Troubleshooting Guide: Constitutional Compliance Problem Solving

Troubleshooting Guide for Enhanced Soil Sampling Tool

This comprehensive troubleshooting guide provides actionable solutions for common issues encountered with the Enhanced Soil Sampling Tool, ensuring constitutional compliance throughout the problem-solving process.

Constitutional Error Handling

The Enhanced Soil Sampling Tool implements constitutional compliance in error handling:

✅ Spatial Analysis Excellence: Detailed spatial error diagnostics
✅ Code Quality Excellence: Comprehensive error classification and solutions
✅ Testing Standards: Validation-driven error prevention
✅ User Experience Consistency: Clear, actionable error messages
✅ Performance Excellence: Efficient error detection and recovery

Diagnostic Tools and System Validation

1. Comprehensive System Diagnostics

library(MLSampling)

# Run comprehensive system diagnostics
diagnostic_results <- run_comprehensive_diagnostics()

print("System Validation Results:")
print(diagnostic_results$system_summary)

if (!diagnostic_results$all_passed) {
  cat("❌ Issues detected:\n")
  for (issue in diagnostic_results$issues) {
    cat("  Problem:", issue$category, "-", issue$description, "\n")
    cat("  Severity:", issue$severity, "\n")
    cat("  Solution:", issue$solution, "\n")
    cat("  Commands:", issue$fix_commands, "\n\n")
  }
}

2. Environment Validation

# Validate R environment for constitutional compliance
environment_check <- validate_r_environment()

if (!environment_check$compliant) {
  cat("❌ Environment issues detected:\n")
  
  # R version issues
  if (!environment_check$r_version_ok) {
    cat("  R Version: Requires R >= 4.3.0, found", environment_check$r_version, "\n")
    cat("  Solution: Update R from https://cran.r-project.org/\n")
  }
  
  # Package dependency issues
  if (length(environment_check$missing_packages) > 0) {
    cat("  Missing packages:", paste(environment_check$missing_packages, collapse = ", "), "\n")
    cat("  Solution: install.packages(c(", paste0("'", environment_check$missing_packages, "'", collapse = ", "), "))\n")
  }
  
  # Package version issues
  if (length(environment_check$outdated_packages) > 0) {
    cat("  Outdated packages:\n")
    for (pkg in environment_check$outdated_packages) {
      cat("    ", pkg$name, ": found", pkg$current, ", requires >=", pkg$required, "\n")
    }
    cat("  Solution: update.packages()\n")
  }
}

Installation and Setup Issues

3. Package Installation Problems

# Diagnose package installation issues
installation_issues <- diagnose_installation_issues()

# Common installation fixes
fix_installation_issues <- function() {
  
  # Issue: terra package installation fails
  if (installation_issues$terra_failed) {
    cat("Fixing terra installation...\n")
    
    # Windows-specific fix
    if (.Platform$OS.type == "windows") {
      cat("Installing Rtools for Windows...\n")
      cat("Download from: https://cran.r-project.org/bin/windows/Rtools/\n")
    }
    
    # GDAL dependency fix
    if (installation_issues$gdal_missing) {
      cat("GDAL installation required:\n")
      cat("  Ubuntu/Debian: sudo apt-get install libgdal-dev\n")
      cat("  macOS: brew install gdal\n")
      cat("  Windows: Install OSGeo4W\n")
    }
  }
  
  # Issue: torch installation fails
  if (installation_issues$torch_failed) {
    cat("Fixing torch installation...\n")
    cat("install.packages('torch')\n")
    cat("torch::install_torch()\n")
    
    # CUDA availability check
    if (installation_issues$cuda_available) {
      cat("CUDA detected - installing CUDA-enabled torch\n")
      cat("torch::install_torch(type = 'cuda')\n")
    }
  }
  
  # Issue: sf package installation fails
  if (installation_issues$sf_failed) {
    cat("Fixing sf installation...\n")
    cat("Required system libraries:\n")
    cat("  Ubuntu/Debian: sudo apt-get install libudunits2-dev libgeos-dev libproj-dev\n")
    cat("  macOS: brew install udunits geos proj\n")
  }
}

# Apply fixes
if (length(installation_issues$issues) > 0) {
  fix_installation_issues()
}

4. Configuration Problems

# Diagnose configuration issues
config_issues <- diagnose_configuration_issues()

# Fix common configuration problems
fix_configuration_issues <- function(tool) {
  
  # Issue: Invalid memory configuration
  if (config_issues$memory_config_invalid) {
    cat("Fixing memory configuration...\n")
    tool$update_config(list(
      memory_limit = "auto",  # Automatic memory detection
      memory_strategy = "conservative"
    ))
  }
  
  # Issue: Parallel processing not working
  if (config_issues$parallel_not_working) {
    cat("Fixing parallel configuration...\n")
    
    # Detect available cores
    available_cores <- parallel::detectCores()
    safe_cores <- max(1, available_cores - 1)
    
    tool$update_config(list(
      parallel_cores = safe_cores,
      parallel_strategy = "simple"
    ))
  }
  
  # Issue: Temporary directory issues
  if (config_issues$temp_dir_issues) {
    cat("Fixing temporary directory...\n")
    
    # Create user-specific temp directory
    user_temp <- file.path(tempdir(), "soil_sampling")
    dir.create(user_temp, recursive = TRUE, showWarnings = FALSE)
    
    tool$update_config(list(
      temp_directory = user_temp,
      cleanup_temp = TRUE
    ))
  }
  
  return(tool)
}

5. Spatial Data Problems

# Diagnose spatial data issues
diagnose_spatial_data_issues <- function(field_data, existing_samples = NULL) {
  
  issues <- list()
  
  # Check CRS consistency
  if (!is.null(field_data) && is.null(terra::crs(field_data))) {
    issues$crs_missing <- list(
      problem = "Field data missing coordinate reference system (CRS)",
      severity = "HIGH",
      solution = "Set CRS using terra::crs(field_data) <- 'EPSG:XXXX'",
      example = "terra::crs(field_data) <- 'EPSG:32633'  # UTM Zone 33N"
    )
  }
  
  # Check data dimensions
  if (!is.null(field_data)) {
    dims <- dim(field_data)
    if (any(dims < 10)) {
      issues$data_too_small <- list(
        problem = "Field data dimensions too small for meaningful optimization",
        severity = "MEDIUM",
        solution = "Ensure field data has at least 10x10 cells",
        current_dims = dims
      )
    }
  }
  
  # Check existing samples format
  if (!is.null(existing_samples)) {
    required_cols <- c("x", "y")
    missing_cols <- setdiff(required_cols, names(existing_samples))
    
    if (length(missing_cols) > 0) {
      issues$samples_format <- list(
        problem = paste("Existing samples missing required columns:", paste(missing_cols, collapse = ", ")),
        severity = "HIGH",
        solution = "Ensure existing_samples data.frame has 'x' and 'y' columns",
        example = "existing_samples <- data.frame(x = c(1, 2, 3), y = c(1, 2, 3))"
      )
    }
  }
  
  return(issues)
}

# Fix spatial data issues
fix_spatial_data_issues <- function(field_data, existing_samples = NULL, crs = "EPSG:32633") {
  
  # Fix missing CRS
  if (!is.null(field_data) && is.null(terra::crs(field_data))) {
    cat("Setting default CRS:", crs, "\n")
    terra::crs(field_data) <- crs
  }
  
  # Fix existing samples format
  if (!is.null(existing_samples)) {
    
    # Standardize column names
    if ("X" %in% names(existing_samples) && !"x" %in% names(existing_samples)) {
      names(existing_samples)[names(existing_samples) == "X"] <- "x"
    }
    
    if ("Y" %in% names(existing_samples) && !"y" %in% names(existing_samples)) {
      names(existing_samples)[names(existing_samples) == "Y"] <- "y"
    }
    
    # Ensure numeric coordinates
    existing_samples$x <- as.numeric(existing_samples$x)
    existing_samples$y <- as.numeric(existing_samples$y)
    
    # Remove NA coordinates
    na_coords <- is.na(existing_samples$x) | is.na(existing_samples$y)
    if (any(na_coords)) {
      cat("Removing", sum(na_coords), "samples with NA coordinates\n")
      existing_samples <- existing_samples[!na_coords, ]
    }
  }
  
  return(list(
    field_data = field_data,
    existing_samples = existing_samples
  ))
}

6. Data Loading Issues

# Diagnose and fix data loading issues
troubleshoot_data_loading <- function(data_path) {
  
  issues <- list()
  solutions <- list()
  
  # Check if path exists
  if (!file.exists(data_path)) {
    issues$path_not_found <- list(
      problem = paste("Data path does not exist:", data_path),
      severity = "HIGH",
      solution = "Check file path and ensure data files are present"
    )
    return(list(issues = issues, solutions = solutions))
  }
  
  # Check for required raster files
  required_files <- c("dem.tif", "ndvi.tif", "slope.tif")
  existing_files <- list.files(data_path, pattern = "\\.tif$", ignore.case = TRUE)
  missing_files <- setdiff(required_files, existing_files)
  
  if (length(missing_files) > 0) {
    issues$missing_rasters <- list(
      problem = paste("Missing required raster files:", paste(missing_files, collapse = ", ")),
      severity = "HIGH",
      solution = "Ensure all required raster files are present in data directory"
    )
  }
  
  # Try loading each raster file
  for (file in existing_files) {
    file_path <- file.path(data_path, file)
    
    tryCatch({
      raster_data <- terra::rast(file_path)
      
      # Check for issues
      if (terra::nlyr(raster_data) == 0) {
        issues[[paste0("empty_", file)]] <- list(
          problem = paste("Raster file is empty:", file),
          severity = "HIGH",
          solution = "Replace with valid raster data"
        )
      }
      
      # Check CRS
      if (is.na(terra::crs(raster_data))) {
        issues[[paste0("no_crs_", file)]] <- list(
          problem = paste("Raster file missing CRS:", file),
          severity = "MEDIUM",
          solution = "Set CRS using terra::crs() or provide CRS in loading function"
        )
      }
      
    }, error = function(e) {
      issues[[paste0("load_error_", file)]] <- list(
        problem = paste("Cannot load raster file:", file, "-", e$message),
        severity = "HIGH",
        solution = "Check file format and integrity"
      )
    })
  }
  
  return(list(issues = issues, solutions = solutions))
}

# Safe data loading function
safe_load_field_data <- function(data_path, default_crs = "EPSG:32633") {
  
  # Diagnose issues first
  diagnosis <- troubleshoot_data_loading(data_path)
  
  if (length(diagnosis$issues) > 0) {
    cat("❌ Data loading issues detected:\n")
    for (issue in diagnosis$issues) {
      cat("  ", issue$problem, "\n")
      cat("    Solution:", issue$solution, "\n")
    }
    return(NULL)
  }
  
  # Attempt to load data
  tryCatch({
    
    # Load raster files
    raster_files <- list.files(data_path, pattern = "\\.tif$", full.names = TRUE)
    raster_stack <- terra::rast(raster_files)
    
    # Set default CRS if missing
    if (is.na(terra::crs(raster_stack))) {
      cat("Setting default CRS:", default_crs, "\n")
      terra::crs(raster_stack) <- default_crs
    }
    
    cat("✅ Successfully loaded", terra::nlyr(raster_stack), "raster layers\n")
    return(raster_stack)
    
  }, error = function(e) {
    cat("❌ Failed to load field data:", e$message, "\n")
    return(NULL)
  })
}

Algorithm-Specific Issues

7. UDL Optimization Problems

# Diagnose UDL optimization issues
diagnose_udl_issues <- function(tool, field_data, existing_samples, n_new_samples) {
  
  issues <- list()
  
  # Check convergence issues
  test_result <- tryCatch({
    tool$run_udl(
      field_data = field_data,
      existing_samples = existing_samples,
      n_new_samples = min(n_new_samples, 10),  # Small test
      max_iter = 10,
      verbose = FALSE
    )
  }, error = function(e) e)
  
  if (inherits(test_result, "error")) {
    issues$udl_execution_error <- list(
      problem = paste("UDL execution failed:", test_result$message),
      severity = "HIGH",
      solution = "Check input data format and parameters"
    )
  }
  
  # Check for slow convergence
  if (!inherits(test_result, "error") && !is.null(test_result$convergence)) {
    if (test_result$convergence$iterations >= test_result$convergence$max_iterations * 0.9) {
      issues$slow_convergence <- list(
        problem = "UDL optimization converging slowly",
        severity = "MEDIUM",
        solution = "Increase max_iter or adjust genetic algorithm parameters"
      )
    }
  }
  
  return(issues)
}

# Fix UDL optimization issues
fix_udl_issues <- function(tool, field_data, existing_samples, n_new_samples) {
  
  cat("Optimizing UDL parameters for better performance...\n")
  
  # Adaptive parameter selection based on problem size
  field_size <- prod(dim(field_data)[1:2])
  
  if (field_size < 1000) {
    # Small field - use simpler parameters
    optimized_params <- list(
      optimization_method = "simulated_annealing",
      max_iter = 50,
      population_size = 20
    )
  } else if (field_size < 10000) {
    # Medium field - balanced parameters
    optimized_params <- list(
      optimization_method = "genetic",
      max_iter = 100,
      population_size = 50,
      mutation_rate = 0.1
    )
  } else {
    # Large field - efficient parameters
    optimized_params <- list(
      optimization_method = "genetic",
      max_iter = 200,
      population_size = 100,
      early_stopping = TRUE,
      patience = 20
    )
  }
  
  return(optimized_params)
}

8. UFN Optimization Problems

# Diagnose UFN optimization issues
diagnose_ufn_issues <- function(tool, field_data, existing_samples, n_new_samples) {
  
  issues <- list()
  
  # Check torch availability
  if (!torch::torch_is_installed()) {
    issues$torch_not_installed <- list(
      problem = "Torch not installed - UFN will use fallback method",
      severity = "MEDIUM",
      solution = "Install torch: install.packages('torch'); torch::install_torch()"
    )
  }
  
  # Check GPU availability if torch is installed
  if (torch::torch_is_installed()) {
    
    gpu_available <- tryCatch({
      torch::cuda_is_available()
    }, error = function(e) FALSE)
    
    if (!gpu_available) {
      issues$no_gpu <- list(
        problem = "GPU not available for UFN optimization",
        severity = "LOW",
        solution = "UFN will use CPU - consider GPU for large problems"
      )
    }
  }
  
  # Test UFN execution
  test_result <- tryCatch({
    tool$run_ufn(
      field_data = field_data,
      existing_samples = existing_samples,
      n_new_samples = min(n_new_samples, 5),  # Small test
      max_epochs = 5,
      verbose = FALSE
    )
  }, error = function(e) e)
  
  if (inherits(test_result, "error")) {
    issues$ufn_execution_error <- list(
      problem = paste("UFN execution failed:", test_result$message),
      severity = "HIGH",
      solution = "Check torch installation and input data format"
    )
  }
  
  return(issues)
}

# Fix UFN optimization issues
fix_ufn_issues <- function(tool, field_data, existing_samples, n_new_samples) {
  
  cat("Optimizing UFN parameters...\n")
  
  # Check if torch is available
  if (!torch::torch_is_installed()) {
    cat("Using statistical fallback method for UFN\n")
    return(list(
      fallback_method = "statistical",
      statistical_params = list(
        method = "kriging",
        variogram = "spherical"
      )
    ))
  }
  
  # Optimize based on data size and available resources
  n_points <- nrow(existing_samples) + n_new_samples
  
  if (n_points < 100) {
    # Small graph
    optimized_params <- list(
      graph_connectivity = "knn",
      k_neighbors = 5,
      hidden_dim = 32,
      n_layers = 2,
      max_epochs = 50
    )
  } else if (n_points < 500) {
    # Medium graph
    optimized_params <- list(
      graph_connectivity = "delaunay",
      hidden_dim = 64,
      n_layers = 3,
      max_epochs = 100,
      batch_size = 32
    )
  } else {
    # Large graph
    optimized_params <- list(
      graph_connectivity = "radius",
      radius = 0.1,
      hidden_dim = 128,
      n_layers = 4,
      max_epochs = 200,
      batch_size = 64,
      early_stopping = TRUE
    )
  }
  
  return(optimized_params)
}

Performance Issues

9. Memory Problems

# Diagnose memory issues
diagnose_memory_issues <- function() {
  
  issues <- list()
  
  # Check available memory
  if (.Platform$OS.type == "windows") {
    mem_info <- system("wmic OS get TotalVisibleMemorySize /value", intern = TRUE)
    total_mem_kb <- as.numeric(gsub("TotalVisibleMemorySize=", "", mem_info[grep("TotalVisibleMemorySize", mem_info)]))
    total_mem_gb <- total_mem_kb / 1024 / 1024
  } else {
    # Unix-like systems
    mem_info <- system("free -g", intern = TRUE)
    total_mem_gb <- as.numeric(strsplit(mem_info[2], "\\s+")[[1]][2])
  }
  
  if (total_mem_gb < 4) {
    issues$low_memory <- list(
      problem = paste("Low system memory:", round(total_mem_gb, 1), "GB available"),
      severity = "HIGH",
      solution = "Enable memory-efficient mode or reduce problem size"
    )
  }
  
  # Check R memory usage
  r_mem_usage <- pryr::mem_used()
  if (r_mem_usage > 1024^3) {  # > 1GB
    issues$high_r_memory <- list(
      problem = paste("High R memory usage:", round(as.numeric(r_mem_usage) / 1024^3, 1), "GB"),
      severity = "MEDIUM",
      solution = "Run gc() or restart R session"
    )
  }
  
  return(issues)
}

# Fix memory issues
fix_memory_issues <- function(tool) {
  
  cat("Applying memory optimization strategies...\n")
  
  # Force garbage collection
  gc()
  
  # Configure memory-efficient settings
  memory_config <- list(
    memory_limit = "1GB",
    streaming_mode = TRUE,
    batch_processing = TRUE,
    cleanup_interval = 60
  )
  
  tool$update_config(memory_config)
  
  return(tool)
}

10. Performance Degradation

# Diagnose performance issues
diagnose_performance_issues <- function(tool, field_data, n_samples) {
  
  issues <- list()
  
  # Benchmark current performance
  start_time <- Sys.time()
  
  test_result <- tryCatch({
    tool$run_udl(
      field_data = field_data,
      n_new_samples = min(n_samples, 10),
      max_iter = 10,
      verbose = FALSE
    )
  }, error = function(e) NULL)
  
  end_time <- Sys.time()
  execution_time <- as.numeric(difftime(end_time, start_time, units = "secs"))
  
  # Check if performance is acceptable
  expected_time <- 30  # 30 seconds for small test
  
  if (execution_time > expected_time) {
    issues$slow_execution <- list(
      problem = paste("Slow execution time:", round(execution_time, 1), "seconds for test"),
      severity = "MEDIUM",
      solution = "Check system resources and optimize parameters"
    )
  }
  
  # Check parallel processing
  if (tool$get_config()$parallel_cores == 1) {
    available_cores <- parallel::detectCores()
    if (available_cores > 1) {
      issues$no_parallel <- list(
        problem = "Parallel processing not enabled",
        severity = "LOW",
        solution = paste("Enable parallel processing with", available_cores - 1, "cores")
      )
    }
  }
  
  return(issues)
}

# Optimize performance
optimize_performance <- function(tool) {
  
  cat("Optimizing performance settings...\n")
  
  # Enable parallel processing
  available_cores <- parallel::detectCores()
  optimal_cores <- max(1, available_cores - 1)
  
  # Configure for optimal performance
  performance_config <- list(
    parallel_cores = optimal_cores,
    caching_enabled = TRUE,
    optimization_level = "balanced"
  )
  
  tool$update_config(performance_config)
  
  return(tool)
}

Error Message Reference

11. Common Error Messages and Solutions

# Comprehensive error message lookup
error_message_lookup <- list(
  
  # Spatial data errors
  "CRS not found" = list(
    problem = "Coordinate reference system missing from spatial data",
    solution = "Set CRS using terra::crs(data) <- 'EPSG:XXXX'",
    example = "terra::crs(field_data) <- 'EPSG:32633'"
  ),
  
  "extent mismatch" = list(
    problem = "Spatial extents of field data and samples don't match",
    solution = "Ensure all spatial data have the same extent and CRS",
    example = "existing_samples <- sf::st_transform(existing_samples, terra::crs(field_data))"
  ),
  
  # Algorithm errors
  "convergence failed" = list(
    problem = "Optimization algorithm failed to converge",
    solution = "Increase max_iter or adjust optimization parameters",
    example = "tool$run_udl(max_iter = 200, patience = 30)"
  ),
  
  "torch not available" = list(
    problem = "PyTorch not installed for UFN optimization",
    solution = "Install torch package",
    example = "install.packages('torch'); torch::install_torch()"
  ),
  
  # Memory errors
  "cannot allocate vector" = list(
    problem = "Insufficient memory for operation",
    solution = "Enable memory-efficient mode or reduce problem size",
    example = "tool$update_config(list(memory_limit = '1GB', streaming_mode = TRUE))"
  ),
  
  # File I/O errors
  "file not found" = list(
    problem = "Required data files not found",
    solution = "Check file paths and ensure all required files exist",
    example = "list.files('data/', pattern = '.tif')"
  )
)

# Function to look up error solutions
lookup_error_solution <- function(error_message) {
  
  # Find matching error patterns
  matches <- sapply(names(error_message_lookup), function(pattern) {
    grepl(pattern, error_message, ignore.case = TRUE)
  })
  
  if (any(matches)) {
    matching_errors <- error_message_lookup[matches]
    
    cat("💡 Possible solutions for error:", error_message, "\n\n")
    
    for (i in seq_along(matching_errors)) {
      error_info <- matching_errors[[i]]
      cat("Problem:", error_info$problem, "\n")
      cat("Solution:", error_info$solution, "\n")
      cat("Example:", error_info$example, "\n\n")
    }
  } else {
    cat("No specific solution found for this error.\n")
    cat("General troubleshooting steps:\n")
    cat("1. Check input data format and validity\n")
    cat("2. Verify system requirements and dependencies\n")
    cat("3. Try with smaller dataset or simplified parameters\n")
    cat("4. Check available memory and system resources\n")
  }
}

Automated Problem Resolution

12. Automated Troubleshooting

# Comprehensive automated troubleshooting function
run_automated_troubleshooting <- function(tool, field_data = NULL, existing_samples = NULL) {
  
  cat("🔍 Running automated troubleshooting...\n\n")
  
  # 1. System diagnostics
  cat("1. System Diagnostics:\n")
  system_issues <- run_comprehensive_diagnostics()
  
  if (length(system_issues$issues) == 0) {
    cat("  ✅ No system issues detected\n")
  } else {
    cat("  ❌", length(system_issues$issues), "system issues found\n")
    for (issue in system_issues$issues) {
      cat("    -", issue$description, "\n")
    }
  }
  
  # 2. Data validation
  if (!is.null(field_data)) {
    cat("\n2. Data Validation:\n")
    data_issues <- diagnose_spatial_data_issues(field_data, existing_samples)
    
    if (length(data_issues) == 0) {
      cat("  ✅ No data issues detected\n")
    } else {
      cat("  ❌", length(data_issues), "data issues found\n")
      for (issue in data_issues) {
        cat("    -", issue$problem, "\n")
      }
    }
  }
  
  # 3. Configuration validation
  cat("\n3. Configuration Validation:\n")
  config_issues <- diagnose_configuration_issues()
  
  if (length(config_issues$issues) == 0) {
    cat("  ✅ No configuration issues detected\n")
  } else {
    cat("  ❌", length(config_issues$issues), "configuration issues found\n")
  }
  
  # 4. Performance assessment
  cat("\n4. Performance Assessment:\n")
  performance_issues <- diagnose_performance_issues(tool, field_data, 25)
  
  if (length(performance_issues) == 0) {
    cat("  ✅ No performance issues detected\n")
  } else {
    cat("  ⚠️", length(performance_issues), "performance opportunities found\n")
  }
  
  # 5. Memory assessment
  cat("\n5. Memory Assessment:\n")
  memory_issues <- diagnose_memory_issues()
  
  if (length(memory_issues) == 0) {
    cat("  ✅ Memory usage within acceptable limits\n")
  } else {
    cat("  ⚠️", length(memory_issues), "memory concerns detected\n")
  }
  
  # Generate summary report
  total_issues <- length(system_issues$issues) + 
                  length(data_issues) + 
                  length(config_issues$issues) +
                  length(performance_issues) +
                  length(memory_issues)
  
  cat("\n📊 Troubleshooting Summary:\n")
  cat("  Total issues found:", total_issues, "\n")
  
  if (total_issues == 0) {
    cat("  🎉 System appears to be functioning optimally!\n")
  } else {
    cat("  💡 Recommendations available for improvement\n")
  }
  
  # Return comprehensive results
  return(list(
    system_issues = system_issues,
    data_issues = data_issues,
    config_issues = config_issues,
    performance_issues = performance_issues,
    memory_issues = memory_issues,
    total_issues = total_issues
  ))
}

# Quick fix function that applies all automated fixes
quick_fix_all_issues <- function(tool, field_data = NULL, existing_samples = NULL) {
  
  cat("🔧 Applying all automated fixes...\n")
  
  # Apply memory optimizations
  tool <- fix_memory_issues(tool)
  
  # Apply performance optimizations
  tool <- optimize_performance(tool)
  
  # Fix configuration issues
  tool <- fix_configuration_issues(tool)
  
  # Fix data issues if data provided
  if (!is.null(field_data)) {
    fixed_data <- fix_spatial_data_issues(field_data, existing_samples)
    field_data <- fixed_data$field_data
    existing_samples <- fixed_data$existing_samples
  }
  
  cat("✅ All automated fixes applied\n")
  
  return(list(
    tool = tool,
    field_data = field_data,
    existing_samples = existing_samples
  ))
}

Constitutional Compliance Troubleshooting

13. Constitutional Compliance Validation

# Validate constitutional compliance
validate_constitutional_troubleshooting <- function(tool) {
  
  cat("📋 Validating Constitutional Compliance...\n\n")
  
  compliance_results <- list()
  
  # 1. Spatial Analysis Excellence
  compliance_results$spatial_excellence <- validate_spatial_analysis_excellence(tool)
  
  # 2. Code Quality Excellence
  compliance_results$code_quality <- validate_code_quality_excellence(tool)
  
  # 3. Testing Standards
  compliance_results$testing_standards <- validate_testing_standards(tool)
  
  # 4. User Experience Consistency
  compliance_results$user_experience <- validate_user_experience_consistency(tool)
  
  # 5. Performance Excellence
  compliance_results$performance <- validate_performance_excellence(tool)
  
  # Summary
  all_compliant <- all(sapply(compliance_results, function(x) x$compliant))
  
  if (all_compliant) {
    cat("🏆 Constitutional compliance ACHIEVED!\n")
    cat("All constitutional principles satisfied.\n")
  } else {
    cat("⚠️ Constitutional compliance issues detected:\n")
    
    for (principle in names(compliance_results)) {
      result <- compliance_results[[principle]]
      if (!result$compliant) {
        cat("  ❌", principle, ":", result$issue, "\n")
        cat("    Solution:", result$solution, "\n")
      }
    }
  }
  
  return(compliance_results)
}

When to Seek Additional Help

14. Escalation Guidelines

# When automated troubleshooting isn't sufficient
when_to_escalate <- function(troubleshooting_results) {
  
  escalation_triggers <- list(
    
    high_severity_issues = any(sapply(troubleshooting_results, function(x) {
      if (is.list(x) && "severity" %in% names(x)) {
        x$severity == "HIGH"
      } else {
        FALSE
      }
    })),
    
    multiple_system_issues = length(troubleshooting_results$system_issues$issues) > 3,
    
    constitutional_violations = !validate_constitutional_compliance(tool)$all_compliant,
    
    persistent_errors = troubleshooting_results$total_issues > 5
  )
  
  if (any(unlist(escalation_triggers))) {
    cat("🚨 Consider seeking additional help:\n")
    cat("1. Check GitHub issues: https://github.com/your-repo/issues\n")
    cat("2. Review documentation: ?MLSampling\n")
    cat("3. Consult constitutional compliance guide\n")
    cat("4. Contact support team with diagnostic results\n")
  }
}

Summary and Next Steps

This troubleshooting guide provides comprehensive problem-solving for the Enhanced Soil Sampling Tool with constitutional compliance.

Quick Start Troubleshooting: 1. Run run_automated_troubleshooting() for comprehensive diagnosis 2. Apply quick_fix_all_issues() for automated problem resolution 3. Validate constitutional compliance with validate_constitutional_troubleshooting()

For specific issues: - Use lookup_error_solution() for specific error messages - Consult individual diagnostic functions for targeted problem-solving - Apply constitutional compliance validation throughout

Related Resources: - vignette("soil-sampling-examples") for usage examples - vignette("performance-optimization") for performance tuning - ?MLSampling for complete API documentation

Soil Sampling Optimization Team

2026-02-26

Troubleshooting Guide for Enhanced Soil Sampling Tool

Constitutional Error Handling

Diagnostic Tools and System Validation

1. Comprehensive System Diagnostics

2. Environment Validation

Installation and Setup Issues

3. Package Installation Problems

4. Configuration Problems

5. Spatial Data Problems

6. Data Loading Issues

Algorithm-Specific Issues

7. UDL Optimization Problems

8. UFN Optimization Problems

Performance Issues

9. Memory Problems

10. Performance Degradation

Error Message Reference

11. Common Error Messages and Solutions

Automated Problem Resolution

12. Automated Troubleshooting

Constitutional Compliance Troubleshooting

13. Constitutional Compliance Validation

When to Seek Additional Help

14. Escalation Guidelines

Summary and Next Steps

Troubleshooting Guide: Constitutional Compliance Problem Solving

Soil Sampling Optimization Team

2026-02-26

Troubleshooting Guide for Enhanced Soil Sampling Tool

Constitutional Error Handling

Diagnostic Tools and System Validation

1. Comprehensive System Diagnostics

2. Environment Validation

Installation and Setup Issues

3. Package Installation Problems

4. Configuration Problems

Data-Related Issues

5. Spatial Data Problems

6. Data Loading Issues

Algorithm-Specific Issues

7. UDL Optimization Problems

8. UFN Optimization Problems

Performance Issues

9. Memory Problems

10. Performance Degradation

Error Message Reference

11. Common Error Messages and Solutions

Automated Problem Resolution

12. Automated Troubleshooting

Constitutional Compliance Troubleshooting

13. Constitutional Compliance Validation

When to Seek Additional Help

14. Escalation Guidelines

Summary and Next Steps