Analysis/data_count.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sra_dump_pickle_dir='/cellar/users/btsui/Data/SRA/DUMP/sra_dump.pickle'\n",
    "\n",
    "\n",
    "sra_dump_df=pd.read_pickle(sra_dump_pickle_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "myDict={'Allelic base counts':'/nrnb/users/btsui/Data/all_seq/snp/',\n",
    "'Genome coverage counts':'/nrnb/users/btsui/Data/all_seq/chip/chip/',\n",
    "        'Transcript counts':'/nrnb/users/btsui/Data/all_seq/rnaseq/'\n",
    "       }\n",
    "myDataDict={}\n",
    "for Type, dataDir in  myDict.items():\n",
    "    #break\n",
    "    fnames=pd.Series(os.listdir(dataDir))\n",
    "    myDataDict[Type]=fnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "for Type, fnames in  myDataDict.items():\n",
    "    srrs=fnames.str.extract('(\\wRR\\d+)',expand=False).unique()\n",
    "    sra_dump_df[Type]=sra_dump_df.index.isin(srrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "sra_dump_df['ScientificName']=sra_dump_df['ScientificName'].astype(str)\n",
    "sra_dump_df['ScientificName']=sra_dump_df['ScientificName'].str.replace('_',' ')\n",
    "sra_dump_df['ScientificName']=sra_dump_df['ScientificName'].str.replace('Drosophila melanogaster','Drosophila\\nmelanogaster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sra_dump_df['LibraryStrategy']=sra_dump_df['LibraryStrategy'].astype(str)\n",
    "\n",
    "sra_dump_df['LibraryStrategy']=sra_dump_df['LibraryStrategy'].str.replace('DNase-Hypersensitivity','DNase-seq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "myDict={}\n",
    "for Type  in myDataDict.keys():\n",
    "    tmpS=sra_dump_df.groupby(['ScientificName','LibraryStrategy'])[Type].sum()\n",
    "    myDict[Type]=tmpS[tmpS>100]\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(ncols=3,sharey=False,figsize=(8,3))\n",
    "\n",
    "tmpS=myDict['Allelic base counts']\n",
    "ax=axes[0]\n",
    "tmpS_sub=tmpS[~tmpS.index.get_level_values('ScientificName').astype(str).str.contains('metagenome')].loc['Homo sapiens']\n",
    "tmpS_sub.sort_values().plot.bar(ax=ax,color='grey')\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "ax.set_ylabel('# of sequencing runs')\n",
    "ax.set_title('From human with\\n allelic read counts')\n",
    "ax.set_xlabel('Library strategies')\n",
    "#ax.set_yscale('log10')\n",
    "\n",
    "\n",
    "tmpS=myDict['Genome coverage counts']\n",
    "ax=axes[1]\n",
    "tmpS_sub=tmpS[~tmpS.index.get_level_values('ScientificName').astype(str).str.contains('metagenome')]#.loc['Homo_sapiens']\n",
    "tmpS_sub.sort_values().unstack().plot.bar(ax=ax,alpha=0.5)\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "ax.set_title('With genome\\n coverage counts')\n",
    "ax.legend(loc=(0.0,0.3))\n",
    "ax.set_xlabel('Scientific names')\n",
    "\n",
    "\n",
    "\n",
    "tmpS=myDict['Transcript counts']\n",
    "ax=axes[2]\n",
    "tmpS_sub=tmpS[~tmpS.index.get_level_values('ScientificName').astype(str).str.contains('metagenome')]#.loc['Homo_sapiens']\n",
    "tmpS_sub.sort_values().unstack().plot.bar(ax=ax,color='grey')\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "ax.set_title('From RNAseq with\\n transcripts counts')\n",
    "ax.legend(loc=(0.3,0.5),labels=[])\n",
    "ax.set_xlabel('Scientific names')\n",
    "fig.tight_layout()\n",
    "\n",
    "#ax.set_ylabel('# of sequencing runs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('../Figures/sra_data_processed.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('../Figures/sra_data_processed.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScientificName           LibraryStrategy\n",
       "Danio rerio              RNA-Seq              2594.0\n",
       "Drosophila melanogaster  RNA-Seq             12133.0\n",
       "Homo sapiens             RNA-Seq            188388.0\n",
       "Mus musculus             RNA-Seq            107545.0\n",
       "Name: Transcript counts, dtype: float64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpS_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b028d53c6d8>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BlMjhSDnHj59g6dKVNGpU54Lyw4ePEhsbS1xcHCNH/kC1apUBiImJoUePd6hZ817atHmK667Lxc6de6hZ8zaqVKmI6i8sWDCJsmVLMW/e+ADMyOFIOe55njHxV3nYDSzAvFnN6fVxpJz2wDj7fZw9Top2wDhVjcX4SbRJ4XgnMGF2R4jIA4DHH6IBMNQu+qcBuUQkJ3A38D2Aqs7EvJ1PjPEekyu7cPb4OlTHKAhHVDUaGGP7BTiHMa8CE/p3iU04uAmTRwTgTqwfiKpuB/YC5RKRYQXQS0ReBUrYDNtgFIYNGH+dYkBZWx6LyVqNneedCfoTjGI2316b3oD367rJ9t91XvImJoMjAKxZG06ZMqUoWbIYYWFhPPTQfUyf4WI9ODI+efPm5rrrjEVwliyZqVfvTlR30bfvq7Rs2RiAggXzx9dv2bIxW7YoAFmzZiFbtqwA1K9/FzExMWzfvoPhw7/nxhurI3IH9es/yI4du2nUqG06z8zhuDTc8zxj4pfZkqq+ndaCXAvYt9/1gAoiEgeEAHEi0sOXSZiIVMIseueLCBjl7Q+M2c8WoCow1Ue7uRgn6rWq+pR1rK6PUUResDIEA7USLnTtOL5k6QQ8bQ+bJjNVn6G9LFFec40FzgKoaqzNYp5c+wtQ1bEisgpoBswVkadsvw0w8/vPmhsltnORcK5BwBZVrZVI/bP23xjs/x9fMqjqQn/n4EhdYmJieKlLb2bNHEtIcDCjRo+/wOzD4cioFCyYnxEj+hMSEkJwcDCTJs1g9uwFPP30I8yYMR+A99/vRaVKtxAXF8fevRG88EJPAPLnz8v06d8RGxvLwYOHeOKJLoGcisORKrjnecbE3wzT+YAeQHm8FmGqWi+N5LpaaQ18q6rPegpEZAnm7fcyH/XbA31UtZ9X/d0iUgIYCqwWkZmqusqeewT4WVUbe9XPgXF0nyUiK4Gd9tQ8jCLxsa1XWVXDgaXAw8C7InIvcAOAqn6GUVo8/SY1z1XAIBHJi9m5aI8xBfIXjwwLRaQcxnRKfVUUkRuBP1R1sP1eCbNTdswqDjcBt3s1Ccb8DuOA/2Ec171RIJ+I1FLVFdaMqZyqbklM2ERkcMpDAJk9ZyGz57ifwHFlsXnzdm6//eJ3M2Fhoaxa9RtAokrB3r0RVKpUN8n+9+6NcDkeHFcc7nme8fDXbGkMsB0oBbwN7MFE/3GkjPbAlARlkzCLWF+081F/CsbJ95A9/4mIqIhsA+7CmCl5kxOYISIbMTb+L9vyzkA1EdkoIlsxdvtgft+7ReQ3oBGwLyUTBFDVSKAnsAjYgPExuGiHJAk+B0JEZBPGxKiDqp5NpG5bYLM1MboJ+BZjFhVq59wXY7rk4RRQXkTWYXZg3vHuTFXPYZSLD63ZUzhQOxl5fcngcDgcqUKLFo8GWgSHw+GIx98M0+tUtap3pmkRWaKq96S5hI6AIiJ7MPkcUhRtKaMiIidVNUcgxnYZph1XO37bGzocVzDuQe64VrjcDNNR9t9IEWkGHORCJ1KHw+FwOBwOh8NxlePvzkNzjE1+MYztei7gbVWdlrbiORwGEWmMCf/qzW5VbeWrfkbE7Tw4rnbczoPjWsA9yB3XContPPilPDgcjsvHKQ/pR+NGdejf/x1CgoMZ+c0PfPTxZ8k3clw2TnlIfcqVK83YMV/EH5cqVZy33/6ExUt+5bOhH5AjRzb27I3gscde4N9/T8bXK1asMBs3LOadvp8yYMBXgRD9qsU9yNMX9zwPHJekPIjIm0n0GaeqfS9XsGsREWmFyRdwMxCGzWmAiSp03H6OqmoDW/9loB9QQFWPe/VTA/gEE5Y1DhM5qLOqenI5pOUcOmCyKjdQ1QUJ5tVGVX1mu74SSS0/Cac8pA/BwcFs27KMJk3bExERycoVs3jk0Y5s2+aSfqc1TnlIW4KDg9m7Zx133Nmc8eOG0ePVvixbtpIO/9eWkqWK06fPx/F1x48fRmxsHKtX/+aUh1TGPcjTD/c8DyyJKQ/JRVs65eMD8CTwaqpJd+3RHrPQb6eqm7ySrE0DutvjBgnqr8FkYQZARAoAPwKvqqpgFJE5pG/yvk1cmOSuHSa6ksMRMGpUr8KuXXvYvXsfUVFRTJgwlZYtGiff0OHI4NSrdyd//LGXffsOUK5caZYtM4Hkfl6wjFatzod4bdmyMbv/2MfWrT4jXDscVwzueZ4xSdJhWlU/9Xy32YdfAh7HxMj/NLF2jsSxeRfuAOpilIU+ydQvDeQAugO9gFH2VCdgtKquALCJ13y+7ReRD4CWQDQwT1W72dwdX2J2OwC6qOovNpHdD0A+YDXQBKiaSLSlZcBdNhdCZqAMJrSpZ9w92EhNIlIN+ERV64jIPcAgWy0OuFtV//Uhdx1M6NhDQGXMrsYmzH2YFbhfVXeJyChghme3w7NTICKFMKFec2Hu9edVdZmINAHexyTpO6qq9UWkD3BSVT+xfWwGmqvqngTydFPV5vZ4KCYR3yhf19jXb+FIewoXKcj+iIPxxxEHIqlRvUoAJXI4Uoe2D93H+PE/AbBli9KiRSOmT59H6webU6xoYQCyZctK926daHJvO7p2fS6p7hyODI97nmdMks3zICK5ReRdYCNmAXabqr6qqofTXLqrk/uBOar6O/C3iNyWTP32mMX8MkBEJL8trwCsS24wEcmN2bEob8PsvmtPDQIGqGp14EFghC1/C1iuqlUwyk1xEicO+BloDNxn6/tDN6CT3W25CzidRN1bMcpCReBRTMK2GlbeF5MZ53/AXDvOrUC4VZqGAw+q6q1AGz9lTpQkrrEjAAQFXbzL6ny7HFc6YWFhNG/eiImTZgDw9DNdef65DqxaOZscObNz7pwJivjWm90YNHg4p06lufWqw5HmuOd5xiTJnQcR+Rh4ABgGVFTVk0nVd/hFe2Cg/T7OHv+WRP12QCtVjRWRyZjFbkq8hU4AZ4ARIjITmGHLGwC3eGWKzmV3l+7G/Oao6kwROZZM/+MwCeeuA17B7I4kxy9AfxEZA0xW1Ygk6q6xSecQkV2YzNhgdiCSTqdqTL1G2p2Rn1Q13O4eLFXV3QCq+rcf8iZHYtfYEQAORETGv4UFKFqkEJGRhwIokcNx+TRpUpf16zdx+LDZBFbdRdNmJr9o2bI30vTe+gDUqFGFBx5oRr/3X+f663MRGxvL2TNn+fyLUYES3eG4ZNzzPGOSXJ6HV4CzQG/gda+FZhDGYTpXGsp21WFNguoBFUQkDmM2EyciPazZUcL6lYCywHx77TMBf2CUhy1AVeCizM0iMhfjRL1WVZ+yjtX1MYrIC1aGYKCWqp5O0BZ8+IOJSCfgaXsYb1yrqqtFpAJwWlV/97pHwJjweHa3sni1+cAuspsCK0Wkgapuv/iKAeb+8xDrdRzL+fs3fhwRCcJcJ1R1qYjcDTQDvrPK8D++5pdA1gvkTa6OqkYnco0dAWDN2nDKlClFyZLFOHDgTx566D4efaxToMVyOC6Ltm3vjzdZAsiXLw9HjvxFUFAQvXq+xLBhJu5G3XoPxNd5442unDx5yikOjisW9zzPmCTn85CsWZMjRbQGvlXVZz0FIrIEuBNjlpSQ9kAfVe3nVX+3iJQAhgKrRWSmqq6y5x4BflbVxl71cwDZVHWWiKwEdtpT8zCL3I9tvcqqGg4sBR4G3hWRe4EbAFT1M7x2PBIoCT0xb94Tsgej4MzGmEZ52pZW1U3AJhGpBdwEJKY8+INnnAkY86kwO04J4ICqDheR7MBtwHvAZyJSSlV3i0huu/uwB/D4MtwGlPIxzl7Mbk1mjOJQH1iexDV2BICYmBhe6tKbWTPHEhIczKjR49m69fdAi+VwXDJZs2ahQf276djxfJySdm3v57nnOwDw00+zGDV6fICkczjSDvc8z5j4lWFaRG4HtnicWu1iqbxn0erwm/bABwnKJmFs830pD+2AexOUTcFEafpQRNoBn1g/iFjMwn9ygvo5gakikgWzY/SyLe+MWUR7fFmWAs9hHJR/EJHfgCXAvuQmpaqzEzn1NvC1iPQCvO+VLiJSF4gBtmKUi8thOGaOq4EFnI8KVgfoLiJRwEngMVU9IiLPAJNFJBg4DDTE/A6PiUg4xtzpoqeTqu4XkQkY/58dwHp7KrFr7AgQs+csZPachYEWw+FIFU6fPkPBQhUuKBsy9GuGDP06yXZ9+/ZPS7EcjnTBPc8zHv5mmF6PcZSOs8fBGJOY5Jx9HVc43hGTAizKFU+r4i2cl5fjquZE7NnkKzkcVzhLDm8JtAgOR7pwqXkePAR52+Srqre9ucPhcDgcDofD4bgG8FcB+ENEOgNf2OOOGMddx1WOqpZM6zFEpCLns2x7OKuqNdN6bIfD4XA4HA6H//irPDwHDMZEXYrD2JU/k1ZCOa4trPN05UDL4chYhGUO470fPyA0UxghoSGsmPUL4/qPpWLtSvzf608QlimUXZt2MrT7YGJjYi9q/0Cn1jRo25DYmFhGvDWM8KXGRaX5Ey1o2L4xBAUx/4e5zPjapCdp/8rD1GhUk7jYOI7/dZzBrwzk2KHUiOTrcCROWOYwBk76lLBMYYSEhLB01jJGf/odPYe8hlQqS3RUDNvDtzPgtUHERMf47CN/4XyMXDSC0f2/48evTK7QMSu+5b9Tp4mNiSUmOoaOzV4AIOf1OXnj89cpUKwAh/Yf4p3n3+XkcReF3ZFxadyoDv37v0NIcDAjv/mBjz5OSbR6R1rgl8+Dw+G4fJzPQ8rJki0LZ/47Q0hoCO9P+pCR74yg22c9eKt9bw7uPkj7rg9z+MBhFoyff0G7omWL0XVId3q07EruAnl4e2xfOt3zHEXLFOWVz3rQvcUrREdF8eZ3b/NVr8+J3BNJ1hxZOX3SRC5u9ngLipUtxpe9Pg/EtK9YnM/DpeF9nw+aMoDP3vqcnNfnZPXCNQC8PrQnG1dtYvp3vlPIvDXsDeJi49i2fvsFysPzTV/gxLETF9R95vWnOPHPv4z7bDztOrUl53U5GP5+0o7XjgtxPg/pR3BwMNu2LKNJ0/ZERESycsUsHnm0I9u27Qi0aNcEifk8JJckroeqfiQiQ/ARG19VO6eSfNcsIlIQkzSuOiaHwR7gJ6Clqjb3UX8x0E1V11pn5n8xkZYOYaIJ/ZmgfjZMNKJKmEhA/wBNUiPhX0JnapuArZsvuTM6IlIYGKyqrQMti+M8Z/4zEYBDQkMJCQ0lNiaWqHNRHNx9EIDw5et5sGObi5SHGo1qsnz6UqLPRXN4/yEi90RStnJZ8hTKi/6mnDtjFrlbVm6mZpNa/PTl5HjFASBztswui6kj3fDc56GhoYSGhhAXR7ziALA9XMlXKK/Ptnc0rk3kvj/j+0iO2o1q0bVNdwDm/Tif/j9+7JQHR4alRvUq7Nq1h927TeDHCROm0rJFY6c8BJjkHKa32n/XAut8fByXgU1oNgVYrKqlVfUWTIbmAinopq6q3or5jXxld34JOKSqFVW1AvAkEHWZol91qOpBpzhkPIKDg+k/exCj1n/HhuXr2RH+OyGhoZSuVAaA2k3vIG/hixdVeQrk4a+D5wOE/RV5lNwF87BP91K+ZnlyXp+TTFkyU7VuNfJ6Lcoe7v4ow1eO5J776/DDp2PSfoIOB+Y+/2ruF0zaMIF1y35j+/rzaW9CQkNo+GB91ixee1G7LFmz0K7jQ3zbP6HLGMTFwUdj+/HFrM9o9nB8Xk9uyHsDfx825nh/H/6b6/NcnwYzcjhSh8JFCrI/4mD8ccSBSAoXLhhAiRyQvM9DW2AGcL2qDkoHea416gJRqvqlp0BVw0XkeqC+iEwEKmAUtUd8ZaH2Yikmd0NCCmGSm3n6V893m1SuMyYj8yqgo6rGiMjjmMRvkZh8B2dV9YWUTExEcgMjgRuB/4BnVHWjiPTBJGArBJQDugK3Y/JZHABaqGqUiNQHPsHco2uA51XVp02EiHwAtMRkgJ6nqt1EpAXGRycT8BfwsKoesuOXBooAxYCPbBK5ksAMVa0gIiGYfBx1gMzAZ6r6ld1Z6QMcJcHv4kuGlFwvh29iY2Ppeu9LZMuVndeG9aJ4ueL0f+EjnnjzKcIyhRG+dL1PO/CgoIt3WuPiIGJnBJO/mMRbY/py5r/T7Nm2mxhSwSnCAAAgAElEQVQvf4kxH3/HmI+/44FOrWnaoTnj+o9N0/k5HGDu82cbP0/2XNl5Z8RblJSS7NE9ALz0/otsXLWJTas3X9Tu/155lInDJ/vcdXipVRf+OmSUg49+6Me+nfvZtGpTWk/F4UhVfD/L3a5woElu56GqzdL7hIjcICK5vT/pIeBVjmcB6osqQBfgFswC/I5k+moO+PrLMBJ4VURWiMi7IlIWQERuxiiHd6hqZUzCtodFpBAmudsdmORptyQz7iIRCbfJ1UZ4lb8NrFfVSpgdkW+9zpUGmmGyQX8PLFLVisBpoJlNtjYKaGvLQ4HnfQ1u78NWmKSFlYB37anlwO2qWgUYB/TwalbJjl8LeNOaLHnzJHBcVatjzMmeFhFPxumLfpckZHCkEv+dOMXmlZuoUqcq+pvyeuvX6NHyFbas2kLknsiL6h/98yh5vHYk8hTKy7FDfwGwYPx8ujXrQu82Pfn3n3+J3H3wovbLflpCrXtrp92EHA4fnDpxivAVG6lepxoAj778CNfnvp4v3v7KZ/2bq9zEM68/xZgV3/Lgk63434vtuK9DSwD+ss7+//z1D8vn/MpNlQWAY0ePkTu/+fOdO39u/vnrn7SelsNxyRyIiKRY0fN/oosWKURk5KEASuSA5JWHL4E5wE1cbLJ08R6qIzVZraoRNqdGOFAykXqL7MI9F9Av4UlVDccscj8GcgNrrOJQH6hqj8Pt8Y1ATYwZ1RFVPQeMT0bOuqpa2SogT3mV34kNv6qqC4E8InKdPTdbVaMwyk4I5h7DHpcEBNitqp4sz6OBuxMZ/wRwBhghIg9gdjkAigJzRWQT0B0o79Vmqqqetr4ai4AaCfpsxPls06uAPEBZe87X75KYDI7LIFfuXGTLlR2ATJkzceudlTmwK4Lr8pjbKDRTKA90fJC535sE5WVvLUvnASa595r5q7mzxd2EZgolf7ECFCpVmB3hxkbW0z5v4Xzc3qQ2y6YtAaBQyULxY1dvWJOIXRHpM1HHNc11ua8ju+c+z5KJqndWYf/O/TRt34Tq91Tl3Rfev+BNq1QWXh1ofBa6PPgKD9d6jIdrPcakr6cwdsg4po6aRpasWciaPStgTJuq3X1b/E7Gr/NX0qhNQwAatWnIr/NWpONsHY6UsWZtOGXKlKJkyWKEhYXx0EP3MX3GvECLdc2TpNmSqg4GBovIF6rq882v47LYAiRmZ+9tohND4r9VXe/szyLSCnjLHj6lqmutc/RkYLKIxAJNgXPAaFXt6d2ZiNyPD+d4a8rj2SWZpqpvJjkz45ydEE+/Z8EkGxSRKC9zLE/yQZ/e/b5Q1WgRqYFRftoBLwD1gCFAf1Wd5mVulFCOxI6DgBdVda53oe3not8lCRkcl8EN+XPTuX8XgkOCCQ4O5pcZy1m7YA3/1+txqtWvTlBwEHO+n82mXzcCkLdIPs6dOQfA/t/38euM5QxZ8Dkx0TEM7/0lsbHGPKnHVz3JeUNOoqNiGPbGF5w6fgqAR1/rQJHSRYiNjeXIgSN82dOFA3SkPXkK5KbHgO6EhAQTFBTMkhlLWLlgFfP2zOZQxCGGTDUWw8tnL+e7gWMoUCR//H2eGDfku563R5g/AyEhISz4aVG8z8S4oeN448ve3NuuCYcPHOad59xGqSPjEhMTw0tdejNr5lhCgoMZNXo8W7f+nnxDR5qSXLSlXKp6Anjdl5mSqrog6JfHQuB9EXlaVYcDiEh14J5L7VBVp2CcsLH93QFsVdVjIpIJY26zGOMMP1VEBqjqYfv75sS8aR8kInkwb9TbABtUNYaU5WJYCjwM9LWL7qOqekJE/Gm7HSgpImVUdSfwKLDEV0URyQFkU9VZIrIS2GlPXYfxoQD4vwTN7hORfkB2jF/DaxjfCA9zgedFZKH1vyjn1VdKZHBcBnu37+GVpl0uKh/9/jeMfv+bi8rLVRZmj54Zfzxx6AQmDp1wUb3XW7/mc7yPnrto487hSHP+2Lab55p0vKi8Ucl7fda/qcpNTB097aJyb6fpyH1/8kwj3+/7TvzzL93bvXqJ0joc6c/sOQuZPWdhoMVweJGcw/RYjC39OszbWe83wnEYMxfHJWIdbVsBA0XkNYzpyx5MqNbUojTwhY3sFAzMBCbZsXsD80QkGBOBqZOqrrROxSswDtO/YUyLUkof4BsR2Ygx40m4gE8UVT1jnbZ/FBGPw/SXiVTPiVGCsmDuz5e9xv9RRA4AKzFO2h5WY65DcaCvqh60DtMeRmDMkX6z1+0IcH8SIicmgyMd8aVQOBxXG8PeHR5oERwOxzWOSxLnSBIR6YDJ5ZCiaEsZFasYnVTVT9J77NBMRdx/NsdVTfV85QItgsOR5qw54sxmHNcGiSWJS85hGgARWeBPmcPhcDgcDofD4bh6SXLnwZphZMNEpKnDebOlXJiIOTentYAOhwcRmcKF5kcAryZ0bM6ouJ0Hx9WO23lwXAu4nQfHtUJiOw/J+Tw8i4lpXxjj9+Dp5ATgQpE40hVVbRVoGRxXBo0b1aF//3cICQ5m5Dc/8NHH7nHlyJjkL5yPNwf1JE++3MTGxjF1zAwmfD2JsuVL0+ODrmTKnImY6Bg+6TWQreHbL2h7S+WbePWjVwCTTOvrT0exZM5yANo93ZoW7ZsRFxfHru1/8F7XDzl3Nopen3TnpluFIGDf7gje7fIBp30kmXM4MgrueZ7x8MvnQUReVNUh6SCPw3HV4nYe0ofg4GC2bVlGk6btiYiIZOWKWTzyaEe2bdsRaNGuetzOQ8rJkz83efLn4ffNO8iWPSvfzPmKV594gy5vd2Lc8ImsXLSaWvVq8sjz7ejU5sJYDJmzZCY6KoqYmFjy5M/Nt/NH0PK21uTOl5svpwzmf3U7cPbMOd798i1+XbiSWRPmki1HNv47aVLRdH6rI8eOHuO7z34IxNSvWNzOQ/rhnueB5VJ3HgBQ1SEiUhsTgSbUq/zbRBs5/EJECgIDMZmMz3I+2lJLVW3uo/5ioJuqrhWRPcC/mPwIh4DHVPXPBPWzAcMxWZWDgH+AJjb3w+XKvgfYr6p3eZWFY3IfVLjc/jMKgXSydqScGtWrsGvXHnbv3gfAhAlTadmisftj48iQ/HX4b/46bKKe/3fqNHt27CNfwbzExUH2nCZ5XI6c2TlqM6R7c/bM+bQzmTJnAq+XgSGhIVa5iCZL1swc/dO09ygOAJmzZMIFTXFkZNzzPGPir8P0d8AnmKzB1e2nWhrKdU1gw4BOwWR0Lq2qtwC9gAIp6Kauqt6Kyfjdy8f5l4BDqlrRLuifxIRlTS1yikgxAJu52uEIKIWLFGR/xMH444gDkRQuXDCAEjkc/lGwaAHKVSjDlvXbGPjWUF7o/Sw/rRnPi288xxf9fIdovaXKzYxZ+A3fLxjJR68NICYmliN/HmXslxOYsno809dP4uSJU6xeuja+zev9ezAzfBIlyhTnx5FTfPbrcGQE3PM8Y+LXzgNGUbjFKxOwI3WoC0SpanwOA1UNF5HrgfoiMhGogPE3eSSZ678U6OyjvBCw16t/9XwXkUdsm0yY5HAdVTXG5ljoicnz8DtwNolQrROAthjlsj3wAyap20VhXkVkhq23DPgac1/FASNVdYCvzu1Oy3qgKpAPeMzKVhEYr6q9bY6GGZ7dDhHpBuRQ1T4i0hl4DojGJMtrZ5O6DfEa/21VnSQiJ1U1h+2jNdBcVTv4kMez85MXWKuqJUWkPPCNvZbBwIOq6l6NBICgoIt3Wd3bVUdGJ2u2LPQb/g4D3/qM/07+xwOP3cegPp+zeNZS6reoQ69Pu9O5XbeL2m1dv42H6z1OiTLFeXPga6xYtIrMWTJzV+PaPHh7e/49cZL3vupD4wcaMHfyzwC81/UjgoOD6fpuZxq0rMvMCXPSe7oOh1+453nGxK+dB2Az4FS91MejGPiiCsZZ/RZMMr47kumrObDJR/lI4FURWSEi74pIWYjfJWgL3KGqlYEY4GERKQS8bcdraMdPionAA/Z7C2B6MvXBZKouoqoVVLUiZtGdFOdU9W5MoripQCfMtetgM2EnxWtAFVWthFEiAN4AjtvdmEqYTN+Xy3PAIHstqwERqdCn4xI4EBFJsaKF44+LFilEZOShAErkcCRNSGgI7w9/h7lTfmbJ7GUANG3TiMWzlgKwYPpibql8U5J97N25j9Onz3CjlKL6XVWJ3Pcn//x9nJjoGJbMXkbFahdaksbGxrJg2iLqNrs7bSblcKQC7nmeMfFXecgLbBWRuSIyzfNJS8EcrFbVCFWNBcIx/ia+WGT9DHIB/RKeVNVwjPLxMZAbWGMVh/qYt/lrbPv6tl5NjBnVEVU9B4xPRs6/gWMi0g7YhskmnRx/ADeKyBARaYKJ3pUUnnttE7BFVSNV9aztp1gybTcCY+wuS7Qta4BXtDBVPeaHzMmxAuglIq8CJVT1dCr06bgE1qwNp0yZUpQsWYywsDAeeug+ps+YF2ixHI5Eef3THuzduZdxw36MLzt66C+q1LoVgGp33sb+3QcAE2HpzUE9AShUrCAhIebPeMEiBSh+YzEi9//JnwcOU/62W8icJXN8+z07zAZ00ZLnF2J3NqzF3p370n6CDscl4p7nGRN/zZb6pKUQ1zBbgNaJnDvr9T2GxH+ruqp61HMgIq2At+zhU6q61jpHTwYmi0gs0BQ4B4xW1Z7enYnI/RhTHhKUh3B+l2Saqr7pdXo8ZjHeIUGzaC5UULOAWayLyK1AY8wuwkPAE4nMD85fi1guvC6xmOvicxxLM+BuoCXwhjUvCvI1xwRlWXycJ8FY8XVUdayIrLLjzRWRp1Q1NXY0HCkkJiaGl7r0ZtbMsYQEBzNq9Hi2bnXRURwZk0rVK3Bv60bs3LqL0fOMX8OXH4ygX/dPePmdFwkJDeHcmXN80ONTAAoUyR/vKH1rjYo82ul/REdHExcbyye9BnL82AmOHzvBoplLGD13GNHRMfy+ZQdTx8wgKCiINwb2JHuObBAUxM6tu/iop0+LUYcjQ+Ce5xkTf6MtLRGREkBZVf3ZRvAJSVvRrgkWAu+LyNOqOhxARKoD91xqh6o6BeOEje3vDoyt/zERyYQxQ1oMbAWmisgAVT0sIrmBnBjfh0HWHOgE0AbYoKoxGHMjX0zB+FbMxeQE8bAH6CgiwUARoIaVKS/GFGmSiOwCRl3qfC2HgPxW5pMYE645dtxiqrpIRJYD/wNyAPOAFzBmYYjIDXb34ZDdlVGgFSaSVUL2YHZsVuOl+InIjcAfqjrYfk8tcyjHJTB7zkJmz3GX35Hx2bhmM7WK1PV57vF7n72orHyVm5k46icA5kyaz5xJ8322HfHpKEZ8Ouqi8mfvf/HShXU4AoB7nmc8/FIeRORp4BmM2UtpzELwS4ypi+MSUdU4u1MwUEReA85wPlRralEa+MJGdgoGZgKT7Ni9gXl2kR0FdFLVlTY06QqMw/RvJKMoquq/wIcAIuJ96hdgN8bcaLPtC8z9840dF4wD9CWjqlEi8g5G8dkNeDIphQDfi8h1mN2GAar6j4i8C3wmIpsxuzpvY3ZmXgNmAPutvDl8DPcJMEFEHuVC5aAt8IiIRAF/Au9czpwcjiuRdUddjIC05vEuFztNOxwOR3rib5K4cMxb41WqWsWWbbLOro6rmIQRkxyXjksS57jaCfYRGcXhuNqIddF+HNcIiSWJ89dh+qx1ngVARELxbTPucDgcDofD4XA4rlL8VR6WiEgvIKuINAR+xL+QnI4rHFUdlR67DiLymYiEJ/g8ntbjOq5OGjeqw5bNS9m+dTk9uncKtDgOh18ULVqIeXMnsHHDIsLXL+CFF568qM6dd9Zk1crZ/HdqDw+0ahZfXrx4EVaumMWa1XMJX7+Ap59+5KK2kyeNZP1vP6fpHByO1MY9zzMe/potBWMyEzfC2I7PBUa4pHEOh/84s6X0ITg4mG1bltGkaXsiIiJZuWIWjzzakW3bnD1+WuPMli6PggXzU7BgfsLDN5MjR3ZWrZxN69ZPsm37+Xu3RImi5MqZk5dffpYZM+YzecpMAMLCwggKCuLcuXNkz56N9b8t4J4698fHxL//vnt54IFmVKx4E1VuaxCQ+V0tOLOl9MM9zwPLZZktqWqsqg5X1Taq2tp+T/Z/j4jE2DfIW0Rkg4h09TjJikgdEYkTkRZe9WeISB0/55RiRGSUzRzsXXYyrcZLD0RkhIjcYr/3SnDu12TaVhORwfZ7HRGpfQnjn0xw3EFEhqa0n4yA9/VwXLnUqF6FXbv2sHv3PqKiopgwYSotWzQOtFgOR7L8+edhwsM3A3Dy5Cm2b99B4SIX5mfduzeCTZu3ERsbe0F5VFQU584Z6+LMmTMRHHz+z3v27Nl46aWn6ddvUBrPwOFIXdzzPGOSZLQlEZmgqg+JyCZ8+DjY7LxJcdpm3EVE8gNjges4n4cgAnidq9gESkRCbJjTNEFVn/I67AW873UuSWVAVdcCa+1hHUyY0yQVjquZBNfDcYVSuEhB9kccjD+OOBBJjepVAiiRw5FySpQoyq23VmD16vV+tylatBBTf/qW0qVL8lrPd+N3Hfr06c7AgcP477TLXem4snDP84xJcqFaX7L/Nr/cgWwugWcwGY372OINQJiINFTVC4JVi8ibQAsgK2ZB+6wNL9oZeA6TrGurqrYTkezAEKCinVMfVZ2aEvlE5DtgoqediIzBJD/LjYn5nxkoBYxV1bdtnUeAzkAmTJjQjqoaY9/G98ckQXtFRJpjkpRFA/NUtZuI5MOEuy1uReiiqr/Ya1Mck+25ODDQ5g7IDkwAimJCkPZV1fEishjohsk5kNVGxtqiqg+LyElVzSEi4zEJ4WZZuUdhFLa/bNsX7DWNsXN6EfgWKGfDoObCZGouq6pRKbimJYCRQD7gCPC4qu6z458GbgJKAI8D/wfUwkT06mDbt8coREHATFV9NZFxQoCvgWoYJXekqg7wCjGcCdgJPKqq/9nxzwDlgQJAV1X17Hp1U9Xmid1TNvpUSyAbJgzuFFXtkZgM/l4rR+oR5MN0xh/zTIcjo5A9ezbGjxtGt259+Pdf/zfHIyIiqVqtIYUKFWDij18zefJMChXMT5nSJene/W1KlCiahlI7HKmPe55nTJI0W1LVSK96h1R1r6ruBQ5jFnQpQlX/sH3l9yp+F+jto/pQVa2uqhUwCoRHgXkNqGJ3PZ6zZa8DC1W1OlAX+Ngu/nzxsbdTrlf5CMwiFpsXoDYwy56rATyMSZLWxpq33IyJ7X+H3V2JsXUAsgObVbUmJhlbK6C8lfldW2cQJu9AdeBBO76HmzCKRw3gLREJA5oAB1X1VntN5nhPSlVfw+70qOrDXMg4Kys2UVx9r7mhqnswiswA234ZJpGcxxuvHSY3hC/FIWuC6+md32Ao8K2d9xjA2yToBqAe8DJGkRmAWcxXFJHKIlIYkzuiHua6VxeT/doXlYEiqlrBhg/+xpZPtvfQrcA2jN+Oh5KYZHzNgC9FJGFG6aTuqcqY61kRaCsixZKQwZHOHIiIpFjR87kKixYpFP8G1uHI6ISGhjJ+/DB+GDeFn6bOvqQ+IiMPsXWrcucdNal5e1WqVKnI77qCRQunULbsjcyf92MqS+1wpA3ueZ4x8Tfa0o+At4FljC27FC5QOuxCFRG5K0G9uiKyyppM1cMsLMG8AR9j35BH27JGwGt28boYyML5N/oJ6W4XyJU9JlVWjiVAGWte1R6zWPb0P19V/1LV05hkYndiFuBVMTsp4fb4Rls/Bphkv5/AvOUeISIPAP/Z8gbAUNt2GpBLRHLaczNV9ayqHsUoagUwidYaiMiHInKXqh5PZH6+mA3UE5HMwL3AUjuXpIhXpuy/iS2GTye4nm96nauFMVUD+A5z3TxMt34zmzCK6SZVjQW2YBb21YHFqnrE/g5jgLsTkeEP4EYRGSIiTTDXHKCCiCyz99DDnL+HACZYX54dtv1NCfpM6p5aoKrHVfUMRjkskYQMjnRmzdpwypQpRcmSxQgLC+Ohh+5j+ox5gRbL4fCLYV99wvbtOxk0aHh82fPPd+D55zsk2a5IkUJkyWLegVx//XXUrl2d33/fxbBh31GyVDXKSS3q1mvFjh1/0LBRm7ScgsORarjnecbErwzTQKh3ngdVPWffYKcIEbkRs7A+DNzsdeo9zJveaFsvC/A5JjnZfmvK43kz3AyziGwJvCEi5TEKyYOqqgnG+waognlj39QPEb/DLDLbAU94lSfcI4uzY45WVV/Zkc94/BxUNVpEamCUi3YYE6F6GMWtVsJFvM3QfNarKAZz/X8XkapAU6CfiMxTVb+yGKvqGWve1BjzxvwHP9r8IiIlReQeIERVN9s37B7/lC9V9Ut/xvfC+zp65hjLhfONxdyX0fiJqh4TkVsx8+sEPIT5/UYB96vqBmtuVCcRWXwdJ3ZP1cT375OYDI50JiYmhpe69GbWzLGEBAczavR4tm79PdBiORzJUrt2dR55pDWbNm1jzeq5ALzx5oeIlGHFr2sAqFr1Vn6cMIIbbriOZs0a8uabXalcpT433VSGjz58k7i4OIKCgug/4Cs2b9keyOk4HJeNe55nTPxVHo6ISEtVnQYgIvcBR1MykJeN/1DruxB/TlXniUhfwLM35VEUjopIDow9/0QbqamYqi4SkeXA/4AcmNCxL4rIi7bvKqq6XlVTmidgFLAa+FNVt3iVNxSR3Bg7/fsxi8L/gKkiMsD6c+QGclqzLu955wCyqeosEVmJsb0HmIdRJD629SqrqrcZ1QVYM56/VfV761PRwUe1KBEJS8S8aBzwFMYm31fbf4FcCcq+xSgafQFUdT/GPMdffsUoTB6lbHkK2q4CBolIXuAYZjdoiK+Kts45VZ0kIrswvyNATiDSmn09DBzwatZGREZj/FhuBBS43eu8z3sqMWGTkMERAGbPWcjsOQsDLYbDkSJ+/XUNmTJf7Jfw7LOP0b372wCsW7eBG0tXv6jOggXLqFqtYZL9790b4cK0Oq443PM84+Gv2dJzQC8R2Sci+4FXgWf9aOexh98C/IxZML+dSN33MM7AqOo/wHCMSctPwBpbJwT43pqhrMfY6P+DWdyGARtFZLM9TjGqeghjG5/QRGc5ZgEcjjFnWquqWzG+GvNEZCMwHyjko9ucwAxbZwnGxh+Mo3U1EdkoIls577+RGBWB1daM5nXO+054MwxzDcb4ODcPs2Pzs/cukhfTgVb29/KYkI3B+CYku1ORCJ2Bx+3cH+W8A36yWH+bnsAijGP9b0k4wRcBFttrM8q2A3gDo4TMBxK+glPM7zEbeM6aIHmT0nsqMRkcDofjsmjVqgNRUX7HqnA4HI40xa8kcR7sW/QgVf037UQKHCKSDaOw3ObxKbDmLtU0HbIsZzTE5MS4T1UfDbQsqYmNtjRDVSem57guSZzjaseliHNcC7gHueNa4ZKSxIlICxtu00NXYLmITBORUqkpYKARkQaYt9NDUuiMfFUiIkOAD7jEXRyHw+FwOBwOx9VHkjsP1tzkdhsbvzkmd0F7jBNyG1V1af4c6YqIrMLk3PDmUVXdFAh5UoLbeXBc7bidB8e1gHuQO64VLmnnAYhTVU9o0QeAr1V1naqOwCT+cjjSFVWt6R0a1n4yvOLgSF8aN6rDls1L2b51OT26dwq0OA6HXwwf9ikHIjawfv0Cn+e7vPQMGzYs4rd185k7ZzzFixeJP9ev3+uEhy9k48bFDOh/PhBfmzYt+W3dfMLDF9Kv3+tpPgeHI7Vxz/OMR3LKQ5CI5LBRjuoD3k+0hEm1HA6HI+AEBwczeNB7NG/xCBVvrUvbtvdz881lAy2Ww5Eso7+dQPPmCXN8nmd9+GZuv/1ebqvakMmTZ9Kvn8mvWuv2atSuVZ3bbmtA5cr1qFatMnffXYvcuW/gg369adS4LZUr16NA/nzUrXtnov07HBkN9zzPmCSnPAzERBhaC2xT1bUAIlIFiEyqoYjEeCIticgGEelqlRBEpI6IxIlIC6/6M0SkzuVMJhl5RlkHYO+yk2k1XnogIiNE5Bb7vVeCc78m07aaiAy23+uISO1LGD9ORL7zOg4VkSMiMiOlfWVkfN07joxLjepV2LVrD7t37yMqKooJE6bSsoWzsHRkfJYvX8Xfx/5J9PySJb9y+rQJDLdq9TqKFjEB/uLi4siSJTOZMmUic+ZMhIWFcvjwEW4sVZwdO/7g6NG/AViwcBkPtPIn5ZHDkTFwz/OMSZLKg6qOBO4BnsQkJ/PwJ+ezDyeGJ/NweaChbf+W1/kITMjRqxYRCUnL/lX1KRsyFqBXgnNJKgM23Gxne1gHSLHyAJzCZHHOao8bcmEuBYcj3SlcpCD7Iw7GH0cciKRw4YIBlMjhSH0e79CeOXMXAbBy1ToWL/6V/ft+Y/++9cybv4Tt23eyc9ceRMpQokRRQkJCaNmyMUWLFU6mZ4cj4+Ce5xmTZJPEqeoBvBaEItJHVfukZBCbRO0ZYI3NFg0mdn+YiDRU1fne9UXkTaAFkBWTaOxZm6irMyYfQjSwVVXbiUh2TPKwinY+fZLIB+AT+/Z8oqedzZMwHsgNtMI46JYCxqrq27bOI5g8BpkwuQQ6qmqM3c3oj8k0/Ip1NG9pZZ6nqt28EuYVtyJ0sRmd+9iyG+2/A1V1sJ3jBEwejBCgr6qOt1mju2GS6GW1OQa2qOrDInJSVXOIyHhMJuxZVu5RmJwOf9m2L9hrGmPn9CImOVw5VY0SkVzARqBsIsnnZmOyfk/EONP/ANxlx+oDnFTVT+zxZqA5cMTXfBL5bfYAY4G6mLwLzwD9gDLAx6r6pd2x6qaqzW2bocBaVR0lIh/4uP4F7PW/0Q7zPHAQE761gu2jG5Aj4b1u5ammqkdFpBrwiarWEZOJe5CtFgfc/f/snXmczdX7wN9zZ8YylkT2falHKdlLIUpoQer7VdrQvifp10KFkqKU4lsiS5C9t+cAACAASURBVGVJWuxLhcgua+EJUYZBshvMzJ35/XHOHXdm7szcYWbu4Lxfr/ua+zmfszznMx/jPOc8y7ka0jivExaW1r8rKyGpHY68zt133079+ldy/Q13AFC9ehVq1ryYKlUbADBr5gSaNLmKX35ZxlNPv8y4sR+TmJjEkiUrqVqtUkZdOxx5Cvf3PG8SbJI4f9qdzkCq+qcdr5Rf8ZuYRGupGaKqDe1CriBmwQnwElBXVWtzKqlaT2CuqjbELDAH2sV2IAZaU6o1dqHtYwT2JEVELsDsws+w9xphshPXwWQlbiAilwJ3Ateqah3Aa+sAFAJ+U9WrgA0Y5aOWldmX2G0wJsFdQ+AOO76PmhjFoxHwus2O3AbYpapX2mcyy39SqvoSp056UhvMTrCyIiL5ML4rM/zabscspN+37RcC8zEKAZgM0d+kozj4+r9LRAoAtTGKVGZkOJ8A7FDVxsBCTAK2/2CyQffNqJHN+h3o+X8I/KyqVwL1gN/T6SIr9ACetO9DU0w2ckcI2BkdQ8UKp3ZXK5QvS0zMnhBK5HBkH9df35SXXnqGDrd3IS7O5Pts374Ny5av4tixWI4di2XW7LlcdVU9AKZP/4Frm7SlabN2/PHHVrZs2RZK8R2OLOH+nudNTkd5OJNofCna2oUqfhmNfbQQkWU2k/T1QC1bvg4Ya3fIE2xZK+AlqwzMxzhyp7e18oJ/lB4/OX4GaohIKczu+Teq6uv/B1X9V1WPA98CTTAL8PqYk5Q19tq3i+0FvrHfDwMngBEicjvgi1zVEhhi204BiopIEXtvuqqeVNV9wF6gNCZxXUsReUdEmmYxD8VM4HoRyQ/cBCywc8mIZGXK/kydcTsZVV0HVME8txnp1UtFVuczxa/dMlU9oqr/ACdEpFgG7dJ7/tcDH1v5vdmU12MRMMiejhXze38cucyKlWuoUaMqVapUJDIyko4d2zN12pxQi+VwnBZPPN6FJx7vAkCdOrX439C3uf32rvzzz7/JdXbs2EWzplcTHh5OREQEzZo2ZtOmLQCULFkCgGLFLuCxxzozcuT4XJ+Dw3G6uL/neZOglAe7g+uj/ukMJCLVMAvrvalu9cPP98HuYP8P+I+qXgEM51Rkp1uAoVaGX0UkAqOQ3OGnFFRS1Y0iMsqeMAS7oP0Cc3qQerGc+nwsyY45xm9M8TNvOaGqXgC7gGyEUSZu49QOuwdo7Ne+vJ+Jy0m/sbxAhKr+Yee8HuhvzbqCQlVPYJSq1pgTiAlBtFkEVLGmOOGq+puIVPQ7tXksVZMpwLsYkyV/Ekj5jhWw/Wd1Pr5nkkjK55OIMVVLb5z0nn8gAvaRSb3kOqr6NvAQ5qRsqYjUzGAsRw7i9Xp5tlsvZkwfx2/r5jNp0lQ2bPgj1GI5HJnyxRdDWbhgCnJJdbb9uZKuXe5CpAb/7j8AwNv9X6Vw4UJMGD+MlSvm8O235r+qb76Zxp9//sXq1T/x668/sG7dBqZPN9bAgwb1Ze3aefz88/cMGDiEzZv/DNn8HI6s4v6e500y9XmwLLO75KMwO9lZws/Gf4j1XUi+p6pzROQNwHcu5VuQ7RORwhgTlUk2UlNFVZ0nIr8AdwOFgdnA0yLytO27rqquVtXMHLpTMxpYDuxWVX8zlhut8nQcswB9ALODPVlE3rf+HMWBIqr6V6p5FwaiVHWGiCwFtthbczC+BgNtvTqq6m9GlQIRKQfsV9UvrU9FlwDV4kUkMh3zogmYhW2DdNoeAYqmKvscowy8AaCqOzCmW4EYCRxS1fWpImZtx5qciUg9jN9IsPPJCn8Bl9nTlQKYk6BfMnj+P2H8HD6wTu2FgD1AKREpARy1cgdSNrZjFJ+ZGJMz7Jyq23wT60WkMcb8bNMZzstxmsycNZeZs+aGWgyHI0vcd1/aGPbffzeGHi/0AaDNTXcFbJeYmMgTT74YdJ8Ox9mE+3ue9wjWbOkS4FPgPmCLiLwlIpdk0qag3aX+HfgRs2Duk07dfhjnWVT1IOa0YT3wPbDC1gkHvrSmTKsxNvoHMYvbSGCddch9I8g5pUBV9wAbSWui8wvmVGINxpxppY1w1AuYIyYL9w9A2QDdFgGm2To/A8/Z8meABiKyTkQ2cMp/Iz2uAJZbBa4np2z3/fkU8wzGBrg3B2gG/KiqcQHuTwU62N+Xz4RsLHAhaU8T0qCq0ao6OMCtb4DiVu7HAd92QTDzCRqr2EzEmrVh3g9I//k/izGNWw/8ivGJiMf4UCwDppH+wr8PMFhEFmJOhnx0E5HfRGQtRtHMspLtcDgcqbmtQ2fi49NzOXM4HI7cJyyrXusi0gL4ErNbuxZ4SVWX5IBsuYqIRGEUlno+G3gR6YKJrPNUKGULBWLyGrRX1ftCLcu5QkS+8i5EhMPhcJzlhHtOx13U4Tj7OHliR0A/56DMlqwpx72Yk4c9mHCeUzBmLF9jzVHOVkSkJcb0ZlA2Oc+e1YjIRxjnapdNyOFwOBwOh8ORTFAnDyLyB8Z0Z5SqRqe696KqvpND8jnOE0TkO9IqoS+q6uxQyJMTuJOH3KN1q+YMGtSXcI+HkaPGM2Dg0FCL5HBkO+49Dw3u5OHMqFChLJ999gFlSpckMTGRzz4bx5ChI1PUue++/9L/rZ7s2rUbgI8/Gc2oUSbeS783X+amm24A4K3+g5k0aWqKtu8P6sv993ekxEUubsmZckYnD4CoasCFj1McHNmBqnYItQyOcwOPx8OHg/vR5uZOREfHsHTJDKZOm8PGjZtDLZrDkW2499xxtpKQ4OXFF99gzZrfKFy4EEuXzODHnxayaVPKd3fSpKl0e+7VFGU3tbmeunUvp2Gj1uTPn48ff5jE7NnzOHLkKAD16tXmgmKp4784sptglYfJ/hGSLIeAlcAwGw7UkcuIiBfjpxEBbAPuU9WDIlLFXj+jqh/ZuslZl+11BLAbGK6qL6fTvwf4AJMXIQmTM6Gjqp5xliEx2bHLYkKv5sM41feyTvCn099jQKyqfn6msgU5XgPgflV9JjfGcwRPo4Z12bp1O9u2/Q3AxImTade2tVtUOc4p3HvuOFvZvXsvu3ebqP1Hjx5j06YtlC9fJo3yEIhLL72YBQuX4fV6iY09zrr1G2jVqjnffDMNj8dD//496dz5adq3a5PT0zivCfbsbRsmfOVw+zmM8X24xF47QoMvq/TlwH7APybfXuBZm1U6EK0ABTqKSHqJ/+7EhNCtbXNudABOa3GfDvfYzM+1MUrE5NPtSFU/OVPFwSpUQdWzUbec4pAHKVe+DDuidyVfR++MoVy5MiGUyOHIftx77jgXqFy5AlfWqcXy5avT3LvttptYuWIO48d9QoUKJqDluvUbad26OQULFqBEiQtpfl3j5AzUTzzehenTfkhWTBw5R7AnD3VVtZnf9VQRWaCqzWwoVkfoWYJZhPv4B5P1uDOBFbxOwGBMCNWrbfvUlAViVDURTEhW3w0RaYUJW5of2Ap0VdWjItIGc1qxD1gFVFPVWzMSXFXjROT/MGGAr1TVtSLyPVARk7dhsKp+asc9auW+FRMStb2q7hGR3sBRVX1XROpg8opEWdkeUNUDgca2JyCLgWuBKSLyuW3ry1LeTVUX2f7LYbJp7xORT4EeqnqrzfMxEpNlPBZ4xGbedoSAsLC0unBWo8o5HHkd9547znYKFYpiwvhh9OjRO9nsyMf06T/w1VeTiYuL4+GH7mXEiPdp0+YufvxxAfXrX8nP879n375/WbpsFQkJCZQtW5rb77iFG2/sGKLZnF8Ee/JQUkR8iyns94vsZaC8AY5cxCY6uwETAcuft4Hn7X3/+gVt/WmYPA6d0ul6ItDW5n94T0Tq2vYXYfJctFTVehjzte42O/hwoC3QFAh6G8xm5V6LSa4GZsFfH5PY7hkb8QtMiOClqnolsAB4OEB3n2OcrWtjzLpez2T4Yqp6naq+h1FM3lfVhpgkcCP86tXHKCt3p2rfB1htx3vFju8IETujY5J3ogAqlC9LTMyeEErkcGQ/7j13nM1ERETw1YRPmTDheyZPTpuPdf/+g8TFmeXlZyPHUa/uFcn33nnnIxpd1Yabb7mHsLAwtmzZRp0ra1G9WhU2bFiI6mKiogqy4feFuTaf841glYfnMRl759md2oXACyJSCBiTU8I5MqWgTbT2L1Ack6wuGeubsByTjdufW4F5qhqLSeTWIbWCYdtHAwK8DCQCP4nIDZiTisuARXb8zkBlzMJ/m6putg72X2ZxPv5bac/YhGtLMScQF9vyOIzSAybBWxX/DkTkAowy8LMtGoNJkJcRX/l9bwkMsfOaAhQVkSL23hRVPR6gfRNMNDJUdS5QwsrhCAErVq6hRo2qVKlSkcjISDp2bM/UaXNCLZbDka2499xxNjNs2EA2bdrM4A9PGUY8/lhnHn+sMwBlypRKLr/11lZs2rQFMIECihcvBsDll9fkissv5YcfFzBz1lwqV6mPyDWIXENs7HEuq9UUR84QlNmSqs4QkYsxi8MwYJOfk/QHOSWcI1OOq2odu1CdhvF5+DBVnbeASZhdeh+dgGtFZLu9LoHJuHwEGGbLXlPVKap6EpMteaaI7AFuw2Ss/kFVU5xYWHOhgOfmIjIbKI1x2n4owP1wTObpjSLSHLOIb6yqsVZhLWCrxvtF/vISvOldRhzz++6x46ZQEmzAAP96/gTyGXH2AyHC6/XybLdezJg+jnCPh9FjvmLDhj8yb+hwnEW499xxtnLNNQ25957/sH79RpYvM6cOr732DiI1WLxkJQBPPtmVW2+5kYQEL/sPHOThh7sDEBkZydyfvgHg8OGjdOn6DF6vNzQTOY8J1kE0CugOVFbVh0XkYhERVZ2WWVtHzqOqh0TkGUxUrI9T3dskIhswpw3LRaQoZqe8olUMEJGuQCdVfRCT+A9bXg/Yraq7bOSl2sA6zGnAUBGpoapb7PtRAdgEVBWR6qq6FT9zKFVtnZ78IhIJ9AN2qOo6EWkPHLCKQ03MSUdWnsUBEWmqqgsxiQ1/zqydH3OAp4CBVrY6qromkzYLgHuAN6zis09VD2dhTEc2M3PWXGbOmhtqMRyOHMW9546zkcWLV5C/QMU05Y8+ej8v/F9fAF599R1efTVtJoCTJ09Sp+4NmY7hcjzkLMGaLY3CmIs0ttfRwJs5IpHjtFDV1RifgbsC3O6HWdwD3A7M9SkOlslAOxHJn6pdKYxz/G8YpSEBGKKq/wBdgPEi4lMmatrTqEeA6SLyC/BXJmKPte1/w/gytLfls4AIe+8N239W6AwMtO3rAH2z0PYZoIGIrLNK12NBtOnta4PxM+mcRXkdDofD4Tiv6XB7V+Lj40MthiMIgs0wvVJVG4jIalX1Oc2utU6rDkdA7C58j8yiLZ0vuAzTDofDcfZTMsq5tDnOD2IObggYyj/Yk4c4G6EnCUBEqmPi8jscDofD4XA4HI7zhGCdTXtjTEkqishYTEz8rjkllOPcQFXnA/NDLAYAIjIU8976M1hVR4VCHofD4XA4HI6zkaDMlgBsnP2rMZFllqrqvpwUzOE413BmS7lH61bNGTSoL+EeDyNHjWfAwKGhFsnhyHbcex4anNlS1hk05E1ubH0d+/7ZT4trjHvjq3170KpNc+Li4/lr2w66PdmTw4eOpGk7btIw6jW8kuVLVnH/XU8kl7/30RtcWbcWYWFh/LllO88+0ZPYY7Hc3/VOujzUCW9iIrFHj/FCt978oVtzba7nEumZLQXr8/CTqt6QWZnD4UgfpzzkDh6Ph42/L6TNzZ2Ijo5h6ZIZ3HvfE2zcuDnUojkc2YZ7z0OHUx6yztXX1OfYsVg+/PjtZOXhuhbX8MuCZXi9Xnr2NqFY+/UelKZtk2ZXUzCqAPd16ZhCeShcpBBHj5gI6r37/R/7/tnPkA9GpChvdVMLujx4F3f/59GcnuI5SXrKQ4ZmSzZjcBRwkYhcyKl49kWBcuk2zGZExIvJFBwBbAPuU9WDIlLFXj+jqh/ZukMwuQRG2+sIYDcwXFVfTqd/DyZfxfUYv44TQEebZO1MZZ8PlMX4iOQDfgR6qerBM+37NGTpDRxV1XcD3FusqtfYZzpNVS/PZdm6AA1U9ancHDcYbP6Kcqo6I9SyODKnUcO6bN26nW3b/gZg4sTJtGvb2i2qHOcU7j13nE0sXfwrFSqlXDb+PG9x8vdVK9dya7vAEd1/WbCUxk0apin3KQgABQoUwLcZ7l8eFVWQIA1sHFkgM4fpRzFZfGvan77PZCA3z0ePq2odu6Ddj0mG5mMv8KyI5EunbStAgY4iElCDAu7EKEO1VfUKoAOQnYv7e1S1NiZPwknM80uBiIRZJSYkqOo1oRr7LKAOcHNWGlil1RECypUvw47oXcnX0TtjKFeuTAglcjiyH/eeO84l7rr3dub+uDDL7d4f2o91fyygxiVVGfnp2OTyLg91YsnqWfTq8zy9XnwrO0V1kMnJg6oOBgaLyNO+nf08wBLMItzHP8AiTGz94QHqdwIGA49jfDaWBKhTFohR1UQAVY323RCRVkAfID+wFeiqqkdFpA3mtGIfsAqolllIUlWNE5H/A7aIyJXAIUz25nmYHBq3icg1wCuYU57pqvqizb78GdAAczIyUlXftzvin2BOh7YCD6jqAXvasQZohDklekBVl1sxLrP3KwEfqOqHdp5HVbWwv7z2FOILTA4GgKdUdTEBsGFZ+wB7MIvtbzGnRc8CBYHbVHWriJS0MleyTbup6qJUfbUFemFOav7FKF977MlJJaBaAPm7Aw/YLkao6ge2/FVMArcdmN/Vr6r6rog8jMlJkQ/YgjnNihWR/wKvY7JXH8Jkuu4LFBSRJkB/TDbvjzAZsSOA3qo62Z6e3ILJhl0Ic5LlyGXCwtLuEQTr2+VwnC2499xxrvDs84/iTfDyzcSpWW773JM98Xg89BvQk3a338RXY78DYPSI8YweMZ4O/7mFbi88yrOPv5LdYp/XBLXTraoficjlItJRRO73fXJauNTYRfQNwJRUt94Gnrf3/esXtPWnAePxy3iciolAWxFZIyLviYgvl8VFmEVsS1WtB6wEultzruFAW6ApEPR2j6p6McncfOkPBfjc5s+IB97BLDrrAA1F5Db7vbyqXm5PRnwRgj4HXrSnGusxi14fhexpwhPASL/ymkBrjGLxus3unB57gRvt3O8EPsxkeldilIUrMJmdL1HVRsAI4GlbZzDwvqo2BO6w91LzC3C1fSYTgP/LSH4RqY+J/nUVRkF8WETqikgDO0ZdTHK8Bn79fKuqDW2uko3Ag7b8NaC1LW+nqnG27Ct7+vUV0BOTaK8h0AKTkM6nYDUGOquqUxxCxM7oGCpWOHU8XqF8WWJi9oRQIocj+3HvueNc4L+d2tOy9XU8+fD/ZV45HRITE5ny3UxuaXtjmnvffzODNjc799zsJijlQURex+y0foRZLA0A2uWgXKkpKCJrMLvQxYEf/G9a34TlwN2p2t0KzFPVWOAboENqBcO2j8Ys4l8GEoGfROQGzEL0MmCRHb8zUBmzgN2mqptVNQn4Movz8d8y+ktVfRmUGwLzVfUfVU0AxgLNgD+BaiLykT3xOCwiFwDFVPVn23aMretjvJ3bAqCoiBSz5dNV9aSNlrUXKJ2BnJHAcBFZD3xtn0VGrFDVGJu9eiswx5avB6rY7y2BIfZ5TrGyFUnVTwVgth33BaCW371A8jcBvlPVY6p6FHPq0dSWT1bV46p6BPDf1rhcRBbaMe7xG2MRMNqeTKR5VyytgJfsHOZjThp8Jyk/qOr+jB+TIydZsXINNWpUpUqVikRGRtKxY3umTpuTeUOH4yzCveeOs50WNzThqWcfokunJzl+/ERyeZmypZg4eWQGLQ1VqlZK/n5jmxZs2WzcVKtWq5xc3rL1dWz7869slNoBwed5+A9mV3m1qnYVkdIE3jHOKY6rah27YJ6G8XlIvQv+FjAJWOBX1gm4VkS22+sSQAsROQIMs2WvqeoUu+CdCcwUkT3AbZjF7w+qmuLEwpoLBTwfFpHZmAXtSlV9KMD9cMzO/EZbdMzvdkCfDGuKdCVmx/1JoCPwXKC6fqSWz3ftn9zPS8bvwHMYM6QrMYrmiQzqpu470e860W8cD9BYVY/7NxQR/8uPgEGqOsWaQ/VOZwyf/On5sqRXDjAaY0q11pobNQdQ1cdE5CqM+dEa+7sO1O8dqqqp5nAVKX+fjhDg9Xp5tlsvZkwfR7jHw+gxX7Fhwx+hFsvhyFbce+44m/jfiIFc06QRxUsU49ff5/Lu20N4+rlHyJcvkgnffwbAqhVrebF7H0qXLok3ISG57fczvqDGJVWJKhTFr7/P5fmnX+XneYsZ/PFbFClSmLCwMDb8prz4fB8AHnjkbppe15j4hAQOHTzEM85kKdsJVnk4rqqJIpIgIkUxO77VclCugKjqIRF5BpgsIh+nurdJRDZgThuWWzmbABWtYoCIdAU6qeqDGFMgbHk9YLeq7rJOy7WBdcBSYKiI1FDVLSIShdkV3wRUFZHqqroVP3MoVQ0cLsCMEwn0A3ao6jrrU+DPMoyPyUXAAdvvR/Y6TlW/EZGtwGj7LA6ISFNVXYgxE/rZr687gXnWTv+QrZ/pM07FBUC0/d13Jv2d+KwwB3gKGAhGEVPVNQHG3Wm/dw6izwWY04K3MQv7DpjnEQEME5H+9vstnPKLKQLE2N/JPb7x7O90GbDM+l5UBI7Y+j5mA09bX6AkEamrqquDfgKOHGfmrLnMnDU31GI4HDmKe88dZwtPPPRCmrLxX3wbsG69hlcyasT45Ovbbr4vYL32be4NWP7qS/1PQ0JHVgg2us9Ka/YyHBNtaRXGTCjXsYu0tcBdAW73wyzuwdi4z/UpDpbJQDsRyZ+qXSlgqoj8hlEaEoAhqvoP0AUYLyI+ZaKmqp7AONtOF5FfgMzOxMba9r9hHGnbpzO3GIzp1Dw7x1WqOhkoD8y3ZjKjbR0wC+uBtu86GMdeHwdEZDHGOflBTo//AZ1FZClwCdmzq/4M0EBE1lll77EAdXoDX4vIQoyTc4ao6irMc1mOUcBGqOpqVV2BMY1aizFlWolxggZ41db9AaMM+hgoIuvtu7DAtp2HcTRfIyJ3Am9gTLrW2XpvZGH+DofD4XA40mHU8HHMmTkv1GI4MiDTJHE2vGkFVd1hr6sARVV1Xc6Ld3ZgTWt6ZBZtKbew0ZR6qOrKUMsSakSksI2OFYVRBh6xykauc+SJm1woFMc5zYUj3H8LjnOf47uyHlLU4TgbibyoWtaTxAFYs4zvgfr2env2iuZw5CifishlGKfmMaFSHBwOh8PhcDjOBTI9eQAQkaEYO/sVOS+SIy8jIldgcj/4c1JVrwqFPGcT7uQhcwrc+xzhVzQi6chBYt98HID8HR4k/IqrwJtA4j8xnPhiEBw/Bp5wCtzbDU/F6hAeTsKyn4ibPTFNnxENriNf6zsBSDr4LydGDyTp2GE85atSoNPTkL8ASfv3cnzUADgRS3jNuuS/rSuER4A3gZPffob3j7W5+hzOVtzJQ+7SulVzBg3qS7jHw8hR4xkwMDdzt56/uJOH4Oj11iAWLFpO8QuL8f2XnwCw6Y+t9B34ESfj4gkPD+fVHk9yxWXCkaPHeKnvAGL2/IM3wUuXu++gwy2tUvR38mQcnZ98gbj4eLwJXm5s0YSnHjL+EEtXrua9oZ+RmJhEVFQB+vV8nkoVyhGzey+vvPkeR44exZuYyHOPdaXZNY1y/VmcraR38hCs8rABY/P+F8buPQxIsvkFHA5HEDjlIXPCa1xO0snjFOjcI1l5CL+0Hl5dA4mJ5LvN5AGM+34kEQ2aE1H7ak6MfBsi81PotWHEvv9/JO3fe6pDj4dC/ccS2/dRko4dJn+HB0iKO0nc9LFEvTiYk9+OwLt5PRGNW+EpUZq4aV/gqVCdpCMHSDq0H0/ZyhR8+k2OvRLYYc+REqc85B4ej4eNvy+kzc2diI6OYemSGdx73xNs3Lg51KKd8zjlIThWrllPVMGCvPLGu8nKw8PdXuH+OzvQtHFDFixezshxkxg9ZACfjpnA0WPH6P7Eg+w/cJBbOz3Mz1PHERl5KhVVUlISx4+fICqqIPEJCdz/eA9eevZRrrz8Um656yE+fPs1qlepxIRvp7F+g9Kv1/P0fmcwNS+pzl0dbmXrtr94vMdrzPlmTKgeyVnHaZstWW7KRlkcfoiIF5MHIQLYhsl0fND6lmwDnvFl9xaRIZgQsKPtdQSwGxiuqi8H6B4bPeoDTOK5JEy41Y42N8aZyj4fP98KK/M0Vb38TPsOBSKy2CbWc4QI75bfCCteKmXZxlOWZonbNhFRt4m9SiIsfwHweCBfPkiIJ+lEbKoew8wnfwE4dhgKRJG0NwYAT6kKeDevN2NsWkW+p/oRN+0LEqO3nhov5i/CIvJBRCQkxGf3dB2O06ZRw7ps3bqdbdv+BmDixMm0a9vaKQ+OPEODOlewM1XiwrCwMI4eM3+njx6LpdRFJZLLj8UeJykpidjjJ7igaBHCw8PTtI2KKghAQkICCQkJyZnWw4Bjtt8jR49R0r9fX/mx2ORyx5kRrPLwpqqm2HoTkS8w4TAdZ8ZxVa0DICJjMHkc+tl7e4FnRWSYzXScmlaAAh1F5BWbsC41dwLlgNo25GoFXC6CgDjFIe8TeU0r4n81EYkTVv1CRO3GFOo/jrB8+Tk56VOIPZqyQaKXkxOGUKjnxyTFnSBx705OTvifuRWznYjaV5OwbikRdZviufCiNONF1G2CN3qrUxwceY5y5cuwI3pX8nX0zhgaNawbQokcjsx58dlHebR7L94dOoKkxCS+HPYeAHff0ZanXuxDi/b3cCz2OO/2fRmPJ21AUK/XS8cHnuHvnbvodPut1K5VE4A+L3Xj8R6vUSB/PgoVimLcp+8D8MQD9/LIcz0ZN2kKx0+cZPgHb+XeZM9hR9YPHgAAIABJREFUglUe/DP8+hKd1c9+cc57lmByTPj4B5PxuDOn8hP40wkYDDyOyYa9JECdskCMqiZCcjZtAESkFdAHyI/JCN3VRiZqgzmt2IcJy1stq5GkRKQA8DHQABP6truqzrMJ2W7D5Iy4HHgPyIdRRE8CN6vqfpuc7RMgysr2gKoeSGesZzAhXxOADap6l4g0snMoCBy3c1M7fgc756rAOFXtY/s5qqqF7fcXMMn48mOyV79uT1ZmAr8A12ByQ7RX1eOBZMjK83JkTr42d5Hk9ZKw3ITwC68ikJjIsZfvISyqMFHPv0vCptUk/bv7VCNPOJFNb+FY/6dI2hdD/o6Pk691R+JmTeDEF++b65vvJmHdUvBLSgTgKVuJ/Lc9QOxHPXNzmg5HUPh2XP0JxgzZ4QglX303nReffoQbWzRh1k8LeK3/B4wY3J9Fy3+l5sXVGPnR2+zYGcPD3V6h/pW1KFyoUIr24eHhfDNmKIePHOXZl99g85/bubhaFT7/6js+frcvtWvVZOTYSQz4cDh9X+7GjB/n0/7mlnTpdAdrftvIy28M5PsvPgmomDiCJ8OnJyIv22zMtUXksIgcsdd7MTkTHNmEVchuwOQl8Odt4Hl7379+QVt/GjAev0R1qZgItLU5Ct4Tkbq2/UVAL6ClqtbD5EDobhf9w4G2QFOgTCaij7V9rwFm+JU/CaCqV1jZxti+wSgNdwONMKcssapaF6P83G/rfA68aP1q1gOvZyDDS0BdW9eXN2IT0Mz2+xomA7mPRpjEcHWA/4pIA//OrFJ1sa1XB6gvIs3s7YuBoapaCzgI3JGBDI5sIuKqlkRc3ogTowacKmvYnIQNKyHRS9LRQ3i3biC88sUp2nkqVgcgaZ8xVUpYtZDwapcBkLgnmuMf9ST27WeIX/kzibYOQFixiyj4yKucGPNucluHIy+xMzqGihXKJV9XKF+WmFQmIg5HXmPKzB9p2fxaAFpf35T1GxSA76b/QMvrriUsLIxKFcpRvmwZtv0VnW4/RYsUpmG92vyydCX7DxxEt/yZfApx0w3NWPPbBgC+nTqb1teb/77rXH4pcXHxHDh0OCeneF6QofKgqv1VtQgwUFWLqmoR+ymRno29I8sUtAvvf4HimKRlyVjfhOWYxbY/twLzVDUW+AbokFrBsO2jAcEklksEfhKRGzAnFZcBi+z4nYHKQE1gm6putmZQX2Yi/z2qWseaXt3sV94EG5VJVTdhnO0vsffmqeoRm4TvEDDVlq8HqojIBUAxVfVlzB4DNCN91mGUmHsxO/9gslR/bZO4vU/K07MfVPVfVT2OSR7XhJS0sp/VmJOXmhilAcyz8WXE/hWokoEMjmwg/LL65Gv1X45/0gfiT+V8TNr/DxFypbnIlx9P1Zok7tkBQMFn+hN2QQmSDu7DU7YSYYUvMH3VrEvibmMj7isjLIz8N91F3EKr+xYsRMEn+nBy8mi8f27InUk6HFlkxco11KhRlSpVKhIZGUnHju2ZOm1OqMVyODKk5EUlWLHa+Jot+3UNlSuWB6Bs6ZIs/dX817pv/wG2/x1NhXJm77Jtp4cB2H/gIIePGNPUEydPsnTFaqpWrkjRIkU4eiyW7X8bZWPxitVUq1zJ9FumFMtWmn63bv+bkyfjKF7sglya7blLhmZLIlIZOOhTFESkBcbkZDtm9zWQHb4jaxxX1Tp2wTwNs2P/Yao6bwGTMEnOfHQCrhWR7fa6BNDCngwNs2WvqeoUm2V7JjBTRPZgfodzMIvoFCcW1lwo4Nm3iMwGSmOcth/KZF4BPfQt/lm/E/2uEwnelM6fWzDKRTvgVRGphcn6PE9VO1hzo/l+9VPPL/V1GNBfVYf5F9p+/GX3YsyiAsqgqk6JyCIFur5I+CW1CStclEL9viBu+hfka3UnREZS8GnjCuTdvomT44cQt2AqBe7rTlSvTyAsjPglc0jcuR3CwvCULEtS7BGIjyNuxlgKdh8AXq8Jyfq5sbGNaNicfM2MNV78msUkLDELr3zXtcVTshz5bupEvpvMP4/jH/Uk6eihtAI7HCHC6/XybLdezJg+jnCPh9FjvmLDhj9CLZbDkcwLr7/NitXrOHjwMDfcdi9PPHgffV58hrcHDyPB6yV/vny8/n/PAPBYl7vp2e89Otz3OElJSTz3xANcWOwCDhw8lGyO98+/B+j55rt4ExNJSkyi9fVNaX6tiRLf+8VneK5nP8I8YRQtUpg3Xn7OyPDUQ7z+zod8PvE7wgjjzZ7dA5r8ObJGZgu1iRj78EN2Ufk10B9jyvE/ILMFpCNIVPWQtZufLCIfp7q3yYbLvRVYLiJFMbvlFa1igIh0BTqp6oOY3w+2vB6wW1V32chLtTG75EuBoSJSQ1W32AzMFTDmPlVFpLqqbsXPHEpVW2dhSgswpkFzReQSoBLGubtekM/igIg0VdWFGH+InwPVtXOqaP0pfsGc0BTGnDzstNW6pGp2o4gUx/hC3AY8kOr+bOANERlrfUDKA+l6zGYgw8HM5upIyYlR76Qpi1+czm7qyROcGJHW+c1TphIJaxZBvNnbiF84g/iFM9LUi583mfh5aa0v42ZNIG7WhCxK7nDkPjNnzWXmrLmhFsPhCMjAPi8FLJ848qM0ZaVKlgjozLz2903cdUdbAKRGVSaNDpzLpOV119LyumvTlFevWpkvP3kvK2I7giAzj5GCquoL53AvMFJV3wO6YuzBHdmIqq4G1gKBnG37YRb3ALcDc32Kg2Uy0E5E8qdqVwqYas131mFMaoZYk6EuwHgR8SkTNVX1BPAIMN0uhP86zen8DwgXkfXAV0CXVPJmRmdgoJWtDtA3nXrhwJd2nNXA+6p6EBgA9BeRRbaOP79gTKrWAN/4Qs36UNU5wDhgie13ElAkA1nTk8ERAhJj/uLkN4HiCzgcDofjbKL5tVdx73/bh1oMRyoyTBInIuutwysisgp4WVVn2+t1LkncuY+INMfkcshStKW8io221EBVn8rtsSPylXehUBwOh+Ms58/aNUMtgsORK1Ra+dNpJYmbKyITgRjgQmAugIiUBZy/g8PhcDgcDofDcR6RmfLQDZNkrCzQRFV9dt9lABf8/DxAVeeT0tk4ZIjIUCC1UeNgVR0VbB82O/fobBTL4XA4HA6H47whQ7MlHyLyFPCls+N2OE4fZ7aUe7Ru1ZxBg/oS7vEwctR4BgwM7GTncJzNuPc8NDizpeAo/loPCja5Gu+Bg+y+08TX8RQtQon+rxJRtjQJMXvY91Jfko4cpch9HSnU5gbTMCKcyCqV2HnjHSQePpKiz1LD3iP8ohIknTAulHufepHEA6eWpgVvaEbJd15n932PE7fxD8LLlqbs16NI+MuE8T7520YO9P8gF2Z/bnC6Zks+ygArrd/DSGC2zQHgcDgceQqPx8OHg/vR5uZOREfHsHTJDKZOm8PGjZtDLZrDkW2499yR1zk2dTZHvppMib4vJpcV7dKJk8tX8c+YCRTtfBcXdOnEwY+Gc+SLiRz5YiIABZs2psjdaRUHH//2eou4jWnDEodFFaTInR04uT5lfp6EnbvYfc+j2TgzR1D5uVW1FyZJ1meYCD2bReQtEameg7KdF4iI12Zo/k1EpopIMVteRUSSRORpv7pDrMOv7zpCRPaJSP8M+veIyIe2//UiskJEqmaT7PNF5G8RCfMr+15EjmZH/3kFEekiIkNCLYcjOBo1rMvWrdvZtu1v4uPjmThxMu3aZiXKsMOR93HvuSOvc3L1ehIPp8zmXPC6azhqkxkenTaHgs3ThleNat2CY7OzHoL4gse6cvjzr0iKcy65OU1QygOAPWnYbT8JGAfqSSIyIIdkO184bjM0Xw7sxySJ87EXeFZE8qXTthUmd0JH/wV8Ku4EygG1beSsDmRv/oGDWD8Eq/iUzca+HY4sU658GXZE70q+jt4ZQzmbqdThOFdw77njbCS8+IUk/rsfgMR/9xN+YbEU98Py56dA44Ycn7sw3T6Kv/4CZcYOo+iD9yaXRUoNIsqU5MQvS9PUjyhXhjJjP6HUsEHkr3NFNs3k/CYosyWbvKwzsA8YAbygqvE2OdZm4P9yTsTziiWYJG4+/gEWYZ59oMD1nYDBwOPA1bZ9asoCMaqaCKCq0b4bItIK6APkB7YCXW1StDbAB5jf9yqgWgahWidg8lL8gsk/8S1Qy/bfHL8wr3b3fqWqjhaRtzHZmBOAOaraI1DnIjIak8ytJlAZk2OkM9AYWKaqXWy9o6pa2H7/D3CrqnYRkf8Cr2OyQR9S1WYiEg68A7TGZJcerqof2WzdDVR1n4g0AN5V1eYB5JmmqpP8x7URyL4CimL+XT1uE9w5cplA2UOD8e1yOM4m3HvuOBcp2KwxcWt/z8BkqT/ef/YRFlWQiwb0ptAtN3Jsxo9c2P1x/u2ddi/bu28/u269m8RDh4mseTEl3+1LzJ0PknQsNqenck4T7MlDCeB2VW2tql/7oi7ZBek5Ef8/1NgF7Q3AlFS33gaet/f96xe09acB4/HLBJ2KiUBbaxr1nojUte0vAnoBLVW1HrAS6C4iBTCKSlugKcbfJSN+AnwL8rswC+jM5loccwJSy+YKeTOTJhcC1wPPAVOB9zEKyhU283lGvAa0VtUrMcoKmCR4VYG6dvyxmckcBHdjfIHqAFdiEtA5QsDO6BgqViiXfF2hfFliYvaEUCKHI/tx77njbMS7/wCeEsUB8JQojvdASkOIqFYZmyx5/9kHQFLscWJnzSVfrZqERUURWb0qpYcNotyUseS//DIuGvQG+S69BOLjSTxkTKfiN20mYecuIitVSLd/R3BkqjzY04U7VDVgpmFV3ZjtUp1fFBSRNcC/QHHgB/+bqroNWI5ZnPpzKzBPVWOBb4AOqRUM2z4aEOBlIBH4SURuwJxUXAYssuN3xuzs1wS2qepma6r2ZSbyezGnDndiMpJvD2LOh4ETwAgRuR3IbAtgqpVlPbBHVddbxfV3oEombRcBo0XkYU5lmm4JfKKqCQCquj8ImTNjBdBVRHoDV6hq4G0TR46zYuUaatSoSpUqFYmMjKRjx/ZMtTa2Dse5gnvPHWcjx39eTOFbWwFQ+NZWHP95cfK9sEKFyF+vdooygFL/G0h4yYsg3IPngqKmMDycgk2vJn7rNpKOHWNny9vZ1e4edrW7h5O/bWBf91eJ2/gHnmIXgMcsdcPLlyWiYgUSdsbkzmTPYTI1W1LVRBFZKyKVVPXv3BDqPOO4qtYRkQswpwhPAh+mqvMWMAlY4FfWCbjWmtqAOR1qISJHgGG27DVVnaKqJ4GZwEwR2QPcBswBflDVFCcWdic/4Nm3iMwGSmNMjx7yuzUB+A7onapJAikV1AIAqpogIo0wJyd3AU9hThbS46T9mej33Xfte4f9ZS7g+6Kqj4nIVcAtwBo7v7B05ugvb4EA91PUsX4m+ew4C0SkmR3nCxEZqKqfZzAnRw7h9Xp5tlsvZkwfR7jHw+gxX7FhQ9rIHA7H2Yx7zx15nRL9elKg/pV4il1AuekTOPTpGA6PmcBF/V+lUPub8O7ey76X+ibXj2rRhBPLfiXpxIlTnYSFEVGxPImHDxMWmY9SQ96BiAjweDi5fBVHv5uRoQz569Xmgke7gNcLiYkc6P9BuiZRjuAJNlRrWeB3EVkOHPMVqmq79Js4soKqHrK+JZNF5ONU9zaJyAbMacNyESkKNAEqWsUAEekKdFLVB4FkUx4RqQfsVtVd9hSpNrAOWAoMFZEaqrpFRKKACsAmoKqIVFfVrfiZQ6lqeqE8FgL9MeZT/vwFXCYi+TGL8RuAX0SkMBClqjNEZCmwJavPKwB7RORSjAN5B+CInX91VV0GLBORtkBFjOL0mIjMt4pMcXv6sB2oj1G07khnHF+diUB7INKOUxnYqarDRaQQUA9wykOImDlrLjNnZT1ah8NxNuHec0de5t+e/QKW733ihYDlx6bN5ti02SnKIqtVJnbuQpJOmghKu+97PNNx9z76fPL343MXZuh87Tg9glUe+uSoFA4AVHW1iKzF7Manftv7Aavt99uBuT7FwTIZGCAi+VOVlwKG2wU8GBOoIap6woZ9He93r5eq/iEijwDTRWQfxiTp8kzkTgLeDVC+Q0QmYpSVzX7yF8EoSQUwpwDPZdR/kLyEObnZAfwGFLblA0XkYjvOT8Bae/8SYJ2IxGN8PIZg3vPPROQVYFk64wy3si+3/fmU6ebAC7a/o8D92TAnh8PhcDjOW+K3bufg+x9nXtGRqwSVYdpx/pI6YpLj9BlQ+V73j81xTvNKzLxQi+BwOByObCIhbufpZ5gWkauBj4BLMTbe4cAxVS2abRI6HA6Hw+FwOByOPE2wZktDMKY0XwMNMCYZF+eUUI68g6rOB+bn9Dgi0hP4b6rir1U1sNGk47wgf9Eo2rzzEBddUgFIYuYLw6narDa1OzUn9l/j9LZw4ET+nLc2Rbui5Utw27BuhHk8hEeGs2r0HNaMnUu+QgXo9PWryfWKlC3Ohu8WMbfvl9S553rq3n8jid5E4mNPMPvlz/h38y4cjrxK61bNGTSoL+EeDyNHjWfAwKGhFsnhyHbce573CMpsSURWqmoDEVln4+IjIotV9Zocl9DhOEdwZktZ5+b3HiV6hbJuwnw8keFEFsxPgwfaEBd7ghWfph9lwxMZTlhYGN64BCKj8vPAnLcZe3sfju5NGVP8/mlvMLfvl0QvV/IVLkjc0eMA1GhZjzr3tWRS57RJhxzp48yWcg+Px8PG3xfS5uZOREfHsHTJDO697wk2btwcatEcjmzDveeh5YzMloBYEcmHCXU5AIgBCmWXcI7sQ0TeB/5S1Q/s9Wxghy+0qoi8B+zkVLK1S4GDmNwLr9uQo6WBzzCRiSKB7ap6c4CxegNHVfVdv7Lt2CzNOTXHnEJERgCDVHVDqGVxQL7CBalwlTDjeRN5ODHey8n44LKCJsZ7k7+H54skzJP279+FVUoTVaIo0csVIFlxAIiMyk86EYsdjjxBo4Z12bp1O9u2mQjqEydOpl3b1m5R5TincO953iRY5eE+TGz7pzCRcSqSfihLR2hZjDH/+cCGZr0I8PdNuQYTmWg6xhF6CoCIXI4xSVsA9MXkgBhs79XOPfFDR6rcFY4QU6xSSY7/e4Sb3n2EUpdVYs/67fzU+wsA6t1/I7Vub8Lu9duY98ZYTh5Oq1QUKVucO0b14MIqpZn/1vg0pw6XtmvMpmlLU5TVvb8lDR66ifDICL7q9FbOTc7hOEPKlS/DjuhTZnXRO2No1LBuCCVyOLIf957nTTJVHmzW4n6qei8mK7AL25q3WYQ5UQCohQlLWlZELsRkcr4Uk0V6iU9xAFDV32xdMHk95vjdW3c6gohId+ABezlCVT8QkSrALEwI2KsxoVNHYd6rUsA9qrpcRIoDI4FqVu5H0pNDRK4DBtvLJKCZ/TkZuBBzetJLVSf7jb8MqAv8AdyvqrEiMh+jUK0UkVZWpvzAVqCrqh61JytjgLa23//aPBxpZHBZps8MT3g4pS+vwo+vf07Mmq1c//p9XPVEW1aNmcPiD78jKQma9vgPLV69h1kvDE/T/kjMfka3eYXCpYrRYfhz6IzlxO47nHy/ZrvGTO+WMgTg6s9/ZPXnP3Jp+8Y0fvq25FMPhyOvERaW9jTNRU90nGu49zxv4smsgqp6gZLWbMmRx1HVXUCCiFTCnDIswSyUG2NOFtYBAqzKoJuhmHwH80Skp4iUy6DucyKyxvcBygGISH2gK3AVRkl4WER82wU1MAvt2hhF5m5M0rsewCu2Th9gtfWxeYWME671AJ5U1TpAU+A4RtHtoKr1gBbAezYjNHb+n9q+DwNP+HcmIhcBvYCWtv1KoLtflX22/GM7dnoyOM6AI7v3cyRmPzFrtgLwx4zllL68CrH7DpOUmARJSawdP4+yV1bLsJ+jew+y74+dVGgkyWUlL62EJ9zDnt+2B2yzccpSLm5VP9vm4nBkNzujY6hY4dSf5grlyxITsyeEEjkc2Y97z/MmmSoPlu3AIhF5VUS6+z45KJfjzFiEURx8ysMSv+vFqSuLyHci8puIfAugqrMxO/7DMYv71SJSMp2x3lfVOr4P4DtfbAJ8p6rHVPUo8C1mUQ2wTVXXq2oi8Dvwk000tx6o4tf+CyvPXKCEiFyQwXwH2QzdxVQ1AZMU7i0RWQf8CJQHStv6O1R1kf3+pR3Ln6uByzDv/BqgM1DZ7/639uevfvIGksFxBhz75xCHY/ZTvFpZACpfW4t/N++kUKliyXUuad2AfRoNQOHSF3LnuJfN9zLFicgfCZiITeUbXMz+rTHJ7S5t15iNU5akGO/CKqWTv1e/vg4Htu/OmYk5HNnAipVrqFGjKlWqVCQyMpKOHdszddqczBs6HGcR7j3PmwTr87DLfjyY7MCOvM1ijKJwBcYUaQfwPGaXfSTGPKiZr7KqdhCRBvhliVbV/cA4YJyITAOaiUg94BZ7v04mMgT00Lf4Z8BO9LtO5NQ7Gah9wLNKVX1bRKYDNwNLRaQlRgEoCdRX1XhrblQgnX5SX4dhfD46ZSK/1ydvIBlUdVM67R1B8tPrY7h18ON4IiM49PdeZvT4lJZ97qfUZZVJSkricPQ+Zr8yEoDCpYqRmGAcpUvUKEeLXndDUhKEhbHi0xnJSgZAzVuvYlKXgSnGqtu5FVWa1MIb7+Xk4WNM7+5Mlhx5F6/Xy7PdejFj+jjCPR5Gj/mKDRv+CLVYDke24t7zvElQyoOq9gEQkaJAkrPlzvMswigLf1qzs/0iUgzjA/EwcAx4WUTa+fk9RPkai8j1wFLrB1AEqA78rarfAD2DlGEBMFpE3sYsxjtgHO+DZQFwD/CGzXK9T1UPB6ooItVVdT2wXkQaY05LLgD2WsWhBSlPDiqJSGNVXQJ0wvhf+LMUGCoiNVR1i4hEARVUNd2/WOnI4JSHM2Tvhr/5vO1rKcqmP/dJwLpl69Vg9ec/AvDXL78xus0rAesBfNo07cHp3D5fnIGkDkfuM3PWXGbOmhtqMRyOHMW953mPYDNMN8A4tRax14eAB1T11xyUzXH6rMdEWRqXqqywL4SqiNyKMbP5ANgDHAHetHXrA0NEJAFz2jRCVVdkRQBVXSUio4HltmiEqq62DsvB0BsYZc2OYjGmQ+nRzSoIXmADMBPzrk4VkZXAGlIu5DcCnUVkGLAZ47vgL/s/ItIFGC8i+W1xL4xzdVZkcOQiq8f8EGoRHA6Hw+E45wk2Sdw6jDPoQnvdBPifL2Gcw3G2YJWXaap6eW6P3bxCSxciwnFO88vejaEWweFwOBzZRHpJ4oJ1mD7iUxwAVPUXzE61w+FwOBwOh8PhOE/I0GzJOsgCLLcmHuMxzqV3AvNzVjSHIyUi0hV4NlXxIlV9Mtg+VHU7kOunDg6Hw+FwOBznApn5PLyX6vp1v+/OBMORq6jqKIzvjeM8IF/+SAZ/8z6R+SIJDw/n5xkLGP3e53To0p7/PHQ75auUp/0Vt3PoQFo/+jIVSjNm/kh2bN0BwIZVGxn08mDyF8hP72GvUb5yWbzeRJb8uJRP+48AoPZVV/BU7yeofmk1+j75Jj9PX5imX4cjL9G6VXMGDepLuMfDyFHjGTBwaKhFcjiyHfee5z0yVB5UtUVuCeJwOBz+xJ2Mp3vHHhyPPUF4RDgfffcBy+etYP2K31ny41I++Dr13kZKdm3fxUOtH0tT/tWwiaxZvJaIyAgGTRhIoxYNWT5vBXt37uXt7gO489GOOTUlhyPb8Hg8fDi4H21u7kR0dAxLl8xg6rQ5bNy4OdSiORzZhnvP8yaZmS3dq6pfppcQTlUH5YxYjtNFRN4H/lLVD+z1bExStIfs9XuY6Er3A/VUNU5EqgM/AHWABExyuNqYEKsHgTY20Zv/OF2ABqr6lF/ZfKCHqq7M0UnmACLSF1igqj+GWhbHKY7HngAgIiKCiIgIkpKS2PL7ltPu7+SJk6xZvBaAhPgE/vhtMyXLmvyHu6NN1tKkxMQzlNrhyHkaNazL1q3b2bbtbwAmTpxMu7at3aLKcU7h3vO8SWYO04XszyIBPoVzUC7H6eNLEIeIeDAhW2v53b8G+BmTR6GHLRsK9LR5FJ4F9qjqFTYi0YNAfC7JHjJU9TWnOOQ9PB4PI2Z/wvdrJ7Fy4a9sXB186owylcowfNYnfDDpPa5olNbNpXDRQlzTsjGrflmdnSI7HLlCufJl2BG9K/k6emcM5cqVCaFEDkf2497zvElmZkvD7M8+qe+JSLecEspxRiwC3rffa2EyTJcVkQsx+RIuBVYDCqyyuRwiVXW8bVMW+MvXmarq6QghIp2AVzCnF9NV9UVbfhSjrLQEDtg6A4BKQDdVnSIiBTC5FxpgTkK6q+q8dMaphfGDyIdRhu9Q1c0i8j1QEZNVerCqfuo3/jCghR3/LpvXYTQmhOskEakPDMIoyPuALqoaY09Wltm2xYAHVXVhejKcznNzpCQxMZGHWj9G4aKFeGNEH6pKFbbp9kzb/bt3P3c2uofDBw9zyRUX8+Znfehy/UPEHo0FIDzcw6tDe/LtyO+I+Tsmh2fhcGQ/YWFpIygGE3rd4TibcO953iTYUK2BCGjK5AgtqroLSBCRSphThiWYBW9jzGJ8narGqepB4B2gP/CEXxcjgRdFZImIvCkiF2cw3J0issb3sf0jIuVs39djTKEaishttk0hYL6q1udUYrobMRmo+9o6T9q5XIHJAD3GKhSBeAyjHNSx40fb8gfsGA2AZ0SkhN/4q1S1HuYExj8IACISCXwE/Me2Hwn086sSoaqNgG5+bdOTwZFNHD18jDVL1tKoecOg6sfHxXP4oHGk/mP9Znb9FUPFahWS7z//Tneit+1k0mff5oi8DkdOszM6hooVyiVfVyhflpiYPSGUyOHIftx7njc5E+UhYOIIR55gEUZx8CkPS/yuF/twEsLVAAAPXUlEQVTVuwnj/3CZr0BV1wDVgIFAcWCFiFyazjhfqWod3wfw+To0xCgI/6hqAjAWaGbvxQGz7Pf1wM+q+v/tnXmYlNWVh99uBDUiOBh0FI2Iy3EHVIyMhACiZowTMaKISxQVH41rXMY1SowTzWgUHfdtcDRGHhXBAddHEFwAF2QRzM8xbnGijiSoiIqmm/njnIKyqKoubaRZzvtPV9+6y/m+e+5X59x77ne/jM+dI70XcGfI80d8JWTrCjJMBs4zs7OBzSR9FumnmNkMYAq+AlFwghqBkfH5rmirGMNf5fp4OEQXAJsUfV+wNl8skreSDEkzaN+hPW3beeRkm7XasEuvnXn7tbcr5t+mm3Hu8LMXl62v98fbRt/biE6bd+IvscJwzFlDWKfdOlx70fXf8hUkybfH8y9MZ8stN6dz501p3bo1Bx+8P/899rGWFitJlimp5ysmTb2qtRq5brTiUtj3sCMetvRn4AzgY3wmHTPbD2gP7AM8YGaPSvoUIDZHjwJGmVkjsK+Z9QOGRv37NtF+NcfyS0kF3WkEFkabjWZW0MeaHVNJd5vZVODHwKNmdmzU2x/oKenTCDeqtHJRqsd1wGxJPSvkXxh/G4jxU04GSeNrvYakPOtv2IFzrzqb+lb11NfVMWHsRCY/MZWfHj2AwScMokPHDtz2+M1MnfAcl591JRt22oCFn3v3dN19J4accSQNDQ00NjRy5TnDmf/hfDpu9F2OOPUw3vqft7jlkRsAeGDEGMb94WGsq3HJrcNo274tPffqyVGnH8mQPY9tyVuQJBVpaGjg1NMu4KFxd9Oqvp4Rd4xkzpxXW1qsJFmmpJ6vmDT1tqX5lHcS6oC1vxWJkmXBM7iz8LqkBuBvZrYevgdiqJmtjZ/hcYCkOWY2BjgfON/M9gDmSJpnZm3wVYknJd2H71UAwMyqtT8VuNrMvovvKxiMhwLVyiTgMGC8mW2N74cou/fCzLrEdV4Tn3cC3gDmheOwDbB7UZF6YCBwD3Ao8HRJlQI6mllPSZMjjGlrSbMrCVtBhnQemsnrr7zB0B8t/arVUbePZtTto5dK37b7toy+YwwAkx56ikkPLX1OwwfvzqXPJv3LtqcZ4qAeg5spdZIsPx5+ZDwPP5KPmmTVJvV8xaOpDdPrLi9BkmXKLPwtS3eXpLWVNNfMfgOMljQnvhsGTI9Nw1sAN5hZHW5ojwPu/zqNx+bic4EJuKP5kKQxX6OK64EbzWwWvmH6KEkLK+QdBBxuZl8C7+H7JhYAx5vZTNwZmFKUfwGwvZm9CHwU5Ytl/8LMBgLXmFl7fIwMByo6DxVkSJYzN15yc0uLkCRJkiSrPHW5az1ZnTCzTyTla4aTJEmSJEm+Ac3ZMJ0kSZIkSZIkyWpErjwkKwVmtg/++tdi3pB0QEvIkyRJkiRJsjqSzkOSJEmSJEmSJDWRYUtJkiRJkiRJktREc855SJJkNcLMDsDP/9g2Du/DzDoDYyXtYGZ9gDMl7VeljsV5zOwnwHaSLqux/VVms7uZnQbcXDhbJWk+ZrYhcBX+auZ5+IGU/y7pgRYVLGkWZtYN2FjSQy0ty6qGma0PPBH//iN+ftEH8f9ukr5oIblOBD6U9PsqefoBn0qaUinPt0W8kn03Sfcs77ZXFNJ5SJKkVgbj52Icgr/et1lIehB4sLn1rKSchp9wns7DMiBeLT0auEPSoZG2GfCTFhVsBcHM1pD095aW4xvSDdgVSOdhGSPpr/j9xcyGAZ9IuqI4T4ytOkmNy0Om0NXrms5JP2AuX30V+/KiC/47mM5DkiRJJcysLbAH0Bc3+Ic1kX8d/GDAHfHnzLDSsz7M7ChgV0knxazxjfhDGeAESc+Wqfd3IcM84BBJH5jZUOA4oA3wGnBEHBB4EHARPpv2kaTeZtYKuAzoA6wJXCfppjLt/Aw4Ez8kc6akI8IYvR3oiM/ODZH0dpyPMjYOUly8QhKrLMPwH7gdgBeBw4GTgY2BCWY2Fz8N/TbcQFoE3C7pqmr3N1mKfsAXkm4sJEh6izicslK/V+ojSYvMbE/gClx/n8d1cqGZvYmfodMXaI3r3qXAlsDlBRnM7Czg4GjvAUkXlQptZj8CfgO0AuZK2tPMOuB61gV3Lo8DXgZeB7pJ+jDKvoaPyUZ87Hwvqj1N0jNhDG4MdAbmmtl5wJ3AOpHvJEnPmlk9cC3wQ/yAzXpcB+8zs12AK4G2cY+OkvRuyTWUHbtmdjpwdKTdKml48UpllD0TP39omJk9iR8w2hdYDzgm/r8YWNvMesV9fg+4OupdBPSWNL/03ibfHDPbEnfGnwa+D+xnZhcBO+MHBI+UdHHkfQe4Fdgf1+OBkl6NlYGr8D5qBH4gaUHo4eBIGyvpfDN7GpgI/AAYFQfMzg2deRp4IeRoCwzBn//HAg3xO/Lz4t8LM1sX1+mdo/0LJY02s8OBs/Hzpx6UdJ6ZrRFtrRdlDwH6SzrWzO4C/gr0wFdmzoiVzMuArcxsOj5WJ8Tf1vj4GSDp9WXRFysquechSZJaGAA8IulV/MTynZvIfz4wXlIP3Bi4PByKSlwDTJTUFX/glzuUbx1gmqSd8R+agjE2SlKPKPsKbnQAXAjsE+mFGehjcEeiB/6DMNTMNi9uxMy2D/n7RdlT46trgf+StBPw+5C5Kbrjqwzb4cbVHpKuAf4C9JXUF5/56yRpB0k7Av9ZQ73JV9kemFbl+2r9vlQfmdlawAhgUPTJGsAJRfX9WVJP4KnINxAPlyoYVHsDWwG74f27i5n1LhbIzDoCtwAHhp4dFF/9Cngp9Ow8XOcagTHAAVH2+8Cbkt7HDemr4toOxA25ArsA+8dqzP8Be8X4GcQS/f0p7mDsiBtkPaON1rjzNVDSLrhx9G9l7u1SYzecjiG4wbd73O/uZcqWsoak3fD+uCjCZi7EjdVukkbiTv2JkrrhxuZnNdSbfH22A26T1F3S/wLnSNoV6ArsZWbbFeV9X1J3XPdOj7SzgOOin3oDn5vZvwD/jIf8dAV+V1RHO0m9JQ0vI8uaMd5OxR3RP0Vbl4delE40DQM+iLHbFZhoZpsAl+C/R93xcV4xxLaIDXAnfQDuvAKcA0yItq8Bfg5cEdfaA3++r9Kk85AkSS0MZskS7T3xfzX2Bs6JmZkngbVYMjNajn7ADQCSGiR9VCZPIzAyPt8F9IrPO5jZU3Ei+WG4IQnwDDAiViZaFcn1s5BrKrA+buSVynKfpLkhz98ivSdLTm2/s6j9ajwn6Z0w/qbjRloprwNdzOw/Yib64xrqTapgZteZ2Qwzez6SqvV7uT4y/FXQr0aeO3ADqEAh3G4WMFXSfEkf4AbSetHe3sBLuFOzDUvr2e7AJElvwFf0rBeuX0gaD6wfp92PxI1+8JCJwljoD1wb1/Yg0C5mXsFnVwvGdWvglhgn9+LGYaG9eyU1SnoPn0Ul7sEOwONR9wXAJixNubHbC19tWSDpE3yv1A/KlC1lVPx9kfJjBXxcX2lmpwDrrcThWCs6f5L0fNH/g81sGq7P27JEf6B8vz0DDDezk3HHoAHX1dsLOlmk81A9BOgPkX88sEGshFejP3BdlFkkaR7uyI6XNFfSl/izvHeVOgqMjjpmAp0q5HkWuMDM/hXYVNLnNdS7UpNhS0mSVCU21fXDjfRFuCG+KB6UlajDZ1RVUteGy1C0wnumR+DLxDNiCbsPgKTjY4b2x8D02HhZB5ws6dEmZK/lHdaFPH8nJmIiPrhNUZ6FRZ8bKPPMlTTPzLoC+wAn4qEuR5fmS6oyG591B0DSiRH68EIkle33CFsq10d1TbRXKNNYUr6xqPyl5ULiiqikZ+XaXgRMBraMFYsB+CwquO71LHISADAzgAVFSb8A3sdnYuuBgoFT6VrrgNkx4/t1qVTn4rESrFXyfeFelh0rAJIuM7NxwL7AFDPrr3iBQ7JMWaw7ZrYVPuu/m6QPI5ynuO+W6jdJl5jZg/jz9/kYa9WerQsqpFOmTFPP53LtVNLJxpLvKulkxTok3Wlmk/FrfdzMjpQ0qQkZV2py5SFJkqYYiIdObCaps6RN8djoajPvjwInhzFNDSELTxBhIWbWyszalclTH7IAHIrH4wKsC7wbYRaHFTKb2RaSpkq6EI/X3jTkOiHyYmZblwmnegI4OJwmIgYdfHbpkPh8WFH7b+LhIeBxv62buFaA+SE3YeTWS7of+CUe+pF8PcYDa5lZcWjRd4o+19LvxfwR6Byx3wBH4KFytfIocHRhhtTMOpnZBiV5JgM/LIRPFenZJEKPw+CaK+ljSYuAB/A9CK/IN7sCPAacVKg0nORytAfejRWWI1iyGvc0cKCZ1Ydz3yfSBXQ0s8VhTBHSV0q5sTsJGGBm34n7fAAe4vU+PnO8vpmtCdQSNrJ4rEQbW0iaJem3uHO4TQ11JM2jHd4PH5vZRvhER1Win2ZKuhRfgTNcV48xs7UjT4dqdRQxKPL3wUOkFlCiFyUsHhNmVmdm/4BvrO4burcG/iyfGONhnpltZb7/p5aDZ0t1souk1yRdDYwDdqrxulZa0nlIkqQpBuNGSzH34wZ8JX6NG9Ezzezl+L8ap+IP9ln40nc5I2UBsL2ZvYivhFwc6b/EQ1Eex42+Apeb2axofxIwA4+TnQNMi/SbKJnhlDQbj+2eaGYzcGMN4BRgiJnNxI2vwl6IW3Aj8Dl8abzaDFqBm4GHzWwCvhT+ZISGjADOraF8UkQY1gPwfngj+uIOfHMk1NDvJfV9jsfs3xs6WdiUXKs8j+FhEZOj/H2UGDoR5nQcvkF0BkvCkIYBu4aeXQYcWVRsJL7pfmRR2imF/GY2Bzi+gljXA0ea2RRga5bo6f3AO/im7JvwsfRR7DcYCPw25JsO/FOZepcau5Km4br8XNR3q6SXIlzk4kgby1fHayUmANuZ2XQzGwScZmYvh0yfAQ/XUEfSPKbh4+dl/Hn3TA1lzox+mgl8CDwmaSzwCPBCPO9+UWP7H5vZs/genKGRNgaf5HnJzEr18lfAhjHWp+Obtd/B9888GWlTJI2L/GeHXE/gY6EpXgJaRWjkKcChZjY7rqkLHla7SpMnTCdJkiTJaoyZtZX0Say2PYdv7H+vpeVKEvO3LZ0kaXpLy5IsIfc8JEmSJMnqzdjY6N0G+HU6DkmSVCNXHpIkSZIkSZIkqYnc85AkSZIkSZIkSU2k85AkSZIkSZIkSU2k85AkSZIkSZIkSU2k85AkSZIkSZIkSU2k85AkSZIkSZIkSU38PyxU3sYHzLz0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpMetaDf=pd.DataFrame(myDict).fillna(0)\n",
    "inHmap=tmpMetaDf[~tmpMetaDf.index.get_level_values('ScientificName').str.contains('metagenome')].astype(int)\n",
    "fig,ax=plt.subplots(figsize=(10,3))\n",
    "sns.heatmap(data=inHmap,\n",
    "            annot=True,cbar_kws={'label':'# of sequencing run processed'},fmt=',',ax=ax,cbar=False)#fmt = ''\n",
    "#ax.set_ylabel('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "#inHmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "sra_dump_sub_df=sra_dump_df[sra_dump_df['SNP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "countS=sra_dump_sub_df.groupby(['ScientificName','LibraryStrategy']).size().sort_values().loc['Homo_sapiens']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-113-bb39aa0a0692>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtmpS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msra_dump_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ScientificName'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'LibraryStrategy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SNP'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Immunoprecipitation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtmpS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtmpS\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1420\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1421\u001b[0m                     return self._cython_agg_general(\n\u001b[0;32m-> 1422\u001b[0;31m                         alias, alt=npfunc, **kwargs)\n\u001b[0m\u001b[1;32m   1423\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mAssertionError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1424\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m   3973\u001b[0m         new_items, new_blocks = self._cython_agg_blocks(\n\u001b[1;32m   3974\u001b[0m             how, alt=alt, numeric_only=numeric_only, min_count=min_count)\n\u001b[0;32m-> 3975\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_agged_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3977\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wrap_agged_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_wrap_agged_blocks\u001b[0;34m(self, items, blocks)\u001b[0m\n\u001b[1;32m   4755\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4757\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4759\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_reindex_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_reindex_output\u001b[0;34m(self, result)\u001b[0m\n\u001b[1;32m   4791\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4792\u001b[0m             \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'copy'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4793\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4795\u001b[0m         \u001b[0;31m# GH 13204\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mPY2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3561\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axis'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3562\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'labels'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3563\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3565\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reindex_axis'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3683\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3684\u001b[0m         return self._reindex_axes(axes, level, limit, tolerance, method,\n\u001b[0;32m-> 3685\u001b[0;31m                                   fill_value, copy).__finalize__(self)\n\u001b[0m\u001b[1;32m   3686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3687\u001b[0m     def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   3496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3497\u001b[0m             frame = frame._reindex_index(index, method, copy, level,\n\u001b[0;32m-> 3498\u001b[0;31m                                          fill_value, limit, tolerance)\n\u001b[0m\u001b[1;32m   3499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3500\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_reindex_index\u001b[0;34m(self, new_index, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   3504\u001b[0m         new_index, indexer = self.index.reindex(new_index, method=method,\n\u001b[1;32m   3505\u001b[0m                                                 \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3506\u001b[0;31m                                                 tolerance=tolerance)\n\u001b[0m\u001b[1;32m   3507\u001b[0m         return self._reindex_with_indexers({0: [new_index, indexer]},\n\u001b[1;32m   3508\u001b[0m                                            \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, target, method, level, limit, tolerance)\u001b[0m\n\u001b[1;32m   2091\u001b[0m                     indexer = self.get_indexer(target, method=method,\n\u001b[1;32m   2092\u001b[0m                                                \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2093\u001b[0;31m                                                tolerance=tolerance)\n\u001b[0m\u001b[1;32m   2094\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot handle a non-unique multi-index!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_indexer\u001b[0;34m(self, target, method, limit, tolerance)\u001b[0m\n\u001b[1;32m   2040\u001b[0m                                       'for MultiIndex; see GitHub issue 9365')\n\u001b[1;32m   2041\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2042\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2044\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ensure_platform_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine.get_indexer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine._extract_level_codes\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/category.py\u001b[0m in \u001b[0;36mget_indexer\u001b[0;34m(self, target, method, limit, tolerance)\u001b[0m\n\u001b[1;32m    591\u001b[0m                 \u001b[0mcodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m         \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ensure_platform_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_indexer_non_unique\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(a, new_shape)\u001b[0m\n\u001b[1;32m   1125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1127\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1128\u001b[0m     \"\"\"\n\u001b[1;32m   1129\u001b[0m     \u001b[0mReturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0mnew\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mspecified\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of sequencing runs from human')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=countS.plot.bar()\n",
    "ax.set_xlabel('')\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "ax.set_ylabel('# of sequencing runs from human')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "myDict={}\n",
    "myDict['SNP']=sra_dump_sub_df.groupby(['ScientificName','LibraryStrategy']).size().sort_values().loc[['Homo_sapiens']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScientificName  LibraryStrategy       \n",
       "Homo_sapiens    RNA-Seq                       1\n",
       "                DNase-Hypersensitivity     2134\n",
       "                ATAC-seq                   3594\n",
       "                ChIP-Seq                  32450\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sra_dump_sub_df.groupby(['ScientificName','LibraryStrategy']).size().sort_values().loc[['Homo_sapiens']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "sra_dump_sub_df=sra_dump_df[sra_dump_df['Immunoprecipitation']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScientificName  LibraryStrategy       \n",
       "Homo_sapiens    RNA-Seq                       1\n",
       "Mus_musculus    ATAC-seq                    794\n",
       "                DNase-Hypersensitivity     1289\n",
       "Homo_sapiens    DNase-Hypersensitivity     2134\n",
       "                ATAC-seq                   3594\n",
       "Mus_musculus    ChIP-Seq                  28383\n",
       "Homo_sapiens    ChIP-Seq                  32450\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sra_dump_sub_df.groupby(['ScientificName','LibraryStrategy']).size().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpS=sra_dump_sub_df.groupby(['ScientificName','LibraryStrategy']).size().sort_values()\n",
    "myDict['Immunoprecipitation']=tmpS[tmpS>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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></th>\n",
       "      <th>SNP</th>\n",
       "      <th>Immunoprecipitation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScientificName</th>\n",
       "      <th>LibraryStrategy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Homo_sapiens</th>\n",
       "      <th>AMPLICON</th>\n",
       "      <td>8754.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATAC-seq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3594.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChIP-Seq</th>\n",
       "      <td>39093.0</td>\n",
       "      <td>32450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNase-Hypersensitivity</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA-Seq</th>\n",
       "      <td>128892.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WGS</th>\n",
       "      <td>65373.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WXS</th>\n",
       "      <td>31521.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mus_musculus</th>\n",
       "      <th>ATAC-seq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChIP-Seq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNase-Hypersensitivity</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1289.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            SNP  Immunoprecipitation\n",
       "ScientificName LibraryStrategy                                      \n",
       "Homo_sapiens   AMPLICON                  8754.0                  NaN\n",
       "               ATAC-seq                     NaN               3594.0\n",
       "               ChIP-Seq                 39093.0              32450.0\n",
       "               DNase-Hypersensitivity       NaN               2134.0\n",
       "               RNA-Seq                 128892.0                  NaN\n",
       "               WGS                      65373.0                  NaN\n",
       "               WXS                      31521.0                  NaN\n",
       "Mus_musculus   ATAC-seq                     NaN                794.0\n",
       "               ChIP-Seq                     NaN              28383.0\n",
       "               DNase-Hypersensitivity       NaN               1289.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(myDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sra_dump_sub_df=sra_dump_df[sra_dump_df['Transcript counts']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScientificName           LibraryStrategy\n",
       "Danio_rerio              RNA-Seq              2594\n",
       "Drosophila_melanogaster  RNA-Seq             12133\n",
       "Mus_musculus             RNA-Seq            107545\n",
       "Homo_sapiens             RNA-Seq            188388\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpS=sra_dump_sub_df.groupby(['ScientificName','LibraryStrategy']).size().sort_values()\n",
    "tmpS[tmpS>2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303829"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###annotate directly the data that fall into each folder\n",
    "#sra_dump_df.index.isin(srrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames=os.listdir('/nrnb/users/btsui/Data/all_seq/rnaseq/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fnames[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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