{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Processing the 2-D NMR data\n",
    "***************************\n",
    "\n",
    ".. currentmodule:: mab_utils\n",
    "\n",
    "Imports\n",
    "======="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:05:14.412632Z",
     "start_time": "2019-05-13T15:05:14.398634Z"
    }
   },
   "outputs": [],
   "source": [
    "#Python modules for numerics and scientific computing\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import math\n",
    "\n",
    "#scikit-learn\n",
    "import sklearn.preprocessing as skpp\n",
    "import sklearn.decomposition as skdecomp\n",
    "\n",
    "#Matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import mab_utils\n",
    "import plot_utils as plu\n",
    "#import tqdm\n",
    "\n",
    "import interlab as inl\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Data Scaling\n",
    "============\n",
    "\n",
    "Here, we define the contour lines that are used for contour mapping, and also the scaling function that will scale the NMR intensity map into a suitable function for interlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:30.254427Z",
     "start_time": "2019-05-13T14:58:30.248621Z"
    }
   },
   "outputs": [],
   "source": [
    "#Contours for contour mapping\n",
    "#Contours go from 2.5 to 35 in logarithmic steps of 1.3 (i.e. 2.5, 2.5*1.3, 2.5*1.69, ...)\n",
    "contoursp = 2.5*1.3**np.arange(0,11)\n",
    "contoursn = -2.5*1.3**np.arange(0,11)[::-1]\n",
    "ssi_contours = np.concatenate((contoursn,contoursp))\n",
    "\n",
    "#Parameters that will be used for the piecewise linear interpolation\n",
    "mid=0.5\n",
    "crush_point = 0.05\n",
    "crush_lo = mid-crush_point\n",
    "crush_hi = mid+crush_point\n",
    "\n",
    "#Piecewise-linear interpolation function for mapping\n",
    "#Data  will be mapped FROM NMR intensity TO 0-to-1 color scale\n",
    "interp_map = ([ssi_contours.min(),\n",
    "               contoursn.max(),\n",
    "               1e-15,\n",
    "               contoursp.min(),\n",
    "               ssi_contours.max()],\n",
    "              [0,\n",
    "               crush_lo,\n",
    "               mid,\n",
    "               crush_hi,\n",
    "               1]\n",
    "                        )\n",
    "\n",
    "#Greyscale conversion function\n",
    "greyscale_conversion = [0.3,0.59,0.11,0]\n",
    "\n",
    "#Colormap for normalization\n",
    "im=matplotlib.cm.ScalarMappable(cmap=mab_utils.RdBu_with_black.reversed(),) \n",
    "#Needs to be reversed because red is positive, but we want red to be negative\n",
    "cm = im.get_cmap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:30.393853Z",
     "start_time": "2019-05-13T14:58:30.256380Z"
    }
   },
   "outputs": [],
   "source": [
    "#Shape of the data in release3\n",
    "windowed_shape = (735,819)\n",
    "nmr_window_region = [1.9,-0.9,9,30.5] #in units of ppm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:30.916475Z",
     "start_time": "2019-05-13T14:58:30.397302Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotsize = (6,4)\n",
    "\n",
    "fig,axes=plu.make_grid_plot(1,2,plotsize=plotsize,\n",
    "                            xlabel='NMR field intensity (% of maximum)')\n",
    "\n",
    "#Create a vector of values at which to evaluate the colormap\n",
    "cmap_range = np.linspace(0,1)\n",
    "colormap_values = cm(cmap_range)\n",
    "grey_values = np.dot(greyscale_conversion,colormap_values.T)\n",
    "\n",
    "#Mapping from colormap space to NMR intensity space\n",
    "nmr_intensity = 100*(2*cmap_range - 1)\n",
    "\n",
    "\n",
    "axes.flatten()[0].plot(nmr_intensity,colormap_values[:,0],'r',label='Red channel',)\n",
    "axes.flatten()[0].plot(nmr_intensity,colormap_values[:,2],'b',label='Blue channel',)\n",
    "\n",
    "axes.flatten()[0].set_ylabel('Color intensity',size=15)\n",
    "axes.flatten()[0].legend()\n",
    "\n",
    "axes.flatten()[1].plot(nmr_intensity,grey_values,'k',)\n",
    "axes.flatten()[1].set_ylabel('Greyscale brightness',size=15)\n",
    "\n",
    "caption_text = ('(Left) Plot of red and blue intensity with respect to NMR ' +\n",
    "'field intensity. (Right) Greyscale brightness based on red and blue channel\\n' +\n",
    "'intensity and the greyscale conversion function')\n",
    "\n",
    "width,height = fig.get_size_inches()\n",
    "\n",
    "t = fig.text(0,-0.25/height,caption_text,size=12,va='top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:32.306308Z",
     "start_time": "2019-05-13T14:58:30.918439Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalization_colormap = mab_utils.RdBu_with_white.reversed()\n",
    "\n",
    "im=matplotlib.cm.ScalarMappable(cmap=normalization_colormap,)\n",
    "cma = im.get_cmap()\n",
    "cm_inset = im.get_cmap()\n",
    "cma = cm\n",
    "\n",
    "plotsize = (6,4)\n",
    "\n",
    "inset_start_x = 0.4;inset_width = -0.5\n",
    "inset_start_y = 22.5;inset_height = -2.25\n",
    "\n",
    "file = './release3/new_ext/089-D2C-S-U-600-1894-104-37C.ft2'\n",
    "\n",
    "fig,axes=plu.make_grid_plot(1,2,plotsize=plotsize)#plt.subplots(figsize=(6,4))\n",
    "mab_utils.nmr_color_scaled(file,fig,axes,cm_inset,cm,extent=nmr_window_region,\n",
    "                           inset_start=(inset_start_x,inset_start_y),\n",
    "                           inset_width=inset_width,inset_height=inset_height,\n",
    "                           colormap_spine_color='k',\n",
    "                           shape=windowed_shape,interp_map=interp_map,\n",
    "                          )\n",
    "\n",
    "# fig,axes=plu.make_grid_plot(1,2,plotsize=plotsize)#plt.subplots(figsize=(6,4))\n",
    "# mab_utils.nmr_color_scaled(file,fig,axes,cm,cm,extent=[1.9,-0.9,9,30.5],\n",
    "#                            inset_start=(inset_start_x,inset_start_y),\n",
    "#                            inset_width=inset_width,inset_height=inset_height,\n",
    "#                            colormap_spine_color='w',\n",
    "#                            shape=windowed_shape,interp_map=interp_map,\n",
    "#                           )\n",
    "\n",
    "caption_text = ('(Left) Color plot of an NMR spectrum using the color map (note that ' +\n",
    "                'the zero-intensity color is shown as white instead of black for ' +\n",
    "                'readability). \\n(Right) Greyscale brightness based on red and blue ' +\n",
    "                'channel intensity and the greyscale conversion function. In both plots, ' +\n",
    "                'the inset captures \\na region of \"negative intensity\" artifacts, and ' +\n",
    "                'the greyscale conversion function is chosen to intensify such artifacts.'\n",
    "               )\n",
    "\n",
    "width,height = fig.get_size_inches()\n",
    "\n",
    "t = fig.text(0,-0.25/height,caption_text,size=12,va='top')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Reading data and metadata\n",
    "=========================\n",
    "\n",
    "Read the spectral metadata table and load it into a pandas dataframe. \n",
    "\n",
    ".. autofunction:: mab_utils.read_all_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:32.338901Z",
     "start_time": "2019-05-13T14:58:32.308210Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/local/NIST/dsheen/nistmab/mab_utils.py:118: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n",
      "  comment='#',\n"
     ]
    }
   ],
   "source": [
    "metadata_table = mab_utils.read_all_metadata(metadata_file='release3/meta.tab',\n",
    "                                             raw_data_path='./release3/new_ext/')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Read the expert scores. These scores represent the visual assessment of the spectra. 0 is a \"good\" spectrum, 1 is a spectrum that has noticable flaws but is still usable for scientific purposes, and 2 is a spectrum with serious flaws rendering it unusable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:32.470091Z",
     "start_time": "2019-05-13T14:58:32.340351Z"
    }
   },
   "outputs": [],
   "source": [
    "expert_scores_table = pd.read_csv('release3/score.tab',\n",
    "                                  skiprows=3,header=None,delim_whitespace=True)\n",
    "#Extract score values from the scores table\n",
    "expert_scores = expert_scores_table[0].values \n",
    "#Find nonzero scores\n",
    "match_expert = np.array([score>0 for score in expert_scores_table[0].values]) \n",
    "metadata_table['ExptScore'] = expert_scores"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Display the first few entries in the metadata table so far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T14:58:32.577788Z",
     "start_time": "2019-05-13T14:58:32.473777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INDEX</th>\n",
       "      <th>DIR_NAME</th>\n",
       "      <th>CODE</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>ExpType</th>\n",
       "      <th>ExpCode</th>\n",
       "      <th>File</th>\n",
       "      <th>ExptScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8822</td>\n",
       "      <td>8822-010</td>\n",
       "      <td>D2C-S-U-900-8822-010-37C</td>\n",
       "      <td>800</td>\n",
       "      <td>D2C-S</td>\n",
       "      <td>./release3/new_ext/001-D2C-S-U-900-8822-010-37...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7425</td>\n",
       "      <td>7425-010</td>\n",
       "      <td>D2A-S-U-900-7425-010-37C</td>\n",
       "      <td>800</td>\n",
       "      <td>D2A-S</td>\n",
       "      <td>./release3/new_ext/002-D2A-S-U-900-7425-010-37...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7425</td>\n",
       "      <td>7425-012</td>\n",
       "      <td>D2C-S-U-900-7425-012-37C</td>\n",
       "      <td>800</td>\n",
       "      <td>D2C-S</td>\n",
       "      <td>./release3/new_ext/003-D2C-S-U-900-7425-012-37...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7425</td>\n",
       "      <td>7425-015</td>\n",
       "      <td>D3A-F-U-900-7425-015-37C</td>\n",
       "      <td>800</td>\n",
       "      <td>D3A-F</td>\n",
       "      <td>./release3/new_ext/004-D3A-F-U-900-7425-015-37...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8495</td>\n",
       "      <td>8495-010</td>\n",
       "      <td>D2A-S-U-900-8495-010-37C</td>\n",
       "      <td>800</td>\n",
       "      <td>D2A-S</td>\n",
       "      <td>./release3/new_ext/005-D2A-S-U-900-8495-010-37...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  INDEX DIR_NAME      CODE                     TITLE ExpType ExpCode  \\\n",
       "0     1     8822  8822-010  D2C-S-U-900-8822-010-37C     800   D2C-S   \n",
       "1     2     7425  7425-010  D2A-S-U-900-7425-010-37C     800   D2A-S   \n",
       "2     3     7425  7425-012  D2C-S-U-900-7425-012-37C     800   D2C-S   \n",
       "3     4     7425  7425-015  D3A-F-U-900-7425-015-37C     800   D3A-F   \n",
       "4     5     8495  8495-010  D2A-S-U-900-8495-010-37C     800   D2A-S   \n",
       "\n",
       "                                                File  ExptScore  \n",
       "0  ./release3/new_ext/001-D2C-S-U-900-8822-010-37...          0  \n",
       "1  ./release3/new_ext/002-D2A-S-U-900-7425-010-37...          0  \n",
       "2  ./release3/new_ext/003-D2C-S-U-900-7425-012-37...          0  \n",
       "3  ./release3/new_ext/004-D3A-F-U-900-7425-015-37...          0  \n",
       "4  ./release3/new_ext/005-D2A-S-U-900-8495-010-37...          0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_table.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Read the raw spectral data into a dictionary, along with the spectral titles.\n",
    "\n",
    ".. autofunction:: mab_utils.read_all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:01:05.327867Z",
     "start_time": "2019-05-13T14:58:32.580560Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Indices to split by\n",
    "full_indices = ['ExpCode','ExpType','DIR_NAME']\n",
    "split_point = 2 #This means ExpCode and ExpType will form the keys that will split the data\n",
    "\n",
    "fulldata,full_data_dict,titles_dict = mab_utils.read_all_data(metadata_table,\n",
    "                                        full_indices,\n",
    "                                        split_point,\n",
    "                                        shape=windowed_shape)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-04T22:09:13.955328Z",
     "start_time": "2019-03-04T22:09:13.951363Z"
    },
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Processing the data\n",
    "===================\n",
    "\n",
    "Process the raw spectral data using the color map and the greyscale conversion function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:01:20.890436Z",
     "start_time": "2019-05-13T15:01:05.329845Z"
    }
   },
   "outputs": [],
   "source": [
    "#Empty container\n",
    "data_dict = dict()\n",
    "thresh = 0 \n",
    "#Not actually using a threshold, but keep it in the code logic in case we decide later\n",
    "\n",
    "for description,nmrdata in full_data_dict.items():\n",
    "    \n",
    "    nmrprocessed = []\n",
    "    for rawdata in nmrdata:\n",
    "        #Crush values below threshold to 0\n",
    "        data=np.zeros_like(rawdata)\n",
    "        data[np.abs(rawdata)>thresh] = rawdata[np.abs(rawdata)>thresh] \n",
    "        \n",
    "        #Convert to colormap and greyscale-equivalent\n",
    "        data,data_colors = mab_utils.colorize(data,interp_map,cm)\n",
    "        \n",
    "        #Check for zero and negative values\n",
    "        data = inl.fix_spectrum(data) \n",
    "        \n",
    "        data = data/data.sum() #Sum-normalize\n",
    "        \n",
    "        #np.dot spits out a 1-D vector, need to add another axis\n",
    "        nmrprocessed += [data[:,np.newaxis]] \n",
    "    nmrprocessed = np.concatenate(nmrprocessed,axis=1).T\n",
    "    data_dict[description] = nmrprocessed"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Preparing the interlab_py analysis\n",
    "================================\n",
    "\n",
    "Create the interlab_py project object\n",
    "-------------------------------------\n",
    "\n",
    "Create the interlab_py object, as described in `Sheen, et al. <https://doi.org/10.1016/j.chemolab.2016.12.010>`_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:01:20.898003Z",
     "start_time": "2019-05-13T15:01:20.892433Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group E1-F-600 has fewer than 3 measurements, not creating\n",
      "Group E1-F-800 has fewer than 3 measurements, not creating\n",
      "Group E1A-F-800 has fewer than 3 measurements, not creating\n",
      "Group E1C-S-800 has fewer than 3 measurements, not creating\n"
     ]
    }
   ],
   "source": [
    "exp_keys = list(titles_dict.keys())\n",
    "\n",
    "jeffries = r'Symmetric Kullback-Liebler'\n",
    "\n",
    "nmr_distance_metrics = [dict(metric=r'Hellinger',function=inl.metrics.hellinger_hyp),\n",
    "                        dict(metric=jeffries,function=inl.metrics.jeffries),\n",
    "                       ]\n",
    "\n",
    "mab_project = inl.Project(distance_metrics=nmr_distance_metrics,\n",
    "                         Sample_names=exp_keys,Data_set_names=titles_dict,\n",
    "                         data=data_dict,rawdata=full_data_dict)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Calculate interspectral distances\n",
    "---------------------------------\n",
    "\n",
    "Pairwise interspectral distance is calculated using the symmetric Kullback-Liebler divergence,\n",
    "\n",
    ".. math::\n",
    "\n",
    "   d_{SKL}(x,y) = \\frac{1}{2} \\left( \\sum_{i} \\left( x_i - y_i \\right) \\log \\frac{x_i}{y_i}  \\right)\n",
    " \n",
    "where :math:`x` and :math:`y` are any two spectra and :math:`x_i` and :math:`y_i` are the elements of those spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:03:54.131729Z",
     "start_time": "2019-05-13T15:01:20.899372Z"
    }
   },
   "outputs": [],
   "source": [
    "mab_project.set_distances()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:14:46.305555Z",
     "start_time": "2019-05-13T15:14:45.349629Z"
    }
   },
   "outputs": [
    {
     "data": {
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DOP2YS/O5RosWLaqW3ylatWrl7etbhaxdu9bb1yezsGrVKm9fX4apT4IjtWzPxCfhEReK6Yuc8rVlRpSk8MkhxOWq+LLI33zzTW/f7t27e9trQ1LZRKE0a9YsS6Il7j3xfTHFSV3UxoIFC6ioqKjaBqqsrOS4445jwoQJ7LjjjoCL9nvggQeYPHky11xzDWbGsmXLWLVqFRMmTOD2228HYMqUKXTu3Jl27doxYsQIRowYwVFHHcXMmTN5+eWXefjhhwGnINC1a1cGDRpEixYt6NGjBzvvvDPvvPMOXbt2ZcaMGVXjq6ysZPDgwXTt2pXKysoa7Zmf2drwKST4PjMQLy9SGzF22D4qHJVinJmN8xy7PTAAeCGjvQtwHK7meWImi6J9GiX1kXSvpLGSTpT0W0njor83l9Rb0q2S7peU8lnsL+kmSX+StG2xxh5ofEJ8e+EsWbKEs88+m3PPPRdJfPbZZxx55JGMGTOmRoj4o48+Sr9+/fjoo494//33+eCDDzjhhBOYNGkSo0eP5tVXX+XVV1+lc+fOPP7441VfwqtWreLdd9+le/fuXHPNNVX9AI499lieeOIJAJYuXcrbb7/NDjvswNChQ5k+fTorVqxgxYoVTJ8+naFDh7LtttvSunVrnn/+ecyMCRMmcMwx3h3mohBjh0tTW4zRj2+iaIVbOVxoZpl3XH8CLjWzREV0lmw0lJn9HTg7Kupze3RMnXIfks4iWrFkircFkkWSVxbpdpi+V99YrF69mv79+7N27VqaN2/OqaeeykUXuVSkm266ifnz53P11Vdz9dVXAzB9+nQmTpzIcccdV+M8J5xwAmPHjuXUU0+t0T579mzOPfdcmjdvzoYNGzjjjDO8eTmpSaFPnz5UVFRw3XXXVeWY/OpXv6o65oorrqhaIY8dO5ZRo0axevVqjjjiiJKJhEqRrx1KaoGbKO42swc9XfYE7om229oDwyStM7PS8uxnULTiR5I6Ar/GbTd9C7iN6iXZQjP7vaSjcQ7wm8zsH5LeAXoDQ4FOZnZHbdfYfffd7aGHHqrRdvjhfrHIF198MastbhvKp6rZlNtQmcJyKXxqtNdee21W2znnnJPVBmQlMEL8NlRqKyOduG2o+fPnZ7V95zvfmW1me3oPAFq2bGmZyVVvvPFGrceUKgMHDrRMQcfDDjvM2zczBwKyt6E22WSTkspFaCokZSWD+rZZ47ahPv3006y2HXbYoUHtUG4GGA8sN7ML486b1v9O4CEzy1aQLDGK6bNYjIsUqAAeNLMJwARJp+OUaDGzybji8A8D/yCS+5C0AtipWGMPNA3BwR0oBfK0w/2AU4HXJaVkoH+BK3+Mmd3asKNrOko2GkrSYFxy3qZAKp4vL7mPtWvXsmhRTRXzuLoTPinwuJWFr6/vDgf8shi+OgD5rBbihOl8Ru2TLPngA78qc6YkBfhXEADvvvtuVltcnQ3fHV1d+NQ+k4qZZcm5+OTgwV+LYWNcRcSR+Tnz5YXEbRvFrf5rI187NLOngZzDucxsVN6DKhLF/DRuwMl8LAX2x8l4bIYLi+2MkwJpiatx8YCkdsAwoAJYYmb+T1ugLAiZs4FSINhhNcX0IPYCHjazHwJ9gN2jx/cCu5nZJDM7ExgFnGRmy81slJmdCnSLHN+BMkUSLVq0qPGTwzGHS3pL0nxJl8X0+Z6keVF27T8afOCBsqIQOyxXirmyeAW4XNJJOOmPFpIex60c0uVLfwncnPojStJ708y8wefpUSg+h20gOeQThRL5vm4GhuCy/1+SNNnM5qX16Qn8HNjPzFZEQRaNQmNGQ2233XYll7jWFMRVk2xskhyV15AUc7I4Dfi1mc2UdD+wiZkdIlc69XRJfwZ+D/zXzF4GiPwY3wEuiTtpFPM8DqB3795hszehpO7o8mBvYL6ZLYiOvwcnCZOeOXsmcLOZrYCqIItGId0O99hjjwa1w1TUmc+nFre/nuvkEtfP94UZpyPlG8Pdd9+d1RYnae9LOo2rUd/YFGCHZUsxp8ypwPmSbgXeB+ZFj38CPIFLhz8UOFHS2ZI64baoWgFjJfm9qYGyIJU5m1GhrH1KnC/6SVcA6AJ8lPZ3ZdSWTi+gl6RnJD0vyR9HHQhExNjhRkkxQ2fnALWpLf4P+EtGW97bBpn/3LiCRr5IoLiCRL7oFF8US9x5fRFZcXcvviirTp06efv6Iq98d59xciE+mYU4wThfFcK4iK5CKhbG3NEtrSUm3ndLnPmPag70BAbjlI2fktTXzJpcnjguF8BnB/nIfcRFTvnO67PZuC9DX7tP9gb84/WtQuIiCH2riLjPYj7yKHHRerURVhbVFDN0tjtwEy4a6m1cNFQPXDTUebgsxyNxE8TNwCzgeqqjoeoMnQ0kmzzv4ipxNU5SdAUyZ7pK4HkzWwu8J+kt3OTxUn3GGShvNubVRDplFw2lUM+iLCggCuUloKekHpI2AYYDkzP6TAIOjs7fHmeDCxp46ETnL1o9i0DDEaKhqinmZPEKrvjR4zgfxSPR47OpqUCbVzRUqGdRHijPOgJmtg44F1cf4A3gXjObK+k3kWwM0XPLJM3D2dxPzcxfkLyeFLueRaBhyNcOy5myi4YKlA/5fjDNbArV2f6ptivSHhsu8z9sYQZyZmOeINIp5mQxFbhS0ghcNNS6KBqqA/AbqqOhtpS0E/Av3BbVv3DRUD8xs2yPbhqSskIB45xq//vf/7La4pyQ22+/fVZbXHWvDz/8MKvNtz3mOyf4ZSHianL4HOc+J+a3vvUt7/GZYncQLy3ii3l/++23vX0zC+TkQioKpRwwsyzHc9yq9/PPP89qi5Od8QUOxNmsT+rCFxARNy7feeMc3D5ntk98M+5avs9HXJiuT6gzbtsv7nNTG+Vkh/WlmJPFSmCT6GcpsAY4DBhpZnMktcH5MppHbbdKGodzeG8gD/2VQDIJd3SBUiDYoaOYk0XKwX2bpAlmNjKaIAAws6dwoY3HUh2t0je95gXw96YfdqApCJo8gVIg2GE1peTgjmMEMDF6/KCkG4EDcKGRWaRHofjUYQPJIOmOxXQ7XLasUXzogSYg6XbYkBRzskg5uA/B5VNkEeVifJ4qS2hmE8zsPOBVwFtpJz0KJa6gUCAZJDlzNt0OfYmZgeSQZDtsSErGwS3pBzgBwd6SfmtmrwOnA39LHZBZ86IIYw40EWH5HygFgh1WU1S5jyg89hRc7YofAa9HT18i6Ydm9mtJ20paABxtZn8CiKKm/kAdIbRLlizhtttuq9F28cUXe/v+7W9/y2qLk/DwyQnEySz4RNV8x8cJuPnO64tiAX+k14QJE7LaRo4c6T3eN9a4JCTfByguYsUX4ZML5XIXt3z58iwhvTvu8FcEnjhxYlabL5II/P+vODvy2ZwvyiouwsnXN59Swr7P3R//+Efv8b4xxEUx+l5vXCngQopwQfnYYX0paimyTCe2md0GEE0inXHCcD8F7ksdI+lEnPTHLk0/4kBTEe7oAqVAsMNqSmXKrHJiS9oF2NTMPpJ0GvAAsDp6rhMwAHg07kTpjsW4O91AMkjyXnG6HcaV8g0kgyTbYUNS9CLH6U5sSX2BC4EfR0/vDfQDBgFbA0/h8iyuAPpJ6mVmNTLB0usIdOjQIdSzSChJv6NLt8Ptttsu2GFCSbodNiRFnyyInNiRMOAjwH+Bv0RO7nMAJF0J3B/Jmt8raXvg3MyJIlBebMx3cYHSIdiho+iThZn9Ou3PbWP6XJnx9/vkoA/VoUMHfvSjH9VoGzFihLfvY489ltUWZyQ+CY+48EjfXYlPrz/OMek7/p133vH2PfDAA7PaRo0aldU2fPhw7/G9e/fOaotz8vteQ9y230MPPZTV9uCDD3r7piinO7p27dpxyimn1Gg75JBDvH2ffvrprLY42/DVD8mnZoPPvuMCNXx94xzJPuHEG264Iavt5JNP9h7vq7cSNy7fexNXz2L27NlZbddee623b/r5y8UO60sx61k0A64G2uAc1p1wYbEdcDkYB5JWz8LMpkfHXQbsaWa1FU4KlAHhQxooBfK1Q0l34NIAFptZ35g+g4E/4er4LDWzg+o5zEanmCuLY3BlL5cDlWY2HkDST4BuZjYJmCSpLfB/wHRJg4BFtZ00KrV5FsA222zTiMMPNCZJv6NLt8Nu3brV0TtQqhRoh3fiCrtlx627c24F3AIcbmYfSsq7AmgxKOZm3M7Ac1HFu3MkbSLpr7jVxAdp/X4J3BzV3D4lNanEETK4y4ckR6GEDO7yIV87NLOZuJvgOEYAD5rZh1H/xQ0z0salmJ/ASiAl3rTezNaY2Rm4CJJj5LiW6noWe+Lkyv+Ei4QaVJxhB5qCoMkTKAVi7LB9Kiw6+jkrz9P2AtpKmiFptiR/lmyJUcxtqAeBG6PKdzOjiWFzXA3ui8moZ2Fmt+JCZ5HU1cyer+sC69evz6rx8PHHmWWZHT5HblymtG9764MPPvD0hK5ds/UOfX3jtPZ9mas+RzbAzJkzs9p8Tue4a/mco3FZr+vXr89qi6t90b9/f297XSRtNRGHmWVlQPvqp8QRF2Sw+eabZ7X5nN5xfX35H77ABfD/L+IqAPrqSfiyveOysn3Xisti991ErFmzxtt3hx128LbXhWc8S81sz4JO5mgODAS+DbQEnpP0fKlHdxZzsvga+ATn4P4C58gGp/u0FPgQJ02+FXCQpHuB64GK6LlAGSPJK2cRCDQljWSHlbgJ50vgS0kzcflkJT1ZFPPWLeXgXotzcJ9mZqcBnwGdzWySmZ0NPA/caWbLzWyUmZ0KdIuiqQJlTJJ9FoHyoRHs8N/AAZKaS9oc2AdXN76kKeatW8rBfVtUg/uxdKmPtH5DgL+k/oi2rd40M28wdXoUSqdOnRpt8IHGJenlLNPt0LcVGUgGhdihpInAYJxvoxL4NS5EFjO71czekDQVeA1X9fOvUcJxSVPMyaISV0oVYL1H6gNJ+wMvpCaGKDb5O9SSkJcus7DzzjsHmYUEk2Sndrod9u/fP9hhgsnXDs3Mn21Ys891wHWFjqkYFPPW7UFgaFT5biZO6qMZTuojdSv2Q+AOqBIRvBdoBYyNQmkDZUoh0VCSDpf0lqT5UfJmXL8TJZmk+jgpAxsBISqvmmLWs/gKpwuV4mZPnx+mPf6Uaid4TjRr1ixLG3/LLbf09vVJDMTJBixYsCCrbdttvUol3uv5rhUX7eE775tveosE0rNnz6w2X4RTnArqFltskdXWuXNnb998oqz+/e9/e9trI99kKEkVOBsaglu1viRpspnNy+jXGjgfeCHvQRWIz0kat0Xqqx8SV1Pkq6++ymrzRT2Bv/ZFmzZtstriZDV8UVJxkVe+8/pqXMTZvO8zExc55fuMxr0H8+fP97bXRtKTQxuSoq0sJA2W9JSkW6PtJSRdFvkvkHR69NzDksZI2lzS3yXdIumXxRp3oOnI07G4NzDfzBaY2RrgHlwQRSZX4wpnBf36QE6EQAtHMV+54UJmNwMqM6U8zOz/RdFQb+PS53sDb5nZj4FtJXk1FNLrCKxYscLXJZAACkiG6oIrlpWiMmpLP+cAnJRMtrJhw4+/yg6XLVvW2JcLNBJhG6qaYjq4nzKzJyNfxC3Ax2Z2nqTvpDpI2gzoYWZvRaGyx0m6HldFL/PLAajpWOzdu3dwLCYYzweztmQonzRr1f8/sp8bgFENMrg6SLfDAQMGBDtMMBvzBJFOMX0Wqc3GFbjEuy/TpTyiDO0TcY7wVP9fQpWqY7bjIFA2FJAMVQmkrza7Aunp+q2BvsCMSNZ6G2CypKPNbFY9hxsoU0JyaDXFlCg/HhiKmyiuNrMZUXu6lMd3geFpx9yCi1eelYv4VvPmzbMciXFOWJ/DNk42wOdAi3Oc+5x4PidinDLpokXZIrtxdSN8r82n99+hQwfv8b7zxkme+Jzhcdt+hQo65rk//BLQU1IPYCHObqqKl5jZ50CVPoWkGcAlTTFR+AIt4gIifA7buEAL3/sTdxfssznfeeMcyb7PQty4fO0+J32cIzqf98D3ejOlVVLEffbrYmP2U6RTtHfBzB7E1d3uDAxPObmBWSknN/AcLpT2P1E47T9w2w2HJEV8K1AYqTu69J/aMLMlQ0DPAAAgAElEQVR1wLnANFw27L1mNlfSbyQd3QRDDpQh+dphOVPsV16Xk/v3AJKOAw42s7uAp6O2B4jRiw8kn0IyZ81sCjAlo+2KmL6DCx5cYKMh6UoCDUmxJ4tcnNytgO8RSSdEbT8F/uY7YbrMQlyOQKD0SfpesULxo7Ig6XbYkBR1yvQ4uWvUq5DUBhgL/MzMVgFIugj4JC78Mb3ojC/5LZAckhzfnm6HcT6iQDJIsh02JEWdMutyckt6EOfQvjySKG8FnA08Lqm7mV1TpKEHGplwRxcoBYIdVlPUdyFycj/oaT8x+n2857DJuZ5/7dq1WdFEcYVk8okMee+997La4qJQfBEfvm2Jjz7KShkB/FFHcXIdvvKdviiY999/33t8ly5dstri5B98kU9xhXPiCkPVRbncxW3YsCErqiwuos33fse9Dz65j7iIH985fPYdN658igz57MAXFfjZZ595j/eNK84OfWOI+ywuXLjQ214X5WKH9aWU5T6GSZoi6dzo705R31slfRhtUQXKlBCFEigFgh1WU8xXXlck1BRJX+ESqVJCgmdL6gC0NLOVvpMq1LMoG5J8R6dQz6JsSLIdNiQlLfcRw2nA+Lgn02UWdtlllyCzkFCSvlcc5D7Kg6TbYUNSzKS8WiOhfMfIpSMfDDzRNKMMFIsg4BYoBYIdVlOych+S9gUuAtpKWmRmD+BKFT5lcd4uD5ld46QAfI7ouMv4pD3iHIO9evXKaqusrMxq88lyAKxfvz6rbZtttvH29Tn7fEvorbbaynu8zzkap5jq67typXdnMFZepC7Kafmf+VrighTy+TLKx2Yz5UbA73TO5z3PR67DJ/fhq7UC/tobcZ9bX1/fZwaCHdaXYgoJPihpGq5KXndJdwIVwIdRl044IbgKYGGkGjocaCnp1ki+PFCmhMzZQCkQ7LCaYm/GXYorlbrczEYBSLpPUjMzmwRMimoQHBiJC/4o6nNH1CfrFiY4uMuHJC/5QwZ3+ZBkO2xIihk6eygwD/g0re0A4M3UJBDJevwVeCz6u0+UqLfaN1FAzczZuO2WQDKQVOMnSaTboS//JZAckmyHDUkx11cHA4NwMtJnSjoEOBaoEn4zs+uAI4CLo7/nRYl6krRd0w850FQEx2KgFCjEDiUdLuktSfMlXeZ5vrukJyS9Iuk1ScMaZfANTDF9FpcDSBqFy7m4B/gXMFbST4BTgH7AlsDtkjoDP8dNcOvwVMnLpKKiIsuZG5fh+uGHH2a1xRmGL3N233339fZ97bXXstp8Gaq+IvcAy5cvz2rz1bgAv3CiL2M97nX5zhuX8e5r940V/O9XLpTLXrGkLAdvz549vX19Tue4u1nf/yCurorvf+A7b5xt+K4V93/1Ob7zqb0Rlxnuw+dMj3OGxwWh1EU+diipArgZGIIryPWSpMlmNi+t2y9xEvpjJfXBKSVvX9DgmpCifholbQGch5sspgCbA1+Z2WpgKU4XamtgLfAJsAmu4tkmcdtQgfIg5VgMAm6BYlKAHe4NzDezBWa2BncTfExGHwNSd4dbUrOiY8lSdg7uQPkQJohAKeCxw/aS0issjouSMAG6UHPXoxLYJ+P4K4Hpks4DtgAObbjRNh7FzLNIObg3S2vzObiHAz+I/u4D/BZYFDdRpEehxJWuDJQ+qb3ipBKiocqDGDtcamZ7xh3iactMfjkZuNPM/hjlk90lqW+p3/yWnYM7PQql0NrPgdIgydtQ6XbYvn37ug8IlCx52mElkH530JXsbabTcTsqmNlzuBvmkjeSsnZwB5JLSIYKlAIF2OFLQE9JPYCFuJ2RERl9PgS+DdwpqTdusljSAMNtVIrts8DM7oweZooD/tXT/bx8zt2sWbMsmYM4mQVfNFJmDYIUvsin5557ztvXFxnyySefZLXFSRT4Ik62284fNew7ry/iJe54H/nUzoiLhjrwwANzvl465TRZZP4ffJIv4H/Ncbbhi3z6/PPPvX19shhffvllVls+Eh6+Wivgt1mfDEnc8T7iIpx8Mia+sYJfeicX8rFDM1sXlVWYhlOfuMPM5kr6DTDLzCbjdkpuj26KDRiVj4RRsSimz6I3cAFu+fVY9LsH0BY3KbTBOYKWAY+Z2f2SrsWtNPYBLjOzaUUYeqAJSLrPIlAeFGKHZjYFF92Z3pa+vT4P2K9BBtiEFFN19o1I3+l7wJ7A7mb2Q9xe3m44X8WNZnYOMDI65tLomIXAo77zSjpL0ixJs+LudAPJIN/M2RySoS6SNC9KhHqsMRM70+1w6dKljXWZQBOQ9AxuSQ0iZVHsPIujgadxK4tHJD2Oq7H9NHAXMFzSdbhci9QxewMvm5l3bZ7uWGzXrl2jv4ZA45GPYzEtGeoIoA9wchQ9l84rwJ5mtjtwP/CHRhg2EBzc5USSAy0i7spskJR3iF5RX7mZTTazb+Gc2UeZ2SHA5cDpZrbYzEYDl+ES9FKcAdzR9KMNNCWNkQxlZk+YWWpD/XlcpEogEEuZJIfOkPS71B/RDff0fE9STJ/FYOB4YFPc/l43SbcCHYDfSNoe+AUuaeW66JjWwNZm9n4u11i/fn2WgzZu/9GndR/n7HvzzTez2uJkFnwSHD5nXZwUga92RZzcR/fu3bPafMb9/vvve48fOHBgVlvcstvnWIyrszFtWmGupUZIhkrndOC/BQ2sADLlMuKc1ptssklWW9wXlM9B7Ts+rt3XFucc9jnI42zW57j2yYXE1T/JZ0fAZ59xdTLmzJmT83nTSegEUUWUz3GvXA2h5sCvcN+9eVHMaKhPcfIdWwMbgC+BjrjVzjKcg3ur6HHqW+iXwBJJrxAc3GWP54ugvslQqfN+H+cnO6jw0QU2FpLopwCQ9CQwO/r5I3A7bpfmQDNbke/5ipln8QZwtlxRo9uBNmb2XUkn4xzcfXAO7qckTQbuN7NLASQ9RIyDO1AeFBDfnksyVEo54HLgIDPLVu0LBNJIeL7PaGAP3I3RWUB34Bvgt5JeNbPb8zlZUfMsIgf3ZcBNQKvIwV0BHIWbDX8d9cnZwZ0usxC3LRJIBg2dDBXpjN0GHG5mixtqnD6C3Ef5kNTJwszmAHOACak2STsBA6KfvCjqZBElqEyW9DCw3swOkbQ/zsH9J2B0FOXyYNphZwC/85wudc5xwDiAPn36lHyiSyCeRkiGug5oBdwXbS18aGZHN/zIa9rhHnvsEewwwSR1svBhZvOB+cB9+R5b1g7uQHIpJKY9h2SoRKh7BkqHpOZWNAbF9FnMAGbU0e2sjGNWASfkeg1J3igOH77Ip7jiLr5oi7jIDp+h+SJD4qJYfHc1cdEevkgW3+uPi/LyjSGzaE9t7XGRZnGRYnVRThncud6d+vrFRSj53p+4vj58ChP53EXH/X985/VFz8XZlm8McV/Yvr5xyhn5yIukU052CCDpGKDSzGbnc1yQ+wiULOGOLlAKlKEdHg/sIWmhmR2e60FlFw0V6lmUBwmPQgkO7jIh6Xbow8xS9YHyWvKXtdxHqGeRbJKsyRPkPsqHpNqhpPHR7+9L2jW6Ma/CzPwSxTGUXTRUoHxI0gczUL4k2A5/Ff3eEzgT2FnS+ziNtJeB56Lw2pwo62io9957j5EjR2a1+ejRo0fOY/dJNbRq1crb11cPwufYi3PE+46Pq1ngkxa58MILs9q+//3ve4/3yTfEOfd8rzeu9sVnn33mba+Nclr+z58/n+OOO65G21tvveXtu/POO+d8Xt//pmtXv9zVO++8k9Xm+x9uttlmWW3gl5j59NNPvX379euX1TZu3ListoMPPth7vM9e4oIyfDI9H374obfvkiX51xdKsh2a2YfR76ovgSjPYg9cnsWZkp4ys4tzOV8xJcpnAH/CTVgHm9lluGSqz3GO7XXR8y2AUySdaGarzOwESadHqeyBMiapy/9AeZFUO5R0W/T7u5J6gsuzMLN7zeznZrY3rrx1ThRbdXaBmZ2e1lRnTYsoQ3drElCGMFA/kvohDZQXCbbDG6Lf3wbukvSJpGck3SzpzOi5obmerOhlVTNIObm9kh+Rg+Zi4CLgH74TpEehxMVxB5JBwj6YNUi3w7itnUAySKodmtmb0e+zAeReyM5Af6Bf9FzON92lNlkcVYeTewecT+MPQD9Jw6Ks3SrSZRY233zzILOQUBJ4F1eDdDts06ZNsMOEknQ7TCeq8/1m9HNPvscXW0hwa+AaYICknwPzanNyR7omJ0XHds2cKALlRbl8SAPJJgl2KKk5rkbLkUBn4AvgXVxawgMNobBc7JXFljgH9itmNkbS36L2L4A5ZrZe0q+BZ4AxkjoBV0V99pbUxsz8OhtAnz59mDVrVo22AQP8YouTJ0/OavPJcoA/iuSbb/z/C19kh8/44hIIfREncdfyjeu2227LavvnP//pPd4nkxAnH+Eb78cfZymCA/DJJ59ktWVGB/lIahRKJr169WL69JqFyQ477DBv35dffjmrLZ+CRHFSF76CW77zxknJ+Gwu7vPh2/69++67s9pmzpzpPT6fL2efRM3q1au9fZctW5bVttNOO9V5jVK3Q0ktgEeAA3FS/e/iagUdAZwG3CDpQjObWJ/rFDvPYgFwuqT7o79PA5D0Z9zs+BHwUyKFRDP7FJf13QFoWdtEEUg+SbijC5Q/CbDDC4BBwIlmlp6ThqTdgJ8Df5e0jZnd4DtBLpTclClpF2BTM/tI0mnAA0DmrcJpwPiY48+SNEvSrELiqgOlQWYESgI+sDUIdlgeJMQOvwfcmjlRAJjZ62Y2AvgZcG0qhLYQSmqykNQXuAQ4P2raG/gucDjwo6iPcLHBT/jOkS6z4EvYCSSHBHxIYwl2WD4kwA53Afx7ehFm9kfgOeDcQi9SbG2orSOH9gBJl+P23ZoBf4kc2OdE2YdTcRXOAAYDT1nc5mygbGjWrFmNn0CgGCTADlvhKSHs4e+478+CUDl/50paAnxQ7HEEvGxnZrG33JKm4mTr01maj6RyqRDssKRJvB1K2gDsY2Yv1dHvIOBfZtauoOuU82QRCAQC5U40WTwLvAC8jqu7PdfMVmf02wd4xswKCmwqduhsIBAIBOrHn4G+wClAR8AAk/QebuJITSCbAgU7XcLKIhAIBMqEKK1g9+hnN6oLyaUSaMzMCqoTGyaLQCAQKGOiCNKeuAmkr5ldWdB5wmQRCAQCgbooyTiwQCAQCJQWZe3g3mqrrcxXPa42fBo6dRGnp1Mb+cZr+6rY1cWmm26a9zGFxJHHVdOrjdmzZy+tI2TRt+SdVkohi7my9dZbW/fu3fM6ppAVfyH/h3yJ06mqjabKTSgkYW5jssP6UtaTRefOnb0CZrXhKx9ZF3379s37mHwnmDfeeCPva/TsmX9mfyET31ZbbZX3MZLqzDvI/PCbWWa8eyLo3r07Tz6ZX2HHOLHI2mjTpk3ex+T7RR5XOrc2tthii7yPKWSCiStNXBsbkx3Wl7KeLALJRVLWh7+QVV85MnDgQD766KNiD6PJ2W677Zg/f36TXjPYYTVhsgiULCUqrVB0Pvroo4K2qZJOsXSZysUOJR0ILE5V0MuX8ngXAmWHJCoqKmr8BAJNTZnZ4QxgrqTHJB2Z78FFW1lI6gNcCSzDVXM6Haef84WZXSJpb1y97Y/M7JLomKOBw4C1wM/NLH+vbyAxJPyDGSgTysgODwY2B/bBKXs/nM/BxdyGOgK40cyekjQZ+Aq30vkUwMxelHQpkaRuVId7NPA/YGWYKMqb1B1dIFBMyskOzSwVZfHfQo4v5jbUXcBwSdcBWwPfNbOzgG0l7e7p3wFobWY/A1ZIOsR30vSiMytWrGi0wQcanyQv/9Pt0FfOs6GpqKigf//+7LrrrvTr14/rr7++Ksz1kUceYeDAgey2224MHDiQxx9/vMaxr7zyCpKYNm1a7Pk//fRTjjrqKPr160efPn0YNmyYt99PfvIT+vfvT//+/enVq1eNSLnx48fTs2dPevbsyfjx1bXLZs+ezW677cZOO+3E+eefX3L+mCTaoaQ+kk6V9AtJ20RtO0lqXeg5i7ayMLPFwOhoxfCgmaUCuBfj9NkzWU61ZvsKwPuizWwcMA6gT58+pWV1gZyRlGjHYrodDhgwoNHtsGXLlrz66qsALF68mBEjRvD5559z1VVX0b59e/7zn//QuXNn5syZw9ChQ1m4cGHVsRMnTmT//fdn4sSJDB061Hv+K664giFDhnDBBRcA8Nprr3n73XBDddXOG2+8kVdeeQWA5cuXc9VVVzFr1iwkMXDgQI4++mjatm3LOeecw7hx4xg0aBDDhg1j6tSpHHHEEQ3yvtSXpNmhpFbAHcAJwDrcd/xU4BPgd8CHuAJzeVO0d0HS9pLGAROA6ySNlzQW2BF4XlIv4BrgUElnmdkaYGZUn/tQ3BsQKGOSeEdXCnTs2JFx48Zx0003YWYMGDCAVHLqrrvuytdff12Vx2Fm3H///dx5551Mnz49Nvlz0aJFdO3aterv3Xf3Lf5rMnHiRE4++WQApk2bxpAhQ2jXrh1t27ZlyJAhTJ06lUWLFrFy5Ur23XdfJDFy5EgmTZpU37egQcnHDiV1k/SEpDckzZV0QS1995K0XtKJDTjc64Fv4b4jW1NTZXYKrupoQRRzZfE+cFZa09MZXd7GSe6mH/OXRh5WoEQop73iYrDDDjuwYcMGFi9eTKdOnaraH3jgAQYMGFCV3f/MM8/Qo0cPdtxxRwYPHsyUKVM4/vjjs843evRoTjrpJG666SYOPfRQTjvtNGpTR/jggw947733OOQQt1u8cOFCunXrVvV8165dWbhwIQsXLqwxCaXaS4UC7HAdcLGZvRxt+cyW9IiZzcs4bwVwLRC/91cYxwMXmNkT0TXS+QDYrtATJ2d9FdioKLOQxaKQufc/d+5cLr30Um677baqtokTJzJ8+HAAhg8fzsSJE73nGjp0KAsWLODMM8/kzTffZMCAASxZsiT22vfccw8nnnhi1f/N54eQFNteKuRrh2a2yMxejh6vAt4Auni6ngc8gNt2b0ha4iJMfbQG1hd64pxWFtE+2B5A6hblU2C2mX1Z6IUDgboIE0ThLFiwgIqKCjp27AhAZWUlxx13HBMmTGDHHXcEYP369TzwwANMnjyZa665BjNj2bJlrFq1igkTJnD77bcDMGXKFDp37ky7du0YMWIEI0aM4KijjmLmzJm8/PLLPPywi8BM+UzATRY333xz1d9du3ZlxowZVX9XVlYyePBgunbtSmVlZY32fPXcGhuPHbaXNCvt73GRj6oGkrYHBuAq2KW3dwGOAw4B9mrIsQIvASPxb9OfiKuoVxC1ThaS2gA34LaDWlBz/2uNpLtwS66VhQ6gMWnWrFneYnorV+b/UgoRV8v3OoXo3hQiJFiIztNnn32W9zF1UU7bUIU4SQvRhkqxZMkSzj77bM4991wk8dlnn3HkkUcyZswY9ttvv6p+jz76KP369asRBfWDH/yASZMmMXr0aEaPHl3V/vjjjzNo0CA233xzVq1axbvvvkv37t054YQTuOaaa2pc/6233mLFihXsu+++VW1Dhw7lF7/4BakIxenTpzNmzBjatWtH69atef7559lnn32YMGEC5513Xuxrq+1zsG7dutzfpByJscOlZrZnHce1wq0cLvR8P/4JuNTM1jfCKuqXwKOSHgXuw1XNGybpJ7jJ4sBCTxz7zkvaBHgC6IWbMB4BKnETRhdgKPBjoL+kb5nZximYEmg0ymWyaApWr15N//79Wbt2Lc2bN+fUU0/loosuAuCmm25i/vz5XH311Vx99dWA+7KeOHEixx13XI3znHDCCYwdO5ZTTz21Rvvs2bM599xzad68ORs2bOCMM85gr738N8Wpra30L8J27drxq1/9quqYK664gnbt2gEwduxYRo0axerVqzniiCNKJhIqRb52KKkFbqK428we9HTZE7gnen/a477M15lZvT37Zva0pG8Dvwduwn1fXwU8DxxqZi8Veu7Y4keSfoxzwOxrZnNi+uyOc0z/zMxuLXQQjUXfvn3t3nvvzeuYuJDA2hg0aFDex6xfn9/W4aeffpr3NXbZZZe8j0l9gPOhkJVF27ZtZ9d2d9ayZUtLbZekmDt3bq3HlCp77LGHzZw5M69jantPu3XrVnK5CE1BnI8jRSErixYtWjSoHUZV6cYDy83swrquL+lO4CEzuz+vgeeApJZAW+AzM/uqvuerbW/jeNxenHeiADCz1yTdjlvelNxkEUgu5bQNFUguBdjhfsCpwOuSUk6cXwDdAZryptrMVgOrG+p8tU0WuwF/zuEcTwDfb5jhBAIOnzR0INDU5GuHZvY0NX27dfUfVcCwYpHUD+hiZlM8zw0DKs0s/+0Tap8s2gLxsXHVLAPy9opK6o7bU1uKy6k4iDQhwajPtsAzwNHAPGAsLjTsKzM7O99rBpJFWFkESoGE2eENwFO4BLxM9sKJs367kBPXFqLRnNxicjdQWHJfL+BhM/sh0IcMIcGIn+I8+pjZBjP7kZmNBDaRFHJEypjUHV36Tw7HHC7pLUnzJV0W0+d7kuZF2bX/aPCBB8qKQuywyOyBu8H28RwulLcg6nrlYyQtr6NP/h5RxyvA5ZJOwokKjjezDZKujxznA3ERBUNSB0Sy5r8FFqVpSdVA0llEmeHbbrttgUMLFJt894qjbNWbcfZSCbwkaXJ65qyknsDPgf3MbIWkjg087PTxVNlheuZyQ9CtW7eSSlxrKrbbruDk44JJoO+sAoirY7sFsEmhJ65tspgZXTi2mHlG33w5Dfi1mc2UdL+Z/S1qTwkJ7g30AwbhVGnPiz74x0u6RdJ2ZpZVPzddwK1v374bX8hIGZHnXdzewHwzWwAg6R7gGNz2ZYozgZvNbAVUiVk2Cul2uMceezSoHT77rD+vqn37/EtD5xtVVUj+R6tWPl3Q2imlO/hSGksOvIS7SfmX57mzgFme9pyIfRfMbHChJ82RqcCVkkYA70saj9uKag78wcyeBZB0JXC/pM64u8JmOP2Vja8I8UZEzB1dbZmzXahpE5W4Ii/p9IrO/QzuRuhKMwuClIFYEriyuBKXlPcCLoT3E2BbXFZ3P9J2avKlmEKCc3Aht3X1uzLtz/jUzkBZEROFUlvmrG9fJvO2uTnQExgMdAWektTXzBo+BT1QFiQtKi/aqTkMGAPciPtcbMBJjgwxs6cKPXdtGdz+6ibxg/R53wOBgsnzjq4SSHcOdKW6/kl6n+cjtYH3JL2FmzwKzmoNlD8JW1lgZjOAfSVtjotqXdHYSXkP4e7McvGkGW5ZHwg0CAXc0b0E9JTUA1gIDAdGZPSZBJwM3CmpPW5bakEDDDdQpiRtZZEiqgfUFdgs+rvquUJv7Gt7F3oUcsJSolmzZrRunV8Vwa++yn8CXrs2f1msL7/MT7C3EEmNQqQ7li+vK/gtm/qI3sWR716xma2TdC6uPkAFcIeZzZX0G2CWmU2OnjtM0jxcWPhPzazRa55KokWLFnkds2bNmryvU4igZb6yM6tX558QXIg4ZSHSHYW8/rpIms8iihj9Jy4dIW5rtqAXVJuDOyvSKBBoKgr5kEZ3TFMy2q5Ie2zARdFPIFAnSZssgNtw4bHH4yIB87/riCF566vARkOSah8HypeE2eEAYLiZPdTQJw6TRaAkSepecaC8SKAdvkvkp2hoEjVlBjYumjVrVuMnECgGCbPDi4FfSNqhoU9ctCkzU0jQzH4v6XRgpJkdJOkg4GxgFfD3KH74ReBl4AMzG1OssQcanwTuFQfKkATa4Rhcguqbkt4HsiJjzGzvQk6caw3uA4GXzewLz3OtgD3MLF/Jj5SQ4G2SJkQhj1tTrXR7IvAzYBFwL05S5Euc8yYzfj5QhiTgLi6wEZAwO5wT/TQ4ua4sngD2BV70PLdz9Hy+02+6kODduOXTRUBKCfQvwOXACpwsOcC3I7HBf0r6j5llxXmmC7h16dIlzyEFSoUE3tHVoDGFBANNR9Ls0MxOa6xz5zpl1paY1wqn6ZQvKSHBQ4AjcIKFfwD6SRpmZu9ENSvGAMvByZRHx64gxoljZuPMbE8z27OQPINA6ZCwveIapNthhw65aHEGSpUk22FDUpvcx4E4DZ0UZ0g6PKPbZsCRwOsFXLuGkGBawaOuZjZF0l7A6UAb4DeS2uIq932Nq28btqLKmKTd0QXKkyTaYbRbcyZuqz/rptrMCpLmr20bah+qhfsM+C5O7TWdNcCbuCJFeREnJGhmJ0a/XyJbs2dkvtcJJJeN+S4uUDokyQ6jm+87gDuBQ6LHzXDVRj8DJhR67toyuK8DrosG8B5wnJm9Gtc/EGhIknhHFyg/EmiHPwWuBn6P85ndYmYvS2oNPEJhLgMgR5+FmfUIE0WgqQl7xYFSIGF22BN4xszW4/TP2gCY2SrgWuDcQk+cc55FVOr0cmBPnJrhvtGMdQ3wtJn9t9BBNBYrVqzgvvvuy+uYQqp6LVq0KO9jvvgiKwq5VoYNy0sxHoApU/IXl2wq0be6SOAdXSyrV69m3rx5dXdMo5DgjEIEHfP93xVSqriQz0fLli3r7pRBvlX/cqEQO5R0B3AUsNjM+sb0GQz8CWiBq9NyUD2HmuJzYNPo8UKgNzAjdVlcekJB5DRNSjoCmA1sg9vzSpfQ/IZQlCjQCFRUVNT4CQSKQQF2eCeQGQxUhaStgFuAo81sV5w/uKGYBewePZ4MXCHpTEk/wLkVXij0xLmuLMYAd5rZmZKaA79Oe+5VXKZ1INBglNPKIpBcClQ/nilp+1q6jAAeNLMPo/4NWQt+DLBd9PiK6PEtuDy4l4AfFXriXCeLXYBLoseZa72VQEhoCDQ4CdgfDmwEeOywtlrwudALaCFpBtAa+LOZFRyllI6ZPQ88Hz3+DDhG0qbApma2sj7nznWyWAzECVPtCnxYn0EEApmElUWgFIixw9pqwedCc+UiVsQAACAASURBVGAg8G2cOsVzkp43s7frcU4AJF0B/DU9D83MvgG+kbQtcKaZ/aaQc+d663YPLjFu/7Q2i0r3XYqT68gLSd0lTZZ0h6TLorbTJT0ZPT5W0q2S7pE0MWq7VtLforZN8r1mIDmkPqTBZxEoJo1kh5XAVDP70syW4nTv+jXEiXEugq4xz3WmpgshL3KdLH6Fc5w8SfUq4t84warXgN8VcO2UkOAPgT6ZQoJmNimS+3ge5zDCzC6NtE8W4pZvWUg6S9IsSbPyLV0aKC2SPFmk2+GKFSuKPZxAPWgEO/w3cICk5pI2xyVAv9EQJ8ZFPMWFhXXFSSUVRE7bUNEy5ihJ38Ytndrj9JoeM7NHCrx2XUKCKYbgRAWRtA3wf7jQsFUxYx0HjAPo1q1bw8fSBZoESYn2WaTbYZ8+fYIdJpRC7DDaCRmM821U4u7mWwCY2a1m9oakqbgb7Q24baOClWKjSKcfRH8aMFZSpn9iM2A3YHqh18mrnoWZPQY8VujFMkgJCc6UdD8ugSRdSHBKtO31QkpA0Mw+Ab4v6Wc4FdwnG2gsgRIkaauJQHlSQDTUyTn0qVLIaAC+ApZFj4XLtchU5F4D/BcXGVUQudaz6A1sGXnakdQStzXVB7e6uLGAa9cqJBj1+SEu/IvIo/9H3My5Ba5wUqBMCQ7uQCmQBDs0s/uA+wAk/Q242swWNPR1cl1Z3AI8SxSShdsKGgU8BVwrabNopsyZuoQEo8c/THv8DfVIVQ8kjwIyZw/HKRNX4Jb2v4/pdyLuw7WXmc3y9QkEUpT6ZJFOXD0LSVtFobQFk+tmXF/gueiiLYDvAxea2eHAL3ArgECgwUjtFeeqySOpArgZVxulD3CypD6efq2B86lHJmtg4yFfOyw2ks6JtulTf/eP/CbLJM2WFBcpVSe5vvItcMl3AIOivx+M/n6Z6ozBQKDByDMKZW9gvpktMLM1uHDvYzz9rsb5xr5u2NEGypWEReWdR/V3NbjgoI+BU3Df997Vdi7kug21ADdJzASOA14xs5RDpT0xkUnFZrPNNqN37955H5MvnTp1yvuYAw88MK/+hYgCdu7cOe9jWrf2RiTXSps2bfI+pi5i9opry5ztAnyU9lwlLiQx/ZwDgG5m9pCkS2giWrRokbeNFGKHhRyTr2DfkiVL8r5GIeKczZvnFXtT8DF1kQSfRQbdgbcAJHUA9sOVo54haQ318PXm+u7egAvH+i4wABfJlGIwLgQsEGhQPEv+2jJnfaV/q0JWJTXD2fGoBhlcYKOh1LeeMvgGSCUsH4yLlHoq+ns5kL+sdESueRb/T9I7wF7AZVEIbYrlOKndQKDBkJTvnWIl0C3t76645XeK1jjf2wxJ4BSUJ0s6Oji5A3EUYIfF5kVgdOSnOB+XKb4+em4Han4m8iLnd8HMZuK2oTLbryz04oFAHAUkQ70E9IyUABYCw3HqngCY2ee4LdPU+WcAl4SJIlAbCUwOvRgnTf46bls2PfjoJOCZQk+cqCkzsPGQ7x2dma2TdC4wDRc6e4eZzZX0G2CWmU1upKEGypikrSzMbB6wk6StgeVWsyLUJcAnhZ67aO9CVCnqamAucE/kgLkM2NPMTpQ0DJdXMcXMbpLUCbgqOnwY0Le+kruB0ibfO7oomXNKRtsVMX0HFzywwEZFwlYWAKQFIKW3vV6fcxZzyjTgC5xmSaWkQUBV/cVI7uMr3D4zZvYpcHbk4W8ZN1FIOgtXqJyOHTs27isINBpJu6PLJN0Ou3TpUuTRBAolCXYo6UVglJnNk/QS8UKCAJjZ3oVcp5jvwlNm9mS0YrgF+NjMzpP0nTqOOw0YH/dkuoBbz549g4BbgkniHV2KdDvs169fsMMEkwA7nAusTnvcKPZWtMkiJQ6Ik8zdCvhS0p9wQoKDUjpU6ciFsRxMwwlwBUqUJNzRBcqfJNhhusSHmY1qrOvk/C5EMgnH4OpQZGX/mNnPsg6q/XzHA0NxE8XVZjYjau9qZs9L2hcnWd5W0iIzewCX0/FUhtMmUIYkMBkqUIYEO6wmV9XZHXEhV5vjpD6W4OpuN8etDD4H8poszOxBqiVD0ttPjH4/Bxyd8dwTwBP5XCeQXMKHNFAKJMUOo52XITi1jZRkwKc4Xb9H63uTnU8G9yzgu8CXuGik/+HidsdEvwOBBiPc0QVKgaTYYSRl809gR1xtoKU4VYOtcd/zb0sabmavFnqNXCeLvYEzcKnkAJtEWYH/kNQeJwv9rUIH0VhIIsrWzZkWLVrkfZ2dd94572PeeuutvPoXoq+zxRZb5H1MIR+M1atX192pAJLwIW0sCnnt+eo8Qf7/u0L27wt5Lfl+bgE2bNhQd6cCKHU7jIKEpuGiSYcBT0Rimqk6QIcA1wLTJO1mZosLuU6ubv7NgJWRU3o5rvB3ijk0XLHxQACovqNLkNpnoAxJiB2eh4uGOsDMpqUmCnB1gMzsv8CBUZ+CawLlOlm8TbUM+Su4fIfNotoWp1MPvZFAII4k1REIlC8JsMPDgFtqS1KOCh+NBQ4v9CK5vvJ7gP7R41/hpJ9X4qTJT6I6szoQaBCSVnQmUJ4kxA53wtUVqovZUd+CyFV19vq0x89L6ouboVoCj0clUgOBBqVEl/yBjYwE2OGWuIjUulgFFFx8JtfQ2QOBl83sCwAz+wi4PXqulaQDI1XaQKBBKCQ4IRBoaBJihyL3rO2CX0yuoQ1PAPvitNIz2Tl6Pu/pV9IWONnzP+M89hXAEjO7SNKxuNVLN5zg4Iu4PbeWwFdmdna+1wskiwTc0QU2AvK1Q0mH477TKoC/mtnvM57vjpMs2irqc1kkglkfpklaV0efeqWi53pwbbNRK1w1pkK4FLgXJ6U7CkDSfZKamdkkYFIUP3xgJP/xo6jPHVGfrFi5ICRYHiSwjkANgpBgeZCvHUqqAG7GJcdVAi9JmhxJh6f4JXCvmY2V1AenlLx9PYbZJD7j2Mki2noanNZ0RjRjprMZcCSu0EZeSDoUmEeadIikA4A3U5OApJ/iitj8IPq7D/BbYJFvooCaAm69evUKsiAJJsmTRRASLB/ytMO9gflmtgBA0j04maT0ycKo9h1sST2jSc2suJMFLuLpvOix4bK3M5c5a4A3gZ8WcO2DcdIhfYDVkRz5kbgCHe6iZtdJGo9LKDktmp2Pl3SLpO3M7IMCrhtICEmeLALlg8cO20tKr7A4Lro5AOiCq1CXohL3XZrOlcB0SefhvgMPbbjRNh6xk4WZXUek7irpPeBYM/tfQ13YzC6Pzj0KNxndA/wLGCvpJ8ApuGS/LYHbJXUGfo4L911HzX9IoMxIisxCoLyJscOlZrZn3CGetsyV5cnAnWb2x0gw9S5JfeN2S0qFXENnezTWAMzszuhhZo2Kv3q6n+dpC5QpYWURKAXytMNK/n977x0vVXX1/78/VCFgAyyIYMEGKiJgiTFiN9hNVMSYH7ZEoybG+CQm8Wswxqgpxhi7xpY8wZpHMRIVC4pGIxaiAVFBRUBEEGkGkLJ+f+w9cO4wM2fm3rl3Cuv9et3XnTl7n33W2bPOWbuttcOinAw9WHuY6XSic5yZvShpPcL+8I0Kw9FSlBKiXMA+5A9RfkMZ5XLWcWp9gtupDxqhh+OB7SRtDcwkzLkOy8rzIXAgcKeknQjv0zllELdZKdbPYlPgaWAnQpcq09VKdq+qzlgsXLiQp59+uqRzfv3rX5d8neeff77kc5YvX15S/nfffbfka0yZMqXkczp27FjyOeuv32g/n4LUi7FYunQpkydPLumcAw44oOTrLFhQjF9WQ1auXFlS/kWLFjX7NQDatWtX8jnt27cv+ZxiKEUPzWyFpHMJgf1aA7eb2URJvwBeMbNRwA8JQ+s/ILxDh9fCHj3F9ix+B8wndK+mEyZsZgPfBL5FmJh2nLLhPQunGmiMHkafidFZxy5JfJ5EGKVpNuLK0QGEd/btZvaxpN7AbDMr3eJTfGyo/QgGY1ZGFjP70Mx+BfyFKuxVOLVPqTF5JB0m6W1JUyRdlCP9AkmTJL0h6SlJvXKV4zhJaiA21GpiRI37CO4MtxEcmjNRwn8F/LyxZRd75xsSPKtXEQIIJr3d/kkV7mXh1D6lPKQJZ6ivEZZjnxRbV0leBwaa2a7AA0DpY47OOkctGQvgasL7+CCgMw1XZ42mBaLOvg9sHj9PJCxrzXAkYY8LxykbmZg8yb8UVjtDxXj+GWeo1ZjZM2aWiTbwEmGliuPkpRF6WGmOA34ct6DOniyaxpqtJkqm2DmLRwkx0+8jeFA/LGkGsBzoSQjbURJxFcD3CUvGniJ0m4YSfCiuIoTSPTnK2MfMvizpK8k8ZjYrV9lOfdAMzlBJTgf+0WQhnbqnBnoTSToAn+ZJ68zaBqRoivWz+Eni8z8k7QMcEwUbE3diKgkze4uwiVIrQgTbA4GphJuZZ2bjgHExoOD4eNr5yTylXtOpLXI8pE11hgoZpW8CAwlzcY5TkBozFuMJi44ey5H2DcK0QaNoVBRCMxvPmhd4o5F0FHARcB0hGNZQ4FBCj+L2mG0YYf9vCB7dufIky1wdwK25lnQ6zU8jVqEU4wyViUn2M2A/M1uWnV4uPKBlfVCDq/IuBp6U9CRwP6HBNCQu0/0GYXvVRpFqLKIz3sHAXsCm8fBsgoV6qinrg+Oa41GSHgXeimuUPyPu5hRD+S5IbBe4Vp4cZa4O4LbZZptV/dplJz/ldoaKEYxvBg5r7Kb1xZLUwx122MH1sIapJWNhZs9LOhC4ktAIF6Eh/hJwUGzoN4qCxiI+XPcC2xKGfubGi3eJ574jaaiZTSj1wpIGEyZj2hNm6edIupEQ8vyCmO104I7EaX/JkcepQ0pt0RXpDPUbgu7cHycqPzSzo8ovvVMv1GDPAjN7AdhXUgdgI4KPXLu4D3ejKRSifFPCgzcLGAI8E1eZIKk9YbOiqwibbuxSakvNzMYCY7MO35eV5+dZ3+/LzuPUL6WuPCnCGaomons61UUNrIBajaSzgc5m9mszWyJpB8LGcZtLmgAcbWYzGlN2IZN5HrAE2NfMHs8YCgAzWxYntb8a85zbmIs7TiFqbH27U6fUmB6eR/CFy3AtYe7uZML7/spcJxVDoWGoQ4AbEvMFa2Fm8+Ow0NeBS/Llc5xSqcXuv1N/1KAe9gTeBpDUjRBW5EAzGyvpC8I8RqMoZCx6A68VUcarNMLPoiXo0aNHyYEBf/SjH5V8ncYEfSt1XcDixYtLvsbxxx9f8jmNCRS3bFnzLCqqsYc0L507dy5ZR0oNgAmw4447lnxOqcybV/qK9T59sh3p0yk10CbAqlXNsx1EjenhMiAThXF/wpbX4+L3eYRoHI2ikLHYACgmjOUi1mwR6Dhlo5bGip36pcb08GXgnOg0/T3gMTPLOOJtQxO2cC1kLEQep6Y8eR2nbNRIaAWnzqlBPfwhMIoQEWM6cFoi7UTghcYWnOZn8bik7H23Sy3DcRpFjT2kTp1SS3oYw5/3ltSFEAkj2eC/EPi4sWUXetFf2thCHacc1NhYsVOn1KIemtla8aHM7M2mlJnXWJhZsxqLGPPpcEK48xuAY2PSfwldqaMJ4XQ3DOLYSfG8iwhhpr/RnPI5laeWWnRO/eJ6GKjYEJKZPQQ8JGkjQgz2VWZ2uqQfAfsk0s8H3gKQtBdrNmBy6pgaHCt26pBa0ENJLxO2Zp0kaTwpc81mtkdjrlMN8w0XA38EBku6huCe/mEi/WDg2ui6frKZnSfpyHyFJQO49ezZs/mkdpqdan9IC+F6WD/UgB5OJDhHZz43SyyyihmLGKDwSuAfZvYa0adD0mWscSr5CvAvM1slaSCwQTQo/STtZWYvZZebDOA2cOBAD+BWw9TAQ5oX18P6odr10MxOTXwe3lzXqWTP4jzC1n8bxI3EtyBshPSJmb0e85xG9AzP7G8BIKlHLkPh1BfV/pA66wa1pIexsf0c8KKZle7JW4BKzllcS4hbUijPaXmO++T2OkAtPaRO/VJjengM8FNglaR/ExrY44DnmxqWvxrmLBxnLWowJo9Th9SaHprZLnHR0L6Jv3OA1pLeBcaZ2ZmNKduNhVO11FiLzqlTak0Pzewzghf3KEntCMP9PyJECd8OaJSxUBM2uqt6JM0BplVaDicnvcysW75ESY8R5rCSzDWzw5pXrPLjeljV1JUeSlqfEGk206sYCCwmhPkYR+hZvNyosuvZWDiO46xLxPBMy4CHgWcJcxUTy1G2D0M5juPUD+OB3Qn+ae2BDnEoaoI1sWfgPQvHcZw6Ijow70WYo9g3fl4B/BN41syualS5biwcx3HqkziHsT/wA4LxMDNr3ZiyfBjKcRynTpC0GQ2Xze5M2G9oInA9a3bNK71s71k4juPUB5JWAV8QwieNI3hzv2Bm85tcthsLx3Gc2kdSW+AnwO1mNqPs5dezsejSpYv16NGjYJ4vvviiYHqbNukjdWkenitXriyYXkwZxTgGTZ48OTVP3759C6YXI2uaLMXU2auvvjo3ZX17LsV8vFrXtxeia9eu1qtXr4J50p7DcjiGFfOsl+M6U6dOTc3Tu3fvgunleC8Vcy/1pIeSWhGizw4xs6fKXX5dz1n06NGDJ554omCeDz/8sGD6RhttlHqdDh06FEz//PPPU8to165dk64BsOeee6bmefbZZwuml0PWjTfeOLUMSalOatkGdNWqVdnOUTVBr169eOmlwnEvly5dWjC9bdu2qz/vuOOOTJtW2z5+r776asnn9OrVi3feeWf197QGVjGNlnrSwxid+11g0+Yov66NhVO7SFrrYU/rBa4rTJs2rSwt71qjEmE3alAPfwZcJenNpm6jmk0l97PoCVwHzAXeAdoCWxM2PzqP4Kae2Xb1euAVwo56rYE5ZnZBBcR2WpBaCuDm1C81pocXA12ACZJmArPJ2gypFnfK2x541MxulnQ30MHMjpd0ErBL1rarvzWzJ4DhAJLul9TKzFZlF5rcoSxtvsKpXnK16GoJ3ymvPqhBPfxP/Cs7layF14GfSToR+DPQVtLThJ7DEYl8FxN6FgBI2heYnMtQQMMdyvr167fu9dXriBpr0TUgqYcDBgxwPaxhakkPk7vmlZtK1sKpwM/N7ADCcNMR8fPPgNMVuIo1264iaTBhc49LKiSz00JIonXr1g3+nPy0bt2a3Xbbjb59+9KvXz+uvvpqVq0K7akxY8YwYMAAdtllFwYMGMDTTz/d4NzXX38dSTz++ON5y589ezZHHHEE/fr1o0+fPgwZMiRnvg8//JD999+f/v37s+uuuzJ69OjVaVdccQW9e/dmhx12aHCtxx57jB122IHevXtz5ZVXNqUayo7r4Roq2bN4DBghaRjwAbBC0k1AN+AXrL3t6v8B98X/N0r6gZktyVmyU/NkHlKnODp06MCECRMA+OSTTxg2bBgLFizg0ksvpWvXrjzyyCN0796d//znPxx66KHMnDlz9bkjR47kK1/5CiNHjuTQQw/NWf4ll1zCwQcfzPe//30A3njjjZz5fvnLX3LCCSdw9tlnM2nSJIYMGcIHH3zApEmTuOeee5g4cSIfffQRBx100OqVTeeccw5jxoyhR48eDBo0iKOOOoo+ffqUs3oaTS3ooaSXgeFmNknSeLLmKLKpuTkLM/sPUGh71H+z9rarmzSfRE61Ue0PabWyySabcMsttzBo0CBGjBhB//79V6f17duXpUuXsmzZMtq3b4+Z8cADDzBmzBj23Xdfli5dynrrrbdWmbNmzeKQQw5Z/X3XXXfNeW1JLFy4EIAFCxbQvXt3AB5++GGGDh1K+/bt2Xrrrenduzcvvxy2VejduzfbbLMNAEOHDuXhhx+uGmMBpemhpC2Bu4HNgFXALWb2hzx5BwEvASea2QNNEHEiwb8i87lZhj1rauamVFasWMGcOXMK5pk/v7AXfOfOnVOv89///rdg+scff5xaRpofxRZbbJFaxvTp01PzLF++vGB6Wn1AWOdfiHnz5qWWkUYttOiKxcxSl1suW7asYHrSz6IYttlmG1atWsUnn3zCppuuWXb/4IMP0r9/f9q3bw/ACy+8wNZbb822227L4MGDGT16NMcdd9xa5Z1zzjmceOKJXHfddRx00EGceuqpqw1BkhEjRnDIIYfwxz/+kc8//5wnn3wSgJkzZ7LXXnutztejR4/VvZstt9yywfF//etfee9rxYoVqz937NixYB0k8zaWRujhCuCHZvaapM7Aq5LGmNmkrHJbA1cB+cf+iiQ5T2Fmw5taXj5qZ+bGWefwseKmke2LMXHiRH784x9z8803rz42cuRIhg4dCoRW/ciRI3OWdeihh/Lee+9x5plnMnnyZPr375+zITZy5EiGDx/OjBkzGD16NKeccgqrVq3K6RciKe/xaqIUPTSzWZk5VjNbBLwF5GrpnQc8CHxSbnmbi6rxszCzKyWdDnzLzPaTtB9wFrAI+IuZPRfH5l4DppnZFZWS3Wl+JNXUKpRq47333qN169ZsskkYuZ0xYwbHHnssd999N9tuuy0QQrs8+OCDjBo1issvvxwz49NPP2XRokXcfffd3HrrrQCMHj2a7t27s/HGGzNs2DCGDRvGEUccwXPPPcdrr73Go48+CsCECRP405/+xGOPPQbA3nvvzdKlS5k7dy49evRo0POdMWPG6p5JvuPVQB497CrplcT3W+Lqt+xztwL6A//KOr4FcCxwADConPLG8k8k7LO9PbDWmKKZNWo4v5JPY8bP4jSgj6StCc4kmebKNwibjH8XOD8e+xxoB3zUwrI6LUxmfXvyzymOOXPmcNZZZ3Huueciifnz53P44YdzxRVXsM8++6zO9+STT9KvXz+mT5/OBx98wLRp0/j617/OQw89xDnnnMOECROYMGEC3bt35+mnn1493Lpo0SKmTp1Kz549ufzyy1fng+BT8tRTISzRW2+9xdKlS+nWrRtHHXUU99xzD8uWLeP999/n3XffZY899mDQoEG8++67vP/++3zxxRfcc889HHXUUS1faXnIo4dzzWxg4i+XoehE6Dmcb2YLs5KvAX5sZumB2EqXdxhwFzAF6AGMAv5OeNcvJDTQG0W1+Fn8L/BD4ALgrzH9WsIy2s+AzID+gTH+yb2SHjGztQbHk85Qm2++eTPfgtOc1HLPIqmHyTH55mLJkiXstttuLF++nDZt2nDKKadwwQUhyMF1113HlClTuOyyy7jssssAeOKJJxg5ciTHHntsg3K+/vWvc+ONN3LKKac0OP7qq69y7rnn0qZNG1atWsUZZ5zBoEFrN4p/97vfceaZZ/L73/8eSdx5551Iom/fvpxwwgn06dOHNm3acP31168e0rnuuus49NBDWblyJaeddlpqsMuWplQ9jNFfHwT+18z+liPLQOCeONzWFRgiaUV0RG4q/wNcBlxJ0L8bEvMnY4DCE6wFqFjUWUkXAi/H4aUHgJXALILPxffNbHTMtz5wo5mdnDj3JuAXZlawh9G3b1+79957C8oxa9asgunFPOhp45hp14DyTHAXI2vahP8nn6QPoZZjgrtLly6vmtnAfOkdOnSwrbbaqsGxyZMnFzynWtl9991t3LjCe84sWVJ4FXinTp1Wf+7QocM6GxsqGeiyHBPcbdu2LaseKliAu4B5ZnZ+rjxZ+e8E/t7E1VDJ8hYTfNbGSloOHGxmY2PascDvzWyrxpRdNX4WZnYhgKQeZjY6Lis7HVgf+EUM+/EHYCnhh/ChqDrHh56caqBEPdwHOAV4U9KEeOynQE8AM7upvNKtxQKgffw8E9gJGBu/izDU3yiqzs/CzL4R/48Hxmclf6sFRHOqgHpaOuvULqXqoZk9T3gpF5t/eCPEKsQrwK6EJbmjgEskrSDsnncJWZPtpVDXTbd27dqlBnFL8zvIrEdvSp6tt946tYw0hSxmnf0uu+ySmiftftOGmCB9k6UNN9wwtYw0ajCAW15atWqV09EtSSY0h1OY5NBTmn9TOfSnBvXwCiCz09Yl8fMNhJh744nzaI2hdmcQnbqnVD8LSYdJelvSFEkX5clzgqRJkiZK+muuPI6TpJb8fczsJTO7N36eb2ZHA52ADc1sTzN7r7Fl15TJdNYdSm3RRY/Y64GDgRnAeEmjkp6zkrYj7FG8j5l9Jqkmw8f06tWr6hzXWoK0rWmbg1rpWUjqAAwBtiIsFHrKzGYDmNkyoHCIgCKoaC3EcOMnRzn6mNmXsxzzzgD2ADYH/p+ZTZA0lbAE7LVc65ud+qARcxZ7AFMyLSdJ9wBHA8kwC2cC15vZZwBmVjPes0nyDQOmLfEsxx7cxRipU09Nj5J9xx13FEwvNbxJc1ELc2eStgGeJBiKDAslnRD3ASoLFR2GMrNxZnYWwWnkrmzHPDO7zcy+TRh7OzKetpjgd5EzEJKkb0t6RdIrn376abPfg9M85HGG6pr5beNfcvx1CxrqxAzWDrOwPbC9pBckvSTpsGaUf7Uepi1XdqqXGnEO/TUhaOG+QEegL8GP7eZCJ5VKtdz5MOAM4Fc0dMxDUhvge6zZw6I/YbXBo8A/sgtKbjrTv3//dW8xeh2Ro0U3t8Ca+FxN3uzfvw2wHTCY4N06TtLOZpYePbFEkno4cOBA18Maptp7FsDehOCFL8Tvb0n6Tvy/uZmlO3oVQcUnuGOMqAWE8OPdCFayn6Qh0RPyBuAaM5sOYGaropv8UkkVl99pHhrRopsBJL0Se7B2WJgZwMNmttzM3gfeJhgPx8lJjfQsNgeyJ66nEhpQm5XrItVw56cDd5jZFOBEaOCYdzVh6OBsSU8BbwA/jueNzbe1qlP7NGKseDywXRzKnAkMJfRYkzwEnATcKakrQbcavTrEqX9qYc4i0uy914obCzP7eY5jGce8C3KcclqzC+VUHEklTXKa2QpJ5xKckVoDt5vZREm/AF4xs1Ex7RBJkwjhZf7HzHxiKZ3R+AAAIABJREFUy8lLqXpYQR6PznfZPJV9vLFRZytuLJqTVatWpTrupG06065du9TrpK1CWbx4cWoZaY59aU5dkB73CWiwEU4uZs+enVpGJux1Psr1cJUawC3GExuddeySxGcjzInlaoQ0G2bGypWFA4ymrVQqpi7SVioVEysp7TrFDMNMnTo1NU+ajqQ5j0L6s1mu5cU1ENDy0pa4SF0bC6d2qaHuv1PH1IIemlmLGIuKmkxJ+0q6SdJtkv4Zj50u6dn4+QxJt0h6RNJu8djUeE6j3dad2qBVq1YN/hynErgeBiraszCzcYTli8cQPG7X8rMAbpPUn+BnMYEUPwunPqgVz1mnvnE9XEO1mMlhwEjCBkjXJBMSfhZ3xkP9CZPc389VkDvl1Q+13KJL6uHcuXMrLY7TBGpZD8tJxe+83H4WZnZLZrvDLl0aHbrdqTCZseJaCeCWTVIPu3btWmlxnEZS63pYTqqhf+V+Fk5O1uVWnFM9uB4GKm4s3M/CyUUtrEJx6h/XwzVU3Fg0J1OnTuWYY44pmCdtz+k0PwyAjTfeuGB6MXtjL126tGD6O++8k1rGzJkzU/Ok7eXdrVu31DLefPPNgunF3G8x1EuLbtq0aZx99tkF86TtW75gwYLU66QNd22//fapZaT5JU2YMKFgOsCLL76YmmfvvfcumL7tttumlvGPf6wVGq6kaxRLvehhU6lrY+HULt6ic6oB18M1uLFwqhZv0TnVgOthoKo2PyJEAYWwSus0M1spaXPgBeAoM/tPPO8mYLGZXVgBsZ0WwFt0TjXgeriGqnLKM7ObAST9AehOcLz7H+D+zDmSvgG8AuzY8hI7LYm36JxqwPUwUC21kHHKQ9KOQHszmy7pVOBBYElM25TglPdkvoKSzlDFBCNzqpMa2UcgL0k9TFu84FQvjdFDSbdL+kTSfwrkGSxpgqSJmfBG1U7Fn8CMU56ZLZS0M3A+8N2YvAfQD9iLEAZkHMF57xKC4972ZtZgmVByh7JOnTr5DmU1iqSabtEl9bBr166uhzVKI/XwTuA64O48ZW5IcDY+zMw+lNSokOEtTcWNBdEpL3pjjyFslXqtpF+a2dkAkkYAD8Q5i/skbQWcm20onPrCx4qdaqBUPTSz5+I7Kh/DgL+Z2Ycxf+H1+1VCxY1FllPe5nnyjMj6/gHgk9t1TK33LJz6II8edpX0SuL7LbEnWSzbA20ljQU6A38ws5y9kGqi4saiOdl222156KGHCuaZPr1w8Nru3bs3WY5iHKrSNnLp3bt3ahk9evRIzZPmUJe2SQ+kb0xTTEtss83Stwaul55Fr169uPHGGwvmSZvX6NChQ+p10jZQKmYOL81AFyPHAQcckJpn3LhxBdPT7qXYPGmkbToGOfVwrpkNbMJl2wADgAMJEbRflPRStY+U1LWxcGoXX7LoVAPNpIczCAbnc+BzSc8R5mar2lh4P9+pWjzap1MNNIMePgzsK6mNpI7AnsBb5Si4OalYzyJOaF8GrE/wm9iUMJbXDTgV+CpwOGH10/Vm9kQ87yJgYCbYoFOf+JyFUw00Rg8ljQQGE+Y2ZgA/B9oCmNlNZvaWpMcIUbRXAbdlHI6rmUoOQx0NbAHMA2aY2V0Akn4AbGlmDwEPSdoI+C3whKS9gFmFCo3brX4b0oPmOdVNLfcmknrYs2fPCkvjNIVGrIY6qYg8vwF+01iZKkElm247AC/GMORnS2on6TZCb2JaIt/FwPWSOgAnZ4xKPnzzo/qgMZvOSDpM0tuSpsQeaL5835BkkpoySVkQ3/yoPvDNj9ZQSWMxA/gsfl5pZl+Y2RkER6ajFbgK+IeZvQYMBDaQdA3BIW+vyojttASlPqSSWgPXA18jxBk7SVKfHPk6E7bp/VcziO3UGW4s1lDJYai/AX+MwQSfi4ahI7ARYS/u84CDCAait5ndRPDgzuyk91KF5HZaiBLHivcAppjZewCS7iEMdU7KyncZYete99NxisLnzgIVMxZm9l+C93Y+ro1/uc71ye06J8+SxULOUFsQAk9mmEFYZZIssz9hPuzvktxYOKn4Eu411LWfhaRUZ7clS5YUTF+0aFHqddIclXbaaafUMiQVTJ8yZUpqGcXshJZ2P23btk0tI21HvmJ22yuGEp2hclXgaq+tuPru98DwsghXImmt0xUrVhRML4dD3Ze+9KXUMtJIe14Adtttt9Q8afdbTGs+zZEx7dkvFjcWgbo2Fk7tkon2WQIzgOR+rj2AjxLfOwM7A2OjYd4MGCXpKDNL9lYcZzWN0MO6pZr8LAbHpFbAacCRwGHAhoQW4jnA1UBrYE5cReXUMSWOFY8HtpO0NTATGEoI2AaAmS0AVi9LinF5LnRD4aThcxaBavKzOBXWbHyU8LM4H3jLzOYRhxAk3S+plZmtqozoTnNTaovOzFZIOhd4nNCguN3MJkr6BfCKmY1qJlGdOsZ7FmuoZC1k/CxulvQA8FRy46NEvoNJTHTH1VOT8xmKpDNUMYH1nOqkMZ6zZjYaGJ117JI8eQc3WrgicKe8+sAjCayhavws4sZHFxLWwAMg6SvAvzKGQdJg4BjC5kc5cae8+qDWd8pzp7z6oNb1sJxU0lj8DThU0h+B5wgbH7UibHyU6RKcBtwOq7dUvQ/oBNwYPbqdOqZVq1YN/hynErgeBqrJz+L6HHlOS3yeTQgq6KwD+Pp2pxpwPVxD3fep0jbzWbWq8Bz5F198kXqNtK5pmg8FpG/kUowc6623XmqeZcuWFUwvpuWUtta+GFmLoZ4e0rTfN00P09KhOD1rKsXI0bFjxyaXU8y9pPlqFOMzVAz1pIdNoe6NhVObeIvOqQZcD9fgxsKpWvwhdaoB18OAO+U5VYm36JxqwPVwDXXnlOd+FvVBrTtDuZ9FfVDrelhOqmbzI4ACTnljMl/SnPLcz6J+kNTgr5ZwP4v6oZb1sJxU0mTOADLLZjJOeecD381kyOOUdyS+F0Hd491/pxpwPVyDO+U5VYs7QznVgOthwJ3ynKplXX4wnerB9TBQ9zM3aWOMG264YcH0YjZQ6d27d8H0YjYuSnMg2mCDDVLLmD59emqe9ddfv2B6MU5Xm2++ecH0Ypyy0ljXAril6Vkxk6xpTpnFbFyU9rwU4+j2zjvvpOZJKyfNiRHSN3Mqx/zCuqaHhfBacKqW1q1bN/hznEpQqh5KOkzS25KmSLooR3pPSc9Iel3SG5KGNIvgZaaa/SwGAD8EppvZhfGcy4Hjgd3NbHFLy+y0HN6ic6qBUvVQUmvCkPrBhEU84yWNMrNJiWwXA/eZ2Y2S+hDC6m9VPqmbh0o+jRk/i+VEP4voazGf4GfxMvDj5Alm9jPgny0uqVMRfGLRqQZK1MM9gClm9p6ZfQHcQ3jXJTFCIxlgAxpu/1u11IKfRUlI+rakVyS98umnn5ZJVKelybToatVYJPVw7ty5lRbHaSR59LBr5reNf99OnLIFkHx/zYjHkowAvilpBqFXcV4z3kLZqGo/i8ZgZrcAtwDstttu6bNkTtVSy/MUST0cMGCA62ENk0MP55rZwDzZc82qZ//+JwF3mtnvJO0N/FnSztW+TXTV+llI2h64HDgoY7klXQDsDVwjqXuF5HZaCPecdaqBEvVwBrBl4nsP1h5mOp3gM4aZvQisB1S9m39V+1kAJ2edczUhmKBT57jnrFMNNEIPxwPbSdoamAkMBYZl5fkQOBC4U9JOBGMxpwziNit17WexYsUK5s2bVzBP2rzGgQcemHqdyZMnF0wvZjOgtHX2nTt3Ti0j7V4hfTOoYmRNK6OY9fzFUGvzFIVIq7O0jXzS/GMAli5d2iQZIL3Oi3lxFjNH09TNoIqhGF+NYihFD81shaRzgccJEbJvN7OJkn4BvGJmowirPG+V9APCENVwK5ewzUhdGwundvGls0410Bg9NLPRhInr5LFLEp8nAfuURcAWxJ9Gp2opdc6iCGeoCyRNio5QT0nq1SyCO3WFz50FKmYsJA2WNE7STTGaLJIukvRA/DxE0gOS7pN0iKRNY96bJH0oKb1f7tQsmbHiYj1nE85QXwP6ACdFh6ckrwMDzWxX4AHg180gulNHlKqH9Uwlh6EMWEyY3JkhaS9gViL9y8DPgIXA+Wb2BHCWpG5ABzNbmKvQ5KYz3bv7gqlapsRW3GpnqHhuxhlqteesmT2TyP8S8M0yiJmTpB5uueWWKbmdamZd7k0kqaSxGGdmz8bQ4zcAH5nZeZKOjOn/B9xJ6P2cmTjvVOCufIUm17fvvPPOVT9p5OQnx1hxV0mvJL7fEn9vyO0MtWeB4k8H/tFkIfPgfhb1g8+dBSq5dDaz3OEzwj7bn0u6BugXexkXAvvFPHcCwxRM/P7Ab1pYXKeFyTOx2FRnqEzZ3wQGska/HCcnvtBiDZUMJHgccCjBUFxmZmPj8R5m9pKkvwG3El4Cj8XTBhN6JN5SWwco8SEtxhkKSQcRhjf3M7NlTRLQWSdwYxGoZM/ibwQv7uzj34j//wr8NSvtGeCZ7HOc+qTEseJUZyhJ/YGbgcPM7JNyyenUNz5nEahrPwszS3UQS3O6e+qpp1Kv06VLl4Lp8+fPTy0jrfWyaNGi1DLat2+fmidtg6RiykhzukrbUKoYSu3+F+kM9RvCtrz3xxfAh2Z2VJOFTZct1elu4403LphejMNl2gZJxThcptV52n1AcRt1pTkQFvOCXrascMewGF1Ow4eh1lDXxsKpbZrBGeqg8kjmrEu4sQhU0s/iGEm3SnpY0iHxWNLPYnNJ10r6o6R94rGbJb1ZKZmdlqWWQ5Q79YPrYaCScxYPAQ9J2gj4raSFNPSzuBBYRBg2mBHP+U7GmDj1zbruLetUB66Ha6gGM3kxcDtwspkl/Sf6AncTNgr5f8UWltx05rPPPiuroE7LUsstOt/8qH6oZT0sJ5UchpKkqwiOUa2ADbL8LGYQfDAyXt5FYWa3mNlAMxu40UYbNYfoTgtRyzF5knrYtWvVb1XgFKCW9bCcVHKC+zzgIMIetBPM7FvQwM9iISF2jwE3xrTLgf6SbgK+7+vk6xdfheJUA66Ha6jknMW1wLU5jmf8LCYBw7PSfkZwqHLWAdblVpxTPbgeBup66WyHDh3o169fwTz//ve/C6YXEwQubY38Fltk79e+NmnRLItZI7/JJpuk5tlqq60Kpnfs2DG1jLZt2xZM79atW2oZxVAvD2mrVq3o1KlTwTyLFy8umF7M75KmQx06dEgtI63Oi9mUKO15AFhvvcIjy8VEd01r8adtKFYs9aKHTaWujYVT2/hD6lQDrocBNxZOVbKuTyY61YHr4RqqxilP0uWS3pHUKZFnc0nvSdo5fj8tOun9slJyOy2Hr0JxqgHXw0DVOOWZ2emSsgf3/we4H0DSJsCJhN3OZuHUPb4KxakGXA8D1VALFxO2w2yApFOBB4FMJMBtgHlmdhHQS9K2uQpLOkPNmTOnuWR2WoBabtG5HtYPtayH5aQqnPLM7LUcWfYAjgcOA75DCDudCb05nxAGZC2SzlDlWpXjtDzZD2itPaSuh/VBrethOakKpzxJvYGOwN7ANZIuMbOzASSNAB4ws+mS5km6GmhnZoXXvDo1z7r8YDrVg+thoNqc8q7OkW9E4nPRMaKc2sfHip1qwPUwoHreoVTSHGBapeVwctLLzPKOz0h6DMgOqjTXzA5rXrHKj+thVbPO6GFTqWtj4TiO45QH7185juM4qbixcBzHcVJxY+E4juOk4sbCcRzHScWNheM4jpOKGwvHcRwnFTcWjuM4TipuLBzHcZxU3Fg4juM4qbixcBzHcVJxY+E4juOk4sbCcRzHScWNheM4jpOKGwvHcRwnFTcWjuM4TipuLBzHcZxU3Fg4juM4qbixcBzHcVJxY+E4juOk4sbCcRzHScWNhdPiSBohyeLfKkmfSRov6XJJmzWyzB9JGlxmUZtEsTJJ2irWxRGJY2MTdbRc0ieSnpJ0jqT2zSq44+TAjYVTKRYAewNfBoYCfwNOAd6UNKAR5f0IGFw26cpDU2V6hlBH+wFnAm8AVwEvStqwydI5Tgm0qbQAzjrLCjN7KfH9cUk3As8B90rawcxWVki2amFeVh09LOkO4J/A74FTKyOWsy7iPQunajCz+YTW+LbAwZnjkq6U9KakxZJmSPrf5HCVpA+ALsDPE0M3g2PaD+MQ1wJJsyU9Iql3miySTpc0UdISSXMlPSupbyJ9PUm/ljRd0jJJ/5Y0pBiZmoKZvQFcB5wsaf2mluc4xeLGwqk2ngFWAHsljm0C/Ao4HDgf2AZ4WlLrmH4sYVjrT4Rhm72B12JaD8LL9WjCUE5r4AVJG+QTQNJXgZuAvwBfA04jtOaT5zwADI9yHQmMB0ZJ2q0ImZrKGKAtsHuZynOcVHwYyqkqzGyZpLnApoljp2U+RwPxIjAD2Ad4zsxel7QCmJE1bIOZ/SDr3DHAJwTjcXceMfYA3jCzKxLHRiXKOZBguAab2bPx8BOStgd+BhxfSKYyMCP+37RgLscpI96zcKoRNfgifU3SPyUtIPQ6Mi/L7VMLkvaSNEbSp/Hc/wKdUs6dAPSX9HtJX5XULiv9IOBjQg+lTeYPeAoYWMwNNhGlZ3Gc8uLGwqkqJK1HGOufHb8PIrTqZxBWS+3NmiGq9VLK6gk8QXi5fofQExlE6FnkPdfMniRMHn8VGAvMlXSDpC/FLF2BzYDlWX8jgC1LuN3GskX8P7sFruU4gA9DOdXH/gS9fDF+PxaYA5xoZgYgqVeRZR0GdASONrPP47ltgI3TTjSzu4C7JHUDjiOsPloIXATMA2YCxxQpR7k5hGCcXq3Q9Z11EDcWTtUQfQeuAqYAT8bDHYDlGUMROTnH6V+wdm+hA7CKMPyU4QRK0HszmwPcLOk4oE88/BTwQ2CxmU0ucHoumZqEpF2Bc4C/mNmicpbtOIVwY+FUijaSMsNJnYEBwNmEnsBhCR+LMcD5kq4BHiE48X0zR3mTgcMlPQYsBt4GniasfrpD0p+AvsCFwPxCgkm6lND7GAvMBfoTHOMuSsj0ODBG0lXARGB9YDdgPTP7ST6ZSnzBbxzrqBVhaG5/woqud4ALSijHcZqOmfmf/7XoH2Fs3+LfKsLL+xXgcmCzHPl/BEwHPif0OLaL556byDMAeCnmMcJKJYBvAVOBJTF9T+AD4LcF5DuC0HuYAywlGJ6LACXytAcuJfSCviBMeD8GHJ4mU47rbRXTj0gcG5uoo+VRlqcJvYr2lf4N/W/d+5NZsnfvOI7jOGvjq6Ecx3GcVNxYOI7jOKm4sXAcx3FScWPhOI7jpOLGwnEcx0nFjYXjOI6TihsLx3EcJxU3Fo7jOE4qbiwcx3GcVNxYOI7jOKm4sXAcx3FScWPhOI7jpOLGwnEcx0nFjYXjOI6TihsLx3EcJxU3Fo7jOE4qbiwcx3GcVNxYOI7jOKm4sXAcx3FScWPhOI7jpNJsxkLSvpLeLlNZiyVtU46yahlJH0g6qMi8wyU9n/hel3Uo6WxJs+P9dWnma2XXqUnq3ZzXrAUkTZQ0uED6WEln5EnrGX+71mWSZYSkv5SjLKchqcYivqCWxB90tqQ7JHVKO8/MxpnZDuUQ0sw6mdl75SirEkjaKr5Y2lRKhmLqUNJgSTNaSqamIqktcDVwSLy/TystU6UopSFRbsysr5mNjXKU9LI2sw/jb7ey1OvWmr7WOsX2LI40s07A7sAg4OKmXLSSL81qxeukUWwKrAdMLPVEBdaZYVjXL6eplPSwmNlM4B/AzgCSTpX0lqRFkt6T9J1M3myrH1s+P5b0BvC5pDMlPZJInyLpvsT36ZJ2i59Xd/clDZE0KV5zpqQLE+ccIWmCpPmS/ilp11z3EV8Uv5f0iaQFkt6QlLmnOyXdJGlMvMazknolzt0xps2T9LakExJpHST9TtK0WO7zkjoAz8Us82MPbe84pPFClGMeMELStpKelvSppLmS/lfShsX8NpK6SBolaaGkl4Fts9IL1qGkL8XftnuUcbGk7pL2kPRirNNZkq6T1C6r3LMkvSvpM0nXS1Ii/cyEjkyStHs83l3Sg5LmSHpf0vcS5+wh6ZV4L7MlXZ3jfrcHMsOc8yU9HY9/WdL4WP/jJX05cc5YSZdLegH4L7DWsJykiyRNTch7bDH1n6Oc4fGZWBTv7+TE8Rck/THKOFnSgYnzNpD0p1jXMyX9Uokhmlz1KenPQE/gkfi7/UhrerOnS/oQyNTP/ZI+jtd+TlLfPPLvL+nNxPcno15lvj8v6Zj4+QNJB0k6DPgpcGKU49+JInvF+14k6QlJXeO5DXrd8Te6LFfeLPly6mtMbifp7nj+REkDE+fl1TsnBTMr+Ad8ABwUP29JaMVdFr8fTngpCdiP8ADuHtMGAzOyypkQy+hAeFDnEwzW5sA0YGbMuw3wGdAqfjegd/w8C9g3ft4ocb3dgU+APYHWwP8Xr9k+xz0dCrwKbBhl3wnYPKbdCSwCvgq0B/4APB/TvgRMB04F2sRrzgX6xvTrgbHAFlGGL8cytor30CYhw3BgBXBeLKsD0Bs4OJ7TjWBkrsn1W+S4p3uA+6KMOwMzM3KXUIcNfrN4bACwV5RxK+At4Pyscv8e67InMAc4LKYdH+UYFOu5N9Ar/uavApcA7eLv/R5waDzvReCU+LkTsFeee25Qr8DGBL05Jcp7UvzeJaaPBT4E+sb0tjnKPB7oHmU8Efg8oRvD89VpVhlfAhYCO8Tvmyd0JPO7/wBoG6+xANg4pj8E3BzL2AR4GfhOofrMpRuJurk7ltUhHj8N6EzQsWuACXnqdj1gCdA11tXHwEfx3A4xrUv2tYERwF+yyhoLTAW2j+eOBa7M8xvmzZtDxsGsra8jgKXAEMIzeAXwUkwrqHf+l2ILUjMERVhMeLFPA27IKF6OvA8B38/1Q8ZyTsvKP53wwh0K3BIfjB0JL+NRuR5KwsP+HWD9rLJuJBqxxLG3gf1yyHkA8A7hJdgqK+1O4J7E907ASoKROxEYl5X/ZuDnURGXAP1yXK/BAxGPDQc+TKn7Y4DXs+pwLWMRH4rlwI6JY78iv7HIV4drPXw5rnU+8H9Z5X4l8f0+4KL4+fGMPmSVsWf2vQM/Ae6In58DLgW6psjSoF4JRuLlrDwvAsPj57HAL0p6QEID5+jEb1assZgPfJ2sZyWW8RGgxLGXo+ybAsuS5xAM3jOF6jOXbiTqZpsC97ZhzLNBnvRxwHGE5+SJ+NseBuwPvJHr2uQ3Fhcnvn8XeCzPb5g3bw751tLXeP0nE9/7AEuK0Tv/K/xX7DDUMWa2oZn1MrPvmtkSAElfk/SSwpDMfII1X6vLmGB61vdn4w/+1fh5LKGHsl/8nouvx+tMUxgi2jse7wX8MA6XzI/ybEloJTbAzJ4GriP0BGZLukXS+rnkNLPFwLxYTi9gz6xrnAxsFu97PUKrqFga1IekTSTdE4cfFgJ/oXB9ZuhGaP0ly5tWIH++OlwLSdtL+nsculhIMELZMn2c+PxfgoGFUP+56qMXYfggWY8/JbwsAU4ntCwnx6GkIwrcS5LurH3f0wg9vQzZOtgASd/SmqHM+YReWjG/wWrM7HNCw+IsYJakRyXtmMgy0+KbKiFjRr/axnMy17+Z0MOA/PVZiNX3K6m1pCvjMNtCwkse8t9fY57PfOTTkabmLeb89eIwV5reOQVo9ASfpPbAg8BvgU3NbENgNKF7nA/L+p5Rxn3j52dJUUYzG29mRxMeoIcIrR0ID8Xl0ahl/jqa2cg85VxrZgMIQxLbA/+TSN4ycZ+dCMMbH8VrPJt1jU5mdjZhOGopWXMFee473/Er4rFdzWx94JsUrs8McwhDG1smjvXMl7lAHeaS80ZgMrBdlOmnRcoEob5y1cd04P2seuxsZkOifO+a2UlRvquAB+IYdRofEV4ISXoShm4y5PstUJibuhU4lzDEsiHwH4q/3zUXMXvczA4mDEFNjuVm2EJSssyerNGvZYQeVaZe1jezzLxCvvosdF/J48OAo4GDgA0IrXrIf3/ZxiL1+SwgR3NQ6rUK6p1TmKasBmlHGPecA6yQ9DXgkBLLeJbQpe1gZjMI3d7DgC7A69mZJbWTdLKkDcxsOWFcOLPk7lbgLEl7KvAlSYdL6pyjnEExX1vCmPTSRDkAQyR9RWEi9zLgX2Y2nTA2v72kUyS1jX+DJO1kZquA24Gr4yRaa4WJ7EwdrSLHhGoWnYlDfpK2oKEBy4uFZYd/I0ySd5TUhzBnsxYpdTgb6CJpgyyZFgKLY+v47GJkitwGXChpQPxNescX8svAQoUFDx1iXe0saVCU8ZuSusU6nR/LKmZp5WjC7zNMUhtJJxKGIf5epLxfIryA5kQ5TiUu5igFSZtKOioauGWE3zQp/ybA96L+HE+YMxttZrMIwz2/k7S+pFYKix72i+flq08Iv10x+rUM+BToSOglFuKfwA7AHoThvYnE3jVrFm1kMxvYSi2z0iyXvhaioN45hWn0D2pmi4DvEVqlnxFaLaNKLOMdwoM0Ln5fSJhwesHyr7s+BfggdqPPIrS+MbNXgDMJw0ufAVMI48O5WJ9gXD4jDAF8SughZfgrYR5iHmGC9+TEPR9CmGP5iNDdvYpgNAEuBN4ExsdzryLMifwXuBx4IXZ/98oj16WEOZwFwKMEA1As5xK66x8T5l3uKJA3Xx1OBkYC70U5u8d7GkaY9L8VuLdYgczsfsJ9/zWe/xBhInclcCSwG/A+oVd2G6G1C6HBMFHSYsICg6FmtrSI630KHAH8kPCb/gg4wszmFinvJOB3hHmO2cAuwAtF3WxDWkUZPiLowX6EsfcM/wK2I9z35cA3bI2PyLcIDbFJBP18gNA7yVuf8bwrgIvj77Z6hWAWdxMXksTyXyp0E3E47TVgopl9EQ+/CEwzs0/ynHZ//P+ppNcKld9U8uhrofxpeucUQA2HTh3yprRJAAAEV0lEQVRJdxImzZrkS+I4uZA0HDjDzL5SaVkcpxTWGackx3Ecp/G4sXAcx3FS8WEox3EcJxXvWTiO4ziplGwsFGIn/TJ+LksYclUgYqY8YqXjOE7RNCkSpZmNI6zDdhzHceoYH4YqA/Lwz47j1DnFbH7UX9JrCuF+7yXEP8qkZYch/7FCXKNFCuG7D4zHR0h6QNK9Me01Sf3yXC9vSGyF8Ne/y8r/iKTz4+dCYa87xCG0zyRNIkTuLHTfh8R7WCDpBoUYSmfEtFzhxVtJulghPPknCiGSN8hVT/HY6qG3tPrJV6+O4zgtRUFjEV/SDwF/JniK3k8IQpcr7w4EL+JBZtaZEAb8g0SWo+P5GxM8UB9SCLeRzUpC+OauwN7Agazxfr0LOCkTSkAhzv2BwMh47BHg34TAcQcC50s6NJ77c0JcnW2jbDnDYSTKfYAQkbILIXrtl7Oy7UnwNt+E4FU7PP7tTwi70IngTV4sOeuniHp1HMdpdtJ6FnsRomBeY2bLzewBQiiLXKwkhL3oI6mtmX1gZskIma+a2QMxHtHVhB7KWmEvzOxVM3vJzFaY2QeEqJv7xbSXCaEwMi3rocBYM5tN6Cl0M7NfmNkXFrYQvTXmATiBEGhwXozzdG2B+x5CCHHwNzNbEfN+nJXnIzP7Y5RzCSEkyNVm9l6MVPsTYGgJQ1T56ietXh3HcZqdNGPRndzhlNfCzKYQ9joYAXyiEGo7GaslGfZ7FTCDHOHDlR4S+y5iLKP4/8/xc1r44e4UH8K7Qd54/9krp7JDXWeHx55GWEBQbPjjnPVTRL06juM0O2nGYha5wynnxMz+GmPe9CJE77wqkZwM+90K6EEItJZNWkjsvwBHxzH9nQjDZJAefngWRYbwjnl7JORV8nvmdrO+Z4fH7kkIGz6bENm2Y6K81oQ9KJLkrZ+UenUcx2l20ozFi4QX3vcUQj4fRwhXvBaSdpB0gEJI7qWEXeOSkWMHSDouDsucTwiVnCvqZcGQ2DGU+XhCj+LBOAQE6eGH7wN+ImkjST0I25nm41FgF0nHRHnPIWxwVIiRwA8kba2wB8avgHvjMNY7hA1YDo/zNBezJlJtwfopol4dx3GanYLGIoYlPo4wcfsZYfevfGGz2wNXEsL+fkyY+P1pIv3heH5mj+Tj4vh8NsWExL6LED46MwRVTPjhSwlDQ+8T9gz4M3mIIa2PB35NCHXdB3iF8ALPx+2xzOfiNZYSDZKZLSBM0t9GCA/9OWsPa+Wrn7z1qrAvxcQCMjmO45SFFokNJWkEYa/ib6blLbK8rxKGo7aK4/vNShwWmgGcbGbPNEP5Iyhj/TiO45SbmnPKi8M43wdua05DIelQSRvG4Z/MvEnBzWIcx3HqlZoyFpJ2ImyzuTlwTTNfbm9gKmH450jgmMT8iOM4zjqFhyh3HMdxUqmpnoXjOI5TGdxYOI7jOKm4sXAcx3FScWPhOI7jpOLGwnEcx0nl/wf37GZkZQXs7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 381.6x360 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_range = ['D2A-S-600',\n",
    "              'D2A-S-700',\n",
    "              'D2A-S-800',\n",
    "             ]\n",
    "\n",
    "f = mab_project.plot_distance_fig(plot_range=plot_range,\n",
    "                                  plot_data=False,\n",
    "                                  ylabel_buffer=0.4,rightlabel_buffer=0.4)\n",
    "\n",
    "for ax,label in zip(f.axes[1::2],plot_range):\n",
    "    ax.text(0.5,0.95,label,ha='center',va='top',transform=ax.transAxes,bbox=dict(facecolor='w'),)\n",
    "\n",
    "caption_text = ('Pairwise spectral distances for all spectra within the \\ndisplayed groups.')\n",
    "\n",
    "width,height = f.get_size_inches()\n",
    "\n",
    "t = f.text(0,-0.25/height,caption_text,size=12,va='top',)"
   ]
  },
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   "source": [
    "Save data tables and Interlab project\n",
    "-------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:03:55.076250Z",
     "start_time": "2019-05-13T15:03:54.920512Z"
    }
   },
   "outputs": [],
   "source": [
    "metadata_table.to_hdf('metadata_table_raw.h5',key='NMR_2D_raw',mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:07:09.627531Z",
     "start_time": "2019-05-13T15:07:08.015206Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('mAb_interlab_project.sav','wb') as f:\n",
    "    pickle.dump(mab_project,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T15:45:08.812217Z",
     "start_time": "2019-05-13T15:45:08.755545Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('mab_analysis_accessories.sav','wb') as f:\n",
    "    pickle.dump((windowed_shape,jeffries,full_indices,split_point,ssi_contours,interp_map),f)"
   ]
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