tttrlib 0.26.2

Read, write & process time-tagged time-resolved (TTTR) data.

tttrlib

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General description

tttrlib is a file format agnostic high performance library to read, process, and write time-tagged-time resolved (TTTR) data acquired by PicoQuant (PQ) and Becker & Hickl measurement devices/cards or TTTR files in the open Photon-HDF format.

tttrlib is a high-performance, file-format-agnostic library to read, process, and write time-tagged time-resolved (TTTR) data from PicoQuant, Becker & Hickl, and Photon-HDF5 files.

Written in C++ with Python bindings, it provides a fast, vendor-independent API for handling photon streams and enables integration into advanced data analysis pipelines for time-resolved fluorescence spectroscopy and imaging.

Key Features

• Fast TTTR file reading (IO-limited)
• Multi-dimensional histogramming
• Correlation analysis
• Fluorescence decay generation and analysis
• Photon distribution (FIDA/PCH)
• Burst and time-window selection
• FLIM and ISM image generation
• Experimental ISM tools (Adaptive Pixel Reassignment, Focus-ISM background rejection)

tttrlib typically outperforms pure Python implementations by ~40× in decay histogramming and ~2–5× in burst selection.

Installation

pip (recommended)

pip install tttrlib

Pre-built wheels are available on PyPI for Linux (x86_64), macOS (arm64, x86_64), and Windows (x86_64) across Python 3.9–3.13.

Conda / Mamba

macOS / Linux (via bioconda)

mamba install -c conda-forge -c bioconda tttrlib

Windows (via tpeulen)

mamba install -c tpeulen tttrlib

We recommend Miniforge with the fast mamba solver.

From Source

git clone https://github.com/fluorescence-tools/tttrlib.git
cd tttrlib
pip install -e .

Pre-compiled packages are available for Windows, Linux (x86_64), and macOS (arm64, x86_64). Legacy 32-bit and Python 2.7 are not supported.

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Usage

See docs.peulen.xyz/tttrlib for the full API and tutorials. Below are minimal examples.

Detailed build instructions for developers are available in BUILDING.md.

Read TTTR data

import tttrlib
data = tttrlib.TTTR("photon_stream.ptu")

macro = data.macro_times
micro = data.micro_times
routing = data.routing_channels

Inspect header

import tttrlib
fn = 'photon_stream.ptu'
data = tttrlib.TTTR(fn)
print(data.header.json)
print(data.header.to_csv())

Cross-correlate photon streams

import tttrlib
fn = 'photon_stream.ptu'
data = tttrlib.TTTR(fn)
correlator = tttrlib.Correlator(
    channels=([1], [2]),
    tttr=data
)
taus = correlator.x_axis,
correlation_amplitude = correlator.correlation

Create intensity images (CLSM)

import tttrlib
fn = 'image.ptu'
data = tttrlib.TTTR(fn)
clsm = tttrlib.CLSMImage(data)
channels = [0, 1]
prompt_range = [0, 16000]
clsm.fill(channels=channels, micro_time_ranges=[prompt_range])
intensity_image = clsm.intensity

# Alternatively
clsm = tttrlib.CLSMImage(fn, fill=True)
intensity_image = clsm.intensity

Minimal burst search

import tttrlib
import numpy as np

fn = 'photon_stream.ptu'
tttr = tttrlib.TTTR(fn)

# Bust selection
L, m, T = 30, 10, 1e-3  # min photons, window photons, window time [s]
ranges = tttr.burst_search(L=L, m=m, T=T)  # flat [start, stop, start, stop, ...]
bursts = list(zip(ranges[0::2], ranges[1::2]))

For PIE/ALEX data, add micro-time gating before burst search; see the tutorial for donor/acceptor prompt examples. For details, parameters, and plotting examples, see the Burst Analysis tutorial.

Supported File Formats

• PicoQuant: PicoHarp/TimeHarp/HydraHarp (ptu, ht3, T2/T3)
• Becker & Hickl: spc132, spc630 (256 & 4096 mode)
• Photon-HDF5: open standard format

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Contributing

To add support for a new format / microscope:

1. Open a GitHub issue describing the format and instrument.
2. Share a small demo file (<100 MB) with expected results.
3. If relevant, document your workflow or analysis steps.

With this information, we can integrate and test the new format automatically.

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Design Goals

• Low memory footprint for large datasets (e.g. FLIM)
• Cross-platform C/C++ library with SWIG bindings (Python, C#, Java, etc.)
• Modular and extendable design for fluorescence spectroscopy and imaging

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Citation

If you use this software, please cite:

Thomas-Otavio Peulen, Katherina Hemmen, Annemarie Greife, Benjamin M. Webb, Suren Felekyan, Andrej Sali, Claus A. M. Seidel, Hugo Sanabria, Katrin G. Heinze. “tttrlib: modular software for integrating fluorescence spectroscopy, imaging, and molecular modeling.” Bioinformatics 41 (2): btaf025 (2025). https://doi.org/10.1093/bioinformatics/btaf025

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License

Copyright 2007–2026 tttrlib developers Licensed under the BSD-3-Clause license.