From aa5d8c0fd5db40910501282ff25244780c1eff4f Mon Sep 17 00:00:00 2001 From: bmeluch Date: Tue, 10 Dec 2024 11:27:24 -0800 Subject: [PATCH 1/6] try putting R functions in separate script --- NOM_visualizations/R/NOM_R_notebook.ipynb | 318 +++++++--------------- utility_functions.R | 80 ++++++ 2 files changed, 185 insertions(+), 213 deletions(-) create mode 100644 utility_functions.R diff --git a/NOM_visualizations/R/NOM_R_notebook.ipynb b/NOM_visualizations/R/NOM_R_notebook.ipynb index 84398937..7c321e47 100644 --- a/NOM_visualizations/R/NOM_R_notebook.ipynb +++ b/NOM_visualizations/R/NOM_R_notebook.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "metadata": {}, "vscode": { @@ -25,7 +25,9 @@ "library(ggplot2, warn.conflicts = FALSE)\n", "library(jsonlite, warn.conflicts = FALSE)\n", "if (!suppressPackageStartupMessages(require('ggExtra', quiet = TRUE, warn.conflicts = FALSE))) install.packages('ggExtra', quiet = TRUE); suppressPackageStartupMessages(library(ggExtra, warn.conflicts=FALSE))\n", - "options(warn = -1)" + "options(warn = -1)\n", + "\n", + "source(\"../../utility_functions.R\")" ] }, { @@ -45,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -54,16 +56,16 @@ }, "outputs": [], "source": [ - "get_first_page_results <- function(collection, filter, max_page_size, fields) {\n", - " og_url <- paste0(\n", - " 'https://api.microbiomedata.org/nmdcschema/', \n", - " collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields\n", - " )\n", + "# get_first_page_results <- function(collection, filter, max_page_size, fields) {\n", + "# og_url <- paste0(\n", + "# 'https://api.microbiomedata.org/nmdcschema/', \n", + "# collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields\n", + "# )\n", " \n", - " response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE))\n", + "# response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE))\n", " \n", - " return(response)\n", - "}" + "# return(response)\n", + "# }" ] }, { @@ -77,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "metadata": {}, "vscode": { @@ -86,28 +88,28 @@ }, "outputs": [], "source": [ - "get_next_results <- function(collection, filter_text, max_page_size, fields) {\n", - " initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields)\n", - " results_df <- initial_data$resources\n", + "# get_next_results <- function(collection, filter_text, max_page_size, fields) {\n", + "# initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields)\n", + "# results_df <- initial_data$resources\n", " \n", - " if (!is.null(initial_data$next_page_token)) {\n", - " next_page_token <- initial_data$next_page_token\n", + "# if (!is.null(initial_data$next_page_token)) {\n", + "# next_page_token <- initial_data$next_page_token\n", " \n", - " while (TRUE) {\n", - " url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields)\n", - " response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", + "# while (TRUE) {\n", + "# url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields)\n", + "# response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", "\n", - " results_df <- results_df %>% bind_rows(response$resources)\n", - " next_page_token <- response$next_page_token\n", + "# results_df <- results_df %>% bind_rows(response$resources)\n", + "# next_page_token <- response$next_page_token\n", " \n", - " if (is.null(next_page_token)) {\n", - " break\n", - " }\n", - " }\n", - " }\n", + "# if (is.null(next_page_token)) {\n", + "# break\n", + "# }\n", + "# }\n", + "# }\n", " \n", - " return(results_df)\n", - "}" + "# return(results_df)\n", + "# }" ] }, { @@ -123,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -132,40 +134,40 @@ }, "outputs": [], "source": [ - "get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) {\n", - " # collection: the name of the collection to query\n", - " # match_id_field: the field in the new collection to match to the id_list\n", - " # id_list: a list of ids to filter on\n", - " # fields: a list of fields to return\n", - " # max_id: the maximum number of ids to include in a single query\n", + "# get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) {\n", + "# # collection: the name of the collection to query\n", + "# # match_id_field: the field in the new collection to match to the id_list\n", + "# # id_list: a list of ids to filter on\n", + "# # fields: a list of fields to return\n", + "# # max_id: the maximum number of ids to include in a single query\n", " \n", - " # If id_list is longer than max_id, split it into chunks of max_id\n", - " if (length(id_list) > max_id) {\n", - " id_list <- split(id_list, ceiling(seq_along(id_list)/max_id))\n", - " } else {\n", - " id_list <- list(id_list)\n", - " }\n", + "# # If id_list is longer than max_id, split it into chunks of max_id\n", + "# if (length(id_list) > max_id) {\n", + "# id_list <- split(id_list, ceiling(seq_along(id_list)/max_id))\n", + "# } else {\n", + "# id_list <- list(id_list)\n", + "# }\n", " \n", - " output <- list()\n", - " for (i in 1:length(id_list)) {\n", - " # Cast as a character vector and add double quotes around each ID\n", - " mongo_id_string <- as.character(id_list[[i]]) %>%\n", - " paste0('\"', ., '\"') %>%\n", - " paste(collapse = ', ')\n", + "# output <- list()\n", + "# for (i in 1:length(id_list)) {\n", + "# # Cast as a character vector and add double quotes around each ID\n", + "# mongo_id_string <- as.character(id_list[[i]]) %>%\n", + "# paste0('\"', ., '\"') %>%\n", + "# paste(collapse = ', ')\n", " \n", - " # Create the filter string\n", - " filter = paste0('{\"', match_id_field, '\": {\"$in\": [', mongo_id_string, ']}}')\n", + "# # Create the filter string\n", + "# filter = paste0('{\"', match_id_field, '\": {\"$in\": [', mongo_id_string, ']}}')\n", " \n", - " # Get the data\n", - " output[[i]] = get_next_results(\n", - " collection = collection,\n", - " filter = filter,\n", - " max_page_size = max_id*3, #assumes that there are no more than 3 records per query\n", - " fields = fields\n", - " )\n", - " }\n", - " output_df <- bind_rows(output)\n", - "}" + "# # Get the data\n", + "# output[[i]] = get_next_results(\n", + "# collection = collection,\n", + "# filter = filter,\n", + "# max_page_size = max_id*3, #assumes that there are no more than 3 records per query\n", + "# fields = fields\n", + "# )\n", + "# }\n", + "# output_df <- bind_rows(output)\n", + "# }" ] }, { @@ -179,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -188,17 +190,17 @@ }, "outputs": [], "source": [ - "get_collection_by_id <- function(id) {\n", + "# get_collection_by_id <- function(id) {\n", " \n", - " # Create API endpoint URL\n", - " url <- paste0(\"https://api.microbiomedata.org/nmdcschema/ids/\", id, \"/collection-name\")\n", + "# # Create API endpoint URL\n", + "# url <- paste0(\"https://api.microbiomedata.org/nmdcschema/ids/\", id, \"/collection-name\")\n", "\n", - " # Retrieve the JSON result from the API endpoint URL\n", - " response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", + "# # Retrieve the JSON result from the API endpoint URL\n", + "# response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", " \n", - " # Extract the collection name from the response\n", - " return(response$collection_name)\n", - "}" + "# # Extract the collection name from the response\n", + "# return(response$collection_name)\n", + "# }" ] }, { @@ -216,75 +218,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 3 × 5
idnamemd5_checksumwas_generated_byurl
<chr><chr><chr><chr><chr>
1nmdc:dobj-11-00dewm521000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv 2a532dca15798e470103ebd752a0937fnmdc:wfnom-11-0mqv1c63.1https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv
2nmdc:dobj-11-00wm33131000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv 3ce562ac512457ea54bdda05a4f01edenmdc:wfnom-11-twkd5a03.1https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv
3nmdc:dobj-11-01kye6251000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv38930c28eae561bc807bd01823f04167nmdc:wfnom-11-ftaq2319.1https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv
\n" - ], - "text/latex": [ - "A data.frame: 3 × 5\n", - "\\begin{tabular}{r|lllll}\n", - " & id & name & md5\\_checksum & was\\_generated\\_by & url\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t1 & nmdc:dobj-11-00dewm52 & 1000s\\_OSBS\\_FTMS\\_SPE\\_BTM\\_3\\_01Nov22\\_Mag\\_300SA\\_p025\\_184\\_1\\_7197.csv & 2a532dca15798e470103ebd752a0937f & nmdc:wfnom-11-0mqv1c63.1 & https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s\\_OSBS\\_FTMS\\_SPE\\_BTM\\_3\\_01Nov22\\_Mag\\_300SA\\_p025\\_184\\_1\\_7197.csv \\\\\n", - "\t2 & nmdc:dobj-11-00wm3313 & 1000s\\_DELA\\_FTMS\\_SPE\\_TOP\\_2\\_29Oct22\\_Mag\\_300SA\\_p025\\_126\\_1\\_7076.csv & 3ce562ac512457ea54bdda05a4f01ede & nmdc:wfnom-11-twkd5a03.1 & https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s\\_DELA\\_FTMS\\_SPE\\_TOP\\_2\\_29Oct22\\_Mag\\_300SA\\_p025\\_126\\_1\\_7076.csv \\\\\n", - "\t3 & nmdc:dobj-11-01kye625 & 1000S\\_CFS2\\_FTMS\\_SPE\\_TOP\\_2\\_run1\\_Fir\\_22Apr22\\_300SA\\_p01\\_12\\_1\\_3369.csv & 38930c28eae561bc807bd01823f04167 & nmdc:wfnom-11-ftaq2319.1 & https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S\\_CFS2\\_FTMS\\_SPE\\_TOP\\_2\\_run1\\_Fir\\_22Apr22\\_300SA\\_p01\\_12\\_1\\_3369.csv\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 3 × 5\n", - "\n", - "| | id <chr> | name <chr> | md5_checksum <chr> | was_generated_by <chr> | url <chr> |\n", - "|---|---|---|---|---|---|\n", - "| 1 | nmdc:dobj-11-00dewm52 | 1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv | 2a532dca15798e470103ebd752a0937f | nmdc:wfnom-11-0mqv1c63.1 | https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv |\n", - "| 2 | nmdc:dobj-11-00wm3313 | 1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv | 3ce562ac512457ea54bdda05a4f01ede | nmdc:wfnom-11-twkd5a03.1 | https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv |\n", - "| 3 | nmdc:dobj-11-01kye625 | 1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv | 38930c28eae561bc807bd01823f04167 | nmdc:wfnom-11-ftaq2319.1 | https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv |\n", - "\n" - ], - "text/plain": [ - " id \n", - "1 nmdc:dobj-11-00dewm52\n", - "2 nmdc:dobj-11-00wm3313\n", - "3 nmdc:dobj-11-01kye625\n", - " name \n", - "1 1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv \n", - "2 1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv \n", - "3 1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv\n", - " md5_checksum was_generated_by \n", - "1 2a532dca15798e470103ebd752a0937f nmdc:wfnom-11-0mqv1c63.1\n", - "2 3ce562ac512457ea54bdda05a4f01ede nmdc:wfnom-11-twkd5a03.1\n", - "3 38930c28eae561bc807bd01823f04167 nmdc:wfnom-11-ftaq2319.1\n", - " url \n", - "1 https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv \n", - "2 https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv \n", - "3 https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "nom_dobj_df <- get_next_results(\n", " collection = \"data_object_set\", \n", @@ -307,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -350,63 +291,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 3 × 3
idhas_inputhas_output
<chr><chr><chr>
1nmdc:wfnom-11-snvsmz18.1nmdc:dobj-11-esgqw196nmdc:dobj-11-0bmapy68
2nmdc:wfnom-11-ph9mfs20.1nmdc:dobj-11-sj597780nmdc:dobj-11-04v02904
3nmdc:wfnom-11-jqhgtg36.1nmdc:dobj-11-zzzrdh93nmdc:dobj-11-09xct845
\n" - ], - "text/latex": [ - "A data.frame: 3 × 3\n", - "\\begin{tabular}{r|lll}\n", - " & id & has\\_input & has\\_output\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t1 & nmdc:wfnom-11-snvsmz18.1 & nmdc:dobj-11-esgqw196 & nmdc:dobj-11-0bmapy68\\\\\n", - "\t2 & nmdc:wfnom-11-ph9mfs20.1 & nmdc:dobj-11-sj597780 & nmdc:dobj-11-04v02904\\\\\n", - "\t3 & nmdc:wfnom-11-jqhgtg36.1 & nmdc:dobj-11-zzzrdh93 & nmdc:dobj-11-09xct845\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 3 × 3\n", - "\n", - "| | id <chr> | has_input <chr> | has_output <chr> |\n", - "|---|---|---|---|\n", - "| 1 | nmdc:wfnom-11-snvsmz18.1 | nmdc:dobj-11-esgqw196 | nmdc:dobj-11-0bmapy68 |\n", - "| 2 | nmdc:wfnom-11-ph9mfs20.1 | nmdc:dobj-11-sj597780 | nmdc:dobj-11-04v02904 |\n", - "| 3 | nmdc:wfnom-11-jqhgtg36.1 | nmdc:dobj-11-zzzrdh93 | nmdc:dobj-11-09xct845 |\n", - "\n" - ], - "text/plain": [ - " id has_input has_output \n", - "1 nmdc:wfnom-11-snvsmz18.1 nmdc:dobj-11-esgqw196 nmdc:dobj-11-0bmapy68\n", - "2 nmdc:wfnom-11-ph9mfs20.1 nmdc:dobj-11-sj597780 nmdc:dobj-11-04v02904\n", - "3 nmdc:wfnom-11-jqhgtg36.1 nmdc:dobj-11-zzzrdh93 nmdc:dobj-11-09xct845" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "nom_analysis_df <- get_results_by_id(\n", " collection = \"workflow_execution_set\",\n", @@ -433,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -521,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -604,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -654,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -773,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -926,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -960,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1007,7 +899,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1040,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1073,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1113,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1242,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1325,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1348,7 +1240,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1464,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1501,7 +1393,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1526,7 +1418,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1563,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1598,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1643,7 +1535,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1686,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1727,7 +1619,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1762,7 +1654,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1796,7 +1688,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1829,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1855,7 +1747,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1909,7 +1801,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -1969,7 +1861,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "metadata": {}, "vscode": { @@ -2048,7 +1940,7 @@ "kernelspec": { "display_name": "R", "language": "R", - "name": "ir" + "name": "kernelspec" }, "language_info": { "codemirror_mode": "r", diff --git a/utility_functions.R b/utility_functions.R new file mode 100644 index 00000000..8c0d781a --- /dev/null +++ b/utility_functions.R @@ -0,0 +1,80 @@ +get_first_page_results <- function(collection, filter, max_page_size, fields) { + og_url <- paste0( + 'https://api.microbiomedata.org/nmdcschema/', + collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields + ) + + response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE)) + + return(response) +} + +get_next_results <- function(collection, filter_text, max_page_size, fields) { + initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields) + results_df <- initial_data$resources + + if (!is.null(initial_data$next_page_token)) { + next_page_token <- initial_data$next_page_token + + while (TRUE) { + url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields) + response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE)) + + results_df <- results_df %>% bind_rows(response$resources) + next_page_token <- response$next_page_token + + if (is.null(next_page_token)) { + break + } + } + } + + return(results_df) +} + +get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) { + # collection: the name of the collection to query + # match_id_field: the field in the new collection to match to the id_list + # id_list: a list of ids to filter on + # fields: a list of fields to return + # max_id: the maximum number of ids to include in a single query + + # If id_list is longer than max_id, split it into chunks of max_id + if (length(id_list) > max_id) { + id_list <- split(id_list, ceiling(seq_along(id_list)/max_id)) + } else { + id_list <- list(id_list) + } + + output <- list() + for (i in 1:length(id_list)) { + # Cast as a character vector and add double quotes around each ID + mongo_id_string <- as.character(id_list[[i]]) %>% + paste0('"', ., '"') %>% + paste(collapse = ', ') + + # Create the filter string + filter = paste0('{"', match_id_field, '": {"$in": [', mongo_id_string, ']}}') + + # Get the data + output[[i]] = get_next_results( + collection = collection, + filter = filter, + max_page_size = max_id*3, #assumes that there are no more than 3 records per query + fields = fields + ) + } + output_df <- bind_rows(output) +} + +get_collection_by_id <- function(id) { + + # Create API endpoint URL + url <- paste0("https://api.microbiomedata.org/nmdcschema/ids/", id, "/collection-name") + + # Retrieve the JSON result from the API endpoint URL + response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE)) + + # Extract the collection name from the response + return(response$collection_name) +} \ No newline at end of file From 83f200c68339b2db3a824ad5c29ce481d6e194a8 Mon Sep 17 00:00:00 2001 From: bmeluch Date: Tue, 10 Dec 2024 12:57:38 -0800 Subject: [PATCH 2/6] fix kernel --- NOM_visualizations/R/NOM_R_notebook.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NOM_visualizations/R/NOM_R_notebook.ipynb b/NOM_visualizations/R/NOM_R_notebook.ipynb index 7c321e47..9c217355 100644 --- a/NOM_visualizations/R/NOM_R_notebook.ipynb +++ b/NOM_visualizations/R/NOM_R_notebook.ipynb @@ -1940,7 +1940,7 @@ "kernelspec": { "display_name": "R", "language": "R", - "name": "kernelspec" + "name": "ir" }, "language_info": { "codemirror_mode": "r", From 6113be7333dfb038d41bc9d7827604d4529ef4a2 Mon Sep 17 00:00:00 2001 From: bmeluch Date: Tue, 17 Dec 2024 11:41:54 -0800 Subject: [PATCH 3/6] test moving funcs out of taxonomic notebook --- NOM_visualizations/R/NOM_R_notebook.ipynb | 1 - .../R/taxonomic_dist_soil_layer_R.ipynb | 315 ++++++++++++------ 2 files changed, 216 insertions(+), 100 deletions(-) diff --git a/NOM_visualizations/R/NOM_R_notebook.ipynb b/NOM_visualizations/R/NOM_R_notebook.ipynb index 9c217355..5951a675 100644 --- a/NOM_visualizations/R/NOM_R_notebook.ipynb +++ b/NOM_visualizations/R/NOM_R_notebook.ipynb @@ -26,7 +26,6 @@ "library(jsonlite, warn.conflicts = FALSE)\n", "if (!suppressPackageStartupMessages(require('ggExtra', quiet = TRUE, warn.conflicts = FALSE))) install.packages('ggExtra', quiet = TRUE); suppressPackageStartupMessages(library(ggExtra, warn.conflicts=FALSE))\n", "options(warn = -1)\n", - "\n", "source(\"../../utility_functions.R\")" ] }, diff --git a/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb b/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb index ba5d4158..acd76991 100644 --- a/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb +++ b/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb @@ -14,7 +14,11 @@ "cell_type": "code", "execution_count": 1, "id": "568d7112-ee43-41ce-aadb-39eaa9585dbd", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [], "source": [ "# Load essential libraries\n", @@ -22,7 +26,8 @@ "library(dplyr, warn.conflicts=FALSE)\n", "library(tidyr, warn.conflicts=FALSE)\n", "library(readr, warn.conflicts=FALSE)\n", - "library(ggplot2, warn.conflicts=FALSE)" + "library(ggplot2, warn.conflicts=FALSE)\n", + "source(\"../../utility_functions.R\")" ] }, { @@ -30,8 +35,8 @@ "id": "21ba287e-e606-4a50-949c-07aac9892473", "metadata": {}, "source": [ - "## Define a general API call funtion to nmdc-runtime\r", - "\r", + "## Define a general API call funtion to nmdc-runtime\n", + "\n", "This function provides a general-purpose way to make an API request to NMDC's runtime API. Note that this function will only return the first page of results. The function's input includes the name of the collection to access (e.g. biosample_set), the filter to be performed, the maximum page size, and a list of the fields to be retrieved. It returns the metadata as a dataframe." ] }, @@ -39,19 +44,23 @@ "cell_type": "code", "execution_count": 2, "id": "4ab6df9b-aaa0-4898-9095-a15efc867bc3", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [], "source": [ - "get_first_page_results <- function(collection, filter, max_page_size, fields) {\n", - " og_url <- paste0(\n", - " 'https://api.microbiomedata.org/nmdcschema/', \n", - " collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields\n", - " )\n", + "# get_first_page_results <- function(collection, filter, max_page_size, fields) {\n", + "# og_url <- paste0(\n", + "# 'https://api.microbiomedata.org/nmdcschema/', \n", + "# collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields\n", + "# )\n", " \n", - " response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE))\n", + "# response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE))\n", " \n", - " return(response)\n", - "}" + "# return(response)\n", + "# }" ] }, { @@ -59,8 +68,8 @@ "id": "8a306a7c-d48c-42bc-8da7-fb134a0c6b2c", "metadata": {}, "source": [ - "## Define an nmdc-runtime API call function to include pagination\r", - "\r", + "## Define an nmdc-runtime API call function to include pagination\n", + "\n", "The get_next_results function uses the get_first_page_results function, defined above, to retrieve the rest of the results from a call with multiple pages. It takes the same inputs as the get_first_page_results function above: the name of the collection to be retrieved, the filter string, the maximum page size, and a list of the fields to be returned. This function returns the results as a single dataframe (can be nested). It uses the next_page_token key in each page of results to retrieve the following page." ] }, @@ -68,31 +77,35 @@ "cell_type": "code", "execution_count": 3, "id": "8af9945b-37c1-49ed-9a8b-dc85a52692eb", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [], "source": [ - "get_next_results <- function(collection, filter_text, max_page_size, fields) {\n", - " initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields)\n", - " results_df <- initial_data$resources\n", + "# get_next_results <- function(collection, filter_text, max_page_size, fields) {\n", + "# initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields)\n", + "# results_df <- initial_data$resources\n", " \n", - " if (!is.null(initial_data$next_page_token)) {\n", - " next_page_token <- initial_data$next_page_token\n", + "# if (!is.null(initial_data$next_page_token)) {\n", + "# next_page_token <- initial_data$next_page_token\n", " \n", - " while (TRUE) {\n", - " url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields)\n", - " response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", + "# while (TRUE) {\n", + "# url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields)\n", + "# response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", "\n", - " results_df <- results_df %>% bind_rows(response$resources)\n", - " next_page_token <- response$next_page_token\n", + "# results_df <- results_df %>% bind_rows(response$resources)\n", + "# next_page_token <- response$next_page_token\n", " \n", - " if (is.null(next_page_token)) {\n", - " break\n", - " }\n", - " }\n", - " }\n", + "# if (is.null(next_page_token)) {\n", + "# break\n", + "# }\n", + "# }\n", + "# }\n", " \n", - " return(results_df)\n", - "}" + "# return(results_df)\n", + "# }" ] }, { @@ -109,7 +122,11 @@ "cell_type": "code", "execution_count": 4, "id": "780eae36-3f46-4c3e-bd26-b2a33c463441", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -204,7 +221,7 @@ "id": "f3a8bc8e-14eb-4844-b8ce-34b1ed7a0773", "metadata": {}, "source": [ - "## Define an API request function that uses a list of ids to filter on\r", + "## Define an API request function that uses a list of ids to filter on\n", "This function constructs a different type of API request that takes a list of ids or similar (e.g. `biosample` ids as retreived above). The `id_field` input is a string of the name of the id field name (e.g. `id` or `has_output`), the name of the new collection to be queried, the name of the field to match the previous ids on in the new collection, and a list of the fields to be returned." ] }, @@ -212,43 +229,47 @@ "cell_type": "code", "execution_count": 5, "id": "ce435021-79f1-4a1d-aef0-fea4bc267817", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [], "source": [ - "get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) {\n", - " # collection: the name of the collection to query\n", - " # match_id_field: the field in the new collection to match to the id_list\n", - " # id_list: a list of ids to filter on\n", - " # fields: a list of fields to return\n", - " # max_id: the maximum number of ids to include in a single query\n", + "# get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) {\n", + "# # collection: the name of the collection to query\n", + "# # match_id_field: the field in the new collection to match to the id_list\n", + "# # id_list: a list of ids to filter on\n", + "# # fields: a list of fields to return\n", + "# # max_id: the maximum number of ids to include in a single query\n", " \n", - " # If id_list is longer than max_id, split it into chunks of max_id\n", - " if (length(id_list) > max_id) {\n", - " id_list <- split(id_list, ceiling(seq_along(id_list)/max_id))\n", - " } else {\n", - " id_list <- list(id_list)\n", - " }\n", + "# # If id_list is longer than max_id, split it into chunks of max_id\n", + "# if (length(id_list) > max_id) {\n", + "# id_list <- split(id_list, ceiling(seq_along(id_list)/max_id))\n", + "# } else {\n", + "# id_list <- list(id_list)\n", + "# }\n", " \n", - " output <- list()\n", - " for (i in 1:length(id_list)) {\n", - " # Cast as a character vector and add double quotes around each ID\n", - " mongo_id_string <- as.character(id_list[[i]]) %>%\n", - " paste0('\"', ., '\"') %>%\n", - " paste(collapse = ', ')\n", + "# output <- list()\n", + "# for (i in 1:length(id_list)) {\n", + "# # Cast as a character vector and add double quotes around each ID\n", + "# mongo_id_string <- as.character(id_list[[i]]) %>%\n", + "# paste0('\"', ., '\"') %>%\n", + "# paste(collapse = ', ')\n", " \n", - " # Create the filter string\n", - " filter = paste0('{\"', match_id_field, '\": {\"$in\": [', mongo_id_string, ']}}')\n", + "# # Create the filter string\n", + "# filter = paste0('{\"', match_id_field, '\": {\"$in\": [', mongo_id_string, ']}}')\n", " \n", - " # Get the data\n", - " output[[i]] = get_next_results(\n", - " collection = collection,\n", - " filter = filter,\n", - " max_page_size = max_id*3, #assumes that there are no more than 3 records per query\n", - " fields = fields\n", - " )\n", - " }\n", - " output_df <- bind_rows(output)\n", - " }" + "# # Get the data\n", + "# output[[i]] = get_next_results(\n", + "# collection = collection,\n", + "# filter = filter,\n", + "# max_page_size = max_id*3, #assumes that there are no more than 3 records per query\n", + "# fields = fields\n", + "# )\n", + "# }\n", + "# output_df <- bind_rows(output)\n", + "# }" ] }, { @@ -256,7 +277,7 @@ "id": "4f512f80-3f14-481c-a28c-f6797fc61882", "metadata": {}, "source": [ - "# 2. Get all Pooling results where the Pooling `has_input` are the biosample ids\r", + "# 2. Get all Pooling results where the Pooling `has_input` are the biosample ids\n", "We use the `get_results_by_id` function above to get a list of all pooling results whose field, `has_input` are the `biosample_id`s we retrieved in step 1. After, the pooling results are unnested to a flat data frame, andthe names are cleaned up so it is clear which collection the results are from. Because `Pooling` is a subclass of `MaterialProcessing` the pooling records are found in the `material_processing_set`." ] }, @@ -264,7 +285,11 @@ "cell_type": "code", "execution_count": 6, "id": "a8fc4389-49d8-42ea-a56b-a561c01900ff", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -362,7 +387,11 @@ "cell_type": "code", "execution_count": 7, "id": "0aa58275-a5e6-418f-a04c-b1ef5e21f65e", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -456,7 +485,11 @@ "cell_type": "code", "execution_count": 8, "id": "37534e80-aab9-4ec7-9829-8cde11e27a51", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -544,8 +577,8 @@ "id": "7075cf7a-9f4d-41d8-9360-383f451e9573", "metadata": {}, "source": [ - "# 3. Get `Extraction` records where `processed_sample_id` identifier is the `has_input` to the `Extraction`\r", - "\r", + "# 3. Get `Extraction` records where `processed_sample_id` identifier is the `has_input` to the `Extraction`\n", + "\n", "The `get_id_results` function is used, again (you can see the pattern), againe querying the `material_processing_set` (becuase `Extration` records are also a subclass of `MaterialProcessing` using the `processed_sample1` identifier as the `has_input` for the `material_processing_set`. The resulting dataframe is unnested and the names are adjusted to make it clear which set the inputs, outputs, and ids are from." ] }, @@ -553,7 +586,11 @@ "cell_type": "code", "execution_count": 9, "id": "832c3ddd-6db6-4292-8325-fc812e156346", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -650,7 +687,11 @@ "cell_type": "code", "execution_count": 10, "id": "8842cde0-c0ab-4bba-8c8e-fcba2b77a4cd", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -752,7 +793,11 @@ "cell_type": "code", "execution_count": 11, "id": "fc982cf2-08a1-4bfe-9cf3-7ca550f46790", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -856,7 +901,11 @@ "cell_type": "code", "execution_count": 12, "id": "e5e627fa-72ad-4e64-baf9-471443ba7168", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -960,7 +1009,11 @@ "cell_type": "code", "execution_count": 13, "id": "a8506d2d-356d-4277-9711-052296ecfae4", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1062,7 +1115,11 @@ "cell_type": "code", "execution_count": 14, "id": "667690ba-a598-4f72-9c21-9ff8a6c28b8c", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1166,7 +1223,11 @@ "cell_type": "code", "execution_count": 15, "id": "be09a8ef-d0fb-4df8-bef3-bc8498f3d444", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1260,7 +1321,11 @@ "cell_type": "code", "execution_count": 16, "id": "2d6c028e-4260-4324-8fb3-0402216d2f7f", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1371,7 +1436,11 @@ "cell_type": "code", "execution_count": 17, "id": "6328c9a8-ee49-4dc4-b1e3-b530f718c371", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1471,7 +1540,11 @@ "cell_type": "code", "execution_count": 18, "id": "a4637e99-8950-441a-9966-e31e7781fa21", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1529,7 +1602,11 @@ "cell_type": "code", "execution_count": 19, "id": "24529f53-c27d-4948-824f-ad5a7a9d405c", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1626,7 +1703,11 @@ "cell_type": "code", "execution_count": 20, "id": "9aa89e83-67de-4620-ba98-a15311882551", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1735,8 +1816,8 @@ "id": "814da9fb-b162-485e-b319-ee47e81920fc", "metadata": {}, "source": [ - "# 7. Get data objects from the metagenome activity result outputs\r", - "\r", + "# 7. Get data objects from the metagenome activity result outputs\n", + "\n", "We query the `data_object_set` using the `matagenome_annotation_has_output` identifiers to match the `id` field in the data objects. We then filter the results for only those results with a `data_object_type` of `Scaffold Lineage tsv` (since this has contig taxonomy results). Note that the `url` is a new field returned that contains the tsvs we will need for the final analysis." ] }, @@ -1744,7 +1825,11 @@ "cell_type": "code", "execution_count": 21, "id": "bf1f3a9c-ba08-43a6-b9aa-577ec7586969", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1842,7 +1927,11 @@ "cell_type": "code", "execution_count": 22, "id": "d3ac4d7e-861f-4553-b05c-751ae08d2304", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -1961,7 +2050,11 @@ "cell_type": "code", "execution_count": 23, "id": "9444e91a-1305-4a15-ae8d-338ca54eb5e4", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -2060,7 +2153,11 @@ "cell_type": "code", "execution_count": 24, "id": "7521a8c4-7b02-4d4b-a0c4-4287bc814516", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -2126,7 +2223,11 @@ "cell_type": "code", "execution_count": 25, "id": "16c7457f-c1dc-4344-8049-581eee81ffd2", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { @@ -2232,7 +2333,11 @@ "cell_type": "code", "execution_count": 26, "id": "8a331e72-182c-4a55-b1e9-705908b6238d", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "name": "stdout", @@ -2372,14 +2477,18 @@ "id": "f425cddd-6a17-4ebb-bf0d-415df07f28ad", "metadata": {}, "source": [ - "## Clean up the relative abundance data to fill in NAs with 0 for unobserved taxa\r" + "## Clean up the relative abundance data to fill in NAs with 0 for unobserved taxa\n" ] }, { "cell_type": "code", "execution_count": 27, "id": "dae25f8c-ffb9-43a7-8b9b-9464d3e9b8b9", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [], "source": [ "# First merge to get the url for geo_loc_name and soil_horizon\n", @@ -2404,7 +2513,7 @@ "id": "b143ea72-6869-4d82-987b-8b7281fb3806", "metadata": {}, "source": [ - "## Plot the average taxa abundance for all M and O horizon soil samples\r", + "## Plot the average taxa abundance for all M and O horizon soil samples\n", "First calculate the average relative abundance for each taxa in each soil horizon. Next, we'll pull out the top ten taxa and lump all others into an \"Other\" category for plotting purposes using the `forcats::fct_other` function. Then we'll calculate the mean relative abundance of each taxa for each soil horizon. Finally, we'll choose an appropriate color palette for the plot, and plot the relative abundance of each taxa for each soil horizon at each location." ] }, @@ -2412,11 +2521,15 @@ "cell_type": "code", "execution_count": 28, "id": "460474ef-2d2e-4553-85fc-78dc5a756bf1", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { - "image/png": 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H9GwKmnntp38Ve+8pWV9cXycYEgUjw4vPzHYEzHME390rjr368u5c/GWa+6WfQw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\nAgJrCUiyECBAgAABAgQIECBAgAABA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QIECBAgQKCv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13X333dVLbrHFFum0006rrve2sHTp0vSR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", 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", "text/plain": [ "plot without title" ] @@ -2465,11 +2578,15 @@ "cell_type": "code", "execution_count": 29, "id": "af8211c8-93cd-4be7-8fc5-1bbcc30187ab", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "r" + } + }, "outputs": [ { "data": { - "image/png": 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mRImekWjZEs3T/+8Y+y+muO6OVilNMGUrBPouaV8tD8dPpNRwmw5eGiBGVUVlBD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mr3NbAhKQgASqE6Ct4P9+RKG0pQgaaENpV6aaaqrqGZRJQd/gtddei15A8FA39dRT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\nPj1xj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IECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQ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\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d5QFwIECBAgQIAAAQIECBAgQIAAAQIE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rt5bKaralU9cVLDv599K1xaCW18ka96K+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\nPPjgg9kWW2yRB8nsuOOO2UYbbRSCJz7xiU8MWFW8WFLvonFa1wEz/+dDOr6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3iJ4e1y234kbHdrbdY0ZcHvUlwJGbrxxryLzGvuB4QAAl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\nUEABBRRQQAEFFFBAAQUU6DuB/wNBDwE4t45oBAAAAABJRU5ErkJggg==", + "image/png": 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IQAACEGhIBP78809fdWaffXbTunVrX1geJxJc2G233dysf//9d9v8khuQwuM1u7TCCiuYtdZaK0VqojZUAueee65PiEtjRn297rrrVl3lk08+2Rx99NG+9K+88oqRaSYcBCAAAQhAoLkRqNc6sLlxpr0QgAAEIAABCEAAAhCAAAQgAAEIFEcA00vFsaaknAnoK7epU6ear7/+2v7Ty7wFFljALLjggmappZYy2jhryK6h1r/oen3//ffmk08+MTNmzDDt2rUznTp1Mh06dGjIXRdZN41Bjclp06YZ+ddYYw2T1GRZZKb/vSATMpMmTTKffvqpadu2rencubNp3759pWSprxfZ/3nychourRBipj7RuJIZlayEPaSOXvlq7EqDhfLXvWeuueZyiq/bccqUKbb5opYtW9raWTReGkK9kgLZa6+9zA033OBGf/DBB81BBx3knifxyMTSkCFD3Kj77LOP6y/a05TmVVH3oqg+mjhxonnooYd8l0899VTTvXt3X1g1JxdddJF55JFH7HntpL/iiivM8ccfbyQoVoTTWNEz8fPPPzezZs0yiy22mFl88cWNTFhm6fK+R6j+H330kb0mkzm1NFp+smxnrXnlzUn1K+JZmJSDNChpHTNhwgRbw5LWZYsuuqi9PkuaR5J4RXBNUo+84uTNschnyq+//momT55s1GdaU2idrvtSpXviH3/8YafTGmzeeec1K664Yub3MWmMU/5ah3Xs2NEsvfTSma3x0oyNvNeDeY+nNG2tNW4Rc7/o50/e/V/kfM/zd1NR65tax2hc+rzHb178ixxDcfyyuJZ3H6iODWldlgUz8oAABCAAAQhAAAIQgAAEAgSsH0k4CORKwHqZWrKGne9v5MiRmZRpaRYo/etf/yrtvvvupYUWWshXhrdMa0Op1LNnz9K9995bsn7oRpZ9zDHHlDbccEPf36hRoyLjey+onZtuuqkv7VlnneWNUubPuv5lBVQZkGe9vP0iv/XC3a6l9aV+yTKDUrI278v6cc011yxddtllJWtDtmKLZs6c6euDLbbYomIab4RevXr50lsbe97LPr+3v8877zz3mrWpWbI2UkuWoISvLZYZj9Kyyy5b2nPPPUsPPPCAGz+px3oxXnrsscdKG220UcnakCjLe5NNNilZggBudquuuqovznvvvedei/Pk1f9F83La+Nlnn5WsDe+SJRTj4+GMxUUWWaRkmVQpqd/SOmszpnTmmWeWVltttdC8de+xtKGULJM/abOuKf5XX31VOuGEE0qWRo2S6uC01TlaAlulXXfdtfT444/HlnP11Ve782GDDTYoyyd4vxTnWl2fPn185RxxxBH2fdva9HLDdZ/45ptvUhWlOee0X0fLXE/p4osv9oVtu+22qfJME7kpzaui7kVJ+B577LG+Ppx77rlLX3zxRZKkieLcf//9vvw1doYPH54obZJIwWfeDz/8YCezNvZKxx13XMnagC4rX/f/Hj16lAYNGlRSX1TjsrpHOGVrfHvvB3pmy1nCr6W9997b98yyBJftsLfffttJbh+33HJLXx5K63UffPCB77q3vGr877//vjf7UH/WnFRIvZ6FoQ0MCfzxxx9L//znP+3nmuaT977p+LW+0DOmmuemisyDq/KtNIYUR88Opx06tmrVSsGZu7w5FvFM8T7TP/zww9Jhhx1WsoRcfPzE0BKeKl1wwQUlS1CljKO1iVk66aSTSpaQVWi6c845p2QJAZalCwvwzp3TTjvNjfL000+XttpqK999xunj5ZZbrnTdddeVfv75Zzd+lKeWsZH3ejDv8RTFJOt1YB5zP4vnT1T7k4bn3f95zfew9mX1u6le65uwNnnDapnneYxfb93kz4p/MN+sx5B+Rzlrr+DvYP3OdK7p2Lt372B1Ej2vyxJZAXn0gffZUsQ7nbB2EQYBCEAAAhCAAAQgAAEI1I+AqV/RlNxcCOQlKDN48OCS9aVg2UtX58Vo1HGVVVYpffzxx6H4tQljfeXsy1ObAY4wR2giK1DCN/qB7S3T0u5RsrRLRCUp5VH/yMJSXMi7Xl5G8ltaHkp9+/YtSYgkeC14vv7665f0wj3O1fLyS/kGhUvefPPNyOK8LwD33XdfO94dd9wRKpgQbIvOJdii+iZxltaE0uqrr16RkTj269fPzjLYliSCMnn2f5G8BEAbyNqAmXPOOStyU3+ofkceeWSizRTlb2mWSNzXyl8b29aX+Uqam7O+2C5de+21JUvLUKI2q156gRklEGaZnkmcj/KSwFGtLkxQRnkGBSJuv/32VEVJMEh11F+XLl3stEUJyjSleVXUvShp51pam9x+Vd9K2DFLJwHN+eef31eGxmhWzntfVP0lKHPbbbeVtMngjNe443bbbWdvGiStT9b3CKdccfLWUxvq1pfQJT23veFev4Rjvc7S7uWLa2kI9F4uSXDZm75W/2uvvebL33uSFyeV4e3zvNcO3jYl8VvaukqWJsbEnNWWE088sSReSVyeXFV+pTGkOLWuE5VHJZc3x6KeKc4z/aWXXkp0T9JvLO86R8K4ScaTpV2mlGSN6p07O+20kz3ukq5TFl544dLQoUNju67asZH3ejDv8RQHJSlf537sjJlgnnnO/SyeP8H6pjnPu//znO/edmb9u8k7XzU+8l7feNsS569mnuc5fp26Zs3fyVfHPMbQW2+9lXitYGnV9VbH9id5XnsT5dkH3rHa0NZlXgb4IQABCEAAAhCAAAQgAIF8CCAokw9XcvUQyENQRl8GBjVqOC/okhz15aMENMKc8g7moc32OBfcbJWwwhNPPBGZJM/6Rxaa4EIR9QqyPfTQQ8t4B+N4z6U56Pnnn49sTTUvv7yZBYVL0gjKWKZhUrVF7eratWtFjQDvvvtuyatNw8sjyn/wwQeXVlhhBV99Km1C5N3/wZdQefFSf3733XclbSBH8YkLl4aqOC0NeiF/wAEHVJW3ZTKlNHr0aO+Qy8w/ffr0RMJUYW2XYM2wYcPK6pLVBklZxjEBUYIyI0aM8DHfZpttYnLxX5JmCstEhJu+f//+doTgvTsPjTJNaV4VdS/y9170mQQng+P57rvvjk5Q5RXLTJevHG386j6QhfPeF9UWaaILtqnSubQ9xWnLc+qZxz3CyTtso1JaJOLqrs1fr6u0aZK1oEyUxsA8Oam93j7Xhkyez0Iv30r+F198sdSiRYvYPovqT2djKa6MvLmq7EpjSHFqXScqjziXN8cinykSepC2luBHBFHjQOESRNUaSvM7iRC8k9faa69d8T7mnTs77rhj6fDDDy8brxKOjhLOkYakhx9+OLL70o6NItaDeY+nSBj/vZDFOjDvuZ/F86cSh7DrRfR/3vPdaVcev5u881XzPM/1jdOOJMe08zzv8as658HfYZHXGCpSUCbvPvCO1Ya0LnP6kCMEIAABCEAAAhCAAAQgkC8BBGXy5UvuFoGsBWVeffXVshevMnFz7rnnlmS+R2rg9bJBKsKfeuop2wROmOr4m266KbJ/ZKbJeXGro348a4MwzGmjxfvjWvGljj7KFVH/qLLjwouql5dr0C/NFuoXmUPQC5FHH33U1vARFIrSS3Cp+w1zaV9+BfOoVlBGJpW8mkuWX355W5uJzL288MILtpaXzTffvGysiIE2IaKc1NiHmRWTMIc2hMePH2//3XXXXaW99trLN26DfOMEZYrof+88yYuXw3GXXXYpY7HkkkuWLr/8clsgRJoKJk2aZH9ht84665TF9ar0d/J0jjK1FGSre9CVV15Z0oaG8tb4HDhwYOmQQw4pu1/JBFRSMwNOmZWO+kpTm0zBemkcShhEwmWaU2+88UZpwIABJQmZBONKg4XUfXud7m8yb6c/aUvyptEGmHPNOUqYpVYXJSijjbdOnTq5ddB4CmqciCpb9XPqrnrruSSXt6BMU5pXRd2LovowLPyee+5x+9Xp33HjxoVFrSkszPxSErM9SQr13hedNjhH3Sdlwkim2zQX1V5pvZLpIieOc5SpnDiX1z3CKTO4USmzb8F6StOe85yUcF7wPlhJyEFa+rTWq/R3/vnnly655JKSTIbofnfyySeX8ZI2ijDhorw5iZe3z/N+Fjr9U+moe+ICCyzg49StWzd7zGmsa20lgUOtI7Tp5r0XO2MwzpRfEVzVxkpjSHFqXScqjyiXN8einykyW+QVntI6/eabb7bXOvqNdemll5akqcUZA87x1FNPtU1aOefSuqn1l+5lElSRgEuYAHglTXHeuRPs6x122MHOXyaK5KQpT/fFYDzdl6KE5NKOjbzXg3mPp6hx7A2vdR1YxNzP4vnjbXNSf979X8R8d9qax+8m73x17gXOMcv1jdOGpMc087yI8at658Ff+eY5hvQbzPn9F5wL+v3pXNMxbH0QvDdH/aYrog+8Y7WhrMvUfzgIQAACEIAABCAAAQhAoBgCCMoUw7lZl6KXfM5LEec4cuTIqpkEN7Wl5lsv7+Pcp59+WlpppZV89dAP+LBNEuXz+eeflwknaBNaKl+9Ti9j9RLZaZeOihdnqqmI+nvrmNRfVL28rLz+o446qiTb2WFOL1eCZij0hX+YS/PyKyx9tYIyTlu0oaANuqgxIMGZ4Be2QfMT3nodd9xxvvGlTcbrr7/eG8Xnv+WWW3ybGk69dIwTlCmi/70voZx6Zc1LMB555BEfM5WlTVGNjTAnAYxjjjmmLE3YhrsE8YKCd/ryLOrlnsobMmRImekWjZEs3T/+8Y+y+muO6OVilNMGUrBPouaV8tD8dPpNRwmw5eGiBGVUVlBDhUzUJHHSFOPUfaONNnKT5C0o05TmVVH3IrdzEni0Gev0q47a6M/DSVDAW478cZrN0tQhOAeVt+aWxmaUZitpOmvXrp2vTtJOFufyvkcENyq9vI4//nh3Y1r3EW2wS4Al6JJumgTTxZ1/8cUXpc6dO/tYSeOFs5keTJs3J5UX1ud5PAuDbYs7V394+0zPtajxp3zEb8011/Sl2XjjjSOLKIKrCk8yhmpdJ0Y20rqQN8d6PlPuvffe0KbL1FLQBJ4zllq1alW68cYbQ9PJjF9QEFzti3Nhc0dlnXLKKZHj9e233y4tscQSvrEqk3Bh4zvN2ChiPZj3eIpjHXatmnVgEXM/i+dPWHvjworo/yLmu9qY1++msPmax/omrp/CrqWZ50WM37z4q+1FjSGZ53Pu+zp6f2uF9YHCkjyvFa+IPggbq/Vel6ntOAhAAAIQgAAEIAABCECgGAIIyhTDuVmXkqWgjLS6eH+ES2hgwoQJifhK04M3rfzSKBHlpM0kGF9fTnqdzNt447Rp08bW7uGN4/UXWX9vuZX8RdbLy8vx9+3bt1IVSy+//LKPtV60jRkzpixdmpdfZYmtgFoFZfRFVSV30UUXlbVF2j6CToIaztf3Dit9yVvJhb1wU/ooQZmi+j/sJVSWvMRFwmyLLbaYj6+EZCo5vfzXJp/DWcdzLc0FQRf84q9Hjx7BKKHn2mjXCzcn/3nnnbdMe0towgSBugcG2UpVfhInbUZOnZzj0KFDQ5NWs0ESmlGFwDhBGX3Z7NRTx6233rpCbqXSV1995ePjFTTLU1CmKc2rou5FFTszECFouk/m5vJwGkPecSf/nXfemUlRwbmrvCtph1HBiuOtk56JqmeYK+IeEbVRqfmc1CXdNEma308//VS2SSTzd9JME+aK4KRyw/o862dhWPviwvbcc0/feIpbHzv5SGOHdwxqvRImnFkUV9UryRiqdZ3otD/smCfHej5TpFEuzum56h0Ljt8xcxiVVhpmnLg6StNUnAubOxKgreQ0VoMarsIEf9KMjSLWg3mOp0rMwq6nXQcWNfezeP6EtTcuLO/+L2q+5/m7KWy+Zr2+ieujqGtJ53kR4zdP/kWNIXHOS1CmiD5Q/cPGar3XZaoXDgIQgAAEIAABCEAAAhAohgCCMsVwbtalZCkoc8EFF/heqMo+fVKnLwfnmWceX/rXX389Nvlhhx3mi6/0+sEuN3jwYN81veCVWYQ4V3T94+rivVZkvbwvxOWXBgCZ9EjittpqKx/z3XbbrSxZ0pdfZQn/G1CLoIy+WNXGXCX3888/l72QCdOydOGFF/raK9NBehmcxIWZ4YkSlCmq/4MvobLmJS5BgThp74lqd5Djk08+6eMtbVFeJwES7/idf/75S1OmTPFGifVLq4I3fe/evWPjJ70ooRhvvtK+FLVhHpZn0NSchA3CtG2l3SAJKytJWJygjNJ7v1zXxmycNh/Fl3CZw0fxZ8yYoWDb5Sko05TmVVH3Iqdfkh533XVXt2/Vx5W0qiTNNxhP64fg/UtjJwsXzFdaOsLmX7AsPQuCQgFRa5oi7hFhG5XS5BImOBFsi3MebE+lue2kCzuKodaIztzXUQKK0i4R5YrgpLKDfZ7HszCqjVHh3vuqWEnbRxInM34yK6n1soTJv/zyy7JkRXFVwUnGUK3rxLIGegLy5FivZ8rqq68eqn3F02y7371zTX6Zr4jSruik1SaxtM5408b9JgjOHX2gkHS9s99++/nK0don6JKOjaLWg3mOp2Dbk5ynXQcWNfezeP4kab8Tp4j+L2q+5/m7KThf81jfOH2S5ph0nhcxfvPkX9QYEvu8BGWK6APVPzhWG8K6TPXCQQACEIAABCAAAQhAAALFEEBQphjOzbqULAVlJGAgLSKPPfZY6aqrripFbQpFAV955ZV9L0n1oz7OhZlWkgYDqfIPqgs/6KCD4rKyrxVd/4oV+m+EIuvlfRkuvzZgk7qgVhmZwAluKCZ9+RVVZi2CMmnaoo0HLwsJaQSdVMN741x33XXBKJHnDz/8sC+t8okSGCmq/4MvobLmJRgnnHCCr936Gjep0waANv00ly+77LKStEopzHEHHnigL+8jjzzSuZToKDXt3v5ccMEFE6WrFClo/k0vRtO4d955p8wcmOoadGk3SILpk55XEpQ57bTTfBxvvfXW2Ky1ietwD2qgyVNQpinNq6LuRbEdGXJxs802c/tWfSxhyrzcwgsv7CtLpqiycMH7Yr9+/RJnK9OTztjW8ZlnnglNW8Q9ImyjMu09MomQQ2gDQwKDmyvSuCMzjnGuCE4qP9jneTwL49oZdm3dddf1jSUJvkiIIQtXFFfVNckYqnWdGMckT471eqacddZZcU12r7Vs2dI3hs444wz3WpwnOD7eeuutyOjBuZO0DGX44Ycf2mbtnHum6hsUbk86NopaD+Y5niIhx1xIuw4M9m1e69Msnj8xzS67VET/FzXf8/zdFJyveaxvyjonQUDSeV7E+M2Tf1FjSMjzEpQpog9U/+BYbQjrMtULBwEIQAACEIAABCAAAQgUQ2BO62URDgKNhoCl0cVYG0P2XzWVtgQrfMmsF2u+8+CJ9cLdDBw40Gy44YbG2jCwL1tqwo1ld9lYX8260VdccUVz7bXXuudRnqLrH1WPYHg969WrV69gdSLPxd0SUHLZW1+qGksQy1hfrUemKfKC9eVn4uIs9fK+uMGxaH1RayxBMF+cnXfe2Xced2KpBDeWxhPz/fffx0Wzr9Wr/7Pk5TTSMiXkeO1jGmaWthETTO/NbPz48d5TYwkP+M4rnai97dq1M5aWBDuq9aLW6M8SmKmUNPL61KlTTbBe1gv8yPhhF9ZYYw2z1lprGWtzyr08duxYk6Z/3IQFePbaay9jaS5wS3rwwQfNIYcc4p57PZZQo7G+1nSD9tlnH9eft6epzKsi70Vp+0Rz1uv0TMjDWZuD7rx18re0oTneTI+WCbjE+enZZ5l1c+PrfhJ09bxHWBscweoUcm4JUhtLsNRXlsK23357X5j3pJ6c0txrK60dvG1K499ggw3MG2+84Sa55ZZbjCVca8455xyzxRZbmOD62Y1YwVNPrhWqlsvlvDiqsvV6pqhNSdwiiyxiLJNdbtSk49rS9OSmkWfWrFm+87iTqGd/WBr9VtM90xIEti+rnBdeeMFst912YdFjw4LrrrzWg3mOp9gGZnCx3nM/z+dPEf1f1HwP/u7J8ndTcBhlvb4J5p/leVHjN0/+RY2hLLl78yqqD7xlOv6kzy/Fz2td5tSFIwQgAAEIQAACEIAABCCQPwH/DkP+5VECBAojoA2jcePG2S/69SJUm6XaNPU6Sx7Nexrqt0w5mLPPPtuce+657vWPP/7Y9VtfJJr77rvPSKgmS5dV/bOsk/LKsl7a5Fx88cVTVbFTp06uoIwSqo/14rshOMs0UuJqWOrifXG1Eet1lokvY5n7cIPmmGMOo02IpM4yOWQstcFm9OjRSZMkipdl/2fJy6m8ZS7C8dpHMcjKBV+Ma6P8gw8+SJX9Msss49twV57dunVLlYc3sqVhy3tqrC/izGKLLeYLS3Ky9NJL+wRlPvroI2Np10mStPA4lup2s/zyy9tzX4UPGzbMZiohpKB74IEH3Hmke7UEyBqia8jzqqHei9SPeh54nZ4HeTjdVyztZb6s0z67fIljTtLc5yUM6XVhG8z1vEdYple81SvEb2lTM3379vWVddRRRxlLA5AvLHhST055PAuD7at0bmngMQMGDDDetYiEdSVEYJnGMZtuuqn9TLDM1Zg0G9D15FqpzXlcz4tj2rpm+UwJ3mej6hIUXNS6IomrVghL6+K0azzFdwRlVDc936pxRa0HG8p4qoZRved+ns+fovo/Kfda5nuev5uC9c96fRPMP8vzosZvkfzj+NQyhuLyreVaUX0QVseGsC4LqxdhEIAABCAAAQhAAAIQgEA+BBCUyYcruRZIQMIvr732mv1VtQRYtFGmP8tmfWa1OPPMM41l0sC8+uqrZXleccUVRhoZqnVF1L+auhVRL2006kV3GqcX9l7NF+rrHj16pMkit7hpBHYsMxCx9ZgxY4bveseOHVOzEt9qBWWK6P8seQmWtOcENUqk3UTxQfecKO9gn+ywww6eGNV5axWUcbTTOKVrflQaW05c7zH45Zw0yjRkJ60yllpsu4rS9mWZyQrVKiMhRsdtu+22Zr755nNO63JsjPMqOO6LvhfFdVRwA1d8Z86cmXk/e4VjnfpUI5DmpI07Br9MjYub5Fo97xFpBCqStKVSHK3R9t9/f5mVdaNKsOOf//ynex7lqSenrJ+FUW2MC9em8o033hh6H7XMN9ja1vTluwSOJPAprjvttJOxTNvFPnPqyTWuvXldy4tjVH2LeKYEBfKi6hIMt8zVBYMyPV900UVNUDinUgHBNaH4pXVFrgeLHk9pWcTFr/fcz+v5U2T/B/lmPd/z/N0UrLvOs17fhJWRVVgR47do/mKT9RjKindYPkX0QVi5CmsI67KouhEOAQhAAAIQgAAEIAABCGRPAEGZ7JmSY0EERo0aZfr3728eeeQRV2NAXkVLmOPmm282q622mq+IDh06mH/84x++sKQnRdY/aZ0Ur8h6pXkJ4bQhuDEatoHpxC36mKUpjuDmdLDdSdpWTZoi+z9LXuIRZKZ5q42ULFxe4yz4VWraugZfIlYzp1RmUFAmL80cadsXFX/PPfd0BWUUR5pjgiYYPv30U1uI0smjSLNLTpnOsSnNq2ruK9WkcdjFHcO0umjsrrPOOnHJUl8Lm/95CcpIVX5S5xUIiUpTz3tE8L4SVccswtVHO+64o89si9Zs999/fyIh03pyyvpZWC3Pgw8+2LRv394ceeSRZtq0aZHZSAuHTFvpT4Jzhx56qG2iKUxooZ5cIxuQ84U8OAarXOQzpVoBU2k2zNNVs94JCspMnz49dRXDngepMwlJELUeLGI8hVSn5qB6z/28nj9F9786Iq/5nufvprABlPX6JqyMrMKKGL9F8s9rDGXFOyyfIvogrFyFNZR1WVT9CIcABCAAAQhAAAIQgAAEsiUQr9Ig27LIDQKZEJD5A22SylzJQw89VFFIRi9S9dJfX8KuuuqqVddBX9oGnV6wXnTRRcHg2PN61T+2UtbFetQrrTYZtUGmZbyu2i9dvXk0RL/X/IHq16JFi9TV1IZXUleP/k9at6TxpFnE67RpV412FW8ejj9LDVVOnjoGXwJ6ryXxB+dQtSYMghvuwXyT1KXIOLqXr7LKKm6Rw4cPL2OpDXKnXTJ1JhMiRbumMK+Kvhel6aMwbW55CHkFN8Z0P05qViRNe/KIG5zLRd4jgs/rPNqnPHUflcYo731aJh6eeOIJEzRzGFWHenKKqlM9wiVspPEubVwyvxcm/OKt1+eff24LLW6xxRZlwqqK11y5Zs3RYV6PZ0qwD5261PtYjQBP2DoxbTu895m0aePix60H8xpPcfWp9Vpw3BT57FHd83r+FNn/ec/3sPmQ1e+mWsdPvdMXMX6L4J/3GMqzn4rogzzrT94QgAAEIAABCEAAAhCAQOMhgEaZxtNX1PS/BA4//HDz4IMPlvHQV0raOF199dXtP22gyb/gggu6cU8//XTXn8YjIZvrr78+NEm/fv3szQQJ7iRx9ah/Q61XNS8bpSXC6yrZoHc2yr1p4vy//PJL3OXCrgXtuE+ePDl12WlUyjfUcZmm0fp6VF8wO30uM0xffvmlyUL9f3BTXF+a6eu8Wl2tX6wFx39wfiStXzDdQgstlDRp3eJJYPLcc8+1y9fL5sGDB9uaDZwKec0uyURImi9ZnTxqPTaFeVX0vSgN865du9pmYKThwnGvvPKK2XfffZ3Tmo9//fWXkSCW10noat555/UGNVh/U79HzJo1y9Yk49XGoLk+ZMgQE9QeEddJTZ1TXNuD11q2bGlk3k5/MmWm8S/zo88++6yZNGlSMLp9/tJLL5m1117b/Pvf//YJkTVnrllydKA3hWeK05Zaj1OnTk2dRXAtHXy+JcmwXuvBPMZTkvZWG6epzv0i+z/v+Z7n76Zqx01DSVfE+C2Cf95jKM/+KqIP8qw/eUMAAhCAAAQgAAEIQAACjYcAgjKNp6+oqUXg6quvNrfeequPxUorrWSHd+/evaLWje+++86X1tlQ9wUGTiTMIbXXXqcXq44QgjZo999/f/POO++Y1q1be6OV+etR/7JKhATUq15ZCMoss8wyIS36X9Bvv/32v5MEvuAYSZAklyjBl/faENCGbZov/YIbAlEVrVf/R9Wn2nBtIsgUy5QpU9wsxC0LQRm9zNSX9c7Xfxonmu9ZmXZyK5zSs9xyy/lSBAVefBdjToLpsmAWU1wml7SJ6wjKKEOZX5IJEDlpFXn77bdtv/7Vw+xSU5lXRd6L3A5L4ZFQzIUXXuimuOWWW8zRRx9tVl55ZTesFo/WHO+//74vCz3zG4tryvcIreF69eplRo4c6XaHhCUHDhxo1l13XTcsiacpc0rS/qg40tqx884723+KM3bsWFto5rHHHjMvvPCCL5metxIev/32291wuP6NolaOyqWpPFPcwVGjJ+ka11uMd32o8ODzzRs3yt8Q1oNZjKeo9mUV3lTnflH9X8R8z/N3U1bjqF75FDF+8+ZfxBjKs3+K6IM860/eEIAABCAAAQhAAAIQgEDjIYDppcbTV9TUIuDVECAgm222mXnttdfM1ltvXVFIRvG/+eYbHVyXRFBGG6+OUIwSrrjiiuatt97yvVyVmvrjjz/ezTfKU4/6R9XFG16veknbhyN44K1PnD+4oR/82ihoJkAqhyVgksQp7vfff58kau5xOnXq5CtD5k8+++wzX1ilkyCrqPj16v+o+tQSHhwPab841gafNtmvvPJKW0OJw1zjKvgVqVcQo5Y615J2ySWX9N37fvrpJ1uLTto8g2OlMQjKrLDCCsZrekebts49XkIzjmvXrp3ZaqutnNPCjk1lXhV5L6qmc4LaY/RMOfbYY6vJqiyNtGmcffbZvnBpgaqHGS9fJVKcNOV7xCmnnGKb4PTiuPTSS82uu+7qDUrkb8qcEgFIGEn33eOOO87WMjNmzBijTWOvC2p8hKuXzv/8aTkqZVN5pvyPQm2+b7/91vz444+pMgkK1wTXdUkya4jrwWrGU5K21hKnqc79ovq/qPme1++mWsZOQ0hb1PjNk39RYyiv/iqqD/KqP/lCAAIQgAAEIAABCEAAAo2HAIIyjaevmn1Nf/jhB/Of//zHx+G0004zSW3US5hFm15eV0mAQl+S66tZx8lW8h133GE6duxoBgwY4ATbx2Bc30XrpB71D9Yh7Lye9ZK2l+eeey6sWqFhb775pvFqoWnTpk2ZtpC55567LG1SLTESgEoruFNWWEYB+sp1rbXW8uX2/PPP+87jTiSEFBR+CItfz/4Pq0+tYcEXjq+++mqqLDWvZWatb9++9marV5PE8ssv78srbd5KfOqpp9paUHQf8Qp2+DJOcaJ7UnCjUqbi0rhp06bZGrG8aTbaaCPvaYP1S6uM4zR3nfu1V1Bm9913N3PNNZcTrZBjU5pXRd2Lqu0YCa8GBVeGDRtWJkBRTf7SjjFjxgxfUmkwCXvO+CI1oJOmeo/QvfqKK67wkZZgs4RnqnFNlVMSFlpXDR061Fx33XW2oOigQYOSJLPNnd51112+uBJc0PrDcc2Ja54cm9IzxRkbWRy92qQq5ffBBx8Yr6CMzOfpg4tqXBHrwTzHUzVtTpumKc/9vPu/yPme5++mtGOmIcUvavzmxb/IMZRXvxXVB3nVn3whAAEIQAACEIAABCAAgcZDAEGZxtNXzb6m0hwjjR+OkwmaTTfd1DmteHz44YfL4sSZ5QnTEnPiiSea9dZbz85nl112Md5NWgUedthhPu0z3gKLrr+37Dh/veuVdENGbfCaWdH5Nttso4PPtWjRwkiVsdd98skn3tNI/7PPPht5rR4XdtttN1+x2rSVZpkk7vLLLzdx49vJo97979Qjq+Paa6/ty0qbqUkFpbQpIWEpx0lzhHcTpWvXrs4l+/h///d/RkImSZ2EwtQvF1xwgW3ObfPNN0+VPqoc557kXD/vvPMSjxOlkdmaWbNmOcltQcANN9zQPW/Inj333NNXPd3nZRpk9OjRbng9zC41tXlVxL3I7bAqPDfddJPRfPW6E044IZGwoDeN1//KK6+Yf/7zn94gWyjtoosu8oU1hpOmdo94/PHHy7QGbbHFFmUCzGn7pqlxStr+Rx991Na6dcwxx9iCojfccEPSpGbNNdcsixs0EdlcuObJsak9U8oGTZUBWhcndVrreDWJ9uzZs2qhxyLWg3mOp6TMao3XVOd+3v1f5HzP83dTreOn3umLGL958S9yDKmfgh8keN/Z1dKPRfRBLfUjLQQgAAEIQAACEIAABCDQNAggKNM0+rFZtKJ9+/a+dkobjGMWxXch5GTChAn2ZnDwUpQggTQT7L///kZmTBynr9a1we11+vp2oYUWcoP0Fe0hhxzinns9RdbfW24lf73rJTX9w4cPr1RN8/rrr5unnnrKjSeBGJlYCHNBDRtJhHE+//xz079//7Ds6hYmQSx9TeU4Cfzceeedzmnkcfr06Yk3DOvd/5GNqPLCwQcfbLw2zaVFSgItSZxMrHg3Ubbffnvfiz9tvC+66KJuVvpaT8JzSZwEnIImXLp3725WW221JMlj4+i+1KpVKzfOxIkTzS233OKex3k0pmRuyuskFBHc6PReb0j+ZZZZxqyzzjpulaQVQdp6HLfYYouZjTfe2Dkt7NjU5lUR96JaOkf9HBRqkdm19ddf31RjIu2RRx6xBQe8awTdi++++24jTWaNzTWle4Q0y0n4zbsJs/rqqxsJyQU3atL2U1PilKbtEvz2mq2Ulg5p30jigvNrwQUXNDJ353XNhWueHJvaM8U7Pmrxv/zyy4l+Q3z44YfGq2lOZe63335VF13EejDP8VR1w1MmbKpzP+/+L3K+5/m7KeVwaXDRixi/efEvcgyp47y/Q3XufYem82pdEX1Qbd1IBwEIQAACEIAABCAAAQg0IQLWpiAOArkSsNRcl6wp4/uzXsKnLvPXX38tWZsgvnyOO+64ivlYL0dLlpCLL51TH2tDNTS9pbnEF9/aNC5ZZlZC495///2+uMrbMt1SFrfI+pcVHhNQdL0c9t5j69atSyNGjIispcaLZf7Dx9kyjRMZ39Ko4YtrvbwpWdoBIuNbggUla6PNl8apn7UpF5kuOB4twYnIuMELO+64o6886wV+MIp9bgli+OKpLRpzUc7aHC5169bNl8Zpy3vvvVeWrMj+L4KXGjhkyBBf+y0NQyVLGKSs7d4Aa5PVl2a22WYrWeZbvFFs/7333uuLJ7YnnXRSydLIUhbXCbA2dEuW5pOydNbXwk6Umo/Wl9K+/C3TMKUbb7wxNl+N7c6dO/vStW3btmSZ7ApNZwn7+OLqvpiH69Onj6+cI444IrYYS8DNF98SonPPNX/i3MUXX+zGVV9uu+22cdETX2uK8yrve1FiuDERLTNbvv5Un2o8WJtapW+++SYm5d+XLMGx0h577FGWh/I588wzK6avJkIt90VLYMRX17j7XN73CEuoyFcXMbMEmlMh0VpA6Zy/r7/+2pd+0qRJZWsB3cMsoWlfvFpO8uakutXS50nXDmkZbL311i538bdMi5QsbWyx2VgbYSXLRKQvnaVZMTRNEVxVcKUxpDi6FzhjTEetq7JyeXFsDM8Uy4SIj+uYMWMSYbU0BvjSxf1GDM4d9Z8lwFyytCdElvXuu++WLAF6Xxk9evQIjZ9mbBSxHsxrPIU2PkFgNevAIuZ+Fs+fBM33Rcmz/4uc72pUXr+bgvM1ze/kNOsbX8ckOEkzz4sYv3nwL3oMWZqYffdY/fbWO4k4l+R5rfRF9EEtYzWvdVkcO65BAAIQgAAEIAABCEAAAtkS0NfzOAjkSiArQRlVUi82vS+35T/ttNNK33//fVkb9OPc+gqlZNmgL0vj5HHFFVeUpZNAjPXluC9NnFCGMth111198eeZZ56SBHSCroj6B8tMcl5kvRz2weP8889fuvLKK0sS5nA217QpdvXVV5e06e+Nb30lVfr2228jm6ZNM+vraF8ajQMJQClPCS7oBZJe4p9yyikly2SHG1cv3L1l1VtQ5scffywtueSSvjqpfhqT1hffLittKFraeUodOnQoi+u0J0xQRhCL6v8iX0JtueWWZRysr/ZKlmmlkl6oO073p0MPPbQkwRiHk45xm+KbbLKJL67iW5phSs8884xvM17jTGFhgkva0Nf1rJwEdZZeeumyeulFs+5pv/zyi12U5tZHH31UskxAlc0rteOhhx6KrFI1GySRmcVcSCsoI8GeYP85fTlq1KiYkkqlvARlVGhTm1dF3ItiOyvBRUsbXOnYY48tmwcaD3ou77DDDiXLHFvp6aeftu8FlqYye4NI88HSPFT27Fc6ja0zzjijpLzzcLXcF9NsJOV9j8hiozJu00T3sJVWWsnXtxLWu+qqq0oSNLW0rdl9q3XdJZdckujvmmuuKevSvDmpwFr6PK8NmRdeeKFs3WRpaipZZphKmvtep2eBhEv13HPutTqqXdosC3NFcFW5cWPIqVeaTVInTdJjnhwb+jOlXoIyGnv6naDfC1rTOW7KlCn2+A2OCW3gRo3TtGMj7/VgnuPJ4ZTmWM06sIi5n8XzJw0HJ26e/V/UfHfaksfvplqedWnWN04bkh7TzPMixq/qnQf/IsfQzz//XLaGkCDz8ccfbwu6nH/++WXdE7w3B4WjnQRF9EEtYzWvdZnTfo4QgAAEIAABCEAAAhCAQP4EEJTJn3GzLyFLQRltiHqFGpwX9PPNN19JXyRa5iFK2223nf3yPijs0qVLF1twxkmj49577+3rH33pZJny8L34X2GFFdxNZl9kz8kXX3xRstTN+9JZNqd9G/KKnnf9PVVK5S2yXl7+EiBYc801fdx0XdpjtEHjjev4tdn55JNPVmyfZTorNL3ykYYBbbA5eTrHrbbaqmSZ0/KF11tQRg195513Sh07dvTVy6mzxp1laqjs2sILL1wmwBUlKFNU/xf5EkqbICuvvHIZF3HThoo2+KQ9xeHoPerFYpwQy7hx40pLLLFEaFrlI8EmabHyajbx5q9xJkGtrJ1lgqAkgTNvWY5fgmPioXulE+Y9aj5cdNFFsVWqZoMkNsOIi2kFZZSNZWKnrF3atKvk8hSUaYrzKu97UaX+SnrdMj1WJozgHe9J/bq/PvHEE0mLrSpeLffFtBtJed4jstiojNs0mTZtWtkcT9qPUfH0DAhzeXJSebX0eZ4bMtKGGMVKawppj+nUqVOkQNk999wThtMNy5urCoobQ05F0mySOmnSHPPi2NCfKfUQlAlqitH41e8HjdOwsazxIe0NUS7t2ChiPZjXeIpiEBde7Tow77mfxfMnrt1R1/Ls/6Lmu9O2PH431fKsS7u+cdqR5Jh2nuc9flXnPPgXPYYsU7ih913dizUWdP/wuiTPayd+3n1Qy1jNc13mtJ8jBCAAAQhAAAIQgAAEIJAvAQRl8uVL7haBLAVlBPSxxx4rtWnTJvKHePDFqH74yuyCNqZnzJjh0zwgdeteNcCHHHKIL19tHsepAPd28MCBA31pVY/TTz/dG8X251n/ssJSBBRVL2//bLHFFrZa3ijTWN648kuA5qWXXkrUKpkECPs6K5inc77TTjvZmolkrsYJ07EhCMqowdKEE/ya3ltPr1+buzI1pa+4vOFRgjLKv4j+L/ollDQQiEGUthEvG/k133UPiNNWJFZyEo4L+5I0mKf3XPWQWZfg1/l/55jNf2kZSjpOnLppA/T555+vWIFqN0gqZhyIUI2gjDRDOO1xjmeddVYg5/LTPAVlVFpTnFd534vKe6m6EGmLkQaZMKFIZ4xEHSVYJk1xamverpb7YjUbSXndI7LYqIzbNClSUEZ9nhcn5V1Ln+e9IaP7ZrB+UfPECZfA0b/+9S81raLLk6sKjxtDTuXSbpI66dIc8+LYkJ8p9RCUkdaYnj17lj3/nbHpPUrgPG5Nr/6tZmwUsR7MazylGdOKW8s6MM+5n8XzJy0LJ36e/V/EfHfaoWPWv5uCzxLvOxdvuWH+atY3YfmEhVUzz/Mcv04ds+avfIscQ++//36pXbt2kffjoLblJM9rh42OefZBLWM173WZlwF+CEAAAhCAAAQgAAEIQCAfAgjK5MOVXD0EshaUUdZ6KaXN1KDWGO8LUWl8kEmVTz75xFObUkmaZbzxnJf7gwcP9oUrzkknneRLW+lEm3LevLVBFybYkUf9K9UtyfUi6uXlI0EZOQm1yFxG8IWJE1dahE488cRQE1tx7ZKZGZljWH755X394uSro4QKbrvtNjebhiooowrqBZrMIISZ2FFbNB923nnnkjYO5NIIyih+3v1fr5dQw4YNs80fSRuRt++9/g033LDiBooYBZ2+TFbauHuRBGRkailOUCmYby3n2siQRo1VVlklsr3O2JfZkihV18E61LJBEswr7rwaQRmZ2gsKROmFbSWXt6CMym+K8yrve1GlfktzXV/p6l4YpU3Jex+QRjmZ7fn888/TFFFT3Frui9VuJOVxj8hiozK4BvDem4oWlFGn5sFJ+dbS50VsyGjdfvTRR5dkosY7P4J+ae447rjjSl9++aWaldjlxVUViBtDTgWr2SR10qY55sWxoT5T6iEoo413aQCUucwoU7uLL754qX///ol+Q9QyNvJeD+Y1ntKM6VrXgXnN/SyeP2k4hMXNq//znu9hbcnqd1Mtz7pq1zdh7QmGVTvP8xq/wfplxd/Jt8gxJJO3a6yxRujaQe/avC7J89obX/68+qCWsVrEuizIgXMIQAACEIAABCAAAQhAIFsCsyk768UnDgKNkoC1iWLGjx9vrI0wM3HiRLPQQgsZy6SKWXXVVY1lgqTBt6mh1r9e9bI0bRhLs4WxvuI3llYP06FDB2MJuZjNNtvMWC8waupPa9PcWC9vzGeffSYBQWO9OLfHimUiq6Z865HY2hQwb7/9tjv2LVNCxlIzbzbffHObWa11qlf/11rvSunFTfeKd99914wdO9ZYXxcbS1DKWCaJjPVFfKXksdc1Xi210MYS2DBfffWVscwuGUugyf6zNt9rzj+28JiLltCUGTFihLFekhrrxbCxvvQzlmkoozrpXokrjkBTnFd534uy7B3VVfNBzxetF3TUc0XzoXPnzvZR91FL4CrLYht8XtwjknVRc+RkCTHbc8XaoDf60/pJa2utzfTstATPa54vzYFrXhyb4jOl0mzU2sraKHWjWYIyxhKQsc/F+bnnnrPHrH5PaA1mmSa1x2mtvyHcAhN48l4P5jWeEjQt0yhNde7n1f/1mO95/m7KdDDVIbMixm/W/IscQ1pj6zex7sWWUK2xPmDL/N1cEX1Qh6FFkRCAAAQgAAEIQAACEIBAHQggKFMH6BQJAQhAAAIQgAAEIAABCEAAAhCAQDICcYIyyXIgFgQgAAEIQAACEIAABCAAAQhAAAIQgAAE/kdg9v958UEAAhCAAAQgAAEIQAAlPfCCAABAAElEQVQCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDpEkBQpun2LS2DAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEPAQQFDGAwMvBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEDTJYCgTNPtW1oGAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOAhgKCMBwZeCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGmSwBBmabbt7QMAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQMBDAEEZDwy8EIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJNl8CcTbdptAwCEIAABCAAAQhAAAIQgAAEIACBxk5g4sSJvia0bt3ad84JBCAAAQhAAAIQgAAEIAABCEAAAhCAAATSEJitZLk0CYgLAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBxkgA00uNsdeoMwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAagIIyqRGRgIIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgcZIAEGZxthr1BkCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAIDUBBGVSIyMBBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBjJICgTGPsNeoMAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJCaAIIyqZGRAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgMRJAUKYx9hp1hgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBITQBBmdTISAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0BgJICjTGHuNOkMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIpCaAoExqZCSAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEGiMBBCUaYy9Rp0hAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABFITQFAmNTISQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQg0RgIIyjTGXqPOEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKpCSAokxoZCSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGiMBBGUaY69RZwhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB1AQQlEmNjAQQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAo2RAIIyjbHXqDMEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQGoCCMqkRkYCCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIHGSABBmcbYa9QZAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCA1AQRlUiMjAQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAYySAoExj7DXqDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCQmgCCMqmRkQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoDESQFCmMfYadYYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSE0AQZnUyEgAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEINAYCSAo0xh7jTpDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCKQmgKBMamQkgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBojAQQlGmMvUadIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARSE0BQJjUyEkAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEINEYCCMo0xl6jzhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACqQkgKJMaGQkgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBojAQRlGmOvUWcIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdQEEJRJjYwEEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKNkQCCMo2x16gzBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBqAgjKpEZGAghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBxkgAQZnG2GvUGQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgNQEEZVIjIwEEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQGMkMGdjrDR1hgAEIJAFgT/++MN8/PHHZu655zadO3c2s802WxbZNsg8fv31VzNt2jTz/fffm06dOpn27ds3yHpSKQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ5EkCjTJ50yRsCEEhF4KKLLjJdunSx/15++eXEaUeMGOGm69evX2y6F1980Rx//PFmyy23tIVjNt54Y9O1a1ez1FJLmW233dacfPLJ5oUXXojNI+riPvvs49bjjDPOiIpWWLgEY/r372/WXXdds/jii5tu3bqZHj16mJVWWslu+6677mrScK624tdcc43LZcqUKdVm06jSPfroo26bNT5xEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAwyCARpmG0Q/UAgIQsAh88803xhGk+OWXXxIz+fnnn910yiPMSaPKEUccYR5//PGwy+ann34yb7zxhv135513mp49e5obbrjBtG7dOjR+MPCDDz4wQ4cOdYPvvvtuW+imbdu2blhRnr/++stcdtll5uqrrzalUim0WLVXQjL622CDDcytt95qFlpoodC4tQZ+++23bv9Ii09zcOJbzVhuDmxoIwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATqSQBBmXrSp2wIQKAwAkcddZQrJCMTS9IeI+01HTt2tOsgoYZRo0aZYcOG2edPP/20Oeigg8y9995r5pyz8q1y0KBBdroOHTqY6dOnm1mzZpn77rvPFs4prJFWQTNnzjS9e/c2r7zyilvseuutZ2uSWWKJJcz8889vJk+ebAsEPfTQQ0aCKyNHjjR77LGHeeyxx+zrbkI8EIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABJoYgcq7v02swTQHAhBofgRee+01WwhELZcgi8ziLLvssqEg3n77bbPnnnua7777zjbB9Mgjj9jnoZH/G/jbb7+ZBx54wD7bbbfdzEsvvWTGjBlj7rrrLnP44YcbCeYU5fr27esKycjckrTiSFAm6A444ACjuDIXNX78ePP+++8bhUlYBgcBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgqRKYvak2jHZBAAIQcAg8+OCDjtc2RxQlJKNIa621lrnuuuvc+DfffLPrj/I8++yzttkoXd9yyy3N9ttvb0edMGGCbdooKl3W4Wrn4MGD7Ww7depkhgwZEiok45S75JJLGqVxTC5Js8yIESOcyxwhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEINDkCCMo0uS6lQRCAQJDAJ5984gaFaVdxL/7X06NHD9O+fXv77IMPPrDNEwXjeM9lnkmuVatWplu3bkZaZRx3xx13ON7cj5deeqlbxiWXXGIkLFPJLbbYYuboo492ow0YMMD1R3lKpZL59NNPzRdffBEVpabwLPKXSamPP/7Y/PrrrxXrMmPGjMRxwzITB/GoxWXR5lrKJy0EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEmgsBBGWaS0/TTgg0YwJeDTISnqjkZCrp4osvNpdffrmR4Miff/4ZmeTzzz83w4cPt69vsskmpkWLFqZz586ma9eudtjTTz+dSKDkmGOOsbW/SJDn3//+d2R5URfeeOMNM3nyZPvy6quvbrbeeuuoqGXhvXr1Mttss40588wzjeoR5WSyShpzllhiCbPOOuuY1VZbzSy//PJml112sc1URaVLGl5t/jfddJPNbocddrCLUr+ttNJKZv311zdLLbWUOf744319+Msvv5h+/frZbV5mmWXMKqusYseVYFGXLl3MIYccUlHw5csvvzTnn3++UXpxEA/l06dPn0T97TCpts1Oeo4QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEA6AnOmi05sCEAAAo2PwBZbbGHuvPNOu+InnniiueWWW4xXeCasRRL+SOLuu+8+89dff9lRd999dzfJXnvtZUaNGmULaAwcOND07dvXvRbmmTp1qpGpJrkff/wxLEpsmGNySZEkIJLGtWnTxtx9992RSb766itz8MEHm1dffbUszrfffmteeeUV+69379628Ejr1q3L4sUF1Jr/N998Y7MTt9tuu83079/fLe733383H374oZljjjnsMPWJhIG8WoacyNLqMmXKFPvv+eefNzK7JQGioJOwlfp62rRpvkvSTKN+eP31113zW74InpNa2+zJCi8EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJCCABplUsAiKgQg0DgJbLTRRq4ZojFjxpgNN9zQHHjggeaee+4xn332WdWNkmDFoEGD7PQLLLCAT6hCgjYtW7a0r0kIJU4rTdUV8CQcO3aseyYtL1m6k046yRWSkdYUCR29++67RlpsbrjhBiPzTXJ33XWXOfXUU1MXnVX+EpS58MIL7fKl5WX77bc3EgLae++97TBdlwCThGTmnntuc8YZZ5iRI0fa5++995556KGHzKabbmrHldaZCy64wKiPvU7hXiGZ7bbbzgwZMsSI/2OPPWZ22mkne0xJyCbOZdXmuDK4BgEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlBNAo0w5E0IgAIEmRmDeeec1zzzzjNlvv/3MO++8Y2uAefLJJ43+5KRdRsI0EqCR+aQFF1wwEQFpWJk4caIdV4IxEr5wnAQ0tt12W/PII4/YghPPPfec6dmzp3O57ChzQT/88IMdvvTSS5ddrxTwxRdfuFFWWGEF11+r54EHHjBPPfWUnY3MOd16662uAJACZWZKGnv23HNPm+39999v9t13X7PBBhskKjrL/H/66Se7TAnvONp9/vjjD1fjj7T/ONp6rrzySltoxqmk+qtjx452/8uEk7TCjB8/3rz11ltm7bXXdqKZ66+/3tUkIy07l156qZGpLjm1WX+LL764Hc9NFPBk2eZA1pxCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVCKBRpgIgLkMAAk2DwMILL2xr/DjggAPMPPPM42uUTOlIS8phhx1mpDFln332MSNGjPDFCTu599573WClCToJjDhO+ce55ZZbznTp0sX+k3aatC4vQRlpVZFr0aKFLRTiaMnx1q9t27bmkksucYPOO+8811/Jk3X+m2++uSsko7LnnHNOu+7yf/TRR7ZmoU6dOpk99thDQWVOQi9egabp06f74kjgSk7mpU4//XRXSMYb6ZRTTjHt2rXzBvn8WbfZlzknEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBALAEEZWLxcBECEGhKBFq1amWkSeT99983N954o22SZ5FFFvE1URpIhg4danbeeWdz1FFHmd9//9133TmR9heZ3JGTqaO11lrLueQeN954Y7Pooova58OHDzeTJk1yr2XtkUCI47x+J6ya4zfffGMcQRHxkKaUKLfOOuuY9ddf3748evRoI46VXB75d+3aNbLYK664wtYQ85///MfMPnv0488r5PLbb7+5+cl8loRt5GRiKUqgSeOsV69ebjqvJ482e/PHDwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIxBOI3imMT8dVCEAAArkSKJVKifNPE1eZyszObrvtZq699lojoQ6ZUJIQhTSJeLXNyESOhGXC3ODBg80vv/xiXwrTJqMLEsbYe++93eR33XWX68/aI405jvv6668db01HmR5ynISBKjknjoRkkggF5ZF/ErNVjqkkteerr74yo0aNMtIOdP7555vtttvOnHDCCW5T//rrL9c/YcIE8+uvv9rnMjkV5xwWwTh5tDlYBucQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEA0gf+pIIiOwxUIQAAChRCQORvHOUIoznnc8bvvvnMve/NwAyt4ll12WaO/3r17mxkzZhiZznnyySftVBKIOfDAA80GG2zgy8VrdunCCy80/fr18113TrxCPIMGDTKnnXaamXvuuZ3LmR07dOjgajuRGaYll1yy5rzHjRvn5iFzRZWct0wJlYhpnMsj/6WWWiquSKP+eP75583tt99u3njjDTNz5szY+N6LY8aMcU8dTUFuQMATdT2PNgeK5hQCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIghgEaZGDhcggAEiiXgNWXz448/Ji7cMQ+kBPPNN58v3axZs8zkyZNtkztJhG+kmeXOO+80ffr0cfN5+OGHXb88Y8eONTLf4zhpHZFZnrA/r0YSaXp54oknnGSZHtdee203v5EjR7r+pB5pu1G7P/74YzeJoz1FAXPNNZcbnsTjFRCKip9H/i1btowqzmgs7LLLLma//fYzw4YNc4VkpPlHGmB23XVXc/XVV5tzzz03NA+vQFYl81bSWhTm8mhzWDmEQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC4QTQKBPOhVAIQKAOBNq2beuW+tlnn7n+Sh6voIw3D6W75JJLzIABA+wsdNxjjz0qZWdf32uvvczNN99s+ydOnOhLc88997jnhx56qFl99dXd8zCPNJE4ed1xxx222aeweLWEbbPNNuaqq66ysxg+fLjPfFClfH/77TcjrTjff/+9HfWll14yK620kvGaMZoyZUqlbIw3Tvv27SvGzzv/YAWOOeYYM2LECDtYppMOP/xws84665gVVljBeAVsvCayvAI/XvNW3rYGy9H5559/HhacO9PQQgmEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARcAgjKuCjwQAAC9Saw6qqrulWQWZykzhs3KLQiTSGOe+yxxxILyrRq1cpJZuadd17X//vvv5sHH3zQPpcJpTPOOMNEaQ9xEv30009GwjU6vv766+bDDz+0BVGc61kc11xzTdOxY0dbQOO1114z0ioTNBcVVc4zzzzjCslIcERCMnJeQZZPPvkkKrkb7hUoUl0qubzz95b/7bffmkcffdQOkomo5557zgSFqpz4X375peM1Xo1AXrNOn376qRsnzBMlSFNkm8PqRRgEIAABCNRG4I8//rC1r2kNIKHL2WabrbYMG3BqaUGbNm2avUaQCcYkQrANuDlUDQIQgAAEIFAXAt988439Uckyyyzje7dQl8rkXChrh5wBkz0EIAABCDQLAqwdmkU300gIQKCBEMD0UgPpCKoBAQgYs9Zaa7kvjv7973+bJ598siKWV155xTarpIjzzDOPnYc3Uffu3Y1jJueFF17wmUzyxgv6hwwZ4gZ5BXief/5589VXX9nXpMWlkpCMIrZu3do26+NkKK0yWTtt1J1yyiluthLgSWK+SppPTj/9dDfdgQce6Pq1KeYICQ0ePNh4TQ+5kf7rGT9+vJEmGrkVV1zRLLbYYv+9En3IO39vyRJQclzPnj0jhWQU58UXX3SiGm2IOm6VVVYxqrOchK7ieATNdTl5FNlmp0yOEIAABJoqAT2Pu3TpYv/tv//+qZrZq1cvN+2MGTNi0+q5cPzxx5stt9zSFo7ZeOONTdeuXY0EKLfddltz8sknG60xqnH77LOPWw89u+vtJBjTv39/s+6665rFF1/cdOvWzfTo0cMWopVgkMwUvvzyy7lX85prrnG5RAmf5l6JgguQQK8znh0NeAVXgeIgAAEINHkCRawdpKn1iiuuMPvuu6+tfVYaTLWG0LpBz9fevXvbJn/jfk/GdQRrh3A6rB3+1p4bTodQCEAAAhColgBrh2rJRafjvUM0m7yv8N4hb8Lk3xgJICjTGHuNOkOgiRKQQMtuu+1mt04mb2Qa580334xs7ejRo31CHgcccICZa665fPEXXXRRs+eee9phMjG0ww47mNtvv90Xx3uicm+88UZz5ZVX2sEtWrQwytdxXrNLSc04Ka1ekjlOGmmCQizSNjNz5kz7zyuc4aRJctxvv/2MNMvIvf/++2b77bc3U6dOjUw6YcIEo41FZ4NQgko777yzG3+OOeYwffv2tc9VN5mxCnOq73nnnedqX3H6MCyuNyzv/L1leTUEeTXGeOPIf84559haf5xwaRDyOr2UlPvhhx/sl5/ea45fAl5R4zbLNmcxZpw6c4QABCDQGAno+SMhCv198cUXqZqg+E5ar/Ywbyb6Kvrggw+2tdHp+f/uu+8ahTlO92FptbvzzjvttYbWCwpL6j744AMzdOhQtx533323kQa0ejgx0HNea4HLL7/cTJo0qawaapuEZCQss9NOO5m452lZ4pQB4uD0T7XropRF1j26+Dpt/uWXX+peHyoAAQhAoCkSyHvtIDPIEqa97LLLjD6yCZrk1fP1qaeeMhdffLEtjPr000+nwszaIRoXawfWDtGjgysQgAAEqifA2qF6dsGUvHcIEin+nPcOxTOnxIZPANNLDb+PqCEEmhWBs88+235xpM2XWbNmmR133NEW/tDLJv1pcSqzN2PGjLFN6TibW4sssohPo4oX2gUXXGBrkhk7dqyR4MOpp55qpNVFQiUy1bTccsuZzz77zHz00Ue2kMRbb73lJld9lLecNtW0oSXXrl07s8UWW9j+JP9k0khmoMaNG2cLyTzyyCM+ARwJrEg7jpw23Lbbbrsk2friSKvMDTfcYHbffXfbVIKEZcRMwi8SmtGX4Irz8ccf22X961//cjWmLLHEErZ5KJmS8Lo+ffqYgQMH2mkkYCQGYiK10RIq0ou60047zRUukfadI444wptFrD/v/J3C1c9qmzY4xV4bgdrkU99qDEnoatCgQea2225zktjH4IblMcccYx566CEjU1Q33XSTLdgkjTwyNSXhmfvuu88WtvFlEjjJqs3aqHTG6gMPPGCkPQkHAQhAAALZETjqqKPM448/bmeo56e0x0jjh2NeUEINo0aNMsOGDbPjaLProIMOMvfee6+rzS6uNnruyHXo0MFMnz7dXvfoOZLmORqXf9JrEobV1+3OOkTp1ltvPVuTjNYH888/v5k8ebItFKRnoNZiMvEogWFpWNN1HAQgAAEIQKC5E5BArTS0OsKO0rKq32zS0Kb3B3rHoXcZ0j4qv8wqHHLIIeb+++830laXxLF2SEKJOBCAAAQgAIHGQYC1A+8dGsdIpZYQaNoEEJRp2v1L6yDQ6Ahos0UvjrTRJI0nEmzR19r6u/7660Pbs9JKK9mbUvPNN1/odeUpQQIJKDgmeCQUoz9tSIU5abeRKSNptXGc8nAEc3bZZZdEm2BOWh2l8eXcc8+1gySo49VU441Xi3/ZZZc1zzzzjP1l+4cffmjzkwYb/UW51VZbzdx8881moYUWKosiDT0SHvnHP/5h89LXb/oTa22U/fzzz24a5SMhn6CwjRshxJN3/k6RCyywgJHAlISkJOBz1lln2X8yEyV1jxJykZM67PPPP99+YamxJwEar2vZsqUtOCQtRdLWoxeV+mvfvr35+uuv7bzVfpnokLrtMFdUm8PKJgwCEIAABJIReO2112whEMWWIIvU0+oZG+befvtt+7krEwoywSSBTEebXVh8hUnLndYVctLEJvOFEgK+66677LWHBHOKctIe5wjJaDNPQrcSlAk6rVsUV9rVZHJRArkKk7AMDgIQgAAEINDcCeh3pCMkow8sZFLRMQPtZaMPLfTxiT5c0W9OPVtfffVVM/vs8Uq/WTt4KeKHAAQgAAEINH4CrB1479D4RzEtgEDjJxD/K6zxt48WQAACjZCABF+kplgvkKTtJcrJ1rdUFusLbm3sxDmZYNJX4QMGDDCbbLJJmYkmJ+28885rf/Wlja4TTjjBCbaP+kLccZU2wJx43qO+vHZelGkzLMo8jzdNNX5pSZHK51tuuSV0o8vJUxt+0oqiL+GjNv8Ud+WVV7Y16Rx77LGmbdu2dnJ9fe4IyehLOWmVUT/oy/O0Lu/8nfrIfIY2/zQWHCdhKQnJLLjggubEE0+0Nyp79uxp245XnGeffbbMTJbGpISRpAXIMfUle7USwFlyySVtwZmtt97aKSL0WFSbQwsnEAIQgAAEKhLwCpheffXVsc9JaSm77rrr3DwlfFrJ6fmiL8nlttxyS1vzm/wSEpZ5o6Kc2jl48GC7uE6dOpkhQ4bErh30nFMaR7hWmmVGjBhRVHUpBwIQgAAEINAgCUjzqvP81rpAJn2d3/7BCssssEwzKZ6ctJU6mmuDcb3nrB28NPBDAAIQgAAEGjcB1g7lQjJOj/LewSHBEQIQKIIAGmWKoEwZEIBAagJt2rSxBRckvCDTBlo8zpgxw9boIpMHEnbwCjwkKUBfZ0tYRX8//vijvRklLSAyr6Py9MW4NIxEaUTR1+W1OG0qBW2UO/k5m1TOea1HvZSTySX9SRBEWlP0JzuUnS0TTEsvvbSRUFBSJyb66k1/6guZXPrzzz+NNtVkUiru6zdJx+svztWSvwSq9JfEySyVmEjl9cSJE+2XlxLMUt97XaWv4xVfG6fSECSTXtIisMYaa7hmupSX1GnHuVrarHz1olRquo8++ujUcyGuXlyDAAQgAIG/N60cDmHaVZxrzrFHjx62djEJTuoZKa1rURtkSuMI32qzrFu3bvbz9NJLL7Wzk9Y5CfUW4ZwyVdYll1xi16NSuRKQ1bPH0ZInIeQNN9wwNpmESWW+Sc8+x6RlbIKUF7PIX302adIkm4HqGee0JpXQsNZBleKG5aO1lMxB6gVgtS6LNldbNukgAAEIQMBPQL8tHafneiWnNcLee+9tpJVOTqYXtJaIc6wd4uikv5bFc5S1Q3rupIAABCAAgb8JsHaIHwm8dzD2XhjvHeLHCVchkAUBBGWyoEgeEIBArgS0CaG/LJ2ERCTY0BychIAkAKS/LJw2uPLY5HLqlnf+eim5zDLL2H9OmdUeZdIpyYvQSvlX2+b//Oc/tpCXhJVwEIAABCCQHQFpWnPMEX388cfuV99RJUgYV1ruJDgp7WsSJo0SlJHQrDS/yUkgpkWLFrYQa9euXc2oUaNsDW0SpKj0rJVZB5mmlNOX6ZtuuqntT/pPaSW8Irf66qubStrQvPn26tXLNhOx9tprx2qgkckqaduRUOmsWbPsLMRnlVVWMdJU1717d2+2qf3V5i+NehJIkhCzNA5efvnltiY+9Z+0xUlz4JVXXmnmmGMOu04ypaFzjQmZndLLKjn1u7QaOtoD4gRfJEAroSKZ2nDSL7zwwraQkcxDJnXVtjlp/sSDAAQgAIH0BPQhiuO0bkjipMlUz0ZpN9VzMc6xdmDtwNohboZwDQIQgEDjI8DaoXKf8d6B9w6VRwkxIFA7AQRlamdIDhCAAAQgAIHCCcjUxUMPPWRvsGmjDgcBCEAAAtkR2GKLLcydd95pZyjtdjJnGGemUBF32WUXO36lf/fdd5+tIU/xpOnMcXvttZctKCMhm4EDB5q+ffs6l0KPU6dOtbXj6aI05aV1Xm1266+/fqrkEsK9++67I9NIs45MHr766qtlcaTJTwIn+uvdu7etda5169Zl8eICas1fZq9k5krcbrvtNtO/f3+3uN9//918+OGHrpCMhJcklCTTGEGnr9Gl+VB/Mhsqs1vbbLNNMJrRpqn6Wtr9vE6aadQPr7/+umt+y3vd66+1zd688EMAAhCAQLYEpHF0tdVWM6NHj7bNKEkwsk+fPpFCsypdmnKPPPLIRBVh7cDawRkorB0cEhwhAAEING4CrB0q9x/vHf5mxHuHymOFGBCohcDstSQmLQQgAAEIQAAC9SFwzTXX2F/x33DDDfWpAKVCAAIQaMIENtpoI1eb3ZgxY2yhxAMPPNDcc8895rPPPqu65XrBMWjQIDu9tJJ5hSokaNOyZUv7moRQJDCTp5OWF8dlrZnspJNOcoVk9JW8hI5kVkJabPTckhplubvuusuceuqpTjUSH7PKX4IyF154oV2uNji33357W1ObzGHI6boEmCQkIxNLZ5xxhhk5cqR9/t5779kCq44mH2mdkWYY9bHXKdwrJLPddtuZIUOG2Fp2ZOpxp512sseUhGziXFZtjiuDaxCAAAQgUD2B/fbbz00s84TSPNqvXz+jDxwkhFmtY+3A2oG1Q7Wzh3QQgAAEGjYB1g619U/ev5Gzyp/3DrX1M6khkDsB6wcXDgIQgAAEIACBRkbAMuFQsmyiN7JaU10IQAAC2RGwzBCU2rdvb/9ZGmBSZbzlllu6aZVPmJs+fXrJG88pS8f11luvZGl8KVnaQEpff/11WPLQMGuzzC335JNPLotjfX3uXn/qqafKrnsDxo0bV7JM8Nl/lpYW76VE/g022MAt67XXXkuUJkmk+++/383XevFXsgRFypJZGl18bMXF68455xw3D0tIxXuplEX+lpksN3/154MPPuiWYW1mln799Vf73NIk5MazvuZ343g9f/31V8nawHLjvfnmm97LJUtbjXvtlFNOKSl+0Fkbqm4c1cfSTuOLkkWbfRlyAgEIQKCZEsh77WAJgJYss3q+e7ru65ZpvpJl1q/0f//3f6W33nor9FkQ1SWsHf4mw9rBP0JYO/h5cAYBCEAgLwKsHXjvUO17Dd475DUryRcC2RJAo0zuokgUAAEIQAACEMiegPXC1TULkX3u5AgBCEAAAtZGl5HGjwMOOMDMM888PiAypSMtKYcddpiRxpR99tnH/mLcFynk5N5773VDlSbo9t13XzdI+ce55ZZbznTp0sX+k3aatO6LL75wk6ywwgquv1aPtKrItWjRwlx66aWulhxvvm3btjWXXHKJG3Teeee5/kqerPPffPPNfSaw5pxzTrvuqsdHH31kaxbq1KmT2WOPPUKrJvOHPXv2dK9ZAlauX55nnnnGPpd5qdNPP92EmUu0BGhMu3btfOm8J1m32Zs3fghAAAIQyI6A1gwyk6S1gdf99NNPZvjw4bbmsR49etjPbj0j9YVxJcfa4W9CrB38I4W1g58HZxCAAAQaKwHWDtX1XN6/kbPOn/cO1fUzqSBQBAEEZYqgTBkQgAAEIAABCEAAAhCAQKMj0KpVK3PllVea999/39x4441GJnkWWWQRXzss7V5m6NChZueddzZHHXVUpHmFH374wTa5o8QydbTWWmv58tHJxhtvbBZddFE7XBtqkyZNsv15/JNAiOO8fiesmqP1tbdxBEXEY/HFF4/MZp111jHrr7++fX306NFGHCu5PPLv2rVrZLFXXHGFsb78N5bmHjP77NE/nb1CLr/99pubn8xnSdhGTiaWogSaNM569erlpvN68mizN3/8EIAABCCQLYHu3bubF1980V4bnHbaaba53OBzdurUqfb6wtJQFytoy9rB3zesHf7Hg7XD/1jggwAEINDYCbB2SNeDef9GziN/3juk62NiQ6BIAtFv+4qsBWVBAAIQgAAEIAABCEAAAhBIQSBMM0eK5KmitmnTxuy2227m2muvNRLqePXVV42EKKRJxKtt5oEHHrCFZcIyt8w0GcsMkX0pTJuMLkgYQ8I4jrNMODjezI/SmOM4y3yU463pOH78eDe9hIEqOSeOhGSSCAXlkf/SSy9dqZo+LTBfffWVGTVqlNEX/ueff76xzC6ZE044wc3DMq3k+idMmGAsM072eefOnd3wMI/DIngtjzYHy+AcAhCAQHMhUOTaYY011jAnnXSSefzxx4000Vlm9MzRRx9tll12WRe3hEulTe7dd991w7we1g5eGn/7necla4e/Ba/LCRnD2iGMCmEQgAAEqiPA2qE6bk4q3jv8TYL3Ds6I4AiBhkfgf58RNry6USMIQAACEIAABCAAAQhAAAKhBGTKxnE///yz4010/Pbbb9143nzcwAoebXLpr3fv3mbGjBlG6u+ffPJJO5U2tQ488ECzwQYb+HLxmk648MILTb9+/XzXnRPL0q7jNYMGDTL6Gn3uued2w7LydOjQwdV2IjNMSy65ZM1Zjxs3zs1D5ooqOW+ZEirxbh6Gpc0j/6WWWiqsKDdM/fH888+b22+/3bzxxhtm5syZ7rVKnjFjxrhRHE1BbkDAE3U9jzYHiuYUAhCAQLMh4H3mF7l2ULlSua+/c8891zz88MPmjDPOMPpiWfVQ2KOPPlrWD6wdypD41iusHf7WQhikxNohSIRzCEAAAtUTYO1QPTul5L3D3/x471DbOCI1BPIkgEaZPOmSNwQgAAEIQAACEIAABCCQC4F5553XOKYMfvzxx1RlOOaB9HWY8vG6WbNmmcmTJ9smdxwNMN7rQb++kLrzzjtNnz593EvaAPO6sWPH2uZ7nDBpHZFZnrA/r0YSaXp54oknnGSZHtdee203v5EjR7r+pB5pu1G79ZW84xztKTqfa665nOBER6+AUFSCPPJv2bJlVHFGY2GXXXYx++23nxk2bJgrJCPNP/qifddddzVXX321vcEZlsl3333nBjtj1Q0IeKS1KMzl0eawcgiDAAQg0BwI5LV20PNapva8z8Q4ntJS99JLL5kWLVrY0UaMGGEktOp1rB28NML9rB1YO4SPDEIhAAEIZEeAtUNtLHnv8Dc/3jvUNo5IDYE8CaBRJk+65A0BCNSNgNTiT5s2zUjqe4kllnBfQNWtQhQMAQhAAAIQgEDmBBZYYAGjZ742qH777bdEz3tpBJEAhJzSB1UpX3LJJWbAgAH2dR332GMP21/p31577WVuvvlmO9rEiRN90e+55x73/P/ZOw84u4qyD49ACIRAqBGQXqQjoSOE3psgRZpIlYCAFAFBQDpIr4L0XqRXCRBARAk1FOlSpUhJCC0BxW+/+4zO8ezd2/buvbt3d5/397u5p8yZM/Ocs5m5M/9531122SUsvvji2X6pDTyRpLwuueSSGPapVLquHFt33XXDqaeeGrO4//7724UPqpYvrPGK8+mnn8akTPYttNBCIe9O+O9//3u1bEI+zYwzzlg1fbPzLy7AXnvtFZi8xAidNGLEiLD00kuHBRZYIOQHuvIhsvKTdnk30/m6Ft+H/ffff7/U4aYzLXlTD0pAAhLowwQa3XdArLvgggtGYqwWJjxfLcYK69VWWy2MHDkyJicE4cwzz5xdat8hQ9FuI9+e2new79Du5XBHAhKQQJMI2HeoH6zjDtXZOe5QnZEpJNBMAgplmknXvCUggW4jwIrs++67L1x55ZXhwQcfzCbAKAATYN/5zncCE1hMcNC57S3GZB7lL7fKuDvqcfrpp4fLL7883urWW28NtYRSyJfr0EMPDXfddVf+ULtt6seq8ymnnDK6Y1x55ZXjpORMM83ULh07uKVeaaWV4vENNtggTtJ1SNTFA8svv3ycbF1xxRXDWWed1cXcvFwCEpCABJpJYNFFF43tPsKNZ555JiyzzDJVb0f4nGSlBCt4CklGu1erUGbQoEHpsnZeav71r3+F66+/Pp4jhBKhFqq1619++WVggozvRx99NLz44otRiJLdoAEbSyyxRJhlllmiQGP06NEBrzLF4aLK3ebuu+/ORDIIRxDJYHkhy+uvv17u8ux4XlBEWapZs/PP35/wXCkMBiGi7rnnnjDddNPlk2TbH330Ubad9wiUd6/81ltvZWlKbeQn/vLnu7PO+fu6LQEJSKCvEmh034GV5oTPe++99wLt2rPPPltVEJvY5sM55D3c2XdIhDp+23f4HxP7Dv9j4ZYEJCCBZhKw71A/XccdKrNz3KEyH89KoDsIGHqpOyh7DwlIoKkEcHGMeGK77bYLTNykVeLppqzsfeedd8Ipp5wShg0blok+0vlW/b7uuusCoo1ygx/dVW46bJSBzzfffNPp27LSP11f6pvwFsQWZ/U8YQ2Iz77YYouF3XffvcP9eJYpD/JthqX885NezbiPeUpAAhKQQNcJrLLKKlkmtB/FfYDsZG7jjDPOyPZKCUNY3Z3C5DzwwAPtQiZlF5bYuO2227KjDKQlu/fee6PXG/ZZTVVNJEM6Js4I65MMrzKNNoSqBx54YJYtAp5aQljh+eTggw/Ortthhx2ybcS0aaLv5ptvDvnQQ1mi/268+uqrMewEu6zER9RczZqdf/7+CJSSrbfeemVFMqRBpJ0s31daZJFFMoExoqtKPIrDdaX8urPO6Z5+S0ACEujLBJrRd1h77bUzZGeeeWZceJEdKLNBnyV5k2HRyLzzzpultO+QoWi3Yd+hHY5g36E9D/ckIAEJNIuAfYf6yTruUJmd4w6V+XhWAt1BQKFMd1D2HhKQQNMIMAnDoFSKBc7k06677hpFMb///e/DxRdfHPBokgadmADaf//94yrtphWqARkT6mHPPfcMfU2sseSSS4Y111yzw4cfHHgBmH766SM9PATdcMMNcdV9A3CahQQkIAEJ9FECG2+8cRYCB08xiCzzHj3y1cbrzMknnxweeeSReBgPMFtvvXU+SdxmVfiWW24Zt7lmo402iv2JDgn/ewAR53nnnRf7HhyafPLJw/bbb58lz4dOqNU7DRdvs802WR54pCkWseBtBs9zfPLijOyiGja23XbbwAov7Pnnnw8bbrhhFBeXuxRhK8LkDz/8MCZBgLzJJptkySeddNLwi1/8Iu5TLsJYlTLKe8QRR2TParPNNiuVrMOxZuefv2HeQ1Cl/tjhhx8evf6ka/ECkLf0jn3++efx/cufS9t33nlneOKJJ9Juu+9G1rkR70y7wrkjAQlIoBcSaEbfgd/u/H+NIYzkHniYKWeEWaL95P9ljPYYsUwy+w6JxP++7Tv8jwVb9h3a83BPAhKQQDMJ2Hdw3KHU+1Vv3ySfl+MOeRpuS6CHCBQGdjUJSEACvZJAwQNJW2Eyq60Qlzp+CoKYtsKkTMm6FIQXbWeffXaWthDWp+2hhx4qmbYVDp544olZWQsTVz1apMIEUFaWQhiFTpdlt912y65/+OGHK14/ceLEtoMOOihLz7MtrNDPrilMEmbnCmG0suON3HjqqafaCpNVbYXVao3M1rwkIAEJSKBJBE477bSsbaDdKHglayuIZtsuuOCCtkJYxrbLLrus7aijjmpbdtll26U755xzypao4PmjrRCCr136gve6tsJEWNv555/fVvA001aYxGo77LDD2gqC3Xbpzj333CzfgveVtqFDh8bzCyywQFtBRJGdq2Wj4PEmy5t65K0wwZadu+OOO/KnOrVNe/e9730vy6sQAqmtIDhqK0zAtNEHeeGFF2JbXPA+0zbzzDNn6Qri17aCYKbDvQrioraCR7wsXUE0FNvUgoCpjf7Yc88911YIn5idX3XVVdsKq+rb5VOp79GI/I877rjs/oWwVu3unXYKHvXaCl5uYjr6jQUxVBvPE6MeTz/9dIc+C+9fIWRlyiJ+07fJv3uF+ONthcnTeI5+K+9Tnit5FDwJtMujEXUmw/y7ev/997e7hzsSkIAE+hOBZvQdaKf5Pzx95pprrraC8Lbt6KOPbiuE8msreL9tK3ibiW1sIaRflq6wYKSt4C01w2/fYcY2+w72HbI/CDckIAEJtAgB+w4ztjnu8GpbZ8Y1HHeYsc1xhxb5D8xiVCQwWQ/pc7ytBCQggS4RwE1xQYCRuTQ+4IAD2oUPKM58kkkmCT/72c8CYYQIuVD4nzGu6h0+fHhxUvd7kMAUU0wRTjjhhDBgwIC4Op+i4Haa1fzdZayO1yQgAQlIoPcQ2HvvvcOECRNCYeAqFprQQHic41PKcP2LN5M99tij1Ol4bMiQIQHPdD/96U8zbyGEeuRz7bXXlryOcE2EMioIObPz5JE83Gy66aZZSKcsQZUNVpgTUgoj/FLeU02VS2s+Pd9888XQlXjRKYhGAh5R8GDDp5wRIrEg8AgFAUmHJLThF110Ueynweuuu+6Kn2mmmSZ6vuFZJSOfSy+9NAwcODAdqvrd7PxTAaaddtpQEFiFgoA39hvxUMiHMFHvvvtuwEMMNvfcc4cjjzwy7LzzzpFdQQiUsojf9G2uvPLK6KWIUKDXXHNN/BQmUsPYsWNj3tR/n332Ketxprvq3K7g7khAAhLowwSa0XegjaYNpe2grcMTXGFyIH7KoVx44YVjOzjDDDNkSew72Hew75D9ObghAQlIoGUI2Hfo2qNw3KE0P8cdSnPxqAS6k4Chl7qTtveSgAQaRqCwIiu8/PLLMb/CKuiKIpn8TQkHkAah/vKXv4Rnnnkmf7rk9tdffx2IhY04pyvGoBkhogjj0AzD3R/5U95qRsiEWtNWy6sZ59dff/0sW0JpdMXgzfOrFDahK/lzbb3PFsHWP/7xj/gseH6aBCQgAQl0ngBi2EMOOSTQNyCMAZMLpQwxAmKQkSNHVhTJpGsJwXT77bcHwiGuvPLKUcSZzuW/Bw8eHH74wx+GgpeZsO++++ZPhauvvjrbT+GcsgM1bBCqCQEOVvCkVzY8Tw1ZVUxS8GgSJ/IKXnhCwRtM2bQMbv3ud78Lo0aNCmyXMyb+Ct58AoOJ0003XUxGKKYkkil4agm//OUvwx/+8IcwxxxzlMum7PFm559uvNNOO4WCh6DAu5AM8Q8iGcJF7rfffqHgoTCst956MYQkaXi/isNkzT///FGMRNgqRC9YwXtAFMkUvApE4cw666wTj5f7p7vqXO7+HpeABCTQlwg0q++AaJJxBtqP2WabrSyyRRddNBxzzDGxPUVwmTf7DvYdeB/sO+T/KtyWgAQk0PME7Dt0/Rk47lCaoeMOpbl4VALdReBb+Jvprpt5HwlIQAKNIsDE0YMPPhizY5VutcmF/H1Z5cyKaSaCCu7+Ayt6i40JjoJ7vDj5gaCk4GI/0CFmsGL11VePkzv5GJL56wuhGmJ6Jk+YFLvwwgtjnPKCi/4otmHCa6GFFoqDZ0yY5O2KK64IhVAQ0fPNuHHj4qnZZ589TD755KEQCiFbHc8kFSvLWcnNJF4hVFNgcqsQKiJOwHDfU045JYuTXnD7H/cLoY+iaITJKoxV9Qzg4UWlEOYgMFlTbKxkZ5IQe+yxx+LK6eI0lfZZWX/jjTfGJExiwqeawZtyIR6hjHgHIOY7MdznmmuuePnmm28eJ69K5TV69Ohw1llnxbq+9dZb2Wp+JjPnnXfeAPef/OQnMe/i61dZZZUoNiqEuwinnnpqu9Ndebb5jB599NFQcLsd/vSnPwWeDcbE2TzzzBOWW265uGI9TSzmr3NbAhKQgASqE6Ct4P9+RKG0pQgaaENpV6aaaqrqGZRJQd/gtddei15A8FA39dRTh29/+9vRw0hnPKKUyb6lDiMEwWsKn9T20kbRjtZjiEILIZxi/4h+zXe/+93Yr6onr1LXNDt/+iO8U2+88UYULtGP49nXY/TVEHvzjdibwcJ6rN46X3fddaEQQizQJyyEA6vn1l4jAQlIoM8RaFbf4c0334wLI/AgxuIKfr/zexbBaF8z+w7tn6h9h/Y83JOABCTQ1wjYd+j6E7Xv0J6hfYf2PNyTQHcRUCjTXaS9jwQk0DACrMBdZJFFMvEDk1a482+UIWIgHMPbb79dNkvEFuedd15YYoklOqRh4oQwC4VY5HFSBaFMOUPwk0QopEHcgavmUsYE35gxY+Kp448/Poo4uBer11mVnbcll1wyrmjmGOKWvfbaK7z++uv5JB22p5xyyhhGYd111213rieEMkz+EI4BY0Ltqaeeittpso6dUkIZvP4ce+yxcbV7NR0oQpibbropExPFGxT+YdASLzRrrLFGh/AaXXm2KX9CgRDKo5Ixacb7VYuoqFI+npOABCQgAQlIQAJ5AoQHu+GGG6LoCzGyJgEJSEACEpCABCoRsO9QiY7nJCABCUhAAhIoJmDfoZiI+61MwNBLrfx0LJsEJFCSAMIYhCgYwoVGimRYKUz4BEQyeJDZfffdY+gAjt97771RlMKkAmUgHaKdcobHEEQyrMA+/fTTYz6s4t1tt92yS66//vroVSQd2GyzzaL3mW222SYdip5Hbr311oAnnGJjdTuCHAxhyYYbbhhXuG+11VbxGOd/9KMfRZEMq90JTYEraEQzzz77bJwowYMKhmcTRDrVBCYxcZP/ue2227I7sGq7Vjv77LOjwIQ64DEI3s8991ysL95bEP0MGTIkZgeH/H1qvQfp6nm2XMdqdFZxY6wkxIPQ888/H9+nyy+/PKTnjlCIMGGaBCQgAQlIQAISaBSBP//5z7HvhxBXkUyjqJqPBCQgAQlIoO8SsO/Qd5+tNZOABCQgAQk0g4B9h2ZQNc9mEvhPwPtm3sG8JSABCTSYACKCZI12GU/4IbyJYIgY1l577XSr6D0GDzIIMLbeeuuAe0DCMxWH50kXfPTRR2GZZZaJ4ZLyIRkI3YTHkCOPPDImRSAxfPjwuD3rrLMGPnQokuGWf+GFF0677b7xsIKde+650cMK27jpS0Kia6+9NiCWwQjFhGgmGSEjCEWx8sorh4022ijgSefVV1+N3luWWmqplKxbvxG4EEoqiX+4+bbbbltTGXBlfemll8a0vBeIZPLhsRZccMEYHgNBEd5oMPhsuummcbsz/9TzbMn/j3/8Y/Z+4RVozTXXzG673nrrBT6EmXrggQcCIb+eeOKJsPTSS2dp3JCABCQgAQlIQAL1EkC4TT+WfqMmAQlIQAISkIAEqhGw71CNkOclIAEJSEACEsgTRHk88AAAQABJREFUsO+Qp+F2byCgUKY3PCXLKAEJtCPQLKEM4pS777473gtxRl4kky8AQpctt9wyiiyuvPLKGKZpvvnmyyfJtk844YSQF8mkEyNGjAgnnXRSmDBhQnjxxRfT4bq+KU8SfpDBZJP977/2l156KYYu4jhhnkoZK4oRaCCUwT744INSyRpyDFf/jz/+eIe8Pv300/Dee+9FLzcIRJLxDNZff/20W/Ebzyx4GIIpoY3yIpn8hXjQ4RzpulLXep5tPvwVnoZKGV59CAmFx5mhQ4eWSuIxCUhAAhKQgAQk0GkCCGSmm266DmEnO52RF0hAAhKQgAQk0C8I2HfoF4/ZSkpAAhKQgAQaRsC+Q8NQmlE3EfjfbGo33dDbSEACEugqgbwQJL/d1XxHjx6dZbHxxhtn26U21l133SiUwQPK008/HUoJZZiIWHzxxUtdHsUseI5BFIJgoyu27LLLlr385JNPjucoZyUX+zPMMEOWR/Kokx1o4AbColoN7zfJ604t1+DtZ9SoUTEp9a1kM844YwyvVW9d6322K6ywQjjnnHNi0RBjIbZZaaWV2k1Y4fmGEFmaBCQgAQlIQAISaCQB+j+aBCQgAQlIQAISqJWAfYdaSZlOAhKQgAQkIAEI2HfwPehtBBTK9LYnZnklIIF2XjbGjh3bMCKvvfZalhchb15++eVsv3jjww8/zA7lvYRkBwsbs88+e363w/ZUU00VjxEqqStWzjNJPs+8SObjjz8OlBmRDqGWHnvssRhuKaVPYZvSfnd94z1lttlmi55U8AjTlfBPqb4IYd5+++1YV57vc889Fx5++OHMk0y9da332SKKwVPMm2++GcuEJ6AhQ4YEvNzgGWiNNdaIYbm6i7n3kYAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEB/I6BQpr89cesrgT5AgPA6yfJhmNKxer/zghfCItVqeYFN/pp8OfPHG70999xzV8wS7yr33ntvuPjii2PYo88++6xi+maevPDCC0MpDzh4aJliiikacmvCKXGfO+64IwqC6hXDVCpMvc8WcdQf/vCHsN1224Unn3wy3oKwU7fddlv8cGDYsGFhm222Cdtvv32YZJJJKhXDcxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQCcJKJTpJDCTS0ACPU9gkUUWCZNPPnnAWwieX/hmv1ZDXHP++efHkDeINgYPHhwv/eKLL+I34Zw6482knGgieTWptVz1pqskMPnqq6/CVlttFf785z+3yx4BBuGiFl100TB8+PAwfvz4ToU5apdZJ3ZwvTfLLLN04orOJb377rvDzjvvHN+J/JUIcRZeeOGw5JJLhrXXXjuMGDEivPvuu/kkndruyrOFwZ133hnuv//+cOONN4YHH3ww5D0jjRkzJvAZOXJkOO+886LHmU4VzsQSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEBZAgplyqLxhAQk0KoE8Mqx8sorh/vuuy9MmDAhPProo1HsUWt5ESecddZZ8bPRRhtFTytci2cWwi39+9//Dtddd11IoZFqzbcV0+21116ZSIaQPwhEll566bDAAgu08+By2WWXZcXHA01vtGeeeSbsuuuumUhm2223DRtssEEUAxWLc/71r3/1aBUnnXTSsNZaa8UPvJ999tkwatSo6PkH8RfG+33ssceGE088sUfL6s0lIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0JcIGNOhLz1N6yKBfkRgvfXWy2p7yimnZNvVNhAlIIJJ9uMf/zhthnnnnTduk+b555/Pjpfa+Oabb8LXX39d6lTLHPvkk0/CLbfcEssz55xzhnvuuSd6W/ne977XTiRDgo8++igrdzNCFWWZN3GD54oHHeyYY44Jp59+ehSiFItkeHawwRBFdbfBl3BLyfBOwzPZb7/9YlimSy+9NJ0KDzzwQLbthgQkIAEJSEACEpCABCQgAQlIoC8SGDduXGDxS/L02xfraJ0kIAEJSEACEpCABCQggdYioEeZ1noelkYCEqiRwI9+9KNw9tlnhzfeeCN6TEEkwbFqdsIJJ4QXX3wxJsPDyqqrrppdQmieZNdee20gLFM5u+iii8Khhx4aCLt08MEHB7yXNNIIjZSsXg8veNpJhrCI8EPljPA/yRCS9EYbPXp0Vuytt9462y7eIAxV8ijTnXXlOW644YZx8I9n8fjjj3cQLFFWvOAMGzYshl/6+9//HsU/lcJrFdfPfQlIQAISaAyBv/3tb2HLLbeMmQ0cODDcdNNNNYcPzHt0e+ihh7Iwj2R2ySWXRK92bF9++eXR8xnbvcneeeedMNtss3W6yB9//HEMgVjtQkSkePabZpppwne+852w/PLLh4033jjMMMMMJS9tBFPExUcddVTMH8+DK664Ysl7eVACEpCABPoXgWb1B3qKYrk2fJlllokLSfDey6KTZhuLRy644ILw1FNPhb/+9a/h/fffz27JWA3jM0sssUTYcccdw7TTTpudK7fx2WefBfoPU089dbkkTT8ON/p22K233hpmn3327J6N6KtkmbkhAQlIQAJdItCs36VdKlSDLua38z//+c/4e5bfta1ku+++e4wMkMrEwlHax1qNsfV111233YLf4va21rx6W7py/bdG1KOV35lG1M88JFCNgEKZaoQ8LwEJtCQBJqyOPvrosN1228XyMSGFl5DddtstDo4UFxrPIQzCnHrqqdmpX//61+3SbrrppuG0004LDIRdccUVYYsttggrrLBClj5tMIBz5plnxl061quvvno61bDvKaecMsuLAZ96bNCgQdlleY8x2cH/bhx++OHtOqlJRFKcrtX38/XluTC5VmyvvfZa2GOPPbLD3VlXBu2YVHzsscfCP/7xj0C4K97XYsMrzgcffBAP8/4pkikm5L4EJCCB7iFAG4FgMdm+++4bENLWYh9++GF2bbGnNtr1lC8DWL3J3nvvvSgUpk55D2i11gGBaqp7rdeQDpES4QhPPvnksMkmm3S4tBFMv/zyy6xsEydO7HAPD0hAAhKQQP8k0Kz+QHfTrNaGv/3224H2vdLYQaPKfP/99wcmy/AiU8refPPNwOeuu+4K5513XhTu5L0KF1/Dwqkjjzwy3HDDDVFgU3y+u/YZk0r9nOJFOY3oq3RXPbyPBCQggb5OoFm/S1uBG+0Q4wzd0Z53tr75cRKuRfzBGDgLkWsxFp0isM1bcXubP9cXtqv13xpRx1Z+ZxpRP/OQQDUCCmWqEfK8BCTQsgTWWWedsPfee0fRCoriww47LE7a7LDDDmHxxRePq77pTLzyyisBDzAvv/xyVpcjjjgievfIDhQ2BgwYEI4//vgokOH45ptvHg455JCAd5Lpp58+rnAaOXJkOPfccwMdO2ynnXaqeXV5vKDGf/IrpgkjxH0mm2yy8IMf/KDGHEJkgKCIEFFMMOGlhOtnnnnmOAD23HPPhWuuuSayyWeawhLlj/WGbTwAJS86vBc8u+WWWy5MOumkYezYseGRRx4Jv/rVr7JnR526u66E+uJZYIQM4znzTHj3MERavMe8txjeZTQJSEACEmgNAqNGjQpXXXVVw73ItUbtaivF97///YCgpBHt00wzzZT1uYrvTr+OgT0GbBgMS2ELmVRjEK2UkLk4D/clIAEJSEACzSDQW/sDjWzDu8KV8EqM2SRRKp7jfvjDH8ZFJfw+pv1/6623wo033hi3EdPsvPPOMYT28OHDO9z6t7/9bWARlCYBCUhAAhKoh4C/S+uh1phr+N1/5513xnmPWnK8+eaba0nWp9K0Sv+tT0G1MhIoIqBQpgiIuxKQQO8igKhg6NChUVxA5wqPIRwrZwhH9tlnn/Czn/2sZBJCMSFMwVsNAhMENXxmnHHGgJeSvG200UYxbf5Yo7ZxeYdXGQaP8EDCB48ka6+9djxey31wT0wIgYMOOijAhlBRfBZccMHw7rvvhs8//zxmM/fcc8fVVww+sVoOAU1vtP322y/cfvvtceUZghkEKDDg/UAshcGQemKIp1DYc+673/1uPNbsf1ZaaaX4DHjHEOkw4ce7yKQfzyMfj33PPfes+YdCs8tt/hKQgAQk8B8C9DFWW221MOuss9aNhFCRtAfYAgssUHc+PXEhIplGGQxZ/V3NEJHiOfCJJ54IrBajX4ZwOW+9mWm+Hm5LQAISkEDvINCI/kB317SRbXhXyk7bn0QytO8scGFRULER4hrOhDJinOIXv/hFXPySD1PNNa1Sr+LyF+/bVykm4r4EJCCB1iDQyN+lrVGj3lGKIUOGBMIwEjqJBcLVjMUzt912W0zGoliiB/QH645+zh133BEXJ/FMNAn0RwKT9MdKW2cJSKBvESB8DUISRAflGnTC12yzzTYxHQMslYz8HnzwwcBqpcknnzwmzYtk5p133nDOOefEUE7FgzSV8u3MuTnnnDPG6Jxnnnmyy5IQKDtQwwYdTTzg5Cf0XnrppSjKwEsO4pKHHnoo4MaYmOQYk095wUYNt2mJJIMHD46dazwBJRs/fnwUwtCBpn533313OOGEEwIip2S4Z+5O+/nPfx7DLhGHFaOjTzgvmFNOYrHjXppVcc16v7qzvt5LAhKQQF8gkP4/RtRICKauGJ7dllpqqfiZaqqpupJVv7h2vvnmi32iqaeeOtYXV8uvvvpqu7rLtB0OdyQgAQlIoEkEGtkfaFIRWzpbQhD/6U9/imXE4y1hoEuJZEhAaOXf/OY30TMu+6+//nq477772OyVZl+lVz42Cy0BCUggI1DL79IssRtVCay//voxDR7gCb9Uzf785z9HL/H0xfCyojWOAH0yxqh4xzUJ9EcCHSX7/ZGCdZaABHo9gbnmmit6T0FcwOALoWv44FEEYQtCkTSoVUtl6RgQIgd1MgMyrGaeZpppAvchLzyTlLNaOndcW22QZ4011oihhFBXI6TgvpQBY3UVn1oM4cgmm2wS3Re/8cYbcSBqoYUW6hD/EwV3KWPFVy0rvktdyzFEH3waYUwoVoqxCiOEQccee2ygrrwLiI7wGJNET5RjxRVXLJsP3nbKWaOeLT8G+JAf9+P54nKa54LXI00CEpCABFqLwMYbbxxDAPF/9v333x+uvPLKsN1227VWITtZGgS4b7/9dmx3mLxpZaN8CF5hj9Evm3/++esuMv0DPAfSR6jXCMOJcArPfJ3pY+bvRxl4BrPPPntA1K1JQAISkEBrE2h2f6ARbTPe1958883YtjTityVt1TvvvBPHQljY0RXjN3oywiRXM0Q0W221VRgzZkxMStgmvOw2wvgNDm/GEOqxRnMuVQY86RCGao455mg3nlEqbfExnhshLAkpUm5BWfE17ktAAhKQQGUC9fwubUTb/tVXX8W2mHad8eN6f39Wrl33nmWu4pprroltMR5Nkgf4cqVIYZdWXnnlwOLfzlgjnkFn7lctLfMBlKlZ4zB40Kf/wLwY/YBmWL11wKsg/ROiN3T2OTajHuYpAQjoUcb3QAIS6FMEGLihw8hkCqF3VllllRjrut4OJPkxEYPHFcQV5F1JJNMMmAxqEC4piWTquQcDTAiG1lxzzUB4KUL99GWjo4USeoMNNgiLLrpopweVuosNz2HJJZeM5VxiiSUUyXQXeO8jAQlIoJME8GZy+umnZ1cRCqCSsDJLWGKDEAKEWOTz/PPPxxSjR4/Ojl166aUlrgpxIIV2nOvok0yYMKFkOoS+Kf+XX365Q5pbbrkl9geYdFl66aXDYostFgWlm266aXjggQc6pOcAfQfyTIbnvXQPYop3tzGolLdSTPPn2UZoi/CX/hB1pu6LLLJI+OlPfxqFtcXpS+2TB2EtEVRzLQzY3mKLLWLoSia0Eperr766VBbRgxxhJgi/hVCH1XB8s8/K+nLPtWRmHpSABCQggW4l0Mj+QL7g9bTNXP+73/0utjvJY+qJJ54YF1+ssMIKUchJ2GnGROppw2lb6XewWIjraT9/+MMfRi9v+bJ3ZjvvMRfRay3GWAzt91lnnRXWXXfd7JIrrrgiluvCCy/MjiFipqz0aUoZ/RzqQOjLxRdfPODllUU1HGOlejmrhXO1EBCV+ir06yj373//+1gE6sQzhRfPkn7C6quvHoXa5cqYjt9zzz1hww03jONgXEs/hb4GnnUpIwuLuBcLszQJSEACEugageLfpfnc6m3bUx54IL/qqqvCOuusE8Wv/J/OGPJss80W27lHH300Je3UN+0EedEW0M6/9tprgWPs88GLfjXbdtttY1rCCtZr/B6nLli5xbspb35n33777XGXNrtWq/cZnH322RkPxMLljDIlbsX9iEsuuSSeSwJfhCtEMeAZMlfBmARjCgiEWMhTbJ0dg2FMieey7LLLxveFth+v9Szs4TkzzlTufU19RSIP5K2rdUh5scDozDPPjHM1jEPR76EvxoJh5u/uuuuulNRvCfQIAT3K9Ah2byoBCUhAAhKQgAQkIAEJ9CYCDFRsv/32gYkOwuUx+XT99dd3ugqffPJJHIziQlb7YkzUILxhpRhxt3fYYYd4PP/Ps88+G1hJnYyBsdVWWy3tZt8MMjHYhRiTyZ9khJEkJCOujYuNMj388MPx85Of/CROSOXDQpEfZUtGnGyOYZ999lk63LRv7kf5kiEgzlsppvnzTMbhYa9Y3IRXGFamwZJJpUpWLg8GfRAOkcepp56acSH8Y7GRZo899oheZPLnGARF1MSHQUo88SGg1SQgAQlIoPUINKo/QM260jZz/bhx42K7Q7/koosuCieddBKHozGp9OKLL0YPuZ1pwxFsEta6OEQxbTFhk/jQxtUTipK+CRNDzz33XPSw+9vf/jYKVsuFX6Iis8wyS2w7/1Or//1LGVJfJB1lhTKW+lfpOPVH6HrBBRekQ9k3fQjqRD+DNhoxa94bLQlr4VzN206lvgp9DPoC3AfPwXnxD/fHew3MYP6Xv/wlwK2UHX300XEiqvgcIStPOeWUQPhKJqjglu/nFad3XwISkIAEShOo9ruUq7ratpMHbcauu+4a/vjHP7LbzmjfabP4/XrooYeGn//85+3OV9oZNWpU2HHHHQMeRxD/IghBCItnE8Qa1A9xxM9+9rOy2bz00kvxNysJyglTy16cO8FCZEQSCHMQeXD/ch5W+L1Nu0/7zKJY9itZV58BC2RSHwPe5YyxkJSOvljeUt+BBdAvvPBC2HLLLTuEmGI8gvEfRDYIgFn4nYx8a+m/kQbP+oh6SwlhKBfjSHwY+2BhVXGfhX4I7wTi6Lx1tQ7kRb8ScdPTTz+dzzpu85z40LfZZpttYj+2uA/W4SIPSKAJBPQo0wSoZikBCUhAAhKQgAQkIAEJ9D0CrGjGuxzG4AyimUbYlFNOGVd8kxdiilJeRYoHg5jUKTYGN9JgGivP8l7w9t9//0wkw8olVhQxWPL444/H1cWpXpdddlk46KCD2mWNICi/yosVaOzzafaKZAafRowYEQduKBQimeIBnHaFLdrBtW9eJMPAGoNRiFIoP4NzhOs8//zzi6783y7PI58Hq9tZnUYeeNRhUIf7MLFYzgg3wQARoZbwdEhawnBy/N57742TXzwvBsRIx4CRJgEJSEACrUmgUf2BrrTNeTJMgiCSwBCiMHnGBBhhizrbhjP5hkgGbyYHHnhg+MMf/hAnxH75y19mHlCPO+642Jbmy1DrNqudkxE6mxBMxxxzTJwkqjQZla5J35tttllsx2mDk7FambYd0VDeKHsSyeBhhb4OwhM+9OVY0czkEpN1eOUpZ5U4l7umM8cpPyIZ2OPJkH7CddddF1egp3x4nqX6gNSZ6zGePcKYxx57LIatQliDSAmPOtRdk4AEJCCBzhOo9XdpI9p2vJ6m3/UsfuH/bhbOIIDFM9jQoUNjBWg/aadrMdoAFsUwZkBIHkQTeHXBEE8SXhLD8wntRzmjXUrWFY8y5JGENrTBhF8qZynsEp5RavG434hnUK4snT3OWAJe4hDfwB9+PAvEzYzLYGPHjo2C3nzetfbfEDux0AaGeLchf/o3r7/+euwv0NdKIRgRpDAW0lmrtw7cB2/MSSSD+AtREGMg9GWOOOKI2OchHR55K70DpNEk0CwCepRpFlnzlYAEJCABCUhAAhKQgAT6FIHBgweHM844I4omqBihcnCFn1wGd6WyiC9GjhwZB64YwGAQKG9JKIOYgkGQUpMkDDqwCgxbf/31s8tx5Z/c2SKgYRJmiimmyM4jPEHwwionBjEYXGHiCXe9GAMueSPEYTqXP96Zbcr5xBNPlLyEleAMRLLyirjlDNZh1P20007rVEx2Jr2SJxk86hB6IAmIqAMfnl8lF9NMPKU8GLRkQDLlgWtjPkyyMQhUznhXGJTEWC2WXDCzj/cYPnDeeuutA15qmITEQ40mAQlIQAKtR6AR/YGuts15KqntZ/IMYSeGFxK8lBSvzK2lDSfUD+EE0kQc+Q0bNix6d0kr1xGkpEk1ztdqhBgYMGBAOOCAA2L5EJDSt+LDRB3CGcIR8qFtTO1tcf6zzjpr4JMPdYCHPsIM5A1RcAqHSDvLxFO+D0T/i5ADTF7R14IhIZzoGxVbJc7FaevZZxKN1eRMCA4cODDLgr4mq+wRaGGIe4YPH56dZ8U3/QaMyU/6k/kwV4SIpI48r7TyPbvYDQlIQAL9nEAjf5c2om2nDUi//fmtyu/hQYMGZU+Jdp72Em+3GL9Vacsq2UMPPRTT8zt7hhlmiIJYwv/kjd+h3AujreQ3brHRr0ge51g8U6qtLL6m0j7tNnm8+eabUcCxyy67dEiOx5Q0llFL2KVGPIMOhejCAUTAfK688soYRitlBX8W8TA+g0c8vOq88sormVfgWsZgyJcFUBihjBjHyb8rLDLig4g69Q+vvfbaTKAUL6zhn3rrQNb0STBCTqW+CvupbLxnaeyKstXyjLlek0AjCehRppE0zUsCEpCABCQgAQlIQAIS6NMEmGhIg1IMqqUJo65WGuFEmgxKA2MpT1bwpFVdaVKKFWWISfJG2B6MCbz8BArhBjAmyxCK5CeI4onCP9NNN104/vjj025c3ZPtNGED974M6JX6bLLJJpExZU0iGSaIGLhLq95qLdLdd98dkzKYSDiDxDh/PSvmGTAsZYiSELZgpCmXB/HGiwcbU35M4qVysJI+L5JJafhmIgyxEsZAGow0CUhAAhJoTQJd7Q80um2mDUmTIBAjnFGxSKZWkrR7eZFMug4PNbSnWFfaKPpRTIakldQpf/pV999/f1xVTVvJpAp9geJwBil9Ld8IVWnLEeecddZZJftATCrhSQaPb4hauWc5ayTnUvfg3nmRTEqDd700+YVHgbyxOjz1Cffee+92IpmUjudZSdCb0vktAQlIoL8RaOTv0ka07bSPycgv/d+fjvHNb+gkpMBzCELLcsZvUQSgCE5oCxC6lvrdivBl7rnnjtmQBnFEsSG4IUQS1lVvMilvfvtjKfxSOp6+8a5G/4D+R7nf0Skt3414Bvn8GrGNGIZPsc0000zZ2BLnCGvVGXv++eejxzg8ybGgp9S7Qn70WdO5Dz74oDO3yNLWUwf6b+l+6d3KMvzvBgJhhMB8EFNrEugJAgpleoK695SABCQgAQlIQAISkIAEei0BfsQnLzIMFjXCjT2DJEsttVRkwiRR3h555JE4ccOE11577RVPsZoLzzN5S0IZvNGkyTFWGafBCQahUrnz16VtRCgMkGG462U1ek8agzl4uoE3oSA6G+bp3//+dzbYRIglVlmXMu7z4x//uNSpGLKA2OEYg4GIkEoZAhxcCZcyBv2SJaFT2i/+XnfddeMhJvWSi+LiNO5LQAISkEBrEKi3P9CMtrnUyu96KOFNBi9ppQwhyRxzzBFPffLJJ6WS1HxstdVWCwiDmQAjNBITfoh78vbOO+/EEEKcy3uOyaeptE0/gBCT2OKLLx5XrZdLzwQOq5uxp556qlyykivsyybu5AlEy5SzlMEGDzoYAuq8JTEux3bcccf8qXbbrFyv1A9sl9gdCUhAAhLICNTyu7QRbTv/v/O7F+O3OR5XyhmeRBBWEA6YsYRSxnhBChM8yyyzRJEMnkfKGYJYjDZ+1KhRHZLhrQUjdDS/rxthSSjD71+82RVbCruEOIj7VrJGPINK+dd7btVVVy17Kf2uZMXtezpe7hvPezwnxFLlxjPStTPOOGPcTF5u0/Fav+upA2Mn6R2+6aab4rhOElrl77vHHnsEPqXERPl0bkugWQTa/wJp1l3MVwISkIAEJCABCUhAAhKQQB8hkEIubLbZZrFGxH1mhfHss8/epRoy+EM4oldffTWG+vnOd74T82MiCWO1DRMoCD7Gjx8fwy8lYQUDZIQvwMgnGXklI755NSMNwhxEMrhAnm+++apdUtd5QgIce+yx2bWsWGNgi1XRSSjERB0rwYvDKGQXVdkgvADupTFcOleycmzgmiwfxiAdy3/PO++8+d1sOx/mgOebzzNL9N+NJMphlwEvTQISkIAEWpdAvf2BZrTN1dqoWikmMUa59NNMM008ldrXculqPc4ECp/9998/rhh/9NFHY/8G8UfyWoPgl4k++ghpwqWW/AllkF8RT1ilSpa8ztGfoh9ULNzh2kZxLlWOav3I5M2nWMic+hV4Cign6E33o/wIkDQJSEACEvgPAf5fbMTv0ka07fz/nIQM5TxwpOdWziNqOk95aDuT+AIPrdXyZGEIns0QrRB+KY01kCfeQe68886Y/YYbbli1vUnlqPaNdznGHGjzaefzi0+4Z1oMtOmmm1bLKo6jpETlft+n83x319hHpfY9eXqhPMXtO8dqtdSH4f2hHwNPxiFYAIX4Ki2eYsFVPVZvHfBmRBhM3qmzzz47fggFxfgZC6EY4yrV36qnjF4jgXoJKJSpl5zXSUACEpCABCQgAQlIQAL9lsDKK68cfvKTn0RvMikE04033lgytE+tkBiIOvroo2PyBx54ILpIZicJZXCZywDISiutFO644444kZTyvvfee+MmoQXWWmutdDjGuU47lQY3Upr8iiYGVpollME9MJ5vio3QRIR82G+//cKTTz4ZB08IQ9RZbzLk+9e//jXLvtrEX7nzeeFKEi5lmRZtpFX2RYfbCV5OOumk4tNl9/MCm7KJPCEBCUhAAj1KoJ7+wCuvvJKVuVFtc7XJr+yGVTZon3vKEIIwccIHETL9qkMOOSQKaZno49gtt9xSc/HyglP6FHxqMTzREPqxlAC2UZxLlePb3/52qcNVj6WQG9X6KWRUrq9S9SYmkIAEJNBHCTTqd2kj2vb0/zmou+oBLC2iYfwAkcKYMWOiSKFS6GjaEcYcGH8YOXJkDOuXBLKIZJLoZuutt27o24BXmZNPPjkglsXjCKIejDIQMgqPa3iiq2aNeAbV7lHP+UrtexK41JNvugYRzIUXXhjHiOj71CuGSfmV+q63DjvssEP0dsz4Dv0rDPEOnzPOOCMMGTIkbLTRRtGjzPzzz1/q1h6TQNMJGHqp6Yi9gQQkIAEJSEACEpCABCTQFwkcccQRmReZP/3pTwH3x10xVjSlCRiEMhgDRSlWNZNxGINX2IsvvpjFI08rrRDR5Ce58iu+EdF0xhhQ6wnDbfDxxx8fb82qKlaV5Vfo1VomvO4kq7ZKKc8sXcP3wIEDs92JEydm26U20uq/4nOshMMow3LLLVfzp9JgVPE93JeABCQggZ4j0Nn+QDPa5immmKJbAHSlbzB27NjYp0meYqoVGM99hLhM4SQJv1TKZX+5fFL7y3k8y3WmDS63qruZnOudLEt8qvVT4FCur8I5TQISkIAE2hPozO/SRrft1X6/ti9p6T3aPsYJpp9++pjgxBNPzMYWSl8RQhLBUJ98KKTrrrsuXoKYhjGHRlq58EuE68EQUtQyltHoZ1CtjrX2iept36vdn/N44FtyySXD6aefHr3IJJEM4qIVV1wxhu7mOdYipq10v67UAc9GiJV/9atfxbLm8/r0008DC6NY7MViME0CPUFAjzI9Qd17SkACEpCABCQgAQlIQAK9nkAKufDDH/4w1uXII4+sy/NJHgReZQgNwMQQgxx//OMf42mEHMSgxpJghm3c6LK6itVX2Prrrx+/0z/5EAGEIKhm+TQpjnW1a5pxfpdddgnEVGdQ5/PPP4/edVhRRtipWo0QBMny9UrH8t/vv/9+fjfbzvN79913s+OlNsqFMkD8RFgEVlAxwJhCJ5TKw2MSkIAEJND7CHS2P5BvW6q1T9DIp+nJtrkrTwbRyoILLhizoF187LHHasoO0Sj9HPoAGGEh00rzahkk8THpCBOBR5q+aLxPhDWo1k+h7uX6Kn2Ri3WSgAQk0AgCtf4ubUTbjrAl2XvvvZc26/pGGMFvadrM4447LowYMSKKJffaa68orph00klL5st4Al5kPvvss4BQBY+viFQZd8AIz5QXOpTMpJMHF1hggdhHYIHQrbfeGhfKIKBIi4fSeEu1bBvxDIrvkbygFB9nPy/ILXW+2cdo+1lUlESwPKsNNtggLLroomGWWWZpd/t8KMp2J7pph/dxn332iZ9PPvkkei0i5DZCHxY44aV5xx13DC+88EKYaaaZuqlU3kYC/yGgRxnfBAlIIBKggUJ9Wu9n991379ckL7nkkoxd3s1/f4bSrAEY3Dym9/Swww7rNOKPP/44uz7lU+p7qaWWihORDKjttttugWfMCjhNAhKQgAQkkCcwfPjwgDtZjB/3e++9d3RtHA/U8U+KA85gAe6R04AUq4HSYBYDQMkVM55sRo0albnXTdenW+cHi/IhCNL54u833ngjO1Q8uJKd6KYN3C+nCUHK/otf/KJTd85PkBFCoZLlJyHz6fL88q6c82nSdrnzKXQDK86ef/75lLzkNyvY8yvhSibyoAQkIAEJtByBzvQH8m1Lb2ub6wWPmCiFOaSv8eyzz9acVV5gSj61Wp4zLv6rWQopUS1dq51P/QyExeWEv5SZyT7DOrba07M8EpBAbyBQy+/SfJtTb9tOO5k8mlYbV3/66afDlltuGX8jM25QbIhTk7AUD20p7DHXnXnmmcXJs308pyVhCp7cGJdArJq8pyCUaYYlrzIIaRHmEOoJcQeC2RVWWKGmWzbiGXCjvDefSr/N86GyaipggxOxCIfQVNgxxxwTvcrgmaV4HIcxBub+sErCn5igCf/QP8l76sPbzaabbhrOOuus2B9kXiZZCjue9v2WQHcQUCjTHZS9hwR6AQEaSSYI6v18+OGHvaCWzSsiKuvELql4m3e31s4ZxftOO+0UDj300KYUlI55Yo3opbNGxyxdX+mbWK6EtGCFPgr6Aw88MLpq7kxM9M6WzfQSkIAEJNA7CbBCeY455oiFZzCJT71GWAAGDjBW2KS88l5kOJfCLyGUSWGXGGBIg2GkwWafffaQJpVuvvnmOND1nzMd/yW8EZ5sMAbWit3zTjLJf34+pkGyjjk09gguonEPnYzVZWlFeTpW6XuRRRbJQmNxbT4UU/F1N954Y/GhuM9gW1oBf/311wfif5cy+hd4AiplCy+8cHb42muvzbZLbVx00UVRBMUqsKuuuqpUEo9JQAISkECLEqi1P9DItrlWFN3dhpcq19prr50dZpKulrETJoBS2z/llFOGJApJGaV6sV/cP0FgM+ecc8ako0ePjt5o0nXF39xn2WWXjf05JhN7euV1cfkq7ee9CZ5zzjllk1bqx5S9yBMSkIAEJBBDF1X7XdqIth1PLYRjxvCuWslTGGMAeFy57LLL2gk7yj0uxD5JeHrSSSfFMe9yaVP4JeaL7r333uj1g7SMVeTFKOWur+d4EsrQlt91113Rswz5IKjIt/WV8m7EMyB/+hvJKglQ06KmlLYZ36nuxX0c7kXfJll6Zmk//82YUurX5AUr+TTN2GY+hXEN3plyYcphfcABB2S3ryfkdnaxGxKok4BCmTrBeZkE+jIBJlMYGOjMB+8bmgQg8P3vf79dDNNWpoIrvz322KPkBy9Jm2++efwRkDqluH3k+COPPNLK1bJsEpCABCTQzQToO51xxhnZXWuZ+MkSF23gNSat9kJUgagTKxbKpH1CEPzhD3+IafITJfFA4R/yS55YEPYef/zx6VS7bwZMjjjiiMwzDavOii0NGJFPdxnxyPNechhE6YyL4zRgxComBgdLGavVnnjiiVKn4qDcL3/5y3iOSTRY4jkob4TIOvroo0PeG0/+PIN78803Xzx0xRVXlO1HMAiXVvchBl599dXz2bgtAQlIQAItTqDW/kAj2+ZakfREG15ctj333DPzjoeAdeONNw6VQkvQx2HiLLW7hBRI9Uh55/dL9U9Y8IKxIvyggw5qt6I55cH3aaedFsWwEydOjKEuBwwYkD/d0ttwZCIKY8I0he3MF5oQkITe0CQgAQlIoD4C1X6XNqpt32+//WIBETYgaCllLFi++OKL4ynEIYsttlipZO2OsQjm8MMPj8fImxBM5UQTLMAhHBKGyJLFOVizvMmQN0LYVI9rrrkmW8CTvNuQppo16hmk3+7c7/TTT+8gxOU4wqmnnnqKzaZa6ueU6uMMGjQou3e5xcR4kmPuI1kSzKT9Zn4vvvji2UIjONLHKmV577+E29Qk0N0EFMp0N3HvJ4FeQIB4mHRIOvM55JBDekHNmldEOorEVOSTOpLNu1tr55wGsVq7lP8pHS4tjzzyyJKfo446Kq4Mv+OOO+Jq/qWXXjpelCYSe0P9LKMEJCABCXQfgZVWWil6VGvEHddbb72YDd7NMNwNF/cvklCG82nAoZRQhvM//elPM6EGA2o/+clPwt/+9rc44IPIg7CRTEQlzzRMtpQKq4mHFwzB6Kmnnhpuu+220B0rfhiESl5xEJMgSqnVGABMq95+97vfxdBYaVUY4pkLLrggEHe+khHnGyEwRl+PFfG//e1v4wo+eMKO/XLGZFteoIQQlxXf48aNi5dQHlZYkU/y0oh3vmKXyeXy97gEJCABCbQOgVr7A41qm2uteU+14fny4d0lvyL/ySefDISWZDyFkAHJcxyu+JnUWXXVVQNpMMIpJuFvPs8ZZpgh2yUPPMSRTzLCUuApBsNTH206YSdYJc9ve1aD77///rFfQ5ohQ4YEBD29yfBAgDcjJggR9W6xxRaxTqzkRkzN2AZ9l9T/6U11s6wSkIAEWolAtd+ljWjbN9xww+y3Jx5G+a2ahAR4FXn88cfj//Njx46NaGj7arUdd9wxLggl/TPPPNNusU9xHmnBCV5rEJsSkonfq820lD/tNG00c1TDhg3r1C0b8QxYqJO8/OJpnn4C3Glj8c5y8MEHRxFTGqPoVAE7mbhS/y31b8iSEOB4IUqhlXg/mNNAaJTGGEiXQjCx3WxDcJTGUfDMS/8KEXQyRDu84/RTMMJu5+uU0vktgWYTUCjTbMLmLwEJ9AsChDnAqw6f5MawX1S8n1SSjt0ll1wSpp566lhjFOPdMTHYT/BaTQlIQAJ9hgArtFIIpq5UilU0k08+eZbF8OHDs+20wSACoYWS0VbNP//8abfdN0INQvqkEEK4MibON9cw8cT9GADCWMXFauQUGz2fUfJwwuALwo+dd945EBe72YZg5LDDDstuQ5tM7PJajAG9K6+8MoYzIj1CcFY2LbTQQnHVGmLv/MqzcnlSz+Rl55VXXokTUky+sTod4RDsEd8mSyu/0j6TfQxiwhWPQ3jvQfxEOSgPnnJSLHtWK3ZmwDPdw28JSEACEmgNArX0BxrVNtda455qw4vLt/3224cTTjghpFXQeIlDwIJnPiYDt9tuuzhhwgr2tAiHEIa0w3lRTMp3+eWXz7zM0DcYMWJE2HXXXTMRMekIjbjMMsvES/gtv9Zaa8WQTIS3wOvb5ZdfHs/RdtNPYGKutxn9jBtuuCFO7DGRSp122223AG+ER4iq6bN873vfy+ra2+poeSUgAQn0NIFqv0sb1bbjZXSJJZaI1UX8yeJNfm/y253FMS+88EI8h4Cj3GKZUqwQVuLZI401nHLKKVlexekRXfI7ORlC0zQuno41+vsHP/hBuyxpoztrjXgGjCHQZsILwyssnPHeg5jnwgsvjP0I+gzNtkr9N7wPpT4L4znwY8wHETLfCKMQyTJuwwdjLILxjO4yFialkN4stKI/xkImxkBmm222sM8++4QJEybEcTTEzvl3rrvK6H0koFDGd0ACEuhVBFDDMolQr5s4FNAIHFAA12soYPl01Rj0eeeddypmwwAHLvJKudereOF/T9IZquTKuJY8SEN9//GPf9SavNPp6KTxXD766KNOX9tdFyCGSoNr3JOV+LUYqm3S8u51xbryDCgD7xFeA7pijXqfulIGr5WABCTQygQQy6bQOV0pJyuTWJGeLO89Jh3jO388eaHJn89vM8l03333xZVGaXUU/QsGJTAGLwgxxMrjcmKfY489Ng4M5UXB3TXIwiBPaofpH+27775xkCdfx3LbDCriCYbJNwbOMFwTkw+r2xngWmeddcpdHo8zWHbeeedFTzCsfEfkwkAhK6QYoMIbDwOXyaaZZpq0mX0zYfXggw8GhE9pcDLvIhl303iaYTAphX3MLnZDAhKQgAR6DYFa+wONaJtrhdKTbXhxGZmsYdUz3tOYJClneLhDODpq1Kh2bWw+Pe04AtrkPY5zaRwlpaNfw6pqBK146cP4fY5nOYxJmW222SaGeUh9jXiil/1D3/Hee+8NP//5z8Mqq6wSpp122sgNoe/vf//76JEnrTTHc44mAQlIQAKdJ1Dtd2kj2nbaNn6Xs5gi/XYfP358JiDltyjiyBQiuDO1YLFM8tBWKQTT0KFDwxprrJFlnTzMZAeasEG98x5kOhN2KV+cRjwD+iqIdvP9C+6B0JdQ2fQrkkglf+9Gb1fqvzFuhJAKj7XJeE8Yo6FvQ5+GcRAEyizGSca7013Ge0QZEDKnxUT0v5hjYJ6CBWCIj+i/8Nw0CfQEgW8Vfjy09cSNvacEJNBaBBikZ0UrxmAEbvXqNfLaaqutAiuDMFzzJzdr+TzpjDFhkdwHsjI5xSFE+coPeFyysVoXF/s0mEz2Y0we4DqWiYl8Byqff9qmHMRifuihh6JggXy5nkkTVLl0KtNqpnQN36SjHBhp4PKzn/0siz/J9b/5zW/iZAerdZLLfVZs51d4p7rQuaWDd/bZZ8fO7pgxY6IbQQaGSIMKPE2wcI5VP7jzS275SXfooYdmq5ljwUr8w7Pj2ueffz67lk415eceqU7FlzK4REgCJnaY7OG5wA3Xgn//+99jcjo3rNiik4ZwJG+sYEKAlJ4Rg4MpDavAUZ7nbfTo0bGcCGS4VxJx0Mljooh3g9AQSb2drkVglDqidARZHdYZQ/CTYp6ymopJw1qNiTFWu2Gsti+l2melFop8XDhTtyRyoh48Q95XVhjS+S+2rj6DfH6IjmDDu/npp5/GU0zo4fWI+6MsTx4KcM/IwGApq/d9KpWXxyQgAQlIoHUI0B6yEo3+DiujWFVdqziDNhvhMuIR2vrJJpusdSpWpSQMHL388suBb/oBqa9S5bKaTrPaPYVruPrqq+OK9XIXwh2GiGnpe9G3ISRkcb+n3PUel4AEJCCBvkegK21zrTRasQ3HDT91Z2EU40QzzTRTbBfTCuRa68bvXiZeaE9LCVZTPoQdePHFF+PvZEQ0/DbvjvAJ6f49+c14AKE98VTAGJsmAQlIQALNJdCItp08GOdnsQXzEY38DVup9ohF8AJCe4xHtlrHCyrl2RPnuvoM6J8Qqor5FeYUemL8o1r/jfmjN954I/an6NcwvpMW5/QE81L3ZN6IReN8WDQNS8NNlyLlse4m0HtGNLubjPeTgATqJoASFPeuiDIwXKj98Y9/zFSjKWPUrEl0gIAkiWQ4z6QBHYB33303bLvttnH1LcdxV8/KH86hRuWHPSu3EdOUMtzOEduagYC8cT2TJHwQhbBCOLk0zKdLog+8shAvMQlGSIMIgkYdY6AlpS32HJLqQqeKshSrdukcMLHy7LPPxjrR8UKlndwMxxsU/iEd6lsEEHwXG50Nysgq5GKjfH/605+ieIMyEGaguLNEh4o6sLKIiTOYFnvOwTMJHWQEPLgdzK+24tq8px7Kn5gksQjlIg1CG0Q5pbSaCJtgwOfmm28OxNVuBbd71AfxS7IUviLt842r57322isLnZA/R115f/jw3p5//vmBmKd56+ozSHnxziEi4u8nbyi2WcnO3wUCtvR8mCwstq6+T8X5uS8BCUhAAq1FgMG1egfYGCBjJVpvNFZXL7fccp0qOkJlxMK4vC4l/k6Z5cNBMTBVyejbMMjJR5OABCQgAQlAoCttc60EW7ENRyzKp6vGWEYtnlKY6KrUnne1HN19Pb/x+bDgaOONNy7LgPGBNEaANwJNAhKQgASaT6ARbXsj8uhsTRkrZu4FY3FlbxXJUP6u8iP0YwqBRH49YdX6b9NPP33g08rGQivGkXrrWFIrs7VsXSNg6KWu8fNqCUigDAGEMsmFPWpWvMXkDaEFXk8wBkTwwFHKiFPND35ECbi0Iy8EKqRHNMNqXLy8MPFfbKRFgINIhs7E7rvvHr2HcByhAi77WbWLWIB0edf3xXnhLQaBA6uMGHjAOwsue/GiUqudfPLJUSSDWhZBC+6DEcgsueSSMQtWNNHxxGsJ5cUt8MiRI6NQ5Mc//nF2m5NOOimugs4O/HcDrzdJJEOHA48nzz33XPzgVQSPQYg1cOl/4oknFl+e7ROCAXd8CHLw6EIZ8SrCfZOnHEQ/1CFvuCPE3V+yFVZYIe5zLO+qEY86CJMoC95pyJ9ysqoaMc+vf/3rbGAHd8wIc3raEPogTkrCKN5H3tu8IfDh2VEP3k3ESJSffURQCKRwf4zhdQZ+pYRCnK/3GaRr8yIZQnHccsstURR25513xneM+/P3UMka9T5VuofnJCABCUhAAr2BAKvn8G6IkBnRcCnDAx8iYgwPgaU8x5W6zmMSkIAEJCABCUigqwQY58HjMgtiyhmL2RhDwzozllUuP49LQAISkEDfJcAcA+PgLPBgEbMmAQlIoK8S0KNMX32y1ksCXSDACpMUq7KWbBCPHHHEER2Snn766WHllVeOggu8hxBvEFEIimQm6REJDBgwIIo7yrm5xe0ubvHxLELYGIyVwAhxWIGLaAVDlIDwJG+El0nCBiYuCNWUDO8xfBBqMOmBpw3CDJUbVMCzCkIThCspniIeRjrj6QSvLqzwQTiS6kJ5cH1LHckPARH542knL8QgRA7iBsQWiDYQXsA2Gd5XcPGPUSdEK6h0kyGWQKSB8AXhESF5CG2Uv0dKC3M+V155ZSZ24hziIMInIYBCNEToJGJephXT3DdvqJiLV2mR76WXXhqTsYIJkUw+7BUCFD6IiVJ8zWuvvTZsuumm+awbtg3zJ554omR+eAaCNd51rrnmmixEGOKq0047rYOSnnKmcGMIuRDNJON540qQZ4YICWEXgi8m3nj+xVbvMyAfPCylVWI//elPY0z3FMZh2WWXDXx4lwmHVc4a+T6Vu4fHJSABCUhAAr2FAH0chDAIWfGUuMsuuwQEwalP+Mgjj8TjSQCL9z5NAhKQgAQkIAEJdAcBxk8YV6GfcvHFF0exLkKYeeaZJ96ekFQsQktjZowDFXu37Y5yeg8JSEACEmhdAiwkfuutt+JcDfMSLHTFGJPvbCjE1q2lJZOABCTQkYBCmY5MPCKBfk8AQQfeSGo1Qi2VEspwnEl7hCiEOsKDCz/MDzjggBg3mvxxZV8q5FH+3ghe8sKSdI4JivXXXz/cddddUTiCOCB13BCcJPeAqJ7zIpl0Pd+4zSPEECIHhCFMbJRz/4Y4Ik2IcO1UU03FV6fswgsv7FAXXAOvuOKKMQQUmeG5pJSABa83KWwTMbzzQhkYJeERnnryIplUQAZO8CTDZA8CIkJf4dmllCGGSR6B8ueJFY5IidBJ2EsvvZQJZfLpym0TTxVPPAzgIOLIi2Ty1yDqSQM9xeGf8um6uk2IIkREtRquGhGAEXqh2GAx++yzx8NbbLFF8em4j2CF+yUPSJXqVs8z4B1Iq9lxC3nwwQdHr0nFhdltt92iSOmvf/1r8am43+j3qeRNPCgBCUhAAhLoJQQOPPDA6PXu6aefjl4J8UyI9zi8xtCWMwGVjH7vDjvskHb9loAEJCABCUhAAk0lwG9/JjR32mmnGOr6oIMOivebbbbZ4njA+++/H7755pt4jLR4CSi3WK2pBTVzCUhAAhJoWQIIZYo9xzA/keYAWrbgFkwCEpBAFwlM0sXrvVwCEuijBPD00plPOQxrrrlm/LHOeTxzMHlAGBiMc9XCvxDqJy8IiRfm/sFDSrL8pD/eTpIlrzNpv/g7raRBZMAESCmDxbBhw0qdqvkYIgu8spQyziVbbrnl0ma7bwY0kuXDROE6l1XO2OKLL15SZJOuIxQAHlswvJmUs0puePOhBBC8dMYQRSGWIhxRPpxUqTwQWmHJK1CpNN1xDMEOoaMIhfXwww+3CyOVvz+htWD65JNPdvA2k0+Xf46V6lbPMxgzZkwWEgKPNuUGvxDs7LrrrvliZdvNeJ+yzN2QgAQkIAEJ9EICCJDxgrf33ntnbSue5/Csl0QyeNhDrIxIXJOABCQgAQlIQALdSQDPtfRV8CCb7J133onegBHJ0JfZbLPNoofhaovV0vV+S0ACEpBA/yGAuDJveEbHez0e4zUJSEACfZmAHmX68tO1bhKokwBijgceeKDOqztehreZhx56KOC9g7A/GF5FWPGSQsLEgyX+ITxPJcuLNghHlLygoIJORmidl19+Oe12+P7www+zYwg4Shn3mWSSrmkLk7eRUvlPPvnk2eF8nbKDhY18mvxxwiARqicZYZUqWWL+9ttvx1VFk03WsSmoVNa8F5i0KqnS/cqdS+VALEJZeD94bs8991wUpCRvK3gjapbhijivjIfjuHHjwm233RbDX3FfwhThYWXhhReuqRipXiRG0MQ7Rd0ItfTYY4+1EyhVqls9zyD/nic3y+UKTRiwUtaM96nUfTwmAQlIQAIS6E0EGCAkbCEeEmnTaS8JJzp06NAoUk5C5N5UJ8sqAQlIQAISkEDfIYDHZD54IOaDUAaBDAuzCPddylNz36m9NZGABCQgga4QYA4G7/z81mVuiMXL+THuruTttRKQgARamUDH2dFWLq1lk4AEeiUBwhWdc845mYiFSuCdI+9Zo1zFitXMxelSqCWO5z3K5AUvJ510UvFlZffzApt8IjyxdNVqHZTIh3eq5Z75uuLNhE8thucQYo+WEkwgZCpnjegkI4IhDNUdd9wRhSSVBCPlytGI4zwTPBsVG64mCWG03377RZ54lCE0F9+VDK9EhGMgLjhefj777LNKySueq+cZ5EVf+b+NUjeaY445Sh2OzyOdaNT7lPLzWwISkIAEJNDbCeCtDS+DXfU02Ns5WH4JSEACEpCABFqTAKEy+GgSkIAEJCCBWglMOumkYamlloqfWq8xnQQkIIG+QEChTF94itZBAr2AAOKBvF100UVhk002CXTCKlnee0mpdHmBxVdffZUl+eKLL+I23lLo5NVq5cQJrMLpqpXy3NLVPLk+1ZVtBkPK1YHzxVbOI0wjxDDF90r7qNN33nnnDiGVpptuuui1Zckllwxrr712GDFiRHj33XfTZd3+TWgowiocfPDB0fMOoYpGjhwZ5p9//pJl4f3baqutwp///Od25/FENN9880U1/vDhw+Pqc4Ri1ayeZzBw4MAs24kTJ2bbpTbKhX1qxvtU6v4ek4AEJCABCUhAAhKQgAQkIAEJSEACEpCABPqccLwAAEAASURBVCQgAQlIQAISkEBPEFAo0xPUvacE+hkBQh+deuqpsdZ4S2ECH28bp59+eth///0r0njvvfdqPo/r+2R4gCEMDV5TiNM81VRTpVN97jvv7WbDDTcMv/71r1u2js8880xAcJJEGnhu2WCDDaKIhNinecuHk8of787tXXbZJfzlL38Jt99+e/j888/DdtttF8Uy0047bYdi7LXXXplIBsESQp+ll1464LoyL7S67LLLsmvxQNNIy4dbqiYywg1zKetN71Op8ntMAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAApUITFLppOckIAEJdJUA3il23333gOeXySefPAoO8K6BERJpzJgxFW/x9ttvVzxP3OVkKV/2UzghhAjPP/98SlLyG68qeA7prZYXRzz33HNVqzFhwoSqaZqVANFS8vxzzDHHRLHUWmutFYpFMjyTTz75JBYDsVNP2sknnxxmnHHGWATCXP3iF7/oUBzKesstt8Tjc845Z7jnnnui1xzigOdFMiT46KOPsuvzHpGyg13YyL8Lr7zySsWcyp3P59Hq71PFCnpSAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAiUIKJQpAcVDEpBA4wgceuihIYlZDjzwwIBw4MwzzwyElUEAgYimknCDMDaVPGPkvXOst956WcEXXnjhbPvaa6/NtkttEAZqttlmi15NrrrqqlJJWvoY3nIQZ2CjR4/OeJcqNCKVZZddNswxxxxhzTXXDM3w2kKoIayUtxTKl2zrrbdOmx2+ee6pbOXCQ3W4qEkHpp9++nDiiSdmud96663Rq0x2oLDx6KOPZru8h4SRKmcPPvhgdqrRdUPksuCCC8b8r7/++vDBBx9k98pvcN9zzz03fyjbbrX3KSuYGxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhJoAAGFMg2AaBYSkEBpAnfeeWdIwpMlllgi7LnnnjHhMsssE3bbbbe4/dprr4XDDjusdAaFo4glTjjhhJLnEV3cfffd8dxSSy0Vvvvd72bpNt1005A8zFxxxRXhkUceyc7lN95///0o3OHYxx9/HFZfffX86V6zjQgJwzPOQQcdFMoJME477bQoniD8Fc9kwIABDa8j4bWwzz77rEPegwYNyo7Bu5TxTuyxxx7ZqSSYyQ70wMZGG20U1l133ezOBxxwQMBbUrJ8vfIeY9L59H344Ye3E9U0um6IlH75y1/G2yGKwvvNl19+mW4fv/Fic/TRR4c33nij3fH8Tiu9T/lyuS0BCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBrhKYrKsZeL0EJND3CLz33ntRbNHZmo0YMSLMPffc8TI8Wey3335xm5BLZ511Vph00kmzLA855JDolYPJ+ssvvzysvfbaYZ111snO5zfwCIMA5Ne//nX4zne+EwUKhLk5+OCDYzLEAYRxyhsCkOOPPz5sscUW8fDmm28euCdeTPAQgkBm5MiR0avGhx9+GNPstNNOHUIA5fNs5e0tt9wy4F3nscceC/fff3/YYIMNwm9+85uw2GKLRc8uiIpuvvnmyJp6DBkyJBMuNbpe8EWcgTjp1FNPjYKlhRZaKMw///zRm03yvrL33nvHZ7LccsvFd2Ps2LHxml/96lchPRPKlkIwNbqcnc0PrzIPP/xwfP94fxCbwBhbfPHFw8CBA+N7etNNN4Vhw4aFH/zgB2HmmWeOYccIYXTNNdcEvBflrRl149l///vfD3/5y1+ikIy/rW233TbwDPh742+nnHAsla2V3qdUJr8lIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0AgCCmUaQdE8JNDHCIwbNy5cfPHFna7VJptskgllEEGQD4b3jRQOJmWK15EzzjgjbLzxxvHQPvvsEx566KEw00wzpSTxe9555w2ff/55FHkg9JhxxhljvnjFwBDhnH322VEQEg/k/ll11VXDMcccEwUNCG2OOOKI+CGPYm8meAwhbW82QukgVnr88cfDU089FdZaa60o3oARDJPBHtHGXHPNlQ419BuvPIh2CK2FWAn7+c9/HgjDhXjq9ttvj+GhEMwgJpl22mnD0KFDwyuvvBLTEpZr5513jtsIS/75z3/Gc3mPQfFkN/8zyyyzRO9HeOzBLrnkkrDZZptF8Q91OOqoo6LAjJBT1JUP7z2hwxJ/hGRHHnlkrB/eZBDQNMOuu+66wN/UjTfeGNkhMssboqXtttsuis84nrwA5dO0yvuUL5PbEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEugqAUMvdZWg10tAAh0IXHjhhdGrCSe+973vhb322qtDGg6ssMIKYZdddonnEK4wsV9sCGXuu+++sOKKKwY8x5AuiWTWWGONQHgnwiyVM0I8Pfjgg2H48OFRVEO6vEiG/M8555xwwQUXxPzL5dMbjs8xxxzhjjvuiEKMb3/727HICISSSAOPPttss00UJBH+qll27LHHBkRTU001VXaLJIIZPHhwuPXWWwMefpKNHz8+ijkoH+UinBbhthAvJbvhhhvSZo9+77jjjrGMFAJBzL777huFPOzjkQhxyayzzsputJdeeinyx8sOIiHEYOutt16WB16N8iGc0nVd/Z5iiinCeeedF9/tH/3oR2GBBRYIU089dfQ0QznuueeeTNTGvaaZZpoOt2yV96lDwTwgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggS4Q+FZhoq+tC9d7qQQkIIGmEEDogSCGsDFXXXVVvAeCgieffDJu410EDx+dMTycvP766+Fvf/tbFAbgUQVRAx5M+qIR1ufFF18Mn376aUD0MOeccwaEKt1lPD94I9ogBNFkk7V3YobHIUIB/eMf/4hl45ni/aa32zfffBPeeuutWDfqTMijJFxqpbrhdWbPPfeMRbr66qujB6JK5evp96lS2TwnAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggVoJKJSplZTpJCCBbiVQSijTrQXwZhLopQQI+UQoq6WXXjp6kClXjf333z9cfvnl8fQTTzwRxUrl0npcAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAn2FQPvl/X2lVtZDAhKQgAQk0E8JPPXUU+Hxxx8PgwYNit+IZoqN81dccUU8PPfccyuSKQbkvgQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQJ8lMEmfrZkVk4AEJCABCfRDAssvv3ys9YQJE8I+++wT7r///jBx4sSMxCOPPBJDLqXIi3vssUd2zg0JSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJ9HUChl7q60/Y+kmglxIw9FIvfXAWu8cJfPXVV2GjjTYKTz/9dFaWgQMHRq8xH3zwQfj000+z41tvvXU488wzs303JCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNDXCehRpq8/YesnAQlIQAL9isAUU0wRrrvuurD33nuHwYMHx7p//fXX4ZVXXslEMt/97nfDWWedpUimX70ZVlYCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSAACepTxPZCABFqSwCeffBLLNWDAgGyyvyULaqEk0MIEvvjii/Dqq6+Gv//972H8+PFh6NChYa655goLLrhgC5faoklAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUigeQQUyjSPrTlLQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCbQQAUMvtdDDsCgSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAs0joFCmeWzNWQISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUighQgolGmhh2FRJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgASaR0ChTPPYmrMEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQAsRUCjTQg/DokhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJNI+AQpnmsTVnCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIEWIqBQpoUehkWRgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEmgeAYUyzWNrzhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACLURAoUwLPQyLIgEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQPAIKZZrH1pwlIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABFqIwGQtVBaLIgEJ9BECbW1t4V//+ldWm0knnTTw0SQggZ4l8H//93/hm2++yQrh32aGwg0JSKCHCdh36OEH4O0lUIaAfYcyYDwsAQn0OAH/f+rxR2ABJFCSQPHf5mSTTRYmmcS1uiVheVACEuhWAsX/Pzku2q34vZkEyhIo/tu071AWlSeaQEChTBOgmqUE+jsBGraxY8dmGAYNGhSGDBmS7bshAQn0DAFEMvm/zamnnjoMHjy4ZwrjXSUgAQnkCBT//zTVVFOFaaaZJpfCTQlIoCcI/POf/wyffPJJdmv+Lvn71CQgAQn0NIHivgO/a/h9o0lAAj1L4Ouvvw7jx4/PCjHttNOGKaecMtt3QwISkEBPEWBh77hx47Lb+9smQ+GGBHqUwFdffRU+/fTTrAzTTTddmGKKKbJ9NyTQTALKuZtJ17wlIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABFqGgEKZlnkUFkQCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoJkEFMo0k655S0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQm0DAGFMi3zKCyIBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAzCSiUaSZd85aABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISaBkCCmVa5lFYEAlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBZhJQKNNMuuYtAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNAyBBTKtMyjsCASkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAs0koFCmmXTNWwISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUigZQgolGmZR2FBJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgASaSUChTDPpmrcEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQMsQUCjTMo/CgkhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJNJOAQplm0jVvCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGWIaBQpmUehQWRgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEmgmAYUyzaRr3hKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACLUNAoUzLPAoLIgEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQTAIKZZpJ17wlIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABFqGgEKZlnkUFkQCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoJkEFMo0k655S0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQm0DAGFMi3zKCyIBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAzCSiUaSZd85aABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISaBkCCmVa5lFYEAlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBZhJQKNNMuuYtAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNAyBBTKtMyjsCASkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAs0koFCmmXTNWwISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUigZQgolGmZR2FBJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgASaSUChTDPpmrcEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQMsQUCjTMo/CgkhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJNJOAQplm0jVvCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGWIaBQpmUehQWRgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEmgmAYUyzaRr3hKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACLUNAoUzLPAoLIgEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQTAIKZZpJ17wlIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABFqGgEKZlnkUFkQCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoJkEFMo0k655S0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQm0DAGFMi3zKCyIBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAzCUzWzMz7Ut6vv/562H333cMMM8wQrr766oZVbcyYMeHGG28Mb731Vvj000/DAgssEBZbbLHw/e9/P8w333w13acRedR0IxNJQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBXkxAoUwND2/ixInhmGOOCRMmTAiDBw+u4YrqSf7973+Ho48+OowaNapd4tGjRwc+l1xySTjiiCPCKqus0u58fqcReeTzc1sCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQn0ZQKGXqrydL/++utw4IEHhldffbVKys6dPuWUUzKRzDzzzBNGjBgRjjrqqPCjH/0oTDPNNOGbb74Jhx9+eLj77rvLZtyIPMpm7gkJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQxwjoUabCA/3rX/8aTjjhhBgWqUKyTp969tlnw+233x6vW2qppcJxxx0XBg0aFPdXW221sMEGG4R99903jB07Npxxxhlh1VVXDVNMMUW7+zQij3YZuiMBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAT6OAE9ypR4wF999VU488wzwx577JGJZL71rW+VSFnfocsvvzxeOGDAgPCrX/0qE8mk3Oaee+5w2GGHxd0vvvgi3HPPPelU9t2IPLLM3JCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAL9gIBCmaKHPG7cuLD99tuH66+/PrS1tYWBAweGAw44IMw///xFKevb/fLLL8Njjz0WL15++eXDTDPNVDIjPM3MNtts8dzNN9/cLk0j8miXoTsSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUigHxBQKFP0kMePHx/ef//9eHSBBRYIF154Ydh4442LUtW/++KLL0YBDjksueSSFTMaNmxYPP+3v/0tfPDBB1naRuSRZeaGBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIS6CcEJusn9exUNRdccMGwww47hBVXXLFT19WS+IUXXsiSzTnnnNl2qY055pgjO/zmm2+Gb3/723G/EXlkGbshAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE+gkBhTJFDxrxygUXXFB0tHG7Y8eOzTKbeeaZs+1SG0OHDs0Ov/fee9l2I/LIMnNDAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJ9BMCCmWKHvSkk05adKSxu19++WWW4eDBg7PtUhuDBg3KDk+cODHbbkQeWWZ1bhCiKl+mOrPxsn5CYMKECYGPJgEJtBaBzz//PPDRJFCOAKLeb33rW+VOd+r4uHHjwtdff92pa0zcfwnQ3833efsvCWsugdYi8NlnnwU+mgTKEWhk3+Hjjz8O//rXv8rdyuMSaEfgiy++CHw0CUigtQgwhsxHk0A5ArPMMku5U50+/sEHH4T/+7//6/R1XtA/Cfjbpn8+d2vd+gQ++eST1i+kJewxAsxVVHNE0pnCTdKZxKbtOoH8gP/AgQMrZpg//9VXX2VpG5FHlpkbEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoJ8Q0KNMNz/o/Gqoat5rJpnkfzqmf//731lJG5FHllkf2Phi1mF9oBZWQQI9Q2Dwe2N65sbeVQISkEAPErDv0IPwvXWvJ9DMvoN/m73+9bACPUSgmX+XPVSllrut/z+13COxQL2IgP9H9aKHZVElIIGGEbDv0DCUZtTPCDS73+DfZj97oaxuwwg0+2+zYQXtZEYKZToJrKvJp5xyyiyLf/7znyHvNSY78d8Nziebaqqp0mZoRB5ZZnVuDBgwoGXcGOpYt86H6GUSKBCo9H+QgPoeAdzPFostJ5vMrkDfe9KtWSP6Dq1i9h1a5UlYjt5IoJl9B/82e+MbYZlbgUAz/y47W79GhWzkvvQd8guIOluWRqb3/6dG0jSv/kaglf6P6m/se6K+LPb85ptvslsz5lBtsWiW2A0JdJEA/9+0Sugl+w5dfJhe3m8JNLvf4N9mv321rHgXCTT7b7PW4jVyzIF7OjtWK/kGpcuLXL7++usw9dRTl82Z88kGDx6cNtsJZerNI8uszg2EO3nxTp3ZNOSysQ3JxUwk0D8JTD/99P2z4v201ggwx4793/+agwYNCvn2pZ9isdrdRKBSn6ebipDd5n9/BdkhNyQggRoJNLPv4N9mjQ/BZBIoItDMv8uiW3Xr7pAhQ7r1fpVu5v9Pleh4TgKVCfTV/6Mq17r/np04cWIYP358BoAxh/x4eHbCDQk0gcC0007bhFzry9K+Q33cvEoCze43+LfpOyaB+gg0+2+zvlJ1/ar/xfbpel7mUAOBGWaYIUuVn6zMDuY28ufzE5mNyCN3GzclIIH/Z+9eoPQo6/uB/zbZJOS2aC4KkYSEXMiBchHkUpRWKNJyaijFHksFxYI9rQW8pSAgpyUKHFstB6xQYwmc1NrKVQF7Uw5WCKhQjC0k3CIhISEXyAVyTzabf2f+7LjZZLO7k91339nnM+e87PPOzDPzPJ/fvi9vdr87Q4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkhAQlKlxmSdOnFicccWKFUV7b4222ydPnlzs0hPHKA6mQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIREBQpsaFnjp1anHGBQsWFO29NVq3Z1eTmTBhQrFLTxyjOJgGAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCARAUGZGhf6sMMOi3e96135WefNmxctLS17HUF226XWoMy73/3uaGhoKPbriWMUB9MgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQiICjTC4XeunVrbNy4MX80NzfvcYbf/u3fztctW7Ys7rzzzj22Z+GZr3/967Fjx45823nnnbfHPj1xjD0OagUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoB8LCMr0QnGvvvrqOOuss/LHo48+uscZsuDL2LFj8/W33npr3H777fHGG2/kz1euXBlf/OIX46GHHsqfn3TSSXH00Uf3yjH2OKgVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF+LNDYj+dWt1MbOnRo/PVf/3VcddVVsWrVqrjjjjvyx6hRo2Lt2rXFuCdNmhR/9Vd/VTxv2+iJY7Q9njYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoL8LuKJMH1V46tSpMWfOnHjve98bgwYNykfRGpJpbGyMD3/4w/ntl0aOHNnhCHviGB0e3AYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQD8TaNj1f0s/m1PlptPc3BwvvfRSLF++PN75znfGoYceGsOHD+/WPHriGN06YR3t/GLDQXU0GkMhUC2BqbtWVmvARrtfAtu3b481a9YUx8jCmCNGjCieaxBIRcBnh1QqbZ69IdCbnx28NnujYo6ZgkBvvi5T8OvKHL0/dUXJPgT2LuA9au8u/XXtli1bYv369cX03va2t0V2ZXQLgdQEfHZIreLm21MCvf25wWuzpyrlOKkJ9PZrs6883Xqpr+TbnDe7gsy0adPyR5vV3Wr2xDG6dUI7EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQqJuDWSxUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTEBQpmIFM1wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyAoIy5dz0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJiAoEzFCma4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5QQEZcq56UWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAxAUGZihXMcAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMoJCMqUc9OLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgKCMhUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTEBQpmIFM1wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyAoIy5dz0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJiAoEzFCma4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5QQEZcq56UWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAxAUGZihXMcAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMoJCMqUc9OLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgKCMhUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTEBQpmIFM1wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyAoIy5dz0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJiAoEzFCma4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5QQEZcq56UWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAxAUGZihXMcAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMoJCMqUc9OLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgKCMhUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTEBQpmIFM1wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyAoIy5dz0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJiAoEzFCma4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5QQEZcq56UWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAxAUGZihXMcAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMoJCMqUc9OLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgKCMhUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTEBQpmIFM1wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyAoIy5dz0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJiAoEzFCma4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5QQEZcq56UWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAxAUGZihXMcAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMoJCMqUc9OLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgKCMhUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTEBQpmIFM1wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyAoIy5dz0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJiAoEzFCma4BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5QQEZcq56UWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAxAUGZihXMcAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMoJCMqUc9OLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgKCMhUrmOESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUExCUKeemFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUEBGUqVjDDJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKCcgKFPOTS8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCQjKVKxghkuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBOQFCmnJteBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQlKlYwQyXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgnICgTDk3vQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBComIChTsYIZLgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDkBQZlybnoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUTKCxYuM13DoR2LFjR+zcubNORmMYBAiUFdi6dWvZrvpVUCB77267NDc3h++BtiLa7QWGDBkSDQ0N7VeXer59+/ZoaWkp1VcnAgTqR8D/N+qnFkZCoFWgnl6XBxxwQOuw9vurzw77TegABOpCoJ7eo+oCpJ8Pov3PHbL38p76N2U/p0t2ej352WHbtm2xa9euZC1NnEB/EPC5oT9U0Rz6o0C9vDazz5XZ7yx6ahGU6SnJxI6zadOm2LJlS2KzNl0C/U9g3bp1/W9SZtRlgex93Ht5l7mS3PGggw7qsXlv3Lgxsh9aWQgQqLaAzw7Vrp/R90+BenpdZp8deuoXom+++Wa0/4Vr/6ygWRHo3wL19B7Vv6Xrc3abN2+O7GEh0JHAwQcf3NGmbq9fv369P9DptpoOBOpLwOeG+qqH0RBoFaiX12b284ae/J2FoExrhX3tlkCW1howoD7u3LWxWyO3MwECbQWGDx/e9ql2PxfIrgTWNvk7aNCgGDx4cD+ftenVi0D2V2KNjfXx0dNnh3r5rjCOKgr05mcHr80qfkcYcz0I9Obrsrvz66mQTHbeoUOH1s1nVe9P3f1OsD+BXwnU03vUr0al1VsC2ZVr2/6BRPYz5Hr5d2Bvzdlx60dg2LBhdXNFGZ8d6uf7wkiqJdDbnxu8Nqv1/WC09SPQ26/Nvpppffy2oq9m77ylBbIfWGWPelhW1cMgjIFARQWampoqOnLDLiOQXfK4bVAmCy6MGDGizKH0IdBtgewHVvWy+OxQL5UwjioK9OZnB6/NKn5HGHM9CPTm67Iv51dPP4jz/tSX3wnOXXWB/voeVfW69Nb4s6vWtg3K1NPPkHtrzo5bPwIjR46sm8H47FA3pTCQign09ucGr82KfUMYbt0I9PZrs68mWh+XBOmr2TsvAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNoK/1wfAABAAElEQVT1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQjICiTTKlNlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQtoCgTNr1N3sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDICgjLJlNpECRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJpCwjKpF1/sydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJCMgKJNMqU2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC2gKBM2vU3ewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCMsmU2kQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkLCMqkXX+zJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgkIyAok0ypTZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLaAoEza9Td7AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAyAoIyyZTaRAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQsIyqRdf7MnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQj0JjMTE2UAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQC8KnPvJr/Xi0R2aQP8VeLr/Ts3MCBCoQwFXlKnDohgSAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAzwsIyvS8qSMSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUoYCgTB0WxZAIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR6XkBQpudNHZEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAOBRrrcEx1MaSNGzfGd77znfjFL34Ry5Yti6ampjjqqKPyx2mnnRZDhgwpNc7/+q//iqeeeqrbfbNzn3nmmbv1e+211+Lb3/72bus6ejJ+/Pj40Ic+1NFm6wkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/V5AUGYvJZ4/f35cc8018eabbxZb16xZE4sXL44HHngg/u3f/i2+/OUvx7Bhw4rtXW0888wz8b3vfa+ruxf7NTc37xGUefrpp+Pee+8t9tlX47jjjhOU2ReQbQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEC/FxCUaVfiLAxz9dVXR3ZFmQEDBsQHP/jBOPbYY/PQzLx58+K///u/IwvSfPrTn44bb7wxRo4c2e4InT9taGjofKf/22PXrl3FfmPGjCnarY0XXnihtdnp166es9MD2YEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUFEBQZl2hbv55pvzkExjY2N88YtfjFNPPbXY49xzz42vf/3rcdddd8Vzzz0X3/3ud+NjH/tYsb0rjUsvvTSyR2dLdsunLIzT0tISRx555F7Ps2jRovwwb3/72/Mr3XR2TNsJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAikLDEh58u3nvnDhwnjqqafy1TNmzNgtJJOtzK7Kctlll8XRRx+d73P//ffHzp0783ZP/mft2rVx7bXX5iGZ0aNHx3XXXReDBg3a4xQvvvhivu7www/fY5sVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECuwsIyrTxePjhh4tnZ599dtFu3zjnnHPyVatXr47HH3+8/eb9ep7dbmnWrFmxZs2a/Dhf+MIXYm+3XcrCNNkjWwRlcgb/IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjsU0BQpg1PdkWZbGlqaorJkye32bJ7893vfnexYt68eUW7Jxr/8R//ET//+c/zQ51xxhlxwgkn7PWwL7zwQrFeUKag0CBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdCjQ2OGWxDZkt1B6/vnn81lPmDAhv81SRwTZFV6GDh0aW7ZsiZdffrmj3bq9fuPGjXHrrbfm/UaMGJHf5qmjgyxatKjYNH369Ni8eXNkQZ8lS5bkt2maMmVKHvYZMmRIsZ8GAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBlAUGZt6qfhVS2b9+ePzvooIM6/Z54xzvekYdSXn311U737eoOc+bMifXr1+e7X3TRRTFq1KgOu7744ov5tuHDh8d//ud/xr/8y7/Em2++udv+WZjn0ksvjX3dRmq3Dt14sm3bttixY0c3etiVAIF6FMje+yzpCDQ3N+822ey93EJgXwLZ54yGhoZ97dLlbVu3bo3234Nd7mxHAgTqRsBnh7ophYEQKATq6XWZ/dFPTy3ZHydlf9RkIUCg2gL19B5VbclqjL79z4uzfwd6L69G7fpqlD352SH7Y+KWlpa+morzEiDQAwI+N/QAokMQ6AWBenltZr+ryH5n0VOLoMxbkps2bSpMu/LhrLUI2Q9uemJZu3Zt3H///fmhRo8e3Wm4pTUok4179uzZeb/smyMb+4YNG/Ln2di+8pWvxGOPPRY33HBDDBw4sCeGWhy7p+beY4NyIAIEui3Q+n7R7Y469AuBLCDaGhLtFxMyiR4XaP280xMHzn5gJZzVE5KOQaBvBXx26Ft/ZyewN4F6el32ZMg2+3lH+1+47m3+1hEgUN8C9fQeVd9S/XN0WVAme1gIdCTQld/FdNS3/frs/UZQpr2K5wSqJeBzQ7XqZbTpCNTLa1NQppe+59oGZbpyu6LBgwfnI8l+4bNr1679/mvru+++u/gB0HnnnRf7GkMWUFm2bFkhkd166WMf+1gce+yxMXLkyFi3bl1+lZl/+Id/yH8B+vjjj8d3vvOdOP/884s+GgQIECBAgACBVAXO/eTXUp26eRPYb4Ef7PcRHIAAAQIECBAgQIAAAQIECBAgQIAAAQJ9KzCgb09fP2dv+xf1XbnyyoABv6Lb35Ry9hfW3/ve93KMAw88MH7v935vnzBZEOaoo46KsWPHxnve8564+eab49RTT81DMlnHt7/97ZGFbf7u7/4uWsd5++23R0/eJmqfA7SRAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCHAm699FZRhg0bVpSnbWimWNmu0bpPduWXrgRr2nXf7ekPf/jDaL2314wZM2Lo0KG7bW//ZNy4cXHLLbe0X73H8yOOOCIP3Xz3u9/NryzzxBNPxDnnnLPHfmVWZJdE7GycZY5bps/GMp30IUAgFxg1ahSJhASyS9e3vURe9j5eL+/lCZUh2almV73ryVs5JQtp4gT6WKA3Pzv4XN/HxXX6ygr05uuyuyjZZZB7amlqasqv4NtTx9uf43h/2h89fVMXqKf3qNRrUYv5Z1dfb3vl9uzfgPu6cnotxuQc6Qhkf0CcXf3fQoBAdQV6+3ODz/XV/d4w8r4V6O3XZl/NTlDmLfm2vyjsyn1Tsw/92dIT99D80Y9+9NYoIs4888yi3RONU045JbKgTLa89NJLPXHI/BiNjY2RPSwECFRbwA8rql2/7o6+/S8usvdx3wPdVbR/WYFBgwaV7aofAQJ1JOD/G3VUDEMh8JZAf31dtt7yWqEJEKi2QH99j6p2VXpv9O2vvJ79O9D3QO95O/LuAj477O7hGYEqCvh/RhWrZswpCPTX1+av7h+UQhX3Mccsbdz6C8S1a9fuY8//v2nNmjV5Y3+DMuvXr4/58+fnx5o8eXJMmjSp03N3Z4cJEyYUu7/yyitFW4MAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkJqAoMxbFc+SUAcffHD+bMWKFfv8PsiuJtMapsnCLfuzzJs3L1qT9h/4wAf251B77bt58+Zi/ZgxY4q2BgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgNQFBmTYVnzp1av5syZIlu91Ltc0ueXPhwoXFqiOOOKJol2m0Xk0m63vcccd16RBz586Nj370ozFjxoxYsGDBPvssXbq02H7ooYcWbQ0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQGoCgjJtKn7qqafmz7IrvGRXeuloeeSRR4pNxx9/fNEu02gbdJk4cWKXDpHdJurll1+O7LZNjz/++D77/Pu//3ux/eijjy7aGgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1AQEZdpUPAvKDB8+PF9z++23F7dXarNLPP/88/HAAw/kq7KQzJQpU9puzttbt26NjRs35o/m5uY9treuyIIuy5cvz5+OGzcuhg4d2rppn19PPPHEaGhoyPe5++67i2O07/Twww/HT3/603z1ySefHIIy7YU8J0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFISEJRpU+1hw4bFxRdfnK959dVX49JLL81vbZRdYWb79u3x6KOPxqc//em8PXDgwLjooova9P5V8+qrr46zzjorf2R9OloWL15cbJo0aVLR7qxx0EEHxR//8R/nu23ZsiU++9nPxk9+8pPIxpkt69atizlz5sS1116bPx8yZEj8+Z//ed72HwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAqgKNqU68o3mfe+65sXLlyrjrrrvilVdeiT/7sz+LkSNHxrZt2/KATGu/mTNn7vcVWl5//fXWw8Vhhx1WtLvSuPDCC2PRokWR3QZqxYoVccUVV8TgwYOjqakp2h43C9XccMMN0Z0gTlfObx8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQNUEXFGmXcWyK8Vcdtll8Zd/+ZcxduzYfOuGDRuKkEwWaPnKV74SM2bMaNez+0/XrFlTdOpukGXAgAFx/fXXx5e+9KU4+OCD8+NkV71pDckceOCBcfrpp8dtt90WU6dOLc6jQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIVcAVZTqo/Ac+8IHIHmvXro3nn38+GhoaYvz48TFu3Li83UG3fPWNN964r83FtvPOOy+yx/4s73//+yN7ZKGbpUuXRhbqyYIxreGZ/Tm2vgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/iQgKNNJNUeNGhW//uu/3slefb959OjRkT0sBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECexcQlNm7i7UECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEuiUw5pSbu7W/nQkQaBX4cGvDVwIECPS6wIBeP4MTECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgDAUGZOiiCIRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPS+gKBM7xs7AwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQB0ICMrUQREMgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPcFBGV639gZCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6kCgsQ7GYAgECBAgQIAAAQIECBAgkLjAuZ/8WuICpk+gnMDT5brpRYAAAQIECBAgQIAAAQIECBBIVkBQJtnSmzgBAgQIECBAIE2BMafcnObEzZpAjwh8uEeO4iAECBAgQIAAAQIECBAgQIAAAQIECBDoKwG3XuoreeclQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoqYCgTE25nYwAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCvBARl+kreeQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoqIChTU24nI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6CsBQZm+kndeAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBmgoIytSU28kIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6SkBQpq/knZcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCmAo01PVu7k+3cuTMWL14cL774YvHYtGlT3H777cWed911V/z+7/9+DBo0qFinQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEuiJw7ie/1pXd7EOAQDuBp9s97y9P+ywoc++998Zf/MVfxMsvv7yb5fDhw4ugzOuvvx5/+Id/GOPGjYtPfepTcfnll8eAAS6CsxuYJwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0SqHnqZMGCBXHaaafFH/zBH+wRkmk/4iVLluSrXn311bjyyivz0Mz27dvb7+Y5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgU4FanpFmdWrV8fpp58e2de2y8CBA6OhoSGam5vbro6lS5fu9vyee+6JrVu3xoMPPrjbek8IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdCZQ0yvKXHzxxUVIprGxMT75yU/GT37yk9iwYUMcddRRe4z17LPPjm9/+9sxbdq0Ytv3v//9+OEPf1g81yBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQFYGaBWXmzJkTWcglW4YMGRLz5s2LW2+9NU4++eQYOnToXseaXWnmIx/5SDz55JPxG7/xG8U+s2bNKtoaBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLoiULOgzN13312MJwvInHTSScXzzhpNTU1x3333xYgRI/JdH3vssVi1alVn3WwnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAjULCjzi1/8Ij/p+PHj46KLLioG0NXG6NGj48ILLyx2f+mll4q2BgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHOBGoSlFm5cmVxBZgTTjihszF1uP2oo44qtgnKFBQaBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXRCoSVBm+fLlxVAmTJhQtLvbGDRoUNFl27ZtRVuDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQGcCNQnKHH744dHQ0JCP5dlnn+1sTB1ub719U7bDkUce2eF+NhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoL1CToMyIESNiypQp+bmffPLJ2LBhQ/txdPq8ubk5Hn744Xy/LHRzxBFHdNrHDgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaBWoSlMlOdswxx+TnXLt2bVxxxRWt5+/y1+uvvz4WLFiQ73/ooYfGyJEju9zXjgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqFpT50z/90+L2S7Nnz45Zs2ZFdpWYzpadO3fGTTfdFNddd12x6yc+8YmirUGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgKwI1C8qcccYZ8alPfSof065du+Laa6+NE088Mb72ta9FdjumLBDTumTthQsXxj/90z/F8ccfH5/97GeLUE32/POf/3zrrr4SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6JJAY5f26qGdvvzlL8dDDz1U3EJp/vz5kT3aLps3b46mpqbIvrZfBg8eHHPnzo3GxpoOu/0wPCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKigQE0TJwcccED86Ec/ii984QsxZ86caGlp2YMsu9rM3kIyxx13XNxyyy1x5JFH7tHHCgIECBAgQIAAAQIECBCotsCYU26u9gSMnkCfCXy4z87sxAQIECBAgAABAgQIECBAgACBKgrU7NZLrThjx46Nb37zm/ntlk499dTW1R1+HT16dHzjG9/I9z/55JM73M8GAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvsSqOkVZdoOJLtCzCOPPBJvvvlmvPjii8Vj3bp1MXny5Jg2bVr+GD9+fAwYUPM8T9uhahMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPQDgT4LyrTaNTU1xfHHH58/Wtf5SoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCnBfrsUi27du3KryjT3Nzc4ZyuuOKKuO+++2LTpk0d7mMDAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAga4I1DwokwVjrr322pg4cWL85m/+Zvzyl7/c6zhbWlripptuig996EMxduzYuPzyy2NfoZq9HsRKAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm8J1DQo88Ybb8RZZ50Vs2bNiqVLl+ZDeO655/ZajOXLl8eOHTvybVu2bImvfvWr8Vu/9VuxatWqve5vJQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF9CdQ0KDNjxox46KGHdhtPa2Bmt5X/9yS7esz73ve+aGxsLDY98sgjcfbZZ0d22yYLAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAge4I1Cwo8/3vfz8effTRYmy/+7u/Gz/72c/isssuK9a1bUyaNCnff82aNTFz5sxoaGjINz/xxBMxd+7ctrtqEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOhUoGZBmeuvv74YzCWXXBIPPvhgnHjiicW6jhpNTU35bZduu+22YperrrrKVWUKDQ0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGuCNQkKNPS0hLz58/PxzNu3Lj4m7/5m+IKMV0ZZLbPRRddFO9973vz3VeuXBkd3bKpq8ezHwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQFoCNQnKLF++PLZt25bLnnbaaTFs2LBSyr/zO79T9Fu4cGHR1iBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQmUDNgjKtA3nHO97R2uz210mTJhV9XFGmoNAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDogkBNgjIHHXRQMZSnn366aHe38fzzzxddJk+eXLQ1CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHQmUJOgzCGHHBIDBw7Mx/LUU0/F1q1bOxvXXrf/7Gc/K9Yfc8wxRVuDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQGcCNQnKNDY2xumnn56PZd26dfGZz3yms3Htsf2ee+6JH/zgB/n67Ao1Y8eO3WMfKwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0JFCToEx28j/5kz8pxjB79uy4/vrru3xlmX/913+Niy66qOh/wQUXFG0NAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0RqFlQ5pxzzokTTjihGNM111wTkydPjhtvvDEeeeSRePnll2PHjh3R0tISK1asiCeffDK+9a1vxXve85744Ac/GBs2bMj7/tqv/Vpcd911xXE0CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHRFoLErO/XEPoMGDYr77rsvjj/++Fi9enV+yFdffTVmzpxZHH7AgAGRPZqbm4t1bRuDBw/OwzNDhgxpu1qbAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKcCNbuiTDaSQw45JH784x/H+973vr0OLLuaTEchmVNOOSXmzZsXxx577F77WkmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgXwI1DcpkA5k+fXp+q6U77rgjzjjjjMiuNLOvZcqUKfHP//zPeUim7a2b9tXHNgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtBWp266W2J25oaIiPf/zj+eONN96IJ598MlauXJnfkmnnzp0xefLkyAIy2dfhw4e37apNgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJRAnwRl2o70wAMPzK8s03adNgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGeFqj5rZd6egKOR4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKArAoIyXVGyDwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOUFBGUqX0ITIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6IpAY1d26ul9Nm3aFPfee288++yzsWHDhti+fXu0tLR06zS33XZbt/a3MwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQNoCNQ3KbNu2La666qq44447Yv369fslLyizX3w6EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSSE6hpUOZzn/tc3HrrrckhmzABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9I3AmFNu7psTOyuBygt8uPIz2NsEahaUueeee/YIyRxwwAExZcqUaGpqiiFDhuxtfNYRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6BGBmgVlZs+eXQy4oaEhbrjhhsiuMDN48OBivQYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB3hKoWVDm5z//eTGHb37zm/GJT3yieK5BgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoLcFBvT2CbLjL1myJNauXZufKrvF0vnnn1+L0zoHAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUKgJkGZVatWFSc86aSTYujQocVzDQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK1EKhJUGbcuHHFXFpaWoq2BgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaCdQsKPOud70rn9OCBQti165dtZqf8xAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIBWoSlBkwYECcf/75+QnXrVsXc+fOxU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgpgI1CcpkM7r66qtjwoQJ+eQ+//nPx8KFC2s6UScjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIW6BmQZkDDzww7rvvvnjnO98Zq1evjmOOOSZmzpwZTz31VLz++utpV8HsCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEel2gsdfP8NYJbr755li+fHm8//3vjzvvvDOam5vjxhtvzB/ZLsOHD48xY8ZEQ0NDl4a0ePHiLu1nJwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKZQM2CMnPnzo358+d3qL5p06bIHhYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECvSFQs1sv9cbgHZMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAVwVqdkWZSy65JFauXNnVcdmPAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI8K1Cwoc/HFF/fowB2MAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHcE3HqpO1r2JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqKyAoExlS2fgBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3RGoXFBm5cqVcdNNN3VnjvYlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgEI19YbB06dJ44IEHYtWqVbFt27Zobm7eYxi7du2KlpaWfFu2z4YNG+KVV16JJ554Inbu3Bmf+cxn9uhjBQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGOBGoalFm2bFlceeWVceedd+41HNPRIK0nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsL8CNQvKZFeFOeuss+KZZ57Z3zHHoEGD9vsYDkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJCWwIBaTXfOnDl7hGSGDRsW06dPj9GjRxfDmDp1amSPMWPGxIABuw9v2rRp8eCDD8batWuL/TUIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdEVg9yRKV3qU3Ocf//Efi54TJ06Mhx9+ODZt2hTPPvts3HDDDcW2b3zjG/HCCy/Ea6+9FmvWrIlvfetbMW7cuHz7okWLYujQoTFixIhifw0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXRGoSVBm8+bN8dRTTxXjmT17dpx22mnF87bt7Ioxrcvb3va2uOCCC+Lpp5+OI488MlpaWuKjH/1obNy4sXUXXwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0SaAmQZlly5ZFc3NzPqCjjjoqzjzzzN0Gl91qaezYsfm67Eoz7ZdRo0bFPffcE4MGDYoVK1bELbfc0n4XzwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjsU6AmQZn169cXg5g+fXrRbts4/PDD86fZrZh27NjRdlPezvqdffbZeXvOnDl7bLeCAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwL4EahKUGTx4cDGGQw45pGi3bbQGZbKQzHPPPdd2U9FuDcr88pe/jOx2ThYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXRWoSVBm2rRp0dDQkI/pjTfe2OvYsn1al//5n/9pbe72deLEifnzlpaWWLBgwW7bPCFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwL4GaBGWGDRsW48ePz8exZMmSvY5n6tSpxfr//d//LdptG61hm2zdM88803aTNgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF9CtQkKJONYPr06flAfvzjH8crr7yyx6DaXlHmscce22N7tmLRokXF+ra3cypWahAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoQKBmQZmjjz46H0Jzc3N85CMfiTVr1uw2pCxIM3z48HzdT3/602gfltm1a1f8/d//fdFn8uTJRVuDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQGcCNQvKXHLJJdHY2JiPZ968eXHEEUfE5ZdfHq+99lq+buDAgXHBBRfk7ZaWljj33HPj/vvvj02bNsXixYvjj/7oj+LJJ5/Mtw8YMCDa3qopX+k/BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPYhULOgzMSJE+PKK68shrJ69er46le/GsuWLSvWzZw5M7IQTLZk288555wYOXJkHHbYYXHnnXcW+1144YUxevTo4rkGAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgc4EahaUyQbypS99KT73uc8VYZhsXdtbKGVXiZk1a1a2uliyWy61XbLgzHXXXdd2lTYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBTgVqGpTJRvO3f/u3+S2UPv7xj8f06dOjqalpt0Fec801MXv27P/H3p3AeVXWi+P/AAPIKgq4oCAKIokbWLmUhVz1ZiqZ3syF3DW9mmtu6a80l6uvsqu3NLFwK7th7pYtpuWaiYqpiAiKKIKibLIoOAz//3Nuc/rOMMMMzMxhlvd5vQ7f5zzPc875PO8z3/Hrlw/PE2kppurbDjvsEH//+9+jX79+1ZscEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFitQNlqW5uoccSIEXHzzTfXevUTTzwxDjnkkHj88cezvVevXjF8+PAYNWpUdO7cudbzNBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoTWCdJMrUFkxpfUqOOeCAA7K9tF6ZAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwNoIFL700toE6RwCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECDRWQKNNQQecTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0CIFGW3pp8uTJcffddxc26AsvvLCwe7kRAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAyxdotESZF198MS666KLCRCTKFEbtRgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBViFg6aVW8RgNggABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoC6BRptRpkePHjF48OC67hfvv/9+LFy4MO/Xrl272HTTTWPzzTfP9g033DBmzZoVb731Vrz55puxePHivO+AAQNi5MiR+bECAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfoKNFqizJe//OVI++q2KVOmxG677ZZ1KSsriyOOOCIuuOCC2GabbWo8bfny5TFu3Li44oorYubMmfHOO+/EfvvtF4ccckiN/VUSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE2gsKWX0kwyKZFm/vz50a1bt3jmmWfilltuqTVJJgXcqVOnOPnkk2Pq1Kmxyy67xIoVK2LMmDHxu9/9rrbxqCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQo0BhiTKXXHJJvPHGG1kQt956awwfPrzGgGqqXG+99eKuu+6Kvn37xieffBLnnntuTd3UESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKhVoNGWXqr1Dv9seOyxx7LSVlttFQcffHBd3Vdp32yzzWL06NHZUkyTJ0/OZqbZYIMNVumnohiBxYsXx7Jly4q5mbsQINBkAnPnzm2ya7tw8xOoqKioEtTSpUv9Lq8i4qC6wIYbbhjt2rWrXr1Wx4sWLYq0rKaNAIGWLeCzQ8t+fqJvnQLN6X3Zu3fvRkNeuHBhlJeXN9r1XIgAgXUj0Jx+R60bgbZ11zQjfOmW/j8wffdgI1CbQGN+dkirGVT/7qu2+6onQKB5Cvjc0Dyfi6gINJf3Zvq7ivR3Fo21FZIokz6gvPzyy1nMu++++1rHnpZfGjduXKxcuTKeeuqp2G+//db6Wk5smED6sspfdjXM0NkEmoOA93FzeArrLob0BVb1L7HWXTTu3NoF0qyAfue09qdsfG1BwPu4LTxlY2xpAs3pfZm+r2msJNv02SHtNgIEWrZAc/od1bIlW2b0vndomc+tpUadft9IlGmpT0/cBP5PwOcGPwkEmqdAc3lvNtb3DZXKhSTKTJs2LUtuSTdtyABSwk3lNnv27Mqi13Ug0LNnz+jRo8c6uPOqt1y8apUaAgTqKbDRRhvVs6durUEgfZhZsGBBPpRu3bpF2m0EahNoyOe26tfs1atX/nmweptjAgRajoDPDi3nWYm07Qg0p/dlY352aE6zCPveoe28n4y08QWa0++oxh+dK1YX+Pjjj+PDDz/Mq9N3yOutt15+rECgKQX69OnTlJd3bQIEChDwuaEAZLcgsBYCrfW9WUiizIABA3Lyv/3tb3l5TQtPPPFEfsqgQYPyskLxAu3bty/+pu5IgECjC3To0KHRr+mCzVeg+vNOv8ur1zXf6EXW0gV8dmjpT1D8BP5PwH83/CQQaH4CrfV92VrH1fx+gkREoGkFvJeb1re5Xb36//f53qG5PaHWHY/fN637+Rpd2xDwPm4bz9koW55Aa31vFpLtsPHGG0flvwRKs8v8+te/XuOfgD/84Q/xu9/9LjuvY8eOsd12263xNZxAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQdgUKSZRJvCeccEKufOyxx0bp7DB5Qy2Fp59+Og477LB8fckjjjgi+vbtW0tv1QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWFSgsUeb000+Prl27ZhF89NFHsccee8R+++0Xjz32WCxZsmSVyFJdWqZp9OjRsdtuu8WCBQuyPt26dYsLLrhglf4qCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKxOoGx1jY3Z1q9fv7jjjjviwAMPjPLy8uzSDz74YKQ9bb169Yr+/ftHWlZpxowZMXfu3Ky+9I+0/tX48eNjyJAhpdXKBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOoUKGxGmRRJmkHmV7/6VfTu3XuVwNKMMS+99FI8//zzNSbJbLLJJnHXXXdl11jlZBUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6hAoNFEmxfK1r30tpk6dGmeccUb06dOnjvAi+vbtG2nZpsmTJ8dXvvKVOvvrQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAmgcKWXiq9+QYbbBD//d//HVdffXVMmDAhHnvssXj33Xdjzpw52bJMG2+8cWy66aaxyy67xB577BFpySUbAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYYIrJNEmcqA27dvnyXDpIQYGwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGmFCh86aWmHIxrEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKhNQKJMbTLqCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWpXAOll6acmSJXHXXXfF5MmTY9GiRbF8+fKoqKhYI9if//zna9RfZwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbYtUGiizLJly+KCCy6Im2++ORYsWNAgeYkyDeJzMgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgzQkUmihz1llnxfXXX9/mkA2YAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg3QsUlihz5513rpIks95668XgwYOjZ8+e0blz53WvIQICBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFWK1BYoszYsWNzxHbt2sUVV1wRaYaZTp065fUKBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJpKoLBEmeeffz4fw4033hjHH398fqxAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKkF2jf1DdL1Z8yYEfPmzctulZZYOuKII4q4rXsQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyAUKSZR577338hvusssu0aVLl/xYgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEARAoUkyvTr1y8fS0VFRV5WIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCUQGGJMptttlk2pkmTJsXKlSuLGp/7ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMgECkmUad++fRxxxBHZDefPnx+33norfgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKFChSSKJNG9J3vfCcGDBiQDe68886LV155pdCBuhkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDbFigsUWb99dePu+++OzbeeOOYM2dO7LjjjnH22WfHc889Fx988EHbfgpGT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0OQCZU1+h3/e4Nprr4133nknRo4cGePHj4/y8vL40Y9+lO2pS7du3aJPnz7Rrl27eoU0ffr0evXTiQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEASKCxR5tZbb42JEyfWqr5kyZJIu40AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAUwgUtvRSUwTvmgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqK1DYjDKnnHJKvPvuu/WNSz8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECjSpQWKLMcccd16iBuxgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBNRGw9NKaaOlLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQYgUkyrTYRydwAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBNRGQKLMmWvoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0WAGJMi320QmcAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgTQTK1qRzQ/qOHTs2Zs+e3ZBLVDn34osvrnLsgAABAgQIECBAgAABAgQIECBAgAABbUCkGQAAQABJREFUAgQIECBAgAABAgQIECBAgMDqBApNlJk4ceLqYlmjNokya8SlMwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgzQtYeqnN/wgAIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0DYHCZpQZMGBALFq0qF6qS5YsiYULF8bSpUur9B89enQMHz68Sp0DAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvURKCxR5t57761PPFX6zJw5M37/+9/H2WefnSXZPPzww3HSSSfFvvvuW6WfAwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ1CTTrpZc233zzOOGEE+LRRx+NTTbZJNJMM1//+tfj9ddfr2tc2gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUEWjWiTKVkablltLMMmlLyzedccYZlU1eCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRLoEUkyqSR7LTTTtG/f/9sUClpZvbs2fUaoE4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkkCLSZRJwe6zzz7pJVasWBHPPvtsVvYHAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfoItKhEmR49euRjeuedd/KyAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIG6BFpUoszvfve7fDybbbZZXlYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUJdAi0mUeeqpp2Lq1Kn5eD7zmc/kZQUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdQk0+0SZ5cuXx/e+970YOXJkPpY0m8wmm2ySHysQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqEugrK4OjdV+xBFHxJQpU+p1uYqKikgJMkuWLIlZs2Zl5dITzzrrrNJDZQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ1ChSWKDN58uSYOHFinQHV1WH06NEhUaYuJe0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVBZr90kuVAffq1SvOOOOMuOWWWyqrvBIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCot0BhM8qMGTMmRo4cWe/A2rVrF506dYoePXrEoEGD4oADDoiuXbvW+3wdCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJQKFJYoY7mkUnZlAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBogVazNJLRcO4HwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOsSkCjTup6n0RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQiUNjSSzXdf9myZfHqq6/Ga6+9lu3Tpk2L7t27x6BBg2Lw4MHZnsodO3as6XR1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOotsE4SZVKCzI033hj/9V//FbNnz15tsP369YvLL788jjzyyGjf3gQ4q8XSSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUKtA4Zkn9957b2y99dZx2mmn1Zkkk6KeNWtWHHPMMfHpT386Hn/88VoHooEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA6gQKTZSZOHFiHHbYYfH222+vElOvXr1i++23j+HDh8eGG264Sns6d++99470aiNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwpgKFJcosWLAgDj744Pj444/zGPfaa694+OGH48MPP4z58+fHiy++GM8//3zMnTs3Fi1aFI899lh86UtfyvunJZu+9rWvZf3zSgUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC9RAoLFHmsssui+nTp2chde7cOR566KFsHzVqVPTo0WOVULt37x577LFH/P73v88SZir7vP7669myTaucoIIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAagQKS5R54okn8jDGjh0baTaZ+m4pYeZXv/pVtG//f+Hed999sXLlyvqerh8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBKCRRZvny5fHCCy9k3FtvvXUcddRRa0y///77x7777pudl5Zxeu2119b4Gk4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBouwKFJMq88sorsWzZskz5c5/73Fprp5llKrcJEyZUFr0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqFOgkESZzTffPA+kXbt2eXlNC+uvv35+Smk5r1QgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUItAIYkyffr0icGDB2chPPHEE7WEUnf1ww8/nHVKyTa777573SfoQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQOCfAoUkyqR7ffGLX8xuOXXq1Bg/fvw/b1//l3nz5sUjjzySnTB06NDo3bt3/U/WkwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoM0LFJYoc9VVV8WWW26ZgR9zzDHxzDPP1Bt/zpw5MXLkyEjJMh07doyrr7663ufqSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJFJYok2aAeeCBB6JHjx7x0UcfZUsnpYSZ6dOn1/okUr877rgjvvCFL8RLL70U7du3j1tvvTX23XffWs/RQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAmgbKaKtem7sEHH4zTTz+93qeuWLEibrnllrjttttis802i4EDB2Z7WVlZfPDBB9mekmMWL16cXzP1+/Of/5zt48aNy+sVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQl0GiJMosWLYpp06bVdb9V2isqKuLtt9/O9scff3yV9tKK1O+mm27KqiTKlMooEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1CVQ2NJLdQWinQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBTCjTajDLbbbddfO9732vKWF2bAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwFoLNFqizLBhwyLtNgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLNUcDSS83xqYiJAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg0QUkyjQ6qQsSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0R4EWlyjz7rvvxjXXXNMcLcVEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQjAXK1kVsb731Vtx///3x3nvvxbJly6K8vHyVMFauXBkVFRVZW+qzaNGiePvtt+OZZ56JFStWxBlnnLHKOSoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1CZQaKLMzJkz4/zzz4/x48fXmBxTW5DqCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRUoLBEmTQrzL777hsvv/xyQ2OOjh07NvgaLkCAAAECBAgQIECAAAECBAgQIECgLoGDTv6furpoJ0CgFoGXaqlXTYAAAQIECBAgQIAAgXUp0L6om48bN26VJJmuXbvG0KFDo3fv3nkYW2+9daS9T58+0b591fCGDBkSDzzwQMybNy/vr0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgPgJVM1Hqc8Za9rntttvyMwcOHBiPPPJILFmyJCZPnhxXXHFF3nbDDTfEa6+9Fu+//37MnTs3fvGLX0S/fv2y9mnTpkWXLl2ie/fueX8FAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvURKCRRZunSpfHcc8/l8YwdOzb23HPP/Li0nGaMqdx69eoVY8aMiZdeeimGDRsWFRUV8Y1vfCMWL15c2cUrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoJFJIoM3PmzCgvL88C2n777WOfffapElxaaqlv375ZXZpppvq24YYbxp133hkdO3aM2bNnx3XXXVe9i2MCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECqxUoJFFmwYIFeRBDhw7Ny6WFbbbZJjtMSzF98sknpU1ZOZ03evTorDxu3LhV2lUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWJ1AIYkynTp1ymPYfPPN83JpoTJRJiXJvPrqq6VNebkyUeb111+PtJyTjQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB9BQpJlBkyZEi0a9cui2nhwoU1xpb6VG7/+Mc/KotVXgcOHJgdV1RUxKRJk6q0OSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwOoFCEmW6du0a/fv3z+KYMWNGjfFsvfXWef2LL76Yl0sLlck2qe7ll18ubVImQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsFqBQhJlUgRDhw7NAnn00Ufj7bffXiWo0hllnnzyyVXaU8W0adPy+tLlnPJKBQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK1CBSWKLPDDjtkIZSXl8fhhx8ec+fOrRJSSqTp1q1bVvf0009H9WSZlStXxk9/+tP8nEGDBuVlBQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ1CRSWKHPKKadEWVlZFs8TTzwR2267bZxzzjnx/vvvZ3UdOnSIMWPGZOWKioo46KCD4r777oslS5bE9OnT47DDDosJEyZk7e3bt4/SpZqySn8QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWI1AYYkyAwcOjPPPPz8PZc6cOfHDH/4wZs6cmdedffbZkZJg0pbaDzzwwOjRo0dstdVWMX78+LzfUUcdFb17986PFQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUJVBYokwK5NJLL42zzjorT4ZJdaVLKKVZYi655JJUnW9pyaXSLSXOXHbZZaVVygQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqFCg0USZFc/XVV2dLKB199NExdOjQ6NmzZ5UgL7roohg7dmykpZiqbzvssEP8/e9/j379+lVvckyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgtQJlq21tosYRI0bEzTffXOvVTzzxxDjkkEPi8ccfz/ZevXrF8OHDY9SoUdG5c+daz9NAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoDaBdZIoU1swpfUpOeaAAw7I9tJ6ZQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrI1D40ktrE6RzCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRUQKJMQwWdT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0CIEJMq0iMckSAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYYKSJRpqKDzCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWoSARJkW8ZgESYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FABiTINFXQ+AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAixCQKNMiHpMgCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGiogUaahgs4nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoEQISZVrEYxIkAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAQwUkyjRU0PkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItQkCiTIt4TIIkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqIBEmYYKOp8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBFCJStyyhXrFgR06dPj6lTp+b7kiVL4qabbsrDuuOOO+KrX/1qdOzYMa9TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCmAussUeauu+6Kb3/72/Hmm29Wiblbt255oswHH3wQX//616Nfv35x2mmnxTnnnBPt25sEpwqYAwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoJFJ4oM2nSpDj11FPjr3/9a50BzpgxI+sza9asOP/88+PZZ5+N22+/PTp16lTnuQ3tsHjx4vj1r38dL7zwQsycOTN69uwZ22+/fbbvueee0blz5wbd4v3338/GUp+L9O/fPw4++OBau06cODFS4lHyWrhwYWyzzTZZnLvvvnsMHjy41vM0ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTakkChiTJz5syJUaNGRXot3Tp06BDt2rWL8vLy0up46623qhzfeeed8fHHH8cDDzxQpb6xD1LiyUUXXRQffvhhfum5c+dmy0Tdf//98eCDD8aVV14ZXbt2zdvXtPDSSy9lyS31OW/EiBE1JsqkpasuvfTSePjhh6tc5umnn46033zzzXHxxRfHF7/4xSrtDggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECbVGg0HWMjjvuuDxJpqysLE4++eT429/+FosWLcpmQKn+AEaPHp3NujJkyJC86be//W089NBD+XFjF6ZPnx7f+c53siSZtMxTiuG73/1unHHGGfHpT386u11KpDn99NOzuNf2/q+99lq9T01JRDVtV199dZ4ks9VWW8VJJ50U3//+97PlqtIMOCnxKMX+hz/8oabT1REgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2pRAYTPKjBs3LlKSS9rSskWPPvpo7LLLLqvFTjPNHH744bH//vvHAQccEI899ljW/5JLLom99957teeubeO1114badmllMiTkk722GOP/FIHHXRQ/OQnP4k77rgjXn311bjnnnviyCOPzNvXpDBt2rSs+wYbbBBplpo13V588cV8Zp2dd945rrjiinyGm7Q01H777RdnnnlmpJlw0phGjhwZ66233preRn8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKsRKGxGmd/85jc52vXXX19nkkze+f8vpNlR7r777ujevXtW/eSTT8Z7771X2qVRyq+88ko899xz2bVSYk5pkkyqTDO7fOtb34oddtgh63PfffdFWv5obbapU6dmp22zzTZrc3rcdttt2XkdO3aMCy+8ME+SqbzYlltuGf/v//2/7DAl/vzpT3+qbPJKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGiTAoUlyrzwwgsZcP/+/ePYY49dY+zevXvHUUcdlZ/3xhtv5OXGKjzyyCP5pdKSS7VtBx54YNY0Z86ceOqpp2rrVmv9vHnzIu1pW5tEmSVLlsQzzzyTnb/rrrtG3759s3L1P9JMM5tvvnlWnWa/sREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2rJAIYky7777bj4DzGc+85m19t5+++3zc5siUSbNKJO2NIPNoEGD8ntVLwwfPjyveuKJJ/JyfQuvvfZa3nVtEmUmT54cK1euzK4xYsSI/Fo1FSpjTUs9NcUsPDXdUx0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoDkKlBUR1DvvvJPfZsCAAXl5TQtpmaHKbdmyZZXFRnlNSyhNmTIlu1aKMS2zVNvWp0+f6NKlS3z00Ufx5ptv1tat1vqUtFK5DR06NJYuXRopSWfGjBmRxjh48OAsUadz586V3aq8Vib0pMotttiiSlv1g1LvFOvGG29cvYtjAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECbECgkUSbNmpIST9IsKGk2lLXdKpdvSucPGzZsbS9T43mLFy+O5cuXZ22bbLJJjX1KKzfaaKMssWXWrFml1fUqT506NevXrVu3+OMf/xj/+7//Gx9++GGVc1Mizqmnnho1LQE1d+7cvG9dsaY4K7e1ibXyXK8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYuUEiiTPfu3bNZUlKCyIQJE2LRokXRo0ePNbIrLy+PRx55JDsnJd1su+22a3R+XZ2XLFmSd0nx1rWlJJe0pVll1nSrTJRJ9xw7dmx2ehpTum+ySVu67g9+8IN48skn44orrogOHTpk9emPNYm1a9eu+XlrE2t+crVCSuxpzOtVu7xDAgQKErAkW0HQzeQ2lcv2VYaTkkRL/5tSWe+VQKVASrhd3Sx7lf3q87pgwYJo7BkB63NffQgQaFwBnx0a19PVCDSGQHN6XzbmLLbz5s2LTz75pDGIXIMAgXUo0Jx+R61DhjZz6+rfOyxcuHCVfyDaZjAMtF4CjfnZ4YMPPoi0coCNAIGWK+BzQ8t9diJv3QLN5b2Z/q6idJKQhqoXkiiTgtxxxx0jJYikLzrOPffc+OlPf7pGsV9++eUxadKk7Jy03NCaJtrUdbPSvyisbcmj0mt06tQpO0x/4ZP+B6C+f4mUkktmzpyZXyotvXTkkUfGTjvtlI1p/vz52SwzP/vZz7IZbp566qn49a9/HUcccUR+zprEWjqWjz/+OL9GQwsVFRWRdhsBAi1bwPu4ZT+/hkaf/vtV/Uushl7T+QRqE/DZoTYZ9QRaloDPDi3reYm2bQg0p/flmnw/UtfT8dmhLiHtBFqGQHP6HdUyxFpXlL53aF3Ps7mPJiXJ+J3T3J+S+AisXsB7ePU+WgmsK4Hm8t6sbz5GfZ3a17djQ/t985vfzJNJ0iwql1xySaRZYura0oeba665Ji677LK86/HHH5+XG6tQuexSul7p7C21Xb99+3/RrckPR0qE2X777aNv377x6U9/Oq699trYY4898sSfDTbYIA499ND48Y9/HJX3uOmmm6J02aTSf1FVV6yV10jjkE1d29NUT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLQFgcJmlNlrr73itNNOyxJDUib5xRdfHPfdd18cffTRsdtuu1VJ4kgJHVOmTInnn38+fvjDH8Y//vGP/FnsvPPOcd555+XHjVUoXaKoNGmmtutX9kkzttSVrFJ6jX79+sV1111XWlVjOS0t9ZWvfCXuueeebGaZZ555Jg488MCsb5cuXfJzUhyls8bkDf8sVMaZDiuXi6reZ22O05jLygr78VmbEJ1DgEA9BLyP64HUirqk//6WJk2mZMrShMpWNFRDaYYCPjs0w4ciJAJrIeCzw1qgOYVAEws0p/dlY/7rrvTZIX1+tREg0LIFmtPvqJYt2TKiT/+gtPQflfreoWU8t9YSZfp9U/rz11rGZRwE2pKAzw1t6Wkba0sSaC7vzcb8ziH5F5rpcOWVV8af//znfAmliRMnRtpLt6VLl0bPnj0jvVbf0nJHt956a5MkaJQmn9RniaK05FLaunfvXj3MRjvefffds0SZdME33ngjv25prCmO1S1DVRlnOrkxY033XN1982ALKCwo4B5uQaC1CqTZrWxtRyAlT86dOzcfcEqgbMz/NuQXViBQg8D6669fQ60qAgRamoDPDi3tiYm3LQi01vdlmnHXRoBAyxdorb+jWv6TaZoRfPTRR7Fgwb++rU3f85d+l900d3VVAv8n0Lt3bxQECLRwAZ8bWvgDFH6rFWit781/rR9UwKNbb7314i9/+UuccMIJtf4L9vSvhWpKkhkxYkQ8+uijMWzYsCaJNH0BU5mFNG/evDrvUfkXjU35F4wDBgzI43j77bfzcukHvso48sZqhdL2poy12m0dEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSanUChiTJp9Cnj6MYbb4wJEybEHnvsUSdISgq54YYbsv677rprnf3XtkNavmjTTTfNTp89e/ZqL5NmaalMphk0aNBq+zaksTRhqE+fPvmlBg4cmJfrirW0vSljzQNSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0U4FCl14qNUgzxDz22GPx4YcfxtSpU/N9/vz5kRI6hgwZku39+/evdfaZ0us1RnnrrbeOWbNmxYwZM2LJkiWRlqSoaXvllVfy6m233TYv16eQlo5Ky0+lKSjTUlSrmyHnrbfeyi+5xRZb5OUUZ+U2adKkGDlyZOXhKq+pPW1pNpnSGWpW6diCKw46+X9acPRCJ7BuBV5at7d3dwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBQqsM4SZSpHmdYp3XnnnbO9sm5dvaYZbtLyThUVFfHEE0/Ev//7v9cYSkrwqdxS7GuypSWe3nzzzeyUp556arWJMr///e/zS++www55eauttorNNtss3nnnnSzOk08+ucZkorTsUmWizPDhw/OlpfILKRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2pBAYUsvpeWWXn311WZNmxJlKmeRuemmm/LllUqDnjJlStx///1ZVUqSGTx4cGlzVv74449j8eLF2V5eXl6l/bOf/WyesPKb3/wmS3ap0uGfB4888kg8/fTT2VFacqo0USZVVibxzJw5M8aPH//Ps/71kpJ9fvKTn8Qnn3ySVR566KH/alQiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRBgcISZW644Yb41Kc+Fbvvvnv87Gc/y5Zcam7eXbt2jeOOOy4LKy3BdOqpp2YzsqSkk+XLl8fjjz8ep59+elbu0KFDHHvssTUO4Tvf+U7su+++2Z7OKd022WSTOOaYY7Kqjz76KM4888z429/+ls1ikyrT0lPjxo2Liy++OOvTuXPn+M///M+sXPpHSnzp27dvVnX99ddHSuxZuHBhdvzuu+/G97///WyJp1Sxyy67rJJok3X0BwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgDQkUvvRSSgpJe0o4Oeigg7KkkVGjRuWzrKxr+xRTSjS544474u23346TTjopevToEcuWLcsSZCrjO/vss9c6+eSoo46KadOmRVrCafbs2XHuuedGp06dIi1D9cEHH1TeIlJSzRVXXBFbbrllXldZ6NKlS1x11VVxwQUXxHvvvRc333xztm+44YZVZsJJ537ve9+rPM0rAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDNChQ2o0xKAind0mwqt99+e+y1115ZIkhK5pg+fXppl3VSTjPFfOtb34rvfve7+YwtixYtypNkttpqq/jBD34QBxxwwFrH1759+7j88svj0ksvjU033TS7TpqxpjJJZv3114+UPPTzn/88tt5661rvk9rS7DOf+9znomPHjlm/efPmZa9lZWVxyCGHZMsvpUQfGwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgrQsUNqPMww8/HGn/xS9+Effcc08sWbIkt58xY0a2VFBKHPniF7+YzTJz8MEHR7du3fI+RRf23nvvSHtKPJkyZUo2403//v2jX79+dc5+86Mf/ahe4Y4cOTLSPnfu3HjrrbciJeSk5JfK5Jn6XCQl1Vx55ZVRXl4eb7zxRrzzzjux8cYbxxZbbLFO/eoTuz4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSIFCkuUSTO17LPPPtm+ePHiuPvuu7OkmUceeSQqKiqyMa9cuTL++te/Zvupp56azYhy9NFHx+c///kiTarcKy1ltNtuu1Wpa+yD3r17R9obsqUZZIYMGZLtDbmOcwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECrVWgsKWXSgG7d+8eRx55ZDz00EPZTCpXXXVVbLfddqVdstlV0rJCe+yxR5b8ccUVV8TMmTOr9HFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoL4C6yRRpjS4zTbbLM4999x46aWXYuLEiXHWWWfFJptsUtolpk6dGhdeeGG2nNCXvvSlGD9+fJV2BwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqEljniTKlAe60005x9dVXZzPH/PGPf4zjjz8++vTpk3dJSzSl+kMPPTSvUyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQH4Gy+nQquk+HDh1in332yfbLL788zjzzzPjVr35VdBjuR4AAAQIECBAgQIAAAQIECBAg0MYF+ux+bRsXMHwCDRE4pCEnO5cAAQIECBAgQIAAAQJNItAsE2XefffduOuuu+Luu++Oxx57LMrLy5tk8C5KgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQdgSaTaLM4sWL45577olf/vKX8fDDD8eKFStWeQobbbRRjBkzJo499thV2lQQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWJ3AOk2USTPFPPTQQ1lyzL333htLly5dJdaysrL48pe/nCXHpNeOHTuu0kcFAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgboE1kmizDPPPJMlx4wfPz7mzJlTY4zbbrttHHPMMfGNb3wjNt544xr7qCRAgAABAgQIECCwpgJ7/fjQNT1FfwIEKgXGVBa8EiBAgAABAgQIECBAgAABAgQIECBAoGUKFJYo8/rrr2fJMbfffntMnTq1Rq31118/Dj300Gz2mM9+9rM19lFJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYG0ECkuU+drXvhYTJ05cJcZ27drFqFGjsuSYr371q9GlS5dV+qggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FCBwhJlqge65ZZbxlFHHRVHH310bLHFFtWbHRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoVIFCE2XSbDEHH3xwNnvMyJEjI80mYyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQhEBhiTKXXXZZfP7zn4+ePXsWMS73IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBFoLBEmS9/+ctVbuyAAAECBAgQIECAwLoQOH/nc9fFbd2TQCsR+FYrGYdhECBAgAABAgQIECBAgAABAgQIECDQVgUKS5Rpq8DGTYAAAQIECBAg0LwEOlw/sHkFJBoCBAgQIECAAAECBAgQIECAAAECBAgQIECgMIFGS5R54IEH4uSTT84DP+200+Lcc//1r3W/9KUvxcsvv5y3N7Qwc+bMhl7C+QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAm1IoNESZZYuXRrvvPNOTrdw4cK8nApz5syp0l6l0QEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJhZo38TXd3kCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECzUKg0WaU2XHHHeOqq67KB7Xbbrvl5VQ4/fTT47333qtS54AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAUQKNligzdOjQSHtt21FHHVVbk3oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECTS5g6aUmJ3YDAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB5iDQ4hJl0vJNjz76aHOwEwMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEALEmi0pZfqGvNBBx0Ur7zySmy77bZx991319W9xvZ+/frF7Nmzs7YFCxbE+uuvX2M/lQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqCxQ2o8ybb74ZU6ZMifS6ttvKlSvzU9966628rECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgLoHCEmXqCqSu9vfffz/SskuV26JFiyqLXgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUKdCoSy/96U9/iueff77Gm1YmuaTXK6+8ssY+NVVWVFRESoq59957o3RGmY022qim7uoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1CjQqIky/fv3j/333z8++eSTGm+WKmfNmhUXXHBBre31aRg4cGAMGjSoPl31IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAJNOrSS5/61KfizDPPbFLanj17xq233hrt2rVr0vu4OAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOsSaNQZZRLNd7/73Xj44Ydj/vz5VaRmzpwZy5cvj06dOsXmm29epW11Bx06dIiuXbtGjx49YtiwYXHSSSfFTjvttLpTtBEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYRaDRE2W6desWzz777Co3GjFiREycODFLdnn++edXaVdBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCkFGnXppaYM1LUJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINESg0WeUqS2YH/zgB9lyTBtssEFtXdQTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaDKBwhJl/u3f/q3JBuHCBAgQIECAAAECBAgQINCyBfb68aEtewCiJ7CuBMasqxu7LwECBAgQIECAAAECBAgQIECgZQq0uKWX3n333bjmmmtapraoCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE1plAYTPKlI7wrbfeivvvvz/ee++9WLZsWZSXl5c2Z+WVK1dGRUVF1pb6LFq0KN5+++145plnYsWKFXHGGWesco4KAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABArUJFJooM3PmzDj//PNj/PjxNSbH1BakegIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINFSgsUSbNCrPvvvvGyy+/3NCYo2PHjg2+hgsQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0LYHCEmXGjRu3SpJM165dY8CAAfH+++/H3LlzM/mtt946e50/f37MmzcvW36p8pEMGTIkrr766hg5cmRllVcCBAgQIECAAAECBAgQaAUC5+98bisYhSEQWBcC31oXN3VPAgQIECBAgAABAgQIECBAgECLFSgsUea2227LkQYOHBg33XRT7LnnnlndjTfeGN/85jez8g033BCjRo3KygsWLIjf/va3cd5558WsWbNi2rRp0aVLl+jevXt+LQUCBAgQIECAAAECBAgQaPkCHa4f2PIHYQQECBAgQIAAAQIECBAgQIAAAQIECDR7gfZFRLh06dJ47rnn8luNHTs2T5JJlZUJM6n8wAMPpJds69WrV4wZMyZeeumlGDZsWDa7zDe+8Y1YvHhxZRevBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOolUEiizMyZM6O8vDwLaPvtt4999tmnSnBpuaW+fftmdY888kiVtnSw4YYbxp133hkdO3aM2bNnx3XXXbdKHxUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEVidQSKJMWkKpchs6dGhlscrrNttskx1Pnjw5Pvnkkypt6SCdN3r06Kx+3Lhxq7SrIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILA6gUISZTp16pTHsPnmm+fl0kJlokxKknn11VdLm/JyZaLM66+/Hmk5JxsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB+goUkigzZMiQaNeuXRbTwoULa4wt9anc/vGPf1QWq7wOHDgwO66oqIhJkyZVaXNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYHUChSTKdO3aNfr375/FMWPGjBrj2XrrrfP6F198MS+XFiqTbVLdyy+/XNqkTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGC1AoUkyqQIhg4dmgXy6KOPxttvv71KUKUzyjz55JOrtKeKadOm5fWlyznllQoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEahEoLFFmhx12yEIoLy+Pww8/PObOnVslpJRI061bt6zu6aefjurJMitXroyf/vSn+TmDBg3KywoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6hIoLFHmlFNOibKysiyeJ554Irbddts455xz4v3338/qOnToEGPGjMnKFRUVcdBBB8V9990XS5YsienTp8dhhx0WEyZMyNrbt28fpUs1ZZX+IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILAagcISZQYOHBjnn39+HsqcOXPihz/8YcycOTOvO/vssyMlwaQttR944IHRo0eP2GqrrWL8+PF5v6OOOip69+6dHysQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqEugsESZFMill14aZ511Vp4Mk+pKl1BKs8RccsklqTrf0pJLpVtKnLnssstKq5QJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1ClQaKJMiubqq6/OllA6+uijY+jQodGzZ88qQV500UUxduzYSEsxVd922GGH+Pvf/x79+vWr3uSYAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwGoFylbb2kSNI0aMiJtvvrnWq5944olxyCGHxOOPP57tvXr1iuHDh8eoUaOic+fOtZ6ngQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBtAuskUaa2YErrU3LMAQcckO2l9coECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE1kag8KWX1iZI5xAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoqIBEmYYKOp8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBFCEiUaRGPSZAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINFShr6AUqz3/ooYfiggsuqDxs8tdnn322ye/hBgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAq1HoNESZebNmxfPPfdc65ExEgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVYlYOmlVvU4DYYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA2gUabUWabbbaJM888s7b7qCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwTgUaLVFmp512irTbCBQt0Gf3a4u+pfsRaEUCh7SisRgKAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEVi9g6aXV+2glQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoJQISZVrJgzQMAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB1Qs02tJLq79Nza0rVqyI6dOnx9SpU/N9yZIlcdNNN+Un3HHHHfHVr341OnbsmNcpECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFhTgXWWKHPXXXfFt7/97XjzzTerxNytW7c8UeaDDz6Ir3/969GvX7847bTT4pxzzon27U2CUwXMAQECBAgQIECAwBoJXLfXR2vUX2cCBP4lcMqf/1VWIkCAAAECBAgQIECAAAECBAgQIECAQEsUKDzrZNKkSbHnnnvGf/zHf6ySJFMdcMaMGVnVrFmz4vzzz8+SZpYvX169m2MCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdQoUOqPMnDlzYtSoUZFeS7cOHTpEu3btory8vLQ63nrrrSrHd955Z3z88cfxwAMPVKl3QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAugUITZY477rg8SaasrCxOOOGEOPLII2PHHXeMz33uczFx4sQq8Y4ePTpuv/32uOSSS+K1117L2n7729/GQw89FHvvvXeVvg4IECBAgAABAgQI1EfgyME/rk83fQgQIECAAAECBAgQIECAAAECBAgQIECAAIFWKFDY0kvjxo2LlOSSts6dO8cTTzwR119/fey6667RpUuXGmnTTDOHH354TJgwIb7whS/kfVLijI0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAmggUlijzm9/8Jo8rJcjssssu+XFdhZ49e8bdd98d3bt3z7o++eST8d5779V1mnYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECuUBhiTIvvPBCdtP+/fvHsccemwdQ30Lv3r3jqKOOyru/8cYbeVmBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF0CZXV1aIz2d999N58B5jOf+cxaX3L77bfPz02JMrvttlt+rECAAAECBAgQIECAAAECBAgQIECgsQX2+vGhjX1J1yPQdgTGtJ2hGikBAgQIECBAgAABAi1HoJAZZd55551cZMCAAXl5TQsdO3bMT1m2bFleViBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQl0AhiTLbbLNNtGvXLotl8uTJdcVUa3vl8k2pw7Bhw2rtp4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAdYFCEmW6d+8egwcPzu49YcKEWLRoUfU46jwuLy+PRx55JOuXkm623XbbOs/RgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEClQCGJMulmO+64Y3bPefPmxbnnnlt5/3q/Xn755TFp0qSs/xZbbBE9evSo97k6EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgsUeab3/xmvvzS2LFj45JLLok0S0xd24oVK+Kaa66Jyy67LO96/PHH52UFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvURKCxRZq+99orTTjsti2nlypVx8cUXx2c/+9n4n//5n0jLMaWEmMotlV955ZX45S9/GTvvvHOceeaZeVJNOj7vvPMqu3olQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUC+Bsnr1aqROV155Zfz5z3/Ol1CaOHFipL10W7p0afTs2TPSa/WtU6dOceutt0ZZWaFhVw/DMQECBAgQIECAQAsW+MHxXVpw9EInsG4Fvr9ub+/uBAgQIECAAAECBAgQIECAAAECBAgQaLBAoRkn6623XvzlL3+JCy+8MMaNGxcVFRWrDCDNNlNTksyIESPiuuuui2HDhq1yjgoCBAgQIECAAAEC9RX4/qffq29X/QgQIECAAAECBAgQIECAAAECBAgQIECAAIFWJlDY0kuVbn379o0bb7wxW25pjz32qKyu9bV3795xww03ZP133XXXWvtpIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILA6gUJnlCkNJM0Q89hjj8WHH34YU6dOzff58+fHoEGDYsiQIdnev3//aN++8Hye0lCVCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWoFAYYkyixcvzhJeunbtWoWtZ8+esfPOO2d7lQYHBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBpRoLCpWq688srYeOON49hjj81mkmnEMbgUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgToFCplRZsWKFXH99ddHmlXm5ptvjilTpsSTTz5ZZ3A6ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGgsgUJmlJk+fXrMnz8/j3n//ffPywoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEihAoJFGmoqKiyli22267KscOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDS1QCGJMkOGDIm0V24PPvhgZdErAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUIECkmUSSM5++yz8wGNHz8+nnzyyfxYgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGV7PwEAAEAASURBVCBAgEBTCxSWKHPiiSfGuHHjokePHjF//vz4whe+EGeccUb84Q9/iGnTpkV5eXlTj9X1CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2rBAWVFjHzt2bMyePTsOOeSQuOWWW2LFihVx7bXXZnuKoaysLAYMGBCdO3euV0ivvPJKvfrpRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJFJooM3HixFrV04wyb7zxRq3tGggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0RKCwpZcaEqRzCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRUoLAZZY477rhs6aWGBux8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmsjUFiizCmnnLI28TmHAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKMIWHqpURhdhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoLkLSJRp7k9IfAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAo0iUNjSS40SrYsQIECAAAECBAgQIECAQKsUuG6vj1rluAyKQFMLnPLnpr6D6xMgQIAAAQIECBAgQIAAAQIEWpeAGWVa1/M0GgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVoEzChTC4xqAgQIECBAgAABAgQIEChO4MjBPy7uZu5EgAABAgQIECBAgAABAgQIECBAgECbFTCjTJt99AZOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhbAhJl2tbzNloCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQJsVkCjTZh+9gRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2paARJm29byNlgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQZgUkyrTZR2/gBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIG2JSBRpm09b6MlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLRZgbI2O3IDb5DAihUroqKiokHXcDIBAute4JNPPln3QYigMIHy8vIq90q/y/0MVCFxUE2gY8eO1WrW/jD9/K1cuXLtL9CIZzbisBoxKpci0DIE/HejZTwnUbYtgeb0vmytnx3a1k+U0RJoXIHm9DuqcUfmajUJpO8ZSrf0/4F+BkpFlKsLNOZnBz9r1XUdE2h5At7HLe+ZibhtCDSn92ZjfnaQKNM2fn4bfZSLFi2Kjz76qNGv64IECBQr8MEHHxR7Q3drVgJLly6NtNsI1CawySabRLt27WprXqP6Dz/8MJYtW7ZG5zRV5003baoruy6B1i/QlJ8durd+PiMk0CQCTfm+XNOAG/Ozw4IFC/zl6po+AP0JNEOB5vQ7qhnytPqQFi9eHGm3EahNYNNG/B/0efPm+ce9tUGrJ9BCBHxuaCEPSphtTqC5vDfT31Wk7x0aa5Mo01iSbew6KVurufyr8DZGb7gEGlVgvfXWa9TruVjzFkgzgS1fvjwPsqysLNJuI1CEQKdOnRot6aaIeN2DAIGaBZrys8MPju9S803VEiCwWoEzm9Fn+sZKsE0DTp8dOnTosNqxF9V4/s7nFnUr9yHQ6gQ+XG9MqxuTAdUuUH3m2vQdcnP5XV571Fpai0D6fxWz4LeWp2kcbVWgKb9zaKumxk2gMQSay3uzMb9zSC7r5G/HlixZEnfddVdMnjw50swk6S/t1vQDzM9//vPGeK6usZYC3bp1i7TbCBBo2QIbbLBByx6A6NdIIP33du7cufk5Xbp0ie7d/fv9HEShSQX8rDUpr4sTKEygKT87fP/T7xU2Djci0LoEWudn+p49ezabx1R1IZFmE5ZACLQIgab87NAiANpYkGkG8jQjWOWWvj9O3z38f+zdC5RV1X0/8N8MAwgIGsEXKYRGUANFo8ao6dKliY3L1lc0VgNGm+hKjY8k9R0TV+sjxlYlEqvRuNTYNKsKPqJmpaurFjVBY0KMGgXjW8AXIqg8RQfm/z8nzuHOMId5cO+de8/93LWus88+++yz92fP4HX4uo8XgWoIbLHFFtW4jXsQIFBBAZ8bKoirawKbIFDUn82qBmWS7fa//e1vx80339zhA3Nf1kVQpi9qriFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINK5AVYMyZ5xxRlx77bWNq23mBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/SZQtaDM7bffvkFIJnme1fjx4yPZTnfw4MH9huDGBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDZV4MCrj93ULlxPoDEFjmvMaZs1AQL9I1C1oMz111+fzbCpqSkuvfTSSHaYGTRoUFavQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBSAlULyvzhD3/I5vDjH/84TjrppOxYgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEClBZorfYOk//nz58fSpUvTWyWPWJo6dWo1buseBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDKBqgRlFi1alN1wr732iiFDhmTHCgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqIVCVoMzo0aOzuaxbty4rKxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBColkDVgjIf/ehH0znNnTs32traqjU/9yFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQClQlKNPc3BxTp05Nb/j222/HLbfcgp8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAVQWqEpRJZnT++efH2LFj08mde+65MW/evKpO1M0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaW6BqQZktttgi7rzzzth2223jzTffjF133TXOPPPMePTRR+Ott95q7FUwewIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYoLtFT8Dh/eYPr06fHqq6/G/vvvH7fddlu0trbGtGnT0nfSZNiwYTFq1Khoamrq0ZBeeumlHrXTiAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAiULWgzC233BKPPfZYrvrKlSsjeXsRIECAAAECBAgQqKTA3TMnV7J7fRMotMDhRxd6eiZHgAABAgQIECBAgAABAgQIECBAgEADCFTt0UsNYGmKBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECNSxQtR1lTj311HjjjTdqmMLQCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEiixQtaDMiSeeWGRHcyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBGhQ48Opja3BUhkSgDgSOq4Mx9mGIVQvK9GFsLiHQIwH/YusRk0YEuhYo6L/cup6sWgIECBAgQIAAAQIECPReYMC143p/kSsIECBAgAABAgQIECBAgACBmhUQlKnZpTEwAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBehI4b49z6mm4xkqghgROr6GxGAoBAkUXqLugzBtvvBG33nprfOtb3yr62pgfAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAHQnYja6OFstQCRAgQKBhBfolKLNgwYK45557YtGiRbFmzZpobW3dYAHa2tpi3bp16bmkzfLly2PhwoXxu9/9LtauXSsos4GYCgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgY0JVDUo88orr8R5550Xt912W5fhmI0N1DkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECmyJQtaBMsivMwQcfHE899dSmjDe9duDAgZvchw4IECBAgAABAgQaU+Cg+6c15sTNmkA5BI7+STl60QcBAgQIECBAgAABAgQIECBAgAABAgT6TaC5Wne+8cYbNwjJDB06NHbeeecYOXJkNowJEyZE8h41alQ0N3cc3o477hj33ntvLF26NGuvQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAnAh2TKD25oo9t/uM//iO7cty4cTFr1qxYuXJlPP3003HppZdm56677rp49tlnY/HixbFkyZL46U9/GqNHj07PP//88zFkyJDYfPPNs/YKBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHoiUJWgzKpVq+LRRx/NxnP99dfHAQcckB2XlpMdY9pfW265ZRx33HHx5JNPxqRJk2LdunXx5S9/OVasWNHexFcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECPRKoSlDmlVdeidbW1nRAkydPjs9//vMdBpc8amnrrbdO65KdZjq/ttpqq7j99ttj4MCB8frrr8c111zTuYljAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhsVqEpQ5p133skGsfPOO2fl0sJOO+2UHiaPYvrggw9KT6Xl5LrDDjssLd94440bnFdBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYGMCVQnKDBo0KBvDX/zFX2Tl0kJ7UCYJyfzpT38qPZWV24MyL7zwQiSPc/IiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FOBqgRldtxxx2hqakrH9O6773Y5tqRN++uJJ55oL3b4Om7cuPR43bp1MXfu3A7nHBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYmEDLxk6W69zQoUNjzJgxsWDBgpg/f36X3U6YMCGr/+Mf/5iVSwvtYZuk7qmnnoo999yz9LQyAQIECBAgQIAAgW4Fzt37lG7baECAQNcC07uuVkuAAAECBAgQIECAAAECBAgQIECAAIG6EajKjjKJxs4775yiPPjgg7Fw4cINgEp3lHnooYc2OJ9UPP/881l96eOcskoFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjkCVQvK7LLLLukQWltbY8qUKbFkyZIOQ0qCNMOGDUvrHnnkkegclmlra4sf/ehH2TU77LBDVlYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0J1A1YIyp556arS0/PlJT7Nnz46JEyfG2WefHYsXL07HOGDAgDjuuOPS8rp16+LII4+Mu+++O1auXBkvvfRSfOlLX4o5c+ak55ubm6P0UU3dTdJ5AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn9OrlTBYdy4cXHeeefFJZdckt7tzTffjCuuuCLdXWbrrbdO684888y44YYbIgnKJOePOOKIaGpqimQ3mdLXCSecECNHjiytUiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhsInLfHORvUqSBAoCcCp/ekUd21qVpQJpG5+OKLY9WqVXHVVVelYZikrvQRSskuMRdeeGFccMEFyan01TkkM3z48Cxs097GVwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0JXAgGvHdVWtjgCBBhWoalAmMb7yyitj6tSpcfXVV8cjjzwSI0aM6ED/3e9+N7bZZps45ZRTYu3atR3O7bLLLnHrrbfG6NGjO9Q7IECAAAECBAgQINBTgStbvt/TptoRILCBwF0b1KggQIAAAQIECBAgQIAAAQIECBAgQIBAPQlUPSiT4Oy+++5x88035zp97Wtfi7//+7+PX//61+l7yy23jN122y0++9nPxuDBg3Ovc4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAnkC/BGXyBlNan4RjDj300PRdWq9MgAABAgQIECBAgAABAgQIECBAoFoC1xy4ulq3ch8ChRM49b7CTcmECBAgQIAAAQIECBAogEBzAeZgCgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6FejXHWXWrl0bL730Ujz33HPZe+XKlXHTTTdlA58xY0Z84QtfiIEDB2Z1CgQIECBAgAABAgT6KjBjz0F9vdR1BBpeYErDCwAgQIAAAQIECBAgQIAAAQIECBAgQKDeBfotKHPHHXfEWWedFS+//HIHw2HDhmVBmbfeeiuOOeaYGD16dHzjG9+Is88+O5qbbYLTAcwBAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAjwSqHpSZO3dunHbaafHAAw90O8D58+enbV577bU477zz4ve//3387Gc/i0GD/F/A3eJpQIAAAQIECBAgQIAAgToSuHvm5DoaraESqB2Bw4+unbEYCQECBAgQIECAAAECBAgQIECgHgSqGpR5880347Of/WwkX0tfAwYMiKampmhtbS2tjgULFnQ4vv322+O9996Le++9t0O9AwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLdCVT1OUYnnnhiFpJpaWmJr3/96/Gb3/wmli9fHpMnb/h/Dx522GHpDjI77rhjNo9f/OIX8b//+7/ZsQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBnghULShz4403RhJySV6DBw+O2bNnx7XXXht77713DBkypMuxJjvNTJkyJebMmRP77bdf1ubCCy/MygoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEeiJQtaDMzJkzs/EkAZm99torO+6uMGLEiLjzzjtj8803T5s+9NBDsWjRou4uc54AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAJlC1oMzjjz+e3nTMmDHx1a9+NRtATwsjR46ME044IWv+4osvZmUFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAt0JVCUo88Ybb2Q7wOy5557djSn3/OTJk7NzgjIZhQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAPBKoSlHn11VezoYwdOzYr97YwcODA7JI1a9ZkZQUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3Qm0dNegHOd32mmnaGpqira2tnj66af73GX745uSDiZNmtTnflxIgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoNwC1xy4utxd6o9AQwicel9DTNMkCRCoEYGqBGU233zzGD9+fDz33HMxZ86cWL58eQwfPrxXBK2trTFr1qz0miR0M3HixF5drzEBAgQIECBAgAABAgQI1K7AQfdPq93BGRmBWhY4+ie1PDpjI0CAAAECBAgQIECAAAECBAjUnEBVHr2UzHrXXXdNJ7906dI455xzeg3xve99L+bOnZte97GPfazXQZte39AFBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEChRKoyo4yidg//uM/xh133JE+fun666+P7bbbLr7zne9ES8vGh7B27dq4+uqr45JLLsngTzrppKysQIAAAQIECBAgQIAAAQIECBAgQKBSAsePv7pSXeuXAAECBAgQKKCAzw4FXFRTIkCAAIHCCWw8pVLG6R544IHxjW98I6ZPn56GZf7lX/4l7r777viHf/iH2GeffSIJxLS/kvIzzzwTf/jDH+KKK66IJ554ov1U7LHHHnHuuedmxwoEztuj9zsUUSNAoF3g9PaCrwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECi8QNWCMonkZZddFvfdd1/2CKXHHnssknfpa9WqVTFixIhIvnZ+DRo0KG655ZZud6HpfJ1jAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAlUNymy22WZx//33p49cuvHGG2PdunUbrEBbW1uXIZndd989rrnmmpg0adIG16ggQIAAAQIECBAgQIAAgfoWOHfvU+p7AkZPoJ8EpvfTfd2WAAECBAgQIECAAAECBAgQIFCvAs3VHvjWW28dP/7xj2POnDmx7777dnv7kSNHxnXXXZe233vvvbttrwEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBrgSquqNM6QCSHWJ+9atfxbJly+K5557L3m+//XbssMMOseOOO6bvMWPGRHNz1fM8pUNVJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKIBAvwVl2u1GjBgRe+yxR/pur/OVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLkF+j0oU+4J6Y8AAQIECBAgQIAAAQIE6k/gypbv19+gjZhATQjcVROjMAgCBAgQIECAAIE/Cwy/bhkKAgQIECBAoMYFPNOoxhfI8AgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMojIChTHke9ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LhA2R699POf/zxOOumkqk33rbfeqtq93IgAAQIECBAgQIAAAQIECBAgQKAxBS4/aUhjTtysCZRB4KIy9KELAgQIECBAgAABAgQIlFugbEGZNWvWxJIlS8o9Pv0RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIE+C1xz4Oo+X+tCAo0scOp9xZy9Ry8Vc13NigABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJNA2XaU6dRvevjxj388DjrooBg8eHBXp9URIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJpA2YIyzc0bbk7z4osvxq233hpHHnlkfOlLX4oDDjggumpXtdm6EQECBAgQIECAAAECBAjUpMCMPQfV5LgMikCtC0yp9QEaHwECBAgQIECAAAECBAgQIECgxgQ2TLf0cYBf/OIXY/bs2XHaaafFtttum/Xy9ttvx4033hgHHnhgfPSjH41vfvOb8cgjj2TnFQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUQ6BsO8o0NTXFX//1X6fvq666Kh544IF0N5k777wzli5dms7ljTfeiB/+8Ifpe9y4cXHsscemO83ssssu1ZirexAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAgwkcP/7qBpux6RIgsDGBsu0oU3qTAQMGxOc+97m44YYbIgnH/OIXv4jjjjsuhg8fnjV7+eWX47LLLotdd901Jk2aFJdcckm88MIL2XkFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAuUUqEhQpnSAAwcOjL/7u7+Ln/70p7Fo0aK4/fbb46ijjoohQ4ZkzebNmxcXXHBBjB8/Pj796U/HD37wg3j11Vez8woECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIENlWg4kGZ0gEm4ZgkJJOEZZLQTBKeSUI0SZim/TVnzpw444wzYuzYsXHAAQfE9ddfH0uWLGk/7SsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPglUNShTOsLkMUzJ45iSxzIlj2dKHtOUPK4peWxT8lq3bl088MADcfLJJ8f222+fBmr+8z//M1asWFHajTIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHgm09KhVhRtttdVWcdJJJ6XvZKeZmTNnxm233RYPPfRQtLW1xQcffBC//OUv03eyK80hhxwSM2bMqPCodE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC9S4w/Lpl9T4F4ydAoIwC/bajTN4ctt122zjttNPi17/+dSxcuDCuuuqq2HfffbPmq1evToM0WYUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgR4I1MSOMl2NM3n00ksvvRQvv/xyLFiwoKsm6gikAgOuHUeCAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCtQE0FZZJwzOzZs9PHKt15553x+uuvdzmBUaNGdVmvkgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECeQL8HZZJwzEMPPZSGY+64447ccMyWW24ZX/jCF+KYY46Jz33uc3nzUU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgS4F+Ccq0tbWlO8fMnDkzbr/99txwzPDhw+Pwww9PwzGf//znY9CgQV1OQiUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB7gSqFpRJwjHJzjHt4ZjXXnuty7ENGzYsDjnkkDQcc/DBB8dmm23WZTuVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHojUNGgTBKOefjhh7PHKr366qtdji0Jw/zt3/5tGo5JQjJDhw7tsp1KAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0VKHtQJgnH/OY3v0nDMcljlfLCMcljlA466KA0HHPYYYdF8pglLwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKVEihbUGbhwoUxbdq0SMIxr7zySpfjbWlpiQMPPDANxxxxxBGx5ZZbdtlOJQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyC5QtKJM8Yumqq67aYHwDBgyI/fffPw3HHHnkkTFy5MgN2qggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUGmBsgVlOg+0PSBz1FFHxbbbbpuefvDBBzs36/NxErrxIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINBTgYoFZdauXRv/93//l757OpjetGtra+tNc20JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaXKC5wedv+gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAg0iICjTIAttmgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRhco26OXDj744Jg7d26je5o/AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAjQqULSgzYsSImDhxYo1O07AIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaXcCjlxr9O8D8CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINIiAo0yALbZoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUYXKNujlxod0vwJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaW+DumZMbG8DsCfRR4PCj+3ihywgQINAHATvK9AHNJQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUnIChTf2tmxAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAn0QEJTpA5pLCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6k+gpf6GbMQEOgpcc+DqjhWOCBDoscCp9/W4qYYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoewE7ytT9EpoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBATwQEZXqipA0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDdCwjK1P0SmgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBPBARleqKkDQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQN0LCMrU/RKaAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQE8EWnrSSBsCBAgQIECAAAECBAgQIECAAAECjShw0acWNeK0zZkAAQIECBAgQIAAAQIECBRWwI4yhV1aEyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgVsKNMqYYyAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT4KHHT/tD5e6TICDS5w9E8aHMD0CRCopoAdZaqp7V4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL9JiAo02/0bkyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBNAY9eytFesWJF3HrrrfH444/HK6+8EiNGjIjJkyen7wMOOCAGDx6cc2XPq1tbW+Pee++Nxx57LBYuXBhvvvlmbLPNNjF27NjYbbfd4tBDD40BAwbkdrh48eL42c9+lnu+9MSYMWPiqKOOKq1SJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0lICgTBfLnQRXvvvd78ayZcuys0uWLImXXnop7rnnnvjlL38Zl112WQwdOjQ739tCEsD5t3/7tzQgU3ptcs/nn38+Zs2aFT//+c/j3HPPjU984hOlTbLyk08+GXfccUd2vLHC7rvvLiizMSDnCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcILCMp0WuIkDHP++edHsqNMc3NzHHLIIfHJT34yDc3Mnj07fv/736c7wHzzm9+MadOmxfDhwzv10P3hokWL0nssX748bbzXXnvFPvvsE9ttt1289tprcf/990cSgnnhhRfirLPOiltuuSVGjRq1QcfPPvvsBnV5FU1NTXmn1BMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGkJAUKbTMk+fPj0NybS0tMRFF10U++67b9biyCOPjH//93+PGTNmxJ/+9Ke466674vjjj8/O97Rw8cUXR3tI5swzz4wjjjiiw6Vf/OIX4+abb07fyQ4z//qv/xqXX355hzbJQbLzTPL6yEc+ku50kx74BwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQJcCgjIlLPPmzYtHH300rTn00EM7hGSSymRXltNPPz0Nyfzxj3+Mu+++O6ZOnRoDBgwo6WXjxcWLF8cTTzyRNvrMZz6zQUgmOZHc5ytf+Uo88sgj8fTTT8dvf/vbWLVq1QaPenruuefSfnbaaaf0q38QIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0n8C5e5/Sfzd3ZwJ1LDC9jsdu6AQI1J9Ac/0NuXIjnjVrVtb5YYcdlpU7F9p3gHnzzTfj4Ycf7nx6o8ePP/54dn6//fbLyp0LSVhmzz33TKvb2tqy3WPa2y1dujSSd/ISlGlX8ZUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkC9gR5kSm2RHmeQ1YsSI2GGHHUrOdCzutttuWcXs2bM32HkmO9lFYcyYMfG1r30t3nrrrfirv/qrLlqsryrdqeb9999ff+L/l5599tnsuNGDMsePvzqzUCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5AkIynwos3bt2njmmWfSo7Fjx6aPP8pDGzVqVAwZMiRWr14dL7/8cl6zLut33nnnSN49eT311FNZs85hmOeffz47l/SXPJopCfrMnz8/Bg4cGOPHj0/DPoMHD87aKRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGllAUObD1V+xYkW079qy3Xbbdfs9sc0226ShlNdee63btn1pkIRk5syZk146bty4GD58eIdunnvuufR42LBh8T//8z/xX//1X7Fs2bIObZIwz2mnnRYbe4xUhwt6cZBYJeEiLwIE6lsgCfx5NY5Aa2trh8l+8MEHaeizQ6UDAiUCm2222UbDwyVNuy367NAtkQYE6kLAZ4e6WCaDbDCBWvq5TH4PUa7XmjVrYt26deXqbpP6KeO0NmkcLiZQjwK19GdUPfrV25jbf7/ePu7Ox+31vhJoFyjnZ4f33nsv2tra2rv2lQCBOhSo9OcGn+vr8JvCkGtCoNI/mz2dZFNTUyR/Z1Gul6DMh5IrV67MTDfffPOsnFdIAirJqxLfGMlYrrjiiuzWSdil86s9KJO0vf7669PTyTdHMvbly5enx8nYLr/88njooYfi0ksvjdJHOXXur7fHyQ42lZh7b8eRtO9+tfrSq2sINIbAO++80xgTNcsuBZJfICRvLwJ5Aj0JD+dd27k+CSUnf+HlRYBAfQv47FDf62f0xRSopZ/LcoZsk99tJMHuWnj5hXotrIIx1KtALf0ZVa+G9Tzu5HfIyduLQJ5AOYMy7777bs2EbPPmq54AgY0LVPpzg8/1G/d3lkCeQKV/NvPu27k+yUKU8+8sBGU+FC4NyvTkcUWDBg1Kr0z+widJKScLU45XkrL/9re/HS+88ELa3SGHHBJ77bVXh66TgMorr7yS1SWPXjr++OPjk5/8ZLrzzNtvv53uMnPDDTeku+Q8/PDDceutt8bUqVOzaxQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKK3Bly/fL26HeCDSIwOK4tkFmapoECNSCQHMtDKIWxlC6DWRPdl5pbl5PV66tgJP/q/+CCy6Ixx57LCXZYYcd0kcndfZJgjCTJ0+OrbfeOj71qU/F9OnTY999980ez/SRj3wkjj322Lj66qujfZw33XRTVOoxUZ3H55gAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUIsCdpT5cFWGDh2arU9paCar7FRob5PsPtOTYE2nyzc4TMIv5513XsybNy89l4Rkrrrqqmh/xFPpBaNHj45rrrmmtKrL8sSJE+Pwww+Pu+66K91Z5ne/+10cccQRXbbtbWXi1b6rTm+vLXf7teXuUH8EGkhgiy22aKDZmmpra2uU7qCWbI3fk13UyBEoh0Dymaaczw/dpDGt2KSrXUygoQUq+tnBz2ZDf2+ZfN8FKvpz2cthlWu33eS2yaOly/U/JvVyGpoTIFBGgVr6M6qM09JVjkDyO/NkN/T2V/JYnVr5HXL7mHwtrsCIESPS3f+LO0MzI1B8AZ8bir/GZlifArXys1nO3zkkKyEo8+H3Y+mzMJOdXbp7JY9cSl7JL2429bVw4cI466yzsh1fkt1iLrvsskg+2G3q6zOf+UwalEn6efHFFze1u+z65D9wauU/cpZno1IgQKC3AqUhwd5eq339CSS/sCoNygwcODB8D9TfOtbriIWy6nXljJtARwH/3ujo4YhALQgU9eeyZgK2tbDIxkCgjgWK+mdUHS9JRYee/OVFaVAm+e/A0t+7V/TmOm94gVr6Xmtt+NUAQKBvAj439M3NVQQqLVDUn831zw+qtGCN9588rqg9hbR06dJuR7tkyZK0zaYGZZ588sk4+eSTs5DM/vvvHz/4wQ/KEpJJBjh27NhsLkkgx4sAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0KgCgjIfrnySbt9+++3To9dff32j3w/JbjLtYZrkEUl9fT344IPxrW99K5YtW5Z2ccwxx8SFF15Y1sdgrFq1KhveqFGjsrICAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDRBARlSlZ8woQJ6dH8+fM7PJqipElanDdvXlY1ceLErNybwq9+9av453/+50geg9Hc3BxnnnlmnHbaaWm5u35uueWW+PKXvxyHHnpozJ07d6PNFyxYkJ3/2Mc+lpUVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKNJiAoU7Li++67b3q0bt26mD17dsmZjsUk5NL+2mOPPdqLPf76wgsvpCGZtWvXxsCBA+Piiy+OI444osfXJ4+Jevnll+Odd96Jhx9+eKPX/fd//3d2fpdddsnKCgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRhMQlClZ8SQoM2zYsLTmpptuyh6vVNIknnnmmbjnnnvSqiQkM378+NLTafm9996LFStWpO/W1tYO59va2uLKK6+M9vqzzz479ttvvw5tujv49Kc/HU1NTWmzmTNnxquvvtrlJbNmzYpHHnkkPbf33nuHoEyXTCoJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBBhFoaZB59miaQ4cOjRNPPDF++MMfxmuvvZY+Cuk73/lOfOITn0iDLb/97W/je9/7Xvq4pAEDBsRXv/rVLvs9//zzY86cOem5iy66KA444ICsXbLDy5NPPpkejxgxIp5++un0nTXIKRx00EExadKk9Ox2220XX/nKVyIJ86xevTr+6Z/+KX3vtdde6aOb3n777bjzzjsjeURT8ho8eHCccsopadk/CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKNKiAo02nljzzyyHjjjTdixowZsXDhwjj55JNj+PDhsWbNmjQg0978zDPP7NMOLUm/7a9ly5bFXXfd1X640a877bRTFpRJGp5wwgnx/PPPR/IYqNdffz3OOeecGDRoUCThm7feeivrKwnVXHrppfGXf/mXWZ0CAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKARBQRlOq16slPM6aefHjvvvHP86Ec/isWLF8fy5cuzVh//+Mfj61//eiSPMurtK3nc0vz583t7WZftm5ub091tHnjggbj22mvTsMz777+fhWS22GKLSB4NdcYZZ0RS9iJAgAABAgQIECBAgAABAgQIEOi9wN0zJ/f+IlcQIJAKHH40CAIECBAgQIAAAQIECNSegKBMzpr8zd/8TSTvpUuXxjPPPBNNTU0xZsyYGD16dFrOuSytnjZtWpenW1pa4v777+/yXF8r999//0jeS5YsiQULFqShngkTJsT222/f1y7r7rrLTxpSd2M2YAK1InBRrQzEOAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAFAUGZbpC32mqr2Geffbpp1f+nR44cGcnbiwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoGuB5q6r1RIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoloCgTLHW02wIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRyBARlcmBUEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFEtAUKZY62k2BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECOQKCMjkwqgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIolIChTrPU0GwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRwBQZkcGNUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLFEhCUKdZ6mg0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECOQEtOvWoCBAgQIECAAAECBAgQIECAAAECDS9w0P3TGt4AAIE+Cxz9kz5f6kICBAgQIECAAAECBAhUSsCOMpWS1S8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBNCQjK1NRyGAwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEClBARlKiWrXwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZoSaKmp0RgMAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoo8DdMyeXsTddEWgcgcOPLuZc7ShTzHU1KwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgU4CdpTpBOKw/gQu+tSi+hu0ERMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVFxCUqTq5GxIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRRYMaeg4o4LXMiUHGBKRW/gxsQIEBgvYBHL623UCJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiwgB1lCry4pkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFGFzjo/mmNTmD+BPomcPRP+nZdjV9lR5kaXyDDI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKI+AoEx5HPVCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQ4wKCMjW+QIZHgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQHgFBmfI46oUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDGBVpqfHyGR4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+ixw7t6n9PlaFxJoZIHpBZ28HWUKurCmRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FFAUKajhyMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGCCnj0UkEX1rQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIOLKlu9jIECgTwJ39emqWr/IjjK1vkLGR4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUBYBO8qUhVEnBAgQIECAAAECBAgQIECAAAECRRQ4d+9TijgtcyJQFYHpVbmLmxAgQIAAAQIECBAgQKB3AoIyvfPSmgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqSGDGnoPqaLSGSqB2BKbUzlDKOhKPXiorp84IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqVcCOMrW6MsbVY4G7Z07ucVsNCRDoKHD40R2PHREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDIAnaUKfLqmhsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAmICiTUSgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUWUBQpsira24ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKZgKBMRqFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQZAFBmSKvrrkRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhkAoIyGYUCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAkQUEZYq8uuZGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQCbRkJQUCBAgQIECAAAECBAgQIECAAAECBDoIXNny/Q7HDggQ6I3AXb1prC0BAgQIECBAgAABAgSqImBHmaowuwkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/CwjK9PcKuD8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBVBARlqsLsJgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAv0tICjT3yvg/gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAlUREJSpCrObECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9LeAoEx/r4D7EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVEWgpSp3cRMCFRQ46P5pFexd1wQKLnD0Two+QdMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAegE7yqy3UCJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiwgKBMgRfX1AgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNYLCMqst1AiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAosICgTIEX19QIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTWCwjKrLdQIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKLCAoEyBF9fUCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE1gu0rC8qESBAgAABAgQIECBAgAABAgQIECBQKjBjz0Glh8oECPRCYEov2mpKgAABAgQIECBAgACBagnYUaZa0u5DgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQrwKCMv3K7+YECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVEhCUqZa0+xAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPSrgKBMv/K7OQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLUEWqp1I/chUCmBc/c+pVJd65dA4QWmF36GJkiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH1AnaUWW+hRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUGABQZkCL66pESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrBcQlFlvoUSAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBgAUGZAi+uqREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKwXEJRZb6FEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQYAFBmQIvrqkRIECAAAEWU3t+AABAAElEQVQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAisFxCUWW+hRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUGCBlgLPzdQIECBAgAABAgQIbCAwbN6UDepUECDQQ4EJPWynGQECBAgQIECAAAECBAgQIECAAAECBGpUwI4yNbowhkWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBeAUGZ8nrqjQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEYFBGVqdGEMiwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoLwCLeXtTm8Eqi9wZcv3q39TdyRQGIG7CjMTEyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB3AnaU6U7IeQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUIICMoUYhlNggABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoDsBQZnuhJwnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAohICgTCGW0SQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6ExCU6U7IeQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUIICMoUYhlNggABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoDuBlu4aOE+AAAECBAgQIECAAAECBAgQIECgUQWGzZvSqFM3bwKbLjBh07vQAwECBAgQIECAAAECBMotYEeZcovqjwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCYFBGVqclkMigABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoNwCgjLlFtUfAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBATQoIytTkshgUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAuQVayt2h/ghUW2DGnoOqfUv3I1AYgSmFmYmJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHsBO8p0b6QFAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAAQQEZQqwiKZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQvYCgTPdGWhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRAQFCmAItoCgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAt0LtHTfRAsCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLoTGDZvSndNnCdAoCuBCV1VqiNAgEBlBOwoUxlXvRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSYgKBMjS2I4RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFRGQFCmMq56JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqDEBQZkaWxDDIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqIyAoExlXPVKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQYwKCMjW2IIZDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQGYGWynSrVwLVExg2b0r1buZOBIomMKFoEzIfAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5AsIyuTbOEOAAAECBAgQIECAAAECVRIQgK8StNsUT0D4vXhrakYECBAgQIAAAQIECBAgQIBARQU8eqmivDonQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoFQFBmVpZCeMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoqICgTEV5dU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFArAoIytbISxkGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBRAUGZivLqnAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoFYEBGVqZSWMgwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKICgjIV5dU5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBArQgIytTKShgHAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBARQUEZSrKq3MCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaERCUqZWVMA4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCgjKVJRX5wQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABArUi0FIrAzGO+hJYtWpVvP/++/U1aKMlQGADgXfeeWeDOhXFFVi7dm2Hya1evTpaW1s71DkgUCqwxRZbRFNTU2lVn8srV66MDz74oM/Xu5AAgdoQ8NmhNtbBKAiUCtTSz+WWW25ZOrRNKi9fvjw6f37dpA5dTIBAvwjU0p9R/QLQYDft/DuG5L8D16xZ02AKptsbgXJ+dnj33Xejra2tN7fXlgCBGhPwuaHGFsRwCHwoUCs/m8nfVSR/Z1Gul6BMuSQbrJ8kJJP8BasXAQL1LeDnuL7Xb1NHn/wCq/MvsTa1T9cXS6CcHzqTX476BWmxvj/MpjEFfHZozHU369oWqKWfy3KGbJPPDUK2tf29Z3QEeiJQS39G9WS82pRXIPlz3J/l5TUtWm/lDMq89957sW7duqIRmQ+BhhLwuaGhlttk60igVn42BWXq6JumyEPdfPPNY+jQoUWeorkRaAiBkSNHNsQ8TfLPAskvp5YtW5ZxJH+ODxkyJDtWINBZoFy7yST9jhgxwi+sOgM7JlCHAj471OGiGXLhBWrp57Kcnx2S0I3/K7zw374m2AACtfRnVANw9/sUk5DjihUrsnEkv0MePHhwdqxAoJICW221lc8OlQTWN4EqCFT8c8P6f0VVYTZuQaA4AhX/2ewnKjvK9BN8vd+2pcW3Tr2vofETSAQGDRoEooEFBgwY4Huggde/2lP32aHa4u5HoDICPjtUxlWvBDZFoKg/lwMHDtwUFtcSIFAjAkX9M6pGeGtuGJ0fmZf8d6DvgZpbpsIOyGeHwi6tiTWQgH9nNNBim2pdCRT1Z7O5rlbBYAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0UUBQpo9wLiNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgvAc/Pqa/1MloCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6IXAsHlTetFaUwIEMoEJWalQBTvKFGo5TYYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBPQFAmT0Y9AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAoQQEZQq1nCZDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQJyAokyejngABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoFACgjKFWk6TIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyBMQlMmTUU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAoAUGZQi2nyRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOQJCMrkyagnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAolICgTKGW02QIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTyBARl8mTUEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFEpAUKZQy2kyBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECeQKCMnky6gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAolIChTqOU0GQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTwBQZk8GfUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKFEhCUKdRymgwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECegKBMnox6AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQgkIyhRqOU2GAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgT0BQJk9GPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKEEBGUKtZwmQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkCcgKJMno54AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBQAoIyhVpOkyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMgTEJTJk1FPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQKAFBmUItp8kQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkCQjK5MmoJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKJSAoEyhltNkCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8gQEZfJk1BMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRKQFCmUMtpMgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnkCgjJ5MuoJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQKJSAoU6jlNBkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIE8AUGZPBn1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEChRIQlCnUcpoMAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAnoCgTJ6MegIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUIJCMoUajlNhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIE9AUCZPRj0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEChBARlCrWcJkOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAnICiTJ6OeAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgUAKCMoVaTpMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIExCUyZNRT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUCgBQZlCLafJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5AkIyuTJqCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiUgKBMoZbTZAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPIEBGXyZNQTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUSkBQplDLaTIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ5AoIyeTLqCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECiUgKFOo5TQZAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPAFBmTwZ9QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoUSEJQp1HKaDAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQJ6AoEyejHoCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFCCQjKFGo5TYYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBPQFAmT0Y9AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAoQQEZQq1nCZDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQJyAokyejngABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoFACgjKFWk6TIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyBMQlMmTUU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAoAUGZQi2nyRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOQJCMrkyagnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAolICgTKGW02QIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTyBARl8mTUEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFEpAUKZQy2kyBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECeQKCMnky6gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAolIChTqOU0GQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTwBQZk8GfUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKFEhCUKdRymgwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECegKBMnox6AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBQgkIyhRqOU2GAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgT0BQJk9GPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKEEBGUKtZwmQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkCcgKJMno54AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBQAoIyhVpOkyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMgTEJTJk1FPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQKAFBmUItp8kQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkCQjK5MmoJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKJSAoEyhltNkCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8gQEZfJk1BMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRKQFCmUMtpMgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnkCgjJ5MuoJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQKJSAoU6jlNBkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIE8AUGZPBn1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEChRIQlCnUcpoMAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAnoCgTJ6MegIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUIJCMoUajlNhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIE9AUCZPRj0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEChBARlCrWcJkOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAnICiTJ6OeAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgUAKCMoVaTpMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIExCUyZNRT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUCgBQZlCLafJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5AkIyuTJqCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiUgKBMoZbTZAgQIECAwP9j7z7g5KrqhgGfNEI6LUAooQUpAQRpAoYmoJSoRLC/YAAR/QQERaRXAUUQeBEpAorCK5EiHRGQpiBFegmhBUIJpBICIaR8/q/OOLvZMrs7dec5v99k7txyynM2d87c+c+5BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaE1AoExrMtYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0KwGBMt2qOzWGAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgNQGBMq3JWE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCtBATKdKvu1BgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHWBATKtCZjPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLcSECjTrbpTYwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFoTECjTmoz1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3UpAoEy36k6NIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaE1AoExrMtYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0KwGBMt2qOzWGAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgNQGBMq3JWE+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCtBATKdKvu1BgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHWBATKtCZjPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLcSECjTrbpTYwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFoTECjTmoz1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3UpAoEy36k6NIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaE2gd2sbGn39e++9l/7whz+kxx57LE2aNCkNHjw4rb/++tlju+22S3379i0J0aOPPpquvvrqNHHixDRz5sy01lprZWVsueWWacSIEUWVUYo8iirITgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBOhYQKNNC50XgydFHH53efffd/NapU6eml19+OV1//fXp5ptvTqeddlrq379/fntHF+bPn59OOumkdMcddzQ59IEHHkjxuPTSS9Pxxx+fttlmmybbC1+UIo/C/CwTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLqzgFsvNevdCIY58sgjsyCZnj17ps997nPp2GOPTd///vfTJptsku0dgTQHH3xwmjVrVrOji395xhln5INkVl999XTAAQekE088MX35y1/OZq+ZN29eVu6tt97aaqalyKPVzG0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHQzATPKNOvQs88+O8Vtl3r37p0FrowaNSq/x5gxY9K5556bxo0bl5577rl07bXXpr322iu/vdiFJ554It1www3Z7htvvHE65ZRT8rPTxG2ddt1113TIIYekmMUm6rPtttumxRdfvEn2pcijSYZeECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6uYAZZQo6+JlnnkmPPPJItmb06NGpMEgmVvbo0SMdeOCBaYMNNsj2ue6661Lc/qij6bLLLssO6dOnTzrqqKPyQTK5fFZbbbV0zDHHZC8jaOe2227Lbco/lyKPfGYWCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINICBQpqCT77zzzvyruOVSa+kLX/hCtuntt99Of//731vbrcX1s2fPTg8++GC27ZOf/GQaOnRoi/vFTDMrrbRSti1mrilMpcijMD/LBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFGEBAoU9DLMaNMpMGDB6c11lijYEvTxY022ii/4r777ssvF7Pw7LPPpoULF2a7fuITn2jzkFw5L7zwQpo8eXJ+31Lkkc/MAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgQQQEyvyno+MWSuPHj89eDR8+PLvNUmt/A8sss0zq169ftvmVV15pbbcW1+eCcWLjKqus0uI+uZVRj1wqLKcUeeTy9UyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaBSB3o3S0Pba+d5776W5c+dmuy2//PLt7Z6WXXbZNHHixPTGG2+0u2/hDlOnTs2/bK+cKCOXCsspRR65fDv7HF5z5szp7OGOI0CgRgSmTJlSIzVRjUoILFiwoEkxcSs/5/ImJF40E1h66aXbDB5utnubL9999938WKvNHW0kQKCmBYwdarp7VK5BBWrp/2X8sKhUacaMGWnevHmlyk4+BAhUSaCWzlFVImioYptfd5g1a1aKaw8SgdYESjl2mDZtWmr+N9haudYTIFCbAsYNtdkvakWgVv5v9ujRI8V3FqVKAmX+I1k4YB84cGC7vgMGDMj2+eCDD9rdt3CHjpTTv3///KGF5ZQij3zGnVyIi1UfffRRJ492GAECtSLg/3Gt9ER16hEXD1xAqI59I5Zq7NCIva7N3VHA2KE79qo21btALf2/jFtNx4WrUiRjh1IoyoNA9QVq6RxVfY3Gq0HM4h4PiUAlBOJ84zpXJaSVQaB8AsYN5bOVM4GuCNTK/81SXW/IWQiU+Y9EYfBJ3759cz6tPi+22GLZtg8//DB15EJQR8oprEfhL/5LkUerDavDDZtttlkd1lqVCRAgQIAAgWoJGDtUS165BNoW8H+zbR9bCRConoDzU/XslUyAAAECBOpRwNihHntNnRtBwP/NRuhlbSRQvIBAmf9Y5W67FC979erVrmDPnj3z+0SUcjHHxAGFEVftHVNYRmHkfSnyyFe+kwsRsVXqqK1OVsVhNSgQwWPNk7+X5iJeE6i8gP+blTdX4n8FjB3+a2FpUQHnp0VNrCFQCwL+b9ZCL9RXHUr5uc/Yob76vtK1dX6qtLjyCBQn4P9mcU72Ko9AfJ/S0t9geUqTa70JtPS3Ucqxa715qC+BWhHwf7NWeqI+6lEYO1GKGguU+Y9i4W2OCoNmWkPO7ROzvrQX8FKYR79+/fIvI4/CWWPyG/6zkCsjXuZu9RTLpcgj8ulKGjJkSIqHRKAlgQjsevvtt/Ob4v+Xv5c8hwUCVROI95WpU6fmyx80aFAq5naD+QMsEOiCwJJLLtmFox3a3QUiELzwXrcx9h08eHB3b7b2Eah5gZjZdPr06fl6xv/Lws+m+Q0WCJRBoJT3HS9D9WRZZYHmn23ic018vpEIEKiuwAcffJBmzJiRr8QSSyzR5Fp2foMFAmUQGDp0aBlylWV3EYi7Q0ybNi3fHJ9t8hQWCFRV4P33308zZ87M1yGuIS+++OL51xYIlFPgv9OilLOUOsi7MPik8DZHrVU93lQjdfQLxsJycnm0V0bzckqRR2tlWk+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6K4CAmX+07MRoZabZq0wqrS1js/9Ir+jgTKFv4jK5dFeGbG9sJxS5NFamdYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLqrgECZ//Rs3AJp2LBh2as333yzzf4unKJtjTXWaHPf5htXXXXV/Kr2yincXlhOKfLIV8ICAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBBBATKFHT0mmuumb2aOHFimj17dsGWpovPPPNMfsW6666bXy5mIVdG7Pv000+3eUhue8wmM3z48Py+pcgjn5kFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECDCAiUKejoUaNGZa8WLFiQ7rvvvoItTRfvueee/IqNN944v1zMwuqrr55WXHHFbNcoI8pqKcVtmXKBMhtttFH+tlCxbynyaKlM6wgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3VlAoExB70agzIABA7I1l1xySZo2bVrB1n8vjh8/Pl1//fXZiwiSGTFixCL7zJkzJ7333nvZY968eYts/8xnPpOtmzRpUrryyisX2R7BM+eee2766KOPsm1f+cpXFtmnFHkskqkVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFuLCBQpqBz+/fvn/bdd99szRtvvJG+973vZbO6RODK3Llz07333psOPvjgbLlXr15pn332KTj6v4tHHnlk2nnnnbNHHNM8ReDL0KFDs9XnnXdeiqCcmTNnZq/feuutdOKJJ6bbb789e7355punDTbYoHkWqRR5LJKpFQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBbizQuxu3rVNNGzNmTIpglXHjxqXXXnstHXDAAWnQoEHpww8/zAJkcpn+4Ac/aDGAJbe9red+/fqln/70p+mII45IkydPTpdeemn2WGqppZrMYrPaaqul4447rsWsSpFHixlbSYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDopgJmlGnWsTFTzIEHHpiOPfbY/Kwvs2bNygfJrL766un0009Po0ePbnZkx16uueaa6eKLL05bbbVV6tOnT3Zw7lZPvXv3Tl/60pey2y9FkE5rqRR5tJa39QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB7ibQY+G/UndrVCnbE8Er48ePTz169Egrr7xyWmGFFbLlUpYxb9689NJLL6XXX389LbfccmmVVVZJAwYM6FARpcijQwXamUAbAvPnz09vv/12fo+4rdmQIUPyry0QIFAdgbiN4NSpU/OFRzDmwIED868tECBAoFoCH330UZoyZUq++BgLDx48OP/aAgEC1RGYM2dOmj59er7w+H/Z0c+q+YMtECBAoIQCzT/bxOeatn5sVsKiZUWAQBsCH3zwQZoxY0Z+jyWWWCLFzOgSAQIEqi0Qd43I/Vg96uKzTbV7RPkE/i3w/vvvp5kzZ+Y5llxyybT44ovnX1sgUE4Bt15qRzduh7TFFlu0s1fXNscMMh/72MeyR2dzKkUenS3bcQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBehBw66V66CV1JECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6LKAQJkuE8qAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHgQEytRDL6kjAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAlwUEynSZUAYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1ICBQph56SR0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6LCBQpsuEMiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgHAYEy9dBL6kiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINBlAYEyXSaUAQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQD0ICJSph15SRwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgS4LCJTpMqEMCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6kFAoEw99JI6EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdFlAoEyXCWVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQDwICZeqhl9SRAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgywICZbpMKAMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF6EBAoUw+9pI4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdFhAo02VCGRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSDgECZeugldSRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOiygECZLhPKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoB4EBMrUQy+pIwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQJcFBMp0mVAGBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC9SAgUKYeekkdCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEuiwgUKbLhDIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoBwGBMvXQS+pIgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQZQGBMl0mlAEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA9CAiUqYdeUkcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEuCwiU6TKhDAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpBQKBMPfSSOhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHRZQKBMlwllQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUA8CAmXqoZfUkQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoMsCAmW6TCgDAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBehAQKFMPvaSOBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXRYQKNNlQhkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUg4BAmXroJXUkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDosoBAmS4TyoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAeBHos/Feqh4qqIwEC9SMQp5X58+fnK9yjR4/Uq1ev/GsLBAhUR6D5/82ePXumeEgECBCotoDzU7V7QPkEWhbwf7NlF2sJEKi+gPNT9ftADQi0JLBgwYIUj1xy3SEn4ZkAgWoLGDtUuweUT6BlgeZjh/guMb5TlAhUQkCgTCWUlUGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFB1AT8jr3oXqAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAlBATKVEJZGQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAlUXEChT9S5QAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUoICJSphLIyCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqi4gUKbqXaACBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEClRAQKFMJZWUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUXUCgTNW7QAUIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQqISBQphLKyiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKi6gECZqneBChAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFRCQKBMJZSVQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUHUBgTJV7wIVIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqISAQJlKKCuDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg6gICZareBSpAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQCQGBMpVQVgYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDVBQTKVL0LVIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKASAgJlKqGsDAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaoLCJSpeheoAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQCUEeleiEGUQINCYAu+//36aPn16yRs/ePDgNGjQoJLnK0MCBFJasGBBmjx5cnrnnXfS1KlT07x589LQoUOzx3LLLZd69zZ08HdCgED5BIwdymcrZwLlEjB2KJesfAkQKEbA2KEYJfsQqC0BY4fa6g+1IdBoAm+99VaaP39+yZu94oorljxPGRIg8G8BYwd/CeUS8G1XuWTlS4BAuvvuu9Mpp5xScomxY8emffbZp+T5ypBAIws8++yz6S9/+Uu68847swCZliwGDhyYtt1227TTTjulDTfcMPXo0aOl3awjQIBApwWMHTpN50ACFRcwdqg4uQIJEGhBwNihBRSrCNSogLFDjXaMahFoMIEDDzwwRbBMqdO9995b6izlR6DhBYwdGv5PoOwAAmXKTqwAAgQIECBQuwLvvfdeOvvss9Ott97abiVj3xtvvDF7bLLJJumoo45KyyyzTLvH2YEAAQIECBDoPgLGDt2nL7WEAAECBAhUQsDYoRLKyiBAgAABAt1HwNih+/RlrbdEoEyt95D6EehGAnH7lvXXXz/17NmzS61affXVu3S8gwkQ+LfAyy+/nH7wgx9kt1kqNFlhhRVSPJZccsm0cOHCNGnSpPT666+nWbNm5Xd7+OGH01577ZVOPPHEFEEzEgECBMohYOxQDlV5Eui8gLFD5+0cSYBAZQSMHSrjrBQCxQoYOxQrZT8CBKoh0KtXr2zW7LgGKhEgUBsCxg610Q+NUose//oCbGGjNFY7CRCorMDf/va3bMaJwnt+xkWr7bffPu24445prbXWqmyFlEaAQF4ggl7222+/9MYbb2Tr+vXrl7761a+mT3/602n48OH5/QoXZsyYka6++up0xRVXpLlz52ab4nZMF198cRZYU7ivZQIECHRGwNihM2qOIVAZAWOHyjgrhQCBjgkYO3TMy94EKilg7FBJbWURIFCswLe//e30zDPP5HePH/VuvPHGaYcddkhbb711imudEgEC1REwdqiOeyOXKlCmkXtf2wlUQODdd99Nd911V7rjjjvSo48+ms1OkSt2pZVWyr6Uj0HoqquumlvtmQCBCggcdthh6YEHHshK+vjHP56OOeaYtNxyyxVV8uTJk7PbNeXuvRtBb+eff37q3dtEdUUB2okAgTYFjB3a5LGRQNUEjB2qRq9gAgTaETB2aAfIZgJVEjB2qBK8YgkQaFMg5g54+umns+8r7rzzzjRt2rT8/n369Emf/OQnsx/5brnllqlv3775bRYIECi/gLFD+Y2V0FRAoExTD68IECijwNSpU1MMPiNoJgajhWmNNdbIBqAxm8Xyyy9fuMkyAQIlFhg/fnw2m0xkG//3zjvvvNS/f/8OlfLRRx+lAw44ID3//PPZcT/5yU+yX110KBM7EyBAoB0BY4d2gGwmUCEBY4cKQSuGAIEuCxg7dJlQBgRKImDsUBJGmRAgUGaBBQsWZD/uje8r4se+MZtFLsXs26NGjcp+6LvZZpv5gWAOxjOBMgkYO5QJVrZtCgiUaZPHRgIEyiXw1ltvZQEzMQidMGFCk2LWW2+9bKrD7bbbLi211FJNtnlBgEDXBU499dR08803ZxlddtllabXVVutUpq+++mrad99905w5c9Lmm2+efv7zn3cqHwcRIECgGAFjh2KU7EOgPALGDuVxlSsBAuUVMHYor6/cCbQlYOzQlo5tBAjUosC8efPSQw89lH1ncc8996QPPvggX83BgwenbbfdNvvOImbmjts1SQQIlFbA2KG0nnIrTkCgTHFO9iJAoIwC8WV7BMzEY+LEifmSYsD5iU98In9/0EGDBuW3WSBAoPMCO++8c3rvvfdKEtzys5/9LN1www2pR48e6ZZbbkkDBgzofMUcSYAAgSIFjB2KhLIbgRIJGDuUCFI2BAhUTcDYoWr0Cm5QAWOHBu14zSbQTQQ+/PDD7Jb18X3F3/72tzR37tx8y5ZeeulslpkddtghrbPOOvn1FggQ6JqAsUPX/BzdOYFex/8rde5QRxEgQKA0AkOGDEkbbbRRGjNmTHbrloEDB6YpU6ZkUx2+8cYb6b777ktXXnlliqnX4sv4FVZYwVSHpaGXSwMKxBSil156adbyL37xi2nkyJFdUnjnnXfS/fffn+URt04zC1SXOB1MgECRAsYORULZjUAJBIwdSoAoCwIEqi5g7FD1LlCBBhIwdmigztZUAt1UoHfv3mnVVVdNMeP9nnvumVZfffUUt6F/88030+zZs9PTTz+d/XDwz3/+c5oxY0Z2PXTJJZfsphqaRaD8AsYO5TdWQssCvVtebS0BAgSqIzBixIgUjwMOOCA988wz6c4778zuDzp58uQsYCaCZsaOHZv22Wef6lRQqQTqXCCmH8+lCDrralpzzTXzWcT/0/j/KxEgQKCSAsYOldRWViMKGDs0Yq9rM4HuLWDs0L37V+uqL2DsUP0+UAMCBEon0L9//7Tjjjtmj/gyP27L9Ne//jU9/PDDKX7kG7e1j8e9995bukLlRKDBBIwdGqzDa6i5AmVqqDNUhQCBpgJrrbVWmjNnTja14c0335xiykOJAIGuCRTeQ3f+/Pldy+xfR/ft2zefRynyy2dmgQABAp0QMHboBJpDCLQjYOzQDpDNBAjUtYCxQ113n8rXqICxQ412jGoRINBlgUGDBmW3so/vKWbOnJmee+65LucpAwIEUjJ28FdQLQGBMtWSVy4BAi0KxBftjz76aBaVHdHZMXVhYYrB6Morr1y4yjIBAh0QWGWVVbJbl82bNy+98MIL2e3OOnD4IrvGLdFyadiwYblFzwQIEKiYgLFDxagV1KACxg4N2vGaTaAbCxg7dOPO1bSaEDB2qIluUAkCBEooMGXKlGzW+5j9/sknn1wk53XWWWeRdVYQIFC8gLFD8Vb2LK2AQJnSesqNAIFOCMRFqsceeywLjrn77rsXCY6J6Q1HjRqVtt9++7TpppumPn36dKIUhxAgEAK5e+xGkMyECRO6jFKYh0CZLnPKgACBIgWMHYqEshuBEggYO5QAURYECFRdwNih6l2gAg0kYOzQQJ2tqQS6sUAuOCZusxTBMQsXLmzS2rgd/ac//em03XbbpVLc3r5J5l4QaDABY4cG6/Aaaq5AmRrqDFUh0EgCcZHq8ccfTxGF3VJwzOKLL5622mqrLDhm8803b3J7l0Zy0lYC5RAYMWJENpvM/fffn1588cW0xhprdKqY+MB4++23Z8euu+66aeDAgZ3Kx0EECBAoRsDYoRgl+xAoj4CxQ3lc5UqAQHkFjB3K6yt3Am0JGDu0pWMbAQK1KhDXOuO7igiOeeKJJxYJjllttdWy4Jj4Qa9Z72u1F9WrXgWMHeq15+q73gJl6rv/1J5AXQksWLCgSXDM9OnTm9R/scUWS1tssUU22IznCJaRCBAovcC2226bbr311hQXjk877bR0/vnnp169enWooPgVxSmnnJLdjzcO3GOPPTp0vJ0JECBQjICxQzFK9iFQfgFjh/IbK4EAgdIIGDuUxlEuBLoqYOzQVUHHEyBQKYGpU6dmwTHxg96WgmOGDx+e/Zg3gmMiUEYiQKA8AsYO5XGVa9sCPf71RVfT+cLa3t9WAgQIdEggd5EqorDvuuuu1Dw4Jm6jtNlmm2XBMTGDTNxmSSJAoPwCP/nJT7JgmSjps5/9bDrwwAPT4MGDiyp4zpw56YILLkhXXXVVtn9ML3r55Zdnt3UqKgM7ESBAoA0BY4c2cGwiUEUBY4cq4iuaAIE2BYwd2uSxkUDVBIwdqkavYAIE2hGYNm1a9l1FfGcRs943/5o0rnXGbZUiOCZmuZAIEKiMgLFDZZyV8l8BgTL/tbBEgECJBf75z3+mE044IcXAszDFzBWbbLJJNtgcNWqU27UU4lgmUCGB2bNnp7322iu9/fbbWYkRJLP//vun0aNHp549e7Zai/gAee655+aP69evXzYjzeqrr97qMTYQIECgWAFjh2Kl7Eeg8gLGDpU3VyIBAu0LGDu0b2QPAtUSMHaolrxyCRBoS+CYY47JZpBpHhyz3HLLZYExESCz1lprtZWFbQQIlEnA2KFMsLJtVUCgTKs0NhAg0FWBW265Jbs1Sy6ftddeOwuO2XrrrdOQIUNyqzv8HLPQxG2aJAIEuiYQv5g44ogj0qxZs/IZxf+tYcOGZY/49UT8X33zzTfTa6+9ll599dUm+8b/xZNOOinFbFASAQIESiFg7FAKRXkQKJ+AsUP5bOVMgEDnBIwdOufmKAKVEjB2qJS0cggQKFZgzz33TG+99Va2+8CBA1N8VxEzx4wcOTL16NGj2GwW2W/AgAGLrLOCAIGOCxg7dNzMnbWQCgAAQABJREFUEZ0XECjTeTtHEiDQjkDzC1bt7F705rFjx6Z99tmn6P3tSIBA6wIzZ85MF154YbrhhhsWmWa09aNSisC3I4880r1520KyjQCBDgsYO3SYzAEEKi5g7FBxcgUSINCGgLFDGzg2EagRAWOHGukI1SBAIBMoDJQpJcm9995byuzkRaChBYwdGrr7K9r43hUtTWEECBAgQIBATQnEjDGHHXZY2m233bJbKD333HPp/fffb7GO8auKjTfeOO20007ZI26jJhEgQIAAAQKNJWDs0Fj9rbUECBAgQKCrAsYOXRV0PAECBAgQaCwBY4fG6u9qtlagTDX1lU2gmwsss8wyaZNNNil5K+N2MBIBAqUVWGedddLZZ5+dZTp16tT8rZamTZuWll566TR06NC05pprZsulLVluBAgQ+K+AscN/LSwRqHUBY4da7yH1I9AYAsYOjdHPWtk9BIwdukc/agWBehfYYIMN0korrVTvzVB/Ag0hYOzQEN1c1Ua69VJV+RVOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQKYGelSpIOQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqKSBQppr6yiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKiYgECZilEriAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJoCAmWqqa9sAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBigkIlKkYtYIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqKSBQppr6yiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKiYgECZilEriAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJoCAmWqqa9sAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBigkIlKkYtYIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqKSBQppr6yiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKiYgECZilEriAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJoCvatZuLIJECBAgAABAgQIECBAgAABApUUWLhwYXrkkUfSzTffnCZMmJAmT56c3n333bTUUkulZZddNq255pppl112SRtuuGHq0aNHJavWpKznnnsuPf7449m6/v37p9GjRzfZ/thjj6Xx48dn66LuO+64Y5PtHX1xzTXXpI8++ig7bOWVV05bbrllR7NIH3zwQbr++uvzx40cOTKtt956+de1ttCeca3VV30IECBAgAABAgQIECBAgACB0gj0+NcFooWlyUouBAgQIECAAAECBAgQIECAAIHaFIjLH5dddlk6+uij06RJk9qtZASLHH/88Wns2LFVCZj52c9+lg4//PCsniuuuOIidT700EPTL37xi2z7xhtvnB5++OF229TWDksssUSaOXNmtsuYMWPS1Vdf3dbuLW4L13DLpWOPPTadcMIJuZc199yecc1VWIUIECBAgAABAgQIECBAgACBkgi49VJJGGVCgAABAgQIECBAgAABAgQI1KrA7Nmz03bbbZe++c1vLhJw0lqdX3vttbTvvvumrbbaKk2bNq213awnQIAAAQIECBAgQIAAAQIECBCoMwG3XqqzDlNdAgQIECBAgAABAgQIECBAoHiBefPmpT322CPdfffdTQ7adNNN00YbbZRitpahQ4emt99+O02cODE99NBD6amnnsrve//996fddtst3X777SlugSQRIECAAAECBAgQIECAAAECBAjUt4BAmfruP7UnQIAAAQIECBAgQIAAAQIE2hC44oor0q233prfI4JjzjnnnPSpT30qv675wnXXXZd++MMfphdeeCHbFMEyJ598cjrllFOa71q211tssUU68sgjs/yHDBlStnIaOWPGjdz72k6AAAECBAgQIECAAAECjSwgUKaRe1/bCRAgQIAAAQIECBAgQIBANxc4/fTT8y0cPnx4uuWWW9Jyyy2XX9fSwuc///k0cuTItMkmm6SZM2dmu5x33nnp8MMPT5UKWhk1alSKh1Q+Acbls5UzAQIECBAgQIAAAQIECBCoZYGetVw5dSNAgAABAgQIECBAgAABAgQIdFYgglwKb6O0//77txskkytrxIgR6dRTT829zAJmIshGIkCAAAECBAgQIECAAAECBAgQqG8BgTL13X9qT4AAAQIECBAgQIAAAQIECLQi8OKLLzbZssEGGzR53d6L0aNHN9nl6aefbvK6mBfvvPNOeuihh1I8S+0LlMNr/Pjx2W205s+f334FqrRHOdq9YMGCNGnSpPTggw+mV155pUotUywBAgQIECBAgAABAgQIEKg9Abdeqr0+USMCBAgQIECAAAECBAgQIECgBAJLLLFEk1wiYKJ58EuTHZq9WGmllbLbLfXs2TMtvfTSadNNN222R8svr7322nT22WenCKyZMmVKfqfll18+RbDO7rvvnr797W+nHj165Lc1X/jtb3+b5RHrl1122XTrrbc236XbvC6F1y9/+ct08cUXZyZhG48LL7wwnXHGGen555/P1i+55JLpu9/9bvrxj3+cBg4cmNoyPv/887PjO4u8/fbbp5///OdtHl6Kdkebo+2R9t5773TwwQdnsx+dcsop6fe//31644038nVYaqml0ic+8YnMZo899sivt0CAAAECBAgQIECAAAECBBpNQKBMo/W49hIgQIAAAQIECBAgQIAAgQYRGD58eBo0aFCaNWtW1uLTTz89ff3rX0/Dhg0rWuC0004ret8IjIlAhTvuuKPFY956660Uj9tuuy2NGzcuXXLJJWnVVVdtcd/JkyenRx99NNu24oortrhPva8spdebb76Z94rlsI1gmcI0ffr0dM4556QjjzwyW92WcQSY5PwL8yh2ubV+jeNL2e74e8rVc8cdd0yPPPJIGjNmTHr11VcXqeq0adPS7bffnj0iYOjMM89Mffv2XWQ/KwgQIECAAAECBAgQIECAQHcXECjT3XtY+wgQIECAAAECBAgQIECAQIMK9O7dO+27777prLPOygTefvvttPHGG6ejjjoqfeMb30hDhgwpmUzc4marrbbKZvPIZRr5R3nrrrtumjBhQnYLpghWiPTXv/41m13muuuuS9ttt13ukIZ5LqdX5B1BIC2lmEmlf//+LW1qsm7AgAFp6NChTdYVvojZgBZbbLEs0GTxxRdPEydOTO+9915+l4997GP55cKFcrY76rDrrrumCACKFDPojBw5MsWMSP/4xz/Shx9+mK/Keeedl9W9Naf8jhYIECBAgAABAgQIECBAgEA3FBAo0w07VZMIECBAgAABAgQIECBAgACBfwvE7CERjPLyyy9nK2K2ke9973vp0EMPTdtuu22KWTi22Wab7JY0vXr16hTbwoUL0z777NMkSCZmlokZbPr06ZPPc8GCBemEE05IJ598corlmOlm//33T0899VRNzezxwQcfpNdeey1f72IXwraYVG6v3/zmN2n+/PlZoMiXv/zltMsuu2TGN954Y/rmN79ZTBWzW24dfvjhRe37l7/8JSsjt/MOO+yQ9XHude653O2+8sors6LiVl0//elPs1sx5W7vFX16+eWXZ39vUY9Il156aTrppJNSBAVJBAgQIECAAAECBAgQIECgkQQEyjRSb2srAQIECBAgQIAAAQIECBBoMIGYFeTmm2/OZtp46aWX8q2fO3dudgukuA1SpMGDB6dRo0ZlwTMR6LDhhhvm921v4Ve/+lWKYIlcuvDCC9O3vvWt3Mv8c8zsEYEykXfcHifSCy+8kOL2Tscdd1x+v2ov3HLLLSluW1WuVG6vCJIJ6xtuuCGb5SfaMXr06HTEEUeUvElPPPFEillq5s2bl+W9zjrrpKuuuirFbEbNU7nbHeVFuTFbUcxiVJj69euX9ttvvzRlypS8w4wZM9LVV1+d9tprr8JdLRMgQIAAAQIECBAgQIAAgW4v0LPbt1ADCRAgQIAAAQIECBAgQIAAgYYWWHvttVMENMQsL63ddufdd99NN910UzrssMPSRhttlOLWOccff3yaPn16m3YRIPGjH/0ov0/caikCEtpKu+++e/rsZz+b3yUCZV5//fX86+68UCmvAw88MB8kUy7P6LO41VH87USKoKz4G2rpll6VavdBBx20SJBMYftjJqW4VVQuPffcc7lFzwQIECBAgAABAgQIECBAoGEEBMo0TFdrKAECBAgQIECAAAECBAgQaFyBuL3MWWedld544430y1/+Mn3mM59JMctGa2nChAnZ7C8RMHPJJZe0tls2I8zs2bPz2+NWNrnb3eRXtrBwyimn5NfOmTMnPfTQQ/nX3XkhZtCphFdhIFI5POO2WREkM2nSpCz7CD6JW3ytttpqLRZXqXZ/7Wtfa7H83MrFFlssjRgxIvcyH+STX2GBAAECBAgQIECAAAECBAg0gEDvBmijJhIgQIAAAQIECBAgQIAAAQIEMoGY7eO73/1u9ogAlfvuuy/dcccd2e1qHnnkkfwtdHJccauafffdN02cODELnMmtzz0/++yzucXseeutt27yurUXMWvNoEGDUgRcRBo/fnxru1Z8/c4775zi9lEdTRGEtPnmm7d5WKW8CoNB2qxQJzbG7DB77rlnevzxx7OjIzDq0ksvTVtssUWruVWq3ausskqrdchtKJzxJm5BJhEgQIAAAQIECBAgQIAAgUYTECjTaD2uvQQIECBAgAABAgQIECBAgEAmELOA7LDDDtkjVkTQyu23356uvfbaNG7cuPThhx/mpU488cTsdkxf//rX8+tioTAAIm69EzPXFJti9pG4JVSkWroFTsy0s9JKKxXbjA7tVwmv3r17p1VXXbVD9erIzt/5znfSn//85/wh8bfxla98Jf+6pYVKtDv+9pZZZpmWim+yLmaVyaUFCxbkFj0TIECAAAECBAgQIECAAIGGEXDrpYbpag0lQIAAAQIECBAgQIAAAQIE2hKIGV523333dNlll6WXXnop7bHHHk12P+GEE1LzwILCAIiOBmcU3qbn+eefb1JWd31RCa/hw4enCJYpR4pbZv3617/OZ73XXnulo48+Ov+6tYVKtLuYIJnW6mc9AQIECBAgQIAAAQIECBBoJAGBMo3U29pKgAABAgQIECBAgAABAgQaUCBmhmke4NIewworrJDNKhMzzuTShAkT0gMPPJB7mT3H7ZtyqU+fPrnFDj/H7XsaIVXCq2/fvmWhvOKKK5oExcRtti666KKiyqpEuxvlb6gocDsRIECAAAECBAgQIECAAIE2BATKtIFjEwECBAgQIECAAAECBAgQIFCfAk8++WR2q6QlllgixS2W/vSnP3W4IRF4EDOGFKYIlilMI0aMyL+cOHFifrmYhcL9l1122WIOqft96tXr7rvvTmPHjk0LFy7M+mDNNdfMbtFVeBujtjqnXtvdVptsI0CAAAECBAgQIECAAAEC9SogUKZee069CRAgQIAAAQIECBAgQIAAgVYFhg0bliKoZebMmdk+48aNa3XftjbstNNOTTbPmDGjyesImMilN998M82dOzf3st3nwkCZoUOHtrt/d9ihHr3itklxS65c3y611FLppptuSvFcbKrHdhfbNvsRIECAAAECBAgQIECAAIF6ExAoU289pr4ECBAgQIAAAQIECBAgQIBAuwLLLLNMKgw+ufHGG9P777/f7nHNd7j//vubrFpjjTWavC4MgIjbOz3++ONNtrf24uWXX07Tp0/Pb1577bXzy915od68Jk+enHbZZZd8X8UMMtdcc00qbEcx/VW4v7+TYsTsQ4AAAQIECBAgQIAAAQIEyicgUKZ8tnImQIAAAQIECBAgQIAAAQIEqihQeNuk2bNnpzPPPLPDtfnjH/+YP6ZXr15po402yr+OhXXXXTf17ds3v+6kk07KL7e10Hy/MWPGtLV7t9lWT14RWLXbbrulV155Je9/4YUXpm222Sb/utiFemp3sW2yHwECBAgQIECAAAECBAgQqFcBgTL12nPqTYAAAQIECBAgQIAAAQIECLQpcOihh6aYASSXjjnmmHTCCSfkb6GTW9/a88UXX5z+8Ic/5Dd/6UtfSiuuuGL+dSwsvfTS6bDDDsuvu+GGG9K9996bf93SwtNPP50uu+yy/KbNN988rbLKKvnX3XmhXrxi1pevfvWr6eGHH853x4knnpj23nvv/OuOLNRLuzvSJvsSIECAAAECBAgQIECAAIF6FRAoU689p94ECBAgQIAAAQIECBAgQIBAmwIrrLBCOvXUU5vsc/zxx6eRI0ems846K7300ktNtn300UfpmWeeSePGjUs77rhj2m+//VIETEQaNGhQOu6445rsn3txxBFHpJVXXjn3Mu28887p8ssvz78uXLjpppvS1ltvnebPn5+t7t27dzrnnHMKd+n2y/XgdfDBB6frr78+3xff+c53UgRadSXVQ7u70j7HEiBAgAABAgQIECBAgACBehHoXS8VVU8CBAgQIECAAAECBAgQIECAQEcFYlaZ1157LQuMyR37wgsvpEMOOSR7LLHEEmn55ZdPPXv2TBMmTEgRLNM89enTJ11zzTVprbXWar4pe92/f/8s/z333DMLrInbPH3jG99IF110Udpiiy2y2zNF3g8++GC67bbb0sKFC/P5RCDPZpttln/dCAu17vXAAw+kc889N98VPXr0SDELUMz8E7djisfcuXOb9GN+54KFCJYqvE1Trbe7oOoWCRAgQIAAAQIECBAgQIBAtxYQKNOtu1fjCBAgQIAAAQIECBAgQIAAgTPPPDOtv/766fDDD09TpkxpAjJjxowUj9bSuuuumy644IL0qU99qrVdsvVjxoxJd911Vxo7dmx68cUXs3V33313ikdLacCAAen888/PAmpa2t7d19Wy15w5c5rwR2DTPffc02RdMS8+/PDDRXar5XYvUlkrCBAgQIAAAQIECBAgQIBANxVw66Vu2rGaRYAAAQIECBAgQIAAAQIECPxbIGYE2WeffdL48ePTySefnM3yEjPItJZiWwTGxKwijz76aLtBMrl8Ro0alR5//PF04IEHZrPU5NYXPi+55JLpoIMOyvaLWWcaOTWqV6O2u5H/1rWdAAECBAgQIECAAAECBGpLoMe/fhXz3/l+a6tuakOAAAECBAgQIECAAAECBAgQKIvAtGnT0jPPPJPeeeedbJaZuF3S0KFD07Bhw9LIkSPTcsst1+VyI+8nn3wyPf/882nppZdOq6yyStpggw3S4osv3uW8u2MGjerVqO3ujn/D2kSAAAECBAgQIECAAAEC9SEgUKY++kktCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEuijQ+jzDXczY4QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqSUCgTC31hroQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUTUCgTNloZUyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBLAgJlaqk31IUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBsAgJlykYrYwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVoSEChTS72hLgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmUTEChTNloZEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1JKAQJla6g11IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKJuAQJmy0cqYAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECglgQEytRSb6gLAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA2QQEypSNVsYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK1JCBQppZ6Q10IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKJiBQpmy0MiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKglAYEytdQb6kKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFA2AYEyZaOVMQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQC0JCJSppd5QFwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbIJCJQpG62MCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEaklAoEwt9Ya6ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlE1AoEzZaGVMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQSwICZWqpN9SFAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgbAICZcpGK2MCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaEhAoU0u9oS4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJlExAoUzZaGRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSSgECZWuoNdSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECibgECZstHKmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJYEBMrUUm+oCwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQNkEBMqUjVbGBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECtSQgUKaWekNdCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEyiYgUKZstDImQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoJQGBMrXUG+pCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQNgGBMmWjlTEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAtCQiUqaXeUBcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGyCQiUKRutjAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGpJQKBMLfWGuhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRNQKBM2WhlTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUEsCAmVqqTfUhQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoGwCAmXKRitjAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBWhIQKFNLvaEuBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECZRMQKFM2WhkTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUkoBAmVrqDXUhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAom4BAmbLRypgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCWBATK1FJvqAsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDZBATKlI1WxgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABArUkIFCmlnpDXQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMomIFCmbLQyJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqCUBgTK11BvqQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUDYBgTJlo5UxAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBALQkIlKml3lAXAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBsgkIlCkbrYwJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqSUCgTC31hroQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUTUCgTNloZUyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBLAgJlaqk31IUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBsAgJlykYrYwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVoSEChTS72hLgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmUTEChTNloZEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1JKAQJla6g11IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKJuAQJmy0cqYAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECglgQEytRSb6gLAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA2QQEypSNVsYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK1JCBQppZ6Q10IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKJiBQpmy0MiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKglAYEytdQb6kKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFA2AYEyZaOVMQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQC0JCJSppd5QFwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbIJCJQpG62MCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEakmgdy1VRl0IECBAgECjCUzosXy3bvKaC9/q1u3TOAIECBQjMP+7/YrZrS736XXeB3VZb5UmQIBAdxE49uHbu0tTFmnHiZvssMg6KwgQIFCPAgdf9mA9VruoOp+912ZF7WcnAgQIECBAgECtCZhRptZ6RH0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKIiBQpiysMiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKg1AYEytdYj6kOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAWAYEyZWGVKQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQK0JCJSptR5RHwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbIICJQpC6tMCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEak1AoEyt9Yj6ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlEVAoExZWGVKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQawICZWqtR9SHAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgLAICZcrCKlMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaExAoU2s9oj4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJlERAoUxZWmRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSagECZWusR9SFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiLgECZsrDKlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoNYEBMrUWo+oDwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQFkEepclV5kSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKIPAO++8k1599dU0cODAtNpqq6XFFlusDKXIkgABAgQIEOiuAmaU6a49q10ECBAgQKBBBXbdddfsAklcJDn44IMrrhAXaVpKa6+9dlavsWPHtrTZujoTaK2f66wZqkugpgW++93v5s/nxxxzTNF1feyxx/LHnXTSSYscV8z5eObMmendd99d5NhiV/zqV7/K1+Hxxx/PH/b+++/n1x966KH59bmFYuqW27denrtqWYp2ttYfpchbHgQI1KfA888/nz8fr7POOun1118vuiExno/PGvGYNWtWk+OKOd/UwnmxSaUb9MW5556b78ennnqqIRSK+ftsCAiNrFuB+fPnpxtvvDF94QtfSP3790/LLrts2mSTTVKMofv165dWXXXVdOyxx6bp06e32sb2xuOtHljhDV19ryjF//dx48blz5N33313E4FS5N8kQy8IECBAgEAVBATKVAFdkQQIECBAgEB5BJ588sl08803p1deeSV7XHTRRWnatGnlKaxZrpMmTUp77rlnOuSQQ5pt+ffLXJ0mT57c4nYr60OgvX6uj1aoJYH6EHj77bfz5/NTTjkl3X///UVV/MMPP8wf19J7QHvn48suuyyttdZaaeLEiUWV19JOcWE7V07UJ5cWLlyYXx+/gG2ecsd0l/eKUlg2N+rM69b6ozN5OYYAge4hMHfu3Pz5+Lnnnkvf+ta3im7YW2+9lT92wYIFTY5r73xTK+fFJpVu0BczZszI92Phe3V35mjv77M7t13b6l/g6aefTuutt14aPXp0uu6669IHH3zQpFFxPo7xewTKR8DMhRde2GR77kV74/HcftV8LsV7RSn+v7/33nv582QEGBWmUuRfmJ9lAgQIECBQDQGBMtVQVyYBAgQIECBQFoFLL700y3fYsGHZc1w4+e1vf1uWsppnuu6666arrrqq+Wqvu5mAfu5mHao5dSMQF77jF/zNL4iXugFnnnlm2nvvvVN3CVQptU9H8mPZES37EiBQbYFbbrklXXLJJWWthvNiWXllToBANxa48sor02abbZYisDHSkCFD0kEHHZQuuOCC9Oc//zm7FnPqqadmwe6xPWaGPOCAA8p+Xo+ySp28V5RaVH4ECBAgQKB1gd6tb7KFAAECBAgQIFA/AvGr0N/97ndZhb/+9a+n22+/PcXtN84///z0/e9/P/Xo0aOsjYlf2rSV7r333hRf9C6xxBJt7WZbjQu01881Xn3VI1DXAuPHj09HH310OuOMM7rUjrbOx6X6P77XXnul7bbbLqvnyJEji65vW3UrOpMa2bFUlqVoTmf7oxRly4MAgfoRiFvi7bTTTmmllVbqdKXbOt/U0nmx0w10YF0LtPX3WdcNU/luLfDEE09kgey5mZ9iFt/jjz8+DR48eJF2/+hHP0oRaHLYYYelmDkmZguL2WW23377Rfat1RWleq8o9//3cudfq/2jXgQIECDQvQQEynSv/tQaAgQIECDQsAI33HBDmjJlStb+nXfeOfuFUQTKPP/88+nOO+9Mn/70p6tqs+mmm1a1fIUTIECgngV69eqV5s+fn84666w0ZsyYtNVWW3W6OZU4H6+wwgopHh1NlahbR+vUHfbvbH90h7ZrAwEC7Qvk3mPiNhLxpWrMLtPZ5HzTWTnHVULA32cllJVRSoE5c+akr33taykXJBMBMscdd1yrRfTs2TP98Ic/zG7BHTPMxI+V4lZM9RQo02rjOrih3P/fy51/B5trdwIECBAg0CkBt17qFJuDCBAgQIAAgVoTyE2VPmDAgPSpT30qu5iSq+OvfvWr3GKHnuPWG3Ef7FmzZnXouFLt/NZbb6U333yzU9nFr6defvnl9MYbb3Tq+MKDXnnllRQXqNpKM2bMSC+++GL2RXZb+5V6W9QrgqHiXuRxEayj6fXXX0+TJk3q6GEd2j8u6sUU0e0ZtpRp9GP0YbRx3rx5Le1iHYGGEPjBD36QtbNSt2DqCGr834z/o7kL+B05tqv7xvtEPCqVIiD1hRdeSB999FFZiizFe1ep+iPGADGLUTX6tSy4MiVAoFWBPfbYIw0fPjzbfuutt6aLL7641X0rvaEj57T4zBJjzs6ct0ox5qx2+dE3EVQb5+5p06Z1qavi80HuRxidyagU7yFd/ZzSkb+dttpYira0lb9tBNoSiFsuxTWZSJtsskmbQTKF+Rx77LFp6NCh2aq77rorPfLII4WbO7Tc1fFp3Dr22WefTVOnTu1QuR3ZuRT/3+PaU1xDKleKWaDjPertt98uVxHyJUCAAAECHRYQKNNhMgcQIECAAAECtSYQFxHjvtSRdthhh7TYYoul1VdfPT/jwHXXXVd0wEl82fn//t//S0suuWRafvnl03rrrZdN6Ru/lolfIjUPmtlwww3T2muvnU3rG+Xfdttt2etYd+2118aqLH384x/P1u+///65VdlzBPHEvptvvnn2Oi5MxK2jVltttTRs2LBsRoJ4/tKXvlRU0Mu4ceNSzEgQAUNhsOKKK6Zlllkmm1En6tZaytVjyy23zHaJ6Y2jHnF81CVuGbXtttumX/7yl/ks4kL0CSeckF2wWnrppdOIESOycr/85S8v8sXtTTfdlHf5+c9/ns+jtYXRo0dn+8fsQM1TfFEegVGf/OQnU79+/bL7kMd0yrEcMwf97W9/a35Ik9fhEH8nceEsptZfeeWVM6NYFxfRWkrF9nPu2Pg7Ofjgg7O/n4EDB6Z11lknxXP8PcUX/u+//35u1xafow2f+9znsmOiD9Zaa63MNo6Pe6139eJ/i4VaSaCGBQ488MA0atSorIYTJkxIRx55ZKdr29L5+Ne//nV2zvnf//3ffL7xfzDOz4Uzkp199tnZuq233jrbL37Vutxyy2X/R2P69/322y/7ku6iiy7K9ovj43xabGqpbnFsnG8jr3jEeT6+BIxzYLw3xXvEuuuum82eFhfic/vF1PNtpbg1VOz7xS9+cZHd4nZRsS2+LI6L2nE7kngd580111wzO99+4QtfSA899NAixxZrWXhgZ9+7StkfYRd/V1tssUV+DBBt7t+/f/Y+GO/D5fzyoNDDMgEClRWI83ecu3IpznmvvfZa7mWHnls6/xd7Xiz2nFZYofhic999983Gs9GO3JgzzuMxFm3+2aXw2Fju6pizWuXnPrtstNFGWZPuueeetOOOO2bn7zh3x2eTGOOffvrp7Y67cybxmTLO9fG+GsfGe16MwceOHZvihwNtpVK9h3Tmc0rUq9i/nZb+Ppu3q1RtaZ6v1wQ6I3DFFVfkD4vgl2LT4osvngXVxGfn3//+9/lgyGKPj/06Oz6NY999993005/+NLsmE9dmYqwe12Xic0OMwf/0pz/Fbk1Sqd8rivn/HkErhx9+eHbujM8VcQ0pzoFf/epX273+VEz+9913X4prO/H5IcbU8R4VBkOGDMmuI8Wt0iMQSSJAgAABAtUScOulaskrlwABAgQIECiZwG9/+9v8TCYR3JFLe++9d3bxN35dExcdjjnmmNymFp/j10r77LNPixdT4yJwXJiJLw0feOCBLIgmMonAmriYmEtxP+n4AjNSTN+eS7lfpceFh8IUvyqKbRGY8+STT6bPfOYziwT1xGwBf/zjH7Mgjgj6iS/xmqd33nknxa9h4yJx8xRlxO2n4hEXiiJQJS7WFKZcPeKCcLRvl112SdOnT8/vEr9Mvfvuu7NHXNSIYJiYAvmqq67K7xMLsV9cUIqL7tGuXDkRZBMXn8MnLmzHdMitpfjF2I033phtjnIKUwSIfOUrX0l/+ctfCldny/Flbq6dMc3yj3/84yb7xIwucREovghvfjEm2n/HHXekv/71r1kgy8knn5wFXOUyKLafY/9o+//8z/8s8oVqfNEdbcu17/LLL88uDuXKyD3H32FcmGpex2hf8+O32Wab3GGeCXRrgZhGPQLkIpAkAs3OOeecLMAjZhDraGrpfBznllhfmHJfihXOBhW/Lo/94uJ3BA5GsGAuxf/Rp556KsUtPHLn1NhWeHxu39aeW6pbbt9c/eJX7nEuy9UvtsevVKP8CCTM7RfvW22lmAUsvgiOIL7mKfKIc9arr76aXdzOBVrGlw5xno9t8X4UQZDRL3HOy6ViLWP/rr53lao//v73v6dvfvObKYKwmqcwDet4RHv/7//+LwtkbL6f1wQI1LdABFlEQPuFF16YneMj8DEXiN+RlrV0/i/2vFjsOS1Xn/hccsghhywSDBOffZ555pnsEefqOE+3dNuRro45q1l+zjm+eI1b8EaAS+79NtbFWCHeLyNo9JprrsnO30sttVSObpHn22+/PZ1xxhnZ+1LhxvgMEI/4HBSfsyLIvnkqxXtIVz6nRH2K/dvJucUxOa9YzqVStCWXl2cCXRWIcWJ8Rs+lXKB67nV7z/EDqM6kro5P4/wTP8JpKaA8AlPiET/QiWtPcW0kfugVqdTvFe39f4/x/k477ZSN9wud4vrTH/7wh+y6RtzytrXUVv5xfokA9Ajii7F08xSfpWKWn3jEe1Gcg+MzlESAAAECBCot0LPSBSqPAAECBAgQIFBKgQgmuPTSS7Ms4+JnzACQSxFkEbOMRIpfu8QXe62lCG6I4IS4qBFfyMaXkHFhIz7Ax2wAERQSKW7xE2Xk8oovD+PCaY8ePbLtcfEmF1DS0mwo2U4t/DN79uwUx8ZFkwhmiWnfH3vssezCSXwxHCku2ES9Wkrf/va380EysX9cEI4vQOOL0MJfUMUvdtq6YBTtjS8Koj7f+c530vXXX5/++c9/pgg86dOnT1Z0lBUBPREkE22MC/CPP/549sVCzD4TKYJifvazn2XL8U8EzOy5557Z65deeinFRdjW0mWXXZZtCtMIdipM0Ue5IJn4VVbM2hMXwcMt2hmzAEWKizJRr8L0ve99L/tyPf5m4pemcWzUM25tFL/oWn/99bOLOPHL05glojAV28/hHbNPxKwDcaEnZo+Jiz/hGn9PRx11VPb3FRfc4+JZ9GlhiuOizVHHNdZYI2tDXKiKW1tFHeNiWqSod/ydSAQaSSBmrYpzUaS44Bq/8I5zdilSnOPj3J37PxZ5xntLrIvgv+YpfqF/xBFHZKvj1+wxK0sEETY/ZzU/rhSvI9gxgjZi5oA4r8aMV/EL0Dh3lzqddtpp2UxpMZtV/CI0zmVxUfyCCy5IETQTX8bmglJzZXfEslTvXV3pjwjgjPeyCJKJNv3kJz/JAo8i2DXeX+I9J2cbf28RhNk8kDHXds8ECNS3QJxfc7dgirFffH4oRerIeTHKK+acFuPLGAvGvjHmjDHmP/7xj+wcHQEdcX6NFJ9d4hyXC6LMVv7rn66OOatdfq4d8WVsfJEbwaInnnhiNq4Pk/hsErM2RIofAUSgfVspzu0RbPKNb3wj+0wRgUYRgBOzt0UKr5Y+25XqPaQrn1MK21XM307h/oXLpWpLYZ6WCXRFID4z5667xFg3xtqVSF0dn0YAYy5I5qCDDsqCFuP/ZgTUx7WGmF0lUgQxxnWbXCrHe0Uu7+bP8WOvwiCZOI/Ge0ecB+PzTwQfxvWkCHTpTIp2/uIXv8g+s8WsoHF9K65hxPg6HOJaUfxYLFIEDTX/AVZnynQMAQIECBDojIAZZTqj5hgCBAgQIECgZgTuvffe9MILL2T1iQugffv2zdctvkTcfffdU0zXGx/yY5aSz3/+8/ntuYX4wit+QZr74isuisaMKrkUARQRhBG/2H/00Uezix7xy6a4sNB8NoOYTrejv3SKcuLibjyi7N122y1XdDZ7Qly0iFszxRej0d6oR0xZm0u/+93v8rd5iiCe+EVOfNmXSzGLTVzY/exnP5vVPWbgiS+DW6pnzBQQjwioyV1gj3zii+C4eBpfIMYXhTHzSlycj19A5dIGG2yQ3e4q9o22xOwuhbMtxJfauaCm8Mzd5il3fDzHl98x00qkuKBSOANPtCs3q0HMUBO/7I9fjOZSzCYUMyPE7UCiL2Oq41x/x8X8XNmRb+RTaBT7xReh8fcS284888zsFiq58ovt5wiMCb8I8olAnV133TVXvWz2mLivepQf6+MiUXypEb9czqX4QjaOjxQz3xRekI86xiMuMMUvnOP+3nHhP3cBP5eHZwLdWSBuwXT11VdnF3Lj3B9BcWeddVaXmxy/EI9HXKjNpY033jgLoMu9LnyO82GkOJflZjKLoJGWfjFZeFwplmOmmHhfii9Ec8GgEdxYjl9hxrk8HOJ8Hu+pkeKidrxnxvtQzGoV59v4EiDOs5GKtSzle1dX+uM3v/lNFgAUdY8AoL322isWsxRtjtvfRQBkvGdGsFC8Bz/44IP5Wybm9vVMgED9CwwaNCibPTIXHBfjuggOzwXPdLaFxZ4Xc/m3d06LsWKcq+J9JwLZY/xYOMtgjDXjsdVWW2X7xbk8AuXjF/u51JUxZ7XLz7UhnuN9N/f5Ib5kzqX4XBJj+rjlR3xBG+2Nz1nxurUUnx0OO+yw/OZ4n/v/7N0HuCtF3YDxQRBBpXdpFxUpAoI0kSZVwEvvXaRIU3oTRXpHpKN0FNALUgRUpIs0QdqlNymCUi9FRD/E++UdnXVPbno2OUnOO89zTpKts79NZndn/zvD/ic4n3MObu4SQMPnlIo4hrR7nZLywmu9705+2vL3RWxL+TL9rEA7AjzQklL+d5eGdeK1iPNTyhoSdTj5QBO6xOOPsjnVhfC7S4F8RR8ravkQGErLkSSOD9Q95B/+4rx3zjnnjIE9tZZTadwHH3yQ1ROxvZTB+Xqb5EC9UTre4lDemnClZTtMAQUUUECBogU+UvQCXZ4CCiiggAIKKNBNAZ7CSYluE8oTwRkpEfxRKXHDi0pPEoEm+SCZND2VBjwVQ5pkkkmG3ExN07T7yrrzQTJpeTPOOGO8KZk+0/1OPqUuhggSooIjHwCSpqO1HboqSSlfCZyGpVcCTfJBMmk4QSQpcSPhyCOPTB+zVyqwUgs4tK6ST1TY0yIEiRYaqEApTwQgEQhCKm+ZgcqTlGgaPV/ZkoYTSMJ6SLQOQEszJLp6ohKdZo1ZTiUjlnf66afHm83cAGimD3TWwZNXqRUbApHyQTKMT4kK99RNCU3W55/wTUFfTJueNEvzpVe2nQAbvvupBZ00zlcFBl2Aspjvfvr9U65V6nKuGw4EH6YgGdbHsSE1nd7p9dPKQQqSYV2pm7tOrJcbhylIJr98ylqOFyRaHiMgtZlU9LGr1f3BMXXUqFHxj5YEKiW+dxxfUqrXrVWazlcFFOg/AVr8S+fBtACw7bbbDstG1CrTaH0xXbuQv3yQTD6znG9y3kniHPvXv/51Nrqdc87hXn+2Ef99Q0B7Pkgmjee4TMto6eYvrTNWS7QMV+n6iEAkgkNTyp+3M6yIY0jR1ym1vjtpOyq9FrEtlZbrMAVaFRiOQJl2z08JVqNFWFKq+yjffrrS5pqev1qt/ZbPV+lzq793Wg4mUa9z2GGHZeVkfh20skvX3M0mWvSaZZZZYgtAu+22W3bdVr4cjrfpGsZz63IdPyuggAIKdEvAQJluSbseBRRQQAEFFChcgC4gLr300rhcAjQWX3zxCdax4oorhtlnnz0OpxUOuv0pT7Q0k1KtigqeKKdSmRZVKgWJpGW0+pqepqk0f2rZhHG0HJASXWCkCiSewKn1xCstj6RWZGgZh6dQK6VqAR48VZ8S3qmp3DQsvdKqDqm8WyGGpWAm8p2vrGcciSe4SNwET1018Tm1YsN7Kpa++MUv8rZiovli1s0fQUY015y6emK+vGX5AujuiCecSLTW0EyitZ+U8nlPw/KvBEWRCN659957s1Fp/zCAJ1558jc1N50mIn+HH3547HaGm7smBUaaAL9TbnyRaM2EwDTKiG4nngYdjkQwTqXjXSfyQuAjx75qKd1MZjzdBTaaOnHsanV/0DIaXWpwfkDXi9VS/kYBrTOYFFBgcAUIjudJehLnYrQ21e1Uq0wbO3Zslp199903e1/pTb7bVh4OSKmdc87hXn/ahvRKyz/VEscxWnUg1TpO5YMhy5fFeUdKb775ZnobX9s9hnTiOqXWd2dI5ss+tLstZYvzowJtCxDsllL+fRpW9GsR56e0cJvqKi655JLYdXalIJA999wz8FerlatGtq+V3zvlDi1kkai3qFavQ53M9ttv30g2hkxDK7q0lEX30dttt92QceUfqK8heW5dLuNnBRRQQIFuCfzvbKNba3Q9CiiggAIKKKBAQQJ0xZNujqYAjPJFc9OLlkkILOCGKhXdPB2fT/R9nVK1p37S+HxFaRpW1GuqkK+0vPSkDePyAS50v5NSvjumNKz8lQAXWl+gNRduCn7uc58rnyS7MVA+It9SwlxzzVU+Ovuc7/4qG/jfNzQTT0stBIjQZUkKGGE0T1+lPrp5qpOnm1J6/vnnsy6J6u2jFKiTnzdf8UJf2bVSeuqUrq6wbrRSLv89uvPOO7MnfSutKz1lxjhavklphRVWCHzHaI2HfUvwFBVXPG3F02J0xcTTWSYFRrrArrvuGrtgoiUnfi88/ZlvNasbPtVafer0ugn2qxXQUeT6OWbUSvnAQwIwG63s78Sxq939kcp+tpdAS8pmWg4gr7fffnvsbilZcAwzKaDA4Arku2Di+oGWRjgPq3WuXrRGrTKNLuBItJBYL2g6X47nW0Np55xzuNdfbp3fxvJxfOZYRXeltJjCNRCtxJQnujyplqaaaqps1D/+8Y/sff5Nq8cQrnGKvk6p9d3J57na+1a3pdryHK5AqwL51lNff/31VhfT8HxFnZ8SHEKgCOeLxx57bGyZeOGFF86u53nwp9E6hnqZb+X3Tr1FKsvq1W81UsdUK4+pPKGcIzCd4xDrJ3CR7rzTQ1+eW9dSdJwCCiigQCcFDJTppK7LVkABBRRQQIGOCuS7XTrggAPCd77znYrro4I7pfPOOy8ceuihIR/M8corr8TRXMTXqiRNy+jUa60AiFTBUL7uVFHN8HoV5UyTD3ChkqJSoEylLjaYN59Styf5YY28p3Ufgj5++9vfBvrufvvtt2OTvMxLkExqLac88CntI6ar1WoO48tTPhCFSnL+GkkEyVCZ02jlU349hxxySCOriNPk5yMgiiAbAohSPseNGxdbTkqtJy2xxBKxNRmage/WzfKGN8YJFeiSAGUix4CFFloolhunnnpqIMCuWvcTnchWvYrlTqyTZXZzvfVuCqcW28hXrSf1GZ9PnTh2tePCecKvfvWrcNppp8UymCdgTQooMLIFaE2LVrPoujV1wXT99ddX7J6iE1K1yrRUhlJGV7tGSHmaaaaZYkuNPFyQD5Rp55xzuNefto1Xtr/etUEaT9eq5J1zh/LUyPVP+TzpczvHkPx1QFHXKbW+OynP1X3N7dwAAEAASURBVF7b2ZZqy3S4Aq0K5OtHUkBFq8tqZL5UtjFtO3UrO+64Y6xv4nqdOgV+VwSU83fUUUfFB2G4bqE1rHnnnbeRrFWdppXfO10jpVSv/qve+LScSq88HETX4NTzUNaVt5RbaR6HKaCAAgoo0G0BA2W6Le76FFBAAQUUUKAQgUcffTQLJGCBjV5085T4L37xi4r92BN0MPHEExeSv1YWUq+iu9Iy05NAjKv0dGSledIwKmwqpaKebqq0bIZts802MVCGvLMv6DaFdOGFF8ZXKrN5yrVaajZ/3NxIiYqkfIVbGl7tladOG01pPeyH1MR7I/OW54fuPX7/+98Hugq7+OKLoxXf25RoNp8/Ao0uuuiiMPXUU6dRviowogR4QpwumL71rW9lXTA99NBDXTOYfPLJu7au/IqKWm+1Y0B+XfnWzPLD0/v805/vv/9+Glz3tRPHrlZdyMsaa6wRn2rNZ5zzgXnmmSek7qfobqNeFyf5+X2vgAL9L0AXTL/5zW8CrQzeeOONMWhmp5126sqG1SrTUhna7rl/q+ecw73+/A7guiDf6mV+XHqfv05s5liV5q91vGz3GJKuH1hXUdcptb47aZsqvba7LZWW6TAF2hHgHIwHnAhy42ESWiWp93vPr4/gGlqcpG6BLoroFqlWSmUb07RbvlLnwQNCtKJ71VVXhXvuuSe2MMOyeRDm7LPPDj/72c/CBRdcENZbbz0Gt5Ra+b3nu5CrV7eSb1GrmQz+8pe/DBtttFHWKnCad7rppovBijz8M3r06LD55puHF154IY32VQEFFFBAga4LGCjTdXJXqIACCiiggAJFCORbk+EmaeoHutqyedL9pJNOiqPpf32zzTbLJqVSku47qESl/+h2nprJFtqlN/nWTqjEr5do3jul1B90+tyt13XWWScGd/DEPv12EyiDO03vkuieqTxoKP+k1J///Oemsprvqoknt8q73mpqYTUmZj006c73iJsq9W4w11hUDNjixi1/VM7fd999cZnXXnttrCRkXlo/OPDAA2MLCLWW5TgFBllgl112iQF3t9xyS+xObr/99gtbbrnlIG9yw9uW76av0kz5m3OVxjPsxRdfrDYqDs+PzzePX3Om0sheOnbRglk6/nCs2WOPPQJN4tOVB92apETXjSnVummapvFVAQX6X4Cbquecc0682cnvnmA5usEc7kQZ+sYbb8QAnnp5Idg6dVVb6dyfoMBmzzmHe/35bSaonZYnywPP89O0eqzKL6Pa+3aPId26TqmW//zwdrclvyzfK1CEANfTtO7FdS8tz/IwyYorrtjwonnohGt//jbYYIPYSmutmYs+P6XlRVo+5o/gFFolo56AIBI+0/U0+aLllUrlc628tjMuf86erx+qtMxm615YBnUXm2yySRYks+2228ZgIAKfZp111iGrSV3PeW49hMUPCiiggAJdFDBQpovYrkoBBRRQQAEFihGgQvQnP/lJXBg3sQ4//PBQr7lsKlYIruHGIBUsDz/8cFhggQXiMvIVlDzNUitQZtddd40V09xA+973vlfMBrWxlHxlztNPP113Sflpyisp6s5c0ATss0033TQQsMTNbZ6oorIotUyw9dZbT7Am9gnz8ZRXvSeO7r333vDd7343djNFEA77KiWaO66XuJnQStdSqRsrtoPmjL/85S9XXRU3sAmoyXcBliZm/nfeeSdrKYagoUUXXTT+ERhzxRVXZE+d0eqMSYGRLMDvg5uYqQum008/vWYZPuhW+adCefq2WuKJ+kYCZeoFYD777LPZKmh9pdHUK8cublKMGTMmZpsWimita9ppp624Ga+++mo2PN86QTbQNwooMJAC3JSlGw3OW7mpybllMy0adAKFMpRuesgPQSJ0r1Qt1Tr3b/Wcc7jXX76tHKtqBcqkY9UUU0wRPvWpT5XP3vLnIo4h+eNhJ69T6m1kEdtSbx2OV6AVAR6yIVCGRL1Po4EyBF6kFmuZd/vtt+elZsr/HvNlZ7WZ8tOU161wPU+dQjo35/xy4403jn+ch9PKzd133x0fiqFb6i222KLaagofnq//SuVjtZXUC6SpNB+t5KTWu374wx+G3XbbrdJksVsqyh6S59YViRyogAIKKNAFgY90YR2uQgEFFFBAAQUUKFSAVjXSDau11lqrbpAMK+dpJJ5qSYnK7pQ+//nPp7dZAE42IPeGi31uyNJE7h//+MfcmBDotonU7Sdh5pxzzmz7yRdBJ9XS448/HpuNZzxBQjzhNFyJJxZJBIywPwmUIS2zzDIhX3ETB5b+cTN8vvnmix9p/Sf/ZGiaJr1ec801sduiM888MzaZzL7nBijptttui61OpGnLXwnEYf3Ms/jii4fyrpdq7ecFF1wwW9z555+fva/05rTTTgs0k0xlfWodie8O28+62T/5pp/zy1h33XUDTRWTqLiqNl1+Ht8rMMgC/L6PPfbYuIn8jg4++OCWNzf9xllAt8vzljOdm5Gbt6kLwZdeeik3ZuhbghRTcOLQMUM/MV2t8jbfysraa689ZOZalr1y7CJwNu1n8l8tSIYN4yZGSvVa60nT+aqAAoMhwDFmrrnmihtDC1SUja2kWuViM8vLn3MSIForcc6ZEi3HkNo95xzu9aftSa/5m+FpWHqlVVGCIEm0BtRsdyppOZVeiziGFHmdUimPjQ4rYlsaXZfTKdCMAK3NpvoByt9av/f8cg866KAwduzYOIgWA1dZZZX86Irvizg/pU6GoBm6LKI+olKiHuD73/9+Nop6mnwq6liRX2b+PQ8YjBo1Kg669NJLa9Yh0SpPs4k6FxJ1OKnep9IyOJamFmU8t64k5DAFFFBAgW4IGCjTDWXXoYACCiiggAKFCqTAAhbaTBcbPAGaEn1F8xQmib6RF1544fj+vPPOC/kng+LA//476qijsqAEghXyKbVA8vbbb+cHd/w9N0RTyzZ0ZZTel6+Yigeai09P6uS7niqfthufCfRIAUrsi5tuuimutlJrMik/tBJDojLlkEMOSYOHvPJUbbphQOVP2q/pxjlBJXTVUq0i5ogjjojdQNGqzGKLLTZBZXqt/Uwg1rzzzhvzQ5/jqYJoSAZLH7h5ffTRR8ebFDSH/9WvfjVOQkXSHHPMEb9jTJO/AZ1fBttAV1Wk5ZZbbkjXIPnpfK/ASBLYaaed4pOZbHOtllTqmaTfONN1uzyvl7dGxlOOpNatuDFIE+/liSbUd9hhh/LBFT9T3nKjoVLiploKcvzSl76UBTOmaWtZ9sqxixuUKaUA3PQ5/7r33nvH1ujSsFSpnz77qoACgy2QumCijCW1epypVS42I0gLN6kFlRNPPLFqEPidd94Zuzll2VNPPXXWbVS755zDvf5yK64Nn3jiifLB8XyfLk9Syl8LpmHtvBZ1DCnqOqUXtqWdPDivApUEaIGVci6lbbbZJtBKSQp0TsPTK/UdjKf1GRLlHcGOqfxO01V6LeL8lC7BX3755bj4I488MmtZpXx9+ZZcUn1AmqaoY0VaXqVXHElc7xx66KGVJokt2XIcaTalspF9RH1HpfTUU08Nqcvz3LqSksMUUEABBbohYKBMN5RdhwIKKKCAAgoUJkCAQGp6d4YZZgirrbZaw8vmRl7qhoemcNPTMTyxkypfqPhecsklwy9+8YtAsASJ7il44oeKDhLj6Toon6abbrr48Xe/+10g2OKyyy4L5U8G5acv8j1N2aYADZ4aXW+99cKTTz4ZK49SN0A07Xv11VfH1RI8sueeexaZhZaWlZ4uovsggj94smqjjTaquiy26ytf+UocT8s+NF38pz/9KX6mEoZKnFVXXTVWxlARlvYpExBQtfTSS8dp6RecllvooomKNIJmeDqNSv9UoTbNNNOEffbZJ06f/1drP/OE6sknnxwnJz88tXb88cfHrroYSPALT5WxL+iHnLTzzjsP6aebJqFTJd5hhx0WLrrooiGt2rBf119//ayFh/KArbhQ/ykwAgX43VAucDOznTT99NNns3NzjeMET1r2U0oV3+R5u+22CzR/TiU1LcNgRNlEsEy9LgvTNtNCFse81LIMQaYsh/KWso6bCvlW2tJ89Sx74djFzQy69SNdcskl4aSTTsoCETl+3nfffeFb3/pWOOGEE9JmxdfUTPyQgX5QQIGBFuD8jfO2dlK9crHRZdOFUGpJjesUuuikDEtd6lFGnXrqqfGcM7UeRgB2/uZrO+ecw73+cidusNLl6ZVXXpkFMXEdxs1nzvtJnD+X34wuX06zn4s6hhR1ndJs/vPTF7Ut+WX6XoGiBHiwaf/994+Lo0zbY489YoA2ATHUvzzzzDOBVmc5H/3CF74Qx6d1U1ZSj9Boavf8lID1VGdB3RUPAuWDYmixluC+/fbbL2ZpxhlnnKDL5qKOFbW2mYeoUldTOBJImFqjpK7slFNOifUttZZRbVyqd2E81yXso/TA1uuvvx4uv/zysNJKK2V1IkznuTUKJgUUUECB4RAwUGY41F2nAgoooIACCrQsQFO76SKbFjxSn8+NLjD/JGH+xh6VGdwIo9sKLtI32GCDeBMxNZvLUzasl65yCIJhunxKATsEXdDyyYYbbthws8D55bTyngCNMWPGxO56mP+KK64I88wzT+xCghuhBMbw5D9pkUUWieN5Mmu4E5XC+f1HBVa9G7dUKtElEoltpssVgleYjwryhx56KI5jH9CfeT7Rcg3TkOgPnOVwU52KKPo6Ty24cBOB7qBSd035ZdTbz9yApqKJG68EXRFsw/Jnmmmm2NUVrV7w9BSJ71g+mIdh3IRJAVlvvPFG7KucACK6yaL5ZvZrChSjcqvdGzas06TAoAjQLUa6cdjqNi277LLZjcTbb789bL755rHbPrre65e07bbbZsGTL7zwQiAokTKIFqsInOHmIcGfBBvWS5Q5M888c+xykPlZDq0SsBxMOJYQ0Jda78ovr55lLxy7CIpMQTDceNl9991j8CLditANEzefudlMk//cgE3H/vvvvz+/qb5XQIERInDMMcdkXTC1ssn1ysVmlrnFFlvEaw6CFWlVktYiKZ9paYZzY4L8OBflXJvA7fJg9HbPOYd7/XkrruO4fiOAnGsCtp8uW1OLlWwrrT0WnYo8hhRxndLO9hW5Le3kw3kVqCZA675cZ6duiWhFioCZ5ZdfPp6nUQ5wbfzII4/ERXA9Th0OrQI2k4o4P6X7Jc6bSQTc0/UT5TPX9Fzbc67+3nvvxePJDTfckHWbmvJZ5LEiLbP8FR9ahqS7KRItK88222zxXJ/y4Nvf/nbMV7WWJcuXl/984IEHxm1mGPVQ7CMCgmhRmFcCFwnK2XXXXeMf03G8euyxx3hrUkABBRRQoKsCBsp0lduVKaCAAgoooEC7Aq12u5TWS3AGlR8k+qy/66670qjYyso999wTllpqqVipTGAMzebyxDzzcAPt0UcfjRUI2Uz/fUOlDTcdecIypW5e6HNTjxZSeNIqtXpCpTkVMCQqamihhFZXRpW6JOqFxA3X1VdfPcsKN3PrJW6E33HHHfEmb9pOKsZTN1pUvtDVSKXmg9luukPiZkFqrp6WbFL3KtxIIJCKvsz5DlRKjexnnkLju8VTUikgiS49+B6ReMqMgC8q0FJFX35d7EOCnbhBS+J7SOsPPNlFHulTnFYuuFlTaf78snyvwEgToGUoAt9aTZQxtCjGE5apdScCKGjNqV8SAR4cDyjPUusBqfwh4IMg0YMPPrihzcGBZXGTkZuxlGUpWJXym8rvagE3jVj2wrGLmyrcoOTmBQmrhx9+OB4bCHQk8JLjwtprr50FW9JCW2q5oSFIJ1JAgYEQoDsJrkXS8aHZjWqkXGxmmencni5NOUfkeJVaLaTMXmutteK571577VVxse2ecw73+tNGcW5PUCOtjdK6TGqZgGsNWoukVRluUnciFXUMKeI6pd3tK2pb2s2H8ytQTYDrbB46oUwjmKNSIhCFc2Cmq9YtdaX58sPaPT+l7KGeiUCedC5OnQPX9JxHEyzC+TN1T6yrPBV9rChffvpMq8TkkwD4FAzOuT7HEh4a4uGhNddcM03e8Ct1Yrfcckt86CcdLymXqUvj2MTDS9RL0WoNDw+lRPC9SQEFFFBAgW4LTFSqBPpPjX231+z6FFBAAQUUUCA8NdHMA60w9/j/dC/TjxvJEy1cyD///POxkoDghtQ9Q63toVLh6aefjtPS+gyV1sORCPDhxh4VMTwlxFOVvRhUwdOtPGVFIA/dKDWbR7aTVmSo2GEbUwBMI+ZU1nAzdNy4cfFpLiqDGu22pdH9jD+VdDzxRgU96+BJrVRhVC+f3OygRQieuOImLhVpKfim3ryO7x2BD3eevHcyU3BOJj69f1paaXbTCTbkt8dvlhad+jFRBnEsoxxZbLHF4lOijWwHxy7mpan71G0fgSG0xkWivKXFtUZTo5bDeeyiRTiOQxzD2X7KW1rTMSnQ6wIH3XtDr2ex5fwdutjKLc/b6zM2Wi42uh3paXy6+KDs4tol331HveW0e87Z7fUTAJNughPQmQLMOefmYQWuf+hyt1vnzUUfQ9q5Tqm3r+uNL3pb6q1vpIzf7cI/DOymnrTVEl3fNs5TOW8k+IQ/Amco9zhvb7ZOoV7m2zk/5cEc6pQ4FyeQj1YYO3EOXW8b6o2nToRrBsoerhmaqVeptWxayeXcGkPqQriGSEE5teZznAIKKKCAAt0SMFCmW9KuRwEFFFBAgQoCBspUQHHQiBGgMoYKGCrW6Qbk4AZbOBgxQG7owAgYKDMwu3LEbEilQJkRs/FuqAJ9JmCgTJ/tMLNbiEC1QJlCFu5CFOiAgIEyHUB1kQoooIACCiigQJsCdr3UJqCzK6CAAgoooIACCrQmQFO7BMlwQ5Z+uk0KKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQKcFhqcvgE5vlctXQAEFFFBAAQUU6DkBuiCiOfiPfvSj4brrrgvHHXdczOMmm2wSuxXquQybIQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFBg4AQNlBm6XukEKKKCAAgoooEBvCjz55JNh9OjRQzL3mc98Jvzwhz8cMswPCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooECnBOx6qVOyLlcBBRRQQAEFFFBgiMAcc8wx5PNss80WrrnmmjDddNMNGe4HBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUKBTArYo0ylZl6uAAgoooIACCigwRGD++ecPd911V3j88cfDwgsvHBZaaKEw0UQTDZnGDwoooIACwy/w6quvxkxMOumkw58Zc6CAAgoooECZwN577x123nnnOHSqqaYqG+tHBRRQQAEFFFBAAQUUUKC+gIEy9Y2cQgEFFFBAAQUUUKAAgYknnjgsueSS8a+AxbkIBRRQQIEOCUw77bQdWrKLVUABBRRQoH2BySabLPBnUkABBRRQQAEFFFBAAQVaFbDrpVblnE8BBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCgrwQMlOmr3WVmFVBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBVoVMFCmVTnnU0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFOgrAQNl+mp3mVkFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBVgUMlGlVzvkUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF+krAQJm+2l1mVgEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUKBVAQNlWpVzPgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIG+EjBQpq92l5lVQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUaFVgovGl1OrMzqeAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQL8I2KJMv+wp86mAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCijQloCBMm3xObMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKBAvwgYKNMve8p8KqCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCrQlYKBMW3zOrIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKNAvAgbK9MueMp8KKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACbQkYKNMWnzMroIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIK9IuAgTL9sqfMpwIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooEBbAgbKtMXnzAoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAL9ImCgTL/sKfOpgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo0JaAgTJt8TmzAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQL8IGCjTL3vKfCqggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgq0JWCgTFt8zqyAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCijQLwIGyvTLnjKfCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAm0JGCjTFp8zK6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCvSLgIEy/bKnzKcCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKBAWwIGyrTF58wKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC/SJgoEy/7CnzqYACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKNCWgIEybfE5swIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooEC/CBgo0y97ynwqoIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKtCVgoExbfM6sgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo0C8CBsr0y54ynwoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAJtCRgo0xafMyuggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgr0i4CBMv2yp8ynAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQFsCk7Q1tzMroIACCiigQFsCC+48pq35e33msadv1OtZNH8KKKBAFwTm68I6hmsVjw3Xil2vAgoooEAUuGiAHTYf4G1z0xRQYCQJ/OvidQd2cyfZ7IqB3TY3TAEFFFBAAQUGW8AWZQZ7/7p1CiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAv8VMFDGr4ICCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKDAiBAwUGZE7GY3UgEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUMBAGb8DCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAiNCwECZEbGb3UgFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABA2X8DiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgqMCAEDZUbEbnYjFVBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQyU8TuggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooMCIEDJQZEbvZjVRAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQwUMbvgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooMCIEDBQZkTsZjdSAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQwEAZvwMKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACI0LAQJkRsZvdSAUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEDZfwOKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCowIAQNlRsRudiMVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFJpFAAQUUUEABBRRQQAEFFFBAgX4T+L//+7/w8ssvh9deey3MNddcYfrpp++3TTC/CiiggAIKKKCAAgoooIACCiiggAIKKDAMArYoMwzorlIBBRRQQAEFWhe49dZb4w1Rboq28/fee+/FTJx66qnZch5++OHWM+acQwT+/ve/Z6577rnnkHGD9uGzn/1s3Nbtttuu5U2bd9554zK22WabIcs46qijMsfnn39+yLgzzjgjG/fggw8OGecHBQZV4LHHHgv77bdfGDVqVJhsssnib2CJJZYIM8wwQ/jUpz4VVltttbDvvvuGJ554YlAJ3K4OCLz99tvhnXfe6cCSi1lkOt/Zcccdi1ngMC/lhRdeGOYchFDtuDvsGTMDXRO44447svOo9Bsrf11ggQXCcsstF77xjW+EMWPGhH/9619dy18v/E66trFNrKjXXdJ3aBDK6144Nnq908SPY0An3WijjWqW1VyLL7744mGNNdYIRxxxRN1rgFrX171E2OtlXRFWnSxjau3nWuM8Pyxiz7oMBRRQoL8EbFGmv/aXuVVAAQUUUGDEC7z//vvhueeea9vh3//+d1zGW2+9lS3vn//8Z9vLdQH/ERg/fnzmSmsPg5z4Pn744YfhlVdeaXkzWQbfv/JlvPnmm5lj+c0ZKpaYj+R3NzL4b4AF3n333UAwGjcqq6W//OUvgb/rrrsunHjiiWGXXXYJJ5xwQph44omrzeJwBcKFF14Yg6uuv/76sOCCC/akSCrry48RPZnZGpn685//HPbYY4/AOdgvfvGLGlN2fhSmlY67nV+za+gVgWauKW677bZw3nnnhVVWWSVcdtllYcopp+zYZvTS76RjG9nCgvvFZVDK6145Nnq908KPZcBm4dw+/a7qbdqvf/3rGCzz05/+NKy33noVJ691fV1xhi4P7Jeyrl2WTpcxtfZzrXF81zw/bHfvOr8CCijQXwIGyvTX/jK3CiiggAIKjHgBWg3gaaFKiW44brjhhjhqiimmCMsuu2ylyeKwSSbxNKgqjiMUUECBHhKgFZl11lknPPnkk1mullxyyfCFL3whBjbMPffcsQL9/vvvD7QQMHbs2PjU/0knnRReeumlGFwz0UQTZfP6RoEk8IMf/CDstdde6aOvHRaYf/75A0Fv1W5edXj1Ll6BqgKzzjprPKbkJ/jggw8CwTQcR7hxRhA4AXVLL7104GbsbLPNlp+8sPf+TipT6lLZpRNDPTZ2QtVlFiHwla98JXz84x/PFkW5TDnNuQWtSf7tb3+LnzfccMNw/PHHx+DcbOI+eTMSyjrLmD75MppNBRRQYIQIeIdohOxoN1MBBRRQQIFBEVh00UXDtddeW3Fz3njjjTD99NPHcTR7XW26ijM7UIFhFOBJZZ6wn3rqqRvOxVZbbRVWWGGFOP3nP//5hudzQgX6SYDK73yQDN0rnX322WH11VevuBlUmFMxfuCBBwZucvLk//nnnx/KuzWrOLMDR5wAN1RM3RPoJe9Wjrvdk3JN3RZYaaWVwgUXXFB1tQ899FDgxisBm3TVesABB4Sf/OQnVadvZ0Qv/U7a2Y6i59WlaNHqy+sla693qu+nkTjm3HPPjd0wVdp2Wng96KCDAt3qcF299957h9GjRwcC6vsp9dLvr1NuvbyNnh92aq+7XAUUUKB3BQyU6d19Y84UUEABBRRQQAEFRogA/ao3mwgY4M+kwCALfOc738lakqEVGZ7in2aaaapuMi3H7LPPPmGhhRYKq622WpyOoBlutNgFU1U2Rygw4gRaOe6OOCQ3OBPgmHLLLbcEWp4hIPPnP/95DNr82Mc+lk3jGwUUKF7A653iTQd1iZznH3HEEYFudc4888wYLEMXTIcccsigbrLb1QEBzw87gOoiFVBAgR4X+EiP58/sKaCAAgoooIACwyZA/9Cvv/56y+unqXaW0Wr617/+FW8Q00dyI4lm4f/xj3/UnPStt94KzzzzTOCJq2YTTRo//vjjsc/mZudtZHq2k+XX24Zayyoqj+z3p59+OrZIUWt9tcaxPU899VRL1rWW2+o4uibD99VXX211Ec6nQFcF+A3SfRJp0kknDTxFWitIJp+5r371q2GZZZaJg/7yl7+Em2++OT+66vtXXnklNt3eaLlbbUGdLo9Z73vvvRdeeOGFalmIw7mhSzn09ttv15yu2sgiPDje0Bw+Ny5aSc0cC4s6BtTKZy8dqyjPX3755VrZrTmuiOMC35FHHnkkdntQc2UtjizC+69//Wvgr51UhFU763fe4ReYZZZZsu6ZaLGsXvmbctzu9UBaTq3Xor6f7Z7/Nvpb49j0pz/9qa3yq5ZHGldE+VHUcWUklNftftebOd6nfVzptYhzl0rLdVh/CKRAeXLLtUS7qZ3ylbKO8zRaI+P73alURDnVzvlcO0bNmLRbxjSzrkan7da2N5ofp1NAAQUUaF7AQJnmzZxDAQUUUEABBQZYgIvvjTbaKFAZPvvss4cZZpghzDPPPLHbDm581ku//e1vw8orrxznm2222eIy6A6KYTyJWi1xM3jeeecNyy23XJzk4IMPDjPNNFNc95RTThm22267GHBxxhlnxOm+/OUvx+loCn7zzTePT7jS3RRd99B392mnnZatipuUPEm12GKLhemmmy589rOfDZ/4xCfCxhtvXPfGETeYt91227gd5GO++eYLn/zkJwNd/ey2225t3xijUoflLLDAAnG5afl83muvvcLf//73bDuqvWknj2wH7uecc06gkmPPPfeMn9nvNNM8+eSTx25f7rnnnmqrn2D4j3/842g9xRRThM997nNxn7D/2XfV0he+8IW43h122KHaJBMMP+uss+I85J/vQaX0+9//Pqy55ppxW+jPHV++V1NNNVXMI0/bUYFnUqAXBS655JLs+8lvc/75528qm3SN8cUvfjF2kUF5XCnRtROt1iy11FIxCGfmmWeOvyt+L5SpHA+4mVcpdas8TuXUxRdfHD2OPfbYsPTSS8f8zjnnnGHUqFHh61//egyCTPmkzKKbkBlnnDErh5iOZdRKRXgsssgicRW/+93vwiqrrBLzSTnF8Yfj6nHHHVe1bG/0WJjfhlaOAXTfRZ5OOeWUbFFrrbVWHEYXLOVpuI9V+fxwTOc7+5nPfCaW57RwgSvf1WeffTY/acX3RRwXuOGzyy67ZL8ZjtmcI/Dk/2GHHTbBucHCCy8cbdPxhnMl/Pm74oorJshnq97YpOWOGTMmBmh96UtfivnivI4y5Kabborra+S4W4TVBBvngL4WSC3IcI5HmVottXI90OzvpJ3vZzqutHr+28xvLW/E75Kn9bkO+fSnPx2vX7hOotzFrFJq1qXV8iO/7laOK/n50/t+K6+bPTayna1815mv0eN9I9c77Zy7kBfTYAmkcpqtWnDBBVvauHbKV1Z4++23B84rqTfhPI36JMo9zpd23HHHisHjzZZ1RZRTzZ7P5TFbMepmGZPPazPvPT9sRstpFVBAgcEQsOulwdiPboUCCiiggAIKFCBwww03hBNOOCG89tprQ5ZGBQJ/t956a+DGX6UbrrSCst9++8WbbulGUFrIG2+8EW688cbYogHBH4cffnhsHSGN55UnOHni/p133olBLvkmggngePjhh2O3ISyL6QjkuOuuu8Iaa6wRxo0bly2KJyfJJ38EQxAMs9lmm4XLLrssm4Y3TEdlNZU4LI+Km/JE5fkee+wxwQ0vnoZ69NFH499VV10VW3lYccUVy2ev+5l1b7nllhPchKZSmafT+bvmmmvCRRddFIM6Ki2w3Tyy7ayPp4IJKEmV9JNNNlk0YhzbeO2118btJL/VEkE9jKeJ53yiD272P3/sK25wlifywT7hpkGjKX0XmL68FR4+sx4qoemjvTzxPfvjH/8Y/+g+gO++3dKUK/l5uAUIlEmJgMBmE+Ujf9XSHXfcEQNMaHGlPPG7ITiSP37/5IUK73xKv8FOl8epnOI4QRdS5WXM888/Hy644IJw3333xeMCv+2vfe1rE5TdTIcjT7Xvvvvu+U2J74vyIMjo6quvjoEbqWxiGGUkraztu+++4fLLL4+u00477ZB8NHosTDO1egygdRtc84l9TUp5jh9K/3rhWJXyQqtwq6++erj++uvToPiK66WXXhqHX3jhhfF4NmSC0oeijgscM77xjW9UDHbips1BBx0Ug085RyHwjMQ5FDcyU+K4mPzLWztq1zstFxPOy9J+Zd2PPfZYDIrlPdNVO+4WZcV6TIMjwPVBCpwmAPqjH/3oBBvHd6fV64FGfydFfD/5/rd7/tvobw0k7DbYYIN4HVWOxrGUADb+uIF8/PHHD7kuadSF5bZbfrCMVo8rzJtP/VheN3NsbOe7jlOjx/t0rsU8rLM8tXvuUr48P/e/AOftKdW6DkjT5F+LKF85T9p0002zYP+0fOp0yus4ll9++TS6qXOlIsqpVs7nyGw7Rt0sYzLYJt94ftgkmJMroIACAyBgizIDsBPdBAUUUEABBRQoRmD//fePlXZbbLFFvBFJMAg3+3gamUSrAtygqpR23XXXcPLJJ8cKkfSENK3T0NTulVdeGZ9m4sYrT9LTWky1xFOQtIJA4qn89ddfPwa8bL311kNmIdCBJ/XpemOnnXYKv/zlL+NN0qOOOiqruP/mN78Z6H6EIBnyTcDHgw8+GGjxhJYSSOSR1gnKEzdaqawmPwRQHHjggeHuu+8OVFYSLMSySdx4Zdmpsrx8OdU+0/0TT49iyvIJIGKdbBc3IVjfRz7ykVhhxM2I8uAllltkHo8++ugYJMNTXjwdRT7Y1h/96EeBoBmCg9gHVMBXS1TwcwOblmgIdLrzzjvDH/7wh/h0PcsgsV3lQUvVltfOcL5nJ554YgySWXbZZcNvfvObuK+5IUnQFfs8dWFDS0fdyFM72+O8I0+A3zw3tUkEJ/LbLDJxo56yiyAZfp9HHHFEXB+/EW6wE4hAGUsiwIPjQ3kQZMpPp8vjtJ5DDz00ljEcG37wgx/EMp/f9pJLLhknGTt2bBg9enSgyXnKTwI/KbcJ0tt+++3TYmL5lA+wZESRHlSgr7feejEggTxzHORYwvFnhRVWiPkgiGKTTTbJ8lT+ppFjYTvHAAJICSgl4COl8847Lw4jiDSlXjtWUV7z3aS1Ms4tCHripgvnFRxLuTG79tprV2xlrIjjAsdAbv7wm+A7RkAAx2x+A7RshiuJcwMCy7gRTyIIFe+JJpoofqb1vBTUmz+vKsI7rqD0j5vtBMnQ0g2tK/GkNi3epN91mq7SaxFWlZbrsP4VIBCR81HO5WltkPP+Sqmd64FGfydFfj+LOP9t5LfGdQPXDySe1idY8sUXX4wtoXHuPMccc8RxtHRIa1X51KhLEeVHO8eVfJ5534/ldaPHRravne8686fUyPE+TVv+WuS5S/my/dx/AgSiEKzL9TuJ8+GFFlqoqQ1pt3ylboM6A64ZaPmP+he6pOP8jPO2dN5JHQx1LfnUaFlXRDnV6vkc+W3HaDjKmLxxu+/b2fZ21+38CiiggAIdFCgduE0KKKCAAgooMEwCC+z08/GD/Ndt1tKTafQhE/9KlSINrb7URUA2D/OWAggmmK9U6TK+1F1RNl3pptSQae69997xpRtGcXwpKGF86anpIeP5UApoGb/qqqvGaUrNAY8vVeYOmea73/1utnzyUao0zsZ/8MEH40tPPcfP5fktVShn06U3pWCMIcsqVcKkUdkr2zDppJPG6ZZZZplsOG9KNznHl7oniONKT8uOL1X0DhmfPpSeWs/WUwp6SYPja6niMhtXCjwaMo4PpRt5cXzpptn4UqsxE4xnQOkG8PjSjb84Xekm75BpisgjC0zLx3zRRRcdX7pJPmQ9fChV7I8nn0xT6sZlgvH5ZZRahBlfqgybYJpzzz038yjdsJtgPN8Jll+6YThk3N57753NV+pjfci4UlBUNq50Mzwbx/e11MVEHFdqWj9+97KRuTelm63Z/KWKxNwY3xYvMG9pkYP6V7wWSyzdlMy+n6XgisJXUupyJ1t+qTWWissv3RAdT/nIb5O/UnDHkOm6UR6zwnwZU2q6fYJyqhT0Mr7UtHuWz1LrLRMcY1gOZXHallIrUgzKUic8Sq2BZctPbzieUd6kfJSCPNOo+NrosbCoY0ApqDHLSynQY0he+NArx6rkxSvHKvZ5eSp1YZRtC/nOpyKOC/we0rkB+Sg9sZ1fRXzPNKVAriwf11133ZBp0rG0FEg1ZHj60K53Kag1Wzd5LHW5ML4U1JMWP55zk5SqHXeLsErr6N4r54yD+tc5RcrB9NsqBWqMX3fddYf88X3k3J1zqVQOl7qzG58/58rnrojrAZZX63dS1PczbU8qU5o9/23mt5a/XigF0FW8TioFp48vdcmU7Y9SIF2eNr6v5cIE7ZYfRR1X0ncq2fZjeV3v2FjEd73R43216x32eRHnLiynW+mDi9YZP6h/nTTMn4tTJpeX1aVWFMdTB1Pq4jMrQ0pdR4/nnLNSqnZ9XUT5WgrSyfLwq1/9qtLqx5ceZMqmKT1YM8E0tcq6Isqpds7nijBig7tRxlTbz6y/1rjBOj9ka00KKKCAAvUEbFGmdOViUkABBRRQQAEFEKD1ln322WcCDJpW32GHHbLh5a2nlC604xOmpcCTcP7558fWCbKJ//uGbidOP/30+MQ3Tf3ztFO1xNNP+W5GJplkkgm6amLeddZZJ2vZJb+sUuVR9nGKKaYIRx55ZPY5vSnd7IpPdPKZJzDziW4baE2HVKpkCvkmgfPT0c0QLdaQaLHg17/+dX501fc8Rc7TVSSeqqKLkEqJZaeujmheOO/eiTwec8wx8cnz8rzQIgvWJJ4o5gnYaontmmmmmSYYzZNl7AtSfjsmmLCAAbTaUAqUiS0R7bbbboHvXqXEk9Gpyy26yzAp0EsCtOqSUqXfVBrHK63A0DIMf3TRkP74rfH3+OOPh2effTY/S2yFY9SoUYE/WhGrlEoV1bF1jjSu1u+kU+VxWnd6pZl0WsjIp6mnnjp85StfyQbRZV6lbtxoCSSl8nKfVkmw4K8Ij9JNjax1kbROXjme0YIBtiSerq2Wah0LO3EMKM9Hrx6raCmIfV6e+A7SUhuJYxEtDKVUxHGBFtLSuQGtxVTqzoD9ytO2JPY1LSo0morwLl/XWWedFVv/SMPTMS99rvRahFWl5TqstwXogrMUbDbkj98RT/hTPqbWkTjHpuXASqno64FK6+jE97OI899avzVaZCOVbj7GLmpTK4v57aMbPlrmTKnS9VgaV+m1iPKjE8eVQS2vi/6u1zreV9rfaVjR5y5pub72rgBlcnlZTVdLt912W2wNlpyXAhrjuSznIc2kIsrX0oMt2SqrHSsoF2hltvQgTdZFZTZTnTdFlFPtnM8VYVRnE+PoosuYRtZZb5pubXu9fDheAQUUUKB4AQNlijd1iQoooIACCijQpwKlJxGr5pymc1Oib+WUqDinb3RSqbWRijcn07Qso/RUavxIdxjV0tJLL11t1JDh1QJMZp111mw6AmJSFzvZwP++mX766eO78m6N8jfX9t133/LZhnym24WUqHRpJFGRlRLdIdRK3IwjlZ58CqWnF7NJi84jzcCnG4zZSnJvUldTDHrggQdyY/73lhvT1bqHoYsKbkCT8t+fOKDgf4sttljsloomnrfbbruaS6cikVR6OqzmdI5UoNsCpScms1WWnv7I3ld6c8kll8SuaOiOptTiSvZHN3j8zTfffFlQX5r/jDPOiF2/EUDD77NammGGGbJRtX4nnSqPs5WX3lC2U1ZVSvlynyCVSimV+YwrL/eL9qA7vWqJbUjdRVUrT5m31rGw6GNApbz24rGK84xqwatsw84775xtSv48o4jjQqn1t2zZ5V2jZCNKbziWcqOI7pkqBermp82/L8I7vzyCl0stVOQHNfS+CKuGVuREPSVAd0oEGZf/TTXVVDHIPWWWLj3oJoiuWfOpE9cD+eWn90V/P4s4/631W6MbU7rfI2288cZZF0tpe/KvdHVLt2yk+++/P3Z7mh9f630R5UfRx5VBLa878V2vdbyvtd+LPneptS7H9YYA167l5TTn6pThKdEtJV19EtBLN9WNpiLK11SGsc4111wzlFouywItUz6oDzr88MPDNttsk9UPpHH1Xosop9o5nyvCqN42dqKMqbfORsZ3Y9sbyYfTKKCAAgoUL9BcaG3x63eJCiiggAIKKKBAzwjMNttsVfNCRXlK+Ru4zz///JAggxNPPDFNVvE1PUX/3HPPxQrgSk86VXv6qHyBc845Z/mg+JkK65Tmmmuu9HaCV57srJQee+yxOJgnPlNwR6XpGEYgTkqNtpRCaw8plZobzp5QT8Pyr/TpnRKtRaRUdB7z25HWkX/Nt85A5T0VX+Wp1veHadN3KP/9KV9G0Z/T940b/PSZzj7Cn5vTN998c3bzgkAkkwK9JJAP/MiXA0XnMf1GWC7BI5QzqRWa22+/PeQDAGv9TjpVHue3t9o6mKaRcr9amZ9fR1EejZSppa6sYksNpabxAy23ladax8KijwHl6+ZzLx6rCPqqlUrdRGaj8/nPBpbepH3c7HEhv7z8evLLTu/zwcVpWL3X/PJbPTfIr4Pjdq0guPy01d63alVteQ7vXQECt0vd8FXMYKmbofDwww+HUlcxgZYLSl14hVL3YaHUhWXWmlcnrgcqZiY3sIjvZyNldVpltfPfWr81WnRLqV75xXTkp9TlaeC4QCArAbCNpCLKj6KPK/W2N1+O5vOf395W93F+efn15Jed3jdbXnfiu17reJ/yWes1OTFNO+dytdbhuN4Q4NyxWv0G+54gRlpWfOedd2Jrt7S2R1ndbErfqWbPlQjQ4TdFy42Uf6usskp8aImWXGk5qdTNcgz0aTY/afoiyqmiyodWjdK2VHvtRBlTbV2tDu/UtreaH+dTQAEFFGhPwECZ9vycWwEFFFBAAQUGSKC8O4tGNi0fvEHFDX+NJCrdCVyoVDHYaIVlI/mt1u1OrTymChhuyqZKgGrT0yUK6+DJ8UYDZfJmpf6pqy16guH5+YrOY60b0GRk9tlnz/JTrQWERvZHtpAuvCG44JRTTgmXX355vPnP01kmBfpFIB949tJLL9XMNt2jnXrqqRWnofuxat99Wqr51a9+FU477bTAjXlaYWo1NfL7b6U8zuenkXUwfavrKcqD4wYtLtRKaTxdEVKeL7TQQhNMXutYWPQxYIKVlwbkjzm9cqyqF7yaP1bl85+2r53jwiuvvBIXw/7N/z7Tstt9zee3Ve98Hmp9f/LTVXvfjlW1ZTq8PwUIal944YUDT+EfcMABsfs4zuNpVTG13JT//hZ1PVBLq6jvZxHnv7V+a6msZlvqlV9Mk78BznVFo4Eyef9Wy4+U16Kuf+ptb7+W13nror7rtb5DfC9qpaLOXWqtw3H9IUDLMnTrTLAKrRTRZSotutA9NAEqjaZ2yle6eOSaglZxU73QuHHjwqWXXhr/yMMSSywRW5Oha+9mA3qLKKeKOJ9rx6jefuhEGVNvnc2M7+S2N5MPp1VAAQUUKE7AQJniLF2SAgoooIACCgywQLWuP959991sq6nkoyngRhNPS1ZK+aaDK41Pwyq1RpPGtfOaWjyp9IR/reVWMyqfJ5mx/NT9Rvk0lT7nbYvOI5VatVK+JYn333+/1qRVxzXqU3UBTYz45S9/GTbaaKPATeh8mm666eINaSroRo8eHTbffPPwwgsv5CfxvQI9IUCLMnQV9Prrr8duXKg0rRRYSGZpwjx1a1eeeZ4qrRQoQxlCk+y0rJRPE088cey6KXVHQVdp9bqgY/5Olcf5vHVyHUV6kM98Czf5bUjv8/ukWpla61hY9DEg5Sv/2ovHqnrfAb6/KZUHTBV1XOCmTn49aX3tvhbhnc9Dre9PfrpK74uyqrRsh/W3wO677x6OOeaYwDkdLY5xjOJYlb6/bF1R1wPVpIr8fhZx/lvrt5bKaralU9cVLDv599K1xaCW18ka96K+67W+Q6ynWiry3KXaOhzefwIE3NGSDF1zkWgJrNFAmSLKVwJ2fv/734frrrsuXHzxxeG3v/3tkC5POXbwR+s3F110UZh66qkbRk5lahHlaavnc0UY1drgTpQxtdbXzLhOb3szeXFaBRRQQIHiBAyUKc7SJSmggAIKKKDACBTIN2e9/vrrx8rzfmfgZvQbb7wR6B6qXqKJY1qTIdFneCMJs0ceeSTevP7Nb34T6lXSV1pm0Xl88cUXK60mG5YfP/PMM2fDe/HNfffdFzbZZJMsSGbbbbeNXQRw4z/fnQ15pzlpUjeDeOIK/adAHQFuxG+88caxtRcm/dnPfha+973v1Zmr8dFf//rXsyAZbvQQULPUUkvFbh/odi6lH/3oR+ntQP9OivQgCJSnVfPBjRnif9+0W6YWfQwozx+fe/FYxTG3VqK5+pT4XqdUxHGB5d16663x2M1T2kW3KlOEd9redl6LsGpn/c7b2wK0pLjAAguEsWPHxozSOiSBMt26Hij6+5kviyvJ58e3cv6bD3Bt5LoiX4Y1el1BvosoP4o+rgxqed2t73ql72P5sCLPXcqX7ef+FqCroxQoQzndSCqyfOU6hoB8/rjOZtnUexC0Q4szJFq1PPDAA7NrnUbyWEQ51c75XJFG1ba3l8qYfB67se359fleAQUUUKB7AgbKdM/aNSmggAIKKKDAAArkK4Dvv//+ultIUEn5U951Z+ryBGwTTQX/7W9/izc7uSlQLT399NPZqPIgjGxE2ZvUjDqttDz44IPhy1/+ctkU//tI0/a0PPCxj33sfwNL74rOY73K+2effTZb/zzzzJO978U3F1xwQUgtNPzwhz8MdD1TKWFLaxmkfOsOlaZ1mALDIbDVVltllccnnHBC4HO9biIaySff+zFjxsRJP/3pT8enOqeddtqKs7766qvZ8EH9nXTCgzK1VqBMKlOnmGKK8KlPfSozbvRN0ceASuvtx2PVM888k21K/kZDEceF/PJoiaxWoMyuu+4aA27nn3/+hgPcivDONr6NN0VYtbF6Z+1xAW54puARuiFLv4tuXQ8U/f3s9Plv3iV/zVBtN+enafS6gmUVUX4UfVypZ9uv5XV+nw7ntW8nzl2qfS8d3n8C+aC7/He21pYUVb5Sx/HOO+9kLcVwrFh00UXjH4ExV1xxRXyIhbzQ6kwzqYhyKh0VLuTIAABAAElEQVS3WG+z53NFGdXa5vz+Gs4ypjyP3dj28nX6WQEFFFCgOwIf6c5qXIsCCiiggAIKKDCYArSGwo1W0m233RbSzb9KW0tTuVRMMM/iiy8eqnW9VGnebg5bcMEFs9Wdfvrp2ftKb0477bRsME9MNZLyyz///PNrzsLyaY6bG6nnnntuNm1+GUXk8ZZbbslufGQryb3Jtyqx9tpr58b03lu+hyQq5XjSslpim1OLMgTNmBToNQG6CNtggw1itt5+++2w6aabBl4bTQ8//HDFIDCaQ0+tKPF7rhYkw3poLj2lQf2ddMLjwgsvTGwTvD7wwAMxOIkRNIXfbPPxzFfUMYBm51NK34n0Ob+OXjlW3XjjjeGtt95KWZzg9cwzz4zD0k2ZNEERx4V892Y/+clP0qIneCVQ85xzzomtQP3xj38cMj55l1szURHeQ1bW4ocirFpctbP1gQDd9aXf4LzzzhummWaamOsirwdq/U6K/n52+vyX4NYpp5wyGtEy3Lhx46ru5ccffzxQxpFotWf22WcfMm0tlyLKj/wyiri26OfyOlmzA8rL6yK/60N2cJMfOnHu0mQWnLxHBQhUueqqq7Lc1XooJ5uo9Kbd8pXfyjLLLBPreijDUjdJ+XXwft111w1c45AI6CmfLv3+yn97TF9EOdXO+Vy7RmwDKW0j78u3s1fKGPKWT0Vte36ZvldAAQUU6A2B/9UK9UZ+zIUCCiiggAIKKNB3AgcffHDMM5Ucu+yyS6h2M/WII44IdFdAqzKLLbZYSzcHu4Gz4447Zi0BnHjiiVWDf2g2+JJLLolZom/tRvv+plsgbi6Qzj777KxSKg7I/XvppZfC0UcfHStPaL78q1/9aja26DwSMHLQQQdly8+/oSKW/qhJX/rSl8J8882XH91z76lcIlHpVK3Z96eeeipsueWWWd5TwEw2wDcK9IjAWWedFeaYY46YG8ocuhBLN9KqZZFWYA444IBYzlJZXp7Sb4Th+RZjyqfbe++9A7//lAb1d9IJDwIbn3jiiUSXvXJ8ZN+k9I1vfCO9beq1qGNAvoW38iCsXjxW/fOf/wzf+ta3QqXvNU8lpyeTCSrL3whJ+7id48Lo0aPDwgsvHPfTeeedF/ItP+R33lFHHZXd9OFmUD4l73JrpinCO7+uVt8XYdXqup2vtwVuv/32sNFGG2WZ5DeRT0VdD9T6nRT9/ez0+S/dj6RuEwkwSu/zbrzn2LDvvvtmwa2bbbZZ+SRZi5ydKj+KOq6kjPdzeZ2+g2xLJe+ivuvJqpXX9FtgXs/lWhEczHloyWWnnXaKXUWyhZ/85CfD8ssv39DGpu9Uq+dKBClzzUKdEPUY+Qdt8hlgPHVCpOWWWy7ku3xlWPr9VfrtFVFOtXM+164R20dK28j7StvZC2UMecunorY9v0zfK6CAAgr0hoCBMr2xH8yFAgoooIACCvSxAAEHSy+9dNwC+p7mSaJ77703VvZS8cvTp1RqHH744XEanj7dZ599enaL6Qrj2GOPjfl79913YzPBBMTwnkRT16eeempYYYUVspt1VATlKzzihFX+0XrAySefHMdSEbXKKquE448/PnbTwEAqlngqnuX/9a9/jdPtvPPOId8EeyfySIsB3FxMTerT9RRPxa+66qox6ITK/tTXecxUj/5L30Wyt80224Tf/e532Y2H119/PVx++eVhpZVWymyZjn1qUqAXBQjCu/LKK8OoUaNi9njycuWVVw50gfbd73430LIFrb5QZuywww6xvKJLGILsuElFokuGn/70p/E9/774xS9mldKUbSeddFJWYU0AAn3QE4xAd0/5NKi/k054cPOVJ3jZd2k/0FoAAY8cJ0nrr7/+kADIvHW990UdA6affvpsVQTwXHzxxeHSSy+Nw3r1WMV3mQCjxx57LOaTm0K0frDWWmvFz9xwIVgln4o4LvD0L8GzJPbpkksuGX7xi1/E4F+GcY7w/e9/Pxx55JF8jOM5pubTdNNNFz9yXCJ4+LLLLgt8L0hFeMcFtfmvCKs2s+DswyBw9913B7oMK//jhivd/hHgzfn9G2+8EXNHNxqHHnrokJwWdT1Q63fSie9np89/6QI0BcjTUuR6660XnnzyyXhuzTGXblg557/66qujJwF5e+655xBbPtRyKaL8KOq4ks94v5bXtY6NbF9R3/W8VbPvO3Hu0mwenL77ApxnlJfTPKjENQABIHQZ/eMf/zjLGIHbtbqRziYsvSmifN1+++1jq64s97DDDgsXXXTRkFaEKfs4/031DeUBxcxXq6wropxq53yuCCO2sR/KGPKZT0Vte36ZvldAAQUU6A2BSXojG+ZCAQUUUEABBRTobwEqQjfffPNwxx13BCrb6VqJm1Uf+9jHhjwlQzDJtddem3XX1KtbvcUWW8TWALjZxhOgPNlJpcqMM844JMBikkkmiTek80/YNrJNBMf88Ic/DPvvv3986orAIf5YPq2gEECTEl2vpJtzaRivReaRm+48zUSz8PyRD26GfPjhh3GV7Ef6pU5P0+fz0Wvv6fucm4/PPPNMbA2Dp+joWmbmmWeON1axZV9SyUgi6Imbntx07fXWcnrN2vx0R2CRRRYJ9FFPJXgKYqCimRvttRKVyVSe8wR7PpCPYEWCYBjHTbrdd9897LHHHrEFDiqu05ONdJVHEB/lG4Ef5GEQUyc8vvKVrwS69OAGwKSTThqf6M0HGnFTlBbF2klFHAOWXXbZ+N2gpTdai+CP8pGbLXT712vHKoIfafqd4xF/3GjANbUww75keGqFKfkWdVxgv/LbIaiI9XJ8JoiUm1A8HZ2O3XSXyHGIfZ9Pq622WgxqI4iYQDcSy0rBNUV459fXyvuirFpZt/MMnwAtYFVqBatSjmhd8Oc//3kWcJmfpojrgVq/k6K/n904/yWIZcyYMfFagi4Rr7jiivhHICzd0L733nsZIcd7Aro57y5PtVyYtojyo4jjSsp3P5fX9Y6NbGMR3/Vk1cprJ85dWsmH83RXoFbXj/mccA1Aa7EbbrhhfnDN90WUr5zfck7DuQ11CZQpW2+9dWytl8Bm/lKiFS0eBipP9cq6IsqpVs/nijBie/uhjCnfL0Vte/ly/ayAAgooMPwCtigz/PvAHCiggAIKKKDAAAjQ2gE3r7ipOssss8QtolnddMOVgBKeAB87dmxYaqml+mKLeQqKrk7oQ5v8cyMutfDCjTGeXmeb99prr5a2hydMH3jggdi6SaoQp+nsdKONViAuvPDCGLjCjctKqag8zj333LEVICq32DbykYJk6FKK7lc23njjSlnouWFUDHKDmko0moAmcUPz0UcfjdtGKw/s11NOOSXe5EwbwBNvJgV6VYAbatxoo7UuWnvJP4mYzzNlBS1dUElN6zME++WDZNK0VExzk2f22WePgyh3uIFHmc2yuYlPeb322mvHllGYiKfdU8taaTmD8lq0B8dCgvBmmGGGGGSUgmQIqKB1NVqVYZ+2m9o9Bsw111yxVRSOAam85FhHIFZKvXSsIr8cj9KxilbCyC/H6BVXXDG2hrTmmmumrGevRR4XaOnhnnvuiecyrJdj5csvvxyP3dwQJ/CM4w0tO5UnAmQ5lpKflFLLOOlzEd5pWa28FmnVyvqdp7cE6GqB8oGbigQ+8PvjHKo8GC3luojrgVq/k6K/n906/11wwQXj8ZsA+dRaAoH4KUgGz1Sepxbkkml6reWSpimi/Ej5aPf6p5/L60aOjUV819N+a/W16HOXVvPhfMMrwLk/D4TQyhDnQMcdd1xssYUuVJtJRZWvlHMEBNLyGInzpD//+c8xSIbzpoUWWii2YHjMMcfE4OzyPDZS1hVRTrVyPleUUb+UMfl9U9S255fpewUUUECB3hCYqFQh+L/HdXsjT+ZCAQUUUECBESOw4M5jBnpbx56+0UBvX62N46YgN13HjRsXqAj49Kc/HZ+orzVPL49LLY48++yzsSKKIJZqN6pb2Q4qkJ566qn4NC83T/HiJlu6cdnIMlvJY7rJR+sBqcl3boTTKhCJFlbyXT41ko9emoYn2Z5++ul4ExNTtqf8Cf9eyu/g5mW+wd208J/uX7q9genmPBXP/PG9ppKcm360oNRoomWLP/3pT/F3QnnAzTyWM1JTOx4EwNB6D4mApnSDgFYaCIaYc845w/zzz1+xpYAivFs5BuTXy01buv7j2DPVVFPlR2Xvh+tYlWUg94buATlW0fINrTDw2kgq8riAOUExBKVxjOHcgNb06iWCezg2MS2tz/Dbq5SK8K603EaHFWnV6Dpbm26QA103b42kR+Zq53qg3u+kne/ncJ//ElxHMCq/cY4NnJ9WC4ov35X1XNL0RZQf7R5XUl76ubxu5NjIdrbzXU9Orb62c+7S6jpbme9fF6/bymx9Mc8km13RF/lsNJPtlK/5dfCQ0QsvvBDPLwnO5zojPSCUn67S+0bLuiLKqVbO54oy6ocypnz/FLXt5cv1swIKKKDA8AgYKDM87q5VAQUUUECBKGCgjF8EBYZXoNKNguHNkWsfTAEDZQZzv7pVeYFqgTL5aXyvgAKdEDBQphOqg7xMz38Hee+6bb0qYKBMr+4Z86WAAgoooIACI1mgchv2I1nEbVdAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQYSAEDZQZyt7pRCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAuUCBsqUi/hZAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQYCAFDJQZyN3qRimggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgqUC0xSPsDPCiiggAIKKKCAAgqMFIFXX301buqkk046UjbZ7VRAAQU6IrD33nuHnXfeOS57qqmm6sg6XKgCCiigQPsCnv+2b+gSFFBAAQUUUEABBRRQoP8FDJTp/33oFiiggAIKKKCAAgq0KDDttNO2OKezKaCAAgrkBSabbLLAn0kBBRRQoLcFPP/t7f1j7hRQQAEFFFBAAQUUUKA7Ana91B1n16KAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwzAIGygzzDnD1CiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAt0RMFCmO86uRQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUGCYBQyUGeYd4OoVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFuiNgoEx3nF2LAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigwDALGCgzzDvA1SuggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgp0R8BAme44uxYFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBYRYwUGaYd4CrV0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFOiOwETjS6k7q3ItCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoMn4AtygyfvWtWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6KKAgTJdxHZVCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAsMnYKDM8Nm7ZgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIEuChgo00VsV6WAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwfAIGygyfvWtWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6KKAgTJdxHZVCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAsMnYKDM8Nm7ZgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIEuChgo00VsV6WAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwfAIGygyfvWtWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6KKAgTJdxHZVCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAsMnYKDM8Nm7ZgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIEuChgo00VsV6WAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwfAIGygyfvWtWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6KKAgTJdxHZVCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAsMnYKDM8Nm7ZgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIEuChgo00VsV6WAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwfAIGygyfvWtWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6KKAgTJdxHZVCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAsMnYKDM8Nm7ZgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIEuChgo00VsV6WAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwfAIGygyfvWtWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6KKAgTJdxHZVCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAsMnYKDM8Nm7ZgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIEuCkzSxXW5KgUUUEABBRQoE1jhp0uXDRmsjzdvcftgbZBbo4ACCrQgcNrKo1qYqz9m2eWG5/ojo+ZSAQUUGFCBqy59aEC3LIS1N1xoYLfNDVNAgZElcPFTGw/sBm82988HdtvcMAUUUEABBRQYbAFblBns/evWKaCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCvxXwEAZvwoKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACI0LAQJkRsZvdSAUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEDZfwOKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCowIAQNlRsRudiMVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFDJTxO6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigwIgQMlBkRu9mNVEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFDBQxu+AAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigwIgQMFBmROxmN1IBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFDAQBm/AwoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIjQsBAmRGxm91IBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQNl/A4ooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKjAgBA2VGxG52IxVQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUMlPE7oIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKDAiBAyUGRG72Y1UQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUmEQCBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgf8I/POf/wwvvvhiGDduXBg1alSYYYYZpFFAAQUUUEABBRRQQAEFFBggAVuUGaCd6aYooIACCiigQAhf+9rXwlxzzRX/dtttt4ZI/v73v2fz7Lnnng3N0+xEn/3sZ+M6tttuu2ZndfqSwNtvvx3eeeednrU46aSTsu/QY4891rP5bDRjveB9xhlnZKYPPvhgo1l3uhEg0Eo53y5LEd/HMWPGZN/pW2+9dUiWilj+kAU2+WG4199kdqtOno7/O+64Y9Vp6o2Yd955437aZptthkw6KEZDNsoPCigwRIDAmEMPPTRw3j755JOHueeeOyyxxBJhxhlnDFNOOWVYeeWVw0033TRkHj90TuDUU0/NjpsPP/xw51bkkhVQoOcEOFdO53XtvL733ns9t235DFU778xP0+r7o446KjN8/vnnW11M1fmKqAPx/LoqryMUUEABBbokYIsyXYJ2NQoooIACCijQeYGxY8eGX/3qV9mKzjrrrPD9738/TDvttNmwSm/Gjx8fnnvuuTjqtddeqzRJ28NY/ocffhheeeWVtpc10hZw4YUXhn333Tdcf/31YcEFF+zJzedp4/Qd+r//+7+ezGOjmeoVb4J1kilPdZsUQKDVcr5dvSK+j3/729+y7zQBmvlUxPLzy2v2/XCvv9n8Vps+lRntHGtZBmVO+TIGxaiancMVGMkC//73v+M1w5FHHhl4Xym9++674cYbb4x/yy+/fCD4kQAaU+cE3nrrrey46blg55xdsgK9KPD+++9nv/928letTG9nmUXOW+28s4h1vPnmm5nhv/71ryIWOWQZRdSBeH49hNQPCiiggALDIGCgzDCgu0oFFFBAAQUU6IzAeeedFxc8yyyzhL/85S+BypULLrgg7LHHHp1ZoUvtuMAPfvCDsNdee3V8Pa7gPwJ6+03odQHL+V7fQ+ZPAQUU6C8BbtKtt956Q1qKWXbZZcPo0aPjk/hTTz11vNF4xx13hIsuuih88MEHgZYOVl111XDLLbcExpsUUEABBYoVoLu7NdZYo+JCeTDlhhtuiOOmmGKKQJldLU0yibe/qtk4XAEFFFBAAQVC8EzBb4ECCiiggAIKDIQAlSU/+clP4rZsvvnmseLkgQceCGeeeWbYfffdw0QTTTQQ2znSNoIWGEzdE+gl76222iqssMIKceM///nPdw/BNfWswHCW853+PnZ6+fV26nCvv17+ujn+tttuiy1KlN/81qibe8F1KdA9AbpqS90pzTnnnOGnP/1pWGaZZSbIwPbbbx8OOuig2M0r3WzSLeS6664bbr755gmmdYACCiigQHsCiy66aLj22msrLuSNN94I008/fRxHt0zVpqs4c48NrHbe2WPZ7Fh2PL/uGK0LVkABBRRoUMBAmQahnEwBBRRQQAEFelvg6quvDq+//nrM5Oqrrx6mmmqqQKDMk08+GSu/V1pppd7eAHOngAJDBD71qU8F/kwKJIHhLOc7/X3s9PKTYbXX4V5/tXwNx/DFF1+84mo1qsjiQAX6WoCgmJ/97GdxG0aNGhVbiCFYplrihux1110XKCfono0WZWhdhq6YTAoooIACCjQrUO28s9nl9Ov0nl/3654z3woooMDgCHxkcDbFLVFAAQUUUECBkSxw7rnnxs3/xCc+EZ8C3WyzzTKOM844I3tf5BsCc55++unYBHury/3nP/8ZnnrqqfDhhx+2tAhaWHj88cfDq6++2tL8aaZ2t+Wvf/1r4K9eevfdd2N+2e5Oppdeein8+c9/bnkV48ePD3/605/Cyy+/3PIymJG+wNm/7KdWEzdinnjiidCq2T/+8Y8YMPb888/HVhJazUet+dr1xomgtla3MeWtXau0HF97U6AT5Xynfh90/0cZ0sn03nvvhRdeeKHmKijLKIPoWqTI1I5bM7/3oo4ZHCPbLc9b8bNMakXNeRTongAtxKR0yimnhFpBMmm62WefPeyzzz7pYzj++OOz9516U1RZwrk6x6dWUlHnxlzzcF775ptvtpKNbB7O89NDEtnALr1hfzzyyCOBY1SziXNdrt04jraairj+KyIfrebf+RToJYFuXcc2c/5bzYeyh/P6f//739UmaWt4UeV8EXUg1TakiPKv2rIdroACCigwMgUMlBmZ+92tVkABBRRQYKAEqNzg6U7SyiuvHCaddNLw6U9/Oiy99NJx2FVXXdVypTALoNuXeeedN5xzzjkx4GHPPfeMn+k3e+655w6TTz55WGeddcI999wT19fIvx//+MdhscUWC/Sp/bnPfS7QxQN5bySo5/e//31Yc80147o//vGPh/nmmy/MNNNMsRUdlkl3U1RyVErtbguV21jwN2bMmFjR/aUvfSm2/DHLLLOE+eefP2u+Pq2fCvltt902cGNhyimnjPn95Cc/GV132223ipXMZ599dlwHNy1SWmutteKwaq0D/fa3v42G7JfZZpstro8mmXHlid9GEtvEU10EXPEdmnXWWWOzzqyT5Tea7rvvvthtEC0bsX/ZbpxOPvnkuot4//33w3e+852w1FJLhWmmmSbMPPPMcbvZ1zzJvNFGG9W9AU/lGUEFrJPv5zzzzBN4Upr3bMvtt98+JB/d9D7ppJPi9iy33HIxDwcffHD8/pJHnLbbbrsYOHbWWWfF6fiuPfTQQ0Pymz4UYZWW5WtvCxRZzjf7+0Cmke8jwRj77bdf/N3ydCRlCOXipptuWjdIo9byU7l98cUXx7L92GOPjcc3ygdu6vLb/vrXvx6eeeaZbCdyPNpwww3DjDPOmB1jmI5lVEq11p+mb8Wt0d97WgevrRwz8vOn9xyvKEs/85nPxDKG8pzjEGXos88+myab4PULX/hCLHt22GGHIeMaMbJMGkLmBwV6WuDOO+/Mzqe++MUvhtGjRzecX7phWnvttcORRx4Zy/00I61act6y4IILVjy/TdPxyrVLOqcu7zaknbKEawmWu+SSS8bVEbRJt7ScQ3JM4vjEK2VhIwGErZ4bp3wsssgiMR+/+93vwiqrrBKPkeRvuummi2XycccdF/7+97/Haer941yAfJN/ynPO+Tl/3GabbcJzzz03wexF7Q8WTED3Lrvskp2bL7DAAvG8Fc/DDjus5v4moIZrHubhGohrN175vNdeezW0/e1c/yWYIvKRluWrAv0s0Gq9QaPntY1Oh2G1887ky/XF/vvvH6addtpYL0DdAu+/+tWvxhaUP/jgg+xYct5556XZmnpttZwvX0krdSCNnF8XUf6V59XPCiiggAIKJAG7XkoSviqggAIKKKBA3wpccMEFWYssVASntPXWW8eAAJ5oIRDge9/7XhrV1CtPPXLDjSf3CVBJAROTTTZZbAGDcQTjUMlNcMKWW25ZdflUBDOept7z6W9/+1u48cYb49+4cePizb38eN7z5CE3/ah4qfQU0TvvvBP++Mc/xr+f//zn4YYbbggTTzzxkMUUsS0sg8STnNwUzldMP/bYY0NaTyG4aI899pig8ph98uijj8Y/7HBbccUVs7zylGlaTxqY1lP+BCafyQdBNeUBQvRfjuvNN98cK6IPP/zwGEiVlpleX3vttbDBBhsEKvHLE8u46aab4t+OO+4YnxwmkKZauvTSS8OJJ544pNKbpzbvvvvu+EfQzvnnnx8r18uXcccdd8Qb3jwpVp7Y5xjwx3ftkksuCQQPlSfsNtlkk3D99deXj4r7Jm3LUUcdFSvdmKib3jz9y77l+3raaaeFQw45JMsnT4g9/PDD8XuLe/oOlO9zZijCKluxb3peoKhyvpXfBzj1vo98V1ddddUJWnjh6X269SA4bb311qvqXGv5LJvjDL+drbbaaoLjB61F4UPl9F133RWPAV/72tcmKHeZjmMkFe677777kLzUWj8TturW6O89ZabVY0aaP72+9dZbgRuk5eUgxy3KaIZfeOGF8Zie5kmveFNmE+iUT/WMLJPyWr5XoPcFUpdL5DQF7zaaawJ7r7zyygkmJ0D5N7/5TRx+xRVXxDJ7gon+O4BrE8obzinzXTe1W5aksopgyrFjx8abqeWtyHBsoizknJTzcIKzy1O758YpHwR603UiAS7pfI5hXBNRJu+7777h8ssvj+e23PytlriuOeGEEwL5yicCWPijCyzO4wmWT6mI/cGyuK76xje+MeTcPq0DW1om4vjFMZgA93zi+M+1X3krcxzXaZWGv2uuuSZcdNFF8SGK/Ly8x6zd6z+W024+WIZJgX4XaLfeoNHz2kanw7PaeWcaV+n6gpYiqZMigIQHsFgGifP1ZlK75Xx+Xa3WgaRjBctKx4i03KLKv7Q8XxVQQAEFFKgkYIsylVQcpoACCiiggAJ9I0BgRHpyhsrVfODAxhtvHFvQYGN4UoUKyXbS0UcfHSskePqPSglu9HNh/6Mf/SgQNEPwRwrOqbYeghQIkqElGgIEeJr1D3/4Q3wSkWWQDjzwwHDZZZdNsAieuCQAg4CJZZddNlbE82QlFSUEF9DKAJXiJCq+Ky0jLbSIbaGpeYI2uFlAywULL7xwfEqVp0VJBO0QWMLTiwTssF0Ei2BGRfY3v/nNOB03b7mhmSp4GEjXWVR4UymdEvuZYTzxlE+77rprbKmF7wJPqHJjAheekuUmBk/1YoYfrZdUSuQlBcnwVBcV9i+++GJsoYH9Ncccc8TZaK2Hp0lrpSOOOCLwJPDee+8d7r///ri9v/71r+PTYsxH/njytTwRLIUDQTJ8F1gOgUfsX24kcGM32XJzgSfLygODWCatV6Sbw7Tww/qYn5vjbEuqwKfSnZsjpG57s06+FwcccABvA08br7/++rFVJH5D9VJRVvXW4/jeECiynG/l91FPgd97vhKbgBjKEyrJKbO4OUh5QpBjO+nQQw+Nv2F+Lz/4wQ9iYAw3ZFOrAdwQpUWE1VZbLXzkIx+JNxQpcwkWpPWDlDj2EJDZTGrXrZHfezvHjPJt4RhIOchTtxwHKP+4GckxgOMRgTS0BlGttary5dX7bJlUT8jxCvSeAAHbKXG+VETiHGaiiSaKiyoPis8vnzKI4BES5z+0LkIqsiyhez4CgCj/OB/nePHAAw/E1is51yVxk5Rg80qpqHNjbnRyXCQYmuMY5+ccEx588MHY+iLrJsCEIO9aifNejqtbbLFFPBay/zAkGIZEIArn0fnU7v5gWQSYcAzk3JtjK1602sZ1IMeQ1N0v1zNch+avN2npjZYcyRvHHlqP4VjHvCyDayOWSaAPLWCWBwGx/iKu/4rIB3kxKdDvAkXUG2DQyHltM9NVcqXMyV9f0IIx57eUg5RLKXiPMrHVVFQ5z/pbrQOplfciyr9ay3ecAgoooIACCNiijN8DBRRQQAEFFOhrgdtuuy08/fTTcRuoYP3Yxz6WbQ8BHOuuu27saoKblDytx42xVhMVvIsuumhsWYRlkwhMoXsGmtDmaVBu6H7729+OlaDV1sNT6uSb7pJSorsfuoVIgSEEg9DCSUo0qZu6ZaIbDiq7eRozJYbxxw3UFExBqyUEC1VKRWwLT1AShMKNWLr0IVEpT0UwT+PT8gHBQx/96EfjDcv807L/3959gElSlQsYLmQvJpCMqOSs4AKr5LgoUYJkWJDoVQTJ4YJkkSCi5KDkKFFZkKxEWdIKCJJEVhEJKkgW9Brufud6ijO11bl6pqfnO88z090VT701c6q66q//EOjDD91jMR31IQCFp0UpPA3KDxeDYsGe9aWFi80xUIrl8WRVDDhiOvY3HvwdMI6by3Ttk2YKuPDCC0MwCdNzgZunRtNlMC0X3rn5zEVtMjewn+o9fUymgvSiFfNSP1Ikc2GL4BWCaGI6etbN/uLCOYXgK1xi4e+Nvw8utrNeArUIoiHIKt4kZ1rqHjMerbrqquHp3PTvhGwS3IzhQht/q9/+9reD0WB6x23iZhCFG0kxExR/L2XZkuI88bUKq7gsX3tfoKp2vt3/j0ZCBA2ScYxCO0Z2q3ijlP9XfugiiYu9nRSCDOlignYxHoNYHm0AXVDwP0X2LP7nyS6TtnNk7CKgh/83btAynvakmVKFW6P/906PGWXbwTGDYwpdG1LoouPQQw8NQYscE2gDyQJQlhWibHn1htkm1dNxnAK9KZB2O1RVoAxd3I0dOzbPRMi5Mt0EFQvtKu0eZbvJXefFUmVbwrk1PwSTpN1KESRD4ArHDgLeOcZyTsl3mViqPDfmvI4fMqbEoBLWM3r06HDOSrZOvtcQ3Ehd+VyrcN6677775qOpM+fW7D++D/LgAAE0cX92uj84TvA9j1cK9VtnnXXy9fO9hOMqfpzX8z2B4FRublMIjGE/c05AcDrZ3mKhu1x++H7AcALjCZwhO0QsVX3/67QesT6+KjCcBaq4bhC3v9F5bavTxenTVx5sit8v6LqNB6bi94vll18+44c2iOy97ZQq2/m4/naugcR5i69VtX/F5fpZAQUUUECBooAZZYoiflZAAQUUUECBYSVAlz2xpBea47A0cwfZQDotXKBNb1DG5XGRk+ADCjcgCcypVbhQmgbJxOl46nG66aYLH9PsKgzgqUsutE8//fShj/s0+CHOzytPI8ZugYpp1tPpeF/FtpCpJwbJsMy4bi6SxCd1d9xxxwEp5ZkuFlKRc4GbwoVlMq+0UsjawsX3aaaZJuPmQhrgEpeD1WmnnZYH8HBjNC08oUohyIob3GXLIFvRSSedlM+WXqTPB/7nDQEqaZBMHI8NF7woXHCnG6i0kO2AC/r8lM3PtFwcS4O9ivsYg1hIT1/2d8L8/L1SyF7Dk8bNliq803URQBSDZBg+atSo0q6x0nl4X4VVcZl+7l2Bqtr5bv1/XHPNNQGP9vuII47IL2KnomQyIVCj08LN1eIxiEAQ2p1YuGCeBsnE4TwRHwtPlzdbqnKr9//ejWMGbWAMkkm3lWN1DBLieEwmnk6LbVKngs6vwOALdCNQhq2I3z3ILEI3mWWFNo9CEGXaflfdlhAAngbJxLrMNttsIQAkfma9aan63HjFFVccECQT18V5H+fG8eZvvcBFMu+UnX8TkE8wSyzF71Cd7A8C0uP3GSzTIJm4PuoeA2HZHoJZKWSUi5kbCbBPg2TCBP/5xfeg2G0v3Tel9a/i+18V9Ujr63sFhqvAUH2PrXf+W8uSawVcZ6Hw/YHrBrGdTOchgIasvu2Uqtt5jmVl1zAaXQOpVfcq2r9ay3a4AgoooIACqcD70g++V0ABBRRQQAEFhpMA2TfoC5nCk4NkZSkWnqLnSXvKTTfdlE2aNKk4SdOfeQIz3lwrmyl2JcQ4UpuXFW5e0nVTWSH1NkESlGL/0jxxyFNQZAIgI0q9wsVvCk+R1ipVbAvBKWXmrDO98bjffvvVqkYYnqZ854J0s4UbEBMmTAiTjxkzpvTGcFzW/PPPHzLu8JkMOLGQoSHeKCH7TuxiKY5PX0ntHrPI8NQo2U/Kyl577VU2OAzjRkHMipPWg5FkDCI1O3+j/C3UKunN9nQfk56ZbBKU5ZZbLsOkVqFrKdK78xP/XmpNG4dX4R2XFV/JKNRO6dSqnXU6z9AIVNXOd+v/g/8LnmCn0AVd7P6uqEXQWtr9UXF8M5/JKhW7yyhOz7hYaGfKyiyzzJIPLuveIR+ZvKnSrd7/e9XHDNq/NItZsknh7c4775wPKrbF+YgW3tgmtYDlpAr0iABBDbGk7+Owdl/J1hIDGsu6XyJQMZ6/kj0wvflZdVsSs0yWbUsaUElGyFi6cW5MRpNaheNazI5Y6/sT86aB4sVlcZ4fS/E7VCf7g2yksdTrepXvh2S04Zh51FFHhVnI1BML5wf1CkE4FIL/J06cmE9axfe/KuqRV8g3CgxTgaH8Hlvv/LcWJ9mpXnrppTCah6li93zF6Tl+kM241dKNdr7dayC16l5F+1dr2Q5XQAEFFFAgFXjvW2E61PcKKKCAAgoooMAwEODJei5IUsqyyTCcgAMuLvAUDk/m0KUNmVTaKTGNd6150wvOBFKUpQ6ni5t6hYwxlHfffbfmZPGCOkESBFbw5CF923NxmUCJGPjBxdZapYptYXtrBXSQgpxCdpYY/NNMXdKnKGtNH4c/++yzA4KBSEdcr0Q30swT5MJNkSeffDKfJU05nw8svMHtzjvvzEgFTEDLQgstVJgiG5C6foqRkwcssMACIZDo+eefD11VxSw8cdpYTz5zM5usL7hQV7ptSoOJ0n2MR0zjzzrqlfSGeb3p0nFVeKfL4/2CCy5YHNTS53atWlqJEw+pQFXtfLf+P2h7Y3ud3qgrQ2umjSmbLw4j80CtQuBiLPPOO298O+A17ZpwwIg6H6p0q/f/XvUxo5F12kayD6sqtklVSbocBbovMPvss2cvv/xyWBGvMatjp2smMJLga7IB8H0g7QqIZdPdRSx8RykrVbUl9Y4b6flnGvzdjXPjZr533HvvvSFjIOfYZIkplnrfoeL3J+aJx+Q4fyf7Iz0+pMeNuOz0tXgOkM57zz335Jlp0nni+3hDnM+c95eV+DfR6ve/qutRVjeHKdDrAkP5Pbbe+W8tt5jJivGN2p6y6xG1lhuHd6Odb+bcm8D4WtdAYt3KXttt/8qW5TAFFFBAAQWKAgbKFEX8rIACCiiggALDRiDtjuOAAw7IvvGNb5TWPfYrz8hzzz03++Y3vxm62SmduM7AehebmS1mruF9rSci4xOmTNNO4UIq3QOREYQLqTwd1U6pYluKF4TTesSbnqwnXthIx6fv6YaKi9gEPbUSKJNeSObiOj/NFG4GEGDERatYT+ZrFNDDNOkNaOpavDBF4FC9rDQsI/074enTNEMEf6vXX399duqpp2ZcVCeDULPlj3/8Yz5pozrkE7bwpgrv4urq/Q0Vpy1+7sSquCw/965AVe18t/4/SAseS72beEzTaHxcTq3XZo8ftKdVlSrd6v2/x7a4qmNGo/Y8bYfTtq0TN9ukTvScV4HBF6BL05gRjCDz9Byv09rQ3U/sNoOsMjHLCMuNgTJ0g1nWLlbZlrCNtUqt8/PYHjNfo7aUaVK3snNj1tPovDSOJ+Cb9Y8ePZpFDyjNHgMHzPSfD+3uj3gMZBtaPYanx5bDDz+8rFqlw9L54gSdfP9Ll9dpPWJ9fFVguAmk/wftXjcobnNZ+12chs/NTpfOmwbPxfYxHZ++b6adTqfnfdXtfKfXQIr1Sz930v6ly/G9AgoooIACtQQMlKkl43AFFFBAAQUU6GkBnrJJAyOaDRghQ8dVV12VjRs3ruXtS5+8LJs5ze7xzjvvlE3ScBgXx2uVa665Jttss83yrCFxuplnnjlcUF566aWzddddN9tqq62y3//+93F06WsV2/LBD36wdNkMjE9zlj0RWnOmySPqbX9xvjfffDMfxAWoejcD8gn/84anVSmxnryvqq5TTz01i6tZ0vHpDW3qss466+TdJ8UFMP3CCy8cAmpI7U5K+UbdWVXZhUCsRxXecVnxtd7fUJym7LVKq7LlO6w3BLrVzlf5/5F28dBouekT7+0IN1p+O8tsZZ5O11/v/z22xVW1w43qWqsdbsUjndY2KdXwvQLDQ4Dufm655ZZQ2TvuuCNrtXsMslRy3jp27NhwnpZuNV1gcu5G4Mgll1ySHXnkkSFw/Oc//3neDWxZNpmq25JawTBpXYvvY3vM8CraZNrjNOtZcX18Tr/HtfMdqtH3h3b3R6wrN4HT40YcXu81njdjGLuWqjd9HFf8PtPp97+q6hHr56sCw1Eg/h9Q93avGxS3u955bTpts9Ol85CVN5aYQTl+Lr6mXTEXx9X6XHU7z3oatZHp+PQaSK06MrzT9q/esh2ngAIKKKBAFDBQJkr4qoACCiiggALDSiDNMrDrrrtmY8aMqVt/MryceOKJYZrTTz+9rUCZ5557ru460vGkc6+yPPjgg9kWW2yRB8nsuOOO2UYbbRSCJz7xiU8MWFW8WFLvonFa1wEz/+dDOr6dbSFbC31f081Ro0LwUrwANNtsszWaPB+fpiHeeOON2+pSK02F3ExdSdscS1ldCZbixvmss84aJ5viNS6Di0XpE2B0H0bXWRQu4O25554ZF/ZJV59eLOPGTCzpPk6fVvvDH/4QJ6nstQrvqirTqVVV9XA53RWosp3v1v9H2j7G/+1aKt34v6y1rqqGd8utWL+qjxkcV+qVdF+l21hvnnrjbJPq6ThOgd4UWH/99UPXrNTuxhtvrJmZsqz2nGuTzfLVV18No+lOYrHFFhswKVlM9t9//4z2ZsKECSEQ5+KLLw7TcJOQ4Pdi6YW2pOpzY4LTycxSDABJt73T7x3psmq9b2d/cHwgiIpAnhdffLGlrDKcNz/22GNhXv6+Gj2kUFbvKr7/VVGPsro5TIHhJNBL32ObcUvb4bR9LJu30QNSZfOkyx/qayBl9WNYFe1frWU7XAEFFFBAgVTAQJlUw/cKKKCAAgooMCwEuOAa05YTQPCtb30ra5SO++2338646crTRDzNSar14gXtRhvf6CLCpEmT8kXwFGmV5fzzz8/iE5YnnHBCtvvuu5cunm6FYoaD9OnM4sTd3hYuvpDx56233goXx+leqVah+6FYikE/cXjZa3qB56GHHiqbZMAwgnGKTy+ly0jrMWDG5EM6Ta26YlsvUOaZZ54JSySNcnxSl312+eWXh+HzzTdfdv/992czzTRTsub33v7pT3/KP6T7mJTw/D/whFijC2YTJ07MDjrooJAuf4cddsiWWmqpfJm13qRW7XrXWnYrw6uwamV9Tjs0AlW38936/0gvvKfHgDK1NDijbHwvDuuWW3Fbqz5mNDrGxXaYeqT7sFivZj7bJjWj5DQK9J7AZz/72RD4QBDjXXfdld15553Zyiuv3FRFeco9BskQ1Fz2neJLX/pSduCBB4ZAiR//+MfZ8ssvH56OZwUEu0833XQD1tUrbUl6vpee9w6obPIhnabeuXG9QJl4/MTk4x//eLL06t62uj9Yc3p84Ny6XvdLX//618NDAgS4H3zwwXn3rATR000j+79W4fsb5/Tvf//7B0xSxfe/2E1sJ/UYUCk/KDAMBdJ2bSi/xzZLl9aXDJv1SqPxZfOmy0/b8LJpGZZOU6+db/UaSK31MbyK9q/e8h2ngAIKKKBAFHhffOOrAgoooIACCigwXASuu+66LAYL8DRooyAZtoun+MjIEgtZZVott99+e1bviZ4008cGG2zQ6uLrTs8FfAop1HnatFahjjGjDBdda5Vub8unP/3pfNWnnXZa/r7szamnnpoPpuuhtJDqPJY0ewrD2KcElVDwiRfZw4DCL4JHuNjNPASFxK6X5p577vzv59JLL81vehRmDx+ffPLJ7Gc/+1l4zw2ROeecs2yy0LVX6YjJAwke4mI5hRs0sRC8FbePv51aQTJMf/PNN8fZsnQf87fxyU9+Mozj6dd6f6s/+clPsptuuik744wz8mAdZuy2d17xDt5UYdXB6p11kASqbuer+P8o2/TRo0fnmaGuuOKKum0I3W8Mt9Itt6JDVceMuFza6tdeey1+nOKVto/C9n3mM5+ZYnwrA2yTWtFyWgV6R4D//8MOOyyv0G677RYC6vMBNd48//zzGdksY9lpp53i2wGvBHysscYaYdjVV1+dEaT8wgsvhM9l5/K90pZ049z4ggsuGGCTfiDrJwHilLXXXnvAeWk6XafvW90frG/RRRfNVxsf0sgHJG94mOHss8/O+C7xi1/8IoxJj2vnnXdeMvWUb/kuRPcs1DHNplfF978q6jFljR2iwPASqOK6wWBuMdctYgDmRRddlL300kulq+dawPe+973ScfUGdqOdp3vzWqXWNZBa0zO8ivav3vIdp4ACCiigQBR4785DHOKrAgoooIACCijQ4wLpBUSeDmy2kDkjFi44kO2klUIAyiGHHFI6Cxe3ebqUsuyyy+YBC6UTtzGQizsUgilqdSnx9NNPZ6lHDJgpW123t4WbBvHJ0eOPP75mEMs999yT/fCHPwxVnGGGGcIF8rS+aQaY119/PR0V3scbHATC7LLLLgMCR9KJjzzyyJAynawyBKjETC50f8RTnxRuqsb36by85yLUfvvtF5725PO4ceN4KS0EYcWL5OkEmJOCnzJq1Kjs8MMPz0fH/cuAGASWj0ze7LPPPiEjUhxU3MdkiaEwPF1+nJ5X0t/H4KV55pknW2KJJfLR3fbOV9TBm6qsOqiCsw6CQDfa+U7/P2ptNt05UGijvvnNb5ZORjYB2rvhWLrlllpUdcyIy/zb3/4WbmTzBH2xECTID2XLLbcccCO0OG0zn22TmlFyGgV6UyDNqkcg80orrVQ3Kx/n2gTpx5uWSy+9dLb55pvX3Lh4fCCLFeeiFAKtx44dO8U8vdKWdOPcmGP6U089NcU2c35NF1axpN/V4rAqX1vZH6x33XXXzc+Tzz333AFZFdJ6HX300SGjI8M23HDDMIoHNBZZZJHw/qyzzspv+oYByS8Cr4455pj8O96aa66Zj41/E518/6uiHnmFfKPAMBbo9LrBYG46D6/E7xQE4nGeTIbktHCOS/uZZntJx9d73412vp1rIPXqWEX7V2/5jlNAAQUUUCAKGCgTJXxVQAEFFFBAgWEhQP/w119/fagrqV3XWmutputNAAvpsClvvPFG1s7T/TwRyI21mK2DYBueIOSJUS5ictGhnWw1jTZihRVWyCfhIi/p4WO3Oy+//HL2ox/9KPvc5z6XX7hnYlK41yvd3BZSpx977LFh9XR3xRP7BMTwnkLdTjnllHCjIN7IJCNPGqjBdLPMMgsvoXAhiH1G1oZYCAyKNjfeeGO24oorhid2seHi+2233RYuLNE9F2XGGWfM9t133zh7eKUbq3ghmyc6SYf/61//OuzPmKacGxrXXnttmJ7Akr322mvAMtIP/G1xkZunh2Mw1mOPPRa2lUwvlK985SsDgqnGjBkTuk1iHE4nnnhiCOzhM3Wgj26eXv7ud7/LoLwU9zF1X3XVVcN4/i65efPb3/42fObvkxv1/K0SbMWT1AQxpWUwvNP1tfO+Kqt21u08gyPQrXa+0/+PWltPEF1MYU7XeNzo48YXhfbg5JNPrnsjtdZye2V4t9zS7avqmJEuk4BY9sUTTzwRBrMvCBLkJjeFruq4udlpsU3qVND5FRg6Ac6FaCvoDpNCsAzt+TbbbBPO4x599NHQXStPytO1DhlGOCejzDvvvOHcsNhdThj5n19plsDx48eHoSw7zeAXp++ltqTqc2MCuOl6iHNjAhkpZGrkfJnzd8rGG28cPocPXfrVyv6gCuyneK5MvZdZZpmQOZLAewrfaw499NDsqKOOCp8Zz/dECkH5J510UnjPOfjqq6+eHXfccaF7JgZynkB2M75jxMCrnXfeOUu7NYnfcZi+3e9/VdSD9VsUGO4CVVw3GEwDgu5WWWWVsEqOHwRmci2A7LKcz9J20KakhWNas6Xqdr6dayD16lpF+1dv+Y5TQAEFFFAgChgoEyV8VUABBRRQQIFhIUDq7hggwhNyZOZopaRPKrYa0LLwwgtns88+e0irzQX1j370oxlZUL785S9nPOnDhfKLL744f/KwlXo1mvbAAw/M5p9//jAZdkfS9QAAQABJREFU2Wu4aDLbbLOFC/a8cnGZC65cxOeHwgXdeIMwDEh+Dca2bL311hmZCAgeIlsLWVjwItPMzDPPHAI/qCP7kIs8m222WVLD/3/Lk70xeObuu+/Ottpqq9CFFt6xcIODi++U++67L3StNO2004Ygm9VWWy2LXWKxHLpzid01xfm5gHz55Zfn6Y3J/IAP3R/RrReBMZhTllxyyYzxtW6KcEH9iCOOCBfBubjFMvghdfKECRPCMrjpXLw5SwBPDIIhMGaPPfYIF8pJl878BBoRWEQaZm4yTDPNNGFZZX2s89Qu3UtR2C62F2+2BadHHnkkjGPffPGLXwzv46/B8I7rave1Sqt26+B83RXoZjvfyf9Hra0m4IKMYqQxp/DU+RxzzBGOEfy90p0H7WCtjGS1lttLw7vhVty+Ko4ZcZncUKS9PP/880OALIG17Asyj3HDlve0j/HmeJyvnVfbpHbUnEeB3hFYaKGFQiBx7KKGNoJudjiPo3s9hm+yySYZwdSx607OB2+44YZwLl5vSzhfK2Yh3HbbbUtn6aW2pMpzYzaWIG6CuzHlfJTzUroLvfXWW4MFN3zJutLt0sr+iHWh7pyjMy/bwN8C20BAy/TTTx+yPvDdlG6TrrzyyvwcnfkJjiGAlvMEvvMQrE9QOt8hySz0ta99LSNLEYXlxqCcMGDyr6q+/3Vaj1gfXxUY7gKdXjcY7O0nkJDrH5THH388I7ssAYacz/LgFO1ovIbANHTh1mypsp1v9xpIvbpW1f7VW4fjFFBAAQUUQMBAGf8OFFBAAQUUUGBYCXCzLpa0m6E4rNEr83BRgPLwww9n9JfcbOEJ04kTJ4and7jpSRc5MWhn7bXXDsEU9dKvN7uesul42v7222/PuJEYnxTiYi0XTKgLARBkCyFzARdaYyFwp6wM1rYQNEK9eAKKgBiCQOJTk9SbJ/vpf3rvvfcuq2Z4WpeneKlv3G6WQcaXWOg+iGUQbBO7e6IrpthVE+slQIqngpdbbrk424BXboKwb+kaiYv3FIJ7YopjbqbGbWF99QoBKARhEcDEDZVXX301TM5FcS6Wsz1cYC8WniLl4h0Xzik8ffqrX/0qbAcX1Vku28DTsDEwiCw3MUtPXB5POBOUwxOucVv4W4nZbXga+pZbbsnTOcf5eGXewfBO19nO+6qs2lm383RfoJvtfCf/H/W2nKxUHE8InIyBbBwjaK8IViNIb7311qu3iJ4e1y234kbHdrbdY0ZcHvUlwJGbrxxryLzGvuB4QAAlGSGq3B+2SVHeVwWGpwBBDrQLl112Weh+qdZW0NaT+Y8uNgmqbqbE7n6YlifkOaetVXqpLany3JhzdAK+CVokEInzUgrnxmR95GYwwfSDUVrZH7E+ZJJ84IEHwvcIjiN8/3vhhRfCuTrfKwlw5zsZQbLFQtYGvnOS+TMG2nN+wHk+hUAtAoQvvfTSKTINVfn9r5N6FLfJzwoMV4EqrhsM5rYTZMf1AdoIgiz5Hk+AHgF8XBu4//7784epqFer7WiV7Xy710BqeVbZ/tVah8MVUEABBRRAYKrJJ+b/f2auhwIKKKCAAgoMusDYi97rTmfQVz4IK7xt67sHYS3dX0W8IEo/9bH7HYITyF5C4UmeNE12t2v0yiuvhL6ouUDLDVjWH2/MNlr3UG5LzHAzadKkkJmHC8NpVz+N6k7gCllzuAjNBaJahYvvBJgQoMLNUozIMNNKwZagFC6EkyUC47I0+Y2WSUDPU089FZ5IjtkmGs1Dl1F0l0R/4+wvLmCRyajdwraQRYa/EbYjBhM1Wt5gejeqS63xVVvVWk+nw0/9/DydLqJn59/lp7/r2bo1U7F2/z/qLZu2hxtmtEWf/exnm/6fq7fMXhvXDbfiNnZ6zIjLI0iQ4zVP2ZIFopWnbeMymn0dLm1Ss9vjdMNDYPwV/58pbnjUtrVabrDp6NZmqGhqupCgm1V+aEPI6kiAS6vnk1SH4wE3NylkTdlxxx3D+3q/erEtafXcmACYgw8+OGwmwehkR6RwXkzGS86L6RI3Bo+EkYPwq539kVaLYxPLePbZZ8N3DL7PcDO7mcL3CjLIYMANbb6j8L0mPgxQbxmdfP8rLreTehSX5efmBS55evPmJx5mU45b8LJhVuP/r24V1w2GesNjEA31IDB/nXXWabtKrbbztVbUzjWQWstieJXtX731OE4BBRRQYGQKGCgzMve7W62AAgoo0CMCBsr0yI5oUI2y4JIGs/Ts6H7alp5FtmIKFAQMlCmA+FEBBRRQoDIBA2Uqo+zKgvbbb7/sO9/5TgiyefHFF9sKtulKxbq80FqBMl1ebcPFj9T90RDGCbouYKBM14n7agV77rlneFiGjLgrr7xyzW3baaed8q6meSCJB4UsCiiggAIKKNC8wKjmJ3VKBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgUYCZCuI3QmOGzduxATJNHIZqvHuj6GSd70KKNCqAN0q0Z3yhz/84eyZZ54JXdUVl0EX12eeeWYYvMACCxgkUwTyswIKKKCAAk0IvK+JaZxEAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQoI7ANddck3Hz8qKLLspWXXXV0GXE1FNPne2999515nJUtwTcH92SdbkKKNBNgZVWWiks/u233w5d9t10003ZO++8k6/yrrvuyrbddtvsX//6VxjmMSan8Y0CCiiggAItCZhRpiUuJ1ZAAQUUUEABBRRQQAEFFFBAAQUUUEABBaYUOOKII7KJEycOGHHcccdlCy200IBhfhgcAffH4Di7FgUUqFbgsMMOy2699dbsgQceyK677rrw84EPfCCbb775Mrrxe/XVV/MVbr/99hldMFkUUEABBRRQoHUBM8q0buYcCiiggAIKKKCAAgoooIACCiiggAIKKKDAAIG55por/zxq1Khs9913z/bYY498mG8GV8D9Mbjerk0BBaoRICjmhhtuyPbff//sIx/5SFjou+++mz3++ON5kMynPvWp7Lzzzsu7+KtmzS5FAQUUUECBkSUw1b8nl5G1yW6tAgoooIACvSMw9qIVeqcyXajJbVvf3YWlDv4i6c+eMs0002TTTjvt4FegwjX207ZUyOKiFOiqwKmfn6eryx/Khe/y098N5epdtwIKKDDiBcZf8UjfGmyw6ehht20vvfRSdt9992X//Oc/s8997nPZ9NNPP+y2oYoKc0P3r3/9a1gUBnQ/NRTF/TEU6q6zTOCSpzcvG9wXw8YteFlfbEevbsRbb72VPfHEE9mzzz6bcT1n9tlnz+aff/5s0UUX7dUqWy8FFFBAAQWGjYBdLw2bXWVFFVBAAQUUUGCoBGaaaaahWnXl6+2nbakcxwUqoIACCiiggAIKKNCBADcwN9hggw6W0B+zkg2Bn6Eu7o+h3gOuXwEFOhXgYa2llloq/HS6LOdXQAEFFFBAgYECdr000MNPCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAn0qYKBMn+5YN0sBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBgoICBMgM9/KSAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCijQpwIGyvTpjnWzFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQYKGCgz0MNPCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAn0qYKBMn+5YN0sBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBgoICBMgM9/KSAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCijQpwIGyvTpjnWzFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQYKTPXvyWXgID8poIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKNB/AmaU6b996hYpoIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKlAgYKFOC4iAFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECB/hMwUKb/9qlbpIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKFAiYKBMCYqDFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBfpPwECZ/tunbpECCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKBAiYCBMiUoDlJAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRToPwEDZfpvn7pFCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAiUCBsqUoDhIAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQoP8EDJTpv33qFimggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgqUCBgoU4LiIAUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIH+EzBQpv/2qVukgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooUCJgoEwJioMUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF+k/AQJn+26dukQIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooECJgIEyJSgOUkABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFOg/AQNl+m+fukUKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACJQIGypSgOEgBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCg/wQMlOm/feoWKaCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCpQIGChTguIgBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgf4TMFCm//apW6SAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCihQImCgTAmKgxRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQX6T8BAmf7bp26RAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQImAgTIlKA5SQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6D8BA2X6b5+6RQoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIlAqNKhjlIAQUUUEABBQZJ4MhlTh6kNQ3Nag68b9ehWbFrVUABBXpI4M2dru2h2lRblenOWK/aBbo0BRRQQIGWBN7debuWph9OE3/gtPOGU3WtqwIKKFBTYPz48TXHDfcRG2ywwXDfBOuvgAIKKKCAAiNUwIwyI3THu9kKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooMBIEzBQZqTtcbdXAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQYoQIGyozQHe9mK6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACI03AQJmRtsfdXgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQYIQKGCgzQne8m62AAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCow0AQNlRtoed3sVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIERKmCgzAjd8W62AgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCigw0gQMlBlpe9ztVUABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFRqiAgTIjdMe72QoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigwEgTMFBmpO1xt1cBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFBihAgbKjNAd72YroIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIjTcBAmZG2x91eBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBghAoYKDNCd7ybrYACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKjDQBA2VG2h53exVQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgREqYKDMCN3xbrYCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKDDSBEaNtA12exVQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBYavwP/+7/9mTz75ZPbPf/4zm2eeebIZZphh+G6MNVdAAQUUUECBQRcwUGbQyV2hAgoooIACClQhcOCBB2aXXHJJw0VNM8002fTTT5/NOOOM2ZgxY7I11lgjGzt2bMP5Oplg3nnnDbOvueaa2RlnnNHJogZl3uFW33oor7/+ejbVVFNlH/nIR+pN1vVxiyyySPa3v/0tW3XVVbNzzz236+tzBQr0o0Dazp999tnZaqut1tRm3n777dn2228fpt1yyy2zo446qqn5WpnI//FWtLoz7WabbZY98MADNRc+9dRTh2P/rLPOmq2wwgrZJptski288MI1p++lEb///e+zueaaa0ir5N/4kPK78kEQmDBhQrbVVlvVXdOHP/zhbKaZZsoWWGCBbK211so22mijbNSo3r2UWuv/9q9//Wu26KKLhm3dcMMNs+9973v5dh999NHZD37wg/CZ4+fcc8+djzv99NOzY489Nny++uqrs8UXXzwf5xsFFFBgqASuvfba7Kqrrsp++ctfZo8//nj297//Pa8KgTK01bvssku4/pOPaPCm1rkX7T9BOJ/73Oeys846q8FSHK2AAgoooIACw02gd7/dDTdJ66uAAgoooIACgyrw8ssvZ7/73e9aWufNN9+cHXPMMdnaa6+dXXrppV0Lpoj1+uMf/9hS/YZq4uFW31pOF1xwQbbffvtlt9xyS/bpT3+61mSDMhxTAmWGy9/AoKC4EgVaFEjb+XfeeafpubkhGNs1ltGN4v94N1RbW+aLL76Y7+dGc95www3ZkUcemV100UXh5kmj6Ydq/B/+8Idszz33zP71r3+FG0BDVQ/W69/4UOq77sEQ4LjC33kz5a677gqBz6uvvnp25ZVXdu07RDN1qTdNrf/bf//73/m2/vnPfx6wiL/85S/5uH/84x8DxhGAzjIpnNdaFFBAgaEUeOONN7LddtstO//882tW47XXXsvOOeec8LPOOutkF154YQh4rDVDo3Mv2kACZfxeX0vQ4QoooIACCgxvAQNlhvf+s/YKKKCAAgooMFmAJ4bmm2++UgsugvN00MMPP5w999xzYRpumG2xxRbZddddF7KPlM7owGElwJOxe++997Cqs5VVQAEFFKhGgOxdH/rQh/KFcVOY4/+bb76ZPfXUU9lbb70VPm+66abZcccdF4JR8ol76M2nPvWpUGfOaywKKDB4Ap/4xCemyJZCdx60I88//3wIFqFdIRibDFV8l5hjjjkGr4KuSQEFFBjhAgQsbr311uHaDhT/9V//lREIs8QSS4T2m2xfkyZNym688cbwwzTXX399tvTSS2fjx4/Ps2oxPC2ee6UavldAAQUUUGDkCRgoM/L2uVusgAIKKKBA3wnssMMO2Re+8IW628XFbtKK8wQST2pzgfumm24KadTrzujIYSHATdBeKlzI4+/MPtJ7aa9YFwUU6FcBnhyO3QgWt5GngA855JCM7kVol/fZZ59s3XXXzRZccMHipEP+uZeOZR7HhvzPwQoMogBdatTLUPDII49kBNr9+te/zn71q19lBxxwQMhSMIhVbGpVVf/fbrPNNnmXtbHrpqYq4kQKKKBAhQKvvPJK9sUvfjEjAxZlqaWWyuiWtSyL7O6775499thj2bhx4zLa7meeeSZc86HtpkvuYumlc69i3fysgAIKKKCAAt0XeF/3V+EaFFBAAQUUUECBoRfgiSP6qU6zjpx33nlDXzFr0JcCXLxbZpllsoUXXrgvt8+NUkABBYaLwNRTTx26XfrqV78aqkywDF0wWeoLeByr7+PYkSUwevTo7Pbbb88zUV522WU92RVR1f+3H//4x8P5LOe0H/7wh0fWTndrFVCgZwQIToxBMttuu212zz33lAbJxAoT2Pfzn/88W3nllcMgulcigMaigAIKKKCAAgoUBQyUKYr4WQEFFFBAAQX6WmDjjTfOt4/uGAaz/OlPf8peeOGFtlf50ksvZfw0KnQ18eSTT3Z8Ab/T+v79738P9WA57Rb6AueJMLapG4V0+lw4a7f84x//CE8X/+1vf2t3EWE+tpO/x06X01ElnFmBES5A5jGyBdB2DVWhjX/xxRfbWj3dgvz2t7/t6DjT1opbmKmT7WthNaWTrrXWWvnw3/zmN/n7em+Gw3GM4wbH/HfffbfeptQd1+z5Rb2FVGFVb/mOU2CoBT72sY/l3TNxvKBr12ZKp+d4/G9zbHr22WdDVqxm1uk0CiigQL8IPPDAAyF7DNsz88wzZ3S5TBB0ozLddNOFwOhpp502TErWMJZVVeH86+mnn87IXNhO6ZXzt3bq7jwKKKCAAgr0k4CBMv20N90WBRRQQAEFFGhJgJuKaXnnnXeyRRZZJPzst99+6agp3o8dOzZMlwbeTDHR5AFcOPnGN76RzT///NlHP/rR7BOf+EQ255xzZptttlnoQ7vWPLEel19+eQigWHbZZTOe6uQiPf1o33rrrQNm5cbqjjvuGJb9kY98JPvkJz+ZcVGIp6l4eqrZQJN26ptWhCe31ltvvdClxYc+9KFQD7abNMef/exnszPOOCMruqfz856bAWT/mXHGGbPZZ589W2yxxTK2ie0/4ogjBmzLWWedFfbDySefnC9m/fXXD8NIo19Wbr755uzzn/98Nuuss2ZzzDFHMJtlllnCMJ4WrlVOPPHEsNz4ZNphhx0W9ilZY6jfl7/85fxC2eKLLx6m/cpXvlK6OP7W+LtYbrnl8u1kn2NG9yH8fXDD26KAAtUL0C7y/3bhhReGhZ9yyinhiVPaKf6faTvHjBmTX5RvpwbXXXddaP9YD0/4cyE9ltNPPz2snyf0Kfyvb7XVVuF/nzY+tvW0A80EV3KcYB087T/ffPOF4wxtGm0g7V2x0E7HYwxtclmhnWaZTIfXX//617LJsksvvTRf1uOPPx6mqXr7SlfcxsD3v//9+VxlqfrjyME+jrHeJZZYIjjG4yP7Le6jH//4x7Fq+SvHdI7tHB/5e43HfD6TOa/W/uIYH5fb6Pyi0XGMylRhlW+UbxQYBgKxHeEG7DzzzFNa4yrO8ch8RZdynP9/8IMfDMcm1sd72va77767dN3N/N+Wzlhj4Jlnnpm3GXRhYlFAAQUGW2D//ffPgwSPPPLIbKaZZmq6Clx32XffffPpL7nkkvx9q+decUa68+a6BseBhRZaKHS1zLUFzn8blcE4f2tUB8croIACCiigwECBUQM/+kkBBRRQQAEFFOhvgZ/85Cf5BnJDKS1clI5ZZho90U9f188991y4QZUuI33/2muvZWuvvXZ2yy23pINDBpMrrrgiDL/gggtCYMmACSZ/iPUg28n//M//ZL/73e/ySZ544okBGQ/on3vPPfccEEDCxGQ74cYlP+PHjw8X3FdbbbV8OcU3ndSXp10J/CCYBMdieeONN7Jf/OIX4Yd09T/96U9LnwRj3A477FB6k499csghh4Sb1/fee28IoiEFc7SK64xWxafr+YwlQTXxZmSch37Pf/azn2W33XZbuMn4rW99K5tmmmni6PD68ssvh3WxLaeeemp2+OGH5+N5kp5+z+PTbTE7DDeti2XChAnZdtttN+DGeZwGO+rPDzfaf/jDH2YE/lgUUKA6Af4/CRjg/3633XYLbUK6dDIFPPTQQyH47Y477shop1spN954Y0YQJU+KEnxDN38LLrhgvgjWSx0IBnz00UezNddcc4osMmT44DhB8B7tN0F1xfLnP/8522STTbI777yzOCpsGwGV/Oy0007Zcccdl3eb8ZnPfCZkQuBm7pVXXhnGFxfA9k+cODEfTEDEGmuskX+Ob2KwBQE+BGtQqtq+uI6qXmlTY1lnnXXi2/x1qI5jVIAAUfZHLG+99VZ+bHv99dfj4PDKDfIvfelLUwRT8jdNBjZ+ONe5+OKLw42cATNP/hCPmY3OL+odx6qyKtbNzwr0sgBtbsxGwE1RunUtlirO8Ti33WKLLab4/sC6ON+MbfvRRx+dcQM5LfX+b9Ppmn0f23OmL55XN7sMp1NAAQU6EYjnox/4wAey//7v/255UZtvvnl26KGHhvk4b/3ud7+bve9972vp3IuZCULm/KvYfSfnbFxH4OfVV18N10TKKjlY529l63aYAgoooIACCtQWeF/tUY5RQAEFFFBAAQX6S4CbjWnmkQ022KCrG8gNToJkeNLo6quvzuiCiBtYZCIhoILAFOpQ7wlNbm4SNEHGkk033TQ8dU62gdVXXz3UneATboLydBLLPPDAA7P77rsv3Kjk5ulXv/rVMB3p2gnaiTfIyja8k/p+5zvfyY4//vgQJLPSSitl3CimWyNu8BFAcuyxx4abwqyX9XBztli4eLTllluGi1BcvCKohRsSBKZgNG7cuDAL20LwCDcFGcaNbIJrYjn33HPDMC6EpeXrX/96dtJJJ4UgGZ6o5yl96kjGBvYPGQYIVGFb2Ee1Ctb0k05Zcsklww1xbobTX3qjwoU09gPZJbjYx1NxBD7hxE1L/l7ivuViHDdAikE9jdbheAUUaE7gmGOOCccEglgIOKQ9pe3aY489sqmmmioshKwzxQxe9ZZOJpANN9wwBMnwxCsXzckIUFbefvvtkMmGYwPtOOt++OGHwxOpZAWgcGOWtrCs0L7HIBmm/9GPfhQCOAnk5CL+XHPNFWYjawxZumIhI0FsZwiAKcs+UsxEU2bADdsYCEqbHM3iejrdvricTl+pJ0GW3//+98Oi6IJp9OjRUyx2qI5jVARvjmXRkMxlfOaHY0Ys7FuySZCFiGM+2WP4u+U4yfGSc4B484cb+fz91CqNzi9qzcfwKqzqLd9xCvSawIMPPhgyD3KeSBvKOWWxVHWOx7lwbFvJIsn5KueIHCto28m2SCFAne82FgUUUKBfBQgc5xyHQtZVznFaLWSLJLsvhe/9sdu8Zs+94vo4F6YN5nsDD8zcc8892f333x8y3vK9nsJ5WNl1jl45f4vb4qsCCiiggAIKvCdgRpn3LHyngAIKKKCAAsNU4Mknnwz9VRerT4ABQQ1cEOFmZfr0D0+TE3jS7cKT+2RPmWGGGcKq6O6HJ5q4qcnNVOrIDTwCNcoKWVQI4CD4hQvzFG4+coOMbAXbbLNNyBzDU61cVF9llVXyxRCwws8KK6wQpuNmITdLqU+t0k59yb4QUw3TRQc3e+lCKBaG8UNQSbw5S4YFnu6KBQe6KYpBIddee22WPvGPAfuPoBIyHXBDkH1KhgO6TyL4Jha2oditBjcSCaChYMKFsXhBi2EELFE39gnj6PucrpTKMsJwI4RCfeguhUL2nrJMOmFk8ovtjhf7uGnL/ouFYCi65uImKDdJuYHN9nIBLnbREqf1VQEFOhf44x//mC2//PIhECZ2p8FSyfBCQGLsgo8U6/WyccWa0CZ98YtfDE/d09bTJseAlzhN+kqbzA/t3brrrpuPYp6NNtoo/N8TKHnXXXeFtiBmbGFCAnhilzwEqZCNK23TaLsIsCAohPby/PPPDwGFses42rxrrrkmHEeKwRgsP96k5YYEbVtZoAztbmwP2e5i6WT7istq9JlAI7qeSgvrp70lqxpZESh0UVjW3dRQH8dWXHHFtOoZXWfFfZWOIDCGYz8BNdwg/8IXvpCPphsAfjjGMZwATG7Y8PdbVuqdX5RNH4dVYRWX5asCvSJAe0a7mxbaPjI9EVTNdw0CtGebbbbQZi+99NLppOF9Fed4tOWch1JWXXXVkF0wPafmvJPu1mhzOWf+9re/Hc5hwwz+UkABBfpMgIx7sRAo025hXoJVKLTpdGXX7LlXuk7Orzkvp2vpWOiqlO/w8cEdHtYh42NaeuH8La2P7xVQQAEFFFDgPYHWw3Dfm9d3CiiggAIKKKBATwjss88+oVsKuqZIf7gByg3P7bfffkCQDJ/p0mYwCql9Y5BMuj4ucBMQQeFmF91v1CpnnnlmHiTDNPFmIN2BcAOQws2/NEgmDPzPL1IE40DhRu4NN9zwnzFTvrRT31/+8pcZ3W6QVWX33XcfECSTroGn22Pdi11bEQwSt4WbvmmQTFwGNwZ5ip0yatSoAcExcZpar/yNcMOD7pS4kZHeUI7zcCPitNNOy4OQCGCqVbj5HINkmIb6FLtqKpuXjEJcmONn6623Lpsk3ABNsx0VrUpncqACCrQlcMopp2RpkExcCN3ZxfaKrFiNCjdZabu4qcrT/nyuFyQTl8c8aZBMHM7NWIIHY6HtSEvsboO6kymtrE0jow1ZtGLZd99949uwzvhUbrwpG0eSYYYMX5R4oZ9sCsUugGJXhgT51Qokanf7Yl2afWUbCBxKf+hqiZsZMUgGU9pd2utiGQ7HMQKaYvYIbsakQTLp9nC857hPIVNSvUxytc4v0uUV31dhVVymnxUYagEyDKTtB+/5f6Ntof0lSIZCQHXalV5a7yrO8ThHjYVz8jRIJg7nHJGAOAoZCsk0Y1FAAQX6USA9hyl7gKXZbU4DW3iIqt3CcSFdVlwOmWWnm2668DGtMwN65fwt1tVXBRRQQAEFFBgoYKDMQA8/KaCAAgoooECfCpByl2wqPBV/zjnnhK6Mur2pY8aMqRm8wrp33nnnvApkjCkrBF/wlFJZSYNrYuaDsukYlnbdQVBKWWm3vjzBTsYWupIiC0u9wo1KCk/6pyXecGVY2kVIOg3vCS76zW9+E7oKOeqoo4qjSz9zc2PChAlhHNtY7yIbaZnJfkOptU8YR5aedgqZd+gyY9KkSXVTR5ONIpaiVRzuqwIKdCYw88wzh0xXZUshmGLOOecMo8jiVa/Q/RHBLgSYkOGKC+J0l9FMiVm2yqZN26q0DgR+xIv8ZOaKXSyVLYNun2JmErJxkf2KQlscM1XddNNNA2Zle8haQhBOPLbQjrJdaYntNplragUKtrN96Tqafc/2ELCZ/tCOxkxsLIebyWPHjg2BmKkn43r9OEYdCfqJpVFGPAKUKASITpw4Mc424LXe+cWACQsfqrAqLNKPCgy5AG1F2n7E9wSBk8UxFrIB0uaSCaxYOj3H4xhy2223hcUS+M85a61CV3t0rcZPPLeuNa3DFVBAgeEqQNdLsZBtr93C+VAs8Vw4fm72lfPyxRZbrHRygs95EIbyl7/8JbzGX71y/hbr46sCCiiggAIKDBSY8lGqgeP9pIACCiiggAIK9LzAYYcdlsUU6KQhp7slspNwMZuuNXjSniwye+21V0YXRYNV0m4yyta5wAIL5IPTtML5wMlvuCATn/pPh/OebnkobF+8MBMGlPxKb9oWn3KKk1dRX7K+UAjuICCEdbFtDz/8cLj4H2/uphermD7d/tSFccUS+xgvDq/1+dlnnx0QmHP88cfXmjQMj9tAlydcSCvLPlDraeK6C05GxnUwiJscPBGMFan9yeSQBjMVrZLF+FaBvhZI/09it2zNbHCz084999x1FxczytS7oM7/LJk9YuAFXTa10kbVq0NcP5VM68A6Y2nUbjMd7T/BL3SZQ5DeQgstFGYnK8E999wTjiXPPfdcHhgUM8yQlY0btWSm4aI/gaYxAINjLG08pazbpTBi8q92ti/O28rrvffem9VKyU8by01tsgTRFRNZ1ahz7F6quJ74d9dLxzHqmB4n2W8xC1ux/nxObyxxfCkr9c4vyqYvG9auVdmyHKbAUAoQfEYXdWWF9pfMYgcddFDoCoku5+imiTaE7pGKJf5fMLyVczzOVwlSpDQ6F+7khnFYgb8UUECBYSBA0GIs8bwzfm7llQdtYqGbpHYKwfD1CoGVlHfffXfAZL14/jaggn5QQAEFFFBghAsYKDPC/wDcfAUUUEABBfpBgKebeaK9WHbbbbeM7n4I0qCbCp6a52ewgmUaBa/EbAXUu9aNrHo3XGOgDDci04vyRQc+kyKY9O08rVorUKbT+nJjji5AeMqV7Ylp6svqUxxGQBOF7Wh0Eao4b6PPqS03U/lppnBjhAtyZUEx9fZLo2VzE//666/PTj311HCTmkw8FgUUmFJg2mmnzQfSdjVb0ic502UU508vvhfH8blRu8o08aI9AY0EtRHkRhdxsWskpqlX6tWh1vpj289yG7XbTJMGkND+x0AZgl5iPQmOoQs/Sgwg4fhJHcjCctVVV4VAmTDB5F90a0QhK0lZV3lh5ORf7WxfnLeqVzLL0FUR20E2MLqz++lPfxoCZornDr16HMMiPZYdfvjhTfOk86UzdXIcYzmdWKX18L0CvS5AwPQSSyyRkUXrgAMOyI455pgQvEi2xmL2wU7O8eK5MB71MoX1upf1U0ABBaoSSK8L1LqG0cy6qgiUoavRdkp6HtYL52/tbIPzKKCAAgoo0M8CBsr089512xRQQAEFFBjhAnSr8bOf/SwjfTlP8pDOfNddd83OOOOMjmWayVhQlokkXXGayp0glrKSdhtRHB+fVmo18KdW3Tup7zXXXJNtttlm+ZOwsa7sg9GjR4eMP3RNstVWW2W///3v4+gpXrnZnLpMMUEbA8gwFAs3BuvduI3TxVcyMJSVevulbPo4jH3GTeWYWj8OZ5vpHmzxxRcP3Utxoz92eRKn8VWBkSYw44wz5puc/h/nA2u8IRAilhlmmCG+neK1ViDKFBM2GEC7cumll4aAzZdffjkjy9l6662Xd+NWb/Z26hDbfpbbSftPNhqyFnDzgCBSAmXI+kXmBApd3VEImCFQhuF0X0Q3H7HbJYJP6t04aGf7wkq78IuAITLJ0D0KhWCfNFCml49j1Df+D7DPY7dZDG9Uah3z2j2Osb6qrBrV3fEK9JrAHnvskX3729/OOJcmMJI2P2Z3qfIcr9E5ea+5WB8FFFCgGwIEKcZg9DQzSyvrop1+9dVXwyx8L+hWIGKtayy9dP7WipvTKqCAAgooMFIEDJQZKXva7VRAAQUUUGCECtBlBDcwl1122dD9Dt0xffrTn8522WWXuiJpNxdlE8YLHmXj4jDSrdcrpFiPpZ0nu8l08sorr2R0EdSoUJeYkYGbnGWl3fo++OCD2RZbbJEHyXCzlZT0BH0UUxvTlQWleCGJ7b/jjjtCFhpucqdPj5XVtZVhafr6jTfeONzgaGX+Kqfdbrvt8iAZtpmuQAjkomsUutCKhb/TWIpWcbivCvS7AO13LH/4wx/i24avaaBMuoyGM7YxARfb6daILpdOPPHEEAxI1xl090f3OFUH/lHFNMtVM+1/eqwptv9klfne974XgkrJiEOmFQrp48nWRiFQhkJbRJDfGmusEbqIY1i9bpcY32uFbYmBMjEbEHXs9eMYdeRY9thjj4Xj5I033pilXXMxfrBKFVaDVVfXo0DVAmRoXGyxxbJHH300LJp2JAbKdHqOl34XaOWYV/U2ujwFFFCgVwRmn332bLXVVgvnpzxIcsUVV2R0lddKIeNtLJtssknLQeZx3nZfe+X8rd36O58CCiiggAL9LvC+ft9At08BBRRQQAEFFFhyySWzgw8+OIcgOOGRRx7JP8c36dOb3OisVd555538ye5a0zC80Q3MZ555Jp89DebIBzZ4E2+WvvXWW1marr1stmbSDbdb3/PPPz/DhHLCCSdkZ511VsiaUgySIfgodolS7JYp3f56GWdYx9e//vVsyy23zI444gg+NizRiQkfeuihhtPHgKKGE7Y4Adt++eWXh7nmm2++8CQyAVtjxowZECTDBGRtiKVoFYf7qkC/C/AUaSwTJkyIbxu+EqASC/9f3SyLLrpoCJJhHePGjcu7IXrggQe6FpSXtmlp215rO9Npiu3yBhtsEGajfaLOMdvVqquumgf50D7TxR+FLG0EadAukS0mzh9GDoNfadBQ6tjrxzFoY5dZBDT98pe/rKvN8bbeeUzdmRuMrMKqwSocrUDPChAw+Nxzz4X60QbG89cqzvEIEo9B043OhSdOnJittdZa2de+9rXQdvcsmBVTQAEFOhTYeuut8yXss88++cM/+cA6b2iv6RI1lnRZcVi3X3vl/K3b2+nyFVBAAQUUGK4CBsoM1z1nvRVQQAEFFFCgJYH9998/izdd6U7ny1/+crjRly5kmmmmyW8MPv/88+moAe9vv/32jBtVjQo3FF977bWak8UuoLjQ/pnPfKbmdLVGkBknltNOOy2+LX099dRT8+F0/VNW2q3vXXfdFRbHdvA0ba2CW8woU8zYw83mWC688ML4dopXAnLOPvvskCXoF7/4RT6elMyxFDOw8NQ9gSkU6jpp0qQ46RSvpM3npgfzLLXUUlmtrpemmLGJAT//+c/zTDrcXK6X6eLmm2/Ol1i0ykf4RoE+F+B/MHbrQ6aTH//4xw23mECP++67L0xHl3YsYzAL7fp0000XVnn44Yfn3RhVWQeCVqILGdNiOvmydTz55JMhuIVxZEGYc845B0y2wgorZHSRR6H7JdppSux2KXyY/Ctmlbn11luza6+9NgxeeumlW+rKLi5rqF45bo8fPz5f/fLLL5+/74XjGJWJx7LicYxx6TH/vPPOY1DNwjGfrpXIdHTOOefUnK6dEVVYtbNe51GgFwQ4xsRz+0UWWSSLXQRWcY7HeTRd4lHIshgDcsq2m+7vaLM55rTaBV/Z8hymgAIK9KoAwS3xfJ4gwgMOOCD/Tl2vzlx32G233fIHelZfffVs5ZVXnmKWeudeU0zcxoBeOX9ro+rOooACCiigwIgQeO+OwojYXDdSAQUUUEABBUaqANliyHQSu8HgyfmTTjppAAcXqOMTP/fff392yy23DBjPB1Khf+UrX5lieNkAnubeddddS4NquLjND4XsKGmgSNmyyobttNNO+U3K448/vmYACNkVfvjDH4ZF0C/32muvXba48PR5O/WN3T9wY69W901PP/109qUvfSlfbwyYiQPWXXfdPJDp3HPPzdIMCHEaXo8++uiMYBbKhhtuGF75xQ3xWF5//fX4Nn897LDDwnvmJYtLreCTI488MqPbFrLK0O1IlTcfohMVSTPGhIolv3hSjhsusRSt4nBfFeh3AdptsrRQCHLYaqutsnvvvbfmZj/88MPhgnicgLa6yv/huNx6rwSiHHPMMWES/ncJHqzV3tRbTr1xHMdiljRu2Mb3xXlY73777ZcHhUbLdDqWFYMnCb6ImcVqBcqQCS0GmwynbpfeeOONkHmBm8+UaaedNltllVVyitg+D+VxjMrEY1nZcYwuDrkxT+F8JgashAHJLwJ9+RuM27LmmmsmYzt/W4VV57VwCQoMvsDdd9+dbbbZZvmKOXeNJf5f8LmTc7yDDjooLJLjB8GWZYUskjFAfp555snPn8umdZgCCigw3AU4V+Uc9f3vf3/YFK7hkFEr7Wq1uI1PPfVU6N746quvDqN4QIVlcL2nWOqdexWnbedzr5y/tVN351FAAQUUUGAkCBgoMxL2stuogAIKKKCAAkGArC277757rsHNxXhTMA7cfvvt49uQdYYuBgj+4KlOMpnwJBLBMvFp/nziGm8uuuiibIcddsieeOKJMAU367i4vf7664fPpFgn+KOdQtaCY489Nsz65ptvhqw0BMTwnkIa+FNOOSUbO3ZsHqzz/e9/P78RFyYq/GqnvmQkiAW/O++8M78x+/LLL2c/+tGPQnaCl156KU6Wd8EUB/AkF8E+FAKMlllmmeyqq67KUyuzTYceemh21FFHhWkYT4BRLLPMMkt8G54yu+SSS0If5nEgQTqxnnQbsuKKK2akraf7EG4m84QwgUff+ta3wiw8IbzvvvvG2St5TbtYYj+deOKJ+QU+ggAefPDBEFj13e9+d8D62I8WBUaqAO3jRz/60bD5ZJQiuIH/ZQJAuPh95ZVXhnaQIBqC2371q1+FaeliKAbIDbYdXWHQxlDIfBUDZ6qsB8eyGDRB9pCNNtoo+/Wvfx2CI2hP6JqHtj9mfyGj2l577VVahdh90m9/+9sw/mMf+1j2qU99asC0BM7Emwuxe7peCpTh+EC3fOkPQZEES3Ezm7+hH/zgB/k2kWUl/l0xMB4feD9UxzHWHbP7cBwlcJO/b7ICUQj6igG+BMFwPnLcccdlr7zyShhPgAzZJdjv8Xi78847Z8XutsLEHfyqwqqD1TurAl0RIBNZ2n7E97Tn22yzTWhvadfj/xvfKb75zW/mdanqHI+2nK7vKHzv2HzzzbPYNvN/T/D7GmusEb6b0CbHc+cwg78UUECBPhXgvPSKK64Igc5sItlXR48eHQLkeciG817acc6DOPejjea7NYUHhS677LK8q9QwMPlV79wrmaztt71y/tb2BjijAgoooIACfS4wqs+3z81TQAEFFFBAAQUGCHBRm8ANAmTefvvt8IT5DTfckE+z4447hm4KuDFFat/tJmcD4EI0F6dj4YbcCy+8kJ155plxUOkrN9t44ptgG34I5iDogZuYFIIxGD7XXHOVzt/MQFIR88QUN5PJLEDGAIJOZptttvxGGcshMwM3a9MnYYvLb7e+Bx54YLiZR6YBMqFwI5untmafffYQIIQddeKmA4XgHYJhCB6KKeYZzo0BgkRIp4zTJptsEjIAcTOTJ8biPqArCW4e0lVWLCuttFIIAOIGLk/88sM6uUFK9xMUgoC4mT5hwoRwIY0UzgQq8XRa+vQ+T5Vdd911eXdNcR2dvrK/2T5u3vI3sMcee2R77rlnyCZEIFasA10/cfOTfcUTxQ899FCnq3Z+BYatABe36XZp4403DoEg/E/wP8xPrUKKc7qlmH766WtN0tXhHDPI+LH44ouHtu6II47ICEZJU693WgEuul9++eWhzSc4iG6p+MGLLuM4vsWy5JJLhuNefBI3Do+vZBxhHO0ypZhNhmGzzjpruCHBjQgKQToxUCcMGOJf9brsS6tGgOkhhxySbbrppungrBeOY1SIJ6S5yUMAZ8wswTExBokSHHPCCSdkdCdJhjQCOvnhmE9QbzxOsiyOod24iV6VFXW0KNArApxL89NMWXbZZcNNV84hY6nyHI9APgJkyH5JO88P59Uc/9566624ytBG9FLAYl4x3yiggAJdEFhvvfVCsCDtHtcdeCDn5JNPrrsmAmYIsJl33nlrTtfo3KvmjC2M6IXztxaq66QKKKCAAgqMKAEzyoyo3e3GKqCAAgoooACp0U8//fQcguwiF198cf6ZC9FkGiELTEzDG288EcDAvM1mKeCCDIEjPN1NymAu5hAgQdDKaqutFp5y4oJPp4WbsDxhuvTSS4dls474NDnrJXsNATt777133VW1W19uPN5+++0ZQTsx4wCBLo8//njY7uWXXz7UjwtZ3LiLJXWPw8h4wI2B5ZZbLmwLGV8ISmIfcGOY4BKWO8ccc8RZwit1JwPNggsumNcBBzIsxDLP5PT0OBCEQsYECjcaY4AK+4X9/uijj4b1x/mqfOXpfgJ26J6FwnZxk5s6EEjFjVHWz0113ChkhIhZgsIAfykwwgQWW2yx0C6Q8SkNrisy0IUdGTdoDzsJQCwut53PCy+8cAjIYF5ubhJ0WXUXTATecLwiaCI+DUvAZAySwSAeH2j/ahW6IeI4FUtZoAzjPv/5z8dJQhuVf+jRNwRLErBJpgeOtd/5zndCdji6tyuWXjiOUSeCYLhBTn1iiRnp4meyCdHNGPspBj/R1Us8V6ELyQsuuCC79NJLQ8BonK+q1yqtqqqTy1GgWwJ8b+DckmBuAso5r691jKnqHI9zWoJBCcyPbTvn1TFIhmMd3cOmGW26tf0uVwEFFOglAb4TcF5E9liCFssK5390Nc3DUXTZSptarzRz7lVv/mbHDfX5W7P1dDoFFFBAAQVGmsBUky+mvPd49EjberdXAQUUUECBIRY4cpn6T8AMcfU6Xv2B9+3a8TKGcgEEaRCUQWYZuvRIu2lotV5c3CYdMNlNeLo/ZjlpdTmNpo+ZWiZNmhRuEHLDLO2WqNH8cXy79SUl/W9+85sQ3DLffPOFm9pp5pe4/GZe2Rb8n3322ZDdhW1Jn96ttQxuFNMFBcE09TJKcNOBIJVXX301XECjvtwwHozCDXNS6WNFgA43vLmha+lPgTd3urY/N2zyVk13RufBfq3i0CYQQEdAIAFxdC3D/3sxgK7V5Q736TEh0I5j19xzzx3aX24WWFoT6IXjGH/XHB845pFFjeNEWWFfP/300yETBtmEOI7xfxCDVsvmqXJYlVZV1mukLevdnbfr203+wGnnDbttq/ocj7b9kUceCZkUCRaNwd7DDsYKKzDCBcaPH9+3ArEbz8HeQB424Ts1P5wrcf5LcHiz3WSn9W323Cudp933Q33+1m69nU8BBRRQQIF+FDBQph/3qtukgAIKKDBsBAyUGTa7yooqoIACbQsYKNM2nTMqoIACCjQQMFCmAZCjFVBAgR4QMFCmB3aCVVBAAQUUUEABBQoCPl5WAPGjAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQH8KGCjTn/vVrVJAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQoCBgoUwDxowIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooEB/Chgo05/71a1SQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUKAgYKFMA8aMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKBAfwoYKNOf+9WtUkABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFCgIGChTAPGjAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQH8KGCjTn/vVrVJAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQoCBgoUwDxowIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooEB/Chgo05/71a1SQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUKAgYKFMA8aMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKBAfwoYKNOf+9WtUkABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFCgIGChTAPGjAgoooIACCiiggAIKKKCAAgoooCekyBEAAAgbSURBVIACCiiggAIKKKCAAgoooIACCiigQH8KGCjTn/vVrVJAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQoCEz178mlMMyPCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgr0nYAZZfpul7pBCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAmUCBsqUqThMAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQoO8EDJTpu13qBimggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgqUCRgoU6biMAUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIG+EzBQpu92qRukgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooUCZgoEyZisMUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF+k7AQJm+26VukAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooECZgIEyZSoOU0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFOg7AQNl+m6XukEKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACZQIGypSpOEwBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCg7wQMlOm7XeoGKaCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCpQJGChTpuIwBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgb4TMFCm73apG6SAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCihQJmCgTJmKwxRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQX6TsBAmb7bpW6QAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQJmAgTJlKg5TQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6DsBA2X6bpe6QQoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAJlAgbKlKk4TAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUKDvBAyU6btd6gYpoIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKlAkYKFOm4jAFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBvhMwUKbvdqkbpIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKFAmYKBMmYrDFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBfpOwECZvtulbpACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKBAmYCBMmUqDlNAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRToOwEDZfpul7pBCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAmUCBsqUqThMAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQoO8EDJTpu13qBimggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgqUCRgoU6biMAUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIG+EzBQpu92qRukgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooUCZgoEyZisMUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF+k7AQJm+26VukAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooECZgIEyZSoOU0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFOg7AQNl+m6XukEKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACZQIGypSpOEwBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCg7wQMlOm7XeoGKaCAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCpQJGChTpuIwBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgb4TMFCm73apG6SAAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCihQJmCgTJmKwxRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQX6TsBAmb7bpW6QAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQJmAgTJlKg5TQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU6DuB/wNBDwE4t45oBAAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] From 130cc06ccad2a2cb57488ad073adfa4c868ad38d Mon Sep 17 00:00:00 2001 From: bmeluch Date: Fri, 10 Jan 2025 12:35:06 -0800 Subject: [PATCH 4/6] remove function defs from notebooks, update narration --- NOM_visualizations/R/NOM_R_notebook.ipynb | 185 +- .../R/taxonomic_dist_soil_layer_R.ipynb | 1879 +---------------- utility_functions.R | 30 + 3 files changed, 90 insertions(+), 2004 deletions(-) diff --git a/NOM_visualizations/R/NOM_R_notebook.ipynb b/NOM_visualizations/R/NOM_R_notebook.ipynb index 5951a675..4943f443 100644 --- a/NOM_visualizations/R/NOM_R_notebook.ipynb +++ b/NOM_visualizations/R/NOM_R_notebook.ipynb @@ -35,180 +35,9 @@ "source": [ "This notebook demonstrates how to use the existing NMDC-runtime API endpoints (as of August 2024) to explore the natural organic matter (NOM) data present in the NMDC database. It traverses the database schema backwards, first pulling all available natural organic matter results, and then identifying the corresponding source samples. The processed data is then evaluated for quality and compared by sample type. Note that retrieving the CSV results files can be time consuming.\n", "\n", - "## 1. Define functions to interact with API\n", + "## 1. Query NMDC API\n", "\n", - "First, we will define R functions to interact with some of the available NMDC API endpoints. Further documentation of available API endpoints is available here: https://nmdc-documentation.readthedocs.io/en/latest/howto_guides/api_gui.html\n", - "\n", - "### Define a general API call funtion to nmdc-runtime\n", - "\n", - "This function provides a general-purpose way to make an API request to NMDC's runtime API. Note that this function will only return the first page of results. The function's input includes the name of the collection to access (e.g. `biosample_set`), the filter to be performed, the maximum page size, and a list of the fields to be retrieved. It returns the metadata as a dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "metadata": {}, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_first_page_results <- function(collection, filter, max_page_size, fields) {\n", - "# og_url <- paste0(\n", - "# 'https://api.microbiomedata.org/nmdcschema/', \n", - "# collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields\n", - "# )\n", - " \n", - "# response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE))\n", - " \n", - "# return(response)\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define an nmdc-runtime API call function to include pagination\n", - "\n", - "The `get_next_results` function uses the `get_first_page_results` function, defined above, to retrieve the rest of the results from a call with multiple pages. It takes the same inputs as the `get_first_page_results` function above: the name of the collection to be retrieved, the filter string, the maximum page size, and a list of the fields to be returned. This function returns the results as a single dataframe (can be nested). It uses the next_page_token key in each page of results to retrieve the following page." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "metadata": {}, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_next_results <- function(collection, filter_text, max_page_size, fields) {\n", - "# initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields)\n", - "# results_df <- initial_data$resources\n", - " \n", - "# if (!is.null(initial_data$next_page_token)) {\n", - "# next_page_token <- initial_data$next_page_token\n", - " \n", - "# while (TRUE) {\n", - "# url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields)\n", - "# response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", - "\n", - "# results_df <- results_df %>% bind_rows(response$resources)\n", - "# next_page_token <- response$next_page_token\n", - " \n", - "# if (is.null(next_page_token)) {\n", - "# break\n", - "# }\n", - "# }\n", - "# }\n", - " \n", - "# return(results_df)\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define an API request function to get object information for a given ID\n", - "\n", - "This function constructs a different type of API request that takes a list of IDs and uses them to retrieve related data. In short, it searches in `collection` for records that have elements of `id_list` in their `match_id_field`, then returns all `fields` for the matching records.\n", - "\n", - "Fields such as `has_input` or `has_output` are likely to be useful values for `match_id_field`, though other fields are also usable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "metadata": {}, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) {\n", - "# # collection: the name of the collection to query\n", - "# # match_id_field: the field in the new collection to match to the id_list\n", - "# # id_list: a list of ids to filter on\n", - "# # fields: a list of fields to return\n", - "# # max_id: the maximum number of ids to include in a single query\n", - " \n", - "# # If id_list is longer than max_id, split it into chunks of max_id\n", - "# if (length(id_list) > max_id) {\n", - "# id_list <- split(id_list, ceiling(seq_along(id_list)/max_id))\n", - "# } else {\n", - "# id_list <- list(id_list)\n", - "# }\n", - " \n", - "# output <- list()\n", - "# for (i in 1:length(id_list)) {\n", - "# # Cast as a character vector and add double quotes around each ID\n", - "# mongo_id_string <- as.character(id_list[[i]]) %>%\n", - "# paste0('\"', ., '\"') %>%\n", - "# paste(collapse = ', ')\n", - " \n", - "# # Create the filter string\n", - "# filter = paste0('{\"', match_id_field, '\": {\"$in\": [', mongo_id_string, ']}}')\n", - " \n", - "# # Get the data\n", - "# output[[i]] = get_next_results(\n", - "# collection = collection,\n", - "# filter = filter,\n", - "# max_page_size = max_id*3, #assumes that there are no more than 3 records per query\n", - "# fields = fields\n", - "# )\n", - "# }\n", - "# output_df <- bind_rows(output)\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define an API request function to identify the database collection for an ID\n", - "\n", - "The functions above rely on knowing the MongoDB \"collection\" to search for relevant objects. But what if you don't know what the collection is called? This function takes an NMDC ID and returns the collection that the object is part of." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "metadata": {}, - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_collection_by_id <- function(id) {\n", - " \n", - "# # Create API endpoint URL\n", - "# url <- paste0(\"https://api.microbiomedata.org/nmdcschema/ids/\", id, \"/collection-name\")\n", - "\n", - "# # Retrieve the JSON result from the API endpoint URL\n", - "# response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", - " \n", - "# # Extract the collection name from the response\n", - "# return(response$collection_name)\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Query NMDC API\n", - "\n", - "Now we will use the functions defined above to retrieve natural organic matter data from the NMDC database.\n", + "First, we will use the functions defined in `utility_functions.R` to retrieve natural organic matter data from the NMDC database. These functions interact with some of the available NMDC API endpoints. Further documentation of available API endpoints is available here: https://nmdc-documentation.readthedocs.io/en/latest/howto_guides/api_gui.html\n", "\n", "### Get all data objects with NOM results\n", "\n", @@ -242,7 +71,7 @@ "source": [ "### Example of identifying a collection\n", "\n", - "We will use the `get_collection_by_id` function defined above to identify the name of the next collection to search in. Here, we have a list of IDs from the `data_object_set` collection, and we need to know the name of the collection containing the data analysis workflow information from the previous step." + "We will use the `get_collection_by_id` function to identify the name of the next collection to search in. Here, we have a list of IDs from the `data_object_set` collection, and we need to know the name of the collection containing the data analysis workflow information from the previous step." ] }, { @@ -540,7 +369,7 @@ "\n", "In this case, we are interested in the environments from which the samples were taken, so the `fields` argument includes the trio of environmental terms required for each NMDC biosample: `env_broad_scale`, `env_local_scale`, and `env_medium`. \n", "\n", - "Passing an empty string to the `fields` argument of the API functions defined above returns all fields for the class being queried, which can be useful but slow. More information about the metadata fields for the `Biosample` class is available here: https://microbiomedata.github.io/nmdc-schema/Biosample/" + "Passing an empty string to the `fields` argument of the API functions returns all fields for the class being queried, which can be useful but slow. More information about the metadata fields for the `Biosample` class is available here: https://microbiomedata.github.io/nmdc-schema/Biosample/" ] }, { @@ -887,7 +716,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Label sample types\n", + "## 2. Label sample types\n", "\n", "In this notebook we want to explore how natural organic matter varies across sample types. We can label the sample types using the environmental metadata terms included in the biosample query earlier.\n", "\n", @@ -1126,7 +955,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Pull and process results files\n", + "## 3. Pull and process results files\n", "\n", "The processed data objects pulled from the API above contain references to the results files, not the results themselves. We can use the `url` field of the DataObject class to retrieve the stored data. The NMDC natural organic matter workflow produces a results data table saved as a CSV at that url. The rows of the CSV correspond to m/z peaks in the mass spectra." ] @@ -1570,7 +1399,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Visualize natural organic matter by sample type\n", + "## 4. Visualize natural organic matter by sample type\n", "\n", "First we will see how many processed data objects from each sample type remain after quality filtering." ] diff --git a/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb b/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb index acd76991..e48db04a 100644 --- a/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb +++ b/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb @@ -15,6 +15,7 @@ "execution_count": 1, "id": "568d7112-ee43-41ce-aadb-39eaa9585dbd", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } @@ -30,84 +31,6 @@ "source(\"../../utility_functions.R\")" ] }, - { - "cell_type": "markdown", - "id": "21ba287e-e606-4a50-949c-07aac9892473", - "metadata": {}, - "source": [ - "## Define a general API call funtion to nmdc-runtime\n", - "\n", - "This function provides a general-purpose way to make an API request to NMDC's runtime API. Note that this function will only return the first page of results. The function's input includes the name of the collection to access (e.g. biosample_set), the filter to be performed, the maximum page size, and a list of the fields to be retrieved. It returns the metadata as a dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4ab6df9b-aaa0-4898-9095-a15efc867bc3", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_first_page_results <- function(collection, filter, max_page_size, fields) {\n", - "# og_url <- paste0(\n", - "# 'https://api.microbiomedata.org/nmdcschema/', \n", - "# collection, '?&filter=', filter, '&max_page_size=', max_page_size, '&projection=', fields\n", - "# )\n", - " \n", - "# response <- jsonlite::fromJSON(URLencode(og_url, repeated = TRUE))\n", - " \n", - "# return(response)\n", - "# }" - ] - }, - { - "cell_type": "markdown", - "id": "8a306a7c-d48c-42bc-8da7-fb134a0c6b2c", - "metadata": {}, - "source": [ - "## Define an nmdc-runtime API call function to include pagination\n", - "\n", - "The get_next_results function uses the get_first_page_results function, defined above, to retrieve the rest of the results from a call with multiple pages. It takes the same inputs as the get_first_page_results function above: the name of the collection to be retrieved, the filter string, the maximum page size, and a list of the fields to be returned. This function returns the results as a single dataframe (can be nested). It uses the next_page_token key in each page of results to retrieve the following page." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8af9945b-37c1-49ed-9a8b-dc85a52692eb", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_next_results <- function(collection, filter_text, max_page_size, fields) {\n", - "# initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields)\n", - "# results_df <- initial_data$resources\n", - " \n", - "# if (!is.null(initial_data$next_page_token)) {\n", - "# next_page_token <- initial_data$next_page_token\n", - " \n", - "# while (TRUE) {\n", - "# url <- paste0('https://api.microbiomedata.org/nmdcschema/', collection, '?&filter=', filter_text, '&max_page_size=', max_page_size, '&page_token=', next_page_token, '&projection=', fields)\n", - "# response <- jsonlite::fromJSON(URLencode(url, repeated = TRUE))\n", - "\n", - "# results_df <- results_df %>% bind_rows(response$resources)\n", - "# next_page_token <- response$next_page_token\n", - " \n", - "# if (is.null(next_page_token)) {\n", - "# break\n", - "# }\n", - "# }\n", - "# }\n", - " \n", - "# return(results_df)\n", - "# }" - ] - }, { "cell_type": "markdown", "id": "c3494f01-ac01-4eac-85d5-728622ddac6b", @@ -115,87 +38,20 @@ "source": [ "# 1. Get all biosamples where soil_horizon exists and the geo_loc_name has \"Colorado\" in the name\n", "\n", - "The first step in answering how the taxonomic distribution of contigs differ by soil layer is to get a list of all the biosamples that have metadata for soil_horizon and a string matching \"Colorado, Rocky Mountains\" for the geo_loc_name. We use the get_next_results function (defined above) to do this. We query the biosample_set collection with a mongo-like filter of {\"soil_horizon\":{\"$exists\": true}, \"geo_loc_name.has_raw_value\": {\"$regex\": \"Colorado\"}}, a maximum page size of 100, and specifying that we want three fields returned id, soil_horizon, and geo_loc_name. Note that id is always returned. Since we will be joining the results of multiple API requests with a field of id for different collections, we can change the name of the id key to be more explicit - calling it biosample_id instead." + "The first step in answering how the taxonomic distribution of contigs differ by soil layer is to get a list of all the biosamples that have metadata for soil_horizon and a string matching \"Colorado, Rocky Mountains\" for the geo_loc_name. We use the `get_next_results` function (defined in `utility_functions.R`) to do this. We query the biosample_set collection with a mongo-like filter of {\"soil_horizon\":{\"$exists\": true}, \"geo_loc_name.has_raw_value\": {\"$regex\": \"Colorado\"}}, a maximum page size of 100, and specifying that we want three fields returned id, soil_horizon, and geo_loc_name. Note that id is always returned. Since we will be joining the results of multiple API requests with a field of id for different collections, we can change the name of the id key to be more explicit - calling it biosample_id instead." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "780eae36-3f46-4c3e-bd26-b2a33c463441", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 4
biosample_idsoil_horizongeo_loc_namegeo_loc_name_type
<chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValue
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValue
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
\n" - ], - "text/latex": [ - "A tibble: 6 × 4\n", - "\\begin{tabular}{llll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 4\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> |\n", - "|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get biosamples using get_next_results function\n", "biosample_df <- get_next_results(\n", @@ -216,142 +72,25 @@ "head(biosample_df)" ] }, - { - "cell_type": "markdown", - "id": "f3a8bc8e-14eb-4844-b8ce-34b1ed7a0773", - "metadata": {}, - "source": [ - "## Define an API request function that uses a list of ids to filter on\n", - "This function constructs a different type of API request that takes a list of ids or similar (e.g. `biosample` ids as retreived above). The `id_field` input is a string of the name of the id field name (e.g. `id` or `has_output`), the name of the new collection to be queried, the name of the field to match the previous ids on in the new collection, and a list of the fields to be returned." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ce435021-79f1-4a1d-aef0-fea4bc267817", - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "# get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) {\n", - "# # collection: the name of the collection to query\n", - "# # match_id_field: the field in the new collection to match to the id_list\n", - "# # id_list: a list of ids to filter on\n", - "# # fields: a list of fields to return\n", - "# # max_id: the maximum number of ids to include in a single query\n", - " \n", - "# # If id_list is longer than max_id, split it into chunks of max_id\n", - "# if (length(id_list) > max_id) {\n", - "# id_list <- split(id_list, ceiling(seq_along(id_list)/max_id))\n", - "# } else {\n", - "# id_list <- list(id_list)\n", - "# }\n", - " \n", - "# output <- list()\n", - "# for (i in 1:length(id_list)) {\n", - "# # Cast as a character vector and add double quotes around each ID\n", - "# mongo_id_string <- as.character(id_list[[i]]) %>%\n", - "# paste0('\"', ., '\"') %>%\n", - "# paste(collapse = ', ')\n", - " \n", - "# # Create the filter string\n", - "# filter = paste0('{\"', match_id_field, '\": {\"$in\": [', mongo_id_string, ']}}')\n", - " \n", - "# # Get the data\n", - "# output[[i]] = get_next_results(\n", - "# collection = collection,\n", - "# filter = filter,\n", - "# max_page_size = max_id*3, #assumes that there are no more than 3 records per query\n", - "# fields = fields\n", - "# )\n", - "# }\n", - "# output_df <- bind_rows(output)\n", - "# }" - ] - }, { "cell_type": "markdown", "id": "4f512f80-3f14-481c-a28c-f6797fc61882", "metadata": {}, "source": [ "# 2. Get all Pooling results where the Pooling `has_input` are the biosample ids\n", - "We use the `get_results_by_id` function above to get a list of all pooling results whose field, `has_input` are the `biosample_id`s we retrieved in step 1. After, the pooling results are unnested to a flat data frame, andthe names are cleaned up so it is clear which collection the results are from. Because `Pooling` is a subclass of `MaterialProcessing` the pooling records are found in the `material_processing_set`." + "We use the `get_results_by_id` function to get a list of all pooling results whose field, `has_input` are the `biosample_id`s we retrieved in step 1. After, the pooling results are unnested to a flat data frame, andthe names are cleaned up so it is clear which collection the results are from. Because `Pooling` is a subclass of `MaterialProcessing` the pooling records are found in the `material_processing_set`." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "a8fc4389-49d8-42ea-a56b-a561c01900ff", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 3
biosample_idpooling_has_outputpooling_id
<chr><chr><chr>
nmdc:bsm-11-5228zz06nmdc:procsm-11-49bwy122nmdc:poolp-11-a1nnyd94
nmdc:bsm-11-1frj0t76nmdc:procsm-11-49bwy122nmdc:poolp-11-a1nnyd94
nmdc:bsm-11-nyxsx333nmdc:procsm-11-49bwy122nmdc:poolp-11-a1nnyd94
nmdc:bsm-11-ex491068nmdc:procsm-11-kngzyt90nmdc:poolp-11-sj9jpg87
nmdc:bsm-11-1byjjh32nmdc:procsm-11-kngzyt90nmdc:poolp-11-sj9jpg87
nmdc:bsm-11-da5wpm57nmdc:procsm-11-kngzyt90nmdc:poolp-11-sj9jpg87
\n" - ], - "text/latex": [ - "A tibble: 6 × 3\n", - "\\begin{tabular}{lll}\n", - " biosample\\_id & pooling\\_has\\_output & pooling\\_id\\\\\n", - " & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-5228zz06 & nmdc:procsm-11-49bwy122 & nmdc:poolp-11-a1nnyd94\\\\\n", - "\t nmdc:bsm-11-1frj0t76 & nmdc:procsm-11-49bwy122 & nmdc:poolp-11-a1nnyd94\\\\\n", - "\t nmdc:bsm-11-nyxsx333 & nmdc:procsm-11-49bwy122 & nmdc:poolp-11-a1nnyd94\\\\\n", - "\t nmdc:bsm-11-ex491068 & nmdc:procsm-11-kngzyt90 & nmdc:poolp-11-sj9jpg87\\\\\n", - "\t nmdc:bsm-11-1byjjh32 & nmdc:procsm-11-kngzyt90 & nmdc:poolp-11-sj9jpg87\\\\\n", - "\t nmdc:bsm-11-da5wpm57 & nmdc:procsm-11-kngzyt90 & nmdc:poolp-11-sj9jpg87\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 3\n", - "\n", - "| biosample_id <chr> | pooling_has_output <chr> | pooling_id <chr> |\n", - "|---|---|---|\n", - "| nmdc:bsm-11-5228zz06 | nmdc:procsm-11-49bwy122 | nmdc:poolp-11-a1nnyd94 |\n", - "| nmdc:bsm-11-1frj0t76 | nmdc:procsm-11-49bwy122 | nmdc:poolp-11-a1nnyd94 |\n", - "| nmdc:bsm-11-nyxsx333 | nmdc:procsm-11-49bwy122 | nmdc:poolp-11-a1nnyd94 |\n", - "| nmdc:bsm-11-ex491068 | nmdc:procsm-11-kngzyt90 | nmdc:poolp-11-sj9jpg87 |\n", - "| nmdc:bsm-11-1byjjh32 | nmdc:procsm-11-kngzyt90 | nmdc:poolp-11-sj9jpg87 |\n", - "| nmdc:bsm-11-da5wpm57 | nmdc:procsm-11-kngzyt90 | nmdc:poolp-11-sj9jpg87 |\n", - "\n" - ], - "text/plain": [ - " biosample_id pooling_has_output pooling_id \n", - "1 nmdc:bsm-11-5228zz06 nmdc:procsm-11-49bwy122 nmdc:poolp-11-a1nnyd94\n", - "2 nmdc:bsm-11-1frj0t76 nmdc:procsm-11-49bwy122 nmdc:poolp-11-a1nnyd94\n", - "3 nmdc:bsm-11-nyxsx333 nmdc:procsm-11-49bwy122 nmdc:poolp-11-a1nnyd94\n", - "4 nmdc:bsm-11-ex491068 nmdc:procsm-11-kngzyt90 nmdc:poolp-11-sj9jpg87\n", - "5 nmdc:bsm-11-1byjjh32 nmdc:procsm-11-kngzyt90 nmdc:poolp-11-sj9jpg87\n", - "6 nmdc:bsm-11-da5wpm57 nmdc:procsm-11-kngzyt90 nmdc:poolp-11-sj9jpg87" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pooling_df <- get_results_by_id(\n", " collection = 'material_processing_set',\n", @@ -385,89 +124,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0aa58275-a5e6-418f-a04c-b1ef5e21f65e", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 6
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typepooling_has_outputpooling_id
<chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70
\n" - ], - "text/latex": [ - "A tibble: 6 × 6\n", - "\\begin{tabular}{llllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & pooling\\_has\\_output & pooling\\_id\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 6\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | pooling_has_output <chr> | pooling_id <chr> |\n", - "|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " pooling_has_output pooling_id \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df2 <- left_join(biosample_df, pooling_df2, by = 'biosample_id')\n", "head(biosample_df2)" @@ -483,89 +147,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "37534e80-aab9-4ec7-9829-8cde11e27a51", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 6
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_id
<chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70
\n" - ], - "text/latex": [ - "A tibble: 6 × 6\n", - "\\begin{tabular}{llllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 6\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> |\n", - "|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " processed_sample_id pooling_id \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df3 <- biosample_df2 %>%\n", " rename(processed_sample_id = pooling_has_output) \n", @@ -584,75 +173,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "832c3ddd-6db6-4292-8325-fc812e156346", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 3
processed_sample_idextraction_has_outputextraction_id
<chr><chr><chr>
nmdc:procsm-11-kngzyt90nmdc:procsm-11-h9s7h174nmdc:extrp-11-v25scb12
nmdc:procsm-11-mr5hf033nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35
nmdc:procsm-11-33n4p085nmdc:procsm-11-6xc6vy98nmdc:extrp-11-j5qc7973
nmdc:procsm-11-2fxf0e98nmdc:procsm-11-x763xr38nmdc:extrp-11-y5ewyv43
nmdc:procsm-11-y8w3sk61nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92
nmdc:procsm-11-5s07gt34nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21
\n" - ], - "text/latex": [ - "A tibble: 6 × 3\n", - "\\begin{tabular}{lll}\n", - " processed\\_sample\\_id & extraction\\_has\\_output & extraction\\_id\\\\\n", - " & & \\\\\n", - "\\hline\n", - "\t nmdc:procsm-11-kngzyt90 & nmdc:procsm-11-h9s7h174 & nmdc:extrp-11-v25scb12\\\\\n", - "\t nmdc:procsm-11-mr5hf033 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35\\\\\n", - "\t nmdc:procsm-11-33n4p085 & nmdc:procsm-11-6xc6vy98 & nmdc:extrp-11-j5qc7973\\\\\n", - "\t nmdc:procsm-11-2fxf0e98 & nmdc:procsm-11-x763xr38 & nmdc:extrp-11-y5ewyv43\\\\\n", - "\t nmdc:procsm-11-y8w3sk61 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92\\\\\n", - "\t nmdc:procsm-11-5s07gt34 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 3\n", - "\n", - "| processed_sample_id <chr> | extraction_has_output <chr> | extraction_id <chr> |\n", - "|---|---|---|\n", - "| nmdc:procsm-11-kngzyt90 | nmdc:procsm-11-h9s7h174 | nmdc:extrp-11-v25scb12 |\n", - "| nmdc:procsm-11-mr5hf033 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 |\n", - "| nmdc:procsm-11-33n4p085 | nmdc:procsm-11-6xc6vy98 | nmdc:extrp-11-j5qc7973 |\n", - "| nmdc:procsm-11-2fxf0e98 | nmdc:procsm-11-x763xr38 | nmdc:extrp-11-y5ewyv43 |\n", - "| nmdc:procsm-11-y8w3sk61 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 |\n", - "| nmdc:procsm-11-5s07gt34 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 |\n", - "\n" - ], - "text/plain": [ - " processed_sample_id extraction_has_output extraction_id \n", - "1 nmdc:procsm-11-kngzyt90 nmdc:procsm-11-h9s7h174 nmdc:extrp-11-v25scb12\n", - "2 nmdc:procsm-11-mr5hf033 nmdc:procsm-11-7qy2y664 nmdc:extrp-11-gnvf5s35\n", - "3 nmdc:procsm-11-33n4p085 nmdc:procsm-11-6xc6vy98 nmdc:extrp-11-j5qc7973\n", - "4 nmdc:procsm-11-2fxf0e98 nmdc:procsm-11-x763xr38 nmdc:extrp-11-y5ewyv43\n", - "5 nmdc:procsm-11-y8w3sk61 nmdc:procsm-11-q086v208 nmdc:extrp-11-9qd5ke92\n", - "6 nmdc:procsm-11-5s07gt34 nmdc:procsm-11-edpstj65 nmdc:extrp-11-76s2tz21" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extraction_df <- get_results_by_id(\n", " collection = 'material_processing_set',\n", @@ -685,96 +213,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "8842cde0-c0ab-4bba-8c8e-fcba2b77a4cd", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 8
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idextraction_has_outputextraction_id
<chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35
\n" - ], - "text/latex": [ - "A tibble: 6 × 8\n", - "\\begin{tabular}{llllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & extraction\\_has\\_output & extraction\\_id\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 8\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | extraction_has_output <chr> | extraction_id <chr> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " processed_sample_id pooling_id extraction_has_output \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", - " extraction_id \n", - "1 nmdc:extrp-11-c0kyyp83\n", - "2 nmdc:extrp-11-76s2tz21\n", - "3 nmdc:extrp-11-faz6a041\n", - "4 nmdc:extrp-11-9qd5ke92\n", - "5 nmdc:extrp-11-qg3zf244\n", - "6 nmdc:extrp-11-gnvf5s35" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df4 <- biosample_df3 %>%\n", " left_join(extraction_df, by = join_by(processed_sample_id))\n", @@ -791,96 +237,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "fc982cf2-08a1-4bfe-9cf3-7ca550f46790", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 8
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_id
<chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35
\n" - ], - "text/latex": [ - "A tibble: 6 × 8\n", - "\\begin{tabular}{llllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 8\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " processed_sample_id pooling_id processed_sample_id2 \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", - " extraction_id \n", - "1 nmdc:extrp-11-c0kyyp83\n", - "2 nmdc:extrp-11-76s2tz21\n", - "3 nmdc:extrp-11-faz6a041\n", - "4 nmdc:extrp-11-9qd5ke92\n", - "5 nmdc:extrp-11-qg3zf244\n", - "6 nmdc:extrp-11-gnvf5s35" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df5 <- biosample_df4 %>%\n", " rename(processed_sample_id2 = extraction_has_output)\n", @@ -899,82 +263,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "e5e627fa-72ad-4e64-baf9-471443ba7168", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 3
processed_sample_id2library_preparation_has_outputlibrary_preparation_id
<chr><chr><chr>
nmdc:procsm-11-7qy2y664nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032
nmdc:procsm-11-x763xr38nmdc:procsm-11-9ghwha16nmdc:libprp-11-gasf6t26
nmdc:procsm-11-h9s7h174nmdc:procsm-11-4z512838nmdc:libprp-11-t70f6032
nmdc:procsm-11-6xc6vy98nmdc:procsm-11-44e5ds31nmdc:libprp-11-8bzn7n07
nmdc:procsm-11-q086v208nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35
nmdc:procsm-11-1qgqxz62nmdc:procsm-11-hxfxnz83nmdc:libprp-11-6zgrcr81
\n" - ], - "text/latex": [ - "A tibble: 6 × 3\n", - "\\begin{tabular}{lll}\n", - " processed\\_sample\\_id2 & library\\_preparation\\_has\\_output & library\\_preparation\\_id\\\\\n", - " & & \\\\\n", - "\\hline\n", - "\t nmdc:procsm-11-7qy2y664 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032\\\\\n", - "\t nmdc:procsm-11-x763xr38 & nmdc:procsm-11-9ghwha16 & nmdc:libprp-11-gasf6t26\\\\\n", - "\t nmdc:procsm-11-h9s7h174 & nmdc:procsm-11-4z512838 & nmdc:libprp-11-t70f6032\\\\\n", - "\t nmdc:procsm-11-6xc6vy98 & nmdc:procsm-11-44e5ds31 & nmdc:libprp-11-8bzn7n07\\\\\n", - "\t nmdc:procsm-11-q086v208 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35\\\\\n", - "\t nmdc:procsm-11-1qgqxz62 & nmdc:procsm-11-hxfxnz83 & nmdc:libprp-11-6zgrcr81\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 3\n", - "\n", - "| processed_sample_id2 <chr> | library_preparation_has_output <chr> | library_preparation_id <chr> |\n", - "|---|---|---|\n", - "| nmdc:procsm-11-7qy2y664 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 |\n", - "| nmdc:procsm-11-x763xr38 | nmdc:procsm-11-9ghwha16 | nmdc:libprp-11-gasf6t26 |\n", - "| nmdc:procsm-11-h9s7h174 | nmdc:procsm-11-4z512838 | nmdc:libprp-11-t70f6032 |\n", - "| nmdc:procsm-11-6xc6vy98 | nmdc:procsm-11-44e5ds31 | nmdc:libprp-11-8bzn7n07 |\n", - "| nmdc:procsm-11-q086v208 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 |\n", - "| nmdc:procsm-11-1qgqxz62 | nmdc:procsm-11-hxfxnz83 | nmdc:libprp-11-6zgrcr81 |\n", - "\n" - ], - "text/plain": [ - " processed_sample_id2 library_preparation_has_output\n", - "1 nmdc:procsm-11-7qy2y664 nmdc:procsm-11-wd4s5f38 \n", - "2 nmdc:procsm-11-x763xr38 nmdc:procsm-11-9ghwha16 \n", - "3 nmdc:procsm-11-h9s7h174 nmdc:procsm-11-4z512838 \n", - "4 nmdc:procsm-11-6xc6vy98 nmdc:procsm-11-44e5ds31 \n", - "5 nmdc:procsm-11-q086v208 nmdc:procsm-11-jkvhv341 \n", - "6 nmdc:procsm-11-1qgqxz62 nmdc:procsm-11-hxfxnz83 \n", - " library_preparation_id \n", - "1 nmdc:libprp-11-wv6p0032\n", - "2 nmdc:libprp-11-gasf6t26\n", - "3 nmdc:libprp-11-t70f6032\n", - "4 nmdc:libprp-11-8bzn7n07\n", - "5 nmdc:libprp-11-24s1rh35\n", - "6 nmdc:libprp-11-6zgrcr81" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "library_prep_df <- get_results_by_id(\n", " collection = 'material_processing_set',\n", @@ -1007,96 +303,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "a8506d2d-356d-4277-9711-052296ecfae4", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 10
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idlibrary_preparation_has_outputlibrary_preparation_id
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244nmdc:procsm-11-t397mj03nmdc:libprp-11-p07zpd31
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032
\n" - ], - "text/latex": [ - "A tibble: 6 × 10\n", - "\\begin{tabular}{llllllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & library\\_preparation\\_has\\_output & library\\_preparation\\_id\\\\\n", - " & & & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244 & nmdc:procsm-11-t397mj03 & nmdc:libprp-11-p07zpd31\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 10\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | library_preparation_has_output <chr> | library_preparation_id <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 | nmdc:procsm-11-t397mj03 | nmdc:libprp-11-p07zpd31 |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " processed_sample_id pooling_id processed_sample_id2 \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", - " extraction_id library_preparation_has_output library_preparation_id \n", - "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", - "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", - "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "4 nmdc:extrp-11-9qd5ke92 nmdc:procsm-11-jkvhv341 nmdc:libprp-11-24s1rh35\n", - "5 nmdc:extrp-11-qg3zf244 nmdc:procsm-11-t397mj03 nmdc:libprp-11-p07zpd31\n", - "6 nmdc:extrp-11-gnvf5s35 nmdc:procsm-11-wd4s5f38 nmdc:libprp-11-wv6p0032" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df6 <- biosample_df5 %>%\n", " left_join(library_prep_df, by = join_by(processed_sample_id2))\n", @@ -1113,96 +327,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "667690ba-a598-4f72-9c21-9ff8a6c28b8c", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 10
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_id
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244nmdc:procsm-11-t397mj03nmdc:libprp-11-p07zpd31
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032
\n" - ], - "text/latex": [ - "A tibble: 6 × 10\n", - "\\begin{tabular}{llllllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id\\\\\n", - " & & & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35\\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244 & nmdc:procsm-11-t397mj03 & nmdc:libprp-11-p07zpd31\\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 10\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 | nmdc:procsm-11-t397mj03 | nmdc:libprp-11-p07zpd31 |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " processed_sample_id pooling_id processed_sample_id2 \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", - " extraction_id processed_sample_id3 library_preparation_id \n", - "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", - "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", - "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "4 nmdc:extrp-11-9qd5ke92 nmdc:procsm-11-jkvhv341 nmdc:libprp-11-24s1rh35\n", - "5 nmdc:extrp-11-qg3zf244 nmdc:procsm-11-t397mj03 nmdc:libprp-11-p07zpd31\n", - "6 nmdc:extrp-11-gnvf5s35 nmdc:procsm-11-wd4s5f38 nmdc:libprp-11-wv6p0032" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df7 <- biosample_df6 %>%\n", " rename(processed_sample_id3 = library_preparation_has_output)\n", @@ -1221,75 +353,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "be09a8ef-d0fb-4df8-bef3-bc8498f3d444", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 2
processed_sample_id3data_generation_id
<chr><chr>
nmdc:procsm-11-43n6yz70nmdc:omprc-11-g1n61y55
nmdc:procsm-11-4jj6k690nmdc:omprc-11-yt96hb84
nmdc:procsm-11-4z512838nmdc:omprc-11-afejca38
nmdc:procsm-11-7cpyc435nmdc:omprc-11-by9r5p41
nmdc:procsm-11-9ghwha16nmdc:omprc-11-bd1eyb41
nmdc:procsm-11-bxg58286nmdc:omprc-11-t2fdcy08
\n" - ], - "text/latex": [ - "A tibble: 6 × 2\n", - "\\begin{tabular}{ll}\n", - " processed\\_sample\\_id3 & data\\_generation\\_id\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t nmdc:procsm-11-43n6yz70 & nmdc:omprc-11-g1n61y55\\\\\n", - "\t nmdc:procsm-11-4jj6k690 & nmdc:omprc-11-yt96hb84\\\\\n", - "\t nmdc:procsm-11-4z512838 & nmdc:omprc-11-afejca38\\\\\n", - "\t nmdc:procsm-11-7cpyc435 & nmdc:omprc-11-by9r5p41\\\\\n", - "\t nmdc:procsm-11-9ghwha16 & nmdc:omprc-11-bd1eyb41\\\\\n", - "\t nmdc:procsm-11-bxg58286 & nmdc:omprc-11-t2fdcy08\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 2\n", - "\n", - "| processed_sample_id3 <chr> | data_generation_id <chr> |\n", - "|---|---|\n", - "| nmdc:procsm-11-43n6yz70 | nmdc:omprc-11-g1n61y55 |\n", - "| nmdc:procsm-11-4jj6k690 | nmdc:omprc-11-yt96hb84 |\n", - "| nmdc:procsm-11-4z512838 | nmdc:omprc-11-afejca38 |\n", - "| nmdc:procsm-11-7cpyc435 | nmdc:omprc-11-by9r5p41 |\n", - "| nmdc:procsm-11-9ghwha16 | nmdc:omprc-11-bd1eyb41 |\n", - "| nmdc:procsm-11-bxg58286 | nmdc:omprc-11-t2fdcy08 |\n", - "\n" - ], - "text/plain": [ - " processed_sample_id3 data_generation_id \n", - "1 nmdc:procsm-11-43n6yz70 nmdc:omprc-11-g1n61y55\n", - "2 nmdc:procsm-11-4jj6k690 nmdc:omprc-11-yt96hb84\n", - "3 nmdc:procsm-11-4z512838 nmdc:omprc-11-afejca38\n", - "4 nmdc:procsm-11-7cpyc435 nmdc:omprc-11-by9r5p41\n", - "5 nmdc:procsm-11-9ghwha16 nmdc:omprc-11-bd1eyb41\n", - "6 nmdc:procsm-11-bxg58286 nmdc:omprc-11-t2fdcy08" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "data_generation_df <- get_results_by_id(\n", " collection = 'data_generation_set',\n", @@ -1319,103 +390,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "2d6c028e-4260-4324-8fb3-0402216d2f7f", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 11
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_iddata_generation_id
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346nmdc:omprc-11-63ajbd04
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60nmdc:omprc-11-769ab655
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35NA
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244nmdc:procsm-11-t397mj03nmdc:libprp-11-p07zpd31NA
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032NA
\n" - ], - "text/latex": [ - "A tibble: 6 × 11\n", - "\\begin{tabular}{lllllllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id & data\\_generation\\_id\\\\\n", - " & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346 & nmdc:omprc-11-63ajbd04\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60 & nmdc:omprc-11-769ab655\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608\\\\\n", - "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35 & NA \\\\\n", - "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244 & nmdc:procsm-11-t397mj03 & nmdc:libprp-11-p07zpd31 & NA \\\\\n", - "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032 & NA \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 11\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> | data_generation_id <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 | nmdc:omprc-11-63ajbd04 |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 | nmdc:omprc-11-769ab655 |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 |\n", - "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 | NA |\n", - "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 | nmdc:procsm-11-t397mj03 | nmdc:libprp-11-p07zpd31 | NA |\n", - "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 | NA |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-0asn5d63 M horizon \n", - "5 nmdc:bsm-11-0djp2e45 M horizon \n", - "6 nmdc:bsm-11-0f43ab20 M horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "5 USA: Colorado, North Sterling nmdc:TextValue \n", - "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - " processed_sample_id pooling_id processed_sample_id2 \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", - "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", - "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", - " extraction_id processed_sample_id3 library_preparation_id \n", - "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", - "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", - "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "4 nmdc:extrp-11-9qd5ke92 nmdc:procsm-11-jkvhv341 nmdc:libprp-11-24s1rh35\n", - "5 nmdc:extrp-11-qg3zf244 nmdc:procsm-11-t397mj03 nmdc:libprp-11-p07zpd31\n", - "6 nmdc:extrp-11-gnvf5s35 nmdc:procsm-11-wd4s5f38 nmdc:libprp-11-wv6p0032\n", - " data_generation_id \n", - "1 nmdc:omprc-11-63ajbd04\n", - "2 nmdc:omprc-11-769ab655\n", - "3 nmdc:omprc-11-597mc608\n", - "4 NA \n", - "5 NA \n", - "6 NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df8 <- biosample_df7 %>%\n", " left_join(data_generation_df, by = join_by(processed_sample_id3))\n", @@ -1434,89 +416,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "6328c9a8-ee49-4dc4-b1e3-b530f718c371", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 6 × 4
idwas_informed_byhas_outputtype
<chr><chr><list><chr>
1nmdc:wfmag-11-2702vw88.1nmdc:omprc-11-bd1eyb41nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37nmdc:MagsAnalysis
2nmdc:wfmag-11-29we8r98.1nmdc:omprc-11-r5w9js52nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91nmdc:MagsAnalysis
3nmdc:wfmag-11-94b5df26.1nmdc:omprc-11-by9r5p41nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851nmdc:MagsAnalysis
4nmdc:wfmag-11-94b5df26.2nmdc:omprc-11-by9r5p41nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762nmdc:MagsAnalysis
5nmdc:wfmag-11-9e4rbw35.1nmdc:omprc-11-sz2d4412nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53nmdc:MagsAnalysis
6nmdc:wfmag-11-9xzvxq91.1nmdc:omprc-11-gaptm502nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78nmdc:MagsAnalysis
\n" - ], - "text/latex": [ - "A data.frame: 6 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & id & was\\_informed\\_by & has\\_output & type\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & nmdc:wfmag-11-2702vw88.1 & nmdc:omprc-11-bd1eyb41 & nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37 & nmdc:MagsAnalysis\\\\\n", - "\t2 & nmdc:wfmag-11-29we8r98.1 & nmdc:omprc-11-r5w9js52 & nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91 & nmdc:MagsAnalysis\\\\\n", - "\t3 & nmdc:wfmag-11-94b5df26.1 & nmdc:omprc-11-by9r5p41 & nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851 & nmdc:MagsAnalysis\\\\\n", - "\t4 & nmdc:wfmag-11-94b5df26.2 & nmdc:omprc-11-by9r5p41 & nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762 & nmdc:MagsAnalysis\\\\\n", - "\t5 & nmdc:wfmag-11-9e4rbw35.1 & nmdc:omprc-11-sz2d4412 & nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53 & nmdc:MagsAnalysis\\\\\n", - "\t6 & nmdc:wfmag-11-9xzvxq91.1 & nmdc:omprc-11-gaptm502 & nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78 & nmdc:MagsAnalysis\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 4\n", - "\n", - "| | id <chr> | was_informed_by <chr> | has_output <list> | type <chr> |\n", - "|---|---|---|---|---|\n", - "| 1 | nmdc:wfmag-11-2702vw88.1 | nmdc:omprc-11-bd1eyb41 | nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37 | nmdc:MagsAnalysis |\n", - "| 2 | nmdc:wfmag-11-29we8r98.1 | nmdc:omprc-11-r5w9js52 | nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91 | nmdc:MagsAnalysis |\n", - "| 3 | nmdc:wfmag-11-94b5df26.1 | nmdc:omprc-11-by9r5p41 | nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851 | nmdc:MagsAnalysis |\n", - "| 4 | nmdc:wfmag-11-94b5df26.2 | nmdc:omprc-11-by9r5p41 | nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762 | nmdc:MagsAnalysis |\n", - "| 5 | nmdc:wfmag-11-9e4rbw35.1 | nmdc:omprc-11-sz2d4412 | nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53 | nmdc:MagsAnalysis |\n", - "| 6 | nmdc:wfmag-11-9xzvxq91.1 | nmdc:omprc-11-gaptm502 | nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78 | nmdc:MagsAnalysis |\n", - "\n" - ], - "text/plain": [ - " id was_informed_by \n", - "1 nmdc:wfmag-11-2702vw88.1 nmdc:omprc-11-bd1eyb41\n", - "2 nmdc:wfmag-11-29we8r98.1 nmdc:omprc-11-r5w9js52\n", - "3 nmdc:wfmag-11-94b5df26.1 nmdc:omprc-11-by9r5p41\n", - "4 nmdc:wfmag-11-94b5df26.2 nmdc:omprc-11-by9r5p41\n", - "5 nmdc:wfmag-11-9e4rbw35.1 nmdc:omprc-11-sz2d4412\n", - "6 nmdc:wfmag-11-9xzvxq91.1 nmdc:omprc-11-gaptm502\n", - " has_output \n", - "1 nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37 \n", - "2 nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91 \n", - "3 nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851 \n", - "4 nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762\n", - "5 nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53 \n", - "6 nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78 \n", - " type \n", - "1 nmdc:MagsAnalysis\n", - "2 nmdc:MagsAnalysis\n", - "3 nmdc:MagsAnalysis\n", - "4 nmdc:MagsAnalysis\n", - "5 nmdc:MagsAnalysis\n", - "6 nmdc:MagsAnalysis" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "metagenome_annotation_df <- get_results_by_id(\n", " collection = 'workflow_execution_set',\n", @@ -1538,54 +445,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "a4637e99-8950-441a-9966-e31e7781fa21", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 'nmdc:MagsAnalysis'
  2. 'nmdc:MetagenomeAnnotation'
  3. 'nmdc:MetagenomeAssembly'
  4. 'nmdc:ReadBasedTaxonomyAnalysis'
  5. 'nmdc:ReadQcAnalysis'
  6. 'nmdc:MetagenomeSequencing'
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 'nmdc:MagsAnalysis'\n", - "\\item 'nmdc:MetagenomeAnnotation'\n", - "\\item 'nmdc:MetagenomeAssembly'\n", - "\\item 'nmdc:ReadBasedTaxonomyAnalysis'\n", - "\\item 'nmdc:ReadQcAnalysis'\n", - "\\item 'nmdc:MetagenomeSequencing'\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 'nmdc:MagsAnalysis'\n", - "2. 'nmdc:MetagenomeAnnotation'\n", - "3. 'nmdc:MetagenomeAssembly'\n", - "4. 'nmdc:ReadBasedTaxonomyAnalysis'\n", - "5. 'nmdc:ReadQcAnalysis'\n", - "6. 'nmdc:MetagenomeSequencing'\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] \"nmdc:MagsAnalysis\" \"nmdc:MetagenomeAnnotation\" \n", - "[3] \"nmdc:MetagenomeAssembly\" \"nmdc:ReadBasedTaxonomyAnalysis\"\n", - "[5] \"nmdc:ReadQcAnalysis\" \"nmdc:MetagenomeSequencing\" " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "unique(metagenome_annotation_df$type)" ] @@ -1600,82 +467,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "24529f53-c27d-4948-824f-ad5a7a9d405c", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 4
metagenome_annotation_iddata_generation_idmatagenome_annotation_has_outputworkflow_type
<chr><chr><chr><chr>
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-vpaxc956nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-ad42v813nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-954m1b13nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-wvtgyb44nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-fs964t51nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-8sttbc64nmdc:MetagenomeAnnotation
\n" - ], - "text/latex": [ - "A tibble: 6 × 4\n", - "\\begin{tabular}{llll}\n", - " metagenome\\_annotation\\_id & data\\_generation\\_id & matagenome\\_annotation\\_has\\_output & workflow\\_type\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-vpaxc956 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-ad42v813 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-954m1b13 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-wvtgyb44 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-fs964t51 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-8sttbc64 & nmdc:MetagenomeAnnotation\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 4\n", - "\n", - "| metagenome_annotation_id <chr> | data_generation_id <chr> | matagenome_annotation_has_output <chr> | workflow_type <chr> |\n", - "|---|---|---|---|\n", - "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-vpaxc956 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-ad42v813 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-954m1b13 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-wvtgyb44 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-fs964t51 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-8sttbc64 | nmdc:MetagenomeAnnotation |\n", - "\n" - ], - "text/plain": [ - " metagenome_annotation_id data_generation_id \n", - "1 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", - "2 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", - "3 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", - "4 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", - "5 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", - "6 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", - " matagenome_annotation_has_output workflow_type \n", - "1 nmdc:dobj-11-vpaxc956 nmdc:MetagenomeAnnotation\n", - "2 nmdc:dobj-11-ad42v813 nmdc:MetagenomeAnnotation\n", - "3 nmdc:dobj-11-954m1b13 nmdc:MetagenomeAnnotation\n", - "4 nmdc:dobj-11-wvtgyb44 nmdc:MetagenomeAnnotation\n", - "5 nmdc:dobj-11-fs964t51 nmdc:MetagenomeAnnotation\n", - "6 nmdc:dobj-11-8sttbc64 nmdc:MetagenomeAnnotation" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "metagenome_annotation_df <- metagenome_annotation_df %>%\n", " filter(type == \"nmdc:MetagenomeAnnotation\") %>%\n", @@ -1701,110 +500,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "9aa89e83-67de-4620-ba98-a15311882551", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 14
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_iddata_generation_idmetagenome_annotation_idmatagenome_annotation_has_outputworkflow_type
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346nmdc:omprc-11-63ajbd04NA NA NA
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60nmdc:omprc-11-769ab655NA NA NA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-b96vap67nmdc:MetagenomeAnnotation
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-hkqqdt25nmdc:MetagenomeAnnotation
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-mn4z5956nmdc:MetagenomeAnnotation
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-jfypfh90nmdc:MetagenomeAnnotation
\n" - ], - "text/latex": [ - "A tibble: 6 × 14\n", - "\\begin{tabular}{llllllllllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id & data\\_generation\\_id & metagenome\\_annotation\\_id & matagenome\\_annotation\\_has\\_output & workflow\\_type\\\\\n", - " & & & & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346 & nmdc:omprc-11-63ajbd04 & NA & NA & NA \\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60 & nmdc:omprc-11-769ab655 & NA & NA & NA \\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-b96vap67 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-hkqqdt25 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-mn4z5956 & nmdc:MetagenomeAnnotation\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-jfypfh90 & nmdc:MetagenomeAnnotation\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 14\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> | data_generation_id <chr> | metagenome_annotation_id <chr> | matagenome_annotation_has_output <chr> | workflow_type <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 | nmdc:omprc-11-63ajbd04 | NA | NA | NA |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 | nmdc:omprc-11-769ab655 | NA | NA | NA |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-b96vap67 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-hkqqdt25 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-mn4z5956 | nmdc:MetagenomeAnnotation |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-jfypfh90 | nmdc:MetagenomeAnnotation |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-06tgpb52 O horizon \n", - "5 nmdc:bsm-11-06tgpb52 O horizon \n", - "6 nmdc:bsm-11-06tgpb52 O horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "5 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "6 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - " processed_sample_id pooling_id processed_sample_id2 \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "5 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "6 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - " extraction_id processed_sample_id3 library_preparation_id \n", - "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", - "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", - "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "4 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "5 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "6 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - " data_generation_id metagenome_annotation_id \n", - "1 nmdc:omprc-11-63ajbd04 NA \n", - "2 nmdc:omprc-11-769ab655 NA \n", - "3 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", - "4 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", - "5 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", - "6 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", - " matagenome_annotation_has_output workflow_type \n", - "1 NA NA \n", - "2 NA NA \n", - "3 nmdc:dobj-11-b96vap67 nmdc:MetagenomeAnnotation\n", - "4 nmdc:dobj-11-hkqqdt25 nmdc:MetagenomeAnnotation\n", - "5 nmdc:dobj-11-mn4z5956 nmdc:MetagenomeAnnotation\n", - "6 nmdc:dobj-11-jfypfh90 nmdc:MetagenomeAnnotation" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df9 <- biosample_df8 %>%\n", " left_join(metagenome_annotation_df, by = join_by(data_generation_id), relationship = \"many-to-many\")\n", @@ -1823,82 +526,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "bf1f3a9c-ba08-43a6-b9aa-577ec7586969", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 6 × 3
data_object_iddata_object_typeurl
<chr><chr><chr>
1nmdc:dobj-11-jp45gr33Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
2nmdc:dobj-11-mmv19z03Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv
3nmdc:dobj-11-8ybd1f87Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv
4nmdc:dobj-11-wn5g7j41Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv
5nmdc:dobj-11-xt9amn82Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv
6nmdc:dobj-11-b6yhf780Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv
\n" - ], - "text/latex": [ - "A data.frame: 6 × 3\n", - "\\begin{tabular}{r|lll}\n", - " & data\\_object\\_id & data\\_object\\_type & url\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t1 & nmdc:dobj-11-jp45gr33 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t2 & nmdc:dobj-11-mmv19z03 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc\\_wfmgan-11-3s20yk38.2\\_scaffold\\_lineage.tsv\\\\\n", - "\t3 & nmdc:dobj-11-8ybd1f87 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc\\_wfmgan-11-2a0ap078.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t4 & nmdc:dobj-11-wn5g7j41 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc\\_wfmgan-11-hv3nyk36.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t5 & nmdc:dobj-11-xt9amn82 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc\\_wfmgan-11-me7h8h69.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t6 & nmdc:dobj-11-b6yhf780 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc\\_wfmgan-11-k5a19412.1\\_scaffold\\_lineage.tsv\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 3\n", - "\n", - "| | data_object_id <chr> | data_object_type <chr> | url <chr> |\n", - "|---|---|---|---|\n", - "| 1 | nmdc:dobj-11-jp45gr33 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| 2 | nmdc:dobj-11-mmv19z03 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv |\n", - "| 3 | nmdc:dobj-11-8ybd1f87 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv |\n", - "| 4 | nmdc:dobj-11-wn5g7j41 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv |\n", - "| 5 | nmdc:dobj-11-xt9amn82 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv |\n", - "| 6 | nmdc:dobj-11-b6yhf780 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv |\n", - "\n" - ], - "text/plain": [ - " data_object_id data_object_type \n", - "1 nmdc:dobj-11-jp45gr33 Scaffold Lineage tsv\n", - "2 nmdc:dobj-11-mmv19z03 Scaffold Lineage tsv\n", - "3 nmdc:dobj-11-8ybd1f87 Scaffold Lineage tsv\n", - "4 nmdc:dobj-11-wn5g7j41 Scaffold Lineage tsv\n", - "5 nmdc:dobj-11-xt9amn82 Scaffold Lineage tsv\n", - "6 nmdc:dobj-11-b6yhf780 Scaffold Lineage tsv\n", - " url \n", - "1 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "2 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv\n", - "3 https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv\n", - "4 https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv\n", - "5 https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv\n", - "6 https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "data_object_df <- get_results_by_id(\n", " collection = 'data_object_set',\n", @@ -1925,110 +560,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "d3ac4d7e-861f-4553-b05c-751ae08d2304", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 16
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_iddata_generation_idmetagenome_annotation_iddata_object_idworkflow_typedata_object_typeurl
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346nmdc:omprc-11-63ajbd04NA NA NA NANA
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60nmdc:omprc-11-769ab655NA NA NA NANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-b96vap67nmdc:MetagenomeAnnotationNANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-hkqqdt25nmdc:MetagenomeAnnotationNANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-mn4z5956nmdc:MetagenomeAnnotationNANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-jfypfh90nmdc:MetagenomeAnnotationNANA
\n" - ], - "text/latex": [ - "A tibble: 6 × 16\n", - "\\begin{tabular}{llllllllllllllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id & data\\_generation\\_id & metagenome\\_annotation\\_id & data\\_object\\_id & workflow\\_type & data\\_object\\_type & url\\\\\n", - " & & & & & & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346 & nmdc:omprc-11-63ajbd04 & NA & NA & NA & NA & NA\\\\\n", - "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60 & nmdc:omprc-11-769ab655 & NA & NA & NA & NA & NA\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-b96vap67 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-hkqqdt25 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-mn4z5956 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-jfypfh90 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 16\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> | data_generation_id <chr> | metagenome_annotation_id <chr> | data_object_id <chr> | workflow_type <chr> | data_object_type <chr> | url <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 | nmdc:omprc-11-63ajbd04 | NA | NA | NA | NA | NA |\n", - "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 | nmdc:omprc-11-769ab655 | NA | NA | NA | NA | NA |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-b96vap67 | nmdc:MetagenomeAnnotation | NA | NA |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-hkqqdt25 | nmdc:MetagenomeAnnotation | NA | NA |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-mn4z5956 | nmdc:MetagenomeAnnotation | NA | NA |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-jfypfh90 | nmdc:MetagenomeAnnotation | NA | NA |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon\n", - "1 nmdc:bsm-11-00m15h97 M horizon \n", - "2 nmdc:bsm-11-06ta8e31 M horizon \n", - "3 nmdc:bsm-11-06tgpb52 O horizon \n", - "4 nmdc:bsm-11-06tgpb52 O horizon \n", - "5 nmdc:bsm-11-06tgpb52 O horizon \n", - "6 nmdc:bsm-11-06tgpb52 O horizon \n", - " geo_loc_name geo_loc_name_type\n", - "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", - "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "4 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "5 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - "6 USA: Colorado, Rocky Mountains nmdc:TextValue \n", - " processed_sample_id pooling_id processed_sample_id2 \n", - "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", - "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", - "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "4 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "5 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - "6 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", - " extraction_id processed_sample_id3 library_preparation_id \n", - "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", - "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", - "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "4 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "5 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - "6 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", - " data_generation_id metagenome_annotation_id data_object_id \n", - "1 nmdc:omprc-11-63ajbd04 NA NA \n", - "2 nmdc:omprc-11-769ab655 NA NA \n", - "3 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-b96vap67\n", - "4 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-hkqqdt25\n", - "5 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-mn4z5956\n", - "6 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-jfypfh90\n", - " workflow_type data_object_type url\n", - "1 NA NA NA \n", - "2 NA NA NA \n", - "3 nmdc:MetagenomeAnnotation NA NA \n", - "4 nmdc:MetagenomeAnnotation NA NA \n", - "5 nmdc:MetagenomeAnnotation NA NA \n", - "6 nmdc:MetagenomeAnnotation NA NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df10 <- biosample_df9 %>%\n", " rename(data_object_id = matagenome_annotation_has_output) %>%\n", @@ -2048,89 +587,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "9444e91a-1305-4a15-ae8d-338ca54eb5e4", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 6
biosample_idsoil_horizongeo_loc_namedata_object_iddata_object_typeurl
<chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountainsnmdc:dobj-11-jp45gr33Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountainsnmdc:dobj-11-mmv19z03Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv
nmdc:bsm-11-0gmd9f09M horizonUSA: Colorado, Niwot Ridge nmdc:dobj-11-xt9amn82Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv
nmdc:bsm-11-0hz4rd27O horizonUSA: Colorado, Niwot Ridge nmdc:dobj-11-8ybd1f87Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv
nmdc:bsm-11-0qa78w81M horizonUSA: Colorado, North Sterling nmdc:dobj-11-wn5g7j41Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv
nmdc:bsm-11-0yw1rj05M horizonUSA: Colorado, North Sterling nmdc:dobj-11-b6yhf780Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv
\n" - ], - "text/latex": [ - "A tibble: 6 × 6\n", - "\\begin{tabular}{llllll}\n", - " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & data\\_object\\_id & data\\_object\\_type & url\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:dobj-11-jp45gr33 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:dobj-11-mmv19z03 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc\\_wfmgan-11-3s20yk38.2\\_scaffold\\_lineage.tsv\\\\\n", - "\t nmdc:bsm-11-0gmd9f09 & M horizon & USA: Colorado, Niwot Ridge & nmdc:dobj-11-xt9amn82 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc\\_wfmgan-11-me7h8h69.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t nmdc:bsm-11-0hz4rd27 & O horizon & USA: Colorado, Niwot Ridge & nmdc:dobj-11-8ybd1f87 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc\\_wfmgan-11-2a0ap078.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t nmdc:bsm-11-0qa78w81 & M horizon & USA: Colorado, North Sterling & nmdc:dobj-11-wn5g7j41 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc\\_wfmgan-11-hv3nyk36.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t nmdc:bsm-11-0yw1rj05 & M horizon & USA: Colorado, North Sterling & nmdc:dobj-11-b6yhf780 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc\\_wfmgan-11-k5a19412.1\\_scaffold\\_lineage.tsv\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 6\n", - "\n", - "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | data_object_id <chr> | data_object_type <chr> | url <chr> |\n", - "|---|---|---|---|---|---|\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:dobj-11-jp45gr33 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:dobj-11-mmv19z03 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv |\n", - "| nmdc:bsm-11-0gmd9f09 | M horizon | USA: Colorado, Niwot Ridge | nmdc:dobj-11-xt9amn82 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv |\n", - "| nmdc:bsm-11-0hz4rd27 | O horizon | USA: Colorado, Niwot Ridge | nmdc:dobj-11-8ybd1f87 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv |\n", - "| nmdc:bsm-11-0qa78w81 | M horizon | USA: Colorado, North Sterling | nmdc:dobj-11-wn5g7j41 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv |\n", - "| nmdc:bsm-11-0yw1rj05 | M horizon | USA: Colorado, North Sterling | nmdc:dobj-11-b6yhf780 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv |\n", - "\n" - ], - "text/plain": [ - " biosample_id soil_horizon geo_loc_name \n", - "1 nmdc:bsm-11-06tgpb52 O horizon USA: Colorado, Rocky Mountains\n", - "2 nmdc:bsm-11-06tgpb52 O horizon USA: Colorado, Rocky Mountains\n", - "3 nmdc:bsm-11-0gmd9f09 M horizon USA: Colorado, Niwot Ridge \n", - "4 nmdc:bsm-11-0hz4rd27 O horizon USA: Colorado, Niwot Ridge \n", - "5 nmdc:bsm-11-0qa78w81 M horizon USA: Colorado, North Sterling \n", - "6 nmdc:bsm-11-0yw1rj05 M horizon USA: Colorado, North Sterling \n", - " data_object_id data_object_type \n", - "1 nmdc:dobj-11-jp45gr33 Scaffold Lineage tsv\n", - "2 nmdc:dobj-11-mmv19z03 Scaffold Lineage tsv\n", - "3 nmdc:dobj-11-xt9amn82 Scaffold Lineage tsv\n", - "4 nmdc:dobj-11-8ybd1f87 Scaffold Lineage tsv\n", - "5 nmdc:dobj-11-wn5g7j41 Scaffold Lineage tsv\n", - "6 nmdc:dobj-11-b6yhf780 Scaffold Lineage tsv\n", - " url \n", - "1 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "2 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv\n", - "3 https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv\n", - "4 https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv\n", - "5 https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv\n", - "6 https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df_final <- biosample_df10 %>%\n", " select(biosample_id, soil_horizon, geo_loc_name, data_object_id, data_object_type, url) %>%\n", @@ -2151,59 +615,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "7521a8c4-7b02-4d4b-a0c4-4287bc814516", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 2 × 2
soil_horizonn
<chr><int>
M horizon266
O horizon 58
\n" - ], - "text/latex": [ - "A tibble: 2 × 2\n", - "\\begin{tabular}{ll}\n", - " soil\\_horizon & n\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t M horizon & 266\\\\\n", - "\t O horizon & 58\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 2 × 2\n", - "\n", - "| soil_horizon <chr> | n <int> |\n", - "|---|---|\n", - "| M horizon | 266 |\n", - "| O horizon | 58 |\n", - "\n" - ], - "text/plain": [ - " soil_horizon n \n", - "1 M horizon 266\n", - "2 O horizon 58" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "biosample_df_final %>%\n", " count(soil_horizon)" @@ -2221,89 +640,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "16c7457f-c1dc-4344-8049-581eee81ffd2", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 3
contig_idtaxainitial_count
<chr><chr><dbl>
nmdc:wfmgas-11-qdbye406.1_scf_10000_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 1
nmdc:wfmgas-11-qdbye406.1_scf_10001_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 1
nmdc:wfmgas-11-qdbye406.1_scf_10002_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 1
nmdc:wfmgas-11-qdbye406.1_scf_10003_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 1
nmdc:wfmgas-11-qdbye406.1_scf_10004_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 1
nmdc:wfmgas-11-qdbye406.1_scf_10005_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT02831
\n" - ], - "text/latex": [ - "A tibble: 6 × 3\n", - "\\begin{tabular}{lll}\n", - " contig\\_id & taxa & initial\\_count\\\\\n", - " & & \\\\\n", - "\\hline\n", - "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10000\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 & 1\\\\\n", - "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10001\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 & 1\\\\\n", - "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10002\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 & 1\\\\\n", - "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10003\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 & 1\\\\\n", - "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10004\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 & 1\\\\\n", - "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10005\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT0283 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 3\n", - "\n", - "| contig_id <chr> | taxa <chr> | initial_count <dbl> |\n", - "|---|---|---|\n", - "| nmdc:wfmgas-11-qdbye406.1_scf_10000_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 | 1 |\n", - "| nmdc:wfmgas-11-qdbye406.1_scf_10001_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 | 1 |\n", - "| nmdc:wfmgas-11-qdbye406.1_scf_10002_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 | 1 |\n", - "| nmdc:wfmgas-11-qdbye406.1_scf_10003_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 | 1 |\n", - "| nmdc:wfmgas-11-qdbye406.1_scf_10004_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 | 1 |\n", - "| nmdc:wfmgas-11-qdbye406.1_scf_10005_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT0283 | 1 |\n", - "\n" - ], - "text/plain": [ - " contig_id \n", - "1 nmdc:wfmgas-11-qdbye406.1_scf_10000_c1\n", - "2 nmdc:wfmgas-11-qdbye406.1_scf_10001_c1\n", - "3 nmdc:wfmgas-11-qdbye406.1_scf_10002_c1\n", - "4 nmdc:wfmgas-11-qdbye406.1_scf_10003_c1\n", - "5 nmdc:wfmgas-11-qdbye406.1_scf_10004_c1\n", - "6 nmdc:wfmgas-11-qdbye406.1_scf_10005_c1\n", - " taxa \n", - "1 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 \n", - "2 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 \n", - "3 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 \n", - "4 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 \n", - "5 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 \n", - "6 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT0283\n", - " initial_count\n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 \n", - "5 1 \n", - "6 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "url <- biosample_df_final$url[1]\n", "\n", @@ -2331,99 +675,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "8a331e72-182c-4a55-b1e9-705908b6238d", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] \"Processing 10 of 114\"\n", - "[1] \"Processing 20 of 114\"\n", - "[1] \"Processing 30 of 114\"\n", - "[1] \"Processing 40 of 114\"\n", - "[1] \"Processing 50 of 114\"\n", - "[1] \"Processing 60 of 114\"\n", - "[1] \"Processing 70 of 114\"\n", - "[1] \"Processing 80 of 114\"\n", - "[1] \"Processing 90 of 114\"\n", - "[1] \"Processing 100 of 114\"\n", - "[1] \"Processing 110 of 114\"\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 6 × 4
taxacountrelative_abundanceurl
<chr><int><dbl><chr>
Acidimicrobiia 3727.050796e-03https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Acidithiobacillia 59.476876e-05https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Actinomycetes 184093.489196e-01https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Agaricomycetes 1512.862017e-03https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Alphaproteobacteria183553.478961e-01https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Anaerolineae 173.222138e-04https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
\n" - ], - "text/latex": [ - "A tibble: 6 × 4\n", - "\\begin{tabular}{llll}\n", - " taxa & count & relative\\_abundance & url\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t Acidimicrobiia & 372 & 7.050796e-03 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t Acidithiobacillia & 5 & 9.476876e-05 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t Actinomycetes & 18409 & 3.489196e-01 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t Agaricomycetes & 151 & 2.862017e-03 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t Alphaproteobacteria & 18355 & 3.478961e-01 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\t Anaerolineae & 17 & 3.222138e-04 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 6 × 4\n", - "\n", - "| taxa <chr> | count <int> | relative_abundance <dbl> | url <chr> |\n", - "|---|---|---|---|\n", - "| Acidimicrobiia | 372 | 7.050796e-03 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| Acidithiobacillia | 5 | 9.476876e-05 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| Actinomycetes | 18409 | 3.489196e-01 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| Agaricomycetes | 151 | 2.862017e-03 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| Alphaproteobacteria | 18355 | 3.478961e-01 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "| Anaerolineae | 17 | 3.222138e-04 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", - "\n" - ], - "text/plain": [ - " taxa count relative_abundance\n", - "1 Acidimicrobiia 372 7.050796e-03 \n", - "2 Acidithiobacillia 5 9.476876e-05 \n", - "3 Actinomycetes 18409 3.489196e-01 \n", - "4 Agaricomycetes 151 2.862017e-03 \n", - "5 Alphaproteobacteria 18355 3.478961e-01 \n", - "6 Anaerolineae 17 3.222138e-04 \n", - " url \n", - "1 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "2 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "3 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "4 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "5 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", - "6 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "urls <- unique(biosample_df_final$url)\n", "results_list <- c()\n", @@ -2519,30 +778,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "460474ef-2d2e-4553-85fc-78dc5a756bf1", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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Ic4wtW7a4gEmBjo5x0kkn2bZt24I+qsL56KOP3I+CFoVnffv2tSlTpgR9tKB+CnYUeui6I4e/S6aHqvUUVnr/0qVLu/PL6pVXXrHp06e7YObss89Oc416o2tTU0XYddddZ6rK823JkiUusPPvFeBpqFBVqoWbKpS+/vpr96PzKQTq0qVL0GXr1q3BefxKf57ICkC913UohFOlWbjps3nvvffsgw8+sCFDhtjtt99uqpaMbD/88IOdfvrpNmPGjMhNpmMoLNTPRRddZPfee2+6zya804svvuhC5l9//TVYrc927ty57kchn4ZLVYgU2WbPnu2CXlVKRjZ9B2SgH30+zz33nClsjGyy0xyG77zzTuQm99n4e7nzzjvt+uuvd31S6a3vop4hfX8VNitg9k1h76JFi9z32H+vtC3yM9e6ZFjpOPmlFc3NC9UvBpW0/uc//3Hp8GWXXZbuv4bQL6+slrzm5r1xbgQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFUCCot8O/HEE/1i0l7/9a9/uYBCgcJRRx1lb775pmnIOQ3NqD++q/pMFT1qCisig6rwhdx6660uVGvdurXdf//9LkjT8TSHk9rChQvt5JNPthNOOMGKFi1q9913nwtBFMpccMEFwaEUAoRDs2DD3wsKClSBp2EmL774Ynv11VfdeRRglChRwnW98MILTSGkrlVmCpUWLFhgjz/+uKtGUifdo+4tsiXT46677nKhWosWLUwVcLp2BRmPPfaYKWRT8HXuueemq2wKX5MCJoU8CohUddiqVStXuSYDNYWeCqIUqinwvPHGG52pzqMASxZqCi1l4QM7rVPwqOcrXMk4btw4t05/vw+3Sy+91P2tX6GaqptURSlDDeP48ssvW8uWLV0wKz9VR0VruhYfqh1yyCE2bdo0+/77710lo8LYunXrut1UHTl48OBohwjWjRw50lRJdfXVV9sXX3zhXJVH6Lhqur6BAwcG/f2CQmo5KFTTZ6DjKKjU864QU0GZt1Vop1AsMkjUsc4666wgVFMFoc6n/RUK615UAaem0FrPn1qqvXVOPRdDhw7Voul72atXL1fhp+cuo5Ysq4zOk5e2F/n7w04bG6fo6qZOneoeZn3Zw03Jv/8vK5SW6r8sUOnq//3f/9k111zjfpGG+7OMQH4RWFHkv78k88v1cp0IIIAAAgikWqDJ3oznAEj1NWX1fC0vSfv/XGb1OOyHAAIIIIBAQRVYOLpPQb017isJAn9dsk8SjpI3D1Fs9K4cu7BTTjnFhUc6gUIQP7RbMk6owgcN1ahhBDVk4KeffmoaCi+yqUrNhw0KxRRghFvx4sWDucs01JyOE64U0vxmGqLQ/31Y51DIpmEAw02VW77aTecMD0eoKqjwfHEKX3xo5I+huegUlPimwElDSIabKrgUMKhq58gjj7SZM2cGm3PC47DDDnNVWGEPnVDn7dSpkwttDj30UBeQ+QtR4YpMfVNopUosDTGoplBRf29XhZb21T0pVFQopGNGtmeeecZVEmq9TGUbbgpEb7nlFrdKwwHqfOGm8K5du3YuOFP4qgo8hVLhphCqZ8+eblupUqXcNYU/3/A1qAJM1Y+Rx1BFl54vP0Rm5POuwC5ceaVjas7BcJONQlVVJBYpUsS56vP27ZFHHjEVAqmpilIVlpFN0Yq+ZwpD1VQh6cNhvde1q1pNTcOPqrIt8nujME3DfappuElVfvmWCm99V/Sd8U3fK1WDqinQVZCuqkKFvz540zOmz9m3ZFj5Y+WX15RXrC1evNiNZ6pSzshQLRJN6bia0mwlvirLzc7EgpHH5z0CCCCAAAIIIIAAAggggAACCCCAAAIIIFAQBFQF45vmPIvXvv32W1eJs3z5cvM/qlDyP0uXLnVVU/4YquLSnF0aOlHzcEWGA76f5tTyQyYqhIvXFDpEhkiaP00BhG8atjAcuvj1qgLybdWqVX4x3asCi8hQTZ0U7PhWrlw5u+OOO/zb4FXVRb6qKfIcOeFx9913p/PQxSig8sGL5gpT5VaspnmwfKimPv6zePrpp12ApXWaXy1aqKZtCiwVNqmpOjAyGHUb4vyjqjAfxGh4xchATLvq2Rk9erSrmlPgd/PNN6c5oh8OUaGbhpOMdoxKlSqlmR9NBTmxmp6nyFBNfWWjsEhNAVk4XNI65Rj169d3P9H2Vx8Fcgq0fYt85mXgm6ouo31vtL8+YzVVx6mSLdGWDO/wuRRW+lBN6xXaRhuqM7yPlpNhFXnMvP6+eCovUA+FxmaNfDhUeqqHUAlouGls2XBTSa7G79R4wTQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB/wqE5z3KaJAyVcZs2rQpLl24cqVNmzZBpVRGx65WrZopuItXIFG7du0gtIq8CG3zTZVi0Vp4/jjNxxWraX62aC18DgVofgjLyL7+PJHnSLaHArxw1V3kdSgc1BCCal9++aWr6ovsowCkbdu2kavde1X9+Xbttdf6xaivmh/trbfecttUUZjosKKqnvPVVqqOixaI+hM2atTIVT6q6k3VT75pWEoV2aipyMYP+ei3h18PP/xwVy2mISM1xKOyhXD1nu971VVX+cV0r3q+VHUnn/B1qKOvYNTzruwiVtOIe76Fn3lV5mkuOTV932QSq2moSzX/vMXqF16fDO/w8bTcsWPHyFUJvc+uVUInyWOdUhqsKQ33oZoeco2HqxJK/eLQh6YvQLip1HPixImubFP/5YTa66+/nmb80nB/lhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcIooLBIQ/2pbdy40c1vlRMOPmRQiKAATVVu+tutAh8FCT4YUeVSrKZhJWO1cIVMgwYNonZTNVMiLdZ5EjmHjp/IeZLhoXAvXguHVPobevfu3dN1Vx/NRxetaW4wNVV/1f+7CiteC19LeJ61ePtom0afCwdLDzzwQNxdvJtGtfOhmColfWvevLlfjPmqa1WwpqE5v/nmG2vatGm6vhkdp3Hjxi5Y0zxwfujM8EH8dWqdAlZVlclF16phJBU++hZ+5uWhijw1nSNey0yg5o+TDG9/LP/apEkTv5il16xaZelkubxTyoK1J5980oViul/9QtK4p+HxRqM5qJJNE/Vpokr9svATFmp8VD9Wb7T9WIcAAggggAACCCCAAAIIIIAAAggggAACCBQmgTp16gS3q5AgXrvzzjtNFTWRTZVK8UYLU2Cn4flUYaOAQVUzWWmRQ0DGOka0ofNi9Y22PpHzZOccyfKIFQD6e9K8c74pwIzWVAUWq/lgTecJhx/R+msYUZno+chMsKbnwTfNNaafRJpCNQW0CnX8dWq/jAJA9QkHr7rWyGBNQWO8qjcdI2y7cuXKNJWUqlZ744037NFHH7U5c+aY5gBMtIUrQjO6hkSPGe6XDO/w8bQc7xmK7Bv5PjtWkcfKD+9TFqy9+OKLgYfGUM0oVAs6/72gX4D6Za0vkyauVBKsBzOjsYLDx2AZAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoqAJ+PjDdn+bGCs+VFHnPAwcOjFzl3u/YsSNmsPbqq69anz59giocf4DKlSvbwQcfbO3atXMFEjpv5BQ/vq9/jTZkn9+WzNecPE8yPfxcaLHuPVwJtWvXrqjdwnOrRXbww4SWKFEiclPc9xkN+xne+eeffw7eKqDRnHyJNlWcqfnr1HKyrlXFO/FaeHs4ZNW1nHTSScFwjv4Y6n/AAQe4AE7Dd27dutUyGl4zJ57DZHj7e/Kv8Z4h3yfaazKtoh0/L65LWbDmk3QlwIMGDcq0hX5Bn3vuuS4d1s4q7SRYyzQjOyCAAAIIIIAAAggggAACCCCAAAIIIIBAARQ488wzbciQIa6K7JVXXjEFMFn9Q3kkz/z5803H90Pbacqf0047zYUL4fnKtJ8fDjAzoUzk+fL6+2R7fP/993FvOby9Ro0acftG26hqMM1fpmEXM2oa7tBXM2q+vERbeLjDXr162d13353orkG/8FCEiVyrhkP0Ldq1KpBU8BWeB83396/+GArMwlVyAwYMCEI1BYVXXnmlmytNw09qSE3fHnvsMb9o4Wc+XP21du3aoE+yFpLhnaxrya5Vsq4jlcdJSbCmklhf+hhrAsVEbloTCfqmYE2T/tEQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHCLqAihH/84x+uWk2jft1www2W0TxXiZpNmDDBBXXq/+CDD9rll18edVcN66cgQy2rw0RGPXAeW5lsj4xCJP0t3DdVS2W2KbDS0Ix6LjIaCU7DIfoWGZr69dFew6GY5oHLqCm8C1eIqX/4GOHriHWscJ9Y1yrbeMHaqlWr3OE1XKOvktMzPHnyZLdec9dpHrVKlSpFvYzNmzcH68PPvIZmVQCnaq6MKjg/++wzGzZsmBvaUkVJiWQoYausegcXno2FZFhl4/S5tmv02QyTfDnhMX2zM56of7B1ef6/jkjypXI4BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgXwrcfvvtVrJkSXftDz30kL3wwgsJ34dCgcWLF0ftP3PmTLde83OpOiVW+/DDD4OKNYVsBbUl20Nu4aq0SLdwVdQpp5wSuTnD9+GCFU3TFK9pPjHfNBRiuGnOMt/C1Vlap+EsFUKpySccBrqVoX8UNqniSvsoRPJDQWoOOD8v3vPPP2/btm0L7ZV2cenSpfbee++5lS1atEgzV1q459SpU8Nv0ywrbFywYIFb16ZNm2DbrFmzguozeccK1bTD22+/HewXfub1XWnevLnb9tFHH8X9fF9//XXT/IZjx44Nwj3tmNPewYVnYyEZVtk4fa7t+r9vQg5eglJ0PylieALCzJ7SDyep/Q466KDM7k5/BBBAAAEEEEAAAQQQQAABBBBAAAEEEECgwAoceuihduedd7r7U/Ch4RsHDx5smjstXnvjjTfs8MMPt0mTJkXt5ucA0zE1VGC0tmLFCjv77LODTX5IyGBFAVpItoe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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 875, - "width": 875 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "options(dplyr.summarise.inform = FALSE)\n", "horizon_taxa <- biosample_taxa_df %>%\n", @@ -2576,30 +819,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "af8211c8-93cd-4be7-8fc5-1bbcc30187ab", "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 875, - "width": 1125 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "geo_taxa <- biosample_taxa_df %>%\n", " group_by(geo_loc_name, soil_horizon, taxa) %>%\n", @@ -2635,7 +862,7 @@ "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", - "version": "4.3.1" + "version": "4.4.1" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/utility_functions.R b/utility_functions.R index 8c0d781a..51a422fb 100644 --- a/utility_functions.R +++ b/utility_functions.R @@ -1,3 +1,10 @@ +# This function provides a general-purpose way to make an API request to +# NMDC's runtime API. Note that this function will only return the first page +# of results. The function's input includes the name of the collection +# to access (e.g. biosample_set), the filter to be performed, the maximum page +# size, and a list of the fields to be retrieved. +# It returns the metadata as a dataframe. + get_first_page_results <- function(collection, filter, max_page_size, fields) { og_url <- paste0( 'https://api.microbiomedata.org/nmdcschema/', @@ -9,6 +16,15 @@ get_first_page_results <- function(collection, filter, max_page_size, fields) { return(response) } + +# The get_next_results function uses the get_first_page_results function, +# defined above, to retrieve the rest of the results from a call with multiple +# pages. It takes the same inputs as the get_first_page_results function above: +# the name of the collection to be retrieved, the filter string, the maximum +# page size, and a list of the fields to be returned. This function returns the +# results as a single dataframe (can be nested). It uses the next_page_token +# key in each page of results to retrieve the following page. + get_next_results <- function(collection, filter_text, max_page_size, fields) { initial_data <- get_first_page_results(collection, filter_text, max_page_size, fields) results_df <- initial_data$resources @@ -32,6 +48,14 @@ get_next_results <- function(collection, filter_text, max_page_size, fields) { return(results_df) } + +# This function constructs a different type of API request that takes a list of +# IDs and uses them to retrieve related data. In short, it searches in +# `collection` for records that have elements of `id_list` in their +# `match_id_field`, then returns all `fields` for the matching records. +# Fields such as `has_input` or `has_output` are likely to be useful values +# for `match_id_field`, though other fields are also usable. + get_results_by_id <- function(collection, match_id_field, id_list, fields, max_id = 50) { # collection: the name of the collection to query # match_id_field: the field in the new collection to match to the id_list @@ -67,6 +91,12 @@ get_results_by_id <- function(collection, match_id_field, id_list, fields, max_i output_df <- bind_rows(output) } + +# The functions above rely on knowing the MongoDB "collection" to search for +# relevant objects. But what if you don't know what the collection is called? +# This function takes an NMDC ID and returns the collection that the object is +# part of. + get_collection_by_id <- function(id) { # Create API endpoint URL From 44cdff0f48ff04222b3a6fb30a60a0a625b92e69 Mon Sep 17 00:00:00 2001 From: bmeluch Date: Fri, 10 Jan 2025 16:13:48 -0800 Subject: [PATCH 5/6] render taxonomy notebook --- .../R/taxonomic_dist_soil_layer_R.ipynb | 1763 ++++++++++++++++- 1 file changed, 1714 insertions(+), 49 deletions(-) diff --git a/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb b/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb index e48db04a..3c149dae 100644 --- a/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb +++ b/taxonomic_dist_by_soil_layer/R/taxonomic_dist_soil_layer_R.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "780eae36-3f46-4c3e-bd26-b2a33c463441", "metadata": { "metadata": {}, @@ -51,7 +51,75 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 4
biosample_idsoil_horizongeo_loc_namegeo_loc_name_type
<chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValue
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValue
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValue
\n" + ], + "text/latex": [ + "A tibble: 6 × 4\n", + "\\begin{tabular}{llll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 4\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> |\n", + "|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get biosamples using get_next_results function\n", "biosample_df <- get_next_results(\n", @@ -83,14 +151,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "a8fc4389-49d8-42ea-a56b-a561c01900ff", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
biosample_idpooling_has_outputpooling_id
<chr><chr><chr>
nmdc:bsm-11-5228zz06nmdc:procsm-11-49bwy122nmdc:poolp-11-a1nnyd94
nmdc:bsm-11-1frj0t76nmdc:procsm-11-49bwy122nmdc:poolp-11-a1nnyd94
nmdc:bsm-11-nyxsx333nmdc:procsm-11-49bwy122nmdc:poolp-11-a1nnyd94
nmdc:bsm-11-ex491068nmdc:procsm-11-kngzyt90nmdc:poolp-11-sj9jpg87
nmdc:bsm-11-1byjjh32nmdc:procsm-11-kngzyt90nmdc:poolp-11-sj9jpg87
nmdc:bsm-11-da5wpm57nmdc:procsm-11-kngzyt90nmdc:poolp-11-sj9jpg87
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " biosample\\_id & pooling\\_has\\_output & pooling\\_id\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-5228zz06 & nmdc:procsm-11-49bwy122 & nmdc:poolp-11-a1nnyd94\\\\\n", + "\t nmdc:bsm-11-1frj0t76 & nmdc:procsm-11-49bwy122 & nmdc:poolp-11-a1nnyd94\\\\\n", + "\t nmdc:bsm-11-nyxsx333 & nmdc:procsm-11-49bwy122 & nmdc:poolp-11-a1nnyd94\\\\\n", + "\t nmdc:bsm-11-ex491068 & nmdc:procsm-11-kngzyt90 & nmdc:poolp-11-sj9jpg87\\\\\n", + "\t nmdc:bsm-11-1byjjh32 & nmdc:procsm-11-kngzyt90 & nmdc:poolp-11-sj9jpg87\\\\\n", + "\t nmdc:bsm-11-da5wpm57 & nmdc:procsm-11-kngzyt90 & nmdc:poolp-11-sj9jpg87\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| biosample_id <chr> | pooling_has_output <chr> | pooling_id <chr> |\n", + "|---|---|---|\n", + "| nmdc:bsm-11-5228zz06 | nmdc:procsm-11-49bwy122 | nmdc:poolp-11-a1nnyd94 |\n", + "| nmdc:bsm-11-1frj0t76 | nmdc:procsm-11-49bwy122 | nmdc:poolp-11-a1nnyd94 |\n", + "| nmdc:bsm-11-nyxsx333 | nmdc:procsm-11-49bwy122 | nmdc:poolp-11-a1nnyd94 |\n", + "| nmdc:bsm-11-ex491068 | nmdc:procsm-11-kngzyt90 | nmdc:poolp-11-sj9jpg87 |\n", + "| nmdc:bsm-11-1byjjh32 | nmdc:procsm-11-kngzyt90 | nmdc:poolp-11-sj9jpg87 |\n", + "| nmdc:bsm-11-da5wpm57 | nmdc:procsm-11-kngzyt90 | nmdc:poolp-11-sj9jpg87 |\n", + "\n" + ], + "text/plain": [ + " biosample_id pooling_has_output pooling_id \n", + "1 nmdc:bsm-11-5228zz06 nmdc:procsm-11-49bwy122 nmdc:poolp-11-a1nnyd94\n", + "2 nmdc:bsm-11-1frj0t76 nmdc:procsm-11-49bwy122 nmdc:poolp-11-a1nnyd94\n", + "3 nmdc:bsm-11-nyxsx333 nmdc:procsm-11-49bwy122 nmdc:poolp-11-a1nnyd94\n", + "4 nmdc:bsm-11-ex491068 nmdc:procsm-11-kngzyt90 nmdc:poolp-11-sj9jpg87\n", + "5 nmdc:bsm-11-1byjjh32 nmdc:procsm-11-kngzyt90 nmdc:poolp-11-sj9jpg87\n", + "6 nmdc:bsm-11-da5wpm57 nmdc:procsm-11-kngzyt90 nmdc:poolp-11-sj9jpg87" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pooling_df <- get_results_by_id(\n", " collection = 'material_processing_set',\n", @@ -124,14 +254,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "0aa58275-a5e6-418f-a04c-b1ef5e21f65e", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 6
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typepooling_has_outputpooling_id
<chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70
\n" + ], + "text/latex": [ + "A tibble: 6 × 6\n", + "\\begin{tabular}{llllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & pooling\\_has\\_output & pooling\\_id\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 6\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | pooling_has_output <chr> | pooling_id <chr> |\n", + "|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " pooling_has_output pooling_id \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df2 <- left_join(biosample_df, pooling_df2, by = 'biosample_id')\n", "head(biosample_df2)" @@ -147,14 +353,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "37534e80-aab9-4ec7-9829-8cde11e27a51", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 6
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_id
<chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70
\n" + ], + "text/latex": [ + "A tibble: 6 × 6\n", + "\\begin{tabular}{llllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 6\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> |\n", + "|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " processed_sample_id pooling_id \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df3 <- biosample_df2 %>%\n", " rename(processed_sample_id = pooling_has_output) \n", @@ -173,14 +455,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "832c3ddd-6db6-4292-8325-fc812e156346", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
processed_sample_idextraction_has_outputextraction_id
<chr><chr><chr>
nmdc:procsm-11-kngzyt90nmdc:procsm-11-h9s7h174nmdc:extrp-11-v25scb12
nmdc:procsm-11-mr5hf033nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35
nmdc:procsm-11-33n4p085nmdc:procsm-11-6xc6vy98nmdc:extrp-11-j5qc7973
nmdc:procsm-11-2fxf0e98nmdc:procsm-11-x763xr38nmdc:extrp-11-y5ewyv43
nmdc:procsm-11-y8w3sk61nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92
nmdc:procsm-11-5s07gt34nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " processed\\_sample\\_id & extraction\\_has\\_output & extraction\\_id\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t nmdc:procsm-11-kngzyt90 & nmdc:procsm-11-h9s7h174 & nmdc:extrp-11-v25scb12\\\\\n", + "\t nmdc:procsm-11-mr5hf033 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35\\\\\n", + "\t nmdc:procsm-11-33n4p085 & nmdc:procsm-11-6xc6vy98 & nmdc:extrp-11-j5qc7973\\\\\n", + "\t nmdc:procsm-11-2fxf0e98 & nmdc:procsm-11-x763xr38 & nmdc:extrp-11-y5ewyv43\\\\\n", + "\t nmdc:procsm-11-y8w3sk61 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92\\\\\n", + "\t nmdc:procsm-11-5s07gt34 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| processed_sample_id <chr> | extraction_has_output <chr> | extraction_id <chr> |\n", + "|---|---|---|\n", + "| nmdc:procsm-11-kngzyt90 | nmdc:procsm-11-h9s7h174 | nmdc:extrp-11-v25scb12 |\n", + "| nmdc:procsm-11-mr5hf033 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 |\n", + "| nmdc:procsm-11-33n4p085 | nmdc:procsm-11-6xc6vy98 | nmdc:extrp-11-j5qc7973 |\n", + "| nmdc:procsm-11-2fxf0e98 | nmdc:procsm-11-x763xr38 | nmdc:extrp-11-y5ewyv43 |\n", + "| nmdc:procsm-11-y8w3sk61 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 |\n", + "| nmdc:procsm-11-5s07gt34 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 |\n", + "\n" + ], + "text/plain": [ + " processed_sample_id extraction_has_output extraction_id \n", + "1 nmdc:procsm-11-kngzyt90 nmdc:procsm-11-h9s7h174 nmdc:extrp-11-v25scb12\n", + "2 nmdc:procsm-11-mr5hf033 nmdc:procsm-11-7qy2y664 nmdc:extrp-11-gnvf5s35\n", + "3 nmdc:procsm-11-33n4p085 nmdc:procsm-11-6xc6vy98 nmdc:extrp-11-j5qc7973\n", + "4 nmdc:procsm-11-2fxf0e98 nmdc:procsm-11-x763xr38 nmdc:extrp-11-y5ewyv43\n", + "5 nmdc:procsm-11-y8w3sk61 nmdc:procsm-11-q086v208 nmdc:extrp-11-9qd5ke92\n", + "6 nmdc:procsm-11-5s07gt34 nmdc:procsm-11-edpstj65 nmdc:extrp-11-76s2tz21" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "extraction_df <- get_results_by_id(\n", " collection = 'material_processing_set',\n", @@ -213,14 +557,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8842cde0-c0ab-4bba-8c8e-fcba2b77a4cd", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 8
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idextraction_has_outputextraction_id
<chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35
\n" + ], + "text/latex": [ + "A tibble: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & extraction\\_has\\_output & extraction\\_id\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 8\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | extraction_has_output <chr> | extraction_id <chr> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " processed_sample_id pooling_id extraction_has_output \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", + " extraction_id \n", + "1 nmdc:extrp-11-c0kyyp83\n", + "2 nmdc:extrp-11-76s2tz21\n", + "3 nmdc:extrp-11-faz6a041\n", + "4 nmdc:extrp-11-9qd5ke92\n", + "5 nmdc:extrp-11-qg3zf244\n", + "6 nmdc:extrp-11-gnvf5s35" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df4 <- biosample_df3 %>%\n", " left_join(extraction_df, by = join_by(processed_sample_id))\n", @@ -237,14 +664,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "fc982cf2-08a1-4bfe-9cf3-7ca550f46790", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 8
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_id
<chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35
\n" + ], + "text/latex": [ + "A tibble: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 8\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " processed_sample_id pooling_id processed_sample_id2 \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", + " extraction_id \n", + "1 nmdc:extrp-11-c0kyyp83\n", + "2 nmdc:extrp-11-76s2tz21\n", + "3 nmdc:extrp-11-faz6a041\n", + "4 nmdc:extrp-11-9qd5ke92\n", + "5 nmdc:extrp-11-qg3zf244\n", + "6 nmdc:extrp-11-gnvf5s35" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df5 <- biosample_df4 %>%\n", " rename(processed_sample_id2 = extraction_has_output)\n", @@ -263,14 +773,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "e5e627fa-72ad-4e64-baf9-471443ba7168", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
processed_sample_id2library_preparation_has_outputlibrary_preparation_id
<chr><chr><chr>
nmdc:procsm-11-7qy2y664nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032
nmdc:procsm-11-x763xr38nmdc:procsm-11-9ghwha16nmdc:libprp-11-gasf6t26
nmdc:procsm-11-h9s7h174nmdc:procsm-11-4z512838nmdc:libprp-11-t70f6032
nmdc:procsm-11-6xc6vy98nmdc:procsm-11-44e5ds31nmdc:libprp-11-8bzn7n07
nmdc:procsm-11-q086v208nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35
nmdc:procsm-11-1qgqxz62nmdc:procsm-11-hxfxnz83nmdc:libprp-11-6zgrcr81
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " processed\\_sample\\_id2 & library\\_preparation\\_has\\_output & library\\_preparation\\_id\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t nmdc:procsm-11-7qy2y664 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032\\\\\n", + "\t nmdc:procsm-11-x763xr38 & nmdc:procsm-11-9ghwha16 & nmdc:libprp-11-gasf6t26\\\\\n", + "\t nmdc:procsm-11-h9s7h174 & nmdc:procsm-11-4z512838 & nmdc:libprp-11-t70f6032\\\\\n", + "\t nmdc:procsm-11-6xc6vy98 & nmdc:procsm-11-44e5ds31 & nmdc:libprp-11-8bzn7n07\\\\\n", + "\t nmdc:procsm-11-q086v208 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35\\\\\n", + "\t nmdc:procsm-11-1qgqxz62 & nmdc:procsm-11-hxfxnz83 & nmdc:libprp-11-6zgrcr81\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| processed_sample_id2 <chr> | library_preparation_has_output <chr> | library_preparation_id <chr> |\n", + "|---|---|---|\n", + "| nmdc:procsm-11-7qy2y664 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 |\n", + "| nmdc:procsm-11-x763xr38 | nmdc:procsm-11-9ghwha16 | nmdc:libprp-11-gasf6t26 |\n", + "| nmdc:procsm-11-h9s7h174 | nmdc:procsm-11-4z512838 | nmdc:libprp-11-t70f6032 |\n", + "| nmdc:procsm-11-6xc6vy98 | nmdc:procsm-11-44e5ds31 | nmdc:libprp-11-8bzn7n07 |\n", + "| nmdc:procsm-11-q086v208 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 |\n", + "| nmdc:procsm-11-1qgqxz62 | nmdc:procsm-11-hxfxnz83 | nmdc:libprp-11-6zgrcr81 |\n", + "\n" + ], + "text/plain": [ + " processed_sample_id2 library_preparation_has_output\n", + "1 nmdc:procsm-11-7qy2y664 nmdc:procsm-11-wd4s5f38 \n", + "2 nmdc:procsm-11-x763xr38 nmdc:procsm-11-9ghwha16 \n", + "3 nmdc:procsm-11-h9s7h174 nmdc:procsm-11-4z512838 \n", + "4 nmdc:procsm-11-6xc6vy98 nmdc:procsm-11-44e5ds31 \n", + "5 nmdc:procsm-11-q086v208 nmdc:procsm-11-jkvhv341 \n", + "6 nmdc:procsm-11-1qgqxz62 nmdc:procsm-11-hxfxnz83 \n", + " library_preparation_id \n", + "1 nmdc:libprp-11-wv6p0032\n", + "2 nmdc:libprp-11-gasf6t26\n", + "3 nmdc:libprp-11-t70f6032\n", + "4 nmdc:libprp-11-8bzn7n07\n", + "5 nmdc:libprp-11-24s1rh35\n", + "6 nmdc:libprp-11-6zgrcr81" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "library_prep_df <- get_results_by_id(\n", " collection = 'material_processing_set',\n", @@ -303,14 +882,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a8506d2d-356d-4277-9711-052296ecfae4", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 10
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idlibrary_preparation_has_outputlibrary_preparation_id
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244nmdc:procsm-11-t397mj03nmdc:libprp-11-p07zpd31
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032
\n" + ], + "text/latex": [ + "A tibble: 6 × 10\n", + "\\begin{tabular}{llllllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & library\\_preparation\\_has\\_output & library\\_preparation\\_id\\\\\n", + " & & & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244 & nmdc:procsm-11-t397mj03 & nmdc:libprp-11-p07zpd31\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 10\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | library_preparation_has_output <chr> | library_preparation_id <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 | nmdc:procsm-11-t397mj03 | nmdc:libprp-11-p07zpd31 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " processed_sample_id pooling_id processed_sample_id2 \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", + " extraction_id library_preparation_has_output library_preparation_id \n", + "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", + "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", + "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "4 nmdc:extrp-11-9qd5ke92 nmdc:procsm-11-jkvhv341 nmdc:libprp-11-24s1rh35\n", + "5 nmdc:extrp-11-qg3zf244 nmdc:procsm-11-t397mj03 nmdc:libprp-11-p07zpd31\n", + "6 nmdc:extrp-11-gnvf5s35 nmdc:procsm-11-wd4s5f38 nmdc:libprp-11-wv6p0032" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df6 <- biosample_df5 %>%\n", " left_join(library_prep_df, by = join_by(processed_sample_id2))\n", @@ -327,14 +989,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "667690ba-a598-4f72-9c21-9ff8a6c28b8c", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 10
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_id
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244nmdc:procsm-11-t397mj03nmdc:libprp-11-p07zpd31
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032
\n" + ], + "text/latex": [ + "A tibble: 6 × 10\n", + "\\begin{tabular}{llllllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id\\\\\n", + " & & & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35\\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244 & nmdc:procsm-11-t397mj03 & nmdc:libprp-11-p07zpd31\\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 10\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 | nmdc:procsm-11-t397mj03 | nmdc:libprp-11-p07zpd31 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " processed_sample_id pooling_id processed_sample_id2 \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", + " extraction_id processed_sample_id3 library_preparation_id \n", + "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", + "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", + "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "4 nmdc:extrp-11-9qd5ke92 nmdc:procsm-11-jkvhv341 nmdc:libprp-11-24s1rh35\n", + "5 nmdc:extrp-11-qg3zf244 nmdc:procsm-11-t397mj03 nmdc:libprp-11-p07zpd31\n", + "6 nmdc:extrp-11-gnvf5s35 nmdc:procsm-11-wd4s5f38 nmdc:libprp-11-wv6p0032" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df7 <- biosample_df6 %>%\n", " rename(processed_sample_id3 = library_preparation_has_output)\n", @@ -353,14 +1098,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "be09a8ef-d0fb-4df8-bef3-bc8498f3d444", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 2
processed_sample_id3data_generation_id
<chr><chr>
nmdc:procsm-11-43n6yz70nmdc:omprc-11-g1n61y55
nmdc:procsm-11-44e5ds31nmdc:dgns-11-ekte1238
nmdc:procsm-11-4jj6k690nmdc:omprc-11-yt96hb84
nmdc:procsm-11-4z512838nmdc:omprc-11-afejca38
nmdc:procsm-11-7cpyc435nmdc:omprc-11-by9r5p41
nmdc:procsm-11-9ghwha16nmdc:omprc-11-bd1eyb41
\n" + ], + "text/latex": [ + "A tibble: 6 × 2\n", + "\\begin{tabular}{ll}\n", + " processed\\_sample\\_id3 & data\\_generation\\_id\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t nmdc:procsm-11-43n6yz70 & nmdc:omprc-11-g1n61y55\\\\\n", + "\t nmdc:procsm-11-44e5ds31 & nmdc:dgns-11-ekte1238 \\\\\n", + "\t nmdc:procsm-11-4jj6k690 & nmdc:omprc-11-yt96hb84\\\\\n", + "\t nmdc:procsm-11-4z512838 & nmdc:omprc-11-afejca38\\\\\n", + "\t nmdc:procsm-11-7cpyc435 & nmdc:omprc-11-by9r5p41\\\\\n", + "\t nmdc:procsm-11-9ghwha16 & nmdc:omprc-11-bd1eyb41\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 2\n", + "\n", + "| processed_sample_id3 <chr> | data_generation_id <chr> |\n", + "|---|---|\n", + "| nmdc:procsm-11-43n6yz70 | nmdc:omprc-11-g1n61y55 |\n", + "| nmdc:procsm-11-44e5ds31 | nmdc:dgns-11-ekte1238 |\n", + "| nmdc:procsm-11-4jj6k690 | nmdc:omprc-11-yt96hb84 |\n", + "| nmdc:procsm-11-4z512838 | nmdc:omprc-11-afejca38 |\n", + "| nmdc:procsm-11-7cpyc435 | nmdc:omprc-11-by9r5p41 |\n", + "| nmdc:procsm-11-9ghwha16 | nmdc:omprc-11-bd1eyb41 |\n", + "\n" + ], + "text/plain": [ + " processed_sample_id3 data_generation_id \n", + "1 nmdc:procsm-11-43n6yz70 nmdc:omprc-11-g1n61y55\n", + "2 nmdc:procsm-11-44e5ds31 nmdc:dgns-11-ekte1238 \n", + "3 nmdc:procsm-11-4jj6k690 nmdc:omprc-11-yt96hb84\n", + "4 nmdc:procsm-11-4z512838 nmdc:omprc-11-afejca38\n", + "5 nmdc:procsm-11-7cpyc435 nmdc:omprc-11-by9r5p41\n", + "6 nmdc:procsm-11-9ghwha16 nmdc:omprc-11-bd1eyb41" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "data_generation_df <- get_results_by_id(\n", " collection = 'data_generation_set',\n", @@ -390,14 +1197,104 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "2d6c028e-4260-4324-8fb3-0402216d2f7f", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 11
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_iddata_generation_id
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346nmdc:omprc-11-63ajbd04
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60nmdc:omprc-11-769ab655
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608
nmdc:bsm-11-0asn5d63M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-y8w3sk61nmdc:poolp-11-pak1ws91nmdc:procsm-11-q086v208nmdc:extrp-11-9qd5ke92nmdc:procsm-11-jkvhv341nmdc:libprp-11-24s1rh35nmdc:dgns-11-j3w06312
nmdc:bsm-11-0djp2e45M horizonUSA: Colorado, North Sterling nmdc:TextValuenmdc:procsm-11-258vbz70nmdc:poolp-11-vfkwpy98nmdc:procsm-11-nrrknt87nmdc:extrp-11-qg3zf244nmdc:procsm-11-t397mj03nmdc:libprp-11-p07zpd31nmdc:dgns-11-jxh3ht55
nmdc:bsm-11-0f43ab20M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-mr5hf033nmdc:poolp-11-ay38nw70nmdc:procsm-11-7qy2y664nmdc:extrp-11-gnvf5s35nmdc:procsm-11-wd4s5f38nmdc:libprp-11-wv6p0032NA
\n" + ], + "text/latex": [ + "A tibble: 6 × 11\n", + "\\begin{tabular}{lllllllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id & data\\_generation\\_id\\\\\n", + " & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346 & nmdc:omprc-11-63ajbd04\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60 & nmdc:omprc-11-769ab655\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608\\\\\n", + "\t nmdc:bsm-11-0asn5d63 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-y8w3sk61 & nmdc:poolp-11-pak1ws91 & nmdc:procsm-11-q086v208 & nmdc:extrp-11-9qd5ke92 & nmdc:procsm-11-jkvhv341 & nmdc:libprp-11-24s1rh35 & nmdc:dgns-11-j3w06312 \\\\\n", + "\t nmdc:bsm-11-0djp2e45 & M horizon & USA: Colorado, North Sterling & nmdc:TextValue & nmdc:procsm-11-258vbz70 & nmdc:poolp-11-vfkwpy98 & nmdc:procsm-11-nrrknt87 & nmdc:extrp-11-qg3zf244 & nmdc:procsm-11-t397mj03 & nmdc:libprp-11-p07zpd31 & nmdc:dgns-11-jxh3ht55 \\\\\n", + "\t nmdc:bsm-11-0f43ab20 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-mr5hf033 & nmdc:poolp-11-ay38nw70 & nmdc:procsm-11-7qy2y664 & nmdc:extrp-11-gnvf5s35 & nmdc:procsm-11-wd4s5f38 & nmdc:libprp-11-wv6p0032 & NA \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 11\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> | data_generation_id <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 | nmdc:omprc-11-63ajbd04 |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 | nmdc:omprc-11-769ab655 |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 |\n", + "| nmdc:bsm-11-0asn5d63 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-y8w3sk61 | nmdc:poolp-11-pak1ws91 | nmdc:procsm-11-q086v208 | nmdc:extrp-11-9qd5ke92 | nmdc:procsm-11-jkvhv341 | nmdc:libprp-11-24s1rh35 | nmdc:dgns-11-j3w06312 |\n", + "| nmdc:bsm-11-0djp2e45 | M horizon | USA: Colorado, North Sterling | nmdc:TextValue | nmdc:procsm-11-258vbz70 | nmdc:poolp-11-vfkwpy98 | nmdc:procsm-11-nrrknt87 | nmdc:extrp-11-qg3zf244 | nmdc:procsm-11-t397mj03 | nmdc:libprp-11-p07zpd31 | nmdc:dgns-11-jxh3ht55 |\n", + "| nmdc:bsm-11-0f43ab20 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-mr5hf033 | nmdc:poolp-11-ay38nw70 | nmdc:procsm-11-7qy2y664 | nmdc:extrp-11-gnvf5s35 | nmdc:procsm-11-wd4s5f38 | nmdc:libprp-11-wv6p0032 | NA |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-0asn5d63 M horizon \n", + "5 nmdc:bsm-11-0djp2e45 M horizon \n", + "6 nmdc:bsm-11-0f43ab20 M horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "5 USA: Colorado, North Sterling nmdc:TextValue \n", + "6 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + " processed_sample_id pooling_id processed_sample_id2 \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-y8w3sk61 nmdc:poolp-11-pak1ws91 nmdc:procsm-11-q086v208\n", + "5 nmdc:procsm-11-258vbz70 nmdc:poolp-11-vfkwpy98 nmdc:procsm-11-nrrknt87\n", + "6 nmdc:procsm-11-mr5hf033 nmdc:poolp-11-ay38nw70 nmdc:procsm-11-7qy2y664\n", + " extraction_id processed_sample_id3 library_preparation_id \n", + "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", + "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", + "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "4 nmdc:extrp-11-9qd5ke92 nmdc:procsm-11-jkvhv341 nmdc:libprp-11-24s1rh35\n", + "5 nmdc:extrp-11-qg3zf244 nmdc:procsm-11-t397mj03 nmdc:libprp-11-p07zpd31\n", + "6 nmdc:extrp-11-gnvf5s35 nmdc:procsm-11-wd4s5f38 nmdc:libprp-11-wv6p0032\n", + " data_generation_id \n", + "1 nmdc:omprc-11-63ajbd04\n", + "2 nmdc:omprc-11-769ab655\n", + "3 nmdc:omprc-11-597mc608\n", + "4 nmdc:dgns-11-j3w06312 \n", + "5 nmdc:dgns-11-jxh3ht55 \n", + "6 NA " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df8 <- biosample_df7 %>%\n", " left_join(data_generation_df, by = join_by(processed_sample_id3))\n", @@ -416,14 +1313,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "6328c9a8-ee49-4dc4-b1e3-b530f718c371", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
idwas_informed_byhas_outputtype
<chr><chr><list><chr>
1nmdc:wfmag-11-2702vw88.1nmdc:omprc-11-bd1eyb41nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37nmdc:MagsAnalysis
2nmdc:wfmag-11-29we8r98.1nmdc:omprc-11-r5w9js52nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91nmdc:MagsAnalysis
3nmdc:wfmag-11-94b5df26.1nmdc:omprc-11-by9r5p41nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851nmdc:MagsAnalysis
4nmdc:wfmag-11-94b5df26.2nmdc:omprc-11-by9r5p41nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762nmdc:MagsAnalysis
5nmdc:wfmag-11-9e4rbw35.1nmdc:omprc-11-sz2d4412nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53nmdc:MagsAnalysis
6nmdc:wfmag-11-9xzvxq91.1nmdc:omprc-11-gaptm502nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78nmdc:MagsAnalysis
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & id & was\\_informed\\_by & has\\_output & type\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & nmdc:wfmag-11-2702vw88.1 & nmdc:omprc-11-bd1eyb41 & nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37 & nmdc:MagsAnalysis\\\\\n", + "\t2 & nmdc:wfmag-11-29we8r98.1 & nmdc:omprc-11-r5w9js52 & nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91 & nmdc:MagsAnalysis\\\\\n", + "\t3 & nmdc:wfmag-11-94b5df26.1 & nmdc:omprc-11-by9r5p41 & nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851 & nmdc:MagsAnalysis\\\\\n", + "\t4 & nmdc:wfmag-11-94b5df26.2 & nmdc:omprc-11-by9r5p41 & nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762 & nmdc:MagsAnalysis\\\\\n", + "\t5 & nmdc:wfmag-11-9e4rbw35.1 & nmdc:omprc-11-sz2d4412 & nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53 & nmdc:MagsAnalysis\\\\\n", + "\t6 & nmdc:wfmag-11-9xzvxq91.1 & nmdc:omprc-11-gaptm502 & nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78 & nmdc:MagsAnalysis\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | id <chr> | was_informed_by <chr> | has_output <list> | type <chr> |\n", + "|---|---|---|---|---|\n", + "| 1 | nmdc:wfmag-11-2702vw88.1 | nmdc:omprc-11-bd1eyb41 | nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37 | nmdc:MagsAnalysis |\n", + "| 2 | nmdc:wfmag-11-29we8r98.1 | nmdc:omprc-11-r5w9js52 | nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91 | nmdc:MagsAnalysis |\n", + "| 3 | nmdc:wfmag-11-94b5df26.1 | nmdc:omprc-11-by9r5p41 | nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851 | nmdc:MagsAnalysis |\n", + "| 4 | nmdc:wfmag-11-94b5df26.2 | nmdc:omprc-11-by9r5p41 | nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762 | nmdc:MagsAnalysis |\n", + "| 5 | nmdc:wfmag-11-9e4rbw35.1 | nmdc:omprc-11-sz2d4412 | nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53 | nmdc:MagsAnalysis |\n", + "| 6 | nmdc:wfmag-11-9xzvxq91.1 | nmdc:omprc-11-gaptm502 | nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78 | nmdc:MagsAnalysis |\n", + "\n" + ], + "text/plain": [ + " id was_informed_by \n", + "1 nmdc:wfmag-11-2702vw88.1 nmdc:omprc-11-bd1eyb41\n", + "2 nmdc:wfmag-11-29we8r98.1 nmdc:omprc-11-r5w9js52\n", + "3 nmdc:wfmag-11-94b5df26.1 nmdc:omprc-11-by9r5p41\n", + "4 nmdc:wfmag-11-94b5df26.2 nmdc:omprc-11-by9r5p41\n", + "5 nmdc:wfmag-11-9e4rbw35.1 nmdc:omprc-11-sz2d4412\n", + "6 nmdc:wfmag-11-9xzvxq91.1 nmdc:omprc-11-gaptm502\n", + " has_output \n", + "1 nmdc:dobj-11-xhmyza67, nmdc:dobj-11-0k8d5s49, nmdc:dobj-11-9xkp2r29, nmdc:dobj-11-dg3pn969, nmdc:dobj-11-0kcq6e37 \n", + "2 nmdc:dobj-11-psevcm91, nmdc:dobj-11-teqrqr23, nmdc:dobj-11-9fmjfj44, nmdc:dobj-11-j4kqn850, nmdc:dobj-11-1tagyf91 \n", + "3 nmdc:dobj-11-jx439j48, nmdc:dobj-11-syzgc354, nmdc:dobj-11-wtcheh80, nmdc:dobj-11-x1np8d24, nmdc:dobj-11-3ysvz851 \n", + "4 nmdc:dobj-11-2bqtt266, nmdc:dobj-11-h5dvmy54, nmdc:dobj-11-kym9yb67, nmdc:dobj-11-sr82ew48, nmdc:dobj-11-vsczzw39, nmdc:dobj-11-2vzahp17, nmdc:dobj-11-2s7gaj21, nmdc:dobj-11-amk45957, nmdc:dobj-11-mxwdb762\n", + "5 nmdc:dobj-11-86qjqq64, nmdc:dobj-11-yrf0br13, nmdc:dobj-11-7rntfb07, nmdc:dobj-11-1n2enq40, nmdc:dobj-11-65kbsc53 \n", + "6 nmdc:dobj-11-pajr6f05, nmdc:dobj-11-5kp8jq15, nmdc:dobj-11-2d2e5340, nmdc:dobj-11-wskvry88, nmdc:dobj-11-4hz03k78 \n", + " type \n", + "1 nmdc:MagsAnalysis\n", + "2 nmdc:MagsAnalysis\n", + "3 nmdc:MagsAnalysis\n", + "4 nmdc:MagsAnalysis\n", + "5 nmdc:MagsAnalysis\n", + "6 nmdc:MagsAnalysis" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "metagenome_annotation_df <- get_results_by_id(\n", " collection = 'workflow_execution_set',\n", @@ -445,14 +1418,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "a4637e99-8950-441a-9966-e31e7781fa21", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. 'nmdc:MagsAnalysis'
  2. 'nmdc:MetagenomeAnnotation'
  3. 'nmdc:MetagenomeAssembly'
  4. 'nmdc:ReadBasedTaxonomyAnalysis'
  5. 'nmdc:ReadQcAnalysis'
  6. 'nmdc:MetagenomeSequencing'
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 'nmdc:MagsAnalysis'\n", + "\\item 'nmdc:MetagenomeAnnotation'\n", + "\\item 'nmdc:MetagenomeAssembly'\n", + "\\item 'nmdc:ReadBasedTaxonomyAnalysis'\n", + "\\item 'nmdc:ReadQcAnalysis'\n", + "\\item 'nmdc:MetagenomeSequencing'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 'nmdc:MagsAnalysis'\n", + "2. 'nmdc:MetagenomeAnnotation'\n", + "3. 'nmdc:MetagenomeAssembly'\n", + "4. 'nmdc:ReadBasedTaxonomyAnalysis'\n", + "5. 'nmdc:ReadQcAnalysis'\n", + "6. 'nmdc:MetagenomeSequencing'\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] \"nmdc:MagsAnalysis\" \"nmdc:MetagenomeAnnotation\" \n", + "[3] \"nmdc:MetagenomeAssembly\" \"nmdc:ReadBasedTaxonomyAnalysis\"\n", + "[5] \"nmdc:ReadQcAnalysis\" \"nmdc:MetagenomeSequencing\" " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "unique(metagenome_annotation_df$type)" ] @@ -467,14 +1481,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "24529f53-c27d-4948-824f-ad5a7a9d405c", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 4
metagenome_annotation_iddata_generation_idmatagenome_annotation_has_outputworkflow_type
<chr><chr><chr><chr>
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-vpaxc956nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-ad42v813nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-954m1b13nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-wvtgyb44nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-fs964t51nmdc:MetagenomeAnnotation
nmdc:wfmgan-11-0nwd1388.1nmdc:omprc-11-2937gz63nmdc:dobj-11-8sttbc64nmdc:MetagenomeAnnotation
\n" + ], + "text/latex": [ + "A tibble: 6 × 4\n", + "\\begin{tabular}{llll}\n", + " metagenome\\_annotation\\_id & data\\_generation\\_id & matagenome\\_annotation\\_has\\_output & workflow\\_type\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-vpaxc956 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-ad42v813 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-954m1b13 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-wvtgyb44 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-fs964t51 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:wfmgan-11-0nwd1388.1 & nmdc:omprc-11-2937gz63 & nmdc:dobj-11-8sttbc64 & nmdc:MetagenomeAnnotation\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 4\n", + "\n", + "| metagenome_annotation_id <chr> | data_generation_id <chr> | matagenome_annotation_has_output <chr> | workflow_type <chr> |\n", + "|---|---|---|---|\n", + "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-vpaxc956 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-ad42v813 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-954m1b13 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-wvtgyb44 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-fs964t51 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:wfmgan-11-0nwd1388.1 | nmdc:omprc-11-2937gz63 | nmdc:dobj-11-8sttbc64 | nmdc:MetagenomeAnnotation |\n", + "\n" + ], + "text/plain": [ + " metagenome_annotation_id data_generation_id \n", + "1 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", + "2 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", + "3 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", + "4 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", + "5 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", + "6 nmdc:wfmgan-11-0nwd1388.1 nmdc:omprc-11-2937gz63\n", + " matagenome_annotation_has_output workflow_type \n", + "1 nmdc:dobj-11-vpaxc956 nmdc:MetagenomeAnnotation\n", + "2 nmdc:dobj-11-ad42v813 nmdc:MetagenomeAnnotation\n", + "3 nmdc:dobj-11-954m1b13 nmdc:MetagenomeAnnotation\n", + "4 nmdc:dobj-11-wvtgyb44 nmdc:MetagenomeAnnotation\n", + "5 nmdc:dobj-11-fs964t51 nmdc:MetagenomeAnnotation\n", + "6 nmdc:dobj-11-8sttbc64 nmdc:MetagenomeAnnotation" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "metagenome_annotation_df <- metagenome_annotation_df %>%\n", " filter(type == \"nmdc:MetagenomeAnnotation\") %>%\n", @@ -500,14 +1583,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "9aa89e83-67de-4620-ba98-a15311882551", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 14
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_iddata_generation_idmetagenome_annotation_idmatagenome_annotation_has_outputworkflow_type
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346nmdc:omprc-11-63ajbd04NA NA NA
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60nmdc:omprc-11-769ab655NA NA NA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-b96vap67nmdc:MetagenomeAnnotation
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-hkqqdt25nmdc:MetagenomeAnnotation
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-mn4z5956nmdc:MetagenomeAnnotation
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-jfypfh90nmdc:MetagenomeAnnotation
\n" + ], + "text/latex": [ + "A tibble: 6 × 14\n", + "\\begin{tabular}{llllllllllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id & data\\_generation\\_id & metagenome\\_annotation\\_id & matagenome\\_annotation\\_has\\_output & workflow\\_type\\\\\n", + " & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346 & nmdc:omprc-11-63ajbd04 & NA & NA & NA \\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60 & nmdc:omprc-11-769ab655 & NA & NA & NA \\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-b96vap67 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-hkqqdt25 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-mn4z5956 & nmdc:MetagenomeAnnotation\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-jfypfh90 & nmdc:MetagenomeAnnotation\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 14\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> | data_generation_id <chr> | metagenome_annotation_id <chr> | matagenome_annotation_has_output <chr> | workflow_type <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 | nmdc:omprc-11-63ajbd04 | NA | NA | NA |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 | nmdc:omprc-11-769ab655 | NA | NA | NA |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-b96vap67 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-hkqqdt25 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-mn4z5956 | nmdc:MetagenomeAnnotation |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-jfypfh90 | nmdc:MetagenomeAnnotation |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-06tgpb52 O horizon \n", + "5 nmdc:bsm-11-06tgpb52 O horizon \n", + "6 nmdc:bsm-11-06tgpb52 O horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "5 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "6 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + " processed_sample_id pooling_id processed_sample_id2 \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "5 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "6 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + " extraction_id processed_sample_id3 library_preparation_id \n", + "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", + "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", + "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "4 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "5 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "6 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + " data_generation_id metagenome_annotation_id \n", + "1 nmdc:omprc-11-63ajbd04 NA \n", + "2 nmdc:omprc-11-769ab655 NA \n", + "3 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", + "4 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", + "5 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", + "6 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1\n", + " matagenome_annotation_has_output workflow_type \n", + "1 NA NA \n", + "2 NA NA \n", + "3 nmdc:dobj-11-b96vap67 nmdc:MetagenomeAnnotation\n", + "4 nmdc:dobj-11-hkqqdt25 nmdc:MetagenomeAnnotation\n", + "5 nmdc:dobj-11-mn4z5956 nmdc:MetagenomeAnnotation\n", + "6 nmdc:dobj-11-jfypfh90 nmdc:MetagenomeAnnotation" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df9 <- biosample_df8 %>%\n", " left_join(metagenome_annotation_df, by = join_by(data_generation_id), relationship = \"many-to-many\")\n", @@ -526,14 +1706,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "bf1f3a9c-ba08-43a6-b9aa-577ec7586969", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 3
data_object_iddata_object_typeurl
<chr><chr><chr>
1nmdc:dobj-11-jp45gr33Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
2nmdc:dobj-11-mmv19z03Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv
3nmdc:dobj-11-8ybd1f87Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv
4nmdc:dobj-11-wn5g7j41Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv
5nmdc:dobj-11-xt9amn82Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv
6nmdc:dobj-11-b6yhf780Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv
\n" + ], + "text/latex": [ + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & data\\_object\\_id & data\\_object\\_type & url\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & nmdc:dobj-11-jp45gr33 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t2 & nmdc:dobj-11-mmv19z03 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc\\_wfmgan-11-3s20yk38.2\\_scaffold\\_lineage.tsv\\\\\n", + "\t3 & nmdc:dobj-11-8ybd1f87 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc\\_wfmgan-11-2a0ap078.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t4 & nmdc:dobj-11-wn5g7j41 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc\\_wfmgan-11-hv3nyk36.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t5 & nmdc:dobj-11-xt9amn82 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc\\_wfmgan-11-me7h8h69.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t6 & nmdc:dobj-11-b6yhf780 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc\\_wfmgan-11-k5a19412.1\\_scaffold\\_lineage.tsv\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 3\n", + "\n", + "| | data_object_id <chr> | data_object_type <chr> | url <chr> |\n", + "|---|---|---|---|\n", + "| 1 | nmdc:dobj-11-jp45gr33 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| 2 | nmdc:dobj-11-mmv19z03 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv |\n", + "| 3 | nmdc:dobj-11-8ybd1f87 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv |\n", + "| 4 | nmdc:dobj-11-wn5g7j41 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv |\n", + "| 5 | nmdc:dobj-11-xt9amn82 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv |\n", + "| 6 | nmdc:dobj-11-b6yhf780 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv |\n", + "\n" + ], + "text/plain": [ + " data_object_id data_object_type \n", + "1 nmdc:dobj-11-jp45gr33 Scaffold Lineage tsv\n", + "2 nmdc:dobj-11-mmv19z03 Scaffold Lineage tsv\n", + "3 nmdc:dobj-11-8ybd1f87 Scaffold Lineage tsv\n", + "4 nmdc:dobj-11-wn5g7j41 Scaffold Lineage tsv\n", + "5 nmdc:dobj-11-xt9amn82 Scaffold Lineage tsv\n", + "6 nmdc:dobj-11-b6yhf780 Scaffold Lineage tsv\n", + " url \n", + "1 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "2 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv\n", + "3 https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv\n", + "4 https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv\n", + "5 https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv\n", + "6 https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "data_object_df <- get_results_by_id(\n", " collection = 'data_object_set',\n", @@ -560,14 +1809,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "d3ac4d7e-861f-4553-b05c-751ae08d2304", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 16
biosample_idsoil_horizongeo_loc_namegeo_loc_name_typeprocessed_sample_idpooling_idprocessed_sample_id2extraction_idprocessed_sample_id3library_preparation_iddata_generation_idmetagenome_annotation_iddata_object_idworkflow_typedata_object_typeurl
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-00m15h97M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-ytthx235nmdc:poolp-11-gxv2dy50nmdc:procsm-11-cd8pg312nmdc:extrp-11-c0kyyp83nmdc:procsm-11-jrykhg31nmdc:libprp-11-2szbj346nmdc:omprc-11-63ajbd04NA NA NA NANA
nmdc:bsm-11-06ta8e31M horizonUSA: Colorado, Central Plains Experimental Rangenmdc:TextValuenmdc:procsm-11-5s07gt34nmdc:poolp-11-5e2asm75nmdc:procsm-11-edpstj65nmdc:extrp-11-76s2tz21nmdc:procsm-11-tq69qx97nmdc:libprp-11-pqjwcw60nmdc:omprc-11-769ab655NA NA NA NANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-b96vap67nmdc:MetagenomeAnnotationNANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-hkqqdt25nmdc:MetagenomeAnnotationNANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-mn4z5956nmdc:MetagenomeAnnotationNANA
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountains nmdc:TextValuenmdc:procsm-11-ez7edj21nmdc:poolp-11-qq41ss20nmdc:procsm-11-p09rbx50nmdc:extrp-11-faz6a041nmdc:procsm-11-v7s6qh96nmdc:libprp-11-4qwse385nmdc:omprc-11-597mc608nmdc:wfmgan-11-3s20yk38.1nmdc:dobj-11-jfypfh90nmdc:MetagenomeAnnotationNANA
\n" + ], + "text/latex": [ + "A tibble: 6 × 16\n", + "\\begin{tabular}{llllllllllllllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & geo\\_loc\\_name\\_type & processed\\_sample\\_id & pooling\\_id & processed\\_sample\\_id2 & extraction\\_id & processed\\_sample\\_id3 & library\\_preparation\\_id & data\\_generation\\_id & metagenome\\_annotation\\_id & data\\_object\\_id & workflow\\_type & data\\_object\\_type & url\\\\\n", + " & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-00m15h97 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-ytthx235 & nmdc:poolp-11-gxv2dy50 & nmdc:procsm-11-cd8pg312 & nmdc:extrp-11-c0kyyp83 & nmdc:procsm-11-jrykhg31 & nmdc:libprp-11-2szbj346 & nmdc:omprc-11-63ajbd04 & NA & NA & NA & NA & NA\\\\\n", + "\t nmdc:bsm-11-06ta8e31 & M horizon & USA: Colorado, Central Plains Experimental Range & nmdc:TextValue & nmdc:procsm-11-5s07gt34 & nmdc:poolp-11-5e2asm75 & nmdc:procsm-11-edpstj65 & nmdc:extrp-11-76s2tz21 & nmdc:procsm-11-tq69qx97 & nmdc:libprp-11-pqjwcw60 & nmdc:omprc-11-769ab655 & NA & NA & NA & NA & NA\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-b96vap67 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-hkqqdt25 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-mn4z5956 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:TextValue & nmdc:procsm-11-ez7edj21 & nmdc:poolp-11-qq41ss20 & nmdc:procsm-11-p09rbx50 & nmdc:extrp-11-faz6a041 & nmdc:procsm-11-v7s6qh96 & nmdc:libprp-11-4qwse385 & nmdc:omprc-11-597mc608 & nmdc:wfmgan-11-3s20yk38.1 & nmdc:dobj-11-jfypfh90 & nmdc:MetagenomeAnnotation & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 16\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | geo_loc_name_type <chr> | processed_sample_id <chr> | pooling_id <chr> | processed_sample_id2 <chr> | extraction_id <chr> | processed_sample_id3 <chr> | library_preparation_id <chr> | data_generation_id <chr> | metagenome_annotation_id <chr> | data_object_id <chr> | workflow_type <chr> | data_object_type <chr> | url <chr> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-00m15h97 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-ytthx235 | nmdc:poolp-11-gxv2dy50 | nmdc:procsm-11-cd8pg312 | nmdc:extrp-11-c0kyyp83 | nmdc:procsm-11-jrykhg31 | nmdc:libprp-11-2szbj346 | nmdc:omprc-11-63ajbd04 | NA | NA | NA | NA | NA |\n", + "| nmdc:bsm-11-06ta8e31 | M horizon | USA: Colorado, Central Plains Experimental Range | nmdc:TextValue | nmdc:procsm-11-5s07gt34 | nmdc:poolp-11-5e2asm75 | nmdc:procsm-11-edpstj65 | nmdc:extrp-11-76s2tz21 | nmdc:procsm-11-tq69qx97 | nmdc:libprp-11-pqjwcw60 | nmdc:omprc-11-769ab655 | NA | NA | NA | NA | NA |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-b96vap67 | nmdc:MetagenomeAnnotation | NA | NA |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-hkqqdt25 | nmdc:MetagenomeAnnotation | NA | NA |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-mn4z5956 | nmdc:MetagenomeAnnotation | NA | NA |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:TextValue | nmdc:procsm-11-ez7edj21 | nmdc:poolp-11-qq41ss20 | nmdc:procsm-11-p09rbx50 | nmdc:extrp-11-faz6a041 | nmdc:procsm-11-v7s6qh96 | nmdc:libprp-11-4qwse385 | nmdc:omprc-11-597mc608 | nmdc:wfmgan-11-3s20yk38.1 | nmdc:dobj-11-jfypfh90 | nmdc:MetagenomeAnnotation | NA | NA |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon\n", + "1 nmdc:bsm-11-00m15h97 M horizon \n", + "2 nmdc:bsm-11-06ta8e31 M horizon \n", + "3 nmdc:bsm-11-06tgpb52 O horizon \n", + "4 nmdc:bsm-11-06tgpb52 O horizon \n", + "5 nmdc:bsm-11-06tgpb52 O horizon \n", + "6 nmdc:bsm-11-06tgpb52 O horizon \n", + " geo_loc_name geo_loc_name_type\n", + "1 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "2 USA: Colorado, Central Plains Experimental Range nmdc:TextValue \n", + "3 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "4 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "5 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + "6 USA: Colorado, Rocky Mountains nmdc:TextValue \n", + " processed_sample_id pooling_id processed_sample_id2 \n", + "1 nmdc:procsm-11-ytthx235 nmdc:poolp-11-gxv2dy50 nmdc:procsm-11-cd8pg312\n", + "2 nmdc:procsm-11-5s07gt34 nmdc:poolp-11-5e2asm75 nmdc:procsm-11-edpstj65\n", + "3 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "4 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "5 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + "6 nmdc:procsm-11-ez7edj21 nmdc:poolp-11-qq41ss20 nmdc:procsm-11-p09rbx50\n", + " extraction_id processed_sample_id3 library_preparation_id \n", + "1 nmdc:extrp-11-c0kyyp83 nmdc:procsm-11-jrykhg31 nmdc:libprp-11-2szbj346\n", + "2 nmdc:extrp-11-76s2tz21 nmdc:procsm-11-tq69qx97 nmdc:libprp-11-pqjwcw60\n", + "3 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "4 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "5 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + "6 nmdc:extrp-11-faz6a041 nmdc:procsm-11-v7s6qh96 nmdc:libprp-11-4qwse385\n", + " data_generation_id metagenome_annotation_id data_object_id \n", + "1 nmdc:omprc-11-63ajbd04 NA NA \n", + "2 nmdc:omprc-11-769ab655 NA NA \n", + "3 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-b96vap67\n", + "4 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-hkqqdt25\n", + "5 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-mn4z5956\n", + "6 nmdc:omprc-11-597mc608 nmdc:wfmgan-11-3s20yk38.1 nmdc:dobj-11-jfypfh90\n", + " workflow_type data_object_type url\n", + "1 NA NA NA \n", + "2 NA NA NA \n", + "3 nmdc:MetagenomeAnnotation NA NA \n", + "4 nmdc:MetagenomeAnnotation NA NA \n", + "5 nmdc:MetagenomeAnnotation NA NA \n", + "6 nmdc:MetagenomeAnnotation NA NA " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df10 <- biosample_df9 %>%\n", " rename(data_object_id = matagenome_annotation_has_output) %>%\n", @@ -587,14 +1933,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "9444e91a-1305-4a15-ae8d-338ca54eb5e4", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 6
biosample_idsoil_horizongeo_loc_namedata_object_iddata_object_typeurl
<chr><chr><chr><chr><chr><chr>
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountainsnmdc:dobj-11-jp45gr33Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
nmdc:bsm-11-06tgpb52O horizonUSA: Colorado, Rocky Mountainsnmdc:dobj-11-mmv19z03Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv
nmdc:bsm-11-0gmd9f09M horizonUSA: Colorado, Niwot Ridge nmdc:dobj-11-xt9amn82Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv
nmdc:bsm-11-0hz4rd27O horizonUSA: Colorado, Niwot Ridge nmdc:dobj-11-8ybd1f87Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv
nmdc:bsm-11-0qa78w81M horizonUSA: Colorado, North Sterling nmdc:dobj-11-wn5g7j41Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv
nmdc:bsm-11-0yw1rj05M horizonUSA: Colorado, North Sterling nmdc:dobj-11-b6yhf780Scaffold Lineage tsvhttps://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv
\n" + ], + "text/latex": [ + "A tibble: 6 × 6\n", + "\\begin{tabular}{llllll}\n", + " biosample\\_id & soil\\_horizon & geo\\_loc\\_name & data\\_object\\_id & data\\_object\\_type & url\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:dobj-11-jp45gr33 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t nmdc:bsm-11-06tgpb52 & O horizon & USA: Colorado, Rocky Mountains & nmdc:dobj-11-mmv19z03 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc\\_wfmgan-11-3s20yk38.2\\_scaffold\\_lineage.tsv\\\\\n", + "\t nmdc:bsm-11-0gmd9f09 & M horizon & USA: Colorado, Niwot Ridge & nmdc:dobj-11-xt9amn82 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc\\_wfmgan-11-me7h8h69.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t nmdc:bsm-11-0hz4rd27 & O horizon & USA: Colorado, Niwot Ridge & nmdc:dobj-11-8ybd1f87 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc\\_wfmgan-11-2a0ap078.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t nmdc:bsm-11-0qa78w81 & M horizon & USA: Colorado, North Sterling & nmdc:dobj-11-wn5g7j41 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc\\_wfmgan-11-hv3nyk36.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t nmdc:bsm-11-0yw1rj05 & M horizon & USA: Colorado, North Sterling & nmdc:dobj-11-b6yhf780 & Scaffold Lineage tsv & https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc\\_wfmgan-11-k5a19412.1\\_scaffold\\_lineage.tsv\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 6\n", + "\n", + "| biosample_id <chr> | soil_horizon <chr> | geo_loc_name <chr> | data_object_id <chr> | data_object_type <chr> | url <chr> |\n", + "|---|---|---|---|---|---|\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:dobj-11-jp45gr33 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| nmdc:bsm-11-06tgpb52 | O horizon | USA: Colorado, Rocky Mountains | nmdc:dobj-11-mmv19z03 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv |\n", + "| nmdc:bsm-11-0gmd9f09 | M horizon | USA: Colorado, Niwot Ridge | nmdc:dobj-11-xt9amn82 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv |\n", + "| nmdc:bsm-11-0hz4rd27 | O horizon | USA: Colorado, Niwot Ridge | nmdc:dobj-11-8ybd1f87 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv |\n", + "| nmdc:bsm-11-0qa78w81 | M horizon | USA: Colorado, North Sterling | nmdc:dobj-11-wn5g7j41 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv |\n", + "| nmdc:bsm-11-0yw1rj05 | M horizon | USA: Colorado, North Sterling | nmdc:dobj-11-b6yhf780 | Scaffold Lineage tsv | https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv |\n", + "\n" + ], + "text/plain": [ + " biosample_id soil_horizon geo_loc_name \n", + "1 nmdc:bsm-11-06tgpb52 O horizon USA: Colorado, Rocky Mountains\n", + "2 nmdc:bsm-11-06tgpb52 O horizon USA: Colorado, Rocky Mountains\n", + "3 nmdc:bsm-11-0gmd9f09 M horizon USA: Colorado, Niwot Ridge \n", + "4 nmdc:bsm-11-0hz4rd27 O horizon USA: Colorado, Niwot Ridge \n", + "5 nmdc:bsm-11-0qa78w81 M horizon USA: Colorado, North Sterling \n", + "6 nmdc:bsm-11-0yw1rj05 M horizon USA: Colorado, North Sterling \n", + " data_object_id data_object_type \n", + "1 nmdc:dobj-11-jp45gr33 Scaffold Lineage tsv\n", + "2 nmdc:dobj-11-mmv19z03 Scaffold Lineage tsv\n", + "3 nmdc:dobj-11-xt9amn82 Scaffold Lineage tsv\n", + "4 nmdc:dobj-11-8ybd1f87 Scaffold Lineage tsv\n", + "5 nmdc:dobj-11-wn5g7j41 Scaffold Lineage tsv\n", + "6 nmdc:dobj-11-b6yhf780 Scaffold Lineage tsv\n", + " url \n", + "1 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "2 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.2/nmdc_wfmgan-11-3s20yk38.2_scaffold_lineage.tsv\n", + "3 https://data.microbiomedata.org/data/nmdc:omprc-11-gaptm502/nmdc:wfmgan-11-me7h8h69.1/nmdc_wfmgan-11-me7h8h69.1_scaffold_lineage.tsv\n", + "4 https://data.microbiomedata.org/data/nmdc:omprc-11-t2fdcy08/nmdc:wfmgan-11-2a0ap078.1/nmdc_wfmgan-11-2a0ap078.1_scaffold_lineage.tsv\n", + "5 https://data.microbiomedata.org/data/nmdc:omprc-11-yt96hb84/nmdc:wfmgan-11-hv3nyk36.1/nmdc_wfmgan-11-hv3nyk36.1_scaffold_lineage.tsv\n", + "6 https://data.microbiomedata.org/data/nmdc:omprc-11-r5w9js52/nmdc:wfmgan-11-k5a19412.1/nmdc_wfmgan-11-k5a19412.1_scaffold_lineage.tsv" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df_final <- biosample_df10 %>%\n", " select(biosample_id, soil_horizon, geo_loc_name, data_object_id, data_object_type, url) %>%\n", @@ -615,14 +2037,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "7521a8c4-7b02-4d4b-a0c4-4287bc814516", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 2 × 2
soil_horizonn
<chr><int>
M horizon277
O horizon 61
\n" + ], + "text/latex": [ + "A tibble: 2 × 2\n", + "\\begin{tabular}{ll}\n", + " soil\\_horizon & n\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t M horizon & 277\\\\\n", + "\t O horizon & 61\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 2 × 2\n", + "\n", + "| soil_horizon <chr> | n <int> |\n", + "|---|---|\n", + "| M horizon | 277 |\n", + "| O horizon | 61 |\n", + "\n" + ], + "text/plain": [ + " soil_horizon n \n", + "1 M horizon 277\n", + "2 O horizon 61" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "biosample_df_final %>%\n", " count(soil_horizon)" @@ -640,14 +2108,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "16c7457f-c1dc-4344-8049-581eee81ffd2", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
contig_idtaxainitial_count
<chr><chr><dbl>
nmdc:wfmgas-11-qdbye406.1_scf_10000_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 1
nmdc:wfmgas-11-qdbye406.1_scf_10001_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 1
nmdc:wfmgas-11-qdbye406.1_scf_10002_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 1
nmdc:wfmgas-11-qdbye406.1_scf_10003_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 1
nmdc:wfmgas-11-qdbye406.1_scf_10004_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 1
nmdc:wfmgas-11-qdbye406.1_scf_10005_c1Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT02831
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " contig\\_id & taxa & initial\\_count\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10000\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 & 1\\\\\n", + "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10001\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 & 1\\\\\n", + "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10002\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 & 1\\\\\n", + "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10003\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 & 1\\\\\n", + "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10004\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 & 1\\\\\n", + "\t nmdc:wfmgas-11-qdbye406.1\\_scf\\_10005\\_c1 & Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT0283 & 1\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| contig_id <chr> | taxa <chr> | initial_count <dbl> |\n", + "|---|---|---|\n", + "| nmdc:wfmgas-11-qdbye406.1_scf_10000_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 | 1 |\n", + "| nmdc:wfmgas-11-qdbye406.1_scf_10001_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 | 1 |\n", + "| nmdc:wfmgas-11-qdbye406.1_scf_10002_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 | 1 |\n", + "| nmdc:wfmgas-11-qdbye406.1_scf_10003_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 | 1 |\n", + "| nmdc:wfmgas-11-qdbye406.1_scf_10004_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 | 1 |\n", + "| nmdc:wfmgas-11-qdbye406.1_scf_10005_c1 | Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT0283 | 1 |\n", + "\n" + ], + "text/plain": [ + " contig_id \n", + "1 nmdc:wfmgas-11-qdbye406.1_scf_10000_c1\n", + "2 nmdc:wfmgas-11-qdbye406.1_scf_10001_c1\n", + "3 nmdc:wfmgas-11-qdbye406.1_scf_10002_c1\n", + "4 nmdc:wfmgas-11-qdbye406.1_scf_10003_c1\n", + "5 nmdc:wfmgas-11-qdbye406.1_scf_10004_c1\n", + "6 nmdc:wfmgas-11-qdbye406.1_scf_10005_c1\n", + " taxa \n", + "1 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. KBS0725;Bradyrhizobium sp. KBS0725 \n", + "2 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Propylenellaceae;Propylenella;Propylenella binzhouense;Propylenella binzhouense L72 \n", + "3 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Tardiphaga;Tardiphaga robiniae;Tardiphaga robiniae 1155 \n", + "4 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium lablabi;Bradyrhizobium lablabi GAS165 \n", + "5 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. SRL28;Bradyrhizobium sp. SRL28 \n", + "6 Bacteria;Pseudomonadota;Alphaproteobacteria;Hyphomicrobiales;Nitrobacteraceae;Bradyrhizobium;Bradyrhizobium sp. AUGA SZCCT0283;Bradyrhizobium sp. AUGA SZCCT0283\n", + " initial_count\n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "5 1 \n", + "6 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "url <- biosample_df_final$url[1]\n", "\n", @@ -675,14 +2219,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "8a331e72-182c-4a55-b1e9-705908b6238d", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"Processing 10 of 119\"\n", + "[1] \"Processing 20 of 119\"\n", + "[1] \"Processing 30 of 119\"\n", + "[1] \"Processing 40 of 119\"\n", + "[1] \"Processing 50 of 119\"\n", + "[1] \"Processing 60 of 119\"\n", + "[1] \"Processing 70 of 119\"\n", + "[1] \"Processing 80 of 119\"\n", + "[1] \"Processing 90 of 119\"\n", + "[1] \"Processing 100 of 119\"\n", + "[1] \"Processing 110 of 119\"\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 4
taxacountrelative_abundanceurl
<chr><int><dbl><chr>
Acidimicrobiia 3727.050796e-03https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Acidithiobacillia 59.476876e-05https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Actinomycetes 184093.489196e-01https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Agaricomycetes 1512.862017e-03https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Alphaproteobacteria183553.478961e-01https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
Anaerolineae 173.222138e-04https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv
\n" + ], + "text/latex": [ + "A tibble: 6 × 4\n", + "\\begin{tabular}{llll}\n", + " taxa & count & relative\\_abundance & url\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t Acidimicrobiia & 372 & 7.050796e-03 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t Acidithiobacillia & 5 & 9.476876e-05 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t Actinomycetes & 18409 & 3.489196e-01 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t Agaricomycetes & 151 & 2.862017e-03 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t Alphaproteobacteria & 18355 & 3.478961e-01 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\t Anaerolineae & 17 & 3.222138e-04 & https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc\\_wfmgan-11-3s20yk38.1\\_scaffold\\_lineage.tsv\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 4\n", + "\n", + "| taxa <chr> | count <int> | relative_abundance <dbl> | url <chr> |\n", + "|---|---|---|---|\n", + "| Acidimicrobiia | 372 | 7.050796e-03 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| Acidithiobacillia | 5 | 9.476876e-05 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| Actinomycetes | 18409 | 3.489196e-01 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| Agaricomycetes | 151 | 2.862017e-03 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| Alphaproteobacteria | 18355 | 3.478961e-01 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "| Anaerolineae | 17 | 3.222138e-04 | https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv |\n", + "\n" + ], + "text/plain": [ + " taxa count relative_abundance\n", + "1 Acidimicrobiia 372 7.050796e-03 \n", + "2 Acidithiobacillia 5 9.476876e-05 \n", + "3 Actinomycetes 18409 3.489196e-01 \n", + "4 Agaricomycetes 151 2.862017e-03 \n", + "5 Alphaproteobacteria 18355 3.478961e-01 \n", + "6 Anaerolineae 17 3.222138e-04 \n", + " url \n", + "1 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "2 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "3 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "4 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "5 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv\n", + "6 https://data.microbiomedata.org/data/nmdc:omprc-11-597mc608/nmdc:wfmgan-11-3s20yk38.1/nmdc_wfmgan-11-3s20yk38.1_scaffold_lineage.tsv" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "urls <- unique(biosample_df_final$url)\n", "results_list <- c()\n", @@ -741,9 +2371,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "id": "dae25f8c-ffb9-43a7-8b9b-9464d3e9b8b9", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } @@ -778,14 +2409,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "460474ef-2d2e-4553-85fc-78dc5a756bf1", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 875, + "width": 875 + } + }, + "output_type": "display_data" + } + ], "source": [ "options(dplyr.summarise.inform = FALSE)\n", "horizon_taxa <- biosample_taxa_df %>%\n", @@ -819,14 +2467,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "af8211c8-93cd-4be7-8fc5-1bbcc30187ab", "metadata": { + "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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olU7nzp2trKyY/uXT0tKCgoJcXFykXhxJT09fsWJFcHCwp6ennZ2dh4fHqFGjmN5xFeXh7t27CxYs8PDwsLW1dXd3nzRp0qlTpyhmGwCAhcoClQUqCwCoESoLVBaoLIAHvl4CAGggqqqqEhMTfX196zojAFD37OzsHjx4QAhZsWLF6tWrdXV15Ubz9fXluWgkJiZmZ2cTQoYPH878Sxw5ciTzty0yMnL69OlqZrK0tDQxMZEQ4uLiwu3QVRFFnVdcvHhxzpw53H+Gubm5ubm5MTExf/755+7du1u1aiUkPyqnc+fOnbFjxxYXF0tN37RpU1hYGPefWFZWVlZWVlJS0qZNm8LDwz09PaUW2bVr1w8//FBZWcn8LCgoiI2NjY2N9fX17d69u0Y3HwA+QKgsUFmgsgCAGqGyQGWBygJ4oHkJAKAhuHbt2qJFi3R1ddXpcREAGowJEyYwXWUePHjw1q1bQUFBgwcPVrZrhaNHjzIBdrhdNzc3W1vbp0+fJicnp6eny+31QrirV68yr0B26NBB5UTu378fHBzMpNO5c+fevXtbWVndv3///PnzOTk5d+7cGT9+/Llz5/T0arjpVTmdN2/eTJ8+/d27d8HBwR06dHB2dmamnz17lnnL0sDAYMSIER06dDAyMiosLIyLi0tISKioqJg2bVpSUhJ3nMUNGzasWbOGCffu3btbt24GBgbXr1+Pi4s7ffp0UlKS5jYfAD5MqCxQWaCyAIAaobJAZYHKAvhIAACg/nv06FFERERJSUldZwQAKNi3b5+lpaWlpeWff/7JHzMjI4OJOWXKFKlZy5cvt/xfXl5eoaGhkZGRubm5NeahrKzM2dnZ0tKyR48eYrGYnR4WFsaktnbtWrkLZmVl3bt37969e3l5efyr2L17N5PUmjVrasyPImPGjLG0tLSysvrtt9+4+Xz9+vXEiROZ9Hfv3s1dxNXV1dLSct68eWqm4+7ubmlp6ejo2LVr10ePHkllzM/Pz9LS0s7OLiUlRWrWqlWrmAQvXbrETszNzbW3t7e0tHRwcDhx4gQ3/vnz5x0dHdnj+Pz5c3WyDQANBioL4VBZoLIA+GChshAOlQUqC1ABxl4CAGgIHB0dR48e3bRp07rOCABoi6+//jo8PNzHx4d9v+zJkyfh4eFz5szp0qWLl5fXd999d//+fUWLx8TEvH37lhASEBDA7bt85MiRTODYsWNyF7Sysmrfvn379u1rHKH35cuXTMDGxkboVv2vCxcuXLlyhRDi5+c3d+5cbj5NTEz++OMPExMTQsj69evLyso0lE5paemXX37p6OgoNT0nJ6dZs2bDhg1zc3OTmuXv788E0tPT2Ynr1q17//49IWTJkiVMT/SsAQMGfPvtt5rbfAD4kKGyQGWBygIAaoTKApUFKgtQBM1LAAAAAA2Tt7f33r17U1JSdu7cOWPGDDc3Nx2d/3/vl56evn379kGDBk2dOlW2a29CSEREBBNg+69gODs7u7i4EEIyMjKSk5PVyR47OLChoaFqKSQkJDCBhQsXys41NjYOCQkhhBQWFj569EhD6TRt2pT9U8d17dq11NTU3377TXbW69evmYBEImEnxsfHE0Jat24dGBgou8jYsWNbtGhBMdsAACxUFqgsFGUbAICFygKVhaJswwcOzUsAAAAADZmJicmQIUO+//776Ojo+/fv7927NyQkxNLSkpkbExPj5+dXXl7OXeTVq1cXL14khHTt2tXW1lYqQfY1Q/aPomrMzc2ZAPunSFlPnz4lhDRt2tTJyUluhC5dujCBjIwMDaVjbW3N/rXmUVJS8uDBg+jo6LVr186cOVNqbmVlZWZmJrMifX192cUbN27s4eFBMdsAAFJQWRBUFqgsAKAmqCwIKgtUFvC/MBgXAAAAwIfC2NjYx8fHx8fnxx9/jIiIWLZs2Zs3b9LS0rZs2cJ9Ty0qKop5AfDG/2PvzuNqTP/48V+nc1RKKUKUaI9q2piiiVH2pjCYGLJVfOzGkmXsX1t8YzSWSUqSZTAklbJVJm2GUkorUaQm7ULLOb8/ru/n/M7nnNPpdNp5Pf+Yx+1e3/fdmfs693lf9/t6/FhNTa2xvQUHB+/YsUPiwV1VVVXpRHZ2tjjrFxUV0YoNGhoaw4YNI/8tATFo0KDGNtHQ0KAT9HmpMS3Zj7q6emNbFRcXBwQExMXFZWdnc+t1CPXq1auGhgZCiIgxjQcOHNiKYQMAiIDGQig0FgAAvNBYCIXGAr42SC8BAAAAdFK8JQ7EXyElJSUtLY0QMmnSJCUlJaEbMhiMGTNmqKiozJkzhxBy584d3odAMTsPlpSU/P3332PGjBFnZUGWlpYMBoPD4cTExIizflBQ0O7duwkhW7dupQ+BTV4fbn890SXCW7IfGRkZoZtcvnx58+bNdH0Gg6Gjo6Otra2hoWFgYKCjo8Ptp0lJS0vTCd4q53x69+7dimEDwJcEjQUvNBatGDYAfEnQWPBCY9GKYcNXDuklAAAAgM6lR48edKKwsFD0mgUFBXSCDrVKPX78eOvWrYQQVVVV0U9oo0ePVlBQqKqq4i1xkJeX9+TJE0LIoEGDVq5cKXTD2NhYOgDv9evXJX4I7N27t4mJSXJy8vPnz8PDwydOnCh6fe6zoo2NDZ3Q1NTMysqixR+E4p6Xtra2iD231n64UlJS1q5dy+FwVFVV3d3d7e3tuX9TwvNX41JTU+vWrVtdXR3vkLx8BMNr9bABoGtBYyEUGou2DhsAuhY0FkKhsWjrsOHrgfQSQOf15MmTsLCw2NjYwsLCsrKyvn37Dho0aPDgwT/++OOIESM6Orr2YGxsXFpaOn36dC8vryZXTkpK+uGHHxpb2rt3b01NTS0tLWdnZ27FWMkOJMKgQYPq6+sXLly4Z8+eluwHAL5y3GIFqampotekT2vkf+sYDB48mE48fPhQ9BNaWVlZdXU1IYR3cFduB8Offvpp9uzZQjccOXIkfQgMDw//+PGjxCPourm5LV++nBCyb9++sWPHiqiG8erVq6ioKEKIsrKyoaEhnUkLPlRXV+fk5Ojo6Ahu9fTpUzohdClXa+2H6/Lly7QDoJ+fn4mJCd9S7sjDXEwmU0NDIzc3Nykpqb6+Xuh1SElJaeuwAaBrQWMhCI0FGgsA4IPGQhAaCzQW0IqaHi4MANpffn6+s7Ozg4PDyZMnnz59WlxcXFdX9+bNm9jY2AsXLsyYMePnn3/OyMjoqPBycnIuXrx48eLFDx8+dFQMzfX+/ft//vnn8uXLjo6Oq1evpl96AAA6J1NTUzo4bXx8/J07dxpb7fXr13/88Qed5u2gZ2lpSTc/ffp0enq6iANduHCBPqvY2tpyZ3IfAqdNm9bYhoMGDTI1NSWEfPjwISIiQoxzEs7R0dHAwIAQkpubu3v37sZqMjQ0NKxbt47NZtNNuHUerKys6MTRo0cFt6qurvbx8SGEKCkpNTZEbevuh6uoqIgQwmKxjI2NBZcmJCQIzhw1ahQh5M2bN+fPnxdcev/+/eTk5LYOGwC6FjQWfNBYoLEAAEFoLPigsUBjAa0L6SWATic1NdXBweH+/fuEECaTaWVlNXfu3MWLF0+ePHnIkCF0nejoaCcnpzdv3nRIhPHx8evXr1+/fn1ZWVmHBCCaoaHh6v+1YsWKqVOn0k43HA7n6tWr/+f//J+ODhMAoFFMJnPVqlWEEDabvXz58sDAwM+fP/Otc+fOnZ9//rmiooIQYm9vz+12RwiRk5Nbu3YtIaSuro72VBDs1Pb582c/P78DBw4QQuTl5RctWkTnP3ny5NWrV4QQc3NzESO7EkKmTp1KJ2hnQ67CwsLMzMzMzMx///23yTOVkpI6deqUgoICIcTX13f+/PmCQ8W+fPnSyckpLi6OEKKkpERPjRo7dix9nTcoKOjUqVO8W1VWVi5btqy0tJQQsn79ejk5ORFhtNZ+uGifvvr6eho2r7CwsG3bttHpuro67vzVq1fTne/bty8sLIx3k+Tk5A0bNrRK2FVVVfSvgyF5Ab4AaCx4obFAYwEAQqGx4IXGAo0FtDpGkyN3AUB7Ki4unjBhQnFxMSFkxowZv/76a9++fXlXSElJ2b9//4MHDwghRkZGN27ckJWVbecgAwMDN27cSAhJSEhQV1dvuwNJVhzv559/PnTokOAKDQ0N58+f37JlC73vXbt2zdLSUoIDiY6BEKKiosJ9/RwAQDJsNnvZsmU3b96k/+zdu7e5ufnAgQO7deuWn5+fkZHBLaVtYGBw9epVZWVl3s05HM7GjRu5vdVUVVWHDx/ev39/WVnZsrKyf//9NzY2trKykhDCZDJ///137niwW7ZsOXv2LCFk7969CxYsEBHhu3fvhg8fzmazWSxWUlJSr1696PxVq1bRXoorVqzYvHmzOCcbGxu7aNGiqqoqQgiDwTAxMTEyMlJWVi4rK0tPT+eW6ZCRkbl48SL31k2lpqb+8MMP9Cn322+/HTlyZP/+/TMyMiIiIt6+fUuvT0REBG9RCKH3fAn2Y2ZmVlxcbG9vz/cARghJSkqaMmVKQ0ODoqKim5ubmZkZh8PJzc0NDw+Pj49XVlYuLy/ncDgmJiZ79uzR1dWlj8FHjx49ePAg3cPo0aMtLCzk5eWTkpIiIiLq6upGjBhBHyl5G9/mhh0cHLx06VJCyODBgx8+fCjOXwcAOjM0Fmgs0FgAQJPQWKCxQGMBbQdjLwF0LsuWLaO5JXd399WrVwuu8M033wQGBs6ZM+fvv/9+9uzZ9evXGytfC3yYTOa8efOys7P9/PwIIVFRUXzfJFrOzMysdXcIAF8tKSmpY8eOfffdd56ensXFxe/fvxesZSEnJ+fm5rZ06VL6CMGLwWB4eHhoa2sfOXKkqqrq3bt33OdJXurq6vv37+fWr6ivrw8ODiaEsFgsBwcH0RGqqqpaWlrGxcXV19ffvHlz/vz5Ep4qISNHjrx58+aaNWuSk5M5HE5ycrJguQZ9ff1jx44NHTqUb76xsfGNGzdWrlz54sWLxMTExMRE3qVTp07dv3+/iMLrrb4fyszMzN3d/cCBA5WVlZ6enryLTE1Nvb29Fy5cmJ6e/vTpU9oJ1NHRkRCyevXqnj177tq1q7a2Njo6Ojo6mrvVhAkTNm3aJFjvvnXDBoAuB40F3zpoLNBYAIAgNBZ866CxQGMBrQifCYBOJCoqinYfMDY2pi8vC8VkMvfu3UtLqQYHB7d6eqmqqkpOTo7JZLbK3goLC/v3798qu2oVkyZNoumlDhy8qnWvMAB8qVgs1ty5c6dPn37hwoWUlJS8vLy8vLyKigplZWUtLS1LS8uFCxfyDpzLh8FgLFmyZObMmaGhoffu3cvOzn7//n1dXd2AAQMGDhw4cODAUaNGjRs3TlpamrtJZGQkrXo6atSo3r17NxnhlClTaLN17dq1ljwEEkJ0dXVDQ0MjIyPDw8MTExOLi4urq6vl5eX79u1rbm4+adKksWPHcguj8zE1Nb19+7a3t/edO3fy8vKqq6t79eplYmIyc+ZMe3t78WNorf1QK1as+O67706cOJGRkZGfn6+oqPjNN9/MmDGDVng/dOjQhg0bsrOzlZSUePuHLliwYNiwYT4+PnFxcf/++6+8vLy+vv7s2bNnzJhBS4u0ddgA0OWgsUBjgcYCAJqExgKNBRoLaCMojgfQiTg5OcXExBBCzp07xzsWolDTpk3LyclhMpnR0dE9e/bkXcRms4OCgm7cuJGamlpWVqaqqqqtre3k5DR58mTBlIaVlVV+fv7GjRtXrVrl6+t76tSpgoICQoiqqqqWltbs2bOnTZvGbXe3bt165swZvj2MGDHi6tWr5L8v827fvn3JkiXZ2dl79uxJSEjQ0tLirfT64sWLS5cuZWVlZWdnFxYW9unTp3///jY2Ns7OznxlAElrF8ejsrKyaB8NExMTbmAiDtSsgPX09D58+LBw4cI9e/ZwZzbrCnPV19dfvXo1NDQ0Ly/v7du3ffr0GThw4IQJE5ydnWVkZJq8GgAAAI3x8PAICwvj7cMIAADAB40FAAA0CY0F4O0lgM7iw4cP8fHxhBADA4Mmc0uEkOvXrwudX1RUtGjRIt6Xf1+/fv369evIyEh9ff2AgIDGRkv69ddf/f39uf989+7du3fvYmNjg4KCzp4921jPDqFSU1N/+uknWnuX17Fjxzw8PNhsNndOQUFBQUHBo0ePjh07FhgYaG1tLf5RJJOenk4n6OiIorVuwOJf4VevXs2aNev169fcOfn5+fn5+bGxsd7e3sHBwZ3qhTAAAOhaKisrBwwY0NFRAABAp4bGAgAAmoTGApBeAugsEhIS6AB6hoaGEu+ktrZ2+vTpL1++JITo6ura2tpqamq+fPkyMjIyKysrMzPTwcEhPDy8X79+fBuGhISkpaVJS0s7OztbWFh07949MTHRz8/v8+fP9+7d8/f3X7hwISFk06ZNq1evvnLlyt69ewkhYWFhAwYM4H39mRBSUVHh6upaU1OzYMECQ0NDfX19Oj8iImL//v2EEFlZWQcHB0NDwx49epSUlERHR8fFxdXW1rq4uDx69Eiwzm8rqqmpOXLkCJ02MDAQvXLrBizmFSaEfPjwYd68eTS3NHLkyO+//75Xr14lJSUhISHPnj17+/btkiVLaAljAACA5qqvr4+Pj0d1CwAAEAGNBQAANAmNBRCklwA6j/z8fDqhoaEh8U58fHxobmnx4sVbtmzp1q0bnb9ly5Z9+/Z5e3sXFxfv3Lnz5MmTfBumpaWpqKgEBgYaGxvTOePHjx8zZoyTkxOHw4mMjKTJjx49evTo0UNRUZGu07t3b8HivKdPn1ZUVLx7966uri7v/BMnThBCZGVlg4KCuEchhKxcudLDw8PLy6uqqio5OdnGxkbi029MQ0PDmzdvnjx5cvjw4dzcXEJI9+7df/zxR9FbtW7AYl5hQkhMTExOTg4hZM2aNRs2bODuYfny5XPmzHnw4MHjx4/z8/MHDhzYjEsAAABASGJi4tq1a5lMpouLS0fHAgAAnRQaCwAAaBIaC6CkOjoAAPh/3r9/TycGDx4s2R5KS0t///13Qoi1tfX27du5uSVCCIvF2rZt27fffksICQ4O5qayeLm7u/MmUeh+jIyMCCE02yGmDx8+bNy4kS+3RAgpLCxUVFScPHky31EIIdOnT6cTL168EP9Ajblw4YLa/9LQ0BgxYsTy5ctpbonJZB49elRVVVX0flo9YDGvMLewIV8CTEpKaunSpRMnTpw4cSL30wIAACA+ZWXltWvXhoWF8Y3aCAAAwIXGAgAAmoTGAii8vQTQWdDKeISQ7t27S7aHsLCwqqoqQsiKFSsEh0piMBiurq6JiYmEkPj4eL53X+Tk5GbNmiW4z8GDB6empvIOPtQkeXl5bvaFFz20UOXl5XSCw+GIfyDJmJqaenh40KSOaK0bsPhXWEVFhU78+eefmzdv5v1Tjho1atSoUWIeEQAAgI+urq5g/w8AAABeaCwAAKBJaCyAQnoJoLPgJhW4qYvmevz4MSFESkpKU1OzrKxMcAXu+zppaWkzZ87kXaSlpcVkMgU3kZJq9juOAwcOFGer6urqgoKCvLy8Z8+eXbx4sblHEcHQ0HDs2LF8M5lM5qBBg3R1dY2NjSU4KdLigMW/wpMmTfL09KyoqDh+/HhERMTkyZOtrKzMzMy4NQkBAAAAAAAAAAAAOhbSSwCdBTf3k52dLc76RUVFDx8+JIRoaGgMGzaMEFJcXEwIYbPZVlZWorcVTD7169evuQE3Rl1dvbFFxcXFAQEBcXFx2dnZbVfezcTExN3dvVV21YoBi3+FBwwYEBgYuHHjxvT09JycHC8vLy8vLwaDoaenZ2dnZ29vb2pq2pJIAAAAAAAAAAAAAFoI6SWhOsggAAAgAElEQVSAzsLS0pLBYHA4nJiYGHHWDwoK2r17NyFk69atNL1UW1sr5rEE1xQspicxGRkZofMvX768efPmT58+0cPp6Ohoa2traGgYGBjo6OhMmTKltQJoLa0bcLOusLm5eURERExMzP3796Ojo7OzszkcTmZmZmZm5okTJ6ZNm+bp6dnYdQYAAAAAAAAAAABoa0gvAXQWvXv3NjExSU5Ofv78eXh4+MSJE0Wvz81C2djY0AktLa3Y2FhFRcXnz5+3bazNl5KSsnbtWg6Ho6qq6u7ubm9v36NHD+7SgoKCDoxNqA4PWEpKijvSUmVlZVJS0t9//33lypWSkpLr16+rqKjs3LmzrWMAAAAAAAAAAAAAEEqSAUgAoI24ubnRiX379tXX14tY89WrV1FRUYQQZWVlQ0NDOlNHR4cQUllZWVJSInSr+vr6qqqqqqoq0TtvC5cvX+ZwOIQQPz8/Jycn3lQNDayd42lSBwacnZ2dmZmZn5/PnaOoqDh69OitW7c+ePBgwIABhJCQkJC2CwAAAAAAAAAAAABANKSXADoRR0dHAwMDQkhubu7u3btpekNQQ0PDunXr2Gw23YRbdc3c3JxOnD59WuiGe/fuNTAwMDQ0pKM0taeioiJCCIvFMjY2FlyakJDQzvE0qQMDXrJkia2t7YwZMwQX9ezZk35Campq2i4AAPgyuLi4qKmpqamp/fzzz02urKenp6amNmvWLN6ZgwYNUlNT27p1a5vF2FYyMzPVRNLX1x8/frybm1tQUJDQ1laCc/f396c774Sv5ALA16nlDUF7srKyUlNTW7x4cSvus66u7ty5c8uXL7e3tzcyMtLV1bW2tp45c6a3t/fbt29b8UASMDY2VlNTW7VqFe/MrtvyAkCHaPmX3jb16NEjGsa9e/fa87hz587lXoHAwEBxNrG2tuZuEhcX19YRdipoeqCFkF4C6ESkpKROnTqloKBACPH19Z0/f/7Lly/51nn58qWTkxNt7ZSUlNauXctdZGFhMXnyZELI8ePHvby8+L46BAcH+/n5EUJsbW3pGzAt19DQIOaa9M2q+vp6wXY6LCxs27ZtdLqurq5VAmu5Dgz4m2++IYQUFBRcunSJb1Fqamp8fDwhZPjw4a1+XAD4UkVHR1+5cqWjo2iRnJycixcvXrx48cOHD62yw+rq6rS0tLCwsOXLl0+YMOHFixetslsAgM6pUzUErX5Lb8yff/45cuTITZs2BQUFJScnl5WV1dTU5OXlxcbG7t6928rK6vDhw4KPM+0WHgBAO8CX3qCgoCbXSU1NzcvLa/tYWhNaK+g8MPYSQOeira3t5+e3aNGiqqqqe/fu3b9/38TExMjISFlZuaysLD09/cmTJ3RNGRkZPz8/FRUV3s137NgRFRVVU1Pj4eERFhZmZWWlr69fUVERGRlJx2rq27fvrl27Whhkt27d6MSZM2cmTZrEZDKHDRsmepPx48cfP368oaHB1dXVzc3NzMyMw+Hk5uaGh4fHx8crKyvX1NRwOJzr169bWFjo6urSHFsH6sCAFyxYcP369fr6+nXr1l29epV2oqmqqkpKSgoNDa2trWWxWP/5z39a63AA8DXYsWPH999/36dPn44ORELx8fEbN24khNjY2MjLy4u/oY6Ojr29Pd9MNptdUlLy4sUL+ipqWlra0qVLQ0NDWSx8MQaAL1bnaQgkvqU3y9WrV7n98AwMDKytrdXV1dls9ps3b1JSUv7555+GhgZPT8/MzExvb+/2Dw8AoHXhS29jEhISioqK+vXrJ2Kdmzdvtls8rQWtFXQeX9ENBaCrGDly5M2bN9esWZOcnMzhcJKTk5OTk/nW0dfXP3bs2NChQ/nmq6urh4aGrlmz5unTp6mpqampqbxLNTU1T506NWjQoBZGaGJiIiUlxWazfXx8fHx8RowYcfXqVdGbmJmZubu7HzhwoLKy0tPTk3eRqampt7f3woUL09PTnz596uDgcPLkSUdHxxYG2UIdGLCpqemePXu2bdtWV1cXFxfH9/qUgoLCgQMHRowY0VqHA4CvQUVFxa+//nrq1KlmbUX7+vH1Y+ha9PX13d3dG1uamJg4e/bsT58+PXv2LCAgYNGiRdxFX8C5AwDwkqwh6KKeP39Ob/6ysrInTpyYMGEC3wpJSUkrVqzIy8sLCQm5cOGCOMUD2wdaHwCQjMRfer9gTCaTwWDU19ffvHnT1dVVxJo0vSQtLV1bW9te0XUiaHqghVAcD6Az0tXVDQ0NDQwMnDt3rp6enpKSEovF6tmzp66urpOTk7+//7179wRzS5Senl5wcDAt+NC7d29paWlNTU07O7vDhw9HR0c3tlWzGBgYHDlyRFdXV0ZGplevXurq6uJstWLFipCQEHt7e21tbWlpaRUVFVtb2xMnToSEhKirqx86dGjo0KHdunXr06ePsrJyy4NsuQ4M2NnZOTY21sXFZdiwYaqqqjIyMoMGDbKxsdm4ceOjR4+mTp3auocDgC+bqakpISQ0NPTWrVvN2tDMzMzMzGzgwIFtE1fH+/bbb9etW0enk5KSeBd98ecOAF8ViRuCLsrf3//z58+EkP379wvmlgghZmZmZ86c6d69OyHkt99+o4PadgZofQCgLYj40vsFY7FYo0aNIoTcuHFDxGrJycmvX78mhNja2rZTZJ0Mmh5oIby9BNB5jRkzZsyYMRJsyGKxXFxcXFxcxFmZjuXTmBMnTpw4cUJw/owZM2bMmME3s8mvKaampo11mTQ1Nb1z5w7fTL63r0QzMzN78+aN+OuLc6DmBpyVlSW4pmRXeMCAAbt37xaxIQCAmDw9PSdMmFBfX79lyxZra2tFRcWOjqgTGTlyJJ14/vx5x0YCANB2OrwhKCws7N+/f7sdLiUlhRDCYDCE5pYoPT09S0vLqKioN2/e5OXlaWlptU9srXIpqqqq5OTkmExmq4QEAF+Dr/NLr6Oj4/379588eZKfn99Y+iQ4OJgQ0rdvX0tLy/Dw8PYNsFGd8D7fCUOCTgJvLwEAAAB8yQwMDFatWkUIKS4ublbeWk9PT01NbevWrfSfLi4uampqampqb9++5Vtz/PjxdNHOnTv5Fl29epUu+v3333nnl5eXHz169IcffjAyMho8eLCFhcXChQv/+usvvi7kW7duVVNTo4XFCSGWlpZqamqC/Rsk1rt3bzrBYDB45/OdO69nz56tXr3a0tJSU1PT1NR0zpw5ISEhoo9SUFCwffv2kSNHamlpmZmZLV68+PHjx4SQZcuWqampCe0Owmazr127Nn/+fHNzc01NzREjRsydO/fmzZuCo9ADADRJ4oaAEvOOTZmZmampqdEBjbKzs+fPn29gYEBvdOLf0l+9erVp0yYrKystLS1zc/MZM2ZcvXpV/HeMaHUjDodTX18vYrWff/7Z2dnZ2dlZVlZWzPCadXMWcSkaI7T1sbKyUlNT8/LyIoT4+vpaWloaGBhoaGhYWFjMnDnz2rVrHA5H6N4kaH0A4EvV2Jdeqln3eaq4uNjDw2PcuHEGBga6urqjR4/esWNHbm6umPEEBASoq6urqanZ29tXVlYSQh4/fkyfGn777Tehm9y8eZOuEBgYKOZRJk6cKC0tTf6bQxLE4XDoN3kHBwcpqUZ/JBf/+jQ0NNAgDx48KHRX9Llp7ty5vDObdZ8Xp7V68eLFvn37FixYYG1traWlZWlpOXXqVE9Pz+LiYsGQWrHpqa+vv3TpkrOzs42Njba2tpWV1cyZM0+fPk1fKYYvFd5eAgAAAPjCrVq1KiQkJCsr6+LFi1OnTv3uu+8k2ImdnR3t0BcTE/PTTz9x51dWVnJ7QcbGxvJtxZ0zduxY7szIyMjly5dXVFRw57x79+7du3e3b9/28fHx9/dXVVWVIEIJvHz5kk5oamqKs/6ZM2d27dpVV1dH//nvv/9GRUVFRUXZ29sPHz5c6CYRERErV6788OED/WdxcTGtT7V+/frGfhAsKipatGgR78iLr1+/fv36dWRkpL6+Pn0aF/MEAQAoiRsCie/YqampP/30E/3RsFnu37+/dOnS6upq+s+ioqKioqK4uLgbN24EBAQI/WGUj5aWVkZGBiFk3759Bw8ebKyrtb29vb29vfiBSXxzlvhS8Pn111/9/f25/6R/iNjY2KCgoLNnz/JdGQlaHwD4gon40ivBfT4kJGTdunXcGzUhJCcnJycn5+zZs9u3b29ybKezZ8/++uuvHA7H1NT00qVLCgoKhBBzc3M1NbU3b97QwcgFt7p27RohRFpa2sHBQcyzVlBQsLW1DQ8Pv3HjxvLlywVXePLkCa2CM2XKlMbq8bTnk0uz7vONOXbsmIeHB2/qq6CgoKCg4NGjR8eOHQsMDLS2tm6LkF69ejVr1ixaaZDKz8/Pz8+PjY319vYODg5uz/eYoT0hvQQAAADwhevWrZunp+eUKVPYbLa7u/u9e/fogBPNwi1HzpdeSkhI4D69pKenl5eXKykpcZfS9FL//v2HDBnCXWfBggW0R7mZmdnIkSPV1NTS09Pv3btXWFiYmpo6a9asu3fvslgsQsimTZtWr1595cqVvXv3EkLCwsIGDBhAOyG2CtqpnBAizrjuv/3226FDh+j0yJEjhw0bJisr+88//0RHR4eGhj569Ehwk9TU1CVLltTV1TGZzEmTJllYWNTU1MTFxcXExBw8eJA+S/Opra2dPn06/QlAV1fX1tZWU1Pz5cuXkZGRWVlZmZmZDg4O4eHh/fr1k/y0AeDrI1lD0Kw7Nq+KigpXV9eampoFCxYYGhrq6+sT8W7pGRkZbm5ubDabjkIqKysbGxsbGBj48ePH+/fvBwYGOjs7Nxn27Nmzw8LCCCGXLl1KSkqaP3/++PHjm/xVS3R4Et+chV4KCYSEhKSlpUlLSzs7O1tYWHTv3j0xMdHPz+/z58/37t3z9/dfuHAhd2UJWh8A+LI19qVXgvt8XFzc0qVL2Ww2g8EYN26chYWFnJxcSkrK9evX6+rqtm3b1qdPHxEZoLNnz27ZsoUe7uLFi9w7EoPBcHBw+OOPPzIyMnJzc7W1tXm3Ki8vv3//PiFk/PjxPXv2FP/Ep0yZEh4enpaWlpOTo6Ojw7eUvtWkpqZmbm4uNL0kcTsoATHv86Jbq4iIiP379xNCZGVlHRwcDA0Ne/ToUVJSEh0dHRcXV1tb6+Li8ujRIzEbAvGbng8fPsybN4/mlkaOHPn999/36tWrpKQkJCTk2bNnb9++XbJkSWPvkEFXh/QSAAAAwJfP3NzcxcXFx8fn1atXBw8e3LFjR3P3oKqqamhomJaW9vfff/POj4uLI4QoKyuXlZVxOJz4+PiJEyfSRa9fv87Pzyf/++rSzp076+vrGQzGpk2bli9fzu3yVlFRsWzZsqioqOzs7PPnz8+fP58Q0qNHjx49enCHCendu3efPn0kOX8ebDb733//zc7OPnnyZFRUFCHE1dX1+++/F71VUVHRsWPHCCFycnKenp6Ojo7cRffv3//Pf/4jtNbEgQMH6urq5OTkvL29ufm5NWvW+Pr67tixo6qqSnATHx8f+vPl4sWLt2zZ0q1bNzp/y5Yt+/bt8/b2Li4u3rlz58mTJ5t/6gDwVZOgIWjWHZvX6dOnFRUV7969q6ury50pzi09NzdXRUUlMDDQ2NiYzhk/fvyYMWPmzp3LZrOjo6PFSS/Z2touW7aMjm+amZm5ZcuWLVu2aGtrj/gvoRl60eFJfHMWeikkkJaWJvTKODk5cTicyMhI3vSSBK0PAHx5xPnS29z7PJvN3rlzJ5vNlpeXP378+Lhx47i7cnFxmTZt2sePH/fs2TNp0iShGZfGckuUo6PjH3/8QQgJDQ2lNV25bt68SXM8vL3cxDFu3Dg5Obmamprg4OC1a9fyLuKtjNfYi0ESt4MSEPM+L7q1om2frKxsUFAQdz+EkJUrV3p4eHh5eVVVVSUnJ9vY2LRiSISQmJiYnJwcQsiaNWs2bNjA3cPy5cvnzJnz4MGDx48fixgBC7o0jL0EAAAA8FVwd3fX0NAghPj6+vLW9hGfnZ0dIaS4uDg7O5s7Mz4+nhDy888/0+dD3vp4gpXx7t+///DhQ0KIo6PjihUreB/kevbs+ccff9DeiIcPH/706ZMEEfIJDQ1VEzBw4EBzc3MnJ6eoqCgFBYV9+/YJDhklyNPT8+PHj4SQzZs38+aWCCG2trbbtm0T3CQhIYE+ybu5uXF/3aNcXFzGjBkjuElpaSkdpMra2nr79u3cny8JISwWa9u2bd9++y0hJDg4mObtAACapVkNQUvu2B8+fNi4caNkCZX169fz/iJGCBk9erSenh4hRPxRPX799dfAwEA7Ozvu75u5ubmBgYHLly83Nze3sbHZsWNHenq6mHtryc25JZeCj7u7O9+Vsba2NjIyIoTQH/UoCVofAOjqJPvSK8F9/tq1a8+ePSOErFy5kje3RAgxNjZevHgxIaSgoCArK0swSH9/f5pbsrCwEMwtEUJMTEwGDRpECLl58ybfIloZT0VFZfTo0c26Mt27d6dxBgUF8S1KTEx89+4dPX2h27bzkwsR+z4vQmFhoaKi4uTJk/n2QwiZPn06nXjx4kWrh8T9UvHjjz/yriwlJbV06dKJEydOnDjx/fv34h8XuhCklwAAAAC+CnJych4eHoSQhoaGdevWiR7wXCiaXiKExMTE0Inq6mr6hGltbT1y5Ejyv+kl+jwmIyPDLfBNX3UihAitqK6goEBrtZeUlAh9KG11/fv319bWFqeOObfKn9CO8z/99JNgH/zbt28TQqSkpIQWoHd1dRWcGRYWRjuV8z3BUgwGg7sVzeoBADRLsxqCltyx5eXluT9jNYu8vPzs2bMF59OsmIhB5gWNGTMmICAgJSXFz8/Pzc3N2NiYO2z7ixcvTp8+PW7cuIULF4ozJFJLbs4SXwo+cnJys2bNEpw/ePBg8r9XRoLWBwC+eEK/9Epwn7979y4hRF5efsGCBYKbODk5rV69evXq1dz7LZe/v/+vv/5KCOnRo8f58+cbK85Gq+qlp6dzB4sihOTn59My1NOmTZOgDN2UKVMIIbm5uXy9CmittkGDBpmYmAjdsJ2fXMS/z4uQmJj4/Plz2iWCT3l5OZ0QfwQ+8UNSUVGhE3/++Sff/keNGuXr6+vr62tqairmcaFrQXE8AAAAgK/FqFGjnJyc/vzzz4yMjN9///2XX35p1uZmZmZKSkrl5eUxMTG0EkJiYmJDQwOLxRo2bFhOTk5ERERGRkZpaWmvXr3If9NL1tbW3BE+6IOivLw87YcuyNzcnE7k5eV98803kp7o/6OjoyN02Pby8vKsrKy4uLisrKxZs2YdP36cPnY2pq6ujlYSNzc35+20ziUjI2NpaUnLa3DRjoF9+/blPm7xGjp0qODMx48fE0KkpKQ0NTXLysoEV+AOHZyWljZz5kwRMQMACCV+Q9CSO/bAgQMFf1sUh6amptCfDiXbGyGkZ8+eEyZMmDBhAiGkqqoqMTExKioqPDz87du3hJDbt287OjpGRETIyMiI2ElLbs4SXwo+WlpaTCZTcL7gziVofQCgq5PsS68E93n6FqmOjo7Q/NCgQYPc3d0F5587d+7OnTt0urq6Ojg4eM6cOUKP6OjoSOtRh4SErFy5ks68fv06zVg0tzIeNWbMGAUFhaqqqhs3bnDvgWw2m47S19irS6Tdn1zEv8+Lr7q6uqCgIC8v79mzZxcvXmy7kCZNmuTp6VlRUXH8+PGIiIjJkydbWVmZmZlxi/jBFwzpJQAAAICvyI4dOyIjI4uLi48ePWpvb9/Yw5JQTCZzzJgx169fj42NbWhoYDKZtJv2N998Iy8vT19RosMvTZ48OTc3t6ioiPzvwEv0Ny9a9UIo2j+d/PdxroX09fWFPuJS9+7dW7BgAZvN3rp1q4ODg4gnt1evXjU0NBBCtLS0GltHsJI4PVk1NTWh66uoqEhLS9fW1vLOpAM4sdlsKyurxg5ECf19EwBAHGI2BC25Y6urq0sWm8QbikNBQcHOzs7Ozm737t1//fXX9u3bKyoq6KgkQnumc7Xk5txaZyR0vCihJGh9AKCrk+xLb3Pv8xwOh040985Gc0sODg63b9/+/Pnz3r17x48fL3REVUNDQy0trRcvXvCml2hlvCFDhkiWIJeWlp40adLly5dv3LixefNmOjM+Pp7e20Wkl9r5yUX8+7xoxcXFAQEBcXFx2dnZLaxHJ35IAwYMCAwM3LhxY3p6ek5OjpeXl5eXF4PB0NPTs7Ozs7e3x6tLXzAUxwMAAAD4ivTs2XPPnj2EkLq6unXr1jWr0BAhhI7iUFlZmZqaSv5bBWjEiBGEEH19fdpRmtaR41bJ4x34oclSDNx3g1qrgrkIdnZ2tP5GaWlpSkqKiDWlpaXphIgyer179+abwy1AIRSDwRDsoS/+7334ZRAAJCZmQ9CSO7bol4FEENpLullSUlIuXrx48eJFETdhBoMxY8YMOv45+e/vniK05OYs8aXgI04dV0qC1gcAvmyNfelt7n2+oaGhrq6OSHRnmzdv3smTJ1etWkUIqaio2LVrV2Nr0nzPs2fPXr16RSfosK+SvbpE0Xe28vPzk5KS6BxaGU9bW1tEyqqdn1zEv8+LcPny5REjRhw5ciQ+Pr60tFRHR2fChAlubm6enp43btxo05DMzc0jIiIuXrzo5uamp6fHYDA4HE5mZuaJEyfs7e1XrFjx+fPn5gYAXQLSSwAAAABfF3t7+8mTJxNCnjx54ufn16xtx4wZQzs8xsTEfPz48enTp4QQ2pubwWDQ4ZdomXJaGU9fX5/3tR5NTU1CCC00J1ReXh6d0NbWblZgkhk2bBidoOP6NkZNTY0+PYoYCFfwpJSUlAghtP6SoNLS0pqaGr6Z9O0oRUXFN005efJkE+cGANA4cRqCznbHFtPjx4/Xr1+/fv167m+IjRk9ejQt7sQ9kcZ0rZuzBK0PAHzxhH7pbe59nsVi0S/2+fn5zTr6nDlz9u/fz2Awli9frqOjQwi5fv36gwcPhK5MM2GEEFp3+q+//iKEMJnMqVOnNuugvL777jtau5umWBoaGpqsjEfaph1s0woEKSkpa9eu/fTpk6qq6uHDhzMyMqKjo/38/Hbu3Dlr1ixuHde2IyUlNWrUqJ07d0ZGRqanp1+4cGHp0qW0A+L169f379/f1gFAh0B6CeD/FxcXp9YcmZmZHR2y5AYNGqSmprZ169aODqSzMDY2VlNTo11pmpSZmSn6s6Gvrz9+/Hg3N7egoCDxR00EAGg3e/fupYWwPTw8mvV8qKysbGZmRgiJiYn5559/6uvrmUzm8OHD6VJaHy8zM7OkpIS+vWRnZ8e7Of2Frrq6OicnR+j+ab6KEEKfPNsat+BDRUWFiNWYTCatfZGUlFRfXy90HcH3n2hfyKKiotLSUsH1MzIyBGfSs66srCwpKRF6lPr6+qqqqqqqqsbCAAAQU5MNQWe7Y4uJjjdO/tvLQYSysrLq6mpCiNACTby61s1ZgtYHAL54Qr/0SnCfpxNZWVlCX+ssLCykNUj5hvmhA+ARQrp163bgwAE6vWnTJqGvsxgYGNCqrSEhIWw2myaERo8e3bdvX/HOVQgWi0XHpgoODmaz2bGxsbRqnOj0UkvaQaE/BFVVVTWW+28Vly9fpsf18/NzcnLq0aMH79I2baGys7MzMzN5v04oKiqOHj1669atDx48GDBgAPlvvhC+PEgvAUAXlpOTQ2tffPjwoaNj+R/V1dVpaWlhYWHLly+fMGGCiN7uAAAdom/fvjt37iSE1NTUuLu7NysRTsdSevToUVRUFCHEyMiIO7Tvd999RwjhcDj+/v70mY134CXy3/ecCCFHjx4V3HN1dbWPjw8hRElJqVmDQrUc/YVRhFGjRhFC3rx5c/78ecGl9+/fT05O5ptJz53NZvv7+wtuQs+UD3d84NOnTwsNY+/evQYGBoaGhrRYPACAxJpsCDrtHVs0S0tL2lH69OnT6enpIta8cOECPWveIq5Cda2bswStDwB8PXi/9Epwn6ebVFRUnDt3TnCTkJCQjIyMjIwMEd0ORowY4eTkRAh59erVb7/9JnQdmvVJSUm5cOECHcy1JZXxKFofr6ioKCEhgVbG4+axGtOSdrCgoEBwE6HPEa2IXisWi2VsbCy4NCEhoe0OvWTJEltb2xkzZggu6tmzp4GBASEE785+qZBeAhDCxMRktRgER1mAdhYfH09rX3TUCOc6OjqCH4yVK1fOnj3b0tKSrpOWlrZ06dLO0JMRAICXk5MTzQY9ePCgWd/16c9wnz59og9IvOOcDx48mPZNoz/AKSoqWlhY8G47duxYOlBTUFDQqVOneBdVVlYuW7aMdrVev369nJyc4KEbGhrEj7NZmkwvrV69moa0b98+WkyDKzk5ecOGDYKbTJkyhQ4F7O3tHRMTw7soICBA6FAfFhYWtFzV8ePHvby8+H7tDQ4OpjWsbG1t6XUGAGgJ0Q1BC+/YTWqjW7qcnNzatWsJIXV1dQ4ODidPnhT8Hv7582c/Pz/ag15eXn7RokWiw+taN2cJWh8A+HrwfumV4D6/cOFCWh/v//7f/8tX3S4rK8vT05MQoqKiQqsdNGbbtm20VN2JEyeysrIEV+C+VLR7925CiKKi4vjx45t7pnwsLS3pK1xXr14VpzIekej6MJlMege+c+cO35vB//zzz5EjR1p4FoJ4Wyua1auvr6e1ynmFhYVt27aNTtMBtFrXN998QwgpKCi4dOkS36LU1FQ6Xi+34gV8YTCiI4AQ5ubm7u7uHR1F2woKCiKE0J59IDF9fX0RH5XExMTZs2d/+vTp2bNnAQEBQh9cAQA60KFDh2xtbT9+/NisrYyMjPr161dUVFRVVUUIoQ9dXNbW1leuXKGLxowZIzh++I4dO3744Yf6+vpdu3bdunVr5MiR/fv3z6JwM3UAACAASURBVMjIiIiIoMUiDAwMnJ2deTfhjpp75syZSZMmMZlMbvn4FqJPtoSQly9fil6zT58+K1asOHjwYHV1tZub2+jRoy0sLOTl5ZOSkiIiIurq6kaMGMH3IMdisU6cODFt2rTq6uo5c+Y4OjqamZnV1NTExcVFRUUZGxvn5+eXl5dLS0vzbrVjx46oqKiamhoPD4+wsDArKyt9ff2KiorIyEj6K2Hfvn1FDIYMANAsohsCCe7YTWq7WzrXvHnz0tLSzp8//+nTpz179pw+fXr48OH9+/eXlZUtKyv7999/Y2NjKysrCSFMJvPQoUPq6upNhteFbs6StT4A8GVr7Etvc+/zMjIy27ZtW7x4cWVl5Zw5cyZNmmRmZqagoPDs2bM///yztraWwWAcOXJE8BGAl7Ky8vbt29esWVNfX79p06a//vqLwWDwrqCtrT1kyJDnz5/TQjWOjo4yMjItvAJSUlKOjo4+Pj6XL19ms9lEjPQSkagdHD16dEBAQFVV1Q8//PCf//zH0NDw/fv3CQkJFy5caGhoYLFYrdL5WGhrNX78+OPHjzc0NLi6urq5uZmZmXE4nNzc3PDw8Pj4eGVl5ZqaGg6Hc/36dQsLC11dXW4JipZbsGDB9evX6+vr161bd/XqVWtrazU1taqqqqSkpNDQ0NraWhaL9Z///Ke1DgedCtJLAF8p0X1JoFV8++2369at27t3LyGkybGFAQDan4aGxsaNG2lxpGaxtbWlFdWlpKS4L2tS33333ZUrV+g038BLlLGx8Y0bN1auXPnixYvExMTExETepVOnTt2/fz/fE6mJiYmUlBSbzfbx8fHx8RkxYsTVq1ebG7NQ3J8U79y5U1ZWpqysLGLl1atX9+zZc9euXbW1tdHR0dHR0dxFEyZM2LRp05gxY/g2MTU19fX1XbJkSU1NzbVr165du0bnDx482NfX9/vvvyeEyMrK8oUUGhq6Zs2ap0+fpqampqam8i7V1NQ8deoU7RQJANByohsCCe7YTWq7WzoXg8Hw8PDQ1tY+cuRIVVXVu3fvbt68Kbiaurr6/v37+SrjNRZe17o5S9D6AMCXrbEvvRLc5+3t7QMCAn755Zf379+HhoaGhoZyF3Xv3n3Tpk1NVhwlhMycOfPPP/+Mi4tLSEi4dOnS7Nmz+VZwdHR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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 875, + "width": 1125 + } + }, + "output_type": "display_data" + } + ], "source": [ "geo_taxa <- biosample_taxa_df %>%\n", " group_by(geo_loc_name, soil_horizon, taxa) %>%\n", From ebdc140525376b752bdaaf0af7cc78cecbb79243 Mon Sep 17 00:00:00 2001 From: bmeluch Date: Tue, 14 Jan 2025 09:19:42 -0800 Subject: [PATCH 6/6] render nom notebook --- NOM_visualizations/R/NOM_R_notebook.ipynb | 174 ++++++++++++++++++---- 1 file changed, 142 insertions(+), 32 deletions(-) diff --git a/NOM_visualizations/R/NOM_R_notebook.ipynb b/NOM_visualizations/R/NOM_R_notebook.ipynb index 4943f443..ce105e68 100644 --- a/NOM_visualizations/R/NOM_R_notebook.ipynb +++ b/NOM_visualizations/R/NOM_R_notebook.ipynb @@ -46,14 +46,75 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 3 × 5
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2nmdc:dobj-11-00wm33131000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv 3ce562ac512457ea54bdda05a4f01edenmdc:wfnom-11-twkd5a03.1https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv
3nmdc:dobj-11-01kye6251000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv38930c28eae561bc807bd01823f04167nmdc:wfnom-11-ftaq2319.1https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv
\n" + ], + "text/latex": [ + "A data.frame: 3 × 5\n", + "\\begin{tabular}{r|lllll}\n", + " & id & name & md5\\_checksum & was\\_generated\\_by & url\\\\\n", + " & & & & & \\\\\n", + "\\hline\n", + "\t1 & nmdc:dobj-11-00dewm52 & 1000s\\_OSBS\\_FTMS\\_SPE\\_BTM\\_3\\_01Nov22\\_Mag\\_300SA\\_p025\\_184\\_1\\_7197.csv & 2a532dca15798e470103ebd752a0937f & nmdc:wfnom-11-0mqv1c63.1 & https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s\\_OSBS\\_FTMS\\_SPE\\_BTM\\_3\\_01Nov22\\_Mag\\_300SA\\_p025\\_184\\_1\\_7197.csv \\\\\n", + "\t2 & nmdc:dobj-11-00wm3313 & 1000s\\_DELA\\_FTMS\\_SPE\\_TOP\\_2\\_29Oct22\\_Mag\\_300SA\\_p025\\_126\\_1\\_7076.csv & 3ce562ac512457ea54bdda05a4f01ede & nmdc:wfnom-11-twkd5a03.1 & https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s\\_DELA\\_FTMS\\_SPE\\_TOP\\_2\\_29Oct22\\_Mag\\_300SA\\_p025\\_126\\_1\\_7076.csv \\\\\n", + "\t3 & nmdc:dobj-11-01kye625 & 1000S\\_CFS2\\_FTMS\\_SPE\\_TOP\\_2\\_run1\\_Fir\\_22Apr22\\_300SA\\_p01\\_12\\_1\\_3369.csv & 38930c28eae561bc807bd01823f04167 & nmdc:wfnom-11-ftaq2319.1 & https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S\\_CFS2\\_FTMS\\_SPE\\_TOP\\_2\\_run1\\_Fir\\_22Apr22\\_300SA\\_p01\\_12\\_1\\_3369.csv\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 3 × 5\n", + "\n", + "| | id <chr> | name <chr> | md5_checksum <chr> | was_generated_by <chr> | url <chr> |\n", + "|---|---|---|---|---|---|\n", + "| 1 | nmdc:dobj-11-00dewm52 | 1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv | 2a532dca15798e470103ebd752a0937f | nmdc:wfnom-11-0mqv1c63.1 | https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv |\n", + "| 2 | nmdc:dobj-11-00wm3313 | 1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv | 3ce562ac512457ea54bdda05a4f01ede | nmdc:wfnom-11-twkd5a03.1 | https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv |\n", + "| 3 | nmdc:dobj-11-01kye625 | 1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv | 38930c28eae561bc807bd01823f04167 | nmdc:wfnom-11-ftaq2319.1 | https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv |\n", + "\n" + ], + "text/plain": [ + " id \n", + "1 nmdc:dobj-11-00dewm52\n", + "2 nmdc:dobj-11-00wm3313\n", + "3 nmdc:dobj-11-01kye625\n", + " name \n", + "1 1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv \n", + "2 1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv \n", + "3 1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv\n", + " md5_checksum was_generated_by \n", + "1 2a532dca15798e470103ebd752a0937f nmdc:wfnom-11-0mqv1c63.1\n", + "2 3ce562ac512457ea54bdda05a4f01ede nmdc:wfnom-11-twkd5a03.1\n", + "3 38930c28eae561bc807bd01823f04167 nmdc:wfnom-11-ftaq2319.1\n", + " url \n", + "1 https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_OSBS_FTMS_SPE_BTM_3_01Nov22_Mag_300SA_p025_184_1_7197.csv \n", + "2 https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000s_DELA_FTMS_SPE_TOP_2_29Oct22_Mag_300SA_p025_126_1_7076.csv \n", + "3 https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/results/1000S_CFS2_FTMS_SPE_TOP_2_run1_Fir_22Apr22_300SA_p01_12_1_3369.csv" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "nom_dobj_df <- get_next_results(\n", " collection = \"data_object_set\", \n", @@ -76,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "metadata": {}, "vscode": { @@ -119,14 +180,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "metadata": {}, "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 3 × 3
idhas_inputhas_output
<chr><chr><chr>
1nmdc:wfnom-11-snvsmz18.1nmdc:dobj-11-esgqw196nmdc:dobj-11-0bmapy68
2nmdc:wfnom-11-ph9mfs20.1nmdc:dobj-11-sj597780nmdc:dobj-11-04v02904
3nmdc:wfnom-11-jqhgtg36.1nmdc:dobj-11-zzzrdh93nmdc:dobj-11-09xct845
\n" + ], + "text/latex": [ + "A data.frame: 3 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & id & has\\_input & has\\_output\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & nmdc:wfnom-11-snvsmz18.1 & nmdc:dobj-11-esgqw196 & nmdc:dobj-11-0bmapy68\\\\\n", + "\t2 & nmdc:wfnom-11-ph9mfs20.1 & nmdc:dobj-11-sj597780 & nmdc:dobj-11-04v02904\\\\\n", + "\t3 & nmdc:wfnom-11-jqhgtg36.1 & nmdc:dobj-11-zzzrdh93 & nmdc:dobj-11-09xct845\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 3 × 3\n", + "\n", + "| | id <chr> | has_input <chr> | has_output <chr> |\n", + "|---|---|---|---|\n", + "| 1 | nmdc:wfnom-11-snvsmz18.1 | nmdc:dobj-11-esgqw196 | nmdc:dobj-11-0bmapy68 |\n", + "| 2 | nmdc:wfnom-11-ph9mfs20.1 | nmdc:dobj-11-sj597780 | nmdc:dobj-11-04v02904 |\n", + "| 3 | nmdc:wfnom-11-jqhgtg36.1 | nmdc:dobj-11-zzzrdh93 | nmdc:dobj-11-09xct845 |\n", + "\n" + ], + "text/plain": [ + " id has_input has_output \n", + "1 nmdc:wfnom-11-snvsmz18.1 nmdc:dobj-11-esgqw196 nmdc:dobj-11-0bmapy68\n", + "2 nmdc:wfnom-11-ph9mfs20.1 nmdc:dobj-11-sj597780 nmdc:dobj-11-04v02904\n", + "3 nmdc:wfnom-11-jqhgtg36.1 nmdc:dobj-11-zzzrdh93 nmdc:dobj-11-09xct845" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "nom_analysis_df <- get_results_by_id(\n", " collection = \"workflow_execution_set\",\n", @@ -153,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "metadata": {}, "vscode": { @@ -241,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "metadata": {}, "vscode": { @@ -324,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "metadata": {}, "vscode": { @@ -374,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "metadata": {}, "vscode": { @@ -493,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "metadata": {}, "vscode": { @@ -646,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "metadata": {}, "vscode": { @@ -680,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "metadata": {}, "vscode": { @@ -727,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "metadata": {}, "vscode": { @@ -760,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "metadata": {}, "vscode": { @@ -793,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "metadata": {}, "vscode": { @@ -833,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "metadata": {}, "vscode": { @@ -962,7 +1072,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "metadata": {}, "vscode": { @@ -1068,7 +1178,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "metadata": {}, "vscode": { @@ -1184,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "metadata": {}, "vscode": { @@ -1221,7 +1331,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "metadata": {}, "vscode": { @@ -1246,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "metadata": {}, "vscode": { @@ -1283,7 +1393,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "metadata": {}, "vscode": { @@ -1318,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "metadata": {}, "vscode": { @@ -1363,7 +1473,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "metadata": {}, "vscode": { @@ -1406,7 +1516,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "metadata": {}, "vscode": { @@ -1447,7 +1557,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "metadata": {}, "vscode": { @@ -1482,7 +1592,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "metadata": {}, "vscode": { @@ -1516,7 +1626,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "metadata": {}, "vscode": { @@ -1549,7 +1659,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "metadata": {}, "vscode": { @@ -1575,7 +1685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "metadata": {}, "vscode": { @@ -1629,7 +1739,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "metadata": {}, "vscode": { @@ -1689,7 +1799,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "metadata": {}, "vscode": {