binary-ensemble 1.0.0

Python bindings for the Binary Ensemble Package.

binary-ensemble

Compress, store, and stream massive ensembles of districting plans.

Redistricting samplers like GerryChain's ReCom, ForestReCom, and Sequential Monte Carlo emit millions of plans. Stored as JSONL, a single ensemble can run to tens of gigabytes — most of it redundant. BEN (Binary-Ensemble) is a compression format and toolkit built for exactly this data: it turns those JSONL mountains into compact binary files you can store, share, and stream sample-by-sample without unpacking the whole thing.

binary-ensemble is the Python interface to the binary-ensemble Rust library.

A real 50k-plan ensemble on Colorado's ~140k census blocks is 13.5 GB as JSONL. Reordered by GEOID20 it compresses to a ~280 MB BEN stream, and then to a 5.6 MB XBEN file — over a 2,400× reduction, fully lossless.

Install

pip install binary-ensemble

Requires Python 3.11+. Pre-built wheels are available for Linux, macOS, and Windows. The only runtime dependency is NetworkX, and the API is fully type-annotated (py.typed).

Quick example

Write an ensemble into one self-describing .bendl file, then read it back:

from binary_ensemble import BendlEncoder, BendlDecoder

plans = [[1, 1, 2, 2], [1, 2, 2, 2], [1, 1, 1, 2]]

# The stream context finalizes the bundle when it closes.
encoder = BendlEncoder("ensemble.bendl", overwrite=True)
encoder.add_metadata({"sampler": "demo", "seed": 1234})
with encoder.ben_stream() as ensemble:
    for assignment in plans:
        ensemble.write(assignment)

# Iterate the assignments straight back out, one at a time.
for assignment in BendlDecoder("ensemble.bendl"):
    print(assignment)

The graph travels with the data

An assignment is just integers — it only means something in a dual graph's node order. A .bendl file embeds the graph, so a collaborator can open one file and reconstruct plans with no risk of pairing the wrong graph:

import networkx as nx
from binary_ensemble import BendlEncoder, BendlDecoder

dual_graph = nx.convert_node_labels_to_integers(nx.grid_2d_graph(4, 4))

encoder = BendlEncoder("run.bendl", overwrite=True)
ordered = encoder.add_graph(nx.adjacency_data(dual_graph))  # reordered for compression
with encoder.ben_stream() as ensemble:
    for step in range(1000):
        ensemble.write([(node + step) % 4 + 1 for node in range(16)])

decoder = BendlDecoder("run.bendl")
graph = decoder.read_graph()        # back as a live networkx.Graph, in assignment order
print(len(decoder))                 # 1000 — read from the header, no scan

for assignment in decoder.subsample_every(100):
    ...                             # every 100th plan, without decoding the rest

More than the basics

• Whole-file converters for existing JSONL ensembles:

  from binary_ensemble import encode_jsonl_to_ben, encode_ben_to_xben
  
  encode_jsonl_to_ben("plans.jsonl", "plans.ben")   # fast working format
  encode_ben_to_xben("plans.ben", "plans.xben")     # smallest, for storage
  

• Shrink for sharing — reorder a finished file and recompress its stream to XBEN, keeping every asset:

  from binary_ensemble import relabel_bundle, compress_stream
  
  relabel_bundle("run.bendl", out_file="run-sorted.bendl", sort="mlc")
  compress_stream("run-sorted.bendl", out_file="run-archive.bendl")
  

• Subsampling by stride, range, or explicit indices on both bundles and plain .ben/.xben streams — skipped samples are never materialized.

• Custom assets: attach scores, notes, run manifests, or arbitrary binary blobs (a zipped shapefile, a GeoPackage) alongside the stream. Every asset is checksummed (CRC32C), large payloads are xz-compressed transparently, and BendlDecoder.verify() validates a whole file in one call.

• Sampler-agnostic: encoders take plain list[int] assignments, so the same API works for GerryChain, ForestReCom, SMC, or your own code.

Documentation

Full docs are at binary-ensemble.readthedocs.io:

• Quickstart — your first ensemble in a few lines.
• Concepts — dual graphs, the BEN/XBEN/BENDL formats, encoding variants, and the compression levers.
• Quick Help — compress a GerryChain run, analyze with NumPy/pandas, subsample, convert formats, shrink a file for sharing, recover a crashed run.
• API reference — every public class and function.
• Tutorial notebooks — executed end to end in CI against the live API, as is every code snippet in the docs.

Command-line tools

The same engine ships as the ben and bendl CLI tools via Cargo:

cargo install binary-ensemble

License

MIT — see LICENSE.