interpylate

interpylate is a Python library for N-linear regular grid interpolation. It provides a flexible and efficient method to interpolate N-dimensional arrays using a N-linear approach, making it particularly useful for image interpolation and its higher-dimensional equivalents.

Installation

pip install interpylate

Dependencies

• numpy: Core dependency for array operations

Architecture

The library is structured with a unified interface and specialized implementations:

• NLinearRegularGridInterpolator: A unified interface that automatically selects the appropriate dimension-specific implementation based on the input dimension
  • For dimensions > 3, it utilizes NLinearRegularGridInterpolatorLarge
• LinearRegularGridInterpolator: Optimized for 1D arrays
• BiLinearRegularGridInterpolator: Optimized for 2D arrays (images)
• TriLinearRegularGridInterpolator: Optimized for 3D arrays (volumes)
• NLinearRegularGridInterpolatorLarge: Less optimized general implementation for any dimensions

This design provides both optimization for common cases and flexibility for higher dimensions.

Usage

from interpylate import NLinearRegularGridInterpolator

# Create an instance of the interpolator
# This will automatically select the appropriate implementation based on dimension
interpolator = NLinearRegularGridInterpolator(dim=3)

# Evaluate the interpolation at specified coordinates
interpolated_values = interpolator.evaluate(NDarray, continuous_inds)

# Compute the gradient of the interpolated array
gradient = interpolator.grad(NDarray, continuous_inds, evaluate_too=False)

# Compute the hessian of the interpolated array
hessian = interpolator.hess(NDarray, continuous_inds, grad_too=False, evaluate_too=False)

Main Features

• Dimension-agnostic API: Work with arrays of any dimension using a consistent interface
• Performance-optimized implementations: Specialized algorithms for 1D, 2D, and 3D cases
• Gradient computation: Calculate first-order derivatives of interpolated arrays
• Hessian computation: Compute second-order derivatives for advanced analysis
• Regular grid support: Designed specifically for regular grid structures

Examples

The interpylate GitHub repository includes several example scripts demonstrating the library's capabilities:

• 1D Interpolation: Basic interpolation for one-dimensional arrays
• 2D Interpolation: Image interpolation techniques
• 3D Interpolation: Volume data interpolation methods
• Speed Contest: Performance comparison between interpylate's TriLinearRegularGridInterpolator and scipy.interpolate.RegularGridInterpolator

Documentation

The full API documentation is available in the docs/ directory of the project or via the online documentation portal.

Contributing

At the moment, I am not actively reviewing contributions. However, if you encounter issues or have suggestions, feel free to open an issue.

License

This project is licensed under the CeCILL License.