Crate scirs2_io 

SciRS2 IO - Scientific Data Input/Output

scirs2-io provides comprehensive file I/O capabilities for scientific computing, supporting MATLAB, NetCDF, HDF5, CSV, WAV, image formats, and more, with streaming, compression, async support, and database connectivity.

🎯 Key Features

• SciPy Compatibility: Similar to scipy.io for MATLAB, WAV, ARFF files
• Multiple Formats: MATLAB (.mat), NetCDF, HDF5, CSV, WAV, images (PNG, JPEG, TIFF)
• Matrix Market: Sparse matrix exchange format
• Streaming I/O: Memory-efficient reading/writing of large datasets
• Compression: GZIP, ZSTD, LZ4, BZIP2 for data compression
• Async I/O: Non-blocking operations with tokio
• Database: SQL/NoSQL connectivity (PostgreSQL, MongoDB, InfluxDB)

📦 Module Overview

SciRS2 Module	SciPy Equivalent	Description
matlab	scipy.io.loadmat, savemat	MATLAB .mat file I/O
wavfile	scipy.io.wavfile	WAV audio file I/O
netcdf	scipy.io.netcdf	NetCDF scientific data format
matrix_market	scipy.io.mmread, mmwrite	Matrix Market sparse format
csv	-	CSV with type conversion
image	-	PNG, JPEG, BMP, TIFF image I/O

🚀 Quick Start

[dependencies]
scirs2-io = "0.4.1"

use scirs2_io::csv::{read_csv, CsvReaderConfig};

// Read CSV file
let config = CsvReaderConfig {
    has_header: true,
    delimiter: ',',
    ..Default::default()
};
let (headers, data) = read_csv("data.csv", Some(config)).unwrap();

🔒 Version: 0.4.1 (March 27, 2026)

Modules

• arff: Support for ARFF (Attribute-Relation File Format) files
• compression: Utilities for data compression and decompression
• csv: Support for CSV (Comma-Separated Values) files
• image: Support for image file formats (PNG, JPEG, BMP, TIFF)
• matlab: Support for MATLAB (.mat) files
• matrix_market: Support for Matrix Market sparse and dense matrix files
• netcdf: Support for NetCDF scientific data files
• serialize: Utilities for data serialization and deserialization
• validation: Utilities for data validation and integrity checking
• wavfile: Support for WAV audio files
• error: Error types for the IO module
• fortran: Support for Fortran unformatted files

Re-exports

pub use advanced_coordinator::AdaptiveImprovements;

pub use advanced_coordinator::AdvancedCoordinator;

pub use advanced_coordinator::AdvancedStatistics;

pub use advanced_coordinator::IntelligenceLevel;

pub use advanced_coordinator::PerformanceIntelligenceStats;

pub use advanced_coordinator::ProcessingResult;

pub use advanced_coordinator::QualityMetrics;

pub use advanced_coordinator::StrategyType;

pub use enhanced_algorithms::AdvancedPatternAnalysis;

pub use enhanced_algorithms::AdvancedPatternRecognizer;

pub use enhanced_algorithms::DataCharacteristics;

pub use enhanced_algorithms::EmergentPattern;

pub use enhanced_algorithms::MetaPattern;

pub use enhanced_algorithms::OptimizationRecommendation;

pub use enhanced_algorithms::SynergyType;

pub use arff::read_arff;

pub use arff::write_arff;

pub use arff::ArffData;

pub use arff::ArffValue;

pub use arff::AttributeType;

pub use arff::SparseArffData;

pub use arff::SparseInstance;

pub use bmp::read_bmp;

pub use bmp::write_bmp;

pub use bmp::BmpImage;

pub use columnar::filter_table;

pub use columnar::read_columnar;

pub use columnar::read_columnar_with_columns;

pub use columnar::select_columns;

pub use columnar::split_into_row_groups;

pub use columnar::write_columnar;

pub use columnar::write_columnar_with_options;

pub use columnar::Column;

pub use columnar::ColumnData;

pub use columnar::ColumnStats;

pub use columnar::ColumnTypeTag;

pub use columnar::ColumnarTable;

pub use columnar::ColumnarWriteOptions;

pub use columnar::EncodingType;

pub use columnar::Predicate;

pub use columnar::RowGroup;

pub use columnar::RowGroupConfig;

pub use columnar::TableStats;

pub use netcdf_lite::NcDataType;

pub use netcdf_lite::NcDimension;

pub use netcdf_lite::NcFile;

pub use netcdf_lite::NcValue;

pub use netcdf_lite::NcVariable;

pub use npy::read_npy;

pub use npy::read_npz;

pub use npy::write_npy;

pub use npy::write_npy_f32;

pub use npy::write_npy_f64;

pub use npy::write_npy_f64_2d;

pub use npy::write_npy_i32;

pub use npy::write_npy_i64;

pub use npy::write_npz;

pub use npy::ByteOrder;

pub use npy::NpyArray;

pub use npy::NpyDtype;

pub use npy::NpyHeader;

pub use npy::NpzArchive;

pub use wavfile::read_wav;

pub use wavfile::write_wav;

pub use wavfile::write_wav_config;

pub use wavfile::WavFormat;

pub use wavfile::WavHeader;

pub use wavfile::WavOutputFormat;

pub use wavfile::WavWriteConfig;

Modules

adaptive_compression

Adaptive compression — automatically selects the best compression algorithm based on data characteristics profiled at runtime.

advanced_coordinator

Advanced Mode Coordinator - Unified Intelligence for I/O Operations

arff

ARFF (Attribute-Relation File Format) handling module

async_io

Async I/O support for streaming capabilities

bmp

Pure Rust BMP image file format (24-bit uncompressed)

columnar

Pure Rust columnar storage format

compression

Data compression module

csv

CSV (Comma-Separated Values) file format module

database

Database connectivity

delta

Delta Lake log-based table format reader and writer.

distributed

Distributed I/O processing

enhanced_algorithms

Enhanced algorithms for Advanced Mode

error

Error types for the IO module

formats

Domain-specific file formats

fortran

Fortran unformatted file format module

gpu

GPU-accelerated I/O operations

harwell_boeing

Harwell-Boeing sparse matrix format module

hdf5

HDF5 file format module

idl

IDL (Interactive Data Language) save file format module

image

Image file format module

jsonl

JSON Lines (NDJSON) format support

lance

Lance-inspired columnar storage format (pure Rust, in-process).

matlab

MATLAB file format (.mat) handling module

matrix_market

Matrix Market file format module

metadata

Advanced metadata management

ml_framework

Machine learning framework compatibility

mmap

Data pipeline APIs

mqtt_broker

In-process MQTT-style topic-based message broker.

netcdf

NetCDF file format module

netcdf_lite

Pure Rust NetCDF Classic format reader/writer

network

Network I/O and cloud storage integration

neural_adaptive_io

Neural-adaptive I/O optimization with advanced-level intelligence

npy

NumPy NPY/NPZ binary file format support

out_of_core

Out-of-core processing for terabyte-scale datasets

parquet

Apache Parquet columnar file format module

pipeline

Data pipeline APIs

quantum_inspired_io

Quantum-inspired I/O processing algorithms with advanced capabilities

realtime

Real-time data streaming protocols

schema_registry

Protocol Buffer schema registry for scirs2-io.

serialize

Data serialization utilities

simd_io

SIMD-accelerated I/O operations

sparse

Comprehensive sparse matrix format support

streaming

Streaming and iterator interfaces for large data handling

table_provider

DataFusion-compatible table provider interface.

tensors

Tensor serialization formats: SafeTensors, ONNX proto I/O, TFRecord

thread_pool

Thread pool for parallel I/O operations

tiledb

TileDB-inspired multidimensional array storage.

validation

Data validation and integrity checking module

visualization

Visualization tool integration

wavfile

WAV file format handling module

workflow

Workflow automation tools

zero_copy

Zero-copy I/O optimizations

Macros

pipeline

Pipeline configuration DSL for easier pipeline creation

stage

Stage creation DSL