TinySpline  0.5.0
Spline Library for a Multitude of Programming Languages

CI Security Language Grade: C/C++

TinySpline is a small, yet powerful library for interpolating, transforming, and querying arbitrary NURBS, B-Splines, and Bézier curves. The core of the library is written in ANSI C (C89) with a C++ wrapper for an object-oriented programming model. Based on the C++ wrapper, auto-generated bindings for C#, D, Go, Java, Javascript, Lua, Octave, PHP, Python, R, and Ruby are provided.


MIT License - see the LICENSE file in the source distribution.


  • Object-oriented programming model
  • B-Splines of any degree and dimensionality
  • Spline interpolation
    • Cubic natural
    • Centripetal Catmull–Rom
  • Evaluation
    • Knots
    • Sampling (multiple knots at once)
    • Components (find y for given x)
  • Knot insertion (refinement)
  • Bézier curve decomposition
  • Derivative
  • Degree elevation
  • Computation of rotation minimizing frames
  • Morphing
  • Serialization (JSON)
  • Vector math


Pre-built Binaries

Releases can be downloaded from the releases page. In addition, the following package manager are supported:

Conan (C/C++):

NuGet (C#):

<PackageReference Include="tinyspline" version="" />


go get github.com/tinyspline/go@v0.4.0

Luarocks (Lua):

luarocks install --server=https://tinyspline.github.io/lua tinyspline

Maven (Java):


PyPI (Python):

python -m pip install tinyspline

On macOS, you may need to change the path to Python in _tinysplinepython.so via install_name_tool.

Compiling From Source

See BUILD.md.

Getting Started

A variety of examples (unit tests) can be found in the [test](test) subdirectory. The [examples](examples) subdirectory contains at least one example for each interface (target language).

The following listing shows a python example:

from tinyspline import *
import matplotlib.pyplot as plt
spline = BSpline.interpolate_cubic_natural(
100, -100, # P1
-100, 200, # P2
100, 400, # P3
400, 300, # P4
700, 500 # P5
], 2) # <- dimensionality of the points
# Draw spline as polyline.
points = spline.sample(100)
x = points[0::2]
y = points[1::2]
plt.plot(x, y)
# Draw point at knot 0.3.
vec2 = spline.eval(0.3).result_vec2()
plt.plot(vec2.x, vec2.y, 'ro')
# Draw tangent at knot 0.7.
pos = spline(0.7).result_vec2() # operator () -> eval
der = spline.derive()(0.7).result_vec2().norm() * 200
s = (pos - der)
t = (pos + der)
plt.plot([s.x, t.x], [s.y, t.y])

The resulting image:

Theoretical Backgrounds

[1] is a very good starting point for B-Splines.

[2] explains De Boor's Algorithm and gives some pseudo code.

[3] provides a good overview of NURBS with some mathematical background.

[4] is useful if you want to use NURBS in TinySpline.