reports/step-2_seq-stats-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're calculating Tajima's pi (average pairwise genetic distance) per OTU and then calculating summary stats from these pi values. These include Hill numbers 1-5 accounting for OTU sampling differences in two different ways*, average, median, standard deviation, and variance.  \n",
    "\n",
    "Accounted for sampling differences: \n",
    "* divide Hill number by the number of unique OTUs per cell (\"OTU richness\")\n",
    "\n",
    "I'm interested in seeing how these two methods for controlling for sampling bias impact the Hill number calculations.\n",
    "\n",
    "\n",
    "*Note: normally I include many more exploratory plots with better annotation, but the remote Mac I'm working on has a weird font problem. Since we don't have physical access to the computers and I don't want to break the machine on accident, I'm just going to make exploratory plots separately on my laptop*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressMessages(library(ape))\n",
    "suppressMessages(library(here))\n",
    "suppressMessages(library(tictoc))\n",
    "suppressMessages(library(raster))\n",
    "suppressMessages(library(data.table))\n",
    "suppressMessages(library(tidyverse))\n",
    "suppressMessages(library(furrr))\n",
    "suppressMessages(library(sf))\n",
    "suppressMessages(library(skimr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to calculate Hill numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get one hill number from a list of genetic distances. Original python code written by Isaac Overcast, with slight modifications (correct = TRUE implemented by CMF)\n",
    "## dists are the OTU Tajima's pi\n",
    "## order is the q order of the Hill number\n",
    "## correct indicates if you want to correct for species richness or not. Default is TRUE\n",
    "hill_calc <- function(dists, order = 1, correct = TRUE) { \n",
    "  if (order == 0) {\n",
    "    return(length(dists))\n",
    "  }\n",
    "  if (order == 1) {\n",
    "    h1 = exp(entropy::entropy(dists))\n",
    "    if (correct) {\n",
    "        return(h1 / length(dists))\n",
    "    } else return(h1)\n",
    "    \n",
    "  }\n",
    "  else {\n",
    "    tot = sum(dists)\n",
    "    proportions = dists/tot\n",
    "    prop_order = proportions**order\n",
    "    h2 = sum(prop_order)**(1/(1-order))\n",
    "    if (correct) {\n",
    "        return(h2 / length(dists))\n",
    "    } else return(h2)\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to calculate pairwise sequence divergence. All sequences have > 50% sequence overlap, so I don't need to account for it here. I'm extracting the relevant sequences from the sequence data frame (sdf) and using dist.dna to calculate average pairwise genetic distances with pairwise deletion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ids is a vector of sequence IDs to be analyzed\n",
    "# sdf is the sequence data frame created after reading in the FASTA sequences\n",
    "pairwise_div <- function(ids){\n",
    "    seq_df <- sdf[recordID %in% ids\n",
    "       ][, seq := str_split(seq, \"\")]\n",
    "    \n",
    "    pi <- as.DNAbin(seq_df$seq) %>% \n",
    "        dist.dna(model = \"raw\", pairwise.deletion = TRUE) %>% \n",
    "        mean()\n",
    "    \n",
    "    return(pi)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in and take a look at metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.757 sec elapsed\n",
      "Observations: 3,048,396\n",
      "Variables: 33\n",
      "$ recordID             \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 797197, 1674336, 1416487, 609365, 797199, 147331…\n",
      "$ bin_uri              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"BOLD:AAA0006\", \"BOLD:AAA0006\", \"BOLD:AAA0006\", …\n",
      "$ order_taxID          \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 113, 113, 113, 113, 113, 113, 113, 113, 113, 113…\n",
      "$ order_name           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Lepidoptera\", \"Lepidoptera\", \"Lepidoptera\", \"Le…\n",
      "$ family_taxID         \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 546510, 546510, NA, 546510, 546510, NA, NA, NA, …\n",
      "$ family_name          \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Depressariidae\", \"Depressariidae\", \"\", \"Depress…\n",
      "$ subfamily_taxID      \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 2479, 2479, NA, 2479, 2479, NA, NA, NA, 2479, 24…\n",
      "$ subfamily_name       \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Stenomatinae\", \"Stenomatinae\", \"\", \"Stenomatina…\n",
      "$ genus_taxID          \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 66559, 66559, NA, 66559, 66559, NA, NA, NA, 6655…\n",
      "$ genus_name           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Stenoma\", \"Stenoma\", \"\", \"Stenoma\", \"Stenoma\", …\n",
      "$ species_taxID        \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 111154, 111154, NA, 111154, 111154, NA, NA, NA, …\n",
      "$ species_name         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Stenoma Janzen44\", \"Stenoma Janzen44\", \"\", \"Ste…\n",
      "$ collectiondate_start \u001b[3m\u001b[90m<lgl>\u001b[39m\u001b[23m NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n",
      "$ collectiondate_end   \u001b[3m\u001b[90m<lgl>\u001b[39m\u001b[23m NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n",
      "$ habitat              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", …\n",
      "$ coord_accuracy       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n",
      "$ elev                 \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 700, 1310, 900, 1220, 700, 710, 980, 710, 680, 6…\n",
      "$ elev_accuracy        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n",
      "$ depth                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n",
      "$ country              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Costa Rica\", \"Costa Rica\", \"Costa Rica\", \"Costa…\n",
      "$ province_state       \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"Guanacaste\", \"\", \"Guanacaste\", \"\", \"Guanaca…\n",
      "$ region               \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"Area de Conservacion Guanacaste\", \"\", \"Area…\n",
      "$ sequenceID           \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 2556142, 3748943, 3538054, 2556256, 2556144, 358…\n",
      "$ markercode           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"COI-5P\", \"COI-5P\", \"COI-5P\", \"COI-5P\", \"COI-5P\"…\n",
      "$ genbank_accession    \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"JQ536825\", \"HQ935432\", \"HM885007\", \"\", \"JQ53682…\n",
      "$ seq_primers          \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"LepR1|LepF1\", \"LepF1|LepR1\", \"LepF1|LepR1\", \"\",…\n",
      "$ marker_codes         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"COI-5P|COI-5P\", \"COI-5P|COI-5P\", \"COI-5P|COI-5P…\n",
      "$ full_name            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"LepidopteraDepressariidaeStenomatinaeStenomaSte…\n",
      "$ n_nucs_all_trim      \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 658, 658, 601, 658, 658, 658, 658, 658, 658, 660…\n",
      "$ numseqs              \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, …\n",
      "$ geometry             \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"-85.428|10.987\", \"-85.462|10.931\", \"-85.436|10.…\n",
      "$ continents           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"North America\", \"North America\", \"North America…\n",
      "$ num_continents       \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …\n"
     ]
    }
   ],
   "source": [
    "tic()\n",
    "bold_meta <- fread(here(\"output\", \"spreadsheets\", \"final_filter_meta.csv\"))\n",
    "toc()\n",
    "\n",
    "bold_meta %>% glimpse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to **sf** object for geographic filtering. I'm also removing a lot of the unnecessary columns to make filtering go faster. If I ever need them, I can just rejoin with the original data frame by the recordID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.423 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "# convert to sf object and project to behrmann equal area crs\n",
    "behrmann_crs <- \"+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +ellps=WGS84 +units=m +no_defs\"\n",
    "\n",
    "# unnecessary columns\n",
    "col_remove <- c(\"order_taxID\",\n",
    "                \"family_taxID\",\n",
    "                \"subfamily_taxID\",\n",
    "                \"subfamily_name\",\n",
    "                \"genus_taxID\",\n",
    "                \"genus_name\",\n",
    "                \"species_taxID\",\n",
    "                \"species_name\",\n",
    "                \"collectiondate_start\",\n",
    "                \"collectiondate_end\",\n",
    "                \"habitat\",\n",
    "                \"coord_accuracy\",\n",
    "                \"elev\",\n",
    "                \"elev_accuracy\",\n",
    "                \"depth\",\n",
    "                \"country\",\n",
    "                \"province_state\",\n",
    "                \"region\",\n",
    "                \"sequenceID\",\n",
    "                \"markercode\",\n",
    "                \"genbank_accession\",\n",
    "                \"seq_primers\",\n",
    "                \"marker_codes\",\n",
    "                \"n_nucs_all_trim\",\n",
    "                \"num_continents\",\n",
    "                \"numseqs\",\n",
    "                \"full_name\")\n",
    "\n",
    "# slim down data table and convert geometry column back to lon and lat\n",
    "bold_meta[,(col_remove) := NULL\n",
    "         ][,lon := str_split_fixed(geometry, \"\\\\|\", 2)[,1] %>% as.numeric()\n",
    "          ][,lat := str_split_fixed(geometry, \"\\\\|\", 2)[,2] %>% as.numeric()\n",
    "           ][,geometry := NULL]\n",
    "\n",
    "tic()\n",
    "bold_sf <- st_as_sf(bold_meta, coords = c(\"lon\", \"lat\"), \n",
    "                         crs = 4326, agr = \"constant\") %>% \n",
    "  st_transform(crs = behrmann_crs)\n",
    "toc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get cell numbers for each geographic resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "template_high <- raster(here(\"data\", \"templates\", \"template_high.tif\"))\n",
    "template_medium <- raster(here(\"data\", \"templates\", \"template_medium.tif\"))\n",
    "template_low <- raster(here(\"data\", \"templates\", \"template_low.tif\"))\n",
    "\n",
    "bold_coords <- st_coordinates(bold_sf)\n",
    "\n",
    "\n",
    "bold_sf_pts <- bold_sf %>% \n",
    "    mutate(cell_high = cellFromXY(template_high, bold_coords),\n",
    "          cell_medium = cellFromXY(template_medium, bold_coords),\n",
    "          cell_low = cellFromXY(template_low, bold_coords))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing the transformed data frame to csv so I can read in the smaller data set and make figures on my laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing layer `bold_sf_pts' to data source `/Users/labuser/Desktop/connorfrench/bigass-phylogeography/BigAss-phylogeography/output/spreadsheets/bold_sf_pts.csv' using driver `CSV'\n",
      "Writing 3048396 features with 8 fields and geometry type Point.\n"
     ]
    }
   ],
   "source": [
    "st_write(bold_sf_pts, here(\"output\", \"spreadsheets\", \"bold_sf_pts.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.874 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "# since I'm going back and forth between remote computer and local, I'm putting this here to read in the bold_sf_pts data frame if it's not already loaded\n",
    "tic()\n",
    "if (!exists(\"bold_sf_pts\")) {\n",
    "    bold_sf_pts <- fread(here(\"output\", \"spreadsheets\", \"bold_sf_pts.csv\"))\n",
    "} else setDT(bold_sf_pts)\n",
    "toc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to filter bold data by the resolution, number of individuals per OTU, and number of OTUs per cell, and calculate summaries of the data\n",
    "# dt = data table to filter, resolution = string (\"high\", \"medium\", \"low\"), min_ind = integer, min_otu = integer\n",
    "filter_occs <- function(resolution, min_ind, min_otu, calc_pi = FALSE) {\n",
    "    \n",
    "    cell_filter <- paste0(\"cell_\", resolution)\n",
    "    \n",
    "    dt_filter <- bold_sf_pts[,ind_count := .N, by = c(cell_filter, \"bin_uri\")\n",
    "           ][ind_count >= min_ind\n",
    "            ]\n",
    "    \n",
    "    \n",
    "    dt_filter <- dt_filter[,.(total_ind = uniqueN(recordID),\n",
    "                             n_ind_min = min(ind_count),\n",
    "                             n_ind_median = median(ind_count) %>% as.double(),\n",
    "                             n_ind_max = max(ind_count),\n",
    "                             n_ind_var = var(ind_count),\n",
    "                             n_otu = uniqueN(bin_uri),\n",
    "                             n_order = uniqueN(order_name)),\n",
    "                             by = c(cell_filter)\n",
    "                          ][n_otu >= min_otu]\n",
    "    \n",
    "    # rename cell column to allow for row-binding data.tables later\n",
    "    setnames(dt_filter, cell_filter, \"cell\")\n",
    "    \n",
    "    # add ID columns\n",
    "    dt_filter <- dt_filter[,res_filter := resolution\n",
    "                          ][,ind_filter := min_ind\n",
    "                           ][,otu_filter := min_otu]\n",
    "    \n",
    "    return(dt_filter)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It took dplyr > 200 seconds to filter for a single filtering regime! data.table is awesome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.706 sec elapsed\n",
      "Rows: 2,795\n",
      "Columns: 11\n",
      "$ cell         \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 22055, 25328, 22781, 27464, 22417, 17731, 18452, 14499, 1…\n",
      "$ total_ind    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 274225, 7514, 5839, 786, 3726, 1067, 727, 3426, 639, 1041…\n",
      "$ n_ind_min    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …\n",
      "$ n_ind_median \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 47, 7, 8, 7, 6, 10, 9, 9, 6, 11, 5, 6, 30, 16, 9, 68, 32,…\n",
      "$ n_ind_max    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 1964, 287, 61, 47, 68, 86, 69, 52, 19, 64, 24, 19, 273, 6…\n",
      "$ n_ind_var    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 47030.42349, 2966.76248, 81.35492, 152.79081, 161.00897, …\n",
      "$ n_otu        \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 13113, 1177, 877, 132, 701, 133, 108, 491, 113, 129, 109,…\n",
      "$ n_order      \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 19, 10, 6, 4, 10, 2, 5, 13, 9, 2, 9, 10, 10, 14, 3, 11, 1…\n",
      "$ res_filter   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"…\n",
      "$ ind_filter   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …\n",
      "$ otu_filter   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 10…\n"
     ]
    }
   ],
   "source": [
    "# since the filtering process is fast, I'm including some liberal and conservative filtering parameters.\n",
    "res_list <- c(\"high\", \"medium\", \"low\")\n",
    "ind_list <- c(3, 5)\n",
    "otu_list <- c(100, 150, 200)\n",
    "\n",
    "# get all combinations of filtering parameters\n",
    "input_list <- expand_grid(res_list, ind_list, otu_list)\n",
    "\n",
    "tic()\n",
    "bold_filtered <- purrr::pmap(list(input_list$res_list, \n",
    "                             input_list$ind_list,\n",
    "                             input_list$otu_list),\n",
    "                             filter_occs)  %>% \n",
    "    rbindlist()\n",
    "toc()\n",
    "\n",
    "\n",
    "glimpse(bold_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fwrite(bold_filtered, here(\"output\", \"spreadsheets\", \"bold_cell_summaries.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 18 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>res_filter</th><th scope=col>ind_filter</th><th scope=col>otu_filter</th><th scope=col>total_ind</th><th scope=col>total_otu</th><th scope=col>num_cells</th><th scope=col>ind_var</th><th scope=col>ind_median</th><th scope=col>otu_var</th><th scope=col>otu_median</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>high  </td><td>3</td><td>100</td><td>2280962</td><td>156071</td><td>257</td><td>112314.93</td><td>11.0</td><td>1016922.8</td><td>281.0</td></tr>\n",
       "\t<tr><td>high  </td><td>3</td><td>150</td><td>2215160</td><td>148027</td><td>190</td><td>147233.06</td><td>15.0</td><td>1263516.7</td><td>481.5</td></tr>\n",
       "\t<tr><td>high  </td><td>3</td><td>200</td><td>2171685</td><td>142419</td><td>157</td><td>177571.38</td><td>21.0</td><td>1435781.2</td><td>578.0</td></tr>\n",
       "\t<tr><td>high  </td><td>5</td><td>100</td><td>1982162</td><td> 84832</td><td>145</td><td>191930.17</td><td>31.0</td><td> 797422.9</td><td>345.0</td></tr>\n",
       "\t<tr><td>high  </td><td>5</td><td>150</td><td>1950115</td><td> 82439</td><td>125</td><td>221457.50</td><td>37.0</td><td> 885496.0</td><td>458.0</td></tr>\n",
       "\t<tr><td>high  </td><td>5</td><td>200</td><td>1910119</td><td> 80249</td><td>112</td><td> 15380.76</td><td>40.0</td><td> 957655.8</td><td>486.0</td></tr>\n",
       "\t<tr><td>medium</td><td>3</td><td>100</td><td>2414781</td><td>168894</td><td>245</td><td> 11660.66</td><td> 8.0</td><td>1303342.7</td><td>285.0</td></tr>\n",
       "\t<tr><td>medium</td><td>3</td><td>150</td><td>2364220</td><td>162173</td><td>189</td><td> 10660.67</td><td>11.0</td><td>1566350.1</td><td>460.0</td></tr>\n",
       "\t<tr><td>medium</td><td>3</td><td>200</td><td>2319823</td><td>157074</td><td>160</td><td> 11684.48</td><td>14.0</td><td>1751718.4</td><td>547.0</td></tr>\n",
       "\t<tr><td>medium</td><td>5</td><td>100</td><td>2096913</td><td> 91068</td><td>140</td><td> 13884.34</td><td>26.5</td><td> 974563.0</td><td>344.5</td></tr>\n",
       "\t<tr><td>medium</td><td>5</td><td>150</td><td>2057410</td><td> 87899</td><td>114</td><td> 15991.74</td><td>33.5</td><td>1119803.1</td><td>486.0</td></tr>\n",
       "\t<tr><td>medium</td><td>5</td><td>200</td><td>2026516</td><td> 85769</td><td>102</td><td> 17438.54</td><td>38.0</td><td>1206054.9</td><td>557.0</td></tr>\n",
       "\t<tr><td>low   </td><td>3</td><td>100</td><td>2560774</td><td>178485</td><td>208</td><td> 12790.43</td><td> 8.0</td><td>2152665.0</td><td>344.5</td></tr>\n",
       "\t<tr><td>low   </td><td>3</td><td>150</td><td>2522813</td><td>173655</td><td>168</td><td> 10906.33</td><td> 9.5</td><td>2507008.7</td><td>444.5</td></tr>\n",
       "\t<tr><td>low   </td><td>3</td><td>200</td><td>2494853</td><td>170213</td><td>148</td><td> 12165.96</td><td>10.0</td><td>2733440.4</td><td>556.0</td></tr>\n",
       "\t<tr><td>low   </td><td>5</td><td>100</td><td>2257253</td><td> 99602</td><td>134</td><td> 13553.40</td><td>17.5</td><td>1410306.0</td><td>353.0</td></tr>\n",
       "\t<tr><td>low   </td><td>5</td><td>150</td><td>2226433</td><td> 96820</td><td>111</td><td> 15932.43</td><td>23.0</td><td>1607383.7</td><td>426.0</td></tr>\n",
       "\t<tr><td>low   </td><td>5</td><td>200</td><td>2185006</td><td> 93280</td><td> 90</td><td> 18966.34</td><td>37.0</td><td>1842518.1</td><td>550.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 18 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " res\\_filter & ind\\_filter & otu\\_filter & total\\_ind & total\\_otu & num\\_cells & ind\\_var & ind\\_median & otu\\_var & otu\\_median\\\\\n",
       " <chr> & <dbl> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t high   & 3 & 100 & 2280962 & 156071 & 257 & 112314.93 & 11.0 & 1016922.8 & 281.0\\\\\n",
       "\t high   & 3 & 150 & 2215160 & 148027 & 190 & 147233.06 & 15.0 & 1263516.7 & 481.5\\\\\n",
       "\t high   & 3 & 200 & 2171685 & 142419 & 157 & 177571.38 & 21.0 & 1435781.2 & 578.0\\\\\n",
       "\t high   & 5 & 100 & 1982162 &  84832 & 145 & 191930.17 & 31.0 &  797422.9 & 345.0\\\\\n",
       "\t high   & 5 & 150 & 1950115 &  82439 & 125 & 221457.50 & 37.0 &  885496.0 & 458.0\\\\\n",
       "\t high   & 5 & 200 & 1910119 &  80249 & 112 &  15380.76 & 40.0 &  957655.8 & 486.0\\\\\n",
       "\t medium & 3 & 100 & 2414781 & 168894 & 245 &  11660.66 &  8.0 & 1303342.7 & 285.0\\\\\n",
       "\t medium & 3 & 150 & 2364220 & 162173 & 189 &  10660.67 & 11.0 & 1566350.1 & 460.0\\\\\n",
       "\t medium & 3 & 200 & 2319823 & 157074 & 160 &  11684.48 & 14.0 & 1751718.4 & 547.0\\\\\n",
       "\t medium & 5 & 100 & 2096913 &  91068 & 140 &  13884.34 & 26.5 &  974563.0 & 344.5\\\\\n",
       "\t medium & 5 & 150 & 2057410 &  87899 & 114 &  15991.74 & 33.5 & 1119803.1 & 486.0\\\\\n",
       "\t medium & 5 & 200 & 2026516 &  85769 & 102 &  17438.54 & 38.0 & 1206054.9 & 557.0\\\\\n",
       "\t low    & 3 & 100 & 2560774 & 178485 & 208 &  12790.43 &  8.0 & 2152665.0 & 344.5\\\\\n",
       "\t low    & 3 & 150 & 2522813 & 173655 & 168 &  10906.33 &  9.5 & 2507008.7 & 444.5\\\\\n",
       "\t low    & 3 & 200 & 2494853 & 170213 & 148 &  12165.96 & 10.0 & 2733440.4 & 556.0\\\\\n",
       "\t low    & 5 & 100 & 2257253 &  99602 & 134 &  13553.40 & 17.5 & 1410306.0 & 353.0\\\\\n",
       "\t low    & 5 & 150 & 2226433 &  96820 & 111 &  15932.43 & 23.0 & 1607383.7 & 426.0\\\\\n",
       "\t low    & 5 & 200 & 2185006 &  93280 &  90 &  18966.34 & 37.0 & 1842518.1 & 550.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 18 × 10\n",
       "\n",
       "| res_filter &lt;chr&gt; | ind_filter &lt;dbl&gt; | otu_filter &lt;dbl&gt; | total_ind &lt;int&gt; | total_otu &lt;int&gt; | num_cells &lt;int&gt; | ind_var &lt;dbl&gt; | ind_median &lt;dbl&gt; | otu_var &lt;dbl&gt; | otu_median &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| high   | 3 | 100 | 2280962 | 156071 | 257 | 112314.93 | 11.0 | 1016922.8 | 281.0 |\n",
       "| high   | 3 | 150 | 2215160 | 148027 | 190 | 147233.06 | 15.0 | 1263516.7 | 481.5 |\n",
       "| high   | 3 | 200 | 2171685 | 142419 | 157 | 177571.38 | 21.0 | 1435781.2 | 578.0 |\n",
       "| high   | 5 | 100 | 1982162 |  84832 | 145 | 191930.17 | 31.0 |  797422.9 | 345.0 |\n",
       "| high   | 5 | 150 | 1950115 |  82439 | 125 | 221457.50 | 37.0 |  885496.0 | 458.0 |\n",
       "| high   | 5 | 200 | 1910119 |  80249 | 112 |  15380.76 | 40.0 |  957655.8 | 486.0 |\n",
       "| medium | 3 | 100 | 2414781 | 168894 | 245 |  11660.66 |  8.0 | 1303342.7 | 285.0 |\n",
       "| medium | 3 | 150 | 2364220 | 162173 | 189 |  10660.67 | 11.0 | 1566350.1 | 460.0 |\n",
       "| medium | 3 | 200 | 2319823 | 157074 | 160 |  11684.48 | 14.0 | 1751718.4 | 547.0 |\n",
       "| medium | 5 | 100 | 2096913 |  91068 | 140 |  13884.34 | 26.5 |  974563.0 | 344.5 |\n",
       "| medium | 5 | 150 | 2057410 |  87899 | 114 |  15991.74 | 33.5 | 1119803.1 | 486.0 |\n",
       "| medium | 5 | 200 | 2026516 |  85769 | 102 |  17438.54 | 38.0 | 1206054.9 | 557.0 |\n",
       "| low    | 3 | 100 | 2560774 | 178485 | 208 |  12790.43 |  8.0 | 2152665.0 | 344.5 |\n",
       "| low    | 3 | 150 | 2522813 | 173655 | 168 |  10906.33 |  9.5 | 2507008.7 | 444.5 |\n",
       "| low    | 3 | 200 | 2494853 | 170213 | 148 |  12165.96 | 10.0 | 2733440.4 | 556.0 |\n",
       "| low    | 5 | 100 | 2257253 |  99602 | 134 |  13553.40 | 17.5 | 1410306.0 | 353.0 |\n",
       "| low    | 5 | 150 | 2226433 |  96820 | 111 |  15932.43 | 23.0 | 1607383.7 | 426.0 |\n",
       "| low    | 5 | 200 | 2185006 |  93280 |  90 |  18966.34 | 37.0 | 1842518.1 | 550.0 |\n",
       "\n"
      ],
      "text/plain": [
       "   res_filter ind_filter otu_filter total_ind total_otu num_cells ind_var  \n",
       "1  high       3          100        2280962   156071    257       112314.93\n",
       "2  high       3          150        2215160   148027    190       147233.06\n",
       "3  high       3          200        2171685   142419    157       177571.38\n",
       "4  high       5          100        1982162    84832    145       191930.17\n",
       "5  high       5          150        1950115    82439    125       221457.50\n",
       "6  high       5          200        1910119    80249    112        15380.76\n",
       "7  medium     3          100        2414781   168894    245        11660.66\n",
       "8  medium     3          150        2364220   162173    189        10660.67\n",
       "9  medium     3          200        2319823   157074    160        11684.48\n",
       "10 medium     5          100        2096913    91068    140        13884.34\n",
       "11 medium     5          150        2057410    87899    114        15991.74\n",
       "12 medium     5          200        2026516    85769    102        17438.54\n",
       "13 low        3          100        2560774   178485    208        12790.43\n",
       "14 low        3          150        2522813   173655    168        10906.33\n",
       "15 low        3          200        2494853   170213    148        12165.96\n",
       "16 low        5          100        2257253    99602    134        13553.40\n",
       "17 low        5          150        2226433    96820    111        15932.43\n",
       "18 low        5          200        2185006    93280     90        18966.34\n",
       "   ind_median otu_var   otu_median\n",
       "1  11.0       1016922.8 281.0     \n",
       "2  15.0       1263516.7 481.5     \n",
       "3  21.0       1435781.2 578.0     \n",
       "4  31.0        797422.9 345.0     \n",
       "5  37.0        885496.0 458.0     \n",
       "6  40.0        957655.8 486.0     \n",
       "7   8.0       1303342.7 285.0     \n",
       "8  11.0       1566350.1 460.0     \n",
       "9  14.0       1751718.4 547.0     \n",
       "10 26.5        974563.0 344.5     \n",
       "11 33.5       1119803.1 486.0     \n",
       "12 38.0       1206054.9 557.0     \n",
       "13  8.0       2152665.0 344.5     \n",
       "14  9.5       2507008.7 444.5     \n",
       "15 10.0       2733440.4 556.0     \n",
       "16 17.5       1410306.0 353.0     \n",
       "17 23.0       1607383.7 426.0     \n",
       "18 37.0       1842518.1 550.0     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interested in:\n",
    "# the total number of individuals (total_ind) and otus (total_otu),\n",
    "# the number of cells\n",
    "# the variance in the median number of individuals per otu per cell (ind_var), \n",
    "# the median of the median number of individuals per otu per cell (ind_median), **this tells us the average across cells for each filtering regime\n",
    "# and the variance in the number of otus per cell (otu_var) for each filtering regime\n",
    "\n",
    "bold_summary <- bold_filtered[,.(total_ind = sum(total_ind),\n",
    "                                 total_otu = sum(n_otu),\n",
    "                                 num_cells = .N,\n",
    "                                 ind_var = var(n_ind_median),\n",
    "                                 ind_median = median(n_ind_median),\n",
    "                                 otu_var = var(n_otu),\n",
    "                                 otu_median = median(n_otu)),\n",
    "                             by = .(res_filter, ind_filter, otu_filter)]\n",
    "\n",
    "bold_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fwrite(bold_summary, here(\"output\", \"spreadsheets\", \"bold_summary_resolutions.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I took a closer look at sampling differences in the `R/resolution_selection.R` file. The gist is that the 193 km resolution (medium) and 150 OTUs per cell with 3 individuals per OTU per cell was the filtering regime that balanced among the criteria we considered- number of cells retained for analysis, median number of OTUs per cell, variation in the median number of OTUs across cells, number of individuals per OTU "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate pairwise nucleotide differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in fasta alignment file names\n",
    "fasta_list <- fread(here(\"output\", \"spreadsheets\", \"fasta_filenames.csv\"))\n",
    "fasta_list$full_path <- here(\"output\", \"alignments\", fasta_list$filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230.106 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "# read in fasta files and keep as two column data frame\n",
    "tic()\n",
    "s <- map(fasta_list$full_path, ~seqinr::read.fasta(.x, as.string = TRUE)) %>% \n",
    "unlist()\n",
    "toc()\n",
    "\n",
    "sdf <- s %>% enframe(name = \"recordID\", value = \"seq\")\n",
    "setDT(sdf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 2,795\n",
      "Variables: 11\n",
      "$ cell         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 22055, 25328, 22781, 27464, 22417, 17731, 18452, 14499, …\n",
      "$ total_ind    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 274225, 7514, 5839, 786, 3726, 1067, 727, 3426, 639, 104…\n",
      "$ n_ind_min    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…\n",
      "$ n_ind_median \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 47, 7, 8, 7, 6, 10, 9, 9, 6, 11, 5, 6, 30, 16, 9, 68, 32…\n",
      "$ n_ind_max    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 1964, 287, 61, 47, 68, 86, 69, 52, 19, 64, 24, 19, 273, …\n",
      "$ n_ind_var    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 47030.42349, 2966.76248, 81.35492, 152.79081, 161.00897,…\n",
      "$ n_otu        \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 13113, 1177, 877, 132, 701, 133, 108, 491, 113, 129, 109…\n",
      "$ n_order      \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 19, 10, 6, 4, 10, 2, 5, 13, 9, 2, 9, 10, 10, 14, 3, 11, …\n",
      "$ res_filter   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", …\n",
      "$ ind_filter   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…\n",
      "$ otu_filter   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…\n"
     ]
    }
   ],
   "source": [
    "bold_final_filter <- bold_filtered[ind_filter %in% bold_summary$ind_filter & \n",
    "             otu_filter %in% bold_summary$otu_filter &\n",
    "             res_filter %in% bold_summary$res_filter]\n",
    "\n",
    "glimpse(bold_final_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_all_pairwise_div <- function(res_filter, ind_filter, otu_filter) {\n",
    "    cell_res <- paste0(\"cell_\", res_filter)\n",
    "    df_out <- bold_sf_pts[,ind_count := .N, by = c(cell_res, \"bin_uri\")\n",
    "           ][ind_count >= ind_filter\n",
    "            ]\n",
    "    \n",
    "    df_out <- df_out[,otu_num := uniqueN(bin_uri), by = cell_res\n",
    "                 ][otu_num >= otu_filter\n",
    "                  ]\n",
    "    \n",
    "    df_out <- df_out[,pi := pairwise_div(recordID), by = c(cell_res, \"bin_uri\")]\n",
    "    \n",
    "    df_out <- df_out[,.(num_ind = unique(ind_count),\n",
    "                       order = unique(order_name),\n",
    "                       pi = unique(pi)), \n",
    "                       by = c(cell_res, \"bin_uri\")]\n",
    "    \n",
    "    setnames(df_out, cell_res, \"cell\")\n",
    "    \n",
    "    return(df_out)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40451.78 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "tic()\n",
    "df_test <- calc_all_pairwise_div(res_filter = \"medium\", ind_filter = 3, otu_filter = 150)\n",
    "toc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cell</th><th scope=col>bin_uri</th><th scope=col>num_ind</th><th scope=col>order</th><th scope=col>pi</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5448</td><td>BOLD:AAA0006</td><td> 21</td><td>Lepidoptera</td><td>0.0058795703</td></tr>\n",
       "\t<tr><td>5448</td><td>BOLD:AAA0007</td><td> 48</td><td>Lepidoptera</td><td>0.0032591444</td></tr>\n",
       "\t<tr><td>5448</td><td>BOLD:AAA0008</td><td> 50</td><td>Lepidoptera</td><td>0.0001830117</td></tr>\n",
       "\t<tr><td>5448</td><td>BOLD:AAA0009</td><td>  6</td><td>Lepidoptera</td><td>0.0040526849</td></tr>\n",
       "\t<tr><td>5448</td><td>BOLD:AAA0010</td><td> 32</td><td>Lepidoptera</td><td>0.0006961860</td></tr>\n",
       "\t<tr><td>5448</td><td>BOLD:AAA0011</td><td>105</td><td>Lepidoptera</td><td>0.0024404010</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " cell & bin\\_uri & num\\_ind & order & pi\\\\\n",
       " <dbl> & <chr> & <int> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 5448 & BOLD:AAA0006 &  21 & Lepidoptera & 0.0058795703\\\\\n",
       "\t 5448 & BOLD:AAA0007 &  48 & Lepidoptera & 0.0032591444\\\\\n",
       "\t 5448 & BOLD:AAA0008 &  50 & Lepidoptera & 0.0001830117\\\\\n",
       "\t 5448 & BOLD:AAA0009 &   6 & Lepidoptera & 0.0040526849\\\\\n",
       "\t 5448 & BOLD:AAA0010 &  32 & Lepidoptera & 0.0006961860\\\\\n",
       "\t 5448 & BOLD:AAA0011 & 105 & Lepidoptera & 0.0024404010\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 5\n",
       "\n",
       "| cell &lt;dbl&gt; | bin_uri &lt;chr&gt; | num_ind &lt;int&gt; | order &lt;chr&gt; | pi &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 5448 | BOLD:AAA0006 |  21 | Lepidoptera | 0.0058795703 |\n",
       "| 5448 | BOLD:AAA0007 |  48 | Lepidoptera | 0.0032591444 |\n",
       "| 5448 | BOLD:AAA0008 |  50 | Lepidoptera | 0.0001830117 |\n",
       "| 5448 | BOLD:AAA0009 |   6 | Lepidoptera | 0.0040526849 |\n",
       "| 5448 | BOLD:AAA0010 |  32 | Lepidoptera | 0.0006961860 |\n",
       "| 5448 | BOLD:AAA0011 | 105 | Lepidoptera | 0.0024404010 |\n",
       "\n"
      ],
      "text/plain": [
       "  cell bin_uri      num_ind order       pi          \n",
       "1 5448 BOLD:AAA0006  21     Lepidoptera 0.0058795703\n",
       "2 5448 BOLD:AAA0007  48     Lepidoptera 0.0032591444\n",
       "3 5448 BOLD:AAA0008  50     Lepidoptera 0.0001830117\n",
       "4 5448 BOLD:AAA0009   6     Lepidoptera 0.0040526849\n",
       "5 5448 BOLD:AAA0010  32     Lepidoptera 0.0006961860\n",
       "6 5448 BOLD:AAA0011 105     Lepidoptera 0.0024404010"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fwrite(df_test, here(\"output\", \"spreadsheets\", \"med_3_150_pi.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to calculate the summary statistics per cell. Hill numbers are corrected by OTU richness per cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_sumstats <- function(df) {\n",
    "   df_out <- df[,.(avg_pi = mean(pi),\n",
    "          median_pi = median(pi),\n",
    "          sqrt_pi = sqrt(mean(pi)),\n",
    "          sd_pi = sd(pi),\n",
    "          var_pi= var(pi),\n",
    "          num_ind = sum(num_ind),\n",
    "          num_otu = uniqueN(bin_uri),\n",
    "          num_order = uniqueN(order),\n",
    "          hill_1 = hill_calc(pi, 1),\n",
    "          hill_2 = hill_calc(pi, 2),\n",
    "          hill_3 = hill_calc(pi, 3),\n",
    "          hill_4 = hill_calc(pi, 4),\n",
    "          hill_5 = hill_calc(pi, 5)),\n",
    "          by = \"cell\"]\n",
    "  return(df_out)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.372 sec elapsed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 6 × 14</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cell</th><th scope=col>avg_pi</th><th scope=col>median_pi</th><th scope=col>sqrt_pi</th><th scope=col>sd_pi</th><th scope=col>var_pi</th><th scope=col>num_ind</th><th scope=col>num_otu</th><th scope=col>num_order</th><th scope=col>hill_1</th><th scope=col>hill_2</th><th scope=col>hill_3</th><th scope=col>hill_4</th><th scope=col>hill_5</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5448</td><td>0.002084775</td><td>0.001234568</td><td>0.04565934</td><td>0.002550134</td><td>6.503185e-06</td><td>277738</td><td>13300</td><td>20</td><td>0.5398728</td><td>0.4006171</td><td>0.3200303</td><td>0.2698272</td><td>0.2362016</td></tr>\n",
       "\t<tr><td>6364</td><td>0.002351639</td><td>0.001361903</td><td>0.04849370</td><td>0.002994169</td><td>8.965047e-06</td><td>  7611</td><td> 1192</td><td>10</td><td>0.5119873</td><td>0.3817164</td><td>0.3080146</td><td>0.2641870</td><td>0.2362316</td></tr>\n",
       "\t<tr><td>5631</td><td>0.002258771</td><td>0.001374665</td><td>0.04752653</td><td>0.002636205</td><td>6.949575e-06</td><td>  5844</td><td>  877</td><td> 6</td><td>0.5505554</td><td>0.4236279</td><td>0.3494859</td><td>0.3032256</td><td>0.2724399</td></tr>\n",
       "\t<tr><td>6892</td><td>0.002583159</td><td>0.001400230</td><td>0.05082479</td><td>0.003207600</td><td>1.028869e-05</td><td>  1541</td><td>  270</td><td> 7</td><td>0.5140907</td><td>0.3942914</td><td>0.3266356</td><td>0.2851218</td><td>0.2579253</td></tr>\n",
       "\t<tr><td>8335</td><td>0.002076913</td><td>0.001054852</td><td>0.04557316</td><td>0.002734620</td><td>7.478144e-06</td><td>  1142</td><td>  189</td><td> 4</td><td>0.4974995</td><td>0.3670452</td><td>0.2928867</td><td>0.2492382</td><td>0.2216627</td></tr>\n",
       "\t<tr><td>5629</td><td>0.002599724</td><td>0.001586310</td><td>0.05098749</td><td>0.003029968</td><td>9.180709e-06</td><td> 11859</td><td> 1862</td><td>10</td><td>0.5523312</td><td>0.4241509</td><td>0.3505987</td><td>0.3039582</td><td>0.2719015</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 14\n",
       "\\begin{tabular}{llllllllllllll}\n",
       " cell & avg\\_pi & median\\_pi & sqrt\\_pi & sd\\_pi & var\\_pi & num\\_ind & num\\_otu & num\\_order & hill\\_1 & hill\\_2 & hill\\_3 & hill\\_4 & hill\\_5\\\\\n",
       " <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 5448 & 0.002084775 & 0.001234568 & 0.04565934 & 0.002550134 & 6.503185e-06 & 277738 & 13300 & 20 & 0.5398728 & 0.4006171 & 0.3200303 & 0.2698272 & 0.2362016\\\\\n",
       "\t 6364 & 0.002351639 & 0.001361903 & 0.04849370 & 0.002994169 & 8.965047e-06 &   7611 &  1192 & 10 & 0.5119873 & 0.3817164 & 0.3080146 & 0.2641870 & 0.2362316\\\\\n",
       "\t 5631 & 0.002258771 & 0.001374665 & 0.04752653 & 0.002636205 & 6.949575e-06 &   5844 &   877 &  6 & 0.5505554 & 0.4236279 & 0.3494859 & 0.3032256 & 0.2724399\\\\\n",
       "\t 6892 & 0.002583159 & 0.001400230 & 0.05082479 & 0.003207600 & 1.028869e-05 &   1541 &   270 &  7 & 0.5140907 & 0.3942914 & 0.3266356 & 0.2851218 & 0.2579253\\\\\n",
       "\t 8335 & 0.002076913 & 0.001054852 & 0.04557316 & 0.002734620 & 7.478144e-06 &   1142 &   189 &  4 & 0.4974995 & 0.3670452 & 0.2928867 & 0.2492382 & 0.2216627\\\\\n",
       "\t 5629 & 0.002599724 & 0.001586310 & 0.05098749 & 0.003029968 & 9.180709e-06 &  11859 &  1862 & 10 & 0.5523312 & 0.4241509 & 0.3505987 & 0.3039582 & 0.2719015\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 14\n",
       "\n",
       "| cell &lt;int&gt; | avg_pi &lt;dbl&gt; | median_pi &lt;dbl&gt; | sqrt_pi &lt;dbl&gt; | sd_pi &lt;dbl&gt; | var_pi &lt;dbl&gt; | num_ind &lt;int&gt; | num_otu &lt;int&gt; | num_order &lt;int&gt; | hill_1 &lt;dbl&gt; | hill_2 &lt;dbl&gt; | hill_3 &lt;dbl&gt; | hill_4 &lt;dbl&gt; | hill_5 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 5448 | 0.002084775 | 0.001234568 | 0.04565934 | 0.002550134 | 6.503185e-06 | 277738 | 13300 | 20 | 0.5398728 | 0.4006171 | 0.3200303 | 0.2698272 | 0.2362016 |\n",
       "| 6364 | 0.002351639 | 0.001361903 | 0.04849370 | 0.002994169 | 8.965047e-06 |   7611 |  1192 | 10 | 0.5119873 | 0.3817164 | 0.3080146 | 0.2641870 | 0.2362316 |\n",
       "| 5631 | 0.002258771 | 0.001374665 | 0.04752653 | 0.002636205 | 6.949575e-06 |   5844 |   877 |  6 | 0.5505554 | 0.4236279 | 0.3494859 | 0.3032256 | 0.2724399 |\n",
       "| 6892 | 0.002583159 | 0.001400230 | 0.05082479 | 0.003207600 | 1.028869e-05 |   1541 |   270 |  7 | 0.5140907 | 0.3942914 | 0.3266356 | 0.2851218 | 0.2579253 |\n",
       "| 8335 | 0.002076913 | 0.001054852 | 0.04557316 | 0.002734620 | 7.478144e-06 |   1142 |   189 |  4 | 0.4974995 | 0.3670452 | 0.2928867 | 0.2492382 | 0.2216627 |\n",
       "| 5629 | 0.002599724 | 0.001586310 | 0.05098749 | 0.003029968 | 9.180709e-06 |  11859 |  1862 | 10 | 0.5523312 | 0.4241509 | 0.3505987 | 0.3039582 | 0.2719015 |\n",
       "\n"
      ],
      "text/plain": [
       "  cell avg_pi      median_pi   sqrt_pi    sd_pi       var_pi       num_ind\n",
       "1 5448 0.002084775 0.001234568 0.04565934 0.002550134 6.503185e-06 277738 \n",
       "2 6364 0.002351639 0.001361903 0.04849370 0.002994169 8.965047e-06   7611 \n",
       "3 5631 0.002258771 0.001374665 0.04752653 0.002636205 6.949575e-06   5844 \n",
       "4 6892 0.002583159 0.001400230 0.05082479 0.003207600 1.028869e-05   1541 \n",
       "5 8335 0.002076913 0.001054852 0.04557316 0.002734620 7.478144e-06   1142 \n",
       "6 5629 0.002599724 0.001586310 0.05098749 0.003029968 9.180709e-06  11859 \n",
       "  num_otu num_order hill_1    hill_2    hill_3    hill_4    hill_5   \n",
       "1 13300   20        0.5398728 0.4006171 0.3200303 0.2698272 0.2362016\n",
       "2  1192   10        0.5119873 0.3817164 0.3080146 0.2641870 0.2362316\n",
       "3   877    6        0.5505554 0.4236279 0.3494859 0.3032256 0.2724399\n",
       "4   270    7        0.5140907 0.3942914 0.3266356 0.2851218 0.2579253\n",
       "5   189    4        0.4974995 0.3670452 0.2928867 0.2492382 0.2216627\n",
       "6  1862   10        0.5523312 0.4241509 0.3505987 0.3039582 0.2719015"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tic()\n",
    "sumstats <- calc_sumstats(med_3_10)\n",
    "toc()\n",
    "\n",
    "head(sumstats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "── Data Summary ────────────────────────\n",
      "                           Values  \n",
      "Name                       sumstats\n",
      "Number of rows             957     \n",
      "Number of columns          14      \n",
      "_______________________            \n",
      "Column type frequency:             \n",
      "  numeric                  14      \n",
      "________________________           \n",
      "Group variables            None    \n",
      "\n",
      "── Variable type: numeric ──────────────────────────────────────────────────────\n",
      "   skim_variable n_missing complete_rate         mean      sd           p0\n",
      " 1 cell                  0             1 4583.        3.31e+3 48          \n",
      " 2 avg_pi                0             1    0.00286   1.17e-3  0.000432   \n",
      " 3 median_pi             0             1    0.00172   1.14e-3  0          \n",
      " 4 sqrt_pi               0             1    0.0526    9.75e-3  0.0208     \n",
      " 5 sd_pi                 0             1    0.00337   1.07e-3  0.000775   \n",
      " 6 var_pi                0             1    0.0000125 8.29e-6  0.000000600\n",
      " 7 num_ind               0             1 2667.        1.29e+4 31          \n",
      " 8 num_otu               0             1  199.        6.45e+2 10          \n",
      " 9 num_order             0             1    4.77      3.33e+0  1          \n",
      "10 hill_1                0             1    0.531     1.03e-1  0.157      \n",
      "11 hill_2                0             1    0.423     1.04e-1  0.106      \n",
      "12 hill_3                0             1    0.365     1.02e-1  0.0896     \n",
      "13 hill_4                0             1    0.331     1.00e-1  0.0820     \n",
      "14 hill_5                0             1    0.308     9.86e-2  0.0776     \n",
      "             p25          p50          p75    p100 hist \n",
      " 1 1903          3107         7318         1.19e+4 ▇▆▃▃▃\n",
      " 2    0.00220       0.00275      0.00333   2.23e-2 ▇▁▁▁▁\n",
      " 3    0.00103       0.00154      0.00205   2.23e-2 ▇▁▁▁▁\n",
      " 4    0.0469        0.0524       0.0577    1.49e-1 ▂▇▁▁▁\n",
      " 5    0.00268       0.00331      0.00392   7.93e-3 ▂▇▅▁▁\n",
      " 6    0.00000716    0.0000109    0.0000154 6.29e-5 ▇▃▁▁▁\n",
      " 7   84           182          619         2.78e+5 ▇▁▁▁▁\n",
      " 8   17            34          102         1.33e+4 ▇▁▁▁▁\n",
      " 9    2             4            6         2.00e+1 ▇▃▁▁▁\n",
      "10    0.469         0.528        0.596     9.60e-1 ▁▃▇▂▁\n",
      "11    0.358         0.411        0.482     9.25e-1 ▁▇▆▁▁\n",
      "12    0.296         0.351        0.423     8.93e-1 ▂▇▃▁▁\n",
      "13    0.260         0.315        0.389     8.64e-1 ▂▇▃▁▁\n",
      "14    0.238         0.293        0.365     8.38e-1 ▃▇▂▁▁\n"
     ]
    }
   ],
   "source": [
    "skimr::skim(sumstats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "fwrite(sumstats, here(\"output\", \"spreadsheets\", \"med_3_10_sumstats.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Richness'"
      ],
      "text/latex": [
       "'Richness'"
      ],
      "text/markdown": [
       "'Richness'"
      ],
      "text/plain": [
       "[1] \"Richness\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "   10.0    16.0    40.0   226.1   151.0  9936.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Hill 1'"
      ],
      "text/latex": [
       "'Hill 1'"
      ],
      "text/markdown": [
       "'Hill 1'"
      ],
      "text/plain": [
       "[1] \"Hill 1\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "   2.881    9.669   23.422  130.826   83.897 5780.860 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Hill 1 corrected'"
      ],
      "text/latex": [
       "'Hill 1 corrected'"
      ],
      "text/markdown": [
       "'Hill 1 corrected'"
      ],
      "text/plain": [
       "[1] \"Hill 1 corrected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.2029  0.5337  0.5891  0.5914  0.6495  0.9723 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Hill 1 resample'"
      ],
      "text/latex": [
       "'Hill 1 resample'"
      ],
      "text/markdown": [
       "'Hill 1 resample'"
      ],
      "text/plain": [
       "[1] \"Hill 1 resample\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's \n",
       "  3.016   5.785   6.251   6.280   6.799   9.747       3 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Average pi'"
      ],
      "text/latex": [
       "'Average pi'"
      ],
      "text/markdown": [
       "'Average pi'"
      ],
      "text/plain": [
       "[1] \"Average pi\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "0.0006159 0.0024941 0.0029882 0.0031632 0.0036133 0.0223606 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Median pi'"
      ],
      "text/latex": [
       "'Median pi'"
      ],
      "text/markdown": [
       "'Median pi'"
      ],
      "text/plain": [
       "[1] \"Median pi\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       "0.000000 0.001420 0.001855 0.002084 0.002433 0.022516 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paste0(\"Richness\")\n",
    "summary(fasta_sumstats$richness)\n",
    "\n",
    "paste0(\"Hill 1\")\n",
    "summary(fasta_sumstats$hill_1)\n",
    "\n",
    "paste0(\"Hill 1 corrected\")\n",
    "summary(fasta_sumstats$hill_1_c)\n",
    "\n",
    "paste0(\"Hill 1 resample\")\n",
    "summary(fasta_sumstats$hill_1_r)\n",
    "\n",
    "paste0(\"Average pi\")\n",
    "summary(fasta_sumstats$avg_pi)\n",
    "\n",
    "paste0(\"Median pi\")\n",
    "summary(fasta_sumstats$median_pi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the resampling method returns a few NAs! I don't know what they're from, but the corrected method of calculating Hill numbers conveys much of the same information, so I'm going to go with the corrected values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 3 × 21</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cells</th><th scope=col>avg_pi</th><th scope=col>median_pi</th><th scope=col>sd_pi</th><th scope=col>var_pi</th><th scope=col>richness</th><th scope=col>hill_1</th><th scope=col>hill_2</th><th scope=col>hill_3</th><th scope=col>hill_4</th><th scope=col>⋯</th><th scope=col>hill_1_c</th><th scope=col>hill_2_c</th><th scope=col>hill_3_c</th><th scope=col>hill_4_c</th><th scope=col>hill_5_c</th><th scope=col>hill_1_r</th><th scope=col>hill_2_r</th><th scope=col>hill_3_r</th><th scope=col>hill_4_r</th><th scope=col>hill_5_r</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 610</td><td>0.001377272</td><td>0.0006611153</td><td>0.001686415</td><td>2.843996e-06</td><td>10</td><td>5.031984</td><td>4.256456</td><td>3.932519</td><td>5.031984</td><td>⋯</td><td>0.5031984</td><td>0.4256456</td><td>0.3932519</td><td>0.3767629</td><td>0.3668295</td><td>NA</td><td>4.361142</td><td>3.963474</td><td>4.025759</td><td>3.790077</td></tr>\n",
       "\t<tr><td>1241</td><td>0.001811893</td><td>0.0000000000</td><td>0.003040131</td><td>9.242396e-06</td><td>13</td><td>4.275040</td><td>3.612409</td><td>3.302378</td><td>4.275040</td><td>⋯</td><td>0.3288493</td><td>0.2778776</td><td>0.2540291</td><td>0.2403825</td><td>0.2315504</td><td>NA</td><td>3.007234</td><td>2.818323</td><td>2.718252</td><td>2.895737</td></tr>\n",
       "\t<tr><td>3904</td><td>0.002221667</td><td>0.0000000000</td><td>0.005483623</td><td>3.007012e-05</td><td>18</td><td>3.651395</td><td>2.665172</td><td>2.309921</td><td>3.651395</td><td>⋯</td><td>0.2028553</td><td>0.1480651</td><td>0.1283290</td><td>0.1191281</td><td>0.1140707</td><td>NA</td><td>     NaN</td><td>     NaN</td><td>2.004682</td><td>     NaN</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 21\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " cells & avg\\_pi & median\\_pi & sd\\_pi & var\\_pi & richness & hill\\_1 & hill\\_2 & hill\\_3 & hill\\_4 & ⋯ & hill\\_1\\_c & hill\\_2\\_c & hill\\_3\\_c & hill\\_4\\_c & hill\\_5\\_c & hill\\_1\\_r & hill\\_2\\_r & hill\\_3\\_r & hill\\_4\\_r & hill\\_5\\_r\\\\\n",
       " <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  610 & 0.001377272 & 0.0006611153 & 0.001686415 & 2.843996e-06 & 10 & 5.031984 & 4.256456 & 3.932519 & 5.031984 & ⋯ & 0.5031984 & 0.4256456 & 0.3932519 & 0.3767629 & 0.3668295 & NA & 4.361142 & 3.963474 & 4.025759 & 3.790077\\\\\n",
       "\t 1241 & 0.001811893 & 0.0000000000 & 0.003040131 & 9.242396e-06 & 13 & 4.275040 & 3.612409 & 3.302378 & 4.275040 & ⋯ & 0.3288493 & 0.2778776 & 0.2540291 & 0.2403825 & 0.2315504 & NA & 3.007234 & 2.818323 & 2.718252 & 2.895737\\\\\n",
       "\t 3904 & 0.002221667 & 0.0000000000 & 0.005483623 & 3.007012e-05 & 18 & 3.651395 & 2.665172 & 2.309921 & 3.651395 & ⋯ & 0.2028553 & 0.1480651 & 0.1283290 & 0.1191281 & 0.1140707 & NA &      NaN &      NaN & 2.004682 &      NaN\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 21\n",
       "\n",
       "| cells &lt;int&gt; | avg_pi &lt;dbl&gt; | median_pi &lt;dbl&gt; | sd_pi &lt;dbl&gt; | var_pi &lt;dbl&gt; | richness &lt;int&gt; | hill_1 &lt;dbl&gt; | hill_2 &lt;dbl&gt; | hill_3 &lt;dbl&gt; | hill_4 &lt;dbl&gt; | ⋯ ⋯ | hill_1_c &lt;dbl&gt; | hill_2_c &lt;dbl&gt; | hill_3_c &lt;dbl&gt; | hill_4_c &lt;dbl&gt; | hill_5_c &lt;dbl&gt; | hill_1_r &lt;dbl&gt; | hill_2_r &lt;dbl&gt; | hill_3_r &lt;dbl&gt; | hill_4_r &lt;dbl&gt; | hill_5_r &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  610 | 0.001377272 | 0.0006611153 | 0.001686415 | 2.843996e-06 | 10 | 5.031984 | 4.256456 | 3.932519 | 5.031984 | ⋯ | 0.5031984 | 0.4256456 | 0.3932519 | 0.3767629 | 0.3668295 | NA | 4.361142 | 3.963474 | 4.025759 | 3.790077 |\n",
       "| 1241 | 0.001811893 | 0.0000000000 | 0.003040131 | 9.242396e-06 | 13 | 4.275040 | 3.612409 | 3.302378 | 4.275040 | ⋯ | 0.3288493 | 0.2778776 | 0.2540291 | 0.2403825 | 0.2315504 | NA | 3.007234 | 2.818323 | 2.718252 | 2.895737 |\n",
       "| 3904 | 0.002221667 | 0.0000000000 | 0.005483623 | 3.007012e-05 | 18 | 3.651395 | 2.665172 | 2.309921 | 3.651395 | ⋯ | 0.2028553 | 0.1480651 | 0.1283290 | 0.1191281 | 0.1140707 | NA |      NaN |      NaN | 2.004682 |      NaN |\n",
       "\n"
      ],
      "text/plain": [
       "  cells avg_pi      median_pi    sd_pi       var_pi       richness hill_1  \n",
       "1  610  0.001377272 0.0006611153 0.001686415 2.843996e-06 10       5.031984\n",
       "2 1241  0.001811893 0.0000000000 0.003040131 9.242396e-06 13       4.275040\n",
       "3 3904  0.002221667 0.0000000000 0.005483623 3.007012e-05 18       3.651395\n",
       "  hill_2   hill_3   hill_4   ⋯ hill_1_c  hill_2_c  hill_3_c  hill_4_c \n",
       "1 4.256456 3.932519 5.031984 ⋯ 0.5031984 0.4256456 0.3932519 0.3767629\n",
       "2 3.612409 3.302378 4.275040 ⋯ 0.3288493 0.2778776 0.2540291 0.2403825\n",
       "3 2.665172 2.309921 3.651395 ⋯ 0.2028553 0.1480651 0.1283290 0.1191281\n",
       "  hill_5_c  hill_1_r hill_2_r hill_3_r hill_4_r hill_5_r\n",
       "1 0.3668295 NA       4.361142 3.963474 4.025759 3.790077\n",
       "2 0.2315504 NA       3.007234 2.818323 2.718252 2.895737\n",
       "3 0.1140707 NA            NaN      NaN 2.004682      NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter(fasta_sumstats, is.na(hill_1_r))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hill 1 when correcting for species richness conveys much of the same information as the resampling-based method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = hill_1_c ~ hill_1_r, data = fasta_sumstats)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.113816 -0.010342  0.000622  0.011435  0.051778 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -0.081103   0.006350  -12.77   <2e-16 ***\n",
       "hill_1_r     0.107326   0.001002  107.14   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.01869 on 470 degrees of freedom\n",
       "  (3 observations deleted due to missingness)\n",
       "Multiple R-squared:  0.9607,\tAdjusted R-squared:  0.9606 \n",
       "F-statistic: 1.148e+04 on 1 and 470 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WMY21etUgHJd5D66j8L96MBDWhAAxrQ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0wLGYFaIfwYj3gTj/0YGZJo7xx0A7+8LM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\nAwYMfAYCGyQpq6HstrsAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(hill_1_c ~ hill_1_r, data = fasta_sumstats)\n",
    "summary(lm(hill_1_c ~ hill_1_r, data = fasta_sumstats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relationship between OTU richness and Hill 1. A strong relationship is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BDoV0CB1O/cGhkBAgQIECBAgAABAjMK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\nCBAgQIAAAQIECBAgQGAtgf8Pp60vKj5GsbwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(hill_1 ~ richness, data = fasta_sumstats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relationship between OTU richness and corrected Hill 1. No relationship is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7DfSSiGeKz3NcnjyV0Jo0s2BRRQQAEF\nFFBAAQVmRaBWBdIcsyLha8eEwDP5lB9Lvp7wtDoeyDApOTd5MrEpoIACCiiggAIKKKBAQ8AC\nqX9+FO7IoZ7QP4frkSqggAIKKKCAAgoo0L2AD2no3sxXKKCAAgoooIACCiigQE0FLJBqemI9\nLAUUUEABBRRQQAEFFOhewAKpezNfoYACCiiggAIKKKCAAjUVsECq6Yn1sBRQQAEFFFBAAQUU\nUKB7AQuk7s18hQIKKKCAAgoooIACCtRUwAKppifWw1JAAQUUUEABBRRQQIHuBSyQujfzFQoo\noIACCiiggAIKKFBTAQukmp5YD0sBBRRQQAEFFFBAAQW6F7BA6t7MVyiggAIKKKCAAgoooEBN\nBSyQanpiPSwFFFBAAQUUUEABBRToXsACqXszX6GAAgoooIACCiiggAI1FbBAqumJ9bAUUEAB\nBRRQQAEFFFCgewELpO7NfIUCCiiggAIKKKCAAgrUVMACqaYn1sNSQAEFFFBAAQUUUECB7gUs\nkLo38xUKKKCAAgoooIACCihQUwELpJqeWA9LAQUUUEABBRRQQAEFuhewQOrezFcooIACCiig\ngAIKKKBATQUskGp6Yj0sBRRQQAEFFFBAAQUU6F7AAql7M1+hgAIKKKCAAgoooIACNRWwQKrp\nifWwFFBAAQUUUEABBRRQoHsBC6TuzXyFAgoooIACCiiggAIK1FTAAqmmJ9bDUkABBRRQQAEF\nFFBAge4FLJC6N/MVCiiggAIKKKCAAgooUFMBC6SanlgPSwEFFFBAAQUUUEABBboXsEDq3sxX\nKKCAAgoooIACCiigQE0FLJBqemI9LAUUUEABBRRQQAEFFOhewAKpezNfoYACCiiggAIKKKCA\nAjUVsECq6Yn1sBRQQAEFFFBAAQUUUKB7AQuk7s18hQIKKKCAAgoooIACCtRUwAKppifWw1JA\nAQUUUEABBRRQQIHuBSyQujfzFQoooIACCiiggAIKKFBTAQukmp5YD0sBBRRQQAEFFFBAAQW6\nF7BA6t7MVyiggAIKKKCAAgoooEBNBSyQanpiPSwFFFBAAQUUUEABBRToXsACqXszX6GAAgoo\noIACCiiggAI1FbBAqumJ9bAUUEABBRRQQAEFFFCgewELpO7NfIUCCiiggAIKKKCAAgrUVMAC\nqaYn1sNSQAEFFFBAAQUUUECB7gUskLo38xUKKKCAAgoooIACCihQUwELpJqeWA9LAQUUUEAB\nBRRQQAEFuhewQOrezFcooIACCiiggAIKKKBATQUskGp6Yj0sBRRQQAEFFFBAAQUU6F7AAql7\nM1+hgAIKKKCAAgoooIACNRWwQKrpifWwFFBAAQUUUEABBRRQoHsBC6TuzXyFAgoooIACCiig\ngAIK1FTAAqmmJ9bDUkABBRRQQAEFFFBAge4FLJC6N/MVCiiggAIKKKCAAgooUFMBC6SanlgP\nSwEFFFBAAQUUUEABBboXsEDq3sxXKKCAAgoooIACCiigQE0FLJBqemI9LAUUUEABBRRQQAEF\nFOhewAKpezNfoYACCiiggAIKKKCAAjUVsECq6Yn1sBRQQAEFFFBAAQUUUKB7AQuk7s18hQIK\nKKCAAgoooIACCtRUwAKppifWw1JAAQUUUEABBRRQQIHuBSyQujfzFQoooIACCiiggAIKKFBT\nAQukmp5YD0sBBRRQQAEFFFBAAQW6F7BA6t7MVyiggAIKKKCAAgoooEBNBSyQanpiPSwFFFBA\nAQUUUEABBRToXsACqXszX6GAAgoooIACCiiggAI1FbBAqumJ9bAUUEABBRRQQAEFFFCgewEL\npO7NfIUCCiiggAIKKKCAAgrUVMACqaYn1sNSQAEFFFBAAQUUUECB7gUskLo38xUKKKCAAgoo\noIACCihQUwELpJqeWA9LAQUUUEABBRRQQAEFuhewQOrezFcooIACCiiggAIKKKBATQUskGp6\nYj0sBRRQQAEFFFBAAQUU6F7AAql7M1+hgAIKKKCAAgoooIACNRWwQKrpifWwFFBAAQUUUEAB\nBRRQoHsBC6TuzXyFAgoooIACCiiggAIK1FTAAqmmJ9bDUkABBRRQQAEFFFBAge4FLJC6N/MV\nCiiggAIKKKCAAgooUFMBC6SanlgPSwEFFFBAAQUUUEABBboXsEDq3mwsvmKpfOhvJjcljyQ3\nJIckCyc2BRRQQAEFFFBAAQUUaAjMoUTtBdbKEf4luTv5VnJb8pLkQ8m7kq2SKYlNAQUUUEAB\nBRRQQAEFFBgVAnvmU/wnmW+IP83c2d/k5OSkuRieN31nJZcnsyU2BRRQQAEFFFBAAQUGIzBn\nXsS17MaDebGvUaCVQK8KpF3zZvcl87d60/QtkzyVbNNmvd0KKKCAAgoooIACCsxMoFYFkt9B\nmtnpHtvrN83HPzt5rM1h3Jn+yxK2symggAIKKKCAAgoo0PcCFkj1/hHgFrvHZ3KIrJ9rJtu4\nWgEFFFBAAQUUUECBvhCwQKr3ab4+h/eqDofIcOh6CU+1symggAIKKKCAAgoo0PcCFkj1/hE4\nMYc3IflAm8PcL/38DPymzXq7FVBAAQUUUEABBRRQQIFhF+jVQxo4EPb9XHJoskLCE+tWSb6X\nPJvskNgUUEABBRRQQAEFFBisQK0e0jBYBF83tAK9LJD4pG9Obk54/OLzjem1mW6R2BRQQAEF\nFFBAAQUUmBWBWhVIzb8bZ1ZgfO3oFTg9H42sliyZTE1uSmwKKKCAAgoooIACCihQEbBAqmDU\nfHajHN8GCeec7x3xC2QZTbIpoIACCiiggAIKKKCAAqNKoJe32K2YI70ooRj6R3Jl8kzCE+7W\nTmwKKKCAAgoooIACCsyKQK1usfMpdrPyozD6X7t4PuJ5yZMJD2ZYI1k3WS65JpmYvCSxKaCA\nAgoooIACCiiggAKjRqBXI0hH5QivS/iFsc2Np9mdmfyueYXLCiiggAIKKKCAAgp0IVCrEaQu\njttNeyjQqwLprnzm93X43FtkHY/6XqDDNq5SQAEFFFBAAQUUUKCTQK0KJG+x63Sqx/Y6Ro2W\nSm7ocBis46ENy3fYxlUKKKCAAgoooIACCvSNgAVSfU/1Uzm0J5KlOxziMo11D3TYxlUKKKCA\nAgoooIACCvSNgAVSvU/1X3J4e3Q4xN2z7urk7g7buEoBBRRQQAEFFFBAAQUUGFaBXn0Hac0c\nBSNJX0qai2EKp+eS7RKbAgoooIACCiiggAKDFajVd5D8RbGD/TEYG6+7Nh/z7clJyU7JnxMe\nyrBVslby4YQ+mwIKKKCAAgoooIACCigwagR6NYJUDpDfffTXhFvp7kkuTDZNbAoooIACCiig\ngAIKzKpArUaQmm+7mlUcXz/6BN6Vj3RRskhyfHJcMn9yVrJzYlNAAQUUUEABBRRQQAEFRpVA\nr0aQNslRckvdp1ocLX2sYxubAgoooIACCiiggAKDFajVCNJgEXzd0Ar0qkDitjpGjNo11rGN\nTQEFFFBAAQUUUECBwQrUqkDyIQ2D/TEY/a+bJx9xs4QHMlQbt1X+b6PjJ5mem7Dtk40+Jwoo\noIACCiiggAIK9K2A30Gq76lfOIfG+b0z2SY5J6EIejq5JvlowgMb2IZtbQoooIACCiiggAIK\nKKDAqBDoxS12jA4+lpyY8F2jiclVyV0JRRPFEoUS2ziSGASbAgoooIACCiigwKAEvMVuUGy+\naLgF+CWwPL2Op9hRCPGo7x8nVyeLJ7slaySTE7a1KaCAAgoooIACCijQ9wKOHNT7R2BcDu/5\nZNVki+TvCW3ZZPPkJcnKyfLJHYlNAQUUUEABBRRQQIG+FuD7J7b6CjBqxDmemlySMJJ0bXJb\nslyyWTJbckBiU0ABBRRQQAEFFFCg7wUskOr9IzBXDo/vGK2WHJswcsRtdeW8vyzztPVemPhf\nBRRQQAEFFFBAAQX6W8Bb7Op9/rltbonkT8naydeT8xIKp+2THyX/mzCKZFNAAQUUUEABBRRQ\nQAEFRoXAnvkU/0nmG+JP8+XsjwLo3mR8077nz/LNCd9RomiyKaCAAgoooIACCigwGIFaPcWu\n3Go1GAhfM/oFDml8xIUyfWPC944WTRg9ujQpI4j02RRQQAEFFFBAAQUUUECBUSHQqxGk1XN0\njEzxe5D4vUfME+YnJg8l3H73VGJTQAEFFFBAAQUUUGAwAo4gDUbN14yIQBkhfF3e/V+NT8BD\nG2ZPeDDD4cmRCd9B8ntIQbApoIACCiiggAIK9LdAuYDub4X6Hv1NObTHk/ckSzUOk+8e8b2j\nk5PvJlslPP6bkSWbAgoooIACCiiggAIKKDDiAr26xY4DuzGhINovmZAsmbw1oZ+HNDCitGti\nU0ABBRRQQAEFFFBgMALeYjcYNV8zIgK75F15et0NCUXY5sm8yXXJicmE5K7kuMSmgAIKKKCA\nAgoooIACCowKgV6NIF2Uo/tGQlF0WPJAwq105Lbk+8nTyQKJTQEFFFBAAQUUUECBwQjUagRp\nMAC+ZugFelUgcfscj/cujYczTEh43DeNwoliaX0WbAoooIACCiiggAIKDEKgVgVS+T04g3Dw\nJWNA4Ll8Rn5gS+O7SFPKQqZlHf02BRRQQAEFFFBAAQX6XsCn2NX7R+CyHN4bOhwio0uMMl3f\nYRtXKaCAAgoooIACCiiggALDKtCrW+y2yVHwS2Jf3+JoVkzf1OTQFuvsUkABBRRQQAEFFFBg\noAK1usVuoAftdr0V6FWBxKc+MKFI+l5CwfTqhEd+35ecmZTb7DJrU0ABBRRQQAEFFFCgawEL\npK7JfMHMBHpZIPHe3GZ3fvJM8r8Jj/3+ZOJ30IJgU0ABBRRQQAEFFJglgVoVSF4gz9LPwph5\n8Rn5pGS2hCfZ8fAGmwIKKKCAAgoooIACCjQJ+JCGJpCaL/JIb4ujmp9kD08BBRRQQAEFFFBg\n8AKOIA3ertMreQDC2clAv98zX6eduU4BBRRQQAEFFFBAAQWGR8ACqTfO07LbA5JxA9z95tlu\n9wFu62YKKKCAAgoooIACCijQIwELpN7A8tS4k7vY9VzZtpcFEoUav/Nog4RzfnVyevJ4YlNA\nAQUUUEABBRRQQIGGgAVS/X8U1swh/jlZMuEhDTSeZPdo8rbk3MSmgAIKKKCAAgoooIACEfAh\nDfX+MVg+h3dpskRyTPK6Ro7OdIHkL8k6iU0BBRRQQAEFFFBAAQUUGDUCvfo9SOfnCHlq3YYt\njnT99HErIL8TyaaAAgoooIACCiigwGAFavV7kAaL4OuGVqBXBRLF0akdPuoPs47b7RbpsI2r\nFFBAAQUUUEABBRToJFCrAslb7Dqd6rG9bul8fH4pLA9jmDfZO7kouSW5JNk3YR3fS3pJYlNA\nAQUUUEABBRRQoO8FfEhDfX8Enm8cGsXPFQnfOTo+GZesnHwyYfSI9tALE/+rgAIKKKCAAgoo\noIACCoy8wJ75CP9JhvIXxjIy9EQj52W6T/Jw8mRyR8L3jyiiKJIYabIpoIACCiiggAIKKDAY\nAW+xG4yarxl2AQounlI3d8JT7A5MPp+8OOHR399NymO/D8+8TQEFFFBAAQUUUEABBRQYFQK9\nGEHiwCh8HksYJSqjRRROhL67khuTp5KFE5sCCiiggAIKKKCAAt0KOILUrZjbj5gAo0c8yY7G\naFEZMaKPgomiiO8mUSy9KbEpoIACCiiggAIKKNDXAj6kod6nn1vrFkwoiDZI+N4R7abkZclF\nCU+7ezBZNbEpoIACCiiggAIKKNDXAhZI9T79FEW0u5Orp8/933+uzeyZyY7JQsmjiU0BBRRQ\nQAEFFFBAgb4W8Pcg1ff088tfV0q4rW655KtJ+YWwFET7JdsnrOfR3xRMNgUUUEABBRRQQAEF\nFFBgxAV68ZCGxXNU5YEM92Sex3vzvSMe/c30vuTgpGxDEWVTQAEFFFBAAQUUUKBbAR/S0K2Y\n24+IAAUQv++I4ojRofKDyy+F5el1jCbx2G++n8QteNMSmwIKKKCAAgoooIACfS3gLXb1Pv3H\n5PB4St1WCb/36LSkjCBdmXkat9gdnTCSZFNAAQUUUEABBRRQQAEFRlygF7fYcVAUR88kjBKV\n34VUfh8Sy/Sz/v2JTQEFFFBAAQUUUECBwQiUO5U2HsyLR9trHEEabWdkaD/PJtnd7Ek5z0wJ\no0a0svyBFxb9rwIKKKCAAgoooIAC/S1QLpz7W6G+R79bDo1iiNEi2neSrZK3JlMSGuvXTV7B\ngk0BBRRQQAEFFFBAAQUUGGmBXt1i90gOrNxKd0DTQfI7sKY01vOEu58nNgUUUEABBRRQQAEF\nuhXwFrtuxdx+xATmb7wzt9n9sOlT8P2jfRNGkBhJZGTJpoACCiiggAIKKKBAXwt4i139T3/5\nvlHzl+aWzKF/tHH4VP0L1p/CI1RAAQUUUEABBRRQoLMAt1nZ6ivA7XWMHvEI71MTlpnn1rv5\nkvuT0iaVGacKKKCAAgoooIACCvSrgCNI9T7zN+Twyu834hfGnpNck9ye0JZOKJqeTY5PbAoo\noIACCiiggAIKKKDAiAv06iENe+fIKIBKKJZIWS7Tm9M3d2JTQAEFFFBAAQUUUKBbAR/S0K2Y\n24+YAI/zfrTy7mU0qXwviVX0rZc8xYJNAQUUUEABBRRQQIF+FvAWu/qefb57tH7CKBHtoeSZ\npIwgTcs8j/fmZ+C0xKaAAgoooIACCiigQN8LWCDV90eAAqmc3wczv2jCbXTzJDygYfnkwIS2\nZbLA9Dn/o4ACCiiggAIKKKBAHwuUC+g+JqjtoS/cODIe3/3bZPfk2oRb6Rg5ujEpj/7mlru3\nJDYFFFBAAQUUUEABBfpawAKpvqf/6cahUfxQCH07oVDaJNk04ba6HZLSGGGyKaCAAgoooIAC\nCijQ1wL+HqT6nv6Hc2jXJy9PXpp8N1kj+XjCqBKP9uY7STx1hNvxLkpsCiiggAIKKKCAAgoo\noMCIC/TqMd+75MjKo7yZ8oAGwugSvw/p+Uoya1NAAQUUUEABBRRQoGsBH/PdNZkvGCmBX+eN\nq4/0LkUSP8SMJj3XWM+tlssmNgUUUEABBRRQQAEF+lrA7yDV+/SX81tGkUqxxCgS89xieV2D\nYO3G1IkCCiiggAIKKKCAAn0r4HeQ6n3qH8vhMUr0RLJ6sl2yRfJI8sdkUnJVwq12Kyc2BRRQ\nQAEFFFBAAQX6WsACqd6nf8kcHg9g4Pce3ZbwiG9GjrjFbpHkkISHNdDHdjYFFFBAAQUUUEAB\nBfpawAKp3qf/lZXDozj6d3J3wi+O3Sxh9GhysmpybWJTQAEFFFBAAQUUUKCvBSyQ6n3635PD\nY3SI7yJxq93LkrUSGt9D4tY7HvnNuvMTmwIKKKCAAgoooIACfS3AhbOtngKc2zckFEJ3JvMn\nFMQ8sIHff0Tj1rulkwsSvodkU0ABBRRQQAEFFFCgrwUskOp7+pfIoTE6dH9CEURhxC+P5Zzz\nHSSWCSNMrB+X2BRQQAEFFFBAAQUU6GsBC6T6nv4yIrRwDpHvH1EM/SjZPdk3OSNhdIm2XLLX\n9Dn/o4ACCiiggAIKKKBAHwtYINX35JcRoadziPMmBySvTY5KPp3wvSOKJNotyc7T5/yPAgoo\noIACCiiggAJ9LOBDGup78u/NoVEEURzxKO/PJIsnNEaUnkzekbAN61dObAoooIACCiiggAIK\n9LWABVJ9Tz+FzzXJOgnn+fbk8ITfd/Tu5F0JhdJ9CQ9teCixKaCAAgoooIACCijQ1wIWSPU+\n/fyeo3UTvo/E6NEhCeecBzPQ5k74/hG34ZXb7TJrU0ABBRRQQAEFFFCgPwX8DlK9z/t2Obx/\nJowaLZ9QHPFghhIKJ4qlCQnfTbIpoIACCiiggAIKKNDXAhZI9T39jBhRFE1oOkRup2PEiEbh\nRIH0aLJLYlNAAQUUUEABBRRQoK8FLJDqe/rLY765jY4RoykJhRG/A4nzzu9HKo3fl7R/sl8y\nT+l0qoACCiiggAIKKKBAvwlYINX3jD+WQ6MwonGeb052T3ZIfpcsljCaxDbXJTwW/GPJhQm/\nO8mmgAIKKKCAAgoooIACCoyIwJ55VwqV+Ybw3Zdp7JNfEMu+JyXPJYws0fevxjzrpiZMV0qu\nTU5JbAoooIACCiiggAIKDESAO5S4ltx4IBu7jQIDEehFgcStdRRCrcIPMP2lWOJR3zzMgbZR\nwvoJiU0BBRRQQAEFFFBAgZkJ1KpA8ha7mZ3usbue33FEEdSqURDxe494QAONW+qOmD73P/9z\naab3JP4LQAPEiQIKKKCAAgoooED/CFgg1ftclwKIozw3eTKh+GH+kqS0JzLzo7KQKcUV/xJg\nU0ABBRRQQAEFFFBAAQWGXaAXt9hR4FRvo2P+2UYft9CV2+zK/IvTR1sxYVt+waxNAQUUUEAB\nBRRQQIGZCXiL3cyEXD8qBHhKHSNI5OHGlN97VBqFEU+6ozE/PmFE8VvJFcmViU0BBRRQQAEF\nFFBAgb4SmKOvjra/DvaBHC6FD43fc8TyBcmSCcXQssn8CY0i6u3Jvsl6yaaJTQEFFFBAAQUU\nUEABBRQYEYFe3GLHgfCdI26XK6FgImWZabWPkaOPJuV2u8zaFFBAAQUUUEABBRToKOAtdh15\nXDmaBB5vfBhGiB5JPpt8OPlFUhoF0q0J26ycfCm5KTkzWSGxKaCAAgoooIACCiiggALDKtCL\nESR+D1IZLapOy3wZRWL5/oQCqrQ1MzMxuT1ZOrEpoIACCiiggAIKKNBOwBGkdjL2jyqBp/Np\nShHEY7x5vHf5xbA8zY7fd3R5QuOx3hdPn3vhP9dmsk1yV3LYC13+VwEFFFBAAQUUUEABBRQY\nHoFejCDxAA4KJIojnmJHUXRU8sHGlF8US18ZUXpt5pvbG9PB95jmaV7hsgIKKKCAAgoooIAC\nDQFHkPxRGBMCfKeIxq123Cp3c/KBRiiefpkwpd2WnD19bsb/MMLE68fP2O2SAgoooIACCiig\ngAL1FCgXyPU8uv4+KkaH7kyWSVZLnkv4vUerJ2slNEaPKKROZaFFW7TR92iLdXYpoIACCiig\ngAIKKFA7AX4xqK2+Avc1Do1b7SiG50+YL99Pyuz0Zb5r1Kq9M53/Tqa1WmmfAgoooIACCiig\ngAJ1E3AEqW5ndMbjWTyLjByV88xDGvhOEYVSKY4ZQdo4aW7bpeNzyW7NK1xWQAEFFFBAAQUU\nUKCuAuXCua7H1+/HtUQAOMeMGPFdohJureMWvNkb2SHTs5IzEgqqrRL6DklOTmwKKKCAAgoo\noIACCvSFgAVSfU8zxdC4pPyeo8syz3ePeCLdPxOKpm0T2knJM8n7Eoqmq5PNkosSmwIKKKCA\nAgoooIACfSNggVTfU83vNuL7RrRlE375673JcsnaycIJt9dRQM2X7JrYFFBAAQUUUEABBRTo\nawELpPqffoogRoxelqySUDSV7x9ldnqR5O85QsKmgAIKKKCAAgoo0PcCFkj98SNAccTT6A5K\n+F7SBsmrkxUSRpDmSmwKKKCAAgoooIACCiigwKgQ2DOfotzqNpQfiAcxMGJEePgC3zMqy9yC\nV27D4+l2OyY2BRRQQAEFFFBAAQW6FZgzL+BattWTkbvd14hvX73VasQ/jB9gyAV4EENpnOsy\nYsgPMD/IhMZteL9IXsmCTQEFFFBAAQUUUECBfhWwQKr3mb8th0fxUxojRYwaVRvryXnJwcmG\nyTbJSolNAQUUUEABBRRQQIG+ErBAqvfp5hfFMlrEbXU0HuHN47857xRFpbHN1GS75NLk98nk\nhMd88/Q7mwIKKKCAAgoooIACfSFggVTv00yBRGtVEFEUlcb82xOKpvEJD21YPbkzuThZN7Ep\noIACCiiggAIKKFB7AQuk+p7ieXNozaNEHC232VX76WP5vOSh5PaEdkPytuS3yc+S5teky6aA\nAgoooIACCiigQL0ELJDqdT6rR1MeyFDtY54HNzzc1Mn3krZKftnUz+I+ySuS9VmwKaCAAgoo\noIACCihQZwELpPqe3UdyaOU2OkZ/nmgcKiNLCyY89pvQuKWO8JCG5sZ3k25LXt68wmUFFFBA\nAQUUUEABBeomYIFUtzP6f8fDwxgojMqtcTyo4bTk/oTCiRGmMsrENqznO0et2vzpfLLVCvsU\nUEABBRRQQAEFFKiTQLlArtMxeSwvCHDbHIUQxQ9Tihy+U0QrI0svLL2wzZVZ4PtJzW2LdCyS\n8EQ7mwIKKKCAAgoooIACtRZwBKnWp3f6wTUXQ+WIq/3Mcytdc1suHT9Mjk9arW/e3mUFFFBA\nAQUUUEABBca0gCNIY/r0dfzwZeSo3GL3TLbm9yDdnTA/ISmNbd6c0P+75NRkjeSDyXXJRxOb\nAgoooIACCiiggAIKKDAsAnvmXRjFmW8I343vILFPvltUwnJJte+m9P80ebaxLdN/J59OxiU2\nBRRQQAEFFFBAAQXaCcyZFVxjbtxug7HU7y12Y+lsdfdZy3eQeBU/sK1aKZb4OVggYUSRwon+\n5ZMvJjslNgUUUEABBRRQQAEF+kLAAqm+p5mCp9xex+O8T0t4Eh2/B+m+hN+FxHqyQrJ1sm5y\nQMJ6HthwVHJc8q7EpoACCiiggAIKKKCAAgoMi0AvbrFbKp+ckSBGhCiGmE5M6OMWOr5vVEaL\n+B1JFFGbJRskZWSJIqns4w+Z93chBcGmgAIKKKCAAgooMIOAt9jNwOHCaBUoj+ymCHo8YaRo\n08aH5WENNIof2gXJWclbk82T0ijcKIoonpZI/pbU4t7SHIdNAQUUUEABBRRQQAEFRqlAL0aQ\nXpZjpQB6IKFYYsSIJ9g9ljyYPJJwux3bUADx5LrfJ3x3ifVM509ok5Ldk2OTKclciU0BBRRQ\nQAEFFFBAAQQcQfLnYEwITG18SkaQ+O4RD2BYLJk3WSih+OHWOtr1yTYJRdW4hO1PSCimWF4y\n4XtJn0kWTd6UjERj5IuRLP4Q2hRQQAEFFFBAAQUUGHIBH9Iw5KSjZoeP5pPckfCwBooiWvP5\nplBiZInCiO1WSdiGgogRpi8mlyXzJFsmyzSW18l0ONvyebOfJQ8l9yTcMviXZP3EpoACCiig\ngAIKKKDAkAk0XzAP2Y7d0agQ+HY+BSNANG6lI+W7SXwniTAaU0ZkGG36d/KD5FPJvslqyYXJ\nxgkjTSskjEZ9OPlZwkjTpxNGdnrRKN6uSJjulqyebJdw6+BFyVsSmwIKKKCAAgoooIACCtRI\nYM8cC8XLUP6iWHiuSSh6SkqRVKaln+8c0cf3jrjtjlGlqQnreXodI0qvSCia6OPWO0anjkt+\nnPAdJUac3poMZaOAvzo5PaEoa26fTwefZZnmFS4roIACCiiggAIKDJsA/9jOtST/oG5TYEgE\nelEgMTpEMcMjvZkycsSUW+8mJ1cmpVCi2KEw4iEOPLCBW9i+mfw1KcUT2/J6wj7Z/s6EESRG\nmShW6N8kGaq2ZXbEPpdus0OO8YaEWwFtCiiggAIKKKCAAiMjUKsCyVvsRuaHaDjelVveKCBm\nT25rvCHFDQ9nWClZI6HooS2bMAq0cMLoEa/ZItks4Qd+74Tb3EpBxWjOkck+Cb9v6fLkuuTE\n5OvJULX1siNGwe5qs0M+z1kJ29kUUEABBRRQQAEFFJhlAQukWSYcEzugAKKYYBSJ0SEKJUaG\nSmMd3/GhOOKBDDytrhRQ82b+wGTxhMZ3j2h7JKcm2ySHJScnpyWvShZJhqrx2To11lMI2hRQ\nQAEFFFBAAQUUmGUBC6RZJhy1O7g3n4xCiDYuoYhgSsFDe/qFyX9HkRg14vcb8T2kLZMpyTnJ\nz5O1kxUTfl4ootgvhdT2Ce2g5KZk24T3YfRqKBq3Aa6VLNlmZ7zXaxNGt2wKKKCAAgoooIAC\nCihQE4FefAcJmr8njLCU8P0iRo74Xk/5ThLFDmEd29HPAxcuTSiGeDjDLsm0hNdStPw5OTP5\nalLalzLDAxXYz4LJUDQKsn8kv0oo4JobI12MiC3XvMJlBRRQQAEFFFBAgWETmDPvxHVkLR7S\n4AjSsP3cjMgbXdL4YeUHlsb55kl5fIeI0ZfSmCcUI2zL6NAGCQ9e4LtFZWTpvsyvmVyUjE+q\nI0X8cln6+E4QBdZQNAq3nZMtk4nJG5NVklcnxyVfS3ZPpiY2BRRQQAEFFFBAAQUUqIlAL0aQ\nKHgeSygySih+SFkuU/oY+SH03ZyUW+kokP7Z6H8iU7Zlv2UE6m+Z5za4KUkZccrskLYVs7eT\nE4ow3p/3nphsktgUUEABBRRQQAEFRlagViNII0vpuxeBXhRIfG+HYuKhpBQzpSCqTtmG5T8m\n9zfmGT3iddwy99PkkITih+817ZgwQsQ+GUm6Jyn72yvzvWz84Vs+YRTMpoACCiiggAIKKDA6\nBCyQRsd5qNWn6EWBxPeAymhLKWCYloKo9JVlHs7waMLoECM1DySMKPEwB76z9HDCCBKFE7fa\nfTQ5MjknoVi6IbEpoIACCiiggAIK9J+ABVL/nfOeH3EvCiQ+dPPIUSmGyrRaMFEEsUxR9I6E\nW/RenJyYlO14IMLtlWX2MzX5ZkIhVZ6Ql1mbAgoooIACCiigQJ8IWCD1yYkezsPsVYFUvkdE\ngVMtlkqBxLTMl/XcRldt3M7GNvsl7G9CsnTyqmS1hEJqmYRtVk1sCiiggAIKKKCAAv0lUKsC\nyafY1fuHl8KIAoZQADGtNoqa0sr8AqWjMeW2Ogojvo80KXlLcldyccJtdbxuyYTG7Xc2BRRQ\nQAEFFFBAAQXGrIAF0pg9dQP64KXoYWMe7V2Wy5T+Ms/vGaKA4pHeX0pKY/05yXuT65OVkubG\numsTHthgU0ABBRRQQAEFFFBAAQVmSaBXt9gx+sMoEuGXvDKl4Cl9zFeXSz/fJ/pOUtoGmeE7\nSpOTg0pnY7pzpnxv6U1N/S4qoIACCiiggAIK9IdArW6x649TNvqPcjgKpFIctSqISmFUptwq\nR5G0fYXuM5lnPbfVUSTtn0xMKI4+kdgUUEABBRRQQAEF+lOgVgUSt13Z6ivACNI8CUVRtXEr\nXemrzpdteET4eclHkt8lKyZ7JNxqd1WyecItecyzDbfe2RRQQAEFFFBAAQUUGPMCFkhj/hR2\nPIA7s3bRxhYUQq0ahRIPYaDyZ4SofC9tvczzmhMTfjnsZcnbEn4f0kAbP1+7JG9OeNLdtOS3\nCftk5MmmgAIKKKCAAgoooMCoEigXw6PqQ/lhhkxgicaemkeJyuhReSOKFbap/jzwsIZSQL83\n869JuimOeO+LkyMTCrXTkruTbyUXJeWzZdamgAIKKKCAAgoooIACCvyfQK++g1R++SsFEaND\nZVrmWS59TJ+vbMc8vxPpiuTJ5NHk7OR1yUDaX7PR5cmSTRsv1eg/t6nfRQUUUEABBRRQQIGx\nKVCr7yCNzVNQv0/dqwKJkSGKoQca02pBRH8plMo8RRFFVSma6Of2ux8m/P6jYxPWH5x0altn\nJQ95mNBmI/rZL6NSNgUUUEABBRRQQIGxLWCBNLbP36j89L0qkB7L0VLkcGtbKYKYlgKouWCi\nQCqjSEzPSO5IKLRuSl6RbJtQ3DQ/1pvb89ZP3p6cnJyTdGqs/1qnDVyngAIKKKCAAgooMCYE\nLJDGxGkaWx+yVwUSRQhF0ONJqwKpXR/FUXl09yKZ5/a6fzWmh2T6q+SCpDRGjG5O2B+/LJbX\nU0R9OmnXKKK+326l/QoooIACCiiggAJjRsACacycqrHzQXtVIFGgVIug5vnmESTWU9zQPyGh\n7ZhwuxyPDKdQuithG7J08saEESYevlC+b7Rf5qcmbH940qrdkM69W60YZB+f5T0Jx8zT9hZK\nbAoooIACCiiggAK9F7BA6r1x371Drwqk4yLZqihq7qPYYcSHwuiPjSkPU9gmofi5MTkq+VAy\nOXlHwj6uTXhC3VeSalspC+zvoITteGR4te2UBdZPqHYOcn72vO6rCfujeOOhEg8mPHHvg4lN\nAQUUUEABBRRQoLcCFki99e3LvfeqQPp3NKvFEIUQyxQ91X4KI0Z7mHLbG99d4rtEvJ7CiO8w\nvS/hCXZPJF9IJicUIU8l8ybNjW3Y9vrku42V4zLdK6F/v0bfKxvzR2T60WT5Rv9AJ8dkw/sS\nftfSbI0X8XhyfoEtI1/s06aAAgoooIACCijQOwELpN7Z9u2ee1Ug8X0gCqFbG9NqUUSRRFFE\nH4URhQ7TsxK+XzStscytczzue8GEooNiideybck1md8iaW58j4n9PpvckDzSyMcznT/5ZcL7\n/z35fTIpYft9k4G09bMRr9+0zca4Uuwt0Wa93QoooIACCiiggAKzLmCBNOuG7qFJoFcFErea\nUcQwksI8xQSjSFOTqxvLpUBiStiOBzKUZW5de2uydHJ/o/+WTF+c/DSh+GEUhymjOKXNk5lP\nJdyC92Ryc3J8skxC+1PCCNVaLFTazpl/POG7RDNr38gG53TYiCfrcay7d9jGVQoooIACCiig\ngAKzJmCBNGt+vrqFQK8KpIfyXhRIFDtXJoz8lMKH/rKu9FEMlf7qutLHducm4xIaBQrr1kkO\nSCigFk6WTf6RUBwxanRS8uXktuSm5P0JRdMqSavG7XwUSYu1Wlnp+3Xmj6ost5o9O50Ht1ph\nnwIKKKCAAgoooMCQCFggDQmjO6kK9KpA4vayUvyU7x+V5WoBRB/F02XJ7ZXXlG0pnLjNjlv1\nGJWh8Qfhxkauz3SFhIck7JFcmFyc/CqhKJovoTE9M6GQOiVp13gP3u9d7TZo9P8o0077YTMK\ntYGMRrGtTQEFFFBAAQUUUKB7gVoVSOVit3sGXzEWBBjp4dY3WnmAwQtLL4z8lPmyfu3M/Cah\nMPleckdCmz35Q3JeQtFEoXNiwveSXpcwUnVtwsMXPpRsmCyZbJpsnzAaRGPKvnldp5893mNy\nsnzSqVFsvT7hvVq19dK5esJ2NgUUUEABBRRQQAEFFBgjAr0YQaIAodBgZIgpKaNGTEs/8880\n1lPoND+Rju8K8VpGo7g97riE0Z3JSbm1jlvp2E/ZP7fPfTlpd4sco1SMPnVq07Jy104bZB2F\n2+UJI1a8F6NYhyR/Ti5I+D7V6YlNAQUUUEABBRRQoHcCtRpB6h2Te+5GoBcFUvlBpRCicGm+\nxa5V0UQfI0PVxogPr6Xo4WEPP0/2SBZIJiYUS19J3pjwBLobEgounlDXPGqVrumNkSi24cEP\nrdqO6eS9ygMdWm1T+pbLzFXJowmjZRRr3PLHPAUS+9k9sSmggAIKKKCAAgr0RqBcd27cm927\n134U6EWBhOO/kubCiOUy0lMtkugr/dUHH5ydfr4zxDpGjX6R8Ijui5Oyr/La8noKJQoWbrdr\n1a5L560Joz8rNG2wZZYfSBgJGmh7VTakILommZickHBrH+0DCUXia1iwKaCAAgoooIACCgy5\ngAXSkJO6w14VSN8ObSmCmDJqc3dTH/0UNmWkidvs6Ns2OTzh1joKHrbZLOFBDBQ/FEdXJLyO\nkRqKmtsTijLeh/4pSXPbJR2M6qybnJ8wMvX75NjkooT3pkDjFsFWbdl08rkornivPyRXJz9P\n2rXvZ8Wl7Vbar4ACCiiggAIKKDBLAhZIs8Tni1sJ9KpAmpg3o+CohpEWiptqXxn5oY/ihW2Y\nUviclVBUTUtohyU8bKG85r5G32KZLp4wMnRGUkaXVso8jVvy9k7Y76cS2mzJG5LvJCclX0nW\nSto1Rpco4K5KPpfslfwg4XNTXPGHs1XbMJ183oVarbRvyAS4ZXJCMm7I9uiOFFBAAQUUUGAs\nCFggjYWzNMY+4575vFzAzzfEn5tChuKBp8sx5T1KYcOoEN8XKsusJ4wYUSAxzwjPwwkF0kEJ\njVGi/RNeNzWZPam2PbJwb/KlhG0olO5J2CfT3ZLBtGXyIoqjbyUUVqUtkhneh8/4zdLZNF0x\ny2wzoanfxaER4DteNycYE35mKHotSINgU0ABBRRQoA8ELJD64CQP9yH2qkCiKKFAYdSGaSmC\nuIhtNc82pFzocgsdBdHFydzJwgnr1k4ouv6WNLeXpINtKFa4UP558vZks2RcMtj2tbzwmqT5\n1juKpQeTIxJu7WMkq7kxSsWtfHM1r3B5lgW4jZOfhf2T1ZIVk52TfybXJxSwNgUUUEABBRSo\nt4AFUr3P74gcXa8KJEZsSiHUbkoxwzoKIwoqLnbpI4xAMWKzcvLx5JiE/o8llyUUHask1bZm\nFtiGkah/JF9PhqLxfl9osyO+Y8Rtd48m2zdtM3uWL0xObup3cdYF3pRdUJRu0mJXC6aP3411\nfIt1dimggAIKKKBAvQQskOp1PkfF0fSqQPpJjq5aGDGSVF1mnmKGaRk5ouihUGIEYJ6EYog+\nbqE6NaEI4QEM/0rYH99B2jvhoQvrJ+ck7OukhG3fkgxFYzTiw212tFT6Genic1LIlcYDHU5P\nKBTHl06nQybw5+zp2A572zrr+FlyFKkDkqsUUEABBRSogYAFUg1O4mg7hF4VSPzrfSmIqoVQ\n6WNKPwVNmS/r+Ey7JYwQ7J6U7/28O/MUInx/iXU8HW5Swn7KvnjfXyT/TsYl/KGZ1e+j/Cb7\n+EnSrm2UFXx2joViitErCrkrk5cltqEXuDu7fEeH3b486zgfjEK+NuFnwaaAAgoooIAC9ROw\nQKrfOR3xI+pVgXR/jox/wS/FUZmWIqg6LesoKuhnNIiHLXw2aW5HpoPRo6caU0ZoHki4GGYk\nh+KE1+6VXJDQz/7vSg5LuP2q2/bmvID3e0WbF/4s/bzvhslHE0a+Nk1KYZdZ2xAL3Jn9vavF\nPimGf5mUn6mbM8/Py6Rks8SmgAIKKKCAAvUSsECq1/kcFUfTiwKJwoDChFEeLlTLxWqZluKo\nukxxxDKv+17CRe2KCbfPjU+qjYKFIqjspxRBvB99Fyfs7/vJFsnayfsTLpYZ4Vks6badkhcw\navG2ZI7Gi1fI9LiEUS2KI9vwCfwub3VC09vxF+QlCef40wk/QxTECydHJxS5GyU2BRRQQAEF\nFKiPgAVSfc7lqDmSXhRIPLGtWvyUeabN89U+Hq5AsXNuQtFBsVPWc9H7xoS2TsLoFKNCNJa5\nMGbbUiyxL4qiauNi+aqEEYZuG0XRoQkX2TxMgmKJ97s22SCxDa/AVnk7iuDXV96W0TsK59UT\nimEK7Wr7aRaurnaM0PyL8r7EpoACCiiggAKzLmCBNOuG7qFJoBcFEm9BAVNGeCgkynynKSNA\nXPTyL/9MN00WSF6aHJHQ94GE7wT9Ozk72TihYOF7R1wY/yyZlNyTUGR9Oam2V2WBz7B8tbOL\neW7hel2yU8LIlG3kBA7IW/NzdlSyeULx85fkjoSCeb6k2lbKAj+LL692DuP8bnmvyxN+jvnc\nlyU7JzYFFFBAAQUUGLyABdLg7XxlG4FeFUgUOe0KI/pLKFaYf7wxZQToxITvFX0iqbY9ssB+\nGR16MPlkQqH0o6S06zJDUcQ+d03YP7fpVdvDWdih2lGZH5f5HZOvJl9L3pHMndDenJyfMIrE\nBe4VCZ/J7xoFYYTa9nlfiqHyc3Rf5imcyjnL7AyNn7PqqNMMK3u0wM/H8Qk/t4cmWyWvTQ5P\nKO6/n9gUUEABBRRQYHACFkiDc/NVHQR6VSBRQFCkcOFaLl6ry6WvOmWeAolb4fZKKESa/4Wd\nW9rYz+Rk24RRp0WT0m7IzGcTtlkjOTPh+yfsc96ERnFFEdTcGBG6KeFC9qzkz8lDyW3JSQnF\n2XeS7ZLXJF9JHklOSV6U2EZOgML2xqS5qK5+IkYj+RnbuNo5DPMfyXtQlK/V4r02TB9F224t\n1tmlgAIKKKCAAjMXsECauZFbdCnQqwKJiz4uRgnFSjWt+umj2CGl7Z0ZihJGhU5IzkgovLhF\nab/kYwkFU7VRyJyfsB0PY6DQ4V/py/vf1JifkGm1rZiF+xNev3BlBbdpUSjx+d5X6S+zq2WG\n0S6KMtvICnwrb39F0q5YpVDhHPMX6XC2yXmzfTu84cFZx8+4TQEFFFBAAQW6F7BA6t7MV8xE\noNcFUilMqkUR89X+Ms/oEam2CVn4XMJtdEcmf0wYKZiU8KV8Lj6rbZMssP9LE4ojCjW2XzfZ\nPLk9ocD6XlJtx2fhwqTVxTW3cPF+FGit2sfTeWfirXatdIavb7m8FcXqMckcTW/7miwzMkhR\nPZxtybwZP9+rd3jTDRrbUIzbFFBAAQUUUKA7AQuk7rzcegACvSiQKBQodEpRVKZcKJZ5ps3L\njBbR/5akVRuXzluSzycUPdck7KPcurRC5n+TUBRRBD2aUBDxgIe3JWw/OXlD8mTyzoTG52Vb\ntmlus6eDfX06eTZpdRG7Uvr5HExtIytAgXxvcnPCeT8woajm5+qwZLjbsnlDfjZW7fDG3NrJ\nNgt12MZVCiiggAIKKNBawAKptYu9syDQiwJprnweLvi4KK2m2sd8WX6qaTtus/tlsltCUVQa\nt1DxJfxFEv5lnhEd9kFhxQgP73V58oqEgueRpLwPy8cmiye0bySMMtG4MGWB+dJxAABAAElE\nQVQ7LlSbG+/PfndM2GaVpLm1uwheORu+NeG1FG+24RHg54MimsJoYsKI0obJSDRGJO9O9urw\n5p/Kuikd1rtKAQUUUEABBdoLWCC1t3HNIAV6USDxUSgqKCgGUiBVt6FY4nUPJdwSReHDbVHn\nJhQ5WybVtlEWpiQPJt9Ldkj2T9gH27O8YlIttLI4/SlijAgxesRF7BPJ9kmrdkM6f5A8nyzY\nYoOd0/dwwh9QGrd6/SnhM/CdFz4b879KFkts/SVwcA73jmTpFoc9Pn0UUPu2WGeXAgoooIAC\nCsxcwAJp5kZu0aVALwqkefIZKAgofCgquOWtLDOlj3Wlr2xHkXJZo//Hmd6esI5C5oTkxQmN\nooZRgvJ9Id5v7+TKhMKKi1H2vX7Srm2VFey37OPUzFPUtGofTyejVJe0WLlo+ridi9Et2pLJ\nlOSCpDoitXGW+XzXJ62KrHTbairAI8cvTG5N3pNQKDHq+L5kWnJ20lzAp8umgAIKKKCAAgMQ\nsEAaAJKbdCfQiwKJT0BhU4qgmzJPkVT6WhVIjNJQIJ2csH6hhEYhxCgMF5MrJScmjCxRAD2Z\nUNi8LKk2/qAworNLtbNp/tAsczteaS/PDPul0OH1pc2emYMSPtN9yR4JF7jcqvf2hGOj8Jk/\nof04uSrhori5URhRTH29eYXLtRfg54HvQFHA87NLHkgOTqo/b1m0KaCAAgoooEAXAhZIXWC5\n6cAEelUgPZe3pyAqxVApjsrFIdNSQDG9M6FAuTQ5M6m2H2WBPi4oz094iMOayZuSPye8bvOk\n2g7PwpRkqWpnY36tTHnNbo3lMtkiM/ckU5MTkuOS2xOKu28n3GbHZyjHwD6+k5TiiD+g9O2Y\ntGt439Vupf21FxiXI3xZ8tJkjtofrQeowP8vsH66+Ick/oGKv++PSl6S2BRQQIHBClggDVau\nj143Psd6YzJpgKEg4IK/1dPZ0j3oxpPESlE0sym3urENt7E9k3wh4da10r6SGUaXjk/KLXFl\nHVP+B3t3sgALjcZtdxclFDjvT16crJbskzyS/C35ZsJ7vTIpbcHMfChhpGpygg37viXhM16Z\nbJ+8Ipk7qTZGuNh+fLWzaX6dxja8j00BBRToJ4Ev5mD5e/QPyWeTzyf8Pc3f/bskNgUUUGAw\nAhZIg1Hrs9fwQ/KeZPcB5mfZrhcF0snZL/8jLOE9CMvV+bK+TP+e9YywPJy8L6Hxr4z8D7Rd\nUTFX1t2Z7JVUGwUMxVUpAnnfBxIKsn8nv00olHjvXyZlJIjX8TmuSzZKSlshM79KuE2KEYDm\ntkQ6eA9GqNq1rbKC0TVHD9oJ2a+AAnUUeGcOin8AY+S/uX0sHfy9vEHzCpcVUECBAQhYIA0A\nyU26E9gzm3NRP193L5vp1j/PFqXoYcp7VKdlnn7yfGM9RcbsyScS/ofJiA/rKFg6Nd7vh202\nYNRp+eTghFvg3p5UGwXNTckZjU7ec1qyeGO5OpktC39MuOWvVaOoOrTVikYfn3Fih/XdruK4\n9k9OSyjyPpv06kl5FI4fTH6TTEx+mmyb2BRQQIGZCfCPUvwd3K6dkhV/aLfSfgUUUKCDwJxZ\nx7Xkxh22cZUCXQn0okBidIQHKDBSUi2EmC/hB5l5tinzTC9ISqOYYP0/E4qSTu2ErPxxhw2W\nzLonkl3abPOS9D+VvCG5NuHWj3Zt/azgs7f6ftPb0s+/kr4xaW68N8fDKNJQNPbHMVGU8V2o\nY5JJyYPJUBcuq2Sf/0q43fB7yYEJBRnHyoUNfznZFFBAgVYCK6STv99bjbyX7RlZ4u8zmwIK\nKNCtgAVSt2JuP1OBXhRIy+RdS9FTLYiqfc9Xtin9ZXp91t2SsA0FxdcTLvrnTVo1CrLbko+0\nWtno2ztT9vHWZHyjr3nyq3T8OOF/0q9rXllZHpd5PuurKn3V2S9mgc/OSMtHk08kf044lk6f\nMasH3LbMluyPfTOqVdrsmWEEi2Pge1JD0ebKTiiO/pQs0LTDNbI8NflJsnqyUGJTQAEFqgL8\n3cDfmYtXO5vm+fuUbfj71aaAAgp0I2CB1I2W2w5IoBcF0jx5ZwojioRSIFWnTzf18z9FwjYP\nJhRIeyUrJZOSfRIuwo9OWrWD08l3ixZpsXJ8+s5K2D+jHfc35k/NtPlWNAoLCpk7k/ck7Vop\nAPmfPsXJO5LTE0aezk/2T7ZJGNVidIf+HydrJ0PVLsuOftBhZ3/IutM6rO9m1Qey8T1Jc3HE\nPrZLuD2xnEOKtjOSlyY2BRRQAIH5E/7+3ZqFNo1/PLq1zTq7FVBAgU4CFkiddFw3KIFeFEgU\nKqXgeT7zFD7lArpaKJVtynqmzyaMBnExfmRS1rEt+7oy2TIZn7w6+UVCwfWGpLktnY47kr8m\n+ye3J7SNEvZD8VK96D8py8clJyZ/Sdo1RqOmJRSCFAOPJ8cmH0so1igYWL9O0otGYYfHKzvs\nfIesYxSJAm5W26+zA27fa27vTwcF0VHJXQnHv1nCSNPDSafPl9U2BRToIwFuyb0wmaPFMTPy\nfEtySIt1dimggAIzE7BAmpmQ67sW6EWBxG0UXMBT3LRKWce0zFe346L70UaeypSRF4qN7yes\no1DidUzPSdZLWrXj0/n3ZKnkvQnF1ycTGv9DnpQwakRbLnkseVuyWvJkckDS3F6bDgoP3CiK\npiSrJNU2LgsUW1OTBasrhmh+1eyH42ckq13DhG3ma7dBF/1/zbZfatp+pSxTmOJAuyL59PS5\nF4oy7P+ZtLoYamzmRAEF+khgfI717oR/QHlx5bj5u4q/P65LGGmyKaCAAt0KWCB1K+b2MxXg\nAneoLqSrb0Yhw36rhU+Zb9fPei66KXweakwfzHT1pDRGjyh0dk0WKJ0tpnOnjyLn9IRbOxjl\noejiPSiM1k+4dYwiZkJyVXJB8qKEtkPCa/gf9wHJZ5PfJny2ryUrNOb5PK3aXOmckuzdauUs\n9nHcGGzRYT+7ZN39HdZ3s+on2ZhbEqvtkCxgQ+NYH0l2ZKHRFs2Uc7lN6XCqgAJ9L0BhdGHC\n/wMY0WfkmfnfJIsnNgUUUGAwAhZIg1HzNR0FelUgUUhQjDSnFEdl2ryeZdbx+sOT05JbkuoD\nGn6S5TOSTu3lWVn2w5Q8kFAI8R4UcBMT+hmlOjdZLKk2iqDDEv6HzkjUT5NXJbT3JNOmz7X/\nzzeyin8t7UX7fXbKvmdrsfNx6eMWQkbchqJtnZ1QkK1Z2Rnvf0Rj+XOZ3pc0/+vv39LXiwKx\n8bZOFFBgjArwdwl/h+6crDxGj8GPrYACnQVWzeojk/MbYZ6+XrQ5s1Ou5zbuxc7dZ38K9KJA\nWiKU/KBSiJDmeZZLyjYULGWedRMTRnMojO5MPpKUxv9U6WvXKBBuSNjPUQmFzvLJ+5I7Eoqd\nfySMevC+r0u6bXwebgnp1L6QlRd12mAW1r0sr304+VmycFLa0pmheJmWMD9U7eTsiH1u29gh\nI3PfTSiOMHxX0tyuTke57a55ncsKKKCAAgooUE+B9+ewuHuHa6ADGmGePtYNdbNAGmpR9zf9\nOyQUEvMNocUi2VdzAdS8XAon+pnnIvuhxjzL3B5WRh+OyTwjSaVxMd6pQNo3659K2N9Xkmpb\nLgtc6H8z4b0vTQbTtsmLeI8FOryY29J+1mH9rK5aPzu4KXky4S+eyxL+8mH0aNVkKNu47Ixi\nk/M0tZHnM2XkiPPR3Mang/WbNK9wWQEFFFBAAQVqK8BXD7hWaFUI0ce6dl9PyKpBNQukQbH5\nok4Ce2YlhcJQFki8H38AShHEtDrfXCyx/MPGNmVbiiKKIG4hOyQ5KyntuMwwStKu3ZoV9yTf\nTygY3phU24eyUIqxA6orupjnDyP30HMLXqu2XjoxeE2rlUPYR+HyhuQLCYXhVgkjb71qy2fH\nuyZfTB5LWh3/3OnnfF2S2BRQQAEFFFCgfwQuzKEe2+FwWcc2Q9kskIZS031NF+hFgTR79lwt\niBgNKsvV4oj50v945nk6HH23JZMa8xMyPSOhYKJtk1B4vJaFtDclv0z4btF5yaEJ+7gx2SfZ\nP2Ek47fJxxNu+bo4YRuKp+2SwTZe+2zCaBQPJaDNkeyUMLLy04TGH1yKsr8k3Np3ZsItenMl\nA20rZ0MKoB8mRyTbJiPduDWRc8b5eUuyUbJ7wq2HFKkTEpsCCiiggAIK9IfAvDlMrus27XC4\nrGMbth2qZoE0VJLu578CvSiQls7eKUD4A1BNtY/5atjuikYfxcO1jfkfZEqBs1dyeEJRc3BC\nIXJK8nRyXPLJhP6bEvb7p4T9zZZw4c427JNC6lcJ2zyVLJSUtkBm3pkckHwqWTuZWdsmG0xO\nKNpuTR5N2C+fhUJx6YT3vDehkPpY8o3knoTPs2wys/bFbEAhRnF1YkKhxXucnyyVjGRbPW+O\nJ8eN6Z0Jt+ItntgUUEABBRRQoH8EuObhWuClHQ6ZdWzDtkPVLJCGStL9/FegFwUSRUe1MCrz\n/IGg2KkuM/+VRh9FBtssn/C5KIbYnj7Cd2veltAOS+5KXsFCpVE4PZgwIvVIQjFCkVRtFCq8\n16GVzjLq80D6zksoXvhspyeLJJ0ahdCmyXuT7ZOFk9IuyMylyaKlozFlnxcllyTNn6+xyfTJ\nJ/JfRmnKcZd14zNzWXJ5Mq50jvB0rhF+f99eAQUUUEABBUZOgOshrr3e3uEjsI5t2HaomgXS\nUEm6n/8KUIhQfMz3355Zn+GHnuKiOaXQob86X4qmMmUkhnkKpAMSRiOqn2+xLDNy9OakVWO0\nidf/IXk4YQTn88kHk18nvP8VCcUUjaKGgmm/hD9kpVF8MWpzcVK2LesGMt06G/E5x7fZeLn0\nP5m8vs36BdPPXyKco1YNlweSPVqttE8BBRRQQAEFFBhmAb4KcGXS6h9N6WMd2wxls0AaSk33\nNV2gFwUSO+aWMAqRklL8lOVWU0Z+Lki4/ezfyTJJq7ZDOmf2rw/8AeQ9JyX3NbbnNXyuiUkZ\ntaGY4ztPX01atQnppMhixGqJpJv2tWx89kxe8OesP6LNNuU4x7VZT/fRye86rHeVAgoooIAC\nCigwXALc+n97wvXPiytvyjx9rBvqrwfUqkB6UQXN2XoJUHSQaisFSZmyjnlCscSUW9PWTE5I\n1k7uTFq1hdJJMUUB1KotmU5GmWgLJNzONn8yb8IfTG6nYwSLtlGybHI4C5VGUULRdF2yYPLZ\nhFv6Tkuqhds8WWYkp3pcWZzeeN39jfl2E9azXavG+0xNKOratSlZMZT38bZ7H/sVUEABBRRQ\nQIGZCdydDV6dULTclPAP1YR5+jZN2MbWRsACqQ1MDbrLua0WDWW+FCbVw2R7vmdDAUJx85nG\nciYtG3/Ilkvajej8Musohv6UsD+GdPlDSSFEQXJKUtr4zNyTUHCVxuf5TfK+ZK/k4wkjWlsk\n7OOyZJfG9PFM703YxzeSarHD51wn6dRYz3Y0Cr8dkw8l2ycPJMsnfPZ2beWsmNZupf0KKKCA\nAgoooMAwC0zJ+22WcI3z1UaYp+/WxKbAqBfo1S12jO4wMtQcCqTSV76HxC1wv094DdNOt5Rl\n9fTf80PBwu1lzW3rdDyTPJsw+nNu8vVkxYQ2IeF7Qa9NaGz/VDI3C41GUfRQskpj+cuZXtiY\np9jiDzef9dvJJsnqCcUUhc71SRm9orhh37smrdq70sln4bNRFD6WPJzckDya8BkoHCmY1k2O\nTM5ITkno43UUdrslY73h/+rk9clLxvrB+PkVUEABBRRQYNgE+Ifk/yQbD9s7+ka1F9gzR8gP\nVfUhCENx0KVAYt8UREzpo3ipFkjMMwpzQHJNwmgIBc3M2ubZgH19N+EWNxojP1cl7JNRnQOT\ng5PLEx6G8M6EdnbytelzL4w0UZRQFJX2t8x8pbHAhfvk5AuN5Q0z5TieSxZp9JUJo0e8PwVM\naZ/MDEXQp5J5Gp1MP55QPO2dHJRQHO2ezJHQ+MP+kYRj5L04pnOSw5JjE0aseP21SXlNZsdc\nmy2f+PPJIwmu/Czws3Jpwm2WNgUUUEABBRRQoJOABVInHdcNSqBXBRIFCRf1XOyWaat51jHa\nc0eyT7JTwqjJdsk2yXJJu7ZFVjBqQwFxS8KoC/ubmvCHpdo+nQWKjQ2SXyTHJKVRvFCgbNvo\nYP4NyQLJ6QkjRhQ/tJOS3yYcy0ZJc9s0HXyGZSsr9sg8t/Zhwudkyu1zeyWrJnz+7ZNWjfdj\nf3z2fya/TP7aWL4702lJc6GWrjHTfpRPyigYxeG8jU+NCcfJeWDkzKaAAgoooIACCrQTmDMr\nuC5zBKmdkP1dC/SqQKIA4MK+hB/cktJXpowcXJdwof+dhH6KBkZImP99snzSqs2ezk2T3ZJb\nkkuS65NWjcLoj8kNyWebNjg0y3yO8xIKtL8mjELdnLw8KW1SZn6UcCxbls7KlBERLuzfWOlj\nllGjbZLdEgoxlmn7J1dOn/v//0PB8HBCIXRgQpF3dHJYwojWPgnrjkp62ZbOzhnNWWqI3wQH\niuP12+yX4vCaBNPR1hbPB1onafdzOdo+r59HAQUUUECBugpYINX1zI7gcfWqQPpXjokiohRB\nTClAmNLfvO6D6Ts7uTVhtIQRHNqGyQUJI0zVUZksztCWyBL7fH3C63dKmhvruCBn/YrNK7O8\nVnJ4Mi25JdkjmSuh8d4UarwHry/HQx+fdXxS2qOZeVNZmMn0Z1n/0zbbvDb93J7HKFYpgt6c\n+bsS3oNbB0shelzm50uGqi2WHVEIPpxwzCUXZ77VyFm6u26n5BUUQe0axQfO67XbYAT618x7\nnpNUf47/kWV+tmwKKKCAAgooMPwCFkjDb177d+xVgURBw0VkSbnAZlouLss6ipYpCbehcaHJ\nNiskpfGDf0nyq9LRYvqS9PE6Cpn9EkaBPpCMS2js46sJ2zBq06m9KiufS97T2GjJTKckFyUX\nJoxs/T55KKkez3lZfnfCcS2XDKR9Kxud0WbDd6SfYoj3PChhVIrP9aWkjEBx/m5Lbk7OTPge\n1qw2CiCOjePgO1VHJNg/k0xKKNqGoiBgdOjjSad2e1bu0mmDYVzHz8Xjya+TDZKFkpcn30w4\nL+9PbAoooIACCigwvAIWSMPr3RfvxgU2F/lDOfoAHBeMpRCqFhHMV5fZhgtupuck3J72fHJf\nwi1xayS0LRMKqUVYaNHmTR/72bax7mOZ8sV/RlpubEwpmugbSPtwNuIYTk3OTygMvp08mfBZ\nmfIeqyRcxH8voWjiNWcnA20UPeyrVUFFoYIF+9wioRA6NKk2Ls7PSiYk2O2ctGqrpfNzCQXZ\nPsnLklaNkTjssdy/aYN1snxvQsHGdMFkVtolefEXO+xgtqzj+0lv6bDNcK3iL99bkh+0ecO9\n0k/hPKHNersVUEABBRRQoDcCFki9ce3rvfaiQKLYoojgwp5pKYiYbxVGJtiGKQXMLsk7E0ZW\nuOjcMWHEhG34l/t2jVu2Lkhmb2ywQKY7JB9M3pb8MzkyGWjbMBsyWsBn5nOdl3w5oWihWDs8\n4fYv+igYJicUKdclXNwPpLHdxQnFwmJNL9g9y7wXoWBhyrGUtkxmKCA4h7TvJ7+dPvd//xmX\n2WMSXnt18pvkmsbydzKdI6m2Q7IwLaEYK47V9TtlgYLunoTPNyvt63nxVUk7q62zjp+hpZOR\nbm/KB2D0qFNRiO+XEpsCCiiggAIKDJ+ABdLwWffNO/WiQOKCtlVR1NzHcglFyK3JhKTa9ssC\nF6brJmz7imT+ZMVk7qTa6OPC/XfJ+MqKl2b+/GRSsmilfyCzvJb3LRfpx2f+tOQjCQUR65rD\nsVCItbvwz6oZGoXOlcn9ydEJIzwUMeyHPoqbixJue6MAOyFZK6EQo78UOR/P/LVJtR2bhTuT\nzaqdmd8ywYr3q7ZLs/D35OfVzso878X5oKBjNGpWGqNmjyRfa7ETzuUtCUXfaGhfyIe4cCYf\n5Kisp6C2KaCAAgoooMDwCVggDZ9137xTLwqkF0WPEQ8u8CkemDbPV/vKNlyYt2qXp5PbyB5M\nJiYUDLyGESde8+6EPxw0Chr62P+/Eooitj0n4YK8m7Z1Nj4p4fV/SN6b3JBQyNFHscL7MMrB\nZ6Jwo5C6L3ki+VEy0DYuG74/obi7KWGfX00YxXlzwrHzXhROrOP9KdQWSkpj+4llIdO1E17z\nqkpfdXbzLPC5V6903pj5icmplb7m2WnpuCo5vHnFIJa3yWu4DfLi5KPJuxL2SzHIOWfkcDS0\nz+VD8HPVqVHM/bLTBq5TQAEFFFBAgSEXmDN75Lpo4yHfszvsW4FeFEhgcrsbF+fN4YKcH+Lm\nfvpY1+q2LW4RoyggjOCwzCjGkwmFCPu6N3l7QmPkZsPkAwlFx1pJN22ubMyF7rMJIwIPJFcm\njHbwXv9MVk0omi5Izk8o1o5LaIzM7JdQJFLcdNu4ED+i6UXLZvmp5OSEz3RnUm185luSfSud\nB2X+kspyq9nL0/mFygq+R3VOwsjV/JX+MrtUZjhP+L+1dM7idEJef3SC69RkYrJHQnE4Wtpm\n+SD8PCzf5gNR4E5JPt1mvd0KKKCAAgoo0BsBC6TeuPb1XntVIFHoNBdCZZlpmW8ulLgIZRSF\nC+R3J99P6OOi/HMJowsUKrsmXJS+KPlFQoFEAbVLMquN97wjWaOxo90ypRiblDBqRHGwYsIx\n7J/wvjhSJJ2RcEy8/u6Ei/5WhUa6WzaOiddzQd7cDkwHxdpOCe9dLtbnzfypyW3JgklpP8rM\nCWWhzZRC9nuVde/IPMfH56fgw7fasMGf0Tk+a780iu7LkjOT5ls7MTgyuS9ZhAWbAgoooIAC\nCgybgAXSsFH3zxtxYc/F9nxDfMg/b+yXi332z7TMl+XqlFEX1n8mOSm5JZma/DnhYv3BpNwy\n9trMV9uiWaCA+WHCdgslg22r5IUUY1s07eBnWebzndOY3pwphRH5aDI+eTjhtdwG996EW+Ao\n7m5KVkoG0ubJRrhs2GJjipVjEt6Dbb6TUNxMS/BaPam2Q7JwfrWjxTzfOTqw0k8hwMgYI1Qc\nDyNQ70nekJydYECRVorHzPZN4xxOSa5P9ko2S3ZO+Jl4NNkysSmggAIKKKDA8ApYIA2vd1+8\nW68KpKujV25/46K6Gi7uSeljntEWlvdPqm27LND/q+TwZGLS3OZIx6SEQoptubBn+1cm3bYP\n5gXsq7lxIcwIASM1jLCUz/6VzHMbHe9NMcS6BRIatpOTs5KrkubRmHS1bBRU+7Rc80LnlzN5\nKuHCnNvtKNAYRWpuFFnPJxSWrdp66eQ41m1aOVeWj04o/ig8OS7OEdPfJtzu169tsRz4N5Lb\nE+zuTxile0liU0ABBRRQQIHhF7BAGn7z2r9jrwqkayP3t4SLSC7SmTbPlz4uvtmG6XcTLvbH\nJ19IKAT4F3tGTk5PjkyqjdudKBS4vY0pozcUMb9JuKDfNemm8Z4XtnjBDunj1jKKnK8nbEMB\nQWHH6AGfn9uv/p6U9vvMnJgskVA4UUgNpH02G92bYNDcuECfnHCRPpD2i2x0S7Jm08brZPm2\n5Pim/uriMlnYKdkj2SyZPRktbY58kNcnX0w4Z9skw/35+FmwKaCAAgoooMDIClggjax/Ld+9\nVwUSF96PJf/bIhRC9DMtocCgj2npuzXzuyaMptycUGyckWydrJzQvp3cljydcJFMYfbphPbh\nhMLpFSwMsL0721GcNF9sU+RQcHFRTuFDwcZ7T0mOTiiOrkiOSGi7JxRQFCK0PyTNxd30FS3+\nw8U/+5uacPy89yLJ25KbEt5nvmQgjWLzlITPQgH54+SvCc4nJRSYY62tmw/8r4TRrQuTixPO\n/z+S1RKbAgoooIACCvSPgAVS/5zrYTvSXhVIB+YISsFTnTJPuGBnStFR+hgtGp+sn6yabJrs\nn1CIUOhwEfyfxjxTRmvo5wKZi2NuJWNfayWl/SUzPy0LA5hSiDySUFw1t++m486Ez755QnFx\nbsKF+j0JtxTulTBqwzbMl0YxcmxZGMB0zmzzleShhGMljyffSeZPum2b5AWHJRSuX0s2TsZi\ne3E+9IPJCQnnqjSKyNMSzsPypdOpAgoooIACCtRegGsmrpPG6rVN7U/QWDzAXhVIZdSCgoUC\nghEL5kvKRT/LXPiXbV6eeS52z04oMv6WTE7K69mOvp0TiiJez0MD+IL8DQkXydX2oSz8q9ox\ngPkPZpunk48k1ZGk12eZIo6i7JvJLsknk/sSPgf9XLz/NtkoqTY+w2eqHQOcH5ftGAHjFrl5\nBviaOm/G+eVnY7YWB8nI26XJT1uss0sBBRRQQAEF6ilggVTP8zqiR9WrAomCgBEVRogobqoF\nEcVEKXgoKlim4OB2qW8ltycUItXXcHvaiQnbEtaVeUZZHk7OTxZKqm3XLEypdrSYXzp9uyWf\nT9h+qeQDCfu8P+Gim9v9KNgYzdojOTfhc1KU/SThWCnGWjW2p7BbrtVK+wYsMHe2pHDdtsMr\nuA2REcBWBVSHl7lKAQUUUEABBcaogAXSGD1xo/lj96pA4lY0LmYpKrhgrRY71dvqKHIOSGiH\nJo8mFE9fTNZKTk9uTChA6L8mYcSG29VuTkqh9I3Mz540tx+mg+//tGpcRB+YUIxNSy5O+Nws\n8/4LJ+9MPpe8P1kxaddYz3Gxv0UbGy2SKfuh/8ONPieDF5iQl3K+O52HNRrblHOQRZsCCiig\ngAIK1FjAAqnGJ3ekDq1XBRIjLxQ/FElMubAlFDll9Ijl5xIKiI0SRpBYR/FT2iWZOSJhpIlR\nmJMTtqE4+lNydWOZaXN7ZTp4/x2aVzSWv57pQ8nbkzLiwJSiiKLukKSbxn7uSPh83PbHlMJr\n58Q26wILZBf8LG3SYVfbZR3nfI4O27hKAQUUUEABBeojYIFUn3M5ao6kVwUSBQYXs9VQEJVi\nqfRT+DBi81hCocQI0l5Jaedmhocx8L2jUnSxH17D9mU/9O2U8Idk8YTb3Sh+fpy0ai9NJwXM\n61qtTN+bEoq3F7dZ366bUSyKvR2TjRMv1IMwhO287Ou4Dvv7ddb9vsN6VymggAIKKKBAvQQs\nkOp1PkfF0fSqQOJf8SleKEIoXiiESjFDH8UHyz9PeEgDy2x3ZXJwUtpBmeH2Ooqjsg373iqh\nGOG7Sbyu+l4scxveZxNGhFq1L6ST9+rUrs1Kbq8rbVxmuFWO7zrdnnC732HJUolteARelbeh\nMOa8VM8tPwtfTiic105sCiiggAIKKNAfAhZI/XGeh/Uoe1UgUQRVi5bTskzRMi15srGuFERl\nW5ZPTG5L5k1oFB+sJxQmbMPta3xhn3U3JDxEgff6RcIFNN9dmtnIzQ+zDe/VqZ2Sld9rbLBQ\nptzuxzF8LXlPQgFGEXVvskFiGx4BbmVkxPGfydHJMcnNCSOGb0hsCvy/9u4E3rpzvhe4SCIk\nMUVMQSKGUHMbocTURK+5pTVUEVMNRbX0apWqmqs1dFJTrqFIUb2GGoKQQSNIjBGhImJIZA4x\nZJDW/f04S7dz9znv+55z9vues/b3+Xx+9trPWnudtb5r9973n2etZxMgQIAAgfkRGFWBdOn5\nuW5zeabDf93vaE9bbznbLWlR03UtaNpazLTo+V7SkYFdk4429Vapbr/Hwvt+X1r8dNuOKH02\nOTnpZ3o7XffZW9uutJCd8rpc64jUNZfbYGF9i7G2g5PLJzdNnp68KXlJ8stJb+l6d3KFZLIN\nx/zwdP5WctXJlZZXLPCv+eQNkjcmV0v6PXl10r73JRoBAgQIECBAgACBFQvMagRpGD3qa2+N\n62uLm74OGQqjYZsWI+cnd0y+mLQQ6shRC6bhM8PrD9P31YX131547f7a37/X1+cnOyTT2l3S\n2eLqhtNWpu/GSfdzh+SXku77V5Jprf/l4pTkjydW3inL/5l0H6ckHd3o33t50u01AgQIECBA\ngACB1QuMagRp9Rz2sBYCsyqQWtgMxUxfh+JoeB36Lsi6Fg4dlWlfi54zkpcl3cexSUeXhn11\n1OYBSQupjhqclHRdtz09aevo0YOT7qejDcNoVhZ/oX0g745P9vyF3ktd6rp5/6VkeNj/cVnu\n31mu/X1Wvmdhgzvm9aKkt35ddaGvo0n3Tk5N3rnQ54UAAQIECBAgQGB1Agqk1fn59BSBWRVI\nF+ZvDUXNUMBMvu/yUCw9KsuvT85LOnL0jKSjR13ftBjq9v+SnJgM/e07M/lO0uXuY7LdJG+6\nnwdNdk4sXznLhycdbTokeUHSv9HPHJZcMWl7avKZny4t/T/Py6p+poXQl5NXJdNaR6YuSH57\n2kp9BAgQIECAAAECWySgQNoiLhtvjsCsCqSOCrVoaTqaMixPvrbQ6fuvLGzTQqd9JyctIp6c\n7J58JDkruSR5dLJHsn/SbZuOHnU/Xbe4vSIdH1zcOfG+o0u/lfR5lg8lb0jum0yOOt0z73s8\nfQZpqda/8Y/JbZMez9WTpdrBWfGupVbqJ0CAAAECBAgQ2GwBBdJmU9lwcwVmVSANhdBQvAzF\nzOLXYbuv54A7UtP1L076uY4O7ZN8Iem6PtPT7b+ffCPptn3f0areXveIZHE7KB3ddjVtp3z4\nm0lv+5vW7prOHsd+yUOSPhO1XHtiVnakbDVt33z4SUld+veXetYqqzQCBAgQIECAwGgFRlUg\n+QfdaL+nPz2xFi8dhWlaPLQNy9tnuQVQX4e2YxaOSfq5v0nunlw26fTgLXCun/S2tDcmt0x2\nTbpt272S9yctYha3K6Sjt8xtSbtcNu7f/6WkI0dHJY9K+jd2Tp6ffDvpMbQA++vkJcmxyTWS\n3po3nGMW/792lfSc///1bl5HR8/elPxa0lv5WhzeNDkleVjyqUQjQIAAAQIECBAgQGCFArMa\nQRpusetrb41rkTL0tWAaRpa6PKTFSLc5NemoUUeK2vfypM8Jdfk9SW+B+1zyyaT7aX9Hm/pf\nENpaoFwtaUF2RPLqZHNbC6PTkv7to5MeR4/v0OQ+yQlJC7NOHNHzOi/prYBD63NNFyW/NXQs\nem3h1H20yNrS1mLvK8l/JDec+PBuWX590mO+2US/RQIECBAgQIDA2AVGNYI09ou1Uc5vVgVS\nC4gWEi0uhtehEBpeh/6LJ7bpCNHwLNLw2R+kr8XIWcnvJB3R6QjOnslHk27X9x1B6a1r3W/T\ngqEF182TzWl3zkbd/q+SjhQN7UZZ6OhQi7KOLt0i+Y3kjslOyeL24nSckSwuVloctVg7O7lq\nsqXtBfnAScmuS3ywzzV9ZIl1ugkQIECAAAECYxRQII3xqm7jc5pVgXRkzquFSzMULH0d+ha/\ndl2LoI4IvSS5TvIXC++Hbbuu27X4+njSgqkjOC1cuk0//87kRUlHl1p4tVBpcbNUUZFVP28t\nrl7183e/uNBRmo4s/e9f7J76bof0vjXpSFILvj9MnpOcmLQ4un2yktbi6A+W+eB+WVeHjp5p\nBAgQIECAAIF5EFAgzcNV3srnOKsCqcXNUNgMhdHwOvRPvg7rWgQ9fcGghUaLjBY5xye9le6B\nyVOTv0wemlwp6a1v3VdHkZrehvbaZJ9k9+Q/k1cmk62f6+f/Mun+fivpMeyVLNWenRVb8oxP\nj+tfkxZwH0taJPV4VtpaAB64zIc76tVzaKGkESBAgAABAgTmQUCBNA9XeSuf46wKpBYG/Qf9\nUAT1H+5dHl57K9uwbnjtun9JWgg9IXlm0meXzk1ekZycPDlZ3D6cju8kf754xcL738zrhckw\nivSwLHfkqSNQhydfSHoMPd4rJku1B2XF6Uut3Ar9Z+ZvPGSZv3P9rKvh9ZbZxioCBAgQIECA\nwJgEFEhjuprr5FxmVSB9PefXkZ/J4mfa8tDX1/7j/i7Js5MWNC1YDk5a/HwpafE07Ra4Fjvd\n/l7JtHa5dHbfv5p0BKr7/eNkh2RoD85Ct/l40meFprWnpPOEaSvWqG+77Gf/pLfRPSm5XTLZ\n3pQ3h012LFp+Qd5/dVGftwQIECBAgACBMQsokMZ8dbfRuc2qQOqtbi04hgJocnnoG4qiFizD\ncrdrhlvt+izPO5MfJi1O/ja5bPJnSYumPmfUz3akad9kWhsKpDtkZYutZ03ZqMVSR2g6enXQ\nwvoWLC1SHps8IjkxeUkyi3aj7PS4pOfd2wn7PFSXP5l0ZKhtn+QHyYuTuyR/lPxh0qKqBd6P\nk/snGgECBAgQIEBgXgQUSPNypbfiec6qQDo85zCtKOo/+lvQDBm2GfpfmXUtCL6WvDBp8XRA\n8uikn+l+W6j0VrdXJ8cm3ebU5KKkI0SL22+mYxhh6jbDrXaLt3tUOrqvTyW3THrrXY+rozIt\nTHqs7092S9ayXTs7OyN5T3KtiR3vmeUPJKcl11zo7zG2EKpFC7p+brDsCJJGgAABAgQIEJgn\nAQXSPF3trXSusyqQ/jLH33+4t+AYip++tkAZ/kE/+drt+r6jKNdLut0FyVuSZyYnJWcn5yTd\nrsVKR4/enTx2Yfmf8trPDCMuWfzppAgtcLrukcnJyXLtvVnZ/bcIaSH2pqTFV0fEHpJ8Numk\nCzsna9X6N45JJm/5G/bd/6OvycHJnkmfm3pf8qLk35MPJq9MjkwmC6m81QgQIECAAAECoxdQ\nII3+Em/9E5xVgdTRl2F0qKM3LY76fuibfG1R04KkhUkzrGth0/dd/6PkWwvvW1DcINklGdpL\ns9Dtus2/JDdJem7fSDrK1FGjeycdCdoxWar9dVZ8N2lB1gLkbcmTkn6+raNHLZae2Tdr0Hos\nvX2wo1xLtQdkxfnJIcl/JNsni1v3c1zSQkojQIAAAQIECMyLgAJpXq70VjzPFhEtSCaLjbX4\n8x0BGgqdoeiZ9r59vV3ukuRpyd8sLLcgat/XkhcmLUyGQqHFwl8ki1tHeL6TdJ9Nbz97QTKM\n9lwhyy2QHpFMazU4Lenx7jdtg4W+HucJy6zfklXXzsY91usv86EWe92mBWCLvKXaA7OivxF1\n6aU20E+AAAECBAgQGJmAAmlkF3Q9nM6sCqQWGYvTUaRm6O8/+ofljtqcnHS0qbfDdUTppslk\nawF0atJCqQXYtZLFrRMXfCG5atJJFha3P01HC6y7Llpxpbz/QNIRqB7XTslS7W5Z0eNci9aR\nqRrsv8zODsi6Fos9rt5+uFSrV7fZfakN9BMgQIAAAQIERiYwqgLJf+Ue2bdzE6fTwqi3pvU3\njRa3H6ajBc8eSQu2FgyHJotHaTq7W7f5TNJRp3sli9ud0vH55KykxcLi1lvoXp18MOlzO3+b\nvClpcbZX8uikrQXWUq3rWtAt1y6flT2+g5I7Jzsm01pHtI5OHjlt5UJf1/XWup7PNRb6pr1c\nM50tpFoAagQIECBAgAABAgQIrECgBUn/4b3LCj673EeGEY8WO03/RjO8n+zrqM+Tkv+btGD4\nUPLyZFo7Jp19NugjyXMXbXBg3rcQu8Oi/mlvb5XOv0nembwxOShpEbNd8s3kGclSrcf35iVW\n9vN/lvQ8Wvh9I7k4aXG41HNGd866ej0xWdz620sdTbt90iLp4GSpdkhW9Ng0AgQIECBAgMC8\nCIxqBGleLtp6P8+tUSBN3lY3WSANRVILiY4OnZV02zOTf0umtVuk8+ykn+nEDH2+6LrJK5Le\n9tbRoB2S1bRH5MPd1z0X7aTFT4uyPh/1S4vWDW87ItXngIaCq/1XTJ6ftAh6QDKtPTyd/ZvH\nJS9K/ir5bNLnjh6ctA2FVIvJydbjelrSQuy2kyssEyBAgAABAgRGLqBAGvkF3hanN4sCadec\nSEeLmhYFLYRaNLSoGfqH4qivL0y2T/oP/dcm/UxHTfZK2nq7WouGjsL08y0E+rkWU5P7+/5C\n32l5HYqKLK6o/WU+1b/R55KekTwv6a17/Rv3Tqa1/dLZz9xl2sr0PTNpEVifaW3vdPY8P5gc\nmrSo2jOZbAflTQup3mbY2wU7CtZnrlq0dZIGjQABAgQIECAwTwIKpHm62lvpXGdRIF09xz4U\nLkOB1OKoxcNkuk3fX5T09rq23006gtT+k5J9kxOSryWPTx6dnJd8KWmB1HTU5kpJW1//PGmB\n9fvJ4tZCbHPbrbPhq5Ojk8OTFyR7JEu1v8uKDy+1Mv07Jd9NHrDMNpuzaq9s1OLp/cn7kuck\n1040AgQIECBAgMC8CSiQ5u2Kb4XznUWB1EJgKISGAmkomIaiqOu73AKnoyp9/6Ckt80dnhyW\nfCPp+hYVr0uOTLrda5LrJC2suv9pt7v1vDqq0oJmv+TdSUd/+vmTkmcnvT1vLVsLlhdvYoct\ntp6xiW2sJkCAAAECBAgQ2DyBURVIl968c7bVBhRo0dLWAmhT17m3w10laTHTUZEWNv+c9Pmb\nFjL9fG9zu2xyTHKb5LHJbyffSj6W/EGyuB2cjlOTFiwfTy5ODkrukvxj0pGo9l85WavW2eN6\nLsu1rjfL3HJC1hEgQIAAAQIECBDYhgItSFrI7LLGx9AioKM1Q/o3Hpl0goGOCg39w/M0HS1q\nX5+96W1wb0yOSjq6NK29Mp1vTZ6WHDdtg/S9N2mx9uQp63dLX58petuUdSvtauHW2wOXesZo\n36yrw00SjQABAgQIECBAYPUCl8ku+u+r261+V/ZA4GcCsyiQtsuuJwukfmmbFkDD67A8vB+K\npo4UdVTn3OSfku8k09pL0tli6ilJR5umtVPS2VGmpVr/D6l/f8+lNtjC/h57R73elXR5svWW\nwC8nLeo0AgQIECBAgACBtRFQIK2No71MCMyiQLpq9j8UPi2Eentb3/eWt6EQGgqkvu8oT99f\nkFwhOSz5YnLzpJ+b9ozRvdPf2/I+lLw+WdyukY7u+3WLVyx6f3beP2BR32re9lhPWcgL8/qk\n5BVJC8bDk87IpxEgQIAAAQIECKyNwKgKpB3WxsRe1qFAZ5AbWgufXusWOtdMOro02fp+6OsX\n/BvJ6ck9klOSI5J/SPp+2O8ts9zZ6r6X3DU5IJlsu+bNIcmFyfGTK6Yst3jbcUr/SrtOzAdb\n2D0h6TH3maOeUwulNyf10AgQIECAAAECBAgQWKcCsxhB6qm2OGkx0MLoawvLfd+00JkcSRq2\nG15bZByYtF036cjTJ5PHJb2drvucvIXvB3n/ouQRyXOS3lb3leQdSW93W6rtkxXd182W2kA/\nAQIECBAgQIDAuhbof2Dvv+c8g7SuL9PGOrhZFEideW4ogIbXfnGbFkGTfX3fdDKGIxbSEaMW\nUfdL2vZI3p0MBVT3c3by0uReSSdGaMH09eTjyZ8knXTi9kn/1t2Sxa2jWh9IPrZ4hfcECBAg\nQIAAAQIbRkCBtGEu1cY50FkUSMMXdRhFGoqgocAZ3rfQ6TbNccnJyfuTtr9IWgT1maS2Y5M+\no9TfROozSocmLYo6KUJHm85Inposbs9LR/f/rOT6yZWT3pLXGfL6mRskGgECBAgQIECAwMYU\nGP7daQRpY16/dXnUsyiQeqLfSYZCaHhtQdQM7/v6uaSjRd9Oeqtcb5Fr2zHpyNBByV2Sbvu2\nZOdkaFfMQp/rOSd5SbLUbHbdx8nJ8Pf7996R7JloBAgQIECAAAECG1dAgbRxr926PfJZFUi9\nZW6yGOqtbh0B+mHSQqYFz5COCnVdC5drJUPrLXAvTj6RdF+9LW5x6+18/5F8JGmBtVzbOytv\nkQyjUsttax0BAgQIECBAgMD6FxhVgdR/2GrjFGgh09nbWtS0bZd0ROntSafmvnHSqb3b39aC\nZaeko0enJkPbNQstrG690LHbsGLitUXW3yUdVu0td8u1r2flF5JNbbfcPqwjQIAAAQIECBAg\nMBMBBdJMWNflTlsodWTooKSFU0eRWiBNto4APX+io9vfNvlysn3S55F+NxnaVbNwj+Q+Sfd3\nueSoRCNAgAABAgQIECBAgMCKBWZ1i11HfvobQy2OOsrT2+d6G10z3Fo3rOvrnsnQOgPdR5Pe\nWtfnjLp9nzHqLXSdke7/JC2wOhrV0aBhPwdkWSNAgAABAgQIEJgfgVHdYjfteZL5uZTjP9OO\n+OyetHhpcdRJF3rN+36y9Ta79n0q6S12l08en7TA6ux030uOTK6Z/HPS55JaGB2cfDP5zWTf\npMXTG5PbJL2dTyNAgAABAgQIECBAgMAWC8xqBOmFOZJhpKjFTpdbCDVdHvq6fFLSAqejQn1G\n6NnJ5EQKd1xY10Jr2Ee3PS35fNLP3jRpkfWWRCNAgAABAgQIEJgPgVGNIM3HJVv/ZzmrAumg\nnHqLmd5mN1kc9Za4ry30teDpuiOSFkSd+a7HM9kumzcfT85IWlR1xrtO9tC+FkkXJb+WtN09\n6ftO7qARIECAAAECBAiMX2BUBdKlx3+95voMH7Vw9i1ihlGfdvUWur0X+rZrR9qdkhZC70z6\njNFke2beXCe5VdLtX598P/li8vTk3OQOSduxSf+PpPvXCBAgQIAAAQIECBAgsMUCsxpBauHS\nEZ+O6Jy0sNxCqX2nJ/8n6ftvL7zeLq/PTT6SDK0FUbd97EJHnz3qrHWT7Ul50320XS/pPhVI\n1dAIECBAgAABAuMXMII0/ms8mjPs7xqdnOyYtMg5NGnx0ueFOlr0yKTtnJ+9/HQyhptl+esL\n7/ty9YUctdB3ZF4fuLA8vLTvWsluSdd9M5ncR95qBAgQIECAAAECBAgQ2DyBWY0gfTp//sKk\nt9h1Jrr+VlFvjXtacuOkhVGfP+oIU59L6i10fSapM9cNrb+Z1KLqlgsdvZWu+3vEwvu+7Jd0\nm96a1xGm30s0AgQIECBAgACB+RAY1QjSfFyy9X+WsyqQ3ppT7+10f5G0EOrytxaWW9C0aGpB\n1Ekcuu7M5C3J4vaf6XjWROfjstwi6f3JE5L3JOcl7XtZohEgQIAAAQIECMyPgAJpfq71VjvT\nWRVIb8gZ9Ha3FkL9TaT+QGzffyVpMdOiaRhB6uvRyY7J4tbj6215t55Y0dGmNySnJC2ujkt+\nPdEIECBAgAABAgTmS2BUBVJ/NFQbr0CLnj47dIukv1F06eTaSdt2yVAcfTbLfUapo0AdUVrc\nXpuO2yTd1yuTDyX9fLe/RvKq5ImJRoAAAQIECBAgQIAAgVULdISmt7ztsuo9/eIOfj9vh1vf\n+mxQn0dqAdTnkDqC1MkV+tp8KfmbZLn24Kw8Jul+mqOTByYaAQIECBAgQIDA/AqMagRpfi/j\n+jrzWRVI985pdpSoxUxfmxZiw3JfWxz1FrkuGwUKgkaAAAECBAgQILBFAqMqkHrLlTZegWfk\n1Foc9Uvb1uKo6fNEQ+vyyUkLpP89dHolQIAAAQIECBAgMI8CCqTxXvWdc2q/muyUDCNEfW6o\n6W8gfXWh/wp5bRH1jmSvZPGPwKZLI0CAAAECBAgQIDAfAgqk8V7nFj5DQfTyLPe3jloIdYru\n/5uckXwu6TY3TfosUZf/NelIUpc1AgQIECBAgAABAnMloEAa7+U+K6fW2+k6evTFpCNKvdWu\nxdL1knclN0q6TacAb2HUiRq+nfx50pnrNAIECBAgQIAAAQIECGx1gVlN0tDfPeqzRZ25riNI\nXW5B1Az9fT0naSHVIul9ya2T/nhsJ3nQCBAgQIAAAQIECCwnYJKG5XSsW1cCX1s4mt4ud8LC\n8il5/fukfdsnbVdMXpbcPulvHB2XvCX5vUQjQIAAAQIECBAgMDcCbrEb96XuKFBbr/MvJy2K\nrps8OWnr+7a3JjdMuv3BSdvRyU1/uuR/CBAgQIAAAQIECMyJwA5zcp7zeJotim4xceLDrXXt\nGgqjYfkBWTg1uWfyw3am7ZJ0UgeNAAECBAgQIECAwNwIGEEa76Vu8dtb6PqM0TELp9nCaNo1\n3zH9f5WcuLBdX+6XdBRJI0CAAAECBAgQIECAwFYVeEz+Wkd4Omqzlu2S7KwjQp2soRM1tFg6\nL/lB0tGhHyXt6wQN7R9Glp6a5a7vLHcaAQIECBAgQIAAgeUERjVJw3Inat3WE5hFgbRbDn+Y\nra4FUmexGyZd6Jf4FUmLo27TyRz6+sLkfUm3fVCiESBAgAABAgQIENiUwKgKJM8gbepyb9z1\nw2hQR5F2Ss5P9kzemeye3DDpqFK/0MNzR3+Q5fckneb7+EQjQIAAAQIECBAgQIDAVheYxQhS\ni6KOEL0l6S10w2jRMKrU90M6vXeX35xoBAgQIECAAAECBLZEYFQjSNMe2N8SDNuuX4HeYtdR\npPssvPZIWxwN7ewstCjqNu9I+mOxZyYaAQIECBAgQIAAgbkVcIvdeC99C54+SzRZBJ+b9/+Q\n7JM8IGnB1Fwj6e12hycaAQIECBAgQIAAAQIEtqnALG6x6wn1eaKOEnVGuj6LNBREXT4r6brh\n9rvOaDdZTOWtRoAAAQIECBAgQGCTAm6x2ySRDdaLwKdzIL2Frs8jPT45LemEDJ9Jvpt01Kit\n21wuuXzfaAQIECBAgAABAgTmVcAtduO+8lfO6XXUqAXQa5LhmaPOUtd2UtKKf49kx4XlvGgE\nCBAgQIAAAQIE5lNAgTTu697C6OKkRVDbcAtd+1ss3SDpiFKfV2pfJ27QCBAgQIAAAQIECMyt\ngAJp3Je+t80NxVHP9LDkvOTOyZWSrtsl6XNInbyho00aAQIECBAgQIAAgbkVGEYU5hZg5Ce+\n38L59Xmjzmh3YHKL5JvJZHHcUaYXJhoBAgQIECBAgACBuRaY/EfyXEOM9ORvunBeu+b17cm1\nkv2TPm/UNowYtUC64Kc9/ocAAQIECBAgQIDAHAsYQRr3xW8B3NvnOnLUiRnukgzF0Zey/EdJ\nm9nrfubgfwkQIECAAAECBOZcwAjSeL8Aff6oEy9sn7wh6Y/BHpP0uaObJC2Urp50mz6XpBEg\nQIAAAQIECBCYewEF0ni/Ar1lrs8dtSDqdN97J2cmbX1/SPJnfZP2jp+9+F8CBAgQIECAAAEC\n8y2gQBr39T89p7dn0hnrTkzemJyb/Ery60lbp/t+1k+X/A8BAgQIECBAgACBORdQII33CzDc\nQteRpI4itUh6UtJRpbbh+bNLstxoBAgQIECAAAECBOZeYPhH8txDjBBg95zTZZOHJKclLYI6\nYUP7+nxSC6fHJi2euq1GgAABAgQIECBAYO4FjCCN9yvQiRdaFH0/2Se5b3LbpJM2fD751+Q2\nSbcxSUMQNAIECBAgQIAAAQIE1ofAY3IY/U2iXdb4cA7N/vr7R0u1FkndRiNAgAABAgQIECCw\nUoHekdR/y95upTvwOQKLBWZVIO2bP3Rh8pykI0dD63L7uq4TNmgECBAgQIAAAQIEViowqgLJ\nLXYr/RpsjM99Ood5/+TNycOSI5M+f9Rb7TrVd9d9JtEIECBAgAABAgQIECCwbgRmNYI0nOCd\ns/DlpJM0dPizry2WOsKkESBAgAABAgQIEFiNwKhGkMxit5qvwsb47AE5zD5n1Nvqvp58O/lC\n0tnsjk7unmgECBAgQIAAAQIECETALXbj/hp00odOxNBCuD8Iu3Ny7YV0JOmHySHJ3sn3Eo0A\nAQIECBAgQIDAXAsYQRr35f+9nF5/IPao5PrJu5MHJ53y+w+THZOuf2qiESBAgAABAgQIECBA\nYF0IzOoZpONyducnFydvS85JOnLUdETp2IXX9iuWg6ARIECAAAECBAhsscConkFyi90WX/8N\n9YFr5WhbHHU67z5r9IakxVGfP7oouXXS1hnt7pB0pEkjQIAAAQIECBAgMLcCRg3Gfekvyem1\n+NktOSl5fHKzpLfV3S0ZCqRud6tEI0CAAAECBAgQIDDXAkaQxn35+ztIeyQdNWoRdMPkm0nb\ndklHjPZP+ixSiyiNAAECBAgQIECAwFwLGEEa9+V/XU6vhVCn+P5x0hGjjhTdN/lw0h+MPTdp\nAdVb7jQCBAgQIECAAAECcy1gBGncl/89Ob2OHPU6Xzv556RTf1+QnJl03deTjh75LgRBI0CA\nAAECBAgQmG8B/yge//Xv7xvtlOyZdKToGt6X9AAAIeNJREFUB8muSdupSfvbup1GgAABAgQI\nECBAYK4F3GI3/sv/pZxifyD275NPJS2W2jqS9B9JZ7jrSNJJiUaAAAECBAgQIEBgrgWMII3/\n8h+SU/zl5AnJB5NHJy2Kbp48MTk7+VFyZKIRIECAAAECBAgQIEBgmws8JkfQ2986qrPWrT/c\n9dnk88m7k+8k5yfHJp3Eoc8j/X6iESBAgAABAgQIEFiJwKh+KHYlAD6z9gKzLJB6tNdIhtvp\nOorUUaXeeteZ6/400QgQIECAAAECBAisVGBUBZJb7Fb6NdhYnzs9h3vHpD8Oe2ByheT1yduT\nbyQaAQIECBAgQIAAAQIRUCDNz9egt/AdupD5OWtnSoAAAQIECBAgQGALBMxitwVYNiVAgAAB\nAgQIECBAYNwCCqRxX19nR4AAAQIECBAgQIDAFgi4xW4LsOZg034ffie5e3KVpM8n9TmljyYa\nAQIECBAgQIAAgdELGEEa/SXe7BO8VrbsD8n+Y/JfSacFv2byoaSz3nV2Eo0AAQIECBAgQIDA\nqAWMII368v785Dpr3cOT2yc7Jccnb0xOTtr6Pfj35IfJDZL+eOzQbpmF9ycvT/rDshoBAgQI\nECBAgAABAgRmKjDL30FqUXRGcl7y1eTLybeTi5Oh4Hlolr+b7J5Maweks6NKe09bqY8AAQIE\nCBAgQGCuBfwO0lxf/s07+etks4667Lh5m//0d4k2c9Mt2myvbN0fhu1xnJYckbT1t5B+nPxd\n0v4+c/TuZHLkKG9/3voMUp9H+l/Jq3/ea4EAAQIECBAgQIDAyATcYjebC9oRm5cll9nM3e+f\n7R62mdtuyWa9LW7n5PnJc5OOArX1uN6W/EZySHJW8vFkufadrOzEDRoBAgQIECBAgAABAgRm\nKjCrW+wuyFF34oWh7ZqFWyefS3rL3TeT/oBsX1s8dca6PqO0uG2fjhZRD1m8wnsCBAgQIECA\nAIG5F+h/fO+/KW83BolLj+EknMNUgcumt8XOvyW9pa4jROcnxyY3Sz6Q/EnS1pGmfqnvlPS2\nu8XtsenovnrboEaAAAECBAgQIECAAIGZCsxiBKlTdLfoeW9ySXJU0gka+uxRi6WvJn3mqNv8\nSnJI0lGijiRdP2nryNHvJxctvOZFI0CAAAECBAgQIPALApfJu9GMIP3CmXmzzQRmUSB1YoYW\nQ/+d9PmhM5OvJJ9J3pxcmLTw6fpu2y/2Kxbet1A6OulrR51aJGkECBAgQIAAAQIEpgkokKap\n6FuVwCwKpB7QfyYtgM5JbpL0FrkXJ7skb0i6rtk3GdqxWXhX8oykzxxdOdEIECBAgAABAgQI\nLCWgQFpKRv+KBWZVIJ2UI2oB1JGg3mZ3RtLb6r6f9Edhf5B0/TOTNpMx/MzB/xIgQIAAAQIE\nCGy+wKgKJJM0bP6F34hbdta6PmvUGetaIDW7JZ1w4bTkjklvw+voUtvjksWTMVwpfR1x0ggQ\nIECAAAECBAiMXkCBNO5L3FGiFkk3TR6QvDY5PunI0ZOTjiS14v9u8sSks9k9LennOqr0zaTF\nVbf/QvKwRCNAgAABAgQIECBAgMBMBWZ1i92bctSdlW5ykoUd875TeXc0qb+T1PUtlL6XdASp\n04MfkXwnaRF1y+S2yQuSHyX/kGgECBAgQIAAAQIEBoFR3WI3nJTXbSswqwLp9jmtFkAthl6X\n3Dn5peRByReTjgy1SHpU0lvp2v466cjRHn2zqHV/nf3u/ov6vSVAgAABAgQIEJhfAQXS/F77\nmZ35rAqkHnBvq+ttcr21rsVS56jvaNExSZ8/emQytH65O633Q4aOKa8dffrYlH5dBAgQIECA\nAAEC8ymgQJrP6z7Ts55lgdSZ6V6U9DeP+ltIJyS9pa5Tfz8smWw3z5sWULtPdi5avkfed9RJ\nI0CAAAECBAgQIFCBURVIO7imoxfoqNGfJYcmD046I92rkt5y18kYJtt2C2869fdSrQXUsN1S\n2+gnQIAAAQIECBAgQIDAigVmOYK0Z47qo0kLm28spMvtu04y2TpBQ59L6jNKS7WXZsXHl1qp\nnwABAgQIECBAYO4ERjWCNHdXb52e8KwKpKvlfDvhwuHJjSbO/cYLfS2Yus1k61TfJyeL+7vN\nrZPOZPe7faMRIECAAAECBAgQiIACyddgzQVmVSC9Jkf6uWSnKUfcvq579aJ1O+f9J5IWVr+X\n3DDps0n9XaQ+u3RwohEgQIAAAQIECBAYBBRIg4TXNROYRYHUyRk6W90DlznKrus23XayXS5v\nXpB0UofejteclDwu0QgQIECAAAECBAhMCiiQJjUsr4nALAqka+TIWthM3lq3+GC7rtt022nt\n0uns7yFdddpKfQQIECBAgAABAgQiMKoCqf8A1sYp0NvhWvwsN2V3C59u098+mtb+O52nJWdN\nW6mPAAECBAgQIECAwNgEFEhju6L/cz6dwvuYZPFvHf3PFpe61EMXtunECxoBAgQIECBAgAAB\nAgTWhcAsbrHrid01uSR5ZN+kXSXZJ9k1aV/XHZhoBAgQIECAAAECBFYqMKpb7FaK4HNrKzCr\nAqlH2YkVWgh1MobeTtf01rn+gOyzEo0AAQIECBAgQIDAagRGVSDtsBoJn90QAhcuHGV/86jF\n0cXJt5KrJ3+cvDf5bKIRIECAAAECBAgQIEBgXQjMagRpz5zdBckTp5zldul7c/LlRKE8BUgX\nAQIECBAgQIDAZgmMagRps87YRjMXmFWB9Jwc+ecXjr6/dXSr5I7JMK13n0m6KLlbohEgQIAA\nAQIECBBYicCoCiSz2K3kK7BxPtOC6KPJk5PTk95Kd2RyWvLBZLfkc0m30wgQIECAAAECBAjM\nvYACadxfgU7EcEDyguS5SX/3aMfktkmfR/pk0hntup1GgAABAgQIECBAgACBdSEwq1vsDs7Z\ndca6/aecZYvjjiJ1/Z2nrNdFgAABAgQIECBAYHME3GK3OUq2WRcCu+coOsX3tGeMOjHDTkkn\nazgn0QgQIECAAAECBAjMvYBb7Mb9FbhBTq+jSE9L3p3cJ9k3eWjS2+u6/qzkZolGgAABAgQI\nECBAYO4FFEjj/gp0hrpTkj5z1Fvp3pEcl7wmOTvpjHYdEh1+KymLGgECBAgQIECAAIH5FVAg\njfvaH53Tu2/SQun6SYukjyVHJR1JarF0+eQTiUaAAAECBAgQIECAAIF1ITCrSRqul7PrD8V+\nL+ktdp3Fbmh7Z+HcpM8o7Td0eiVAgAABAgQIECCwhQKjmqShD+pr4xU4Oaf20eTuSSdkuEdy\nXvIryZOSE5L+JtLLkt5upxEgQIAAAQIECBAgQGCbC8xqBKkndmryrOTNC8vn5/XTyR8mrfZb\nLPU3ka6WaAQIECBAgAABAgS2VMAI0paK2X6bCfQZs2smfeboeUscxUkL/dfO65lLbKObAAEC\nBAgQIECAwFwI9B/Q2ngFOilDZ6u7zjKnOKw7Y5ltrCJAgAABAgQIECAwFwIKpPFf5vfmFB+f\nLHWtn5B1n0t6K55GgAABAgQIECBAgACBbS4wy2eQrpuz68QMr00ulwxtuyw8NeksdgcOnV4J\nECBAgAABAgQIbKGAZ5C2EMzm21bglPz5zl73b8l9ksOS/i5SZ63bIzko+UiiESBAgAABAgQI\nEJh7gaVuu5p7mJEB9Idg90melbQ42jF5TXKD5JBEI0CAAAECBAgQIECAwLoRmOUtduvmJB0I\nAQIECBAgQIDAKAVGdYudEaRRfkedFAECBAgQIECAAAECKxFQIK1EzWcIECBAgAABAgQIEBil\ngAJplJfVSREgQIAAAQIECBAgsBIBBdJK1HyGAAECBAgQIECAAIFRCuwwyrNyUtMELp/O/ZKd\nkxOTryUaAQIECBAgQIAAAQIE1p3ALGex65TeL04uSC5Ovp/8JDkquVGiESBAgAABAgQIEFiN\ngFnsVqPns1tVoLdQviPpj8E2uyYdSbpZcn5yTKJICoJGgAABAgQIECBAgMD6EZjVCNLDcoo/\nSPojsYvbdul4X3LE4hXeEyBAgAABAgQIENgCgVGNIG3Bedt0hgKzKpA+kmP+22WO++ZZ19vt\nrrvMNlYRIECAAAECBAgQWE5gVAWSWeyWu9Qbf92NcwqfWuY0js+6PpvU7TQCBAgQIECAAAEC\ncy+gQBr3V+BHOb0rLHOKl826nZJupxEgQIAAAQIECBCYewEF0ri/Akfm9B64zCn+dtZdmBy3\nzDZWESBAgAABAgQIECBAYKsKzOoZpM5Q1wLo6VPO5ibpOzN57pR1uggQIECAAAECBAhsrsCo\nnkHa3JO23WwFZlUg9ag7StTnjI5Inph0uu9/Snpb3duT7RONAAECBAgQIECAwEoFFEgrlfO5\nJQVmWSD1j+6TvCY5MflGcmjyoKRTfWsECBAgQIAAAQIEViOgQFqNns9OFZh1gTT5Rw/Im44c\nfTnpLHYtnHq7nUaAAAECBAgQIEBgJQKjKpBM0rCSr8DG/cxLc+gfSv4r6e8jHZxcL/lc8ohE\nI0CAAAECBAgQIECAwDYX2BojSP0bfe7ozlPO9vHp+3FymynrdBEgQIAAAQIECBBYTmBUI0jL\nnah1W09g1gVSnzX6VvKny5zS27LuPcust4oAAQIECBAgQIDANIFRFUhusZt2icfXd4Oc0rWT\nty5zai2Q7rLMeqsIECBAgAABAgQIjF5AgTT6S/zTE9x14TTPW+Z0u27nxMx2yyBZRYAAAQIE\nCBAgMG4BBdK4r+9wdidn4ZLk1kPHlNeuOyn5yZR1uggQIECAAAECBAjMhYACaS4u86W+l9N8\nV/K8ZIcpp3zV9D0leeOUdboIECBAgAABAgQIECCwVQVmPUlDT+Y6yXeSw5KbJW29ne6ApL+J\n9MnksolGgAABAgQIECBAYEsETNKwJVq2XTcCncXudkmLov5A7DnJ+cmHk08kd00uTDQCBAgQ\nIECAAAECcysw7XarucWYgxM/Jed4YNJZ7W6R9LePPpWckWgECBAgQIAAAQIE5l5AgTSfX4FO\nxtBoBAgQIECAAAECBAhMCJikYQLDIgECBAgQIECAAAEC8y2gQJrv6+/sCRAgQIAAAQIECBCY\nEFAgTWBYJECAAAECBAgQIEBgvgUUSPN9/Z09AQIECBAgQIAAAQITAgqkCQyLBAgQIECAAAEC\nBAjMt4ACab6vv7MnQIAAAQIECBAgQGBCQIE0gWGRAAECBAgQIECAAIH5FlAgzff1d/YECBAg\nQIAAAQIECEwIKJAmMEa8eL+c2+HJ+Qvpcvs0AgQIECBAgAABAgQmBBRIExgjXfz7nNdbkxOS\nhy6ky+3rOo0AAQIECBAgQIAAAQLrSuAxOZqfJLus8VEdlP39KNl/yn7b13XdRiNAgAABAgQI\nECCwUoHL5IP9t+ztVroDnyOwWGBWBVJHip67+I9NvO+6bqMRIECAAAECBAgQWKnAqAokt9it\n9Guw/j93xRziTZJ3L3OoXddtuq1GgAABAgQIECBAYO4FFEjj/QrstHBqFyxzisO6Vv0aAQIE\nCBAgQIAAgbkXUCCN9ytwVk7tjOSOy5xi13Wbs5fZxioCBAgQIECAAAECcyOgQBrvpe6Dcq9N\nnplcbcpptu/Pk27TbTUCBAgQIECAAAECBAisC4FZTdJwuZzdJ5KvJfdPLr+QLrev67qNRoAA\nAQIECBAgQGClAqOapGGlCD63tgKzKpB6lDsn/b2jTundkaKmy+3rOo0AAQIECBAgQIDAagQU\nSKvR89mpArMskIY/2GJo34UojAYVrwQIECBAgAABAqsVGFWBtMNqNXx+wwh01OjTG+ZoHSgB\nAgQIECBAgACBbSBgkoZtgO5PEiBAgAABAgQIECCwPgUUSOvzuqz1UXVyhs5md1xyWtKRpGcn\nfiA2CBoBAgQIECBAgACBQcAtdoPEeF+vm1P7cLJjcnDS2ev2Th6dPDz5X8lJiUaAAAECBAgQ\nIECAAIF1ITCrSRo6QthRo8OSXRadaaf3/kByfLL9onXeEiBAgAABAgQIENhcgVFN0rC5J227\n2QrMqkC6Vw77guQaSxz+VdL//eS3l1ivmwABAgQIECBAgMCmBEZVIHkGaVOXe2Ovv1MO/2PJ\n6UucxjnpPzzpdhoBAgQIECBAgACBuRfwDNK4vwL9vaPzF06x13r/pM8fnZsclXw36Xq/ixQE\njQABAgQIECBAgIACadzfgRNzer+R3Cd5RdJb7U5Ndk967f8quU3yqkQjQIAAAQIECBAgQIDA\nuhCY1TNIV8vZ9Qdi/yt5UXKlpK3F0e8kP0h+nOyRaAQIECBAgAABAgRWIuAZpJWo+cw2EegE\nDBcv/OX/zmu/vG0tlG6c7JT0ObTrJRoBAgQIECBAgACBuRcwScO4vwK/ntPraNGDk4ckZyQt\nms5K+jtIv5u8P3loohEgQIAAAQIECBCYewHPII37K3DDnN6Xk7cn70hulfR2us5q95mko0o3\nTzp5g0aAAAECBAgQIEBg7gUUSOP+CnSGuqssnGKLoRZFzWTr+mGmu8l+ywQIECBAgAABAgTm\nTsAtduO+5P2No72S2y9xmp3e+37JR5ZYr5sAAQIECBAgQIDAXAkokMZ9uU/K6f1z8qakv380\n2TpBQ9d1EofXTa6wTIAAAQIECBAgQGBeBdxiN/4r/4Sc4ruS45O3JCck/T2kTtywfXLPpFOB\nawQIECBAgAABAgTmXsAI0vi/Ai1+7pb0t5ZaGHX2utslr05ulnwx0QgQIECAAAECBAgQILBu\nBFq8/CTZZd0ckQMhQIAAAQIECBAgsHkCfih285xsRYAAAQIECBAgQIAAgY0l4Ba7jXW9HC0B\nAgQIECBAgAABAjMUUCDNENeuCRAgQIAAAQIECBDYWAIKpI11vRwtAQIECBAgQIAAAQIzFFAg\nzRDXrgkQIECAAAECBAgQ2FgCCqSNdb0cLQECBAgQIECAAAECMxRQIM0Q164JECBAgAABAgQI\nENhYAgqkjXW9HC0BAgQIECBAgAABAjMUUCDNENeuCRAgQIAAAQIECBDYWAIKpI11vRwtAQIE\nCBAgQIAAAQIzFFAgzRDXrgkQIECAAAECBAgQ2FgCCqSNdb0cLQECBAgQIECAAAECMxRQIM0Q\n164JECBAgAABAgQIENhYAgqkjXW9HC0BAgQIECBAgAABAjMUUCDNENeuCRAgQIAAAQIECBDY\nWAIKpI11vRwtAQIECBAgQIAAAQIzFFAgzRDXrgkQIECAAAECBAgQ2FgCCqSNdb0cLQECBAgQ\nIECAAAECMxRQIM0Q164JECBAgAABAgQIENhYAgqkjXW9HC0BAgQIECBAgAABAjMUUCDNENeu\nCRAgQIAAAQIECBDYWAIKpI11vRwtAQIECBAgQIAAAQIzFFAgzRDXrgkQIECAAAECBAgQ2FgC\nCqSNdb0cLQECBAgQIECAAAECMxRQIM0Q164JECBAgAABAgQIENhYAgqkjXW9HC0BAgQIECBA\ngAABAjMUUCDNENeuCRAgQIAAAQIECBDYWAIKpI11vRwtAQIECBAgQIAAAQIzFFAgzRDXrgkQ\nIECAAAECBAgQ2FgCCqSNdb0cLQECBAgQIECAAAECMxRQIM0Q164JECBAgAABAgQIENhYAgqk\njXW9HC0BAgQIECBAgAABAjMUUCDNENeuCRAgQIAAAQIECBDYWAI7bKzDdbSrELhSPnvXZI/k\n9OSw5NxEI0CAAAECBAgQIECAwLoSeEyO5ifJLjM6qqdkv99Pzks+n7Qw+mHyJ4lGgAABAgQI\nECBAYDUCl8mH+2/Z261mJz5LYFJglgXSM/KHWgw9OhlGDLfP8kFJi6bnJhoBAgQIECBAgACB\nlQookFYq53NLCsyqQNorf/Hi5IFL/OXfSP8lyQ2XWK+bAAECBAgQIECAwKYERlUgmaRhU5d7\nY6+/fw7/a8nblziN96T/+GSpAmqJj+kmQIAAAQIECBAgME4BBdI4r+twVntn4YvDmyVeu77b\naQQIECBAgAABAgTmXkCBNO6vwHdzelfbxCl2fbfTCBAgQIAAAQIECMy9wPDQ/txDrDHAdbK/\n3r6242but1Nwt3X2j7VsH8rO/jTpM0ZfnbLjPqN0l+SlU9bpIkCAAAECBAgQIDB3AtvN3Rlv\nnRPeKX+ms8T1gbXNaTfIRn+U9HOdVGEt26HZ2TWTeyanTuz46ll+f3J+8msT/RYJECBAgAAB\nAgQIbIlA/817UXL75Jgt+aBtCSwl0DnjO3q0uQXVUvuZ1r9bOj+WdErv1yXPTA5Ovpd8Irlq\nohEgQIAAAQIECBBYqYBZ7FYq53PbRKA/Cnvn5LHJrsk9kismT0r2T85KNAIECBAgQIAAAQIE\nCKwbgVmOIK2bk3QgBAgQIECAAAECoxQwgjTKy+qkCBAgQIAAAQIECBCYewHTfM/9VwAAAQIE\nCBAgQIAAAQKDgAJpkPBKgAABAgQIECBAgMDcCyiQ5v4rAIAAAQIECBAgQIAAgUFAgTRIeCVA\ngAABAgQIECBAYO4FFEhz/xUAQIAAAQIECBAgQIDAIKBAGiS8EiBAgAABAgQIECAw9wIKpLn/\nCgAgQIAAAQIECBAgQGAQUCANEl4JECBAgAABAgQIEJh7AQXS3H8FABAgQIAAAQIECBAgMAgo\nkAYJrwQIECBAgAABAgQIzL2AAmnuvwIACBAgQIAAAQIECBAYBBRIg4RXAgQIECBAgAABAgTm\nXkCBNPdfAQAECBAgQIAAAQIECAwCCqRBwisBAgQIECBAgAABAnMvsMPcC6wvgMvM+HC2z/4V\nxTNGtnsCBAgQIECAwDoX+PEaH9+s/w27xoe7/O4USMv7bK21w5f0+1vrD/o7BAgQIECAAAEC\nBNZY4OI13t822d122+Sv+qPTBG6dzh2nrVijvv2yn5cmT1ij/dnNxhO4VQ754clTNt6hO+I1\nErht9nO/5OlrtD+72XgCd8ohH5g8e+MduiNeI4Ffz376b4IXrtH+7GbjCdw7h7x38rg1PvQW\nR59e433aHYGZCvT/QRxFVT9TpXHv/L45vfPGfYrObhMCD836b21iG6vHLfD4nN6J4z5FZ7cJ\ngT/O+uM2sY3V4xbofyA5YtynuLqz8zzK6vx8mgABAgQIECBAgACBEQkokEZ0MZ0KAQIECBAg\nQIAAAQKrE1Agrc7PpwkQIECAAAECBAgQGJGAAmlEF9OpECBAgAABAgQIECCwOgEF0ur8fJoA\nAQIECBAgQIAAgREJKJBGdDGdCgECBAgQIECAAAECqxNQIK3Oz6cJECBAgAABAgQIEBiRgAJp\nRBfTqRAgQIAAAQIECBAgsDoBBdLq/HyaAAECBAgQIECAAIERCSiQRnQxN3EqF2f9jzexjdXj\nFuj17/dAm1+BXn/fgfm9/j1z/3/BfF//nr3/v8B3wHfAd4DAgsB2eb0ejbkW2D5nv/dcCzj5\nHUKwF4a5FrhMzv46cy3g5C8XgmthmGuBXXL215xrASdPgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgACBaQL/D9TEUAIDGjqDAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(hill_1_c ~ richness, data = fasta_sumstats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relationship between average pi and richness. Hard to see with outliers, but I doubt there's a strong relationship. I'll filter out the outliers and see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = avg_pi ~ richness, data = fasta_sumstats)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0025648 -0.0006649 -0.0001720  0.0004611  0.0191802 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  3.182e-03  6.421e-05  49.548   <2e-16 ***\n",
       "richness    -8.113e-08  9.229e-08  -0.879     0.38    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.001324 on 473 degrees of freedom\n",
       "Multiple R-squared:  0.001631,\tAdjusted R-squared:  -0.0004795 \n",
       "F-statistic: 0.7728 on 1 and 473 DF,  p-value: 0.3798\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sBu5h7jDEOvBAgQIECAAAECBAhsR8AM0na0tnfuD3J6szvth7tzknMIECBAgAAB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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(avg_pi ~ richness, data = fasta_sumstats)\n",
    "summary(lm(avg_pi ~ richness, data = fasta_sumstats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still no relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = avg_pi ~ richness, data = fasta_sumstats_filtered)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0025421 -0.0006531 -0.0001364  0.0005422  0.0036203 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  3.161e-03  5.532e-05   57.14   <2e-16 ***\n",
       "richness    -2.974e-07  2.342e-07   -1.27    0.205    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.001005 on 449 degrees of freedom\n",
       "Multiple R-squared:  0.003579,\tAdjusted R-squared:  0.00136 \n",
       "F-statistic: 1.613 on 1 and 449 DF,  p-value: 0.2048\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AY0oAENaEADfWfABKm/XvLp6O4TA3Q5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oaqaPpsJ+OTfu+/BxTn4psvahEgX2HBmV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\nBRRQQAEFFFCghAL/D7uUW1mMA80pAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fasta_sumstats_filtered <- filter(fasta_sumstats, richness < 1000, avg_pi < 0.02)\n",
    "\n",
    "plot(avg_pi ~ richness, data = fasta_sumstats_filtered)\n",
    "summary(lm(avg_pi ~ richness, data = fasta_sumstats_filtered))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about median pi?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = median_pi ~ richness, data = fasta_sumstats)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0021088 -0.0006652 -0.0001983  0.0003514  0.0204076 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  2.110e-03  6.649e-05  31.735   <2e-16 ***\n",
       "richness    -1.146e-07  9.556e-08  -1.199    0.231    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.00137 on 473 degrees of freedom\n",
       "Multiple R-squared:  0.003031,\tAdjusted R-squared:  0.0009229 \n",
       "F-statistic: 1.438 on 1 and 473 DF,  p-value: 0.2311\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ECAAAECBAjMVECBNNOJdVkECBAgQIAA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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(median_pi ~ richness, data = fasta_sumstats)\n",
    "summary(lm(median_pi ~ richness, data = fasta_sumstats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still nothing! Something to note: Judging by the p-value, the relationship becomes stronger at broader resolutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = median_pi ~ richness, data = fasta_sumstats_filtered_m)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0021017 -0.0006446 -0.0001470  0.0003811  0.0042700 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  2.106e-03  5.600e-05  37.611   <2e-16 ***\n",
       "richness    -4.490e-07  2.371e-07  -1.894   0.0589 .  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.001018 on 449 degrees of freedom\n",
       "Multiple R-squared:  0.007925,\tAdjusted R-squared:  0.005716 \n",
       "F-statistic: 3.587 on 1 and 449 DF,  p-value: 0.05888\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IFFbAoooIACCiiggAIKlFfAAqm8+7av\nLXuRBU7tayHHFVBAAQUUUEABBRSokoBXsavS3nZbFVBAAQUUUEABBRRQoKOABVJHHgcVUEAB\nBRRQQAEFFFCgSgIWSFXa226rAgoooIACCiiggAIKdBSwQOrI46ACCiiggAIKKKCAAgpUScAC\nqUp7221VQAEFFFBAAQUUUECBjgIWSB15HFRAAQUUUEABBRRQQIEqCVggVWlvu60KKKCAAgoo\noIACCijQUcACqSOPgwoooIACCiiggAIKKFAlAQukKu1tt1UBBRRQQAEFFFBAAQU6ClggdeRx\nUAEFFFBAAQUUUEABBaokYIFUpb3ttiqggAIKKKCAAgoooEBHAQukjjwOKqCAAgoooIACCiig\nQJUEJq7SxlZ8W1MMz1YzmMDtfyru4eYroIACCiiggAIKKPAeAWeQ3kNSuo4Uwd8nD5MHa3mk\n1meBDIRNAQUUUEABBRRQQAEFRpfAiqxOZnQ+MMyrlQLobPIY2ZEsUEvup+8sYpEEgk0BBRRQ\nQAEFFFBg0AL5DJvPsvlMa1NgWASKKpC+y9o9TuZpsZbpy1iWsSmggAIKKKCAAgooMFgBC6TB\nyvm8tgJFFUgP8I67tH3X/xvLMjYFFFBAAQUUUEABBQYrUKoCyXOQBvtjMPqfNyOrOCc5r8Oq\nZizLZFmbAgoooIACCiiggAKVF7BAKu+PwNu1TZuowybWx+rLdljUIQUUUEABBRRQQAEFyi9g\ngVTeffw0m3YvWbfDJmbsHpJlbQoooIACCiiggAIKKKDAqBAo6hykr7N1z5IPt9jK9GUsy9gU\nUEABBRRQQAEFFBisQKnOQRosgs8bXoGiCqQxrOZfyHNkb7JCLbmfvoxlGZsCCiiggAIKKKCA\nAoMVsEAarJzPaytQVIGUN0wBtAO5jeT69Enup8/iCASbAgoooIACCiigwJAELJCGxOeTWwkU\nWSA1vt9kPEhsCiiggAIKKKCAAgoMl0CpCqSJh0vF1+kJgVd6Yi1dSQUUUEABBRRQQAEFRkjA\nAqk4+Fl56bH9fPmZ+7mciymggAIKKKCAAgoooECBAhZIxeAuwMveNYiX9rLrg0DzKQoooIAC\nCiiggAIKDJeABdJwSb77de7m4Zwkx2P2p23OQgeQ+he39uc5LqOAAgoooIACCiiggALDLGCB\nNMygDS/3UMP9vu4+2dcCjiuggAIKKKCAAgoooEDxAh7SVbyx76CAAgoooIACCiiggAI9ImCB\n1CM7ytVUQAEFFFBAAQUUUECB4gUskIo39h0UUEABBRRQQAEFFFCgRwQ8B6lHdlQBq7kYr7kE\neZtcRe4hNgUUUEABBRRQQAEFFFBgxAW2Yw3+Q6bowprMy3tcXHu/Cdw+Wrv/d25nIjYFFFBA\nAQUUUEABBQYikCs357PsigN50mhd1kPsRuueKWa9ZuNlLyevk0XI7CRfaLsMydglZGpiU0AB\nBRRQQAEFFFBAAQVGTKBbM0h/Ygv/Rca22NKp6Mv3Nx3YYswuBRRQQAEFFFBAAQXaCTiD1E7G\n/lEtMBlrtxkZR35CdiQfJPX2And+Srasd3irgAIKKKCAAgoooEDVBDzErhp7fFo280yS6j4X\nZ1iIfIfkwgw7k3q7njspmiavd3irgAIKKKCAAgoooECVBLyKXfn3dorgf5D6BRi24f7NZAzJ\n/d+QV8gRZAbyBnmV2BRQQAEFFFBAAQUUUECBEREo8hykL7BFOXwuF2S4lfyYNLZv8OAZMiX5\nIzmH2BRQQAEFFFBAAQUU6K9Aqc5B6u9Gu1yxAkUWSCez6r+rrf4m3GaGaKPa49zkBzoF1GHk\nTbIysSmggAIKKKCAAgoo0F+BUhVIHmLX393eu8tl5ihfBJuWYmnv2m1mis4n+RnI4XZfJSnU\nLic2BRRQQAEFFFBAAQUqKeBFGsq/2x9jE+dq2MwcYrc8eZh8nmRWKUXSruRoYlNAAQUUUEAB\nBRRQQAEFRlSgyEPs8tpPkenbbOFW9L9EpmszbrcCCiiggAIKKKCAAp0ESnWIXacNdax7AkUW\nSGPZjBvI5aTxe4+ydeuRnH/03TywKaCAAgoooIACCigwCIFSFUiegzSIn4Aee0ouypBC6BRy\nNzmPPE4WJ8uQA2vhxqaAAgoooIACCiigQLUFPAepGvs/5xutSHLO0YNkMnI2+QjZk9gUUEAB\nBRRQQAEFFFAAAWeQqvNj8Dab+vdaqrPVbqkCCiiggAIKKKCAAgMQcAZpAFguqoACCiiggAIK\nKKCAAuUWsEAq9/516xRQQAEFFFBAAQUUUGAAAhZIA8ByUQUUUEABBRRQQAEFFCi3gAVSufev\nW6eAAgoooIACCiiggAIDELBAGgCWiyqggAIKKKCAAgoooEC5BbyKXbn3r1s3vAIT8XKfIquT\nKcgd5ATyELEpoIACCiiggAIKlEDAGaQS7MQBbEI+4Ce2gQvMy1OuJX8k85N8l9T25C6yE7Ep\noIACCiiggAIKKKDAMAlsx+v8h2RWooi2NS+aD/dv1pL76bP1T2AqFrubnEtmanrKV3j8BsmX\n8NoUUEABBRRQQIEqCnyAjc5n2RWruPFuczECRRZIR7PKL5EfkzVryf30ZczWt8CeLDKetCtg\nv8/Yw8RDVkGwKaCAAgoooEDlBCyQKrfLi9/gogqkr7LqL5AlW2zCUrWxHCZm6yzwL4b37rDI\ntIy9RfyrSQckhxRQQAEFFFCgtAKlKpA8B6m0P6fvbNhu/PdAcn2LzbyuNvbtFmN2vVvggzwc\n/+6udz16lkfPkVnf1esDBRRQQAEFFFBAgZ4TsEDquV3W7xWejiUXJGd0eEbGskyWtbUXyOFz\n87Ufft/0jE1DspxNAQUUUEABBRRQoIcFLJB6eOf1ser1q9W93mG5+lh92Q6LVnroVLY+F2PI\nxRpatW/S+Qi5ptWgfQoooIACCiiggAIKKDAwgaLOQcr383y9w6pkzO/w6QBUG6p/59FFPM7h\ndvWWPzDsSHJ1wE3rnd4qoIACCiiggAIVEyjVOUgV23ejdnOLKpD+hy3OzMYcLbY8fRnLMra+\nBeZkkavIK+Sf5CRyH3mRZHbJpoACCiiggAIKVFXAAqmqe77A7S6qQMoP6wVkAvkSmb2W3E9f\nxrKMrX8CY1hsXfITcijJDNwsxKaAAgoooIACClRZwAKpynu/oG0vqkDK6k5C9idPk3yBV5L7\n6cuYTQEFFFBAAQUUUECBoQhYIA1Fz+e2FCiyQKq/Yb7EdKFa/ELTuoq3CiiggAIKKKCAAkMV\nKFWB5Aflof449M7z32RVx/XO6rqmCiiggAIKKKCAAgp0X8DLfHff3HdUQAEFFFBAAQUUUECB\nUSpggTRKd4yrpYACCiiggAIKKKCAAt0XsEDqvvloeMdpWIkDyF0kXxb7MPk9mZfYFFBAAQUU\nUEABBRSorIDnIFVv1+f7fC4kuZrdL8kdJJf/znf53EA2IJcSmwIKKKCAAgoooIACCigwIgLd\nuIpdfcMu485FZIp6R+023/HzK/I4yQyTTQEFFFBAAQUUUECB/giU6ip2/dlglyleoFsF0sps\nSq5mN3ebTRpL//1klzbjdiuggAIKKKCAAgoo0CxQqgLJc5Cad2+5H6/E5t1IUgS1am/QeRbJ\ncjYFFFBAAQUUUEABBSonYIFUrV2e6v7VPjb5FcaznE0BBRRQQAEFFFBAgcoJWCBVa5ffzOYu\nQabqsNmrMZblbAoooIACCiiggAIKVE7AAqk6u3wdNnUTkn2eq9V9jUxKGtvWPPgIOaax0/sK\nKKCAAgoooIACCiigQDcFirxIQw6XO57k/KITyVG1+zmUbjxZlMxHfkxyAYediE0BBRRQQAEF\nFFBAgf4K5PNmvkJmxf4+weUU6EugyAIp33X0MFm8YSWW4v6VJD/I9Yzj/meITQEFFFBAAQUU\nUECBgQhYIA1Ey2X7JVBUgTQH755ZoU+QfM/R+uQQcgzZi3ycPEX2JjYFFFBAAQUUUEABBQYj\nUKoCyXOQBvMj0DvP+Rirmi9+zaW98wWxJ5EFSWaNNiK5pPe9ZEliU0ABBRRQQAEFFFCg8gIT\nV16g3ADTsXmPkTPJa2QBMoHU24bcSdE0fb3DWwUUUEABBRRQQAEFqixggVTuvX8fm7cIyQUZ\nFiJPksZ2Gg8yu5RLf09LniU2BRRQQAEFFFBAAQUqK+AhduXe9eexeTn36E7SXBxly3NJ7xRH\nr5O1iE0BBRRQQAEFFFBAgUoLWCCVe/e/xOY9SJYje5BJSL2tw52zySnkfjIrsSmggAIKKKCA\nAgooUGkBC6Ty7/6b2cSLyG4ks0jXkZyHlPOS/ka+TOYkjecm8dCmgAIKKKCAAgoooED1BDwH\nqfz7PEXQr0kOpct3Ic1LniHnk8wu7UjeJhcQmwIKKKCAAgoooIACCigw4gLbsQa59PYUBazJ\nRLzm5SQXY8iFGhrbljx4lXytsdP7CiiggAIKKKCAAgoMQOADLJvPsisO4DkuqkBHgSILpLxx\nLuOd7zzKl8ZeRnLe0d0kxVEOvbMpoIACCiiggAIKKDBYgVIVSB5iN9gfg9563tOs7rpkFfJx\nMg25kJxMJhCbAgoooIACCiiggAIKIGCBVK0fg8weJTYFFFBAAQUUUEABBRRoIeBV7Fqg2KWA\nAgoooIACCiiggALVFLBAquZ+d6sVUEABBRRQQAEFFFCghYAFUgsUuxRQQAEFFFBAAQUUUKCa\nAhZI1dzvbrUCCiiggAIKKKCAAgq0ELBAaoFilwIKKKCAAgoooIACClRTwAKpmvvdrVZAAQUU\nUEABBRRQQIEWAhZILVDsUkABBRRQQAEFFFBAgWoKWCBVc7+71QoooIACCiiggAIKKNBCwAKp\nBYpdCiiggAIKKKCAAgooUE0BC6Rq7ne3WgEFFFBAAQUUUEABBVoIWCC1QLFLAQUUUEABBRRQ\nQAEFqilggVTN/e5WK6CAAgoooIACCiigQAsBC6QWKHYpoIACCiiggAIKKKBANQUskKq5391q\nBRRQQAEFFFBAAQUUaCFggdQCxS4FFFBAAQUUUEABBRSopoAFUjX3u1utgAIKKKCAAgoooIAC\nLQQskFqg2KWAAgoooIACCiiggALVFLBAquZ+d6sVUEABBRRQQAEFFFCghYAFUgsUuxRQQAEF\nFFBAAQUUUKCaAhZI1dzvbrUCCiiggAIKKKCAAgq0ELBAaoFilwIKKKCAAgoooIACClRTwAKp\nmvvdrVZAAQUUUEABBRRQQIEWAhZILVDsUkABBRRQQAEFFFBAgWoKWCBVc7+71QoooIACCiig\ngAIKKNBCYOIWfXYNj8D0vMzYfr7UVP1czsUUUEABBRRQQAEFFFCgQAELpGJwF+Bl7yrmpX1V\nBRRQQAEFFFBAAQUUKErAAqkY2bt52RRJ/Z1B2oxl9y1mVXxVBRRQQAEFFFBAAQUU6K+ABVJ/\npQa+3D0DeMqjA1jWRRVQQAEFFFBAAQUUUKAgAS/SUBCsL6uAAgoooIACCiiggAK9J2CB1Hv7\nzDVWQAEFFFBAAQUUUECBggQskAqC9WUVUEABBRRQQAEFFFCg9wQskHpvn7nGCiiggAIKKKCA\nAgooUJCABVJBsL6sAgoooIACCiiggAIK9J6ABVLv7TPXWAEFFFBAAQUUUEABBQoSsEAqCNaX\nVUABBRRQQAEFFFBAgd4TsEDqvX3mGiuggAIKKKCAAgoooEBBAhZIBcH6sgoooIACCiiggAIK\nKNB7AhZIvbfPXGMFFFBAAQUUUEABBRQoSGDigl7Xly23QArreclEZDx5ndgUUEABBRRQQAEF\nFOh5AWeQen4XdnUDUhDtQR4hd5M7yRPkf8kUxKaAAgoooIACCiiggAIKDFlgO17hP2Q0Fxkp\npv9GniQ7krnJB8nnyT3kajKa15/VsymggAIKKKCAAgoUIPABXjOfZVcs4LV9yYoK9EKB9DX2\nzbNk4Rb7aAb6UiQd3GLMLgUUUEABBRRQQIFyC1gglXv/jsjW9UKBdBMy+3TQ2YKx50n+B7Ep\noIACCiiggAIKVEegVAWS5yBV5wd3KFuai3ksRi7o8CIXMjYVycUbbAoooIACCiiggAIK9KSA\nV7Hryd02qJXOBRZWIPORZ8il5DnSn/Y2CyWdZofG1l7orf68oMsooIACCiiggAIKKKCAAu0E\nij7E7pO88X3kTfIAeZG8TH5A+juLeCXLdjrHKOcoPT6A12NRmwIKKKCAAgoooEAJBEp1iF0J\n9kcpNqHIAml9hFIYHUSmr2lltidXn3uaHF7r6+tmUxZ4lazWYsEF6XuC7NlizC4FFFBAAQUU\nUECBcgtYIJV7/47I1hVVIE3K1kwgBzRtVS7H/THyXZJD4loVPXS/p+X7jlIk5TbPX5XsTXJ1\nu1NJDuOzKaCAAgoooIACClRLwAKpWvu7K1tbVIG0HmufQ+lSEKXlcLq9yEvkNfIUyTXr891G\ny5P+tMwk/YtkVirnJd1M8r1I/T1Uj0VtCiiggAIKKKCAAiUSsEAq0c4cLZtSVIG0Mxt4Q8NG\nHsX9HFa3JZmk1p/ZoEfIK2SlWl9/blIQeZGP/ki5jAIKKKCAAgooUG6BUhVIfsAt9w/rC2xe\n/byjdbifwijfcHwNqbccFpfHj5IjyWIkM0N9tSzTn+X6eh3HFVBAAQUUUEABBRRQQIF3CRQ1\ngzQf75IiZhXyZ/IX0thyjtKDZFcyM8lhcwOZRWJxmwIKKKCAAgoooEDFBUo1g+R5I+X+ab6X\nzTu2lo9we3XD5uYH+eja499xm0t0P0AWrvV5o4ACCiiggAIKKKBA5QQ8xK78u3wnNjFXmFuL\nbEFycYZZSC7zPRnJhRzyvUhjyDQkF3CwKaCAAgoooIACCiiggAIjJlDUIXb1DcpM4d/I8+QW\ncinJ1eymJfW2NndyiN0H6x3eKqCAAgoooIACCijQD4FSHWLXj+11kS4IFF0gZRNyjlGuYHco\naT60cl76xpPfkjK2XKjiQ2S6Mm6c26SAAgoooIACCoywgAXSCO+AMr59NwqkuK1GniHXke+Q\nL5FfkFzt7hySQ+7K1PLdTpeQXKgi3/eU2wvJ0sSmgAIKKKCAAgooMDwCFkjD4+irNAh0q0DK\nW85GDiL5fqRcxOFcshVpnlWiq6fb+qx9zrf6E1mOZAZtBXI8eZWsQ2wKKKCAAgoooIACQxew\nQBq6oa/QJNDNAqnprd/zMIXSuuQAcjDZntS/S4m7PdFybtVTZL82a3sg/blq31Rtxu1WQAEF\nFFBAAQUU6L+ABVL/rVyynwKjoUBKsfBDkvOUcrGG+0kOR5tAniVbkF5pO7CiWe+xbVZ4Evof\nIznE0KaAAgoooIACCigwNIFSFUhe5ntoPwxlefYybMg/yEzkEZLD0hYja5DMtuS8pT+S58iZ\nZLS3j7KCuVLfG21WNIfeXUGWaDNutwIKKKCAAgoooEBFBSyQKrrjGzY75+ak6MmMS87NyRfK\n1r8L6ZPcP5nsTg4huaBDLxRIb7Ge7WaPGHqnZTzL2RRQQAEFFFBAAQUUUGCUCWzH+uQqa1OM\nwHplhuhmcgPZo8X7f52+p8g8JOuYAmq0t1x04knSznNqxnLY4ObEpoACCiiggAIKKDA0gVId\nYle2K5cNbddW89mZJfoDmZ3c04LgWPqmrY3n0LQsN9rbiazgy+SXpPlnPI8PIznX6lRiU0AB\nBRRQQAEFFFDgvwIeYvdfisreyRXqct7Ro2TuFgr5jqQXyXwkFzfIcqO9vcIKbkbOJguSX5Hx\nJNvwDbII+ThJwWdTQAEFFFBAAQUUUOC/As1/Xf/vgHcqIzCeLV2U5CIN25IUQY1tDh7kkLRV\nSK5sdyPphfZvVnIpMp4cQfL4N2QcWZJcR2wKKKCAAgoooIACCigwCgVG8hyknGP0BFmIPED+\nTnJIXb2lqHiY5NLfm9Q7e/B2sh5cZ1dZAQUUUEABBRToBYFSnYPUC+BVWMduF0jzg7oWWYJk\nxiizKzeRT5HbyXMkhdIt5G2Sq9ttT2wKKKCAAgoooIACCjQLWCA1i/h4yALdKpCWZ02vJrka\nXWaEcjuB7EhOqT2+g9u7yesk5x7l8LReuDADq2lTQAEFFFBAAQUUGAGBUhVInoM0Aj9BI/SW\na/C+F5PMEOUiBblAx8zkUHIwuYbkEt55fDjJ4XQzkswcpYiyKaCAAgoooIACCiiggAJdESh6\nBilV/XiS4qdV24jOfGnqh1sN2qeAAgoooIACCiigQAcBZ5A64Dg0OgXWYbVmJTm3aC+yHmm8\nxHu+D+hy8iViU0ABBRRQQAEFFFCgsgIeYlf+Xf9RNvFIMpasTlIsnUxyAYaM1dsV3Fms/sBb\nBRRQQAEFFFBAAQWqKGCBVO69Pi+bdwF5gNxFVq0lF13I9wBlLMukTUFeeeee/1FAAQUUUEAB\nBRRQQAEFRlCgqHOQ/sI25cIM+cLUXLHuQ6TeUhxnLMtkdmk82Y3YFFBAAQUUUEABBRQYiECp\nzkEayIa7bHECRRRIKXpeJhvWVvssbnOJ7+lqj3OTsSyTq9Y9Thq/IJaHNgUUUEABBRRQQAEF\n+hQoVYHUeKJ+n1vuAj0lkEt0T0burK31VtyeR24mvya3kZyDlGU2J+uTZ4lNAQUUUEABBRRQ\nQIHKClgglXfXP8Om5dLdc5Bx5AmyIvkmSUE0D8ns0dtkGZIvh7UpoIACCiiggAIKKKCAAiMu\nUMQhdtmoc8gJHbYuY1nGpoACCiiggAIKKKDAYAVKdYjdYBF83vAKFFUgZWboNbI3mahhlXM/\nfRnLMjYFFFBAAQUUUEABBQYrUKoCyUPsBvtj0BvPu4bV3Iz8kdTPQcqar01mIhnLMjYFFFBA\nAQUUUEABBRRAwO9BKv+PwT/YxAXI4SRXqUtyP30ZsymggAIKKKCAAgoooEBNwBmkavwo5AIN\nP2vY1Cm5vzFZguQiDVeRU8nrxKaAAgoooIACCiigQGUFnEGq3q7/GJucK9b9nMxPPkR+R24n\n+UJZmwIKKKCAAgoooIACCigwogJFXaSheaOWpOMVkuJo0obBqbmf85SeInM39HtXAQUUUEAB\nBRRQQIG+BEp1kYa+Ntbx7gh0q0DKF8We2GaTMpt4GTmmzbjdCiiggAIKKKCAAgq0ErBAaqVi\n35AEulEgTcMa5otjV+6wpp9l7LkO4w4poIACCiiggAIKKNAsUKoCyXOQmndveR8vyqZlf+fS\n3t8mi5Pmdi8dOdxuiuYBHyuggAIKKKCAAgooUAUBC6Qq7OX3ve8bbGYOr/sPWZFsSW4gfyG5\nol29zcOdF8lL9Q5vFVBAAQUUUEABBRRQQIFuCxR5iN0ObMyrZFtyMTmOpOWKdXeRs8gYsgi5\nmvyV2BRQQAEFFFBAAQUU6K9AqQ6x6+9Gu1yxAkUVSDlcLucU7VRb/eW4fY3sS8aSXOY7332U\nq9dldil5k5xM5iQ2BRRQQAEFFFBAAQX6ErBA6kvI8QELFFUgbcqaPEsavxB4fR4/TR4id5Bc\nuOEN8jBZi6xBLiWPknmJTQEFFFBAAQUUUECBTgIWSJ10HBuUQFEF0q6szXUt1mg6+n5BUhzd\nRlIoTU7qLQXVP8n59Q5vFVBAAQUUUEABBRRoI1CqAqlxZqHN9trdwwKPs+6zk5xjlMPn6u0Z\n7qT/eJILMkxPXib1lsPsUlzdTBYgdxObAgoooIACCiiggAKlF/AqduXexbly3bTkMy028yP0\nXUNyGN4ZLcZvoS+FVJazKaCAAgoooIACCihQCQELpHLv5sfYvJ+R35PVmjY1h9d9ndxP/tQ0\nlof52ch0ac5PsimggAIKKKCAAgooUAkBD7Er/27em03MLNJF5GKSmaFZSA6de4WsSloVQWvR\nPxm5itgUUEABBRRQQAEFFKiEgDNI5d/Nb7OJmSlaluSQug+S58juJAXQRqS5pYD6NTmG5Dwm\nmwIKKKCAAgoooIAClRBwBqkSu/mdjbyW/yaNLecY5fC7NckJJJcEX4bsQu4lOxObAgoooIAC\nCiiggAIKKNBVgaIu892fjcjM0ikkxVIOtbuVfIdMQmwKKKCAAgoooIACCvQl4GW++xJyvKcE\nrmZtW13lrqc2wpVVQAEFFFBAAQUUUGA4BDwHaTgUfQ0FFFBAAQUUUEABBRQohYAFUil2oxuh\ngAIKKKCAAgoooIACwyFggTQcir6GAgoooIACCiiggAIKlELAAqkUu9GNUEABBRRQQAEFFFBA\ngeEQsEAaDkVfQwEFFFBAAQUUUEABBUohYIFUit3oRiiggAIKKKCAAgoooMBwCFggDYeir6GA\nAgoooIACCiiggAKlELBAKsVudCMUUEABBRRQQAEFFFBgOAQskIZD0ddQQAEFFFBAAQUUUECB\nUghYIJViNw7LRozhVbYml5FnyWPkFLISsSmggAIKKKCAAgooUAkBC6RK7OY+N3IiljiR/Ipc\nSVIo7UJeJ5eQbxCbAgoooIACCiiggAKlF7BAKv0u7tcG7slSa5B9yfTk82QhcjDZqna7Mrc2\nBRRQQAEFFFBAAQUUUKBwge14h/+QKQp/p/e+wVi6XiavkjfJW+QN8nYt6X+IXERsCiiggAIK\nKKCAAgo0C3yAjnyWXbF5oBcfO4NU3F6bkpeetp+ZrLjV6POVf84Sef/8LBxOziQpik4jr5AU\nT5lVWo2kkLMpoIACCiiggAIKKKCAAgMSWIClMwOTSnog6dYM0sysV843eqJh/e7n/m9ILs6Q\nw+vS1iKZUcpymVVKliE2BRRQQAEFFFBAAQXqAqWaQcqVy2zFCCzKy+bwtf60jVlob5JZp5f6\n84QhLDM/z72YPEVuJOuQWcgtZDFyENmd1Nvl3JmNZJlzSIqkz5Jutbzvt8knSGay7iMnkCPI\n68SmgAIKKKCAAgooMLICKZBeIyuRXPDLpsCQBXLoWmaaip5BSkF8NTmTTEJSDKUAynvfVrt9\nhtsZSL0dz50URSlGtiQTSLfacrxRZq9SyO1GvkAOIOn7F8khjDYFFFBAAQUUUECBkRUo1QzS\nyFL67nWBbhVIa/CGKXZmq73xntzeRFIgPU5yWGAKoO+SqclW5BWSsQdJZroy89SNlvd/hBxF\nchnyxjYzD24mJzd2el8BBRRQQAEFFFBgRAQskEaEvdxv2q0CaXcYM4NUb0tzJ0VRip9f1+7n\ncZKi6QWSmaPbyBEkF3S4hHSjfYs3yXlR+R+uVVuCzqxjDmW0KaCAAgoooIACCoycQKkKJK9i\nN3I/SCPxzhPzpplBqrdrufM3kp+DFGnXkUfJZSTn/NxJniSZccqhdl8lvyfdaKvzJn8nKdBa\ntRvovJus1mrQPgUUUEABBRRQQAEFFOhdgW7NIK0HUQ6Zazx3Zyoen0tyYl2uWJfZo9xPYZLL\nfadA2o/kMLsc0tatC3v8k/f6EWnVciW975EcgvdXMhOxKaCAAgoooIACCoyMQKlmkEaG0Hdt\nFuhWgTSWN76LHE0aC53c/wzJrEyKpBRRKZRyP4exPU9+SDID1a12KG+UIqmxpbDLrFLWK7Nd\n+WLbXLDhRbIDsSmggAIKKKCAAgp0X8ACqfvmpX/HbhVIgVyWpOA5g+TwtMy+LEEOIyk4tiZp\nuRBCxpcjk5But/r5UevU3jiHAeb8p1vJYiTnQz1AJiU59C8zXl8iNgUUUEABBRRQQIHuClgg\ndde7Eu/WzQJpfURPJU+RzBJlhii5hqxNRlP7CSvzEtmJfJmksMvhdYeTFESN65uLOmSbJic2\nBRRQQAEFFFBAge4JWCB1z7oy79SNAikzLbkgQ84v+gvZi+SCCykqxpH5yGhsX2el6sVc1j3F\n3C1kVdLYsn0pplIA2hRQQAEFFFBAAQW6J2CB1D3ryrxTNwqkI9DMIWkfalKdmsfnklzKOz/c\ni5MUT78l+5ClyEi3rNft5EjykQ4rcwdjOdzOpoACCiiggAIKKNA9AQuk7llX5p2KLpAyO/QW\nWb2N6DT052p1F5EcdncVySzTlSTPO5ZkhmYk2xm8+S86rEAuIPEcycUmbAoooIACCiiggALd\nE7BA6p51Zd6p6AJpeyTvI5OQVckGZFHS2DKDlKvXrdLYyf1lSWae/tjU3+2HX+MNHye5kl2r\nthWdOcSu3Xir59ingAIKKKCAAgooMHQBC6ShG/oKTQJFF0i5RHfO48k5PJkhyvcb5Vye68ny\nJFeFq88ccfc9LVeUy0xSrmg3Ui3FXc49uojMRBrbJ3iQCzjs0djpfQUUUEABBRRQQIGuCFgg\ndYW5Wm9SZIGU2aJc8S0F0GXkaHIvyeFo55MUS78lz9ZuuWnZLqb3gJYj3euck7fK9x+9QE4k\nvyFXkBRvB5IxxKaAAgoooIACCijQXYFSFUg5b8NWXoHM/OTKddnP+TLVU8jPyURkb/JdknN7\nPk+mJH8h7VoO0ftgu8Eu9T/I++SQv43J2mQ6ciHZnmR2yTYwgVyw49NkdvIoOY3cRGwKKKCA\nAgoooIACCoyoQFEzSGezVZktyvcebUXeIN8hOVwt7a/kBvI2yYxSp1bEDFIuDrErOYmcTjIL\ntAixFSuQgvlQkpm3HGYZ/2tIfg5y6ff6zwd3bQoooIACCiigQJ8CpZpB6nNrXaArAkUUSLnq\nXAqizKx8r7YVX+T2SZJD1FIYPU3yoTiH1+W2+QINdL3TluS/+TCd85WGq63MCz1G7ie/Jj8j\nV5I3ybeJrTiBFEe54MWaTW+xIo8nkGOa+n2ogAIKKKCAAgp0ErBA6qTj2KAEiiiQcjhcLsRw\nOflxw1pNwf0cVrUzyWxSlsnhVReQR8hKpLEtzYMUMcc1dg7x/lw8/xmSc4jyP1Rj+ywPcs5U\nDvuzDb9ADqtLsdtcHNXfKYcwZnyZeoe3CiiggAIKKKBAHwIWSH0AOTxwgSIKpLGsxivkT+RO\nkvOOmtsGdOQiDZk9WpD8rnb/X9ymILqM5MNyXiMzUvU2hjspcvL9Sjlca6AthVHe4/1tnrgX\n/bm0eN6nrzY1C2RmKx/oG9exr+dVdXwPNjyH1XVqlzK4f6cFHFNAAQUUUEABBRoESlUgtfuA\n2rC93u1RgRxe93cyD5mR/II0FhzZ93uSzNakALqLbEeWIjl36SWSWaUVSA7NSyGV5+Twtwnk\nfnIPyaFaPyWTkf62T7LgUSSFWat2JJ1zklx+vF2bloG8xhMkxdbVJIcP/ox4Dg0Ibdrs9Ge/\ndWoZn63TAo4poIACCiiggAIKKFCkQAqTHOo2xTC/yXy8Xs4zOpfkPKOciL8LyfvdQt4iV5Ep\nSV8txdWJJK+Xw/PmJ3ORrcl4kiJlctKflu8s2rDDginEUjyt3maZaejP+ifrkRRnU5HPkVzp\n7gIyltjeK/B9uvJz0KldyGDjYZmdlnVMAQUUUEABBRQo1QySu3N0CBRVIGXrcoGFceRlkuLh\nRZLiI49/RPp7iNz2LPscyTkszW0mOu4jBzUPtHl8M/27txlLd97jP2Qe0qodRucdJIfXNbc5\n6HicfLt5wMfvCCzOf7P/c0GGVi3juVDGSq0G7VNAAQUUUEABBVoIWCC1QLFraAJFFkhZs8ym\nZMZmH5LvQco5QClm9iBLkP60G1lo3w4LfoGxFFD9mbnZi+VyiF6rAofu9/2RZGarVcvhc7kK\n32atBmt9Kb5y3pWttcDv6Z5Alm0aTnGUQveEpn4fKqCAAgoooIACnQQskDrpODYogaILpPpK\nfZc7OZfoXnIGuZZkNuF40unwvokYz+F4q5N2rX7VvIXbLdDQn0P6biNXkAUb+nOY3C9J1nG5\nhv7GuwvxILNLeb92LbMfWSb/s9reK5Ai8xiSfXpJ7f6Ftccpjvp7qCSL2hRQQAEFFFBAgXc+\nc+WzV7sjVCRSYMAC3SiQdmOtXiKfb1q7HIJ3N0nB1K7lnKAcdvWxdgvQPyfJ/xgLdFimcWhW\nHpxPUqDdRK4kWb/MLK1B2rW5Gcj7zN9uAfrXJlnfrLetvcAyDO1PjiY/Jh5WB4JNAQUUUEAB\nBQYs4AzSgMl8Ql8CRRdIM7ACKT62abMiC9KfWZsN2oynOxdhyOF57dpXGXiCZLZpIG1ZFt6V\n7EE2JJnd6NRysYiHSAq+du1XDFzWbtB+BRRQQAEFFFBAgWEVsEAaVk5fLAJFF0hf5D1y4YJO\nMyo5tOoo0q5tzkCKqJVbLJDZnMfIXi3Giuj6Bi+a851ySfLmti4db5AUWzYFFFBAAQUUUECB\n4gVKVSBNXLyX7zAKBGZnHXLeUQ5na9dymF1mc9q1FFApjs4jh5LTSQqRNUmuGHcFyWFa3WiZ\nIUpxlFmiI8gFZCxZn2xJ9ienEZsCCiiggAIKKKCAAgr0oEDRM0hfwmRCC5fM/KSoOYM8QK4n\nM5FObQsGryEptnIu0B0k34s00EPreMqQ2+d4hUtIZpOeJmeTdYhNAQUUUEABBRRQoHsCpZpB\n6h6b79RJoOgCKTNIr5NPN6zEDrW+q7k9jLxGHib5QtlPkr7aWBbo63yhvl7DcQUUUEABBRRQ\nQIHeF7BA6v19OOq2oOgCKRucmaKnyOokh6LlKm+ZWcrV5zIjdCXJIZf7k5fJosSmgAIKKKCA\nAgoooEBfAhZIfQk5PmCBbhRIOQQu5+7k0LgXyY3k7+QVcilpPLTuLB7nnCObAgoooIACCiig\ngAJ9CZSqQOp0VbO+IBzvLYG3WN2vkxw+NwV5iDxINiOrkSdIvR3Jnf4cZldf3lsFFFBAAQUU\nUEABBUoh4FXsSrEbB7QRj9SWztXecmGDVi3LTE1ynlGuVGdTQAEFFFBAAQUUUKASAhZIldjN\n79rIzBrlMLucY3TZu0b+/4OMTSBzkxRSHyI5LymH4h1H8n1INgUUUEABBRRQQAEFSifgIXal\n26V9blCuUncu2ZOMabH0ZPTtSvK9SLeRDUgOv8vPSi70cDtZnNgUUEABBRRQQAEFFFBAgUIE\nunGRhsYVX4QHz5M/kBkaBubh/gXkcZLLguc7jxpbzl06nuQQvMaLOjQu430FFFBAAQUUUECB\nagmU6iIN1dp1o3dru10gRWJpki95zfcf5fZRkkPvxpHHSGaYWrWxdN5CDmw1aJ8CCiiggAIK\nKKBA5QRKVSB5iF3lfn7/u8HXcm8tkkPmFiA5x+hKMh2ZmTxDWrVctOFosn6rQfsUUEABBRRQ\nQAEFFOhlAQukXt57Q1v3SXn6mSQzSAuS+cjKZBuSw+sOJRuTVi0XevAQu1Yy9imggAIKKKCA\nAgr0tIBXsevp3Tekld+eZ3+Q5Hykxtmi+3mcadJDyC/IhWQNMiuZQHKO0kIkRZJNAQUUUEAB\nBRRQQIFSCVgglWp3DmhjNmHpo0hjcZQXyPlFOSdpcjIbSVGUw+pyOyd5i+RcpZ8SmwIKKKCA\nAgoooIACpRLwELtS7c4BbcwcLH1Xm2d8nf5tSS4DfizJle7y3Ui5sMNTZBpiU0ABBRRQQAEF\nFFCgdAIWSKXbpf3eoFypbq42S+fwuf/Uxj7P7dnk3yTfi/QQ2YXsQ2YnNgUUUEABBRRQQAEF\nSiNggVSaXTngDTmdZ2xDcihdc9ucjnzX0dMks0m5ut3fyRpkdZILOKRQymF63W7z84ab1TJf\nt9/c91NAAQUUUEABBRRQQIHiBbbjLTJjky9iLaLl6nT5gtccUpfk/trkXpIr2eXS3o3tIh68\nSbZt7Gy6/zce5yIO3Wq5oETWNU5PkCdr9//B7SzEpoACCiiggAIKKDAyAn4P0si4+66DFPg+\nz7uETERyYYUk988hp5C5yX3kRHIEuZ6sSlJI/Z60azMz8Gy7wQ79mfXZmuxI1iQTk75azoG6\nnOTcpyVJLjE+I1mGZD0uI81FHl02BRRQQAEFFFBAAQUU6EWBomaQNgLjDbJhC5T0ZSzfdbQF\nOZwcR/YmmTl6hcxOWrUF6cwM0yqtBtv0TUV/Xj9XwHuI3EryfUt3kxRkndpvGLyZTNZiocy6\n3UZ+2WLMLgUUUEABBRRQQIHiBUo1g1Q8l+/QH4GiCqRrefOfd1iBjGWZ5par111Bcu5RZm8a\nWw51u4Hkwg39bWNZ8FKSy4ev0PCk6bmf4ufVpv6GRd6Z7XqOjs0bO5vub8njp0jW26aAAgoo\noIACCijQXQELpO56V+LdiiiQpkIu5+ssT+Yk+5Gcw5PkfvoylmWybHNLIZTD7VJ4HEZ2J5ll\nSrGS4inFTX/bDiyYCz7ke5VatWPoTNHVquU5WccFWw3W+hatLZND72wKKKCAAgoooIAC3RUo\nVYH0/u7a+W5dFJi89l45DG4c2ZDkkLYUPjuRXKChfhW6+rJ0/bflKnbLke+SFFNZdmbyDbIq\nScHT3/Z5FjyCPNzmCfvQ/1GSQqe5vVjr6FSQZSxFVH3Z5tfwsQIKKKCAAgoooIACCvSQQBEz\nSCl+M9uTc4X2IVeTFBC5+lxmgh4kOR8oRdNsZCBtEhb+JEmxtA2Zh3RqOefoC50WYOwFskGb\nZa6h/5A2Y+nODFdmtWwKKKCAAgoooIAC3Rco1QxS9/l8x1YCRRRIeZ8HSIqkm8mlZBaSYugM\nkhmXFEj1HMf9aUlfbT0WSMHzCrmpdj+vcSRpdREFut93C9k1d9q0XGjhLdLuog8pnFLobUqa\n2xZ0ZOwTzQM+VkABBRRQQAEFFOiKgAVSV5ir9SZFFEgpVlJ05PuCcrW6dUkOkRtPUtjkggm5\nn2VS7NxKch5QipV2bR0GUoz8hEzZsNBq3L+XnE1aXSjhYPqvJe0O6fwqY8+QSUi7thsDWdcU\nd7uQFFx5v/R9k9gUUEABBRRQQAEFRkbAAmlk3Ev9rkUUSDnXKLNEF5NxJLM8KZRSUOT+CWR5\nkmWeJVuR8SQXcGjVJqLzPvLzVoP0zUtyCF9mdJpbLheec5Z+TSZuGlyNxzm87ltN/a0eLk3n\nUSSFXPJ7sgSxKaCAAgoooIACCoycgAXSyNmX9p2LKJBSiKRguZN8l8xDcjntfUmKp7TPkCyT\n2aMda3mQ21ZtZTozezRDq8Fa32+5/Ueb8VXoz2xWirUDyfdJlk3BdgixKaCAAgoooIACCvSm\nQKkKpHaHPPXmrnGtGwVSzJxMZiQLkbEkh7CliHmEpIBK4XQKmYvkvKJryByk1VXt5qP/YfIU\nadduZiDLtWqX0bkwOZYsSXLI3+MkM0i7EJsCCiiggAIKKKCAAgoo8I5AETNIeeEUO8+SHFq3\nDsnhdIuSXKghszePkr1Izv/JOUVZJsu2Kpw3oD+zTSms2rV9GcjFIGwKKKCAAgoooIAC1RH4\nAJuaz5krVmeT3dKiBYoqkLLei5DnydvkNZKiKLNLuWjCniR9ef+0I8kF79x773+moSsXc/jc\ne4fe6cn/GHeRFFw2BRRQQAEFFFBAgeoIWCBVZ193bUuLLJCyEdOSzOzkfJ8USi/X7ufx5WQH\n8qda38bctmv7MJBD7JZrWiCH7h1HHibTNY35UAEFFFBAAQUUUKDcAhZI5d6/I7J1RRdI2ahc\n4jszPJkFSpGU+5lFqhdN6ctMU2aU9ietDrNL3+9IZqBy7lJmiw4mD5Ccw7QEsSmggAIKKKCA\nAgpUS8ACqVr7uytbW3SBNIatuJ5k9udGksIoBU2Ko1yw4XaSy3BnpilXtstyh5F2bQ0GjiSX\nkHwv0W5kKmJTQAEFFFBAAQUUqJ6ABVL19nnhW1xkgTQpa59CJifOnUR2Jb8kKY5SCH2YjCX5\njqNvk7RVSWaU8r1DNgUUUEABBRRQQAEFOgmUqkDKIVO2cgscyuYtRu4mm5IcEncTyczRheR0\nkh/q3K5C0nK+0sWk3QUZsoxNAQUUUEABBRRQQIHSCVgglW6XvmuD5uHRl8lfSS73XW/zcuc2\nsiXJ7NFXyEsks031dgt3spxNAQUUUEABBRRQQIHKCFgglXtXr8nmPUxOIzmUbmqSlvONZiW5\nYMPfyNpkJXIrqbdZuPNM/YG3CiiggAIKKKCAAgpUQcACqdx7eRo270lyHnmMHEDSziUpmHKO\nUcbnIyuTo0laiqN1yTl5MMi2EM/7GfknOZvsS2YnNgUUUEABBRRQQAEFFFCgo0BRF2nYgHfN\noXOTk9XJq+SPZBHyZ5ILM4wjuWz3PiSF0YzkMnI1GWwB/TWe+wa5kuxPDiQ57+lFkqvk2RRQ\nQAEFFFBAAQXKI/ABNuU/ZMXybJJbMtICRRVIk7FhuRhDZm8mIt8nj5P8AOcqdfXbfHFs7ie5\nul2WSRHVqU3MYNY7F3N4iOTwvFwdb2uSgmsb0tjG8CDvn+9ZWrJxwPsKKKCAAgoooIACPS1g\ngdTTu290rnxRBVK2diOSguVR8hw5nhxL7iMpkpJcsOE6kkPhDid3kDvJTKRVy4zUhSTnKP2U\nfIHsTK4iea+TSLuWc57+3m7QfgUUUEABBRRQQIGeE7BA6rldNvpXuMgCaRI2/36SCzJk9uZu\nkiva5X6Koxz2thdpbFPy4BpycmNnw/0juZ/XmbOhL3dnJpmFSiGWw/VatQ3pzLpkhmotsigZ\nrjY9L7QDOYwcTD5LxhKbAgoooIACCiigQHECFkjF2Vb2lYsskFIwPE5yblF/J/DSAABAAElE\nQVQKklzSe1NyAjmVbEFSsKS4aGwr8CDFTnMRNAd9b5E1SHNL0ZPnZAZqv+bB2uNvcJtlksw2\n5fYukvOlhtI25smZ0crhfn8luXJfCrWcY/VhYlNAAQUUUEABBRQoRsACqRjXSr9qkQVSCoVD\nyUdJZoRSNGTmKDNIZ5FpSC77naKpuWXZTzd1fo7HKbhatcw8vU7+QHJuUnPbnI4UV5m1mrs2\nmALspyTF0ta1voHerMET3iA5xynnRtXbtNw5kTxKZq13equAAgoooIACCigwrAIWSMPK6YtF\noMgC6V+8fgqWFC5P1m5TIKVQycUZMtuT7EiaW6sC6cssdE/zgg2Pc45RZoRyFbzGlhmqHNqX\nYuWQxoHa/cwspXAaTCGT86d+W3ud5pscYnc9+WXzgI8VUEABBRRQQAEFhkXAAmlYGH2RRoEi\nC6TTeaPMzqQoym2KhbNJipH0pXBKf/MhbivQl8PfMsPT2FbiQYqrWRo7G+7Pz/3MTo0njcVO\nZndeJfeRGUhzy1XuUnjt0jzQx+O5GM96fqjDcvF9oMO4QwqMFoEFWJHMpO5AViXvJzYFFFBA\nAQVGu4AF0mjfQz24fkUWSOfgkUIos0UpTu4mOf8o75lCJ0mBsRSptxwqlxmgzAY1t3xgu40c\n1TxQe5zCKgVX3ie3N5HbSdbhCTInadf+xMDv2g226V+e/qz/pG3G0702eaPDuEMKjLRADgfN\nuXP5WX6Q3EHy/0/+X1uO2BRQQAEFFBjNAhZIo3nv9Oi6FVkg5YtiU5y8QH5I8l45LyeFUb67\nKGP5UJZZnxQRD5HHyDjS7jLfKUpScB1HMmOUNjnZluSwvN+QFFKrkhw6l8P3jiUp1jq1UxnM\n+VIDaXOxcNbfGaSBqLnsaBLIlSavIjeTZRtWbGbuH0Py//ASxKaAAgoooMBoFbBAGq17pofX\nq6gCaSNMUgDl3KMURbl/A/knear2OIVSvUhKoZEPY0k+sGUmqV1bmoFrSJ6T4iuvk+Jod5LD\n5ZrbhnTkdWdqHqg9npbbPH/zNuOdunPY4G/bLDAx/Z6D1AbH7lEh8C3WIufmtft/I//vXj4q\n1tSVUEABBRRQoLWABVJrF3uHIFBUgXQt65TDdHJoW1r+Cv1t8iPyPyRjmTFKgZSiaVaSmZ9Z\nyJ3kKNJXW4QFNiCZLep0mNtEjN9IzmyxXP6nOoXksKKxZKBtDZ6Q2a89SQqiepuGO38l+fCZ\nbbMpMBoF8oeGvTus2GKM5Q8Rc3dYxiEFFFBAAQVGUsACaST1S/reRRRIU2GVD1U5XC4F0GdI\nvWVmKMVPZomyTMa/Qxrb2jzIrFAO8xmutgAvNJ6k+MpfzT9Ndia3khRqnQ6TY7hlm43en5K7\nSAq+l8kV5B8kM1I5VPDDxKbAaBV4hhXLbG+7lhnZ/Gyv1W4B+xUYokD+sDQdaTX7P8SX9ukK\nKFARAQukiuzobm5mEQVSZoFS/BxMUgBlhuVsch9Jf5LzjnKbD18pqBpbZpJypbvMDg1nyz/C\n+5PbyLMks0YHkhnJQNuaPCGvcT3Zg+xKLiXZ1hRMW5DBzEjxNJsCXRPI/5Pbd3i3GRjL/6dL\ndVjGIQUGI7AMTzqLvE7yM5bfp0cQZ9xBsCmgwIAELJAGxOXC/REookBKgfMU2ZrcT1IkZUYo\n/wgmeZzCKLc3kVZ/OcwXyH6GjMaWmaP8Y54CsHndMxP1CPkZsSkw2gXygTSFfbuWwj8zwTlM\n1abAcAlswgulMDqBfIIsTvJHpXyv3AQyP7EpoIAC/RWwQOqvlMv1W6CIAilvnuIhh7P9iGRW\nJcVQY4GUxzeT50nzX7A/Uls2h8WNxpbD6m4gzcVRfV1T2GWGbLp6h7cKjFKB+VivXOgkBX3+\nsNHY1uZBDhvdobHT+woMUSB/YHqJfK/F6+RDzpkkh2C3+/3a4ml2KaBAxQUskCr+A1DE5hdV\nIE3DyuZQthQKvySrkUPJP8mTJAXSJ8neJMvVW45HP4+cX+8YhbdXs06t/nGvr2r+2p5DBNev\nd3irwCgWSCGUGdGcj3cA2ZOcQfL/6E+ITYHhFNiHF7uFtCuA5mQsRxysTGwKKKBAfwRKVSDl\ng7CtvALPsWmbknzo+jr5CkmbjJxIniEnk9+THJaW85YWJfuRHF6xEhmtbUpWLOvfruUf9/xV\nvvncqnbL26/ASArkDxILkx3JKmRSkv9vVyT/JjYFhlMg5x6dQ3JEQav2IJ0poJYll7dawD4F\nFFCgzAIWSGXeu/+3bfmrdNqqJDNKaTnG/NF37v3fYWr71u6n703yD7IFyT+So7WNY8WW7rBy\nczCWgi/L2RToBYHHWMkf9MKKuo49L5BDOTM72allvN0MU6fnOaaAAgoooMCwCBR1iF1WLv/A\n5YIF+ct0u5YrwI0nS5FpyWhoi7ESOdQoM11Hki+QxoJ+Ex6/QjLj1aodS+dNxH/gW+nYp4AC\nVRb4ERufP5S1a/njUs5bXaPdAvYroIACTQKlOsSuadt8OIwC+UHJoWz9yddYLoc6TEGKaHvx\nopkdmqvFiy9I39Nk56axotal6W1aPtyf3hwidwX5FfkLyUxYDvnIoX/1luIp27UxqRdPmTlK\ncZTiaTliU0ABBRR4t8A8PHyV7PTu7nceTcR/TyI3kuaLhryzgP9RQAEFWghYILVAsevdAvkQ\nnw/49SvG9fe2qKJkLOuSCzOkmEgx9lGSWaOLyMvkKjIpmZscQ1KMZJ1zDs9xZAHSrfYt3uhF\nsl7TG+ZqdGeTu8jktbFs10/Ja+Qlku3LYSGZObI4AsGmgAIKtBHYmv4cUn0EWZ7k9/+65GKS\ni/gsRmwKKKBAfwUskPorVfHllmD78yG9P9mf5VKQFFUg8dLvfGFqZpIyW5T3SlIc3UZSED1U\nu72B28zcPEFSqGQsy61M0lKo5MTdRchw/3UxF154nmxLWrX4ZD13axqclscpqDYnSzaNDdfD\nxXmh9Uk+SEw8XC/q6yiggAIjKLAm753f9/nDUv5NyKzSCWQeYlNAAQUGImCBNBAtl+2XwHYs\nVXSBlBVZiuTQs9+SecgYkjY9SWGSY86TU8kXyafJT0hmaPIP52kkf3GsF1gPcz8zUsPV8tfL\nFGOTdHjBrM8FHcaHe+hjvOAdJNucgjEfJDJT9SViU0ABBcogkAv4zEMmLcPGuA0KKDAiAhZI\nI8Je7jftVoF0PowntqDcjL4cTpcP//lrYnNbh46MPUVWIznEbXaSmZwUDb8gw9G25kXG9/FC\nX2f85j6WGa7hT/FCKRh/SbK9aZlB252kaNyD2BRQQAEFFFBAgaoLWCBV/SeggO3vRoGUvxDm\nvKhW3230M/onkGvJc6S5XUTHMyTFQg5za2yr8CCzSms3dg7y/lo8L4VHp+8uSrFy5iBffyBP\ny+F+j5MftnnSpvRnuxduM263AgoooIACCihQFYFSFUjDfQ5JVX4IenE7Uxhlf+ccnQ81bUCu\nWpTzeM4gU5PGImghHq9OriRZbinS2O7nwdXkILIJ6VTcMNyxXcZozpHauc1Ss9K/JTmpzfhw\ndq/Pi40lB7R50axDCsqt2ozbrYACCiiggAIKKKCAAoMUKHIGaQ7W6RKSc2hymFzOJcr99GUs\nLR/yM7uU2ZnnSWPLYW+5UEPOu2mcKUrxkEPr0pfZpZzblNmnFDh5zmBbLrSQmapdSAqyektR\ndxO5gnTjIgn78D4Xkk4tXid3WsAxBRRQQAEFFFCgAgKlmkGqwP7qiU0sqkCaia3PYWIpfm4n\nD5DbavfTl7EskxmjFE4pjv5E0nLOTWZJUgCloEpSYO1HxpDjyCPkEyTFTIqX/M/xLZICZyhF\n0jY8P8VWirJzSWZq8t6nkelIN1rOM8r7dmpHM1j36rTcRxnMIXnrkCk7LeiYAgoooIACCijQ\ngwIWSD2400b7KhdVIJ3Chqew+E4NYDluc47Pj8h3ScayTC66UC9CLuX+RuRhkqLnIpLlxpNc\nHCGzReeQFEFLkLQLyOHv3Pu//6RIynI5XG+wbXqeuA3Zn2RdlyXdbCvwZikiF2jzpjF7jGzf\nZjzdeY0Yprh8gqQIfZnsQxpnx3hoU0ABBRRQQAEFelbAAqlnd93oXfEiCqR8AE8Rc3HTZq/H\n46dqqV+2OrM1D5IryOskBVF9xuh67j9J8lpLkRRFuf9vkvZ1kucskge1NpbbFEib1Tt69PY8\n1jvb2Txrle37CxlPJiOtWs75SkGUWaY5awvkl8fnSTyPrfV5o4ACCiiggAIK9LqABVKv78FR\nuP5FFEjzsJ0pcr7YtL2r8DjFUGYyniVZJgVODsFbnBxIGgukzKKcTq4hOeTteySH5uX+ySTF\nUvN70PVOYZHD1Hq5zcLKZwboYbIP2YJ8m9xKHiEfJa1aLoYRz9+1GqRvSRLzdduM262AAgoo\noIACCvSSgAVSL+2tHlnXIgqk+dj2FD+fazD4MPcza5Rzee4m9ULoTe5ntiMzSVn+NbIMWZXM\nTNImITlU7xaSoinP/RtZmrRqubrdtq0GeqwvM0Qpiv5NUiilYPoJmZG0aysyEKMUWO3acQwc\n327QfgUUUEABBRRQoIcELJB6aGf1yqoWUSBNzMan8DmrASH380E//YeQ82v3p+V2F5IP9XfW\n+vKD3q7lsLG8Rp7Xqq1FZ15rnlaDFejbhm28r4/tjHcOX7QpoIACCiiggAK9LlCqAimHAtnK\nKZAC5gLyCbI1yQUTPk4WJAeQG8iaJMs8Sw4h/0synuJmA9KuZfbkRXIimaZpocxS/YEcTsaT\nKrbYxGVMh42flrEsZ1NAAQUUUEABBRRQQIEmgSJmkPIWc5AcNpfD4XKVuhxy9wq5nKQvY/OS\neluGO1km5xvlPKW5SXP7Mh057ygF1G0kV2dLMfRDkoIp59b8lXSagWK41G1Wti4FaorTVi1/\nmMh5TPu1GrRPAQUUUEABBRToMYFSzSD1mH1pV7eoAilgC5N8GM+sUIqi3CZP1pKC6DUyjhxD\nUuCcTs4lT5MUPrmYwGdJCp988M/6puV/htw/iWQm6ijSrihgqFLtMLb2fjJ/01anODqUxLZ+\nflfTIj5UQAEFFFBAAQV6SsACqad2V2+sbJEFUgTGkFVJiqKXyWPkPpJDvHJFustJ+lJAZXbo\nbJJzmHYimXnKc3Jp8FPI8sTWt8CkLHImifGvyFfI7iTnHT1DViNVa1OxwWuQT5I5iU0BBRRQ\nQAEFyiFggVSO/TiqtqLoAqm+sTk8LkXQBJKZoswOjSX5oc45SOnLeD7Uz0VsQxPIbNEXSArO\nu8l15Gfkg6RKLT9fB5FXSWYgc5hnZi7PIfMQmwIKKKCAAgr0toAFUm/vv1G59t0qkFZn61MA\nJS+Q35NjyEMkM0kXkBRJOTQsY1OTSYitNwRmYTVzOOR6ZLZRssoTsR5nkfyMbULyCzQtXzp8\nPnmUzE1sCiiggAIKKNC7AhZIvbvvRu2ad6tAyiFe+R6f/PX+JXIHSTH0MHme5KINfycPkvyl\nP8ulmLqKfIbYRqfAdKzWcSTnluVwyOzb7LeTycxkJNv2vHmukjhvi5XIYZwXkdNajNmlgAIK\nKKCAAr0jYIHUO/uqZ9a0WwXSnojcQFL45Ipz+UCdCzTk/KNrSIqlel+WWZasSg4mmVn6IbGN\nLoEpWZ0UvckaZAxJW4lcTcaR6clItSt54wM6vPmKjKWYG+lCrsMqOqSAAgoooIACfQhYIPUB\n5PDABbpVIOXQq/r5H5kh+mrTqv6Gx7lIQ5KrrDW2nFif56zd2On9ERf4CWtwL8n3KjW3Kei4\nlfy6eaCLjzN79OkO75dD8FIgpRC3KaCAAgoooEBvClgg9eZ+G9Vr3a0CaSwKd5PMBt1WE1mK\n25PIkyQfVDODlCLqHtLcjqXj9OZOH4+oQM7hyc9Pu7Y5Azl0MoezjUR7mDfdssMbp7DLbOXS\nHZYZytDCPHlfcjz5PclFM/L/gU0BBRRQQAEFhk+gVAVSrrJlq45AZoa+SfIBMR8cLyb/InOQ\nJ0haTqbPD/lcJDMQje0sHuSwO9voEEhxMQvJOWLtWsZysY3Z2i1QcP8lvP6mHd4jYyngbu6w\nzGCH9uaJmUH7JMml1XM44mHkRrIAsSmggAIKKKCAAgqMUoHMAOSv6M0FSRGr+5Hae93ObWaM\n8r5J/X6KqD/U+vIX98b2WR483tjh/REVmIR3z35brcNaLMFY9u8MHZYpcmhJXjw/Uzu2eJPF\n6cuhnHu2GBtqV97vZbJR0wtNx+MzSWZIUzDZFFBAAQUUUGDoAqWaQRo6h68wHALdLJDyATGH\n0WXmqH77F+7/mrxJchhdPtC+VHs8E7f1dgR38t01ttEjcAWr8qsOq3MAY7d0GO/G0Fa8SQ7r\nzAzk9uQLJD9vr5A/keGeyZ6U18yM0U6kVZuczvvJHq0G7VNAAQUqKpAPuJ8ie5HvkbXIGGJT\noD8C+fnJH2Rz8SWbAsMi0K0CKeehzEouICmO8oG1sR3DgxySlMPsMp4iaX2StjLJh9wN88A2\nagTWY01S2G7cYo0+Qd9rZPMWY93uysxliu+7SX6+UmhvRopoH+NFs90phNq1/Ri4tN2g/Qoo\noEDFBFZie+8j+Xc/h0bnj6j5N/8aMj+xKdCXgAVSX0KOD1ig6AJpNtboKPICSXWf4udtkvM+\nch5LvS3KnedJfimOJ/kL/85kb5LDlX5BbKNP4DusUvbp30hmaLYlx5P07Uuq1rZggx/uY6Pj\ndGcfyzisgAIKVEEgf8B6kRxOpm7Y4Hx2yB9SHyQzNfR7V4FWAhZIrVTsG5JAkQXSAqxZPixe\nTTYhC5P8Jf9VkiIpf2m/guSvRJmJyHIpptKfYioZR75EBtom4QmLkUXIxG2e/EH6dyVHkENI\n1nEiYhuYwAos/mdyF8kszYlkdVLFtgobncNEG4v/ZoeD6DivudPHCiigQAUF/sk2n9pmu/Pv\n+E3EP5C2AbL7vwIWSP+l8M5wCRRZIP2blcxJ6WMbVvZ73L+DpCDKB8mfksxCrEZyvHEKlAkk\nXyA7M6m3JblzMDmDnEC+RiYjzW0KOvLLtD5jlSIrJ+PvQxrXI9udmal8qD+OnEbynJtJCjub\nAoMRSDGen98ftnnyjPQ/SfLza1NAAQWqLDA9G5+jDVbsgPBlxvKHVpsCnQQskDrpODYogRQK\nKSJSWAxnW54Xyy++uZpeNH9hT3GUmaTcnkcyk5NiZ1WS44/zvD1IvR3Incw4XUBSUP2OPErG\nk0zP11u2IbNRd5Oc+5Jp+bz2tiTLp1hLAbYJyXvvQFKU1dsM3Mky95FOMwD15b1VoJVA/efr\nWww2zkguyONrSX5G88vcpoACClRZYHE2Pp8/puuAsHJtmfzxyaZAOwELpHYy9g9aoKgCaUfW\n6LY2a3UR/ZeTFEc53C6/IJMUQZnVeZbUC59duZ/jkz9OGtvkPPgreYjUf7n+L/fvJSl0mtt8\ndDxFdiH3k3bnx6RQS4HVbgaAIZsCfQpsyRL5OU5hfja5iqTwP4dkFsmmgAIKVF0gf8DMv/31\nf+9beXyWzlwZ1KZAJwELpE46jg1KoOgCKT+0O5HLyARyC8k5P3eS50iKlRxyVC+MXuJ+lsnh\nd98nWWZ70qrlte8iPyBjST6QfoG0a3sykAIqv5Azu9SuZfbq+naD9ivQT4EcPvIl8mOSn70V\niU0BBRRQ4P8L5N/aQ///w/fcyx9Sj3tPrx0KvFvAAundHj4aBoGiCqT6IXY5pCgFUGZ3TiM5\nHygzRflremaPcqjbKySzTTmUrl64bMb910kKpczqtGt7M/AvsgBJ4TMHaddWYiDLpAjr1HJ4\nXv7yP5S2EE/OsdMpDtckHh4Agk0BBRRQQIEGgXW4n88B+beyseWPnjnv+AWSf09tCnQSsEDq\npOPYoASKKpCyMimMnifLknvIHSTn/axGbif1Qml97rdqf6QzhVSn4mJbxjOLNDdJ8ZND6dq1\nNRjI62W5WUi79j0Grms32Ef/tIyfRPIe40lmw1LojSPOIIBgU0ABBRRQoEFgG+7nD6U3k1+R\nI0j96JL8gdGmQF8CFkh9CTk+YIGiCqSFWZMUCU+Q/OJ7kWRW5m6SX4L5i1EKhxROOTSuPnPE\n3f+2tbiXIuqT/+15752f03UeGUMeJjuTdi3L/pvcR3LYU6uWc5vuJXu3GuyjbxLGrybZvqUb\nlp2B+78jOYywsb9hEe8q8I5ALuqwDTmN3EDOJd8knWZRGbYpoIACPS0wJ2u/F8kfGI8nu5D8\nwdGmQH8ELJD6o+QyAxIoqkDKzM54kkPnUuSkGErBlBmcF0iKohzqluOPcz7SnqS5vZ+OLH9K\n80Dt8azc5pC97WuPd6s9Xqz2uPFmdR7kkL7NyKdICrQUU3mPesus0j9Jirip650DuM0FJR4j\nrYq9vMyfSQo0mwKtBHKxkStI/t/IX1Hz83kQeYRk9nUeYlNAAQUUUECBdwtYIL3bw0fDIFBU\ngZS/ej9D8uHudpLD5FLQ5C/h+St5ZlRSpGQa/RDyD9LcsnyKqiy3D5mU1Nvi3Mnha/lAmddO\nS7GTvzylANuffIx8gvySvEYyg1RvW3Eny+X98xerc0hmeK4j85L8NevLZHeyDfkBuYBkPMtv\nQsaQxpbZoyzXri3CQLZnvnYL9Eh/tnt1Epvvkw1IfjnZhiZwOk+/iXyw6WWm4vH55EaS/3ds\nCiigQDcEtuRN/kVyFEj+oHkR+RSxKTDaBCyQRtseKcH6FFUgfQmbFAOHkWtaOI2lL0VLkgLp\nTNLcUtBkNmcjkpmZXNHucnIbyWv/jUxLGls+vH+FXEveIHn9y8jGpLllpmdHcij5Kal/0M/6\npCh7kOQDa2axkrNIZomOIvkH4zTSWLQ9yeMUTp1aZtLW7rTAKB9LcZdLVsc2BWH2x8vkHrIC\nsQ1OYHmelp+xD7V5+oz0P082bzNutwIKKDBcAvl39A8kRdHPyCdJ/n3Mv+f5Nyz/XtoUGE0C\nFkijaW+UZF2KKpC+ik9+kZ5KUqRMTxrbx3iQQ+9eJC+QY8j7SdpY8j8kRUp+MadNQVJ85FC8\nb5KPkL5aXi+/6AfS8o/CoyQzT1OSB8hfyS4k25HtSluQ3E8uJMeQk8nTZF/SruUQqhR2y7Rb\nYJT350N6PM4hszes6zTc/x3JvvxwQ793+y+Qn+sUnJ3aiQwe0WkBxxRQQIFhEMi/sfmD5Edb\nvNaa9OXfwk1bjNmlwEgJWCCNlHyJ33c7ti0f2lOADGfLL9jM/uQX6bPkJJJiZSqyE8lfpm4l\nmX3I+yeZlUjR8RR5kvQ1G8Miw9pW49VSlC1Ve9XMFmUWqT5LtCP381f8FDoLkxQLKfJSHP2S\nPFJ7vB+3rVqKrMdJ/ZDAVsuM5r5DWLlbyCRtVvLv9J/bZszuzgI/YTgzlJ3abxg8vtMCjpVO\nIL97tiaHk8xc53fSLMSmQJEC+Xfv2x3e4OeMXdVh3CEFui1ggdRt8Qq8X1EF0jrYvUo+TsaR\negGUgiLnJp1Hcj85iGxBTiApjCaQeUm32695w9Mb3vQM7qcoqLeJuPME+Qq5j5xGUhRtRdLm\nJin8Xic7k8YWhxSDOzR29tj9h1nfbHu7thIDOUwsBaRtYALbsnh+7t/f4WlXMvajDuMOlUsg\nf6gZT/I7Mb8bjyU5lPVF8nliU6AIgTl40fx7vWCHF1+dsfzbPbbDMg4p0E0BC6RualfkvbZj\nO/PLcLhnkPKL8z7yC5K2IbmW5AN03i/JL9jmv1JNSd81JLMyQ2lT8+TdSIqci8nvyWqkUzuT\nwQMbFriU+//T8Dh3LyPnkPFkMnIT+QaptzW4kw8wb5L/JXuTzAxkW39MerVl1iv7bOUOG5DC\nKMss3mGZoQxNwpPz83oiOY/ksL61SBnajGzECySzq63aenTm/51FWw3aVzqB2dmi/DHmD6Tx\nd3MK6G+R/H75OLEpMNwC8/GC+T0+V4cXXr62TP4NtCkwGgQskEbDXijZOhRVIIVpTZJD7PJB\ntv7Ldk7up6h4gxxEWrWc7J9f0HPUBvPh8XMkh+1tTFYhnyXrkpz/0tjyCzsFS2ap8gHjtyRF\nyqkkHzDzOB8yWrU/0ZkPJPV2NHdOqj+o3d7NbbIvyQeXzAplPRpb1jvbdzW5hOQ9lyW93p5l\nA+Lern2Ygey32dotMIT++XnuHST79AiyHzmFvEn+SFKQ93rL/4v5ufkumby2Mfml/xWSonv/\nWp835Rf4DZuYQ5gmarOph9Kf36M2BYZbIH8My+/6L3Z44e8wdleHcYeqIZA/2OXf4/wuyr/P\nfyVrk5FoFkgjoV7y9yyyQArdyuRmkg/Oz5MUKflQm/+RGtumPMjMzARyH3md7EL2Iq+QJ8k4\nkudmNiavleLkVXIAyS/1z5F8gM54fsE/RVKgZTwfNDYied7BpFXLPwh53gy1wY9xm/dbuvZ4\nNW7z2veSr5J8YM36TkW2ISmosl37kcfIFqRM7S9szLkdNiizhTd0GB/sUM7DuJOcTTIz2NiW\n5MGjJLN1ZWj5GXyc5Of6HpLC6AWyG7FVR+AhNvXLHTZ3EcbyO3XuDss4pMBgBfL7NL9/pm/x\nAnPRl39nv91izK7qCGzFpuZz2vlkZ5LTB/5M8pmp3WcshgprFkiF0Vb3hYsukOqymV3YkKxC\n7iA7krT8UP+T5H+0S8n/kG1rj9/iNuf0pPBZkaRQOobsSfIB8ltkM5IPlFeQ/I95HhlHxpL3\nk41Jip78Qs8HinrygXt90thSZOUDfg7Jq//DcAz389z8AniQHE4uJP8meb+vkfwlLcXYn8hh\n5FqSQuoIUqa2MBuTD+wHkVg1tvxyjMcnGjuH6X6MU3CmEG3V1qPzDTJbq8Ee7JuUdV6H5APy\nBqTddjNkK6lAfp4/1mHbJmMsv8uW67CMQwoMVmBKnngdyb+lm5BpyYwkH4rzR8H8m938bwBd\ntooILMt25ndU/XNc42avxoN8bssfkbvZLJC6qV2R9+pGgTQLlnmfA8h3yOnkVLI8eZKkmMgH\n4Edq9/OLOf/4p0BKdiUpqo4i9fYl7qRgyut+k+Q1jiYplH5A6i0fMPM/csb3IV8kKa7+RvKB\nfhvS2ObkwY3kaXIk+RG5neT5r5N7SJ6XbEseIKeQqUm97cedbNdr5Lck65B/XMrQ8qEtxeC9\n5Dcks0bximmrv3jPR3+K1E+T2clgWn5WUnh2avnZ2bLTAo4pMACBaVh2XZJDSpcYwPOGa9H7\neaFOHzA+zHh+R84xXG/o6yjQJDAlj/N7N0dq5GcteY7k3/F8GLVVVyD/Jp/YYfPzOS+F9JgO\nywz3UH4m8zOaP6bbFBgWgaILpBQvL5EHyTnkBpJiI6kXLvXH+eGu389tHueDd4qRPE7BMglZ\nmdxK0pdf2ClEcv82kmImsxlp+ct7Zn/+RervlcOVsuzz5DiSX/7NMw9j6duGHE/OI3nf/BUt\nH/RTBKxNriL5UJ4PMlmntInJ7iTrewfJ+r9FMuuS988/Npkd6PWW2bUc8pVfkCkO9yFzk8aW\nD25nkRik2Mx+intMpyMDaZew8F59PCE/Vzv3sYzDCvQlkP+Hf0Lyeye/G/KHm/wM30iWJd1q\nB/NGN5F2H0T/H3tnAW83kf7vP9KlUKRIoQVaiktxL1ug+LK4OxQpbovLQnFZ3KVQ3N21pVCK\nFrciFQotUpxCkeW3/+cpZ3ZDSHLtnHvPvWfez+dpJjOTyeRNMvN+J+fCAMocg6JFD1TaA36t\ndJFgMch7Hivdh9h+dXngG7qzeUGXulLmuLlgQZ1yF0WBVG6PxvYmfYHxQe5QAV/sRZvhy0Jy\nJWEp8hUOBsye+zpYFhRTQcBY9ilsDYoZhc4YeA4URP1B8fIvOBoMZBQhCjGFiHYoKFZs63P4\nGAx03B8C9s1863WETWAPWA9cPSsyRcLXYFsvw0Cwjw4cCqK7YQnw/KvAujAa7PMU0NLmYOL1\nHg/9wP6Vq1+z0Zb3SmGzJATrRUL/G/jV5d9wjNtrQWGVZwpUn5uN8irE/OiBenrgVuo5ljj5\nK5Y0J3ufQQXT8tAc1omTOF7dA7MkTuh7exL4NXulRH5MRg9ED0QPNJcHjLXWKDiZ87ux3TIF\ndcpdFAVSuT0a26uYQJoB334Le2f4eDfyFBbiSxRQcPhZViaC5dY1bVvzgYJjEGjD4EjYE/zZ\nl8G35QYyi4LHKMQOhyvgFjgXFF6Ko0vB8jfgBzDIVuCEfvnlw/rJAIXd/9ooUseBAusE2B6e\nhAcgCEL7tSlo3cBrVIS1pK3Iye27Qu4JUMjo4zdhEWiqXU0DL4HCJW0dyfDcJ6cLCvb/Tpmi\nOK9v3t8voBIin2aj1YgHNuE6HRd65FzvNeQr7sO7nVOtbNkL0pJjk+PZY3AffA4uxKwH0aIH\nogeiB1rCA29zUufdPOtNgbGY831zWRRIzeXpGjpPX671P1Du4HJz2lRsTAlpe5oMzxl+GjeG\ntF+G/lXKX6u0Vag8A6+AKxaHgYJmKPi53/JlYYFS2gDmJvgEFCYG/QY8ijUD6IfAdtaEg8BA\nwzq+yKeAAmkQ7ADng3XHgv2bF9KmsPC4YF1JeF1LljJcBbaPy5X23ZwK9r+lbGFO7HX2h+kT\nnZiF9B3g6vkcifyGJttzgAHdhgUH7kXZxwXlWUX27SNYLVH4F9KKU+/Tton8mIweaIwH7uYg\nF1LybE4KfJ+XzqtQgXy/6m4AjjNnwk4wHUSLHogeiB5oKQ8ojoyzZs3ogDHfEHDObk4zHjD+\n6tmcJ43natse6Mvl+VCVWyD9gzb96VmWfUim51TsKE4USqPhOzAAWaCUtlwOgXPBry+KGoP4\nd+BOmBwUMwohhYyBhOd1pdW2REFgOwbYfwPNIMPA2nLbst3zIGm7szMBBsIwSK8c70+efZkJ\ntDXANkO900mPAvsYbDMSirWWMr9uSZY5sL0ARUFi1nHJvPnZ8d6m/64rWUfBaJ1pkpl1pKei\n/BLwPvr8vAh+IfwStodo0QNN9YCroor3IjMo2LqoQiyLHogeiB5o4x5wPnZR+10w7gkxz6Kk\nH4NPoRs0p0WB1JzerpFzVUogbYf/FCnhxUm6MwghxclIUPgY9BrsmqeoMBAO4ukr0v1BgWO5\nKGr6wavg8WuBgb8CxSDGtkaDdRVgXqcvdbApSFjXcwwFxVU7SJrCxnMq0PzN/+qQNNt7BQzW\n54MVwMB/RjgSbHt9SNpu7Hh9LWHTclKvuXfByQ3+9GdjrQsH6oMeBQ2sTZn90L8Nte4csCt4\nTxSbXlO06IFyeMDFAd/bPHPMcMEk/U7n1Y/50QPRA9EDbdUDLjJfBcZqLkIbczn3D4K5obkt\nCqTm9ngNnE/h4EPdoczXOhvtKUwMYpO2ATuKFsWD25uhKyhu7Id5AY/fCXYBvxY9D5Z9A+/C\nMPgXdAHNAOZjeAP8umDa8xjwj4UTYF3YAxRWtmWg/jT0hyx7iMzTwNWSozIq+In5MXCQsD+2\npwB0sNgS0vY4GVelM+u5r4BbGlYG/dtQm58D9HHR1x3bt84RoM8fhnNgCaivDafiiQWVB1A2\nsKA8FkUPtIQHHEtegaxFHfuzETgmzeROtOiB6IHogeiBSbGIcd2mYIzRUhYFUkt5vg2fty/X\nZkBcboGkywyS/fqzKrSHS0EhEQRQ2L5HnsHJleAKrfkGIm/ClBCsHwkFyFhQ6JwKt8EVsC1c\nBl/ALHAy/AK29RO4wuF1Kpj8WhXEjGLrdrgQsuxFMo+EJ+HYrAqlvJ5sjwADf6/Ba07b0WRM\nhIXSBXXsG7AdCvrSawh+U1TODfU1v2x57AoFBxxSqjOGrT7Rx0+A9+14qI9tQ6Wf4W8ZlXck\nz3vQO6MsZkUPtKQHXDhwPDgbfOeS5js7Ds5MZsZ09ED0QPRA9EBVeCAKpKq4DW2rE5UUSJPj\nqvPBoNwvKooDxYr7Bhs/gILHfQOT8WDw7L4oXPzaY9D+LFj/n+DXIcttT3HlFyOPsy2/WCka\nvgDrvQn+bcFncBWMhu/A4xUcE+A0sI209SBDYaDYsd7GUJcZWCn0FGdXg/0/GIbCj7AJNNQG\ncID+sS1Fjl/KVgTFmD6bF+prfi2zvSzrQqbCZhQ42CRtPXb09+7JzIK0YkrfeQ/3hv3hYfA+\n7QXRogeq0QOr0ynftZfhcPB5vxR8d++EdhAteiB6IHqgvh7oSkXna+OSaJXzgDHLf6Bn5U4R\nW641D1RSIAVfGpArSAyYgwBSvHwCt4JpyxQh74AC5iO4C94Ff/ai0LoBrKcQGgIG8goRg/rr\n4SkYDvfAE9CltLX+SFA0BUGmIJoeXoLnwODfQD7YHCTeAtu6BEbDVFBfU1AYUNmfV+ECmAca\nahtygNe3TMaBCqVHYFBGWV5WLwr0gcGfAjaYgd8w0L+LhszU9iD2P4fkV71UlT/seq4bQYGq\nv6+EJSBa9EA1e8B3/wxwbHgX7octwcWPaNED0QPRA/XxwF+p9CIYtAfeJL0ORCu/B6JAKr9P\na77FSgukzfGw4sgg/yxQNLhvkK4I8quQP5dT6PSGDcDyyyBpx7LzNbiSu1OioD3pm+FTuAZs\n1+MVN/uCgf/KcAycDruA4mAUaF3BQSuINIXXbaBIexnuBftosN8S5vkVFnmmmHHwnTevQkb+\nFuR9D35908+2/yHou4sgzzpS4LmWz6sQ86MHogeiB6IHogdq3AMukBrTOLf2ABdXF4BzwUXi\n7SFaeT0QBVJ5/RlbwwOVFEjT0b5fZhRHh4I2I7iviDHYDluFz93wUynPQSXYDCQsV2ApXNJf\nMNYm7zcYA34Nst0z4QsYCvYjaSuz43n9AqM5ePmzL49VDHl86NtA0i351WMk51fUFZnizi9N\nDbHZqHwY3Ah+fTsA/Dq0FRSZ90FBGy16IHogeiB6IHogeuCPHjDeGA8n/TH7v3sHkpoAnf+b\nExPl8EAUSOXwYmzjDx6opEA6kjMpRF6BU0tnvZStQbaCxkHC8iSuupwPC0F70NYH6+4B70PS\nZmXna3gW/PpzAihu5gcHoHfgBkjaJuz4BSVtC5KheFoRloSZoaXtbTqwb0EnFHk/wToFdepb\n9DwVjy+oPDdl+nbhgjqxKHogeiB6IHogeqBWPbATF65AMmDPssnJNI45OKsw5jXaA1EgNdp1\n8cA8D1RSIA3mpAbUp8EoWA/8GdfO8AikvyT56Vk8RhRLN4Nfd/w6pAjwi9Q0EMyAPoiId0k/\nAbarKLsJHgQF2GIQ7A4Sfq0KthEJB6xwXrf+vK4XtLT1pwN+xcqzDSjQT36Za6odRAOfQp4w\nvIKy15t6knh89ED0QPRA9ED0QBv1wBlc1/11XNvVlF9VR51Y3DAPRIHUMH/F2vXwQCUFkl8k\nFDwKo49BkTMCwhcjvwr5Jcn9sFWcDIZrYRSY7xci2+kCrswcDsEGkVCAvQWh3dCWX1aeAI/1\nZ2i9wVUbBcXSoO0OltvGXDA5+CVJYWK9v0NLml9rFHwHZHSiG3mj4YKMssZkteegl0Bx2CPR\nwLSkzwb98ddEfkxGD9SaBybjgl1QuRJcfLkC/MIdLXogeiB6QA+cDAPrcMUtlF9WR51Y3DAP\nRIHUMH/F2vXwQCUFkn/bMhb8WrM4KEQUMQojAwvz3Ve8BHHzJeltINhuJCxT7Lgysz38Cv7s\nTDHzLLwHiqKvwLpHgF+WQpvW92+LrGM74e9supb2FUlZ5kD3GSgQWtK25eSKk/tgB9gQ/H2z\n1/s4KGzqsnZU8Hqnr6OiX488j0LVnye+APpuDKwJ0aIHatUDHblw3ze/YhvgnA63gWPKw1DX\nu0WVaNED0QNt3AMumPwInXKuswP5X0CfnPKY3TgPRIHUOL/Fowo8UEmB5NcXA3sHi6tAwaJI\n+QT8KmSZeYqib0r7lm8KSbuSHev9GxQte8N3YDseF4SQ7VwL2j/A+t+D5Z+DX2I8pwJDOwr8\n8pRnU1Fgm9vlVWjG/CU4100wDhSUCpe9YEoosi4U6ntFqaJHnoe1ocgWp1AReig44OuLaNED\nteyBB7h4x4vuKSfMw/5wcMEnWvRA9EBte8C/C/an6C40pudNy66D0VCfhU2qRaunB6JAqqej\nYrX6e6CSAsle3AyKGIVPEDLJ7Sul/K/YGvRbth4kbVZ2DOwvhLHwAzgAuQoT2jqMtHWWhU3A\nr0Zbg4LJc/8NFF6u9roCvBjcCJdDkT1C4SlFFaq4bG76pr9eBH1iILcCXASKRwVWtOiB6IG6\nPbAqVRxTFsipugj5vlMr5ZTH7OiB6IHa8YDjxEfwJjjPrgX+Gsa5eDwsDdHK64G/0JwxYM/y\nNhtbq2UPVFogGTgoSoKQcatgURA9DQ4W7o8DAwyDkPQXm3bkedwhoFg6Ai4GvxK9Ck+BosmX\nY17wp2EKH7+4/AijYE9YDqzzANwGA+B6KLIhFPYrqlDFZfb9UXDgSNtOZOjrhdMFcT96IHrg\nTx44jZyBf8r9Y4bv2wl/zIp70QPRAzXqgVm47rPA+OMX+AiMW+aAaOX3QBRI5fdpzbdYaYH0\nIB7+GBRJipMgkF4i7bmHg4G64kih5HY9SNrO7HjsHeBAYx1/Lmdbn8IlcHhp33oBf0rmSs2X\nsCVsCwqzzcGfqe0B/tTvYHB1Z0FI2uzseJ41k5mtJL0k/dQP8xX090nKzi8ob0tFU3AxTlhu\no0UPNNQDV3DA9XUcdAvlBkDRogeiB6IHogea1wNRIDWvv2vibJUUSH6dUMS8BzeU0v4s7i5Q\nDPn16LtSOggnxc8MEKwzCX+ip4gaAVfD2+C+x04Aj+kGj8IwUBgojLSN4BfoAv6Erz+sDNYZ\nBZ5X0WTaPL8sdYRp4DHwmMmgtdkudHhkHZ3+J+VP11GntRcrem+HieD9dXsrLADRogfq64Fj\nqOjYUmSvU+hCTbTogeiB6IHogeb1QJsSSJM3r+/i2VrAA4oixcXf4V0YAzOV8KvNdLAF+Cwo\nVKzrT+VWhJXgUFBcTQ/+xxYURGvD1bAu7AZ+BfF4v0gZ8AdRNjPpZUBBdAlcAHPBcbA+GCw/\nBfuU0oqkU2E58Gd7Mi/45cm6rc3sc13CTr+1xmur773oRUWD2vCcLUra+zkjvAg9IVr0QH08\ncDuVlgLHnyxzTFkE7sgqjHnRA9ED0QPRA9ED0QOtywN96a5Bcocyd3sF2lP0+KXIQHUH+Bz8\nCYr5ftXxZ3c/lvat58/ZwjH26d+gYBoLb8F4uB+uhV1gKtBuA4+z3ADFtm3PvE/Ac3h8D5gW\nLDfPcwTs2zjwi5Rfpx4BvyS1VlucjnttfkHJs6EUnJNX2Mrzp6H/Pjc+b2lTOF4G/iZ86nRh\n3I8eyPHAmeR/DZukyrdg35/snpLKj7vRA9ED0QPRA83jgb9wGmOeuPDZPP6uibNUSiDtjffe\nhsFwHfjlyC9A+4Bfee4Cgw1FkMLEfEWN22PhdFBUeZxCR16H8+Aa+ALeB9vqBL4Yz8Pd8AIo\nckbBWbAe+LcnioWRYN2bSvu+VAuBX5k8ZlM4CZ6B1m6DuIDB0D7jQvYgT6E4f0ZZW8jqw0WM\nh6lzLkYB9SVsn1Mes6MH0h7wi+uJ4HujuB4CH4MLO/2gri+2VIkWPRA9ED0QPVABDxjLGdtF\ngVQB59Zqk5USSAodBZI/lzOgUKhcBwoit0eAIkfhY6ChWOoPaduXDOv8kCqYjn3F0IewBPhi\nvAbB/IKioPJ8b8J7YB337U+WHU2m/dgVPsmq0MryutLf0aAftoVFYTUYAPphZ2irdiEXdkcd\nF+fzc24ddWJx9EDaA3OQ4RjxT/BLdheIFj0QPRA9ED3Qch6IAqnlfN9mz1wpgaQw8ouQXygG\ngiJHYeLfEpkOmGc99y+C5CqsXz4ULGMhLZDImvRlZATbh8G/IUoKJHYn/W3SymwVWX4xOQUU\nPp5rekjblGR4rltAcdcWbBYuwp+Z6UcFor5+AlaBtmw+S7fVcYF3Ue4XyWjRA9ED0QPRA9ED\n0QOt1wNRILXee1e1Pa+UQFLo+JO3z2AUHAX3wE/gz1JGg0LFvyfqDL3gG/ArTrDVSSig9gKD\n+/0hbX518mvIy3B9ujC1fyn7ih9/WqVoyrLbybTP9fmyMAX1tgR/nue5j4X5oBrN++F/uGKq\nauxcBfrkCr//Cfi861V8+xO8PhCtvB6Ylebmhayfdpb3TLG16IHogeiB6IHogd//f4/GifEn\ndvFpKJsHKiWQ7KA/s1MEKXLErzx+wVDQuFWMfA5Tg7YD+KVobTgThpT2FU+KLNvyZ1P+NGx7\nuAJ+BdsO5xlOuh9MC2nza4EibV/wPGvCavAgKM5+BAWc7c0JwRYkcR0onPx7g3fgIfB6rPsu\n2C9Fmv05EqK1rAem4/Ter7NyuuGz8Al0yCmvxewuXLQ/TRwDPtej4RxQ8NTHNqfSm+AkJb5P\n14ALINGiB6IHogeiB6IHKuWB+AWpUp6t4XYrKZAUOAoXA1UFkUGT+4qR70DRYRD1d9BmAAWG\ndV39/wKs7/6D4HG2ZWDrT+FeAcuscx8orA6CD8FArRMkbSt2vgXPczp4nLwKCifP6b4i6S6Y\nEtYFxdRjoCjrA/bBegaRBoBPgf2wTYPEibAHRGtZD6zB6X2+vJcK4bnBr5Lea+9pb4j2uweW\nYONixUvgmLAW7An+/Zrv2kJQZEdR6PtwKiwOLjBsCMPgI+gG0aIHogeiB6IHogcq4YEokCrh\n1Rpv02BI4VLulfSpadOASRQPYVXZ9NegCPk3jIa9QPMrTBAoA0gfC/7Nkm0oOhRJ90K6PYVW\n0mZg5wV4IJlJ2hfofbgV/Hrk+f0S9ERp+wbb92BhMFg8AxRyJ4Omjz6A4WDQaDB5P2iKM/t6\nKOwPX0F7aGmbjA7MBFO1dEda6PwG695jhbfPoFufi8Ug2u8e8NkYATeCiwJJ8525G96CdFmo\ntzwJ30kFUdps2/fr8XRB3I8eiB6IHogeiB4okwecq5zje5apvdhM9MCk1WIfqnILpE1pU7Fj\nQKoo+QE+A3++o9ixTIGiUNoKDLLMk2SgtQH71lOwWLYczAGDQeH0NmQF/4uS73W5TZqBsV+K\n/JL0DKwNrpS/BuNgEdB2Afvpl6jJQTsYPoanQdFkW56jB2g7gyJpVvgZ/IKRZ+0oWBM897bg\nNZXTFEXnw5dgHw1gn4TVoBZtGi66Gyjco/3RA9uxq6Cf7o/Z/92bmdQE2Pi/OX9MXM3ufX/M\n+sNeeBcX/ENu3IkeiB6IHogeiB4ojwf+QjPGOlEglcefsRU80Bd8qMotkBQftvsrPAv+fcMO\n8B0sCUHwKHo6gV+BTCtApoCkbcmOX50sV9gomEzbtunRsB6k7QMydk9nst8ZFGYKNoXMaDgX\nFDbBDArt/5Uhg+1AOB3ehdDuSNK7geYLaiBpX8bBNpBl65D5IXjud8B+eB0Xw1TQVOtCA177\n26DPFXK9oT94nj0gWvRA8MBFJO4IOznbh8n/V07Zq+QfmFMWsn3GXQiJFj0QPRA9ED0QPVBu\nDxh/tRmBNGW5vRPbqxoPTEZPFEGKGNN+PZIboA/cCg/CjqD5LKw5KfX7f0ChF+mpQfGgkLC+\nAZqialq4E+YBV639SnIY+HclBmDJQE8R5EuTNr8g2bc+8BBk2cRSpoIimELu4xLzlzKtF87h\nF63x0BWsOxbSthYZ/ixPQXYiKBg1r38AzAkbgS96Y812DEg914+JRgaTfgYuhyGggIoWPeDz\nG573PG/kvUvW912aIu/AUr7l1osWPRA9ED0QPLA8iS2hO/gV23n+bmjusWJWzrk1LAKOhU+D\nMUVy/mc3WvRA9EAteaAvF2swXs4vSA42tukg9ygMB4XDFXAmKDIsD3UckPyaYhD2IzgomWf5\n4zAfLAW2Z1vaBTBoUur3f45h4zkUUJoCxS9Mq7qTYc+Td2pGfsj6Gwn78UTIYKuwuRD2Ac+1\nIFhnFdA8t9cxAMZBO0iaQeJIODuZmUjPT9rr3zyR19CkfdJvixcc6DVdXFBeLUXT0xF9uzr4\nTEWrjAf2p9lREH5Kmj6Lz7GLCrumC0r7l7L162qeLUeBz2T3vAoxP3ogeqCmPDAlV3sZ/B84\ndjif3w4T4FnoDM1lO3Ki78Ex8CZw4dX9t8D5NFrr8IALfc4zPVtHd2MvW4MHKiGQ/I8k+KA6\n+H0DrsrsBq4MDYEr4Q2wzg9wCYwA6/eDqUBbEh6BL0BxYvlZoC0Bv8HG7mAe47m2cAe7Dt4G\nRUmWGez59cYB0MBwXrCf7WEaeBUcKD2HX3S0PuBP/OaCl8F+vQchsDyWtOUKsy0hbSuTYdlM\n6YLE/hWk70zsNzS5HQcozorscApfKKrQwmX6XyGq2NT/+sztrRCFEk4os3WiPd+FQ3LaPY78\nL8H3Ost6kOk92iWjUJE7DJryTGc0G7OiB6IHWrEHzqDvLjKmg9ku5Ll46fyUN3dTVDZbn5Zc\n5DwAwjxu487R98KYUppNtCr3QBRIVX6DWmP3KiGQ9IOrL34RckXIQPcrUBCJQseAyq0C6Ryw\nnkHw17AaBHOQfBEss/48EOwoEr+AwqQbDIWLwE/jrgAtA3nmYGjQZj2FVeibX64cuEfALHA0\n2P9joDs8DR+A5/IaxLTXa/88fhfIsp3IHJ1VkMjbn/Rrif2GJrfngLF1HHQo5fq0Gs1BbgiM\nhI3Afe/VKmCf9b33JVp5PbA1zRkoXAAuGrjgsAhcDj7j3osi251Cj78K1gLfvb7g/XKhIt4z\nnBCtWTywAGe5EAyyXwafyRUgWnV4YE664ZiyXk53ZiPfhcZtc8rLmf0+jf0rp0HHwOFwSk55\nzK4uDxgrGMelRXd19TL2plV5wCDGh6pDmXu9I+0pLBQNonBQzDgwhjxFzwR4Cs6GE8A65r8B\nruC4dd8+mk6bg+hosFxseyD0gOnhOPA4RZCDoWLMz/euEN0FP4IiLRxv/6zrwNgRtO1gNIQ6\nof/vkPcSeA0/wZVQFAgaZH4HRStjJ1L+JDTWDGrt58IFDTxK2WUF5S1ZdDgn9+dcXTI6MQ15\niscBGWUxq+keWJMmXofwnLv1+V4F6mOrUsl3z3fIYz+B08H3MFr0QHN4YEdO4hzinOJYchDc\nB84hx0K0lvfALnThozq6cQ3lN9RRp6nFxgiOUwq2PPsHBS7wRKt+D0SBVP33qNX1sFICSUcM\nhf9L4GAk5o0vbZ24voKtQDsMgmj5jLTixX1Xp62XJ+Q2oMy2e4PWFd6DkeAXE8XJPjAaQgBn\nfVeqzgNXHZYFgzl5C66EYJORWBBWgNlgKTgEFHU7QBBTJHNNUaaQ2iynRjvy34djcsrrmz2Q\nik/AVBkHbE3eb7BERlk1ZL1LJ7xfebYhBT4PU+dViPlN9kB3WlgRujWyJRcApm3ksfGw6IHG\nesCx2bHdcT5t65HxM2yTLmhD+46JCkLn3Q/hRegH9ZmbqNZsdgRneraOs51MuQt5lTQXhHxe\nimwTCo07olW/B6JAqv571Op62JceKxQ6lLnnK9OegbhiKLl1kjJPBpS2lv8DgikkFEwHlraz\ns7X+x3AHpAP/LuS9CXdCsGdIDIZkoOandAXK62A/PP8OoCjxK9OMEMzfJls3eXwoa8r2FA7+\nHBRYSfPlvh4+gWQ/knXqm+5KxTHwMmwK88BycC4oNPVrNdqUdMr73Kugc50o83n1S1m06IHo\ngeiB4IEHSNwUdjK2Lma5aNYWrTMX5bzm/OGvEHaEo+EDUCwtBNVi29GR8TB5QYduo+zKgvJy\nFDmHOJc4X+bZQRS8lVcY86vKA8ZQ3s+eVdWr2JlW7YFKCSQHa4NdV4Hc+tXkWDgLroDfwDqK\nE78IFE1c21M+AZYGA/8RcBzsDufA1+CqWTdQ2JwEv5b22UyydfjXvNVgGrBP54M2AyiQrnWn\nZAojXza/KpXTXF33PPblFnA17QwYBePAayyH+ZXLCUa/eR0yDNaDajW/0ilci/o4D+Vey9wQ\n7XcPdIiOiB6IHpj0M25/SZBnC1Lg2FEUpMJPQAAAQABJREFUEOcdW+35g+ngs5D+WuR/dOge\neBcMIKvBZqITzku75XRGMec8sG5OeTmzjTvOzGlQ3+k3v2ZFq34PRIFU/feo1fWwEgIpCJDP\n8cZD4CqWYuQ5cDVLPgNFylh4GxRMB0HaXOWxnRNKBYqZf4KCyMFLAeY1nAx+8XHg/QZs2/Ou\nDtr9cNWk1O//OAAPSuyvRfrf4OCt+RXHyXRJdypga9PmdeCk9ggcDl5bua0dDRoQpCfOcp+n\nqe1NRQNHwg/gvVM0PwwrQ9IOZecjUEzVshlE3ApBAPszkMthdogWPVBrHgjB0UoFF96JMsd0\n55Sm2Kwc7ELcJjBvUxoq07Gr0I4LbnmLRs4rjg/bQ7XYAXRkIuwEybF8efZHgaKuOezvnMR5\n39hj8sQJZyH9ABirGAtEq34PhDGgZ/V3NfawtXhAceGkUc5V6LDK7yD3PRjwflnaei73xbQ4\nuIe8N0nvAdvCBWDAfBv45cWB1EluGkjaNeyMhy3An2n5BelJUJTZ9powDmwz2BgSI8IOW9t3\noOwN2i7wHfjSRausB/xap1D8BC4D78NpcG0pvRdbbQXweQr75tWirc5FK4wUkAZpy4LP9ovw\nKTQ1AKSJaNEDrc4Do+nxPgW9Xpuyn8HxpjE2HQddBS7m+f59Dc5fj8Fc0FJ2DCd2/Cyymyns\nX1ShBcoUJS6EueD1KPgrDuMAx/30HE9WxWw7WnauVwwZazwI3l/7Mz9Eax0eiAKpddynFu+l\nQmLyerI79RzkO0C5bA4ass0hEISP219hFNxXyreO+b/AeeDP7wyOv4Cx8BhsA379OBcUWeGY\n50m7+uOkZ7tLQzCPccVsKjgHPKftbQ/BFEeea91ShsLK/SfAwdHzKM4uhtkgWuU80J+m34NZ\nS6c4mK334lZQMBmQ3AEGN5dALZvvwng4O8MJPsN3wpug4I8WPVBLHnBhzLHeLyZp890YCrek\nC+q53556zjnDYQ1wjtUWh0EwDuaAljAXkx6q48SOozfWUaclip1b+8LJoGDqAS1hs3DSveFC\nOBM2hjiG4oRWZH+hr8ZtPVtRn2NXm9kD83I+BYMPSkMop0BSnPlzN8WPga5bccVN4RL23Q4p\n7SuAtK3A/s/vDuYXIyel0fAWGCx7XaFdV3lug6S5QvgZOOh2BgXYULgOtD5gsK148lyKoAFg\nfz4AV5NckdsZXoGxsAC0NpuCDv8VdgK/NDgJVJvNTIe8l+ukOrYS+7fDRzARvAcbQa3bfjhg\nDLTLcYT3WH8F4Z9TLWZHD7Q5DzjuOx+8DMsnrs6x+xHw62rXRH5DkkdSeRyERZzksb6Lz0Bj\nxVeyrcak+3CQ850iMM/0Sb+8wpgfPdAGPPAXrsHYMAqkNnAzK3UJrmytACvXk9Op50NVToFE\nc5P+rkjBYbDmViFiIOy53E/yDfuKoGDDSJxQ2rmTraJFkXMDrALzwF7gcbZjm05eCh4D7tng\nfghliqp3wfPbhn3ZF7T14HkI/VGEHQBhsvGluw8USgq/1mK96ej74DWPhm9BH54JecE1Rc1u\nCiPFtGIuz/ak4L28whrLv47rHVDHNT9Feb866sTi8nhgWZpRtB4MPsvV9G7RnZozx3+/Njsn\nfA5jS2nfCeeNxprz0yEFB69JmePr9AV1KlXUkYa/hsNyTrAV+c558+WUx+zogbbggSiQ2sJd\nrLJr6Et/nEzKLZBcrTM4D8JDkfN9ad/zieUyvrR1kP8QnIweA1f7rOdP3Y6GYOuTUHgpXH4G\nJyav423wi4PnVtCcDAbWthHEkluPWwkWhz3gKzBInwmyrDOZnsdJsDXYanRSn1wIs5Q6rLjb\nED4Bf7pWLaZAnVBHZ/ySN7KOOrVSfCMXenkdFzuI8hPqqBOLm+aBLhw+EBxP3gQXdRxDHOdW\nhGgt64G5Ob3CYDvo0cSuOHa6yNa7oB1FivPMEgV1Klm0DY07l54G/upCU6wdAs5dh0K06IG2\n7IEokNry3W2ha6uEQJqBa3GycNVKUWTaIFhCvj9jM22AEVAoKVbC/u2kfwRFjpOU5tchjz3e\nHexBsP5GMDVY9iX4smiKGieOLeEKsA+KJ88tCga/Gl0JRfYshUcVVaiSMr/EGKRdkNMfgwUn\nTMVSluk3v849CvrFIPBA0LeVsK406n1YtqDxaym7u6C8looO42KT70P62qcjw3du03RB3C+b\nBww8h8NQmD/R6oyk+8MP0FKBcqI7MVlGD/hObVzQXnfKHMda8ivNBpx/FDgfOo8q6vyK5gJT\ntOiBtu6BKJDa+h1ugeurhECaletwslCYDCml72IbxIwDuOUSxJCrr9fAh6U8jzWQV2RdCsGO\nIfEOBMF0CGnbeBnWgF/AiWEFcLKyvUtAmxJGgMcY8BvQaLfBRZNS+f88RdGx+cVVU9KLnui7\nLhk9Ujxp+tlrTpviUz8qVM8C/2j1dBgHb0M3qITdT6M+J1NlNO71+Aysk1FWi1mduegJoFDK\nssvIHA1ZvsyqH/Ma7oETOWQkKEaz7A4yB2cVxLxW64H76PlNBb0/nLKPYLKCOs1R5Ly4HPg3\np3+FdhAteqAWPBAFUi3c5Wa+xkoIJCeJz8AJw2B9LChigiAy4A1p8wOuyvrV6BtQ6LhSbj3F\nVTCD6bPDDtt74Etwhc/Vso/B8w0FV3Kt3x6CKZbSPzE7mry3IG9ym54y2zoDPN/rMBiOghmg\nGsxgzZ9X6Dt9pkh8GjaCk2AU6Ge/sL0G+jptT5DxAsycKvD6LXsJgjBNVWnS7hwcPQZehL/B\nTDA3GHTo9/Mg2v88sCVJ36ErYTlQNK0CBnGKp5UgWuU84Lh0QEHzS1PmO+h9idY2PLAil+Fc\ntnvG5axK3o/gXBqtvB5wTnbhbsbyNttqWluenl4Db4BxxwBYCqL92QNRIP3ZJzGniR5wUHcy\n79DEdtKHn0KGwsWAPAig5NZzhv1PEumJpJcEBYtfkKzjVgGgPQQKAc2fPCgEfgKDxoHwLThZ\nOaBsBWnRcz55d0DSDNA9Ji/ouapUbvB5KewHJ8NIUAQuCi1pM3Fyr3cEXAz6fDWw3/rvC9gX\nVoZNYTjot80hmPV/ge4hI7V1klKs6PNKWGcavR681z4bomjKCkjIrnnzXj4DwVcGb74bPSBa\nZT3ge7JWwSlckPG+rFBQJxa1Pg/sQpddmHgUnAMcm24E373koh270ZrogWk4/hRw0TOMcW+T\n3glqxQ7nQp2n7wWft/3hQTAvL1ahqGbtL1y5z0rPmvVAvPCye6BSAskB7hUIIsgH14kkiB73\nLTNP4fFlaX8i2/7g4Gj59+CA4EC5DVwGb4ITkpPVa+DAOQWsBz+CAczqkGUKiWNTBQabg8Hz\nGZRfAAuA+feB7fllqjskzZ8x3QYfQrkFZvI8daVvpoJ+mAE6gf3dAO6H90H/LQvalPAuOMnr\nq7lAOxGenJTK/8eB+vz84rKUTEsri8E8MFlZWmzbjczE5S0If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8G0UE02D53xOVAA+GwPA99L34slIdrvX3/uKXCE45KCOOuZ9L1TcB4I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\nXN0DXoCvoCdo04Bifwd3cuw58i/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jIQ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\nNSnJtY+ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fasta_sumstats_filtered_m <- filter(fasta_sumstats, richness < 1000, median_pi < 0.02)\n",
    "\n",
    "plot(median_pi ~ richness, data = fasta_sumstats_filtered_m)\n",
    "summary(lm(median_pi ~ richness, data = fasta_sumstats_filtered_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "fwrite(fasta_sumstats, file = here(\"output\", \"spreadsheets\", paste0(\"sumstats_\", res, \".csv\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unused functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to calculate per-site number of nucleotide differences between two sequences. If two sequences have less than 50% sequence overlap, I am not calculating pi.  \n",
    "*Not used because I already determined that all sequences have < 50% overlap, and ape's dist.dna function is much faster than this function*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_div <- function(seqname1, seqname2, df) {\n",
    "  \n",
    "  # get the sequence vector\n",
    "  seq1 <- filter(df, recordID == seqname1) %>% pull(seq) %>% unlist()\n",
    "  seq2 <- filter(df, recordID == seqname2) %>% pull(seq) %>% unlist()\n",
    "\n",
    "  # TRUE if site is not a gap or ambiguous site\n",
    "  bp1 <- seq1 != \"-\" & seq1 != \"n\"\n",
    "  bp2 <- seq2 != \"-\" & seq2 != \"n\"\n",
    "  \n",
    "  # number of shared sites that contain nucleotides / longest sequence sans gaps or ambiguous\n",
    "  seq_overlap <- sum(bp1 == bp2 & bp1 & bp2) / max(sum(bp1), sum(bp2))\n",
    "  if (seq_overlap >= 0.5) {\n",
    "      \n",
    "      # remove sites with gaps or ambiguous characters\n",
    "      s1 <- seq1[bp1 & bp2]\n",
    "      s2 <- seq2[bp1 & bp2]\n",
    "    \n",
    "      # calculate per-site pairwise divergence\n",
    "      div <- (1 - sum(s1 == s2) / length(s1))\n",
    "  } else div  <- NA\n",
    "  \n",
    "  return(div)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to correct for uneven OTU sampling through a resampling method. Preliminary exploration showed that this method was nearly perfectly correlated (R2 = 0.96) with Isaac's alternate correction by dividing by OTU richness, but this method is much slower. So I'm going with the other method. The other method scales from 0 to 1 as well, which is more intuitive to interpret."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_hill <- function(x, q, n = 100) {\n",
    "    s <- replicate(n, base::sample(x, size = 10, replace = TRUE), simplify = FALSE)\n",
    "    hill <- map_dbl(s, ~hill_calc(.x, q, correct = FALSE))\n",
    "    hill_avg <- mean(hill)\n",
    "    return(hill_avg)\n",
    "}"
   ]
  }
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