tttrlib

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.

The library facilitates the work with files containing time-tagged time resolved photon streams by providing a vendor independent C++ application programming interface (API) for TTTR files that is wrapped by SWIG (Simplified Wrapper and Interface Generator) for common scripting languages as Python as target languages and non-scripting languages such as C# and Java including Octave, Scilab and R. Currently, tttrlib is wrapped for the use in Python.

• Multi-dimensional histograms
• Correlation analysis
• Time-window analysis
• Photon distribution anaylsis
• FLIM image generation and analysis

tttrlib is programmed in C++ and wrapped for python. Thus, it can be used to integrate time-resolved data into advanced data analysis pipelines.

Capabilities

• Fast reading TTTR files (IO limited)
• Generation / analysis of fluorescence decays
• Time window analysis
• Correlation of time event traces
• Filtering of time event traces to generate instrument response functions for fluorescence decays analysis without the need of independent measurements..
• Fast photon distribution analysis
• Fast selection of photons from a photon stream

Generation of fluorescence decay histograms tttrlib outperforms pure numpy and Python based libraries by a factor of ~40.

Documentation

Installation

In an anaconda environment the library can be installed by the following command:

conda install -c tpeulen tttrlib

Alternatively, you can use pip to install tttrlib

pip install tttrlib

Usage

The API of tttrlib as well as some use cases are documented on its web page. Below you find a small selection of code snippets.

Access photon data as follows:

import tttrlib
fn = 'photon_stream.ptu'
data = tttrlib.TTTR(fn)

macro_times = data.macro_times
micro_times = data.micro_times
routing_channels = data.routing_channels

Print header-information:

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

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 from CLSM data:

import tttrlib
fn = 'image.ptu'
data = tttrlib.TTTR(fn)
clsm = tttrlib.CLSM(data)

channels = [0, 1]
prompt_range = [0, 16000]
clsm.fill(channels=channels, micro_time_ranges=[prompt_range])

intensity_image = clsm.intensity

tttrlib is in active development. In case you notice unusual behaviour do not hesitate to contact the authors.

Supported file formats

PicoQuant (PQ)

• PicoHarp ptu, T2/T3
• HydraHarp ptu, T2/T3
• HydraHarp ht3, PTU

Becker & Hickl (BH)

• spc132
• spc630 (256 & 4096 mode)

Photon HDF5

Design goals

• Low memory footprint (keep objective large datasets, e.g., FLIM in memory).
• Platform independent C/C++ library with interfaces for scripting libraries

Building and Installation

C++ shared library

The C++ shared library can be installed from source with cmake:

git clone --recursive https://github.com/fluorescence-tools/tttrlib.git
mkdir tttrlib/build; cd tttrlib/build
cmake ..
sudo make install

On Linux you can build and install a package instead:

Python bindings

The Python bindings can be either be installed by downloading and compiling the source code or by using a precompiled distribution for Python anaconda environment.

The following commands can be used to download and compile the source code:

git clone --recursive https://github.com/fluorescence-tools/tttrlib.git
cd tttrlib
sudo python setup.py install

In an anaconda environment the library can be installed by the following command:

conda install -c tpeulen tttrlib

For most users, the latter approach is recommended. Currently, pre-compiled packages for the anaconda distribution system are available for Windows (x86), Linux (x86, ARM64, PPCle), and macOS (x86). Precompiled libary are linked against conda-forge HDF5 & Boost. Thus, the use of miniforge is recommended.

Legacy 32-bit platforms and versions of programming languages, e.g., Python 2.7 are not supported.

License

Copyright 2007-2024 tttrlib developers. Licensed under the BSD-3-Clause