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#scientific-computing

10 packages tagged with “scientific-computing”

pkg

Linear.Net

Pure C# linear algebra and scientific computing library. Supports dense and sparse vector, matrix and general n-th order tensors over both primitive and custom types. 11 matrix storage types supported. Mathematical computing package includes BigDecimal, BigComplex and BigRational implementations. Targetting .NET framework 4.5 and higher, and .NET Core 3.0 and higher.

v1.0.0.5↙ 6.6K
Linear-algebraScientific-computingMathematics

Dew.Math.Linux

Dew.Math.Linux provides the high-performance numerical computation capabilities of Dew.Math, but with native acceleration binaries compiled for Linux. It is designed for compute clusters, scientific servers, containerized deployment environments, and performance-critical Linux workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS/LAPACK): decomposition and eigenvalue routines optimized for AVX/AVX-512 - Sparse matrix operations with Pardiso and UMFPACK direct solvers and Krylov-based iterative solvers - Complex-valued linear algebra and spectral computations - Polynomial operations, splines, interpolation, approximate function models, and spectral transforms - Probability distributions, stochastic simulation, and random number engines - Special function library suitable for numerical physics, statistics, and differential systems - Optimization algorithms for non-linear fitting, gradient models, linear programming, and statistical inference Performance Architecture: - Linux-native accelerated BLAS/LAPACK libraries - Multithreaded vectorized math with CPU feature dispatch (AVX/AVX2/AVX-512) - Low-overhead memory allocator for stable scaling under parallel workloads - Optional OpenCL GPU integration for Linux compute environments Platform Model: - Contains Linux native runtime binaries - For Windows native acceleration use: Dew.Math - For portable managed-only computation use: Dew.Math.Core Use Dew.Math.Linux for Linux HPC compute nodes, microservice model engines, AI research pipelines, and scalable distributed scientific processing.

v6.3.10↙ 37.4K
dotnetcsharplinuxnativenative-acceleration

Dew.Math

Dew.Math is the Windows-optimized high-performance numerical computation library for .NET. It provides a vectorized matrix and vector math environment with native runtime acceleration, multithreaded execution, and extensive algorithm libraries for scientific, engineering, financial, AI/ML and signal processing workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS, LAPACK): SVD, QR, LQ, LU, eigenvalue problems, least-squares, rank reveals - Sparse matrix support: direct solvers (Pardiso, UMFPACK), iterative solvers (CG, BiCG, GMRES), preconditioning strategies, structured sparse formats - Complex number computation with fully vectorized math operations - Polynomial arithmetic, interpolation, splines, rational approximations, Chebyshev basis transforms - Numerical differentiation, root solving, non-linear systems, ODE support for stiff and non-stiff cases - Probability distributions (over 30 families), random number generators, Monte Carlo methods - Special mathematical functions (Airy, Bessel, Gamma-related, elliptic integrals, Legendre, etc.) Optimization and Modeling: - Non-linear curve fitting with Levenberg-Marquardt and trust-region refinements - Direct and constrained optimization (Simplex/Nelder–Mead, BFGS, Conjugate Gradient, LP, dual-phase simplex, Gomory cutting plane) - Vectorized expression parser for dynamic formula construction and symbolic-style evaluation Performance Architecture: - Native accelerated BLAS/LAPACK kernels with automatic CPU dispatch (AVX, AVX2, AVX-512) - Scalable multithreading with a lock-free memory allocator for low-GC overhead - Optional OpenCL GPU offloading for supported device targets Platform Model: - Contains Windows native acceleration binaries - For Linux native acceleration use: Dew.Math.Linux - For a pure managed, portable edition use: Dew.Math.Core Use Dew.Math when you require **maximum numerical performance on Windows** for HPC, simulation, economic modeling, data analytics, or scientific visualization workflows.

v6.3.10↙ 54.8K
dotnetcsharpwindowslinuxmacos
pkg

FpZip.Net

Pure managed C# implementation of the FPZip floating-point compression algorithm. Provides lossless compression for multi-dimensional float and double arrays, optimized for scientific and numerical data with high spatial correlation.

v1.0.1↙ 203
compressionfpzipfloating-pointscientific-computingnumerical-data

Dew.Lab.Studio

Dew Lab Studio bundles the Dew.Math, Dew.Signal, and Dew.Stats libraries into a unified package suitable for scientific, engineering, financial, and real-time signal-processing applications. It includes full numerical computation, vectorized signal analysis and filtering, statistical modeling, probability distributions, spectral transforms, optimization, and advanced data workflows. This package also integrates visualization via Dew.Math.TeePro, Dew.Signal.Tee, and Dew.Stats.Tee, which extend TeeChart for high-performance plotting of large datasets, spectrograms, signal traces, matrices, probability distributions, and live streaming data. No hard WinForms linking is introduced into your own code; the visualization libraries depend on WinForms internally while allowing you to use WinForms, WPF, Avalonia, or custom UI frameworks. Included Components: - Dew.Math (Windows native-accelerated numerical computation) - Dew.Signal (real-time DSP, filtering, transforms, spectral and streaming signal analysis) - Dew.Stats (probability distributions, statistical modeling, hypothesis testing, inference) - Dew.Math.TeePro (high-speed charting extensions for numerical data) - Dew.Signal.Tee (spectral / time-frequency / oscilloscope plotting extensions) - Dew.Stats.Tee (statistical visualization and histogramming helpers) Usage Model: - Use Dew Lab Studio for Windows desktop and server applications requiring interactive visualization, scientific and engineering debugging workflows, data interpretation, or real-time signal monitoring. - For Linux HPC systems: use Dew.Lab.Studio.Linux (accelerated native Linux builds). - For maximum cross-platform portability: use Dew.Lab.Studio.Core (managed-only .Core builds). Platform Notes: - Dew.*.Tee packages depend on WinForms internally. Therefore, projects targeting net8.0-windows or net9.0-windows must enable the Windows Desktop framework (WinForms).

v6.3.10↙ 6.4K
dotnetcsharpwindowsmathsignal-processing

Dew.Signal.Linux

Dew.Signal.Linux is the Linux-native accelerated edition of the Dew.Signal digital signal processing library. It provides a full suite of DSP algorithms built on top of Dew.Math.Linux, delivering high-performance numerical processing with multithreaded AVX/AVX2/AVX-512 hardware acceleration. This package is intended for scientific servers, compute clusters, HPC pipelines, digital instrumentation, real-time data acquisition, industrial analytics, embedded Linux platforms, and cloud CPU workloads. Filter Design and Processing: - IIR filters: Butterworth, Chebyshev I/II, Elliptic, Bessel - Transformations: bilinear, matched-Z, frequency remapping, pole-zero and state-space formulations - FIR filters: window methods, Remez exchange, Hilbert transformers, differentiators, integrators, Savitzky–Golay smoothers, envelope detection - Multirate DSP: decimation, interpolation, half-band polyphase filters, zoom-spectrum workflows Spectral and Frequency-Domain Analysis: - FFT-based spectral estimation and spectrum analyzer infrastructure - Parametric estimators: Yule–Walker, Burg, Covariance, Modified Covariance - Chirp-Z transform, time-frequency spectrograms, bispectrum, bicoherence, coherence, transfer function estimation, phase unwrapping - Real/complex cepstrum and inverse cepstrum - Spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR Signal Modeling, Streaming, and Synthesis: - White, pink, brownian, blue, violet and deterministic test signal generators - Continuous streaming components and dataflow processing units for real-time measurement systems - High-performance convolution, correlation, DCT/IDCT, interpolation and filtering kernels - Spectral forecasting based on controlled peak selection Integration and Platform Model: - Uses Dew.Math.Linux for native-accelerated numerical backend - Does **not** require WinForms or TeeChart (visualization is optional and external) - Suitable for server, embedded, batch compute, containerized, and headless execution Dew.Signal.Linux provides the same API surface as Dew.Signal, but is optimized specifically for Linux compute environments where high throughput and deterministic performance are required.

v6.3.10↙ 9.6K
dotnetlinuxdspsignal-processingfiltering

Dew.Signal.Core

Dew.Signal.Core is the cross-platform, managed-only edition of the Dew.Signal DSP library. It provides a comprehensive suite of digital signal processing algorithms and components without requiring native binary acceleration. This maximizes compatibility across platforms including Windows, Linux, macOS, mobile, embedded, and WebAssembly environments. Filter Design and Processing: - IIR filters: Butterworth, Chebyshev I/II, Elliptic, Bessel; analog and discrete design workflows - Frequency transformations in Z- and S-domains, bilinear and matched-Z transforms, pole/zero and state-space forms, group delay analysis, stability filters - FIR filter design: window methods, Remez exchange algorithm, Hilbert transformers, differentiators, integrators, Savitzky–Golay filtering, fast envelope detectors - Multi-rate filtering: half-band multistage FIR designs, zoom-spectrum, decimation, interpolation Nonlinear Processing: - Sample-and-hold, sample-and-decay, and median filtering Spectral Analysis and Estimation: - FFT-based spectral analysis framework - Parametric estimators: Yule–Walker, Burg, Covariance, Modified Covariance - CZT, real and complex cepstrum and inverse cepstrum - Bispectrum, bicoherence, coherence and transfer function estimation - Peak detection, peak interpolation and tracking, phase unwrapping - Advanced spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR Time-Domain Modeling and System Representation: - Conversion between state-space, zero-pole, numerator–denominator transfer models - System zero/pole determination and stability analysis Signal Synthesis, Streaming, and Measurement: - Signal generators using stack-based vectorized function evaluation - Support for multiple noise models: white, pink, brownian, blue, violet - Components for continuous streaming, recording, playback, triggering and monitoring - Scalable FIR and IIR convolution, auto-correlation, cross-correlation, DCT/IDCT Forecasting and Time-Series Modeling: - Spectral forecasting based on user-defined peak selection for interpretable predictive modeling Platform and Integration Notes: - Uses Dew.Math.Core as its numerical foundation (managed vector/matrix computing) - Visualization is optionally provided through Dew.Signal.Tee (Windows-only), not included here - Compatible with cross-platform UI frameworks (MAUI, Uno, Avalonia), compute services, and WASM Dew.Signal.Core is ideal when portability, sandbox safety, or plugin isolation is required.

v6.3.10↙ 6.8K
dotnetcsharpcross-platformdspsignal-processing

Dew.Lab.Studio.Linux

Dew.Lab.Studio.Linux bundles the native-accelerated Linux editions of the Dew libraries: Dew.Math.Linux, Dew.Signal.Linux, and Dew.Stats.Linux. This package is intended for Linux desktops, servers, HPC clusters, and container-based compute environments requiring high numerical throughput and scalable parallel execution. Included Components: - Dew.Math.Linux: dense and sparse linear algebra (BLAS/LAPACK, Pardiso, UMFPACK), complex arithmetic, interpolation and splines, spectral and polynomial transforms, optimization, curve fitting, special function library, numerical integration/differentiation - Dew.Signal.Linux: FIR/IIR filtering, FFT and spectral analysis, convolution/correlation, windowing, resampling, time-frequency transforms, streaming-friendly DSP operations - Dew.Stats.Linux: probability distributions, random sampling, inference tests, regressions, statistical modeling and simulation workflows Performance Architecture: - Native-accelerated BLAS/LAPACK for Linux - CPU feature dispatch (AVX / AVX2 / AVX-512) - Scalable multithreading with low-GC memory allocator - Optional OpenCL GPU acceleration when available Use Dew.Lab.Studio.Linux when you require a unified math + DSP + statistics environment on Linux, with full native acceleration and container/HPC-friendly runtime deployment.

v6.3.10↙ 6.0K
dotnetcsharplinuxnativenative-acceleration
pkg

ZiggyMath

ZiggyMath is a comprehensive mathematics library optimized for high-performance computing with: 🚀 Performance Features: - 5-29x faster memory operations through SIMD acceleration - Zero GC pressure design for real-time applications - Hardware acceleration with AVX2 and ARM64 NEON support - Intelligent workload-aware memory allocation 🧮 Mathematical Coverage: - Linear Algebra: Gaussian elimination, LU/QR decomposition, eigenvalue solvers - Signal Processing: FFT, convolution, filtering with hardware acceleration - Statistics: Regression, probability distributions, descriptive statistics - Calculus: Numerical integration, optimization, differentiation - SIMD Operations: Vectorized mathematical functions 🏗️ Advanced Features: - MathComputationContext for workload optimization - Comprehensive performance monitoring and benchmarking - Matrix layout optimization for cache efficiency - Advanced memory pooling strategies Perfect for scientific computing, data analysis, machine learning, and real-time systems requiring high-performance mathematical operations.

v3.0.0↙ 226
mathematicsmathlinear-algebrasignal-processingstatistics
pkg

cscompress

CsCompress is a .NET library for compressing and decompressing arrays of floating-point numbers (float and double) with various precision levels, efficient SIMD optimizations, and a fluent API for easy integration.

v1.0.2↙ 615
compressionfloating-pointquantization.NETC#