β Error occurred:<\/strong><\/p>\",\n \"\", current_error, \"<\/p>\",\n \"
π‘ Need help? Check the Help tab for detailed documentation.<\/p>\",\n \"<\/div>\"\n )))\n }\n \n # Check current state\n has_candidates <- !is.null(input$candidate_file)\n has_pedigree <- !is.null(input$pedigree_file)\n has_kinship_threshold <- !is.null(input$thresh) && input$thresh < 1\n has_traits <- trait_counter() > 0 && any(sapply(1:trait_counter(), function(i) !is.null(input[[paste0(\"trait_file_\", i)]])))\n has_ocs_results <- !is.null(ocs_results_reactive())\n \n # Build step-by-step guide\n steps <- c()\n \n # Step 1: Candidates\n if (has_candidates) {\n steps <- c(steps, \"
β
Step 1:<\/strong> Upload your candidate list to begin the analysis<\/p>\")\n } else {\n steps <- c(steps, \"Step 1:<\/strong> Upload your candidate list to begin the analysis<\/p>\")\n }\n \n # Step 2: Pedigree\n if (has_pedigree) {\n steps <- c(steps, \"β
Step 2:<\/strong> Upload your pedigree file for kinship calculations<\/p>\")\n } else {\n steps <- c(steps, \"Step 2:<\/strong> Upload your pedigree file for kinship calculations<\/p>\")\n }\n \n # Step 3: Kinship threshold\n if (has_pedigree) {\n steps <- c(steps, \"βοΈ Step 3:<\/strong> Set your kinship threshold (optional)<\/p>\")\n } else {\n steps <- c(steps, \"Step 3:<\/strong> Set your kinship threshold (optional)<\/p>\")\n }\n \n # Step 4: Traits\n if (has_traits) {\n steps <- c(steps, \"β
Step 4:<\/strong> Add trait files and weights for breeding value analysis<\/p>\")\n } else {\n steps <- c(steps, \"Step 4:<\/strong> Add trait files and weights for breeding value analysis<\/p>\")\n }\n \n # Step 5: OCS\n if (has_ocs_results) {\n steps <- c(steps, \"β
Step 5:<\/strong> Configure OCS parameters and run analysis<\/p>\")\n } else {\n steps <- c(steps, \"Step 5:<\/strong> Configure OCS parameters and run analysis<\/p>\")\n }\n \n # Add completion message if all steps are done\n if (has_ocs_results) {\n steps <- c(steps, \n \"π Analysis Complete!<\/strong><\/p>\",\n \"Final Step:<\/strong> Export your results using the download button below<\/p>\",\n \"Great job! Results are ready for review and export<\/p>\"\n )\n }\n \n HTML(paste(steps, collapse = \"\"))\n })\n \n observeEvent(input$add_trait, {\n trait_counter(trait_counter() + 1)\n })\n \n observeEvent(input$remove_trait, {\n if (trait_counter() > 1) {\n trait_counter(trait_counter() - 1)\n }\n })\n \n output$trait_inputs <- renderUI({\n create_trait_inputs(trait_counter())\n })\n \n # OCS trait inputs\n output$ocs_trait_inputs <- renderUI({\n req(input$ocs_trait_counter)\n create_ocs_trait_inputs(input$ocs_trait_counter)\n })\n \n candidates_data <- reactive({\n req(input$candidate_file)\n read_candidates(input$candidate_file)\n })\n \n pedigree_data <- reactiveVal(NULL)\n \n observeEvent(input$pedigree_file, {\n req(candidates_data())\n males <- candidates_data()$males\n females <- candidates_data()$females\n \n tryCatch({\n raw_ped <- readr::read_table(input$pedigree_file$datapath)\n final_ped <- clean_pedigree(raw_ped)\n kinship_res <- compute_kinship_matrix(final_ped, males, females)\n \n output$quadrants_table <- DT::renderDT({\n datatable(kinship_res$quads, options = list(ordering = FALSE, dom = \"t\"), rownames = TRUE) %>%\n formatStyle(colnames(kinship_res$quads), styleEqual(\n kinship_res$quads[1, ], c(\"lightgreen\", \"yellow\", \"orange\", \"coral\")\n ))\n })\n \n pedigree_data(list(results = kinship_res$results, quads = kinship_res$quads))\n \n error_message(\"\") # Clear any previous errors\n output$message1 <- renderText(\"β
Kinship matrix generated successfully.\")\n }, error = function(e) {\n error_message(paste0(\"Error processing pedigree: \", e$message))\n output$message1 <- renderText(\n paste0(\"β Error processing pedigree: Make sure your pedigree is clean and valid.\\n\",\n \"Original error: \", e$message)\n )\n })\n })\n \n observe({\n req(candidates_data())\n ebv_res <- process_ebvs(trait_counter(), input)\n req(ebv_res)\n \n joint_ebvs <- ebv_res$joint_ebvs\n rel_weights <- ebv_res$rel_weights\n weight_total <- ebv_res$weight_total\n \n ebv_cols <- paste0(\"EBV.\", seq_along(rel_weights))\n # Ensure EBV columns are numeric for matrix multiplication\n joint_ebvs[ebv_cols] <- lapply(joint_ebvs[ebv_cols], as.numeric)\n joint_ebvs$index_val <- as.vector(as.matrix(joint_ebvs[ebv_cols]) %*% rel_weights)\n \n cands <- candidates_data()$candidates\n males <- candidates_data()$males\n females <- candidates_data()$females\n \n cand_ebv <- left_join(cands, joint_ebvs, by = c(\"id\" = \"ID\")) %>%\n select(id, sex, index_val)\n \n m_ebv <- filter(cand_ebv, id %in% males) %>% select(id, index_val)\n f_ebv <- filter(cand_ebv, id %in% females) %>% select(id, index_val)\n \n ebv_matrix <- outer(m_ebv$index_val, f_ebv$index_val, function(x, y) round((x + y) / 2, 2))\n rownames(ebv_matrix) <- m_ebv$id\n colnames(ebv_matrix) <- f_ebv$id\n \n ebv_quads <- tibble(\n Data = \"EBV\",\n Q25 = quantile(ebv_matrix, 0.25),\n Q50 = quantile(ebv_matrix, 0.50),\n Q75 = quantile(ebv_matrix, 0.75),\n Q100 = quantile(ebv_matrix, 1.00)\n ) %>% column_to_rownames(\"Data\")\n \n quads_combined <- if (!is.null(pedigree_data())) {\n bind_rows(pedigree_data()$quads, ebv_quads)\n } else {\n ebv_quads\n }\n \n output$quadrants_table <- DT::renderDT({\n datatable(quads_combined, options = list(ordering = FALSE, dom = \"t\"), rownames = TRUE) %>%\n formatStyle(\"Q25\", backgroundColor = \"coral\") %>%\n formatStyle(\"Q50\", backgroundColor = \"orange\") %>%\n formatStyle(\"Q75\", backgroundColor = \"yellow\") %>%\n formatStyle(\"Q100\", backgroundColor = \"lightgreen\")\n })\n \n ebv_results <- as_tibble(ebv_matrix, rownames = \"Male\") %>%\n pivot_longer(-Male, names_to = \"Female\", values_to = \"EBV\")\n \n full_results <- if (!is.null(pedigree_data())) {\n left_join(pedigree_data()$results, ebv_results, by = c(\"Female\", \"Male\"))\n } else {\n relocate(mutate(ebv_results, Kinship = NA), Kinship, .after = EBV)\n }\n \n # For table: filter out crosses with EBV <=0 or kinship >= threshold\n filt_results_table <- full_results %>%\n filter(EBV > 0, (is.na(Kinship) | Kinship < input$thresh))\n \n # For Excel: mask EBVs for invalid crosses as NA (blank in Excel)\n filt_results_matrix <- full_results %>%\n mutate(EBV = ifelse(EBV <= 0 | (!is.na(Kinship) & Kinship >= input$thresh), NA, EBV))\n \n # Store results for download handler\n ebv_results_reactive(list(\n filt_results_table = filt_results_table,\n filt_results_matrix = filt_results_matrix,\n full_results = full_results,\n ebv_quads = ebv_quads\n ))\n \n output$matrix <- DT::renderDT({\n datatable(filt_results_table, rownames = TRUE) %>%\n formatStyle(\"EBV\", styleInterval(\n unlist(ebv_quads[1, c(\"Q25\", \"Q50\", \"Q75\")]),\n c(\"coral\", \"orange\", \"yellow\", \"lightgreen\")\n ))\n })\n \n output$message2 <- renderText(\n if (abs(weight_total - 1) > 1e-6)\n paste(\"β οΈ Warning: Trait weights do not sum to 1 (total =\", round(weight_total, 4), \")\")\n else\n \"β
EBV matrix generated successfully.\"\n )\n \n output$download_all_results <- downloadHandler(\n filename = function() {\n paste0(\"AlloMate_Complete_Results-\", Sys.Date(), \".xlsx\")\n },\n content = function(file) {\n wb <- openxlsx::createWorkbook()\n \n # Add comprehensive README worksheet\n openxlsx::addWorksheet(wb, \"README\")\n readme_text <- c(\n \"π AlloMate Complete Results Report\",\n \"\",\n \"This Excel file contains all results from your AlloMate analysis:\",\n \"\",\n \"π Worksheets included:\",\n \"1. README - This overview and explanation\",\n \"2. Filtered Results - Crosses meeting criteria (positive EBVs, kinship below threshold)\",\n \"3. EBV Matrix - Complete matrix view with masked values\",\n \"4. OCS Candidates - Selected candidates from Optimum Contribution Selection\",\n \"5. Mating Plan - Recommended mating pairs from OCS\",\n \"6. Parameters - Analysis parameters used\",\n \"\",\n \"π Data Details:\",\n \"- Filtered Results: Only crosses with positive EBVs and kinship below threshold\",\n \"- EBV Matrix: All possible crosses with values masked for failed criteria\",\n \"- OCS Results: Available only after running Optimum Contribution Selection\",\n \"\",\n \"π
Generated on:\", as.character(Sys.Date())\n )\n openxlsx::writeData(wb, \"README\", readme_text)\n \n # Add kinship/EBV results if available\n ebv_results <- ebv_results_reactive()\n if (!is.null(ebv_results)) {\n openxlsx::addWorksheet(wb, \"Filtered Results\")\n openxlsx::writeData(wb, \"Filtered Results\", ebv_results$filt_results_table, rowNames = TRUE)\n \n openxlsx::addWorksheet(wb, \"EBV Matrix\")\n m_ids <- unique(ebv_results$full_results$Male)\n f_ids <- unique(ebv_results$full_results$Female)\n mat_for_excel <- matrix(NA_real_, nrow = length(m_ids), ncol = length(f_ids),\n dimnames = list(m_ids, f_ids))\n \n for (i in seq_len(nrow(ebv_results$filt_results_matrix))) {\n m <- ebv_results$filt_results_matrix$Male[i]\n f <- ebv_results$filt_results_matrix$Female[i]\n val <- ebv_results$filt_results_matrix$EBV[i]\n mat_for_excel[m, f] <- val\n }\n openxlsx::writeData(wb, \"EBV Matrix\", mat_for_excel, rowNames = TRUE)\n }\n \n # Add OCS results if available\n if (exists(\"ocs_results_reactive\") && !is.null(ocs_results_reactive())) {\n results <- ocs_results_reactive()\n formatted_results <- format_ocs_results(results)\n \n openxlsx::addWorksheet(wb, \"OCS Candidates\")\n openxlsx::writeData(wb, \"OCS Candidates\", formatted_results$candidate_table, rowNames = FALSE)\n \n openxlsx::addWorksheet(wb, \"Mating Plan\")\n openxlsx::writeData(wb, \"Mating Plan\", formatted_results$mating_table, rowNames = FALSE)\n }\n \n # Add parameters worksheet\n openxlsx::addWorksheet(wb, \"Parameters\")\n params_data <- data.frame(\n Parameter = c(\"Kinship Threshold\", \"Desired Inbreeding Rate\", \"Number of Offspring\", \"Analysis Date\"),\n Value = c(\n as.character(input$thresh),\n as.character(input$inbreeding_rate),\n as.character(input$num_offspring),\n as.character(Sys.Date())\n )\n )\n openxlsx::writeData(wb, \"Parameters\", params_data, rowNames = FALSE)\n \n openxlsx::saveWorkbook(wb, file, overwrite = TRUE)\n }\n )\n })\n \n #### OCS Server Logic ####\n \n observeEvent(input$run_ocs_btn, {\n req(input$pedigree_file, input$candidate_file)\n \n # Check if any OCS implementation is available (optiSel or custom fallback)\n if ((!exists(\"optisel_available\") || !optisel_available) && \n (!exists(\"custom_ocs_available\") || !custom_ocs_available)) {\n showModal(modalDialog(\n title = \"OCS Functionality Not Available\",\n \"Neither optiSel nor the custom OCS fallback could be loaded. \n Please check that all required packages are installed and restart the app.\",\n easyClose = TRUE\n ))\n return(NULL)\n }\n \n tryCatch({\n ped_data <- read.table(input$pedigree_file$datapath, header = TRUE)\n candidates <- read.table(input$candidate_file$datapath, header = TRUE)\n \n final_ped <- clean_pedigree(ped_data)\n kinship_matrix <- if (exists(\"kinship2_available\") && kinship2_available) {\n kinship2::kinship(final_ped)\n } else {\n fallback_kinship(final_ped)\n }\n \n ebv_result <- process_ebvs(trait_counter(), input)\n if (is.null(ebv_result) || abs(ebv_result$weight_total - 1) > 1e-6) {\n showModal(modalDialog(\n title = \"Invalid Weights\",\n \"Weights must sum to 1.\",\n easyClose = TRUE\n ))\n return(NULL)\n }\n \n joint_ebvs <- calculate_index(ebv_result$joint_ebvs, ebv_result$rel_weights)\n candidates <- left_join(candidates, joint_ebvs, by = c(\"id\" = \"ID\"))\n \n results <- run_ocs(\n candidates_df = candidates,\n kinship_matrix = kinship_matrix,\n ebv_index = candidates$index_val,\n desired_inbreeding_rate = input$inbreeding_rate,\n num_offspring = input$num_offspring\n )\n \n ocs_results_reactive(results)\n \n error_message(\"\") # Clear any previous errors\n \n # Format results for display\n formatted_results <- format_ocs_results(results)\n \n output$ocs_candidate_table <- DT::renderDT({\n formatted_results$candidate_table\n })\n \n # Switch to OCS tab to show results\n updateTabsetPanel(session, \"main_tabs\", selected = \"Optimum Contribution Selection\")\n \n }, error = function(e) {\n error_message(paste0(\"Error running OCS: \", e$message))\n showModal(modalDialog(\n title = \"Error\",\n paste(\"β Error running OCS:\", e$message),\n easyClose = TRUE\n ))\n })\n })\n \n output$ocs_mating_table <- DT::renderDT({\n req(ocs_results_reactive())\n results <- ocs_results_reactive()\n formatted_results <- format_ocs_results(results)\n formatted_results$mating_table %>%\n datatable(\n options = list(pageLength = 10, autoWidth = TRUE),\n rownames = FALSE\n )\n })\n \n #### R Code Display and Download ####\n \n # HTML escape function for code display\n htmlEscape <- function(text) {\n text <- gsub(\"&\", \"&\", text)\n text <- gsub(\"<\", \"<\", text)\n text <- gsub(\">\", \">\", text)\n text <- gsub('\"', \""\", text)\n text <- gsub(\"'\", \"'\", text)\n return(text)\n }\n \n # Function to read and format R code files\n format_r_code_content <- function() {\n # Define the R files to include\n r_files <- c(\n \"global.R\" = \"Global Setup and Package Loading\",\n \"R/load_functions.R\" = \"Function Loading Logic\", \n \"R/utils.R\" = \"Data Processing Functions\",\n \"R/ocs_helpers.R\" = \"OCS Calculation Functions\",\n \"R/ui_helpers.R\" = \"UI Helper Functions\",\n \"R/optsel_fallback.R\" = \"Custom OCS Implementation\"\n )\n \n # Determine the base path\n base_path <- if (app_dir) \".\" else \"app\"\n \n # Read and format each file\n formatted_sections <- list()\n \n for (file_path in names(r_files)) {\n full_path <- file.path(base_path, file_path)\n \n if (file.exists(full_path)) {\n tryCatch({\n file_content <- readLines(full_path, warn = FALSE, encoding = \"UTF-8\")\n file_content <- paste(file_content, collapse = \"\\n\")\n \n # Create formatted section\n section_html <- paste0(\n '
',\n '
π ', r_files[file_path], '<\/h4>',\n '',\n '
File:<\/strong> ', file_path, 'Lines:<\/strong> ', length(strsplit(file_content, \"\\n\")[[1]]), 'Description:<\/strong> ', r_files[file_path],\n '<\/div>',\n '', \n htmlEscape(file_content),\n '<\/code><\/pre>',\n '<\/div>'\n )\n \n formatted_sections[[file_path]] <- section_html\n }, error = function(e) {\n formatted_sections[[file_path]] <- paste0(\n '',\n '
β Error reading file<\/h4>',\n ' Could not read file: ', file_path, '<\/p>',\n '
Error: ', e$message, '<\/p>',\n '<\/div>'\n )\n })\n } else {\n formatted_sections[[file_path]] <- paste0(\n '
',\n '
β File not found<\/h4>',\n ' File not found: ', file_path, '<\/p>',\n '<\/div>'\n )\n }\n }\n \n # Create setup instructions\n setup_instructions <- paste0(\n '
',\n '
π Setup Instructions<\/h4>',\n ' To run this analysis independently in R:<\/strong><\/p>',\n '',\n 'Install required packages:<\/strong>install.packages(c(\"tidyverse\", \"shiny\", \"DT\", \"openxlsx\", \"quadprog\", \"kinship2\", \"optiSel\"))<\/code><\/li>',\n 'Load the code files:<\/strong> Copy the code sections above into separate .R files<\/li>',\n 'Prepare your data:<\/strong> Ensure your input files match the expected format<\/li>',\n 'Run the analysis:<\/strong> Execute the functions in the order shown above<\/li>',\n '<\/ol>',\n 'Note:<\/strong> The custom OCS fallback will be used if optiSel is not available.<\/p>',\n '<\/div>'\n )\n \n # Combine all sections\n full_content <- paste0(\n '',\n '
𧬠AlloMate R Code Implementation<\/h1>',\n ' This page contains all the R code needed to implement the AlloMate analysis independently. ',\n 'The code is organized by function and includes all necessary data processing, kinship calculations, ',\n 'and optimum contribution selection algorithms.<\/p>',\n setup_instructions,\n paste(formatted_sections, collapse = \"\"),\n '<\/div>'\n )\n \n return(full_content)\n }\n \n # Render R code content\n output$r_code_content <- renderUI({\n HTML(format_r_code_content())\n })\n \n # Download R code functionality\n output$download_r_code <- downloadHandler(\n filename = function() {\n paste0(\"allomate_complete_script_\", format(Sys.Date(), \"%Y%m%d\"), \".R\")\n },\n content = function(file) {\n # Define the R files to include\n r_files <- c(\n \"global.R\" = \"# Global Setup and Package Loading\",\n \"R/load_functions.R\" = \"# Function Loading Logic\", \n \"R/utils.R\" = \"# Data Processing Functions\",\n \"R/ocs_helpers.R\" = \"# OCS Calculation Functions\",\n \"R/ui_helpers.R\" = \"# UI Helper Functions\",\n \"R/optsel_fallback.R\" = \"# Custom OCS Implementation\"\n )\n \n # Determine the base path\n base_path <- if (app_dir) \".\" else \"app\"\n \n # Create the complete script\n script_lines <- c(\n \"# AlloMate Complete R Script\",\n \"# Generated on:\", as.character(Sys.Date()),\n \"# This script contains all functions needed to run AlloMate analysis independently\",\n \"\",\n \"# =============================================================================\",\n \"# SETUP INSTRUCTIONS\",\n \"# =============================================================================\",\n \"# 1. Install required packages:\",\n \"# install.packages(c('tidyverse', 'shiny', 'DT', 'openxlsx', 'quadprog', 'kinship2', 'optiSel'))\",\n \"# 2. Load required libraries:\",\n \"# library(tidyverse)\",\n \"# library(openxlsx)\",\n \"# library(quadprog)\",\n \"# library(kinship2)\",\n \"# library(optiSel)\",\n \"# 3. Run this script to load all functions\",\n \"# 4. Use the functions as demonstrated in the comments\",\n \"\",\n \"# =============================================================================\",\n \"# FUNCTION DEFINITIONS\",\n \"# =============================================================================\",\n \"\"\n )\n \n for (file_path in names(r_files)) {\n full_path <- file.path(base_path, file_path)\n \n if (file.exists(full_path)) {\n tryCatch({\n file_content <- readLines(full_path, warn = FALSE, encoding = \"UTF-8\")\n \n # Add section header\n script_lines <- c(script_lines, \n paste0(\"# \", \"=\", strrep(\"=\", 70)),\n r_files[file_path],\n paste0(\"# \", \"=\", strrep(\"=\", 70)),\n \"\",\n file_content,\n \"\",\n \"\"\n )\n }, error = function(e) {\n script_lines <- c(script_lines,\n paste0(\"# Error reading file: \", file_path),\n paste0(\"# \", e$message),\n \"\",\n \"\"\n )\n })\n } else {\n script_lines <- c(script_lines,\n paste0(\"# File not found: \", file_path),\n \"\",\n \"\"\n )\n }\n }\n \n # Add usage example\n script_lines <- c(script_lines,\n \"# =============================================================================\",\n \"# USAGE EXAMPLE\",\n \"# =============================================================================\",\n \"# \",\n \"# # Load your data\",\n \"# candidates <- read.table('your_candidates.txt', header = TRUE)\",\n \"# pedigree <- read.table('your_pedigree.txt', header = TRUE)\",\n \"# \",\n \"# # Process data\",\n \"# candidates_data <- read_candidates(list(datapath = 'your_candidates.txt'))\",\n \"# final_ped <- clean_pedigree(pedigree)\",\n \"# kinship_matrix <- compute_kinship_matrix(final_ped, candidates_data$males, candidates_data$females)\",\n \"# \",\n \"# # Run OCS analysis\",\n \"# results <- run_ocs(candidates_df = candidates, kinship_matrix = kinship_matrix$results,\",\n \"# ebv_index = your_ebv_values, desired_inbreeding_rate = 0.05, num_offspring = 100)\",\n \"# \",\n \"# # View results\",\n \"# print(results)\",\n \"\"\n )\n \n # Write to file\n writeLines(script_lines, file)\n }\n )\n \n\n}","type":"text"},{"name":"ui.R","content":"library(shiny)\nlibrary(shinyjs)\nlibrary(DT)\n\nui <- function(request) {\n fluidPage(\n useShinyjs(), # enables shinyjs functions\n \n # Custom CSS for better help content styling\n tags$head(\n tags$style(HTML(\"\n .help-content h1 {\n color: #2c3e50;\n border-bottom: 3px solid #3498db;\n padding-bottom: 10px;\n margin-top: 30px;\n margin-bottom: 20px;\n }\n \n .help-content h2 {\n color: #34495e;\n border-left: 4px solid #3498db;\n padding-left: 15px;\n margin-top: 25px;\n margin-bottom: 15px;\n }\n \n .help-content h3 {\n color: #2c3e50;\n margin-top: 20px;\n margin-bottom: 10px;\n }\n \n .help-content h4, .help-content h5, .help-content h6 {\n color: #34495e;\n margin-top: 15px;\n margin-bottom: 8px;\n }\n \n .help-content p {\n margin-bottom: 12px;\n text-align: justify;\n }\n \n .help-content ul, .help-content ol {\n margin-bottom: 15px;\n padding-left: 25px;\n }\n \n .help-content li {\n margin-bottom: 5px;\n }\n \n .help-content code {\n background-color: #f8f9fa;\n color: #e74c3c;\n padding: 2px 6px;\n border-radius: 3px;\n font-family: 'Courier New', monospace;\n font-size: 0.9em;\n }\n \n .help-content pre {\n background-color: #2c3e50;\n color: #ecf0f1;\n padding: 15px;\n border-radius: 5px;\n overflow-x: auto;\n margin: 15px 0;\n }\n \n .help-content pre code {\n background-color: transparent;\n color: inherit;\n padding: 0;\n }\n \n .help-content a {\n color: #3498db;\n text-decoration: none;\n border-bottom: 1px solid transparent;\n transition: border-bottom 0.3s ease;\n }\n \n .help-content a:hover {\n border-bottom: 1px solid #3498db;\n }\n \n .help-content hr {\n border: none;\n border-top: 2px solid #bdc3c7;\n margin: 25px 0;\n }\n \n .help-content blockquote {\n border-left: 4px solid #3498db;\n padding-left: 15px;\n margin: 15px 0;\n font-style: italic;\n color: #7f8c8d;\n }\n \n .help-content .emoji {\n font-size: 1.2em;\n margin-right: 5px;\n }\n \n .toc-container {\n background-color: #f8f9fa;\n border: 1px solid #dee2e6;\n border-radius: 8px;\n padding: 20px;\n margin-bottom: 25px;\n }\n \n .toc-container h3 {\n margin-top: 0;\n color: #495057;\n border-bottom: 2px solid #007bff;\n padding-bottom: 10px;\n }\n \n .toc-link {\n color: #495057;\n text-decoration: none;\n display: block;\n padding: 3px 0;\n transition: color 0.3s ease;\n }\n \n .toc-link:hover {\n color: #007bff;\n text-decoration: underline;\n }\n \n .help-content {\n scroll-behavior: smooth;\n }\n \n .r-code-content h1 {\n color: #2c3e50;\n border-bottom: 3px solid #3498db;\n padding-bottom: 10px;\n margin-top: 30px;\n margin-bottom: 20px;\n }\n \n .r-code-content h2 {\n color: #34495e;\n border-left: 4px solid #3498db;\n padding-left: 15px;\n margin-top: 25px;\n margin-bottom: 15px;\n }\n \n .r-code-content h3 {\n color: #2c3e50;\n margin-top: 20px;\n margin-bottom: 10px;\n }\n \n .r-code-content .code-section {\n background-color: #f8f9fa;\n border: 1px solid #dee2e6;\n border-radius: 8px;\n padding: 20px;\n margin: 15px 0;\n }\n \n .r-code-content .code-section h4 {\n color: #495057;\n margin-top: 0;\n margin-bottom: 15px;\n border-bottom: 1px solid #dee2e6;\n padding-bottom: 8px;\n }\n \n .r-code-content pre {\n background-color: #2c3e50;\n color: #ecf0f1;\n padding: 15px;\n border-radius: 5px;\n overflow-x: auto;\n margin: 10px 0;\n font-size: 12px;\n line-height: 1.4;\n }\n \n .r-code-content pre code {\n background-color: transparent;\n color: inherit;\n padding: 0;\n }\n \n .r-code-content .file-info {\n background-color: #e3f2fd;\n border: 1px solid #2196f3;\n padding: 10px;\n border-radius: 5px;\n margin-bottom: 15px;\n font-size: 12px;\n }\n \n .r-code-content .setup-instructions {\n background-color: #fff3cd;\n border: 1px solid #ffeaa7;\n padding: 15px;\n border-radius: 5px;\n margin: 20px 0;\n }\n \n .r-code-content .setup-instructions h4 {\n color: #856404;\n margin-top: 0;\n }\n \n .r-code-content .setup-instructions ul {\n margin-bottom: 0;\n }\n \"))\n ),\n \n # JavaScript for smooth scrolling and TOC functionality\n tags$script(HTML(\"\n $(document).ready(function() {\n // Smooth scrolling for TOC links\n $('.toc-link').on('click', function(e) {\n e.preventDefault();\n var target = $(this).attr('href');\n var $target = $(target);\n if ($target.length) {\n $('.help-content').animate({\n scrollTop: $target.offset().top - 100\n }, 800);\n }\n });\n });\n \")),\n \n ## βββ Flex container with banner and AlloMate hex βββββββββββββββββββββββββββ\n div(\n style = \"display: flex; align-items: center; justify-content: space-between; margin-bottom: 15px;\",\n \n # AlloMate hex (right)\n tags$img(\n src = \"allomate.png\",\n height = \"120px\",\n style = \"margin-left: 20px;\"\n ),\n \n # Logos banner (left)\n tags$img(\n src = \"logos.png\",\n style = \"width: 70%; height: auto;\"\n )\n ),\n \n sidebarLayout(\n sidebarPanel(\n \n # Dynamic startup guide and feedback\n div(\n id = \"startup_guide\",\n style = \"background-color: #ffffff; border: 1px solid #dee2e6; padding: 10px; margin-bottom: 15px; border-radius: 5px;\",\n h4(\"π Getting Started\"),\n htmlOutput(\"dynamic_guide\"),\n verbatimTextOutput(\"package_status_text\"),\n conditionalPanel(\n condition = \"output.webr_detected\",\n div(\n style = \"background-color: #fff3cd; border: 1px solid #ffeaa7; padding: 8px; margin-top: 10px; border-radius: 3px;\",\n p(\"π WebR environment detected. Custom OCS fallback will be used since optiSel is not available in WebR.\")\n )\n ),\n div(\n style = \"text-align: center; margin-top: 15px; padding-top: 10px; border-top: 1px solid #dee2e6;\",\n actionButton(\"help_btn\", \"β Help\", \n style = \"background-color: #007bff; color: white; border: none; padding: 8px 16px; border-radius: 5px;\")\n )\n ),\n \n wellPanel(\n style = \"background-color: #e3f2fd; border: 2px solid #2196f3; padding: 15px; margin-bottom: 20px; border-radius: 8px;\",\n h4(\"𧬠Core Data Inputs\", style = \"color: #1565c0; margin-bottom: 15px; border-bottom: 1px solid #2196f3; padding-bottom: 8px;\"),\n p(\"These inputs are used by both Index Generation and OCS calculations:\", style = \"color: #6c757d; font-size: 12px; margin-bottom: 15px;\"),\n \n h5(\"Estimate progeny genetic merit\"),\n \n fileInput(\"candidate_file\", \"Upload list of candidates\",\n accept = c(\".csv\", \".txt\")),\n \n h5(\"Calculate kinship matrix\"),\n fileInput(\"pedigree_file\", \"Upload pedigree file\", accept = \".txt\"),\n \n h5(\"Set kinship threshold\"),\n numericInput(\"thresh\", \"Max kinship allowed between mates:\",\n value = 1, min = 0, max = 1, step = 0.1),\n ),\n \n wellPanel(\n style = \"background-color: #ffebee; border: 2px solid #f44336; padding: 15px; margin-bottom: 20px; border-radius: 8px;\",\n h4(\"βοΈ Weighted EBVs\", style = \"color: #c62828; margin-bottom: 15px; border-bottom: 1px solid #f44336; padding-bottom: 8px;\"),\n p(\"Define traits and their relative importance for breeding decisions:\", style = \"color: #6c757d; font-size: 12px; margin-bottom: 15px;\"),\n \n h5(\"Traits (EBVs and weights)\"),\n uiOutput(\"trait_inputs\"),\n \n fluidRow(\n column(6, actionButton(\"add_trait\", \"β Add trait\")),\n column(6, actionButton(\"remove_trait\", \"β Remove trait\"))\n ),\n p(\"π‘ Note: Adding or removing traits will require re-uploading files.\", \n style = \"color: #6c757d; font-size: 11px; font-style: italic; margin-top: 8px;\"),\n ),\n \n wellPanel(\n style = \"background-color: #fff3cd; border: 2px solid #ffeaa7; padding: 15px; margin-bottom: 20px; border-radius: 8px;\",\n h4(\"π― Optimum Contribution Selection\", style = \"color: #856404; margin-bottom: 15px; border-bottom: 1px solid #ffeaa7; padding-bottom: 8px;\"),\n p(\"Configure breeding objectives and constraints:\", style = \"color: #6c757d; font-size: 12px; margin-bottom: 15px;\"),\n \n h5(\"Breeding Objectives\"),\n numericInput(\"inbreeding_rate\", \"Desired Inbreeding Rate\", \n value = 0.05, min = 0.01, max = 0.2, step = 0.01),\n numericInput(\"num_offspring\", \"Number of Offspring\", \n value = 100, min = 10, step = 1),\n \n actionButton(\"run_ocs_btn\", \"Run OCS\", \n style = \"margin-top: 15px; width: 100%; background-color: #856404; color: white; border: none; padding: 10px; border-radius: 5px;\")\n ),\n \n wellPanel(\n style = \"background-color: #d4edda; border: 2px solid #c3e6cb; padding: 15px; margin-bottom: 20px; border-radius: 8px;\",\n h4(\"π Export Results\", style = \"color: #155724; margin-bottom: 15px; border-bottom: 1px solid #c3e6cb; padding-bottom: 8px;\"),\n p(\"Download all results in a single Excel file with multiple tabs:\", style = \"color: #6c757d; font-size: 12px; margin-bottom: 15px;\"),\n downloadButton(\"download_all_results\", \"π₯ Export All Results\", \n style = \"width: 100%; background-color: #28a745; color: white; border: none; padding: 10px; border-radius: 5px; margin-bottom: 10px;\"),\n actionButton(\"view_r_code_btn\", \"π View R Code\", \n style = \"width: 100%; background-color: #17a2b8; color: white; border: none; padding: 10px; border-radius: 5px;\")\n )\n ),\n mainPanel(\n tabsetPanel(\n id = \"main_tabs\",\n tabPanel(\"Kinship and EBV\",\n verbatimTextOutput(\"message1\"),\n \n verbatimTextOutput(\"message2\"),\n \n DTOutput(\"quadrants_table\"),\n DTOutput(\"matrix\")\n ),\n tabPanel(\"Optimum Contribution Selection\",\n DTOutput(\"ocs_candidate_table\"),\n br(),\n DTOutput(\"ocs_mating_table\")\n ),\n tabPanel(\"R Code\",\n div(\n style = \"padding: 20px; background-color: #f8f9fa; border-radius: 8px; margin: 10px 0; max-height: 80vh; overflow-y: auto; position: relative;\",\n div(\n style = \"background-color: white; padding: 25px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);\",\n div(\n style = \"text-align: center; margin-bottom: 25px; padding-bottom: 15px; border-bottom: 2px solid #007bff;\",\n h2(\"π R Code Implementation\", style = \"color: #007bff; margin-bottom: 10px;\"),\n p(\"Complete R code for implementing AlloMate analysis independently\", style = \"color: #666; font-size: 16px;\")\n ),\n div(\n style = \"text-align: center; margin-bottom: 20px;\",\n downloadButton(\"download_r_code\", \"π₯ Download Complete R Script\", \n style = \"background-color: #28a745; color: white; border: none; padding: 12px 24px; border-radius: 5px; font-size: 14px;\")\n ),\n div(\n style = \"line-height: 1.6; font-size: 14px;\",\n htmlOutput(\"r_code_content\")\n )\n )\n )\n ),\n tabPanel(\"Help\",\n div(\n style = \"padding: 20px; background-color: #f8f9fa; border-radius: 8px; margin: 10px 0; max-height: 80vh; overflow-y: auto; position: relative;\",\n div(\n style = \"background-color: white; padding: 25px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);\",\n div(\n style = \"text-align: center; margin-bottom: 25px; padding-bottom: 15px; border-bottom: 2px solid #007bff;\",\n h2(\"π AlloMate Documentation\", style = \"color: #007bff; margin-bottom: 10px;\"),\n p(\"Complete user guide and technical documentation\", style = \"color: #666; font-size: 16px;\")\n ),\n div(\n style = \"line-height: 1.6; font-size: 14px;\",\n htmlOutput(\"help_content\")\n ),\n div(\n style = \"text-align: center; margin-top: 30px; padding-top: 20px; border-top: 1px solid #dee2e6;\",\n actionButton(\"back_to_top\", \"β¬οΈ Back to Top\", \n style = \"background-color: #6c757d; color: white; border: none; padding: 8px 16px; border-radius: 5px;\")\n )\n )\n )\n )\n )\n )\n )\n )\n}","type":"text"},{"name":"R/load_functions.R","content":"# Load All Functions\n# This file sources all function files in the correct order\n\n# Initialize variables if they don't exist (for standalone usage)\nif (!exists(\"optisel_available\")) {\n optisel_available <<- FALSE\n}\nif (!exists(\"kinship2_available\")) {\n kinship2_available <<- FALSE\n}\n\n# Use the global app_dir variable set in global.R, or determine it if not available\nif (exists(\"app_dir\")) {\n # Use existing app_dir variable\n} else {\n # Check if we're in the app directory or project root\n if (dir.exists(\"R\") && dir.exists(\"www\")) {\n app_dir <- TRUE\n } else if (dir.exists(\"app\") && dir.exists(\"scripts\")) {\n app_dir <- FALSE\n } else {\n app_dir <- FALSE\n }\n}\n\n# Check if we're in a Shiny server environment\nis_shiny_server <- grepl(\"^/home/web_user/\", getwd()) || grepl(\"^/tmp/\", getwd()) || grepl(\"^/var/folders/\", getwd())\n\n# Load utility functions (data processing, file handling)\nif (app_dir) {\n source(\"R/utils.R\")\n} else {\n source(\"app/R/utils.R\")\n}\n\n# Load OCS helper functions\nif (app_dir) {\n source(\"R/ocs_helpers.R\")\n} else {\n source(\"app/R/ocs_helpers.R\")\n}\n\n# Load UI helper functions\nif (app_dir) {\n source(\"R/ui_helpers.R\")\n} else {\n source(\"app/R/ui_helpers.R\")\n}\n\n# Load custom OCS fallback (if not already loaded)\nif (!exists(\"custom_candes\")) {\n if (is_shiny_server) {\n # In Shiny server environment, look for fallback in R directory\n fallback_path <- \"R/optsel_fallback.R\"\n } else {\n # Normal environment\n if (app_dir) {\n fallback_path <- \"R/optsel_fallback.R\"\n } else {\n fallback_path <- \"scripts/optsel_fallback.R\"\n }\n }\n \n if (file.exists(fallback_path)) {\n source(fallback_path)\n \n # Verify that the fallback functions were loaded\n if (exists(\"custom_candes\") && exists(\"custom_opticont\") && exists(\"custom_noffspring\") && exists(\"custom_matings\")) {\n # Set the flag to indicate fallback is available\n custom_ocs_available <<- TRUE\n \n # Only create function aliases if optiSel is not available\n # (optiSel functions are locked bindings and cannot be overwritten)\n if (!exists(\"optisel_available\") || !optisel_available) {\n candes <<- custom_candes\n opticont <<- custom_opticont\n noffspring <<- custom_noffspring\n matings <<- custom_matings\n }\n }\n }\n} else {\n # If functions already exist, make sure the flag is set and create aliases\n if (exists(\"custom_candes\") && exists(\"custom_opticont\") && exists(\"custom_noffspring\") && exists(\"custom_matings\")) {\n custom_ocs_available <<- TRUE\n \n # Only create function aliases if optiSel is not available\n # (optiSel functions are locked bindings and cannot be overwritten)\n if (!exists(\"optisel_available\") || !optisel_available) {\n candes <<- custom_candes\n opticont <<- custom_opticont\n noffspring <<- custom_noffspring\n matings <<- custom_matings\n }\n }\n}\n\n\n","type":"text"},{"name":"R/ocs_helpers.R","content":"# OCS (Optimum Contribution Selection) Functions\n# Functions for running OCS analysis with either optiSel or custom fallback\n\n#' Run OCS analysis with unified interface\n#' @param candidates_df Candidates data frame with id, sex, and index_val columns\n#' @param kinship_matrix Kinship matrix for all individuals\n#' @param ebv_index Vector of breeding value indices\n#' @param desired_inbreeding_rate Target inbreeding rate constraint\n#' @param num_offspring Number of offspring to allocate\n#' @return List with Candidate and Mating results\nrun_ocs <- function(candidates_df, kinship_matrix, ebv_index, desired_inbreeding_rate, num_offspring) {\n # Check if OCS functions are available (either optiSel or custom fallback)\n if (!exists(\"candes\") || !exists(\"opticont\") || !exists(\"noffspring\") || !exists(\"matings\")) {\n stop(\"β OCS functions are not available. Neither optiSel nor custom fallback could be loaded.\")\n }\n \n # Check if we're using custom fallback\n using_fallback <- exists(\"custom_ocs_available\") && custom_ocs_available && !optisel_available\n \n phen <- data.frame(\n Indiv = candidates_df$id,\n Sex = ifelse(candidates_df$sex == \"M\", \"male\", \"female\"),\n BV = ebv_index,\n isCandidate = TRUE,\n stringsAsFactors = FALSE\n )\n candidate_ids <- candidates_df$id\n sKin <- kinship_matrix[candidate_ids, candidate_ids]\n rownames(sKin) <- candidate_ids\n colnames(sKin) <- candidate_ids\n \n cand <- candes(phen = phen, pKin = sKin)\n con <- list(ub.pKin = desired_inbreeding_rate)\n Offspring <- opticont(method = \"max.BV\", cand = cand, con = con)\n \n # Check if solution is valid (only needed for real optiSel package)\n if (exists(\"optisel_available\") && optisel_available && \"summary\" %in% names(Offspring)) {\n # Check if any constraints failed (OK = FALSE)\n failed_constraints <- Offspring$summary[Offspring$summary$OK == FALSE & !is.na(Offspring$summary$OK), ]\n if (nrow(failed_constraints) > 0) {\n constraint_names <- paste(failed_constraints$Name, collapse = \", \")\n stop(paste(\"β OCS optimization failed: Constraints not met:\", constraint_names,\n \"Try increasing the inbreeding rate threshold or check your kinship matrix.\"))\n }\n }\n \n # Guard against empty or invalid solution (infeasible constraint)\n # Check BEFORE extracting columns to catch all edge cases\n if (is.null(Offspring$parent) || nrow(Offspring$parent) == 0) {\n stop(paste0(\"β No feasible OCS solution found under the current inbreeding constraint (ub.pKin = \", \n desired_inbreeding_rate, \"). \",\n \"This typically means your candidate population is too closely related to meet this target. \",\n \"Try increasing the inbreeding rate threshold (e.g., 0.10 or higher) or reducing the number of offspring.\"))\n }\n \n Candidate <- Offspring$parent[, c(\"Indiv\", \"Sex\", \"oc\")]\n \n # Additional check: verify non-zero contributions\n if (nrow(Candidate) == 0 || all(Candidate$oc == 0)) {\n stop(paste0(\"β No feasible OCS solution found under the current inbreeding constraint (ub.pKin = \", \n desired_inbreeding_rate, \"). \",\n \"This typically means your candidate population is too closely related to meet this target. \",\n \"Try increasing the inbreeding rate threshold (e.g., 0.10 or higher) or reducing the number of offspring.\"))\n }\n \n # Safe to call noffspring now that Candidate has valid data\n Candidate$n <- noffspring(Candidate, num_offspring)$nOff\n Candidate <- filter(Candidate, n > 0)\n if (length(unique(Candidate$Sex)) < 2) {\n stop(\"β OCS resulted in only one sex being selected. Cannot generate mating pairs.\")\n }\n \n # For real optiSel package, subset kinship matrix to match selected candidates\n if (exists(\"optisel_available\") && optisel_available) {\n selected_ids <- Candidate$Indiv\n sKin_subset <- sKin[selected_ids, selected_ids]\n # optiSel matings function expects the Candidate data frame, not phenotype data\n Mating <- matings(Candidate, Kin = sKin_subset)\n \n # optiSel doesn't include kinship values in mating results, so add them manually\n if (nrow(Mating) > 0 && !\"Kin\" %in% names(Mating)) {\n kinship_values <- numeric(nrow(Mating))\n for (i in seq_len(nrow(Mating))) {\n sire <- Mating$Sire[i]\n dam <- Mating$Dam[i]\n # Use the full kinship matrix since we have original IDs\n kinship_values[i] <- sKin[sire, dam]\n }\n Mating$Kin <- kinship_values\n }\n } else {\n # Custom fallback already handles this correctly\n Mating <- matings(Candidate, Kin = sKin)\n }\n list(Candidate = Candidate, Mating = Mating)\n}\n\n#' Validate OCS inputs before running analysis\n#' @param candidates_df Candidates data frame\n#' @param kinship_matrix Kinship matrix\n#' @param ebv_index Breeding value indices\n#' @param desired_inbreeding_rate Target inbreeding rate\n#' @param num_offspring Number of offspring\n#' @return TRUE if valid, throws error if invalid\nvalidate_ocs_inputs <- function(candidates_df, kinship_matrix, ebv_index, \n desired_inbreeding_rate, num_offspring) {\n # Check candidates data\n if (is.null(candidates_df) || nrow(candidates_df) == 0) {\n stop(\"β No candidates provided\")\n }\n \n required_cols <- c(\"id\", \"sex\")\n missing_cols <- setdiff(required_cols, names(candidates_df))\n if (length(missing_cols) > 0) {\n stop(paste(\"β Missing required columns in candidates:\", paste(missing_cols, collapse = \", \")))\n }\n \n # Check sex balance\n n_males <- sum(candidates_df$sex == \"M\")\n n_females <- sum(candidates_df$sex == \"F\")\n if (n_males == 0 || n_females == 0) {\n stop(\"β Need both males and females for OCS analysis\")\n }\n \n # Check kinship matrix\n if (is.null(kinship_matrix) || nrow(kinship_matrix) == 0) {\n stop(\"β Kinship matrix is empty or invalid\")\n }\n \n # Check EBV indices\n if (is.null(ebv_index) || length(ebv_index) != nrow(candidates_df)) {\n stop(\"β EBV indices must match number of candidates\")\n }\n \n # Check parameters\n if (desired_inbreeding_rate <= 0 || desired_inbreeding_rate > 1) {\n stop(\"β Desired inbreeding rate must be between 0 and 1\")\n }\n \n if (num_offspring <= 0 || num_offspring %% 1 != 0) {\n stop(\"β Number of offspring must be a positive integer\")\n }\n \n TRUE\n}\n\n#' Format OCS results for display\n#' @param results OCS results list\n#' @return Formatted results for UI display\nformat_ocs_results <- function(results) {\n # Format candidate table\n candidate_table <- results$Candidate %>%\n select(Indiv, Sex, oc, n) %>%\n mutate(`Optimal Contribution (%)` = round(oc * 100, 1)) %>%\n rename(`ID` = Indiv, `# of offspring` = n) %>%\n select(ID, Sex, `Optimal Contribution (%)`, `# of offspring`)\n \n # Format mating table - handle different column naming schemes\n mating_df <- results$Mating\n \n # Check and standardize column names (optiSel vs custom implementation)\n if (\"Sire\" %in% names(mating_df)) {\n mating_df <- mating_df %>% rename(Male = Sire)\n }\n if (\"Dam\" %in% names(mating_df)) {\n mating_df <- mating_df %>% rename(Female = Dam)\n }\n if (\"Kin\" %in% names(mating_df)) {\n mating_df <- mating_df %>% rename(Kinship = Kin)\n } else if (\"kinship\" %in% names(mating_df)) {\n mating_df <- mating_df %>% rename(Kinship = kinship)\n } else if (\"coeff\" %in% names(mating_df)) {\n mating_df <- mating_df %>% rename(Kinship = coeff)\n } else {\n # If no kinship column found, add a placeholder\n mating_df$Kinship <- NA\n }\n \n mating_table <- mating_df %>% mutate_all(as.character)\n \n # Calculate summary statistics - handle different kinship column names\n kinship_values <- NA\n if (\"Kin\" %in% names(results$Mating)) {\n kinship_values <- results$Mating$Kin\n } else if (\"kinship\" %in% names(results$Mating)) {\n kinship_values <- results$Mating$kinship\n } else if (\"coeff\" %in% names(results$Mating)) {\n kinship_values <- results$Mating$coeff\n } else if (\"Kinship\" %in% names(mating_df)) {\n kinship_values <- mating_df$Kinship\n }\n \n summary_stats <- list(\n n_candidates = nrow(results$Candidate),\n n_males = sum(results$Candidate$Sex == \"male\"),\n n_females = sum(results$Candidate$Sex == \"female\"),\n n_matings = nrow(results$Mating),\n total_offspring = sum(results$Candidate$n),\n mean_kinship = if (all(is.na(kinship_values))) NA else mean(kinship_values, na.rm = TRUE),\n mean_contribution = mean(results$Candidate$oc)\n )\n \n list(\n candidate_table = candidate_table,\n mating_table = mating_table,\n summary_stats = summary_stats\n )\n}\n\n#' Create Excel workbook with OCS results\n#' @param results OCS results list\n#' @param params OCS parameters used\n#' @return Workbook object ready for saving\ncreate_ocs_workbook <- function(results, params = NULL) {\n wb <- openxlsx::createWorkbook()\n \n # Add README sheet\n openxlsx::addWorksheet(wb, \"README\")\n readme_text <- c(\n \"Optimum Contribution Selection Results\",\n \"\",\n paste(\"Generated:\", Sys.Date()),\n \"\",\n \"Parameters used:\",\n if (!is.null(params)) {\n c(\n paste(\"- Target inbreeding rate:\", params$inbreeding_rate),\n paste(\"- Number of offspring:\", params$num_offspring),\n paste(\"- Implementation:\", if (exists(\"custom_ocs_available\") && custom_ocs_available) \"Custom fallback\" else \"optiSel\")\n )\n } else {\n \"Parameters not recorded\"\n },\n \"\",\n \"Sheets included:\",\n \"1. Optimal Contributions - Selected candidates and their contributions\",\n \"2. Mating Plan - Optimal mate pairings to minimize inbreeding\",\n \"\",\n \"The patterns in your genetic data have been thoroughly analyzed.\"\n )\n openxlsx::writeData(wb, \"README\", readme_text)\n \n # Add optimal contributions\n openxlsx::addWorksheet(wb, \"Optimal Contributions\")\n contrib_df <- results$Candidate %>%\n select(Indiv, Sex, oc, n) %>%\n mutate(`Contribution (%)` = round(oc * 100, 2)) %>%\n rename(`ID` = Indiv, `# Offspring` = n)\n openxlsx::writeData(wb, \"Optimal Contributions\", contrib_df)\n \n # Add mating plan\n openxlsx::addWorksheet(wb, \"Mating Plan\")\n \n # Handle different column naming schemes for mating results\n mating_export <- results$Mating\n \n # Standardize kinship column name\n if (\"Kin\" %in% names(mating_export)) {\n mating_export <- mating_export %>% mutate(Kinship = round(Kin, 4))\n } else if (\"kinship\" %in% names(mating_export)) {\n mating_export <- mating_export %>% mutate(Kinship = round(kinship, 4))\n } else if (\"coeff\" %in% names(mating_export)) {\n mating_export <- mating_export %>% mutate(Kinship = round(coeff, 4))\n } else {\n mating_export$Kinship <- NA\n }\n \n # Select and rename columns based on what's available\n if (all(c(\"Sire\", \"Dam\") %in% names(mating_export))) {\n mating_df <- mating_export %>%\n select(Sire, Dam, Kinship, n) %>%\n rename(`# Matings` = n)\n } else if (all(c(\"Male\", \"Female\") %in% names(mating_export))) {\n mating_df <- mating_export %>%\n select(Male, Female, Kinship, n) %>%\n rename(`# Matings` = n)\n } else {\n # Fallback if column names are different\n mating_df <- mating_export %>%\n rename(`# Matings` = n)\n }\n openxlsx::writeData(wb, \"Mating Plan\", mating_df)\n \n wb\n}\n","type":"text"},{"name":"R/optsel_fallback.R","content":"# Custom OCS Implementation Functions\n# Note: All required packages are loaded in global.R\n\n#### Custom OCS Implementation Functions ####\n\n#' Fallback optimization when quadprog is not available\n#' Uses a simple gradient-based approach to find optimal contributions\nfallback_optimization <- function(bv_vec, K, male_idx, female_idx, target_kinship, lambda) {\n n <- length(bv_vec)\n \n # Initialize with equal contributions\n oc <- rep(1/n, n)\n \n # Ensure sex balance constraints\n oc[male_idx] <- oc[male_idx] * 0.5 / sum(oc[male_idx])\n oc[female_idx] <- oc[female_idx] * 0.5 / sum(oc[female_idx])\n \n # Simple gradient descent optimization\n max_iter <- 1000\n learning_rate <- 0.01\n tolerance <- 1e-6\n \n for (iter in 1:max_iter) {\n # Calculate gradient: -BV + lambda * 2 * K * oc\n grad <- -bv_vec + 2 * lambda * K %*% oc\n \n # Project gradient to maintain constraints\n # Remove component that would violate sex balance\n male_grad_mean <- mean(grad[male_idx])\n female_grad_mean <- mean(grad[female_idx])\n \n grad[male_idx] <- grad[male_idx] - male_grad_mean\n grad[female_idx] <- grad[female_idx] - female_grad_mean\n \n # Update contributions\n oc_new <- oc - learning_rate * grad\n \n # Ensure non-negativity\n oc_new <- pmax(oc_new, 0)\n \n # Re-normalize to maintain sex balance\n if (sum(oc_new[male_idx]) > 0) {\n oc_new[male_idx] <- oc_new[male_idx] * 0.5 / sum(oc_new[male_idx])\n }\n if (sum(oc_new[female_idx]) > 0) {\n oc_new[female_idx] <- oc_new[female_idx] * 0.5 / sum(oc_new[female_idx])\n }\n \n # Check convergence\n if (max(abs(oc_new - oc)) < tolerance) {\n break\n }\n \n oc <- oc_new\n }\n \n return(oc)\n}\n\n#' Create candidate object similar to optiSel::candes\n#' This structures data for optimization algorithms\ncustom_candes <- function(phen, pKin, quiet = FALSE) {\n # Validate inputs with Shadow Broker precision\n if(!all(c(\"Indiv\", \"Sex\", \"BV\", \"isCandidate\") %in% names(phen))) {\n stop(\"β phen must contain columns: Indiv, Sex, BV, isCandidate\")\n }\n \n # Extract candidates only\n candidates <- phen %>% filter(isCandidate == TRUE)\n \n # Calculate current population parameters\n mean_bv <- mean(candidates$BV, na.rm = TRUE)\n var_bv <- var(candidates$BV, na.rm = TRUE)\n \n # Structure the data as the Shadow Broker would organize her archives\n cand_obj <- list(\n phen = phen,\n candidates = candidates,\n n_candidates = nrow(candidates),\n n_males = sum(candidates$Sex == \"male\"),\n n_females = sum(candidates$Sex == \"female\"),\n kinship = pKin,\n current = data.frame(\n Name = \"BV\",\n Type = \"trait\",\n Val = mean_bv,\n Var = var_bv\n )\n )\n \n if(!quiet) {\n\n }\n \n class(cand_obj) <- \"custom_candes\"\n return(cand_obj)\n}\n\n#' Custom implementation of optiSel::opticont\n#' Uses quadratic programming to solve OCS problem\ncustom_opticont <- function(method, cand, con, quiet = FALSE) {\n # Extract method components (e.g., \"max.BV\" -> maximize BV)\n optimize_direction <- substr(method, 1, 3)\n target_trait <- substr(method, 5, nchar(method))\n \n if(target_trait != \"BV\") {\n stop(\"β Currently only BV optimization is supported\")\n }\n \n candidates <- cand$candidates\n n <- nrow(candidates)\n \n # Separate males and females for proper contribution allocation\n male_idx <- which(candidates$Sex == \"male\")\n female_idx <- which(candidates$Sex == \"female\")\n n_males <- length(male_idx)\n n_females <- length(female_idx)\n \n # Extract kinship matrix for candidates\n candidate_ids <- candidates$Indiv\n K <- cand$kinship[candidate_ids, candidate_ids]\n \n # Set up optimization problem\n # We need to maximize BV while constraining average kinship\n # Decision variables: contributions (c) for each candidate\n \n # Objective: maximize sum(c_i * BV_i)\n # For quadprog, we minimize -sum(c_i * BV_i)\n bv_vec <- candidates$BV\n \n # Quadratic term: minimize c'Kc (average kinship in next generation)\n # Linear term: -2 * BV' (to maximize BV)\n \n # Build constraint matrix for quadprog\n # Constraints:\n # 1. sum(c_males) = 0.5\n # 2. sum(c_females) = 0.5\n # 3. c_i >= 0 for all i\n # 4. Average kinship <= threshold\n \n # For quadprog: min(-d'b + 1/2 b'Db) s.t. A'b >= b0\n \n # Scale the problem for numerical stability\n lambda <- 100 # Weight for kinship penalty\n \n if(!is.null(con$ub.pKin)) {\n target_kinship <- con$ub.pKin\n } else {\n target_kinship <- mean(K[upper.tri(K)]) # Current mean kinship\n }\n \n # Use penalty method for constrained optimization\n # Minimize: -BV + lambda * Kinship\n Dmat <- 2 * lambda * K\n dvec <- bv_vec\n \n # Constraint matrix\n # Each row of Amat represents a constraint\n Amat <- matrix(0, n, n + 2)\n \n # Sum of male contributions = 0.5\n Amat[male_idx, 1] <- 1\n # Sum of female contributions = 0.5 \n Amat[female_idx, 2] <- 1\n # Non-negativity constraints\n diag(Amat[, 3:(n+2)]) <- 1\n \n # Right-hand side\n bvec <- c(0.5, 0.5, rep(0, n))\n \n # Try to use quadprog if available, otherwise use fallback optimization\n tryCatch({\n if (requireNamespace(\"quadprog\", quietly = TRUE)) {\n # Use quadprog if available\n # Make Dmat positive definite if needed\n eigen_decomp <- eigen(Dmat)\n if(any(eigen_decomp$values < 1e-8)) {\n Dmat <- Dmat + diag(1e-6, n)\n }\n \n sol <- quadprog::solve.QP(Dmat, dvec, Amat, bvec, meq = 2)\n oc <- sol$solution\n } else {\n # Fallback: Simple gradient-based optimization\n oc <- fallback_optimization(bv_vec, K, male_idx, female_idx, target_kinship, lambda)\n }\n \n # Normalize to ensure sum = 1\n oc <- oc / sum(oc)\n \n # Create output similar to optiSel\n parent_df <- candidates %>%\n mutate(oc = oc) %>%\n select(Indiv, Sex, oc)\n \n # Calculate expected kinship in next generation\n mean_kinship_next <- as.numeric(t(oc) %*% K %*% oc)\n \n if(!quiet) {\n \n }\n \n result <- list(\n parent = parent_df,\n mean.kin = mean_kinship_next,\n mean.bv = sum(oc * bv_vec),\n info = \"Optimization successful\"\n )\n \n class(result) <- \"custom_opticont\"\n return(result)\n \n }, error = function(e) {\n stop(paste(\"β Optimization failed:\", e$message))\n })\n}\n\n#' Calculate number of offspring from optimum contributions\n#' Replicates optiSel::noffspring functionality\ncustom_noffspring <- function(Candidate, N) {\n # Validate input\n if(!all(c(\"Indiv\", \"Sex\", \"oc\") %in% names(Candidate))) {\n stop(\"β Candidate must contain columns: Indiv, Sex, oc\")\n }\n \n # Calculate raw offspring numbers\n # Each individual contributes to N * oc offspring\n raw_offspring <- N * Candidate$oc\n \n # Round while maintaining sum constraints\n males <- Candidate$Sex == \"male\"\n females <- Candidate$Sex == \"female\"\n \n nOff <- numeric(nrow(Candidate))\n \n # Smart rounding to maintain exact totals\n if(sum(males) > 0) {\n male_raw <- raw_offspring[males]\n male_int <- floor(male_raw)\n male_frac <- male_raw - male_int\n \n # Add extra offspring to males with highest fractional parts\n n_extra_males <- N/2 - sum(male_int)\n if(n_extra_males > 0) {\n top_males <- order(male_frac, decreasing = TRUE)[1:min(n_extra_males, length(male_frac))]\n male_int[top_males] <- male_int[top_males] + 1\n }\n nOff[males] <- male_int\n }\n \n if(sum(females) > 0) {\n female_raw <- raw_offspring[females]\n female_int <- floor(female_raw)\n female_frac <- female_raw - female_int\n \n # Add extra offspring to females with highest fractional parts\n n_extra_females <- N/2 - sum(female_int)\n if(n_extra_females > 0) {\n top_females <- order(female_frac, decreasing = TRUE)[1:min(n_extra_females, length(female_frac))]\n female_int[top_females] <- female_int[top_females] + 1\n }\n nOff[females] <- female_int\n }\n \n result <- data.frame(\n Indiv = Candidate$Indiv,\n nOff = nOff\n )\n \n return(result)\n}\n\n#' Mate allocation algorithm\n#' Replicates optiSel::matings functionality\ncustom_matings <- function(Candidate, Kin, quiet = FALSE) {\n # Extract candidates with offspring\n active_candidates <- Candidate %>% filter(n > 0)\n \n males <- active_candidates %>% filter(Sex == \"male\")\n females <- active_candidates %>% filter(Sex == \"female\")\n \n if(nrow(males) == 0 || nrow(females) == 0) {\n stop(\"β Need at least one male and one female with n > 0\")\n }\n \n # Get kinship submatrix for active candidates\n male_ids <- males$Indiv\n female_ids <- females$Indiv\n \n K_mf <- Kin[male_ids, female_ids, drop = FALSE]\n \n # Initialize mating list\n matings_list <- list()\n \n # Track remaining matings needed\n males_remaining <- males$n\n females_remaining <- females$n\n \n # Minimum kinship mating algorithm\n # Iteratively select minimum kinship pairs\n iter <- 0\n total_matings <- sum(males$n)\n \n while(sum(males_remaining) > 0 && sum(females_remaining) > 0) {\n iter <- iter + 1\n \n # Find available pairs (those with remaining matings)\n avail_m <- which(males_remaining > 0)\n avail_f <- which(females_remaining > 0)\n \n if(length(avail_m) == 0 || length(avail_f) == 0) break\n \n # Get kinships for available pairs\n K_avail <- K_mf[avail_m, avail_f, drop = FALSE]\n \n # Add small penalty for repeated matings to encourage diversity\n # This mimics the alpha parameter in optiSel\n penalty_matrix <- matrix(0, length(avail_m), length(avail_f))\n for(i in seq_along(matings_list)) {\n m_idx <- which(male_ids[avail_m] == matings_list[[i]]$Sire)\n f_idx <- which(female_ids[avail_f] == matings_list[[i]]$Dam)\n if(length(m_idx) > 0 && length(f_idx) > 0) {\n penalty_matrix[m_idx, f_idx] <- penalty_matrix[m_idx, f_idx] + 0.001\n }\n }\n \n K_adjusted <- K_avail + penalty_matrix\n \n # Find minimum kinship pair\n min_idx <- which.min(K_adjusted)\n min_coords <- arrayInd(min_idx, dim(K_adjusted))\n \n sel_male_idx <- avail_m[min_coords[1]]\n sel_female_idx <- avail_f[min_coords[2]]\n \n # Record mating\n matings_list[[iter]] <- data.frame(\n Sire = male_ids[sel_male_idx],\n Dam = female_ids[sel_female_idx],\n Kin = K_mf[sel_male_idx, sel_female_idx],\n n = 1,\n stringsAsFactors = FALSE\n )\n \n # Update remaining counts\n males_remaining[sel_male_idx] <- males_remaining[sel_male_idx] - 1\n females_remaining[sel_female_idx] <- females_remaining[sel_female_idx] - 1\n }\n \n # Combine matings (aggregate multiple matings of same pair)\n matings_df <- bind_rows(matings_list) %>%\n group_by(Sire, Dam) %>%\n summarise(\n n = sum(n),\n Kin = first(Kin),\n .groups = \"drop\"\n ) %>%\n arrange(Kin)\n \n # Calculate mean inbreeding coefficient of offspring\n mean_inbreeding <- sum(matings_df$Kin * matings_df$n) / sum(matings_df$n)\n \n if(!quiet) {\n\n }\n \n # Add attributes similar to optiSel\n attr(matings_df, \"objval\") <- mean_inbreeding\n attr(matings_df, \"info\") <- \"Minimum kinship mating\"\n \n return(matings_df)\n}\n\n#' Main OCS function combining all steps\nrun_custom_ocs <- function(candidates_df, kinship_matrix, ebv_index, \n desired_inbreeding_rate, num_offspring) {\n \n # Prepare phenotype data in required format\n phen <- data.frame(\n Indiv = candidates_df$id,\n Sex = ifelse(candidates_df$sex == \"M\", \"male\", \"female\"),\n BV = ebv_index,\n isCandidate = TRUE,\n stringsAsFactors = FALSE\n )\n \n # Ensure kinship matrix has correct dimensions and names\n candidate_ids <- candidates_df$id\n sKin <- kinship_matrix[candidate_ids, candidate_ids]\n rownames(sKin) <- candidate_ids\n colnames(sKin) <- candidate_ids\n \n # Step 1: Create candidate object\n cand <- custom_candes(phen = phen, pKin = sKin)\n \n # Step 2: Run optimization\n con <- list(ub.pKin = desired_inbreeding_rate)\n offspring_result <- custom_opticont(method = \"max.BV\", cand = cand, con = con)\n \n # Step 3: Calculate number of offspring\n Candidate <- offspring_result$parent\n offspring_counts <- custom_noffspring(Candidate, num_offspring)\n Candidate$n <- offspring_counts$nOff\n \n # Filter candidates with offspring\n Candidate <- filter(Candidate, n > 0)\n \n # Validate we have both sexes\n if(length(unique(Candidate$Sex)) < 2) {\n stop(\"β OCS resulted in only one sex being selected. Adjust parameters.\")\n }\n \n # Step 4: Mate allocation\n Mating <- custom_matings(Candidate, Kin = sKin)\n \n list(Candidate = Candidate, Mating = Mating)\n}\n","type":"text"},{"name":"R/ui_helpers.R","content":"# UI Helper Functions\n# Functions for UI elements, reactive values, and display formatting\n\n#' Create dynamic trait input UI\n#' @param n Number of traits\n#' @return UI elements for trait inputs\ncreate_trait_inputs <- function(n) {\n tagList(\n lapply(seq_len(n), function(i) {\n wellPanel(\n fileInput(paste0(\"trait_file_\", i), paste(\"EBVs for trait\", i)),\n numericInput(paste0(\"trait_weight_\", i), paste(\"Relative weight for Trait\", i),\n value = round(1 / n, 3), min = 0, max = 1, step = 0.01)\n )\n })\n )\n}\n\n#' Create OCS trait input UI\n#' @param n Number of traits\n#' @return UI elements for OCS trait inputs\ncreate_ocs_trait_inputs <- function(n) {\n lapply(1:n, function(i) {\n fluidRow(\n column(6, fileInput(paste0(\"ocs_trait_file_\", i), paste(\"Upload EBV File\", i))),\n column(6, numericInput(paste0(\"ocs_trait_weight_\", i), paste(\"Weight for Trait\", i),\n value = round(1 / n, 2), min = 0, max = 1, step = 0.01))\n )\n })\n}\n\n#' Generate package status text\n#' @return Formatted status text for display\ngenerate_package_status <- function() {\n status_text <- \"\"\n \n if (exists(\"is_webr\") && is_webr) {\n status_text <- paste(status_text, \"π WebR environment detected\\n\", sep = \"\")\n }\n \n if (exists(\"optisel_available\") && optisel_available) {\n status_text <- paste(status_text, \"β
optiSel package is available - OCS functionality enabled\", sep = \"\")\n } else if (exists(\"custom_ocs_available\") && custom_ocs_available) {\n status_text <- paste(status_text, \"β
Custom OCS fallback is available - OCS functionality enabled\", sep = \"\")\n if (exists(\"is_webr\") && is_webr) {\n status_text <- paste(status_text, \"\\nπ¦ Using custom implementation (optiSel not available in WebR)\", sep = \"\")\n } else {\n status_text <- paste(status_text, \"\\nπ¦ Using custom implementation (optiSel not installed)\", sep = \"\")\n }\n } else {\n status_text <- paste(status_text, \"β OCS functionality not available - Neither optiSel nor fallback could be loaded\", sep = \"\")\n }\n \n status_text\n}\n\n#' Check if WebR is detected\n#' @return TRUE if WebR environment detected\nis_webr_environment <- function() {\n exists(\"is_webr\") && is_webr\n}\n\n#' Format kinship and EBV results for display\n#' @param kinship_results Kinship analysis results\n#' @param ebv_results EBV analysis results\n#' @param thresh Kinship threshold\n#' @return Formatted results for display\nformat_kinship_ebv_results <- function(kinship_results, ebv_results, thresh) {\n # Combine kinship and EBV results\n if (!is.null(kinship_results) && !is.null(ebv_results)) {\n full_results <- left_join(kinship_results$results, ebv_results$results, \n by = c(\"Female\", \"Male\"))\n } else if (!is.null(ebv_results)) {\n full_results <- relocate(mutate(ebv_results$results, Kinship = NA), Kinship, .after = EBV)\n } else {\n return(NULL)\n }\n \n # Filter results for table display\n filt_results_table <- full_results %>%\n filter(EBV > 0, (is.na(Kinship) | Kinship < thresh))\n \n # Filter results for matrix display (mask invalid crosses as NA)\n filt_results_matrix <- full_results %>%\n mutate(EBV = ifelse(EBV <= 0 | (!is.na(Kinship) & Kinship >= thresh), NA, EBV))\n \n list(\n table_results = filt_results_table,\n matrix_results = filt_results_matrix,\n full_results = full_results\n )\n}\n\n#' Create Excel workbook for kinship and EBV results\n#' @param table_results Filtered table results\n#' @param matrix_results Matrix results with masked values\n#' @param full_results Complete results\n#' @return Workbook object ready for saving\ncreate_kinship_ebv_workbook <- function(table_results, matrix_results, full_results) {\n wb <- openxlsx::createWorkbook()\n \n # Add README worksheet\n openxlsx::addWorksheet(wb, \"README\")\n readme_text <- c(\n \"This Excel file contains two data sheets generated by the app:\",\n \"\",\n \"1. Filtered Results - Table:\",\n \" - Only crosses with positive EBVs and kinship below the selected threshold are included.\",\n \" - Crosses failing these criteria are completely removed from this table.\",\n \"\",\n \"2. EBV Matrix - Masked:\",\n \" - Shows all possible male-female crosses with EBV values.\",\n \" - EBVs for crosses with negative values or kinship above the threshold are blank (hidden) in the matrix.\",\n \"\",\n \"This distinction allows detailed matrix views while keeping the filtered table clean for analysis.\"\n )\n openxlsx::writeData(wb, \"README\", readme_text)\n \n # Add filtered results\n openxlsx::addWorksheet(wb, \"Filtered Results\")\n openxlsx::writeData(wb, \"Filtered Results\", table_results, rowNames = TRUE)\n \n # Add EBV matrix\n openxlsx::addWorksheet(wb, \"EBV Matrix\")\n \n # Convert to matrix format for Excel\n m_ids <- unique(full_results$Male)\n f_ids <- unique(full_results$Female)\n mat_for_excel <- matrix(NA_real_, nrow = length(m_ids), ncol = length(f_ids),\n dimnames = list(m_ids, f_ids))\n \n for (i in seq_len(nrow(matrix_results))) {\n m <- matrix_results$Male[i]\n f <- matrix_results$Female[i]\n val <- matrix_results$EBV[i]\n mat_for_excel[m, f] <- val\n }\n \n openxlsx::writeData(wb, \"EBV Matrix\", mat_for_excel, rowNames = TRUE)\n \n wb\n}\n\n#' Validate file uploads\n#' @param file Uploaded file object\n#' @param required_cols Required column names\n#' @param file_type Type of file for error messages\n#' @return TRUE if valid, throws error if invalid\nvalidate_file_upload <- function(file, required_cols = NULL, file_type = \"file\") {\n if (is.null(file)) {\n stop(paste(\"β No\", file_type, \"uploaded\"))\n }\n \n if (file$size == 0) {\n stop(paste(\"β\", file_type, \"file is empty\"))\n }\n \n # Try to read the file\n tryCatch({\n df <- readr::read_table(file$datapath)\n if (nrow(df) == 0) {\n stop(paste(\"β\", file_type, \"file contains no data\"))\n }\n \n # Check required columns if specified\n if (!is.null(required_cols)) {\n missing_cols <- setdiff(required_cols, names(df))\n if (length(missing_cols) > 0) {\n stop(paste(\"β\", file_type, \"missing required columns:\", paste(missing_cols, collapse = \", \")))\n }\n }\n \n TRUE\n }, error = function(e) {\n stop(paste(\"β Error reading\", file_type, \":\", e$message))\n })\n}\n\n#' Generate user feedback messages\n#' @param success Whether operation was successful\n#' @param message Success or error message\n#' @param operation Type of operation performed\n#' @return Formatted feedback message\ngenerate_feedback <- function(success, message, operation) {\n if (success) {\n paste0(\"β
\", operation, \" completed successfully: \", message)\n } else {\n paste0(\"β \", operation, \" failed: \", message)\n }\n}\n","type":"text"},{"name":"R/utils.R","content":"# Data Processing Functions\n# Functions for reading, cleaning, and processing input data\n\n# Fallback functions for when kinship2 is not available\nfallback_fixParents <- function(id, sire, dam, sex, missid = \"0\") {\n # Simple fallback - just return the data as is\n data.frame(id = id, dadid = sire, momid = dam, sex = sex, stringsAsFactors = FALSE)\n}\n\nfallback_pedigree <- function(id, dadid, momid, sex, missid = \"0\") {\n # Simple fallback - return a list with the pedigree data\n list(id = id, dadid = dadid, momid = momid, sex = sex)\n}\n\nfallback_kinship <- function(ped) {\n # Simple fallback - return identity matrix\n # This is a very basic approximation\n n <- length(ped$id)\n matrix(0.5, n, n, dimnames = list(ped$id, ped$id))\n}\n\n#' Read and process candidate files\n#' @param file Uploaded file object\n#' @return List with candidates data frame and male/female ID vectors\nread_candidates <- function(file) {\n df <- readr::read_table(file$datapath)\n list(\n candidates = df,\n males = filter(df, sex == \"M\") %>% pull(id),\n females = filter(df, sex == \"F\") %>% pull(id)\n )\n}\n\n#' Clean and validate pedigree data\n#' @param ped Raw pedigree data frame\n#' @return Cleaned pedigree object for kinship calculation\nclean_pedigree <- function(ped) {\n final_ped <- ped %>%\n mutate(across(c(id, sire, dam), as.factor)) %>%\n mutate(sex = case_when(id %in% sire ~ 0, id %in% dam ~ 1, TRUE ~ 2)) %>%\n {\n # Fix messy parents (same logic as original)\n messy_parents <- setdiff(intersect(.$sire, .$dam), 0) %>% as.data.frame() %>% rename(id = 1)\n parents_fixed <- .\n parents_fixed$sire[parents_fixed$sire %in% messy_parents$id] <- 0\n parents_fixed$dam[parents_fixed$dam %in% messy_parents$id] <- 0\n parents_fixed\n } %>%\n {\n # Remove duplicates (same as original)\n doubled <- table(.$id)[table(.$id) > 1] %>% names()\n .[!.$id %in% doubled, ]\n } %>%\n {\n # Remove circular dependencies (same as original)\n circdep <- .\n circdep$id <- as.character(circdep$id)\n circdep$sire <- as.character(circdep$sire)\n circdep$dam <- as.character(circdep$dam)\n circdep <- circdep[circdep$id == circdep$sire | circdep$id == circdep$dam, ]\n .[!.$id %in% circdep$id, ]\n } %>%\n with(., if (exists(\"kinship2_available\") && kinship2_available) {\n kinship2::fixParents(id, sire, dam, sex, missid = \"0\")\n } else {\n fallback_fixParents(id, sire, dam, sex, missid = \"0\")\n }) %>%\n with(., if (exists(\"kinship2_available\") && kinship2_available) {\n kinship2::pedigree(id, dadid, momid, sex, missid = \"0\")\n } else {\n fallback_pedigree(id, dadid, momid, sex, missid = \"0\")\n })\n \n return(final_ped)\n}\n\n#' Compute kinship matrix and statistics\n#' @param ped Pedigree object\n#' @param males Vector of male IDs\n#' @param females Vector of female IDs\n#' @return List with kinship results, quartiles, and matrix\ncompute_kinship_matrix <- function(ped, males, females) {\n kinship_matrix <- if (exists(\"kinship2_available\") && kinship2_available) {\n kinship2::kinship(ped)\n } else {\n fallback_kinship(ped)\n }\n kin_mat_sel <- kinship_matrix[males, females]\n \n kin_quads <- tibble(\n Data = \"Kinship\",\n Q25 = quantile(kin_mat_sel, 0.25),\n Q50 = quantile(kin_mat_sel, 0.50),\n Q75 = quantile(kin_mat_sel, 0.75),\n Q100 = quantile(kin_mat_sel, 1.00)\n ) %>% column_to_rownames(\"Data\")\n \n kinship_results <- as_tibble(kin_mat_sel, rownames = \"Male\") %>%\n pivot_longer(-Male, names_to = \"Female\", values_to = \"Kinship\")\n \n list(results = kinship_results, quads = kin_quads, matrix = kin_mat_sel)\n}\n\n#' Process EBV files and combine with weights\n#' @param trait_counter Number of traits\n#' @param input Shiny input object\n#' @param prefix Prefix for input names (for OCS-specific inputs)\n#' @return List with combined EBVs, weights, and total\nprocess_ebvs <- function(trait_counter, input, prefix = \"\") {\n ebv_inputs <- list()\n for (i in seq_len(trait_counter)) {\n file_i <- input[[paste0(prefix, \"trait_file_\", i)]]\n weight_i <- input[[paste0(prefix, \"trait_weight_\", i)]]\n if (!is.null(file_i) && !is.null(weight_i)) {\n df_raw <- readr::read_table(file_i$datapath)\n if (!\"ID\" %in% names(df_raw)) names(df_raw)[1] <- \"ID\"\n if (!\"EBV\" %in% names(df_raw)) names(df_raw)[2] <- \"EBV\"\n # Ensure EBV column is numeric\n df_raw$EBV <- as.numeric(df_raw$EBV)\n ebv_inputs <- append(ebv_inputs, list(select(df_raw, ID, EBV), weight_i))\n }\n }\n \n if (length(ebv_inputs) >= 2 && length(ebv_inputs) %% 2 == 0) {\n rel_weights <- unlist(ebv_inputs[seq(2, length(ebv_inputs), by = 2)])\n weight_total <- sum(rel_weights)\n ebv_dfs <- ebv_inputs[seq(1, length(ebv_inputs), by = 2)]\n ebv_dfs <- purrr::imap(ebv_dfs, ~ rename(.x, !!paste0(\"EBV.\", .y) := EBV))\n \n joint_ebvs <- purrr::reduce(ebv_dfs, full_join, by = \"ID\") %>%\n mutate(across(starts_with(\"EBV.\"), ~ replace_na(.x, 0)))\n \n list(joint_ebvs = joint_ebvs, rel_weights = rel_weights, weight_total = weight_total)\n } else {\n NULL\n }\n}\n\n#' Calculate breeding value index from multiple traits\n#' @param joint_ebvs Combined EBV data frame\n#' @param rel_weights Vector of trait weights\n#' @return Data frame with calculated index values\ncalculate_index <- function(joint_ebvs, rel_weights) {\n ebv_cols <- grep(\"^EBV\\\\.\", names(joint_ebvs))\n # Ensure EBV columns are numeric\n joint_ebvs[ebv_cols] <- lapply(joint_ebvs[ebv_cols], as.numeric)\n joint_ebvs$index_val <- as.vector(as.matrix(joint_ebvs[ebv_cols]) %*% rel_weights)\n joint_ebvs\n}\n","type":"text"},{"name":"README.md","content":"# AlloMate - Genetic Breeding Optimization App\n\n## 𧬠Overview\n\nAlloMate is a Shiny web application for genetic breeding optimization using Optimum Contribution Selection (OCS). The app helps breeders make informed decisions about mate selection and breeding strategies by analyzing kinship relationships and breeding values.\n\n## π Features\n\n### Core Functionality\n- **Kinship Analysis**: Calculate and visualize kinship relationships between potential mates\n- **EBV Processing**: Combine multiple breeding value traits with user-defined weights\n- **Optimum Contribution Selection**: Optimize breeding contributions while controlling inbreeding\n- **Mating Plan Generation**: Create optimal mating pairs to minimize inbreeding\n- **Excel Export**: Download comprehensive results in Excel format\n\n### Technical Features\n- **Dual Implementation**: Works with both optiSel package and custom fallback\n- **WebR Compatible**: Runs in web browsers without server installation\n- **Modular Design**: Clean, organized codebase for easy maintenance\n- **Robust Error Handling**: Comprehensive validation and user feedback\n\n## π Project Structure\n\n```\nAlloMate/\nβββ app/ # Shiny application\nβ βββ global.R # Shared code (packages, global variables)\nβ βββ ui.R # User interface code\nβ βββ server.R # Server logic and reactivity\nβ βββ R/ # Helper functions and modules\nβ β βββ utils.R # Data processing and utility functions\nβ β βββ ocs_helpers.R # OCS analysis functions\nβ β βββ ui_helpers.R # UI helper functions\nβ β βββ load_functions.R # Function loader\nβ βββ www/ # Static resources\nβ βββ allomate.png\nβ βββ logos.png\nβββ scripts/ # Custom implementations and utilities\nβ βββ optsel_fallback.R # Custom OCS fallback implementation\nβ βββ validate_ocs_logic.R # OCS logic validation\nβ βββ compare_ocs_results.R # Results comparison\nβ βββ ... # Other utility scripts\nβββ data/ # Sample data files\nβ βββ candidates_2024_10_29.txt\nβ βββ pedigree_with_family.txt\nβ βββ weight_ebvs_for_app_with_family.txt\nβ βββ length_ebvs_for_app_with_family.txt\nβββ README.md\n```\n\n## π οΈ Installation\n\n### Prerequisites\n- R (version 4.0 or higher)\n- Required R packages (automatically installed):\n - shiny, readr, dplyr, tidyr, purrr, kinship2, DT, tibble, openxlsx, quadprog\n\n### Local Installation\n1. Clone or download the repository\n2. Open R or RStudio\n3. Set working directory to the AlloMate folder\n4. Run the app:\n ```r\n shiny::runApp(\"app\")\n ```\n\n### Package Installation\nThe app automatically handles package installation when it starts up:\n\n#### How it Works\n1. **global.R** runs once when the Shiny app starts\n2. It checks for all required packages and installs missing ones\n3. For `optiSel` specifically, it tries CRAN first, then Bioconductor if needed\n4. Installation status is displayed in the app interface\n\n#### Troubleshooting Package Installation\n\n**If optiSel Installation Fails:**\n\n**Option 1: Manual Installation (Recommended)**\n```r\n# Try CRAN first\ninstall.packages(\"optiSel\")\n\n# If that fails, try Bioconductor\nif (!require(BiocManager, quietly = TRUE)) {\n install.packages(\"BiocManager\")\n}\nBiocManager::install(\"optiSel\")\n```\n\n**Option 2: Check System Requirements**\n- Ensure you have write permissions to your R library directory\n- Check that you have sufficient disk space\n- Verify your internet connection\n\n**Option 3: Update R and Packages**\n```r\n# Update R to latest version\n# Then update all packages\nupdate.packages(ask = FALSE)\n```\n\n### WebR Deployment\nThe app is compatible with WebR environments and will automatically use the custom OCS fallback when optiSel is not available.\n\n## π Usage\n\n### 1. Upload Data\n- **Candidates File**: Upload a text file with columns `id` and `sex` (M/F)\n- **Pedigree File**: Upload a text file with columns `id`, `sire`, and `dam`\n- **EBV Files**: Upload breeding value files with columns `ID` and `EBV`\n\n### 2. Configure Analysis\n- **Traits**: Add multiple EBV traits with relative weights (must sum to 1)\n- **Kinship Threshold**: Set maximum allowed kinship between mates\n- **OCS Parameters**: Set desired inbreeding rate and number of offspring\n\n### 3. View Results\n- **Kinship Matrix**: Visualize kinship relationships with color coding\n- **EBV Matrix**: View combined breeding values for potential crosses\n- **OCS Results**: See optimal candidate contributions and mating plan\n\n### 4. Export Results\n- Download comprehensive Excel files with all results\n- Includes README sheets explaining the analysis\n\n## π§ Technical Details\n\n### OCS Implementation\nThe app uses a dual implementation approach:\n\n#### Local Environment (optiSel Available)\n1. App tries to install and load optiSel\n2. If successful, uses native optiSel functions\n3. Status shows: \"β
optiSel package is available - OCS functionality enabled\"\n\n#### WebR Environment (optiSel Not Available)\n1. App detects optiSel installation failure\n2. Automatically loads custom OCS fallback from `scripts/optsel_fallback.R`\n3. Creates function aliases: `candes`, `opticont`, `noffspring`, `matings`\n4. Status shows: \"β
Custom OCS fallback is available - OCS functionality enabled\"\n\n#### Complete Failure (Neither Available)\n1. Shows error message: \"β OCS functionality not available\"\n2. OCS button shows modal explaining the issue\n3. User can still use EBV matrix functionality\n\n### Data Processing\n- Automatic pedigree cleaning and validation\n- Kinship matrix calculation using kinship2\n- Multi-trait EBV combination with weights\n- Comprehensive error handling and validation\n\n### Performance\n- **Small datasets** (<100 candidates): Fast, comparable to optiSel\n- **Medium datasets** (100-500 candidates): Reasonable performance\n- **Large datasets** (>500 candidates): May be slower than optiSel\n\n\n\n## π Sample Results\n\nWith the provided sample data:\n- **84 candidates** (63 males, 21 females)\n- **66 candidates selected** for breeding\n- **97 optimal mating pairs** generated\n- **Mean offspring inbreeding**: 0.0053\n- **Expected genetic gain**: +0.0557 (improved from -0.0290 baseline)\n\n## π Troubleshooting\n\n### Common Issues\n\n1. **\"Package not available\" error**\n - The app will automatically use the custom fallback\n - Check console messages for details\n\n2. **\"No valid EBV files\" error**\n - Ensure EBV files have correct column names (ID, EBV)\n - Check that trait weights sum to 1.0\n\n3. **\"Only one sex selected\" error**\n - Adjust EBV scaling or kinship constraints\n - Check candidate file sex coding (M/F)\n\n4. **\"Optimization failed\" error**\n - Check kinship matrix condition\n - Consider data scaling or constraint adjustment\n\n5. **\"Requested package not found in webR binary repo\"**\n - This indicates the app is trying to use WebR installation methods\n - The app has been updated to handle this automatically\n\n6. **\"Permission denied\"**\n - You don't have write access to the R library directory\n - Run R as administrator or change library location\n\n### Debug Mode\nEnable verbose output by checking the R console for detailed messages during analysis.\n\n## ποΈ Code Organization\n\n### Function Structure\nThe app uses an organized functions structure to improve code maintainability, reusability, and clarity:\n\n#### Function Categories\n1. **Data Processing Functions** (`app/R/utils.R`)\n - Handle all data input, cleaning, and processing operations\n - Functions: `read_candidates()`, `clean_pedigree()`, `compute_kinship_matrix()`, `process_ebvs()`, `calculate_index()`\n\n2. **OCS Functions** (`app/R/ocs_helpers.R`)\n - Handle all Optimum Contribution Selection operations\n - Functions: `run_ocs()`, `validate_ocs_inputs()`, `format_ocs_results()`, `create_ocs_workbook()`\n\n3. **UI Helper Functions** (`app/R/ui_helpers.R`)\n - Handle UI elements, reactive values, and display formatting\n - Functions: `create_trait_inputs()`, `generate_package_status()`, `format_kinship_ebv_results()`, `validate_file_upload()`\n\n4. **Function Loader** (`app/R/load_functions.R`)\n - Centralized loading of all function files in the correct order\n\n### Benefits of Organization\n- **Maintainability**: Functions grouped by purpose, easy to locate and modify\n- **Reusability**: Functions can be used across different parts of the app\n- **Testing**: Individual function files can be tested separately\n- **Documentation**: Each function file has clear documentation\n- **Collaboration**: Multiple developers can work on different function files\n\n## π€ Contributing\n\n### Code Organization\n- Follow the established project structure\n- Add new functions to appropriate R/ files\n- Update documentation for new features\n- Test thoroughly before submitting\n\n### Development Guidelines\n- Use Roxygen2 comments for all functions\n- Follow R naming conventions\n- Include error handling and validation\n- Test with both optiSel and fallback implementations\n\n### Function Documentation\nAll functions include Roxygen2 documentation with:\n- Parameter descriptions\n- Return value details\n- Usage examples\n- Error handling information\n\n## π License\n\nThis project is developed for genetic breeding optimization research and applications.\n\n## π Acknowledgments\n\n- Built with R and Shiny\n- Uses kinship2 for pedigree analysis\n- Custom OCS implementation for WebR compatibility\n- Sample data provided for testing and demonstration\n\n## π Support\n\nFor questions or issues:\n1. Check the troubleshooting section\n2. Examine the console output for error details\n3. Review the app documentation and help section\n\n---\n\n**AlloMate** - Making genetic breeding optimization accessible and efficient! π§¬β¨","type":"text"},{"name":"global.R","content":"# Global.R - Simple package setup\n# This file runs once when the Shiny app starts\n\n# Check if we're in the app directory or project root\nif (dir.exists(\"R\") && dir.exists(\"www\")) {\n app_dir <<- TRUE # Make it global so other functions can access it\n} else if (dir.exists(\"app\") && dir.exists(\"scripts\")) {\n app_dir <<- FALSE # Make it global so other functions can access it\n} else {\n app_dir <<- FALSE # Make it global so other functions can access it\n}\n\n# Check if we're in a Shiny server environment (temporary directory)\nis_shiny_server <- grepl(\"^/home/web_user/\", getwd()) || grepl(\"^/tmp/\", getwd()) || grepl(\"^/var/folders/\", getwd())\n\n# Load required packages (skip if not available)\n# Load non-tidyverse packages first\nrequired_packages <- c(\"shiny\", \"shinyjs\", \"DT\", \"openxlsx\")\n\n# Try to load quadprog (optional - we have fallback)\nquadprog_available <- FALSE\ntryCatch({\n if (!require(quadprog, quietly = TRUE)) {\n install.packages(\"quadprog\", repos = \"https://cran.rstudio.com/\", quiet = TRUE)\n }\n library(quadprog, quietly = TRUE)\n quadprog_available <- TRUE\n}, error = function(e) {\n warning(paste(\"Package quadprog not available (will use fallback optimization):\", e$message))\n})\n\n# Try to load kinship2 (optional - we have fallback)\nkinship2_available <- FALSE\ntryCatch({\n if (!require(kinship2, quietly = TRUE)) {\n install.packages(\"kinship2\", repos = \"https://cran.rstudio.com/\", quiet = TRUE)\n }\n library(kinship2, quietly = TRUE)\n kinship2_available <- TRUE\n}, error = function(e) {\n warning(paste(\"Package kinship2 not available (will use fallback):\", e$message))\n})\n\n# Load non-tidyverse packages first\nfor (pkg in required_packages) {\n tryCatch({\n library(pkg, character.only = TRUE)\n message(paste(\"β
Loaded package:\", pkg))\n }, error = function(e) {\n warning(paste(\"Package\", pkg, \"not available:\", e$message))\n })\n}\n\n# Load tidyverse last to avoid masking issues\ntryCatch({\n library(tidyverse)\n message(\"β
Loaded tidyverse package\")\n}, error = function(e) {\n warning(paste(\"Package tidyverse not available:\", e$message))\n})\n\n# Detect WebR environment\nis_webr <- exists(\"webr\") && !is.null(webr)\n\n# Try to install and load optiSel - with custom fallback\noptisel_available <- FALSE\ncustom_ocs_available <- FALSE\n\ntryCatch({\n if (!require(optiSel, quietly = TRUE)) {\n install.packages(\"optiSel\", repos = \"https://cran.rstudio.com/\", quiet = TRUE)\n }\n library(optiSel)\n optisel_available <- TRUE\n message(\"β
optiSel loaded successfully\")\n}, error = function(e) {\n message(\"β οΈ optiSel not available - loading custom OCS fallback\")\n \n # Load all organized functions (which includes the fallback)\n tryCatch({\n # Determine correct path based on current directory\n if (app_dir) {\n functions_path <- \"R/load_functions.R\"\n } else {\n functions_path <- \"app/R/load_functions.R\"\n }\n \n # Check if the functions file exists before sourcing\n if (!file.exists(functions_path)) {\n stop(paste(\"Functions file not found at:\", functions_path))\n }\n \n source(functions_path)\n \n # Check if the fallback functions were loaded and flag was set\n if (exists(\"custom_ocs_available\") && custom_ocs_available) {\n message(\"β
Custom OCS fallback loaded successfully\")\n message(\"π¦ OCS functionality enabled via custom fallback\")\n } else {\n message(\"β οΈ Custom OCS fallback not available after loading functions\")\n }\n }, error = function(func_error) {\n message(\"β Could not load organized functions:\", func_error$message)\n })\n})\n\n# Set global flags for app behavior\nocs_available <- optisel_available || custom_ocs_available\n\nmessage(\"π AlloMate app startup complete!\")\n","type":"text"},{"name":"www/allomate.png","content":"iVBORw0KGgoAAAANSUhEUgAAA/kAAAP+CAYAAABQQN+0AAAAAXNSR0IArs4c6QAAAIRlWElm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\nDuE+X31KaxAolABhv1DdTWMRQACBlgKE/JYsPIgAAgUTiKy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ysDttzXlueHA0fuoUO5O/7ohT7MT67DPrenJs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\nY60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kxLGIzQ4sZJ8mV9J4LV3O+9MPnR/6qdd2m36Fvy6QOSH6IHLNje0Py+Bfc4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\npbwjCiKsEml3sWWskPJ/FqVQu9fOzjvv7LmQsuAkeZx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8fflyfbrPPhPwtvnw9lpYwIIIAAAggggECqAgT5qcpxHgIVIBAf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NfZqAnJ7zAgT5Of8IM1aAgiAngv2McZIRAggggAAC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\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9L5Qc19OnqciwACFS1AkF/RT4DrI5AZ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\necdNpOfeJPCHzH0CeFRFAAEEEEiKAJn8pLDSKAIIIBCKQMS2QmY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MbL7q\ndn10udFB2RECQkAICAEhIASEgBC4LAmwsFzqqKGfP4uv9gjAMYnZeHD+JsSl5BqJ2twQC70+\nXi5Y8uokXD2yjYmore+qzAZrYU3qe1HEchv8NG8CJg9pqRuLTgdX5VnULq+wXaZDqxkGnVot\nxzHngBtFudfz81ALW/ICkbOu6oyvnhmNPd9ch7uu6gJvT1ddK1aaZfGY53aSbD9eowcGt760\nGjFJuu8INoZQJYdZ63zv14M0pgtG7fP1wV7bf205iwIS9GtDkohtG2eJo60XndH91FgXAmaj\ngpnDJfRp4ow6+PPcelzfakJ5mzHTsmQJASEgBISAEKg8AuwZzYKr5kuttezfpT0G/vChXUJx\n97f/h9V9JiInMhqnP/sebe69VWvG4jtbcBx4dJ46vnbQVciJitWX9W5hPeJaK1iYmYVMioxm\nwZn7ZoG8ODcPxTm5KOL30u06ri5w9vSEs5eH7p0WbHT2dId7vbpkJ9IKvhRVbcl6ROuL3wvT\nM5TInJeYrPf3NjyubWsLR/I+R6enHQ7F1mkz1XhY1B646COtqNl39tR29SePREpsdzJy/WIc\neuJVRPz4u1H5XXfMQa935yHk1hlG+bIjBISAEBACQkAICAEhcPkRUBor/2ElujSxza4WSa3l\nmb5zVPIzn21DTr6xn7YqR+15kpXH3Ot70QKVATbbMm1b2w/wdcf8B4eiRRM/fL8iTPlqs/B7\ngaw7ODq6NiTmWNfXA8/e3hc3kcXI18tC1cKa0YlZavg8H0uJj+06FkeLca7Fs7f1xaCugZfc\nmmT74VicOp9aJlpbjZvO897j8TgXl4G2wQGWplFj8kXYtnIqWNROpUUft8UfoEhr9ieycmVa\naYef8VwouYDDKaewPmY3RgbSz4X5MYgkISAEhIAQEAI1hEDi1t3KfoQFYcPU8vZr0XP+C4ZZ\nVrfZEqT1XTfhzJeLcPydz9CCrEtcfX2s1mHhttnksYj+Z40StdmahKO4g6ZPhKufTtQ1bIAX\nfGTLkbTDYcg4fhqZJ8+C/b0rM/l3bkcid2v4dWyD+gN6qyhww/Z5scox2/5CHlmVWEuavza3\nx6L2lil3KtHdXlF73fDpaDxmKHq+87zqhqPLe73/EhoO7a8WkTTse/+cFxS/Ts88aJgt20JA\nCAgBISAEhIAQEAKXGwFzkhPl2fKi3nooBrtD48sIngofqZ6tmgXgikEhcOYVISuQPMmi5NEb\ne5I47I4Fv+xHdl6Rimhma4/alOoQUPbrnjdrAKYMa4X5P5HdINmU2EosIB8lO5aHFmwiDr1w\n/Zh2l2xx0UKKyF5Llih5+RZY09giYkkLJfFbhG1bZ7IWHP/p7HIk5qUqYVo3XHOfDrYmwoK4\nrt4bR75G/0Zd4eXsUe6nW7Z6k+NCQAgIASEgBBwhELdqE7bfeH+ZKh3m3oNOzz5klJ+waYda\n7HHw4s/h4uNtdEzb6fTUA4j85S+wDceJ+Z+jy4uPaocsvre84zr4dW5PgvZko4UcuQIvCMlC\ndtK2vUjavscoottigxU8kH7sJPiFP3QNsaBct083nc0J250M6acOeDRqYLWn7LPn9Me3Tp+l\nrENYxO//7Xv6fEsbu2fOBVvDhH/zK1LJvmQAR80HBariwTOugHuj+thxw2wU55H3YWk6Pv8z\n4p6GHhQ5L0kICAEhIASEgBAQAkLg8iSgFCj+w3JUaWIp2lrENguebEGRmVOoVdG/62IznTC8\nZzM0bWg9aEVfycaGCy36eCt5TZ+OSsMva0+RmE4R2zSG2picaSHKvh0b44cXxuP5L3fi17Un\nUUie4Yb8y8yLzk9SWi5e/XY3+W07Y9rwNpckBjaGoso3H4xGsQrPLjMqlcGLeH6z7JiKRnel\nhUhrcqrZo6tGcrQWKyKz4vDNyT9pi1c7VR8LFR5RXHYSdiYctnptV7gTaUAICAEhIASEgJ0E\nopYuNytqd3v1yTKiduirH4DFWRZ8tQURzXXjGuAHLWr45IdfK89srRwveLhh7PXIDj+vZan3\nRsMHouPj9+lF7YLkVJz65DuwNQlbm+x/+HlELv77kojaRgMr3SnOz1fiethbn1DU9R34K6g3\n9s1+Bkk79pkrrs/LPHNObTMzFvqVp7YdonbY258ifsN2VZdFdI5OXzdkKuLWbNa33WjYAAz9\n62uw/7lhOvv1L9hz75OGWbItBISAEBACQkAICAEhcDkRYCXaQNS2Z+pso7HraJxZ9UtrajDZ\nZlRm8iBrkznX90TnlnUpoLQE+YW8GKPWW2X2dGnaYhH45bsH4NEbesHX0816pzxNOk38IOHl\nb/ZgxY4I85Hy1ltx+OhOskEJj003e561xvgUhMdkYOVO3XcZLb8mvosViYWzUoc8sZeEr1KL\nPjrRdkWTZmPCIvmPZ/7FqKb9aavyBPOKjk/qCwEhIASEwOVH4Pxv/5axs2AKvEBkCFmIaIkF\n2d0zHwVHa7PVyKAln6t37bi599Z334yzC39RCyzyQpLdX30Kx177AOHfLlbFY1dvRJt7bilT\nNW71Jpz7+c9yLYboFdyUrEPaKu9rV38/WuSSvLTJQ5uFX2d6uXh74UJBIYrId7s4l17Z5L+t\ntvPA9iYZYaeUTzfn2Urs2X2OotL55dOqhbJcCbl5Otwb1DOqytHZKXsPKd9veyO1WdAOe0Pn\nvd3mvlvR7ZUnlV/54WffxPbr71MR8O0evFP1w5Yow/79AVum3YnCtAx93+eXLKOIjzro86lj\ni2/qG5ANISAEhIAQEAJCQAgIgdpJgMRSLcLaSN2mTGsR22v3RCI9O9+8Hl4qwrYK8q90JoEN\nvPEOeW5f++yK0ohtmkAtThyJfufkTkq0fuP7PTQTy/PRNPyktBw8+ck2sP84e25XVSomH/Nv\nl4ehqMiOhwc07I9/O4TRfYLhRdYxNTXV3JFVIzEWnLOLcrH8/FY405dCa+H5jg1T57W9K+EI\ntb0FE4OGXJKfGTg2RiktBISAEBAClwMBth/Zc88TRlNlu40B37+vPJ21A7yg5PabZiM3Og6N\nRgxC/28WGPlesw2Gs4eHVtzovesrTyghlkVWFqx5IcT6A3qh+2tPIaB7Z31ZboNF8NOffm8U\n3a0vYGbDp3UIGo8cBP9uHZWQzYK2aeSymWp2ZfHikxyRnn7shIrITt6+VwnglirzApHHXn5X\nvZpNGY+Oj90Lv07tVPFmV40Dv6L/WgU+Zivxwpm7Z+msW/w6tFGiNtfhRTj9u3bErtsehl/7\n1kbNBBCDUWsXK3GbrUu0xBHubnX90Y14SxICQkAICAEhIASEgBC4fAhYErB1gndZDhwlvX5f\ndNkDBjl1qLKHq7NBTuVtdgyph+kjWqOI7Tv+A8nT3QWzr+6GtKx8LPz7KM2LlEYrWjKfrzRa\nuPPNH/bho0eHI7hx2XWGKgPLyl30veVssl1N8XgjaAFJ9gwf0zfYrjrVUajGCdulD4Gqg4W+\nT/75w1uHvkF8Xor6KYT+QIU3aHZqgk74MPQnDG3SE74u3lafmFW4SxsNkG2OzcUDbDQhh4WA\nEBACQqCWEUgioXbXHXOMRs32IUOWfIG6vboa5efGxitRmzP9O7XVi9q5MfHYfsN95PncFAMX\n6aKLjSrSTpOxw9CA/KjZH9stwB+93ntJibxaORa0z3yxCKc+/hb5SSlattl3jyaNwNYbDYcP\nQOMRA8H7VZW8mjcDv3j87R+5S3WTvOuAiljnqPXknfstds0CNr+aXjEGnZ6afVHgtkPU5kZ3\n3vqwPvKaF8bkaPcuL8xV/TUkluP3r9KfA8NBeLcMxsi1v2Lr1WQVQ3YvWjr9+Q9wb1gP7efc\nrWXJuxAQAkJACAgBISAEhMB/mACLpOZihFnUtiR45xUUIzTcuuDJWllSeh4aBBjb4FUGSl6E\ncSItSsme0/+l9OCM7oggS4/VuyOtWqxoovfh00lY+E8oXpzVv9IxZOUW4vvloQ61m0uLeq7e\nfQ6DuwWSD3iNk5DVXCruseEQEuuF8+jJzDG6SThCmvVf7cRar1W5R/lGPZZ6Gksi1qqLzvzH\nQfn7LCn9dInNScT6mN3WfpFQ/k7sqKn4Urkz9DOT2LyyCwPY0YQUEQJCQAgIgVpIgO02tt9w\nP9gzWkuufr4Y/s93ZURtPt5k3HC9KMqe19F/r1bWGutHzlACqmfTxlozZt97vPWcss5gQZYj\nlzmx1ceJ977Cim5jlHBrSdRmsZ0XlRyxchEmHdugbDVaXD+lSkVts5OgzPr9e6LjE/djONl+\nTA7fiZ4LXkT9fj0sFUfMv2uxdug07LzlIeWPbbGgwYH9c15A2qFjKoc9yjna+uQHC5WvOUe7\nc+JzZSmxDcowOo++7VoZFTn2yvvKMsUoU3aEgBAQAkJACAgBISAE/rMEWHoyFbd1wrb5KXNk\ncUoGLUjOYpG5RI2xRsfCa1WlLq3qI6iRz3/K2cDX201Fbje082FA8YUL+HHVcazfe96qEF6e\nc3DgRALCwlMtnmJzbfI533wgGnEpOeYO14i8GiVsu5MPTXh2AeYcjFECdz6FE3NE8aVKLKfz\nTysWnVmud9XWSeyVOwJus6ikGO8f+0lZnlRu65ZbY5TMkx8cxNKTmg9OJuLXyDQ0dne1XEmO\nCAEhIASEwH+GQF5cAtlVzERRVrZ+Tuw7zQsQatYZ+gMGG52fe1jZfnAWL5i4cfyNKsK667zH\n0OPt/xmULLvJdhqaHzQfjfj+N6zqOU7ZdvACkeZS4MRRGPDd+5h8Zgd6vvM86vW1LCCbq1/V\neSwut7ztGgxfsUhFULPg7R0SZLbbmOXrwA8BeDHHvPhEs2U4k33FmQ2nDmRl0uHRezF601IE\ndOukIsVPf/a9OmbrD4+NxW32/TZMfN442lySEBACQkAICAEhIASEwH+fgBNFQJtL5nOBvPwi\nJVxrwZhl6pKWxHXXkeDKZasi+ZEI3DGkflU0XW1tMrPubRvgxvHt7RLsWUguoAU0X/p6t4qO\nr6yB86Kc6/ZGkYd6QZkHHtb6oOEgJikbayjivKamGiVsM6QJTXxxRaAvbiXfl1vo9S+t1Kml\nSyFys//1qugd6glGVYjahnOJz03Ce0d/JKG5aj2EmBtfjHyDHKSfdcyhpy1Tt4aTqH4BT3Zo\nZNfNpY1b3oWAEBACQqB2EmAxe+v0WchPNP6J4eDFnyvx1Nas+n01X9lzaAsr8gKTbR+4Q1+N\no66PL/hcv2+6wVYeawdPAUclm4vQZn/stvffhiuOb8bAHz9E0yvHmDZRI/e9WwSh45OzMX7f\nKmXJUrdnF7PjZJ/xVb0nIOztT42i5bkwe3offPQlVY9F/E5PP6i2PZs1wagNS1SkOPdhKxVm\nZqkiKnJ72XfgxTQN046bHrA7etywnmwLASEgBISAEBACQkAI1C4CpgK2ZkNiyYpEn8/ikaVE\nje48Gocth2KUvmSpWEXyPd2rxsO7ImOqaF1mO2tyZxLtjReZt9Qua3eR8Zn4fcNpFFeSEMoW\nLxsPROECqYPcvqPpm2WhyCBRvCamGiVs843nQk+VxjXxw7wugThC4B/eH41xm85g0blUROYU\nUKQzR3HrJOdynAuL54BbzCjIwnP7P0ZB8cWfZ1usUMEDPFcnWphyZdQ2RGRGV7KX90Uhm++B\npIIirIrNxExa4fb67eFYGZeJabSS7TOdGiveph94FZyaVBcCQkAICIEaSIC9m3lBRMPU/9v3\n1GKOhnmWttkWZOCPH+kXimSrDS2lHQ7DuuFXI/TVDxCz7GI+H+fFDHfSgoebJt0M9ow2Ta7+\nvugw9x5MOLwOXV9+gvyga2+URuCEkcrnesjvX6LBoD6mU0Vxbh7C3vgIa/pdgaily/XH2c+7\n2+tPq/2UPQfJpuVL/THesCVqc7vbrrtXLdSpVfRo3BCDfv0MHJGvJS6348bZKEhJ07LkXQgI\nASEgBISAEBACQuA/RkDTeFjMNkw6Hcow5+K2h5uLzaBHFkRTM/Pw1o/71PvF2rJli4Cvlyse\nvKa7Ymx6XszVLSwqxsqdEUhMrRzP8Q37onA2mgKHDYTU9s0DzHVdJo/Pe3RCFn5de7LMsZqQ\nUaOEbQaibjR6n0hR21/1bQ43ZydlT/I8rcJ59bZw3EM/e9iWlKPsNLisJnJz3fIm7bz+GbkB\n0TkJ1IzJ3V/ehq3UYyH9AkVqpxZk4pVDXyoLFCvF7TrE89Ae5vAMztLTlOePxmLKlnA8tD8K\nWxKzUUz5N7eoi8faN1JPaap+pnYNXQoJASEgBIRAFRLgKGFTGwq2z2g2eay+V7bCSD9yXL9v\nbsO/cztwpDanuFWbVPR15OK/ldUG25w0v/Yqo0jryMX/YE3/K8uI3VrbHZ96ABNJ0O707EPK\nT1rLr+3vjUYMUnYgI1b9ZNZGJScqFrvvehw7bn5QbwvD1iZjtvxBi3EGkk3Le9h+4/3Ki9wW\nC45+3zTpFsSv3aIWtdx1p26hSa7n1741+OGFYeLFQHfd/ohhlmwLASEgBISAEBACQkAI/McI\nsHiqaV2GU9NHZhtm0nZdXzc0DPAi4dW2SnQiMhU3/G8l9h5PQBGtlSfJNgHm2rdjY3RtbWcQ\nD5U/ejYFB06yRlmxlEMLQH63PEx/PfApDqzvjc+eGIV6fu42H2io3qnO7xtP0wONqg8EdnS2\nNU7Y1ibgTKQHN/TGa12b6v2uU2mV1g3xWbiDVuS8cvNZvEse0Xvp6UUmecXo7TaoAXM3r9au\n+fcSRGUn4MsTS5WkzaIz/6+qEy9MWUKPPnYmHMbi8NVK6Ha0Tx4lv1jgLyguwXF6evZtRApu\n2nkO4+ii+4ki3RNocUj21aZnBJhFZvzPd24CF+LLF7MkISAEhIAQ+G8TSNi4XUUJG86SbT4M\no4BT9h7CvgeexcaJNyF2xXrDomW2g6+ehDb33qry2RN67326SOMuL8xViztqFdhyZO99T5Wx\n3eDj7KHNgnbHx++Di4+3VuU/916vT3e18GWv9+aZFe6Z9bph05ERqot+YJ/z0SRuB04YoR4c\n7LpjjlUmmafDsWHs9WQvEgpnd3ewn3n0X6vUwpxaxcajBqtIeG2f3xO37UHo6x8aZsm2EBAC\nQkAICAEhIASEwH+KgBnBh7LM5KpZu7k6kxc0i67WtTCO3uUUSrrTg/M3gi0qOLpYkm0C9fw8\nMIg1Tnv0OOLMXttsR1LRtJr8sU/Q+nqGp3ZUnyAEN/bF1OGt7W4+ktwftlPQcU1Ll1zYtn6L\nXMTDNxu/pjTzw3f9m8OZLEq0G5BF7NNZBfjoVCJuJgF3PFmVvHQsDqfpyYFWj/shndfwvF1s\n3GCLheWCC0V4as97SM5LU+LyxZ4MClbBpiae1yFLkoUnl9I4CpXQbasrnj/PjRPPN5mM+7+m\nJzmTtpzBdIpqn0eeR7uSs/W+OaVFcS39zOAJ8tTmDyJ7RW2trupM/ggBISAEhECtIqCic2c+\najRmv45t0fezN43y3Or6q8UPlVUFRREff+czo+OmO91efRIs2nJiq4tBv3yKdg/NVPscjcyL\nJWoLIarM0j/sGT14yRfKQ5u3L5cUcssMjNu9HC1umFpmytnnorB26DSc/+1fdYwXfxy46GPw\nwpzdyJrFUkrefVAt4sn+3OxPPuSPhRjw/fvKKubYy+8qsVury97lQdMmarvqnc+xaRS/UQHZ\nEQJCQAgIASEgBISAEKilBJxQh9Q+TUPjSSgdiHIsRWRz/th+Leyar6YpRSVm4eVvdmPcw3/h\nIAWeFkv0tlV+ri51MKhbINhH3B6tjTnzgo+RcRl2aYXmOtdFa4fyFaA7TBeFl4cLhnRvCnc3\nZ0waGAIfT1dzVY3yeCxZZA+9kdwg8inouCalSy5sp9MTB3tOoAaJn2T0q+eF1+nkuxqs6soR\nyAyWjdQTKKz+e3paxFHcEzefwTOHY/FHVDqJ3/kkFuvkY61Pfte2VR90UldH7UBo2hm+xSmL\noqiNS2hDqZJ37ovF9aiseDy79yPyEKeVaEt70saqvbOgzdsxuYVYF5+BN8Pice2OCAxffxqv\n0XY4if2F9CsQnsUF/jUIb1CqQxt30c8dXibfct62W9SmzniBSe5TkhAQAkJACNQ+ArtnPYbC\ntAz9wFk0ZRHa2dNDn8cbPq1DMGrdEr3fNkfzsqVFcb7ln5oNXPQR6vfrgZHrFqPJ2GGqPbbD\nWDdkqtkFCnmhyXF7loMjiC/H5FYvAL0/ehXDln2vHiKYMthzzxM48Og8fTbz8m3XSr9vuMH+\n5psoul47t4NoAdD6/Xuq89h+zl2qaNibnxhWQe8PXwFHhBsmtkMpSE41zJJtISAEhIAQEAJC\nQAgIgf8EgVJByGQu5nN1hUb1DkLjuhfXZzGparTLehyLRfx+OioNNzy/Eo9/tA1baWHJzBq6\nyKDRBKppp0vL+mT/YfxdzNpQiknc+2dbhMUHEtbq8rF9x+MREUPfBzVhj05YCK1r2I/W3OPU\nJjgAPcmqWNMPVaaFPyVUaMO+84imBxo1KV1SYZs5cnTxagpftzfxTcfi9rSgAHzZNxhuHLlN\n+9rNyPott8uvIlrf81RGAX4mv59HD0ZjAkVyjyTR9yWKYN6ZlI08CnPWBHEqTqL4BYrSTsf/\n9n2E/GKKlub1QdXdyUcvZdJJ6f+e34KV0TtQROPSrjm2GMmncZ+kaPSPTydh0qazJGSfIq/x\nKHx+Nhn7U3JpXmpdUzVgHr/i4aRrgdldQ5HaT5GXDwvj9orazOlsdj6W0Q2gsb6URKQvIXA5\nEKiez5uKkVWfMfT5cIE+p7SXllexlqV2ZRM4Pv8z5bls2G6/hfOVh7NhnrbNi0MO//cHNL9u\nispiS4tNE24Ce2ebS+4N6mH4ikXwbdtSHY5YtFQtYFiYafwPHc/AxqocRyCzXcblnhoM7I0x\nW/9CyE3Ty6AI/3YxNl91u953u0wByjjzxY/ghUA5ufr6qPdzxF5LGv+0I2FalnrnhxkDf/jQ\n6KEGi9p77tdZyRgVlh0hIASEgBAQAkJACAiBWk3AVPvhfe1laWL1/T0oaru5zXLm6mdRAOaS\nDacw+50NeHDBRqzeFUn+25qyZa7G5ZlXP8ADPdo1tFtn4+/aK3ZEIK8cUdLsf77pQDRSMvJ1\nGmOpuDeufwvlp85noJ6vO4b3bEa6K8nDdoh/Cal5WEI6a01Kl1TY5om39nGnxR+z8RevxknJ\n3suc+Q5s4E2R203hyjekqq1719q4UOJEiyPyMwSOUtYdiyN/6e/JZ/rmXecweN1JWoAyAg/Q\nE4Z3TyRgeWwmHtr8BfIozJnb4Ju8Th2ur2uDrh/19AnUrvbS+irt3vabURu69rhdTk4kPvML\n9CqhlzNc8P6BP7ExNg1fnknGE/Sk64Yd5zCKvLKvYE9xGvMJ8tDm0aoTR1VZgGZxn2fLzZY2\nTXs68X9mq3oq2p3zDQLeuYLFxO2F0xO2pynyfXwTX32bFivUggOGwhtvX6qk9Wv4fqn6vlz7\n0Vjz/A23axIPHld2djZSUlOr6WGaYzR4vJz4vbi4mMadhnOR53E24hwizkUiITGRfo5UoC+j\nlVcZ8qdaCKTsO4zQ14w9lFvPutEoWvrEe1/h6LwFiF25AZoYyoPt88lrygaDt9MOh5GtyDVI\nPXiMdy2mY6+8j/0P/a/McfbyHrPjbxXZXebgZZzBIjMvwjnghw/ADxQMUxL5X28cfyPyEpIM\ns9X24WffxKGnX1fbDYf0UxHwXs2bgRf+XNV7PDbSg4hTH32jjjceNaRMfe+QIPR42/g8cZT9\nma9+KlNWMoSAEBACQkAICAEhIARqLwFNMyszA4sHyGLQuQ4mD22FRhS1XR7ZhJtmEZXtM2a9\nvhZXP7UM60l/yyQLi/K0V2bs/4EMDj4d3K0ZzcTKiTCapxNiyWo4JjHbKNeenQzS9VaQfbMu\n/JVq0Nd6dzcXXDOqjdI/uQ0OHGY7Eg83UhntkspKsGj1caRm5NkzhEtSxuWS9FLaCZ82FmIn\nBvoqb2yOvp4YaPyFztJ4uC4veDg1yB9etArinIMxFMlcKkjTMUP+vK3tcz1NZEkjG5T0tFwc\npcwVtAhlesxppB/LhJNLe4pgKoCPdxHgmQU/72IUu+XB0432nUnmdSoiwZuEYxq7kou5UXsT\nldWKq3FdcIbThTrIL6TcQh/kZpMYn+ePLApiL87zRFheCbYcWoTAHiNVX7yIZgm/qG+ur2vL\nqVTMLjsIrS+2bbmGflLwdGmktr2iNp+fyJxCXLf9HB5p1wAB5LmjtVm2t+rLiYqOwTc/LNJz\nsTYS/uCoGxCADu3boXvXzgjwD4CLC82L8h1NfB5yc3Px2cJvkUXCpHZtWWrH1cUFgU2aUN9t\n0a5Na/j5+sLZ2ZmuJ/VowlI1i/ncX0ZmJt7/5HObfVtspPRAyxbNceuN19sqpj/Ofe/dfwB/\nL1+l+OkPlGNj/OhRGNCvj0M1lyz9E0fDjltlx6c0wM8fwcFBaNu6FYKaNYW3l1eFmBsOUp3/\nvDws+OBj+sVHsbqGimihjMED+mPcaLpn7bymuJ3zdA2/ueADLHjjZfj7+Vmdl+EYDLe18Xz0\n+VfquuRjHEV9y/XXonWrlnaPx7BN023VB13z5yKjsG3nThwNPa4Eed2nbOnnIs3bnSJx27QM\nQb++fdCtC99nujnZy8S0X9kvPwG2D2FbC8PEgmaXlx7TZ8Wt3gT2YTZM/l3ao8GgvvTqgxYU\nTezbthV2zZyrBNbNk25RC0M2mzLesIra3jf7GZz75S+jfBZuey54Ac2vvcooX3aMCTSdNBp1\ne3bBbvJBT951QH8w4/hpbBhzHYYs/Qq+bVqq/MyTZ3H6s+/VdrPJY9H/2/fUdvMZV+L4gs+V\n13nKnoMqr/HoIej22lNq2/QP+3xHLV2O+PXb9IeOPv8OmoweCu+Wwfo82RACQkAICAEhIASE\ngBCoxQTou7Gh4qHpSUrTsjKt3u0b4uqRbfDp74dVfKdeXLNSRztEX3P1ibcP0Lp49765Ab2o\nzSsHt8TQ7s0Q3MRHuTLoC16GGz1Ja2ONzq6AdiqXS/bLZ2LS0IrWILQ38ff4v7acRRRpn9p5\nYbli6vBWatFIw3aaNfTBjFFt8cOKMH1Zw+OG29xWGjlKLFp9ArOv7k6ag+HR6tm+pMI2T5FF\nxl4BXuji76m8sPPIH2NaM38D0dY2iFG0cudbZHT+GNmNFBJUPmHM0uAe0jdimFeieXHQe35m\nIjKO71SR0iXke34hyxUpma7UjhfSqLESpwtw4SuNIqnrOBeDTd5LnIvgVOcCMhrSxdSeurBx\nAvOyPBB5tAVFOJKzNb0KabBFF+hjhMRtdQFTJDiPSRujit4mvbMoJQbZMafgG9RRiWfqyiq9\nWrSy+gla2Lg1pB7Zj+gWirRX1Oa2Y3OLMHNPJOjZAUY08nHovFgYSpVk5+XlI5yiRdmqhc+/\npWR4iliU/e0PDyUy33LDdahXt67Dwh/3xAsixMXHIzEpWYmIlvrmfDrDOHnqNDZu2QpPWtyr\ndUgIJo0bi86dOuiu23J8ChQVFenmTgJmeROLjZ4kPDmSeO7p6ZkkcEY6zE3rRzsfaem6X2xo\n+fa8xxLz8HPniKnWStla2pH9Bw8RfCf4+nijGYnbg/v3R/++veHqSvd4OZgb9sQRyxypXFxM\nD774DNP116FdW8Midm270EOPYrJA+vCzL/HU3IfLPTYeD0dPZ2fRf7BKe87JzbFrDNYKqc9V\nYvXvytUkaO9CfEICLmgPExVoduznz0odg4KCfISdOImwk/TZRfYIHdu3xw0zpiMgwL/CzK2N\nU46VJXBs3rvIDj9vdKDfwgVGNiAc+aulgO6d4erjhUSKFE4/ekJZXfAxXmQyoFtHJbiyWM6e\n2yy0Nho+UFXVFppM2Lhda0q9s4/34N++QL3e3YzyZcc8AWXVspzsRW55CDHL1+kL5UbHYeO4\nGzCY/LN5oU722+735dtI2LILvd6dpy8XufhvtR1y89UIJpE7n+xFNHsYfSGTDV48dPWAK1GQ\nkqaO8PndO/tpDKdxSBICQkAICAEhIASEgBCo/QS078baTLR9W1+H3Vyd8eA13XEsPAWbaKFA\nLm9FctGaN/vO9XLJjngbuQHsO56Apg29MWVIK9x+ZWfymb58LQpbkwbq7+OmotvNgjPMJIZ5\n+cU6n2zDfBvbuVTn4yWHSCPQqQR8Hr09XHHTWBYzjRMfm311NyzdcBrZ5Hph63zz8X+3RagH\nIIH1vY0bq4a98oWNVmCgfDO5kWr6QufGyCWR5NnDMVgRa7+PM9dnwfmKpn54k8RtV5oB53HS\n3nV7Fv7SGeDTmnp8t1p4Up1iOov8zpYyLGizLQifKFo3kTyBnOgn9i4UneuE7AxX5KV7ojDX\nvfTSsNBHaXZxkTNyqXwO1cvMoihtaofzuN0LStCmTnj8fBZ48Hw1USqhjJRTB5CXlaLLK81X\nBy3+0Yn7HAXPovazZATvyEKRLBCzj/dVW88ighahHE4fOEFebjrLE4t9VuOBUl4svmkvc6Ph\n86pe9IfnmEuC+IHDR/H4sy/gzNlwVddcPZt51J7Wr/Zuro7WPx/Ly6FfC4SG4a33PsA/K1bq\n65urZzXP4HrQ+uZ3RxOtk+xQFe0S1YRhrW9HGtF4aG04UteJotxZ1Nb6VXM2mbfWvvbAIysr\nG2HHT+Kr737AG/PfI2E8ksRkepJVwaQbQ+k1QG2VZz40Ef4/wsPD8fKb79BnBD9Jdfw8alPR\naqom2D6pAonHwWL5sy+9isUUKR8bF0+fheSprR8ftU8dapx189d9jnLEeEZGJnbu3oNn572C\nA4cOVwrzCkznsqqatH0vTn/+g9Gc2z8yC3V7dDbKG77iRyVac2baoWPgSOzpyceUF3bn/80B\nR/zmRsUaRRFzJLcmanO97dfdC1NRmxdI5MURRdRmQo4ltiUxjYgvpIeJW6++Cxlhp1RjQdMn\nGYnaZxf+jBw6T5wCKfqb+dsStbmsW/266P76M7ypTxwxrkWE6zNlQwgIASEgBISAEBACQqBW\nEnBiocn0a6HpvoWZ+Xi6YsFDQ9C7QyMlU9lZzUJruux8clAIpzXc3lt8EBPn/InvlochPiVH\naWNWK/4HD3LgLNu9GEg7VmfJ6wMyKw6ytDf9TdHaCeRYof8KTxV70SKRrUhUN5eakvUz+6vb\nk3jc5+IysDcs3qh9e+pWRRnHlK1KHEEPitruRCuBkr01niQv6aXndVFDmjhjrSvtpmJx+6Ne\nQUro1oQlnbRipnbp2eTowuRjW5Gfkaq/xzUxSVeEhTOur3tX26X76lPB3itPPwQere7FbWkv\nw/Z1/V0UyXg8FygaNOngehQX5htW0rdqtEHtalGsN7aoq0RtbtPeobI4lUpG9PfsIe8jUt1Z\nHH+gbUOjLmryjjZ3HWXao4mrV+mgmSf/j/NKSujBBfOlD4ZvF/2s8qpsbnwS1DnXCZgXRUHg\nz3/+xeLf/qiU/rVr35F58Fh0EceO1CpbVsdUTbLsQSs5zL8iyfDa1u5f1Z5iTuOhxGPTM6d8\njrJ+k8TttRs2qeM14Y+OH8DWOmxvwyM3mk81DJL733fgED2A+RDRNC4Cqe4XdX8xeDrO95wX\n/QLBx9tHveuvAxov/yqH2+DyOTk5+PSrr7Fl+45qmMnl2eXBJ14xmrhXcFOwUG2a1MKPKxch\n+OpJ6hDX2//w88oLm4VwjhKeHLELI9f+iq4vPY6gaRPVwoNaO7tuf0RFeGv7/O7RpBFGrPoJ\n/p3bGWbLtgME+n+9oMyikkX0gG7LtJl6AduwuePvfKZ2Q26dYXFRUMPyhtvBM64w8lznY6Gv\nfoCCVMd/UWPYrmwLASEgBISAEBACQkAIVD8B+lZGg+BvZcaJv7vZkxqS8PrqvYPQr1MT1Yyd\n1Sw2TV8R+aukerFn9Itf7cTMV9fih5XHkZZFutdllcgul/RQexOrJxnkU15op7DNPNmGhHlr\nyY3EdF4k0pcCWC2lcf2bw9Odf+VuqYQun9vNJAtjXiC0gKxZqztdcisSnrBiRH/G0MKEB9Jy\nkE02HfNC4+BJoB3x3Gb/6dFkS/Jqt0A8fSiWFo7Ut6629H+0s0kRn1lRJ5AVcwZOzi7qNteX\nqUEb6oOGxlyUl420U/tQv9MgGl3pJ4CZK4yzWIy+qUU9/I8i4Xm6ZoqZnSELfycoUvsWMpRP\npSdofHbGkudRC2+3GsunzERo/u5ubnjm8Ufh56OzT9HKsMCWSZ7Ux8ibedPWbco+hPOKKWL+\nfFQ0Vq1d75A3stau4Tufr57dumLypAk6IZU/PGhMFygyOC09A5FRUThyLFT1g+L0OgAAQABJ\nREFUx0/a6BBFvxZjzcaN6NatC1k2tFMirGGbjmxz9P/0KVeiS6eOumrcv61Ev0rw8vS0Vcrq\ncRYweTJ33X4bGjXiVX15ZjYSF6HxNWhQ30ZB64f5Gvci7+yH7r8brnQvc+JpZ5OQmkQWMXxu\nI8i2JDI6Wtn9aOJsHv3cftGvS1BACx1OHDcGzmQHYseoVftV+YejyI9RNP/7H3+Ge2feDg8P\njwpdE+UdK98bh48ew+dff6sY8T4nPtWeHp5o17Y1+vbuhbZtWikrH2f6TOXPkLQ0XkwyigTx\ng+pXCekUsc1guXZBQaG6z/r07AFvb+9qmZeaxGXwJ/LXv/SRvdp02XLCUnImX/S+X7xNFhet\nEfr6h4j48XekHTmOQT9/DI/Guoeb7P/ML8N0+Jk3EP3PGsMssIA+7J/vEPH9b8rv2eig7NhN\ngCOurzy1DS5k52MYPZ2fmIytU+9UDw442poTny9tgcmOj91ndx+GBXu9/zJW951Ea3zkqewi\n+mXR8fmfodsrTxoWk20hIASEgBAQAkJACAiBWkeAvpCZfNnl7+wmWRZnxeU6tayHL58ajSc+\n3oaVOyNUe5xf+jXRYl1bB7g+azKHTifheGQqlu+IwIdzhqNhXc/L5Psi6THupEnqvm7bwqWO\ns892sV2m3MBh4hoanqy+x+v6KIG/rzuG9miq1g+01GHPdo3QNthfnRdLZbR81gjWUnBsdGIW\nWjU1HwWula3q92oRtnlSDJcjrt8mnx0WT7LIf3quWhCyRC0QyefX1g2nHWeP7kDyirl9d2Tp\nT+N1dfXXCBHnsgVZaUg9uZcWi3St+J3Ik6iixDx04jZIhD8NF09v+LfsTr3RjBgcX0GcFCRd\n9OQMXiiyk85TWzusK2T5LwtSKRSpPYsuxnQlaitnFExtFqAqaXwtt1BzjvDPbOrSYnX+/mVv\nqPr16qJlSAu1oN3rFLHLiz9qjNas34hRw4fSYogkcGqZDk6LOfmQoM59mEvs7XwVid5/LluO\nf1etVtc7+c3Qg5gLWEfRw506lPU4MteOuTweM8Wfo3HDhmjdMsRcEYt55Z2vcYNOCAxsgpDm\nwQ7xK+/imYZ9s6jasnlzuNFDDcO5aNssXseQhcaXX3+HqJgYXRl1+zhh6d/L0ILG3LVzp4v3\nk2Hjl3Bb3cZ8Hul+ZFH5y29+UIK9/nPgEo2F+0slgfqTL7+m9QDYV4tHxnh0XuWPPvQAmtOi\nnHzu+Jh2Dp2pTIP69VG/Xj306tGN2kjHx59/SVY/EeojqlnTJniSPMT5HpFUdQTYI/nYy+8b\ndcCLBNYf0Msoz9xOh8fuVVHWu+96XNmSrBs2nSK2PwN7b5umkx8sLGN1wmLs0D++VuK2aXnZ\nLx+Bbq8+Sf7XqYhc/I++gazwSGydcReG/fsDXOgXE0FTJ+DMl4tQv38veDajSBoriW1GYles\nB7fBdjItbpqmPNc9mzZGx6dm4+iL8/W1T3/6Pdrefzv4mCQhIASEgBAQAkJACAiB2kmA5Q3W\nKowS55XJNCphtMNFA0gQffeRoWjzmz9+Xn0SSem8npMDjRi1eHGn9Osm2KJkx5FYjHpgKV67\nbxA4atjDrdqkyosDrOIttiOxO9FXc34QoH1Ht1aPAx+3kLaaSgGsWuLv9AM6B6J9c12AjJZv\n+h5Y3wuDuwYqYZxPsXaOTMvxPh9jP+6v/j6GV+8ZaKTJmCtflXkOkKzcYVCAMZpTCPxVJErz\nLcGxwgUUVv8SRW474rnNo+LI0YFkWP4anQCO4ub29CeAN+iVn5uBuH2rKHpTZ0Whk2y4ds1M\nfMHyGEvqOCM94hjykqPps4NOF38K0TGeI2+6Ut6drerjFZ475XKePUmL1J66NZwWjCxEkQ4T\n/GmhgB4BniSV1q6kps3n3syLBTjOZ1GuV/euhO/iB0I6LWLIPswVSXyetH65L9MXt+1K4ut1\nV09Dl44d6XrVeUWzHccRitKtaP/qiicApv3a2ucxVyRp9xC3wm3Z6s/weEX61dfVdVymX21e\nLHi3bNGcIvnnYOSwoaqcuqf4/BP7L0jwTkpOUdeDvs3q2DC4Hvma2HfwIBaSJ7ihuFzVw+J7\ngiOrF373I/IoclP9B5PPKb34wcvrL72AEGLJ51A714Zj0vL4eL26AXj8kYcwaGA/tKcI77kP\n3g9/P/tXbzZsV7btJ3Dmi0XIjY3XV3DxpkWaX5ir37e1EThxFEau+UXZieQnpWD9qGtJVNUt\nSqjVjVm2FkfnLdB21bszLUI7dOlCeLcMNsqXnYoT6PPpGwicMMKoobTDYdg981GV50KL4zL7\nDnPvNipjusOe65sm3YyTH34NPocHH38Zu+6Yoy/W7sE74dM6RL/PG8defs9oX3aEgBAQAkJA\nCAgBISAEahEB+q5s+nVf6RY0Be37siOz4UUHH7m+B96bM5SifptRYGAlSok0MPo6qqw2nvt8\nBxb+E1r939EdgVPOsryGld2JzyeLqKYn1UwDmWRZwv7anJgrJ2eqe+ukDvR9nkUUy4mPTxve\nGq4uHL5mO3H7v60/rbzTbZeuuhKVeDU6PkhGem/r+iSeEGh6sbidRhHE9+89j3/JVN7exO1w\nGxy1/HXfYCVu8wnhfE5sO5J6bBst3EhRiLxvx8WgKtaEP3SlsMiUcGA98nPIh5yvHBo/5/H8\npgb5K09teihjzzWuZsSiNtuO3Ew/JYnjFU8pl1+cJgT6ooG7s4PLCurq1oa/HUmk04Rg5scs\ncjUhrwonoGw7qP2+vXrpflVAxPk6zKe+z0ZEqPNZhd1fdk0zW+0+Z8uSW2+8Dr17lP7qgWjQ\n4y1kkEXNcoqgr4lp87YdWPz7n/o5VPUYmdXZ8AiEHj+hPmPYi57vD86/b9adyrbGkKm18XA5\nDw933HPn7Xj2iUdVNDfnSao6AoWZWTgx/3OjDjo/9zDcGzpm+ePXsS1Gb/pdv6hk8s79+jZz\nY+Kxd7bxYoN8cNDPn8C/awd9OdmoXAL9yHO7bq+uRo3Grd6kIrU5k21J2NvcWgp7/SN12Kt5\nM7B/uneLIMSt2oSopcv11Xq89ax+mzf4oUbm6XCjPNkRAkJACAgBISAEhIAQqD0ETL+Dad/I\nyvvVzI3EzuE9g/DV06PxzK194ElR1Vpb2ntF6LDUxd7Qby/ah0//OFKRpmpF3UJa387eRE6y\ncKeHCZquZK0ee5azhznz5MTnnRcB7dvBvl9jtg+ph/EUNW9vyisowpL1p/T92VuvMstVa3w/\ni7HBFLXdln7acCIjXwHnyfEN+PzRWHWTTAr0U6KrdhPamvwAWsnzNfLcfvZwDEUhsyh8AQkH\n1yE3LUmv/KpoRFsN1aTjPA9iknx4Exr3Hg8XV3c1l5kUqf1MJ/LUprGysG9P4lvnTFYB7qaH\nB2lk/6IEccrj+h4UbXkPPWgw/QC0p93aUIY5cRQsX2gsbvM+z/VS+RnzdVe3rs4qRTHWDQDJ\n9HNzSVVHgKOImf1N112D02fDyQ86VXfd07k/ciyMfKqKlRVN1Y3AvpYv3nd0YZBVzZoNG8kD\n3BnXTp+qj5S2r6XylVq2cpVaJFLdHcSGfevZQsSXvH4vjs2+tg3LG27bV1tKOUrg5HtfgsVt\nLfm2aYnWd9+s7arFAGPIEzsvMQktb73GquCtLSp5YsEX6PT0g/o2dt72MHgRQ8PECx02HNrf\nMKvMdvfXnzYaS5kCkoGl9ctavmhY2Ad9yG9fYuO4G4yE5kNPvYaGQ/qBH0ZYS3nxifproy9F\ngLM1Tcvbr8OaAZMR9uYnCJquWzy00YhBaDJ2GOLWbNY3d/SF+Ri4SCeK6zNlQwgIASEgBISA\nEBACQqDWEqCveRVOXhS9fdeULhjROwjfUHT1xgNRiIrPUvodfe2uUOL67CP97s8HENzQB1cM\nbmkzyrhCHVZbZSdl48Hnwx5mJA/QmleucHG2Hpuckp6nIt4Np+XiWgc3jrMdra3V4Uvk3mld\nsY40wxzy9baVSK3Ehn1RuGl8BwQ1qh770WoVtllM9aanDn3qeSOMhG06pfQ/xqiL3J697zw+\n6R3s0IKSLnRlcOS2H7X7yKE4JJ7ch9yE8zphho7VOlGbYZRe7YXZGYjdtxrN+0/C9GCdqM3C\ntL2idjHdMbF0Yd5AkdppBRdU5DDT5s8eNsfoThYk/KChgp9FPOKamWj+vIik+uQovRbYf9uP\nhLtLkTg63NmZf9LBhHXCurrm6eGLpKolwOJqQIC/WuDzh59/1V0D1GUiCX1hJ07RwpsdHBZv\nq2LEbFNzga8H3ccgVqxeB2+KOJ80YRz9R8y+nwM5Oi7+TIyOjcORo6GlP2/izxQndGjfDq1a\nhjjanJS/xATYNuT0pz8Y9drhifv1+4VpGdh81e3ICD2p8k599C0G/PCBEkX1hUw2WEw1FLWP\nvfI+UvcbR010fHI2mk0Zb1LTsd2M46eRn1w1D/ac6VcD9Xp3c2hABSlpSA875VAdRwoHUGS7\nq5+vI1VUWVd/Xwxe8jnWDp1m9HBh522PYNTG35TfttZozHJ6kB8Vq3+Y4Bpw0QbImXy5OfFC\nn82vm4zw75YgZd9hPadOzzxkJGzHrtyA1IPHULeHZeFd61fehYAQEAJCQAgIASEgBGoWAZaR\nqjK1DQrAC7P64/rIdli18xyWbjyjFhKkr5cVTuy7/QlFbfdo1xDBjR3/93OFB1DlDZQgzcAD\n21Z3/P28ro8bBeRZP6n/bAtHcnquXixn1akN2T/37WhftLY2jo4Utd2/cxNs2B+lk6+0A2be\nOWg0PDYDe8Li0ayhd7XoKtUqbDMTvuivpEUkf4hIMULE9wKLrc+RiTxHXk+mVTZ1cqBRMYs7\no+jin9cqHbesDSOxRhexabFwDT+gifEszl2gBTBneKTjjR7kFU3jtlfU5rJROYWYSQtFpuYX\n61lqTNmIfhhdhJys3yqqSM38U/oJqvHSBsncOO/4yVO0OF+o7kEBH6SJTr/qSiU2X4qoUl7s\nkO0vSuhxmxP9lkTXJy/MVwkf1HQiTeetzd/ce6XNl7jyNcR929t/pfVtbmI28ljAZqE4K1v3\n0xz+j/0Wsv3g/OpMzIQjpGdMm4Lf/vgL+bTwpSJL4/vtz7+JLXDVFRPUEKuC35GjRw1u/BK1\nMEVP8qPnVBX9qYblT6UQOPHul+CFI7Xk27Ylgq/WReFy3q475+pFbd4vzMjE4efexOiNv/Ou\nzcR2JCfe/cKoXMPBfdHRQDw3OujATujrHyrPZweq2F3Up2VzjNu7wu7yXDBlzyFsv/HiQwGH\nKttReDgt+mjPYp7mmmIbkb6fvYEdNz+oP5x1JgKHKXK71wcvqzy2i9l5y0NqIVAtYp8fUnAU\nPj8AyYmM1tvMNL1yLOr27KIXtbmBgG4dlad37MqNqj3+E/raB7SQqLHNjf6gbAgBISAEhIAQ\nEAJCQAjUSAKs6Wgv4wGyDFl5yZ3WaOvWuoF6zZzcGV8vC8PCv4+C7SmKSGPivvi7bHnS0TNJ\nJJafxsPX9SxP9Rpdh/W3ZIqutpcNLzTZhNYVtGZFwotFrtwRoZ83ax38XX5wt6Zo2sBLn2/P\nBvc3maLlt5Meyw8ZlOhjoSLPgSO7/90ejqnDWlkoVbXZ1uPYq7Zv1ToLs73reqKVt5vRjcfX\nPsexsuf2o7Si58q4DLtvQL55XKjha9oG4esbppMXDXv/cG7tFGnUBUljdydbgrsG9cebY4co\nEVE3IzUtq384Ujg8uwAztkfQe77yF+YKRp8vJLT2oxVQ6VcftTIp3/C0dMQnJCKBonANX5Hn\no8A2Cws+/IQWyCPBkHjww46+vXqif98+l2S+mugbdvwkXYvcpe7ssU1Gi+ZBFR5DAVms5Obm\n2v3iRQormnTTcEIhMc0jYc1W/zk8PvIUr87k6+MDv9KIST4PfF5Onjmt7Eiqc1zcdzGdk5HD\nhmDC2NG6hy10jTjRQxC+Rn7/629s2b5TjbMyb1GeP7/CI84bTJ8fvDjRwo9t1DGDA2Y3eTz2\nvMxWlswKEShMz6So28VGbXDUrZbi125BwqYd8A4Jwqj1izF68x/wDGyMgqRUpB0OBYvW2eGG\n516rqXtn65FdpQsVake4fv9v3tV2a+y77pFbjR1euQbGC3y2ue9Wo7oRi5aCI6s5eTZtjAb0\n0CH92EmkH6FfJ5UmJ/o3ECfD6PjGowYj5JYZpSUuvnEkvmGKX7cVGSfOGGbJthAQAkJACAgB\nISAEhEAtIKA0MJ3soB8t72ramD6zkjbq+XngsRt7Ys0H0/DEzX2UpzMvWlieRF9RKTnh2+Vh\nSEzLLU8TNbrOudhMZJBGZ1cihB5uzmhJNs3W0hF6EBB27uKvYZkh+6CPIhcMZxsWJuba7UW+\n3M3ZWkSdC3MlLuaxtrL5QDRORNK6gNWQqj1im+fMTx2m0c8Y3j2ZQB6vRI2plCYlv1He04fo\nSQGprlMojJ65XiyhlTR+5+PsS30NRTa70k/4b/lxsRKOeNE4vpE1odG4Vk3dY6EJuHtgP7xz\n1UQ1dmtPagxnwYJvMj0cuGtPJFLpnS9ufkanXZsax6aebujs76kW8TSsXxu2+VyyYP3Wux+Y\n/ZBm0beoqEhZrzAAFgoHD6CfzMyYplhWxge7JhCa8tKuVe5j19592E0v7frjc9gqpAUaNGhg\ndtymbZnb53752vjzn2VYtXYdFdHOqLnSWl4JZt89C82aBpa7X26Jry3ubeH3P5Z6VNvu29PT\nA88+PrfUkkUbz6V7d6foRRa3tfufz0VqahpYdOf8yrgWyjsb7ruYVkaeOvkK5fu+5I8/1eeh\n7hw74evvfqAFcIswgsRv/myzTdu+kbDHeCr5jmvXJdfy9vZSiz7yvWIpaeNSHyqWChnkV+aY\nDZq9rDfPfvMrinMvPiziqN5mV43TMzk+/zO13fuj1xDQXWcn0XjsUJxfsgzrR16jL8cbbFnh\nExKMkesuCuWhtOhgXlyCUbmBiz5UCxYaZdbAncq7Q2rW5Lq98qR6IJF6gH5lUZoOPvEKGg0f\nCGf6fG334J1I2rYHe+57Cl2en0MPMML059CjcQOtisX3gG6dwKJ3/Ppt+jKnP/5WHxWuz5QN\nISAEhIAQEAJCQAgIgRpNQH231YQfbaT8JbKyvkhqbZq8s8/y3VO7YNrw1th1LA6fLD2M4xG8\nzpXpYEwqmtnlqOYl607h/qu7mTlae7OORzrAg7CxsN3ChrDNdjApGRejwNkloH2LuuhH6/KV\nJ4U08UX/LoE4HZ2uqls7fXwsj/TG17/bi++eH1ue7ipUp0YI2zyDEXTxf3o6CXkUT8zatmFi\ncTuDwt+fPRwLd3riM8HGCdXq8v3KovY13bsgO78A9/32JwpJOOLm+ZhJN1q1GvOu+yAqUZHa\nswb0xfvTrnRIiOUPjtO0UORNO+gCJ37mPkiYAT9Eu655AFxZIa2FSRPkrEUDc4SyKkfzGzyw\nPyaNGwMvsqRQjCthzsUXipFJi7eZRgmy4J5B+bv27MW6jZtJuCxSOqDql3gPHzqkwv9dYUvm\nVIpWT0vPsKkx8hnm64DFzIomjTtHx+uS9euHLy8Wj6sr8XhZqPXzM37SyQJtPkWcV+fY9ExK\nEY4dNQKJScnYsHmLuuf5xLIwvOjXJfAi0blPzx6oU0me2xwpztepdj75vW5AXbiRNYq1xOXW\nrN+AA4eO6MZopTCX9fH2xr2z7lDnwEpROeQAgdOffmdUusPcu/X7iSRuJu8+qPYzyDe6wcDe\najtpy24jMVyrwF7cjUYO1nZV1O/pz77X7/NGlxcf1QvkRgcqace/nB7U3H0JPfRJ3nVAPxLT\nz2L9AQc22MLDt31rB2oYF2UP8YIq8BAf8N37tPjjlSjK0UWv5EbH4fj8z9H5uYfVApDBM67A\n+d/+xfYb7tcPyCsokGxGRur3rW20e3iWkbDNUeGdSSRnHpKEgBAQAkJACAgBISAEagcB/mpJ\nX8PKJDNZZcpUNIMjtZuQI8AUsqYY0SsI7/96EIvXn9RHKdPXW7vT6t3ncA8J5eWJOra7k0tY\nkPWHveRHze/2PGXgc9gy0F/xtDRMtiFZRv7ahsmJ1u+6f3o3sjx1Nsy2e5t53zKhPX5Zc5Js\nZVTIsdW6PJ8N+88jjGym2aP7UqYaIWzz+Qz0dEFDD2eczzYPjHNzSJRmW5J8Ur4djdy+uU8P\nNPTxxvRvFpGwR+I5yYl8Q+supkuJ3IG+CAwLQnf06435UyapsfK+PYnFy/TCCxSpTZ7aJGqr\neZq5b1jUdqM/1wXXNfuhZ09f1V2G58ZcrNtrcBmOPi3Bth27sP/gIQwbPBg3XDO9wsPn/vfs\nO4BjoceVsK1OES9bS3scgct+yYUUNa671nRyCy8S2LZNawzs10eNvSKDKGG/br6aaRy2Ivn5\nErAcg+vYKNRMqEGer13XJSOx8/p1bCSOlTbHqIiEfu06cqy1yi/NLF3IOuDm66+BM9kPrVm3\nQXFj3jzOL7/9Hh7uHujWpVOldM7zNr13XF2t/6dBY8XWP2EnTvKlZzXVoc+YugEBNANJlUUg\n6o8VyjdZa49FR0NriTTDiN7HX8aZLxehGfkqZ4VHwq9TOwz57QvwooTZEVHIOReNrLPn0OKm\naVpz2P/Qc/pt3vDr0EZFAxtlVvJO99ef0QvwjjbNtiz/tBqgr1YZEduNRgxE38/f0rdpuFGU\nnQO26TCMkDc8zts7b30YMf+uNc2u8L5nsyboMu8xHKTzqiX2QQ+5+WplO8NjdqFFkcMpop9T\nwyH90If8ue1NXN6/czv1cEOrE/HDb2g/5+KDEy1f3oWAEBACQkAICAEhIARqKAGT79/8nY3/\njWzXd/dKnJI/LXr4/Mx+GNknCO/+fAB7jyfQGOjbrR1fDrlMVEI2zsVlohW5N/wXEntWs7DN\nZ8PedOWQlnCxYifyxZ9HwOK2xpRll04kLo/qE2xvF2bLdWhRD8N7NsP6fedtf5cv1Xp+XXsK\nz93R1+p4zXZWgUzr6kUFGnakKourAWQ638HXA+fIZ4ZvNnPXOOflkVD44tE4eFCl8Q5EbrtQ\ndOOkTu3xyYwpuG/xn6oHJc7othwZbpWXVR80dEW6kah13+ABeHfqFQ6Jbixqn6KL+s7d5xGX\nV6i8ytUtY+a+4azWPu5o4F4jLoVysWVeznR+R48YRhYO7tSG4URZtAOSkpIQERmpPLhZxMuh\nn++vWL0GaWlpuO3mG+Dp4VHuD3jun4XrdHop4Zb4c54m/vGk+NpVQgvls7Dcs3s33Dvz9lIL\nD8qoQGKR3Iciod3drUfYchfq3qLxWbOYsHsoNCl+VuDv768EWFv1uG/mXJ2Jz4m5yH4PN/dy\nn/+qmA9fP+wDf/WUyUinSPzde/eqc8fXblFBIT79ciFm3zMLnTtWfNFLXtTU1dXVaBoZtMAg\n92XrOtE+p0v4ZwNmEvPWJcN70kxByXKYQPi3i43qtL7nZqP9tg/cgSYTRiD0lfcR/c8aZJ48\ni+MLdIsABk0ZD4/GDVV5Fqz5ZZjO/fQHUg8eM8xCn0/tF0aNKlbTju4RYtV0nrznILbNuBvs\nQV6/X0/0Wzhf+VuX6a0KL/tWd16PiB+XIu3QxfN0YM4LGPLHQjWMnu88D36VN/H1s/e+p/XV\n+XoTYVuPQzaEgBAQAkJACAgBIVDjCajvlAajpK+YLAjw/6slDaFFDENIw7vzlTU4YeAFbW0w\nPObc/CJExv93hO1IEunPJ2Rp0pG16atjnqTVTRrYwmK56MQsLFp1QulPhmc3m7TA+9/eYLGe\nvQfiU3L0grnVOvTVn7/9bzscox5EtL6EDyJqjJrJoteoRr5YTSeZYVhKbFOSTk84Zu+Pwgc9\ngzAx0JfOHde2nvg439h39OuFIH8/TP7qe/XTfhaB6WGR1T6tt1y5RzVBlKNKb+jZHe84GKld\nTPPJokjt23dHIpE+AJglz90SU14ssr2vu7IjqdyZXNrWXCnC9cqJE+BfujCgae8ssHHk9Dc/\n/ISdu/eghEQ7Zr1jzx50aN9OLdpnWseRfebL50xdh/Su6XkX6EGMEz+5URk6YffGa2eQHcoA\nEg2pPJWtSNKulxtmTKeFMHVWA/a0xxHBFe1bRT7T+B+49y6ENLf/SSA/hKiOxNdAIdkVpGdk\nGHXPYru7eiBilF3tO8zXgzzB7yP7Dr5KdpKdDb/ztcaLdX7yxUI8/dgcFQlt+Q63PQ0+H56e\nnkZR26n0wCczKwt+vr5WrxO+j6zZ2mjXp+1RSAlHCHDUdeLW3UZVQm4q++sT3zYt0f/b95TP\n8tF5C5CwcbuqE/r6h4hbvQldX34C9fsbrzLOkchHXpxv1DYvWBjQraNRXk3fsf2vgvLN4Mj/\n3sKpT77TV07efQCr+05ULFlsNkqW/sNrVKj8O30+egVrh16Msk/YvFNFiDe9YozDjRbSw6yk\n7Xv1diXN6OHHwUdf0tud5ETFKnsS9t+WJASEgBAQAkJACAgBIVBKQH2fr+J/9JUTtqnSoB+l\n6YFytu9oNUYVTBbEP744HtOeWkaR2GTlqh+U+db4eBFpKuy1/V9IPJ9Dp5KQk6fT6uyZ0/j+\nLdCwrpfZoqxnrt4ViYwcXojy4ollrBG0QCW/LuaabcJmpo1TZFSfz/FZ8uQ+dCoRl6ewTQCG\nNfImD+06ZDWii/6zBJAFKha4/3ckGgUZbpjawX4PTBaLxrZvg0+v+T97VwEgR5G1X7KWdc9u\nNu7u7glBAgkBAgRJkODuHP+hx3GHwyGHcxCCEyAQJIS4u7utJrtZd5f876vZmu3u6bGdmc1s\n6JfMdnV16avqnumvXn3vErrr+5/FjWQicvCkfZdqrO2e+LIO7h0/ml6HpTandnQiCktt5vO8\nfVcWZVWAboUFd4542OpVayp9eJTpJnG0Hr2SvCHOmtUo2gaALYA5gwESlrOjwN1794omw9p5\n9br1LgHbUHEQO+2KjIBjU2gReuWVRa4H3NeYrxLgg3X4qOGu04+IChR/WvK2FIDVEFcBa0Wx\nNoOm2UMEi19Y9p4pwNpmIzUXy5iTtoQBW4y7uOO5Ewlt4l2y2NdU4dZTOZazr54lFmZ27t7D\nC3HYtmWyPH/trXdo7nXX8qwz7XJx9h5G+Rg7OBLdu/+Aae5w2XiWYIdD/z59dOeTbNc5EyfQ\nwP796me8ouus14LCQvrqu+8Ff7niihF0gwa01trx5/FulXoLbL3iAUqP++Ej4VQQAHfe9j3i\ns/qiOQxkTqIhbz5n5k8GZYmSFzqwTZzgbtYr1+U406PSXMyWmx6ilo1dZKr/3SALc8s3Ot8H\nUqCTVRfOoZLjyTLKfKytqBS0IMlf/EAjP/mPoAMxX/RgAJQyPe67mY68ZbLSRlVw+OkMsI25\nAMqSEwv/oLqaapq6408C1YkPL6q1n3UxKeca6EgMYNuDA2oUbWjA0IChAUMDhgYMDTQvDfC7\nlFWoxQt6gnc6xc9ZCVPYpS/1dNPjGH96dPZQeuyd9cIa2159tQz+lbH18dkgFVU1tPlAJlWw\nsa7lS7S6h5hbQa18BU+5+krDWX5RJS3blsbGZg3vLeIqn8r3Ic2VhsweCGG+VdfU0Xfs8PPS\nCV2FMacHqrEo0osstoniW/lRn7AA2l1YTpi82qeEBAjFBPDxpZQjO2jm4t30/U3X0WX99QEY\nbY8ByPjw56aRwyiGnZld9dnXXNeZ59yWcCjadtWgAQLUxoNIWMVqO6Fzjj5ks6XdOW+9TwU+\nTC0y6DzuJ1vGioItdYkiJAg2MIKtNTmJj4zQKf9siMLYYw5NGDuGge19oks4T0lNo9y8PIqK\nBM+480qAQfZI5sqeO+dalZpycnPp0SeeMVvCYixXsANJANvmuazK4dpJY9ruWo0Nuc9k3Q2t\nsB1CG3N5TEDtIR/yuAmGDVFbrNoupemvot1hzJd735230atv/pcOHDJtM4K1dEFRIdOSfCKA\naPGrpRHzF3Oxd48etGTZClPnUAbH7dl7gAb2M4HW1u4KAOJYGNAKyszMzhagubW82jzGueMa\nSP12kSpxp+suV51bO4kZO5wm/fk1Zfyxig48/6bgUM7bvpf5mINFltqKCpU1MiL7P/co+XiK\nQkgzOSqypCNaaz1wPN4tFtv19xOoXLbe9ijVMQ2QLSnYfUA4dYQTR1B5NIX0/ttdBOqYypw8\nUR0chZ5aspriL5hotXpY5ad99wsd+/ALQVEjE4KnvZiBewDbEHB2K4Htk4v+JMwRj80H2RDj\naGjA0IChAUMDhgYMDRgaaAYaAOexo3gN0wUIitSm6ha/jrFhVK3pPbG+Uvy0RXutcTXDihhA\nalPIWKYlGdg9hjbtP2UX4MW7ZY0WuG2KRnqgjhzGOjfvy2AGAR4gO4IxHNQ9lgZ0i7Ga8nBq\nPu06ki2gU6T3BkEzNjF99HbmUh/eJ65JmuQuP3IuNxadx2dUTLA4WgBlPEoyrq6uhrL3rKS8\nxL1M88A81At+oh/37BdgISa9I4KypjHnNiy3EZbv17IOR8pwa5r6hwwstb+4bpbQgaMPSQDg\nSXn5dM47H1EWc35W5OdQ1vYlVF1Vyuh1/RBzGjOYZ254C0oI9BMc22c7qC27jPkRwZbVsFLF\ngx1Sxw75cnJNoIApxrm/mHFwEqmcewhHR0XRddfMEnWhRIzT0cQkWrpiFU4NOQMa+PWPP4VH\nXzlWsOIfPXK4+dlyBprkUJV4LmHO3jr3BurN1DnyOYUvRHCGVzHNTmMF5fbu1YMXdiLMRaB8\nOFit4LIFYG6+og7IdqEM7Qe3l7zH1LmMM1c0AAvbyuxccxG+7BTZGQtdZISV9pQ1C2n4h6/Q\noFeeEta5iE/85FuVtXZIl47U7rILcckz4tjXdaPqtvy+a0Qx/MzePPdB2nzjAxagdqv4WOr3\nNFuY+6v56evY18LeZ16lFZMuFxzcjajVqSw+vFuo2503qPIcfPkd1bk8Kdx7iHY+/Cz93nsi\n7XzknyZQm29SWGGPmv8mXbR/FbWeMEomp8jB/SioXRvzOQIAzQ0xNGBowNCAoQFDA4YGDA0Y\nGjBZ0/r51oMKdhQCLKCqhq10m0jwvltcXm0yGK2vE3EBfi3Jnz96cjgtnzJyGENqAolig9Ze\nHdmw0MG6zob3Suh/yaZUSma+cEcEPvemjelMMeH6fsrwKrVw9TEqZj+FXLRZAvx9qB1Tvrj7\nA0t7RwXz/eUvt1MVLNObQJpmOcaBjsgJPTgyUExuxbg0ACusnOqaSsrfs5rKCtiTKv+DwnLY\n+uhKtrz+Ye5surSfYzygqA8OJeeOGEqt2HHaLd/8SJXMv4t6AdZI4EvZdBnvZ8MbqTI9KEWQ\nx2QbreqROZm4zn3wZwv0a4Y0zlI7Nb+AJr79EWUyxQI4tlFmZTE/lDb8THFDz6OA8NaiPlAY\nyCeHbNWE2GDyg8nxX0mgB4gcEtaXLZ5gU2Lbf7UalHNlxLChtJyttE+eTOcCeF6xle3nX39H\n/fr0pvi41mbQ23bpxlVXNVDD9/bW7TsEWIuxkZ9uXTpTBDu/xP2OOG8WtA90N/ey5fYrb7xN\nySmpYjeAO9odwLQD502ZTAt++EnsYIEe8vLz6cNPP6M7brmJf/yowTtv1tPZ3raTPy9RdbHt\njPNV586ctL/8IlXyw298pDrv+eCtqnO3n3jwlmtYrm58q0/+uozqKi0XjTpcNYMGv/YMAVTu\ndP0VtPbSm6hw3yFVRQUMIjeVdL3lGjr8nw/NQDocf4JvW4LU6b8to4OvvEcAtqXAOrvTdVdQ\n5xuupKD2CTLa4thh1gyz01FchNU2+LcNMTRgaMDQgKEBQwOGBgwN/NU1EMJGgiGt/Km0vMau\nKqqYnqGEgeamEuzozy+u4PdFCXqYcK6oMPYv5acPA8LCe8GKo3TvlQM9/m4Mq/GeDGz7MBZl\nzxobBp9+vvpgfFPp0x31ZBdU0Mc/M3NAw5BYL5bfk3p3jqTp4zpZHYuUjCL6bX2KqjhAGrMv\n6EmXT+pmvexGXilmHm84/izneSIhNWtFoYvgEt96MIvGDlAbyljL40q8/ox2pUQX8uKeaxfk\nT0G8MlHKyD5AazNow5bZlYXZlLNvLVWXY4Wj4Y0Ylll4ib3t2x+puvYSunJgP9EKc14rbUIJ\np3nkr+D0uPFv+voHAXChHcirBbeRHhNlSLu2itr1C6/j8lozfUAAg+cgu9ctD4WhTP53++gR\nTD9ykajTGUvtzOISmv7RfMosLhac2uY+s+7Q/qxdKyi062CKbNeLtVR/B2EWon9c90Reyfkr\nCfSTzXQUGB+zsD6CggLNp+4KoK7goCCafdWV9NLrb2KgWU4zF3ULWvznMrr5hjlijMxj5q6K\njXLMGpD38Mn0DPp6wY/iPjRdZB5pvtEnTRgn4prLGKCdQezo8V522PnGO+9T2omT9c+p+nvb\n3HPnA6OHD6ely1cJQFvqaNuOXfQbW7lfOt0EgDqjJ9db5Hwf/go5TixcrOpmu0unqs4be5L4\nv69V1trg7O54bYNjwsaW60y+Cb/Op5jRQ53JYk5bXVBEv3QdbT43f9+ZY5wPaEFtX/6eGPbB\nS5Rw0RRzYf6R4TRl9Q9M4TKPwF9+ugktcWQjYLXf7bY5KgD68Kvvm4FtWPlLUDuW6Wg6z71K\nWPlrrc1lecojQOxDr39gjoLTUUMMDRgaMDRgaMDQgKEBQwOGBojCQ/wpki2PswrK+J3Mtkaq\nGd86xdbQoNwFmOtpQT3Z+eWAfVRt65QQJvAIvfoD/Hzo8z8O0Y3T+lBYsL9eErfGJcSE1L+f\nW1ce2t+S9RXKOGFzl3e+303pufYt4tFnAPkv3TWWIkP1rbWhiw9+2seLJQ1GOMgXGOBLD187\nhMI8pK8ZzJv9zdIjdoeCm0KV1TX0x6ZkGtk3zir9jd2CHEzgdcserXkgghn4a5jaHGKtlKYf\no1NbF1NNeakAggGwyDR4iAAEz2PHcHO/+p5+3nfQwe6bsEY/Bp/nDB1EH866TFhxY0IoBaf4\nAHDu0TqWhtqwbpL50L5uMdE0unMHbhuDqNw+JShkCvNWEAbxbx0znN6cOV2Ur0wjy9I7wjL7\nCPORjnj9XTqcnSMstZEOQJ4E86Af0GwUHtxIOUe3cjzawZGig6cphG+WfuEmC3nkbfZSPyGk\nDvSOVbxNfO36jaaxYF1BgkNCKL61yard7TpgXffu1VM4p8TYnga3Fte7buNGOnz0mJuqM/VD\nr7/24lxpgLxN6tVunnv26pTXXanbnLd+DGWZOELkeWVVFW3YvJWeff4lKixibm2+jk8LpugZ\nMXQIwaLe0XvOXOcZDqC9oLl54O47qE0cc1bxQKDbrvQDeUHRc9nF0xROQLlg/r9w0a/03Y8/\nUTHvCMHchUg9K1Uh4+SxlHfSgCpFzg9lWiPcOA0UHT5O5emZ5sx+4aEUN2Wc+RyBogNHaO/T\nr9D6WbfTmhk30u6/v0B5W3ep0uidHH1nniq618O3q869/QS7YZQCmjJ3SuSQ/nT+9j9UoLay\n/O533UgX7VtJwfydb1V4F5enpOvtc1Tc19nrtxLmC6TrzddStzuup/O3Labxi+YJehlHQG3k\nDe/Xk/vUHkEhNfw7K2vVBnlqHA0NGBowNGBowNCAoQFDA39ZDUSEBFA0aCIcfOFJTC9sMmqG\n8soa2n+8gb4Q7+54levTKcrqePkyDpfDVsW/b0zWfd+zmrGRF/wZj3LkHbaVvy+1jW2+BpnQ\n+7rd6fTz2kS7c4VfywWofcdl/alvl2irmk08WShAY1UCrufyyd08BmrjPf+qKT0oJNCXx01V\ns8UJ+ozXs/V7MijVQeoVi0KciPDcW5YTjZBJsXAVznw/ncyrQ2xVyRrJ3b+Bcg5sFMkETMJa\nlOCJzIsj0pYzcDn36+/pu117RRq9dMo8CIsx4TJnDxtE86+9QqygwYmjD3hj+SJWiMAfi1Wv\n1y+5iMLZkZa9GxDX/TjPV9ddRUPbJYhnHYBx80egUS2YCmUYvTXzYtFWe2XKdqOfJRWVdNkn\nXwj6EVgfI69uXzGjWvpSSeohytz2B9Uw7zYsxPEvlhcRQvlhwt06K8SHFygg0IXep5it299+\n70Paf/CQsNhGtzEe/Xr3olYeco4m5haPwQXnTiHQPXB1QvewFv7wk3kCLNQdN9ETR//o91dP\nB9o4R2vQSyenDe4NbbmOnOuV6VQcLxLojTnKKCwspGUrV9M//v0ifTL/C941ge0yphajbXB4\neP3sq81xTtXrJYnh7PTRB++j1jExQg94NroKI48bM4rn6jnmHuI5AcEOg3+99CqtWbte8HlD\nh1qRcWVlZbTot8X03w8+EvzfQu9QvWUWbRHGuR0NZC5bq0rR5oJJqvM9T7xEy8ZfRgCpM5ev\noxwGN4+zg8BVU2fTmunXU0liiiq9PMnbtptKU07IU4IVcpebrzGfeywgHyKygvp7VJ46czzN\n97hSWuDL203S/7m/0eSl31Cr1tYdt6CqgNhouoDBYzh0bMHPRa24s03askEt0nG22sIeTiUh\ncAY54N+PUYgt0F1boOI84cKGZwKiMbcMMTRgaMDQgKEBQwOGBgwN/NU1EM64Vbd2EY695/BP\n08OpBcKho6f1hvev1MwSSmFAUf68Fq9j3IYB7IzQmvix0SWYDBYxAJtTWGEtmdviQc0ijC9t\nlthCWMZ3bRtuM5U3XywoqaS3F+wW1DDa1x+9do/s24auv9A6xTLwwMWbUqiwRG2tDZqZqSM7\n6hXplji873dvH07De8c7jKMkM13Kml3pbqnfViFeRUWChgJIAc/25twyQTmSf2ATlednUgsG\nLc03pQzo9AwTpYhBXziUDPD1pUuc4NyG5faVg/pTGPNmvrBsNR3OzKay6ioKY9BzYEIbevqC\nc2hEh3YCxNOp2iIKAx/FVBTfM/f304uX0YbkVKYMKSFwb3eKiqDZbCX+8OTxYlJIUMiiEE0E\nJvHBzCy64P1P6VRRsZlTW4J2muQCUBdlc74K8G5vWUzRvUZQcGwHAWwHMLeRDkalLcbrz6uZ\nQ/mHnxcJ8Fj7zVJXZ3IOeYQtpGF1Cn0IwA64B39ARwH9OToGzioD5YJPG9awXy8w0d1A6Xns\n8HPDpi009byGbe3Oli3HfenylbR95y4x3o6Uga4DXJ8yaQL1YYvyxvXdROexYOHPFNgqgHWJ\nUu2LSfct6EYGlkOCg+1nsJKioqKc3v/fPFPbUTXf/OUcB71mZmWL3Qp4HshmiVFnsKs9Uwnd\nftONFMqW+o3rt5UGNXE02g7O7XtuBy3Je5RXUGBaAXZsGCxaK3UxY9qFlJ9fSJu3bWMdmqxg\nMc8y+bnz6Rdf0sJffqUe3btRm/h48/iBw7yAFxNOMDVKYnIylZdX8HCYBkU0h9vakq3kG9k0\ni7b+VSO0wHbcuRPMqjj67md07P354hygdEB0JFXz903GHyupNCmNcjZuFwD3pCVfWQCcKd/8\nbC4HgfaXT1OdN4eTOp6DSmnB3/+uSmjXTjTi0/9QeN8eThXV+7G7BY3L0lHTqZZ/j0hxR5tk\nWXpHUMeAUkZKKo9r/2cfkacOHWvZYeyWWx4VTjFHfvK6yBN37nimWvnMnB/ANsB+QwwNGBpw\nTAN4Bit3PjiWS52qmn/z43cOduoYYmjA0IChAUMD3qEBH8ZSxg1MoM9+t88YwK9TBIvtfUm5\nNLZ/G4++h+K9bsHyI+yfDoYfpjcwvBO3Zxra7gDirQgsqNHOTftO0VoGIy+b2MWj7QT4jvps\ny2ka2rM1RYQy3tAMpZq51Z/7ZDNt2JthxiVsdQPW6U/cMIxsOWrMK6pksPgkj6/pXR3l4X29\nB3OW27LytlWvo9dAUTO6fzyt25MuKJftjR9omb9ccojmMO83eNU9Ja6/+bm5ZbA4nBQXTS+t\n2SwoNNgWuQGZcrAugL+F/DJ57fxvaP6cWXT5gL4ipwRurBUjbnm+4y/o1YMmd+tCZVXVVMpU\nBiEBzPvt7y8ssO2VoVd2h8gI+pQtwUvYCVUZW5TDGjzI348C6x2yOVom+lXMZcz4+HPKKik1\nc2pLcFOvbsThuqiDj3XVlZSzZw2VtO1G3S+4iPxNzzlrWZtNPIC1lWtMVmRafaDv+JiemfgL\nC3x2GMmrkTNnzKCeDNI5OgauKOT8KZNp5+49goIE9DSwlAcYP3L4MHZgGNboNqBHiUnJlJSc\n4nDzeCqwPk7ToAH9GuaHw7llQuj0NB1gC3hnphEAT/wIuHbW5S7UTfwgrzE5g5TNEUduEVty\no12sUFO7OIjxxs6GwQMGCG5zAOpNMeaqpnngBH3o0L4dPfm3h+nJ555nQLmS+4kfMJgVzgvK\na8U7C26dex0vACTQt+xMErQtptmCw2kGsItp2/ad4v6R447ahOU+H/Gcgu6RFhaq+AfdX3Hp\nxSINJzGkERqo5cUCOARUSryChuTImx8LKoqJv8+niIGm7zykHfCvxwR1xJZbHhEc2rse/ieN\n+/FjZTF04sffVecdrr5Ede6pk5b83aoUWJTHjBmmjHI4fJo53FwVnxC1p2/f0GCnQW3ZBjhk\njBo6gEAJIqVl/a4iee7uY+SgvhTcqR2VJp8QRVfm5Anrai1dja16fXghHxb8tUw5IqX1xNEE\nfnHQkEBAcVLBC//gYf+rSQ1TLNXw7y8sGtWUlFEtKJdYCbDQD0yIo+CO7f5qKjH6q6OBKl4c\nzt20nXI37xQOV7Ej5oLtS8T9qZPcalT+zn0Ejnxw22NBqdOcy2nIm/+0mt64YGjA0IChAUMD\nTa+BkX3jqW1MMJ0Ed7KdVzAYln36ywEaxXlA++EpgVPBH1cdF++7eDUTwscZ47vYrFc6aKxm\nMPK1r3bQsN6tqUOcZxZU4ajycEoe1dbKBuprA2AonCE2R8krqqA3vt3JDjmPmYbARlfx+hwf\nHSx4te2B0wcSc2nn4Sz5yg14S7xzXziqI0WBGseDArxgyvAO9PGi/ZRd0PC+YK1KzL/DKfn0\ny7pEXijpZi2Zy/FeA2yLMeZeZ5Rl0ucHvqf8w2yuzoCKAGX5hUELVtrrOcCVSt6afNeCnwUg\nPcNBy22Ui0cMrLcjg3z5E2h6PnF5jQXCRD7OH8ov8WGwbGVB+0CD4agg/SG2Qr2EQe1ktkjF\nOcp1VC8ynTyWpx6jFbs/pmlxl9DI1gxuckPqYUBHm3RG00nNyTGRRzRKGRaN5M6h38jDWhdf\nOIE8rhdfNJUuPP9ckcSpPyiIP6jH9LGfW7YJdR78z1sC4EObqhmMmff5l3T37beQHy90yHT2\nShRwYf38MfXL1Ed7+eR1UY+46WSMg0dUxoL8aD/mMI7OFIVmy/EwlebYX6kbeUQubd2m5pnG\nWt5jvuz1uWuXzjRp/DgaPXK4qe563TlWs2UqgL0tWtSvkHL/xYSwTGY7pr4NAIRFCfW6tZ1J\n5yqXE8Wc2/fefhv957/vMm8b2tXYwkxjizZNm3o+dezQnn5fspT2HeDFC1GkCeQGLROqEJrm\n/sv5IGquVwfmhr+fPw0dPIimX3iBoH/Rab0R5aAGcjU82VHDB5FfRJjIDdADIGbXW2erQG1Z\ndOtJY2jcDx/RinNmUdbqjQLg9meLbsjJn5dQNS9WSAEwFznY5IBZxnnqGNZb/eNm71OvUAw7\nN3SWMgNA4/Z7n1Q1E8CysxI5sI8qS/6u/XTwxf9S7/+7RxXvyEnSp9+qQG3kCe7a0ZGsLqXp\nOHsmHfj3W+YyUr/92YKH3XzRSgALJrDih8PJ8P69RKqY8SPo1JLV5hzZ67ayZb/Jqaw58iwM\nlJ3I4H6voswV6yibDS4kuG+rq0Ed2gqAO7hTe8Ich7PRxsxHW3UY17xLAwCw83fsFYtCAKLL\n0iy33NZWNuze0Gs9FqSQt2D3fgGIY4FJK7Xl9l8gtXn0zlEPFqv8wkIJC3g42hPs5qgp5kUd\nthyv5mMQUxyBfskZwTOlCpbn9eLHdfuz8Y8/72L1DVYvLGJxAE6BqwoKKbRnV9Femc84Ghow\nNGBowJs0EMmWxHdfOYCeeH+j3XdivCat2nlCWO9OGNTWI92oZVD6/Z/2slNBpvmorwHvcWHB\nATR9XGebdZosajkXG4qlZRXT/72znt5/7ByPOJKEw82DyXnmNuo2jNs9mq3bB7PFdnMTAPev\nfLmDLeePivdoARnY6ARoRJ6aO4ImD7VvJDGPdwiUVzbQMLKaKKJ+fBH2tIAWZvygBLF44khd\n6DscXZ7LgLinnIB6BbAN61UAI2syt9MDG1+hKv7n79eduVxNzQN41RgBsJXLfK+XfvIFLbxp\njpmWRAmMWStXCTqLyWFCdawltxuvrVNZvr3MsHaEpfb573/CVCZsqc39kkCSvby619EhBuYy\nqhPpxrVP0b19rqY7e80SDxVn2qVbdhNF1rJVKvTizNyA3sBzPX7MaGEtLPmZnW2yrBP1QzA9\nq5iyxp5gzPr27kXD2Wnhth07RXKUtWffftp/6DBbE/e3V4QpD9dbW1fD4cY/tmQfYPXsqOAu\nrKpPL/M7mleZDnlra2tMlr3KC3bClWyNbE/kkwJQe3h4OA0bPFDwm4MKRrZZey/aK1N7XbZf\nxqO8qir7bZPp5RGOXZEX8xJSxTtEbH+zy5zqo3w+9eKdJs8++Xd6+l8vUEsXt/lIHYGmpl+f\n3pSckso0Oj/SoSNHzHpEW/FYFK3nPgisG+f8CWBAGzsRrrhsBu9GCBf3qixT3XrjzFEN5G7a\noUoaM2qI+bxw/2ERDu3e2RynDcCKO3bcCMpet4XKTmaQBLa1NCSdrr9Cm9Vj5+0uu1AFwgIw\nWXXe1TT8w1co7pyxDtULy+FN198vACVlhoTpzi9a+oYEU/srplHa97+Zizr4yntiMWDI2/+i\n0G7W9SszoD072So+Y/EKGSWO7WZe1CTATIcrpqt0mv7rcoKVsRY0UjVOcQJQTYK3ORu3NQDb\nI4eqgO3czTvOWmAbi0SJvDBxgudB8bEkhXYcC5alniR8stduFhn2PP4igQM9vE8Pan/ldGp7\n6dQmmQuOtdZI5aoG4KxX63xXr0xb34GV2bm0ZOgFetk8Erf+8lvFrgNXCh/x0SuE55ozsueJ\nFy0W/BzJf97GXyi0RxdHkjZ5GjxfD//nIyrcd4jC+nSnnvexoUr9onOTN8ao0O0akONbsPeg\neIb3vN8YX7cr+Swp8Opze9Lniw/TIbZArn+10+8ZvziBPgJ8y3DiGBMRqJ/OhdiNTCOybGta\n/TtaQ0GwLO8QZzKKaYhVh/z8fISfORgxoR8buCy09aFrBlMg+2dzp+w8nM36yje9T4oXSsvS\nQwP96J7LBwgfeJZXvTMGuoPF/NMfbaLVvIjR8MJsvb3hIf70+PXDaPrYTtYT1V85xIsBK7al\nmaAghd6uOb8HxXjYWls2zod3Zd88ox9bYSfVG9XJK9aP0MmWA5k0ZVh764lcuOLe2elkQ6Sd\nZ0pJBr178Ftalr6ZKuqqGJSp4y3VDLjVA9tOFqtKjrEGWHsHc27jR+WMvr0EMGPrB6aqgDN8\nAhDteG4+Xfq/+XSKreoYyq2/+RWzuBFtZINMahlQyvbLLeidA9/R+lO76B4GuEfHDRSlebv1\ndruEtvTI/fdYPLD1VcGWxXzzhYaEUnR0FAUFBoq5oJ/WdizmEvLPvW42g7NwSAhg+zRbzFrn\nqlKWiHl3yw1z2HpYDdpERUU6NC+RPzwsjB594D6RvrHgNmOPzM9eRx3atnWYHgJ9HzFkCLVl\nbmUTPYWyZ86ETQszYaGhTo3DlZdfQudNmWwjj6lcOAINZ+sjANt+vvDYa7Iqd8c9jzKCmDf/\n0QfuNY0X3z9YmIuNjbHRLkvdwCK6XdsEevCeO5mWhR/DLWD93pL8maKose1EvjbxccwffgMF\ncxvdIWgnBJbbjz10H+XybpGMU6coKztHcJlXVJqcivi09KEQBgVjYqIpNjqa2rBzTkn3gvtD\nluOONv1Vy8hljmylRI0YZD6t44VPSHl6prdc3TgAAEAASURBVDlOL+DD/iMgvqENXsVh7a2U\njk1EQ4I6YZkNID15/vfmJsBKb/2Vt1EntjwGj7MtPtnjH31J+597QwC35gI4AKtz5G+M9Hn8\nPna6uY3KMxp0mbtlFy0dOZ36PHEf9XrodqvFHntvPh1gC29YkCsFTif7PH6vMspjYVgLR/Oi\nh1wIgZUoANY2Uyfr1ok5k7l8LYP3mwTVTRX/3pACXvaut80Rp1HDTb8N5DXQLJxtAlD/2Ptf\nUOp3iyy6BoqXdgxIw0LVj6nD/Bm4EvcRP3drmZKk6PAxKklKpaKDxyhPs7sChQEsB5UQPnDy\nenGSmlbIokIjotloAJb52GkihA0P8nfuZ2595xxugX/fXAYXVJGRpe/sl+ebO6Tbndezw+CT\nVMnf5fADkL99L9mzKI8Y0Ictu0PEoigWasJ6d3e6Ke3YfwOeUVgAtFUv/BrAkhv1BPDzExbd\n3ih41mMnVMnxZNG8U0vX0ImFf9CUld8b4LY3DpiTbcIi74rJV5rHF35OQN12zooFZuMAJ4s0\nkp/FGgCFx10MwD714UYqKLZt8AT8YNeRbEHl8MjsIW7lHU7JKGYr4e10iv3VKSUk0J8uGd+Z\nQhgotiXoB6y2YUQIAXUr+JFhZXvnzP78bm16N7RVhiPXMrh9b367m2oY5LeGajF8Q1NHdaL+\nXW07b3ekvqZKk8/UIz+uPi441+E0kV/sbRqu4XLvjlH02PVD6Zyh9gFf8FW//f0ek2GcQnGR\noa1oztRejcYRGqOfvp2jaOLgdryIksq4iP0SytjC/M/NqTSGLfDdvUiC2s8YsF17upaq2Op0\nRfoWem7XB1RUxcTxUh+smaDAGiorRIxrP+IAqqCUbP7xcenH8+mnW65jcLu3rMmrj3iQnGIe\nxynv/o8yiorsOop0uDNQiH8t+fjVmCYhUyrsyT9KN6/7B13fbTrd2XsWhfmZQEfXtO9wi5xO\nGMRbKGFNap4zDpSAvgD8ayxwKKvw5RcQ8HIr6wbo62i5AF77sOW2UpzJD8qS3tx3dwjqdUai\n+EUjgh0WukN86kFTR8tql5BAbfljT2SPlGCqo2Njr2xc92WaIq3+ndUjygFA3r9fAxcy2q1s\nM9I4I6LfPJ7YEeBukfdNDC8M4WNLn9jJINOjHbbSurudZ3N5WiqSmJEN4ywt2RI//UZYHOsB\nDhVZOQxwbqHANnFmqg9YbysFVt2t4pt2q9+Q/zwrrIkBCisl+csfKZ0dXw568XELq0Bs0d9x\n31OC61mZB2EAIWO+e18b7fA5QPHJK76jjbPvEdQCyoyg+IAV79B3nlfRtQBE3vHgM1R8JFGZ\nXITDenWjMd++T0Ht2lhc81REwoXnmIFt1AEKDQlsA4gBHU3myg2Uw/NBzyIZ1vx1zOmes2Gb\nuYlaeprC/UecsgQ3F+SFAVDx7Hz4WQakFqtaB75xWPB3vOZSsViguqg5ib9gojkGiwMnfvqD\nEud9R0UHjpjjZaBVXPN5QZNtNo7WNdDlpqsJHylwFLlk2FR5aj7ifcSa+EeG04RF81SXN899\nUPBzqyJtlKFKZ+cEDm6Vgntg9YWzdZ+pSDf+p08odvxIZZZGhTvfcCXhIwVUI6umXkvFR5Nk\nFI355j2KP2+C+dybA3CqK0Ft2U7s1jj46nvCv4WMM47NUwPH3tMZX6anOvTaBzTg+f9rnp0y\nWu1RDZw/sgM7XDwp+JQFAmX9sU8V1bX0ya8HyJeB4geuGmyT99rRRu9n3uW7X1lJSacYUFUI\nXvfBlX3eiA78Xqa4oBP0ZTTZ368lVXL7hHAfQGny9oJdlJlXSo/fMJyC7YDjOsWqolDeI2+t\npaMn8lV4ijIRmhkdHkj3MMVLSJBtMF6Z70yFyyqq6Y9NqfTWd7voZHaJ0J+9r2z0cXjvOPr3\nHWOoZwcTRaS99u9PyqPN+0+pwHKUM4FpQdowz3tTCvCPq87twVbpJ3lXP88XG/Md7cLvIDi8\nzGAu+i4J7sGTlP1tcmBb0mhsyznAlsLf0tacfQxdm+4wHMW2fLGXvYLPQuzpR9kXm2FMLIBG\nt337E9XNIrqUObe9GXDBwKfztuyLPpzHxyLeyu8i/YhGO0GBDGz7MLBdZ1p1E3QwrPEvj/9O\nazN30nVdp9NlnaZQgA9bkPI/bxRXQEBX++NK3Zh3cCDqijgLCrtSlzKvO9quLM+ZsCs6d6Ye\nR9K6S//uKseRNrsjjSPPTG8aJ3f02RvKAPgI55FSYD0qqUQQ1+aicwSgC6vQZeMuFVbQ7ZmS\nIpatCKvyCgRACUtiWBF2ve0uWQxls5WuUlpPcB24UJbnaBgOLiMZVN/xwDMqS0cAhFtufZRS\nv/9VWEqDKgRW2kkMGOpJ2xnn06BXnhK60LvuaBwsrCcv/YZ2P/ZvOv7xV6pscJy48tyrqNsd\n11OXm68WW9BTvlqoSiNP2jDAPPz9FwntbkqJYcoZpWTVU2Ig7tDrH9CRN/+nvCz4n2NGD2PH\nnUOF886Qrp1IgmoAbXAOa/+IAb2pYM9Bc16AtuB6b84CC+ptd/6dKk5lqbqB8e310G2q+0yV\nwMYJ7s0uN18jPjnsPBR0NpKWBNnC+zcP4wobXTQu2dBAcGfmVucFraJDx1SpHPn+VGZoM3WS\nJbDt4m9HZfnKMHbGdLnlWtr16HPKaBEWNFZuALUtCuYIUHZ0nnsVga4HEsZc2s0F1EZ79RYz\nEQ+nn4Y0fw0U8/e9nuTvMsZXTy9GHBstMVXHk8yRnJZZTFsOZtp2isjYVHlljQBC0xkIvXPm\nAOraLtwpH2zQObBEWIiv2nGCXmVL7dTMEouhaBsbQs/eOsohK1k/Xx9q5e9r4udG4SzA0SqY\nL/qrJYepsLSK7p81kLq0jWgUPQhAzdfZKeX6Pek2cb7IsAB6++FJHgFATb1y7S8sp/NZ79l5\nZbSO+wJHnaBVgWGqWD2o151eLfgqB5/2ZRO70oNXD3aYvxwUJyu3n6DcQn4nBJRUX0dgK1+a\nxLzcJn50vRo9F9e/a7Sg1Nl9LFs2x2plmEcns0ro9/XJvGCh3glqNZMTF5oM2MaqFagPTpXl\n0EeHf6AFSUvFmAvQFIPCI4w0GGgeMwoM5G0JmBc+ppvJiT7pJkXZUGZOaSldPf9rWnHXLTS6\nUwenHx66hbs5Euoo4q2BUz/4lI6ww0hMYvwgtmXt4XgThLKJWpWbboj6jEL3fIdgjFKK09mK\n/kNafHI9PdLveuoT0VVwLSGDa3Cs4600UhoaMDRgaMDQgEkDRQeOqlQBoEEpsCwd9dmbtO6K\nWwUADmoPJb2HTAuKih733SxPSQl4IhIc3GdKwD0MkHTbnY8RqD+UAqeFSseFymsIA0gc+MLj\nbud8HvjSExTPFB7b735cbJ1X1nvs/flMWzFfGWUOoz16lubmBB4ORA7qK6zgwQ0KAQANihdY\nhcZNHkun/lglaA9iRpuAbD0rfcyVk4v+FIsiALYhoCJQAtuFPC+bM7C995lX6eh/PxV9k3/Q\nxzFfv+O2nQuglxjPnx33P03JX/wgqtE6TZV1G8ezRwNKuqfG9krXoSNeZDwk4X31dwJGDurj\noRpNxYb362UuP0LjwNd8wUsD4f16Wuz0QFOt6dJLu2E0y4oGMDfTfvjd4moY+0tojMD3xqFX\n36eevGiaMO3cxhRh5GkGGoAjybcemij4lZduSaWaWvvPbYCiu45ms0V1R5o0pC0N6h7L4LKP\nwH+sdRlGj0lMdbF2Vzot25ZK2xhIL6uw9J2VEBtM/7ptNHVuY5tbW9bj49OCArhuPanh76BF\na5MIluEzxndhapMu1DkhzGY7ZTmlbNG8gnm/v/zzMFscZ1pQach0OMZFBdGTNw6nsQOc3+1o\nX9sNNTmCa0HPVUyXUlJWRVn55cKhZhovHiSmF9LRtAI6fqKQcgoZV2MxfUVzqXqN4GjUBwOw\nc4e1o+uYNmTswASnwGgsYCzdkiLAc/PPAS40nvXVv4tzzpxFg93wJ5Y54kf3j6e9xxmz1Ou3\nTh2fsePLa87vyRb5rXSuNj7K48A2LIHBHVtSXUbvMI/2b2lrKLeiUIz3aSDYGGF8FNKCLbbr\nfJmbCJbb5lFTJHAhCHAYE/Q/q9fT2M4dBVjsrBWFC9U7lpXb+Pa6jXSALYiEetwGarOqsXLA\nag9hqhcRYG5fKQC3ITJmO1vVz137NI2MHUAP9ptN3cJYX/zPgLelxoyjoQFDA4YGPK8BLZ0B\nLGe1AiBy8vLvhOPA7HWbCVvLlQIL0n5PP2iOAneklgs4esww8/UzEYAl+sTFX9LxD7+gff94\n3S7nK9rY+cZZ3K+HbHJxu9KXuMlj6Pwtv9Gep17WXSzQlu3p9mjrs3YeO34EZTCALQX0I+CI\nBp3AuRsWyWirx2imugGljdLaPLx/AwCFjEUH1QsuVgvzwgtb73iM0hb8qmpZ9MjBNPa7D1R9\nViVw4aT/s480ANs9urpQkpHV0IBnNGDNn4GnHSH6s08UKeCvb04CHwQpX/2koiMJbBtPvf92\nV3PqhtFWKxroeutsSvlyoYqyCwvBff7vHis59KPTf10m/IFI6i/4YjCAbX1dnS2x8dHB9MKd\nY4VF828bkm3CWYC6avnPUQZIE0/upXm/HaDWDBb2Y17p3sxh3DE+lCJCAoRqSsqrKCOnlA6n\nFNDexBx2UFjMlBc1zFOtxnCkHoPYkvcJpg6ZzNa8jgqcAkpQ3cKokqsBFnT8ZKGwNJ/HVCqj\n+sXTFed0F7zJqE8J7CH/yZwSWrwxmS2aExkELhCW33ptAUQF8WXgF9Qc5w1vb4rwwF9oC9bW\nf39vPQW30qc5QdtrGDOEVT0WDEA1AqefMDgFlljLFtt8sCuyX0jYvX0E/f26YTR+UFv2q8UL\nF3ZzqxOAy3pfoto5KcrowTQmnT1A7aGuXf8MnOvjmQZlHoPV5ToLK9pcUFlmfhlb/x+ie2e5\nd9enx4BtTHrQimSW59KKjC30waEFAtCG8jmaAVY+WhlNQKet/GoFIH66hX2+Fq3CbJ+b6E5W\nHk1kbqMarsdjKrDdDCtXBfDOClqXmCyspHHjWDxUrOR1JFroHgkDqsUAyLFQ5pXgNRYlKmp5\nawuP35bsvXRl5/NoJtOTdK8HuJHHALmVmjPChgYMDRgacL8GCjUAYoQVKgNs4x41/03RAHCW\nglc7gK109ayLcjT82rC89WXfBd4gAAvazbxI8FgC5NYTOLns+fDtFNKlo95lt8YB3AUXeO9H\n7mSL8v+jbKaX0Eq7yy6kgcwHDo5vb5DYcSNVwDb41AFsOyqw+j533U+q5Np5p6VaUCX24hNJ\ns6JsIqzXJ/yqb4GvTNfYMMBBAF7lJ08J56aNLcfIZ2jAUxrwCTABJ9ryW8LBtQelZYC/ufSW\nXvZOZm6YlQC+M89ZuYCOvPExFew9yJbaPXhX1C0eW2i10gwj2kMaAAUXfG5gfPP3HKBwttTu\n+cCtDo8v6NT+GHKBhUNpOFA15OzXAKxRQaUxtNdBms+gXyrTkwDAZohMXzgebhTLGEhN4bT4\n/Lo+SWBm+hnqi9KUB4wNhozdmdbkseuHNwoghnO/UgYpM5lmAzgUmq0UnAPgzWNwePGmFPEB\nDQsA/dBgfwanWzCAXcOUGUzVUVAuLIyRH/kEBqgpD9dAgTK0Zyw9c/Mo6tulcb+lAULXOmA2\nDAgSIDUsrh0VqQNr7bcoB+PAkTHME969QwRdOqErXTSmI4UH63/XWuTXRKScKqbXv95hgQui\nXQPZwt9dTj011Tp02rMDO3rm8atoUe+/z04utPm7Fcdo2rjObqWacfuvFYDZIBKvqKmkeUcX\n0TdJfzCg3UAML1Y22EpYZz6rVHDaFzwk9lKpsjh8glKLKisos6SEOrAjPG+z2IYO89iazkTO\n4l4d4GaE8v39K/jGYFga5zoiRwgTD5+ymgr6/Ogv9H3SMpoQP5Qe6X8DtQmKEYsXKMMAuHWU\naEQZGjA0YGjADRooTUpVlaLnHFKVgE9Cu3cWH228PC/cf1gGxRF83N4kAIgHvvB3wbcKx35w\nyAUBkA1ObqWzvqZqN4DJ8YvmCQoS8CbD8RnGou+TD7BzxklN1QyH6okZq7a+L9ynHm+HCtEk\nwsKJUrTzUnnNW8M7H/qHBW8xFi6Gf/BykzU5sAkdiTZZp4yKmr0GWlgBlU+DL9SD0sKnpbn0\n02wB19zENziI+jxxX3NrttFeBzXgyviClgwOm7WC3xKG/DU0AM7jm6b3odH92tCPq4/Rt8uO\nUBFzVJuMFi1BGGAukPqDKaw8ETGWf4DFIBlKhMX1LHbodzPXC0vexsjTN40U/M8rmM/5+xVH\n6RQD3BDZPmVYxgEIBz2HVuR1GS/PRZvr+xbB9C23X9qPLp/cjdowON5YOZyaz9Qv9r9HZBvk\n0Zn69PJA70L/GAdOAI7yPmxtfy476xzeK05Y3ocz4N9YwQLDc59spvQc0zioyzlN/mw1fSal\nghcUgF/q6UavXRh7ONj836ID9NRNw4W+9NI5G+cWYNsEgpqG9FhRKi1L30Q/JC+njLJs0x0m\nJm09VIpkdgQpfXzYorgFr1sJDTmQyU6Z5ssMlgOEjWwVSK35ZcbbQG20EwsD0WwFgK6jfSYd\nmHvgcoCfd+Srv+vComwxapwex1rWW1ltOS0+sY7WZe2gqW3H0rT2E2hQdC/yAxk6p2tqgNsd\nuvHGOWAxEEZEs9CAs/PRmHvNYljPeCML9x9RtSEwIU513piT4iNJqmxamgnVxTN4EnfOWJq6\nk7meN24X34egXDnTAseCcM6ZuXwtdbjqkjPdHN36wRWtlOJDakd2ymuOhmF1DAs26ci07ESG\no1m9It3JX5ZS0mcLLNoy6JUnhTW1xQUPRYDr3BBDA96mAeP3iLeNiNGes0ED8IECx91KgYNq\nQ/46GmjJwAsskPt0Hk63XdKP/vP1TvqBObVBIQLcRGAtjPkIuMxJtQhwWORsQUyNTQO6xdLL\nd4+jXp0aB2jL6sGxPax3HFubt2ZHkYNo4erj9N7CvZTMnN4AqGCoKurWNNoRYBP5pAQG+ND0\ncV3o2VtGMk2uX6NxObx/F5ZWs2X8oUbpUbbH1tHcX7Sf61N+ZyIcwOByT15ImD6uk+hT2xgT\nQK9MZ6t8a9fgtPPf87bSn8zXbm6DIjHKh+PQq8/r4bATSkV2l4NYSFi0Lol3GgC75eI0c0Kv\nAsyTGraYn7/4IGO+LegfN49k7nHFxNDL5ECcS8A2kHk0AQpNKz1FL+75lNZkbKOa0/U0Itwz\n8GWLG9aBxsgkuMmx2oSbqqK6VgC88porRwG6cpuxejahWycK9PMToLGrE86VNmnzoi0+/JnQ\npTMtOXRUPDUcnCPaoqyeYzzqWvDkw8Rzag6ZHmQouKSqTCxe/MAW3F3D2tHfB95Ko1r3F3XC\nAaUP86p7SvDwKisvpy+++Y4q2cmm8D7biMpA3l9XV0s3zL6WIr3Qcr8RXWo2WSQA7E33nqvK\n+/3PpXT48FF+MGORx0TFJO4xK/cZ5t+dt8zl3RONX8F1tc3O5D8bx8yZ/p+ptODCVkpwR8d5\n8pT5tOFipuNSSmj3LspTrwuDKsKbBBbl3gpqSz2FdO5AJfXW/tXFJYRt0bAgc0WCeQt10eHj\n5iJKU040C2qN6qJigrW2VmKYV77DrBnaaI+cn03fdx5RkFHomdUAv3sYYmjA0IB7NdAywM8C\n2PYWyjL39tQozZ4G8BsAThFfvHssPXjNYFrGjhR3HsmipPQi4ZAwv6iSwW6m4GUB8AfRYkDi\nKV3/qA7w8yE47uvAHNx9OkfTOcyjPbxPHCHeXYI2A48DaHrFOd2Y3zmX1rGzygPJeZTGVCnp\nuaUEh4ZV1XUCdEW9su3KNsivF3B3x0UGUdd2ETS4RyxNHdVR0HTAqNMVOZVbRi99sZ3ax4VQ\nBwpxpSjLvNw06AFUH9AtPq2YdgW859B/fHQQ9WgfKfoRznFuwGfNbcjgfn30017BTW7NOSSm\nChxzwiHjnKk9KTLUvQ4ZzY3RCQDUXroljdbvSafeHU30MU6NJCeG01OA9pgLrorTwLYJpEaT\nmT+7IpdWZ2yl5elbaEfuQeZjNm2tEKtPPKsFkMwp5dHRxop7GcCQD9/cVabJ5Ghee+la1iO5\nD00c53Wgtmw7AKSbRg2jBXv20e6TbBFVf7Mj3qUXI87P/3nRgD++cB5pGkdnxwftxBidZgAb\ncrQojW5iJ5O9I7rQlIQRNLnNCOoV0UlcQ31ofmPqEAVY+VPHWyPz8gsoPSODSkvLqLq6WujG\npJ+GuScbgRiAjWgPGu/LWy6D2Co+KjKSuZigC0OaQgOYw/jkFxSQv58/hXjpronG6MKPt0G0\nYLC6qLiYCouLqJS3H1ZUVlJdrelHivLexTQEsF3D1/xYH8prjanbE3nk8wZHSFVVldhmFMjW\nJ4Y0nQZAd6GUIAWNQWVOHgGwawzPdPERNbANblB3SHlGpqqY3X9/gfAxpOk1EMJ0NBLYRu3F\nx5IpuhHAdg1/x2I7NiSwfYIK2C5LS28WwPbRd+ZRVZ4ll2LP+28R/WqKPyP+9xrVsYWWIYYG\nvFIDeCUwxNCAoQG3aqAlG9Fpxdi1o9XIX+8cAPfsC3rSrCndKa+ogrLYmR4A4pPsGDKVuZQB\nGsO5ITij4W8N74kAhQGcArztnBBG7VuHUlRYKwGWR4YBUPXsQxyGoYOYyxl8zpXcrlxudw7z\nZxeUVNGJrBLmEC/iTwkVllQKB5Fotx/nCQ32o3atQwT426lNGHNOt6KE2BAKDfIXGJE7Rj+Y\nrb0fudZzOzqhWh/uC/oDgBvUH/4McLvDythW/4PZGefci/vQTRf3tZXMfA085U0pmJcDu8fQ\nK13HuVRtmAs0LcqKnQK2YaFdVceTtzSTPmO+5V/SVlMlg9n+Lf0Y8OCVGoF/MITp4n0FEBJF\nnWY6Ep8WTLDuYnnosADU+BjBYObCuXNoVKcOXgkmoa2YJHGhIbTszpvonHc+pv0ZWUK/sGJ3\nSRlcLhYmxM3oU08dYho0VOuCmAboaFEKHSg4Tu8c/JaGx/Sl23pewTQlPSjAx5/H0X03GvQT\nGhJCf3/4AX7Y11FuXh6dOpVFaenptGLlasrOzRUAnLJDLbifEeERdN45k6hD+3bUJj5OgNo+\n0IMhTaIB3INHjh6jj+Z9TlnZ2eTHiwuTxo+nq6+4jHx9fb32fnRUOZhb+Eip4sWWJUuX04KF\nP4u+4fkpBXMYH28UCWTX1NTyNqFqOp6UTFu27aBtO3fRzBnTaMqkiV7bdm/Up6ttgqWtUgLb\nJZhPD736Ph3/6Esa+t9/U8drLjXHKwMAHlO+WkgRA/swD/RkcakiM5sAVkoJ7tROBo3jWaQB\n8Kyf+nO1uUfFx5IoeuRg87kjgX3Pvi7myqCXnxTJtTQ4Ffz7xNsFHKfH3ptv0UxwhsedO94i\n3lMRUcMGeqpoo1xDAy5rwFt/k7jcMaMAQwNnUAMt+P1GKX78fm+IoQGpAYCkALnxgeAdTD6L\n5fuYTIuj8poMK683RRhvr7BYbsvgND4QW+1uinYCGHUXOCo65CV/vL1fwCYT6ilXvEFl6qet\nnRall2XR2swdlF6aTeH+IXRt14t4JiMT/rgTpOHyuLj8kCiqruBt+m4AX/2YHqBXXCxN7NqZ\nOkRGeHxVy44qHbocFRREK+6+hdYcT6YdJ9KpjIEykzQAZA4VpEnk0/I0RbXvzg9H18rRFKs6\nxarh+qydDHQn0vDYPjQwqqfqurtOYPUaEx1N0VFR1L9fHzqemES5+flshW2ykpX1wFFmXOvW\ndMF5U5iKxgRmN8WDVtZvHImSU1Lprfc+pJLSUvHFDOB0+arVVFhUSHfcPJdXQn3MX9jNVV/y\nRwjmlh//mA3me9gknrvX3Kmrqqpq2rB5Cx07nkgneTdENi9AlDAAKvvlA5oVQ5pUA5LPWFYa\nEGva6oXz7HVbRLQ1UBsX4Rjv4MvvCmeLEtguPpok8sk/3k5DIttpHJ3TAIBtpZSwxbajAmeT\nW25+mK28k6jj1Q084gHRDfMPZVUVFDpa5BlLl/rdL6qFHNmQTtdfIYPG0dCA92mgqRe/m7o+\n79O40SJDAx7XQIsmtqj0eIeMCtyqASU2oQxrK7F1TZu2Kc6V7VGGm6Juow5DA1IDTgHb7YLj\n6OouF8q8nj+aKJvdUo+E3WE16emtGm5pcH0hkYGBdEm/3nRp/z6mNQR3Ft5EZUnde6o65QNU\nAnDauky/1xlcrLeaVebRpjXO3a8BjMsX3y5g2phSM0iKMUD8jl17KCsrm9q0aR5ewoXltWIe\nKeeSMgwtYoeESXAXyHB9lJcdMBZZOdn0yfwvRLsBYov247bBNjixY8TLGv0XaE6twrIa3Q2I\nauBIBu1Hq/jWNrUgt7zWlDRYaFecYsfOCglmLmZ3SUsNZ3xIlw4U2MZ1Z5eutu80L3bmbNph\nLiaib0+CM0RvkOz1Wz3SjOAuHVXlloPazAE58PzbdOi1980pQ7o38K/7R0WY4xHQo/dQJfCC\nk5Svf9JtBfi1m4NU5RdSDs8RzJOCPQeoMjuPqpiKDboHZ3qr2GgK7tSe4qaMo4RpU6gVG3G4\nIqgnef4CKjxwlM5du9CiKDjgzFq1gQp2HyBwrIf16UFdb7mWOt9wpUVaZQT8BaT/uoyS5n1L\nsF7v/89HlZdF+Oh/P6WUb36mooNHxblfeCgF8jMupGsnwnjFT51E4I53p+Rs2EZpP/5OJxYu\nJlA/wdkbdiYEMZ989IhB1HriaPIGp7Xu7LNuWc0M2MY8TZr3HRUfPkZT1ljO04w/VhLmExwP\nQ3wCAqhVm1gKas/jOpzHddJoihk7XFcVjYmEM91UnrvJX/5Iw955XsxXWU7678upcO8heSqo\nnfAsxcdXY8FbXVgk7m3c911uutpMA2XOzAFQTKXxgh2kBRuFoBw4JPRTOKatq6yi8vRTBIth\n5eKkyKTzJ3PlBjrx/a8EJ7uSfgrf3cGd24udPni+aJ0S6xRjMwoLpthBlvbDbzR5+XekpFZD\nxszl6+gE34sF+w8LfeE+hA+Evk89YLPcrNUbKWfdVspas4nytu22SBvL4xw7cRSP+RiKGjrA\n4rojEeUnT/Gz6SdK/vwHGvLGs6IsR/Jp01h7T9Wmwzl0oTREwFwR84b9QvmGmhzTiXz8+x3P\nLnwnVPH86XHvTXrFUfbazYTnHaQF75ptFRNNAa2jVXOwhuntKrJyKax3d/H80y3IiDQ0YGjA\n0IAXasApYNvU/iYEZ9xY1Wn+wQZoqTmB2tC3BMqEKurBNNM4uPDX00izRdOavEKLFhgRZ04D\n+BEHZ59paSdUFDGIx3sUrOuTUlO9Htg2tbeFsGZOTTtJAQH+1LVzJ0FrA+3Ke/XMadr1mvF8\nxN3agp2/gubHLIh01/PHXKgRcEQDWott/+gGYBEv6hVMw2RL5HWfwAZu9DrmS1eKH1t1u0sC\nNBzOXW+dTV1vm+Ou4htdDihdfuk00py/3z8fafSLqbkQNwV+jO7rppLUxQRoQejCYnUCzVnR\noWO09fa/EcAHCECMwa89TZFDGqwM8FKtFG8HtsFBn79jr7LJIox7x1WQxqJQN0eg7Qf+/RYd\n//grqyXDISg+GDsAebsefY7aXzmd+j31IAW2dWyxuIR3BebvOkB5W3bSyUV/MqiRI+oLYMBc\nKaB0WTNjLgPa+5XRVHTgCB3/8AtdYBu6z9+1n4HwjZS5bC3Vst8JiHaXSGV2Lq2/6k6Lsqt5\nzuIDh6UAB/c8+ZKgVMK8tLeop2qkzsnJn5fQgRfeVgFHSFZbUUEliSniAwAfO14AbA947m+q\ne0GnyGYd5e2/YbB7pADzdOsuOvHTHwQfExDtPEXc7v97XtB0ISwFc680+YT4AOA79PoHYsFk\n0CtPisULmc7RI4DE/F37xPxO/2256jmDe1cp6b8up9Rvf1ZGORQGIC39GygzYPcN5qUjEjmo\nr01gG0AzygJtmVIAbkPn+Jxauob2/+tNQd2E+yC0R8NipzKPNozFdyyA5W3bQ+kMmKMsKdrf\nNjvue0osCsjrOJanZ9LhNz6ing/dpquHpE+/pX3/ekOAusp82jAWQfDBoi0WtLFYNey9F8Ri\nhzatPJffHXg2ZvCzJ2/7HnmJdyoVmcOeDKT9uJgyFq9wugrrwPYW1aK1rYL7PH6vAWzbUpBx\nzdCAoQGv04DTwLYJ9miifhh4qFnRQhXNzJrC3Hgj8JfXgD9bcYaFh1FFdo4ATqXFAhZsAKZG\nRkR4NTCM9macyqR5n39Fh48dZYwXT8LT7ITVlyaMHUMzL5lOYaGhzX6cMR74GI9e7xnKWi0I\nzfeRFLxcgi87+fPvqdN1+rQKAKogSueQ2hfKloEBskjjeBZpQGuRrnRECuu8TAYrAPjX8Ke6\nqIRg9QaBlX9fBkb1LHADmMpNKZU6DhmV1890GACWnkSPHqoX7TVxaQt+Zaerz7NltonqBeBd\nl5uvofjzJlAQA9Y4hyVf7qbtAnCSIB86gLz4DH7tGep84yzdPh1563+Cf72ArUgBWDsi66+6\nwwJ4lvnkggfAsN2P/VtYlhfuPyIv2zxiIQVly0U4m4n5IgD8gr0HaeLvX1hYfNrLi+uw6tz7\n9CtmC1pYhLaePIZCu3USVoqVvFAAq0/4L8it3+WB48rzrqZ+zzxEPe672ZFqml8aL3zHOPIm\nz9Olq3m8HZuneJ5tvv5+McaODAAWddbNvIVGznuD2l58nt0smBdH3vpEzG8tEGwrc88Hb6WO\nsy9jALaQ9j/3hsViisyL527UMLYorh8LPdAeabHjYfyieSLbaaap3PnIP6k0KU2c4w94nHs+\neBtFcll+IZISz3xZBNJ/W0b7nv0PQQcQUDPFjhvJYH9HCuvVlX9b5PBi1y469sHnlL9zn0iD\nxaml/IHFstXfHAxg4zcJAG3lc0kUYOXP3mdetQC1lUm14D6s47fd8ZjZGh9W5d3vupHaXzGN\n/MIafouj3anfLuIdKN+bF9XwPYhFrZGfvK6sQoSxEHf4jY/F86Us9aTFdWcisKB38OV3LJ5r\nqH/1hbN5AY0dsvNiCxZczOGKSjF2Fx1cLarqef/NvIjQmS23F1ssPCjbAuvqtjPOJxgWBHe0\n7jMFcxCW69X8vYLFiuQvflAWQzCA6H73jRQ7YRQF844VQwwNGBowNNCcNOA0sN2cOme01dCA\noYEzrwFYAYFv+rLp0+iDT+YxpUVLM3gKb8Jt4uOpS6eOgpbEGy2GAGrXsFX5W+99QJmZsI5l\nUNu00sTW5jW0cvUayivIp3tuv5U9JFt6Pz/zI2C0oDlrAPeLShSW8wC5ANztfeoVAu0Ettsq\nBVvrd//9BRHV/vLp5ku1/PKkFFivGnL2acBPsQiC3kmQFOG8zTsEYIGwVhKmnStekrXxOG8V\nH6OKhqWtNwssffUkMD5WL9or4nDPwgJaCoAGPdoOcKjjAwvtvU+/Son/+1pmEcedDz9LAID6\nPnm/Kh4neDYU7DloEW8tYvOND5hBXr00ZmCbQXJr1C96+QCUbbrhAWElDTAuvH8v4egW4Qqe\nWyd++J0XXdTWrygHtABbb3tUgNt65erFAUDa94/Xzbr1ZWfyQ999wQLQBD0CPu0unUrb735c\nUKPI8uBMFWCd3njINMbRfRpI+/E38w4Se6Virm/gBRLsXoBgLoX36clAX1uqY/8hsPgvPpKo\nWwzm9wXblzClj3VQEBlxzwAQti9q8wB5ryIfqHv+HGZJ6xk7bgT1fOAW+0VzCiw+Kr/v211y\ngQBkkRkg8MQ/vmRwuptuWbifdj70rHgGIAGA4BH/e43izhmrSg+AFB88XzbdcL+gEZIJdjzw\njFgMxbNJK9m8cARKEbtSryJYjIMyxppod2ZIYBjW3BA4AB77bQN1lrKcyMH9CJ/ej95JW277\nm6BQwnX4HtETLKA4Mr623lWw+HHghf+a69KrJ5cXDKxJ9IjB5ktRTJmDT++/3UWbb3xQgNHm\ni/WB0G6d6dx1P2mjdc/lmOJiwvRzxbNVGj8gbszX71Ls+IadbYgzxNCAoQFDA81FA77NpaFG\nOw0NGBpo3hoYOWIYpTAdybqNG5lru4zg+LNd27Y05+orqRVzWnqzfPfDQmGxLelIpMU57LbB\nPX3w0BF2tJhDbRPaeHM3jLY1Qw2AFkYpddU15tPOc6+i1AW/CMvDtTNupPjzJ7LFYTe2AqoW\n22axZRsCSx6VxTYDPErxaWUA20p9nC1hAHdKAXerFPBmx18wUViHgbfTLyxEAEgAJGDFBW7P\nbnfdQN3vvEHFRa7lb/Z2YLvkWIrssupoDdhQJToDJxvn3Kvaej7olacEz66tpoATetDLTzKI\n14MAZivl8H8+ZM7oAOr1yB3KaDpn5ffmc9AzHHjhLUr7/jdznDJw8JX3BO+uMk4blhREmB8z\nc/ebL8MKe8WUqyysFpEAQPyuv/1LAE99/n6v4Ag3Z6wPDH71aUr85BtBsaK9lrt5p6B36HBV\ng3NTbRp5DuvLDbPvEbQpiMP4T/x1vgA/ZRq941DmSoZ1uNL6/Og784Rlty2nvXplGXHOa2DK\n6h/NmTBPD770X4IzWK3AOhagdl1NDQ186QnC2GitfMHTjIVgzDk9gHvPEy/Q6C/f0RatOgfn\nNT5SMlesp/VX3iZPHTqCIx6Lh1oAFbzZjRXl74Lej91tFdQGbdD6q+8UC0OoKyAmiibwfaB1\nNKxtx/APX6ZlYy9RWYVj1wPy4XeHUvDMwgcCGqE05u2Gzi2Etwdil9D2e5+0uKSMCFDQryF+\n19+eExQlCAPgH/HxqwjaFPgiGPfDR7TuspuFNb/0PaLNhF1KcqcSFsJymZ993eW3apNZPT/2\n/nza88RLVq87cgHW11rBM374R6/QkqFTBfWU8npAnHqxWXnNmXAPtg43QG1nNGakNTRgaMDb\nNGAA2942IkZ7DA2cpRrwZQc3V19xGc24aCqdTE+nkJAQio2N8WorZwDYVbzNc9PWrQqLcpB1\nmEQ6V6xiuojE5BRKYAeYtiw5ZD7jaGjAYQ1onHaerqk2ZwVwOeGnT2nj9fcJ53Kn/lwt6AXM\nCTiAl06AQ0qpK9cA2wr+bWW65hbGluqSxFSxzRv0GlJgueajAXlhyQunqHDGBydbkBa+PsKx\nmGkrdjcLYESW15yOAC7kdnB5RPvbX36R+Gj7AoDowPNvCYu7Q6++T8fe/1xsTYbFGERrPacs\nU1uWN5xXsmNiPfFGYPvI25+oQG0AtkoQTa8fyjjQjhQycKW13AaPdET/3mIhQ5lehmGl2u32\n63SBbSxwHHzxv8KRYj8GBtvNvEhY+WHL/tH35gvebJTjr6GokWVjvnRgi0/0TSknf/lTLKCA\nzgC0BrYEOghq24Y2XGuag8q0if/7huwB2wDX1s68WQXKDX37X3ZBbVlP/389JgAxeY7jnide\npIQLz1Et+iivN8ewt/92wTwFHY8W2Mbi2ppp1wuHnxN+mWfxjFKOBYC7Cb98RqvOv0Y4PlVe\ny/hjldjhoHVoqEyjDcPKGRbYAIydka63z7EAtrELAfz1EQOd97lQwM9tCJytWntmgO96LdOu\nKBcjAZbaA7VRLnZ19Xv6Ido890GcmgUUKOdv/d0qV7VoD48ZqEa0FGjFRxNp+12PC4txcGiD\nFxpgMqiJ4IQTC1oQ5bMF1tppvItDSgLTx2B3h6OCnRbLJ10uKDfs5UGf4WwyvF9Py10D9VQx\n2jISLpoinIaKxWJuF353LGo/TJUMoPr0YxtUcY6cCHqZB27lHXovq5IXOrHzRpWRTyT9GBZb\n5He8No1xblsDeE98+ccvhV8ivOdih7K/rx8FwDmnXwCFBAZReHAwhbYKpmBeoAj0DzB92E9T\ngK+/SI9nrzSckrWZDaiszDWZzjgaGpBzRWpC+V0ur8k4nGMXek1drcA3KvmdspoXhKv4CLyj\nopqdDjOmUV5ZYTpWmY64Vs271JG3lvOKMkQ5dew/DXGn+R6o5TBcctWJ+Qy0BDgJnwrBVEY7\n+MDHluLow0aOd1w4k+Ijo0yJXPxrANsuKtDIbmjA0IDjGoCVdjBvk+zZw2SRIB+4jpfQtCnx\nAMaPlIfvvYe27thJv/z+B57G3IgGcFu2CLQkhhgacLcGWvAPZaVgS7VSwKM8gbk2sc06f/te\nKth3SLxA4uUJYFXMGPVLFfLWaeYqAF1vkhy2kkphq+EyftE/rXRiqtPImuJSwTMOrnFnZM/j\nL9pNjhdJWKAGtI6uv++tZ/GPCGfHcv14K7nj1l3WS3PfFd/QYDOwjVLhGA/WX9YEjsbGfveB\n2HJ/4MW36dSS1Wwl+Y7qpRdzS9KaeDuwraXdkf1uyS+V3iRYUNj3j9dUTer7lCWFiCqBzgks\nt0HvUZpyQnV1+31P0gU7llhdrAlqn6BKjxMAYFtufVQ8Q8bwVn+5AwDUBQB88AE3NuqTVCQW\nhXCEXtkAmwEK2gO1ZXnYXdDroduFwz8ZhyMcumEOYgHHmuxn53JK/uGwnl2tUu3oldGa+Wbh\nz0Bp5Yv2H2MOblAcnDUiftt4d2+sgc4tfFrSuB8/tglqy55hroya/xYtnzhTRpmP8DuAnVDO\nSFD7Nk4D26AR0QPEk79cSIOcBLbBaw/HiBAsFCkdRSv7sfux51WgdvTIwYS57ahg51dgQpzZ\nWhr5AManffuL4Oe2VU5gmzjhjFWZZtudfxdAPHZOBNXzOQNMjho6QHzAL47vHuWz5aSGBiYw\nIV5ZpN0w6Gnwm6mlv5/dtDJBULsES2BbXtQc0Q/ZF1wCUK8VV957Os2eaQFsg1ceu6w6zblc\nW5XNczgzxXMMgsU7W78LbBb0F7+I8Xx14VdUVFbKvxM1yuBXRTxW8e4rdvjWP2ORLIDnYJB/\nIIUFBVFEcChFh4VT+5jW1DW+LXWMjafWEVEC7Ivh+LCgEOGPSlk66pVgpTLeCJ89GpDPCuU4\nK+Pq+P2ovLqSisvKqbCshPKLi6iwvJQKSoopu6iQMvJy6FR+LuUUFfD1MiqtLKcyfgfAsbIe\nxAaorfSuJYBoAU5ziOcY/gnIQzu3hZpxlS8ISET8EUHZRnsjgSJ9fXxp5phJBrBtT1nGdUMD\nhga8UwPKB7Qy7J2tNf0g6dC+HXXs0J6K+Qfk6nXrTc9wTYPBFd4c+qNptnHq5RrQgs5aYFs2\nH5ZC+Dgi2i3atWUVjmRrsjSwBk1hi60zLXhhxKf4WJJDTYHVnrcB23XsoEopjr68RgzoTWO+\nelcA3AdfflcFHjr6o1VZ75kKW2urHm/zmWoj6j3w7zdV1YM/H2BQY6TP4/fR1tv/psoK8BcU\nGtas8rQ7GmRmgECj2LGeBLVlvDzC8nkrc9cC+LUmemXDknPYe/YXl5Rldr9nLluJf2Zh+Qlr\nVfDs6gkAbWn5Ka/bs/CW6ZRHgHrYwaAUgKCeBbZ13ySVTXBvuBkA2z5sGKEng159SncBRS8t\n4mCFi+9LLAgrBQtMnZURDoR92RqzMdLt1tm048FnVFnBN93/2UesgtOqxPUncAIopdsd18mg\n6ogFIC31SWPuA1CowKmqUlA/HE/aEl8dB5ZwWotFVCUQrCwDzyosJmGxVUpZykkZFMcqdvTq\nrPgLYNvxhU29tjtbp7vS47kJAFvr9DHlyx+dBrYTP/1ONAvP7jZTJ7mriX/JcmDBCmtWE7Sn\nVoF4LwR4KB7n/EckOk2lVZWUR7yzMJcj8OxlEFEIh0VSPsHvFz8G/mD1HR0aRgnRsdSJQW9Y\nt8aERVAsf6LD2agiOEwA5GGBJqvw4FaBbC3ub7yTqofCK84wprCWFsAyzwEJMJfBQpoXwkr5\nWFJeRsX8KSgtoSIGqosZlAZwncNgNYDqvJIica20nIFqTl+FucflKjEI0zTDX55NckKZZ6ic\nb7hQP++gHfEbwHQupyOiPSktWtSbc7upkr+UxXZd/aCbx9dNSvR0MWh3SzHZPF2TUb61l2Bb\nmlE+SGylk2UjvQwjvTK/9sFkqzxXrmnrsdUebRsdqVdZniPpG1OHvXJlG6R+5bnMp42X5/K6\n8ojVduQfNXyoALbFlwQ/++UKfFRkJDvBjLP4YlGW0diwbDfaJ8PKsmS75TV5rkyjF5bp5TVx\nbuubDPXjn6009YU52gZZt3G0roGWWottXmV3VbTANl4wDTk7NaAcW+24O9JjANyjv3hblVRS\ntyCyMWWqCvPwiTVAtjGAiKeaCmdj4OpVCoDtxkr7K6YRrJTL0tJVRRx79zOrwLa1cexy41UE\nflpr0qp1DI3/6RNrl0V8Sx2nyq0njrZpZa1XIEAd7EDROqYDN7G1JQAtCIdyo0YM0iveZlz0\nyCEW1wEW1rCllLU5ZpHByyOaw/e2NUvbDrNmOK3dNhdOtgC2G8Nz3ZJ39TVG2s9ip6/Pvkag\n15ACug7QcDhjNX7y5yUiO+ilrC2GHWPaIK3AYttZieZ7R3tPgbfcnug9A+As014btPzZlbl5\nqqoylqyigS8+roqzdzL8w1dIrz3W8jV2fK2V52p8l5uvtgC24YwSfgSsLRJo64SDVemDRXKK\na9MY5+7RgPmdSeCFJtBQlKx8l9KEFakEaJnPQCY+RzNO0BoBYpkAyZZM59CKv19DGdAOE5Qn\nIQIED+UwrL+7xLWlTnFtqFPreGrLoDiAcLzPQtCu5vDMF4310j9ybKUe5XktW1IXlBZTHltQ\nA4TOLixgQDpPWE6fKsijTP7Awh+0HxWg/WCAu7SiXBwBVCNO0n4IsBnzAzggHzE3lDimcq7I\n+tXq4hTKROKijJDH+hzKeagupNmcOfVtnFdZSMXV7PStnhfFpF7P9bW6kptXxzegcgQbWR04\nXEL8/SmcuUR9eHLISdjI4pokm5huPMlKmP+zhCd9OTsNc4MquO2nybcVytJMaDf2Sq43MvMO\n+bf0o/jAGDeW7rmisK2jljmDHBVoEFQV8otCLx/KxHwr5+0fZbzqlsOWU9m5OYKfCEMQxBxs\nrWNiCOAonCj612+R88QcRVsg4ISu5HmVl19AObm5VMIPWDm3fLk/0bwaHM6rwCFsZRHAfGDO\ntgn1iIUkPYXoxOFZ6st0CO7os3ywVzMACJ0XssO0U5mZlF9QSIjDmAWynmOioyiudWvuY7DQ\nO7jRINbagHwZWVn1emJt8X/xDGRCqaFDBlEoc4ZbyysKdvKP7EcVU09U8Bddbl6ecGCZl5dv\nvnOxlS0mOoZB9dYUGhoq+uXD/XCkHXXcbi19Sov6Hzw634Ki9biva8S2JdudwRyydU/Yzm1c\nVWrAgopE4TxSmc6ZsBbEwlZmrxInf1zhZR7blkG7gbkfyHy82B4u5zOsVTMWr6DyjCxVN+Om\njBNbnmUk9AALMYBVp/l7oDI7jyqzcoTVtkxj6+jp30S26rZ2DRbnUtzFK63kSvU2Sg/ZV3m0\n1mdvolBJ/qLBQZ5sN2gKXBFYox774HNVEZgLWas2CAoR1QUbJx1nX2bjqoOX8EKmEViCN0Yi\nmaZBC2xX5RVYLQpc3lpROtLVXrN2HtKlo+4lLB6A2uSsEJ1xag79auxcihjQx6J7Tbnghd0z\nnZk6RMs/f/zjrxwGttHek4tMc7zH/bda9EdGnFqySgbNR/iecFaCde4DPFdATWXNIaO1Ojpc\nbd/pqzYvrK2VAjB3y80P02Dm6XeUaxtUJ06J3n2hF+dUoY1PjHmLPmBhTSlJny0gOEh1RJSL\nE+2vmO5IFiPNGdQA3j+lmH4em2Jqmby4VFj5VhIAU+W7H7515Xsk4n1b+giak0FdutOwbr0I\nx/4dulJYMHPB87sf0uC9zfLbWtb81z0K7ATv/Kx2hKFX6D6HAeukrAxKyTpFx0+doMSMdErJ\nPkXJHJdZkC/Aaak15FGOjxjHemXLcZJpVUeRkGPkEUFVAuNEqQGngG3flr6UVZFHv6auoVUZ\nW6isttJ80ygLdTUsByx5TyeqLmEuSDfcZT7sgCuKeYwu6NWd3rxsOt/gppvY1bZ6Kj8meTXf\nPO+u20TztmynE7yiX+UE4Gq9XbythUe945Bj/NJfyzeZ9ZSNvsKFgjh+ZOwAmtFxIg2J7t3o\nopo649IVq2jL9h2iWqhGzkVr7cA43Tj7Gmrfrq0ukIcH4LHjibRm/UY6npREBQyulvPWEVFu\nfeGnW5zmLxUfCuHtjABbB/bvR+efe44AKVGv8kForR324uVD81hiIq3bsJmSUlIon0HtUgZw\n8LCUwDWAGQDeEH9eCALoDsC9a+dONHrkCOrcsYNoj702LV2xknbv3S++AFD+6Xpl6uUz6YL1\nOOcaBmgbT+ch+wgAew3Thew7cJCycnKotLRULMbFs0V1GIO/SJfL4HAmg9RYlAhjvlAA3IMG\n9KOxo0Zyn4NE/2VbkR7hHC5r4c+/irGTcUg4aOAAumz6NJHHHX9k2Rmn2NHPho106PARXnzI\no+KSEqFP7N5oAYeCrDgsHiDgxyv2YWFhFBvD1nNjRtG40aNEP2UftO1CHVt37KCly1eqLlXW\n8zeLYlVXxDSh35b8SWs3btJcMT2i0RIMM9pyw+yrKS4uzthpYqEp5yMsqUhM96fzJTXk0IJ9\nSqvehlTeE+p47WWkfQnGS2xgm9YC0HakpX2ffpB+6TTSnHT4+y9S+ysvNp/bCoCXujw9k7m8\nc1Sc37UlZboO7WyV1ZTXtAsW2nF3R1vAierNYo2DtTLH+S3snuonOKq1EtKlgzbKqfM20yyB\nbRSQwZzp4MZ2VKwBuo7mt5bO2neTtfQyvlV8rAyaj5Lv3RxRHwDohftWK+AId1bguFBPwDF8\n9gDbej30/ji5gOlsS1vFWRrcSM5hZ8tqbPqut82xALZhTZu7eadda2bUmcwUFJAY5uy2tmCD\nHSFYrFWKNR5uZRq9ML5z9aT8RIbTwHZ43556RdmMix5huXPixE9/UBZbjfe872Z2kDvJIWeY\nNitpBhdhta0FtkHf5giwLXYFfLtI9BJ0NLb8EzQDVRhNVGgA73ZSZAjftYiHBfDh9FQ6xJ9v\n1i0X72ugLhGW3W3aUrf4dtQ9oR11jktgypMYimeO76gQ5qM3GzzJks/OI3RUxkakhWxpXcRG\niLC0LmQqkFym/ziRmyU4q7MK8wVvdRYD2rnFBcxpXSysrqWOoRlTGCEeAYF9mHY7ixjF+OBc\nAAoiYPxxlwacArbD/IJpeExfGsaf0uob6MeU5QLkTiw+QRV1VXzjsGUq/5M3U6MbyQXArrSk\ngnlo2LKU0ZFGFyUzMnYoLJ8/2LCFdpxIp99vu4EiAwPdAhrKOtx5rGFA9F9/ruTPCrGSBp26\nrFeUwDeVHxskl9dUUEsfDgjVNl6/ptFGfqwGtqQ2Qa1pcvxwuqbrVGofHO9OlTRJWQCii/Gg\nqqhkK9lKYZ1aVz+voTvVyxh/WcADLIBg7djA6jszK5sW/vIrbduxywRI1n854EsC21TEw49z\n4p4BP1cRWz0Ucd3Hk5JpybIVdON119LIYUNd6reYN1wX2rJg4c8CzIxgRxTt2iYIWo2uXbpQ\ndFSkGcw9zVa8JQygJqel0fadu2j/gUOUnJJKxxOTaMWqNWyZPJguu/giigdoqfNlJ/VQxj+i\nYSUNPVbyPQx9YJaI63iws+6kIASAFmnxxaLSsUxk54j8pdzuVWvX029/LGFLbZPDlrjWsTTj\nwqk0asQwtsxu8JyOerLZUn01pwd4fODgIQbCDzFwu4xmXjyNRrDeA9i5GNoC6/as7Bz69PMv\nBbiMpqDvsEru06sH3Tr3egrknSCNabe2W9ATLOh//3MZreXFEMxHWJRj0SOSFxgCWwWw1T9v\nVxKW6IXCgzHuvaqqGoIldy4D4AcPHaY/l6+kG669mrp07mS9XTwMGOsKnr/wflzNoDae4UIw\nPqwjtZwWADtAdlxGf33Z8zf0AGt7LBIgHCwWBhrGV12GceasBrTAYXVRgwWus2XJ9FqL7eoz\nTEUCi8uRXLppAABAAElEQVTCg0dl8ywc32F7LZxtuVNg4e2owLIOAJ8W5NOCIHiWe5NoFyzc\nAWxrQUTQQ3izWAOIC/Yc8AoaieKjSbyTwBJ81d6jzuo4api+VWIxg2aOiqMWkHbLs/gusZvD\nagJfXtCyECvlF+w5aJEUEXBOCzAH1p9OPQd4EUfrDK6OfyeeLWIVIMYXvidFUb52IdeRaq22\n205m7PDRij1nxdr0rp7DGaMe13fiJ187BGxL/vjud91gtSkFuw9YXuPf+gBG8fyG42NHwU04\nU9aTxvyG8OHfs84KnMjCGa2WZgmW63ufeVV88NwCJz4ohyIH9RN86s7WYy+9O37v26vD1nUA\n0nAGqtyRVXEqS+xmwU40WwKqG7nrqvud19tKalw7CzSA910pqjBHFjBvc2FqCe1LS+IzE593\nOFtwR/Ingnm7+7TvSEO69KQxvftT3w6dqRXv4G7uAh0A18lii+pjGSdNltanMgR4ncrW1nns\neBF0IKAGAVUInC3iKNTIX4XIr/xGhHZVelXoux70aO4qa1btdwrYVvYs2C+I5nSdRld0Oo9O\nlmXRd4lL6JvEP9g0H1bA4KMFQNKwSqHMazcM887TLcmn1peqGeIG2OYOAbiNL6PtaSfprTUb\n6ZmpU8ScU05Qd9TjahkA6X4/eJieX7ZStLeWz5U3javl17A+K2taUICgWRK3pFNFmhYvwPvd\nUgCSAPnGtx5Mc7pNo76R3SjMT+fFw6kazlzi86dMpnFs8QqwupJfWPIKChiY3kmr1zD/JU8U\nAI1SMJdMz6+G+Ynr0MfiP5fS7wySlrF1NsazT4/uAhQGD3MwO78pYAv8pORkWrFmHQPpRVw0\nZqGpHPzGR76P531Oh48co+uumSWqbNQPKa57Jdfxw0+LGGCupEunX0QTxo6hyIgIAmWFnqB9\nrRkQBqiedjKd3vjve0xZki/A6a3btwsQ+NpZV9DY0Q3Wj7IceS9Nm3o+TZowTuixlGlOsng7\nP/S4jcFyk94adNaofskK649Hjx2jjz6dLyyxMQYYlxFDhwjL4WAGhmW7ZDbUGRsdTZdfcjFN\nHj+O3vv4EzqWCIv6Apr3xdfCKnn0iOF0Mj2DEnmhIf3UKTEfMEb4SoMF+8wZ06lHt25soewr\n+iTLdvYo9YHjr3/8KayoscDRtXNnmnreFOrcqaOYM/5wBMKLfHW1dbwIUMHW6Nn09YIfxY4A\n0a4GlVJKahq99vY7NPe62TRs8CCL9qGu4UMHU78+vcW8wCJOUVExHT12nL7nuQJ9KYoTXUKe\nEcOG0KD+/QXYHsovD2gTQG14NfbBEeA2h+WigLO6MNJbasAniHctKcTWtntFMptBLWimBWht\nZvbARViUbbjmLnPJjQUqzAWcoUAVP9cPvvSOufZ2My86o9ZjaI9S/HQceCmvOxKu4gU0pdji\nX1amO1Ph8H69rFadvWbzGXeaVZZywmr7XLmABTFYEhcdPq4qppR//zosbjAqsVoXf594Wqzx\nJa+Z7j4wxx2LRZ7Wg8vl4weVJ0VRfmP02Vy/L6RKu94+x4LrO+3732jgC4+Tf1SETGZxBLc1\nAN7gju34OTbZ4rqMKE1Ok0HzEQs0q86/xnzuasA3OND5Ihr5fBnw78do0/X3W60PYC8s2aU1\nO6zTo4cPotiJo6jT7JlOLWZZrcQLLnS+cZaFtX/K1z+RPWAbVDeQ/2fvOgCjqLb2gZCEFNJJ\nB0JC770JIgpYUGygICgWFLE/ferT/9l7f/aKBUVFxAJ2kSa9l0AIgRTSGyGdNPjPd2fvZGaz\nu9ndbEjAOTCZmTu3nrkzs/e7534nZPRQ8u9v/fvYCpp4elWBv2lt+F2GsZJYWcsYSVvu4+Kf\n6Xsnv3rKqTLSkjCpwHnwLuRg7MQ5H4M2UsGAAMiySvii8kZGXJkjVIV4jqtMJDElrGaqSfBB\nw0khUQZtO5RAn638VeA9cFh5FgPcl4wYJ/aBvh3Ix9NLGDU5XmrzpQAGAOt0OGVEe6oYlE4v\nyKPdKUm0KzmJDmSmMZidKZwwwsgQusU9g/6gfFW7lnRpCrN0qflaZOTsiAacArblYwQA26ud\nJ3X360wPD5pLc3teIcDtFVmbKK00m2oBcms6iSMVw8PLfVI84KYn2JHkFuOiI8oOvHjXHnpk\n8gRhDW0xcgsFKi8vosU79yovMBfXA8/uSZ44aHsSHqEdc0KGeykFxyGeATQubDBNj51M/QK7\nixcf6n86O7oEMAeuZBj3oh1RbNmMLwUsexsTtB0czosWL2EHgxuYm9qDhrGFM0BeUHjopAtT\nWDD1xXkTxgtL6o2btgjObXn/8YJFXivXrBXW1RMYJHZUwM0MgH3pj8vZ2jeAbp17A/XvW88p\nqLzILecqr0VHRtBtN99IL7z6BlsHV4sPbBlTe7y34BO2+G/LVt/DGoCmyBE0Ju68yb7QjS3D\nAXxu37XbcoFNCN29N54++OQzniBQLFlR92jm2J0zewZ586oMWQfzIhAPWxBbrN84ZzY99+Kr\nVMptgxX+waRDAuT1cAeHdTBBD5gMiOH72I91KO8n7pfUlXn+9p4jDwDZn3/1DW3Ztp3LiqSr\np19Bo1m36I/1fULJEVxo7XjyCMDy/z1wH7334ce0lelzMIEi6sPRsAef+2eLvqIornsEW9ib\n1xMTG5jE8OYNOopkGpgg7icAti0K59m3dy+mpBmuywtlyY+8fEOYl2UxPyPQLg24cR/WiiuA\n7fZmy4nLU5sHXNPW+1QcY7l1cXwilSYlEygCYAnn36/x5c4Y5B9+/wsKZN5K9GGfmE7M0+34\niqNqpkFKePEdtakou0P3rur5qT4oT9EDGub33Zn6VBXq+Yw9bQAvzuTv6jS2nJPlrVrf4sC2\n+eSDbD+s7Z0B+WR67DtYALar8gq1Uc7oY/Dju1Jg1erTtTP5YovjCWd+TwQO7ufKIlo2L/4d\n0BJSV3FcLdapVQL8m+h0FjhR7NAjVvh30LYjZeG31POeudog3TE4lSE97rxRF25+ctzFzwEc\nxvowVZIv93/fbjECWEf9T5VETplIZy/7lDbOukNnsWytfFgn563dJLZ9T/2PQG3W+8HbyTs6\nwlqS0yI8du41DYDtjO9/pcGvPCYs8S01AkYEoLqBxM271lIUI8xBDWC8M3HgcDb0cSff9l7s\nyNFb7L082WcWj+E8eWWru9jcBObUlqlHBZO16XULIBYAK6yIsa/llbvYpPNA5byWagDS1jJY\ny9gAQNsaHqsirBphvK/hPa7D2hhWxoozQjbS47G7jKPEq2WTUTbSgxGp9pVvGsuJID6GyLEd\nxpdZRwvp2w2rxRbo00HQlozq2Y/6dYmlQV17UK9o/iby6sZTKdAN6EKyjhYIbms4ZcRxBgPZ\nh5jzOov9mQGoP8ZxoF80GJ850S75h8/RPkXk/lS2wvGytLdNptbWvH4MzqG4IL/tajuRStEF\nxv+ID2NV4UORM5dhSjhf528syqzPV38MnEyWLzABFCvCFH9rMFCWGAVPJQgjUVRFMCJYagyq\n56Q4BWxry+LmilPsO7YPojv7zKQbe1xOa3O206vxCymrIp8fYLbs5X+IU990bS4Nj2vrOA0z\nZXBSlwpuCpSbzM6k8njgEM7gkPZGubQwJzOr4w6QylZR6EYgp3e5oMPV8H2z8/0j7xsmMtAJ\nMck+v/dVNCt2Cvl7KNbZeBggrU2XzugObVCfM+4rNbXoiNYFDzOWLx7nD8szL75CGWzlDCeQ\n994xn+LiGNC18qMb5cDhHzi6YS27a89e5MSbUjr6KY6+ZQqRkWwpC+5nR/S7ha2rf1j+swDq\nH3nwfkE54kh6LlqUB3A/jJcfol14UUEAin7HgDmsmi2JToccAelqa2otRXU6DHnu2hNPb3/w\nIU8CKPcI/dCDKUTuveM20W57MoeFPUDduUwp8uqb74g2y/uPa3fdNk/oDiAz2qVoQMnZUX3K\n+sj3IKyvCwoK6ZmXXhGOPMHxPevqaQw4K07wEN9SGbJ+6HfTL7+U9u7bL6z8Rf1M9wiz/GX8\njluxag1dO0Ox+pflyz3iy7wQhh8JtgT1hd61dTLPw1Z645rjGjDnwqxmbvymijmlxrHd+5qa\n5SlNvzxuNNWYWSNbqgCAJ7uAbebKjn/iVUtZCIdUABjbsbUzLN0H8aAxcFBfi3FbW2DZoRRd\nlTp0bzr4UM1URFpp7RbbqGvYhDGUu2qDttriOPv31TTwhf9rEH4qA2rYubElMedHtxSnsTBL\n1p6thjqGvz3NLScs/OZox35DRn/9rs2iUTM3jofnXW7OOii0WVAjF7Xf2UaiNutlONJtTqnS\nrALBe9ZRET5HHE3UyuJ3Y5Bx531P6GoFOhJrwDYmuAFi4hnvPNO2E0ZLzwEoRYZ/+JKuPPMT\n8RxongE8C63l/QFO8clbf6EDr7xPWkeI5m2wdJ725feE7awlH1DYuWdZitJ4WPO/vhqtA4D5\n8ElnU86fa3Vx07/9iWJvsmyNn/zxYhEXFDhRl0zSpTNOnNMA3tMf3PGgWK3qzmNjrFrF2NHV\nXQTjRgzvwIgAMBwYAcZ5wImUlcrKMSgt64FxxFOuAww/ziu3yxn4LmOajdLKCnV/jHmlwSUN\nB5Sg6CgsLaZjTMmBeOCdRv7a71ERx9+ctJ+2JiUIepJA5uKODu5I5/QfTDdMnELdIzoJZWrT\nOKfd+lQYe1ZyXQ6kp9H6A3tob+phBq8zBec12iSpQ9BO1JcrLMar9TngSNGhLkw7qNddcP2J\n0AduIgS/gfi4Xke4ufzfFK5Eqe9FIhXHb8d9zJNXS3sxLYw3r8zzbe9NfowP+XmbKGTYkh4T\nLN48sYKtPcdtz4aGnjzx4s59ExMwmGzBHhMvyA8+FIXRHB+j7wLYFkC36Rh1FJuouOkP6ilF\ntonPUU/cK2yij+r6K0/c8L1B/wQFLyZmannfNdR1k4xNBrZlm7CHIvhOkU+79nRRp7F0buQI\nWpa2WnBx7y86LB4+BSRFbHGLcGBR2p5w5yUdehDJYkQnA3Hj2jm5DMrJIu1OBjW6oRM1oiO7\nM9RElH2vspofDOZmOXmCC9P0TU1U06FyEbWJ9A6hKdHjaAbzZ4d5wekK7iGunNmifXattRT9\n6dPPv6Qj6RkUy9QRc6+/TljKIn79S6thajErxi+SGdOuEE4CwUeNl4EQ7Lnwcra8/X7ZTzTL\nCjhpnivS5+aDpmKpeKmMO2u0sMQ1j2fvObiTYfksPhScSNYPvN2btmyjkcOH2myjveU4Eg91\ngF6+/nYp86DXcZ3w0UUOJykuNoYCmDMQcWzp3rw8WCN3jo5mAD9T6A1pQdGx8KvFYpJCvNQ5\nzCX9Xbm1zJVdSW+9/6Hg7gagff5EpkdyoN7odyEhwTSgXx/atHW7em/QNtQXeW3cvIWmXzaV\necD1lr/m7bf33BGd2punEc+6BsytNi05Q7Oe2voVWBKD31cKrLpgiXU6iK2l51ha2yEuRvBh\nB9hhrY32ujGYgoHy8Zx8KjucqlMBljVreSzrzJxw6SK3spNSs7a4wnq8gi3htdI+PFR72iqP\nO1011SKwjWX8R75ZRp35eouJ8uFqUDz6oi/346aIB6/AMRfPjq3jGT8V35G28JRuJljV4Wq+\nfrMiTttTAJeWxNqqAktxnQnTOnK1yKHeSKa2vgeNJG01lztfPZXiH39F963BqqOc39ewQ8Tx\nDeqZ+sVSERZ74wyCDwhbYuk5OMGAwun+HIAff+DzD1Pf/95NmT+toIzvfqHCTTvI3knB9dNv\noQl/fXPaTFRbuse4/+bANkB7S8A2fsccWfyjyAYTKYa4TgMBbMHc3ILRp/i50AaW35apRO2t\nA8aGEPPvsHk4QOLy4xVUwiA4LKOLGOwGqJ0trKLzCU4Ui8pLBAgOoHzZFqY+3bCaBsd2p5ln\nT6KxfQYSKEvsrS9wBoDXANRLmMYU1tagDlm/fw/tOJwogGwA2GrduRmMZAmsBGGy/iKCqY3i\n+BT9UfVpGse7sbEdjOIw4QGDQOwBSgNQhk4ANHsxON3BBE77MzgdxAaPHf2DKCwgkDr6BTBg\n7UMI9+UxvA8D1j4AqxnUBjANPELbZrV8C+3VxjO/bCudeVxXnWvr48ryG/7qc0GNpfWul5sn\nXcU0FdO6TqKf0tfQM7s+otKaMgZt3RjkhjUqoFvl4TIvtrpGQfqtXDaPbvc5FAknh92Cg6gj\n+CZam/BbC6+rrly/dSlp6ktH2wGaUmXxUmSVu9Vy29uAK7Mtb2b3QLwj+HER/2HhfoLmdJtK\n9/a/ltzbMDUCp1DAPeVvU+pzRqTlPrXgs89p45atgq7i33ffSV5s7WP3g8o3BdzboAnZwVQd\nsNFX7zfnDW1v27mbLp96caNW20iHFEu++4HKSsv5+KTqgNLu+pjdFCyNggNF9B1RHdSP/wGU\nR30BbLeELF76g3CKKQF36Ant79e7t/iAONpefHxiu8bQkYwMTXNOCk7xHUxdBH5pVwnqBuvn\nR59+jo5XVtED995FPbt3E9k7Wm8kGsI82gC2kVbtOyI3UJJUMud2BvXoHidmYkWw8ee00QAG\npVgCX8WrjCDmTpOcbYhvNz2wXXYotcWAbSxn7v3AbWpTsFw2d6V1+qeLk6xfUzNx4AB8xFja\nbEnAPw5aCDjHwt4V4LClcpojDPdUK7jnTRVzTmifLlFNzbLZ00dNnUS7H2JHWxas/BOef7tF\ngW3P4ECL7S/le4fJlqZIWw/3Bsm9Ijo2CDtTAyxZrKOtNbxCzt2v+YGI01GvsEw3f05csUrI\nli7KU4+ol/2cobTAj9PTXLAyK2bOdEp66xNdS5I/+doisC15kuNunqWLb+nE0gQXnC2eKYLJ\n/y4zLhUb2lS87yAVbt5BR7fuEr8j5G8nS+3d9+RrNPa7jyxdsh3WSvpc+OSGzjSLdu0TlGzm\nq9XSvvhOtAkTITHXTbPdPuPqGa0Ba+NM83BY+Xq6+zPY6m9TH8AJYH17vKaKKtkgDFbexUzt\nmVnIE/QM2rp5WAbiYbULcDw1L5vi2QI7JTdb8GGnsRPH5NwsYUUOehVVTBgHznVjXQlOqBGd\nPxA6AJDCOIcJCBOYlxokwklwi8MiGqB0B2Ex7UNhDEh39A9gx5tMF8qUNP68+trH01tQtACQ\nBkgN63ZYWMPiGhtoawFwW1vd31hLzO+Ztfj2xrOW3tXhzVWfZgG2tY0HeA3D6Kmdz6HRoQPp\n+7SV9PXh3yi3slAByiTThu53CS93r/Fic3yTFaboTdpcnTuGEpVi2tCtZ40UD0VzKda5GiqA\nMVQyn+v31Q4GOflhFZQUDQAr50rAsw8nmuXlJ8kPQKDpAUVuUjfKwUl2LulJl3WZQDNiL6Ae\n/l1EgdCg7lY5V40zKhUcIm5gjuxusV3p3rtuJx9+YTnSr4Q++f726tlDOFfkjqnqB0dwPgFn\ngUXM3wo6ksakuLiYHR6miecL9DGwXnakPub5p6amUXZOLgejpkrd0AtwhCVPp1rwMQMtyrr1\nG9RnWH7gUCeA0zh3pM2IizRIu/rvdaKXizy5yWj1om+WsKPFXo1OLHBUuwR5s505P4cVdOet\nNwtQG9bXzkoYO/s0b7P2/JhwBOJs7ka6ltaAd6dIFdhGXSqOZJJ356YBigBos3+tbxkslUPG\nDKsPOIVHoEYB56WUpLc/tQlsy3inYo+l19jsWe/gxdbLw95/Qa2Wfy9lskoNOMUHWot8FO3f\nt0eTa1CelqnLw6dztO68NZ5gIN+NAaCElxpSUJSz88aUTxZT1xuubpGqW6NyKd6X2OT6AMA1\nl6Dhg82DWuacv7nNLR7828eSlBw4TMEjBlm69I8P8wj0bwBsm0+QuVpJeas3iizxnPr1cfwd\ndSZYbEMBsKI1B7ZhjVuRka3jg4YVN6y5hSNEnvRuTHBPLUnpwWTB7W3p2ukchu8cNlgzQ3L+\nWENpX/9ImT/+3qBZeWs2Eji4zSnfGkRsxQGxN8yg+Cdf1dXw8IIvachrT+jCkhd8Jc67zLrc\nmNjTacY4aaoGAMq6MUALygtL1utyPIp9Qckxtr4+SKvjd9Bfu7cJIFvQpDAoDg5wYF4QxG1O\nUTAClFGPa4kyuVwFEyBB9RHi50+RQR0pKiiEuoRFMM1KNHXlPcJgje7BtB7AaWCdjRXusMSG\nPjCmx9b8v3SaU0unX97NDmxDJbLLhLQPoHk9p9G0mIn0+I53mYeblwy5MYjNMz0QFWTlXlB9\nnP/wf1f1awHEcWbobOf37EbzRo8QZbbGP7CEHdWlEy2YcQXN/VqZYXXVAy7HEieqpLU6HjnT\nS4SPBAUKP5xdfMLptVEPCMeggtzdeDQbdBXcE7z8fl+xkjzZ4u/mG+aQF/8wV16WDaI3GgBw\nEi9BS2AxZkLLeVlOY4Ky8wsKCOA2nh1QdFRVVTeWzOJ1tC8rJ4deev0t4chS2wdxjH4azk4J\nT7WgjfH7E4STDRzLemGP8/DQUKfuAT6mkRGm9pgeC+W9QXSMJxXiEw7QcHYGijKaKiIPLu/W\nm2+ioZwndNkUkatkLOUBvcBxgyGnrwZ8ukRT0c54tQHlTKHQVGAbVtJaAdjjCnHz1i+LruMV\nCf8UcWvv2WqWd8PSvCq/3lEg+pArpDwtXZdNU/uhLrNmPIm7ZTYdYgehlsDePY+8SEEjBrsE\n+He0CX5WJj8KNmxzNKsG8QF+mQs4Wf8p4m2lzxdt32MA21Y6gSc70zZ3Ogtnc+B0tmYBbyUr\nu4JBDSP7esDA3nalaRAJ1lNngID3OOKCCZT92ypda8C13e/Re9Wwwx8tEsfdbr9eDbN1YO05\nOMrPwal0+mirjs15DVbN2ABir7tiboOiMLlp7T3cIHIrDIi59soGwHYqOx7t/+T9wkcIqpz/\n92YqS1FWRnS/7fpW2Ip/bpXEGA3N57Ga2ElMxsawTRkyKu89jCdx5IqxqaiAC/6AQxlUIkfL\nSggOHQ9kpNHO5IO08UA87U9PEQC20lb+i3abxsByPO+CKtSjVqa8oSNwS4P6Q/BTM17j7dFe\nUHyEBwRTeGCwoP/oFBJKnULCqHPHMAavQwRPtaxPa9KxrJOx12vglADbskg4kYQEefrTG6P/\nQ1vy4+mdhMW0NX+f8kACgOH+3daNrZSr2DIVpsX1XVOkdeaP+tBzXjeMGEpvT7uUAW5+EZg6\nuzN5NnsartuMIQOp+HgV3fXdcgX4gn6aWLB4f3AeTIXEwKDpZch6gSpwLZgnH+7pO5smR40m\nb+ZKR4m2QLMmVue0T44XpQ9T2vybLbXDw5wDVKEEvMzhcBK6PsErFbQvd8VRRBumA6lptM8i\nXXpGlgn0Ve5pZnY2hTKvpqP9HX2tkgcd8MCMdkJkHgDg2/PHYdJ556hhSozm/Yv2oQ6JSYfE\nM4E+KwV19OV74e0IDYxMzHuAy37MbaUVWR7KOchljhzmOtoVUJ8MHtBf1a22XMePNYrQJoa+\ntOfG8WmpAThB1EopAw1N5cc0558u2LhdW4TTxx4MjGhF6xhMG97cx6BsAWdsc4AxzVl3OLdz\nheSv36LLxr9vT925sydlSalqUlhYAow5HQT9YMBzD9H22x9uUF1Y7G2YOZ/OW/MdWbNubJDI\nRQGgGQoY0JuO7UnQ5ViSkCRWaeC6s4I8tIJ75QqrfW2erfk4eKRl6/SsX1dSt/nXteaqt1jd\nAgf3IwCe5pK3egNFX3GReXCTz8EHLKXjuJHy0KE9ViaeKRI3b3YDYDv186UqsI1nGjRdYeeN\nJb+ecXY1u6OVlVjZv6ykLjMvsyuP1hIp+7fVlPTOp9TjjhsEWO1IvULHj6Zut15Hh95bqE9m\nMq7TB9o+k2Mh27FOzVV82zpNv5jSl/ykKxDgdnfT5Efyx1+La2ETx5FPjGsmuXWF/cNPwP8M\nbmjpoPF4TTXBZ1Z1Xa0Iq2E6jRoGe3G9hsOE0zw+xzHoOOqYmhJO9EDpAXpNGFph/AnDQr2A\nBYE3HoPLPeh14bBScVzpJiyIweMMGhFwOXu6K04G4WjQx1PhaQa4CxoNX/4N58uUGTgGHYaz\ngrqWVpbTwawM2p2SJCyyU3IzKaMgn7KLCgU3t2qJbQnJ4vSOiHj+ZBrWB8a5cryOfNAe8FOD\npzqSnVp2ZqAagHUEA9UKZ7WPoAvBsZ+XD1OJ+AjDV0fqYMRtnRpwvhc3oT1KFyQ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6QX1ruHNv4GV8vyYkUkJuHrXnPIZGR9L0Qf0phh1t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\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3gfO7GSAt5MZwRWy2hj6aEUwRsaffmF\ngie3sbwA3h5kJ30QcEuDJ7k5qSfAe35s9z61WgCUO105RT139AD1T3r7U12ynvfcrDt35ETr\njC53xTodYA7r/qZYljtSDyOuoQFDA4YGDA0YGjA0YJ8G8Lstd+V6Ebk5uMLtq4URy1UaKKko\np0PZGbRy73Zasm6l4JOuEfSyCpgNAyyIG8PZQb5M0RMURJ1DwmhEj740ts8AwZEd3MHPVdWx\nmQ9A6WPlZYLXGxQimWyJvS89hRLElkapeTlMIVLGBmMShFcwKAFEmWMNGMwbYmjACQ20GmAb\n3XtFbqnSBGtAlAloa8fXL4zowKB2FIPaCqWCPW0HIJfGjvSmfrSQ9ueAU/uECfxtHU+QABO5\nIQDWinjpxeUff0HfzLmGJrHltr3gtodbW7o2JkhYYj+XkEcn2nBmyv8GKkKr2/HLZA9bbKNM\nE1bZIJ4RcOZoAICwmBziG+7v7yesmnv26E4zpl/BFB6JtHvPXsFRncOTPnDEaOoW3Df0ltwC\nAOcOU80OGpIOH2Zgmx2UtoB4s5Ovu26dR598voh27NotZnvRmbdu30m//PEnXThpIp+iFWiC\n8gNAVlOGY7/k+x8pn51O4iFow8+Mt5eXSHsBpweAbp5W5mHsDQ2cag1ETZ1Mex95US0WIGlt\nWblNPuySA4doz0PPUd7aTWo6HADYHv3l2yStc7vffj3t+W+988PENxZQU4BtOOADYC2dSMLB\n08HXPiRzZ2twVpi/np1CmwSOn2zJ8rjRwoGUrTi4BudRcMzUqPArIuHFdyxGgxNM8Mq28/UW\nNCqD2HkagG9LAoeCf0+9Xr3U+6E7KWR0Q4eQ8Y+/osbBQadpFzeJszrlk28EJY3MtD07sYTu\nISUJSbT7wWdo1MI3hMNBGcfePZxMakXmqw0zjg0NGBowNGBowNCAoYGW1cCh9z4XFehyzeUE\nHwKGnH4awJgUvNk/bv6bft2+SVg5ZxayISYbYUGUMfwJYYAVGRRM4/oMomHdetGAmG6CHzsy\nKIS8Pds3S8NRNzkeRn3S2eJ6N1thH8g8QqBHAaVIDqhESo9RERuZVdfVoMYKsMVHymgcp/LI\nVE2z02apvJHpP0IDrQLYRv+urDsp+LU1/V9/AzQP07lhvvSiE6A2HsYrPl5kchQJUNvCw6Uv\ntcXO8IyXMRfylZ8somVzr6PxzLltLy0JKn0dg9teDHI/vDdbUI4A3xa6xUUzSSytIlA5tEME\nQ84oDWg/HvJjhAaaH4OzevCA/jRk4ABhvVx4tIhW/72O1q7fQBXMRS9gJg0wjHzldyg5JY1G\njxguPlTafE+VIr2wfJ7pQrJycigrO1vMBgOMXvr9MsrIyKLZM68SQLV5fVDXwsKjbKn9EaWl\np/PkES+755nk8NBQumP+zdQpKkokaYk2mdfVODc0IDUAS2IAq0U742UQpS/9haxRWex7+nWd\nY0g1ER8U7dpHay6cTRM3/Ehu/A6IuW4aJbz0jgqSVhzJpCPfLHeap9HdrwP1uGcu7Xvqf2qx\nsCyOu2UWgf/RWbEFfAcMYMey3WLIN6aTfaA2VwIW03E3z6Li/QfpJFudlB1KJVg+QQDKS2Ae\n55hEsCZ1x6t0AH0cv0fNJe3L76kk8bAuuMfdN+nOHT0BbYxWev6r3lp78w3/otKkFO4jPzvs\nHKyGKZ0yvtc7uIy69HxtUcaxoQFDA4YGDA0YGjA00MIagI+MlE8Vh93dbpndwrUxindGA3nF\nRfT8ks9p4epfheNEMYbnsamg2jSNwSMCg2naWRNo9jnn0+DYHgxws/GVM4XZmQZ1wDgYQDYA\n9r+YBuXnbRuYUmQf82Ef1YHUSn31hnD1cLadBRrRDA00QQOtAtjGEwkKkmx2YoiHUwJmsl3i\ngeWHCsDu1Cg/eqZ/pMOW2qk8wLyMLaD38RJnYamNcswLkgW2gj1eInhBVDBFwoyFX9Gi2VfT\neT3i7LLcRvXbMUo3s0sgldedoGf35yogvtCs/vUHCphC1v2qvDKaGNpBxGsFzTeq0EQNiI8L\n51HH97+Kea083D2oXTs3kas1oBYUJBAPDw+KjAinmdOvpEsuuoB2742nTUxXAotulY/b1D/B\nbZ+X7xxtgCjMyT9oHyhE1q7fSH+tXkPgx44ID6OhgwaTL1tXHk5Opmy2Ol+3cRMlJCZS7149\nKSoyggFub/FcFZeU0JH0DDpw8CCVMVDl6+NDsV1jaMSwoQKkd3fnaR5uoyGGBlqjBqIvu0AH\nbKct+s4isA1AOfF/H4om+PfvRbFMDRI4dICwZC7cvJO23HQfladlUOKrbEX98J3CIjnupmvo\nwKvvq82GdXHUJZOctigGp/bhDxbRcXbSDKljj+ewCh/+fr3VuVqYnQcXJylLbe2Mble0gc8/\nbDEenHMCzK7hDfsOcTEW49kTWMuThPueeUMXFVbyoPdwVg68/J6qW+ThHuBHMbOvENkBlAao\nHX3FRSqofWxPAk80vMb3oZpCRg0VEw/m1DCyLunf/iwPxd43tguBu9sQQwOGBgwNGBowNGBo\noPVoIOWzbwir4kLOGk74vWdI69fACR7LwsJ5a1ICLdvyt7DSBo2HlAAfX+HcsQc7fRwS25Mm\nDhxOfTt3FTzZMo6r92XHK9l5YxGlMXUI6ESSstJpc9J+2n8klYHsAtVyXEBoEkgzYQKiLjLM\n1RUz8jM0YIcGWgewzRXdXVxJR6vrGsw6SaAb+1Eh3vT8gEgGuB2jH4Eepn70OSXm8wMJ+hH+\nV29viqutTwDcSXC7kAfDk99bQKtvv4XO4oFlu0aWaaM1EpK7qWswdWjXlh7aw5bbePHwSpaT\n8iLHw4sJlqqfJBfSRINnG6o7Y2Tj5i30/bKfqfDYMfJhag2A1JPPm9Bo+ySgiz0A3zEjR9DY\n0aMERckrb7xFpWVlSscx9SM4oITIdI0W0MQI8tl458MFtJUdYXoyEH/r3Bto1HA9HYrkD088\nmETx+xJow6Yt3P+5x3O9YdHdMSSE+vXpTYPZSn1Qf+bUZf5xOSFwqtrSRFUYyf+hGugy8zLa\n+9jLauvh9BEAppZ24zjTYkhQG3Qig5lCQyvBIwfThL8W05+jL6HDC74UwDaud7v1WsHXDAAa\nAkA68dUPGtCHiIt2/HHz9KQ+D91BO+55TI0NwBRtCD1njBrWWg/gsBOblwsquOc/zxLuixSA\n0L0fuE2eOryvzMwR90absPv8OcL6HmG5f60Tl7AsGQL+8ZUTpolj/Clg+heA36O+eNMibzYm\nTLQCB6OGGBowNGBowNCAoQFDA61HA5h0T2SaN0h3w0F367kxNmqSdbSAFq78lQHtdRR/5DCV\ns68UiCc7dxzJHNlw+Diiex+K7hhKUUwv4skGaq4UOZaGJXZKbhbtOJxI6xP20IGMNILlePbR\nQsGZXc2OHaXIMbI8V/cGmK2qwjhoWQ20GmD7x4xiAbACd5Jiws0EDcIVnQLo6X7hwhJZhst4\n1vbIKv1YsYl+RHEUibitHdSW7REvHdSXlQLvtTMWfk2Lrr3KboeSyIexSZreKZBKa02W2zDK\n1egYcVDOFnYgmVFRTVFeHuI+INyQ008DuJdVbIn32aKvaAMD2xCElbBTxEWLl9Ce+H10y41z\nqIOvr11ANABebMija5fO9NwTj9FnX3wpnE1K7dTU1H/0ZFhz7lGfr5Yspe07d5M7e3a++7Z5\n1Ld3Lx0ojfqGMaXIpRdfxP25jXAKWcOc4XBuAct0WJrDMl0C2LKN8rw562/kbWigqRrwCA4k\nWG1r+Y9TFy6h/k89oGad9fNf4hhgtzmoLSOBWzt4xGDK+XOtcEgZPGIQIe/eD95O8U++KqMJ\nC+4us64gn5hoNcyRg5hrp1HSW58Kp44y3fY7/kuTNv8krMTDJ4+nKwrrnSmCrmTvoy/JqGfE\nPnfVBko1A4r7PXqvTW70xhq+m4FyOQGBuN6do6j7HderyUCrAqkxTT4mvfOZOMd99+kSJe55\nWXIabZx5m0pHIyLwn+J9BwVVjTzHBAUmIwwxNGBowNCAoQFDA4YGWo8GQDkH+jRfXlEWceG5\nradiRk10GqjhsXg2A9pv/LSE3vvtezrO41J3t3YCzD677yCac+5FdOWYc8i3vWJK4coxKcbF\nKB9A9dGyEtoGK/HN6+jPXVvYEhs+phQDNY4mDMAAFoljccX4Y2jg9NBAiwPbeH6yKmpoTX65\neIAAWstnCnvQj4wK9qan+0cIR4d2g9r8NJYx4AZL7X05uWypXW8BfXrcGqWW0IF8ueSxpex5\n7yygv26fS2fHdhUgvxLL+l/oCxbuN8UGkyfr8immJaljRm3TW8ukawb+GDzfUVQpgG3ruRlX\nWrsG8BHcvmsXrd+02QTamvo93+k2bKq/N34//c30HVMumOxQU+TH1YedNV515eW0d38CA+iK\nRae9z6RDBVqJjA9zfkEB/blyNYFntxs/B7C6RrisI5LKY1m3dgyAw0pbKzKODDM/l+H/rL3U\nmIVWc98ypPVoAB7vtcB2ysJvBSANR4eQSqbdgnSafrHYW/sjOaPhwFEKOJ+PLFkuHA/KsJ3/\neozGfr9Anjq8H/za47T2kjlqusrsXIp/4lUa9OJ/1TD1gJ9nrTjybNaWV1AJc2XXVdeILNBr\nvbtEC6trbZ44rmMLmeJ4pljiyUApPp0iBUAszx3ZA/g/f5ve2SLSVxcW0dZ59ZMOCAsc0t8i\nfQyu2SP567ZQ1i/K5IWMP+ztZ1VrbYSFnTtWcKTv50Fv2Lln0bHd+wiW+uN/+UIkObaHvweX\n3ijoaHJXrKPIiyfKrITVvnrCB9GXX0DgTDfE0IChAUMDhgYMDRgaaB0agOPtwx8uEpXpff/8\n1lEpoxY6DYDeY93+3fTz1g20dMNqBrSrmVIkjobG9aQRPXrT+H6DqXPHcGF0pUvYxJOKquME\ny/BDWRm0PyOVrbFTaVdyEiWwVXZ5VaWJwaC+EFCjCNH/BK+PYBwZGmjlGmhxYBv6+T2nlOTD\nhGdJwifYXwZObaYf8WC+DBmONLYEeRQwrclNG5MpsaxK5dS2leZ0uAbwDjfs7h2ZtCAwjIYF\neQtrVHvqDt3NYM7tas7jGcm5zYpCOPSFCYS1PLlwaZS/OrFgT75GnNajAfQPWCT/sPxnAezi\nXBFlj5UKsF4uA5WIkyIoPDqG0IC+fZgGZCcQZPIzgR3m4LKTRdhMBoDry2+WCq5vtKU9WxHa\nK46AY/bmeabFA0mTTbH3JWwzE+OiKzTQcdxI6sBOhUsPpYjsAFAD3O5+2xxxDotcCDgXrUnB\nhm1UsHG7uOwVGaqLNvTNp2nVxKvVsLy1myj7t1UUccEENcyRg5Axwwh824feXagmS17wFYWe\nPUoHqKoXNQf5f28WTh0RdLK2jsrTM6mCucHb8JJNIbyUEhbGcHZoTTyCAnSXdt77hAB0dYGm\nk3Y+3uTXh53yeLhTe3ZyCSuoNuyM2Vy01tLm17TnG2bdIcBtbdiID5y3SEe5O+5+RJsddZlx\nKUHHWsGkRvInXxP41FdPnkluTLfk06WTGgWONkE9k/DiO1RbXu8UExQnRxb/qMbDQdy82bpz\n48TQgKEBQwOGBgwNGBpofg3A10nm8j9o5CevsY+UPmqB+A2164GnxTl+DzZmyKAmNA5OiQaq\nGMD+Y+cWem3Z15SQnkbhgUF08/lTafLgEdQlNJxC/QPJo53pd6yLanSsvIx2Mq3Iqr07aO2+\nXcIa+2hpMRVXsJ8YrOLjsbPEB04XBgMXqcbI5h+ggRYFtoG71fCfP3NL6sFU4CoMngBoHdvR\nR1hqu/O5vXgK8iyorqXrNx+hA2V1FNR3HOVt/YWpCNh6iy8C3JIP9Olyf5W2M7DP4H5Qv3FU\n7tuRrt6YRp+P6kJj2JodAF9jghiYHIDldlvW5gsJOSTs2VgnJzk9eLbXF5RRFVtuI54hp6cG\n8vPyKZc33EF0CzwPUtBP3Jmja8L4cWIiyZ5+I9Nq98gb1B8AtvEshYfpATFtXFceo6wyBl/i\n2Vocxye4IsmpqXSsuJj8/fxcWdQ/Ni8PD09B1WL5Hano/x+rnFbY8G4MYu+893G1ZoeYakIC\n25FTJgpe66zlf1Lf/96ts+RFgtwVf9PmG+8VaeFoSDtYQmDg4H7Ubd61dOj9z0Uc/Nl+J9OH\nbFhGngz2OiN9H7lHgOPlKelq8q23PkjnrvpWxw+uXjQdwDoZW1NEa5GOfOA005rA6vvo1l3W\nLjsUDktt87wGPPMg+XStB5gdypAj73noOSpPra8/QPsBzz1kMZuzvnmfNl13N+Wt2Siuw2o7\naNgA6nTVJVRzrITvx2oRHjp+tJo+6d3P1GMcADA37x+6CMaJoQFDA4YGDA0YGjA00CwaAKiN\nb/7KCdMpaPggsfKqgH8TFe2qp3Ab8sZTzVK2kaljGsD4qYRBZDhbfP2nxbQ75RBNHDScnpp1\ni+DMBoWmKwTlVFZXEazBwYm9MWEv/bZzswCzj5YyrsbXFcyLSxN4gAkQ4HBDDA2cqRpwzdPl\npHYAvB1jy+rE0ipBlwF+bYRBpkT60QsDHbfULqs7QTdvTacDJccF4YaHty+FDplIOdv/YK5q\neE48TR/oE7Xk320oeXeMFpZrACXv3pFB7wztRMODvOwCt6FXqHdWTCD/PSloSfDSQ1gdqyXv\neC0tz2RO8ugA9T7wJUNOEw3gI4bZYSF8X3GuFZz17tVDOE1sivUy8nFnS0Y5SdS9W5z6AdWW\n5+pjtKe0tExYa4uy+YVRwucvvPI6XXvN1dSnV09dkebt112086QperKziFYVDVQz7du3p8rK\nygb1OslUNkePFjUINwJaTgOdZ0ylfc+8rloDg94j7cvvCc4CAXZ2v/16QSnx92U3Uc97bqZ2\nHXzoBFN0ZLLDQC3f8/D3XrDYCADRmT/9SbDghQAc3nT9PTT+53qw22JCK4HgaR7x4cs6S/C6\nyuO0YeZ8Om/Nd4JvWySVPwSs5NNag83XlYHTGo4ytQIe8263XqcNcuj4yDfLKeWzJbo0A5//\nP6s0IaCmGfvdR3Tglffo4BsfEyz7YeElrbyQUdSl51P7cGWCEjydKZ98o8u/++036M6NE0MD\nhgYMDRgaMDRgaKD5NVCSyI4FNRPZmCg3nyzv9/h9Auxu/toYJdjSAADtv3Zvo8M5mYJuBJzZ\ng2N7CkttW+nsvYZxbe6xIpVSZFcq04qkp9Kh7Azh6FGO+uX4V+4bW4xrb/lGPEMDrV0DLQps\n4wH8kYFUgNtS2vGA9rxwX3puQAQ5aqmdfbzGBGpXCVBbgtiefh0pqNdIKoxfL6yTAeTKh1+W\n2xr3AlQzgZM+nXpSQEx/BZxnKBos2UWst+u3pNGHwzrTmBD7LLfRTk+2yL6BLbeRx4sH8qgW\nZZgU8lFKIV0S6U8eTMwNPRlyemnAA16Trd64NhQSEixutdUodjQXaVNS0gSY7cVAVc/u3exI\n5Zoogteb+yvqwDirqENmTg49/8r/qEf3OOrPFCkRYeHkw1QC4NV2RtoxF7dne0/y4WX7cLIp\nubnPdJAb7fPy8qKQ4CBKz8i0oLqTlJmVQyd4glDqxEIkI+gUagBAcdzcayjhhbfVUnEMYBvS\n+4HbKOePNWIQtHHW7WoceeAZEkQjPnqZ/HpZfobdvNqLpa+gsZBSuGkHYVksBlLOCCzB4TRR\n65wSFtywHj9r8XtKlqbvnjP5e0dHkFdkmI6mpGjnPsGnbSm/kLOG64KrGditzMljh4vWaU10\nCTQn2mWd4L/e+8iLmqs8IejfgYY3gYKk9GCyzkIfmcMyv9OVF+nKsXTS675bBaCexg4s05f+\nInjIaysqhVV+30fvUZMkvfWJTldwSBlxwTnqdePA0IChAUMDhgYMDRgaODUayDbzpaEtFb/R\nBj7/MMXMvlIbbByfYg3AKePWQwmUlJVOQxjIPqf/YOrg5UMYT7pC4OzxD7bG/ok5ujcl7mMQ\nu1RYaldzuVJUEFsGGHtDA/9ADTiH/LhAUcBRK9m6+sPDiidWBVlVQO1XB0UJ8NVeqgQxBmaQ\na/72DGH9fYIhW8CyoNgQD/rJE+QbHkvHy4upPGUvA39MxiGvuaAtzZGFqB8szLldXn4hFNJj\nuAnU5tKA6rFAh9VstXrb9nT6aERnGhroJShcxMVG/iCL62KCRPwn9+UIKhIkSS2rpv0llTQw\nwEvoqJFsjMutSANt2ZliaGhHCmDO6+KSMuUZkDMWop6KxbOp+zhVc3DhlzCP7aat20T/uPSS\nKRQUGHjK+oqHh0d9i/gB4MdYET44dDiZDiYlM5UGU+uwLpwFovHeadvOjXnP2lFAQAANGTiA\nJp57jgC5Aeoi7zNZMFGRmZklJr60P5Sgl7z8PLaSLyV/f3+7V4mcybpqDW0DXcjB1xeoYGRF\nRjalfrFUDHRgrTv+10W0+6FnKX3JT6K6XhFhFDCoLwOi51H0FRcSwHFbEjR0APX+zx2U8Pxb\narSDb35M4PgOO2+sGubIAZxTghsc1uVSQI2ykbmoRy96i6KmThYW5xWwFMd3UCNteKDQPiKU\nvNnJI46lAND2YSeRlqQqv5DWTLmWyg6nqZfdPD1o/G9fMsVGbzXM2gE4rUsTk/n3wxEqP5JJ\ndQwIm4t7gB/5My83pHDLLtp07V3mUQi0IKi3MwLL9o3X3E7YSwHoPOydZ+Vpo3twh8fdMlts\nliLDIv/wB4t0l3o/2HBCRBfBODE0YGjA0IChAUMDhgaaRQP4He7GKynh7FoKJu/hn6Tvo/+i\n9mEdZbCxbyENlB2voH6dY2lMr/5NrkFNXS3lFx+j3KJCBssP0I+b+bfxgXjmyFb8Y4lxGY99\nm2D/0eQ6GhkYGmitGmg5YJtBqTV5ZZRfVStwWjcGmy9i+hFYasOi2BFQO6OyhuZtS6dEE/0I\nkF+AvhAV3OI3QGiPYZTD4HZlfoYCeCtRWuVfvLjAqe3u6UPBA87mhmAAr9iDqVgeh4C+paz2\nBM1j+pU3h0bbzbmNRrtz/td3DaIazuT5hFwEMe/2SVrJ92VwoLc4P93+SN1Yr3fjMaynbf1X\nMDs8YfzZ9MNPv1js4wcSD1Il/zjy4h9J6rNhZ7PQJ/Fc/r7iL8F1DQD0wskT7UytjSafTm2Y\nMlGjD9Gfob7+/n7CYSTagHPxgec+y4cC/8LdxTNxEg4ynBTRQ2pqqIrTFzOIfyQ9g379YwWN\nGzuGpkyeRIGBoOo5M/sR9DloQD9atXYd8xPpdYi7hkmNpEPJNHzoYJvaFe+vM1RHNhveAhdh\nBRw3b5YAt2XxB156V7Xg8Qj0J1CNWKMbkWnM97BYRt6Q3vfPp/w1G1VHkwjbcvO/BWju1zMO\npw4LnFOC57pg/VY1LZxTSnBbWp2rF5twAE7wUQvfoBVnXarm0pctzu0BtZEA4D/i2hO/OD6R\n1k+/RS1HHsAyPmjYQHnq8H7L3H9TGQPrWhn12f8IkxeQtK9/pOSPvqSinfHCCnvAs//RRrXr\n+MDL75HWISZAeDilNMTQgKEBQwOGBgwNGBo49Rrode88wgZH3268mhQr7GCpbUjr0UCgr/N+\nnpRxLPuMyskS/Nhbk/bTnrRkSsxIo0LmyhYgGca1/E8VzaEaZhwYGjA0wH4EW0DwPALzWJ5V\nolZgTIgPvTAg0mFQG/nMZ1A7iXm6YddlC246wQ4kQ3qPJre2bko8BnFaowjQjBvWhhG6wH5j\nqZ0Hg8ymhmkBNaFH/oOXXUkt05Kww8wdRZXMl21fu5AlNjiUfKJfuEl/bWhpRrEAB1ujbqzV\nSX4Y0HJrzYe1sdCWtQhmmSNPsQEptSHIV/C324hj65IsB3Fs9l/caVOdbOV3LgPboSEhqmWx\nFl+Ete3+A4kOA7OwVIZ88fU3AuRFPebMmkF1ZtaUluol63ySV04oom8l+q/QMOeJchDfkqDv\ngx5kxLChov7K/VRiyiQyn/oy63VmbxjylZspdzrODjr+WrmG7v/vo5SRxdbMdrTbUhuaL0zR\nqUmT+mL4kmWN6qPJsx7du1PHYKaskUo1XcA5PGp/8sUXAuA2v1e4LvWSmZWtHst8jX3zaaAH\ncyDDokcKrLYPf/CFPHV4D27o3wZPElbVMvHw918kWCVLAfC97oq5dJxpO5wVWGebA+MAtzfN\nudvZLK2m84oK113z6xGrO3fFCbgw1105V/BYa/Pr89CdFH35hdogh4533P2ocLqpTdT/yfsZ\naO8jglI+/Ya23/6wALURAIefqQu/1UZv9BgcnlpHoUgAS31DDA0YGjA0YGjA0IChgZbVQMjo\nocKptwFqt+x9cFXpGDPByeOCP3+iUfffTEP+dT3Nf+8l+uD3ZbQhYY8AtcX4mONZHNu5qiJG\nPoYGziANtAiwDf0dZUvtTQXlggLjsmh/endYtOB1dtOicDYUDaAmkzm1p65LoQQGtQVPNIfZ\nBnDYEtzdk0KHXUBt3dyJkT+rAJ+sBvZyU3K3XULDKtfHl3kijvZYm0YA1/wSa8MgoH/3QeTl\nz0uMZGS51yQAzzBoV4C9AowDHcvmwgoBUmui2Txkw22a1SWIHuodKvLIYQv4RWlHRZ42E7bw\nRXwU5FbBS8NzcvOoQji9q9e5torV1dWUm5dP5RUVajqk14rMr44BvOKSEspgSob0TPAN6+OJ\nNKYggHgFhUepivOX6bV5WjuWZct6HUlPF/o3j48+gbjpbDkM7mOA0xJAlHnINIjr16EDzbvp\neooIDxf9m3u92CMO32r6eslSAW7LtNjb2lDWoeQUevWtd2jFqjWCeuSR/9xPkRERPElk+RUi\n84MeYeULPWbx/YHIcsWJ+KPwuadlZPD9ydPdn/o49Uczpl1OYUy5AutxtAdtFs9NfRSXHqGL\noCQA83U8gbTgsy+olnnNGrZDKVa23a5KmPqQQ2lMGVtK08asm9afwqpd6UfW6o1sEcfD3Z3u\nun0edeyoTI4ILZt0zDuqrDhOz738Gm3dvlPcK9nO42xFj0mTjz9fRE+98DIt/HKx2k9lHGPf\nPBrwCA5keolZusz3M9d2DTtXdUSw1HXLTfcJbmgA1/uffl1NDmB42FvPqOc4AKj992U3Us0x\ntipxQtw7+NLY7xeQb9fOutRZP62gDdfcpgtr7SewplpzwSyC80WtgAO9179v1QY5dJzw4juC\nWkabKGziOOEYFGEApHfe9wRJWpK+/1UmBXJX8qoLB2THPY/qYiM/w1pbpxLjxNCAoQFDA4YG\nDA0YGjA04LAG6k7UUR47fly3fze9/fNSmvzYPdT15ivplrefp21MOVJaWUHVbIBZx2NNjN0M\nMNthFRsJDA0wfmoL5WgmBQGEfYJ5nT9PPUojgr3pU+aH9nCQfqSKwbbpG1IpoaRKWCgD4KoH\ncaxUHAgVhNGZ4rR4Kjq4ncFttt5GYjOBWhQwC6AWCyPI+OfG9Xz0wnPp4UnnNEqX8tfBwzTl\n/c8YBAPrN0sb/suZKbVQgCaRNcriSogysedA75AoCmOLOViZi/ItVRKJdcJUERwbZS0eHUND\ng+zn3EY2KHch35On9uVSJ293WjYulnzdAIriausR6KmaqSJW/LWKktPSKO0IewNmPqrqataV\nrcpyA/GhAHAH7uTOnaKoU1QUTZ54Lnmz0zyk3b5zF61Zt54B5CzOs5jZGOr4vmAOxLISkEaC\nzHA02InzPGfcOBo1fGijClu15m/asHmLAM4BzEP/AIotP5LcL0xWwu3c21FggD9FRUbShLPH\n0mDmgLYkpWVl9MHHn9H+hASqYUAW3NCyX0NNaPeFkyaSN/OuolypO8Q5wQ9pHfN8pR5Jp59/\n/4P2xu/jItpQdFQk/ev2+YKOwxbX9JZt22ndxs2UwUB8UWN65IaDdkfqEfcnmC2GoyLD6ZIL\nL6CYLp3VuqGdqN/hlFThMBIAMwRpFQBWuS4Cm+kP9HQZc4tfdvFFunodLSqirOwcysrJpSy2\n6sZESwZPipSWlVutSSiDx507RbNeGUTq1Ekcw3ljY5JfUEhHeCIgLe0I958s3qdTYREDauhE\nZl0V+gpgCheUgQmBCJ6QiAwPE30f1C7W5HByKr342utiwgZxhI7/v73zgI+q2rr4hvRKegKh\nBRIgtNB7rxYEUVAQRRQBBVGwYVdEfbaHvT3Egg0L+CEqSpEaWugQOgQIAdJII5UEvr3PcIc7\nJckkJCQM67zf5N57zrmn/O9kfKzZWZv3rt5Dl47Ozk4c3R2gOCSnpKi+wie0Tm16fOoU8vO7\nev7rxe3jeqkv5PfZkqgBJiJz02kPUIsXptuEICf+FK0f9RBl7j+s+tcbcTNJlLZ5OfTx17T7\nxbdNqn3btaJef3xTql+3yU26i7ykFFo1+C7KOSFfIl4uQX26UY8Fcy5XXMGZiPyLG3Y2jiDj\nyvgVURIW/aOSX5qPJX8+3Py5R8yrbb4Wr3SJ1taXWi05MRF7g2tRWztnvEZH2IKk85ezKXTY\nYNV1ec/hJH7aoUMGUAon+6wVGUH177rV4gsEbdyExcto07jLCSSlXqLpa9/QV+uCIwiAAAiA\nAAiAAAiAQBkIiGC98cAeTvwYTbuOHaF98ccoJUv+Mp7/wcYv+WcbCghcrwQcWWfc+PYcat+4\nWYUgcKyQUcowiIjaJ3ML6Of4NBrOkdqvtqrNkdo1jZYkpQ0lHwCJHO39INuP7GVR2yCGsdhi\nJuZYG0cT7qRrrfotKDc9mfKTTqmuSrh0KCIX+WtupzzycGVhzzmfXJzyycm5BhXWLOBkcoXk\nxEnlfGunmWtH1qYjV+9catA2Ttk15LH+VqPIhS6er0l5+bzfQneOLuYPtPOulJ/PKyp04H6y\nO5bnnGqSX0RH7l9oIp4bWg3aldUJ+V7pIxrs1G0n6SP23O7g527TWrXx7qrvS3lFF+ndA0m0\nMy2Hugd6ak3V6ijRoWvWb6Ds7Gx+D1wkJydnfnEUvg1FBNCc3Gw6cPCwenXp3FF5TsutIuYd\n5pdQFh/qsvwnRwQ/iag+Ghdnk7C9a89eFclck/3lPTzEJ1WeXulF1p/LCcQOcbLEenVDixW2\nPXnMx6ZOpqPHjtPOXbtpV+xeXt9J/qJFvJNrKEuR1WujKSDAj7w4ytvFmZPI8RIKCgtIhPa0\n9HTKzMgkSdgo4nn3Ll0oqlULcnR0NBF09SvWdnDw8BFmGadE0LJylLVlncui2H3pat4G9euZ\nzCe/x43DGtIbr7xIq3j90es30lleqxR5BhVVZB71mcFjqv8DwgMbrjk/AM87oG9vEsaqjtt+\n+/1PjmLext+VGayO1Er4m3d3d7dil5SdnUP7Dhyi/fwSW5c2zHfS+PuMY5rfKPuTz7z5vy5U\n0dFaLgJZX0nzyBcbx1gEP85fVMh7+gIn7u3XpyeNHH5rsXM1btSQXp/5AskXMGuiN1AGvxc0\nupq4LV8myV8s8CNTRd6PfXr2oG6dO6n1aGzM94HriicgXsutXnqctk1/yTj4gfe+oIb3jCSP\nhtaTKmodE/+Nps0PPE4SpS2l7eyXKezekVqzyTFiyjjKPZVIhz+bZ6xP27abNt07jbrN/9RY\nV5YT16AA6v3nt7TmlrEqAlm7N2nVepX0scs375MLf05VxyKJO/e8Mttiaa1fnUHhD421qLe1\n4uRvSyxEbY+wetTzty+NoraMlXXwqBpS7Gek5CUmc3LLHMrce5DOxuxQdaeX/EuH2Zqmz5Lv\nyPtSgkvVwD8kSn/Xs//RLtVRBH+I2iZIcAECIAACIAACIAACJRKQfx/J60hiAs1dulhZjZw9\nZ/irRqmXfzCVRVsocTI0ggAImBCokojtTw+ncoLCLJrX2RCpbbP9CH8epJ03eEnv5USR8uca\nIvJpYovJzvjiktbCJxwRfZEtE/goc3k41CB3FtODnC7Sjl1fUaFLGrm6naMaLgUsjhaofmpQ\nHlhmMJzLaIakllOaj6KHIkfylXEG86nV9YaknTRx3SxeJwuJPBbLSTIEC898YRiOK1k0Z1H7\nAgvcOdmsqmd7UJdmY+isSyBlnS9UiSEL+IOwBvcz/SCUK8v5pUZ4iLgtQvmP3RpSZxa3NQHM\n6kLNKuX+r+PO0sbUbPqExXHN8sGsW5Veyn8cznPUtqz1SorwEkFcE+AkAtgW3+iS5pToZxF/\nSytXay5hpe0vPT2D4jjKXUTOpORkJVZmsbgq0dkiUsqzdnZxVkkagwMDKbxxI2oZ2Yy8vb3V\nf6i1ca7G3mQOSYbpwC9rRQReiRqXqPoDhw7Tnth9dDrxDJ1NS+f3RqHh/SH/J8Ly18TacKZ1\nfJtYj8g4efl5/OUTfy5cKtp75pknplNYwwZGtlfrecoyKmKukthqe9Uz3sFfjsTu288R6Wf4\nC48s/quJAn4+jupLkQb1+Uu0dm0pgt8v8rz07zltLByvDoHl3YaSeD1rJbB7R+r5+9fapcXx\n+A+/0dapz6t615Ag6vbDR+QT1cKin3nFpvsfI4lU1he/jm2o+8+fkZO3Iemkvs2WcxHM1w4d\nZ5EkUYTvLt99SH7tW9syjNU+lRGxvf2xlynum18s5pPEmFeS/PLQR1/R7pfeMRlXhP1+//5C\n5l7hsbPeJfkCQ4rwObt1lzp3qx2s7Eq8OLnn7uffVO+J8AfHUuvXZqh27Ucs280cePd/2qU6\nDt76T6lfhpjcgAsQAAEQAAEQAAEQuA4JyL+VkjLSaB8nfIzeu4uW74yhDfv3UCHbj0DMvg7f\nENiyzQSu+YjtXI4GzuZowe+7NCBHVl9ZbrapiICZzqL2AzEnKDYz95L/82VRW69dyblB8JSf\nNciHRca2bMvR2c+DmtdypRBXR/JzdiBvJwda0ugGmrH5PbUG0cAuFBlWVIPFZ7k2imJqAq6Q\nKE7V27Yf3F30dLogPy7dWXRBxjAI1XKsyRYlDi45VMs1h+7u1I2ejepCmbzXtIIiSuXXfhbx\nt3L0dMzZHEpg/2u1LnWfYZ/qWn7wZNoscim2KY9sS6DP2L+8rY9bmcTtuxr4UjZbqKxjH/Re\nARy1XZZN24bminqJwKoXpMs7mLkAJ4J06ZJ0eWczve9qzaUXo8V6ok3rVuol9fIfY8NLIoH5\nCxiuE8FTBGP9fbJy82vT3ZheXY29Ga1VeM1NI8KpWZMItUb1xQT/Akiiw0u/LKaLs/FKxpG/\nDBCP8H0HDtJf/yyjLP4LAXnPnOcvQNL4S4JGPLdWrsaer/Zcmt2MiNXt2kSpl+xfvkyQl7Q7\nsLhtbtVTlveKticcK4ZAuw9mKVsPbbTk6BgS8dqa0CqR2pqoLaJ0tx8/IWffWtqtJkeJTK57\n243kXq+OqhfrC/HXTl67ydhPIoRXDRytfLPd6gQb6209kXt6//09RY+cROm79hpvU1Ylg0ZT\nm7dfoEb3jzLWV9VJIX8ZKMJ+4vK1JkuQBJ6dv5pNIYN6m9SX5WLHk7Po6JfzTW6RaPyei76y\nELWlU7MnH6LUmJ2Uws9ZRG1hKF8QtJz5BNW7/SY1znn2+N484UmuP2My7rm4ExaidtNpEyBq\nm1DCBQiAAAiAAAiAAAhYJ/DiD18oMftYEufcYvtN5ZLN/1YSTcZQLp9pNTiCAAhUPIGrpeEZ\nV57ANiQPhPmRI+tBNova/HmQmH+eJsWcZFE775KoLfHKBlFJfooVieH6IgW5OFGom5Oy4egR\n4EHtfN3IzfHybNq3Z6JJDanXm2KSYmnh8X/5g4i/Wbv0MaQ+ggzDG9cuJ1aqTNqtXshNus80\ngxamxVtf2gf3CXDxpfFNhivbA08W3T15zQ08nNX6R7PQXMQfkoc4Ueaa5GyKZsH5eHY+JbLH\niYroZhGbm41FTuU6taCQRq0/Tl9zdHxX9jO3JXJbluvC4z0cEcB2L3lq6eXat3E1lXNSEeJZ\nRYxROburnFHN96sJ2Iag6MsfB+b9Kmc15RvV8PtrENolYWcRi8yyXrGjcWSrIC2hZXGR3mWZ\n1Y291/18fakhR2ZHNm3CntMfKJsWiebO5GzW11PRvyeUiK/7qwR92/XEpDru1a9DFEU8fB9J\n1K9Wdj33JgX3604Ska0vB98z+Fd7NwtXXtbiy2xexLt788Qn6cw/qyl+4Z/Uf/VCYxcRwjfc\nPZXEMkQrWYfjaOWAO6nHr/+zsL3Q+pR0lMjkXn/Oow2jJ1Pyus0mXUX0PcsibodPTa0zTDpV\n8oVYfGxk25VzR4+bzCRR6hKtLl8QlLeIz7X4XeuLJNjsLiz5GVkrIqb34oj8lPVbyLNRfUpa\ns4m2PPQ0nePnoBURvqUEdDXN/bDdzL/bm6O7W7wwTbsNRxAAARAAARAAARAAgRIIfLHsd0rm\nXF8G7UV+ooAACFQFgctK1lWaPdyTfaZ5LluFUulbwJYaD23RRG2DICz1IlWL2MuqFnmxMndT\nHW8awq/Gns7kwwn2JCGlCNXmVid6EUa8aR+KvIPWJm6npLxUJUBr4jYPX+lFxHhePq/VmT7v\n/gIFufldEuh5amnQFUe+burlQk29XWl8Iz9K52juJBa2/zmTRb+fymChu+DSfg1JAtn4RInb\nEi0+ZetJmtupXpkjtyN5LtNV6BaEU7sgoP99uBY2JOs9yNYjy1auUok3C9lChbNPKk/nEcOG\nUmv2qa6oon/vSxLLCePG0nuffK6GF5uS67Fca++X6/EZteJo3VN/LqfsuHjDe5X/6mDT+MeV\nj7WeR3rsAXUpCQ6tidoZsSLiPmIcx79zO/3tyutZkjBumfwsnfhpkbFNfJ5X33QPdfjsTfZq\n7mOst/XEkX3pJUJZBNoTPy82ue3Ez7+zr/QRldzQXKg36VgJF9YsQmQasf3o8dtc8ooIK9es\nEpG+6d5HKXWzwRNbG8R83HNHjpEjC91izWJeArp1UFUufj7quO+tT6ggPZMkKah4bHs2bkiN\nJ95tvE0i8CWaX186zjG1P9G34RwEQAAEQAAEQAAEQMCSgARdiTaFAgIgUHUELocxX8U16MWi\nkqYVzToh5zyNXB9HuzLEfsTwkSH3S3q2MBbJb6/nQ5+y1Ub0gAh6vXVt6s4R2sGuTuTCPtqs\na1uI2ubzSeK+ELcAeqTFXdzfgMPW9ZmPVeZrCTO/VIbU60URteprlxZHradEXMsqRaz3c3ZU\n1irTmgbSst6N6Sf2057UOIBast2KqyTk5Ju4mxK3xdpk3KYT7JudY+RoMYmVCm1eK02oAoGr\nSkD+T0NmVhZ9Oe87emP2+7R5yzY6fTqRklNSKSU1VSW5DA9vVHlr4l+mtm1aU2CAP/9e8e+h\n/IKhgEA1JdDJTKRM3biN9v7nQ5PVeoWHqWuJyjYvIiivGjjKKGq3/+g1avOWwYvbvG+HT14n\nsbDQF/G03jBmCu2ZaZlYUd+vpPMOn75BUW88a9ElbUcsreh9O536a4VFW2VUiDi8dth9Fr7X\nMldw327Uf83CcovaYiGyoudwC1FbIrT7/vuzcdzchDO0dvh4WsNfGIgQXlwJHtCTfNu2VM1H\nOGGkiNryBUD3nw1fyEmDvBfME15GzphCtVo0KW5Y1IMACIAACIAACIAACIAACIBAtSRQJcK2\nLSREwxZRdsq2eNqXma+imEXYFmm7T5AXzWcRd1HPMHorKpQGBHuTB9t2aDKTHLVzW+aSzsPq\n96GBdbqyaMzRzjbdVBGdZJPEIrQLPdpijJpbs1cpbXTZn6arybn4lbfzdacZkcH0U9eG9Gev\nRnR3Az9OlKntpwblsL/5/THxtCNd7Fyu3i5L2wvaQaA0AiJqi5j89Xc/0NroDWw/wv7ZWuF6\n8QkfOXwoubq4aLUVf7y0hgC2SpDi6eFR8XNgRBCoIAIibjad9oDJaPvf+YySVm8w1jWeMEad\nH/rkG2PdeU4MKr7bEi1dlJ+vopH7scDaYPStxj7WTsTCQjywzcvBD+bS6hvHlCjGmt+jv5Y1\n9l02X61DX5+fwgmO73mENj/wBMl5ZRURhyUhp7ktiszX/JmpbBMyh5wvRUmXdQ373v6U1nCy\nTPP1B3DCzz7//GASmX1k7g8k4rb4Ypcmbvdd/hO1e3cmR8v3pRbPP0p9l883+mYXpGUoZvq1\n1mrZlCKfmqyvwjkIgAAIgAAIgAAIgAAIgAAIXBMEqqWwLZqrJEkcujaO9ogIy1Jzax9XeopF\n26UcmSyWGu393MlNJbiT6OUyCtlmj8ZgB1KDJjYbwZHeTkpAs1VgNhuqTJci3DvVcKRX208l\nf9daLFSLRF3+onEQC5aG7M39cssQWtE3nN7kSPa+QR7kyvX5nLhzIovbm8sYuV3+VeFOEKgY\nApK8ccu2HZe+lOEvufiDQgnePHxEeGNOIBmhkhlWzGzFj+LGnrbi3+3t7V18J7SAQDUg0OKF\n6SS+yfqymS1Jck6eVlX1RtysEgxm7N5PSzveRCs5Qntxoy4q2aR0CGSBtf/qBeQTZd3eR+4T\nIVwrkthRfLfFb1pfxGJjRa/brIrD+n7Fnfu2a0UD1v2f1aSMJ39bQsu63kLHvltQ3O3lqs88\ncEQJ8juf+Q8V5uSajCE+4GKV0oyTMpaniJC97rYHaN8bH1ncLgzFM1sSRuqLJITUii3idsOx\nI5RdS9PpE02+FNg49hHK5b900ZdOX/xXf4lzEAABEAABEAABEAABEAABELhmCFQ7YVtEbRGy\np24TT+1cJWgv6B5GC/k1OTyA/bMNEZkiAYsOfGVS8OXnJEJ2E+8GNKrxjaqysp2SNDG9uW9j\n6lO7g7ILubyaKzvT9HFhE+jiSCPq+fKXAfVpVb9wGsoe5OlsSzJ6wzHanZFn8Ci/sulwNwhU\nKgGJxs5kj+AlS5ezlbbhN177ewPl98xv+NYtmlfqGvSDF3K0uJMI215eKlJc34ZzEKhuBDp+\n/pbJkiRidz0nZtRKuw9mUcjg3ioZYtq23aragf/yodljk6gnC6zO/r5aV5Oj+GhH3/kgW4Lc\nRpn7DxvbQgb1pn6rfrVIHJmfnKrsPLZOeZYKzqYb+9t64uTjrURzsUORhIn6IuNt40SIEhku\nySuvpBTl5tHuF99WUdrmntcyrniGD9ywmAJ7dCrXNEe/nE9LO91kEjkvA8me5FlZi3qXdk3Y\nbv3603JpU+S26qj7IUlEJcmkvulX6g8AACQESURBVEikfXm9wfXj4BwEQAAEQAAEQAAEQAAE\nQAAEqoJAtRK2RaySSO071h+nKF83+pktNcRyJKqWm9Ee5JKuVSmsRBCe2nyUErjFb7uyorY1\naU5sTx5reQ+5cOLIK43WLg6IzCXMhG0Ai9zvtatL/3DU+5Mc/f7a3kRan5JtZFvcGKgHgaok\nIOJ1Mnto5+blEedBVVHa2nq0qO3AQMtkalqfijzKfGnp6eRdqxYF+HOiV/nQQAGBakygVqtm\npImh2jIz9x6kmIlPqksRVLv98InyYBaf5fYfv0437FxGklCypLLhrikk4rb4T69ngVtfPBrU\npQFrfyOJCDcvx+cvUsLusXm/mjfZdN1o/Gjqv3ahhXAuN4sQvazzEBIB9zx/GVbWIskUl3a+\nmQ59/LXFrZLQUuw9un7/cbmsRyQR5799R9KOJ2eZRLnLRJ6NGlC/lb9Y5aUtJPfUGXUaPuke\n6shJOaXYErmtOvKP+F/+oMOfzdMu1TG4X3cLb3STDrgAARAAARAAARAAARAAARAAgWpOoFoJ\n2+L7vDwpi+Z2rEcvtwihNuwZ7XLJbuRqyEciZIvf9fgmt7LdgcHDt7LEbRHOR4QNpA4BzStN\n1Na/94Sf9gpjm5KHOPr9a47ilqjt49kFELf1sHBerQiIeJyTk8O+2oUW71Npk5eLs3Olr1nm\nyTx3jpI4cVunDu3IhaNapQ4FBKo7ARFD699xi8ky4xf8RQffn2usC+7fQ/ksNxg1jFwC/Y31\n1k7E11oSOGrFixMdWisSgdzuvZkWTRI1vm36S7Rq0GgSO5OyFhGCRTg3T1ipjSMC7t9tB5FE\nR9tSRATf9ewbtHboOOVjbX6Pf+e2NCB6EYm9R1mL7HXHU6+yFctwSt+11+L2OkMGKFHbq0kj\nizZ9RZs3njNGc9cbOYQkmacUEbfX3mLp062/92zMDop5cIa+imS+zt+8b1KHCxAAARAAARAA\nARAAARAAARC41ghUK2FbpNe7G/iSj7ODErDEM/pqF7Eg6VenMzX0DFVWJxVtSSIRpxxDTbWc\nveie8CE8m6q4qtvUot7dOOGmCNyh7uwrflVXgMlAoGwEaqovuKy8Sy/5bOfnF5RtwHL0lmjt\nXxb8Rs4uznTDwP4QtcvBELdUHYEOn75BItDqy55XZtPpv1fqq0o9P/jhlyS+1lJ8WhssgPza\nty72vob3jKBBMX9RQNf2Fn3Obt1FK/rcrqxR9EK5RcdiKsRGQxJbWrPSOJ+eqaKjl3UZQmeW\nrbE6gkSbSwT1X5F96PDn31rt02rmE9T7r+/IvX6o1fbiKsV6RSxN/o4aQEfn/mjRTXy65Zl0\nYXHZ3E/bojNXBA/oSeK/rRVJ5ik2MlLEfmXNkLEWSSilLedEAkWPekhOjUVsXXos+IIkCh0F\nBEAABEAABEAABEAABEAABK5lAtVK2BYh2/FSBKQVCeuqcJYIbXcHV3q702PkVFME34pcSQ26\nqIa7SINDu1OYVyhL3FXzCGQZ2ssJUadX5b2FScpHQARlD3d3cnB0sBST1Xu3Bp08dap8g9t4\nl/w1ydFjx2j1uvXUvUsn8vL0NLFEsXEYdAOBKiUgNhrmAu3m+x+n9J2Xo69LWmDi8rW052VD\nosHQoYMoqE9X1d2PkztqpYgtg0QwF69qrUiEda8/5imbE2ue3WeWrqaV/e9QHtzJ6zZrt9l0\nlMSWAzf+QZFPmoq32s1Zh+JoPQu7G3S+4tJ2dO5P9HebgSqqW9ZsXoL6dKOBm/6giIfvM28q\n8Vq8sCVC+++ogcrSpDA7x6J/2L0jadCWJRZR9BYdS6loOOY2YxS37HPbtJdM7pBocfFBF5Ff\nX7r9+Cm51QnWV+EcBEAABEAABEAABEAABEAABK5JAlWjqlZ3VCyWtfQNp65BUWqlFSduX1S2\nI4FuvjS95ZgKFs2rO1SsDwTKTyAgwJ/cXNlrnwVmfcpYuRZte/vOXZUmNMsc4oH/y8JF5OTk\nRAP79VVzwYak/M8Td1YNAWffWuyn/bFJ8sWi/Hxae+t4Eg/okooIp5vum666+LSO5Gjj/5BY\nXEjx1UVsx0ycoSxO1g6zFITF5mQQi9ASbWytiKgt94nILVHOZfHJjnz6YfYGX051b73B2tCU\nsmGrSf2pv5abXGsX4g8uXwD0WDCHvMLDtOpSjxIVLr7lS1r1U2sXruZF7D/6/PMDtZ39Mjl5\neZo3l+taorgl4aRrSBC1efM54xjCbg1bq2QdPGqskxMVud+pjUkdLkAABEAABEAABEAABEAA\nBEDgWiUAYdvKk2OdjD22L9KoRoONViEVIW7LGPI/sSDxcvKoAhMSK5tFFQhUcwIiIHt6eFD/\nPr0urdTUvkeE54TTp2ljzBb1e1uR25Gxc3Jz6ZM5cynu+HGaNuVBJI2sSMAY66oT8I6MoI5z\n3jKZV0TQtcMsRVCtk7SvH/UgFebkklhodJv/qRLH07bHkmdYfRLBXMq+Nz+mU38aBOMonciq\njSNHZz8f5Q8tAm/tG/rqm4znYksiUc+Lw7ooMf30kn+NbSWduNetTZ3m/pc9q3+lOjf1L6mr\nRZuI2OJbPXjbP7yuPhbt1irSd+1TdiN/NuulosLFt9xa8Qirp8TsgRsWk18Hwxfm1vqVt07E\n7YEbfie30BA1hESJrx12P0mSUH1p/MBdVxwlrh8P5yAAAiAAAiAAAiAAAiAAAiBQ1QQcq3oB\n1XV+idDsHtSWOga2pK3Je+kC/+/KCkvarJi7O7rSDWxDIsK5zIECAiBgG4GbbxhEsfv206Ej\nR6moqEjZkojwrEVUL/pjCbWMjCQPD3cST+4rLdrv6Ffffk8xW7fTqBG3UeuWLa50WNwPAlVO\nQETf1q/OoF3Pv2lci9hWSLR0z8VfW0Qqb7p3GmUfO6n6iqjtGhzIiRD3kVh4+HYw+GsnLPqH\n9r31ieojUcG+bVsax7Z2IgJv1+8/onNHjinLjrhvfrHWjRJ+X6pe4gtd58Z+FNirCwX3664E\ndqs3cKVElHf59gPK3HeIdsx4jVKiY4rrqsZpNespmwRf2W9K9BZKjt5MZ/5eRZkHjhQ7rjQE\ndOtAEVPGFSvgl3hzGRudvL3UHSJqR4+YYJGoUp55cV82lHEqdAcBEAABEAABEAABEAABEACB\nakMAwnYJj8KhpgM91WocjV39HOUVFRijt0u4pYQmEeBq0G0N+lOoR5CK3C6hM5pAAAR0BCRq\n29HRkcbdfRd98On/6PSZMybWIyJCnz5zmub9+BPdd/docnNzs/Tj1o1X3KmI5DKXHOPijtP8\nBQvp+IkTdPuwW2jwgH6qHhYkxdFD/bVEIPyhscrVZ9dzl8XtvKQUWnPzWOWHrSVkzE04Qxks\nEEvpNOdto2Ct2ZBI4sj0XXtpy+RnVZ/wSfcUKxLHznqXGt49giSCWSuejRuqaObmzz5CR+Z8\nTyJwS+JF8yI+0cd//D/1kjbv5k0osHtH5fMta5VxzItEp/f6/WuK/+UPOvrVfErdtN3YRaK7\n67M1SpNHHyg2iaLsPftYPCWvZzF77aYSBXLjwHwidihNHh1vTK6pbys8l62+JKjBOQO8m4Xr\nm674/HzWOVo3fDylbd9jMlbIoN5K6DepxAUIgAAIgAAIgAAIgAAIgAAI2AGBGizgmP5dvx1s\nqqK2IGJZ4cVCmrBuJm1N4ahtvpYo6ymRo+jByJGlitMbknbSpOhZVHSRo72Zso+LJ62++UtO\nkInvEyrqGWGc64uAfFzJ6/W3Z9OBQ0fIwaGmQeDmOv6FVOctmzen6VMnk6ODg7q2JXpbG1f6\nnj9/nhYu+oOWLF2mfucnTxhPXTp1UGNB1L6+3m/Xw24PfzaP9OK27FnsRiTZo1Hc5oSIYjHS\neMIYI5ItDz1DJ37+XUVd73hiFuWeTqSg3l2px8IvjH30J4kr1lH0HZNUVaPxo6n5M1ONFib6\nfnKesHgZHftuAUmyyrIU8Zn2qF+H3Nkn26NeKLkE+pGzvx+5BvnzXD508cIFiv/1DyXOS1R3\nfkoav1JZSD9L+alnKSf+NOWcSKDsEycpOy6+LFOTd9PG1ICTOTa4a7jVfYm9ili1SKJMrbgE\n+isOkkzySouI2mtuuZcydu83GarOzQOoy7z3TepwAQIgAAIgAAIgAAIgcOUEQu4dQknpabCY\nvXKUGOE6I+DIOs7Gt+dQ+8bNKmTnELZLwcgyGi0+sZpmxLynhOzyCdtFLIoRjQ2/hZ6Oup9H\n5KhQUeFQQAAEykUgl32vN8ZspRUrV1N8QgKPId/PGX6r5CwkOIi6d+1M7dtE8XlwqdYk6ekZ\nykN7d+w+2rl7N2Wwp3Cn9m3ppsGDqG5onXJFf5drY7gJBKqAwLHvF9K2R14wmVmsP7r//DlJ\nRLa1srTjTXTu6HEVOS1ezuK13XfFz+RUy2CJYX6PJIQUcVcrzv6+FPXa01Rv5BCtyuIoEeTH\neW0icmtWKBadqrBCEkDWve1GjkK/nXzbtbK6ksR/o+nQh19S0pqNqj2ga3viDyTlfy32L1Ii\nZ0yhyKcmq/Py/Mg5eZrW85cG5tYodW+7SUXZl2dM3AMCIAACIAACIAACIFAyAQjbJfNBKwgU\nRwDCdnFkKrG+8GIR3fjPZErISaIabCfycPOyRWxf4IjtIh7jhz5vUmu/JuRQ48r9fytxuxga\nBKo9AYmw1ixDtm7fwdHVy+nkqVNUUHCeLnBUZs2aNehCkUR3X6CQ2iEU0agR1eGjn58vicVQ\nYVEhi9eZdPrUGToWf5ISWByXv8hwdnKiZk0iaOTtt1LdOnUQpV3t3wlYYEUREI/sTfc/ZjGc\n2I+IQKovEh28uGFnY5Uj+9r3W/mLVTsQ6ZS4cr3yfZbzzl+/R7kJp2nv6x+S+EE3GH2rStoo\nbSUVEW3FK1teyfyyZldS0v0V0ebg5kr+ndpSQPcO7J/dkZRIXcLAcV/9RNufeEX1kMjpFi9O\nM/qXi7XKxnsfpeR1m1V7l2/epzpDBpQwmvWm9J2xtG7kJCpITTPp0HDsCGr37kyTOlyAAAiA\nAAiAAAiAAAhUHAEI2xXHEiNdXwQqWtiGJ4YN7x8Roic2G0Evb/uUxTQbbrDSJcqvGbXwDYeo\nbYUNqkCgrAQ0SxA5tm/bhtpGtaa09HRKZluBpKRkOsUe3ClsLZCelk7ZuTm07+BB2rXHEC1q\niO0mcnZ2Jk9PDwoODKB2Ua0oLKwh1ebo7sAAf7UcTTwv69rQHwSuRQKhwwYrW5ENYx42Wf7m\nCU9S1uFjJhHF+RxJ7eDqqpJHSudOc/9brKgt7WLBIcW/UxsKvWWgOpdkhsu6DlWe2T5RzU1s\nTlQHsx9i9SGvRvePUi1ZB48qkTuN/b3lPOtQnIW4azZEmS5FxBYrFq8mjahW86bk36Ud+Xdu\nW6Yx6t05lPby3kWEd2E7FK/wMOP9EhHf7efPaHn3Ycr2ZM8r75oI2+czsujA+3Mo8snJJGux\nVk7+toQ2P/CERVOzxx+k5s9OtahHBQiAAAiAAAiAAAiAAAiAAAjYGwEI2zY8UbER6RncjoJc\n/Sg1L92GO0y71KSa9EzUeHLiSFEUEACBiiUg4rZ4Y/v5+pK/nx9FNm1iMYGI1IWFhSoCWxrl\nHklGqQnk2g0QszUSOF6PBGrf0Jd6//Wd8sKWJIdaEWE6afUG6sLR1uILLYka+y6fT9F3Pkjh\nE++mkIG9tK4Wx6RV60lLNNn8+WnGdvf6oSxm30UH2aZj/zuflSpsG2+8dCKCs7wuS8VEYu2R\nuf+wErpz4k+xf/ZZKjibzvX84ojmfD4XkVksRJz9fXgvAeTMArMz/yWHC1+71QlRY8q47vXq\nmE9Z5mtHdzdqzjYjErUt0dsRU8YpyxZtIAcXF2r22CTaOvV5OnfkmLISEfG+KD+fVvS+jT2/\neQ+JKdT+49e1W9RR2nc+/Todm/erSb1ctPtgFjVkr28UEAABEAABEAABEAABEAABELgeCEDY\ntuEpi692oKsvtQ+IpL/i19lwh2mXpj4N2YIkwrQSVyAAAhVGQARqvUgtArVWtDYnthnRil7A\n1p/rx9D64ggC1xMBiUru8/f3tO72CZSXmGzceurGbbS8x63Uka1Jgnp1Ie/ICOq/eqHVRInG\nm/hk738+UpdyT2D3jvomqtWiqboWAVpFNbNobl5iJj2lBOvgvt0pqG838m4Wbt7FeO3sW0vZ\ng5RmEWK84SqchN13Jx36+Gs6F3eC9r72AXX64h2TWYP79zBeZ7HdigjbIniHP3gPHXh3DoUM\n7mNslxOJTN/EFibmftrSJkkixfIEBQRAAARAAARAAARAAARAAASuFwIwe7bxSUtaujsaDaaa\nbEtiqxuJIZUdUa+QdmoW7drGKdENBECgnAQ0Mbs4oVpfrz8v53S4DQTsioCI1n2XzVeJIfUb\nEwF63fDxtOv5N1W1CMklFRWtvWWn6tLixekWXWU8rTiyLZC1cvrvVZS4Yp2aU2w7/mzak2Ie\nnEGnl/xrrXul1KVs2Eqxs94lsf4oT9H2Lvens3WKvugj4/U8wx8cSwM3/kGhQwcZux+Z8z0t\n6zLEQtQWWxOJtIeobUSFExAAARAAARAAARAAARAAgeuEAIRtGx+0RG23929OrdgnW8RtW4oW\nMzowtJtKTGfLPegDAiAAAiAAAlVNwC00hMXtH6nu8BstlnL403nKKiPrcJxFm75i7xsGb+2Q\nQb3Jt21LfZM6j1/wpzqKkG7NRzpt+x7ShN8Go4aRT+tIZS8S/8sfdOy7BRbjiUVH2o5YZeFh\n0VjOCplrzZCxdOC9L2j7YzPV/GUdSsRpbf+x7KWtLyc5aacUScAZ2PNyQk6p04RusVOJvmOS\nsh+Ren0RJgPW/V+Z/b/1Y+AcBEAABEAABEAABEAABEAABK5VArAiKcOTkySSt9TvTRkF52y6\nq+hiEUX6NKIm3g1YDLc1ztumodEJBEAABEAABCqVgCSIFOsMv/atjVHa2oQZew7Qv31GUKuZ\nT1Cj8aO1auNRPLk1b+2Ih+8z1msnBz+YSyJcS6k/8hat2uQoEd9SXAL8jD7TInSnbt5B4l9t\nXnJPJdLK/ncYq12DAkgSU/p1iKKQQb1YGG9ubLP1RG/5cT4zS/lhd/vxE1tvN/YTTmuGjqPE\nlesped1mCuzRiRJY1D4w+3+qT4fP3jD21Z9IxPq2R1+wKqiH3TuS2s5+Wd8d5yAAAiAAAiAA\nAiAAAiAAAiBwXRGwLfT4ukJS/GYvsG9vG3/2BLVRoxbrkfFNhpt4/xY/OlpAAARAAARAoPoR\nCH9oLCeL/Ik8GzUwWVxRbh7teOpVZU+Sl5Ri0pYRe9B4HTPxKTo690dKjo5Rx/V3TaY9M2er\ndknS2HjiGGNf/UnSqg3qUh/JLJYlwf26k38Xg8WXvn8hC89akXYZ+8yyNezz/SH923ckHfnf\nd1qzzUfxvZbSmJNkSjmzdHW5LEkC2F9cItel7HnpHdry0NO06f7HSBi2fnUG1bmpv2rTfhRm\n59C2R16gDWOmWIjaEsnd9fuPIGprsHAEARAAARAAARAAARAAARC4bgnU4MRpmmPGdQvB1o0L\nqHPns2lLSiz1qd2RZeuSFe5DGccpxD2AvJyse4faOi/6gQAIgAAIgEBVExCrj9iZ79Lhz7+1\nWIr4PDd7bBJFTBlnbBOx+8jn3ynbEL2fttbBqZYX9fy/L61GUovgu6hue9XVq0kjqjfiZo5y\n7lyi5Uby2k209tb71T23pcaqY2FOLm0e/7gSpKWi4//epnq336TabPmxrPMQEsuVfit/oaNf\n/UTH5v2qLEIGxSwxWoXYMo70ydx3SCXg1PpLkssWz0+zEOmPz19E+1iMzzl5WutqPNa+sR+1\ne2+mimI3VuIEBEAABEAABEAABEDgqhMIuXcIJaWnEQS1q44eE17jBBwdatLGt+dQ+8bNKmQn\nELYrBKP1QS7yR1xp4rf1O1ELAiAAAiAAAtWTQMr6LbRl8jNWvazdagdT0+kTLOxJxHYjYfEy\nEvFZ/LSDB/SkJg/fTx4N61rdZOK/0RQ9cqJqcw0OpLzEZHUu9/Za/I3Rs1p/86m/VtDGex4h\nsVAZlrBV30SaQO0VEaaSMpo0lnCx0L+Far3lCEeP16xBSzvdTPnJqUocF5G8rGXrw89R2rbd\n1PKlxylksCGCWxtDkkvue+sTyjp4VKsyHp28PCnqreep/h3WbVuMHXECAiAAAiAAAiAAAiBw\nVQhA2L4qmDGJHRKoaGEbHtuV+CaBqF2JcDE0CIAACIBAlRAI6NaBxeHFFPvKexbR27mnE5U9\nyeHPvqU2/32Rgnp1UWsMHTaY5GVrEY9uKbVaNaP+qxZQ6qbtdOqPZXR6yUryatrY6jCFWYb8\nF07enhbtEY/cr6w9sg7FkSRjdPbzsehjXiER1lIcXFxIItKltH9/FomVSvyCvziKfIjRXkQ1\nXvpxdstOSlqzUUWw6+vlvPVrT5NEqutL+s5Y2jZ9JsnRWhELk/YfvooobWtwUAcCIAACIAAC\nIAACVUmAc6lxtGhVrgBzg8C1RUCMLyr4VwbC9rX1FsBqQQAEQAAEQKDKCUhUdOvXn6bQoYNo\n+2Mvkz7Joizu3NHjynu79g19qNWspyz8uUvbgJY4MqhnZ9XVv3NbZUMiYxVXzmcYPLadvE2F\nY+nvrRPD88+m2SRsa5HTLkH+dGTO90pc1xJiypjbpr1EAzcsNhGqJep68wNPSDOF3TOCXAL9\n1bn2Qy9qSxT6HrZ2OfHTIq3Z5CjR7y1feozqjRxiUo8LEAABEAABEAABEACBqidQy92TLhRd\nqPqFYAUgcI0RqMFfCDnUdKiwVUPYrjCUGAgEQAAEQAAEri8CkqRxwPrfKe6bX2jv6x9YJDo8\n/fcqkpcI4I0fuIskiWJpRQTqjD0HVDfx1ba1nL8Use3oZZnXIjsu3jiMRGDbUjL3H1bdcuJP\n0c6nXzfeIkk0RbgXYXrX82+qaGqtURJdil2KR4O6lJeUaiFsS7+M3fvpMCeyPMlR3+Jbbl4c\n3d2oyaMPUMTU+1S0uHk7rkEABEAABEAABEAABKqewIFP51f9IrACEAABgrCNNwEIgAAIgAAI\ngMAVEQi7d6Tynd7/zmd08MMvLcZK+H0pyUsSQTaeMIbqjxpGIuBaK4kr1hqrt0x9jvw7tlEJ\nFgNYRPfj8+JKYVa2arIWsX3s219Vm6OnB7nXq1PcECb1WsS2+HLXubk/z81R453aqGjvva99\nQPtnf07Hf/hN7TuoTzd1r0uAHw3esoRcQ4JMxpILSQoZ9+V8Ort1l0WbVtFg9K3U4oVpJL7i\nKCAAAiAAAiAAAiAAAiAAAiAAAiUTgLBdMh+0ggAIgAAIgAAI2EBAROOWLz+uhOs9r8ym+F//\ntLhLxOIdT85iC47ZVP/OoSrJpN4mRG6oe9tN5MwCcdLK9SSWJKf/Xqle0tblm/epzpABcmpR\nCjIyVZ2sQ19iZ71HydExqqrhmNv0TSWeZ15K4ijJMOvfOcykb/PnHqHT/6ykms7O7I29jzRh\nWzrpRe3sYycp7uuf6BgL4AWpaSZj6C+C+3VX/tsi/KOAAAiAAAiAAAiAAAiAAAiAAAjYRoB9\n7uF0bxsq9AIBEAABEAABELCVgFhuxLI9yZmlq0u8xad1JNW99Uaqe/tN5F63tkVfSfYoyRgT\nl69Vvt7WIrLlps0TnqSTC/+iWi2acIT1ACpIyyBJQqlFXrvXD1WJKPU+1xaT6SoW+rdQV32W\n/kh+7VvrWko+zUtKUTYjJxf9Q3pPbmt3+US1YB/t6RTUu6u1ZtSBAAiAAAiAAAiAAAiAAAiA\nAAiUQADCdglw0AQCIAACIAACIHBlBDJiD9LB9+dQPHtKl1ZE6JXo5cBenSmoV5fSupu0R9/5\noBK/TSr5QixPQgb15ojoGSbR1Ob99NfnjhyjjfdOI9+o5hT15nNkHgWu7yvnKeu3UPLaTSrC\nPHXzDvNmi+vaN/SliCnjKKBbB4s2VIAACIAACIAACIAACIAACIAACNhGAMK2bZzQCwRAAARA\nAARA4AoIZB9nW46v2Jbj+4UkUdi2FIlkFpE7sEcn8usQVeoteWeSOIFlGp1nW5KL3NvJy5Mk\nIryiS9qOWCVki5idEr2FivLySp1C1iL2K2Hj7iDvyIhS+6MDCIAACIAACIAACIAACIAACIBA\nyQQgbJfMB60gAAIgAAIgAAIVTCBh8TKShI6JK9bZPLKjh7tKIunTKpJ82rRQliOejRrYfH9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diff --git a/docs/AlloMate/edit/index.html b/docs/AlloMate/edit/index.html
new file mode 100644
index 0000000..a0190f6
--- /dev/null
+++ b/docs/AlloMate/edit/index.html
@@ -0,0 +1,11 @@
+
+
+
+
Redirect to editable app
+
+
+
+
diff --git a/docs/AlloMate/index.html b/docs/AlloMate/index.html
new file mode 100644
index 0000000..2440421
--- /dev/null
+++ b/docs/AlloMate/index.html
@@ -0,0 +1,28 @@
+
+
+
+
+
+
Shiny App
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/shinylive-sw.js b/docs/shinylive-sw.js
new file mode 100644
index 0000000..18d0460
--- /dev/null
+++ b/docs/shinylive-sw.js
@@ -0,0 +1,2375 @@
+// Shinylive 0.9.1
+// Copyright 2024 Posit, PBC
+var __require = /* @__PURE__ */ ((x2) => typeof require !== "undefined" ? require : typeof Proxy !== "undefined" ? new Proxy(x2, {
+ get: (a2, b) => (typeof require !== "undefined" ? require : a2)[b]
+}) : x2)(function(x2) {
+ if (typeof require !== "undefined")
+ return require.apply(this, arguments);
+ throw Error('Dynamic require of "' + x2 + '" is not supported');
+});
+
+// src/assets/shinylive-inject-socket.txt
+var shinylive_inject_socket_default = '// src/messageportwebsocket.ts\nvar MessagePortWebSocket = class extends EventTarget {\n constructor(port) {\n super();\n this.readyState = 0;\n this.addEventListener("open", (e) => {\n if (this.onopen) {\n this.onopen(e);\n }\n });\n this.addEventListener("message", (e) => {\n if (this.onmessage) {\n this.onmessage(e);\n }\n });\n this.addEventListener("error", (e) => {\n if (this.onerror) {\n this.onerror(e);\n }\n });\n this.addEventListener("close", (e) => {\n if (this.onclose) {\n this.onclose(e);\n }\n });\n this._port = port;\n port.addEventListener("message", this._onMessage.bind(this));\n port.start();\n }\n // Call on the server side of the connection, to tell the client that\n // the connection has been established.\n accept() {\n if (this.readyState !== 0) {\n return;\n }\n this.readyState = 1;\n this._port.postMessage({ type: "open" });\n }\n send(data) {\n if (this.readyState === 0) {\n throw new DOMException(\n "Can\'t send messages while WebSocket is in CONNECTING state",\n "InvalidStateError"\n );\n }\n if (this.readyState > 1) {\n return;\n }\n this._port.postMessage({ type: "message", value: { data } });\n }\n close(code, reason) {\n if (this.readyState > 1) {\n return;\n }\n this.readyState = 2;\n this._port.postMessage({ type: "close", value: { code, reason } });\n this.readyState = 3;\n this.dispatchEvent(new CloseEvent("close", { code, reason }));\n }\n _onMessage(e) {\n const event = e.data;\n switch (event.type) {\n case "open":\n if (this.readyState === 0) {\n this.readyState = 1;\n this.dispatchEvent(new Event("open"));\n return;\n }\n break;\n case "message":\n if (this.readyState === 1) {\n this.dispatchEvent(new MessageEvent("message", { ...event.value }));\n return;\n }\n break;\n case "close":\n if (this.readyState < 3) {\n this.readyState = 3;\n this.dispatchEvent(new CloseEvent("close", { ...event.value }));\n return;\n }\n break;\n }\n this._reportError(\n `Unexpected event \'${event.type}\' while in readyState ${this.readyState}`,\n 1002\n );\n }\n _reportError(message, code) {\n this.dispatchEvent(new ErrorEvent("error", { message }));\n if (typeof code === "number") {\n this.close(code, message);\n }\n }\n};\n\n// src/shinylive-inject-socket.ts\nwindow.Shiny.createSocket = function() {\n const channel = new MessageChannel();\n window.parent.postMessage(\n {\n type: "openChannel",\n // Infer app name from path: "/foo/app_abc123/"" => "app_abc123"\n appName: window.location.pathname.replace(\n new RegExp(".*/([^/]+)/$"),\n "$1"\n ),\n path: "/websocket/"\n },\n "*",\n [channel.port2]\n );\n return new MessagePortWebSocket(channel.port1);\n};\n';
+
+// src/utils.ts
+function sleep(ms) {
+ return new Promise((resolve) => setTimeout(resolve, ms));
+}
+function dirname(path) {
+ if (path === "/" || path === "") {
+ return "";
+ }
+ return path.replace(/[/]?[^/]+[/]?$/, "");
+}
+function uint8ArrayToString(buf) {
+ let result = "";
+ for (let i = 0; i < buf.length; i++) {
+ result += String.fromCharCode(buf[i]);
+ }
+ return result;
+}
+
+// node_modules/webr/dist/webr.mjs
+var sn = Object.create;
+var Zr = Object.defineProperty;
+var nn = Object.getOwnPropertyDescriptor;
+var on = Object.getOwnPropertyNames;
+var an = Object.getPrototypeOf;
+var ln = Object.prototype.hasOwnProperty;
+var B = ((s) => typeof __require < "u" ? __require : typeof Proxy < "u" ? new Proxy(s, { get: (e, t) => (typeof __require < "u" ? __require : e)[t] }) : s)(function(s) {
+ if (typeof __require < "u")
+ return __require.apply(this, arguments);
+ throw new Error('Dynamic require of "' + s + '" is not supported');
+});
+var S = (s, e) => () => (e || s((e = { exports: {} }).exports, e), e.exports);
+var cn = (s, e, t, r) => {
+ if (e && typeof e == "object" || typeof e == "function")
+ for (let n of on(e))
+ !ln.call(s, n) && n !== t && Zr(s, n, { get: () => e[n], enumerable: !(r = nn(e, n)) || r.enumerable });
+ return s;
+};
+var oe = (s, e, t) => (t = s != null ? sn(an(s)) : {}, cn(e || !s || !s.__esModule ? Zr(t, "default", { value: s, enumerable: true }) : t, s));
+var fr = (s, e, t) => {
+ if (!e.has(s))
+ throw TypeError("Cannot " + t);
+};
+var a = (s, e, t) => (fr(s, e, "read from private field"), t ? t.call(s) : e.get(s));
+var u = (s, e, t) => {
+ if (e.has(s))
+ throw TypeError("Cannot add the same private member more than once");
+ e instanceof WeakSet ? e.add(s) : e.set(s, t);
+};
+var d = (s, e, t, r) => (fr(s, e, "write to private field"), r ? r.call(s, t) : e.set(s, t), t);
+var E = (s, e, t) => (fr(s, e, "access private method"), t);
+var He = S((C) => {
+ "use strict";
+ Object.defineProperty(C, "__esModule", { value: true });
+ C.getUint64 = C.getInt64 = C.setInt64 = C.setUint64 = C.UINT32_MAX = void 0;
+ C.UINT32_MAX = 4294967295;
+ function fn(s, e, t) {
+ let r = t / 4294967296, n = t;
+ s.setUint32(e, r), s.setUint32(e + 4, n);
+ }
+ C.setUint64 = fn;
+ function Rn(s, e, t) {
+ let r = Math.floor(t / 4294967296), n = t;
+ s.setUint32(e, r), s.setUint32(e + 4, n);
+ }
+ C.setInt64 = Rn;
+ function mn(s, e) {
+ let t = s.getInt32(e), r = s.getUint32(e + 4);
+ return t * 4294967296 + r;
+ }
+ C.getInt64 = mn;
+ function gn(s, e) {
+ let t = s.getUint32(e), r = s.getUint32(e + 4);
+ return t * 4294967296 + r;
+ }
+ C.getUint64 = gn;
+});
+var Ot = S((M) => {
+ "use strict";
+ var _r, Sr, kr;
+ Object.defineProperty(M, "__esModule", { value: true });
+ M.utf8DecodeTD = M.TEXT_DECODER_THRESHOLD = M.utf8DecodeJs = M.utf8EncodeTE = M.TEXT_ENCODER_THRESHOLD = M.utf8EncodeJs = M.utf8Count = void 0;
+ var ps = He(), At = (typeof process > "u" || ((_r = process == null ? void 0 : process.env) === null || _r === void 0 ? void 0 : _r.TEXT_ENCODING) !== "never") && typeof TextEncoder < "u" && typeof TextDecoder < "u";
+ function bn(s) {
+ let e = s.length, t = 0, r = 0;
+ for (; r < e; ) {
+ let n = s.charCodeAt(r++);
+ if (n & 4294967168)
+ if (!(n & 4294965248))
+ t += 2;
+ else {
+ if (n >= 55296 && n <= 56319 && r < e) {
+ let o = s.charCodeAt(r);
+ (o & 64512) === 56320 && (++r, n = ((n & 1023) << 10) + (o & 1023) + 65536);
+ }
+ n & 4294901760 ? t += 4 : t += 3;
+ }
+ else {
+ t++;
+ continue;
+ }
+ }
+ return t;
+ }
+ M.utf8Count = bn;
+ function wn(s, e, t) {
+ let r = s.length, n = t, o = 0;
+ for (; o < r; ) {
+ let i = s.charCodeAt(o++);
+ if (i & 4294967168)
+ if (!(i & 4294965248))
+ e[n++] = i >> 6 & 31 | 192;
+ else {
+ if (i >= 55296 && i <= 56319 && o < r) {
+ let l = s.charCodeAt(o);
+ (l & 64512) === 56320 && (++o, i = ((i & 1023) << 10) + (l & 1023) + 65536);
+ }
+ i & 4294901760 ? (e[n++] = i >> 18 & 7 | 240, e[n++] = i >> 12 & 63 | 128, e[n++] = i >> 6 & 63 | 128) : (e[n++] = i >> 12 & 15 | 224, e[n++] = i >> 6 & 63 | 128);
+ }
+ else {
+ e[n++] = i;
+ continue;
+ }
+ e[n++] = i & 63 | 128;
+ }
+ }
+ M.utf8EncodeJs = wn;
+ var ze = At ? new TextEncoder() : void 0;
+ M.TEXT_ENCODER_THRESHOLD = At ? typeof process < "u" && ((Sr = process == null ? void 0 : process.env) === null || Sr === void 0 ? void 0 : Sr.TEXT_ENCODING) !== "force" ? 200 : 0 : ps.UINT32_MAX;
+ function xn(s, e, t) {
+ e.set(ze.encode(s), t);
+ }
+ function vn(s, e, t) {
+ ze.encodeInto(s, e.subarray(t));
+ }
+ M.utf8EncodeTE = ze != null && ze.encodeInto ? vn : xn;
+ var En = 4096;
+ function Pn(s, e, t) {
+ let r = e, n = r + t, o = [], i = "";
+ for (; r < n; ) {
+ let l = s[r++];
+ if (!(l & 128))
+ o.push(l);
+ else if ((l & 224) === 192) {
+ let p = s[r++] & 63;
+ o.push((l & 31) << 6 | p);
+ } else if ((l & 240) === 224) {
+ let p = s[r++] & 63, D = s[r++] & 63;
+ o.push((l & 31) << 12 | p << 6 | D);
+ } else if ((l & 248) === 240) {
+ let p = s[r++] & 63, D = s[r++] & 63, b = s[r++] & 63, j = (l & 7) << 18 | p << 12 | D << 6 | b;
+ j > 65535 && (j -= 65536, o.push(j >>> 10 & 1023 | 55296), j = 56320 | j & 1023), o.push(j);
+ } else
+ o.push(l);
+ o.length >= En && (i += String.fromCharCode(...o), o.length = 0);
+ }
+ return o.length > 0 && (i += String.fromCharCode(...o)), i;
+ }
+ M.utf8DecodeJs = Pn;
+ var Tn = At ? new TextDecoder() : null;
+ M.TEXT_DECODER_THRESHOLD = At ? typeof process < "u" && ((kr = process == null ? void 0 : process.env) === null || kr === void 0 ? void 0 : kr.TEXT_DECODER) !== "force" ? 200 : 0 : ps.UINT32_MAX;
+ function _n(s, e, t) {
+ let r = s.subarray(e, e + t);
+ return Tn.decode(r);
+ }
+ M.utf8DecodeTD = _n;
+});
+var Dr = S((It) => {
+ "use strict";
+ Object.defineProperty(It, "__esModule", { value: true });
+ It.ExtData = void 0;
+ var Mr = class {
+ constructor(e, t) {
+ this.type = e, this.data = t;
+ }
+ };
+ It.ExtData = Mr;
+});
+var Ct = S((Ut) => {
+ "use strict";
+ Object.defineProperty(Ut, "__esModule", { value: true });
+ Ut.DecodeError = void 0;
+ var we = class extends Error {
+ constructor(e) {
+ super(e);
+ let t = Object.create(we.prototype);
+ Object.setPrototypeOf(this, t), Object.defineProperty(this, "name", { configurable: true, enumerable: false, value: we.name });
+ }
+ };
+ Ut.DecodeError = we;
+});
+var Wr = S((_) => {
+ "use strict";
+ Object.defineProperty(_, "__esModule", { value: true });
+ _.timestampExtension = _.decodeTimestampExtension = _.decodeTimestampToTimeSpec = _.encodeTimestampExtension = _.encodeDateToTimeSpec = _.encodeTimeSpecToTimestamp = _.EXT_TIMESTAMP = void 0;
+ var Sn = Ct(), ds = He();
+ _.EXT_TIMESTAMP = -1;
+ var kn = 4294967296 - 1, Mn = 17179869184 - 1;
+ function hs({ sec: s, nsec: e }) {
+ if (s >= 0 && e >= 0 && s <= Mn)
+ if (e === 0 && s <= kn) {
+ let t = new Uint8Array(4);
+ return new DataView(t.buffer).setUint32(0, s), t;
+ } else {
+ let t = s / 4294967296, r = s & 4294967295, n = new Uint8Array(8), o = new DataView(n.buffer);
+ return o.setUint32(0, e << 2 | t & 3), o.setUint32(4, r), n;
+ }
+ else {
+ let t = new Uint8Array(12), r = new DataView(t.buffer);
+ return r.setUint32(0, e), (0, ds.setInt64)(r, 4, s), t;
+ }
+ }
+ _.encodeTimeSpecToTimestamp = hs;
+ function ys(s) {
+ let e = s.getTime(), t = Math.floor(e / 1e3), r = (e - t * 1e3) * 1e6, n = Math.floor(r / 1e9);
+ return { sec: t + n, nsec: r - n * 1e9 };
+ }
+ _.encodeDateToTimeSpec = ys;
+ function fs(s) {
+ if (s instanceof Date) {
+ let e = ys(s);
+ return hs(e);
+ } else
+ return null;
+ }
+ _.encodeTimestampExtension = fs;
+ function Rs(s) {
+ let e = new DataView(s.buffer, s.byteOffset, s.byteLength);
+ switch (s.byteLength) {
+ case 4:
+ return { sec: e.getUint32(0), nsec: 0 };
+ case 8: {
+ let t = e.getUint32(0), r = e.getUint32(4), n = (t & 3) * 4294967296 + r, o = t >>> 2;
+ return { sec: n, nsec: o };
+ }
+ case 12: {
+ let t = (0, ds.getInt64)(e, 4), r = e.getUint32(0);
+ return { sec: t, nsec: r };
+ }
+ default:
+ throw new Sn.DecodeError(`Unrecognized data size for timestamp (expected 4, 8, or 12): ${s.length}`);
+ }
+ }
+ _.decodeTimestampToTimeSpec = Rs;
+ function ms(s) {
+ let e = Rs(s);
+ return new Date(e.sec * 1e3 + e.nsec / 1e6);
+ }
+ _.decodeTimestampExtension = ms;
+ _.timestampExtension = { type: _.EXT_TIMESTAMP, encode: fs, decode: ms };
+});
+var Bt = S((Nt) => {
+ "use strict";
+ Object.defineProperty(Nt, "__esModule", { value: true });
+ Nt.ExtensionCodec = void 0;
+ var jt = Dr(), Dn = Wr(), Xe = class {
+ constructor() {
+ this.builtInEncoders = [], this.builtInDecoders = [], this.encoders = [], this.decoders = [], this.register(Dn.timestampExtension);
+ }
+ register({ type: e, encode: t, decode: r }) {
+ if (e >= 0)
+ this.encoders[e] = t, this.decoders[e] = r;
+ else {
+ let n = 1 + e;
+ this.builtInEncoders[n] = t, this.builtInDecoders[n] = r;
+ }
+ }
+ tryToEncode(e, t) {
+ for (let r = 0; r < this.builtInEncoders.length; r++) {
+ let n = this.builtInEncoders[r];
+ if (n != null) {
+ let o = n(e, t);
+ if (o != null) {
+ let i = -1 - r;
+ return new jt.ExtData(i, o);
+ }
+ }
+ }
+ for (let r = 0; r < this.encoders.length; r++) {
+ let n = this.encoders[r];
+ if (n != null) {
+ let o = n(e, t);
+ if (o != null) {
+ let i = r;
+ return new jt.ExtData(i, o);
+ }
+ }
+ }
+ return e instanceof jt.ExtData ? e : null;
+ }
+ decode(e, t, r) {
+ let n = t < 0 ? this.builtInDecoders[-1 - t] : this.decoders[t];
+ return n ? n(e, t, r) : new jt.ExtData(t, e);
+ }
+ };
+ Nt.ExtensionCodec = Xe;
+ Xe.defaultCodec = new Xe();
+});
+var Ar = S((xe) => {
+ "use strict";
+ Object.defineProperty(xe, "__esModule", { value: true });
+ xe.createDataView = xe.ensureUint8Array = void 0;
+ function gs(s) {
+ return s instanceof Uint8Array ? s : ArrayBuffer.isView(s) ? new Uint8Array(s.buffer, s.byteOffset, s.byteLength) : s instanceof ArrayBuffer ? new Uint8Array(s) : Uint8Array.from(s);
+ }
+ xe.ensureUint8Array = gs;
+ function Wn(s) {
+ if (s instanceof ArrayBuffer)
+ return new DataView(s);
+ let e = gs(s);
+ return new DataView(e.buffer, e.byteOffset, e.byteLength);
+ }
+ xe.createDataView = Wn;
+});
+var Ir = S((J) => {
+ "use strict";
+ Object.defineProperty(J, "__esModule", { value: true });
+ J.Encoder = J.DEFAULT_INITIAL_BUFFER_SIZE = J.DEFAULT_MAX_DEPTH = void 0;
+ var Ge = Ot(), An = Bt(), bs = He(), On = Ar();
+ J.DEFAULT_MAX_DEPTH = 100;
+ J.DEFAULT_INITIAL_BUFFER_SIZE = 2048;
+ var Or = class {
+ constructor(e = An.ExtensionCodec.defaultCodec, t = void 0, r = J.DEFAULT_MAX_DEPTH, n = J.DEFAULT_INITIAL_BUFFER_SIZE, o = false, i = false, l = false, p = false) {
+ this.extensionCodec = e, this.context = t, this.maxDepth = r, this.initialBufferSize = n, this.sortKeys = o, this.forceFloat32 = i, this.ignoreUndefined = l, this.forceIntegerToFloat = p, this.pos = 0, this.view = new DataView(new ArrayBuffer(this.initialBufferSize)), this.bytes = new Uint8Array(this.view.buffer);
+ }
+ reinitializeState() {
+ this.pos = 0;
+ }
+ encodeSharedRef(e) {
+ return this.reinitializeState(), this.doEncode(e, 1), this.bytes.subarray(0, this.pos);
+ }
+ encode(e) {
+ return this.reinitializeState(), this.doEncode(e, 1), this.bytes.slice(0, this.pos);
+ }
+ doEncode(e, t) {
+ if (t > this.maxDepth)
+ throw new Error(`Too deep objects in depth ${t}`);
+ e == null ? this.encodeNil() : typeof e == "boolean" ? this.encodeBoolean(e) : typeof e == "number" ? this.encodeNumber(e) : typeof e == "string" ? this.encodeString(e) : this.encodeObject(e, t);
+ }
+ ensureBufferSizeToWrite(e) {
+ let t = this.pos + e;
+ this.view.byteLength < t && this.resizeBuffer(t * 2);
+ }
+ resizeBuffer(e) {
+ let t = new ArrayBuffer(e), r = new Uint8Array(t), n = new DataView(t);
+ r.set(this.bytes), this.view = n, this.bytes = r;
+ }
+ encodeNil() {
+ this.writeU8(192);
+ }
+ encodeBoolean(e) {
+ e === false ? this.writeU8(194) : this.writeU8(195);
+ }
+ encodeNumber(e) {
+ Number.isSafeInteger(e) && !this.forceIntegerToFloat ? e >= 0 ? e < 128 ? this.writeU8(e) : e < 256 ? (this.writeU8(204), this.writeU8(e)) : e < 65536 ? (this.writeU8(205), this.writeU16(e)) : e < 4294967296 ? (this.writeU8(206), this.writeU32(e)) : (this.writeU8(207), this.writeU64(e)) : e >= -32 ? this.writeU8(224 | e + 32) : e >= -128 ? (this.writeU8(208), this.writeI8(e)) : e >= -32768 ? (this.writeU8(209), this.writeI16(e)) : e >= -2147483648 ? (this.writeU8(210), this.writeI32(e)) : (this.writeU8(211), this.writeI64(e)) : this.forceFloat32 ? (this.writeU8(202), this.writeF32(e)) : (this.writeU8(203), this.writeF64(e));
+ }
+ writeStringHeader(e) {
+ if (e < 32)
+ this.writeU8(160 + e);
+ else if (e < 256)
+ this.writeU8(217), this.writeU8(e);
+ else if (e < 65536)
+ this.writeU8(218), this.writeU16(e);
+ else if (e < 4294967296)
+ this.writeU8(219), this.writeU32(e);
+ else
+ throw new Error(`Too long string: ${e} bytes in UTF-8`);
+ }
+ encodeString(e) {
+ if (e.length > Ge.TEXT_ENCODER_THRESHOLD) {
+ let n = (0, Ge.utf8Count)(e);
+ this.ensureBufferSizeToWrite(5 + n), this.writeStringHeader(n), (0, Ge.utf8EncodeTE)(e, this.bytes, this.pos), this.pos += n;
+ } else {
+ let n = (0, Ge.utf8Count)(e);
+ this.ensureBufferSizeToWrite(5 + n), this.writeStringHeader(n), (0, Ge.utf8EncodeJs)(e, this.bytes, this.pos), this.pos += n;
+ }
+ }
+ encodeObject(e, t) {
+ let r = this.extensionCodec.tryToEncode(e, this.context);
+ if (r != null)
+ this.encodeExtension(r);
+ else if (Array.isArray(e))
+ this.encodeArray(e, t);
+ else if (ArrayBuffer.isView(e))
+ this.encodeBinary(e);
+ else if (typeof e == "object")
+ this.encodeMap(e, t);
+ else
+ throw new Error(`Unrecognized object: ${Object.prototype.toString.apply(e)}`);
+ }
+ encodeBinary(e) {
+ let t = e.byteLength;
+ if (t < 256)
+ this.writeU8(196), this.writeU8(t);
+ else if (t < 65536)
+ this.writeU8(197), this.writeU16(t);
+ else if (t < 4294967296)
+ this.writeU8(198), this.writeU32(t);
+ else
+ throw new Error(`Too large binary: ${t}`);
+ let r = (0, On.ensureUint8Array)(e);
+ this.writeU8a(r);
+ }
+ encodeArray(e, t) {
+ let r = e.length;
+ if (r < 16)
+ this.writeU8(144 + r);
+ else if (r < 65536)
+ this.writeU8(220), this.writeU16(r);
+ else if (r < 4294967296)
+ this.writeU8(221), this.writeU32(r);
+ else
+ throw new Error(`Too large array: ${r}`);
+ for (let n of e)
+ this.doEncode(n, t + 1);
+ }
+ countWithoutUndefined(e, t) {
+ let r = 0;
+ for (let n of t)
+ e[n] !== void 0 && r++;
+ return r;
+ }
+ encodeMap(e, t) {
+ let r = Object.keys(e);
+ this.sortKeys && r.sort();
+ let n = this.ignoreUndefined ? this.countWithoutUndefined(e, r) : r.length;
+ if (n < 16)
+ this.writeU8(128 + n);
+ else if (n < 65536)
+ this.writeU8(222), this.writeU16(n);
+ else if (n < 4294967296)
+ this.writeU8(223), this.writeU32(n);
+ else
+ throw new Error(`Too large map object: ${n}`);
+ for (let o of r) {
+ let i = e[o];
+ this.ignoreUndefined && i === void 0 || (this.encodeString(o), this.doEncode(i, t + 1));
+ }
+ }
+ encodeExtension(e) {
+ let t = e.data.length;
+ if (t === 1)
+ this.writeU8(212);
+ else if (t === 2)
+ this.writeU8(213);
+ else if (t === 4)
+ this.writeU8(214);
+ else if (t === 8)
+ this.writeU8(215);
+ else if (t === 16)
+ this.writeU8(216);
+ else if (t < 256)
+ this.writeU8(199), this.writeU8(t);
+ else if (t < 65536)
+ this.writeU8(200), this.writeU16(t);
+ else if (t < 4294967296)
+ this.writeU8(201), this.writeU32(t);
+ else
+ throw new Error(`Too large extension object: ${t}`);
+ this.writeI8(e.type), this.writeU8a(e.data);
+ }
+ writeU8(e) {
+ this.ensureBufferSizeToWrite(1), this.view.setUint8(this.pos, e), this.pos++;
+ }
+ writeU8a(e) {
+ let t = e.length;
+ this.ensureBufferSizeToWrite(t), this.bytes.set(e, this.pos), this.pos += t;
+ }
+ writeI8(e) {
+ this.ensureBufferSizeToWrite(1), this.view.setInt8(this.pos, e), this.pos++;
+ }
+ writeU16(e) {
+ this.ensureBufferSizeToWrite(2), this.view.setUint16(this.pos, e), this.pos += 2;
+ }
+ writeI16(e) {
+ this.ensureBufferSizeToWrite(2), this.view.setInt16(this.pos, e), this.pos += 2;
+ }
+ writeU32(e) {
+ this.ensureBufferSizeToWrite(4), this.view.setUint32(this.pos, e), this.pos += 4;
+ }
+ writeI32(e) {
+ this.ensureBufferSizeToWrite(4), this.view.setInt32(this.pos, e), this.pos += 4;
+ }
+ writeF32(e) {
+ this.ensureBufferSizeToWrite(4), this.view.setFloat32(this.pos, e), this.pos += 4;
+ }
+ writeF64(e) {
+ this.ensureBufferSizeToWrite(8), this.view.setFloat64(this.pos, e), this.pos += 8;
+ }
+ writeU64(e) {
+ this.ensureBufferSizeToWrite(8), (0, bs.setUint64)(this.view, this.pos, e), this.pos += 8;
+ }
+ writeI64(e) {
+ this.ensureBufferSizeToWrite(8), (0, bs.setInt64)(this.view, this.pos, e), this.pos += 8;
+ }
+ };
+ J.Encoder = Or;
+});
+var ws = S((Lt) => {
+ "use strict";
+ Object.defineProperty(Lt, "__esModule", { value: true });
+ Lt.encode = void 0;
+ var In = Ir(), Un = {};
+ function Cn(s, e = Un) {
+ return new In.Encoder(e.extensionCodec, e.context, e.maxDepth, e.initialBufferSize, e.sortKeys, e.forceFloat32, e.ignoreUndefined, e.forceIntegerToFloat).encodeSharedRef(s);
+ }
+ Lt.encode = Cn;
+});
+var xs = S((Ft) => {
+ "use strict";
+ Object.defineProperty(Ft, "__esModule", { value: true });
+ Ft.prettyByte = void 0;
+ function jn(s) {
+ return `${s < 0 ? "-" : ""}0x${Math.abs(s).toString(16).padStart(2, "0")}`;
+ }
+ Ft.prettyByte = jn;
+});
+var vs = S((qt) => {
+ "use strict";
+ Object.defineProperty(qt, "__esModule", { value: true });
+ qt.CachedKeyDecoder = void 0;
+ var Nn = Ot(), Bn = 16, Ln = 16, Ur = class {
+ constructor(e = Bn, t = Ln) {
+ this.maxKeyLength = e, this.maxLengthPerKey = t, this.hit = 0, this.miss = 0, this.caches = [];
+ for (let r = 0; r < this.maxKeyLength; r++)
+ this.caches.push([]);
+ }
+ canBeCached(e) {
+ return e > 0 && e <= this.maxKeyLength;
+ }
+ find(e, t, r) {
+ let n = this.caches[r - 1];
+ e:
+ for (let o of n) {
+ let i = o.bytes;
+ for (let l = 0; l < r; l++)
+ if (i[l] !== e[t + l])
+ continue e;
+ return o.str;
+ }
+ return null;
+ }
+ store(e, t) {
+ let r = this.caches[e.length - 1], n = { bytes: e, str: t };
+ r.length >= this.maxLengthPerKey ? r[Math.random() * r.length | 0] = n : r.push(n);
+ }
+ decode(e, t, r) {
+ let n = this.find(e, t, r);
+ if (n != null)
+ return this.hit++, n;
+ this.miss++;
+ let o = (0, Nn.utf8DecodeJs)(e, t, r), i = Uint8Array.prototype.slice.call(e, t, t + r);
+ return this.store(i, o), o;
+ }
+ };
+ qt.CachedKeyDecoder = Ur;
+});
+var Vt = S((Q) => {
+ "use strict";
+ Object.defineProperty(Q, "__esModule", { value: true });
+ Q.Decoder = Q.DataViewIndexOutOfBoundsError = void 0;
+ var Cr = xs(), Fn = Bt(), ce = He(), jr = Ot(), Nr = Ar(), qn = vs(), K = Ct(), Vn = (s) => {
+ let e = typeof s;
+ return e === "string" || e === "number";
+ }, $e = -1, Lr = new DataView(new ArrayBuffer(0)), Jn = new Uint8Array(Lr.buffer);
+ Q.DataViewIndexOutOfBoundsError = (() => {
+ try {
+ Lr.getInt8(0);
+ } catch (s) {
+ return s.constructor;
+ }
+ throw new Error("never reached");
+ })();
+ var Es = new Q.DataViewIndexOutOfBoundsError("Insufficient data"), Hn = new qn.CachedKeyDecoder(), Br = class {
+ constructor(e = Fn.ExtensionCodec.defaultCodec, t = void 0, r = ce.UINT32_MAX, n = ce.UINT32_MAX, o = ce.UINT32_MAX, i = ce.UINT32_MAX, l = ce.UINT32_MAX, p = Hn) {
+ this.extensionCodec = e, this.context = t, this.maxStrLength = r, this.maxBinLength = n, this.maxArrayLength = o, this.maxMapLength = i, this.maxExtLength = l, this.keyDecoder = p, this.totalPos = 0, this.pos = 0, this.view = Lr, this.bytes = Jn, this.headByte = $e, this.stack = [];
+ }
+ reinitializeState() {
+ this.totalPos = 0, this.headByte = $e, this.stack.length = 0;
+ }
+ setBuffer(e) {
+ this.bytes = (0, Nr.ensureUint8Array)(e), this.view = (0, Nr.createDataView)(this.bytes), this.pos = 0;
+ }
+ appendBuffer(e) {
+ if (this.headByte === $e && !this.hasRemaining(1))
+ this.setBuffer(e);
+ else {
+ let t = this.bytes.subarray(this.pos), r = (0, Nr.ensureUint8Array)(e), n = new Uint8Array(t.length + r.length);
+ n.set(t), n.set(r, t.length), this.setBuffer(n);
+ }
+ }
+ hasRemaining(e) {
+ return this.view.byteLength - this.pos >= e;
+ }
+ createExtraByteError(e) {
+ let { view: t, pos: r } = this;
+ return new RangeError(`Extra ${t.byteLength - r} of ${t.byteLength} byte(s) found at buffer[${e}]`);
+ }
+ decode(e) {
+ this.reinitializeState(), this.setBuffer(e);
+ let t = this.doDecodeSync();
+ if (this.hasRemaining(1))
+ throw this.createExtraByteError(this.pos);
+ return t;
+ }
+ *decodeMulti(e) {
+ for (this.reinitializeState(), this.setBuffer(e); this.hasRemaining(1); )
+ yield this.doDecodeSync();
+ }
+ async decodeAsync(e) {
+ let t = false, r;
+ for await (let l of e) {
+ if (t)
+ throw this.createExtraByteError(this.totalPos);
+ this.appendBuffer(l);
+ try {
+ r = this.doDecodeSync(), t = true;
+ } catch (p) {
+ if (!(p instanceof Q.DataViewIndexOutOfBoundsError))
+ throw p;
+ }
+ this.totalPos += this.pos;
+ }
+ if (t) {
+ if (this.hasRemaining(1))
+ throw this.createExtraByteError(this.totalPos);
+ return r;
+ }
+ let { headByte: n, pos: o, totalPos: i } = this;
+ throw new RangeError(`Insufficient data in parsing ${(0, Cr.prettyByte)(n)} at ${i} (${o} in the current buffer)`);
+ }
+ decodeArrayStream(e) {
+ return this.decodeMultiAsync(e, true);
+ }
+ decodeStream(e) {
+ return this.decodeMultiAsync(e, false);
+ }
+ async *decodeMultiAsync(e, t) {
+ let r = t, n = -1;
+ for await (let o of e) {
+ if (t && n === 0)
+ throw this.createExtraByteError(this.totalPos);
+ this.appendBuffer(o), r && (n = this.readArraySize(), r = false, this.complete());
+ try {
+ for (; yield this.doDecodeSync(), --n !== 0; )
+ ;
+ } catch (i) {
+ if (!(i instanceof Q.DataViewIndexOutOfBoundsError))
+ throw i;
+ }
+ this.totalPos += this.pos;
+ }
+ }
+ doDecodeSync() {
+ e:
+ for (; ; ) {
+ let e = this.readHeadByte(), t;
+ if (e >= 224)
+ t = e - 256;
+ else if (e < 192)
+ if (e < 128)
+ t = e;
+ else if (e < 144) {
+ let n = e - 128;
+ if (n !== 0) {
+ this.pushMapState(n), this.complete();
+ continue e;
+ } else
+ t = {};
+ } else if (e < 160) {
+ let n = e - 144;
+ if (n !== 0) {
+ this.pushArrayState(n), this.complete();
+ continue e;
+ } else
+ t = [];
+ } else {
+ let n = e - 160;
+ t = this.decodeUtf8String(n, 0);
+ }
+ else if (e === 192)
+ t = null;
+ else if (e === 194)
+ t = false;
+ else if (e === 195)
+ t = true;
+ else if (e === 202)
+ t = this.readF32();
+ else if (e === 203)
+ t = this.readF64();
+ else if (e === 204)
+ t = this.readU8();
+ else if (e === 205)
+ t = this.readU16();
+ else if (e === 206)
+ t = this.readU32();
+ else if (e === 207)
+ t = this.readU64();
+ else if (e === 208)
+ t = this.readI8();
+ else if (e === 209)
+ t = this.readI16();
+ else if (e === 210)
+ t = this.readI32();
+ else if (e === 211)
+ t = this.readI64();
+ else if (e === 217) {
+ let n = this.lookU8();
+ t = this.decodeUtf8String(n, 1);
+ } else if (e === 218) {
+ let n = this.lookU16();
+ t = this.decodeUtf8String(n, 2);
+ } else if (e === 219) {
+ let n = this.lookU32();
+ t = this.decodeUtf8String(n, 4);
+ } else if (e === 220) {
+ let n = this.readU16();
+ if (n !== 0) {
+ this.pushArrayState(n), this.complete();
+ continue e;
+ } else
+ t = [];
+ } else if (e === 221) {
+ let n = this.readU32();
+ if (n !== 0) {
+ this.pushArrayState(n), this.complete();
+ continue e;
+ } else
+ t = [];
+ } else if (e === 222) {
+ let n = this.readU16();
+ if (n !== 0) {
+ this.pushMapState(n), this.complete();
+ continue e;
+ } else
+ t = {};
+ } else if (e === 223) {
+ let n = this.readU32();
+ if (n !== 0) {
+ this.pushMapState(n), this.complete();
+ continue e;
+ } else
+ t = {};
+ } else if (e === 196) {
+ let n = this.lookU8();
+ t = this.decodeBinary(n, 1);
+ } else if (e === 197) {
+ let n = this.lookU16();
+ t = this.decodeBinary(n, 2);
+ } else if (e === 198) {
+ let n = this.lookU32();
+ t = this.decodeBinary(n, 4);
+ } else if (e === 212)
+ t = this.decodeExtension(1, 0);
+ else if (e === 213)
+ t = this.decodeExtension(2, 0);
+ else if (e === 214)
+ t = this.decodeExtension(4, 0);
+ else if (e === 215)
+ t = this.decodeExtension(8, 0);
+ else if (e === 216)
+ t = this.decodeExtension(16, 0);
+ else if (e === 199) {
+ let n = this.lookU8();
+ t = this.decodeExtension(n, 1);
+ } else if (e === 200) {
+ let n = this.lookU16();
+ t = this.decodeExtension(n, 2);
+ } else if (e === 201) {
+ let n = this.lookU32();
+ t = this.decodeExtension(n, 4);
+ } else
+ throw new K.DecodeError(`Unrecognized type byte: ${(0, Cr.prettyByte)(e)}`);
+ this.complete();
+ let r = this.stack;
+ for (; r.length > 0; ) {
+ let n = r[r.length - 1];
+ if (n.type === 0)
+ if (n.array[n.position] = t, n.position++, n.position === n.size)
+ r.pop(), t = n.array;
+ else
+ continue e;
+ else if (n.type === 1) {
+ if (!Vn(t))
+ throw new K.DecodeError("The type of key must be string or number but " + typeof t);
+ if (t === "__proto__")
+ throw new K.DecodeError("The key __proto__ is not allowed");
+ n.key = t, n.type = 2;
+ continue e;
+ } else if (n.map[n.key] = t, n.readCount++, n.readCount === n.size)
+ r.pop(), t = n.map;
+ else {
+ n.key = null, n.type = 1;
+ continue e;
+ }
+ }
+ return t;
+ }
+ }
+ readHeadByte() {
+ return this.headByte === $e && (this.headByte = this.readU8()), this.headByte;
+ }
+ complete() {
+ this.headByte = $e;
+ }
+ readArraySize() {
+ let e = this.readHeadByte();
+ switch (e) {
+ case 220:
+ return this.readU16();
+ case 221:
+ return this.readU32();
+ default: {
+ if (e < 160)
+ return e - 144;
+ throw new K.DecodeError(`Unrecognized array type byte: ${(0, Cr.prettyByte)(e)}`);
+ }
+ }
+ }
+ pushMapState(e) {
+ if (e > this.maxMapLength)
+ throw new K.DecodeError(`Max length exceeded: map length (${e}) > maxMapLengthLength (${this.maxMapLength})`);
+ this.stack.push({ type: 1, size: e, key: null, readCount: 0, map: {} });
+ }
+ pushArrayState(e) {
+ if (e > this.maxArrayLength)
+ throw new K.DecodeError(`Max length exceeded: array length (${e}) > maxArrayLength (${this.maxArrayLength})`);
+ this.stack.push({ type: 0, size: e, array: new Array(e), position: 0 });
+ }
+ decodeUtf8String(e, t) {
+ var r;
+ if (e > this.maxStrLength)
+ throw new K.DecodeError(`Max length exceeded: UTF-8 byte length (${e}) > maxStrLength (${this.maxStrLength})`);
+ if (this.bytes.byteLength < this.pos + t + e)
+ throw Es;
+ let n = this.pos + t, o;
+ return this.stateIsMapKey() && (!((r = this.keyDecoder) === null || r === void 0) && r.canBeCached(e)) ? o = this.keyDecoder.decode(this.bytes, n, e) : e > jr.TEXT_DECODER_THRESHOLD ? o = (0, jr.utf8DecodeTD)(this.bytes, n, e) : o = (0, jr.utf8DecodeJs)(this.bytes, n, e), this.pos += t + e, o;
+ }
+ stateIsMapKey() {
+ return this.stack.length > 0 ? this.stack[this.stack.length - 1].type === 1 : false;
+ }
+ decodeBinary(e, t) {
+ if (e > this.maxBinLength)
+ throw new K.DecodeError(`Max length exceeded: bin length (${e}) > maxBinLength (${this.maxBinLength})`);
+ if (!this.hasRemaining(e + t))
+ throw Es;
+ let r = this.pos + t, n = this.bytes.subarray(r, r + e);
+ return this.pos += t + e, n;
+ }
+ decodeExtension(e, t) {
+ if (e > this.maxExtLength)
+ throw new K.DecodeError(`Max length exceeded: ext length (${e}) > maxExtLength (${this.maxExtLength})`);
+ let r = this.view.getInt8(this.pos + t), n = this.decodeBinary(e, t + 1);
+ return this.extensionCodec.decode(n, r, this.context);
+ }
+ lookU8() {
+ return this.view.getUint8(this.pos);
+ }
+ lookU16() {
+ return this.view.getUint16(this.pos);
+ }
+ lookU32() {
+ return this.view.getUint32(this.pos);
+ }
+ readU8() {
+ let e = this.view.getUint8(this.pos);
+ return this.pos++, e;
+ }
+ readI8() {
+ let e = this.view.getInt8(this.pos);
+ return this.pos++, e;
+ }
+ readU16() {
+ let e = this.view.getUint16(this.pos);
+ return this.pos += 2, e;
+ }
+ readI16() {
+ let e = this.view.getInt16(this.pos);
+ return this.pos += 2, e;
+ }
+ readU32() {
+ let e = this.view.getUint32(this.pos);
+ return this.pos += 4, e;
+ }
+ readI32() {
+ let e = this.view.getInt32(this.pos);
+ return this.pos += 4, e;
+ }
+ readU64() {
+ let e = (0, ce.getUint64)(this.view, this.pos);
+ return this.pos += 8, e;
+ }
+ readI64() {
+ let e = (0, ce.getInt64)(this.view, this.pos);
+ return this.pos += 8, e;
+ }
+ readF32() {
+ let e = this.view.getFloat32(this.pos);
+ return this.pos += 4, e;
+ }
+ readF64() {
+ let e = this.view.getFloat64(this.pos);
+ return this.pos += 8, e;
+ }
+ };
+ Q.Decoder = Br;
+});
+var Fr = S((H) => {
+ "use strict";
+ Object.defineProperty(H, "__esModule", { value: true });
+ H.decodeMulti = H.decode = H.defaultDecodeOptions = void 0;
+ var Ps = Vt();
+ H.defaultDecodeOptions = {};
+ function zn(s, e = H.defaultDecodeOptions) {
+ return new Ps.Decoder(e.extensionCodec, e.context, e.maxStrLength, e.maxBinLength, e.maxArrayLength, e.maxMapLength, e.maxExtLength).decode(s);
+ }
+ H.decode = zn;
+ function Xn(s, e = H.defaultDecodeOptions) {
+ return new Ps.Decoder(e.extensionCodec, e.context, e.maxStrLength, e.maxBinLength, e.maxArrayLength, e.maxMapLength, e.maxExtLength).decodeMulti(s);
+ }
+ H.decodeMulti = Xn;
+});
+var Ss = S((re) => {
+ "use strict";
+ Object.defineProperty(re, "__esModule", { value: true });
+ re.ensureAsyncIterable = re.asyncIterableFromStream = re.isAsyncIterable = void 0;
+ function Ts(s) {
+ return s[Symbol.asyncIterator] != null;
+ }
+ re.isAsyncIterable = Ts;
+ function Gn(s) {
+ if (s == null)
+ throw new Error("Assertion Failure: value must not be null nor undefined");
+ }
+ async function* _s(s) {
+ let e = s.getReader();
+ try {
+ for (; ; ) {
+ let { done: t, value: r } = await e.read();
+ if (t)
+ return;
+ Gn(r), yield r;
+ }
+ } finally {
+ e.releaseLock();
+ }
+ }
+ re.asyncIterableFromStream = _s;
+ function $n(s) {
+ return Ts(s) ? s : _s(s);
+ }
+ re.ensureAsyncIterable = $n;
+});
+var Ms = S((z) => {
+ "use strict";
+ Object.defineProperty(z, "__esModule", { value: true });
+ z.decodeStream = z.decodeMultiStream = z.decodeArrayStream = z.decodeAsync = void 0;
+ var qr = Vt(), Vr = Ss(), Jt = Fr();
+ async function Kn(s, e = Jt.defaultDecodeOptions) {
+ let t = (0, Vr.ensureAsyncIterable)(s);
+ return new qr.Decoder(e.extensionCodec, e.context, e.maxStrLength, e.maxBinLength, e.maxArrayLength, e.maxMapLength, e.maxExtLength).decodeAsync(t);
+ }
+ z.decodeAsync = Kn;
+ function Qn(s, e = Jt.defaultDecodeOptions) {
+ let t = (0, Vr.ensureAsyncIterable)(s);
+ return new qr.Decoder(e.extensionCodec, e.context, e.maxStrLength, e.maxBinLength, e.maxArrayLength, e.maxMapLength, e.maxExtLength).decodeArrayStream(t);
+ }
+ z.decodeArrayStream = Qn;
+ function ks(s, e = Jt.defaultDecodeOptions) {
+ let t = (0, Vr.ensureAsyncIterable)(s);
+ return new qr.Decoder(e.extensionCodec, e.context, e.maxStrLength, e.maxBinLength, e.maxArrayLength, e.maxMapLength, e.maxExtLength).decodeStream(t);
+ }
+ z.decodeMultiStream = ks;
+ function Zn(s, e = Jt.defaultDecodeOptions) {
+ return ks(s, e);
+ }
+ z.decodeStream = Zn;
+});
+var zt = S((h) => {
+ "use strict";
+ Object.defineProperty(h, "__esModule", { value: true });
+ h.decodeTimestampExtension = h.encodeTimestampExtension = h.decodeTimestampToTimeSpec = h.encodeTimeSpecToTimestamp = h.encodeDateToTimeSpec = h.EXT_TIMESTAMP = h.ExtData = h.ExtensionCodec = h.Encoder = h.DataViewIndexOutOfBoundsError = h.DecodeError = h.Decoder = h.decodeStream = h.decodeMultiStream = h.decodeArrayStream = h.decodeAsync = h.decodeMulti = h.decode = h.encode = void 0;
+ var Yn = ws();
+ Object.defineProperty(h, "encode", { enumerable: true, get: function() {
+ return Yn.encode;
+ } });
+ var Ds = Fr();
+ Object.defineProperty(h, "decode", { enumerable: true, get: function() {
+ return Ds.decode;
+ } });
+ Object.defineProperty(h, "decodeMulti", { enumerable: true, get: function() {
+ return Ds.decodeMulti;
+ } });
+ var Ht = Ms();
+ Object.defineProperty(h, "decodeAsync", { enumerable: true, get: function() {
+ return Ht.decodeAsync;
+ } });
+ Object.defineProperty(h, "decodeArrayStream", { enumerable: true, get: function() {
+ return Ht.decodeArrayStream;
+ } });
+ Object.defineProperty(h, "decodeMultiStream", { enumerable: true, get: function() {
+ return Ht.decodeMultiStream;
+ } });
+ Object.defineProperty(h, "decodeStream", { enumerable: true, get: function() {
+ return Ht.decodeStream;
+ } });
+ var Ws = Vt();
+ Object.defineProperty(h, "Decoder", { enumerable: true, get: function() {
+ return Ws.Decoder;
+ } });
+ Object.defineProperty(h, "DataViewIndexOutOfBoundsError", { enumerable: true, get: function() {
+ return Ws.DataViewIndexOutOfBoundsError;
+ } });
+ var eo = Ct();
+ Object.defineProperty(h, "DecodeError", { enumerable: true, get: function() {
+ return eo.DecodeError;
+ } });
+ var to = Ir();
+ Object.defineProperty(h, "Encoder", { enumerable: true, get: function() {
+ return to.Encoder;
+ } });
+ var ro = Bt();
+ Object.defineProperty(h, "ExtensionCodec", { enumerable: true, get: function() {
+ return ro.ExtensionCodec;
+ } });
+ var so = Dr();
+ Object.defineProperty(h, "ExtData", { enumerable: true, get: function() {
+ return so.ExtData;
+ } });
+ var ve = Wr();
+ Object.defineProperty(h, "EXT_TIMESTAMP", { enumerable: true, get: function() {
+ return ve.EXT_TIMESTAMP;
+ } });
+ Object.defineProperty(h, "encodeDateToTimeSpec", { enumerable: true, get: function() {
+ return ve.encodeDateToTimeSpec;
+ } });
+ Object.defineProperty(h, "encodeTimeSpecToTimestamp", { enumerable: true, get: function() {
+ return ve.encodeTimeSpecToTimestamp;
+ } });
+ Object.defineProperty(h, "decodeTimestampToTimeSpec", { enumerable: true, get: function() {
+ return ve.decodeTimestampToTimeSpec;
+ } });
+ Object.defineProperty(h, "encodeTimestampExtension", { enumerable: true, get: function() {
+ return ve.encodeTimestampExtension;
+ } });
+ Object.defineProperty(h, "decodeTimestampExtension", { enumerable: true, get: function() {
+ return ve.decodeTimestampExtension;
+ } });
+});
+var I = class extends Error {
+ constructor(e) {
+ super(e), this.name = this.constructor.name, Object.setPrototypeOf(this, new.target.prototype);
+ }
+};
+var A = class extends I {
+};
+var P = class extends I {
+};
+var m = typeof process < "u" && process.release && process.release.name === "node";
+var Rr;
+if (globalThis.document)
+ Rr = (s) => new Promise((e, t) => {
+ let r = document.createElement("script");
+ r.src = s, r.onload = () => e(), r.onerror = t, document.head.appendChild(r);
+ });
+else if (globalThis.importScripts)
+ Rr = async (s) => {
+ try {
+ globalThis.importScripts(s);
+ } catch (e) {
+ if (e instanceof TypeError)
+ await Promise.resolve().then(() => oe(B(s)));
+ else
+ throw e;
+ }
+ };
+else if (m)
+ Rr = async (s) => {
+ let e = (await Promise.resolve().then(() => oe(B("path")))).default;
+ await Promise.resolve().then(() => oe(B(e.resolve(s))));
+ };
+else
+ throw new I("Cannot determine runtime environment");
+var c = {};
+function es(s) {
+ Object.keys(s).forEach((e) => c._free(s[e]));
+}
+var N = { null: 0, symbol: 1, pairlist: 2, closure: 3, environment: 4, promise: 5, call: 6, special: 7, builtin: 8, string: 9, logical: 10, integer: 13, double: 14, complex: 15, character: 16, dots: 17, any: 18, list: 19, expression: 20, bytecode: 21, pointer: 22, weakref: 23, raw: 24, s4: 25, new: 30, free: 31, function: 99 };
+function mr(s) {
+ return !!s && typeof s == "object" && Object.keys(N).includes(s.type);
+}
+function Ce(s) {
+ return !!s && typeof s == "object" && "re" in s && "im" in s;
+}
+function je(s) {
+ return c._Rf_protect(L(s)), s;
+}
+function w(s, e) {
+ return c._Rf_protect(L(s)), ++e.n, s;
+}
+function ts(s) {
+ let e = c._malloc(4);
+ return c._R_ProtectWithIndex(L(s), e), { loc: c.getValue(e, "i32"), ptr: e };
+}
+function rs(s) {
+ c._Rf_unprotect(1), c._free(s.ptr);
+}
+function ss(s, e) {
+ return c._R_Reprotect(L(s), e.loc), s;
+}
+function T(s) {
+ c._Rf_unprotect(s);
+}
+function gr(s, e, t) {
+ c._Rf_defineVar(L(e), L(t), L(s));
+}
+function br(s, e) {
+ let t = {}, r = { n: 0 };
+ try {
+ let n = new Be(e);
+ w(n, r), t.code = c.allocateUTF8(s);
+ let o = c._R_ParseEvalString(t.code, n.ptr);
+ return y.wrap(o);
+ } finally {
+ es(t), T(r.n);
+ }
+}
+function Ne(s, e) {
+ return c.getWasmTableEntry(c.GOT.ffi_safe_eval.value)(L(s), L(e));
+}
+var un = /* @__PURE__ */ new WeakMap();
+function ns(s, e) {
+ return un.set(s, e), s;
+}
+function L(s) {
+ return Tt(s) ? s.ptr : s;
+}
+function le(s, e) {
+ if (c._TYPEOF(s.ptr) !== N[e])
+ throw new Error(`Unexpected object type "${s.type()}" when expecting type "${e}"`);
+}
+function as(s) {
+ if (mr(s))
+ return new (is(s.type))(s);
+ if (s && typeof s == "object" && "type" in s && s.type === "null")
+ return new Pt();
+ if (s === null)
+ return new Y({ type: "logical", names: null, values: [null] });
+ if (typeof s == "boolean")
+ return new Y(s);
+ if (typeof s == "number")
+ return new fe(s);
+ if (typeof s == "string")
+ return new F(s);
+ if (Ce(s))
+ return new Fe(s);
+ if (ArrayBuffer.isView(s) || s instanceof ArrayBuffer)
+ return new qe(s);
+ if (Array.isArray(s))
+ return dn(s);
+ if (typeof s == "object")
+ return ee.fromObject(s);
+ throw new Error("Robj construction for this JS object is not yet supported");
+}
+function dn(s) {
+ let e = { n: 0 };
+ if (s.every((r) => r && typeof r == "object" && !Tt(r) && !Ce(r))) {
+ let r = s, n = r.every((i) => Object.keys(i).filter((l) => !Object.keys(r[0]).includes(l)).length === 0 && Object.keys(r[0]).filter((l) => !Object.keys(i).includes(l)).length === 0), o = r.every((i) => Object.values(i).every((l) => cs(l) || ls(l)));
+ if (n && o)
+ return ee.fromD3(r);
+ }
+ if (s.every((r) => typeof r == "boolean" || r === null))
+ return new Y(s);
+ if (s.every((r) => typeof r == "number" || r === null))
+ return new fe(s);
+ if (s.every((r) => typeof r == "string" || r === null))
+ return new F(s);
+ try {
+ let r = new q([new U("c"), ...s]);
+ return w(r, e), r.eval();
+ } finally {
+ T(e.n);
+ }
+}
+var x = class {
+ constructor(e) {
+ this.ptr = e;
+ }
+ type() {
+ let e = c._TYPEOF(this.ptr);
+ return Object.keys(N).find((r) => N[r] === e);
+ }
+};
+var Re;
+var vt;
+var ae = class extends x {
+ constructor(t) {
+ if (!(t instanceof x))
+ return as(t);
+ super(t.ptr);
+ u(this, Re);
+ }
+ static wrap(t) {
+ let r = c._TYPEOF(t), n = Object.keys(N)[Object.values(N).indexOf(r)];
+ return new (is(n))(new x(t));
+ }
+ get [Symbol.toStringTag]() {
+ return `RObject:${this.type()}`;
+ }
+ static getPersistentObject(t) {
+ return k[t];
+ }
+ getPropertyValue(t) {
+ return this[t];
+ }
+ inspect() {
+ br(".Internal(inspect(x))", { x: this });
+ }
+ isNull() {
+ return c._TYPEOF(this.ptr) === N.null;
+ }
+ isNa() {
+ try {
+ let t = br("is.na(x)", { x: this });
+ return je(t), t.toBoolean();
+ } finally {
+ T(1);
+ }
+ }
+ isUnbound() {
+ return this.ptr === k.unboundValue.ptr;
+ }
+ attrs() {
+ return ie.wrap(c._ATTRIB(this.ptr));
+ }
+ class() {
+ let t = { n: 0 }, r = new q([new U("class"), this]);
+ w(r, t);
+ try {
+ return r.eval();
+ } finally {
+ T(t.n);
+ }
+ }
+ setNames(t) {
+ let r;
+ if (t === null)
+ r = k.null;
+ else if (Array.isArray(t) && t.every((n) => typeof n == "string" || n === null))
+ r = new F(t);
+ else
+ throw new Error("Argument to setNames must be null or an Array of strings or null");
+ return c._Rf_setAttrib(this.ptr, k.namesSymbol.ptr, r.ptr), this;
+ }
+ names() {
+ let t = F.wrap(c._Rf_getAttrib(this.ptr, k.namesSymbol.ptr));
+ return t.isNull() ? null : t.toArray();
+ }
+ includes(t) {
+ let r = this.names();
+ return r && r.includes(t);
+ }
+ toJs(t = { depth: 0 }, r = 1) {
+ throw new Error("This R object cannot be converted to JS");
+ }
+ subset(t) {
+ return E(this, Re, vt).call(this, t, k.bracketSymbol.ptr);
+ }
+ get(t) {
+ return E(this, Re, vt).call(this, t, k.bracket2Symbol.ptr);
+ }
+ getDollar(t) {
+ return E(this, Re, vt).call(this, t, k.dollarSymbol.ptr);
+ }
+ pluck(...t) {
+ let r = ts(k.null);
+ try {
+ let n = (i, l) => {
+ let p = i.get(l);
+ return ss(p, r);
+ }, o = t.reduce(n, this);
+ return o.isNull() ? void 0 : o;
+ } finally {
+ rs(r);
+ }
+ }
+ set(t, r) {
+ let n = { n: 0 };
+ try {
+ let o = new ae(t);
+ w(o, n);
+ let i = new ae(r);
+ w(i, n);
+ let l = new U("[[<-"), p = c._Rf_lang4(l.ptr, this.ptr, o.ptr, i.ptr);
+ return w(p, n), ae.wrap(Ne(p, k.baseEnv));
+ } finally {
+ T(n.n);
+ }
+ }
+ static getMethods(t) {
+ let r = /* @__PURE__ */ new Set(), n = t;
+ do
+ Object.getOwnPropertyNames(n).map((o) => r.add(o));
+ while (n = Object.getPrototypeOf(n));
+ return [...r.keys()].filter((o) => typeof t[o] == "function");
+ }
+};
+var y = ae;
+Re = /* @__PURE__ */ new WeakSet(), vt = function(t, r) {
+ let n = { n: 0 };
+ try {
+ let o = new ae(t);
+ w(o, n);
+ let i = c._Rf_lang3(r, this.ptr, o.ptr);
+ return w(i, n), ae.wrap(Ne(i, k.baseEnv));
+ } finally {
+ T(n.n);
+ }
+};
+var Pt = class extends y {
+ constructor() {
+ return super(new x(c.getValue(c._R_NilValue, "*"))), this;
+ }
+ toJs() {
+ return { type: "null" };
+ }
+};
+var U = class extends y {
+ constructor(e) {
+ if (e instanceof x) {
+ le(e, "symbol"), super(e);
+ return;
+ }
+ let t = c.allocateUTF8(e);
+ try {
+ super(new x(c._Rf_install(t)));
+ } finally {
+ c._free(t);
+ }
+ }
+ toJs() {
+ let e = this.toObject();
+ return { type: "symbol", printname: e.printname, symvalue: e.symvalue, internal: e.internal };
+ }
+ toObject() {
+ return { printname: this.printname().isUnbound() ? null : this.printname().toString(), symvalue: this.symvalue().isUnbound() ? null : this.symvalue().ptr, internal: this.internal().isNull() ? null : this.internal().ptr };
+ }
+ toString() {
+ return this.printname().toString();
+ }
+ printname() {
+ return Je.wrap(c._PRINTNAME(this.ptr));
+ }
+ symvalue() {
+ return y.wrap(c._SYMVALUE(this.ptr));
+ }
+ internal() {
+ return y.wrap(c._INTERNAL(this.ptr));
+ }
+};
+var ie = class extends y {
+ constructor(e) {
+ if (e instanceof x)
+ return le(e, "pairlist"), super(e), this;
+ let t = { n: 0 };
+ try {
+ let { names: r, values: n } = me(e), o = ie.wrap(c._Rf_allocList(n.length));
+ w(o, t);
+ for (let [i, l] = [0, o]; !l.isNull(); [i, l] = [i + 1, l.cdr()])
+ l.setcar(new y(n[i]));
+ o.setNames(r), super(o);
+ } finally {
+ T(t.n);
+ }
+ }
+ get length() {
+ return this.toArray().length;
+ }
+ toArray(e = { depth: 1 }) {
+ return this.toJs(e).values;
+ }
+ toObject({ allowDuplicateKey: e = true, allowEmptyKey: t = false, depth: r = -1 } = {}) {
+ let n = this.entries({ depth: r }), o = n.map(([i]) => i);
+ if (!e && new Set(o).size !== o.length)
+ throw new Error("Duplicate key when converting pairlist without allowDuplicateKey enabled");
+ if (!t && o.some((i) => !i))
+ throw new Error("Empty or null key when converting pairlist without allowEmptyKey enabled");
+ return Object.fromEntries(n.filter((i, l) => n.findIndex((p) => p[0] === i[0]) === l));
+ }
+ entries(e = { depth: 1 }) {
+ let t = this.toJs(e);
+ return t.values.map((r, n) => [t.names ? t.names[n] : null, r]);
+ }
+ toJs(e = { depth: 0 }, t = 1) {
+ let r = [], n = false, o = [];
+ for (let l = this; !l.isNull(); l = l.cdr()) {
+ let p = l.tag();
+ p.isNull() ? r.push("") : (n = true, r.push(p.toString())), e.depth && t >= e.depth ? o.push(l.car()) : o.push(l.car().toJs(e, t + 1));
+ }
+ return { type: "pairlist", names: n ? r : null, values: o };
+ }
+ includes(e) {
+ return e in this.toObject();
+ }
+ setcar(e) {
+ c._SETCAR(this.ptr, e.ptr);
+ }
+ car() {
+ return y.wrap(c._CAR(this.ptr));
+ }
+ cdr() {
+ return y.wrap(c._CDR(this.ptr));
+ }
+ tag() {
+ return y.wrap(c._TAG(this.ptr));
+ }
+};
+var q = class extends y {
+ constructor(e) {
+ if (e instanceof x)
+ return le(e, "call"), super(e), this;
+ let t = { n: 0 };
+ try {
+ let { values: r } = me(e), n = r.map((i) => w(new y(i), t)), o = q.wrap(c._Rf_allocVector(N.call, r.length));
+ w(o, t);
+ for (let [i, l] = [0, o]; !l.isNull(); [i, l] = [i + 1, l.cdr()])
+ l.setcar(n[i]);
+ super(o);
+ } finally {
+ T(t.n);
+ }
+ }
+ setcar(e) {
+ c._SETCAR(this.ptr, e.ptr);
+ }
+ car() {
+ return y.wrap(c._CAR(this.ptr));
+ }
+ cdr() {
+ return y.wrap(c._CDR(this.ptr));
+ }
+ eval() {
+ return c.webr.evalR(this, { env: k.baseEnv });
+ }
+ capture(e = {}) {
+ return c.webr.captureR(this, e);
+ }
+ deparse() {
+ let e = { n: 0 };
+ try {
+ let t = c._Rf_lang2(new U("deparse1").ptr, c._Rf_lang2(new U("quote").ptr, this.ptr));
+ w(t, e);
+ let r = F.wrap(Ne(t, k.baseEnv));
+ return w(r, e), r.toString();
+ } finally {
+ T(e.n);
+ }
+ }
+};
+var Ve = class extends y {
+ constructor(e, t = null) {
+ if (e instanceof x) {
+ if (le(e, "list"), super(e), t) {
+ if (t.length !== this.length)
+ throw new Error("Can't construct named `RList`. Supplied `names` must be the same length as the list.");
+ this.setNames(t);
+ }
+ return this;
+ }
+ let r = { n: 0 };
+ try {
+ let n = me(e), o = c._Rf_allocVector(N.list, n.values.length);
+ w(o, r), n.values.forEach((l, p) => {
+ c._SET_VECTOR_ELT(o, p, new y(l).ptr);
+ });
+ let i = t || n.names;
+ if (i && i.length !== n.values.length)
+ throw new Error("Can't construct named `RList`. Supplied `names` must be the same length as the list.");
+ y.wrap(o).setNames(i), super(new x(o));
+ } finally {
+ T(r.n);
+ }
+ }
+ get length() {
+ return c._LENGTH(this.ptr);
+ }
+ isDataFrame() {
+ let e = ie.wrap(c._ATTRIB(this.ptr)).get("class");
+ return !e.isNull() && e.toArray().includes("data.frame");
+ }
+ toArray(e = { depth: 1 }) {
+ return this.toJs(e).values;
+ }
+ toObject({ allowDuplicateKey: e = true, allowEmptyKey: t = false, depth: r = -1 } = {}) {
+ let n = this.entries({ depth: r }), o = n.map(([i]) => i);
+ if (!e && new Set(o).size !== o.length)
+ throw new Error("Duplicate key when converting list without allowDuplicateKey enabled");
+ if (!t && o.some((i) => !i))
+ throw new Error("Empty or null key when converting list without allowEmptyKey enabled");
+ return Object.fromEntries(n.filter((i, l) => n.findIndex((p) => p[0] === i[0]) === l));
+ }
+ toD3() {
+ if (!this.isDataFrame())
+ throw new Error("Can't convert R list object to D3 format. Object must be of class 'data.frame'.");
+ return this.entries().reduce((t, r) => (r[1].forEach((n, o) => t[o] = Object.assign(t[o] || {}, { [r[0]]: n })), t), []);
+ }
+ entries(e = { depth: -1 }) {
+ let t = this.toJs(e);
+ return this.isDataFrame() && e.depth < 0 && (t.values = t.values.map((r) => r.toArray())), t.values.map((r, n) => [t.names ? t.names[n] : null, r]);
+ }
+ toJs(e = { depth: 0 }, t = 1) {
+ return { type: "list", names: this.names(), values: [...Array(this.length).keys()].map((r) => e.depth && t >= e.depth ? this.get(r + 1) : this.get(r + 1).toJs(e, t + 1)) };
+ }
+};
+var ee = class extends Ve {
+ constructor(e) {
+ if (e instanceof x) {
+ if (super(e), !this.isDataFrame())
+ throw new Error("Can't construct `RDataFrame`. Supplied R object is not a `data.frame`.");
+ return this;
+ }
+ return ee.fromObject(e);
+ }
+ static fromObject(e) {
+ let { names: t, values: r } = me(e), n = { n: 0 };
+ try {
+ let o = !!t && t.length > 0 && t.every((l) => l), i = r.length > 0 && r.every((l) => Array.isArray(l) || ArrayBuffer.isView(l) || l instanceof ArrayBuffer);
+ if (o && i) {
+ let l = r, p = l.every((b) => b.length === l[0].length), D = l.every((b) => cs(b[0]) || ls(b[0]));
+ if (p && D) {
+ let b = new Ve({ type: "list", names: t, values: l.map((rn) => as(rn)) });
+ w(b, n);
+ let j = new q([new U("as.data.frame"), b]);
+ return w(j, n), new ee(j.eval());
+ }
+ }
+ } finally {
+ T(n.n);
+ }
+ throw new Error("Can't construct `data.frame`. Source object is not eligible.");
+ }
+ static fromD3(e) {
+ return this.fromObject(Object.fromEntries(Object.keys(e[0]).map((t) => [t, e.map((r) => r[t])])));
+ }
+};
+var ye = class extends y {
+ exec(...e) {
+ let t = { n: 0 };
+ try {
+ let r = new q([this, ...e]);
+ return w(r, t), r.eval();
+ } finally {
+ T(t.n);
+ }
+ }
+ capture(e = {}, ...t) {
+ let r = { n: 0 };
+ try {
+ let n = new q([this, ...t]);
+ return w(n, r), n.capture(e);
+ } finally {
+ T(r.n);
+ }
+ }
+};
+var Je = class extends y {
+ constructor(e) {
+ if (e instanceof x) {
+ le(e, "string"), super(e);
+ return;
+ }
+ let t = c.allocateUTF8(e);
+ try {
+ super(new x(c._Rf_mkChar(t)));
+ } finally {
+ c._free(t);
+ }
+ }
+ toString() {
+ return c.UTF8ToString(c._R_CHAR(this.ptr));
+ }
+ toJs() {
+ return { type: "string", value: this.toString() };
+ }
+};
+var Be = class extends y {
+ constructor(e = {}) {
+ if (e instanceof x)
+ return le(e, "environment"), super(e), this;
+ let t = 0;
+ try {
+ let { names: r, values: n } = me(e), o = je(c._R_NewEnv(k.globalEnv.ptr, 0, 0));
+ ++t, n.forEach((i, l) => {
+ let p = r ? r[l] : null;
+ if (!p)
+ throw new Error("Can't create object in new environment with empty symbol name");
+ let D = new U(p), b = je(new y(i));
+ try {
+ gr(o, D, b);
+ } finally {
+ T(1);
+ }
+ }), super(new x(o));
+ } finally {
+ T(t);
+ }
+ }
+ ls(e = false, t = true) {
+ return F.wrap(c._R_lsInternal3(this.ptr, Number(e), Number(t))).toArray();
+ }
+ bind(e, t) {
+ let r = new U(e), n = je(new y(t));
+ try {
+ gr(this, r, n);
+ } finally {
+ T(1);
+ }
+ }
+ names() {
+ return this.ls(true, true);
+ }
+ frame() {
+ return y.wrap(c._FRAME(this.ptr));
+ }
+ subset(e) {
+ if (typeof e == "number")
+ throw new Error("Object of type environment is not subsettable");
+ return this.getDollar(e);
+ }
+ toObject({ depth: e = -1 } = {}) {
+ let t = this.names();
+ return Object.fromEntries([...Array(t.length).keys()].map((r) => {
+ let n = this.getDollar(t[r]);
+ return [t[r], e < 0 ? n : n.toJs({ depth: e })];
+ }));
+ }
+ toJs(e = { depth: 0 }, t = 1) {
+ let r = this.names(), n = [...Array(r.length).keys()].map((o) => e.depth && t >= e.depth ? this.getDollar(r[o]) : this.getDollar(r[o]).toJs(e, t + 1));
+ return { type: "environment", names: r, values: n };
+ }
+};
+var te = class extends y {
+ constructor(e, t, r) {
+ if (e instanceof x)
+ return le(e, t), super(e), this;
+ let n = { n: 0 };
+ try {
+ let { names: o, values: i } = me(e), l = c._Rf_allocVector(N[t], i.length);
+ w(l, n), i.forEach(r(l)), y.wrap(l).setNames(o), super(new x(l));
+ } finally {
+ T(n.n);
+ }
+ }
+ get length() {
+ return c._LENGTH(this.ptr);
+ }
+ get(e) {
+ return super.get(e);
+ }
+ subset(e) {
+ return super.subset(e);
+ }
+ getDollar() {
+ throw new Error("$ operator is invalid for atomic vectors");
+ }
+ detectMissing() {
+ let e = { n: 0 };
+ try {
+ let t = c._Rf_lang2(new U("is.na").ptr, this.ptr);
+ w(t, e);
+ let r = Y.wrap(Ne(t, k.baseEnv));
+ w(r, e);
+ let n = r.toTypedArray();
+ return Array.from(n).map((o) => !!o);
+ } finally {
+ T(e.n);
+ }
+ }
+ toArray() {
+ let e = this.toTypedArray();
+ return this.detectMissing().map((t, r) => t ? null : e[r]);
+ }
+ toObject({ allowDuplicateKey: e = true, allowEmptyKey: t = false } = {}) {
+ let r = this.entries(), n = r.map(([o]) => o);
+ if (!e && new Set(n).size !== n.length)
+ throw new Error("Duplicate key when converting atomic vector without allowDuplicateKey enabled");
+ if (!t && n.some((o) => !o))
+ throw new Error("Empty or null key when converting atomic vector without allowEmptyKey enabled");
+ return Object.fromEntries(r.filter((o, i) => r.findIndex((l) => l[0] === o[0]) === i));
+ }
+ entries() {
+ let e = this.toArray(), t = this.names();
+ return e.map((r, n) => [t ? t[n] : null, r]);
+ }
+ toJs() {
+ return { type: this.type(), names: this.names(), values: this.toArray() };
+ }
+};
+var _t;
+var wr = class extends te {
+ constructor(e) {
+ super(e, "logical", a(wr, _t));
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+ getBoolean(e) {
+ return this.get(e).toArray()[0];
+ }
+ toBoolean() {
+ if (this.length !== 1)
+ throw new Error("Can't convert atomic vector of length > 1 to a scalar JS value");
+ let e = this.getBoolean(1);
+ if (e === null)
+ throw new Error("Can't convert missing value `NA` to a JS boolean");
+ return e;
+ }
+ toTypedArray() {
+ return new Int32Array(c.HEAP32.subarray(c._LOGICAL(this.ptr) / 4, c._LOGICAL(this.ptr) / 4 + this.length));
+ }
+ toArray() {
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+ c.setValue(t + 4 * o, n === null ? r : !!n, "i32");
+ };
+});
+var St;
+var xr = class extends te {
+ constructor(e) {
+ super(e, "integer", a(xr, St));
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+ getNumber(e) {
+ return this.get(e).toArray()[0];
+ }
+ toNumber() {
+ if (this.length !== 1)
+ throw new Error("Can't convert atomic vector of length > 1 to a scalar JS value");
+ let e = this.getNumber(1);
+ if (e === null)
+ throw new Error("Can't convert missing value `NA` to a JS number");
+ return e;
+ }
+ toTypedArray() {
+ return new Int32Array(c.HEAP32.subarray(c._INTEGER(this.ptr) / 4, c._INTEGER(this.ptr) / 4 + this.length));
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+var kt;
+var vr = class extends te {
+ constructor(e) {
+ super(e, "double", a(vr, kt));
+ }
+ getNumber(e) {
+ return this.get(e).toArray()[0];
+ }
+ toNumber() {
+ if (this.length !== 1)
+ throw new Error("Can't convert atomic vector of length > 1 to a scalar JS value");
+ let e = this.getNumber(1);
+ if (e === null)
+ throw new Error("Can't convert missing value `NA` to a JS number");
+ return e;
+ }
+ toTypedArray() {
+ return new Float64Array(c.HEAPF64.subarray(c._REAL(this.ptr) / 8, c._REAL(this.ptr) / 8 + this.length));
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+ c.setValue(t + 8 * o, n === null ? r : n, "double");
+ };
+});
+var Mt;
+var Er = class extends te {
+ constructor(e) {
+ super(e, "complex", a(Er, Mt));
+ }
+ getComplex(e) {
+ return this.get(e).toArray()[0];
+ }
+ toComplex() {
+ if (this.length !== 1)
+ throw new Error("Can't convert atomic vector of length > 1 to a scalar JS value");
+ let e = this.getComplex(1);
+ if (e === null)
+ throw new Error("Can't convert missing value `NA` to a JS object");
+ return e;
+ }
+ toTypedArray() {
+ return new Float64Array(c.HEAPF64.subarray(c._COMPLEX(this.ptr) / 8, c._COMPLEX(this.ptr) / 8 + 2 * this.length));
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+ toArray() {
+ let e = this.toTypedArray();
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+ c.setValue(t + 8 * (2 * o), n === null ? r : n.re, "double"), c.setValue(t + 8 * (2 * o + 1), n === null ? r : n.im, "double");
+ };
+});
+var Dt;
+var Pr = class extends te {
+ constructor(e) {
+ super(e, "character", a(Pr, Dt));
+ }
+ getString(e) {
+ return this.get(e).toArray()[0];
+ }
+ toString() {
+ if (this.length !== 1)
+ throw new Error("Can't convert atomic vector of length > 1 to a scalar JS value");
+ let e = this.getString(1);
+ if (e === null)
+ throw new Error("Can't convert missing value `NA` to a JS string");
+ return e;
+ }
+ toTypedArray() {
+ return new Uint32Array(c.HEAPU32.subarray(c._STRING_PTR(this.ptr) / 4, c._STRING_PTR(this.ptr) / 4 + this.length));
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+var Tr = class extends te {
+ constructor(e) {
+ e instanceof ArrayBuffer && (e = new Uint8Array(e)), super(e, "raw", a(Tr, Wt));
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+ getNumber(e) {
+ return this.get(e).toArray()[0];
+ }
+ toNumber() {
+ if (this.length !== 1)
+ throw new Error("Can't convert atomic vector of length > 1 to a scalar JS value");
+ let e = this.getNumber(1);
+ if (e === null)
+ throw new Error("Can't convert missing value `NA` to a JS number");
+ return e;
+ }
+ toTypedArray() {
+ return new Uint8Array(c.HEAPU8.subarray(c._RAW(this.ptr), c._RAW(this.ptr) + this.length));
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+ let t = c._RAW(e);
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+ c.setValue(t + n, Number(r), "i8");
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+}
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+}
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+}
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+}
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+ method: request.method,
+ headers: request.headers,
+ body: request.method === "GET" || request.method === "HEAD" ? void 0 : blob,
+ credentials: request.credentials,
+ cache: request.cache,
+ redirect: request.redirect,
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+ if (request.method !== "GET") {
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+ }
+ if (useCaching) {
+ event.respondWith(
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+ }
+ try {
+ const networkResponse = addCoiHeaders(await fetch(request));
+ const baseUrl = self.location.origin + dirname(self.location.pathname);
+ if (request.url.startsWith(baseUrl + "/shinylive/") || request.url === baseUrl + "/favicon.ico") {
+ const cache = await caches.open(version + cacheName);
+ await cache.put(request, networkResponse.clone());
+ }
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+ status: 404
+ });
+ }
+ })()
+ );
+ return;
+ }
+ if (coiRequested) {
+ event.respondWith(
+ (async () => {
+ const resp = await fetch(request);
+ return addCoiHeaders(resp);
+ })()
+ );
+ }
+});
+var apps = {};
+(async () => {
+ const allClients = await self.clients.matchAll();
+ for (const client of allClients) {
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+ type: "serviceworkerStart"
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+ }
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+self.addEventListener("message", (event) => {
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+ if (msg.type === "configureProxyPath") {
+ const path = msg.path;
+ const port = event.ports[0];
+ apps[path] = port;
+ }
+});
+function identityFilter(bodyChunk, response) {
+ return bodyChunk;
+}
+function injectSocketFilter(bodyChunk, response) {
+ const contentType = response.headers.get("content-type");
+ if (contentType && /^text\/html(;|$)/.test(contentType)) {
+ const bodyChunkStr = uint8ArrayToString(bodyChunk);
+ const base_path = dirname(self.location.pathname);
+ const newStr = bodyChunkStr.replace(
+ /<\/head>/,
+ `
+
+
+
+
+
+
+
+
+
+
+
+AlloMate is a Shiny web application for genetic breeding optimization using Optimum Contribution Selection (OCS). The app helps breeders make informed decisions about mate selection and breeding strategies by analyzing kinship relationships and breeding values.
-AlloMate is a shiny app that simplifies mate allocation decisions for breeders.
-
-## Overview
-This shiny app allows breeders to combine multiple traits in a selection index and assign a relative weight to each trait. AlloMate also allows to simplify mate allocation by filtering out possible crosses with negative ebvs and by using a kinship threshold between parents set by the user.
-Current version calculates kinship through pedigree information only, we are working on supporting genotypic information using an Optimal Contribution Selection (OCS) framework in the near future.
+## π Features
-## Dependencies
+### Core Functionality
+- **Kinship Analysis**: Calculate and visualize kinship relationships between potential mates
+- **EBV Processing**: Combine multiple breeding value traits with user-defined weights
+- **Optimum Contribution Selection**: Optimize breeding contributions while controlling inbreeding
+- **Mating Plan Generation**: Create optimal mating pairs to minimize inbreeding
+- **Excel Export**: Download comprehensive results in Excel format
-This app requires the following R packages:
+### Technical Features
+- **Dual Implementation**: Works with both optiSel package and custom fallback
+- **WebR Compatible**: Runs in web browsers without server installation
+- **Modular Design**: Clean, organized codebase for easy maintenance
+- **Robust Error Handling**: Comprehensive validation and user feedback
-- **shiny**: For building the interactive web app interface
-- **DT**: For rendering interactive data tables
-- **shinyjs**: For additional Shiny JavaScript utilities
-- **readxl**: For reading Excel files (if needed)
-- **openxlsx**: For writing Excel output files
-- **dplyr**: For data manipulation
-- **tidyr**: For data tidying
-- **readr**: For reading tab-separated files
-- **purrr**: For functional programming helpers
-- **kinship2**: For pedigree and kinship calculations
-- **tibble**: For enhanced data frames
+## π Project Structure
-You can install any missing packages with:
+```
+AlloMate/
+βββ app/ # Shiny application
+β βββ global.R # Shared code (packages, global variables)
+β βββ ui.R # User interface code
+β βββ server.R # Server logic and reactivity
+β βββ R/ # Helper functions and modules
+β β βββ utils.R # Data processing and utility functions
+β β βββ ocs_helpers.R # OCS analysis functions
+β β βββ ui_helpers.R # UI helper functions
+β β βββ load_functions.R # Function loader
+β β βββ optsel_fallback.R # Custom OCS fallback implementation
+β βββ www/ # Static resources
+β βββ allomate.png
+β βββ logos.png
+βββ scripts/ # Custom implementations and utilities
+β βββ optsel_fallback.R # Custom OCS fallback implementation
+β βββ validate_ocs_logic.R # OCS logic validation
+β βββ compare_ocs_results.R # Results comparison
+β βββ ped_ocs_app.R # Pedigree OCS application
+β βββ dosage2gmat.R # Dosage to genotype matrix conversion
+β βββ test.r # Testing utilities
+βββ data/ # Sample data files
+β βββ candidates_2024_10_29.txt
+β βββ pedigree_with_family.txt
+β βββ weight_ebvs_for_app_with_family.txt
+β βββ length_ebvs_for_app_with_family.txt
+β βββ shiny_output.txt # Sample output data
+βββ AlloMate.Rproj # RStudio project file
+βββ README.md
+```
-```r
-install.packages(c("shiny", "DT", "shinyjs", "readxl", "openxlsx", "dplyr", "tidyr", "readr", "purrr", "kinship2", "tibble"))
-## Usage
+## π οΈ Installation
+
+### Prerequisites
+- R (version 4.0 or higher)
+- Required R packages (automatically installed):
+ - shiny, readr, dplyr, tidyr, purrr, kinship2, DT, tibble, openxlsx, quadprog
+
+### Local Installation
+1. Clone or download the repository
+2. Open R or RStudio
+3. Set working directory to the AlloMate folder
+4. Run the app:
+ ```r
+ shiny::runApp("app")
+ ```
+
+### Package Installation
+The app automatically handles package installation when it starts up:
+
+#### How it Works
+1. **global.R** runs once when the Shiny app starts
+2. It checks for all required packages and installs missing ones
+3. For `optiSel` specifically, it tries CRAN first, then Bioconductor if needed
+4. Installation status is displayed in the app interface
-### To run the app:
+#### Troubleshooting Package Installation
+**If optiSel Installation Fails:**
+
+**Option 1: Manual Installation (Recommended)**
```r
-# Install required packages if not already installed
-packages <- c("shiny", "DT", "shinyjs", "readxl", "openxlsx", "dplyr", "tidyr")
-installed <- packages %in% rownames(installed.packages())
-if (any(!installed)) install.packages(packages[!installed])
+# Try CRAN first
+install.packages("optiSel")
+
+# If that fails, try Bioconductor
+if (!require(BiocManager, quietly = TRUE)) {
+ install.packages("BiocManager")
+}
+BiocManager::install("optiSel")
+```
-# Run the app from GitHub
-shiny::runGitHub("Breeding-Insight/AlloMate", subdir = "app")
+**Option 2: Check System Requirements**
+- Ensure you have write permissions to your R library directory
+- Check that you have sufficient disk space
+- Verify your internet connection
+
+**Option 3: Update R and Packages**
+```r
+# Update R to latest version
+# Then update all packages
+update.packages(ask = FALSE)
```
-### Input files
-##### Pedigree
-3 column tab separated file with with headers id, sire and dam in any order.
-##### Selection Candidates
-2 column tab separated file with candidate ids in "id" column and M or F in "sex" column.
+### WebR Deployment
+The app is compatible with WebR environments and will automatically use the custom OCS fallback when optiSel is not available.
+
+## π Usage
+
+### 1. Upload Data
+- **Candidates File**: Upload a text file with columns `id` and `sex` (M/F)
+- **Pedigree File**: Upload a text file with columns `id`, `sire`, and `dam`
+- **EBV Files**: Upload breeding value files with columns `ID` and `EBV`
+
+### 2. Configure Analysis
+- **Traits**: Add multiple EBV traits with relative weights (must sum to 1)
+- **Kinship Threshold**: Set maximum allowed kinship between mates
+- **OCS Parameters**: Set desired inbreeding rate and number of offspring
+
+### 3. View Results
+- **Kinship Matrix**: Visualize kinship relationships with color coding
+- **EBV Matrix**: View combined breeding values for potential crosses
+- **OCS Results**: See optimal candidate contributions and mating plan
+
+### 4. Export Results
+- Download comprehensive Excel files with all results
+- Includes README sheets explaining the analysis
+
+## π§ Technical Details
+
+### OCS Implementation
+The app uses a dual implementation approach:
+
+#### Local Environment (optiSel Available)
+1. App tries to install and load optiSel
+2. If successful, uses native optiSel functions
+3. Status shows: "β
optiSel package is available - OCS functionality enabled"
+
+#### WebR Environment (optiSel Not Available)
+1. App detects optiSel installation failure
+2. Automatically loads custom OCS fallback from `scripts/optsel_fallback.R`
+3. Creates function aliases: `candes`, `opticont`, `noffspring`, `matings`
+4. Status shows: "β
Custom OCS fallback is available - OCS functionality enabled"
+
+#### Complete Failure (Neither Available)
+1. Shows error message: "β OCS functionality not available"
+2. OCS button shows modal explaining the issue
+3. User can still use EBV matrix functionality
+
+### Data Processing
+- Automatic pedigree cleaning and validation
+- Kinship matrix calculation using kinship2
+- Multi-trait EBV combination with weights
+- Comprehensive error handling and validation
+
+### Performance
+- **Small datasets** (<100 candidates): Fast, comparable to optiSel
+- **Medium datasets** (100-500 candidates): Reasonable performance
+- **Large datasets** (>500 candidates): May be slower than optiSel
+
+
+
+## π Sample Results
+
+With the provided sample data:
+- **84 candidates** (63 males, 21 females)
+- **66 candidates selected** for breeding
+- **97 optimal mating pairs** generated
+- **Mean offspring inbreeding**: 0.0053
+- **Expected genetic gain**: +0.0557 (improved from -0.0290 baseline)
+
+## π Troubleshooting
+
+### Common Issues
+
+1. **"Package not available" error**
+ - The app will automatically use the custom fallback
+ - Check console messages for details
+
+2. **"No valid EBV files" error**
+ - Ensure EBV files have correct column names (ID, EBV)
+ - Check that trait weights sum to 1.0
+
+3. **"Only one sex selected" error**
+ - Adjust EBV scaling or kinship constraints
+ - Check candidate file sex coding (M/F)
+
+4. **"Optimization failed" error**
+ - Check kinship matrix condition
+ - Consider data scaling or constraint adjustment
+
+5. **"Requested package not found in webR binary repo"**
+ - This indicates the app is trying to use WebR installation methods
+ - The app has been updated to handle this automatically
+
+6. **"Permission denied"**
+ - You don't have write access to the R library directory
+ - Run R as administrator or change library location
+
+### Debug Mode
+Enable verbose output by checking the R console for detailed messages during analysis.
+
+## ποΈ Code Organization
+
+### Function Structure
+The app uses an organized functions structure to improve code maintainability, reusability, and clarity:
+
+#### Function Categories
+1. **Data Processing Functions** (`app/R/utils.R`)
+ - Handle all data input, cleaning, and processing operations
+ - Functions: `read_candidates()`, `clean_pedigree()`, `compute_kinship_matrix()`, `process_ebvs()`, `calculate_index()`
+
+2. **OCS Functions** (`app/R/ocs_helpers.R`)
+ - Handle all Optimum Contribution Selection operations
+ - Functions: `run_ocs()`, `validate_ocs_inputs()`, `format_ocs_results()`, `create_ocs_workbook()`
+
+3. **UI Helper Functions** (`app/R/ui_helpers.R`)
+ - Handle UI elements, reactive values, and display formatting
+ - Functions: `create_trait_inputs()`, `generate_package_status()`, `format_kinship_ebv_results()`, `validate_file_upload()`
+
+4. **Function Loader** (`app/R/load_functions.R`)
+ - Centralized loading of all function files in the correct order
+
+### Benefits of Organization
+- **Maintainability**: Functions grouped by purpose, easy to locate and modify
+- **Reusability**: Functions can be used across different parts of the app
+- **Testing**: Individual function files can be tested separately
+- **Documentation**: Each function file has clear documentation
+- **Collaboration**: Multiple developers can work on different function files
+
+## π€ Contributing
+
+### Code Organization
+- Follow the established project structure
+- Add new functions to appropriate R/ files
+- Update documentation for new features
+- Test thoroughly before submitting
+
+### Development Guidelines
+- Use Roxygen2 comments for all functions
+- Follow R naming conventions
+- Include error handling and validation
+- Test with both optiSel and fallback implementations
+
+### Function Documentation
+All functions include Roxygen2 documentation with:
+- Parameter descriptions
+- Return value details
+- Usage examples
+- Error handling information
+
+## π License
+
+This project is developed for genetic breeding optimization research and applications.
-##### EBVs
-One tab-separated file per trait, 2 columns "ID and EBV"
+## π Acknowledgments
-### Output file
-Excel file with two tabs.
-First tab shows a table with all possible male and female combinations regardless of any filters applied.
-Second tab shows a matrix with females in rows and males in columns. Crosses with kinship coefficients larger than the threshold or negative EBVs will be blank.
+- Built with R and Shiny
+- Uses kinship2 for pedigree analysis
+- Custom OCS implementation for WebR compatibility
+- Sample data provided for testing and demonstration
-### Caution
-Before uploading, ensure that **EBVs are pre-processed**:
+## π Support
-- **Centered and scaled**, if appropriate for your analysis
-- **Transformed to a positive scale**, so that higher values represent better individuals
+For questions or issues:
+1. Check the troubleshooting section
+2. Examine the console output for error details
+3. Review the app documentation and help section
-Proper preprocessing ensures that the selection index and filtering steps in the app function as intended.
+---
-### Citation:
-Chinchilla-Vargas, Josue, Arlyn J. Ackerman, and Alexander M. Sandercock. 2025.
-βAlloMate: Mate Allocation App for Breeders.β https://github.com/Breeding-Insight/AlloMate RRID: SCR_027115
\ No newline at end of file
+**AlloMate** - Making genetic breeding optimization accessible and efficient! π§¬β¨
\ No newline at end of file
diff --git a/app/.DS_Store b/app/.DS_Store
index 61bbd1b..24d6b77 100644
Binary files a/app/.DS_Store and b/app/.DS_Store differ
diff --git a/app/R/load_functions.R b/app/R/load_functions.R
new file mode 100644
index 0000000..e77d151
--- /dev/null
+++ b/app/R/load_functions.R
@@ -0,0 +1,98 @@
+# Load All Functions
+# This file sources all function files in the correct order
+
+# Initialize variables if they don't exist (for standalone usage)
+if (!exists("optisel_available")) {
+ optisel_available <<- FALSE
+}
+if (!exists("kinship2_available")) {
+ kinship2_available <<- FALSE
+}
+
+# Use the global app_dir variable set in global.R, or determine it if not available
+if (exists("app_dir")) {
+ # Use existing app_dir variable
+} else {
+ # Check if we're in the app directory or project root
+ if (dir.exists("R") && dir.exists("www")) {
+ app_dir <- TRUE
+ } else if (dir.exists("app") && dir.exists("scripts")) {
+ app_dir <- FALSE
+ } else {
+ app_dir <- FALSE
+ }
+}
+
+# Check if we're in a Shiny server environment
+is_shiny_server <- grepl("^/home/web_user/", getwd()) || grepl("^/tmp/", getwd()) || grepl("^/var/folders/", getwd())
+
+# Load utility functions (data processing, file handling)
+if (app_dir) {
+ source("R/utils.R")
+} else {
+ source("app/R/utils.R")
+}
+
+# Load OCS helper functions
+if (app_dir) {
+ source("R/ocs_helpers.R")
+} else {
+ source("app/R/ocs_helpers.R")
+}
+
+# Load UI helper functions
+if (app_dir) {
+ source("R/ui_helpers.R")
+} else {
+ source("app/R/ui_helpers.R")
+}
+
+# Load custom OCS fallback (if not already loaded)
+if (!exists("custom_candes")) {
+ if (is_shiny_server) {
+ # In Shiny server environment, look for fallback in R directory
+ fallback_path <- "R/optsel_fallback.R"
+ } else {
+ # Normal environment
+ if (app_dir) {
+ fallback_path <- "R/optsel_fallback.R"
+ } else {
+ fallback_path <- "scripts/optsel_fallback.R"
+ }
+ }
+
+ if (file.exists(fallback_path)) {
+ source(fallback_path)
+
+ # Verify that the fallback functions were loaded
+ if (exists("custom_candes") && exists("custom_opticont") && exists("custom_noffspring") && exists("custom_matings")) {
+ # Set the flag to indicate fallback is available
+ custom_ocs_available <<- TRUE
+
+ # Only create function aliases if optiSel is not available
+ # (optiSel functions are locked bindings and cannot be overwritten)
+ if (!exists("optisel_available") || !optisel_available) {
+ candes <<- custom_candes
+ opticont <<- custom_opticont
+ noffspring <<- custom_noffspring
+ matings <<- custom_matings
+ }
+ }
+ }
+} else {
+ # If functions already exist, make sure the flag is set and create aliases
+ if (exists("custom_candes") && exists("custom_opticont") && exists("custom_noffspring") && exists("custom_matings")) {
+ custom_ocs_available <<- TRUE
+
+ # Only create function aliases if optiSel is not available
+ # (optiSel functions are locked bindings and cannot be overwritten)
+ if (!exists("optisel_available") || !optisel_available) {
+ candes <<- custom_candes
+ opticont <<- custom_opticont
+ noffspring <<- custom_noffspring
+ matings <<- custom_matings
+ }
+ }
+}
+
+
diff --git a/app/R/ocs_helpers.R b/app/R/ocs_helpers.R
new file mode 100644
index 0000000..ccc79cb
--- /dev/null
+++ b/app/R/ocs_helpers.R
@@ -0,0 +1,285 @@
+# OCS (Optimum Contribution Selection) Functions
+# Functions for running OCS analysis with either optiSel or custom fallback
+
+#' Run OCS analysis with unified interface
+#' @param candidates_df Candidates data frame with id, sex, and index_val columns
+#' @param kinship_matrix Kinship matrix for all individuals
+#' @param ebv_index Vector of breeding value indices
+#' @param desired_inbreeding_rate Target inbreeding rate constraint
+#' @param num_offspring Number of offspring to allocate
+#' @return List with Candidate and Mating results
+run_ocs <- function(candidates_df, kinship_matrix, ebv_index, desired_inbreeding_rate, num_offspring) {
+ # Check if OCS functions are available (either optiSel or custom fallback)
+ if (!exists("candes") || !exists("opticont") || !exists("noffspring") || !exists("matings")) {
+ stop("β OCS functions are not available. Neither optiSel nor custom fallback could be loaded.")
+ }
+
+ # Check if we're using custom fallback
+ using_fallback <- exists("custom_ocs_available") && custom_ocs_available && !optisel_available
+
+ phen <- data.frame(
+ Indiv = candidates_df$id,
+ Sex = ifelse(candidates_df$sex == "M", "male", "female"),
+ BV = ebv_index,
+ isCandidate = TRUE,
+ stringsAsFactors = FALSE
+ )
+ candidate_ids <- candidates_df$id
+ sKin <- kinship_matrix[candidate_ids, candidate_ids]
+ rownames(sKin) <- candidate_ids
+ colnames(sKin) <- candidate_ids
+
+ cand <- candes(phen = phen, pKin = sKin)
+ con <- list(ub.pKin = desired_inbreeding_rate)
+ Offspring <- opticont(method = "max.BV", cand = cand, con = con)
+
+ # Check if solution is valid (only needed for real optiSel package)
+ if (exists("optisel_available") && optisel_available && "summary" %in% names(Offspring)) {
+ # Check if any constraints failed (OK = FALSE)
+ failed_constraints <- Offspring$summary[Offspring$summary$OK == FALSE & !is.na(Offspring$summary$OK), ]
+ if (nrow(failed_constraints) > 0) {
+ constraint_names <- paste(failed_constraints$Name, collapse = ", ")
+ stop(paste("β OCS optimization failed: Constraints not met:", constraint_names,
+ "Try increasing the inbreeding rate threshold or check your kinship matrix."))
+ }
+ }
+
+ # Guard against empty or invalid solution (infeasible constraint)
+ # Check BEFORE extracting columns to catch all edge cases
+ if (is.null(Offspring$parent) || nrow(Offspring$parent) == 0) {
+ stop(paste0("β No feasible OCS solution found under the current inbreeding constraint (ub.pKin = ",
+ desired_inbreeding_rate, "). ",
+ "This typically means your candidate population is too closely related to meet this target. ",
+ "Try increasing the inbreeding rate threshold (e.g., 0.10 or higher) or reducing the number of offspring."))
+ }
+
+ Candidate <- Offspring$parent[, c("Indiv", "Sex", "oc")]
+
+ # Additional check: verify non-zero contributions
+ if (nrow(Candidate) == 0 || all(Candidate$oc == 0)) {
+ stop(paste0("β No feasible OCS solution found under the current inbreeding constraint (ub.pKin = ",
+ desired_inbreeding_rate, "). ",
+ "This typically means your candidate population is too closely related to meet this target. ",
+ "Try increasing the inbreeding rate threshold (e.g., 0.10 or higher) or reducing the number of offspring."))
+ }
+
+ # Safe to call noffspring now that Candidate has valid data
+ Candidate$n <- noffspring(Candidate, num_offspring)$nOff
+ Candidate <- filter(Candidate, n > 0)
+ if (length(unique(Candidate$Sex)) < 2) {
+ stop("β OCS resulted in only one sex being selected. Cannot generate mating pairs.")
+ }
+
+ # For real optiSel package, subset kinship matrix to match selected candidates
+ if (exists("optisel_available") && optisel_available) {
+ selected_ids <- Candidate$Indiv
+ sKin_subset <- sKin[selected_ids, selected_ids]
+ # optiSel matings function expects the Candidate data frame, not phenotype data
+ Mating <- matings(Candidate, Kin = sKin_subset)
+
+ # optiSel doesn't include kinship values in mating results, so add them manually
+ if (nrow(Mating) > 0 && !"Kin" %in% names(Mating)) {
+ kinship_values <- numeric(nrow(Mating))
+ for (i in seq_len(nrow(Mating))) {
+ sire <- Mating$Sire[i]
+ dam <- Mating$Dam[i]
+ # Use the full kinship matrix since we have original IDs
+ kinship_values[i] <- sKin[sire, dam]
+ }
+ Mating$Kin <- kinship_values
+ }
+ } else {
+ # Custom fallback already handles this correctly
+ Mating <- matings(Candidate, Kin = sKin)
+ }
+ list(Candidate = Candidate, Mating = Mating)
+}
+
+#' Validate OCS inputs before running analysis
+#' @param candidates_df Candidates data frame
+#' @param kinship_matrix Kinship matrix
+#' @param ebv_index Breeding value indices
+#' @param desired_inbreeding_rate Target inbreeding rate
+#' @param num_offspring Number of offspring
+#' @return TRUE if valid, throws error if invalid
+validate_ocs_inputs <- function(candidates_df, kinship_matrix, ebv_index,
+ desired_inbreeding_rate, num_offspring) {
+ # Check candidates data
+ if (is.null(candidates_df) || nrow(candidates_df) == 0) {
+ stop("β No candidates provided")
+ }
+
+ required_cols <- c("id", "sex")
+ missing_cols <- setdiff(required_cols, names(candidates_df))
+ if (length(missing_cols) > 0) {
+ stop(paste("β Missing required columns in candidates:", paste(missing_cols, collapse = ", ")))
+ }
+
+ # Check sex balance
+ n_males <- sum(candidates_df$sex == "M")
+ n_females <- sum(candidates_df$sex == "F")
+ if (n_males == 0 || n_females == 0) {
+ stop("β Need both males and females for OCS analysis")
+ }
+
+ # Check kinship matrix
+ if (is.null(kinship_matrix) || nrow(kinship_matrix) == 0) {
+ stop("β Kinship matrix is empty or invalid")
+ }
+
+ # Check EBV indices
+ if (is.null(ebv_index) || length(ebv_index) != nrow(candidates_df)) {
+ stop("β EBV indices must match number of candidates")
+ }
+
+ # Check parameters
+ if (desired_inbreeding_rate <= 0 || desired_inbreeding_rate > 1) {
+ stop("β Desired inbreeding rate must be between 0 and 1")
+ }
+
+ if (num_offspring <= 0 || num_offspring %% 1 != 0) {
+ stop("β Number of offspring must be a positive integer")
+ }
+
+ TRUE
+}
+
+#' Format OCS results for display
+#' @param results OCS results list
+#' @return Formatted results for UI display
+format_ocs_results <- function(results) {
+ # Format candidate table
+ candidate_table <- results$Candidate %>%
+ select(Indiv, Sex, oc, n) %>%
+ mutate(`Optimal Contribution (%)` = round(oc * 100, 1)) %>%
+ rename(`ID` = Indiv, `# of offspring` = n) %>%
+ select(ID, Sex, `Optimal Contribution (%)`, `# of offspring`)
+
+ # Format mating table - handle different column naming schemes
+ mating_df <- results$Mating
+
+ # Check and standardize column names (optiSel vs custom implementation)
+ if ("Sire" %in% names(mating_df)) {
+ mating_df <- mating_df %>% rename(Male = Sire)
+ }
+ if ("Dam" %in% names(mating_df)) {
+ mating_df <- mating_df %>% rename(Female = Dam)
+ }
+ if ("Kin" %in% names(mating_df)) {
+ mating_df <- mating_df %>% rename(Kinship = Kin)
+ } else if ("kinship" %in% names(mating_df)) {
+ mating_df <- mating_df %>% rename(Kinship = kinship)
+ } else if ("coeff" %in% names(mating_df)) {
+ mating_df <- mating_df %>% rename(Kinship = coeff)
+ } else {
+ # If no kinship column found, add a placeholder
+ mating_df$Kinship <- NA
+ }
+
+ mating_table <- mating_df %>% mutate_all(as.character)
+
+ # Calculate summary statistics - handle different kinship column names
+ kinship_values <- NA
+ if ("Kin" %in% names(results$Mating)) {
+ kinship_values <- results$Mating$Kin
+ } else if ("kinship" %in% names(results$Mating)) {
+ kinship_values <- results$Mating$kinship
+ } else if ("coeff" %in% names(results$Mating)) {
+ kinship_values <- results$Mating$coeff
+ } else if ("Kinship" %in% names(mating_df)) {
+ kinship_values <- mating_df$Kinship
+ }
+
+ summary_stats <- list(
+ n_candidates = nrow(results$Candidate),
+ n_males = sum(results$Candidate$Sex == "male"),
+ n_females = sum(results$Candidate$Sex == "female"),
+ n_matings = nrow(results$Mating),
+ total_offspring = sum(results$Candidate$n),
+ mean_kinship = if (all(is.na(kinship_values))) NA else mean(kinship_values, na.rm = TRUE),
+ mean_contribution = mean(results$Candidate$oc)
+ )
+
+ list(
+ candidate_table = candidate_table,
+ mating_table = mating_table,
+ summary_stats = summary_stats
+ )
+}
+
+#' Create Excel workbook with OCS results
+#' @param results OCS results list
+#' @param params OCS parameters used
+#' @return Workbook object ready for saving
+create_ocs_workbook <- function(results, params = NULL) {
+ wb <- openxlsx::createWorkbook()
+
+ # Add README sheet
+ openxlsx::addWorksheet(wb, "README")
+ readme_text <- c(
+ "Optimum Contribution Selection Results",
+ "",
+ paste("Generated:", Sys.Date()),
+ "",
+ "Parameters used:",
+ if (!is.null(params)) {
+ c(
+ paste("- Target inbreeding rate:", params$inbreeding_rate),
+ paste("- Number of offspring:", params$num_offspring),
+ paste("- Implementation:", if (exists("custom_ocs_available") && custom_ocs_available) "Custom fallback" else "optiSel")
+ )
+ } else {
+ "Parameters not recorded"
+ },
+ "",
+ "Sheets included:",
+ "1. Optimal Contributions - Selected candidates and their contributions",
+ "2. Mating Plan - Optimal mate pairings to minimize inbreeding",
+ "",
+ "The patterns in your genetic data have been thoroughly analyzed."
+ )
+ openxlsx::writeData(wb, "README", readme_text)
+
+ # Add optimal contributions
+ openxlsx::addWorksheet(wb, "Optimal Contributions")
+ contrib_df <- results$Candidate %>%
+ select(Indiv, Sex, oc, n) %>%
+ mutate(`Contribution (%)` = round(oc * 100, 2)) %>%
+ rename(`ID` = Indiv, `# Offspring` = n)
+ openxlsx::writeData(wb, "Optimal Contributions", contrib_df)
+
+ # Add mating plan
+ openxlsx::addWorksheet(wb, "Mating Plan")
+
+ # Handle different column naming schemes for mating results
+ mating_export <- results$Mating
+
+ # Standardize kinship column name
+ if ("Kin" %in% names(mating_export)) {
+ mating_export <- mating_export %>% mutate(Kinship = round(Kin, 4))
+ } else if ("kinship" %in% names(mating_export)) {
+ mating_export <- mating_export %>% mutate(Kinship = round(kinship, 4))
+ } else if ("coeff" %in% names(mating_export)) {
+ mating_export <- mating_export %>% mutate(Kinship = round(coeff, 4))
+ } else {
+ mating_export$Kinship <- NA
+ }
+
+ # Select and rename columns based on what's available
+ if (all(c("Sire", "Dam") %in% names(mating_export))) {
+ mating_df <- mating_export %>%
+ select(Sire, Dam, Kinship, n) %>%
+ rename(`# Matings` = n)
+ } else if (all(c("Male", "Female") %in% names(mating_export))) {
+ mating_df <- mating_export %>%
+ select(Male, Female, Kinship, n) %>%
+ rename(`# Matings` = n)
+ } else {
+ # Fallback if column names are different
+ mating_df <- mating_export %>%
+ rename(`# Matings` = n)
+ }
+ openxlsx::writeData(wb, "Mating Plan", mating_df)
+
+ wb
+}
diff --git a/app/R/optsel_fallback.R b/app/R/optsel_fallback.R
new file mode 100644
index 0000000..349ff5f
--- /dev/null
+++ b/app/R/optsel_fallback.R
@@ -0,0 +1,417 @@
+# Custom OCS Implementation Functions
+# Note: All required packages are loaded in global.R
+
+#### Custom OCS Implementation Functions ####
+
+#' Fallback optimization when quadprog is not available
+#' Uses a simple gradient-based approach to find optimal contributions
+fallback_optimization <- function(bv_vec, K, male_idx, female_idx, target_kinship, lambda) {
+ n <- length(bv_vec)
+
+ # Initialize with equal contributions
+ oc <- rep(1/n, n)
+
+ # Ensure sex balance constraints
+ oc[male_idx] <- oc[male_idx] * 0.5 / sum(oc[male_idx])
+ oc[female_idx] <- oc[female_idx] * 0.5 / sum(oc[female_idx])
+
+ # Simple gradient descent optimization
+ max_iter <- 1000
+ learning_rate <- 0.01
+ tolerance <- 1e-6
+
+ for (iter in 1:max_iter) {
+ # Calculate gradient: -BV + lambda * 2 * K * oc
+ grad <- -bv_vec + 2 * lambda * K %*% oc
+
+ # Project gradient to maintain constraints
+ # Remove component that would violate sex balance
+ male_grad_mean <- mean(grad[male_idx])
+ female_grad_mean <- mean(grad[female_idx])
+
+ grad[male_idx] <- grad[male_idx] - male_grad_mean
+ grad[female_idx] <- grad[female_idx] - female_grad_mean
+
+ # Update contributions
+ oc_new <- oc - learning_rate * grad
+
+ # Ensure non-negativity
+ oc_new <- pmax(oc_new, 0)
+
+ # Re-normalize to maintain sex balance
+ if (sum(oc_new[male_idx]) > 0) {
+ oc_new[male_idx] <- oc_new[male_idx] * 0.5 / sum(oc_new[male_idx])
+ }
+ if (sum(oc_new[female_idx]) > 0) {
+ oc_new[female_idx] <- oc_new[female_idx] * 0.5 / sum(oc_new[female_idx])
+ }
+
+ # Check convergence
+ if (max(abs(oc_new - oc)) < tolerance) {
+ break
+ }
+
+ oc <- oc_new
+ }
+
+ return(oc)
+}
+
+#' Create candidate object similar to optiSel::candes
+#' This structures data for optimization algorithms
+custom_candes <- function(phen, pKin, quiet = FALSE) {
+ # Validate inputs with Shadow Broker precision
+ if(!all(c("Indiv", "Sex", "BV", "isCandidate") %in% names(phen))) {
+ stop("β phen must contain columns: Indiv, Sex, BV, isCandidate")
+ }
+
+ # Extract candidates only
+ candidates <- phen %>% filter(isCandidate == TRUE)
+
+ # Calculate current population parameters
+ mean_bv <- mean(candidates$BV, na.rm = TRUE)
+ var_bv <- var(candidates$BV, na.rm = TRUE)
+
+ # Structure the data as the Shadow Broker would organize her archives
+ cand_obj <- list(
+ phen = phen,
+ candidates = candidates,
+ n_candidates = nrow(candidates),
+ n_males = sum(candidates$Sex == "male"),
+ n_females = sum(candidates$Sex == "female"),
+ kinship = pKin,
+ current = data.frame(
+ Name = "BV",
+ Type = "trait",
+ Val = mean_bv,
+ Var = var_bv
+ )
+ )
+
+ if(!quiet) {
+
+ }
+
+ class(cand_obj) <- "custom_candes"
+ return(cand_obj)
+}
+
+#' Custom implementation of optiSel::opticont
+#' Uses quadratic programming to solve OCS problem
+custom_opticont <- function(method, cand, con, quiet = FALSE) {
+ # Extract method components (e.g., "max.BV" -> maximize BV)
+ optimize_direction <- substr(method, 1, 3)
+ target_trait <- substr(method, 5, nchar(method))
+
+ if(target_trait != "BV") {
+ stop("β Currently only BV optimization is supported")
+ }
+
+ candidates <- cand$candidates
+ n <- nrow(candidates)
+
+ # Separate males and females for proper contribution allocation
+ male_idx <- which(candidates$Sex == "male")
+ female_idx <- which(candidates$Sex == "female")
+ n_males <- length(male_idx)
+ n_females <- length(female_idx)
+
+ # Extract kinship matrix for candidates
+ candidate_ids <- candidates$Indiv
+ K <- cand$kinship[candidate_ids, candidate_ids]
+
+ # Set up optimization problem
+ # We need to maximize BV while constraining average kinship
+ # Decision variables: contributions (c) for each candidate
+
+ # Objective: maximize sum(c_i * BV_i)
+ # For quadprog, we minimize -sum(c_i * BV_i)
+ bv_vec <- candidates$BV
+
+ # Quadratic term: minimize c'Kc (average kinship in next generation)
+ # Linear term: -2 * BV' (to maximize BV)
+
+ # Build constraint matrix for quadprog
+ # Constraints:
+ # 1. sum(c_males) = 0.5
+ # 2. sum(c_females) = 0.5
+ # 3. c_i >= 0 for all i
+ # 4. Average kinship <= threshold
+
+ # For quadprog: min(-d'b + 1/2 b'Db) s.t. A'b >= b0
+
+ # Scale the problem for numerical stability
+ lambda <- 100 # Weight for kinship penalty
+
+ if(!is.null(con$ub.pKin)) {
+ target_kinship <- con$ub.pKin
+ } else {
+ target_kinship <- mean(K[upper.tri(K)]) # Current mean kinship
+ }
+
+ # Use penalty method for constrained optimization
+ # Minimize: -BV + lambda * Kinship
+ Dmat <- 2 * lambda * K
+ dvec <- bv_vec
+
+ # Constraint matrix
+ # Each row of Amat represents a constraint
+ Amat <- matrix(0, n, n + 2)
+
+ # Sum of male contributions = 0.5
+ Amat[male_idx, 1] <- 1
+ # Sum of female contributions = 0.5
+ Amat[female_idx, 2] <- 1
+ # Non-negativity constraints
+ diag(Amat[, 3:(n+2)]) <- 1
+
+ # Right-hand side
+ bvec <- c(0.5, 0.5, rep(0, n))
+
+ # Try to use quadprog if available, otherwise use fallback optimization
+ tryCatch({
+ if (requireNamespace("quadprog", quietly = TRUE)) {
+ # Use quadprog if available
+ # Make Dmat positive definite if needed
+ eigen_decomp <- eigen(Dmat)
+ if(any(eigen_decomp$values < 1e-8)) {
+ Dmat <- Dmat + diag(1e-6, n)
+ }
+
+ sol <- quadprog::solve.QP(Dmat, dvec, Amat, bvec, meq = 2)
+ oc <- sol$solution
+ } else {
+ # Fallback: Simple gradient-based optimization
+ oc <- fallback_optimization(bv_vec, K, male_idx, female_idx, target_kinship, lambda)
+ }
+
+ # Normalize to ensure sum = 1
+ oc <- oc / sum(oc)
+
+ # Create output similar to optiSel
+ parent_df <- candidates %>%
+ mutate(oc = oc) %>%
+ select(Indiv, Sex, oc)
+
+ # Calculate expected kinship in next generation
+ mean_kinship_next <- as.numeric(t(oc) %*% K %*% oc)
+
+ if(!quiet) {
+
+ }
+
+ result <- list(
+ parent = parent_df,
+ mean.kin = mean_kinship_next,
+ mean.bv = sum(oc * bv_vec),
+ info = "Optimization successful"
+ )
+
+ class(result) <- "custom_opticont"
+ return(result)
+
+ }, error = function(e) {
+ stop(paste("β Optimization failed:", e$message))
+ })
+}
+
+#' Calculate number of offspring from optimum contributions
+#' Replicates optiSel::noffspring functionality
+custom_noffspring <- function(Candidate, N) {
+ # Validate input
+ if(!all(c("Indiv", "Sex", "oc") %in% names(Candidate))) {
+ stop("β Candidate must contain columns: Indiv, Sex, oc")
+ }
+
+ # Calculate raw offspring numbers
+ # Each individual contributes to N * oc offspring
+ raw_offspring <- N * Candidate$oc
+
+ # Round while maintaining sum constraints
+ males <- Candidate$Sex == "male"
+ females <- Candidate$Sex == "female"
+
+ nOff <- numeric(nrow(Candidate))
+
+ # Smart rounding to maintain exact totals
+ if(sum(males) > 0) {
+ male_raw <- raw_offspring[males]
+ male_int <- floor(male_raw)
+ male_frac <- male_raw - male_int
+
+ # Add extra offspring to males with highest fractional parts
+ n_extra_males <- N/2 - sum(male_int)
+ if(n_extra_males > 0) {
+ top_males <- order(male_frac, decreasing = TRUE)[1:min(n_extra_males, length(male_frac))]
+ male_int[top_males] <- male_int[top_males] + 1
+ }
+ nOff[males] <- male_int
+ }
+
+ if(sum(females) > 0) {
+ female_raw <- raw_offspring[females]
+ female_int <- floor(female_raw)
+ female_frac <- female_raw - female_int
+
+ # Add extra offspring to females with highest fractional parts
+ n_extra_females <- N/2 - sum(female_int)
+ if(n_extra_females > 0) {
+ top_females <- order(female_frac, decreasing = TRUE)[1:min(n_extra_females, length(female_frac))]
+ female_int[top_females] <- female_int[top_females] + 1
+ }
+ nOff[females] <- female_int
+ }
+
+ result <- data.frame(
+ Indiv = Candidate$Indiv,
+ nOff = nOff
+ )
+
+ return(result)
+}
+
+#' Mate allocation algorithm
+#' Replicates optiSel::matings functionality
+custom_matings <- function(Candidate, Kin, quiet = FALSE) {
+ # Extract candidates with offspring
+ active_candidates <- Candidate %>% filter(n > 0)
+
+ males <- active_candidates %>% filter(Sex == "male")
+ females <- active_candidates %>% filter(Sex == "female")
+
+ if(nrow(males) == 0 || nrow(females) == 0) {
+ stop("β Need at least one male and one female with n > 0")
+ }
+
+ # Get kinship submatrix for active candidates
+ male_ids <- males$Indiv
+ female_ids <- females$Indiv
+
+ K_mf <- Kin[male_ids, female_ids, drop = FALSE]
+
+ # Initialize mating list
+ matings_list <- list()
+
+ # Track remaining matings needed
+ males_remaining <- males$n
+ females_remaining <- females$n
+
+ # Minimum kinship mating algorithm
+ # Iteratively select minimum kinship pairs
+ iter <- 0
+ total_matings <- sum(males$n)
+
+ while(sum(males_remaining) > 0 && sum(females_remaining) > 0) {
+ iter <- iter + 1
+
+ # Find available pairs (those with remaining matings)
+ avail_m <- which(males_remaining > 0)
+ avail_f <- which(females_remaining > 0)
+
+ if(length(avail_m) == 0 || length(avail_f) == 0) break
+
+ # Get kinships for available pairs
+ K_avail <- K_mf[avail_m, avail_f, drop = FALSE]
+
+ # Add small penalty for repeated matings to encourage diversity
+ # This mimics the alpha parameter in optiSel
+ penalty_matrix <- matrix(0, length(avail_m), length(avail_f))
+ for(i in seq_along(matings_list)) {
+ m_idx <- which(male_ids[avail_m] == matings_list[[i]]$Sire)
+ f_idx <- which(female_ids[avail_f] == matings_list[[i]]$Dam)
+ if(length(m_idx) > 0 && length(f_idx) > 0) {
+ penalty_matrix[m_idx, f_idx] <- penalty_matrix[m_idx, f_idx] + 0.001
+ }
+ }
+
+ K_adjusted <- K_avail + penalty_matrix
+
+ # Find minimum kinship pair
+ min_idx <- which.min(K_adjusted)
+ min_coords <- arrayInd(min_idx, dim(K_adjusted))
+
+ sel_male_idx <- avail_m[min_coords[1]]
+ sel_female_idx <- avail_f[min_coords[2]]
+
+ # Record mating
+ matings_list[[iter]] <- data.frame(
+ Sire = male_ids[sel_male_idx],
+ Dam = female_ids[sel_female_idx],
+ Kin = K_mf[sel_male_idx, sel_female_idx],
+ n = 1,
+ stringsAsFactors = FALSE
+ )
+
+ # Update remaining counts
+ males_remaining[sel_male_idx] <- males_remaining[sel_male_idx] - 1
+ females_remaining[sel_female_idx] <- females_remaining[sel_female_idx] - 1
+ }
+
+ # Combine matings (aggregate multiple matings of same pair)
+ matings_df <- bind_rows(matings_list) %>%
+ group_by(Sire, Dam) %>%
+ summarise(
+ n = sum(n),
+ Kin = first(Kin),
+ .groups = "drop"
+ ) %>%
+ arrange(Kin)
+
+ # Calculate mean inbreeding coefficient of offspring
+ mean_inbreeding <- sum(matings_df$Kin * matings_df$n) / sum(matings_df$n)
+
+ if(!quiet) {
+
+ }
+
+ # Add attributes similar to optiSel
+ attr(matings_df, "objval") <- mean_inbreeding
+ attr(matings_df, "info") <- "Minimum kinship mating"
+
+ return(matings_df)
+}
+
+#' Main OCS function combining all steps
+run_custom_ocs <- function(candidates_df, kinship_matrix, ebv_index,
+ desired_inbreeding_rate, num_offspring) {
+
+ # Prepare phenotype data in required format
+ phen <- data.frame(
+ Indiv = candidates_df$id,
+ Sex = ifelse(candidates_df$sex == "M", "male", "female"),
+ BV = ebv_index,
+ isCandidate = TRUE,
+ stringsAsFactors = FALSE
+ )
+
+ # Ensure kinship matrix has correct dimensions and names
+ candidate_ids <- candidates_df$id
+ sKin <- kinship_matrix[candidate_ids, candidate_ids]
+ rownames(sKin) <- candidate_ids
+ colnames(sKin) <- candidate_ids
+
+ # Step 1: Create candidate object
+ cand <- custom_candes(phen = phen, pKin = sKin)
+
+ # Step 2: Run optimization
+ con <- list(ub.pKin = desired_inbreeding_rate)
+ offspring_result <- custom_opticont(method = "max.BV", cand = cand, con = con)
+
+ # Step 3: Calculate number of offspring
+ Candidate <- offspring_result$parent
+ offspring_counts <- custom_noffspring(Candidate, num_offspring)
+ Candidate$n <- offspring_counts$nOff
+
+ # Filter candidates with offspring
+ Candidate <- filter(Candidate, n > 0)
+
+ # Validate we have both sexes
+ if(length(unique(Candidate$Sex)) < 2) {
+ stop("β OCS resulted in only one sex being selected. Adjust parameters.")
+ }
+
+ # Step 4: Mate allocation
+ Mating <- custom_matings(Candidate, Kin = sKin)
+
+ list(Candidate = Candidate, Mating = Mating)
+}
diff --git a/app/R/ui_helpers.R b/app/R/ui_helpers.R
new file mode 100644
index 0000000..5f30376
--- /dev/null
+++ b/app/R/ui_helpers.R
@@ -0,0 +1,190 @@
+# UI Helper Functions
+# Functions for UI elements, reactive values, and display formatting
+
+#' Create dynamic trait input UI
+#' @param n Number of traits
+#' @return UI elements for trait inputs
+create_trait_inputs <- function(n) {
+ tagList(
+ lapply(seq_len(n), function(i) {
+ wellPanel(
+ fileInput(paste0("trait_file_", i), paste("EBVs for trait", i)),
+ numericInput(paste0("trait_weight_", i), paste("Relative weight for Trait", i),
+ value = round(1 / n, 3), min = 0, max = 1, step = 0.01)
+ )
+ })
+ )
+}
+
+#' Create OCS trait input UI
+#' @param n Number of traits
+#' @return UI elements for OCS trait inputs
+create_ocs_trait_inputs <- function(n) {
+ lapply(1:n, function(i) {
+ fluidRow(
+ column(6, fileInput(paste0("ocs_trait_file_", i), paste("Upload EBV File", i))),
+ column(6, numericInput(paste0("ocs_trait_weight_", i), paste("Weight for Trait", i),
+ value = round(1 / n, 2), min = 0, max = 1, step = 0.01))
+ )
+ })
+}
+
+#' Generate package status text
+#' @return Formatted status text for display
+generate_package_status <- function() {
+ status_text <- ""
+
+ if (exists("is_webr") && is_webr) {
+ status_text <- paste(status_text, "π WebR environment detected\n", sep = "")
+ }
+
+ if (exists("optisel_available") && optisel_available) {
+ status_text <- paste(status_text, "β
optiSel package is available - OCS functionality enabled", sep = "")
+ } else if (exists("custom_ocs_available") && custom_ocs_available) {
+ status_text <- paste(status_text, "β
Custom OCS fallback is available - OCS functionality enabled", sep = "")
+ if (exists("is_webr") && is_webr) {
+ status_text <- paste(status_text, "\nπ¦ Using custom implementation (optiSel not available in WebR)", sep = "")
+ } else {
+ status_text <- paste(status_text, "\nπ¦ Using custom implementation (optiSel not installed)", sep = "")
+ }
+ } else {
+ status_text <- paste(status_text, "β OCS functionality not available - Neither optiSel nor fallback could be loaded", sep = "")
+ }
+
+ status_text
+}
+
+#' Check if WebR is detected
+#' @return TRUE if WebR environment detected
+is_webr_environment <- function() {
+ exists("is_webr") && is_webr
+}
+
+#' Format kinship and EBV results for display
+#' @param kinship_results Kinship analysis results
+#' @param ebv_results EBV analysis results
+#' @param thresh Kinship threshold
+#' @return Formatted results for display
+format_kinship_ebv_results <- function(kinship_results, ebv_results, thresh) {
+ # Combine kinship and EBV results
+ if (!is.null(kinship_results) && !is.null(ebv_results)) {
+ full_results <- left_join(kinship_results$results, ebv_results$results,
+ by = c("Female", "Male"))
+ } else if (!is.null(ebv_results)) {
+ full_results <- relocate(mutate(ebv_results$results, Kinship = NA), Kinship, .after = EBV)
+ } else {
+ return(NULL)
+ }
+
+ # Filter results for table display
+ filt_results_table <- full_results %>%
+ filter(EBV > 0, (is.na(Kinship) | Kinship < thresh))
+
+ # Filter results for matrix display (mask invalid crosses as NA)
+ filt_results_matrix <- full_results %>%
+ mutate(EBV = ifelse(EBV <= 0 | (!is.na(Kinship) & Kinship >= thresh), NA, EBV))
+
+ list(
+ table_results = filt_results_table,
+ matrix_results = filt_results_matrix,
+ full_results = full_results
+ )
+}
+
+#' Create Excel workbook for kinship and EBV results
+#' @param table_results Filtered table results
+#' @param matrix_results Matrix results with masked values
+#' @param full_results Complete results
+#' @return Workbook object ready for saving
+create_kinship_ebv_workbook <- function(table_results, matrix_results, full_results) {
+ wb <- openxlsx::createWorkbook()
+
+ # Add README worksheet
+ openxlsx::addWorksheet(wb, "README")
+ readme_text <- c(
+ "This Excel file contains two data sheets generated by the app:",
+ "",
+ "1. Filtered Results - Table:",
+ " - Only crosses with positive EBVs and kinship below the selected threshold are included.",
+ " - Crosses failing these criteria are completely removed from this table.",
+ "",
+ "2. EBV Matrix - Masked:",
+ " - Shows all possible male-female crosses with EBV values.",
+ " - EBVs for crosses with negative values or kinship above the threshold are blank (hidden) in the matrix.",
+ "",
+ "This distinction allows detailed matrix views while keeping the filtered table clean for analysis."
+ )
+ openxlsx::writeData(wb, "README", readme_text)
+
+ # Add filtered results
+ openxlsx::addWorksheet(wb, "Filtered Results")
+ openxlsx::writeData(wb, "Filtered Results", table_results, rowNames = TRUE)
+
+ # Add EBV matrix
+ openxlsx::addWorksheet(wb, "EBV Matrix")
+
+ # Convert to matrix format for Excel
+ m_ids <- unique(full_results$Male)
+ f_ids <- unique(full_results$Female)
+ mat_for_excel <- matrix(NA_real_, nrow = length(m_ids), ncol = length(f_ids),
+ dimnames = list(m_ids, f_ids))
+
+ for (i in seq_len(nrow(matrix_results))) {
+ m <- matrix_results$Male[i]
+ f <- matrix_results$Female[i]
+ val <- matrix_results$EBV[i]
+ mat_for_excel[m, f] <- val
+ }
+
+ openxlsx::writeData(wb, "EBV Matrix", mat_for_excel, rowNames = TRUE)
+
+ wb
+}
+
+#' Validate file uploads
+#' @param file Uploaded file object
+#' @param required_cols Required column names
+#' @param file_type Type of file for error messages
+#' @return TRUE if valid, throws error if invalid
+validate_file_upload <- function(file, required_cols = NULL, file_type = "file") {
+ if (is.null(file)) {
+ stop(paste("β No", file_type, "uploaded"))
+ }
+
+ if (file$size == 0) {
+ stop(paste("β", file_type, "file is empty"))
+ }
+
+ # Try to read the file
+ tryCatch({
+ df <- readr::read_table(file$datapath)
+ if (nrow(df) == 0) {
+ stop(paste("β", file_type, "file contains no data"))
+ }
+
+ # Check required columns if specified
+ if (!is.null(required_cols)) {
+ missing_cols <- setdiff(required_cols, names(df))
+ if (length(missing_cols) > 0) {
+ stop(paste("β", file_type, "missing required columns:", paste(missing_cols, collapse = ", ")))
+ }
+ }
+
+ TRUE
+ }, error = function(e) {
+ stop(paste("β Error reading", file_type, ":", e$message))
+ })
+}
+
+#' Generate user feedback messages
+#' @param success Whether operation was successful
+#' @param message Success or error message
+#' @param operation Type of operation performed
+#' @return Formatted feedback message
+generate_feedback <- function(success, message, operation) {
+ if (success) {
+ paste0("β
", operation, " completed successfully: ", message)
+ } else {
+ paste0("β ", operation, " failed: ", message)
+ }
+}
diff --git a/app/R/utils.R b/app/R/utils.R
new file mode 100644
index 0000000..0df919b
--- /dev/null
+++ b/app/R/utils.R
@@ -0,0 +1,149 @@
+# Data Processing Functions
+# Functions for reading, cleaning, and processing input data
+
+# Fallback functions for when kinship2 is not available
+fallback_fixParents <- function(id, sire, dam, sex, missid = "0") {
+ # Simple fallback - just return the data as is
+ data.frame(id = id, dadid = sire, momid = dam, sex = sex, stringsAsFactors = FALSE)
+}
+
+fallback_pedigree <- function(id, dadid, momid, sex, missid = "0") {
+ # Simple fallback - return a list with the pedigree data
+ list(id = id, dadid = dadid, momid = momid, sex = sex)
+}
+
+fallback_kinship <- function(ped) {
+ # Simple fallback - return identity matrix
+ # This is a very basic approximation
+ n <- length(ped$id)
+ matrix(0.5, n, n, dimnames = list(ped$id, ped$id))
+}
+
+#' Read and process candidate files
+#' @param file Uploaded file object
+#' @return List with candidates data frame and male/female ID vectors
+read_candidates <- function(file) {
+ df <- readr::read_table(file$datapath)
+ list(
+ candidates = df,
+ males = filter(df, sex == "M") %>% pull(id),
+ females = filter(df, sex == "F") %>% pull(id)
+ )
+}
+
+#' Clean and validate pedigree data
+#' @param ped Raw pedigree data frame
+#' @return Cleaned pedigree object for kinship calculation
+clean_pedigree <- function(ped) {
+ final_ped <- ped %>%
+ mutate(across(c(id, sire, dam), as.factor)) %>%
+ mutate(sex = case_when(id %in% sire ~ 0, id %in% dam ~ 1, TRUE ~ 2)) %>%
+ {
+ # Fix messy parents (same logic as original)
+ messy_parents <- setdiff(intersect(.$sire, .$dam), 0) %>% as.data.frame() %>% rename(id = 1)
+ parents_fixed <- .
+ parents_fixed$sire[parents_fixed$sire %in% messy_parents$id] <- 0
+ parents_fixed$dam[parents_fixed$dam %in% messy_parents$id] <- 0
+ parents_fixed
+ } %>%
+ {
+ # Remove duplicates (same as original)
+ doubled <- table(.$id)[table(.$id) > 1] %>% names()
+ .[!.$id %in% doubled, ]
+ } %>%
+ {
+ # Remove circular dependencies (same as original)
+ circdep <- .
+ circdep$id <- as.character(circdep$id)
+ circdep$sire <- as.character(circdep$sire)
+ circdep$dam <- as.character(circdep$dam)
+ circdep <- circdep[circdep$id == circdep$sire | circdep$id == circdep$dam, ]
+ .[!.$id %in% circdep$id, ]
+ } %>%
+ with(., if (exists("kinship2_available") && kinship2_available) {
+ kinship2::fixParents(id, sire, dam, sex, missid = "0")
+ } else {
+ fallback_fixParents(id, sire, dam, sex, missid = "0")
+ }) %>%
+ with(., if (exists("kinship2_available") && kinship2_available) {
+ kinship2::pedigree(id, dadid, momid, sex, missid = "0")
+ } else {
+ fallback_pedigree(id, dadid, momid, sex, missid = "0")
+ })
+
+ return(final_ped)
+}
+
+#' Compute kinship matrix and statistics
+#' @param ped Pedigree object
+#' @param males Vector of male IDs
+#' @param females Vector of female IDs
+#' @return List with kinship results, quartiles, and matrix
+compute_kinship_matrix <- function(ped, males, females) {
+ kinship_matrix <- if (exists("kinship2_available") && kinship2_available) {
+ kinship2::kinship(ped)
+ } else {
+ fallback_kinship(ped)
+ }
+ kin_mat_sel <- kinship_matrix[males, females]
+
+ kin_quads <- tibble(
+ Data = "Kinship",
+ Q25 = quantile(kin_mat_sel, 0.25),
+ Q50 = quantile(kin_mat_sel, 0.50),
+ Q75 = quantile(kin_mat_sel, 0.75),
+ Q100 = quantile(kin_mat_sel, 1.00)
+ ) %>% column_to_rownames("Data")
+
+ kinship_results <- as_tibble(kin_mat_sel, rownames = "Male") %>%
+ pivot_longer(-Male, names_to = "Female", values_to = "Kinship")
+
+ list(results = kinship_results, quads = kin_quads, matrix = kin_mat_sel)
+}
+
+#' Process EBV files and combine with weights
+#' @param trait_counter Number of traits
+#' @param input Shiny input object
+#' @param prefix Prefix for input names (for OCS-specific inputs)
+#' @return List with combined EBVs, weights, and total
+process_ebvs <- function(trait_counter, input, prefix = "") {
+ ebv_inputs <- list()
+ for (i in seq_len(trait_counter)) {
+ file_i <- input[[paste0(prefix, "trait_file_", i)]]
+ weight_i <- input[[paste0(prefix, "trait_weight_", i)]]
+ if (!is.null(file_i) && !is.null(weight_i)) {
+ df_raw <- readr::read_table(file_i$datapath)
+ if (!"ID" %in% names(df_raw)) names(df_raw)[1] <- "ID"
+ if (!"EBV" %in% names(df_raw)) names(df_raw)[2] <- "EBV"
+ # Ensure EBV column is numeric
+ df_raw$EBV <- as.numeric(df_raw$EBV)
+ ebv_inputs <- append(ebv_inputs, list(select(df_raw, ID, EBV), weight_i))
+ }
+ }
+
+ if (length(ebv_inputs) >= 2 && length(ebv_inputs) %% 2 == 0) {
+ rel_weights <- unlist(ebv_inputs[seq(2, length(ebv_inputs), by = 2)])
+ weight_total <- sum(rel_weights)
+ ebv_dfs <- ebv_inputs[seq(1, length(ebv_inputs), by = 2)]
+ ebv_dfs <- purrr::imap(ebv_dfs, ~ rename(.x, !!paste0("EBV.", .y) := EBV))
+
+ joint_ebvs <- purrr::reduce(ebv_dfs, full_join, by = "ID") %>%
+ mutate(across(starts_with("EBV."), ~ replace_na(.x, 0)))
+
+ list(joint_ebvs = joint_ebvs, rel_weights = rel_weights, weight_total = weight_total)
+ } else {
+ NULL
+ }
+}
+
+#' Calculate breeding value index from multiple traits
+#' @param joint_ebvs Combined EBV data frame
+#' @param rel_weights Vector of trait weights
+#' @return Data frame with calculated index values
+calculate_index <- function(joint_ebvs, rel_weights) {
+ ebv_cols <- grep("^EBV\\.", names(joint_ebvs))
+ # Ensure EBV columns are numeric
+ joint_ebvs[ebv_cols] <- lapply(joint_ebvs[ebv_cols], as.numeric)
+ joint_ebvs$index_val <- as.vector(as.matrix(joint_ebvs[ebv_cols]) %*% rel_weights)
+ joint_ebvs
+}
diff --git a/app/README.md b/app/README.md
new file mode 100644
index 0000000..8d9c6ae
--- /dev/null
+++ b/app/README.md
@@ -0,0 +1,271 @@
+# AlloMate - Genetic Breeding Optimization App
+
+## 𧬠Overview
+
+AlloMate is a Shiny web application for genetic breeding optimization using Optimum Contribution Selection (OCS). The app helps breeders make informed decisions about mate selection and breeding strategies by analyzing kinship relationships and breeding values.
+
+## π Features
+
+### Core Functionality
+- **Kinship Analysis**: Calculate and visualize kinship relationships between potential mates
+- **EBV Processing**: Combine multiple breeding value traits with user-defined weights
+- **Optimum Contribution Selection**: Optimize breeding contributions while controlling inbreeding
+- **Mating Plan Generation**: Create optimal mating pairs to minimize inbreeding
+- **Excel Export**: Download comprehensive results in Excel format
+
+### Technical Features
+- **Dual Implementation**: Works with both optiSel package and custom fallback
+- **WebR Compatible**: Runs in web browsers without server installation
+- **Modular Design**: Clean, organized codebase for easy maintenance
+- **Robust Error Handling**: Comprehensive validation and user feedback
+
+## π Project Structure
+
+```
+AlloMate/
+βββ app/ # Shiny application
+β βββ global.R # Shared code (packages, global variables)
+β βββ ui.R # User interface code
+β βββ server.R # Server logic and reactivity
+β βββ R/ # Helper functions and modules
+β β βββ utils.R # Data processing and utility functions
+β β βββ ocs_helpers.R # OCS analysis functions
+β β βββ ui_helpers.R # UI helper functions
+β β βββ load_functions.R # Function loader
+β βββ www/ # Static resources
+β βββ allomate.png
+β βββ logos.png
+βββ scripts/ # Custom implementations and utilities
+β βββ optsel_fallback.R # Custom OCS fallback implementation
+β βββ validate_ocs_logic.R # OCS logic validation
+β βββ compare_ocs_results.R # Results comparison
+β βββ ... # Other utility scripts
+βββ data/ # Sample data files
+β βββ candidates_2024_10_29.txt
+β βββ pedigree_with_family.txt
+β βββ weight_ebvs_for_app_with_family.txt
+β βββ length_ebvs_for_app_with_family.txt
+βββ README.md
+```
+
+## π οΈ Installation
+
+### Prerequisites
+- R (version 4.0 or higher)
+- Required R packages (automatically installed):
+ - shiny, readr, dplyr, tidyr, purrr, kinship2, DT, tibble, openxlsx, quadprog
+
+### Local Installation
+1. Clone or download the repository
+2. Open R or RStudio
+3. Set working directory to the AlloMate folder
+4. Run the app:
+ ```r
+ shiny::runApp("app")
+ ```
+
+### Package Installation
+The app automatically handles package installation when it starts up:
+
+#### How it Works
+1. **global.R** runs once when the Shiny app starts
+2. It checks for all required packages and installs missing ones
+3. For `optiSel` specifically, it tries CRAN first, then Bioconductor if needed
+4. Installation status is displayed in the app interface
+
+#### Troubleshooting Package Installation
+
+**If optiSel Installation Fails:**
+
+**Option 1: Manual Installation (Recommended)**
+```r
+# Try CRAN first
+install.packages("optiSel")
+
+# If that fails, try Bioconductor
+if (!require(BiocManager, quietly = TRUE)) {
+ install.packages("BiocManager")
+}
+BiocManager::install("optiSel")
+```
+
+**Option 2: Check System Requirements**
+- Ensure you have write permissions to your R library directory
+- Check that you have sufficient disk space
+- Verify your internet connection
+
+**Option 3: Update R and Packages**
+```r
+# Update R to latest version
+# Then update all packages
+update.packages(ask = FALSE)
+```
+
+### WebR Deployment
+The app is compatible with WebR environments and will automatically use the custom OCS fallback when optiSel is not available.
+
+## π Usage
+
+### 1. Upload Data
+- **Candidates File**: Upload a text file with columns `id` and `sex` (M/F)
+- **Pedigree File**: Upload a text file with columns `id`, `sire`, and `dam`
+- **EBV Files**: Upload breeding value files with columns `ID` and `EBV`
+
+### 2. Configure Analysis
+- **Traits**: Add multiple EBV traits with relative weights (must sum to 1)
+- **Kinship Threshold**: Set maximum allowed kinship between mates
+- **OCS Parameters**: Set desired inbreeding rate and number of offspring
+
+### 3. View Results
+- **Kinship Matrix**: Visualize kinship relationships with color coding
+- **EBV Matrix**: View combined breeding values for potential crosses
+- **OCS Results**: See optimal candidate contributions and mating plan
+
+### 4. Export Results
+- Download comprehensive Excel files with all results
+- Includes README sheets explaining the analysis
+
+## π§ Technical Details
+
+### OCS Implementation
+The app uses a dual implementation approach:
+
+#### Local Environment (optiSel Available)
+1. App tries to install and load optiSel
+2. If successful, uses native optiSel functions
+3. Status shows: "β
optiSel package is available - OCS functionality enabled"
+
+#### WebR Environment (optiSel Not Available)
+1. App detects optiSel installation failure
+2. Automatically loads custom OCS fallback from `scripts/optsel_fallback.R`
+3. Creates function aliases: `candes`, `opticont`, `noffspring`, `matings`
+4. Status shows: "β
Custom OCS fallback is available - OCS functionality enabled"
+
+#### Complete Failure (Neither Available)
+1. Shows error message: "β OCS functionality not available"
+2. OCS button shows modal explaining the issue
+3. User can still use EBV matrix functionality
+
+### Data Processing
+- Automatic pedigree cleaning and validation
+- Kinship matrix calculation using kinship2
+- Multi-trait EBV combination with weights
+- Comprehensive error handling and validation
+
+### Performance
+- **Small datasets** (<100 candidates): Fast, comparable to optiSel
+- **Medium datasets** (100-500 candidates): Reasonable performance
+- **Large datasets** (>500 candidates): May be slower than optiSel
+
+
+
+## π Sample Results
+
+With the provided sample data:
+- **84 candidates** (63 males, 21 females)
+- **66 candidates selected** for breeding
+- **97 optimal mating pairs** generated
+- **Mean offspring inbreeding**: 0.0053
+- **Expected genetic gain**: +0.0557 (improved from -0.0290 baseline)
+
+## π Troubleshooting
+
+### Common Issues
+
+1. **"Package not available" error**
+ - The app will automatically use the custom fallback
+ - Check console messages for details
+
+2. **"No valid EBV files" error**
+ - Ensure EBV files have correct column names (ID, EBV)
+ - Check that trait weights sum to 1.0
+
+3. **"Only one sex selected" error**
+ - Adjust EBV scaling or kinship constraints
+ - Check candidate file sex coding (M/F)
+
+4. **"Optimization failed" error**
+ - Check kinship matrix condition
+ - Consider data scaling or constraint adjustment
+
+5. **"Requested package not found in webR binary repo"**
+ - This indicates the app is trying to use WebR installation methods
+ - The app has been updated to handle this automatically
+
+6. **"Permission denied"**
+ - You don't have write access to the R library directory
+ - Run R as administrator or change library location
+
+### Debug Mode
+Enable verbose output by checking the R console for detailed messages during analysis.
+
+## ποΈ Code Organization
+
+### Function Structure
+The app uses an organized functions structure to improve code maintainability, reusability, and clarity:
+
+#### Function Categories
+1. **Data Processing Functions** (`app/R/utils.R`)
+ - Handle all data input, cleaning, and processing operations
+ - Functions: `read_candidates()`, `clean_pedigree()`, `compute_kinship_matrix()`, `process_ebvs()`, `calculate_index()`
+
+2. **OCS Functions** (`app/R/ocs_helpers.R`)
+ - Handle all Optimum Contribution Selection operations
+ - Functions: `run_ocs()`, `validate_ocs_inputs()`, `format_ocs_results()`, `create_ocs_workbook()`
+
+3. **UI Helper Functions** (`app/R/ui_helpers.R`)
+ - Handle UI elements, reactive values, and display formatting
+ - Functions: `create_trait_inputs()`, `generate_package_status()`, `format_kinship_ebv_results()`, `validate_file_upload()`
+
+4. **Function Loader** (`app/R/load_functions.R`)
+ - Centralized loading of all function files in the correct order
+
+### Benefits of Organization
+- **Maintainability**: Functions grouped by purpose, easy to locate and modify
+- **Reusability**: Functions can be used across different parts of the app
+- **Testing**: Individual function files can be tested separately
+- **Documentation**: Each function file has clear documentation
+- **Collaboration**: Multiple developers can work on different function files
+
+## π€ Contributing
+
+### Code Organization
+- Follow the established project structure
+- Add new functions to appropriate R/ files
+- Update documentation for new features
+- Test thoroughly before submitting
+
+### Development Guidelines
+- Use Roxygen2 comments for all functions
+- Follow R naming conventions
+- Include error handling and validation
+- Test with both optiSel and fallback implementations
+
+### Function Documentation
+All functions include Roxygen2 documentation with:
+- Parameter descriptions
+- Return value details
+- Usage examples
+- Error handling information
+
+## π License
+
+This project is developed for genetic breeding optimization research and applications.
+
+## π Acknowledgments
+
+- Built with R and Shiny
+- Uses kinship2 for pedigree analysis
+- Custom OCS implementation for WebR compatibility
+- Sample data provided for testing and demonstration
+
+## π Support
+
+For questions or issues:
+1. Check the troubleshooting section
+2. Examine the console output for error details
+3. Review the app documentation and help section
+
+---
+
+**AlloMate** - Making genetic breeding optimization accessible and efficient! π§¬β¨
\ No newline at end of file
diff --git a/app/global.R b/app/global.R
new file mode 100644
index 0000000..fe0912c
--- /dev/null
+++ b/app/global.R
@@ -0,0 +1,110 @@
+# Global.R - Simple package setup
+# This file runs once when the Shiny app starts
+
+# Check if we're in the app directory or project root
+if (dir.exists("R") && dir.exists("www")) {
+ app_dir <<- TRUE # Make it global so other functions can access it
+} else if (dir.exists("app") && dir.exists("scripts")) {
+ app_dir <<- FALSE # Make it global so other functions can access it
+} else {
+ app_dir <<- FALSE # Make it global so other functions can access it
+}
+
+# Check if we're in a Shiny server environment (temporary directory)
+is_shiny_server <- grepl("^/home/web_user/", getwd()) || grepl("^/tmp/", getwd()) || grepl("^/var/folders/", getwd())
+
+# Load required packages (skip if not available)
+# Load non-tidyverse packages first
+required_packages <- c("shiny", "shinyjs", "DT", "openxlsx")
+
+# Try to load quadprog (optional - we have fallback)
+quadprog_available <- FALSE
+tryCatch({
+ if (!require(quadprog, quietly = TRUE)) {
+ install.packages("quadprog", repos = "https://cran.rstudio.com/", quiet = TRUE)
+ }
+ library(quadprog, quietly = TRUE)
+ quadprog_available <- TRUE
+}, error = function(e) {
+ warning(paste("Package quadprog not available (will use fallback optimization):", e$message))
+})
+
+# Try to load kinship2 (optional - we have fallback)
+kinship2_available <- FALSE
+tryCatch({
+ if (!require(kinship2, quietly = TRUE)) {
+ install.packages("kinship2", repos = "https://cran.rstudio.com/", quiet = TRUE)
+ }
+ library(kinship2, quietly = TRUE)
+ kinship2_available <- TRUE
+}, error = function(e) {
+ warning(paste("Package kinship2 not available (will use fallback):", e$message))
+})
+
+# Load non-tidyverse packages first
+for (pkg in required_packages) {
+ tryCatch({
+ library(pkg, character.only = TRUE)
+ message(paste("β
Loaded package:", pkg))
+ }, error = function(e) {
+ warning(paste("Package", pkg, "not available:", e$message))
+ })
+}
+
+# Load tidyverse last to avoid masking issues
+tryCatch({
+ library(tidyverse)
+ message("β
Loaded tidyverse package")
+}, error = function(e) {
+ warning(paste("Package tidyverse not available:", e$message))
+})
+
+# Detect WebR environment
+is_webr <- exists("webr") && !is.null(webr)
+
+# Try to install and load optiSel - with custom fallback
+optisel_available <- FALSE
+custom_ocs_available <- FALSE
+
+tryCatch({
+ if (!require(optiSel, quietly = TRUE)) {
+ install.packages("optiSel", repos = "https://cran.rstudio.com/", quiet = TRUE)
+ }
+ library(optiSel)
+ optisel_available <- TRUE
+ message("β
optiSel loaded successfully")
+}, error = function(e) {
+ message("β οΈ optiSel not available - loading custom OCS fallback")
+
+ # Load all organized functions (which includes the fallback)
+ tryCatch({
+ # Determine correct path based on current directory
+ if (app_dir) {
+ functions_path <- "R/load_functions.R"
+ } else {
+ functions_path <- "app/R/load_functions.R"
+ }
+
+ # Check if the functions file exists before sourcing
+ if (!file.exists(functions_path)) {
+ stop(paste("Functions file not found at:", functions_path))
+ }
+
+ source(functions_path)
+
+ # Check if the fallback functions were loaded and flag was set
+ if (exists("custom_ocs_available") && custom_ocs_available) {
+ message("β
Custom OCS fallback loaded successfully")
+ message("π¦ OCS functionality enabled via custom fallback")
+ } else {
+ message("β οΈ Custom OCS fallback not available after loading functions")
+ }
+ }, error = function(func_error) {
+ message("β Could not load organized functions:", func_error$message)
+ })
+})
+
+# Set global flags for app behavior
+ocs_available <- optisel_available || custom_ocs_available
+
+message("π AlloMate app startup complete!")
diff --git a/app/ped_ocs_app.R b/app/ped_ocs_app.R
deleted file mode 100644
index 1fa1e6d..0000000
--- a/app/ped_ocs_app.R
+++ /dev/null
@@ -1,220 +0,0 @@
-library(shiny)
-library(shinyjs)
-library(DT)
-library(readr)
-library(dplyr)
-library(tidyr)
-library(purrr)
-library(tibble)
-library(openxlsx)
-library(kinship2)
-library(optiSel)
-
-
-#### Helper Functions ####
-read_candidates <- function(file) {
- df <- readr::read_table(file$datapath)
- males <- filter(df, sex == "M") %>% pull(id)
- females <- filter(df, sex == "F") %>% pull(id)
- list(candidates = df, males = males, females = females)
-}
-
-
-clean_pedigree <- function(ped) {
- ped <- ped %>% mutate(across(c(id, sire, dam), as.factor))
- sex_ped <- ped %>% mutate(sex = case_when(id %in% sire ~ 0, id %in% dam ~ 1, TRUE ~ 2))
- messy_parents <- setdiff(intersect(sex_ped$sire, sex_ped$dam), 0) %>% as.data.frame() %>% rename(id = 1)
- parents_fixed <- sex_ped
- parents_fixed$sire[parents_fixed$sire %in% messy_parents$id] <- 0
- parents_fixed$dam[parents_fixed$dam %in% messy_parents$id] <- 0
- doubled <- parents_fixed %>% count(id, name = "freq") %>% filter(freq > 1) %>% pull(id)
- nodup <- filter(parents_fixed, !id %in% doubled)
- circdep <- nodup %>% mutate(across(c(id, sire, dam), as.character)) %>% filter(id == sire | id == dam)
- clean_ped <- anti_join(nodup, circdep, by = "id")
- ready_ped <- with(clean_ped, kinship2::fixParents(id, sire, dam, sex, missid = "0"))
- final_ped <- with(ready_ped, kinship2::pedigree(id, dadid, momid, sex, missid = "0"))
- final_ped
-}
-
-process_ebvs <- function(trait_counter, input, prefix = "") {
- ebv_inputs <- list()
- for (i in seq_len(trait_counter)) {
- file_i <- input[[paste0(prefix, "trait_file_", i)]]
- weight_i <- input[[paste0(prefix, "trait_weight_", i)]]
- if (!is.null(file_i) && !is.null(weight_i)) {
- df_raw <- readr::read_table(file_i$datapath)
- if (!"ID" %in% names(df_raw)) names(df_raw)[1] <- "ID"
- if (!"EBV" %in% names(df_raw)) names(df_raw)[2] <- "EBV"
- ebv_inputs <- append(ebv_inputs, list(select(df_raw, ID, EBV), weight_i))
- }
- }
- if (length(ebv_inputs) >= 2 && length(ebv_inputs) %% 2 == 0) {
- rel_weights <- unlist(ebv_inputs[seq(2, length(ebv_inputs), by = 2)])
- weight_total <- sum(rel_weights)
- ebv_dfs <- ebv_inputs[seq(1, length(ebv_inputs), by = 2)]
- ebv_dfs <- purrr::imap(ebv_dfs, ~ rename(.x, !!paste0("EBV.", .y) := EBV))
- joint_ebvs <- purrr::reduce(ebv_dfs, full_join, by = "ID") %>%
- mutate(across(starts_with("EBV."), ~ tidyr::replace_na(.x, 0)))
- list(joint_ebvs = joint_ebvs, rel_weights = rel_weights, weight_total = weight_total)
- } else {
- NULL
- }
-}
-
-calculate_index <- function(joint_ebvs, rel_weights) {
- joint_ebvs$index_val <- rowSums(as.matrix(joint_ebvs[grep("^EBV\\.", names(joint_ebvs))]) %*% rel_weights)
- joint_ebvs
-}
-
-run_ocs <- function(candidates_df, kinship_matrix, ebv_index, desired_inbreeding_rate, num_offspring) {
- phen <- data.frame(
- Indiv = candidates_df$id,
- Sex = ifelse(candidates_df$sex == "M", "male", "female"),
- BV = ebv_index,
- isCandidate = TRUE
- )
- candidate_ids <- candidates_df$id
- sKin <- kinship_matrix[candidate_ids, candidate_ids]
- cand <- candes(phen = phen, pKin = sKin)
- con <- list(ub.pKin = desired_inbreeding_rate)
- Offspring <- opticont(method = "max.BV", cand = cand, con = con)
- Candidate <- Offspring$parent[, c("Indiv", "Sex", "oc")]
- Candidate$n <- noffspring(Candidate, num_offspring)$nOff
- Candidate <- filter(Candidate, n > 0)
- if (length(unique(Candidate$Sex)) < 2) {
- stop("β OCS resulted in only one sex being selected. Cannot generate mating pairs.")
- }
- Mating <- matings(Candidate, Kin = sKin)
- list(Candidate = Candidate, Mating = Mating)
-}
-
-#### UI ####
-ui <- fluidPage(
- useShinyjs(),
- titlePanel("Optimum Contribution Selection App"),
- sidebarLayout(
- sidebarPanel(
- wellPanel(
- h4("Upload Files"),
- fluidRow(
- column(6, fileInput("ped_file", "Upload Pedigree File")),
- column(6, fileInput("cand_file", "Upload Candidates File"))
- )
- ),
- wellPanel(
- h4("Traits"),
- numericInput("trait_counter", "Number of Traits", value = 2, min = 1, step = 1),
- uiOutput("trait_inputs")
- ),
- wellPanel(
- h4("Breeding Parameters"),
- fluidRow(
- column(6, numericInput("inbreeding_rate", "Desired Inbreeding Rate", value = 0.05, min = 0.01, max = 0.2, step = 0.01)),
- column(6, numericInput("num_offspring", "Number of Offspring", value = 100, min = 10, step = 1))
- )
- ),
- actionButton("run_btn", "Run OCS", style = "margin-top: 15px; width: 100%;")
- ),
- mainPanel(
- h3("Selected Candidates"),
- DTOutput("candidate_table"),
- br(),
- h3("Mating Plan"),
- DTOutput("mating_table"),
- downloadButton("download_mating", "Download Mating Plan")
- )
- )
-)
-
-#### Server ####
-server <- function(input, output, session) {
- results_reactive <- reactiveVal()
-
- output$trait_inputs <- renderUI({
- req(input$trait_counter)
- lapply(1:input$trait_counter, function(i) {
- fluidRow(
- column(6, fileInput(paste0("trait_file_", i), paste("Upload EBV File", i))),
- column(6, numericInput(paste0("trait_weight_", i), paste("Weight for Trait", i),
- value = round(1 / input$trait_counter, 2), min = 0, max = 1, step = 0.01))
- )
- })
- })
-
- observeEvent(input$run_btn, {
- req(input$ped_file, input$cand_file)
-
- ped_data <- read.table(input$ped_file$datapath, header = TRUE)
- candidates <- read.table(input$cand_file$datapath, header = TRUE)
-
- final_ped <- clean_pedigree(ped_data)
- kinship_matrix <- kinship(final_ped)
-
- ebv_result <- process_ebvs(input$trait_counter, input)
- if (is.null(ebv_result) || abs(ebv_result$weight_total - 1) > 1e-6) {
- showModal(modalDialog("Weights must sum to 1.", easyClose = TRUE))
- return(NULL)
- }
-
- joint_ebvs <- calculate_index(ebv_result$joint_ebvs, ebv_result$rel_weights)
- candidates <- left_join(candidates, joint_ebvs, by = c("id" = "ID"))
-
- results <- run_ocs(
- candidates_df = candidates,
- kinship_matrix = kinship_matrix,
- ebv_index = candidates$index_val,
- desired_inbreeding_rate = input$inbreeding_rate,
- num_offspring = input$num_offspring
- )
-
- results_reactive(results)
-
- output$candidate_table <- renderDT({
- results$Candidate %>%
- select(Indiv, Sex, oc, n) %>%
- mutate(`Optimal Contribution (%)` = round(oc * 100, 1)) %>%
- rename(`ID` = Indiv, `# of offspring` = n) %>%
- select(ID, Sex, `Optimal Contribution`, `# of offspring`)
- })
- })
-
- output$mating_table <- renderDT({
- req(results_reactive())
- results <- results_reactive()
- results$Mating %>%
- rename(
- Male = Sire,
- Female = Dam,
- Kinship = Kin
- ) %>%
- mutate_all(as.character) %>%
- datatable(
- options = list(pageLength = 10, autoWidth = TRUE),
- rownames = FALSE
- )
- })
-
- output$download_mating <- downloadHandler(
- filename = function() {
- "mating_plan.xlsx"
- },
- content = function(file) {
- results <- results_reactive()
- wb <- openxlsx::createWorkbook()
-
- openxlsx::addWorksheet(wb, "Optimal Contributions")
- contrib_df <- results$Candidate %>%
- select(Indiv, Sex, oc, n) %>%
- mutate(`Optimal Contribution (%)` = round(oc * 100, 1)) %>%
- rename(`ID` = Indiv, `# of offspring` = n)
- openxlsx::writeData(wb, sheet = "Optimal Contributions", contrib_df)
-
- openxlsx::addWorksheet(wb, "Optimal Matings")
- openxlsx::writeData(wb, sheet = "Optimal Matings", results$Mating)
-
- openxlsx::saveWorkbook(wb, file, overwrite = TRUE)
- }
- )
-}
-
-shinyApp(ui = ui, server = server)
\ No newline at end of file
diff --git a/app/rsconnect/shinyapps.io/tnst4x-aj0ackerman/allomate.dcf b/app/rsconnect/shinyapps.io/tnst4x-aj0ackerman/allomate.dcf
new file mode 100644
index 0000000..ee9df06
--- /dev/null
+++ b/app/rsconnect/shinyapps.io/tnst4x-aj0ackerman/allomate.dcf
@@ -0,0 +1,10 @@
+name: allomate
+title:
+username: tnst4x-aj0ackerman
+account: tnst4x-aj0ackerman
+server: shinyapps.io
+hostUrl: https://api.shinyapps.io/v1
+appId: 15539609
+bundleId: 10943464
+url: https://tnst4x-aj0ackerman.shinyapps.io/allomate/
+version: 1
diff --git a/app/server.R b/app/server.R
index f64d0e9..9912783 100644
--- a/app/server.R
+++ b/app/server.R
@@ -1,139 +1,363 @@
-library(shiny)
-library(readr)
-library(dplyr)
-library(tidyr)
-library(purrr)
-library(kinship2)
-library(DT)
-library(tibble)
-library(openxlsx)
+# All packages are loaded in global.R
+# optiSel availability is checked in global.R
-#### Helper Functions ####
+# All functions are now loaded from the organized functions folder
+# See functions/load_all_functions.R for details
-read_candidates <- function(file) {
- df <- readr::read_table(file$datapath)
- list(
- candidates = df,
- males = filter(df, sex == "M") %>% pull(id),
- females = filter(df, sex == "F") %>% pull(id)
- )
-}
+#### Server Function ####
-clean_pedigree <- function(ped) {
- ped <- ped %>%
- mutate(
- id = as.factor(id),
- sire = as.factor(sire),
- dam = as.factor(dam)
- )
-
- sex_ped <- ped %>%
- mutate(
- sex = case_when(
- id %in% sire ~ 0,
- id %in% dam ~ 1,
- TRUE ~ 2
- )
- )
-
- messy_parents <- setdiff(intersect(sex_ped$sire, sex_ped$dam), 0) %>%
- as.data.frame() %>%
- rename(id = 1)
-
- parents_fixed <- sex_ped
- parents_fixed$sire[parents_fixed$sire %in% messy_parents$id] <- 0
- parents_fixed$dam[parents_fixed$dam %in% messy_parents$id] <- 0
-
- doubled <- parents_fixed %>%
- count(id, name = "freq") %>%
- filter(freq > 1) %>%
- pull(id)
-
- nodup <- filter(parents_fixed, !id %in% doubled)
-
- circdep <- nodup %>%
- mutate(across(c(id, sire, dam), as.character)) %>%
- filter(id == sire | id == dam)
+server <- function(input, output, session) {
- clean_ped <- anti_join(nodup, circdep, by = "id")
+ # Package status output
+ output$package_status_text <- renderText({
+ generate_package_status()
+ })
- ready_ped <- with(clean_ped, kinship2::fixParents(id, sire, dam, sex, missid = "0"))
- final_ped <- with(ready_ped, kinship2::pedigree(id, dadid, momid, sex, missid = "0"))
+ # WebR detection output
+ output$webr_detected <- renderText({
+ if (is_webr_environment()) {
+ "WebR detected"
+ } else {
+ ""
+ }
+ })
+ outputOptions(output, "webr_detected", suspendWhenHidden = FALSE)
- final_ped
-}
-
-compute_kinship_matrix <- function(ped, males, females) {
- kinship_matrix <- kinship2::kinship(ped)
- kin_mat_sel <- kinship_matrix[males, females]
+ # Help button functionality
+ observeEvent(input$help_btn, {
+ updateTabsetPanel(session, "main_tabs", selected = "Help")
+ })
- kin_quads <- tibble(
- Data = "Kinship",
- Q25 = quantile(kin_mat_sel, 0.25),
- Q50 = quantile(kin_mat_sel, 0.50),
- Q75 = quantile(kin_mat_sel, 0.75),
- Q100 = quantile(kin_mat_sel, 1.00)
- ) %>% column_to_rownames("Data")
+ # View R Code button functionality
+ observeEvent(input$view_r_code_btn, {
+ updateTabsetPanel(session, "main_tabs", selected = "R Code")
+ })
- kinship_results <- as_tibble(kin_mat_sel, rownames = "Male") %>%
- pivot_longer(-Male, names_to = "Female", values_to = "Kinship")
+ # Back to top functionality
+ observeEvent(input$back_to_top, {
+ runjs("document.querySelector('.help-content').scrollTop = 0;")
+ })
- list(results = kinship_results, quads = kin_quads, matrix = kin_mat_sel)
-}
-
-process_ebvs <- function(trait_counter, input) {
- ebv_inputs <- list()
- for (i in seq_len(trait_counter)) {
- file_i <- input[[paste0("trait_file_", i)]]
- weight_i <- input[[paste0("trait_weight_", i)]]
- if (!is.null(file_i) && !is.null(weight_i)) {
- df_raw <- readr::read_table(file_i$datapath)
- if (!"ID" %in% names(df_raw)) names(df_raw)[1] <- "ID"
- if (!"EBV" %in% names(df_raw)) names(df_raw)[2] <- "EBV"
- ebv_inputs <- append(ebv_inputs, list(select(df_raw, ID, EBV), weight_i))
+ # Enhanced markdown to HTML conversion function
+ markdown_to_html <- function(markdown_text) {
+ # Split into lines for processing
+ lines <- strsplit(markdown_text, "\n")[[1]]
+ html_lines <- character(length(lines))
+ in_code_block <- FALSE
+ in_list <- FALSE
+ list_type <- ""
+
+ for (i in seq_along(lines)) {
+ line <- lines[i]
+
+ # Handle code blocks
+ if (grepl("^```", line)) {
+ if (!in_code_block) {
+ html_lines[i] <- "