trattoria 0.3.7

The fastest streaming algorithms for your TTTR data

🍕 Trattoria 🍕

Trattoria delivers you the fastest streaming algorithms to analyze your TTTR data. We currenlty support the following algorithms:

• Second order autocorrelations: Calculate the autocorrelation between two channels of your TCSPC.
• Third Order autocorrelations: Calculate the coincidences between 3 channels. A sync version is provided were it uses the fact that the sync channel is periodic and known.
• Intensity time trace: Calculate the intensity on each (or all) channels versus time.
• Zero finder: Given two uncorrelated channels (e.g. a laser behind a 50/50 splitter) compute the delay between the input channels.
• Lifetime: Compute the lifetime histogram from a pulsed excitation experiment.

Supported file formats

Currently Trattoria can only read PTU files from PicoQuant. If you want support for more or want to help providing it please put a ticket on the tttr-toolbox project.

Installing

pip install trattoria

Examples

The entry point to Trattoria is the PTUFile class. This class has methods that give us access to the algorithms. Each of the algorithms takes as input a parameter object and returns a results object. For a complete list of the functionality see the examples folder.

from pathlib import Path

import trattoria

import matplotlib.pyplot as plt

ptu_filepath = Path("/path/to/some.ptu")
ptu = trattoria.PTUFile(ptu_filepath)

timetrace_params = trattoria.TimeTraceParameters(
    resolution=10.0,
    channel=None,
)
tt_res = ptu.timetrace(timetrace_params)

plt.plot(tt_res.t, tt_res.tt / timetrace_params.resolution)
plt.xlabel("Time (s)")
plt.ylabel("Intensity (Hz)")
plt.show()

The examples folders contains examples of all the functionality available in Trattoria. For more details check the docstrings in core.py.

Design

Trattoria is just a very thin wrapper around the trattoria-core library which itselfs provides a lower level interface to the the tttr-toolbox library. A Rust project that provides the compiled components that allows us to go fast.

Changelog

0.3.5

• Bug fix. The last 1024*16 where being ignored for performance reasons. This has has been fixed upstream in tttr-toolbox and this version of Trattoria uses the upgraded version of trattoria-core.
• trattoria-core dropped support for Python 3.6 and 3.7 and therefore Trattoria too.

0.3.4

• The g2 algorithm now supports a mode flag. With "symmetric" we use the prefered version of the algorithm that returns negative and positive delays. "asymmetric" returns only positive delays but is faster. Default is "symmetric".

0.3.3

• The underlying TTTR Toolbox and Trattoria Core were refactored to support multiple custom ranges or records at once. start_range and stop_range have disappeared in favor of record_ranges. It takes a list of tuples of integers or None.

Citing