shufflesnap
shufflesnap assigns large 2D point clouds to regular grids: every point gets its own grid cell, and the solver seeks a small total squared movement using multiscale local descent. It is a Python package with a native C++ core and nanobind bindings. The main use case is turning embeddings (UMAP, t-SNE, Isomap) into image atlases.
Instead of building an n x n cost matrix, shufflesnap refines a trivial legal assignment by solving exact linear assignment problems inside small grid windows at multiple scales — coarse-to-fine strided windows repair global structure in a handful of rounds, then stride-1 windows polish. The assignment is legal after every round, the cost never increases, memory is linear in the number of points plus grid cells, and random initialization works well on the tested distributions. Final accuracy and the number of polishing rounds have no worst-case guarantee.
The public API has three functions:
snap_to_grid(points, width=None, height=None, cleanup_seconds=None, ...)window_cleanup(points, initial_assignment, rows, cols, budget_seconds=None, ...)linear_sum_assignment(cost_matrix)
Install
python -m pip install shufflesnap
To run the matplotlib example from the source tree:
python -m pip install -e '.[examples]'
python examples/basic_usage.py
API
snap_to_grid(points, width=None, height=None, cleanup_seconds=None, ...)
High-level wrapper for snapping a 2D point cloud onto a destination grid. Inputs must use the same coordinate system as the target grid (default range [0.03, 0.97] per axis); the API does not normalize inputs.
Behavior:
- chooses a destination grid automatically when
widthandheightare omitted: an exact factorization ofnwith aspect ratio in[1:1, 2:1]when one exists, otherwise a slightly larger near-square grid - supports any
n <= width * heightdirectly: the occupied cell subset is selected before cleanup and remains fixed - by default cleanup runs until none of the four scheduled half-offset tilings can improve the assignment;
cleanup_secondscaps the time instead, and0.0returns the raw seed (a deterministic random permutation, with a fixed random cell subset when the grid has more cells than points) polish_all_offsets=Trueadds one stride-1 sweep over every complete window placement after normal cleanup, reducing the remaining local error at additional cost- pass
mask(a(height, width)bool array) to restrict which cells may be used — shaped atlases (circles, cut corners, a half-empty last row) work out of the box
Returns:
grid_points:(n, 2)float64 NumPy array of assigned destination points in original point orderassignment:(n,)int64 NumPy array of destination-grid cell ids in original point order(width, height): destination-grid size tuple
window_cleanup(points, initial_assignment, rows, cols, budget_seconds=None, ...)
Improve any legal assignment with the native multiscale window cleanup kernel.
Key options:
budget_seconds=Noneruns until converged (a full stride-1 round changes nothing)strides=Noneuses the automatic coarse-to-fine schedule; pass a list to overridewindow_size=6all_offsets=Trueuses allwindow_size ** 2tiling offsets instead of the default four and is substantially slowernum_threads=Noneto usestd::thread::hardware_concurrency()fixed_suffix_countto keep a suffix of target cells fixed;cell_maskto mark which cells may be used at alltrace_rounds=Trueto record per-round cost, elapsed time, and stride
Returns a dict with assignment, rounds_completed, elapsed_s, final_cost, and converged.
linear_sum_assignment(cost_matrix)
Solve a dense square linear assignment problem exactly with the native C++ Jonker-Volgenant implementation.
Returns:
row_ind:int64NumPy array of shape(n,)col_ind:int64NumPy array of shape(n,)total_cost: Pythonfloat
More
- Source repository: https://github.com/kylemcdonald/shufflesnap
- Issue tracker: https://github.com/kylemcdonald/shufflesnap/issues
- Example scripts: https://github.com/kylemcdonald/shufflesnap/tree/main/examples
Metadata
Release files for shufflesnap 0.3.0
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