tdfpy

tdfpy reads Bruker timsTOF .d folders (analysis.tdf and analysis.tdf_bin) in pure Python, with no Bruker native library. DDA, DIA and PRM acquisitions (PASEF and diaPASEF) come back as familiar objects: MS1 frames, precursors, isolation windows, PRM targets and transitions.

Spectra are read lazily and centroided by a Numba-accelerated pipeline that keeps ion mobility: region exclusion, smoothing, noise filters, and a choice of two centroiders. Peaks are NumPy arrays of [m/z, intensity, 1/K0].

from tdfpy import DDA

with DDA("sample.d") as dda:
    for frame in dda.ms1:
        peaks = frame.centroid()  # shape (N, 3): m/z, intensity, 1/K0

Installation

pip install tdfpy

Requires Python 3.12+. Extras: tdfpy[viz] (plots), tdfpy[mcp] (MCP server).

Where next

• Getting started: DDA, DIA and PRM walkthroughs on real data.
• Spectrum batches and file checks: batch window extraction and .d validation.
• MCP interface: let an AI agent query and extract timsTOF data.
• API reference: every public class and function.

Related packages

The tacular-omics mass spectrometry stack:

• tdfpy (this package) reads Bruker timsTOF .d data.
• mzmlpy reads mzML files.
• spxtacular processes the spectra from both: centroiding, deconvolution, matching, scoring and plotting.