diff --git a/ubiome.ipynb b/ubiome.ipynb new file mode 100644 index 0000000..41f7392 --- /dev/null +++ b/ubiome.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What's in my gut? Comparing your *ubiome* data to public datasets\n", + "\n", + "## Introduction\n", + "\n", + "This notebook uses your *ubiome* data to visualise and compare your microbiome to other people's microbiome.\n", + "\n", + "The analyses presented in this notebook are exploratory and only descriptive. Our microbiomes are not yet well understood, and are vastly different between people. Do not be suprised to see differ from the public ones!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data import\n", + "\n", + "First, we load the necessary packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading tidyverse: ggplot2\n", + "Loading tidyverse: tibble\n", + "Loading tidyverse: tidyr\n", + "Loading tidyverse: readr\n", + "Loading tidyverse: purrr\n", + "Loading tidyverse: dplyr\n", + "Conflicts with tidy packages ---------------------------------------------------\n", + "filter(): dplyr, stats\n", + "lag(): dplyr, stats\n" + ] + } + ], + "source": [ + "library(httr)\n", + "library(tidyverse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The *ubiome* data is available as a raw fastq file or a json result file \n", + "The fastq files are the \"raw\" data that contain the DNA sequences from a bacterial gene called \"16S\". While we could analyse those files and retrieve the bacterial composition of your samples, we will rather use the json file, which contains the result, as analysed by *ubiome*\n", + "\n", + "We are not interested in the names of the users, so let's \"anonimize\" our dataframe first by dropping some columns" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
idbasenamecreateddownload_urluser.idmetadata.user_notes
37844 taxonomy.json 2018-04-10T19:21:12.089631Z https://www.openhumans.org/data-management/datafile-download/37844/ 08868768 NA
37870 taxonomy-1.json 2018-04-10T19:21:42.698608Z https://www.openhumans.org/data-management/datafile-download/37870/ 35269294 GENITAL (F)\r\n", + "Sampled on\r\n", + "12/03/2015\r\n", + "Kit No. 407-018-198 Registered on 12/03/2015
37872 taxonomy-2.json 2018-04-10T19:21:42.709555Z https://www.openhumans.org/data-management/datafile-download/37872/ 35269294 GENITAL (F)\r\n", + "Sampled on\r\n", + "5/09/2016\r\n", + "Kit No. 620-054-275 Registered on 5/10/2016
37874 taxonomy-3.json 2018-04-10T19:21:42.723138Z https://www.openhumans.org/data-management/datafile-download/37874/ 35269294 SKIN\r\n", + "Sampled on\r\n", + "5/09/2016\r\n", + "Kit No. 620-054-275 Registered on 5/10/2016 EXPLORE SAMPLE
37876 taxonomy-4.json 2018-04-10T19:21:42.734174Z https://www.openhumans.org/data-management/datafile-download/37876/ 35269294 MOUTH SET DATE\r\n", + "Kit No. 938-066-987 Registered on 7/31/2016
37878 taxonomy-5.json 2018-04-10T19:21:42.746854Z https://www.openhumans.org/data-management/datafile-download/37878/ 35269294 GUT SET DATE\r\n", + "Kit No. 938-066-987 Registered on 7/31/2016
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|llllll}\n", + " id & basename & created & download\\_url & user.id & metadata.user\\_notes\\\\\n", + "\\hline\n", + "\t 37844 & taxonomy.json & 2018-04-10T19:21:12.089631Z & https://www.openhumans.org/data-management/datafile-download/37844/ & 08868768 & NA \\\\\n", + "\t 37870 & taxonomy-1.json & 2018-04-10T19:21:42.698608Z & https://www.openhumans.org/data-management/datafile-download/37870/ & 35269294 & GENITAL (F)\r\n", + "Sampled on\r\n", + "12/03/2015\r\n", + "Kit No. 407-018-198 Registered on 12/03/2015 \\\\\n", + "\t 37872 & taxonomy-2.json & 2018-04-10T19:21:42.709555Z & https://www.openhumans.org/data-management/datafile-download/37872/ & 35269294 & GENITAL (F)\r\n", + "Sampled on\r\n", + "5/09/2016\r\n", + "Kit No. 620-054-275 Registered on 5/10/2016 \\\\\n", + "\t 37874 & taxonomy-3.json & 2018-04-10T19:21:42.723138Z & https://www.openhumans.org/data-management/datafile-download/37874/ & 35269294 & SKIN\r\n", + "Sampled on\r\n", + "5/09/2016\r\n", + "Kit No. 620-054-275 Registered on 5/10/2016 EXPLORE SAMPLE\\\\\n", + "\t 37876 & taxonomy-4.json & 2018-04-10T19:21:42.734174Z & https://www.openhumans.org/data-management/datafile-download/37876/ & 35269294 & MOUTH SET DATE\r\n", + "Kit No. 938-066-987 Registered on 7/31/2016 \\\\\n", + "\t 37878 & taxonomy-5.json & 2018-04-10T19:21:42.746854Z & https://www.openhumans.org/data-management/datafile-download/37878/ & 35269294 & GUT SET DATE\r\n", + "Kit No. 938-066-987 Registered on 7/31/2016 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "id | basename | created | download_url | user.id | metadata.user_notes | \n", + "|---|---|---|---|---|---|\n", + "| 37844 | taxonomy.json | 2018-04-10T19:21:12.089631Z | https://www.openhumans.org/data-management/datafile-download/37844/ | 08868768 | NA | \n", + "| 37870 | taxonomy-1.json | 2018-04-10T19:21:42.698608Z | https://www.openhumans.org/data-management/datafile-download/37870/ | 35269294 | GENITAL (F)\r\n", + "Sampled on\r\n", + "12/03/2015\r\n", + "Kit No. 407-018-198 Registered on 12/03/2015 | \n", + "| 37872 | taxonomy-2.json | 2018-04-10T19:21:42.709555Z | https://www.openhumans.org/data-management/datafile-download/37872/ | 35269294 | GENITAL (F)\r\n", + "Sampled on\r\n", + "5/09/2016\r\n", + "Kit No. 620-054-275 Registered on 5/10/2016 | \n", + "| 37874 | taxonomy-3.json | 2018-04-10T19:21:42.723138Z | https://www.openhumans.org/data-management/datafile-download/37874/ | 35269294 | SKIN\r\n", + "Sampled on\r\n", + "5/09/2016\r\n", + "Kit No. 620-054-275 Registered on 5/10/2016 EXPLORE SAMPLE | \n", + "| 37876 | taxonomy-4.json | 2018-04-10T19:21:42.734174Z | https://www.openhumans.org/data-management/datafile-download/37876/ | 35269294 | MOUTH SET DATE\r\n", + "Kit No. 938-066-987 Registered on 7/31/2016 | \n", + "| 37878 | taxonomy-5.json | 2018-04-10T19:21:42.746854Z | https://www.openhumans.org/data-management/datafile-download/37878/ | 35269294 | GUT SET DATE\r\n", + "Kit No. 938-066-987 Registered on 7/31/2016 | \n", + "\n", + "\n" + ], + "text/plain": [ + " id basename created \n", + "1 37844 taxonomy.json 2018-04-10T19:21:12.089631Z\n", + "2 37870 taxonomy-1.json 2018-04-10T19:21:42.698608Z\n", + "3 37872 taxonomy-2.json 2018-04-10T19:21:42.709555Z\n", + "4 37874 taxonomy-3.json 2018-04-10T19:21:42.723138Z\n", + "5 37876 taxonomy-4.json 2018-04-10T19:21:42.734174Z\n", + "6 37878 taxonomy-5.json 2018-04-10T19:21:42.746854Z\n", + " download_url user.id \n", + "1 https://www.openhumans.org/data-management/datafile-download/37844/ 08868768\n", + "2 https://www.openhumans.org/data-management/datafile-download/37870/ 35269294\n", + "3 https://www.openhumans.org/data-management/datafile-download/37872/ 35269294\n", + "4 https://www.openhumans.org/data-management/datafile-download/37874/ 35269294\n", + "5 https://www.openhumans.org/data-management/datafile-download/37876/ 35269294\n", + "6 https://www.openhumans.org/data-management/datafile-download/37878/ 35269294\n", + " metadata.user_notes \n", + "1 NA \n", + "2 GENITAL (F)\\r\\nSampled on\\r\\n12/03/2015\\r\\nKit No. 407-018-198 Registered on 12/03/2015 \n", + "3 GENITAL (F)\\r\\nSampled on\\r\\n5/09/2016\\r\\nKit No. 620-054-275 Registered on 5/10/2016 \n", + "4 SKIN\\r\\nSampled on\\r\\n5/09/2016\\r\\nKit No. 620-054-275 Registered on 5/10/2016 EXPLORE SAMPLE\n", + "5 MOUTH SET DATE\\r\\nKit No. 938-066-987 Registered on 7/31/2016 \n", + "6 GUT SET DATE\\r\\nKit No. 938-066-987 Registered on 7/31/2016 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# download the public data and store in a data_frame\n", + "url <- paste0(\"https://www.openhumans.org/api/\",\n", + " \"public-data/?source=direct-sharing-132\")\n", + "resp <- GET(url)\n", + "results <- content(resp, as = \"parsed\", type = \"application/json\")$results\n", + "results <- bind_rows(lapply(results, as.data.frame.list, stringsAsFactors=FALSE))\n", + "jsons <- filter(results, metadata.tags..json. == 'json') %>%\n", + " select(-metadata.tags..fastq., -user.name, -user.username,\n", + " -metadata.tags..uBiome., -metadata.tags..16S.,\n", + " -source, -metadata.description, -metadata.tags..json.)\n", + "head(jsons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a data frame containing all the public json files, we will read them and combine them in a data frame.\n", + "\n", + "The block below defines a function that parses our data files, and the block below the function applies said function to all the public datasets. \n", + "*Note: the data is quite messy and applying the cuntion will generate several warnings. You can probably ignore them.*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# this function assumes a valid data format and fails on unquoted json.\n", + "read_json_or_tbl_or_csv <- function(data_file) {\n", + " tryCatch({\n", + " jsonlite::fromJSON(data_file)$ubiome_bacteriacounts %>%\n", + " select(taxon, parent, tax_name, tax_rank, count, count_norm) %>%\n", + " mutate(id = strsplit(basename(data_file), \".\", fixed = TRUE)[[1]][1])\n", + " },\n", + " error = function(cond) {\n", + " paste(cond)\n", + " tryCatch({\n", + " tab <- read_table2(data_file) %>%\n", + " select(taxon, parent, tax_name, tax_rank, count, count_norm) %>%\n", + " mutate(id = strsplit(basename(data_file), \".\", fixed = TRUE)[[1]][1])\n", + " assertthat::assert_that(ncol(tab) > 1)\n", + " return(tab)\n", + " },\n", + " error = function(bla) {\n", + " paste(bla)\n", + " csv <- read_csv(data_file) %>%\n", + " select(taxon, parent, tax_name, tax_rank, count, count_norm) %>%\n", + " mutate(id = strsplit(basename(data_file), \".\", fixed = TRUE)[[1]][1])\n", + " return(csv)\n", + " })\n", + " } \n", + " )}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "TRUE" + ], + "text/latex": [ + "TRUE" + ], + "text/markdown": [ + "TRUE" + ], + "text/plain": [ + "[1] TRUE" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“90 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 5 6 columns 7 columns 'data/37852.json' file 2 11 6 columns 7 columns 'data/37852.json' row 3 14 6 columns 7 columns 'data/37852.json' col 4 15 6 columns 7 columns 'data/37852.json' expected 5 16 6 columns 7 columns 'data/37852.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“7 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 8 6 columns 7 columns 'data/37870.json' file 2 9 6 columns 7 columns 'data/37870.json' row 3 12 6 columns 7 columns 'data/37870.json' col 4 13 6 columns 7 columns 'data/37870.json' expected 5 15 6 columns 8 columns 'data/37870.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“9 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 3 6 columns 7 columns 'data/37872.json' file 2 6 6 columns 7 columns 'data/37872.json' row 3 9 6 columns 7 columns 'data/37872.json' col 4 10 6 columns 8 columns 'data/37872.json' expected 5 13 6 columns 7 columns 'data/37872.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“16 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 3 6 columns 8 columns 'data/37874.json' file 2 4 6 columns 7 columns 'data/37874.json' row 3 7 6 columns 8 columns 'data/37874.json' col 4 19 6 columns 8 columns 'data/37874.json' expected 5 20 6 columns 8 columns 'data/37874.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“103 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 8 6 columns 7 columns 'data/37876.json' file 2 9 6 columns 7 columns 'data/37876.json' row 3 10 6 columns 7 columns 'data/37876.json' col 4 12 6 columns 7 columns 'data/37876.json' expected 5 16 6 columns 7 columns 'data/37876.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“78 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 5 6 columns 7 columns 'data/37878.json' file 2 6 6 columns 7 columns 'data/37878.json' row 3 18 6 columns 7 columns 'data/37878.json' col 4 20 6 columns 7 columns 'data/37878.json' expected 5 23 6 columns 7 columns 'data/37878.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“73 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 5 6 columns 7 columns 'data/37880.json' file 2 6 6 columns 7 columns 'data/37880.json' row 3 19 6 columns 7 columns 'data/37880.json' col 4 22 6 columns 7 columns 'data/37880.json' expected 5 24 6 columns 7 columns 'data/37880.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“87 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 7 6 columns 7 columns 'data/37882.json' file 2 8 6 columns 7 columns 'data/37882.json' row 3 9 6 columns 7 columns 'data/37882.json' col 4 11 6 columns 7 columns 'data/37882.json' expected 5 20 6 columns 7 columns 'data/37882.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“24 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 5 6 columns 7 columns 'data/37884.json' file 2 6 6 columns 7 columns 'data/37884.json' row 3 9 6 columns 7 columns 'data/37884.json' col 4 19 6 columns 7 columns 'data/37884.json' expected 5 20 6 columns 7 columns 'data/37884.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " `tax_name,tax_rank,count,count_norm,taxon,parent` = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“70 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 11 1 columns 2 columns 'data/37902.json' file 2 14 1 columns 2 columns 'data/37902.json' row 3 15 1 columns 2 columns 'data/37902.json' col 4 16 1 columns 2 columns 'data/37902.json' expected 5 17 1 columns 2 columns 'data/37902.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_integer(),\n", + " count_norm = col_integer(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Parsed with column specification:\n", + "cols(\n", + " `tax_name,tax_rank,count,count_norm,taxon,parent` = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“124 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 13 1 columns 2 columns 'data/37904.json' file 2 16 1 columns 2 columns 'data/37904.json' row 3 18 1 columns 2 columns 'data/37904.json' col 4 21 1 columns 2 columns 'data/37904.json' expected 5 22 1 columns 2 columns 'data/37904.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_integer(),\n", + " count_norm = col_integer(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Parsed with column specification:\n", + "cols(\n", + " `tax_name,tax_rank,count,count_norm,taxon,parent` = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“79 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 10 1 columns 3 columns 'data/37906.json' file 2 11 1 columns 2 columns 'data/37906.json' row 3 16 1 columns 2 columns 'data/37906.json' col 4 27 1 columns 2 columns 'data/37906.json' expected 5 30 1 columns 5 columns 'data/37906.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_integer(),\n", + " count_norm = col_integer(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Parsed with column specification:\n", + "cols(\n", + " `tax_name,tax_rank,count,count_norm,taxon,parent` = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“117 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 6 1 columns 2 columns 'data/37908.json' file 2 10 1 columns 2 columns 'data/37908.json' row 3 13 1 columns 2 columns 'data/37908.json' col 4 14 1 columns 2 columns 'data/37908.json' expected 5 15 1 columns 2 columns 'data/37908.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_integer(),\n", + " count_norm = col_integer(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“92 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 11 6 columns 7 columns 'data/37914.json' file 2 12 6 columns 7 columns 'data/37914.json' row 3 13 6 columns 7 columns 'data/37914.json' col 4 17 6 columns 7 columns 'data/37914.json' expected 5 24 6 columns 7 columns 'data/37914.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " `tax_name,tax_rank,count,count_norm,taxon,parent` = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“96 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 10 1 columns 2 columns 'data/37922.json' file 2 11 1 columns 2 columns 'data/37922.json' row 3 12 1 columns 2 columns 'data/37922.json' col 4 13 1 columns 2 columns 'data/37922.json' expected 5 17 1 columns 2 columns 'data/37922.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_integer(),\n", + " count_norm = col_integer(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“76 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 9 6 columns 7 columns 'data/37944.json' file 2 12 6 columns 7 columns 'data/37944.json' row 3 13 6 columns 7 columns 'data/37944.json' col 4 14 6 columns 7 columns 'data/37944.json' expected 5 19 6 columns 7 columns 'data/37944.json'\n", + "... ................. ... ................................................... ........ ................................................... ...... ................................................... .... ................................................... ... ................................................... ... ................................................... ........ ...................................................\n", + "See problems(...) for more details.\n", + "”Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_character(),\n", + " taxon = col_character(),\n", + " parent = col_character()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“76 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 9 6 columns 7 columns 'data/my_microbiome.json' file 2 12 6 columns 7 columns 'data/my_microbiome.json' row 3 13 6 columns 7 columns 'data/my_microbiome.json' col 4 14 6 columns 7 columns 'data/my_microbiome.json' expected 5 19 6 columns 7 columns 'data/my_microbiome.json'\n", + "... ................. ... ........................................................... ........ ........................................................... ...... ........................................................... .... ........................................................... ... ........................................................... ... ........................................................... ........ ...........................................................\n", + "See problems(...) for more details.\n", + "”" + ] + } + ], + "source": [ + "invisible(map2(jsons$download_url, paste0(\"data/\", jsons$id, \".json\"), download.file))\n", + "file.remove(\"data/37844.json\") # invalid json\n", + "data_path <- dir(\"data\", pattern = '.json', full.names = TRUE)\n", + "data_container <- map(data_path, read_json_or_tbl_or_csv)\n", + "full_data <- map(data_container, mutate_all, as.character) %>%\n", + " bind_rows()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
taxonparenttax_nametax_rankcountcount_normid
1 0 root root 82710 1000024 37852
2 131567 Bacteria superkingdom 82708 1000000 37852
481 206351 Neisseriaceae family 2 24 37852
482 481 Neisseria genus 2 24 37852
24 496 Neisseria macacae species 2 37852
543 91347 Enterobacteriaceaefamily 853 10313 37852
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllllll}\n", + " taxon & parent & tax\\_name & tax\\_rank & count & count\\_norm & id\\\\\n", + "\\hline\n", + "\t 1 & 0 & root & root & 82710 & 1000024 & 37852 \\\\\n", + "\t 2 & 131567 & Bacteria & superkingdom & 82708 & 1000000 & 37852 \\\\\n", + "\t 481 & 206351 & Neisseriaceae & family & 2 & 24 & 37852 \\\\\n", + "\t 482 & 481 & Neisseria & genus & 2 & 24 & 37852 \\\\\n", + "\t 24 & 496 & Neisseria & macacae & species & 2 & 37852 \\\\\n", + "\t 543 & 91347 & Enterobacteriaceae & family & 853 & 10313 & 37852 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "taxon | parent | tax_name | tax_rank | count | count_norm | id | \n", + "|---|---|---|---|---|---|\n", + "| 1 | 0 | root | root | 82710 | 1000024 | 37852 | \n", + "| 2 | 131567 | Bacteria | superkingdom | 82708 | 1000000 | 37852 | \n", + "| 481 | 206351 | Neisseriaceae | family | 2 | 24 | 37852 | \n", + "| 482 | 481 | Neisseria | genus | 2 | 24 | 37852 | \n", + "| 24 | 496 | Neisseria | macacae | species | 2 | 37852 | \n", + "| 543 | 91347 | Enterobacteriaceae | family | 853 | 10313 | 37852 | \n", + "\n", + "\n" + ], + "text/plain": [ + " taxon parent tax_name tax_rank count count_norm id \n", + "1 1 0 root root 82710 1000024 37852\n", + "2 2 131567 Bacteria superkingdom 82708 1000000 37852\n", + "3 481 206351 Neisseriaceae family 2 24 37852\n", + "4 482 481 Neisseria genus 2 24 37852\n", + "5 24 496 Neisseria macacae species 2 37852\n", + "6 543 91347 Enterobacteriaceae family 853 10313 37852" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(full_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we download our own private microbiome data. Once again, you can safely ingore the warnings." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# now our private data\n", + "access_token <- Sys.getenv(\"OH_ACCESS_TOKEN\")\n", + "url <- paste(\n", + " \"https://www.openhumans.org/api/direct-sharing/\",\n", + " \"project/exchange-member/?access_token=\",\n", + " access_token, sep=\"\")\n", + "resp <- GET(url)\n", + "user <- content(resp, \"parsed\")\n", + "my_files <- c()\n", + "for (data_source in user$data){\n", + " if (data_source$source == \"direct-sharing-132\"){\n", + " my_files <- c(my_files, data_source$download_url)\n", + " }\n", + "}\n", + "my_files <- subset(my_files, grepl(\"json\", my_files))\n", + "download.file(my_files, destfile = \"data/my_microbiome.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Parsed with column specification:\n", + "cols(\n", + " tax_name = col_character(),\n", + " tax_rank = col_character(),\n", + " count = col_character(),\n", + " count_norm = col_integer(),\n", + " taxon = col_integer(),\n", + " parent = col_integer()\n", + ")\n", + "Warning message in rbind(names(probs), probs_f):\n", + "“number of columns of result is not a multiple of vector length (arg 1)”Warning message:\n", + "“20 parsing failures.\n", + "row # A tibble: 5 x 5 col row col expected actual file expected actual 1 5 6 columns 7 columns 'data/my_microbiome.json' file 2 11 6 columns 7 columns 'data/my_microbiome.json' row 3 14 6 columns 7 columns 'data/my_microbiome.json' col 4 15 6 columns 7 columns 'data/my_microbiome.json' expected 5 16 6 columns 7 columns 'data/my_microbiome.json'\n", + "... ................. ... ........................................................... ........ ........................................................... ...... ........................................................... .... ........................................................... ... ........................................................... ... ........................................................... ........ ...........................................................\n", + "See problems(...) for more details.\n", + "”" + ] + } + ], + "source": [ + "my_data <- read_json_or_tbl_or_csv(\"data/my_microbiome.json\") %>%\n", + " mutate_all(as.character)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploratory analysis\n", + "\n", + "We have loaded the datasets! Now we can compare our microbiome data to others'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+Pjn3zySWZ35kC8/5q8/y7sw3Pg4fA9BBkXF7dkyZLFixcnJCQwezHzHmj3y524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Lry3HcU6nM7yVByiKGjFixBX3n4hHH3/8\n8bPPPms0GgkhGo3m3nvvveGGG2Kd1OATloD24XA43G63p3sFwCOowo7juA0bNrzwwgsul8sT\nVKvVmzZteuCBByKWGwBA4njzzTc//vjjsJvbbLZHH300vLY/+MEPfvCDH4R9anGqqqp67LHH\nPDfNZvNTTz2Vk5MzZcqU2CUVEQF3IaMoCitRQEBBFXabN2/evHnzDTfcsHTp0uzsbI7jmpqa\ndu7cuWHDhszMzNWrV0c6SwCAeGe1WgkhixYtUqvVobblOI6iqDC2Ge3q6vr888+FUyeYgNP1\n3nrrrcQr7HQ6XVtbm09Qq9Vi21kIKKjC7k9/+tO6des2b97sHVy7du1dd921ZcsWFHYAAEG6\n8cYbs7KyQm3FsixFUWGMu1VXV3/++eehtooLBoPBP9jS0hL9TCJNrVZnZma2trZ6IlKpND8/\nP4YpgZgF1ZFbV1e3ZMkS//iyZcvOnj072CkBAABcQcDtZTMzM6OfSRRkZ2eXlJSkp6enpqbm\n5uZWVFRIpdJYJwUiFVSPnUQiCdiT73K5cOUmAABE34oVK7788kuf4C233BKTZKJAo9FoNJpY\nZwFxIKjCbty4cc8+++yCBQu851XZ7faXX3554sSJEcsNAADg3/bv319TU+MdmT9//pdffinM\n6pNIJDNmzOjo6NixY4f3fTiOI4SEMc8g4FxUAPELqrDbuHHj0qVLS0tLFy9enJuby/N8Q0PD\nRx99ZDAY9uzZE+kUAQAA/vnPf/oHU1JShMJOKpVWV1dXV1dHPS8AcQmqsFu8ePHOnTs3btz4\n6quveoKjRo16/fXX58+fH7HcAAAA+kPTtFwuj3UWACIS7ALFy5cvX758eXNzc1NTE0VR+fn5\niXqNKgAAxJecnJzhw4fL5fLu7u5Tp045nc5YZwQQMyHsPEEIycnJycnJ8dz86quvtm/f/j//\n8z+DnRUAAMB/uP/++/030aqrq6upqUlPT/dEZsyYUVpa6unGc7vdhJAw5vn19vZiBX6IR6EV\ndj5OnDjx0ksvobADAIAIcTgcBw4cOHfu3N69e0eMGJGdne350aeffvqPf/zDZzUul8tlMBg8\nyxS73W6e5yWSkP/YYYQX4hQ2JAEAAJGqr68fOXLkK6+8cv78+SeffLK8vPyDDz4QftTV1bVt\n27a8vDz/Vl1dXSzLRjdTALFAYQcAACK1evVq7yVOTCbTmjVrhD0nqqur+1lLVRiBBRiCUNgB\nAIAYNTQ07N+/3yfY09Oza9cu8r8L1HV0dPg3VCqVGEiFIQuFHQAAiFF3d3c/8bKyMkLI5cuX\nm5ubfe5QWVkZ6dwARKu/60kPHjzYf+P6+vrBzAUAAOB/CSuYOBwOn7hQt2VkZNx00007duz4\n8ssvR44cWVhYqFAo5HL5hAkT0tLSYpEvgCj0V9hNnTo1ankAAAB402g0jzzyyKOPPuodvOaa\naxYtWiT8f8WKFbm5ufv27WtpaeF5/rrrrhs9enQsMgUQkf4Ku02bNkUtDwAAAB8bN26USCS/\n/e1vLRaLVCq99dZbn3nmGe+NX6dOnYo+CABv/RV2jz32WLTSAAAA8MUwzEMPPZSXl/fnP//5\nqaeemjBhQqwzijGz2dzZ2UkISUtLU6vVsU4HxGhACxQDAABEgUKhCGOR4QRz4sSJ06dPC9OB\naZoeNWrUyJEjY50UiA5mxQIAAIjdpUuXTp48KVR1hBCO444fP97Y2BjbrECEUNgBAACInfdC\nzR4XLlyIfiYgcijsAAAAxM5mswUZhCEOhR0AAIDYaTQa/6BWq41+JiByKOwAAADE7qqrrvKJ\nMAzjHwRAYQcAACB2GRkZ06dPVygUwk2lUjlt2jS9Xh/brECEhvrscQAAgLhQWFhYUFDQ29tL\nUVRSUpL3Qs0AHijsAAAA4gNN0ykpKbHOAkQN9T4AAABAgkBhBwAAAJAgUNgBAAAAJAgUdgAA\nAAAJAoUdAAAAQILArFgAEKPDhw8fPnzYaDTm5OQsWrQoIyMj1hkBAMQBFHYAIDrvvPPO3r17\nhf/X1tYePHjwv/7rv4qLi2ObFQCA+GEoFgDEpa6uzlPVCVwu15tvvhmjdAAA4gkKOwAQl+rq\nav+gwWDo6uqKfjIAAPEFhR0ARIPVar333nu3bNlyxXvyPB+FfAAAElIcXGPX1NT03HPP1dTU\nvP/++56g2Wz+wx/+cOLECZfLVV5efvfddwvXVvcVB4CYs1gsDofjincrKyvzD6anp2MnJQCA\nKxJ7j93+/fsffvjhvLw8n/jzzz/f1ta2adOmp59+WqVSPf744xzH9RMHgHhRWlo6c+ZMn+Ca\nNWsoiopJPgAAcUTshZ3L5XrmmWeuvvpq72BHR8fhw4fXrl1bVFSUk5Nz9913NzU1nTx5sq94\nrJIHgPD86Ec/WrNmTUVFRW5u7uTJk3/961+Xl5fHOikAgDgg9qHYuXPnEkJqa2u9gxcuXJBK\npUVFRcJNjUaTl5d37tw5q9UaMD5mzJiAB3e73ZHrzxOuE3K5XBE6/kC43W7hX3Gm53noRNhD\nIzx0HMeJ86ETXs8sy4qwo9rziAX50E2ZMmXKlCn+zSNEeGZZlo3cq054UnieD+8iwvAaCk2u\n+Ir1/DTsU4TXVsCybP/pDfChI1fKjWXZEydOtLW1paSkjB49WqlUkqAfuoEQXnU8z4v588Tl\nctG06PqAovDsDITnozhyVwxLpdK+fiT2wi4go9Go1Wq9P391Ol1vb69OpwsY7+s4DofDarVG\nNNV+zh5zdrvdbrfHOos+GY3GWKfQJ6fT6XQ6Y51Fn0wmU6xTCMBmsxFCOI4L6U3BsuyxY8cM\nBsPw4cMrKysjlt2/RfQDQfgLxHGc8Oc8DGE0FP7AOByO/h924TXD83zYuYX3XUL4s2c0GmUy\nWT93Ez6pIvTQdXR0vPjii21tbcLNpKSkn/zkJ6WlpZ5aP3If48Lv5Xa7xfyXQpyfJwKXyyXm\nh85sNkfoyAzD9HPNcVwWdoSQvr5Vh/RtWyqVqtXqQcrIl81m43lepVJF6PgD4Xa77Xa7TCbr\np+SPIbvd7na7VSqVCHvsOI6z2WwSiUQul8c6lwCcTqfL5VIqlSL8hi2kRFFU8G+6s2fPrl69\n+vTp08LNuXPnbt26Va/XRyI94aFTKBQMw0Ti+IQQiURCCKEoKoxnRyibwmgovImu+FnnqXvC\nOIXQkUZRVNhvWJVK1X96QtlH03QkHro33njDU9URQoxG4xtvvPHYY48JTRiG6Ss3juPq6+s7\nOzs5jktNTS0qKhKe4uB5fq/I/SUaCDF/FPM8b7Vah+xHcf/PSFwWdsnJyUajUfgoESK9vb0p\nKSl9xfs6jlQqjVxlI3wVE7r0xcbpdNrtdqlUKtr03G63UqkU4acJy7JCYSfOh04YXo9odRI2\n4e8rRVFBPnQ2m23VqlXea9rt27fvnnvu+eCDDwYrJZZlT58+3d7erlarCwoK5HK5TCbrv+to\nIIQnJezqJLyKUGhyxVes0AMd3imEfr7w2grvcYVC0X96A6mJhbqzr4YGg+HixYs+wZ6enrNn\nzwo7nTAMEzA3juO++OKLzs5O4WZ7e3tTU9P8+fND+psi3JmmaXF+nrhcLrfbrVAoRPhFkeM4\nq9Xa17MTc8IYsVwuD7XWHxRxWdiVlpa6XK7a2tqSkhJCiNFobGhoqKioyM7ODhiPdb4AELJP\nP/3Uf6XiDz/8sL6+vrCwcODHN5lMf/3rXz3jOAzDTJ482fuqvvjidrvr6uoMBoPT6VSr1YWF\nhVlZWbFOKg70NVhmsVj6b3jhwgVPVec51MmTJ8ePHz9oyQGERXRluI/u7u6Ojg5hjL+jo6Oj\no8Nut6empk6dOvWll166ePGisMpdcXHxVVdd1Vc81r8EAISsqakpYLyxsXFQjr97927vq3Pc\nbvehQ4fid3OLkydPXrp0yeFw8DxvNptPnTrV3Nwc66TiQEZGRsCRgSuWxa2trUEGAaJM7D12\nDzzwgOfqhx//+MeEkJ/85CfXX3/9L37xiz/84Q+PPfaY2+2urKx85JFHhDdnX3EAiC/Dhg0L\nKR4Sh8NRX1/vE3S73bW1tfHY0dXZ2dnR0eETPH/+fDz+LlGWlJQ0d+5cn72Jr7rqqrKysp6e\nnn4aBpztGOoUyBEjRrz44otJSUkhtQLon9gLuz/+8Y8B4yqV6pe//GXwcQCIL/Pnzx87dmxV\nVZV3cOXKlfn5+QM/eF+TmltaWgZ+8OgLOG+RZVkxT3sXjxUrVkil0r179wrrekyZMuWWW265\nYo9AWlqa95QLQXp6ekinFmZmRO6yThiaxF7YAcDQJJPJ3n333TVr1hw4cECIrFix4tVXXx2U\ng2s0GpVK5b+4iUKhGJTjR1lfc2VEOIdGhCQSyU033bR8+fLOzs6UlJQgZz+Ul5c3NDR4l9QK\nhWLUqFERSxMgWCjsAECkSkpKvv766+rq6suXL5eXlw/KnAkBRVHXXHPNxx9/7B3s7e0NtcdF\nJPR6PU3TPivJ6XQ6ca4EIU4Mw4S0sbhEIpk3b97Zs2fb2to4jktLS6uoqIjTLwaQYFDYAYB4\nURRVUVERibntI0eOpCjq22+/7erqksvlDMMcPHjw+uuvH/QTRYFKpSorKzt//ryntpPJZFFY\nz3mIk0qlo0ePjnUWAL5Q2AHAEFVZWVlZWcmyrEQi+fOf/8yybKwzCl9eXl5ycnJra6vD4dBo\nNDk5OTFZQAsAYg7vfAAY0hKmANJoNBqNJtZZAECMiX0dOwAAAAAIEgo7AAAAgASBwg4AAAAg\nQaCwAwAAAEgQCXLVMABEx9tvv/3555+H0VDYbam6unrt2rVhNGcY5r777hsxYkQYbQEAhg70\n2AFACKqqqgwGQ0hN3G630Wjs7u5WKpXhbYtps9mampouXIjgkGsAACAASURBVLgQRlsAgCEF\nPXYAELJXX32VpoP6Wnjq1KlNmzbZbDbhpsPhWL9+/YwZM0I63VdfffXMM8+EnCUAwNCDHjsA\niBSWZZ9++mnvLVmdTueWLVuMRmMMswIASGAo7AAgUmpra9vb2ymK8g5aLJbjx4/HKiUAgMSG\nwg4AIsXhcIQUBwCAAUJhBwCRUlhYGHDDrtLS0ugnAwAwFKCwA4BISUpK+uEPf+gTXLJkybBh\nw2KSDwBAwsOsWACIoJtvvlmn073//vvNzc1paWkLFy688cYbY50UAEDCQmEHABFEUdTChQsX\nLlxICGFZlqIohmFinRQAQMLCUCwAAABAgkBhBwAAAJAgMBQLAABx4Nlnn5XL5WE05HneZzHF\nYHAcF8a5AGIOhR0AAIjaiBEjUlJSXC6Xy+UKta3VauU4TqPRhHFenU43cuTIMBoCxBAKOwAA\nELWJEydu3bo1vLb33ntvU1PTX//618FNCUC0cI0dAAAAQIJAYQcAAACQIDAUCwAAEADLsjab\nTSKRKBSKMKZfAMQECjsAAABfly5damlp4XmeEKJSqYqLi8ObgQEQZRiKBQAA+A9NTU3Nzc1C\nVUcIsVqt1dXVYczJBYg+FHYAAAD/obm52Sficrna29tjkgxASFDYAQAA/B+3282yrH/cbrdH\nPxmAUKGwAwAA+D8MwzAM4x8Pb98LgCjD5AkASBAsy/7lL38xm81htD1//jwh5OOPP/7uu+/C\naF5QUPC9730vjIYgTpmZmT6jsQzDpKWlxSofgOChsAOABFFfX79jx46BHOHYsWPhNaRpGoVd\nIsnPz3c6nR0dHcJNqVRaXFw8pHrsTCbTyZMnzWZzeXl5YWFhSG2tVmtbWxvLsmlpacnJyZFJ\nEPqEwg4AEoQwh3Hu3LkrV64MtS3HcTzP0zQdxnJlTz31VH19faitQMxomi4tLc3NzbVarRKJ\nRKvVBhycTVTffffde++9d/HiRZqm3W73okWLVq9eLZEEVTCcP3/+6NGjbrebEMLzfElJyZQp\nU7AKYDShsAOAhKJSqbKyskJtxXEcx3EMw4TxF0gqlQZ/59dee02hUIR6CqFmDSM3k8kUahPw\nUKlUKpUq1llEm8Fg2L59e29vb2ZmpvDC+/TTT9PS0q6//vortm1vbz98+LDnJkVRtbW1Op2u\noqIighnDf0JhBwAQPUeOHIl1CgD9+e677wwGg16vF3rdJBJJcnLywYMHgynsamtr/YM1NTUo\n7KIJs2IBAADg38xmM03/R23AMIzNZgu4BIyPgCvC2Gy2QUsOgoAeOwCA6ElPTw/jai2e58O7\nSsnlcnV2dobREIastLQ0nz02nE5nRkZGMNfYabXaIIMQOSjsAACi58knnwzjEkCWZSmKCqMi\nrK6u3rBhQ6itYCi7+uqr9+/ff+nSJa1Wy/O8zWbr6em54447gmlbXl5eW1vrUxeOGjUqMplC\nYBiKBQAAgH/TaDR33nnnqFGjWlpaDAZDcnLyPffcM2XKlCDbXnPNNUlJScJNmUw2ZcqUvLy8\nSOYLvtBjBwAAAP8nLy/vv/7rvxoaGsxmc3FxsUwmC75tRkbG0qVLLRYLy7JJSUk+l+tBFKCw\nAwCAIcHpdFIUFdLyNEOZTqdTKpVBLl/njaIojUYTiZQgGCjsAAAgwZlMpvb2dmFep1QqzczM\nHIIL1MEQgT5SAABIZMOHD29pafGs1uFyuZqbm51OZ2yzAogQFHYAAJDI5s6d6xPhOK6rqysm\nyQBEGgo7AABIZGlpaf5BnyU5ABIGCjsAAEhkZrPZPxjGooAAcQGFHQAAJDLvbek9dDpd9DMB\niAIUdgAAkMgOHTqUnJzsuUlRVFpamlqtjmFKAJGD5U4AACCR8TyfkZGRnJxss9koilIqlVjK\nDhIYCjsAAEh8MpkspB0UAOIUhmIBAAAAEgQKOwAAAIAEgcIOAAAAIEGgsAMAAABIECjsAAAA\nABIECjsAAACABIHCDgAAACBBoLADAAAASBAo7AAAAAASBAo7AAAAgASBwg4AAAAgQaCwAwAA\nAEgQKOwAAAAAEoQk1gkAAAym2traHTt2hNqK53me5ymKoigq1LZdXV2hNgEAiBAUdgCQUM6e\nPXv27NkonzSMchAAIBIwFAsAAACQINBjBwBBcTqdRqNx5cqVHR0dVquVoii73c7zvFQqVavV\nEgk+TAAAYg+fxQBwZXa7va2tjRCi1+v1er3RaPQMPrpcLpvNptfrpVJpTHP8t4ULF65ZsybU\nVhzHcRzHMEwYg6qPPvpoXV1dqK0AACIBhR0AXJn3/ACapn2qH57nT5w48Ze//KX/g/A8T8K6\nHM1sNgd/Z6lUqtFoQj3FQAo7hmFCbUIIMZlM3d3dhJCUlBStVhvGEQAA/KGwA4ArYFmWZVnP\nzYClT3Z29qVLl1wuVxTzimNnz56tr6/33CwsLKyoqIhdOgCQODB5AgAGh9AhB1fU3NzsXdUR\nQurr65ubm2OUDgAkFPTYAcSf+vr6r7/+uq2tTaFQlJWVzZgxQ6FQRO50EolEIpF4Ou04jqNp\n3++EBoOhsrKy/+OEPRTb3d19+fLlUFuJVmNjY8BgTk5O9JMBgASDwg4gzly8ePHdd98V/m82\nm48ePdrS0rJq1Sr/YmsQ6fX6trY2oTLjed6ntqMoavTo0ePHj+//ICzLUhQVxhVpX3311TPP\nPBNqK9FyOp1BBgEAQoWhWIA4s3fvXp9IS0vL6dOnI3pSuVyelZWl0WgMBsPx48eTk5N1Op1c\nLpfJZCqVKj09HcudBE+tVgcZBAAIFQo7gHjicrkCbmDV2tpKCOno6Kivr49Q349UKk1NTd2x\nY8frr7+uVCpVKlVqaqper9fpdOFNCx2yiouLfR4xhmGKi4tjlQ8AJBIUdgDxhGGYgEOuQofZ\njh07Hn/8caHIA9FKSkoaN26cSqUSbqpUqnHjxiUlJcU2KwBIDBg9AYgnNE0PHz68pqbGJ15a\nWhqTfCA86enps2fPttvthJCITnwBgKEGPXYAcWbBggU+vTtTp07Nzc2NVT4QNoVCgaoOAAYX\neuwA4oxGo7njjjtOnDjR2tqqVCpLS0vz8vJinRQAAIgCCjuA+COVSidMmBDrLAAAQHQwFAsA\nAACQIFDYAQAAACQIDMUChInn+XPnztXX17vd7szMzDFjxkil0lgnFSXNzc2fffZZa2urXq+f\nM2cO1mADABAJFHYAYdq1a1ddXZ3w/5qamlOnTq1cuXIoTHJkGObhhx92uVzCzb17965du3bO\nnDmxzQoAAAiGYgHCc+bMGU9VJ+jt7d2/f3+s8okanuflcrmnqhNs3bq1u7s7VikBAIAHCjuA\ncNTX1wcZTDBOp5OiKJ+gw+E4e/ZsTPIBAABvGIoFCEFjY+MTTzwxffr0jIwM/59yHBf9lKKM\n5/mAcZZlo5wJAAD4Q48dQAhYlm1vbzeZTFlZWf4/zc7Ojn5KUSaTyQLGS0pKopwJAAD4Q2EH\nEI6xY8fq9XrviFQqnTlzZqzyiRqapp1Op0/we9/7Xk5OTkzyAQAAbxiKBQiHRCK5+eab//Wv\nf9XX17Msm5WVNXXq1JSUlFjnFQ0ul+uhhx7avXu3wWBIS0ubO3fu7NmzY50UAAAQgsIOIGxy\nuXzWrFmzZs2KdSIxMGHChEmTJsU6CwAA8IWhWAAAAIAEgR47AABIKDzPd3Z22mw2jUYT61wA\nog2FHQCE7JtvvqHpkPv73W43RVFhNDx37lyoTWDI6u3tPXjwoNFoFG6OGTOms7MztikBRBMK\nOwAIgUQiIYQ8/fTTsTo1QD/cbve3335rMpk8EZ1Od/XVV8cwJYAowwclAITgxz/+cVVVVRgN\nnU7n22+/nZ2dvWDBgjCaMwyDubdwRW1tbd5VnSA7O9tsNmNYFoYIFHYA4nL58uVf//rX/mvF\nBcPhcFAU9dBDD4Ux3EkImThx4rp16/q/T3FxcXFxcRgHt1gsb7/9dnp6+k033RRGc4Bg2Gy2\nvuIo7HyYzebLly+7XK6cnJz09HSfn1osljNnzpjN5vLy8vz8/JhkCOFBYQcgLg0NDV1dXWlp\nacnJyWE053nefy/XYFy8eBH7vUK8U6lUAeNqtTrKmYjcsWPHPvnkE5fLJdwcN27c0qVLPR8d\nx44de/fdd2tra2maZln2e9/73q233oprIeIFnicAMbr++uuXL18eaiuO4ziOYxgmjNpu9erV\noTYRp+rq6q1bt4baiud5nufD6+ns6OgIoxVEQkZGRkpKSnd3t3fw0qVLfRV8Q1Nzc/Pu3bu9\n93c+duyYXq+fNm0aIaStre2dd97p6enJzMwU9ob++OOPU1NTly5dGrOMIRQo7AAgQahUKoqi\nampqampqonxqDPOJBE3TU6dOPXTokKfabm9vP3z4cGyzEptjx455V3WCI0eOCIXd4cOHm5ub\n09LS3G43IUQikaSkpHz77bco7OIFCjsASBC5ubnPPfec/7Xzwfj000+/+uqrVatWjRgxIozm\nmZmZYbSCSFCr1XPmzDGbzVarVaPRPPjgg54BRxBYLBb/oNlsFv5jMpkYhvH+EcMwVquVZVmM\nxsYFPEkAkDiGDx8eXsPjx48TQgoLC8eMGTOoGUFsaDQadKP2JeCu1p6gXq/3KYWdTmdaWhqq\nuniBLcUAAACGkEmTJsnlcp/gzJkzhf9MnTp1+PDhPT09HMcRQux2e29vb3irFEFMoLADAAAY\nQpKTk1euXKnX64Wbcrl80aJFlZWVwk2NRnPnnXdWVFQYDIaWlpakpKQ777xTuPwO4gJ6VgEA\nAIaWYcOG3XPPPd3d3Q6HIz093WeYtaCgYP369ZcvXzYajWVlZQqFIlZ5QhhQ2AEAAAw5FEWl\npqb29VOaplNTUzUajUwmi2ZWMHAYigUAAABIECjsAAAAABIECjsAAACABIHCDgAAACBBYPIE\nAAAMmu7u7qNHj1oslvLy8vLy8linAzDkoLADAIgei8Xi2bspeCzLUhTls9FTMGw2W6hNBuKf\n//zn73//e88vOG/evMcff1wqlUYzB4AhDoUdAED03H///dE/KUVRwdytvb19z549oR6c53me\n5ymK6ujoeOWVV7x3o9q7d6/D4Vi4cGFfbQ0GQ6inA4D+DenCjuf5BDhFGISshI/jWOfSJ3Hm\n5skqcunF9hePwu8l5mc2om+KOXPmGI1Gt9sdRtuTJ09KJJKKioow2kql0unTp/f/ewk/vXjx\n4ksvvRTGKQRms9lnj1FCyIEDB2pqavpvGIXPIjG/6ohY0xOI8y8FHrp+vq0N6cLObrfb7fYI\nHZzjOJ7ne3p6InT8gRBeana73eFwxDqXAIQNCnt7e2OdSAAWi4UQ4na7I/fMWq1WQgjHcWFU\nAMIzKzyAYeA4LnK/lzAmKNo3BcuyhBC73R659AoLC++7777w2t5zzz1arXbdunVhn73/38to\nNIZ9ZI+Ar9hgXo1GozFyq+AKbwpxvuo8H8VOpzPWuQQgPHeD8tqIEKfTKc5nVnjowrjoIkg0\nTet0ur5+OqQLO6VSqVQqI3Tw7u5unudTUlIidPyBcDqdRqMxor/+QPT29rpcruTk5CDHj6Kp\ns7OTEMIwTOSeWbVaTQihaTqMC6o4juM4jqbp8B46mqYj93sJf7kpihLnm0K4DkypVIozPUHk\ncqPpQVghIeArNpgj63S6yP1qwntBnE+ry+Xq7e1VKBTCu15sjEaj0+nU6XSD8vIYXBzHdXV1\nyWQyrVYb61wCsFgsNptNq9X67NUWHUO6sIMI6erqMhgMDodDqVTm5uaK8403KFiWraqqamlp\n4TguOzt7zJgxcrk81kklFJfL1dTUZDabaZpOTk7OysoS4d+YhFFZWXnHHXeE2orneY7jKIoy\nm80PPfSQTw/K7bffPm/evL7a/vnPf66qqgonVwDoAwo7GGQNDQ0XL14U/m8ymdra2ioqKtLT\n02ObVSSwLLtz586uri7hZktLy4ULF1asWDEotd0nn3xy5MiRUFsJIzvhddeZzWaxVaUul+v4\n8eOey7Z6e3u7uroqKytF2JWbGNRqdUlJSaiteJ53u900TdM0vWXLlt/+9rcXLlwghCiVyh//\n+Mdr1qzpp20Cf+sDiBUUdjCY7HZ7fX29T/DChQt6vT7xOlqOHTvmqeoEJpPp8OHDM2bMGPjB\nm5ubm5ubB36cuFZfX+9zMb7JZDIYDNnZ2bFKCfpXUVHx1ltvtbS0mEymwsJCsX1VABgKUNjB\nYDIajf6TgFiWNZvNSUlJMUkpcgIWXk1NTdHPJFEFvGq7t7cXhZ2YURSVk5MT6ywAhi4UdjCY\n+praHdKU79/97ndnz5599913h/LX/bS0tOTk5DAaCiuKhdHQM4AOAADxC4UdDKaAE7AlEklC\nXkmTk5PT0tLiHxyUg19//fXLly8PtZUwK5ZhmDBqu9WrV4faJNKSkpI6Ojp8gv1M8gcAgES7\n7AliS6FQFBYW+gRLSkoS7wI7Qsi4ceN81lDQarWTJ0+OVT6JZ9iwYT67UWk0mqysrFjlE7bu\n7u4LFy5cuHBBWC4njnAc19bWVl9f39LSIs611gDAB3rsYJAVFBSo1WqDwWC321UqVW5ubuJd\nXSeQSCQ33nhjVVVVU1MTz/PZ2dnjxo0bysPHg04mk40ZM6apqclkMgnLnWRnZ8fdlNjjx497\nzyjKy8ubMGFC7NIJgd1uP3nypGe32bq6uoqKitTU1NhmBQD9Q2EHg0+v1+v1+lhnEQ1SqXTS\npEmTJk2KdSIJSyqV+vcBx5GGhgafeeKNjY0pKSnDhw+PUUYhqK6u9lR1hBC3211dXT1x4sTI\n7RIBAAOHwg6GIpvNFt701YaGBkKI0Wi84vaXAWm12szMzDAaQpxqbGwMGBRnYVdfX+/ZK1Yq\nlY4YMcLnDizLvvfee93d3T5xYYHiMDpThRXvAGAQobCDoejhhx+ura0Nu/nhw4cPHz4cXtun\nn366vLw87FNDfPFZh6+fYGwpFAqFQtHW1rZnzx4hkpGR4V/YEUJqamq+++67QTy1TCZTqVSD\neECAIQ6FHQxFPT09CoVi9uzZYbQNu3Pi4sWL58+fF+eW1RAhWq3Wv39LhJPE5XL5K6+84p0q\nz/P+k74JITfddNOtt97qHTl48OD27duXL18+a9asME6dnJys0WjCaAgAAaGwgyFKo9H87Gc/\nC7WV9+5JobZ97733zp8/H2oriGtlZWXNzc0sy3oiDMME7AmLOf9LY2Uy2aVLl7wjycnJlZWV\nPg3r6uoIIWlpaWFsRwYAgw6FHUAATqfTbrdTFKVUKiUSvE2AEELq6+urq6tVKtWYMWOCXE5P\nrVZPnTr15MmTQk9tUlLSqFGj4mWeeEFBAU3TDQ0NLMtSFJWZmVlUVBTrpADgCvAXC8BXa2tr\nb2+v8H+aptPT07Eo7hDHcdxvf/vbXbt2CTe1Wu2DDz64YMGCYNqmpqbOnj2bZVme532W5RM5\niqLy8/Pz8/OdTqdUKo27hWYAhqYEXDYWYCC6u7s9VR0hhOO41tZWu90ew5Qg5t58801PVUcI\nMZlMTzzxREjzbyQSSXxVdd5kMhmqOoB4gcIO4D94V3X9B2Ho2LFjh0/E4XB8+OGHMUkGAKAf\nGIqFiOB5/uLFixzHhdFWWBO1trY2vHVQc3NzlUplGA0F/5+9+wyI4uoeBn53WfqigPSi0hFp\nigiKioUaXSzEhsYee2LFXhKjxvg8iYkmsUUTERVBBI0KQhSxAIooAha6ooggvW+d98P9P/Nu\ndhcCxJ0d8Pw+hdld9+TCzJy55Vzxee7tHwQ9yeDBgzU1Nfv27Sv9EkEQMrcCk97HFgAAFA4S\nOyAXN27cOHjw4L/5FzZt2tS1D3p5eW3cuLHL36usrMzlcqUPdvkfBN2CpaWlkZGRzGUNDAbD\nxMREutSwqakpJaEBAEAnQGIH5KK+vh4h5Onp2YWbH0EQBEF0oZ6IUCiMjY3FX91lffr0efv2\nrfgRJpOpo6Pzb/5N0N3Nmzdv9+7d4kd69eo1ZcoURcUDAABtgcQOyNHo0aOHDx/e2U8JhUKC\nILpQZITH48XGxnb2UxLYbLaBgUFlZSUeR1ZWVjY0NIQeu49cUFBQdXX1yZMn8TKavn37btu2\nDXaHAwDQECR2AEjS1tbu3bs3j8djMBiw3znA5s2bN23atKKiIjabbW5urqSkpOiIAABABkjs\nAJCBwWCoqqoqOgpALxoaGo6OjoqOAgAA2gPlTgAAAAAAegjosQOAjuLj4zMyMjr7KYIgEEJd\nqyXb2NgInZQAANDdQWIHAL0YGhqyWKy3b99KLM6lgJmZGcXfCAAA4MOCxA4AerG2to6IiODx\neF347C+//HLv3r19+/bJLLTbPiaTqaGh0YUvBQAAQB+Q2AFAOyoqKl1bjYtrxGhoaLDZ7A8d\nFAAAgG4AFk8AAAAAAPQQkNgBAAAAAPQQkNgBAAAAAPQQMMcOyFFaWlpZWVlnP4X38uraXrGd\n/QgAoMfg8/l4wxhVVdUObg0iEAi4XC5BEF2e2NpZtbW1BQUFfD7f2Ni4sbHx5cuXenp6Li4u\n6urqFHw7+BhAYgfk6NatW4oOAQDwUaipqcE7+SKEGAyGlpaWpqZm+x+pr6+vq6vD1R8RQmw2\nW1dXV65BpqamXrt2TSAQ4B+bmprwttS2trafffaZjY2NXL8dfCRgKBYAAED31tjYSGZ1CCGC\nIOrr69uvGdTa2lpbW0tmdfgfaWhokF+QJSUlly9fJrM6hJCmpqaxsbGhoeHbt29Pnz7d1NQk\nv28HHw9I7AAAAHRvzc3N0gdbWlra+YjMLKqxsfGDxSTl8ePH0gdxiSItLa38/PycnBz5fTv4\neMBQLJCj1atXDx06tLOfEgqFBEHg612n8Hi8efPmdfZTAIDuDk/MlXlw4sSJMvvhZE7Jles8\nXZmpJDmZmMViQY8d+CAgsQNypKam1oVKuf8msevsRwAAPQCLxeLz+dIHEUJDhgyRma7JvMIo\nKyvLIzxMT09P+iDOPgmC4PF4+vr68vt28PGAoVgAAADdm/QD5D9ukderVy/ppfe9e/f+wJGJ\nGTZsmPR6Dh6PJxAIqqqqPDw8HBwc5Pft4OMBiR0AAIDuTU1NrXfv3mSipqysrKur237FExaL\npa+vT3bRKSkp9enTR01NTX5BamlpzZs3z8TEhAxSKBS+fv2axWJ5e3svXLiwgyVaAGgfDMUC\nAADo9jQ0NDQ0NAQCAZPJ7GAVTFVVVWNjY7xMtQtzP7rAzMzsiy++aGho4HK5urq6XC735cuX\n+vr6MkdpAegaSOwAAAB0FUGoVVT0r69Xf/8eiUSo83XFP6wu5GfUpHTitLS0tLS00P96DaE0\nMfiwILEDAADQeVwuCg9HUVFet265C4UqaWkoMhIFB6PPPkOQqQCgODDHDgAAQCdVV6NFi9D6\n9aioqNncvFRTs9HUFBUXo02b0MKFqLJS0fEB8PGCHjvwUROJRNXV1a2trSoqKjo6OtLFDuLj\n49PS0tTU1Hx9fd3c3BQSpDzweLzz588/ffpUX19/8uTJlpaWio4IdB8iEdqwAV2/jiwsEJNJ\n1NcjhAgGA/XqhbS00I0baP16dOIEUlJCCP3111937txRUlIaPXr0qFGjFB06oCk+n//q1auG\nhgY2m923b19VVdV23iwSiTIzM/Pz87W0tNzc3AwNDSmLs1uAxA58vFpbW/Py8sjqd6WlpRYW\nFtra2vhHPp8fFBQUHx+Pf9y8efOGDRv27dunmFg/qLdv344ZMyYvLw//uG3btsOHD0NtZ9BR\n8fEoKgpZWsqYUcdgIFNTFBODJk0SBQVNnz79woUL5Ivz588/efIkpaGC7qC6ujopKYncFC4z\nM9Pb29vAwEDmm5ubm3/99df09HRlZWWRSJSUlMThcOCZQRwkduAj1dzc/PDhQ/HqBkKhMDc3\nNz8/H5czjYmJIbM6bP/+/Twez9nZmcFgMBiMzn5jbm7uvw/7g1i0aBGZ1SGEWltbV6xY4eXl\nBXuQgw65ehVpa6O2anMwmUhXF129evDlS/GsDiH0+++/jxgxYsGCBVQECboJkUh09+5d8a1+\neTzevXv3OByOzHUt0dHRmZmZRkZGIpGIwWA0Njb++eefVlZWpqamFEZNa5DYgY+UhoaGdM0q\nJSWl58+fZ2dnI4Ru3rwp/amoqKjCwkIq4pOb2tpaiYQVIdTc3BwbGxsaGqqQkEA3k5uL2q39\nizQ00IsXEdnZ0q+cO3cOEjsgrrq6WnrPt+bm5oqKCrLmH0kkEmVlZeGahXjTDjU1tfLy8szM\nTEjsSJDYATn68ccff/75Z8q+jiCIjr+5rTkcZLYnvUNRWwfpY8KECe7u7u1vTFRfXy+zoerq\n6uQWF+hZWlrEB2HV1dWNjIz+dkIxmYjLlblDK/yZAQltbQUp8zifzxcIBBKVnBkMhniHH4DE\nDsjFwIEDzc3Nu5YGVVdX83g8Q0PDLgx3amtrDx06tIPfIhKJpAuZlpeX4//Q0tKqqqqSeBVX\nn6ItU1PTf6yeb2JioqurW11dLXHc0dFRnqGBHsTQEL1/T9Y0YbFYDAbjb6cSj4f69h3Qt++z\nFy8kPgp/ZkBCW9u4kdOdxamqqurq6r5+/ZrcnA1vs2tkZCTHELsbSOyAXNjZ2f3yyy9d++zG\njRufP3/+888/t78w6l/S0NAwMDCo/HtdBjabTS6PuH//vo+Pj/ir+vr68fHxenp6HS9tL+7y\n5csRERH/JuYPgsVi7d27d+nSpeIHPT09g4ODFRUS6GY8PdG9e6hXrzbfUF+Phg3bNWVKXFxc\nc3Mzebh3797bt2+nIkLQfWhqatrZ2UlMQRZfxyaBw+Hs3bsXIcRisUQiUVNTk6urawef5z8S\nUMcOfKQYDEa/fv3MzMzw/Fwmk2lgYGBjY8P+n3Hjxl27dm3gwIEIISUlpTFjxty4ccPKyord\nVSoqKor+n/4/S5YsOXr0qLm5OUJIXV19zpw5sbGxdi8n4wAAIABJREFU0qVeAJBt1ixkZYVk\njbQihFBjI+rfH332mYODw/Xr14cOHcpkMpWUlLy8vP766y8LCwtqYwXdwKBBgxwdHfEliMVi\nDRgwoJ1EzdXVdePGjebm5kpKShoaGj4+PkuWLIHLlzjosQMfLwaDYWRkZGRkJBQKZW6/HRgY\nGBgYWFdXp6qqisc3OzWNj84WL168ePHi2tpaLS0t2HocdI6pKVq/Hs2di4yMkMQ4Wn09KitD\nX3+N+vVDCI0YMeL+/fvNzc1MJrP9GQLgY6akpOTi4uLi4tLa2tqRvxM3N7dBgwaVlZWx2ey2\nRnI/ZpDYAYDaz2x68IWjrcEOAP7BlClIXR3t34+ePEFsNoPJZIhEqKkJOTmh//4XBQWJv1ej\n/SW0APxPp7J/VVXVLkyJ+RhAYgcAAKDzAgORlxeKixM9eMB//56lp8caMgR98gmCpwUAFAoS\nOwAAAF3SqxeaPl0waVJjQwObzWbBYCsANADdmAAAAAAAPQQkdgAAAAAAPQQkdgAAAAAAPQTM\nseshRCJRaWmpUCjE1X0UHY68iESi2tpaoVCoq6vbg/83AQAAgK6BxK4nuHfv3nfffffu3TuE\nkK6u7tq1a/38/BQd1IdXXFyckJCA95pUV1cfM2YMbE8EAAAAiIPErhvj8/mvXr0qKSnZsWMH\nuV9ydXX1tm3buFzugAED2vlgU1OTurp61/bsMjExob4wVVVVVWxsLLn5bEtLy7Vr1zQ1NaGQ\nPQAAAECCxK4be/bs2fbt2+vq6sisjvTdd9/p6urK6Xs///xzDocjp3+8Lenp6WRWR0pNTYXE\nDgAAACDB4oluDG+ELBAIpF8SCoXy+97ffvutsrJSfv/+oEGDvL29JabQ1dbWSr9T5kEAAADg\nowWJXTc2Z84chJDMPVXkurAgICCgT58+8vv3AwMD586dK/G/oKmpKf1OmQcBAACAjxYMxXZj\nY8eOZbFYeXl5p0+flnhpwoQJgwYNauuDAoGgtbVVVVVVWVm5C987ePBgBoPRhQ/+G87Ozs+f\nP5c46OLi0uV/UCgU4uUmnUIQhEgkYjAYXdijsLGxsbMfAQAAADoFErtuTF1d3d/f39/fn81m\nnzhxgpxpN2vWrFWrVrXzQR6PV19fr6mpqa6uTkmkH0C/fv3GjBlz584dcujZzc3N1dW1a/8a\ng8GoqalZvHjxhwuwE19N/ZcCAAD4SEBi1xPMnz8/ICAgMzNTIBC4uLj07dtX0RHJhbu7u729\n/Zs3b4RCoYmJyb9ZHTJz5sxHjx514YONjY1PnjwxMDCwsbHpwse1tLQGDhzYhQ8CAAAAHQGJ\nXQ9hbGxsbGys6CjkTktLq50yLh3n6+vr6+vbhQ8WFBSsXbvWycmp/T5RAAAAQCFg8QQAAAAA\nQA8BiR0AAAAAQA8BiR0AAAAAQA8BiR0AAAAAQA8BiR0AAAAAQA8BiR0AAAAAQA8BiR0AAAAA\nQA8BiR0AAAAAQA8BiR0AAAAAQA8BO08AAAC9vX/fv6ZGncdDNTVIR0fR0QAAaA0SOwAAoKu0\nNHT0KMrJ2ZmZyWAwkL8/GjwYrViBnJwUHRkAgKZgKBYAAGjpxAnk44Nu3kQ8XlmvXu9690at\nrejqVbRkCbp4UdHBAQBoCnrsPgyBQFBWVtbc3KympmZoaKimpqboiECnEQTR1NTE5/OVlJQ0\nNDRYLBZ5vKWlRSAQMJlMJhOehf4/gUBQXV3N4/FUVFR0dXXJFgMfwLVraPVq1K8fUlf//wdV\nVJChIaquRnPmIFNT5OEh/ona2tqwsLD8/HwzM7MZM2b069eP6pgBADQAF+IPoLGx8dGjRzwe\nD/9YVFTk6OgIN7nuRSAQlJeXC4VC/GNdXZ2urq6mpqZIJKqqqhIIBPi4urr6uHHjFBcmjTQ2\nNhYXF5MtVl5ebmFhwWazFRtVD8Hlop9+QoaGf8vqSGw20tND//0vioxEDAY+9uTJEz8/v4qK\nCvzj119/HR4ePmXKFMpCBgDQBCQf/+DmzZuvX79u/z0GBgbKysrkj0KhMDMz8+XLlyKRqGtd\ndyYmJr6+vl34IOiyqqoqMkdBCBEEUV1draqq2tDQQGZ12OTJk5OSkigPkF5EItGrV6/EW0wo\nFL569WrAgAHQqfkB3L+P0tKQjU2bb9DVRXFx6Plz5OCAECIIIiQkhMzqEEItLS0LFiwYMWKE\ngYEBBfECAOgDErv25Ofnnz59uqqqqp339OnTZ9asWRIHGQxGXl5ebm5u175XW1vbzMxswIAB\nXfs46CyhUMjlciUO4hHY1tZWiePKysqmpqZUhUZTzc3NfD5f4iCfz29qatLS0lJISD1Kfr7s\nvjoSk4nU1FBuLk7snj179uzZM4m31NXVJSQkzJ49W35hAgBoCBK79mRnZ7ef1SGEVFRUOnW8\nI2prazMzMyGxo4xIJOrUcXt7e3mG0w2I99WJa6vFEEJqamo7duzo1auX3ILqQVpbyTFWzNDQ\nkPH3I4jJRC0t+D8bGhpk/jNtHQcA9GAwaNKeKVOmuLq6tv+e6upqmTezysrKLn+vg4PDjBkz\nuvxx0FksFkvyrokQQkhFRUVJSUn6OMwkU2+jP6mduQdMJrN///5GRkZyC6oHMTRE/5uzi6mo\nqEg+K/J4yNgY/6e9vb3MJ0kXFxe5hQgAoCnosfsHu3bt+sf3FBcXFxYWih8xMDDYt28fQRC6\nurpyCw18MAwGQ1tbu6amRvygmpqauro6g8GQOM5gMGRmex8VFRUVfX399+/fix/U09NTVVVV\nVEg9yrBhiMtFXC5qqz2bm5GDA3J3xz9pa2vv3Llz69at4m+ZNm3a8OHD5R0pAIBuoMfuA+jf\nv7+trS2+pSkrK/fr12/gwIGKDgp0jpaWFlmwg8lkamlp6enpIYTU1NS0tbXxcQaDoaam1lb3\n3sfGxMTE2NgYLxtSVlY2NjaGqYcfjKkpWrUKvXuHCELGqyIRevcOTZ2KxHqON23adOjQIVzi\nRF9ff+PGjSdPnqQsXgAAfUCP3QfAYDD69u3bt29foVAIfTndF5vNZrPZBEFI5G3q6urq6ur4\nuEAgqK2tVVSEtMJgMAwNDQ0NDUUiEayE/fA2bEDPn6O7d5GJCRKvncTjobdv0YQJaPly8bcz\nmcyVK1euXLmytbUV6mgC8DGDy/GHBFldD9BWbxz00rUFsjq56N0b/fYbmj4dFRai0lJmdTWj\nqgq9eYOKitDixejw4bZGaSGrA+AjBz12AABAS7q66NAhtGABunWLl5+PEFIbOBCNHYtgvTwA\noG2Q2AEAAI0NGoQGDWqqqmIymWo6OoqOBgBAdzCGAgAAAADQQ0BiBwAAAADQQ0BiBwAAAADQ\nQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQ\nQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQ\nQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ0BiBwAAAADQQ7AUHUCPxWQy\nCYJQdBSyMRgMJSUlBoOh6EBkYzKZSkpKio5CNtx0TCZNn4hweIqOok10/quj+UlB59ho3nT0\nvxTT9npC50sxQojOTafYSzGDtn/xAAAAAACgU2ia7QIAAAAAgM6CxA4AAAAAoIeAxA4AAAAA\noIeAxA4AAAAAoIeAxA4AAAAAoIeAxA4AAAAAoIeAxA4AAAAAoIeAxA4AAAAAoIeAxA4AAAAA\noIeALcUAvQiFwoyMjPr6eg8PDy0tLUWHI4nO4dE5NpqDpgOge6HzOavw2JS++uor6r+1x3j3\n7t2dO3dsbGzED/J4vOTk5Dt37igpKRkYGCgqNkT78KRlZWVt3769pKQkNzcXIWRnZ6foiP6G\nzuHROTaEEI/HS0pKunHjRnV1df/+/Wm1wyP9m44mJyzNryfS4dE5NkTv8OgTm0x0PmfpEBv0\n2P0rxcXFTU1N4kdyc3P/85//9O3bV11dfevWrdu2bXN3d4fwOqKgoOC7777bvHmzo6OjomOR\ngc7h0Tk2hNCbN2/27NljYmJiZGQUFhZ27969nTt30mTPeJo3Ha1OWJpfTyTCo3NsNA+PVrFJ\no/M5S5PYILH7V4YNG4YQIggC36Xq6up27dq1YsWK4cOHI4SEQuGvv/56+PBhNTU1CO8fpaam\n2tra4vOhtLT0xYsXCCEvLy8I7x/ROTaRSLRr166JEyeOHz8eIWRtbX3gwIG//vrL19dX0aEh\nRL+mS0pKYrFYI0eORPQ7YWl+PREPj86xIWi6f4du56w4msQGQ7H/llAo3LRpk6qqar9+/XJy\ncrKzs5ctW9bc3Hz06NGcnJzW1lahUOjs7AzhSZMYnkMInT179vHjxxERERcuXCgtLb13796j\nR48UlQHQOTw6xybh9evXUVFRW7Zsqa2tPXLkSGJi4pIlS8aOHauoHjuaN115efnx48fZbPa9\ne/eYTCatTlhE7+uJeHgNDQ20jQ2arrPofM7SNDYC/AtcLrelpeXgwYPz589vbW1tbW0VCASl\npaXz58//9ddfm5ubw8PDg4OD3717JxKJIDxxr1+/Xrp06a5du44dOzZ79uydO3eKRKLMzMyY\nmJi0tLSmpiaCIEpKSjgcTnV1NcWx0Tw8OsdGEASXy71x48apU6eys7MJgqioqOBwOIcPH541\na1ZYWFhLSwt+W319PfWx0bzpCIJ4+vQph8NZsGBBbm4urU5Ygk7Xk3fv3l27dk3mSzi8uro6\naLpOxUbQtenofM7SNjboseu61tbWtWvXstnswMDAmJgYkUg0aNAgJpO5c+dOZ2fnZcuWKSsr\nV1RUZGdn37p16+bNm+PGjVNSUvpow0tKSnrz5k2/fv0QQiKRKDQ0NCgoaNGiRW5ubjo6OjEx\nMfr6+sOHD7e3tzczM1NWVkYIPX78uLCwcNq0aRT37tAtvG7UdLm5uVu3bq2rq+NyuX/88Ye1\ntbWNjU16enpOTs4PP/zg5eXFYrEQQkePHn369Kmbm5u84+lGTYc9ePBAQ0OjsLBwypQpvXv3\nhuuJTNnZ2T/++KO7u7uuri6fz7906VJERERxcbGtra2Tk1NMTAyDwXBxcYGm63hslpaW9vb2\nCm868RMW0e+c7S7XE5hj1xVCoXDz5s2urq4eHh7jxo1DCE2fPv3MmTO+vr76+vovX7708/PD\n70xNTd25c2daWpq7u7uKisrHHJ66uvqRI0e4XG5ZWZm3t3d5ebm/v391dTXu2lmzZs3o0aMR\nQnw+PyUlRUdHJzk5OSsra8uWLZSdDzweTyQSqampvXnzhlbhdZem43K5MmfnzJ07d/v27UlJ\nSTNnzkQIPX/+PDk5+ZtvvqEgMPo3HUKIy+XeunUrPz/fzMzsk08+CQgIWLly5cmTJzdv3owQ\nguuJtGHDhjk5OR0/fnzv3r3btm1js9k2NjZxcXFZWVn79u0jw4Om61RsvXv3VnjTiZ+wn332\nGVyKuwZ67LqCyWQWFRVdv349ICCgb9++CCFbW9tbt269evXKy8urpKTk1q1b2traMTExAoFg\n8uTJLi4u+vr6H3l49fX1f/75Z1FR0fTp07W0tC5dutTQ0HD06FFnZ+cNGzbY2NgwGIyGhobS\n0tLw8PCMjAx7e/tVq1ZRttJe/MmVx+PRKrzu0nRNTU0yZ+f4+vqqq6uHh4enpKSkpqZevXr1\nyy+/dHJyoiA2mjcdQqi4uHjTpk2NjY19+vS5fPlyRkbG6NGjTU1Nz5w54+joaGhoCNcTmays\nrMLDwxsaGthsdmhoqLOz89ChQ6Oiopqbm2fMmIHDMzc3h6breGzi4Smq6cRP2D59+sCluGsg\nsesoiTmSDg4O165dYzKZHh4eCCEmk2loaBgeHu7s7Dxu3LiCgoK0tDR7e/vFixdT06lO5/Aq\nKipevXpVXFxMjjEZGBi0NTzn6+vr7+8fGBjo6OiIu7LlDc8arqmpMTExmTx5MkJIU1OTPuGh\nvw/P0bnp+vTpExQU9O7du9DQUHNz861btwqFwpiYGG9vbzc3t2HDhmlqag4YMGDZsmXkUIu8\n0bbpMKFQuH79+gkTJixdunTQoEFubm4XLlxobGzkcDh5eXl37twJCAhwcXGB64k0bW3tmpqa\nuLi48ePHW1paIoR69+6NEIqJifHz87OwsAgPD//ss8/q6uqg6ToYm3h4Cmk69PcTls1m0+dS\nTPO7mARI7DrkzZs3W7ZsqaurY7PZly9fzs7O9vX11dTUvHDhwsiRI3v16oUQMjU1zc3NffDg\nAYfDGTly5Pjx452dnak5H2ge3tmzZyMjI1etWuXl5ZWcnFxUVDRy5EgjI6Pr16+z2Wzcc/P8\n+fOwsLCFCxfq6upSEJI4mU+uCg9P/BLs6+s7bNgw+jcdi8VqZ+ZQcHDwwIED+/fvj6+A8tMt\nmg4rKiqKiYlZs2aNuro6QkhHR0dJSenixYt+fn5OTk4RERFVVVUZGRkTJ06cM2cOXE/wupz4\n+Pi3b99aWlo6ODgkJCSoqKh4enriN1hYWMTGxvbp02fMmDG5ubmZmZnffPMNXIo7HhsZnvya\njsvlSlwB2jlhEQ0uxRjN72ISILGTrSNzJP38/FJTU3Nzc729vfGn7O3tPTw8yDOEgthoGB5G\n/K9iE0LI1tb20qVLeMG8kZERHmNydnZW1PAc6sCTq5GRkQLDk74Ejxkzprs03dGjR8eNG2dt\nbY0QOnPmzIoVK1gs1sSJE42MjCgIj85Nh6RaTyAQ/Pnnn0OGDDE0NMRv6N+//8WLF/v27evi\n4qKnp5eUlGRra+vt7S2/vTq6xfUEyRqz9vX11dDQiI2NHT16NJvNRggpKysnJyfr6uq6urpS\nEB7Nm65rdzG5hlddXb1s2TI7OztydLL9E9bQ0FCBl2Ka38XaQ+US3G4kNTV17ty5iYmJYWFh\nr169CgoK4vP5VVVVP/zww/z582/evIkXfmdnZ3M4nIyMDEXFRhAE3cIjCILH44WGht6/f588\ncuXKlU8//fT9+/cEQXz11VcrV64UCoUEQRQXF8fGxiYlJZFVMCggc416XFzcpEmT3rx5I/5O\nasK7efPm7du3yR+FQuHnn39+5coV8lUOh5OQkEB0k6bbv3//vHnzkpKSfvjhh927d1MWG0Hv\npiPaaL3Vq1dv2bJF/G1z586Ni4ujLCr6X08IghAIBPPnz7948SL+saioaOrUqceOHRMIBMuX\nL1+3bh2unpOTkzNp0qSsrCxqoqJ509HzLvb111+vWrUKf3UHT1hCEecsze9i7YMeO9k6OEfS\n3Ny8pKTkzp07gYGBComt/emlCgkPIaSkpJSdnZ2YmBgYGIh7Gqytre/evfvy5cvhw4fb2Njg\nMaaUlBRLS0svLy95D891+clVW1vb3t5e3uGJ16R1cXFpp6Iv9U0nroNN5+zsrJBJV6jdYsiK\nbTrUbnGE8+fPq6mpDRgwACH04MGD5OTkxYsXa2hoUBMYna8nPB6Pz+ezWKy2xqwDAgIsLCyi\no6MzMjISEhKSk5OXLl06dOhQasKjc9Mhut7FrK2tz507p6OjY21t3cETVldX18rKioJLsTi6\n3cU6BRK7v+Hz+ampqQ8fPszKyjIxMfnHOZJubm729vZDhw7F83apj6396aXyC0/mntZkhKqq\nqp6enhcuXFBWVsa3KwaDoampGRER4erqamlpSc0YE0k8c9LV1W3rUmJubn769Gk7OztjY2N5\nhySu47cHPT09iptOXDtXYfGmU1VVpWzmkMRW5bRtOtR26xkbG7NYrFOnTmVmZt69e/evv/7a\nuHGjhYWFvOOh1fVEJomF6m2NWQ8fPjw3N1dFRWX37t1TpkyhYF0OzZuOzncxgiB4PJ5AILhy\n5UpAQABBEAo8YbvXXaxTILH7/woLCzdt2lRRUVFeXq6rq7t8+fKOzJHU0NCg4HxoJzbU7vRS\nOYWXnZ1dVVXl4OAgM8KqqqoRI0aIRKLo6GgfHx+8TZ6qqmpCQkJ+fr6fn5+VlRWHw3F1daXm\nfKDnkyup4wvB3NzcLC0tKWu6jqdNCmm6zhZDprLpUGdaD6+HZbFYeMmwqampvGOj2/VEgvRC\ndTab/eDBg7y8PFxxDSGkrKwcFxc3YMAA/HsfMmSIjo6OvANDtG86Wt3FJDKnrKysHTt2ZGRk\n1NfXv379WiAQjBgxQoEnbPe6i3WOoseC6UIgECxcuBCP7pPS09M5HA6esRETExMUFLRy5cpt\n27bNmTNHfOhd4bEpKjxyhxmZEXK53IULF+7fvx//GB0dffHixS1btlRUVFAQG0EQPB7vzp07\n0dHRhw4d2r9/P94VhyCINWvWzJgxo6ysjHznkSNHjh07RhDE+/fvS0pK5BFMWVmZxCZIAoHg\n/v37iYmJ9fX1AoFg6dKle/fuxS9lZmZyOJyzZ8/iH589ezZz5syCggJ5BNaWFy9eLFy48Ouv\nv96/f39QUNCDBw8IBTWdTLW1tSEhIffu3cM/fvvtt/PmzWtpaaFD0xGdbz0q0fZ6Qp6whYWF\nR44cmT59+t27d8lXHz9+zOFwyGl29+/fnzp1amVlJQWBkWjbdB0Mj+LYUlJSoqKi8H9XV1cH\nBwffuHGDIAiRSPTbb7/hibkKP2FpfhfrGuix+z8lJSUXLlwIDQ0VHyY3MTEhq0kNGDBAIYW4\nOhIbg8Gwt7enODzxPa1lRqikpGRubh4WFlZUVJSSkvLkyZMVK1b4+/tramrKOzZEsydXJPV0\nmJWVtX379pKSktzcXISQvb09TRaCYXV1dRs2bFiyZMns2bO9vLxevXoVHx8fEBBgZmZGZdNV\nVFTcuHHDzs5O+qW2tlFXYDFkUhdaj8rwaHI9kejOkegsmTVrlvRCdYWMWYujSdN1OTyK72Lm\n5uYODg4EQTAYjOzs7KSkpHXr1rFYLLxrWUpKSm5u7vTp0xV4wtL8LtZlH29iJzFQIhAILl26\n5OrqSs7hQAiJRKL+/ftHRUVRPEeyC7FRPL2Ux+MJhcKCggJ8x0IIyYxQT0/Pysrq1atXVlZW\nK1euxBOfKSAUCjdu3Dh9+vRFixaNGTPGzc2NyWQqdo26+DWuoKBg9+7doaGhISEh/v7+OHGh\n/vZA/7QpNzf3hx9+wCXvBQJBdHT0H3/88fbtW0dHR319fcUWQ6Z/65HoeT0Rf9SRPmFVVFQ0\nNDQkSqwNHDiQ4jFrejbdvwmP4pn+ZOZkYmISFxfn5OSEyx4xmUwNDY3o6Gg7O7sxY8YoKhVm\nMpkvXryg513s3/hIEzuZs3MeP378/Plzcg4HQmjFihWurq4ODg5UzpGkc2yY9J7WHh4eMiO0\ntLR0c3MbOXLkwIEDqSzATbcnV4y8xuXk5DCZzGnTpiGESktL09PTi4uLTUxMBgwYQOVCMDqn\nTZixsXF+fn5KSoqfn9+uXbtqa2vt7OwuX77c1NTk7u6u2GLI9G89Mk56Xk/EH3VknrBWVlbS\nC9X79Onj4OBgYWFBQV5C26brFuFhZOY0Y8aM9PT0goICMjYmk5mQkJCXl+fv76+rq0vlotfC\nwsIff/zx/PnzOjo6vr6+9LyL/SsKHgpWhLZm5+Tk5EycOJGcE5Camjp79uzm5maIjSQQCEJD\nQ8+cOXPq1Cl85OLFi8HBwRUVFYqNEJekxzsxEwTx/v178VkvmFAoLCoqmjRp0qFDhw4cOJCb\nm0tNbARBvHv37uHDhwRBHDx4cP78+WlpaRwOJzQ0dMGCBRMnTly+fPmMGTNCQ0MJgkhMTFy8\nePEff/zB5/MpCOzrr79es2aNSCTasWPH7t27T58+HRwc/Ntvv+FX16xZc/jwYfzfiYmJISEh\n8+fP/+KLL7hcLgWxYW/evJk0aVJYWNjPP/+MjyQkJAQFBRUWFhIEMXnyZLLk265du3Jzc0+d\nOvXs2TNqYqN/63WL60lycnJbJ2xGRoaiKufRvOnoHF5ra2t8fPyhQ4diYmK4XG5tbe306dPP\nnDnz6NEjDodDVq2Ljo4+ffr01q1bxWedUqC6unrRokVXrlz59ddfJ0+eXFhYSJO72AfUY3vs\nysvLpVcyY20NlIwbN05NTe2PP/7Izs7Gs9q3bNkij3L5dI6tfe3saT158mRFRUjnJ1eCIBob\nGxMTE0+dOoW3DoyJiTEwMMDrc0ePHr18+fJJkyZ5eHiEhYUFBAQMHDiQypVW1tbWZ8+exaXC\n1q1b5+zsrKOjc/r0aQ8PDx0dHcXuIYH16tWrubn50qVLQUFB/fv3RwhZWlpmZGRkZmb6+Pgo\ndht1mrRet76e4O6cKVOmZGdnS5+wnp6era2t8lttTfOmo3l4MklvEOLn56eqqhoZGRkSEqKk\npHTmzJk3b97cvn378ePHX3zxRWBgIN41RN54PN7Vq1dxTdORI0f6+Pi4u7s/ffo0LS1t+fLl\nCr+LfVg9NrHLzs7+8ccf3d3ddXV1+Xz+pUuXIiIiiouLbW1tjY2N2xkocXd3x9URly1bJqff\nKJ1jk9bxPa1HjRo1ZMgQiiNsa6J63759IyIiWCwWnsSTlpZ29+7duXPn2tnZUZY5PXnyZPv2\n7cnJyaWlpe/evROJRJ6enkpKSpGRkTNnznRzczMzM8N9+48fPy4sLJw2bRq5gw01aJs2SVST\nwluC4sKzDAajf//+Z8+eNTMzmzBhAvXFkMnY9PX1VVVVFd563et6Ij4K1rdvX3t7ezwQ5u/v\nL/OEdXR0lF+JNZo3Hc3DE4dLSTMYjPXr10+YMGHp0qW4iM+FCxcaGxtnzJiBM6eVK1caGhqW\nlZVZWlquXLmSmpQOISQUCrdu3YpX7t+9e3fcuHF4gqatrW14eLiJiYmHh4cC72IfXI9N7MzN\nzZ8+fZqenj527FjcnWNlZZWQkJCWlubj46OiotLW7Jxp06Y5OztbWlrKY7AfLwQbM2YMDWOT\nqVN7Wvv5+fXp02fgwIEfPMLu+ORaU1Ozfv2MuOsqAAAgAElEQVT6efPmrVy5cuzYsVVVVQkJ\nCd7e3oMHD8bXuKFDh969e7e+vj4yMvKvv/7asmULNasj+Xx+bGzsuXPnioqK7OzsnJycaJU2\nIakFkp6enpqamlFRUaNGjdLS0kII9enTp7y8PCEhYeLEiaNHj6Zyl3eJ2EJCQhTeevS81slU\nU1Ozc+dOHx8fvIjE3d3d2NgYP+rg6tzSJ6xcy7/RvOloHh6JnHVNEERbG4T0798fZ04eHh5e\nXl6UzVcTCoWbN2+urq7W0dFZs2bNuHHjUlJSKisrcd1B/GR7+fLlBQsWFBYWyvUuRilFjwXL\n0cuXLydOnHjkyJFDhw7hIyUlJcHBwcePHycUNDuHrOtDw9iktbWRn0gk+vLLL3ft2kW+s6ys\nTGKX1Q8rJSUlKCgoPz+fIAgejxcdHb1jx44TJ040Nja2trYKBILS0tL58+f/+uuvzc3N4eHh\nuGSdSCTKy8vDaROVu/hxudyWlpaUlBQOh0NOqBIIBCtXrsR7pz548IDD4Vy6dGnNmjVffvll\nREREU1MTNbEJBIL169fv3bv39OnTISEhq1ev5vF48fHxkydPfvv2Lfm2AwcOLFiwgMrZYOIR\nSleTEolEq1at+uabb8gj1dXVGzZsoHh2jszY6NB6NL+ecLncS5cuHT58+OzZs+S2yNu3bw8N\nDRWJRAKBYMmSJd999x1BENSfsDRvOpqHJzHruqysjMPhPHnyhHxDc3Pz5MmTcfm6nTt3rlu3\nTk6RSBcKJf53KT569OikSZNwLUnif1VCc3Jy8I9NTU2zZ88+ffq0vO9iVOqxPXYIIW1t7Zqa\nmri4uPHjx1taWiKE8JNfTEyMj49PZWUl9cNM5EIwGsYmrYNbSCGE2Gw2WZJAHrrLkysSe3g1\nMTG5fv36oEGDDAwMEEJMJlNVVTU6OtrBwWHw4MG5ubn5+fnff/89nnhHwcMrn8/fvHlzWVkZ\nm81eu3ats7PzkCFDoqKiuFxucHBwWlpabm7uqFGj8Jvt7OwePXrk6upK2VgJSeYCSfwnFxYW\nZm9vj//k1NXVfX19KZudgyfStbV4U+GtR+frSfujYEZGRpaWluR0Dnt7e4o7S+jcdPQPT2LW\ndfsbhNjb23t4eMjpTiG9jQR5KR4/fnxCQgKLxRoyZAhCyMjIqLi4+M6dO/7+/gwGQ1lZWVtb\nu7W11dPTU653MSp178SOy+VKnP94dWR8fPzbt28tLS0dHBzwQImnpyd+g4WFRWxsbJ8+fT79\n9FOKh5nIWQi45oWfnx99YsMEAkFsbGxYWFhhYaGrqyuuikSTLaSsrKzCw8MbGhrYbHZoaKiz\ns/PQoUOjoqKam5sHDx5Mh2n+Evsg6erqpqen5+fnjxkzBr+hpaXlzp07eXl5uN6K/K5xMikp\nKb148eLatWsTJkzAZV21tbWZTCYeKLGxsVFU2iRxzhIEIbOalI6OTmlp6e3btyne8E18x1KZ\ndcIIgsAlEiluPTpf67AOjoIFBAT069ePnM5BTWzirRcYGHjjxg36NB3Nf7Ptz7o2MjI6f/68\nmpoa3l/1wYMHycnJixcv1tDQkOvzv3j1HIlLsbKysoaGRlRUlLe3Nz4rbWxszp07p62tjW8Z\nFhYWjo6OcgpMIbpxYlddXb1s2TI7OzvcI4Jkrcfx9fXV0NCIjY0dPXo0/o0qKysnJyfr6uoO\nHTqUsq3KkdjtwcrKCi8EmzhxIk1iI+3YsaO+vt7W1vbatWvNzc3tb+RH5XbgiPZPrkjWkmF9\nff2IiAhc3xIhlJiYOHjw4JSUFD09PWdn5w9+jZO5p7VQKHz48OHz58/19fWdnZ3j4+PZbLa7\nuzt+1dLSMiYmRkdHZ8SIESUlJdSnTdLnbEBAgMwFkpaWlqNGjaLyT056x1INDQ2Zq63x4k0q\nW4/O1zoSk8ksLi6OjY0NDg42NTVlsVi4p9/Z2RlftHFVQh6Ph/vqKHvUkWi9nJwcHx+fK1eu\n0KHpaP6b/cdZ1xRvEEIQxPXr1xMTE93d3clCoRYWFhKXYlwTMS8vDz9UsNlsLpebmZk5duxY\nOQWmWN04sVNXV8/Jybl37x7uUBUKhTLX40ybNu3u3bsPHjwYNmyYqqrq06dPL126FBISIv7M\nLVfStwdyIdjkyZMVGxsZ4ebNmxsaGhBCa9eudXJy0tbWxlUbBgwYoMA9rWn+5Ir+6eHV2Ni4\noaHhzJkzb9++TU5OzsvLW7FiRVlZWUFBwejRoz94MNKDERIblzk7O7NYrEuXLpFPriwW686d\nOzjRpDJTb38NHYfDkblAsnfv3pRldUhWpo4QMjExoX7xpgTaXuuQ1Dnr6OjYkVEwR0dHeU/n\nIMlsPSMjo+bmZjpcimn1m8XFQcgy2iKRKDQ0NCgoaNGiRW5ubjo6OjExMfr6+n5+fuKlpCnb\nICQ7O3vv3r3l5eUzZszA4w/kNhKOjo7il2IGg2FmZnbq1CkHBwc8jOPg4ICnFckpNsXqxokd\nQsja2vrcuXM6OjrW1tZFRUVtrcexsLCIjo7OyMhISEhITk5eunQpXsJGDenbg5qaGl4INm7c\nOCsrKypjwz3V0hEWFxdHR0dLV22YNWuWojbyo/mTK+rYkuHBgwdra2uXlpbi5f1424nGxkZ5\nJHbigxEIIZkbl9nY2Ny+fTs9PR3fIV68eBEbGxsSEmJgYCCnTF26H/Ef19DNnj1bR0dHUSua\nxbdp8vb2ltix1MDAQOZqawqec0i0vdZJn7Pjxo3T0tKi1SiYzNaLiYmZO3futWvXFNV07cSm\nwN9seXn58ePH2Wz2vXv3XFxcOj7rWt4bhFRUVBw6dOj69eufffbZ3LlztbW18XGy08TNzU1i\nSzoDAwM8fQjvHqakpNRTszrUrRM7giB4PJ5AILhy5UpAQABBEH/++eeQIUPIh5j+/ftfvHix\nb9++w4cPz83NVVFR2b1795QpU6jZP6r92wNZ1/fTTz+lJjaRSBQfH//LL7/4+voymUyJrqaB\nAwfiB2vy+Yas2uDv76+Q3bdo9eQqrYMPr7ge0siRI9XU1PT19VNTUyMjIz///PMPGzD5YC2+\np/XVq1elNy4zNTU1NzfHd4jr168nJycvWbKEHJmVB4ktQcV7r3k8XlvnbGBgoEKqSUkUux4w\nYICNjY3EjqX29vbUxyY+pM5gMGh1rSMjbOucVewomPTUYZmt5+HhwePxqL9NiF+H24pNUb/Z\n+vr6P//8s6ioCBej4fF4dJh1HRkZ+eOPP3p5ea1du5Zsh9bW1urq6j59+uBOkzFjxjg7O0ts\nSYcHJXrMCol2dI/ETvqhPysra8eOHRkZGfX19a9fvxYIBCNHjmxnPY6Njc2QIUN0dHSoCfgf\nbw/idX29vLzkHVt2dvaePXvev3+/YsUKHR0d6a4mHx8fiecbslRYYGBgnz59KNvIDw/SlZSU\n0OrJVVrHH14RQmlpadu3bz9//nxpaemqVas+eJcn+WCdmpqqqqqKByOYTObZs2cfP34cERFx\n4cKF0tLSe/fuPXr0aM6cOfn5+Xw+f9++fRTcIcT7ETu1ho6yalIVFRV5eXnGxsYyi12vWLEi\nPT1desdSKhdvSgypu7m50eRaJ15HuqamRuY56+/vb2NjQ80omMxpphJTh9u5U+DHCcpuE9LX\n4fHjx6enp9PhN4s9ePBAQ0OjsLBwypQpbDZbU1OTDrOus7KysrKyli5dir+LIIikpKQ9e/ao\nqqo6ODiQnSYjRoyQuBRraGh8DFkdQt2kjh1Z/g2rrq4ODg7GpXFEItFvv/02adKkN2/ePH78\nmMPhXLx4Eb/t/v37U6dOrayspDhambv4NTc3S9R+I+Rc1wd79+7d3r17ORwOWeOnnep0q1at\nEo+Q+lJhLS0ty5cv/+uvv/6xHtLWrVsprrLG5/Ojo6M3bdp05MgRPp9fUVHB4XAOHz48a9as\nsLAwsuxWfX09QRD79u374osvxD9eW1tbXl4up9iePn3K4XAWLFiQm5tL7sxIEERmZmZMTExa\nWhquk1dSUsLhcKqrq/EGrBL12OSH3BKUIIjGxsZZs2b99NNP+CU6nLPff//9/v37CYJIT0//\n/PPPRSJRY2PjwYMH582bN2PGjPDw8OzsbEXtWEoQRH5+fkhICN4EmUSHdisoKFi4cOH27dt3\n7Nhx7Nix9s/Zffv2rVq1St4hid8pBALBhg0boqOjyV2Gr1+/jncZVlTr3bx5kyzj19Z1mA6/\nWR6Pd+fOnejo6MLCQoFAsHTp0r179+KXcBG4s2fP4h+fPXs2c+bMgoICKsPjcrkLFizAVS2f\nP3++Zs2adevWie/9jQuF4kp10pfij0H36LGTmDyUnZ2dlJS0bt06FovFYDBcXFxSUlJyc3On\nT59O2XqcioqKGzdu4HlLEtraC+GTTz6R6MiR60Kw1tbWc+fO/frrr0OHDn358qWGhgYeZm2n\nq8nMzEw8QiprXkgM0rXfl0P9kyv610uG1dTUNDU15RSb+IO1+GCEpaWlvb299MZl5E5iAQEB\nFJTQE5/UrK6uLt43TPEaOpnKy8uzsrJwz7TMbZqmTp1aXV1NZXEfhFBFRcWrV6/09fVlDqkP\nHjxYTU1Nge0mFAo3btw4ffr0RYsWjRkzxs3N7R9rmFEwCibRPdzW1OHZs2cr5K9OfMqarq6u\nzOuwsbGxYs8Iib1VhgwZgqv5ODo6GhoaGhkZKWrWNUlJSQm33rNnz+Lj46dOnbp48WI9PT3y\nDeKbIVFcvYEuFJ1ZdpT4Q39xcTGHw3n8+DH5alJSEvlInZubGxUVdePGDbnWLs/IyOBwOE+f\nPiUIgs/nR0ZGrl+//vfff+fz+e3shUDl00NUVNSBAweqq6sJgrh8+TK5c0Nnu5o+rHfv3knX\nB8eOHDkyffr0u3fv4h/p8ORaXl6enp7e1nO/Yh9eJXoQxR+sxUv583i8W7duPXny5ODBg4sW\nLSosLMQfb2pqWrp0KVl+/YPj8XgXLlzYtm3b8ePHGxsbxfsRpXcuoeacFf868d7K7Ozs4OBg\nkUiEf1yzZs3hw4fxfycmJoaEhMyfPz8kJIRsOmocO3Zs/vz5XC4XnwihoaELFiyYOHHi8uXL\nZ8yYERoaSlDebgRBlJeX44sevgi3traKv6rAc5bsDBO/U+BNBQ4ePEi+LTc3NygoCL+T+tYT\n71lv/zpMfWyYzL1VCIL46quvVq5cKRQK8Y+4ik1SUhLF4YnbuHHjjBkz2tqzpydtI9EFdO+x\nI+dwqKurl5eX44d+PT291NTUgoIC8tGQyWQmJCTk5eX5+/vr6+vLdT0OZmxsnJ+fn5KS4ufn\nt2vXrtraWlyTqampyd3dnclkytwL4e3bt1u2bKGmq8nBwcHT0xPPd7G2tr53715OTg5etkn9\nPAly7ks721o7OztLFLpUeF/O0aNHi4qKjI2N6bZkGEn1ILq5uZEP1kZGRuQMzsbGxvDw8IyM\nDHt7+1WrVpFFH5WVlcePH0/++GHhAjrNzc2WlpaJiYn379//5JNPVFRUcD+ipqYmxWvoxOFV\nTSdPnqysrHRzc2MymWw2GweGO6dlFrueOXOmra2tPOIhxApxEWKL1m1tbS9duiQUCn19fR0c\nHLS0tEaPHr18+fJJkyZ5eHiEhYUFBASYmZlR1m7Y2bNnIyMjAwMDCVl1pA0MDAiCCA8Pp/6c\nlTnNVKJ7GP196rCBgQHFrSfes25gYNDOdZjKM0KczL1VEEI2NjYRERFVVVUpKSm6urpWVlaU\nzbpui4WFxZUrV/r06SPzxKSseg490Tqxk95vG69kdnJywuWte/XqhX+pN27csLa2bmpqwvWQ\nqAnP2tr67NmzeK+hdevWOTs76+jo4PJvOjo6FN8e2sdkMk1MTKKiokxNTfv162dkZERxdTpy\naWQ7m4P5+fn16tVL/CpMWT2ktuBBurVr19JnyXA7RQcHDhyYl5eH1/ObmZnhwYgZM2b4+/vL\n3LjsA85ef/fuXWFhoba2NkEQbW1cNm3aNDyp2cvLiyw9oJBtJAYPHszhcM6fP//w4UN3d3ct\nLa3bt29bW1ubmJgghEpKSigrdi1eiEtTU3Pz5s29e/fGf+eqqqpqamrnzp0bO3ZsW0Pq1NRr\nkJluenh4yKzVzOFwAgMDqT9nxddvenl5kXcKcsM3cu0LxRu+iRf2CwgI8PLySk5OLioqGjly\nZDvXYQoCw8SrNxgYGMjcW0UkEqmrqxsaGiYlJdna2np7ezOZTMoibIuurm5lZWVcXJyfn5+q\nqqqiw6EX+iZ20nM4yPJvY8aMsbKyam1tPXPmzJs3b27fvv348eMvvvgiMDCQyq0tyYlK0n05\nPj4+VN4eOsLY2LigoODWrVsBAQGmpqYUdzWJz31pZ3OwqVOnSixQp+DJlUxKWCxWXl7eo0eP\n8EYRCCFcfSAkJIQmS4ZRu0UHfXx8xB+shw0bNn78eHk/tj579uzAgQORkZElJSWOjo46Ojpt\nbVwWGBjYr18/3I9oYGBA8dwXiUmcBgYGo0aNSk5Ovnz5srOzc1lZmZKSEt4EydnZmYJi19KF\nuJSUlLKzsxMTEwMDA/GN09ra+u7duy9fvhw+fDifz7979259fX1kZORff/21ZcsWam7/eIth\nmemmtbW1zFrNRkZG1PQ2ia/Jzc/PlznNVFNTU4FTh6UL+40ePdrU1BT3rDs7Oyt2vppE9QY8\nfVlmvm5paenp6cnhcFxdXemQ1WH29vaXL19ubGzE5a8BiS6JXU5OTlVVlXjeI7NPmFzJ7OXl\nNWjQIENDw7KyMlz6lbKUTvxq4unpifdCwBU3xPtyJkyYoJC9ENphY2Nz8eJFBoPh5ORkb29P\nQVcTIWu/FxcXl7Y2B/P19bW1tZVYYiI/EklJr169UlJSjh07Jj1I5+LiosDn/o4XHXR0dNTT\n08MP1uPHjyfrdsoDQRAnT56MjIycMmXK6tWrcYcrQsje3r6tjcvGjBlDTmqmrKIvXoVgaGgo\nUSdcQ0Nj7Nixr1+//v3339lsNu6FQgipqqrKu9h1W4W49PX14+LilJWVcYrJYDA0NTUjIiJc\nXV0bGhpkDqnLFY/HE4lEz58/l5luTpo0SWatZgoCQ1LjOTNmzPD09CQ7w8TvFIrqHm6rsB+H\nwyF71gcMGEB9lVBMZnEffHbIzNcpWGXVWbivJzY2NiAgADrt/kZhs/vEZGdnT5o0afLkyTEx\nMeTB9+/fczicrKws8XcKhcK7d++SK5mpJ7HCnyCI+Pj4yZMnv337lnzPgQMHFixYQHExjg46\nefJkcHBwRUUFBd+VlZX15Zdfbt269eXLl/jIwYMH58+f39raWldXN3PmzB9//JF8c1NTU3Bw\n8OXLlwlKFqjjKjkLFixISkoSCATiL2VnZ8+ePXvTpk21tbUEQSxdujQ9PR0fV0jNi9evXy9d\nunTXrl3Hjh2bPXv2zp07RSJRXFwcLvFDvo36v7rw8PB169Y1NjZKvxQTE4NXC5FHVq5cefr0\naUIRk5rJVQgSlVZI165dmzRp0vr16ykL6ezZs+IXDZFIdOPGjblz50ZFRUVEREyfPr2mpga/\n9Pbt2+Dg4FWrVpHz1ilD1h6qqqqaOnVqdHQ0+dKtW7c4HM6zZ88IgsjLy8OdiFTOo29rjn96\nejp51xCvefH+/fuSkhJqYuNyubgp8vPzcXUh8qXo6OjJkyeT9YYOHTp04MAB8Tod8vP8+fPd\nu3dPnz592bJleXl5RNvFfQiCiImJ4XA4mzdv3rNnz/z581+8eEFBhF0jEAg+5kUSbaFFj11r\na2t8fPzatWtPnjxZUFDg5uamrKzc1n7bI0eOrKmpwQ/9FMcpPTqMECLncIwaNQq/jeI5HJ1i\nZ2eXmppqa2sr1wfrf9zvZciQISoqKjI3B3N1daVgkO7s2bPZ2dn79++3tbWVGFloa5BOIc/9\n7exvodiZQwRBfPPNN4sWLcJ9rlh5efnLly+VlZWdnZ3b2rhMrpOauVwu7uAnZE0LGzx4sMSQ\nOmZjY2NmZnbv3r1JkybJKTAJdnZ2N27cKC4uHjVq1IsXL7799tuCgoLVq1fjrqbExETc1YQQ\nSkxMdHZ2rqysdHFxkV+5HHE8Hq+lpWXbtm3ksLW6urpIJIqOjvbx8VFTU0MIqaqqJiQk5Ofn\n+/n56enpybtWcwfHcxBCJiYm0tNMqeweJrfLs7S0bGdjFRcXF7JnXd5T1ioqKn7++edr166N\nHTt2woQJjx8/fvr0qY+PT1vFfby9vd3c3Nzd3anf96ULmEzmx7xIoi20SOxUVVUjIyNnzpwZ\nGBh49erVuLg4PFmnrf22nZyc5Ff+TYL4wGtDQ4P01QTvNBAWFmZvb0/9HI7OUlZWDgwMlOuJ\n2vH9XtraHEzeV+H2kxK8kk7mIB31JZHoWXQQIcRgMOLj4/HQcGFh4ZUrVw4fPhwREXHjxo0r\nV65YWFi4u7tTuXEZ3jHv22+/xX9ObU0Lc3JykpjEieEZ7sHBwfKLUFw7hbiUlJTw9aSoqCgl\nJeXJkycrVqzw9/enJqvDeQm+tIoPWysw3czJydm+ffvNmzdVVVXt7e3xwbbm+PP5fHt7eyqn\nmdbW1r5//75Xr14ikUh8EidCqP3CfpaWlhRMWRO/GtvZ2RkaGiorK7948cLf35/FYrVVveHm\nzZvTpk1zdnambG8V8GHRIrFjsVhXr161t7cfOHDguHHjCgoKfv/9dx0dHU9PT5lzOChbySwx\njcPFxUXm1URHR6e0tPT27dsUz+HoGnmvpOvgfi8jR47EZUSo3xys/aSkX79+ZmZmSkpKHh4e\nmpqacXFxLBYLdw/LL+M8f/58VlaW9LxpmmzOKJOZmVlsbOzly5dTU1M1NDT8/Pw+//zzkJCQ\nt2/f3rx5c/HixZRtXIbXlr5//37dunX4d9fWKgQvLy/pDd8QQrdu3aqvr/f19ZVfkBL69ev3\n5MmT4uLio0eP2traip+VxsbG/fv3f/XqlZWV1cqVK3HFInmTWFwyYMAA8dpDCkw3OzWeY2lp\naW1tTc0005ycnO+///7kyZNXrlx5+PCht7f369evxbNhhBCu3qCmpoYnTT548CA5OXnx4sUa\nGhpyikqCxNVYIBCcO3fOwsKipqZGU1OTzWbLrN4wceJEOvfSgX9Ei8QOIYQrcQ8YMKCuri4r\nK6u6uvru3bsVFRUzZ8708PBQSJ+w9MBrO1eTUaNGfYzlrWVpZ5gJ/X2TXBcXF0VtWN5+UsLh\ncPDbKBuky87OvnDhgre3t0SXG002Z5TJxMRk4sSJAQEBn3322ZgxY6ytrdlstoqKikAgSElJ\n+fTTT3E9IENDQ3KV8QdHDvrPmTNnzpw55F3czs7uwoULMlchDBw4UCIhTk1NPX369OrVq8WL\n11OgnUJc5ubmI0eOHDhwIGUz1iW28VVRUZEYtlZIuok6P56jrKws784wPLgZHx8/ZcqU9evX\njxgxIjIyUk9PD+/cSGbDiAbFOMWvxs+fP9+7d29dXZ2xsfH9+/fxytzW1lZaVW8AH4Zip/iR\n9u3b9/3330dERISEhJw6daqlpeXZs2dz5sz54osvSktLFRKSzNLqOTk5EydOJLcjTE1NnT17\ndnNzsyICpK/bt29zOJxt27bNnz//5s2bZE1/ErlJ7ps3b16/fq2IGAkej/f+/XuJlRNJSUkz\nZswQP5KXlxcSEiLvYPDuh7t375Z+iQ6bM3acQCDYtGnT8ePH8Y8nTpyYPXt2W9Xh/6Xz58/P\nnDkzKiqKx+ORB5ubm/Hc+XZWIUhMpW9qasLl/ql38ODBkJAQRX07l8tNTEw8duxYQkKCQCCQ\nWFwivUGIosyaNQtvvd3S0rJ///7g4GC8bEIhc/xl/tUtWrTo+vXrBEFIr2oiFLeNBEZejefN\nm3fjxg18NRYIBKtWrdq0aVN9ff2ePXuWL18eHh4u/n8EujW69NgVFBTExcVpampu2rQJ90zo\n6+t7e3tXVVUNGDCAmplDHSnVqKenp6SkdPr0aYWs8O8u2hlmwshNcjGFBKmkpKShoSH+TC8U\nCo8fP+7m5jZ48GDyIDWDdHjSVWRkpIODg8SfEx02Z+ygoqKivXv36ujorFixAjesnZ3drVu3\nbG1t5VGeQ3rQ/8aNG3v37kUIDRkypJ1pYfr6+uLdnMrKyoqqlUBxIa6mpqYnT57gqYdv3rzZ\nsmVLXV0dm82+fPlydnY23paG7KXDs4cpqz3UDlqN50j/1Z05c6a4uHjJkiUqKipWVlbSkzgV\ntY0EJvNqzGQya2pqcnJypk6dKu/iPoB6dEnsysrKXr169d///lc8h1NXV8d7S1MQQMdLNX7y\nySeffPJJt1gxpEDdbr8X6aQEUTtIh6+/aWlpAQEBEqkwNUUHu+z69etpaWnR0dFXr16dPHny\n3LlzyQaU68Zl0oP+RUVFq1ev9vf3R4pehdBBFBfiOnbsWHp6+rhx49pZbS2elyiqApyEzMzM\nhoaGkpKSH3/80dnZedOmTW5ublFRUbdv3x47dqynpyeVc/yl/+qqqqq2b9+ON4qkTzYsTvpq\nLBQKw8PDhwwZ4urqqtjYgFwousvw/zx8+DAoKIiyruCysrLMzEyyY7y2tjYkJAT39hME8e23\n386bN6+lpQUGXv8NxQ4zdVB8fPzp06d37NixYMGCxMREiVcpHqQrKCgICgq6cuWK+MGUlBTp\nwOijpaUlPDz8yJEjd+/e5fP5FH+7+KB/UlKS9KB/SkrK3r17z58/T9vTlspCXOfPn1+4cCFB\nEK9evQoKCuLz+VVVVT/88IP4lAmJeo1UVoBry++//87hcPbs2VNWVkYerKqqOnbsmHitRMr8\n418dBZU4O0v8alxcXLxu3bo9e/ZITEQBPQZdVjIbGhoSBPHu3Ttzc3O5ftGzZ8/Cw8NfvXpl\nbm6+cuVKMzMzhFB+fr6mpuawYcOamppOnjyZm5vb2toaHR09a9asefPmnTx58tGjR2w2u6Cg\nYNu2bZTNGu7u5syZc+/evbNnzy5ZsrZ+AucAABKwSURBVETRscjW2tpaWVnZ1NTk5+fn4eEh\n/dBP2eI1zMrKysfH5+zZs6NGjdLS0nr58uXx48cbGxsXL15MZRidoqamNmvWLEV9+8iRI69e\nvVpQUHDixAmJXxafz8/MzBw2bNiwYcMUFV5HKCkpUbahqrGxcWVlpVAoVFdXJwjit99+u3v3\nrr+//6+//ooL1DU0NDg6Onp5ef3xxx94QgLFC0pkMjQ01NfX37Jli/hBXV3dzz//XCHxtPNX\nhy1cuLClpYX6wNqBr8YnT55UVlZOTU0NCQnpFjUcQNfQZShWTU2tsLBw1KhR+PoiD0QbOyAh\nhHpGqUa6of9+LywWC+9S37dvX5psgIgnXVVWVj5+/Pj06dPjx49fuXIlNftHdVMyB/1TU1P3\n7NlTU1Pj5eUl7/o+9NTa2lpYWCiRlvH5/Pj4eB8fHwMDA9qutpZWX18fFxf36aef0mcSWPtT\nTSirh9xx+Gp86dIle3v7TZs2DRw4UNERATmiS2KnpKQ0evRo+WV1qN3NBqBUo5zY2tqOHDkS\nFs93HHn9tbOz27x5s4ODw8eZl3Scrq5uZWVlXFycn5+fqqrqy5cv//vf/z58+HD58uXBwcEf\nbeudOnXq559/FolETk5OZCOoqalduHDB09PT0NDQyMjo+vXrbDYbL8R5/vx5WFjYwoULdXV1\n6ZaXiESiq1event70ycqib86RYfTIba2tqNGjRo9erSKioqiYwHy9bFkKgRBxMbGrlu3Tnzq\ndHl5+fv3701NTfG815cvX5LblKWmpu7cuTMtLc3d3R1Ogy6jcpipxwgKCho6dCi0W8eRw0wq\nKiqpqakzZ8709/enSResoixcuNDY2PjEiRN5eXnr16/X0tJCCGloaPTq1au8vNzJycnFxWXB\nggW///57SkqKtrZ2SUnJ6tWr5Vdx8N8wMDBwc3PD/wv0Qf+pJhLgavzxYBAEoegYKLJgwQIL\nC4s5c+ZUVlZmZGQ8ePCgoqICIcRisTZs2ODp6fmf//zn2bNnc+fOffz4cXNz89atWxUdMgCg\nQ2JiYsLCwj755JOQkBBarXtVrMLCwu+++w7vdoV3F1i/fv2gQYPIaZEvX7588uRJ79698TY/\nCg22+4mJiTlz5szJkyfptsYffOQ+osQuIyPjP//5T3Nzs4qKCt5t1tXVVUtL6+effy4sLDx2\n7FhDQ8OhQ4dKS0uHDx8+bdo0ymq+AwD+JaFQ+O7dO+iQkNbc3PzTTz89fPhw8eLF/v7+Bw4c\nQAitWbNG0XH1BPBXB+jpI0rsEEJ8Pr+urk5HR0d8Eu6tW7eOHj167tw5BQYGAADyc/ny5d9/\n/33cuHGGhoaPHj369ttvFR0RAEBePpY5dpiysrLEMjGhUHj9+nXxEsQAANDDBAUF2dvbf/fd\ndzweD8YiAOjZemCPnVAozMjIqK+v9/DwaH++bVFR0S+//KKnpxcaGgorXgEAPVtDQ8OxY8cc\nHR3x5hwAgB6ppyV2WVlZBw8e1NfX5/F4o0eP5nA40u+5fv36+/fv8/Pz37x5M3PmTB8fH+rj\nBAAAAAD44HpUN1VBQcF33323efNmR0fHtt7zj5sNAAAAAAB0Uz0hramoqKisrHRwcEhNTbW1\ntcVZXWlp6YsXLxBCXl5e4sv4FbsDEgAAAACA/PSEodjjx4+npqYeOXLk2bNnO3bssLe3r6qq\nqqqqMjU1ra6uNjc3379/v6JjBAAAAACQu+6a2BEEQe6T09DQsHTp0gkTJsycOfPJkyfFxcXG\nxsZOTk4aGhqvX79esWLFqVOn8N4SAAAAAAA9WHfadScpKenOnTsIIT6fv3HjxgcPHuDjWlpa\nISEhFy9erKysdHFxmTRpkoeHh4aGBkKosLDQwMBAW1tbkXEDAAAAAFCiO82xU1dXP3LkCJfL\nLSsrMzIyOnHixODBg/Hqh8DAwPj4+D/++GP9+vV8Pj8lJUVHRyc5OTkrK2vLli0f7UbgAAAA\nAPiodKceu169elVXV587d87Dw2PevHk1NTWXL1/GLzGZzE8//fT27dvPnz9/9erVpUuXTpw4\nYWBg8NNPP1laWio2bAAAAAAAanSnHrvi4uKRI0fev3+/d+/eurq6wcHBkZGRY8eOxSOttra2\nKioqR48e/eGHH3744QdFBwsAAAAAQDV69dhxuVyJIzwe7+bNm2FhYTk5OePHj1+7dq2+vv7J\nkycRQpMnT2az2cePH8fvTE1NnTVrlqamZlVVFdVxAwAAAADQgNJXX32l6Bj+T3V19bJly+zs\n7AwMDPCR3NzcrVu31tXVcbncP/74w9ra2szMzMjI6MyZM46OjiYmJubm5mFhYUVFRSkpKU+e\nPFmxYoW/v7+mpqZi/0cAAAAAABSCRomdurp6Tk7OvXv3/P39GQxGXV3dhg0blixZMnv27P/X\n3r3HVF3/cRz/HM6Bo3ITkfulEx0YhDBncezIURyZwizAsWw0BwqyUVpapu5UQ8emLUeXkcq0\nQC3GVcGEwkjThAlmQBOaIpZcIqAQg0QuHji/P85+zKlMLDjY8fn463y/38/3+31//jl77XM+\nn88JDg5ubm4+fvx4WFiYp6fn5cuXy8vLw8LCXF1dFQpFc3PzE088sX79+unTp091JwAAAKbM\nQxTshBBKpTInJ8fOzk6pVNbX19fV1b3yyis3b97ct29ffX39wMDA8PBwYGCgt7d3bm7utWvX\nzp49a9jfxN/f39zcfKrLBwAAmEpTNseuo6OjtLT09jN6vV4ul4eHh2dlZd28eTMgICA9Pb29\nvf21116TyWR79+59/vnni4qKOjs7XV1d161bV1dXZ2dnx6JXAAAAgylYFTs4OCiXy69evdrX\n1zd68sKFC7t37545c6ZMJuvp6cnNzY2PjxdCpKamqlSqpKQkIYSTk5NcLtdqtVZWVqmpqUuW\nLDF+8QAAAA8tYwe77u7u119/XavVqtVq8f9/Brt+/XpKSsqrr74aGhqq1+szMzOLi4uXLVvm\n5ubW1NS0dOlSw72VlZXbtm2rqqoKCgqysLAwcuUAAAAPOWP/FDtr1iwfH59PP/1Ur9cPDw9v\n3br1zJkzjY2NQ0NDwcHBQgiJRLJ69Wp3d/eMjAwhhFqtzsvLO3369EcffWRmZubj4xMbG+vn\n52fksgEAAB5+UzDHLiEhobm5uaysTCqVenh4HDx40NbWVghx8eJFQwOpVBodHf3jjz/W1NQk\nJSV5e3sXFBQ4Ojpu2bLF+NUCAAD8Vxh7Vaxerx8aGtLpdCUlJWFhYQEBAUVFRTY2Njdu3Lhy\n5cqzzz5raGZmZlZWVnb58uWIiIiQkJDly5cHBgZKpVJjlgoAAPDfYtRgd+HCheTk5Orq6t7e\n3tbWVp1Op1arpVJpfn5+TExMSUmJjY2Nj4+PEOLkyZNKpbKvr2/OnDlWVlZGqxAAAOC/S6LX\n643zpuvXrycmJt6+QqKkpGT37t3Ozs7r1q3z8vJycHA4evSoRqPR6XQtLS27du2ytrY2Tm0A\nAAAmwHhz7MZaISGVShMSEioqKlQq1YYNG4QQSqXygw8+INUBAAA8kEnc7kSn0x07duz8+fMK\nhSIhIcHwD7AXL16cO3eu+P8KiQ8//LCmpiYoKGjevHkHDhxITU0NDQ2dvJIAAABM2CSO2G3f\nvr2hoSEgIODEiROff/65QqFQKBQFBQWjDby8vGQy2WeffabT6ZKSkt54443JKwYAAMDkTfzi\nieHhYa1W+/fffwsh3nzzzYCAgJkzZ37xxRfz58/38/PLy8u75woJZ2dnGxubia0EAADgkTIp\niyf2799fWlq6cePGkJAQIYRer9+8ebO5ufl777134MABVkgAAABMhokZsRsaGvr+++/Ly8ul\nUqmjo6Ofn19ZWZlMJps/f74QQiKRKBSK7Oxsd3f3yMhIJyen9vZ2Ly+v9evXs5UJAADARJmA\nYNfQ0PDOO+/09PQMDg4ePHhQqVQqFIoZM2YcPnx44cKFhh9Y7e3tOzs7y8rKwsPDlUplcHCw\nv7+/ubn5BPQAAAAAQoh/v3iip6cnJSUlPj4+OTl58+bNarV67969AwMDy5Yte+yxxzIzM0db\nxsXFzZ49u7u7+1++EQAAAPf0YMGura2tqqrq0qVLIyMjhjONjY2WlpZqtbqvr++TTz5paGgY\nGBg4cuSIRCJZu3bt+fPna2pqDC3t7Ozef/99Z2fnCe4BAAAAhBDjXzzR2NiYkZHR2tpqaWnZ\n2dnp7++/Y8cOiUQyODgok8k6OzvffffdoKCg1atXFxYWFhUV7dmzx9HRcdeuXW1tbWlpaZPd\nDQAAAIxrg+KKiop9+/atXbtWo9FIpdK2trbBwUGJRCKEkMvlQojU1FSVSpWUlCSEcHJyksvl\nWq3WyspKq9XqdLpJ7QAAAAAM7h/sOjs709LStm/f/uSTTxrOuLm53dGmqalp6dKlhs+VlZXb\ntm2rqqoKCgpycXGZ2HIBAAAwlvsHuxMnTnh6eo6muntSq9V5eXnTpk2rra01MzPz8fExbEEM\nAAAAo7n/4omOjg5bW9t7Xurq6mpraxNCJCUleXt7FxQUODo6btmyZYJrBAAAwDjcf8TOzs6u\nrq5Or9cbJtXdLjMz087OLjEx0dra+u23356cCgEAADAu9x+xW7BgQXd396lTp+44r9Pp6uvr\nAwMDJ6cwAAAAPJj7BztfX1+VSmXY62T0pF6vz8jIcHNzCwoKmszyAAAAMF7j2seut7dXq9V2\ndXVFRUX5+vr+9ddfx48ft7CweOutt8aafgcAAAAjG+8Gxf39/fn5+efOnevv7/fw8AgNDQ0J\nCbl71h0AAACmyniDHQAAAB5yD/ZfsQAAAHhoEewAAABMBMEOAADARBDsAAAATATBDgAAwEQQ\n7AAAAEwEwQ4AAMBEEOwAAABMBMEOAADARBDsAAAATATBDgD+iSVLligUin9wo0aj8fX1nehy\nAEAIgh0AAIDJINgBAACYCIIdANzbU089pVarv/vuO5VKNWPGjFmzZsXHx/f09Iw2kMlkV69e\nDQ8Pt7a2tra2fumll7q7u4UQGo1m9uzZQ0NDtz9t8eLFDg4Ot27duuMtubm5hufb2Ng8/fTT\nubm5RugaAFNFsAOAe5PL5b/88svWrVs//vjjlpaWtLS0rKysNWvWjDYYHh5esWLFokWLsrKy\nkpKSCgoKNm3aJISIj4+/du1acXHxaMuOjo7y8vKXX37Z3Nz89lfk5eXFxMS4u7sXFBTk5OQ4\nODjExMR89dVXRusjABMjm+oCAOAhZWZm9ueffx45cmTBggVCiFWrVp0+fTojI6O1tdXDw0MI\n0dTUVFhYuGLFCiFEZGTk2bNnv/76ayHEypUrN2zYcOjQoejoaMOjDh8+PDIyEhcXd8crfv31\n19DQ0NzcXAsLCyHEwoUL7e3tc3Jyli9fbsyeAjAZjNgBwJgsLS01Gs3o4aJFi4QQ9fX1hsNp\n06ZFRUWNXlUqlV1dXUIIKyurF198sbS09I8//jBcys/PnzNnzrx58+54vlarPXnypCHVCSFs\nbGycnZ1bWlomrUMATBzBDgDG5OTkJJFIRg/t7e2FEJ2dnfe8am5uPjIyYvgcHx+v0+mysrKE\nEL///ntFRUVsbOzdz+/t7U1OTg4ICLC1tZXJZDKZ7Lfffht9CAA8KIIdAIyXTqcTQpiZ3f+b\nU6PR+Pj4HDp0SAhRUFBgZma2atWqu5u98MILO3fujIiIKCkpqa2t/emnn1xdXSe8bACPDubY\nAcCY2tvbh4eHpVKp4dAwVufk5DSee9esWaPVan/++efs7OznnnvOxcXljgZXrlw5c+ZMYmLi\njh07DGd0Ol13d/fjjz8+cT0A8GhhxA4AxtTf319WVjZ6WFpaKpfLVSrVeO6Ni4uTSqU7d+78\n4Ycf7l42IYQwbH3i7u4+eiY9PX1gYGB4ePhfFw7gEcWIHQCMycPDY+PGjc3NzUql8ptvvjl6\n9GhsbKydnd147nVxcQkLC8vOzraxsYmMjLy7gVKp9PDw2L9//9y5c+3t7YuKiqqrqxcvXlxd\nXX3q1CmVSmVpaTnRHQJg4hixA4AxWVpaZmdn5+TkREZGZmRkJCYm7tmzZ/y3x8fHCyFWrlw5\nffr0u6+am5sXFhZ6enrGxMRER0ffuHHjyy+/3LRpk1wuj46Obmtrm7BuAHhkSPR6/VTXAAAP\nI41G09XVdenSpX/8hOLi4oiIiHPnzo3z11sA+JcYsQOASXHr1q2UlJRnnnmGVAfAaJhjBwAT\nrLW1tba2Nj09vba2trKycqrLAfAIYcQOACbYt99+GxUV1dDQcOzYsaCgoKkuB8AjhDl2AAAA\nJoIROwAAABNBsAMAADARBDsAAAATQbADAAAwEQQ7AAAAE0GwAwAAMBEEOwAAABNBsAMAADAR\nBDsAAAAT8T9oteU4i4FquAAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "phyla <- full_data %>% filter(tax_rank == \"phylum\")\n", + "\n", + "ggplot(phyla) +\n", + " geom_boxplot(aes(reorder(tax_name, as.numeric(count)), as.numeric(count)), alpha = 0.8, colour = \"grey30\") +\n", + " geom_jitter(aes(reorder(tax_name, as.numeric(count)), as.numeric(count), colour = id)) +\n", + " scale_colour_grey(start = 0, end = .9) +\n", + " geom_point(data = filter(my_data, tax_rank == \"phylum\"),\n", + " aes(reorder(tax_name, as.numeric(count)), as.numeric(count)), colour = \"red\",\n", + " size = 3, alpha = 0.8) +\n", + " theme_minimal() +\n", + " theme(axis.text.x = element_text(angle = 30, hjust = 1),\n", + " legend.position = \"none\") +\n", + " ggtitle(\"Distribution of phyla in my microbiome\",\n", + " subtitle = \"compared to OpenHuman public datasets\") +\n", + " xlab(\"phyla\") +\n", + " ylab(\"Log of counts\") +\n", + " scale_y_log10()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot represents the log of the bacteria counts for each phyla found in the samples. Public data is in grey, you are in red!\n", + "\n", + "The public datasets are mostly from gut samples. If you see outlier in grey, they may come from genital or skin samples. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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Var1WobGxujf2XyBGq1WqVSKRaLbWyMHC+eQCKqqanhOM7o02SBVSYyoFAodDqd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\n+Bt0K5G3YCMOqn4zP/roo5MnT0qS5O+IbkE1TwN5+dmzZyVJ2rt37xtvvCE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m5eUJIQoLCwcNGjRw4MCXXnopKSlp9+7dc+fOHTNmjNFo\nFEK4jvLjH/84lPUCAKgkAQiimTNnur4AzWZzWlraxIkTjx8/7rragQMH+vfvHx0d3aFDh0mT\nJt24caO4uDghISEuLu748eNlZWV33323yWTq1q1bvSv/5z//ycvLS0xMNJlMKSkpeXl5hw8f\ndg60e/fuQYMGtW7d2mQyZWVlFRYWWq1W+Sa3UQAAzZ9OkqRQ9ZQAAABoRHzGDgAAQCNo7AAA\nADSCxg4AAEAjaOwAAAA0gsYOAABAI2jsAAAANILGDgAAQCNo7AAAADTi/wFUccdA9q0BMgAA\nAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# merge both datasets for the stacked bar chart\n", + "data_4_barchart <- bind_rows(full_data, my_data) %>%\n", + " filter(tax_rank == \"phylum\")\n", + "\n", + "# random color scale up to 433 colors\n", + "n <- distinct(data_4_barchart, tax_name)\n", + "color <- grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]\n", + "col <- sample(color, length(n$tax_name))\n", + "\n", + "ggplot(data_4_barchart) +\n", + " geom_bar(aes(id, as.numeric(count), fill = tax_name),\n", + " stat = 'identity', position = 'fill', alpha = 0.6) +\n", + " ggtitle(\"Distribution of phyla between datasets\") +\n", + " theme_minimal() +\n", + " scale_fill_manual(values = col) +\n", + " theme(axis.text.x = element_text(angle = 30, hjust = 1, size = 6)) +\n", + " xlab(\"Dataset\") +\n", + " ylab(\"Percentage\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}