rangefinder 0.2.1

Unsupervised ranging, ion identification, and composition for atom probe tomography mass spectra

Rangefinder

Automated ranging, species identification, and composition for atom probe tomography (APT) mass spectra. Rangefinder goes from an APT event list (.pos/.epos, represented internally by reconstructed positions and m/z) to ranged peaks, identified positive ionic species, and isotope-resolved composition — with no user-supplied ranges, no candidate element list, no interactive tuning, and no manual curation.

It is deterministic (operator variance is exactly zero), pure Python (NumPy/SciPy/pandas), and ships its own benchmark harness against the open-source APT ecosystem.

Scope

Rangefinder is an APT-specific end-to-end system, not a general-purpose mass spectrometry or spectroscopy toolkit. Its physical and chemical model assumes:

• event-level, one-dimensional time-of-flight m/z data;
• positive atomic and small molecular ions typical of field evaporation;
• APT-like peak widths and one-sided thermal tails;
• natural-isotope family patterns, APT charge-state priors, and elemental atomic percent as the final quantity; and
• reconstructed x_nm, y_nm, and z_nm coordinates for the public pipeline data model and optional spatial analyses.

The histogramming, local peak detection, centroid refinement, and area-integration code is potentially reusable for other one-dimensional TOF mass spectra after adaptation and domain-specific validation. The MassBank and ToF-SIMS studies in this repository test that detector only. They do not validate Rangefinder's species assignment or composition outside APT; the benchmark adapters convert profile/centroid data to pseudo-events and disable APT family-assignment features.

Rangefinder does not currently model negative-ion assignment, adduct and fragment chemistry, tandem MS, chromatography or ion mobility, arbitrary profile/centroid inputs, instrument-pluggable resolution models, or non-mass-spectrometry axes. For those use cases, use a domain-specific toolkit or extract and validate only the low-level detector.

Range and overlap auditing

Every detected peak now carries two independent range estimates:

• the production fixed/FWHM or crowded-window mixture-fit area; and
• a clean-room equal-error range estimate with asymmetric boundaries, estimated purity, recovery, background, missed signal, counting uncertainty, and the fixed-versus-adaptive discrepancy.

Equal-error ranging was inspired by the public Atom-Probe-Toolbox, but no GPL source was copied. It remains a shadow diagnostic, because the bundled ablation found a large synthetic-data improvement but regressions on seven of ten real composition controls.

Composition uses a guarded natural-isotope-envelope deconvolution only for identifiable connected overlap groups with high-confidence species anchors, full-rank and conditioned envelope matrices, adequate observed coverage, optimizer convergence, and a residual pass. Rejected groups retain their assignment probabilities unchanged. Output tables preserve the uncorrected atomic_percent_assignment beside the promoted atomic_percent_weighted, and diagnostics.json records every accepted or rejected component.

Install

python -m pip install rangefinder

For development:

git clone https://github.com/kylemcdonald/rangefinder.git
cd rangefinder
python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"

Quick start

from pathlib import Path

import rangefinder
from rangefinder.io.pos_loader import PosSampleData, PosSampleMetadata
from rangefinder.benchmark.truth import load_pos_arrays

sample_path = Path("your_sample.pos")
x, y, z, mz = load_pos_arrays(sample_path)
meta = PosSampleMetadata(path=sample_path, sample_name="s", sample_slug="s",
                         event_count=len(mz), file_size_bytes=len(mz) * 16,
                         verified_big_endian_float32=True,
                         verified_columns=("x_nm", "y_nm", "z_nm", "m_over_z_da"))
sample = PosSampleData(metadata=meta, x_nm=x, y_nm=y, z_nm=z, m_over_z_da=mz)

config = rangefinder.load_default_config()
artifacts = rangefinder.run_custom_pipeline(sample, Path("out"), config)
print(artifacts.elemental_composition)

Layout

• src/rangefinder/ — the package.
  • analysis/ — the Rangefinder pipeline (custom_pipeline) plus the comparator front-ends (naive, PyCCAPT, pyOpenMS) and shared assignment/composition stages.
  • benchmark/ — ground-truth registry, metrics, and detection-ablation harness.
  • io/, references/, validation/, utils/ — POS loading, isotope tables, the synthetic generator, config/paths helpers.
  • bridge.py, bridge_runner.py — run foreign-toolchain comparators (APAV, ms_deisotope, APyT) in a separate environment.
  • config/defaults.yaml — the bundled default pipeline configuration (rangefinder.default_config_path()).
• scripts/ — the benchmark and paper-table generators.
• paper/ — rangefinder.tex and its auto-generated tables/.
• controls/ — public CC-BY reference datasets (fetch large files with scripts/download_benchmark_controls.sh; reproducible raw data and generated synthetic controls are git-ignored, while provenance and range files are tracked).

Reproduce the benchmark and paper

scripts/download_benchmark_controls.sh          # fetch public controls
scripts/bootstrap_env.sh                        # install comparator environments
python scripts/run_benchmark.py                 # all methods x all datasets
python scripts/run_detection_ablation.py        # detection-stage ablation
python scripts/run_ablation.py                  # pipeline-stage ablation
python scripts/run_quantification_ablation.py   # range/deconvolution promotion test
python scripts/build_paper_tables.py            # regenerate paper/tables/*.tex
tectonic paper/rangefinder.tex

Every number in the paper regenerates from these commands; table numbers are never hand-edited.

License

The software is released under the MIT License. The reference data and range files under controls/ remain CC-BY-4.0 as documented in controls/README.md.