splotrs

splotrs fits the yields and shared shape parameters of normalized component probability-density functions, then calculates per-event sWeights. The numerical core is written in Rust and uses Ganesh; the Python API accepts vectorized NumPy callbacks.

The library is intended for unbinned mixture fits where a discriminating variable separates components and the resulting sWeights are used to study another, component-independent variable. It supports signed input event weights and exposes both the sPlot yield covariance and the full joint fit covariance.

Features

Joint extended maximum-likelihood fits of component yields and shared PDF shape parameters.
Vectorized Python PDF callbacks with runtime validation of their output type and shape.
A native Rust API for allocation-conscious fitting without Python.
Per-event sWeights in the same row order as the input data.
Signed input event weights.
Fit diagnostics, uncertainties, and covariance matrices.

Installation

Install the Python package from PyPI:

python -m pip install splotrs

Or add the Rust crate from crates.io:

cargo add splotrs

Python 3.10 or newer is required.

Python quick start

Each component PDF must be callable as pdf(events: NDArray[np.float64], parameters: dict[str, float]) -> NDArray[np.float64]. It receives the complete two-dimensional event array and must return one finite, nonnegative float64 density per event.

from collections.abc import Callable

import numpy as np
from numpy.typing import NDArray

from splotrs import ShapeParameter, splot

Pdf = Callable[
    [NDArray[np.float64], dict[str, float]],
    NDArray[np.float64],
]


def signal(events: NDArray[np.float64], parameters: dict[str, float]) -> NDArray[np.float64]:
    mean = parameters['mean']
    residual = events[:, 0] - mean
    return np.exp(-0.5 * residual**2) / np.sqrt(2.0 * np.pi)


def background(
    events: NDArray[np.float64], _parameters: dict[str, float]
) -> NDArray[np.float64]:
    residual = (events[:, 0] - 2.5) / 0.7
    return np.exp(-0.5 * residual**2) / (0.7 * np.sqrt(2.0 * np.pi))


data = np.array([[-1.2], [-0.3], [0.2], [2.1], [2.8]], dtype=np.float64)
pdfs: list[Pdf] = [signal, background]

result = splot(
    data,
    pdfs,
    shape_parameters=[ShapeParameter('mean', 0.0, -5.0, 5.0)],
)

print(result.yields)
print(result.shape_parameters)
print(result.sweights)  # (n_events, n_components)

shape_parameters are fitted jointly with the yields. Every PDF receives the complete parameter dictionary, so multiple components can share the same parameter.

Inputs

data: finite values with shape (n_events, n_features).
pdfs: one callable per mixture component, in output-column order.
shape_parameters: optional ShapeParameter objects with initial values and bounds.
initial_yields: optional nonnegative starting yields, one per PDF.
weights: optional signed event weights with shape (n_events,).
max_steps and tolerance: optimizer controls.

Result

splot returns an immutable SPlotResult containing:

yields and yield_errors;
sweights with shape (n_events, n_components);
covariance, the event-summed sPlot yield covariance;
shape_parameters and shape_errors keyed by parameter name;
fit_covariance, the full joint yield-and-shape covariance;
convergence status, message, minimum negative log-likelihood, and evaluation counts.

Invalid inputs and callback contracts raise ValueError. Numerical PDF, optimizer, and covariance failures raise SPlotError. Exceptions raised inside a PDF callback are preserved.

Complete example

The included example fits a Gaussian bump over a quadratic background and uses sWeights to recover the component distributions of a control variable:

just examples

It writes the resulting plot to target/examples/bump.png.

Rust usage

Implement ParametricPdf or use compatible closures, then call splot with event rows, PDFs, shape parameters, and an SPlotConfig:

use splotrs::{ParametricPdf, SPlotConfig, ShapeParameter, ShapeParameters, splot};

let data = vec![vec![-1.2], vec![-0.3], vec![0.2], vec![2.1], vec![2.8]];
let signal = |event: &[f64], parameters: &ShapeParameters| {
    let residual = event[0] - parameters["mean"];
    (-0.5 * residual * residual).exp() / std::f64::consts::TAU.sqrt()
};
let background = |event: &[f64], _parameters: &ShapeParameters| {
    let residual = (event[0] - 2.5) / 0.7;
    (-0.5 * residual * residual).exp() / (0.7 * std::f64::consts::TAU.sqrt())
};
let pdfs: [&dyn ParametricPdf; 2] = [&signal, &background];
let shapes = [ShapeParameter::new("mean", 0.0)?.with_bounds(-5.0, 5.0)?];

let result = splot(&data, &pdfs, &shapes, SPlotConfig::default())?;
println!("yields: {:?}", result.yields);
# Ok::<(), splotrs::SPlotError>(())

Statistical assumptions

Component PDFs must be normalized over the fitted discriminating variables.
Components must be identifiable; linearly dependent PDFs produce a singular covariance error.
A variable studied with sWeights should be independent of the discriminating variables within each component.
Signed input weights are supported, but some weighted datasets can produce an indefinite or singular information matrix.

Development

Enter the reproducible Nix development shell:

nix develop

The shell supplies Rust, Cargo, Clippy, rustfmt, Python, uv, and Just. It synchronizes the locked Python development environment on entry. Run just to list all recipes; the main workflows are:

just build       # Build the Rust crate and Python distributions
just test        # Run Rust and Python tests
just lint        # Run Clippy, rustfmt, Ruff, ty, and Yamloom checks
just fmt         # Format Rust and Python sources
just examples    # Run the complete plotting example
just clean       # Remove generated build and test artifacts

Without Nix, install uv, Rust, and Just, then run uv sync before using the same recipes.

Before publishing, inspect both artifacts locally:

cargo publish --dry-run
uv build

License

Licensed under either of the following, at your option:

Apache License, Version 2.0 (LICENSE-APACHE)
MIT License (LICENSE-MIT)

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Size: 116.0 kB
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splotrs 0.1.4

splotrs

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Complete example

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