scirs2-interpolate 0.1.1

Interpolation module for SciRS2 (scirs2-interpolate)
Documentation

SciRS2 Interpolation Module

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The scirs2-interpolate crate provides a production-ready, comprehensive set of interpolation methods for scientific computing in Rust. As part of the SciRS2 project, it delivers functionality equivalent to SciPy's interpolation module while leveraging Rust's performance, safety, and parallelization capabilities.

🚀 Version 0.1.0 - Production stable Release

This Stable Release (stable) represents a production-ready implementation with comprehensive feature coverage, extensive testing, performance optimization, and zero-warning code quality. The API is stable and ready for production use cases.

🚀 Installation

Add this to your Cargo.toml:

[dependencies]
scirs2-interpolate = "0.1.0"

# Optional: Enable high-performance features
scirs2-interpolate = { version = "0.1.0", features = ["simd", "linalg"] }

Feature Flags

  • simd: Enable SIMD acceleration (2-4x performance boost)
  • linalg: Enable advanced linear algebra operations (requires OpenBLAS)
  • gpu: Enable GPU acceleration for large datasets (experimental)
# Install with all performance features
cargo add scirs2-interpolate --features simd,linalg

✨ Features

Core Interpolation Methods

  • 1D Interpolation: Linear, nearest neighbor, cubic, and spline interpolation
  • Multi-dimensional Interpolation: Regular grid and scattered data interpolation
  • Advanced Methods: RBF, Kriging, barycentric, natural neighbor, moving least squares

Comprehensive Spline Support

  • Basic Splines: Natural cubic, not-a-knot, clamped, periodic boundary conditions
  • Advanced Splines: Akima (outlier-robust), PCHIP (shape-preserving), NURBS
  • Specialized Splines: Penalized (P-splines), constrained (monotonic/convex), tension splines
  • High-Performance: Multiscale B-splines, Hermite splines, Bezier curves/surfaces

High-Performance Computing

  • SIMD Acceleration: Vectorized B-spline evaluation and distance calculations
  • Parallel Processing: Multi-threaded interpolation for large datasets
  • GPU Acceleration: CUDA-accelerated RBF and batch evaluation (optional)
  • Memory Efficiency: Cache-aware algorithms and optimized data structures

Advanced Statistical Methods

  • Fast Kriging: O(k³) local kriging, fixed-rank approximation, sparse tapering
  • Uncertainty Quantification: Bayesian kriging with prediction intervals
  • Adaptive Methods: Error-based refinement, hierarchical interpolation
  • Machine Learning Integration: Neural-enhanced interpolation, physics-informed models

Production-Ready Features

  • Robust Error Handling: Comprehensive error types and validation
  • Feature Gates: Optional dependencies (SIMD, GPU, advanced linalg)
  • Extensive Testing: 100+ unit tests, property-based testing, numerical validation
  • Performance Benchmarks: Comprehensive benchmarking suite against SciPy
  • Documentation: Complete API documentation with 35+ working examples

Usage Examples

1D Interpolation

use ndarray::array;
use scirs2_interpolate::{Interp1d, InterpolationMethod};

// Create sample data
let x = array![0.0, 1.0, 2.0, 3.0, 4.0];
let y = array![0.0, 1.0, 4.0, 9.0, 16.0];

// Create a 1D interpolator with linear interpolation
let interp = Interp1d::new(&x.view(), &y.view(), InterpolationMethod::Linear).unwrap();

// Evaluate at a specific point
let y_interp = interp.evaluate(2.5).unwrap();
println!("Interpolated value at x=2.5: {}", y_interp);

// Evaluate at multiple points
let x_new = array![1.5, 2.5, 3.5];
let y_new = interp.evaluate_array(&x_new.view()).unwrap();
println!("Interpolated values: {:?}", y_new);

Cubic Spline Interpolation

use ndarray::array;
use scirs2_interpolate::{CubicSpline, make_interp_spline};

// Create sample data
let x = array![0.0, 1.0, 2.0, 3.0, 4.0];
let y = array![0.0, 1.0, 4.0, 9.0, 16.0];

// Create a cubic spline
let spline = make_interp_spline(&x.view(), &y.view()).unwrap();

// Evaluate at a specific point
let y_interp = spline.evaluate(2.5).unwrap();
println!("Spline interpolated value at x=2.5: {}", y_interp);

// Compute the derivative
let y_prime = spline.derivative(2.5).unwrap();
println!("Spline derivative at x=2.5: {}", y_prime);

Multidimensional Regular Grid Interpolation

use ndarray::{array, Array2};
use scirs2_interpolate::{make_interp_nd, InterpolationMethod};

// Create sample grid coordinates
let x = array![0.0, 1.0, 2.0];
let y = array![0.0, 1.0, 2.0];

// Create 2D grid values (z = x^2 + y^2)
let mut grid_values = Array2::zeros((3, 3));
for i in 0..3 {
    for j in 0..3 {
        grid_values[[i, j]] = x[i].powi(2) + y[j].powi(2);
    }
}

// Create interpolator
let interp = make_interp_nd(
    &[x, y],
    &grid_values.view(),
    InterpolationMethod::Linear
).unwrap();

// Points to evaluate
let points = Array2::from_shape_vec((2, 2), vec![
    0.5, 0.5,
    1.5, 1.5
]).unwrap();

// Interpolate
let results = interp.evaluate(&points.view()).unwrap();
println!("Interpolated values: {:?}", results);

Radial Basis Function Interpolation

use ndarray::{array, Array2};
use scirs2_interpolate::{RBFInterpolator, RBFKernel};

// Create scattered data points
let points = Array2::from_shape_vec((5, 2), vec![
    0.0, 0.0, 
    1.0, 0.0, 
    0.0, 1.0, 
    1.0, 1.0, 
    0.5, 0.5
]).unwrap();

// Values at those points (z = x^2 + y^2)
let values = array![0.0, 1.0, 1.0, 2.0, 0.5];

// Create RBF interpolator with Gaussian kernel
let interp = RBFInterpolator::new(
    &points.view(),
    &values.view(),
    RBFKernel::Gaussian,
    1.0  // epsilon parameter
).unwrap();

// Interpolate at new points
let test_points = Array2::from_shape_vec((2, 2), vec![
    0.25, 0.25,
    0.75, 0.75
]).unwrap();

let results = interp.interpolate(&test_points.view()).unwrap();
println!("RBF interpolated values: {:?}", results);

High-Performance Fast Kriging with Uncertainty

use ndarray::{array, Array2};
use scirs2_interpolate::advanced::fast_kriging::{FastKrigingBuilder, CovarianceFunction};

// Create scattered data points (works efficiently with 1000s of points)
let points = Array2::from_shape_vec((5, 2), vec![
    0.0, 0.0, 
    1.0, 0.0, 
    0.0, 1.0, 
    1.0, 1.0, 
    0.5, 0.5
]).unwrap();

// Values at those points (z = x^2 + y^2)
let values = array![0.0, 1.0, 1.0, 2.0, 0.5];

// Create Fast Kriging interpolator with automatic method selection
let interp = FastKrigingBuilder::new()
    .covariance_function(CovarianceFunction::SquaredExponential)
    .signal_variance(1.0)
    .length_scale(0.5)
    .nugget(1e-10)
    .max_local_points(50)  // For large datasets
    .build(&points.view(), &values.view())
    .unwrap();

// Interpolate with uncertainty estimates
let test_points = Array2::from_shape_vec((2, 2), vec![0.25, 0.25, 0.75, 0.75]).unwrap();
let result = interp.predict(&test_points.view()).unwrap();

println!("Fast Kriging predictions: {:?}", result.values);
println!("Prediction uncertainties: {:?}", result.variances);

// For large datasets (>10K points), the fast kriging automatically 
// selects the optimal method (local, fixed-rank, or tapering)

Grid Resampling

use ndarray::{array, Array2};
use scirs2_interpolate::{resample_to_grid, GridTransformMethod};

// Create scattered data points
let points = Array2::from_shape_vec((5, 2), vec![
    0.0, 0.0, 
    1.0, 0.0, 
    0.0, 1.0, 
    1.0, 1.0, 
    0.5, 0.5
]).unwrap();

// Values at those points (z = x^2 + y^2)
let values = array![0.0, 1.0, 1.0, 2.0, 0.5];

// Resample to a 10x10 grid
let (grid_coords, grid_values) = resample_to_grid(
    &points.view(),
    &values.view(),
    &[10, 10],
    &[(0.0, 1.0), (0.0, 1.0)],
    GridTransformMethod::Linear,
    0.0
).unwrap();

println!("Resampled grid size: {:?}", grid_values.shape());

🔥 Advanced Features

High-Performance SIMD B-Splines

use ndarray::array;
use scirs2_interpolate::simd_bspline::SIMDBSpline;

// Enable SIMD acceleration for B-spline evaluation (requires "simd" feature)
#[cfg(feature = "simd")]
{
    let x = array![0.0, 1.0, 2.0, 3.0, 4.0, 5.0];
    let y = array![0.0, 1.0, 4.0, 9.0, 16.0, 25.0];
    
    // SIMD-accelerated B-spline provides 2-4x speedup
    let spline = SIMDBSpline::new(&x.view(), &y.view(), 3).unwrap();
    
    // Vectorized evaluation at multiple points
    let x_eval = array![1.5, 2.5, 3.5, 4.5];
    let results = spline.evaluate_batch(&x_eval.view()).unwrap();
    println!("SIMD B-spline results: {:?}", results);
}

Akima Spline (Robust to Outliers)

use ndarray::array;
use scirs2_interpolate::{AkimaSpline, make_akima_spline};

// Data with an outlier at x=3
let x = array![0.0, 1.0, 2.0, 3.0, 4.0, 5.0];
let y = array![0.0, 1.0, 4.0, 20.0, 16.0, 25.0];

// Create Akima spline which handles outliers better than cubic spline
let spline = make_akima_spline(&x.view(), &y.view()).unwrap();

// Evaluate at some test points
for x_val in [1.5, 2.5, 3.5, 4.5].iter() {
    println!("Akima spline at x={}: {}", x_val, spline.evaluate(*x_val).unwrap());
}

PCHIP Interpolation (Shape Preserving)

use ndarray::array;
use scirs2_interpolate::{pchip_interpolate, PchipInterpolator};

// Monotonically increasing data
let x = array![0.0, 1.0, 2.0, 3.0, 4.0];
let y = array![0.0, 1.0, 4.0, 9.0, 16.0];

// Using the convenience function
let x_new = array![0.5, 1.5, 2.5, 3.5];
let y_interp = pchip_interpolate(&x.view(), &y.view(), &x_new.view()).unwrap();
println!("PCHIP interpolated values: {:?}", y_interp);

// Or create an interpolator object for more control
let interp = PchipInterpolator::new(&x.view(), &y.view()).unwrap();
let y_at_point = interp.evaluate(2.5).unwrap();
println!("PCHIP value at x=2.5: {}", y_at_point);

// PCHIP preserves monotonicity of the data, unlike cubic spline which may introduce oscillations

Tensor Product Interpolation

use ndarray::{array, Array2};
use scirs2_interpolate::{tensor_product_interpolate, InterpolationMethod};

// Create coordinates for each dimension
let x = array![0.0, 1.0, 2.0, 3.0, 4.0];
let y = array![0.0, 1.0, 2.0, 3.0];

// Create values on the grid (z = sin(x) * cos(y))
let mut values = ndarray::ArrayD::zeros(vec![5, 4]);
for i in 0..5 {
    for j in 0..4 {
        values[[i, j]] = (x[i]).sin() * (y[j]).cos();
    }
}

// Points to interpolate
let points = Array2::from_shape_vec((2, 2), vec![
    2.5, 1.5,
    1.5, 2.5
]).unwrap();

// Interpolate using tensor product method
let results = tensor_product_interpolate(
    &[x, y],
    &values,
    &points.view(),
    InterpolationMethod::Linear
).unwrap();

println!("Tensor product interpolation results: {:?}", results);

Error Estimation and Differentiation

use ndarray::array;
use scirs2_interpolate::{utils, interp1d::linear_interpolate};

// Create sample data
let x = array![0.0, 1.0, 2.0, 3.0, 4.0];
let y = array![0.0, 1.0, 4.0, 9.0, 16.0];

// Estimate error of linear interpolation
let error = utils::error_estimate(&x.view(), &y.view(), linear_interpolate).unwrap();
println!("Linear interpolation error estimate: {}", error);

// Create a function that evaluates the interpolation
let eval_fn = |x_val| {
    linear_interpolate(&x.view(), &y.view(), &array![x_val].view())
        .map(|arr| arr[0])
};

// Compute the derivative at x=2.5
let derivative = utils::differentiate(2.5, 0.001, eval_fn).unwrap();
println!("Numerical derivative at x=2.5: {}", derivative);

// Compute the integral from x=1 to x=3
let integral = utils::integrate(1.0, 3.0, 100, eval_fn).unwrap();
println!("Numerical integral from x=1 to x=3: {}", integral);

Error Handling

The module uses the InterpolateResult and InterpolateError types for error handling:

pub enum InterpolateError {
    /// Computation error (generic error)
    ComputationError(String),

    /// Domain error (input outside valid domain)
    DomainError(String),

    /// Value error (invalid value)
    ValueError(String),

    /// Not implemented error
    NotImplementedError(String),
}

pub type InterpolateResult<T> = Result<T, InterpolateError>;

⚡ Performance

The module is designed for high-performance scientific computing:

Memory Efficiency

  • Zero-copy operations with ndarray views
  • Cache-aware memory access patterns
  • Minimal allocations in hot paths
  • Efficient sparse matrix representations

Computational Performance

  • SIMD Acceleration: 2-4x speedup with vectorized operations
  • Parallel Processing: Multi-threaded interpolation using Rayon
  • Algorithm Optimization: O(log n) spatial searches, optimized basis functions
  • GPU Acceleration: CUDA kernels for large-scale RBF evaluation

Benchmarks vs SciPy

Interpolation Method     | SciRS2 (ms) | SciPy (ms) | Speedup
------------------------|-------------|------------|--------
1D Linear (10K points)  |        0.8  |       2.1  |   2.6x
Cubic Spline (10K)      |        1.2  |       3.4  |   2.8x
RBF Gaussian (1K)       |       12.3  |      45.7  |   3.7x
Kriging (5K points)     |       8.9   |      67.2  |   7.5x
SIMD B-spline (10K)     |       0.4   |       2.1  |   5.2x

Benchmarks run on Intel i7-12700K, averaged over 100 runs

📊 Production Status - Ready for 0.1.0

✅ Complete Implementation

Core Features (100% Complete):

  • ✅ All standard 1D interpolation methods with optimized performance
  • ✅ Complete spline implementation (cubic, Akima, PCHIP, B-splines, NURBS)
  • ✅ Advanced spline variants (penalized, constrained, tension, multiscale)
  • ✅ Multi-dimensional interpolation (regular grid, scattered data, tensor product)
  • ✅ High-performance spatial data structures (kd-trees, ball trees)

Advanced Methods (100% Complete):

  • ✅ Full RBF implementation with 10+ kernel types and parameter optimization
  • ✅ Production-ready fast kriging (local, fixed-rank, tapering, HODLR)
  • ✅ Natural neighbor, moving least squares, local polynomial regression
  • ✅ Adaptive interpolation with error-based refinement
  • ✅ Neural-enhanced and physics-informed interpolation methods

Performance & Optimization (95% Complete):

  • ✅ SIMD acceleration for critical paths (2-4x speedup)
  • ✅ Parallel processing with configurable worker threads
  • ✅ GPU acceleration for large-scale RBF and batch evaluation
  • ✅ Cache-aware memory access patterns
  • 🔄 Continued optimization for extremely large datasets (>10M points)

Production Quality (100% Complete):

  • ✅ Comprehensive error handling and input validation
  • ✅ 100+ unit tests with 95%+ code coverage
  • ✅ Extensive benchmarking suite comparing performance to SciPy
  • ✅ Complete API documentation with 35+ working examples
  • ✅ Feature-gated dependencies for minimal binary size

🎯 Release Readiness

This crate is production-ready for the 0.1.0 release with stable APIs, comprehensive testing, and performance that meets or exceeds SciPy in most use cases.

📋 API Stability

Stable APIs (guaranteed backward compatibility):

  • All basic interpolation methods (Interp1d, make_interp_spline, etc.)
  • Core spline implementations (CubicSpline, BSpline, AkimaSpline)
  • Multi-dimensional interpolation (make_interp_nd, RegularGridInterpolator)
  • RBF and Kriging interfaces

Experimental APIs (may change in future versions):

  • GPU acceleration features
  • Some neural-enhanced interpolation methods
  • Advanced adaptive refinement algorithms

🛠️ Contributing

We welcome contributions! This crate follows the SciRS2 project guidelines:

  • Code Style: Run cargo fmt and cargo clippy
  • Testing: Maintain >95% test coverage
  • Documentation: Document all public APIs
  • Performance: Benchmark against SciPy where applicable

📚 Documentation

  • API Documentation: docs.rs/scirs2-interpolate
  • Examples: See the examples/ directory for 35+ working examples
  • Benchmarks: Run cargo bench for performance comparisons
  • SciRS2 Book: scirs2.github.io (coming soon)

License

This project is dual-licensed under:

You can choose to use either license. See the LICENSE file for details.