peacoqc-py

Python bindings for peacoqc-rs flow cytometry quality control.

What this crate is for

Use peacoqc-py when you want PeacoQC from Python (PyO3 + Polars) without rewriting the QC pipeline. The Rust algorithm crate remains the source of truth.

Use a sibling instead when you need:

• Rust library API → peacoqc-rs
• CLI → peacoqc-cli
• Long QC + unmix orchestration → tru-ols CLI

Note: This package may sit outside the root Cargo workspace members; build via its own Cargo.toml / maturin (or project) workflow.

How it works

A cdylib named peacoqc wraps peacoqc-rs (with flow-fcs) and exposes QC entry points to Python, bridging event tables through Polars/pyo3-polars.

Related crates

• peacoqc-rs — algorithm implementation
• flow-fcs — FCS loading behind the bindings
• flow-density — intended shared KDE (PeacoQC still vendors density today)

Demo / API

Build from this directory with your usual PyO3/maturin flow (see crate Cargo.toml). Version 0.1.0. Prefer calling into the same config/result concepts as peacoqc-rs (PeacoQCConfig, good-cell masks, exports).

import polars as pl
import peacoqc

# Load your data as a polars DataFrame
df = pl.read_csv("events.csv")

# Run PeacoQC quality control
result = peacoqc.run_qc(
    df,
    channels=["FL1-A", "FL2-A"],
    channel_ranges={"FL1-A": (0.0, 262144.0), "FL2-A": (0.0, 262144.0)},
)
print(f"Removed {result.percentage_removed:.2f}% of events")

# Apply the mask to filter good cells
good_mask = result.good_cells
clean_df = df.filter(pl.Series(good_mask))

Performance

Same algorithmic costs as peacoqc-rs; Python overhead is binding/conversion only. GPU features follow the Rust crate defaults when enabled at build time.

License

MIT