cubinterpp

Cubic and linear interpolation with C++ in Python.

Introduction

This C++ header library features tools for piecewise cubic interpolation.

Currently, only 1D interpolation is supported, however, future released are planned to extend the library to higher dimensions.

The library features three kinds of different interpolation types:

• Linear interpolation
• Monotone cubic interpolation
• Akima spline interpolation
• Natural cubic spline interpolation

All classes are templatized and support the STL's vector types.

The accompanying python script in cubinterpp compares the interpolation types.

Usage

The following instructions show how to build and test the cubinterpp header library in a python environment.

Prerequisites

• C++ compiler, e.g. gcc
• cmake: to use the provided cmake configuration
• pybind11: to compile the library header into a python module
• mlpyqtgraph: to plot the example's results

Building

To build the header library for usage in Python, it's recommended to use cmake. An appropriate cmake configuration is provided in the main CMakeLists.txt. Prior to compilation, the required external libraries are downloaded automatically using the cmake FetchContent module. Prior to building, make sure cmake is installed and configured with a C++ compiler like e.g. gcc. In order to create the python module, the development python library is also required.

In order to do so on a Debian based system, install cmake, gcc, g++ and python3.11-dev (change the python version depending on your configuration):

sudo apt install cmake gcc g++ python3.11-dev

Set the appropriate environment variables (it's recommended to add these lines to e.g. your .bashrc):

export CC=/usr/bin/gcc
export CXX=/usr/bin/g++

Then create the build directory, configure and build using:

mkdir build
cmake ..
make

This should build and automatically copy the library file cubic_spline.*.so into the cubinterpp directory.

Testing

This library comes with severals tests. To run all tests, first build and then run (while remaining in the build directory):

ctest -V

Interpolating and plotting the results

A python program is provided to compare the three interpolation types. Data preparation and visualization is done in python with mlpyqtgraph.

In order to run the python program, it's recommended to install uv and issue:

uv run cubinterpp 

This should install all required python dependencies automatically and run the python program that does the interpolation and plotting, resulting in the comparison plot shown at the top of this document.

License

An MIT style license applies for cubinterpp , see the LICENSE file for more details.