trattoria 0.1.1

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.
• 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.

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 three methods that give us access to the algorithms. Each of the algorithms takes as input a parameter object and returns a results object. For example:

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 thing 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.

Citing