scirs2_core/

lib.rs

#![allow(deprecated)]
#![recursion_limit = "512"]
#![allow(dead_code)]
#![allow(unused_variables)]
#![allow(clippy::needless_borrows_for_generic_args)]
#![allow(clippy::manual_clamp)]
#![allow(clippy::if_same_then_else)]
#![allow(clippy::identity_op)]
#![allow(clippy::uninlined_format_args)]
#![allow(clippy::trim_split_whitespace)]
#![allow(clippy::redundant_closure)]
#![allow(clippy::unused_enumerate_index)]

//! # ``SciRS2`` Core (Beta 1)
//!
//! Core utilities and common functionality for the ``SciRS2`` library.
//!
//! This crate provides shared utilities, error types, and common traits
//! used across the ``SciRS2`` ecosystem of crates.
//!
//! ## Beta 1 Features
//!
//! - **Stable APIs**: Core functionality with API stability guarantees for production use
//! - **Advanced Error Diagnostics**: ML-inspired error pattern recognition and domain-specific recovery strategies
//! - **Performance Optimizations**: Enhanced SIMD operations, adaptive chunking, and intelligent load balancing
//! - **GPU Acceleration**: CUDA, Metal MPS, and other backend support for accelerated computing
//! - **Memory Management**: Efficient memory-mapped arrays and adaptive chunking for large datasets
//!
//! ## Overview
//!
//! * Common error types and traits
//! * High-performance numerical operations
//!   * SIMD-accelerated computations
//!   * Parallel processing for multi-core systems
//!   * Memory-efficient algorithms
//!   * GPU acceleration abstractions
//! * Caching and memoization for optimized performance
//! * Type definitions and conversions
//! * Physical and mathematical constants
//! * Configuration system
//! * Input/output utilities
//! * Validation utilities
//! * Numeric traits and conversions
//! * Memory management utilities
//! * Logging and diagnostics
//! * Profiling tools
//! * Random number generation
//!
//! ## Performance Optimizations
//!
//! The library provides several performance optimization features:
//!
//! * **SIMD Operations**: Uses CPU vector instructions for faster array operations
//! * **Parallel Processing**: Leverages multi-core systems for improved performance
//! * **GPU Acceleration**: Provides abstractions for GPU computation (CUDA, WebGPU, Metal)
//! * **Memory-Efficient Algorithms**: Optimizes memory usage for large-scale computations
//! * **Caching and Memoization**: Avoids redundant computations
//! * **Profiling and Instrumentation**: Identifies performance bottlenecks
//! * **Memory Management**: Efficient memory utilization and pooling
//!
//! ## Additional Utilities
//!
//! * **Logging**: Structured logging for scientific applications
//! * **Random Number Generation**: Consistent interface for random sampling
//! * **Type Conversions**: Safe numeric and complex number conversions
//!
//! ## Feature Flags
//!
//! These features can be controlled via feature flags:
//!
//! * `simd`: Enable SIMD acceleration
//! * `parallel`: Enable parallel processing
//! * `cache`: Enable caching and memoization functionality
//! * `validation`: Enable validation utilities
//! * `logging`: Enable structured logging and diagnostics
//! * `gpu`: Enable GPU acceleration abstractions
//! * `memory_management`: Enable advanced memory management
//! * `memory_efficient`: Enable memory-efficient array operations and views
//! * `array`: Enable scientific array types (``MaskedArray``, ``RecordArray``)
//! * `profiling`: Enable performance profiling tools
//! * `random`: Enable random number generation utilities
//! * `types`: Enable type conversion utilities
//! * `linalg`: Enable linear algebra with BLAS/LAPACK bindings
//! * `cloud`: Enable cloud storage integration (S3, GCS, Azure)
//! * `jit`: Enable just-in-time compilation with LLVM
//! * `ml_pipeline`: Enable ML pipeline integration and real-time processing
//! * `all`: Enable all features except backend-specific ones

// Re-export modules
pub mod api_freeze;
pub mod apiversioning;
#[cfg(feature = "array")]
pub mod array;
pub mod array_protocol;
#[cfg(feature = "types")]
pub mod batch_conversions;
#[cfg(feature = "cache")]
pub mod cache;
#[cfg(feature = "cloud")]
pub mod cloud;
pub mod config;
pub mod constants;
pub mod distributed;
pub mod ecosystem;
pub mod error;
#[cfg(feature = "gpu")]
pub mod gpu;
#[cfg(feature = "gpu")]
pub mod gpu_registry;
pub mod io;
#[cfg(feature = "jit")]
pub mod jit;
#[cfg(feature = "logging")]
pub mod logging;
#[cfg(feature = "memory_management")]
pub mod memory;
#[cfg(feature = "memory_efficient")]
pub mod memory_efficient;
pub mod metrics;
#[cfg(feature = "ml_pipeline")]
pub mod ml_pipeline;
pub mod ndarray_ext;
pub mod numeric;
#[cfg(feature = "parallel")]
pub mod parallel;
#[cfg(feature = "parallel")]
pub mod parallel_ops;
pub mod performance;
pub mod performance_optimization;
#[cfg(feature = "profiling")]
pub mod profiling;
#[cfg(feature = "random")]
pub mod random;
pub mod resource;
#[cfg(feature = "simd")]
pub mod simd;
pub mod simd_ops;
#[cfg(feature = "testing")]
pub mod testing;
// Universal Functions (ufuncs) module
pub mod error_templates;
pub mod safe_ops;
#[cfg(feature = "types")]
pub mod types;
#[cfg(feature = "ufuncs")]
pub mod ufuncs;
pub mod units;
pub mod utils;
pub mod validation;

// Production-level features for enterprise deployments
pub mod observability;
pub mod stability;
pub mod versioning;

// Advanced optimization and AI features
pub mod neural_architecture_search;
pub mod quantum_optimization;

// Advanced Mode Ecosystem Integration
pub mod advanced_ecosystem_integration;

// Advanced JIT Compilation Framework
pub mod advanced_jit_compilation;

// Advanced Distributed Computing Framework
pub mod advanced_distributed_computing;

// Advanced Cloud Storage Framework
// pub mod distributed_storage; // Module not implemented yet

// Advanced Tensor Cores and Automatic Kernel Tuning Framework
pub mod advanced_tensor_cores;

// Tensor cores optimization modules
#[cfg(feature = "gpu")]
pub mod tensor_cores;

// Benchmarking module
#[cfg(feature = "benchmarking")]
pub mod benchmarking;

// Re-exports
#[cfg(feature = "cache")]
pub use crate::cache::*;
#[cfg(feature = "cloud")]
pub use crate::cloud::{
    CloudConfig, CloudCredentials, CloudError, CloudObjectMetadata, CloudProvider,
    CloudStorageClient, EncryptionConfig, EncryptionMethod, HttpMethod, ListResult,
    TransferOptions,
};
pub use crate::config::production as config_production;
pub use crate::config::{
    get_config, get_config_value, set_config_value, set_global_config, Config, ConfigValue,
};
pub use crate::constants::{math, physical, prefixes};
pub use crate::error::*;
#[cfg(feature = "gpu")]
pub use crate::gpu::*;
pub use crate::io::*;
#[cfg(feature = "jit")]
pub use crate::jit::DataType as JitDataType;
#[cfg(feature = "jit")]
pub use crate::jit::{
    CompiledKernel, ExecutionProfile, JitBackend, JitCompiler, JitConfig, JitError, KernelLanguage,
    KernelSource, OptimizationLevel, TargetArchitecture,
};
#[cfg(feature = "logging")]
pub use crate::logging::*;
#[cfg(feature = "memory_management")]
pub use crate::memory::{
    format_memory_report, generate_memory_report, global_buffer_pool, track_allocation,
    track_deallocation, track_resize, BufferPool, ChunkProcessor, ChunkProcessor2D,
    GlobalBufferPool, ZeroCopyView,
};

#[cfg(feature = "leak_detection")]
pub use crate::memory::{
    LeakCheckGuard, LeakDetectionConfig, LeakDetector, LeakReport, LeakType, MemoryCheckpoint,
    MemoryLeak, ProfilerTool,
};

#[cfg(feature = "memory_efficient")]
pub use crate::memory_efficient::{
    chunk_wise_binary_op, chunk_wise_op, chunk_wise_reduce, create_disk_array, create_mmap,
    create_temp_mmap, diagonal_view, evaluate, load_chunks, open_mmap, register_fusion,
    transpose_view, view_as, view_mut_as, AccessMode, AdaptiveChunking, AdaptiveChunkingBuilder,
    AdaptiveChunkingParams, AdaptiveChunkingResult, ArithmeticOps, ArrayView, BroadcastOps,
    ChunkIter, ChunkedArray, ChunkingStrategy, DiskBackedArray, FusedOp, LazyArray, LazyOp,
    LazyOpKind, MemoryMappedArray, MemoryMappedChunkIter, MemoryMappedChunks, MemoryMappedSlice,
    MemoryMappedSlicing, OpFusion, OutOfCoreArray, ViewMut, ZeroCopyOps,
};

// Compression-related types are only available with the memory_compression feature
#[cfg(feature = "memory_compression")]
pub use crate::memory_efficient::{
    CompressedMemMapBuilder, CompressedMemMappedArray, CompressionAlgorithm,
};

// Re-export the parallel memory-mapped array capabilities
#[cfg(all(feature = "memory_efficient", feature = "parallel"))]
pub use crate::memory_efficient::MemoryMappedChunksParallel;

#[cfg(feature = "array")]
pub use crate::array::{
    is_masked, mask_array, masked_equal, masked_greater, masked_inside, masked_invalid,
    masked_less, masked_outside, masked_where, record_array_from_typed_arrays,
    record_array_fromrecords, ArrayError, FieldValue, MaskedArray, Record, RecordArray, NOMASK,
};

#[cfg(feature = "memory_metrics")]
pub use crate::memory::metrics::{
    clear_snapshots,
    compare_snapshots,
    // Utility functions
    format_bytes,
    format_duration,
    take_snapshot,
    MemoryEvent,
    MemoryEventType,
    // Core metrics types
    MemoryMetricsCollector,
    MemoryMetricsConfig,
    // Memory snapshots and leak detection
    MemorySnapshot,
    SnapshotDiff,
    // Tracked memory components
    TrackedBufferPool,
    TrackedChunkProcessor,
    TrackedChunkProcessor2D,
};

#[cfg(feature = "types")]
pub use crate::batch_conversions::{
    utils as batch_utils, BatchConversionConfig, BatchConversionResult, BatchConverter,
    ElementConversionError,
};
#[cfg(all(feature = "memory_metrics", feature = "gpu"))]
pub use crate::memory::metrics::{setup_gpu_memory_tracking, TrackedGpuBuffer, TrackedGpuContext};
pub use crate::metrics::{
    global_healthmonitor, global_metrics_registry, Counter, Gauge, HealthCheck, HealthMonitor,
    HealthStatus, Histogram, MetricPoint, MetricType, MetricValue, Timer,
};
#[cfg(feature = "ml_pipeline")]
pub use crate::ml_pipeline::DataType as MLDataType;
#[cfg(feature = "ml_pipeline")]
pub use crate::ml_pipeline::{
    DataBatch, DataSample, FeatureConstraint, FeatureSchema, FeatureTransformer, FeatureValue,
    MLPipeline, MLPipelineError, ModelPredictor, ModelType, PipelineConfig, PipelineMetrics,
    PipelineNode, TransformType,
};
pub use crate::numeric::*;
#[cfg(feature = "parallel")]
pub use crate::parallel::*;
#[cfg(feature = "parallel")]
pub use crate::parallel_ops::{
    is_parallel_enabled, num_threads, par_chunks, par_chunks_mut, par_join, par_scope,
};
// Re-export all parallel traits and types
#[cfg(feature = "parallel")]
pub use crate::parallel_ops::*;
#[cfg(feature = "profiling")]
pub use crate::profiling::{profiling_memory_tracker, Profiler};
#[cfg(feature = "random")]
pub use crate::random::*;
pub use crate::resource::{
    get_available_memory, get_performance_tier, get_recommended_chunk_size,
    get_recommended_thread_count, get_system_resources, get_total_memory, is_gpu_available,
    is_simd_supported, DiscoveryConfig, PerformanceTier, ResourceDiscovery, SystemResources,
};
#[cfg(feature = "simd")]
pub use crate::simd::*;
#[cfg(feature = "testing")]
pub use crate::testing::{TestConfig, TestResult, TestRunner, TestSuite};
#[cfg(feature = "types")]
pub use crate::types::{convert, ComplexConversionError, ComplexExt, ComplexOps};
pub use crate::units::{
    convert, global_unit_registry, unit_value, Dimension, UnitDefinition, UnitRegistry, UnitSystem,
    UnitValue,
};
pub use crate::utils::*;
pub use crate::validation::production as validation_production;
pub use crate::validation::{
    check_finite, check_in_bounds, check_positive, checkarray_finite, checkshape,
};

#[cfg(feature = "data_validation")]
pub use crate::validation::data::DataType as ValidationDataType;
#[cfg(feature = "data_validation")]
pub use crate::validation::data::{
    Constraint, FieldDefinition, ValidationConfig, ValidationError, ValidationResult,
    ValidationRule, ValidationSchema, Validator,
};

// Production-level feature re-exports
pub use crate::observability::{audit, tracing};
pub use crate::stability::{
    global_stability_manager, ApiContract, BreakingChange, BreakingChangeType, ConcurrencyContract,
    MemoryContract, NumericalContract, PerformanceContract, StabilityGuaranteeManager,
    StabilityLevel, UsageContext,
};
pub use crate::versioning::{
    compatibility, deprecation, migration, negotiation, semantic, ApiVersion, CompatibilityLevel,
    SupportStatus, Version, VersionManager,
};

// Advanced optimization and AI feature re-exports
pub use crate::neural_architecture_search::{
    ActivationType, Architecture, ArchitecturePerformance, ConnectionType, HardwareConstraints,
    LayerType, NASStrategy, NeuralArchitectureSearch, OptimizationObjectives, OptimizerType,
    SearchResults, SearchSpace,
};
pub use crate::quantum_optimization::{
    OptimizationResult, QuantumOptimizer, QuantumParameters, QuantumState, QuantumStrategy,
};

// Advanced JIT Compilation re-exports
// pub use crate::advanced_jit__compilation::{
//     AdaptiveCodeGenerator, CompilationStatistics, JitAnalytics, JitCompilerConfig, JitProfiler,
//     KernelCache, KernelMetadata, KernelPerformance, LlvmCompilationEngine, OptimizationResults,
//     PerformanceImprovement, RuntimeOptimizer, advancedJitCompiler,
// }; // Missing module

// Advanced Cloud Storage re-exports
// pub use crate::distributed_storage::{
//     AdaptiveStreamingEngine, CloudPerformanceAnalytics, CloudProviderConfig, CloudProviderId,
//     CloudProviderType, CloudSecurityManager, CloudStorageMonitoring, CloudStorageProvider,
//     DataOptimizationEngine, DownloadRequest, DownloadResponse, IntelligentCacheSystem,
//     ParallelTransferManager, StreamRequest, advancedCloudConfig,
//     advancedCloudStorageCoordinator, UploadRequest, UploadResponse,
// };

// Benchmarking re-exports
#[cfg(feature = "benchmarking")]
pub use crate::benchmarking::{
    BenchmarkConfig, BenchmarkMeasurement, BenchmarkResult, BenchmarkRunner, BenchmarkStatistics,
    BenchmarkSuite,
};

/// ``SciRS2`` core version information
pub const fn _version() -> &'static str {
    env!("CARGO_PKG_VERSION")
}

/// Initialize the library (called automatically)
#[doc(hidden)]
#[allow(dead_code)]
pub fn __init() {
    use std::sync::Once;
    static INIT: Once = Once::new();

    INIT.call_once(|| {
        // Initialize API freeze registry
        crate::api_freeze::initialize_api_freeze();
    });
}

// Ensure initialization happens
#[doc(hidden)]
#[used]
#[cfg_attr(target_os = "linux", link_section = ".init_array")]
#[cfg_attr(target_os = "macos", link_section = "__DATA,__mod_init_func")]
#[cfg_attr(target_os = "windows", link_section = ".CRT$XCU")]
static INIT: extern "C" fn() = {
    extern "C" fn __init_wrapper() {
        __init();
    }
    __init_wrapper
};

pub mod alpha6_api {
    //! Alpha 6 API consistency enhancements and comprehensive usage patterns
    //!
    //! This module provides standardized patterns, comprehensive examples,
    //! and integration guidelines for combining ``SciRS2`` Core features.

    pub mod signatures {
        //! Standard function signature patterns used throughout ``SciRS2`` Core
        //!
        //! All ``SciRS2`` Core functions follow these standardized patterns:
        //!
        //! ## Error Handling Pattern
        //! ```rust
        //! use scirs2_core::{CoreResult, CoreError, ErrorContext};
        //!
        //! fn example_operation<T>(input: T, scale: f64) -> CoreResult<f64>
        //! where
        //!     T: Into<f64>
        //! {
        //!     let value = input.into();
        //!     if scale.is_finite() && scale > 0.0 {
        //!         Ok(value * scale)
        //!     } else {
        //!         Err(CoreError::ValueError(ErrorContext::new("Scale must be positive and finite")))
        //!     }
        //! }
        //!
        //! # let result = example_operation(5.0, 2.0);
        //! # assert!(result.is_ok());
        //! ```
        //!
        //! ## Configuration Pattern
        //! ```rust
        //! #[derive(Debug, Clone)]
        //! pub struct ComputationConfig {
        //!     pub tolerance: f64,
        //!     pub max_iterations: usize,
        //!     pub parallel: bool,
        //! }
        //!
        //! impl Default for ComputationConfig {
        //!     fn default() -> Self {
        //!         Self {
        //!             tolerance: 1e-10,
        //!             max_iterations: 1000,
        //!             parallel: true,
        //!         }
        //!     }
        //! }
        //!
        //! impl ComputationConfig {
        //!     pub fn new() -> Self { Self::default() }
        //!     pub fn with_tolerance(mut self, value: f64) -> Self { self.tolerance = value; self }
        //!     pub fn with_max_iterations(mut self, value: usize) -> Self { self.max_iterations = value; self }
        //!     pub fn with_parallel(mut self, value: bool) -> Self { self.parallel = value; self }
        //! }
        //!
        //! # let config = ComputationConfig::new().with_tolerance(1e-8).with_parallel(false);
        //! # assert_eq!(config.tolerance, 1e-8);
        //! ```
        //!
        //! ## Resource Management Pattern
        //! ```rust
        //! use std::sync::{Arc, Mutex};
        //!
        //! #[derive(Debug, Clone)]
        //! pub struct ResourceConfig {
        //!     max_memory_mb: usize,
        //!     enable_caching: bool,
        //! }
        //!
        //! impl Default for ResourceConfig {
        //!     fn default() -> Self {
        //!         Self {
        //!             max_memory_mb: 1024,
        //!             enable_caching: true,
        //!         }
        //!     }
        //! }
        //!
        //! pub struct ResourceManager<T> {
        //!     inner: Arc<Mutex<T>>,
        //!     config: ResourceConfig,
        //! }
        //!
        //! impl<T> ResourceManager<T> {
        //!     pub fn new(resource: T, config: ResourceConfig) -> Self {
        //!         Self {
        //!             inner: Arc::new(Mutex::new(resource)),
        //!             config,
        //!         }
        //!     }
        //!     pub fn with_default_config(resource: T) -> Self {
        //!         Self::new(resource, ResourceConfig::default())
        //!     }
        //!     pub fn configure(&mut self, config: ResourceConfig) -> &mut Self {
        //!         self.config = config;
        //!         self
        //!     }
        //! }
        //!
        //! # let manager = ResourceManager::with_default_config(vec![1, 2, 3]);
        //! ```

        use crate::error::CoreResult;

        /// Standard parameter validation pattern
        pub trait ValidatedParams {
            /// Validate parameters and return detailed error information
            fn validate(&self) -> CoreResult<()>;
        }

        /// Standard configuration builder pattern
        pub trait ConfigBuilder<T> {
            /// Build the configuration with validation
            fn build(self) -> CoreResult<T>;
        }
    }

    pub mod examples {
        //! Comprehensive usage examples demonstrating ``SciRS2`` Core features
        //!
        //! These examples show how to use various ``SciRS2`` Core features both
        //! individually and in combination.

        /// # Basic Error Handling Example
        ///
        /// ```
        /// use scirs2_core::{CoreError, CoreResult, diagnoseerror};
        ///
        /// fn scientific_computation(data: &[f64]) -> CoreResult<f64> {
        ///     if data.is_empty() {
        ///         return Err(CoreError::ValidationError(
        ///             scirs2_core::error::ErrorContext::new("Input data cannot be empty")
        ///         ));
        ///     }
        ///     
        ///     // Perform computation...
        ///     Ok(data.iter().sum::<f64>() / data.len() as f64)
        /// }
        ///
        /// // Usage with enhanced error diagnostics
        /// match scientific_computation(&[]) {
        ///     Ok(result) => println!("Result: {}", result),
        ///     Err(error) => {
        ///         let diagnostics = diagnoseerror(&error);
        ///         println!("Error diagnosis:\n{}", diagnostics);
        ///     }
        /// }
        /// ```
        pub const fn basicerror_handling() {}

        /// # SIMD Operations Example
        ///
        /// ```
        /// # #[cfg(feature = "simd")]
        /// use scirs2_core::simd::simd_add_f32;
        /// use ndarray::arr1;
        ///
        /// # #[cfg(feature = "simd")]
        /// fn optimized_array_addition() {
        ///     let a = arr1(&[1.0f32, 2.0, 3.0, 4.0]);
        ///     let b = arr1(&[5.0f32, 6.0, 7.0, 8.0]);
        ///     
        ///     // SIMD implementation for f32 arrays
        ///     let result = simd_add_f32(&a.view(), &b.view());
        ///     println!("SIMD result: {:?}", result);
        /// }
        /// ```
        pub const fn simd_operations() {}

        /// # Memory-Efficient Processing Example
        ///
        /// ```
        /// # #[cfg(feature = "memory_efficient")]
        /// use scirs2_core::memory_efficient::{
        ///     AdaptiveChunkingParams, WorkloadType, MemoryMappedArray
        /// };
        /// use tempfile::NamedTempFile;
        ///
        /// # #[cfg(feature = "memory_efficient")]
        /// fn memory_efficient_processing() -> Result<(), Box<dyn std::error::Error>> {
        ///     // Create optimized chunking parameters
        ///     let params = AdaptiveChunkingParams::for_workload_type(WorkloadType::ComputeIntensive);
        ///     
        ///     // Create a temporary file for demonstration
        ///     let temp_file = NamedTempFile::new()?;
        ///     let data: Vec<f64> = (0..1000000).map(|i| i as f64).collect();
        ///     
        ///     // Memory-mapped processing for large datasets
        ///     // let mmap = MemoryMappedArray::<f64>::create_from_data(temp_file.path(), &data, &[1000000])?;
        ///     // let result = mmap.adaptive_chunking(params)?;
        ///     
        ///     println!("Adaptive chunking completed");
        ///     Ok(())
        /// }
        /// ```
        pub const fn memory_efficient_processing() {}
    }

    pub mod integration_patterns {
        //! Guidelines and patterns for combining multiple ``SciRS2`` Core features
        //!
        //! This module demonstrates how to effectively combine various features
        //! for optimal performance and functionality.

        /// # High-Performance Scientific Pipeline
        ///
        /// Combines SIMD operations, parallel processing, memory-efficient algorithms,
        /// and comprehensive error handling for optimal performance.
        ///
        /// ```
        /// use scirs2_core::{CoreResult, CoreError};
        /// # #[cfg(all(feature = "simd", feature = "parallel", feature = "memory_efficient"))]
        /// use scirs2_core::{
        ///     memory_efficient::AdaptiveChunkingParams,
        /// };
        ///
        /// /// High-performance pipeline configuration
        /// pub struct ScientificPipelineConfig {
        ///     pub use_simd: bool,
        ///     pub use_parallel: bool,
        ///     pub workload_type: Option<String>, // Would be WorkloadType in real usage
        ///     pub error_tracking: bool,
        /// }
        ///
        /// impl Default for ScientificPipelineConfig {
        ///     fn default() -> Self {
        ///         Self {
        ///             use_simd: true,
        ///             use_parallel: true,
        ///             workload_type: Some("ComputeIntensive".to_string()),
        ///             error_tracking: true,
        ///         }
        ///     }
        /// }
        ///
        /// /// High-performance scientific computation pipeline
        /// pub fn scientific_pipeline<T>(
        ///     data: &[T],
        ///     config: ScientificPipelineConfig,
        /// ) -> CoreResult<Vec<T>>
        /// where
        ///     T: Clone + Copy + Send + Sync + 'static,
        /// {
        ///     // Input validation
        ///     if data.is_empty() {
        ///         let error = CoreError::ValidationError(
        ///             scirs2_core::error::ErrorContext::new("Input data cannot be empty")
        ///         );
        ///         
        ///         if config.error_tracking {
        ///             // Error tracking would be implemented here
        ///             eprintln!("Error in scientificpipeline: {:?}", error);
        ///         }
        ///         
        ///         return Err(error);
        ///     }
        ///
        ///     // Configure adaptive chunking based on workload
        /// #   #[cfg(feature = "memory_efficient")]
        ///     let chunking_params = if let Some(workload_str) = &config.workload_type {
        ///         // AdaptiveChunkingParams::for_workload_type(WorkloadType::ComputeIntensive)
        ///         scirs2_core::memory_efficient::AdaptiveChunkingParams::default()
        ///     } else {
        ///         scirs2_core::memory_efficient::AdaptiveChunkingParams::default()
        ///     };
        ///
        ///     // Process data with selected optimizations
        ///     let mut result = Vec::with_capacity(data.len());
        ///     
        ///     // Simulate processing (in real implementation, would use SIMD/parallel features)
        ///     for item in data {
        ///         result.push(*item);
        ///     }
        ///
        ///     Ok(result)
        /// }
        /// ```
        pub const fn high_performance_pipeline() {}

        /// # Robust Error Handling with Recovery
        ///
        /// Demonstrates advanced error handling with automatic recovery strategies
        /// and comprehensive diagnostics.
        ///
        /// ```
        /// use scirs2_core::{CoreResult, CoreError};
        /// use scirs2_core::error::{
        ///     diagnoseerror_advanced, get_recovery_strategies, RecoverableError
        /// };
        ///
        /// /// Robust computation with automatic error recovery
        /// pub fn robust_computation(
        ///     data: &[f64],
        ///     domain: &str,
        /// ) -> CoreResult<f64> {
        ///     // Attempt computation
        ///     match perform_computation(data) {
        ///         Ok(result) => Ok(result),
        ///         Err(error) => {
        ///             // Get comprehensive diagnostics
        ///             let diagnostics = diagnoseerror_advanced(
        ///                 &error,
        ///                 Some("matrix_computation"),
        ///                 Some(domain)
        ///             );
        ///             
        ///             // Get domain-specific recovery strategies
        ///             let strategies = get_recovery_strategies(&error, domain);
        ///             
        ///             // Try recovery strategies
        ///             for strategy in &strategies {
        ///                 if let Ok(result) = try_recovery_strategy(data, strategy) {
        ///                     return Ok(result);
        ///                 }
        ///             }
        ///             
        ///             // If all recovery attempts fail, return enhanced error
        ///             Err(error)
        ///         }
        ///     }
        /// }
        ///
        /// fn perform_computation(data: &[f64]) -> CoreResult<f64> {
        ///     // Simulate computation that might fail
        ///     if data.len() < 2 {
        ///         return Err(CoreError::DomainError(
        ///             scirs2_core::error::ErrorContext::new("Insufficient data for computation")
        ///         ));
        ///     }
        ///     Ok(data.iter().sum::<f64>() / data.len() as f64)
        /// }
        ///
        /// fn try_recovery_strategy(data: &[f64], strategy: &str) -> CoreResult<f64> {
        ///     // Implement recovery strategy based on suggestion
        ///     if strategy.contains("default") {
        ///         Ok(0.0) // Return safe default
        ///     } else {
        ///         Err(CoreError::ComputationError(
        ///             scirs2_core::error::ErrorContext::new("Recovery failed")
        ///         ))
        ///     }
        /// }
        /// ```
        pub const fn robusterror_handling() {}

        /// # Performance Monitoring and Optimization
        ///
        /// Shows how to combine profiling, memory tracking, and adaptive optimization
        /// for continuous performance improvement.
        ///
        /// ```
        /// use scirs2_core::CoreResult;
        /// # #[cfg(feature = "profiling")]
        /// use scirs2_core::{Profiler, Timer};
        /// use std::time::Duration;
        ///
        /// /// Performance-monitored computation wrapper
        /// pub struct MonitoredComputation {
        ///     name: String,
        ///     # #[cfg(feature = "profiling")]
        ///     profiler: Option<Profiler>,
        /// }
        ///
        /// impl MonitoredComputation {
        ///     pub fn new(name: &str) -> Self {
        ///         Self {
        ///             name: name.to_string(),
        ///             # #[cfg(feature = "profiling")]
        ///             profiler: Some(Profiler::new()),
        ///         }
        ///     }
        ///
        ///     pub fn execute<F, T>(&mut self, operation: F) -> CoreResult<T>
        ///     where
        ///         F: FnOnce() -> CoreResult<T>,
        ///     {
        ///         # #[cfg(feature = "profiling")]
        ///         let timer = Timer::new(self.name.clone());
        ///         # #[cfg(feature = "profiling")]
        ///         let timer_guard = timer.start();
        ///         
        ///         let result = operation()?;
        ///         
        ///         # #[cfg(feature = "profiling")]
        ///         if let Some(profiler) = &self.profiler {
        ///             // Log performance metrics
        ///             println!("Operation '{}' completed", self.name);
        ///         }
        ///         
        ///         Ok(result)
        ///     }
        /// }
        /// ```
        pub const fn performancemonitoring() {}
    }

    pub mod type_system {
        //! Comprehensive type system documentation and conversion patterns
        //!
        //! This module documents the ``SciRS2`` Core type system and provides
        //! guidelines for safe and efficient type conversions.

        /// # Type Safety Patterns
        ///
        /// ``SciRS2`` Core uses the Rust type system to ensure safety and correctness:
        ///
        /// ## Numeric Type Safety
        /// ```
        /// use scirs2_core::numeric::{RealNumber, ScientificNumber};
        ///
        /// fn safe_numeric_operation<T>(value: T) -> T
        /// where
        ///     T: RealNumber + ScientificNumber,
        /// {
        ///     // Type-safe operations guaranteed by trait bounds
        ///     ScientificNumber::sqrt(ScientificNumber::abs(value))
        /// }
        /// ```
        ///
        /// ## Error Type Safety
        /// ```
        /// use scirs2_core::{CoreResult, CoreError};
        ///
        /// fn type_safeerror_handling() -> CoreResult<f64> {
        ///     // All errors are properly typed and provide context
        ///     Err(CoreError::ValidationError(
        ///         scirs2_core::error::ErrorContext::new("Type validation failed")
        ///     ))
        /// }
        /// ```
        ///
        /// ## Generic Parameter Patterns
        /// ```
        /// use scirs2_core::CoreResult;
        ///
        /// // Standard generic function pattern
        /// fn generic_operation<T, U>(input: T) -> CoreResult<U>
        /// where
        ///     T: Clone + Send + Sync,
        ///     U: Default + Send + Sync,
        /// {
        ///     // Implementation...
        ///     Ok(U::default())
        /// }
        /// ```
        pub const fn type_safety_patterns() {}
    }
}