gasp-python 0.0.4

High-performance proportional sampling from point cloud clusters with geometric size measures and sampling measures.

GASP: Geometric Allocation of Sample Points

High-performance proportional sampling from point cloud clusters with geometric size measures and sampling measures.

Installation

pip install gasp-python

Features

Proportional Cluster Sampling

Sample from multiple point cloud clusters with automatic proportional allocation based on geometric size measures:

from gasp import sample_from_clusters

# Sample 1000 points proportionally from clusters
# Each cluster gets samples proportional to its convex hull volume
sampled = sample_from_clusters(
    directory="./my_clusters",  # Directory with .npy files
    total_samples=1000,
    size_measure="hull_volume",
    sampling_method="centroid_fps",
    seed=42
)

# Returns: {cluster_name: sampled_points_array, ...}
for name, points in sampled.items():
    print(f"{name}: sampled {len(points)} points")

Geometric Size Measures

• bbox_volume - Axis-aligned bounding box hypervolume
• hull_volume - Convex hull hypervolume
• covariance_volume - Covariance determinant (ellipsoid proxy)
• mean_dispersion - Mean pairwise Euclidean distance

Sampling Methods

Multiple strategies for representative point selection within each cluster:

• uniform - Pure random sampling
• centroid_uniform - Guarantees centroid + random
• centroid_fps - Farthest point sampling
• centroid_kmedoids - K-medoids clustering
• centroid_voxel - Voxel grid sampling
• centroid_stratified - Stratified sampling

Grid Search

Test multiple configurations:

from gasp import grid_search_sample_from_clusters

results = grid_search_sample_from_clusters(
    directory="./my_clusters",
    total_samples=1000,
    size_measures=["bbox_volume", "hull_volume"],
    sampling_methods=["uniform", "centroid_fps"],
    seed=42
)

# Access specific configuration
best_result = results[("hull_volume", "centroid_fps")]

Direct Point Cloud Operations

You can also use the low-level functions directly:

import numpy as np
from gasp import bbox_volume, hull_volume, sample_centroid_fps

# Single point cloud
points = np.random.randn(1000, 3)

# Compute geometric measures
volume = bbox_volume(points)
hull_vol = hull_volume(points)

# Sample representative points
indices = sample_centroid_fps(points, count=100, seed=42)
sampled = points[indices]

How It Works

1. Load clusters: Reads all .npy files from a directory
2. Compute sizes: Measures each cluster using the specified size measure
3. Proportional allocation: Distributes N samples across clusters proportionally (minimum 1 per cluster)
4. Sample points: Uses the specified sampling method within each cluster

Performance

• Fast: Rust core for high-performance computation
• Flexible: Works with Python lists or NumPy arrays
• N-Dimensional: Supports 2D and 3D. Work ongoing for N-dim support.
• Robust: Handles edge cases (empty clusters, degenerate geometries)