sampeuler 1.0.0

SampEuler: Euler Characteristic Transform and related topological data analysis tools

SampEuler

A Python package for computing Euler Characteristic Transforms (ECT), Smooth Euler Characteristic Transforms (SECT), SampEuler and related topological data analysis tools for geometric simplicial complexes.

Installation

pip install sampeuler

Features

• ECT (Euler Characteristic Transform): Compute ECT using evenly spaced or custom directions
• SECT (Smooth Euler Characteristic Transform): Cumulative/integrated version of ECT
• SampEuler: ECT with randomly sampled directions as empirical measures of ECT pushforward measures
• SampEuler Vectorization: Convert SampEuler output to 2D histogram images for vectorization and interpretation
• Distance Metrics: ECT metric and Wasserstein distance for comparing shapes
• Parallelized: All main functions use Numba JIT compilation with parallel execution

Quick Start

import numpy as np
import sampeuler as se

# Define a simplicial complex (list of simplices, each simplex is a list of vertex indices)
simp_comp = [
    [0], [1], [2],           # 0-simplices (vertices)
    [0, 1], [1, 2], [0, 2],  # 1-simplices (edges)
    [0, 1, 2]                # 2-simplex (triangle)
]

# Define vertex coordinates
data = np.array([
    [0.0, 0.0],
    [1.0, 0.0],
    [0.5, 1.0]
])

# Compute ECT with 100 evenly spaced directions
ect_result = se.ect_2d(simp_comp, data, k=100, interval=(-2., 2.), points=500)

# Compute SECT (mean across directions)
sect_result = se.sect_2d(simp_comp, data, k=100, interval=(-2., 2.), points=500, mode='mean')

# Compute SampEuler with 1000 random directions
sampeuler_result = se.SampEuler_2d(simp_comp, data, k=1000, interval=(-2., 2.), points=500)

Examples

For more detailed examples including visualizations, see the examples/toy_example.ipynb notebook.

API Reference

ECT Functions

ect_2d(simp_comp, data, k=20, interval=(-1., 1.), points=100, factor=3)

Compute ECT in 2D using k evenly spaced directions and evaluate at points places. The simp_comp variable is a list of lists representing the abstract simplicial complex information, and the data variable stores the list of coordinates for vertices as the embedding of the simplicial complex. The interval is the filtration interval the algorithm will be evenly sampling points to evaluate the Euler characteristic curves at. This interval should be chosen large enough so that all vertices have height within the interval along all directions. The algorithm uses factor to more finely sample filtration values to allow more accurate results.

ect(simp_comp, data, thetas, interval=(-1., 1.), points=100, factor=3)

Compute ECT using provided direction vectors thetas for geometric simplicial complex of any dimension. The remaining setup is the same to ect_2d.

SECT Functions

sect_2d(simp_comp, data, k=20, interval=(-1., 1.), points=100, factor=3, mode='full')

Compute SECT (Smooth Euler Characteristic Transform) in 2D using k evenly spaced directions. The SECT is the cumulative integral of the Euler curve, providing a smoother representation that captures the same topological information. Parameters are the same as ect_2d, with an additional mode parameter:

• mode='full': Returns the full (k, points) array with one SECT curve per direction
• mode='mean': Returns the mean SECT curve across all directions as a 1D array of shape (points,)

sect(simp_comp, data, directions, interval=(-1., 1.), points=100, factor=3, mode='full')

Compute SECT using provided unit direction vectors for geometric simplicial complexes of any dimension. The directions parameter is an array of shape (k, dim) containing unit vectors. Other parameters are the same as sect_2d.

SampEuler Functions

SampEuler_2d(simp_comp, data, k=20, interval=(-1., 1.), points=100, factor=3)

Compute SampEuler with k randomly sampled directions in 2D as an empirical measure drawn from the ECT pushforward measure. The variables are the same to ect_2d

SampEuler(simp_comp, data, dim, k, interval=(-1., 1.), points=100, factor=3)

Compute SampEuler with k randomly sampled directions in arbitrary dimension. The dim parameter specifies the dimension of the ambient space the simplicial complex is embedded in, which determines how random directions are sampled (uniformly on the unit sphere in R^dim). Other parameters are the same as ect_2d.

Vectorization

SampEulerVectorization(simp_comp, data, k=20, xinterval=(-1., 1.), xpoints=100, yinterval=(-1., 1.), ypoints=100, resolution=1, factor=3, precomputed=None)

Convert SampEuler output to a 2D histogram image for vectorization and visualization. The x-axis represents filtration values (xinterval divided into xpoints bins), and the y-axis represents Euler characteristic values (yinterval divided into ypoints bins). Each cell counts the proportion of directions whose Euler curves fall within that bin. The resolution parameter controls internal SampEuler resolution; when resolution > 1, a direction is counted in a bin only if ALL its values within the corresponding x-interval fall within the y-bin. Alternatively, pass precomputed SampEuler/ECT data directly to skip recomputation.

# From simplicial complex
vec = se.SampEulerVectorization(simp_comp, data, k=1000, xpoints=100, ypoints=50)
image = vec.image  # Access the 2D histogram (shape: ypoints x xpoints)
vec.plot()         # Visualize the result

# From precomputed SampEuler data
precomputed_se = se.SampEuler_2d(simp_comp, data, k=1000, interval=(-2., 2.), points=100)
vec = se.SampEulerVectorization(precomputed=precomputed_se, xinterval=(-2., 2.), xpoints=100, yinterval=(-1., 3.), ypoints=4)

Distance Metrics

ect_metric(simp_comp1, data1, simp_comp2, data2, k=20, interval=(-1., 1.), points=100)

Compute the ECT metric between two geometric simplicial complexes. The metric is defined as the supremum over k evenly spaced directions of the integrated L1 difference between the two Euler curves. This provides a rotation-invariant distance measure between shapes based on their topological signatures.

sampeuler_wasserstein_distance(empirical1, empirical2, p=2, delta_x=1.0)

Compute the p-Wasserstein distance between two SampEuler empirical measures using optimal transport. Each SampEuler output is treated as an empirical measure over the space of Euler curves. The function computes the optimal matching between curves using the Hungarian algorithm, with the cost between curves defined as the Lp distance scaled by delta_x (the spacing between sample points, typically (interval[1] - interval[0]) / (points - 1)).

# Compare two shapes using Wasserstein distance
se1 = se.SampEuler_2d(simp_comp1, data1, k=1000, interval=(-5., 1.), points=3000)
se2 = se.SampEuler_2d(simp_comp2, data2, k=1000, interval=(-5., 1.), points=3000)

delta_x = 6.0 / (3000 - 1)  # spacing between sample points
distance = se.sampeuler_wasserstein_distance(se1, se2, p=2, delta_x=delta_x)

Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• Numba >= 0.50.0
• SciPy >= 1.6.0
• scikit-learn >= 0.24.0
• Matplotlib >= 3.3.0

License

GPL-3.0 License

Citation

If you use this package in your research, please cite:

@software{sampeuler,
  title = {SampEuler: Euler Characteristic Transform Tools},
  author = {Haoche Yang},
  year = {2025},
  url = {https://github.com/reddevil0623/SampEuler}
}