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.
- 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.
In an anaconda environment the library can be installed by the following command:
conda install -c tpeulen tttrlibAlternatively, you can use pip to install tttrlib
pip install tttrlibThe 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_channelsPrint 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.correlationCreate 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.intensitytttrlib is in active development. In case you notice unusual behaviour do not hesitate to contact the authors.
- PicoHarp ptu, T2/T3
- HydraHarp ptu, T2/T3
- HydraHarp ht3, PTU
- spc132
- spc630 (256 & 4096 mode)
- Low memory footprint (keep objective large datasets, e.g., FLIM in memory).
- Platform independent C/C++ library with interfaces for scripting libraries
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 installOn Linux you can build and install a package instead:
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 installIn an anaconda environment the library can be installed by the following command:
conda install -c tpeulen tttrlibFor 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.
If you use this software please also check the pre-print:
tttrlib: modular software for integrating fluorescence spectroscopy, imaging, and molecular modeling; Thomas-Otavio Peulen, Katherina Hemmen, Annemarie Greife, Benjamin M. Webb, Suren Felekyan, Andrej Sali, Claus A. M. Seidel, Hugo Sanabria, Katrin G. Heinze; https://arxiv.org/abs/2402.17252
Copyright 2007-2024 tttrlib developers. Licensed under the BSD-3-Clause
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