flow-density

FFT-accelerated kernel density estimation for flow cytometry.

Overview

flow-density provides 1D and 2D kernel density estimation optimized for the event counts typical in flow cytometry (10K–10M events). The FFT-based algorithm avoids the O(n²) cost of naive KDE, making real-time density plots feasible even for large files.

Features

Public API

1D Density Estimation

use flow_density::KernelDensity;

let kde = KernelDensity::estimate(&data, 1.0, 512)?;
let peaks = kde.find_peaks(0.1);       // locate density peaks
let d = kde.density_at(42.0);          // query density at a point

2D Density Estimation

use flow_density::KernelDensity2D;

let kde2d = KernelDensity2D::estimate(&x_data, &y_data, 1.0, 256)?;
let contour = kde2d.find_contour(0.5); // extract contour at threshold
let d = kde2d.density_at(100.0, 200.0);

Utilities

use flow_density::common::{standard_deviation, interquartile_range, gaussian_kernel};

Algorithms

• 1D KDE: Gaussian kernel with Silverman bandwidth selection, evaluated via FFT convolution (realfft). Supports bandwidth adjustment factor and configurable grid resolution.
• 2D KDE: Separable Gaussian kernel on a regular grid, FFT-accelerated along each axis. Contour extraction via threshold-based boundary tracing.
• Peak finding: Local maxima detection with configurable minimum prominence (peak removal fraction).

Scope

This crate owns:

• 1D and 2D kernel density estimation
• Bandwidth selection heuristics (Silverman, Scott)
• Peak/mode detection in density estimates
• Contour extraction from 2D density fields
• (Future) Adaptive bandwidth KDE
• (Future) GPU-accelerated KDE for interactive use

It does not own: plotting/rendering, clustering, gating logic, or FCS file I/O.

Tests

cargo test -p flow-density

License

MIT