analysis.ipynb

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    "# <a name='TOP'>Evaluate fungal genomic variant calling pipelines</a>\n",
    "\n",
    "## Table of Content\n",
    "* [Prepare analysis environment](#PREP)\n",
    "* [VCF preprocessing and standardization](#VCF)\n",
    "    * [Table S2: Total number of SNPs of each pipeline after each filtration step](#TS2)\n",
    "* [Generating summary statistics from each callset](#SUM)\n",
    "    * [Figure 1: Number of SNPs of each pipeline by clade](#F1)\n",
    "    * [Figure S2: Missingness distribution of SNPs from each pipeline](#FS2)\n",
    "* [Sensitivity and Specificity analysis of each pipeline](#SEN)\n",
    "    * [Figure 2: Scatterplot of sensitivity and specificity of each pipeline](#F2)\n",
    "* [Site level consensus analysis](#SITE)\n",
    "    * [Figure 3: Comparison of SNPs called across pipelines](#F3)\n",
    "    * [Figure 4: High confidence SNPs missed by each pipeline](#F4)\n",
    "    * [Figure S3: Private SNPs show low mapping quality and read depth in ds7 and ds12](#FS3)\n",
    "* [Sample level consensus analysis](#SHA)\n",
    "    * [Figure 5: Sample level SNP concordance by clades](#F5)\n",
    "    * [Figure S1: Percentage of private SNPs for each pipeline by clade](#FS1)\n",
    "* [Similarities of SNPs discovered from controlled isolates](#SNP)\n",
    "    * [Table 2: SNP calls from each pipeline for control isolate comparisons](#T2)\n",
    "* [Comparison of SNPs discovered from 16B25 reference genome: Figure S4](#EVAL)\n",
    "    * [Figure S4: Scatterplot of SNP number from B8441 vs. 16B25](#FS4)\n",
    "\n",
    "Note: click each subtitle above will navigate you to the corresponding analysis codes, while click the title of each analysis session will bring you back to the top. "
   ]
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    "## <a name='PREP'>[Prepare analysis environment](#TOP)</a> \n",
    "The analyses depend on following Python3 libraries, as well as those command line (CLI) tools :\n",
    "```\n",
    "    vcftools-v0.1.15\n",
    "    zlib-1.2.11\n",
    "    bcftools-v1.8\n",
    "    htslib-v1.10.2 (bgzip and tabix)\n",
    "```\n",
    "Note that all the tables and plots can be reproduced without the above CLI tools. You could **only** need the CLI tools if you wanted to reproduce all the tables and plots from scratch (from the VCF files). To do that you need to uncomment the bash cells for corresponding analysis. "
   ]
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   "source": [
    "import os\n",
    "import sys\n",
    "import subprocess as sp\n",
    "from io import StringIO\n",
    "from glob import glob\n",
    "\n",
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "import statistics as stat\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import statistics as sta\n",
    "import matplotlib.font_manager\n",
    "\n",
    "from funpipe.vcf import siteinfo, gt_pair, vcf\n",
    "from funpipe.utils import run\n",
    "\n",
    "\n",
    "class snp_share:\n",
    "    \"\"\" SNP counts in categories \n",
    "    \n",
    "    To do: add missing categories in this module\n",
    "    Description\n",
    "    -----------\n",
    "        Of all discovered SNPs for a paticular pipeline, which shared SNP category\n",
    "        does it belongs to. It inputs a \n",
    "        \n",
    "    Example\n",
    "    -------\n",
    "    >>> pipe_dos =  [0,  1, 0, 0, 1, 1, 1]\n",
    "    >>> np_called = [9, 10, 8, 8, 7, 7, 6]   # number of pipelines called\n",
    "    >>> pipe = snp_share(pipe_dos, np_called)\n",
    "    \n",
    "    >>> pipe.hit_cates\n",
    "    >>> pipe.miss_cates\n",
    "    \n",
    "    \"\"\"\n",
    "    def __init__(self, var_series):\n",
    "        var_series = var_series[var_series!=0]\n",
    "        self.counts = var_series.value_counts()\n",
    "        self.counts.index = self.counts.index.astype('int32')\n",
    "\n",
    "        self.total = len(var_series)\n",
    "        self.private = len(var_series[var_series==1])\n",
    "        self.share = len(var_series[var_series>10])\n",
    "        if self.total != 0:\n",
    "            self.pct_private = round(self.private / self.total * 100, 2)\n",
    "            self.pct_share = round(self.share / self.total * 100, 2)\n",
    "        else:\n",
    "            self.pct_private = np.nan\n",
    "            self.pct_share = np.nan\n",
    "    \n",
    "    def plt(self, log=False):\n",
    "        if log:\n",
    "            lg_count_table = self.counts.apply(math.log)\n",
    "            lg_count_table.sort_index().plot.bar()\n",
    "        else:\n",
    "            self.counts.sort_index().plot.bar()\n",
    "\n",
    "            \n",
    "callsets = [\n",
    "    'ds01-cfsan',\n",
    "    'ds02-gatk3',\n",
    "    'ds03-gatk3',\n",
    "    'ds04-gatk3',   \n",
    "    'ds05-gatk3',\n",
    "    'ds06-gatk3',\n",
    "    'ds07-gatk4',\n",
    "    'ds08-gatk4',\n",
    "    'ds09-samtl',\n",
    "    'ds10-samtl',\n",
    "    'ds11-smtrm',\n",
    "    'ds12-pilon'\n",
    "]\n",
    "\n",
    "samples = ['CA'+'{0:02d}'.format(i) for i in range(1,36)]\n",
    "\n",
    "clades = pd.read_csv('data/sample_clades.tsv', sep='\\t', header=0, index_col='Sample')\n",
    "\n",
    "clade_map = {\n",
    "    1 : 'South Asia',\n",
    "    2 : 'East Asia',\n",
    "    3 : 'Africa',\n",
    "    4 : 'South America'\n",
    "}\n",
    "\n",
    "# font = {'family' : 'normal',\n",
    "#         'size'   : 11}\n",
    "# matplotlib.rc('font', **font)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## <a name='VCF'>[VCF preprocessing and standardization](#TOP)</a>\n",
    "Including:\n",
    " - Standardize contig, sample names and sample orders in the VCF.\n",
    " - Diploid to haploid.\n",
    " - Mask heterogygote genotype calls.\n",
    " - Remove sites with asterisk marks (*) in the ALT field.\n",
    " - Remove INFO field and 'GT' FORMAT fields to reduce file size.\n",
    "\n",
    "Note that there are heterozygous sites in some diploid call sets, such as `CA02` in `ds07` `scaffold00012-31812-T-A,G 1/2` `scaffold00012-31813-T-G,C 0/2` etc. "
   ]
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     "text": [
      "Generate sample maps for each call set\n",
      "Remove monomorphic sites, standardize contig name, sample name and sample orders.\n",
      "Remove heterozygous genotypes, change diploid to haploid\n",
      "Remove sites with asterisk marks\n",
      "Remove unused information\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "### VCF standardization ###\n",
    "# The following script assumes availablility of high number of cores, recommond to run on a UGES machine.\n",
    "\n",
    "set -euo pipefail\n",
    "\n",
    "# vcfstats () {\n",
    "#     # Get summary statistics for a set of VCFs\n",
    "#     SUFFIX=$1  # VCF suffix\n",
    "\n",
    "#     TOTAL=vcfstats/total_sites_${SUFFIX}.tsv\n",
    "#     MNP=vcfstats/total_mnps_${SUFFIX}.tsv\n",
    "#     MULTI=vcfstats/total_multi_sites_${SUFFIX}.tsv\n",
    "#     SAMHAP=vcfstats/sites_per_sample_hap_${SUFFIX}.tsv\n",
    "#     SAMDIP=vcfstats/sites_per_sample_dip_${SUFFIX}.tsv\n",
    "\n",
    "#     echo \"\" > haptmp.tsv\n",
    "#     echo \"\" > diptmp.tsv\n",
    "#     echo \"\" > ${TOTAL}\n",
    "#     echo \"\" > ${MNP}\n",
    "#     echo \"\" > ${MULTI}\n",
    "    \n",
    "#     for STAT in vcfstats/*${SUFFIX}.stats; do\n",
    "\n",
    "#         # total number of SNPs\n",
    "#         grep '^SN' $STAT | awk -F '\\t' 'NR==4 {print $4}' >> ${TOTAL}\n",
    "#         # MNPs\n",
    "#         grep '^SN' $STAT | awk -F '\\t' 'NR==5 {print $4}' >> ${MNP}\n",
    "#         # total number of multiallelic SNP sites\n",
    "#         grep '^SN' $STAT | awk -F '\\t' 'NR==9 {print $4}' >> ${MULTI}\n",
    "\n",
    "#         # number of SNPs for each sample\n",
    "#         paste haptmp.tsv <(grep '^PSC' $STAT | cut -f3,13 | sort | cut -f2) > ${SAMHAP}\n",
    "#         paste diptmp.tsv <(grep '^PSC' $STAT | cut -f3,5,6 | sort | cut -f2,3 \\\n",
    "#             | perl -e 'while(<>){ chomp($_); $tot=0; foreach (split(/\\t/, $_)) {$tot+=$_;} print $tot,\"\\n\"}') > ${SAMDIP} \n",
    "#         cp ${SAMHAP} haptmp.tsv\n",
    "#         cp ${SAMDIP} diptmp.tsv\n",
    "#     done\n",
    "#     rm *tmp.tsv\n",
    "# }\n",
    "\n",
    "\n",
    "# cd /gsap/garage-fungal/fungal-research-projects/data/benchmark/pipelines_comparison\n",
    "\n",
    "echo \"Generate sample maps for each call set\"\n",
    "# while IFS=$'\\t' read -r -a entries; do \n",
    "#     # generate sample map\n",
    "#     if ! [[ $(bcftools query -l ${entries[1]} | head -1) == CA* ]]; then\n",
    "#         paste <(bcftools query -l ${entries[1]} | sort) samples.tsv | sed 's/\\t/ /g' > ${entries[0]}.sample_map.tsv    \n",
    "#     fi\n",
    "#     # summary statistics\n",
    "#     bcftools stats -s- ${entries[1]} > vcfstats/${entries[0]}.raw.stats &\n",
    "# done < snp_vcf_list.tsv \n",
    "# vcfstats raw\n",
    "\n",
    "\n",
    "echo \"Remove monomorphic sites, standardize contig name, sample name and sample orders.\"\n",
    "# while IFS=$'\\t' read -r -a entries; do\n",
    "#     if [[ -f ${entries[0]}.sample_map.tsv ]]; then\n",
    "#         # rename samples\n",
    "#         bcftools annotate --rename-chrs chr_maps.txt -x ^FORMAT/GT ${entries[1]} \\\n",
    "#           | bcftools reheader -s ${entries[0]}.sample_map.tsv \\\n",
    "#           | bcftools filter -e 'N_ALT==0 || TYPE!=\"SNP\"' -O z > ${entries[0]}.snps.vcf.gz \\\n",
    "#         && bcftools stats -s- ${entries[0]}.snps.vcf.gz > vcfstats/${entries[0]}.snps.stats &\n",
    "#     else\n",
    "#         # reorder samples\n",
    "#         bcftools annotate --rename-chrs chr_maps.txt -x ^FORMAT/GT ${entries[1]} \\\n",
    "#             | bcftools filter -e 'N_ALT==0 || TYPE!=\"SNP\"' -O z > ${entries[0]}.tmp.vcf.gz \\\n",
    "#         && bcftools view -s $(echo \"CA\"{01..35} | sed \"s/ /,/g\") -O z ${entries[0]}.tmp.vcf.gz \\\n",
    "#              > ${entries[0]}.snps.vcf.gz \\\n",
    "#         && bcftools stats -s- ${entries[0]}.snps.vcf.gz > vcfstats/${entries[0]}.snps.stats \\\n",
    "#         && rm ${entries[0]}.tmp.vcf.gz &\n",
    "#     fi\n",
    "# done < snp_vcf_list.tsv\n",
    "# vcfstats snps\n",
    "\n",
    "\n",
    "echo \"Remove heterozygous genotypes, change diploid to haploid\"\n",
    "# for i in {01..12}; do\n",
    "#     VCF=dataset${i}.snps.vcf.gz\n",
    "#     OUT=$(echo $VCF | sed 's/vcf/hap.vcf/g')\n",
    "#     STATS=$(echo $OUT | sed 's/vcf.gz/stats/g')\n",
    "#     bcftools filter -e 'GT=\"het\"' $VCF \\\n",
    "#       | sed 's/\\([0-9\\.]\\)\\/[^\\t]\\+/\\1/g' | bgzip > $OUT \\\n",
    "#     && bcftools stats -s- $OUT > vcfstats/$STATS &\n",
    "# done\n",
    "# vcfstats hap\n",
    "\n",
    "\n",
    "echo \"Remove sites with asterisk marks\"\n",
    "# for i in {01..12}; do\n",
    "#     VCF=dataset${i}*hap.vcf.gz\n",
    "#     OUT=$(echo $VCF | sed 's/vcf/no_star.vcf/g')\n",
    "#     STATS=$(echo $OUT | sed 's/vcf.gz/stats/g')\n",
    "#     zcat $VCF | grep -v '*' | bgzip > $OUT \\\n",
    "#     && bcftools stats -s- $OUT > vcfstats/$STATS &\n",
    "# done\n",
    "# vcfstats no_star\n",
    "\n",
    "echo \"Remove unused information\"\n",
    "# patch dataset09 header DPT4L not defined in header\n",
    "# bcftools reheader -h dataset09.header.txt dataset09.snps.hap.no_star.vcf.gz > dataset09.patch_header.snps.hap.no_star.vcf.gz \\\n",
    "#     && tabix dataset09.patch_header.snps.hap.no_star.vcf.gz\n",
    "\n",
    "# echo \"Remove unused INFO and FORMAT fields\"\n",
    "# for file in dataset*.no_star.vcf.gz; do\n",
    "#     OUT=$(echo $file | sed 's/vcf/mini.vcf/g')\n",
    "#     STATS=$(echo $OUT | sed 's/vcf.gz/stats/g')\n",
    "# #     bcftools annotate -x INFO,QUAL $file | bcftools reheader -h mini_header.txt | bgzip > $OUT\n",
    "# #     tabix $OUT\n",
    "#     bcftools stats -s- $OUT > vcfstats/$STATS\n",
    "# done\n",
    "# vcfstats mini\n",
    "\n",
    "# echo \"Remove unused Alt alleles\"\n",
    "# for i in {01..12}; do\n",
    "#     VCF=dataset${i}*mini.vcf.gz\n",
    "#     OUT=$(echo $VCF | sed 's/vcf/trim_alt.vcf/g')\n",
    "#     STATS=$(echo $OUT | sed 's/vcf.gz/stats/g')\n",
    "#     bcftools view --trim-alt-alleles $VCF | bcftools filter -e 'N_ALT==0 || TYPE!=\"SNP\"' | bgzip > $OUT\n",
    "#     bcftools stats -s- $OUT > vcfstats/$STATS\n",
    "# done\n",
    "# vcfstats trim_alt\n",
    "#\n",
    "# for file in *trim_alt.vcf.gz; do \n",
    "#     FREQ=$(echo $file | sed 's/.vcf.gz//g')\n",
    "#     vcftools --gzvcf $file --freq2 --out vcfstats/$FREQ \\\n",
    "#     && cut -f4,5 vcfstats/${FREQ}.frq > vcfstats/${FREQ}.freq.tsv &\n",
    "# done\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='TS2'>[Table S2: Total number of sites for each call set](#TOP)</a>"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds01-cfsan</th>\n",
       "      <th>ds02-gatk3</th>\n",
       "      <th>ds03-gatk3</th>\n",
       "      <th>ds04-gatk3</th>\n",
       "      <th>ds05-gatk3</th>\n",
       "      <th>ds06-gatk3</th>\n",
       "      <th>ds07-gatk4</th>\n",
       "      <th>ds08-gatk4</th>\n",
       "      <th>ds09-samtl</th>\n",
       "      <th>ds10-samtl</th>\n",
       "      <th>ds11-smtrm</th>\n",
       "      <th>ds12-pilon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total SNPs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Initial</th>\n",
       "      <td>223736</td>\n",
       "      <td>228820</td>\n",
       "      <td>223268</td>\n",
       "      <td>222172</td>\n",
       "      <td>223322</td>\n",
       "      <td>223110</td>\n",
       "      <td>237277</td>\n",
       "      <td>224100</td>\n",
       "      <td>202854</td>\n",
       "      <td>231765</td>\n",
       "      <td>239476</td>\n",
       "      <td>228137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remove monomorphic &amp; MNPs &amp; InDels</th>\n",
       "      <td>223736</td>\n",
       "      <td>228820</td>\n",
       "      <td>223268</td>\n",
       "      <td>222172</td>\n",
       "      <td>223322</td>\n",
       "      <td>223110</td>\n",
       "      <td>237277</td>\n",
       "      <td>224100</td>\n",
       "      <td>202854</td>\n",
       "      <td>231765</td>\n",
       "      <td>239476</td>\n",
       "      <td>228121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remove het sites</th>\n",
       "      <td>223736</td>\n",
       "      <td>228820</td>\n",
       "      <td>223268</td>\n",
       "      <td>222172</td>\n",
       "      <td>223322</td>\n",
       "      <td>223110</td>\n",
       "      <td>224812</td>\n",
       "      <td>220197</td>\n",
       "      <td>202854</td>\n",
       "      <td>216782</td>\n",
       "      <td>230151</td>\n",
       "      <td>228121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remove * sites</th>\n",
       "      <td>223736</td>\n",
       "      <td>228820</td>\n",
       "      <td>222361</td>\n",
       "      <td>222172</td>\n",
       "      <td>222415</td>\n",
       "      <td>222170</td>\n",
       "      <td>224812</td>\n",
       "      <td>220197</td>\n",
       "      <td>202854</td>\n",
       "      <td>216782</td>\n",
       "      <td>230151</td>\n",
       "      <td>228121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minimize VCF</th>\n",
       "      <td>223736</td>\n",
       "      <td>228820</td>\n",
       "      <td>222361</td>\n",
       "      <td>222172</td>\n",
       "      <td>222415</td>\n",
       "      <td>222170</td>\n",
       "      <td>224812</td>\n",
       "      <td>220197</td>\n",
       "      <td>202854</td>\n",
       "      <td>216782</td>\n",
       "      <td>230151</td>\n",
       "      <td>228121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remove unused ALT alleles</th>\n",
       "      <td>223736</td>\n",
       "      <td>228820</td>\n",
       "      <td>221724</td>\n",
       "      <td>222152</td>\n",
       "      <td>221790</td>\n",
       "      <td>222170</td>\n",
       "      <td>224812</td>\n",
       "      <td>220197</td>\n",
       "      <td>202854</td>\n",
       "      <td>216782</td>\n",
       "      <td>230151</td>\n",
       "      <td>228121</td>\n",
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      "text/plain": [
       "                                    ds01-cfsan  ds02-gatk3  ds03-gatk3  \\\n",
       "Total SNPs                                                               \n",
       "Initial                                 223736      228820      223268   \n",
       "Remove monomorphic & MNPs & InDels      223736      228820      223268   \n",
       "Remove het sites                        223736      228820      223268   \n",
       "Remove * sites                          223736      228820      222361   \n",
       "Minimize VCF                            223736      228820      222361   \n",
       "Remove unused ALT alleles               223736      228820      221724   \n",
       "\n",
       "                                    ds04-gatk3  ds05-gatk3  ds06-gatk3  \\\n",
       "Total SNPs                                                               \n",
       "Initial                                 222172      223322      223110   \n",
       "Remove monomorphic & MNPs & InDels      222172      223322      223110   \n",
       "Remove het sites                        222172      223322      223110   \n",
       "Remove * sites                          222172      222415      222170   \n",
       "Minimize VCF                            222172      222415      222170   \n",
       "Remove unused ALT alleles               222152      221790      222170   \n",
       "\n",
       "                                    ds07-gatk4  ds08-gatk4  ds09-samtl  \\\n",
       "Total SNPs                                                               \n",
       "Initial                                 237277      224100      202854   \n",
       "Remove monomorphic & MNPs & InDels      237277      224100      202854   \n",
       "Remove het sites                        224812      220197      202854   \n",
       "Remove * sites                          224812      220197      202854   \n",
       "Minimize VCF                            224812      220197      202854   \n",
       "Remove unused ALT alleles               224812      220197      202854   \n",
       "\n",
       "                                    ds10-samtl  ds11-smtrm  ds12-pilon  \n",
       "Total SNPs                                                              \n",
       "Initial                                 231765      239476      228137  \n",
       "Remove monomorphic & MNPs & InDels      231765      239476      228121  \n",
       "Remove het sites                        216782      230151      228121  \n",
       "Remove * sites                          216782      230151      228121  \n",
       "Minimize VCF                            216782      230151      228121  \n",
       "Remove unused ALT alleles               216782      230151      228121  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsnps = pd.read_csv('data/total_snps_summary.tsv', sep='\\t', header=0, index_col='Total SNPs',\n",
    "                    thousands=r',')\n",
    "nsnps.columns = callsets\n",
    "nmult = pd.read_csv('data/total_multialleles_summary.tsv', sep='\\t', header=0, index_col='Multi-allelic sites',\n",
    "                    thousands=r',')\n",
    "nmult.columns = callsets\n",
    "nsnps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='SUM'>[Generating summary statistics from each callset](#TOP)</a>\n",
    "* Calculating number of SNPs after standardization\n",
    "* Missingness distributions\n",
    "* **Calculate unique and shared SNPs within each pipeline**\n",
    "    - Shared SNPs\n",
    "    - Unique SNPs: include and exclude NAs\n",
    "* **Pairwise comparison of call-set similarity amongst all pipelines**\n",
    "    \n",
    "* **General summary of call sets: MAF and missingness distributions**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of number of SNPs from each pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5lP8xFBHp0qWLtGrVymB9+fPnl+3bt4vI/z6w+i+zIiL//vuvAJCQkBB58OCBAJDr168b1ChXrpz88ssvyutSsmRJSUxMTPU5JiQkSJ48eZT16lWrVk3mzZuX4vNJze7du6VHjx5SsmRJ0el08sEHH8jff/+tjCcN7ObNm8vgwYNFJOXmRq5cuaRAgQLi4OAgtWrVkjVr1oiIyJo1a6RcuXJy9OhRg9czNc7Ozga/cxGRDz74QObMmSMiIufPn5dcuXLJgwcPRERk5cqV8t5776X6mo0ZM0Z69+5tsGzdunVStmzZVOcwduxY+eijj0Tk9QYEgPj7+6d435TeK5MmTZI2bdqkWn/QoEFSq1Ytg2WTJ09Wwj2l92OOHDkkKipKuf9ff/0lNjY2Eh8fL+fOnRMbGxuleSgiEh8fL7lz55bjx48r63xbc+Po0aPK+J9//il58uRRbtvZ2Rl88RYRadmypfIZI3Wk9zOY3i+y+masiMj48ePFzc3N4DFVqlSRb7/9VkT+977YsWOHMv706VOxsrKSY8eOicjb3xf6L7KpfYHVc3NzU9ar16lTJxk2bFiKzycln332mdStW1fOnDmT6hevtJob33zzjdSrV89g2YkTJyRnzpwSHx8vR44cEQcHB/nrr7/k+fPnaT4fEZFGjRrJwoULJTExUYoUKSJbtmxRcmfmzJnSqVMn5b7pbW40a9bMYFnt2rXl66+/TnUOzs7O8vnnnyu3Y2JiBICcOnVKREQ6duxo8GVa5HVToUWLFiKS8rbE2G3Z3LlzpUaNGgaP2bp1q+TJkydZfusbA9euXVOWjR07Vrp3767cbtSokbJdP378uOTIkUOePn2qjO/cudPgS3WrVq1k/PjxBusZO3astG7dWkTS19zImzevfP/99yn+MeBNlStXlgULFrz1fnppfd958/fQtGlTGTx4MJvL7yBuE9K/TdDPVz83EZFvv/1W8ubNK/Hx8cqyDh06KNlgyebGm3x8fAy+G6b1ve1t26s37//m82rfvr2MGjUq3XN78OCBzJw5U+rUqSO5cuWSggULysSJE+XFixciYvgaHTt2THLlyiVBQUGpNjcKFCgg9vb24uTkJP3795ewsDAREXFxcZHp06crt+l/eFhKJtKfFKZs2bLKMhsbGzg7O6d4eITey5cvMWnSJGUXNHt7e+zevdvgsI70srGxgY+PD77++mvcuXMn408Cr49ptLGxUW7ny5cPJUqUMLhP3rx5ER0dbbDsvffeS/bv0NBQZVewunXrwt7eXvkJDw9HWFiY8hhnZ+dkx7Al9eDBA8TGxhq8vgBQrly5NF/flLRp0wabNm1CeHg4bt++DRcXF7Rr1w5PnjxJdt/Fixfjl19+Mdj1OKlJkyYhKioKDx48wLlz55STVfbv3x/Dhg3Dp59+qhyipN/9LDVJX0P9bf37qmbNmqhZs6ayO7Gvry+GDh2a6msWGBiIbdu2GbzmI0aMQEREBIDXu5p/9dVXqFy5MgoWLAh7e3v4+voq77vbt28DQJpnvU7pvfLm+yIjzzElBQsWRIECBQzuHxcXh3v37iEwMBDx8fFwdHRUnqN+V8+MnKQp6W70+fLlQ2xsLOLj43Hv3j08ffoUPXr0MHgdT548ifDw8HTXJ9O87TOYHkkzLKVMS+m9m/S9mj9/fhQuXBihoaHpfl8ULVoUefPmTXNeoaGhJmfap59+ilatWmHIkCFwcHBAjx49MnQizMDAQJw7d87gubRr1w46nQ4RERFo0qQJ/u///g9ff/01ihUrhsaNG2P//v2p1mvVqhX27duHCxcuKIcpRkdH4+bNm9i/fz9atWqV7rnpJf2MAunLmjc/1wCUxwQGBmLx4sUGz/nrr79WThKXEmO3Zan9jmNjY3H//n1l2R9//IGJEyfizz//RIUKFZTlI0aMwI4dO3Dv3j1cu3YNJ0+exJAhQwAA4eHhKFSoEPLnz6/c38XFxWBdpr7H8ubNiz179iiHflatWhWLFy9O9f63b99Odbth6vedNWvWQKfToXnz5nB0dMT48ePTvRs4ZR/cJhj3HIsUKaKcXDq152hO+qt1vXkYpK+vL2rVqgUHBwcUKFAA06dPT3cGvG179TZp5VNKHBwcMGfOHJw5cwZPnjzBqlWrsGLFCsybNy/ZfRs1aoTOnTtj0qRJqdZ78OABHj9+jP/++w+//PKLcr6/HTt24NatW3j//fdRrlw5zJ49O8tchTKzsbmRiUqXLg0ABsfgxcfHIyQkBE5OTgCAHDmS/4p8fHywc+dO7Ny5E48fP0ZUVBTatm1rcIxtRrRt2xZNmjRJ8fJQtra2BselGdsASUnS43v1/3Z0dFSOS7t8+TKioqKUn+fPn2PKlCnKY1J6bZIqXLgwcufObfD6Aq9fb/3rawxnZ2fMmDEDT548SVYbAKpUqYIhQ4Yoxwynl5WVFSZNmoTTp08jPDwcFStWROfOndN8TNLXUH876bGLI0aMwIoVK3DhwgVcuHABn3zySaq1ihcvjr59+xq85k+fPlW+CP7222/49ttv8fPPP+PBgweIiorCsGHDlPed/gtyQEBAhp7327ztOb7p8ePHBk2n4OBg2NjYoFixYihevDhy5syJ+/fvGzzP2NhY9OnTx+S52tvbI3fu3Pjzzz8N6j979gw//vijyfUpfdL6DObPn98imRYTE4MHDx4ojbT0vC/elmnA6+2GqZmWN29ezJkzB5cuXcLNmzdhbW2d6hWBUppT8eLF0bBhQ4Pn8uTJE7x48UL54vXJJ5/g6NGjuH//Prp06YKOHTum+qXY3d0dx48fxx9//IHWrVtDp9PB3d0dGzduhL+/P9zd3VN9Lmk1uM2pePHimDJlisFzjo6OxtWrVwGk/joBGd+WlS5dOtl5gG7evIk8efKgSJEiAF7/T8CAAQOwceNG1K1b1+C+FSpUQIMGDbB69Wr4+vqiffv2SmaWKlUKjx49Mvhd/Pfff8nWn9Z7TN8YSetz1KhRI2zbtg0PHjzAkiVL4O3tnWqDy8XFJdXthqnfd5ydnbFixQr8999/OHLkCPbv35/i/2RQ9sZtgvmlJwfe9Lbnk9IFF/z8/DB69Gh88803iIiIwJMnT/DVV1+lOwPSs71KS1r59Da5cuVCly5d0LJlS5w/fz7F+yxYsAB79+7FoUOHMlS7atWq+PXXXxEREYHNmzdj2bJlWL16tVHzzG7Y3MhEJUqUQLt27TBx4kTcu3cPsbGx8Pb2Rs6cOdG+fXsArz+UISEhePHihfK4J0+eIFeuXChSpAgSExPx+++/Y9++fSbNxcfHB5s2bcL169cNlteuXRtr1qzBixcvcO/ePXz++ecmrSepadOm4dGjR4iKisKnn36K5s2bw8nJCc7OzujSpQtGjRqlfOmKjo7G7t270/wr2Zty5MiBTz75BLNmzcKtW7fw6tUrLFq0CEFBQejXr1+666xatQobN25UusT379+Hj48PihQpgooVK6b4mDlz5uDKlSvKCT7T49ChQzh79ixevXqF3Llzw9bW1qBjnpKff/4Zfn5+iI+Px5o1a3DhwgWD59azZ0/cv38fQ4YMQZcuXVCsWLFUa40cORKbN2/G77//jlevXiEhIQFBQUHYs2cPgNfvO2traxQtWhQ6nQ6HDx/GunXrlMcXKVIEffr0wahRo5QTtt29ezfVQE+vS5cu4aeffkJ8fDzOnDmDFStW4OOPP071/jqdDhMnTsSzZ89w584dzJ49GwMGDICVlRUaNmyIKlWqYMSIEcrv8/Hjx9iyZUuGTk6Vmly5cmH48OGYPHkyrl27BhFBbGwsjh07ZvamD6Uttc9g7dq1sWHDBqV5l/R/Mk315ZdfIiwsDM+fP8fEiRNRrlw51K9f36zviyFDhmDhwoW4ePEi4uPjsWnTJvz111/KX+bTY8eOHbh69Sri4+ORN29e5MmTB9bW1ineN6Vt0Mcff4wLFy7ghx9+wPPnzyEiCA0NVU5YeebMGRw7dgyxsbHImTMn8ufPD51Ol2qe1alTB7lz58bixYuVvTRatWqF//u//0Pp0qXh5uaW6nMpXry4RU4QOW7cOCxZsgQHDx5EfHw84uPjceXKFRw7dgzA6/zLkSOHwVyM3Zb17dsXN27cwJIlS/Dq1SvcvHkTM2fOxJAhQ6DT6fDPP/+gc+fO+OGHH9CmTZsUa4wcORLLly/Hzz//jGHDhinL69Wrh7Jly2Ly5Ml4/vw57ty5g7lz5xo8dsiQIVi1ahWOHDmChIQEHDp0CCtXroSnpycAKCf28/X1RWJiIi5evIjly5crj4+IiMDvv/+OqKgo6HQ62NvbQ6fTpfoeGzduHObPn49Dhw4hISEB0dHRysmwTf2+s2bNGoSFhUFEYGdnB2tr61TnQdkbtwnm5eDggPfee0/5fnbr1i0sXLgwzccUL148w8/vyZMnsLKyQpEiRWBjY4Pz58/j+++/T/fj37a9eptx48Zh5cqV2LJlC169eoUXL16kepJ4/VUQL1++jJcvXyIhIQEHDx7E4cOH0bhx4xQf4+TkhEmTJmH69Onpfk6vXr3C6tWrlT35ChQoACsrK2bb/8fmRib75Zdf4OLiglq1asHR0RFXr17FgQMHlI5or169UL58eZQsWRL29vY4ceIEJk2ahNKlS8PZ2RklS5bEwYMH07wCRnqUL18eo0aNwsOHDw2WL126FBEREShcuDDc3d3NegmiXr16oXbt2nB2dkaOHDmwfv16ZezXX3/F+++/D3d3d+UM6StWrMjw3ikLFy5Eq1at0KxZMxQtWhRbtmzB/v37lb1m0qNQoUJYvnw5qlSpgnz58qFatWqIiorCgQMHkCdPnhQf4+DggM8++wwPHjxI93oiIyPh4eGBQoUKoUiRIjh69GiaV7IBXp99evr06bC3t8eCBQuwbds2g90Tc+fOjY8//hjnz59Xzt6fmjp16mD//v1YsWIFSpUqpZwRWv+l3MPDAy1atEDVqlVRuHBhLFu2DP379zeosWLFCjRp0gRt27aFra0tGjRooPxl01hdu3bFqVOnULhwYXTr1g2TJk1Ktt6kihcvjqpVq8LNzQ1VqlRBxYoV8e233wJ4vXfM/v37kTdvXtStWxf58+dH9erVsW3bNrP9BXjhwoXo06ePsrupi4sL5s2bh7i4OLPUp/RJ7TP45Zdfws7ODqVLl8b777+Prl27mm2dgwcPhru7O4oVK4aAgADs3LlT+bJhrveFl5cXRo0ahe7du6NQoUKYP38+tm7ditq1a6e7xu3bt9GlSxfY29ujVKlSyuWtU5LSNsjJyQl+fn7Yv38/ypYtC3t7e7Ru3Rr//PMPgNd/ofTy8kLRokVhb2+P5cuXY9u2banuXp0jRw60aNECz58/R7NmzQC8bm48ffo0zb02AGDevHmYP38+7O3t0aFDh3S/BhnVpUsX/PLLL5g1axaKFi2KokWLYsiQIcr7K0+ePJg7d65yZaeRI0cCMG5b5uzsjH379mHjxo0oWrQomjdvjrZt2ypXitqyZQuioqIwfPhwg124k25Du3TpghcvXsDOzs6gAWJtbY2dO3fi2rVrKFasGFq0aIG+ffsCeL29AIAePXrgm2++wciRI2Fvb48xY8Zg8eLFyhXC8ufPj7Vr12L58uWws7PD1KlTlcYH8PoQxmXLlqFMmTKwtbVF9+7d8dVXXym/2zd5enpi3rx5GD9+vHIlGv3/eJj6fefw4cP44IMPYGtri+rVq+PDDz9McS9Vyv64TTC/n3/+GUeOHIG9vT369+//1obKxIkTcePGDeXQ5vRo1aoVhg8fjqZNm6JAgQKYNm1aqnsapuRt26v0rH/Dhg34+uuvUaRIETg6OqZ4BUgAyJkzJx48eIAePXqgcOHCcHBwwLhx4+Dt7Y2JEyemuo4pU6Yo+ZtemzdvRuXKlZEvXz40adIEHh4eGXpdsjOdGHssAxFpwo8//ohFixbhxo0bFtuF21w8PDwQHx9vsIdIWtasWYMZM2YYHM9ORPQuqlu3Ljp16vTWvwhu374dvXv3RmxsrOa2EURERElx/xWibOzRo0dYvHgxJkyYwC+tRETviL/++gtXrlzBrl27ko35+fnBwcEBrq6uCAgIwKxZs9C3b19uI4iISPN4WApRNjV16lQ4OjqiatWqGDp0aGZPh4iILKB06dIYOHAgli1bhsKFCycbv3v3Llq1aoV8+fKhRYsWqFevHhYtWpQJMyUiIjIvHpZCRERERERERJrGPTeIiIiIiIiISNPY3CAiIiIiIiIiTeMJRd9Cf311IqLMdv/+fbx8+TKzp6E65i4RZRXvSu4CzF4iyhpMyV02N96iSJEivKwkEWUJjo6OmT0Fi2DuElFW8a7kLsDsJaKswZTc5WEpRERERERERKRpbG4QERERERERkaaxuUFEREREREREmsbmBhERERERERFpGpsbRERERERERKRpbG4QERERERERkaaxuUFEREREREREmsbmBhERERERERFpGpsbRERERERERKRpbG4QERERERERkaZZZ/YEiIiyApcpuzJ0/+Cv26s0EyKirIPZSBnB9wsRZSY2N7IBbkiIiIiIiIjoXcbmBhGRBWSkCckGJBERERFRxvCcG0RERERERESkaWxuEBEREREREZGm8bAUoiyI51EhIiIiIiJKPzY3LIT/s0pERERERESkDh6WQkRERERERESaxj036K3UvMqDmnu0qL23DK9+QURERERElDVwzw0iIiIiIiIi0jTuuUH0juH5X4iIiIiIKLvhnhtERO+QsWPHwsXFBTqdDhcvXkw2vnr1auh0Omzfvl1ZFhkZiTZt2sDV1RVVqlTBsWPHTB4jIiIiIjInNjeIiN4h3bt3x4kTJ+Ds7JxsLDg4GCtWrEC9evUMlk+ZMgX16tVDYGAgVq9ejb59+yIuLs6kMSIismzDmYgou+NhKUSkGTykxnSNGzdOcXliYiKGDBmCJUuWYOLEiQZjmzZtQlBQEACgTp06KFmyJI4ePYqWLVsaPUZERK8bzpMnT0bDhg2Tjb2t4bxnzx74+/uja9euuH37NmxsbNIcIyLK7rjnBhERwcfHBw0aNMD7779vsPzhw4eIi4tD8eLFlWUuLi4ICQkxeoyIiF5r3LgxHB0dky1P2nDOlSuXwdimTZswfPhwAIaN47eNERFld9xzg4joHXflyhVs2bIl03Zf9vHxgY+Pj3I7JiYmU+ZBRJRVmLvhTET0LuCeG0RE77jjx48jODgYrq6ucHFxwalTp+Dp6Ykff/wRDg4OsLa2RkREhHL/4OBgODk5GT32Ji8vL4SFhSk/tra26j5hIqIsTN9wnjFjhqrr8fHxgaOjo/LDxjIRaR2bG0RE77gRI0bg7t27CA4ORnBwMOrVq4fly5djxIgRAIAePXpg2bJlAAB/f3+Eh4ejSZMmJo0REVHK1Gg4p4SNZSLKbtjcICJ6hwwbNgyOjo4ICwtD69atUa5cubc+Zv78+Th58iRcXV3h4eGBdevWKSenM3aMiIhSplbDmYgou+M5N4iI3iG+vr5vvc+RI0cMbhcrVgz79u1L8b7GjhER0euG865duxAREYHWrVsjf/78ylWmUjN//nwMGDAArq6uyJkzZ7KmcmpjRETZHZsbRERERESZwJINZyKi7I6HpRARERERERGRprG5QURERERERESaxuYGEREREREREWkaz7lBRERERERZmsuUXRm6f/DX7VWaCRFlVdxzg4iIiIiIiIg0TdXmxosXL9ClSxe4ubmhevXqcHd3Vy5vFRkZiTZt2sDV1RVVqlTBsWPHlMdZeoyIiIiIiIiItEv1w1I8PT3Rtm1b6HQ6fP/99xgyZAiOHDmCKVOmoF69etizZw/8/f3RtWtX3L59GzY2NhYfIyLzychuo9xllIiIiIjIcrLzd3VV99zInTs32rVrB51OBwCoV68egoODAQCbNm3C8OHDAQB16tRByZIlcfTo0UwZIyIiIiIiIiLtsug5NxYvXozOnTvj4cOHiIuLQ/HixZUxFxcXhISEWHyMiIiIiIiIiLTNYldLmTt3LoKCgnDw4EHExsZaarUZ5uPjAx8fH+V2TExMJs6GiIiIiIiIiN7GIntuLFy4EFu3bsXu3buRN29eODg4wNraGhEREcp9goOD4eTkZPGxN3l5eSEsLEz5sbW1NffLQURERERERERmpHpzw8fHBxs2bMD+/fthb2+vLO/RoweWLVsGAPD390d4eDiaNGmSKWNEREREREREpF2qHpYSFhaGiRMnokyZMmjWrBkAIFeuXDh9+jTmz5+PAQMGwNXVFTlz5sS6deuUK5dYeoyIiIiIiIiItEvV5oajoyNEJMWxYsWKYd++fVlijIiIiIiIiIi0y6JXSyEiIiIiIiIiMjeLXS2FiIiIiIiItMNlyq503zf46/YqzoTo7bjnBhERERERERFpGpsbRERERERERKRpPCyFiIiI3nkZ2fUa4O7XREREWQ2bG0RERERERERkksz+QwEPSyEiIiIiIiIiTWNzg4joHTJ27Fi4uLhAp9Ph4sWLAIAXL16gS5cucHNzQ/Xq1eHu7o6goCDlMZGRkWjTpg1cXV1RpUoVHDt2zOQxIiIiIiJzYnODiOgd0r17d5w4cQLOzs4Gyz09PXHjxg1cunQJnTt3xpAhQ5SxKVOmoF69eggMDMTq1avRt29fxMXFmTRGRESWbTgTEWV3POcGEdE7pHHjxsmW5c6dG+3atVNu16tXDwsXLlRub9q0SfliXadOHZQsWRJHjx5Fy5YtjR4jIqLXDefJkyejYcOGBss9PT3Rtm1b6HQ6fP/99xgyZAiOHDkC4H+N4z179sDf3x9du3bF7du3YWNjk+YYZU+ZfY4DoqyEzQ0iIjKwePFidO7cGQDw8OFDxMXFoXjx4sq4i4sLQkJCjB4jIqLXLNlwprRlpEnABgFR1sTmBhERKebOnYugoCAcPHjQYuv08fGBj4+PcjsmJsZi6yYiyurM0XBOCbOXMhv3OiFz4zk3iIgIALBw4UJs3boVu3fvRt68eQEADg4OsLa2RkREhHK/4OBgODk5GT32Ji8vL4SFhSk/tra2Kj5LIiLt0Dec582bZ/bazF4iym7Y3CAiIvj4+GDDhg3Yv38/7O3tDcZ69OiBZcuWAQD8/f0RHh6OJk2amDRGRERpM2fDmYjoXcDmBhHRO2TYsGFwdHREWFgYWrdujXLlyiEsLAwTJ05EVFQUmjVrhho1aqBu3brKY+bPn4+TJ0/C1dUVHh4eWLdunXJyOmPHiIgodWo0nImIsjuec4OI6B3i6+ub4nIRSfUxxYoVw759+8w6RkRErxvOu3btQkREBFq3bo38+fPjyJEjmDhxIsqUKYNmzZoBAHLlyoXTp08DeN04HjBgAFxdXZEzZ85kTeXUxoiIsjs2N4iIiIiIMoElG85ERNkdmxtEREREREQq4VVBLI+v+buJ59wgIiIiIiIiIk1jc4OIiIiIiIiINI3NDSIiIiIiIiLSNJ5zg4iIiIiIiCgL4PlCjMc9N4iIiIiIiIhI09jcICIiIiIiIiJNY3ODiIiIiIiIiDSNzQ0iIiIiIiIi0jQ2N4iIiIiIiIhI09jcICIiIiIiIiJNY3ODiIiIiIiIiDSNzQ0iIiIiIiIi0jQ2N4iIiIiIiIhI09LV3PD19cWTJ08AAKNGjULt2rVx7NgxVSdGRERERERERJQe6WpuLF26FAUKFMDff/+NK1eu4KuvvsKkSZPUnhsRERERERER0Vulq7lhbW0NADh06BAGDhyI1q1bIz4+XtWJERERERERERGlR7qaGzly5MDGjRuxceNGtGzZEgDw6tUrVSdGRERERERERJQe6T4sZcOGDRg6dCicnZ0REBCA5s2bqz03IiIiIiIiIqK3sn7bHXbt2oUbN25gwoQJaNKkCQDAzc0N3333neqTIyIiIiIiIiJ6mzT33Jg5cyZGjx6N06dPo1+/fli+fLml5kVERERERERElC5pNjc2b96MCxcuYOPGjTh16hRWrlxpqXkREZEKxo4dCxcXF+h0Oly8eFFZHhgYiPr168PNzQ116tTB1atXVR0jIiLLZjIRUXaXZnMjT548sLe3BwA4OjoiLi7OEnMiIiKVdO/eHSdOnICzs7PB8mHDhsHT0xMBAQHw9vaGh4eHqmNERGTZTCYiyu7SbG5ERUVhx44dys+TJ08MbhMRkbY0btwYjo6OBssiIyNx9uxZ9O/fHwDQrVs3hIaGIigoSJUxIiJ6zVKZTET0LkizueHk5IRFixYpP0lvf/vtt+laQWq727m4uKB8+fKoUaMGatSogY0bNypj3D2aiMhyQkNDUaJECVhbvz7HtE6ng5OTE0JCQlQZIyKi1DF3iYiMk+bVUo4cOWLyCrp3747JkyejYcOGycY2btyIGjVqJFuu36XOw8MDmzdvhoeHB/z9/VUbIyKizOPj4wMfHx/ldkxMTCbOhojo3cDsJTKey5Rd6b5v8NftVZwJJZXmnhvmkNLudmnh7tFERJZVunRp3L17F/Hx8QAAEUFISAicnJxUGXuTl5cXwsLClB9bW1sLPXMioqzHErkLMHuJKPtJs7mRI0cOWFlZKT9Jb+t3eTPFwIEDUbVqVQwePBj3798HwN2jiYgsrWjRoqhVqxbWrVsHANiyZQscHR1Rrlw5VcaIiCh1zF0iIuOk2dyIjo7G06dPlZ/o6Gj88ssvcHZ2RtOmTU1a8bFjx3D58mWcP38ehQsXxqBBg0yqZy4+Pj5wdHRUfriLHhFlJ8OGDYOjoyPCwsLQunVr5Uuvr68vfH194ebmhq+//hqrV69WHqPGGBERWTaTiYiyuzR3v8iXL5/ybz8/P3h7e+PZs2fw9fWFu7u7SSvW7yJnY2OD8ePHw83NDYDhrnjW1tYGu9TZ2dmZfexNXl5e8PLyUm5n5JAaIqKsztfXN8Xl5cuXh5+fn8XGiIjIsplMRJTdvfWcG9evX0eXLl0wYMAAjBgxAufOnTO5sfHs2TNERUUptzds2ICaNWsC4O7RRERERERERJQxae65MXToUOzevRtTp07Fli1bYGVlleEVDBs2DLt27UJERARat26N/PnzY9++fejWrRsSEhIgIihTpgx+/vln5TG+vr7w8PDA3LlzYWdnl2x3O3OPEREREREREZF2pdncWLlyJfLmzYsZM2Zg5syZynIRgU6nw6NHj966gtR2t7tw4UKqj+Hu0URERERERESUXmk2N27fvm2peRARERERERERGSXN5oazs3OyZVFRUbC3t1drPkREREREREREGZJmc+Pbb79F69atUbFiRSQmJqJTp07466+/4ODggB07duDDDz+01DyJiIiIKBtxmbIrQ/cP/rq9SjMhIqLsIM2rpfz0008oW7YsAOD333/HzZs3cffuXaxZswbe3t4WmSARERERERERUVrSbG5YW1sjZ86cAICDBw9iwIABKFasGNq3b4/o6GiLTJCIiIiIiIiIKC1pNjfi4+MhIgCAv//+G/Xr11fG4uLi1J0ZEREREREREVE6pHnOjRYtWqBXr14oWrQooqOj0bBhQwBAREQEcuXKZZEJEhERERERERGlJc09N7755hvUrVsXNjY22LNnD6ytX/dCAgMD4eXlZZEJEhERERERERGlJc09N6ytrTFx4sRkyxs1aqTahIiIiIiIiIiIMiLNPTeIiIiIiIiIiLI6NjeIiIiIiIiISNPSbG7s378fAPD06VOLTIaIiIiIiIiIKKPSbG5MmTIFANC0aVNLzIWIiIiIiIiIKMPSPKFoXFwc5s+fj8jISHz33XfJxseOHavaxIiIiIiIiIiI0iPN5saKFSuwdu1axMbG4sKFCwZjOp1O1YkREREREREREaVHms2NunXrom7dunB2doa3t7el5kRERERERERElG5pNjf0vL29cebMGRw4cAAA0KpVK9SuXVvViRERERERERERpUe6LgW7fPlydO/eHZGRkbh//z66deuGn376Se25ERERERERERG9VbqaG99//z3OnTuHb7/9FosWLcLZs2dTPMEoERFp119//YVatWqhRo0aqFKlCtauXQsAiIyMRJs2beDq6ooqVarg2LFjymOMHSMiIiIiMqd0NTcAoEiRIin+m4iItE9E0L9/f6xZswYXL17En3/+iWHDhiE6OhpTpkxBvXr1EBgYiNWrV6Nv376Ii4sDAKPHiIgobeZuOBMRZXfpOueGq6srpk+fjmHDhgF4fRUVV1dXVSdGRESWpdPpEBUVBQB4+vQpHBwckCtXLmzatAlBQUEAgDp16qBkyZI4evQoWrZsafQYERGlTt9wPnLkCKpVq4bg4GBUqFABH330kdI43rNnD/z9/dG1a1fcvn0bNjY2aY4REWV36WpuLFu2DGPGjEGtWrWg0+nQsmVL/Pjjj2rPjYiILESn02Hjxo346KOPkC9fPjx+/Bhbt25FdHQ04uLiULx4ceW+Li4uCAkJwcOHD40aIyKitzN3w5mIKLtLV3OjSJEi+O2339SeCxERZZL4+Hh8+eWX2Lp1Kxo3bgx/f3906tQJFy9eVH3dPj4+8PHxUW7HxMSovk4ioqzM3A1nIqJ3QbrPuUFERNnXxYsXcefOHTRu3BjA67/4OTo64vLly7C2tkZERIRy3+DgYDg5OcHBwcGosTd5eXkhLCxM+bG1tVXxmRIRZX1JG87//fcfDh48iAEDBiA+Pt5s6/Dx8YGjo6Pyw8YyEWkdmxtERITSpUvj7t27uHbtGgAgKCgIN2/eRPny5dGjRw8sW7YMAODv74/w8HA0adIEAIweIyKi1Jm74ZwSNpaJKLthc4OIiFCsWDEsX74cPXv2RPXq1dG1a1d8//33cHJywvz583Hy5Em4urrCw8MD69atU05OZ+wYERGlTo2GMxFRdvfWc24kJCSgdevWOHDggCXmQ0REmaRPnz7o06dPsuXFihXDvn37UnyMsWNERJS6pA3nHDlyIDEx0aDhPGDAALi6uiJnzpzJmsqpjRERZXdvbW5YWVnh+fPnSExMRI4c3NGDiIiIiEht5m44ExFld+m6WkqdOnXQoUMH9O/f3+B4vE6dOqk2MSIiIiIiIiKi9EhXc+Py5csAgBUrVijLdDodmxtERERERERElOnS1dw4fPiw2vMgIiIiIiIiIjJKuk6iER8fj2+++QYjR44EANy8eROHDh1SdWJEREREREREROmRrj03Ro8ejYSEBJw4cQIA4ODggF69euHs2bOqTo6IiIiIiIiI6G3S1dw4deoULl68iJo1awIA7O3tERcXp+rEiIiIiIiIiIjSI12HpeTOndvgdkJCAhITE1WZEBERERERERFRRqSruVGtWjWsW7cOiYmJCAoKwvDhw9G0aVOVp0ZERERERERE9Hbpam74+Pjg+PHjiIiIQP369ZEjRw7Mnz9f7bkREREREREREb1Vus65YWtrC19fX/j6+qo9HyIiIiIiIiKiDElXc+Ply5dYtGgRDhw4AJ1OB3d3d4wbNw65cuVSe35ERERERERERGlKV3Nj+PDhePjwIcaMGQMAWL16Na5fv45Vq1apOjkiIiIiIiIiordJV3PDz88P165dg06nAwB06NABlStXVnViRERERERERETpka4Tijo4OCA2Nla5/fLlSxQuXDhdKxg7dixcXFyg0+lw8eJFZXlgYCDq168PNzc31KlTB1evXs20MSIiIiIiIiLSrjSbG9999x2+++47VKhQAXXr1sUXX3yBL774Ah9++CEqVKiQrhV0794dJ06cgLOzs8HyYcOGwdPTEwEBAfD29oaHh0emjRERERERERGRdqXZ3Lhw4QIuXLiAxMRE1K5dG7du3cKtW7dQq1YtJCQkpGsFjRs3hqOjo8GyyMhInD17Fv379wcAdOvWDaGhoQgKCrL4GBERERERERFpW5rn3Fi9erUqKw0NDUWJEiVgbf169TqdDk5OTggJCUGBAgUsOlauXDlVniMRERERERERWUa6TigKALt370ZgYCDi4+OVZV5eXqpMKjP5+PjAx8dHuR0TE5OJsyEiIiIiIiKit0lXc6Nv3764du0aatasCSsrKwBQrpxijNKlS+Pu3buIj4+HtbU1RAQhISFwcnKCnZ2dRcfe5OXlZdC0efOQGiIiIiIiIiLKWtLV3Dh//jyuXr2qNDZMVbRoUdSqVQvr1q2Dh4cHtmzZAkdHR+UQEUuPEREREREREZF2pau54eLigpcvXyJv3rwZXsGwYcOwa9cuREREoHXr1sifPz+CgoLg6+sLDw8PzJ07F3Z2dgbn97D0GBERERERERFpV7qaG9988w1atmyJpk2bInfu3MryWbNmvfWxvr6+KS4vX748/Pz8ssQYEREBL1++xMSJE7F3717kzp0b1atXx7p16xAYGIhBgwbhwYMHKFCgANasWYPKlSsDgNFjRET0dubOZSKi7CzNS8HqTZ06FTlz5sSLFy8QHR2t/BARUfYxZcoU6HQ6BAQE4J9//sHChQsBvN4Dz9PTEwEBAfD29oaHh4fyGGPHiIjo7cydy0RE2Vm69ty4ceMGbty4ofZciIgokzx79gwrV65EWFiYcsLo4sWLIzIyEmfPnsW+ffsAAN26dcPo0aMRFBQEOzs7o8Z4viMiorczdy4ze4kou0vXnhvly5fH06dP1Z4LERFlkps3b6JQoUKYO3cuateujUaNGuHgwYMIDQ1FiRIlYG39uheu0+ng5OSEkJAQo8eIiOjtzJ3LRETZXbr23MiTJw9q1aqFVq1aGZxzw8fHR7WJERGR5cTHx+O///5DpUqV8PXXX+PChQtwd3fHrl27VF+3j4+PwfYkJiZG9XUSEWV1aucys5eIspt0NTcqVaqESpUqqT0XIiLKJE5OTsiRIwf69esHAKhZsybee+89/Pfff7h79y7i4+NhbW0NEUFISAicnJxgZ2dn1NibvLy84OXlpdx2dHS02PMmIsqqzJ3Lb2L2ElF2k67mxuzZs9WeBxERZaLChQujRYsW2Lt3L9q1a4fbt2/j9u3baNCgAWrVqoV169bBw8MDW7ZsgaOjo3LstrFjRESUNjVymYgoO0tXc2POnDkpLk/PpWCJiEgbli1bhsGDB8Pb2xs5cuSAr68vSpUqBV9fX3h4eGDu3Lmws7PD6tWrlccYO0ZERG9n7lwmIsrO0tXcSHrZ1xcvXuCvv/7Chx9+qNqkiIjI8sqUKYPDhw8nW16+fHn4+fml+Bhjx4iI6O3MnctERNlZupobCxYsMLj92Wef8ZrZRERERERERJQlpOtSsG9ycHDArVu3zD0XIiIiIiIiIqIMS9eeG999953y74SEBJw5cwbFixdXbVJEREREREREROmVrubGhQsX/vcAa2vUqFEDnp6eqk2KiIiIiIiIiCi90tXc4FmWiYiIiIiIiCirSrO5cezYsTQf3LhxY7NOhoiIiIiIiIgoo9JsbkycODHZMp1Ohzt37uDu3btISEhQbWJEREREREREROmRZnPD39/f4PajR4/w5ZdfYt26dfj8889VnRgRERERERERUXqk61KwL168wLx581CpUiUAwLVr1zBjxgxVJ0ZERERERERElB5pNjcSExOxfPlyuLq64vr16zh9+jR8fHzg4OBgqfkREREREREREaUpzcNSqlSpgpcvX2Lu3LmoXr06njx5gsuXLyvj1apVU32CRERERERERERpSbO58fz5c+h0OsyaNSvZmE6nw61bt1SbGBERERERERFReqTZ3AgODrbQNIiIiIiIiIiIjJOuE4oSEREREREREWVVbG4QERERERERkaaxuUFEREREREREmsbmBhERERERERFpGpsbRERERERERKRpbG4QERERERERkaaxuUFEREREREREmsbmBhERERERERFpGpsbRERERERERKRpbG4QEZGB1atXQ6fTYfv27QCAyMhItGnTBq6urqhSpQqOHTum3NfYMSIiSh9zZTIRUXbH5gYRESmCg4OxYsUK1KtXT1k2ZcoU1KtXD4GBgVi9ejX69u2LuLg4k8aIiOjtzJnJRETZHZsbREQEAEhMTMSQIUOwZMkS5MqVS1m+adMmDB8+HABQp04dlCxZEkePHjVpjIiI0mbuTCYiyu7Y3CAiIgCAj48PGjRogPfff19Z9vDhQ8TFxaF48eLKMhcXF4SEhBg9RkREb2fOTCYiehdYZ/YEiIgo8125cgVbtmzJlOOzfXx84OPjo9yOiYmx+ByIiLISS2Qys5eIshs2N4iICMePH0dwcDBcXV0BABEREfD09MTnn38Oa2trREREKH8NDA4OhpOTExwcHIwae5OXlxe8vLyU246Ojmo/XSKLcpmyK0P3D/66vUozIa0wdyanhNlLRNkND0shIiKMGDECd+/eRXBwMIKDg1GvXj0sX74cI0aMQI8ePbBs2TIAgL+/P8LDw9GkSRMAMHqMiIhSp0YmExFld9xzg4iI0jR//nwMGDAArq6uyJkzJ9atWwcbGxuTxoiIyDjMXSKilLG5QUREyRw5ckT5d7FixbBv374U72fsGBERpZ85MpmIKLvjYSlEREREREREpGlsbhARERERERGRpmVqc8PFxQXly5dHjRo1UKNGDWzcuBEAEBgYiPr168PNzQ116tTB1atXlceoMUZERERERERE2pXpe25s3LgRFy9exMWLF9GrVy8AwLBhw+Dp6YmAgAB4e3vDw8NDub8aY0RERERERESkXZne3HhTZGQkzp49i/79+wMAunXrhtDQUAQFBakyRkRERERERETalunNjYEDB6Jq1aoYPHgw7t+/j9DQUJQoUQLW1q8v5KLT6eDk5ISQkBBVxoiIiIiIiIhI2zK1uXHs2DFcvnwZ58+fR+HChTFo0KDMnA4AwMfHB46OjspPTExMZk+JiIiIiIiIiNJgnZkrd3JyAgDY2Nhg/PjxcHNzQ+nSpXH37l3Ex8fD2toaIoKQkBA4OTnBzs7O7GNv8vLygpeXl3Lb0dHRYq8HEREREREREWVcpu258ezZM0RFRSm3N2zYgJo1a6Jo0aKoVasW1q1bBwDYsmULHB0dUa5cOVXGiIiIiIiIiEjbMm3PjXv37qFbt25ISEiAiKBMmTL4+eefAQC+vr7w8PDA3LlzYWdnh9WrVyuPU2OMiIiIiIiIiLQr05obZcqUwYULF1IcK1++PPz8/Cw2RkRERERERETalelXSyEiIiIiIiIiMgWbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRER48eIFunTpAjc3N1SvXh3u7u4ICgoCAERGRqJNmzZwdXVFlSpVcOzYMeVxxo4REVHa1MhlIqLsjM0NIiICAHh6euLGjRu4dOkSOnfujCFDhgAApkyZgnr16iEwMBCrV69G3759ERcXZ9IYERG9nblzmYgoO2Nzg4iIkDt3brRr1w46nQ4AUK9ePQQHBwMANm3ahOHDhwMA6tSpg5IlS+Lo0aMmjRERUdrUyGUiouyMzQ0iIkpm8eLF6Ny5Mx4+fIi4uDgUL15cGXNxcUFISIjRY2/y8fGBo6Oj8hMTE6PukyMi0iBTc/lNzF4iym7Y3CAiIgNz585FUFAQ5s2bZ5H1eXl5ISwsTPmxtbW1yHqJiLRCjVxm9hJRdsPmBhERKRYuXIitW7di9+7dyJs3LxwcHGBtbY2IiAjlPsHBwXBycjJ6jIiI0s9cuUxElN2xuUFERABe76K8YcMG7N+/H/b29sryHj16YNmyZQAAf39/hIeHo0mTJiaNERHR25k7l4mIsjPrzJ4AERFlvrCwMEycOBFlypRBs2bNAAC5cuXC6dOnMX/+fAwYMACurq7ImTMn1q1bBxsbGwAweoyIiNKmRi4TEWVnbG4QEREcHR0hIimOFStWDPv27TPrGBERpU2NXCYiys54WAoRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERaRqbG0RERERERESkaWxuEBEREREREZGmsblBRERERERERJrG5gYRERERERERado71dwIDAxE/fr14ebmhjp16uDq1auZPSUiomyNuUtEZHnMXiJ6F71TzY1hw4bB09MTAQEB8Pb2hoeHR2ZPiYgoW2PuEhFZHrOXiN5F70xzIzIyEmfPnkX//v0BAN26dUNoaCiCgoIyeWZERNkTc5eIyPKYvUT0rnpnmhuhoaEoUaIErK2tAQA6nQ5OTk4ICQnJ5JkREWVPzF0iIstj9hLRu8o6syeQ1fj4+MDHx0e5fefOHTg6Oqq2vpiYGNja2iZb7rju3a2tdn2t1la7vlZrq10/K9W+f/++6SvNgrJK7r7LtdWunx3nntXzJa36atbObq9Lds1dwLLZm93eL+/695fU6vN14etijvom5a68I+7duyf58+eXuLg4ERFJTEyUYsWKSWBgYJqPK1WqlKrzUrO+VmurXV+rtdWur9XaatfXau2sgLmrndpq1+fcLV9b7fparW2J+pktK2avVmurXV+rtdWur9XaatfXam1L1Nd7Zw5LKVq0KGrVqoV16163h7Zs2QJHR0eUK1cuk2dGRJQ9MXeJiCyP2UtE76p36rAUX19feHh4YO7cubCzs8Pq1asze0pERNkac5eIyPKYvUT0Lnqnmhvly5eHn59fhh7j5eWl0mzUr6/V2mrX12pttetrtbba9bVaO6tg7mqjttr1OXfL11a7vlZrW6J+VpDVslertdWur9XaatfXam2162u1tiXq6+lERCyyJiIiIiIiIiIiFbwz59wgIiIiIiIiouyJzQ0iIiIiIiIi0jQ2N4iIiIiIiIhI09jcIKIsR+1TAfFUQ0REhpi7RESWxdw1PzY3NMicb9R///0XO3fuVK6Fbu51BAcH4/Tp09i3b5/Z66s5dzXnbYn6WnXs2DEAgE6nQ2Jiotlfj3PnzhnUV9u7/vvMTpi7rzF3sx/mLmVV5v5dajW/tLzNYO6mjLmrnnfqUrDZhU6nA/D6jaT/tzHCw8MxYMAANGnSBMePH0eVKlVQqVIl5MyZU/kw5MhhfP8rPDwcffv2Rbly5XDnzh24ubnBxcVFeQ6mzF/Nuas5b0vU/+OPP1CuXDlUqFABVlZWRtexdO2AgAC0bNkSjRo1wnfffYfKlSsDgMnvQ72rV6+iW7duaNasGWbMmIGyZcuatX5SN27cQFxcHKpUqWJSnYSEBFhZWSEmJganT5+GiKBly5ZmmiVlBHOXuZsW5m7KmLtkCnPlLqDd/NL6NkOruatmfeZucubMXV4KNovT/7KvXbuG3bt34+7du2jQoAG6dOlicu1PPvkErq6umDp1KqZOnYr79+8jMTERISEh+OGHH+Dm5mZS/Y8//hhubm6YOnUqhg8fjgIFCsDa2hp3797FZ599Bicnpyw5dzXnrXb9J0+eoFixYqhYsSIGDRqEXr16oUSJEnj48CHy5MmDvHnzZsnaehEREfDx8cFPP/2Enj174ttvv0Xu3LkBmB7Kz58/x8WLF7Fp0yYcOXIEXbt2xdSpU5EzZ06z1Nc//vfff8eiRYvwzz//oEmTJpgwYQIaNmyIXLlyGV37o48+wsuXL3Hs2DHUqlULX3zxBRo3bmx0PUobc9fyc2fuWr62HnM3Zcxdy1IzdwHt5peWtxlazV1L1GfupswcucvDUrI4fadw9OjROHPmDHLlygVfX18MGDAA/v7+Rtc9deoUTp06halTpwIADhw4gHz58mHUqFFwcXHBrFmzTJq3n58fzpw5o9T38/PD48ePUa1aNSQkJOC7777LknNXc96WqG9jYwNPT09UqlQJmzdvxkcffYStW7fik08+QWRkZJatre+xJiQkYMSIETh48CCCg4Ph7OyMJUuWAIBJQSwiyJs3L8qWLQsPDw/MmTMH58+fR+PGjbF+/XqT6yd9/LJly7BgwQJER0ejcuXKGDlyJEaPHo1///03Q/USEhIAAIGBgXj8+DF27dqF6OhoNGrUCH369EGnTp1Mft0pZcxdy86duZs5tZm7yTF3M49auQtoN7+0vM3Qcu6qWZ+5m5zZc1coy0pISBARkYMHD4qnp6eIiDx69Ej8/Pxk2rRpUqlSJdm+fbtRtR88eCB///23iIicPXtWunTpooyFhYVJp06dJCwszOi537hxQ44cOSIiIn///bc0btxYGfPz85Pu3bvLgwcPstzc1Zy3JeqLiJw/f15mz54tIiI//fSTFCpUSAoVKiTXr1+X+Pj4LFf75cuX8vfff8v9+/elV69ecuDAAWVsy5YtUqFCBXnvvfckJibGqPqvXr2Sf//9V2JjY2XAgAFy+vRpERH577//5Mcff5T69etLixYt5Pnz50bVF/nfZ/Xhw4eyZMkSefjwoTJ248YNadq0qaxcudKo2uPGjZP58+cbLAsPD5euXbtKaGio0XOmlDF3LT935q7lazN308bctSw1c1dEu/ml5W2G1nNXjfrM3bSZK3fZ3NCAfv36SadOnQyW3blzR/bt2yevXr3KcD39GzOppG/0PXv2SLNmzTI+0TTWk/SDumfPHmnRooXJNfXMPXc15m2p+gkJCZKYmCi9e/eWoKAgERFxdnaW/v37i62trRw/fjzL1b59+7Z888038uGHH0rVqlWT/Y4fPHggx44dM3re165dkzlz5kiHDh2kZs2aBmMJCQly5swZ2bt3r9H1kwb96NGjpUiRIjJjxgwJDw836vOpl5iYKHfu3BEHBwfJmTOnbNy4Mc31k3kxd1OuqcfcNazN3DXE3CVjmDt3RbSbX1reZliqtlq5q1Z95m7KzJ27bG5kcaGhoTJnzhypXLmy9OnTR/bv36+MJSYmGlVT/7gTJ07I8+fPDeq8fPlSPvjgA2U9xnQm9fUuX76shENSdevWNbq+mnNXc96WqJ90HatWrZKRI0dKu3bt5NNPPxURkatXrxpV0xK1T548KY6OjtKyZUuZMmWK7NixQ0REIiIiZM6cOUa/1/W2b98uRYoUEXd3d1m+fLmcOnVKRF5vSH766SeT64uI9O3bV0aNGiVLliyR+vXrS+/evWXv3r0SGRlpcm1fX1/Jly+fNGnSRK5cuWJyPUobc9dyc2fuZl5t5m7amLuWpUbuJn2s1vIrO2wztJq7atZn7qbNHLnL5oYGvHr1Sk6dOiXTpk2Tjh07yrBhw+TWrVsm1bxx44ZUr17dYNnz58/lt99+Ew8PD5Nqi4hcuXJF6tSpY7AsJiZGVq5cKf379zeptppzV3PeatbXh5V+98XExEQZMWKE1KxZU168eGH8hC1UW+T1Lpd+fn4yYcIE6du3r0ydOlVq1qwpy5cvN7l+YmKi/PXXX/Lrr79Kr169ZOjQobJy5UqpVauW/PTTT0bX13eRw8PDDX5/UVFRMm3aNHFxcZEff/zRqJoRERGydetWuXr1qsTFxUlCQoIMGTJEdDqdnDx50ug5U/owdw0xd5Nj7qZdn7lLGaVG7opoN7+0vM3QYu6qWZ+5m3pNc+cumxtZ2Pnz5+XUqVNy9uxZiYuLk+fPn8uOHTukf//+cvHixQzXi46ONthItGzZUm7cuKHcfvXqldy6dUtiY2NFJOO7XcbGxsrjx49FROTFixfSunVruXPnjtKVffXqlVy7dk2io6MzXF/Nuas5b0vU13tzI+jn5ydnzpwREeM742rW1j/PJ0+eyKVLl+TUqVMSHR0tMTExsmHDBpk9e7ZMmDDB6Dnr68fExEhISIjcuXNHRF4H8xdffCGjRo1Sju011fjx46VWrVqyatUqiYqKUpZfvnxZwsPDMzznGzduSKNGjeTDDz+UevXqyejRoyUkJEREXh87Seph7lpm7szdzKnN3E19zszdzGPu3BXRbn5peZuRHXJXjfrM3dTnrEbuWmfodKakOv2lsA4fPow5c+YgX758ePToERo0aIB+/fqhY8eOaNSoEezt7TNce+nSpbh9+zbat2+PDz/8EHXr1kVUVBRiY2Oxe/du/P777+jTpw/ee+89ABk/m+6XX36JHDlyoGfPnnB0dISLiwuioqJQokQJHD9+HOvWrUPfvn1RoUKFDNdXc+5qzlvt+jExMbh//z7ee+89uLm5oUiRIrhx4wbKly+PevXqITExEU+fPoWdnV2G5qx2beB/z3PMmDG4fPkySpcujWLFiqFdu3bo1q0bbGxsjKqrp792+oQJE+Dn54eiRYuiUaNG6Ny5M2bMmAHgf2doNob+s3rmzBkUKFAA5cuXx8aNG5GQkIBGjRrBxcUFVatWzVBN/Wsye/Zs9OrVC6NGjcK///6L+fPno0uXLti/fz+cnJxMvjY8GWLuWn7uzF3L1waYuylh7mYONXMX0G5+aXmbodXcVbs+czc5NXOXzY0sRn8prK+++gpLlizBrl27sH//foSFhWHo0KHo3LkzpkyZYlTtDz74AOHh4Vi3bh2CgoJw9epV+Pj4oGvXrnj8+DE6d+6M9u3bGz336tWr4+eff8bZs2cxdOhQPH/+HO7u7qhfvz4iIiLQrl07NGzYMMvNXc15q10/pY3gkydP8OzZM+zZswebNm1Cv3790KlTpyxVW+/cuXMIDAzE+fPncfToUfz999/Yvn07du7cibFjx6JGjRpG19bpdDh9+jTOnj2L8+fPY8uWLTh06BAWL14MNzc39OvXD87OzkbVFhHlszp+/Hhs27YNxYoVg6+vLzZu3Ih9+/Zh4MCB6NChQ4ZrR0ZG4sGDB3B3dwcAVKpUCWvXrkW3bt0QGRmJQoUK8Qu2mTF3LT935q7la+sxd5Nj7lqemrkLaDe/tLzN0GruWqI+czc51XLXqP09SFV//PGHdO/eXZ4+fSrVqlWTmJgYOX/+vNStW1c58UxG6Y/Fevr0qWzZskUGDRokjRo1ko8//jjZ8UzG7CaW9FiylStXSpMmTaRChQoyevRoOX/+vMTFxRldX825qzlvS9Q/dOiQjBkzRnr27Ck+Pj7SpUsXyZMnj/Tt21fatm0rvr6+Ru+ip2ZtvcjISPHx8VFuP3nyRLZu3SoTJkyQ+/fvm1Rb5PWl0pIeA3j//n359ttvpVevXgaXr8oo/e9q3bp1MmXKFIOxhw8fytixY2Xbtm3prvfmWaaHDBkiQ4cOVW7fuXNHXF1dzXKyJkoZc9dyc2fuZk5tPebua8zdzKdG7opoN7+ywzZDjdoi6mej2vWZu69ZInfZ3MiC9MdM7dy5Uz766CMREQkKCpK2bdsaFQj6D01oaKh06NBBRF5fP3zp0qXSv39/GThwoOzatcvo+T569EhERO7evSs9evQQkdcnh5kxY4a0aNFCxo4da/Qxk2rOXc15W6K+JTbeatTWbxx++eUX6dixo9jb28tnn32mHGMnIsoxm8bQ19+yZYv07NlTnJycZNWqVXLv3j3lPkn/bawXL15I7dq1xcbGRrZv325SrUGDBskXX3yhhPn169elatWqUqJECfn000/F3d1d5s2bJyLmOZ6UkmPuWmbuzN3Mqc3cTY65m/nMnbsi2s0vLW8ztJy7atZn7iZnidxlcyOL0H9Yzp07pywLCwuT9u3by9SpU6Vq1aqyevXqDNdNTEyU77//XurUqSPNmjWT7777zmD8ypUr8umnnxqcrCij9X/88Udp3769tG3bNtmZcv38/KRXr15GXc5HzbmrOW9L1FdzI6hmbf0G5OXLl1KrVi3x9fWV7777TkaNGiUDBw6UZcuWKSfJMqX+ixcvpEqVKrJgwQIZM2aMDB06VKZNmyY7d+406VrcSZ07d04iIyNlxowZYmtrKz169MjwRkQ/399++01q164t9evXl02bNinj69evl6lTp8rBgweTPYZMx9y17NyZu5lTm7mb8nyZu5lDrdwV0W5+aX2bodXcVbM+czfl+Void9ncyAL0namAgACpVq2a0tFLTEyU5cuXy+effy5fffWVSesYOHCg2NjYSLt27SQ0NFRZfvToUZN2V9Lr0KGD2NjYyMcffyzPnj1Tlp89e9bkLwVqzl3NeatVX+2Nt9obWBGRFStWyGeffSYir4P/9OnT4uPjIx07djTpcnv6+itXrpQZM2aIyOuzd2/fvl0mTJggH330kfzzzz9G1799+7bcunVLjh8/Lr1791aWBwUFSd++fUWn08kvv/ySoZoPHjxQ/v31119LmTJlpGvXrsq1yZPiF2zzYe6mjblriLn79vrMXXobS+SuiHbzS8vbDK3lrtr1mbvJWSp32dzIQjp16iSrVq0SkdfHIXbq1En27t1rUk19h/zgwYNy8OBB6du3r1hZWcmkSZPk3LlzYm9vb5Zdln799VdZtWqVfPTRR1K0aFFZsGCBhIWFiYODg9H1LTF3NeZtqfpqbgTVrB0VFSWdOnUSFxcXOX78uLL80aNH4ufnZ1Jtkdfh2b17d6lbt65cvnxZWX7nzp0MHRf4pri4OPnrr7/E09NTypQpIwsWLEh2nxMnTkhERES6a/7222/i5eVlcDmtBw8eyCeffCKurq7i6ekpd+/eNXrO9HbMXcvPnblr+drM3f9h7mY+NXJXRLv5peVthiVqq930Uas+c/d/LJm7bG5ksps3b0piYqJcuXJFatasKfHx8bJixQrx9PQUT09PadGihdHX+U16XFjSE/mcOXNGqlWrJk2bNpUffvhBREy7bvObtm3bJmXLlpXKlSvLt99+a1R9Neeu5rwtWV+NjaAlNrAPHz6UnTt3yscffyzt2rWTWbNmSVhYmEk1kwoLC5Nvv/1WunbtKkOGDJEVK1aY5WRNIq83VJ6enlK4cGEZNmyY/Pjjj8rcly5dKhcuXMhwzYSEBBk5cqTodDpZv369svz06dNSv359o/8iQalj7r69NnM35frM3ZQxd+lt1MxdEe3mV3bZZpi7dtL6amWj2vWZu4Yslbs6EZGMX2OFzMHf3x/Tpk3D/v37AQAjR47E7t270bhxY8yYMQOurq6oUaMGjhw5kuHrfCcmJiJHjhyIjo7GzJkzcf/+fQDArFmzUL58eQBAWFgYHB0dASDD1xHW14+JicHChQvx9OlT5M6dG8OHD4eTkxMAICgoCOXKlctwfTXnrua8LVkfAOLj42Ft/fpqzv7+/hgyZAgKFSqEnj17YsSIEcp1qbNabb1Hjx5h37592L9/PwICAvD555+jefPm6a6ZVEqv49WrV7Fjxw4EBAQgMTERY8eOxfvvv29U/aRu3bqFu3fv4tKlS/Dz84OdnR3y58+Pffv2wd/fP92vy5uvia+vL7y9vVGmTBn89NNPqFWrVprPj4zD3LX83Jm7mV9bj7nL3M0MauYuoN38yg7bDC3mrpr1mbvJWTp32dzIROPHj0fBggXh5OSE4OBg9O/fH0FBQfjwww9hb2+PAQMGwM3NDTNnzsxwbf2bY/To0bCzs4ODgwPmz5+PuLg4DBkyBF9++SVy5cpl9Nz19UeOHIncuXNDRPDHH3/Azs4OgwYNwqhRo5AzZ06TaqsxdzXnrXZ9S2y81drA6i1ZsgTPnj2Dvb09mjZtigoVKiAoKAi7d+/GwIEDUaBAgQzXTOr3339HREQEKlasiJo1a6JgwYI4fPgw9u3bh+nTp8POzs6ouvrXJzAwECKC3Llzw8nJCf7+/ti9ezdCQ0PRpk0bdOvWLV31kr5+9+7dQ86cOVGwYEEAwMSJE7Fo0SIMGDAAa9eu5RdsM2PuWn7uzF3L106KufsaczfzqJm7gHbzKztsM7SWu5aoDzB39TIld03f+YOMkZCQIFu3bpV27dqJi4uL7Nu3TxmLj4+XI0eOSKtWrUxax9WrV6V+/foiItKqVSs5cOCAfPfdd6LT6ZRLbpni8uXLSv2WLVvKhg0bZNKkSVKgQAEZOXJklp27mvNWs77+5DqjRo2SqVOnysKFC6VIkSJib28vkyZNkhcvXmTJ2vpdEefPny+9e/eWTz75RMqVKyd9+vSRlStXmnyMnb7+vHnzpHfv3uLu7i5ubm4yatQoOXjwoMTGxpp0xmj9bosBAQFSoUIFadasmfTt21fmz58vd+7cMWnOixcvlg4dOkjhwoVlxIgRSg4EBgYqZ5HmyezMh7mbeXNn7lq2NnM39Tkzdy3LErkrot380vI2Q4u5q2Z95m7qc7Zk7rK5kck6dOggzs7OMnToUNm6davyxn/06JGEh4ebVHvz5s3y+++/y+HDh6Vt27Yi8vqYx5kzZyrrMeXa7T/++KP88ssvsnfvXmnfvr2IiFy8eFHGjx+vnGTG2OuUqzl3Neetdn01N4Jq1o6KipJatWpJfHy8jBo1SiZPnixjxoyR4sWLi7e3t0m1k9aPi4uTUaNGyZgxY6RTp05SqlQpWbRokcn1RUT69OkjP/74o4SGhsqKFStk6NChMnDgQFmyZInBcbJvow/vhw8firOzs9y8eVPOnTsns2fPlurVq8uhQ4dSvD+ZD3PX8nNn7lq+NnP3f5i7mU/N3BXRbn5peZuh1dxVsz5z938yK3etTd/3gzJKv8tPXFwc+vbtiyZNmmDZsmVYtGgR/Pz80LZtW9SvX1/ZbceY2gDQrVs3vHr1CidPnkRsbCyio6OxfPlyREVFoXjx4gBg0jFqw4cPx/Pnz7Fr1y6lzubNmxEXF4dixYoBQLLjzjJr7mrO2xL19a5du4YJEybgyJEjsLKyQosWLfDee+/h/v37GDlyJAAYdeyh2rWPHTuGnj17IiAgAGfPnsWpU6fw6tUr3Lx5E/369ctwvTft3r0bvXr1QkBAAPz9/XH69GlERkaiZ8+eaNiwodF1kz5fd3d3tGvXDsWKFcOQIUNQo0YN7NixA9bW1sqxmhlx8OBBeHp6okyZMgCAWrVqQafTYd++fWjcuLGyXu4abR7MXcvPnbmbubWZu8kxdy1LzdxNWh/QVn5ll22GVnNXzfrM3eQsnrtmaZGQUSZOnCjnzp1Tbl+4cEEGDhwo9erVM/nM6NOnT5dbt26JiMjz58+lS5cuUqZMGWncuLE8evRIREz7K9ns2bOVf0dGRoq7u7u8//77UrNmTZPrqzl3NeetVv037//y5Us5fPiwNG3aVJ4+fSre3t4ybNgwo+ZrydrPnj2T/fv3S5cuXeT+/fuyZs0a6dmzp1G1RQw7vImJiRIVFSVbtmyRQYMGiYjI6tWrZfDgwUbXT8rT01M++OADadiwoRw5ckRZbuwugLdv3xadTielS5eWwMBAZfmiRYukR48eZpkzpYy5a/m5M3czrzZz93+Yu5lHzdwV0W5+aXmbobXcVbM+czd1mZG7bG5YmH7Xsi1btkjdunVFJPluOJcuXTKqtv7DtW/fPsmdO7fY29vLzJkz5eXLlyLy+g2mv8yWKZdkOnr0qOh0OilevLgsXbpURESio6PlwoULcvXqVaPqqzl3Nedtifp6am4E1ay9YMEC5dJUjx8/lp49e0rfvn3F2dnZ4MuOsdasWaNcN/v27dvStGlTGTVqlJQqVUrOnj1rdF398126dKk0bdpUVq1aJZMnT5bevXvLrFmzlGuKG7sb3fXr16VFixZiZ2cn3t7esmzZMundu7dcuXLFYP1kOuau5efO3M3c2szdlDF3LUfN3BXRbn5lh22GlnNXzfrM3ZRZOnfZ3MgkTZs2lcOHD4uISExMjIiI3Lhxw+Qv2Hfv3pUOHTrIjh07ZN26dVK5cmUpU6aMbNu2zaT5Jq3frVs3Wb58ucybN09KlSol1atXN8uHSo25qzlvS9ZXc+OtRm39/Tdv3iwNGjRQur33798XPz8/OXDggPj7+2eoZkr1f//9d3F3d1fm/PLlS/H395d169Ypny9j6AP8+fPn0r9/f3nw4IGIiAQFBcn69evlk08+kVGjRhldP2n3+88//5QyZcqIlZWVcj14Ugdz1zJzZ+5mTm3mbtqYu5nD3Lkrot38yi7bDK3lrpr1mbtpy4zcZXMjEyQmJoqnp6esWrXKYHnHjh1lxYoVJtUeP368TJgwwWBZu3btJE+ePNK3b1/lQ2GskSNHytSpU5XbUVFRUr16dcmdO7dMmDAhQyeaeZOac1dz3mrVt9TGW40NrF6tWrUkKChIHj58KNOnT5fcuXMb7Mpoqg8++ECuXbsmz549kzlz5ki+fPlk4cKFZqv/22+/SZEiRWTatGkGy/39/SUoKMjouvqN1fHjx5Wzcs+bN09y5swpjRs3lufPnxs/aUoRczdlzF1DzN23Y+5SeqmZuyLazS8tbzO0lruWqC/C3E1NZuSucWd5IZPodDo0atQIa9euxfLly3Hjxg1s374djx49wpAhQ0yqXalSJTx9+tRgWefOnfH5558jNjYWt2/fNrq2iKB48eJ4+fKlsqxAgQL4+OOPMWvWLISHh+PBgwdZbu5qz1ut+vqTMM2fPx+urq7o2LEj+vXrhytXrqBChQro27cv+vXrh1evXmWp2noXL16EnZ0dAgIC8OWXXyJPnjw4deoU/Pz8EBgYaHRdvePHj8PW1haPHj3CV199hejoaPz11184cuQI7t27Z3RdEVH+Xa1aNYwfPx7+/v74+OOPcejQIQBA7dq1UbZsWaPqx8fHw8rKCvfu3YOXl5fy3pgyZQru3buHVq1aIU+ePEbPn1LG3LXs3Jm7lq2tx9xNGXM3c6iZu4B280ur2wwt5q4l6jN3U5ZZucvmRiYQEfTu3Rs9evTAhQsX0KRJE+zatQuff/65ybVbtmyJf/75B4MHD8aePXsQGhqKL7/8EoMHD0ZiYiIuXbpkdG2dToeOHTvi9OnTmDt3Lk6fPo3o6GgsWrQIH3/8MaKjo3H27NksN3e15612fTU3gmrWrlGjBlq1aoVx48ahUKFCmD59OhISEvDw4UO4uroaXVevYcOGqF27NoYOHQpra2vMnTsXtra2uHPnjnKWbmPoz9a8bt06ODs7Y8yYMZgxYwaKFy+OhQsXYvTo0RnaAEZHR+P27dvK+1d/puklS5agefPmKFWqFOLj4xEfHw97e3tMnz7d6LlT6pi7lp07czdzajN3X2PuZg1q5i6g3fzS6jZDy7mrZn3m7mtZJnfNvi8IpZufn5+Eh4dLZGSkUWegfZN+t6tz587JV199JU5OTsquf//++6/UqVPHpPr647I2b94sEydOFDc3N2nevLksWbJEAgICTKqv5tzVnLcl6t+6dUs++OAD+eSTT2T37t0SEhIipUuXlocPH0rnzp1l48aNWa62/jWJiYmRJ0+eiMjr33Ht2rVl586dRs83pfkHBwcrtz/44AOT6ic9trFly5bKrqGxsbGSkJAgv/32m/zyyy/prvfixQvp2LGjdO/eXerWrSvffvutJCYmSmxsrOzdu1d5nd78L6mHuWuZuTN3LV+bufsaczfrMXfuimg3v7S8zdBy7qpVn7n7WlbKXTY3LEz/Zlq/fr307dtXldp+fn7y77//Snx8vERFRUlsbKwMHTpUduzYYZb6165dk8uXL0tkZKSEh4dLbGys9OjRw6T6as5djXkn/VCq+bqouRFUewOrt3v3bomIiJCjR4/K559/bpaaSZ07d04eP34sf/zxh3z66admqak/tjEmJkbmzJkjefPmlS+//FJEMhbI48aNk3HjxklISIhs2LBB2rRpI9HR0WaZI6Ufc9fyc2fuWr52Usxd5m5mUzN3k9bXSn5ZYt5qzD075K4l6oswd7NK7rK5YQEpXeKmZcuWsm/fPhEx7XJJevoT+Ny9e1fq1q0rERERBus/f/68SfX1c7xz5458+OGH8vjxY4OxQ4cOGV1b//rcuXPH7HPXfzDVmLda9ZOejMncG8GUNlLm3sAmJiYqtTds2CAfffSR8rzM9Zdy/Wu0YcMG6dWrl4i8vq64OU5MdOzYMWnevLn8/fffMm3aNPn000/l2LFj0qFDB4mMjEx3nVu3bkn58uUlNjZWWdamTRvlxGqPHz+Wo0ePmjxfShlzN23MXUPM3bQxdyk9LJG7Iupmr1ZzV0Td7NVa7iads5r1mbtZL3fZ3FDRzZs3Dd4c+lALDw+X9evXm1T76dOncuvWLbl48aLB8unTp4u3t7fB+ozx4MEDuXjxopw+fdpguZeXl8n1L126JPfu3Uu2fObMmTJ58mSTap89e9Zgty19sJlj3iIi27dvl9WrVyc7G/SkSZPMUj+ppGd3VnPjbY7akZGRcuTIEQkICDBY3qpVK+VLjSkePHggp06dktu3bxssb968uVnqJ5WYmCiTJ0+WSpUqyaxZsyQuLk7OnTsntWrVylCdK1euyMKFC+X58+fKRu6bb76R/v37i4jIxx9/rHTHyXyYuylj7qYPc/d/mLuUXmrmroi62avV3BVRN3uzS+6KmDd7mbvJZbXcZXNDRSVLlhSdTmewa9KbxxoZEwwZPa4po2JjY6VZs2byySefSJUqVQw2Jr///rvyxjWm/qtXryR//vzi7u4uO3bsUHZZevr0qezatUt5PYypHRsbK5UqVZIbN24oy/SBtmnTJpPmra9fsWJF8fPzU5bp579+/XqT6y9evFhq164tISEhIvK/94Y5Nt4BAQGydu3aZMfnTZs2zeQN7JEjR6RRo0bi4eEhOXPmlE6dOsnJkyflxYsXsn//fqNqJnX48GGpV6+e9OjRQ/Llyyd9+vSRq1evyrNnz2TDhg0m10+JuY5tTPqXDRGR06dPS58+fcTPz09q165t0vudUsbcTY65mzrmbsqYu5QRauWuiLrZq9Xc1c9drezVcu6KqJe9zN3UZaXcZXNDJb/++qs0b95cTp48Ka6urlKyZEnZtGmTMn7s2LFkXb/0Uvu4psmTJ8vo0aPl2bNnMnnyZPnyyy9lxowZMm3aNCU4jX1zPnr0SHr06CENGjSQMmXKSM+ePeX69evi7e0tN2/eNGne06dPlzFjxoiIyMOHD2Xnzp3i7e0tixYtMsuH6rPPPpMhQ4aIiEhoaKh88cUXUrFiRfHw8JC7d++aVP/FixdSvXp1adKkiSxbtkxZ/vLlS7NsvKtVqyYjRowQe3t7OXHihHLioIMHD5q8kfrwww/lt99+U+qVLFlSihUrJt9++61R9d70/vvvy6+//ioirzvmAwYMkGLFisn333+v3EeNsDT12MaU5pSYmCgdOnQQR0dH2bx5s4iYbzddYu6mhrmbMuZu6pi7lF5q5q6Iutmr1dwVUTd7tZq7IupmL3M3ZVktd9ncUMmZM2dk7dq1yu0lS5ZIvnz5pGHDhnL16lWpVKmSUcenqX1c040bN6RcuXLKhqNly5bSqVMnWb9+vTRo0ED69Olj8gfr9OnTsnz5comOjpYxY8aIg4ODFChQQB4+fGh0zWvXrom1tbVypuKBAwdKr169ZPr06dK6dWuZMWOGSXMWed1p9vHxERGR3r17i7e3t/j7+0u3bt2kSpUqJm1svb29Zfz48XLs2DGxtbWVBQsWmDxfvbFjx8ro0aNFRGTlypUyfPhw+eCDD2TAgAFy584dk2r/+++/0rZtW4Nl3t7esmbNGqlRo4acPHnSpPp3796Vdu3aJQvE48ePS8WKFZWNgLkkPXbS2GMb//33X+XfiYmJyudFX3vw4MFSpUoVc02ZkmDupo65mxxzN2XMXcoItXJXRN3s1WruiqifvVrNXRH1spe5m1xWzV02N1SW9IQycXFxMnnyZNHpdNKtWzej6l25ckUWLFggz58/VzqR5jyu6fr168rxkRcuXDA4e3BQUJD07t1b7t+/b3T9+Ph4efDggbRs2VLCw8NFRMTFxUXq1asnpUqVMjocAgMDpWTJklK5cmWZPHmytGzZUhnbs2eP9O/fX6Kjo03aUO3du1dq164t27Ztk8GDBxuMde3aVa5fv25U3WvXromrq6s8e/ZMRET++OMP6dChg1y4cMHoueo9e/ZMOnbsqBzz2b59e/nkk0/k3Llz0q1bN6lYsaJERUUZXf/Ro0dSo0YN+fTTTyU8PFx++eUXad68uYi8PuYzabfZGAkJCdKpUyfp2rVrsl3e1q5dK2PGjDG5E/y2E6BlxKNHj0Sn04mTk5Ps2rVLWZ50jo8ePZLQ0FARkWTHspJ5MHcNMXeTY+6mjrlLxjB37oqom71azV0R9bNXi7krom72MncNZeXcZXPDzF68eCEPHz6UU6dOJVuuV7x4cfnvv/+MXkdMTIzBRkSN45r0b059IIuInD9/XurWrWuWXaKWLl0qc+bMkQkTJsigQYNE5PUxgqbWXrRokVhZWcmSJUuUZefOnZMGDRqYZd5z5syRxo0bS7Vq1eTy5csi8vr1r169utHHBx48eFDWrVsnIq+/HDx9+lSGDRsmZcuWlUuXLpk034SEBBk5cqQ4ODjIpEmTpGDBggbjHTt2lKtXr5q0jr///lt69+4tRYoUkUGDBikn5Zo2bZpMmDDBpNoir49PHTBggMFfa0Re/y70X3CMocYJ0Ly8vGTo0KHyww8/SK5cuaRly5YSFBSkjO/du9cgC8g8mLvpw9z9H+Zu2pi79DaWyF0R9bNXq7krom72ai13RdTPXubu/2Tl3GVzw8w8PDykR48eUqVKFXFzc1OOzRJ5Hbzff/+9zJw506jaMTExcubMmWTLzXVcU0xMTLKNVNJwbNOmjfzyyy9G1Y+JiTE4E3VMTIz069dPnJycDDYob64zvbVPnDhhsCzpLoxt27aVn3/+2ah56+sn7bBv3rxZGjZsKM7OzjJq1Chp3ry5skumKV3VpLt0iYhMmDBBhg0bJk+fPjW6pt7nn38uGzZskD59+ih/iTh79qzUqFHD6JMrPnr0SHk/xsfHS0hIiLKrYkBAgFSsWFFu3bpl9JwTExOVLzS3bt2SpUuXSocOHaRChQoyYsQIKVeuXLKzSWeEGidAW79+vezZs0dERJ4/fy79+/eXHDlyyKRJk+TatWvi5OQkDx48MHrOlDLmbuq1mbtpY+4aYu5SeqmZuyLqZq9Wc1dfT63szQ65K2Le7GXupiwr5y6bG2a0fft2qVOnjty9e1fi4+Plu+++kzJlykjr1q1N/gUvXbpU2rRpI82bN5eCBQvK3LlzDXatMvW4ppTq63eLio2NlQ0bNhjs9mZK7S+++EIeP34sJ06ckK1bt4qI8bsrJa1tb28vn3/+uRKO8fHx8vvvv0uzZs2Mqv1m/UKFCsnChQvl/v37EhcXJ6dOnZIffvjB4JizjEpp463fYFy8eFGKFSsmx44dM7p20jNdi4h8+umn0rp1a5k8ebI0atRIVq9ebbDO9NJ/qalUqZJUrFjR4EtNXFyczJ8/X+bMmWPUvEVe74r6ppiYGAkNDZWdO3fKL7/8YnCG8IxS8wRoL1++NNhoX758WRo1amSwYeHJ7MyHuZu+2szd/2Hupoy5S+mlZu6KqJu9Ws3dN+ubO3u1nLv6+mpkL3M3dVk1d9ncMKOvv/5a5s6dKyL/64C9fPlSevXqJW3btjX6OK/Hjx9L2bJl5eTJkxIfHy/79++XunXrSsWKFZUguHPnjoSFhYlIxoMzrfrHjx8XEZGQkBDlutAZeaOmVrtatWpy9uzZDM3TmHkHBwcrZ3bO6AcspfoffPCBVKlSRalvCv2GpEWLFsk2sHr6uRtbW7+B/eqrryQmJkaeP38uS5culcGDBxu9K9qbX2oWL14sZcqUkVatWpnlS83vv/8uOp1O3N3dDXZxMydznwBN/3lP+h5LSEhQjhE+e/aslCtXThnjJQjNh7mb/trMXeZuapi7lBFq5a6Iutmr1dxN79yNzV4t527S+ubOXuZuclrIXTY3zOjQoUPi5OSk7KajFxsbK927dzc63I4cOZLiCZm+/vprqVixovzzzz9G1U1vfVOOT0ut9ty5c6VixYrKcXzmrG2OeadVf968eVKxYkWD66FnVHo2UsYe05hW7aS7SuplNHjU/FIjItKwYUNZtmyZDB8+XHLkyCGjR4822OXyzz//NKl+UkmfuyknQNPXGT16tGzcuDHZ+ODBg5VuP09mZ17M3fTXZu4yd1PD3KWMUCt3RdTNXq3mblr11Zx7Vs/dt9U3NXuZu6nXycq5y+aGmS1evFgaNGgg3t7eEhISoiyvWLGinDt3zqiakZGRUr58efn666+VZfo3l5eXlyxfvtykOb+t/ooVK1Sr/dNPP6lWW+3XxZS5Z8bGW7+RMnUDq+aXmufPn8uiRYskODhYRF7vrle7dm2xs7OT3377TdauXSsNGzY0af4JCQnJ/qKh7ziLZPwEaPpaZ8+elffff185vnPjxo3KsatJN1ZkfszdjNVm7hpi7jJ3KePUyF0RdTNGq7mbnvpqvi5ZNXfTqm+O7GXuGtJK7rK5YQb6X7b+ZDB//vmnDBs2TKpXry5du3aVnj17Sp8+fUxax9GjR6VZs2YycOBAgxMJffTRR/LNN9+YVFvt+lqtrWb9zPyfD1M3sCLqfanRe/M629u2bZPChQuLTqdLdiKtjEh6bGNKfykw5QRo3bp1kz179khMTIx88cUXUrJkSWncuLGcP39eRLhLtLkxd7NnbTXrM3fTxtylt7FE7opoN7+0mo1q1s7spo+p9Zm7yWX13NWJiICMJiLQ6XQAgE6dOmHx4sV47733cP/+fYSEhGD37t1o2LAh6tSpg3z58mWodmJiInLkyIG4uDjY2NjA398ff/zxBw4dOgRra2vUrl0be/fuxdWrV5PNJbPra7W2JerrHTt2DJ999hlKly4NT09PNGjQAADQrVs3NGjQAF5eXhmuqWbthIQEWFlZKa/Lrl27sHPnTpw6dQplypSBjY0NrKys8Ouvvxo1Z339+Ph4WFtbAwDi4+Oh0+lgZWWFKVOmIDQ0FOvXrzeq/ubNm9GzZ0+0bNkSP/74I8qWLausQ7++jJozZw4++OADtGjRAhMnTsTdu3chImjQoAEmTJgAT09P1KlTB0OHDjWqPqWMuZu9aluivh5zN+X6zF16GzVzF9Bufmk5G7ND7qpRn7mbnKZy14KNlGxJ38VesGCBsltUVFSUbNmyxeizz+olPa5Jf5blR48eybVr12Tx4sWyefNm5QQ0xpyNVs36Wq2tdn1991T/V48zZ87I9OnT5cMPP5RGjRrJhAkTpFKlSsnmktm1k963Y8eOyiWvIiMj5ezZs/LFF1/I4cOHJSYmJt01U6vfoUMHuX79usH4kydPpHnz5iadcOptxzbu3LlTRDJ27OfkyZOlSJEi4uXlJZcvX5YZM2bIV199JSIiQUFBUrVqVeWYzMzuZGcnzN3sVVvt+szdt9dn7tLbqJm7ItrNLy1no1ZzV836zN2UaSl32dwwg+joaKlWrZokJCTIuXPnZPDgwVK8eHHp2bOnch3kjEp6XFOtWrXk3r17IiKyZcuWFE+Qk5Xqa7W2JeprdSOl9pealOo/fPhQNm3aJBcuXFBuG0vNYxs3b94sJUqUEDs7O5k7d67ymW/durUsXLjQ4PmR+TB3s0dtS9Rn7qa/PnOX0qJG7opoN7+0nI1azl016zN3U6eV3GVzwwRjx46VgIAAuXv3rrRq1Uo8PDykd+/eyvWDGzdubNIZhkUMj2uaM2eOlCxZUho2bGhyXUvU12pttepreSMlot6XmtTqDxkyREqUKCG9e/eWJ0+emOEZmO/YRn23Ozg4WNq2bSvbt2+XX3/9VapVqyZOTk7y448/yqlTp8wyZzLE3M2etdWqz9zNWH3mLqXEErkrot380mI2qllby00fEeZuUlrM3RyZfViMFokIoqOjERAQAHd3d2zfvh1fffUVrKys0K9fP/To0QPr1q1Dnjx5UL169QzXnzNnDvbs2YO4uDiULFkSP/30EwYNGgRbW1uEh4ejYsWKOHPmjNHzV7O+Vmtbor6VlRUAYN68eZg7dy7y5cuHL774AmPGjMHEiRNx6dKlLFl73LhxCAwMRExMDIoXL47BgwdjwYIFaN26Ne7evYuIiAjcvHnT7PVbtWqFO3fu4M6dO7h9+7bR9RMSEgC8PtYwT548yr8TEhLQpUsXDB48GH369FGO0UyPHDleR+eiRYtQqVIldO7cGX369MGlS5dQrVo1jBw50qhjjil1zN3sV9sS9Zm7GavP3KWk1M5dQLv5peVs1HLuqlmfuZucFnOXzQ0j6HQ65M+fH7t370bv3r0xfvx4DBgwAO7u7ujQoQMiIyMxe/ZszJ8/36j6z549w8CBAzFlyhQMHToUFSpUQK1atTBhwgTcvHkTp06dQs+ePQG83vBkpfpara12fS1upNT+UmOJL00iomwEu3btihs3bgAArK2tYWVlhadPn8Lf3x/ffPONUfWrVKmCyMhIg2Xt2rXDTz/9hCpVqhhVk1LG3M1+tdWuz9y1fH39Opi72YPauQtoN7+0nI1azV216jN3305TuWuhPUSyFf3uUOvWrRMPDw/Zu3ev+Pj4iIODg3z44Yfy119/mbyLjtrHNalZX6u11ayv5ol4LHGSH29vb8mVK5dUqFBBfvvtNxERuXfvnpQpU8Ysu0WqVV/tYxuDg4Plww8/FA8PD/nzzz/l1q1b4ujoKDdv3hSRzD+pUnbC3M2etdWsz9zNnPrM3ezDErkrot380mo2qllb7WxUuz5zN2Vayl02N0xQp04dOXr0qHL7v//+k7Jly4qzs7O8fPkyw/XUPq5JzfparW2J+npa20ip/aXGUl+a1D628dq1a/LZZ5+Js7OzfPTRR8rrnZGzUFP6MXe1X9sS9fWYu5atr8fczV7Mnbsi2s0vLWdjdshdNeozd9NHK7nL5oaREhIS5JNPPpElS5YYLJ86daocOnTIpNrjxo2TiRMnGizr0KGD6HQ6+eeff0yqrXZ9rdZWq3522Eip8aXGEvUtdQI0kf/9LpKeaCordbGzC+Zu9qqtVn3mbubVZ+5mP2rmroh280uL2ahmbS03ffSYu2+nhdzlOTeMlCNHDnTp0gWrV6/GlClTcPHiRWzatAm7d+9Gs2bNTKqt9nFNatbXam216qt5Ih5LnOQnMTERVatWxeXLl5VlTk5O6NmzJ1avXo2cOXNmufpigWMb36T/Xdja2irLdDqdWWrT/zB3s1dtteozdy1fn7mbfamZu4B280uL2ahmbbWzUe36zN300UTuZnJzRfMOHDggo0ePliJFioiHh4ccOHDA5JpqH9ekZn2t1la7/ooVK2TAgAEGy3744QdZuXKlSXNWu7aIyI4dO6RWrVri7e0tFy5ckI0bN0qNGjXMUlvt+mofO0mZg7mbPWqrXZ+5mzn1mbvZkxq5K6Ld/NJyNmo1d9Wuz9zNHtjcMJOYmBiz7LKkp/ZxTWrW12ptNetrdSOlp9aXGjXqW+rYRsp8zF3t11azPnPXcvWZu+8Oc+euiHbzS6vZqGZtLTd9RJi72YFOxIjrE5FFJCYmIkeOHIiJiVF2/xERs+3+o2Z9rdZWs/7169exceNGrF69Gu+//z7q16+PiRMnKuvLqrWTevbsGWxsbEzePc8S9T/44AMsXLgQjRs3BgCEhISgefPmiI+PR0BAgGrPgbRNq/mi5dpq1mfuWrY+c5eMpdX80mo2qllb7Wy0RPYyd7WLzQ0iC9LiRkqLEhMTMXToUNSsWROjR49Wlk+bNg3u7u5mOU6YiLSBuWsZzF0i0tNy00dLmLvJsblBRNnSzp078dlnn8Hd3R29e/dGQEAA5s2bhwsXLmT21IiIsiXmLhGRZTF3DVln9gSIiNTQsWNH5M2bF9u3b0erVq3Qvn17LFy4MLOnRUSUbTF3iYgsi7lriHtuEFG2p/axk0REZIi5S0RkWcxdNjeIiIiIiIiISOPMdzpvIiIiIiIiIqJMwOYGEREREREREWkamxtEREREREREpGlsbhARERERERGRprG5QURERERERESaxuYGEREREREREWkamxtEREREREREpGlsbhARERERERGRpv0/TqjR12RGKb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1280x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,4), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "# Total number of SNPs by each pipeline\n",
    "plt.subplot(1,3,1)\n",
    "nsnps.iloc[-1].plot.bar()\n",
    "plt.title('Total Number of SNPs by each pipeline')\n",
    "plt.ylabel('Number of SNPs')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "\n",
    "# Number of sites with heterozygous calls\n",
    "plt.subplot(1,3,2)\n",
    "(nsnps.iloc[1] - nsnps.iloc[2]).plot.bar()\n",
    "plt.title('Number of sites with heterozygous calls')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "\n",
    "# Number of multi-allelic SNPs\n",
    "plt.subplot(1,3,3)\n",
    "nmult.iloc[-1].plot.bar()\n",
    "plt.title('Number of multi-allelic SNPs')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <a name='FS2'> [Figure S2: Missingness distribution of SNPs from each pipeline](#TOP)</a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1760x1040 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(22, 13), dpi=80, facecolor='w', edgecolor='k')    \n",
    "\n",
    "for i in range(1, 13):\n",
    "    plt.subplot(3,4,i)\n",
    "    ds = 'dataset{0:02d}'.format(i)\n",
    "    af = pd.read_csv(glob('data/vcfstats/'+ds+'*trim_alt.freq.tsv.gz')[0], sep='\\t', header=0, \n",
    "                     names=['N_CALL', 'Ref_AF'])\n",
    "    af['F_MISS'] = (35 - af['N_CALL'])/35\n",
    "    plt.hist(af['F_MISS'], bins=40, range=(0,1))\n",
    "    plt.title(callsets[i-1])\n",
    "    if i in [1,5,9]:\n",
    "        plt.ylabel('Missingness')\n",
    "fig.savefig('figs/FigureS2_miss_dist.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='F1'>[Figure 1: Plot number of SNPs for each clade](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Reformat dataset\n",
    "# read stats snp per each sample\n",
    "snp_per_sample = pd.read_csv('data/vcfstats/sites_per_sample_hap_trim_alt.tsv.gz', \n",
    "                             sep='\\t', usecols=range(1,13), header=None, names=callsets)\n",
    "snp_per_sample.index = samples\n",
    "\n",
    "# data frame with metadata \n",
    "nsnp_meta = pd.concat([snp_per_sample, clades], axis=1)\n",
    "\n",
    "nsnp_meta_melt = nsnp_meta.melt(id_vars=['Clade'], value_name='nSNPs', var_name='pipelines')\n",
    "\n",
    "# Generate the plot\n",
    "fig = plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='black')\n",
    "plt.subplots_adjust(wspace=0.5, hspace=0.8)\n",
    "for clade_i in range(1,5):\n",
    "    clade_data = nsnp_meta_melt[nsnp_meta_melt['Clade']==clade_i]\n",
    "    plt.subplot(2, 2, clade_i)\n",
    "    ax = sns.swarmplot(data=clade_data, x='pipelines', y='nSNPs', color='grey')\n",
    "    plt.title('Clade '+str(clade_i))\n",
    "    plt.setp(ax.get_xticklabels(), rotation=60)\n",
    "    \n",
    "    # boxplot no fill\n",
    "    ax = sns.boxplot(data=clade_data, x='pipelines', y='nSNPs', color='white')\n",
    "    plt.setp(ax.artists, edgecolor = 'black', facecolor='w')\n",
    "    plt.setp(ax.lines, color='black')\n",
    "    plt.ylabel('Number of SNPs')\n",
    "\n",
    "    \n",
    "plt.show()\n",
    "fig.savefig(\"figs/Figure1_n_snps_per_clade.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='SNP'>[Comparison of SNPs from same isolate, patient or outbreak (Table 2)](#TOP)</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate shared and unique SNPs within each callset amongst 3 sample pairs: samples from same isolate, patient and outbreak.\n",
    "\n",
    "- CA25 vs. CA26: same isolate\n",
    "- CA28 vs. CA29: same patient\n",
    "- CA08 vs. CA27: same outbreak\n",
    "\n",
    "Note: the below analysis involved in computing from VCF files, which would take some time and compute resources. To reproduce the results, have the VCF files inplace and uncomment the below cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def mat2df(mat, dt_int=True, prefix=None):\n",
    "#     \"\"\" Transform numpy matrix to dataframe and add column and row information \"\"\"\n",
    "#     if dt_int:\n",
    "#         df = pd.DataFrame(mat, dtype='int32')\n",
    "#     else:\n",
    "#         df = pd.DataFrame(mat)\n",
    "#     df.columns = ['isolate', 'patient', 'outbreak']\n",
    "#     df.index = callsets\n",
    "#     if prefix is not None:\n",
    "#         df.to_csv('data/'+prefix+'.tsv', sep='\\t')\n",
    "#     return df\n",
    "\n",
    "\n",
    "# np.set_printoptions(suppress=True)\n",
    "\n",
    "# share = np.zeros((12, 3))\n",
    "# unique_w_na = np.zeros((12, 3))\n",
    "# unique_wo_na = np.zeros((12, 3))\n",
    "\n",
    "# pairs = ((24, 25), (27, 28), (7, 26))  # same isolate, same patient and same outbreak\n",
    "\n",
    "# for i in range(1, 13):\n",
    "#     name = '{0:02d}'.format(i)\n",
    "#     file = glob('data/mvcfs/'+'*'+str(name)+'*trim_alt.vcf.gz')\n",
    "#     fungal_vcf = vcf(file[0], prefix='dataset'+str(name)).cal_dos()\n",
    "#     # compare 3 pairs\n",
    "#     for j in range(0, 3):\n",
    "#         share[i-1, j], unique_w_na[i-1, j] = fungal_vcf.samples_concord(pairs[j][0], pairs[j][1])\n",
    "#         share[i-1, j], unique_wo_na[i-1, j] = fungal_vcf.samples_concord(pairs[j][0], pairs[j][1], na_ignore=True)\n",
    "\n",
    "# share = mat2df(share, prefix='shared_snps')\n",
    "# unique_na = mat2df(unique_w_na, prefix='unique_snps_w_na')\n",
    "# unique_no = mat2df(unique_wo_na, prefix='unique_snps_wo_na')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='T2'> [Table 2: Comparison of SNPs from same isolate, patient or outbreak](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shared SNPs\n",
      "            isolate  patient  outbreak\n",
      "ds01-cfsan     1135     1147      1142\n",
      "ds02-gatk3     1020     1065      1027\n",
      "ds03-gatk3      955      974       961\n",
      "ds04-gatk3     1027     1053      1024\n",
      "ds05-gatk3      956      977       961\n",
      "ds06-gatk3      974     1010       980\n",
      "ds07-gatk4      971     1000       990\n",
      "ds08-gatk4      923      948       942\n",
      "ds09-samtl      878      894       894\n",
      "ds10-samtl      991     1031      1011\n",
      "ds11-smtrm     1025     1073      1042\n",
      "ds12-pilon     1146     1176      1182\n",
      "\n",
      "Unique SNPs with NA\n",
      "            isolate  patient  outbreak\n",
      "ds01-cfsan       21       23        28\n",
      "ds02-gatk3       61       90        49\n",
      "ds03-gatk3        9       23        17\n",
      "ds04-gatk3       63       51        50\n",
      "ds05-gatk3        9       20        18\n",
      "ds06-gatk3       39       53        41\n",
      "ds07-gatk4        8       26         8\n",
      "ds08-gatk4        3       10         5\n",
      "ds09-samtl        0        3         2\n",
      "ds10-samtl       27       38        21\n",
      "ds11-smtrm       30       53        33\n",
      "ds12-pilon      111       75        51\n",
      "\n",
      "Unique SNPs without NA\n",
      "            isolate  patient  outbreak\n",
      "ds01-cfsan        5        4         6\n",
      "ds02-gatk3        0        0         0\n",
      "ds03-gatk3        1        4         2\n",
      "ds04-gatk3       33       24        24\n",
      "ds05-gatk3        1        4         2\n",
      "ds06-gatk3       23       26        31\n",
      "ds07-gatk4        0        0         0\n",
      "ds08-gatk4        0        0         0\n",
      "ds09-samtl        0        3         2\n",
      "ds10-samtl        0        0         0\n",
      "ds11-smtrm        0        0         0\n",
      "ds12-pilon        1        3         3\n"
     ]
    }
   ],
   "source": [
    "print(\"Shared SNPs\")\n",
    "share = pd.read_csv('data/shared_snps.tsv', sep='\\t', header=0, index_col=0)\n",
    "print(share)\n",
    "\n",
    "print(\"\\nUnique SNPs with NA\")\n",
    "unique_na = pd.read_csv('data/unique_snps_w_na.tsv', sep='\\t', header=0, index_col=0)\n",
    "print(unique_na)\n",
    "\n",
    "print(\"\\nUnique SNPs without NA\")\n",
    "unique_no = pd.read_csv('data/unique_snps_wo_na.tsv', sep='\\t', header=0, index_col=0)\n",
    "print(unique_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"SHA\">[Sample consensus analysis](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "### Per-sample consensus analysis ###\n",
    "# cd data\n",
    "# [ ! -d sample_comp ] && mkdir sample_comp\n",
    "\n",
    "# cd sample_comp\n",
    "\n",
    "# ## Per-sample consensus ##\n",
    "\n",
    "# bcftools merge ../*trim_alt.vcf.gz --force-samples | bgzip > all.callsets.vcf.gz\n",
    "# bcftools reheader -h all_merge_header.txt all.callsets.vcf.gz > all.callsets.patch_name.vcf.gz\n",
    "\n",
    "# # calculate allele frequencies\n",
    "# for i in {01..35}; do\n",
    "#     bcftools view -s $(echo {1..12}:CA${i} | sed 's/ /,/g') all.callsets.patch_name.vcf.gz | \\\n",
    "#         bcftools filter -e 'AC==0' | bgzip > ca${i}.vcf.gz\n",
    "#     vcftools --gzvcf ca${i}.vcf.gz --freq2 --out ca${i}\n",
    "#     cut -f1-5 ca${i}.frq | grep -v 'nan' > ca${i}.af.tsv\n",
    "# done\n",
    "\n",
    "\n",
    "# ## Generate consensus VCF ##\n",
    "# for i in {01..35}; do\n",
    "#     # generate per-sample vcf include reference calls\n",
    "#     bcftools view -s $(echo {1..12}:CA${i} | sed 's/ /,/g') all.callsets.patch_name.vcf.gz | \\\n",
    "#         bgzip > ca${i}.with_ref.vcf.gz \n",
    "\n",
    "#     # update consensus VCF header\n",
    "#     cat consensus_header.txt > ca${i}.cons.final.vcf\n",
    "#     sed -i \"$ s/$/\\tCA${i}/\" ca${i}.cons.final.vcf\n",
    "\n",
    "#     # filter VCFs\n",
    "#     bcftools filter -i 'AN > 9 & (AC >9 | AC<3)' ca${i}.with_ref.vcf.gz | \\\n",
    "#         bcftools query -f '%CHROM %POS %REF %ALT %AN %AC %AC{0}\\n' | \\\n",
    "#         awk '{if($7>9) {print $1\"\\t\"$2\"\\t.\\t\"$3\"\\t\"$4\"\\t.\\t.\\tAN=\"$5\",AC=\"$6\"\\tGT\\t1\"} else {print $1\"\\t\"$2\"\\t.\\t\"$3\"\\t\"$4\"\\t.\\t.\\tAN=\"$5\",AC=\"$6\"\\tGT\\t1\"}}' \\\n",
    "#         >> ca${i}.cons.final.vcf\n",
    "#     bgzip -f ca${i}.cons.final.vcf\n",
    "#     tabix -f ca${i}.cons.final.vcf.gz\n",
    "# done\n",
    "\n",
    "# # generate final vcf\n",
    "# bcftools merge ca*.cons.final.vcf.gz | bgzip > cauris.consensus.vcf.gz \\\n",
    "#     && tabix cauris.consensus.vcf.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "snp_counts = [0] * 35\n",
    "for i in range(1, 36):\n",
    "    sample = 'ca{0:02d}'.format(i)\n",
    "    af = pd.read_csv('data/sample_comp/'+sample+'.af.tsv.gz', header=0, sep='\\t', \n",
    "                     names=['ID', 'AC', 'N_ALLELES', 'N_CHR', 'AF'])\n",
    "    af['MAJOR_FREQ'] = af['AF'].apply(lambda x: x if x > 0.5 else 1 - x)\n",
    "    af['AC'] = (af['N_CHR']*af['MAJOR_FREQ']).astype('int')\n",
    "    snp_counts[i-1] = af['AC'].value_counts().sort_index()\n",
    "samp_share = pd.DataFrame(snp_counts).fillna(0)\n",
    "samp_share.index = samples\n",
    "samp_share = pd.concat([samp_share, clades], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='F5'>[Figure 5: Sample level SNP concordance by clades](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')    \n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "for i in range(1,5):\n",
    "    plt.subplot(2,2,i)\n",
    "    sns.boxplot(data = samp_share.loc[samp_share.Clade==i, [x for x in range(1,13)]], color='grey')\n",
    "    plt.title('Clade '+str(i))\n",
    "    plt.xlabel('Datasets supporting SNP (n)')\n",
    "\n",
    "    if i in [1,3]:\n",
    "        plt.ylabel('Number of SNPs')\n",
    "plt.show()\n",
    "fig.savefig('figs/Figure5_sample_consensus.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='SOU'>[Source of private SNPs from each pipeline in each clade](#TOP)</a>\n",
    "\n",
    "Get source of private SNPs (shared snps == 1) in each clade1 sample, and summarize in a boxplot. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### calculate percentage of private SNPs for each sample ###\n",
    "\n",
    "# # for each clade, sample x pipeline count matrix\n",
    "# ct_mat = [np.zeros((len(clades[clades.Clade==clade_i].index), 12)) for clade_i in range(1,5)]\n",
    "# ct = [0] * 4\n",
    "\n",
    "# for clade_i in range(1,5):\n",
    "#     cl = clades[clades.Clade==clade_i].index\n",
    "#     n_samples = len(cl)\n",
    "#     # for each sample, calculate number of private SNP from each pipeline\n",
    "#     for smp_i in range(0, n_samples):\n",
    "#         dosmat = vcf('data/sample_comp/'+cl[smp_i].lower()+'.vcf.gz').cal_dos()._dosage_matrix.fillna(0)\n",
    "#         for call_i in range(0,12):\n",
    "#             # number of SNP called in each site\n",
    "#             n_called = dosmat.apply(sum, axis=1)   \n",
    "#             # percentage of private SNPs for each \n",
    "#             ct_mat[clade_i-1][smp_i, call_i] = snp_share(n_called * dosmat[call_i]).pct_private\n",
    "#     # matrix to dataframe \n",
    "#     ct[clade_i-1] = pd.DataFrame(ct_mat[clade_i-1]).fillna(0)\n",
    "#     ct[clade_i-1].columns = callsets\n",
    "#     ct[clade_i-1].index = cl\n",
    "#     # output dataframe\n",
    "#     ct[clade_i-1].to_csv('data/sample_comp/clade'+str(clade_i)+'.pct_private.tsv', sep='\\t')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='FS1'>[Figure S1: Percentage of private SNPs for each pipeline by clades](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Source of private SNPs for each clade ###\n",
    "fig=plt.figure(figsize=(10, 8), dpi=80, facecolor='w', edgecolor='black')    \n",
    "for clade_i in range(1,5):\n",
    "    ct = pd.read_csv('data/sample_comp/clade'+str(clade_i)+'.pct_private.tsv', sep='\\t', header=0, index_col=0)\n",
    "    plt.subplot(2,2,clade_i)\n",
    "    sns.boxplot(data = ct.melt(value_name='percentage of private SNPs', var_name='callsets'), \n",
    "                y='percentage of private SNPs', x='callsets', color='grey')\n",
    "    plt.title('Clade '+str(clade_i))\n",
    "    if clade_i in [1,2]:\n",
    "        plt.xticks([])\n",
    "    else:\n",
    "        plt.xticks(rotation=60)\n",
    "plt.show()\n",
    "fig.savefig('figs/FigureS1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='SITE'>[Site Consensus Analysis](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "### Data preprocessing ###\n",
    "# for VCF in data/mvcfs/*trim_alt.vcf.gz; do\n",
    "#     OUT=$(echo $VCF | sed 's/vcf.gz/sites.tsv/g')\n",
    "#     bcftools query -f '%CHROM:%POS:%REF:%ALT\\n' $VCF > $OUT\n",
    "# done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### get var ids ###\n",
    "# os.chdir('data/mvcfs')\n",
    "# for i in range(1, 13):\n",
    "#     name = '{0:02d}'.format(i)\n",
    "#     with open(glob('dataset'+name+'*trim_alt.sites.tsv')[0]) as fh, open('data'+name+'.varid.tsv', 'w') as ofh:\n",
    "#         ofh.write(\"varid\\tds\"+name+'\\n')\n",
    "#         for line in fh:\n",
    "#             chr, pos, ref, alt = line.strip().split(':')\n",
    "#             alts = alt.split(',')\n",
    "#             for i in alts:\n",
    "#                 if i != '*':\n",
    "#                     ofh.write('-'.join([chr, pos, ref, i])+'\\t1\\n')\n",
    "\n",
    "# # merge variant ID tables\n",
    "# varf = sorted(glob('data*.varid.tsv'))\n",
    "# allvar = pd.read_csv(varf[0], sep='\\t',header=0)\n",
    "# for i in range(1, 12):\n",
    "#     allvar = allvar.merge(pd.read_csv(varf[i], sep='\\t', header=0), how='outer')\n",
    "# allvar = allvar.fillna(0)\n",
    "# allvar = allvar.set_index('varid')\n",
    "# allvar['n_called'] = allvar.sum(axis=1)\n",
    "# allvar.to_csv('all_variant.tsv.gz', sep='\\t', compression='gzip', )\n",
    "# os.chdir('../..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Bar plot of SNP sharing across the samples ###\n",
    "\n",
    "# count table\n",
    "snpmetr = pd.read_csv('data/mvcfs/all_variant.tsv.gz', sep='\\t', header=0, index_col='varid', \n",
    "                      names=['varid']+ callsets + ['n_called'])\n",
    "\n",
    "count_table = snpmetr['n_called'].value_counts().sort_index()\n",
    "count_table.index = count_table.index.astype('int32')\n",
    "\n",
    "# count table for each callset\n",
    "counts = [0] * 12\n",
    "for idx in range(0,12):\n",
    "    var_series = snpmetr['n_called'] * snpmetr[callsets[idx]]\n",
    "    counts[idx] = snp_share(var_series)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.27233507393132"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### percentage of common SNPs ###\n",
    "pct_cate = count_table.sort_index()/snpmetr.shape[0] * 100\n",
    "pct_cate[9:].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='F3'>[Figure 3: Comparison of SNPs called across pipelines](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1280x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plot log counts for each SNP category ###\n",
    "lg_count_table = count_table.apply(math.log).sort_index()\n",
    "lg_count_table\n",
    "\n",
    "fig=plt.figure(figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "# SNP sharing amongst call sets\n",
    "plt.subplot(1,3,1)\n",
    "count_table.sort_index().plot.bar(color='grey')\n",
    "plt.title('SNP sharing amongst call sets')\n",
    "plt.xlabel('Shared SNPs')\n",
    "plt.ylabel('Number of SNPs')\n",
    "\n",
    "# percentage\n",
    "plt.subplot(1,3,2)\n",
    "(count_table.sort_index()/snpmetr.shape[0] * 100).plot.bar(color='grey')\n",
    "plt.title('% SNP sharing amongst call sets')\n",
    "plt.ylabel('% SNP sharing')\n",
    "plt.xlabel('Shared SNPs')\n",
    "\n",
    "# \n",
    "plt.subplot(1,3,3)\n",
    "lg_count_table.sort_index().plot.bar(color='grey')\n",
    "plt.title('log SNP sharing')\n",
    "plt.xlabel('Shared SNPs')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('figs/Figure3_site_consensus.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "pct = {\n",
    "    'callset': callsets,\n",
    "    'pct_share': [],\n",
    "    'pct_private': [],\n",
    "    'total': []\n",
    "}\n",
    "\n",
    "for idx in range(1,13):\n",
    "    pct['pct_share'].append(counts[idx-1].pct_share)\n",
    "    pct['pct_private'].append(counts[idx-1].pct_private)\n",
    "    pct['total'].append(counts[idx-1].total)\n",
    "\n",
    "pct = pd.DataFrame(pct)\n",
    "pct.index=callsets\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "pct.total.plot.bar(color='grey')\n",
    "plt.title('Total number of SNPs')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "pct.pct_share.plot.bar(color='grey')\n",
    "plt.title('Percentage of shared SNPs (>10)')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "plt.ylabel('% of shared SNPs')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "pct.pct_private.plot.bar(color='grey')\n",
    "plt.title('Percentage of private SNPs (=1)')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "plt.ylabel('% of private SNPs')\n",
    "plt.show()\n",
    "\n",
    "fig.savefig('figs/Figure3_site_private_snps.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='F4'>[Figure 4: High confidence SNPs missed by each pipeline](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def miss_source(snpmetr, n_share):\n",
    "    miss = abs(snpmetr[snpmetr['n_called'] == n_share] - 1)\n",
    "    return miss.iloc[:, range(0,12)].sum(axis=0)\n",
    "\n",
    "snpmetr = pd.read_csv('data/mvcfs/all_variant.tsv.gz', sep='\\t', header=0, index_col='varid', \n",
    "                      names=['varid']+ callsets + ['n_called'])\n",
    "\n",
    "fig=plt.figure(figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "miss_source(snpmetr, 11).plot.bar(color='grey')\n",
    "plt.ylabel('Number of SNPs')\n",
    "plt.title('Missing SNPs in 11 category')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "miss_source(snpmetr, 10).plot.bar(color='grey')\n",
    "plt.title('Missing SNPs in 10 category')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "miss_source(snpmetr, 9).plot.bar(color='grey')\n",
    "plt.title('Missing SNPs in 9 category')\n",
    "plt.xticks(range(-1,12), rotation=60)\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('figs/Figure4_site_miss_common_snps.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='ACC'>Access quality of unique SNPs discovered only by a single pipeline</a>\n",
    "Use dataset07 as an example, to show if distribution of DP, MQ, FS and QD change with the SNP sharing. \n",
    "\n",
    "[[to top](#TOP)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "### Extract info from vcf of dataset07 ###\n",
    "# printf \"varid\\tMQ\\tQD\\tDP\\tFS\\n\" > data/private_snps/dataset07.info.tsv\n",
    "# bcftools query -f '%CHROM-%POS-%REF-%ALT\\t%INFO/MQ\\t%INFO/QD\\t%INFO/DP\\t%INFO/FS\\n' data/dataset07.*.vcf.gz |\n",
    "# perl -e '\n",
    "# while(<>){\n",
    "#     if(/,/) {\n",
    "#         @fields=split(/\\t/, $_); \n",
    "#         ($chr, $pos, $ref, $alts) = split(/-/, $fields[0]); \n",
    "#         @alts = split(/,/, $alts); \n",
    "#         foreach $alt (@alts) {\n",
    "#             print join(\"\\t\", \"$chr-$pos-$ref-$alt\", $fields[1], $fields[2], $fields[3], $fields[4]),\"\\n\";\n",
    "#         }\n",
    "#     } else { print $_; }\n",
    "# }' >> data/private_snps/dataset07.info.tsv\n",
    "# gzip dataset07.info.tsv.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='FS3'>[Figure S3: Private SNPs show low mapping quality and read depth in ds7 and ds12](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Generate barplot of MQ, DP, QD, FS ###\n",
    "snpmetr = pd.read_csv('data/mvcfs/all_variant.tsv.gz', sep='\\t', header=0, index_col='varid', \n",
    "                      names=['varid']+ callsets + ['n_called'])\n",
    "qual = pd.read_csv('data/private_snps/dataset07.info.tsv.gz', sep='\\t', header=0, index_col='varid', \n",
    "                   dtype={'MQ': np.float32, 'QD': np.float32, 'DP': np.float32, 'FS': np.float32}, \n",
    "                   na_values='.').fillna(0)\n",
    "\n",
    "# MQ, QD, DP, FS\n",
    "var_series = snpmetr['n_called'] * snpmetr['ds07-gatk4']\n",
    "var_series = var_series[var_series !=0]\n",
    "var_ct = var_series.to_frame()\n",
    "var_ct.columns = ['counts']\n",
    "var_ct = var_ct.astype({'counts': np.int32})\n",
    "\n",
    "ds7 = qual.merge(var_ct, left_index=True, right_index=True)\n",
    "ds7.columns = ['MQ', 'QD', 'DP', 'FS', 'snp_share']\n",
    "\n",
    "\n",
    "fig=plt.figure(figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.subplot(1,3,1)\n",
    "ax = sns.boxplot(x='snp_share', y='MQ', data=ds7, color='grey')\n",
    "plt.title('Map Quality')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.boxplot(x='snp_share', y='QD', data=ds7, color='grey')\n",
    "plt.title('Quality by Depth')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.boxplot(x='snp_share', y='DP', data=ds7[ds7.DP < 3000], color='grey')\n",
    "plt.ylim(0,3200)\n",
    "plt.title('Depth')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig.savefig('figs/FigureS3_1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='PIL'>[Private SNPs from dataset 12](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "### Extract info from vcf of dataset12 (pilon) ###\n",
    "# cd data/private_snps\n",
    "\n",
    "# printf \"varid\\tMQ\\tQD\\tDP\\n\" > dataset12.info.tsv\n",
    "# bcftools query -f '%CHROM-%POS-%REF-%ALT\\t%INFO/MQ\\t%INFO/QD\\t%INFO/DP\\n' ..mvcfs/dataset12.*.no_star.vcf.gz |\n",
    "# perl -e '\n",
    "# while(<>){\n",
    "#     if(/,/) {\n",
    "#         chomp;\n",
    "#         @fields=split(/\\t/, $_); \n",
    "#         ($chr, $pos, $ref, $alts) = split(/-/, $fields[0]); \n",
    "#         @alts = split(/,/, $alts); \n",
    "#         foreach $alt (@alts) {\n",
    "#             print join(\"\\t\", \"$chr-$pos-$ref-$alt\", $fields[1], $fields[2], $fields[3]),\"\\n\";\n",
    "#         }\n",
    "#     } else { print $_; }\n",
    "# }' >> dataset12.info.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "### quality scores of Sites in dataset 12 ###\n",
    "qual = pd.read_csv('data/private_snps/dataset12.info.tsv.gz', sep='\\t', header=0, index_col='varid', \n",
    "                   dtype={'MQ': np.float32, 'QD': np.float32, 'DP': np.float32, 'FS': np.float32}, \n",
    "                   na_values='.').fillna(0)\n",
    "snpmetr = pd.read_csv('data/mvcfs/all_variant.tsv.gz', sep='\\t', header=0, index_col='varid', \n",
    "                      names=['varid']+ callsets + ['n_called'])\n",
    "\n",
    "# MQ, QD, DP\n",
    "var_series = snpmetr['n_called'] * snpmetr['ds12-pilon']\n",
    "var_series = var_series[var_series !=0]\n",
    "var_ct = var_series.to_frame()\n",
    "var_ct.columns = ['counts']\n",
    "var_ct = var_ct.astype({'counts': np.int32})\n",
    "\n",
    "ds = qual.merge(var_ct, left_index=True, right_index=True)\n",
    "ds.columns = ['MQ', 'QD', 'DP', 'snp_share']\n",
    "ds_priv = ds[ds.snp_share==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Generate barplot of MQ, DP, QD ###\n",
    "fig=plt.figure(figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.boxplot(x='snp_share', y='MQ', data=ds, color='grey')\n",
    "plt.title('Map Quality')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.boxplot(x='snp_share', y='QD', data=ds, color='grey')\n",
    "plt.title('Quality by Depth')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.boxplot(x='snp_share', y='DP', data=ds[ds.DP < 3000], color='grey')\n",
    "plt.ylim(0,3200)\n",
    "plt.title('Depth')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('figs/FigureS3_2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='EVAL'>[Comparison of SNPs discovered from 16B25 reference genome](#TOP)</a>\n",
    "Regenerate variant call set aligning to *Candida auris* 16B25 nanopore assembly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "### Run variant calling and generate summary statistics ###\n",
    "# cd /gsap/cdcfungal/WGS_pipelines/onprem_calls_16b25\n",
    "\n",
    "# uncomment the following code chuck to re-run variant calling\n",
    "# # on-prem variant calling\n",
    "# run_gatk.sh gatk_cauris_onprem.json\n",
    "# id: f7aa662b-0dee-4ff3-aefc-b60dfc3d7502\n",
    "\n",
    "# # preprocess snp vcf\n",
    "# grep -v '*' filtered_SNPs.vcf | bgzip > cauris_16b25_snps_nostar.vcf.gz \\\n",
    "# && tabix cauris_16b25_snps_nostar.vcf.gz\n",
    "\n",
    "# bcftools stats -s- cauris_16b25_snps_nostar.vcf.gz > cauris_16b25_no_star.stats\n",
    "\n",
    "# # Extract sites with all alt calls\n",
    "# bcftools filter -i 'MAF==0' cauris_16b25_snps_nostar.vcf.gz | bgzip > cauris_16b25_snps_nostar_maf0.vcf.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Number of SNPs for each sample from reference genome (B8441 vs. 16B25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def per_sample_snps(file, prefix):\n",
    "    stats = (sp.check_output('grep \\'^PSC\\' ' + file + '| cut -f3,13', \n",
    "                            shell=True).decode())\n",
    "    return pd.read_csv(StringIO(stats), sep='\\t', header=None, names=['sample', prefix], \n",
    "                      index_col='sample')\n",
    "\n",
    "c_16b25 = per_sample_snps('data/refdiff/cauris_16b25_no_star.stats', '16b25')\n",
    "c_b8441 = per_sample_snps('data/refdiff/dataset03.snps.hap.no_star.stats',\n",
    "                          'b8441')\n",
    "data = pd.concat([c_16b25, c_b8441, clades], sort=False, axis=1)\n",
    "\n",
    "# Calculate difference and percentage of differences within each clade\n",
    "data['diff'] = data['16b25'] - data['b8441']\n",
    "b8441_mean = []\n",
    "for clade_i in range(1,5):\n",
    "     b8441_mean.append(sta.mean(data[data.Clade==clade_i]['b8441']))\n",
    "\n",
    "data['mean'] = data['Clade'].apply(lambda x: round(b8441_mean[x-1], 0))\n",
    "data['pct_diff'] = round(data['diff'] / data['mean'] * 100, 0)\n",
    "\n",
    "data.to_csv('data/refdiff/n_snps_16b25_vs_b8441.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='FS4'>[Figure S4: Scatterplots of number of SNPs in b8441 vs. 16b25](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZQ2JiIhaLhTFjxjBixAgAMjMzmTZtGqdOncJisRAXF0dkZKTDz1cLqEUqjyUnh+9ffImGQwZT+7ZWeNRz/X5XQdlBtFWbSMUpvLtFbmoaPo0aOrukKik3N5dHH32UmTNn0qdPH3bs2MGkSZNYs2YNTzzxBG+88Qbt27dn9erVzJgxg9WrV5Oamkp2djbbtm0r8cv80qVLMZvNfPbZZ2RlZTFixAhatWpFhw4diI+Pp0WLFrz22mskJycTExNDmzZtSr3gU2UqWEBdsBVnYQULqNUPi9y6wnOSfZs0ofZtrZxdkkNo6oWIVCuFQ3KbubMVkm9g165d1K9f336xpp49e7J8+XI+//xz7r77btq3bw/AsGHDiI+PB2Dfvn34+PgwevRo+vfvT0JCAjk5OQBs3bqVmJgYDAYD9erVIzo6mo0bN2I2m9m+fbt9dLlhw4Z0796dTZs2OfyctYBapOIVXbjXj8YP/d7ZJTmMRpRFpNooHpI1J/nGTpw4QVBQEDNmzODw4cP4+fkxZcoUTpw4ga+vL5MnT7bfZ+rUqQBcvXqVbt26MX36dAwGA1OmTGHBggXEx8eTkpJCSEiI/flDQkI4ePAgmZmZ5ObmlmhLSUkpUVNaWhrnzp0rcTwpKalCzlkLqEUqVvGQ7OpzkotTUBaRasFmsfDjm28pJN8Cs9nM7t27WbVqFfPmzWPnzp2MHTuW++67j+3bt7N69WqaNWvGmjVrGDduHF988QWDBg1i0KBB9ucYN24cY8eOJT4+HpvNVuIHpNFotC+QK62tuLVr17Js2bJKOFvsNcwd27XErhchAT5aQC1SBmk7dhbaAq5mhWRQUBaRasJgMtF61nOYL1/Gr6lC8s0IDg4mPDyczp07A9emXri5uZGenk6HDh1o1qwZAIMHD2b27NlkZGTwzTff0LhxY9q1awdc20XCze3aj4oGDRqQmppKw4bXprukpqYSGhpKQEAAnp6epKWlERwcbG9r3rx5iZpiYmLo1atXieNJSUmlXm21LLSAWqTihPS9Hw//APzv6lwj/w9pjrKIVGmZ+xPJ/O9+ALyCghSSb0GPHj1ISUkhMTERuDb/OC8vj8mTJ7N//35OnjwJwJYtW2jcuDF169bl5MmTLFmyhLy8PMxmMytXriQ6OhqAqKgo1q5di9VqJSsri82bN9OnTx9MJhO9e/fmvffeA+D06dN8/fXXpe56ERQURJs2bUr8KQjtFaVgAXXkXY1p0zSgRv6AFykrS04OP69dj9VsxmAwENDlzhr7f0gjyiJSZRVccc/k68sdr7+KydPT2SVVK4GBgbz++uvMnz+f7OxsTCYTS5cupVWrViQkJDBx4kTMZjN+fn4sXboUo9HI2LFjOX/+PAMGDMBsNtO1a1f+/Oc/AzB+/HjmzZtH//79yc/PJzY2lq5duwIwc+ZMZs2aRb9+/TCbzUydOpXw8HDnnbyIlEnhOcme9QMJ6nWfs0tyKgVlEamSCl+WuvXM6QrJZXTHHXewbt26EscjIyNLHfH18PBgzpw5pT6Xt7c3CQkJpbb5+/tX6txjEal8xS9LXf++e51dktNp6oWIVDmFQ7IW7omIVL7iITlizOgaO92iMAVlEalSclNTFZJFRBzsh2XLFZJLoakXIlKleAUH03hkLHVub6uQLCLiII1/PwLvRg1pFDNMIbkQjSiLSJVw6ftjWK5eBaDBwAcVkkVEKpklJ4fLP1y72I93gzAajxiukFyMgrKIOF3m/kQOzpjND0tfdXYpIiI1QsGc5O+em0VOKVfRlGsUlEXEqQov3GswaKCzyxERcXmFF+4FR0XiVejy81KUgrKIOI12txARcayiu1v0q5GXpb4VWswnIk6R9e13CslSIWw2my5XLXITrPn5Csm3SEFZRJzCOzQUnyaNafb4nxSSpczSMrKZtWIPqRnZuJkMmC02gv19mDu2K0H+PuV+foVwcSUGNzdqtWyBb9OmCsk3SUFZRBzKfPkKbn6+eNYPpN3iBeqopcxsNhuzVuzhbPoVLFYbZsu142fTrzD7jT387Zle5fp8VXYIF3EUS24uBjc3jG5uNBn1EID63pukOcoi4jBZiQfYO3Ycmfv+C6ijlvI5fCKD1IxsLFZbkeMWq42z6dkcPpFR5ucuHMLNFiu5eRbMFqs9hNtstl9/EpEqwJKTw+G5CXy/eAk2iwWDwaC+9xYoKIuIQ2QlHuBIwgIMBiPudes6uxxxASnnr+BmKv0HvpvJQMr5K2V+7soM4SKOUnjhnmf9QDAq9t0qvWMiUukKQrIW7klFCg30xWwpfWTXbLERGuhb5ueuzBAu4ghFd7fQZanLSkFZRCqVQrJUltYR/gT7+2AyFv3hbzIaCAnwoXWEf5mfuzJDuEhlK7kFnEJyWSkoi0ilunLyJ4VkqRQGg4G5Y7sSEuCLm8mIl4cJN5OR0EBf4sd2LVcwqMwQLlLZ8i9eIvfsWW0BVwG064WIVKoGAx+k/r098ahbx9mliAsK8vdh+bO9KnwLt4IQXnzXi5AAn3KHcJHK5hUcRPslf8G9Tm19VstJQVlEKlxW4gHObP6ElnFPY/L0VEiWSmUwGGjTNIA2TQMq9HkrK4SLVAZLTg7fL36RBoMGUuf2tup3K4iCsohUqMJzkq+mpuHTuJGzSxIps8oK4SIVqfCcZJ8mTahze1tnl+QyNEdZRCpM8YV7CskiIpWr+O4WBRcUkYqhEWURqRCZ+xM5On+hFu6JiDiItoCrfBpRFpFys1ksnHz73WshOX6WQrKIiAOc+3q3QnIl04iyiJSbwWSi9aznyL9wAb+mCskiIo4QHNUbD/961Lujk0JyJdGIsoiUWVbiATL+/R8APAMCFJJFRCqZJSeHU++twZqfj8FgwL/zHQrJlUgjyiJSJgUL90w+PtzR7nZMXl7OLklExKUVnpPsGVSf4Mjezi7J5WlEWURuWeHdLVrPek4hWUSkkhVfuBfUu5ezS6oRNKIsIrdEu1uIiDiWdrdwHgVlEblpV8+dU0gWEXGwH/72+i8huR8RY/6gkOxACsoictM869cn/A8PU6tVK4VkEREHaTJyBD6NGtJw2BCFZAfTHGUR+VWXvj+GJTcXgNDoBxSSRUQqmSUnh0vfHwPAKySERsOHKiQ7gYKyiNxQ5v5EDs6YzfGXX3F2KSIiNULBnOSDM2aTnXza2eXUaArKInJdhRfuNRw21NnliIi4vMIL94Lvj8K7QZizS6rRNEdZREql3S1ERByr6O4WWrhXFWhEWURKuPDdQYVkF3D8+HEefvhhBg4cyODBg0lMTARg7969DB8+nAcffJDY2FhOnDhhf8zKlSvp27cvUVFRxMfHk5+fD4DVamXhwoXcf//9REZGsmzZMmw2GwC5ublMnTrV/rg1a9Y4/FxFqjtrfr5CchWkoCwiJXg3aIBvRIRCcjWWm5vLo48+ysMPP8yHH37IU089xaRJk0hNTeWJJ57gueee4+OPP6Zfv37MmDEDgJ07d/L++++zfv16Pv/8czIyMli1ahUAa9as4eDBg2zatInNmzeze/duPv30UwCWLl2K2Wzms88+Y926daxatcoeykXk5hjc3KjTto1CchVTpYLyyy+/zHPPPWe/PWrUKO6//34GDBjAgAEDmDdvHqCRDZHKkn/pEgAe/vW4fWGCQnI1tmvXLurXr0+fPn0A6NmzJ8uXL+fzzz/n7rvvpn379gAMGzaM+Ph4ALZu3Up0dDS1atXCZDIRGxvLxo0b7W1DhgzBw8MDLy8vhg4dWqQtJiYGg8FAvXr1iI6OtreJyI1ZcnOx5udjMBhoFBujkFzFVIk5ysnJybzwwgvs2rWLfv36AZCfn8/BgwfZsWMHtWvXLnL/wiMbVquV0aNHExERQXR0dJGRjaysLEaMGEGrVq3o0KGDE85MpPrI3J/I9wv/wm8mTSSgy53qqKu5EydOEBQUxIwZMzh8+DB+fn5MmTKFEydO4Ovry+TJk+33mTp1KgApKSl07NjR/hwhISGkpKTY20JCQm667eDBg6XWlZaWxrlz50ocT0pKKv9Ji1QzBXOSTd7etJr2DEa3KhHLpJAq8S+ydu1aunXrxm9+8xt7B3r48GE8PDyYMmUKKSkptG3blmeeeYZ69eoVGdkA7CMb0dHRbN26lYSEhBIjGwrKItdXeOGeZ2CAs8uRCmA2m9m9ezerVq1i3rx57Ny5k7Fjx3Lfffexfft2Vq9eTbNmzVizZg3jxo3jiy++ACjxC1LBbZvNVqLNaDT+altxa9euZdmyZRVyjiLVWfGFewaTydklSSmqRFB++umngWvz3ApcuHCBu+++m9mzZ1OrVi1eeOEF4uLiePPNNytkZEOjGiLXZCUe0MI9FxQcHEx4eDidO3cGrk29cHNzIz09nQ4dOtCsWTMABg8ezOzZs8nIyCAsLIzU1FT7c6SmphIWdm1rqgYNGpRoCw0NLdLWsGHDEm3FxcTE0KtXrxLHk5KSiIuLq4AzF6n6tLtF9VElgnJpevToQY8ePey3J0yYQNeuXcnOzq6QkQ2NaohoCzhX1qNHD+bPn09iYiIdOnRg37595OXlMXnyZB555BFOnjxJeHg4W7ZsoXHjxtStW5eoqCgWLVrEyJEj8fPzY82aNfY5zlFRUWzYsIG+fftitVr54IMPGDRokL1t7dq1dOzYkYsXL7J582bmzJlTal1BQUEEBQU56m0QqXIUkquXKhuUd+7ciZubG/fccw9wLQAbjUbc3NwqZGRDoxoikHP6jEKyiwoMDOT1119n/vz5ZGdnYzKZWLp0Ka1atSIhIYGJEydiNpvx8/Nj6dKlGI1GevToQVJSErGxsZjNZjp16sTjjz8OwPDhw0lOTmbQoEHk5+cTGRnJkCFDABg/fjzz5s2jf//+5OfnExsbS9euXZ15+iJVlvnyFa6eO6eQXE1U2aCckZHBm2++ydq1a/Hz82PFihVERkbi4eFRISMbGtWQmqzgm5ewfg9Qv0d33IstmBXXcMcdd7Bu3boSxyMjI4mMjCz1MaNHj2b06NEljptMJuLi4kodSPD29iYhIaH8BYu4sIJ+17N+IO1fXIRbrVoKydVAlQ3KgwYN4tSpUwwbNgyr1UrLli2ZO3cuoJENkfLISjzA6Q8/ptWzUzB5eyski4hUMktODkcX/oWwAf2p17GD+t1qpEoF5SeffLLI7YkTJzJx4sQS99PIhkjZZCUe4EjCAoweHuSmncO3SWNnlyQi4tIKz0n2jQinXscOzi5JbkGVuuCIiFSewiG5zdzZCskiIpWs+MK9JqMecnZJcosUlEVqgOIhWQv3REQql3a3cA0KyiIuzma1cvLdvyski4g4UPqefykku4AqNUdZRCqewWik9aznyMvIxK9phLPLERGpEYJ63Yt7vbrU7dBeIbka04iyiIvKSjzA+W/2AOBRt65CsohIJbPk5PDT/63GmpcHQL2OHRSSqzmNKIu4oII5ySZvL+p17IDJ29vZJYmIuLTCc5I9g4MI6RPl7JKkAmhEWcTFFIRkg7s7rWfPVEgWEalkRRfuRRMcVfoFfaT6UVAWcSGFQ3JbLdwTEal0xUNyxJjRmm7hQhSURVzE1fT0oiG5eTNnlyQi4vKSXntDIdmFaY6yiIvwDAgg4rHR+DVrppAsIuIgTR6KxbtBGA2HDVFIdkFlHlH+4IMPitxevXo1o0aN4o9//CNbtmwpd2EicnMuHjmKOTsHgJD7+ygkV3PqW0WqPktODhePHAXAs359Gg0fqpDsosoclBMSEux/f+ONN3jzzTe599576dSpE/Hx8axZs6ZCChSR68tKPMChWfEcW/Kys0uRCqK+VaRqK5iTfHDmHLJP/ezscqSSlXnqhc1ms//9/fff59VXX6V169YA3HPPPUyePJkRI0aUv0IRKVXhy1I3jo1xdjlSQdS3ilRdxRfueTdq6OySpJKVOSgX/orhypUrtGjRwn779ttvJz09vXyVich1FQ7Juiy1a1HfKlI1FQ3Juix1TVHmqRd5eXn87W9/Y+fOnbRr146vv/7a3vbpp58SHh5eEfWJSDEXDh5SSHZh6ltFqh6r2ayQXEOVeUR5ypQpHDp0iM2bN3Py5EkuXbrEfffdx2uvvcbf/vY3li5dWpF1ijiEzWbj8IkMUs5fITTQl9YR/lWuM/Ru2BC/3zQnYsxohWQXpL5VpOoxurlRt2MHfJs2VUiuYcoclP/whz/Y/56Tk8P58+cB+O1vf8v9999PREREuYsTcaS0jGxmrdhDakY2biYDZouNYH8f5o7tSpC/j7PLI//iRdxr18ajbh3aJsxVR+2i1LeKVB2WnBwwGjF5etJo2BBsNpv63hqmXBccycjI4JtvvuHKlSs0atQIgDZt2hAREcGXX35ZIQWKOILNZmPWij2cTb+C2WIlN8+C2WLlbPoVZr+xp8gCK2fI3J/I3j8+zvndewDUUbs49a0izlcwJ/no/IVY8/MB9b01UZmD8t69e4mKimLSpEnce++9rFixokh7XFxcuYsTcZTDJzJIzcjGYi0aiC1WG2fTszl8IsNJlV0LyUfnL8To5oZXSLDT6hDHUN8q4nyFF+55N2qEwU3XZ6upyhyUFy9ezPTp0/nXv/7F22+/zTvvvMNbb71lb3f2CJzIrUg5fwU3U+kjBW4mAynnrzi4omvsIVkL92oM9a0izqXdLaSwMgflH3/8kcGDBwPQuXNnVq5caV+pDfp6QqqX0EBfzJbSA0i+2UpooK+DK1JIrqnUt4o4j0KyFFfmoFyrVi1+/vl/V6Rp1aoV8+fPJy4ujuPHj1dIcSKO0jrCn2B/H4yl9IcGAwTW8XJ4TVfT0jB6KiTXNOpbRZzHnJ1DXnqGQrLYlTkoDx8+nMcee4wPP/zQfqxPnz784Q9/YOTIkeTl5VVEfSIOYTAYiP/j3RhLScpWK8x5858O+8q74HVC7u9Dp+XLFJJrGPWtIo5X0O96BvjT7i8LFJLFrsyz08eNG4e/vz9paWlFjj/xxBPUq1ePv/3tb+UuTsSRzl/IBQxA0UBstf1vQV+bpgGVWkPm/kROf7CRVtOfxc3HB/datSr19aTqUd8q4liWnByOLlhMaL8H8L+zs/pdKaJcyziHDx9u/7vNZiMzMxN/f39iY2OJjY0td3EijlSwoM9sKdlmMl5rr8ygXHhO8tW0c7iFN6m015KqTX2riGMUnpPsGxGO/52dnV2SVDHl2kcZ4PLlyzzzzDO0a9eOe+65h44dO5KQkMDVq1croj4Rh7nRgr6r+Va8PEyV9trFF+75KiTXeOpbRSpX8YV7TR552NklSRVU7qAcHx9Peno6f//739mxYwdvvPEGR44cYeHChRVRn4jDtI7wJ6ie93XbV39xtFLmKWt3CymN+laRyqPdLeRmlTsof/XVV/z1r3+lffv2BAcH07lzZ1555RU+//zziqhPxGEMBgOjHrjtuu2VceERm83GqffWKCRLCepbRSpPxn/2KiTLTSn3pWbCwsK4cOECfn5+9mNXr17Fy8vx22mJlFfOVQteHiZy80pOVC648EhFzlM2GAy0njmdvPQMfCPCK+x5pfpT3ypSeer3+C3udetS5/a2CslyQ2UOylu2bAGga9euPPbYY/zpT38iNDSUtLQ0VqxYwQMPPFBhRYo4yo3mKZsttgq78EhW4gHyL1ykfs/f4l67Nu61a1fI80r1p75VpHJYcnL4ed37NBoxHJOnJ3Xb3e7skqQaKHNQXrBgQZHbr7zySpHbn332GVOmTCnr04s4RcGFR1LOX8ZaKC+bjAZCAnxoHeFf7tfISjzAkYQFGL28qHfnHbj5+JT7OcV1qG8VqXiF5yR7BgUR+rv7nV2SVBNlDspfffVVRdYhUiWcy8zBbLEWCckA9et5Ez+2a7m/orOHZA8P2syZqZAsJVR033r8+HHmzp3LpUuXMBqNzJo1iw4dOjBq1ChSU1PtUznuvPNOZsyYQV5eHnfddRdNmvxv55XRo0czcOBAMjMzmTZtGqdOncJisRAXF0dkZCQAP//8M9OnTycjIwOj0cjcuXPp2LFjhZ6LSFkUX7gX0rePs0uSaqRcc5S///57GjRogJ+fH0ePHuXLL7/EZrPxu9/9jmbNmlVUjSIOYbPZmLViD+ezcoocNxqujSjXr3v9HTFuRpGQrIV7cgMV1bfm5uby6KOPMnPmTPr06cOOHTuYNGkSW7Zs4eDBg+zYsYPaxab9fPfddzRt2pQNGzaUeL74+HhatGjBa6+9RnJyMjExMbRp04bQ0FAmT57M0KFDiYmJ4eDBg4wbN44tW7bgo18GxYm0u4WUV5l3vfjggw+IiYkhNTWVbdu2MWLECL777jsOHDjAkCFD2LZtW0XWKVLpDp/IIDUjG4u1+JX5IDUjp1w7XuRlZCoky02pyL51165d1K9fnz59ro2g9ezZk+XLl3P48GE8PDyYMmUK/fv3Z9q0aWRmZgKwb98+8vPzeeihh+jfvz/Lli3DYrFgNpvZvn07I0aMAKBhw4Z0796dTZs2kZqaytGjRxk8eDAAbdu2JTw8nB07dlTsmyNyi3584y2FZCmXMo8ov/baa7z99ts0a9aMyZMn89e//pWePXsCsGPHDhYtWmT/Sk6kOrjRlfnKu+OFh389mo77I77h4QrJckMV2beeOHGCoKAgZsyYweHDh/Hz82PKlClcuHCBu+++m9mzZ1OrVi1eeOEF4uLiePPNN4FrgXrixIlkZ2fzpz/9CQ8PDwYNGkRubi4hISH25w8JCSElJYWUlBQCAwNxd3e3twUHB3PmzJkSNaWlpXHu3LkSx5OSkm7pfRK5GY1HxuIdFkqDIYMUkqVMyhyUz58/T4cOHQBITU2le/fu9rZ7772Xp59+utzFiThSZex4cfHIUXwaNcLNz5fg3r3KW6LUABXZt5rNZnbv3s2qVauYN28eO3fuZOzYsWzbto0ePXrY7zdhwgS6du1KdnY2Y8eOtR+vU6cOo0eP5s0332TgwIEAJcKG0WjEZrOVGkKMxpJfWq5du5Zly5bd9DmI3CpLTg6Xf0iizu1t8Qzwp+HQwc4uSaqxMk+9aNGihX0O2z333MP27dvtbZ999pnmKEu1U7DjhclYLAgYKNOOF1mJBzg0K57vX3ypIssUF1eRfWtwcDDh4eF07twZuDZS7ObmxjvvvMPu3bvt97PZbBiNRtzc3Hjvvff46aefirS5ubkREBCAp6cnaWlp9rbU1FRCQ0MJCwvj/PnzmM1me1taWhqhoaElaoqJiWHDhg0l/ixevPimz0vkegrmJB+a8zxXTp50djniAso8ojx9+nTGjh3Lli1baNCgAU8//TRdunQhPz+fffv2sXLlyoqsU6TSGQwG5o7tynOv7eZserb9uNUG+WYr5zJzCPK/uYVJhRfuNXno95VVsrigiuxbe/Towfz580lMTKRDhw7s27ePvLw8wsLCmD9/PmvXrsXPz48VK1YQGRmJh4cH3333Hd9++y3z588nNzeXd999l379+mEymejduzfvvfcekyZN4vTp03z99deMGzeO4OBgWrVqxcaNGxk2bBiHDx/m+PHjdOvWrURNQUFBBAUFVeRbJgIUX7gXjU+hnVtEyqrMQbl9+/Z88sknfPjhhxw8eJBOnTphMBho1aoV06ZNo0WLFhVZp4hD1K/njdFgwGigyBZx57NymP3GHv72TK9fneem3S2kPCqybw0MDOT1119n/vz5ZGdnYzKZWLp0KV26dOHUqVMMGzYMq9VKy5YtmTt3LgBTp04lPj6e/v37k5eXxwMPPEBsbCwAM2fOZNasWfTr1w+z2czUqVMJDw8H4MUXX2TmzJm8++67ACxZsoQ6depU7Jsjch3FQ3LEmNGakywVolzbwwUGBvLYY49VVC0iTnf4RAZpmTkl9lG2WG2cTc/m8ImMGy7ou3Do8P9CcvwshWQpk4rsW++44w7WrVtX4vjEiROZOHFiieN16tRhyZIlpT6Xv7//decXN2rUiLfffrtctYqUhc1iUUiWSlOuoCziasq784VPo0bUatWS8D+MUkgWEXEAg8mEf5c78W3aVFvASYUrc1DesmXLr96nYO9OkeqirDtf5GVdwKNuHdxr16Lt83MqsUJxdepbRW6OJScHDAZMXl40GPCgs8sRF1XmoPzWW29x4MCBUlc1w7WFUerMpbop2PnibPqVIhceMRog2N+71J0vshIPcOSFRTR/Yhz1e/7WkeWKC1LfKvLrCuYkYzDQZvYMjB4ezi5JXFSZg/Lbb7/NH/7wBx544AFGjRpVkTWJOE3BzhfTl+8mNaPozhc5Vy2kZWQTHPC/UeXCC/e8GzZwRsniYtS3itxY8YV7hkIXuhGpaGXeR9nLy4uFCxfy+uuvk5OTU5E1iThV/XreuJmMFJ/mlnExl3ELvyQ1/Qqg3S2kcqhvFbk+7W4hjlbmoAzQpEkTVq1ahdVqrah6RJzu8IkMUjOysZUyVdlssTHtb7vJ3J+okCyVRn2rSEkKyeIM5d71Qvsli6v5/qdMzJbrB5T0C7mc+P5njJ6e2gJOKo36VpGiLFevkn/hgkKyOFSV2h7u5Zdf5ty5cyQkJADw0UcfsWLFCsxmM7fddhvz5s3Dz88PgJUrV7J+/XosFgvdu3dn+vTpuLu7Y7VaWbx4MV999RUWi4WBAwcyfvx4/YeSm2Kz2di068cb3QE3NyMZTdtz72u9cPMrfRcMERGpGDabDYPBgEfdurRb+AImXx/9TBeHKdfUi4qSnJzM+PHjWbVqlf3Y8ePHWbhwIW+99RZffPEFISEhLFq0CICdO3fy/vvvs379ej7//HMyMjLsj12zZg0HDx5k06ZNbN68md27d/Ppp5865byk+jl8IoPMi7mltoVnn2Hk6S9wy88lNNBXIVlEpJJZcnI4NCue9D3/BMDNz1chWRyqSgTltWvX0q1bN0aPHm0/tm3bNnr27ElwcDAAI0eOZNOmTVitVrZu3Up0dDS1atXCZDIRGxvLxo0bAdi6dStDhgzBw8MDLy8vhg4dam8rLC0tjUOHDpX4k5SU5JiTliop5fwV3N1K/rcIzz7DkJTtBOZl0dTPWuo2cSIiUnEK5iRf+PY7Lh793tnlSA1V4VMv8vPzcb/FrVqefvppAJYuXWo/lpKSUmQf0ZCQELKzs8nKyiIlJYWOHTsWaUtJSbE/LiQkpNS2wtauXXvdS7FKzRUa6Eu+uej85IKQbDaYWBMWxR8G36MRDXG4svStItVV0YV7/Qj/g7ZKFOco94jyTz/9xKRJkwD46quvuOOOO7jnnns4cOBAuYsrLYwUHCveVnC7YC5TYUZjydOMiYlhw4YNJf4sXry43HVL9dU6wp96tb3st4uH5FSvAP6yep92I5BKV5l9q0hVVjwk67LU4kzlHlF+/vnnCQoKwmazsXjxYiZMmECtWrVISEhg3bp1ZX7esLAwkpOT7bdTU1Px9fWlTp06hIWFkZqaWqQtLCwMgAYNGpRoK+0KV0FBQQQFBZW5PnFNBoOB/t2bsmrzIbDZuCfjQJGQDGCxwrptxxjRp5WTqxVXVll9q0hVl/nf/QrJUmWUe0T56NGjPP/88/z888/8/PPPjBw5khEjRvDDDz+U63l79+7Nzp077aF39erVREZGYjQaiYqK4pNPPuHixYtYrVbWrFljv6RrVFQUGzZsIC8vj9zcXD744ANd7lVuSbC/97W/GAy8H9qL9xrcbw/JBdZ/eZxDP57HVtpmyyIVoLL6VpGqLvCebrSdP1chWaqEco8oW61Wrl69yo4dO7j99tvx9fUlNTUVHx+fcj3vb37zG5555hkee+wx8vPziYiIYMGCBQD06NGDpKQkYmNjMZvNdOrUiccffxyA4cOHk5yczKBBg8jPzycyMpIhQ4aU9zSlhrDZbGx9bwu3X8zgu9rNuWry5JzJs8T98sxWnlv+DSEBvswd25Ug//J93kWKq6y+VaQqsuTkcOofa2kcG4PJ25s6bdo4uyQRoAKCcu/evXnooYc4ffo0U6ZMISkpiT//+c/07dv3lp/rySefLHK7f//+9O/fv9T7jh49usguGQVMJhNxcXHExcXd8uuLHPh8F/d+/xn5BjeO+Tbmqsnjuve1WG2cTb/C7Df28LdnemnkQypURfatIlVZ4TnJXsHBhEb/ztklidiVOyjPnj2bDz/8EE9PT/r3789PP/3E4MGDGTVKK1SleslKPMDlN1/FYjCxNizyhiG5wLWwnM3hExm0aRrwq/cXuVnqW6UmKL5wL+QB/SIoVUu5gvKlS5c4deoU/fv3x9Pz2tfTTZo0KXWkV6Qqy0o8wJGEBeDuzj8C7isxJ/lG3EwGUs5fUVCWCqO+VWoCS26udreQKq/Mi/n27dvHvffey5AhQ4iMjOTw4cMVWZeIw+RlXeDI/IUYPTz4tHn0LYVkALPFRmigrtInFUN9q9QUJ1auUkiWKq/MQfnll1/mqaeeYv/+/QwdOpQXX3yxIusScRiPunVoPv5xvMZO5EjerQVek9FASICPrtQnFUZ9q9QUjUfG0uSRhxWSpUorc1A+cuQIjzzyCN7e3jz22GMcOXKkIusSqXQXDh0m/+IlAOr3/C3nvANxM91cZ+1mMuBmMhIa6Ev82K7q5KXCqG8VV2bJySEr8dpFczzq1qXh4IHqP6VKK/Mc5cL7x/r6+mI2myukIBFHKJiTXKtVS9o+Pwe4dvlqs+Xm9kVu2bgeDz/QmtYR/urkpUKpbxVXZV+4d+Qo7V9ciF/Tps4uSeRXlXlEWRdakOqqICQbPTwI/8P/dhBoHeFPsL8PJuOvB9/fdmhAm6YBCslS4dS3iisqsrtF9AP4RkQ4uySRm1KuEeXDhw/bO3WLxVLkNkAbbRguVUzhkNxm7mz8mv1vRMNgMDB3bFdmrdjD2fTLWKylP4ebycDvuoU7pmCpcdS3iqspvgWc5iRLdVLmoJyTk8PgwYOLHCt822AwaG6dVCkXjxy9bkguEOTvw/Jne3H4RAb7jqSyYccPWKz/CyhuJgPzn7gHo7HcV38XKZX6VnElNouFw/NeUEiWaqvMQfno0aMVWYdIpfNp1IjabdvQ5KHflxqSCxgMBto0DaBN0wAe+l0rPvvmJEmnL9CsQR1+1y1cIVkqlfpWcSUGk4nAbl3xaxpB+KMKyVL9lPvKfCJVXV5GJh7+9XDz86XN7Bm39Fij0Uh0dy04ERG5FZacHGw2G24+ProktVRrZQ7KvXr1uuFvhgaDgW3btpX16UUqRFbiAY7MX0jTPz1GcO9ezi5H5Fepb5XqrmBOss1ioc3c2Zh+ubqkSHVU5qD87LPPlnp89+7drFu3js6dO5e5KJGKUHjhnm94uLPLEbkp6lulOiu6cC8ao4eHs0sSKZcyB+X777+/yO38/HwWL17Mxo0bmTRpEn/84x/LXZxIWd1odwuRqkx9q1RXxUNyxJjRmpMs1V6FzFE+fvw4Tz/9NHl5ebz33nvcfvvtFfG0ImWikCyuQn2rVBcKyeKqyr18/5133mHo0KF06NCBjRs3qiMXp8u/cBGjlxdt4mcpJEu1pb5VqhNrfj7my5cVksXllHlE+fz58zz77LMcOXKEJUuW0Lt374qsS+SW2Ww2DAYD9Xv+lnp33oGbj4+zSxK5ZepbpTop6Hfda9fm9gUJmLy9FZLFpZQ5KPfr148LFy7Qp08f9uzZw549e0rcZ8aMW9uKS6SsshIP8NPqf9B65nTca9dWSJZqq6L71uPHjzN37lwuXbqE0Whk1qxZdOjQgVGjRpGamoqXlxcAd955JzNmzMBqtbJ48WK++uorLBYLAwcOZPz48RgMBnJzc5kzZw6JiYlYLBbGjBnDiBEjAMjMzGTatGmcOnUKi8VCXFwckZGRFfOmSJVkycnhyPyFBEdFUr9Hd/W74pLKHJTvvfde+2+NV65cqbCCRG5V4TnJeekZuNeu7eySRMqsIvvW3NxcHn30UWbOnEmfPn3YsWMHkyZNYsuWLRw8eJAdO3ZQu9j/lzVr1nDw4EE2bdqE1Wpl9OjRREREEB0dzdKlSzGbzXz22WdkZWUxYsQIWrVqRYcOHYiPj6dFixa89tprJCcnExMTQ5s2bQgNDS3XOUjVVHhOsm9EOPV7dHd2SSKVosxBecGCBRVZh0iZFF+45xsR7uySRMqlIvvWXbt2Ub9+ffr06QNAz549Wb58OYcPH8bDw4MpU6aQkpJC27ZteeaZZ6hXrx5bt25lyJAhePyyrdfQoUPZuHEj0dHRbN26lYSEBAwGA/Xq1SM6OpqNGzfStm1btm/fzmeffQZAw4YN6d69O5s2bWLs2LFFakpLS+PcuXMlak1KSqqw85bKVXThXj/CRz/i7JJEKk25dr3IysoiPT2dZs2aAfDBBx9w+PBhIiMj6dq1a4UUKHI92t1CXFVF9a0nTpwgKCiIGTNmcPjwYfz8/JgyZQoXLlzg7rvvZvbs2dSqVYsXXniBuLg43nzzTVJSUggJCbE/R0hICCkpKQClth08eJDMzExyc3Ov+7jC1q5dy7Jly275PZGqoXhIjhijy1KLayvzrhdHjhwhKiqKd999F4CVK1eSkJBAdnY2Tz/9NNu3b6+wIkWKs9lsJL+/QSFZXE5F9q1ms5ndu3czcOBANmzYwJgxYxg7diydOnXi5Zdfpl69eri5uTFhwgR27dpFdna2fXFWYUbjtR8V12uz2WwA131cYTExMWzYsKHEn8WLF9/0eYnzZB34ViFZapQyjyi/8sorjBo1iieffBKAVatW8eyzzxITE8Pu3btZunQp9913X4UVKlKYwWCg1fRnuZp2Dt/wJs4uR6TCVGTfGhwcTHh4uP1qfj179sTNzY133nmHDh06cM899wDXArDRaMTNzY0GDRqQmppqf47U1FT7POOCtoYNGxZpCwgIwNPTk7S0NIKDg+1tzZs3L1FTUFAQQUFBZXx3xNkC7u7C7QsSqNWqpUKy1AhlHlH+73//a79C1A8//EB6erp9G6POnTtz7NixiqlQpJDM/Ymc/WILAG4+PgrJ4nIqsm/t0aMHKSkpJCYmArBv3z7y8vIICwtj/vz5XL58GYAVK1YQGRmJh4cHUVFRbNiwgby8PHJzc/nggw/sc5yjoqJYu3YtVquVrKwsNm/eTJ8+fTCZTPTu3Zv33nsPgNOnT/P1119r1wsXYcnJ4cc3VmL+ZXFp7dtaKSRLjVHmEeW8vDz7tkL79++nYcOGBAYGXntSNzf9J5IKl7k/kaPzF2L09CCgW1fca9VydkkiFa4i+9bAwEBef/115s+fT3Z2NiaTiaVLl9KlSxdOnTrFsGHDsFqttGzZkrlz5wIwfPhwkpOTGTRoEPn5+URGRjJkyBAAxo8fz7x58+jfvz/5+fnExsba50zPnDmTWbNm0a9fP8xmM1OnTiU8PLwC3xlxhsJzkr1CQgjrH+3skkQcqsxBOSwsjO+//56WLVuyY8cOunXrZm/75z//af9qTqQi2EOyhwdt4mcrJIvLqui+9Y477mDdunUljk+cOJGJEyeWOG4ymYiLiyMuLq5Em7e3NwkJCaW+jr+/vxbpuZjiC/dC+z3g7JJEHK7MQTk2NpbHH3+cdu3asXPnTt5//30ANm7cyNKlSxk1alSFFSk1W5GQrIV74uLUt0pVoN0tRK4pc1B+6KGH8PPz48CBA7z55pu0atUKgL/+9a9ER0fzyCPaV1HKL//iRY4uWKyQLDWG+lapCk6+/a5Csgjl3Ed54MCBDBw4sMixHTt2lOcpRYpwr12b3zw1Hq+QEIVkqTHUt4qzNR4Zi1doKGED+iskS41WrqAMYLFY+OKLLzh58iRWq7VI24QJE8r79FJDXTh4CO+GDfGoW4fAe7r9+gNEXIz6VnE0S04OF48cpV6njrjXrk2DgQ86uyQRpyt3UJ4xYwZffvklt99+O+7u7hVRk9RwBXOS/X7TnLYJczWaITWS+lZxJPuc5CNHabfoBWr9puQe2CI1UbmD8vbt2/nHP/5hv9SqSHkUXrgXMWa0QrLUWOpbxVGKL9zza67PnEiBMl9wpICnpyeNGjWqiFqkhtPuFiL/o75VHEG7W4jcWLmD8mOPPcbs2bP58ccfycrKKvJH5GZdPHJUIVmkEPWtUtlsViuH572gkCxyA+WeerF48WLy8vLYuHGj/T+YzWbDYDBw5MiRchcoNYNPkybUad+OxrExCskiqG+VymcwGgm6twd+zZoSPvoRhWSRUpQ7KH/22WcVUYfUUFfT0/EMCMDNx5vWM6Y5uxyRKkN9q1QWS04ONosVNz9fgqMinV2OSJVW7qDcoEGDiqhDaqCCOckRj40m5P4+zi5HpEpR3yqVoWBOsjUvj7bz4jF5eTm7JJEqrdxzlEXKovDCPb/m2oZIRKSyFV64V6tVK4yens4uSaTKU1AWh8tKPKCFeyIiDqTdLUTKRkFZHCor8QBHEhYoJIuIOIhCskjZlXuOssitMF/JxuTtRevZMxWSRUQcwGo2Y8nJUUgWKQMFZXEIm9WKwWgk8J6u1OvUAZO3t7NLEhFxaQX9rnutWtw+/3mMXl4KySK3SFMvpNJl7k/kwJRnyfvlQgkKySIilcuSk8PBmXNI+2oHcK3fVUgWuXUKylKpChbuXU1NIy8j09nliIi4PPuc5IOHuHLypLPLEanWFJSl0pRYuNc0wtkliYi4tOIL98JHP+LskkSqNQVlqRTa3UJExLGKhuRoLdwTqQBVfjHftGnT+Ne//kWtWrUAaNKkCa+88gorV65k/fr1WCwWunfvzvTp03F3d8dqtbJ48WK++uorLBYLAwcOZPz48eosHMhms3F640cKySIiDnTh0GEuHj6i3S1EKlCVD8r79u3jtddeo0WLFvZjO3fu5P3332f9+vX4+PgwefJkVq1axdixY1mzZg0HDx5k06ZNWK1WRo8eTUREBNHR0U48i5rFYDDQamocuWnn8G3S2NnliIjUCP6d76Ddohfw+01zhWSRClKlp16cP3+eM2fO8Morr9C/f3+efPJJTp8+zdatW4mOjqZWrVqYTCZiY2PZuHEjAFu3bmXIkCF4eHjg5eXF0KFD7W1SuTL3J3Jm86fAtRXWCskiIpXLkpND0mtvkH/pEgC1WvxGIVmkAlXpEeW0tDS6d+/OtGnTCAsL480332TcuHEEBgbSsWNH+/1CQkJISUkBICUlhZCQkFLbij/3uXPnShxPSkqqhDNxfYUvS12/R3fca9d2dkkiIi6t8Jxkr9AQGgzo7+ySRFxOlQ7KrVu35rXXXrPffuyxx1i+fDlBQUElfmMuuG2z2Uq0GY0lB87Xrl3LsmXLKqHqmqf4wj2FZBGRylV84V7Yg/2cXZKIS6rSQXn//v2kpqbSt29f+zGbzUZ+fj6pqan2Y6mpqYSFhQHQoEGDEm2hoaElnjsmJoZevXqVOJ6UlERcXFxFnoZL0+4WIiKOVXJ3i9GabiFSSar0HOW8vDyef/550tLSAPi///s/mjVrxpgxY/jkk0+4ePEiVquVNWvW0KdPHwCioqLYsGEDeXl55Obm8sEHH9jbCgsKCqJNmzYl/jRr1syh51id5V+6xNEFixWSRUQc6OS7f1dIFnGQKj2i3KVLF5544glGjx6NxWIhLCyMv/71rzRo0IAff/yR2NhYzGYznTp14vHHHwdg+PDhJCcnM2jQIPLz84mMjGTIkCFOPhPX5F6rFr+Z9BSegYEKySIiDtJkZCzeoaGE9o9WSBapZFU6KAOMHDmSkSNHljg+evRoRo8eXeK4yWQiLi5O0ycq0YXvDuLdoAEe/vUI6HKXs8sREXF5lpwcLnx3EP+77sTNz09zkkUcpEpPvZCqJ3N/IofnJnB0wWJsNpuzyxERcXkFc5KPzF/Ipe+PObsckRpFQVluWub+RPsWcE3/9Ji+8hMRqWRFFu71i8avxW+cXZJIjaKgLDelcEjWwj0RkcpXdHcLXZZaxBkUlOVXXfr+mEKySDV1/PhxHn74YQYOHMjgwYNJTEws0r5161batm1b5Fjv3r3p168fAwYMYMCAAaxYsQKA3Nxcpk6dSt++fYmKimLNmjX2x2RmZjJu3DgeeOAB7r//frZt21bp5+bKbFYrRxIWKCSLOFmVX8wnzufTpDH17uhIw2FDFZJFqpHc3FweffRRZs6cSZ8+fdixYweTJk3iq6++wmAwkJSUxIIFC7BarfbHpKamkp2dzbZt20oEs6VLl2I2m/nss8/IyspixIgRtGrVig4dOhAfH0+LFi147bXXSE5OJiYmhjZt2pS6j738OoPRSFBkL3ybRhA++hGFZBEn0YiyXNfVXy7xbfLyotXUZxSSRaqZXbt2Ub9+ffte8j179mT58uXYbDYuX75MXFwczz33XJHH7Nu3Dx8fH0aPHk3//v1JSEggJycHuDb6HBMTg8FgoF69ekRHR7Nx40bMZjPbt29nxIgRADRs2JDu3buzadMmx56wC7Dk5JB/8RIAQff2JOJRjSSLOJOCspQqc38i/33iKVI++dTZpYhIGZ04cYKgoCBmzJjB4MGDeeSRR8jLy8NoNDJt2jQeeeQRWrRoUeQxV69epVu3bixfvpz169eTkpLCggULAEhJSSEkJMR+35CQEFJSUsjMzCQ3N7fUtuLS0tI4dOhQiT9JSUmV9C5UHwVzkg/Nisfyyy8nIuJcmnohJRReuFerVStnlyMiZWQ2m9m9ezerVq1i3rx57Ny5k7Fjx/Lwww8TGBjIgAEDSE5OLvKYQYMGMWjQIPvtcePGMXbsWOLj47HZbCVGN41Go32ryNLailu7di3Lli2rqFN0GcUX7hm9vJxdkoigoCzFaHcLEdcRHBxMeHg4nTt3Bq5NvXBzc2PZsmU0b96cAQMGkJ+fj8ViYcCAASxdupRvv/2Wxo0b065dOwBsNhtubtd+VDRo0IDU1FQaNmwIXJvPHBoaSkBAAJ6enqSlpREcHGxva968eYmaYmJi6NWrV4njSUlJNfZCUdrdQqTq0tQLsVNIFnEtPXr0ICUlxb7Txb59+8jLy+M///kPmzZt4qOPPmLFihWYTCY++ugjGjduzMmTJ1myZAl5eXmYzWZWrlxJdHQ0AFFRUaxduxar1UpWVhabN2+mT58+mEwmevfuzXvvvQfA6dOn+frrr4mMjCxRU1BQEG3atCnxp1mzZg57X6oShWSRqk0jymJnzcvH5OND61nPKSSLuIDAwEBef/115s+fT3Z2NiaTiaVLl+Ln53fdx4wdO5bz588zYMAAzGYzXbt25c9//jMA48ePZ968efTv35/8/HxiY2Pp2rUrADNnzmTWrFn069cPs9nM1KlTCQ8Pd8BZVm82qxVrXr5CskgVpaAs2CwWDCYTAV3upG772zFpbpyIy7jjjjtYt27dddsbNmzI4cOH7bc9PDyYM2dOqff19vYmISGh1DZ/f3/NPb4FBf2um68vbRPiMXp4KCSLVEGaelHDZe5PJHFyHFfT0wEUkkVEKpklJ4eDs+I5u2UrACZPT4VkkSpKQbkGK5iTnHc+nfwLF5xdjoiIy7PPST54iJyfk3/9ASLiVArKNVSJhXtNNSdZRKQyFV+4F/7oH5xdkoj8CgXlGigr8YB2txARcSDtbiFSPSko10BnNn+ikCwi4kAXjxzl4pGjCski1Yx2vaiBWsY9zdXUNHwaN3J2KSIiNUK9Th1pv3gBvs2aKiSLVCMaUa4hshIPcPrDj4FrK6wVkkVEKpclJ4cf/vaafbG0X/NmCski1YxGlGuArMQDHElYgNHDg/r39sSjbh1nlyQi4tIKz0n2Dg2lwaABzi5JRMpAI8ournBIbjN3tkKyiEglK7pwL5qwgQ86uyQRKSMFZRdWPCRr4Z6ISOUqHpIjxozWdAuRakxB2UWZL1/h6KK/XAvJ8bMUkkVEHOCn1f9QSBZxIZqj7KLc/Hxp+fQk3OvWVUgWEXGQxr8fgVdICKHRv1NIFnEBGlF2MVnffsfVc+cBqHdHJ4VkEZFKZsnJIX3PPwFw8/EhrN8DCskiLkJB2YVk7k/kyPPzObpwMTabzdnliIi4vII5yUcXLObikaPOLkdEKpiCsovI3J9ovyx1s8f/pNEMEZFKVvyy1LVatXR2SSJSwRSUXUDhkKzdLUREKl/xkKzLUou4JgXlau7S98cUkkVEHMhms3Fk/sJCu1soJIu4Ku16Uc35hDfBv8udNBg0UCFZRMQBDAYDIfdH4ds0gvA/jFJIFnFhCsrVVG5qKl7BwZg8PWk5ZbKzyxERcXmWnByseXm416lDYPd7COx+j7NLEpFKpqkX1VDm/kT2T/gzpz/82NmliIjUCAVzkg/OmI05O9vZ5YiIgygoVzOFF+7Vub2ts8sREXF5hRfu1WnfHpO3t7NLEhEHUVCuRrS7hYiIY2l3C5GaTUG5mlBIFhFxLIVkEdFivurCZsPk60vrmdMVkkVEHMBmA5vVqpAsUoMpKFdxNosFg8lEvU4dueP1VzF5ejq7JBERl1bQ77r5eNMmfhZGDw+FZJEaSlMvqrDM/YnsnziZ3LQ0AIVkEZFKZsnJ4eCseFI++RS41u8qJIvUXArKVVRW4gGOzl9IfmYW5suXnV2OiIjLs89JPniI3LOpzi5HRKoABeUqKCvxAEcSFvxv4V5TzUkWEalMxRfuhT/6B2eXJCJVgIJyFVMiJGvhnohIpdLuFiJyPQrKVczZz79QSBYRcaBLx45z8chRhWQRKUG7XlQxLSb/mdzUNHwaNXR2KSIiNULd9u1o/+IifCPCFZJFpAiNKFcBmfsT+Xn9BwAYPTwUkkVEKpklJ4fjS/9GXmYmAH5NIxSSRaQEBWUnK7ji3pkPPyYvI9PZ5YiIuLyCOclp274kbftOZ5cjIlWYgrITFWwBVzAn2cO/nrNLEhEXcvz4cR5++GEGDhzI4MGDSUxMLNK+detW2rZtW+TYypUr6du3L1FRUcTHx5Ofnw+A1Wpl4cKF3H///URGRrJs2TJsNhsAubm5TJ061f64NWvWOOT8yqL4wr0GgwY4uyQRqcIUlJ1Eu1uISGXKzc3l0Ucf5eGHH+bDDz/kqaeeYtKkSfZwm5SUxIIFC7BarfbH7Ny5k/fff5/169fz+eefk5GRwapVqwBYs2YNBw8eZNOmTWzevJndu3fz6afXLsqxdOlSzGYzn332GevWrWPVqlUlQnlVoN0tRORWuWRQ/vrrrxkwYAB9+/Zl9OjRpP1yZbuqwpydzfeLlygki0il2bVrF/Xr16dPnz4A9OzZk+XLl2Oz2bh8+TJxcXE899xzRR6zdetWoqOjqVWrFiaTidjYWDZu3GhvGzJkCB4eHnh5eTF06NAibTExMRgMBurVq0d0dLS9rSo5tWadQrKI3BKX2/UiIyODKVOm8O6779KyZUveffddpk2bxsqVK51dmp2bjw8t4ybjVquWQrKIVIoTJ04QFBTEjBkzOHz4MH5+fkyZMgWj0ci0adN45JFHaNGiRZHHpKSk0LFjR/vtkJAQUlJS7G0hISE33Xbw4MFS60pLS+PcuXMljiclJZX9ZG9S4xHD8QoOJuR39yski8hNcbmgvGvXLlq2bEnLli0BGDFiBIsWLeLcuXPUr1/fqbVlffsdXsHBeAUHUbdDe6fWIiKuzWw2s3v3blatWsW8efPYuXMnY8eO5eGHHyYwMJABAwaQnJxc4nHFA2TBbZvNVqLNaDT+altxa9euZdmyZWU+r1tlyckhc99/Cex+DyZvb0If6Ouw1xaR6s/lgvLZs2cJDQ213/bw8KBevXqkpKQUCcqOHtUomJPs3bAB7Zcs1miGiFSq4OBgwsPD6dy5M3Bt6oWbmxvLli2jefPmDBgwgPz8fCwWCwMGDGDp0qWEhYWRmppqf47U1FTCwsIAaNCgQYm2gr62oK1hw4Yl2oqLiYmhV69eJY4nJSURFxdXMSf/i8Jzkt3r1qVO2zYV+vwi4vpcLiiXNrIBJUc3HDmqUXjhXvMJTygki0il69GjB/PnzycxMZEOHTqwb98+8vLy+M9//oOfnx8AycnJ9OnTh48++giAqKgoFi1axMiRI/Hz82PNmjX2Oc5RUVFs2LCBvn37YrVa+eCDDxg0aJC9be3atXTs2JGLFy+yefNm5syZU2pdQUFBBAUFVfr5F1+4V7tN60p/TRFxPS4XlMPCwvjnP/9pv52Xl0dmZqZ9VKSAo0Y1iuxuET9Lc5JFxCECAwN5/fXXmT9/PtnZ2ZhMJpYuXWoPyaXp0aMHSUlJxMbGYjab6dSpE48//jgAw4cPJzk5mUGDBpGfn09kZCRDhgwBYPz48cybN4/+/fuTn59PbGwsXbt2dch5lqZoSI7Wwj0RKTOXC8r33HMP8+bN49ixY7Ro0YL169fTvn17/P39i9zPEaMal47/oC3gRMRp7rjjDtatW3fd9oYNG3L48OEix0aPHs3o0aNL3NdkMhEXF1fqQIK3tzcJCQnlL7gC2Gw2jsxfWCgkj1ZIFpEyc7mg7O/vz0svvcSzzz7L1atXCQgIYNGiRU6pxTe8CQHduhL2YD+FZBERBzAYDIRG/w7fphGE/2GUQrKIlIvLBWWAbt26OXUPz5yUFLxCQjC6u9Ni0lNOq0NEpKaw5ORgyc3Fo149Au7uQsDdXZxdkoi4AJe84IgzZSUeIPGpyZz+oOptti8i4ooK5iR/N30W5stXnF2OiLgQBeUKVHjhXt2OHZxdjoiIyyu8cK/eHR0x+fo4uyQRcSEKyhWkyO4WWrgnIlLpSu5uoYV7IlKxFJQrgEKyiIhjKSSLiCO45GI+hzMacavlx23PTVNIFhFxBIMBg9GokCwilUpBuRxsFgsGk4m67W7njtdexejh4eySRERcWkG/a/LyovXsGRjc3BSSRaTSaOpFGWXuT+S/E/5MTspZAIVkEZFKZsnJ4eDMOZzeeO2S20Z3d4VkEalUCsplkLk/kaPzF2K+eBFLdrazyxERcXmF5yRfTc/AZrM5uyQRqQEUlG9RVuIBjs5fqIV7IiIOUnThXj8ixvxBI8ki4hAKyregyO4W8bMUkkVEKplCsog4kxbz3YLUbV9qJFlExIEuJ/3Ipe+PKSSLiFMoKN+C30x8ktzUNHwaNnB2KSIiNUKdtm1ov2QxPo0bKSSLiMNp6sWvyNyfyKn31mCz2TC6uyski4hUMktODsf/upSr6ekA+DZprJAsIk6hoHwDBQv3Uj75jLyMDGeXIyLi8qxXr3L4+fmkfbWDc9t3OrscEanhNPXiOi59f4wj/1hnn5PsGRDg7JJERFzej2+8ReCZFEL796PBkEHOLkdEajiNKF/HiZVva+GeiIiDXfnxRy3cE5EqQyPKxVy9ehWAs2YzzUeN5KfcHDh0yMlViUhVlpSUBPyv/5BbV/De5bRpTfbdd3L48GEnVyQiVZmj+l0F5WKSk5MBeO3kD7z21JNOrkZEqpPk5GQ6derk7DKqpYK+d9Enm+CTTU6uRkSqi8rudw02XQe0iIyMDHbt2kXDhg3x9PSs0OdOSkoiLi6OxYsX06xZswp9bmdz5XMDnV91V9nnd/XqVZKTk+nevTv+/v4V/vw1QWX2vVB9PuPVoc7qUCOozopUFWt0VL+rEeVi/P39efDBByv1NZo1a0abNm0q9TWcxZXPDXR+1V1lnp9GksvHEX0vVJ/PeHWoszrUCKqzIlW1Gh3R72oxn4iIiIhIKRSURURERERKoaAsIiIiIlIKBWURERERkVIoKDtQ/fr1mTBhAvXr13d2KRXOlc8NdH7Vnaufn/y66vIZqA51VocaQXVWpOpQY2XR9nAiIiIiIqXQiLKIiIiISCkUlEVERERESqELjjjI119/zV/+8heuXr1KaGgoCxcuJCgoyNll3ZJp06bxr3/9i1q1agHQpEkTXnnlFVauXMn69euxWCx0796d6dOn4+7ujtVqZfHixXz11VdYLBYGDhzI+PHjMRgMTj6Tol5++WXOnTtHQkICAB999BErVqzAbDZz2223MW/ePPz8/ACq3bkWP7dRo0aRmpqKl5cXAHfeeSczZsy4Yf25ubnMmTOHxMRELBYLY8aMYcSIEc48Lf7xj3/w3nvvYTAY8Pb25rnnnqNdu3Zl+vepiucn13f8+HHmzp3LpUuXMBqNzJo1iw4dOtjbt27dyqRJkzh48KD9mKM/F2WpsXfv3nh7e2MymQCIjo5m7NixN6wjMzOTadOmcerUKSwWC3FxcURGRpb7vazofqI8dd5qjXl5edx11100adLE/hyjR49m4MCBN6zj559/Zvr06WRkZGA0Gpk7dy4dO3Ys93u5d+9eFi1aRG5uLr6+vsyfP5+IiAjA8Z/LstbpjM9mlWKTSpeenm676667bEePHrXZbDbbO++8Y3v00UedXNWti4qKsn3//fdFju3YscPWt29f28WLF21ms9n21FNP2V5//XWbzWazrV692vbQQw/Zrl69asvJybGNGDHCtnnzZmeUXqqff/7Z9sQTT9jatWtnmz59us1ms9mOHTtm69q1q+3s2bM2m81me+GFF2wzZ8602WzV61xLO7e8vDxbx44dbRcuXChx/xvVv2jRItvTTz9ts1qttoyMDFufPn1s+/fvd+TpFLFv3z7bvffea0tPT7fZbDbbV199ZbvnnnvK/O9T1c5Pri8nJ8fWvXt32xdffGGz2Wy27du32+69916b1Wq12Ww22w8//GDr1auX7bbbbrM/xtGfi7LUePbsWdvdd99tv09hN6pj4sSJthdffNFms137P9+tWzfbmTNnyvVeVkY/UdY6y1Lj3r17bYMGDSr1+W5Ux9ChQ21r1qyx2Ww223fffWe75557bFeuXPnVGm9U59mzZ2133nmnLTEx0Waz2Wx///vfbb///e9tNpvjP5dlrdMZn82qRlMvHGDXrl20bNmSli1bAjBixAj+9a9/ce7cOSdXdvPOnz/PmTNneOWVV+jfvz9PPvkkp0+fZuvWrURHR1OrVi1MJhOxsbFs3LgRuDZqMmTIEDw8PPDy8mLo0KH2tqpg7dq1dOvWjdGjR9uPbdu2jZ49exIcHAzAyJEj2bRpE1artVqda2nndvjwYTw8PJgyZQr9+/dn2rRpZGZmAjeuf+vWrcTExGAwGKhXrx7R0dFOPbc6derw/PPP4+/vD0C7du1IT08v879PVTs/ub5du3ZRv359+vTpA0DPnj1Zvnw5NpuNy5cvExcXx3PPPVfkMY7+XJSlxn379uHj48Po0aPp378/CQkJ5OTk3LAOs9nM9u3b7SN4DRs2pHv37mzatKlc72VF9xPlqbMsNe7bt4/8/Hweeugh+vfvz7Jly7BYLDesIzU1laNHjzJ48GAA2rZtS3h4ODt27CjXe/n5559z99130759ewCGDRtGfHy8/f1ydH9Vljqd8dmsahSUHeDs2bOEhobab3t4eFCvXj1SUlKcWNWtSUtLo3v37kybNo2PP/6Ydu3aMW7cOE6fPl3k3EJCQuznlZKSQkhISKltVcHTTz/NyJEj7V8nwbWai59PdnY2WVlZpbZV1XMt7dwuXLjA3XffzcKFC9m4cSM+Pj7ExcUBN66/qp1bs2bN6N69OwBWq5X58+dz7733lvnfp6qdn1zfiRMnCAoKYsaMGQwePJhHHnmEvLw8jEYj06ZN45FHHqFFixZFHuPoz0VZarx69SrdunVj+fLlrF+/npSUFBYsWHDDOjIzM8nNzS3zZ/d6dVZ0P1GeOstSI1wLgKtWreLvf/87u3btYuXKlTesIyUlhcDAQNzd3e1twcHBnDlzplzv5YkTJ/D19WXy5MkMGjSIJ5980t4nO6O/KkudzvhsVjUKyg5gs9lKnatqNFaft79169a89tprNGjQAIPBwGOPPcbp06cxGo0lzq3gdmnnXR3OubR/q4Jj1flce/Towcsvv0y9evVwc3NjwoQJ7Nq1i+zs7BvWX1XP7fLly0yYMIHTp0+zcOFCoGz/PlX1/KQks9nM7t27GThwIBs2bGDMmDGMHTuWV199lcDAQAYMGFDq4xz5uShLjYMGDeL555/H29sbLy8vxo0bx9atW29Yh+2XnV3L+tm9Xp2dOnWq0H6iPHWWpcaxY8cyZcoU3N3dqVOnDqNHj2br1q03rKO8P6OvV+fVq1fZvn0748ePZ+PGjdx3332MGzfO/jhH91dlqdMZn82qpnpWXc2EhYWRmppqv52Xl0dmZiZhYWFOrOrW7N+/n88//7zIMZvNRn5+fpFzS01NtZ9XgwYNSrQV/g26Kir+b5Wamoqvry916tQpta06nevOnTvZvXu3/bbNZsNoNOLm5nbD+qviuZ04cYKhQ4fi5+fHO++8Q+3atcv871MVz09KFxwcTHh4OJ07dwaujRy6ubmxbNky9u7dy4ABAxg7diwWi4UBAwZw6tQph38uylLj5s2b+fbbb+3PYbPZcHNzu2EdAQEBeHp6kpaWdss13qjOd955p0L7ifLUWZYa33vvPX766acibW5ubjesIywsjPPnz2M2m+1taWlp5X4v09PT6dChA82aNQNg8ODBnDx5koyMDKf0V2Wp0xmfzapGQdkB7rnnHg4fPsyxY8cAWL9+Pe3bt7fPsawO8vLyeP755+0f/P/7v/+jWbNmjBkzhk8++YSLFy9itVpZs2aNff5TVFQUGzZsIC8vj9zcXD744AN7W1XVu3dvdu7caf/Pv3r1aiIjIzEajURFRVXrc83IyGD+/PlcvnwZgBUrVhAZGYmHh8cN64+KimLt2rVYrVaysrLYvHmzU8/tzJkzjBw5kmHDhrFo0SI8PT3tdZbl36eqnZ9cX48ePUhJSSExMRG4Nn8yLy+P//znP2zatMm+Y43JZOKjjz6icePGDv9clKXGkydPsmTJEvLy8jCbzaxcuZLo6Ogb1mEymejduzfvvfceAKdPn+brr7++6Z0FrldnWFhYhfYT5amzLDV+9913LF++HKvVSnZ2Nu+++y79+vW7YR3BwcG0atXKPtf38OHDHD9+nG7dupXrvZw8eTL79+/n5MmTAGzZsoXGjRtTt25dp/RXZanTGZ/NqkZX5nOQb775hsWLF3P16lUCAgJYsGABDRo0cHZZt2T16tW89957WCwWwsLCeP7552nQoAGrVq3i/fffx2w206lTJ+Lj4/Hw8MBisbBkyRJ27NhBfn4+kZGRxMXFOX3LtOKWLl3K2bNn7Vuobdq0iRUrVpCfn09ERAQLFiygTp06ANXuXIuf21//+lc+//xzrFYrLVu2ZO7cudStW/eG9efk5DBv3jwSExPJz88nNja2yCJBR3v++edZu3atffSjwIoVK/j0009v+d+nqp2f3Ni+fftYuHAh2dnZmEwmpk+fTpcuXeztycnJ9OnTh8OHD9uPleX/bXk+F7daY15eHvPnz+df//oXZrOZrl278txzz+Hp6XnDOjIyMpg1axYnT57EbDYzfvx4+vfvX+73sqL7ifLUeas1Xrhwgfj4eL7//nvy8vJ44IEHmDhxIkaj8YZ1/Pzzz8ycOZP09HTg2naoNxuUb1Tntm3bWLp0KWazGT8/P+Lj42nVqhXg+M9lWep01mezKlFQFhEREREphaZeiIiIiIiUQkFZRERERKQUCsoiIiIiIqVQUBYRERERKYWCsoiIiIhIKRSUpVr5+9//zgMPPEDHjh256667GDduXJFtoKZOnUqXLl2KbHQO8Pnnn9OrVy/77ZYtW9K+fXs6duxIx44d6dSpEyNHjmTv3r23XNPDDz/MypUrr9v+4YcfEhUVRadOnRg8eDB79uwpcR+r1cpDDz3E3LlzS32O9evX07Fjx1Lbtm7dWm233RGRqk/9bknqd2sOBWWpNj788ENWrlzJiy++yH//+1927NhB69atGTVqFBkZGfb7ZWVlMXXqVH5t58O///3v7N+/n/3797N3716ioqIYO3YsFy5cqLCav//+e+bNm8err77Kvn37iI2N5YknnuDq1atF7rd8+XL27dtX6nP8+OOPvPDCCyWOWywW3n33XSZNmvSr5yoiUhbqd4tSv1vzKChLlZGcnMztt9/Ou+++S48ePejSpQtTp04lPz8fgMTERG6//XZuu+02DAYDPj4+PPnkkzzwwANFOuz777+fw4cP884779z0axuNRoYPH86VK1c4deoU+fn5zJw5k65du9KtWzfGjBljv2pRaX744QeGDRtGx44deeyxxzh79iwAJ0+exGKxYLFY7JdZ9fT0LNLB7t+/n08++YSoqKgSz5uXl8eUKVMYOXJkibYpU6bw1Vdf8dhjj930eYqIFKZ+V/2u3JiCslQpeXl5HDp0iC+++ILVq1fz5ZdfsmXLFuBaR/zVV1/xxBNPsG7dOo4fPw7A3Llzad68uf05AgMDmTdvHkuWLOH777+/qde9fPkyb7zxBvXr16d58+Z8+OGHfPfdd2zdupWdO3cSFBTESy+9dN3H79ixg/j4ePbs2UNAQACTJ08GoHv37rRs2ZKBAwfStm1b5s6dy4svvoiXl5f9dadOncqCBQvw8fEp8bwvvvginTp1onv37iXapk6dyttvv03jxo1v6hxFREqjfrco9btSmIKyVDl//OMf8fb2pnnz5rRr144TJ04A0LVrV9avX09gYCCvv/46/fr1o3v37rz55pslniMyMpIBAwYwZcoU8vLySn2dUaNG0blzZzp37kxkZCSJiYksX74cb29vPD09OX36NB988AFnzpwhISGBv/71r9etOTY2ltatW+Pl5cWUKVPYt28fKSkp9stgr169msTERGbPns2f//xnfv75ZwBmz57NgAEDaNeuXYnn/Prrr/nmm2+Ii4sr9TWDg4N/9b0UEbkZ6nevUb8rxbk5uwCR4vz9/e1/d3NzK/J12W233WZfeJGWlsaXX37JokWLqFOnDsOGDSvyPNOmTWPQoEEsXryYO+64o8TrvPvuu9x+++2l1vDggw+Sk5PDxo0bWbRoEQ0bNuSZZ56hd+/epd4/LCzM/vf69evj5ubGuXPn+Oijj6hbty6dO3cGYPDgwWzatIkPP/yQRo0acebMGRYtWlTi+dLT05k9ezZ/+9vf8PT0vN5bJSJSIdTvqt+V0ikoS7XRs2dPnnrqKYYMGQJAUFAQsbGxHDhwoMgK7AI+Pj4sXryY3//+9+Tm5t7Sa508eZLOnTsTExPD5cuXee+99/jzn//Mvn378PDwKHH/wqu9z549i9lspkGDBpw5c4YGDRoUua+7uzsmk4lNmzZx9OhRunTpAmCv8T//+Q+PPfYY6enpPPTQQ8C1BSTZ2dl07tyZ1157zf4DQESkMqnfVb9b02nqhVQb0dHRLFu2jF27dmE2m8nNzWX37t3s2LHjuiMO7dq1Y/z48axbt+6WXuurr75i4sSJpKam4uvrS61atfDz88PNrfTfLf/xj39w7Ngxrly5wgsvvMB9991HQEAAvXr1YuPGjezduxer1crWrVv5z3/+w/3338/KlSvtK7/37t1Lv379GD58OJs2bWLAgAEcOHDA3vbaa6/h4+PD3r171VmLiMOo31W/W9NpRFmqjSlTplC/fn0WLVpEcnIyNpuNFi1aMG/evFIXXRQYO3YsX3/9tX1F9M0YNWoUycnJDB48mOzsbJo1a8arr76K0Vj675aRkZFMnDiR8+fP0717d/u2QsOGDePKlStMnz6d9PR0mjRpwquvvkqzZs1u7eRFRJxA/a7UdAabNgIUERERESlBUy9EREREREqhoCwiIiIiUgoFZRERERGRUigoi4iIiIiUQkFZRERERKQUCsoiIiIiIqVQUBYRERERKYWCsoiIiIhIKRSURURERERKoaAsIiIiIlIKBWURERERkVIoKIuIiIiIlEJBWWqEf/7znzz22GN06dKFO++8kxEjRrB9+3YApk6dyty5c8v0vH/6059YunTpTd1369at9OvXj44dOxIVFcV7771XptcUEakOqkK/WyA7O5u+ffuycuXKMr2m1FwKyuLyPvzwQyZNmkRMTAxff/0133zzDSNHjmTSpEls3brVITX89NNPPP3008yYMYP9+/fzyiuvsGDBAr799luHvL6IiCNVhX63sHnz5vHTTz85/HWl+nNzdgEilSknJ4fnn3+eBQsWEBUVZT/ev39/srKyOHHiRJH7X7hwgXnz5rFv3z7S09MJCQkhLi6OyMhIAPbs2cP8+fP5+eef+e1vf8vly5ftj7VYLKxcuZJ169Zx6dIlOnXqxOzZswkJCaFJkyZ88803+Pn5YbVayczMxGQy4evr65g3QkTEQapKv1vgs88+4+TJk3Tq1KmSz1xckUaUxaXt37+f/Px87r333hJtDz/8MGPHji1y7MUXXyQ7O5vNmzezb98+oqOjef755wHIyMhg/PjxjBo1ir1793L//fezd+9e+2PfffddNm7cyMqVK/l//+//ERERwRNPPIHNZgPAz8+PrKws2rZty+jRo3nooYdo1qxZ5Z28iIgTVKV+98yZMyxevJhFixZhNCryyK3TiLK4tIyMDOrUqYO7u/tN3X/ixIm4u7vj4eFBSkoKvr6+pKamArB9+3aCg4MZNmwYAP369WP16tX2x65bt45x48bRpEkTACZPnsydd97Jd999R7t27QCoVasWiYmJHD16lD/+8Y9EREQwePDgijxlERGnqir9bps2bYiLi2PixIk0bNiwgs9SagoFZXFp9evXJysri/z8/BKd9pUrV0qMMKSlpZGQkMCxY8cIDw8nJCTEPjJx7ty5Il/nAUU63zNnzjBnzhz7SAiA1Wrl9OnT9qBsMpkwmUy0a9eOoUOHsmXLFgVlEXEpVaXf/X//7/8RHBzMgAEDKvoUpQZRUBaX1rFjR7y8vPjyyy/p27dvkbYVK1bw5Zdf0rZtW/uxyZMnM2DAAN59912MRiO7du3iiy++ACA4OJgzZ84UeY7U1FQaN25sb58+fXqRrxuTkpJo2LAh27dvZ+XKlfz973+3t+Xl5VG7du2KPmUREaeqKv3uX//6V9LS0ujcuTNwbeeLAwcO8O9//5vXX3+9Mk5dXJAm7IhL8/Dw4JlnnmHOnDls2bKF/Px8cnNzWb9+PW+99RZPPfVUkftfvHgRT09PjEYjycnJLFu2DLgWau+77z6ysrJ45513MJvNbNu2jX//+9/2xw4ZMoRly5Zx+vRprFYrq1evZtCgQWRlZXH77bdz7Ngx1qxZg9Vq5T//+Q8bNmxg6NChDn0/REQqW1Xpdz///HP++9//snfvXvbu3csdd9zBxIkTFZLllmhEWVzesGHDqF27Nm+99RYzZszAarXSokULli1bRs+ePfnqq6/s901ISGD+/Pm88sor1K9fnxEjRnDo0CGOHz9OmzZtWLFiBfHx8SxZsoSOHTvSs2dP+2PHjBmD2Wxm1KhRZGZmEhERweuvv05wcDAAr7/+Oi+88AKLFy8mLCyMBQsWcNdddzn8/RARqWxVpd8VKS+DrWAikIiIiIiI2GnqhYiIiIhIKRSURURERERKoaAsIiIiIlIKBWURERERkVJo14tiMjIy2LVrFw0bNsTT09PZ5YhINXD16lWSk5Pp3r07/v7+zi6nWlLfKyK3wlH9roJyMbt27SIuLs7ZZYhINbR48WIefPBBp73+yy+/zLlz50hISGDZsmVs3brV3paVlUVmZib//Oc/MRgMzJ49m0OHDmG1Wvntb3/Ls88+i8lkIjMzk2nTpnHq1CksFgtxcXFERkYC8PPPPzN9+nQyMjIwGo3MnTuXjh07AnDw4EHi4+O5cuUKvr6+LFy4kKZNm9507ep7RaQsKrvfVVAupuDSmIsXL6ZZs2ZOrkZEqrq8rAt887flvLL330UuretIycnJvPDCC+zatYt+/foBMGHCBCZMmADA5cuXiYmJYerUqfj4+PDSSy9hMBjYvHkzZrOZ3//+93z00UcMHjyY+Ph4WrRowWuvvUZycjIxMTG0adOG0NBQJk+ezNChQ4mJieHgwYOMGzeOLVu24ObmxoQJE5g3bx7du3fnyy+/ZMKECXzyyScYDIabOgf1vSJys2w2G/9+5/9Y+PGHld7vKigXU/CVX7NmzWjTpo2TqxGRqir/wgXc69QBwL1ObV4ZMsRpUwbWrl1Lt27d+M1vfsO5c+dKtC9ZsoT27dvzu9/9DoBJkyaRn5+PwWAgKyuLy5cvU7duXcxmM9u3b+ezzz4DroXX7t27s2nTJgYMGMDRo0cZPHgwAG3btiU8PJwdO3YQHByMyWSie/fuAPTu3Zt58+bx7bff0r59+yK1pKWllVpjcnIyoL5XRK7PfPkKRk8PjO7u8MjD8PGHld7vKiiLiNyi89/s4fjLS2n17BTq3dHppkdNK8vTTz8NwNKlS0u0nTx5kk2bNvHFF18UOe7u7s68efN4//33adu2LV27diUzM5Pc3FxCQkLs9wsJCSElJYWUlBQCAwNxd3e3twUHB3PmzBlsNhuhoaFFnr+grXhQXrt2rf0SxSIiNyv/0iUOzZ6LZ2AArZ6Nc1i/6/BdLw4cOEDbtm05e/YsAHv37mX48OE8+OCDxMbGcuLECQDy8/OZO3cu0dHR9OvXj/Hjx3P+/HkAzpw5w+23386AAQPsf7755hvg2hy6hx9+mOjoaPr378/+/fsdfYoi4sLOf7OHY395CZOPN57V4DK577zzDsOHDy91scuMGTP497//TXBwMHPmzKHgQq3FfwAZjUZsNlupP5h+ra24mJgYNmzYUOLP4sWLy3qKIuLiCkLylaQf8W3aFIPJ5LDXduiIcnp6OrNnzyY/Px+A1NRUnnjiCd544w3at2/P6tWrmTFjBqtXr2blypWcOXOGDz/8EHd3dxYuXMi8efN4+eWX2bt3L/fddx+vvPJKide43hw6Hx8fR56qiLiggpDsVrsWbefNxadhA2eXdENWq5XPPvuM9957r8jxPXv20KhRIxo2bIiHhwcPPvggf/nLXwgICMDT05O0tDSCf/klIDU1lebNmxMWFsb58+cxm824uV370ZGWlkZkZCTBwcGkpqYWeY20tLQSo8wAQUFBBAUFVdIZi4irKRySG8XG0HjEcIe+vsNGlM1mM5MnTy6yqvnzzz/n7rvvtn81N2zYMOLj4wFo3749kydPtn/Nd/vtt3P69GkA9u3bR2pqKiNGjGDgwIGsXr0auNahX28OnYhIeZz/Zg/fL15SbUIywLFjx/Dw8Cix+8SXX37JkiVLsFqtmM1mNm3aRJcuXTCZTPTu3dserE+fPs3XX39tD8OtWrVi48aNABw+fJjjx4/TrVs32rVrx9WrV+3f7O3YsQODwaC5xjfBZrNx6Md0tv37FId+TLeP6ouI80MyOHBEedGiRXTp0oV77rnHfuzEiRP4+voyefJkTpw4QVBQEFOnTgWga9eu9vtlZWXx6quvMmzYMODagrv777+f0aNHc/bsWf7whz9Qr149wsLCrjuHrrjrLShJSkqqsHMWEddxNe0c7nXq0HZefLUIyXBtfnKDBiVrnTx5MnPnzqV///4YDAY6d+5sn+c8c+ZMZs2aRb9+/TCbzUydOpXw8HAAXnzxRWbOnMm7774LXFskWOeXBY3Lly8nPj6ehIQEvL29Wbp0KSYHfj1aHaVlZDNrxR5SM7JxMxkwW2wE+/swd2xXgvz1LaiIJTub/KwLTgvJ4KCgvHnzZk6dOsW0adOKHC9YYb169WqaNWvGmjVrGDduXJFFJz/++CMTJkzgrrvu4pFHHgFg+vTp9vbQ0FBiYmLYsmULjzzyyE3Pk9OCEhG5GQXzbxsMfJDgyF64+fk5u6TrevLJJ4vc7tu3L3379i1xPx8fHxYsWFDqc/j7+1+3b2zUqBFvv/12qW2tW7dm7dq1t1awi7PZbBw+kUHK+SuEBvrSOsLf/jPKZrMxa8UezqZfwWK1YbZce8zZ9CvMfmMPf3uml9MXiYo4S0G/6xUcTMdXlji133VIUP7ggw9ITU1l4MCB9mNjxowhMDCQDh062PfMHDx4MLNnzyYjIwN/f3+2b9/O1KlTGT9+PKNGjQKuvXkrVqxg2LBh9sUpNpsNNze3G86hKy4mJoZevXqVOJ6UlKRN70UEuDbdIvWLrbSa/iwmT88qHZKlavm10eLDJzJIzcjGYi061cJitXE2PZvDJzJo0zTASdWLOE/+pUscnb+QRrEx1G13u9P7XYcE5VWrVhW53bJlS1auXElWVhaPPPIIJ0+eJDw8nC1bttC4cWPq1q3Ljh07eOaZZ1i2bBldunSxP9ZgMLBr1y5yc3OZOHEiGRkZrFu3jmnTphWZQzds2LAic+iK04ISEbmRgjnJ7nVqk5eRiXdoyK8/SISbGy1OOX/llwBd8vFuJgMp568oKEuNU3hO8uVjx6nb7nZnl+TcfZRbtWpFQkICEydOxGw24+fnx9KlSzEajbz44osYDAbmz59vv39QUBBvvPEGCxcuZPbs2fTr14/8/HxGjhzJvffeC9x4Dp2IyM0oHJLbzpurkCy35GZGi0MDfTFbSl+4Z7bYCA30dUSpIlVG8YV7DYcOdnZJgJOC8vfff2//e2RkZKlTIzZt2nTdx4eFhfHGG2+U2najOXQiIr+maEiuPgv3pOq4mdHi3nc2Itjfxz7qXMBkNBAS4EPriJL7Xou4qqqwu8X1OPyCIyIiVZXNYuHnf6wtFJIbOrskqYZuZrTYYDAwd2xXQgJ8cTMZ8fIw4WYyEhroS/zYrlrIJzXK+V27q2RIBl3CWkTEzmAy0XrOLCw5ORpJljJrHeF/U6PFQf4+LH+213V3xhCpKUL63o9Pw4bUub2ts0spQSPKIlLjnf9mD+n/+jcAngH+CslSLrcyWmwwGGjTNIDIuxrTpmmAQrLUGPmXLnHy7Xex5udjMBiqZEgGjSiLSA1XMCfZM8Cfeh07YPTwcHZJ4gI0WixyfYXnJPs2bUr9Ht2dXdJ1KSiLSI1VeOFe6zkzFZKlQhWMFmubN5H/Kb5wryqHZNDUCxGpoUrubqGFeyIilakq725xPQrKIlLj5KSkcOwvLykki4g40A9L/1atQjJo6oWI1EDeoaFE/HEMdW5vo5AsIuIgEWP+QJ3b2xDWv5+zS7lpGlGuwmw2G4d+TGfbv09x6Md0bLbS9+UUkZuT9e13WHJzAQj93f0KySIilSz/0iUuHj4CgFdwcLUKyaAR5SorLSObWSv2kJqR/csVnmwE+/swd2xXgvx9nF2eSLVTMCc5sFtXWsZNdnY5IiIuL//SJQ7Niicn+TQdl76EV0iIs0u6ZRpRroJsNhuzVuzhbPoVzBYruXkWzBYrZ9OvMPuNPRpZFrlFhRfuNYqNcXY5IiIuryAkX/nxBA2GDKqWIRkUlKukwycySM3ILnJFJwCL1cbZ9GwOn8hwUmUi1U/R3S3m6mIi4jCaPic1VeGQXJ0W7pVGUy+qoJTzV36ZblGyzc1kIOX8Fe3LKXITMvbuK7YFnEKyOIamz0lNZbl61WVCMmhEuUoKDfTFbCl95MFssREa6OvgikSqJ79mTanTto22gBOH0vQ5qclMnp74d7nLJUIyaES5Smod4U+wvw9n068UmX5hMhoICfChdYS/E6sTqfryMjPxqFcPj3r1aPv8HGeXIzXMzUyf07eC4mrMly9j9PDA6OHhEgG5gEaUqyCDwcDcsV0JCfDFzWTEy8OEm8lIaKAv8WO7YjAYnF2iSJV1/ps97Bv7BOn/+rezS5EaqmD6XGkKps+JuJL8S5c4OHMORxcswmYpZd5oNaYR5SoqyN+H5c/24vCJDFLOXyE00JfWEf4KySI3UHjhnncDzUcW59D0OalJCi/c84+NwWAyObukCqWgXIUZDAbaNA3QV3QiN0G7W0hVoelzUlO40u4W16OpFyJS7RUNydrdQpxL0+ekJqgJIRk0oiwiLiA/MxP3OnVoO2+OdreQKkHT58TVWXNzMV++4tIhGRSURaQas9lsGAwGQqMfoP69PXHz1dxPqTo0fU5cUUG/61m/Ph1e/ovL97sOn3px4MAB2rZty9mzZwHYu3cvw4cP58EHHyQ2NpYTJ07Y77ty5Ur69u1LVFQU8fHx5OfnA2C1Wlm4cCH3338/kZGRLFu2zL4vZW5uLlOnTrU/bs2aNY4+RRFxgPPf7OHQzDmYs3MAXL6zFhFxtvxLl/hu6nNk/nc/UDP6XYcG5fT0dGbPnm0PvKmpqTzxxBM899xzfPzxx/Tr148ZM2YAsHPnTt5//33Wr1/P559/TkZGBqtWrQJgzZo1HDx4kE2bNrF582Z2797Np59+CsDSpUsxm8189tlnrFu3jlWrVpGYmOjI0xSRSnb+mz0c+8tLZCcnk38hy9nliIi4vPxLlzg0ey6Xjn7P5aQfnV2OwzgsKJvNZiZPnkxcXJz92Oeff87dd99N+/btARg2bBjx8fEAbN26lejoaGrVqoXJZCI2NpaNGzfa24YMGYKHhwdeXl4MHTq0SFtMTAwGg4F69eoRHR1tbxOR6q8gJLvVrkXbeXPxDg11dkkiIi6tICRfSfqRRrExNBo2xNklOYzD5igvWrSILl26cM8999iPnThxAl9fXyZPnsyJEycICgpi6tSpAKSkpNCxY0f7fUNCQkhJSbG3hYSE3HTbwYMHS9STlpbGuXPnShxPSkoq55mKSGUpHpK1u4WISOUqHpJdeeFeaRwSlDdv3sypU6eYNm1akeNms5nt27ezevVqmjVrxpo1axg3bhxffPEFQInVwQW3CyaSF2Y0Gn+1rbC1a9eybNmy8p2YiDiMzWIhed37CskiIg6U8c9/1diQDA4Kyh988AGpqakMHDjQfmzMmDEEBgbSoUMHmjVrBsDgwYOZPXs2GRkZhIWFkZqaar9/amoqYWFhADRo0KBEW+gvX78WtDX8ZYuowm2FxcTE0KtXrxLHk5KSikwPqYpsNpu2HJIax2Ay0XrOLCxXruDdIMzZ5YiI1AjBUZF4hYVRp01rZ5fiFA4JygWL8Aq0bNmSlStXkpWVxSOPPMLJkycJDw9ny5YtNG7cmLp16xIVFcWiRYsYOXIkfn5+rFmzhj59+gAQFRXFhg0b6Nu3L1arlQ8++IBBgwbZ29auXUvHjh25ePEimzdvZs6cOSVqCgoKIigoqNLPvaKlZWQza8UeUjOycTMZMFtsBPv7MHdsV4L8fZxdnkiFO//NHrBaCex+Dx5160DdOs4uSUTEpeVfusTPa98nfNRIjB4eNTYkg5P3UW7VqhUJCQlMnDgRs9mMn58fS5cuxWg00qNHD5KSkoiNjcVsNtOpUycef/xxAIYPH05ycjKDBg0iPz+fyMhIhgy5NrF8/PjxzJs3j/79+5Ofn09sbCxdu3Z15mlWGJvNxqwVe+yXRTVbrh0/m36F2W/s4W/P9NLIsriUgivuefj7U+/Ozpg8PZ1dkgigb/bEdRWek+zXvBlB9/ZwdklO5ZSg/P3339v/HhkZSWRkZKn3Gz16NKNHjy5x3GQyERcXV+oUCW9vbxISEiqu2Crk8IkMUjOysVhtRY5brDbOpmdz+ESGNrYXl1H4stRt4mcpJEuVoW/2xFUVX7hX00MyOOGCIzWdzWbj0I/pbPv3KQ79mG6/UMrNSDl/BTdT6SMWbiYDKeevVFSZIk5VOCRr4Z5UJYW/2TNbrOTmWTBbrPZv9m6lTxepSmr67hbXo0tYO1B5RyFCA30xW0rvhM0WG6GBrn+FHHF9uampHHvx5V9CcrxCslQp+mZPXFXS315XSC6FRpQdpCJGIVpH+BPs74PJWHRU2WQ0EBLgQ+sI/8oqX8RhvIKDafb42F9CckNnlyNShL7ZE1cV8egfiPjjGIXkYhSUHeRmRiF+jcFgYO7YroQE+OJmMuLlYcLNZCQ00Jf4sV21kESqtazEA5izswEIjuytkCxVkr7ZE1eSf+kSF767dlE2z/qBhPV7wMkVVT2aeuEgBaMQBTtVFFYwCnEzX9cF+fuw/NleWm0tLqVgTnJAlztpNfUZZ5cjcl0F3+wV7D5UQN/sSXVTMCc5+6dTdFz2Mt6lXHNCFJQdpiJHIQwGA22aBmgenLiEwgv3Gj800tnlVGsvv/wy586dIyEhgWXLlrF161Z7W1ZWFpmZmfzzn//EYrEwZ84cjh07htVqpV27dsycORMfHx8yMzOZNm0ap06dwmKxEBcXZ9+Z6Oeff2b69OlkZGRgNBqZO3cuHTt2BODgwYPEx8dz5coVfH19WbhwIU2bNnXK+1CZCr7ZK77eJCTAR9/sSbVRfOGeQvL1KSg7iEYhREoquruFFu6VVXJyMi+88AK7du2iX79+AEyYMIEJEyYAcPnyZWJiYpg6dSo+Pj7MnDmT2rVr8/HHH2O1Wnn66ad59dVXiYuLIz4+nhYtWvDaa6+RnJxMTEwMbdq0ITQ0lMmTJzN06FBiYmI4ePAg48aNY8uWLbi5uTFhwgTmzZtH9+7d+fLLL5kwYQKffPKJSwZHfbMn1Zl2t7g1mqPsIJpfLFJU5r7/FgvJmpNcVmvXrqVbt26l7jsPsGTJEtq3b8/vfvc7AHr27Mljjz2GwWDAZDLRpk0bTp8+jdlsZvv27YwYMQKAhg0b0r17dzZt2kRqaipHjx5l8ODBALRt25bw8HB27NjBd999h8lkonv37gD07t2bnJwcvv32WwecvXMUfLMXeVdj2jQNUB8u1YI1P18h+RZpRNmBNAoh8j9+zZtRt93tRPzxUYXkcnr66acBWLp0aYm2kydPsmnTJr744gv7scIXeUpOTuadd95h9uzZZGZmkpubS0hIiL09JCSElJQUUlJSCAwMxN3d3d4WHBzMmTNnsNlshBb76ragrX379kWOp6Wlce7cuRJ1JiUl3eJZi8itMrq7E9itKwFd7qJRzDBnl1MtKCg7mOYXS013NT0DzwB/3OvUoU38LGeX4/Leeecdhg8fjr9/yeld+/fvZ+LEiYwaNYqoqCjS0tIASvzybjQasdlspf5S/2ttxa1du5Zly5aV9XREpAzMly9jcHfH5OlJw6GDnV1OtaKgLCIOc/6bPRxb8ldaTJpI4D1dnV2Oy7NarXz22We89957JdrWr1/P4sWLiY+Pt0/JCAgIwNPTk7S0NIKDgwFITU2lefPmhIWFcf78ecxmM25u1350pKWlERkZSXBwMKmpqUWePy0trcQoM0BMTAy9evUqcTwpKYm4uLhyn7OIFJV/6RKHZsXj5udH69kzMLop+t0KzVEWEYcoWLjn5ueLT5PGzi6nRjh27BgeHh4ldp/4xz/+wV//+lf+7//+zx6SAUwmE71797YH69OnT/P111/bw3CrVq3YuHEjAIcPH+b48eN069aNdu3acfXqVb755hsAduzYce3bszZtStQUFBREmzZtSvxp1qxZZb0NIjVWQUi+8uMJardprZBcBnrHRKTSaXcL5zh58iQNGhR9r61WKy+++CKenp4888z/9qxu27YtCQkJzJw5k1mzZtGvXz/MZjNTp04lPDwcgBdffJGZM2fy7rvvAtcWCdapUweA5cuXEx8fT0JCAt7e3ixduhSTyeSYExWREgqHZC3cKzsFZRGpVCVDshbuVZYnn3yyyO2+ffvSt2/fIseMRiN79+697nP4+/tfdw5xo0aNePvtt0tta926NWvXrr21gkWkUigkVxxNvRCRSmW+dAn3OnUUkkVEHMSal48lN1chuQJoRFlEKoXNasVgNBJyfx8Cf/tb3Hy8nV2SiIhLK+h3PQP8af/iYvW7FUAjyiJS4c5/s4fvps/EfOUKgDprEZFKln/pEt9NfY6Mf/8HUL9bURSURaRCFcxJzj17lvwLF5xdjoiIyyuYk3zp+2NcOfmTs8txKQrKIlJhii/c8w4Lc3ZJIiIurfjCvUbDhzq7JJeioCwiFUK7W4iIOJZ2t6h8CsoiUm42i4XTH2xUSBYRcaDM/+xTSK5k2vVCRMrNYDLRevZMzJcu4d1A0y1ERBwhqNe9eIWFUrtVS2eX4rIcHpQPHDjAyJEj2bZtGyEhIYwaNYrU1FS8vLwAuPPOO5kxYwbjxo0jJSXF/rgzZ87QokULVq9ezX/+8x/+9Kc/0ahRI3v7iy++SPPmzTl48CDx8fFcuXIFX19fFi5cWOLyrSJSMc5/swdrXh5B9/bEvXYt3GvXcnZJIiIuLf/SJX7+x1qajHoIk5eXQnIlc2hQTk9PZ/bs2eTn5wOQn5/PwYMH2bFjB7Vr1y5y39dee83+96NHjzJ27FgSEhIA2Lt3LzExMTz77LNFHpOXl8eECROYN28e3bt358svv2TChAl88sknGAyGSj47kZrl/Dd7OPaXl3CvW4eAu7tg+uWXXRERqRz5ly5xaPZcriT9iF/z5gT1utfZJbk8h81RNpvNTJ48mbi4OPuxw4cP4+HhwZQpU+jfvz/Tpk0jMzOzxOOeffZZJk+eTHh4OAD79u3j4MGDDBkyhKFDh/L5558D8N1332EymejevTsAvXv3Jicnh2+//bZEPWlpaRw6dKjEn6SkpEp6B0RcR0FIdqtdizZz5ygki4hUssIhuVFsjEKygzhsRHnRokV06dKFe+65x37swoUL3H333cyePZtatWrxwgsvEBcXx5tvvmm/z4cffoi3tzcDBw60H6tVqxb9+/dnwIAB/PDDDzz88MMEBwdz9uxZQkNDi7xucHAwZ86coX379kWOr127lmXLllXOyYq4sKK7W8zFp2EDZ5ckIuLSiodkLdxzHIcE5c2bN3Pq1CmmTZtW5HiPHj3o0aOH/faECRPo2rUr2dnZ+Pj4ALBq1SqeeeaZIo976aWX7H9v3rw5DzzwANu2beO2224rdYqF0Vhy4DwmJoZevXqVOJ6UlFRk1FtE/ufquXMce/FlhWQREQf68fU3FJKdxCFB+YMPPiA1NbXIqPCYMWN44IEH6NChg32U2WazYTQacXO7Vtb333/PxYsX+e1vf2t/3JUrV3jnnXcYO3as/X42mw03NzfCwsJITU0t8tppaWklRpkBgoKCCAoKquhTFXFpnvXr0/zJJ/Br3lwhWUTEQSIeHU3t1q0JfaCvs0upcRwyR3nVqlV8+umnfPTRR3z00UcArFy5krCwMObPn8/ly5cBWLFiBZGRkXh4eAD/v707j4u62v8H/hqGfXFBZMclCUkUxSwC+bqCC0u4IZrmcjXjpl4SRcENIXHBq1lii0WmN00qIxM1l5J+ltxuetVU3EIJUBxkMxaBWT6/P4y5DCAiMgvwej4ePh7M+czyPgwcXn7mnPMBfvnlF3h6eqqcETY1NcX+/fvxzTffAABycnLw3XffYezYsXB3d0dVVRVOnz4NAEhLS4NIJIKbm5smuknUZhWfOw/ZX7+n1sOGMiQTEamZtLQUJecvAAAMLTszJGuJVvdRHj9+PLKzsxESEgKFQoHevXsjLi5OeTwrKwsODqp/kEUiEbZv34633noLu3btglwux8qVK+Hq6goAeP/99xEbG4v4+HiYmJhg27ZtEIvFGu0XUVtSMye58/MD0Wdl9OMfQERET6VmTnJF1h8Y8O4WXsRJi7QSlK9du6b8Ojw8HOHh4Q3eb/Xq1Q22u7q6Ys+ePQ0e69OnD5KTk5++SCJSWbjXY9YMbZdDRNTm1V24x5CsXbyENRE1SHV3i1hOtyAiUjPubqF7GJSJqJ7i/56rE5J5RoOISJ0UUilDsg7S6hxlItJN5s86o/NAD/SYPYMhmYhIA/QMDGA9bCjkni/CKTRE2+XQXxiUiUip6l4BjLpawcDCAn1WLdd2OUREbZ6srAwisRhiExPYvxyo7XKoDk69ICIAD+cknw2bj3s/ntJ2KURE7YK0tBSXVsciIy4eCqlU2+VQAxiUiUi5cE/fwhxmvXpquxwiojav9sK9jv3doWdgoO2SqAEMykTtXP3dLTgnmYhInbi7RevBoEzUjjEkExFpFkNy68KgTNSOKSorYdipE0MyEZGGCHI5FFXVDMmtBHe9IGqHBIUCIj09WI8Yji7eXhAbG2u7JKLHEgQBGbeKkFdQDjsrM/TpaQmRSKTtsoiapGbcNezUCf03b+S420owKBO1MwWn03H76wPoE7MCBhYWHKypVcgvqsDqHemQFFVAXyyCTC7AxtIUcfO8YG1pqu3yiBolLS1FRmw8HCYEw4onJ1oVTr0gakdq5iRXFdyDrLRU2+UQNYkgCFi9Ix13C8shkytQWS2HTK7A3cJyxHyUDkEQtF0i0SNJS0txeXUsym7cwIPc29ouh54QgzJRO6G6cC8OJvb22i6JqEkybhVBUlQBuUI1EMsVAu4WViDjVpGWKiNqXE1ILr95C05TQ+E0eZK2S6InxKBM1A7U393CQdslETVZXkE59MUNz0XWF4uQV1Cu4YqIHq9uSObCvdaJQZmojRMUCtz55iC3gKNWy87KDDJ5w9MrZHIBdlZmGq6I6PFKzl1gSG4DuJiPqI0T6emhz+oVkP55n9MtqFXq09MSNpamuFtYrjL9Qqwngm0XU/TpaanF6oga1nWID4ztbGHxrLO2S6GnwDPKRG1Uwel0SI6fAADom5sxJFOrJRKJEDfPC7ZdzKAv1oOxoRj6Yj3YWZkhdp4Xt4gjnSEtLcXv730IWcUDAGBIbgN4RpmoDaqZk2zYqROsfAZDbGKi7ZKInoq1pSneXzaC+yiTzqo9J9mi97OwGTlC2yVRC2BQJmpjai/cc3trDUMytRkikQhuz3SB2zNdtF0KkYq6C/cYktsOTr0gakO4uwURkWZxd4u2jUGZqI2oKizE9S3vcHcLIiINuvXRJwzJbZjGp15cuHAB06ZNw4kTJ2Bra4sZM2ZAIpHA+K/LOb7wwgtYuXIlqqur8eKLL6J79+7Kx86ePRvjxo1DcXExoqOjkZ2dDblcjsjISPj6+gIAcnJysHz5chQVFUFPTw9xcXHw8PDQdDeJNM6oSxe4vLkQpj26MyQTEWlIj7/NgsVzrrAbO1rbpZAaNBqU9+/fj4kTJypv79mzB0ePHoWRkRFCQkIwatSoJ3qxwsJCxMTEQCqVAgCkUikuXbqEtLQ0dOjQQeW+Fy9exDPPPIOvv/663vPExsbCxcUFH3zwAXJzcxEaGgo3NzfY2dkhIiICkyZNQmhoKC5duoSwsDAcO3YMpqamT1QrUWtR/N9zMHfuBYMOHWDlM1jb5VATtfT4SkSaIy0tRdn1G+j8/EAYdurIkNyGNTr1Ij4+Xvn1Rx99hI8//hjDhg3DwIEDERsbi3379jX5hWQyGSIiIhAZGalsy8jIgKGhIZYsWYKgoCBER0ejuLgYAHD27FlIpVJMnz4dQUFBSExMhFwuh0wmw8mTJzFlyhQAgKOjI3x8fHDw4EFIJBJcvXoVEyZMAAD07dsXPXr0QFpaWpPrJGpNCk6n48ra9bj+9rvaLoWeUEuOrwCwdetWrFixAgCQmJiI4OBg5b+hQ4fC3d0dFRUVyvvfv38fQUFBOHPmjLKtsrISUVFRGDNmDPz8/FRqKC4uRlhYGPz9/TF69GicOHFCeSwnJwevvvoqAgICEBQUhHPnzj3x90PXCIKAyzcLceI/2bh8sxCC0PAFT6j9kZaW4nJMHDLWrkdFdra2yyE1a/SMcu2B4auvvsL27dvRp08fAMDgwYMRERGhDKyPk5CQAE9PTwwe/L8zXvfv38dLL72EmJgYWFhYYP369YiMjMTHH38MABg6dCjCw8NRUVGB119/HYaGhhg/fjwqKytha2urfB5bW1vk5eUhLy8PVlZWMDAwUB6zsbHBnTt36tWTn5+Pe/fu1WvPzMxsUn+ItK3gdDqu//Nt6HewQM85s7VdDj2hlhpfc3NzsX79evz0008IDAwEACxYsAALFiwAAJSVlSE0NBRRUVHKT9ZOnDiBjRs3Ii8vT+W5tm3bBplMhiNHjqCkpARTpkyBq6srBgwY0K4+ycsvqsDqHemQFFVAXyyCTC7AxtIUcfO8YG3ZOvtELaMmJJdn3oTT1FCYduum7ZJIzRoNyrX3pywvL4eLi4vydr9+/VBYWNikF0lNTUV2djaio6NV2ocMGYIhQ4Yoby9YsABeXl6oqKjAvHnzlO0dO3bE7Nmz8fHHH2PcuHH1agMAPT09CILQ4J6aenr1T5wnJycjMTGxSfUT6ZraIbnv2jjubtEKtdT4mpycDG9vbzz77LMN/ud/y5Yt6N+/P8aOHQsAqK6uxueff47NmzfjzTffVLnv8ePHER8fD5FIhM6dOyMgIAApKSno27cvTp48iSNHjgBQ/SQvODj4kZ/k+fv716tH109SCIKA1TvSkVdQBoUAyOQP2/MKyhDzUTreWzqCeze3U3VDMhfutQ+NBuXq6mq89957cHNzg7u7O06dOoXhw4cDAA4fPowePXo06UX2798PiUSiDLkAMGfOHPj7+2PAgAHKs8yCIEBPTw/6+vrYu3cvBg8erFzMJwgC9PX10aVLFxgZGSE/Px82NjYAAIlEAmdnZ9jb26OgoAAymQz6+g+7lp+fr1zoV1toaChGjKi/z2FmZqbK9BAiXVNy/kKtLeAYklurlhpfFy9eDODh2eC6srKycPDgQRw9elTZZmhoiKSkpAafKy8vr96ndZcuXUJxcXGLfJIH6P5JioxbRbhbWA5FnZkWCgHIKyhHxq0i7uPcDilkMlyOeYshuR1qNCgvWbIEly9fRmpqKrKyslBaWorhw4fjgw8+wHvvvdfgwNyQnTt3qtzu3bs3kpKSkJ6ejnXr1iE5ORnm5ubYsWMHfH19YWhoiIsXL+K3337DunXrUFlZid27dyMwMBBisRgjR47E3r17sWjRIty+fRunTp1CWFgYbGxs4OrqipSUFISEhCAjIwM3btyAt7d3vZqsra1hbW39BN8qIt1g/qwzLF8YhO4zpjMkt2ItNb42ZteuXZg8eTIsLS2bdP+GPpWr+bQOePpP8gDdP0lx514ZFHVT8l8UCgF37pU1GJQFQeBVA9swPX192PiNgMzzBTiFhmi7HNKgRoPyrFmzlF8/ePAABQUFAID/+7//w+jRo9GzZ8+nevHx48cjOzsbISEhUCgU6N27N+Li4gAAUVFRiI2NRVBQEKqrq+Hv74+pU6cCAFatWoXVq1cjMDAQMpkMUVFRyrMvmzdvxqpVq7B7924ADz927Nix41PVSaQLKiX5MLaxhr6ZGZ5bvkzb5dBTUvf4qlAocOTIEezdu7fJj3FwcIBEIoHjX9sLSiQS2NnZtdgneYDun6SolsrxqGV7wl/H6+Kc5rZLWloKkVgMfVNT2I0do+1ySAseu49yUVERrl69ChcXFzg5OQEA3NzcAADff/89Ro4c+cQveu3aNeXX4eHhCA8Pr3efjh07YsuWLQ0+3tLS8pEf3Tk5OeHTTz994pqIdFnB6XRc37wVzvPDYD1iuLbLoRaijvG1xvXr12FoaIhnnnmmyY/x8/NDcnIyPDw88OeffyI1NRVr1qxpsU/yWgMjQzFEQINhWSR6eLy2mjnNdwvLIVcIyjnNdwvLOae5lauZk6ynr4++8XHQqzW9iNqPRreHO3PmDPz8/LBo0SIMGzYMO3bsUDmuCx+TEbV1NZel1rcwh3mtBV/Uuql7fM3KyoKDw5NNzZk/fz4MDAwQFBSEyZMnY8qUKfDy8gLw8JO8zMxMBAYGYs6cOfU+yTt06BCCgoKwbNmyVv1Jnp2VOcTihoOtWE8EOytzlbaMW0WQFFVAXme6hlwh4G5hBTJuFamtVlKf2gv3Og30YEhuxxo9o7xp0yYsX74cEydOxJkzZxAeHg59fX387W9/AwDuK0mkZjUhmQv32p6WHl8XLlyocnvMmDEYM6bxj4p/+OEHldsmJiYq+zvX1l4+yevT0xI2lmbKM8Q1xHoi2HZ5OPe4tryC8r+mW9R/Ln2xCHkF5Vz818pwdwuqrdEzyjdv3lRu+TNo0CAkJSXhvffew48//gig/sIOImo5qiE5liG5jeH4qptEIhHi5nnBtosZ9MV6MDYUQ1+sBzsrM8TO86r3vthZmUEmb/g/NTK5ADsrM02UTS2EIZnqavSMsoWFBXJyctDtrw21XV1dsW7dOkRGRmLPnj0aKZCovRJkchh27gy3uNUw/WtxFbUdHF91l7WlKd5fNqJJu1g8PANt+ogz0Kb1zkCTjhMECHI5QzIpNXpGefLkyZg7dy6++eYbZduoUaMwa9YsTJs2DdXV1equj6jdEeQPP8PtOsQHA9/fxpDcRnF81W0ikQhuz3SB74vd4PZMl0ee4X/SM9Ckm2rGXYMOHeCesJ4hmZQaPaMcFhYGS0tL5Ofnq7S/8cYb6Ny5M9577z21FkfU3hScTkful/vhtmYVDDp2hNjISNslkZpwfG07nuQMNOkeaWkpMmLXwj4oEF2H/h/HXVLx2O3hJk/+3/+qBEFAcXExLC0tMXXqVOW+xkT09GrPSZaVlcOgle4aQE3H8bXtqDkDzYV7rUvtOcmVEom2yyEd1OjUixplZWVYunQp3N3dMXjwYHh4eCA+Ph5VVVXqro+oXai7u4WJg722SyIN4fhKpB3S0lJcXh2rXLjnNHmStksiHdSkoBwbG4vCwkJ89tlnSEtLw0cffYQrV65g48aN6q6PqM3jFnDtG8dXIs1ThuSbt7hwjxr12KkXwMO9Nn/88UeYmz/caN3GxgbvvvsuAgMDsXr1arUWSNSWCYKAvENHGJLbMY6vRJp3/+IlhmRqkiYFZXt7e9y/f185kANAVVUVjI2N1VYYUXsgEonw3IooSO/fh4mdnbbLIS3g+EqkeVbeXjB+exPMn+AS79Q+NRqUjx07BgDw8vLC3Llz8frrr8POzg75+fnYsWMH/P39NVIkUVtTcDod0pL7sPMfA31TU+ibmmq7JNIwjq9EmiUtLUXWp/9Cz9kzoW9uxpBMTdJoUN6wYYPK7XfffVfl9pEjR7BkyZKWr4qoDfvfnOSO6DpsCENyO8XxlUhzas9J7uDqAhs/X22XRK1Eo0H5hx9+0FQdRO2C6sK9NQzJ7RjHVyLNqLtwjyGZnsRj5yhfu3YNDg4OMDc3x9WrV/H9999DEASMHTsWvXr10kSNRG2CakiO5RX3iOMrkZpxdwt6Wo1uD7d//36EhoZCIpHgxIkTmDJlCi5evIgLFy5g4sSJOHHihKbqJGrVqouKcePtdxmSSYnjK5H6ZX3yKUMyPZVGzyh/8MEH+PTTT9GrVy9ERETgnXfewdChQwEAaWlpSEhIgK8vP8IgehxDy85wiXgTJk4ODMkEgOMrkSb0mD0LFq69YTt6lLZLoVaq0TPKBQUFGDBgAABAIpHAx8dHeWzYsGGQ8HKPRI0qPvtfVJeUAAC6eHkyJJMSx1ci9ZCWlqLwl18BAAYdLBiS6ak0GpRdXFzw9ddfAwAGDx6MkydPKo8dOXKEc+iIGlFwOh1X4jfg+pZ3tF0K6SCOr0Qtr2ZO8tX1G1GelaXtcqgNaHTqxfLlyzFv3jwcO3YMDg4OWLx4MTw9PSGVSnH27FkkJSVpqk6iVqXgdDqu//Nt6HewwDPz5mq7HNJBHF+JWlbdhXtmPXpouyRqAxo9o9y/f38cOnQIgwYNQmFhIQYOHAiRSARXV1d89dVXeOGFFzRVJ1GrUTsk87LU9CgcX4laDne3IHV57PZwVlZWmDu35c6IXbhwAdOmTcOJEydga2uLGTNmQCKRKC/X+sILL2DlypUoLS3FmjVrcP36dSgUCri7u2PVqlUwNTXFr7/+itdffx1OTk7K5928eTOcnZ1x6dIlxMbGory8HGZmZti4cSOe4dV3SENKLvxWaws4hmRqXEuPr0TtkSCX43LMWwzJpBaPDcotqbCwEDExMZBKpQAAqVSKS5cuIS0tDR06dFC5b0JCAjp06IBvv/0WCoUCixcvxvbt2xEZGYkzZ84gNDQUy5YtU3lMdXU1FixYgLVr18LHxwfff/89FixYgEOHDkEkEmmsn9R+Wbg8iy5enuj2ylSGZCIiDRCJxbDzH43qwiI4hYZouxxqYxoNyseOHXvsE4wa1bTVpDKZDBEREYiMjMTf/vY3AEBGRgYMDQ2xZMkS5OXloW/fvli6dCk6d+6MoUOH4rnnnoNIJIJYLIabmxsuX74MADh79iyqqqowceJEiEQizJ07F2PGjMHFixchFouVq8dHjhyJtWvX4rfffkP//v2bVCdRc1TevQtjW1uITUzgupSXHabHa8nxlag9kpaWQiQSQd/cHDa+I7VdDrVRjQblTz75BBcuXICdnV2Dx0UiUZMH8oSEBHh6emLw4MHKtvv37+Oll15CTEwMLCwssH79ekRGRuLjjz9W2T80NzcXu3btQkxMDADAwsICQUFBCA4Oxu+//45XX30VNjY2uHv3br1abWxscOfOnXpBOT8/H/fu3atXZ2ZmZpP6Q1SjZk7yM2GvwXaUn7bLoVaiJcdXovZGWlqKyzFxAERw37AWeoaG2i6J2qhGg/Knn36KWbNmwd/fHzNmzGj2i6SmpiI7OxvR0dEq7UOGDMGQIUOUtxcsWAAvLy9UVFTA1NQUAHDu3DmEh4djxowZ8PN7GELefvtt5WOcnZ3h7++PEydOKM9A16WnV3/NYnJyMhITE5vdJyJA9bLUHfr00XY51Iq01PhK1N7UhOTyzJtwmhrKkExq1WhQNjY2xsaNG/HKK68gJCQEJiYmzXqR/fv3QyKRYNy4ccq2OXPmwN/fHwMGDFCeZRYEAXp6etDXf1jWl19+iU2bNiE2NhZjx44FAJSXl2PXrl2YN2+e8n6CIEBfXx/29vb1NunPz89v8IxNaGgoRowYUa89MzMTkZGRzeontS+1QzIX7tGTaqnxlag9qRuSuXCP1O2xi/m6d++OnTt3QqFQNPtFdu7cqXK7d+/eSEpKQnp6OtatW4fk5GSYm5tjx44d8PX1haGhIT7//HNs374d//rXv9C7d2/lY01NTbF//35YW1tj0qRJyMnJwXfffYdPPvkEzs7OqKqqwunTp+Ht7Y20tDSIRCK4ubnVq8na2hrW1tbN7hO1bwzJ1BJaYnwlai8YkkkbmrTrhYuLi1pefPz48cjOzkZISAgUCgV69+6NuLg4KBQKbN68GUZGRli6dKny/n379kV8fDy2b9+Ot956C7t27YJcLsfKlSvh6uoKAHj//fcRGxuL+Ph4mJiYYNu2bRCLxWqpn9ovkUgEoy6W6LNmNUMyPRV1ja9EbU3N1EqGZNIkjW4PV+PatWvKr8PDwxEeHl7vPmfOnHnk411dXbFnz54Gj/Xp0wfJyclPXyRRAwS5HCKxGF28XkLn5wdybhwRkZrVjLv65uZw3xDPcZc0qtEr8xHR/xScTsf5RUtQXVQMABysiYjUTFpait+WRkNy4gcAHHdJ8xiUiZqgZk6y9M8/IX/wQNvlEBG1eTVzkst+z0R1YaG2y6F2qllBuebKekTtgerCvViYONhruyRqwzi+EtVfuMcr7pG2NCko//HHH1i0aBEA4IcffsDzzz+PwYMH48KFC2otjkjb6oZkU0dHbZdEbQzHVyJV3N2CdEmTgvJbb70FExMTCIKATZs2YcGCBViwYAHi4+PVXR+R1giCgLvfHWNIJrXi+Eqk6s+Mqyi/eYshmXRCk3a9uHr1Kj788EPk5OQgJycH06ZNg6mpKTZt2qTu+oi0RiQS4bnly1BdXAITO1ttl0NtFMdXIlVdPF/AgK3/hFmPHtouhahpZ5QVCgWqqqqQlpaGfv36wczMDPn5+crLTBO1JQWn03H7wEEAgNjYmCGZ1IrjK9HD6RY33kmEtLQUABiSSWc06YzyyJEjMX36dNy+fRtLlixBZmYm3nzzTYwZM0bd9RFp1P/mJHeEzcgR0Dc303ZJ1MZxfKX2rvacZAtXF9iOHqXtkoiUmhSUY2Ji8M0338DIyAhBQUH4448/MGHCBMyYMUPd9RFpTN2FewzJpAkcX6k9q7twjyGZdM1jg3JpaSmys7MRFBQEIyMjAED37t0xe/ZstRdHpCn1d7fgZalJ/Ti+UnvG3S2oNWh0jvLZs2cxbNgwTJw4Eb6+vsjIyNBUXUQaU11yHze2buPuFqRRHF+pvcv69F8MyaTzGg3KW7duxT/+8Q+cO3cOkyZNwubNmzVVF5HGGHbqiN5LFzMkk0a19Pi6detWrFixAgCQmJiI4OBg5b+hQ4fC3d0dFRUVUCgU2LhxI0aPHg1fX18kJiZCEAQAQGVlJaKiojBmzBj4+flh3759yucvLi5GWFgY/P39MXr0aJw4cUJ5LCcnB6+++ioCAgIQFBSEc+fOPVVfqH3oOXsmnBf8nSGZdFqjQfnKlSuYOXMmTExMMHfuXFy5ckVTdRGpXdGZs6gqLAIAWA56niGZNKqlxtfc3FzMnz8fO3fuVLYtWLAABw4cwIEDB7Bnzx6Ym5tj48aNMDU1xb59+3Dp0iUcPHgQqamp+Pnnn3H48GEAwLZt2yCTyXDkyBF88cUX2LlzJ86fPw8AiI2NhYuLCw4fPoykpCTExMQgLy8PABAREYHAwEAcOnQI69evx8KFC1FRUfF03yBqk6SlpSg4nQ4A0Dc3g42fr5YrImpco0G55iwDAJiZmUEmk6m9ICJNKDidjivxG3B9y1Ztl0LtVEuNr8nJyfD29n7kvOYtW7agf//+GDt2LADg+PHjmDhxIgwNDWFsbIxJkyYhJSVFeSw0NBQikQidO3dGQEAAUlJSIJPJcPLkSUyZMgUA4OjoCB8fHxw8eBASiQRXr17FhAkTAAB9+/ZFjx49kJaW1qz+UNslLS3F5dWxuLbxnyi7eVPb5RA1SaOL+WoP5ERtRe2Fe73+/rq2y6F2qqXG18WLFwN4eDa4rqysLBw8eBBHjx5VtuXl5cHW9n97g9va2irPDDd07NKlSyguLkZlZWWDj8vLy4OVlRUMDAyUx2xsbHDnzp0G683Pz8e9e/fqtWdmZja1y9QK1YTkmivumT/zjLZLImqSxwbljIwM5YAul8tVbgOAm5ubeiskakHc3YJ0hSbG1127dmHy5MmwtLRUeV2RSKRyPz09vUaP1dT0qGN122s/Z13JyclITEx88s5Qq1U3JHNOMrUmjQblBw8eKD9Oq1H7tkgk4rxlajVKfrtYJyRzTjJpj7rHV4VCgSNHjmDv3r0q7Q4ODpBIJMrbEokEdnZ2Kscc//rdqDnWpUsXGBkZIT8/HzY2Nspjzs7OsLe3R0FBAWQyGfT1H/5Jyc/Ph69vw3NPQ0NDMWLEiHrtmZmZiIyMbHZ/STcJcjky1rzFkEytVqNB+erVq5qqg0jtLHq7wMpnMJxCJzEkk9ape3y9fv06DA0N8Uydj7j9/Pzw9ddfY8yYMVAoFNi/fz/Gjx+vPJacnAwPDw/8+eefSE1NxZo1ayAWizFy5Ejs3bsXixYtwu3bt3Hq1CmEhYXBxsYGrq6uSElJQUhICDIyMnDjxg14e3s3WJe1tTWsra3V2nfSHSKxGHZBgajKz4fT5EnaLofoiTXpynxErdmDO3dgYm8PsZERei9+U9vlEGlEVlYWHBzqTy2aPHkycnNzMX78eEilUvj6+mLixIkAgPnz52Pt2rUICgqCVCrF1KlT4eXlBQBYtWoVVq9ejcDAQMhkMkRFRaFHjx4AgM2bN2PVqlXYvXs3gIcLCDt27KiZjpJOkpaWAoIAgw4dYD1siLbLIWq2RoPyiBEjGpx7VkMkEqnspUmkawpOp+P6P99GzzmzYRcwVtvlECm19Pi6cOFCldtjxozBmDFj6t1PLBYjMjKywWkOJiYmiI+Pb/D5LS0tHzm32MnJCZ9++mmTa6W2rWZOsqBQwD1hPcR/XXWSqDVqNCgvW7aswfaff/4ZX3zxBQYNGqSWoohaQk1I1u9ggY793bVdDpEKjq/UFtVduMeQTK1do0F59OjRKrelUik2bdqElJQULFq0CK+99toTv+CFCxcwbdo0nDhxAra2tpgxYwYkEgmMjY0BAC+88AJWrlwJhUKBTZs24YcffoBcLse4ceMwf/58iEQiVFZWYs2aNTh//jzkcjnmzJmj3N+zuLgY0dHRyM7OhlwuR2Rk5CMXlVDbxd0tSNepY3wl0ibubkFtUZPnKN+4cQOLFy9GdXU19u7di379+j3xixUWFiImJgZSqRTAwz8Mly5dQlpaGjp06KBy39pXj1IoFJg9ezZ69uyJgIAAlatHlZSUYMqUKXB1dcWAAQOUV4/64IMPkJubi9DQULi5uSlXdVPbVz8kc+Ee6baWGF+JtIkhmdqqRq/MV2PXrl2YNGkSBgwYgJSUlGYN4jKZDBERESrz4jIyMmBoaIglS5YgKCgI0dHRKC4uBtDyV4+i9kPPwABGVl0YkqlVaInxlUjbRHp60DMwYEimNqfRM8oFBQVYtmwZrly5gi1btmDkyJHNfqGEhAR4enpi8ODByrb79+/jpZdeQkxMDCwsLLB+/XpERkbi448/bvGrR9XFq0O1PQqZDHr6+rB8YRA6DegPvVpXCiPSNS05vhJpS824q29mhr7xcRx3qc1pNCgHBgbi/v37GDVqFNLT05Genl7vPitXrnzsi6SmpiI7OxvR0dEq7UOGDMGQIf/bNmbBggXw8vJCRUVFi189qi5eHaptKTidjuw9n8MtNgZGVl04WJPOa6nxlUhbpKWluBzzFmz8RsJu7GiOu9QmNRqUhw0bpgyd5eXlzX6R/fv3QyKRYNy4ccq2OXPmwN/fHwMGDFCeZRYEAXp6etDX12/xq0fVxatDtR21d7eQV1ZquxyiJmmp8ZVIGx6G5DiUZ96EzPMFbZdDpDaNBuUNGza0yIvs3LlT5Xbv3r2RlJSE9PR0rFu3DsnJyTA3N8eOHTvg6+sLQ0PDFr96VF28OlTbUDsk910bx90tqNVoqfGVSNNqh2SnqaFwCg3RdklEavPYXS9KSkpQWFiIXr16AXh4djgjIwO+vr7KKzY11/jx45GdnY2QkBAoFAr07t0bcXFxANRz9ShqW1R3t2BIptZHneMrkTrUDclcuEdtXaNB+cqVK5gxYwb8/f0RGxuLpKQkbN++HaNHj8bixYsRHx+P4cOHP/GLXrt2Tfl1eHg4wsPD691HHVePorZDEATkn/iBIZlaLXWNr0TqVHb9BspvZTEkU7vRaFB+9913MWPGDOWlUXfu3Illy5YhNDQUP//8M7Zt28aBnLRCJBKh97IlkBYXw7jWLidErQXHV2qNOj8/EB7vbIZpt27aLoVIIxrdR/m///2v8upQv//+OwoLC5VbGA0aNAjXr19Xf4VEtRScTkfuV18DAMRGRgzJ1GpxfKXWQlpaiutvv4PqkvsAwJBM7UqjQbm6ulp5aelz587B0dERVlZWAAB9ff1627ARqVPNnOQ7qYcgLS3VdjlET4XjK7UGNXOS76X9PxSm/1vb5RBpXKNB2d7eXjmfOC0tDd7e3spj//73v5XbsxGpW92FewYWFtouieipcHwlXVd34Z7d2NHaLolI4xqdozx16lT8/e9/h7u7O3788Ud89dVXAICUlBRs27YNM2bM0EiR1L6phuRYLtyjNoHjK+ky7m5B9FCjQXn69OkwNzfHhQsX8PHHH8PV1RUA8M477yAgIAAzZ87USJHUfkn//BM33kmsFZJ5lo3aBo6vpMv++NdehmQiNGEf5XHjxqlcUQ94+DEhkSYYdOgA16hIGHW1YkimNofjK+mqHrNmwMLlWdj41r+CLVF78tigDAByuRxHjx5FVlYWFAqFyrEFCxaopTBq34p+PQOzHt1h1LUrOnsM0HY5RGrD8ZV0hbS0FCXnLqDrEB/om5owJBOhiUF55cqV+P7779GvXz8YGBiouyZq52rmJFv0dkG/9Wu5+p/aNI6vpAtqz0k2trOFxbPO2i6JSCc0KSifPHkSn3/+ufIyq0TqUnvhnvOCvzMkU5vH8ZW0TVpaisurY1F+8xacpoYyJBPV0uj2cDWMjIzg5OSk7lqonau/uwXnJFPbx/GVtKluSObCPSJVTQrKc+fORUxMDG7evImSkhKVf0Qt4f6lywzJ1C5xfCVtERQKZMTGMyQTNaJJUy82bdqE6upqpKSkKD8KFwQBIpEIV65cUWuB1D5YuDwL62FD4DBxPEMytSscX0lbRHp6cBgfjAe3b8Np8iRtl0Okk5oUlI8cOaLuOqidqsjNhamjI/QMDfFs+EJtl0OkcRxfSdOkpaUQ5HIYduoEq8Fe2i6HSKc1KSg7OPBKaNTyauYk95g1Aw7BQdouh0grOL6SJtXMSVZIpej/z40QGxtruyQindakOcpELa32wr3Oz3touxwiojav9sI9K5/BDMlETcCgTBrH3S2IiDSLu1sQNQ+DMmkUQzIRkWYxJBM1X5PmKBO1FLGJCYy6WqHP6hUMyUREGqCnrw+xsTFDMlEzMCiTRiikUugZGKCzxwAM3P4u9HipXiIitaoZd8UmJnCLi+G4S9QMnHpBaldwOh3nFr6JSkk+AHCwJiJSM2lpKX5bthx3Dh4CwHGXqLk0HpQvXLiAvn374u7duyrtx48fR9++fZW3V69ejeDgYOU/b29vDBs2DABw584d9OvXT+X46dOnAQA5OTl49dVXERAQgKCgIJw7d05jfaP6auYkyysroZBWa7scIqI2TzknOfMm5BUV2i6HqFXT6NSLwsJCxMTEQCqVqrRnZmZiw4YNUCgUyra4uDjl1xKJBKGhodiyZQsA4MyZMxg+fDjefffdeq8RERGBSZMmITQ0FJcuXUJYWBiOHTsGU1NTNfWKHoUL94iINKvuwj2n0BBtl0TUqmnsjLJMJkNERAQiIyNV2svKyhAZGYkVK1Y88rGrV69GSEgIBg4cCAA4e/YsJBIJpkyZgnHjxmHPnj0AHgbqq1evYsKECQCAvn37okePHkhLS1NPp+iRGJKJiDSLu1sQtTyNnVFOSEiAp6cnBg8erNIeHR2NmTNnwsXFpcHHpaen48aNG9i2bZuyzcjICKNHj8bs2bNx9+5dzJo1C507d4a9vT2srKxgUGsulo2NDe7cuVPvefPz83Hv3r167ZmZmc3tIv1FEATcS/t/DMlERBpUnnkTFX9kMyQTtSCNBOXU1FRkZ2cjOjpapf3DDz+ElZUVgoODkZub2+Bjd+7ciblz58LQ0FDZtnz5cuXXdnZ2CA0NxbFjxzBz5kyIRKJ6z6GnV//EeXJyMhITE5vbJWqESCRC78gIVBcVwdjGRtvlEBG1C50G9MeAd7fw5ARRC9JIUN6/fz8kEgnGjRunbJszZw5u3rwJZ2dnBAcHQyqVQi6XIzg4GNu2bUO3bt1QUlKCX3/9FZs3b1Y+ThAE7NixAyEhIbC0tFS26evrw97eHgUFBZDJZNDXf9i1/Px8+Pr61qspNDQUI0aMqNeemZlZb3oINU3B6XRUZOeg25TJ0DMwYEgmIlIzaWkpbn74EXr+bTYMLTszJBO1MI0E5Z07d6rc7t27N5KSkmBra6tsy83NxahRo3DgwAFl29mzZ+Hq6goLCwtlm0gkwk8//YTKykqEh4ejqKgIX3zxBaKjo2FjYwNXV1ekpKQgJCQEGRkZuHHjBry9vevVZG1tDWtrazX0tn2qPSfZzn8MDDp00HZJRERtmrS0FJdj4lCeeRMd+vSBnf8YbZdE1Obo9AVHsrKy4ODgUK9948aNiImJQWBgIKRSKaZNm6bcOm7z5s1YtWoVdu/eDQDYsmULOnbsqMmy252C0+m4/s+3/5qTHMeQTESkZrVDstPUUIZkIjXRSlC+du1avTZHR0dkZGSotM2ZM6fBx9vb2+Ojjz5q8JiTkxM+/fTTp66RmqYmJOt3sEDftXEwdaz/HxsiUr+tW7fi3r17iI+PB/BwG82EhARUVlbCzMwM69atQ8+ePVFdXY3169fj9OnTMDQ0hKenJ5YuXQpDQ0NUVlZizZo1OH/+PORyOebMmYMpU6YAAIqLixEdHY3s7GzI5XJERkYqp7Xl5ORg+fLlKCoqgp6eHuLi4uDh4aG170VbVzckc+EekfrwynzUbNLSUvy+7T2GZCItys3Nxfz581WmuEkkErzxxhtYsWIFvv32WwQGBmLlypUAgB07duDWrVs4cOAAvv32W1RWVir3pN+2bRtkMhmOHDmCL774Ajt37sT58+cBALGxsXBxccHhw4eRlJSEmJgY5OXlAXi4f31gYCAOHTqE9evXY+HChajghS7UJufzZIZkIg1hUKZmM7CwgGv0UoZkIi1KTk6Gt7c3Zs+erWz77rvv8NJLL6F///4AgJCQEMTGxgIALl68iDFjxsDY2BgikQi+vr44fPgwgIdXSA0NDYVIJELnzp0REBCAlJQUyGQynDx5Unl22dHRET4+Pjh48CD3r9eC7jOm49nwhQzJRBqg03OUSTcV/vIrzLo7wdjWFp3c+2m7HKJ2bfHixQCgstf8rVu3YGZmhoiICNy6dQvW1taIiooCALi7u+Po0aMICgqCoaEhDh06hPz8fABAXl6eyiJrW1tbXLp0CcXFxaisrKx3LC8vD3l5eU3evx7gHvbNJS0tRfGZs7AePgxiY2NYjxim7ZKI2gUGZXoiNbtbmDv3gnvC+gb3rSYi7ao5A7xnzx706tUL+/btQ1hYGI4ePYrXXnsNZWVlCA0NRceOHTF8+HCcOHECwMOtNuv+Tuvp6UEQBAB45LGm7l8PcA/75qg9J9nE3h4WvRu+QBcRtTwGZWqy2lvAPRu+gCGZSEfZ2NhgwIAB6NWrFwBgwoQJiImJQVFREeRyOWbNmoVly5YBAI4ePYqePXsCABwcHCCRSOD41168EokEdnZ26NKlC4yMjJCfnw+bv/ZHl0gkcHZ2fqL96wHuYf+k6i7cY0gm0izOUaYmqR2SeVlqIt3m5+eHc+fOISsrCwBw7NgxdOvWDZ06dUJaWhqio6OhUCjw4MED7Ny5U3kxKD8/PyQnJ0OhUKCkpASpqakYNWoUxGIxRo4cib179wIAbt++jVOnTsHX11dl/3oAje5fDzzcw97Nza3ev5pQT//D3S2ItI9nlOmx7l/OYEgmakVcXV0RHx+P8PBwyGQymJubY9u2bdDT08OECRNw8eJFBAUFQSqVIjAwEK+++ioAYP78+Vi7dq3y2NSpU+Hl5QUAWLVqFVavXo3AwEDIZDJERUWhR48eALh/vToICgWuvLWOIZlIyxiU6bEsXJ6FzcgRsB8XxJBMpKMWLlyoctvX17fB6Q9isRhxcXENPoeJiYlyH+a6LC0tHzm3mPvXtzyRnh4cJk5AxR9/wGnyJG2XQ9RuMSjTI5X/kQ3Tbk7QMzCA84K/a7scIqI2T1paCkW1FEZdLNHF8wV08XxB2yURtWuco0wNKjidjvNvLsbtr7/RdilERO1CzZzkSytXQ1bxQNvlEBEYlKkBtRfuWfJsBhGR2tVeuNd16BDom5pouyQiAqdeUB3c3YKISLOkpaW4vDoW5TdvceEekY7hGWVSYkgmItIsWVkZQzKRDuMZZVIysLCAsbU1nlsVzZBMRKQBIgMD6JubMyQT6SgGZYJCKoWegQE69usLj+3vQE+fPxZEROpUM+6KjYzQJ2Ylx10iHcWpF+1cwel0/PeNf+BBXh4AcLAmIlIzaWkpflsajdy/dhXiuEukuxiU27GaOckKaTUEuVzb5RARtXm1F+4JUqm2yyGix2BQbqe4cI+ISLPq7m7hFBqi7ZKI6DH4eU87pBqS42Dq6KDtkoiI2jRuAUfUOvGMcjtU8NNphmQiIg0qv5WFiuwchmSiVoZnlNshl4hwVBcVwdjaWtulEBG1C53c+8EjcStM7Oy0XQoRPQGNn1G+cOEC+vbti7t376q0Hz9+HH379lVpGzlyJAIDAxEcHIzg4GDs2LEDAFBZWYmoqCiMGTMGfn5+2Ldvn/IxxcXFCAsLg7+/P0aPHo0TJ06ov1OtQMHpdPzxrz0QBAF6+voMyUREaiYtLcXVjf9EVUEhADAkE7VCGj2jXFhYiJiYGEjrrPTNzMzEhg0boFAolG0SiQQVFRU4ceIERCKRyv23bdsGmUyGI0eOoKSkBFOmTIGrqysGDBiA2NhYuLi44IMPPkBubi5CQ0Ph5uYGu3Y8QNWek2wXFAjDTh21XRIRUZtWe05yx759YBfgr+2SiKgZNHZGWSaTISIiApGRkSrtZWVliIyMxIoVK1Taz549C1NTU8yePRtBQUGIj4/HgwcPADw8+xwaGgqRSITOnTsjICAAKSkpkMlkOHnyJKZMmQIAcHR0hI+PDw4ePKiZTuqgurtbMCQTEalX3YV7DMlErZfGzignJCTA09MTgwcPVmmPjo7GzJkz4eLiotJeVVUFb29vLF++HCKRCEuWLMGGDRsQGxuLvLw82NraKu9ra2uLS5cuobi4GJWVlfWO5f11MY3a8vPzce/evXrtmZmZT9tVncEt4IiINIu7WxC1LRoJyqmpqcjOzkZ0dLRK+4cffggrKysEBwcjNzdX5dj48eMxfvx45e2wsDDMmzcPsbGxEASh3nQMPT09CIIAAA0eqys5ORmJiYlP1S9dJisrx++J7zMkExFpUE7ylwzJRG2IRoLy/v37IZFIMG7cOGXbnDlzcPPmTTg7OyM4OBhSqRRyuRzBwcHYtm0bfvvtN3Tr1g3u7u4AAEEQoP/XZT4dHBwgkUjg+Ff4k0gksLOzQ5cuXWBkZIT8/HzY2Ngojzk7O9erKTQ0FCNGjKjXnpmZWW96SGukb26GPiujod/BgiGZiEhDur86DebOzrAeNkTbpRBRC9BIUN65c6fK7d69eyMpKUllikRubi5GjRqFAwcOAAC+/fZbfPXVV9ixYwf09PSQlJSEgIAAAICfnx+Sk5Ph4eGBP//8E6mpqVizZg3EYjFGjhyJvXv3YtGiRbh9+zZOnTqFsLCwejVZW1vDug3u/FD4y39g6ugIEwd7dOjznLbLISJq86SlpSj65T+w8R0JsZERQzJRG6Kz+yjPmzcPBQUFCA4Ohkwmg5eXF958800AwPz587F27VoEBQVBKpVi6tSp8PLyAgCsWrUKq1evRmBgIGQyGaKiotCjRw/tdUSDauYkmz/TE+7/3FhvCgoREbUsaWkpLsfEoTzzJkzseYKCqK3RSlC+du1avTZHR0dkZGQobxsaGmLNmjUNPt7ExATx8fENHrO0tGzTc48fpfbCvWcXhTMkExGpWe2Q7DQ1lCGZqA3S2TPK1HSqu1vwstREROrG3S2I2geNX5mPWtafV64yJBMRaZAgCLjy1nqGZKJ2gGeUWznzZ51hO3oU7AL9GZKJiDRAJBLBaUoIyjJvwilkorbLISI1YlBupcpvZcG0R3fo6eujV9hr2i6HiKjNk5aWQlFZBaOuVug80AOdB3pouyQiUjNOvWiFCk6n43xEJHK/+ErbpRARtQs1C/curlgFWXm5tsshIg1hUG5lai/c6zLYS9vlEBG1ecqFe5k3YT1iOPTNzLRdEhFpCINyK6K6uwUvS01EpG7c3YKofWNQbiUK0//NkExEpEGysjKGZKJ2jov5WgmDTp1gbGuL51YsY0gmItIAPUNDGHbuBEuGZKJ2i0FZx8mrqiA2MkKH51wxMHErRGKxtksiImrTasZdPUNDPLcimuMuUTvGqRc6rOB0Ov77xj9QkZsLABysiYjUTFpaiovRK5G97wsAHHeJ2jsGZR1Vs3BPkMu0XQoRUbtQe3cLIiKAQVkncXcLIiLN4u4WRNQQBmUdw5BMRKRZDMlE9CgMyjqm6JdfGZKJiDSoIjsHD3JvMyQTUT3c9ULHPPuP+aguKoJR167aLoWIqF3o6NYHHtvehrGtrbZLISIdwzPKOqDgdDpu7dwFQRAgEosZkomI1ExaWoor6zaiUpIPAAzJRNQgBmUtq5mTfO/H/wdpSYm2yyEiavNq5iQX/fIfFP3nV22XQ0Q6jEFZi+ou3DPs3FnbJRFRK7V161asWLFCefvMmTOYPHkyXn75ZUydOhW3bt1SHktMTMTYsWMRGBiIiIgIlJeXAwAqKysRFRWFMWPGwM/PD/v27VM+pri4GGFhYfD398fo0aNx4sQJ5bGcnBy8+uqrCAgIQFBQEM6dO6eBHjdP3YV79kEB2i6JiHQYg7KWcHcLImoJubm5mD9/Pnbu3Klsk0gkeOONN7BixQp8++23CAwMxMqVKwEAv/zyC1JSUrB//34cPHgQMpkMSUlJAIBt27ZBJpPhyJEj+OKLL7Bz506cP38eABAbGwsXFxccPnwYSUlJiImJQV5eHgAgIiICgYGBOHToENavX4+FCxeioqJCs9+IJuDuFkT0pBiUtUBWUYHM9z5kSCaip5acnAxvb2/Mnj1b2fbdd9/hpZdeQv/+/QEAISEhiI2NBQAoFApUV1ejsrISMpkMVVVVMDY2BgAcP34coaGhEIlE6Ny5MwICApCSkgKZTIaTJ09iypQpAABHR0f4+Pjg4MGDkEgkuHr1KiZMmAAA6Nu3L3r06IG0tDQNfheaJverrxmSieiJcNcLLdA3NUWfVcshNjNlSCaip7J48WIAD88G17h16xbMzMwQERGBW7duwdraGlFRUQAALy8vDB8+HMOHD4exsTEcHBwwbdo0AEBeXh5say1qs7W1xaVLl1BcXIzKysp6x/Ly8pCXlwcrKysYGBgoj9nY2ODOnTsN1pufn4979+7Va8/MzHyK70LTdJ/+Csx79ULXIT5qfy0iahs0HpQvXLiAadOm4cSJEyqD7vHjx7Fo0SJcunQJACCVSrF+/Xr88ssvEIlE6N69O2JjY2FlZYU7d+5g9OjReOaZZ5SPX7ZsGby9vZGTk4Ply5ejqKgIenp6iIuLg4eHh6a72aDCf/8CE3t7mHZzgkVvF22XQ0RtVM0Z4D179qBXr17Yt28fwsLCcPToUXz11VfIzMzEqVOnYGpqivj4eERGRuK99957uPOOSKTyXHp6ehAEAQAeeaxue82xhiQnJyMxMbGFevp40tJSFPx0GrZjRkHPwIAhmYieiEaDcmFhIWJiYiCVSlXaMzMzsWHDBigUCmVbUlIS7ty5g2+++QYGBgbYuHEj1q5di61bt+LMmTMYPnw43n333XqvERERgUmTJiE0NBSXLl1CWFgYjh07BlNTU7X3rzE1c5LNundH/7c3NfiHhYioJdjY2GDAgAHo1asXAGDChAmIiYlBUVERjh8/Dn9/f3To0AEAMH36dEyaNAkA4ODgAIlEAse/PumSSCSws7NDly5dYGRkhPz8fNjY2CiPOTs7w97eHgUFBZDJZNDXf/gnJT8/H76+vg3WFhoaihEjRtRrz8zMRGRkZIt+H2rPSTZ1dEDHfn1b9PmJqO3T2BxlmUyGiIiIegNhWVkZIiMjVVZrA0D//v0RERGh/DivX79+uH37NgDg7NmzkEgkmDJlCsaNG4c9e/YAwBPNlcvPz8fly5fr/VPHx3+1F+65LHmTIZmI1MrPzw/nzp1DVlYWAODYsWPo1q0bOnXqBDc3Nxw7dgzV1dXKYwMGDFA+Ljk5GQqFAiUlJUhNTcWoUaMgFosxcuRI7N27FwBw+/ZtnDp1Cr6+vrCxsYGrqytSUlIAABkZGbhx4wa8vb0brM3a2hpubm71/tWE+pZSd+EeQzIRNYfGzignJCTA09MTgwcPVmmPjo7GzJkz4eKiOhXBy8tL+XVJSQm2b9+OkJAQAICRkRFGjx6N2bNn4+7du5g1axY6d+4Me3v7Js+V09THf9zdgog0zdXVFfHx8QgPD4dMJoO5uTm2bdsGPT09hIWFISEhAUFBQTA0NES3bt2wYcMGAMD8+fOxdu1aBAUFQSqVYurUqcqxeNWqVVi9ejUCAwMhk8kQFRWFHj16AAA2b96MVatWYffu3QCALVu2oGPHjlrpO8DdLYio5WgkKKempiI7OxvR0dEq7R9++CGsrKwQHByM3NzcBh978+ZNLFiwAC+++CJmzpwJAFi+fLnyuJ2dHUJDQ3Hs2DHMnDmzyXPlNPHxX+m167VCchxMHR1a5HmJiOpauHChym1fX98Gpz8YGhoqt4qry8TEBPHx8Q0es7S0fOTJBScnJ3z66adPVrCaCIKAq+s2MiQTUYvQSFDev38/JBIJxo0bp2ybM2cObt68CWdnZwQHB0MqlUIulyM4OBjbtm1Dt27dcPLkSURFRWH+/PmYMWMGgIeD4I4dOxASEgJLS0tlm76+/hPNlbO2toa1tbVa+23u3At2AWNhO2Y0QzIRkQaIRCI4TQ1F2fUbcJw0QdvlEFErp5GgXHsjfADo3bs3kpKSVHa9yM3NxahRo3DgwAEAQFpaGpYuXYrExER4enoq7ycSifDTTz+hsrIS4eHhKCoqwhdffIHo6GiVuXIhISGPnSunLmWZN2H2TE+IxGI8M/dvGn1tIqL2SFpaCnlFBYxtbNDJvR86uffTdklE1Abo7D7Kmzdvhkgkwrp165Rt1tbW+Oijj7Bx40bExMQgMDAQUqkU06ZNw7Bhw5SP0+ZcuZo5yY6TJqD7tKkae10iovZKWlqKyzFxkN7/Ex7vbIG+uZm2SyKiNkIrQfnatWv12hwdHZGRkaG8ffDgwUc+3t7eHh999FGDx7Q5V67gdDqu//NtGHTsgK5Dh2ilBiKi9qTuwj2GZCJqSbyEdQupCcn6HSy4cI+ISAO4uwURqRuDcgsoTP83QzIRkQbJysoZkolI7XR2jnJrYmhlBWM7O7hGL2VIJiLSAD0jQxh1tYKl54sMyUSkNgzKT0FeVQWxkREsnnWGx7tbIBKLtV0SEVGbJq+qgp6hIfQMDOC6LJLjLhGpFadeNFPB6XScfX0+yv/IBgAO1kREaiYtLcXF6JXI/mwvBEHguEtEaseg3Aw1W8ABAkRifguJiNStZgu48sybEBkYNHgVViKilsaU94RqQvLDy1LHwtTRUdslERG1abVDMhfuEZEmcY7yE2BIJiLSLG4BR0TaxDPKT6D47DmGZCIiDaq8k4cHt+8wJBORVvCM8hNwfuN1VBeXwMiqi7ZLISJqFyx6u8AjcSuMra21XQoRtUM8o/wYBafTcXPHxxAUCojEYoZkIiI1k5aWImPtejzIywMAhmQi0hoG5UbUzEkuOJ2O6uISbZdDRNTmySoqcDkmDsW/nkHxr2e1XQ4RtXOcevEIJRd+Q8mXXyvnJBt1sdR2SUREbV7mBztgU1AEp6mhsH85UNvlEFE7xzPKj/DH7s+4cI+ISMMqc29z4R4R6QyeUa6jqqoKAJAv1kPvGdNw6/594P59LVdFRLosMzMTwP/GD3pyNd+7qoEeKO3nhsuXL2u5IiLSZZoadxmU68jNzQUAvP/7dbz/979ruRoiak1yc3MxcOBAbZfRKtWMvRu+/gr4+istV0NErYW6x12RIAiC2p69FSoqKsJPP/0ER0dHGBkZabucp5KZmYnIyEhs2rQJvXr10nY5GtHe+sz+6oaqqirk5ubCx8cHlpZcz9Ac6h57dfVnp6lae/1A6+9Da68faP19qF2/o6OjRsZdnlGuw9LSEi+//LK2y2hRvXr1gpubm7bL0Kj21mf2V/t4JvnpaGrs1cWfnSfR2usHWn8fWnv9QOvvQ039mhh3uZiPiIiIiKgBDMpERERERA1gUCYiIiIiagCDMhERERFRAxiU27CuXbtiwYIF6Nq1q7ZL0Zj21mf2l6hpWvvPTmuvH2j9fWjt9QOtvw/aqJ/bwxERERERNYBnlImIiIiIGsCgTERERETUAF5wpJW4cOECpk2bhhMnTsDW1lbZfvz4cSxatAiXLl1Sto0cORImJiYQi8UAgICAAMybNw+VlZVYs2YNzp8/D7lcjjlz5mDKlCkAgOLiYkRHRyM7OxtyuRyRkZHw9fXVbCfrqNvnGTNmQCKRwNjYGADwwgsvYOXKlVAoFNi0aRN++OEHyOVyjBs3DvPnz4dIJGpVfW5qf6urq/Hiiy+ie/fuysfOnj0b48aNa7RPOTk5WL58OYqKiqCnp4e4uDh4eHhopa9A/f6eOXMGCQkJqKyshJmZGdatW4eePXsCAJKSkvDll19CLpfDx8cHy5cvh4GBQbPfe2p9tm7dinv37iE+Ph4AGv15SUxMxKFDhyAWi+Hi4oK33noLZmZmzR4PWuJ3p6n1V1dXY/369Th9+jQMDQ3h6emJpUuXwtDQUKv11+1DYmIijh8/rjxWUlKC4uJi/Pvf/4axsXGLj8kt/R40Vr+pqSkA4P79+5g+fTpiYmIwaNAgAGg174FcLseaNWtw/fp1KBQKuLu7Y9WqVTA1NW12nZcuXUJsbCzKy8thZmaGjRs34plnnlFL/SKRCDExMbh8+TIUCgX+7//+D8uWLYNYLNZ8/QLpvIKCAiE4OFhwcXER8vLylO2///67MGLECOG5555Ttt29e1d46aWXBIVCUe95EhIShMWLFwsKhUIoKioSRo0aJZw7d04QBEEIDw8XNm/eLAiCIOTk5Aje3t7CnTt31NuxRtTtc3V1teDh4SHcv3+/3n337NkjTJ8+XaiqqhIePHggTJkyRUhNTRUEofX0+Un6e+bMGWH8+PENPk9jfZo0aZKwb98+QRAE4eLFi8LgwYOF8vJyNfWocXX7e/fuXeGFF14Qzp8/LwiCIHz22WfCK6+8IgiCIKSlpQljxowR/vzzT0Emkwn/+Mc/hA8//FAQhOa/99R65OTkCG+88Ybg7u4uLF++XBAEodGfl3//+9/CiBEjhPLyckGhUAgLFy4U3nnnHUEQmj8ePM3vzpPWv23bNmHmzJnCgwcPBIVCIaxYsULYtGmT1up/VB9qKy0tFfz9/YXDhw8LgqCeMbml34PG6hcEQTh+/Ljg6+sruLm5Cb/++quyvbW8BytXrhTWrFkjKBQKQSaTCeHh4UJCQkKz66yqqhKGDh0qnDp1ShAEQThx4oQwduzYBrNGS9S/ZcsWYenSpYJCoRCqq6uFSZMmCfv379dK/Zx6oeNkMhkiIiIQGRmp0l5WVobIyEisWLFCpf3s2bMwNTXF7NmzERQUhPj4eDx48ADAw7PPoaGhEIlE6Ny5MwICApCSkgKZTIaTJ08q/1fs6OgIHx8fHDx4UDOdrKOhPmdkZMDQ0BBLlixBUFAQoqOjUVxcrOzXxIkTYWhoCGNjY0yaNAkpKSnKY7re5yft79mzZyGVSjF9+nQEBQUhMTERcrm80T5JJBJcvXoVEyZMAAD07dsXPXr0QFpamk7097vvvsNLL72E/v37AwBCQkIQGxsL4OF7GBAQAAsLC4jFYkydOlXl/X3S955al+TkZHh7e2P27NnKtsZ+XhQKBaqrq1FZWQmZTIaqqirlpzLNGQ+e9nfnSeu/ePEixowZA2NjY4hEIvj6+uLw4cNaq/9Rfahty5Yt6N+/P8aOHaussyXHZHW8B43VX11djc8//xybN2+GtbW1yn1by3swdOhQzJ07FyKRCGKxGG5ubrh9+3az67x48SLEYjF8fHwAPPzk+sGDB/jtt9/UUv+iRYuwdu1aiEQilJSUoKysDJ06ddJK/QzKOi4hIQGenp4YPHiwSnt0dDRmzpwJFxcXlfaqqip4e3vj/fffx5dffom8vDxs2LABAJCXl6cybcPW1hZ5eXkoLi5GZWVlg8e0oaE+379/Hy+99BI2btyIlJQUmJqaKoPWo/rV2DFd6vOT9hd4OAju3LkTn332GX766SckJSU12qe8vDxYWVnBwMBAeczGxgZ37tzRTCdraai/t27dgpmZGSIiIjB+/HgsXLhQOXUoLy8PdnZ2yvs25f193DFqPRYvXoxp06Ypfx6Axn9evLy8MHz4cAwfPhw+Pj64d+8epk2bBqB548HT/u48af3u7u44evQoysvLIZVKcejQIeTn52ut/kf1oUZWVhYOHjyIJUuWKNtaekxWx3vQWP2GhoZISkqCu7t7vfu3lvfA19cXDg4OAIDc3Fzs2rULAQEBza7z7t27KuPwk/bhSesHAAMDA6xduxZ+fn7o0qULvLy8tFI/g7IOS01NRXZ2Nv7+97+rtH/44YewsrJCcHBwvceMHz8eb731FkxMTGBsbIywsDDlHCBBECASiVTur6enB+GvHQIbOqZpj+rzkCFDsHXrVnTu3Bn6+vpYsGABfvrpJ1RUVDyyX4Du97k5/Z03bx6WLFkCAwMDdOzYEbNnz8bx48cb7VND34eaY5r0qP7WnCWYP38+UlJSMHz4cISFhSmP16295nZz3ntq/Rr7efnqq6+QmZmJU6dO4eeff0b//v2V/8lsznigjt+dxup/7bXX4OrqitDQUMyaNQu9e/dW/uHXlfpr27VrFyZPngxLS0tlW0uPyersQ0P1N0bX6gca78O5c+fwyiuvYMaMGfDz82t2ndp6D1auXIn//Oc/sLGxwZo1a7RSP/9q6LD9+/cjOzsb48aNU4biOXPmYOvWrThz5gyCg4Mxb948yOVyBAcHIzs7G6mpqSofJQiCAH39h2s2HRwcIJFIlMckEgns7OzQpUsXGBkZKc9a1D6maY/q8/bt2/Hzzz8r7ycIAvT09KCvr//IfgG63+fm9Hfv3r34448/VI7p6+s32id7e3sUFBRAJpMpj+Xn5+tMf3NycjBgwAD06tULADBhwgRkZWWhqKgI9vb29d5De3t7AI9+fx93jFo3GxubR/68HD9+HP7+/ujQoQP09fUxffp0pKenA2jeeKCO353G6r9//z5mzZqF1NRU7NmzB05OTspFirpSfw2FQoEjR45g4sSJKu0tPSarqw+Pqr8xulT/4/rw5Zdf4vXXX0d0dDTmzZsHAM2us+443FJ9eFT96enpyM3NBfDwDP/LL7+MjIwMrdTPoKzDdu7cicOHD+PAgQM4cOAAgIer/69cuYKDBw/iwIED2LFjB8RiMQ4cOIBu3bohKysLW7ZsQXV1NWQyGZKSkhAQEAAA8PPzQ3JyMhQKBUpKSpCamopRo0ZBLBZj5MiR2Lt3LwDg9u3bOHXqlFZ2gHhUn+3t7bFu3TqUlZUBAHbs2AFfX18YGhrCz88PX3/9tXJe4v79+zFq1KhW0efm9PfixYt4//33oVAoUFFRgd27dyMwMLDRPtnY2MDV1VU5TzAjIwM3btyAt7e3TvQ3Ojoa586dQ1ZWFgDg2LFj6NatGzp16gQ/Pz8cOnQIf/75JxQKBfbt26fy/j7pe0+tn5+f3yN/Xtzc3HDs2DFUV1crjw0YMED5uCcdD9Txu9NY/WlpaYiOjoZCocCDBw+wc+dOjBs3Tqfqr3H9+nUYGhrW2zmgpcdkdfXhUfU3Rpfqb6wPn3/+Od555x3861//Us77BdDsOt3d3VFVVYXTp08DANLS0iASieDm5qaW+r///nts2bIFCoUCMpkMBw8ehKenp3bqf/xaRdIVdXe9EISHKz5r73pRVVUlxMTECGPGjBF8fX2FVatWCZWVlYIgCEJFRYWwfPlywd/fX/Dz8xM++eQT5eMKCwuF+fPnCwEBAcLo0aOFb7/9VjOdeozafd66daswZswYYdSoUcLChQuF4uJiQRAEQSaTCQkJCcp+bdy4UbmStbX1uSn9LSkpERYtWiT4+/sLvr6+wpYtWwS5XC4IQuN9ys7OFmbOnCkEBgYKgYGBws8//6zx/tVVu7/Hjx8XXn75ZcHf31+YPHmycOXKFeX9PvnkE8Hf318YNWqUEBUVJVRVVQmC0Pz3nlqfd999V2W1/KN+XqqqqoS33npLGDVqlBAYGCi88cYbwt27dwVBaP540BK/O02tXyaTCatWrVLW+M477zz1eNZSv/t1+3DkyBFhypQp9e6njjFZHe/Bo+qvbfjw4Sq7XrSG90AulwvPP/+84O3tLbz88svKfzWPa26dly9fFiZPniz4+/sLEydOFC5fvqyW+gVBEMrLy4Vly5YJ/v7+QkBAgBATEyNUVFRopX5ewpqIiIiIqAGcekFERERE1AAGZSIiIiKiBjAoExERERE1gEGZiIiIiKgBDMpERERERA1gUKZW5bPPPoO/vz88PDzw4osvIiwsDBkZGcrjUVFR8PT0VNmMHAC+++47jBgxQnm7d+/e6N+/Pzw8PODh4YGBAwdi2rRpOHPmzBPX9OqrryIpKemRx7/55hv4+flh4MCBmDBhgvLiB7UpFApMnz4dcXFxDT7Hl19+CQ8PjwaPHT9+HEFBQU9cNxFRU3DcrY/jbvvBoEytxjfffIOkpCRs3rwZ//3vf5GWloY+ffpgxowZKCoqUt6vpKQEUVFReNzOh5999hnOnTuHc+fO4cyZM/Dz88O8efNw//79Fqv52rVrWLt2LbZv346zZ89i6tSpeOONN1BVVaVyv/fffx9nz55t8Dlu3ryJ9evX12uXy+XYvXs3Fi1a9Ni+EhE1B8ddVRx32x8GZdIZubm56NevH3bv3o0hQ4bA09MTUVFRkEqlAIDz58+jX79+eO655yASiWBqaoqFCxfC399fZcAePXo0MjIysGvXria/tp6eHiZPnozy8nJkZ2dDKpVi1apV8PLygre3N+bMmaO8ilZDfv/9d4SEhMDDwwNz587F3bt3AQBZWVmQy+WQy+XKy1AbGRmpDLDnzp3DoUOH4OfnV+95q6ursWTJEkybNq3esSVLluCHH37A3Llzm9xPIqLaOO5y3KXGMSiTTqmursbly5dx9OhR7NmzB99//z2OHTsG4OFA/MMPP+CNN97AF198gRs3bgAA4uLi4OzsrHwOKysrrF27Flu2bMG1a9ea9LplZWX46KOP0LVrVzg7O+Obb77BxYsXcfz4cfz444+wtrbG22+//cjHp6WlITY2Funp6ejSpQsiIiIAAD4+PujduzfGjRuHvn37Ii4uDps3b4axsbHydaOiorBhwwaYmprWe97Nmzdj4MCB8PHxqXcsKioKn376Kbp169akPhIRNYTjriqOu1QbgzLpnNdeew0mJiZwdnaGu7s7bt26BQDw8vLCl19+CSsrK3z44YcIDAyEj48PPv7443rP4evri+DgYCxZsgTV1dUNvs6MGTMwaNAgDBo0CL6+vjh//jzef/99mJiYwMjICLdv38b+/ftx584dxMfH45133nlkzVOnTkWfPn1gbGyMJUuW4OzZs8jLy4NUKkXPnj2xZ88enD9/HjExMXjzzTeRk5MDAIiJiUFwcDDc3d3rPeepU6dw+vRpREZGNviaNjY2j/1eEhE1BcfdhzjuUl362i6AqC5LS0vl1/r6+ioflz333HPKhRf5+fn4/vvvkZCQgI4dOyIkJETleaKjozF+/Hhs2rQJzz//fL3X2b17N/r169dgDS+//DIePHiAlJQUJCQkwNHREUuXLsXIkSMbvL+9vb3y665du0JfXx/37t3DgQMH0KlTJwwaNAgAMGHCBBw8eBDffPMNnJyccOfOHSQkJNR7vsLCQsTExOC9996DkZHRo75VREQtguMux11qGIMytRpDhw7FP/7xD0ycOBEAYG1tjalTp+LChQsqK7BrmJqaYtOmTXjllVdQWVn5RK+VlZWFQYMGITQ0FGVlZdi7dy/efPNNnD17FoaGhvXuX3u19927dyGTyeDg4IA7d+7AwcFB5b4GBgYQi8U4ePAgrl69Ck9PTwBQ1vjrr79i7ty5KCwsxPTp0wE8XEBSUVGBQYMG4YMPPlD+ASAiUieOuxx32ztOvaBWIyAgAImJifjpp58gk8lQWVmJn3/+GWlpaY884+Du7o758+fjiy++eKLX+uGHHxAeHg6JRAIzMzNYWFjA3Nwc+voN/9/y888/x/Xr11FeXo7169dj+PDh6NKlC0aMGIGUlBScOXMGCoUCx48fx6+//orRo0cjKSlJufL7zJkzCAwMxOTJk3Hw4EEEBwfjwoULymMffPABTE1NcebMGQ7WRKQxHHc57rZ3PKNMrcaSJUvQtWtXJCQkIDc3F4IgwMXFBWvXrm1w0UWNefPm4dSpU8oV0U0xY8YM5ObmYsKECaioqECvXr2wfft26Ok1/H9LX19fhIeHo6CgAD4+PspthUJCQlBeXo7ly5ejsLAQ3bt3x/bt29GrV68n6zwRkRZw3KX2TiRwI0AiIiIiono49YKIiIiIqAEMykREREREDWBQJiIiIiJqAIMyEREREVEDGJSJiIiIiBrAoExERERE1AAGZSIiIiKiBjAoExERERE1gEGZiIiIiKgBDMpERERERA34/7oPHzHx2ETTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('data/refdiff/n_snps_16b25_vs_b8441.tsv', sep='\\t', header=0)\n",
    "\n",
    "fig=plt.figure(figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.22)\n",
    "\n",
    "sns.set_style(\"ticks\")\n",
    "for clade_i in range(1,5):\n",
    "    plt.subplot(2, 2, clade_i)\n",
    "    plt.gca().set_aspect('equal', adjustable='box')\n",
    "    subdata = data[data.Clade==clade_i][['b8441', '16b25']]\n",
    "    plt.scatter(subdata['b8441'], subdata['16b25'])\n",
    "    plt.xlabel('nSNPs b8441')\n",
    "    plt.ylabel('nSNPs 16b25')\n",
    "    plt.title('Clade'+str(clade_i))\n",
    "\n",
    "    lim_min = min(subdata.min())-200\n",
    "    lim_max = max(subdata.max())+200\n",
    "    plt.xlim(lim_min, lim_max)\n",
    "    plt.ylim(lim_min, lim_max)\n",
    "    x=np.linspace(lim_min, lim_max,101) \n",
    "    plt.plot(x,x,'r--')\n",
    "fig.savefig('figs/FigureS4_1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot distribution of SNP number differences between reference genomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('data/refdiff/n_snps_16b25_vs_b8441.tsv', sep='\\t', header=0)\n",
    "\n",
    "fig=plt.figure(figsize=(8, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "sns.set_style(\"ticks\")\n",
    "plt.subplot(1,2,1)     \n",
    "sns.boxplot(x='Clade', y='diff', data=data, color='grey')\n",
    "plt.ylabel('16b25 - b8441')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(x='Clade', y='pct_diff', data=data, color='grey')\n",
    "plt.ylim(0,50)\n",
    "plt.ylabel('Percentage of SNP differences')\n",
    "plt.show()\n",
    "\n",
    "fig.savefig('figs/FigureS4_2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='SEN'>[Evaluation of sensitivity and specificity of each pipeline](#TOP)</a>\n",
    "Using SNPs from CA05 and CA06 assemblies for this analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_with_labels(coord, labels, main, minx, miny):\n",
    "    assert len(coord) == len(labels), 'coord len is not equal to labels len'\n",
    "    for i, label in enumerate(labels): #get (0, label)\n",
    "        x, y = coord[i] #2 dim\n",
    "        plt.scatter(x, y, color='grey')\n",
    "        plt.title(main)\n",
    "        plt.annotate(label,\n",
    "                xy=(x, y), #show point \n",
    "                xytext=(5, 2), #show annotate\n",
    "                textcoords='offset points',\n",
    "                ha='right',\n",
    "                va='bottom')\n",
    "    plt.xlabel('Specificity')\n",
    "    plt.ylabel('Sensitivity')\n",
    "    plt.xlim(min_x * 0.998, 1)\n",
    "    plt.ylim(min_y * 0.998, 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Calculate Precision, recall and F1 score from GATK's VariantEval output tsv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conf_mat_cal(eval_df):\n",
    "    \"\"\" Calculate Precision, recall and F1 score from a confusion matrix \"\"\"\n",
    "    eval_df['TP'] = eval_df['HOM_VAR_HOM_VAR']\n",
    "    eval_df['FP'] = eval_df['HOM_VAR_UNAVAILABLE'] + eval_df['Mismatching_Alleles']\n",
    "    eval_df['TN'] = eval_df['NO_CALL_UNAVAILABLE'] + eval_df['HOM_REF_UNAVAILABLE']\n",
    "    eval_df['FN'] = eval_df['NO_CALL_HOM_VAR'] + eval_df['HOM_REF_HOM_VAR'] + eval_df['UNAVAILABLE_HOM_VAR']\n",
    "\n",
    "    eval_df['spec'] = eval_df['TN']/ (eval_df['TN'] + eval_df['FP'])\n",
    "    eval_df['recall'] = eval_df['TP'] / (eval_df['TP']+eval_df['FN'])   # recall = sensitivity\n",
    "    eval_df['precision'] = eval_df['TP'] / (eval_df['TP']+eval_df['FP'])\n",
    "    eval_df['f1'] = 2 * (eval_df['recall'] * eval_df['precision']/(eval_df['recall'] + eval_df['precision']))\n",
    "    eval_df['harm'] = 2 * (eval_df['spec'] * eval_df['recall']/ (eval_df['spec']+eval_df['recall']))\n",
    "    return eval_df\n",
    "\n",
    "refs = ['CA05', 'CA06']\n",
    "params = ['recall',  'spec', 'precision', 'f1', 'harm']\n",
    "\n",
    "for param in params:\n",
    "    p_out = pd.DataFrame(index = callsets)\n",
    "\n",
    "    for ref in refs:\n",
    "        eval_df = pd.read_csv('data/'+ref+'_var_eval.tsv', sep='\\t', header=0)\n",
    "        conf_df = conf_mat_cal(eval_df)\n",
    "        p_out[ref] = conf_df[param].values\n",
    "    p_out.to_csv('data/'+param+'.tsv', sep='\\t')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='F2'>[Figure 2: Scatterplot of sensitivity and specificity of each pipeline](#TOP)</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sens = pd.read_csv('data/recall.tsv', sep='\\t', header=0)  # recall = sensitivity\n",
    "spec = pd.read_csv('data/spec.tsv', sep='\\t', header=0)\n",
    "\n",
    "refs = ['CA05', 'CA06']\n",
    "\n",
    "# ca05 plot\n",
    "fig = plt.figure(figsize=(10, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "for ri in range(2):\n",
    "    plt.subplot(1, 2, ri+1)\n",
    "    min_x = min(spec[refs[ri]])\n",
    "    min_y = min(sens[refs[ri]])\n",
    "    coord = [ (spec[refs[ri]][i], sens[refs[ri]][i]) for i in range(12) ]\n",
    "    labels = [str(i) for i in range(1,13)]\n",
    "    plot_with_labels(coord, labels, refs[ri], min_x, min_y)\n",
    "fig.savefig('figs/Figure2_1.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lSjBt2jRy5MhBbGws33//PeHh4eTNm5eZM2dSoEABfaYvhBBvnF6PeE+dOsXXX3/NtWvXXvj8kCFDGDVqFDt37kQpxfr16wEYN24cHTp0YMeOHVSsWJH58+cDMHPmTBwcHAgICMDNzU2nK7GEEOJt02vhXb9+PWPGjMHGxua5527dukVCQoJ2trdVq1bs2LGDpKQkjh07po1lTVsOT8cHurq6AuDi4sLBgwdfeBJDCCHeZXod1fCqI9Lo6Oh03QQFChQgKiqK+/fvkzNnTm3IUtryf7/GxMSEnDlzEhMTo11gAC8e35eYmEh4eDjFixd/6bhFIYTIrJSUFO7cuUPFihXTjf/OyFsbTvairuWXDaL/96DrZ2XLlv6gffny5cydOzfrCQohxGtatWrVc1egvspbK7y2trbcvXtXe3znzh1sbGzImzcvcXFxpKSkYGxsrC0HsLGx4e7du9jZ2ZGcnExcXBzW1tbp4nbu3JmWLVumW3br1i3c3d1ZtWqVdomqEEJkVWRkJB07dsz0Sf63VngLFy6Mubk5f/31F9WqVcPX15f69etjamqKg4MD27dvx9XVVVsOTwdR+/r60rt3b7Zv346Dg8NzA6OtrKy0Gaj+zc7O7oWXXgohRFZktgvT4ON4PTw8tHlHp02bho+PD05OTsTHx2sTQY8ZM0Ybh3f8+HEGDhwIPJ3f9OTJkzRt2pTVq1czevRoQ6cvhBBZ9kFcuXbz5k2++OIL9uzZI0e8Qog3RtfaInM1iHdObGws0dHRH/RQQVNTU2xsbF7abSb+26TwindKbGwsUVFRFC5cGAsLi1eOaHlfKaWIj4/n1q1bAFJ830MyV4N4p0RHR1O4cGFtRrEPkZGREZaWlhQuXJjo6Oi3nY7QAym84p2SlJSEhYXF207jnWBhYfFBd7e8z6TwinfOh3qk+2/SDu8vKbzinZeYpPvdOt5E3Li4OMaNG4eLiwvNmzfnm2++4ezZs9rzFy9epGzZsto99tJERERod3X+9ttvefToEQDHjh2jZs2aNG/enObNm2v3DxMfDjm5Jt55ZqbGuA72y3jFTPKf3jzDdVJTU/Hw8KBmzZr4+vpiYmLCkSNH8PDwYNu2beTJk4dNmzbRpEkT1q5dq03uBP+bZa9p06bMmzeP+fPnM2TIEEJCQujWrRu9evV649sk/hvkiFeIV/jzzz+Jjo5mwIAB2sRNtWrVwsfHR7vx5pYtW/D09OTcuXParXdeNcteSEgIhw8fpkWLFvTu3Zvbt2+/nY0Tb40UXiFe4dy5c1SqVOm5yZgcHR3Jly8f+/fvp1ChQpQoUYJGjRqxdu1agFfOspcrVy7c3d3x9fXF0dERT09Pw26UeOuk8ArxCtmyZXvhjHlpNm3ahIuLCwDOzs5s3ryZxMTEV86y5+3trd3w8uuvvyYsLIyHDx/qIXvxrpLCK8QrVKxYkXPnzj1XSH/66Se2b9/OwYMHWbp0KZ9//jkjR44kNjaWXbt2pZtlD/43+15qaioLFix47jbsaUfG4sMghVeIV3BwcCBfvnzMnTtXK5Z//PEHmzZt4ubNm9SqVYuDBw+yd+9e9u3bR+/evVm3bl26WfYAbZa9bNmyERgYqI2A8PX1pXLlyjJ2+QMjhVeIVzAyMmL+/PncuHEDFxcXXF1dWbx4MYsWLcLf358OHTqkW79Dhw6cPn2ay5cvv3SWvSlTpvDbb7/RtGlTfv/9dyZMmPAWtky8TTI7mXinnD9/nvLly6dblpiUgpnpm79lk77ivkkvag/x7tC1tsgRr3jn6as4vutFV7y/pPAKIYSBSeEVQggDk8IrhBAGJoVXCCEMTAqvEEIYmBReIYQwMCm84p2Xmpz4VuO+6fl44+LiGDx4MC1atKBFixbpYokPg1wgLt552UzMuDKx9RuPW3LE7xmuo4/5eH18fChYsCDTp0/n4MGDjB07lg0bNrzx7RPvLjniFeIV3vR8vEopdu3aRc+ePQGoX78+kyZNejsbJ94aKbxCvMKbno/33r17mJmZsXLlSlq0aIG7u/tzM5WJ959euxr8/f1ZsGABSUlJdOnShY4dO6Z7/sCBA0ybNg2AMmXK4O3tTY4cOWjVqpW2MyYkJBAeHs7BgwdJTEykadOmfPTRRwDkz5+fJUuW6HMTxAcus/Pxfv/99wwcOPCl8/GmpKRw9+5dcufOja+vL4cPH6Zv377s2bNHb9sg3j16K7xRUVHMmDGDTZs2YWZmRvv27alZsyalS5cGIDY2Fi8vL1asWEHp0qVZvHgxM2bMYOTIkWzatEmLM3ToUFq2bEn+/PnZuXMnrq6ueHt76yttIdKpWLEiq1evRimV7q6/P/30E+XKlePgwYOcOXOG3377DaWUNh9vkyZNtPl4jY2Ntfl48+TJg4mJiVasP/vsMx4/fsy9e/fIly/f29pMYWB662oICgqiVq1aWFtbY2lpSZMmTbR7TgFcu3aNQoUKaYW4YcOG7N69O12M4OBgQkND8fDwAJ7eq+rixYu0atUKd3d3Lly4oK/0hQDe/Hy8ZmZm1KlTh23btgFw8uRJLCwsyJMnz1vbRmF4ejvijY6OpkCBAtpjGxsbTp8+rT0uXrw4kZGRhIaGUq5cOQICArh79266GLNnz8bT0xNj46ezSJmbm9OiRQvat2/PgQMH6Nu3L9u3b8fMzEx7TWxsLLGxseniREZG6mMThYGkJie+1ggEXeJmMzF75Tpp8/H6+Pjg4uKCiYkJefLkYdGiRQwbNuy5+6V16NCBX375RZuP18vLiwULFlCwYEF++uknACZOnMjo0aNZvXo1JiYmzJgx47k+ZPF+01vhfdU9pwCsrKyYMmUKo0aNIjU1lbZt22Jqaqo9f+nSJe7fv0/Dhg21Zf3799d+d3R0ZPr06Vy5coVy5cppy5cvX87cuXPf9Obo7EVzvv4X5oF9l2RUHPUdN2/evPz444/PLff3939uWb58+Th16pT2eMWKFc+tY2Njw8KFCzOR6YftZf8v/+X/I70VXltbW44fP649jo6OxsbGRnuckpKCnZ2dNn7x7NmzFC1aVHt+9+7dODs7p4u5YsUKXFxctK9lSqnn7lXVuXNnWrZsmW5ZZGTkcyf2DMXM1BjXwX7plvlPb/5WchHiv+hF/0Pw3/4/0tv3mzp16hAcHExMTAzx8fHs2rWL+vXra88bGRnRrVs3oqKiUEqxdOnSdIX25MmTODg4pIt57NgxNm7cCMDRo0dJTU2lZMmS6daxsrKiSJEi6X7s7Oz0tZlCCJFpej3i9fT0xN3dnaSkJNq0aYO9vT0eHh4MGDCASpUq4e3tTY8ePUhMTKR27dp0795de314eDi2trbpYo4YMQIvLy/8/PwwNzdn+vTp0jf2Hvr3CIIP1QdwV64Pll7H8bq6uuLq6ppu2eLFi7XfGzRoQIMGDV742rSzwc+ytbVl2bJlbzRH8W4xNTUlPj4eS0vLt53KWxcfH5/uvId4f8jhonin2NjYcOvWLR4/fvzBHvEppXj8+DG3bt1Kd15EvD9kkhzxTrGysgKezuyVlJT0lrN5e0xNTbG1tdXaQ7xfpPCKd46VlZUUHPFek64GIYQwMCm8QghhYFJ4hRDCwKTwCiGEgUnhFUIIA5PCK4QQBiaFVwghDEwKrxBCGJgUXiEyITHpxTemfNlyIV7kg79y7X2cZPlDp8/J59/HuWGF4X3whVf+kd4/Mvm8eNdJV4MQQhiYFN7/MOlvFOK/6YPvavgv+692k0i/uvjQSeEVBvdf/cDQN/lA+nBI4RXiHSEfSB8O6eMVQggDk8IrhBAGJoVXvJCMmBBCf6SPV7yQ9DcKoT9yxCuEEAYmhVcIIQxMr4XX398fZ2dnGjduzKpVq557/sCBA7i6uuLq6srgwYN59OgRAMeOHaNmzZo0b96c5s2bM3z4cABiY2Pp2bMnTk5OdOzYkTt37ugzfSGE0Au9Fd6oqChmzJjB6tWr8fPzY926dYSFhWnPx8bG4uXlxYwZM/D396dcuXLMmDEDgJCQELp164afnx9+fn74+PgAMHPmTBwcHAgICMDNzY2JEyfqK30hhNAbvRXeoKAgatWqhbW1NZaWljRp0oQdO3Zoz1+7do1ChQpRunRpABo2bMju3buBp4X38OHDtGjRgt69e3P79m0A9u/fj6urKwAuLi4cPHiQpKQkfW2CEELohd4Kb3R0NAUKFNAe29jYEBUVpT0uXrw4kZGRhIaGAhAQEMDdu3cByJUrF+7u7vj6+uLo6Iinp+dzMU1MTMiZMycxMTHp3jc2NpabN2+m+4mMjNTXZgohRKbpbTiZUuq5ZUZGRtrvVlZWTJkyhVGjRpGamkrbtm0xNTUFwNvbW1vv66+/Zvr06Tx8+PCF75MtW/rPjuXLlzN37tw3sQlCCKEXeiu8tra2HD9+XHscHR2NjY2N9jglJQU7Ozs2bNgAwNmzZylatCipqan8/PPP9OzZE2Pj/00MYmJigo2NDXfv3sXOzo7k5GTi4uKwtrZO976dO3emZcuW6ZZFRkbSsWNHPWylEEJknt66GurUqUNwcDAxMTHEx8eza9cu6tevrz1vZGREt27diIqKQinF0qVLcXZ2Jlu2bAQGBrJz504AfH19qVy5MhYWFjg6OuLr6wvA9u3bcXBw0I6S01hZWVGkSJF0P3Z2dvraTCGEyDS9HvF6enri7u5OUlISbdq0wd7eHg8PDwYMGEClSpXw9vamR48eJCYmUrt2bbp37w6gdUHMmzePvHnzMnXqVAC+++47vLy8aNq0Kbly5WLatGn6Sl8IIfRGr5cMp43RfdbixYu13xs0aECDBg2ee93HH3/M2rVrn1tubW3NwoUL33ieQghhSHLlmhBCGJgUXiGEMDApvEIIYWBSeIUQwsCk8AohhIFJ4RVCCAOTwiuEEAYmhVcIIQxMCq8QQhiYFF4hhDAwKbxCiA9WYlJKppa/KXJ7dyHEB8vM1BjXwX7PLfef3lyv7ytHvEKILHvREaK+jxr/y+SIVwiRZS86ctT3UeN/mRzxCiGEgUnhFeIdl5qcmKnl4t0nXQ1CvOOymZhxZWLr55aXHPH7W8jm3ZGanEg2E7MMl72LpPCKd8bL/mnexD+TPmO/LM5/pQj8V73oA+m/8mEkhfc99F8tAvo8stP3UeN/uQgIw5PC+xbo++hLioAQ7zYpvG+B9NkJ8WGTUQ1CCGFgGR7xhoWFsWTJEh48eIBSSlsut1kXQgjdZFh4hw4dyqeffkr16tUxMjIyRE5CCPFey7DwJiUlMXLkSEPk8k7R9wkwIcSHK8PCW6xYMaKjo7Gxscl0cH9/fxYsWEBSUhJdunShY8eO6Z4/cOAA06ZNA6BMmTJ4e3uTI0cOLl++zKhRo3j06BHZs2dn7NixlC9fnoiICJo2bcpHH30EQP78+VmyZEmm83odcgJMCKEvGRbe1NRUXFxcqFChAubm5tryjPp4o6KimDFjBps2bcLMzIz27dtTs2ZNSpcuDUBsbCxeXl6sWLGC0qVLs3jxYmbMmMHIkSMZOXIkPXv2pGHDhgQHBzNs2DC2bNlCSEgIrq6ueHt7Z3GzhRDi7cmw8DZu3JjGjRtnOnBQUBC1atXC2toagCZNmrBjxw769esHwLVr1yhUqJBWiBs2bEiPHj0YOXIkbm5u1K9fH4CyZcty+/ZtAEJCQrh48SKtWrUiZ86cjBgxgrJly2Y6NyGEeJsyLLwtW7ZM91gpxfXr1zMMHB0dTYECBbTHNjY2nD59WntcvHhxIiMjCQ0NpVy5cgQEBHD37l0AWrVqpa03e/ZsGjVqBIC5uTktWrSgffv2HDhwgL59+7J9+3bMzP7X5xobG0tsbGy6XCIjIzPMVwghDCXDwrt27VqmTp1KfHy8tixv3rwcPnz4la97duhZmmdHRVhZWTFlyhRGjRpFamoqbdu2xdTUNN3rp06dyqlTp/jtt98A6N+/v/a8o6Mj06dP58qVK5QrV05bvnz5cubOnZvRZgkhxFuTYeFdtGgRy5YtY8GCBQwcOJB9+/a91hGkra0tx48f1x7/+wRdSkoKdnZ2bNiwAYCzZ89StGhRAJKTkxk2bBhRUVH89ttv5MqVC4AVK1bg4uJCnjx5gKfF2cQk/SZ07tz5uaP0yMjI507sCSHE25LhlWvW1tZUrlyZ8uXLc+/ePb799ltCQkIyDFynTh2Cg4OJiYkhPj6eXbt2af228PTot1u3bkRFRaGUYunSpTg7OwMwZcoU4uLiWLp0qVZ0AY4dO8bGjRsBOHr0KKmpqZQsWTLd+1pZWVGkSJF0P3Z2dq/XGkIIYQAZHvGamJjwzz//UKxYMU6fPs1nn33Go0ePMgxsa2uLp6cn7u7uJCUl0aZNG+zt7fHw8GDAgAFUqlQJb29vevToQWJiIrVr16Z79+7ExMSwatUqihQpgpubmxbPz8+PESNG4OXlhZ+fH+bm5kyfPp1s2eSqZyHEf0uGhbdt27b06tWLhQsX0qJFCwIDAylVqtRrBXd1dcXV1TXdssWLF2u/N2jQgAYNGqR7Pm/evJw7d+6F8WxtbVm2bNlrvbcQQryrMiy8bdq0wdnZGUtLS9atW0dISAh169Y1RG5CCPFeyvB7empqKmvWrGHYsGHaVWXPjj4QQgiRORkW3qlTp3LhwgVOnz6NUoo//vgDHx8fQ+QmhNAzuZHm25FhV0NwcDCbN2+mVatW5MqVi6VLl9K8eXND5CaE0DOZk+TtyPCI18TEJN3IATMzs+fGzgoh3m2JSSlvOwXxjAwraJkyZVi1ahUpKSlcuXKFX3/9Nd2VYkKId5+ZqTGug/2eW+4/Xb69vg0ZHvGOGDGCs2fPcu/ePb7++msePXrEDz/8YIjchBDivZThEW/OnDmZNGmSIXIR/wEyQbwQWffSwjthwoRXvvBDvCuFkJMxQrwJLy28q1atIl++fDg5OZE7d25D5iSEEO+1lxbe3bt3s3nzZnbs2EGpUqVo1aoV9erVk7kRhBAii15aRQsXLky/fv3YunUr7u7u7N69m6ZNmzJ16lQuX75syByFEOK98loDcqtVq0a1atWIjIxk6NChLFu2jPPnz+s7NyGEeC9lWHgTExPZs2cPfn5+hISE8MUXXzBw4EADpCaE+C+TETAv99LC++eff7Jlyxb27t1LlSpVaNmyJbNnz053fzMhhHgZGQHzci8tvJ07d6ZQoUK0bt2avHnzEhERwapVq7Tnu3btapAEhRDiffPSwtuiRQuMjIy4d+8e9+7dM2ROQgjxXntp4Z08ebIh8xBCiA+GDMoVQggDk8IrhBAGJoVXCCEMLMPCGxoaSs+ePQG4cOECzZs358qVK3pPTAgh3lcZFt6xY8fi5uYGQNmyZenfvz9jxozRe2JCCPG+yrDwxsfH07hxY+1xo0aNiIuL02tSQgjxPsuw8BoZGXHhwgXt8eXLl2WGMiGEyIIM52r47rvv6NSpE2XKlAHgypUrTJs27bWC+/v7s2DBApKSkujSpQsdO3ZM9/yBAwe0WGXKlMHb25scOXIQGxvL999/T3h4OHnz5mXmzJkUKFCAxMRERowYwZkzZ8iePTvTpk2jVKlSmd1mIYR4qzI8dG3YsCE7duygS5cudO/ena1bt/LZZ59lGDgqKooZM2awevVq/Pz8WLduHWFhYdrzsbGxeHl5MWPGDPz9/SlXrhwzZswAYObMmTg4OBAQEICbmxsTJ04EYMWKFVhYWBAQEMAPP/yAl5eXrtsthBBvzUsLb3BwMAC7du3ir7/+QilFcnIyf/31F7t27cowcFBQELVq1cLa2hpLS0uaNGnCjh07tOevXbtGoUKFKF26NPC0wO/evRuA/fv34+rqCoCLiwsHDx4kKSmJ/fv306xZMwCqV6/O/fv3iYiI0HHThRDi7XhpV8O2bduoXbs2K1aseO45IyMjvvzyy1cGjo6OpkCBAtpjGxsbTp8+rT0uXrw4kZGRhIaGUq5cOQICArh79+5zrzUxMSFnzpzExMQ8F7NAgQJERkZSqFAhbVlsbCyxsbHpcomMjHxlrkIIYUgZ3uzyRYX3dSilnltmZGSk/W5lZcWUKVMYNWoUqamptG3bFlNT05fGe9kJvX8vX758OXPnztUpZyGEMIQMT66FhYWxZMkSHjx4kK6YLly48JWvs7W15fjx49rj6OhobGxstMcpKSnY2dmxYcMGAM6ePUvRokWBp0fHd+/exc7OjuTkZOLi4rC2tsbGxoY7d+5QrFgxAO7cuZMuJjydzrJly5bplkVGRj53Yk8IId6WDE+uDR06lBw5ctC4cWOaNGmi/WSkTp06BAcHExMTQ3x8PLt27aJ+/fra80ZGRnTr1o2oqCiUUixduhRnZ2cAHB0d8fX1BWD79u04ODhgamqKo6Mjfn5+ABw/fhxzc/N03Qzw9Ei6SJEi6X7s7Oxeu0GEEELfMjziTUpKYuTIkZkObGtri6enJ+7u7iQlJdGmTRvs7e3x8PBgwIABVKpUCW9vb3r06EFiYiK1a9eme/fuwNMhbF5eXjRt2pRcuXJpQ86++eYbRo8eTdOmTTEzM2Pq1KmZzksIId62DAtvsWLFnusmeF2urq7a6IQ0ixcv1n5v0KABDRo0eO511tbWL+zKMDc3Z8qUKZnOQwgh3iUZFt7U1FRcXFyoUKEC5ubm2vKM+niFEEK8WIaFt3HjxunmahBCCJE1GRbeli1bcuvWLY4ePUpycjI1atTQRhUIIYTIvAxHNfzxxx+0bt2a3bt3s2fPHtq0aaNdYSaEECLzMjzinTVrFitXrtQu7b106RJDhgyhUaNGek9OCCHeRxke8SYlJWlFF+Djjz8mJSVFr0kJIcT7LMPCmz17dkJCQrTHISEhWFhY6DUpIYR4n2XY1TBkyBB69+5NsWLFUEpx7do1Zs2aZYjchBDivZRh4XVwcGDbtm2cOnUKpRSVK1cmT548hshNCCHeSxkW3oSEBA4fPsyDBw8AuHXrFoBMOiOEEDrKsPD27t2b2NhYihQpoi0zMjKSwiuEEDrKsPBGRUWxffv2dHPpCiGE0F2GoxrKlCmj3RlCCCFE1mV4xPvVV1/h5OREmTJlMDH53+q//fabXhMTQoj3VYaF98cff6RXr1589NFHhshHCCHeexkW3hw5cuDh4WGIXIQQ4oOQYR9vw4YNWbVqFdHR0Tx48ED7EUKI91VqcmKmlmdWhke8v/76K4mJiYwfP15bZmRkxPnz599IAkII8a7JZmLGlYmtn1tecsTvbyR+hoV39erVVKxY8Y28mRBCiNfoahgyZIgh8hBCiA9GhoW3bNmy+Pv7ExERIX28QgjxBmTY1bBnzx527NiRbpn08QohhO4yLLzPzsUrhBAi6zIsvImJiRw4cIBHjx4BkJKSwo0bN/D09NR7ckII8T7KsPB6enoSHh7OnTt3+OSTTzh16hQ1atQwRG5CCPFeyrDwnj9/nl27djF27Fi6du2KUopx48a9VnB/f38WLFhAUlISXbp0eW4qybNnzzJ69GiSkpIoWLAgP/74I1ZWVrRq1Uq7r1tCQgLh4eEcPHiQxMREmjZtql2+nD9/fpYsWZLZbRZCiLcqw8JrY2ODiYkJxYsX5+LFizg5OREfH59h4KioKGbMmMGmTZswMzOjffv21KxZM92NMydOnMiAAQNwdHRk8uTJLFmyBE9PTzZt2qStM3ToUFq2bEn+/PnZuXMnrq6ueHt767i5Qgjx9mU4nMzS0hJ/f3/KlStHQEAAFy5ceK3hZEFBQdSqVQtra2ssLS1p0qTJc6MjUlNTtb7j+Ph4smfPnu754OBgQkNDtbkiQkJCuHjxIq1atcLd3Z0LFy687nYKIcQ7I8PCO2rUKM6fP89nn31GtmzZ6NSpE926dcswcHR0NAUKFNAe29jYEBUVlW4dLy8vRowYQd26dQkKCqJ9+/bpnp89ezaenp4YGxsDYG5uTosWLdi0aRPdu3enb9++JCamv3Y6NjaWmzdvpvuJjIzMMF8hhDCUDLsaSpQowdChQwGYOXPmawdWSj237Nm7WCQkJDBixAiWL1+Ovb09y5YtY9iwYSxatAiAS5cucf/+fRo2bKi9pn///trvjo6OTJ8+nStXrlCuXDlt+fLly5k7d+5r5ymEEIb20sLbu3fvV75w4cKFr3ze1taW48ePa4+jo6OxsbHRHl+8eBFzc3Ps7e0BaNeuXbrbxu/evRtnZ+d0MVesWIGLi4t2l2OlVLrJ2QE6d+5My5Yt0y2LjIyUe8QJId4ZLy28TZo00X6fPXs2AwYMyFTgOnXqMGfOHGJiYrCwsGDXrl3pZjgrVqwYkZGRXLlyhZIlS7Jnzx4qVaqkPX/y5Ek6d+6cLuaxY8dISEjAw8ODo0ePkpqaSsmSJdOtY2VlhZWVVaZyFUIIQ3pp4X32qHH58uXPHUVmxNbWFk9PT9zd3UlKSqJNmzbY29vj4eHBgAEDqFSpEj4+PgwcOBClFPny5WPSpEna68PDw7G1tU0Xc8SIEXh5eeHn54e5uTnTp08nW7YMu6mFEOKdkmEfL6DzHYZdXV1xdXVNt2zx4sXa746Ojjg6Or7wtdu3b39uma2tLcuWLdMpFyGEeFfI4aIQQhjYS494nx2rm5KSwj///JNupIK1tbU+8xJCiPfWSwtvrVq1MDIy0optzZo1tedkWkghhNDdSwtvaGioIfMQQogPhvTxCiGEgUnhFUIIA5PCK4QQBiaFVwghDEwKrxBCGJgUXiGEMDApvEIIYWBSeIUQwsCk8AohhIFJ4RVCCAOTwiuEEAYmhVcIIQxMCq8QQhiYFF4hhDAwKbxCCGFgUniFEMLApPAKIYSBSeEVQggDk8IrhBAGJoVXCCEMTAqvEEIYmF4Lr7+/P87OzjRu3JhVq1Y99/zZs2dp3bo1zZo1o1evXsTGxgJw7NgxatasSfPmzWnevDnDhw8HIDY2lp49e+Lk5ETHjh25c+eOPtMXQgi90FvhjYqKYsaMGaxevRo/Pz/WrVtHWFhYunUmTpzIgAED2LJlCyVKlGDJkiUAhISE0K1bN/z8/PDz88PHxweAmTNn4uDgQEBAAG5ubkycOFFf6QshhN7orfAGBQVRq1YtrK2tsbS0pEmTJuzYsSPdOqmpqTx69AiA+Ph4smfPDjwtvIcPH6ZFixb07t2b27dvA7B//35cXV0BcHFx4eDBgyQlJelrE4QQQi/0Vnijo6MpUKCA9tjGxoaoqKh063h5eTFixAjq1q1LUFAQ7du3ByBXrly4u7vj6+uLo6Mjnp6ez8U0MTEhZ86cxMTEpIsZGxvLzZs30/1ERkbqazOFECLTTPQVWCn13DIjIyPt94SEBEaMGMHy5cuxt7dn2bJlDBs2jEWLFuHt7a2t9/XXXzN9+nQePnz4wvfJli39Z8fy5cuZO3fuG9oKIYR48/R2xGtra8vdu3e1x9HR0djY2GiPL168iLm5Ofb29gC0a9eOo0ePkpqayoIFC0hJSUkXz8TEBBsbGy1mcnIycXFxWFtbp1uvc+fO7NmzJ93Pi07sCSHE26K3wlunTh2Cg4OJiYkhPj6eXbt2Ub9+fe35YsWKERkZyZUrVwDYs2cPlSpVIlu2bAQGBrJz504AfH19qVy5MhYWFjg6OuLr6wvA9u3bcXBwwNTUNN37WllZUaRIkXQ/dnZ2+tpMIYTINL11Ndja2uLp6Ym7uztJSUm0adMGe3t7PDw8GDBgAJUqVcLHx4eBAweilCJfvnxMmjQJgClTpjBq1CjmzZtH3rx5mTp1KgDfffcdXl5eNG3alFy5cjFt2jR9pS+EEHqjt8IL4Orqqo1CSLN48WLtd0dHRxwdHZ973ccff8zatWufW25tbc3ChQvffKJCCGFAcuWaEEIYmBReIYQwMCm8QghhYFJ4hRDCwKTwCiGEgUnhFUIIA5PCK4QQBiaFVwghDEwKrxBCGJgUXiGEMDApvEIIYWBSeIUQwsCk8AohhIFJ4RVCCAOTwiuEEAYmhVcIIQxMCq8QQhiYFF4hhDAwKbxCCGFgUniFEMLApPAKIYSBSeEVQggDk8IrhBAGJoVXCCEMTAqvEEIYmF4Lr7+/P87OzjRu3JhVq1Y99/zZs2dp3bo1zZo1o1evXsTGxgJw+fJlOnToQPPmzWnXrh3nz58HICIigqpVq9K8eXOaN29O9+7d9Zm+EELohd4Kb1RUFDNmzGD16tX4+fmxbt06wsLC0q0zceJEBgwYwJYtWyhRogRLliwBYOTIkXh4eODn58fAgQMZNmwYACEhIbi6uuLn54efn5+2vhBC/JforfAGBQVRq1YtrK2tsbS0pEmTJuzYsSPdOqmpqTx69AiA+Ph4smfPDoCbmxv169cHoGzZsty+fRt4WngvXrxIq1atcHd358KFC/pKXwgh9MZEX4Gjo6MpUKCA9tjGxobTp0+nW8fLy4uuXbsyadIkLCwsWL9+PQCtWrXS1pk9ezaNGjUCwNzcnBYtWtC+fXsOHDhA37592b59O2ZmZtr6sbGxWpdFmsjIyDe+fUIIoSu9FV6l1HPLjIyMtN8TEhIYMWIEy5cvx97enmXLljFs2DAWLVqkvX7q1KmcOnWK3377DYD+/ftrr3d0dGT69OlcuXKFcuXKacuXL1/O3Llz9bVZQgiRZXorvLa2thw/flx7HB0djY2Njfb44sWLmJubY29vD0C7du2YNWsWAMnJyQwbNoyoqCh+++03cuXKBcCKFStwcXEhT548wNPibGKSfhM6d+5My5Yt0y2LjIykY8eOb34jhRBCB3rr461Tpw7BwcHExMQQHx/Prl27tH5bgGLFihEZGcmVK1cA2LNnD5UqVQJgypQpxMXFsXTpUq3oAhw7doyNGzcCcPToUVJTUylZsmS697WysqJIkSLpfuzs7PS1mUIIkWl6PeL19PTE3d2dpKQk2rRpg729PR4eHgwYMIBKlSrh4+PDwIEDUUqRL18+Jk2aRExMDKtWraJIkSK4ublp8fz8/BgxYgReXl74+flhbm7O9OnTyZZNhiILIf5b9FZ4AVxdXXF1dU23bPHixdrvjo6OODo6Pve6c+fOvTCera0ty5Yte7NJCiGEgcnhohBCGJgUXiGEMDApvEIIYWBSeIUQwsCk8AohhIFJ4RVCCAOTwiuEEAYmhVcIIQxMCq8QQhiYFF4hhDAwKbxCCGFgUniFEMLApPAKIYSBSeEVQggDk8IrhBAGJoVXCCEMTAqvEEIYmBReIYQwMCm8QghhYFJ4hRDCwKTwCiGEgUnhFUIIA5PCK4QQBiaFVwghDEwKrxBCGJheC6+/vz/Ozs40btyYVatWPff82bNnad26Nc2aNaNXr17ExsYCEBsbS8+ePXFycqJjx47cuXMHgMTERIYMGYKTkxMtW7bk8uXL+kxfCCH0Qm+FNyoqihkzZrB69Wr8/PxYt24dYWFh6daZOHEiAwYMYMuWLZQoUYIlS5YAMHPmTBwcHAgICMDNzY2JEycCsGLFCiwsLAgICOCHH37Ay8tLX+kLIYTe6K3wBgUFUatWLaytrbG0tKRJkybs2LEj3Tqpqak8evQIgPj4eLJnzw7A/v37cXV1BcDFxYWDBw+SlJTE/v37adasGQDVq1fn/v37RERE6GsThBBCL0z0FTg6OpoCBQpoj21sbDh9+nS6dby8vOjatSuTJk3CwsKC9evXP/daExMTcubMSUxMzHMxCxQoQGRkJIUKFdKWxcbGal0WaW7dugVAZGTkC3NNehzz3LKbN28SFZf43HKzmzdfud2vE1+fsV8WX5+xdYkvbZ612LrElzbPWuwXxU+rKSkpKZl6TyOllMrUK17TwoULiY+Px9PTE4ANGzYQEhKCt7c3AAkJCbRu3RofHx/s7e1ZtmwZwcHBLFq0iIoVK3Ly5ElMTJ5+LtSvX5/ff/+dTp06sWjRIooVKwbA119/zbBhw6hSpYr2vnPmzGHu3Ln62CQhhHihVatW4eDg8Nrr6+2I19bWluPHj2uPo6OjsbGx0R5fvHgRc3Nz7O3tAWjXrh2zZs0Cnh4d3717Fzs7O5KTk4mLi8Pa2hobGxvu3LmjFd47d+6kiwnQuXNnWrZsmW5ZYmIi4eHhFC9eHGNj4wxzj4yMpGPHjqxatQo7OzvdGuAtxNZ3fIlt+PgS2/DxMxM7JSWFO3fuULFixUy9h94Kb506dZgzZw4xMTFYWFiwa9cuxo8frz1frFgxIiMjuXLlCiVLlmTPnj1UqlQJAEdHR3x9fenduzfbt2/HwcEBU1NTHB0d8fPzw8HBgePHj2Nubp6umwHAysoKKyur5/IpWbJkprfBzs6OIkWKZPp1bzu2vuNLbMPHl9iGj/+6sdMOBDNDr0e8np6euLu7k5SURJs2bbC3t8fDw4MBAwZQqVIlfHx8GDhwIEop8uXLx6RJkwD47rvv8PLyomnTpuTKlYtp06YB8M033zB69GiaNm2KmZkZU6dO1Vf6QgihN3orvACurq7a6IQ0ixcv1n53dHTE0dHxuddZW1uzcOHC55abm5szZcqUN5+oEEIYkFy5JoQQBiaF9wWsrKzo16/fC/uK3+XY+o4vsQ0fX2IbPr6+cwc9DicTQgjxYnLEK4QQBiaFVwghDEwKrxDipVJTU992Cu8lKbxviHSVi4wkJye/7RQybc2aNZw9e5YnT5688dgbN24kLi7ujcd9mXfpf1ROrukgNTWVbNmykZqaSnx8PDly5Ei3PCtCQ0N58OABT548STfG+U3E1nf8EydOcPv2bR49eoSbm5u2XCmFkZFRlmKHh4fz+PFjlFKUK1dOi/lfaPPAwEAaNWqktUFycjImJiZvpF22bt1KpUqVdLp6KiOxsbEMHz6cxMREPv/8c+rVq0fBggVf67L7V1FKERMTg7u7O5aWlnTt2hUnJ6cst8WzUlJSMDY2Ji4ujsePH2NhYUGuXLm093+T76ULKbw6SPvDzZ07lxMnTgDg4+OjzRuR9kfPrBs3bjBw4EAqV67MgwcPGDlyJElJSdr14lndYfQZ//r16/Tr14+vvvqKkJAQvv/+e4yMjChVqlSWY9+4cYPhw4eTN29erKysGDNmDI8fP8ba2lqneP+Orc82T0lJwdXVlZiYGAYPHqx9IL2pf/5vvvmGq1ev0qtXL1xdXbG2tiY+Ph4LC4ssxVVKkZCQoM1/vXnzZiwsLHBxcaFatWrkzZs3y7nD05slLFy4kKJFi9KnTx9t7pY3pW3btnz00UcYGxvToEEDnJycshQv7cP4zJkzXL16lQYNGmgFPTOk8GZS2j/MX3/9xciRI5kxYwZLly7F39+fLl26MGzYMJ1j9+3blxo1atC5c2f69u1LSkoK9+7dIz4+nilTplChQoUs5a7P+IMGDaJ8+fJ4eHgwfPhwbt68SXR0NKampowZM4bq1avrHLtXr17Ur1+fdu3a0a1bNwoVKkRMTAxGRkYMGzZMp3k40ui7zQF8fX1ZvHgxcXFxGBsbM3HiRHbs2MGYMWNISUnB1NRU59gHDhzAx8eHHDlyoJSiR48e7NmzhzFjxmRpHOqRI0f4888/KVCgAKamprRp04ZVq1axc+dOypYtS6NGjahSpYo2h3Zm7N69m/Pnz1O+fHlMTU2pW7cuM2bMYPv27TRq1IiuXbtSsGBBnXNP+x/19/cnKCiI7t27ExQUxNmzZ7GysuLzzz+ndu3amY6bVnSvXLlCnz59yJUrF3fv3qVLly60a9cuU20hfbyZlHaUcurUKTp16kS5cuWYOnUqW7du5c8//6RKlSra5O6ZVbhwYe0TPyYmhooVK7J06VK++uor1qxZk+Xc9Rm/ZMmSFC1aFICIiAgcHR3ZuXMnbdq0YeHChTqfpElOTiZv3rzUrl0bExMT4uLisLW1pV+/fnz88cds3bo1S3nru80BmjVrRrNmzQgICGDAgAF4eHiwY8cOUlNTs1R04ell923btmXBggUMHjyY8ePHc+TIEe7fv5+luJ988glJSUlMnDiRXbt28c8//9CpUydmzZqFsbEx8+bNIykpSafYVatW5erVq/Tr14/169djbGzM999/z4oVK4iIiMDd3V3nvt/U1FSMjIx49OgRcXFx1KxZk9KlS9OqVStat26NmZkZf/zxh06x07qd1q9fT8+ePdmwYQPe3t5s27aNLl26EBgY+PrBlMi006dPqw4dOih3d3d16NAh9c8//2jPXbhwQee4S5YsUUuWLFFKKRUWFqYtf/DggerRo4e6deuW7knrOf7mzZvVunXrtHjP6tatm7p69arOsWfOnKkWL16slFIqODhYWx4aGqp69+6t7ty5o3Nsfbf5kydPlFJKzZo1S61evVoppVTz5s3VoEGDVNmyZdXJkyd1jp2YmKiUUurXX39V06dP12KPHz9eValSRZ04cUKnuKmpqUqpp20wb9489cMPP6iuXbuqVatWKaWUOnfunAoNDc1S7KioKDV27FjVsWNH1bp1a7Vjxw6llFJ37txRly9f1in2s0aNGqVcXV2Vs7Oz2rNnj4qNjVVKKRUdHa3u3bunc9wrV66oPn36qHnz5mntr5RSCxcuVD4+Pq8dR7oaXpP6V59cWFgY69at4+HDh1SuXJmKFStSvnx5bfL2zHhZn3BCQgLZs2cnICCATZs2pZtg6F2J/7LYjx8/xtjYGH9/fwICArT76b2J2ImJiZiZmbFv3z7WrFnDokWL3ljsN93maf2tt27dYurUqdy7dw9HR0c8PDwIDw/XviXoEvvRo0fkyJGDJ0+e8MMPPxAeHs6XX35Jjx49iIyM1Gme2rSv02lzWMfGxlK1alX++OMPli1bRrZs2bh9+zb+/v6ZPvH4bOyHDx/y+PFjihYtiq+vL/Pnz9fm4d6+fbtOJzXT2uX8+fNMmzaNRYsWsWLFCvbt20fp0qVp3Lgx1atXz9LJwRMnTrBt2zYiIiKoUqUK1atXp2rVqi/czleRwvua0hozICCA0NBQSpUqRbNmzfjjjz/w9/cnJSUFT0/PLM0NOmHCBJo2bUrVqlVJSkrCz8+P5cuXU6BAAfr06YODg4POJ+70HX/EiBF8+eWXODo6kpiYyJYtW9i4cSN58+alZ8+eVKlSRefYs2fPxtnZmdKlS5OUlMS2bdtYvnw5OXLk4LvvvqN69eo6x9Z3m0+aNImmTZtSuXJlli1bRlBQEAsXLtTi/fsDPTNGjhxJ48aNcXR0ZOPGjezevZt58+ZlqbCk5TNu3DhCQkIoUqQIuXPnpl27dpQrV44zZ86QI0cO7aSpLrEnTZrEsWPHKFGiBOXLl6dFixbkzp2bgwcPYmdnl+lJxf9t4MCBJCYmMn/+fABu377N4sWLCQ0N5eeff870ybBnRzH9888/KKUIDAzkxo0bPHnyBDs7O9zc3LCysnr9v6XOx9wfkJSUFKXU068Z9evXVytWrFA1atRQ7u7u6uzZs+rx48dq+/btOsX++++/1aNHj5RSSo0ePVqtX78+3fOnTp1S8fHxSqn/fU17V+IfOHBAPXz4UCml1IIFC9S8efPSPR8aGqrFzGzssLAwLa9vv/1W7d+/X3vuyZMnKigoSN29e1en2IZu87QumKSkJHXz5k2l1P/2qcz6d5vPnTtXey4mJkYppVRycrJOsdO2NTY2Vg0ZMkRFRUWpM2fOqJ9//lkNGjRITZ48WUVERGQp9r1799S3336rrl27prZv364mTpyoxowZo9asWZOuyy6z0rY5PDxcbd68WdWuXVt988036ty5c9o64eHhmY6b9nd6+PChGjRokHJxcVF9+vRR69atU3///bdauXKl1s2TGXqdj/d9kfa1YdasWQwcOJCqVaty+fJlbGxs6Nq1Kw4ODsyYMSPTcf/55x9WrlyJhYUFrVu3pkGDBuzevZscOXJw5coVAJKSkrQ7c2T2yEif8R89esTGjRtZunQpX3/9NfXr12fx4sUsXryYqKgoHj9+jJmZGaNGjcLY2DhTse/evcvixYspXrw4Li4u1KtXj6CgIKytrTl79iw5cuTgwYMH2pnpzMR+W22eK1cubt68SUJCAk+ePGHw4MGZiguvbvOYmBgePHigtbku0rY1KCiI7Nmzc//+fSpUqECRIkU4efIkBw8e1PlCirTY586do1ChQhQsWJBixYrxySefcOjQIU6cOEG9evV0GomRmpqKsbExiYmJeHh4sHbtWr744gt++eUXBg4cSJUqVRg1apRO30bT8p41axZ2dnZMnz6dwMBAfH19CQkJYfz48fzzzz9aHq/bRSKF9zUlJSWRP39+7O3tOXXqFGXLlqVJkybcuHEDGxsbzMzMMh0zd+7ceHh4EBQUxO+//05sbCy7du2icOHCREREYGtrS8GCBXX+KqrP+Dly5GDKlCkEBgaybds2YmJiOH78OA4ODuTKlYuSJUtiYWGBsbFxpr9OW1lZ4ejoyOnTp1mzZg0PHz5k/fr1KKW4fv06+fPn55NPPsl0zvD22rxQoULcunVLu53Mm2zzatWqkT17duzt7bGwsNDp4oy0orF//37mzZun3evQzc2NqlWr4ujoSOXKlXUaO50We9++fSxatIhLly4RHR3N4MGDKVasGMWKFaNevXoULlw407HhfwdGK1eupEqVKuTOnRsAT09PmjVrho+PD0FBQXz55Zc6xY+JieH8+fOMHDkSgMaNG1OjRg08PT2JiIjQbj+WmX5pKbyvydTUlIoVKxIREaENacqTJw9Xr17lhx9+yHS8S5cu8ffff9OwYUPy5s1LqVKlOHLkCKVKlUIpxbhx49INNcrsP5I+44eGhrJr1y6aNWvGkydPGDhwIIGBgdy5c4crV67Qr18/8uTJ8/qN8YyrV69y48YN6tWrR2pqKvfv3+fOnTuUK1eO4sWLP9fWmcn7bbY5oLc2TxuapWubw/+Kxq5du5gwYQL29vbMmDGD+fPnU6FCBZo3b0758uWzFHvjxo0MGzYMe3t7RowYgYeHB05OTnh4ePDRRx/pFDvtRGtSUhJnz57lzz//ZOnSpXTq1AkzMzNKlSrFL7/8kum4Dx8+JDQ0lOrVq5M3b16qVavGwYMHKVeuHPD0AzYiIkLnbwAyjvcV0q6tDw0NZffu3TRq1Ih69epx9+5dxo4dS8+ePalWrZpOV65ky5ZNG7N44sQJHB0d6du3L3369CEiIgJPT0/Cw8O19TN7hKTP+Pny5UMpxTfffMO+ffsoWbIkvXr1YurUqSQlJdGlSxfCwsJ0in3v3j2OHj1K586duXr1Kp06daJfv344OTlx5swZJk+ezJ07d3SKLW3+an/88QcBAQGcO3cOeHrEOHLkSG7cuMGlS5cyHe9ZR44c4fz589y9e5ds2bLh4+PDtGnT2Lt3L9u3b9cpZtoJtDNnzrBu3TqmTZvG+PHj8ff3p0ePHgQFBemcb0hICH369OH7778nKiqKVq1asXnzZtzd3Vm3bh0DBw7ks88+o0SJEjrNASGjGjLw8OFDOnbsSPPmzWnUqBExMTEUK1aMkJAQ7t27R6tWrXSOfejQIQYPHkytWrWwsbGhSZMmODg4cPToUR4/fkyDBg2ylLs+41+6dInevXtTvnx5kpKSaNmyJV999RVXr14lLi5O6yPNLKUUvr6+TJgwgS+++IJKlSpRt25dSpQowdatW0lISKBNmzY65y1t/nKPHz9m5cqVrFixgmrVqjFkyBCdv/7/W1hYGGvXruXPP/+kbt26dO3aVbvEPis2btzITz/9RExMDIGBgdrwvCVLljB37lyGDBlChw4dMhUzJiYGeDq08Ndff2X37t10796djh07snbtWi5fvkyZMmVo2rQplpaWOs3pIYX3JW7fvk3BggWZPHky+fLlo2vXrixevJjff/+de/fusXz58jdyXXlcXBwPHjxg48aNXL9+ncKFC3Pz5k1++OEHbGxssnxNvz7jP3nyBCMjIzZs2MDevXvJmTMnUVFRzJgxI0uXfD569Ih79+5x8+ZNduzYgbGxMeXKlePatWt4eHiQN2/eLOUtbf4/aUUjNDQUU1NT8uTJQ0JCAsuXL2fv3r3UrVuXMWPG6JRzWuzw8HBy5MiBsbExFy9eZMeOHYSFhVGrVi2+/fbbLLf33LlzCQsL49y5czRq1IihQ4cCT7utChQoQM6cOTMV7+eff8bJyQlzc3OMjY2JiIhg7ty53L17l++//546depo6+qauxTeF1iwYAEFChSgTZs2LF68mIcPH/LXX39Rvnx5hg4dysaNG0lISKBbt26Zjp02JvTQoUNs27aN+/fvM3HiRPLly8fBgwc5duwY5ubm9OvXT6c/qj7jp8Xet28fq1evJiEhgSFDhlC+fHmuX7/OoUOHSE1NpVu3bjrHPnr0KEFBQcTFxTFw4EAA/Pz8OHPmDHnz5mXIkCGZao9nY0ubp5dWGM+dO4enpycFChSgaNGiNGnShGrVqhEWFsaBAwe0v0NmPBt7+PDhJCUl0ahRI5ycnMibNy/Hjx/n2rVr9O3bN9Ox4X/t8uDBAx4+fEjRokW5fPkyU6dOJTIykpSUFL755hvatWuX6djR0dHky5ePYcOGUaRIERwdHSlZsiTBwcHMmDGDSpUqMW3aNJ3y1mR6ANp77smTJ8rd3V3t3r1brVy5Uh05ckTNmDFDTZ06VcXGxqqrV6+qli1bqtOnT2c69rNjQps2bap2796tnJycVPny5dXkyZNVQkJCuvUzO9ZT3/HTuLq6qj///FO1bdtW1a5dW40aNUpdunQpXfzMxH42bycnJ7VmzRrl4uKivvjiC7Vs2bLnxgLrGlva/MV69eql9u/frzZv3qxatmyp+vfvr8aPH698fX3TXRara+x9+/aplStXqs8//1x17dpVLVmyRO3fv18bK51Zz443dnZ2Vv369VMTJkxQx44dU0op9eeff6oNGzZkKbZST8dzT5o0SQ0fPlytXLlShYWFqTt37qgbN24opXRvb6WUkpNr/2JmZkbXrl3p27cvkyZNonLlygwcOJAhQ4awbds2Ro4cSe3atXXqT0s7GlmxYgX29vZ88cUXODo6smzZMgICAqhfv752YgMyNzzFEPEB1q1bR7ly5ahRowY1atRg5syZnD59mq5duxIaGqpT7LS8ly1bRvXq1XFzc6NKlSoMGTKENWvWUKtWLS5duqStp0tsafMXCwsLIyEhgVq1ahEbG4unpyeNGjXiwIEDKKWyNInPmTNnePToEXXq1CExMREfHx/q16/Pzz//TGxsrE4zm8H/2tzPz48vvviCPn36kCdPHjZt2sTcuXPJkSOHTucBUlJSMDIyIioqisDAQM6cOUObNm2oWrUqZ86cYf78+Vy+fFnrR87KXM0ynOwFGjRoQKNGjVBK8emnn9KxY0dGjBiBs7MzLi4uOu8waWxsbEhOTubEiROYmZlRs2ZNevbsyenTp3Uen2qo+B999BG3b9/W5iGuUaMGPXv2JDAwkMqVK2cpds6cOalYsSIhISHY2tpSr149rly5QlRUFB9//HGWYkubv1jp0qWxt7fn9OnT3Lt3j9q1a1O1alXWrFmj87jXNB9//DEVK1bk7NmzxMTEUKBAAZydndm7dy+ff/65TjHTuhhiY2OJi4vD3t6e8uXLU7RoUY4cOcLBgwc5cuSITtN5pl1q3b9/fypWrMilS5fYtWsXLVq0YPz48SxfvjxLU5A+S454n5GSkgI8PavZrl075s2bh6+vL6dOnaJ27dps376dnDlz6vRJp/6/K/3x48fUr18fOzs7zM3NiY6OBmDHjh3aCIm0PN6V+M/Grl27NrVq1cLKyorLly+TkJDAunXraNu2bZZiP3nyhLp165KYmEhKSgoPHz7E0tKSoKAgmjVrluW8pc3/J22Kzrt375KYmEjbtm21qRqDgoL4/vvvadKkCZaWlpmK+2zs2NhYzM3NadGiBRUqVODy5ctcv36dcePG0ahRI+2uLZmVVhzHjBlDYGAgI0eOZPfu3eTMmZNGjRrx7bff0rp160zHTWvDEydOYGdnx+jRo1mxYgV9+vRh9uzZHDhwgO7du1OgQIE3cgshKbz/L+2yw3v37tGlSxd2795NaGgoZcqUYf369UycOJHff/8dyPxXjLQ5QsPCwvjqq6/YuXOndpJhx44d9O7dGzMzM2rUqAGQ6UlO9Bk/LfaFCxdo0KABixcvplatWhQsWJCbN2/y/fffY2FhoV2+q0vsq1evauN2O3bsiKWlpbbTm5ub8+mnn+ocW9r8+djZsmUjJiaGIUOGMGfOHPLkyUNcXBxVqlQhKiqKXLly0aVLl0y1x79jjxw5kqlTp1K8eHGSkpIoWrQoO3bsAMDd3T3TsdPiA9rJ199//x1PT0+mT59O3759+fvvvylYsKBOV9elXWG5fPlynjx5ws2bN0lMTKRGjRoMHTqU48ePa+u+iTuHyKiGfxk+fDh2dnZ89913wNNb2sycOVOnuRj+bd68eYSGhhIZGcmTJ08YN24cxYsX59q1a5QoUQJra+sszYSlz/iTJ0/m7t27XLhwgfj4eMaOHYuDgwN37tzB2tqaXLly6Rx77ty5HD9+HKUUOXPmZNCgQVpR+/TTT8mfP7/OsaXNX2zQoEFUrVoVBwcHrly5gq+vLyVKlKB79+7Y2NhkqbikzWdib29PbGwsvr6+VK1alSZNmpAnTx6dLq9/1rBhw4iLi2PevHnA0wspJk+eTFBQEH5+fpibm2c6plKKlJQUfv75Z4KCgqhZsybOzs5YWFjg4+ND7dq16dix4xu7D5+ManhGcnKyGjt2rAoKClJKPR3h8OTJEzVo0CDtjKmuNm3apDp16qQ9DgwMVA0aNFCjRo3SzpK+q/H9/f1Vhw4dtMf79+9XlStXVj179lSXLl3SKWba2eP169erzp07q/v376uoqCj1+++/q9atW6s5c+boPPtYGmnzF3v48KH67rvvVHx8vBo5cqT64Ycf1LJly9SIESPU0aNHsxQ7JiZG9ezZUyml1JQpU9SwYcPUpEmT1KBBg9T58+ezFFsppeLj49X69etVs2bNVK9evdTBgwe157Iyu9mzbty4oUaPHq3at2+v2rdvr0aNGvVG4j5LuhqeYWxsTIkSJRgxYgTBwcHaNeCXL1+mQIECWYqdnJxMrVq1tN8bNWpEx44dCQsLY8KECdoMR+9ifBMTE+0a9aSkJBwdHZkwYQLR0dF4enpy69atTMdMO6KKjY3lyy+/xNramrx58+Ls7EzVqlU5ceIEc+bMITExUeejL2nzF7OwsCB//vw0b96c+Ph4hg4dSpcuXQgNDc3y1WQ5cuTA2tqaWrVqcfPmTQYNGsTw4cO5ceOGzv26aV0MCQkJPHjwAEdHR+bMmUOtWrVYuXIlgwcPJjw8XOd7zKX17yYmJgKQPXt2PDw8GDx4MAULFiQ6Oho/P78s7y/PksL7L+7u7vTo0YORI0fStm1bRo4cSZ06dbJ8++wKFSqwceNGli1bpt2l4tSpU/Tq1YtSpUqlG3L0rsWvWLEip06dYu7cuRgbG/PkyRN27tzJmDFjcHZ25vz58zrHLleuHHPnzmXz5s2YmJiQPXt2IiMjcXFxAeDChQs6x5Y2fzFjY2NGjhzJwoULmTZtGr/++ivffffdG7lNvJmZGVOmTOGXX35h9uzZbN68mcGDB/PJJ5/odLcN+N+H9PTp0xk1ahSXLl3C1taWTz/9FA8PD3LmzMm9e/d0iq2U0rpq0v6G/fr102ba++mnn6hfvz4BAQE6nWx81RuL/5d2f6yQkBAVExOjtm7dqm7evJmlgdLPOnbsmGrbtq1q2LChGj16tPaVrFWrVurKlSvvdPyLFy+qZs2aKRcXF/XDDz+ovn37qidPnqiWLVumu1eZLnbs2KH69++vnJyc1Pjx41W3bt20vK9fv56l2NLm/5O2H6dddLF371516dIltXjxYnXgwAGVlJSkc65pE5GnxQ4ODlYRERHqp59+Ups2bcryhRh///23cnNzU/Hx8Wr//v2qV69eqk+fPsrf31+nvG/cuKFmzpypnJ2d1fz587XlAQEBqnv37kqp9BdIpNWGN+WDP7mmlNKGh2TLlo179+7RsmVLNm/eTL58+bIUO+3ER1JSEqampkRFRZGSksKVK1coVqwYf/31F2fPniUpKYmxY8dmOn5aR7++4sPTr9AmJibExMQQERFBcnIyJUqU4PDhwxw/fhwjIyOdJ95Oy/vy5cskJSVx5coVihQpwoULF7h8+TKPHz/G29s7UzH13ebq/y/L1Uf8tNz12eZp7t69S9u2bVm3bl2Wu9HSpLXN3bt3cXNzY926dW9kIhyAwMBAwsLCKFWqFBs3bqRmzZrkz5+foKAgxo8fn+kTdn379qVy5cp8+umnjB8/ngYNGmizx6WmplKsWDHt7/HGTqg9642W8f+Q4OBgdfbsWe1x2if2uHHj1KxZs9It08WzJ4TSPjnbtm2rNm/erC0/dOiQCggIyPLRQBo3N7csx3/ZJ3ubNm3Sxf7777/V0aNHM3W08eeff6rU1FStPdLaqEOHDsrX11dbLzAwUK1cuTLTRxkv+mbyJtpEKaXWrVv3wqPMN/U3fdEJxNatW2e5zffv369++ukn1apVK7V06VIVEhKilFJqzpw52m2DdD3S3b9/v/rll19U165d1datW1VkZKRSSilvb281Y8aMLMX+t+vXrytXV1fVuXNn7UT3rFmz1I8//pjpWGfPnlWtW7fWHgcHByt3d3ftlkpxcXFvJOdX+WAL76JFi1STJk3UnDlz0t3u+c8//3yuMGTG63yFyYqbN2+qpUuXql69eqmAgIB08Xv06JGl2OHh4WrMmDHq3Llz2j3DlHr6DzZgwIAsxQ4LC1MuLi7p4ir19Lbwz569T5OZtr9x44aaOnWqaty4cbriuH37duXh4aF70v/v+vXrqmzZsmrEiBFKqf8V+DfxN71x44b66aeflKurq1q5cqW2/MCBA2rgwIFZip2QkKCcnJzUsWPH1Pbt25Wzs7Nq37692rJlizp16pS2ni77eUJCgvr888/Vtm3b1KpVq9TXX3+tBgwYoA4dOqTOnDmTpdiv8vDhQ3XkyBHVu3dv1bx5c53me0hISFBDhgzRRlnExMSoVq1aaaNoPDw80m2DPnywlwwXKFBAu0X2wIEDcXJy4uuvv9YGvGdl7GXlypUZN24c48ePJyEhAU9PTypUqKDN4J+VcaM+Pj6UKlWKevXqceDAAYoUKUJUVBR2dnZMmDBB5/hpX6e2bt3K9evXadKkCQ0aNCBfvnxYWFgwbNiwLOU+Z84cvv/+e27fvs327ds5ePAgbdu2pXTp0vz444/Pxc7MSIaJEydib2/PkiVLyJ49O8ePHycuLg47OzutqyIrbT5r1iw6depETEwMISEh2jwdb+JvOnHiRKpWrcp3333Hhg0bKF++PLdv38bMzEybiU3X2Js2baJUqVI4ODgAUKJECebPn8/atWupXLky9vb2On+N3rVrF+XLl8fZ2RmADh06sHz5cnx8fGjUqBEVKlTI0ld09cxMa8/ORLZp0yaaNm1Ks2bN+PTTT3W6fN/IyIgyZcpol6LnyZOH3Llzc/bsWZRSJCcn63TJcWZ8sIW3ePHilCtXjnbt2nHmzBl27tzJH3/8Qbt27XB0dNRpRz937hxRUVH07NkTeHoxxoIFC4iLi6No0aI8evQIyPxVUmnOnDmjzQ365MkTfHx8UErx119/8cknn2i3xdElvpGREYUKFaJVq1ZcvnyZgwcPcvz4cR4/fkyVKlV0vsIL4PDhwxw4cABvb288PDxo3749LVq0wN/fnwIFCjBmzBidY9+8eZMnT57g4eGBqakp3bp1w9bWlhMnTlCmTBmGDx+uc2yAAwcOcOfOHaZPn87SpUvZsGGDVnifPUuvS/zLly/z4MEDunbtipmZGcOHD2fDhg1cv34dOzs77XY4uub+6aefcuTIEaKiosifPz/79++nSpUqNG/enIEDBxIZGYmdnZ1OsWvWrMmBAwf4+++/qVKlCtmyZaNz5840bNiQcePGcePGDZ1v5wNP98eUlBSyZcumFe8pU6ZgbW2Nra0tTk5OmY6ZmJjInTt3KFy4MD169CAhIUFr29q1a+Pn58eNGzcYMWIEkLUP64x8sMPJqlSpQps2bShevDhNmjRhyJAh1K5dm5kzZxIYGKhTzFKlSlG8eHFtxqiyZcsSFxen3ZfJ09OTs2fP6pxzmTJlKFasGOHh4Vy/fp1atWoxefJkAgMDsbS05MCBAzrHTju6+Oabb6hatSrz58+nePHiBAcHc+XKFf7++29tnGNmlStXjhYtWjBy5EhKly5Ny5Yt6dixI7/99htAlm7RUqRIEYoUKcLOnTs5efIkOXPmxMfHhx07dmBlZaXzbWXSbNmyha5duwJPb3J46dKlN3IVIzzdX+zt7Rk+fDhDhgyhVKlS+Pj4sHr1agoWLKjzfpimePHiWFpa0r9/f4YNG8ahQ4dwdnYmX7585MyZU5t0RxdWVlYUL16cuXPnEhgYSGJiIomJiXz00UeYm5tz8uRJneImJiYyYcIEYmJitLtTGxkZce3aNe7evat9C1A6jAk4fPgwo0aNYuvWrTx+/Djd0XL9+vXZtm0blStXpkqVKumGmenDB3XEGx4ezr179zhx4gTNmzfXrnU3NTXlo48+ok2bNlSvXl0buJ5Z5ubm1KpVi9u3b1OqVCny5MmDlZXVG/sKY2pqyldffaUdaS1YsEB7rkaNGvz111/axCm6yp8/P1evXuXq1as8ePCAzp07k5iYiJ+fnzZnQmbly5ePLl26sHjxYiwtLbVbv5uYmFClShWCgoL46quvdM75iy++YNq0adjb26ebCczBwYGDBw/qFDPtq+748eO127sULVqUSZMmMX36dFatWqXNzZAVrVq14uDBgzx+/BgLCwttecWKFdm1a5dOMdP288uXLzN69GhCQkLIkSMHefPmJSkpiV9//ZWoqCiaNGmS6di3b9/G2toaCwsL+vXrx/r165k9eza7du3i008/5e7du0RFRWnjsDNDKYWJiQl79+7ljz/+wMXFhf79+wNPP0RGjhxJtmzZdD4Stba25uTJk9jZ2XHkyBEaN26Mo6Mj8HSWtp49e9K5c2ctlzcxJ8PLfFDDydLmeY2JieHgwYO4ubnRp0+fTN8a5N+ePHnCxYsXuXDhAlWrVtXuKguwaNEiLly4oH2FqVKlSqZ3nGfjP9uv+OjRI27dukV4eDjz5s1j1KhRVK1aNVN9a0+ePOHSpUucOnWKihUrUrlyZU6cOIGXlxeWlpZs3rxZe68cOXJkOvbFixc5c+YM1apVI3/+/ERHR1OyZElOnz7NgwcPmDdvHqNHj9Y577Nnz1KnTh0ePHjA6NGjCQsLY/To0QCsXbuWH374gWrVqmW6v/HJkydcuHCBkJAQrV3S7N+/n8DAQEqXLk3Hjh0zPZQpLfeQkBCqVatGmTJlePz4MZ6enjg5OWFiYsKSJUsYNWoUn376aaZzd3Nzo3Llyty5c4fg4GDc3d3p2rUrOXLkwMfHB2NjY2rWrKkVncyYO3cuN27coFmzZtStWxd4epS6fPly4uLiyJMnD5UqVaJatWqZjg1PC963335LrVq1OHr0KOHh4fTq1Usr5FnpN75y5QrTpk2jTZs2hIWFcenSJfLnz4+Li0u6AyK9DB/7lw+m8Pr6+rJ9+3YWLVoEwLVr15gyZQrnzp1j9OjRfPHFFzp/yo0ePZp79+5RuHBhfH19qVmzJt999x2lS5fm8uXLNG3alE6dOjFy5Eid3iMtfpEiRfD19aVGjRpa/PHjx2vjPLt06ZLp+GmxP/roIzZt2kTNmjUZOnQohw4dokSJEtSsWVMbV5pZz+a9efNmGjRoQM+ePSlcuDCjR4/G0tIyy3kXLlyYzZs34+LiQs+ePTlz5gz+/v6UKVOGIkWK0KJFiyy1eVq7PNvm8PTkUkREhE6zeP27XapXr87QoUO5desWS5YsoWzZshQuXJiOHTtmOvd/7+dXrlxh6tSpnD9/nsmTJ1OzZk2dpzU1MjJi0aJF/PbbbzRs2JAcOXLg7Oys3Xsw7VbrWZGcnMwvv/xCzpw56dSpE76+vixbtoz8+fMzYMCALM0/rJRiw4YN2gnBv/76ixMnTnDx4kU+++wzOnbsmKXcM5vMB+HEiRNq1KhR6vHjx+mW+/r6qu7du6vY2Fid4l67dk01bdpUG3OalJSkJk2apGrWrKnmzJmjlFJqxowZ2lCVzF4F96r4y5Yte279zMR/Uezx48erWrVqaeMwdfWi2BMnTlQ1a9ZUv/322xvP29vbW9WuXVutW7fuufUzO6TpVW0+e/bsLMV/WbvUrl1brV+/Psu5v2w/37x5s+ratavO+3magIAANXjwYHXw4EH1448/qkGDBqmff/5ZG8Orq4iICC3n1NTUdEM8Hzx4oH788cd0Y2919e+x+eHh4er333/Xhpa96eFvL/PBFN6YmBj17bffqsGDB2sDpdP+2fv166f27dunU9wHDx4oLy8vdfHixXTLL126pDp16pTuIg1dLj1+WfyLFy+qDh06qJMnT+qU96tiX7p0SX3zzTfqr7/+euOx9Zn3hQsXVIcOHdSJEyd0jv2q+Gntcvz48Tce+03lntF+vn///izFj4yMVBs3blRKKXXr1i21detWNWHCBNWrV68stcucOXPUkCFD1KFDh9Itf/Z/Jm17MiPtAo6oqCi1d+9edeTIkefWMVSxfdYHM6ohT548TJs2jezZs1O7dm3mzZvHzZs3tbP2utxDDSB37tzkz5+fcePGcebMGW156dKlqVevnjb5M+h2j6aXxf/4449xdHRk3759OuWdUe5169bV+cTU28q7TJkyODo6sn//fp1jvyp+Wrv88ccfbzz2m8o9o/28YsWKWYpva2ur3eGhUKFCODk50bp1axo0aEDZsmUzHU/9f0+nmZkZQUFBBAQEaF2A8PR/Rv3/Zf26nItJ6yIbPnw4J0+eJDU1lWvXrnH48GFtHX2eRHspg5d6A0v7xDt9+rTat2+fSk5OVqdOnVJ9+vRRPXr0UF5eXmrt2rVZfp/ly5erSpUqKU9PTxUWFqYuXryoOnXqpF1dltVPVX3Gl9j//dz1uZ+nxT579qzavn37C+cazupl7y/qwli8eLGKiorSOWbaBEtBQUHq22+/VUoptWrVKuXu7q4qVKigdQW+DR/EybX79+/j7u5Oq1atcHR0JCkpCWtra3LkyKHziIa0E06RkZFcuHCBkiVLkjt3bmbOnEloaCilSpXCwsJCu6jhXYovsd+/3EE/+/m/Y7dp04aGDRty69YtChQooJ1szKqoqCgOHTpE69atiYiI4MSJE5w8eZLw8HA8PDwyPUoiPDyc2bNnU6tWLe7evcvNmzd58OABJiYmeHh4YGVlxaxZs5g8ebJex+u+zHtdeK9fv06xYsWYOXMmuXLlwt3dnUWLFuHn58edO3eYM2eONiRGV927d6dChQo4ODhQvnx57t+/T6FChciZM6c2LCUrw1P0GV9ivx+563M/f1nsrVu3EhkZyezZs6lXr55OsV8lNTWVixcvcvLkSVxcXDL9wREXF8e2bds4evQoOXPmpFChQoSGhjJ27Fj++ecf5syZw8cff0zPnj0NMnzs397bPt7du3fz888/s2PHDmJjY7VP7AcPHrB9+3aGDh1KWFiYTrFv3LgBQHBwMObm5gwaNIibN2/i6elJy5YtWbFihXbDQsh8364+40vs9yt3fe7nr4rt7+/P0KFDuXz5sk6x4em3AHh6qX1AQADh4eHac9myZaNcuXK0bt0600U3JSWFnDlz0rx5c6KjowkJCeHs2bNUqVKFEydOMG3aNJKTk7VL+w1ddOE9vnKtTJkyhIaGsnfvXqysrDAzM6NSpUoMHjyYK1eusHbtWnx8fDIdNzw8XLvtyN27d8mXLx/9+/fHxMSEH374ASsrK2bOnIlSSqc/qD7jS+z3L3d97ef6jg1PT3zdv3+fYcOG0aZNG5RSBAcHp+vCMDU1zXTctK6DcePG8fnnn9O1a1f++usvAgICCAgIoGHDhtpYXn3Ox/Aq7+URb3JyMh999BH29vZERERw+PBh4uLiKF68OHv27GH8+PE0btw43eWlrytPnjw4ODgQFBREREQERYoUwcTEhLFjx5IzZ05mzZpFmTJltAmU36X4Evv9yl2f+7k+Y8PTLgyA5cuX06JFCzp06IC/vz/e3t64ubllaeQIoM2PknbH4WrVqjFy5Ejtsuy0y+7fRtGF97iPVylFy5YtmT59OqVKlWL37t1s3ryZlJQU6tevT5s2bTJ9lU3ap2NCQgIeHh48evSIIkWKUK1aNYoVK8amTZswNjbWeRIVfcaX2O9f7qCf/VzfsXfv3s3evXupX78+R48exdLSkr/++ouKFSsybNgwNmzYwJMnT3S6KvBZhw4dYsOGDbRs2ZJSpUpRqFAh3NzcmD17NkWKFHkrfbtp3tuuhlu3bpE7d24SEhIAtDlCe/bsSfHixXXaYfT9FUaf8SX2+5c76Gc/13dsfXdhpEmbsGrnzp1ERkaSmppK/fr133rRhfe48BYpUoR69eqxYcMG4uPjKVeuHLdu3SJ//vzUqVNH57gv+gpTrVo1berArH6F0Wd8if3+5a6v/VxfsZ/twjhy5AghISHUq1dP68JYs2ZNlrownmViYoKbmxuNGzfm7t272iyE8JYumng2t7f67nrWrl07Fi1axKZNm7h69SomJib06NEjSzHNzc1p2rQpGzZsoFChQtpXmH/++YemTZsCWZvdSJ/xJfb7lzvoZz/XV2wTExOUUvz0008v7MJwcnKiTZs2byT3NNbW1lhbW6dbJoVXj3LlysXgwYOJiIjg8ePHmJubp7trgK70/RVGn/El9vuXu772c33F1mf3yH/Fe3tyzRAePHiQ7iuMkZHRG51AWZ/xJbbh4+s79/+SX375hZs3b+Li4kK5cuUIDQ1l3rx5LFu27G2nZhBSeIUQBvfw4UMWLVrEvXv30nVh6DI5+3+RFF4hxFujj+6R/wIpvEIIYWDv5ZVrQgjxLpPCK4QQBiaFVwghDEwKrxBCGJgUXvGflpKSwrJly2jVqhXNmzfH2dmZH3/8kcTExAxfW7ZsWWJiYti0aRO9evV6I/mcPn2a0aNHv5FY4v0lhVf8p40dO5YTJ06wfPly/Pz82LhxI1evXmXEiBFvJZ+wsDCioqLeynuL/473+pJh8X4LDw/H39+fQ4cOaXcpsLS0ZNy4cZw4cQKAq1ev4u3tzePHj4mOjqZcuXLMnDlTm7TmVe7cucOwYcO4f/8+AI6OjgwcOBCADRs2sGbNGlJTU7G2tmbUqFFYWloye/ZsHj58yPDhwxk5ciTDhw/n+vXrZMuWjQoVKuDt7f1WZ8US7wbZA8R/1rlz5yhduvRzt4YpUKAAX375JQDr16+nRYsWrFu3jl27dnHz5s3XvoX6+vXrKVKkCJs3b2bVqlVcv36dhw8fcvToUXx9fVm1ahW+vr706NGD/v37U7BgQQYMGICDgwM+Pj4EBgby6NEj7UgcSHd7G/HhkiNe8Z+VdmPIVxkyZAiHDx9m8eLFXLt2jejoaB4/fvxa8evVq0fPnj25ffs2derUYfDgweTKlYv9+/dz/fp12rdvr637zz//8ODBg3Svr1atGjNmzOCbb76hTp06dO7cmWLFimV6O8X7R454xX+Wvb09V65cIS4uLt3yqKgoevbsSUJCAoMGDWL9+vUULlyYLl26UKFCBV73Yk17e3v27NlDu3btuHXrFm5ubvz999+kpqbSvHlz/Pz88PPzY/Pmzfz+++/kzp073euLFi1KYGAgPXv2JC4ujq5du7Jjx443tv3iv0sKr/jPsrW1xdXVlR9++EErvnFxcYwdOxZra2uyZ8/OoUOH6Nu3L87OzhgZGXHq1ClSUlJeK/60adOYP38+jRo1YsSIEZQuXZpr167x2WefsW3bNqKjowFYs2YNnTt3Bp5OaJ5299zVq1czfPhw6taty5AhQ6hbty6XLl3SQ0uI/xrpahD/aWPGjGH+/Pm0b98eY2NjEhMTadSoEf379wfA09OTvn37kjt3biwsLKhevbp2u/WMdO7cGS8vL1xcXDAzM6Ns2bLa7x4eHnTr1g0jIyNy5szJ3LlzMTIyomrVqsycOZO+ffvy448/cvToUZydnbGwsKBQoUK4u7vrsznEf4RMkiOEEAYmXQ1CCGFgUniFEMLApPAKIYSBSeEVQggDk8IrhBAGJoVXCCEMTAqvEEIYmBReIYQwsP8DvuoxGzq+0rwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "harm = pd.read_csv('data/harm.tsv', sep='\\t', header=0, index_col=0)\n",
    "\n",
    "harm.plot.bar(figsize=(5, 5))\n",
    "plt.title('Harmonic Mean of Sensitivity and Specificity')\n",
    "plt.ylabel('Harmonic Mean')\n",
    "plt.xlabel('Call sets')\n",
    "plt.ylim(0.8, 1)\n",
    "plt.xticks(rotation=60)\n",
    "plt.savefig('figs/Figure2_2.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}