVbreG8B0E2wrLxD3FcM2JcGx7GUxuU1sQTGEmYfE0z0e7KsFcB5yPu24B2lXk1My6bus9XgbmtXK239ZzkigsMRBGRMXlBth7idiGcdjMgj2hbn6T5geyDgDgCNB1B/Ay7DNd+XIEZihNUKiZ1bIP/E8dvw9mCnrbNOa+PAL7EAx34+A9DbqTV3J8VT0G4CqYIFsgU8U77YGfxPFF5Lli3NdRAP8G0ELNlPMBc3yZ8xWMdMpPj4qi5u/cWqCqtpkwnQoyvPETBJ7Hjtyo6lcw56u/Hw28NgPwA4p4eLF1x6qnVXUIgOuR/0D2Othnn/wnvGcadpcD02PFNoNxfjwF3z2Ynb4CcFsheoMBAFR1McwwsmAG+305BDPUPr+9ytIAPKiq7+Rjm5MwPYpW+inzF4BBqlpah0UCcAU2msPMrpuf4yEF9hyKxeUrANeqqvXzSpCvOba8WP6kA7jHcX54tisVwNP5rK+keQSA30loiOgsDo0kouJiC1hlwP8XEBdVPSAiPwC4xmPV3SLyvGOGPU9TAYyG96+W2QgwcKCqZwD8n4iMgRnW0gHmF8tqML82noT5IvAbTHf7xQBWOHrGBI2qfi0il8J8meoO02X/FICdMEP83lfVVBG5MR91ZgN4UERmwMxq2Bam19ZhmIT641R1kYj0smyeqarWaekdswH+XURehnndr4H5RbUqTG+IP2GGdWwFsAZmYoI1Pl7DIqOqS3wM5SxQdTCBv9MwH7aPwsyS9RvMl7Yv3fOlFMAkADf5WLfC0QukRHAMK/4Z9uGcfgPQqvq9iNSGme2rNUyvh/owQfRKMM9vGkwOpVUAPlNV26/gFCBH79kWItIJpifR1TCzz8bCHNfpMNeZTTC5nL5V1YLMaBg0jh6t34hIa5jr4TUwPWPjYHqunIS5jm3B2fPPmgdLVf8SkR4A7oe5Xl0Cc53aD2AJgA9UdZ0UfjKPHFV9VEQ+h7netoPpdZYB897xP1UN5g8omwFcLSLtYIaTXg3TM6sqTMD5AMw1aikCfB/2sZ/dAK4RM3nNtQDa4Ox565zttQzM4zwIk4/sWwCfFySRvaoeEpFrYfJg3QPTAzAa5segbwG85eh5VFjsZIEAACAASURBVOo5gjN9RKQRTG/I9jC9wKvCfK7JgOk1tgUm6PmdqtoS7RdJ82DOs2Mw73NrAHyhqrZhh7k3DN415wGYHp3XwrzfNIH5bBQL0zvsJMxxvhnm2JjqyFXmq13vicivAB6GOY7jUQJ6wwZKVX8UkRYwk+l0gnlOmTCfyAcp5I9ORER0DnMkjF4Ik/Te3UJV7R6CJhER0TlORB6ESYLvbruqcnZYIiIqNA6NJCIKUyIyRET+IyLdRMTrV0NHD7SZ8A6CAaV3Ni4iIiIiIgpjHBpJRBS+4mCGADwMIMcx29IxmKS1dWCGX9j8ghKSnJ2IiIiIiCg/GAgjIiLA9BC+wHHzZxeAGx35xYiIiIiIiEoVDo0kIgpf+UkSmQPTC6ylqtpmXiMiIiIiIirx2COMiCh8vQIz8143AK0A1IKZJSkKZmbHozDTla8C8GmoZ4ojIiIiIiIqLM4aSUREREREREREYYFDI4mIiIiIiIiIKCwwEEZERERERERERGGBgTAiIiIiIiIiIgoLDIQREREREREREVFYYCCMiIiIiIiIiIjCAgNhREREREREREQUFhgIIyIiIiIiIiKisMBAGBERERERERERhQUGwoiIiIiIiIiIKCwwEEZERERERERERGGBgTAiIiIiIiIiIgoLDIQREREREREREVFYYCCMiIiIiIiIiIjCAgNhREREREREREQUFhgIIyIiIiIiIiKisMBAGBERERERERERhQUGwoiIiIiIiIiIKCwwEEZERERERERERGGBgTAiIiIiIiIiIgoLDIQREREREREREVFYYCCMiIiIiIiIiIjCAgNhREREREREREQUFhgIIyIiIiIiIiKisMBAGBERERERERERhQUGwoiIiIiIiIiIKCwwEEZERERERERERGGBgTAiIiIiIiIiIgoLDIQREREREREREVFYYCCMiIiIiIiIiIjCAgNhREREREREREQUFhgIIyIiIiIiIiKisMBAGBERERERERERhQUGwoiIiIiIiIiIKCyUC3UDwln16tU1ISEh1M3IlxMnTqBy5cqhbgaVAjxWKBA8TihQPFYoUDxWKBA8TihQPFYoEDxOSp6ffvopVVVr2NYxEBZCCQkJWLduXaibkS9Lly5Fx44dQ90MKgV4rFAgeJxQoHisUKB4rFAgeJxQoHisUCB4nJQ8IrLL1zoOjSQiIiIiIiIiorDAQBgREREREREREYUFBsKIiIiIiIiIiCgsMBBGRERERERERERhgYEwIiIiIiIiIiIKCwyEERERERERERFRWGAgjIiIiIiIiIiIwgIDYUREREREREREFBYYCCMiIiIiIiIiorDAQBgREREREREREYUFBsKIiIiIiIiIiCgsMBBGRERERERERERhgYEwIiIiIiIiIiIKC+VC3QAqnOzsbKSnpyMjIwOZmZnIyckp0v3FxMRg8+bNRboPOjfwWKFA8DgJvTJlyiAyMhJRUVGIjo5G2bJlQ90kIiIiIqIiw0BYKXb69Gns2rULlSpVQmxsLOrUqYMyZcpARIpsnxkZGYiKiiqy+uncwWOFAsHjJLRUFTk5OThx4gQyMjKQmpqKevXqoXz58qFuGhERERFRkWAgrJTKzs7Grl27UL16dVStWjXUzSEiolJIRFC2bFlER0cjOjoax44dw65du3DhhReyZxgRERERnZOYI6yUSk9PR6VKlRgEIyKioKlatSoqVaqE9PT0UDeFiIiIiKhIMBBWSnE4ERERFYWoqChkZGSEuhlEREREREWCgbBSKjMzE5UrVw51M4iI6BxTuXJlZGZmhroZRERERERFgoGwUionJwdlyvDlIyKi4CpTpkyRz0BMRERERBQqjKSUYkU5OyQREYUnvrcQERER0bmMgTAiIiIiIiIiIgoLDIQREREREREREVFYYCCMiIiIiIiIiIjCAgNhREREREREREQUFhgIIyKyGDlyJEQEIoKlS5eGujlEREREREQUBOVC3QCi0s7XDGvly5dHdHQ0YmJiUK9ePbRs2RJt2rRBjx49EBkZWaxtnDhxInbu3AnABHioZElLS8OYMWMAAM2bN0evXr1C3CIiIiIiIqJzEwNhREXk9OnTSE1NRWpqKrZv347FixcDAGJjY9G/f3+MGjUKMTExxdKWiRMn4vvvvwfAQFhJlJaWhlGjRgEA+vfvz0AYERERERFREWEgjCiIZs+e7bqvqjh+/DiOHTuGpKQkLFu2DDt37kRaWhreeecdzJw5E9OmTUP79u1D2GLyZeTIkQwaEhERERERnWMYCCMKIn89eVQV8+fPx+OPP46UlBTs2bMHPXr0wMqVK9GsWbNibCURERERERFReGKyfKJiIiK44YYbsG7dOlcvsPT0dPTp0wc5OTkhbh0RERERERHRuY+BMKJiFh0djRkzZiA2NhYAsHnzZkyfPt1aNjMzE7Nnz8YjjzyCNm3aoFq1aoiIiEBMTAyaNWuGwYMH45dffvG5r44dO0JEXPnBALhmQnS/eQ4BPHPmDBYsWIBhw4ahffv2iI+PR/ny5REVFYWLL74YAwYMwLJlywr/ZADYuXOnqx0DBgxwLXvyySfRqFEjVK5cGXFxcWjXrh3Gjh2L7OzsgOo9evQoXnrpJVx99dWoUaMGypcvj1q1aqFr16547733cOrUKb/b5zVrpK3dqampGDlyJC699FJERUUhKioKLVu2xKuvvoqTJ0/6rKN+/fquZZMmTbK+Rs7JDpxOnTqFDz74AN26dUOtWrVQoUIFVKlSBQkJCWjdujWGDh2K+fPnIysrK6Dni4iIiIiIKBxwaCRRCNSqVQsPPfQQ3njjDQDA//73P9x5551e5Zo2beoVAAFMT7Lk5GQkJydj3LhxGD58OF555ZWgta9bt27W4E9WVhZSUlKQkpKCSZMmoX///vjwww9Rvnz5oO174cKF6Nu3L44fP+5advLkSaxcuRIrV67EhAkTMH/+fFSrVs1nHXPmzMGAAQOQlpaWa/mBAwdw4MABLFq0CG+++SYSExPRokWLoLR73bp16NWrF/bu3Ztr+fr167F+/XrMmDEDixYtQlxcXKH3tX37dlx33XXYtm1bruWnT5/GiRMnsGvXLqxduxbvvvsu1q9fj+bNmxd6n0REREREROcCBsKIQqRfv36uQNjKlSuRlZWFiIiIXGUyMzMRFxeHbt26oUWLFqhTpw4iIiKwd+9e/Pzzz5gxYwaysrLw6quvIj4+Ho8//niu7V9++WWkpqbi+eefx6ZNmwDkTujv1LhxY6/9VqlSBV26dMEVV1yBhIQEVKxYEfv378emTZswdepUnDhxApMmTUJsbCzGjBkTlOdk165duP32211DRrt3745KlSphw4YN+Oijj5Camoq1a9eiR48eWLFiBcqV876Eff3117jttttcPceuvfZa9O7dG+eddx527dqFKVOmYOPGjdi9ezc6dOiANWvWeD3+/Prjjz/Qo0cPHD16FHfddRc6deqEKlWqIDk5Gf/5z39w5MgRJCUl4fHHH8fkyZNd28XHx2P27Nk4dOgQBg0aBADo1KkThgwZ4rWP+Ph4ACbXXJ8+fVxBsObNm6N379648MILERERgWPHjmHz5s1YsmQJkpKSCvW4iIiIiIiIzjmqyluIbldccYUWVHJycoG3LYz09PSAys3+eY+2fXWRJjw7T9u+ukhn/7yniFsWOgBct/w4c+aMVq5c2bVtUlKSV5n58+drVlaWzzp27typjRs3VgAaFRXl8/Xp0KFDvtr43Xff6cmTJ32uT01N1fbt2ysALVOmjP7+++9eZQI9Vnbs2JHrOSxXrpwmJiZ6lTt48KA2a9bMVe7NN9+07jM+Pt5vmaysLB04cKCrTKtWraztevHFF11llixZkme7Y2Nj9ccff/Qq9/vvv2tsbKwC0LJly+revXv91tW/f39re5zWrl3rKnvjjTfqmTNnfJbdtGmTpqam+q0v1AI9Tqh4heo9xh/beUhkw2OFAsHjhALFY4UCweOk5AGwTn3EYpgjjIIucf1eDJ+1EXvTMqEA9qZlYvisjUhcvzfPbcNJ2bJlUbduXdf/hw8f9ipz3XXXWXs9OdWrVw8ffPABACAjIwNz5swJStu6dOmCyMhIn+urVauGSZMmAQBycnIwderUoOwXAJ566in07NnTa3l8fDw+++wzlC1bFgAwZswYr3xhEyZMwKFDhwAAffv2xbBhw7zqKVeuHMaOHYvLL78cgBnS+N133xW63e+++y7atGnjtbx+/fp45JFHAADZ2dlYtGhRofbjPhzy/vvvdz0fNk2bNvU7hJSIiIiIiCjcMBBGQTd6wVZkZuUOUGRmZWP0gq0halHJVbVqVdf9I0eOFKiOtm3buu6vXr260G0K1IUXXoiaNWsGdb9ly5b1Gt7p7pJLLkH37t0BAHv27MHatWtzrZ81a5br/rPPPut3P0899ZR1u4KoUaMG+vXr53N9586dXfeTk5MLta9KlSq57juHuxIREREREVFgGAijoNuXlpmv5eEsJyfHdV9ErGUOHTqEN998E3/7299Qt25dVK5cOddsghUrVnSV3bNnT9Dalp6ejrFjx+Kmm25CQkICqlSp4jWT4YEDB4K632bNmuG8887zW8Y9qOQeCFNV1//Vq1dHy5Yt/dbjDKgBhQ/ktWrVym/PrDp16rjuHzt2rFD7at++vau33qhRozBs2DBs2LChUHUSERERERGFCybLp6CrHRuJvZagV+1Y30PtwpX7rIa22QSnT5+OQYMG5ZpB0Z/09PSgtGvJkiXo16+fK9BVXPtt0KBBvsrs27cvVxtOnjwJAGjYsGGe9dSoUQMxMTE4fvw49u/fX4DWnlW9enW/6ytUqOC6f+rUqULtKy4uDm+//TYGDx6MM2fO4K233sJbb72F+Ph4tG3bFtdccw2uv/56NGnSpFD7ISIiIiIiOhcxEEZB93T3Rhg+a2Ou4ZGREWXxdPdGIWxVyZOdnZ2rJ1WNGjVyrV+2bBn69evn6jXWsmVLdO3aFRdddBFiYmJyBVduueUWV52FlZKSgh49eiAz0wQzGzVqhOuvvx4NGzZEXFxcrh5oDz30EA4fPhyU/QK5h/35UrlyZdf9P//803U/IyPDWsafKlWq4Pjx47m2LYgyZYq3c+2gQYPQuHFjvPTSS1iyZAlycnJw6NAhJCYmIjExEcOGDUPbtm3x9ttvo3Xr1sXaNiIiIiIiopKMgTAKul4tzDCw0Qu2Yl9aJmrHRuLp7o1cy8nYuHGjqwdT5cqV0bRp01zrR44c6QqCffjhhxg4cKC1nhMnTgS1Xa+++qorCDZixAi89NJLPodt+mpTQTmfD3/cH2+VKlVc96Oioqxl/HEG0ty3LS06dOiADh064MiRI1i+fDlWrVqF77//HmvXrkVOTg5WrlyJ9u3bY+HChejYsWOom0tERERERFQiMBBGRaJXizoMfOXh008/dd1v27YtIiIiXP+fPn0ay5cvB2DyT/kLOO3atSuo7XLOoBgfH49//vOfPoNgGRkZOHr0aFD37T4jYiBlateu7bofHR2NSpUq4eTJk0hJScmznsOHD7uGnLrXU9pUq1YNvXr1Qq9evQAAe/fuxTPPPINPP/0UWVlZeOqpp7Bu3boQt5KIiIiIiKhkYLJ8ohDYv38/xo8f7/r/gQceyLX+yJEjOHPmDADgoosu8lvXggUL8tyf+9A9VfVb9uDBgwCA+vXr+x3y99133+VK9h8MmzZtcu3flyVLlrjuX3nlla77IuL6PzU1FUlJSX7rWbhwoet+qIcP5uf1yUudOnUwadIk14yeP/30k6uHHxERERERUbhjIIyomGVkZKBv376uRPlNmjRBnz59cpVxz5W1fft2v3W9/fbbee7TfQhhXsMGnfv+/ffffQZlsrOz8corr+S53/zKzs7Gu+++63N9cnKyK/BXt27dXIEwALjttttc90ePHu13P//+97+t24VCfl6fQJQrVw5169Z1/e8MqhIREREREYU7BsKIiomqYv78+WjVqhVWrFgBwAzn+/zzz716XsXExLhmPly3bh1mz57tVd+ff/6JPn364I8//shz3/Xr13fd//nnn/2WdQaXDh8+jDFjxnitz8rKwsCBA4tsuN3o0aMxb948r+WHDx/GHXfc4QrqPP744yhbtmyuMgMGDEB8fDwAM/TUFlTLzs7Go48+ivXr1wMwj7dLly7Bfhj5EhcXh5iYGABAUlKS315hU6dOxYQJE/z28vrxxx9dj+/CCy8slTnQiIjyK3H9XrR7bTEGfHMC7V5bjMT1e0PdJCIiIiqBmCOMKIgSExNd91XVlUcrKSkJy5Ytw44dO1zr69ati2nTpqFZs2bWuh577DEMGTIEANC7d2/cddddaN++PaKiovDrr79i4sSJ2LdvH+69915MnjzZb7u6dOniCgo98MADeOKJJ1CvXj1XIKlBgwZo0KCBa7/ffvstAODJJ5/E0qVL0b17d1SrVg0pKSmYPHkyUlJS0KlTJ6SkpOSa+bKwOnbsiKSkJNx8883o06cPunfvjkqVKmHDhg346KOPcPjwYQBmKOPQoUO9to+KisKECRNw8803Izs7G0OHDsXs2bPRu3dv1KhRA7t378aUKVOwYcMGV/m8nrvi0rlzZ8yePRvbt2/H7bffjltvvRWxsbGu9R06dEBkZCRSUlIwatQoPPbYY+jWrRuuvPJKnH/++ahQoQIOHTqE5cuXIzEx0TWT53PPPReqh0REVGwS1+/NNWP13rRMDJ+1EQCYs5SIiIhyYSCMKIhuueWWPMvExsbi3nvvxahRo3IFOjw9+uijWL16NaZOnYqcnBxMmTIFU6ZMyVWmZ8+eGDduXJ7BnB49eqB9+/ZYsWIFtm3bhkceeSTX+hdffBEjR44EANx0000YPnw4Xn31VQDA3LlzMXfu3Fzl27Vrh+nTp3sNTSysevXqYcSIEejTpw9mzJiBGTNmeJW58sor8fXXX6NcOfvl64YbbsDMmTPRv39/HD9+HEuXLsXSpUu9yl1wwQWYPXs2GjduHNTHUFAvvPACvvnmG2RmZuLzzz/H559/nmv9jh07kJCQ4Oo9eOLECSQmJuYKvrqLiIjASy+95JV/jojoXDR6wVZXEMwpMysboxdsZSCMiIiIcmEgjKiIREREIDo6GtHR0UhISEDLli3Rpk0b3HjjjYiMjMxzexHBJ598gh49emD8+PFYv349Tp48ifj4eDRv3hz33HMP+vbtG1BbypYti2+//RbvvPMO5syZgy1btiA9Pd3Va8jTK6+8gmuvvRbvv/8+Vq9ejePHj6N69epo0qQJ7rzzTgwYMMBnIKqwunbtivXr1+Odd97B119/jT179iAiIgJNmzbF3XffjYceeijPfffs2RPbt2/HBx98gK+++grbtm1Deno6qlatiksuuQQ9e/bEwIEDA3odikvz5s3x008/4a233sLy5cvxxx9/4OTJk17lRowYge7du2Px4sVYtmwZtmzZgoMHDyIrKwtRUVFo2LAhOnXqhAcffNDVy4+I6Fy3L80+XNzXciIiIgpfUtgZyqjgWrVqpQXNs7R582Y0adIkyC3KW0ZGBvMNUUACPVZ27tzpymHWv39/TJw4sYhbRiUJryklU6jeY/xZunQpOnbsGOpmUAnV7rXF2GsJetWJjcQP/+gcghZRScdrCgWKxwoFgsdJySMiP6lqK9s6JssnIiIiolLt6e6NEBmRewKVyIiyeLp7oxC1iIiIiEoqDo0kIiIiolLNmQds9IKt2JuWiTqxkXi6eyPmByMiIiIvDIQRERERUanXq0Ud9GpRh8NTiIiIyC8OjSQiIiIiIiIiorDAQBgREREREREREYUFDo0kopBKSEgAZ68lIiIiIiKi4sAeYUREREREREREFBYYCCMiIiIiIiIiorDAQBgREREREREREYUFBsKIiIiIiIiIiCgsMBBGRERERERERERhgYEwIiIiIiIiIiIKCwyEERERERERERFRWGAgjIiIiIiIiIiIwgIDYUREREREREREFBYYCCMiIiIiIiIiorBQLtQNICIiIiIqTRLX78XoBVuxLy0TtWMj8XT3RujVok6om0VEREQBYCCMiIiIiEKqNAWWEtfvxfBZG5GZlQ0A2JuWieGzNgJAiW0zERERncWhkUREREQUMs7A0t60TCjOBpYS1+8NddOsRi/Y6gqCOWVmZWP0gq0hahERERHlBwNhRERERBQypS2wtC8tM1/LiYiIqGRhIIzoHDZx4kSICEQEEydOLFRdO3fudNU1YMCAoLTvXDNy5EjXc7R06VJrGef6jh07FmvbisOAAQNcj2/nzp2hbk6pMG3aNHTv3h3x8fGIiIhwPX+JiYkAgnsOE5VUpS2wVDs2Ml/LiYiIqGRhjjCiINu7dy9mzpyJRYsWITk5GampqThx4gRiYmJQt25dXHnllbj++uvRo0cPlC9fPtTNLdWOHz+OTz/9FF999RU2bNiAI0eO4PTp06hSpQrOP/98NGrUCK1atULHjh1x5ZVXokwZxv4pf5KSklxBqV69eqF58+ZBq3vw4MEYN25coesZOXIkACAhIYFBaiqVasdGYq8l6FVSA0tPd2+UK0cYAERGlMXT3RuFsFVEREQUKAbCiILk+PHjeP755zF+/Hj89ddfXutTU1ORmpqKpKQkjB8/HjVq1MDzzz+PwYMHIyIiIgQtLt1mz56NQYMG4fDhw17r0tLSkJaWho0bN+KLL74AADzzzDN4/fXXi7uZVMolJSVh1KhRAEygKViBsJ9//tkVBKtevTqGDBmCiy++GBUqVAAAtGnTJuC6nO3r0KEDA2FUKpW2wJIzIX5pSe5PREREuTEQRhQE27Ztw0033YQtW7a4lrVu3RrdunVDQkICYmJicOTIEWzfvh3ffPMNfv31Vxw+fBhDhw7FZZddViqGySUkJEBVQ90MAEBiYiJ69+6NnJwcAEDDhg1x6623okmTJqhSpQrS09Px22+/YdWqVfjhhx9w5swZZGdn51Fr8SgpzyGF1tdff+26/9577+GOO+6wlhswYACDW3TOK42BpV4t6pTo9hEREZFvDIQRFdKRI0fQpUsX7N69GwBw2WWXYdy4cbj66qut5UePHo01a9bg+eefx7ffflucTT0nnDp1Cn//+99dQbARI0Zg1KhRKFu2rLX80aNH8cknn3AYKpUof/zxh+t+ixYtQtgSopIhV2BJFdi+HZg/H3D2+q1dG2jUCDj//NA1koiIiM4JDIQRFVL//v1dQbCrr74a33zzDaKjo/1u07p1ayxcuBBjxoxhgCafFi1ahIMHDwIArrrqKrz88st+y8fFxWHIkCHF0TSigLkPn3YOhyQKe5s3A//5D/Dll4DjfdVLs2bArbcCjz4KxMcXb/uIiIjonMDM0USFsGrVKnz11VcAgKioKEybNi3PIJi7xx9/HG3bts21LD8z7xVklr4ffvgB/fr1Q0JCAipWrIiaNWuiZ8+emD9/vt/t8jtr5M8//4whQ4bg8ssvR7Vq1RAREYG4uDi0adMGw4YNw+rVqwNqryf34afXXnttgepw17FjR9fjAoAzZ85g7NixaN++PWrUqIHIyEhcfPHFGDp0aK5ePAWV16yRttd0wYIF6NWrF+rWrYsKFSqgdu3a6NOnT76ew02bNuHJJ59E8+bNERcXhwoVKqBOnTq4+eabMXXqVFcPu2CbOnUqunbtilq1aqFixYpISEjAgw8+iOTk5IDr2L17N0aMGIHWrVujRo0aKF++PGrWrIlu3bph7NixOH36dJ51LF26FP3790ejRo1QpUoVVx2XXHIJevfujY8++ggHDhxwlXfO1njfffe5lt13332u18Z5S0hICPhxuJ9DkyZNci2vX79+rjrdzy9/s0a6H7cA8P3333u1z98MpkQlRkoKcPPNQNOmJhDmKwgGAJs2AS+9BNSvDzzzDPDnn8XXTiIiIjonsEcYUSGMGTPGdf++++5DvXr1QtiavL322msYMWJErqDHwYMHMXfuXMydOxcDBw7EuHHjCjW74okTJ/DQQw/h008/9Vp37NgxrFmzBmvWrMFbb72FnTt35vs5c8/1ZUuUXxjHjh3DTTfdhB9++CHX8pSUFKSkpGDChAn47LPPcMMNNwR1v77k5OTg4YcfxtixY3Mt379/P7744gvMmjULH374IR544AGfdZw5cwbDhg3D+++/7xXs2rdvH/bt24cvv/wS7777LubMmYOaNWsGpe2nT5/GLbfc4ppx0WnXrl34+OOPMWXKFHzwwQfo27ev33peffVVjBo1ymsCioMHD+LgwYP47rvv8Pbbb2PevHm4+OKLvbbPycnBoEGD8NFHH3mtc9axadMmzJw5E0OHDs11ThNRETtzBnjlFXNzP8e71gKuuxBocxFQMwbIUWBPOrByJ5C4GfjpMHDyJDB6NDB7NjBlCnDVVSF7GERERFS6MBBGVECqikWLFrn+v+eee0LYmrwlJiZizpw5qFy5Mh544AFceeWVyM7OxrJlyzB58mScOXMG48ePR3R0NN58880C7ePUqVPo1KkT1q5dCwCoWLEi+vbti3bt2qFq1apIT0/Hr7/+iq+//hq//fZbgRLHX3TRRbke06hRo3B+kHLG3H///fjhhx/QtGlT9O/fH/Xq1cOBAwcwbdo0rF69GhkZGbj11luxYsUKtGrVKij79Of555/HtGnTcPHFF+Pee+9FgwYNkJGRgVmzZmH+/PmuQFm7du3QuHFjr+1VFX379sXs2bMBADVq1MCdd96JFi1aoHLlyti1axemT5+OdevWYc2aNejSpQvWrl2LSpUqFbrtzz77LBITE1GvXj3cf//9aNSoEY4ePYrExEQsXLgQp0+fxsCBA1GlShXcfvvt1jqeeOIJV2AqKioKd9xxB1q3bo2YmBgcOHAAiYmJWLx4MVJSUnDtJfVW6AAAIABJREFUtdciKSnJK5D33nvvuYJgsbGxuPvuu9GiRQvExMTg5MmT2LlzJ1avXo0lS5bk2q5z586YPXs2Fi9ejPfeew8A8Nhjj6Fz5865yuXnuYqPj3e9Fu+++65rn//9738R7zbE64ILLgioPmddt9xyCwCgWbNm1qHCl1xyScBtJCo2hw4Bt98OuPdYvL8h8ExnoJElIH9xNaBzfWBER2DpTmDEd8CqfcC2bUD79qYn2aBBxdR4IiIiKtVUlbcQ3a644gotqOTk5AJvWxjp6ekh2W9JlJycrAAUgEZGRmpWVlZQ6u3fv7+r3h07dhSq7IQJE1zrAWjt2rX1t99+8yq3atUqjYqKUgBapkwZXbNmjVeZHTt2uOrp37+/tT2DBw92lbn00kt1165dPtu+aNEiPXbsmN/HZ5Oenq5xcXGu/dSsWVNfeeUVTU5O1pycnHzX16FDh1zPUb9+/fT06dO5yuTk5OjTTz+d67HZ9vXiiy+6yixZssS6P+f6Dh06WNe7v6YA9N5777UeW0OGDHGVGTx4sLWuMWPGuMr06tVLjx8/bi333HPPuco9++yz1jKB8Gx7p06dNCMjw6vce++95ypz3nnnWcskJia6yrRt21b3799v3ed///tfV7nbb7/da32zZs0UgMbExOjWrVt9tv348eO6fv16r+Xu59CECRP8PPr8CfQ8D2T/eR1TBRGq9xh/fJ1TVApt3Khat66qSYuv2ixGdWU/VX0x8Fv2C6qju6qWL3O2nhEjVHNyeKxQQHicUKB4rFAgeJyUPADWqY9YDHOEERXQ3r17Xffr1auHcuVKfgfL//3vf2jYsKHX8quuugpvvPEGADOU7K233sp33bt378b48eMBANWqVcPMmTP99mzp3LkzYmNj872fqKioXMM3Dxw4gOeeew5NmzZFXFwcunbtiuHDh2PevHk4ceJEvupOSEjAxx9/jIiIiFzLRQSvv/66aybQjRs3YuHChflue341btwY48ePtx5bL7/8MiIjIwGYHGKeTp06hVdeecVVz/Tp033mr/vXv/6Fa665BgAwduxYnDp1qtBtj4mJwWeffYYqVap4rXv00UddvcAOHjyIqVOnepV54YUXAADVq1fHl19+6XPI5kMPPeTqjfnFF1945XHbtm0bAKBDhw7WoZNO0dHRaN68eQCPjIgKZe1aoEMHYM8e8//dFwFrBgNXe783+VVGgKfaAcvuA6qbayH+9S9g2JMmLEZERETkQ8n/5k6F9/jjQFJSUKqKzM4GypYNSl1FrnlzoAjz/Rw5csR1vyABneLWrFkzdO/e3ef6+++/H8899xyOHTuGuXPnIjs7G2Xz8VpPnz4dZ86cAWCGkAUr15RNnz59EB8fj6FDh+KXX35xLU9LS8OiRYtcQ1YrV66Mu+66CyNHjkStWrXyrPeRRx5BxYoVretEBE888QRWrVoFwAxL8/d8BsPgwYN9zioaFRWFVq1aYfny5dixYwdOnTqVq+0LFizAoUOHAABDhgzJc3bSu+++G8uXL0d6ejp+/PFHn8n8A3X33XfnGu7nadiwYZg+fToA81wOchvS9Msvv2DDhg0AzHEZFxeX576mTJmC7OxsLFq0KFey+UqVKuGvv/5CSkoKsrKyvIKcRFSM1qwBunYFMjLM/6+3Bp6+DnCb9CHf2tQFVj0IdJ8C/J4GvD0GCUeOAp06BafNFDKJ6/di9IKt2JeWidqxkXi6eyP0alHHXvjECWDHDuD4caBKFeC884Ai/BxCRESlGwNh4SApCfj++6BUxQOm9OrSpYvf9eXLl0e7du0wb948nDx5EsnJybj00ksDrn/FihWu+zfffHOB2xmoDh06YP369Vi5ciUSExOxfPlyJCUl5UqqfuLECXz44YeYNWsW5s6d6+rR5Utez5H7emcetKJ0VR7Jn+vUMV8IVBVpaWm5go/Lly933f/zzz+9ktZ7cu/huHnz5kIHwvJ6Llu1aoXo6Gikp6d7PZfubc/Jycl3291169YNM2bMwObNm9G1a1c89dRT6Nq1q6s3HREVky1bgBtuMEEwATC2AzCoY3DqbhAHLO4PXDMB+CMdCZMnA23aAA8/HJz6qdglrt+L4bM2IjPLTJCzNy0Tw2dtBICzwbCkJOCzz4C5cwGPaz8AoEEDE3h9+GEgH59niIjo3Me4RjgI4nCfM9nZKFeaeoQVoWrVqrnup6WlFem+gqFBgwb5KrNv3758BcL2OIe5AGjSpAmysrLy18ACEBG0a9cO7dq1AwBkZWVhw4YNWL58OaZPn44ff/wRAJCamoqePXtiy5YtfnsX5fUcxcXFITY2Fmn/z96dx0VVvQ8c/1x2FBQJFUEFt8w9911RM0xNUUxyNy3btOxrlNqvMsu0XMoWNct9z7VMDRWlElNRcVdyX8ANFUUE2e7vj8sMIAwzrMPyvF8vX5y5c+49zwy3gGfOeU5UFBEREXn3QgxwcXHJ8nlbW1t9+8nljJcuXdK3P/jgg2yNe+/evWz1z4yx91JRFKpXr86RI0e4e/cujx8/1r+etLHPmDEjW5s3PBn7V199xZ49e4iIiODvv//m77//xtbWlmbNmtG2bVs6d+5M586dZaaYEPnp+nXw9gbdTOq5HfIuCabj4QSBQ7Vk2M0YeOcdePppLREiCg1TZ3lNDwjTJ8F0YhOSmB4Qhk/MRfj8czBWouDcOe3fvHnwwgvaKoEslsgLIYQoOSQRVhLk4fLA2OhoHB0d8+x6RZmbm5u+ffnyZRITEwt1nTBTdrcrXbq0vv3w4cNsXf/BgwcAWFpaYmdnVyCJsCdZW1vTtGlTmjZtytixY1m1ahWDBg1CVVVu377NnDlz+L//+z+D55v6HkVFRWX7/ckJXR20nLh//36Oz42Pj8/xuTo5ud90ibC8jN3T05PQ0FA+//xzli9fTlRUFI8fPyY4OJjg4GC+/vprKlSowIQJE3j33XdRcrNESwiR0ePH4OsLV65ojz9rCq/n07LFWk/Bby+T3H4pFgkJ8JIvHDgImdTGFAXPpFleKSKiYjOc7/zoPv/bMgsm7Er/REsX8PaA2pXApQw8TICwW7D7AgReg2QVtm3Tdij9+mt4++3cLccVQghR5EmxfCFyqE6dOvpZYbGxsRzJozps2ZGcnGxy30ePHhntk7a4fGZFzrOiK8SelJSUJ8XW88KAAQMYNWqU/rGudpgh2XmPsvv+FLS08V26dClbO9pOmjQp1+Pn5n5L2w4KCspW7IsXL84wToUKFfj++++5desWe/fuZcaMGfj4+Ojv2Vu3bvHee++lq1MmhMgj774LKbUVGVELPu6Rv+O1rMyZD1L+vx/1APz6ack4kalNoeG0nbaLauO30HbaLjaFhhs/KYeymuX1JDen9MvXO1w4xI5f3sT3REoSzM4SxtSFS6Nh39vwWU8Y2BSerwV968IEL9g+Av4bDaMaa8txY2NhzBgYPhzM8GGdEEKIwkMSYULkkKIo6eogLVu2LE+um3a5m7GZOZGRkSZfV7d7nql90s54M0XlypX17SfrNJlT2u+RseWMxt6ju3fv6pfBZvf9KWi6+mEAJ0+eLPDxjb2Xqqpy4cIFQFtymva+z6/Yra2tad26NePGjWPjxo3cvn2bhQsX6sf++eefOX78eJ6NJ0SJt2QJ/PST1m7pAnNeKpCZOLeeawvjUmpChh6DD7O3PLyk0M3QCo+KRSV1hlZ+JcMym+Vl6Li/d23srS1R1GTG7lnB4rWTeCpWm3lOz8pw5g347iXweCrDuenUcIafemk15DxSdk5euhR8+2IhCVIhhCixJBEmRC68++67+vaiRYu4fPlyrq+ZdgfKrBI3SUlJHDx40OTrGpsNFR8fT3BwMKAtWatbt67J1wZo3769vv37779n69z8lHa3RGOzuHbt2mXy882bN89dYPmsY8eO+vbGjRsLfHxj7+WhQ4f0y2mffC8LKnYbGxteeeUVxowZoz+m+29AJ+3yVFVV8y2W3NAt5yys8YkS6tw5bQkaQEU7WD8AbAuwFt+XXaB5ygcWs7+DzZsLbuwiIjsztPLCk7O8sjru09idaS/W5qeA2YwNXoUFKol21rD0Ofh9BHhkXUMzAy9PCBkFTVM2ldn8B3WmTIFszKwXQghRfEgiTIhcaNOmDd27dwcgOjqaAQMGEK3bFt4Es2fPZu/evemOpU1AZZVMWL16Nbdv3zZ5rJMnT7Jjxw6Dzy9evFhfaLxXr15YZnNTBD8/P33B8e+//54bN25k63xTRUZGZmtJaNqknLHk3pw5c9LtOvmkb775Rt/u27evyTGYQ/fu3fXF9pcuXVrgs8KWL1+e5f05a9YsffvJ97JZs2bUq1cPgJ07d2Z53+YFT09PfTsxMTHdc2mTp2mXchYmuhgLa3yiBEpIgIEDQXdPLu0O7oY3KskXNpawuh+USZlt+spQSLOpi8jeDK28oJvllZa9tSX+3rUzdo6JoffE13j+aMqHeE87YnVoKAxpm/NZheVLw67h0NFDe/jPPzJbUAghSihJhAmRS0uWLNEvC/z3339p166dfrdCQ0JCQnj++ecZO3ZshuWPXbt21Sehfvzxx0xnmR08eDDdLBZTjRgxgvPnz2c4fuDAAfz9/QFtBsx7772X7WtXqVKF1157DYA7d+7Qr18/ruiKI2fir7/+ytFum+vWraNevXr88ssv+hlFmVFVlTlz5rBo0SL9scGDB2d57QsXLvDaa69lSIaoqsrEiRP1ScuGDRvStWvXbMdekEqXLs2nn34KaLP9unfvbnQGYUhISLZ3mDQkKiqKAQMGZJqcmTdvHqtWrQKgYsWKDBw4MN3ziqIwdepU/WM/Pz8CAgKyHO/06dO8+eab6Y5dv36d999/n4sXLxo879GjRyxZskT/uFGjRumer1atmr59+PDhLGMwF12MZ86cITY2f/6AFSJbpk6FkBCt/V49eN70HYjzVPVyML+n1r4TBa+NAJk5qZedGVp5waexO1P7NsDdyR4FcHeyZ2rfBhl3jYyNhV69QDeTvbUL7H0N6lbOcM1sK2MLm16GuikzymbMhExqSwohhCjeCu8Wd0IUES4uLgQGBvLiiy/y33//cezYMVq3bk3Lli3p2rUrnp6elClThrt373L+/Hn+/PPPLOsQubm5MXDgQJYtW8bdu3dp3rw5b731FnXr1uXhw4cEBQWxatUqypUrR+fOnY0uQdPx8fFh06ZNPPvss4wcOZLmzZuTlJTE33//zdKlS/W7PL733ns5XvY3c+ZMQkJCCAkJ4dixY9SuXRs/Pz/atGmDs7Mz0dHRnDx5kq1bt3L69GkuXryYbimoqc6cOcNrr73GmDFj6NChA61atcLDw4OyZcsSExNDWFgYv//+OydOnNCfM2jQILy9vbO8ro+PD8uWLSM0NJRhw4ZRtWpVbt68yapVq/g3pdizra0tCxYsKBK7C44ePZqQkBCWLl3KlStXaNGiBd26daNLly5UrlwZVVWJjIzk+PHjBAYGcv78eWrUqMHXX3+d67F191v9+vUZOXIktWrVIioqio0bN+qTWoqiMHv27EyXrL744ot88sknTJ48mXv37tGtWzfat2/PCy+8gIeHB1ZWVty9e5eTJ0/y119/cfz4cSwtLZk7d67+Go8fP2bmzJnMnDmT5s2b0759e+rUqYOTkxP3798nLCyMlStXEh6u1cNp37497dq1SxdHgwYNqFChArdu3WL58uWUL1+eVq1aYW+v/aFob2+fbimnOXTp0oVjx44RExPDiy++yLBhw3BxcdHfoy1atMDZuYBn44iS6/RpmDJFazdwgqm9zRuPX33Ydg6WHIU/d2h1y4YPN29MhYS/d+10uzhCFjO08ohPY/eMia+0Hj+Gvn1B97uNtxtsGAqlbA2fk11OdvDHQOKbLsbm3gMY/Ra0awc1a+bdGEIIIQo1SYQJkQeefvpp9u/fz8SJE1mwYAHx8fHs37+f/fv3GzzH1dWVjz/+OMMf3gDffvstx48f58iRI9y+fZvPPvss3fOVKlVi48aN6f7oN6Z37960atWKiRMnMnv27Ez7jBw5MldJEDs7O3bt2sUrr7zCunXriIuLY8mSJelm3KSVtv6SqapUqUK5cuW4d+8ecXFxbN++ne3btxvsb2VlxdixY5k2bZrRay9atIjbt28THBysnyGXlqOjI6tWraJZs2bZjttcFi9eTK1atfjiiy94/Pgx27ZtY9u2bQb7p930IDe++uorFEVh48aNfPzxxxmet7Gx4ccff9QvLc7MZ599RpUqVRg3bhwPHjzgn3/+4Z9//jE59rTJSl2C1pBOnTqxdu3aDAlOKysrPv/8c15//XUSEhIy/Pfh4eHBpUuXDF63IIwbN06/FDUwMDBDPcDdu3fj5eVlnuBEyZKcDKNGQXw8WCiwoGfB1gUz5Btv2H4erj+E996B55+HQr7hSUHQJaSmB4QRERWLm5M9/t61s05U5aeEBPDzgz//1B53doWNw8DeJuvzcqJaOU5PfJtG/lMhJhaGDIR/9oKV/GkkhBAlgfzfXog84uTkxJw5c5g4cSLr1q0jMDCQU6dOERkZyaNHjyhbtixVq1alefPm9OjRg+7du2Nl4BcuZ2dn9u7dy3fffceaNWs4e/Ysqqri4eFBnz59ePfddylfvny2EmEAH374Ie3ateOHH35g79693Lhxg7Jly9KyZUveeustXnjhhVy/Dw4ODqxdu5adO3eydu1a/vrrLyIiIoiNjaVs2bI8/fTTtG/fngEDBlC1atVsX79Hjx7cunWLf/75h6CgIEJCQvjvv/+4efMmjx49olSpUjg7O1O3bl06duzIgAED8PDwMOnaTk5O7N69m59//pkVK1YQFhbGw4cPqVy5Mt27d8ff358qVapkO2ZzUhSF//u//2PkyJH88ssvBAYGEhYWxt27d7GwsMDFxYVnnnmG1q1b0717d1q1apUn49rY2LBhwwZWrFjBokWLOHHiBPfu3cPV1ZXnnnuOcePGUbduXaM19V599VX69evHwoULCQgI4MSJE9y5cweAcuXK8fTTT9OyZUu6deuWIdnj4eHBlStXCAwMZPfu3YSGhnLlyhUePnyIra0t7u7uNGvWjIEDB9KzZ0+DMYwaNQoPDw/mzZvHwYMHuX37dpa15Aqau7s7hw8fZsaMGQQGBnLp0iViYmKkeL4oeD//DHv2aO136kLzGuaNR6ecPczrCb1XQ1Q0vPEa/PZHgexgWdgZnaFVUFQVxoyB337THrerAL/nUxIsxb1mDeCdFvDdAdgXAtO/hgkT8208IYQQhYcivyibT7NmzdTs7PqX1unTp6lTp04eR2RcdHQ0jo6OBT6uKHqKyr3i5eXFX3/9Bciue+ZQVO6TksZcP2OyEhQUJDPbCrOICKhTBx48gKql4eQYcMjD5WzZEBRUGy+vTHY+HLQBVqaUJli5EgYMKNjAhGGzZsG4cVq7iTPsfhXK5E+tMp2goNp4tTwBTefD6UgoZQdn/oMi9oGXyH/y80eYQu6TwkdRlEOqqma6lEeK5QshhBBCiNz54AMtCQYwt6vZkmBZmt0NKpTW2u+9A/fvmzceofn9d3j/fa3tXgo2D8r3JJievTXM7aG1H8WBf/Y3CxJCCFH0SCJMCCGEEELk3IEDsGKF1u7nAd0bZd3fXFxKwczntfbNSPjk/8wbj4Bjx7SZeaoKpa3gj5fArYA39+joCX71tPaa9RAUVLDjCyGEKHCSCBNCCCGEEDmjqvBeyiwaGwv4uod54zFmUAPomFI38oc5EBpq3nhKsvv3wdcXHj3SNldY3R2e9TRPLNO7QqmUjR0+eE+7r4UQQhRbkggTQgghhBA58+uvsHev1h5bH6qVN288xigKzOkBVhbaLpdvvaZ9FQVLVWH4cDh3Tnv8eTPo2dh88VQpC/9L2Swm5Ahs3Wq+WIQQQuQ7SYQJIYQQQojsi4uDDz/U2uVt4aNu5o3HVHXLw3spSY99h2DRQvPGUxLNnAmbNmntnpVhfCG4d/7XGsqk1LabNFFmhQkhRDEmiTAhRIkWFBSEqqqyY6QQQmTXd9/B5cta+/NWBVfgPC980hEql9HaH/rDnTvmjackCQ6G8eO1tmdpWOoHFoXgT5Jy9qkJ0oPH4I8/zBuPEEKIfFMIfuoIIYQQQogi5cED+OorrV23LIxsb954ssvBBr711tp3omDih+aNp6R4+BCGDoWkJLC1hPW+UM7B3FGlGtsKyqbMCvtykllDEUIIkX8kESaEEEIIIbLn22/h7l2t/Xl7sLI0bzw50bcOeNfQ2j8vhMOHc33JTaHhtJ22i2rjt9B22i42hYbn+prFir8/XLigtb9sAU2qmTeeJznZwegWWnvfYdi/37zxCCGEyBeSCBNCCCGEEKa7dw9mzdLajZ2hTxPzxpNTigKzu4G1hVYPasybuaoLtSk0nAkbjhMeFYsKhEfFMmHDcUmG6QQEwLx5WrtDRRjb1bzxGPJWc20zBYBvvzJvLEIIIfJFkUyEKRo/RVH+UBTlmqIojxVFua4oSqCiKK8qimKVD2P2UBRlpaIoZxVFeagoSryiKJGKouxVFOVLRVFq5PWYQgghhBCFzsyZcP++1v68o5ZQKqpqu2jL4QD2HoAVK3J8qekBYcQmJKU7FpuQxPSAsNxEWDzcuwcjRmhtBytY3A8sCul94+YIfvW09trf4No188YjhBAizxW5RJiiKOWAncBqoAfgDtgArkBn4Gdgv6IoVfNoPBdFUXYDfwADgJpAacAaeApoDUwATimK8kFejCmEEEIIUSjdvq0tiwRo6QLdG5g3nrzwcQdwTalT9cE4iI7O0WUiomKzdbxEGT0aIiK09jedoJqLeeMxRpccTUqGH2ebNxYhhBB5rkglwhRFsQF+Q0t4AVwFPkZLUPkDp1OONwG2KYpSJpfjWQHbAK+UQ3Foiba3gYHAh8CelOdsgK8URXkrN2MKIYQQQhRaM2ZATIzW/tyraM8G03G0ha+f09rXb8Hnk3N0GTenzHfNNHS8xFi3Dlau1No9qsDI1uaNxxTN3KBNFQDu/vALtfx/k5pvQghRjBSpRBjwJqDblugw0EhV1S9UVV2tquoMtARYQMrzddGSZLkxAGiW0r4KPKOq6ihVVeeoqrpKVdWvVVVtD4xKc85n+bE0UwghhBDCrO7dgzlztHa7CvBcXfPGk5cGN4TWlbX2t99CWPaXM/p718beOv2mAfbWlvh7186LCIumGzfgjTe0trMN/NyvyCRPD3l3AMD5YRSdzx+Qmm9CCFGMFJlEWEpy6aOUhyowVFXVe2n7qKoaBwwFUj6qZIyiKE/lYljvNO1pqqpezqyTqqo/A4dSHroAdXIxphBCCCFE4TNnDjx8qLUntisyCQ2TKAr80B0UICERxo7JduF8n8buTO3bAHcnexTA3cmeqX0b4NPYPV9CLvRUFUaNgjt3tMdzu0GlXC3WKFAf0J1oG202X/9jOwCp+SaEEMVFkUmEoS2HLJ/SDlRV9WRmnVRVvYVWPwzAFuidizErpGmfNdL3vzTt0rkYUwghhBCicHn0CGan1EpqVA661TdvPPmhSSV4LWUHzD93wB9/ZPsSPo3dCR7fmYvTehA8vnPJTYIBLFoEmzdr7ZdrQP/G5o0nmy48KsPmOh0B8LpwiIrRkYDUfBNCiOKgKCXCnk/T/tNI37TPd8vFmDfTtGsZ6at7Pon0STEhhBBCiKJt4UKtUD7A+FbFazZYWlO6gJOd1h47BuLizBtPUXXpEowdq7Ur2cOPfc0aTk64OcWzpmFXACzVZHxP7Eo5XsJrvgkhRDFQlBJhaT96PGSwl+aggfOy67c07fGKonhk1klRlFdJrSW2TFXVu7kYUwghhBCi8EhI0IrkA1R3gH7Nsu5flLmUgs87ae0Ll2HWTPPGUxQlJ8Mrr6TuvvlLT3AuZd6YcsDf+xr/Va3BGRft13/fE7uwt7Io2TXfhBCimChKibCn07QvGel7DW1mFkAtRcnxx5brgY0p7SrAGUVRflIU5U1FUV5WFMVfUZR/0HaSJKXvmByOJYQQQghR+KxeDZdTyqR+0AysitKvjznwRjNokFIdY8oUuHrVvPEUNd9/D0FBWntUHeheNDdV8Gl8l6m+lwlsphXNr3H3Gj82sCrZy12FEKKYKEq/yTilaUdm1VFV1UTgQcpDK3JYs0tVVRV4CfgSiAbs0HaInAOsAr4G2qHtYNkT8FVV9WFOxhJCCCGEKHRUFWamzIqqaAfD2pg3noJgZQHfv6C1H8WC//vmjacoOXMGxo/X2tUcYEYvk07bFOpM22kNqTa+GW2nNWRTqHM+Bmk6n8Z3eXtuaiydjxirziKEEKIoUNRs7ohjLoqixAPWKQ+tU5JdWfUPB9xSHrqpqno9F2M7ASOBKWgF+DOzB/hQVdW9Rq41Ci2ZRsWKFZuuXr06q+4GlS1blpo1a+bo3NxISkrC0tLSeEdR4sm9Ikwh90nhdO7cOe7fv2/uMNJ5+PAhDg4O5g6jxHEKDeXZ//0PgAsjfbky2NfMERn38KEtDg6Pc32dupO/o8LufQCEfvMN9599NtfXLM6UpCQajx5NmTNnUBWFI99+xP2GxmeD7Y2wYvEJe+KTUxdw2FioDK8fSxu3LH/dz5Xs3CeNR39K2ZNniXWtyP6Vq4pvjTyRKfn5I0wh90nh06lTp0OqqmZaz0ESYcbH7Ya2C2VZIAiYCuwHYoGqaDPGPkKbdRYHvKyq6m+ZXuwJzZo1Uw8ePGi8YyZOnz5NnTp1cnRubkRHR+Po6Fjg44qip6TcK7qV1x07diRItxREmKww3ieXLl2iWrVqAAwbNozFixebNyAzMNfPmKwEBQXh5eVl7jBKnt694fffwc4Sro4FF/P/kr8p1JnpAZWJiLLBzSkef+9r+DROLc8aFFQbL6+w3A909T488yM8SoCUEZSMAAAgAElEQVQG9eDwEbCyyv11i6vPP4dPPtHa4xrCjD4mndZ2WkPCozJ+zuzu9Jjg8cfyMsJ0snWffL8f3kmZDbZvH7RsmW9xicJHfv4IU8h9UvgoimIwEVaUlkamXXJoZ0L/tFu6ROdkwJQk2Ba0JNg6oIuqqttVVb2vqmq8qqrnVFWdCnQGHqfEtUxRFNecjCeKj5EjR6IoCoqiYGFhwcWLF80dkhBCCJE9587B5s1ae0iNQpMEm7DBk/AoW1QUwqNsmbDBM3+W0lUpCxPbae3jJ+Gnn/J+jOLi8GGYPFlr1y0LX/Q0+dSIKJtsHTeLl+qBRcossFWLzRqKEEKI3CtKH2tFAeVS2k+RPjGWjqIoVkCZlIeJQEwOx5yJlixMBt5VVTU5s06qqh5QFGUx8DrgCAwHpuVwzHzyCXAl11exs0sgdWJeYVYVmGyWkWNiYli7dq3+saqqLF68mM8++8ws8QghhBA58v33Wo0wgHc7mDeWFNMDKhObkH45dWyCJdMDKqebFZZnxrWBhaFwIQo+/gj8/MDFJe/HKcri4mDIEEhMBCsFlvUBO9N/V3Rzis90RpibU3xeRpk7rg7g5Qm7LsKGDfDNHFkeKYQQRVhRSoT9B1RLaXsCl7PoWxnQ/ZZ0Vs3B+k9FUaoBusIGp1RVjTByyk60RBhAi+yOl/+uoL1tuZOc/BjDZdIKk0tmG3ndunVER6efhLhkyRImTZpEzjcwFUIIIQrQ/fuwcKHW7loJ6hWOnfIKfPaQnRV80w16r4Z79+H/PoJ5MjMsnY8/hlOnUtpNoYlHtk73977GhA2e6RKc9tZJ+Htfy8soc6/PM1oi7OotCA2FJk3MHZEQQogcKkpLI0+kaWe6ztPA8ycM9sqaW5r2A4O9UqWtKpyjXSpF8bBo0SIArK2tGTRoEACXL19m165d5gxLCCGEMN3ChfAwZfL92FbmjSUNQ7OE8nX20ItPQ7caWnv+z9oyQKH555/UXUWbPwUTvLN9CZ/Gd5na9xLuTo9RUHF3eszUvpfyZ4ZfbvSqndr+ba3hfkIIIQq9opQIC0jTNvZTtluadk73OU6b/KpiQv+0H3/dyeGYooi7cOECf//9NwDdunXjfyk7bUFqgkwIIYQo1JKT4ccftfbTZaBbA/PGk4a/9zXsrZPSHcv32UOKAt92A2sLbanomLdTl4yWIJtCw2k7bRfVxm+h7bRd/LEnDIYN094LO0tY6gvWOVts4tP4LsHjj3Fx2kGCxx8rfEkwgKploXFKGeDf1ps3FiGEELlSlBJhu4HbKe3nFEWpl1knRVEqAC+nPIwDTNrBMRPnUs4HqKIoShsj/V9O087ZVpCiyFu8eDG6lbhDhw6lSZMm1Kun3aobNmzg/v37WZ0OgJeXl77QPkBSUhKLFy+ma9euuLu7Y2VlhaenZ6bnPnz4kG+//ZauXbvi5uaGra0tzs7ONG/enE8++YTbt29nel5ahw8f5vPPP6dbt25UrVoVOzs77O3tqVKlCj4+PixfvpykpCSj1zHm0qVL+tc5fPjwXPf19PREURT9e5OYmMj8+fNp164dLi4u2NvbU6tWLUaPHs21a6b9wXT58mXGjBlDzZo1sbe3p0KFCrRv356ffvqJxMTsb+n+77//8uabb1K3bl2cnJyws7OjatWq+Pn5sWXLlizPDQoK0r8HkyZNAiAsLIyxY8dSp04dypQpg6IoudrhcOPGjfTo0YOKFStiZ2eHp6cngwcPZv/+/YB2f+tiyGyczL5P4eHhfPTRRzRs2JBy5cqli1/nzJkzTJ8+nV69elG9enVKlSqFra0tlSpVolu3bsyZM4e4uLgM4xl7f44fP86oUaOoUaMG9vb2lC9fnueee45Vq1Zl+72JjIxk0qRJNGjQAEdHRxwdHWnSpAlTp07l0aNH2b6eEIVaYCCcP6+132qQWiS8EDDb7KHaLqkz4/bugxUr8ne8QmZTaDgTNhwnPCoWFQiPiiXmnfdAtxnQ1JbwTCWzxlggeqfMCjt6NvW1CyGEKHKKTI0wVVUTFUWZAnwLKMBSRVGeU1X1nq6Poih2wBJSlyb+oKpqprOzUorbD0t5+JmqqpOeGC9WUZTfAL+UQ0sURemiqmqGivOKokwEuqQ8fAz8moOXKIo4VVVZsmQJAE5OTrz44osADBkyhPHjxxMbG8uaNWsYNWqUyde8e/cuvXv3Zs+ePUb7btu2jeHDh3Pr1q10x+Pj4zl48CAHDx7k22+/Zfny5fTq1SvTa3z22WcZkhQ6165d49q1a/z22298++23/P7777i5uWXa19wiIyPx8fEhODg43fFz585x7tw5Vq5cyY4dO2jatKnBa2zatInBgwcTE5O610ZcXBy3b99mz549LF++nM263dSMiImJ4dVXX2X16tUZnrt69SpXr17l119/pUePHqxatQpHR0ej11y6dClvvPEGsbGxJsWQlYSEBAYNGpRukwfQEoGXL19m9erVfPXVVzz11FPZum5AQAADBgzg3r17BvusXLmSN954I9Pnbty4wY0bNwgICGDWrFls3ryZOnXqmDT2smXLeO2113j8+LH+WFxcHIGBgQQGBrJixQrWrVuHnZ3xTYgPHjyIj48P4eHh6Y6HhoYSGhrKr7/+SmBgIM7O+bBrnRDmMG+e9tXeEoYa+xyw4Pk0vmueGUMfd4BlR+FGDHwwDnr3BhP+f10cTA8IIzYh9UMwr/MH8QvdlvKgIrzTxcCZeW9TqDPTAyoTEWWDm1M8/t7XCu5+6P0MTPpLa/++Ad4dVzDjCiGEyFNFJhGWYi7gC7QHmgBHFUX5CW32VmVgJKD7K+kU8EUux5sIdAWcgZrACUVRlgP7gFi0rQlfAlqmOeczVVULWXVPURACAwO5ckXLk7700kvY2mqbCgwePJiJEyeSnJzMokWLspUIGzx4MHv27KFRo0YMGDAAT09PHjx4wLFjx9L1W79+PX5+fiQlJWFpaUnPnj3p0qULrq6uREdHs3v3btasWUN0dDR9+vRhx44ddO7cOcN4sbGxWFlZ0bp1a9q2bUvNmjUpU6YMd+/e5eLFiyxfvpzw8HAOHTpE79692bt3L9bWhWsX0cTERHx9fQkODqZTp074+PhQqVIlwsPD+eWXXzh58iT37t3j5Zdf5uTJk9jYZCywHBwcTP/+/UlISACgbdu2+Pn5UaFCBS5dusSSJUvYs2cPI0aMMBrP48ePee6559i3bx8AVatWZcCAAdSrVw9bW1vOnTvH0qVLCQsLY8uWLfj4+LBjxw4sLAxP2A0ODmbKlClYWloycuRI2rZti52dHWFhYbi6umb7PRs1apQ+CWZnZ8fw4cNp3bo1lpaWHDx4kAULFvD+++/Tr18/k6957tw5+vfvz8OHD/Hz86NLly6UKVOGixcv4u6eWnQ7NjYWRVFo2rQpHTp0oHbt2pQrV44HDx5w+fJl1qxZw3///cf58+d54YUXOHLkCE5OTlmOHRISwpdffgnAiBEj6NChA5aWloSEhLBgwQJiYmLYsmULgwcPZt26dVle6+rVq/To0YO7d+8yaNAgOnXqhIODA6dOneLHH3/kzp07HDlyhLFjx7J06VKT3x8hzGFTaDjTA8KIiIrFzckef+/a+DR+ogh+eDj8ljKZ/uXqUK5UwQdaWDnawtddYegmuH4LJk+C6TPNHVWBiIhK/dCl3KP7fL1tNgDRNvY4LuoLWfzMykubQp3TFdYPj7JlwgZPgIJJhjWqCB5l4fJ9rU6YJMKEEKJIKlKJMFVV4xVF6Q2sAzqj1e7KLNl1GOijqqrxdWhZj3dBUZSuwGqgFuAIvJny70mJwCRVVafmZkxRdKWtATZ06FB9293dnU6dOhEYGMi+ffs4ffq0ybNatm3bxtixY5k5c6bBxMjVq1cZMWIESUlJVKxYkc2bN9O8efN0fUaMGME777xD165duX//PsOGDePChQsZkli+vr6MHTvWYDJl8uTJfPDBB8yePZuDBw+ycuVKhg0blmlfcwkPDyc8PJx58+bx+uuvp3vujTfewMvLi/3793Pu3Dk2bdpE//790/VJSkpi5MiR+iTYxx9/zOTJk9P1ee+99xgyZAi//mp88uf48eP1SbA33niD2bNnZ0i++fv78+qrr7J06VJ27drF/PnzDc6SAti5cyeurq4EBgZSt25dg/1MERgYqF/m6OLiwu7du6lfv77++UGDBvHuu+/i5eVlNGmUVnBwMA4ODuzevZsOHToY7NemTRvOnz9PtWrVMn1+0qRJzJo1C39/fy5fvszs2bP59NNPsxx769atODo6sn37dlq1Si3yPXjwYEaPHo2XlxcRERGsX7+e9evX4+vra/Bau3btwsnJiT179tCyZct0zw0fPpwmTZoQFRXFypUrmTZtWqGdJSmEbmmbblZPeFQsEzYcB0ifDFuwAHTL398ofLPBzG5wQ5h7EP69Bt98C34DoJmxPZyKPjcne8KjYkFVmfbn91SI0Wb6fvficD7yrFBgcUwPqJxud0mA2ARLpgdULphEmKJomyf8EAJ7DmobSjg45P+4Qggh8lRRqhEGQMpSyOfQanJtASKAeOAmsAsYBbTMbAljDsc7DDREW0a5EbgEPEJLfN1Bmx02DaitquqUvBhTFD33799n48aNAFSrVo22bdumez5tYiw7NZyaNm2aZRIMYPr06Tx4oO3tsHbt2gxJMJ3mzZsza9YsQFvm+OQyOF2frGYU2djYMHPmTH3SYtmyZSa/loI0YsSIDEkw0GY7ffFFau48ICAgQ5/NmzcTFhYGaPXankyCgfY+LFq0iKpVq2YZx/Xr15kzZw4AXbp0Ye7cuZnOQLO2tuaXX36hevXqAPrvU1Z++umnXCfBAL755ht9+4cffkiXBNPx9PTMUe2xKVOmZJkEA6hTp47BJBiAhYUF77//vv46pt5z06dPT5cE06lVqxYLFizQP54xY4bRa3333XcZkmCg/bf+9ttvA1oCNTAw0KTYhDCHJ5e2AcQmJDE9ICz1QGIi/Pyz1m7iDM09EE9QFPipp1Y4PykZXhkKaZZgF1f+3rWxt7bkpeM78D6rfbiz/ZlW1JtY28iZeSsiKuPP0KyO5wvvmtrXhCTYLTuCCyFEUVTkEmEAqmaNqqo9VVV1V1XVVlVVV1VVu6iq+rOqqkarWKuqOlxVVSXl3yQjfeNUVV2qqmpfVVWrqapaWlVVa1VVXVRVba2q6gRVVS/k2QsURc6qVav0tZoGDx6sL3Sv4+vrS+nSWum6ZcuWmVxs/u23384yCaaqKitSCva2aNGC9u3bZ3k9Pz8/rKy0iaDbt283KYYnWVpa6pMCBw4c0G8OUJi8++67Bp/r0KGD/j04depUhud1CU2AceMML3koVaqUPgliyK+//kp8fLzRa4GWDPPz00oSnj17lkuXLhns6+Hhoa9BlxtxcXH6+6BSpUq89NJLBvt6eXnRsGFDk69dqlQpRo4cmesYddq00WamnD9/nsjIyCz7litXjldeecXg8926ddMnEfft28eNGzcM9i1fvjwDBw40+HzaJcaZ3U9CFBZpl7YZPL51K+g2E3njWS3pIzJqUBE+6ai1T5yGLz43bzwFwKexO7ObO/JZoJYojXQsR+KP3fFpYrgGZH5wc4rP1vF84eUJNimz0v7cmGVXIYQQhVORWhopRGGVdlnkkCFDMjxfunRp+vTpw/Lly7l+/Trbtm2jZ8+eRq9rLLF18uRJ7t7VlgI4OzuzadMmo9d0cHAgKiqK06dPZ/p8cnIymzZtYv369YSGhhIREUF0dDTJyckZ+kZHR/PgwQPKli1rdNyCUqpUKRo0aGDweRsbG1xcXLhx40amRdxDQkIAbSZSp06dshyrS5esiwP/888/+vatW7eMfn/SxnP69GmDu4O2a9cuQ7I1J44ePapfAtqxY8csk66gJcOerE9nSOPGjfXJX1Ps3LmT1atXExISwpUrV4iOjjaYMA4PD8fFxcXgtdq3b5/pzLu0OnfurE9chYSEGEwsNmvWDEtLy0yfA9LVO8tqUwAhzE2/tC2T43q6IvmO1jCgRQFFVkR92BY2nIbQGzB1GvTpC02amDuq/JOYyPPT/CFeu4dc1nake+fc7yCdXf7e19LVCAOwt07C37sAy/M62EC7qrDrIgTsKLhxhRBC5BlJhAmRS6dOneLAgQMAtGrVilq1amXab+jQoSxfvhzQEmemJMLS/pGdmbSzhv7880/+/PNPE6PO/I/2a9eu4ePjw6FDh0y+TmFLhD311FNGk0S6jQzi4uIyPBcREQGAq6ur0UROzZo1s3w+7fdn+PDhWfZ9UlZJFWP3hal0rxXQL8vMiil9dEyN8f79+/Tr1y9bMxR1S4ENMfZ9ebJP2vfhSVkl3CD1XoLM7ychCgt/79rpaoQB2Ftb4u+dsrTt4kXQ/QwZWhMcbDO5itCztoTFPtB0PiQmwdBBcOAQlCqmmwt8+SX8+6/WHlMHvBuZJQxdHTCz7Rqp062Glgg7Hw7nzoEJP3eEEEIUHpIIEyKXjM0G0+nSpQvu7u6Eh4ezefNmIiMjjf6RbW9vn+Xz9+/nfD8I3ZI9nYSEBLy9vfWzZFxcXOjVqxf169enYsWK2NnZ6WcMfffdd+zevRvA5GWeBcXYrCZjHj58CGgzy4wxlijLy+9PWsbuC1PFxMTo23nxetMyNcahQ4fq7yVHR0defPFFnn32WSpVqkSpUqX038/Vq1ezZs0awPg9l93XovueZya395MQhYWuIL7BXSPnzwfdUvfX2xq4ikinYUX4uAN8GgQnz8D/3oN5P5k7qrwXFASffaa165SFr3qbNRyfxncLPvH1JO+a8MFOrR2wBWoaLskghBCi8JFEmBC5kJiYqJ/lBVpNL2N1o0BLOq1YsSLLWlamcEizU9GkSZOM7qaXlVWrVumTYF27dmXjxo0GEx+6umQFJbNlmfnFwcGB+/fv8+jRI6N90yaSDF0LwMrKitjYWH1tssIi7fc3L15vdv3999/6JFijRo3YsWMH5cuXz7RvcHCwydfN7mtxkB2/RAnh09g9/Q6ROgkJoPtQp20FaJA3s05LhIntYcd52HMVfpoPXZ6DLOotFjm3bsHAgZCcDHaWsNoH7GW2IA0qQCUHuP4Q/vwN3pZEmBBCFCXyUbcQubBt27YsC21nJe1MspxKu/zs5MmTubrWzp079e1vvvkmy9k/ly9fztVYkH5JWVaznwCjxdHzkpubGwA3btwwmvg5d+5cls/rvj+JiYn8999/eRNgHtK9VoALF4zv92FKn+xIe89NmTLFYBIMsnfPGfu+PNkn7fsgRIn0559w86bWftX0TTEEYGUBK32hnJ32+LWR2jLT4iA5GQYPhuvXtcfft4OGnmYNqdBQlNTdI//ap+24KoQQosgoXNMThChi0iazhg0bZrC4eVorV67k7NmzHD16lNDQUBo3bpzj8Rs3bkyZMmV48OAB27dvJyYmJlvL19K6qfsjCKhRo4bBfrdu3eLIkSM5GiMtJycnfTurGk0A+/fvz/V4pmrRogWnT58mOTmZoKAgevToYbBvYGBgltfq2LEjW7ZsAbTdKHU7FRYWjRo1wtramoSEBP7++2+Sk5OzXAoYFBSUp+Obes/Fx8dna+w9e/YQHx+fZcF83Uw0gObNm5t8bSGKJd3PstJW0K+ZeWMpiqqUhUW9wWcN3I8Gv5fg7z1gZ2fuyHJnyhTYkVIMfmA1GNnRvPEUNp08YfERiI6FQ4cgZUdtIYQQhZ/MCBMihyIjI/njjz8ArbbR3LlzmTRpktF/aZdO5nZWmKWlJYMGDQK0elRffvlljq+Vtq7S+fPnDfabOnWqfqfB3LC3t9cnDkNCQgzWaUpISGDu3Lm5Hs9Uffr00bdnzZplsF9sbKzRuF5++WV9Muabb77J8ezB/GJnZ8fzzz8PaMnItWvXGuwbFBRk8o6RpjL1nps7dy63b982+bp3795lyZIlBp/fvn27fgZl69atcXV1NfnaQhQ7t27B5s1a26+aFMnPqd7PwJiUnTZDDsGo11JrrhVFmzeDrtzC044wr582C0qk6uSZ2t4dYK4ohBBC5IAkwoTIoeXLl+sTQr6+viYXBx8wYIC+VtTKlSuNLgs0ZuLEifrZVVOnTmXGjBlZ1tS6ffs2X3zxRYakRtpZMR9//HGm15g/fz7fffddruJNq1u3boBW0ymz+maJiYm8/vrrnD59Os/GNKZnz57Urq3torZr1y4mT56coU9CQgIjR45MtytkZqpUqcKYMWMAuHPnDt7e3lku21NVlcDAQKZMmZLzF5BNY8eO1bdHjx7NiRMnMvS5dOlStne9NEXae27y5Mk8fvw4Q5/Nmzczfvz4bF/7/fffJyQkJMPx8+fPM2LECP3jcePGZfvaQhQrK1akLut6pYV5YynqZjwP7apo7WXLYeZM88aTU6dOwaBBWiLPwQrW9wPHYrobZm5UKQs1ymltSYQJIUSRIksjhcghU3eLfFKFChV4/vnn2bp1K3fu3OH333+nX79+OY6jcuXKrF69ml69ehEfH4+/vz/z58/H19eXOnXqUKpUKR48eMDZs2fZt28f//zzD0lJSXh5eaW7zogRI/jyyy+JiYlh48aNNGnShCFDhlC5cmVu3rzJhg0b+Ouvv3B1daVBgwbs0C2XyIV33nmHhQsXEh8fz6xZszhz5gx9+/bF0dGRc+fOsXTpUsLCwnj55ZdZvXp1rsczhaWlJQsWLKBTp04kJCTw6aefsmPHDvz8/KhQoQKXL19m8eLFnDp1ij59+rBx48Ysrzd16lSOHDlCYGAgx44do27duvTu3ZsOHTrg6upKQkICN2/e5OjRo+zYsYOIiAi6dOnCRx99VCCv97nnnmP48OEsXryYyMhImjdvzvDhw2nTpg0WFhYcPHiQhQsX8uDBA/r168e6deuAvNlNsU+fPri5uREREcGBAweoW7cuI0eOpHr16kRFRbF161Y2b95MqVKl6Nu3Lxs2bDDput27d2fHjh20bduWYcOG0b59eywtLQkJCWHBggX62Yd9+/bF19c3169DiCJLVVOXRdYqA20NL1EWJrCxhPV+0Hw+XHkAH3wAdetC9+7mjsx09+5B794QHa09Xv481K9q3pgKs06ecP4e7DkE8fGQxZJ8IYQQhYckwoTIgcOHD+tnVLm7u2dIKhkzZMgQtm7dCmgJtdwkwgC8vb3566+/GDRoEBcuXODs2bNMmzbNYH8HBwfKli2b7pirqysrVqzg5ZdfJi4ujqNHj3L06NF0fdzd3dm4cSM//vhjruLVqVOnDnPmzGHUqFEkJyezdetW/fui8+qrrzJhwoQCS4QBtG3bljVr1jBkyBBiYmLYs2cPe/bsSdenffv2LFy40GgizNramq1btzJu3Djmzp1LQkIC69at0yeUMpN2E4SCMH/+fB4+fMi6deuIi4tj3rx5zJs3T/+8hYUFM2bMoGzZsvq4HR0dcz2uvb09S5cu5aWXXuLevXtcuHAhQwLQycmJFStWcODAAZMTYc2bN2fAgAG8+uqr/PLLL/zyyy8Z+nTv3r3Adz8VotA5dAiOH9faw5+RpW95oUJp+G0AtF0AjxLBrz/s2g1FoRbh48fQrx/oZi5PbgK9i0Dc5tSpGvwSCo8eQ0gItG1r7oiEEEKYQJZGCpEDaWeDDRw4MNuzY3r37k2ZMmUACAgIMFos3hStWrUiLCyM5cuX079/f6pVq4aDgwNWVlY4OzvTrFkzXnvtNdasWcONGzdo0KBBpnEdPnyY4cOHU6VKFaytrXnqqado2rQpkydP5ujRo3leWHzkyJH8+++/9O/fn0qVKmFtbY2rqys9evTgjz/+4Oeff86T2UfZ1adPH06ePMno0aOpXr06tra2uLi40LZtW+bOncuuXbvSFfzPio2NDd9//z1nzpxh/PjxtGzZkvLly2NlZUWpUqWoVq0a3bt358svv+TYsWNZ1rfKD9bW1qxdu5b169fTrVs3ypcvj62tLVWrVmXQoEEEBwczbtw47ty5oz/H2dk5T8Zu0aIFR48eZfTo0dSoUQMbGxvKli1L/fr1+fDDDzl69CjdczCbYvDgwYSEhPDqq69SvXp17OzscHZ2pnPnzqxYsYItW7ZgV9QLWQuRW7qfZRYKDG1t3liKk2ddYVlfUICHMdDNW1tuWJglJWk7RO7apT32rQofGd4sRqTo5Jna3v2nuaIQQgiRTYpalAt5FnHNmjVTDx48mKNzT58+TZ06dbJxxifAlRyNlVZCQgLW1ta5vk7+qwpkrO0kCk50dHSezBoShYevr69+VtadO3fyJBmWV/dJUFAQnTp1AuDTTz9l0qRJub5mSZb9nzH5LygoKNuzb0UW4uKgUiWIioJu7rDtVXNHlGeCgmrj5RVm7jBg3kF4U9s5GLdKsCcYqlUzb0yZUVV480346SftcbvyEDASShXvjRPy7D555gcIuwOdWsCugtvlWhQc+fkjTCH3SeGjKMohVVUz3Q5blkaWGHmTFIqLi8baWpIbQpQ0ly5d0u+S2qhRozybESaEMJNNm7QkGMArz5o3luLqjWZwLxYm7oKI69ClM+zYCTUKUS02VYWPPkpNgjVwgt+HFPskWJ7q5KklwvYdkTphQghRRMjSSCGEKOHOnz/PtWvXDD4fHh5Onz599Ducvv766wUVmhAiv+iWRTrbQu9G5o2lOBvfDvzbaO2Ll6BdW3hi12azUVXw94epU7XHnqXhz8FQTj7wzJZ2KZsJxMZDaKh5YxFCCGESmREmhBAl3L///ssrr7xChw4daN++PTVq1MDe3p47d+6wb98+fv31Vx49egRotehGjRpl5oiFELkSEQE7d2rtAdXBtiiUPCiiFAW+eg6sLGDqHrhxEzq0hz+2QLt25osrMRHefhvmz9ceVykFOwaB21Pmi6moapdmV83g3dCypfliEZw9nBEAACAASURBVEIIYRJJhAkhhCAxMZFdu3axS1coORNeXl6sX78eS0vLAoxMCJHnVq+G5GStPUR2Bcx3igJfdoGn7OH9HXD/ATz3nLYccdiwgo/nwQPo3x8CArTH1R0gcDB4Viz4WIqDqmXB3RHCoyF4F/xvvLkjEkIIYYQsjRRCiBKuV69erFixgiFDhlC/fn1cXV2xsbHBwcGBGjVqMHDgQH7//Xd2794ttcGEKA6WL9e+1ioDLTzNGkqJMq4NLOoNlgo8fgzDh8Po0drGBQXlxAltxpIuCda4HPz9iiTBckNRUmeF7QnRlpwKIYQo1GRGmBBClHBlypRh4MCBDBw40Nyh5JiXlxeyC7IQJjh5MrWO0eCntT/iRcEZ/ix4lIX+ayEyFn78EYKCYMUKaJSPtdqSk+Hnn+G99yA2Vjv2YmVYORAc7PNv3JKibRVYcxJuRcH581CzprkjEkIIkQWZESaEEEIIUVKsWJHaHtTCfHGUZJ2qwaHXoaWb9vjkSWjWDN5/H6Kj8368sDDo3BneeENLginApCawcbgkwfJK2jphe/4yXxxCCCFMIokwIYQQQoiSIDk5NRHWujzUKG/eeEqyqmVhz0iY1FFbKpmYCDNnQq1a8M03kLJBSa6Eh2vJr3r14K+U5EzlUrDTFz59EaTeY95pUBEcbLR28E7zxiKEEMIoSYQJIYQQQpQEe/bAlStae3Bd88YitJ0kP/WCg6Ogjbt27OZN+N//wMMD/P3h1Kns1ZxKStKWWg4cCJ6eWkH+pCRtFtjbz8Cpt6Fz/bx/LSWdlQW0rqy19/xr3liEEEIYJTXChBBCCCFKAl2RfCsF+stukYXGs67wz0hYfQI+2wX/RUFkJMyYof2rWRO6dtWWT9atC66uUKaMNsPv/n0tuXnyJOzbB9u3w+3b6a//gjtM7QqNPMzz+kqKNlVgxwU4cxmiosDJydwRCSGEMEASYUIIIYQQxV1cHPz6q9Z+oTK4lDZvPCI9CwUGNgC/erDuFMzdB3+Fa8+dO6f9yw4rBfpXg3faQMsaeR+vyKiFe2o75AB0fd58sQghhMiSJMKKMFVVUWS3JyGEEHlIdt8sprZu1WYPAQxuYN5YhGGWFuBXX/t39g78dhr+OA0HbkJsUtbnuthCmwrg8zT0ehaeciiYmIUmbSLswD+SCBNCiEJMEmFFlIWFBcnJyVhKoVMhhBB5KDk5GQsLKSFa7OiWRTpaw4uNzBuLME2tp+D9dtq/xGQIi4SL9+BGFDyM04rsl7aBquWgenmoVg7kA1LzcSkFNcrB+Xuwf4+5oxFCCJEFSYQVUfb29sTExFCmTBlzhyKEEKIYiYmJwd7e3txhiLwUFQVbtmhtXw+wtzF6yqZQZ6YHVCYiygY3p3j8va/h0/huPgcqDLKygHoVtH+i8GrhnpIIO6JtciCJSSGEKJTkI98iytHRkejoaHOHIYQQopiJjo7G0dHR3GGIvLRpE8THa+2BjY13D3VmwgZPwqNsUVEIj7JlwgZPNoU653OgQhRxLVOWR96KSt2hVQghRKEjibAiqkyZMjx69Ih79+6ZOxQhhBDFxL1793j06JHMNi5u1qzRvpa3g07PGO0+PaAysQnpSy/EJlgyPaByfkQnRPGRtk7Y/n/NF4cQQogsSSKsiLK0tMTDw4PIyEjCw8N58OABSUlJUuRYCCGEyVRVJSkpiQcPHhAeHk5kZCQeHh5Sf7I4iYyEnTu1dj9PbYmdERFRmS+dNHRcCJGicaXU/8b2B5k1FCGEEIZJjbAizMbGhurVq/PgwQOioqK4fv06ycnJ+TpmXFwcdnZ2+TqGKB7kXhGmkPvE/CwsLLC3t8fR0RFXV1dJghU3GzZAYqLW9mti0iluTvGER9lmelwIkQU7K2hUEQ5dhwP7zR2NEEIIAyQRVsRZWlpSrlw5ypUrVyDjBQUF0bix8foiQsi9Ikwh94kQ+Uy3LLKSPbSrYdIp/t7XmLDBM93ySHvrJPy9r+VHhEIULy3dtUTYoVNaEtpK/twSQojCRpZGCiGEEEIURzduQFCQ1u5fHSxN+7XPp/Fdpva9hLvTYxRU3J0eM7XvJdk1UghTNHXTvsbGQ1iYeWMRQgiRKfmIQgghhBCiOFq3DnQlE0xcFqnj0/iuJL6EyImmlVLbh0OgXj3zxSKEECJTMiNMCCGEEKI4Wr1a++pRGlpVM28sQpQUdcuDbcqy4kN7zBuLEEKITEkiTAghhBCiuLl6FYKDtXb/GqAo5o1HiJLC2hIaVtTahw+ZNxYhhBCZkkSYEEIIIURxs3ZtatuvqfniEKIkapKyPDL0TOryZCGEEIWGJMKEEEIIIYob3bLImo7QpIp5YxGipNElwh7Gwdmz5o1FCCFEBpIIE0IIIYQoTi5cgJAQre1XU5ZFClHQ0hbMP3TAfHEIIYTIlCTChBBCCCGKkzVrUtsvNzNfHEKUVPUrgHXKn1mHg80bixBCiAwkESaEEEIIUZzoEmF1naC+m3ljEaIksrXSkmEAhw6aNxYhhBAZSCJMCCGEEKK4OHsWjh7V2n41zRuLECWZvmD+aVBV88YihBAiHUmECSGEEEIUF+vXp7Zfkt0ihTCbxq7a1/uP4PJl88YihBAiHUmECSGEEEIUF+vWaV/rlIU6ruaNRYiSrGHF1PbRI+aLQwghRAZW5g5ACCGEEELkgUuX4NAhre1bw6yhCFHipUmE/fLDRqb8a42bkz3+3rXxaexuxsCEEELIjDAhhBBCiOIg7bLIfk3MF4cQAsraEePmDIDblXOoQHhULBM2HGdTaLh5YxNCiBJOEmFCCCGEEMWBLhFWwxEaym6RQpjbISdtZmad2xf1x2ITkpgeEGaukIQQQiCJMCGEEEKIou/aNfj3X63drwYoinnjEUJwxKk6AB73blAqPlZ/PCIq1tApQgghCoAkwoQQQgghirqNG1Pbvs+aLw4hhN7NalUAsECl9u3UnSPdnOzNFZIQQggkESaEEEIIUfTplkVWLQ3Nqpo3FiEEAF4v2enbuuWR9taW+HvXNldIQgghkESYEEIIIUTRdvMm/P231vatJssihSgkur5oQUIpWwDq3LqIu5M9U/s2kF0jhRDCzKzMHYAQQgghhDBuU2g40wPCiIiKxc3JHn/v2tof1Js2gapqnfo1Nm+QQohUFgrWjcrDv9cYYn+NIeM7mzsiIYQQSCJMCCGEEKLQ2xQazoQNx4lNSAIgPCqWCRuOA+Czbp3Wya0UtKpmrhCFEJlpWBH+vQbH/oPkZLAwvCDHYLJbCCFEnpKlkUIIIYQQhdz0gDB9EkwnNiGJnzYcgN27tQN9PMFClkUKUag0rKh9fRgHV68a7KZLdodHxaKSmuzeFBpeMHEKIUQJIokwIYQQQohCLiIqNtPjDQ4FQVJKgqxfo4ILSAhhmnrlU9snjhvsZijZPT0gLL8iE0KIEksSYUIIIYQQhZybk32mx30u7Nca5e2gfc0CjEgIYZJ6FVLbJw8Z7GYo2W3ouBBCiJyTRJgQQgghRCHn710be2vLdMcqJMXS6vxh7UEfD7CUX+uEKHRcSkHF0lr7RKjBboaS3YaOCyGEyDn5jUkIIYQQopDzaezO1L4NcHeyRwHcneyZUzYCi8QErYNvQ7PGJ4TIgm5W2MnTBrtkluy2t7bE37v2/7N353FWlvf9/18XMzAM+y6yyb6oGFETE5eEGFM0LoymxvySfNU0adO0SX7daGKbfpOm2Wn6aNom/cVvaprNn0kMwa1KFMWoRI2ICyoKAgIDRhFHAUcYZu7vH/e5nUFh1nPOdZbX8/GYx/2Zw+Hc77ZU4M11XXchk0lSVfKpkZIkSWWgYcHEQ58gd9G/p9eRA+Dd/mVZKlnHj4U7NsOTW9Iz/Wpq3vSW7P+3fWqkJBWeRZgkSVK52bsXbr01nRdPgf5v/ou1pBKRrQhrPgCbN8PMw5/n96ayW5JUEG6NlCRJKjcrVsBrr6XzxfPjZpHUuY5Pjnz8yE+OlCQVh0WYJElSubn++vQ6uBbOnhs3i6TOdXxy5LoH4+WQJAEWYZIkSeWlpQVuuimdF02E+gFx80jq3IiBMHFoOj/+SNwskiTPCJMkSSord98NL72Uzg2uBtOhlq8dxdIVk9jRNIAJIw6wZNF2Ghbsjh1Lx42Dxj3w+PrYSSSp6rkiTJIkqZxk2yJrApx3QtwsKinL147iymVTaWyqIyHQ2FTHlcumsnztqNjRdHzunLD1z8LBg3GzSFKVswiTJEkqF0kCy5en8zuPglGD4uZRSVm6YhLNLYc+QbS5pYalKyZFSqTXHZsrwg4cTJ8cKUmKxiJMkiSpXDz8MGzdms4Ns+NmUcnZ0XT48+KO9LqKaO6Y9vnJJ+LlkCRZhEmSJJWNbDUYwOK3xMuhkjRhxIEeva4i6liErX84Xg5JkkWYJElS2cjOB1swCo7x3Ccdasmi7dT3bz3ktfr+rSxZtD1SIr1u9CAYm9vKvP6xuFkkqcpZhEmSJJWDzZvhkUfSefGMuFlUkhoW7OZrF29h4oj9BBImjtjP1y7e4lMjS0W2Kmz9U3FzSFKVq40dQJIkSd2QrQYDaDgxXg6VtIYFuy2+StXcMXD3Vli/OX3wRQixE0lSVbIIkyRJKgdZETZ1CJxwdNwsknouWxH20j7+545H+crvXmRHUzMTRtSzZNEcGhZMjJtPkqqEWyMlSZJK3Ysvwm9+k86Lp7qSRCpHHQ7Mv/aHK2hsaiYBGpuauXLZYyxf2xgvmyRVEYswSZKkUnfTTdDWls4NPi1SKkvz2ouwSc9vPeSHmltaWbrCs8MkqRjcGilJklTqli9Pr6Pq4IzpcbNUmeVrR7F0xSR2NA1gwogDLFm03TO41DtThsPAWnjtIDNefPOTPHc0NUcIJUnVxxVhkiRJpezVV2HFinS+YArU+se3Ylm+dhRXLptKY1MdCYHGpjquXDaV5WtHxY6mclTTD2aPBjhsETZhRH2xE0lSVfJPUpIkSREsX9vI6V+/g2mfu5nTv37Hkc8Huv12aM6tFFl8fPECiqUrJtHcUnPIa80tNSxdMSlSIpW93DlhM3cfWoTV969hyaI5MRJJUtVxa6QkSVKRLV/byJXLHqO5pRVoPywbePOT47JtkQNr4A/mFjNm1dvRNKBHr0tdmpuuCJv4yvNMHxTY/GriUyMlqcgswiRJkops6YqnXi/BMtlh2Yf8Zbi1FW68MZ3/YAIMtoAppgkjDtDYVHfY16VemZOuCAtJwh0XTYb58yMHkqTq49ZISZKkIjvSodhven31ati1K50XzytwKr3RkkXbqe9/aGFZ37+VJYvefL6T1C2zOpwv9/T6eDkkqYpZhEmSJBXZkQ7FftPr2bbIfgEucOVIsTUs2M3XLt7CxBH7CSRMHLGfr128xadGqvdmjW6fNzwWL4ckVTG3RkqSJBXZkkVzDjkjDA5zWHaSwPXXp/Pp42DskCKnFKRlmMWX8mbEQBgzCHa9ChuejJ1GkqqSRZgkSVKRZeeALV3xFDuamg9/WPbjj8Mzz6Tz4tkRUkoqiNmjc0XYhthJJKkqWYRJkiRF0LBgYudPicu2RQIsfkvhA0kqjlmjYPU22PBs7CSSVJU8I0ySJKkUZUXY8SNg5ujO3yupfGQH5j/XBK+8EjeLJFUhizBJkqRSs20brFmTzg0z42aRlF8dD8zfuDFeDkmqUhZhkiRJpeaGG9rnxSfEyyEp/7IVYeCB+ZIUgUWYJElSqcm2RU4cBCdPiptFUn51XBG24bF4OSSpSlmESZIklZKmJli1Kp0bpkIIMdNIyrchA+DoIem8YX3cLJJUhSzCJEmSSsn//A8cPJjObouUKlO2KuxpzwiTpGKzCJMkSSol2bbI4f3hXR6UL1Wk7JywDVvj5pCkKmQRJkmSVCr274dbbknn8ybDgJq4eSQVRlaEvbgHXnopbhZJqjIWYZIkSaXijjtg7950Xnxc3CySCmdmhydHbtoUL4ckVSGLMEmSpFKRbYsc0A/OsQiTKtaMDkXYM0/HyyFJVcgiTJIkqRS0tcENN6Tze46GYXVx80gqnGkj2udNT8TLIUlVyCJMkiSpFDzwADz3XDo3zI2bRVJhDR8Io+vTeZMrwiSpmCzCJEmSSkG2LRLgghPi5ZBUHNNHptdnPCNMkorJIkySJKkUZEXY28fC0cPiZpFUeNk5YZu2xs0hSVXGIkySJCm29evhqafSuWFm3CySimN67pywrS9AS0vcLJJURSzCJEmSYrv++vZ58YnxckgqnmxrZFsCW10VJknFYhEmSZIUW7Ytcs4wmDsubhZJxZFtjQR4ZmO8HJJUZSzCJEmSYtq5E+6/P50bpsfNIql4shVhAJuejJdDkqqMRZgkSVJMN94ISZLOi31apFQ1Jg6F/rm/jm1aHzeLJFURizBJkqSYsm2RRw2EU6dGjSKpiGr6wdTcgflujZSkorEIkyRJimXPHli5Mp0XHwP9Qtw8koorOyds07Nxc0hSFbEIkyRJiuXWW+HAgXRumB83i6Tim55bEbapsX2LtCSpoCzCJEmSYsm2RQ6phbPmxM0iqfiyA/NfaYYXX4ybRZKqhEWYJElSDC0tcPPN6XzuJKirjZtHUvFN6/DkyC1bosWQpGpiESZJkhTDXXfByy+nc8O8uFkkxZEdlg+wZVO8HJJURSzCJEmSYsi2RdYGeJ/ng0lVqWMR9uxT8XJIUhWxCJMkSSq2JGkvwhaOhxH1cfNIimPkQBgyIJ23bIibRZKqhEWYJElSsa1ZA42N6dwwO24WSfGE0L4q7Nln42aRpCphESZJklRs11/fPl/4lng5JMWXFWFbtsXNIUlVwiJMkiSp2LJtkSePhskjO3+vpMp2zPD0umVnum1aklRQFmGSJEnF9MwzsG5dOjdMj5tFUnzZirA9r0FTU9wsklQFLMIkSZKKqeO2yMVui5SqXscnR27ZEi2GJFULizBJkqRi+tWv0uuMoXD8hLhZJMWXbY0EeHZTvBySVCUswiRJkorl+efh3nvTueGY9IlxkqrbISvCnoqXQ5KqhEWYJElSsdx4Y/th2A1ui5QEjBkEg/qn85YNcbNIUhWwCJMkSSqW7GmRYwfCOzwoXxLpytBse+Szz8bNIklVwCJMkiSpGPbuhdtuS+cLJ0ONfwyTlJNtj9yyNW4OSaoC/glMkiSpGFasgP3707nhuLhZJJWW14uwnXFzSFIVsAiTJEkqhmxb5OBaOPvYuFkklZZsa2TTq/Dyy3GzSFKFswiTJEkqtJYWuOmmdD53IgzsHzePpNLS8cmRnhMmSQVlESZJklRod90FTU3p3DA3bhZJpWfy8PZ5m+eESVIhWYRJkiQVWrYtsjbA+06Im0VS6Zk8rH3etjFeDkmqAhZhkiRJhZQk7UXYwvEwclDcPJJKz9FDoSak87Zn4maRpApnESZJklRIa9ZAY2M6N8yKm0VSaartBxOGpvM2zwiTpEKyCJMkSSqkbDUYwOIT4+WQVNqyc8K2boubQ5IqnEWYJElSIWVF2FvHwKSRcbNIKl3ZOWHbnoubQ5IqnEWYJElSoWzYAI8/ns4N0+JmkVTasiJs+y5oa4ubRZIqmEWYJElSoXTcFtngtkhJnZiS2xp54CC88ELcLJJUwSzCJEmSCiUrwmYNg3lHx80iqbRlZ4QBbPOcMEkqFIswSZKkQnjuOfjtb9O54RgIIW4eSaUt2xoJsHVzvBySVOEswiRJkgrhxhshSdK54YS4WSSVvkNWhG2Ml0OSKpxFmCRJUiFk2yKPGghvnxE3i6TSN3YQ1NWk87ZNcbNIUgWzCJMkScq3PXvg9tvTefEU6Oe2SEldCKF9Vdi2rXGzSFIFswiTJEnKt1tugQMH0rnh+LhZJJWP7Jywrdvj5pCkCmYRJkmSlG/ZtsghtXDW3LhZJJWP11eEPRc3hyRVMIswSZKkfDpwAG6+OZ3fNwnq+sfNI6l8ZCvCdr4EBw/GzSJJFcoiTJIkKZ9WrYJXXknnBleDSeqBKbkVYW0J7NgRN4skVSiLMEmSpHxatiy99u8H554QN4uk8jJpWPu8bVu8HJJUwSzCJEmS8qW1FX71q3R+z9Ewoj5uHknlZeLQ9rnRJ0dKUiFYhEmSJOXLvffC88+n8/vnxM0iqfxM7LAirHFTvBySVMEswiRJkvLll79Mr/0CLD4xbhZJ5Wd0PdTVpHPjlqhRJKlSWYRJkiTlQ1tb+/lg7zoKxg7t/P2S9EYhwITcfzsat8fNIkkVyiJMkiQpH373O9ie+4vr+2fHzSKpfGXbIxt3xs0hSRXKIkySJCkfsm2RABctiJdDUnnLDsxvfC5uDkmqUBZhkiRJfZUk7UXYaeNgwoi4eSSVr9eLsBfT/7ZIkvLKIkySJKmvHnkENuWe8Pb+mXGzSCpv2dbI/Qdh9+64WSSpAlmESZIk9VXHbZEXuy1SUh9M7PCgjcbGeDkkqUJZhEmSJPXVddel15NHw9QxcbNIKm/ZijCAxm3xckhShbIIkyRJ6osnnoD169P5/TPiZpFU/g5ZEbYpXg5JqlAWYZIkSX3RcVvk+90WKamPJnQswrZEiyFJlcoiTJIkqS+yIuz4ETB7fNwskspfXS2MGZTObo2UpLyzCJMkSeqtZ55JnxgJ8P7pcbNIqhzZ9kgPy5ekvLMIkyRJ6i23RUoqhOzA/Mbn4uaQpApkESZJktRbWRE2axgcPzFuFkmV4/UVYS/EzSFJFcgiTJIkqTe2bYMHHkjn90+DEOLmkVQ5siJs1x7Yvz9uFkmqMBZhkiRJvbFsWfvstkhJ+ZRtjQTYuTNeDkmqQBZhkiRJvfHzn6fXYwbDyVPiZpFUWbIVYQCN2+PlkKQKZBEmSZLUU9u2werV6fyB6W6LlJRfHVeENW6JFkOSKpFFmCRJUk/94hft86Unx8shqTIdsiJsc7wcklSBLMIkSZJ66mc/S68zhsJJbouUlGej6qGuJp0bn42bRZIqjEWYJElST2ze3P60yEvdFimpAEJo3x7pGWGSlFcWYZIkST3RcVvkB9wWKalAsu2RjT41UpLyySJMkiSpJ7JtkXOGwQmT4maRVLleXxH2+7g5JKnCWIRJkiR118aN8NBD6XzpTLdFSiqc11eEvQhJEjeLJFUQizBJkqTuylaDAXzgpHg5JFW+rAjbfxB2746bRZIqiEWYJElSd/385+n1uBFw3MS4WSRVtglD2+fGxng5JKnCWIRJkiR1x/r18Oij6XzpzLhZJFW+7IwwgMZt8XJIUoWxCJMkSeqOjtsiL/VpkZIKbGLHFWGb4+WQpApjESZJktSVJGkvwk4cBbPHx80jqfIdsjVyS7QYklRpLMIkSZK68vjj8OST6XzprLhZJFWHuloYMyidG7fGzSJJFcQiTJIkqSuHPC3SbZGSiiTbHtm4I24OSaogFmGSJEmdSRK45pp0PmU0TB8bN4+k6pEdmN+4M24OSaogFmGSJEmdue8+2LQpnT88N24WSdXl9RVhL8TNIUkVxCJMkiSpMz/9aXrtF+CDb4ubRVJ1yYqwXXtg//64WSSpQliESZIkHUlLS/v5YO85GsYPi5tHUnWZ2OG/OTs8J0yS8sEiTJIk6Uhuuw127UrnDx8bN4uk6pOtCANobIyXQ5IqiEWYJEnSkWTbIutr4CKfFimpyDquCGt8Nl4OSaogFmGSJEmHs3cvLF+ezhdOhmED4+aRVH0OWRG2OV4OSaogFmGSJEmHc/318Oqr6fzhE+JmkVSdRtVDXU06uyJMkvLCIkySJOlwsm2Ro+pg0fy4WSRVpxDat0du3xY3iyRVCIswSZKkN3r+efj1r9P5A1NhQG3UOJKq2KRcEda4M24OSaoQFmGSJElv9LOfQWtrOn/YQ/IlRZSdE9b4XNwcklQhLMIkSZLe6Cc/Sa/HDIbTZsbNIqm6ZUXYjt2QJHGzSFIFsAiTJEnq6Ikn4IEH0vmy2dAvxM0jqbplZ4QdOAi7dsXNIkkVwCJMkiSpo//+7/b58ndEiyFJQPuKMIDGxng5JKlCWIRJkiRlDh6EH/84nc88CmaMjZtHkrIVYQCN2+PlkKQKYREmSZKU+fWv4bncgdRXHBc3iyTBG1aEbYkWQ5IqhUWYJElS5gc/SK+DauGSt8bNIkkAR1uESVI+WYRJkiQBvPgi3HBDOv/hMTB0YNw8kgQwoAbGDU7nxq1xs0hSBbAIkyRJArj2WjhwIJ2vODluFknqKNse6WH5ktRnFmGSJEnQ/rTIYwbDu+ZGjSJJh8gOzN++M24OSaoAFmGSJEnr1sGDD6bz5bOhX4ibR5I6en1F2PNxc0hSBbAIkyRJylaDAVz+jmgxJOmwJuVWhL20D5qb42aRpDJnESZJkqrb/v3wwx+m8zuPgulj4+aRpDea2PHJkZ4TJkl9YREmSZKq2/LlsGtXOv/xW+JmkaTDyc4IA4swSeojizBJklTdvve99DqqDv7wlLhZJOlwDlkRti1eDkmqABZhkiSpej39NNx5ZzpfPhMG9o+bR5IO55AVYZvj5ZCkCmARJkmSqtf/+T/t85+cHi+HJHVmeB0MyhX1jVuiRpGkcmcRJkmSqtP+/e1Pi3znUTD36KhxJOmIQmjfHtm4PW4WSSpzFmGSJKk6LVvWfkj+JzwkX1KJy7ZHNu6Im0OSypxFmCRJqk7ZIfmj6+D9HpIvqcS9viLs93FzSFKZswiTJEnVZ/16uOuudL5iFtR5SL6kEpcVYTt2Q1tb3CySVMYswiRJUvU55JD80+LlkKTuyrZGHmyF55+Pm0WSyphFmCRJqi779sHVV6fzwvEw20PyJZWBScPa58bGeDkkqcyVZREWUpeGEG4KIWwPIewPIewMIawMIXw8hFBboPvWhBAuCSFcG0LYGELYG0J4JYSwIYRwSwjhb0MIswpxb0mSlCc//Sk0NaXzp06Om0WSuivbGgk+OVKS+qAghVEhhRBGAtcBUSru8wAAIABJREFUZ73hh8bnvs4CPhlCuChJkq15vO8C4L+ABYf54aHATOAcYALwF/m6ryRJyqMkgX/7t3SePBgWH+63dUkqQRM7rgjbEi2GJJW7sirCQggDgOuBM3MvbQOuAjYCk4A/AuYBJwG3hBDekSTJK3m472nALUD2u8/vgBuBzbnvJ+fu+76+3kuSJBXQnXfC44+n858fB7U1cfNIUneNHwL9ArQlFmGS1AdlVYQBn6S9BHsIODtJkpeyHwwh/AewHFgEHAv8A7CkLzcMIRwF3EBagr0GfDRJkmuP8N4aYFxf7idJkgro299Or/U18PEz4maRpJ6o7QdHDYade6ExbxtfJKnqlM0ZYblzv/4+920CXNaxBANIkuQ14DJgX+6lT4cQRvfx1t8Gss+44kglWO7+rUmS7Ozj/SRJUiFs2gQ33pjOH5kBo4d2/n5JKjXZ9kgPy5ekXiubIoz07K+xuXllkiSPH+5NSZI8D2RlVR2wuLc3DCEcA1yS+/Y3SZL8rLefJUmSIvvOd9IzwgA+7WowSWUoOzC/8bm4OSSpjJVTEfYHHeZbu3hvxx8/pw/3vJz2/x19vw+fI0mSYnr5Zfh+7rfyd4+H+ZPj5pGk3ni9CHuhzx+1fG0jp3/9DqZ97mZO//odLF/rKjNJ1aGcirDjO8xrunjvg0f4eT31zg7zHSGE0SGEfwwhPBZC2BtCeCWE8HgI4d9DCLP7cB9JklRIV10Fr+Sen/OXb4ubRZJ6K9sa+fKrsHdvrz9m+dpGrlz2GI1NzSRAY1MzVy57zDJMUlUopyKsY9G0pYv3bgdac/OsEELo5T1PyV1fBo4B1gH/m7RcGwwMJT2U/1PAEyGEPh3ML0mSCmD/fvjXf03nY4fDeSfGzSNJvTWxw9mGfTgnbOmKp2huaT3kteaWVpaueKrXnylJ5aKcirARHeZdnb0xSZKDQO6ffaklLa16JIRQBwzPfdsK3ASMBzYBnwc+SPoUy2wbZg3wzRDC/9vTe0mSpAK65hrYsSOdl5wM/Xr772OSFFm2Igz6VITtaGru0euSVEnKqQgb0mF+rRvv7/hf8d48Fmpkh3lU7vtbgeOSJPlKkiQ/S5Lk/0uS5FzgTzu895shhEm9uJ8kScq3tjb45jfTeUI9fOgdcfNIUl9M6lCEbd/c64+ZMKK+R69LUiUJSfb0pBIXQjgA9M992z+36quz9zcCE3LfTkiSZGcP7zcB6PjPLHuAaUmSvHiE918HvD/37VeTJPn7I7zvT4A/ATjqqKNOvvbaaw/3tpK1d+9ehgwZ0vUbVfX8taLu8NeJuqu3v1ZG33sv8z//eQCe+dNL2XZprx8mrTKxd28dQ4bsjx1DJa5cf530a36Nd77vjwDY9LEr2PqRy3v1Oat3tPDf6w5woK39tQH94IrjB3DahP5H/olVyD+rqDv8dVJ63v3ud69JkuSUw/1YbbHD9MFe2ldpDcx935mO/5yxpxf3e+PPWXakEiznKtqLsLOBwxZhSZJclXsvp5xySrJw4cJeRItn1apVlFtmxeGvFXWHv07UXb36tZIk8Pe5346H92fGN45hxjDPv6l0q1bNYeFC/++szpX1r5NR9bC7mek1zUzv5e+hC4Fj1zaydMVT7GhqZsKIepYsmkPDgon5TFoR/LOKusNfJ+WlnIqwJtqLsNF0UoSFEGqBbN3wQWBfL+63N/dzs/8dPdTF+zs+yXJGL+4nSZLy6Y47YPXqdP7kPBjmlh9JFWDKcNjdDNu29+ljGhZMtPiSVJXK6YywpzvMU7t47yTSw+sBNiS92P+Z+zkbOrz0chc/peOPDz/iuyRJUuElCXzxi+k8qBb+6j1R40hS3kzO/Xv/1t4fli9J1aycirB1HebD7vM8wo+vO+K7uvZoh3nYEd+V6lh+dVWaSZKkQlq5Eu65J50/NQ/GdvXbuCSViSm5v3Zs+33cHJJUpsqpCFvRYV7UxXvP6TDf2od73tJhPrmL93b88aeP+C5JklRYHVeDDa6Fv3E1mKQKkq0Ie6UZXvbf3yWpp8qpCLsTeCE3nx1COO5wbwohjAM+mPv2NeD6PtzzeuDV3HxRCGF0J+/9kw5zX8o3SZLUFytXwr33pvOn5sFYTyyQVEGmdPhv2rZt8XJIUpkqmyIsSZKDwFdy3wbgRyGEkR3fE0IYCPwQGJx76T+O9KTHEMJ/hxCS3NcXj3DPJuBbuW+HAT/O3eONn/UntD8xch/wn93+H0ySJOVPksAXvpDOg2vhr10NJqnCdCzCtj4bL4cklalyemokpAXT+4EzgZOAR0II3wM2kh6Q/zFgXu69TwBfzsM9vw6cS3ru2LnAuhDC1bl7jgQW517PfCJJkhfe9CmSJKnwrr++/UmRnzrW1WCSKs/kjivCNsXLIUllqqyKsCRJDoQQFgPXAWcBkzl82fUQcFGSJH3eNJ8kyashhPcBPwcWAjNoX5nWUTPwySRJftrXe0qSpF5oaYHPfjadRw6Az743bh5JKoQJQ6FfgLYEtm6MnUaSyk7ZbI3MJEnyEnA26TlgNwM7gAPA74E7SM/qOjVJkq15vOcLpMXbB0jPDduWu+fLwFrga8DMJEl+mK97SpKkHvr+9+Hp3PNqPn8SjBwSN48kFUJtv7QMA9jm1khJ6qmyWhGWSZIkAX6W++rtZ1wBXNHDe/4i9yVJkkrJnj3tT4qcNgT+/KyocSSpoKYMh+2vwFYPy5ekniq7FWGSJElv8s1vwvPPp/NXT4O6/nHzSFIhTR6WXrftiJtDksqQRZgkSSpvmzfDt3IPeX7raLj01Lh5JKnQsidHbt8FbW1xs0hSmbEIkyRJ5StJ4NOfhubm9Pt/ORuCf7yRVOGyFWEHDravhpUkdYt/UpQkSeVr+XK4+eZ0vmImnDE3bh5JKoZsRRjANs8Jk6SesAiTJEnlac8e+Mxn0nlUHXzzvLh5JKlYJncowp7dHC+HJJUhizBJklSevvhF2L49nb/5dhg7ImocSSqaqR3+e/fsU/FySFIZsgiTJEnl57774F//NZ1PGwsffWfcPJJUTCMHwtAB6bx5Q9wsklRmLMIkSVJ52bcPLrssfVLagH7wvfOhn3+kkVRFQoBpI9N5s1sjJakn/FOjJEkqL5/9LGxIV0B8/bTLOP2m81m+dlTkUJJUZNNy2yM3b42bQ5LKTG3sAJIkSd22fDl85zsAPDDpWK5660W0NdVw5bKpADQs2B0xnCQVUVaEbXkOkiRdJSZJ6pIrwiRJUnnYtAmuuAKAVwYM4q/P+yva+tUA0NxSw9IVkyKGk6Qiyw7Mbz4Azz8fN4sklRGLMEmSVPL67d8Pl1wCL78MwJL3/QXbRow/5D07mgbEiCZJcWRnhIHnhElSD1iESZKk0pYkzP3GN+ChhwD42envY8Wc0970tgkjDhQ7mSTFk22NBNj8TLwcklRmLMIkSVJp+9KXGHfnnen8rqMY+C9nUd+/9ZC31PdvZcmi7RHCSVIkUzsWYevj5ZCkMuNh+ZIkqXR9//vwxS+m84wh8MsPsXj0HpL+W1i6YhI7mgYwYcQBliza7kH5kqrL0DoYXQ8vNsPmDbHTSFLZsAiTJEml6brr4BOfAKBl6GD633gpjB4GpE+HtPiSVPWmjUyLsC1bYieRpLLh1khJklR6li2DD30I2tpgcC2Pff1vYJ5PhZSkQ2TnhG3eGjeHJJURizBJklRarrkGPvABaGmBAf1g+YW8cuyc2KkkqfRk54RtfR5aWzt/ryQJsAiTJEmlIkngn/8ZPvKR9C909TVw44Vw9vzYySSpNGUrwlpaobExbhZJKhMWYZIkKb79++FP/xSWLEkLsSG1cMvF8AdviZ1MkkrXtJHts+eESVK3WIRJkqS4tmyBM8+Eq65Kv588CO79MLzr2KixJKnkZSvCADY9HS+HJJURizBJkhRHksDVV8OCBfC736WvvW003P9xOGFq1GiSVBamjoB+IZ03roubRZLKhEWYJEkqvk2b4L3vhY99DJqa0tc+cyzc/adw9MjOf64kKVVXC1OGp/NGV4RJUndYhEmSpOJ58UX427+F446DlSvT1ybUww3nw7cvgQG1cfNJUrmZOSq9bngmbg5JKhP+aVOSJBXeCy/Af/4nfOtb8Mor7a9/YjZ84wIYPiReNkkqZ7NGwe2bYMO2dMt5CLETSVJJswiTJEmFkSTwwAPw3e/CtdfCgQPtP3bGOPj6e+D02Xm73fK1o1i6YhI7mgYwYcQBlizaTsOC3Xn7fEkqSbNyK8L2NKf/6DBuXNw8klTiLMIkSVL+tLXBmjVw3XXp16ZNh/74glHw5TPg3BPzumph+dpRXLlsKs0tNQA0NtVx5bKpAJZhkipbtjUSYMMGizBJ6oJFmCRJ6r3mZnj00fSpj3feCatWwe43FE81AS4+Bj51Kpw5pyDbdpaumPR6CfZ6tJYalq6YZBEmqbLNGt0+b3wKTj89XhZJKgMWYZIkqWt79sDGjenXhg3w9NPw0EPwxBPQ2nr4n3PaOLhkJnzgrTBhREHj7Wga0KPXJaliTBsB/QK0JbBhXew0klTyLMIkSVJq3772kuvppw8tvp5/vuuff1Q9nDUezpoG58yHSYUtvzqaMOIAjU11h31dkipaXS1MGQ5bmmDD+thpJKnkWYRJklRtWlvTomvNmvTrkUfS7xsbu/8Z4wbCCSPhpLFw8mQ4aSrMGB3taWVLFm0/5IwwgPr+rSxZtD1KHkkqqlmj0iJs4zOxk0hSybMIkySp0rW1pdsYV65Mv1avTld/deXoepg5LPc1CmaNhZnjYcYYGPbm1VcxZeeA+dRISVVp5ii4bRNs2JY+sTfSP0pIUjmwCJMkqRK1tcG998LPfw6//CXs3Hn49w3oB/NHwrEjYNYYmD0OZo+HmaNhaGmVXV1pWLDb4ktSdZqVe3LknmZ44QWfHClJnbAIkySpkuzeDVdfDd/9Lmze/OYfnzEUzjoaTj0GTjoGjhsPA2re/D5JUvno+OTIDRsswiSpExZhkiRVgp074ctfTkuw115rf70mwLvHwyXzYNFxcMyoeBklSYUxs8N/2zc8CaefHi+LJJU4izBJksrZSy/BN74B//Zv0Nzc/vrc4fDnJ8AH3wZjhsTLJ0kqvOkj03/4aE1g/cOx00hSSbMIkySpHCUJXHstfOYzsGtX++uLJsCSM+CsuR6WLEnVYkBNuirsqRfhycdjp5GkkmYRJklSudm+HT75SbjppvbXThsLXzsb3jk7Xi5JUjzzxuaKsA2xk0hSSbMIkySpnNxwA1x+OTQ1pd+PHQj/vhA+8DZXgElSNZs3BpYDz+yA/fuhrrye/CtJxdIvdgBJktQNra3wuc/B4sXtJdj/mgFP/jlceqolmCRVu7lj0mtbAhs3xs0iSSXMFWGSJJW6ffvgQx9KV4MBDO0PV58Nf/i2uLkkSaVj3pj2+ckn4Ljj4mWRpBJmESZJUinbtQvOOQfWrEm/P244LP8gzBwfN5ckqbTM7ViEPQxcEi2KJJUyizBJkopg+dpGlq54ih1NzUwYUc+SRXNoWDCx85/03HNw9tnweO4JYO89Gn7xYRg+uPCBJUnlZWgdTBoG21+BJx+NnUaSSpZFmCRJBbZ8bSNXLnuM5pZWABqbmrly2WMARy7Dfv97WLgQnnoq/f7D0+EH/w/097duSdIRzBuTK8Keip1EkkqWh+VLklRgS1c89XoJlmluaWXpiiP8RaWpKd0OmZVgH58NP/yQJZgkqXPZOWFPPQttbXGzSFKJsgiTJKnAdjQ1d//1116DCy+Ehx9Ov/+jWfC9S6GmpoAJJUkVYd7Y9Np8AJ59Nm4WSSpRFmGSJBXYhBH13Xs9SeBjH4O7706/v3hKWoL187drSVI3dHxy5Pon4+WQpBLmn6wlSSqwJYvmUN//0BVd9f1rWLJozqFv/MpX4Jpr0vnMcXDNR6DWlWCSpG7KVoQBPLE2Xg5JKmEeNiJJUoFlB+J3+tTIm2+Gf/iHdJ42BJZ9GOr6R0grSSpbYwelXy+8Co89FDuNJJUkizBJkoqgYcHEIz8hcutWuOyydB5SCzddCmOGFS+cJKkyhADzj4I7NsOj62KnkaSS5NZISZJiammBSy+F3bvT77//Hjh2UtxMkqTyNX9cen1iMxw8GDeLJJUgizBJkmL68pfhvvvS+c/mwqVvj5tHklTeTjgqve5vgY0b42aRpBJkESZJUiwPPJAekA8wfwR86+K4eSRJ5S9bEQbw6CPxckhSibIIkyQphubm9Fyw1lbo3w9+3AADPRxfktRHx42DkJsf+13UKJJUiizCJEmK4Z/+CZ56Kp2/dAq85Zi4eSRJlWFQf5g5Kp0fezhuFkkqQRZhkiQV27p1sHRpOp8yGpb8Qdw8kqTKMj93Ttijj8fNIUklyCJMkqRiamuDT3wifZJXvwBXnQ81NbFTSZIqyQm5c8I2Pwd79sTNIkklxiJMkqRi+tGPYPXqdP6L42DB1KhxJEkVKFsRBukqZEnS6yzCJEkqln374O/+Lp0nDoJ/PC9uHklSZTqhQxH22EPxckhSCbIIkySpWJYuhZ070/mrp8OQgXHzSJIq0/SRMDj3JOKH74+bRZJKjEWYJEnF0NjYfkD+SaPgI++Im0eSVLn6BThxfDo/5IowSerIIkySpGL4/Ofh1VfT+VvvTf+SIklSoZx0dHp95On0AS2SJMAiTJKkwnv4YfjhD9N58WRYODduHklS5cuKsNdaYP36uFkkqYRYhEmSVGhf/CIkCdQE+Oa5sdNIkqpBVoQBPPRgvBySVGIswiRJKqS1a+H669P5ipkw++jO3y9JUj7MGwN1Nem85p64WSSphFiESZJUSF/6UnqtCfB3742bRZJUPfrXwAlHpfNDa+JmkaQSYhEmSVKhPPwwLF+ezpfPhOlj4+aRJFWXbHvk2vXQ1hY3iySVCIswSZIKpeNqsL93NZgkqciyImzfa7BhQ9wsklQiLMIkSSqERx6BX/0qnS9zNZgkKQIPzJekN7EIkySpEJYuTa81Af7+7LhZJEnVaf44qM39le/Bu+NmkaQSYREmSVK+bdsG116bzpdMhRnjosaRJFWpulo4cXw6339f3CySVCIswiRJyrdvfxtaW9P5r98ZN4skqbqdOjG9rnkCWlriZpGkEmARJklSPr38Mlx1VTq/6yg4ZWrUOJKkKvf2Sen1tRZ49NG4WSSpBFiESZKUT9//PuzZk85/c1rcLJIkZSvCAO5fHS+HJJUIizBJkvKlpSXdFgkwZxi8b37cPJIkzRwFo+rT+b5VUaNIUimwCJMkKV+uuy49KB/gr0+GfiFuHkmSQmhfFXb/g3GzSFIJsAiTJClfvvOd9DqmDj7y9rhZJEnKZEXY01th9+64WSQpMoswSZLy4ZFH4N570/njc6F+QNw8kiRlsgPzAR54IF4OSSoBFmGSJOXDf/5neg3AJ86IGkWSpEO8rcOB+ff9Jl4OSSoBFmGSJPXVyy/DT36SzudNgqlj4uaRJKmjkfUwZ3Q6339P3CySFJlFmCRJffXjH8O+fen8Z6fGzSJJ0uGcmtseef9aSJK4WSQpIoswSZL6Ikngu99N52lDYNFxcfNIknQ4b89tj3xpL2zYEDeLJEVkESZJUl/cdRc8+WQ6f/J46Bfi5pEk6XBO7XBg/v2/jZdDkiKzCJMkqS+uuiq91vWDj54eN4skSUcyfxzU16bz6pVxs0hSRBZhkiT11ksvwbJl6XzxMTBmSNw8kiQdSf+a9lVh99wbN4skRVTb2Q+GEJ4uwD2TJEnmFOBzJUkqrmuugf370/ljb4ubRZKkrpwxGVZtgXWbYPduGDUqdiJJKrpOizBgJpDPR4qEPH+eJEnx/Nd/pdepQ+Dd/huPJKnEnXkMcHc633sPXHBh1DiSFEN3tkaGPH5JklQZ1q5NvwA+OtdD8iVJpe8dk9p/v7rHc8IkVaeuVoTNKkoKSZLKzdVXp9cAXPGOqFEkSeqWoXWwYDys2Ql3r4qdRpKi6LQIS5LkmWIFkSSpbLz2Gvz0p+n83gkwxTNWJEll4owpaRH24OPQ3Az19bETSVJR+dRISZJ6avny9ImRAH90YtwskiT1xJlT0mtLK9x/f9wskhRBp0VYCOEvQghjihVGkqSy8KMfpdeRA6DBIkySVEbOmNI+3+05YZKqT1crwv4FaAwhLAshXBBCqClGKEmSStbzz8Ovf53Ol06Huv5x80iS1BNHDYHZo9P5njvjZpGkCLqzNbIWWAwsB7aHEL4ZQji2sLEkSSpRP/sZtLam80dOiZtFkqTeOGNyel39EBw8GDeLJBVZV0XYj4FXSZ+JFYBxwF8Dj4UQ7g8hfCKEMLzAGSVJKh3ZIflTh8Bp0+NmkSSpN848Jr3ubYZHHombRZKKrNMiLEmSy4HxwMeA3+RezkqxU4DvAjtDCD8NIZxdyKCSJEW3YUP7wcIfngUhxM0jSVJvnNnxnDC3R0qqLl1ujUySZF+SJD9IkmQhMAP4J2AL7YXYQOCDwIoQwrMhhH8MIfhP5JKkynPNNe3zh98WL4ckSX0xfSQcPSSdf/PruFkkqci6c0bY65Ik2ZIkyReSJJkOnEW6dXIf7aXYJODzwIYQwqoQwmUhhEH5Di1JUtElCfzkJ+l80iiYNz5uHkmSeisEWDg1ne+6D9raosaRpGLqURHWUZIkqzpsnfw4cHfuh7JS7EzgB8BzIYTvhxDO6GtYSZKieeAB2LgxnT88L24WSZL6KivCdu+BdeuiRpGkYup1EZbJbZ28OkmSdwEzgS8Dz9JeiA0BPgrcFUJ4OoRwZV/vKUlS0WWH5PcL8MG3xs0iSVJfZUUYwKrbo8WQpGLrcxHWUZIkm5Mk+d9JkkwD3gv8lEOfOpkVZZIklY+WFrj22nQ+azxM8IHJkqQyN2tU+zlhq1bEzSJJRZTXIqyjJElWJknyv4CLgZ1AUqh7SZJUULffDi+8kM4fmR83iyRJ+XDIOWG/9ZwwSVWjIEVYCGFiCOHKEMJ64BbSc8QkSSpP2dMiB9bARQviZpEkKV88J0xSFarN1weFEAaSrv66nPSJklnJFnLXV4Ffkh6gL0lSeXjtNbjhhnQ+fxIMGxg3jyRJ+XLIOWEr4YQTokWRpGLp84qwEMLpIYSrSLc//hg4G6ih/Vyw1cAfA+OTJLk8SZJVfb2nJElF8+tfwyuvpPMlx8fNIklSPs0aBROGpvOdt8TNIklF0qsVYSGEycBlpKu/ZmQvd3jLDuBHwA+SJNnQp4SSJMX0i1+k1/oaOM/zwSRJFSQ7J+yax+Cu+9JzwvoV7BhpSSoJ3f6vXAihPoTwkRDC7cBm4EukJVi28ms/8AvgfcCUJEn+zhJMklTWOm6LPG8SDK6Lm0eSpHxbeEx6fWkPPPZY3CySVARdrggLIZwJXAH8ITAke7nDW9aSnvv10yRJXsp3QEmSojlkW6SrwSRJFeiN54S95S3RokhSMXS6IiyEsBFYRVqEDaV99dcu4NvAiUmSnJwkyX9YgkmSKs4h2yI9H0ySVIFmdjgnbNWtcbNIUhF0tTVyeu4agIPATcD7gQlJkvxlkiSPFjKcJEnR7N/fYVvkZLdFSpIqU3ZOGMBdv03PCZOkCtadM8LWA38LTE6S5MIkSX6VJMnBAueSJCkunxYpSaoWr58TttdzwiRVvK7OCHtHkiT3FyWJJEml5Oc/T69ui5QkVbpDzgm73XPCJFW0TleEWYJJkqrSIdsifVqkJKnCzRwFE3PnhN1xS9wsklRgXT418nBCCP2Ak4C3AmOB0UAC7AZeAB4AHkqSJMlTTkmSisdtkZKkahICnDUNfvworPotHDwItb36q6Iklbwe/dcthDAR+BxwGTCki7fvCSH8N/DNJEl29C6eJEkRHPK0yPlxs0iSVAxnT0+LsFdehQcfhLe/PXYiSSqI7hyWD0AI4VJgA/BnwFDSJ0l29jUM+DSwIYTwgfzGliSpQFpa4MYb0/lct0VKkqrEe6a1z7e7PVJS5erWirAQwseB72Xf5q6vAL8DtgAvkZZqI4CppFsms7KsHvj/QwhDkyT5r3wFlySpIH7zG2hqSueL5sbNIklSsUwcBnPHwPpdsPIW+Pw/xk4kSQXRZREWQpgF/CvtBdj9wFeAm490BlgIIQAXAFcCp+Z+7rdDCHclSbIxH8ElSSqIX/0qvdYGOO+EuFkkSSqms6elRdjqtbBvHwweHDuRJOVdd7ZGfgUYRHoY/n8AZyRJclNnB+EnqRuAM4Dv5F6uz32WJEmlKUlg+fJ0XjgeRg6Km0eSpGJ6z/T0euAg3HNP3CySVCCdFmEhhLHAYtIS7NdJknwmSZLW7n54kiStSZJ8GriNdFXY4hDCmL4EliSpYNasgcbGdG6YHTeLJEnFtnAq9MttBLr95qhRJKlQuloRdiHQPzd/rg/3+dvctX/uMyVJKj3ZtkiAxSfGyyFJUgwjBsJbJ6TzytviZpGkAumqCDs5d12bJMkjvb1J7uc+lPv2lN5+jiRJBZVti3zraJg0Im4WSZJiyJ4euXY97NoVN4skFUBXRdgC0m2Rq/Nwr3tJt0f6T+ySpNLz9NPwxBPp3DAzbhZJkmI5e3r7fMfKeDkkqUC6KsLG567r83Cv7DOOzsNnSZKUX9lqMICLFsTLIUlSTO+YDANr03ml54RJqjxdFWHDctemPNwr+4zhefgsSZLyKyvCZg+DuePiZpEkKZaBtXDmlHS+3RVhkipPd4uwvXm416u565A8fJYkSfmzcyf89rfp3DANQoibR5KkmLLtkZt2wObNcbNIUp51VYTVFOCehfhMSZJ674Yb2ueL3hIvhyRJpSA7MB98eqSkitNVESZJUuXLtkUeXQ9vmxo1iiRJ0Z04HkbVp/NtN3T+XkkqM7XdfN+8EEJfn507r48/X5Kk/Nu7F+64I50vmAz93BYpSapyNf3SVWG/eAJu/w20tkI21rp8AAAgAElEQVSNG3skVYbuFmFfLWgKSZJiuf12OHAgnS84Lm4WSZJKxTkz0yJs9x548EE49dTYiSQpL7q7NTLk6UuSpNJy003pdWANnDU3bhZJkkrFohnt8603xsshSXnW1Yqw1UBSjCCSJBVdWxvcfHM6v+doGDQgbh5JkkrFxGEwfxw89nxahH3hy7ETSVJedFqEJUlyRrGCSJJUdA89BM89l87nz4qbRZKkUrNoRlqEPfAY7N4No0bFTiRJfeZTIyVJ1SvbFglw3vx4OSRJKkXnzEyvbQncflvcLJKUJxZhkqTqlRVhbxkJk0fGzSJJUqk5YwoM6p/Oty6Pm0WS8sQiTJJUnXbsgDVr0vn8aXGzSJJUiupq4azc75ErbofE46MllT+LMElSdfqf/2mfz3dbpCRJh3VO7umRO3bBunVxs0hSHnR6WH4I4bRC3DRJktWF+FxJkrrtxtyj4McOhLdOiZtFkqQeWL52FEtXTGJH0wAmjDjAkkXbaViwuzA3y84JA7j1JpjvPx5JKm+dFmHAPUC+178m3bivJEmF09wMt9+ezu+bBDUukJYklYfla0dx5bKpNLfUANDYVMeVy6YCFKYMmzEKZo6Cjbvh1htgyZX5v4ckFVF3/uQfCvAlSVI8q1bBq6+m8wXHRo0iSVJPLF0x6fUSLNPcUsPSFZMKd9NFue2Rdz8Ie/cW7j6SVARdrcz6rzzcow64CBiEJZgkqRRkT4vs3w/eOy9uFkmSemBH04AevZ4X58yE7/wOWg7Cqjvh/AsKdy9JKrBOi7AkSf64tx8cQgjAFcAXSEuwzIu9/UxJkvosSdqLsHcdBcMGxs0jSVIPTBhxgMamusO+XjALp8KAGjjQCrcutwiTVNYKcihKCOFiYB3wfWAy6UqwfcA/ATMKcU9Jkrpl3TrYujWdz5/Z+XslSSoxSxZtp75/6yGv1fdvZcmi7YW76ZABcGbuwTI335L+o5Iklam8FmEhhLNDCA8AvwDmkhZgB4BvA9OTJPlCkiR78nlPSZJ6JFsNBnD+CfFySJLUCw0LdvO1i7cwccR+AgkTR+znaxdvKdxTIzPnzUqvW3bCk08W9l6SVEB5eXpjCOFU4KvAwuwloBX4EfDFJEm25eM+kiT1WVaEzR0OM8bEzSJJUi80LNhd+OLrjc6fDX/163S+aTkc68NmJJWnPq0ICyEcG0L4FbCatATLngr5S+D4JEk+ZgkmSSoZu3fDffel83lT4maRJKmczBoNc0an843L4maRpD7oVREWQpgaQvgh8AhwIe0F2G3AW5MkuSRJkqfyF1OSpDy47TZoa0vnc/2XbEmSeuT82el19UPwos9Ak1SeelSEhRCOCiH8O7Ae+AhQQ1qA3QeclSTJoiRJ1uQ/piRJeXDLLel1cC2c4UH5kiT1SFaEtSVw6y1xs0hSL3WrCAshDA8hfAXYCPwZMIC0AFsHNCRJclqSJKsKllKSpL5qa4Nbb03ns46GurwckylJUvU4fTIMr0vnG38RN4sk9VKnRVgIYWAI4bPAJuBzwGDSAmwzcBnwliRJbih4SkmS+uqRR+D3v0/nc2fEzSJJUjnqXwPn5FZU37oSWlri5pGkXuhqRdgm0qdBjiQtwHYCfw7MSZLkJ0mSJAXOJ0lSfmSrwQDOOT5eDkmSylm2PfLlfXDvvXGzSFIvdLUvZDyQlV37gFuAecC/hBB6fdMkST7T658sSVJvZOeDzRkG00bHzSJJUrk6dyb0C+k5YTctg4ULYyeSpB7p7gEpCTAI+Gie7msRJkkqnqYmWL06nc+ZEjeLJEnlbPQgOG0y3LMVbrwe/vnfYieSpB7pzmH5Ic9fkiQV18qV0NqazufOi5tFkqRyd/6s9Pr0Vnj66bhZJKmHuloR9t6ipJAkqZCybZH1NfCu2XGzSJJU7s6fDZ9bmc433wCz/yZuHknqgU6LsCRJVhYriCRJBZEk7QflLxwPA7t7KoAkSTqsY8fC1BGwpQluvA7+0iJMUvnoztZISZLK17p10NiYzufOiJtFkqRKEAJckFth/ZvfwYsvxs0jST1gESZJqmzZtkiAc+fHyyFJUiW5aG56bW2Dm26Im0WSesAiTJJU2bJtkTOGwswxcbNIklQpzjwGRten86+ujZtFknrAIkySVLn27IF77knncyfHzSJJUiWp7QcXzEnnFatg376ocSSpuyzCJEmVa+VKaGlJ53Pmxc0iSVKlybZHvnYAVtwaN4skdZNFmCSpcmXbIuv6wbvnxM0iSVKlee90GNw/nd0eKalMWIRJkipTkrQXYe8aD4P6x80j/V/27jxOy6ru4/jnMAM47CIuMIjghhsk7oo7CJqVRGWLaZqtmm2GSZvtUOTT/pTLk22amRGWpqDivpuYqIma+6DmhgoMscx5/jj33YCxzNwzzLmXz/v1mtf1u6+5lu/ovJiZ333OuSSp2jR0hyO3T/XlV7WOwpakMmYjTJJUnR55BJ58MtUTR+TNIklStSpOj1y0GK6/PmsUSWoLG2GSpOo0Z05rPWGXfDkkSapmR++YFs4H+NPv82aRpDawESZJqk7FRtjgBth1cN4skiRVqwGbwOGFkdezLoOWlrx5JGkDbIRJkqrP8uVw3XWpntAIIeTNI0lSNStOj3z2RbjzzrxZJGkDbIRJkqrP7bfD4sWpnrBD3iySJFW7Y0ZC8T0np0dKKnMdboSFELYKIbwvhPCNEMJPQgjndEYwSZJKtvr6YONdH0ySpI1qcF/Yb2iqZ/4xPblZkspUyY2wEMKgEMKvgaeA3wBfAE4BPrSWY38SQlgaQlhQclJJktrq6qvTdsxA2KJP3iySJNWCyTun7aNPw3335c0iSetRUiMshLA9MA84DqgnDYRd3wIsPwM2AbYPIRxSyj0lSWqTl1+Gu+5K9YRhebNIklQr3rXaCOxLLsyXQ5I2oN2NsBBCPfAXoJHU/PodcDTwyXWdE2N8ALi/8PLI9seUJKmNrr22dUrGETvlzSJJUq3YZgDs25jqSy52eqSkslXKiLAPAiOBCJwaYzwuxngl8PQGzruB1Djbt4R7SpLUNsX1wRrqYOx2ebNIklRLjt01bR99Gu69N28WSVqHUhph7yhsr4sx/qwd5z1Q2O5Ywj0lSdqwGFsbYYdsBZvU580jSVIteefq0yMvypdDktajlEbYaNJosFntPO+lwnZgCfeUJGnDHn4Ynnoq1RNG5M0iSVKFmTVvIGOnj2bEmXsxdvpoZs1r559uw/rD/oWnRzo9UlKZKqURVvzX8Nl2nldXwr0kSWq74mgwgAm75sshSVKFmTVvIFNnDqdpUU8igaZFPZk6c3j7m2HF6ZGPPQP33NP5QSWpg0pphL1a2PZr53nbFLYvrfcoSZJKdfXVaTukF+yyZd4skiRVkBmzh9K8Ys2xC80r6pgxe2j7LvROnx4pqbyV0gh7orDdp53nFZ8W+cB6j5IkqRTLl8N116V6QiOEkDePJEkVZOGiHu3av05D+8HYrVN9ye+dHimp7JTSCLua9PTH94QQBrXlhBDCOOAQ0tpiczZwuCRJ7Xf77bB4caon7JA3iyRJFWbIgOXt2r9exemRTyyEu+/uQCpJ6nylNMLOBZaTpkb+IYTQd30HhxAOAX5XePk68H8l3FOSpPVbfX2wcTvnyyFJUgWaMvEZGrqvWmNfQ/dVTJn4TPsv9o6d09AJcHqkpLLT7kZYjPFJ4Jukf9oOBh4OIXwD2K94TAhhvxDCh0IIVwBzgUGk0WBTYoyvruWykiR1TLERNmYgbNEnbxZJkirMpDEvM23yEzQO+DeBSOOAfzNt8hNMGvNy+y/W2A8OHJbqS34PLS2dG1aSOqC+lJNijN8MIWwJnApsCXyh+KnC9pbVDi++F/DtGON5JaWUJGl9XnqpderFhGF5s0iSVKEmjXm5tMbX2rx7V7jpKXjqObjlFjjooM65riR1UClTIwGIMZ4GvAt4kNTsWtfHAmByjPHLHU4rSdLazJ3buhjvhJ3yZpEkSWmdsLrCmIgLL8ibRZJWU9KIsKIY4x+BP4YQ9gAOAoYD/YHFQBNwA3BHjD4qRJK0ERWnRTbUwdjt8maRJEmweW+YuD389RG45FL40c+hRzufQClJG0GHGmFFMcZ7gHs641qSJLVLjK2NsEO3gp6d8qNNkiR11PtHpUbYK6/DlVfCMcfkTiRJpU+NlCSpLDzyCDz1VKqPGJE3iyRJavW2kdC7e6ov/EXeLJJUYCNMklTZrr22tR6/c74ckiRpTb17wNsLP5v/fCW8+mrePJKEjTBJUqW75pq03WIT2G1w3iySJGlN7x+Vtv9eATP/mDeLJFFCIyyEsLzEj2UhhBdCCAtCCH8IIZwSQui/Mb4oSVKNWLUKrrsu1eOGQAh580iSpDWN2xa26J3q3/5f3iySRGkjwupL/OgBbAbsAEwGfgw8HUL4UMe+BElSzZo3D155JdXjts2bRZIk/bf6bvDe3VJ93W3Q1JQ3j6SaV0oj7FbgFuDvb9i/GLgfuKOwXbza5yJwb+Fzjxb2BaAPcE4I4VMl5JAk1bo11gfbJV8OSZK0bscVpkfGCL+7MG8WSTWv3Y2wGOOBwMeAXoVdFwL7xBj7xRhHxxj3L2z7AfsAF5GaXr2Ak2OMOwKbA2cBKwqf+04IYVjHvxxJUk0prg+2fV/YZtO8WSRJ0trtNQR2GJjqCy/Im0VSzStljbBNgcuB7YEPxxiPjzHevbZjY4x3xxjfD3wU2BH4SwhhQIzx5RjjN4DjCod2JzXXJElqm2XL4OabUz2uMW8WSZK0biHA+0en+t6H4L778uaRVNNKmRr5CWAb4KoYY5tWO4wxngfMBoYXzi/uvxS4iTQqbFwJWSRJterWW1MzDGD8jnmzSJKk9Tt+dGt9wbn5ckiqeaU0wt5BWvPrsnaeN4vU8HrHG/b/tbAdUUIWSVKtKk6LDMBhNsIkSSprIzaFwwt/8v32Qli+PG8eSTWrlEbY8ML2lXae93Jh+8aG1xOFbf8SskiSalVxofwxm8FmvfNmkSRJG3bS7mn74iK44oq8WSTVrFIaYcVz2vuc+u3Wcc+WwnYxkiS1xaJFcHdhecpxQ/NmkSRJbTN5Z+jXM9UX/DxvFkk1q5RG2GOkiSgnhhC6t+WEwnEnrnb+6rYqbF8sIYskqRZdfz20FN5HGb9T1iiSJKmNenWHd++a6r9eA889lzePpJpUSiNsVmG7I3BhCKHX+g4ufP6iwvERmPmGQ/YsbJ8oIYskqRYV1wfr0Q0O3G79x0qSpPJRnB65qgV+86u8WSTVpFIaYWcDzxbqdwCPhBC+HkI4LISwTQhh88L2sBDC14GHgcmF4xcC3y9eKIRQD7yZ1CC7uuSvQpJUW4rrgx2wRXp3WZIkVYb9hsJOg1J9wbkQY948kmpOuxthMcbXgaOAl0hTJLcCvghcQ5r2+Fxhe01h/+DCcS8Aby6cX7Q38ABwI/CXkr8KSVLtaGqChx5K9bht8maRJEntE0LrqLB/PAZ33pk3j6SaU8qIMGKM9wGjgN+TFrsP6/loAS4G3hRjnP+G69wWYzys8LGg5K9CklQ7iqPBAMbvnC+HJEkqzfGjoS6k+oJz82aRVHNKaoQBxBifjzG+F9gG+BhwPvBX4IbC9vzC/m1ijO+LMboSoiSp44rrg/XrDnttnTeLJElqv8F94cjtU/2738PSpXnzSKop9R29QIxxIXBu4UOSpI0nxtYRYYduBfUlv58jSZJy+uAYuOIReG0JXPJ7OPGk3Ikk1Qj/gpAkVY6HHoKFC1M9ftu8WSRJUuneuiNs1SfV5/w4bxZJNcVGmCSpchSnRQKMc30wSZIqVvc6+GBh0fzb58F99+XNI6lm2AiTJFWO4rTIwQ2w8xZ5s0iSpI758J7p8WoA5/w0axRJtaNDa4SFEIYC7wP2BYYC/YC6DZwWY4wjO3JfSVINWrkSrrsu1eOGpMevS5KkyjV8QFo0/8pH4Te/he+cDX365E4lqcqV1AgLIdQB04BP09r4Wv0vkrj64W84PdJBIYQAHAscD+wObA68DDwI/A74ZYxxZUfvs4EMmwL/ALZcbfeIGOMTG/O+klSz/vY3eO21VI/fIW8WSZLUOT66Z2qEvb4ULv4dfOjDuRNJqnKlTo08Bzid1EgLwEuF/bFQv1p4HVbb/yzQBCws8Z7pgqkBdQ1wMXA00Aj0ALYCDgfOA+4IIQzryH3a4H9YswkmSdqY1lgfbKd8OSRJUuc5ekdo7JtqF82X1AXa3QgLIYwFPlh4eSswMsa4+kItJ8cYBwIDgXcD95IaYvOB0THGrUsNG0LoAVxGangBPA18GXgvMIU0QgtgD+DKEEK/Uu+1gRxHACcCLcCyjXEPSdIbFNcHG9kPhvbPm0WSJHWO+m5w8phU3z0f7rknbx5JVa+UEWEnF7ZLgbfFGB9Z20ExxldjjH8A9gZ+CxwBzCxMayzVx4GDCvU9wJtijN+MMV4cY/weqQE2u/D5XUhNsk4VQuhFGhEH8FPg+c6+hyTpDZYuhVtuSfX4oXmzSJKkzvWhPaBb4c/Ec36SN4ukqldKI2wsaarjhTHGlzd0cIxxFal59jhwMHBCCfckhFAPfLF4WeCEGOMrb7jXssL1lxR2nRZC2KyU+63HN4ERpGmeX9zAsZKkznDLLbB8earH7Zg3iyRJ6lxb94c3F9b/vPB3rWuCStJGUEojbHBh+8A6Pr/JG3fEGFcAvyRNkTyuhHtCmg65eaG+Nsa41vvHGP9FWj8MoCdwTIn3+y8hhH2ATxVenhZjfL2zri1JWo/i+mDdAhxqI0ySpKrzsT3Tdsky+NUvs0aRVN1KaYT1LGzfuOh9cRTWpus47+HCdpcS7gkwYbX6qg0cu/rnjyzxfmsIIXQHzif9N7ssxvinzriuJKkNio2wPQfCpg15s0iSpM535PawbeFPyZ98H1pa8uaRVLVKaYQtKmzfOPLrxcJ2XW/VF6coDirhngC7rVb/bQPH3r2O8zriTGAUsBj4RCddU5K0IS+9BPPmpXr8xn4gsCRJyqKuG3xi71Q//ARcfXXWOJKqVymNsOLi+MPfsP9+0tTHies474jCttQJ36s32J7YwLHPAKsK9Q4dXKCfEMLOtK4H9sUY4zMduZ4kqR2uuw5iTPW4nfJmkSRJG89JY6BX91T/6Ht5s0iqWqU0wu4gNbz2eMP+vxa2O4cQ1nhaYwjhVGASaZH7O0u4J8CA1eoX13kUEGNcSWvDrR7oXeI9CSF0I02J7EkaaeZjTCSpK117bdpuUgdjt82bRZIkbTwDNoEPvCnVf70GHnlk/cdLUglKaYTNKWzHhRB6rLb/QuD5Qv3VEMIzIYQbQwhNwI9WO67URlKf1eplbTi+ebW6b4n3BDgFOIA0wuwjMUYnq0tSVyquDzZ2C9ikPm8WSZK0cX1in9b6pz/Ml0NS1QqxON2krSeEUA9cR1oj7PMxxrmrfW488Gda1w8rXrw4NfG7McYzSwoawnKgME6W7oVRX+s7vgkYUng5JMb4bAn3HEZ6OmYf4OwY4+fWcswTwDaFlyNijE9s4JofAT4CsOWWW+558cUXr+/wsrN48WL69Omz4QNV8/xeUVts6Puk53PPsf973wvAYx9+F0+97+1dFU1lZvHinvTp8+/cMVQB/F5RW/h9Ut5Gf+7bDPzb/azs3YvbLvkDq3r1ypbF32nVFn6flJ/DDjvsbzHGvdb2uXa/tV5oQB20js9dE0LYHfgSMA7YElgK3AX8OMY4q733W81iWp9IuUnh9fqs/lix10u8589ITbAngbNKvMYaYoznAucC7LXXXvHQQw/tjMt2meuvv55Ky6w8/F5RW2zw++QXv/hPue2H+7Dt3gs2fiiVpeuvH8mhh/r/Xxvm94rawu+TMveV3eCY+6lfspSDHn8MTs33rDJ/p1Vb+H1SWUqZGrleMcaHY4wnxBgbY4z1McZ+McZxHWyCQevTKqH1CZRrVRi11q/wciWwpL03CyEcB7y58PLUGGO7ryFJ6qDi+mADesAeQ/NmkSRJXePoHWBEYYnon3y/9aE5ktQJ2j0iLIRQnG64PMa43kXrO9nDwIhCPZw0SmtdhgJ1hfqR2N75n8mHCtvngDEhhDHrOK7/avUnQgjFht15Mcbn13aCJKkNYmxthB0+OD1WXZIkVb+6bmmtsNPnwEOPwVVXwVFH5U4lqUqUsurwM6S1v34GdOUY1fuBiYV6L+CG9Ry7+jzQ+0u8X3Fds62Ab7TxnNNXqy+n9eEBkqT2euABeL7wz+i4Ees/VpIkVZcPjoGzrofFy+HsaTbCJHWaUt5eL64qeUdnBmmD2avVE9d5VHLkavVVGyGLJGljKz4tEmD8LvlySJKkrjdgE/jwHqm+9iaYNy9vHklVo5RGWPHpi+t9auNGcB3wQqEeH0LYdW0HhRC2AN5TeLkMuKyUm8UYD40xhg19sOYUzRGrfe7eUu4rSSooNsKG9oIdBuXNIkmSut6n9oW6wkSds6fnzSKpapTSCLuzsF1rI2pjKTyt8luFlwH4dQhh09WPCSFsAvwK6F3Y9ZMY40tru14I4ZchhFj4+OpGii1JKsWKFXBDYQb8+CEQwvqPlyRJ1WebAXBs4c/Oiy+Fp5/Om0dSVSilEXY+qRF1Ygih94YO7mQ/A24q1HsAfw8hfDGE8O4QwunAPbROi3wQ+GYX55MkdYY774TFi1M9bvu8WSRJUj6n75+2q1rgB2fnzSKpKrS7ERZjvAY4BxgC/DmE0GXzVWKMy4FjgLmFXVuTml0XA98Ddi7svwc4Ksb4aldlkyR1ouLTIgHG7bzu4yRJUnXbcwgcNjzV554HixblTCOpCrT7qZEhhAOA3wLDgKOAR0MIfyCN1GoCmjd0jRjjre2972rnvhJCGA8cCxwPjAEGAa8AD5CaYhcUplJKkipRcX2wXQbA4H55s0iSpLw+dwBc9wQsXgrnnQtTzsidSFIFa3cjDLgZiIU6Av2ADxY+2iKWeN/WC8QYgd8XPkq9xonAiR3JUbjO8I5eQ5K0msWL4fbbUz2+MW8WSZKU35Hbwy6bw4MvwA/Phk99Gnr0yJ1KUoUqZY0wSGuEFT/e+LotH5Ikrd1NN6XF8gHG7Zg3iyRJyq9baF0rrOlfcOFv8+aRVNFKGZn1rQ0fIklSiYrrg9UFOGSHvFkkSVJ5OG4UfPk6WPg6TP8mnPABqKvLnUpSBWp3IyzG+OWNEUSSJKB1fbB9BkH/hrxZJElSeehZD5/bHz47Bx5+HP74Rzj22NypJFWgUqdGSpLU+V54Af7+91SP2zpvFkmSVF4+sicM6pXqb38NYlz/8ZK0FjbCJEnlY+7c1nrcyHw5JElS+endAz69b6r//iBccUXePJIqUqc1wkIIdSGEQSGEIZ11TUlSjSlOi2yog/23zZtFkiSVn1P3gX49U/2trzoqTFK7dagRFkLYMYTwkxDCw8By4HngqbUc964QwhkhhBM6cj9JUpUrLpR/8JZpLRBJkqTVDdgETt071bf/Da6/PmscSZWn5EZYCOELwP3Ax4HtgbDaxxv1BqYD54cQtij1npKkKvbYY/D446ket03eLJIkqXx9Zj9oKLxh9q2vZo0iqfKU1AgLIXwF+AbpqZMRuAu4dT2nXAwsAeqAt5VyT0lSlSuOBgMYv3O+HJIkqbxt3jstnA9w7Y1w++1580iqKO1uhIUQdgG+Unh5H7BbjHFfYMa6zokxLgMKC79wWHvvKUmqAcX1wTbrCW8amjeLJEkqb587AHrUpfqrX8ybRVJFKWVE2KmF814BJsQYH2rjeXeTpk2OLuGekqRq1tLS+sTIwwdDt7XNspckSSoY2g8+vEeqZ8+FW27Jm0dSxSilEXYYaTrkL2OM/2rHecVF9H2bX5K0pvvugxdfTPW4EXmzSJKkyjD1QOhZGBV21hfyZpFUMUpphBUbWX9r53lLC9veJdxTklTNXB9MkiS1V2M/+Nheqb72Rrjhhrx5JFWEUhphhZY7q9p53oDC9vUS7ilJqmbF9cG26QPbDsqbRZIkVY4zD2x9gqSjwiS1QSmNsOJ0yOHtPK8wgZuFJdxTklStli+HG29M9fjBEFwfTJIktdFWfeCUvVN9w61w3XV580gqe6U0wu4kLXp/TFtPCCH0Bt5NWlvs5hLuKUmqVrffDksLs+fHbZc3iyRJqjxnjIVe3VP9lakQY948kspaKY2wmYXtfiGE49p4zjnAwEJ9cQn3lCRVq9XXBzt8l3w5JElSZdqiN5y2T6pvvgPmzMmbR1JZK6URdgkwnzQq7BchhDMKI77+Swhh7xDCNcB7SaPBro8xuoKhJKlVcX2w0ZvCln3zZpEkSZXpcwdA3x6p/sIUaGnJm0dS2Wp3IyzGGIF3Ai8D3YFppHXDflA8JoQwO4TwDHA7cBipafYs0NYRZJKkGlC3ZAnccUd6MW5I3jCSJKlyDeqVmmEA98yHP/whbx5JZauUEWHEGB8BDgDuIzW5GoBhpFFfAOOBwYXPBeAe4IAY43MdDSxJqh4D7rsPVhUeQjx+ZN4wkiSpsn1mP9i8V6q/dCasWJE3j6SyVFIjDCDG+DDpSZDvAS4HXqW18RWAZmAOaRTY3jHGpzqcVpJUVQbcc08q6gMcvGPeMJIkqbL17QlfPjjVjz4B55+XNY6k8lRyIwwgxtgSY7wkxvi2GONAYFNgODAI6BtjPDLG+LvCdEpJktaw6d/+lor9Noc+PfOGkSRJle+je8GIAan++ldhyZKscSSVnw41wt4oxvhqjPGpGOPLNr8kSev13HP0efzxVI8bmjeLJEmqDj3q4BuHpfq5F+AH38+bR1LZaXcjLITgIi6SpI6bO7e1Hr9zvhySJKm6vHcUjN4y1d+dDi+9lDePpLJSyoiwB0MId4QQPhFC2LzTE0mSasO116Zt73rYZ0TeLJIkqXp0CzBtXKpfWwLf+B2vA2kAACAASURBVFrePJLKSimNsADsBfwQaAohXB5CeHcIYZPOjSZJqloxwjXXpPqQLdM0BkmSpM5y1PZw6PBU//R/4eGHs8aRVD5KaYRdDqwkNcTqgaOAi4DnQwj/F0I4rBPzSZKq0T//CU8VHiY8bpu8WSRJUvUJAf5nQvqrdeUqmPLZ3IkklYl2N8JijG8DBgOnAreR/mkJQF/gROCaEMJTIYRpIYRdOzGrJKlaFEeDAYzfJV8OSZJUvcYMhhN3T/Wfr1hzfVJJNaukp0YWngr5sxjjWGA74KvAw7Q2xYYCZwD3hRDuCSF8JoSwVSdlliRVusL6YMsH9IXdhmQOI0mSqtY3D4fe3VP92U/CqlV580jKrqRG2OpijI/HGL8eY9wJ2Bf4MfAvWptibwK+BzwdQrgqhHBcR+8pSapgLS3/eUf2lT12SQvaSpIkbQxD+sLnx6b67w/Ar36VN4+k7DrcCFtdjPGuGOOngEbgaOBioJnUEKsDJgC/7sx7SpIqzL33wssvA7BoD2fQS5Kkjez0A2Bov1R/8Ux4/fW8eSRl1amNsKIY46oY45UxxvcBWwKfB5ZvjHtJkirMauuDvbLn6IxBJElSTejVHaaNS/VzL8B3v5M3j6SsNkojDCCE0KswDfJS4FtA9411L0lSBSk2wrbty7KttsibRZIk1Yb3jYK9CuuSfm9G69OrJTFrXhNjp89lxJlXMHb6XGbNa8odaaPq1EZYCKFbCGFiCOE3wPOkaZATgHrS9Mhm0nRJSVItWrYMbr451eMH580iSZJqR7cA35+Y6mXLYcpn8+aRysSseU1MnTmfpkXNRKBpUTNTZ86v6mZYpzTCQgh7hhC+DzQBfwXeB/QmNb8iMBc4Cdgyxuhi+ZJUq267DZqbUz1uh7xZJElSbTlwGBxbWJ/0kj/+5+E9Ui2bMXsBzSvWfJpq84pVzJi9IFOija++1BNDCMOB4wofI4u7VzvkfuA3wIUxxoWl3keSVEWuvba1Pnzn9JNCkiSpq5w9Aa54GJasgNM+DvfeD91dxUe1a+Gi5nbtrwbtHhEWQvhoCOEm4J/A10lNsFD4eBY4G9g9xjg6xjjDJpgk6T+K64ONGQiDeufNIkmSas/QfvDlg1P94MPwox/mzSNlNmRAQ7v2V4NSpkb+DDiA1ubXEtLIr4nA1jHGKTHG+zovoiSpKrz6Ktx1V6rHDc2bRZIk1a7P7A87bpbqr54FCx27odo1ZeJIGrrXrbGvoXsdUyaOXMcZla/UNcJagNnA8cBWMcYPxBivjjG2dF40SVJVuf56aCn8mBhfvT9YJUlSmetRBz8+KtWLl7pwvmrapDGNTJs8isYBDQSgcUAD0yaPYtKYxtzRNppS1gg7Hbgoxvh8Z4eRJFWxq69O2x7d4MDt8maRJEm1bcJ2MHlnmPkPuOj38NFT4OCDc6eSspg0prGqG19v1O4RYTHG73ekCRZC2KrUcyVJFazYCBu7BfTumTeLJEnS9ydCQ2FsyCc+BitX5s0jqUuUOjWyXUII9SGEySGEvwBPdsU9JUll5Mkn4eGHU33ENnmzSJIkAQzrD188KNXz/wE/+XHePJK6xEZthIUQ3hRC+AGwEPgDcDSlTceUJFWy4mgwgAm75MshSZK0us8dANsPTPWXvwRPP503j1SCWfOaGDt9LiPOvIKx0+cya15T7khlrdMbYSGEgSGE00II9wD3AKcBg0hPmAR4rbPvKUkqc3PmpO1mPWHM1nmzSJIkFfWsh58dnerFS+G0U/Lmkdpp1rwmps6cT9OiZiLQtKiZqTPn2wxbj05phIXkzSGEP5BGf/0A2J3U/ArASuCvwHuBwZ1xT0lShVi1Cq69NtXjBkO3sP7jJUmSutL4beH9o1N92eXwpz/lzSO1w4zZC2hesWqNfc0rVjFj9oJMicpfhxphIYSRIYRpwNPAX4DJQA9S8ysCDwOfBhpjjG+JMf4+xrisg5klSZVk3jx4+eVUT/BpkZIkqQydPQEGNqT6tFPgNScyqTIsXNTcrv3rNHs23HQTNLfzvArU7kZYCKFvCOFDIYRbgAeBM0ijvIqjv1Yff3dRjPFHMcYXOiWtJKnyFKdFAhyxa74ckiRJ67JFb5hxRKqbnoMvfTFvHqmNhgxoaNf+dfrc5+Dgg+Gtb+2EVOWtzY2wEMJhIYRfA88C5wD70dr8Wgr8FpgA+DgwSVKr4kL5O/aDYZvmzSJJkrQuJ+0OBw9L9U9+CnfdlTeP1AZTJo6koXvdGvsautcxZeLItl/k1VfhgQdSve+OnZiuPK23ERZC2CaEcFYI4THgGuA4oBetUx+vBT4AbBljPCHGeE2MsWVjh5YkVYglS+CWW1I9oTFvFkmSpPUJAc55K3TvBjHCR04mrFq14fOkjCaNaWTa5FE0DmggAI0DGpg2eRSTxrTjd+8770zf8wD7V/8T3us38PnHCtvVVzZ+APgN8NsY48KNkkqSVB1uuAFWrEj1EdX/7pIkSapwOw2CqQfC12+Ee+cz9NJLYdy43Kmk9Zo0prF9ja83uu221nq/0R0PVOY2NDWy2ACLpKmPe8QYR8UYv2sTTJK0QcVpkfUBDt0pbxZJkqS2mHoQ7LgZAMN/8Qt45JHMgaSNrNgI26EPDKr+pUzas1j+scBXQwhvDyF031iBJElVpNgI229z6LdJ3iySJEltsUk9nJ8WDK9bvhw+dBK0uAKQqlRLC9x+e6r37583SxfZUCPsAmAxaWRYD+CtwKXAcyGEn4UQDtjI+SRJlaqpqXXRzSOG5c0iSZLUHgdtA6funeobb4FzzsmbR9pYFiyARYtSvf9mebN0kfU2wmKMJwODgZOAGwq7A7Ap8BHgphDCo4UF9bfbqEklSZXlmmta6wk758shSZJUimnjWLbloFSf8Tl48sm8eaSNYfX1wfavjTevNzg1Msa4NMb4qxjjYcD2wDeBp0gNsQCMAL4CPBxCuDmE8LGNGViSVCGK0yL7d4e9hmeNIkmS1G59e7Lg9A+levFS+OiHWp+sV2ZmzWti7PS5jDjzCsZOn8useU25I6lSFBthfephN0eE/ZcY4+Mxxq/EGIcDRwAXActobYrtD/x0tVOGhxB6dlJWSVKlaGlpbYQdPhjq2/XjRpIkqSy8svdo+ODu6cXsa+BXv8obaC1mzWti6sz5NC1qJgJNi5qZOnO+zTC1TbERts/mUFcbv7OX/FXGGK+NMb6fNHXyFOBOWhtixTb5B4DnQwjnhxAO7WBWSVKlmD8f/vWvVE/YNm8WSZKkjjh7Igzuk+rPfAqefTZvnjeYMXsBzStWrbGvecUqZsxekCmRKsYrr8CDD6Z6/8F5s3ShDrf7YoyvxRh/HmPcD9gVOBt4ntamWD/SGmPXhhCeCiFM6+g9JUllbs6c1vqIXfLlkCRJ6qgBm8DP35LqRa/BKR8tqymSCxc1t2u/9B+33db6vXxQ7Sz73qnj3mKM/4gxTgG2Bt4G/AlYQWtTbChwRmfeU5JUhorTIkf0ge02z5tFkiSpo942Et6zW6pn/QUuuSRvntUMGdDQrv3Sf9x8c9oGYL8RWaN0pY0yATTGuCrGeHmM8R3AEOCzwH0b416SpDLT3Aw33ZTqCY15s0iSJHWWHx0Jg3ql+tSPw/PP581TMGXiSBq6162xr6F7HVMmjsyUSBXjllvSdvRA6F87jdONvhJajPGlGOMPYoy7A3ux5mL6kqRqc/PNsGxZqo/YMW8WSZKkzrJ5b/jpm1P90ivwkfJ4iuSkMY1MmzyKxgENBKBxQAPTJo9i0hjfkNR6/PvfcOedqT5wy7xZulh9V94sxngPcE9X3lOS1MWK64N1C3D4TnmzSJIkdaZjd4WZ/4DfPwB/vhx+8xs44YTcqZg0ptHGl9rnnnta37w+sHamRUIXjAiTJNWYq65K230Hwaa98maRJEnqbD99M2zVO9Wf/AQ8/XTePFIpiuuDAYzdPl+ODGyESZI6zzPPwP33p/rIbfJmkSRJ2hg26wXnvS3Vr74OJ59UFlMkpXYprg82rDdsvWneLF3MRpgkqfPMnt1aH7lrvhySJEkb01t2hA/unuqrr4VzzsmbR2qPGFtHhNXY+mBgI0yS1JmK0yI36wl7DsubRZIkaWP6/pEwrF+qP/dZ+Oc/8+aR2mrBAnjppVQfWHu/s9sIkyR1jpUr4ZprUj1hCNT5I0aSJFWxfj3hF8ekekkznHQCrFqVN5PUFjW8PhjYCJMkdZY774RFi1I9sfZ+oEqSpBo0blv4xN6pvulW+OEP8uaR2qLYCOvfA3YdnDdLBjbCJEmdozgtEmCC64NJkqQaMX08bD8w1V/4AvzjH3nzSBtSXCj/gM1rchZH7X3FkqSNo9gI230gDO6fN4skSVJX6d0DfjUJugX493I44bi0ZESZmjWvibHT5zLizCsYO30us+Y15Y6krrRwITz6aKoPHJo3SyY2wiRJHffCC3D33ak+cuu8WSRJkrraAVvD5/ZP9d3zYNq38+ZZh1nzmpg6cz5Ni5qJQNOiZqbOnG8zrJbccENrfciO+XJkZCNMktRxV1+dHsMMcOTOebNIkiTl8PXDYNfNC/XX4Z578uZZixmzF9C8Ys0F/ZtXrGLG7AWZEqnLFRthDXWw9zZ5s2RiI0yS1HHFaZF9u8P+LpQvSZJqUM96+M3bob4brFyVpkguW5Y71RoWLmpu135VoWIj7IAtoEdd3iyZ2AiTJHVMSwvMmZPqcVvV7A9USZIkxgyGsw5J9QMPwZe/lDfPGwwZ0NCu/aoyzz0HDz2U6kNqc30wsBEmSeqov/8dnn8+1RO3y5tFkiQptzMPhH2GpPrs/4Ebb8ybZzVTJo6kofuab1o2dK9jysSRmRKpS63+vXjIDvlyZGYjTJLUMcVpkQATd82XQ5IkqRzUd4Nfvx02qU9rqJ54PLz+eu5UAEwa08i0yaNoHNBAABoHNDBt8igmjWnMHU1doTgtsmc32GdE3iwZ1ecOIEmqcMVG2Mj+MGJQ3iySJEnlYOQg+O54+ORV8PhTcPpn4Nzzc6cCUjPMxleNKjbC9t8iNWprlCPCJEmle/VVuPXWVB9Zu+sMSJIk/ZdT94HDC6Nuzvs/uOKKvHlU2154AR54INWH1HYj1EaYJKl0c+fCypWpPnLnvFkkSZLKSbcAFxwD/Xqm1x86CV56KW8m1S7XB/sPG2GSpNJdeWXa9uwGB9f2D1RJkqT/Mqw//PioVD/3Anz8I2ndMKmrFadF9ugG+22bN0tmNsIkSaWJsXWI/+GDoVePvHkkSZLK0fGjYdJOqf7DTLj44rx5VJuuvz5t990cGrpnjZKbjTBJUmnuvRcWLkz10dvlzSJJklSuQoBz3gKb90qvT/0YNDXlzaTa8uKLMH9+qmt8fTCwESZJKtXqC74ePTpfDkmSpHK3RW84762pfuU1OPkDTpFU17nuutb68B3z5SgTNsIkSaUpNsJ2GQDDN8ubRZIkqdwdsxN84E2pnn0tnPPzvHlUO669Nm03qYP9nclhI0yS1H4vvAB33JHqo7fOm0WSJKlS/PBI2Lpfqk//LDz6aN48qg3FRtiBW8Am9XmzlAEbYZKk9rvqqtbh/EfvmjeLJElSpei/CfxyUqqXLoMPHAerVuXNpOr21FOtDddxw/JmKRM2wiRJ7VecFtm/Oxywfd4skiRJleTwEfDJfVJ9653wvRl586i6FUeDAYzbKV+OMmIjTJLUPitXwuzZqZ7YCN3r8uaRJEmqNNPGw8jCGqtf+Qrcd1/ePKpexUbYgB6whyPCwEaYJKm9br0VFi1K9dE75M0iSZJUiXp1h1+/HeoCLF8Bx78X/v3v3KlUbWJsbYQduhXU2QICG2GSpPYqTosMwFGjskaRJEmqWPs0whcOSvV9D8LXzsqbR9XnH/+A555L9bjhWaOUExthkqT2ufzytN1nEGzeN28WSZKkSvalg2GPwan+zgy47ba8eVRd1lgfbOd8OcqMjTBJUts98QQ8+GCq3zIiaxRJkqSK16MOfj0JetZBSwuc8D5YsiR3KlWLYiNscAPstGXeLGXERpgkqe2K0yIBjt4tXw5JkqRqsesW8K3DU/3oE3DG6VnjqEqsXAnXX5/qcUMghKxxyomNMElS2xUbYUN6we5b580iSZJULT69Hxy8Tar/9xyYMydvHlW+u+6CV19N9bht82YpMzbCJElts3QpXHddqt881HeVJEmSOktdN/jlMdCnR3r9wQ/AK6/kzaTKNnt2az1h13w5ypCNMElS28ydC8uWpfronfJmkSRJqjYjNoXvT0x103Nw2il586iyFRthozaFIf3zZikzNsIkSW1TfFpkj24wfpe8WSRJkqrRyWPg6B1SfeHFcOmlefOoMr3yCtx5Z6onDs2bpQzZCJMkbVhLC/z5z6k+bCvo0zNvHkmSpGoUApz3VhjYkF5/7MPw/PN5M6nyXHtt+v0dYKIzOd7IRpgkacPuvhuefTbVx+yQN4skSVI1G9wXfn50ql9a5BRJtV9xWmRDHRzo7+5vZCNMkrRhl13WWr9t93w5JEmSasG7doV3Fpai+MNMmDkzbx5VjhhbG2GHbgWbdM+bpwzZCJMkbVixEbbXZtA4IG8WSZKkWvCTo1qnSJ7yUXj55bx5VBkeegiefjrVE0bkzVKmbIRJktbvn/+EBx5I9THb5s0iSZJUK7bsAz8oPEXy+Rfhs5/Km0eVoTgaDGDirvlylDEbYZKk9Vt9WuQxo/PlkCRJqjXvHw1vLqzx9KvfwlVX5c2j8ldshG3dG3baMm+WMmUjTJK0fsVG2Ig+sFtj3iySJEm1JIS0cH7fHun1R06G117Lm0nla9kyuOGGVE9sTN8/+i82wiRJ6/bii3Dzzak+Zht/mEqSJHW1rfvDjCNS/fRCOPOMvHlUvq6/HpqbUz1xZNYo5cxGmCRp3a64AlpaUn3MbnmzSJIk1aoP7wmHDk/1z85pHfUjre6KK9K2PsARO+fNUsZshEmS1q04LXJgTzhwx7xZJEmSalW3AOe/FRrq0+uTT4SlS3MmUrmJsbURdtCW0L8hb54yZiNMkrR2zc2ti20ePRTq/ZEhSZKUzXYD4VuHp/qfT8A3vpY1jsrMQw/B44+n+mif9L4+/lUjSVq7a69tfadx0i55s0iSJAk+uS/sPSTV3zsb5s/Pm0flozgaDODoUflyVAAbYVIXmzWvibHT5zLizCsYO30us+Y15Y4krd2sWWnbsxtM2DVvFkmSJEFdNzj3rVAXYOUq+OjJreu5qrYVG2Hb9oWRW+bNUuZshEldaNa8JqbOnE/TomYi0LSomakz59sMU/lZubK1EXbEEOjTM28eSZIkJbtvBZ/eL9W33QXnnZs3j/JbtAhuuinVb9naJ71vgI0wqQvNmL2A5hWr1tjXvGIVM2YvyJRIWocbb4SXXkr1O3bKm0WSJElr+uqhMKx/qj8/BZ57LmscZTZnDqwq/J15tEuabIiNMKkLLVzU3K79UjZ//GPa1gd425i8WSRJkrSmPj3gp29O9auL4TOfzJtHeRWnRfauh0N80vuG2AiTutCQAWt/hO269ktZtLTAzJmpPnwwDOyVN48kSZL+21t2hHfsnOqL/wBXXZU3j/JoaYErr0z1+MHQs3vePBXARpjUhaZMHElD97o19jV0r2PKxJGZEqnalfRwhltvbR1e/06/NyVJksrWj46Cvj1SfcpHWp/4rdpxxx3wwgupPnqHvFkqhI0wqQtNGtPItMmjaBzQQAAaBzQwbfIoJo1pzB1NVajkhzNcemnadgswyWmRkiRJZWtIX5g2LtWPPw3f/mbePOp6xQdcBeAto7NGqRT1uQNItWbSmEYbX+oS63s4wzq/B1taWtcHO2RL2LzvRk4pSZKkDvnYXvDLv8PdC2HG9+ADJ8EOjgyqCTHCn/6U6v02h8H98+apEI4Ik6QqVdLDGe66C555JtXv8BcoSZKkslfXLS2cH4DlK+CTp6QGiarfQw/BI4+ketK2ebNUEBthklSlSno4Q3E0WADevkfnh5IkSVLn26cRTi4saXHVNXDZZXnzqGsUp0UCTNo9X44KYyNMUs0oaeH4CtbuhzPE2Lo+2AFbwJABGzmhJEmSOs208bDpJqn+9CdcOL8WFKdF7twfdtwqb5YKYiNMUk0oeeH4CtbuhzPcey88/niq37F9l+WUJElSJxjUC751eKqfbILp386bRxvXM8+kZU0AJg3PGqXSuFi+pJpQ0sLxVaBdD2cojgYDeMeeGyeQJEmSNp6P7Annz4N7noXvfhdOOBG29w3OqvTnP7fWb39TvhwVyBFhkmpCSQvH15IY4fe/T/Xem8GwgXnzSJIkqf2KC+cD/HsFfOpUF86vVsX1wRp7wZ7Ds0apNDbCJNWEkhaOryV33w3//Geq37OONcQkSZJU/vYbCh8sLJz+1zlw+eV586jzLVoE112X6mOGQbeQN0+FsREmqSa0e+H4WvO736VtAN69V9YokiRJ6qDp42FAYeH80z8Fy5fnzaPOdcUVsHJlqiftljdLBbIRJqkmtHvh+FqyalXrtMhDtoLGTfPmkSRJUsds3hvOOiTVjzwOP/1J3jzqXMVpkf27w6G+sd9eLpYvqWa0a+H4WnLjjbBwYarfu1PeLJIkSeocp+wNP7sbHn4JvnYWHH8CDBqUO5U6atkyuPLKVL9la+huW6e9HBEmSbWuOC2yPvi0SEmSpGrRow6+d0SqX10MZ30pbx51jjlzYMmSVB+zS94sFcpGmCTVsuXL4dJLUz2xETbrkzePJEmSOs9bdoTx26b65+fBAw/kzaOOu+SStG2ogze7PlgpbIRJUi2bMwdeeSXV79s1bxZJkiR1rhDgfyakpwq2tMBnT4MYc6dSqZqb4bLLUv2WraF3z7x5KpSNMEmqZcVpkQ118LYxebNIkiSp843aEj6yR6rnXNe6vpQqz1VXweLFqX63b2KXykaYJNWqJUtanzjztq2hj+8oSZIkVaWvHwb9Cr/rffY0WLEibx6Vpjgtsnc9HDU6b5YKZiNMkmrVX/4CS5em+r3+IJUkSapam/eGrxyc6gWPwc9/ljdPF5s1r4mx0+cy4swrGDt9LrPmNeWO1H5Ll6bf3wHeujX06pE3TwWzESZJteqii9J2QA840oU2JUmSqtpp+8J2m6b661+F117LGqerzJrXxNSZ82la1EwEmhY1M3Xm/Mprhv31r61Pi3RaZIfYCJOkWvSvf7WuD/HO4dCze9Y4kiRJ2sh61MG3x6X6xVdgxnfy5ukiM2YvoHnFqjX2Na9YxYzZCzIlKlFxWmSfejjS2RwdYSNMkmrRRRfBypWpPnGvvFkkSZLUNd65C+w1JNX/8z/w7LN583SBhYua27W/LC1eDJdfnupjhsEmvondETbCJKkW/fKXabt9Xzhg+6xRJEmS1EW6Bfju+FQvXQZf+3LePF1gyICGdu0vS1dcAc2Fxt27XdKko2yESVKtufde+PvfU/2BHSGEvHkkSZLUdQ4bAUcV3gg9/wJYUGFTBNtpysSRNHSvW2NfQ/c6pkwcmSlRCYrTIvt1hwk2wjrKRpgk1Zpf/SptA3DC/lmjSJIkKYPp49PvgqtaYOrncqfZqCaNaWTa5FE0DmggAI0DGpg2eRSTxjTmjtY2r7+eFsoHmDTMtX07QX3uAJKkLrR8Ofz2t6k+fDAM2yxvHkmSJHW90VvC8W+CX/8d/nQ53HYb7F+9b5BOGtNYOY2vN5o1C5YtS/WxLpLfGRwRJkm15Mor4cUXU/0Bh1VLkiTVrG8cBj0LUwbP+AzEmDeP1q74JvZmPeGIXfNmqRI2wiSplhQXye9TD5P3zBpFkiRJGQ3rD6ftk+qb74C//CVvHv23Z5+Fa65J9Xu2hR516z9ebWIjTJJqxbPPtv6Cc+wI6N0zbx5JkiTlNfUgGLBJqr/0eWhpyZtHa7rootb/J8fvlTdLFbERJkm14he/gFWrUv2R/fJmkSRJUn4DG+CMA1I9/yH4wyVrfHrWvCbGTp/LiDOvYOz0ucya15QhZA0rTovcoR/sMyJvlipiI0ySakFLC5x3XqpHb+oPUkmSJCWn7Qub90r1WV+ElSuB1ASbOnM+TYuaiUDTomamzpxvM6yr3H8/3Htvqt+/A4SQN08VsREmVRHfsdE6zZkDTz6Z6o/u5g9SSZIkJX16wJkHpnrBY3DRhQDMmL2A5hWr1ji0ecUqZsxe0NUJa9NvftNav3+ffDmqkI0wqUr4jo3W69xz07ZXPRzntEhJkiSt5uN7weA+qf7al2HFChYual7roevar07U0gIXpoYkY7eAbbfIm6fK2AiTqoTv2GidFi6EP/851e8ZAf175c0jSZKk8tLQHb54UKofexp+eQFDBjSs9dB17Vcnuv56aCoMaDh+56xRqpGNMKlK+I6N1umCC1ZbJH/fvFkkSZJUnj60B2zdL9XfOIvPHzachu51axzS0L2OKRNHdn22WlOcFtmjG7xr77xZqpCNMKlK+I6N1mrlytZpkW/aFPbZNm8eSZIklaee9fCVQ1L99HO87a4rmDZ5FI0DGghA44AGpk0exaQxjVljVr2lS+HSS1N99FAY2DtvnipUnzuApM4xZeJIps6cv8b0SN+xEZddBk89leqPjXaRfEmSJK3bB94E02+Gf74C3/o6k/55MpPGHJ47VW2ZNQsWL0718W/Km6VKOSJMqhKTxjT6jo3+2w9/mLab9oDj98+bRZIkSeWtex2cVRgV9tyL8LP/zZunFv3f/6XtZj3hzaPyZqlSjgiTqsikMY02vtRq3jy46aZUf3gk9O6ZN48kSZLK3/tGwbdvhodehBnT4ZRTocHlVrrEY4/B3LmpPn576Nk9b54q5YgwSapWxdFgdQFOPThvFkmSJFWGum7wpcITJJ9/Cc4/L2+edpo1r4mx0+cy4swrGDt9LrPmNeWO1HYXXNBan+xsjo3FH7VjmgAAIABJREFURpgkVaPnn4ff/S7Vbx8GwwblzSNJkqTK8e7dYPuBqf7Ot+Df/86bp41mzWti6sz5NC1qJgJNi5qZOnN+ZTTDVq1qbYTtMwh2c6bPxmIjTJKq0TnnwPLlqf6U7yZJkiSpHeq7wRcOTHXTv+BXF6z/+DIxY/aCNR4eBtC8YhUzZi/IlKgdZs+GpkLD7kO75c1S5WyESVK1aW6G/y0sbLrHQBi7Y948kiRJqjzvHw3b9E/1tG/AihV587TBwkXN7dpfVoqL5Peqh3fvkzdLlbMRJknV5oIL0tRIgM/uBSHkzSNJkqTK070OziyMCntiIVz427x52mDIgLUv6r+u/WXj+efhz39O9bHDoV/H886aN5Cx00cz4sy9GDt9NLPmDezwNauFjTBJqiYrVsCMGake0cd3kyRJklS6k3aHIX1T/e2vpXWsytiUiSNp6F63xr6G7nVMmTgyU6I2+s1vYOXKVJ+8d4cvN2veQKbOHE7Top5EAk2LejJ15nCbYQX1uQNIyqSlBZ58Ep56Ks1FX7IEli2DXr2gXz/o3x9GjEgf9f5TUTEuvhieeCLVZ4yB+rr1Hi5JkiStU896OOMA+PRseORJuOT38N735U61TpPGpAXmZ8xewMJFzQwZ0MCUiSP/s78sxdg6LXJkPxi7Q4cvOWP2UJpXrPl3QPOKOmbMHsqkMS93+PqVzr9upVrR0gJ33QVXXgnXXQfz5sHrr2/4vB49YIcdYO+94dBD08c222zstCpFSwtMm5bqrRrgxIPy5pEkSVLl+/Ce8O2b4V9L4FtfhXe/B7qV7+SySWMay7vx9Ua33goPPZTqk3fplGVNFi7q0a79tcZGmFTtnnoKzj8ffv3rNAKsvZYvhwceSB+//GXat8su8K53wbHHplrl4bLL4B//SPXpo2GT7nnzSJIkqfL16g6n7w+fvwYeeARmzYLJk3Onqh4//3na1gc4Yb9OueSQActpWtRzrftlI0yqXg8/DN/+Nlx4Yet886LdBsABW8LuQ2D7rWDoptC3J/Ssg6Ur4PXl8HIzPPICPPQ8zH8ebnkWFheu8+CD8LWvpY8xY+BjH4P3vQ/69On6r1NJjOn/N8CmPeCjh+TNI0mSpOrx8b3gO7ekvxG++RV4+9t9IFNneOEFuOSSVE/eBrbs3ymXnTLxGabOHL7G9MiG7quYMvGZTrl+pbMRJlWbl19ODar//d81G2B7bwYn7AqTxsDQAW271sGrTYFc2QLznoWrH4VL74d5L6b98+bBRz8KU6bASSfB6afD1lt33tejtpk1C+6+O9Wn7Qp9y/zJOJIkSaocfXvCp/eFr1wP8x6AOXNg4sTcqSrfBRekGTgAp+zbaZctrgM2Y/ZQFi7qwZABy5ky8RnXByuwESZVkz/9KY3O+te/0usAvGMb+PwhsNeIjl27vhvs3Zg+vnAIPPoyXPR3OO9v8MwSeO01+OEPUwPuhBNoOOywDn85aqOVK+GLX0z1wJ7w2fF580iSJKn6fGIf+O6tsHg5fPurNsI6atWq1mmRu/SHgzv3yZaTxrxs42sdyneFO0ltt2QJnHBCmqtfbIIdthXcc+L/s3ffYVKVd//H3zewFEEEFFEExYIYVHQVCyIWLCixYO/Yey8Y0VijgmKNGFs09mD0ISSWiLHwGEs0KvqzlygqoI8Krkpdyvn9cc9mVwLsLDu7Z2bP+3Vdc/E9Z86c88U9zg6fOee+4eEj6h+CLc46neCi7eGzs+AvB8IOq8f18+bBnXey+dChcNJJ8H//V/hj6+fuu696bLDhG8MKy6XbjyRJkpqejm3iLZIAz/8TXnwx3X5K3fjx8NlnsT6pj7eaNiKDMKnUffABbL55DEMgjg91307wzHGwcSPM7tiiGezRC54+El49BvZaF4CwcCHccgusvTZcfHF+M1Sq7ubMgUsuifVqy8HJXoknSZKkBnLmlnFcYYARl6XbS6n73e/in21bwGH90u0lYwzCpFI2YQJsuWUcvB5g4Crw7glw6FbpfKOw2Wow9iB483imb7ZhXDdzJlx2GfTsCfffHwd1V+HcemucGRTg4r7Q5r9nh5EkSZIKYtXl4ajyWD/+FLz1Vrr9lKrPPoMnnoj1oWtDe8f3bUwGYVKpGjMm3pf/ww9x+YKN4aljYNWO6fYFsNEq/L+rh8PTh8EmXeK6//s/OOww2G47eOedVNtrMr7/Hi6/PNbrtocjt0m3H0mSJDV9w7aC5rkv3Uf+Jt1eStXtt1dfIHDiVun2kkEGYVIpuv56OOigOMNIWTN4YGe4fE9o3rz21zamHdaCfx0P9w6BLrlxq55/HjbeGM45J14tpmX361/DtGmxvmJraFFkP39JkiQ1PWt2hINyd3/86c/w8cfp9lNq5s6F3/8+1v1Xho1WT7efDDIIk0rNNdfAWWfFun0ZPLkPHFzE95Q3C3DYRvDhqXD65nF5wQK49lro0weeey7tDkvTxInVs8wMXAX26ZtuP5IkScqO8/rHPxcuhKtHpNtLqXn4Yfjuu1ifVJ5uLxllECaVkhtugGHDYt25FfzjEBjYO92e8rVCa7hhV3jjOOjXNa779FMYOBCOP776Fk/VbuFCOOWU+GeLZjD6l84yI0mSpMaz/sqwZ69Y33MfTJmSbj+lIkngxhtj3bk17LNpuv1klEGYVCpGj4Yzz4z1Sq3g2UOgTyPMClloG60C/zgabhgEy7WI626/HdZfHx57LN3eSsV998FLL8X6jN7wi27p9iNJkqTsGb51/HPefLh2VLq9lIqXXoLXXov1Sb2hVVm6/WSUQZhUCh56CE49NdadWsLTB8MG3dPtqT6aN4PTt4S3T4IdesR1U6bA7rvD0UfDjz+m2l5RmzYNzj031qu2gYt2TbcfSZIkZdMW3WDgmrG+7bbq2/20ZDfcEP9s2QxOcKKrtBiEScXu+edh6NBYty+Dpw9sOgMqrtUR/j4U7tgd2reM6+66K44dNmFCqq0VrdNPh2++ifW128Dyy6XbjyRJkrLr/NxVYbPmwE2/TbeXYvf55zB2bKwPWhNWWSHdfjLMIEwqZu+9B3vuWT075Ng9oXzNtLsqrBDgmE3gnZNgxx5x3eefw/bbx0kBZs9Otb2i8te/wgMPxHq3bnDgFun2I0mSpGwbuCZsnhv/97c3wk8/pdtPMRs9Oo7xC3D6gHR7yTiDMKlYffstDB4MFRVx+c6BsMP66fbUkLqvAOOHwuhdoU1u7LDrr4dNN4XXX0+3t2Lw7bdxUgGADi3htr0cIF+SJEnpCgGG50Kdih/h1lvS7adYzZgBd9wR6227QHkJjvXchBiEScVo3jzYf/94ZRTAb/rCYf3T7akxNAtw8ubw5gmwRe6bpfffhy23hMsui/9dsihJ4Mgj4euv4/INA6Brp3R7kiRJkgD26AW9O8f6umtgzpx0+ylG99wDP/wQ6zM2S7cXGYRJRWnYsOoxsg5eCy4YnGo7jW7dFeGFo+Hy7aFFgPnz4eKLYaut4IMP0u6u8d10Ezz+eKz3WwOGZiAUlSRJUmloFqpnkPz6W7j77lTbKToLF8KNN8Z6rXaw+8bp9iODMKno3HNP9RtleUe444Bs3gLXohlcsA28eiysv1Jc99prUF4e//tU3V/f1L3ySgxGAdZoC7fvl83zQZIkScXrwA2gR4dYXz0ifpGt6Ikn4OOPY31aH2jePN1+ZBAmFZW33qoeB2qlVvDng2G5lun2lLbyVeG14+GcfhCIl1qfcQbsuCNMmpR2dw3r669hn33iZAktmsEDu0OHtml3JUmSJP1ci2Zw7lax/uwLeOihdPspJtdcE/9cvgyO3DrdXgQYhEnFY+ZMOOAAmDs3Xl78p91hjZXS7qo4tG4Bo3aGCUfAmrlphp97DjbYAG65pWleHTZ3Luy3H0yZEpev7wf9e6XbkyRJkrQkR5ZDl9yXtiMub5qf0evqlVfgf/831sevB+3bpNuPAIMwqXiccgp8+GGsL9kUtm/CM0Quq23WgLdOhGPL4/LMmXDSSbDDDvDpp+n2VkgLF8IRR8ALL8Tlw9eGk3dItSVJkiRpqVq3gLP6xfrdD+Cxx9LtpxiMGhX/LGsGp2+XaiuqZhAmFYP7768eVHL7LnD+Lqm2U9SWbwW37wHjD4XVl4/rJkyADTeMg8o3hW+efvUrGDMm1v07w637Oy6YJEmSit8JfaFD61hfcVmc/TyrPv4Yxo6N9SFrQTdnfS8WBmFS2j7+GE48MdadW8H9BziAYj52XhvePhlO2CQuz5oFp50G221X2jNLXnpp9TgCvdrDXw6F1hkfJ06SJEmloX0rOGWzWL/6ehzOJKuuvbY6CDxn23R70c8YhElpmjsXDjwQZsyIy/cMhq4d0+2plLRvBbfsDs8MhR7t47p//AP69IHzz4+3TpaSyy+HSy6Jddc28OTBsGL7VFuSJEmS6uT0LWG5sliPuCLdXtLy9dfVd/zs1g3W75ZqO/o5gzApTZddBm+8EetzNoRd+6TbT6kauGa8OuyUzeLMkvPmwYgR0Ls3jBtX/JdkL1wIZ58NF14Yl1dtA88dCj26pNuXJEmSVFcrLQfH5e7aePpZePXVdPtJw003xYseAH7lTJHFxiBMSssrr8DIkbEu7whX7JFuP6WuXUu4aTC8eixstkpc98UXsNdesNtu1RMRFJvZs+HQQ+G66+Lyqm3g2YNh3a7p9iVJkiQtq7O3igPEQ/auCvvpJ/jd72LdrzP0XzfdfvRfDMKkNMyaBUOHxiuBWjaDe/eCli3S7qpp6NsVXj4Wbv0ldGwV1z3xBKy/PpxwAnz1Vbr91TRpEmy9Nfzxj3F5vfbw8hGwnpdOS5IkqYR1aw9DN4r1uL/Ce++l209juuMOqKiI9blbOOlVETIIk9Jw/vnw0Uex/s1msEH3dPtpapo3g+P7woenwlEbxdslFyyA226DtdeGCy6A779Pt8c//Qk23bT61tgBK8MLR8MaK6fblyRJklQI5/aPn8MBRl6ZaiuNZs6cn098tccm6fajxTIIkxrbc8/BjTfGul9nOHundPtpyjq3hTuHwJsnwOC14rrZs+HKK2H11WHYMJg6tXF7mjIFDjggPqZPj+tO7w3PHOvA+JIkSWo61l0R9ls/1g+Ogc8+S7efxnDnndV3oAzfHJp5NVgxMgiTGtNPP8GRR8Z6uRZwz97QvHm6PWVBny7w+GEw4XDYIjd+2IwZ8duaNdeEww6DF15o2EH1f/wxzgrZq1e8GgxgpVbwyK5ww35Q5q2xkiRJamKG5waKX7AArhmVbi8NrbISrroq1mu2g4O3SLcfLZFBmNSYLrgAPv881lf3g56rpNtP1mzbA14+Dp48BLbNjcNVWQn33w8DBsCGG8ZfXp98UrhjfvEFXHQRrLFGnBVy5sy4ft814J3jYZ/NC3csSZIkqZhsvArsuk6s77wTvv463X4a0r33wpdfxnp4X7/oLmIGYVJj+ec/YfToWG+zMpy4XartZFYIMGgdmHA0vHQUHLBe9Yw2774L550HPXvCxhvH+okn4Icf8t9/ksC//x1/1jvtBD16wG9+Uz1g5oYd4Jl94OEjoEvHQv/tJEmSpOJy/oD459xKuP66dHtpIGHBAhgxIi50bwuH90+3IS2VEaXUGCor4ZhjYkjSqjncvic0M4dOXb/u0O8A+GYm/OENuPsN+CAXWL31VnxcdVUMz3r0gHXXjSHZSitBu3bQtm382f70E0yeHAOwiRPhu+/++1ibrQjn94sDZjpWgCRJkrJi69VhwOrwjy/glt/BecOhY9P6Qnjlp5+GTz+NC78qh5ZGLcXMn47UGK6+Ol5tBPDrTaBX13T70c+t3BZ+NSA+3vsWHnkb/vo+TJwGC5MYYH72WXyMH5//fldbDvZaA47aAsrXaLj+JUmSpGI2fGv4x4Pw00y4eTT8+sK0OyqcBQtY44EHYr1qGzh6m3T7Ua0MwqSG9uGH8dY4gA06wLk7p9uPlq53Z7hoYHz8OBde+gJe+gw+/AY+nA6f/AQz5//36zq2hB7tYP2OsHlXGLAubLRavJpMkiRJyrJd1onjhb35NdxwHZx2OrRvIjOmP/wwy1WNDTZsY2hdlm4/qpVBmNSQFi6E446Lt88F4Pe7eZlsKWnfCnbpGR81zV8Is+bBzEpo2RzatoTW/lwlSZLU8MZN7MSo8d2YWtGSrh0qGTZoMkPKp6fd1tKFAL8eAPs+DNMq4Ppr4eJL0+6q/ubPh0tzf4/OreH47VJtR/lxkCKpIf3+9/D887E+pTdssXZBdjtuYif6j+zDmuf1pf/IPoyb2Kkg+1WeWjSLIdmqy8OKyxmCSZIkqVGMm9iJ4WN7MKWiFQmBKRWtGD62R2n8e2CvX8Amq8b62msXP65uqbn/fvjgg1ifuzEs1zLdfpQXgzCpoXz1FZx7bqy7LwdX7FaQ3Zb0Lz9JkiRJy2zU+G7Mntf8Z+tmz2vOqPHdUuqoDpoFuHJgrH+aCSOuSLef+po7Fy65JJYrdYCTt0+3H+XNIExqKOeeCz/8EOvf7QjLtynIbkv6l58kSZKkZTa1YvFXHC1pfdHZeW3YNjeJ1M2/g6qxtUrR738Pn38OwKTDhkCbEvkZyCBMahAvvhgvkwXYa3XYrbxguy75X36SJEmSlknXDpV1Wl90QoArd4j13Eq47JJU21lms2bB5ZfHeq12fD3Yq8FKiUGYVGgLFsApp8S6dXO4rjC3RFYp+V9+kiRJkpbJsEGTaVO24Gfr2pQtYNigySl1tAy26g67rRvrP9wNH32UajvLZPRo+PrrWF/aj6SFM0WWEoMwqdDuuAPefDPWv9oYenQu6O6bxC8/SZIkSXU2pHw6I/aexGod5hJIWK3DXEbsPan4Z41c1BUDIQALFsKFw9Pupm6+/RauvDLW63eAg7ZItx/VmVOdSYU0bRpccEGs12gLv9q54Ieo+iXXEFMml+RUzJIkSVKGDCmfXvqf0ft0gYM2hAffhj+NhTP/CVtumXZX+bn00uqxoK/eFpo3X/r2KjoGYVIhXXghTM/9UrpuhwYbMLEhfvlVzUZZNRB/1WyUVceTJEmSpIK5fHt45D2oXABnnAIvvQrNivymtfffh1tvjfVOq8KuG6Xbj5ZJkZ9lUgl580247bZY77gq7LVxuv3UkbNRSpIkSWo0a3aEs3JXgb3yOjz4YLr95OOcc+KY0M0CXLtLHPxfJccgTCqEJIFTT4WFC6FFgN/uUXJvis5GKUmSJKlRnT8AVmkX6/OGwcyZ6fazNE89BU88EetjesKGq6fbj5ZZSQZhIToghPBYCGFyCGFuCOGrEMIzIYRjQggFu+UzhLB6COH4EMIDIYR3Qgg/hhAqQwjfhhBeCCFcFkLw/4Cse/BBeOGFWJ+2IfxilXT7WQbORilJkiSpUS3fCkbsEOspX8NVI9PtZ0kqK+GMM2K9fBlctmu6/aheSi4ICyF0BJ4GxgC/BFYDWgKrAAOBO4BXChFOhRDGAZOAW4GDgfWB5YEyYCWgP3Ah8FEI4Zz6Hk8l6qefYNiwWHdpDRftkm4/y8jZKCVJkiQ1uqEbQd+usR51Nfz73+n2szijRsXxwQB+XQ5dOqTbj+qlpIKwEEJL4C/EwAvgS2IQdRAwDMidmWwC/C2E0L6eh9yAOKkrwL+Aq4FjgP2Bs4AXc8+1AkaFEC6o5/FUii6/HL76KtYjt4UV2qTbzzJqMlMxS5IkSSodzQLcmLuYYE4lHH9MHHqmWHzyCfzmN7HesAOcuVO6/ajeSm3WyBOBAbn6DWDHJEm+r3oyhDAaGAcMAnoTQ7Jh9TjeHOBm4KYkST5czPPXhxDOAK7PLV8SQng4SZKP6nFMlZIPP4Trcz/+LTvD0C3S7aeemsRUzJIkSZJKy1bd4fhN4bbX4ZkJcN99MHRo2l3FQO6kk2Du3Lh86y5QVmoxihZVMleE5cb9qrriKgGG1gzBAJIkmQMMBapG2Ds1hLBiPQ47IEmSU5YQglUd8wbgf3KLLYBD6nE8lZIkifeJz5sXrxu8abf4bYYkSZIkqW6u2hFWzQ2cf9YZ8O236fYDMGYM/P3vsT5uXdiqV14vGzexE/1H9mHN8/rSf2Qfxk3s1IBNqq5KJggj3g7ZOVc/kyTJu4vbKEmSb4jjh0G8ZXHPZT3gokHbUjxco95wWY+nEvPoo/Dkk7E+ej3o65wJkiRJkrRMVmgNowfHetr3cObp6fbz9ddw6qmxXrk1jNw9r5eNm9iJ4WN7MKWiFQmBKRWtGD62h2FYESmlIGznGvWTtWxb8/nGGLn8pxp1aQ4QpbqZMwfOPDPWHVrClb9Mtx9JkiRJKnV7rQd75q66euCPMG5cOn0kCRx3HEybFpd/uy10bJfXS0eN78bsec1/tm72vOaMGt+t0F1qGZVSELZBjfr1WrZ9bQmvayg1j/F5IxxPabvmGvj001hfthV0zu9NUZIkSZK0BCHAzYOhQ+u4fMxR1ROTNaa77op3AAEc2AMO2DLvl06taFmn9Wp8pRSErVujnlTLtpOBBbm6ZwihwQZuyo1ddmSNVY831LFUJL74Aq68MtYbdoATByx9e0mSJElSflZrD7fm7riZ9j0ceTgsXNh4x3/3XTjttFh3bQM371Onl3ftUFmn9Wp8pRSEdahRf7e0DZMkmQ/8mFtsAbRtqKaAc4D1cvXbGIQ1feecA7Nnx/qmwdCilP43kiRJkqQid8AGcFifWI//O4y4snGOO2MG7LsvzJoVJ0S7Z1foVLe7f4YNmkybsgU/W9embAHDBk0uYKOqj1L6F3zNs29OHtvPrlEvX+BeAAghbA/8Jrc4Hzg+SZJGjKrV6J59Fh7OzY1wwJqwbc90+5EkSZKkpuimXWGdjrG+8CIYP75hj7dwIRx9NHzwQVy+uBx2rPtceEPKpzNi70ms1mEugYTVOsxlxN6TGFI+vcANa1mFJEnS7iEvIYRKoCy3WJa76mtp208BuuYWuyZJUtAbi0MI6wEvACvmVp2bJMmoPF53HHAcQJcuXTYdM2ZMLa8oLjNmzKBdu2yOhxXmz6fvscfSdtIkFrRuyav3XsPcziul3VbRmjGjFe3azU27DRU5zxPly3NF+fJcUT48T5Qvz5V0tf3sSzY56SKaz5nLvPbL88bom5ndvXuDHKvHXXfR4777AJjed33+38jzoHnzWl4VNZ3zpBJYmaYwB+D222//epIkfRf3XCkFYdOBXBzM8kmSzCjk9nXsZU3geaBq2ocbkiQ5s6776du3b/Laa6/VvmERmTBhAtttt13abaTjxhvhjDNifcUWcH5jTEhauiZM6MV2232Ydhsqcp4nypfnivLluaJ8eJ4oX54rRWDMO3DQ/8R6zTXgpX/CKqsU9hh33w1H5ob+7rk8vHwMrNg+75c3nfPkC+BMoO5XwhWbEMISg7BSujWyoka94hK34j8D2FedtfOBmYVqIoTQHXiW6hDs1mUJwVRivvkGLr441msvD2fvkG4/kiRJkpQFB24AF20T688+h8G7wvffF27/Y8bEWyIBOrWCxw+oUwim0lNKQdhHNeoetWzbDai6hvHjpECXvYUQuhJDsKrj3wWcVIh9q8gNHw4//BDrG3aEVmVL316SJEmSVBiXbAdHl8d64pswcHv49tv67/ePf4RDDonjgy3XAv4yBHquVv/9qqiVUhD2To16sZe3LeH5d5a4VR2EEFYhhmDr5FY9ABxbqJBNRezVV+Guu2I9uBvs1ifdfiRJkiQpS0KAW3eDfX8Rl998C7bdBv7972XbX5LAlVfCwQfHEKxNc3hsT9h6vcL1rKJVSkFYzSkiBtWybc3Bm56s74FDCJ2BZ4BeuVUPAYc7Q2QGLFwIp5wS65bN4Ibd0+1HkiRJkrKoRTP4475wWO7ChPc/gE03hUcfrdt+pk2DAw+ECy6Iy8uXwWNDYPsNCtuvilYpBWHPAVXXPu4YQlh/cRuFEFYGDswtzgH+Up+DhhA6AU8DvXOrxgKHJkmyoD77VYm4+274179ifVYf6Llyqu1IkiRJUma1aAZ3D4Fh/eLyDz/AHnvAQQfBl18u/bXz58O990Lv3vCnP8V1PdrCy4fCQEOwLCmZICxJkvnAFbnFANwbQuhYc5sQQmvgHqBtbtXoJEmmLW5/IYS7QwhJ7nHJErZZAXgKqLoX7q/Agble1NRVVMB558V6teXgAmeJlCRJkqRUNQtw9c7wyH7xai6IA96vs0680uvRR2MotmAB/PgjvPIKXH459OwJhx8eJ0ID2L0bvHosrL96en8XpaJF2g3U0S3APsAAYBPgrRDCbcAnxAHyjwZyNw3zHnB5PY/3N2DTXD2VOC7YL0MIS3vNrCRJnqrncVUMLrmkegDGUdtAu1aptiNJkiRJytmnN2y+GgwbDw+9D5WV8NBD8bE0XVrDdQPgoH5x7DFlTkkFYUmSVIYQ9gQeAQYC3Vl82PUGsFeSJD/U85D9atRdiWOD1eZzap/VMpPGTZzCqPEfMrViNl07tGHYoF4MKS/SGTneeQdGj471Nl3gwM2Xuvm4iZ0YNb4bUyta0rVDJcMGTWZI+fRGaFSSJEmSMqr7CjBmfzjtS/jdy/A/H8GcJYxi1LsDnNoHDu8PbVo2bp8qKiUVhAEkSfJ9CGFHYH/gMKAcWAn4HngXGAP8wdsXi8u4iVMYPvZtZs+Lb0pTKmYzfOzbAMUXhiUJnHZavJS2WYDfDl7qNwXjJnZi+NgezJ7XHIApFa0YPrYHgGGYJEmSJDW0rbrHx81z4PWp8O5X8N2P0L4MurSHbXtC946170eZUHJBGECSJAnx6qx8rtBa0j6OAI6oZRuvkyyQUeM//E8IVmX2vAWMGv9h8QVhjzwCzz0X6xPXg42Wfs/4qPHd/hOCVZk9rzmjxnczCJMkSZKkxrJCaxi4VnxIS1Ayg+WrtE2tmF2n9amZNQvOPjvWK7aCywbX+pKpFYvPx8XHAAAgAElEQVS/rHZJ6yVJkiRJUjoMwtQounZoU6f1qRkxonra3Su3hE7tan1J1w6VdVovSZIkSZLSYRCmRjFsUC/alP389sE2Zc0ZNqhXSh0txiefwNVXx3qTTnD0gLxeNmzQZNqU/fy2zzZlCxg2aDLjJnai/8g+rHleX/qP7MO4iZ0K3bUkSZIkScpTSY4RptJTNQ5Y0c4amSRw+ulxyl2A0btA8+ZLf01O1Thgi84aCTiIviRJkiRJRcQgTI1mSPlqxRN8LerRR+GJJ2J9VE/o17NOLx9SPv2/wq3+I/s4iL4kSZIkSUXEWyOl2bPj1WAAHVrCyF8WZLcOoi9JkiRJUnExCJOuugomTYr15ZtB5xUKslsH0ZckSZIkqbgYhCnbPv0URo6M9cYd4YTtC7brpQ2iLxWaEzNIkiRJUu0cI0zZdsYZMHdurG8elPcA+flY0iD6jg+mQhs3sZMTM0iSJElSHgzClF2PPx4HyQc4Yh3YqlfBD7G4QfSlQhs1vpsTM0iSJElSHrw1Utk0Zw6cdlqsVyiDq3ZLtx+pHpyYQZIkSZLyYxCmbBo1Ko4PBvCbzWHlwgyQL6XBiRkkSZIkKT8GYcqejz6CK66IdZ+OcGLhBsiX0uDEDJIkSZKUH8cIU7YkCZxwQhwgPwC37gotCjdAvpQGJ2aQJEmSpPwYhClb7rsPnnsu1iesB/16ptuPVCBOzCBJkiRJtfPWSGXHd9/BWWfFetU2MMIB8iVJkiRJyhKDMGXHOefAtGmx/u22sELbdPuRJEmSJEmNyiBM2fDss3DPPbHerRvss3m6/UiSJEmSpEZnEKamb86cOEA+QNsWcPPuEEK6PUmSJEmSpEZnEKam78or4eOPY33ZprD6yun2I0mSJEmSUmEQpqbtzTdhxIhYb9IJTtsp3X4kSZIkSVJqWqTdgNRgKivhiCNg/nxo0Qx+vxu0aJ52V5IkSZKUeeMmdmLU+G5MrWhJ1w6VDBs0mSHl09NuSxlgEKam68or4a23Yn3+RlC+Zrr9SJIkSZIYN7ETw8f2YPa8eKHClIpWDB/bA8AwTA3OWyPVNL35JlxxRaz7dIQLBqfbjyRJkiQJgFHju/0nBKsye15zRo3vllJHyhKDMDU9i94Sefdu0NKLHyVJkiSpGEytaFmn9VIhGYSp6fmvWyLXSrcfSZIkSdJ/dO1QWaf1UiEZhKlpeflluPzyWHtLpCRJkiQVnWGDJtOmbMHP1rUpW8CwQZNT6khZ4v1iajp++AEOPhgWLICWzeDePbwlUpIkSZKKTNWA+M4aqTSYEqjpOPlkmDQp1ldtDhv1SLMbSZIkSdISDCmfbvClVHhrpJqG+++HBx6I9S5d4bSd0u1HkiRJkiQVHYMwlb5PP4WTTop159Zw977QzFNbkiRJkiT9nGmBSltlZRwX7Kef4vLdO0OXjun2JEmSJEmSipJBmErb2WfDK6/E+tTeMLg83X4kSZIkSVLRMghT6XrgARg9OtabrQijhqTbjyRJkiRJKmoGYSpN77wDxx0X6xVbwSP7Q6uydHuSJEmSJElFzSBMpWf6dNhrL5g1CwLw4K6w+sppdyVJkiRJkoqcQZhKS2Ul7LsvfPJJXL50U9h5o3R7kiRJkiRJJcEgTKUjSeCkk+C55+LyfmvABYPT7UmSJEmSJJUMgzCVjuuugzvvjPVmK8LdB0EzT2FJkiRJkpQfUwSVhnvvhXPOiXX35eCvB8NyrdLtSZIkSZIklRSDMBW/cePgqKNi3b4MHt0PVumUbk+SJEmSJKnkGISpuD3zDBxwACxYAK2bw2NDYKMeaXclSZIkSZJKkEGYitdTT8Eee8SZIls0g7GDYUDvtLuSJEmSJEklyiBMxenPf4bdd4dZs6BZgPt3hF03SbsrSZIkSZJUwgzCVHzuvRf22y9eCVbWDB7aGQ7ol3ZXkiRJkiSpxBmEqXgsXAgXXQSHHx7HBGvTHP66G+y7ZdqdSZIkSZKkJqBF2g1IAFRUxJkh//znuLxCGTw6xDHBJEmSJElSwRiEKX3//CccdBBMmhSXey4Pj+4Pvbql2pYkSZIkSWpavDVS6ZkxA848E/r3rw7BBq8GrxxnCCZJkiRJkgrOK8LU+ObNg7vvhksvhSlT4rqyZnD1FnD6jhDMZyVJkiRJUuEZhKlexk2cwqjxHzK1YjZdO7Rh2KBeDClfbfEbf/st/OEPcMst1VeAAfTvDLftDut3b5SeJUmSJElSNhmEaZmNmziFsdc/wL2P38QbXdfj9dXW4+H316Dt3luw0wZd462Pn34Kb78Nf/87vPRSnBmySvfl4NIt4fD+0MyrwCRJkiRJUsMyCNMyGzX+Q/b8/F3Wnj6ZtadPZr93no5P3FvLC3t3gJP7wNFbQ6uyBu9TkiRJkiQJDMJUD1MrZvPlCl343zU3oXzKB7SvnLXkjXutAL/sBkP6wNY9IYTGa1SSJEmSJAmDMNVD1w5teLT3tjzae1tCspC1p02m64/f0pNvuXC3T6FtS+jaAdZfBdq3SrtdSZIkSZKUcQZhWmbDBvVi+Ni3mT1vAUloxicrrc6UVVdj770nQfmKabcnSZIkSdISjZvYiVHjuzG1oiVdO1QybNBkhpRPT7stNTCDMC2zqtkhR41/l6kVlb5xSJIkSZJKwriJnRg+tgez5zUHYEpFK4aP7QHgv2mbOIMw1cuQ8tUYUh6AC4HuabcjSZIkSVKtRo3v9p8QrMrsec0ZNb6bQVgTZxAmSZIkSZJS15i3Kk6taFmn9Wo6mqXdgCRJkiRJyraqWxWnVLQiIfznVsVxEzs1yPG6dqis03o1HQZhkiRJkiQpVUu7VbEhDBs0mTZlC362rk3ZAoYNmtwgx1Px8NZISZIkSZKUqsa+VbHqlktnjcwegzBJkiRJkpSqrh0qmVLRarHrG8qQ8ukGXxnkrZGSJEmSJClV3qqoxuIVYZIkSZIkKVXeqqjGYhAmSZIkSZJS562KagzeGilJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKBIMwSZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMaJF2A2p6xk3sxKjx3Zha0ZKuHSoZNmgyQ8qnp92WJEmSJEnKOIMwFdS4iZ0YPrYHs+c1B2BKRSuGj+0BYBgmSZIkSZJS5a2RKqhR47v9JwSrMntec0aN75ZSR5IkSZIkSZFBmApqakXLOq2XJEmSJElqLAZhKqiuHSrrtF6SJEmSJKmxGISpoIYNmkybsgU/W9embAHDBk1OqSNJkiRJkqTIwfJVUFUD4jtrpCRJkiRJKjYGYSq4IeXTDb4kSZIkSVLR8dZISZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKBIMwSZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKBIMwSZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKBIMwSZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKBIMwSZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImlGQQFqIDQgiPhRAmhxDmhhC+CiE8E0I4JoTQogGO2TaEcFYI4cUQwjchhDkhhM9DCA+FEAYV+niSJEmSJEkqrIIHRg0thNAReAQYuMhTq+QeA4ETQwh7JUnyRYGOWZ475lqLPLV67rF/COFB4MgkSSoLcUxJkiRJkiQVVkkFYSGElsBfgAG5VV8CtwOfAN2Ao4BfAJsAfwsh9EuS5Md6HnMN4G9Al9yqV4H7ge+ADYHjgBWBg4GFwGH1OZ4kSZIkSZIaRkkFYcCJVIdgbwA7JknyfdWTIYTRwDhgENAbuBAYVs9j3kB1CHYXcGySJAtzy38MIdwK/IN4ZdihIYQxSZI8Xs9jSpIkSZIkqcBKZoyw3LhfF+QWE2BozRAMIEmSOcBQYGZu1akhhBXrccyNgCG5xS+Ak2uEYFXH/IIY0FW5ZFmPJ0mSJEmSpIZTMkEYceyvzrn6mSRJ3l3cRkmSfAOMyS22AvasxzEPqFHfngvaFudvxNszAfqGENauxzElSZIkSZLUAEopCNu5Rv1kLdvWfH6Xhj5mkiQJML7GKmeRlCRJkiRJKjKlFIRtUKN+vZZtX1vC6/IWQmhGHHgfYD7wVkMfU5IkSZIkSQ2nlIKwdWvUk2rZdjKwIFf3DCGEZTheN2C5XD0lSZL5tWz/eY163SVuJUmSJEmSpFSUUhDWoUb93dI2zIVWP+YWWwBtG/J4OdOW8FpJkiRJkiQVgRCHtyp+IYRKoCy3WFbbFVohhClA19xi1yRJvqrj8bYCXswtvpgkyda1bN8T+Ci3+FGSJL2WsN1xwHEAXbp02XTMmDGL26xozZgxg3bt2i2ydh4wBViWC+/UVM2Y0Y527Wak3YaKnOeJ8uW5onx5rigfnifKl+eK8tF0zpOFwCpAm7Qbqbftt9/+9SRJ+i7uuRaN3UyJKlhamCTJ7cDtAH379k222267Qu26UUyYMIH/7nk+8C7xfxopmjDha7bbbq2021CR8zxRvjxXlC/PFeXD80T58lxRPprWebIeTSEIW5pSCsJmAB1zdevc8tLU/Mn9tIzHW9y+Gup4JawFsFHaTajoTADK025CRW8CnifKzwQ8V5SfCXiuqHYT8DxRfibguaLaTcDzpHSU0hhhFTXqFZe2YQihBdA+tzgfmNmQx1vMNhVL3EqSJEmSJEmpKKUg7KMadY9atu0GNM/VHyfLNhDaZGBW1f5y4drSrFGj/miJW0mSJEmSJCkVpRSEvVOjXuyAZ0t4/p0lbrUUSZIsBN7PLeZz71+9jylJkiRJkqSGU0pB2Pga9aBatt2lRv1kQx8zhBAWeX78kraVJEmSJElSOkopCHsO+DZX7xhCWH9xG4UQVgYOzC3OAf5Sj2P+qUZ9fAih9RK22xVYJ1e/liTJv+txTEmSJEmSJDWAkgnCkiSZD1yRWwzAvSGEjjW3yQVV9wBtc6tGJ0kybXH7CyHcHUJIco9LlnDMt4BxucXVgdEhhJ/9NwshrA7cUmPVYvclSZIkSZKkdNU2AHyxuQXYBxgAbAK8FUK4DfiEOED+0cAvctu+B1xegGOeAfQDuuT2v0EI4T5gGrAhcDzVM0Y+kCTJ4wU4piRJkiRJkgqspIKwJEkqQwh7Ao8AA4HuLD7segPYK0mSHwpwzM9DCLvmjrkWsEXusagHgaPqezxJkiRJkiQ1jJK5NbJKkiTfAzsSxwF7HJgKVAL/BzwLHAdskSTJFwU85kSgD3A28DLwHTAX+BJ4GNg1SZJDkiSpLNQxJUmSJEmSVFgldUVYlSRJEuCh3GNZ93EEcEQdtp8JXJd7SJIkSZIkqcSU3BVhkiRJkiRJ0rIwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKBIMwSZIkSZIkZYJBmCRJkiRJkjLBIEySJEmSJEmZYBAmSZIkSZKkTDAIkyRJkiRJUiYYhEmSJEmSJCkTDMIkSZIkSZKUCQZhkiRJkiRJygSDMEmSJEmSJGWCQZgkSZIkSZIywSBMkiRJkiRJmWAQJkmSJEmSpEwwCJMkSZIkSVImGIRJkiRJkiQpEwzCJEmSJEmSlAkGYZIkSZIkScoEgzBJkiRJkiRlgkGYJEmSJEmSMsEgTJIkSZIkSZlgECZJkiRJkqRMMAiTJEmSJElSJhiESZIkSZIkKRMMwiRJkiRJkpQJBmGSJEmSJEnKhJAkSdo9ZFYI4Vvg87T7qKOVgO/SbkIlwXNF+fA8Ub48V5QvzxXlw/NE+fJcUT48T4rPGkmSdF7cEwZhqpMQwmtJkvRNuw8VP88V5cPzRPnyXFG+PFeUD88T5ctzRfnwPCkt3hopSZIkSZKkTDAIkyRJkiRJUiYYhKmubk+7AZUMzxXlw/NE+fJcUb48V5QPzxPly3NF+fA8KSGOESZJkiRJkqRM8IowSZIkSZIkZYJBmCRJkiRJkjLBIExLFaIDQgiPhRAmhxDmhhC+CiE8E0I4JoTQIu0e1TBCCCuEEPYPIdwSQnglhDAthDAvhPB9COGtEMLvQgib5bGfu0MISb6Pxvi7qbBCCBPq8DOelOc+dwkhPBRC+DyEMCeE8E0I4cUQwpkhhLYN/FdSgYUQLqnL+0CNx90F2FePRv8La7FCCM1DCBuEEI4IIdwUQng5hDCrxs/qkmXYZ8HeK0II/UIId4UQ/p3ra3oI4fUQwq9DCCvVtTctu0KdKyGENiGE3UMI14cQXsidH5UhhB9DCO+HEP4QQtghz3353lNkCnieNMhnVd9TikchzpXca5fls8yEAu1vuwL/Z8k0QwwtUQihI/AIMHCRp1bJPQYCJ4YQ9kqS5IvG7k8NJ4RwLnAZ0GoxT3fIPfoQf/73A8cnSTKrEVtUExVCaAX8AThokac65x5bASeHEPZOkuT/NXZ/anSfpt2ACupPwN6F2FEh3ytCCAG4FjgDCDWeagN0BDYBTgkhHJwkybOF6F+1qve5EkI4BLgVaLeYp8uA9XKPI0IITwJDkyT5tj7HVKMr2HtKIfmeUpTSPFf8LFOEDMK0WCGElsBfgAG5VV8SZ8L4BOgGHAX8gvhG/rcQQr8kSX5Mo1c1iHWpDsE+BZ4G3gS+I/4C3wHYB2gOHAqsHELYNUmShbXs93jgmwbpWMVir1qery0wvQc4IFdPI77vvA2sRDzXNgfWBp4MIWyRJMmX9ehVjWcM8T2kNisAd+fqhHg+LM2FwDu1bON7TvFovsjydOL/5z2XYV+FfK8YAZyZq2cCdwKvEgOUfYCdgC7AX0IIA5IkyedcVv0U4lxZk+oQ7Cvg78C/iO8JbYmfcQ8CWgO7AE/nPs/m88We7z3FoZDvKVUK8VnV95TiU4hz5Vlq/5wL8a67+4nBJ8QvbWpzU27/S1Pbe47qwCBMS3Ii1SHYG8COSZJ8X/VkCGE0MA4YBPQmfiAY1thNqsEkwOPAKOD55L+nl709hDAAeIL4S31n4HBqf6N/KkmSSQXuVUUkSZJxy/raEMKeVP/D9gtgQM2rTUMINwO/B44EVgWuA/Zb9m7VWJIk+QD4oLbtQggn1Fh8JkmSz2t5yQtJkkyoT29qVK8C7wOvA68nSfJZCOEI8vtHwn8U8r0ihFAOnJtb/AHYZpEryG7L3TJzMfH33e25YM1b+RtWQc4V4EVgJPC3JEkWLPLcH0II1xC/7FuVeKX7r4g/69r43lMcCnWe1FSvz6q+pxStep8rud8ztd4FFULYheoQ7JMkSf6Rx+7fqM9naNWdQZj+S4jjfl2QW0yIl4p/X3ObJEnmhBCGEq8WagucGkIYmSTJtMbtVg3k3EV/5otKkuQfIYThxG8wAI6gfh88pEtq1Ccuest1kiQLQwgnE69IXB3YN4SwQZIkfkPWdBxVo/b9pIlJkuTKAu3qkhp1fd8rLqL61qXzl3Ab5aXArsSrzDYDBhO/LFIDKdC5cnOSJJfXcpz3QgjHAY/mVh1BfkGYikAB31MKyfeUItTI54qfZUqAg+VrcQYSx9eA+I38u4vbKEmSb4i3u0C8jW7PRuhNjaC2EKyGh2vUGzZEL8qGEEJPYOPc4sdJkjyxuO2SJJkN3FFj1f4N3ZsaRwhhfeI/CAAqgLEptqMiVcj3ihDC8sR/jAL8SPVtuYvuK6H6Sx+ovhpNRawOn2X+Rrx9DWD1EEL7BmpJTZzvKQohdAL2yC0uoPYhHpQSgzAtzs416idr2bbm87s0QC8qbj/VqNsscSupdoNq1ONr2db3naap5jeof0ySZE5qnaiYFfK9Yluqx8N8vpaxoWoea9clbqWSk7tlsubP3s8zWla+p+gQqs+Bp5IkmZJmM1oygzAtzgY16tdr2fa1JbxO2VDzZ17bWD4Ad4QQvgghzA0hVIQQ3gsh3BFC2KahGlTjCiE8HkL4KsTp6aeFEN7MTVO9cS0vrcv7zpvEb9kAeudmZ1IJy92Sf2iNVXfl+dLLctPSzwkh/BhC+DiEcF8IYXfPiyarkO8Vee8rN5tg1e+5lUIIK9fWqEpD7mdZdSfELCCfmSN972m66vNZ1fcUHVmjzvezDMBJIYQPQgizQggzQwiTQggPhxAOyX1GUoEZhGlx1q1RT6pl28lUf8js6S//zDmuRp3P2AY7At2BlsTZ4X4BHMP/b+++w+2q6jSOf1/AEAi9l4zEAKKCNBEkFEMACRYECSBSpDt2ZHAExYoIowMi4jCUoYP0rgMiJCPVENoQUCA06S20AAGS/OaPtc6cnZPT7r375tx7z/t5nvPctfdee+117jlZ2fe3V4H/kXRN7k5sg9ungZVIS9MvA6wHfAO4W9Lpkho9aW+73YmIWUDlCdsIYNW+VNgGhM8ClT8C7ouIKc0yF2wBjCY9fV0cWIMUULsKuE3SamVX1DquzLaiJ/c7MPcDnw82zGWDTfFe5to2VsAGtz1DWV/uVd2mdDFJ6wEb5M2XSO1Buz4OrEXqkboosBowgbT65FRJ65ZYVcOT5Vt9SxXSLzXLGBGzJL0OLE36Po0AZvRj3WyAkDSG6lOPmcDxTbK/QVq2fDLwJCl4OpI0DLcyFPczpJuMzSLi9X6ptPWnl0nd/O8EniFNFDuKFOAYk/PsS5p/ZXz+A7Wo7XancL33F859qnfVtgGip09QZwITgduAx4B3SQHYT5Lmq1wQ2AS4VdLGHpowpJTZVvSmrHrn2iAlaTRweGHXMS1OcdszdJVxr+o2pbsVp3g4LyLebeOcWaTVbf8CTCP1Sl2edO88ARhOCpDdLGnzBosvWC84EGb1LFZItzNHy9ukQBikp2IOhA1xklYCLqLaq/SHEfFkg+y/Bb4eEW/WOXaspC2AS0i9QdYBjgUOLLnK1r8OB6ZExHt1jh0taSfSE61FSau4fQ84qiZfb9qdisV7UFcbYCStSOpJCOmPynNbnHIJcEJETK9z7IQ86f4VpB4aq5AmK962nNraAFBmW+F2p4tJGgFcTvq/CdIqk3c0OcVtz9BV1r2q25QuJWkYaX6winYe6t0MrBYRz9Q5dpKk7wOXknqLLQ5cmFdAnl0nv/WQh0aaWY/kG8crqQ4x+QPphqCuiLizwY1F5fhNwBeAyLv2leShboNIRNzWIAhWOX45c98wflfSwo3yU/0uWHfYi+qDuasjolVP5KkN/hCtHL+fNDF65Q+MbSRtUkpNbaAps61wu9NFJC0InA9UhhvdBRza7By3PUNXP92ruk3pLjsAy+b0ne303IqIaQ2CYJXjT5IWUngu7/oQqZeYlcCBMKun2KNreBv5i3P+vNEwlw16koaTxrtvnHfdAuyWl4HutYi4BfhT3lyQuVcFsyEgIs4HHsybSwKb1WQptjvtrNjldiv6sxsAABMbSURBVGfo6O3Esg1FxCPA2YVdnymjXBsQymwr3O50IUkLkHpr7ZB3PQhsX8ZKtW57hq4271XdpnSv4rDIUu5lACLiZeA3hV1uU0riQJjV82ohvWzDXPz/Sl9L5M1ZQMOnKTa45S6/lwHj8q7JwKebPUHroUmF9FollWkDy6RCuvYzbrvdqZPn1Ya5bEDLvSU+kjefYe7l5PtqUiHtNmXoKLOtcLvTZfKiTidTXaX2EWDriHihxMtMKqTd9gwtkwrpep+t25QuJGkVqvPIzST1Ni3TpELabUpJHAizeh4qpEe1yDuS9FQE4OG+9gyygUnS+4CLSd1zAe4Gxpc8qX1xwtClG+aywazZZ9x2u5MD8JUhCW9SXRXOBp/iE9SzSp73wm3K0FRmW9GT+x1Iq3jVO9cGjxNJKwBCWrFvXD9MaO+2Z+hq9dm6TelOX6b69/DlEVF2UNNtSj9wIMzqmVpIb9Qib/H41Ia5bNDKf0j8nuoQgvuAbSPilZIv5adiQ1+zz7gn7c76VG84HnAAfnCStAiwW2HXGSVfwm3K0FRmW9F2WZKWp/pH60sl9yCy+UDS8cDX8uZTpCDYP/rhUm57hq5Wn63blO60TyFd2rDIArcp/cCBMKunODSl1VxN4wvpa/uhLtZBeTLZc4Cd864HgG3yePWyfbKQ9lOxoanZZ+x2p/vsTJovDuCmiHi45PLdpgxNZbYVk4B3cnrLHJxtpHit/25xXRtgJP0S+HbefJYUBHu0ny7ntmfoavXZTsJtSleRtDnwwbz5BHBDP1zGbUo/cCDM6pkIvJjT2+TloOchaQXgi3lzJmklQRsi8mSyp1P9jB+i/Hk0KtcaQ/WGYA7lzhNkA4Ck3Umr3UCaEPbm4vEcBLk7b64paXvqyAs2FFegvKjkqtr8U/ok+RWSVicNVaj4Y5nlW+eU2VZExAyq340lmPupfrEsAd8o7LqwZ7W2TpL0c+C7efN5UhCs7MB75Vpue4aodu5V3aZ0peK9zJllj1KQtAzVID64TSmNA2E2j4iYBRyVNwWcLWmu8cj5BvMsYETedWI/9RKyDihMJrt33jUN2Coinmt8Vt1y9pa0bS6vUZ7NgctJ3zWAs/NywTYISPpWq+XhJe0InFbYdWyD1bl+WkifJOn9NeUsAPwOqOy/JCI8JHsQkjQK2CpvvkGag7Cd8z4naULurdooz9qk3j+VJ/GTIuLW3tfWBqAy24ojgcofLkdLWrdOnh8BlXbuDvyHyKAh6QjgB3nzRdIDvb/3ohy3PUNUP9yruk3pEpJGALvmzSCtRtvuuZtKOkDSwk3yjCT1Flw573oYPwAuzUKdroANWCeRhq1sAWwI3CvpZFJAZCSwP/DhnPcB4OedqKT1m6OoTib7HnACsHGTe4SKP0XEW4XtDUlPMZ6UdB1pfrEXgdmk79Gn8qtS8P3Ad8p4AzbfjAN+I+lBUnfw+0mTeoo0UezngDGF/BOBo+sVFBFXSrqQNG/UasCdud2ZSpofYW9g45z9WeCQst+MzTf7UP13f1EPVp9dHfg18KKka4F7gOdI7dSKwFjg81Tvb55h7t4Z1kGSPkC6fygq/pE4Ls9LWXRpRNxd3FFmWxERd+dhc98jDdW9VdJppJWRFyPdC1VWA5sBHOR5CftfGd8VSQeSghIVJ5J6Ea7Z4vI3R8RLNfvc9gxAJbUppd6ruk0ZmMr6/6fGLqTPFODGiHi8B1VaETgVODZ/7+4kLegyk/T/2GbABKqB9RnAbrnDipUhIvzyq+6LtCrFDaQId6PXncD7O11Xv0r/7Ce1+NwbvUbVlHN8D869DFi20+/drx5/V65o8/OdQ+pluGiL8hYmLc7QrKxpwHqdfu9+9fo7I+Cxwuc5pgfnHtyDNuUvwGqdfr9+zfX5je3F/yv7NCirtLYifyePy+1Uo7IqQ+o6/nvshlcZ3xVS74ze3MuMrVMftz0D8FXS96T0e1W3KQPvVcZ3pU6Zfynk/VIP67NjD+pxH7Bup3+HQ+3lHmHWUES8ImkbUpfPvYANgOWAV0hPQy4AzghHpq2xX5GCpZ8gPXFbkfQdGg68Rvpj+FZSF/O7OlVJ65N/Aa4BNiU9WVuB9BkvRFrZ5iHSfGBnRETLCT4j4h1gd0lnAfuRvjsrkIbPPUwaQndKtN+DyAaecVSXlX8wejZ06BzSE9NNSStyrUL6vo0AXgeeBG4HLoiISSXV1wagMtuKSH+VHCLpYuAgYEvSd2sm8Cgp4H9SzNtLyLqH256hq/R7VbcpQ5+kNUgjpyB9Ty7rYRF/JvUi3ZTUg3kk6Xu3BKn317OkXoSXAn+IiDklVNsKlCOSZmZmZmZmZmZmQ5onyzczMzMzMzMzs67gQJiZmZmZmZmZmXUFB8LMzMzMzMzMzKwrOBBmZmZmZmZmZmZdwYEwMzMzMzMzMzPrCg6EmZmZmZmZmZlZV3AgzMzMzMzMzMzMuoIDYWZmZmZmZmZm1hUcCDMzMzMzMzMzs67gQJiZmZlZHZIelxSSHu90XQYKSTvl38lMSav2sawzc1khaVQ5NRy4JN2Q3+vvO10XMzOzbuZAmJmZmQ16kkYVgip9fe3T6fczEEkaDhyXN0+JiKc7WZ9B6Mf55xclbdnRmpiZmXUxB8LMzMzMrB1fA0YBM4FjOluVwScibgZuzJv+/ZmZmXXIQp2ugJmZmVkJXgB2anJ8HPDNnJ4InNAk710AETGqlJoNAZIWAQ7Lm2dGxDOdrM8g9gvSd3FTSeMj4tpOV8jMzKzbOBBmZmZmg15EvAVc0ei4pKUKm/+IiIZ5ra69geVz+uxOVmSQuxF4ChgJHAI4EGZmZjafeWikmZmZmbXy1fzzkYi4raM1GcQiIoDKZPnbSlqzk/UxMzPrRg6EmZmZmdXRatVIST8pTLA/Nu/bWtKlkp7MKys+IukUSavVnDtc0lck3SrpRUlvSbpP0mGSFm6zfotJOljS9ZKekfSOpOmS7pD0M0nLty6lret8FFgvb57f5jkLSfpGfn+vSJoh6e+Sjq39XbQoZ3lJB0g6V9JUSa9Lek/SS5Jul3SkpJWanH9h4TPasI3rDcufR0h6WtKCNccXlLSXpKsLn/HbOX1X/qy/IGnRJpc5r5Des/VvwczMzMrkoZFmZmZmJZB0DPC9mt2j82uCpK0j4u4cuLka2Kgm7zrA0cCnJW0XEW83udb2wJnACjWHhuVyNwIOlrRnRFzV2/eU7VhIT2yVWdIKpCF/G9QcWiu/9pO0SxvljAYeAhasc3jZ/NoE+I6kvSPisjr5/hPYNacPIE3438xOwHI5fXpEzC7UZzngj8DH65w3Mr82AA7M5dQdfhsR90p6Odf/81RXkzQzM7P5wIEwMzMzs777OjABeAw4gxTAWQrYC9gMWBq4RNI6wB+ADUlBlWuAl4EPAd8iBUe2AH4AHFHvQpJ2Bi4kBYhm5zJuAJ4DFge2AnbL6cslbRsRN9Yrq03b5p9zgCnNMkoaxtxBsBeA04CpwAhgPLAzcDFwT4vrDiO9x0dJ728q8CJpRMP7gW1IE8+PAC6QtHlETC4WEBETJT1ICsDtIenQPJ9cIwdWTgX+q+bYqVSDYNNIQxwfAt4GlsjX2JIUnGtlMrA9sJ6klSLiuTbOMTMzsxI4EGZmZmbWdxNIAaldImJmZaekU0mBr/GknmE3AesDe0TEXMMMJV1ACg4tAnxd0pER8U5Nnn8CTicFiJ4HPhcRd9TU5XRJJwDXA0sCZ0kaHRHv9fRN5aGBH8ubf4uIN1qc8q9Ug2D3ANtExMuF46cVAnljW5T1ArB5RNzS4PjRkrYi9a4bAfwbKQhY6xTgWFKwaldST7p55B5o4/Lm9RHxeOHYCsAOeXMKMDYi3mxQTjtDP/9KCoQBbAz0tdeemZmZtclzhJmZmZn13QvAnsUgGEBEzAF+Vtj1MeDk2iBYzvsQcG7eXIoUIKn1XVJAB1LQrTYIVinrDtKqhJCG7LUcitjAaKAy39WDzTLm3mDfzJvvArvWBMEqdbsU+HWrC0fE9CZBsEqeiaQgF8DYHCisdSZQ+VwOaFLc/oBy+tSaY6Op3jef3ygIluv0REQ80azewN8K6XVb5DUzM7MSORBmZmZm1nfnRMRrDY7dARR7Y/2uSTk3F9IfKR6QJGCPvDk5Im5qUacLgVk5/akWeRsp9m6a3iLvZlTnLLs6Ih5ukvc40lDLMtxaSM8TPIyI6aShmACbSfpIbR5JCwH75M0XgCtrshSHU67d65pWvVJIt714gJmZmfWdh0aamZmZ9d1fGx2IiFl5cvSVgDeBB5qU83whvXTNsbWBZXJ6uqQdaW0GqXfZh9vIW88yhXSrQFhxEvkbmmWMiGclPUBaIKApSWsDXyYF2tYkDfcc1iD7yAb7TybN1wapV9ghNcc/A6yS02fWGUZ6P/BMzrN/DkqeSgpI9iagV+wpV/s5m5mZWT9yIMzMzMys7+YZAlijMtfX9IiINvIBDK85NqqQHp9f7eptsGXhQrrV/GCrFNLT2ih7Gk0CYTnYdAxwKO2PYlii3s6IuEXS1Hy9vSQdFhHvFrIUh0yeVuf82ZK+AlxKCsLtl1+vSrqN1JPvuoi4s816vl5IL9LmOWZmZlYCD400MzMz67t2ewX1ZTjgkn04t1EPqlaKgbm6QaaCxQrpZiszVjScZyv7Pmny/QVIq2NeB/wY2Jc06f1O+fXDwjkLNinv5PxzuXweAJJWpTpx/aRGQzoj4hrS0MsrqA51XSqfexQwRdJ9ktoJUBY/y7fbyG9mZmYlcY8wMzMzs8FhRiH9k4j46Xy4ZnE45DINcyXF+i3aMFfViEYHJC0CHJ433wC2atTbSlK7q2GeTephNgI4kDSHGqSeXZUAWu0k+XOJiHuBnSQtThqqOQbYMv98H6nH2R8l7RUR5zUpqidDTs3MzKxE7hFmZmZmNjg8XUiXMWF7Ox4vpFsFwp4ppNdoo+xmeTalGig7ucWQw7Ymm4+I14EL8uY4SaPz8Mv98r7ppKGP7ZT1RkRcGxE/ioixwMpUV8IUcJykZr3Tir/LVitMmpmZWYkcCDMzMzMbHO6mOrfUpyQ17FFVoseoDmFcq0XeyYX0uGYZJa1M8wn8VyykH2lx3e1aHC+qDI8UsD+wLdW5186OiHfqndRKRLwcEYcAU/KuFUgT+zdSfO//25trmpmZWe84EGZmZmY2CETEbKAy3G5J0hxa8+Oald5YH5LUbJ6wW6iuermDpNWb5D2Y5vN5FecYa1iOpA2BzzYpZy4RcQfV97Mv8M+Fw/NMkt8LjxfSzaYg2aSQntwwl5mZmZXOgTAzMzOzweMXwKs5fbikQyU1vJ+TtLykIySt24drXp9/LgBs1ChTRLwH/DZvDgMukjTPcEpJOwKHtLjmlEL6AEkfqFPOmqShjD29n630CluZ6qT5t0bE/Y1OkLSdpG9LarhggaQ1SD3MIM2X1qwn28b55z0R8XyTfGZmZlYyT5ZvZmZmNkhExFOSvghcRQo2/Qo4SNKlwN9IPamWIA3L+wSwBann1aQ+XPZy4MicHgvc2CTvr4AJwPrAhsADkk4F7ifN+TUe2Bl4DbgnlzePiHha0mXAF0grM94r6WTSMMIFSJPT7w0MJ02Cv3cP3s/5wL8z9yqYp7Q4Z2XgeOCXkiYCfwUeJf2+lwM+TlrJsjJc9fiIqLsapKT1qM4RdkUP6m1mZmYlcCDMzMzMbBCJiOskfZI0THI0Keh1WJNTZpACT7293v2S7iEFt74E/KhJ3nclbQdcC2xAmuvriJpsrwK7AHu2uPRBpPf2UWBx4NCa43OAHwI304NAWES8Kek84Kt512vAxS1Om5N/DiPNSdZoXrIATgB+3KSsLxXS57a4rpmZmZXMQyPNzMzMBpmIuJ00ef2ewEWkSe1nALNIqx9OAU4FdgNWioj7+njJ/8g/V5c0pkXdXiDNgfVN4HbSBP9vAQ+SVlZcPyL+3OqCEfEyqVfb4aSFAt7Kr0eAM4AxEfHzXr0bKF7/vIh4q2HO5BxgPdKQziuBaaRFBGZT7d12IvCxiDg4IubUKySvUrl73rw+IlotBGBmZmYlU0R0ug5mZmZmNoBJGg48QVoN8ZSI+EqHq9Qnks4F9sibG0TEPfPpuuOAG/Lm9hFx7fy4rpmZmVW5R5iZmZmZNRURM4Fj8ubeklbpZH36QtLypHnMACbPryBYVlnp8zYHwczMzDrDgTAzMzMza8dJpCGYw0nDFQerw4GFc/qE+XVRSZsBW+fNZnO6mZmZWT/y0EgzMzMza4ukHUmrSL4DrB4RT3e4Si1JWpU04f6ipEDUVwEBfwfWiYjZ86kef87XvyAidm+V38zMzPqHA2FmZmZmNmRJ2oc0uX7RO8BWEXHb/K+RmZmZdZKHRpqZmZlZt3gOuAr4hINgZmZm3ck9wszMzMzMzMzMrCu4R5iZmZmZmZmZmXUFB8LMzMzMzMzMzKwrOBBmZmZmZmZmZmZdwYEwMzMzMzMzMzPrCg6EmZmZmZmZmZlZV3AgzMzMzMzMzMzMusL/AR9ki+1+DzYRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "crop_extent = 'Gaulunia/GauluniaCVD'\n",
    "croperfile = crop_extent[8:]\n",
    "\n",
    "datafile = crop_extent + \"/\" + croperfile + '.txt'\n",
    "datafilew2 = crop_extent + \"/\" + croperfile + 'w2.txt'\n",
    "\n",
    "from numpy import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import *\n",
    "from scipy import interpolate\n",
    "from scipy.integrate import trapz, simps\n",
    "from matplotlib.pyplot import *\n",
    "\n",
    "data = pd.read_csv(datafilew2, delim_whitespace = True, skipinitialspace = False)\n",
    "data.columns = ['TIME', 'Average_ndvi']\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "data = data.sort_values('TIME').drop_duplicates('TIME')\n",
    "\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "\n",
    "\n",
    "list_x = list(x)\n",
    "#list_x = sorted(list_x)\n",
    "list_y = list(y)\n",
    "#list_y = sorted(list_y)\n",
    "y = np.array(list_y)\n",
    "x = np.array(list_x)\n",
    "xp = np.linspace(x.min(), x.max(), 1000)\n",
    "#---------------------------------------------------------------------------------------------\n",
    "#                  Cubic Spline Interpolation fit\n",
    "#---------------------------------------------------------------------------------------------\n",
    "s1rev = interpolate.InterpolatedUnivariateSpline (x, y)\n",
    "# Use a smallish value for s\n",
    "if len(x) < 33:\n",
    "    s2 = interpolate.UnivariateSpline (x, y, s = 0.04)\n",
    "else:\n",
    "    s2 = interpolate.UnivariateSpline (x, y, s = 0.11)\n",
    "\n",
    "\n",
    "yc = s2(xp)\n",
    "Area1c = simps(yc, xp)\n",
    "yc_max = yc.max()\n",
    "xc_max = xp[yc.argmax()] \n",
    "\n",
    "print('Plotting Cubic Spline Curve')\n",
    "figr = plt.figure(figsize = (20, 20))\n",
    "plt.rc('xtick', labelsize = 30)\n",
    "plt.rc('ytick', labelsize = 30)\n",
    "plt.xlabel('Time (days)', fontsize = 30)\n",
    "plt.ylabel('Average NDVI', fontsize = 30)\n",
    "plt.plot(x, y, 'o', label='Data points')\n",
    "plt.plot (xp, yc, 'r-', label='Cubic Spline best fit', linewidth = 2.5)\n",
    "#plt.minorticks_on()\n",
    "plt.title('Average NDVI vs Time: Cubic Spline Best fit\\n',fontsize = 30, fontweight = 'bold')\n",
    "\n",
    "#plt.rc('xtick', labelsize = 40, fontweight = 'bold')\n",
    "#plt.rc('ytick', labelsize = 40, fontweight = 'bold')\n",
    "plt.fill_between(xp, yc, color = 'yellow', alpha=.5, label = 'Area under graph')\n",
    "plt.grid()\n",
    "plt.legend(loc = 'best', fontsize = 30)\n",
    "plt.savefig('CubicSpline.png')\n",
    "\n",
    "\n",
    "print(yc_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting Gaussian Process\n",
      "0.8178613461911795\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import gaussian_process\n",
    "from sklearn.gaussian_process.kernels import Matern, WhiteKernel, ConstantKernel\n",
    "#-----------------------------------------------------------------------------------------------\n",
    "#   Gaussian Process Modelling\n",
    "#-----------------------------------------------------------------------------------------------\n",
    "\n",
    "kernel = ConstantKernel() + Matern(length_scale=0.5, nu=0.6) + WhiteKernel(noise_level=0.2)\n",
    "X = x.reshape(-1, 1)\n",
    "\n",
    "gp = gaussian_process.GaussianProcessRegressor(kernel=kernel)\n",
    "gp.fit(X, y)\n",
    "\n",
    "x_pred = np.linspace(x.min(), x.max(), 1000).reshape(-1, 1)\n",
    "y_pred, sigma = gp.predict(x_pred, return_std=True)\n",
    "x_pred1 = np.reshape(x_pred, x_pred.size)\n",
    "ygp_max = y_pred.max()\n",
    "xgp_max = int(x_pred[y_pred.argmax()])\n",
    "Areagp = simps(y_pred, x_pred1)\n",
    "\n",
    "print('plotting Gaussian Process')\n",
    "figv = plt.figure(figsize = (20, 20))\n",
    "plt.rc('xtick', labelsize = 30)\n",
    "plt.rc('ytick', labelsize = 30)\n",
    "plt.xlabel('Time (Days)', fontsize = 30)\n",
    "plt.ylabel('Average NDVI', fontsize = 30)\n",
    "plt.title('Average NDVI versus Time: Gaussian Process fit\\n', fontsize = 30, fontweight = 'bold')\n",
    "plt.plot(x, y, 'o', label = 'Data Sample')\n",
    "plt.plot(x_pred, y_pred, 'g', label = 'Gaussian Process best fit')\n",
    "\n",
    "plt.fill_between(x_pred1, y_pred, color = 'silver', alpha = 0.5, label = 'Area under graph')\n",
    "plt.grid()\n",
    "plt.legend(loc = 'best', fontsize = 30)\n",
    "plt.savefig('GaussianProcess.png')\n",
    "print (ygp_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'iamondale'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop_extent = 'Gaulunia/Diamondale'\n",
    "croperfile = crop_extent[-9:]\n",
    "croperfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crop_extent = 'Gaulunia/DiamondaleF1'\n",
    "croperfile = crop_extent[9:]\n",
    "\n",
    "datafile = crop_extent + \"/\" + croperfile + '.txt'\n",
    "datafilew2 = crop_extent + \"/\" + croperfile + 'w2.txt'\n",
    "\n",
    "from numpy import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import *\n",
    "from scipy import interpolate\n",
    "from scipy.integrate import trapz, simps\n",
    "from matplotlib.pyplot import *\n",
    "\n",
    "data = pd.read_csv(datafilew2, delim_whitespace = True, skipinitialspace = False)\n",
    "data.columns = ['TIME', 'Average_ndvi']\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "data = data.sort_values('TIME').drop_duplicates('TIME')\n",
    "\n",
    "x = data['TIME']\n",
    "y = data['Average_ndvi']\n",
    "\n",
    "\n",
    "list_x = list(x)\n",
    "#list_x = sorted(list_x)\n",
    "list_y = list(y)\n",
    "#list_y = sorted(list_y)\n",
    "y = np.array(list_y)\n",
    "x = np.array(list_x)\n",
    "xp = np.linspace(x.min(), x.max(), 1000)"
   ]
  }
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