Cluster_Tetramers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PseudoCode\n",
    "* Lets figure out how PCA works\n",
    "* Lets see if we can easily plot it\n",
    "* Lets generate a fake, simulated dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [45 10 11]\n",
      " [ 1  4 15]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "testDict = {'a':[1,2,3], 'b':[45,10,11], 'c':[1,4,15]}\n",
    "testArray = np.array( list(testDict.values()))\n",
    "print(testArray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(testArray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.98629249,  0.15759031,  0.04891232],\n",
       "       [-0.07333995,  0.15313457,  0.98548011]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PCAdata = pca.components_\n",
    "PCAdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explaination of PCA\n",
    "I think every row that gets output is the component with the most significant impact. Because I chose \"n_components = 2\", we are getting only two rows. These are now my axes, so if I were to plot this, the stuff in the first row would be my x-coor, and the stuff in my second row would be my y-coor. I can easily plot this by passing this to a plt.scatter function, and theoretically this just gives me a nice plot with points :))) The documentation also talks about getting the variance contained in each component, which I think will be useful for the complete design\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.patches as mplpatches\n",
    "import argparse\n",
    "\n",
    "def plotMyPCA(pcaInput):\n",
    "    figureWidth=5\n",
    "    figureHeight=2\n",
    "    plt.figure(figsize=(figureWidth,figureHeight))\n",
    "    \n",
    "    xData = pcaInput[0]\n",
    "    yData = pcaInput[1]\n",
    "    \n",
    "    plt.scatter(xData,yData,marker='o',\n",
    "    s=3, c=[(0,0,0)], linewidth=0)\n",
    "\n",
    "plotMyPCA(PCAdata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fake simulated dataset time\n",
    "\n",
    "# This block creates the tetramerDictionary (I know it kinda inefficient, sue me)\n",
    "legalBases = ['A','C','G','T']\n",
    "tetramerDic = {}\n",
    "\n",
    "for base in legalBases:\n",
    "    tetramer = base\n",
    "    for base2 in legalBases:\n",
    "        tetramer += base2\n",
    "        for base3 in legalBases:\n",
    "            tetramer += base3\n",
    "            for base4 in legalBases:\n",
    "                tetramer += base4\n",
    "                tetramerDic[tetramer] = []\n",
    "                tetramer = tetramer[:-1]\n",
    "            tetramer = tetramer[:-1]\n",
    "        tetramer = tetramer[:-1]\n",
    "    tetramer = ''\n",
    "len(tetramerDic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['AAAA', 'AAAC', 'AAAG', 'AAAT', 'AACA', 'AACC', 'AACG', 'AACT', 'AAGA', 'AAGC', 'AAGG', 'AAGT', 'AATA', 'AATC', 'AATG', 'AATT', 'ACAA', 'ACAC', 'ACAG', 'ACAT', 'ACCA', 'ACCC', 'ACCG', 'ACCT', 'ACGA', 'ACGC', 'ACGG', 'ACGT', 'ACTA', 'ACTC', 'ACTG', 'ACTT', 'AGAA', 'AGAC', 'AGAG', 'AGAT', 'AGCA', 'AGCC', 'AGCG', 'AGCT', 'AGGA', 'AGGC', 'AGGG', 'AGGT', 'AGTA', 'AGTC', 'AGTG', 'AGTT', 'ATAA', 'ATAC', 'ATAG', 'ATAT', 'ATCA', 'ATCC', 'ATCG', 'ATCT', 'ATGA', 'ATGC', 'ATGG', 'ATGT', 'ATTA', 'ATTC', 'ATTG', 'ATTT', 'CAAA', 'CAAC', 'CAAG', 'CAAT', 'CACA', 'CACC', 'CACG', 'CACT', 'CAGA', 'CAGC', 'CAGG', 'CAGT', 'CATA', 'CATC', 'CATG', 'CATT', 'CCAA', 'CCAC', 'CCAG', 'CCAT', 'CCCA', 'CCCC', 'CCCG', 'CCCT', 'CCGA', 'CCGC', 'CCGG', 'CCGT', 'CCTA', 'CCTC', 'CCTG', 'CCTT', 'CGAA', 'CGAC', 'CGAG', 'CGAT', 'CGCA', 'CGCC', 'CGCG', 'CGCT', 'CGGA', 'CGGC', 'CGGG', 'CGGT', 'CGTA', 'CGTC', 'CGTG', 'CGTT', 'CTAA', 'CTAC', 'CTAG', 'CTAT', 'CTCA', 'CTCC', 'CTCG', 'CTCT', 'CTGA', 'CTGC', 'CTGG', 'CTGT', 'CTTA', 'CTTC', 'CTTG', 'CTTT', 'GAAA', 'GAAC', 'GAAG', 'GAAT', 'GACA', 'GACC', 'GACG', 'GACT', 'GAGA', 'GAGC', 'GAGG', 'GAGT', 'GATA', 'GATC', 'GATG', 'GATT', 'GCAA', 'GCAC', 'GCAG', 'GCAT', 'GCCA', 'GCCC', 'GCCG', 'GCCT', 'GCGA', 'GCGC', 'GCGG', 'GCGT', 'GCTA', 'GCTC', 'GCTG', 'GCTT', 'GGAA', 'GGAC', 'GGAG', 'GGAT', 'GGCA', 'GGCC', 'GGCG', 'GGCT', 'GGGA', 'GGGC', 'GGGG', 'GGGT', 'GGTA', 'GGTC', 'GGTG', 'GGTT', 'GTAA', 'GTAC', 'GTAG', 'GTAT', 'GTCA', 'GTCC', 'GTCG', 'GTCT', 'GTGA', 'GTGC', 'GTGG', 'GTGT', 'GTTA', 'GTTC', 'GTTG', 'GTTT', 'TAAA', 'TAAC', 'TAAG', 'TAAT', 'TACA', 'TACC', 'TACG', 'TACT', 'TAGA', 'TAGC', 'TAGG', 'TAGT', 'TATA', 'TATC', 'TATG', 'TATT', 'TCAA', 'TCAC', 'TCAG', 'TCAT', 'TCCA', 'TCCC', 'TCCG', 'TCCT', 'TCGA', 'TCGC', 'TCGG', 'TCGT', 'TCTA', 'TCTC', 'TCTG', 'TCTT', 'TGAA', 'TGAC', 'TGAG', 'TGAT', 'TGCA', 'TGCC', 'TGCG', 'TGCT', 'TGGA', 'TGGC', 'TGGG', 'TGGT', 'TGTA', 'TGTC', 'TGTG', 'TGTT', 'TTAA', 'TTAC', 'TTAG', 'TTAT', 'TTCA', 'TTCC', 'TTCG', 'TTCT', 'TTGA', 'TTGC', 'TTGG', 'TTGT', 'TTTA', 'TTTC', 'TTTG', 'TTTT'])\n"
     ]
    }
   ],
   "source": [
    "print(tetramerDic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the randomizer maker lelel\n",
    "group1Means = np.random.dirichlet(np.ones(256),size=1)[0] ; group1Means = {key:group1Means[i] for i,key in enumerate(tetramerDic.keys())}\n",
    "group2Means = np.random.dirichlet(np.ones(256),size=1)[0] ; group2Means = {key:group2Means[i] for i,key in enumerate(tetramerDic.keys())}\n",
    "\n",
    "group1SD = np.random.dirichlet(np.ones(256),size=1)[0]    ; group1SD = {key:group1SD[i] for i,key in enumerate(tetramerDic.keys())}\n",
    "group2SD = np.random.dirichlet(np.ones(256), size=1)[0]   ; group2SD = {key:group2SD[i] for i,key in enumerate(tetramerDic.keys())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanList(inputList):\n",
    "    list1 = inputList\n",
    "    for item in list1:\n",
    "        if item < 0:\n",
    "            list1[ list(list1).index(item) ] = 0\n",
    "        elif item > 1:\n",
    "            list1[ list(list1).index(item) ] = 1\n",
    "    return list(list1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tetramer in tetramerDic.keys():\n",
    "    list1 = cleanList( np.random.normal(group1Means[tetramer], group1SD[tetramer], 70) )\n",
    "    list2 = cleanList( np.random.normal(group2Means[tetramer], group2SD[tetramer], 30) )\n",
    "    \n",
    "    tetramerDic[tetramer] = list1+list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Okay now that we have our tetramer data dictionary, lets PCA it!\n",
    "tetramerArray = np.array( list(tetramerDic.values()))\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(tetramerArray)\n",
    "PCAdata = pca.components_\n",
    "plotMyPCA(PCAdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "####################################################################\n",
    "###########################################################################\n",
    "# Following the PCA tutorial\n",
    "import pandas as pd \n",
    "tetramerDF = pd.DataFrame(tetramerDic)\n",
    "groups = 70*['group1'] + 30*['group2']\n",
    "groups = pd.DataFrame(groups, columns = ['group'])\n",
    "unstandDf = pd.concat([tetramerDF, groups], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AAAC</th>\n",
       "      <th>AAAG</th>\n",
       "      <th>AAAT</th>\n",
       "      <th>AACA</th>\n",
       "      <th>AACC</th>\n",
       "      <th>AACG</th>\n",
       "      <th>AACT</th>\n",
       "      <th>AAGA</th>\n",
       "      <th>AAGC</th>\n",
       "      <th>...</th>\n",
       "      <th>TTCT</th>\n",
       "      <th>TTGA</th>\n",
       "      <th>TTGC</th>\n",
       "      <th>TTGG</th>\n",
       "      <th>TTGT</th>\n",
       "      <th>TTTA</th>\n",
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       "      <th>TTTG</th>\n",
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       "      <th>group</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.002757</td>\n",
       "      <td>0.003445</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.004256</td>\n",
       "      <td>0.017841</td>\n",
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       "      <td>0.005660</td>\n",
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       "      <td>0.007340</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002445</td>\n",
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       "      <td>0.006949</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.003540</td>\n",
       "      <td>0.009678</td>\n",
       "      <td>0.007592</td>\n",
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       "    <tr>\n",
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       "      <td>0.004724</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.004051</td>\n",
       "      <td>0.019946</td>\n",
       "      <td>0.014272</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006116</td>\n",
       "      <td>0.002257</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>0.004795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>0.001336</td>\n",
       "      <td>group1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.004552</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.004037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>0.021914</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.005745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007420</td>\n",
       "      <td>0.002809</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>group1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.004426</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.006670</td>\n",
       "      <td>0.001068</td>\n",
       "      <td>0.004377</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.010238</td>\n",
       "      <td>0.005688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.012025</td>\n",
       "      <td>group1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.007689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004637</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.004147</td>\n",
       "      <td>0.018496</td>\n",
       "      <td>0.008980</td>\n",
       "      <td>0.005659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.012994</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013814</td>\n",
       "      <td>0.011278</td>\n",
       "      <td>group1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 257 columns</p>\n",
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      ],
      "text/plain": [
       "       AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "0  0.002410  0.006968  0.000000  0.002757  0.003445  0.000823  0.004256   \n",
       "1  0.001967  0.003545  0.000000  0.004724  0.000553  0.000712  0.004051   \n",
       "2  0.001655  0.004552  0.000190  0.004037  0.000000  0.000009  0.004031   \n",
       "3  0.002066  0.004426  0.002105  0.001141  0.006670  0.001068  0.004377   \n",
       "4  0.002983  0.007689  0.000000  0.004637  0.002755  0.000825  0.004147   \n",
       "\n",
       "       AACT      AAGA      AAGC  ...      TTCT      TTGA      TTGC      TTGG  \\\n",
       "0  0.017841  0.000000  0.005660  ...  0.007340  0.002100  0.002445  0.000000   \n",
       "1  0.019946  0.014272  0.005381  ...  0.006116  0.002257  0.002380  0.004795   \n",
       "2  0.021914  0.006809  0.005745  ...  0.007420  0.002809  0.002742  0.000000   \n",
       "3  0.017778  0.010238  0.005688  ...  0.000127  0.000000  0.002711  0.000000   \n",
       "4  0.018496  0.008980  0.005659  ...  0.000000  0.000000  0.002718  0.007065   \n",
       "\n",
       "       TTGT      TTTA      TTTC      TTTG      TTTT   group  \n",
       "0  0.006949  0.001663  0.003540  0.009678  0.007592  group1  \n",
       "1  0.000000  0.000000  0.000000  0.008093  0.001336  group1  \n",
       "2  0.000000  0.003577  0.000000  0.010204  0.000000  group1  \n",
       "3  0.000000  0.000000  0.004757  0.005694  0.012025  group1  \n",
       "4  0.012994  0.006468  0.000000  0.013814  0.011278  group1  \n",
       "\n",
       "[5 rows x 257 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstandDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TTTC</th>\n",
       "      <th>TTTG</th>\n",
       "      <th>TTTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.000888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.000866</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.001012</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.000363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.000635</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>0.000952</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000748</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.000687</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "0  0.000457  0.000678  0.001040  0.000858  0.000586  0.000449  0.000494   \n",
       "1  0.001288  0.000904  0.000866  0.000515  0.000586  0.001049  0.000905   \n",
       "2  0.000332  0.000565  0.000635  0.000343  0.000512  0.000824  0.000494   \n",
       "3  0.000665  0.000565  0.000693  0.000629  0.000952  0.000599  0.000494   \n",
       "4  0.000748  0.000904  0.000751  0.000687  0.000659  0.000824  0.000987   \n",
       "\n",
       "       AACT      AAGA      AAGC  ...      TTCG      TTCT      TTGA      TTGC  \\\n",
       "0  0.001131  0.000517  0.000769  ...  0.000736  0.001098  0.001134  0.000438   \n",
       "1  0.001044  0.001034  0.000769  ...  0.000368  0.000784  0.000670  0.000384   \n",
       "2  0.000435  0.001182  0.000769  ...  0.000460  0.000784  0.000567  0.000603   \n",
       "3  0.000609  0.000813  0.000604  ...  0.000184  0.000549  0.000825  0.000055   \n",
       "4  0.000522  0.001108  0.000714  ...  0.000552  0.000549  0.000980  0.000603   \n",
       "\n",
       "       TTGG      TTGT      TTTA      TTTC      TTTG      TTTT  \n",
       "0  0.000657  0.000467  0.000791  0.000826  0.000894  0.000888  \n",
       "1  0.000493  0.001012  0.000528  0.000590  0.000752  0.000363  \n",
       "2  0.000438  0.000467  0.000330  0.000590  0.000329  0.000323  \n",
       "3  0.000767  0.000467  0.000725  0.000531  0.000329  0.000808  \n",
       "4  0.000712  0.000467  0.000264  0.000590  0.000847  0.000606  \n",
       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tetramerDF = pd.read_csv('tetraFrequency.csv')\n",
    "tetramerDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = tetramerDF.columns\n",
    "x = tetramerDF.loc[:, features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AAAA</th>\n",
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       "      <th>TTGA</th>\n",
       "      <th>TTGC</th>\n",
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       "      <th>TTGT</th>\n",
       "      <th>TTTA</th>\n",
       "      <th>TTTC</th>\n",
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       "      <th>TTTT</th>\n",
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       "      <td>-0.609798</td>\n",
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       "      <td>1.284642</td>\n",
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       "      <td>0.330979</td>\n",
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       "      <th>2</th>\n",
       "      <td>-1.047863</td>\n",
       "      <td>-0.503166</td>\n",
       "      <td>-0.175356</td>\n",
       "      <td>-1.239765</td>\n",
       "      <td>-0.595304</td>\n",
       "      <td>0.557152</td>\n",
       "      <td>-0.721492</td>\n",
       "      <td>-0.872381</td>\n",
       "      <td>1.412217</td>\n",
       "      <td>0.364265</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.766025</td>\n",
       "      <td>0.317623</td>\n",
       "      <td>-0.535455</td>\n",
       "      <td>-0.337394</td>\n",
       "      <td>-1.007506</td>\n",
       "      <td>-0.729649</td>\n",
       "      <td>-1.170098</td>\n",
       "      <td>-0.327748</td>\n",
       "      <td>-1.634365</td>\n",
       "      <td>-1.096246</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.049720</td>\n",
       "      <td>-0.503166</td>\n",
       "      <td>0.045608</td>\n",
       "      <td>-0.189820</td>\n",
       "      <td>0.954633</td>\n",
       "      <td>-0.319482</td>\n",
       "      <td>-0.721492</td>\n",
       "      <td>-0.256089</td>\n",
       "      <td>0.370441</td>\n",
       "      <td>-0.322181</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.721171</td>\n",
       "      <td>-0.408278</td>\n",
       "      <td>0.674967</td>\n",
       "      <td>-2.695938</td>\n",
       "      <td>0.355645</td>\n",
       "      <td>-0.729649</td>\n",
       "      <td>0.146991</td>\n",
       "      <td>-0.540545</td>\n",
       "      <td>-1.634365</td>\n",
       "      <td>0.386628</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.199816</td>\n",
       "      <td>0.960952</td>\n",
       "      <td>0.266572</td>\n",
       "      <td>0.020169</td>\n",
       "      <td>-0.078658</td>\n",
       "      <td>0.557152</td>\n",
       "      <td>1.176627</td>\n",
       "      <td>-0.564235</td>\n",
       "      <td>1.203862</td>\n",
       "      <td>0.135450</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.408278</td>\n",
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       "      <td>-1.389613</td>\n",
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       "      <td>0.767722</td>\n",
       "      <td>-0.231236</td>\n",
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      ],
      "text/plain": [
       "       AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "0 -0.673560 -0.015127  1.371393  0.650136 -0.336981 -0.903905 -0.721492   \n",
       "1  1.821798  0.960952  0.708500 -0.609798 -0.336981  1.433786  0.860274   \n",
       "2 -1.047863 -0.503166 -0.175356 -1.239765 -0.595304  0.557152 -0.721492   \n",
       "3 -0.049720 -0.503166  0.045608 -0.189820  0.954633 -0.319482 -0.721492   \n",
       "4  0.199816  0.960952  0.266572  0.020169 -0.078658  0.557152  1.176627   \n",
       "\n",
       "       AACT      AAGA      AAGC  ...      TTCG      TTCT      TTGA      TTGC  \\\n",
       "0  1.592788 -0.462980  0.364265  ...  0.189121  1.285490  2.127474 -1.044957   \n",
       "1  1.284642  0.995507  0.364265  ... -1.084407  0.317623 -0.051286 -1.280812   \n",
       "2 -0.872381  1.412217  0.364265  ... -0.766025  0.317623 -0.535455 -0.337394   \n",
       "3 -0.256089  0.370441 -0.322181  ... -1.721171 -0.408278  0.674967 -2.695938   \n",
       "4 -0.564235  1.203862  0.135450  ... -0.447643 -0.408278  1.401220 -0.337394   \n",
       "\n",
       "       TTGG      TTGT      TTTA      TTTC      TTTG      TTTT  \n",
       "0 -0.098739 -0.729649  0.366506  0.523440  0.986093  0.633774  \n",
       "1 -0.780314  1.126261 -0.511554 -0.327748  0.330979 -0.972673  \n",
       "2 -1.007506 -0.729649 -1.170098 -0.327748 -1.634365 -1.096246  \n",
       "3  0.355645 -0.729649  0.146991 -0.540545 -1.634365  0.386628  \n",
       "4  0.128453 -0.729649 -1.389613 -0.327748  0.767722 -0.231236  \n",
       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data=x, columns=features).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "principalDf = pd.DataFrame(data=principalComponents, columns = ['pc1', 'pc2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2.465220</td>\n",
       "      <td>1.249726</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.197430</td>\n",
       "      <td>1.028781</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.831509</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.272447</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>4.537644</td>\n",
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      ],
      "text/plain": [
       "        pc1       pc2\n",
       "0  2.465220  1.249726\n",
       "1 -0.197430  1.028781\n",
       "2 -2.831509  4.601236\n",
       "3  2.272447  2.246411\n",
       "4 -2.378367  4.537644"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'unstandDf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-132-9057c38309ae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0munstandDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'group'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'unstandDf' is not defined"
     ]
    }
   ],
   "source": [
    "unstandDf['group'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finalDf = pd.concat([principalDf, unstandDf['group']], axis=1)\n",
    "finalDf = principalDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.465220</td>\n",
       "      <td>1.249726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.197430</td>\n",
       "      <td>1.028781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.831509</td>\n",
       "      <td>4.601236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.272447</td>\n",
       "      <td>2.246411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.378367</td>\n",
       "      <td>4.537644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pc1       pc2\n",
       "0  2.465220  1.249726\n",
       "1 -0.197430  1.028781\n",
       "2 -2.831509  4.601236\n",
       "3  2.272447  2.246411\n",
       "4 -2.378367  4.537644"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'group'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'group'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-6c2a42188bd9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'g'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mindicesToKeep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfinalDf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'group'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     ax.scatter(finalDf.loc[indicesToKeep, 'pc1']\n\u001b[0;32m     13\u001b[0m                \u001b[1;33m,\u001b[0m \u001b[0mfinalDf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindicesToKeep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pc2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'group'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 Component PCA', fontsize = 20)\n",
    "\n",
    "\n",
    "targets = ['group1', 'group2']\n",
    "colors = ['r', 'g']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['group'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'pc1']\n",
    "               , finalDf.loc[indicesToKeep, 'pc2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mhq9LMml82dJAh9MEGYbJBFkn9hqUCiEuAkDkd0mGx5O1mBWTiViIJhnBzpYGOtnkTmAYZvJAej7QTEFE5QDejPHZXxNC3BWz/RMhxOdUjvsagK8BQGlp6cOvvfZaegZskRs3biA/Pz8l575+/Tpu3LihuT0/Px933nknbt68iZGREdWJARGhoKAA06dPt3z9VN4bANy+fTsqiEKIqPgXFRWZEmwhBEZHRxEMBqEoCqZNm6Y5gYjFrvtK1fueKKn+vDJJrt4b39fEI5339vjjj78jhKiNfz0bffZqXCKi2UKIi0Q0G8Cg2k5CiJ8B+BkA1NbWilWrVqVxiOY5cOAAUjU2r9eLlpYWTd/0mjVrUFNTg9bWVnR3d2ueZ/ny5QmNMZX3Jrl9+3baO97ZdV8+nw+NjY2qlpO8vLy0++zT8Xllily9N76viUc23NtEMeP/G4BnI/9+FsC/ZnAsWY1ZU3c6ytimCpnG53a7UVNTM6EC2rLFncAwzOQi61b2RPQqgFUAionoQwDfBvA9AE1E9GUA/QCeztwIsxuzFeuqq6uxZ88e1XOkM2DNLBMpa8AIO4sMMQzDmCHrxF4I8YzGpoa0DmQCY0ZMpkyZArfbjebm5nHHu93urBKeXMxLNyoylEuTG4ZhMk/WiT1jD2bExOPxqG7zeDxYsmRJVgj+ZMxLz8XJDcMwmWWi+OwZm7GSopfJLnGTLS99IqZEMgyT/fDKfpJiJt87G1aYmchLVzOhpwszk5t01vBnGCY3YLGfpBiVjy0oKMgK83m6y9xqTXAefvhhW6+jBRfdYRgmFbAZf5JilKIHQHOFGQwG0dTUlBazvt6q2u6sAT0T+tWrV6MTn1S6NiZySiTDMNkLr+wnKUYpet3d3ZorzGAwiN7eXvT399tq1lczn1+6dEl1X6fTmXReevz1AoGAble9jo4OFBcXp9S1MdFSIhmGmRiw2E9i9FL0hoaGNM3nEjvN+r29vXj11VcRCoUghIDT6URLS8uY68TicDgwa9ashK+nZq4PBoMIhUKq+wshcPnyZezZsyelrg2zdRIYhmGswGI/ydFK0dNbYcaTbODYuXPnsH379jGvBQKBlF1TL51PCyLC6OhoWoLnuOgOwzB2w2LPqKK2wtTC7/fj8uXLCV3H5/Nhx44dlo/z+/3wer0AYLngjF7Eux5Tp061PXhOq3iOUZ0EhmEYK7DYM5rErjC7urrQ19eHYDCoum9bWxsWLVpk+RqdnZ2apnMjPvroIwwODkZ95qWlpaaqzulFvAOAoihwOBxjTOhFRUUoLi62NTMgG1IbGYaZHLDYM7rIFWZ1dTUaGxs1xT4YDGL79u2or6+3dP4rV64kLPbAZ+b3bdu2jXlNTziN0vncbjecTucYE/rhw4dtDZ6bjJUBGYbJHJx6x5hCmvWdTu35oewVb4XCwkIoipLs8OD3+6M/8m+tqnNGaYcPPvigalc9OzvWTbbKgAzDZBZe2Wcp2dgIpaysDA8//DCOHTumut3v92uu/NXo7+/Hvn37TB2jKAruvPNOXLt2zfT5AfXAuWQi3u0KnuPiOQzDpBMW+ywkm325JSUluiZws6t0NTO2HsFgEDNmzMCnn35qGDkfi9/vR1dX17hJUzKibUfwXLorAzIMM7lhsc8yst2Xa+S3njZtmqnzWI2Id7lcqKysxAcffGD6GElfXx96e3vhcrmwe/duLFy4EHPnzkV1dbWqaOvVxrfL4sLFcxiGSScs9llGtjdCMTKBnz9/3tR5jCLi45G+9FmzZqleOxQKaebmSzeBvF5HRwfOnDkzzlri8/lw6NAhHD16FESEYDA4pja+nRYXLp7DMEw6YbHPMiaCL1fPBG5W7PXM2EDYRy/FNlYAta49MDAwRjjl8VrIiYG0lgwMDGDbtm3jxiP/vnr16rjtyVpcuHgOwzDpgsU+y5govtxk/dZ6Zuy8vDw0NDRgZGREVQDVrh0vnAMDA+jt7TUch9/vxxtvvKHbCwAIW1X0SukmanHh4jkMw6QDFvssY7L4co3M2IkEIsYKp9frRX9/v6GrQAhhOn5Ay1KQLRYXhmEYLVjss4zJ5MtNpRnbam1/M2i5BrLJ4sIwDKMGi30WMpl8uakyY0+ZMgVutxvNzc22nI+I4HA4VMU+lywuDMPkJiz2WQr7ctXRS32L3VZYWAiPx2PLNfPy8lBUVIRHH310UlhcGIbJPVjsmawlXtgLCgqwc+dO1dQ3AJai8c2iKArWr1+PDz74AGVlZXjuuefg8XiiY3K73bjzzjuTvk4iZGOVRYZhshMWeyZKNolHfE670+kcl0cvg+9eeeWVaJc6iR1CL8/T1NSE+vr66JiCwSCCwSAGBgZw5syZjFQ2zOYqiwzDZB8s9gyA7BIPtSqCWgVzAETFNxkURQERqV5HNviJz7OX1922bRteeOGFaGS/kYsh2YlUtldZZBgm+2CxZzImHj6fDydPnsTZs2cBAJWVlViyZAlOnjxp28rcLKFQSDMq3+/349atW7rFjvbv3w+v1xut5EdEaG5uxjPPPAOXy2XrRCrbqywyDJN9sNgzaREPNf97U1PTGAHt7e3F3r17dYU3VciWt2rXdblchpOPo0ePjvlbCBFd9ce7IJKdSE2EKosMw2QXLPZMysXDjP9dku4VvUSrOh7wWdpdomjda6ITqYlSZZFhmOwh8ScYkzNI8VAjWfGIdRFIcdLzvyeL2Ra7ZsnLy8PGjRuTEnstEp1IVVdXRy0R8XDOP8MwarDYMykVD6utbPVwOBzYsGGDpqC7XC6sXr0a9fX1toi+oihoaGhAWVmZ7ZMIIDzegoICeL1etLa2wuv1wufzGR4nqyzm5eVFJ2kulys6MRFCWD4nwzC5DZvxmZSW6LXaylYLKbw3b95EVVUVurq6otHz8TX1+/v70d7envQ1g8EgRkZGAEBzMiTHlmicgSz8I2sD7Nq1C/X19VixYoVutL5e97/GxsasyKpgGCZ7YLGfBJhJ+0pViV6jVrZmISIcOHBgjIgJIXD//fejvLw8OlbpNrDDVSBdGD6fD59++qnmfoqi4Itf/CJ27NhheL7YiZSM3JfIeIXDhw+jra0NmzZt0hXo+CqLnJLHMIwWLPY5jpX8+VSU6LXSkAb4LN+diKLjlaiJWHd3N9atWxcVMTvdBgCwePFidHR06O5TW1uLhQsX4pFHHsGxY8dU93E6naiqqkJ+fj6KiooQCAR0y/n6/X7LAs0peQzDaMFin8Nkw0pPz0Xw5JNPorOzEx9++CFcLhcefPBB1NbWAsAYC4OeMMaLmF1uA8nAwACuXLmiO4GQJn49v34gEEB+fj7cbjcAoLW11VT7XSsCzSl5DMNowWKfw2TLSk/NRVBYWIimpqYxE4CDBw9izpw5KCsrGzMuPWGMFzEjt4GVmvl+vx8vv/wyqqqqNH32sdkKZlPifD4fbty4ASLSnURYFWhOyWMYRguOxs9hsmmlJ10Ebrcb1dXVaGpqGpOO5/f7cfv27XGWCMBaaqBeZoGM1l++fDmeeOIJU+l0oVAIp0+f1hTl2GwFM1kN/f39aGxs1D2n1r0ZwSl5DMNowWKfw6Qyfz4ZzFgcgPAK2Ov1YnBw0JTYAvppaZs2bUJdXR3cbjceffRRPPvss8jLy9ONtAfGFvpxOp3Rc7pcLtTV1eHgwYPwer0AoHnt2tpavPXWW9i6deuYSY4eVgXaKCWPg/MYZvLCZvwcRi84Lt0rvdiMgEuXLhlaHOI7zMlVuKy+p5caaDazQO73xhtvmArsczgcmDVrFubMmQOHw4H29nYcP358XOBj7LUB4Pjx42hrazMUeGnWTybtMVVZFQzDTGxY7HOYVObP6xGf6hfvn9cLZJOFZuI7zMlytkIILFu2DMXFxboiJoU7/nc8AwMD6O7uNhXBHwqF8OGHH+LSpUsAMGZ88YGPNTU18Pl8aGxsNB0wOG/ePMyaNStpgU5FVgXDMBMbFvscJ90rvdu3b48r6hIvdnoBcrJQjpZABoNBfO5znxsnZrETDABob283TDf0+XzjJhVm0NtfCIHm5mbccccduHHjhm7N/VhcLheqqqqySqTtbMvLMExmYbGfBKRrpefz+XD16lXVVD8tZHR8rMXh0KFDusd0d3ejrq4u+nd8LYF4tNINDx06ZGuanrzWqVOnIISAw+EwLfbZFkBnpT4DwzDZD4s9YxudnZ2WjykvL7dsuo4NqFOrJaBFbLqhz+cb15bWLqRLwMqqPpsC6LKhPgPDMPbCYs/YhlHxmXi0TNeVlZXo7e3VPK6ioiJqYu7q6rKUNy+D5jo7Ow2j8NPJrFmzMj2EKNlSn4FhGPtgsWc0seqznTFjRtRnbgYt0/WSJUvQ2tqqWt/e6XRixowZY+ICzBKbbnjlyhXTk4R0kE0Cmk31GRiGsQcWe0aVRHy28+fPx9mzZ01fY/369arm4ClTpmDz5s3Ytm0bQqEQgsEgFEWJtriVBXmsEju5sKtBT/z51VbEiqKgqKgIly9fVj0uWQG1O5COK/ExTO7BRXWYccT6bM1UuAPCk4OXXnrJtBnf6XRieHhYc3tZWRleeOEFrF27FsuXL8fatWvxwgsvYHh42HKjG7XCMnrV5hJFa1zBYNAw+O7ixYsJ9Z6XFflaWlpw+PBhtLS0oLGxEf39/ZbOEwtX4mOY3IPFnokiK9bt3LlT08QdDAbR1NQ0RpisBMlJAoGA4Wo2tsRuTU0N8vLycPHiRdOrcYfDgbvuugvTp09HRUUF7rrrrjHb6+rq0ua37+vr091+/vx5y0KdyKTMDFyJj2FyDzbjMwCM09ckwWAQvb296O/vj5r1h4aGElptWzUHt7W1ob293fT+oVAI165dAwAMDw+js7MTa9euRWlpabQ6n9q4raTMmeXChQuG+1iNeE9lIB1X4mOY3ILFnkloZR4rTA899JBl37eeOVjNB+3z+dDc3GzpGmo0Nzcb+uqJKCX+fIfDYSoo0KxQpzqQjivxMUzuwGLPoLOzM+GVrBACt27d0hRHp9MZLTBjplyvVmDg5z//+YTGp4ZalH8swWAQ9957L86fP2/bNYUQpt9js0LNgXQMw5iFxZ7B4OCgoQBq4ff7MW3aNE3ft8PhwPPPP4+zZ88amoP1irno5d1bxYzLwU6hB8Ire6P+9RKn02lKqLOp0RHDMNkNB+gxGB0d1d2en5+v2bzG5XJh5syZ0ZW6FP3YgK78/PxxgXZqmOk8ZweZKKZjZWUvhDAl1BxIxzCMWXhlz2DatGm62xcuXIhTp06p+pvlCjIvLw9btmyBx+PB8uXLEwro0vNB20k6JhTJUFVVFX3fjHLoOZCOYRgzsNgzKCkp0YxAVxQFs2fPxv3332/YKjcvLw/Tp0/HqlWrxpzDbNEXPR+0XRHysvFOssT2nrdzguJyuVBeXg7AfGEjDqRjGMYIFnsGBQUFmkKqKMqYlbvVFaSVSnx6PuhkmTNnDhYtWoQDBw7YJvZEhHvvvRd9fX0JxzyonXfx4sVJN6Ph9rQMw8TCPvtJjs/nw86dOzW3x5a0VStyY3RuK0Vf9HzQ1dXVutfS88MrioLNmzfj008/Nb0Kl+V5tQiFQgiFQjh//rxtQh/b/e7kyZOakxKZmqdFKqrqMQwzsWGxn+ToBcW5XC7dkrbJnFtLsKQP2u1247777kNZWRkaGhowZ86c6ARA63xaVFZWoqOjA4WFhbrniCUYDOKRRx7BF77wBcycOVNzP5k/bxezZs2KFizSEnu91LxUVdVjGGZiw2b8SU4qC7Mkeu6BgQHs27cvavq/cOGCYdqaw+GAw+EAEcHv90f3VxQFp0+fxrlz50ynvgHhlX1xcTFqampw9epVfPLJJ6r72d0574033kBXV5dufIJeDj23p2UYRg1e2U9yZFCcGskWZtE7txZqK1PZ+U5PAEOhEGpra7FmzRrU19dHV9tSjOXqVlbHMyIUCiEQCOBXv/oVjh8/rrmfkbnfCn6/Hx0dHYaBiMFgEIFAQLVpDrenZRhGDRb7SU4qO5wZdZY7fvz4OLPyr3/964RMzTLfv6amBjNnztQV4Mcff9xQoB0OBzweD9577z1da4DRJCQVhEIheDweVT98KidvDMNMXFjsJxFsRfAAACAASURBVDmpLMwyZcoU1NbW6u4T67f3+Xw4cuRIQtfy+/0YHByEz+czXN2eOHHCsLBOMBhMS85/osT74WXHwsHBQc3JCVfVY5jJC/vsmYwVZok3K3d2diZV3e7YsWM4ceIEamtrdfPfE+nSl60IIXDo0CG0tbVFYxxktUOn04lAIGDYj4BhmNyHxZ4BkLrCLFaatVy5csUw4E2a37VM57dv3zZsg6sn9FaC+NKF3pj8fj+OHDky5v2Q76EQAsuWLUNxcTFX1WOYSQ6b8bMEaYZtbW2F1+tVDb6aaPh8PgQCAU0Bjzcrmw3oM/KRCyGwdOlS5OXladb010JRFDid9s2BXS6XoStDD4fDgerqas0xKYqi24RIZhSw0DPM5IbFPgvIxSIo8p48Hs84cdaKCTAK6HO5XKivrzcUY7/fj5GREWzZsiVaelaL+MY9GzZssDVvnojwxBNPYMOGDZaPdTqdePbZZ7Fu3TrNMQkhEsrHZxhmcsFin2GyvQhKIhYHtXuSKIoCt9uNLVu2jCuXqxYsqCgKFEXBo48+ihdeeAFz5841VbGuq6sLQLipjF50+gMPPIDly5djzZo12LJlC+bPnz9uDFaQloT4Cc3ChQuxadMmOJ1O05OJhx56CGVlZbpBlPX19Rx9zzCMIeyzzzDZXATFSl37WPTuyeFwwOl0apqVtYIFhRB49913TdfODwaDOHjwIFasWKHb8/3uu+/G5cuX4fV60dbWhpKSEjQ0NGDLli3Yv38/jh49aup68t4WLVqEwsJC1SDH++67D3/6p3+Kjo4ODA4Ooq2tTdclceLECSxevBhlZWW674tWjAJH3zMMI2GxzzDZWgQlmUYsyd5TfLCgnHRYzWk/duwYVq5ciY0bN0aPDwaDUT93KBTC3r17x5jBBwYGcOrUKTzxxBNoa2szfS0gHEvQ1dWFZ599VnMyJO+tv78f77zzju79BAKBMe+1VhClvD+9joR2wQ12GGZiwmKfYaxEq6eTZCwOdt6T2qTDCh0dHSguLh5zL3p+bklra2tC1wuFQoaTIXlPZtwR8e+1mtimK3UyUUsPwzCZh8U+w+i1dc2kGTaZ1bmd96Q36TAiGAzi8uXL2LNnz5h7SXXFu2AwiF27diE/P3/M6lcKdVdXl+ma+n6/H+3t7Xj//ffh9/vR09MTrf+vKAp27dqFuro6FBUVYXh4OHq9VKzok2m5yzBMZmGxzzAy+CpdZlizGK3OCwoK4PV6Vc25dt2Tz+dDV1dXwpXsXC4XRkdH0543HwwGozXu5erX7XbD4/FE3w8rXLx4ERcvXlS9DhB2V0jk9davX4/h4WHbzO3ZHFvCMIwxLPZZQKYq2OmhtzoXQozpSudyubB7924sXLgQ06ZNg8/nS/qeYv30ejgcDs2VOhFh6tSpGSl7K8ckr93c3JyW68rrbdu2LTpZs8Pcnq2xJQzDmINT77IEGXzldruzogiKVrqXXKHHpwoGAgF0dHRgZGQkWiMg0XuKNRnrib2iKFi9ejU2bdqkWdu/pKQkoRS6XMDOVE5usMMwExte2TOaqK3OA4EAPB6P5jFCiKiwJOrH7ezs1PWry9z72JWqlhWhtLTUdLperpOMuT1bY0sYhjEHiz2jS3y6V2trqymzuJ6wGKVvXbhwQTdSvby8HOvXrx8zkdBKS9OLH5g/f360+E6iSDdCbOW/bKutL0nG3J6tsSUMw5iDxZ6xhF7gXiyxwhIr7kSE48ePR/eJ9yf7fD6cPn1a87xOpxNVVVWG4hI/oXjuuefQ09MzZuXf0dGBnp4eW3z6DocDRGQqnS5TJGtuz8bYEoZhzMFiz1hCz5wbixSW+NzseOLTt06ePKnrpw+FQpomYynwfX196Orqiopv7IQidvVv9l70kO4Gs6l0mcQOc3uquiMyDJNaWOwZS6iZc9UgIlRUVODFF180FRQmzf5nz57V3W/27NnRlaSexSAWrXxwtXuRPeCzhWRa7iqKgmAwyOZ2hmFY7JkwVsqgxppz41fRRBSNhO/p6TEtVGb9yVOnTgUwvpqbGdTiCOS9vPvuuzh9+jSGhoZw48YNU+dLB4sWLUo4rmDu3LkoLS1lczvDMCz2TGJlUKU5t6amBuvWrYv6cW/fvh1dPXd3d5sWYmn2LygoQG9vr+Z+CxYsSLiErtaEYmBgYFyN/GzA5XLB6XQmZG1wuVxYtGgRm9wZhgEwwfLsiaiPiN4joneJSL3VF2MJO1rsSuF/7LHHAAAHDx6E1+tFYWGh6Rx36U9esmSJbj73kiVLEi6hqyjKuAA1n8+HrVu32ib0ssWtXUyfPj0htwKnwzEME8tEXNk/LoQYyvQgcgW7yqBK68DcuXPR3d1tWuQVRYEQAnV1dbh16xbOnTuHBQsWRCPyZZc6mVefl5enW81NDyHEOAHs7Oy0dUV/77334vz587aca+nSpSgqKjKV/RAL++ezE+4YyGSSiSj2jI3YUQY11jogJw7ynLJ3fXxudmVlZVTQQ6EQjh07hrfffjsaVCaZPXs2HnzwQTz44INR8TKb/hfPsmXLIIQYU9N/cHDQ1rz4999/P6GxaZFIxkB5eTl3ocsyuGMgk2lI70FHRPcA+AMAJQA6AewQQnwSt88iAC8JIf59KgcaudYFAJ8AEAB+KoT4Wdz2rwH4GgCUlpY+/Nprr6V6SAlx48YN5OfnZ3oYAICbN29iZGREVfCICAUFBZg+fbrpc8jubvHnAD5bpU+dOtWSyBIRioqKomIvhMClS5csiTQR4a677sK1a9eix8lCOGbOE39fiSKj642i7GPf+9u3b0cnXWbGescdd0TfcyOy6btoN9lyb3rfVyJCaWnpmKJMRmTLfdlNrt4XkN57e/zxx98RQtTGv64p9kRUAeAYABeA9wHMB3AdwJeFEP8Ws98jAA4LIex1VqqP6W4hxMdEVAKgFcDzQohDavvW1taK9vbsdOsfOHAAq1atyvQwAIRX5Y2Njaq++by8PFMlb1tbW3H48GEAQGVl5bj0ueXLl8Ptdkf/9nq9aGlpsbT6jR+LXCkFAgHDlrWKomDlypV46623TF8vHrX7ssqGDRtw8+bNqHAfO3ZM1x//yCOPQFEUzJgxAxUVFejp6cGRI0cwNKTvxTL7uQHA/v37UVhYiMHBQYyOjmLatGkoKSnJCRNztvw/0/u+u1wurFmzxlIgZbbcl93k6n0B6b03IlIVe70Avb8H0A2gTAixGMDnAewG8Csi+i+pGaY+QoiPI78HAbwOYGkmxpFLaDW8kelzeoLh8/ng9Xpx6dIlzcA0taptifjcZdtYiUyZu//++w1XRQ6HQ1fonU6npZVVIjidTty8eTMayNjW1mYYeHfs2DEcPnwYu3btwo9+9CMUFBRg2bJlhvEQMtbCiP7+fly6dAnNzc04duwYTp06hWPHjmH37t3RZkZmkd+F1tZWeL1eW6wguQJ3DGSyAT2f/TIAX5NmeyHEZQD/JxEdAfAjIrpXCPHNdAwSAIjoDgAOIcT1yL9/C8Bfp+v6uYzVMqg+nw+HDh3C0aNHQUS6AW5qUeGJ+NyDwSC8Xi8ARFedeXl5+MIXvoDTp0/rZg0YXScQCGDZsmX4+OOP8f7775sekxUCgUD0oW41myAUCiEUCmH79u3YsGGD4cTEjIDIOIvy8vJxn5+chJhtZsT+aH30vu/cMZBJF3or+2kAbsa/KIT4CYCnAHyFiHYCmJqiscVTCuA3RHQSwHEAu4QQLWm6ds5jth1tf38/fvjDH+Lw4cMIhUKaQq9nHaiurk5oJf3RRx+hpaUFjY2N6O3thdfrxaFDh1BbWzvGMpEIRIQHHngg4XMoigKn02nKwpFoNgEANDU14cknn4TTqT1PNyMgZiYcZiwEdqRu5jp633dOkWTShZ7YdwN4TG1DxGf/BIB/D+DlFIxL7ZrnhRBLIj/VQoi/Tcd1mc+QD3YjoZo/fz7WrFmDLVu2qK7s1FwHeuIVixSSbdu2oaWlBYcPH0ZbWxuEEFi6dCnuuece6zcGoL29HRUVFQmb8xsaGvCNb3xDU+yDwSACgQB8Pp9ub3gjhBD45S9/iaeeekrzWloCEmtq7+rqstTMSAszqZupYqK4DpJxlTGMXeg9YVsQXr3/nRBi3P8iIcRhIloR2Y+ZBJg1P5eWlhoGHKm5DqZPn46dO3caBtxJYleSAHD8+HEsWLAAFy9eNH0OiRACPT09pur+q+F0OnHnnXdGjw+FQmN88qFQCB6PB/v27cNDDz2UVG5/IBDA66+/jmeeeQZNTU2mWs7Gm9rNFP8xYyHIlD96orkOuGMgk2n0xP4HAJqgs/oXQnQSUQ2AKrsHxmQfZs3PRmlf8cVFHnvssWjk97e+9S28++676OnpwdDQEK5du2Z6fH6/H11dXZaFPvbY0tJSNDQ0AADOnj2Lvr4+U8J88uRJ1NXVoaysDOvXr8err76qeg0gHHiXLEIIDA8PmxIQtfLCZu7JjIk5E/5otfvRanaUTXDHQCaTaIq9EOI6wrn1ukQC9w7aOSgmOzEbWLdv3z7MmjVLdYV17tw57NixIxp05nK50NLSgqVLl0IIgRkzZmDJkiVYunTpmJQ+s6gJvVzxPvTQQ2hvb9cUur6+PvT29kb3X7BggekV+EcffYTr168jLy8PTU1NKa+zL1fNWgISO6G6ceOGYU5/7HaHwwGn02nKxKxX9CdV/mi7qj4yzGSCK+gxhkjhMFsIRwZnxa+went7sX379jH7yonD22+/DSBsDm9ubsaCBQvQ3d2d9NiJCFVVVVi7di2EEDhx4oSmEMvX5Zi6urosZQ38wz/8A+rr6xOqyGe1la3eqjnexG107rvvvhsff/wxgM+K9gSDQbS3t2NoaAjV1dUAoFrqVa1NsNmWuomWj+VUNoaxDos9o4uWr9eoE1sgEMDWrVtxzz33oKSkBPPnz1c1basdByDhtq7xCCGQn58fFR0tn7oWVtMDjxw5kpAbweoEQS8IL97EbbSqHxgYGLOPHP97772HM2fOoKWlBUIICCGiVRBj/eOJ+KOT8blzKhvDWGdCdb1j0otaWpVc/QohcPfdd2seGwqF8OGHH+LYsWNobm7Gj3/844y0kFUUBQMDA9FobelTB8Lmaj0SGa8shZtKYpsCxWM1h99oX7/fD7/fj0AgEH0/gsFgNCNCTirMpm4CyafrcSobw1iHxT4HsSslSU84HA4HiouLTaWQybSzTBAMBtHb2xvNzz9z5gxee+01U2V2E0GugFPNrFmzVF9PtDphIvj9fpw8edLyccmm63EqG8NYx5QZn4j+EsD/lOVq47bNBvBVIQRXs8sC7ExJMvKNTps2LeWrWLuQ97Fjx44MjyR5QqFQ1gShdXd3o66uztIxdvjcOZWNYaxhdmX/bQBzNLbdHdnOZBi7q5npFX9xuVwoKSmJrqSMTOKMfQghVAXR5/Ohra0trWNJZLKnd4wVn7sV1wHDTHbMPqEJ4bayasxBuO0sk2HsrmZmxjca25DGCCKKlpWdyLhcLtTX18PhcNhm2ZCTJUVRDM/rdDrHCKJ027zyyisJl+FNlIqKCkv7+3w+HD9+XHM7+9wZJjVoPnWJ6FkAz0b+FAB+QkQjcbtNBXA/gL2pGR5jBbtTksymVcmGNJ2d+mUZHA4Hvv71r+Ott95CV1eX5sTEapOcdOFwOOBwOLB+/XrMnz8fjz/+OJqamtDb25v0eefOnYsLFy4ACJvpFUXR9KXHCqJ025jJLnA4HIYxBTNnzsQnn3wCIop+3lqfhdPpxIMPPmjmFqMYfUdknwOGYexFb4l1E8CVyL8JwDCAeLW4jXDb23+0f2iMVVKRkmTWNzplyhRs2LBB14zc0NCAn/70pwgGg5qC43Q6UVVVhby8PN1z6YlhqgiFQnA4HNi5cyc2btyI0tJSfPDBB0mfVwiBDz74YEzAoN69hUIhvPnmm5gzZw48Ho+piZGiKFi9ejU+/PBDvPfee6rvv9PpRH19PRYvXjzm8y4sLIwWQpKpdw6HA5s2bbIszEYBhBMlBoRhJhp6FfR2AtgJAET0vwF8VwhxPl0DY6yTqmpmZsp8+nw+jIyMYOrUqWNWkNJ0/9RTT+H11183jBsIBALIz8+H2+3GzJkz0dzcPG6fefPmobq6Gjdu3MD+/fsTuqdEiW3/2tDQYEvk/X333WeptW4wGMR7772Hzs5O0xkFoVAIS5YswYIFC3Dq1CnVfQKBACorK1U/7xdeeMGWYDjOkWeYzGDKeSqE+P1UD4RJnmSqmSVDbAbA3Llzo2JfVVWF8vLy6ErRqjDW1dVh0aJF8Hg8GBoaQnFxMdxuN/Lz8+Hz+fDmm2+m5H7MEAqFcPbsWcNVqtE95+XlYcaMGTh37lxCYzCLw+HAwMAAhoaGNK0iiqLg7NmzqhM7u+q6Z6K8LsMwFiroEVEtgN9FOCBvXA97IcR6G8fFJEi6U5LUKrbJ1W93dzfWrVuHvLw8S/nfx48fx4oVK5CXl4f8/Hz8zu/8zpjtcnKRqdx9IHyPo6OjmqtURVFQWFhoGCdRW1uLO+64I1XDjBIMBrF9+3bdjnvBYFB3vImWt40lUxNShpnsmM2z/yMALwEYAtCDsK+eyVLkKkw+nA8ePJjww9kIs01JzDbRkWjlkatNLjLFpUuXNH3MoVDIVEBkW1tb2oIRhRC4deuWZqnj+Cj/WOys38A58gyTfsyu7F8A8HMAfyiEyNxyijFNuvp9m80A0DPf6h0Xj9VysGZQFAWLFi2ynJqoZ6Y3O8Z0Zh34/X7k5eVpWkSkzz6eVLSU5XavDJNezObZlwB4lYV+YmB3cR09jArvyJWiWolTLfQCtfr6+lIikEuWLDFV+jeW2HrxEwGXy4Xbt29HmxnFI3328dhdv4FhmPRjVux3A3gklQNh7COdD2crTUmk+XbNmjWor6/XFB29jm6JdMNTFEXzWkDYV93U1IQNGzaMq7eeC0WAJESEqVOnWvbZc0tZhpn4mH2KvQTgZ0TkAtAK4Fr8DkIIe3qSMgCSC4ZK58M5PuAKgG7AVaz5dtGiRZYCtTo7OxPKwy4vL0dlZSX27NmjGcEeCoUwPDw8zpdcUVGBF1980fI1sw3ZJGZoaMhy6hunyzHMxMes2Mtk5m8D+Mu4bbKUrvbSibFEsv72dD+cYwOuPvroI6xZs8ZUwJVeoJbaZOfKlSuWI/BlkZ4rV67opqrJVa2aL3njxo04fPhw9D11uVwIBoMp6ZpnBqOUPhmApygKhBBYtmxZNLuhtLTUcuobp8sxzMTHrNg/ntJRMFHsCIbKxMNZiuTIyMgYsTSyUKiJq9Zkp7a21nIp3UAggFOnTuHatXHGqDEoiqI5CSorK0Nvby8qKyujk5KLFy+ivb3d9DjsxCj4TwiBpUuX4tatW5g+fTqKioqix1i1xKgdw+lyDDPxMFtU52CqB8KEMZvKpoeVh7MdudNaJGKh0JvsJCquZqrTORwO3UkQEY15371er2YKW6pxOp0IhUK6Lom2tjY4HA4Eg0E4nU40NzejtLQU06ZNQ2VlJZ577jn09PSYtsRwuhzDTGwsRR4R0RcA1AL4PIC/EUL0E9EKAOfUet0z1rHL327m4Zysu0BtohC7LRELxcmTJzUDyIQQqKmpwbFjx0y9B2ZxOp2m67zLex4cHLR1DFYIBAKYPXs2Ll68qLpdThbl+ygnJB9/HP4v2tvbi9bWVmzevHmMJSYRKwzDMBMDs0V1SgH8G4CHAfQBmAvgnwD0A/h9ALcA/FFqhji5sNPfrvdwTtZdoDVRePjhhwEkZqHo7+/XDaLz+/22NJ6JJxgMoru7G6WlpbpWjfh71ovwTyUulwslJSUYGhpKOA0xEAhg27ZtWLZsGYD01WVgGCYzmE29+zGAfAALIz+xIdEeAA02j2vSYiWVLRmSSc/Ty+O/evUqbt++bdlCIc+pF/TmdDo1V7PJIITA4cOH8cMf/hD9/f2a+8Tfc6Zy7IkIDQ0NSXeIkxkILS0teOWVVwzrMvh8Pni9XrS2tsLr9cLn8yV9LwzDpAezZvw1AJ4VQpwjovjlzIcA7rF3WJOXdAVDJeMuMKpi19HRYdlCYaaDmxAi6odOBX6/Hy+//DJWr16NJUuWjFnlj46OZkUBHZfLhY0bN+LOO++E2+1W7QpolmAwiNHRUZw8eVJzn1AohF27dkEIga6uLhARAoGA6ZW/mZiQVMaNMAwTxorPXutJVwxg1IaxMBHSEQyVjLtgcHBQc6IgRaG4uFhTvNUsFIODg7rBbg6HA1VVVXjvvfc097GDUCiElpYW7NmzB8uWLcNjjz2GKVOmZEW1vJkzZ+IrX/lKNDXR4/Gk/JqBQAAdHR3jPkszLh81t8euXbtQX1+PFStWYMqUKew+YJg0YdaM/2sAz8et6uXS7g8AvGXrqJiov93tdqOmpsb2qGcjd0FFRYWqyba/v98wKr6vrw/Hjh2Lnl/6tl0uV7S4S/z9jI7qzxerqqowZ84cU/eWLEIIhEIhvP3222hsbER/f3/GcupjmT9/fvR9S0WPAC307l3L5aPm6pG1CaTLpLe3N21lnRlmsmN2Zf8tAL8B0AHgdYSF/qtEtBjAYgD1qRkekyr03AVutxsvvvjiuNXW008/jZ07dxqucOOjwIkI9fX1mDlzpqaFYtq0abrnzM/PT/BOk0MKT1VVVUauL3E6nSguLo7+baVlcCrRcvkYTUb8fj9effVVOBzq6w2zaaZqSLfA9evX4fV62S3AMDCfZ99BRA8D+A6A30PYpP+7APYB+LIQoidVA2RSh5q7QJaHVYvS37FjR0LXISLMnDlT98FdUlICRVFUJxKKomDmzJm4cuWK7nUcDgdCoVC0chygvyo1ixACQoiU5NUrioLFixdj+vTpuH79Orq6ulTHHF8HwGrLYMm8efMwffp0nDlzRvdejKr0xVJYWDjuNTOTkVAopDlxTLSsc6xbYO7cuVGXDLsFmMmOaZ+9EKIXwOYUjoXJAPHpeV6vV/Mhr1fIRQ8zD25Z9U9L7BcvXoyDB7VrOzmdTlRXVyM/Px9FRUUYHBy0LR/f7/fD4XBorkKTQVEUrF27NmrtqKurMxWcaaVlsMTlcqG6uhrV1dWq3e2A8PtYW1uLmzdv4vTp06YmE2rfFzOTERlwqfadSqSscypa8TJMrmD/04uZ0OityEKhkKafX6+znJkHt1oL3FgfvxBCN1aAiLB27Vq43W5UV1ejra1N93pW+fTTT+F2u23LrZf3tn79enR0dERjI0pLS6OdAZcvX441a9Zgy5Yt41alWu+X3oRETrpij5WfpxzP5s2bsXr1aqxdu9b0yn5kZGTca3oxIRKn06k53kTSTLkVL8NoY3plT0RfRNh0PwfA1PjtQoilNo6LMYndaUtGKzKth6nD4Ui6PoBeFoKexQEAli5dGo1Sf/PNN1MSUOfxeLBixQrs37/feGcdFEVBXV0d5s2bh6amJtVIdD2XR+xn3tAQLnExMjKCoqIiBAIBeDwewywL+V57PB4sX748+l4LIeD1enHx4kVT2QdaPQViY0K0Au0cDgfWr18/7j1INM2UW/EyjDZmK+h9B+FudycBdAHgMNksIBVpS2bNw9J/TURwOBzYsGEDrl69inPnzo3b1+12Rx/cseVmR0dHMW3aNJSUlEQnKVpV/8z4gPv7+7Ft27aUBa6FQiHNojtWCAaDaGtrQ3t7u6rJWSvXH1D/zKU4lpWVwefzYd++farXjZ905eXlYfr06Vi1atWYcweDQdNphsFgEJWVlarb5ITi0KFDOHLkCIgIwWBw3JjtSjPlVrwMo43Zlf2XAXxPCPHnqRwMY55U+Sflimzr1q2aAVyKoiAUCkX9rQ6HA01NTQiFQpg3b964/T0eD5YsWYKBgQFVMXE6nYaTFL0HudPpxLVr1/Dyyy+nNEUuEAhgdHRUcxxEhMLCQsMOe4B+4GBsrn9sTrqZzzzRokxq5zaD0+nE2bNnNS0ReXl5cLvdWLFiBTo6OqKTvOnTp2NoaChaojiZqHtp1Zo/f35aqk8yzETErNjfiXDkPZMl2NEdT4uysjI8/PDDmgFu8as+o5V0MBhEe3s7Dh48qComclKhN0nRszgEAgF0dXWlJe/80qVLmn5mRVFw/fp1U+cxWjnLDIDDhw+jra0NmzZtwtDQkKnPPJHVcqJ5+4FAwJR5PC8vD8XFxdizZ48tligtC4fb7YbH4zHdvpdhJgtmA/ReQ7hkLpMlpNo/WVJSEg38SpZgMAiPx2NK4LSCqNQC0mJFN50FZkpKSlQF38oY9AIa4/H7/di+fbtu5cL4z9xqUaZE8/bNmsdHRkawdetWWwro6PVm8Hg8eP7557FmzRrk5+drBjgyzGTD7Mp+H4C/J6JiAK0AxtkphRCJF+lmLJNq/2QiqV16CCEMxd5okhK7Yh0cHLQ94t4MQgh89NFHqtuslNOVAY1mjxFC4NatWyn7zBPN2wdgaB7v7+/HK6+8otu62IolysiqJd0Kse17GWayY1bsZTWVcgDPqmwXADLT73OSoifGifon432gsmJeKBSKBuMB4VWp3cVlAHOCJVesRtH5VnE4HNGVdior08l0s40bNwIIuy4CgYBhrIHf78fg4KClfgNWMjUSndzJLAgt5Cpcb1Jj1RLFUfcMYx2zYj83paNgLGN3dzwtH+iqVavw1ltvRYPx9KrIJWv215qkqEXw6/mvE702EaG2thYnT57Ep59+atu5gfBkora2FqWlpWP8508++aTpyoQXL16E0xn+LxtbbZCIMH/+fHR0dEQF3WqmRuz3yUo0vhFmYgGsWiU46p5hrGO2XO77qR4IYx270pb0orz37t07Zt9YoZcP3NhJRkdHh2bZW6fTGX3wx26X/u/a2tpxwqCVDmZ3NTt5/vb2dlRWViZdgEXtvZGpcR0dHbhy5QoKCwvR0tJi6bzy/Y991azK6wAAIABJREFUL2SnwbNnz47pYWA1UyP2+3T58mWMjo7i+vXreP/991U/TzPCaiYWwKolKhVWLYbJdawU1XECeArAvwNQBOAqwt3wfiWEsN+my5hCKy/dColEYjudTlRVVUXL08pJRm9vr6bYOxwOPP/88zh79iwuX76My5cv48KFC1Hftcw9jxVGreAtsyl2JSUlKCgoQDAYxIULFwz3F0Jg9uzZSYm9oiiq741ay1c7rRNyIqBnKZD96fPz8zFjxoxx15ffp1hrilZtATPCahQL4HQ6LVui7LZqMcxkwGxRnRIAewE8AKAPwCUAywD8MYCTRPRbQojLqRokk1oSicQOBALIz8+H2+0e87p84Go9iPPz86Ni0tjYOEa041efybZxVRQFX/7yl6MV+D788EPD+/T7/bh58ybWrl2L5ubEYk6DweC490Zt4mKXqTwevR4Gsf3pXS4X5s2bh/7+/jGmfbVJCfBZcxwrwqq3ClcUBd/85jcT6mhoZzEehpkMmF3ZNwKYAeARIUQ0BJqI6gD8MrKdm+RMUBKJxNYz4Zp5EJupE5BsG9dnnnkmek2zAWiKoqCgoAAPPPAA9u7dm1Agotp7k+jERctKoofsYaDX0AgIT2yEEGNM+yMjI+Mi5+W/hRAgIoRCITzzzDPjfP9aAYFGk79EscOqxTCTBbNivxbAc7FCDwBCiDYi+q8Afmz7yJi0kUgkNhEhEAigtbVVNdLb6EFsFFHd19eH8vJyw0mIjANwOBxjutN96Utfwn333Rfd79KlS6ZM/7ImwPDwsGEjFy2ICBUVFfB6vVHh08uR1yNRy4bD4bCU1tfR0YHi4mJs3bpV9ziZQtnU1DTG928UEMircPuxuy8Gk9uYFfspALRKg10HwP9rJzB6q6/YimTydSC8OpQNV2If7IC5h5CRNaGrqwtPPPGE4SQkNg5AS0ikCd3sKt3v9+Po0aOWS+/KtDq3240XX3xxzHsmhEholQ6EJ05m0vNiqa2txYkTJ6Jj0Fvpy7S+PXv2mH6PYnPjzZZunoir8GwV1FT0xWByG7NifxTAt4joLSFENCeJiO4A8K3IdmYCo7f6WrJkSfT1goICeDyeMaIQ+2CvqalBY2Oj4UOouroau3fv1hwPEaGnp0czHczhcESDu2QcgBaJmNCJSFec5apfrqBldsCTTz6J119/XVX4EsHhcKChoQEff/wxTp06Zfo+nE7nmM/z+vXrmv3pXS4XRkdHLb1HsfnsqSzdnArMCni2Cqp0vdjdF4PJbcyK/RYA+wF8QER7EQ7QKwGwGgABWJWS0TFpRWv1Ffu61+vVPD4UCuGTTz4x9RCaMmUKPv/5z2tGyMua6zU1NdiyZQsOHjyIo0ePRmvG661U40nE9x8r4PHIAjx+vz86GZBBcf/8z/+s26Pd6qQjEAjg7Nmz+O3f/m2cPn3aVFlZGTMQ+7n5fD6cOXNGc1zTp09POGZjIhW5MSvgqWo0ZQd6E7NsnFwx2YGpZGUhxLsAKgD8DMBMAE8gLPb/BKBCCHEyZSNksgq9B3sgEDB8CEl8Ph8++OADzes4nc6omAgh0N7ejlAoNCZPX9aMjxdAn88Hr9eL1tZWeL1eFBYWmq5DL3E4HFi2bNmYWvxOpxOKomD27NmaJvVQKKT5/iTqf+/r68NLL70Et9tt6j7UUuLUegvEBsrNnDnTUlGk2GtIl4wa2VTkRq+mfvz3yIy1Iv575vP50nIf8ruvRrZNrpjswXSevRBiCMCfpXAszAQg0Rrq0i8sg9Zu3Lihu78QIiomVszE8Ss3GcBn1VcuhEBBQQGee+459PT0RHPNHQ6HZm18eZwWidaely6M1tZWw30VRdFMiVNz1QwPD6OsrAylpaW68RHSpaGWdjdRitxY+R6ZCSC1q4OfVRRF4QqCjGVMiz0AENFdABYDmA3gYwCdQgjj5t1MzpBoDXVFUfDOO++AiKJR83oBZ1VVVVExMWsmVjO9JlrDXwgBj8eDffv24emnn8atW7eS7gdARCgpKcHg4GBCx4dCIcNJy9KlS3XFJt5Vc+DAgei/a2trcfTo0WiRIyns69evx/DwsGYA5EQpcmPF3WBUkrerq2vMZ5FOE/+0adM0M0WyaXLFZBdmi+o4AfwtwkV0psdsuklE/wjgvwkhUtc9hMka1B7sevXyJfEipSf0LpcL5eXl0b/N1kJPtghPPPJ6O3bsGJPGlwhOpxNutzupToJGQk9EKC4u1t0nPjhNCDHGGhIKhaAoChwOB5YuXYoVK1aYEq5Mp9epBd3FY6Wmvt6kVsaMaG1Ltc/cqHBVtkyumOzCSlGdrwH4awC/AjCIsM/+KQB/AWAqgG+kYoBM9hH/YNeL9AY+i2w3uzKOX50YRe7LfZMtwqNFMBhMehLx4IMPwuPxpKxqHhAWmsrKSs3tasFp8+bNw5EjR8a8b3KMbW1tWLFihenrZyq9Tivo7uGHHx6znxV3g561Qq93Qrp85pmeXDETD7NivxnAnwshGmNeuwrgb4noFoD/Dhb7SUXsg721tVVXZGfPno2PP/5Yc7tRGdYPPvhAc6IgRdjn8+HGjRsJRbwboXc+ucLT20dRFM1mMnYje7nHoxVdLoVMjYkQ2a0XNX/16lXcvn17TAaIlRWxlqB2dHSgu7s74z7ziVq7gMkMZsU+BKBTY1sHwv3smQyTqQIgRubRmTNn4vLly5rb1ZrGSHw+n2EL2IMHD6K9vT2alpdOZPlYPYLBIC5fNtc6IrbNrp4QaxEbvxD7XdDLlNAikVVqur+DRq6b+MmK1RWxmqBOlIBEhonFrNhvBfAVAGrf8K8C2GbbiJiEyGQBEKOHX0NDA7q6ujSPX7t2rebDtrOzU1dMA4EAjh07lvJVs3RFAGHxVhQFiqKgrq4Ox48ft819IISA1+vFggUL4HK5MDg4iIsXL5o+vqioSPW7EAwGLVcEtLpKzcR3UM91I4RQnawkuyKeKAGJDBOL2abg7wOoJ6JOIvo7IvrPkd9dAB4BcJ6Ivh75+aPUDZdRw0r+cCqIzeGWZm2Xy4W8vLzo63opT3pcuXLFskhZxel0Ii8vD/PmzdPcRwavxb82d+7chGvoaxEMBtHV1RVtMet0mpuTK4qCiooK1e9CIu+hlVVqpr6Dejn+RJQyk7q0EKxZswbLly/HmjVrsGXLFi5Vy2QtZlf2P4z8vgfAIpXtsb58AeAnyQyKsUY2lCuVDz+Px4Ply5ePMY96vV7dVCG98ZnJ6092VR8MBvHYY4+hv79f1+cfm68vc9+bmpqwfv16NDU12S5oVlP9nnnmGfT09Fg218ua/smsUjP1HTRKBU2lSX2y+MyztT8AYw1TYi+EMGsBYDJAtpQrzcvLw/Tp07Fq1aoxryczPqOHeVVVFXp6epIyowshsH///oSPHR4eRkNDA/bu3ZsSd4KiKCCiaI2CWIgIDocDzzzzDO677z7DYMnYUr9S1Ddv3oxZs2YlFdmdqe+gnkldlgzWg4VMn2ztD8BYx1JRHSY7sZI/nApiH5h+vx8+n2/MAzOZ8U2ZMgVPP/00tm/frrq9p6dH02rgcrlMFaJJBilkiVTpM0swGER9fT1mzpwZbUZERBgeHh4nzEbv9RNPPAFFUcZV0ANgepWqJpCZ/A5qBd0dPnxY9zgWMn2yuT8AYx2rFfQWIGzKnxq/TQjRbNegGGtkMjo4/oG5YMEC/OAHP8DChQsxd+5cVFdXJz2+kZER3cI9tbW1aGtriwq7LAqzadMmAFDtmmcXsUJmphyuXIlbGYvD4cDMmTNNibHRey2EGCPSR44csbS61RLI9evXZ7Sqm1WTOguZMdngHmTsw2wFvfsBvIqwv17tf7QAYK3TCGMbmYoOVntgCiEQCATQ0dGBM2fORFdKyYzvypUrmkLv9/tx/fp1VaHp7u4GADQ0NCAQCGDfvn22B/tJIRNCGFbHUxQFK1aswKFDhyxdw+FwmBZLre8CEK5a6PF4xoj0kiVLTLUkBvQFMjZ2YSJEqLOQGZMt7kHGHsyu7H8OwA9gHYBzAFIb3s1YJhMVtYxynKVAy5VSouPTMxE7nU6cPn16zGRAruKlGVeKzpo1a7Bnzx5bVvjSelBXV4eDBw9ixowZWL9+PXbs2KH5gHQ4HPjNb35jOXr/S1/6kqXPMf67UFBQAI/HM+Y9kmMcHh42vbo1Esjh4eEJU9WNhcyYTLsHGXsxK/aLADwlhEi8sDeTctIdHWy2PG3sSil+fGZMyEZ1yrX6x0vkGD0eD77+9a/jJz/5SVJNbRwOB2pra3HixIlojr2cUGzYsAHnz5/HsWPHAGBMQ5lQKKR6XYfDgcrKSvT09ESPAcJWg0WLFmF4eHhcHIQW8e/nY489hs5OrXpY2qitbo0EcnBwEB0dHWM+y2wUeoCFzAxcPCi3MCv2xwFwxAozBrPtbrVWSmYDpPTcFAsWLMB7771narxCCPT19WHz5s1J+fHr6upw4sQJTXP2li1bsHLlyjEr3EAgAI/Ho3o+mR//5JNPoqOjA319fTh9+jSICF1dXeju7sauXbuwbNkyPPbYY5b96ZWVlQm1JI7/zPQ+7/iuhtke7MZCZgwXD8otzKbUfQ3A14hoIxHdTUTT439SOUgmO6murjZlklZbKVktwqJVxKS8vFyzqEo8sQKWTFnd8+fPG/p7pZXF7XajpqYGw8PDuqvijo4OCCFQXV2N7u5uBAKB6P6y+t3bb7+NxsZG9Pf3jzuH3vt55swZ04V5JGqfmd7nHQwGx4w5nUWdEiG2EJT8/sQXgmK4eFAuYfYJMASgD8ArOvtwgN4kQ23mr4baSimRAKl4N4XP50MgEDC9One5XCgoKMD27duTysvXq3OvZcUwsoJcuHAB3//+91FXV6c7EZECasWfbqZ+v9ox8Z+Z1kovFAqBiFRdFNkc7Mad48wxWYoH5TpmxX4bgGUAfgAO0GNikA/Md999F6dOnQIQ9kGHQiFdk1+yAVLxPdjNYKZDXbJo+XuNigMB4Wh56evXIxF/+v3334/u7u5x5tjCwsJoOWMzZlo1gbx8+TKOHj2qeW27gt20etYnUxSHhYyZLJgV+8cBfFUI8f+lcjDMxMPn8+HQoUM4evQoiAj33XdftGlMXV0dVq5cqSoayQRIqaWAGSHNs1qtSe0iFAqhoqIi+nesQNXW1trStMeqP93lcqG8vBzr1q1TLTwTL94VFRXo6elBd3e3qoDGC6TX6015sJtaPEJLSwuEEBMmToBhMolZse8DcDOF42AmIP39/di2bdu4h7wUs/b2dqxcuXLccUbmdyJCRUUFvF4vBgcHMTo6imnTpqGkpATV1dWGKX9qrFy5EmVlZRgaGjIVVGiW+Fr6RIQXX3wRGzduhN/vx6uvvopQKAQhBJxOpy15/lr+dKOAM61VbOzr/f39ePHFFy1VlTMb7JZoaVq9/P5Y4tMGGYb5DLNi/6cA/oqI3hVC9KVwPEwGsfIwlg9gPdFUMzfrmd+lCdntduPHP/7xuB7sTqcz4ejyCxcu4NFHHzVlTrdCvNhLv/XWrVvH+bCTSfeLv6aaP93tdqO5eXwhy1WrVkWtK/GfcezYE60qZyZqO5nStFYnd/J7xzDMZ5gV+79COPXuLBH1AbgWv4MQYqmN42JsxIyIW30Ym3kAx5ub9czviqLA7XZj0aJF+NGPfqQqjPI1GV1uRTzlWKUwvfzyy7assrWi0+0S9lgURYGiKKr+dJ/Pp5nat3fvXpSUlMDlco37jOfNm4f+/n6UlZUlVVVOL9gt2dK0Zus5xJ776tWrljMQGCaXMfu/oSPyw0wwzIh4Ig9jMw/geHOznpg4HA7VanhqJBJdvmDBgui/y8rKsHr1alu61KWyyU485eXlWL9+vaowdnZ26r4nr776KhwOx5jPzO/3QwgR/YyNgvwuX74Mr9erOWnUchMkW5rWbD0HifzejYyMmNqfYSYDZlvc/n6qB8LYj1kR13sYy57tVVVVYx7uhYWFUBRFV+zizc1mIvAHBgYM78vv9+Oee+7BRx99ZLgvEH74L1myZMxrS5Yswb59+5ISa0UJZ5umQ/BdLheqqqo0V8B6/QMA6E4EpOAalSVua2uLThismOGTzbyw6nqR3zujrncMM5mw3KeeiIqJqIKIZqRiQIx9mFlRAfoP42AwiN7eXrS0tEQLuvT39xsKpVpxEikmaliJ2na5XJg6dVzjRU1qamrGvQ+XLl1K2oxvplSvXRhVdZsxY4ZugSO9FrxScPWK5siAykSK5iT7uWsVwHG5XHA6nZOiKI7P54PX6/3/2Xv34CiuPM/3ezKrShYW0hgkhF8yGJBtVWMwloSAsMFGYKzx3enpGYumDe65s3d7Yu7iJuLiud67szExezdmZ3dvjzx20PPwxJ3ttqFty9fjnu6xECC5gQkDlmQZjNDLyNDCYBAStiSMKNXj3D/Eyc6qyjx5MiuzXjqfCAJJlY+TWZnnd35vHDp0CDdu3EAoFMr0kCQ5hrBTixCyFcCfA6jU/W0QwJ9RSt9xf2iSVBHVqETMpOyzN954A4qicCf4devW4fHHH0+acEWitiORCIaGhrjXRQhBZWUlhoeHhUy7XV1d+OSTT9DY2IixsTH09/fj3LlzlvtZQSn1NGcfQFKgmxnBYBAtLS2mAp1nhWAC1yzQjl2n0b4iZng3StOaxQQASGtRHKcZBalg1Ea6qalJphhKbCHa4nYbgH0A9gP4SwBXAJQD2ArgLUKISil9y7NRShwhmstux0waiURMtVlCCIqLi1FfX2/4uZk27ff7NWG2YsUKrQ2rEWzb8vJytLe3C42Z1cDfu3ev0PaisHa+XuDz+VBdXY2ysjIhAVZQUIDf+73fQ3Nzs+HnqqqaCmy9wDUSqiMjI6bFfkTM8G7VWDeLCUhXUZxUMgqcYtZG2qySokRihqgN8k8BvEYp/W1K6euU0gO3/v9tAP8A4D95N0SJU3hmWf0Eb2Qm5WFm/qaUYmJiglu73Uw4Lly4UBvL9u3bEQgENG2UEAJFUbB27Vq8+OKLqKioQEFBQU5UPrPjbgBmhLzP58ODDz6IsrIy4c5xw8PD+PnPf67dM/3xmGmb3Vf2HbNtq6ur4ywUiXX9WSS/2XgnJydx6NAhdHd3m5qXc73Gut1eDm4h6oqTSKwQFfZLAbxr8tm7tz73HELIFkLIACHkLCHkP6TjnLmMnWYf+smYVcFzitHkZ5Wqp5+02FgaGhqwbt06PPPMM3jppZewadMmbcwTExOmJVqzBUIIbt68aWsfVlugp6cnLk7CjFAohI6ODvz0pz/F9PR0kuZOKcULL7yAiooK7b7W1tbGWWc6Ozu557Hy5ff19eHYsWOW401cROSSRpopoZtqcKNEwhAV9lcAVJt8Vn3rc08hhKgAfgzgaQBVALYRQqq8Pm+uY0ejYpPxs88+m5KwN5r87E5aVoJB1ISfSZz685nAZprjG2+8gcnJyaTthoeH0dTUhIMHD5paWxRFweDgoPb7zZs3ceLECcRisaTz7N27Fx0dHUlautGiUZ/Dniud7lIhU0LXraBWiUQ0QO9/AvjzWwL3/8OMcF8A4FnMmPD/0pvhxVEL4Cyl9HMAIIS8BeB3APSm4dw5jd1mHwUFBVizZg0+/PBDw8+tCto4qd1ud9IaHR21tX0uE4lE8Morr+D5559HeXk5zpw5g5GREXR1dVmm/em/i+HhYbzxxhvcqHxWeyDRH53oy5+cnERfX5/h95lKp7tMBMCJ4PbzK4obwY0SCSCu2f/fmOl49x8AnMFMy9veW7//6NbnXnM3gAu637+49TeJBzz22GOmZlZFUbBp06Yk/zDDbi90J5NWaWmp8LapWCmyhWg0ijfeeANNTU1obW0VbqjDvgurmAn9eQBjLV1vbSkqKnJd02WWitbWViG3QDpx+/kVxciqQgjJ2xRDiXcQO6ZGQsgdAL4F4E4AXwLooZR+5dHYEs/9LICnKKX/263fdwCopZS+oNvmBwB+AADl5eWPvvVWdiYIXL9+HUVFRZkehiXT09PapM26iwHAvHnz4Pf7ceXKlSRTdUFBAUKhEEpKSlBYWBg3QfKOZ2fSopTixo0baa2Qxq4r1yCEoLy8HFNTU5iYmDD9vnj7FxcXY86cOXF/Z/ffaP4w24cHpdTwedJfg91Fm9vvmVvPrxMopZiamkI0GgWlFHPnzs2LRayeXJkXnZDOa3viiSc+ppQmud1tFY++Jdj/1bVR2eMLAPfqfr8HwCX9BpTS1wC8BgDV1dV0w4YNaRucHQ4fPoxsHVsi09PTpnnMialIAFBZWYnBwcG41Cp9fADveCIYnZPH6tWr0d3dLVxq1efz4YknnsDhw4fj9mHX5RaJDXS8YvXq1Vi7di2OHj2KgYGBpM9FrmvdunVJz2soFEJTU5Ohbz4QCMSlhImY5ru7u3H8+HFTM3llZaVtt4AX71mqz68b5NL8YYd8vS4gO67N1IxPCHmIEDJGCGngbNNACBklhKww28ZFOgEsI4QsJoQEAHwXwC/ScN5ZTSAQQDAYxLx58zA2Noaenh5NE2R+3Pr6+qTceyMzcCgUQk9PD8bGxjBv3jxuWpm+YhgLFjNKf+JRVVUFVVVtNVHZvHkzHn30UU+1pvvvvz8tgh4AOjo60NTUBEKIUFplImb+aNFMD1HTfK5EnedyRoFkdsPT7P9PAMcopck9M29BKW0hhPwrgN0Annd7cAnnihBCdgI4AEAF8I+U0jNenlNiXUgkEAjA5/NBVVXDiHAWrFVaWop9+/ZpBW5UVTUtSGJ2zpqaGltC8rPPPsOGDRssa/jraWtrw4oVK5KKwLjBPffcg61bt2JwcBDDw8OeFeTRwwqwdHR0mG6jqmpSkxwGzx/N63QH2GuwlKkAOLtkawChRGIFT9hvxowQt+ItAH/lznD43Fp4mC4+JO4iOllbaWUjIyNobW2N20Zf1e7FF1/UWqGeOnUKBw4ciFs4sP2OHz9uq549pRQffPCBrUY109PT+OUvf4lnnnlGE2S9ve4kfNx3330oKirSStumE7P7RgjB88/PrNOdVLjjZXrY6Xa3dOlS7hizIeo8ExX0JBK34An7Usz4ya24CKDMneFIrHBbs+AdT7QbXklJiWk6nt/vxzfffMNdDJw6dQrl5eWa5s+b9O1o6U41Zybgt23bhlWrViEYDOK9995zdCyGXjstKChAVVUVTp8+ndIx7cC7ZwsXLtT87G76o0VN80yIJrpOVFWFqqpZEXXupA20RJJN8IT9NYiltt19a1uJx7itWVgdT6Qb3vDwcFyBlkQopYYFYfT09/fj0KFDluZyZv5PB7FYDPv27cPWrVtx48aNuCIyTkjUThctWoT+/n7XXASpwDRsu/UYrODFPSSmBBoF+hFC8MILL2RFhLYdK4VEko3w8uyPAPi3Asf4w1vbSjzE7drcIsfjVe9ihMNhrtYYi8Us86R5iwo9fr8fq1evTgoK85K33347yQVhFyPtlJe3nYiXwYKUUk+C31gZXzPY4ocnRAkhrmZApIKVlaK3t9eyPwAPo4BUicRNeOrKfwPwESHkHwG8SCmNmxEIIb+FmYI66wGs9m6IEsB9zcLqeCdPngTAN/+KIOJjHx8fFzoWIQTr16/H+vXr48zNJSUlaG5u1iwUVhX+7JKKoCeEYPPmzUmWF30nOBa/YITf70dVVRVGR0dx8eJF2+dXFAWEEBBCDO8JIcST4LczZ/ixs9XV1ULxHtkShW/VBvr8+fMYGhpyZG2TsQCSdGCq2VNKTwLYBuD3AVwkhPwrIWQfIWQvIeQoZnLcGwF8j1J6Kj3Dnb24PSlaHe/gwYNoa2uzFRDnBEVRLE3ziRXDEtOflixZotX/r6urS1tamwiKomDlypWGn7Fo9s2bN3PbBjc0NGDVqlW2rRiKomDLli3YtWuX6fEBeBL8ZmWtYdaKXKn9bmWJ4VUe5GHXYhcKhXDjxg1pRZDYhlsul1L6TwAewEzt+xCAVQAeBTAN4L8CeODWNhKPcXtStDLRR6PRpMmatZp1A3asefPmWVoPKKVYv349ysvLudsAwNWrV10Zn1tYmeADgQBqa2vx/e9/n5uzbsfsz/b9/ve/j5qaGsydO9c0J96r6m+iz2umytDaxaiuAG+RKtoJz043PVazYGJiwnE5YZG6B3IxkJ9YRh1RSr9EemrfSzi40RBDH3lfUlJieww+n881TZ9SClVVhRvafPDBBzhy5AgaGxsxPj4elz1w5coV13PiRWGLH959MXOxsFRD5pdev349fD4fJiYmkqLh9Wb/WCwWZ5JnUevV1dWaWT4xkr68vBwbN27UzlVZWYmVK1fi2LFjqd8EA0SfV/112U37SzeJdQUuX76MoaEhw21FrW2iFju9BYAtDuxmA4hkFFy+fFm6FPKU1EKMJWkj1UnRyC8IzAhwQoj2N17qWzgcxvLlyzEwMOCKYLXjV9fn5TPfKZuIEoVfuiCEoKqqipuHH4lEkhY0oVAIR48exfHjx+O0Oubz3b59u+HEqhc2o6OjmJycxDfffANFUVBZWYkVK1YYpmEaffcXLlzAwoULU7h6PnaeV6viPNmEPmOhu7sbw8PDKRUCEi0m5EbMjkicTnt7u0wvzFOksM8hnE6KvBV9IBDAxo0bMTExgeLiYly4cMF0UvD7/Vi0aBGeeeYZnDx5UmuHaoSdfHi76H2bmcTv9+Pee++1DEa7ceOG9vPw8DD27t3L1eZ4EysTNokCfHh4GO3t7UkamJU2V1dXZ+ua7WDneXU77S8duGFtEz2GGzE7VscYHByU6YV5jDsOWEnacFKb22pF7/P5UFlZifb2dk1rNyIWi2HZsmVaiVwz/72b5v5sxe/347nnnsP4+LhlQGBhYSGA3whekXoCPH+vnaAuq+9+amqKe55Ufbf5XEtetD+AG8dwI2bH6hiA+QI6mzIjJM6Qmv0hx68wAAAgAElEQVQsQKSc7YEDByyjh6PRKF5++WWsXbsWkUjE9JhOTeosUCuboukTIYTgzjvvRDAYxOXLl/Hll19a7jM1NaXFS4hcWzQaRW9vr2mVRDsmXavv3sz6MhvTwZxUp3TDBSFyjHRYESorK1N2S0iyFynsZwFWfsGpqSlhAUspxYcffghVVV3PZ8+UkL/zzjvxyCOPIBKJ4JNPPsHo6Ci30MulS5dw6dIlw8+NOH36NPr6+lBZWSnseuDlbdsx6Vp990YR5bOxNGwqixs3XBBWx9DHQLBFsd1ARqs4ivLycrS3txvum02ZERJn8FrczrHzL52DltjDKr1pzpw5tv3f0Wg0I0FxbsOi2GtqarBmzRosW7aMu+hw4p6IxWKYnp621VCHl7dtx6Rr9d0zF4MeO+lg+YDb1Sm9glkAiouLsW7dOmzZsgW7d++2ZWlhx9iyZUvSMdxwS0iyF55mfx2AHVUrPUXLJbaxWtGPjo5yq4OZoaqqVp1NH+Evuq9XAXx2iEajWLRokfa7VaW0VEgljkFvnrdj0rX67j///POkY+RKVTu3yKW694FAAHPmzMGGDRtSOobZ9eRSZkQ2kQutj3nC/g9hT9hLPMKNB4n3EpeXl5sKDx7RaBR1dXUoKyvDtWvXUFxcbCg8zPbNFn71q1/hmWeewZkzZzAyMpJ2dwIhBAsXLsTly5dNz60XsnbTMHnfvdH3lSu95d1iti1urMjFzIhMkivxLabCnlL6kzSOI69wc5Xn5oNk9hIbCQ8R/H4/ysrK4vKOM4U+AyAWi1nWDNBz/vx5NDU1adeers56wExRnu9973sYHx9HS0sLN5VRL2StNDCjZ1B0AncjGCyXSGVxkwsancQ7cim+RQbouYybwlnkQaKUujLZVFRUYOfOnWhra8PY2Bhuu+02nDt3jissEyf+sbGxjAXZKYqCXbt2YXBwUBN+kUiEWwuAcf369bjf02l1iMVi+NnPfob77ruPe95oNIply5YJCRfRZ1BfZ11/rFyqaucGThc3uaLRSbwjl1xAwsKeELIVwL8DUAngtsTPKaULXBxXTuL2Ks/qQTpy5Ai6urpcmWyMJi6fz4dHHnkEX3zxBS5dugRFURCNRk0n/vnz52NsbMz0HKwDmxfCNBaL4dq1a3Ev1sTEBPbv32+5r6IoGa0LEIvFcO7cOe42iqLg1Vdf1YLtzL5vq2dw586d+Oyzz3Du3Dn09/djyZIlGBgYSDrWbPLdOlnc5JJGJ/GOXHIBCQl7Qsj3APwjgJ8AePLWzwqAfwPgawCvezS+nMLtVZ7Vg/TRRx/FCU6nkw1v4vrkk0+we/duALCc+IPBoGn/cVVV8cd//Md47bXXUhL2LCAwUThHIpGk6z579qxQeqCXgp4Q4oq1IxaLJY3TyMrT0tJier2RSAQvv/xy3P3j1VmfTb5bu4ubXNLoRJEuCfvkUnyLaAW9PwHwXwD8+1u//w2l9A8BLAYwCuCG2Y6zCbdXebwUKzc6bjFEJi428T/22GMAgCNHjiRVVSsoKNC6qCWm7jz//POYP3++pin5fPY9SIqiYO3atabXHo1GcfLkSe33sbExS0FfXV3tiY+eEIIlS5bgmWeewfbt213rFmgEpRRHjx5FU1MTTp8+bbp4icVioJRyFzf5mFonip1qf7mk0Ykg0g1PkkyudG0ExM34ywB8SCmNEkKiAIoBgFI6SQj57wBeBvAjj8aYM7i9yuP5EnmTduJkw1bsIyMjmJqaQmFhIRYsWKCt3EUnLhEfZSAQMNWQQqEQRkdH8fDDD+Pjjz+2dS8AoKqqilu5LxqN4uDBg1i4cCEqKios0+gIIejq6rI9DhEopfj2t7+NoqIiAMDGjRtx6NAhT84VDodx/PhxVywUuSioMkEuaXRWSJeEc3IpvkVU2I8DYPaciwAeAnD41u8EwHx3h5WbuB3FzHuQampq0NHRYTnZMAHNusYxfD6fJqhFJi47EwLrv85MgidPnsS1a9fQ2dmZks++r6/PUqBFo1FtPCUlJdzMAi+DCRVFweDgoGbK/eabbzw7l5uWiVwTVJkinzIW8tElkU5yJb5FVNh3AXgYwAEAvwDwZ4SQCIBpAH8G4CNvhpdbeLHKM3uQKKXo6Ogw3IdNNhMTE3jjjTcMTdnsbyxoy2ri6unpEZ4QEi0AbiG6SNC368wUsVgMPT09mvXEy2I9VqZ5O+SaoMoUuaTRWZFvLolMkAvxLaLC/i8B3Hfr5z+79fPfYKZqXieAH7g/tNzEi1We0YNk5kvz+Xx47rnncPnyZbz++uuWApJSis8++4w7cVFK0dvbKzQhUEozXmI0HA5rWQqZ5Ny5c/jRj36EHTt2YOnSpSkJZEVRtGwGAHHfUXV1NTo7O7nuCqt7kUlBlauBYbmi0VmRTy4JiTlCwp5SegLAiVs/fw3gdwghBQAKKKUTHo4vJ/F6lcdrlUoIwR133IE9e/YIacJMUK9atcpw4rp8+TKampq4x9JPCFNTU1lRHe/q1auZHgKAGQvK66+/DlVVHS8+CCFYvnw5GhoaACRnRVBKTWMPAoGApYVl+fLlWLRoUUYEVa7nqueCRmdFPrkkJObYDokmM6pFKYBRSqn9BteSlOH52MLhMP75n/9ZWLDoBXXixGXkpzeDTQiRSCQrhH02kRgvYRe/34+GhgbDAkos8HHp0qWGjXbq6+tRXl4eJ1CZj3/16tXw+Xx44oknEAqF0NPTk1btWgaGZQf55JJwQq5aluxip6hOA4D/BODRW/tFCCEfA/gLSun7Ho0v60nlQXG6L8/HBgCff/65rZa1Zit30f7r+m0yWZwmE/j9fhQUFCRV4XMDQgj8fr/mljHSgOvr63Hw4EHTFMO2tjbs3r3b1Nx8+PDhjGnXMjAse8gXl4Rdct2yZAfRojp/hBkffTuAXQBGACwA8B0AvyCE/O+U0r/3bJRZSioPSir7zp8/37JrnGhXuYceesj0hbZaVOjp6elBaWkppqamhLbPF8LhsCeWDEIIHn74YU2jb2pqMtSAW1pauMdhgYKrVq0yFJxGMRZuaNciC1kZGJZd5INLwg6zzbIkWunjPwJ4jVK6mVL6d5TSf7r1/2YA/wDgT70bYnaSSg9s/QTrpH92MBjkatyUUmHNftGiRQiFQuju7sahQ4fiCuXwivroiUQimuaZCnZa5GYTXlgzFEVBQ0MDAoGAsIXFiEgkwhWaU1NTrveuFy3Qwnu+ZGCYxGtELEv5hKiwnw/gn0w+exfArHsrU3lQUp1gCwoKsGbNGtPP/X4/1qxZY5l/raoqSkpK0NTUhJaWFhw7dgwtLS3axMyrDpXIl19+mVL0u6Io2LRpk+fV5nKF1atXa1qFHQtLIoQQrtCMRqOW2rXZYtAIO4vgXKo+Jsk/ZptlSXRW/RWA9SafrQdw1J3h5A6pPCgiE6wVjz32mKmJiRCCxx9/HLt27eKWpf393/99vP3225ientZM0dFoFNPT09i7d29cgI6VAOZdkwiqqkJVVSxZsgRPPfVUWtvMesHtt9/ueNHi9/uxfv1vXjdRC4sRhBCMjIyYCmlVVbnaNQBbZVTtLIJZYJhReeXZEBiWCews3PKd2WZZEp2NXgWwgxDyt4SQpwghj9z6/+8A7ADwMiGkiv3zbrjZQyoPitUEa7Rv4ksKwHKinDt3Lnbs2BG3jaIo8Pl82L59OyYmJriLjlOnTmmBO8uXLze9HkVRsGDBgpTM8PpFzooVK2wLe1VVs8oicOPGDUurSOJ4VVVFIBDA9u3b4wRdMBh0PA5KKT766CNTIV1YWMjVrjs6Omy5m+wugtnztWXLFqxbtw5btmzB7t278y44KhuQ9e/jmW2WJdFofJaE+Ue3/lHMlMlltN76n9z6LLfVMgFSyU21mmAT9+UF81lF0PKibI8fP869xoGBAdTU1IBSirvuusu0yUosFsODDz6Ivr4+7vF4+P1+FBcXo7u7G2NjY1o5YCC+gEx9fT3a2tqSUoQaGxvx5ptvOj6/CKJBj8CMkLXa9v7770dZWRlu3LiBwsJClJWVxfURYAFuqXTO43W1A6BZb4zSrlixHiPC4TDef/99NDQ0xAXeOSnQMtsCwzKBaDDabElDA2ZfyqGosH/C01HkIEYPCiucUl1dzZ2ceRNs4kPGe0n37t2L+vp6jI+Px+VdJ2I0mYZCIUxM8OshEULiFhq8QLR3330XlZWVll3mzIhEIjh48KDWmc3n80FRFFRXV2t+ZyYIV6xYEbd4WbZsGU6fPm07Kp4JUZ/PB0opqqqqUFxcbLjImDdvHs6fP2/rHDwhraoqHnroIUMhx+55LBZzfD/NMEppM1sQHjlyxFRLZ8fp7++PyyDhLYKj0SgikQhCoVDeCpBsRcS9UlpaOmvS0BizKeVQtILeEa8HkouwB+Xo0aOalhyLxdDZ2Ymuri7uSyL6kFkV0Dl48CCi0aijtD8rwbV48WLhojqRSAS9vb144IEHuNux/vKKosQtHhI1YSbkurq6klJg9I12zp07h5aWFstrSTwf+xsA1NbWYv369do5Hn/88aTv5dixY4aLO7PzsgWE2ed6C45emyopKUFbW5snNfQB85gQowWhVT3/WCymmfTZd2S0CNZv39bWhvb29rwWINmIlXtlZGQEBw4cmDVpaHpmi2Upe5ycOQqlFJ2dnYjFYtrELppGJ9I/2yoSW/SczOff2tqKN954Iy4ozwhCiKMIe/32zIfv8/mgqiqWL1+Op59+Gjt37hT2r7M8cT3Dw8P4q7/6K7z//vvo6ekR0rbvu+++pDgAVtkusdQsW0zMmzcPY2NjWhMgtkCrr6/HkiVLUFFRYRpboCgKtm3bZhrHQAjB5cuXk/yoBw8e9EzQs/NOTk4KBWaJZmMkBt6Vl5dj48aNuOeee5L2t5NiKnEPqxgj0QwhGeCXu5hq9oSQEQBPUUo/IYRcxYwv3hRK6QK3B5cLeF0FzG63NKNzOulCx0qzOvUV+3w+VFVVoaioKMlq0d3dLZzSl5gnHgqFTDv58cYyd+5cKIpiuDCw6trn9/tx//33a4FM7e3t3HvJqt5VVFTghRdewCuvvJJ03nA4rGU86IWeHTeBkbXCCkop+vr6NPM7D72WHg6HuRYm9h2JPmuyQl56sYoxmjNnjmVg5WyqNpeP8Mz4PwZwRfdzZluIZSle52ryXlKRc9qpb59IKnnzkUgERUVFqK+vT/rMTt54Yp74qVOnbPuxKaVCkxlgHiNBKTUUzkZEo1H09fVhdHRUc1kYCfFUi/H4fD40NjbirbfeSronqqpqC6rEz/Tm2bq6Ou45mDWDWVGMxswC7+w8a/mYx5yN6F1E1dXVWjfIxDih0dFRbmBlcXHxrKo2l4+YCntK6X/W/fznaRlNDuJ1e0izQEAzDTDxnKlo56nAu3Y71gp9nngwGMTg4KDtsVRVVeGOO+4wvW/6sfLul6hwjsViOHHihCZszY7ntMwuSzOsqanB559/jk2bNiESieDcuXOglOKBBx7AihUrAIArpCmlQuWNA4EAGhoa0N/fbyjICSFYtmwZWlpahBdi+ZjHnG0YaeLATIwKgDiLW3l5OVfzB8yf49lqpcm1zAXR2vj3AiijlHYbfLYKwFVK6QW3B5cLpKM9ZGIwX3FxMdrb200nXv05U6m+ZoTf79ci5nnCz+zaQ6EQIpGILe38o48+0kyGpaWltsarKApuv/12tLW1cYUrGyvvftkVzlaLLObvt3NcVVVRXV2NTz75BB0dHUkaGjOnsolodHTU9HuyU9efl6ZUX1+PPXv2cE39ieRjHnM2wcvi6ezsTNLErdLQBgYGZlW1OSty0aUhmnr3twAGASQJewDfA/AAgP/FrUHlEunK1UyMGF24cCH3nGyyv3LlinB+uNV2hBBUVVVh48aN2LNnj6m51qwCmj6lTFQoMEHFJporV67wNjfc/8SJE9xt9GPhWR2cCGceiqKAECJ8PJ/Ph+985zv4+c9/zjWnJnbIM8Pv90NVVa22gZV2YpRBsmzZMu6zYHTOfM1jziacxBLxMoSszPyzyUrjRgOdTFgFRIV9HYC/M/nsVwC+785wcpNM5GryzukkIA+wNlNTSlFUVIS5c+ea1hgoKioyfNhTiR3Q41WHOTb58Sw1LIMgFfM7S5NkAg+AlgYpctx3333XNLiRUoqTJ0+aWn2Mtp+YmMDJkyeFtZPERWd3d7fQwk1VVdTW1qK0tDRv85izCaexRGZpaOmwYOYKqQZlZ8oqICrs54AfoHe7C2PJaURyNdlqbnJyUvNBW63meCtAs2I5XgXk6VfwZouNY8eOGU7kmYodEEE/+fEsNZs2bcLBgwcdn2fRokUoLS3FN998g2+++QZHjx5FZWUldu7cib6+Phw4cIC74LJyfYTDYQwODgp9jwz9gtBJwJWVm4iVZ85m82Y+4nYskRcWzMS5TWR+yAY/eSpB2Zlsqysq7E8D2AbgfYPPtgE449qI8hT9am7x4sVobW21XM2JrgD1L8D169c9E6qUUpw7dw4ff/wxSktLsXHjRuGgHLdjB9wkcfIzWsh8/fXXaGtrc1zRzu/3Y+HChZqfnTE0NIQDBw4gGAwiGAzi9OnTKV0HAO59vvvuu7Fq1SpEIhG0tbUZbmMn4IonVAghWL58udaqV5I+vNDE3bRg8tJbU50PvSaVhZTXqdo8RIvq/DcA3yOEvEMI+W1CyKpb/zdjRtj/hSejyxPstP20u09iUZbTp0+7JlSZj5r5diORCHp6enDp0iV8+umnePnlly3r6zNS6dzmNWzy0xcM6enpQTAY1Aoe3bx5k7uIsmrcw5rKGH03sVgMp0+fTknQs3NUVlaa3mdVVfHwww9j1apVGB8fdyXgild4x+/3S0GfIbzqKChSCMwKs7mNUpryfJgOUmmgk8m2ukLCnlL6Hmb88msA/BJA563/1wDYTin9uWcjzAPstP20s4/RC+CmVr948WKsW7cOGzZsMPUnHzx4EP39/ZbHEq3Glk4URdEmv8uXL3M7glm18F20aBHWrVuH1atXx3XgYxNsdXV1ynn1DFVV4fP5DCfxFStWmN7naDSKQ4cOYXh42LX2nrJNbfaSrR0FvZoP00Uqz3wm2+qKmvFBKX2DELIXM5H38wGMARig2eqIzSJEV3N6c/yVK1cs9/nXf/1Xz1a0fr9fa9Ty3nvvcbd955138NJLL3EfctFqbOlk8eLFaGxsBKUUTU1NXD8aa0tsZrqrqqrSzG9PPvmkYVMZt4ILVVXFCy+8gN7eXq3uQGVlJcrLy7X7bFZlMBKJ4PXXX8euXbtMzbyUUixbtkx4PLOpmUiukY11351ot5nUiI1w+sxnMtBRWNgDwC3Bbq3GSeIQ8fEk+qN4ZmFW0cqqsp6TcqoM/YM3OjrK3ZbVr2eTilEQDTvOI488gosXL+LSpUuuabpO8Pv9qKysRE9PD3p7e00FMdMa7LQlNmsqY6dFrtmYWUDUtWvX4sr2Dg8Paw1mysvLuYupaDSKDz/8EM899xyOHTumNSfSX/OePXu4sSHz58/H0qVLcfbs2ZwpKiJxj1QC5Zz4vL0uXuYEJwupTLbVFRb2hJC7ADwD4B4AtyV8TCmlL7k5sHzCajVnlKtsle+u/98IRVGwfPlyFBYWorOz0/R4iQJIVVWoqhr34JWWluLSpUvmFwhoK+vp6Wk0NTXFPcitra2glIIQgnA4DJ/Pl1FBD8wINKsa98BvtAYWUe6kpTHwm2fAibBXFAW1tbVav3srS8T69estz9PR0YEnn3wSCxYswNmzZ+M+Y2mA+uhgo8VoNBrVFgq5UFRE4g6pBso50W7zKfUvU5YwIZ89IeR3AXyOmRr5/xbAswb/JCZY+Xg+++wzoeAv/T7j4+PcCT0Wi+Guu+7Chg0b8Pzzzxuee/v27XjyySdRWlqKwsJClJaW4sknn0zy623cuJF7fT6fT6uNfu3ataQgmnA4jEgkov3N7R7tdvH5fFqNe6tgRqN0w9raWs0nz1oa6/37RrBnwEmQYjAYxFNPPaUFRPH8l9Fo1DTKXg+zWNy8edO0AyEvNoQ9e+y7lN3sMk86OtK5EShnNh/ytNt8iw1xI9DRLqKa/X8FcBDAH1BKZ1ddRJfQr+YuXryILVu2aKs5XilKYCb4a+HChcIVrRgHDx7EgQMHUFdXh507d+Kzzz6LW0meOnUKhw4d0rafmprCoUOH4Pf7UVNTo/29uLgYmzdvNs0xVxQFkUgE77zzjmVUuh5CSFp994QQrFmzRusZL7oPqx8AxLc0ZojmyVZUVODFF1/EqVOnMDAwgJs3b+Ly5csAzC05fr8f99xzT1yVu5GREVdK+l67do0beMisGnZqJMzWOumZJl1paW6ljhlpt+Pj49yxytiQ1BAV9vcCeEEK+tRgq7mJiYm4F8LKH6UP/mKIdMNjE/+xY8dw/PhxrF27Fo899hgKCgowMTGBlpYWw/1aWlrw0EMPoaioSPPNXb9+HdXV1fj444+1l51pyLFYDG1tbQiHw6isrBS+H24K+kS/sxGrV6/Gpk2bcOjQISGN3kjTSHWyCwQCqKmp0RZT09PTOHnyJA4ePGjafpctTNgkHovFhK6XB7PGXL582dIXaqdGwmysk55p0lmoxc1AuUSf9+HDh23vIxFHNM/+GGai8CUe4CRv08isxYNSig8//FAzN7e3t3O3b2trS8rhP3XqFHw+H5YsWYK7774bDz74IGKxWJyJPhP4fD4t5c0MVVVRVlYGQCznf9OmTYZpSm5HBQcCAdTW1uL555+Hz5e89mb3Vm8yjUajKbtCFEXBt771LaHAQzs1EmZbnfRsIJ1paZlMHZOkhqiw/z8A/IAQ8n1CyF2EkDmJ/7wcZL7j1B9VUVGBxsZGW8FuzLd29epV7nZXr1419M2Fw2EMDQ3h4sWL3Cj2dMLcCLyxUEq1RVMwGORaFXw+H1RVNbzvdiY7ng9V/1lHRwe++OILW9+jUa69me898dr0z5XeemH27NmpkZBrwVL5QDrT0lIpKCPJLKJm/E9v/f8/YV4jX9xZK0nCiT8qFAqhubnZUetVK8GgKIqlmT3TEfUMSilu3rzJjWGoqalBT0+P5veurKxEb2+v4baRSMR0ghSNCub5UAE4alSkJxqNoq6uDmVlZdrzMjAwoOXdG1FaWoo1a9YkPVdWz55RupAZjY2N0oeaZtKZlpbJ1DFJaogK+z8EvxGOxAXs+qOcNpcJh8O48847cfHiRdNtFi5ciC+++ML2sTNBOBzG5OSk6b1gbVzZtszvbZb3zpsgjSa7xBQ8ng917969WiZAKvj9fpSVlWnPy/DwMH75y19y9ykoKDB9vqyePbYgaGlpwaeffmp4r30+H8bHxw33z4YGJvmKSGqvaBtjEWSgXG4iJOwppT/xeBwSE3iTpNPmMqqq4quvvsLy5csN67E3NDRwK8ZlI59//rnpZ4ntY62uKdEcGQqFcOPGDRw6dEj7Dnbu3Ilf/OIXOHv2LGKxmBal39XVhXvvvdf0HG5ZQ5jfnrkG9u3bZ7nP9PS0cLdFIwKBAG6//XbTRZWZRSRbGpjkKzxtu76+Hnv27HH93stAudzDVgU9SXqxmiR55jse0WgUQ0ND8Pv98Pv9qKiowM2bN1FaWor6+notCt8q2j/XURQFiqJoxX7YBNnY2KiZ/FkDm/vvvx8DAwPw+/3Yv38/gN/kmDPhx76HoaEh03O6FePAMiDa29tRU1MjZOG5evWqULdFHnZNxpls6TmbMNK2jYp1yXs/ezEV9oSQDszk1fcSQjphYcanlNa6PbjZjMgkyTPfqaqKRx55BJ988gkAYyHDjnfhwoWkF1+vLeRrkZRYLIaHH34Y9957L65du4bi4mJcu3YNP/vZz0AIibtniQI9G2BjOX78uLDFIPEZsovdSmaZbOk520jUtru7u+W9l2jworTOAJjS/Wz1T+IiIpOkPoo/Me2MEIJPP/0U27ZtQ0NDA5YsWWKammaWnlNRUYGNGzcKRXnnKqdOnUJpaSkqKyvR3t6Ojz76CLFYLCuyDEQhhNgqZgTMCP3333/fdsyH3cyRbGtgMpuQ916ix1Szp5T+r7qf/yAto5FoiL6oFRUV2LlzJ1599dW4bZiJubm5Gbt378bY2JipeZn34l+4cCFrou69gFLqWtAcD0KIVoCIh1O3jF1hzxZ4y5Ytw/DwsC2Tvp0ALZ7ZnxCCr7/+Gh0dHRgfH5eBey6Tjc1jJJnDUmUjhNxGCAkRQr6djgFJZrCTz/3ZZ5+Z5r6ySd1JMYxQKCTUqz7XiUQintbrVxQFDz/8MJ566inT70BRFCxduhRbtmzB1q1bbR3f7/dj9erVti0wLLDwjTfewOTkpK19RWt78/KyKaU4c+YM9u/fj2PHjqG1tdWyx4BEHJkTL9FjOTtQSm8CGAGQ2e4lsww7L6qIFcBq0jXqX37mzBmhYiqBQAB33XWX5XbZCqXUU+uFz+dDQ0MDVqxYYXo/fT4fnn32WaxatQo3btyw1TCHEIL169fjqaeesq3hAzOLnVdeecUzIVtdXa0FQ/IQaaiSjmYv6cTL68m35jGS1BCNxv97AD8khByglGZPhFKeYJZeJ1q8QsRcpz8eK3HLSOxfzsbT1dUllKa2ceNGPPjgg3j55Zfduyl5gFG7YJHvVDSlMnHfFStWoL293VG8QWJLWzfQZ5PEYjHhKnxmwWP5lsKXjuvJtZx4WY/BO0SF/W8B+BaA84SQdgBXEB+dL/vZO8Tqhbd6UUOhENcMrS8Ty/z7r7zyStw2LA997969WLVqFTo7OwGI5YRTSvHFF1+gtrYWDQ0NSb3RrfD5fIhGo2ntfpcOVFXF5s2bsXLlSlBK44qa7Ny5E319fVq1u8rKSpSXl2v7WqVUKoqCYDCIe+65BwAwMDCA0dFRBIPBpMWEnYY509PTOHr0KOrr61O8etpyd6QAACAASURBVONsEtHv2CiGJJ0pfOkQOOm8nlzJiZ+enkZTU1PeLOayDVFh/3sAmH3pMYPPKYC8FfZevfyiL3wwGNTO39PTo52fLRSY79WIRG3qs88+g6Iopql4H330ke3r6O3txaZNm6CqKm6//Xbccccd+Oqrr4T2jUQiuOuuu3Dp0iXb5zWjuLgYRUVFWLhwIb766iucO3cube10CSHw+/3aBGW0mGttbdXGEolEMDQ0hNbWVjz00ENYsmQJli5dytWCY7EY+vv7tXiKxIlRv0CcnJxEX1+fcNDf8ePH8fjjj6csaJxWdwSMY0jSlcKXa61i84VQKIRr1655vviZzZYD0Qp6i70eSLbi5csv8sKXlpYanv/ZZ5/FO++8IxRBrp84nFbd4xGNRvHXf/3XUFUVixcvthXs5ff7sWLFCly9etVwXIFAAI888gi6urq0czH/I6VUK4jDSt/6fD5MTEzgxo0bcQuIdAn6hx9+GA0NDQgEAtzFXCKUUvT29mJwcBCKomDVqlU4ceKE6bkSj5M4MbLvW6Sdb+I1JAoaJxNkKs+ZUfBYOtLIcrVVbD5w5ox59rZbi598cwPZhSvsCSGFABoALALwJYB2SumVNIwrK/D65bd64UdGRnDgwAHD87/99ttC50icOJxW3bMiFotpZn87ke2EEKxcuRILFy409WVXVFTgySefTHJnADMLmZGREXz88cdx5/Yyut4MSinq6+u1Z8KJdsvGzRP0PGKxWNzEaPf7jkajcc+L0wly/vz5tlwIAJJ6DCQez+s0snRq2zItLp6xsTHTe+/G4kdWcuRE4xNC7sdMsZx3APw/APYCGCCEbE7T2DKO132irdLhpqamTM+fGGRnhn7iYP79bCgYkxgVzOITtmzZgnXr1mHLli1x/eSNUr3Y3xYsWCAc/OUlqqri0KFDWmT1yMhI2ivuRSIRnD9/XvvdTntaIPl5MWpzbBUxz87LW+ioqoqlS5fi6aefRl1dnRapH4vF0NnZmZSCl440MtkqNnPMnz/f9H64sfjxei7PBXi5MP8DQAwzPvo5AIIAPsFMZP6swOuX3+qFnzNnDrehisgkziaO4eFhNDU1oa2tLeNFchRFwaZNm+KEeSgU0rT0yclJjIyMoKenRygVKVXXBLuPPl9qrSKi0Sh6enq0nPGurq6Uj+mEvr4+TRCbpV+ZoRc0qUyQBQUFqKqqMv08Go2ivLwcK1asQHd3d1zVQqMFRTrSyJzUonCKTIuLJxgMmn7mxuJHuk34Zvw1AHZTSj+89XsfIeSPbv1/J6X0S++Hl1m8NrVZpdeNjo5yz88r66o/DqU0q2rcs5Q0NqExU3FidzqfzxdnMjbzHc+fP9+0Xa0VPp8PwWAQRUVFmDdvHi5evKi1w3UCW0hluoa+3uRslNVRUlKC5uZmbaFjNwVQZIJctGgR+vv7ue+PHdO512lkduv+myEa45BraXFeUlBQgHnz5iEQCFimGjtBuk34wv5OAIl9Q4cAEAALMePDz2vcevl58F748vJy7vm3bduG5ubmuDQrSimqqqqwaNEi7Ti8hhiZQC8ojHxpDOam2LdvHxobG+OulfmOq6ur8fXXXzt2TRBC0NDQAEopjh49qjUOymUikQhGR0fj/maUfrV79260tbVh3bp12nOnTxO8fv26qd9dZIIUeX+OHDlia0HhZRqZndoWZtiNcciVtLh0EAgEPFv8pGMuz3asbIzZIyEygBsvvwhmL7zV+UU1A7ci8BVFccUFoBcUIkFssVgMb731VpzQYddz7NixlMYSi8XQ1dWFw4cP275Hqd4PplV7sRC7ceOG5TaBQABz5szBhg0bABgLKrO4EJEJUuT94WlcPp8v7RpXKtq2DAJLHa8WP+may7MZK2F/gBBi9La3J/6dUrrAvWFlD5k2tVmdX+TlcCMC3+fzYceOHQiHw9i7d6/j4wDxgkJkIRKJRDzrvBeNRnHo0CGhbfW5+qqq4uGHH8bJkycdC+t58+Zh7dq16Onpwblz57jb2l1YTExMIBQKCecQW6UJMg2fTZDV1dU4cuSIZSqe1fPL07gikQhKSkpEL9k1nAocmTuf3WR6Ls80PGH/n9M2iiwn06a2VM/Pm1DNUFUV9913HxRFwbJly7By5UrtpXj66adx8OBB26Zzo5W06EIk00GFQLwGHo1GUzb5f/XVV/jWt76F4uJiS2EfDAYxMDCgaSVWsIBM0RxinqBSVRV33HEHiouLUVpaiu7ubnR2dgqn4umfXxaIqfdnNzY2mi4gWdfGXJiQZRBY9pPpuTyT8FrcSmGfoxgFCBmZsHhCo7q6GqqqavvrJ9vi4mJbgn716tWaSTZxJS26EHHLhZBNqKqKkydPor29nbsda4+7c+dOfPjhh0JVDlmwo6j5mCeootEorl69iq+//jqpTbIdM3Wim0BRFOzfvx+PPvqo6fOYSxqxlRWiuLg4TSORSJJJf16QRAinZR15AUK7d+/GyZMnMTg4iImJCYyNjZkK0K6uLq1anV5zO3v2rHBBH2BGUE1NTaGhocFw/MyX9vrrr3MXEPkm6IEZQTk4OGjpBmCtYPv6+mxbU0SFpYiFhfeZ1XmM3ASsEBNv8cI04nTVq/fyHNlQC0Iye5HCPgtxWrXMKkCosbER7e3tQqZgfc4z2/8HP/gB3nzzTVvXwoRAf39/0vj1k2tFRYWlKTsTqKrq2UKD5VeLmOUT0xJFETUfO3H12DmP01r57B553SDFjVKq4+PjKX0ukXiJN1FPEsekUrXMKkDoZz/7Wdxx7RCNRvE3f/M3jgRfLBZLGj/zKbe2tuLYsWOe9VK3AxMsqqpCURSsW7cOu3btctQjnh2P/TOCEILKykpbveudjEEkot2oyIub53GaEUIIQUdHh6P3QZRU3jk96SzKI5HYRQr7LCOVqmVWAUKpaKjRaDRlDZeN32hyzVQJX5/Ph0AggO3bt2ulehsaGvDSSy+hvr4ec+fOxdatWx0dOxaLYevWrdi+fbtppbQVK1Z4at5lmQ+hUAjd3d1aKV+jyoT6ksVLliyxtcixSsWbP3++ZUaFqqpJ96i6utp0e7fKnLpVSlWWwJVkMzlhxieE/DmAfwfg6q0//UdKaUvmRuQdVgK7t7fX1JfoVZMbtwiHw7h69SpaWloy0qiGUVNTg4ULFwqn3yxduhS9vb22zxONRrVocl7KT2LwpBvtePWZD5cvXzY1USfCopWDwSCamppMF2GJqXhWucrBYBD79+83XTD6fD5s2rQJPp8v7h7ZLbrjBLei6GUutySbyQlhf4uXKaU/yvQgvMZKYJ8/f940pcqp35UQgpKSEnz99deOxszYunUr+vv7TSPnfT4fOjs7EYvFMlbRz+fzxXWmM0oFM1pIOdW+9YFriW1jOzo6MDg4CABYv3691p53fHycq02y7nBmgpMQgqqqKq0yYFNTk2kcR11dneExeIKrsbER4+Pjposls0C3rVu3Yt++fab36auvvkJZWRkee+wx7TtIR5lTN88x23O5JdlLLgn7nCVx8uMJOiuBzUupMpugrUzwlFLU1NQ4qiLH2L59O5YsWYIvv/wSPp+PW/42Xegr1LF6/HoNy05QllO/vZFmODw8nJR9MDQ0BL/fj+3bt6O8vBwDAwOmwueHP/whfv7znyelwTEopSgqKrIslUwpxdTUlOnYnQgu3j1dunQptm/fjjfffFNb8LGFISEEJ06cSPoOeO9DNBpFJBKxVTzICLdLqc7mXG5J9kKyqWa6GbfM+H8AYAJAF2Ya9HxlsN0PAPwAAMrLyx9966230jhKY6anp7XJnlIKQggKCgpw++23m06abB/ed0MIQXFxMebMmZP0GZvEo9GoJqQmJiYsj8f2tUtxcTFuv/12AMD169cRCASSrtnpsZ1CCEFZWRkmJycRiUTg8/lQXFys+Y0ppbhy5YrhmAghKC8vj9PmJyYm8M033zgaB8uvZt8HLyqbnZstEvTjI4RozUJu3Lhh+p3qn43JyUlcv37d9Hxz5sxxrUqd6D1lz2ckEsGNGzcst098hxK3A6DdFz3Xr19HUVGR0NiN3lOz42YaO9eVS+TrdQHpvbYnnnjiY0ppUrBL1mj2hJA2zDTYSeRPAfwtgP+CmVr9/wXAXwH4w8QNKaWvAXgNAKqrqymr+Z0pQqFQkgkVACorK9Hf388tQjI9PY233nqLm462Zs0aiFyj2TgS8fl8UBQlruiJoij4zne+g3fffdfQf+vz+fAnf/In2nUcPnwYGzZswPT0dJxGODQ05MjvbQQhBIqicDv+bdq0CW1tbaY9Bbq7u3H8+HFT7bmysjJOO/vlL3+pmdztoI/OZveUZ2VRVVU7d+I91GvVvO+UNRRhmn1ra6vpda5cuVLoGRLB7j21Gpt+++npaZw8edK0cqP+mhnsWRSFd7+zCbvXlSvk63UB2XFtWSPsKaX1ItsRQv4BwL94PBxXSKVWdiAQwNy5c7nHF2l2AsSb93n52oQQPPLII+jq6gIhBLFYDIqimAp6RVGwY8cOwwmRXTelFJFIBAMDA9wx2glKYz3iWWW5cDisWTBWr16Nuro67Nmzx9BP/ZOf/ATBYNB2UBZzA9jJGmBdCPXuC6uMhmg0qp2bZw4WDQazMlEXFhYKX48Vdu+pne0DgYC2GDX6DtyotCfN75J8JmuEPQ9CyJ2UUtZS93cBpJ5vkwZSjfK1mohFJ+pQKITR0VE88sgjOHv2LMbGxkzHxCrnMXh+9lgsZhi8ZFQW1UrI2THxs3saCASwceNGTExMxGliVn5qq1QqQggmJyfjfMGFhYVca4LZuewG9jFTvQgiPnWrRcHnnyd2sXaO3UA3u9vL2vMSiXNyQtgD+B+EkJWYMeOfB/BHmR2OGKlG+S5YsMBUm1RVFWVlZZZjSBS8PJwEobW1teHpp5/WAhBZVzz9uUTy8+1qzcCMMGXR9XpSbelLKUVfX19c1T9CCFatWiVUl15/HLvXpCiKrYAwEW2ULQpYqWRgxpVUXl7uqrC3G+hmd/t0ROZLJPlKThTVoZTuoJQup5Q+TCn9NzotPy2IFCQxItUiG8Fg0FQAq6pqub9R8RoeToTTpUuX4irhjY+POxK0ToL3zLQ5XiUzO8dOrKA2MjJi6xgsytwO27Zt88RPfPnyZbS3t2N4eBhDQ0Noa2sTiuOwg1EVPn0BocTrsru9LFojkTgnVzT7jJFKzexUi2ykur+deuSqqqKurg4dHR22hDWvmY4oLG/cLmbaXKp13vXEYjG8//778Pl8uHr1qvUOCeOLxWLCC6jNmzdjyZIlTobJhdcz4dq1a5iennZtgWHkWli2bBk+++wzDAwMJNUysJPel2tFa9LRvEciEUUKew5WjWVEWocaTWbj4+PCzTUqKiqwc+dOtLW1aZMGK+NqNZnYMWfX1tbiscceQ0dHh9D2DLdazzopWqMvBWvW0jccDqeU8heJRNDT04OlS5faTr0jhGDbtm1obm5GLBazrDNw+PBhPProo64LLatFn9stZPWuheHhYezZs4e7WLYTGJcrRWvcaKwjkbiJFPYcUomm15M4mR0+fNh020TBVVJSgubmZm3SGBkZQX9/P+rr65NSyxInE9HyuX6/H6Wlpdp1iXL//fen7PNliwXeecvKyvDVV19pkfeipWB3796Nn/70p7h06VJKY2SLGTtWElbAhwmn999/Hz09PZbFjdgz5aZWyFv0UUrR3d0NAK5rnm4slo1IR9R8Kvffq+uWSFJBCnsO6Y7+NdIGEs/Pfm9paTH8O5tMWMqXiAmZVSI7deqUqYbt8/kwd+5chMNh/NZv/Ra+/e1v49e//jUuXLjgOBiOdZfj7e/3+1FXV4dvfetbSdqcVSnY3bt3OxqXU0pLS1FcXIwHHngAK1eu1Cb0QCCAwsJCSwsIe6bc1gqtFn0XL17EyMiI65qnW4vldJvDU73/bl23ROImUthzSGf0L08bsAOlFEeOHEFXVxe3frqeWCyGtrY2blndSCSCr7/+GpRShEIhvPbaa3j22Wcd14z3+/2oqalBZ2cndztCCJYtW5ZUv140xa60tNSRZi+a988sEz6fD6OjoxgfH8cXX3yB+fPnY3x8HGNjYyCEWF4nMHNPiouLbWuFicJw6dKlWool+93qe2LneP3117Fr1y7LGg8iuLFYdiJ42f2YnJxEd3d32rVymSIoyUaksOfgds1sHnaC6XiEw2F89NFHphq9oih49NFH8fHHH8cJdtFoff2277zzDhobG9Hc3Mwt1uPz+fDggw/i5s2boJTigQcewIoVK/Av//Iv3PMSQrBhwwZTn6/VpHrmzBncd999ltelZ/Xq1fD5fJicnERfX5/pQq+qqgq33Xabdh+ZP55tv3fvXtsdCK3KCkciEbS0tODpp5/WhFeiMGQpjIld6RLdPmZEo1G8+uqr2LFjR8oafqqLZSeCV38/Fi9ejNbW1rRr5VbXXVxcjO7ubhm4J0krOZF6lynspgalQqq54QyrXPlYLIZPPvnEsre4CJRSjI+PY/fu3Vi0aJHpdpFIBCUlJXjuueewfft21NTUgFIqVD734MGDcamD+pS4kpISrZqeEZ9//jl+9atfCV+Pz+fDggULUF9fj4aGBm6aV0NDA+bNm8ddoIl+n6qqas8UL3UxFovh008/RVNTE4aHhw1TK9mCS7/4mJ6eRltbG1544QVs2bIFd999N3c8kUgkScg6IdVUObt95o3uh1EKJQ83tHLedVNK0d7erqWqtra2at+nROIlUthbwAKstmzZgnXr1mHLli3YvXu36xG1buSGA2K58pFIhBsZTgjRxsJbFLDJLxAIoLKykntO1gyGcebMGUvTMk+QUkpx7do1VzvpRSIRLb3OaqF3+fJltLa22q5LkMidd96JhoYG7Zmyeg4opZrwOnXqlLA1iFKKwcFBrdWu1bMmUmnQilQXy3YFr93FgRG8+y/qujO7bmZlSWUxIpE4RZrxBUhH9K8bueF+vx+1tbW2c+UTYXXxq6qqMDExgYsXLxpOonbiFiKRSJzpcmRkJCVBHQ6Hhfzgduns7MRDDz2EioqKuDSvixcvYsuWLVpg4I9+9KOU3S5+vx/V1dVxz5boc8CEt+j3rBeOIudwy7ecSqpcJsrpuuW6M7ruSCSCtrY2w+1l4J7Ea6RmnyXotQEnZWsVRcGmTZvw2GOPOQ6aY7BCML29vaaCnm139epVdHR0oL+/n3vMRNNlV1cX1wRvhVv5/YlEo9E4LYst9ObOnYtVq1YhEAjg1KlTrlgUjISH/jmwsqoAELYGJdb7f+6557jPmZsBqOwe1tfXa/dQBLtuAC+1cieuu8Tr5rloZOCexGuksM8imDawefNm2z71WCyGa9euxU1WbmAk6JmQIITgxIkT2L9/P7cVLxuf3nTJ0v3cHJcoIu4DnsnXSatbPVbCgz0Hy5cvNx0rawErurBj9f6Zf7iiogK7du3K6vKzBQUFSX0PGPX19VqNAFbGmpd14EQrd9t158ZiRCJxijTjZxmBQAC1tbVYuHChcAMbxtTUFIDfTFZHjx7F8ePHQSl1JdKfwY6Vqnarqmpcm1o7+y1cuBAXL160fU7Wdpbnaw+Hw/jyyy/j3A6p3D9FUUAIQXl5OQoLC1FZWRmXh29EIBDA008/jb6+PkNfLiEEK1euTHpOWDS+UWOhxEj2uXPnYt68eQgEAllZfjYUCpmavQ8ePJhUVCox6wCA4+vxwnWXzuweiSQRKeyzFL3P7+rVq+js7LQMBpszZ472cyAQQH19PR5//HG0tLTg1KlTro3NTiU5Xq5/NBpFXV0dysrKMDIygtHRUZw7dw6EEG4a365duzQXgyiqqiIWi2n/rGBuBpa+dv/992sacWVlJYaGhoTPDcxM5pcuXYKiKPj1r3+NefPmYenSpdx9RGrBG/mGKysrcejQIdOKfdFoFCdPnkRtbS0CgUDWlp/lBdwlLjTZQoZlHQwODsbFWWTD9eRabX9JfiGFfRaj1y4eeughvP7661whyEreJh7j6aefRk9PT8qR4wxRYb9o0SJUVlbi4MGD3Da9paWlOHDggLYw0Gv8iqLE5Ys/99xzKCoqwooVK9DW1mZoEVAUBbW1tbjjjjvw1VdfafX+7Vo49OlrlFJNI+ad2wj9AoP9vG/fPtTV1WHDhg3cHGuRADcjLbSoqIi7yDpw4AAWLlyouSwSCxZlA07SUfVZBxMTE1kX8JYrtf0l+YcU9jkC87G++uqrhuZzXh/0goICVFVV4fTp09xz8EzARtvxYIVnli5div379xtuE41Gcd999+G1116LM1WzY/v9fqxfv16rv896sLNr2r59O/bt26cV9GE16Z999llMTExgZGQEXV1drgXyUUpx8uRJ+Hw+PPDAA+jr69PG6yRg8MSJE+ju7rYs+OLEpGxVIjcWi2Hv3r24//77cfz48ZTL8npR0la0t4OeXAh0S0d2j0SSiBT2OcTcuXOxY8cOR2bARYsWob+/33TiVFUVmzdvxsTEBIqLi7nauKIolsKe+SB7eno0c3giiqLg7bffNj1WLBbDBx98oNXPHx4eRnt7uyaMjLSkxMZBbhIOh3HgwAGoqopwOKz5/5cvX4577rnHlrbPYDnWbjdHEU2v098np81avOrw5iQdVVXVpJoOEolERuPnHE4jhYPBINeETQiBz+dDfX09amtr8fzzz8Pv92uR96qqwu/34/nnn0dNTQ33XIQQrUre+fPnTQP5WOqembBnjXx4BUj06U3BYBDNzc1xRUvcRp9VwMbX29ubcmZBqgVsEmH+YSeVEu2MJxQKYe/evZ4UiuEVpzGLao9Go2hra5MV6SSSBKRmn4M4MQMWFBTgwQcfNJ3EI5FInPmzoqICL774oqFvcWBggHsuSik6OjrQ2dnpaoU7RjQaRXNzM6qqquLMxW71F3AyHn0EuF2yzfRsZzxHjx7lts9NtVCMmY+btTY2WkyEw2EtJkIikcwghf0sYvHixejv7zcUwEZ5vmaLChFfqleaNTAjXIeGhjA8PBxnLnarv4ATUllkeJFjzerEO4lXEB1PKBTCiRMnTD93axFj9BxWVFRg48aNpu4mSqmWiporpLuVr2R2Ic34s4hgMGhq1rWT58urbOYGfr8fPp/PspJgornYSX8BVrLWS3w+H9ec7kWOtZWVQ1VV0wqGouOx6m+gqqqnhWLGx8dNXUCscFOuMDw8jKamJtkgR+IZUtjPIpyUAg2FQnFVykKhEACguroaiqI4Ku1rhqqqWLJkCbZs2YIf/vCHwsdm5mInixBCCMrKylIq3atHUZSke7tjxw689NJLWLt2rZYxoP/cixxrKytHTU0NduzYEdf0yO54xsbGuAKVUuppoRirinRuPpte4ka3PonECmnGzyNEzIB28nyNoqxbW1tBKdWa5ehz4lNNcVNVFY2NjdpYEguQmMHMxWwxs3fvXktzvj6LYWBgwJXYAkVRsGXLFqiqanhvN23ahPXr16eUYy1q6rVqIlNWVoaKigoMDQ2hsrLS0Xis3Dlr1qyJO5bbZmqrinSFhYWOj51ORLr1yVQ9SapIYZ8n2El/Egnw02sbDKNJnWl2dnzWeosAK/Eai8Vw77334oMPPsCCBQsQDAbjFiZnzpzBuXPnLLvvVVRUoL6+3tSXCwB33303Vq1apQm20dFRyxgEQog2ZrPjKoqCFStWWJbBdTpx2/mORUuzEkIcj4d3jkAggMcff9zR2EWxqkjH6jNkO25065NIrJBm/DzACzOgV5Htqqriqaeewosvvoja2looiqJVthsaGsJHH32E/fv3a/7KQCCA0tJSXLhwwXQ8iT5mni8XAL788ss4DZZn/vf5fKirq0NxcTF27drFNQ1/97vf9awSmt3v2M3ubWaInsNLM7VXTWvSiWyQI0kHUrPPA7wwA3oV2a6qKlauXAlKKTo7Ow1N/8ykvm/fPuzcuRP79u3jjqW+vj5OeM2fP59b5Y8QEndPrDTEiooKHD58GHPnztW2i8ViiEQiWknfbdu2YcmSJQC8iap28h2nozSryDm8NlPnekU62SBHHJmx4Bwp7PMAL8yATkqV8lAUBT6fT9P4uru7LX38lFI0NzdbjqGtrS3OfB4MBvH++++bbh+NRpPuiahgtNrOq2pyTr9ju4LQyWQaCAQQDAa1/ViwJNtPmqn5yAY5Ynj1bs0WpLDPA6yCsZyYAZ2UKjWDEILly5ejoaFBm7jOnTtnGRQXDofxxRdfWB4/UTssKChAXV0djh07ZjqeyclJhEKhOEFmJBiZ8JucnER3d7cmxIwEKC/OIdWSuF58x4k4nUyt9kvH2HMd2SCHj5fv1mxB+uzzAJ7P2akZUO+PZWlpLEhNnz4mgt/vjxP0oVAI/f39tsdkhpF2uHr1atPcdkop+vr6LPOY9bnP169ft8x9FjFXO8WL71iPU7+6yH5ejz1f0Jd+XrVqlRReOrx8t2YLUtjnAV4FY1VUVKCxsREAtEA6FklfXV1tKfDNxmBVjMUuiqLg+vXrWg2A4eFh/PjHP+YWsrESZE6En5W5ure3N6legSheB9w5nUxF9ktHsGA2YFaTQpI60hWUOtKMnyd4YQYMhUJobm6OM7ezF+6TTz7Btm3b4jrMMT/j2rVrQQgxHYPbwX+xWAxnzpxBX18fGhsbtWY4IpgFiDkJKrOKczh//jyGhoY0M3djYyPGx8eF/eNemnqdTqai++W7mdquC0QGmtlDuoJSRwr7PMLtqGQrgTc+Pp40gY+Pj+OJJ57QJrMjR44kTWa8F5cQgrvvvhuXL1829ekTQpLGxbZ98803bXV6MxNkToSfVZwDyw5gx927d692H0T9415FnjudTEtKSkwzHxL3y/WoeTPs+pNloJl9ZMZC6kgzvsQUEbM0pTTOz0gIsazzzfPh+v1+NDY2mgpsv9/P3V/fztpajgAAFtFJREFUglYEM0HmJPfZyFwtUt+f/Z/J8qhO/OrDw8Nob2/npjjOhknYjgtElsZ1xmxxBXmJFPYSU6way5w/fz4pYI1SajmZWb24LJ/d6PPt27fjtttu406udjR7M4HkNKgsscjLokWLhMcCGAuHdPiB7U6mRtqsHr/fP2smYTtWIBlo5px8KKCUSaQZX2KKiFk6Go3GmSqnpqaEfN1mPlxKKbq7uzE2NoaNGzcCACYmJuL6mHd3d7t2jdXV1aYCqaamBsePH9eEvmjus95c3d3djeHhYWFrg144pNvca8evzhNaqqpi06ZNKY0xl3zadlwgMtAsNfLVFZQOpLCXmKIv9sEEuxF6IR6NRoUns8QX10i46avYifZof/DBB3H27FnLBjoA8Otf/zoufz5xHKzZDzAj/NevX29LW7Vbr4AJh0zlFYtOpjyhFY1GMT4+rv1uV3Dnmk/bjj9ZBppJMoU040u4MG2PZ47WC3FVVR3V+RbxZYrU62dtcpm57+677+Zuf/HixbiYAqNxsEVOV1cX91hGGJnHee10mXDIdnOvaEyD3T7tuejTtuMCkTUH8p9sTcGUmr3EkkAggKqqKlNztH5yLywsdDSZiQg3kZQ9VVU10/OqVasQDAbR1NTEFRJ6jXnjxo2u13E3Mo+XlJQYpi0y4ZDt5l4RbdaJdSJX272KukBkadzcctHYxcwq9eijj2Z6aFLYS8Sw0zLVyWQmItxKSkq4Y1RVNekcBQUFaGxsxJtvvolYLMa1DFBKMTg4KCRk7U5YRuZxnnBwuzeB24gIre7ubtuCO9sXOTxEXSD5XnOAB89Fk+vwFrfXrl3D9PR0Rr9jKewlQtjRSJxMZiK+TKta+hs3bkzy6Q4PD6O5uRmKonDb3gK/eTGtxuGWT5knHKx8/R0dHXj88cczOnlYfc9OBPds8WnPxkAzK0tPXV1dpobmClZuxkxbpaSwlwhjR4jbncxELAdHjhzhHuObb76J+90qPSwRv9+PyspKXLhwwXQcy5Ytw549ezwPnCsoKEB1dbVpMx8g85MHYP49h0IhXL9+3bAAEmAuuGXxlPzFykUzNTWV5hG5C29xSynNuFVKBuhJbOFVsw6RICe7hW5EAvr0EEKwcuVKw3EQQvDss8+ira3N1MKQzsC5bDZps6C8vr4+0/tvJrhl8ZT8xcrSY2V5y3Z48xMrH55JpGYvyRqsLAd2tT6rgD5FURCLxZLcEUbjuHr1Kt555x2Ew2FTAWZHAIv4/HPRpG1lTfH5fFAUhSu4Z7NPO5+xep7tdNLMRqxcb5m2SklhL8kqeOZ/u5HMvMnF5/MhGAyiqKjIUJjoxxEKhfDee+9ZugNEBbCozz8XTdo8a4qiKAgGg3Htjs2YjT7tfMfqeS4sLEzziNyFNz/Nmzcv44tVKewlOYUdrY83uSiKIiR0gBkBJoKIALaTjpaLaVo8a0osFkNRUVFWjlviPVbP8+eff57pIaaM2fzEi71JF1LYS3IOUa3PLWE5NjbG9f0rigKfzyd0TLt55Llm0s5F14MkffCe53wQ9kD2WqWksJfkNW4Iy/nz52NsbMzwM0IIli9fLmwlcJKOlq2ThxHpcD3kc1GW2UAuPc/5hBT2krwn1cklGAxicHDQ8DO/3y8s6IH813y9dj3kWt18iSRbkMJeIrGgoKBAC7BJVYBlW9Ad6zI4MjKCqakpFBYWYsGCBSlpy165HjLVHEgiyQeksJdIBAgEAq4IsGwKuhseHsaVK1dw7NixuNoBqqqmrC17YarN1br5Ekk2IIW9RCKIWwIsG4LumJa8aNGipCJBrJ1xtmnLuVw3XyLJNFLYSyQZINNBSiLVBbNNW873eAeJxEtkuVxJ3pOt/aUziUi74GzTlmUveInEOVKzl+Q1MnrbGF4db0YmtWWz9LpsiXeQSHINKewleYuM3jbHqo43kDlt2WqBlul4B4kkF5FmfEneIhK9PVthWjIhBD5f/JpfVdWMdZnTL9DYwiwcDmN6elr7u1edFyWSfEZq9pK0YWaa9aoimoze5lNRUYGhoSFUVlZidHQUN27cQGFhIcrKyjKmLcv0OonEG6Swl6QFM9NsfX092trakv7e2NiI8fHxlBYAMnrbGkJIVglPuUCTSLxBCnuJ5/B85y0tLXHbsr/v3btXE9ROg+qyrVqdxBq5QJNIvEH67CWeI5LTbYSZz1YU5pcOBAJa5Lnf78+YP1pijUyvk0i8QWr2ElPc8qWL5HSL4MRnK6O3cwuZXieReIMU9hJD3MxP55lm7eDUZ5vpanUSe8gFmkTiPtKML0lCJP3JDjzTrB2kz3b2INPrJBJ3kcJekoTb+el637mqqtxtFcX8kUzVZyvL5kokktmKNONLkvAi/YmZZpubmzE0NGS63eLFi7FmzRo0Nze76rOVZXMlEslsRgp7SRJepT8FAgFUVVVheHjY9NhVVVVYsmSJqz5bL8rmelUISCKRSLxACntJEl7mp4se282gOrersuW7lUAuZCSS/EP67CVJeJmfnoncdzfdEm4HL2Ybw8PDaGpqQmtrK44dO4bW1lY0NTVheHg400OTSCQpIDV7iSFepj+lO7XKTbdEPtdul10CJZL8RQp7iSle5qenM/fdTbdEPtduz+eFjEQy25FmfEne46brgFkJjMj1OgD5vJCRSGY7UrOXzArcch3kc3Md2YRGIslfpGYvmTW4UZUtn5vryCY0Ekn+IjV7icQm+Vq7XTahkUjyFynsJRIH5GtznXxdyEgksx0p7CUSSRz5upCRSGYz0mcvkUgkEkmeI4W9RCKRSCR5jhT2EolEIpHkOVLYSyQSiUSS52SNsCeEPEsIOUMIiRFCqhM++78IIWcJIQOEkKcyNUaJRCKRSHKRbIrG7wHwHQB/r/8jIaQKwHcBBAHcBaCNEFJJKY2mf4gSiUQikeQeWaPZU0r7KKUDBh/9DoC3KKUhSuk5AGcB1KZ3dBKJRCKR5C5ZI+w53A3ggu73L279TSKRSCQSiQDErKWlJycjpA3AQoOP/pRS+s+3tjkM4EVKadet338M4DildO+t3/9fAC2U0ncNjv8DAD8AgPLy8kffeustT64jVa5fv46ioqJMD8MT8vXa5HXlHvl6bfK6co90XtsTTzzxMaW0OvHvafXZU0rrHez2BYB7db/fA+CSyfFfA/AaAFRXV9MNGzY4OJ33HD58GNk6tlTJ12uT15V75Ou1yevKPbLh2nLBjP8LAN8lhBQQQhYDWAagI8NjkkgkEokkZ8gaYU8I+V1CyBcA1gB4nxByAAAopWcANAPoBdAK4N/LSHyJRCKRSMTJmtQ7Sul7AN4z+ewvAPxFekckkUgkEkl+kDWavUQikUgkEm+Qwl4ikUgkkjxHCnuJRCKRSPIcKewlEolEIslzpLCXSCQSiSTPkcJeIpFIJJI8J2tS7yQSicQOoVAIZ86cwdjYGObPn49gMIiCgoJMD0siyUqksJdIJDnH8PAw9u3bB0opwuEw/H4/Dhw4gOeeew4VFRWZHp5EknVIM75EIskpQqEQ9u3bh+npaYTDYQBAOBzG9PS09neJRBKPFPYSiSSnOHPmDMy6dVJK0dPTk+YRSSTZjxT2EokkpxgbG9M0+kTC4TCuXbuW5hFJJNmPFPYSiSSnmD9/Pvx+v+Fnfr8f8+bNS/OIJJLsRwp7iUSSUwSDQRDy/7d37zF6VHUYx78PbbeoYNLloqVQKggGXA1iQVEj1YAoqFiEIlSkGoIKGMUSbyjUW4IoxgteuNWKQqtAUQSMIFC8JAitopRLsWDF0gKFFbCIYOnPP8556XR49+3udndnZ3w+yeR958y8M7/znn33zDlzOWq7TBI9PT0jHJHZ6OfK3sxqZfz48cycOZOurq5nW/jjxo2jq6vr2XQz25hvvTOz2pk8eTKzZ89m6dKl9Pb20t3dTU9Pjyt6sz64sjezWurq6mLvvfeuOgyzWnA3vpmZWcO5sjczM2s4V/ZmZmYN58rezMys4VzZm5mZNZwrezMzs4ZzZW9mZtZwruzNzMwazpW9mZlZw7myNzMzazhX9mZmZg3nyt7MzKzhXNmbmZk1nCt7MzOzhlNEVB3DsJC0Bvh71XH0YVvg4aqDGCZNzZvzVT9NzZvzVT8jmbedI2K7cmJjK/vRTNLiiJhadRzDoal5c77qp6l5c77qZzTkzd34ZmZmDefK3szMrOFc2Vfj3KoDGEZNzZvzVT9NzZvzVT+V583n7M3MzBrOLXszM7OGc2U/giQdIel2SeslTS0t+7Sk5ZKWSTqoqhg3l6Q5ku6XdGueDq46ps0h6a25TJZL+lTV8QwlSSsk3ZbLaXHV8QyWpLmSHpK0tJDWLelaSX/NrxOqjHGw+shb7X9jknaSdIOkO/P/xI/m9FqXW4d8VV5m7sYfQZL2ANYD5wCnRMTinL4nMB/YF9gB+DWwe0Q8U1WsgyVpDrA2Ir5WdSybS9IY4G7gQGAlcAtwVETcUWlgQ0TSCmBqRNT63mZJbwTWAhdGRE9OOxPojYgz8kHahIj4ZJVxDkYfeZtDzX9jkiYCEyPij5K2BpYA7wJmUeNy65CvGVRcZm7Zj6CIuDMilrVZdCiwICKeioi/ActJFb9Va19geUTcGxFPAwtIZWWjSET8BugtJR8K/DC//yHpH27t9JG32ouI1RHxx/z+X8CdwCRqXm4d8lU5V/ajwyTgH4X5lYySP5BBOknSX3IXZK264UqaVi5lAVwjaYmk46sOZoi9KCJWQ/oHDGxfcTxDrSm/MSRNAV4F/IEGlVspX1BxmbmyH2KSfi1paZupU4tQbdJG7fmVTeTxe8CuwF7AauCsSoPdPLUql0F4fUTsDbwNODF3Gdvo15jfmKStgMuAj0XE41XHM1Ta5KvyMhs70jtsuog4YBAfWwnsVJjfEVg1NBENvf7mUdJ5wJXDHM5wqlW5DFRErMqvD0m6nHTa4jfVRjVkHpQ0MSJW5/OoD1Ud0FCJiAdb7+v8G5M0jlQhXhQRC3Ny7cutXb5GQ5m5ZT86XAG8R9J4SS8BdgNurjimQck/0JbpwNK+1q2BW4DdJL1EUhfwHlJZ1Z6kF+QLiJD0AuAt1Lusyq4Ajs3vjwV+XmEsQ6oJvzFJAi4A7oyIrxcW1brc+srXaCgzX40/giRNB74NbAc8CtwaEQflZacCHwDWkbp+fllZoJtB0o9IXVUBrAA+2DoHV0f5FplvAGOAuRHx5YpDGhKSdgEuz7NjgYvrmjdJ84FppJHFHgROB34G/BSYDNwHHBERtbvQrY+8TaPmvzFJbwB+C9xGukMJ4DOk89u1LbcO+TqKisvMlb2ZmVnDuRvfzMys4VzZm5mZNZwrezMzs4ZzZW9mZtZwruzNzMwazpW9/d/LI1JFYVol6TJJu/bjs/OGY8S4HNOwDFAjaVbO51b9WHcvST+R9ICkp/N3My8P3mSbIGmGpFn9XPdISQslrc7l06/PmfWHK3uz5DFgvzydQron9rr8wJlOvkgaqWuonQ9UOtSxpMNID3faBjgZOID03WwL/L7C0OpkBv3/+zgcmEJNn4hno5sfl2uWrIuIm/L7myTdR3o4xsHAJeWVJT0vIp6MiHuGI5iIWEl6XG8lJO1AGnVsPjArNn4gx8WS3l5NZI12ZESszz0ux1UdjDWLW/Zm7S3Jr1Mgjf0u6SxJn5O0Eng8p2/UjV/oIn+FpGslPSHprtxK3oik6ZJulvSkpEckXS1p57xso258SdPydt8i6cq83fskfai0zf0kXZG725+QdKukmYPI/3FAFzA72jx5KyKebX1KGpPjvU/SU5Jul3R0Ka55khZLOkTSHZL+LekqSd2SXirphhzvYkmvLH02JH1c0jcl9Up6VNK38yOMi+vtJem6vO1/SrpI0osKy6fkbc2QdI6kxyStlPR5SVuUttWT4/tXni6R9OLC8lZ5TMvL1kq6V9IJxTwD7wb2L5wimtPXFx4R6/taZra5XNmbtTclvz5QSDsa2B84AThyE5+/mPSc7+nAX4EFknZsLZR0DLAQuIfU1ft+4G7So5Q7uQD4C3AY8Evge6VW9s6kLvbjgHeQBuT4gaSjNrHdsv2BxRHRn+sGvgCcCpwLvDPv/6I2+5yc1/0scDzwuvyZBXk6nNTbuEBSecTB2aSBiGYCX8qff/bxvpK2AxYBzyeV00dyHq4tHxQAZwJr8/5+DJyW37e29dKchy2BY0jd8C8HftEmrvOAP5PKeRHwHUn75mVfBG4A/sSGU0TnY1aFiPDk6f96AuYAD5MqmrHA7qR/0o8DE/M6K0hDU25Z+uw8UqXYmp9Fev71Bwpp25DGPPhQnt8CuB9YuKmYCvPT8nbPLa13LXBTH9tQzs85wPVtYtyqw/7vAub347vrBp4ATi+lXw0sK31P64BdC2ln5jjeV0g7OKftUUiLHM8WhbRTgX8D3Xn+DNJ4Ey8srLNv/uxReX5Knr+wFOutwILC/I+AZUBXIW034BngkFJ5fKGwzjhgDXBGIe1SYNEA/x63ytueVfVvw1NzJrfszZJtgP/maRmwC+kcanGwiusi4j/93N41rTcR8QhpqM5Wy/5lwA7ADwYR5+Wl+YXAqyWNAZA0QdK3JP2dDfk5nnQAM1D9GTijh9SaLl/X8BNgd0nbF9JWxMbXOCzPr9e3SZtU2t7PY+Nu7oXA8/L+IVXs10RhTPSIuJl0kPaG0rauKc3fwYaygXQh4uXAekljJY0F/pa3NbWvbUXEf0m9ODtiNsq4sjdLHgP2If0z3xGYEs8defDB53yqb4+W5p8mdQtDOrCA1FMwUOXxvR8itd63zfPzSKcYvkoatnYfYG5h3/11P6nbfVNaQ3eWv5vW/IRCWrvvpJzeSivH2y7fxf1PbBNDK47uUlqnsoH0XX6SDQdLrWkXYKcBbstsVPDV+GbJuojY1P3yQzVE5CP5dWLHtdrbvs38OuBhSVsChwAnRcT3WyuULz7rp0XAqZK6o/MQo60Dlu3ZkC+A1oVxQzU8abt8F/e/us06rTiWtEnvpJfUsm93fn1Ynn1gNtzcsjcbectILedjB/HZ6W3ml0TEM8B4YAzwVGuhpK1JF80N1AWk1uzX2i2UdEh+u5R07vyI0iozgLsjYs0g9t3OoaWDlsOAJ/P+IY2DflDObyvGfUjn6X83wH1dRzo9sCQiFpemFQPcllv6Niq4ZW82wiLdS/0J0hXrF5HuZQ/gzaSL4jr1MLxN0peBG0kV3oHAoXm7j0m6BThN0uPAeuBTpFMULxxgjKuUnuA2P99FMJd0gDKJdJpgf9LFcb2SvgF8VtI6YHGO62BgoHcAdLI1cImk80hXxp8GnF3odfg68GHgV5K+QrrI7QzgNtIdCQMxh/QwoaskzSW15ieRvut5EbFoANu6i3Sg8i7ScxNWRcSqdisqPZVwTzYcHEyVtBZYExE3DjAPZhtxZW9WgYi4WNJ/SFeVX0q6ov0m0tXcnRwHfIz0RLte4MSIuKKw/GjS7WwXkrrVzyZdQHfSIGK8TNJrgE8D3ySd+15DuqDugMKqp5FOJXyY1G2+HHhvRCwY6D47OIt0znw+qUfyfOAzhVjXSHpTXm8+qUV9NXByRDz93M31LSLulvRa0i1+55IuBLyf1OJf3umzbXwXeBXpYGkC8HnSwUQ7M4DTC/Mn5ulG0tX/ZoOmiKE6DWlmw0XSNNLtgK+IiKWbWL1RJAXwkYg4u+pYzOrK5+zNzMwazpW9mZlZw7kb38zMrOHcsjczM2s4V/ZmZmYN58rezMys4VzZm5mZNZwrezMzs4ZzZW9mZtZw/wMmilto23fydQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 Component PCA', fontsize = 20)\n",
    "\n",
    "ax.scatter(finalDf['pc1']\n",
    "               , finalDf['pc2']\n",
    "               , c = 'grey'\n",
    "               , s = 50)\n",
    "#ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# on command line write\n",
    "#     pip install pandas\n",
    "\n",
    "# Write this and give it your dictionary (hope the shape is as expected)\n",
    "import pandas as pd\n",
    "tetramerDic = #Your dictionary of lists\n",
    "# should be shaped like\n",
    "#     {'AAAA':[num,num,num...], \n",
    "#      'AAAT':[num,num,num...], ... }\n",
    "\n",
    "df = pd.DataFrame(data=tetramerDic)\n",
    "df.to_csv('give_to_matthew_plz.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAAA</th>\n",
       "      <th>AAAC</th>\n",
       "      <th>AAAG</th>\n",
       "      <th>AAAT</th>\n",
       "      <th>AACA</th>\n",
       "      <th>AACC</th>\n",
       "      <th>AACG</th>\n",
       "      <th>AACT</th>\n",
       "      <th>AAGA</th>\n",
       "      <th>AAGC</th>\n",
       "      <th>...</th>\n",
       "      <th>TTCG</th>\n",
       "      <th>TTCT</th>\n",
       "      <th>TTGA</th>\n",
       "      <th>TTGC</th>\n",
       "      <th>TTGG</th>\n",
       "      <th>TTGT</th>\n",
       "      <th>TTTA</th>\n",
       "      <th>TTTC</th>\n",
       "      <th>TTTG</th>\n",
       "      <th>TTTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002410</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>0.003445</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.004256</td>\n",
       "      <td>0.017841</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007340</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006949</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.003540</td>\n",
       "      <td>0.009678</td>\n",
       "      <td>0.007592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004724</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.004051</td>\n",
       "      <td>0.019946</td>\n",
       "      <td>0.014272</td>\n",
       "      <td>0.005381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005501</td>\n",
       "      <td>0.006116</td>\n",
       "      <td>0.002257</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>0.004795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>0.001336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.004552</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.004037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>0.021914</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.005745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007420</td>\n",
       "      <td>0.002809</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.004426</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.006670</td>\n",
       "      <td>0.001068</td>\n",
       "      <td>0.004377</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.010238</td>\n",
       "      <td>0.005688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029487</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>0.012025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002983</td>\n",
       "      <td>0.007689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004637</td>\n",
       "      <td>0.002755</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.004147</td>\n",
       "      <td>0.018496</td>\n",
       "      <td>0.008980</td>\n",
       "      <td>0.005659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.012994</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013814</td>\n",
       "      <td>0.011278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004063</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.010115</td>\n",
       "      <td>0.023998</td>\n",
       "      <td>0.002863</td>\n",
       "      <td>0.001891</td>\n",
       "      <td>0.001292</td>\n",
       "      <td>0.001228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.002208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004879</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004644</td>\n",
       "      <td>0.012041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.015335</td>\n",
       "      <td>0.002549</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>0.008152</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.002369</td>\n",
       "      <td>0.003123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006707</td>\n",
       "      <td>0.006357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004592</td>\n",
       "      <td>0.006821</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008337</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.011616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.004093</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.014785</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>0.001361</td>\n",
       "      <td>0.002838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004448</td>\n",
       "      <td>0.005112</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004093</td>\n",
       "      <td>0.007001</td>\n",
       "      <td>0.005362</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.012413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.001942</td>\n",
       "      <td>0.011997</td>\n",
       "      <td>0.003898</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.006963</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>0.001372</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029420</td>\n",
       "      <td>0.002639</td>\n",
       "      <td>0.006413</td>\n",
       "      <td>0.009874</td>\n",
       "      <td>0.009806</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.004697</td>\n",
       "      <td>0.001689</td>\n",
       "      <td>0.002767</td>\n",
       "      <td>0.002821</td>\n",
       "      <td>0.003238</td>\n",
       "      <td>0.002587</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004645</td>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.008493</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.001669</td>\n",
       "      <td>0.010546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AAAA      AAAC      AAAG      AAAT      AACA      AACC      AACG  \\\n",
       "0   0.002410  0.006968  0.000000  0.002757  0.003445  0.000823  0.004256   \n",
       "1   0.001967  0.003545  0.000000  0.004724  0.000553  0.000712  0.004051   \n",
       "2   0.001655  0.004552  0.000190  0.004037  0.000000  0.000009  0.004031   \n",
       "3   0.002066  0.004426  0.002105  0.001141  0.006670  0.001068  0.004377   \n",
       "4   0.002983  0.007689  0.000000  0.004637  0.002755  0.000825  0.004147   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "95  0.000000  0.000000  0.004063  0.000543  0.010115  0.023998  0.002863   \n",
       "96  0.015335  0.002549  0.000003  0.000518  0.009569  0.008152  0.002766   \n",
       "97  0.002625  0.004093  0.004195  0.000668  0.014785  0.000000  0.002962   \n",
       "98  0.001942  0.011997  0.003898  0.000553  0.006963  0.000158  0.003033   \n",
       "99  0.000000  0.007082  0.000000  0.000531  0.004697  0.001689  0.002767   \n",
       "\n",
       "        AACT      AAGA      AAGC  ...      TTCG      TTCT      TTGA      TTGC  \\\n",
       "0   0.017841  0.000000  0.005660  ...  0.000000  0.007340  0.002100  0.002445   \n",
       "1   0.019946  0.014272  0.005381  ...  0.005501  0.006116  0.002257  0.002380   \n",
       "2   0.021914  0.006809  0.005745  ...  0.000000  0.007420  0.002809  0.002742   \n",
       "3   0.017778  0.010238  0.005688  ...  0.029487  0.000127  0.000000  0.002711   \n",
       "4   0.018496  0.008980  0.005659  ...  0.007768  0.000000  0.000000  0.002718   \n",
       "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
       "95  0.001891  0.001292  0.001228  ...  0.005749  0.002208  0.000000  0.004879   \n",
       "96  0.001453  0.002369  0.003123  ...  0.006707  0.006357  0.000000  0.004592   \n",
       "97  0.001812  0.001361  0.002838  ...  0.000000  0.004448  0.005112  0.000000   \n",
       "98  0.001837  0.001037  0.001372  ...  0.029420  0.002639  0.006413  0.009874   \n",
       "99  0.002821  0.003238  0.002587  ...  0.000000  0.004645  0.006036  0.000313   \n",
       "\n",
       "        TTGG      TTGT      TTTA      TTTC      TTTG      TTTT  \n",
       "0   0.000000  0.006949  0.001663  0.003540  0.009678  0.007592  \n",
       "1   0.004795  0.000000  0.000000  0.000000  0.008093  0.001336  \n",
       "2   0.000000  0.000000  0.003577  0.000000  0.010204  0.000000  \n",
       "3   0.000000  0.000000  0.000000  0.004757  0.005694  0.012025  \n",
       "4   0.007065  0.012994  0.006468  0.000000  0.013814  0.011278  \n",
       "..       ...       ...       ...       ...       ...       ...  \n",
       "95  0.005377  0.000000  0.004674  0.000000  0.004644  0.012041  \n",
       "96  0.006821  0.000000  0.000000  0.008337  0.000181  0.011616  \n",
       "97  0.004093  0.007001  0.005362  0.003611  0.000730  0.012413  \n",
       "98  0.009806  0.000000  0.010720  0.000000  0.000000  0.011462  \n",
       "99  0.006282  0.000292  0.008493  0.000684  0.001669  0.010546  \n",
       "\n",
       "[100 rows x 256 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Working with the Matthew Data for real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 0.0, 0.0] [0.9607843137254902, 0.7137254901960784, 0.25882352941176473]\n",
      "[0.9607843137254902, 0.7137254901960784, 0.25882352941176473] [0.9607843137254902, 0.9254901960784314, 0.25882352941176473]\n",
      "[0.9607843137254902, 0.9254901960784314, 0.25882352941176473] [0.25882352941176473, 0.9607843137254902, 0.32941176470588235]\n",
      "[0.25882352941176473, 0.9607843137254902, 0.32941176470588235] [0.25882352941176473, 0.27058823529411763, 0.9607843137254902]\n",
      "[0.25882352941176473, 0.27058823529411763, 0.9607843137254902] [0.9607843137254902, 0.25882352941176473, 0.9490196078431372]\n"
     ]
    }
   ],
   "source": [
    "# I kind of want to color the fragments as a rainbow from beginning to end to see if any pieces are colored similarly\n",
    "red = [x/255 for x in [255,0,0]]\n",
    "orange = [x/255 for x in [245,182,66]]\n",
    "yellow = [x/255 for x in [245,236,66]]\n",
    "green = [x/255 for x in [66,245,84]]\n",
    "blue = [x/255 for x in [66,69,245]]\n",
    "violet = [x/255 for x in [245,66,242]]\n",
    "\n",
    "colors = [red,orange,yellow,green,blue,violet]\n",
    "transLen = int(finalDf.shape[0]/5)\n",
    "transMatrix = []\n",
    "\n",
    "for n in range(0,len(colors)-1):\n",
    "    color1 = colors[n]\n",
    "    color2 = colors[n+1]\n",
    "    print(color1, color2)\n",
    "    R = [x for x in np.linspace(color1[0], color2[0], transLen)]\n",
    "    G = [x for x in np.linspace(color1[1], color2[1], transLen)]\n",
    "    B = [x for x in np.linspace(color1[2], color2[2], transLen)]\n",
    "    \n",
    "    for x in zip(R,G,B):\n",
    "        transMatrix.append(x)\n",
    "while len(transMatrix) < finalDf.shape[0]:\n",
    "    transMatrix.append([0,0,0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.465221</td>\n",
       "      <td>1.249727</td>\n",
       "      <td>(1.0, 0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.197430</td>\n",
       "      <td>1.028783</td>\n",
       "      <td>(0.9998656997045393, 0.0024442653773838305, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.831510</td>\n",
       "      <td>4.601235</td>\n",
       "      <td>(0.9997313994090787, 0.004888530754767661, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.272448</td>\n",
       "      <td>2.246408</td>\n",
       "      <td>(0.999597099113618, 0.007332796132151492, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.378368</td>\n",
       "      <td>4.537644</td>\n",
       "      <td>(0.9994627988181574, 0.009777061509535322, 0.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pc1       pc2                                              color\n",
       "0  2.465221  1.249727                                    (1.0, 0.0, 0.0)\n",
       "1 -0.197430  1.028783  (0.9998656997045393, 0.0024442653773838305, 0....\n",
       "2 -2.831510  4.601235  (0.9997313994090787, 0.004888530754767661, 0.0...\n",
       "3  2.272448  2.246408  (0.999597099113618, 0.007332796132151492, 0.00...\n",
       "4 -2.378368  4.537644  (0.9994627988181574, 0.009777061509535322, 0.0..."
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color = pd.Series(transMatrix, name='color')\n",
    "finalDf = pd.concat([principalDf, color], axis=1)\n",
    "finalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9YvBLktQjBr8kST1i8EuS1CMGvyRJPWLwS5LUIwa/JEk9YvBLktQjBr8kST1i8EuS1CMGvyRJPWLwS5LUIwa/JEk9YvBLktQjBr8kST1i8EuS1CMGvyRJPWLwS5LUI3MGf5KtkrwxyT8lOTDJPUYss2OSr02uREmSNC6zBn+SHYAfAn8LPBZ4H3Bekr8cWvRuwK4Tq1CSJI3NXHv87wDOBbatqgcC2wBfBI5P8jfTKE6SJI3XRnPM2wU4oKquBKiqXwEvSPJt4P1J7l1Vr55GkZIkaTzmCv47AdcNT6yqDyb5OXBMknsBH5hUcZIkabzmOtR/LvCYUTOq6gTgicDjgI9MoC5JkjQBcwX/l4D9k9xx1MyqOhX4C2DJJAqTJEnjN9eh/ncD/485vhxU1dlJHgrsNO7CJEnS+M0a/FX1a+Dsta2g7fR3yjiLkiRJk+HIfZIk9YjBL0lSjxj8kiT1iMEvSVKPdAr+JIe0g/WMmrdlkkPGW5YkSZqErnv8bwK2nmXevdr5kiRpPdc1+APULPO2Bq4cTzmSJGmSZr2OP8k+wD7tywI+mOSaocU2Bv4U+PJkypMkSeM018h91wGXtz8HuBq4YmiZG2lu1XvE+EuTJEnjNtfIfZ8CPgWQ5MPAYVV1/rQKkyRJ4zfXHv/vVdV+ky5EkiRNXqfgB0jycOAZNJ35Nh6eX1XPHmNdkiRpAjoFf5KXAocDlwH/RXNuX5IkbWC67vH/b+Bo4MCqWjPBeiRJ0gR1vY5/C+AYQ1+SpA1b1+D/IvDISRYiSZImr+uh/sOBFUluD3wFuGp4gao6Z5yFSZKk8esa/Ce3z28Chm/IMzOc75JxFSVJkiaja/A/dqJVSJKkqeg6gM8pky5EkiRNXtfOfQAk2S3JG5OsSLJtO+0vktxrMuVJkqRx6jqAzz2BE4CHARcA2wNHAhcC+wHXAy+dTImSJGlcuu7x/xNwF+D+7SMD8/4dePyY65IkSRPQtXPfU4B9qmp1kuHe+xcBW423LEmSNAnzOcd/0yzTNwd+O4ZaJEnShHUN/m8Arxza26/2+YXA18ZalSRJmoiuh/oPAr4JnAV8hib0X5zkgcADgUdNpjxJkjROnfb4q+osmh79ZwD70hz2fwbwM+CRVXVe1w0meUqSc5OsTnLwLMssT3JmkrOTOIaAJElj0nWPn6r6CfD8ddlYe6rgcOCJNJ0CT09ywuA4/0k2AY4AnlJVFybZYl22KUmSbjGvAXzGYGdgdVWdX1U3AscCewwtszdwfFVdCFBVl065RkmSFq3Oe/xJnklzeH9rYOPh+VW1c4fVbEVzemDGRfzh7X7vB9w+ySrgrsA/VtVHu9YpSZJm13XkvkNp7sr3feAc4MYFbi8jptXQ641o+hM8HrgT8O0kpw33I0hyAHAAwNKlS1m1atUCS1IX1157rW08Bbbz5NnGk2cbr9+67vG/CHh7Vb1uHbd3EbDNwOutgYtHLHNZVf0G+E2SrwMPBm4V/FW1AlgBsGzZslq+fPk6lqa5rFq1Ctt48mznybONJ882Xr91Pcd/V+CrY9je6cAOSbZPcgfguTT3ABj0OeAxSTZKcmeaUwE/GsO2JUnqva57/MfSDNu7TuFfVWuSvAI4CVgCHF1VZyc5sJ1/ZFX9KMmXgB8ANwNHtZcTSpKkddQ1+L8KvCPJ5sBXgKuGF6iqE7usqF3uxKFpRw69fhfwro61SZKkjroG/yfb5+2AfUbML5o9eEmStB7rGvzbT7QKSZI0FZ2Cv6r+e9KFSJKkyZvPAD4bAXsCjwY2Ba6guWvf8VW1ZjLlSZKkceo6gM8WwJeBBwEXAL8EdgFeDnw/yZOq6leTKlKSJI1H1+v43wtsRnMnvvtU1S5VdR+aa+w3a+dLkqT1XNfg3x04qKpOH5zYvv474KnjLkySJI1f1+C/I/DrWeb9GrjDeMqRJEmT1DX4TwMOSvJHgxPb1we18yVJ0nqua6/+1wInAz9L8mWazn1bAE+muePe8olUJ0mSxqrTHn9VnQnsQHM3vKXAE2mC/0hgh6r6/sQqlCRJY9P5Ov6qugw4eIK1SJKkCesc/ABJNgEeCGwJXAycXVV/cMMeSZK0fuo6gM9GwFtpBuy588Cs65IcAby+qn43gfokSdIYdd3jfy9wAPAW4HjgUppz/HsCbwQ2Bl41iQIlSdL4dA3+5wOvq6rBEfquAN6a5HrgDRj8kiSt97pex38zcPYs884CajzlSJKkSeoa/B8D9p9l3ouBj4+nHEmSNEldD/X/N7BnkrOBE7jlHP8ewF2B9yR5WbtsVdUHx16pJElaZ12D/z3t81bAjiPmD577L8DglyRpPdQp+Kuq6ykBSZK0HjPQJUnqkfmO3LeM5nD/xsPzqurEcRUlSZImo+vIfX8KHENzfj8jFilgyRjrkiRJE9B1j/9o4HfA04DVwI0Tq0iSJE1M1+DfEdizqk6aZDGSJGmyunbu+w6w7SQLkSRJk9d1j/8A4Jgk1wEnA39wK96qum6chUmSpPHrGvyXARcAH51jGTv3SZK0nusa/B8HdgHejZ37JEnaYHUN/scCL66qf51kMZIkabK6du67APAcviRJG7iuwf9/gNcn2W5ypUiSpEnreqj/zTSX852X5AJG9+rfeYx1SZKkCega/Ge1D0mStAHrelve/SZdiCRJmrx53Z0PIMnmwD2AK6rq8vGXJEmSJqVr5z6SPCfJj4BfAj8GLk3yoyTPmlh1kiRprLrelncv4BPAF4G30YT/PYHnAMcmWVJVx06sSkmSNBZdD/W/HlhRVQcOTf9okiOBNwAGvyRJ67muh/rvCxw3y7zj2vmSJGk91zX4fwk8fJZ5D2/nS5Kk9VzXQ/0fBg5NsgT4NE3QbwE8i+Yw/9smU54kSRqnrsH/FuD2wME0o/jN+C3NHfveMua6JEnSBHQdwOdmmrH63w08ENgSuAQ4q6qunGB9kiRpjOY1gE8b8t+YUC2SJGnCZu3cl2THJJcn2X2OZXZPclmSB0+mPEmSNE5z9er/W+DUqjpxtgXaed8AXjvuwiRJ0vjNFfxPohmtb22OBR43nnIkSdIkzRX8mwMXdVjHz4Gl4ylHkiRN0lzBfwWwVYd1bNUuK0mS1nNzBf8pwIs6rOOF7bKSJGk9N1fwvx3YNcnRSTYdnplkkyRHAbviyH2SJG0QZr2Ov6rObG/HuxLYK8kZwIVAAdvSjNG/Bti7qr4/hVolSdI6mvMmPVV1PLCMZo/+BuChwMOAG4F/AJa1y0iSpA3AWkfuq6pLcCx+SZIWha635ZUkSYuAwS9JUo8Y/JIk9YjBL0lSjxj8kiT1yKy9+pPceT4rqqrr1r0cSZI0SXNdznctzWA9XS1Zx1okSdKEzRX8L2R+wS9JktZzcw3Zu3KKdUiSpCmwc58kST2y1iF7ZyR5DvBi4H7AxsPzq2qLMdYlSZImoNMef5K9gY8Aq4GtgROAL7Tvvwb4wKQKlCRJ49P1UP//AQ4DXt6+PqKqXghsD1wGeCmfJEkbgK7BvwPwraq6CbgJuBtAVf0aeAfwismUJ0mSxqlr8F8N3LH9+efAjgPzAmw2zqIkSdJkdO3cdwbwIOAkmvP7hyRZA9wIHAL8x2TKkyRJ49Q1+N8G3Lv9+ZD25yNoRus7HThg/KVJkqRx6xT8VXUacFr781XAHknuCNyxqq6ZYH2SJGmM5j2ATxpLgRsNfUmSNiydgz/J7klOBa4HfgFcn+TUJE+dWHWSJGmsug7g8xLg8zR37Hs18Kz2+VrghHa+JElaz3Xt3Pc6YEVVvXRo+pFJjgReD3xorJVJkqSx63qofzPg+FnmHQds2nWDSZ6S5Nwkq5McPMdyj0hyU5Jndl23JEmaW9fgPxnYdZZ5uwJf77KSJEuAw4HdgJ2AvZLsNMty76AZN0CSJI1J10P97weOSrIZ8FngUmAL4Ok0Ib7/YIBX1TmzrGdnYHVVnQ+Q5FhgD2B4+VfSHEl4RMf6JElSB12Df2bP+yXto2iG6p3xpfY57bwls6xnK+BnA68vAh45uECSrWi+UDwOg1+SpLHqGvyPHdP2MmJaDb1+H3BQVd2UjFq8XVFyAO2IgUuXLmXVqlVjKlGjXHvttbbxFNjOk2cbT55tvH7rOnLfKWPa3kXANgOvtwYuHlrm4cCxbehvDuyeZE1VfXaophXACoBly5bV8uXLx1SiRlm1ahW28eTZzpNnG0+ebbx+67rHPy6nAzsk2Z7mLn/PBfYeXKCqtp/5OclK4AvDoS9JkhZm1uBPcinw5Kr6XpJf8YeH5G+lqrZY28aqak2SV9D0GVgCHF1VZyc5sJ1/5LyqlyRJ8zLXHv/hwC8Hfp4z+LuqqhOBE4emjQz8qtp3HNuUJEmNWYO/qt488POhU6lGkiRNVNex+rdJ8tBZ5j00yTaj5kmSpPVL15H7Pgj81Szz9gaOGE85kiRpkroG/6OAr80y7+R2viRJWs91Df47M3fnvj8aQy2SJGnCugb/D4G9Zpm3F3D2eMqRJEmT1HUAn7cDxyW5I7ASuATYEtgH2LN9SJKk9VzXIXs/k2Qf4G00IT9zk56fA3/lyHqSJG0YOg/ZW1UfS/JxYBmwGXA5cG5VjWVgH0mSNHnzGqu/DfkfT6gWSZI0YZ2DP8m9gKfR3FFv46HZVVUHjbMwSZI0fp2CP8nTgWNobqxzKXDj0CIFGPySJK3nuu7x/wPwZWDfqrpigvVIkqQJ6hr82wCvNPQlSdqwdR3A51Sa3vySJGkD1nWP/2+ATyS5FvgKcNXwAlV13TgLkyRJ49c1+H/QPn+Y2cfsX7Lu5UiSpEnqGvwvZO6b9EiSpA1A1yF7V064DkmSNAVdO/dJkqRFYNY9/iTfoblu/5wkp7OWQ/1VtfO4i5MkSeM116H+s4HfDvzsOX5JkjZwswZ/Ve038PO+U6lGkiRN1FrP8SfZOMkNSf7XNAqSJEmTs9bgr6rraW7Ms2by5UiSpEnq2qv/Q8Crktx+ksVIkqTJ6jqAzybAA4ELknwV+CW37uxXVeVteSVJWs91Df49gRvanx8zYn4BBr8kSeu5riP3bT/pQiRJ0uTNGfxJ7gTsDmwHXAJ8tap+OYW6JEnSBMw1ct99gH+nCf0Z1yR5dlV9edKFSZKk8ZurV/87gZtpzunfGXgA8D2aHv6SJGkDNFfw7wK8oaq+VVXXV9WPgJcA2ybZcjrlSZKkcZor+LcEzh+a9hMgwB9PrCJJkjQxaxvAxxvzSJK0iKztcr6Tkowaqverw9OraovxlSVJkiZhruB/89SqkCRJUzHXbXkNfkmSFpmuN+mRJEmLgMEvSVKPGPySJPWIwS9JUo8Y/JIk9YjBL0lSjxj8kiT1iMEvSVKPGPySJPWIwS9JUo8Y/JIk9YjBL0lSjxj8kiT1iMEvSVKPGPySJPWIwS9JUo8Y/JIk9YjBL0lSjxj8kiT1iMEvSVKPGPySJPWIwS9JUo8Y/JIk9YjBL0lSjxj8kiT1iMEvSVKPGPySJPWIwS9JUo8Y/JIk9YjBL0lSjxj8kiT1iMEvSVKPGPySJPWIwS9JUo8Y/JIk9YjBL0lSjxj8kiT1iMEvSVKPGPySJPWIwS9JUo9MPfiTPCXJuUlWJzl4xPznJflB+zg1yYOnXaMkSYvVVIM/yRLgcGA3YCdgryQ7DS32U2DXqnoQcBiwYpo1SpK0mE17j39nYHVVnV9VNwLHAnsMLlBVp1bVle3L04Ctp1yjJEmL1rSDfyvgZwOvL2qnzeZFwBcnWpEkST2y0ZS3lxHTauSCyWNpgv/Rs8w/ADgAYOnSpaxatWpMJWqUa6+91jaeAtt58mzjybON12/TDv6LgG0GXm8NXDy8UJIHAUcBu1XV5aNWVFUraM//L1u2rJYvXz72YnWLVatWYRtPnu08ebbx5NnG67dpH+o/HdghyfZJ7gA8FzhhcIEk2wLHA8+vqvOmXJ8kSYvaVPf4q2pNklcAJwFLgKOr6uwkB7bzjwQOATYDjkgCsKaqHj7NOiVJWqymfaifqjoROHFo2pEDP+8P7D/tuiRJ6gNH7pMkqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDIL6qfwAAAzxSURBVH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5JknrE4JckqUcMfkmSesTglySpRwx+SZJ6xOCXJKlHDH5Jknpk6sGf5ClJzk2yOsnBI+Ynyfvb+T9I8tBp1yhJ0mI11eBPsgQ4HNgN2AnYK8lOQ4vtBuzQPg4APjjNGiVJWsymvce/M7C6qs6vqhuBY4E9hpbZA/hoNU4DNkmy5ZTrlCRpUZp28G8F/Gzg9UXttPkuI0mSFmCjKW8vI6bVApYhyQE0pwIAbkhy1jrWprltDlx2WxfRA7bz5NnGk2cbT8eyhbxp2sF/EbDNwOutgYsXsAxVtQJYAZDkjKp6+HhL1SDbeDps58mzjSfPNp6OJGcs5H3TPtR/OrBDku2T3AF4LnDC0DInAC9oe/c/Cri6qi6Zcp2SJC1KU93jr6o1SV4BnAQsAY6uqrOTHNjOPxI4EdgdWA1cB+w3zRolSVrMpn2on6o6kSbcB6cdOfBzAS+f52pXjKE0zc02ng7befJs48mzjadjQe2cJmclSVIfOGSvJEk9skEFv8P9Tl6HNn5e27Y/SHJqkgffFnVuyNbWxgPLPSLJTUmeOc36Fosu7ZxkeZIzk5yd5JRp17ih6/D/xd2TfD7J99s2ts/WPCU5Osmls12yvqDcq6oN4kHTGfAnwH2AOwDfB3YaWmZ34Is0YwE8CviP27ruDenRsY3/HLhH+/NutvH423hgua/R9Id55m1d94b26PhZ3gQ4B9i2fb3FbV33hvTo2MavA97R/rwUuAK4w21d+4b0AP4CeChw1izz5517G9Iev8P9Tt5a27iqTq2qK9uXp9GMs6DuunyOAV4JHAdcOs3iFpEu7bw3cHxVXQhQVbb1/HRp4wLumiTAXWiCf810y9ywVdXXadptNvPOvQ0p+B3ud/Lm234vovmmqe7W2sZJtgKeDhyJFqrLZ/l+wD2SrEry3SQvmFp1i0OXNv4AsCPNIGw/BF5dVTdPp7zemHfuTf1yvnUwtuF+NavO7ZfksTTB/+iJVrT4dGnj9wEHVdVNzY6SFqBLO28EPAx4PHAn4NtJTquq8yZd3CLRpY2fDJwJPA74E+ArSb5RVddMurgemXfubUjBP7bhfjWrTu2X5EHAUcBuVXX5lGpbLLq08cOBY9vQ3xzYPcmaqvrsdEpcFLr+f3FZVf0G+E2SrwMPBgz+brq08X7A26s5Gb06yU+B+wPfmU6JvTDv3NuQDvU73O/krbWNk2wLHA883z2jBVlrG1fV9lW1XVVtB3waeJmhP29d/r/4HPCYJBsluTPwSOBHU65zQ9aljS+kOaJCknvS3FTm/KlWufjNO/c2mD3+crjfievYxocAmwFHtHuka8qbcXTWsY21jrq0c1X9KMmXgB8ANwNHVZV3+eyo42f5MGBlkh/SHJI+qKq8a988JDkGWA5snuQi4E3A7WHhuefIfZIk9ciGdKhfkiStI4NfkqQeMfglSeoRg1+SpB4x+CVJ6hGDX72S5NAkNfC4OMlxSf6kw3tXJjljQjVN5BKnJPu2v+ddOiz7kCSfTPKLJDe2bbMyyU6TqG2xSfLsJPt2XPY5SY5Pckn79+n0PmkcDH710dXALu3jfwMPAb6a5I/W8r7DgH0nUM9RNEOb3maSPINmNLXNgL8GnkDTNpsD37oNS9uQPJvun49nAtsBX5hUMdJsNpgBfKQxWtPexQrgtCQXAt+gGQTjU8MLJ7lTVf22qn4yiWKq6iKaYTdvE0nuBXwEOAbYt249uMe/JnnabVPZovacqrq5PRKz/21djPrFPX4Jvts+bweQ5IIk70nyxnakrGva6bc61D9wGP1Pk3wlyW+S/Ljde76VJE9P8p0kv01yeZITk9y7nXerQ/1JlrfrfVKSL7TrvXBmRLSB5XZJckJ7SP43Sc5M8rwF/P7709xP/bU1YkSvqvr9XmmSJW29Fya5IcnZSfYeqmtlkjOSPDXJOUmuS/JvSTZNct8kJ7f1npHmvg+D760kf5PkH5NckeSqJP/UDgk7uNxDkny1XfeVST7RDgk7M3+7dl3PTvKhJFcnuSjJm5PcbmhdD2zr+3X7+FSSPx6YP/P3WN7OuzbJ+UleNvg7A3sCuw6cRjp0tgb3DnW6LRn8Uhv4wC8Gpu0N7Aq8DHjOWt7/rzTjZT8d+C+aG+xsPTMzyfNp7m/wE5rDwfvR3Ahm6VrW+y80w8k+g+b2xx8c2vu+N81h+P2B/wkcB3w4yV5rWe+wXYEzOg6l+hbg9cAK4C/b7X9ixDa3bZd9A3AA8Ofte45tH8+kOeJ4bPIHtyB8Lc2NRp4H/H37/rfOzEyyFFgF3Jnm7/TK9nf4yvAXBOCdwLXt9j5OM+T0MwfWdd/2d9gYeD7NofoHAJ8fUdc/A9+n+TuvAg5PsnM77zDgZOB73HIa6Sik9VFV+fDRmwdwKHAZTehsRHNP9pNp9uq3bJe5ALgE2HjovStpAnLm9b40t7984cC0zYA1wIHt69sBPweOX1tNA6+Xt+tdMbTcV4DTZllH2t/nQ8DXRtR4lzm2/2PgmA5ttynwG+BNQ9NPBM4daqc1wJ8MTHtnW8cLBqbt3k7bcWBatfXcbmDa62nGIN+0ff124CrgbgPL7Ny+d6/29Xbt648O1XomcOzA648B5wJ3GJi2A3AT8NShv8dbBpa5PfArmjvPzUz7NLBqnp/Hu7Tr3ve2/rfhoz8P9/jVR5sBv2sf5wL3oTnnOnhHq69W1fUd1/flmR+quU3xpTR7rNDcjexewIcXUOdnhl4fDzwsyRKAJPdI8v4k/80tv88BNF9m5qvLTTseSLOXPdwP4pPA/ZJsMTDtgrp1n4jV7fPXRkzbamh9n6tbHwo/HrhTu31oQv7LNXBP96r6Ds0XtkcPrevLQ6/P4Za/DTSdGD8D3JzmLn0bAT9t1zV886nBv/PvaI7ubI20gTH41UdXA4+g+Y99a2C7qvri0DK/nMf6rhp6fSPNoWNovmRAcwRhvi4d8Xojmp720OxZPwd4F/Akmt/p6IFtd/VzmkPza7Nl+zzcNjOv7zEwbVSbDE+fmTZc76jfe3D7W46oYaaOTYemzfW3gaYtD+KWL04zj/tw63ucd1mXtEGwV7/6aE1Vre16/HHdtvLy9nnLOZcabYsRr9cAlyXZGHgq8IoauJXvcMe1jlYBr0+yaVVdMcdyM19etuCW3wtgplPdXO+dj1G/9+D2LxmxzEwd3x0xfS5X0Ozxjzof7+1jtSi5xy9N1rk0e9T7LOC9Tx/x+rtVdRNwR5p7oN8wMzPJXWk63M3Xv9Ds5b571MwkT21/PIvmXPuzhhZ5NnBeVf1qAdseZY+hLzDPAH7bbh/gP4Ant7/vTI2PoDmv/815buurNKcQvltVZww9LpjnujwCoA2Ce/zSBFVzrfbf0vR8/wTNtfIFPI6mQ91cRx52S/JW4BSa8HsisEe73quTnA4ckuQa4GbgYJrTGHebZ40Xpxk57pj2aoSjab6sbEVzKmFXmo51VyR5H/CGJGuAM9q6dgfmeyXBXO4KfCrJP9P0sD8E+MDA0Yj3Ai8FTkryDpoOcm8HfkhzZcN8HEozcNG/JTmaZi9/K5q2XllVq+axrh/TfGn5XzTjMlxcVRePWjDNaIg7ccsXhYcnuRb4VVWdMs/fQZoXg1+asKr61yTX0/RO/zRNz/jTaHqFz2V/4DU0I+ldAby8qk4YmL83zSVyH6U59P4Bms53r1hAjccleSTwd8A/0pwr/xVNZ7wnDCx6CM3phpfSHFpfDfxVVR07323O4T0059iPoTkqeRTwuoFaf5Xkse1yx9DsaZ8I/HVV3fiHq5tdVZ2X5FE0lw2uoOlE+HOaIwGr53rvCEcAf0bzxekewJtpvliM8mzgTQOvX94+TqG5ikCamFSN61SmpHFIspzmEsM/raqz1rL4opKkgFdW1Qdu61qkxcpz/JIk9YjBL0lSj3ioX5KkHnGPX5KkHjH4JUnqEYNfkqQeMfglSeoRg1+SpB4x+CVJ6pH/DzZrbU1fbqMSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 Component PCA', fontsize = 20)\n",
    "\n",
    "finalDf.plot.scatter(x='pc1'\n",
    "               , y='pc2'\n",
    "               , c = 'color'\n",
    "               , s = 10)\n",
    "#ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.09218538, 0.0778975 ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.4652208130955406 1.2497265428536102 (1.0, 0.0, 0.0)] \n",
      "\n",
      "[-0.1974303397928557 1.0287832043495724\n",
      " (0.9998656997045393, 0.0024442653773838305, 0.0008863819500402901)] \n",
      "\n",
      "[-2.831509667246983 4.601234797979482\n",
      " (0.9997313994090787, 0.004888530754767661, 0.0017727639000805803)] \n",
      "\n",
      "[2.2724476032662326 2.246407906462441\n",
      " (0.999597099113618, 0.007332796132151492, 0.0026591458501208703)] \n",
      "\n",
      "[-2.378368032363736 4.537643563999223\n",
      " (0.9994627988181574, 0.009777061509535322, 0.0035455278001611606)] \n",
      "\n",
      "[2.8646332828894456 4.6920430442010606\n",
      " (0.9993284985226968, 0.012221326886919152, 0.004431909750201451)] \n",
      "\n",
      "[6.073621343718839 4.221108641367134\n",
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      " (0.8550094010206821, 0.2605962933118453, 0.9507923717432178)] \n",
      "\n",
      "[-2.2142681230971015 -5.973628849460964\n",
      " (0.8574133763094278, 0.2605560032232071, 0.9507520816545796)] \n",
      "\n",
      "[4.225936578075235 -1.3968940498114513\n",
      " (0.8598173515981735, 0.2605157131345689, 0.9507117915659414)] \n",
      "\n",
      "[-2.675766254716615 2.1759608633046192\n",
      " (0.8622213268869192, 0.26047542304593074, 0.9506715014773032)] \n",
      "\n",
      "[-0.3555135759938531 1.6158202499976426\n",
      " (0.8646253021756647, 0.26043513295729254, 0.950631211388665)] \n",
      "\n",
      "[-4.494676500862185 -1.0919833506832146\n",
      " (0.8670292774644104, 0.26039484286865433, 0.9505909213000269)] \n",
      "\n",
      "[-6.308114625495282 -1.4071928456045395\n",
      " (0.8694332527531561, 0.2603545527800161, 0.9505506312113886)] \n",
      "\n",
      "[-4.298339199279193 5.25144989539509\n",
      " (0.8718372280419018, 0.2603142626913779, 0.9505103411227505)] \n",
      "\n",
      "[0.8043508241238327 0.5489250972854333\n",
      " (0.8742412033306473, 0.26027397260273977, 0.9504700510341122)] \n",
      "\n",
      "[-3.4220796035677146 -5.459726248150563\n",
      " (0.876645178619393, 0.26023368251410156, 0.950429760945474)] \n",
      "\n",
      "[-7.017664918779186 -3.663090133860767\n",
      " (0.8790491539081386, 0.26019339242546335, 0.9503894708568359)] \n",
      "\n",
      "[-5.473301121468651 -3.5985802485223592\n",
      " (0.8814531291968841, 0.26015310233682515, 0.9503491807681976)] \n",
      "\n",
      "[-4.87601751010261 1.612800817977139\n",
      " (0.8838571044856298, 0.26011281224818694, 0.9503088906795595)] \n",
      "\n",
      "[-1.8433034805930046 -3.0285869680778617\n",
      " (0.8862610797743755, 0.2600725221595488, 0.9502686005909212)] \n",
      "\n",
      "[-2.083261032889576 -0.07568841218282668\n",
      " (0.8886650550631212, 0.2600322320709106, 0.9502283105022831)] \n",
      "\n",
      "[-7.850319117708645 -4.093614974705204\n",
      " (0.8910690303518667, 0.2599919419822724, 0.9501880204136449)] \n",
      "\n",
      "[0.32962776120858833 -1.7189193321486431\n",
      " (0.8934730056406124, 0.2599516518936342, 0.9501477303250067)] \n",
      "\n",
      "[-1.7091478216905873 -0.8479245988334877\n",
      " (0.8958769809293581, 0.25991136180499597, 0.9501074402363685)] \n",
      "\n",
      "[-1.8520585483194483 2.582621496901012\n",
      " (0.8982809562181036, 0.2598710717163578, 0.9500671501477302)] \n",
      "\n",
      "[-1.7794681537045158 -3.767446064352512\n",
      " (0.9006849315068493, 0.2598307816277196, 0.9500268600590921)] \n",
      "\n",
      "[-0.4124679792638838 -2.9937019976273236\n",
      " (0.903088906795595, 0.2597904915390814, 0.9499865699704539)] \n",
      "\n",
      "[0.046687072905552844 -5.530249731619423\n",
      " (0.9054928820843406, 0.2597502014504432, 0.9499462798818157)] \n",
      "\n",
      "[4.611582064956056 -4.974029866821452\n",
      " (0.9078968573730861, 0.259709911361805, 0.9499059897931775)] \n",
      "\n",
      "[-1.85338092425812 -4.324715049814137\n",
      " (0.9103008326618318, 0.25966962127316684, 0.9498656997045393)] \n",
      "\n",
      "[0.21207827295716472 -4.576266073582363\n",
      " (0.9127048079505775, 0.25962933118452863, 0.9498254096159011)] \n",
      "\n",
      "[2.570490074324893 -1.2180113228812885\n",
      " (0.9151087832393232, 0.2595890410958904, 0.949785119527263)] \n",
      "\n",
      "[-6.368546927790766 -4.139028212560379\n",
      " (0.9175127585280687, 0.2595487510072522, 0.9497448294386247)] \n",
      "\n",
      "[-3.809978998251845 4.024995604859612\n",
      " (0.9199167338168144, 0.259508460918614, 0.9497045393499866)] \n",
      "\n",
      "[-3.4640250547734825 -3.797871931554957\n",
      " (0.9223207091055601, 0.25946817082997586, 0.9496642492613483)] \n",
      "\n",
      "[-3.2863039855529514 -1.4021541830111557\n",
      " (0.9247246843943056, 0.25942788074133766, 0.9496239591727101)] \n",
      "\n",
      "[-4.297335492152521 6.489109401608107\n",
      " (0.9271286596830512, 0.25938759065269945, 0.949583669084072)] \n",
      "\n",
      "[-5.311246117329595 5.506612017796374\n",
      " (0.929532634971797, 0.25934730056406124, 0.9495433789954337)] \n",
      "\n",
      "[-3.3201268984998595 4.277348340013322\n",
      " (0.9319366102605426, 0.2593070104754231, 0.9495030889067956)] \n",
      "\n",
      "[-1.796258157993566 -3.5804007712891934\n",
      " (0.9343405855492881, 0.2592667203867849, 0.9494627988181573)] \n",
      "\n",
      "[1.0720830028596242 3.589629019339688\n",
      " (0.9367445608380338, 0.2592264302981467, 0.9494225087295192)] \n",
      "\n",
      "[-0.25295156688696685 1.4814917915635288\n",
      " (0.9391485361267795, 0.2591861402095085, 0.949382218640881)] \n",
      "\n",
      "[-7.567519522252636 -2.761756116124037\n",
      " (0.941552511415525, 0.25914585012087027, 0.9493419285522428)] \n",
      "\n",
      "[-5.323571706372392 2.5546937000659646\n",
      " (0.9439564867042707, 0.2591055600322321, 0.9493016384636046)] \n",
      "\n",
      "[-6.420916982227314 6.500813816623362\n",
      " (0.9463604619930164, 0.2590652699435939, 0.9492613483749663)] \n",
      "\n",
      "[-7.229438645033734 7.716172515604429\n",
      " (0.9487644372817621, 0.2590249798549557, 0.9492210582863282)] \n",
      "\n",
      "[-3.1412233315256723 5.281454751279012\n",
      " (0.9511684125705075, 0.2589846897663175, 0.94918076819769)] \n",
      "\n",
      "[0.4518425811344925 -2.511824790677064\n",
      " (0.9535723878592532, 0.2589443996776793, 0.9491404781090518)] \n",
      "\n",
      "[-1.2203351413602517 3.850118666473298\n",
      " (0.9559763631479989, 0.25890410958904114, 0.9491001880204136)] \n",
      "\n",
      "[-2.5837394068691797 6.784709969677402\n",
      " (0.9583803384367446, 0.25886381950040294, 0.9490598979317754)] \n",
      "\n",
      "[0.4683585716726325 -0.07966152896897165\n",
      " (0.9607843137254902, 0.25882352941176473, 0.9490196078431372)] \n",
      "\n",
      "[-2.998704939606236 -4.774405586084176 list([0, 0, 0])] \n",
      "\n",
      "[-4.899448733327189 -6.446365041757125 list([0, 0, 0])] \n",
      "\n",
      "[-6.134498015903404 -4.24121025832514 list([0, 0, 0])] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for color in finalDf.values:\n",
    "    print(color, '\\n')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mplpatches\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "inFile = 'tetraFrequency.csv'\n",
    "outFile = 'deino_radio_r1_PCA.png'#'hodg_cicad_PCA.png'#'vibrio_cholera_PCA.png'\n",
    "chunkSize = 1000\n",
    "n_components = 10\n",
    "\n",
    "####\n",
    "# Which PCA to plot\n",
    "x_axis_pca = 1\n",
    "y_axis_pca = 4\n",
    "\n",
    "####\n",
    "# Which points to highlight\n",
    "left_coor= 1400\n",
    "right_coor= 1410\n",
    "\n",
    "# coor around 53,200 in hodgkinia looks like HGTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S1BCbcUmSJKkhNuOSJElSQ2zGJUmSpIbYjEuSJEkNsRmXJEmSGmIzLkmSJDXEZlySJElqiM24JEmS1BCbcUmSJKkhNuOSJElSQ2zGJUmSpIbYjEuSJEkNsRmXJEmSGmIzLkmSJDXEZlySJElqiM24JEmS1BCbcUmSJKkhNuOSJElSQ2zGJUmSpIbYjEuSJEkNsRmXJEmSGmIzLkmSJDXEZlySJElqiM24JEmS1BCbcUmSJKkhNuOSJElSQ2zGJUmSpIbYjEuSJEkNsRmXJEmSGmIzLkmSJDXEZlySJElqiM24JEmS1BCbcUmSJKkhNuOSJElSQ+Y1XUCrHXAALF067ajoI66fx0iSJGmI9NjQuWdckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhoSmdl0Da0VEePApTXFbwncbG6t2W3LrTO7bbl1Zltz/bl1Zrctt87stuXWmd223Dqz25ZbZ3ZduXtl5ths7zyvhgI2JJdm5pI6giNiaR3ZbcutM7ttuXVmty23zmxrrj+3zuy25daZ3bbcOrPblltndtty68yuM7eX+3uYiiRJktQQm3FJkiSpITbj1RzXwuy25daZ3bbcOrPblltntjXXn1tndtty68xuW26d2W3LrTO7bbl1Zg9FridwSpIkSQ1xz7gkSZLUEJtxSZIkqSE245IkSVJDbMYlSZKkhtiMz1JEzIuI10TEdyPiooi4MCK+ExGvjYj5A57WbweZN2gR8YiO2/Mj4l0RcUpEHB0Ri4ct15rNlbThiIgjI2LL8vbuEfHjiLg9Is6OiIdvKLltrHmDnReZ6c8sfoATgU8DBwE7lD8HlcO+XCF3HLij/Bkvfyamhles+REdt+cD7wJOAY4GFlfIPa/j9ieALwBPAD4JnDBsudZs7gzZRwJblrd3B34M3A6cDTx8GLPbltvGmp0XrZ8Xl3Tc/l/g+eXtJwI/21By21jzhjov+n5SG9oPcOkM435bIfdfgROArTuGXTmgmutqjs7vuH0BML+8HcBFw5ZrzebOkL1BrvidF86LpnNrrvnSjtvndI2rsi5qVW4ba95Q54WHqczebRHxwoi4e55FxEhEvAi4rd/QzHwj8CngxIh4U5mf1cstSuy4/WTgVZl5JvDXwL4VcjeLiOdHxKHAwsxcA5DFklel9rpyrdncdZnXcftBmfmNMvsMYGxIs9uWW2d223LrzG5bbp3ZJ0XEFyJiV+AbEfGWiNgpIo4ArtmActtY8wY5L2zGZ+8w4AXATRHx24i4DLgROKQc17fMPBd4SvnnmcBGVfI61NXEnAk8F3gOcFZEbA0QEdsANw9hbp3Z1tzeXNhAV/zrObeNNTsv6s+tLTsz3wmcQXF46V8DHwC+C+wBvGRDyW1jzRvqvPAbOPsQEQ+kmHdVG4HpsrcF9svMUweQdXzXoKMy86ayifliZj656jSktouIlwOvA3YDFgLXAicDH83MPw5jdtty21iz86L+3LqzpbawGe9BRGwGPAPYnmLP8vXAaZl5+zDm1qmN88KazZW0YWjbusjtSP25dWZXzfUwlVmKiJcC51GcWLIY2Bh4EnBuOW6ocjvyN4uIF0XEX0fEW8vbm1fMbN28sGZzZ8gf+Guk7uy25baxZudF/bl1ZbdtXeR2pP7cYa/ZPeOzFBGXAo/ufpcTEVsAZ2fmnsOUW2a8FHgv8D3g9+XgHYCnAv+QmScMU801zwtrNne67FpeI3Vmty23jTU7L+rPrbnmVq2L3I7UnzvsNc+7vzvobsH0Jz1Ocu+rlgxLLsA7gQPWtYBQXFKxH22cF9Zs7nTqeo3Umd223Dqz25ZbZ3bbcuvMbtu6yO1I/bl1ZlfOtRmfvQ8B50XE9yhOMAHYieId/AeGMBfqW/DaOC+s2dzpbJAr/vWcW2d223LrzG5bbp3ZbVsXuR2pP7fO7Mq5HqbSg/Ld+tMpDtAP4DqKA/T7vs54zbkvA95D8RHgfRaQzPzCENZcS26d2dbc6tw6XyO1ZLctt401Oy/qz10P2W1bF7kdqTm3zuyquTbjc1ydC7U0F2yIK/71nVtndtty68xuW27d2VJb2Iz3KSK+lpmHtiW3Tm2cF9ZsrqQNQ9vWRW5H6s+tM7ufXC9t2L9dW5ZLRHytpujWzYsas625vbl1vkZqy25bbp3ZbcutM7ttuTVnt21d5Hak/tw6s3vO9QTOHkTETlM3gfkRsWN5m8zs+6t768qdxsAWvDbOC2s2dxY2iBV/w7l1Zrctt87stuUONLtt6yK3I/Xn1pldNddmvDf/SXHmdwAPLv+eOhv8T4cwt86FunXzosZsa25v7ga54l/fuXVmty23zuy25dac3bZ1kduR+nPrzK6U6zHjfYqI8zNzv2HPjYjTuWcBWQKcU97OzKy6UE9NoxXzYn1kW3P7cut8jdSV3bbcNtbsvKg/t+7sjmm0Yl1Ud26d2W3LrTO7n1z3jM9xmfmkqdvlAjKQlZs0V9T5Gqkru225dWa3LbfO7Lbl1p0ttYUncPbvUy3LrVMb54U1mytpw9C2dZHbkfpz68zuOdfDVDYgEfHyrPAlCtJcV+drpK7stuXWmd223Dqz25Zbd7Y0zNwz3qOI2DMifhgRF5d/PyIi3jWsuZ0GvZJr47ywZnNnUmcjUFd223LrzG5bbp3ZbcutI7tt6yK3I/Xn1pldKTcz/enhBzgTOBA4v2PYxcOaW+bsCfxwKg94BPCuYa255nlhzeZOl13La6TO7LbltrFm50Xr50Wr1kVuRzbceeGe8d4tzsxfdA1bO8S5AP8BvANYA5CZFwGHDSC3jfPCms2dTl2vkTqz25ZbZ3bbcuvMbltundltWxe5Hak/t87svnNtxnt3c0TsRnEpJiLiBcANQ5wL9S14bZwX1mzudDaoFX9DuXVmty23zuy25daZ3bZ1kduR+nPrzO4710sb9u4NwHHAQyLi98CVwEuGOBfqW/DaOC+suZncw2vKHfbXSJ3ZbcutM7ttuXVmty23zuy2rYvcjtSfW2d237leTaVPEbExMJKZ48OeGxG7UiwgjwVuo1xAMvPqAeW3Zl7UnW3N7cyt8zVSV3bbcuvMbltundlty50h+/DMvKpqdpnfmnVRnbl1Zrctt87sfnJtxnsQEQ8BDga2p3gHfz3wzcz8TcXcAym+beyciNgbeAbw68z8TtWau6ZT50J9Qma+dMCZf0JxMsQvM/P7FbMeTTFP74iIRcBRwP7Ar4CjM/OPfeYuoDi28frM/EFEvJhig/Jr4LjMXFOx7t2A5wM7UnxsexlwYr/1duTWsizPML0jMvP4Co+fC6+RoVnxN5lbZ3bbcuvMbltu3dkd06i6LqqrD6hrXV/LNioi3gR8IzOvrVLfNLm1bKvrzu6aTs+9i834LEXE3wF/AXwJuK4cvAPFQv6lzPxIn7nvBZ5JccjQ94FHA2cATwFOy8wPVax74E1MRJzSPQh4EvAjiok9t8/cX2TmgeXtV1F85PMN4GnAt/qdx2XeJcAjM3NtRBwH3AWcBDy5HH5In7lfpPjfLQZuBzYBvl7mRma+rELNbwL+jOIM7WcBF1DsNXo+8PrMPKPP3FqW5fuZ5jWZuVOfj637NVLbG5M2bbjrfGPZxg1313QGtmNgHfkD2ZlR15vWOt8Ml6+R7YGzM3N5x/BnZOZ3q+ZPM70q66K6+oBa1vVldi3bqIj4I3AncDlwIvDVzFzWb50dubVsq+vMHkTvYjM+SxHxW2Cf7g1SuQG7JDP36DP3l8C+wELgRmCHjg3L2Zn5iAo119LERMR5FBu7z1I0GEHxYjwMIDPP7DP3/Mzcr7x9DvCszFxW7i05KzMf3k9umffrzHzoVP2ZuX/HuAsyc98+cy/KzEdExDzg98B2mTkREQFcWPH/90tg3zJvMXBqZj4xInaiaOj26zO3rmX5onWNAvbMzIV95tb5GqntjUnbNtw1v7Fs1Ya75h0Dde3MqGt9X9ub4XJZfgPFG759gTdn5jfLcfdaT/eYW9e6qNY+YNDr+jK7lm1URJwPHECxHLwIeC5wLsXr++v9frpR17a6zuyB9C45gGs2bgg/wG+AB08z/MHApRVyz5/udvn3BRVr/iUwSrFxvQPYtBy+CLioQu4I8FaKFfO+5bArBjCPLwS2AB4ILF3XfOoz+6vAEeXt44El5e09gXMq5F4MLCjrHgceUA7fiGLPUdX/38Ly9hbAuZ3TrZBb17J8E8UG9cFdPztT7G3tN7fO18hvgfnTDF8AXDaM2VOv6/L2YuCM8vZOVV4nU+sEiqbrpo5pRJX1xdT/rVxvPA34HLAM+C7wMmCsQu6vO26fN6hlo2uZOwfYqry9McXe8Srz4jzgv4EnAk8of99Q3n5C1eWCwa/va8ntyN6kvL0zsJSiIb/Pa73H3LrWRXWtO2tZ1089nhq2UdO83uZTNOQnAssq5Nayra4zmwH0Ll5NZfbeAvwwIi4Dpj5q3QnYHTiyQu7qiFicmXdRvMsEICI2AyYr5AKszcwJ4K6IuDwz7wDIzBUR0Xd2Zk4Cn4yIr5a/b2IwV+bZjOKddQAZEdtk5o0RsUk5rIpXAp+K4tuwbgZ+HhHXUvwvX1kh93MUK+hR4J3AVyPiCuAgij2iVXwWOCcizgIeD3wUICK2Am6tkFvXsvxtig3rBd0jIuKMCrl1vkYmge2A7pPQth3y7HnABMWnBWMAmXlNRMyvkDlS7uHbmKLx2oxiOVtIsaGtIsv1xveA75V1PpPik4OPA1v1mXtx3HMM8IURsSQzl0bEnpTXrO7TSERsQfEGIrLci5+Zd0ZE1UvuLQHeTLG+eFtmXhARK7LPTxQ71LK+rzEXijd8y8u8qyLiicBJEfFgqq3z61oX1bXurGtdD/Vto+71/8ni04JTgFPKTy37Vde2us7syr2Lh6n0ICJGKI4b3J5iBl9H8W5qokLmwsxcNc3wLYFtM/OXFbLPBp6UmXdFxEi5MZxqYk7PPj8CnGY6zwYel5l/P4i8afIXA1tn5pUDyBoDdqVoZq7LzJsGkLkdQGZeHxGbU3xsd03e97q5/WTvAzyUYu/IwE6urGNZrkvNr5FnAP9Gccz1fTauWeGY1bqyI+LNwCuAuzfcmXl8ueH+WmY+vs/ctwJvpNhof4LiWPepjfZJmfkP/eSW2Xd/jDvNuEWZuaLP3M2ATwH/j2Ljuj/FvL4WeFNmXthn7lUUb5iC4lC8x3ZsXH+aFT4q75jGDsAnKfbiPjf7PI65I6+W9X2d25GI+BHw151Nc3k4xecprtQy2m92Xepad9a1ri+zB76Niog9M/O3g6pxmvyBb6vXR3bXdGbdu9iM9ygitqbjZKy6/onltDbJjhNa+nh8LU1MRGyembf3W1cP09kdeCTFR2m/qiH/AZlZda/DdLnPzczu40IHlV1XzbXk1q3qa6TMqO2NSds23HW9sWzzhrtrOgPbMdCROZCdGTWu7+t8M7wDxZ73G6cZ97jM/Fm/2WVGa7bX6zu3Dm5T787svXeZzbEs/iQUx5+dRXGiyfeBH1B89HMWsH9N07xmABnzOm5vQvER6QMqZq4tn/8rgM0H+HxPB7Ysb/8lxTG3n6U4nu6NFbPf1XF77zL7SuAq4NEVcg/p+jmU4iTDQ4BDKtb8uHJ5u4TipKnvU+ypvBZ4zBDmPqJ8PVxLcc3gLTrG/WJQy0nXNCu/RsqcrSn2qu5H0WwNvNau6W3Sptyaaq20HtYJrcwAACAASURBVJoh97ktq3f3cr2xd8Wcga2Lp8ke+HakK3+r8rX38EEsw2XW1Pb6B23ZXteRW9d6ucbtSPc29ZABblPr6gMq9y4eMz57XwBek5lndw6MiIMoTgR4ZD+hEfHX6xpFsdLrW0S8HPhERNxCcYziMRQL3p4R8fbMPLHP6F8D/0xxvOfHIuKnFCdtfDP7/Li5tFVm3lzefhPFC/qWcm/UWcC/Vsg+BPhgefsfKU4S+k4Ul+z6Z4pLuPXjKxQnov2Be44N25jiahdJcTWKfn0S+HOK5eB/gedl5k8jYn+KefG4Icv9d+B9FP+rVwI/LfdoXE6FY45rfo3sCxxLcczfdWXmDhFxO8WVSc6rkj+DX1EcsjI0uRHxCIqN9fbAd4C/y8zbynF3X12kz+zHUWycJoG/ongt7lYeO/7nmfnzPnOnu1rKv5eHOpCZfb3+IuJdmfnB8vbewMnA/IgI4EXd24Ees08HXpiZN0fEXwLvBn4MvC8ijsvMftdzN5fHQ59IcbjSQD69rHE7MjVv/4XixMqdKE70fVBEnEmxju730pTH06LtdZ3rOGpaL1PfdqTObWpdfUDl3sVmfPY2nm4FnJlnRXH5mn4dTbFQTHdS0EiFXIC/AfaiOMHrQmC/zLy8/Oju+xQr7X6sycxvA98uT9T4M4rLtR0TEadl5ov7zY2I7TPz98ByikuhAayiOI51ULbL8vq4mfmLiiebPAb4CMUVF47NzIyIJ2bmEQOoc36WHwFHxLLM/ClAZp5Xsea6cjfJe46D/nhEnAt8t2w4qhwPV+dr5AvUsNEuM9q24a5row3t23DXtdGG+nY61LWTpK7tCBTHhr8sMy8t5+0bMvPRUVxO8nPAC/rMbdv2us51XF3r5bq2I3VuUzsNsg+o3LvYjM/edyLif4ETuOdkrB2Bl1JsDPp1HnByZp7bPSIiqp45PFGu9G+OiOXlRpXMvKnYwdO3ux9cruS/AnylPKHneRVy30pxpYWvUXz09aOI+C7FyVnHV8gF2DWK6/tO7fmcujoHVGg0svgSjKdSnPj2oyiuLT2oEzE6V8Lv6Bq3YAhzIyI2m9qblZmnR8ShwNeAB1TIrfM1UtdGG9q34a5row3t3nAPcqMN9e10qGsnSV3bEYBFmXlpmfeLiDi2vP0fUZxQ3K+2ba/rXMfVtV6uZTtS8za1lj6AAfQuNuOzlJlviohncs+36U2djHVMZp5aIfoI4JZ1jFtSIRfgmoj4MMUejd9ExCco9hQ9heK6tv364nQDyxf7f/YbmplnRMRjgRdT1HwuxQbqjVn9JLWDu/4egbtP8Pl0leAsri7wqSgu9fjPVbK6vHtqZZGZJ08NjOLbF08YwtyPUpxUeNbUgMy8KCKeTPFRfL/qfI3UtdGG9m2469poQ/s23HVttKG+nQ517SSpazsCcHlEvBv4IcWnERcAlIcv9d2ftHB7Xec6rq71cl3bkTq3qbX0AYPoXbyayhwWEZtSfLtZUlxi7ekUL/qrgQ9mZtUVqdR669hon1Jxo01E7AXc0nFIQue4rbPPKzvUmPtiii/vOqtr+E7AuzPzVf3klhnPBX7Q0dBODd8NODQzP9ZvdkfWdhQb7iWZuWvFrCd0DTo3M5eXG+0XZOYxFfM3o9hw70l5BRiKw0n63ukQEX+bmR+vUtc6cmvbjkRxxZ6/pziZ7kLgI5k5Xs6fh3Yvi9JcZTM+ABHx6sw8ri25dWrjvLBmcyVtGNq2LnI7Un9undmzza16YoAKlQ+cW8+5RMSr64puWW6d2dbc3tw6XyO1Zbctt87stuXWmd223Jqz27YucjtSf26d2bPK9ZjxHkTEQ7jn4+wErqf4OPszFXN3A55PcazqWopv7Duxau79TbbSg4t5sT1wdt77Cwm6v/q719wDKb46+5woLnv1DOA3dcyLiDghM186yOyI+BOKL3m5eADLxaMpvjTgjvKErKMoroX9K4qT+PrNfRPwjcy8tntclZqj+Cr1wyi+XOMH5WEPj6W4ykOlPQ5tfI00lN223Dqz25ZbZ3bbcitn17iNalVundlty60zu2que8ZnqTwx6EsUK4hfUJy9H8CJEXFUhdw3UVzneCPgUcAiiobj5xHxxIplz2R1vw8sa/4mxUlTF0dE50kRVZrE91Jcc/bT5QlD/0ZxKbSjIuKd/eaW2ad0/XwLOGTq7wq5v+i4/aqy5jHgvVWWi9LngaljbD9FcS3sj5bDqpzo9QHg7Ij4SUS8PoqvUR+E44FnA2+OiP8CXgicTbFc/0e/oW18jTSY3bbcOrPblltndttyK2XXuI1qVW6d2W3LrTN7ILlZwzdGzcUfim9Umj/N8AXAZRVyfwmMlrcXA2eUt3cCzq/x+fT9rV5lzZuUt3cGllJch5cqNU/Ni3I+3AFsWg5fBFxU8fmeB/w38ETgCeXvG8rbT6iQe37H7XMoriEMxbWOf1mx5l931t817oIqNVO8EX8axbV8l1FcOeRlwFiF3IvK3/OAmzqW66jy/2vja6Sp7LbltrFm58Xwz4uat1GtyW1jzRvqvPAwldmbBLbjvh85bFuOq2IeMAEspNirSmZeU17eqW8RcdG6RlF8/Xe/RrP8GCYzryr3Tp4UEQ+m2keLazNzArgrIi7PzDvKaayIiKrzeAnFt8e9E3hbZl4QESsy88yKuSMRsQVFcxuZuQwgM++MiOmuA92LiyPiiMw8HrgwIpZk5tKI2BNYUyE3s7h01PcoLrE2H3gmxZeFfJziq6n7MVIeqrIxRdO8GXArxXJd9XJwbXuN1Jbdttw6s9uWW2d223Jrzq5rG9W23DbWvEHOC5vx2XsL8MOIuIx7rke8E7A7cGSF3M8C50TEWcDjKQ5DoDx04NYKuVCszJ4O3NY1PID/q5B7Y0Tsm5kXAGRxya/nUBxW8fAKuavjnuv5HnB3scVlrio142Xz+ckorlv6yYi4icEs/5tRXFM0gIyIbTLzxojYhOorjldSXGv1XcDNFIdlXEux/FW6nnTnH5m5BjgFOCWqfaHJ54DfUHy68U7gqxFxBXAQxSFe/Wrja6TO7Lbl1pndttw6s9uWW2d2XduotuW2seYNc15U2eW/of1Q7P08CDiU4mt6D6L8+Lxi7j5l3kMGXO/ngD9Zx7j/qZC7A7DNOsY9rkLuwnUM3xJ4+IDnzbOBo2tcVhYDuwwoa4zia9kPALYeQN6eNT7v7Si+sRBg83K5PnAAua16jdSZ3bbcNtbsvGj9vKhrG9Wq3DbWvKHOC68zLkmSJDXEq6lIkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhpiMy5JkiQ1xGZckiRJaojNuCRJktQQm3FJkiSpITbjkiRJUkNsxiVJkqSG2IxLkiRJDbEZlyRJkhoyr+kC2mzLLbfMnXfeuekypIE499xzb87MrZquQ7MziPXP5OQkIyPV9snMpYxhqmVYMoaplrmUMUy1DEvGMNVSNaPX7anNeAU777wzS5cubboMaSAi4uqma9DsDWL9Mz4+ztjYmBlDWMuwZAxTLXMpY5hqGZaMYaqlakav21MPU5EkSZIaYjMuSZIkNcRmXJIkSWrInGvGI+IZEXFpRPwuIo6aZvxDIuLnEbEqIv62l8dKkiRJgzSnmvGIGAWOAZ4J7A38RUTs3XW3W4E3AR/v47GSJEnSwMypZhw4EPhdZl6RmauBLwEHd94hM/+QmecAa3p9rCRJkjRIc60Z3x64tuPv68phdT9WkiRJ6tlca8ZjmmE5yMdGxKsjYmlELF22bFlPxUmSJEmd5lozfh2wY8ffOwDXD/KxmXlcZi7JzCVbbeWXFUqSJKl/c60ZPwfYIyJ2iYgFwGHAKevhsZIkqUeTa5exctl7WLnsPUyu9dNmbZjmNV3AIGXm2og4EjgNGAU+n5mXRMRry/HHRsQ2wFJgU2AyIt4C7J2Zd0z32GaeiSRJc9/qW45mctXFxe3J5Wy09T81XJG0/s2pZhwgM08FTu0admzH7RspDkGZ1WMlaRhNTk4yPj5eKWP58uWV65hLGYPKmUsZg8pZV8aK8RWwtmxFVq9gzeJ1L9PDMk/aMF/bmDGonGHJ6MWca8YlaUMwMjLC2NhY5Rwz6smZSxmDypkuY/GCt7L65vcDsHCrtzKyYObpDMs8Gfb52taMQeUMS8Zs2YxLkqRGjC7ci0Xbf7HpMqRGzbUTOCVJkqTWsBmXJEmSGmIzLkmSJDXEZlySJElqiM24JEmS1BCbcUmSJKkhNuOSJElSQ2zGJUmSpIbYjEuSJEkNsRmXJEmSGjKv6QLmsiVLlvR0/6VLl9ZUiSRJkoaRzbgktdDk5CTj4+OVMpYvX165jrmUMaicuZQxqBwz6smZSxmDyhmWjF7YjEtSC42MjDA2NlY5x4x6cuZSxqByzKgnZy5lDCpnWDJmy2PGJUmSpIbYjEuSJEkNsRmXJEmSGmIzLkmSJDXEZlySJElqiM24JEmS1BCbcUmSJKkhNuOSJElSQ2zGJUmSpIbYjEuSJEkNsRmXJEmSGmIzLkmSJDVkXtMFSJJ6Nzk5yfj4eKWM5cuXV65jLmUMKmcuZQwqx4x6cuZSxqByhiWjFzbjktRCIyMjjI2NVc4xo56cuZQxqBwz6smZSxmDyhmWjNnyMBVJkiSpITbjkiSpsqUXreJdH7uVb3znTjKz6XKk1vAwFUmSVMmtt0/wnn+8lVWr4fyLV7PNg0Z5zAEbNV2W1AruGZckSZXcedc9e8Izk9v+ONlgNVK72IxLkqRKdth2lGf+6WIWzIc9dp3Pkx7rXnFptjxMRZIkzUpmkhPXESObEiOb3T08InjjEZvxxiM2m+HRkqZjMy5JkmZl9fjHmVh1OhAs3PxjjM7fp+mSpNbzMBVJknS/MieZWHUasApYydoV/9t0SdKcYDMuSZLuV8QIMboLsADYiJH5j2y6JGlO8DAVSZI0Kxtt/knWrjqdkZEHMbrw0U2XI80JNuOSJGlWYmQT5i/6s6bLkOYUm3FJaqHJyUnGx8crZSxfvrxyHXMpY1A5cyljUDlm1JMzlzIGlTMsGb2wGZekFhoZGWFsbKxyjhn15MyljEHlmFFPzlzKGFTOsGTMlidwSpIkSQ2xGZckqWXGL57gqn9ayS1nrGm6FEkVeZiKJEktsubWSS77uxVMroJbf7iW+ZsHm+7r5lxqq573jEfE4yPigxHxHxHxkHLYJuXwzQdfoiRJmrLmtoS45+/VN2ZzxUiqbNbNeESMRsSXgdOBvwf+CtiuHL0WOBl4/cArlCRJd1u0ywibHTgKAQu3H2GL/+decanNetkz/nfAocBfAw+l4315Zq4EvgE8a6DV9SEinhERl0bE7yLiqGnGR0T8Szn+oojYv2PcWyPikoi4OCJOjIiN1m/1kiTNLEaC3d6ziP2/szH7HLeY0Y3j/h8kaWj10oy/FDghMz8F3DzN+F8Duw2kqj5FxChwDPBMYG/gLyJi7667PRPYo/x5NfDp8rHbA28ClmTmw4BR4LD1VLokST0ZmWcTLs0FvTTjOwM/n2H87cAWlaqp7kDgd5l5RWauBr4EHNx1n4Mp3lRkZp4FbB4R25bj5gGLImIesBi4fn0VLkmSpA1PL834OPCAGcbvDiyrVk5l2wPXdvx9XTnsfu+Tmb8HPg5cA9wA/DEzv1djrZIkSdrA9dKM/xQ4PCLu87lYRGxBcULn6YMqrE/TfWbXfZr5tPcpn8PBwC4UJ6ZuHBGH32cCEa+OiKURsXTZsqbfe0iSJKnNemnGP0RxnPWPgOeUwx4ZEa8BzgM2Bj4y2PJ6dh2wY8ffO3DfQ03WdZ+nAFdm5rLMXAN8HXhs9wQy87jMXJKZS7baaquBFi9J0v1Zk2s4Zc1pfG3Nt1mZq5ouR1JFs74eUmYujYhDgM8Bx5eDP06xp/kPwPMz81eDL7En5wB7RMQuwO8pTsB8cdd9TgGOjIgvAY+mOBzlhoi4BjgoIhYDK4AnA0vXX+mSJN2/Y1Z/nu9P/JgkuWDiEj6w0d81XZKkCnq6OGlmnhoROwNP5Z7LG14GnJaZdw28uh5l5tqIOBI4jeJqKJ/PzEsi4rXl+GOBUykuwfg74C7giHLc2RFxEsVe/rXA+cBx6/9ZSNL9m5ycZHx8vFLG8uXLK9cxlzIGlVN3xmUrrgAmCeAqrmZ8zfTLwYY0T9qWMaicuZQxqJxhyehFz98UkJmrgG+XP0MnM0+laLg7hx3bcTuBN6zjse8F3ltrgZI0ACMjI4yNjVXOMaOenDozXrLoBXxo9SdJkhfPP5Sx+eue1oYyT9qYMaicuZQxqJxhyZitWTfjEbEf8NjMPGYd498A/CwzLxhUcZIk6d4eO+9RfGX0s0wyyVhs0nQ5kirq5QTO9wLPnmH8M4H3VCtHkiTdn41jsY24NEf00ow/CjhzhvFnUnzpjiRJkqRZ6KUZ3xK4dYbxt5f3kSRJkjQLvTTjfwD2mWH8w5i5WZckSZLUoZdm/AfAKyPiPg15ROwNvKK8jyRJ0lC444ZJLv7WGm65YrLpUqRp9XJpww8ChwDnRMTngQsovmp+P+CvgNXABwZeoSRJUh9W3pF89XUrmFgDEfD0f5xk7KFNVyXdWy/fwHl5RDwZ+ALw+q7RlwBHZOZlA6xNkiSpb7dfN0lOwsRqmLcR3HrFBDvZjGvI9PoNnEuBh0XEvsAeFN/AeWlmXlhHcYIlS5b0dP+lS5fWVIkkSe2y5e4jbLJVMP6HZP5GsN0jev6uQ6l2fS2V5Rf7+OU+kiRpaM1bELzw2EXcfm2y6bbByrXr92vOpdnoqxmPiMXAAyn2jN9LZl5TtShJkqRBGJ0fPHDXol1ZOd5wMdI0Zt2MR8QI8HbgjcA2M9x1tGpRkiSpOZnJzz69mit+MsEOf7KKJ71+EyLus/9N0gD0smf8I8DfUpys+TXglloqkiTdr8nJScbHq+3mW768+kf2cyljUDlzIeOGSya4+AerWLsKfn3mnez4qHls89D+97U1/XyGLWNQOXMpY1A5w5LRi16a8cOB72bms+oqRpI0OyMjI4yNjVXOMaOenLZn3Dk2weia+eRqiAWw6dgYY2PVPvhu+zwZdMagcuZSxqByhiVjtnr50p8tgG/WVYgkSRoO2+wzygEvmc8Ddw0eeeg8tq6wV1zSzHrZM/5LYNu6CpEkScNj/8MWsP9hCxgfn2i6FGlO62XP+D8Ar42IHesqRpIkSdqQ9LJn/ADgauBXEfEN4Eqg++1yZuYHBlWcJEmSNJf10oy/r+P24eu4TwI245IkSdIs9NKM71JbFZIkSdIGaNbNeGZeXWchkiRJ0oamlxM4JUmSJA1QL4epEBHzgOcBj6a47nh3M5+Z+YoB1SZJkiTNabNuxiPiAcDpwMOAoDhZM8rR2THMZlySpA3c2lXJH34zyWY7BCxouhppePWyZ/yDwEOAVwJnAJcDTweuAd4N7FH+LUmSNmATa5OvvW4Fd96cADztHycZ26vhoqQh1csx488GTsjM44E7ymETmXlpZh4OrAA+POgCJUlSu9xxfbJ8WbJmBUyshesv8Fs8pXXpZc/4NsA55e215e+NOsafDLwNeN0A6pIkzWBycpLx8fFKGcuXL69cx1zKGFTOXMroO2fjZHTzlUzelWTC2M5rGB+fv/7rGNKMQeXMpYxB5QxLRi96acZvBTYub48Da4AdO8avoTipU5JUs5GREcbGxirnmFFPzlzK6DfnL/59jOvOm+CBu44wuvmdQ/N8hiVjUDlzKWNQOcOSMVu9HKbyW2BvgMycBM4HXh4RCyNiMfBS4IrBlyhJktpm4Viw2xPmsfmOs281br1ykt9+fw0r/5g1ViYNl172jH8P+NuIODIzVwH/BHyJYo95AouAVw++REmSNNctu3SCb/3NSghYsHg1h52wmHkL4/4fKLVcL8340cDHy0aczPxKRKwFDgcmgJMy88s11ChJkua4Gy+ZZHISJtcACcv/kGy+o8245r5ZN+OZmcCqrmFfB74+6KIkSdLclZnkJIyM3tNs73TgKEv/E0bnwdi2wabb2Yhrw9DTN3BKkiRVcfPlE3zrbStZswIed+QC9nl2cZWVzXYY4bD/XMwd10+y5e4j92rUpblsnc14RLy0vPlfmZkdf88oM08YSGWSJGm9uPXKSW69cpIdHzXKwrF6m+ClJ6xhVXlVzrM+s/ruZhxg0ebBos1Ha52+NGxm2jP+BYoTM78ErO74e6ZXaQI245IktcRNv5rg1LcXJ05utGnw519YxOj8+hryzXYIRhfA5FrYeCv3fkszNeNPAsjM1Z1/S5Kk9lv1uVWs+d81TD4gmFyTTE4GKyaTu25JNtoMfnbMasZvmGSfF08wdsDgpnvgyxewYFFw163Jfn9R7YuApLlgnc14Zp4509+SJKmdJq6aYPXXVsMq2Hhl8uDR4LqFsOn2I2y8VbD0+NX87kdrmVwDN129il2+mkQMZi/26PzggMMXDCRLmgtmdQJnRGwCXAj8a2b+c70lSZKkOsXCKA4sBSLgMW9ZwPi2o2y1Z3Hi5OoVkJPF+KlLDc54kKqkvs2qGc/M5RHxQGB5zfVIkqSajWw7wsK/Wciak9cw78B5LHjKfDbu2PO9/+ELWPbbSe78Q7LvK+YTI3biUl16ubThWcAS4LM11SJJkmo0sTq55fJJNtt+hIVPWcCCp0x/uMjiLYLn/+siAMbHJ9ZnidIGp5dm/CjgRxFxNvCF8kuAJEkNmJycZHx8vFLG8uXVP+ycSxmDyhnWjMm1yXePWsHym5ORgGd8ZBGbbD3SSC0besagcuZSxqByhiWjF7004/8E3EaxZ/xjEXE5cFfXfTIznzyo4iRJ0xsZGWFsbKxyjhn15Axjxq1XTrDy+hFYCbEAbrtkAdvuPrsTKYfx+bQ9Y1A5cyljUDnDkjFbvTTju1KcwnFN+ffWgy9HkiTVYWybEeZtFEBCwtb7+OU60jCYdTOemTvXWIckSUPllssnuPbsCbbdd5St9x6OxnVyIomgrxMq5y8KDjluEdedM8ED9xjhAbsMx3OSNnS97BmXJGmDcOfNk3zrLSuZWA2j/7OGg/9tEVvsfP/HV9fp8tPXcObHVjMyH57xoY3Y5uG9N9OLthhhj6c1+zwk3ZuvSEmSutzx+2IPdE4CI3Db1ZNNl8TZn1nD5FpYuwJ+8dnV9/+AlpqcSH7yz6v40l/exYVfmbvPU5rS057xiNgNeCvwaGAL7tvMZ2buNqDaJElqxIMeOsKm249w+9WTLNo82GFJ84d0jG0brLg9GRmBzXacu/vSrv6/CX73w7WsXQnnnrCGBx80j813mrvPV5p1Mx4RDwd+CiwELqU4ofMS4IHANsDlwHU11NiTiHgG8ClgFPhsZn6ka3yU459FcTWYl2fmeeW4zSmuFvMwipNV/yozf74ey5ckDYHRBcHB/7YRd92SLH5gMDLa/JfePPV9G3HBiauZvyh45IvmN11ObaKz7078DF9zXi97xt8PrAYOBG4B/gC8OTN/FBGvAo4GDh58ibMXEaPAMcBTKd4YnBMRp2Tmrzru9kxgj/Ln0cCny99QNOnfzcwXRMQCYPF6K16SNFRGRoNNHtR8Ez5lo82Cg167cNpxa1cn33nXSm64ZJI9nzKPJ7xldpcsHEYPfswoD3n2PK47Z4K9nzuPzXewG9fc1ssS/ifAcZl5KcV7VYAAyMz/AL4DfGQdj11fDgR+l5lXZOZq4Evc9w3CwcAJWTgL2Dwito2ITYHHA58DyMzVmXn7+ixekjT3TK5I6v6evCt/OsGNv55kYjVc9sO13HJ5/ce4r5mYrOV5xUjwmNcu5IWfW8w+B7f3TYU0W70042MUh6JAsYccYOOO8T+jaNibtD1wbcff15XDZnOfXYFlwPERcX5EfDYiNkaSpD5kJjcdvYKrnruca19yJxO31dcgL9r83nvwF47Vu0f/n0+9lGd++Axe8q8/55bxVbVOS5rremnGb6I4NpzMHAfuBPbsGL8FxXHaTZpu7dP9tn1d95kH7A98OjP3o3h+R91nAhGvjoilEbF02bJlVeuVJM1Ra29M7vrJWpiEtbcmy3+0trZp7bD/KI99zQIefNAoT33XQsZm8TX3/bp1+Wq+e8H1ZMLN46v43kU31DYtaUPQyzHjFwCP6vj7TODNEfELiqb+SODCAdbWj+uAHTv+3gG4fpb3SeC6zDy7HH4S0zTjmXkccBzAkiVL6v3cUZLUWqObRbGVXQ0xCvNrPvZ5n+fMZ5/n1H9i58YLR5k/b4Q1ExPMHw2228LTq6QqemnG/wd4Q0QsyswVwLspGvLTy/ErgL8fcH29OgfYIyJ2AX4PHAa8uOs+pwBHRsSXKE7c/GNm3gAQEddGxF7lcfFPBn6FJA2hyclJxsfHK2UsX768ch1zKWNQOZ0ZY0dPcOcZa1i45ygTe8Ns/2XDPk8+/IK9+P5FN7Lr1huz3w4b3e+yOCzPZ1gyunNuvX6SX/1kDVvvMsoeB86+NRuW5zOsr78mM3ox4388Ig4Dvl6ezPhl4MtT4zLz/IjYB3g+MAF8JzOvqLXa+5GZayPiSOA0ikNmPp+Zl0TEa8vxxwKnUlzW8HcUlzY8oiPijcAXyyupXNE1TpKGxsjICGNjY5VzzKgnZypj7OGw5cObr2PQOQ8bG+Nhu26z3muZDc3VXQAAIABJREFUSxlTOWtWJSe+4y5WLp/H/AWw8TsXstdjZt+QT1fL7y+cYM2dyU6PHp3VZTmHbZ7MlYzZur//9v8At0XEFyka2ws6R2bmtcC/1FVcPzLzVIqGu3PYsR23E3jDOh57AbCk1gIlSZJKK8aTNSuBhIm18IerJ9nrMf3nXXjSas75zzUAxTkE79xoMIWqNvd3ANsHgDsojgc/NyLOi4g3RMQW9ZcmSZI0t409MHjI40YZmQeLNoVH/GlPX45+H1f+bIK1K2HtSrhu6cSAqlSdZvyPZ+Z7gfdGxJOBvwKe9//ZO+8wK4qsD7/V4cbJMJHMkHNSQFQQEyomjIuuOayuq65r2G9ds2sOq6usaV1zVsScQUBRJOcchoHJ+cYOVd8fdwRxQAEBw/b7PPPA7a46fbr6hl9XnzoH+BdwtxDiTeA/SqlP97ybHh4eHh4eHruTSJXEjkFWe0GqOLXHT8FJfokTexnN7IsZPgchdmzBrhCCk64NEGtU+EOgGz/tWvQYY1C90gIBXQ/5acLeY++wQ1epWXB/2lwY53ekYqlPBU4RQmwAngSeUkqV7DFPPTw8PDw8PHYaS9ncUvsoS+01nJx2OCenHc76GQ6f/COJAHocabDfxduu7Lm7mfOCxaKJNoX9dEb/1Y9u/jZuAlZ+2sCmeRvoclAj2e0mohmdMAIH75SNUMbuGYseh5vk99CxE4rcbl710l8DO3WVlFKNSqlHlVLDgN7AfYAPuBFYI4T4aPe76OHh4eHh4bGrfBybwezkEupkI082TqTKrWXxJBs3CU4Slr3/4/nPpZJMbprNuw1fYkl7l/xo2CSZ87xNvB5KZrqsnrJzedeX1lbwj5kf887aJXu8ounOsO5Lh6n36yz74Eg+vPFO7IQPpWJAqvDT6s8d5r5kEattWfRJKRc3MQvXWr5bfcruoJHXXfeeePxK2OXnF0qppcBVQoibgSeAk0ilA/Tw8PD4TSOEaEtqofifvVA9jx1BKcXMJU3YjmR430x0be+JJJ8wEQJQIBAkpY05OIGxOBOAgt7bnpermGRRN9UhONLi1RGf83r9ZBSK6dH53F500U77oX9PcRiBHR+DhmSCf3zzCRFN8WXZOloFQgwv7Ljd9k2yhlfjdxBRdRwZuIguxuCd9ndHqS+VSFcAAtcxcZL7YgQOA2DZBw5fTLCQNix512H8M8GtBLJVfydu4ktAYmZcghk+co/56fHLZZfFuBBiKKlwlVOATMAC3txNfnl4eHj8kvEDfYCMn9sRj18H/323jNc/rwZg1MAsrhrfHoB5L1usmeLS9VCdvuN8e+TYo4NDWW1vYIG1gqNCB/CnyA3Yox06tR7MH5yz6D6yZbaNyBKXjY9byATUrrYpya0mkWsBsCyxfpf8SMvTGPkXHwvfcGg7SKfTiB0v2h2xk5tnw6VSVMV/OA/05ORzbJKrUEjeiN/D1ekv7pLPO0K3QwwWT3KIViu6jAqRU/zXzYK7aoXESTSfQ4VCulvflLiJGaDiqf/HP9ssxp1GRd10h0B7jfQ+P3dxc489zU6JcSFEPvB7UiK8B6nS8gtIxYw/p5Sq3e0eenh4eOxlhBCNP9ak+d/nhBAuqaypmXvYLY9fMV8taSRhpcIUZi1LFcgpX+Qy9zkbJwENGyQFfXRyu+1+4aULjT9kngzAx8np2EmbuEiyrv9sKtmPW99ZQX0ywaX7DOWILt0AcCPfCQMRcKg+nK/FAkAxLmvULvvSdbRJ19E7XyW0KJzBgW2K+bBiDe3Tszm4XbcfbG8IPwINhcQQe7YqaShHY/yzQRwLzO/N9vc+2mTlZw6uDT3HGC0WZ+qBEbiJ6YBED6aCC5RULPljDKtWIYDi6wNkDfUWYv6W+dGrK4TQgaNJZVMZ09ynAXiUVO7xWXvUQw8PD4+9TxrQBMzZzv4gsC+wFqjaW055/DppkLX0HVbLhrdCCARHDs8BwLW+00h87/UeoodRjAIC+NGFzrTFNVTFoijggZlfUdb2fXJFAWMGjSNzuE7jTJesETo99+nG8+6N2Moh38xpYVfZDaCHENqeEb5CCC7sO5y/DD90u3HQFc4CIrKMduYIRvtPJ66aaJI1HBY4d4/4tJV/msDcRjrvVp01zng5hB1ThHJahgP5sq5GWocjtDQ0sysAbhSSlQpcUEDTAtcT479xfqwC533AaUDr5k1Tgf8ArymlEnvYNw8PD4+fi3uAy0lNPPypucDZZoQQXYAVwPVKqTd+Bv88fsFIpwqr7kWEUYQVHsntscuxB9l0bpfN2b5rGNKmEICigRpdDzVY/4VL8UE6+duJ3d4ZqmUZX9jvkSfa0lu1rBzTTi/kkcxbWOysYrDZm0nhdZi6juW64I/xtTMVAxMdnSOvPRGApiaFEIIcY9tRWckVd+JWfwZ6mEC/h9CCRT/5PLbH9oT4BvsLZsTuBWBZciJHpv2bE4JX7TE/dgYzIFrMmH+LEBq6f+BW2/Q0yBikE1mYyhHe6iBPiP/W+bErfDmwEbid1Cz4z1ru3sPDw2NvoJS6WgjxAvAYsEQIcSNwv1Lq23QIv5xUDh57HXvjKzjVUzHyx2AWjN1qn1KKZNV1mIFKECab1CYczcEiQSC3Dte/GugApITl/pf62f/SXfdFKRcQCKEhleRf8b8SUY2Y+EjaNqMY26JPO72IdnpKMJ/RLwuAimiE1V2eRKFwsKlV1Tt2fLsxJcSVA04jTuWH+DqcvesntIuUO/NxSQLQKEuR2OjsmRj8PY0Qgm7/CBBfKzFba5iZO7bQVTkJ7C/uRdWvxxx6MVrBgD3sqcfu4sduw8cCHZRSf/eEuIeHx/8SSql5wDDgelLpW+c2L1z3+B/GbViAXfI0KrIUe+0EZPT7P40SZH3qX5WkwGkkXWTiJ4iBQXe9/27zxS6bRPyrI4jPPB4ZWYmLTUw1AQobi2q16Udt+HSd8wYO5tr9RzI+5yR0DLJFK8b4jt8xJ/QQGGmABpofLdz5J53TrtLZPBhd+dAtQZu12WhWy/vlzyZZPPC3OB+9uhfigX4iQhOEivUdFuIAzrznkKs+RFUswHrvz2yZO/D4pfNjFTjf21uOeHh4ePzSaJ4Jv18I8Qbwb+ALIcQTpNK5/qxIKWlqavpJNiKRH85I8b9mY0fsuI11WAkTlAThw2qsQ5dbX4e4GAuxd0EE8acdxSVuLmVyA3laIXrURxM/ft125Hziy58CqQMW0RXPEuh6FcNKBvF15jyCEZdetfk0+Xf8PTKUkQxlZOq5T5TNfv6YL7LTXTg1U7CDBXzECpLV8xljHkeWlr1T5/NDvP9Rglcm1pKTnc7frw6TnbX1XKKPIkb9vpxkrIyMUpum8Tcjrvjr5v2la1zefCqJo6K886KisJOfjt13bbHs3nqv7awNOxLBtQ1QApSG1diA0H48xOXX9Pn7NdnYGbxAJA8PD48fQSm1HjhSCPE74H5gPD9zqIqmaaSnp/9kO56NnbOj0g7ASnyNW/sVRu7BmAWDWsYxF51OWvgsED5SORCg1ealV1BV4/LWh1EKCwzGjAqibSfn+I+dj5GZjYpvICFD3D75EFa8vJ5xKy1u/eZ9dEsSHRoh/a1jt9u/TG5gnvMVxXpPuul9Nm+PRxS6Dr6gQFVsQt5xDWE7ibj2HkSb9ttyFHKLeTz5IF8703Fc+GhJFBFpzeld9+WYDv136Hy2h2UrnnjGRdfCNDaGeP9jHxeeE2zRLm3BSog2i6g1KxHfOZ6hu0jHREpQWhq6CJCevuuZa/bGe21nbahhZ2E1LEU1lqJ3OQHD70MEwnvdj1+CnV+KjR3FE+MeHh4eO4hS6kUhxAekwlbak1pT4/E/hBAa/q5X/3g7raVYhFRM+aXXVVNdKzFNSCYlxx+Rxtcra/hqRQ2jeufRv2NWi34NVpw0w4+ubZkR9ve+C2fjy0xdXMziTdkkbclLRcdyuPk8jfl+ajoUM0ypbS56jKhG7opdQ5IEJj7+HLyZjno3vn7T4qNHbYQGv7s5QKdHLoaFcyEeQV1zLuK5jwGQrmLDly66H9rsk6r0WCOrcHBoKiuioc6PklEmLJ7CCLMCM7zPjg5xC3QNAn6w7VSO7uys7YRu3HwH/PXPEE6Dq/6+1a4uvTVGjDH4ZqqN6rCMtxKraZM8hkz/NlKg7CCuW0nc+gRD70jAt98u29ldiEAWvsPvw/nrkTD5PpzAIxh3fohIz/7xzh4/K54Y9/Dw8NgJlFJ1wGU/tx8evzxk0wrc+gVIf19I777tNgqqaiRKgZWENSUOK8uauOnVxSRtyYfzy/nPRfuQ1vzrrJTiljnv80X5ajJ8ASbsfypxfx3PW8+TLbI5s+M5ZNYlEaI54Y/PZMopp/FMcTE+TePAL7/mihHDWvhRK6uQKFTzA55NsoSOejc+f87Gba5S//lzFp1iEZBuKiwnFt3c/4t7kpRMSzXsdaLJwLP8nOI7k3sTt5DQNAwMbCQoB3fdw8jgQBh44y6Nq64L7rktjedeTNC+vZ9xx/i32U6ceR7q9LNB01rcgAghOOWCMr5q/zxLEq1YXAaNX7/HbQeOa2Gn6dUksY8dQiMN0k/bciylHJS7EaXCKBWkpvGPSNUImGSlXUPAd8Aund/uRK2eD021kEhdK7VkBmKoV9Xzl44nxj08PDy2gRDiD0CtUuqVH2hzCpCplHps73nmsSewlI3Fri/sk9F1JOZeDsolaYdQB/4H4WuZj1vXBL87NszLb0cJBgQnHBmmpK6ebyNVNAFVjUnSclIhFFWJCDMq1uAoSaOV4NONy/ms6DFqVS0GBiYm5ww8h5KKBAtWRTlpdC4vrhtBsrIK5ThMXV+yTTHeRutIO60T6+UqwiKdfsa+AOR10tiwWCI0KOqmwR/uQ914MaohinbLw5v7l812N1eW3Pi1y8CzoJPehYfCT+MUuzyc+Jwl5TM5xVxAGhEiTYt3eWwBunUxuOryMOnp237i8C1C337oiYyX0mj5kGigoDYRbdHGWuHS9F8LlYTakiQ3fLMSvV+SG07vgB65GuVWsCYeYh0hgkTog4uOxHZW/6AYL3HX8LH9Nu20ThxijkUTPz2N5bYQbVO5yvEFQClEx97bbFcnq7FIkq+12SN+eOwcnhj38PDw+B5CiOOBh0kVOvsh6oAXhBAblVLv7nnPPH4qjqOYsyRO62yDzu1Sqe9WybVcmbgBC4tx1lGcx+932q6MrAE0UKn0ejJWir4NMQ5w7vgMfnd8Gj5TYBiCAjuHdq1CrCyP0LNNJr3bZRCLpoRihi+ATzewpYuhaXRMb0VMxZpTEDo0qkaEEJx5RMFm++VaR1bV1uID9mvfbps+6ELniuCtNKhaMkQWukjJgVNvDDDrHRt/SDDoMIvkovuwzs/Cbj0Yf8++m/t3PdJg8as2AN2O3lpKGJrOZX1HYxco7NVTAT967qE7Paa7E+e9CbgfPMxZR/Tln4kRaELn0kEHt2inbDbX13VciZOExWtiTPjmQc7vVoFOggWaiaaqiWk6JRI6EyDoP2S7x7aVzV3x60gQZzZfERZpjDBH75HzFJm5GLe9i1o0HdFjX0R+hxZt5thf8kzyAQBGm8dwEMfsEV88dhxPjHt4eHi05DTgK6XUxz/USCn1kRDiC+BMwBPjvwKu/Wc5i1cmkAr+dmEu+w9O43X7HWLEAZjkfsC56vTNYQ4xFWWTXE9brRMBsf1ZWT1nCMJMQ9kSbB35xQdoozshgtteBBYKbpkZDZg6/75gCJbj4jO2ntkN6CYPjTiFj0uX0S0rj2H5nVD2H3nUepQMkcF43/it2icqJIV3t+Wq6iLUKTWMHNFSjH2LJjSyReuttgXCgv1PSd2kOFXTUMlqUC4yshoZWYaekVroOfAsPx0PNNB8gsy2257lNXMPRk/vDTJBLJnJ5HVryQuH6Z2bt12ffghnkUPTfREIQOjmIHqnHV+AKd97GFyHnrEynu/9Jlrmgfhb5bdo5+ulET7GJPKJzRdmPYvToxiAGYhRKyS5Eras3TZID5xEXuB3CLH9nOYWSWxSNy4ODjVyzxbtFfntEfnjt7t/iv0OdvNToGn2B54Y/wWwXTEuhJDsfLYApZTyBL6Hh8evnaHAIzvY9gPgD3vQF4/dhFKK2YvjqOZfts+/ibL/4DSKtY5Md7/CwSGf1jRGJJYF/uwmbopdhoNNUIS4IfQgQRFCORGsNQ+i7AZ8nS5CC3VEmBkE9n0G+6VL0RbPR6kXcCrXY569JbRDuQp7g8TI1dDCAqVc3PhKpJODmZ7bQoh/S7u0bM7psaWa5nBzOMPNltU1AdY9bxErVSAF1ssBxPY1WcqndUtRG9cgBo5EhNK22lcV6UC0No+MYBmgEP6txWt25x8Xw1ogNWN/yycTWRWL4rqKYYUdOLZHdwYWtRTDSxtnEW34hH6hYsycExDfCedITEgSrk1JjMRDScL3hrY+F5lgTsWdfOhbS7/gIRwVPG1L7HirtqhABXQIgO4go98gY3PQv7ewVAhB5oUBMi8M0GZBHW2mldC+82o6FK8hs6EAwy1hQMJhnV+RbfSlR+BUhPAhyxciy+aidTgALafTVjbDIo3DzWP4wH6TXJHPSPOwrfbL6tU4s19Ey+uKPuDkzT4rpUDKHwy92RV66gPYIFP58Yv1nuDsVvMeu8APCednaCnGBwN9gOXAUlIPc3oA3YFFwOw94KOHh4fH3iaPHc+Usqm5vcceZuMayXvPWeS1ERx1hg/D3PGCKJASWkN6B1nUPDN+0NCU+BxnHEWaCFMpq2m1aiinPlKOAkYfU4c9xiJJAhSsd1fRtTyJteFRlCoBJMmlfyc4+LmUfd0H5eUI1wbloKrXbz62kopNV8RJrnIRBrT9dxgrdisLbzmRWJmfrD5VDLg7F03fuXP6PmYWCN1BSQ3dnwrqnma9zwznE/rpQzncd9IWsbfoK9ybzgChQetC9Ic+RTRna/nwkyT/nNAauJeTD5nDGb9vi+bP3SWfpFKsrK0hqRtgmUxbW8rMDWX886hD6dZ6SyjPxNqpPF7xOgLFQZFlXCZ0zJwtCyxFSKRUhwYiQ1BTJVk4V9Ktp0ZRO43qTf/m7sz5WJpgoTuRArcHQ4whqXG54lnsrx4BYyYp9SlBz/hBvw/u1559CpZQvnEq2YsM0tv1wI1vpCAq6dKYgKo56GN9yOqVWO/8CVwb5jyFf/xriOCW81JKMs5/Osf7TmuxsFS5DtaL56GI4saCSLMKX59LkCUrqZh0Gokcl3zfOEInXbtLY78txvhOokjvQELFGWSMIBFJ7DbbHrvGdsW4Uuqs774WQhwKnAgcp5R663v7jgOeBf6yB3z08PDw2NvEgB/+pd5CBjTHOHjsMZRSPHBlnGgTmD4IBAWHj9/5cue3Xl7AvGWpmPGObVL9NaExxkjF8P7l43VYqYgCpn+QTccx4CeAhkZhTRz7vatRbYFMHYRAubGt7BtHXwVPXw0ojLFbUiA65YrkChdlgTIgMi1JxJUkqvNA6jSuMGhcKsnq8+OzoEopZP1M4tZGGrLakOvrjU+kbizaHT2XZGkJyZrW5Ix+jQ3PN/Dm2AS2oaiUG+mkd6e7kcr7LedPh2TzW7d8PTTWQlYqbOWNtywsC0Djk1k9Ofviop0e62/RhGDfNm35urJ68xJZAayva9hKjH/SMJtkc2Hw6U4Wg9dV00U00S47FeoTvDKA8YwOQYFzhp+rLkzgNM/q3jEhgOXWbBkjFHWqfvNrkZmH7/DrMTa9i6amoaeNQg+0zHhTMdWmbo5L4WEmmb10zKVP0zaRBJLIshK00ECoXgBJBZqVOlLtqtQZKTd17IaNiGAOsr4U65ULIFZDZN8/8nXaKDq2Tqd30XdSDToJZFcbBqSBC07TJPTIECrnT6DsxEyUT9DY8Cl9nWvQjN0TeCCEoL+xpZhwAk+M/9zszJW9BXj0+0IcQCn1phDiMeBW4AdjLD08PDx+BawERgIP7kDbA5vbe+xBpIR4s+51HGio3bWaS4YhGNIntN39PbqYLFsrkNKhW345V8mLWBdUdNN7E94wAwcQm5KoQAjCGfi7/t9W/bVuIzCvfg8zPX3zLHO53MRbkXs40Lgc0w4hNJNATxOnrrlaIoDSCeT+8Kx4YvJLTHuullhOAYOPfIVp/ZqQMY1IdSFrFp/I8vIIbcIafz3yVTqadUSaTCJzS0EWQ7PItb+TMUbb91DcNx8HIaB9V8hstXnf4IEGpZtSbXt0F6yI3k3MXUeH4NlkGIOYVbuGbF+YbumFOzTufx66H6WJOA/PmMua2nqygwGGtd86k8fozEGsTZSCtHFnD+ZeOx05+xPuO2YkPfJz0Ao0Qrekrt2yRS6uC8kE+AOwerlkvxEXMar2Cian23SkgP2cbJRWi0yUYVe+gBbsipF2HIGMo7bpY918lyV3JpFJKPvEYb9nQ4hAXipuHg0tWIje9Xc40RuRsUb83S5FCA2t/X4QyEDFJFXrz6Lp3s4UHGuTkXyO6csHsbB+GF9X+WjMW4yMmYTL8zF9Qa46I4NBvdNhQACEAh0ISNz4Mqw2AZQpQBM4Gca3l8/jN8rOiPF+wNM/sH8VXtykh4fHb4N3gOuEEMOVUjO210gIMQw4Drhpr3n2P4quC0662MfExyyycgWHnLRnliedPDadDq1W07D+Yw7uMZnAEsG+Q15GaOmoTgfizPoPH3SPs6hTmP5pR3J0YMhW/ZVygBhCy9y87c7EDdQWVrPkn9dz7As92ScUINjvMgLySnqpxdQtKKLg4HwC+dp37CiiXzu4jYq0kQYiVsl7j8aZVXsKcoPBitpOFN39d2yhmDR9CMmGWkCwPAH/DZzIVV2nob09hzZ1SUbYFrN1Hz1ch56hfpvt06Uv+oTPiK/ZyNq6vuStkeQWp2bmzzszQM/uBratKOz+DtXWVBQ2SyM3827peObXlyCV5IrigzmkzY8X9NGEoHdePg8fczh18QQZPomSTcCWG4ATckbSK9iRssYG7rJWYbsSTcDs0gp65G+dmaZjsUZmlgAUpgl9BujogTb8oehlLlQOiY0XopwbiCMg6oBrISNzcTMyIGPr3OJKKexVj9EwNQnydCD1xMSaPI/gshKscQo0gSg8gNrEVSTagJHmEshMzaxLuQJx2Egi8w+g7K2eyIQisjiJ/4ShPLe0P5YTQGx0sQ/4ClmWi9OQTlyXPPJqNY/1yUZqAZLE8SnQMTEyRpPf9xAaqi7G9icptI9FWnHeYgYNKsJx/kPJ1HZPdUhXuTjK3i22PHadnfk2qwMOA/69nf1jgIaf7JGHh4fHz8+DwEXAe0KIq4FnlGrOWQcIIfzA74G7gArgXz+Ll/9jHHiMSV1uDQ+/vZ4Fj+nce0EvOuZvf5b7h7AdxfRv4mSkaQzuu6UKo6YJDuq/HjtzMqmYEh/KiSCMdEQgk9JTb2JW4jYsksxwPkSLbWSY/xRaGz2QTimJqj+RaLIJqEPxZaVqQzWqBtAE9XkNLD/yU4ZmBIhvnIM/9wbyRg0hb1RL/xretrBeSM2URz7RKbxWozLRCUcFEEoRrUotjNRcA8s2t+obCHdCK96P2uPuxa2Zw+GJJGNsC/AhMupw3XqiTVeDsjADV/H8A8NJNEmUSnDiXQGKeulomuCA/VJ2l1V+d4WfYlbdWhwlAZi88iUObl2M8G87jeN3ca0luMmZhGjNNQ/HmL+umB7tFnPfn/bHZ6ZuRHoGO9DNr3gxs4KypgjKdRiir0aprUNKAkHB3Y8GKFkradNeIxTe8lRB2ZtQTjmoBGCC7oJLKiuM21KmyIbFJDdNInqQiZg9Gkrb06q4jrTzR5N4pRfoQcAm2fBfCNmAi0LDdpaguRbJihtAJUlWJ0D1AFKx7Y2B4TjNISBKgGaZdNqYT3o0VaJe5lagcCj1hXGUi0CjS87DaL72BIC+hZNI2A3cte485gSmEJcmaAZf2XOYkHZTKtb/J7DSXcyE+C3ImOC04IXsa476SfY8dp2duZIvAMcKIf4jhOgphNCb/3oKIZ4ExgLP7xk3PTw8PPYeSql64FjAIpVVpV4IMU8IMVUIMReoBx4FbODY5vYee4FH3l2PKxUNUYfnPtvRNbYtuf7eGu55tJ7r76nmtVfmodzN91oYuYdAsB2YAj3vEIR/Sw5vQ9tSkVEqm03ON3wavZaEbMCJvgOyCXBxou/iJteilOL3qy7nuGv/xqEPncvh4SzwRVFOKVbtP7frX3yBRCVSejK+2EVk5HLYaYJWIskgFL0SYRa98kfeW3goh3dtjwjYGAYMapvPBUP7s77pCiKZq6nrl0FTwAciiDDbU76xFQ9eF+XlR84iETOo2/gK8QaFnQDpQOkCt4Uv+YExZBr98Ys8itcOZWh1BQHl4FcuI1UVbv2C7Z6Hqi3DnfkObu0ikrVX4kSfZf7St1m2sR0Kwdpyk2+WNW7VR9cED50wmmszpzAh7WnaL/8nzvJXW9j2BwRde+qEwoIV71i8fFwT745fTez9T0ELgwiC0DAyjwUtgAh2wsw+rIUdNIPKPElTYQL//VcSfuZUuo25GveQDChLgC3BFRhaeyJaOnGCCGHgMwei7ApA41XZigv6l7Ou6xpEUJE31mTfcT7CuREUimR+BSqrjqxIGF1p6EqjXVgRd9fgqgQKF4VOTK3Crn6f2LzjSS67hC8rnmV1rosldFxN4OKywSoh+eB+JJ84ikjjGu5M3MWV8atY4i7Z7nXYFm8ln8MiicRhovXMTvX12L3szMz434EuwNnAWYBs3q6RWovxdnMbDw8Pj189SqmZQoh+wNXAOFKhet+yHngDuEspVfFz+CelpKmp6SfZiEQiP9mPvW0jL11SXZ9E1wX5ae7mMdhZP5atrEO6qbjzr78pZWTBfwgMvI1IJIJSQZIZPpSTBu4M/PVr0YxUJpFWtGH/5PHMtT/FL+uwETSiUaOR47fzAAAgAElEQVTKCFltsWNpRGMKlE2k6lLw98a46TJaJQStV+cR840mMuZNEBrCDWFv5xoao5JEl6WEcfohBk1NkDtmEEUfbcTdmAVAxyldmFe0hAXJlbx+3Hi0b7Ok2HEiTRaJmAk4mOZBBPwj0XzFPHhNHbXVRehaHkqzGPu76WR2jlK/KTUW+f0DNDUlka6idpUkmCNQQUm78NXIyhLcCWdwieaweGwPMnVBW90iKtqjbeM8VEM19j2ngZI0tQsTOj4dUASNGAZJfEJhYJPut1q8l5VSFCeWpPq6oG9ajpV54DbHyrUUXzwSAakRs33MfaWJ3vHeiFH7opkdsMw2vJFsz7pkOWPqy+lrfG9ttmhDzOxDLLIMpQmEUlR2ixC4vi/OYxt5pmI0VTmdGTg8wCxtFcIOcYx7AgMjAk11o94q5tWEQiJ49uz/clzWSHrnHILtWFx3t5/rJ71KU9zPiKL1tOq7DyXrXJQSrOn/OrMbBxKOBVHKBBSOakPdiqtSi0Gj69FFJoYyyDChydHQlM7JC6ppck1obOL1jU8y36jBweFO7uah0A8vc1HKRibXo5l55NpFbHDXQ1SjlVZIk9z175Pd8T2wu+z8UmzsDDssxpsf0R4vhDiMVIxkJ1IifDUwSSn10Z5x0cPDw+NnQwKvABNIhaNkAI1Kqb37Tb0NNE0jPf2nx43+2mzcdcEgnp+8kdxMH6eOLMI0tjzg3Rk/Ru3n8uGUBpR0ObzbVMLWUoLhVLhKOFCD4V8LvgTgwzQXYKYdt7nvEemncrAay12RcyjDRgiX/f1RijKPwk0LQNkHpLnzIZhEyYX4oyGUq4PmIOqzefLJM6iTmZw6YDkDxhsIcxvFhPaH9kPCyKjC7KBtKULUpprM0nyQgsbMamyfhhb0k5mxtcBsb55GSfXzpIc08prKCOdlooVyiDVGkUmQQpKM9aSg3WGcfl82FSslWYUaoezUcT75a5zKRS5KwdC/VVOx5mXmPX0ugs/pHvovIz58AjH+bPRBv0MLFrRwH0CunYUjE5CIoMoEYV8eyozTNTPA3053mbogyf4DetCv27Yzgwb7nYSz5EUw/PgHnErUTCMNF/f/ToeSFYjT/4x+8kWUJtchSEO3ArimxfL8dHpULya34AgA3q77gteS00gqm4UNa3i5w634tK3lT4/w9dQ0nIqFRAC2dGkdlzx12EVMWdkeu1ay6BOL4jPj6Bh87H8G3EfxKY2MoqG4FbVYysUvTPKzcklPT8eOT6dOTaOsVSssmWSmnsuVl0dZsPo17JwaZEEp5eF2HJ81gagzn6DeHZ+WTzwISAuEj+GZfYioNixKzOJgMYpe7U9Ef++4VCYXw4/ICmCn2bi42Nikh7f/GXBfewKr4GVUax1MH6e0fYA8CogYEY5odSJh8dM+x7vje2B32fml2NhRdnoFTLPo9oS3h4fHbxaRqjQyATiPzcWxmQEc/0sQ4v/L5Gf7uWJcZyA1e6qWzodQGHK2LiBjV72DU/M+RtaBmAWntLBz6dmZHDF4A8bauynIrEXLGoLQTCCB0PNB+HFifppW9iCrf2/MrevhYBCiVnxbO10y151GT3MwRugQfGkBaFgCuAjXpW3R9WyquxJfTiW3LxnGplgWIJhX2o9n8/9L3piLAYhEFG++bZGWJhi5v8LI1eB7qb37/EUx9bW3kZZBRR+bPkY+F/Yc3OL8WgdPgWSUUPXLIOaRaLqKYJ83OftKjafuaSAcbuSYcQ+CvBHdzKGo15aUik5Ssmm2iy4VAiiZWkLJ9NMRCASwInY2A/uXovUclsoCAijlYtdOwE0uwMg4FTPtYETnAWD6AYUQfvxZD6VyhWtZDG2tMbRfC7e3wux/AUbP34HuI9LoY8qrtbQu+YbBKxaCm03y5VzcFe/QcOYblNyYT+ZTJ1IesJg7LMLk0BCeUwohBLVOI05z2kFbOSRXzcYs7IpIz0HJRtzEDNDbYgkFCBTQgKBQKqLVNbiyLQDSMTDxIZQkR6YWPVq4TGQ2xTkhaicdQcZHh1MxIJPHui/m1alBcjL2xSmshPpskqZDUy/oM8zHXKuEYJ3G/hszMAflkOU7aPN5+7vegb3pv2iBjpj5JzJW8zH2O+MiT34cd84LbMgqQKos8iIBSK/hPN95qXN0K3BVPX696+aiSWr+TOSLt6Nuag+6BKnQYrMZk30STVbTTxbiHj+NXVqOLoToAuQDi5RS3qJNDw+P3xqXABeQKugzA+gK7EcqTnzcD/Tz2AMopXi1cjpzmlZxfO5+DP02i8X9f4dJz4FSyMtvg3Gnp7bHVmOXTgCVxE6sQwv3RE/fWvkJIejWvy+y252oZDVaRq8t+7QgZtajzL0SnCY/oDFkgiTcbsssvCY0irVelMrVSBR99GFb+vvaoKV1RzlR9Ls+I3fjAlr1+IDoQYdR9titpKI7FdI2ebIeWPQuZ3Tejztv9LNsuUQXsH7tGi6+KIBmJZDxxejp+0NlhH3feJxeBVmo40dQ/8X7zN+nmmXuy2iJM+geOHGzD66l2PBchGDeQbQd+hmaigGS/vts4vaHLwMVB/y4yXloRjviKkqV3Eih1oH7piyifaAdA2MBBIKkFqA83ISd9KMAzRS4+1ThrvoHoAj0/ieOXMXC2i9QtVl0zr+X7OBARFoO5i0fojYsw8xui5a+JXPKjiJ86TSpBm79SzmyykBzhxEV5zGUg3Ab+sBUSVHMpPPVD/Le2Q4Vpe0BQaVtYUsXn25wbPYBTI8sZKNVxUlzNhGYdjaOYaDfMpGkfj2bVAOTfDrSyKGf24QmHVqXOaDDqX1nsGpeV2qSWVx0UA/8psOmxueJbGpPNK+cYFqURsBXHyb06tE4to8502w2LTeRmRa1TZlIxwfhBCqpUbpBccnSTtTcfC+qziU3/hFq2TpEVir0yN0gid3VCbSbCV0T3JweE8Bd4aKqFfo+HXAOupLL59+NU7kWIiY3FF/KwFA3ovYcNjXdAAjC5r4UpTdHDzc1IOpciLmpco5+gRbss9PXw2PPsFNiXAgxFngA6Ni86VDgMyFEHvAl8Fel1Gu71UMPDw+Pvc8ZpKoMD1NKNQEIIR4HzhJCZHkLNvcuMxqW8XTZpySkxYKmtTzb50qy3TrcTe+giQQiIVEfvbZZjCuVTOXO/jYVudx+TSYtWAjBlrmyE2U52I0xZEKg+aBurku4nZZKCUhKzF8YvJFl7lyyRGva6p0397Wq7mVDdQxX6hSPy8N4qhRjrUPm4UMZNtTkq5kOSkFmu7VMbh3DKV/M51VLMdZfgD+pEwJmfZzPk6F3OeGASYRUArv2JfSXGhDVVWiH5KESS1g53CCuayRxWV7+X7qP+BOcdz7cdDNf356gZOYxmDKNhpJiBnR4HjFQB6MtQqShmmf0df8AGmUtd8cuw8UhLLOZvOIw/mYUojc/FBLzijnsT1cz9clrkW4Rwy+djZJlCGkDOm79LBbWVNLl/x4EJajvupLQ7ZKFczeSl+ejQ/d9EduJjd9UKrnzhiTxuOLSq/30GdCy6NHziUdxyi5E0xK4tsmm9kch6zIRDTq4OtRnc4SdoH2rNdxV1hZX+RjZpgs+PSVxsow0Hu90NfZrD1P31kDsyEMI31zk/I+4o0uA6XYuIpAglFtHK2M0x6n98KvrQHNoFY7xz9GfEOz4KACxeC6PPBukLpkFQnHNBffRK17PMv93Ms4oG1NLVfk0NRdputiAz9DJDvtZFl/Im68NIFiqOONPc7CrK8hIC1Lu1JBzW/rmqgWxO+OkP5DKvGJPsYnflcBWDiVtG7GuW4fdvOhYKsn6eCVDs7rRmPwIRWp7xJ6Gan46wPDRiKGH4LtpMvLU/dFPug490GW7nwuPvcsOi3EhxChgIjCPVL7xG7/dp5SqFEKsBk4FPDHu4eHxa6c7cPO3QryZfwHnAt2AmT+LV79h7DJJ02c2/k464f22/mlqdGObRTBAJLqMYOmNMD4Xxmbiu3k9Ytjozfu1UE+MVkfi1H2KnjEMLePH82B/n7X+23HDpwFhcHSyB4T4/Iskd/wzQsAvuOOGDLp3Nelj7AuAshLIuy5GLZ/D+6NO56Uvj0ZIwXB/jFaFSQ448Et6r6zils8OpXTcpdw/2mJhQwkyaQACsybMcDdOQKWxAmhSGh+8OY61JW24fvw9oCT4LZQGKt+AVD0YVk08HmPKAQyydGq7BPG/9QyBvv+iYuGZSAJIJ0D9N50xDzgVACfmY+G/niCyIUmP06vJ77iE5W0sHGwsEigBrdNN1uY0kBcNIYCyzFKK7RoOP/cKZHcNNBMpHbT1AYQEPWsw/nfXYiRSM+k5y3pw619qmFuWjlKSGy6eS4/hxWxsaKLRV8sdzoMklcVfgxfx9IOt2bghDAgeuKOBBx++Bt23D2ba+Zvj5Be781EnTCIw6WBUMM7QS1dg181Bv/8yGoXgH6PmEK4rpkv+Jg4e/jkHmn9nYNqAra6nUooV74XJsUYABsrqRWljBrOjy8mf0p1kaSf6G2FGz+5HNKgRuOkfKPF3QODLPX+znXUVLlE7k4QVIGAkaXyjiNMWzKImdwOLe7/KtLrTKe74DYNGPM/bS/enU76k39BTeWb6Gtq3CnHCPh24PbacpPATMQyuuPUKKhdMxV04GaNLCf+In0VHVYAQAuVCWYkkt1DgTHMQSfBh0GltFhfO/4Yh+bXMJYs0I0j/jI5IJQmbQ4lYXwACv9F58xgKXUe//Ykdf/+vlnwwyaa4m8ahRxmb7XjsGXZmZvx6YD4wFMjmO2K8mRmkZpM8PDw8fu2ESYWofJdN39nnsRtRlmLDH6PIJhA+KLg2uJUgPyi7H5/UzGNpbANjW+9Dkb0GWzlgKsgJIu56EK37FjEuhMDX7o/42v1xl/yRyqY8/AXcNxextCc5rdYgtCgPPvIQlhXCshSPPR3l3lu3FPZRUyaimsvLT/+qD0nbT3FDGBkrpAaNdzd0oPO5J6OdmiAx4TW6TH6I9sEEU054j6bsRvp+PpyMppT47Q7MAmylMa+kG7Yw8Qe6sH7kFdwwrQLrRYPLDvqQpYkwvDaObgk/BUqCIUi2Oh/343P43HcEQ2JhHCGJHJyBdlRKjK9+2aJqNijHz7x7chl60cW0XuGDURn48KMLnftPHs7F5idsWFaBqUnm9JnPG23akW44XEQtuSRBC2J2Pg0jNBItUER+fxP3TQuFgkzFzJJc3GYB9/mUOt6Mf8386kZaRQ0uWXAOhhI8evZblDEaXeuBUBpGoArlrsGJb0Lz9cbwjwAgKELUnfom9uhP6dSqPYV6mKQ1j2fuuJF3atohEYjKthTkVVDpkzy26kUqX1F0yS7kb3/IJztDZ8YHDp+VHc9ZRFMXTDMIdu1Az09yCDbWQKiGfVcejOZqqIii8YVMcm9/HaEFEGLLbH37/AA+wyDo11AqQN8uQ9EWvkduo49Drh3OoZ1DOJE2WKvKuHz4K2D58PmGcNsRhWhpQdAkfi1IUsVx3DBldiaulIDCrQ/w0BlvcMNj40lD55GaLNZekiArR3DDWQbWFwlcFAvalBMTOkf7NnLwqhCPdVdctvrfdA+25b4uF9BWy8OWlaT59tul938yobjhigTxOHwxxSUcFow4aM8U2fJIsTOjOwS4QSklt3OHVApse0m1h4eHx6+P79db//a1N0W0m3GjChkTIFN1dpJrXeivs2aNpHNnjXDY5J5u525uLxNrsSufBXwIswCt71iIxHb6uLXlkveetAilCY4810eguXCMwCCY0EkEo4hBs8iqCmIt7UBBeiWNkQ7ouqAg73vhFP4grjKYbV9Azzo/1WEHn5CIVJFIZNIH2SYyLYOvi+4nEA3jj4YY9OW+rDlrOoQshOaCBEeXRMwkcQRinzk836qQ83Nu46Ev51OpZYIF9844kk4DZ5INJL/jhkLQ4PhpV5tLrR6jIWjRsX/qpiESk7hKbX4HK11RPsxAmA7nxltRkz6S7r6DKS8JMkDry7Su36DSm0jLrOeke/9Chw1FzD30cw478zGEloWZdRxCpDLB5A7thHO3hbvexbd/gB5XLWR1RR5KafQf6PJQeTVJoXP+jMF0rs5GUxoXPnoSV/7pKYIofEkfx53/AA4pYSKlRZW7jnStNRcGruTpxMME0kKcaadhRNfiBHPJNQWmpmNJiaFLLKEjkax94gzcaJh5FXGeeHwlV17RnXVLJRWOwRumxj7AoL+kkd3LJPyWSo07UJKznl6VvRCGjZY9E7uqHHNBR+TU99GOOBkx4jDSgjr3XFLMmnKNbu2DFLbqjzr6BNANhJZ6TwgnAUl/c3iUxPr4CuihQ67OXDeEz7cv2Rl55JsGK4RLDHCFwAha9PhkBMvqQqA0LD2J5QZorJOsCmk498xk4leVLCqo5MB2q1l7wwRsLA4zTF666t+sFptYtuZWOjVNxxfujSjeb5e+reJxsJujbhwbKiu+/1XosbvZGTGus/Vn/vu0JlUgw8PDw+O3wJFCiO9OMIRICfKThBADvtdWKaXu33uu/bbQswTh4QbRGQ5aCMS+BuecEyeRgEAAHn88SEbGFlWhBToR6PY0smIe3P0AsukQ1J9ug4HDd+q4T16XoGK9QjPASkpOviSCtCVu7UoGB65jU8XN+GvTsG9+mBrb5M8+i4m9llC3oi/V7wo+DiUZooNyFZmnHM2sjmnMXDAIK24xUrn0+MO/WDPxRGK1eYw+5pHUQSUIv0SLKwwUHdOKuLjr+QTHvcvUmgo2aa34KEsj6Y+A9JPh6DgrC6kdPIeMYABdE6mQHX+CePcVmEd9xKrPR1DYkE6Bo7FEM5ja7iK6lgJoZFt+DioO8PoHjTz2ch1+pbi0fQDD9pE2/k4IKJSCoFxB36YyKq1jueKe9TiOQDeG4J71HEPmdKNDSRF+28fAj0ahDp+Ir91+m4X4t4huJmtLBfoslztOrmTejKnkDelCh0MP5aln3sR2JEqkSpQoFFm+MEZaEuuMlxmdUYbhq2Zjo6JD3gFMlB9THl2JQGN86Hb+FvHRWLsQI2CzwA7RLmpx7AoDraAVJelFjC3sRZn5JR8672zxx3EQH70Hq+9g/8v/w5wpDuuEIO8wH8PH+Hnt8wqq7bbkGGWgQPXtQSD/M7S0CoKHTkTVtUHd+SAk4sgvP0Z7ejKiXWey0kxGDtySfUSYqUJQH71o8eFLNkUde3L2qe0IaOshYYGTROWEeaWkN+/UdGLUxgKsvAb2378/B3S+jUfsjtRmx7DDSXrP6g9uKsVmRyXZYFpICW06BsnOHY3q/DCDnflUT+mNa/nQEWi2SV5JG+q6l5ITmQ1IZHwlbsOX6PXtYPIHMGQ4ol/LrDvbIitbcOhRBh9McsjNFxx0uDcrvqfZmRFeChxAKt3XthhLKozFw8PD47fA+Oa/73PhNrYpwBPju4gQgoLrA7h1Cj1N8PUcSTKZmqETApYulQwduvVMtGa2Rk14BhbOBimR914Fz03fqeM21UuUEri2on71YhJTr2XO2kE88dbVaJrLNdc/RvaG1tTbSTQE0vIzuMTgw4SOAsRLFvUGoBSJb1ZTUxjEUT7AxmrMZPXL4znyLzeT26oKgYGo7YYe6MGiXB/HlAAIVi8SnPn629wztDMzr72Zef5smt45El9lOp1Wt6fN3D7UfDSKW4+ZzhWjMiie25nCBXnorS2m9FpE/e8n0n9MGYv+eDaLMJDKxUgEULoC4WL4JbltDZ65vwHHAQfB++unc0QryZpNXenZZxG6zyaoFKgoazbG0YTAlYoAiqJaH7GNeSgnJRd0IWnyxfC5q1qM5yd3Jln/lQvSpVfVYkZUXAcvpME3K7ig+ADmf/kZoT53Ysw7DbR02t7Qj2cL/0b1U2dTWljGp8Ny0dKhhymospfjYiMQLI+/ysDkMmyl+EukJ3XSRALHt17G4SWvc/KBD0GoPZ2XvkiebdLjzK+Y9lhv8stWct6Su2FhI+0e/i+3vhSistzCLXqbxbFNuBxBU/1QelUZxITO1zlVnPKHNHy1j4AwMKqHgpgM/D977x1eR3Wub99ryq7qvVdLttyLXDHuNr13QwgECJBAaElISEgICTVAAhxCJ6F3TMcYA27g3otcJVuS1fvu09b3x1YIKfwCJ+GcnI99X5cvae9ZM3tpZtbys9951/NCfUY+T6/fRkFrH6dXFeENrwIlGdV9BEIIgv2Sd542sU1o2gcbN17F7KkbsY09OMYyFteP5oUtc7l9xUQKQl5sl0ln70EK0gdQ5x8iXRE4QM/IOjJ21KBJGD77TaJZYcIT69mWMpZZfIsJvqup6viUR/Kex4fA1A1UW2NEdTanFQ0ltWnjXy5IxIU8eRaHixUO1aUw6uJHSR0a/8Lq9B7AXHs3uJKQI6+EQU9ts38FewcepeaMVE676Gekuv9+cXOCfz9fRYw/DtwnhFgKvDn4nhRC+IDbgakkcsYTJEjw/w9m//MmCf6dCCHQMuLR76qq+E9PPEDIkCFf8KxdUfnsObxQ/nGbL6DP6cK47HG47zsIb5QZsx4HafHqioUYVjzS+bvHO/jxibvQmEw8qxeEpwNNNTFtQboATACBGdAYfcXDNGz5LVFTIhCEmkt48I7f8JNbv49bFbiqLkd1jaYo2IU22O8hAT8yAsv6YU+1ByugwuwV0JxF7taxqKYLAN+nw/lYf4TqT3+ClC5kwMXMX/yMKa/Aruxn2ZDdhdOTjmOrHJqwndaRe8jsdTMwo57L/TdSWqCxZ28I1bbwxKp4r7MY8RyEdmVx3AVPEu3Pxlc1g4n9O0gRqTi6m/zUCJf1tPPrhmIW+QxqbIWlx3/A4bXHMmnPbI46ymTMdP2zc9q2w8GKAaiszzuR3RVbuWDTW7z/7AAvv69hRxbgZHkZNeVaSCtBK32NbNLIOvY+Pqm/HMsT/8LVs6OPis5ZNE5cje02KVCHAitoddz0ODpRVBAOi4qyWTq0nyeMAZTdFyHNFiolZPW8xob5YfZELJ5Ju5OyvgzGH/dbMuueYOCWibQdG8DBIGvkBk6/835cMRVLODhbBRvn+smN3E/Wxl+QJl5BmVCD6W3gxxWXMNAXxBXYjRJcynk1y0DaKN2VuIp+hOYuQ1HABoS08G57Fqf1PUTlZFwzf0b9un4sW6Mg5EWXCnrMjdXo4JUKLkPS7xaEFIU1P/o9lavHcUxdE3vKMnn19UtRP4nS+8MHKKkcQWkkiNZ7C+cmSW647BFSDhXSP6SJu5Rl+NoVhC8DoVWjph6B2uiisdLNHQ+PBAke9T5uk+NwCw/Gxz9FBg+DUDEtNyz4NdIOUtd7G2vTVGzRy97o1Zzvev4zr/IEXx9fpQLng0KII4BHgbuJz0vPA5nEU1j+KKV89mvpZYIECRL8DyKlXP6/3YdvMpmZCg8/7GHHDodRoxQyM/+xGFCuuhUnFIDQAMr3fv2Fx7MNSeMyC1eKoGCyihCCRnsv+rjtVH3rOXTHhVPQCgFBRlIXHb2lICRabhuLp/wXk/QtmOYldOJQ3zGF6TXvEMg9kbxRPswHokjDZGfpAfzNpdTMfZfNi4/57LMNW6OlJ4uK7EwUfQQAGcd2Yzwbjzj2+8PEXDEak/bS3VaIPWg6OFbfi3/4SqIbFiAAV0kT2Q3TcaT4SxqwDW7Dz9iUs+m76x66t6aSEvSyq7iT5tRWgo5BjSteNejW63J545bFJL27iM3Z1xOSbrCh59Awll77CNIWZLjqWLD9JJ7sk3QeeRzZd97DW+8WYAuF930G7+smbrdF+XOn0mS6eHKzwY/+SyW/LH59xpyusfaPJoZls6W6k/a841l6SR7aWz04sRwsy8O2nmmIjDLEMd+nc+BaBG7SC65jVM5VNMQeo/zTscy59xJUoTCy5GSU3/yefGM9Stq3yTN34ouALQ1M6SB9MUxNo9sFWa2dcft2B161q9mvqJSuXEBaexFZQCMzaCs7F6v7uzjSAQGO2o1HUXCI1y7SLIWnnnTo7hQI55fcO/zHZJxiEB1STOgtHRBYtk1vJATE4sewd2Is+i7OeQ9w6s/Xs/61aVRG1lPrfh0MBxo2ohx7Lcf1/ITl2rm8XtHIyQ3F2C6Tw/MWU5X3FNPuaWVzwUHWn70IR40ys/9tSspUrn/1KayYHyvsZc+zJ+LsPBnzxnE4fhvdBXfnL6Fey6AytR+34gA20u5Hy5rFyiRoH7oP/7GVSAGmV0VI6HTaKVJLkc5gVrF0kPafM4wlPbrAEoAQRGQEGxMNN7K9CfueH4LjoF7zW0RB2Wf3eOtai/oVUYbM9pJbm0hp+e/wlc6alPI8IcSrwHnAMOIhibXAU1LKV7+G/iVIkCBBgm8gubkKublfHJEzLJsPmh1c37mfOSPyEKEvLoy64hdROnfEF+mNOt9FzZkuStVhTH3wW5StmgAIkiadhP6tj7n4Bx7+9P52ulwtFJ7+Cs09aXxyrp9Ln4Lpjk6tpTMw9WxGnRWPWMfmqFx7cifBjUei75jCmae9jdDBMSSGbtKeu4e8H2/GE/Ujlscjv2/Xvo47KYXs9kx2ljZDaQs9uoFugKWoeKTNkbEIE4bfya+rdpHSO5pj3puNsDRsIXCkxAKKztBwpwkglaOzf4Xz1NGwezsLMlTeP76UAdXFCfvXEhvWjDpS55TJB1CGp5Bbt4aXNx6PEJIRJfvp2FQAUqU7OoLgkBKSP95Ox3aNta+pPJGUizOkEXHQRdbYNszk2GdVNxUFBnok+WVx68BRk1eTkxfh8lUqQVcMFJU+lwfv3A/wNZ6OG41pM1txX/gK7f2XYlkNgKA3eA+TU24lu6GB2Joc1Fj8yYS2N5Ws0A4cxQa7n9Sc23jMq7BsYBNPK0/iuKBKGUahVkNM9YBpAhJ/bwihKeR0ZQ5aHwlUQAqF9KWZhI9rwUjXKXFMXJf/hj2P/gAjSWPU+V6WPmcRs9zoQhbFpngAACAASURBVLC+fwJz81ejKA4XjVnNUzsmkaVHODFjB9IGISQ0W0jLYn/oepKHhpk9PIj12mm8Y8wjyVJJ1h0q736Q8ooDPD37v3ioZD73fzSfMfsqce69gFVGGr5oKpMYRuauKl760cNEjgyDaZC2ZIBgLBlFtcnrPsyQ91roOSmH10/MJ6ZAYVoyU+1k3HZ/XI1JAU6Ud1vu4rkSH6YCaeeU4rODKAgylWzylAIAXEfehPnpraAnoY+NV+4UajIjfOdxQD6PhaBGX4Am4tfCvuMHsH0dILFv+z7a/e8A0F9vs/bmKFFp07ksytyHfCQXJyLpX5Wv/BVGSrmIuN94ggQJEiRI8L/CrxftYGN9N0LA/vYA503OJ9gdXxyY9DeR9K6dNnY0/nvrBpuaMyFVyWDovqnYRjzObNe70YecS3ogwLdPVfiw+SAiohP7wI1fS2GEo6AAHsDXYn927GhMIaKlIC2wTIWwdgLn3PYD+noz2HYwkzU7J3DOlHu5aM2fODMWQ7jdjEgu5sPy7RwqbwSXgZ4a4fzudp7qG42hSnozeygrtnCNzeDXkZV0Pz6WLsMFCBQkN+d1YWcE+On4IoYTX0S5r7OTTZ4MJnrTKIl1c8yaVgbSk/B6JVZtI6Rr7PcU8tyaaZSOS+b6E67E5eqi+9BQ2rZPQ2JiJAcJhQfYXPUzVpZcjfWcTnX2fOpOeR33xAZuGJbLzpa97CroZaAxj6qxKkPGxM+1Vfci1paHSXdszi44iqf6h+Ok9aEPr0N6DCqz93PGgEr1hGnAaJAG0f1DiO4YRUptEEaD8cBuPuyYxzHCQXUZmKO3stsryTNA2VFInz6Aa2qM49OncRST6JE95IlcFKHgqX4Yu+sjnI/f4Yy6OnalF7C38iBDt9WQh8QSErcwSO/eTNnl/RhPVyMUycvTdtB1xGXgwNSdEr+4A+GoSEVhZCl4U86lr30J1Q98j1/0ZNA3awk/23QFgXAml058mWnRnaw5fB3Gexuprl0DL56BarooJZ7B5JgqB/adz9jUF6irq2DF0uNZ2FAMUsGOutANEMSfdpQ2lCJDHhZ/cjb3u6H/229QvnQOo9oczlr3CwIVfhoPSmKKiiMcWvQggZQoSS4dRcxERg4QqO+jITwOvAdxcrvplX3ctXYu3ZU26zL3cXn4O4xUx3FZ9hU8vuB41gR3McfcwYVUAZCdvpAL5BmYRPGIvyxSlUYUIePjC+MvXh7hDhnPELPjmWLhDichxv8bJJ4nfAOora39Su03bNjwNfUkQYIE/y4cxyHwBVUNvyzB4BdHk//Tj7G/qQNpGkhgZ0MbG/s8rH06bm8440KdkXP/kstceFSMhqVxr7bCo90EAvHfPWeYdD0Uf0RvzrW48gc7IaLg6ShG0eZRmDqCUT3JDDM9DLiCaMQFVlql67NjKBqMmx5my1oHty9G1dhlWE4QWdjDir3DsF0qKjbPTT2HI6Mt9IXbuECdR/86g0BykIYZa1FR0Zcu4JrlR+M4Gq1SoaFmC/4rHqBPOoTHfEps03gs080et4FXiXJaYyryphi7T+uCmYe5/r1t2GOO5fnRR3P3W78no3IowUgbUgni+Hw4McFNH51GxHKxKyjx+o7i2Lw36B+yn/033IbWUkBozHZKxc9pvn8mZr0JmHj7dfy2oED34d71NhsfuxEpFZJzujn9yjTCYROA2NYlOKbC+9oQFnsVykrWU1O5m+2mit/0c7pbkF9ZSEyfjxEIYDVfy8FbUpC2RvtbCt7f9fN44xUETT9NniBDhtZRU9RA0o/uoCG1l/7GakLaAC1r6hi44Bmu9lyNL7CPPvNTtNQjCLU8T6R3FUkj0nHf08SC+Yu5d3QxLxU2km5LysdtY1bbfnKeU1C1dMxwEhClTXcjB+2am5JNbpv5c+qeSqUsvJ+crFG0Hb6GnuerCLdmIRAo7x+NL6eViN/k8U3Hs6bn26h9ScjNZax46STOUaKoLgN78F5xkITSunjvrHI8z5ThUm109wDFtkqrK8BAioF/IH6vNhQd5pRf/oCZETdCSJ6auomDJywiGq7gV3Ou5swXT8G7XsO16A3C8z9ElRI7aBP0eXBlT6Gnu4o3/jgeKQUTXobtv76DaXVNsPRhIlflsSSSTMxW+ehwK5v4HV16O05vCotiW7FCbs4uOPKvxphJACklRveDOJcJ6BiD9oqFdvntn1VU9VZJPFVRAvUhfFUKngqHQOC/7/76f3lO+lf4SmJcCOEn7i5QRTxX/G/PuJRSXvR3OyZIkCBBgn8riqKQnJz8zxv+E/4TjiGNGP69qxGZBYiykV9qn1OnVfP4sgMAaGmCla9K9HASANsWCaae7Pus7bQrkhk+P4SumbjSNmMdfAbFP4z8Y64md7oGSH74s3Xsai+l2LBQw/G/x+qvxKWZ6JaLA1LSp9o0qAYFAz/j441FzGudyPDVM5hcfYCa++5H9w8QxMHfL3g7XSOQEcE8rIKjkJ0e5BVxHULVSfrVzQxrOgVTtVDDGfQmhyhccgaaoxIvMA876mt4tnkM3WN3kD6ujfr6jcR2DaMt4md6MI2ikB9NKnQ/D0bVLmxVJYILr2rQe9cTVI4ai9q8H/dr12EP9GFl6gQdL7ZQsbAZiDZAZi/hFBexsn2ExuxHCBdD9bkcHpGF2CdRhGDm6SoXHHk6ZamCT59ux4qlYhoewkaMlTv3cfKsuF2eJ5rGYa2JZ5NGYAqVnqCHwnAHx2UfZrpdhLvq5r+6T6I9NXicKE4UVAFanwfh1nBCMKDYuGQWhe+OQYl5SG12cKSCdGsUfzSb57/9Bo6xHFfHQyAddjd/yI1vfgvTms7Jo5Zz8cxVmEMsKk5+l96IH9UfZkA4qOo8Ump/SnL5EBylmVj3VQxzRdivaiBhbG+UfGkhomcR8BzB8rokdv9Uo1pWU2y4UBBYgCdgYHiDkNwPu3PjfUMSiQneVA2OckNdSMck/iTlw36F3BUncfdPz2Pbbz5h6r5S3FIhXbH57YLV3DTmBO57sostkQwe6M/EPyitjtk6ls51k1FNFxN1g5yIFwXB+BcWEp3zJuW2jepLJsnv4M0cza/XfESemYIWc+Py2BQtOZ1XMw/RXBFi7v4WYlPq6WqqQBpugsTASAV33Ah/WXAflyQf+3fjzDEOEWU5+GJQ7sZ1+zVoKX8d4DvqnhT6ez2kpqd8qbH7z/hPmJP+Xcf4snxpMS6EmAS8Q1yEfxGSeLnoBAkSJEiQ4EthP3gVduM2kBLlsntQJywA4nnIduB9HLMVLfV4FC37s33OmFLKrOG5vLitjrf37Gdoip+c3jTcmkbRyL+2QZQrlpF66olID0RfmQyKgx1rxUoahp5zAiAQjoEAgrpFqnBwu2J48g4jWgpBjxGUgj+WtuA+/Q1MXy5KVCf/T5PxWjbqruGYSdOJLviQPRvGsVUEaC8P0d5QDrpBwUASU4dupjsq2NqRx1FNOaiWim6pZO6q4qSBJLTBNBgdGAAsKWjMHiAgNdJ+cDeTQz6aFUlXQTuhpBBO0AcSBBajX7iblHGX40hBpifCiKE1AIjUXNQZZ+Csfwg9yeTS5A/4U2QBmd4e9pV3cnmkhlmuNo7wBLGFxiTvhbzzYjpvrAogU2FIicbcb2URCKSie5KpmjQSY5GCoxtIzeYJ+01ObNYRhSNQ516AfstliKMkaPHUi9qIwYx+AxHqwyz568Ix6eNVXGkCw5a48wT+7hWMmVbBxjVZFKWouHYNwTEdFABVYltgINkhQH3vSPRRi4B47tGi3eOJGi5AYdG2WVx0imTCE6+z+wQHX1IQDY1RopTarhnYd90FyckoN9+GN+8tjrd76JIh3K2b8GX30t08mx5PFioqJTY0RCR1uoKZHCIp4Ge9gP6kIPjCcMlztN1wPXk96dgIOoEuTTDjYg+LH3aIGXE/dSwX9qez4Kib+P6cCIGNI8Hw4AiHAjOZUIHNLi2Cjc5uzWaEFb9/+9P7cTWUIFBwWyq24oCjEHQb1PZbYEFUHIun6FSkmsrO3K1kuY9AIlEUh7rkZsY053PRx5MRSyVXP9fLb89bRF9K5C8XQkhUoTAtfVh8LEZ3Iu1+VN8khNAQ6ucFtkBoOf9wDCtaohbav8JXiYzfQ3yeOBP4SErZ8/V0KUGCBAkSfFOQjo2s3wZ2PMVEbl4Kg2Lc6n8ds+dRkCZ28AO8pc/91b5ZqoU0IzhSsm9MHf3ZUa6ZPpHSWQadRpRs12C5+jtug3AYNI3urgzufeYWBoLpnHP+AWafGW9y5Xd9PH3HAAVRnar5hzkw4gVSaurI7Mhk+5Za3qzswE4JEtZjaMEkVEehwFbIAIRU8L1xPL9dejSBnkxQI7RXN0NuB5P2lzHyUCHa5hE0HrGenvmr2DljDSOXT0XXBNbRnxJ7YxbOoNCSKSahSSE2j/0jAyUtZG0cSU7Ih46g2BE09KewZeRekg2d0WhMP/kOkj7u4dH1t9M+ZSJFZ92Ny+X+7BwpRZMQGx+DiMIMbI6YVcOLnl/yjpOJbSu81VPED/L3UKTkUeQ6ivrGPoxBc42mLvuvzvc+z3jqJ2zHE0gilt9Gwf4yeno30F1+J8/mB0n6ZSnfDdbybksTo90O0weaoNOEhnqkeyek/KUok5YkmPBHH0a3xHXtabx8UyXP5l2OpfWR15RMp6VSj6BYk2Se5uGRd4LowuawMBneYyMOHoLyeB59eV4rG/eMxLI1clJ60C67nWzdxVVNd2JPmIBLxD0y5dxxDPS2YBNjd/cAafc+RFFGDrkAJeUAbH4pQiY2fzbNdHQbQ3V48txXca2pxQ76wTdAzfDtpO/cywbHoC05DIZKssfNnJNNRsyLMmUbbF+WxFThcFCPIMduxwquQ60Q6AXNOAcr0W2N7/qmkLT8fLzK9UivhwfzHL6VoTJtTg61SaVsvjOGZWs0e6N0FreQGvVSefyDIB0GNvTx1OR3CEXfY77rfKak5rH62vtIas3jHNd2PgpMZuqBYtx2XOoVtWfzo0fP4+fXPIpUJKfsb2fszPMwFC+zSsZgDryH2XM/IFA84/Dk/QYneghkGggTPf0cVN/Ef2W4J/gCvooYnwDcKqV85evqTIIECRIk+GYhFBVRMwX2rQHpoEw+4bNtjlEP0gAk0upAShsh4lFDufxDuOBMznN72X/d7XQnebnoomLIamLh7j/iIDkt+wguKTwGxo2HNasRUYvFT59NV18eUio89cRIcqotRozVeLmlg3F9FUhhEPgog6XDUultr2Z6WiuNE7cSMlNxBf1Mf+RS/D1ptA7bRyynE9Geg0DgCmqEoxlIW0VRYrh70oglB6loy0a3NbA1yrcNZ938T9g2fxX7pq/lx8ve47RSD2svqaPj6W/hl4KOS/6EUthGZZ2gK6ZQEu5DUyxwdECSO7GdXlthbXkrs49/gUBZPdEp5eSdVUdpWSnq54q0SEfS1FSJNu5FYg2dfPJEKc7bJs74C+CseKVKRYBbSWeY9yacwzBvhJfDGwRBzeI7Z/8l1QfbZsVHOzHdCqarj6NXjycjqvG20Kj7+W4aU/ajyBjepA/5tu8CNtVv48CeNIbGDoPmATXuPuNISVu4nwyPH4+m484BVnzAlrEnEFO9IKEfB58i2YMkWl7PsO9OIJj6CtanlSA9DKgaTkTgyb0BJbOMc0YESfG/S3dfGvNeeoRNx2ZSuDNA3u9uwfXsu4T2GJh7HPZYp7C84iTCHpsNQ1txHl3OT4+qYKqrHnv7G6hVsxnIPplmAcUS6hSb4IXPYI/Zhli6gEhmLySH8OUc4KblvyOtJcjKgRu4q/B2knL9jL/yUxZ7PuLdQzbnL2znyo23IEJ+ZNjNmrHriPqr8YcakFoUB4FEIN40cH2vhweO+TU7gkOoyGomJy2IXvRdAikOedduwmoqo3p6KXrFNNQdH+CIQ+CRrKlOIpihIrH4oPM1jn//VObl7KI0dwOZEYOsLJNDNS7sQ4UojoKFIDnk5c5X15HceYDyth749ACRu19GEQIjvAJkfHFmOLaRVmsHqS2/xGMHARUn0gBpX99c8E3mq4jxAaD76+pIggQJEiT4ZqJecidq2z5Eeg4it+yz9/XUU7FDK8CJoKWd8ZkQB+B3tyGtKKnC4Hf3/oTQ6jp8fj8Xvf06sZ4qqDzIa3zCxXkzsX4wF6UmFaVFkJ48F/UtBcsE24Hf3mjwq98rtPb2M2zwSbuDgxLxYkiVrcFMvu2r5z4rmcLtw/D1JSOkQu7uKp4vP8hl4SCemIvYtK1UrJqOKR1acIhoFmpvKn0ZLWRY5aCohCbsIZ7NCTm5bTT23EPPdZVUHLmS6KyP+WRsHWmpvfhRmJ0+j2+99Cb7yutZduGTuFZPpSRjMRfOGcrerHqS9Z20pnRzCBXhUjF/WEFxylCUWBjhjovojx8xqF+hImUSKUlutJgAXKRvGEPJBS8inEouzzuJse4qui8JE2kKMgLBEKHT53FRPdRgY6SRYieb5LMvYObBAJ+ccC2qdJEW8sUdQZD4t4yFsQeQSJp7Xbyz9kNMW/J+ynE8IdeQWTMPM7saR0p+tPZV6nrbcKsaDx25kBxPMl0LjmbbKa3wUgwsDf+4VxgZVBC2zZi8PxFZmsnpI7J4YXUF9KTRvnEmz9dEOW/Pw7gn3Y43qYbTZ9cQlWHumvwG5mA606UvltC4up6Mm9IQjkJa2jVElRBmxEdqm0ZbURvvrfiEifIJsGJY7Ts5dn4NjxysZkWbQ/qpjyDnrUITYAXjfy+6hTWQxapRmRxnmRzxbYfpo99DG30Zp+1bSdQyoKmAxe01HBf2oVo6tmIhd1ZTn5fOaPNjtPxdxBqHYGoGuyrakbl+pgfCjHUdpvfAaCJluxCdL9M5qZekCTZM2EDs7R+jfF8i5RyEexr84sckF7aiCBXbBP91t7A85EOzjyRv4S8IFe9hQtVxVJU+StOQfXgevgrHUkkev5nqDU1YHIvNftQd6z4bUlrS0RjRrUSE4GGzCOvgQ7iyJRd3gM+ykaH2r3Ma+EbzVcT4a8BRwB++pr4kSJAgQYJvIELVUIZN+rv3FXc53rJXwYki1KS/2ibLy5ENmwAJagzZ28NL27tp2ZkKdgp0ZFN+4gGiO7+PNDphqIP7+JuZpTdR3+mw85NyLEdBSJPrl37MiCNcHFx/gIKDOYSS96HvKybFE2Xa0EaGZvYyNqcOpc+DXDwbKSUg6Ggo5ya3Qeb9P2bITbchHBUXDnmeAfZ7THA0FidJime9ykWeNI4J3YUvM5+DSQr5K6bh2lODbuikLj0K/8rZzPZGefzB66h1B0nJOwHPZVezsf8KIuIjIsd9RH2/hRMzKE/fAFjsRscWAjcwkKthv3sv8r3H0X/2Jn3Cx761Fs6gpWMXNlmKQAL6yJ3MSz/MxKRr0JQknAaJbHbQiW+fAHyoSG7c+RwxZR850VQeWrqYyT8u4VdznqYhlo7WkEWoMx+Jm3ljs+kyPbjVJPaE2jHJADSkohKYdws+3w4ioa0EwiOp620j5lg40uH9hn0sfUelJedHiOZDcO2DuAxBTeduJnd1YUtJV6pGuiuE2l1D+f4yYmYSzWkB9vYVIcOtGLsfxT3hJnbYm+hyDmMmuTGFgW4pNF10LBsebOQomY5rcHGsHzAlqIqBG4Mp3nqI/NmKT6DVW1w2Q8Xd9Sqdzz7IsyOrML0quWka7d3x645mU9ocwPx+MUEXrHwrB++qrdQePYKP97fhNOUT6MjgoOpQJk2iukXWkvnor3upqyij6MYf8dTIMCLgYdWcDVSZyQxpzmbXQ3cO3vggT/oFjqcSe8MoMvI6SHtmAoozaG4Z9WBsms60c2BDZCWhbhci6AfDja1YPGaN42BtG7VsYbxLYMxYj5h8IfmHkyio+g3h81+FSCooDm79pvh4ajmI2unGXf1fLFncSe+j1QB4zn2Ow7WvUzVgIkwVhvzbhn2Cz/FVxPj1wPtCiPuB3wP1Mj4j/UchhDgauJf4gvTHpJS3/812Mbj9WCAMXCCl3PS57SqwATgspTz+f6zjCRIkSJDg7xBCg78R4gDMngOr3gLTAI8HDh+krS8XxwbVUSjpTuOXOzchi5v4czS6LbCUl1JWY10uSG77Jb0HKjGTAvSU7GFVp82jOe/TdrCbxa0PMaHNhf3JVI79w8WEhUDXLawpG1AKD5N6sISQjAujTEsh46c3YVgGCA8KNpm+TlAHUzySQzQVdvNH7SATbA9nHArwm/2ns3vnOLIdhT/7NbhjbhAS2ZaPr6wPpXEZkR23UDAknW63hnAs0vtL+ChYS4nZzEBeG3/SfThAyfvT2PboJfiVILeVXU3y1tVcut5NVVouWUEfqiLYW9DJFm8EnyU4/qL7CHSqXKHcjMRhgT+bheI32DK+8LIdSTQqsA4UEh65nS5H0vrt45Bn9vB0dhhJmJzbb+acdWPIqjoTT/u9jN3s8Em1ZE9+H+JAOTLopzBHx6PcQ2doF+GgTo77PNyqhi0dHCF5dd82wn3DkFJBNhZBZT3JRYeZ5A4R7YLvZR1Nj+ols3g7hZf9jGQtit9RoT+JnNOW4nRpqO50Xjee4Y3wYjpMnWThYqgWQxVQ7ZrI8nGbMT+0wVYZQNAvJP6yMG15Xbg1i9wjj0PZ0IfTug6zfR6BxdVIGUNRZ/P7IZexZdEwph/awM/zXuaq9Pk4hhtiOrd7f8XZbetZ9sfr8MTcKKpNcmc6cthS9KiLcZ9M4h1LJUOPEazdyux1E9AtHXt/KV1vn0z/8cswN4zjursvBl83dzYMZ77pxiUVLFeMpYdPo3RRLZqp0eoyqLLF4F0ssTWbp8YcoELpQJERRFY/TmkTamMhOA59o+so3FfOtvJGyhUFvwDpNrH8GrKnCUJjwNEAB+v4S5Hb1uL88uJ4FadRk9jT+yiY8S8oxrvHkO97AxHRUKfN/1JjNiYNWp12CpQ8XEL/5zsk+EpivI/4jDYJ+B6AEH+3elZKKf/XvMsHhfQDwHygGVgvhHhTSrnrc82OIW7NWAVMBh4c/PlnrgLqgH+PR0+CBAkSJPi3I6bPR6amQygABSVQNZyz1CTW7eviomUVlIfdRLfehvvin7BvUjs9Pjjo2kWzpbM9lInzw4ep3p1KvZaCVFREWx5rCiBlciVjPnaR6ahgKHS/fgrF5z1JhWlzqL2E1JZ8BAIvkkLVxGNpKH3pRFxR8sv3kJpmMOHkdt5s7EKGfJDfgUCSpUdAgXf3TmJXYzWm38BX0E4g4qKgNw1TNwmk95FTEGGmOZ1Y45s0uG0easwiJIZyiqXzxvITCEZtpHMx5QtfwSpuBmDLkxciHZ0BJ5Wn27/DDFGAbbezq7KBpNxe0o74lIGGIUQ7Mji2eAVHH+zl6glpGCJexGW5r53iG25BPnkxrVE3m3szMRyB9705WPPWkKIq5P72jyztWIglIGNfJdXvzUUZNQq/thjbjrtzFHd3oySlISZtRBMOP02/BWPgBSQxJA6Ws4E/jD+fp1rW8rF3K8GuFKAaEINVPRVujYTxPXYzDc2pZIgsIrqka/xWihwVEW+JF0itPojpn41/6KVsjl5Pc8yFRGDgorRD4ZRek5RRXVw5Yy6vp3xMbFEfPfVDqajayrPePBxUwqaXGz89wAsrlpPS2Uck5v/sqYetJnMwswZbVdmcP4Fx/R/gWbCG8PojoDuVLsvPH947n9GmC4FA2hr9rQI5MYDemh0v4yMEPVKlrycTYcftD4Wj0rjoDFw9eXx7ZS0ZkTQsxaLSZxATxM+TkLSkhCi3FQSCmKHzpiIZ70hkSoAVN/yOxpoDdEoHHR00CP/mJoYuyWLInjAn/+puFFuhL6eXw+ftY4+9lpEjN1Oq9mPrdYgaG7lvNCgOzHwIe5UbYoMOK+uXUX26Rl+HgYNF+YideCefjds9DSVz2D8dlwEZ5JLgTwjIIJkinYeTbsM7uIA2wRfzVYTzU/w5vPCfyyRgv5SyHkAI8QJwEvB5MX4S8NRgVH+NECJNCJEvpWwVQhQBxwG3ANf+D/c9QYIECRL8ExxHsr/OISk5g/y3NkJLI5RUIqJRCpK9PL1wGs3vBcBSkKj07plM59z3sIVJVPbTHEnHlgoIODCkn1m7Q2ztGE/fvir+KKuo9LZwtiMG/3MUWBsmo81ezGnmYXavO8Qe28EBFLeBftLb8MYJYLiRCpTNWUJrVwoth5vJGh2k83ARKJLs1G6ude+ADsAGZ1BV7srtoS2jm7SqldR6wkQq6nHpCrcFP6JXHUOfrdFPDAS8FVVwgjEMR0FRwG6uhMJmbCRKRi9OqwtdkaSMHYdU3UjzMMJS6M3owz95MzlHruHm9QP4YipK+ljGO/tpkhoSKMOiaMRWhl93PeubX2DzH2yIgUuDq9PHMcI7C7fuZ1jWDXzQ+wAn3vgT9JiHgdUQumIaHvdrAPijKUTDXhwEKg4tRj1D3QsIxJYg0EkKD0HbeA21aV5WZGZglvXD+G3QUAIxFzTnk/LcscjGYkoQXItgj4AXt4wk6I6RLOMiJJzWT4E+jrTh1yOEYIo2m094M37FJPhN8FgSoWfh6e3gjBw3oSmPs+0aCAXciFcuAysesZ2zaQSbWz8iXa5lmHIvEfNcpNdHf3EL3V5BfkcG399Sg0cewSXdrXyQHqTLUOlwqThSoTF5gPL+VBzF4cgzwmxvy8KoPkRSdwORdRUEkkNsHr4PJznISevGEnHAcgRWew6aPbgGwlHxC4eX8joo8oZpOm4pwc5cmsoPUVpfhiUkOxyVrQrYHgvv0P2otqB7/QS6W7MoGFtHWukhBo7poCp8O+E1XhRDIbPVzX1Pumh0VbFh7RQePu0enP5FKBcBZi6k9SN8BmLimfDe6/ESmuVDOen7HvyV9+M4USqmfEq/exjJKd/5UuNzi7WLoAwRJUav7KPO3s947cvVDvgmBo5DIgAAIABJREFU86XFuJTygq+xH/8uCoGmz71u5q+j3l/UphBoJZ5+82Pgf87pPUGCBAm+gTRts2nablM1TcOTFX/vmTd7eXtZgNqRXq65IAtV+Xvv4kfuNli/PIa0bS6at5hpV5+OUHWIxpOj1zWH8CkSv2qjCxt9Si5/jiOVOg6Nejo9sRjEXBQoJlcX9HDzbp11dtztozGaiTZ7Cc7KOUhL50BnDltvvJPQHZfTdY7AO+63THrrbAqnF+CZ2cAOz2t4PpxJzvBttD11KT5TY7+3j5xf/A5t0jpUAWWdOg3NPkYZUaq7djIkqZIeK5/5n07AcarpXjWJK059F9udT4HTROCTI1Cigu6STgiEEalBMpL7cfs6aYmlgYA5ZRt5HA0L0H5xFxWPHksoPIQl9W4+fCyMJcpxZ7VAzIUI+ChKD7ErWeXJ0UmkuUyus46gPLSMRo/CqM4kqh67GKSHGfM/ZeCsaezftpOj5z9NUXg/TQO9ZKVcQqV7MldZSXQ7g9LBdjCbVFJOuwUih4mlqojAS0A83aXL2s8RyVeS4j6ekCJwH3gK24kxuSfGzGSNddkpVI1fy/pVU5CWhtqdSSSURDIKAokGuBDURFx8mN1LZq+bVH8P3/kJ7Nl6Fot89Zw0upx57uO43/UmhumAAlkZ43EXn4Tz8wuhcS8yM5vIhZUgO1F0ybnHvcVr7x3NiLXjGdeTikShj0n0hkvIVc8i8u2h/Nhbiad+GDO2DMflCAQeipvKmN9iEdIiLCrpoTt1gJ6kML3FbUwZvYFXHzoOf+w4UtPdXPCbW9EuWcN3Vp2FjHlJtw1GOPE7cYelkVM3jJ0YjEVyWHFoGLWF04/YRN7OodiPnkdPUogXj1rG3qM+otiR8NiFIKFr2noW9Pnoa8tl49YxnPvpGHKe/BYtcz+Gyx9i67g/UP3KrxEeScyWtEvAdNHSVEmj0CmWEhGyoLgFFCAiUMacivJftdDWhD1pNut2BtGLdEqKVwA6Hm3Elx7XFWoJEomOjgRKlIKvOjX8UxxbsvrGKO0bbHInqky92YOi/t/2Of9fSyn5mvhHV+Nvo/n/sI0Q4nigQ0q5UQgx6ws/QIjvAt8FKCkp+e/2M0GCBAm+sbTttXn5hii2CWtfNFn4gKR7wOC5t/uJGZKP14aYPNrHkbX+v9t33QoLw9AAjVXr8piy+Q3U2tMBMEyHmx9vxlWoMD7iYswcP0ePHIHx4ELC0sXoma8wqeBSLvt5NuF+jfIZkhVt+5ixfShDfVFeGVrPgsoWxi0o4QPPxzS/Pw+PrZEcTqF3ywKMEz7EGLkHs/BGRmRcwhjvTwieF6DnhId54plKckwNl6Pis3SmX38z0ewudl79e9Y7KWzQobgnnUa1GhzBAl1FkQLNcIM0SevMpt1rwOpahq2aBHqQ1ppGDh7zERNcPVyuHSRwajWfBFcwMu8g+5PiyTJgY2X3cnDuDqwPcpB9uTgGgBstkERqbzYlSxdQespL3DPchSEE7bTxtLuC64wfYrx9K8aSG5Btw3GkJLp1HccuPBvnkn7ahJuLGo7EifST3vYnHjriPHKVm3Gmn0nfsvm4/I0kd1yHXDEB/eRbKXfCzAi/wHJboUzYzPefiBACt1aBoQZQ8o7E7liDgsJpriTGeTrpDiexCYlFPKr90TFLOHrJbGRXOk0SLFWjISWIo9v053Uyd/QKntw1mkM9ATRVoS8S48TJeUjdBN1CoNCZOgal3sA+XA9mDNHXQ4Z5FbrrbYxYjOrCAU7rmUpjr4e4IgWEhie3FPGrs1Bb7sBll+ELJNOJJF56aDBj29ZBD5MXcdOd3o8+YQNzR5t8vM6HHvMhYx4C/Tb7l2gMnyP57eg3eHftMI7ecBzKoPwokwq7TTeH3BaHFJUPUBGbpjJj6ga0JfPQbI28iJfL35uF63sX8lpWNbtOXAy2iprex9z9xfDyQk5qyqPbsVEQFC4/ko6zXqCn4gA7bvspYzaWsVTVia0YXPo2eicv5HvIFqmc0d9K0kE7vrLOzsXIgG1J+RTVVvGHZ7pZuyOIlMdw7gnjOH6GQbJr7j8cx5ZjowgF5XMpy4VKHvf7b2arvYtadTRZSsa/Ol38HZ2bbbq220gHurbZdG62ya39vy1n/2/3/u9pBoo/97oIaPmSbU4HThRCHEu8gm2KEOIZKeV5n99ZSvkI8AhAbW3tf3raToIECRL8x7F9mY0Zz75AOhDodnDnCT5vCaAORrrqDzi0tTqMm+DgEjsZMaqYXZtAIhiXtQKcv8ljdUxCqs6alADl2l5WPjyG1k+PQkqFXfvKMWe5iQ64kA7Ur7AZTzkaCtm2wl0zqsg/IgMneCu5R0val8xFibtBU/TiqQwcuxQpoFo3MAcewo59SnLmHbzSXsr2/F7GAZZwUKTARMHVm07yyhnEpm9HMzXaDg8lJuIR+JUYHCfA0E0cxaE3vQ9Uh6wdQ1EtHRSdrAPlNJY0cabVRFMkjVuDvUA6e3rSuChnJi/EHgNLhZZcnEAyMrMbOrIGqyraFDQUQyyV7lePomv0FpQR+z47TToaWt4ctPPnYC4L8mc/hl5fLm+P8VOrR3jIKSToaGhSoT3Uxa6fncmwH0TIPONJvLPeJaVnD8q2EHbDSjTpoCo+rs9+iGutBvZZDj/reoYCLYsr08+Jf2bhHFR/ETLWjT/NQER+T1p6DyPHbmbrxklkZnWTd8yH7D7lHUatnEhF3Vj2DqvlwJIgGDooDpMDq3idCmwpsS2bhu4BMkUNM7TJLLPWkCqSmadPh7zBgkWKivQKtMKh1CadQIe1m9yU4RyQkCxtmoAMHWovTibj1F9gr3oJ0RbjhxVreXCkh7a1Y3nfsUhPClGuRlG7UpG6yaGp6yEpjEuFD40mrKIUVClQdQNL2mweuYvs1UFyQwMktR1DvaKQOtilPmDQPR8TiRAKaDZtPVkkyz8LWwF96WzfcAW5qRq7bBsQOL1ppG2Yib6/FBwFjytIGxKhm0h/CAFEShux9X2cb0io2M96UUBq9XYUVdJNHy+PLGRhu8ByR/CmXsJPX/+E1uhg53ZVE4nF74UN28tZOL/0H47h11vW8YeGJXhVF/eMOp8c/vLFuUItoUL9fwcrTUPy/nMmfd2SoxfqZOUr/8/2n8eTIZDx5Q5IJ/76/zpfKMaFEA7gAD4ppTH4+p+Jz//VBZzAeqBKCFEOHAbOBhb+TZs3gSsG88knA/1Sylbgp4P/GIyM//BvhXiCBAkS/KfgOA6BQOBfOkYwGPyX+/HfOcYny8LorsHAnAc8mTapfpWLT3Xx/icBxtd4GVlp8cmqPu67x0AokJvZyg3X3Mw55wr2DTsX/4FVVAzVCQ2ZiwgECAbjgvLKsc/w8t65FCZ1Mreyg49Wl2Cq8ehcf8xPeUkKbk8AKwaqFBiOwHEFsbUYvtxeehpfxq92kK9IWqZ9SO+GyUhbkOzr4uSGDNLdHWRqKnV9mYSMXlY3PMDOVRPxtxXwTsU+MjJ6qKorxQXYik1RagNlTWmc9eypaJbG9rxO3q86iEjrou66e6nsnMVWp5Uk92Fk+gDhqStxf3wkaBF6pm4jVQ1T3K3wQKwUohYAuyOQU1LOTfJqbluyATPgQZECf/phzLF9yIxeZFII9dmTkVJF6Ab9hzKoLZLsVjRK1BrmiFIaw+tJ14bhXG4Rur2PsB3l/oVLqDfyWdmTSVpaD7oh0U0LWxEo0RY6109i5dPXIW2VZP9Bpp58K8KvwvLv4hlzC6hupJPOL9vvJEyUAxwiLeLjBPXI+MVX88GXjy8mKTTPpc/Zw9Unjib1lFIMmUKPuZBkWY57VjnuWTBeSs4c6GbTtmZmR1ZR0NDMwjkxnm8T6KrCqTWFBINBruQCviPPxCPdqCGVoAbypueJ7X0EmdIIh26EVwOYAwr9x12Jt2cKKa64APRMUkhfIBkIRDFT/j/2zjs8rura2+8+ZfpIoy5Z3ZJc5d5wBYxtmuk9gdBCJ7RAqCEkJPReEjC9d2w6trGxwca9d8mWbFlW75p62v7+GAdCknsvyc39ktyrV4+eZ0Znzj57zj7ae5111vqtfMQmi+wN+/nZRQu576GtrGvPIVF4gHzD4cibJ/K5MQv1k8PwTF6NTO9F3T0BJaMD+7S3kN0p9J/0De39HOZl5HHUVxrtagofprRzeNxDCMnGUC/RzjS8eoT9uokuBN60TkZMzWZl25dULhiPimCHYrNlyWS8aTb+8SswHQuPy6DR7SNdjyCkguFrw1e4hNCpQbriks62DFKagyjeA0SE4JSgYGLxVD6Mb0KGk+ZZBz185JUMtxNE6p+ns/sobKmiKgplxTH27U0ae9NHBf/DOeaFXV+gShuDGK9ULeaS3MP/pjlg/hsJvvnUxrageqvg+se8wA+bT5QsGHyNSeM3FnmTNJQsyZ928581r/13+M8M5z8mbNp/9v5fFimlJYS4EphPcp5/Xkq5TQhx6cHtTwGfkpQ13E1S2vD8f1Z/++ijjz7+XhRFIRj876e3/DPa8HpVmlslmgrDRiukhiyCwSCnHB3klKO/+9zWzQaRcNKTHO3tj64mkAED4/i5oJXg9V2LV8n4Xj9mHT2dw0ueQHgyaOj/Sz4v3cm4ulxUW2HC8W9ROvlSEl/tQK3LIdaYwzJLpUQxyZv9KZmprxPuEATSbdAUpp/1CbtyZ5Polow8Lw9/9vPY8RW8u+xDXl82E6O2GNGaSchWUaRCpC5IfdnXNIypobi2lM60Dk6bso6KuaejhtMQKEzYF+Tzgna6NpWyYeN4jr3Wz0W5eXxZM491upu0I2pZOLABxdtJeHQ146IJUpuzGTlqOCtrqxECMjwRPlRvJ1+MJNKbl1yYBUzO2Mv0Ud9QHVBJGCoHFo6kafdElKwefFPW0rplLL2vXURdWgu119xPS6iLIa7LKRg3C88bQZ7c8iDb9XZAYAuN3Kxe+o3dTKIpjQnpjWR91kPn8pNQIlk4liCWCKGoufg99SAa0RPfYKqv4Tg9RHz9MZHYjkHPzgUszG8iLZTBSLMaf2cmoeCZDM469c+ujBQy+UtP7EWnjaf2ixVsbrmVtaaHo84TnHJSCE1R0NTvPKrBP0/3GjyKaKwa3AdNl3ESsSiMx/8kTvMEdCMpmam1RGmM2vz8nWUkTJufTjqCk3wB9PZapu0Zz4rKWvaE4gSNVOb2nkZcEVhGALllFIalQXfyuE7/vTiFjexsLiW7oo5wRza3zb0HB5BCsrGolhGjg5w8yOCFVxqRngjX/XQO6DZ5eojUee18UDyZyWIqesLFFASbhSDcaXLCmMF81vgVVmkNCw6Jc2ZLCd6GDKJp+ymu+ApaJlNUN4ilr94IQOe4Ds66ugYlOIVs3U880cli4wVs6SAQ1CHplYKjvZ2kR3IIhgO09mvh8h8XY4U9+D0qJf3cfzEWsrcD65UbCVVkccDrQWwazvb2ErYc4TBr8g+fB3padYyoDRJ6WiEY/M6z/kPmk4Ezk7//Ef8qc+MP5T80xv88YfPfJIETKeWnJA3uP/3bU3/yWgJX/BdtLAGW/A90r48++ujj/zRr4q+R+ovV2C+fT1FwACdd7sHhr3uhJk9VWfCZhVCgLK+O7cuO4cDQXYiBNQi7m3Wx3zPF/8vv7aMXHoVeeBQA27fvpjOjm49PnI9HMZmZsZ9E834mNaRT2JhJsyXYBnTogutGLAOXBK8K+8JQkYE7fQpjLtaTYQQHUT0TWTM/hcmbikAKdiBI/PEpuRSIQIRut2TDyB1o7hgHsqJkDdxN6vJpYGkoqV0Ye0qhNVkl8s2nDQIXfsBDnlwSXWlw91Xopk560Q6uyPo1k1e1ob1Xy+wPHydiXUGLbUKoh7CEPXI5lYVnUd0okU6cwzOqafIrSF3g0h0GXPMEY50l7MrbjsRh/TPn45gKvb1ZLPvgaGae+yKt1ipWby/j6WXbUGQ57mExrLQwJ8oDFMXjtGoGuG08N/6apa1F5A7oBiFRXBLNJ/GkdB/87hJH3wd2N4pwuFZr4hkjj8zuHrblRljiW42dsDlGRjlKC6Pe9ijBs59DmTTt23MrrThO20aw/Yhe6P1oIfu2DCZ46DB2d/0MSRhbeql+X+WQQ74zX/bXOjxwW4J4THLpL1yMOrhNSgfcCihJn6L0K+ziCHoWDqT/se+w851zUUkwLHIrv195CVEzKa84p/4QSgduIvjAvYxPKIzWbJbe9BtOXb6GR8q91OyNYqsWpHUiqsqTlTkB0ZUKBY0YrdnovSVEf/MrShNuIghqhKSmO5faFSbz13v5yQkmL39m8MDrF/HLC/9A5kvLkbkuBtUci2ZqKAh0JKoiaTvsa6ym94gMTcMIe/l89wAGx72U2h7iDSMw9k7BctlE3IdimkkZwU0r8jg99dc4Ndcghkyl8MyLKa8rpC63ibjfAATtUjB/2bEMWDUUYfjJ6B3KwOwAZB8cD7MdJ7EPxTcYoSQ91/a8+5E7V3B3rc4j/c5kU20F7abgibebGVuZQ3rqDwuOOOZsF7s3xYj0wqmXuX7QPv+b+d8WM95HH3300ce/KD1OExuN9yDPIPvGOxnruQS/axZdDZKGWyKYdQ6hc92knpxcnCuHqTw5x0NDncMX95Tw1Tvn4rxrUnTn7XjK9iK/fXD7l3RX2aS/kcv48GBWu+O4DIOMNY8Rd3qoqL8fTIUCxUaUV5F+wifUD2kk1CnxxCyo9IISw0q8htPegyfzmu+1nbszE3HQACsB1gibQHoXcuRmLpj+MXWWwlZSyMpuQug2ndOXENLjrF1wDAtdkryqMk62BBbw8T44EKsHr4RNQyDhwrR0IgcKqXhKxxsOk7i4FGXV9Zy2LEpn2xbeuaE/9sHEw59OC2DvWEuquYZsd5jdCYWtevLuoFATDCu+jrbYHfTYbbhdCjETFMWhx5+gAxejXUfyq5U7sRwJaIzekMKv4h+jDs9B2hAzJA++djUD24vQUWit8lB0wr1EsrLwj19PIuMcUlo7UNNHIX0R7J6FQIKhLoPf6bVIr8XVehZKVGAIwT6pIRWBlQ7yZ6fjLN6BkpqGZTUQ2XotaksHVotC2ztp7Incj0UqyhsGSgiEBYoLgiXfjy9+fY5BR2vS+/3cwwaPvZmgpfMaLLkXdygXX08zSIdtX0xm0caLsRJp+AK9XPnY6bge2YciMyhKD7CypgEHFYnCnDWTuE7q4AhsYePfXkh61UJuOX0hHyyFlOatlBXP58GS62haPAPpqDiFB0BAVqbC4VUP8H7URAApSMY7kNEVoNkbZ2talA/ehMk9ebTFU3jm+cuYFO+g6vPxDCxdhZHahrsri8WHrKNm2hoU1aLWysHekQ8JNzgqwZgHl6MQkipBqdCeUNCMNFxuG0cqeH0GnbVtpCNpr93FZS+3YjEDRXGYcMYbWJ4Eq60gQ9cNJssQKIZC+67vzqmTOEC85lIAhBbCU/YcQnElg7SRZMZiTN3fzlahYB0MmjDtHx48kVuscNfbSV33v1Kz5v8cfcZ4H3300Ucf/19Q+VMPmEAXySqVvfMNrCo3WND2hzi3rejiyCP9zJ7hJzdPgajEscA2FVS3DjVjSR+gM9p7OY4jWfyZxZaaDobNbuDwknKErbDy+hhWFMYqFURx2Cwc7km5nxtSn0RFYAPCUXDXF5AzZCumrbN652QKNzQz6Ozd6B4DJDitH5C44zkIZaJf+geUgkGEHZWD9TWJKBbDzn6d4cd9TPyLWXx9zeMINYr/nLnEj9lPq1TIFTYfpnexIhQD08W5tiAbcJCc4Ooms2gn3rYUEkX1KEIidBvbbTL3+gLO3SMIGhGcnmqU4YVkvKUw7Z12Nh6TR37WYZT3tOG4loDmgCMod/qRHW1EYqNaXoJKJj8JPAnAjDssHnnpAJHMDeSd+BE1qo5HZFDkqWXcosnk9KQQHlKKe7ZAHXcaZtvLbK1WaRNuBh78vg4CeUw1wfR1AHSo1WSW3wSAlBJXio1j7cGyNyCd3aAqzEpE+NIO4UZhphHBVRvDJ11YR2ax+t7f8+jua/D5Ilx/vU7GYJ1PnnqSRMxHKT4EAhs3B3okhhsmTFMpO+P7VR1T0wSaBrYNIR+8cUkvsZ5bGHP542RVbMIvjieetoDGQDmW1EGqxKMpyE9zUHwq6qV3c37/QeysWc2mjnQkgv3BGD2F3Xj2+Yj5YiyaUsPo6jG0DKsm27+TKZ+u49MJmRSOfIXsEz8D1WB0x9m4/AXM7H8YXRsjST17NIZIKAL8CRdOQmd8QpIXC5JwRdAtjXlrRrBdNzBNL5v2TKHj6nv48XMfcWDIYQhRSqZlsWHPaGxHA+GgBHp4e+JqfrngMCCpB5OGZGu/FmYcV8QHzyXo7la4J/wsv+1/LDu1fBwUEqYbt56g8ECIzpIWQLD/8OVkbC1FFRbZE+vpsgOE1GLsyDqQFkgDaUqksR/hKcM++nqa9wkynR3MPON01iwJsL0myglTM8hJ13E+fQf50C8hvxjlkdcQaZn/6ZzQZ4gn+ZuMcSHEZJJJjhOANP5SJvCfncDZRx999NHH/wBSWkQSCwAbv/tIhPjbHy37lXSO8FzPZnMe+eoI+muTAVADAltNrv2mhKo6iz0vdzN8sIuifJ2sYoXMIoW2WgPdjnBcWwsh7/2YdYI1r8X58BuLduFmxVIPS5/+A7/yXYGdOHhQB7woWIrGXqeC1Eu7yflSYe8Cm16pYEqFxO4SFr5yAYX7C9jmCrNya4Tz770CRTWp8QTYdXkRo80mCt+/HddVb9OeZdJ8IPnYvsFlceTYtThxP8vnnkjA0hGKinj1TJSjF5JqOOR1SF4ISTiolOFoFtLUQEhEbhMr9V6i+Qbu/B5ys1+BXS7Shq6jObWdF/p7OL0mRq4pELkVqDdcRmWkiaGhfih6Be1tP+HdlkoEklPTdpGWdTtp0ddwrCYS6ed9z9gZVKFxya+q+Sz+OiYGAhexrt9zmSyiPuxHcRT8VQXcUlzPL8Q6MvvfTHlqgpYlG1iZ30xBr4cT5F3Q1EQ4NRVUhXR9SvL6cCLYsU0orsFonulkdH9Gg/MoUsAFoYs4MzGGjJQMXI3V2PfMxhiXSv0p6dx/64VEI4Lu7jzeePM8zj7mPRKJAKb00YYkEzAQ1Dkw3Ja0fWXzzaook57x4clMesh/coULt9sgGoa8HpsD6wNAkDWPXcekO26iuaaaRzZeTWdTHqNdJsJ0GHmkIHL5OUi1iFRtMApw+2mncdv7S6nrtjBym7ljzDZSEgKzZC8X/eJ2ug2Ngp8KOst3svIYD/m169kxPIiS1Ur+1kFU1u9lwFGz0Ku3Ezh6Mj1PX0bu6mMZtG4EarImJyqCIkfDVBw0BCqQKhwiB82p4abGtPtuo8dzI8dkfcwZ+97l9rJxWHbyBkRIhcnmfvalNbB8SD7janKwcWgOROm+vQ7X+iIMx8ZGJ2oH2eQ6lqGnH4XyVQIl6iFhBGh4Np8fV3/MkhdnEh1Zy7Y7nuRQy0Dx9jK/0eTk/N+j+oZjooDwIlQfDv34+oYoLRu8mNxGXbbg2mIfv7k02e/e3l6kbSPv+BnVk1PYeIZB/pZfMGXqs98L8+rjr/ODDWchxDTgC6AbWEUyCXIxECBZ+XILsP5/oI999NFHH338k+mMPEbUWAxIEtZmMgK3/l3tlOoTyds6PqnVNSb5t8BMHatTp3mdyR+6Y1gkjRYzKR6CqgnO/00vLdefT4qsw7VRkvi4koY5U0kz4FIHHvUIusJ+vunZyvzAmww872SqXzFRQ4JtXSYagrNmvwFpNmPO7mHDN0FsM45bi1PYGUE2JsvcKwh62rOprx6EXbaHm7bMQEp4WXG4Z/w3qO1r2J0LensZEjBC3YSryhiTspFPDQ0fEoFEpHUwyUwwucfBNvIRaTE4bj5sHci7MZ3jaguxdYttF7xKVKhYigBMmgauYHxJnHhLANe8Y2kL9XDVpDU83VBMv4FXgLCI77kLGmwQHh6oG82GcA4COGAVc+eo/iit5yB/dh6GdS3y7sehrBCraSWOVcWglAp2pY6k3tnJIfrxpMaXYmQ2o6k2jqUjhWRvoJV3Y89zkQjRL2MaN86CPfc9ztg9XzHgqEpk/pN0p0RwKRkEtAqkNIjXXYy0ewAHd78H0RY+ToEdBc2NZ3wqWiiIR7hhUCXyN8/Q2fIb4tk6Hl+MaCSAqtoE/L2k5tWjaiY4Ju2WRo1iYdoaKbqBCmCAo0DHJpt+RySNPJ9fcP7VbqRpsuD4+QhxKFIqxEydl294HMdRyHQU0gTs6F/FY78Ksdd/NVWxLgAqfbeT4owixevj/iOX8m5TJ6+0F2HtLaJ99Whmpdl4TS85CQUPEllTQvDDKygRl2GXdRHuOZTQE1fTI1W2zO1gaOXVKIcEKDaXQGUprB8GUkUiEYpF9eSvyF95KLZjk1AtDi++m86BuXy9YxaH1hYkbx6jCsY9p/DQZRZmV4y0tnTi3jgxf5Qr5BJ2bVd4YGIqve7BFIhMohc9SW6WQsnX8/DzS7rRiKqCuw5cxvPO77lk3Ok8MT+GgWR5YAqn1H/IiwvHsvuMKcStuXR9dDjuNWORiqT1ng76VZbgKX8GJ74b1TeSqndV2jYYKIALCLXaPLlwA9ceN+q7Gz4hCOf6+eKWcmy3SrvZTo61nAH61L9rrvhTpJRYXfNwYtvQ0k5C9f7wQkT/DvwtXuxbSVapHEtSVaUFuEtKuVgIMQt4F7j8H9/FPvroo48+/tkY1nYgcfD1zh+2j+mwqz5CfqaH9GDSs9f1gkHPewYAgWN0Mq7wIFRB+sUeUh03hXMsmjYlOPpwH2XFyX02zjdZ/IyXtPjNnJr5M4j3kHj2Q2AyqqNgIskQDo3DtsM343lt4GaOOzyVnOklTMsayIR992JGNxLwRcByEczJ5JIXNJr3uMjN6yLsv5muV2MU9waxAb8j2PwWQJEGAAAgAElEQVTxyXhOfjdpcDs6CJNl2R5cLU8hg6cTyUwDS0UXFieKzdy9/Ria3CamP4Zbi3HGeU8zLOLGceIUVj7PNeFPeKp+GWG3QbMQPFvUjOJKcEbnEDILDPY4vdiOYLo6lVM+f52tC28mpaEcR3UIdofYf2oJ+a5UzK4FyNRIMiwlZlBlZGHLZFn1HXHJxp0zKHtawb9xLU2hCmpuu4+KY1tQKiQoAlo8nDrw52jZtwBgp44ifdwvKex8lyW7h7Bu9gKsYITUHgvL2MdL8d9SNXATKQ/3csLRuxGv7ELc/yzpLs+34yzNJqTdBTIO6Djh1UgzTnznDOzuQhR7Mxw96tvPqyNnotdvQIgvufim3/LhqxeQ7w1x7iEvEayNcsZF19HqegRfXia1O7fiT3+bSFsGPW+eCiKOA+j9e7n+D71s2xvmyHEZXHJUIVTvYm3UIeoWaAi6EWhW8gmOAiAhuy2TQJpNIt6OxMIxNd76eQbtVREyBxicee1WKtwelEQ5ypsncIGhMAFJXDgI4SSTdCXotsOW+zIpbPSxbdk4SCTPR6LNxpYxmJDJFa/vZ9GI17DSZqF0peGuWElB+eOs7e/mjSM2klGXiSUMykt20eQ4dH+Ziw+BAigIFAnOu0czJq2T1JoiBNB92AI618fQnSD3LnqT+lHj2DbVJM3bg9Gm81nNHRRIlTThsNSTQFcEHW0NpMs5CHlGcnw0Hd/55xE65wpGPXYVzbvXs63qBoRMliXqfGIz/Z4qQHH1Q3ElK2gaXXtIZnYKQJJAssRZx/TOTEamJzXFhaLA3U+B8ljyutA1TJngH4Ed/gqz9VmQcezwCrxlbyHUwD+k7X8F/hZjfDzwkJSyVQjxx5JKyetbygVCiFeAO4Hp/+A+9tFHH3308U8m6P0RnZEHkq89f16+Icncd01eecEkM0tw5z1ubn19G02dCYQDP80YTLxJYUK9DcnK9cSWW9/TtlIVwQ2Xpn2vTcuQfPKIgWMpxJThrO46m2nao3i6FiFSwtgeP72pnWw9/R0ikUxA0LpuBG987MZnt7HUbXPeMTfTr+hFpNGKK/8ShKLjS5Xszz7Aqq4I00M5mA/fyu7L7qRISsYgEdtGENkzBHnkl7gKGnCrFtkpXeiKTUFBHbW2Rm40yq+1h4j5R1G2ZTROQqfelyCkxRk151LUjgyahu5g1c03kevagT8xkh5/LBmuotjoNhzu9ND/90U47R8w56ohLFJWkJWdRmFzHpqlgwVFO4sp23c/Pb5S1r9cQES/mOjxnzA2rRkjvQsaDxb6yWshmqNRdZGBXDKRO4fdi4WXoduruGPAo0k7yjFwYg04dTtI7LoSWS4RQYWC2R8xUd9MvTQYFTE4Nuajw1vE7sR7OKpOb7aLLbPzmPh5Atzfl7wTeh6mzGLD3kxyUtoZUDiVREuA3hVTwdaJbOsk2PQUwatu+HafrPxrsKN+fCV13H2rijbnSmxDAAJvisPAyekIVaNo2EgsM40Xrg3SQS9BW2XYZU/y1VrB9j1HYEid5Z/F6Xg2iiJK2O0qpAUNBGRqNrmahWKrSRei4jCgfDvK/hcIpmcQUbvp3jaMrn1pSEfQutfhrX2TOadiOT8P7eZLB2YCXgSGAguPWMzQnYMx9B56rpzDbhPuCXgJTV7HBV9NRkMQSl2D4ksgLQUdmP3KOtpnvUzrjFm8UL6Q6oxyTuzez2GBIro+monTGaJAXsi0uJtWCRZJz7OFZKsqKSpyyFhfyh+jggO15VyW/wAoDvn9WnF1mdR/kImDoLx/DWXShUu6CEqHVAfKBmxAL2xgT7aLSnUJjXsqmFEZpXDKVRyoc1C27ccT68an7iNu5yGEJFUu4c9NuaJJd9G0+kq6D5TSHOxl0fFfEPf3fFtV9I+kDp3JhEQnz5vvUy0c1lof86g+nlSR8p9PMP8F0mon+TgNwEE60f+zxribZCEd+KN7hO+Jem4E+ork9NFHH338L8Tvno5HHwM4qEraX2w3TcmLz5k4NjQ3SV5/PU5De4KE6VDalkH1eom0HHyKZLgbEOA/Uv+LdpyWHchEL0rBWIRQEAqoKjgWKJqKO2Ei2g3ayt08c9f1KK35rE/vRtZmQSwdLJXQpzPRo24GkfQYvbXC5tSHTqOj5EsyxX76k8OC2moeW7cC07H5aPdOHi4ZyGvn/pbQuxeiGn4UR8UXE4QOZFFpNhM0dBLFGXSn9eIZvZHUYbsobu3AieSw/cFbKTPcFCk2n/Tfz9mZW1HXHYFia2TuGMiCe4+nZXI2yqhtEA3C1BUMjDiclraZJ6KVJEIdRMYNxnQnPdyflAQ4onwzg3ZMxlEcOgZsw+PYfHGHi6796dhKP+o683jplE8xerzgiwBQltqaPIk+nS+H/ARD9WDYXtZ3DsfuEKgZgDsDLfcorOd+ijz0oPSfkDjEKTF2caMZwJPxOEp2Edx3DOIiF0gQiiBr1LFwwzUIIehsdJAS0vspSKly4weXsb9FQUqN36RmUKodg7QTCBQkaXwyYSXnSBtFqNQ7DWy0tzLceyL9lXzsle9ht3ajLreQaX70k+9BqC6cqKT3iThmSxaJKoe47iFuu2ioLiNj4FKsg4ZgYXMm0k4m5Ra7bHqwSUiNtmFV9Dd9nLcxSG+mQf34dYyaMQ/hDTOovher7AaipaPZ6hxUAZEKnbkNOJm/ZJqsIly+FnaOAwSOhE9sHx8ds5AjN/bHeOTnrDzxcyIjdxIpauSBB35DMOpleGAv16zxYAMrTijALjyUxJ6rsOokI9Sb2TRjOa8M38rk1r2oERV/zMUIJCoCv4CVUjJMONT4Y+Qfr3H5jwp554Y22vYKHEdhq0tJ/vM4Cg3tPrwGmI4bFJsml80wW8ObDIhh+qFLMS98mUV4sBUoGb+WgePXoEl45Zkf8cUHIJ1XOS/lVsZXXkJzvxPwpjWRWtlEOPoSulaB2zUJAHeqn8k33siD7ZUsCecdPF9QGcqnqtpm5y6TqVMc0kIKZfosaq15SBwaZTOfm0s4w3X83z33RGUMNXg4ovtzZGIvWupsFD37727vX5G/xRhvJFk6HillRAjRBVQCcw9uLyB5U9dHH3300cf/QlQl9T/epoLPB+Fe0HUoLFBJiWn0xixcf/RMAlWKwvT73GgugXuA+r02rO0fYH39EAiBUnoYrpl3oGqCs37n4cuXDLJLFCaccBbc+g4bD9OJ+GPIwB5CpgdPoY+gvwW9w2B/woX7oKEmAAeHj3e/BnlfoqAxmxvZ1WESt5NLVmssQq6Zwi+6V9E8ron9Sx4mYWt0eAx6E27WzT8bx1FwLT2V8PVzMDwmAGtzFdKbcykUyS/nUi3uynseX7mBWHsEEok0dZo2DWfvrkH03PowysS14ICqxHhx40gOtClIeyjq5kpcWUtQ0rsYsb+HpSO38+modgzdIi0zzFfZKvEeH0iBYqt4ulOxDRfBsIe8XRWEM9uoHLCGSFeQfHeCQ66qYNlzApeiUObS6Eq7g/xxgxC+TIQQtJg5aKZBipnAciUXbylB0SK4EitRXSV4q/Zz8bwg64pDlLVHKL/gYYQQrPmgi4VzVEBh+oU6ZTPms/tAP+RBuccVW7spPs5P5Is43rCPJccvoDkforKLmIQr4zdi46CYCnM8D5HVb0ByrCISYVoomRXIRAfd99eQ+KYcLJXZGrzucZAywUAWUebew/D+u9hYM4iIN47XcKFpFjkj1jNs0iIG9/jpd/y1WGdYEFfwNxkUsxLLZYAFwjHwdh0gtXg6R9xRxeKvt6GN20RqoYXfPQ7RbzQ5Qz4itiuZA/BhaT31/gSz5x6H3uXGZwQ48vELeW3mMnpMndikVcSG72SxULjUo/L6YD9fF3gZqM9gxG6BYmu4HRtfzEPv7v5YR3+KigSXg2IoJHVXJPWK5KHSBoRuITZCrbGH6+4aQNXXvRyYr+LZn8cy1aIlGCHF6sK208FWQXPoHbAHGQwjukIIwL2lEhNJalUFGRuGszi3nd1pPZSXV1P/aQTLCAAaS7MvZNKF/cjrWoAUECkREH8VcJEa+BVu13jc+b/GaHuJMVKwPGbh4FDgT2XHTskNN8dw6Qavvh7j1Rd9eHUPGhomFhoa2cp/rqjyn/G+8SlPm6+gonFP4c0MV4f83W39K/O3GONrgMl/8n4BcK0QYh9J58OVJBM7++ijjz76+D+GogjufsDDO2+YFBQJTjpFZ1aikrW7uikIeljzmEJHvcOU81z4K//60mNXzQcrGcPi1H6FNBMsWvwIc3SDgrNyuWPk2eh6MVZvG0XVAXQjG+lSKNNiXC73oVYUU2Ot5PNpX7J96eHEJPgAnz9K3dYy/K5u0ieso9WpYXb5TL7YtxvDtplVWoFvxFjMjmr8zXvJvu1l7n93CvsDUdSWNIR0cKQLGw2lKwi5nWhNmXjq8mkYs5oMKXAjSZg6XS1ugpOriJw0h/UfXcTmuJeIEGiOm5QXz0EZvJ3YuW+Rpki6ANmYA4052C4T9yfH89Oc3zNzSw1LWhweOf4EhOYwbsB2YrqLkVdHWXtfgLAWYesxi8hWYhzy2iloCRdSSDa1prJeU9FdBg+etoE7fnYyn/xGYMfg+QeHcOa45xl4xw3MWbCHuT0/hg9MflU5l8FTI3y2JZeXlh5FqjfCA+dmUhwEdcAJ9Fv1PoGdFqGRZyKEwDGqWPWexDIKAFg9z6L/zF2UF5jUNfdDSsHEPe/SluvhjhcW4dhubE+ckZ0CH6nskFsAgYGBFy+1zj5yisaiXTwHWbseZdgMhD9E7KtLsFtmg10OgNcFx17tIXfu/aR/9jGJEYO45sTXeP6LEzmQ00ChV5CSup/iWQtwywQDtmXSJV9BWqcno7BtBXnAA91KMtrBAKtuLq6Cc5g4dgClo1TanXTKtLEoQgVVJbjgSLqADiSe3gA5nQFSY27EH38kpG4bQI8vAXOPA7eJt381W/aOZ2OmSqJkN7WT1jD03WNRDJ3WYJiwL0xqZgteXwfi5nuYtu1+tFeSN40CQZliMS7iZU1KmP5hD0M+KGDlkmaihRl0bPeQgWB2dRm7Jj5IWqybL+1jQVFQHBXHZVF16EqGfTYdBUlixiKGLB3DsCevQdoKA4XkMUXQEIjiK68mvmMQAhhQ/g2kGBBTkV77uwB7bGx7LzAeoaXhzr2GwrT1pAWfIm4oOBlh1s0/E9tWSDhgmJLWHY3k3Xkm94d6+eC2oxjc71AOUyf+4HnE7HAwog6unOSN3UvmO1jYWNi8YrzH/d4+Y/w54DwhhFdKGQNuAaYCLx7c3gT84h/bvT766KOPPv5dKClVuOGW7+KJU3wa00cly9UPeAzssCTyjUV8u41niPoX+6sDjsJq3AKJBEqDTeLDB7g/w8ZSVLojTbxVv4KLSqcjDjuZwcs+5iePHGDLhQOY6ulAlQrBrd+QXTmLEy6fy+yZbxDu9JNuKDz85N0Yi6ajLJuE79ZHGTT+UFLd6bx9wo+ImAYZ3qRquD71WpSaFbz2+RBqmzRkRjtadjd6SxG2YRIqqEXL3E9dex4Zz56N4ii0LzyUZksHBAgFIzyDN3bNZOe6UlIMHx4h0ADDAqMmH8+BPAq8cUrOeIf0ol00vn5GUuYQQTSlh2c77mTc7ChTI02MnzWQxfGbUYVAF0EqpqQzeKqbrn3PM61tCRk9Op8ndBw7qT8dryvFKW4kEdO4a3kOt42vxwyX4FguEA67DA8Vc19j3rYiDDtZ5OfjxCWMSo3w0tIEtqPSGQnw5jdBbjwV1BvvQtlxFi4p0YYMB8CKL6N4qI/ejuS4FhauJbCwjRtOWcCupjIKNu2ldGc18m2NolvHU+2PEEBSIFTanT0MVgYQSoSQLklA8VGpDkZaYepz09ELTyXPlU7cNmmOtpMx6RWctmJkPIuUX2TiGx4nkPdj7Evfw31fFcE7p3HZ7AXoNR6Uzwx2nOHQpFiU7khnWeOd9H71KkMve5LAy+dBWjueQfOI1NmQmhwrEfgu1CFXLSNXLQNA1lYhd2xETzkKI6ogVMjtDHHk6hGgm9iKjYakyxej0ZOsZomtoOwcxLDPf8IT60LYXwrEj94jNnsxc3/yDqI5i7A/ypV2NxM3hmhtH0PqsScRkh72a4mDcQWSLFvllNY0hOIwrTkDj6Nid+TR3fFdbLbtSHKu3ELthmE4i1WwQRUSHYctsxfTMmIXZ1g9jPPup/uOJ5C2koztlpDpKLRHvWQM28RhoxcR8EYpK9uEUCfSIv0oMYu1r53E1pWHMfCQdZxyxeHfHlduWkPzR5fgubQIJaQicDFkQg+uN0JoKuTlKmS/cSdUbWegY/OLM15FWXX3D54/uhaZ1D4Ww2uqZJ/vIvM0F0VKPlXOHlRUKpTSZD+sduL7rkOaTWiZ5+DK/PePkP7BxriUciGw8E/e1wghBgBHADawTErZ/Y/vYh999NFHH//uSEdSf0UEq02ChNxfefFPSC5BrfUOLz4Tw+2ZxWnzf01KdDfKvjjGRQL1hMOxFBBS4laSMebKNQ+ROO5CHtpQReuyCHPVGPcM+ZD0e1dTevh4dp36GNd/thLD0Tg9dz3yjxrPFozZORZ/8Xpkziw8moZHO1g63YwS/+xctuwvY/WyUUjbDTEPnhyLzD9cRzyiofrDzGoNE9rj4U3FImH6EC6DtBE99G4PIYXk85wAsbo4JiZBkcxV1XWwHJFcKS2VQZGjma4JWjdXsUBKEjJpULGnlKgU/KIHnrh3EJmeFI7SH6HTriFXG40m3Ehp07XPjdbWn0DRJnIqttBSMxpHgb3ZHd+e70Y6mde2lozSk2jeU4HfFqzYfCyHqk9RXjKE6sYwioBhxSHc7mGk+L6gMyxQNJsU99fAYADE4GGI3t5v21XdI0nJ+pK46ULTE4wc8TbqzmqKnt5DpruG3/Z/iJreCmbPmscV+2r5akAEgQHouMwQa64TnLjnLnxDDabc58PdvYEXa3/P22o2CI1zM4/lzd3rSHjGMjq7mVvOuwXPIfcj3A696x8hPvdkEmINWqFCR06cXR98SMbb7/LgiCuIPaUTOmkRwTd/hKs2GyGupPuUtxn/3OVk7ktFqW6HsAs8btTSI3EV/ZhPPjBZs8rmyGM1Jk7WkLu345x/FAgocj9N67QPiCVUmtY76IYbU7VZdtkceipqOdx3PJ7HKwhHHOjXhEzpZteaFBwrGTPef/EoDitYw9OGh0QgQbHh4vD3foxmugi4bMKZP0fkdBNOeQZ/h34wkkvBJSVH5bYSa08BQ0UoDg0IkApeKVleto+Ou27CsVXySqoh6uWYEcvJVfezRPooKWphrK+LxKaRJBQLGx2BJAzUIFGFzYmLljFsdjVyaJCwlcN79TN5fe1Ajl4/hPzaQgypsXHhMYye6qHioBCOM+c+Jmyq54vjstjf308/BvDbwG8JPpLBuS0XMH1ENup9wYNJHjZ4ffwttL9jIk2QBrS/EyV02Ap+l3YdHzhfkiICzNZmAGB2zkOajYCN1fYyevopCMX7Nx3rX43/VoEeKWUE+PAf1Jc++uijjz5+II7j0PsnRtLfQzgc/m/344e2YUck3e2RbwUR2tbFcYYk5eDefLSDtnpfMtnSfws/3nM+eH3IaD9u2baLt/r1Iz+tiGNSh377nbcLH92RKI5lY5uSrfdA9nYDhjbz8hc7sW1QsfhsfyVpWhyhmig4FAffpmN7D2LnfMSmfShZ5WhH34gM15OIO/TGVNx6Lygmmi65OrycN81eIq3F9GwazItIrs7eRkpKI+FwiNQsN0XXbMEdTqVhRw+xzTZ6HNAj9BbVELL9XHZhKh1VX/D+x4eQGuxi0sTX2NheRWKwC9/iI9C1GEZPELQISIWeDkntXigvE8hEjECkjl5d0KmOZOf8Bax6/2gQR1M+YiPTb9GJRWwsj8XS92IY3Qak9iJCLcTjLkad8iqdj13LKYYOtoed3Rdy+rH72LtdkBXIZuKgVBq7m7GO+gjXpqGIQJim4avo6jkVVeh/ZYwr+PL9TGwthi0dvl59PMeOW4KyqpPPtdnsbB+IZeq899HJjLp+DmPntNE02EVW8em07HLR0dSFoyn07rNpWSvwuV5nvpUBQgUk83YsxjTcuCNetroLaD/kBtK0IPE1N9FTa2LvygM9Aq0O8+bsYmWxin3IZTiOC9WCyIJxhA6k4RABCa3Vk0i1C3GlZdHL74g4CnrJFWi5E9i43ua1lzsxErBrh4SsTgZtWI5weSCRAHkA36wdzH+iCB0HXGBrCczcBvoFe6nlXZ676VEuXPo65oEM9G4XGWovtmIjAffkZSxy0pBfjcbtCIZoCSLuMKpwgx4n0ppNFzbzjAjSpTNUsSmTAs0bpmzWk0QmZbDtzavp1GyWT1xOZNUESOlFTenC6cxgeNhDeesQvFMW82JbGaK7iMsK1jMgr5OeuJve/ruJFu4j0pCDRiP7Sv7AWHsmZuVuvpwapmOFhwnPZ2Ff9RAfLl/GCZtKmXAgDVWLko7gM10hljDp7VWJ2S3sPy2Aq7yMX1xVBSWDuPTOdhIyTuWHg+lc7DDf18q4m67BE4lCWxPi0hu+dyP3X1KZINHWi9BNPPmb6NjyHMJbxImVvwUgShQAMx7CinuTVrvwYYXjCPFdyuL/z3ntH0Vftcw++uijj39DFEUhGAz+1x/8L/h72oh2OLj8As0t/qKNWFximBLNY7I33E5JIAO/7kYGJBlDNOLbbAByjvDhCSZDVRwjhkz4cQTIgRMJtuZBTg78+gGm9OvHFJJFP+C78tkVIoGl2iAEhuZl0IEGgjl5cMW1FG8x2LDTwIn4CGpRFMeFafqQWoLP6yZzQea7RNv2sbNqKAXNtRSXzUcdexqJgJ+xBVVsbNrJupbJzJgYY2z8JaLtmdzXXASKCorDIjWXOy65jo51Ad69cDQrP+pHYKeX/LE1mCk5EAthhBKoUuH2n5XQX1+H4XuBw8Y/AwKWZ+oYLgUhLE69/mGMPaVEu4O8//ylCClQsMnv5yXgtYk1XEd7NIXrH/0N4ViEw5xDIJJUF963aRLr0z+mI7SYae4fcf2o3/O7wlTa60tJUeGcijMRsYWIWCppdjIGN1Dl4TnfjehjHGbHbALeh9kvBYm8MEbeChQkHS6JFlAIKN+N65+OcVDvpSvsRdUtsnI78I6pwzN0DsF1Iaz5AQxDRzEg8prJpBc/pxLonWbQde8khOFGN9w4IoFS2EvIGciQjo2sUlJAwsB9dUTWXUTBxiFI1UGvCBAcqKO5YtjBHizLD6YH20rQ6jWIaC4UXUPEVGxV4AokMGYtx/PhUShIjj99KAWZwyETZNE8PL29pKQkZfZMw8I2DUwDLJHg2fjzVEyq5+qqINoaB8UV4oBSSazDIWZINKBr9DbSc9sgYOEAaYEQo7adxtoVPlRbgGmRI9x4sYkT5MD6/hhWgIub0iiPu/AgcSkC0nuIZzQTCPSA6Ueabra543Rd/hTnFa+ka+5ZRFfNoCHu5gtVhRWH4n3wNnyOwHjnBDLrSzi1KRtHj6K+fREuTdJTuZM3WjK4/4gXqM9V2C8cuu75NVpPCNXpZMBV+VT328nO43dghRS+KcxkpHIrASXAwL27KeoYTSieTNLWFZh2vJthE3SEEGzuvpL4yFaUynS8YyopG3YPXu6hKx5h3EfHYWkxZMRP2zKdoXc9AcD+3k56zQiD03J+UNl77wUHUNLfxSdVAuOXIJotsLbhDfi/V8VTBk7E9CaQRi16+hkonr9Ud/pnzY1/L/+hMS6EWPx3tCellEf8N/rTRx999NHHvzDfPBxn9wIL1QXHPOJF+xOhhE3bDG6+qxPbBn3sNgYFuxhVXcKRpw4i8xAf/e72ktjjoGUKtPTvFtdTrnQx93GB7oZTf5kL+bsA6OmUPHpRlJYGh7aZS+k6bCW3lZzF5NAQAuE3uLdyCU/ESzEzewgf/wIifQQAh4djfPxYBg6SjpQecjUFj2NxmKHh/vRHfL5nMGv2lRKPBwCV44a+ypQht+M+8jFqG9+i8LjXGKmuYOqarUgjlYa6AYioF4lA1RIMNhrRG7rJa2ygc9kkil8/ATXhIbqukosPuZ2XleMBiYbDS/dtIFV0cPYpPmyXQkvtYJqdTNTDl6H6o6R6wgwo34DiwJizGtlTX0Fgeg+f4GZiYjBFusmCVYfREwkgpUKV4WKIK4EQDqmH7GGt+QkWBnXRbXhzE7hyusjL2YCQkOlcTUr2FXBKHOs9Myl/N6QKQ0gksEiXpMXfYpz/WkJKKu1OBwIY4xqOX6QibYkd/ctr4IKnclny3EYCgTUcMvM9kAoMGsBRgzLY1tXDwgUCBPxu62087fqCgngNRMLYJS003jQPz9ohRCduYljOCVB3Cdf1PM/y5o9x7etleFqC19dXJtu0YfvHDnkDwT30JpTYvaRc8AfMFbOItn9ATXY5HlujqMxmkL+QuGMwWNxHxSiL1KlzcSkx0q6vRW5rhzkvII486ntG4YRJKosWKGzebKAeugyndDd7kNQfEyS/fy7eU98jM+rGMqNIITCBzupy8lvi1G8YyATfDMS4/exY5cUxdAwpSRMKGUgUVHxzT6BsxiKMqM7geDLx84CU+L2rWd85msRLv6a4/0bOOOcOFm46Hi23iUO0Toy6HxNZcQRew8s0YJsjaAwH8AZiZEqL0pPfIlSXidr8/9g7zzirqrNvX2u306cXpsAMZWDoHQEFQVTA3hF7iSVqYjQxtmiKvfcSNRpj19i7YkNQehvaAMMAM0zvc+pu6/1wEESwJZr3eZ6c69P8Zu+z9jprrXPOf9/7Xv87b8eDJkHQUeheXU5dTRG3KDFOmfViUuEJsDM7qHv1eOYnDsXdoqDcUk3GTdcjVUlCXUvA7c3VLz7Bl3lbsY0bUNAwjvcw8wxj51glZDsgcVVJZECYhL6VC/XpvCaW4sxHyPIAACAASURBVAYtiIOqQ6AoOb6f1G7k1uUfIYADivrzu1HfX4JGOhUERi8g6I8ln6DpBkpo7G5CHEAIFSNn77UO/rfyXZHxPuw0o0qRIkWKFP/t2KZk47s20gXXgsq3LQafvuv4c69GSOyoQpG7sJyzurx4bJ2ONTZpf3Uweql4+++5cbO4TOWqJ/bML/30dZOGGhfXEaS/O4nm8Qt5oPZN9s0YhJQOn2dLNvsbsRC8+P6X+FsH0OsIg8UL1iJlP4Rt4IRD9LnsJsIPXIjRmoUiFdgwAktIJDoCePvpUxg49nyChU9SoS9CEKfRWc+mbIuB9YJPzVJk0mICr9AZmlWNXRHDUxenfLlC3E0eU2zBlK6NLNWrWSYGY9UWs7A5C0VKFtwzloDroU/ET65wMN85nJ5/uYIxiXYCjkqn6rBlyha69a1U6DpORKPKXcav1GxKCmowdBPTMujKEcy6aDuOnUHlkBU07rBnNIkxIrAfy+2FSCHIVwp3Cs/e53lJjNZY3rKUv427m5BMend8aHgQYjUbE4/wRM5NVFpV5CpBCtSeWB2SVb+MYbVLvONjjPhTcGd7mfkKR15WSHzb++AqKP5xCDUbTQgOnBzg87lRYjGB4lGoLp9KcZeAv9xML3UwGaNfYuuIpylWysm6dzDNH8dBnsR+M7PRE0/TMS2T4Gf1RJpzUVSX/J4u9vOPIXJ74R1zJ2np6cj9NhG66T2eXKfTUjyU0rP/irYj93/l630Jx7bRK7MVYUmoqcFVXeyFf0D03orMO5avSqRYagczrnmdkU6Mv73qo+2ui0gfsAE59WW6MPF7vOid75M5Np3KZYOJCpsBgUXELpzCuvQTWefzEZ+xlAlDuvhs1VSQgtLCDuJbc0EKMhM6wz6YhuWqRDUbVXVQpaAtSyJrNNINg8iaqQyY/gxjClfx6bun89acI5g8QqPI+ipNQoIvTvC8Rzm4Oo1jvA0oshv39Hupu/9vtNXCGgGNSFAkWAYVW4dgWS9QbNpIQ0Hz9KV1w2GY+ECCUt2HdMdlMBaK+TdsNwP1435kz/Px/KMu6SiUvmjTVh1j2LVeNJ+gzPsLNsb+iua65Lc38ZJ+LQnXR28Fxt45m+2v+MhSNOJPW2x8w+LDI9diOsmnYB9sX/eDxLjqnQA8l9wLbeSjl/8GNe3/pnvKN/lWMS6lLP0P9iNFihQpUvwPR9XBnyOItkpUHXL67x6xGtBPZ9VaE8eFHCOO7E7mhAtVYDdJjF57b7fOrWeR+R7laj/GqrvKpicCnyDUceB4kLqdFGd6suqekXsm8eZ1uMQYNG8c+7wwk21hi81zulj0l2fhpauRuo3I6KRxwXgSMT8uoCCRvhjCH0W0JMt7q6pNTVUJObG1iJykuFVQ8GbuAy3zmZS3jVfry3GFoLy4mroBQaKqzfiFbRzd/CJvBacQ7u5D87S5PNffz6wVL7DqkAmYD5wNroYDdMYyyMdhlBR4pAZNebzy1ulkHZrJfmIeC31z6fBBl1BwvoqDuQ6y6BamZa5E9VVS0zycmQdmUZifdAHJcY9ja6yCbreFaZ6zGFU8nSx7MW2yhfHaZNywu3Msa0Zu4Z743ZhYdOOhWPqw6AIsNrsb8QoPw41dwqflYxOr3UY6Kp0rHBL1Em/hrqiyovfA1/t5pNuFULN3CvUhg1Ryg800mOmke9sYcrEfMXUNorsbVWic7L8eR9qoQqP2w25IWrYTazqEwHXH4e96gUl/uZaaz6aQUZBL/3fuRnY0IHUDNxKFI85BFPZDv/Ej0lprSS8eiNghxB+o+JxXF52EWGoz44i3+dWKzSi1NvG7hyN7eKHmVaxIAkb+GoB3otfQ7tZQv7kfic1HIC2VaGUZmwf2pChm8fFDtfRQl3HcjI9Z0WsSblRldM5czm6+hTgBsOCzFSXcfuoFTI4FUNePwSMaWZrfTVdnPv2yqjFrh2IAYSRzLnyCKZ5q+qwIkVkzDgeJB9j495uoizlYpgeEYOE6mzOyX6Q9dgDLRm9Dn/UasaxO3m7PRldP5ihvLb7C/en/SAb1Da387eMFuKu9uJt6gwBj+kcMvmQDMk1lZDiA8ezbrD3OoXpVHNt1aD/ocxbEspiqN7J9UxkVn3WRkTedfM0Gj0Z/U6AALcsTPPXiPE45bQo9vUeQ19lBrO1panMVoiq4xFHQaC1YhufYkXRcqOKLG9jtMPGzfiycvBWERGS1Y0kLXejIHQWWhLJn2oqiF+PNvw+PrxvF6IsQexYF+79KKmc8RYoUKf4LaYg9x7bON8kR/SkNXIUijO99jRCCQ+/zsekDm7RChZLJKuFwYufx044PkJ+r0tXtMnach9h1FmzU8JZpGEMUHn/MpGqTy+yTdIYMU3FjVYTj67na+iddegTDNrjOczkj1aG40qTwgPsZ0n40zVtLMacsYaCvgVliJZvX7k/UGsgpg29jU+RuCut7otsGIFBiBlpeAz3vvpzeVSWMHLSEx/94E20o9NQcPP4wmTf8mXJfO3XXXkdbfU+CBdu59b7LQQgOPfYxco/6iDytnIFF1yJzNjC76XcMz2xkrccgM6cFCcTz/dBkow8J47n+Sl7z9qLF9qBQRHPxEMCBwz6Ev88CVwF/lHZb4LU0FAQSKKrN5b3aZqZO/AVqRwXQQVC6RBFEJYz4sIG8/C0ok47i4IN3zYPtOlRG6ujhyeD8wAO7zdFIbezOv7vZtXmuU3ai7Ijuq2iM1Q/iYyvpv3Cofuwec+2teh3hTEXiQThx9Iw9xZNQDMQ3Crp4PIL7zr6Dpu1t5ARa0UXRHq9TRVJ6GINUrMpk9NQ7TsNqehWl7knyNehxYCP+HufgPHsFSBfMBLJx865rh7IQoazd2n3ziXRkY9LVZs7rx3Dp78ph6WXInKqkd7Zr4yZad57f5TYgcdC83bvSAKQg4Jrce9utROLp6Np54FqMH/oZhAFXZfzEbuYsSxZJOvAAD3ZbH7T145CWB7OmjGlHPETayM+Jay+y8hoH13KIqBZj5pWy35Q3MJZXU5t+Pm7MQAPctlx64hIWLptRabSjOBd9zov3H4W1eAz+RWPJn/ERg+ZOZG1OK4/PaGPQuHncIEawOr6B7Qc+j3fr8aSrDm3ZbZgvH8lFBeO5qc/1+DNmAjBolMpNT3s5cdtVWLktIBXeXDuS1hsvx1VjOGYQelcxpLgONpfsGAqXOlHHXHse0/SpGNlH0mm/iU/pwIskIZMpI0Xxnvz28085W52BBx0pJPl5fjJGrcW2FHrkgoZGeKnN1j/GwYWe13hJm7CnBBVqGqpnzzXzf51/SYwLIQaQTGMB2CylrPzpupQiRYoUKX5OEk4DDfFncaSky1pGu/kJ2Z7pP+i1/myFYbP3LtxVVXDItK9ZjD0E0pQIQ/DOWxZvvW6TSMC6NQme/kcN1F5Ck65gZ4SQSFxctrg1jFSHItDxqlnknvAaH2oGLnBoOM7idybz3Cs9UZQEYyd6uO+Ka4kd4/LZvGak6VIzYw52IEKWL8Lv+z7Kto3r6JXTwfk1heQ6Aq3bT928OCUrK9Du/DUuCn/53b1YTtIfff6nMzlj2GqGFEqUdBXpKUcLTGGw+ISe3jJWKHFc6TA09wLckx+CeA0KgrCrIUmWZO9UWznT6uSt3gnE0S9Q29AHEgbNjkJzbU+ymzJwJWzpvY1DPF8SX3cX4zXBvOLeVMtOxra0M+SNOopurYSrl8GkXV7P7ucfc0nLe1Tn+pG6zj1DzqJfoGDncceuAhlH0XZ/vD9cHc5gdTArnZWMVEdynHEihxiHIaUkTcnYYy7Tu9+kr/IWYTkMr+dTVP9bP2h9APiGXUwPeRVoHpR+s3Cludfzcm/yEZ1ro2YJvKM1Yus/BDeBagKtq1CKVOQRF+K++SD4QqhTZn/3dd0A1g4/7VBU4kYrUR96BL1+DtbaOxF6CL1kFrKrEdlZz5ick1hkPUlBYTtnzkhnWYXD/to7DNzQSSyRhpQCyzaoay4BWwXHAUXj6Jl1tA3wohiSgw4ahmfdxXispP1m3JUovih6r7PxF2Yw/JfzefneQUSdANqyyWxZ+CWxoq28d8azTH3odNhhuwkKDoJKoNv08tSiI7BifqTpQddM9ntjBqqrkNEZxHz+MHLuy+FTaZHwlTJ2zCy0+eOT66Mtgy961lPn9uCN4NGcOc3E+nAW0k0QHPkH0vNtWmyBEJK2LQNxXQVFFbgIGrb24YQXTsRYfhKNT4+muWQbaw77hCniPACElk4w9/dEw39mX0fQqaTTV1yFuPMsOstO5q/TvuDoxcPwZqnM+GUJv/edRYNsZH9tMkIIGh4xkcmaXjQ8lNirGP9v5UeNhBDiAOA+oPwb/18P/FpK+dFP2LcUKVKkSPEzoAgdK+qjbv4oMnu2UzLW+7NdSxhJqdHdJdmRQorjgNm1BkM69DATlMd9rErTCBFkspas1ieEYGjaPbwZvh5tRQg3GOHV9nS0l89Ds5KPrxfNt4m/eTZEGhn727Mx8lp4R3uPgQk4OKIR8zr86lEXozuNHq7YkSGuM6T+THyn1dFhSxwglNtEa0M+qJL2QJzbKqZRWqNz/zHJfniyL4Psy/AD06WD3fAqrrkJJ78e6VEZsSXGvkNa+CSeh47OiS4MVZsZ2KsFq3Adj60ZxyYZQB21npD3RIZ+8THrtBjn5zQy1NsMEQg4ksVOE/UeldUFQepn+ZnZUyV/3NE7x1K2ttD+69PZeM/pWCrgWMxvW79TjJux1zGjfwUESnwg8cUNeHqUoY+9Ek01uMp71W5zEyIdvsXkQhz3SzJXnUam+znhI3/14+bcF4DsOB0+mwb9buh4gHx5BSH22/08jyBw0K5UBC1zClZ8KwBqerLgt3rMb1AOPQ90AxHZy27Sr3HDhcX88e6tqE4nfyy7G/OzFrzHvAahaWhj98Nt/T1m3e+ItUVQllmU5w9g4GkvoAodZZzKieNAypHgmEypF3z6noM/qHDQL07F65+AWfUsSqgPD23RWC6XIRKCmxfWc83yXHrIHkgcHHsTGb3GoxYeidO6FJFfSUIZBFKAouCedhF3ju2g1VfDMNVGQRIA0ATzVIcuS0eoLkvzEvTSbTRXRcikeAbwuIKLGnNQSQb7F9ow8otxVACaVLBx8Vs6CU+cnFAbTlsFxJP+86vnvECZ9wRaxBIwLPTBS8GYiSMlCi4DBq7BsSE07Eu0h3uzxP6UQ9TpTFD32TnGfmMsBWm3Yznb6WOMh3VLeHu4j2D5Spo3wt/6VXP/pBPRQgr7MA7Z1oT74OU4ro2RcxuJbTpSglGkfHP6/qv5wWJ8hxB/D0gAjwJrSX6MBwGzgXeFEDOklP+KC0uKFClSpPgPoYksFl3zIJ3tMRQrjYLL/WRO+unad60anMhnKJ5yVN8YAA45XGfRQpeabS4nn6qTlj+GePfjKOhc2pGN1usG0kQ6mtCIyRgf2+/jwYt+9wX0WJwBUtCY3U6H6qBaEhUYUNCA2VaPriZwKv5G4KSXOKKmApnYiqLvQ0unhWW7RFVJu26TjYYmBMrA13hv4wTCa71s78pnQySEm9OG19tBYtg6TEdnfSvEo9tQI4tRA0NR/P0BiNc+SrztdQzbQgz1ApLqAp2RapiRnjA9wkWU9LqGhdufpLO1mn659fxy2CLsT1zS3yym96yJ2BMEWa13gPBAuwK4IGG7R8EVAILtikbHpAzysgK79HIkTKg7SnZHmI7MAFJVGBxeDyQj53bifeIkaBIKOcpyiCk4dU0oW95F63vkj5pDZfhExHMrwIwjFIPWaIwMrwdV+X4R5cY3gxA0p0mkEIBFY/wlir4hxr+Jnn8CSmAQuHGU0Kid/xeeH1bQZVCZh+d+tQJr+cPJHcZSp/FNmzWPhjGR9J9VRGjYRshXIRBBbq9Aj8cR/l03o0IooHk5+8Ioxw6+GSNWiSdxBkrBTLyj/gRA2/qncaQEJE0tG7EXFSHwIADF6IMyZR/c7ioSK67B4yaYML2DVct+QY/BfvqfEqK5Tkfi8soVDzDhk8OZ3mcMg4/XWbDmdT57Ix2jz2ZC68ppzOwk1qOJRG4nJ/Sawsan2ggEuqCxGAOBDQQUSV1mO25nGrajYnkSDOsxn9JB9UwfsQbR1Rfp7eTL1pHc8fYFJFAhNAV5yCdsj6Zz+dVns+L5s9iyYRJs6sfmB89jn4PvJb1PkH3zbtzrOHu0chy1J82yi9xeQwmuVPCEOsk+8FMMqZBNNlKejBAazq0XIyu/wD6+iKzQqWj5dyA8peSd9P1pcf9N/JjI+I1AIzBeSrn96weEENcBC4AbgAk/XfdSpEiRIsVPjWNBR60PV3dwTY0tK1wG/kRiXLoR4nUXImWMTk3Hl3Uxmf7phEKCO+5Jip5O2cHVsTto7O3nCKZwgH0iacqu/N974rdQ6a5FQSFj8cMo8WT0tMTOpSsURaqS3KiXjrps7nv3Ln5z6IWgenHjDcjmTQC4fEZucALDcrNZsV1yX69KbiuxyVr5DI81lfP2pn1xpUC1VRKuB4IJLC/4FAef5qFvuopbdSGutLBQ8PZ/EFOHTf7Xwe/gTQgKWyRbVA/vW+kUYNJfjeIEg/xj3RY+qOuNKwvZv7SKwwqXMHDuNixRy8IBN5E/7gyKvX/AWn8PGBmQ4UFRvYxHYZFMRoaHOBaBBIicXULR6pnPk3OmMsxXTVd3Osdt72BA0xLcomkoaf2xjPG8aG3DAYQXDjdMQgLsNc/Ttvwe5k/KxfYFmOS/lB7akO+ex1gH9rLniBshLqsOsC2WoCgtxP2HT8enf7d0UIMjEWoIj92GowLCwKf2SlolbnFR8hWU4N5D8mrwu/v1fah9D8OpnY/dvpFVNRfT/x2FERIqEWx993iGDPs0eaJjQGYe+NJ2f99mHHQP9qbX8XZ9Aa6FtewO1KL9EXrS8eesIeO4adU8BHCuXIIYaOE09cFFRU5KQygKTrQWdljyDRz5MSPOOhA1cxgAU/yjWdBVgep3OOyyQgZ6kilSFw6fQdbAv7PutrFElpVjOBI9HGDEgVsYNPwtyjIeRbb6CP/9ryRsH0IBZcbbrBtYx6q2fHztWRQa67l9zQtQAcKahHbaH4itPYGlS4eTsJPXoT0DBRjohhlshvmsrQjH9oANnauG4Z3s4L50J+qFf985Lk1OFS3OFnprY2mSzdwcuwYXl9HqPpx9xIs01D3GZnsxM8wuDOcFLBSM4KnQ3Y4zIRNZ4EVRY2SMvBjffm//IN/x/yZ+jBgfBtz4TSEOIKWsFUL8FbjyJ+tZihQpUqT4WdAMQf8JKlUVSb0w7MCfLndT2q2ATaVP0GhIpPkAg1Uo2ZGTLp0I77ffQL23FldIXucTJorj+LokqnG3YrPD1WRsJe4Xg5ESxk5QmECIzfWSqsWCREKn0SklkjkTz9BDkJH1yQYEICXiiRu48bYFtPqzCOhh4sMDiA1hNmU/hKklI3OaSICyI1VicCWZmVWc793CwFAHi16bRUtrMeWHvkzfgo1s6OjGSVfQdJeYF7qE4AqlL9FF/fGtLad+Uyll+XksL59DXHMAjfXNwzjjibWsbTmT985uom7gBmTsWg5cnE1mYy/ytCjPzLkSPZjDOVfYjEm/FNwaAvEsCnMvhVgU68unUbJL2dY/l0iWisAkx2jHG0yA7WBbn2LQn3ZjCK79BhYJdEWnJS+TfCsDWbua5eN0Oj1hkBHmRm/jhLQnv3MezVd+g2xaz0q9N82e6ViqQWM4wor6Rib02nODnZSS+YknqLLmU6ZPZnzZ3+lrbqFZLkIRfrzsT+fFUZxqF6FC+sMB1MKkWI2bX5CwVuDzTMPQBvyLK2/H1Ot+PAfew1J7IX2O7IMqk6KvQEBLX4kS3BfdNxhlmhelbNJOD2u3eQvOA5fAymWI/N5w0dnsyuERIATRsOTTNyxUTw6vHnUmigKJRZtZVWWzQAHFUWhd6HDVk0+jXXMp4oZyZJ6OSBgITylr4pv5c8Mj5G4KcPMdV2BjkVm+DHvWXNSxJ+FTfZxpHcftmzuosgUCQb4a4tyi22l9exSNLz6D6u2i6NS/gJLGuvGbialxJnsloepytL6jOWPe+mRxKlVD9ChDGGkQU5gyaD6fVuwL0iWzRyczi20OD2wEJ4+R+7cw51UHKW36jnsbZXkntO7K86+z1/JK9FoE8IV4Go8+ngTJ5O+FzjzODFzIYT0uQI0cB9iAg7STUlG9+Bbsty/YZZStaEAqReWb/Jhv4E7gu+qadgEd/153UqRIkSLFf4Lj/uiheo2HvGI/wcyfLkol9GIUz2Ca9NU4QgA22805O8V4YvPNpIm1aPkGphDoGOjf+Ck6Sj+BZ60nECj4SoNUbGwlYAnM1zNQvAp9vFCjS1AF6dkaOTOvZFN4I//UlhEs8nNyfRQfKtrjSxGOQ7rRwaL398HxqShxl8OueIH1hWW4qMwe9iaxXq28LEsRngTTazYxMquBVV8cy9ovD8G1PDRtHsx9GSbd7en40kZxxOW3kB0MM++BXzKieihRXwzpCpSYj+pal36RcpomLAUrwfS5C/lw69+xbZW8+21K5q0md30ZXwCLg1EKOtJwYwEQkqfu0bnizscA6Na7UYJBEg/OgGgbjmqQIc5DFrsoqEjFIVO6dKuC59X3iEe3cpxxEorwYEgFIRSKht2A3lWLue0qNFsi3GTqsqZ9/x4B2bENXJsiqwnXIxGAi6Rn+t6rEm53VrPafBebOCvN19HcMMO9p1ConApAR00nzmYXEiA1MOfb+I43qOlcwvL6xynPqCMt8Q65GU+hKntWVPzWfjoW9pxbcBvWoUw4jy/Xj6etUeLbr53qvpvps6kvwlEIl6+ifPpdxLsHo/U/arfIrGzegn37MWDGkKU+RO121BqJ2usg3I6N6IPPQmg+7v1DjC2VLprXorvN5LhzPHjGXUfXmhYaMXCFQDUhcfEV6NFWtH9shCwPSA1ZezO3H5BG+/0nckRlCbqt4hgROiv2J6PHDGS4GW3ab9n+4TmMH53Bts4bcU2DknMewZIxGl84D2n6sBNe2hYchnvh3dhpKkQNdM3ixF5b6J1/EwyYjjP3HxDMQt13NhGnFrtlOEPyv+ChX15G25YQZVtXogQLQMZBc4kdXk3zlHewXIfm3Dpm/j3EsouOQrXnM1Edz3ZnDQ5Wcou1lPQVRRjoSGmTKdLR0IEEeuhcrO6/ggihB2fTYa8hXLKF7IteQK1+HhmrRS895390VNxMSD57wyKeMJl+nMTw/mf6+mPE+EvAbCHEA1JK++sHRNIMcvaOc1KkSJEixf9whCLILVUJhn7aHxshFDz5t5IfuZEGeykgKdB35QrLeC37xeOEVZdafxpH5PwZLbq7n/BBxkwm6pNRUXjA7SLi7aIk7kdIgR0D1YWLrvXSZcLA0SqKKrg3fhe1iTjmouksRfLnsZPplXcssnEbXcPTcLwqTlDji/ZCPrmgN9L9iEmFGzByGjl6fZxJbKep2uHp7H584C3i6Gg+rpMsUOTEvbQ3eXBdBbsji8eePh2vYTKkZgDCVfDEPdiKi0AgpSCXJu7oXIqyaAFpLXnMcSzAIENKeiwfhmbpTEOyRoO4ZWDgIgBN/0bE0DEh0gpIsOOk1TUye8DNbLVXUBTfgF9+zkVGOvWWRpqsokPezzWBB2lwKslXy3AjGkpxT7Tmnox5fhnsk4alCvbZ99bvnUdtvwuxP76NAtXk2pGlrFFzGFNUQHF62l7PT9omJsOfrrTYar5H2K7ggLSHk8ezBK5fIHecow1S2d7dyflzlgMj8ahDuWPix7hu648S487KV3HWvAN2gjn3LuGDLaNxLMh4bxRNd11A/6WTcENd2MMW8esNUWTXaqTZjPDk7WzDrV4OApyBAeSINLAkSsCHMeL3u12rYYuDYUFCSLZu2FH7UgjGHpHNkjlxulolY2dqBJfFIQrCUMF0AROat+PM3w+xqTd1joqFi6s4hDNaEU6CzlVQWfcl1cPzSEzZzpjZv0BRBRo+Il39UANhbMuDEBaabMFsDPDi/CNx44LDJn9AQSwL0UNHBkKIqRMRah611jtU17+CVphGXrqHwng7uUPbwAixK1Qt6cLCLGigeFV/cpcP5pmBTXQ9lk7zsGWsm76OY7WDWZR4CZBkySz2M3MIdkVoURxGJ2JIfTOQhx44Fs1/FKDQ6axhZfhqJC5bxDNMKPsHqvj5Nor/VDx1R4Ll8x0UzaZuc4Lzrv3P9PnHiPGHgYnAXCHEXcB6krM5CLgEUIGHhRC7lXWQUm77ifqaIkWKFCn+FyCEYETgCprt5WjCT6Y6cOcxo+f5JDb9iUNawZN5Karabzc/7K8IiAAAx06H9VujxHwOWlSgKuDLEfQeraJou24k2tfl0X3Xr8BVqC1o5KLmlTx3Zxl6x3ACj9WDrqCYks/bSnFQQYGlbT0pKWzCVgXteXEeTh9Ce3sAUxG8Pa2e/RtidDeqrDQsnK50QOI4grg3ga24OG5SrGtImgdspqgmj+xgO1MOf44+rTmISDuu1kaGp4Z2txyPV0NL7CrEo3sS5A6qINiahWH4mX1pKVXOeoqUpM8z1ZWI7TFkgQcciar3pIfalx5qX6ThMEdk0pD4EhfotA3CmolfSaePMg5I+owLIVD7H4Xvg4Xst7IbbY6JeL8UZ/lLoPtQBs1EKHtWRdWGH4s66FBQNPpHY4wO7T0i/hUF6iBGGcdSYb6KLiMYSMJ2DW55KaJfGTz8OBmPFGF+YaP1U9HKVSqqGwCFuJO8GWtKjKJU7bOzzcqnE2x50yZnlMrI33n2fmHHTBp+Aw3dpVimBCnoCPsYtq2dqqkfAYJ+UUBooHgRegauTN48CSFQysbjaDpyWAAUAR6BHVyDxvFsTLzFRvMt8sx9mK4cjwN0CBg9a9cNpDc7zqwnK8lySkn3BpD7fUDXNU8jcsBnvk17yOgfHQAAIABJREFU30wCp1/IERVlPKZIPhaCmCLJGr+E/YNPEW4dyfp55+LYkqKXrmfzw7/CDoTxuQJdxskI7k/G9Sr1Ny9BYwv5o5fw59cPodrfG91xeP35Uzn0kmTqUKLx97jmRsClqaqcvrfdDkBsSAWcdgNSwPpx/WjLsOgTU+nlPZIp6lhal3jY99bjUB2BbetsRFD05WjWZz1P9n69OCPwCC1bLyO7ezuW8icGBwRggvAh7SYgDyklbc/aRBbb2Id0Isc5SGwcqZBw2/Crhd+5hv4nUFPlYpvJRJrtm93vPf+n4seI8dUkxbcAnv/GMfG1c77Jnp/yFP/rGDNmzI86f8mSJT9TT1KkSAHgui7d3d+VOfj9hMPh7z/p32gjHC3ij/Pm0B7/nBMHDufIssGgDkL2fw4AR6jQ3b3XNqQbo75pDVe+EkV6JZn9VCZePJ5EI2SWqYSj3Ty04WPmNlbSLy2frM8OpE0kkr84zUHcuEqFopIXcLjv6kE0tSaYuCWGDxe5IwBtJrxk1mh0WH5WezXUdhd9Rw0jzQdH3RmgvmM77/xJgieM19YxSjrxB5OFY/qf8jC+ijx69V1PSVkFBAJgeIgZ04iNOhw3ngYdzUw+NEBMc/CmQd2zFl0VDv6Jgl+UV5JW8BCa6iHdfwl3m7/CbrbwCh/nOlciEybOdgW5NQy6F3VqNsrX5lxNDMYXWYaJjQCOUU7ZbU18Na7msBhOXn/AgYPTUF+5Elm7EoRAqa1E3/fc75hh63vXiZSSZW4FMZnPNHkFy6K3YGPT69lthLu7YfUqwn97DHHp72D/HUU3u6GfL4THdvEg8BOgd3A24XAEgO4ahzX/jOOaEFkAoTkGoWGJPa/d52CsqqW4LZuZeJyHdU92kIgrTD70DcYUFzN/exEiK48pzhDiOQ1YnhG80fERj9rP4MPHtZ5LKNV6Ii/6J/HNVyVtAIWG6hbT3rGBxZEncbEIL6/DRyeuoeIPRPAnPHQ2bSUR28hL+rtYJADBCYHraZrbk2bj99AJzqx9qZ/8IpLbmTTuUo6q6cP6tdsZN+5ZyoorycgdQfuG80msjuICrpZA21ZEYU+NLWygS8Dc+FNMzBtH7j1jwBqCqR9HXdNCPM0KBVVFuGaAuy7bxrl/3oAZrqQj6ufjdaMZsGA46TsSGeTqAXQ3Z9CQHqRK78SJWLShk6aMQkQUDtlwLK3SxFYErpHARWBrCUY2jqW7uxvpmPga64jiYiF4Ly1Is+owzs5hjNOfSCRMZKlNw0vxpOfe5t641/XEzm4mqPbGVgJ0iz2/r7bZq2lwquinjSVLLfzZv5O+j+mzLZ67z0LoEQ4+Sae72/m3+/ND+DFi/C/seq6RIkWKFCn+P6IoCqHviVb+EH6qNuIRyXN/iNO01WXyyToTjjV4cmMFDXYcV1P4x6bVnDhiHNq3WON9vR9SOsS3/ZqKqh7E3MmYro4ZNYmqNZRNTDpSbA4381nXZhIeQaXZjCdXYZw5mIhUWJHeTfHExXgVyVt5Ptal2Tg1vXht+QGgODB0LQTi+FSDgwuvwM7ZTtj/D/r7trJ+cwmaL52r+80m5AsRCpVzzNAO0t5W0YDeR+XTNCSdnEADQ4OZWH3fQLIOsEAKlJzeEPkMnxZEP2OXp0FISmzXZdCvvh6fmoGUB2F1fcxy85+YwQgJTKS0aLcb6T1kPM7Jv0a+/zxi35koEw7cmW8r3TDj4w9S7bNYpgQ4OngWU33773VcEx1NOP44yZ/wNpSWFoTbBYBoXoXxA9bAV/MjnQQgEWry8b27bSVPtzzAC2VdoGqMU0ZydeaTWFY33rvKIBYDrxcCvj3WWigU4tEjTqa6s43yrDyChoF0LD54rJtFLxsMkUF0CaoCoZCXYDC2l/UaghPvAiAX+POk20lEPsLwmIBgdsnjCKES6ZIomaDLMI+ptxIjQYwELypvcp33cgiFCGXfg9XwT4SRjZZ3FF3OdnRpslV1sfvVMSIRRDoqjgU5RS2Iuqto9YBdoGEroOOl3buZyOfj0HfoTufz3nBIBAFst6/n3HOexKz6K25kDeG4gd9ZRtrUHFpeiGIlJEZPhSGjzmBr/CUkFooQ1AQlil9iKWHSGxJsiFg0BmL4KnvhJkK4ZoiNtQNwnrqctLMG8NuXZtLQmcm4qI/zLQNN2mghk+z9X6Sz/hdo/iiKAgoSv8cmuuZRcvJU4uqlSFdgBl30VoW0Qj/TZ/RCOPOxO99Cy0gHs4vFaVCT5SDrc1h6//k0CA+TLtBId/34TQ1pghDQP/0OZEErfqUQIXat+8T7JubHNuEpTby13504msly3uKi4GMECe4xx7a0mWvPA2B/bRKq+P4Y77d9r0m7ExQfQjGQUu6Rvz7hQBi9n6Srq5ucvL2nZP0c/GAxLqX808/YjxQpUqRI8f+Zvf04/VCWvGWxvdLFseCjxyxGHKyT7w+iqyqm4xDQDdQf2La0W5F2PYNyYkhlEqgmlm6hezeSNPaCNH1XLqeNzRkrhlBqq7hCUjBkGa2b67iqz2joVBD+BnhnGkp3kD51eXjX9MdzSgVn7zsGb1oBrhWB6LN4DZthxdvomz6Mnr5dZd7L6zy0yGSErO65BDNeK0EovQFQMoYQX3FG0tfacHDt1eCA1fl3VP8+KHoJWzo6uOTdDwibFmeMGMbJw4cm36cVwYmuwG66iyLFBE8Qw5EIJ0bu0s9h+njUo8+Bo8/5xvhESDTcBVo7s4XFbJnAowS/dTz1vFk4XV+C3YFecDZiHxNn7oMgBOr4M3/QnAA4LQtIVPwZpEQfdBlaxjisJ85l0Yk5xFU/4LDcXIBdBfPqhlJ5yVWMWFvBvmlBOP3svbaZ7fOT7UtaBsp4Jx3/vIB5rzyIKwVbkfTNFPSZqdFjgsoPCXgagcOQ9qeAgeo5CCFUvnzb4pUHTISAYy+xyNk3i1pZj4ZKkbKreqnQszB67npKELrmZswxrXw2PR+KG2m68UEuXHspviFedN886joVAok4mquCklyPBc/MRelsoVk9CDQdd+JaVCnpaMjnlbcOZVHafM4/9BC06CYQBlrmNIwMwbin/JjtEk9OAKHkERHNqLGVONJFEz4ej12OY0XpWdnJoEdAmXIxZlEDsimIopgE1A788a3ouW/Q2FmBKxUWZMTp413MbL+D99rpYDdT0NFAk+LSbij0YwRazcvItuXo3hi9LtiMWvYEi/4UI90HSqsgvvJZ1Ix/ADYIL3rByXgDAuG+gv++81Ery2hE8MkDCfaf4BK1NDxIlL4Cf6kf8O82P/YGh+g9CUiAUZFBdmExjX2qcHHodtvwsud+gYfMR5hnzwdgjbuWX3ku+P6FsBfM+vux298EYSDS05FOA1rwSIzM3YtaGV6Bx/rPbjL9SWuRCiHSpZSdP2WbKVKkSJHi56W52eU3l8VoapKcerLOaSd/S37ud+ALiq9slRFJZzWO6T8E03Wo7e5kVvnwHyz0u1w/X0QH0DtQjzb7FeyWHETP7bwYGsVXW+pyPCGuH3Esb1d+TpvvY/IaT0LfkcPdr64v7x3UFxcJrkuvcF+2RQKUNORS1JKNIgWBx6cyYGYQt30z8YpzmFCkMj/Hg+pKxlTGSKw4AlEVRTvtFtJKh9C2Mo50wa9swv5wPvr03wCgGLkYpZdjLrsONBPSVOCrfOLk+31x9Vq6EyYSeGLFCuzytcyK9MddcjX4bcgTZDmS3zaE2boxQdm6CBqfwfTL9jo+icrrcCPLoKeTvIaQCG237VrItkZkNAqhEIqnCN+gFwE3GaHMA3XQTFB1hC/jh00wYFY9Dm7S8s6uehxt8GCQDoct7aCqhxcUwfT6GJvXLWFxZRTLdpnbfyD9zjqLgPf7N8I5tfPRnBZ0NUHCVegwoPBsD4Nm6N/72q9Q9XJ8Wc8jZQShJDdpfviMhWMlj8991ebWA67hGetlMkUGs/WjiTS6bP3cJqufQo8RX5NFn7xLZ9+cpMmmApVlyygeBmZY5cbuehYEe2HgcmOLg1F6OvkVnYRuPZP0aIzcjIPQ/nI9/kOnsbHjH/zp0XNp78xgg+riC2Zz4dEPYna1oeeNBEDRBd68XZ+PnuoY9omPJOxsIxLszzK5AHTYNizEwbEVlJSsZ7O/kEjJBgr8ca4Y8Am6/wIUN8rxAz/hpXX741EtRp3dQvPI/SkWAiJRVMdiVLMERcHT+zhs962dOfeqp5uECWYnODGQuknjJ9UUHG4jNMC2kM88xaiBp9I0ZRpVrg+JAAluvIva1+PEZGky/amxgzLpQRG7F/eR3XKns6EiFELhXFrYRpE6gGyliAh7Vlld56wjQWLn3/8KUtrYba8BEnQb6SSfFNnht9FCJ6Jouf9Suz8VP6YC50fAyVLKhm85vi/wNND7J+pbihQpUqT4D/Dq6xZNTRLXhaeftTjhWAPvNyy9pLTAjSDUvYu3kTM02upc6ipdJp9sYPgEIDh50Mgf1Zea1ii/fPljbN9YREEj3qEbMcur0NAIKrtfe1RWCSP36Un01Wd4tnQTw9Zk4QqJGJdLthGizQyjKyqz/AfxhAWGqaNIgYLA7HTYtmEBaYuvQR8kKcElryOO3miRLRfiDABKXbj7lwy6dyHa2gcxQ2n0GvcGdotA5ze7OtJUh9ts0pEmCTQKyMpFzzgSRU8K5F7p6RiqSsKxkb4YLzjvYXeHmO0kIAykCfAKcqOF5C3fgMAgMuTbq1XK2FawHKgF0rPx9rkVRd0lJpwPnsZ98jps1Yfzi2tRJx+z40Zo1+N9Efzx4kMJ9sGJ1iQ93AOliIweKGOPZ+qC5xnoSqJ9YpREElSTvnt/5Q/LcFUy+6IpFqcedDkL1x9L8dRJjDz4x8cMhRJEsOtJQXF/hXCngxBQ1E8hV8nmN55kBNxOSN6+IIIVTXruT73eS+Zwl1ua/0HVw4cw860VZFUWMuC+X+MXPjr/oGFlt/NlfCO2ENioLM6eyZnGfsjYJ8ikpw5ZiXmI4m6ENogyz4nE4z4kCrYr6Ay7KL5SVDt7rzeoVeanfB69CwybsU0W6R11rCoOIa043rCD19QoHFVDJLgENawR6kxDqe7NurQg298JcFjZNo4acAudXosry9Ow4u9SqvTiprpSaJbghYgB662boSfkRHoSsiLog36Dm7gDoZ6K4vHgWAqPfHIx+vLZXHTJFWQ01KK8VY14dy2HlX9I++X9ePfmBFJK9hn/IJGagcS+7AFCoo5eStQpJ/gN33hthIo+XsP60kafqHPsPpcRU7oIiqxvvVk/Sj+SR82/AXC0/uMqye5CReh5SKuFnZtGcEGoCCXwL7b50/FjVvlEYKUQ4kwp5Ttf/VMkR+8PwDVA/U/cvxQpUqRI8TOTny/QdTBN8PlA/0Yg0rUbiLdeAG4E1TsZT+bVe7ShqIKDzvnxEfXdruNKLr6vkli4AJR8hAOjiqtoDrikS5tTvIfv8RopHFpnns778/281asBS5H0rvNw/0nn8kHzCorjefguLebXEUmDJ8I6TxQ94UeoLi9dXkL6/ens43QiAgIpwJQaWfUmQktG/AgoKLqg9Og1uG6yzLsMKTtTeqSU1OTVsPKQAI4AxYUJXIGeNmhnH48fPBCPpvJA+4vIsk2YmDQF/KB6wXVQwn1R+tyH5gXSl4DroOUO/tZx0krOwdp4K9gCT+7vULSS3cfx9YfBSoBUcF97GHXyMf/WvHyFUX4pVrAPuDZ6z6MB0A+9HA69nBLAqnkDe+PT9ImajMrOYIMpGTFiBAUFBT9os7GSPQDj4LsoblxJyZmDUTLSv/c1P4RTrvSw8F0bVYNB++6+IS/eLrFjySwjRYO2DS7zes/ly+gqrHTJkyeNZNb119K2PZlu8f7tCQ6/xUuaGqDLiaAJlTJvabKxfafAiafDe2/AkSfAqKSrTaLhdH7XGCUcdpnbv5uzjs7eo4921WdY8/8CmsGKmT1wFQeEYE2WxiFbExwUHUO4oYH+i+rRrriT03JGcnfiJjLWZzHjlt9RLRW6LIMPdZOX027m3pMeYH7DByT6G1g6bHG30ZgxhrwaD0QttvfRsGQbEljRS8WXXsIQtZp09wNG3ryEFx64lOp1Q4gKgdKVw5IPLuSQhWcibYl9SDZu518IFR7HyQ8eBUDr3HSyC/5JdOIGcDwYozewKHYJ97RcRkgJcEvhhRTpuQhVEPyDb7f3HmLP8fg6B+sHMkYdBUDW1yr1/hiEEHh734fV/i7CKECqEaS5Fi14FELxf38DPzM/RozvQ9JF5U0hxD3A5UAeyWj4/sBrwN6TwlKkSJEixf9YDj9UJx6HLVtdTjhOR1V3j1DZsQ/B7QZcnPjnSKcdof5wL+hvo2Wdw4Lb4shgjAOuDqBmSrqjO1IvXAURDjA6vZUsO4KCFyIbSGz8O8LIQlnfRp0vzm+nrKeLOAV9j6BxXRAFDxOGB8gx0jipaDL1L7azpdtGOBpFip+e+73L/M8PwrE8SNvGbOrB1t4RStyk+HEUQPUSxWRZsRf3iv6Md7bgL52Eu6UWpI0wMpFApxnBZQ3b9HnYAqQQoGo0xyrID+cjgkmRoSoKRw8sR7HG8ZC1jnSRxSmZl2IMXo80O1j1zxls+3MUI00w9f4x+PMVxHeIVz33ALTsZORcKMYex8WA0cjOVtC8iIFj/+152tmuamCUnPCtxzX/eJQrL4B4lP/H3nmHSVFlffi9lTpOjkxigCHnJBJMgFnAnPO66hpZdf10dQ27rmvOAV2zu7omzLrmiCKCgOScB4bJ09Op0v3+6IFhnAGGaNh+n4eH6a57T92qru46de85v3OwdyqHPvACYtj+O7QPNa8/al7/XR1qCwyPYL+jE0+YoVCsxbZAniCnj0rlPAd0l2dLXyReG2rWjhMKnjR/YhUC0D0CVag8Uvh/fNr4PaVGB4b5+wAJh0/ceAfc2FLHfcn9cagTBFE5riGDgpyfhG5UV2C9dzUEVXCiZFb4aOygg2uSGnO5uX4EG6d2pkvXtfTbuAgr9iQdD3iXe/2PM2faS0TMRAhQCnCMBa80SMLnPUG33rUox4/CUHQ8qkFW1mgq+q3HE42h+1cTc5eyQAg2EibdmUmlM5fxSGau7sJ7lQUUiUSSp4JNxsi+bCzbn9QFsxFjMsG3HmvjJNSUASjeUvT+l+KvGUxfrY5oThbpxkSOX3kbYRmj0Y3yRPVb3Ji/8y7izjrhWyL0LIzc07d4Z/wu29xd7EgC549CiMHAQ8BEYAxQAASAS6SUj+yZISZJkiRJkj2JoghOOqG1U7d5u94JMEjoCvthi2TByoYok6asIMPv4YzhZXj09qvZTrklRrhCYntcvn8gzoF/9zF+eA5vf7sR3Yhx4Zi3SfM1IvDhVfJRvroTJ1YHLigL63lx3wxqyQAhqDz2fY5dtoSBGdcwuFdTQqCUGO/fhM++ERsLB/BN6AzfWqC6KNnVaMVrSJVuojqlIskyjsI/6Ay+i9xEpVwArOWryD84qtNDIF1kbD12ycn8bsZjlMdq6ej3c1pHgVABmfDfUl5/DLP+HtSDLkMbdtrm452gH8Y4ZQxYdQglC1FYRLxesvqTMNKBeL1k1QcWPc9sXmGQkTrc2e9BVjFqt/2aT55QkeYa0PMQSsuZRvWiO5F9RqBaLurYE9r9eewyNVUtXsp1q9hWloB0EvHDP2dFRiEEY2/zUl1uck7FrUSMCEZYo1OwhJgW5uz0cQy8xseH9yZilg+e6AFscrR0Tko/uF370NNEIkJIgtZUZMv50cZcZOKOcWHW14iog/QnwieGLcgkr3QCdt13LFkUZdl9VyBNDz9oh/LIuL9yafWXAEjpEuw1jcgnY8E2mgJkoE+OQ6angsDFfblpYYjlwXT26/MPZkZuoUFfDjr0NE7lRzvKMrkRhCAkVDpJF7ciiBZV8LiC1YEoWabChILXaRz6X2YLiTqujH0itSQk/i1cezUKpQhFR+t0FCkkHgoAgqqfiB1HQyVT3XvKJL9GdigYS0oZFUJcCHQDRpJ4drw06YgnSZIkyW8XzTsKMq7FtZaj+caSKLqc4JZ3Z7EmZKMpCnHb4eLRvbZhqSWbFMqESIQIOM5afnfwaxw49FuEN4SquhCXFJQfRbD/ucTjR7DJ45U+QXY4ji7BQuJXXI4Y0JPCYHP8p7QswuvOJxUXUKjf7x3uLnkPdZJG0fJcDixaikcxSbclBYslht4Fp+dpCC2AVDzQFNEgcRGKjlGWUB6ZMn0pYqkPu6CK9Q0OS789mYKOU8ntlEHp+4uR1RtBmDjfPoU27DSklEhnI9gO8e+uQFp1KKk98Ay5G92voPnAagTVgJSSZulHKSXmY6dD/QYQCvLom9D6H4GUFrE1lyDNtaB48ZU8jtCal/qFZiBGn4QSCiHUlrd516nAtRej6v0Qyu4JA9lMWU844FD48A0o7oQ4ZOvxvbEPTBrviSM8kHqHH73Hz1eSRCgCXweJWRcHCRLJfv5BnJo7OtEgCMff1vzA89NFC2lLInMd9FwFo6CldOf739byjlrD2NIMuhb6KLvQg/2DTez6KJZmEn0lgu/OQaiP1OP0CQIKyqAD6PTGnSBgwYYjE0mSABKm1Izl3KIMPCSq3aYO6kHqDdey+qGrsGrz8aWonHm3xoapx7K6eB3FEZdudTH06jqspQJRaGCnhahxl1Almq4NkcjvKDLGELXOpfoxlxNtwbJgLT1HvkfZH75lnYjh4gIe6jUFr+IipQKs4rn4SyyOLuVU/zH0U5u//7d3uJgnat4iS03lvKwdm4WWUlL5qkVkgUPOcQaB3r/tkjU75IwLIbqQCFUZBPwbGAXcL4TIAm6R7c3SSJIkSZIkvyo07yjwtkwqXPODQ5dPu5BjwKxeS6hoiO6Qzf1u9DLt3jhuUGHIpQbh+j8iZS05XqhVBMKFlAoL338moQy4EL3377Hm/RNUP+rKag7q66HWiVOtKOwfj5Ox+gXcPvuh+IoBcN55Fds5nMStTlLjKcEUFqRZLBmwkv9bNgSl8ExY8wVKjkk0eiLrXopTNMLPsB6X8HXkdmwZZ4T/is1jXv66SeMTeRzhjmfO4B+pWNGJhQ2ZLJDjuPBWL6kZ91GvrwMkSodE3LdZcy9O+COgSUnCtXBDS5CNK1BSyzjoQT8r3q3Cn/UqmWUzcM0/A7ngWFCzZrPahVw9E/ofgYyvQJrrsN04GhInPBUt7cjtnm/XKSdW2yTdJzz4Mp7brclrQgjUO55E3voYQtu2exGeFAcbpA2Rp+Kk3fHzxu0GVC8XdxjPMxUfUurNZ3zW8Hb3XX1NjNgiB+lC8d+9BJoUWSprLR54qRzTkqz0x/jjYYX0yfVhfmQlKh9JkKYEf0e0+z9AXTADMXAUsQ/PAlWCEIzN+py3U8+ksUqjl+IS/GoE/wrsz9lHuKTmKhjT8whe+BIZDc9hpvVAn/0RlYv/wCuj4kjhYZqEk+ZquFcVUFZzLVLYLL7reko7H4HqVvG6+SAS8Ao/+3vPZdorKo6ZWOHpYWfS7w/vEMZEShWBAOGwall37r79RixLZ/9L/8MXoz5Eui6zGxfRPzSciBvn8rwT6OIp5Ob832/r1LXCdGyW1leSMi1IxbMOMgahaVF6vRxA9f98Kyh7mh1RUzkNeARwgZOllK8IIdKAJ4GbgdFCiFOllMkkziRJkiT5DePGVmCF5vDeDaNJkUFSbD9OmsU5vyvcITsZXVQOfchPKOTgC0JDTUIZ1wd46iVKrYsxLQy+xBK33u1ktK4nIoSCPMrGF3uSw803kU4c09VBSpyGWZudcfHNK3hFkLgcBkDeYbkYeFAQdFd74u9/fWIgnUuJrnWZ+8cIURml9vUogx7L5bCie1uNefWHNjIu0NHpP68fX8V0TDvhJMyfMoeCwz/EzctBMQ9F6ToS16rDCb/H5pp56QpEFUBBeBOKJsEiha7jbsY1F4IN8ao7IOUuhGag9DsCd97HoKiogxNJk2j53LZiCN/UdaDYG+KBvM78tMSJtKM4NbNw9e4o3oTEn2ttKqQdAylwnZWoSstEUbP2KeyGN1G8vfDk3NxmTPr22J4jDqDkKThhF1RQi9suBPVTXLlny5OPzxrB+KwRO9THNSWR2c7mj7fhC3uzM+5Kufl9CThu4oW2v475UsLjVbupiGyBEJ0RRZ0TjX05YK4GCX5PlKceqOPpqbXUPlKEcFTMKCye4jDkGAUWLULUxxGWhTe0BKdiGlVGHAE0KoLFQiOSvi+nVYMSM8CjM3TxHWR0zyAH6Kj1Yq29kl7BIejCILN7nMofHaQDKfmrkTIxzmxcLMCQkudfvIBIJPEQ983Tx+CM+goFsOr9zIgtxsXlpvIneb7TDTt0LmO2xVXfTqZSmhz4Qx8OthNKTNIBNyZ/0854+74BCZ4H5gMDpZSvAEgp66WUxwMXAfsAs3f/EJMkSZIkyS8FN7aG72b8jcs/WYllJ+I4dEXhuAGd6JTTdtW7eFyybLlDLLb1xVMhVDy+cwEFoWQSqD4S39pBaP790C58enNcsWgSMxeqRp7/LNKNQ/BHdXI36CAU1NQBzTYPOpacwJXkBc6m8JCH6NOzF//w3sXhyiCy7CV8Fn0E2eTgRctdfFJSLCVFcUlkjt3mOAv211C9CSGUzgf4KOys4vGB4YFu3Z4AbFAjWGmfEl98FbHZpyPUPEAH4UXNOR697Gy8wx5CGFuEiQidTbrkguYwIO34WzEunYzxpw9RChIhACvDNtNDxUgEFVY6X1X9RIbStYh9dwHmgnuJfXsObuNKABS9f1NskB+hBFC0zi36udY67IZXQDbixmbjhD/d6ue1q6Td6sM7Xsd3skHggm2r8FQ6NZxafSWn1lzJ0+HX9tiYdgbFEHi7KwgfCA+kDG9+EMnLNDj/mDzyMnX2H5DGmKEJaU6lWMF360K08a9h/H5Wq5h57yH3sSh8Ljf86zVuefXXww4NAAAgAElEQVQNKhu6c86Ynvi8KmriMie/a5P7Nm48ZGaCYcChh6F0Hk5ho8BjwwKhUqEoTC34kbA3nBijEHjFV3zy8gkcP28if1nzAhmUojfpgfc+w2DQJR56n2kx7MLryah38bgSVUp8liTd6kpRUSm67qCqLsX5AboqpfjwUiwKkE1PH5Zsu4y8G9uAvfEzZLy61bZFdRXUm1GijsV3PRdjF1gIDbKP09Ezd8Rd/fWxI2EqdwLXSSlb/UJJKScJIb4GXtxtI0uSJEmSJL84rPBi/rb0AOKuRmzE9wxe0JWCklQGnrjFDGp5OaSkQEoKkYjkvIsaaWiQBIOCJx4JEgy2PcPl9Z+MRzsGMXwfWPNRQkbw628hmNlmdVBFeCkMTER6zqNezsCb0xPFk7t5u3rkGSjd+jNf+4A5md8QDF3EIO/vqLK+xSbOIuszOuvD6KgNJq2/Sp4NMTUx+xf7YgMcXvLTIdLtFIPM3ipuXJIzWKWPDcvmumTlg99eB26TnndsIzTNhCrO4Sjp+QgtD9U3ss2ERSP7Gszqu0BKjOwrsZpEP4QQkFXcom2Gx5cIGQAECh0CLZPjZHR9wtlxXHAlTs0MlGApipqHL+MZXHs5itYTIVomfiK2dIpFi0Td3Y2SqRC8ZPuFgADeqH2bSqcKDZUXI+9wsv9IfKJ9ffcGHe/2EZ7uoOcLvF1axjYfc2A2xxyY3eI9t2ol9uSLwQJ73XOIC/6Nktd183YRyOHlj04iEpNEYvDapDiX3eHj7Ed8LJ7i0KG7QlGfpv0UFMCyVVBbCzk5ROMfYvZ1mODqzHYCVCkxYr4Yjz70ILcuuBk9vxr3nVu566ihmCrUx9byqvMZV2QkEo0VVdD5cB0pU4gt9aBHFErCNub8CI4FgfhaLr00SCCzipWNCxl90kLG+q4n5tiEvSZ/LX+GsBvlT/mntjpPbqyC2Izzmg5Sw9v7IfhxFqKkG6JjN0pSMhEIDEUj4ouT94CgKLjnrsFfEjuipvJ/29k+Vwix+zSUkiRJkiTJz0qFWceH1T9Q4s1h//Q+Cem24CAcuQ6A8pL1DB/tZcKoLXSur74K+fgkytNG4Hv4Dpbm9yEUkkRjIITkx7k2I/ZNzPw++3KIF98I07Ewzj+uXktaMIqy0gOrV0I4jEz3Y006HWQc0X042u8fRSitZ8iEFkRNG4TiaT0zb5d1Y3bDX5A4NLjrWGVNabFdaboNqh6BngfRKhepOtR5FqBa68nXh7Wymd2v2eHSDOg+KPHaNe/Fqn8BVc2D6k9AqUu0SR2CmtrSjmMtQLoVqMZwhPCg6Pl48+9qbhDburRhhtfHnQccyQcrFzMgpwMDcwtang9fPsJIBzMKShw1s7nwklAyUY22ZeIULRsj+zrs0GQU7yBU/8itjmFvkvfiq+hHp4Kr4IuDQfsrcu4NFI8gZeS23Sm3cQ3mwscQRhqaZ0hiehs3kZhbVw5NznhMhlhsfYWRMgxR7UVRISUj8eCVlq8w9Lg2Zoh1HXITD6GhyP2AiRBxfl8f4YGUII6Rxh9yzybQQUeGVOKA4biYqooKGKGFuNE1iBWVuI/eAkWdUC7/G95Ok7CePhe3aileJXGNi075GB5QujxNByfCnLWN+DwvMlg/gXw9i0c6Xrn1cxBaABKiKzpR89Up+CIfkhe6E4GFevvLZPQYxG37Hs3CaA19swooCra/OuyvnR0vbbUVhBB+IB9YvrtsJkmSJEmStnFdt12FVLZFY2MjqxtriNhxuqflt5ixdaTLxAWP0OBE0IVKpDDMqIzegMbEwf341+z5dEjJYExxcctxPPs0n/V4gLVpByIf1Rl4Xj2aFifoT/gfebmSUEgh1OjyyltVCAkbNzYy+Y0vGD/qUxRjOJ7cPJxwlFeHncwKpxvj+C9dl/6IvmIeIrd0q8eyiS1n0V1pQ2MAmygqBkFPMQM4haX2l5SoQ0ijEyGRGL99dSWVTy1BD9iUn/ISsn48AU/71WEgC7yXYtqN6CWH4dR/h/AW4qi9WkhwWPGpWOGHAIFQJxMOnM7C+GTSlBJ6eU9GFXqL42mLYsPPed0SITltXQey9z3E1/+AJ7sMR+a0lgDZKv0hkND5jjeNYXtjaQ+7YmPUG0torM5kdWEmR5SnEbmidcn0vTGOXbER/eYGiFaAUFByXURub8LrliKyOqLn9EaEQkgpeTV8DY2ymtRL3iH19cvJCmRx+OkGoZDVrrFEwxm4sg6kJKtGcvuiOGrhCIySDoQIAV7sg/6P62a/yYtd08ghzNjaFVSFrkK/fQEi3ACL5kFKJuoZlyH7XYZz60U4HRVEWS/0A6/lzfuqWfvVSTimh1rVZk6aRfFN27++pNKZSCSF1a9chbQ9CAqwlIWkq2+hTP8KpbArQVfhwKxSoO3ruj383NfrzrBNZ1wIYQJnSin/0/Q6hYSKynVSyjk/aX4M8Bxb1ttNkiRJkiR7BEVRSElpO0a7vXxevohHV36LEILRHbpzZd9m3eSwE6Naj+DoLjFc1ii1m/d3WJ/+HN632RFsMY6iItYFjsZ1PWBC7SyVB+5OZ9Zsh/79VEqKE7cIw5BIEcO0JYZmkZteS9AXAmUa/s+n8M4L05m8Kp+4ozCLvrwkJhLo0BHhb33MVfUmP66qZWAPgwzjDazwkwglB0/6PShqHkcG/sq/K/7NxliA4elDGBgoZjjHtbIT7BNk1TWTKfd8RkApoFvwEAxl585xSkoKZLZd+TIuZmKLxM0+1LCaKZX/RM8rJ8wisowCunuPbrax06QgtFG7fI1strYb7OysDffciZx0742EgtWk3fccYhfH8nMci6rUgxEHBIoewnvBk+g1laRm5mxuY0uLMGsBiRaM0PfqTxlRcT7RH1waNzhseMmi3AX7RJUJv29Wn9lyLEuX3MXsmXMZ3PtrOlvfIIJpeLuMQ/FuMd6hx7DP0GPot/w+rKp3mVfbnQDVZIZrEHETbBOiIdSUFBh2APKl73HCX2M776J4PmT59ycjG30ogB+oU2DDOosuXbZ3TlLwD5vExrsVcATgwWvkkKI5qCPGbv5cf85rbXfbaC/bmxnXaJnkaQBHAfftsRElSZIkSZK9wucbFhN3E2lAX2xY3MIZD6heDs4cyMe1s/ApBodnJ6IQzepHsUOTEXox3vx7aaUD8OGnFF22gfKaQqSqUbafSnFR4t8m3LmfoXzzEreNO5JXV42iJK+cklHfMQ8v3pXjKS7Lorb7QMzZYahKJ+4xid7yPEF/68IhtY0Wv7t7LooTxX6nkofPfYXsFAfpbsSOvoIRvIRFIYsPNjrE3GpmNTzBS32vJaAmYo6dcJwll64hUp5F9uGCPudeQCA4EYG6x4rRaJ6DsWMfs2xub56783psqZB/yId0PuffWIT3yD5/CUSjrxKPf4HHcwg+37h291NOuxB57Bko0Rgic9ul03+pGP2uxpxzJ0ILYPS4AACht4x714ROV20/lttTESh033AoKy+NgiuR8YTj2xH4+mWLuqMt0nNahussWezwjxvTMM2RvO8dyaSnID3TAyjMj9zPBvNT0rW+9DMuQdVzIb2SOx+6kRUri7HNdP5y1gy6v3IJ5BWinP3HZsO6hRm9FzBxraUMHD2KKW8UYZoQ0gAERR3b913R0tLocKFJxbMmnkKdrNP3Qy07DZFTsP3Ov2F2W5hKkiRJkiT5dTEitzML11QjEOyTU9pq+59Kj+P3hYfiV70YioYbXY7d8Bq1DWmsrUilxHiNZTJKntGJMuPwpmTDLI54NpPV0x38GYK87i0XS2XNOuznrgArTnfje24890GmZ/zIel3l+7/9mbol3VCJctI1GcgKH0iBIjU+nZPJSd1bH8OydRFcV2JaLqoBi9eXkZ0yHdARSj4A680anCatbke6LF3fyIOPb6Qx4nKVvhSxpj9S6lS+a+I7TJDSfc/eGlV9IJo5hm/fHoLVVMp8w4eHMvS86XTz/HJKdO9OLGshpvkMECMSWY6u90TTytrdX/gCCHvXpQ3bWw6l0Yzz4Iyp1MWi/GHQMErTMnZpv1revsxc9hKT7jYJBAXX/N0lpY2aS4f6rqLOLcenpBH/zkOIODIOCXHEptAr4aK75SRc82bWrGwqiCUTzauqPWRkqTTYS9hgfoaLSZ05g/INZ5MjOxH1rGb+/GvIViN0j6vMWb8vvd6dv9meKx0WOTPQnBgFm0oPScnoUxbT96AyamokFXXQu69CINi2ekpb5BxjkHPMpoTvfbfZNiwb+Nh8DceN0IVUCvSB5Gh92r2vXwtJZzxJkiRJ/kc5pKg3PfJLaLTiDMnp2GabdL1ZzcAqf5+1FQVccdvdAHiyaznqxmtZ55EoQqOLcQgAqibotG/btxcZDTUlrwG2jawpJ55WjR3TqJnXG+mqOMDyGWBoAtMCVRXoW8nZ614cwGuoOChIVWFg7/NRlXSEWozmS+hyj80cwJuV31Ier2F0Rn9emWyystxESnjVLOGETWLQuCjevRNpaeReSJceH7N0YRyJTmEXD2OCd+6VfW8NKR1WRh4iZM+lg/cEcjyH7Ebb8S1eCaSM7Tbb7aEmEuOPk7+kqraOCYN7cv6IvttsP2nmND5fvRzHdbnuiw/59/iTdnkMk+42aQxBuFHywhMmF1wJLz5r8vlHDvsMVzn3Ih0hBBlqIdJ1Ee9cihK5GlfxoaTp1MajLBUqi0ZMwU0Zi7TjuPUrifo6UCNX029YGWnP69Q6ks5lCp27JL5nmgiyWfAc0B0baa3B40+hW95aztqYRop00T93cM5yUZuqiL4ee5i59hRMXNLSOjAkVsdwuqEFDqGgTKUA2OQW72L6ylZ5JnYny535SOmwQLr0MF9ndPAOMtT2P8j9Gkg640mSJEnyP0zvjPYvDwstnR+mDca2NSzHwKpSaKzMwZO+lgZn3Tb7Sil599saZs6q4bBeQ+jj/IjyTTXyn49Rds99zOdWgkXriG0sBKnSZ1+Vv+zj45l/xelapnLUES2Lz7jxNdj1n+DzdePpP+3DnCUb6V2WS6pfA65p0TZNC/BsrytxcNGEyq3eDSgKuA6sy4U89RnC9QPIOdqDnjmo3edjVxBaGmPOO5bcXg4NNZIhY37+23GV+QnV5ie4xFkZeYA0fQiG0rbyyo6i631RlAMxzW8xjAPRtN7b7bMrTIk9zY/mu+SoXRjvv4n3569ifUMYVUpem72MkwZ2I823dX3zRjOO47pIIGK1Tp6cMcPh4YdNcrIF1/7ZQ3r69sM0AkFBuFGiapCaLlixzOHtV23icfj0A5tBw1QGDml6GFw6H23m23SMvYvl64F76tlcUmlguxJNgTdmTuO4yocJmRZfHASu4UMROvc+9ShmQyoZmWwOs/KrHejjv5p10cmk1c0nLQ4oEo82kat6P0Z99R/xIECTxL6z8RygoWUqLHZmECdGHYJaUUe5z0O6dwKDxLZ14Xcn1e4GXBwQEAVwHerslUlnPEmSJEmS/G+iF5/AgP6voHwMPs3G8NsEs2vwigy6ebZdjn3qvBCPTl5HzAzwrfo7nhp5M5meGsTM2Xgr6xgxZTWD972ERZFT6XDY2ZQ0hbdskkG0ZYS5kScxZQPdPKeirJwIbhiEga/oJvp06klgwRfY/30WMWB/lCN+1yLmWwiB1qQvcPGpOdgOhMIOF5+aQ2H+VSAdhKrvskLNlsiYJPx8HBmGwJkGyk8Klwgh6LcdSby9y09DONoX0tEehFAIBq/abfa2Rb27gdnmOziYVDpLWWR9Tm5Kb3RVwbXBUBV8+rbP+/kD92FNqJ6GeJw/Ddu/1fZb/hYnEoEN6yXPPmNy+cTtO6jX/N3DC0+YpKYLzjjfYPmy5tUBCahbLspk5yUqdNKIKudhdSxBrd6I7Too2BiN70NWiJoGFUdo2ETRJdSwiJKsfVrtO9cYSa4xEkebgxuYhZo6HMVXRuphDo3fR8F1kKisnhTFnuTyzTnz6Df+IKY67wKJvBIHB/9jqdS/FULprBC804/YSs2A3cUE41z+Fb8bKS06OQ5GHPJNDXK33/fXRHt+BY4QQuQ3/e0ncc2cIIQY8JN2g3fryJIkSZIkyR5BSkmsSuIgaVVHfRsIxaDHQafx8Pe3sfz7cgYqU5Erriaz43HbTXasqjNxm4rgSClotP34dS8PXHgxq+d8xml6LvuwgT4lT6MXlSDlAbjWIsy6u0HxsNAw2OCuROIQspcxVJqEwwF03UWPr0Q2ZuLcezGYMdzF32N1mosM1KJlnI6eenCLsaQFVW74Qz6V8x2mXh9jUYrNqOu9BHLaGjlYa9/HrZmJVjwONaN1eIO0Lfj2U8jIRvRpvhU2Phwj9pENLtiLHDIeDbT/ZP8MZBmjabBn02jPo4PnRAzlV5osSXNRJFDwiTQOKC1ihqtRGa3id8cXY2jbDkcqCKby5BGtFXc2sUnuXohEWFZ7KChSuOqm5qTNklKFk8/S+fRDm31HqvQd0PywJjJzUB55A/f9l1GG7I93yAj+llfBM1+9T2f/Cg7r8CO14SAPfnUuaz7twJDDPqbHPrPIVhN65Xaty9qro1jlLllnG2SekHhYUAN9UQPN17DWbyTpjzbgrwmw7PIwiq1goJD7SSbe/ffhwrxRfGi+yXRnCiVVZXR4uwRscFe6mB9aqAdMxW2cheM7EFL6tes87Aj99eH0jt1BdMolmFocj+tF7514OFhjbuStuq8pNfI5Im34Hku43hu0xxk/tenfllywlba77zF6JxFCHAbcT0Ji8Qkp5W0/2S6ath8BRICzpZQ/CCGKSUgz5gMu8LiU8v69OvgkSZIk2QvMvj3Ohi9s7ECEA+4OktJxx0pNF815liIRBRsap7yLOOh4AKTjINS2nZzRgzN455salq+LMLp0EUVd+jO59Ay+rluFiFn8I30EL4o3UQZrWLH7WGM9Q9RpoKMbxa9IYraGFAoIMAnz4ZeX8tbrI1FVhyv/Jinwr928L3egH1ebD5aDVXk3WmAfhNo6W+7Lv8aIVklQJNMfjrH/Da3H7lR+h7XwIXBiOBVf4zvgBdD8IFSEaCr2c/VZMPMbkC7iT3fA/olVAqdcgrXJzs9+e9wuitDpErj65x7GLuNT0jjSfx2zzbcpVPvQRRvOA7ebzP46HU3X+Ag/vbZZxnD73HSzh0cfMcnJFZx11s4XIRp/vM7445v7N5px/vLFx6xqqOecfoMYd1WzCzO4OI9+h2VjrX8FhIfXF5xMeTQfy/Hx3dtHcfWB5+NXEommdZNNzFUuOFD1T5P0cQaKt21nVXgEWjcVtTtEFlggYElpOaO0ThSqXTjHdyVnyYkIFBqUJv1tBUxzGnLl39F0G3PDh7jpT6IYeTt9LraGGuyEp+N41DXvoWT0Qc3fH0c6XLb6PhrcMF5hoAqVw9JaF+j6tbA9Z/ygvTKK3YRI/DI+DBwMrAW+F0K8JaWcv0Wzw4GuTf+GAY82/W8DVzY55inADCHERz/pmyRJkiS/auyIZP1nNtIBOwJr/2vR84IdiwEVfUch504BKREDD0KacdzLT4BZU2GfA1DufgGhtXRQAj6Vx67u1lSQJ7Gw6i79DmpXbbKKU5iCosVYKyxWyxpcRVDt0RnpmHSSNhFh4OClr28iN77bH8cBx9F591WF804wwfICJmQXgmqB3LbCw+bZTSRu4xSic+7ETDkDUs7Y3EbGKpvkKRJY5a9gVrxCbYaGJ/8kclJOh+lfgplIUJSfvr3ZGQ/83kP9/0WQcQhevPfibJNAiTaQEq258uiSRS6mmZhpW7po11VZ+vZVeeRRX7vb2zLGd7GnCLuVDPGeSabaqc12ry+az/zqSmzX5aEZUxlb2gXfFtnLeuZRKJ5SpNNAVlEX1LmrwQGvGsNT+REUnAyAmiEQWuIroHhAtGPqtfNtKXw3eTXfxJeS2jeV7luMUREqBCFwm4/4ZIsVKtz1dD9U7UUu/f1NFOQvQZprYRed8egih413NCIEdLjZh69HQmLU6HkhRs8LN7eLuzHCbhQAU1qsMSt2ab8/N9v8eKSUX+ytgewm9gGWSimXAwgh/gNMALZ0qCcAz8mEvtFUIUS6EKKDlHI9sB5AShkSQiwACn/SN0mSJEl+1ahe8GQI4nUS1YD07js2Kw6gXvEIcuZniSp9hd1h2hew8EeQkti8WXh/+AZ1nwPa7LvlUvJxpQNZVFfBmuoKzu57AGnZ+xMJ3UAccJuaOVIghUEAhWEECaQ+iVCCFJTEWLXMRSgST24M94bLUZeuxO0axO0ZAwzQctAzz9k8K246Du8vWYqmKIwprmDYFZ8w84kzMDxh+k+YBDg4lS9D52ZnXO1wEGLN28jQMtanHMbHUypI69iVrllLwP4PsWiMwgOPhK8+ANdFHNmsuqH3UMl6I9jquJPsfU44XePReyw0DU44be/H6f8Qe4El5kc4WFSFl3Jq6vNttgvoBkrTtaIKgaq0vm7UQELD5PjRLpVrp7NuYyOnDPgvzvp1yLzxCNVP+gQDpw7iKxwyT/Mg2hFKo3gFw0/tSM07BTx2o8V7xLj0cp2DD2l+GND6aWj9NB45IYIjNRxL4+3/nsYFZ91GvcdgxrKJpP3jArz1BZT80U/6iB1bNah6PI5WnVhx2nhPjI6Ptx3a5Ve8HJN+AJPrPiddTWFc+sgd2s8vjV9S5sjuoBBYs8XrtSRmvbfXppAmRxxACFEKDAS+2xODTJIkSZKfC6EIRjzso/xjG3I8dDiw7ZvlwroNXPv968Rdm+v6H87I/Gb1AqHpiKFNsnehEOQVgOvy0PDjeb/ncFKWruehXo3kBROOqC3j1DjLSVOK8GxR0dKnGfxtyPgWVTyD6U/QtW4adfJ1YqKOHr5zSPUchuusQVGLESIRc3vVrR7un7SR6es38L69kUinQ7hq6Xysi4shzQEZRahF6KmHbt7frV9+zXfr1iGABesWcFGfbzjob1NR3IG4FVGQChgtA8eFFsA34jFips2VD0yhMWahrbE41f82vTotI2zPR7npUfhxGqRnIUq7ttB5SzrhvwwOGKMzaKhGY6NNh4KdDyvZWWKyHqcpEdKUWy/sNK5rD8obG1hWW8OZfQdiqFuRCJWSmdNcuqcGObvvJDQRAeEDkTg2oQqyz21ejXHDG4h/+3/EwyF8Ay/CUzJ2q2N49WWbTQIyr75st3DGN5FfpBAJuyjCpbg0FU/PfzIjdgPefx+JtqoAR6qsui1O+ls7dq7VFDbXEVPTEt+d2rUuXz1q4gnCARd78KYm3v9D7tH8LvtIdKH96r9nvzVnvK1P46eBettsI4QIAq8BE6WUDa12IMT5wPkAJSUlOz/S/0GGDBmyQ+2nT5++h0aSJMn/Nt4shc4nGYRC8Rbvb6hwWL7KoV8vjUcXfEGDlVB7eGDeZy2c8Z8iuvZhzVkvkf9ST343T/D5sJm812Mp5wwagC3jvNl4CTG3DoHKhJQHCShbyZQE5NTP8Lx/D6MAddw1qEP2AzeOqnVt0S6QIlibu4x6twFsmFEyGErLEg4JAhAI4W/RZ1FVFaaTCF1ZWJu9aY8owX6oylCkVYnHeyhtEYrZxCwHicB1NDbW5NC702o6eE9BKAoM2HbxkiQ/PympIpFx+TMwyHsalc4SIvEKhj8fxlH/ijzr8lbtdFXlkiHDt2vvhWcs3n3dRtV7Mqf3nVxy5ovo+acglJbOr2UvRwgDZ+HzvPrZSN6YfjJ+T5RbH3IpLml7VaxXb4Xa2sT3pHfftttccbOH/0628AfgkKN7EI01omLg+qNI1QVbAa/NR6E/kKp2ZLBvIprwtmmrUTaw2JlLidKF3Mu8RJ/TEAJyLko8TLx7Q4zatTIRVibjHPrnZjuGsvcfrPYEvzVnfC1QvMXrIqC8vW2EEDoJR/zfUsrJbe1ASvk48DjAkCFDfvkZOUmSJEnSDlatsbnoT/UoAgIBhT4XpKErFTiuS7Y3uN3+K37oh+4kfhL3ndWbDmcnZofrnFVE3Tpsomh4KLdn0tXYejEZ5+vnwU48JFgbn8Rc9yQgMLL/guYf0aLtoX0LWVnViBCCkd1zUa6cjseqwKx8INEnt6Wzc2q/vjw87fvE331KEUoeQuuC7p+ACCTif+NbkTbMTvEwsnsOXy+sJFsLcMT8iQRW6aScuucVUiqs71lrfk4HfTgFxqg9vr8ku5+gksux0b/gjhsM8Rj45iI79YBxJ7Zqa8sYKp7Ns73SbcQxpyDUElS9JwAzpzvEYqC5MH9RCd6ym1vZaYg8QSQ2GSwTT6XCG9Nfw5UqjbEgb71qcfEVrfMYpLS4fGKUfv2DKAqMHtM6qVlaJr5Hr+boed8jTrkUVT+ZutAsDLGchlPX4omlkV43lGWn30jMXUWjW0660pnu3tbHGpNR/ha5DFPGkcBl6TfT+S/dWrQxI4AE14V4Y3vOdvtxnvoH8rXHoKQr6u0vI1J3rdLqzvJbc8a/B7oKIToB64CTaa0E8xZwSVM8+TCgXkq5vkll5UlggZTynr056CRJkiTZUVzX3WVN7MbG5jvb9zNiqEoEy0z4Cod5BpGeoxB1LE7sNHir+9pkw1Maw15t47oSb6nFPjnZhEIhhExDhr0gVWwkPtmRUDzUpg0Aq2Qobjjx2i3VIZJY2hfrX8Kb21JWcHTXTDql96HWCvG5503+XPlfzjZOpENKouiPFQVo3teInDzSeh9IXpZOQaYPm9GJdo325nZbjuWnTDy4lHNHFVJxt031/DjVIk79+gidLm+ZyLctG+1lk42wU843jXcgMVnNNIYGUknT2k7+25qN3TWWpI1dsyHr6nE9CSUeDC/hUAhli++VlJK50cdZb03BIzIYFrwJj0gjVjcRZC0gMYJXoRoDGHO4xYZyG1UPM+ZwlVCodbJyVd1HTbnHBrEyl6y01dRHstB0yCtQWbfGww9f2QQzwwwZCa5TQbzuzyAjDO47HIjv3foAACAASURBVE/qZUQirY/D/eR13CkfgRmDh29GyS9mre8j4gGJhzixsx4iP+1JfgyV4zbqKOhETJOQ1fo3ZI2zHCvmYGGj42GROY8cpUOLNiMvc/jiYRPdBwPP8hAKtS7CtCXt/XxkbRXOW88nQnvWr0V55wWUcWfukI3dxW/KGZdS2kKIS4APSEgbPiWlnCeEuLBp+yTgPRKyhktJSBue09R9JHAGMEcIMavpvT9LKd/bm8eQJEmSJO1BUZTNcda7wiYbw4b6eeY/CpoOPr9Cvx7pDNnGDPZPbQy7LEh2kYUdl5QdZ2BsLgaSwnHB+1lvzyJb7UaaWrTNccgTb8Sdsy8IgZX3JW5sNgiBFuyNGvTzmPkk850FHKtPYLR+IP1SUrgxdiczGn4kGohTKar5p+9uAJzGBcjoctT0kdgijYm3r6ai2kZR4ky6MYOCXJ3aOpebb41SWely2UVeevVgm+c1JQUq14XxRBOrAMo6hZQUfxvtds9nE7fieHBwsFBR0QJRUvT2294d49hddv5XbITsxSyO3IUqPHT3X4tPbapym9ID98yLkC9Mgv7DUA6d0MJO1K2i2v0KzWPhUkWdZxplxiFo5jog4Wxr3kUYgf04/CgYNNilvsFDt+6tZTsBHNGPWOwzEBJDgesvvIlPlj4PGW9TeNDT3HnZ40RrM9C8FjV6NfsM+4gsfy0JzZkv8QUuRijprey6hoZrRcGMIr0O3H8euf0zqD4zExSNgNaN9JRsRngnMjf+DGlKKX39x7cZplIme+DXfU3KR5JezoBW57b7iMS/HaE9n7FUBY40E0/tqg8lvwBli36767vTHn5TzjhAk/P83k/em7TF3xK4uI1+X9N2PHmSJEmS/OYpLlR5+qF0Vqx26NNTxzB27OdQNQTdTjba3OZXMulijG6XHaFqqAOOJB6yefPiHlRvzCRn5BKGXtKZZfZXfGZ/QZw4D5uPMVDtT4aSQSORRMlsIJwomo3TMJv4kmsTNtc9x/qsp9hQbROLSwwdps0Jc/SYdJ79V5z5Cx0cB/56W5T/PLP94y650GDZ3+MIFUrOb/uYdxeZWm+ytb5U2NNJV7uRqw3ao/tLsm2kayFde5ttFkVuJ+auA2Bp9AH6Bpu1wpXzroDzrgBA/GS1SRdBFHRcLBRUgmohiBQUvR+utQAAzdNcDTSvg4I/uHU1pLTANYg1XyN8JqoF3owYx56ziMWNL2DGNRqrU5EuSFMyed5s/jX8C65VvfRxYiACuN9+jahYjxh7HCI1c7NdccgJiLnTkHOng1oLZpSMmfVk+TvgnPNHMvREBdAiYxRF2wmrMoSH6/33s9JZQoFSgghvuxjT7kT4g6i3vog7+XHoORhx0NF7bd8/5TfnjCdJkiRJkp0jJ1slJ7t9N8Nqt5rnrRfR4hrnBM8gIHZv7PSCp2dRs6EzrtQp/7o7V+R9xNhDPLBF7qfblHt/Qs05PLzxacxu5ZyljOGd8M0UxVy6uVailZTkpTXi8yggXbw1QVZ+a1DVx8UwxOacPq3pjrgg+gwrzffIVPswNHAtimiZJJY5SifjHQ1EQp1mT6IIlWHBm5v02ZPzRT8n9oavMGf/nZhpENj3OtSctovMKKib/1Jof4KhJrzsl3IXK+P/JVPrTgc9kcjpSb0d116KouYglMztWGlGCBVPtCduzRwQAiVtOB4lG3DRPCZlI6ewYup+ON4YobGfYWPznW8IA9xOKNNiyHsnIh0b3n4O7emvmu1qOurV9yXOyV2/R87/BlQfKbkHorYzpyHqxlhjbaSjno9X8dFDS1TvDLHjoXexqOSt501iUZhwpoGyA56t6D0UtffQHd7n7ibpjCdJkiRJkh3m7/E7WeYux3AMovEof/L+cadtOR+/if35GygHH4dy1CkA+Dx1COGCBCkkESVO1Wofw/L3YaG7kGP1CWQpmbzzaT2hewNc4JyPleNl8T0XYOtR1nkNAkEvhREXxV+GJyWHx292efy+GIvXKMz8GtYtjXPjw16qql0qNrpc9HsvYWcZy5w3cDGpsmeyzvqK4jZm9YW6dx3jpCP+82MtehxcC1yBueif+LbijPcIXMeSyP2owkOZ/7Id2keqWko//4Ut3hNCRdW779SYPX1vxFr5Aige9NJTEKqPbsEbqTa/5PxrMpF1fv5qPoGWWYWKwTDPURjqYJxZVyFjTQHj65ZvtbquevlDyKnvogoDZeSR7RpTvdPIeetuIerGSVWDPFl4HT6lbaWV9vD8Aybff2HjurB6mcvlf99pUz8bSWc8SZIkSZLsMLWyDhcXB5sqWb3Tdty532L9+BB2iYX45Bb0Lj1Reg6g65nDCC15jY+rBjKn30rwR+mY/RlHiVPI8jerpMx4M8LYWAB0C896lbQf+1A9+HtAwe10EV63G8JXihAKmWkKAUXDthNOfm21JBgQ3HBtc8z3xnrPlmK36KJ1PHiS/01EoBgZ3QiKhhIs3Wo7v9qR/il7XwdirbOCec5Meqj96ah2AUDoaRhd/9CiXVAdQFqgKdwpF65vmMgiYwleuxLiX7JKq6PkyDNwPn8THBsx9vg2HfGEfQ9iv2NRQqF2PzDOji0h6saJyjjCEcyPr2Swr8dOH3fVBhe7KaezZuOvU+Qu6YwnSZIkSZId5mLjfO6M34cXL+cZZ++0HXfZZ+BVQAhkgRcqlkLPASi+FIbcdTa5jY/x5dqppPoiFKTWUW1+TJaneaa6qC8o8wUuoAlBQV4Z9cwmX+1BF88YFNEypvuYMwwWzYkRboTT/tA6hMCnZDHQcyUr4m+Tqw8kT2t79jPJ/x6e/tdhrXgFNepi9D5lt9v/5juTZ16I0a1M5fIL/eh6+1dDqt2N3Bm9Fgeb93iZ6/z3kKsUtGjjuC5//vwjpm9YRyAzwhkHdOAE33F4UOm3/t98ElyGowhWWh8zLz+dfV94mYxIDuQU7tbjLDOKkUg8TeFfnfQO2+nRjOVUELVn4tV6Y6gJleoTzze4988xLBNOu0RnU7Lrr4mkM57kV0WycFCSJL8MhmiDeEl7jpAbIkXdBdWBPvvD4vdBSlBU2KdlZUCPmkrPvPXIpunqDL05ge3Bxud5Z8Jn9FAO5vAFwxk6PpOlyuFUvTeUHr0z0Pq2Tq4sKlV48KWEFOHWZvIKjf0oNPbb+WNK8ptE6AGMbmdjhEIIzbf9DjtAJCL56x1hLAvWrHPo0knlmKPaDt3YWOFy49VxaqokJ51lceyJUOGWI1BwcNDQWe+uaeWMz62sYE7VBgDC9Tovrf2SPql9yHfCWNZy5OavgyRGLT/IRxmb+/huPU6AAj2bRwuuYU5sKQN9PcjU2laD+SmOW8fahguR0gYExWmT0NUCuvRUeXByYgVLCEEoFNvtY97TJJ3xJEmSJEnys6F1GYU29mrUmvmofSag+FtKqeV4j8Nya2i055PjHU+WJ1Eh05Uub8Y+RgrJ3PHvU3B4Hb21s7n5wcXELZcp82vokOmlW2HrgkXJ+OskvzRcyebwKCnZXI6+Ld542WJjhUS68PK/bCYcJylTe5IpsqmSFaSKdLqr/Vr1y/EHcOWmnQjOevQ0lPn5rE0L0/HKTPL9lZR71URiMqCJ3fvAsSUlRj4lRv4O9TGdVUgpkcQR+Ijbi9GbZCN/7d/ppDOe5H+O5Ox6kiS7Fyld7Mi7SGcDWuBoFHXr5e7bQis7ED1lXJvbFGFQFGilRosiFIrUfDY4laiolKml1IRMNombKAKq6k267d4V9iRJ9gjBgGDiRX6efSFKl84q4w5vXR1zE9k5Al1LOOwBPygKqMLDdf57qZVVZIgsVNHavStISeWW/Q/mnys+QFMqKXu2K8JSsMOS+sW3MKTz58S1jswTX+JKm/7+i3boGKTr4Dz8R+QPnyAGjUG9+F6EsvukCj1aN1QlFceVCOHBq/ffbbZ/bpLOeJIkSZIk2SXs8GSsxqdAWjixz/DlvtDuvvKbz3FffwF3vzEo409qfn/+D7gPXk84s4Rlx/2Vzt2ySAu2vLHfn3Y9H8S+Jk/NYmC8B4HsFAZ0SWPqwlq6FwUZ2q2NgiXxDbjhJagp/RB6y+VxN1yBG25IVPZJkmQvc9hYD4eN3boT/oP5KTPsj+kxfgQTzENYv1ZyxNGezbPCqlDJFnnb3MeQDoUM6XAublSy9P/bu/M4uaoy/+Of59bSS1KdPSErCYQkJDFgCPwEAkQcdgd+Co4BBwFHA4g4447+xlFQcAZ1VGQRXBAUcYGENSzzU1bZ0cSQSCAb2UlCtq7uqq7lnvnj3pBOJ92pqq6u6u58369XvdJ1657nPvdWuvup0+eeE28kn8uTtTw3RJ/lG/1nk4jW8j7eX1L+bvHzuL/+EVqacX/5I27x89h7ZpJN/o5c6nEidScR63PhXr3YO7c7XvtLnrHjPRIdrEbvWR2j+91GJr+KeGQ0XpmnU60mFeMiItIpfm4luAzgcPnNOOdj1v5iJLu4DWtxV5wPePDHh3DDR2FHHx/E/NqFNO1I8cnRXyd18xYidUl+/q0xDOq/+9dWPy/BR2pPBDySmRwRz/j2xye1Oye3n15LetGlwfEiNdQdcScWCcaa5pY/TPbV/6YlGyc7/SJih88uw5URKY9N+dU80HILWTKsYxkXXzCOsdEpNDZmSorn1Rmrv/0Wi+avZ+XQ1aw4ZBNr01uY1HffK+QWwvr2D8fbAM5hiQHks0vJJu8E0uSaNhKJH0EkvrtHO51yfPWyFC3NwfCcL33HZ9LUDvK2Wmqjpc+80l2pGBcpgYa6iOwW6/MR8ulnwaWI9rmgoEIcgK1bwCwYK+sc7qnHYPBQbNxhkMvxZs1EUl49zX6cupxj0RspZh2zu9c6u/NRslt+AOaRq/8SJIJZVtobP+o3vhZ+0Qw4/NRqIn2DX+y5pb+HfAZ8yL9xj4px6VbSpDCC7yvDSNHU6ZhHTBjDjemH8FJ5BsQSjKvvuFe9Necc+dRz4CeJ9Al60m3cVLxPXIN77gHsuLOxsVMgs6RVI8Jl73fbvNGRboaWNMTisHxpvsNivLdSMS4i0gP5vk9jY/Gr1bWWTCaLbuMyGWhKYgMGtooxCFd/B7gMeaujpcC83KhxuA98kOSfnwRzcN/dMPcu7PqfwlU/ZuitP6AmliIaM8wzxgzL7nHOqXV3QT4oUJp23Eu0vv2V9FY8lGHVw9NoGD6Hw8/6FV5NHbncwHeXJM/0n05+2xaa/BiRhiPIduLalnJdu2uMcsVRjM7F6e+GM6VlJktyL3FIZCoj3AQarbFTudTicfP4f2HFO+uYOHgMmaY0GQqbiSSz417yyfsAh9U8Ra72yuCF954WPAAaG3FuFFl3Dvn0M0RqjibXMh7L7P7e6tvPMfigNNu2BjeNHjw+W5Wfa10RoxgqxkVEeiDP80iUYWxzMTHcWytwF/wDNCXhzHPxrrvl3Rguu5X061fhMhuJjfwEsYPOKyzod2+DdWtJnHkk5HLBtheexPviNTTMPJVbt+dY9EaKww+pZdjgPecFj/Ufg5/aHCz1bWPbPZemjT4rf2X4mRp2bjuZ1MzDGXvWSCy6e8ypO/ZK8qPeQyzZTP9Jp2PFrKm9D5V+b7oyRrniKEbn4pzHlWXPJUGCRLSu6BjppoX4dWHR7C0m37dv+zEa5gBz2o31rR8nWPmmz/BRHhZJFpSLczn8ncsxbzBeYtBer3en97gQBf4tUUREDnTu/t/Aju2QzcDD9+B2bH/3tezGe3DpteC3kF1zG84vfCyrJRpg0jSo7wu19dis0959bVD/KLOOSexViAPUDPt3ogMvITZwDvGB/9J+/Nb3fZoRbRi3RyEebPaIjjmZ6OiTOl2Ii/R02dTjpLd+lkzT3TjnyKQcS1/M8c56H4Bo4sNgcbA40b5nd+pY8Rpj4tQIDf0Lm57Q+SnSb3yUlo2X0bL4HPKP3N2p43cH+okjUiUady49jU2YgquthZYWaOgPffpCKhW8FhsAFg3GhEZq21TA+4lrht05H156FkaPxcaOL6ydV0e8fzADS7qDP23XD/F477/GefPeHEOOjDDqJP3qE2mPn1tLtvGHQAt+bjlmh3HzZyezY5PD9+FT369l1MQT8GruBJfGi40ueGhaWfJrWYxz26DOwzVE8ef9iMgZ5V8RtZL0E0mkB1NBL5Vkp38IPA+3fCl29kexaPArZMPWFramT+XQYUlIryI+4mNYAcW489Nk1t9Mevvb9Dn0k3gn/MN+25Tq4NPiHHza3ityikgbLk0wgjvQuC3L1vWOXBbMgzdeyTNqYgQvWtx6AuVisdEQ8SCbh4hh9ROqkkc5qRgXEZGC2ann0PqPyQuX7+S6P2zAMzhqwkyu/vglBcfKvn0n+W2P45qN9IqvUD9lbvkT7oBLNeOu/TKsWoZ97hvYUcdW9Pgi3ZFFDyVadza59KNE4tPpN/BoBo7IvNszPmFG+RbyKYUXHUbN8BvJv3grke1D8a76XFXzKQcV4yIiUrKnFm2lJRuMI/3z4m17zPHt/AzZNbfit6wnPvISvD579mC53M5wqrMo+KlKp4772Q/hwd9DpgU351x4eW3FcxDpbsyMeOJS4olL39326RsjrFyYZ/Boj0Ejuu52w3zyNbLN64gMOAkv3n7Pe6TvJCIf+EGX5VFpuoFTRERKdtT4BmpjHrVxj8lj+u4xx3d2/V3kNs/H3/ES6aVfxjm3R9vYsIuw2rEQSRAf/eUKZw40N4Hvh8lmg1VHRA5gjzyU5d8uS/Hr2zN7fL/Ga42J/yfatYX4zlfIrPpPsut/Svr1y4u6CbynU8+4iIiU7PipAxkzYhCbd2R43+F7Lj/vcjvAhdMV+imCVT92F+tefAh1E35KrrGRaBWWoLc5n8ctfAXWroSvXIdFqvvnd5Fy2plbTHP+LQbFjiPm9d/v/uvW+Nx+a5ZMC2xcn2PSlAgzjqnc94TfvDT4eeFykE/icjuwDnrHexMV4yIi0imTD+67z+2xEf+Mn1yMy2wmNubTha/MWYAd2VdZ1fR9opZgfN9vAvvOoSM2YBD2m8fKlpNId7Et+yqvJ68BYI3dzYx+v9zvTdXZHHvcD5LLVPYvRZH+s/Cjc9k+MIfFBhGP1BwwwzcOlPMUEZEK8+KDqZv6U+qn30ds8Klljb2y6b/IundI+W+xJnVbWWOL9HQ7s0vwyeDTQtZtI+v2P/Xg2HEeH/qnKIMGw8xZEY4+tv3i3fmOppV5sjs7Ltj9zUvILf4dfuO6/R7fqx3JzpGH0dzg0VS3jU3N39tvm95CPeMiItLjeFYHbgdGhIj1CUbAiAgAQ2pOYEPLXByORHQSMetXULvZF8aZfWHHU4A651j0tTQ7X8tjHhz5ozoYvPd+/paltDxyBTgfFvyc2vPuxWo6Ho6Wd1vxyAOQ898uKOfeQMW4iIj0OIf1vYY1zbcQsX6MrruUVFO1MxLpPuojYzmq351k3BbqvTF73FjdWdkdsGNB/t3bQTY/lWPQuXvv529dGn6RBT+KS67HaiZ2GHtQ/UU0cj1gDK6/vGw5d3cqxkVEpMepixzMhMR/4ha+gnvsetyM4+DkM6qdlki3EfMSxCj/jdGxBMQHGtkdwZ+jGibvezhLZNRxZGO3ghnWbww24ND9xq6LTWNI3/sAyvoBortTMS4i0gP5vk9jJ5egTiaTnc6jmjHcpg24y2dDKkVy/jxo6I9NnFqVXHprjHLFUYyuiVOtGOOvd2x9KUf9KI/Y4al2YtTgTv8lrmkzlhhOtmn/awkkk0k2PJVl2S8z1A72mPbVGmoGFnd7Y3e5rsVQMS4i0gN5nkeiDNMB9uQYbtkSXEsK0k1QV0/i7fXYjM6votmTr0lXxChXHMXomjjFxHhwXpaH5uaYMs3jis/HiUSstDwSMGhUIXkkYEDh0xM637HoRo9ILk52J2y6P8rkz9YWl1u7uVQ+RqE0m4qIiFSE833822/E/8oc3JKFnQ84dTpMORIiURg2AmaVd8YWkd5k00afX/88y6a3Hc89k+eZJ/LVTmlvBpGw9vaiEGs4MIaqqGdcREQqY95dcON1kGrGPfEIPLsMi9eUHM5iMbjjIUjuxJxhfSq/cJBIT9F2mv/uuMaVmTHj+jqW3Z6hfqTHIRd0PLNLb6FiXEREKsJtXAeZcInrdBrSKehEMQ7hTV6Jflgnx8+L9HZDhnp88ooYD87N8Z4jIhx3YjesxoF+EyIc9Z26aqdRUSrGRUSkIuyfLsE9fA+sWQWf/DesYf9LdItI+ZxyRoxTzohVOw1pQ8W4iIhUhA0Zhs1/BefcATVtmYhIR1SMi4hIRakQF6mM9bnXuT/1HQDOqfsqI6KTqpyR7ItmUxERERHphR5P30ST20aT28b/pG+udjrSDhXjIiLS7bhsmsyDV9Hyiw+Te/2xaqcj0mlrVvtcdnGKiz/azMK/VmZawXrrh+FheNRZv4ocU4qnYlxERLqd3MJ78Fc8jdu+mtxj38Rlmvfaxz3/FP7x4/FPnoJ7Y3EVshQp3M9uzvD2Bsf2bfCj77ZU5Jhn1X2BidGZTIzO5Ky6z1fkmFI8jRkXEZHup4Bx5e4/PgvbtgRf33At3PFgV2clUrJEwvA88H3o06cy90308QbwwfovVuRYUjoV4yIiUnXOz9Hyt2/jv7OA6KgziE27CLdxCW7zUiLHXY7F6/duNGAgbFgDXgQGDKp80iJFuPTKOJFIhuZmxycujQO5aqck3YSKcRERqbr828/gb3kZ8mlyq+8nOuIU4mdd22Eb+9GvcN/9OtTWYZ++qkKZipQm0WB87qrdi1xpnSrZRcW4iEgP5Ps+jZ38bZ5MJjudR7li5Fs8Mi2x4G/4XpRsKo/Hfs6vbz+4+gYAmpLJsqzC2Z2uSXeIUa44itE1cXpTjHLF6S4xiqFiXESkB/I8j0Qi0ek43SVG34NOJOuvx9/8ApEx5xAbelhV8ihXnN4Uo1xxFKNr4vSmGOWK011iFErFuIiIVJ2ZET/kfDjk/GqnIiJSUZraUERERESkSlSMi4iIiIhUiYpxEREREZEqUTEuIiIiIlIlKsZFRERERKpExbiIiIiISJX0umLczE43s6VmtszM9lqSzQI3hK//zcymF9pWRERERKScelUxbmYR4CbgDGAycL6ZTW6z2xnAYeFjDnBLEW1FRERERMqmVxXjwDHAMufcCudcBvgtcE6bfc4B7nSBF4D+Zja8wLYiIiIiImXT24rxkcCaVs/XhtsK2aeQtiIiIiIiZROtdgJlZvvY5grcp5C2mNkcguEtAEkzW1pUhoHBwJZ9xFYMxahmjIOLOYBU16uvvrrFzN6qdh4iIrKXon6f9rZifC0wutXzUcD6AveJF9AW59xtwG2dSdLMXnHOzVAMxejOMaR7c84NqXYOIiLSeb1tmMrLwGFmNs7M4sBs4IE2+zwAfDycVeV9wA7n3IYC24qIiIiIlE2v6hl3zuXM7DPAY0AE+IVzbrGZXRa+/hNgPnAmsAxoBi7pqG0VTkNEREREDhC9qhgHcM7NJyi4W2/7SauvHXBFoW27SKeGuSiGYlQohoiIiHQxC2pTERERERGptN42ZlxEREREpMdQMV4GZpY3swVm9pqZ/cHM6sPtB5nZb81suZktMbP5ZjahVbvPmVnazPq1ivF3M9tkZkkzu7HEGCvNbFuYz6tmdm4JMZab2XYzWxg+Likhxq7rMSE8n6tLiLE0/Hph+PyOEvN43MxeNLPF4TX+fZEx1rS6HgvMzDezR0vIY1X479/N7Nr9nIsfPpaE17HezOJhm+1m1hLGa/fYrbYNMrMnwvfhxjb/f48ys0VmtszMbjArbg5FERERKZ2K8fJIOeeOdM5NBTLAZWFBMw940jl3qHNuMvA1YFirducTzOLyoV0xgBnAAuCRcJ9SYnwYeAL4BXAR8JsSYryHYPz8HcDpBGOQny4mRqvr8YfwfC4qIY/TgMYwj/cCE0qIcSRwNPAMMBVIAk8UeS6jgYfDPC4Mz+u+IvO4LnztF8BRwBeARR3EcMCfgevD410GfAo4GbiKYCrOd4D/18Gxd0kDXwe+yN5uIZg7/7Dwcfo+9hEREZEu0Otu4OwGngGmAe8Hsm1uHl2w62szOxToC3yJoAjbtU+Tmc0FPkLw/pQS469m9niYxwKCD123Fxmj2cyebnUujlY3BRYSI7QT8AlmrvGLPZdQEzCeEq8pcCrwBlAfxkg5524pIY9d7+1RwKYS8nDh9TgszMknKITbi2HAv4cx5oXHHgFs3XVsM9sOxJxzz7Rz7F+GsZuAZ81sfOsTMrPhQINz7vnw+Z3A/2X3h0ERERHpQuoZLyMziwJnAIsIemBf7WD384G7CQq8iYQrgLaKsRYY1MkYi4CPAeudcy0lxDifoDC7HXjUOZcrMkYD8FGC3uSh4TkVdS4E00wOA/6R4MPAhhJiTCIoYmeFuXQ0DKOQa3oa8GQJecwDBgL/DPwOeM45t7WDGNlWMc4Jj50GPDOLmtk4gg8Go9u0e/fYZja0gzwBRrLn+7I23CYiIiIVoGK8POrMbAHwCrAa+HkBbWYDv3XO+cDcfcR4ugwxniMo4v5YYoy/AWOB7wHHmFltkTGWAS8CN3fiXOYSfBg4lKCYPTMs8ouJ8QWgAfgA8EPgUDP7QAnXY3V4TTIEw0OKPZdFBL38w4CrgelmdkgHMaLAXwh69OsI/l+9QDDM5pXwXJ4Dcm3atT72RzrIE/b9wURTLImIiFSIhqmUx64xwe8ys8XAefva2cymEQxV+J/wXrk4wRToR7ba52JgK0HPZ9ExzGwU8Cfgms7kEe73J4K52acSFIEFxTCzZ4DDCYaIDAZqzOwzzrm2NxAWmsejBGOmJxSZx2zgdOfchrAozgDTafMhpcD35QcEQziKfl/M7CbghXAI0IthHjOAFe3EMKA/QbEddc5lzGwRkHTOnRju+xzwZgfHXgHctK9cQ2uBq+1i/gAABEZJREFUUa2ejwLWd7C/iIiIlJF6xrvOnwiKz0/t2mBmR5vZSQRDCb7pnBsbPkYEL9vBbWKsLSWGmfUnuNnwq8CPS4wxLhyaAUEPdz1wQjExnHMn7NpO0Lu+HmgpMo8hZhYJm6wM8zi+yGv6GDDNgllungIG0GpoR6Hvi5l5BD3N15b43q4GTg5v7n2BoLd+cnsxgOZWMUaGMZ4n6Gn/lJmdQlCo9+ng2Lva7ZNzbgPQaGbvC/P6OHB/e/uLiIhIeakY7yLhSp8fAk6xYOq6xQQF1nqCoQTz2jTJhdsxs1XAfwMXExSN5xYZ4zMENzx+Hfgr0Af4YJExZgILw57kecClwLHFnksbd5RwPU4E/mZmC4F7CHrGjy8mhnNuG8H1fJnghtZfAweVcC4nAmudcyso7b29ieDmytfCXL4PTC4wxrxw+1CCDxPfJyiaDyqw3R7/r8xsrZnt+iBwOfAzgg9dy9HNmyIiIhWjFThFRERERKpEPeMiIiIiIlWiYlxEREREpEpUjIuIiIiIVImKcRERERGRKlExLiIiIiJSJSrGRURERESqRMW4iIiIiEiVqBgXEREREakSFeMiIiIiIlWiYlxEREREpEpUjIuIiEinmdksM3NtHkkze9XM/tXMIm32H2Bm/2FmL5vZdjPLmNlaM7vXzD5sZtZq31PM7Cfhvukw9qyKn6RIF4hWOwERERHpVe4G5gMGjAAuBn4ITAHmAJjZMcD9wFDgAeAuYCcwEjgTuBe4Arg5jPkx4ALgNeDvwJEVORORCjDnXLVzEBERkR4u7Kl+AviSc+57rbY3EBTQw8OHAQuBWuAs59yz+4h1GjDAOffb8PlIYItzrsXMvgh8F3i/c+7JLj0pkQpQz7iIiIh0GefcTjN7HjgXOAQ4j6BH/PJ9FeJhm8faPF/X5YmKVInGjIuIiEiXCcd+jw+fbiEoyjPAHVVLSqQbUc+4iIiIlFO9mQ0mGI4yHLgSOAJ4AdgIHAwscs6lqpeiSPehYlxERETK6erwsYtPcJPmHKAh3Laz0kmJdFcqxkVERKScbgP+ADigCXjDObcVwMwS4T6JdtqKHHBUjIuIiEg5vemc+//7esE512hmbwGTzKxOQ1VEdAOniIiIVNZcIA5cWO1ERLoDFeMiIiJSSdcDm4HrzezYfe1gZqea2ezKpiVSHRqmIiIiIhXjnNtoZh8kWIHzWTO7D3ia4KbOEcDpwEzg8l1tzGwacHb49Pjw3wvNbGb49Y+dczsqkb9IuakYFxERkYpyzr1kZlMIpj38R+AaoA7YRDAF4jnOuQdaNZkOfKtNmE+0+vrXgIpx6ZHMOVftHEREREREDkgaMy4iIiIiUiUqxkVEREREqkTFuIiIiIhIlagYFxERERGpEhXjIiIiIiJVomJcRERERKRKVIyLiIiIiFSJinERERERkSpRMS4iIiIiUiUqxkVEREREquR/AcL8Zhz1eVyOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figureWidth = 10\n",
    "figureHeight = 12\n",
    "\n",
    "plt.figure(figsize=(figureWidth, figureHeight))\n",
    "\n",
    "##############\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "# Data Panel\n",
    "##############\n",
    "dataPanelHeight = 4 ; relativedataPanelHeight = dataPanelHeight/figureHeight\n",
    "dataPanelWidth = 5 ; relativedataPanelWidth = dataPanelWidth/figureWidth\n",
    "\n",
    "\n",
    "dataPanel = plt.axes([0.45, 0.05,relativedataPanelWidth,relativedataPanelHeight])\n",
    "dataPanel.tick_params(bottom=False, labelbottom=False,\n",
    "    left=False, labelleft=False,\n",
    "    right=False, labelright=False,\n",
    "    top=False, labeltop=False)\n",
    "\n",
    "def curateData(tetramerDF, n=2):\n",
    "    '''Given a dataframe of 256 dimensions, returns a df PCA of n dim and a pca for explainedVariance'''\n",
    "    features = tetramerDF.columns\n",
    "    x = tetramerDF.loc[:, features].values\n",
    "    x = StandardScaler().fit_transform(x)\n",
    "    pca = PCA(n_components=n)\n",
    "    columnNames = []\n",
    "    for column in range(0,n):\n",
    "        columnNames.append(f'pc{column+1}')\n",
    "    principalComponents = pca.fit_transform(x)\n",
    "    principalDf = pd.DataFrame(data=principalComponents, columns = columnNames)\n",
    "    return principalDf, pca\n",
    "\n",
    "tetramerDF = pd.read_csv(inFile) # should be a csv file of just tetranucleotide counts/freq (the standardization fixes any discrepency)\n",
    "finalDF, pca = curateData(tetramerDF, n_components)\n",
    "\n",
    "def makeRainbowList(finalDf, colors=None):\n",
    "    '''Returns a list to make a rainbow'''\n",
    "    red = [x/255 for x in [255,0,0]]\n",
    "    orange = [x/255 for x in [245,182,66]]\n",
    "    yellow = [x/255 for x in [245,236,66]]\n",
    "    green = [x/255 for x in [66,245,84]]\n",
    "    blue = [x/255 for x in [66,69,245]]\n",
    "    violet = [x/255 for x in [245,66,242]]\n",
    "    \n",
    "    if colors:\n",
    "        colors = colors\n",
    "    else:\n",
    "        colors = [red,orange,yellow,green,blue,violet]\n",
    "    transLen = int(finalDf.shape[0]/(len(colors)-1))\n",
    "    transMatrix = []\n",
    "\n",
    "    for n in range(0,len(colors)-1):\n",
    "        color1 = colors[n]\n",
    "        color2 = colors[n+1]\n",
    "        R = [x for x in np.linspace(color1[0], color2[0], transLen)]\n",
    "        G = [x for x in np.linspace(color1[1], color2[1], transLen)]\n",
    "        B = [x for x in np.linspace(color1[2], color2[2], transLen)]\n",
    "\n",
    "        for x in zip(R,G,B):\n",
    "            transMatrix.append(x)\n",
    "    while len(transMatrix) < finalDf.shape[0]:\n",
    "        transMatrix.append([0.5,0.5,0.5])\n",
    "        \n",
    "    return transMatrix\n",
    "\n",
    "color = pd.Series(makeRainbowList(finalDF), name='color')\n",
    "finalDF = pd.concat([finalDF, color], axis=1)\n",
    "\n",
    "'''\n",
    "for n, index in enumerate(finalDF.values):\n",
    "    x = index[x_axis_pca-1]\n",
    "    y = index[y_axis_pca-1]\n",
    "    color = index[len(index)-1]\n",
    "    if left_coor < n < right_coor:\n",
    "        linewidth = 7\n",
    "    else:\n",
    "        linewidth = 0\n",
    "    dataPanel.plot(x,y,marker='o',markerfacecolor=color,\n",
    "        markeredgecolor='red', markersize=3, markeredgewidth=linewidth,\n",
    "        linewidth=0)\n",
    "'''\n",
    "# I think scatter is faster? The stuff above does basically the same\n",
    "# thing as this, but can also highlight certain points\n",
    "x_pca_data = finalDF[f'pc{x_axis_pca}']\n",
    "y_pca_data = finalDF[f'pc{y_axis_pca}']\n",
    "\n",
    "dataPanel.scatter(x_pca_data,y_pca_data,c=finalDF['color'],s=7)\n",
    "\n",
    "xmin = min(list(x_pca_data))\n",
    "xmax = max(list(x_pca_data))\n",
    "dataPanel.set_xticks([x for x in np.linspace(xmin,xmax,20)])\n",
    "\n",
    "ymin = min(list(y_pca_data))\n",
    "ymax = max(list(y_pca_data))\n",
    "dataPanel.set_yticks([y for y in np.linspace(ymin,ymax,20)])\n",
    "\n",
    "dataPanel.set_xlabel(f'\\nPC{x_axis_pca}', fontsize=18)\n",
    "dataPanel.set_ylabel(f'PC{y_axis_pca}\\n', fontsize=18)\n",
    "dataPanel.grid(color='grey', linestyle='-', linewidth=1, alpha=0.1)\n",
    "\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "# Variance Panel\n",
    "##############\n",
    "varPanelHeight = 4 ; relativeVarPanelHeight = varPanelHeight/figureHeight\n",
    "varPanelWidth = 2.5 ; relativeVarPanelWidth = varPanelWidth/figureWidth\n",
    "\n",
    "\n",
    "varPanel = plt.axes([0.05, 0.05,relativeVarPanelWidth,relativeVarPanelHeight])\n",
    "\n",
    "# pca.explaned_varaince_ratio_ gets plotted here\n",
    "rectangleWidth = 0.7\n",
    "centeringShift = 1-rectangleWidth/2\n",
    "xTics = []\n",
    "xTicLabels = []\n",
    "for n, variance in enumerate(pca.explained_variance_ratio_):\n",
    "    rectangle=mplpatches.Rectangle( [n+centeringShift,0],rectangleWidth,variance,\n",
    "        facecolor=( 0.2,0.2,0.2 ),\n",
    "        edgecolor='black',\n",
    "        linewidth=0)\n",
    "    varPanel.add_patch(rectangle)\n",
    "    xTics.append(n+1)\n",
    "    xTicLabels.append(f'PCA{n+1}')\n",
    "\n",
    "varPanel.set_xlim(0.2,len(pca.explained_variance_ratio_)+0.8)\n",
    "varPanel.set_ylim(0,max(pca.explained_variance_ratio_)*1.1)\n",
    "\n",
    "varPanel.set_xticks(xTics)\n",
    "varPanel.set_xticklabels(xTicLabels)\n",
    "\n",
    "varPanel.set_ylabel('Explained Variance\\n', fontsize=18)\n",
    "\n",
    "\n",
    "##############\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "# Colorful Genome Panel\n",
    "##############\n",
    "genomePanelHeight = 2 ; relativegenomePanelHeight = genomePanelHeight/figureHeight\n",
    "genomePanelWidth = 9 ; relativegenomePanelWidth = genomePanelWidth/figureWidth\n",
    "\n",
    "genomePanel = plt.axes([0.05, 0.5,relativegenomePanelWidth,relativegenomePanelHeight])\n",
    "genomePanel.tick_params(bottom=True, labelbottom=True,\n",
    "    left=False, labelleft=False,\n",
    "    right=False, labelright=False,\n",
    "    top=False, labeltop=False)\n",
    "\n",
    "n_chunk = finalDF.shape[0]\n",
    "for i in range(0,n_chunk):\n",
    "    left = i*chunkSize\n",
    "    rectangleHeight = 1\n",
    "    rectangleWidth = chunkSize\n",
    "    color = finalDF['color'].iloc[i]\n",
    "    rectangle=mplpatches.Rectangle([left,1],\n",
    "                                   rectangleWidth,\n",
    "                                   rectangleHeight,\n",
    "                                   facecolor=color,\n",
    "                                   edgecolor='black',\n",
    "                                   linewidth=0)\n",
    "    genomePanel.add_patch(rectangle)\n",
    "\n",
    "genomePanel.set_ylim([0,3])\n",
    "genomePanel.set_xlim([0,chunkSize*n_chunk])\n",
    "\n",
    "xmin = 0\n",
    "xmax = chunkSize*n_chunk\n",
    "genomePanel.set_xticks([x for x in np.linspace(xmin,xmax,30)])\n",
    "genomePanel.set_xticklabels([\"{:.2e}\".format( round(x,0) ) for x in np.linspace(xmin,xmax,30)], rotation=90)\n",
    "genomePanel.set_title('Colorful Genome Reference', fontdict={'fontsize':18})\n",
    "\n",
    "##############\n",
    "\n",
    "\n",
    "plt.savefig(outFile, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 2761\n"
     ]
    }
   ],
   "source": [
    "print(chunkSize,n_chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets call this program HoG finder! (Horizontal Gene finder!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backup in case I fuck up\n",
    "figureWidth = 10\n",
    "figureHeight = 12\n",
    "\n",
    "plt.figure(figsize=(figureWidth, figureHeight))\n",
    "\n",
    "##############\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "# Data Panel\n",
    "##############\n",
    "dataPanelHeight = 4 ; relativedataPanelHeight = dataPanelHeight/figureHeight\n",
    "dataPanelWidth = 5 ; relativedataPanelWidth = dataPanelWidth/figureWidth\n",
    "\n",
    "\n",
    "dataPanel = plt.axes([0.45, 0.05,relativedataPanelWidth,relativedataPanelHeight])\n",
    "dataPanel.tick_params(bottom=False, labelbottom=False,\n",
    "    left=False, labelleft=False,\n",
    "    right=False, labelright=False,\n",
    "    top=False, labeltop=False)\n",
    "\n",
    "def curateData(tetramerDF, n=2):\n",
    "    '''Given a dataframe of 256 dimensions, returns a df PCA of n dim and a pca for explainedVariance'''\n",
    "    features = tetramerDF.columns\n",
    "    x = tetramerDF.loc[:, features].values\n",
    "    x = StandardScaler().fit_transform(x)\n",
    "    pca = PCA(n_components=n)\n",
    "    columnNames = []\n",
    "    for column in range(0,n):\n",
    "        columnNames.append(f'pc{column+1}')\n",
    "    principalComponents = pca.fit_transform(x)\n",
    "    principalDf = pd.DataFrame(data=principalComponents, columns = columnNames)\n",
    "    return principalDf, pca\n",
    "\n",
    "tetramerDF = pd.read_csv(inFile) # should be a csv file of just tetranucleotide counts/freq (the standardization fixes any discrepency)\n",
    "finalDF, pca = curateData(tetramerDF, n_components)\n",
    "\n",
    "def makeRainbowList(finalDf, colors=None):\n",
    "    '''Returns a list to make a rainbow'''\n",
    "    red = [x/255 for x in [255,0,0]]\n",
    "    orange = [x/255 for x in [245,182,66]]\n",
    "    yellow = [x/255 for x in [245,236,66]]\n",
    "    green = [x/255 for x in [66,245,84]]\n",
    "    blue = [x/255 for x in [66,69,245]]\n",
    "    violet = [x/255 for x in [245,66,242]]\n",
    "    \n",
    "    if colors:\n",
    "        colors = colors\n",
    "    else:\n",
    "        colors = [red,orange,yellow,green,blue,violet]\n",
    "    transLen = int(finalDf.shape[0]/(len(colors)-1))\n",
    "    transMatrix = []\n",
    "\n",
    "    for n in range(0,len(colors)-1):\n",
    "        color1 = colors[n]\n",
    "        color2 = colors[n+1]\n",
    "        R = [x for x in np.linspace(color1[0], color2[0], transLen)]\n",
    "        G = [x for x in np.linspace(color1[1], color2[1], transLen)]\n",
    "        B = [x for x in np.linspace(color1[2], color2[2], transLen)]\n",
    "\n",
    "        for x in zip(R,G,B):\n",
    "            transMatrix.append(x)\n",
    "    while len(transMatrix) < finalDf.shape[0]:\n",
    "        transMatrix.append([0.5,0.5,0.5])\n",
    "        \n",
    "    return transMatrix\n",
    "\n",
    "color = pd.Series(makeRainbowList(finalDF), name='color')\n",
    "finalDF = pd.concat([finalDF, color], axis=1)\n",
    "\n",
    "for n, index in enumerate(finalDF.values):\n",
    "    x = index[x_axis_pca-1]\n",
    "    y = index[y_axis_pca-1]\n",
    "    color = index[len(index)-1]\n",
    "    if left_coor < n < right_coor:\n",
    "        linewidth = 7\n",
    "    else:\n",
    "        linewidth = 0\n",
    "    dataPanel.plot(x,y,marker='o',markerfacecolor=color,\n",
    "        markeredgecolor='red', markersize=3, markeredgewidth=linewidth,\n",
    "        linewidth=0)\n",
    "\n",
    "xmin = min(list(finalDF['pc1']))\n",
    "xmax = max(list(finalDF['pc1']))\n",
    "dataPanel.set_xticks([x for x in np.linspace(xmin,xmax,20)])\n",
    "\n",
    "ymin = min(list(finalDF['pc2']))\n",
    "ymax = max(list(finalDF['pc2']))\n",
    "dataPanel.set_yticks([y for y in np.linspace(ymin,ymax,20)])\n",
    "\n",
    "dataPanel.set_xlabel('\\nPC1', fontsize=18)\n",
    "dataPanel.set_ylabel('PC2\\n', fontsize=18)\n",
    "dataPanel.grid(color='grey', linestyle='-', linewidth=1, alpha=0.1)\n",
    "\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "# Variance Panel\n",
    "##############\n",
    "varPanelHeight = 4 ; relativeVarPanelHeight = varPanelHeight/figureHeight\n",
    "varPanelWidth = 2.5 ; relativeVarPanelWidth = varPanelWidth/figureWidth\n",
    "\n",
    "\n",
    "varPanel = plt.axes([0.05, 0.05,relativeVarPanelWidth,relativeVarPanelHeight])\n",
    "\n",
    "# pca.explaned_varaince_ratio_ gets plotted here\n",
    "rectangleWidth = 0.7\n",
    "centeringShift = 1-rectangleWidth/2\n",
    "xTics = []\n",
    "xTicLabels = []\n",
    "for n, variance in enumerate(pca.explained_variance_ratio_):\n",
    "    rectangle=mplpatches.Rectangle( [n+centeringShift,0],rectangleWidth,variance,\n",
    "        facecolor=( 0.2,0.2,0.2 ),\n",
    "        edgecolor='black',\n",
    "        linewidth=0)\n",
    "    varPanel.add_patch(rectangle)\n",
    "    xTics.append(n+1)\n",
    "    xTicLabels.append(f'PCA{n+1}')\n",
    "\n",
    "varPanel.set_xlim(0.2,len(pca.explained_variance_ratio_)+0.8)\n",
    "varPanel.set_ylim(0,max(pca.explained_variance_ratio_)*1.1)\n",
    "\n",
    "varPanel.set_xticks(xTics)\n",
    "varPanel.set_xticklabels(xTicLabels)\n",
    "\n",
    "varPanel.set_ylabel('Explained Variance\\n', fontsize=18)\n",
    "\n",
    "\n",
    "##############\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "# Colorful Genome Panel\n",
    "##############\n",
    "genomePanelHeight = 2 ; relativegenomePanelHeight = genomePanelHeight/figureHeight\n",
    "genomePanelWidth = 9 ; relativegenomePanelWidth = genomePanelWidth/figureWidth\n",
    "\n",
    "genomePanel = plt.axes([0.05, 0.5,relativegenomePanelWidth,relativegenomePanelHeight])\n",
    "genomePanel.tick_params(bottom=True, labelbottom=True,\n",
    "    left=False, labelleft=False,\n",
    "    right=False, labelright=False,\n",
    "    top=False, labeltop=False)\n",
    "\n",
    "n_chunk = finalDF.shape[0]\n",
    "for i in range(0,n_chunk):\n",
    "    left = i*chunkSize\n",
    "    rectangleHeight = 1\n",
    "    rectangleWidth = chunkSize\n",
    "    color = finalDF['color'].iloc[i]\n",
    "    rectangle=mplpatches.Rectangle([left,1],\n",
    "                                   rectangleWidth,\n",
    "                                   rectangleHeight,\n",
    "                                   facecolor=color,\n",
    "                                   edgecolor='black',\n",
    "                                   linewidth=0)\n",
    "    genomePanel.add_patch(rectangle)\n",
    "\n",
    "genomePanel.set_ylim([0,3])\n",
    "genomePanel.set_xlim([0,chunkSize*n_chunk])\n",
    "\n",
    "xmin = 0\n",
    "xmax = chunkSize*n_chunk\n",
    "genomePanel.set_xticks([x for x in np.linspace(xmin,xmax,30)])\n",
    "genomePanel.set_xticklabels([\"{:.2e}\".format( round(x,0) ) for x in np.linspace(xmin,xmax,30)], rotation=90)\n",
    "genomePanel.set_title('Colorful Genome Reference', fontdict={'fontsize':18})\n",
    "\n",
    "##############\n",
    "\n",
    "\n",
    "plt.savefig(outFile, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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